ambiser
/
human
mirror of https://github.com/vladmandic/human
9
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
human/dist/human.esm.js

5068 lines
1.3 MiB
Raw Blame History

/*
Human library
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var m8=Object.create,mh=Object.defineProperty,A8=Object.getPrototypeOf,y8=Object.prototype.hasOwnProperty,g8=Object.getOwnPropertyNames,x8=Object.getOwnPropertyDescriptor;var Z1=e=>mh(e,"__esModule",{value:!0});var Eg=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),pr=(e,t)=>{for(var n in t)mh(e,n,{get:t[n],enumerable:!0})},w8=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of g8(t))!y8.call(e,r)&&r!=="default"&&mh(e,r,{get:()=>t[r],enumerable:!(n=x8(t,r))||n.enumerable});return e},Ah=e=>w8(Z1(mh(e!=null?m8(A8(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e);var Rg=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)},ve=(e,t,n)=>(Rg(e,t,"read from private field"),n?n.call(e):t.get(e)),ea=(e,t,n,r)=>(Rg(e,t,"write to private field"),r?r.call(e,n):t.set(e,n),n);var _6=Eg(w6=>{Z1(w6);pr(w6,{MediaPipeFaceMesh:()=>_2,load:()=>Cae});var _2=class{constructor(t,n,r,a){this.facePipeline=new w2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Gr))l[h]=Gr[h].map(d=>i[d]);let u=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[1],s.box.endPoint[0])-s.box.startPoint[0],Math.min(t.shape[2],s.box.endPoint[1])-s.box.startPoint[1]]:0,c=s.box?[Math.max(0,s.box.startPoint[0]/t.shape[2]),Math.max(0,s.box.startPoint[1]/t.shape[1]),Math.min(t.shape[1],s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],Math.min(t.shape[2],s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:u,mesh:i,boxRaw:c,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Mi=[null,null,null];async function Cae(e){Mi=await Promise.all([!Mi[0]&&e.face.enabled?p6(e):null,!Mi[1]&&e.face.mesh.enabled?Ft(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Mi[2]&&e.face.iris.enabled?Ft(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new _2(Mi[0],Mi[1],Mi[2],e);return e.face.mesh.enabled&&e.debug&&Ce(`load model: ${e.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),e.face.iris.enabled&&e.debug&&Ce(`load model: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}w6.triangulation=Fi});var g0=Eg(H2=>{Z1(H2);pr(H2,{NUM_KEYPOINTS:()=>$ae,connectedPartIndices:()=>Oae,partChannels:()=>Pae,partIds:()=>j2,partNames:()=>Mae,poseChain:()=>zae});var Mae=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],$ae=H2.partNames.length,j2=H2.partNames.reduce((e,t,n)=>(e[t]=n,e),{}),Dae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Oae=Dae.map(([e,t])=>[j2[e],j2[t]]),zae=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]],Pae=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});function Ce(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function Fg(){let e,t;if(typeof navigator!="undefined"){let n=navigator.userAgent.match(/\(([^()]+)\)/g);n&&n[0]&&(e=n[0].match(/\(([^()]+)\)/g)[0].replace(/\(|\)/g,""),t=navigator.userAgent.replace(n[0],""),e[1]&&(t=t.replace(n[1],"")),t=t.replace(/ /g," "))}else typeof process!="undefined"&&(e=`${process.platform} ${process.arch}`,t=`NodeJS ${process.version}`);return{platform:e,agent:t}}var yh={};pr(yh,{Abs:()=>Zi,Acos:()=>Yi,Acosh:()=>Ji,AdadeltaOptimizer:()=>Ud,AdagradOptimizer:()=>Hd,AdamOptimizer:()=>jd,AdamaxOptimizer:()=>Gd,Add:()=>Na,AddN:()=>os,All:()=>vh,Any:()=>kh,ArgMax:()=>ls,ArgMin:()=>lu,Asin:()=>Qi,Asinh:()=>eo,Atan:()=>to,Atan2:()=>ro,Atanh:()=>no,AvgPool:()=>us,AvgPool3D:()=>uu,AvgPool3DGrad:()=>Ih,AvgPoolGrad:()=>Nh,BackendWasm:()=>x3,BatchMatMul:()=>cs,BatchToSpaceND:()=>cu,Bincount:()=>Sh,BroadcastTo:()=>qg,Callback:()=>cv,CallbackList:()=>l7,Cast:()=>hs,Ceil:()=>ds,ClipByValue:()=>Ia,Complex:()=>Th,ComplexAbs:()=>hu,Concat:()=>ao,Conv2D:()=>ps,Conv2DBackpropFilter:()=>Ch,Conv2DBackpropInput:()=>fs,Conv3D:()=>du,Conv3DBackpropFilterV2:()=>Eh,Conv3DBackpropInputV2:()=>Rh,Cos:()=>ms,Cosh:()=>so,CropAndResize:()=>io,Cumsum:()=>As,CustomCallback:()=>c7,DataStorage:()=>xh,DenseBincount:()=>Fh,DepthToSpace:()=>oo,DepthwiseConv2dNative:()=>ys,DepthwiseConv2dNativeBackpropFilter:()=>Mh,DepthwiseConv2dNativeBackpropInput:()=>$h,Diag:()=>Dh,Dilation2D:()=>pu,Dilation2DBackpropFilter:()=>zh,Dilation2DBackpropInput:()=>Oh,ENV:()=>fr,EarlyStopping:()=>dv,Elu:()=>lo,EluGrad:()=>Ph,Environment:()=>jg,Equal:()=>co,Erf:()=>uo,Exp:()=>xs,ExpandDims:()=>ho,Expm1:()=>po,FFT:()=>Lh,Fill:()=>fu,FlipLeftRight:()=>fo,Floor:()=>ws,FloorDiv:()=>_s,FromPixels:()=>ed,FusedBatchNorm:()=>bs,FusedConv2D:()=>ei,FusedDepthwiseConv2D:()=>ti,GPGPUContext:()=>up,GatherNd:()=>Ao,GatherV2:()=>mo,GraphModel:()=>Vv,Greater:()=>yo,GreaterEqual:()=>vs,History:()=>u7,IFFT:()=>Wh,Identity:()=>ks,Imag:()=>Bh,InputSpec:()=>Kt,IsFinite:()=>go,IsInf:()=>xo,IsNan:()=>wo,KernelBackend:()=>su,LRN:()=>yu,LRNGrad:()=>Uh,LayerVariable:()=>r7,LayersModel:()=>ha,LeakyRelu:()=>Ns,Less:()=>_o,LessEqual:()=>bo,LinSpace:()=>Vh,Log:()=>Is,Log1p:()=>vo,LogSoftmax:()=>Xg,LogicalAnd:()=>ko,LogicalNot:()=>mu,LogicalOr:()=>Au,MathBackendCPU:()=>Zd,MathBackendWebGL:()=>El,Max:()=>Ss,MaxPool:()=>Cs,MaxPool3D:()=>gu,MaxPool3DGrad:()=>jh,MaxPoolGrad:()=>Hh,MaxPoolWithArgmax:()=>Gh,Maximum:()=>Ts,Mean:()=>Es,Min:()=>Rs,Minimum:()=>Fs,MirrorPad:()=>xu,Mod:()=>No,MomentumOptimizer:()=>qd,Multinomial:()=>qh,Multiply:()=>Ms,Neg:()=>Io,NonMaxSuppressionV3:()=>To,NonMaxSuppressionV4:()=>Co,NonMaxSuppressionV5:()=>Eo,NotEqual:()=>So,OP_SCOPE_SUFFIX:()=>s5,OneHot:()=>$s,OnesLike:()=>Ro,Optimizer:()=>oa,Pack:()=>Fo,PadV2:()=>Ds,Pool:()=>Ek,Pow:()=>Os,Prelu:()=>zs,Prod:()=>Mo,RMSPropOptimizer:()=>Xd,RNN:()=>Hr,Range:()=>wu,Rank:()=>uf,Real:()=>Xh,RealDiv:()=>gs,Reciprocal:()=>$o,Reduction:()=>pn,Relu:()=>Ps,Relu6:()=>Ws,Reshape:()=>Do,ResizeBilinear:()=>Ls,ResizeBilinearGrad:()=>Zh,ResizeNearestNeighbor:()=>_u,ResizeNearestNeighborGrad:()=>Kh,Reverse:()=>Bs,RotateWithOffset:()=>Ko,Round:()=>Vs,Rsqrt:()=>Us,SGDOptimizer:()=>ec,ScatterNd:()=>Oo,Select:()=>zo,Selu:()=>Po,Sequential:()=>Wl,Sigmoid:()=>js,Sign:()=>Bo,Sin:()=>Hs,Sinh:()=>Wo,Slice:()=>Lo,Softmax:()=>Xs,Softplus:()=>Vo,SpaceToBatchND:()=>bu,SparseToDense:()=>Yh,SplitV:()=>Uo,Sqrt:()=>Gs,Square:()=>vu,SquaredDifference:()=>Ks,Step:()=>Ta,StridedSlice:()=>Ho,Sub:()=>Zs,Sum:()=>qs,SymbolicTensor:()=>kr,Tan:()=>jo,Tanh:()=>Ys,Tensor:()=>Ue,TensorBuffer:()=>Ot,Tile:()=>Sa,TopK:()=>Go,Transform:()=>Jh,Transpose:()=>Js,Unique:()=>Qh,Unpack:()=>qo,UnsortedSegmentSum:()=>ku,Variable:()=>Ru,ZerosLike:()=>Xo,_FusedMatMul:()=>Qs,abs:()=>zt,acos:()=>Df,acosh:()=>Of,add:()=>se,addN:()=>Ma,all:()=>dd,any:()=>Ou,argMax:()=>zu,argMin:()=>zf,asin:()=>Pf,asinh:()=>Lf,atan:()=>Wf,atan2:()=>Bf,atanh:()=>Vf,avgPool:()=>Lu,avgPool3d:()=>jf,backend:()=>W5,backend_util:()=>R,basicLSTMCell:()=>lI,batchNorm:()=>li,batchNorm2d:()=>H5,batchNorm3d:()=>j5,batchNorm4d:()=>G5,batchToSpaceND:()=>Wu,bincount:()=>q5,booleanMaskAsync:()=>pC,broadcastTo:()=>Bu,browser:()=>rl,buffer:()=>We,callbacks:()=>Mne,cast:()=>me,ceil:()=>Gf,clipByValue:()=>vn,clone:()=>Rr,complex:()=>Ca,concat:()=>st,concat1d:()=>X5,concat2d:()=>ll,concat3d:()=>K5,concat4d:()=>Z5,constraints:()=>E3,conv1d:()=>fd,conv2d:()=>ra,conv2dTranspose:()=>md,conv3d:()=>Xf,conv3dTranspose:()=>EI,copyRegisteredKernels:()=>Mk,cos:()=>Vu,cosh:()=>Ad,cosineWindow:()=>_m,cumsum:()=>yd,customGrad:()=>$r,data:()=>Uv,denseBincount:()=>J5,deprecationWarn:()=>Mf,depthToSpace:()=>Kf,depthwiseConv2d:()=>ul,deregisterOp:()=>Dne,device_util:()=>Mu,diag:()=>PI,dilation2d:()=>Zf,disableDeprecationWarnings:()=>bN,dispose:()=>Ee,disposeVariables:()=>vN,div:()=>ge,divNoNan:()=>Yf,dot:()=>Q5,dropout:()=>wx,elu:()=>cl,enableDebugMode:()=>_N,enableProdMode:()=>wN,enclosingPowerOfTwo:()=>_x,engine:()=>Fr,env:()=>J,equal:()=>Da,erf:()=>Jf,exp:()=>qn,expandDims:()=>hn,expm1:()=>Qf,eye:()=>em,fft:()=>Ju,fill:()=>Uu,findBackend:()=>$f,findBackendFactory:()=>CN,floor:()=>hl,floorDiv:()=>hd,forceHalfFloat:()=>F_,fused:()=>La,gather:()=>ui,gatherND:()=>xx,gather_util:()=>If,getBackend:()=>SN,getGradient:()=>sf,getKernel:()=>td,getKernelsForBackend:()=>Yo,gpgpu_util:()=>n_,grad:()=>pS,grads:()=>fS,greater:()=>rr,greaterEqual:()=>za,ifft:()=>Al,imag:()=>gd,image:()=>Je,inTopKAsync:()=>kC,initializers:()=>z3,input:()=>Z3,io:()=>bn,irfft:()=>$d,isFinite:()=>ex,isInf:()=>tx,isNaN:()=>nx,keep:()=>Gt,kernel_impls:()=>Pr,layers:()=>K3,leakyRelu:()=>Hu,less:()=>xd,lessEqual:()=>ci,linalg:()=>Mx,linspace:()=>rx,loadGraphModel:()=>Ft,loadLayersModel:()=>Qte,localResponseNormalization:()=>tm,log:()=>Mn,log1p:()=>wd,logSigmoid:()=>sx,logSoftmax:()=>bd,logSumExp:()=>am,logicalAnd:()=>ar,logicalNot:()=>ju,logicalOr:()=>vd,logicalXor:()=>ux,losses:()=>BE,matMul:()=>Ge,math:()=>_5,max:()=>Xn,maxPool:()=>Gu,maxPool3d:()=>sm,maxPoolWithArgmax:()=>cx,maximum:()=>Dr,mean:()=>kt,memory:()=>cd,metrics:()=>ov,min:()=>pl,minimum:()=>fl,mirrorPad:()=>im,mod:()=>om,model:()=>Yte,models:()=>lv,moments:()=>kd,movingAverage:()=>AC,mul:()=>P,multiRNNCell:()=>US,multinomial:()=>hx,neg:()=>vt,nextFrame:()=>Kd,norm:()=>Pd,notEqual:()=>di,oneHot:()=>nl,ones:()=>Or,onesLike:()=>$n,op:()=>O,outerProduct:()=>XS,pad:()=>aa,pad1d:()=>YS,pad2d:()=>QS,pad3d:()=>tT,pad4d:()=>rT,pool:()=>dx,pow:()=>sa,prelu:()=>Xu,print:()=>m5,prod:()=>Nd,profile:()=>gr,rand:()=>dT,randomGamma:()=>AT,randomNormal:()=>px,randomUniform:()=>ml,range:()=>Id,ready:()=>IN,real:()=>Ku,reciprocal:()=>cm,registerBackend:()=>sl,registerCallbackConstructor:()=>ene,registerGradient:()=>Kg,registerKernel:()=>ni,registerOp:()=>$ne,regularizers:()=>uv,relu:()=>zr,relu6:()=>Sd,removeBackend:()=>TN,reshape:()=>G,reverse:()=>Dn,reverse1d:()=>NT,reverse2d:()=>ST,reverse3d:()=>CT,reverse4d:()=>RT,rfft:()=>Qu,round:()=>hm,rsqrt:()=>Td,scalar:()=>be,scatterND:()=>gx,scatter_util:()=>Sf,selu:()=>Cd,separableConv2d:()=>dm,sequential:()=>Jte,serialization:()=>re,setBackend:()=>NN,setPlatform:()=>EN,setWasmPath:()=>XZ,setWasmPaths:()=>KZ,setWebGLContext:()=>sp,setdiff1dAsync:()=>fx,shared:()=>Nm,sigmoid:()=>Fn,sign:()=>pm,signal:()=>WE,sin:()=>Ed,sinh:()=>Rd,slice:()=>Re,slice1d:()=>Fd,slice2d:()=>fm,slice3d:()=>Md,slice4d:()=>Zu,slice_util:()=>cn,softmax:()=>Yu,softplus:()=>dl,spaceToBatchND:()=>qu,sparseToDense:()=>wm,spectral:()=>LE,split:()=>Lt,sqrt:()=>en,square:()=>lt,squaredDifference:()=>Dd,squeeze:()=>Pa,stack:()=>dn,step:()=>yl,stridedSlice:()=>mm,sub:()=>ye,sum:()=>Te,sumOutType:()=>sd,tan:()=>Am,tanh:()=>ol,tensor:()=>yr,tensor1d:()=>on,tensor2d:()=>Nn,tensor3d:()=>ld,tensor4d:()=>rC,tensor5d:()=>aC,tensor6d:()=>sC,tensor_util:()=>mr,test_util:()=>z5,tidy:()=>B,tile:()=>Oa,time:()=>kN,topk:()=>ym,train:()=>fi,transpose:()=>at,truncatedNormal:()=>Od,unique:()=>zd,unregisterGradient:()=>Fk,unregisterKernel:()=>Rk,unsortedSegmentSum:()=>gm,unstack:()=>sr,upcastType:()=>nr,util:()=>v,valueAndGrad:()=>mS,valueAndGrads:()=>AS,variable:()=>mx,variableGrads:()=>ax,version:()=>wae,version_converter:()=>$re,version_core:()=>xN,version_cpu:()=>uw,version_layers:()=>HA,version_wasm:()=>_3,version_webgl:()=>R_,webgl:()=>dL,webgl_util:()=>Ew,where:()=>kn,whereAsync:()=>xm,zeros:()=>Et,zerosLike:()=>He});var _8=Object.create,gh=Object.defineProperty,b8=Object.getPrototypeOf,v8=Object.prototype.hasOwnProperty,k8=Object.getOwnPropertyNames,N8=Object.getOwnPropertyDescriptor,I8=e=>gh(e,"__esModule",{value:!0}),et=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),ze=(e,t)=>{for(var n in t)gh(e,n,{get:t[n],enumerable:!0})},S8=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of k8(t))!v8.call(e,r)&&r!=="default"&&gh(e,r,{get:()=>t[r],enumerable:!(n=N8(t,r))||n.enumerable});return e},qi=e=>S8(I8(gh(e!=null?_8(b8(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),T8=et(()=>{}),C8=et((e,t)=>{(function(n,r,a){function s(u){var c=this,h=l();c.next=function(){var d=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=d-(c.c=d|0)},c.c=1,c.s0=h(" "),c.s1=h(" "),c.s2=h(" "),c.s0-=h(u),c.s0<0&&(c.s0+=1),c.s1-=h(u),c.s1<0&&(c.s1+=1),c.s2-=h(u),c.s2<0&&(c.s2+=1),h=null}function i(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function o(u,c){var h=new s(u),d=c&&c.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var u=4022871197,c=function(h){h=h.toString();for(var d=0;d<h.length;d++){u+=h.charCodeAt(d);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),E8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var d=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^d^d>>>8},l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),R8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=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^(d^d<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,h==c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),F8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.x,d=u.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,u.i=d+1&7,f};function c(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,h.i=0,p=256;p>0;--p)h.next()}c(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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.x&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),M8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.w,d=u.X,p=u.i,f,m;return u.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,u.i=p,m+(h^h>>>16)|0};function c(h,d){var p,f,m,A,y,g=[],_=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,_=Math.max(_,d.length)),m=0,A=-32;A<_;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}c(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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.X&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),$8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.b,p=u.c,f=u.d,m=u.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,u.b=d=d<<20^d>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-d|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var h=0;h<c.length+20;h++)u.b^=c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Y1=et(()=>{}),D8=et((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",u=r.pow(s,i),c=r.pow(2,o),h=c*2,d=s-1,p;function f(w,x,I){var S=[];x=x==!0?{entropy:!0}:x||{};var T=g(y(x.entropy?[w,b(n)]:w==null?_():w,3),S),M=new m(S),D=function(){for(var z=M.g(i),W=u,U=0;z<c;)z=(z+U)*s,W*=s,U=M.g(1);for(;z>=h;)z/=2,W/=2,U>>>=1;return(z+U)/W};return D.int32=function(){return M.g(4)|0},D.quick=function(){return M.g(4)/4294967296},D.double=D,g(b(M.S),n),(x.pass||I||function(z,W,U,H){return H&&(H.S&&A(H,M),z.state=function(){return A(M,{})}),U?(r[l]=z,W):z})(D,T,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(w){var x,I=w.length,S=this,T=0,M=S.i=S.j=0,D=S.S=[];for(I||(w=[I++]);T<s;)D[T]=T++;for(T=0;T<s;T++)D[T]=D[M=d&M+w[T%I]+(x=D[T])],D[M]=x;(S.g=function(z){for(var W,U=0,H=S.i,X=S.j,j=S.S;z--;)W=j[H=d&H+1],U=U*s+j[d&(j[H]=j[X=d&X+W])+(j[X]=W)];return S.i=H,S.j=X,U})(s)}function A(w,x){return x.i=w.i,x.j=w.j,x.S=w.S.slice(),x}function y(w,x){var I=[],S=typeof w,T;if(x&&S=="object")for(T in w)try{I.push(y(w[T],x-1))}catch(M){}return I.length?I:S=="string"?w:w+"\0"}function g(w,x){for(var I=w+"",S,T=0;T<I.length;)x[d&T]=d&(S^=x[d&T]*19)+I.charCodeAt(T++);return b(x)}function _(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),b(w)}catch(S){var x=a.navigator,I=x&&x.plugins;return[+new Date,a,I,a.screen,b(n)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=Y1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),O8=et((e,t)=>{var n=C8(),r=E8(),a=R8(),s=F8(),i=M8(),o=$8(),l=D8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),z8=et((e,t)=>{(function(n,r,a){function s(u){var c=this,h=l();c.next=function(){var d=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=d-(c.c=d|0)},c.c=1,c.s0=h(" "),c.s1=h(" "),c.s2=h(" "),c.s0-=h(u),c.s0<0&&(c.s0+=1),c.s1-=h(u),c.s1<0&&(c.s1+=1),c.s2-=h(u),c.s2<0&&(c.s2+=1),h=null}function i(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function o(u,c){var h=new s(u),d=c&&c.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var u=4022871197,c=function(h){h=h.toString();for(var d=0;d<h.length;d++){u+=h.charCodeAt(d);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),P8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var d=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^d^d>>>8},l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),L8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=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^(d^d<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,h==c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),W8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.x,d=u.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,u.i=d+1&7,f};function c(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,h.i=0,p=256;p>0;--p)h.next()}c(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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.x&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),B8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.w,d=u.X,p=u.i,f,m;return u.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,u.i=p,m+(h^h>>>16)|0};function c(h,d){var p,f,m,A,y,g=[],_=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,_=Math.max(_,d.length)),m=0,A=-32;A<_;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}c(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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.X&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),V8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.b,p=u.c,f=u.d,m=u.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,u.b=d=d<<20^d>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-d|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var h=0;h<c.length+20;h++)u.b^=c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),U8=et((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",u=r.pow(s,i),c=r.pow(2,o),h=c*2,d=s-1,p;function f(w,x,I){var S=[];x=x==!0?{entropy:!0}:x||{};var T=g(y(x.entropy?[w,b(n)]:w==null?_():w,3),S),M=new m(S),D=function(){for(var z=M.g(i),W=u,U=0;z<c;)z=(z+U)*s,W*=s,U=M.g(1);for(;z>=h;)z/=2,W/=2,U>>>=1;return(z+U)/W};return D.int32=function(){return M.g(4)|0},D.quick=function(){return M.g(4)/4294967296},D.double=D,g(b(M.S),n),(x.pass||I||function(z,W,U,H){return H&&(H.S&&A(H,M),z.state=function(){return A(M,{})}),U?(r[l]=z,W):z})(D,T,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(w){var x,I=w.length,S=this,T=0,M=S.i=S.j=0,D=S.S=[];for(I||(w=[I++]);T<s;)D[T]=T++;for(T=0;T<s;T++)D[T]=D[M=d&M+w[T%I]+(x=D[T])],D[M]=x;(S.g=function(z){for(var W,U=0,H=S.i,X=S.j,j=S.S;z--;)W=j[H=d&H+1],U=U*s+j[d&(j[H]=j[X=d&X+W])+(j[X]=W)];return S.i=H,S.j=X,U})(s)}function A(w,x){return x.i=w.i,x.j=w.j,x.S=w.S.slice(),x}function y(w,x){var I=[],S=typeof w,T;if(x&&S=="object")for(T in w)try{I.push(y(w[T],x-1))}catch(M){}return I.length?I:S=="string"?w:w+"\0"}function g(w,x){for(var I=w+"",S,T=0;T<I.length;)x[d&T]=d&(S^=x[d&T]*19)+I.charCodeAt(T++);return b(x)}function _(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),b(w)}catch(S){var x=a.navigator,I=x&&x.plugins;return[+new Date,a,I,a.screen,b(n)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=Y1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),H8=et((e,t)=>{var n=z8(),r=P8(),a=L8(),s=W8(),i=B8(),o=V8(),l=U8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),au=et(()=>{}),j8=et(()=>{}),G8=et(()=>{}),q8=et((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};function s(){return Q.buffer!=Be&&Jt(Q.buffer),yn}function i(){return Q.buffer!=Be&&Jt(Q.buffer),_t}function o(){return Q.buffer!=Be&&Jt(Q.buffer),gn}function l(){return Q.buffer!=Be&&Jt(Q.buffer),Hn}function u(){return Q.buffer!=Be&&Jt(Q.buffer),un}var c=typeof a!="undefined"?a:{},h,d;c.ready=new Promise(function(N,C){h=N,d=C});var p={},f;for(f in c)c.hasOwnProperty(f)&&(p[f]=c[f]);var m=[],A="./this.program",y=function(N,C){throw C},g=!1,_=!1,b=!1,w=!1;g=typeof window=="object",_=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!g&&!b&&!_;var x=c.ENVIRONMENT_IS_PTHREAD||!1;x&&(Be=c.buffer);var I="";function S(N){return c.locateFile?c.locateFile(N,I):I+N}var T,M,D,z,W,U;if(b){_?I=au().dirname(I)+"/":I=__dirname+"/",T=function(N,C){return W||(W=require("fs")),U||(U=au()),N=U.normalize(N),W.readFileSync(N,C?null:"utf8")},D=function(N){var C=T(N,!0);return C.buffer||(C=new Uint8Array(C)),pe(C.buffer),C},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(N){if(!(N instanceof ru))throw N}),process.on("unhandledRejection",Yr),y=function(N){process.exit(N)},c.inspect=function(){return"[Emscripten Module object]"};var H;try{H=j8()}catch(N){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),N}global.Worker=H.Worker}else w?(typeof read!="undefined"&&(T=function(N){return read(N)}),D=function(N){var C;return typeof readbuffer=="function"?new Uint8Array(readbuffer(N)):(C=read(N,"binary"),pe(typeof C=="object"),C)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(y=function(N){quit(N)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||_)&&(_?I=self.location.href:typeof document!="undefined"&&document.currentScript&&(I=document.currentScript.src),typeof r!="undefined"&&r&&(I=r),I.indexOf("blob:")!==0?I=I.substr(0,I.lastIndexOf("/")+1):I="",b?(T=function(N,C){return W||(W=require("fs")),U||(U=au()),N=U.normalize(N),W.readFileSync(N,C?null:"utf8")},D=function(N){var C=T(N,!0);return C.buffer||(C=new Uint8Array(C)),pe(C.buffer),C}):(T=function(N){var C=new XMLHttpRequest;return C.open("GET",N,!1),C.send(null),C.responseText},_&&(D=function(N){var C=new XMLHttpRequest;return C.open("GET",N,!1),C.responseType="arraybuffer",C.send(null),new Uint8Array(C.response)}),M=function(N,C,L){var q=new XMLHttpRequest;q.open("GET",N,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){C(q.response);return}L()},q.onerror=L,q.send(null)}),z=function(N){document.title=N});b&&typeof performance=="undefined"&&(global.performance=G8().performance);var X=c.print||console.log.bind(console),j=c.printErr||console.warn.bind(console);for(f in p)p.hasOwnProperty(f)&&(c[f]=p[f]);p=null,c.arguments&&(m=c.arguments),c.thisProgram&&(A=c.thisProgram),c.quit&&(y=c.quit);var ee=Atomics.load,Y=Atomics.store,ae=Atomics.compareExchange,te;c.wasmBinary&&(te=c.wasmBinary);var ie=c.noExitRuntime||!0;typeof WebAssembly!="object"&&Yr("no native wasm support detected");var Q,he,oe=!1,fe;function pe(N,C){N||Yr("Assertion failed: "+C)}function ke(N){var C=c["_"+N];return pe(C,"Cannot call unknown function "+N+", make sure it is exported"),C}function Ie(N,C,L,q,de){var le={string:function(_n){var Gi=0;if(_n!=null&&_n!==0){var Cg=(_n.length<<2)+1;Gi=Ui(Cg),nt(_n,Gi,Cg)}return Gi},array:function(_n){var Gi=Ui(_n.length);return Ze(_n,Gi),Gi}};function ue(_n){return C==="string"?$e(_n):C==="boolean"?Boolean(_n):_n}var we=ke(N),rt=[],Ht=0;if(q)for(var $t=0;$t<q.length;$t++){var ba=le[L[$t]];ba?(Ht===0&&(Ht=nu()),rt[$t]=ba(q[$t])):rt[$t]=q[$t]}var ji=we.apply(null,rt);return ji=ue(ji),Ht!==0&&Vi(Ht),ji}function Me(N,C,L,q){L=L||[];var de=L.every(function(ue){return ue==="number"}),le=C!=="string";return le&&de&&!q?ke(N):function(){return Ie(N,C,L,arguments,q)}}function Oe(N,C,L){for(var q=C+L,de="";!(C>=q);){var le=N[C++];if(!le)return de;if(!(le&128)){de+=String.fromCharCode(le);continue}var ue=N[C++]&63;if((le&224)==192){de+=String.fromCharCode((le&31)<<6|ue);continue}var we=N[C++]&63;if((le&240)==224?le=(le&15)<<12|ue<<6|we:le=(le&7)<<18|ue<<12|we<<6|N[C++]&63,le<65536)de+=String.fromCharCode(le);else{var rt=le-65536;de+=String.fromCharCode(55296|rt>>10,56320|rt&1023)}}return de}function $e(N,C){return N?Oe(i(),N,C):""}function tt(N,C,L,q){if(!(q>0))return 0;for(var de=L,le=L+q-1,ue=0;ue<N.length;++ue){var we=N.charCodeAt(ue);if(we>=55296&&we<=57343){var rt=N.charCodeAt(++ue);we=65536+((we&1023)<<10)|rt&1023}if(we<=127){if(L>=le)break;C[L++]=we}else if(we<=2047){if(L+1>=le)break;C[L++]=192|we>>6,C[L++]=128|we&63}else if(we<=65535){if(L+2>=le)break;C[L++]=224|we>>12,C[L++]=128|we>>6&63,C[L++]=128|we&63}else{if(L+3>=le)break;C[L++]=240|we>>18,C[L++]=128|we>>12&63,C[L++]=128|we>>6&63,C[L++]=128|we&63}}return C[L]=0,L-de}function nt(N,C,L){return tt(N,i(),C,L)}function ot(N){for(var C=0,L=0;L<N.length;++L){var q=N.charCodeAt(L);q>=55296&&q<=57343&&(q=65536+((q&1023)<<10)|N.charCodeAt(++L)&1023),q<=127?++C:q<=2047?C+=2:q<=65535?C+=3:C+=4}return C}function Ze(N,C){s().set(N,C)}function pt(N,C){return N%C>0&&(N+=C-N%C),N}var Be,yn,_t,Un,Yt,gn,Hn,Rn,un;function Jt(N){Be=N,c.HEAP8=yn=new Int8Array(N),c.HEAP16=Un=new Int16Array(N),c.HEAP32=gn=new Int32Array(N),c.HEAPU8=_t=new Uint8Array(N),c.HEAPU16=Yt=new Uint16Array(N),c.HEAPU32=Hn=new Uint32Array(N),c.HEAPF32=Rn=new Float32Array(N),c.HEAPF64=un=new Float64Array(N)}var Tr=c.INITIAL_MEMORY||16777216;if(x)Q=c.wasmMemory,Be=c.buffer;else if(c.wasmMemory)Q=c.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Tr/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw j("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(Be=Q.buffer),Tr=Be.byteLength,Jt(Be);var Jn,Qn=[],Aa=[],Kr=[],ya=[],Di=[],dr=!1,Gc=!1;x||Aa.push({func:function(){lh()}}),x&&(dr=!0);function D0(){if(!x){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)Kc(c.preRun.shift());zi(Qn)}}function qc(){dr=!0,zi(Aa)}function O0(){x||zi(Kr)}function Xc(){x||(Gc=!0)}function xn(){if(!x){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)z0(c.postRun.shift());zi(Di)}}function Kc(N){Qn.unshift(N)}function z0(N){Di.unshift(N)}var Zr=0,ga=null,ns=null;function P0(N){pe(!x,"addRunDependency cannot be used in a pthread worker"),Zr++,c.monitorRunDependencies&&c.monitorRunDependencies(Zr)}function L0(N){if(Zr--,c.monitorRunDependencies&&c.monitorRunDependencies(Zr),Zr==0&&(ga!==null&&(clearInterval(ga),ga=null),ns)){var C=ns;ns=null,C()}}c.preloadedImages={},c.preloadedAudios={};function Yr(N){c.onAbort&&c.onAbort(N),x&&console.error("Pthread aborting at "+new Error().stack),N+="",j(N),oe=!0,fe=1,N="abort("+N+"). Build with -s ASSERTIONS=1 for more info.";var C=new WebAssembly.RuntimeError(N);throw d(C),C}function Zc(N,C){return String.prototype.startsWith?N.startsWith(C):N.indexOf(C)===0}var Oi="data:application/octet-stream;base64,";function Yc(N){return Zc(N,Oi)}var W0="file://";function Jc(N){return Zc(N,W0)}var wn="tfjs-backend-wasm-threaded-simd.wasm";Yc(wn)||(wn=S(wn));function B0(N){try{if(N==wn&&te)return new Uint8Array(te);if(D)return D(N);throw"both async and sync fetching of the wasm failed"}catch(C){Yr(C)}}function Qc(){if(!te&&(g||_)){if(typeof fetch=="function"&&!Jc(wn))return fetch(wn,{credentials:"same-origin"}).then(function(N){if(!N.ok)throw"failed to load wasm binary file at '"+wn+"'";return N.arrayBuffer()}).catch(function(){return B0(wn)});if(M)return new Promise(function(N,C){M(wn,function(L){N(new Uint8Array(L))},C)})}return Promise.resolve().then(function(){return B0(wn)})}function V0(){var N={a:F1};function C(ue,we){var rt=ue.exports;if(c.asm=rt,Jn=c.asm.F,he=we,!x){var Ht=Ne.unusedWorkers.length;Ne.unusedWorkers.forEach(function($t){Ne.loadWasmModuleToWorker($t,function(){--Ht||L0("wasm-instantiate")})})}}x||P0("wasm-instantiate");function L(ue){C(ue.instance,ue.module)}function q(ue){return Qc().then(function(we){return WebAssembly.instantiate(we,N)}).then(ue,function(we){j("failed to asynchronously prepare wasm: "+we),Yr(we)})}function de(){return!te&&typeof WebAssembly.instantiateStreaming=="function"&&!Yc(wn)&&!Jc(wn)&&typeof fetch=="function"?fetch(wn,{credentials:"same-origin"}).then(function(ue){var we=WebAssembly.instantiateStreaming(ue,N);return we.then(L,function(rt){return j("wasm streaming compile failed: "+rt),j("falling back to ArrayBuffer instantiation"),q(L)})}):q(L)}if(c.instantiateWasm)try{var le=c.instantiateWasm(N,C);return le}catch(ue){return j("Module.instantiateWasm callback failed with error: "+ue),!1}return de().catch(d),{}}var eh={8991:function(N,C){setTimeout(function(){vg(N,C)},0)}};function U0(){Ne.initRuntime()}function zi(N){for(;N.length>0;){var C=N.shift();if(typeof C=="function"){C(c);continue}var L=C.func;typeof L=="number"?C.arg===void 0?Jn.get(L)():Jn.get(L)(C.arg):L(C.arg===void 0?null:C.arg)}}function Pi(N,C){if(N<=0||N>s().length||N&!0||C<0)return-28;if(C==0)return 0;C>=2147483647&&(C=Infinity);var L=Atomics.load(o(),Hi>>2),q=0;if(L==N){var de=Atomics.compareExchange(o(),Hi>>2,L,0);if(de==L&&(--C,q=1,C<=0))return 1}var le=Atomics.notify(o(),N>>2,C);if(le>=0)return le+q;throw"Atomics.notify returned an unexpected value "+le}c._emscripten_futex_wake=Pi;function H0(N){if(x)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in killThread!";o()[N+12>>2]=0;var C=Ne.pthreads[N];C.worker.terminate(),Ne.freeThreadData(C),Ne.runningWorkers.splice(Ne.runningWorkers.indexOf(C.worker),1),C.worker.pthread=void 0}function j0(N){if(x)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in cancelThread!";var C=Ne.pthreads[N];C.worker.postMessage({cmd:"cancel"})}function G0(N){if(x)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!N)throw"Internal Error! Null pthread_ptr in cleanupThread!";o()[N+12>>2]=0;var C=Ne.pthreads[N];if(C){var L=C.worker;Ne.returnWorkerToPool(L)}}var Ne={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var N=8,C=0;C<N;++C)Ne.allocateUnusedWorker()},initRuntime:function(){for(var N=as(228),C=0;C<228/4;++C)l()[N/4+C]=0;o()[N+12>>2]=N;var L=N+152;o()[L>>2]=L;for(var q=as(512),C=0;C<128;++C)l()[q/4+C]=0;Atomics.store(l(),N+100>>2,q),Atomics.store(l(),N+40>>2,N),ph(N,!_,1),bg(N)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ne.threadExitHandlers.length>0;)Ne.threadExitHandlers.pop()();x&&Bi()&&_g()},threadExit:function(N){var C=Bi();C&&(Atomics.store(l(),C+4>>2,N),Atomics.store(l(),C+0>>2,1),Atomics.store(l(),C+56>>2,1),Atomics.store(l(),C+60>>2,0),Ne.runExitHandlers(),Pi(C+0,2147483647),ph(0,0,0),x&&postMessage({cmd:"exit"}))},threadCancel:function(){Ne.runExitHandlers();var N=Bi();Atomics.store(l(),N+4>>2,-1),Atomics.store(l(),N+0>>2,1),Pi(N+0,2147483647),ph(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var N in Ne.pthreads){var C=Ne.pthreads[N];C&&C.worker&&Ne.returnWorkerToPool(C.worker)}Ne.pthreads={};for(var L=0;L<Ne.unusedWorkers.length;++L){var q=Ne.unusedWorkers[L];q.terminate()}Ne.unusedWorkers=[];for(var L=0;L<Ne.runningWorkers.length;++L){var q=Ne.runningWorkers[L],C=q.pthread;Ne.freeThreadData(C),q.terminate()}Ne.runningWorkers=[]},freeThreadData:function(N){if(N){if(N.threadInfoStruct){var C=o()[N.threadInfoStruct+100>>2];o()[N.threadInfoStruct+100>>2]=0,tu(C),tu(N.threadInfoStruct)}N.threadInfoStruct=0,N.allocatedOwnStack&&N.stackBase&&tu(N.stackBase),N.stackBase=0,N.worker&&(N.worker.pthread=null)}},returnWorkerToPool:function(N){Ne.runWithoutMainThreadQueuedCalls(function(){delete Ne.pthreads[N.pthread.threadInfoStruct],Ne.unusedWorkers.push(N),Ne.runningWorkers.splice(Ne.runningWorkers.indexOf(N),1),Ne.freeThreadData(N.pthread),N.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(N){o()[Tg>>2]=0;try{N()}finally{o()[Tg>>2]=1}},receiveObjectTransfer:function(N){},loadWasmModuleToWorker:function(N,C){N.onmessage=function(L){var q=L.data,de=q.cmd;if(N.pthread&&(Ne.currentProxiedOperationCallerThread=N.pthread.threadInfoStruct),q.targetThread&&q.targetThread!=Bi()){var le=Ne.pthreads[q.targetThread];le?le.worker.postMessage(L.data,q.transferList):console.error('Internal error! Worker sent a message "'+de+'" to target pthread '+q.targetThread+", but that thread no longer exists!"),Ne.currentProxiedOperationCallerThread=void 0;return}if(de==="processQueuedMainThreadWork")q1();else if(de==="spawnThread")ih(L.data);else if(de==="cleanupThread")G0(q.thread);else if(de==="killThread")H0(q.thread);else if(de==="cancelThread")j0(q.thread);else if(de==="loaded")N.loaded=!0,C&&C(N),N.runPthread&&(N.runPthread(),delete N.runPthread);else if(de==="print")X("Thread "+q.threadId+": "+q.text);else if(de==="printErr")j("Thread "+q.threadId+": "+q.text);else if(de==="alert")alert("Thread "+q.threadId+": "+q.text);else if(de==="exit"){var ue=N.pthread&&Atomics.load(l(),N.pthread.threadInfoStruct+64>>2);ue&&Ne.returnWorkerToPool(N)}else if(de==="exitProcess")try{f8(q.returnCode)}catch(we){if(we instanceof ru)return;throw we}else de==="cancelDone"?Ne.returnWorkerToPool(N):de==="objectTransfer"?Ne.receiveObjectTransfer(L.data):L.data.target==="setimmediate"?N.postMessage(L.data):j("worker sent an unknown command "+de);Ne.currentProxiedOperationCallerThread=void 0},N.onerror=function(L){j("pthread sent an error! "+L.filename+":"+L.lineno+": "+L.message)},b&&(N.on("message",function(L){N.onmessage({data:L})}),N.on("error",function(L){N.onerror(L)}),N.on("exit",function(L){})),N.postMessage({cmd:"load",urlOrBlob:c.mainScriptUrlOrBlob||r,wasmMemory:Q,wasmModule:he})},allocateUnusedWorker:function(){var N=S("tfjs-backend-wasm-threaded-simd.worker.js");Ne.unusedWorkers.push(new Worker(N))},getNewWorker:function(){return Ne.unusedWorkers.length==0&&(Ne.allocateUnusedWorker(),Ne.loadWasmModuleToWorker(Ne.unusedWorkers[0])),Ne.unusedWorkers.length>0?Ne.unusedWorkers.pop():null},busySpinWait:function(N){for(var C=performance.now()+N;performance.now()<C;);}};function q0(N,C){Ig(N,C),Vi(N)}c.establishStackSpace=q0;function X0(){return ie}c.getNoExitRuntime=X0;function K0(N,C){return Jn.get(N)(C)}c.invokeEntryPoint=K0;function Z0(N,C,L,q){Yr("Assertion failed: "+$e(N)+", at: "+[C?$e(C):"unknown filename",L,q?$e(q):"unknown function"])}function Y0(N,C){var L=_main(N,C)}var rs;b?rs=function(){var N=process.hrtime();return N[0]*1e3+N[1]/1e6}:x?rs=function(){return performance.now()-c.__performance_now_clock_drift}:typeof dateNow!="undefined"?rs=dateNow:rs=function(){return performance.now()};function J0(N){return o()[xg()>>2]=N,N}function Q0(N,C){if(x)return xa(1,1,N,C)}function e1(N,C){if(N==C)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:N,cmd:"processThreadQueue"});else{var L=Ne.pthreads[N],q=L&&L.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function t1(){Yr()}function n1(N,C,L){var q=o1(C,L);return eh[N].apply(null,q)}function r1(N,C){}function a1(N,C,L){if(N<=0||N>s().length||N&!0)return-28;if(g){if(Atomics.load(o(),N>>2)!=C)return-6;for(var q=performance.now(),de=q+L,le=Atomics.exchange(o(),Hi>>2,N);;){if(q=performance.now(),q>de)return le=Atomics.exchange(o(),Hi>>2,0),-73;if(le=Atomics.exchange(o(),Hi>>2,0),le==0)break;if(q1(),Atomics.load(o(),N>>2)!=C)return-6;le=Atomics.exchange(o(),Hi>>2,N)}return 0}else{var ue=Atomics.wait(o(),N>>2,C,L);if(ue==="timed-out")return-73;if(ue==="not-equal")return-6;if(ue==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ue}}function s1(N,C,L){i().copyWithin(N,C,C+L)}function i1(){return b?require("os").cpus().length:navigator.hardwareConcurrency}function xa(N,C){for(var L=arguments.length-2,q=nu(),de=L,le=Ui(de*8),ue=le>>3,we=0;we<L;we++){var rt=arguments[2+we];u()[ue+we]=rt}var Ht=Ng(N,de,le,C);return Vi(q),Ht}var Kl=[],Zl=[];function o1(N,C){Zl.length=0;var L;for(C>>=2;L=i()[N++];){var q=L<105;q&&C&1&&C++,Zl.push(q?u()[C++>>1]:o()[C]),++C}return Zl}function l1(N,C,L){Kl.length=C;for(var q=L>>3,de=0;de<C;de++)Kl[de]=u()[q+de];var le=N<0,ue=le?eh[-N-1]:R1[N];return ue.apply(null,Kl)}function u1(){return i().length}function c1(N){try{return Q.grow(N-Be.byteLength+65535>>>16),Jt(Q.buffer),1}catch(C){}}function h1(N){var C=u1();if(N<=C)return!1;var L=2147483648;if(N>L)return!1;for(var q=1;q<=4;q*=2){var de=C*(1+.2/q);de=Math.min(de,N+100663296);var le=Math.min(L,pt(Math.max(N,de),65536)),ue=c1(le);if(ue)return!0}return!1}var Le={inEventHandler:0,removeAllEventListeners:function(){for(var N=Le.eventHandlers.length-1;N>=0;--N)Le._removeHandler(N);Le.eventHandlers=[],Le.deferredCalls=[]},registerRemoveEventListeners:function(){Le.removeEventListenersRegistered||(ya.push(Le.removeAllEventListeners),Le.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(N,C,L){function q(ue,we){if(ue.length!=we.length)return!1;for(var rt in ue)if(ue[rt]!=we[rt])return!1;return!0}for(var de in Le.deferredCalls){var le=Le.deferredCalls[de];if(le.targetFunction==N&&q(le.argsList,L))return}Le.deferredCalls.push({targetFunction:N,precedence:C,argsList:L}),Le.deferredCalls.sort(function(ue,we){return ue.precedence<we.precedence})},removeDeferredCalls:function(N){for(var C=0;C<Le.deferredCalls.length;++C)Le.deferredCalls[C].targetFunction==N&&(Le.deferredCalls.splice(C,1),--C)},canPerformEventHandlerRequests:function(){return Le.inEventHandler&&Le.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Le.canPerformEventHandlerRequests())for(var N=0;N<Le.deferredCalls.length;++N){var C=Le.deferredCalls[N];Le.deferredCalls.splice(N,1),--N,C.targetFunction.apply(null,C.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(N,C){for(var L=0;L<Le.eventHandlers.length;++L)Le.eventHandlers[L].target==N&&(!C||C==Le.eventHandlers[L].eventTypeString)&&Le._removeHandler(L--)},_removeHandler:function(N){var C=Le.eventHandlers[N];C.target.removeEventListener(C.eventTypeString,C.eventListenerFunc,C.useCapture),Le.eventHandlers.splice(N,1)},registerOrRemoveHandler:function(N){var C=function(q){++Le.inEventHandler,Le.currentEventHandler=N,Le.runDeferredCalls(),N.handlerFunc(q),Le.runDeferredCalls(),--Le.inEventHandler};if(N.callbackfunc)N.eventListenerFunc=C,N.target.addEventListener(N.eventTypeString,C,N.useCapture),Le.eventHandlers.push(N),Le.registerRemoveEventListeners();else for(var L=0;L<Le.eventHandlers.length;++L)Le.eventHandlers[L].target==N.target&&Le.eventHandlers[L].eventTypeString==N.eventTypeString&&Le._removeHandler(L--)},queueEventHandlerOnThread_iiii:function(N,C,L,q,de){var le=nu(),ue=Ui(12);o()[ue>>2]=L,o()[ue+4>>2]=q,o()[ue+8>>2]=de,X1(0,N,637534208,C,q,ue),Vi(le)},getTargetThreadForEventCallback:function(N){switch(N){case 1:return 0;case 2:return Ne.currentProxiedOperationCallerThread;default:return N}},getNodeNameForTarget:function(N){return N?N==window?"#window":N==screen?"#screen":N&&N.nodeName?N.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function d1(N){var C=ot(N)+1,L=as(C);return nt(N,L,C),L}function p1(N,C,L,q){var de=nu(),le=Ui(12),ue=0;C&&(ue=d1(C)),o()[le>>2]=ue,o()[le+4>>2]=L,o()[le+8>>2]=q,X1(0,N,657457152,0,ue,le),Vi(de)}function f1(N,C,L,q){C=C?$e(C):"",p1(N,C,L,q)}function m1(N){return N>2?$e(N):N}var A1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function y1(N){N=m1(N);var C=A1[N]||(typeof document!="undefined"?document.querySelector(N):void 0);return C}function Yl(N){return y1(N)}function th(N,C,L){var q=Yl(N);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=C,o()[q.canvasSharedPtr+4>>2]=L),q.offscreenCanvas||!q.controlTransferredOffscreen){q.offscreenCanvas&&(q=q.offscreenCanvas);var de=!1;if(q.GLctxObject&&q.GLctxObject.GLctx){var le=q.GLctxObject.GLctx.getParameter(2978);de=le[0]===0&&le[1]===0&&le[2]===q.width&&le[3]===q.height}q.width=C,q.height=L,de&&q.GLctxObject.GLctx.viewport(0,0,C,L)}else if(q.canvasSharedPtr){var ue=o()[q.canvasSharedPtr+8>>2];return f1(ue,N,C,L),1}else return-4;return 0}function nh(N,C,L){return x?xa(2,1,N,C,L):th(N,C,L)}function g1(N,C,L){var q=Yl(N);return q?th(N,C,L):nh(N,C,L)}function x1(N){}function w1(N,C){}function _1(N){var C=N.getExtension("ANGLE_instanced_arrays");if(C)return N.vertexAttribDivisor=function(L,q){C.vertexAttribDivisorANGLE(L,q)},N.drawArraysInstanced=function(L,q,de,le){C.drawArraysInstancedANGLE(L,q,de,le)},N.drawElementsInstanced=function(L,q,de,le,ue){C.drawElementsInstancedANGLE(L,q,de,le,ue)},1}function b1(N){var C=N.getExtension("OES_vertex_array_object");if(C)return N.createVertexArray=function(){return C.createVertexArrayOES()},N.deleteVertexArray=function(L){C.deleteVertexArrayOES(L)},N.bindVertexArray=function(L){C.bindVertexArrayOES(L)},N.isVertexArray=function(L){return C.isVertexArrayOES(L)},1}function v1(N){var C=N.getExtension("WEBGL_draw_buffers");if(C)return N.drawBuffers=function(L,q){C.drawBuffersWEBGL(L,q)},1}function k1(N){return!!(N.multiDrawWebgl=N.getExtension("WEBGL_multi_draw"))}var Qe={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(N){Qe.lastError||(Qe.lastError=N)},getNewId:function(N){for(var C=Qe.counter++,L=N.length;L<C;L++)N[L]=null;return C},getSource:function(N,C,L,q){for(var de="",le=0;le<C;++le){var ue=q?o()[q+le*4>>2]:-1;de+=$e(o()[L+le*4>>2],ue<0?void 0:ue)}return de},createContext:function(N,C){var L=N.getContext("webgl",C);if(!L)return 0;var q=Qe.registerContext(L,C);return q},registerContext:function(N,C){var L=as(8);o()[L+4>>2]=Bi();var q={handle:L,attributes:C,version:C.majorVersion,GLctx:N};return N.canvas&&(N.canvas.GLctxObject=q),Qe.contexts[L]=q,(typeof C.enableExtensionsByDefault=="undefined"||C.enableExtensionsByDefault)&&Qe.initExtensions(q),L},makeContextCurrent:function(N){return Qe.currentContext=Qe.contexts[N],c.ctx=wa=Qe.currentContext&&Qe.currentContext.GLctx,!(N&&!wa)},getContext:function(N){return Qe.contexts[N]},deleteContext:function(N){Qe.currentContext===Qe.contexts[N]&&(Qe.currentContext=null),typeof Le=="object"&&Le.removeAllHandlersOnTarget(Qe.contexts[N].GLctx.canvas),Qe.contexts[N]&&Qe.contexts[N].GLctx.canvas&&(Qe.contexts[N].GLctx.canvas.GLctxObject=void 0),tu(Qe.contexts[N].handle),Qe.contexts[N]=null},initExtensions:function(N){if(N||(N=Qe.currentContext),!N.initExtensionsDone){N.initExtensionsDone=!0;var C=N.GLctx;_1(C),b1(C),v1(C),C.disjointTimerQueryExt=C.getExtension("EXT_disjoint_timer_query"),k1(C);var L=C.getSupportedExtensions()||[];L.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&C.getExtension(q)})}},populateUniformTable:function(N){for(var C=Qe.programs[N],L=Qe.programInfos[N]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=L.uniforms,de=wa.getProgramParameter(C,35718),le=0;le<de;++le){var ue=wa.getActiveUniform(C,le),we=ue.name;L.maxUniformLength=Math.max(L.maxUniformLength,we.length+1),we.slice(-1)=="]"&&(we=we.slice(0,we.lastIndexOf("[")));var rt=wa.getUniformLocation(C,we);if(rt){var Ht=Qe.getNewId(Qe.uniforms);q[we]=[ue.size,Ht],Qe.uniforms[Ht]=rt;for(var $t=1;$t<ue.size;++$t){var ba=we+"["+$t+"]";rt=wa.getUniformLocation(C,ba),Ht=Qe.getNewId(Qe.uniforms),Qe.uniforms[Ht]=rt}}}}},N1=["default","low-power","high-performance"];function I1(N,C){var L=C>>2,q=o()[L+(24>>2)],de={alpha:!!o()[L+(0>>2)],depth:!!o()[L+(4>>2)],stencil:!!o()[L+(8>>2)],antialias:!!o()[L+(12>>2)],premultipliedAlpha:!!o()[L+(16>>2)],preserveDrawingBuffer:!!o()[L+(20>>2)],powerPreference:N1[q],failIfMajorPerformanceCaveat:!!o()[L+(28>>2)],majorVersion:o()[L+(32>>2)],minorVersion:o()[L+(36>>2)],enableExtensionsByDefault:o()[L+(40>>2)],explicitSwapControl:o()[L+(44>>2)],proxyContextToMainThread:o()[L+(48>>2)],renderViaOffscreenBackBuffer:o()[L+(52>>2)]},le=Yl(N);if(!le||de.explicitSwapControl)return 0;var ue=Qe.createContext(le,de);return ue}function S1(N,C){return I1(N,C)}var Li={mappings:{},buffers:[null,[],[]],printChar:function(N,C){var L=Li.buffers[N];C===0||C===10?((N===1?X:j)(Oe(L,0)),L.length=0):L.push(C)},varargs:void 0,get:function(){Li.varargs+=4;var N=o()[Li.varargs-4>>2];return N},getStr:function(N){var C=$e(N);return C},get64:function(N,C){return N}};function rh(N){return x?xa(3,1,N):0}function ah(N,C,L,q,de){if(x)return xa(4,1,N,C,L,q,de)}function sh(N,C,L,q){if(x)return xa(5,1,N,C,L,q);for(var de=0,le=0;le<L;le++){for(var ue=o()[C+le*8>>2],we=o()[C+(le*8+4)>>2],rt=0;rt<we;rt++)Li.printChar(N,i()[ue+rt]);de+=we}return o()[q>>2]=de,0}function T1(N){var C=Ne.threadExitHandlers.pop();N&&C()}function C1(N,C){Ne.threadExitHandlers.push(function(){Jn.get(N)(C)})}function ih(N){if(x)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var C=Ne.getNewWorker();if(C.pthread!==void 0)throw"Internal error!";if(!N.pthread_ptr)throw"Internal error, no pthread ptr!";Ne.runningWorkers.push(C);for(var L=as(128*4),q=0;q<128;++q)o()[L+q*4>>2]=0;var de=N.stackBase+N.stackSize,le=Ne.pthreads[N.pthread_ptr]={worker:C,stackBase:N.stackBase,stackSize:N.stackSize,allocatedOwnStack:N.allocatedOwnStack,threadInfoStruct:N.pthread_ptr},ue=le.threadInfoStruct>>2;Atomics.store(l(),ue+(64>>2),N.detached),Atomics.store(l(),ue+(100>>2),L),Atomics.store(l(),ue+(40>>2),le.threadInfoStruct),Atomics.store(l(),ue+(80>>2),N.stackSize),Atomics.store(l(),ue+(76>>2),de),Atomics.store(l(),ue+(104>>2),N.stackSize),Atomics.store(l(),ue+(104+8>>2),de),Atomics.store(l(),ue+(104+12>>2),N.detached);var we=wg(),rt=we+40;Atomics.store(l(),ue+(172>>2),rt),C.pthread=le;var Ht={cmd:"run",start_routine:N.startRoutine,arg:N.arg,threadInfoStruct:N.pthread_ptr,stackBase:N.stackBase,stackSize:N.stackSize};C.runPthread=function(){Ht.time=performance.now(),C.postMessage(Ht,N.transferList)},C.loaded&&(C.runPthread(),delete C.runPthread)}function E1(N,C,L,q){if(typeof SharedArrayBuffer=="undefined")return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!N)return j("pthread_create called with a null thread pointer!"),28;var de=[],le=0;if(x&&(de.length===0||le))return kg(687865856,N,C,L,q);if(le)return le;var ue=0,we=0,rt=0;C&&C!=-1?(ue=o()[C>>2],ue+=81920,we=o()[C+8>>2],rt=o()[C+12>>2]!==0):ue=2097152;var Ht=we==0;Ht?we=Sg(16,ue):(we-=ue,pe(we>0));for(var $t=as(228),ba=0;ba<228>>2;++ba)l()[($t>>2)+ba]=0;o()[N>>2]=$t,o()[$t+12>>2]=$t;var ji=$t+152;o()[ji>>2]=ji;var _n={stackBase:we,stackSize:ue,allocatedOwnStack:Ht,detached:rt,startRoutine:L,pthread_ptr:$t,arg:q,transferList:de};return x?(_n.cmd="spawnThread",postMessage(_n,de)):ih(_n),0}function oh(N){if(x)return xa(6,1,N);switch(N){case 30:return 16384;case 85:var C=2147483648;return C/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return J0(28),-1}x||Ne.initMainThreadBlock();var wa,R1=[null,Q0,nh,rh,ah,sh,oh],F1={e:Z0,r:Y0,x:e1,b:t1,y:n1,j:r1,c:a1,d:Pi,f:rs,p:s1,z:i1,u:l1,q:h1,v:g1,i:x1,t:w1,w:S1,m:rh,n:ah,g:sh,o:U0,a:Q||c.wasmMemory,k:T1,l:C1,h:E1,s:oh},gg=V0(),lh=c.___wasm_call_ctors=function(){return(lh=c.___wasm_call_ctors=c.asm.A).apply(null,arguments)},M1=c._init=function(){return(M1=c._init=c.asm.B).apply(null,arguments)},$1=c._register_tensor=function(){return($1=c._register_tensor=c.asm.C).apply(null,arguments)},D1=c._dispose_data=function(){return(D1=c._dispose_data=c.asm.D).apply(null,arguments)},O1=c._dispose=function(){return(O1=c._dispose=c.asm.E).apply(null,arguments)},z1=c._Abs=function(){return(z1=c._Abs=c.asm.G).apply(null,arguments)},P1=c._Add=function(){return(P1=c._Add=c.asm.H).apply(null,arguments)},L1=c._AddN=function(){return(L1=c._AddN=c.asm.I).apply(null,arguments)},W1=c._ArgMax=function(){return(W1=c._ArgMax=c.asm.J).apply(null,arguments)},B1=c._AvgPool=function(){return(B1=c._AvgPool=c.asm.K).apply(null,arguments)},V1=c._BatchMatMul=function(){return(V1=c._BatchMatMul=c.asm.L).apply(null,arguments)},U1=c._Ceil=function(){return(U1=c._Ceil=c.asm.M).apply(null,arguments)},H1=c._ClipByValue=function(){return(H1=c._ClipByValue=c.asm.N).apply(null,arguments)},j1=c._Conv2D=function(){return(j1=c._Conv2D=c.asm.O).apply(null,arguments)},uh=c._Conv2DBackpropInput=function(){return(uh=c._Conv2DBackpropInput=c.asm.P).apply(null,arguments)},ch=c._Cos=function(){return(ch=c._Cos=c.asm.Q).apply(null,arguments)},Jl=c._CropAndResize=function(){return(Jl=c._CropAndResize=c.asm.R).apply(null,arguments)},Wi=c._Cumsum=function(){return(Wi=c._Cumsum=c.asm.S).apply(null,arguments)},G1=c._DepthToSpace=function(){return(G1=c._DepthToSpace=c.asm.T).apply(null,arguments)},Ql=c._DepthwiseConv2dNative=function(){return(Ql=c._DepthwiseConv2dNative=c.asm.U).apply(null,arguments)},K=c._Equal=function(){return(K=c._Equal=c.asm.V).apply(null,arguments)},ne=c._Exp=function(){return(ne=c._Exp=c.asm.W).apply(null,arguments)},Se=c._FlipLeftRight=function(){return(Se=c._FlipLeftRight=c.asm.X).apply(null,arguments)},Ye=c._Floor=function(){return(Ye=c._Floor=c.asm.Y).apply(null,arguments)},It=c._FloorDiv=function(){return(It=c._FloorDiv=c.asm.Z).apply(null,arguments)},At=c._FusedBatchNorm=function(){return(At=c._FusedBatchNorm=c.asm._).apply(null,arguments)},Ve=c._FusedConv2D=function(){return(Ve=c._FusedConv2D=c.asm.$).apply(null,arguments)},je=c._FusedDepthwiseConv2D=function(){return(je=c._FusedDepthwiseConv2D=c.asm.aa).apply(null,arguments)},Qt=c._Gather=function(){return(Qt=c._Gather=c.asm.ba).apply(null,arguments)},Jr=c._GatherNd=function(){return(Jr=c._GatherNd=c.asm.ca).apply(null,arguments)},Qr=c._Greater=function(){return(Qr=c._Greater=c.asm.da).apply(null,arguments)},hh=c._GreaterEqual=function(){return(hh=c._GreaterEqual=c.asm.ea).apply(null,arguments)},eu=c._LeakyRelu=function(){return(eu=c._LeakyRelu=c.asm.fa).apply(null,arguments)},jn=c._Less=function(){return(jn=c._Less=c.asm.ga).apply(null,arguments)},_a=c._LessEqual=function(){return(_a=c._LessEqual=c.asm.ha).apply(null,arguments)},dh=c._Log=function(){return(dh=c._Log=c.asm.ia).apply(null,arguments)},v4=c._LogicalAnd=function(){return(v4=c._LogicalAnd=c.asm.ja).apply(null,arguments)},k4=c._Max=function(){return(k4=c._Max=c.asm.ka).apply(null,arguments)},N4=c._MaxPool=function(){return(N4=c._MaxPool=c.asm.la).apply(null,arguments)},I4=c._Maximum=function(){return(I4=c._Maximum=c.asm.ma).apply(null,arguments)},S4=c._Mean=function(){return(S4=c._Mean=c.asm.na).apply(null,arguments)},T4=c._Min=function(){return(T4=c._Min=c.asm.oa).apply(null,arguments)},C4=c._Minimum=function(){return(C4=c._Minimum=c.asm.pa).apply(null,arguments)},E4=c._Multiply=function(){return(E4=c._Multiply=c.asm.qa).apply(null,arguments)},R4=c._Neg=function(){return(R4=c._Neg=c.asm.ra).apply(null,arguments)},F4=c._NonMaxSuppressionV3=function(){return(F4=c._NonMaxSuppressionV3=c.asm.sa).apply(null,arguments)},M4=c._NonMaxSuppressionV4=function(){return(M4=c._NonMaxSuppressionV4=c.asm.ta).apply(null,arguments)},$4=c._NonMaxSuppressionV5=function(){return($4=c._NonMaxSuppressionV5=c.asm.ua).apply(null,arguments)},D4=c._NotEqual=function(){return(D4=c._NotEqual=c.asm.va).apply(null,arguments)},O4=c._OneHot=function(){return(O4=c._OneHot=c.asm.wa).apply(null,arguments)},z4=c._PadV2=function(){return(z4=c._PadV2=c.asm.xa).apply(null,arguments)},P4=c._Pow=function(){return(P4=c._Pow=c.asm.ya).apply(null,arguments)},L4=c._Prelu=function(){return(L4=c._Prelu=c.asm.za).apply(null,arguments)},W4=c._Prod=function(){return(W4=c._Prod=c.asm.Aa).apply(null,arguments)},B4=c._RealDiv=function(){return(B4=c._RealDiv=c.asm.Ba).apply(null,arguments)},V4=c._Relu=function(){return(V4=c._Relu=c.asm.Ca).apply(null,arguments)},U4=c._Relu6=function(){return(U4=c._Relu6=c.asm.Da).apply(null,arguments)},H4=c._ResizeBilinear=function(){return(H4=c._ResizeBilinear=c.asm.Ea).apply(null,arguments)},j4=c._Reverse=function(){return(j4=c._Reverse=c.asm.Fa).apply(null,arguments)},G4=c._RotateWithOffset=function(){return(G4=c._RotateWithOffset=c.asm.Ga).apply(null,arguments)},q4=c._Round=function(){return(q4=c._Round=c.asm.Ha).apply(null,arguments)},X4=c._Rsqrt=function(){return(X4=c._Rsqrt=c.asm.Ia).apply(null,arguments)},K4=c._ScatterNd=function(){return(K4=c._ScatterNd=c.asm.Ja).apply(null,arguments)},Z4=c._SelectV2=function(){return(Z4=c._SelectV2=c.asm.Ka).apply(null,arguments)},Y4=c._Sigmoid=function(){return(Y4=c._Sigmoid=c.asm.La).apply(null,arguments)},J4=c._Sin=function(){return(J4=c._Sin=c.asm.Ma).apply(null,arguments)},Q4=c._Softmax=function(){return(Q4=c._Softmax=c.asm.Na).apply(null,arguments)},e8=c._Sqrt=function(){return(e8=c._Sqrt=c.asm.Oa).apply(null,arguments)},t8=c._Square=function(){return(t8=c._Square=c.asm.Pa).apply(null,arguments)},n8=c._SquaredDifference=function(){return(n8=c._SquaredDifference=c.asm.Qa).apply(null,arguments)},r8=c._Step=function(){return(r8=c._Step=c.asm.Ra).apply(null,arguments)},a8=c._StridedSlice=function(){return(a8=c._StridedSlice=c.asm.Sa).apply(null,arguments)},s8=c._Sub=function(){return(s8=c._Sub=c.asm.Ta).apply(null,arguments)},i8=c._Sum=function(){return(i8=c._Sum=c.asm.Ua).apply(null,arguments)},o8=c._Tanh=function(){return(o8=c._Tanh=c.asm.Va).apply(null,arguments)},l8=c._Tile=function(){return(l8=c._Tile=c.asm.Wa).apply(null,arguments)},u8=c._TopK=function(){return(u8=c._TopK=c.asm.Xa).apply(null,arguments)},c8=c._Transpose=function(){return(c8=c._Transpose=c.asm.Ya).apply(null,arguments)},h8=c.__FusedMatMul=function(){return(h8=c.__FusedMatMul=c.asm.Za).apply(null,arguments)},as=c._malloc=function(){return(as=c._malloc=c.asm._a).apply(null,arguments)},tu=c._free=function(){return(tu=c._free=c.asm.$a).apply(null,arguments)},xg=c.___errno_location=function(){return(xg=c.___errno_location=c.asm.ab).apply(null,arguments)},wg=c._emscripten_get_global_libc=function(){return(wg=c._emscripten_get_global_libc=c.asm.bb).apply(null,arguments)},Bi=c._pthread_self=function(){return(Bi=c._pthread_self=c.asm.cb).apply(null,arguments)},_g=c.___pthread_tsd_run_dtors=function(){return(_g=c.___pthread_tsd_run_dtors=c.asm.db).apply(null,arguments)},q1=c._emscripten_main_thread_process_queued_calls=function(){return(q1=c._emscripten_main_thread_process_queued_calls=c.asm.eb).apply(null,arguments)},d8=c._emscripten_current_thread_process_queued_calls=function(){return(d8=c._emscripten_current_thread_process_queued_calls=c.asm.fb).apply(null,arguments)},bg=c._emscripten_register_main_browser_thread_id=function(){return(bg=c._emscripten_register_main_browser_thread_id=c.asm.gb).apply(null,arguments)},vg=c.__emscripten_do_dispatch_to_thread=function(){return(vg=c.__emscripten_do_dispatch_to_thread=c.asm.hb).apply(null,arguments)},kg=c._emscripten_sync_run_in_main_thread_4=function(){return(kg=c._emscripten_sync_run_in_main_thread_4=c.asm.ib).apply(null,arguments)},Ng=c._emscripten_run_in_main_runtime_thread_js=function(){return(Ng=c._emscripten_run_in_main_runtime_thread_js=c.asm.jb).apply(null,arguments)},X1=c.__emscripten_call_on_thread=function(){return(X1=c.__emscripten_call_on_thread=c.asm.kb).apply(null,arguments)},p8=c._emscripten_tls_init=function(){return(p8=c._emscripten_tls_init=c.asm.lb).apply(null,arguments)},ph=c.__emscripten_thread_init=function(){return(ph=c.__emscripten_thread_init=c.asm.mb).apply(null,arguments)},nu=c.stackSave=function(){return(nu=c.stackSave=c.asm.nb).apply(null,arguments)},Vi=c.stackRestore=function(){return(Vi=c.stackRestore=c.asm.ob).apply(null,arguments)},Ui=c.stackAlloc=function(){return(Ui=c.stackAlloc=c.asm.pb).apply(null,arguments)},Ig=c._emscripten_stack_set_limits=function(){return(Ig=c._emscripten_stack_set_limits=c.asm.qb).apply(null,arguments)},Sg=c._memalign=function(){return(Sg=c._memalign=c.asm.rb).apply(null,arguments)},Tg=c.__emscripten_allow_main_runtime_queued_calls=9880,Hi=c.__emscripten_main_thread_futex=11368;c.cwrap=Me,c.PThread=Ne,c.PThread=Ne,c.wasmMemory=Q,c.ExitStatus=ru;var fh;function ru(N){this.name="ExitStatus",this.message="Program terminated with exit("+N+")",this.status=N}ns=function N(){fh||K1(),fh||(ns=N)};function K1(N){if(N=N||m,Zr>0)return;if(x){h(c),postMessage({cmd:"loaded"});return}if(D0(),Zr>0)return;function C(){fh||(fh=!0,c.calledRun=!0,!oe&&(qc(),O0(),h(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),xn()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),C()},1)):C()}c.run=K1;function f8(N,C){if(!(C&&ie&&N===0)){if(!C&&x)throw postMessage({cmd:"exitProcess",returnCode:N}),new ru(N);ie||(Ne.terminateAllThreads(),fe=N,Xc(),c.onExit&&c.onExit(N),oe=!0),y(N,new ru(N))}}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();return x&&(ie=!1,Ne.initWorker()),K1(),a.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}),X8=et((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};var s=typeof a!="undefined"?a:{},i,o;s.ready=new Promise(function(K,ne){i=K,o=ne});var l={},u;for(u in s)s.hasOwnProperty(u)&&(l[u]=s[u]);var c=[],h="./this.program",d=function(K,ne){throw ne},p=!1,f=!1,m=!1,A=!1;p=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",A=!p&&!m&&!f;var y="";function g(K){return s.locateFile?s.locateFile(K,y):y+K}var _,b,w,x,I,S;m?(f?y=au().dirname(y)+"/":y=__dirname+"/",_=function(K,ne){return I||(I=require("fs")),S||(S=au()),K=S.normalize(K),I.readFileSync(K,ne?null:"utf8")},w=function(K){var ne=_(K,!0);return ne.buffer||(ne=new Uint8Array(ne)),X(ne.buffer),ne},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),c=process.argv.slice(2),process.on("uncaughtException",function(K){if(!(K instanceof G1))throw K}),process.on("unhandledRejection",dr),d=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):A?(typeof read!="undefined"&&(_=function(K){return read(K)}),w=function(K){var ne;return typeof readbuffer=="function"?new Uint8Array(readbuffer(K)):(ne=read(K,"binary"),X(typeof ne=="object"),ne)},typeof scriptArgs!="undefined"?c=scriptArgs:typeof arguments!="undefined"&&(c=arguments),typeof quit=="function"&&(d=function(K){quit(K)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||f)&&(f?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),r&&(y=r),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",_=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.send(null),ne.responseText},f&&(w=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),b=function(K,ne,Se){var Ye=new XMLHttpRequest;Ye.open("GET",K,!0),Ye.responseType="arraybuffer",Ye.onload=function(){if(Ye.status==200||Ye.status==0&&Ye.response){ne(Ye.response);return}Se()},Ye.onerror=Se,Ye.send(null)},x=function(K){document.title=K});var T=s.print||console.log.bind(console),M=s.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(s[u]=l[u]);l=null,s.arguments&&(c=s.arguments),s.thisProgram&&(h=s.thisProgram),s.quit&&(d=s.quit);var D;s.wasmBinary&&(D=s.wasmBinary);var z=s.noExitRuntime||!0;typeof WebAssembly!="object"&&dr("no native wasm support detected");var W,U=!1,H;function X(K,ne){K||dr("Assertion failed: "+ne)}function j(K){var ne=s["_"+K];return X(ne,"Cannot call unknown function "+K+", make sure it is exported"),ne}function ee(K,ne,Se,Ye,It){var At={string:function(jn){var _a=0;if(jn!=null&&jn!==0){var dh=(jn.length<<2)+1;_a=Jl(dh),he(jn,_a,dh)}return _a},array:function(jn){var _a=Jl(jn.length);return oe(jn,_a),_a}};function Ve(jn){return ne==="string"?ie(jn):ne==="boolean"?Boolean(jn):jn}var je=j(K),Qt=[],Jr=0;if(Ye)for(var Qr=0;Qr<Ye.length;Qr++){var hh=At[Se[Qr]];hh?(Jr===0&&(Jr=uh()),Qt[Qr]=hh(Ye[Qr])):Qt[Qr]=Ye[Qr]}var eu=je.apply(null,Qt);return eu=Ve(eu),Jr!==0&&ch(Jr),eu}function Y(K,ne,Se,Ye){Se=Se||[];var It=Se.every(function(Ve){return Ve==="number"}),At=ne!=="string";return At&&It&&!Ye?j(K):function(){return ee(K,ne,Se,arguments,Ye)}}var ae=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(K,ne,Se){for(var Ye=ne+Se,It=ne;K[It]&&!(It>=Ye);)++It;if(It-ne>16&&K.subarray&&ae)return ae.decode(K.subarray(ne,It));for(var At="";ne<It;){var Ve=K[ne++];if(!(Ve&128)){At+=String.fromCharCode(Ve);continue}var je=K[ne++]&63;if((Ve&224)==192){At+=String.fromCharCode((Ve&31)<<6|je);continue}var Qt=K[ne++]&63;if((Ve&240)==224?Ve=(Ve&15)<<12|je<<6|Qt:Ve=(Ve&7)<<18|je<<12|Qt<<6|K[ne++]&63,Ve<65536)At+=String.fromCharCode(Ve);else{var Jr=Ve-65536;At+=String.fromCharCode(55296|Jr>>10,56320|Jr&1023)}}return At}function ie(K,ne){return K?te(Ie,K,ne):""}function Q(K,ne,Se,Ye){if(!(Ye>0))return 0;for(var It=Se,At=Se+Ye-1,Ve=0;Ve<K.length;++Ve){var je=K.charCodeAt(Ve);if(je>=55296&&je<=57343){var Qt=K.charCodeAt(++Ve);je=65536+((je&1023)<<10)|Qt&1023}if(je<=127){if(Se>=At)break;ne[Se++]=je}else if(je<=2047){if(Se+1>=At)break;ne[Se++]=192|je>>6,ne[Se++]=128|je&63}else if(je<=65535){if(Se+2>=At)break;ne[Se++]=224|je>>12,ne[Se++]=128|je>>6&63,ne[Se++]=128|je&63}else{if(Se+3>=At)break;ne[Se++]=240|je>>18,ne[Se++]=128|je>>12&63,ne[Se++]=128|je>>6&63,ne[Se++]=128|je&63}}return ne[Se]=0,Se-It}function he(K,ne,Se){return Q(K,Ie,ne,Se)}function oe(K,ne){ke.set(K,ne)}function fe(K,ne){return K%ne>0&&(K+=ne-K%ne),K}var pe,ke,Ie,Me,Oe,$e,tt,nt,ot;function Ze(K){pe=K,s.HEAP8=ke=new Int8Array(K),s.HEAP16=Me=new Int16Array(K),s.HEAP32=$e=new Int32Array(K),s.HEAPU8=Ie=new Uint8Array(K),s.HEAPU16=Oe=new Uint16Array(K),s.HEAPU32=tt=new Uint32Array(K),s.HEAPF32=nt=new Float32Array(K),s.HEAPF64=ot=new Float64Array(K)}var pt=s.INITIAL_MEMORY||16777216,Be,yn=[],_t=[],Un=[],Yt=[],gn=!1;_t.push({func:function(){Qc()}});function Hn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Tr(s.preRun.shift());ga(yn)}function Rn(){gn=!0,ga(_t)}function un(){ga(Un)}function Jt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)Jn(s.postRun.shift());ga(Yt)}function Tr(K){yn.unshift(K)}function Jn(K){Yt.unshift(K)}var Qn=0,Aa=null,Kr=null;function ya(K){Qn++,s.monitorRunDependencies&&s.monitorRunDependencies(Qn)}function Di(K){if(Qn--,s.monitorRunDependencies&&s.monitorRunDependencies(Qn),Qn==0&&(Aa!==null&&(clearInterval(Aa),Aa=null),Kr)){var ne=Kr;Kr=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function dr(K){s.onAbort&&s.onAbort(K),K+="",M(K),U=!0,H=1,K="abort("+K+"). Build with -s ASSERTIONS=1 for more info.";var ne=new WebAssembly.RuntimeError(K);throw o(ne),ne}function Gc(K,ne){return String.prototype.startsWith?K.startsWith(ne):K.indexOf(ne)===0}var D0="data:application/octet-stream;base64,";function qc(K){return Gc(K,D0)}var O0="file://";function Xc(K){return Gc(K,O0)}var xn="tfjs-backend-wasm.wasm";qc(xn)||(xn=g(xn));function Kc(K){try{if(K==xn&&D)return new Uint8Array(D);if(w)return w(K);throw"both async and sync fetching of the wasm failed"}catch(ne){dr(ne)}}function z0(){if(!D&&(p||f)){if(typeof fetch=="function"&&!Xc(xn))return fetch(xn,{credentials:"same-origin"}).then(function(K){if(!K.ok)throw"failed to load wasm binary file at '"+xn+"'";return K.arrayBuffer()}).catch(function(){return Kc(xn)});if(b)return new Promise(function(K,ne){b(xn,function(Se){K(new Uint8Array(Se))},ne)})}return Promise.resolve().then(function(){return Kc(xn)})}function Zr(){var K={a:wn};function ne(Ve,je){var Qt=Ve.exports;s.asm=Qt,W=s.asm.g,Ze(W.buffer),Be=s.asm.m,Di("wasm-instantiate")}ya("wasm-instantiate");function Se(Ve){ne(Ve.instance)}function Ye(Ve){return z0().then(function(je){return WebAssembly.instantiate(je,K)}).then(Ve,function(je){M("failed to asynchronously prepare wasm: "+je),dr(je)})}function It(){return!D&&typeof WebAssembly.instantiateStreaming=="function"&&!qc(xn)&&!Xc(xn)&&typeof fetch=="function"?fetch(xn,{credentials:"same-origin"}).then(function(Ve){var je=WebAssembly.instantiateStreaming(Ve,K);return je.then(Se,function(Qt){return M("wasm streaming compile failed: "+Qt),M("falling back to ArrayBuffer instantiation"),Ye(Se)})}):Ye(Se)}if(s.instantiateWasm)try{var At=s.instantiateWasm(K,ne);return At}catch(Ve){return M("Module.instantiateWasm callback failed with error: "+Ve),!1}return It().catch(o),{}}function ga(K){for(;K.length>0;){var ne=K.shift();if(typeof ne=="function"){ne(s);continue}var Se=ne.func;typeof Se=="number"?ne.arg===void 0?Be.get(Se)():Be.get(Se)(ne.arg):Se(ne.arg===void 0?null:ne.arg)}}function ns(){dr()}function P0(K,ne,Se){Ie.copyWithin(K,ne,ne+Se)}function L0(){return Ie.length}function Yr(K){try{return W.grow(K-pe.byteLength+65535>>>16),Ze(W.buffer),1}catch(ne){}}function Zc(K){var ne=L0(),Se=2147483648;if(K>Se)return!1;for(var Ye=1;Ye<=4;Ye*=2){var It=ne*(1+.2/Ye);It=Math.min(It,K+100663296);var At=Math.min(Se,fe(Math.max(K,It),65536)),Ve=Yr(At);if(Ve)return!0}return!1}var Oi={mappings:{},buffers:[null,[],[]],printChar:function(K,ne){var Se=Oi.buffers[K];ne===0||ne===10?((K===1?T:M)(te(Se,0)),Se.length=0):Se.push(ne)},varargs:void 0,get:function(){Oi.varargs+=4;var K=$e[Oi.varargs-4>>2];return K},getStr:function(K){var ne=ie(K);return ne},get64:function(K,ne){return K}};function Yc(K){return 0}function W0(K,ne,Se,Ye,It){}function Jc(K,ne,Se,Ye){for(var It=0,At=0;At<Se;At++){for(var Ve=$e[ne+At*8>>2],je=$e[ne+(At*8+4)>>2],Qt=0;Qt<je;Qt++)Oi.printChar(K,Ie[Ve+Qt]);It+=je}return $e[Ye>>2]=It,0}var wn={a:ns,d:P0,e:Zc,f:Yc,c:W0,b:Jc},B0=Zr(),Qc=s.___wasm_call_ctors=function(){return(Qc=s.___wasm_call_ctors=s.asm.h).apply(null,arguments)},V0=s._init=function(){return(V0=s._init=s.asm.i).apply(null,arguments)},eh=s._register_tensor=function(){return(eh=s._register_tensor=s.asm.j).apply(null,arguments)},U0=s._dispose_data=function(){return(U0=s._dispose_data=s.asm.k).apply(null,arguments)},zi=s._dispose=function(){return(zi=s._dispose=s.asm.l).apply(null,arguments)},Pi=s._Abs=function(){return(Pi=s._Abs=s.asm.n).apply(null,arguments)},H0=s._Add=function(){return(H0=s._Add=s.asm.o).apply(null,arguments)},j0=s._AddN=function(){return(j0=s._AddN=s.asm.p).apply(null,arguments)},G0=s._ArgMax=function(){return(G0=s._ArgMax=s.asm.q).apply(null,arguments)},Ne=s._AvgPool=function(){return(Ne=s._AvgPool=s.asm.r).apply(null,arguments)},q0=s._BatchMatMul=function(){return(q0=s._BatchMatMul=s.asm.s).apply(null,arguments)},X0=s._Ceil=function(){return(X0=s._Ceil=s.asm.t).apply(null,arguments)},K0=s._ClipByValue=function(){return(K0=s._ClipByValue=s.asm.u).apply(null,arguments)},Z0=s._Conv2D=function(){return(Z0=s._Conv2D=s.asm.v).apply(null,arguments)},Y0=s._Conv2DBackpropInput=function(){return(Y0=s._Conv2DBackpropInput=s.asm.w).apply(null,arguments)},rs=s._Cos=function(){return(rs=s._Cos=s.asm.x).apply(null,arguments)},J0=s._CropAndResize=function(){return(J0=s._CropAndResize=s.asm.y).apply(null,arguments)},Q0=s._Cumsum=function(){return(Q0=s._Cumsum=s.asm.z).apply(null,arguments)},e1=s._DepthToSpace=function(){return(e1=s._DepthToSpace=s.asm.A).apply(null,arguments)},t1=s._DepthwiseConv2dNative=function(){return(t1=s._DepthwiseConv2dNative=s.asm.B).apply(null,arguments)},n1=s._Equal=function(){return(n1=s._Equal=s.asm.C).apply(null,arguments)},r1=s._Exp=function(){return(r1=s._Exp=s.asm.D).apply(null,arguments)},a1=s._FlipLeftRight=function(){return(a1=s._FlipLeftRight=s.asm.E).apply(null,arguments)},s1=s._Floor=function(){return(s1=s._Floor=s.asm.F).apply(null,arguments)},i1=s._FloorDiv=function(){return(i1=s._FloorDiv=s.asm.G).apply(null,arguments)},xa=s._FusedBatchNorm=function(){return(xa=s._FusedBatchNorm=s.asm.H).apply(null,arguments)},Kl=s._FusedConv2D=function(){return(Kl=s._FusedConv2D=s.asm.I).apply(null,arguments)},Zl=s._FusedDepthwiseConv2D=function(){return(Zl=s._FusedDepthwiseConv2D=s.asm.J).apply(null,arguments)},o1=s._Gather=function(){return(o1=s._Gather=s.asm.K).apply(null,arguments)},l1=s._GatherNd=function(){return(l1=s._GatherNd=s.asm.L).apply(null,arguments)},u1=s._Greater=function(){return(u1=s._Greater=s.asm.M).apply(null,arguments)},c1=s._GreaterEqual=function(){return(c1=s._GreaterEqual=s.asm.N).apply(null,arguments)},h1=s._LeakyRelu=function(){return(h1=s._LeakyRelu=s.asm.O).apply(null,arguments)},Le=s._Less=function(){return(Le=s._Less=s.asm.P).apply(null,arguments)},d1=s._LessEqual=function(){return(d1=s._LessEqual=s.asm.Q).apply(null,arguments)},p1=s._Log=function(){return(p1=s._Log=s.asm.R).apply(null,arguments)},f1=s._LogicalAnd=function(){return(f1=s._LogicalAnd=s.asm.S).apply(null,arguments)},m1=s._Max=function(){return(m1=s._Max=s.asm.T).apply(null,arguments)},A1=s._MaxPool=function(){return(A1=s._MaxPool=s.asm.U).apply(null,arguments)},y1=s._Maximum=function(){return(y1=s._Maximum=s.asm.V).apply(null,arguments)},Yl=s._Mean=function(){return(Yl=s._Mean=s.asm.W).apply(null,arguments)},th=s._Min=function(){return(th=s._Min=s.asm.X).apply(null,arguments)},nh=s._Minimum=function(){return(nh=s._Minimum=s.asm.Y).apply(null,arguments)},g1=s._Multiply=function(){return(g1=s._Multiply=s.asm.Z).apply(null,arguments)},x1=s._Neg=function(){return(x1=s._Neg=s.asm._).apply(null,arguments)},w1=s._NonMaxSuppressionV3=function(){return(w1=s._NonMaxSuppressionV3=s.asm.$).apply(null,arguments)},_1=s._NonMaxSuppressionV4=function(){return(_1=s._NonMaxSuppressionV4=s.asm.aa).apply(null,arguments)},b1=s._NonMaxSuppressionV5=function(){return(b1=s._NonMaxSuppressionV5=s.asm.ba).apply(null,arguments)},v1=s._NotEqual=function(){return(v1=s._NotEqual=s.asm.ca).apply(null,arguments)},k1=s._OneHot=function(){return(k1=s._OneHot=s.asm.da).apply(null,arguments)},Qe=s._PadV2=function(){return(Qe=s._PadV2=s.asm.ea).apply(null,arguments)},N1=s._Pow=function(){return(N1=s._Pow=s.asm.fa).apply(null,arguments)},I1=s._Prelu=function(){return(I1=s._Prelu=s.asm.ga).apply(null,arguments)},S1=s._Prod=function(){return(S1=s._Prod=s.asm.ha).apply(null,arguments)},Li=s._RealDiv=function(){return(Li=s._RealDiv=s.asm.ia).apply(null,arguments)},rh=s._Relu=function(){return(rh=s._Relu=s.asm.ja).apply(null,arguments)},ah=s._Relu6=function(){return(ah=s._Relu6=s.asm.ka).apply(null,arguments)},sh=s._ResizeBilinear=function(){return(sh=s._ResizeBilinear=s.asm.la).apply(null,arguments)},T1=s._Reverse=function(){return(T1=s._Reverse=s.asm.ma).apply(null,arguments)},C1=s._RotateWithOffset=function(){return(C1=s._RotateWithOffset=s.asm.na).apply(null,arguments)},ih=s._Round=function(){return(ih=s._Round=s.asm.oa).apply(null,arguments)},E1=s._Rsqrt=function(){return(E1=s._Rsqrt=s.asm.pa).apply(null,arguments)},oh=s._ScatterNd=function(){return(oh=s._ScatterNd=s.asm.qa).apply(null,arguments)},wa=s._SelectV2=function(){return(wa=s._SelectV2=s.asm.ra).apply(null,arguments)},R1=s._Sigmoid=function(){return(R1=s._Sigmoid=s.asm.sa).apply(null,arguments)},F1=s._Sin=function(){return(F1=s._Sin=s.asm.ta).apply(null,arguments)},gg=s._Softmax=function(){return(gg=s._Softmax=s.asm.ua).apply(null,arguments)},lh=s._Sqrt=function(){return(lh=s._Sqrt=s.asm.va).apply(null,arguments)},M1=s._Square=function(){return(M1=s._Square=s.asm.wa).apply(null,arguments)},$1=s._SquaredDifference=function(){return($1=s._SquaredDifference=s.asm.xa).apply(null,arguments)},D1=s._Step=function(){return(D1=s._Step=s.asm.ya).apply(null,arguments)},O1=s._StridedSlice=function(){return(O1=s._StridedSlice=s.asm.za).apply(null,arguments)},z1=s._Sub=function(){return(z1=s._Sub=s.asm.Aa).apply(null,arguments)},P1=s._Sum=function(){return(P1=s._Sum=s.asm.Ba).apply(null,arguments)},L1=s._Tanh=function(){return(L1=s._Tanh=s.asm.Ca).apply(null,arguments)},W1=s._Tile=function(){return(W1=s._Tile=s.asm.Da).apply(null,arguments)},B1=s._TopK=function(){return(B1=s._TopK=s.asm.Ea).apply(null,arguments)},V1=s._Transpose=function(){return(V1=s._Transpose=s.asm.Fa).apply(null,arguments)},U1=s.__FusedMatMul=function(){return(U1=s.__FusedMatMul=s.asm.Ga).apply(null,arguments)},H1=s._malloc=function(){return(H1=s._malloc=s.asm.Ha).apply(null,arguments)},j1=s._free=function(){return(j1=s._free=s.asm.Ia).apply(null,arguments)},uh=s.stackSave=function(){return(uh=s.stackSave=s.asm.Ja).apply(null,arguments)},ch=s.stackRestore=function(){return(ch=s.stackRestore=s.asm.Ka).apply(null,arguments)},Jl=s.stackAlloc=function(){return(Jl=s.stackAlloc=s.asm.La).apply(null,arguments)};s.cwrap=Y;var Wi;function G1(K){this.name="ExitStatus",this.message="Program terminated with exit("+K+")",this.status=K}Kr=function K(){Wi||Ql(),Wi||(Kr=K)};function Ql(K){if(K=K||c,Qn>0||(Hn(),Qn>0))return;function ne(){Wi||(Wi=!0,s.calledRun=!0,!U&&(Rn(),un(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Jt()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}if(s.run=Ql,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return Ql(),a.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}),K8=et((e,t)=>{(function(n,r,a){function s(u){var c=this,h=l();c.next=function(){var d=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=d-(c.c=d|0)},c.c=1,c.s0=h(" "),c.s1=h(" "),c.s2=h(" "),c.s0-=h(u),c.s0<0&&(c.s0+=1),c.s1-=h(u),c.s1<0&&(c.s1+=1),c.s2-=h(u),c.s2<0&&(c.s2+=1),h=null}function i(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function o(u,c){var h=new s(u),d=c&&c.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var u=4022871197,c=function(h){h=String(h);for(var d=0;d<h.length;d++){u+=h.charCodeAt(d);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Z8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var d=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^d^d>>>8},l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Y8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=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^(d^d<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,h==c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),J8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.x,d=u.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,u.i=d+1&7,f};function c(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,h.i=0,p=256;p>0;--p)h.next()}c(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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.x&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Q8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.w,d=u.X,p=u.i,f,m;return u.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,u.i=p,m+(h^h>>>16)|0};function c(h,d){var p,f,m,A,y,g=[],_=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,_=Math.max(_,d.length)),m=0,A=-32;A<_;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}c(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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.X&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),ek=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.b,p=u.c,f=u.d,m=u.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,u.b=d=d<<20^d>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-d|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var h=0;h<c.length+20;h++)u.b^=c.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 c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),tk=et((e,t)=>{(function(n,r,a){var s=256,i=6,o=52,l="random",u=a.pow(s,i),c=a.pow(2,o),h=c*2,d=s-1,p;function f(w,x,I){var S=[];x=x==!0?{entropy:!0}:x||{};var T=g(y(x.entropy?[w,b(r)]:w==null?_():w,3),S),M=new m(S),D=function(){for(var z=M.g(i),W=u,U=0;z<c;)z=(z+U)*s,W*=s,U=M.g(1);for(;z>=h;)z/=2,W/=2,U>>>=1;return(z+U)/W};return D.int32=function(){return M.g(4)|0},D.quick=function(){return M.g(4)/4294967296},D.double=D,g(b(M.S),r),(x.pass||I||function(z,W,U,H){return H&&(H.S&&A(H,M),z.state=function(){return A(M,{})}),U?(a[l]=z,W):z})(D,T,"global"in x?x.global:this==a,x.state)}function m(w){var x,I=w.length,S=this,T=0,M=S.i=S.j=0,D=S.S=[];for(I||(w=[I++]);T<s;)D[T]=T++;for(T=0;T<s;T++)D[T]=D[M=d&M+w[T%I]+(x=D[T])],D[M]=x;(S.g=function(z){for(var W,U=0,H=S.i,X=S.j,j=S.S;z--;)W=j[H=d&H+1],U=U*s+j[d&(j[H]=j[X=d&X+W])+(j[X]=W)];return S.i=H,S.j=X,U})(s)}function A(w,x){return x.i=w.i,x.j=w.j,x.S=w.S.slice(),x}function y(w,x){var I=[],S=typeof w,T;if(x&&S=="object")for(T in w)try{I.push(y(w[T],x-1))}catch(M){}return I.length?I:S=="string"?w:w+"\0"}function g(w,x){for(var I=w+"",S,T=0;T<I.length;)x[d&T]=d&(S^=x[d&T]*19)+I.charCodeAt(T++);return b(x)}function _(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(n.crypto||n.msCrypto).getRandomValues(w)),b(w)}catch(S){var x=n.navigator,I=x&&x.plugins;return[+new Date,n,I,n.screen,b(r)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(a.random(),r),typeof t=="object"&&t.exports){t.exports=f;try{p=Y1()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return f}):a["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}),Mg=et((e,t)=>{var n=K8(),r=Z8(),a=Y8(),s=J8(),i=Q8(),o=ek(),l=tk();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),nk=et(()=>{}),rk="3.3.0",ak="3.3.0",sk="3.3.0",ik="3.3.0",ok="3.3.0",lk=1e-7,uk=1e-4,xh=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 er("refCount")}incRef(e){return er("incRef")}timerAvailable(){return!0}time(e){return er("time")}read(e){return er("read")}readSync(e){return er("readSync")}numDataIds(){return er("numDataIds")}disposeData(e,t){return er("disposeData")}write(e,t,n){return er("write")}move(e,t,n,r,a){return er("move")}memory(){return er("memory")}floatPrecision(){return er("floatPrecision")}epsilon(){return this.floatPrecision()===32?lk:uk}dispose(){return er("dispose")}};function er(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 $g(e){let t=e.length,n=0,r=0;for(;t>0;)r=Math.random()*t|0,t--,n=e[t],e[t]=e[r],e[r]=n}function ck(e,t){if(e.length!==t.length)throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);let n=e.length,r,a,s=0;for(;n>0;)s=Math.random()*n|0,n--,r=e[n],a=t[n],e[n]=e[s],t[n]=t[s],e[s]=r,t[s]=a}function iu(e,t,n){return Math.max(e,Math.min(t,n))}function hk(e){return e%2==0?e:e+1}function dk(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function pk(e,t){let n=Math.random();return t*n+(1-n)*e}function fk(e,t){let n=0;for(let r=0;r<e.length;r++){let a=Number(e[r])-Number(t[r]);n+=a*a}return n}function F(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function an(e,t,n=""){F(ta(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ss(e){F(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function is(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||sn(e)&&!n)for(let r=0;r<e.length;++r)is(e[r],t,n);else t.push(e);return t}function Dt(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function mk(e){return e.length===0}function ta(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function jt(e){return e%1==0}function Ak(e){if(Math.tanh!=null)return Math.tanh(e);if(e===Infinity)return 1;if(e===-Infinity)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function yk(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function gk(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return $g(t),t}function ou(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function xk(e,t=r=>0,n){return new Promise((r,a)=>{let s=0,i=()=>{if(e()){r();return}s++;let o=t(s);if(n!=null&&s>=n){a();return}setTimeout(i,o)};i()})}function wk(e,t){let n=1,r=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(r!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${r} and dim ${s}`);r=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(r===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let a=e.slice();return a[r]=t/n,a}function tr(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),F(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),F(e.every(r=>jt(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function Dg(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:tr(t,e).sort(),i=0;for(let o=0;o<e.length;++o){if(s!=null){if(s[i]===o&&e[o]!==1)throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);(s[i]==null||s[i]>o)&&e[o]===1&&(n.push(e[o]),r.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),r.push(o))}return{newShape:n,keptDims:r}}function Og(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else throw new Error(`Unknown data type ${e}`);return n}function zg(e,t){let n=null;if(e==null||e==="float32")n=new Float32Array(t);else if(e==="int32")n=new Int32Array(t);else if(e==="bool")n=new Uint8Array(t);else if(e==="string")n=new Array(t);else throw new Error(`Unknown data type ${e}`);return n}function Pg(e,t){for(let n=0;n<e.length;n++){let r=e[n];if(isNaN(r)||!isFinite(r))throw Error(`A tensor of type ${t} being uploaded contains ${r}.`)}}function Lg(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function _k(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function sn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function J1(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function Wg(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function va(e){return typeof e=="string"||e instanceof String}function Bg(e){return typeof e=="boolean"}function Vg(e){return typeof e=="number"}function wh(e){return Array.isArray(e)?wh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Vg(e)?"float32":va(e)?"string":Bg(e)?"bool":"float32"}function ka(e){return!!(e&&e.constructor&&e.call&&e.apply)}function _h(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Xi(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function Ug(e,t,n){let r=new Array;if(t.length===1){let a=t[0];for(let s=0;s<a;s++)r[s]=n[e+s]}else{let a=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<a;o++)r[o]=Ug(e+o*i,s,n)}return r}function Ki(e,t){if(e.length===0)return t[0];let n=e.reduce((r,a)=>r*a);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return Ug(0,e,t)}function Q1(e,t){let n=bh(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function bh(e,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool")return new Uint8Array(e);throw new Error(`Unknown data type ${t}`)}function bk(e,t){let n=e.reduce((r,a)=>r*a,1);if(t==null||t==="float32")return Ki(e,new Float32Array(n));if(t==="int32")return Ki(e,new Int32Array(n));if(t==="bool")return Ki(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function ef(e){e.forEach(t=>{F(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function vk(e,t,n){if(t===0)return 0;if(t===1)return e[0];let r=e[e.length-1];for(let a=0;a<e.length-1;++a)r+=n[a]*e[a];return r}function kk(e,t,n){if(t===0)return[];if(t===1)return[e];let r=new Array(t);for(let a=0;a<r.length-1;++a)r[a]=Math.floor(e/n[a]),e-=r[a]*n[a];return r[r.length-1]=e,r}function tf(e){return e&&e.then&&typeof e.then=="function"}var Hg="tfjsflags",jg=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`),this.platformName=e,this.platform=t}registerFlag(e,t,n){if(this.flagRegistry[e]={evaluationFn:t,setHook:n},this.urlFlags[e]!=null){let r=this.urlFlags[e];console.warn(`Setting feature override from URL ${e}: ${r}.`),this.set(e,r)}}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(tf(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=Nk(this.global.location.search);Hg in e&&e[Hg].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=Ik(n,r)})}};function Nk(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(Sk(t,r[0],r[1]),r.join("="))),t}function Sk(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function Ik(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 ${e}.`)}function J(){return fr}var fr=null;function Tk(e){fr=e}var nf;function Gg(){if(nf==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");nf=e}return nf}function Ck(){let e=Gg();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function rf(e,t){let n=Ck();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var Zi="Abs",Yi="Acos",Ji="Acosh",Na="Add",os="AddN",vh="All",kh="Any",ls="ArgMax",lu="ArgMin",Qi="Asin",eo="Asinh",to="Atan",no="Atanh",ro="Atan2",us="AvgPool",Nh="AvgPoolGrad",uu="AvgPool3D",Ih="AvgPool3DGrad",cs="BatchMatMul",cu="BatchToSpaceND",Sh="Bincount",qg="BroadcastTo",hs="Cast",ds="Ceil",Ia="ClipByValue",Th="Complex",hu="ComplexAbs",ao="Concat",ps="Conv2D",Ch="Conv2DBackpropFilter",fs="Conv2DBackpropInput",du="Conv3D",Eh="Conv3DBackpropFilterV2",Rh="Conv3DBackpropInputV2",ms="Cos",so="Cosh",As="Cumsum",io="CropAndResize",Fh="DenseBincount",oo="DepthToSpace",ys="DepthwiseConv2dNative",Mh="DepthwiseConv2dNativeBackpropFilter",$h="DepthwiseConv2dNativeBackpropInput",Dh="Diag",pu="Dilation2D",Oh="Dilation2DBackpropInput",zh="Dilation2DBackpropFilter",gs="RealDiv",lo="Elu",Ph="EluGrad",uo="Erf",co="Equal",xs="Exp",ho="ExpandDims",po="Expm1",Lh="FFT",fu="Fill",fo="FlipLeftRight",ws="Floor",_s="FloorDiv",bs="FusedBatchNorm",mo="GatherV2",Ao="GatherNd",yo="Greater",vs="GreaterEqual",ks="Identity",Wh="IFFT",Bh="Imag",go="IsFinite",xo="IsInf",wo="IsNan",Ns="LeakyRelu",_o="Less",bo="LessEqual",Vh="LinSpace",Is="Log",vo="Log1p",ko="LogicalAnd",mu="LogicalNot",Au="LogicalOr",Xg="LogSoftmax",yu="LRN",Uh="LRNGrad",Ss="Max",Ts="Maximum",Cs="MaxPool",Hh="MaxPoolGrad",gu="MaxPool3D",jh="MaxPool3DGrad",Gh="MaxPoolWithArgmax",Es="Mean",Rs="Min",Fs="Minimum",xu="MirrorPad",No="Mod",qh="Multinomial",Ms="Multiply",Io="Neg",So="NotEqual",To="NonMaxSuppressionV3",Co="NonMaxSuppressionV4",Eo="NonMaxSuppressionV5",Ro="OnesLike",$s="OneHot",Fo="Pack",Ds="PadV2",Ek="Pool",Os="Pow",zs="Prelu",Mo="Prod",wu="Range",Xh="Real",$o="Reciprocal",Ps="Relu",Do="Reshape",_u="ResizeNearestNeighbor",Kh="ResizeNearestNeighborGrad",Ls="ResizeBilinear",Zh="ResizeBilinearGrad",Ws="Relu6",Bs="Reverse",Vs="Round",Us="Rsqrt",Oo="ScatterNd",zo="Select",Po="Selu",Lo="Slice",Hs="Sin",Wo="Sinh",Bo="Sign",js="Sigmoid",Vo="Softplus",Gs="Sqrt",qs="Sum",bu="SpaceToBatchND",Uo="SplitV",Xs="Softmax",Ks="SquaredDifference",vu="Square",Zs="Sub",Yh="SparseToDense",Ho="StridedSlice",jo="Tan",Ys="Tanh",Sa="Tile",Go="TopK",Jh="Transform",Js="Transpose",Qh="Unique",qo="Unpack",ku="UnsortedSegmentSum",Xo="ZerosLike",Ta="Step",ed="FromPixels",Ko="RotateWithOffset",Qs="_FusedMatMul",ei="FusedConv2D",ti="FusedDepthwiseConv2D",Zo=rf("kernelRegistry",()=>new Map),Nu=rf("gradRegistry",()=>new Map);function td(e,t){let n=af(e,t);return Zo.get(n)}function sf(e){return Nu.get(e)}function Yo(e){let t=Zo.entries(),n=[];for(;;){let{done:r,value:a}=t.next();if(r)break;let[s,i]=a,[o]=s.split("_");o===e&&n.push(i)}return n}function ni(e){let{kernelName:t,backendName:n}=e,r=af(t,n);Zo.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),Zo.set(r,e)}function Kg(e){let{kernelName:t}=e;Nu.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Nu.set(t,e)}function Rk(e,t){let n=af(e,t);if(!Zo.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);Zo.delete(n)}function Fk(e){if(!Nu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Nu.delete(e)}function Mk(e,t){Yo(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});ni(r)})}function af(e,t){return`${t}_${e}`}var v={};ze(v,{arraysEqual:()=>ta,assert:()=>F,assertNonNegativeIntegerDimensions:()=>ef,assertNonNull:()=>ss,assertShapesMatch:()=>an,bytesFromStringArray:()=>Wg,bytesPerElement:()=>J1,checkConversionForErrors:()=>Pg,clamp:()=>iu,computeStrides:()=>Xi,createScalarValue:()=>$k,createShuffledIndices:()=>gk,decodeString:()=>rd,distSquared:()=>fk,encodeString:()=>Su,fetch:()=>Dk,flatten:()=>is,getArrayFromDType:()=>zg,getTypedArrayFromDType:()=>Og,hasEncodingLoss:()=>_k,indexToLoc:()=>kk,inferDtype:()=>wh,inferFromImplicitShape:()=>wk,isBoolean:()=>Bg,isFunction:()=>ka,isInt:()=>jt,isNumber:()=>Vg,isPromise:()=>tf,isScalarShape:()=>mk,isString:()=>va,isTypedArray:()=>sn,isValidDtype:()=>Lg,locToIndex:()=>vk,makeOnesTypedArray:()=>Q1,makeZerosNestedTypedArray:()=>bk,makeZerosTypedArray:()=>bh,nearestDivisor:()=>_h,nearestLargerEven:()=>hk,now:()=>Iu,parseAxisParam:()=>tr,randUniform:()=>pk,repeatedTry:()=>xk,rightPad:()=>ou,shuffle:()=>$g,shuffleCombo:()=>ck,sizeFromShape:()=>Dt,sizeToSquarishShape:()=>yk,squeezeShape:()=>Dg,sum:()=>dk,tanh:()=>Ak,toNestedArray:()=>Ki,toTypedArray:()=>nd});function $k(e,t){return t==="string"?Su(e):nd([e],t)}function Ok(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function nd(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=is(e)),J().getBool("DEBUG")&&Pg(e,t),Ok(e,t))return e;if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"){let n=new Uint8Array(e.length);for(let r=0;r<n.length;++r)Math.round(e[r])!==0&&(n[r]=1);return n}else throw new Error(`Unknown data type ${t}`)}function Iu(){return J().platform.now()}function Dk(e,t){return J().platform.fetch(e,t)}function Su(e,t="utf-8"){return t=t||"utf-8",J().platform.encode(e,t)}function rd(e,t="utf-8"){return t=t||"utf-8",J().platform.decode(e,t)}var Lk=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new Pk)}profileKernel(e,t,n){let r,a=()=>{r=n()},s,i=Iu();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(a);else{a();for(let o of r)o.dataSync();s=Promise.resolve({kernelMs:Iu()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<r.length;o++){let l=r[o];l.data().then(u=>{zk(u,l.dtype,e)})}return{kernelName:e,outputs:r,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:a,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),r,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],a,o[2])})})}};function zk(e,t,n){if(t!=="float32")return!1;for(let r=0;r<e.length;r++){let a=e[r];if(isNaN(a)||!isFinite(a))return console.warn(`Found ${a} in the result of '${n}'`),!0}return!1}var Pk=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?ou(`${r}ms`,9):r.error,o=ou(e,25),l=t.rank,u=t.size,c=ou(t.shape.toString(),14),h="";for(let d in a){let p=a[d];if(p!=null){let f=p.shape||t.shape,m=f.length;h+=`${d}: ${m}D ${m>0?f:""} `}}console.log(`%c${o} %c${i} %c${l}D ${c} %c${u} %c${h} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function Wk(e,t,n){let r={},a={};for(let l=0;l<t.length;l++)r[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],c=u.inputs;for(let h in c){let d=c[h],p=!1;for(let f=0;f<t.length;f++)if(r[d.id]){u.outputs.forEach(m=>r[m.id]=!0),p=!0,a[u.id]=!0;break}if(p)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let u=e[l],c=u.inputs;for(let h=0;h<u.outputs.length;h++)if(s[u.outputs[h].id]){for(let d in c)s[c[d].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 c={};for(let d in u.inputs){let p=u.inputs[d];r[p.id]&&(c[d]=p)}let h=Object.assign({},u);h.inputs=c,h.outputs=u.outputs,o.push(h)}}return o}function Bk(e,t,n,r){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=n(()=>o[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=s.inputs[l];if(!ta(u.shape,c.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=u;else{let h=e[c.id];e[c.id]=r(h,u),h.dispose()}}}}var Zg=20,Tu=3,of=7;function Uk(e,t,n,r){let a=Xi(t),s=Vk(e,t,n,a),i=t.length,o=ad(e,t,n,a,s),l=["Tensor"];return r&&(l.push(` dtype: ${n}`),l.push(` rank: ${i}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(o.map(u=>" "+u).join(`
`)),l.join(`
`)}function Vk(e,t,n,r){let a=Dt(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Eu(e):e;if(o>1)for(let u=0;u<a/s;u++){let c=u*s;for(let h=0;h<s;h++)i[h]=Math.max(i[h],Cu(l[c+h],0,n).length)}return i}function Cu(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(of))} + ${parseFloat(e[1].toFixed(of))}j`:va(e)?r=`'${e}'`:n==="bool"?r=Yg(e):r=parseFloat(e.toFixed(of)).toString(),ou(r,t)}function Yg(e){return e===0?"false":"true"}function ad(e,t,n,r,a,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=Eu(e);return[Cu(m[0],0,n)]}return n==="bool"?[Yg(e[0])]:[e[0].toString()]}if(l===1){if(o>Zg){let A=Tu*i,y=Array.from(e.slice(0,A)),g=Array.from(e.slice((o-Tu)*i,o*i));return n==="complex64"&&(y=Eu(y),g=Eu(g)),["["+y.map((_,b)=>Cu(_,a[b],n)).join(", ")+", ..., "+g.map((_,b)=>Cu(_,a[o-Tu+b],n)).join(", ")+"]"]}let m=n==="complex64"?Eu(e):Array.from(e);return["["+m.map((A,y)=>Cu(A,a[y],n)).join(", ")+"]"]}let u=t.slice(1),c=r.slice(1),h=r[0]*i,d=[];if(o>Zg){for(let m=0;m<Tu;m++){let A=m*h,y=A+h;d.push(...ad(e.slice(A,y),u,n,c,a,!1))}d.push("...");for(let m=o-Tu;m<o;m++){let A=m*h,y=A+h;d.push(...ad(e.slice(A,y),u,n,c,a,m===o-1))}}else for(let m=0;m<o;m++){let A=m*h,y=A+h;d.push(...ad(e.slice(A,y),u,n,c,a,m===o-1))}let p=l===2?",":"";d[0]="["+d[0]+p;for(let m=1;m<d.length-1;m++)d[m]=" "+d[m]+p;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function Eu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Ot=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Dt(e),n!=null){let r=n.length;F(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||zg(t,this.size),this.strides=Xi(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Cr().makeTensor(this.values,this.shape,this.dtype)}},Cr=null,Jo=null,Hk=null;function jk(e){Cr=e}function Gk(e){Jo=e}function qk(e){Hk=e}var Ue=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Dt(e),this.strides=Xi(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Jo.buffer(this.shape,this.dtype,e)}bufferSync(){return Jo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Ki(this.shape,e)}arraySync(){return Ki(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Cr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>rd(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Cr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>rd(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 Cr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Cr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Jo.print(this,e)}clone(){return this.throwIfDisposed(),Jo.clone(this)}toString(e=!1){let t=this.dataSync();return Uk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Jo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cr().makeVariable(this,e,t,n)}};Object.defineProperty(Ue,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return rf("Tensor",()=>Ue)}Z();var Ru=class extends Ue{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!ta(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Cr().disposeTensor(this),this.dataId=e.dataId,Cr().incRef(this,null)}dispose(){Cr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Ru,Symbol.hasInstance,{value:e=>e instanceof Ue&&e.assign!=null&&e.assign instanceof Function});var mr={};ze(mr,{assertTypesMatch:()=>Jg,getTensorsInContainer:()=>lf,isTensorInList:()=>Xk,makeTypesMatch:()=>bt});var uf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(uf||(uf={}));var cf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(cf||(cf={}));var hf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(hf||(hf={}));var df;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(df||(df={}));var pf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(pf||(pf={}));var Kk={float32:df,int32:cf,bool:hf,complex64:pf};function nr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Kk[e][t]}function sd(e){return nr(e,"int32")}function bt(e,t){if(e.dtype===t.dtype)return[e,t];let n=nr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Jg(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Xk(e,t){return t.some(n=>n.id===e.id)}function lf(e){let t=[],n=new Set;return Qg(e,t,n),t}function Qg(e,t,n){if(e==null)return;if(e instanceof Ue){t.push(e);return}if(!Zk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),Qg(s,t,n))}}function Zk(e){return Array.isArray(e)||typeof e=="object"}function ff(e){return e.kernelName!=null}var e5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Fu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new e5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new Lk(this.backendInstance),!0}setupRegisteredKernels(){Yo(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Yo(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof su)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Fu.nextTensorId++}nextVariableId(){return Fu.nextVariableId++}clone(e){let t=$.runKernel(ks,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(hs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(td(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=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=ff(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ff(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=td(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:I}=b;return this.makeTensorFromDataId(w,x,I)});if(r){let b=this.getTensorsForGradient(p,f,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:u,attrs:c}=e,h=ff(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),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(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=sf(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(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=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&va(e[0])&&(a=e.map(o=>Su(o)));let s=r.write(a,t,n),i=new Ue(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Wg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ue(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Ru(e,t,n,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 n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*J1(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Ru||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*J1(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,r,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},o=sf(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((u,c)=>{if(u==null){let h=n[c],d=bh(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return u}),r(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=lf(e),n=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&&!n.has(s.id)&&s.dispose()}let r=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===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Ue,()=>"The result y returned by f() must be a tensor.");let s=Wk(this.state.activeTape,t,a);if(!r&&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]=n==null?Yk(a.shape):n,Bk(i,s,l=>this.tidy(l),Jk);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 F(ka(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof Ue),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),F(n.value instanceof Ue,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(ka(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];F(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(...)."),F(u.every(h=>h instanceof Ue),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return u.forEach((h,d)=>{c[d]=()=>h}),c};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=Iu(),n=await this.backend.time(e);return n.wallMs=Iu()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new e5;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}};Fu.nextTensorId=0;Fu.nextVariableId=0;function Yk(e){let t=Q1(Dt(e),"float32");return $.makeTensor(t,e,"float32")}function t5(){let e=Gg();if(e._tfengine==null){let t=new jg(e);e._tfengine=new Fu(t)}return Tk(e._tfengine.ENV),jk(()=>e._tfengine),e._tfengine}var $=t5();function Jk(e,t){let n={a:e,b:t};return $.runKernel(Na,n)}var Mu={};ze(Mu,{isBrowser:()=>n5,isMobile:()=>Qk});function e9(){return typeof navigator!="undefined"&&navigator!=null}function Qk(){if(e9()){let e=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/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(e.substr(0,4))}return!1}function n5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Ar=J();Ar.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.")});Ar.registerFlag("IS_BROWSER",()=>n5());Ar.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Ar.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Ar.registerFlag("PROD",()=>!1);Ar.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Ar.getBool("DEBUG"));Ar.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Ar.registerFlag("IS_TEST",()=>!1);Ar.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);Ar.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Er(e,t){let n=e;if(sn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||sn(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&r5(e,r,[]),r}function r5(e,t,n){if(n=n||[],!Array.isArray(e)&&!sn(e)){F(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}F(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),F(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)r5(e[a],r,n.concat(a))}function a5(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function E(e,t,n,r="numeric"){if(e instanceof Ue)return a5(r,e.dtype,t,n),e;let a=wh(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),a5(r,a,t,n),e==null||!sn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Er(e,a);!sn(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?nd(e,a):is(e,[],!0);return $.makeTensor(i,s,a)}function $u(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>E(a,`${t}[${s}]`,n,r))}var s5="__op";function O(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+s5;let a=(...s)=>{$.startScope(n);try{let i=r(...s);return tf(i)&&console.error("Cannot return a Promise inside of tidy."),$.endScope(i),i}catch(i){throw $.endScope(null),i}};return Object.defineProperty(a,"name",{value:n,configurable:!0}),a}function t9(e,t){let n=E(e,"real","complex"),r=E(t,"imag","complex");an(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);let a={real:n,imag:r};return $.runKernel(Th,a)}var Ca=O({complex_:t9});function Ea(e,t,n,r){if(r==null&&(r=wh(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!sn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){ef(t);let a=Dt(t),s=Dt(n);F(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Dt(t.slice(i)):!0;F(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!sn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?nd(e,r):is(e,[],!0),$.makeTensor(e,t,r)}function yr(e,t,n){let r=Er(e,n);return Ea(e,t,r,n)}var mf={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},id=4;async function r9(e,t){let n=[],r=[],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 c=new Promise(async h=>{let d=await l.bytes(),p=d.reduce((A,y)=>A+y.length,0)+id*d.length,f=new Uint8Array(p),m=0;for(let A=0;A<d.length;A++){let y=d[A],g=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(g,m),m+=id,f.set(y,m),m+=y.length}h(f)});r.push(c)}else r.push(l.data());t!=null&&(u.group=t),n.push(u)}let s=await Promise.all(r);return{data:n9(s),specs:n}}function i5(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,u=Dt(l),c;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 d=mf[h.dtype],p=e.slice(a,a+u*d),f=h.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){c=new Float32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];c[m]=A*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=a9()),c=r(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 ${h.dtype} for weight type int32.`);c=new Int32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];c[m]=Math.round(A*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=u*d}else if(o==="string"){let h=Dt(s.shape);c=[];for(let d=0;d<h;d++){let p=new Uint32Array(e.slice(a,a+id))[0];a+=id;let f=new Uint8Array(e.slice(a,a+p));c.push(f),a+=p}}else{let h=mf[o],d=e.slice(a,a+u*h);if(o==="float32")c=new Float32Array(d);else if(o==="int32")c=new Int32Array(d);else if(o==="bool")c=new Uint8Array(d);else if(o==="complex64"){c=new Float32Array(d);let p=new Float32Array(c.length/2),f=new Float32Array(c.length/2);for(let y=0;y<p.length;y++)p[y]=c[y*2],f[y]=c[y*2+1];let m=yr(p,l,"float32"),A=yr(f,l,"float32");n[i]=Ca(m,A),m.dispose(),A.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=u*h}o!=="complex64"&&(n[i]=yr(c,l,o))}return n}function n9(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(s=>{if(t+=s.byteLength,n.push(s.byteLength===s.buffer.byteLength?s:new s.constructor(s)),!(s instanceof Float32Array||s instanceof Int32Array||s instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`)});let r=new Uint8Array(t),a=0;return n.forEach(s=>{r.set(new Uint8Array(s.buffer),a),a+=s.byteLength}),r.buffer}var Af=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function o5(e){return Af?Buffer.byteLength(e):new Blob([e]).size}function s9(e){if(Af)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let r=0,a=t.length;r<a;r++)n+=String.fromCharCode(t[r]);return btoa(n)}function i9(e){if(Af){let r=Buffer.from(e,"base64");return r.buffer.slice(r.byteOffset,r.byteOffset+r.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let r=0;r<t.length;++r)n.set([t.charCodeAt(r)],r);return n.buffer}function yf(e){if(e.length===1)return e[0];let t=0;e.forEach(a=>{t+=a.byteLength});let n=new Uint8Array(t),r=0;return e.forEach(a=>{n.set(new Uint8Array(a),r),r+=a.byteLength}),n.buffer}function l5(e){let t="/";for(e=e.trim();e.endsWith(t);)e=e.slice(0,e.length-1);let n=e.split(t);return n[n.length-1]}function Du(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:e.modelTopology==null?0:o5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:o5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function o9(){let e=n=>{let r=n<<13,a=0;for(;(r&8388608)==0;)a-=8388608,r<<=1;return r&=~8388608,a+=947912704,r|a},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function l9(){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 u9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function a9(){let e=o9(),t=l9(),n=u9();return r=>{let a=new ArrayBuffer(4*r.length),s=new Uint32Array(a);for(let i=0;i<r.length;i++){let o=r[i],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(a)}}var St=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return St.instance==null&&(St.instance=new St),St.instance}static registerSaveRouter(e){St.getInstance().saveRouters.push(e)}static registerLoadRouter(e){St.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return St.getHandlers(e,"save")}static getLoadHandlers(e,t){return St.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?St.getInstance().loadRouters:St.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},c9=e=>St.registerSaveRouter(e),h9=e=>St.registerLoadRouter(e),d9=e=>St.getSaveHandlers(e),p9=(e,t)=>St.getLoadHandlers(e,t),gf="tensorflowjs",xf=1,ri="models_store",Ra="model_info_store";function u5(){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 wf(e){let t=e.result;t.createObjectStore(ri,{keyPath:"modelPath"}),t.createObjectStore(Ra,{keyPath:"modelPath"})}var ai=class{constructor(e){if(this.indexedDB=u5(),e==null||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,e)}async load(){return this.databaseAction(this.modelPath)}databaseAction(e,t){return new Promise((n,r)=>{let a=this.indexedDB.open(gf,xf);a.onupgradeneeded=()=>wf(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(ri,"readonly"),o=i.objectStore(ri).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),r(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),r(o.error)),i.oncomplete=()=>s.close()}else{let i=Du(t),o=s.transaction(Ra,"readwrite"),l=o.objectStore(Ra),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),c;u.onsuccess=()=>{c=s.transaction(ri,"readwrite");let h=c.objectStore(ri).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>n({modelArtifactsInfo:i}),h.onerror=d=>{l=o.objectStore(Ra);let p=l.delete(this.modelPath);p.onsuccess=()=>(s.close(),r(h.error)),p.onerror=f=>(s.close(),r(h.error))}},u.onerror=h=>(s.close(),r(u.error)),o.oncomplete=()=>{c==null?s.close():c.oncomplete=()=>s.close()}}},a.onerror=s=>r(a.error)})}};ai.URL_SCHEME="indexeddb://";var c5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ai.URL_SCHEME)?f9(e.slice(ai.URL_SCHEME.length)):null;St.registerSaveRouter(c5);St.registerLoadRouter(c5);function f9(e){return new ai(e)}function m9(e){return e.startsWith(ai.URL_SCHEME)?e.slice(ai.URL_SCHEME.length):e}var A9=class{constructor(){this.indexedDB=u5()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(gf,xf);n.onupgradeneeded=()=>wf(n),n.onsuccess=()=>{let r=n.result,a=r.transaction(Ra,"readonly"),s=a.objectStore(Ra).getAll();s.onsuccess=()=>{let i={};for(let o of s.result)i[o.modelPath]=o.modelArtifactsInfo;e(i)},s.onerror=i=>(r.close(),t(s.error)),a.oncomplete=()=>r.close()},n.onerror=r=>t(n.error)})}async removeModel(e){return e=m9(e),new Promise((t,n)=>{let r=this.indexedDB.open(gf,xf);r.onupgradeneeded=()=>wf(r),r.onsuccess=()=>{let a=r.result,s=a.transaction(Ra,"readwrite"),i=s.objectStore(Ra),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return a.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=i.delete(e),c=()=>{l=a.transaction(ri,"readwrite");let h=l.objectStore(ri).delete(e);h.onsuccess=()=>t(o.result.modelArtifactsInfo),h.onerror=d=>n(o.error)};u.onsuccess=c,u.onerror=h=>(c(),a.close(),n(o.error))}},o.onerror=u=>(a.close(),n(o.error)),s.oncomplete=()=>{l==null?a.close():l.oncomplete=()=>a.close()}},r.onerror=a=>n(r.error)})}},na="/",Qo="tensorflowjs_models",h5="info",y9="model_topology",g9="weight_specs",x9="weight_data",w9="model_metadata";function d5(e){return{info:[Qo,e,h5].join(na),topology:[Qo,e,y9].join(na),weightSpecs:[Qo,e,g9].join(na),weightData:[Qo,e,x9].join(na),modelMetadata:[Qo,e,w9].join(na)}}function _9(e){let t=e.split(na);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(na)}function b9(e){return e.startsWith(si.URL_SCHEME)?e.slice(si.URL_SCHEME.length):e}var si=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=d5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=Du(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,s9(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${r.modelTopologyBytes}, weightSpecsBytes=${r.weightSpecsBytes}, weightDataBytes=${r.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(e.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let t={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);t.modelTopology=n;let r=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(r==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=r;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)}let s=this.LS.getItem(this.keys.weightData);if(s==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=i9(s),t}};si.URL_SCHEME="localstorage://";var p5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(si.URL_SCHEME)?v9(e.slice(si.URL_SCHEME.length)):null;St.registerSaveRouter(p5);St.registerLoadRouter(p5);function v9(e){return new si(e)}var k9=class{constructor(){F(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Qo+na,n=na+h5;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=_9(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=b9(e);let t=d5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},el="://",Gn=class{constructor(){this.managers={}}static getInstance(){return Gn.instance==null&&(Gn.instance=new Gn),Gn.instance}static registerManager(e,t){F(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(el)&&(e=e.slice(0,e.indexOf(el))),F(e.length>0,()=>"scheme must not be an empty string.");let n=Gn.getInstance();F(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function od(e){if(e.indexOf(el)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Gn.getSchemes().join(",")}`);return{scheme:e.split(el)[0],path:e.split(el)[1]}}async function f5(e,t,n=!1){F(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=St.getLoadHandlers(e);F(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),F(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=St.getSaveHandlers(t);F(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),F(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=od(e).scheme,l=od(e).path,u=o===od(e).scheme,c=await a.load();n&&u&&await Gn.getManager(o).removeModel(l);let h=await i.save(c);return n&&!u&&await Gn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function N9(){let e=Gn.getSchemes(),t={};for(let n of e){let r=await Gn.getManager(n).listModels();for(let a in r){let s=n+el+a;t[s]=r[a]}}return t}async function I9(e){let t=od(e);return Gn.getManager(t.scheme).removeModel(t.path)}async function S9(e,t){return f5(e,t,!1)}async function T9(e,t){return f5(e,t,!0)}var C9=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 C9);try{Gn.registerManager(si.URL_SCHEME,new k9)}catch(e){}try{Gn.registerManager(ai.URL_SCHEME,new A9)}catch(e){}}var E9={importFetch:()=>T8()},_f,R9=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return J().global.fetch!=null?J().global.fetch(e,t):(_f==null&&(_f=E9.importFetch()),_f(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().setPlatform("node",new R9);function We(e,t="float32",n){return t=t||"float32",ef(e),new Ot(e,t,n)}function F9(e,t){let n=E(e,"x","cast");if(!Lg(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let r={x:n},a={dtype:t};return $.runKernel(hs,r,a)}var me=O({cast_:F9});function M9(e){let t={x:E(e,"x","clone","string_or_numeric")};return $.runKernel(ks,t)}var Rr=O({clone_:M9});function m5(e,t=!1){console.log(e.toString(t))}t5();var $9={buffer:We,cast:me,clone:Rr,print:m5};Gk($9);var bn={};ze(bn,{browserFiles:()=>D9,browserHTTPRequest:()=>z9,concatenateArrayBuffers:()=>yf,copyModel:()=>S9,decodeWeights:()=>i5,encodeWeights:()=>r9,fromMemory:()=>P9,getLoadHandlers:()=>p9,getModelArtifactsInfoForJSON:()=>Du,getSaveHandlers:()=>d9,http:()=>vf,isHTTPScheme:()=>bf,listModels:()=>N9,loadWeights:()=>O9,moveModel:()=>T9,registerLoadRouter:()=>h9,registerSaveRouter:()=>c9,removeModel:()=>I9,weightsLoaderFactory:()=>A5,withSaveHandler:()=>L9});var W9="model",B9=".json",V9=".weights.bin";function y5(e){return new Promise(t=>setTimeout(t)).then(e)}var tl=class{constructor(e){if(!J().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(tl.URL_SCHEME)&&(e=e.slice(tl.URL_SCHEME.length)),(e==null||e.length===0)&&(e=W9),this.modelTopologyFileName=e+B9,this.weightDataFileName=e+V9}async save(e){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"}));if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer);let a=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),s=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(s.download=this.modelTopologyFileName,s.href=a,await y5(()=>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 y5(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Du(e)}}}};tl.URL_SCHEME="downloads://";var U9=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{let a=new FileReader;a.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){r(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let l=i.weightsManifest;if(l==null){r(new Error(`weightManifest field is missing from file ${e.name}`));return}let u;try{u=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let c=[],h=[],d=[];l.forEach(p=>{p.paths.forEach(f=>{h.push(f),d.push(null)}),c.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=A=>{let y=A.target.result,g=h.indexOf(f);if(d[g]=y,d.indexOf(null)===-1){let _={modelTopology:o,weightSpecs:c,weightData:yf(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(_.signature=i.signature),i.userDefinedMetadata!=null&&(_.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(_.modelInitializer=i.modelInitializer),n(_)}},m.onerror=A=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(u[f])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>l5(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=l5(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),r.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);a[i]=t[r.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return a}},j9=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(tl.URL_SCHEME)?H9(e.slice(tl.URL_SCHEME.length)):null;St.registerSaveRouter(j9);function H9(e="model"){return new tl(e)}function D9(e){return new U9(e)}function g5(e,t,n,r){i(e),n=n==null?0:n,r=r==null?1:r,o(n,r);let a=0,s=l=>(l.then(u=>{let c=n+ ++a/e.length*(r-n);return t(c),u}),l);function i(l){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),F(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function x5(e,t){t==null&&(t={});let n=t.fetchFunc==null?J().platform.fetch:t.fetchFunc,r=e.map(u=>n(u,t.requestInit,{isBinary:!0})),a=0,s=.5,i=(t.onProgress==null?await Promise.all(r):await g5(r,t.onProgress,a,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await g5(i,t.onProgress,o,l)}async function O9(e,t="",n,r){return A5(a=>x5(a,{requestInit:r}))(e,t,n)}function A5(e){return async(t,n="",r)=>{let a=t.map(()=>!1),s={},i=r!=null?r.map(()=>!1):[],o=[];if(t.forEach((p,f)=>{let m=0;p.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=mf[y]*Dt(A.shape),_=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:A,groupOffset:m,sizeBytes:g})};r!=null?r.forEach((b,w)=>{b===A.name&&(_(),i[w]=!0)}):_(),o.push(A.name),m+=g})}),!i.every(p=>p)){let p=r.filter((f,m)=>!i[m]);throw new Error(`Could not find weights in manifest with names: ${p.join(", ")}.
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=a.reduce((p,f,m)=>(f&&p.push(m),p),[]),u=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;u.push(m)})});let c=await e(u),h={},d=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let _=0;_<f;_++)m+=c[d+_].byteLength;let A=new ArrayBuffer(m),y=new Uint8Array(A),g=0;for(let _=0;_<f;_++){let b=new Uint8Array(c[d+_]);y.set(b,g),g+=b.byteLength}s[p].forEach(_=>{let b=A.slice(_.groupOffset,_.groupOffset+_.sizeBytes),w=i5(b,[_.manifestEntry]);for(let x in w)h[x]=w[x]}),d+=f}),h}}var G9="application/octet-stream",q9="application/json",kf=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?(F(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,F(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&F(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:q9}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:G9}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:Du(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(p){let f=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?f+=" 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.":f+=" Please make sure the server is serving valid JSON for this request.",new Error(f)}let n=t.modelTopology,r=t.weightsManifest,a=t.generatedBy,s=t.convertedBy,i=t.format,o=t.signature,l=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let u,c;r!=null&&([u,c]=await this.loadWeights(r));let h={modelTopology:n,weightSpecs:u,weightData:c,generatedBy:a,convertedBy:s,format:i};o!=null&&(h.signature=o),l!=null&&(h.userDefinedMetadata=l);let d=t.modelInitializer;return d&&(h.modelInitializer=d),h}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=X9(t),a=this.weightPathPrefix||n,s=[];for(let u of e)s.push(...u.weights);let i=[],o=[];for(let u of e)for(let c of u.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(c)):i.push(a+c+r);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await x5(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,yf(l)]}};kf.URL_SCHEME_REGEX=/^https?:\/\//;function X9(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),a=n>t?e.substring(n):"";return[r+"/",a]}function bf(e){return e.match(kf.URL_SCHEME_REGEX)!=null}var w5=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>bf(r)):n=bf(e),n)return vf(e,t)}return null};St.registerSaveRouter(w5);St.registerLoadRouter(w5);function vf(e,t){return new kf(e,t)}function z9(e,t){return vf(e,t)}var Nf=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},K9=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function P9(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new Nf(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 Nf({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 Nf({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function L9(e){return new K9(e)}var _5={};ze(_5,{confusionMatrix:()=>Z9});function Y9(e,t,n=!1,r=!1){let a=E(e,"a","matMul"),s=E(t,"b","matMul");[a,s]=bt(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return $.runKernel(cs,i,o)}var Ge=O({matMul_:Y9});function J9(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:E(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return $.runKernel($s,a,s)}var nl=O({oneHot_:J9});function Q9(e,t){let n=E(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{F(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},a={perm:t};return $.runKernel(Js,r,a)}var at=O({transpose_:Q9});function eN(e,t,n){let r=E(e,"labels","confusionMatrix"),a=E(t,"predictions","confusionMatrix");F(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),F(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),F(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),F(r.shape[0]===a.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${a.shape[0]}. Labels and predictions should have the same number of elements.`),F(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=nl(me(r,"int32"),n),i=nl(me(a,"int32"),n),o=at(s),l=Ge(o,i);return me(l,"int32")}var Z9=O({confusionMatrix_:eN}),rl={};ze(rl,{fromPixels:()=>rN,fromPixelsAsync:()=>tN,toPixels:()=>nN});function ld(e,t,n){if(ss(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=Er(e,n);if(r.length!==3&&r.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}var al;function b5(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,a=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!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 ${e.constructor.name}`);if(a){let d=2;if(a&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(td(ed,$.backendName)!=null){let d={pixels:e},p={numChannels:t};return $.runKernel(ed,d,p)}let[l,u]=a?[e.videoWidth,e.videoHeight]:[e.width,e.height],c;i?c=e.getContext("2d").getImageData(0,0,l,u).data:r||n?c=e.data:(s||a||o)&&(al==null&&(al=document.createElement("canvas").getContext("2d")),al.canvas.width=l,al.canvas.height=u,al.drawImage(e,0,0,l,u),c=al.getImageData(0,0,l,u).data);let h;if(t===4)h=new Int32Array(c);else{let d=l*u;h=new Int32Array(d*t);for(let p=0;p<d;p++)for(let f=0;f<t;++f)h[p*t+f]=c[p*4+f]}return ld(h,[u,l,t],"int32")}function aN(e){return e!=null&&e.data instanceof Uint8Array}function sN(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function iN(e){return e!=null&&e.width!==0&&e.height!==0}function oN(e){return sN()&&!(e instanceof ImageBitmap)&&iN(e)&&!aN(e)}async function tN(e,t=3){let n=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&oN(e)){let r;try{r=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(a){r=null}r!=null&&r.width===e.width&&r.height===e.height?n=r:n=e}else n=e;return b5(n,t)}async function nN(e,t){let n=E(e,"img","toPixels");if(!(e instanceof Ue)){let u=n;n=me(u,"int32"),u.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[r,a]=n.shape.slice(0,2),s=n.rank===2?1:n.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(a*r*4);for(let u=0;u<r*a;++u){let c=[0,0,0,255];for(let d=0;d<s;d++){let p=i[u*s+d];if(n.dtype==="float32"){if(p<0||p>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${p}.`)}else if(n.dtype==="int32"&&(p<0||p>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${p}.`);s===1?(c[0]=p*o,c[1]=p*o,c[2]=p*o):c[d]=p*o}let h=u*4;l[h+0]=Math.round(c[0]),l[h+1]=Math.round(c[1]),l[h+2]=Math.round(c[2]),l[h+3]=Math.round(c[3])}if(t!=null){t.width=a,t.height=r;let u=t.getContext("2d"),c=new ImageData(l,a,r);u.putImageData(c,0,0)}return n!==e&&n.dispose(),l}var rN=O({fromPixels_:b5}),If={};ze(If,{prepareAndValidate:()=>v5});function v5(e,t){let n=e.shape.length,r=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);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[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(Dt(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<n;++h)u*=o[h],l.push(o[h]);let c=[...Xi(e.shape).map(h=>h/u),1].slice(0,s);return[l,i,u,c]}var Sf={};ze(Sf,{calculateShapes:()=>k5,validateInput:()=>Cf,validateUpdateShape:()=>Tf});function Tf(e,t,n){let r=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: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${r}, and batchDim: ${a}.`;if(n.rank<a)throw new Error(s+` update.rank < ${a}. `);if(e.length<r+(n.rank-a))throw new Error(s+` Output shape length < ${r+(n.rank-a)}`);if(n.rank!==a+e.length-r)throw new Error(s+` update.rank != ${a+e.length-r}`);for(let i=0;i<a;++i)if(n.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<n.rank-a;++i)if(n.shape[i+a]!==e[i+r])throw new Error(s+` updates.shape[${i+a}] (${n.shape[i+a]}) != shape[${i+a}] (${e[i+a]})`)}function Cf(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);if(n.length===0){if(t.size===0)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(e.size===0)throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`)}Tf(n,t,e)}function k5(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=Dt(t.shape)/o,u=[...Xi(n.slice(0,a)),1],c=Dt(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:u,outputSize:c}}var cn={};ze(cn,{assertParamsValid:()=>lN,computeFlatOffset:()=>cN,computeOutShape:()=>N5,getNormalizedAxes:()=>S5,isSliceContinous:()=>uN,maskToAxes:()=>ud,parseSliceParams:()=>M5,sliceInfo:()=>hN,startForAxis:()=>R5,startIndicesWithElidedDims:()=>T5,stopForAxis:()=>F5,stopIndicesWithElidedDims:()=>C5,stridesForAxis:()=>E5,stridesWithElidedDims:()=>I5});function lN(e,t,n){let r=e.shape.length;F(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),F(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let a=0;a<r;++a)F(t[a]+n[a]<=e.shape[a],()=>`Error in slice${r}D: begin[${a}] + size[${a}] (${t[a]+n[a]}) would overflow input.shape[${a}] (${e.shape[a]})`)}function ud(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function N5(e,t,n){let r=[];for(let a=0;a<e.length;a++)r[a]=Math.ceil((t[a]-e[a])/n[a]);return r}function I5(e,t,n,r){let a=[...e];for(let s=a.length;s<r.length;s++)a.push(1);for(let s=0;s<n;s++)s===0?a[t]=1:(a.splice(t,0,1),a.pop());return a}function $5(e,t,n){return n<=e?n:n-(t-1)}function D5(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function S5(e,t,n,r,a,s,i,o,l){let u=e.length,c=new Array(u),h=new Array(u),d=new Array(u);if(t.length&&n>0){let p=t[0],f=n+1;c=T5(i,p,f,r,e),h=C5(o,p,f,a,e),d=I5(s,p,f,e)}else for(let p=0;p<u;p++)c[p]=R5(i,r,s,e,p,l),h[p]=F5(o,a,s,e,p,l),d[p]=E5(s,p,l);return{begin:c,end:h,strides:d}}function T5(e,t,n,r,a){let s=[...a],i=D5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=$5(t,n,o),u=r[l];e&1<<l&&(u=0),s[o]=u}return s}function C5(e,t,n,r,a){let s=[...a],i=D5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=$5(t,n,o),u=r[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]=iu(0,s[o],a[o])}return s}function E5(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function R5(e,t,n,r,a,s){let i=t[a],o=n[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let l=r[a];return i<0&&(i+=l),i=iu(0,i,l-1),i}function F5(e,t,n,r,a,s){let i=t[a],o=n[a]||1;(e&1<<a||s&1<<a||i==null)&&(o>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let l=r[a];return i<0&&(i+=l),o>0?i=iu(0,i,l):i=iu(-1,i,l-1),i}function uN(e,t,n){let r=n.length;for(let a=0;a<n.length;a++)if(n[a]>1){r=a;break}for(let a=r+1;a<n.length;a++)if(t[a]>0||n[a]!==e[a])return!1;return!0}function cN(e,t){let n=e.length>0?e[e.length-1]:1;for(let r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function M5(e,t,n){let r,a=e.shape.length;typeof t=="number"?r=[t,...new Array(a-1).fill(0)]:t.length<a?r=t.concat(new Array(a-t.length).fill(0)):r=t.slice(),r.forEach(i=>{F(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(a).fill(-1):typeof n=="number"?s=[n,...new Array(a-1).fill(-1)]:n.length<a?s=n.concat(new Array(a-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(F(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function hN(e,t,n,r,a,s,i,o,l){let u=t.slice(),c=n.slice(),h=r;r==null&&(h=new Array(u.length));let d=ud(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-u.length,f=ud(o),m=e.slice();f.forEach(x=>{u[x]=0,c[x]=1,m.splice(x,0,1)});let{begin:A,end:y,strides:g}=S5(m,d,p,u,c,h,a,s,i);u=A,c=y,h=g;let _=ud(l);_.forEach(x=>{c[x]=u[x]+1,h[x]=1});let b=N5(u,c,h),w=b.filter((x,I)=>_.indexOf(I)===-1);return{nonStrided:h.every(x=>x===1),$begin:u,$end:c,$strides:h,size:b,newShape:m,outShape:w}}var re={};ze(re,{Serializable:()=>O5,SerializationMap:()=>ii,registerClass:()=>Fa});var O5=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ii=class{constructor(){this.classNameMap={}}static getMap(){return ii.instance==null&&(ii.instance=new ii),ii.instance}static register(e){ii.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Fa(e){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ii.register(e)}var z5={};ze(z5,{TEST_EPSILON_FLOAT16:()=>P5,encodeStrings:()=>L5,expectArrayBuffersEqual:()=>yN,expectArraysClose:()=>dN,expectArraysEqual:()=>fN,expectNumbersClose:()=>mN,expectPromiseToFail:()=>pN,expectValuesInRange:()=>AN,testEpsilon:()=>Ef});var gN=.001,P5=.1;function dN(e,t,n){return n==null&&(n=Ef()),Rf(e,t,(r,a)=>Ff(r,a,n))}function Ef(){return $.backend.floatPrecision()===32?gN:P5}function Rf(e,t,n){let r=!0;if((sn(e)||sn(t))&&(r=!1),sn(e)&&sn(t)&&(r=!0),r){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Er(e),o=Er(t);if(!ta(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=sn(e)?e:is(e),s=sn(t)?t:is(t);if(a.length!==s.length)throw new Error(`Arrays have different lengths actual: ${a.length} vs expected: ${s.length}.
Actual: ${a}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=a[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
Actual: ${a}.
Expected: ${s}.`)}}function pN(e,t){e().then(()=>t.fail(),()=>t())}function fN(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return va(e)||va(e[0])||va(t)||va(t[0])?Rf(e,n,(r,a)=>r==a):Rf(e,t,(r,a)=>Ff(r,a,0))}function mN(e,t,n){if(n==null&&(n=Ef()),!Ff(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Ff(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function AN(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function yN(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function L5(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?L5(n):e[t]=Su(n)}return e}var xN="3.3.0";function wN(){J().set("PROD",!0)}function _N(){J().set("DEBUG",!0)}function bN(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Mf(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}qk(Mf);function vN(){$.disposeVariables()}function Fr(){return $}function cd(){return $.memory()}function gr(e){return $.profile(e)}function B(e,t){return $.tidy(e,t)}function Ee(e){lf(e).forEach(t=>t.dispose())}function Gt(e){return $.keep(e)}function kN(e){return $.time(e)}function NN(e){return $.setBackend(e)}function IN(){return $.ready()}function SN(){return $.backendName}function TN(e){$.removeBackend(e)}function $f(e){return $.findBackend(e)}function CN(e){return $.findBackendFactory(e)}function sl(e,t,n=1){return $.registerBackend(e,t,n)}function W5(){return $.backend}function EN(e,t){J().setPlatform(e,t)}function RN(e,t){let n=E(e,"a","add"),r=E(t,"b","add");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Na,a)}var se=O({add_:RN});function FN(e,t){let n=E(e,"a","floorDiv"),r=E(t,"b","floorDiv");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(_s,a)}var hd=O({floorDiv_:FN});function MN(e,t){let n=E(e,"a","div"),r=E(t,"b","div");if([n,r]=bt(n,r),n.dtype==="int32"&&r.dtype==="int32")return hd(n,r);let a={a:n,b:r},s={};return $.runKernel(gs,a,s)}var ge=O({div_:MN});function $N(e,t){let n=E(e,"a","mul"),r=E(t,"b","mul");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Ms,a)}var P=O({mul_:$N});function DN(e){let t=E(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return $.runKernel(hu,n)}else{let n={x:t};return $.runKernel(Zi,n)}}var zt=O({abs_:DN});function ON(e){let t={x:E(e,"x","acos")};return $.runKernel(Yi,t)}var Df=O({acos_:ON});function zN(e){let t={x:E(e,"x","acosh")};return $.runKernel(Ji,t)}var Of=O({acosh_:zN});function PN(e){F(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),F(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>E(a,`tensors${s}`,"addN")),n=t[0];t.forEach(a=>{if(a.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(a=>{if(!ta(a.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return $.runKernel(os,r)}var Ma=O({addN_:PN});function LN(e,t=null,n=!1){let r={x:E(e,"x","all","bool")},a={axis:t,keepDims:n};return $.runKernel(vh,r,a)}var dd=O({all_:LN});function WN(e,t=null,n=!1){let r={x:E(e,"x","any","bool")},a={axis:t,keepDims:n};return $.runKernel(kh,r,a)}var Ou=O({any_:WN});function BN(e,t=0){let n={x:E(e,"x","argMax")},r={axis:t};return $.runKernel(ls,n,r)}var zu=O({argMax_:BN});function VN(e,t=0){let n={x:E(e,"x","argMin")},r={axis:t};return $.runKernel(lu,n,r)}var zf=O({argMin_:VN});function UN(e){let t={x:E(e,"x","asin")};return $.runKernel(Qi,t)}var Pf=O({asin_:UN});function HN(e){let t={x:E(e,"x","asinh")};return $.runKernel(eo,t)}var Lf=O({asinh_:HN});function jN(e){let t={x:E(e,"x","atan")};return $.runKernel(to,t)}var Wf=O({atan_:jN});function GN(e,t){let n=E(e,"a","atan2"),r=E(t,"b","atan2");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(ro,a)}var Bf=O({atan2_:GN});function qN(e){let t={x:E(e,"x","atanh")};return $.runKernel(no,t)}var Vf=O({atanh_:qN});function XN(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=B5(a);return Pu(e,o,n,s,r,null,null,l)}function V5(e,t,n,r,a,s,i="channelsLast"){let[o,l]=pd(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 Pu(e,u,n,r,a,s,!1,i)}function KN(e,t,n,r,a,s,i="NDHWC"){let[o,l,u]=Uf(t),c,h;if(i==="NDHWC")h="channelsLast",c=[o,l,u,e[4],e[4]];else if(i==="NCDHW")h="channelsFirst",c=[o,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return U5(e,c,n,r,a,!1,h,s)}function Pu(e,t,n,r,a,s,i=!1,o="channelsLast"){let[l,u,c,h]=[-1,-1,-1,-1];if(o==="channelsLast")[l,u,c,h]=e;else if(o==="channelsFirst")[l,h,u,c]=e;else throw new Error(`Unknown dataFormat ${o}`);let[d,p,,f]=t,[m,A]=pd(n),[y,g]=pd(r),_=il(d,y),b=il(p,g),{padInfo:w,outHeight:x,outWidth:I}=ZN(a,u,c,m,A,_,b,s,o),S=i?f*h:f,T;return o==="channelsFirst"?T=[l,S,x,I]:o==="channelsLast"&&(T=[l,x,I,S]),{batchSize:l,dataFormat:o,inHeight:u,inWidth:c,inChannels:h,outHeight:x,outWidth:I,outChannels:S,padInfo:w,strideHeight:m,strideWidth:A,filterHeight:d,filterWidth:p,effectiveFilterHeight:_,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:g,inShape:e,outShape:T,filterShape:t}}function U5(e,t,n,r,a,s=!1,i="channelsLast",o){let[l,u,c,h,d]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,h,d]=e;else if(i==="channelsFirst")[l,d,u,c,h]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,f,m,,A]=t,[y,g,_]=Uf(n),[b,w,x]=Uf(r),I=il(p,b),S=il(f,w),T=il(m,x),{padInfo:M,outDepth:D,outHeight:z,outWidth:W}=YN(a,u,c,h,y,g,_,I,S,T,o),U=s?A*d:A,H;return i==="channelsFirst"?H=[l,U,D,z,W]:i==="channelsLast"&&(H=[l,D,z,W,U]),{batchSize:l,dataFormat:i,inDepth:u,inHeight:c,inWidth:h,inChannels:d,outDepth:D,outHeight:z,outWidth:W,outChannels:U,padInfo:M,strideDepth:y,strideHeight:g,strideWidth:_,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:I,effectiveFilterHeight:S,effectiveFilterWidth:T,dilationDepth:b,dilationHeight:w,dilationWidth:x,inShape:e,outShape:H,filterShape:t}}function JN(e,t,n,r,a){r==null&&(r=Hf(e,t,n));let s=e[0],i=e[1],o=oi((s-t+2*r)/n+1,a),l=oi((i-t+2*r)/n+1,a);return[o,l]}function QN(e,t,n,r,a,s){a==null&&(a=Hf(e,t,r));let i=e[0],o=e[1],l=e[2],u=oi((i-t+2*a)/r+1,s),c=oi((o-t+2*a)/r+1,s),h=oi((l-t+2*a)/r+1,s);return[u,c,h,n]}function Hf(e,t,n,r=1){let a=il(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function pd(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Uf(e){return typeof e=="number"?[e,e,e]:e}function il(e,t){return t<=1?e:e+(e-1)*(t-1)}function ZN(e,t,n,r,a,s,i,o,l){let u,c,h;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let d=JN([t,n],s,r,e,o);c=d[0],h=d[1]}else if(e==="same"){c=Math.ceil(t/r),h=Math.ceil(n/a);let d=Math.max(0,(c-1)*r+s-t),p=Math.max(0,(h-1)*a+i-n),f=Math.floor(d/2),m=d-f,A=Math.floor(p/2),y=p-A;u={top:f,bottom:m,left:A,right:y,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-s+1)/r),h=Math.ceil((n-i+1)/a);else if(typeof e=="object"){let d=l==="channelsLast"?e[1][0]:e[2][0],p=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:d,bottom:p,left:f,right:m,type:d===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},c=oi((t-s+d+p)/r+1,o),h=oi((n-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:c,outWidth:h}}function YN(e,t,n,r,a,s,i,o,l,u,c){let h,d,p,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=QN([t,n,r,1],o,1,a,e,c);d=m[0],p=m[1],f=m[2]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/s),f=Math.ceil(r/i);let m=(d-1)*a+o-t,A=(p-1)*s+l-n,y=(f-1)*i+u-r,g=Math.floor(m/2),_=m-g,b=Math.floor(A/2),w=A-b,x=Math.floor(y/2),I=y-x;h={top:b,bottom:w,left:x,right:I,front:g,back:_,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),p=Math.ceil((n-l+1)/s),f=Math.ceil((r-u+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:d,outHeight:p,outWidth:f}}function oi(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 $a(e){let[t,n,r]=pd(e);return t===1&&n===1&&r===1}function Mr(e,t){return $a(e)||$a(t)}function B5(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function eI(e,t){let n={x:E(e,"x","reshape","string_or_numeric")},r={shape:t};return $.runKernel(Do,n,r)}var G=O({reshape_:eI});function tI(e,t,n,r,a){let s=E(e,"x","avgPool","float32"),i=1;F(Mr(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&F(jt(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(us,u,c);return h=me(h,s.dtype),l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Lu=O({avgPool_:tI});function nI(e,t,n,r,a,s="NDHWC"){let i=E(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]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(jt(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(uu,u,c);return h=me(h,o.dtype),l?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var jf=O({avgPool3d_:nI});function rI(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=$u(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${s.dtype}. `)}),n.length===1)return Rr(n[0]);let r=n,a={axis:t};return $.runKernel(ao,r,a)}var st=O({concat_:rI});function aI(e){let t={x:E(e,"x","sigmoid")};return $.runKernel(js,t)}var Fn=O({sigmoid_:aI});function sI(e,t,n){let r=E(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return $.runKernel(Lo,a,s)}var Re=O({slice_:sI});function iI(e){let t={x:E(e,"x","tanh")};return $.runKernel(Ys,t)}var ol=O({tanh_:iI});function oI(e,t,n,r,a,s){let i=E(e,"forgetBias","basicLSTMCell"),o=E(t,"lstmKernel","basicLSTMCell"),l=E(n,"lstmBias","basicLSTMCell"),u=E(r,"data","basicLSTMCell"),c=E(a,"c","basicLSTMCell"),h=E(s,"h","basicLSTMCell"),d=st([u,h],1),p=Ge(d,o),f=se(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Re(f,[0,0],y),_=Re(f,[0,A],y),b=Re(f,[0,A*2],y),w=Re(f,[0,A*3],y),x=se(P(Fn(g),ol(_)),P(c,Fn(se(i,b)))),I=P(ol(x),Fn(w));return[x,I]}var lI=O({basicLSTMCell_:oI});function uI(e,t,n){let r=E(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(cu,s,i)}var Wu=O({batchToSpaceND_:uI});function cI(e){let t;return e.rank===0||e.rank===1?t=G(e,[1,1,1,e.size]):e.rank===2?t=G(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function hI(e,t,n,r,a,s){s==null&&(s=.001);let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;r!=null&&(c=E(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:cI(i),scale:u,offset:c,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(bs,h,d);return G(p,i.shape)}var li=O({batchNorm_:hI});function dI(e,t,n,r,a,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;return r!=null&&(c=E(r,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),li(i,o,l,c,u,s)}var H5=O({batchNorm2d_:dI});function pI(e,t,n,r,a,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;return r!=null&&(c=E(r,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),li(i,o,l,c,u,s)}var j5=O({batchNorm3d_:pI});function fI(e,t,n,r,a,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;return r!=null&&(c=E(r,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),li(i,o,l,c,u,s)}var G5=O({batchNorm4d_:fI});function mI(e,t,n){let r=E(e,"x","bincount"),a=E(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return $.runKernel(Sh,s,i)}var q5=O({bincount_:mI});function AI(e,t){let n=E(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=G(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return Rr(n);let i={x:n},o={reps:s};return $.runKernel(Sa,i,o)}var Bu=O({broadcastTo_:AI});function yI(e){let t={x:E(e,"x","ceil")};return $.runKernel(ds,t)}var Gf=O({ceil_:yI});function gI(e,t,n){let r=E(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Ia,a,s)}var vn=O({clipByValue_:gI});function xI(e){return st(e,0)}var X5=O({concat1d_:xI});function wI(e,t){return st(e,t)}var ll=O({concat2d_:wI});function _I(e,t){return st(e,t)}var K5=O({concat3d_:_I});function bI(e,t){return st(e,t)}var Z5=O({concat4d_:bI});function vI(e,t,n,r,a="NHWC",s=[1,1],i){let o=E(e,"x","conv2d"),l=E(t,"filter","conv2d"),u=o,c=!1;o.rank===3&&(c=!0,u=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(jt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?u.shape[3]:u.shape[1];F(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),F(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:u,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=$.runKernel(ps,d,p);return c?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ra=O({conv2d_:vI});function kI(e,t,n,r,a="NWC",s=1,i){let o=E(e,"x","conv1d"),l=E(t,"filter","conv1d"),u=o,c=!1;o.rank===2&&(c=!0,u=G(o,[1,o.shape[0],o.shape[1]])),F(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(jt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(Mr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=G(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=G(u,[u.shape[0],1,u.shape[1],u.shape[2]]),p=ra(d,h,[1,n],r,"NHWC",[1,s],i);return c?G(p,[p.shape[2],p.shape[3]]):G(p,[p.shape[0],p.shape[2],p.shape[3]])}var fd=O({conv1d_:kI});function NI(e,t,n,r,a,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${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]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];F(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),F(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(jt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=$.runKernel(fs,d,p);return u?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var qf=O({conv2DBackpropInput_:NI});function II(e,t,n,r,a,s){let i=E(e,"x","conv2dTranspose"),o=E(t,"filter","conv2dTranspose");return qf(n,i,o,r,a,"NHWC",s)}var md=O({conv2dTranspose_:II});function SI(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=E(e,"x","conv3d"),o=E(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]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(Mr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let c={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=$.runKernel(du,c,h);return u?G(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Xf=O({conv3d_:SI});function TI(e,t,n,r,a){F(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];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel(Rh,c,h);return o?G(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Y5=O({conv3DBackpropInput_:TI});function CI(e,t,n,r,a){let s=E(e,"x","conv3dTranspose"),i=E(t,"filter","conv3dTranspose");return Y5(n,s,i,r,a)}var EI=O({conv3dTranspose_:CI});function RI(e){let t={x:E(e,"x","cos")};return $.runKernel(ms,t)}var Vu=O({cos_:RI});function FI(e){let t={x:E(e,"x","cosh")};return $.runKernel(so,t)}var Ad=O({cosh_:FI});function MI(e,t=0,n=!1,r=!1){let a={x:E(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(As,a,s)}var yd=O({cumsum_:MI});function $I(e,t,n,r=!1){let a=E(e,"x","denseBincount"),s=E(t,"weights","denseBincount");F(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),F(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(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:n,binaryOutput:r};return $.runKernel(Fh,i,o)}var J5=O({denseBincount_:$I});function DI(e,t,n="NHWC"){let r=E(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${a} and ${t} for depthToSpace with input shape
${r.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${s} and ${t} for depthToSpace with input shape
${r.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);let o={x:r},l={blockSize:t,dataFormat:n};return $.runKernel(oo,o,l)}var Kf=O({depthToSpace_:DI});function OI(e,t,n,r,a="NHWC",s=[1,1],i){let o=E(e,"x","depthwiseConv2d"),l=E(t,"filter","depthwiseConv2d"),u=o,c=!1;o.rank===3&&(c=!0,u=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(jt(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h={x:u,filter:l},d={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},p=$.runKernel(ys,h,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ul=O({depthwiseConv2d_:OI});function zI(e){let t={x:E(e,"x","diag")};return $.runKernel(Dh,t)}var PI=O({diag_:zI});function LI(e,t,n,r,a=[1,1],s="NHWC"){let i=E(e,"x","dilation2d"),o=E(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,u=!1;i.rank===3&&(l=G(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let c={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=$.runKernel(pu,c,h);return u?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Zf=O({dilation2d_:LI});function WI(e,t){let n=e.length,r=[];for(let a=0;a<n;a++){let s=n-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&i===1&&r.unshift(s)}return r}function Pt(e,t){let n=[];for(let r=0;r<t.length;r++){let a=e[e.length-r-1],s=t.length-r-1,i=t[s];(a==null||a===1&&i>1)&&n.unshift(s)}return n}function yt(e,t){let n=[],r=Math.max(e.length,t.length);for(let a=0;a<r;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)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}function BI(e,t){let n=E(e,"a","equal"),r=E(t,"b","equal");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(co,a)}var Da=O({equal_:BI});function VI(e,t,n){let r=E(t,"a","where"),a=E(n,"b","where"),s=E(e,"condition","where","bool"),i=yt(r.shape,a.shape),o=Bu(r,i),l=Bu(a,i);s.rank===1&&F(s.shape[0]===r.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&an(s.shape,l.shape,"Error in where: ");let u={condition:s,t:o,e:l};return $.runKernel(zo,u)}var kn=O({where_:VI});function UI(e){let t={x:E(e,"x","zerosLike")};return $.runKernel(Xo,t)}var He=O({zerosLike_:UI});function HI(e,t){let n=E(e,"a","div"),r=E(t,"b","div");[n,r]=bt(n,r);let a=ge(n,r),s=He(a),i=Da(r,s);return kn(i,s,a)}var Yf=O({divNoNan_:HI});function jI(e,t){let n=E(e,"t1","dot"),r=E(t,"t2","dot");F((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let a=n.rank===1?n.size:n.shape[1],s=r.rank===1?r.size:r.shape[0];if(F(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=G(n,[1,-1]),o=G(r,[-1,1]),l=Ge(i,o);return G(l,[])}else if(n.rank===1&&r.rank===2){let i=G(n,[1,-1]),o=G(r,[r.shape[0],r.shape[1]]),l=Ge(i,o);return G(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=G(r,[-1,1]),o=Ge(n,i);return G(o,[o.size])}else{let i=G(r,[r.shape[0],r.shape[1]]);return Ge(n,i)}}var Q5=O({dot_:jI});function GI(e){let t={x:E(e,"x","elu")};return $.runKernel(lo,t)}var cl=O({elu_:GI});function qI(e){let t=E(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return $.runKernel(uo,n)}var Jf=O({erf_:qI});function XI(e){let t={x:E(e,"x","exp")};return $.runKernel(xs,t)}var qn=O({exp_:XI});function KI(e,t=0){let n=E(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return $.runKernel(ho,r,a)}var hn=O({expandDims_:KI});function ZI(e){let t={x:E(e,"x","expm1")};return $.runKernel(po,t)}var Qf=O({expm1_:ZI});function YI(e,t){let n=E(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return $.runKernel(Sa,r,a)}var Oa=O({tile_:YI});function JI(e,t,n,r="float32"){t==null&&(t=e);let a=We([e,t],r),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(n==null)return i;if(n.length===1)return Oa(hn(i,0),[n[0],1,1]);if(n.length===2)return Oa(hn(hn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Oa(hn(hn(hn(i,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var em=O({eye_:JI});function Uu(e,t,n){let r={shape:e,value:t,dtype:n};return $.runKernel(fu,{},r)}function QI(e){let t={x:E(e,"x","floor")};return $.runKernel(ws,t)}var hl=O({floor_:QI});function eS(e,t,n=0,r=0){let a=E(e,"x","gather"),s=E(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return $.runKernel(mo,i,o)}var ui=O({gather_:eS});function tS(e,t){let n=E(e,"a","greater"),r=E(t,"b","greater");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(yo,a)}var rr=O({greater_:tS});function nS(e,t){let n=E(e,"a","greaterEqual"),r=E(t,"b","greaterEqual");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(vs,a)}var za=O({greaterEqual_:nS});function rS(e){let t={input:E(e,"input","imag")};return $.runKernel(Bh,t)}var gd=O({imag_:rS});function aS(e){let t={x:E(e,"x","isFinite")};return $.runKernel(go,t)}var ex=O({isFinite_:aS});function sS(e){let t={x:E(e,"x","isInf")};return $.runKernel(xo,t)}var tx=O({isInf_:sS});function iS(e){let t={x:E(e,"x","isNaN")};return $.runKernel(wo,t)}var nx=O({isNaN_:iS});function oS(e,t=.2){let n={x:E(e,"x","leakyRelu")},r={alpha:t};return $.runKernel(Ns,n,r)}var Hu=O({leakyRelu_:oS});function lS(e,t){let n=E(e,"a","less"),r=E(t,"b","less");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(_o,a)}var xd=O({less_:lS});function uS(e,t){let n=E(e,"a","lessEqual"),r=E(t,"b","lessEqual");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(bo,a)}var ci=O({lessEqual_:uS});function rx(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return $.runKernel(Vh,{},r)}function cS(e,t=5,n=1,r=1,a=.5){let s=E(e,"x","localResponseNormalization");F(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${s.rank}.`),F(jt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=G(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},u={depthRadius:t,bias:n,alpha:r,beta:a},c=$.runKernel(yu,l,u);return o?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var tm=O({localResponseNormalization_:cS});function hS(e){let t={x:E(e,"x","log")};return $.runKernel(Is,t)}var Mn=O({log_:hS});function dS(e){let t={x:E(e,"x","log1p")};return $.runKernel(vo,t)}var wd=O({log1p_:dS});function pS(e){return F(ka(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=E(t,"x","tf.grad","string_or_numeric"),a=n!=null?E(n,"dy","tf.grad"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(r),[r],a);return a!=null&&an(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),_d(i),i[0]})}}function fS(e){return F(ka(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=$u(t,"args","tf.grads","string_or_numeric"),a=n!=null?E(n,"dy","tf.grads"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(...r),r,a);return a!=null&&an(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),_d(i),i})}}function mS(e){return F(ka(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof Ue,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof Ue,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=$.gradients(()=>e(t),[t],n);return _d(r),{grad:r[0],value:a}}}function AS(e){return F(ka(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(a=>a instanceof Ue),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof Ue,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=$.gradients(()=>e(...t),t,n);return n!=null&&an(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),_d(r.grads),r}}function ax(e,t){F(ka(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(u=>u instanceof Ru),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in $.registeredVariables)t.push($.registeredVariables[u])}let r=n?t.filter(u=>!u.trainable):null,a=t.length;t=t.filter(u=>u.trainable),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=$.gradients(e,t,null,s);F(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()."),F(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,c)=>{o[c]!=null&&(l[u.name]=o[c])}),r!=null&&r.forEach(u=>l[u.name]=null),{value:i,grads:l}}function $r(e){return $.customGrad(e)}function _d(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 yS(e){let t={x:E(e,"x","neg")};return $.runKernel(Io,t)}var vt=O({neg_:yS});function gS(e){let t={x:E(e,"x","softplus")};return $.runKernel(Vo,t)}var dl=O({softplus_:gS});function xS(e){let t=E(e,"x","logSigmoid");return $r(n=>({value:vt(dl(vt(n))),gradFunc:r=>P(r,Fn(vt(n)))}))(t)}var sx=O({logSigmoid_:xS});function wS(e,t=null,n=!1){let r={x:E(e,"x","max")},a={reductionIndices:t,keepDims:n};return $.runKernel(Ss,r,a)}var Xn=O({max_:wS});function _S(e,t){let n=E(e,"a","sub"),r=E(t,"b","sub");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Zs,a)}var ye=O({sub_:_S});function bS(e,t=null,n=!1){let r=E(e,"x","sum");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(qs,a,s)}var Te=O({sum_:bS});function vS(e,t=-1){let n=E(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return $r((r,a)=>{let s=!0,i=Xn(r,t,!0),o=ye(r,i),l=ye(me(o,"float32"),Mn(Te(qn(o),t,s)));return a([l]),{value:l,gradFunc:(u,c)=>{let[h]=c,d=!0,p=qn(h);return ye(u,P(Te(u,t,d),p))}}})(n)}var bd=O({logSoftmax_:vS});function nm(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function ix(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function ox(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function hi(e,t){let n=t.map(r=>1);return ix(e,n,t)}function kS(e,t,n){F(nm(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function lx(e,t){if(nm(e,t))return null;let n=[];for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);return e.forEach(r=>n.push(r)),n}function rm(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function NS(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function IS(e,t=null,n=!1){let r=E(e,"x","logSumExp"),a=tr(t,r.shape),s=Xn(r,a,!0),i=ye(r,s),o=qn(i),l=Te(o,a),u=Mn(l),c=se(G(s,u.shape),u);if(n){let h=hi(c.shape,a);return G(c,h)}return c}var am=O({logSumExp_:IS});function SS(e,t){let n=E(e,"a","logicalAnd","bool"),r=E(t,"b","logicalAnd","bool");yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(ko,a)}var ar=O({logicalAnd_:SS});function TS(e){let t={x:E(e,"x","logicalNot","bool")};return $.runKernel(mu,t)}var ju=O({logicalNot_:TS});function CS(e,t){let n=E(e,"a","logicalOr","bool"),r=E(t,"b","logicalOr","bool");yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Au,a)}var vd=O({logicalOr_:CS});function ES(e,t){let n=E(e,"a","logicalXor","bool"),r=E(t,"b","logicalXor","bool");return yt(n.shape,r.shape),ar(vd(e,t),ju(ar(e,t)))}var ux=O({logicalXor_:ES});function RS(e,t,n,r,a){let s=E(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]])),F(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),F(Mr(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&F(jt(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(Cs,u,c);return l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Gu=O({maxPool_:RS});function FS(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=E(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]])),F(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(jt(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(gu,u,c);return l?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var sm=O({maxPool3d_:FS});function MS(e,t,n,r,a=!1){let s={x:E(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:a},o=$.runKernel(Gh,s,i);return{result:o[0],indexes:o[1]}}var cx=O({maxPoolWithArgmax_:MS});function $S(e,t){let n=E(e,"a","maximum"),r=E(t,"b","maximum");[n,r]=bt(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Ts,a)}var Dr=O({maximum_:$S});function DS(e,t=null,n=!1){let r={x:E(e,"x","mean")},a={axis:t,keepDims:n};return $.runKernel(Es,r,a)}var kt=O({mean_:DS});function OS(e,t=null,n=!1){let r={x:E(e,"x","min")},a={axis:t,keepDims:n};return $.runKernel(Rs,r,a)}var pl=O({min_:OS});function zS(e,t){let n=E(e,"a","minimum"),r=E(t,"b","minimum");[n,r]=bt(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fs,a)}var fl=O({minimum_:zS});function PS(e,t,n){F(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=E(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");F(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let a=n==="reflect"?1:0;for(let o=0;o<r.rank;o++)F(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),F(t[o][0]>=0&&t[o][0]<=r.shape[o]-a&&t[o][1]>=0&&t[o][1]<=r.shape[o]-a,()=>`Padding in dimension ${o} cannot be greater than or equal to ${r.shape[o]-a} or less than 0 for input of shape ${r.shape}`);let s={paddings:t,mode:n},i={x:r};return $.runKernel(xu,i,s)}var im=O({mirrorPad_:PS});function LS(e,t){let n=E(e,"a","mod"),r=E(t,"b","mod");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(No,a)}var om=O({mod_:LS});function WS(e){let t=E(e,"x","square"),n={};return $.runKernel("Square",{x:t},n)}var lt=O({square_:WS});function BS(e,t=null,n=!1){e=E(e,"x","moments");let r=tr(t,e.shape),a=kt(e,r,n),s=a.shape;n||(s=hi(a.shape,r));let i=lt(ye(me(e,"float32"),G(a,s))),o=kt(i,r,n);return{mean:a,variance:o}}var kd=O({moments_:BS});function VS(e,t,n,r){let a=E(t,"data","multiRNNCell"),s=$u(n,"c","multiRNNCell"),i=$u(r,"h","multiRNNCell"),o=a,l=[];for(let h=0;h<e.length;h++){let d=e[h](o,s[h],i[h]);l.push(d[0]),l.push(d[1]),o=d[1]}let u=[],c=[];for(let h=0;h<l.length;h+=2)u.push(l[h]),c.push(l[h+1]);return[u,c]}var US=O({multiRNNCell_:VS});function HS(e,t,n,r=!1){let a=E(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 ${i}`);n=n||Math.random();let o={logits:i===1?G(a,[1,-1]):a},l={numSamples:t,seed:n,normalized:r},u=$.runKernel(qh,o,l);return i===1?G(u,[u.size]):u}var hx=O({multinomial_:HS});function jS(e,t){let n=E(e,"a","notEqual"),r=E(t,"b","notEqual");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(So,a)}var di=O({notEqual_:jS});function Et(e,t="float32"){if(t==="complex64"){let r=Et(e,"float32"),a=Et(e,"float32");return Ca(r,a)}let n=bh(Dt(e),t);return $.makeTensor(n,e,t)}function Or(e,t="float32"){if(t==="complex64"){let r=Or(e,"float32"),a=Et(e,"float32");return Ca(r,a)}let n=Q1(Dt(e),t);return $.makeTensor(n,e,t)}function GS(e){let t={x:E(e,"x","onesLike")};return $.runKernel(Ro,t)}var $n=O({onesLike_:GS});function qS(e,t){let n=E(e,"v1","outerProduct"),r=E(t,"v2","outerProduct");F(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let a=G(n,[-1,1]),s=G(r,[1,-1]);return Ge(a,s)}var XS=O({outerProduct_:qS});function KS(e,t,n=0){let r=E(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:n},s={x:r};return $.runKernel(Ds,s,a)}var aa=O({pad_:KS});function ZS(e,t,n=0){return F(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),aa(e,[t],n)}var YS=O({pad1d_:ZS});function JS(e,t,n=0){return F(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),aa(e,t,n)}var QS=O({pad2d_:JS});function eT(e,t,n=0){return F(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."),aa(e,t,n)}var tT=O({pad3d_:eT});function nT(e,t,n=0){return F(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."),aa(e,t,n)}var rT=O({pad4d_:nT});function aT(e,t,n){let r=E(e,"x","spaceToBatchND");F(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),F(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),F(r.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let a={x:r},s={blockShape:t,paddings:n};return $.runKernel(bu,a,s)}var qu=O({spaceToBatchND_:aT});function oT(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=E(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2]])),F(Mr(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let u=V5(o.shape,t,s,a,r),c=[u.dilationHeight,u.dilationWidth],h;r==="same"?h=iT([u.filterHeight,u.filterWidth],c):h=[[0,0],[0,0]];let d=c[0]===1&&c[1]===1,[p,f]=sT([u.inHeight,u.inWidth],c,h),m=d?r:"valid",A=d?o:qu(o,c,p),y=(n==="avg"?()=>Lu(A,t,s,m):()=>Gu(A,t,s,m))(),g=d?y:Wu(y,c,f);return l?G(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function sT(e,t,n){let r=n.map(c=>c[0]),a=n.map(c=>c[1]),s=e.concat(r,a),i=t.map((c,h)=>(c-s[h]%c)%c),o=a.map((c,h)=>c+i[h]),l=t.map((c,h)=>[r[h],o[h]]),u=t.map((c,h)=>[0,i[h]]);return[l,u]}function iT(e,t){let n=e.map((s,i)=>s+(s-1)*(t[i]-1)).map(s=>s-1),r=n.map(s=>Math.floor(s/2)),a=n.map((s,i)=>s-r[i]);return n.map((s,i)=>[r[i],a[i]])}var dx=O({pool_:oT});function lT(e,t){let n=E(e,"base","pow"),r=E(t,"exp","pow");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Os,a)}var sa=O({pow_:lT});function uT(e,t){let n=E(e,"x","prelu"),r=E(t,"alpha","prelu"),a={x:n,alpha:r};return $.runKernel(zs,a)}var Xu=O({prelu_:uT});function cT(e,t=null,n=!1){let r=E(e,"x","prod");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Mo,a,s)}var Nd=O({prod_:cT});function hT(e,t,n){let r=Dt(e),a=null;if(n==null||n==="float32")a=new Float32Array(r);else if(n==="int32")a=new Int32Array(r);else if(n==="bool")a=new Uint8Array(r);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<r;s++)a[s]=t();return $.makeTensor(a,e,n)}var dT=O({rand_:hT}),lm=qi(O8()),um=class{constructor(e,t,n,r,a){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=r,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let s=a||Math.random();this.random=lm.alea(s.toString())}nextValue(){if(!isNaN(this.nextVal)){let r=this.nextVal;return this.nextVal=NaN,r}let e,t,n=!1;for(;!n;){let r,a,s;do r=2*this.random()-1,a=2*this.random()-1,s=r*r+a*a;while(s>=1||s===0);let i=Math.sqrt(-2*Math.log(s)/s);e=this.mean+this.stdDev*r*i,t=this.mean+this.stdDev*a*i,(!this.truncated||this.isValidTruncated(e))&&(n=!0)}return(!this.truncated||this.isValidTruncated(t))&&(this.nextVal=this.convertValue(t)),this.convertValue(e)}convertValue(e){return this.dtype==null||this.dtype==="float32"?e:Math.round(e)}isValidTruncated(e){return e<=this.upper&&e>=this.lower}},pT=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let a=r||Math.random();this.randu=lm.alea(a.toString()),this.randn=new um(0,1,n,!1,this.randu()),e<1?this.d=e+2/3:this.d=e-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let e,t,n,r,a,s;for(;;){do r=this.randn.nextValue(),s=1+this.c*r;while(s<=0);if(s*=s*s,e=r*r,t=1-.331*e*e,n=.5*e+this.d*(1-s+Math.log(s)),a=this.randu(),a<t||Math.log(a)<n)break}return s=1/this.beta*this.d*s,this.alpha<1&&(s*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(s)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},fT=class{constructor(e=0,t=1,n,r){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,r==null&&(r=Math.random()),typeof r=="number"&&(r=r.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=lm.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function mT(e,t,n=1,r="float32",a){if(n==null&&(n=1),r==null&&(r="float32"),r!=="float32"&&r!=="int32")throw new Error(`Unsupported data type ${r}`);let s=new pT(t,n,r,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var AT=O({randomGamma_:mT});function yT(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let s=new um(t,n,r,!1,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var px=O({randomNormal_:yT});function gT(e,t=0,n=1,r="float32",a){let s=We(e,r),i=new fT(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var ml=O({randomUniform_:gT});function Id(e,t,n=1,r="float32"){if(n===0)throw new Error("Cannot have a step of zero");let a={start:e,stop:t,step:n,dtype:r};return $.runKernel(wu,{},a)}function xT(e){let t={input:E(e,"input","real")};return $.runKernel(Xh,t)}var Ku=O({real_:xT});function wT(e){let t={x:E(e,"x","reciprocal")};return $.runKernel($o,t)}var cm=O({reciprocal_:wT});function _T(e){let t={x:E(e,"x","relu")};return $.runKernel(Ps,t)}var zr=O({relu_:_T});function bT(e){let t={x:E(e,"x","relu6")};return $.runKernel(Ws,t)}var Sd=O({relu6_:bT});function vT(e,t){let n={x:E(e,"x","reverse")},r={dims:t};return $.runKernel(Bs,n,r)}var Dn=O({reverse_:vT});function kT(e){let t=E(e,"x","reverse");return F(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Dn(t,0)}var NT=O({reverse1d_:kT});function IT(e,t){let n=E(e,"x","reverse");return F(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Dn(n,t)}var ST=O({reverse2d_:IT});function TT(e,t){let n=E(e,"x","reverse");return F(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Dn(n,t)}var CT=O({reverse3d_:TT});function ET(e,t){let n=E(e,"x","reverse");return F(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Dn(n,t)}var RT=O({reverse4d_:ET});function FT(e){let t={x:E(e,"x","round")};return $.runKernel(Vs,t)}var hm=O({round_:FT});function MT(e){let t={x:E(e,"x","rsqrt")};return $.runKernel(Us,t)}var Td=O({rsqrt_:MT});function be(e,t){if((sn(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"&&sn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Ea(e,[],[],t)}function $T(e){let t={x:E(e,"x","selu")};return $.runKernel(Po,t)}var Cd=O({selu_:$T});function DT(e,t,n,r,a,s=[1,1],i="NHWC"){let o=E(e,"x","separableConv2d"),l=E(t,"depthwiseFilter","separableConv2d"),u=E(n,"pointwiseFilter","separableConv2d"),c=o,h=!1;if(o.rank===3&&(h=!0,c=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");F(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),F(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),F(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),F(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let d=l.shape[2],p=l.shape[3];F(u.shape[2]===d*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*p}, but got ${u.shape[2]}.`);let f=ul(c,l,r,a,i,s),m=ra(f,u,1,"valid",i);return h?G(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var dm=O({separableConv2d_:DT});async function OT(e,t){let n=E(e,"x","setdiff1d"),r=E(t,"y","setdiff1d");F(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),F(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),F(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let a=await n.data(),s=await r.data(),i=new Set(s),o=0;for(let c=0;c<a.length;c++)i.has(a[c])||o++;let l=new Ot([o],n.dtype),u=new Ot([o],"int32");for(let c=0,h=0;c<a.length;c++)i.has(a[c])||(l.values[h]=a[c],u.values[h]=c,h++);return[l.toTensor(),u.toTensor()]}var fx=OT;function zT(e){let t={x:E(e,"x","sign")};return $.runKernel(Bo,t)}var pm=O({sign_:zT});function PT(e){let t={x:E(e,"x","sin")};return $.runKernel(Hs,t)}var Ed=O({sin_:PT});function LT(e){let t={x:E(e,"x","sinh")};return $.runKernel(Wo,t)}var Rd=O({sinh_:LT});function WT(e,t,n){let r=E(e,"x","slice1d");return F(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Re(r,[t],[n])}var Fd=O({slice1d_:WT});function BT(e,t,n){let r=E(e,"x","slice2d");return F(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var fm=O({slice2d_:BT});function VT(e,t,n){let r=E(e,"x","slice3d");return F(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Md=O({slice3d_:VT});function UT(e,t,n){let r=E(e,"x","slice4d");return F(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Zu=O({slice4d_:UT});function HT(e,t=-1){let n=E(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let r={logits:n},a={dim:t};return $.runKernel(Xs,r,a)}var Yu=O({softmax_:HT});function jT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Lh,t)}var Ju=O({fft_:jT});function GT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Wh,t)}var Al=O({ifft_:GT});function qT(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=G(e,[n,t]);r=Al(a)}else{let a=[n,2*(t-1)],s=G(Ku(e),[n,t]),i=G(gd(e),[n,t]),o=Dn(Re(s,[0,1],[n,t-2]),1),l=P(Dn(Re(i,[0,1],[n,t-2]),1),be(-1)),u=st([s,o],1),c=st([i,l],1),h=G(Ca(u,c),[a[0],a[1]]);r=Al(h)}if(r=Ku(r),e.rank===3&&e.shape[0]!==0){let a=r,s=e.shape[0];r=G(r,[s,r.shape[0]/s,r.shape[1]]),a.dispose()}return r}var $d=O({irfft_:qT});function XT(e,t,n=0){let r={x:E(e,"x","split")},a={numOrSizeSplits:t,axis:n};return $.runKernel(Uo,r,a)}var Lt=O({split_:XT});function KT(e,t){F(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,a;if(t!=null&&t<n){let f=e.shape.map(A=>0),m=e.shape.map(A=>A);m[e.shape.length-1]=t,a=Re(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,a=st([e,Et(f)],e.shape.length-1),n=t}else a=e;let s=He(a),i=G(Ca(a,s),[r,n]),o=Ju(i),l=Math.floor(n/2)+1,u=Ku(o),c=gd(o),h=Lt(u,[l,n-l],u.shape.length-1),d=Lt(c,[l,n-l],c.shape.length-1),p=a.shape.slice();return p[a.shape.length-1]=l,G(Ca(h[0],d[0]),p)}var Qu=O({rfft_:KT});function ZT(e){let t={x:E(e,"x","sqrt")};return $.runKernel(Gs,t)}var en=O({sqrt_:ZT});function YT(e,t){let n=E(e,"a","squaredDifference"),r=E(t,"b","squaredDifference");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r},s={};return $.runKernel(Ks,a,s)}var Dd=O({squaredDifference_:YT});function JT(e,t){let n=E(e,"x","squeeze");return G(n,Dg(n.shape,t).newShape)}var Pa=O({squeeze_:JT});function QT(e,t=0){let n=$u(e,"tensors","stack","string_or_numeric");F(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&F(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return $.runKernel(Fo,r,a)}var dn=O({stack_:QT});function eC(e,t=0){let n={x:E(e,"x","step")},r={alpha:t};return $.runKernel(Ta,n,r)}var yl=O({step_:eC});function tC(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let u={x:E(e,"x","stridedSlice")},c={begin:t,end:n,strides:r,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return $.runKernel(Ho,u,c)}var mm=O({stridedSlice_:tC});function nC(e){let t={x:E(e,"x","tan")};return $.runKernel(jo,t)}var Am=O({tan_:nC});function on(e,t){ss(e);let n=Er(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Ea(e,null,n,t)}function Nn(e,t,n){if(ss(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Er(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Ea(e,t,r,n)}function rC(e,t,n){if(ss(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Er(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}function aC(e,t,n){if(ss(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Er(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}function sC(e,t,n){if(ss(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Er(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Ea(e,t,r,n)}function iC(e,t=1,n=!0){let r=E(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=r.shape[r.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);let s={x:r},i={k:t,sorted:n},[o,l]=$.runKernel(Go,s,i);return{values:o,indices:l}}var ym=O({topk_:iC});function oC(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new um(t,n,r,!0,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Od=O({truncatedNormal_:oC});function lC(e,t=0){let n=E(e,"x","unique","string_or_numeric");F(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=$.runKernel(Qh,r,a);return{values:s,indices:i}}var zd=O({unique_:lC});function uC(e,t,n){let r=E(e,"x","unsortedSegmentSum"),a=E(t,"segmentIds","unsortedSegmentSum","int32");F(jt(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return $.runKernel(ku,s,i)}var gm=O({unsortedSegmentSum_:uC});function cC(e,t=0){let n=E(e,"x","unstack","string_or_numeric");F(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},a={axis:t};return $.runKernel(qo,r,a)}var sr=O({unstack_:cC});function mx(e,t=!0,n,r){return $.makeVariable(e,t,n,r)}function Ax(e,t){let n=[];for(let s=0;s<t.length;s++)t[s]&&n.push(s);let r=We(e,"int32"),a=We([n.length,e.length],"int32");for(let s=0;s<n.length;s++){let i=r.indexToLoc(n[s]),o=s*e.length;a.values.set(i,o)}return a.toTensor()}async function hC(e){let t=E(e,"condition","whereAsync","bool"),n=await t.data(),r=Ax(t.shape,n);return e!==t&&t.dispose(),r}var xm=hC;async function dC(e,t,n){let r=E(e,"tensor","boolMask"),a=E(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;F(i>0,()=>"mask cannot be scalar"),an(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)),c=G(r,u),h=G(a,[-1]),d=await xm(h),p=Pa(d,[1]),f=ui(c,p,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),p.dispose(),c.dispose(),h.dispose(),d.dispose(),f}var pC=dC;function fC(e,t="euclidean",n=null,r=!1){e=E(e,"x","norm");let a=yx(e,t,n),s=a.shape;if(r){let i=tr(n,e.shape);s=hi(a.shape,i)}return G(a,s)}function yx(e,t,n=null){if(e.rank===0)return zt(e);if(e.rank!==1&&n===null)return yx(G(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Te(zt(e),n);if(t===Infinity)return Xn(zt(e),n);if(t===-Infinity)return pl(zt(e),n);if(t==="euclidean"||t===2)return en(Te(sa(zt(e),be(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Xn(Te(zt(e),n[0]),n[1]-1);if(t===Infinity)return Xn(Te(zt(e),n[1]),n[0]);if(t===-Infinity)return pl(Te(zt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return en(Te(lt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Pd=O({norm_:fC});function mC(e,t,n,r,a=!0){let s=E(e,"v","movingAverage"),i=E(t,"x","movingAverage"),o=E(n,"decay","movingAverage");Jg(s,i),F(ta(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=be(1),u=ye(l,o),c=P(ye(i,s),u);if(a){F(r!=null,()=>"When using zeroDebias: true, step is required.");let h=E(r,"step","movingAverage");c=ge(c,ye(l,sa(o,h)))}return se(s,c)}var AC=O({movingAverage_:mC});function yC(e,t,n){let r=E(e,"indices","scatterND","int32"),a=E(t,"updates","scatterND");Cf(a,r,n);let s={indices:r,updates:a},i={shape:n};return $.runKernel(Oo,s,i)}var gx=O({scatterND_:yC});function gC(e,t,n,r){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(n.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===a))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${a}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function xC(e,t,n,r=0){let a=E(e,"sparseIndices","sparseToDense","int32"),s=E(t,"sparseValues","sparseToDense"),i=E(r,"defaultValue","sparseToDense",s.dtype);gC(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return $.runKernel(Yh,o,l)}var wm=O({sparseToDense_:xC});function wC(e,t){let n=E(t,"indices","gatherND","int32"),r={params:E(e,"x","gatherND"),indices:n};return $.runKernel(Ao,r)}var xx=O({gatherND_:wC});function _C(e,t){if(t==null)return e.shape.slice();if(ta(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let r=0;r<e.shape.length;r++)t[r]==null&&e.shape[r]!=null?n.push(e.shape[r]):n.push(t[r]);return n}return t}function bC(e,t,n,r){let a=E(e,"x","dropout");if(F(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.`),F(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ue?a.clone():a;let s=_C(a,n),i=1-t,o=ge(hl(se(ml(s,0,1,"float32",r),i)),i);return P(a,o)}var wx=O({dropout_:bC});function _x(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function _m(e,t,n){let r=1-e%2,a=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+r-1);a[s]=t-n*Math.cos(i)}return on(a,"float32")}async function vC(e,t,n=1){let r=E(e,"predictions","inTopK"),a=E(t,"targets","inTopK");F(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),F(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),an(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];F(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,u]=[i.length/s,s],c=Og("bool",l);for(let h=0;h<l;h++){let d=h*u,p=i.subarray(d,d+u),f=[];for(let m=0;m<p.length;m++)f.push({value:p[m],index:m});f.sort((m,A)=>A.value-m.value),c[h]=0;for(let m=0;m<n;m++)if(f[m].index===o[h]){c[h]=1;break}}return e!==r&&r.dispose(),t!==a&&a.dispose(),yr(c,a.shape,"bool")}var kC=vC,La={};ze(La,{conv2d:()=>NC,depthwiseConv2d:()=>IC,matMul:()=>SC});function TC(e,t,n,r,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]])),F(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),F(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),F(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),F(c===n[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`),i!=null&&F(jt(a),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let h={x:o,dy:l},d={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,filterShape:n};return $.runKernel(Ch,h,d)}var bm=O({conv2DBackpropFilter_:TC});function Ld(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return P(e,yl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Wd(e,t){let n=t,r=Pt(e.shape,t.shape);return r.length>0&&(n=Te(n,r)),G(n,e.shape)}function Bd(e,t,n,r){if(t==="linear")return e;if(t==="relu")return zr(e);if(t==="elu")return cl(e);if(t==="relu6")return Sd(e);if(t==="prelu")return Xu(e,n);if(t==="leakyrelu")return Hu(e,r);throw new Error(`Unknown fused activation ${t}.`)}var Vd=(e,t)=>!(e>0)||t==="linear";function CC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Vd($.state.gradientDepth,l)===!1){let w=ra(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Bd(w,l,u,c)}let h=E(e,"x","conv2d"),d=E(t,"filter","conv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=G(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),F(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&F(jt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),F(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=Pu(p.shape,d.shape,n,s,r,i),A;o!=null&&(A=E(o,"bias","fused conv2d"),[A]=bt(A,h),yt(m.outShape,A.shape));let y;u!=null&&(y=E(u,"prelu weights","fused conv2d"));let g=(w,x)=>{let[I,S,T,M]=x,D=Ld(w,T,l);F($a(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let z=qf(S.shape,D,I,n,r),W=bm(S,D,I.shape,n,r),U=[z,W];if(M!=null){let H=Wd(M,D);U.push(H)}return U},_={x:p,filter:d,bias:A,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:c};return o==null?$r((w,x,I)=>{let S=$.runKernel(ei,_,b);return I([x,w,S]),f&&(S=G(S,[S.shape[1],S.shape[2],S.shape[3]])),{value:S,gradFunc:g}})(p,d):$r((w,x,I,S)=>{let T=$.runKernel(ei,_,b);return S([x,w,T,I]),f&&(T=G(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(p,d,A)}var NC=O({fusedConv2d_:CC});function EC(e,t,n,r,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},c={strides:r,pad:a,dimRoundingMode:i,dilations:s,filterShape:n};return $.runKernel(Mh,u,c)}var bx=O({depthwiseConv2dNativeBackpropFilter_:EC});function RC(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},c={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=$.runKernel($h,u,c);return l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var vx=O({depthwiseConv2dNativeBackpropInput_:RC});function FC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(Vd($.state.gradientDepth,l)===!1){let w=ul(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Bd(w,l,u,c)}let h=E(e,"x","depthwiseConv2d"),d=E(t,"filter","depthwiseConv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=G(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),F(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),F(p.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),F(Mr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&F(jt(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let m=Pu(p.shape,d.shape,n,s,r,i,!0),A;o!=null&&(A=E(o,"bias","fused conv2d"),[A]=bt(A,h),yt(m.outShape,A.shape));let y;u!=null&&(y=E(u,"prelu weights","fused depthwiseConv2d"));let g=(w,x)=>{F($a(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[I,S,T,M]=x,D=Ld(w,T,l),z=vx(S.shape,D,I,n,r,s,i),W=bx(S,D,I.shape,n,r,s,i);if(M!=null){let U=Wd(A,D);return[z,W,U]}return[z,W]},_={x:p,filter:d,bias:A,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:c};return o==null?$r((w,x,I)=>{let S=$.runKernel(ti,_,b);return I([x,w,S]),f&&(S=G(S,[S.shape[1],S.shape[2],S.shape[3]])),{value:S,gradFunc:g}})(p,d):$r((w,x,I,S)=>{let T=$.runKernel(ti,_,b);return S([x,w,T,I]),f&&(T=G(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(p,d,A)}var IC=O({fusedDepthwiseConv2d_:FC});function MC({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Vd($.state.gradientDepth,s)===!1){let M=Ge(e,t,n,r);return a!=null&&(M=se(M,a)),Bd(M,s,i,o)}let l=E(e,"a","fused matMul"),u=E(t,"b","fused matMul");[l,u]=bt(l,u);let c=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?u.shape[u.rank-1]:u.shape[u.rank-2],d=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=r?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),A=Dt(f),y=Dt(m);F(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),F(ta(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),F(c===h,()=>`Error in fused matMul: inner shapes (${c}) and (${h}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([d,p]),_=n?G(l,[A,c,d]):G(l,[A,d,c]),b=r?G(u,[y,p,h]):G(u,[y,h,p]),w;a!=null&&(w=E(a,"bias","fused matMul"),[w]=bt(w,l),yt(g,w.shape));let x;i!=null&&(x=E(i,"prelu weights","fused matMul"));let I=(M,D)=>{let[z,W,U,H]=D,X=Ld(G(M,U.shape),U,s),j,ee;if(!n&&!r?(j=Ge(X,W,!1,!0),ee=Ge(z,X,!0,!1)):!n&&r?(j=Ge(X,W,!1,!1),ee=Ge(X,z,!0,!1)):n&&!r?(j=Ge(W,X,!1,!0),ee=Ge(z,X,!1,!1)):(j=Ge(W,X,!0,!0),ee=Ge(X,z,!0,!0)),a!=null){let Y=Wd(H,X);return[j,ee,Y]}else return[j,ee]},S={a:_,b,bias:w,preluActivationWeights:x},T={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?$r((M,D,z)=>{let W=$.runKernel(Qs,S,T);return z([M,D,W]),{value:G(W,g),gradFunc:I}})(_,b):$r((M,D,z,W)=>{let U=$.runKernel(Qs,S,T);return W([M,D,U,z]),{value:G(U,g),gradFunc:I}})(_,b,w)}var SC=O({fusedMatMul_:MC});function $C(e){return _m(e,.54,.46)}var DC=O({hammingWindow_:$C});function OC(e){return _m(e,.5,.5)}var kx=O({hannWindow_:OC});function zC(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Re(e,s,t)),s+=n;if(r)for(;s<e.size;){let o=s+t-e.size,l=st([Re(e,s,t-o),Uu([o],a)]);i.push(l),s+=n}return i.length===0?Nn([],[0,t]):G(st(i),[i.length,t])}var Nx=O({frame_:zC});function PC(e,t,n,r,a=kx){r==null&&(r=_x(t));let s=Nx(e,t,n),i=P(s,a(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(Qu(Re(i,[l,0],[1,t]),r));return st(o)}var LC=O({stft_:PC});function WC(e,t,n,r,a="bilinear",s=0){let i=E(e,"image","cropAndResize"),o=E(t,"boxes","cropAndResize","float32"),l=E(n,"boxInd","cropAndResize","int32"),u=o.shape[0];F(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),F(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),F(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),F(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),F(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let c={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:r};return $.runKernel(io,c,h)}var BC=O({cropAndResize_:WC});function VC(e){let t=E(e,"image","flipLeftRight","float32");F(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return $.runKernel(fo,n,{})}var UC=O({flipLeftRight_:VC});function HC(e,t,n=0,r=.5){let a=E(e,"image","rotateWithOffset","float32");F(a.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);let s={image:a},i={radians:t,fillValue:n,center:r};return $.runKernel(Ko,s,i)}var jC=O({rotateWithOffset_:HC});function gl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),F(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),F(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),F(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),F(t.rank===1,()=>"scores must be a 1D tensor"),F(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function GC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=E(e,"boxes","nonMaxSuppression"),i=E(t,"scores","nonMaxSuppression"),o=gl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:a};return $.runKernel(To,{boxes:s,scores:i},l)}var qC=O({nonMaxSuppression_:GC});function KC(e,t,n){let r=XC(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function XC(e,t,n){return YC(e,t,n||ZC)}function ZC(e,t){return e>t?1:e<t?-1:0}function YC(e,t,n){let r=0,a=e.length,s=0,i=!1;for(;r<a;){s=r+(a-r>>>1);let o=n(t,e[s]);o>0?r=s+1:(a=s,i=!o)}return i?r:-r-1}function Ix(e,t,n,r,a){return vm(e,t,n,r,a,0)}function Sx(e,t,n,r,a,s){return vm(e,t,n,r,a,0,!1,s,!0)}function Tx(e,t,n,r,a,s){return vm(e,t,n,r,a,s,!0)}function vm(e,t,n,r,a,s,i=!1,o=!1,l=!1){let u=[];for(let A=0;A<t.length;A++)t[A]>a&&u.push({score:t[A],boxIndex:A,suppressBeginIndex:0});u.sort(Cx);let c=s>0?-.5/s:0,h=[],d=[];for(;h.length<n&&u.length>0;){let A=u.pop(),{score:y,boxIndex:g,suppressBeginIndex:_}=A;if(y<a)break;let b=!1;for(let w=h.length-1;w>=_;--w){let x=JC(e,g,h[w]);if(x>=r){b=!0;break}if(A.score=A.score*QC(r,c,x),A.score<=a)break}A.suppressBeginIndex=h.length,b||(A.score===y?(h.push(g),d.push(A.score)):A.score>a&&KC(u,A,Cx))}let p=h.length,f=n-p;o&&f>0&&(h.push(...new Array(f).fill(0)),d.push(...new Array(f).fill(0)));let m={selectedIndices:h};return i&&(m.selectedScores=d),l&&(m.validOutputs=p),m}function JC(e,t,n){let r=e.subarray(t*4,t*4+4),a=e.subarray(n*4,n*4+4),s=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),o=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),u=Math.min(a[0],a[2]),c=Math.min(a[1],a[3]),h=Math.max(a[0],a[2]),d=Math.max(a[1],a[3]),p=(o-s)*(l-i),f=(h-u)*(d-c);if(p<=0||f<=0)return 0;let m=Math.max(s,u),A=Math.max(i,c),y=Math.min(o,h),g=Math.min(l,d),_=Math.max(y-m,0)*Math.max(g-A,0);return _/(p+f-_)}function QC(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function Cx(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function eE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=E(e,"boxes","nonMaxSuppressionAsync"),i=E(t,"scores","nonMaxSuppressionAsync"),o=gl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],c=l[1],{selectedIndices:h}=Ix(u,c,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),on(h,"int32")}var tE=eE;function nE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},c={maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s},h=$.runKernel(Eo,u,c);return{selectedIndices:h[0],selectedScores:h[1]}}var rE=O({nonMaxSuppressionWithScore_:nE});async function aE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppressionAsync"),o=E(t,"scores","nonMaxSuppressionAsync"),l=gl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),c=u[0],h=u[1],{selectedIndices:d,selectedScores:p}=Tx(c,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:on(d,"int32"),selectedScores:on(p)}}var sE=aE;function iE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gl(i,o,n,r,a,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,d={boxes:i,scores:o},p={maxOutputSize:u,iouThreshold:c,scoreThreshold:h,padToMaxOutputSize:s},f=$.runKernel(Co,d,p);return{selectedIndices:f[0],validOutputs:f[1]}}var oE=O({nonMaxSuppressionPadded_:iE});async function lE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=E(e,"boxes","nonMaxSuppressionAsync"),o=E(t,"scores","nonMaxSuppressionAsync"),l=gl(i,o,n,r,a,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,[d,p]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=Sx(d,p,u,c,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:on(f,"int32"),validOutputs:be(m,"int32")}}var uE=lE;function cE(e,t,n=!1,r=!1){let a=E(e,"images","resizeBilinear");F(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),F(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},u=$.runKernel(Ls,o,l);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Ex=O({resizeBilinear_:cE});function hE(e,t,n=!1,r=!1){let a=E(e,"images","resizeNearestNeighbor");F(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},u=$.runKernel(_u,o,l);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Rx=O({resizeNearestNeighbor_:hE});function dE(e,t,n="nearest",r="constant",a=0,s){let i=E(e,"image","transform","float32"),o=E(t,"transforms","transform","float32");F(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),F(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"),F(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},u={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return $.runKernel(Jh,l,u)}var pE=O({transform_:dE});function fE(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=E(e,"a","bandPart");F(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let a=r.shape,[s,i]=r.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=G(Id(0,s,1,"int32"),[-1,1]),l=Id(0,i,1,"int32"),u=ye(o,l),c=ar(ci(u,be(+t,"int32")),za(u,be(-n,"int32"))),h=Et([s,i],r.dtype);return G(dn(sr(G(r,[-1,s,i])).map(d=>kn(c,d,h))),a)}var mE=O({bandPart_:fE});function AE(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)F(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Lt(e,e.shape[0],0).map(a=>Pa(a,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let a=0;a<e.length;++a)n.push($.tidy(()=>{let s=r[a];if(a>0)for(let i=0;i<a;++i){let o=P(Te(P(n[i],s)),n[i]);s=ye(s,o)}return ge(s,Pd(s,"euclidean"))}));return t?dn(n,0):n}var yE=O({gramSchmidt_:AE});function gE(e,t=!1){if(F(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return Fx(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),r=sr(G(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[u,c]=Fx(l,t);a.push(u),s.push(c)});let i=G(dn(a,0),e.shape),o=G(dn(s,0),e.shape);return[i,o]}}function Fx(e,t=!1){return $.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=em(n),s=Rr(e),i=Nn([[1]],[1,1]),o=Rr(i),l=n>=r?r:n;for(let u=0;u<l;++u){let c=s,h=o,d=a;[o,s,a]=$.tidy(()=>{let p=Re(s,[u,u],[n-u,1]),f=Pd(p),m=Re(s,[u,u],[1,1]),A=kn(rr(m,0),Nn([[-1]]),Nn([[1]])),y=ye(m,P(A,f)),g=ge(p,y);g.shape[0]===1?o=Rr(i):o=st([i,Re(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let _=vt(ge(Ge(A,y),f)),b=Re(s,[u,0],[n-u,r]),w=P(_,o),x=at(o);if(u===0)s=ye(b,Ge(w,Ge(x,b)));else{let T=ye(b,Ge(w,Ge(x,b)));s=st([Re(s,[0,0],[u,r]),T],0)}let I=at(w),S=Re(a,[0,u],[n,a.shape[1]-u]);if(u===0)a=ye(S,Ge(Ge(S,o),I));else{let T=ye(S,Ge(Ge(S,o),I));a=st([Re(a,[0,0],[n,u]),T],1)}return[o,s,a]}),Ee([c,h,d])}return!t&&n>r&&(a=Re(a,[0,0],[n,r]),s=Re(s,[0,0],[r,r])),[a,s]})}var xE=O({qr_:gE}),pn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(pn||(pn={}));function wE(e,t,n=pn.SUM_BY_NONZERO_WEIGHTS){let r=E(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=E(t,"weights","computeWeightedLoss"));let s=a==null?r:P(r,a);if(n===pn.NONE)return s;if(n===pn.SUM)return Te(s);if(n===pn.MEAN){if(a==null)return kt(s);{let i=r.size/a.size,o=ge(Te(s),Te(a));return i>1?ge(o,be(i)):o}}if(n===pn.SUM_BY_NONZERO_WEIGHTS){if(a==null)return ge(Te(s),be(r.size));{let i=P(a,Or(r.shape)),o=me(Te(di(i,be(0))),"float32");return ge(Te(s),o)}}throw Error(`Unknown reduction: ${n}`)}var ia=O({computeWeightedLoss_:wE});function _E(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=E(e,"labels","absoluteDifference"),s=E(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=E(n,"weights","absoluteDifference")),an(a.shape,s.shape,"Error in absoluteDifference: ");let o=zt(ye(a,s));return ia(o,i,r)}var bE=O({absoluteDifference_:_E});function vE(e,t,n,r,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"labels","cosineDistance"),i=E(t,"predictions","cosineDistance"),o=null;r!=null&&(o=E(r,"weights","cosineDistance")),an(s.shape,i.shape,"Error in cosineDistance: ");let l=be(1),u=ye(l,Te(P(s,i),n,!0));return ia(u,o,a)}var kE=O({cosineDistance_:vE});function NE(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=E(e,"labels","hingeLoss"),s=E(t,"predictions","hingeLoss"),i=null;n!=null&&(i=E(n,"weights","hingeLoss")),an(a.shape,s.shape,"Error in hingeLoss: ");let o=be(1);a=ye(P(be(2),a),o);let l=zr(ye(o,P(a,s)));return ia(l,i,r)}var IE=O({hingeLoss_:NE});function SE(e,t,n,r=1,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"labels","huberLoss"),i=E(t,"predictions","huberLoss"),o=null;n!=null&&(o=E(n,"weights","huberLoss")),an(s.shape,i.shape,"Error in huberLoss: ");let l=be(r),u=zt(ye(i,s)),c=fl(u,l),h=ye(u,c),d=se(P(be(.5),lt(c)),P(l,h));return ia(d,o,a)}var TE=O({huberLoss_:SE});function CE(e,t,n,r=1e-7,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"labels","logLoss"),i=E(t,"predictions","logLoss"),o=null;n!=null&&(o=E(n,"weights","logLoss")),an(s.shape,i.shape,"Error in logLoss: ");let l=be(1),u=be(r),c=vt(P(s,Mn(se(i,u)))),h=P(ye(l,s),Mn(se(ye(l,i),u))),d=ye(c,h);return ia(d,o,a)}var EE=O({logLoss_:CE});function RE(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=E(e,"labels","meanSquaredError"),s=E(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=E(n,"weights","meanSquaredError")),an(a.shape,s.shape,"Error in meanSquaredError: ");let o=Dd(a,s);return ia(o,i,r)}var FE=O({meanSquaredError_:RE});function ME(e,t){let n=E(e,"labels","sigmoidCrossEntropyWithLogits"),r=E(t,"logits","sigmoidCrossEntropyWithLogits");an(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=zr(r),s=P(r,n),i=wd(qn(vt(zt(r))));return se(ye(a,s),i)}function $E(e,t,n,r=0,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"multiClassLabels","sigmoidCrossEntropy"),i=E(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=E(n,"weights","sigmoidCrossEntropy")),an(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let u=be(r),c=be(1),h=be(.5);s=se(P(s,ye(c,u)),P(h,u))}let l=ME(s,i);return ia(l,o,a)}var DE=O({sigmoidCrossEntropy_:$E});function OE(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return $r((r,a,s)=>{let i=am(a,[n],!0),o=ye(me(a,"float32"),i);s([r,o]);let l=vt(P(o,r));return{value:Te(l,[n]),gradFunc:(u,c)=>{let[h,d]=c,p=hi(u.shape,[n]);return[P(G(u,p),ye(me(h,"float32"),qn(d))),P(G(u,p),ye(qn(d),me(h,"float32")))]}}})(e,t)}function zE(e,t,n,r=0,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"onehotLabels","softmaxCrossEntropy"),i=E(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=E(n,"weights","softmaxCrossEntropy")),an(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let u=be(r),c=be(1),h=be(s.shape[1]);s=se(P(s,ye(c,u)),ge(u,h))}let l=OE(s,i);return ia(l,o,a)}var PE=O({softmaxCrossEntropy_:zE}),LE={fft:Ju,ifft:Al,rfft:Qu,irfft:$d},WE={hammingWindow:DC,hannWindow:kx,frame:Nx,stft:LC},Je={flipLeftRight:UC,resizeNearestNeighbor:Rx,resizeBilinear:Ex,rotateWithOffset:jC,cropAndResize:BC,nonMaxSuppression:qC,nonMaxSuppressionAsync:tE,nonMaxSuppressionWithScore:rE,nonMaxSuppressionWithScoreAsync:sE,nonMaxSuppressionPadded:oE,nonMaxSuppressionPaddedAsync:uE,transform:pE},Mx={bandPart:mE,gramSchmidt:yE,qr:xE},BE={absoluteDifference:bE,computeWeightedLoss:ia,cosineDistance:kE,hingeLoss:IE,huberLoss:TE,logLoss:EE,meanSquaredError:FE,sigmoidCrossEntropy:DE,softmaxCrossEntropy:PE},oa=class extends O5{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return Ee(a),t?r:(r.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return ax(e,t)}dispose(){this.iterations_!=null&&Ee(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:be(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(oa,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Ud=class extends oa{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:B(()=>He(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:B(()=>He(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;B(()=>{let l=se(P(i,this.rho),P(lt(s),1-this.rho)),u=P(ge(en(se(o,this.epsilon)),en(se(i,this.epsilon))),s),c=se(P(o,this.rho),P(lt(u),1-this.rho));i.assign(l),o.assign(c);let h=se(P(u,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ud.className="Adadelta";Fa(Ud);var Hd=class extends oa{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:B(()=>Uu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;B(()=>{let i=se(s,lt(a));s.assign(i);let o=se(P(ge(a,en(se(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Hd.className="Adagrad";Fa(Hd);var jd=class extends oa{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=ye(1,this.accBeta1),r=ye(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:B(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:B(()=>He(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,c=this.accumulatedSecondMoment[s].variable,h=se(P(u,this.beta1),P(l,1-this.beta1)),d=se(P(c,this.beta2),P(lt(l),1-this.beta2)),p=ge(h,n),f=ge(d,r);u.assign(h),c.assign(d);let m=se(P(ge(p,se(en(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(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),B(()=>{this.accBeta1.assign(sa(this.beta1,this.iterations_+1)),this.accBeta2.assign(sa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};jd.className="Adam";Fa(jd);var Gd=class extends oa{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=ye(1,this.accBeta1),r=ge(-this.learningRate,se(P(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:He(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:He(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,c=this.accumulatedWeightedInfNorm[s].variable,h=se(P(u,this.beta1),P(l,1-this.beta1)),d=P(c,this.beta2),p=zt(l),f=Dr(d,p);u.assign(h),c.assign(f);let m=se(P(ge(r,n),ge(h,se(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ee(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)}};Gd.className="Adamax";Fa(Gd);var ec=class extends oa{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];B(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ec.className="SGD";Fa(ec);var qd=class extends ec{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:B(()=>He(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&B(()=>{let i,o=se(P(this.m,a),s);this.useNesterov?i=se(P(this.c,se(s,P(o,this.m))),r):i=se(P(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ee(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};qd.className="Momentum";Fa(qd);var Xd=class extends oa{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:B(()=>He(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:B(()=>He(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:B(()=>He(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;B(()=>{let l=se(P(i,this.decay),P(lt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,c=se(P(u,this.decay),P(s,1-this.decay)),h=ge(P(s,this.learningRate),en(ye(l,se(lt(c),this.epsilon)))),d=se(P(o,this.momentum),h);i.assign(l),u.assign(c),o.assign(d);let p=ye(r,d);r.assign(p)}else{let u=se(P(i,this.decay),P(lt(s),1-this.decay)),c=se(P(o,this.momentum),ge(P(s,this.learningRate),en(se(u,this.epsilon))));i.assign(u),o.assign(c);let h=ye(r,c);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ee(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ee(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ee(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Xd.className="RMSProp";Fa(Xd);var pi=class{static sgd(e){return new ec(e)}static momentum(e,t,n=!1){return new qd(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new Xd(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new jd(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Ud(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Gd(e,t,n,r,a)}static adagrad(e,t=.1){return new Hd(e,t)}},fi={sgd:pi.sgd,momentum:pi.momentum,adadelta:pi.adadelta,adagrad:pi.adagrad,rmsprop:pi.rmsprop,adamax:pi.adamax,adam:pi.adam},VE=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Kd(){return new Promise(e=>VE(()=>e()))}var R={};ze(R,{ERF_A1:()=>QE,ERF_A2:()=>eR,ERF_A3:()=>tR,ERF_A4:()=>nR,ERF_A5:()=>rR,ERF_P:()=>JE,PARALLELIZE_THRESHOLD:()=>km,SELU_SCALE:()=>Dx,SELU_SCALEALPHA:()=>$x,applyActivation:()=>Bd,assertAndGetBroadcastShape:()=>yt,assertAxesAreInnerMostDims:()=>kS,assertParamsConsistent:()=>UE,assignToTypedArray:()=>hR,axesAreInnerMostDims:()=>nm,calculateShapes:()=>k5,combineLocations:()=>ix,complexWithEvenIndex:()=>lR,complexWithOddIndex:()=>uR,computeConv2DInfo:()=>Pu,computeConv3DInfo:()=>U5,computeDefaultPad:()=>Hf,computeDilation2DInfo:()=>XN,computeOptimalWindowSize:()=>jE,computeOutAndReduceShapes:()=>ox,computeOutShape:()=>HE,computePool2DInfo:()=>V5,computePool3DInfo:()=>KN,convertConv2DDataFormat:()=>B5,eitherStridesOrDilationsAreOne:()=>Mr,expandShapeToKeepDim:()=>hi,exponent:()=>pR,exponents:()=>dR,fromStringArrayToUint8:()=>AR,fromUint8ToStringArray:()=>mR,getAxesPermutation:()=>lx,getBroadcastDims:()=>WI,getComplexWithIndex:()=>cR,getFusedBiasGradient:()=>Wd,getFusedDyActivation:()=>Ld,getImageCenter:()=>GE,getInnerMostAxes:()=>NS,getPermuted:()=>XE,getReductionAxes:()=>Pt,getReshaped:()=>qE,getReshapedPermuted:()=>KE,getSliceBeginCoords:()=>ZE,getSliceSize:()=>YE,getUndoAxesPermutation:()=>rm,log:()=>sR,mergeRealAndImagArrays:()=>iR,prepareAndValidate:()=>v5,prepareSplitSize:()=>fR,segment_util:()=>Ox,shouldFuse:()=>Vd,slice_util:()=>cn,splitRealAndImagArrays:()=>oR,tupleValuesAreOne:()=>$a,upcastType:()=>nr,validateInput:()=>Cf,validateUpdateShape:()=>Tf,warn:()=>aR});function UE(e,t){let n=e[0].length;e.forEach((a,s)=>{F(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)F(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function HE(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var km=30;function jE(e){return e<=km?e:_h(e,Math.floor(Math.sqrt(e)))}function GE(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function qE(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),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 XE(e,t,n=!0){let r=[];if(n){r.push(t);for(let a=t+1;a<e;++a)a<=2*t?(r.push(a),r.push(a-(t+1))):r.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);r.push(...a),r.push(0),r.push(...s)}return r}function KE(e,t,n,r=!0){let a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?r?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function ZE(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function YE(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var $x=1.7580993408473768,Dx=1.0507009873554805,JE=.3275911,QE=.254829592,eR=-.284496736,tR=1.421413741,nR=-1.453152027,rR=1.061405429;function aR(...e){J().getBool("IS_TEST")||console.warn(...e)}function sR(...e){J().getBool("IS_TEST")||console.log(...e)}function iR(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function oR(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function lR(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function uR(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function cR(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function hR(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function dR(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function pR(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}function fR(e,t,n=0){let r=[];if(typeof t=="number")F(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var Ox={};ze(Ox,{collectGatherOpShapeInfo:()=>xR,computeOutShape:()=>gR,segOpComputeOptimalWindowSize:()=>yR});function yR(e,t){let n=!1,r;for(e<=km?(r=e,n=!0):r=_h(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=_h(e,r+1);return r}function gR(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function xR(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${a}, ${a}], but got ${r}`);if(r<0&&(r+=a),r>s)throw new Error(`batchDims (${r}) must be less than rank(x) (
${s}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let h=0;h<r;++h)if(e.shape[h]!==t.shape[h])throw new Error(`x.shape[${h}]: ${e.shape[h]} should be equal to indices.shape[${h}]: ${t.shape[h]}.`);let i=e.shape[n],o=[],l=1,u=1,c=1;for(let h=0;h<r;++h)o.push(e.shape[h]),l*=e.shape[h];for(let h=r;h<n;h++)o.push(e.shape[h]),u*=e.shape[h];for(let h=r;h<a;h++)o.push(t.shape[h]);for(let h=n+1;h<s;h++)o.push(e.shape[h]),c*=e.shape[h];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:i,outputShape:o}}function mR(e){try{return e.map(t=>rd(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function AR(e){return e.map(t=>Su(t))}var Pr={};ze(Pr,{nonMaxSuppressionV3Impl:()=>Ix,nonMaxSuppressionV4Impl:()=>Sx,nonMaxSuppressionV5Impl:()=>Tx,whereImpl:()=>Ax});function _e(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var wR=Pr.whereImpl,Zd=class extends su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new xh(this,Fr())}nextDataId(){return Zd.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&R.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 r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return Fr().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){_e([e],"where");let t=this.readSync(e.dataId);return wR(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Zd.nextDataId=0;var Nm={};ze(Nm,{addImpl:()=>Px,bincountImpl:()=>Im,bincountReduceImpl:()=>Lx,ceilImpl:()=>Wx,concatImpl:()=>Sm,expImpl:()=>Bx,expm1Impl:()=>Vx,floorImpl:()=>Ux,gatherV2Impl:()=>Hx,greaterImpl:()=>jx,lessImpl:()=>Gx,linSpaceImpl:()=>qx,logImpl:()=>Xx,maxImpl:()=>Kx,maximumImpl:()=>Zx,minimumImpl:()=>Yx,multiplyImpl:()=>Tm,negImpl:()=>Jx,notEqualImpl:()=>Qx,prodImpl:()=>ew,rangeImpl:()=>Em,rsqrtImpl:()=>tw,simpleAbsImpl:()=>zx,sliceImpl:()=>Yd,squaredDifferenceImpl:()=>nw,stridedSliceImpl:()=>rw,subImpl:()=>aw,tileImpl:()=>sw,topKImpl:()=>iw,transposeImpl:()=>Cm,uniqueImpl:()=>ow});function zx(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var _R=e=>{let{x:t}=e.inputs,n=e.backend;_e(t,"abs");let r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=zx(a),n.makeOutput(r,t.shape,"float32")},bR={kernelName:Zi,backendName:"cpu",kernelFunc:_R};function Rt(e){return(t,n,r,a,s)=>{let i=R.assertAndGetBroadcastShape(t,n),o=i.length,l=v.computeStrides(i),u=v.sizeFromShape(i),c=v.getTypedArrayFromDType(s,u),h=t.length,d=n.length,p=v.computeStrides(t),f=v.computeStrides(n),m=R.getBroadcastDims(t,i),A=R.getBroadcastDims(n,i);if(m.length+A.length===0)for(let y=0;y<c.length;++y)c[y]=e(r[y%r.length],a[y%a.length]);else for(let y=0;y<c.length;++y){let g=v.indexToLoc(y,o,l),_=g.slice(-h);m.forEach(I=>_[I]=0);let b=v.locToIndex(_,h,p),w=g.slice(-d);A.forEach(I=>w[I]=0);let x=v.locToIndex(w,d,f);c[y]=e(r[b],a[x])}return[c,i]}}function On(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",s),imag:n.makeTensorInfo(a.shape,"float32",i)},o}var vR={kernelName:Th,backendName:"cpu",kernelFunc:On};function Jd(e,t,n="float32"){if(n==="complex64"){let a=Jd(e,t,"float32"),s=Jd(e,t,"float32");return On({inputs:{real:a,imag:s},backend:e})}let r=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Lr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var kR={kernelName:ks,backendName:"cpu",kernelFunc:Lr};function mi(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var NR={kernelName:Xh,backendName:"cpu",kernelFunc:mi};function Wa(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Lr({inputs:{x:a},backend:n});let i=Jd(n,a.shape,a.dtype),o=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=On({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=mi({inputs:{input:a},backend:n}),o=Wa({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Lr({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(a.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=n.data.get(a.dataId).values,o=v.toTypedArray([0],a.dtype),[l,u]=Rt((c,h)=>c!==h?1:0)(a.shape,[],i,o,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var IR={kernelName:hs,backendName:"cpu",kernelFunc:Wa};function qt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;_e([i,o],e);let u=l.data.get(i.dataId).values,c=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,u,c,h);return l.makeTensorInfo(p,h,d)}:({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=Wa({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),h=c.complexTensorInfos.real,d=c.complexTensorInfos.imag,p=l.data.get(h.dataId).values,f=l.data.get(d.dataId).values,m=Wa({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),A=l.data.get(m.dataId),y=A.complexTensorInfos.real,g=A.complexTensorInfos.imag,_=l.data.get(y.dataId).values,b=l.data.get(g.dataId).values,[w,x,I]=n(i.shape,o.shape,p,f,_,b),S=l.makeTensorInfo(I,"float32",w),T=l.makeTensorInfo(I,"float32",x),M=On({inputs:{real:S,imag:T},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(S),l.disposeIntermediateTensorInfo(T),M}else{let u=l.data.get(i.dataId).values,c=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,u,c,h);return l.makeTensorInfo(p,h,d)}}}function Rm(e){return(t,n,r,a,s,i)=>{let o=R.assertAndGetBroadcastShape(t,n),l=v.sizeFromShape(o),u=o.length,c=v.computeStrides(o),h=v.getTypedArrayFromDType("float32",l),d=v.getTypedArrayFromDType("float32",l),p=R.getBroadcastDims(t,o),f=R.getBroadcastDims(n,o),m=R.mergeRealAndImagArrays(r,a),A=R.mergeRealAndImagArrays(s,i),y=t.length,g=v.computeStrides(t),_=n.length,b=v.computeStrides(n);if(p.length+f.length===0)for(let w=0;w<h.length;w++){let x=w%m.length,I=w%A.length,S=e(m[x*2],m[x*2+1],A[I*2],A[I*2+1]);h[w]=S.real,d[w]=S.imag}else for(let w=0;w<h.length;w++){let x=v.indexToLoc(w,u,c),I=x.slice(-y);p.forEach(z=>I[z]=0);let S=v.locToIndex(I,y,g),T=x.slice(-_);f.forEach(z=>T[z]=0);let M=v.locToIndex(T,_,b),D=e(m[S*2],m[S*2+1],A[M*2],A[M*2+1]);h[w]=D.real,d[w]=D.imag}return[h,d,o]}}var Px=Rt((e,t)=>e+t),SR=Rm((e,t,n,r)=>({real:e+n,imag:t+r})),tc=qt(Na,Px,SR),TR={kernelName:Na,backendName:"cpu",kernelFunc:tc};function Im(e,t,n,r,a){let s=v.sizeFromShape(r),i=v.makeZerosTypedArray(a,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function Lx(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=We([a,n],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>=n||(r?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 xl(e){return(t,n,r)=>{let a=v.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function it(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(_e(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=v.sizeFromShape(i.shape),c=n||i.dtype,h=v.getArrayFromDType(c,u);for(let d=0;d<u;++d)h[d]=t(l[d],a);return o.makeTensorInfo(i.shape,c,h)}}function wl(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(_e(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=n||i.dtype,c=t(l,u,a);return o.makeTensorInfo(i.shape,u,c)}}var Wx=xl(e=>Math.ceil(e)),CR=wl(ds,Wx),ER={kernelName:ds,backendName:"cpu",kernelFunc:CR};function Sm(e,t,n,r){let a=v.getArrayFromDType(n,v.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=v.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?R.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let u=0;u<i.shape[0];++u){let c=u*t[1]+s;for(let h=0;h<i.shape[1];++h)a[c+h]=o[l++]}s+=i.shape[1]})}return a}var Bx=xl(e=>Math.exp(e)),lw=wl(xs,Bx),RR={kernelName:xs,backendName:"cpu",kernelFunc:lw},Vx=xl(e=>Math.expm1(e)),FR=wl(po,Vx),MR={kernelName:po,backendName:"cpu",kernelFunc:FR},Ux=xl(e=>Math.floor(e)),$R=wl(ws,Ux),DR={kernelName:ws,backendName:"cpu",kernelFunc:$R};function Hx(e,t,n){let r=We(n,e.dtype);for(let a=0;a<r.size;++a){let s=r.indexToLoc(a).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);r.values[a]=e.values[u]}return r}var jx=Rt((e,t)=>e>t?1:0),OR=qt(yo,jx,null,"bool"),zR={kernelName:yo,backendName:"cpu",kernelFunc:OR},Gx=Rt((e,t)=>e<t?1:0),PR=qt(_o,Gx,null,"bool"),LR={kernelName:_o,backendName:"cpu",kernelFunc:PR};function qx(e,t,n){let r=(t-e)/(n-1),a=v.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var Xx=xl(e=>Math.log(e)),WR=wl(Is,Xx),BR={kernelName:Is,backendName:"cpu",kernelFunc:WR};function Kx(e,t,n,r){let a=v.getTypedArrayFromDType(r,v.sizeFromShape(n));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];u>o&&(o=u)}a[s]=o}return a}var Zx=Rt((e,t)=>Math.max(e,t)),VR=qt(Ts,Zx),UR={kernelName:Ts,backendName:"cpu",kernelFunc:VR},Yx=Rt((e,t)=>Math.min(e,t)),HR=qt(Fs,Yx),jR={kernelName:Fs,backendName:"cpu",kernelFunc:HR},Tm=Rt((e,t)=>e*t),GR=Rm((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),Fm=qt(Ms,Tm,GR),qR={kernelName:Ms,backendName:"cpu",kernelFunc:Fm};function Jx(e,t,n){let r=v.createScalarValue(-1,n);return Tm([],t,r,e,n)}function XR(e){let{inputs:t,backend:n}=e,{x:r}=t;_e(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=Jx(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var KR={kernelName:Io,backendName:"cpu",kernelFunc:XR},Qx=Rt((e,t)=>e!==t?1:0),ZR=qt(So,Qx,null,"bool"),YR={kernelName:So,backendName:"cpu",kernelFunc:ZR};function Cm(e,t,n,r,a){let s=t.length,i=v.sizeFromShape(t),o=v.computeStrides(t),l=v.computeStrides(a),u=v.getTypedArrayFromDType(n,v.sizeFromShape(a));for(let c=0;c<i;++c){let h=v.indexToLoc(c,s,o),d=new Array(h.length);for(let f=0;f<d.length;f++)d[f]=h[r[f]];let p=v.locToIndex(d,s,l);u[p]=e[c]}return u}function ir(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{perm:s}=n;_e(a,"transpose");let i=a.shape.length,o=new Array(i);for(let c=0;c<o.length;c++)o[c]=a.shape[s[c]];let l=r.data.get(a.dataId).values,u=Cm(l,a.shape,a.dtype,s,o);return{dataId:r.write(u,o,a.dtype),shape:o,dtype:a.dtype}}var JR={kernelName:Js,backendName:"cpu",kernelFunc:ir};function ew(e,t,n,r){let[a,s]=R.computeOutAndReduceShapes(e,r),i=nr(t,"int32"),o=v.makeZerosTypedArray(v.sizeFromShape(a),i),l=v.sizeFromShape(s);for(let u=0;u<o.length;++u){let c=u*l,h=1;for(let d=0;d<l;++d)h*=n[c+d];o[u]=h}return{outVals:o,outShape:a,outDtype:i}}function QR(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"prod");let o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=R.getAxesPermutation(l,o),c=l,h=a,d=[];u!=null&&(h=ir({inputs:{x:a},backend:n,attrs:{perm:u}}),d.push(h),c=R.getInnerMostAxes(c.length,o));let p=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:A}=ew(h.shape,h.dtype,p,c),y=m;return i&&(y=R.expandShapeToKeepDim(m,l)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,f)}var eF={kernelName:Mo,backendName:"cpu",kernelFunc:QR};function Em(e,t,n,r){let a=e===t,s=e<t&&n<0,i=t<e&&n>1;if(a||s||i)return v.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=v.makeZerosTypedArray(o,r);t<e&&n===1&&(n=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+n;return l}var tw=xl(e=>1/Math.sqrt(e)),tF=wl(Us,tw),nF={kernelName:Us,backendName:"cpu",kernelFunc:tF};function Yd(e,t,n,r,a){let s=cn.isSliceContinous(r,t,n),i=v.sizeFromShape(n),o=v.computeStrides(r);if(s){let h=cn.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?R.fromUint8ToStringArray(e):e,u=We(r,a,l),c=We(n,a);for(let h=0;h<c.size;++h){let d=c.indexToLoc(h),p=d.map((f,m)=>f+t[m]);c.set(u.get(...p),...d)}return a==="string"?R.fromStringArrayToUint8(c.values):c.values}function Ai(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r;_e(a,"slice");let[o,l]=cn.parseSliceParams(a,s,i);cn.assertParamsValid(a,o,l);let u=n.data.get(a.dataId).values,c=Yd(u,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,c)}var rF={kernelName:Lo,backendName:"cpu",kernelFunc:Ai},nw=Rt((e,t)=>{let n=e-t;return n*n}),aF=qt(Ks,nw),sF={kernelName:Ks,backendName:"cpu",kernelFunc:aF};function rw(e,t,n,r){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]*n[l]+r[l];a.set(t.get(...o),...i)}return a}var aw=Rt((e,t)=>e-t),iF=Rm((e,t,n,r)=>({real:e-n,imag:t-r})),Mm=qt(Zs,aw,iF),oF={kernelName:Zs,backendName:"cpu",kernelFunc:Mm};function sw(e,t){let n=new Array(e.rank);for(let a=0;a<n.length;a++)n[a]=e.shape[a]*t[a];let r=We(n,e.dtype);for(let a=0;a<r.values.length;++a){let s=r.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);r.values[a]=e.values[o]}return r}function iw(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=v.getTypedArrayFromDType(n,i*r),u=v.getTypedArrayFromDType("int32",i*r);for(let h=0;h<i;h++){let d=h*o,p=e.subarray(d,d+o),f=[];for(let g=0;g<p.length;g++)f.push({value:p[g],index:g});f.sort((g,_)=>_.value-g.value);let m=h*r,A=l.subarray(m,m+r),y=u.subarray(m,m+r);for(let g=0;g<r;g++)A[g]=f[g].value,y[g]=f[g].index}let c=t.slice();return c[c.length-1]=r,[We(c,n,l),We(c,"int32",u)]}function ow(e,t,n,r){let a=v.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let f=0;f<a;f++)s[0]*=n[f];s[1]=n[a];for(let f=a+1;f<n.length;f++)s[2]*=n[f];let i={},o=new Int32Array(n[a]),l=new Ot(s,r,e),u=[],c=s[0]===1&&s[2]===1;for(let f=0;f<n[a];f++){let m;if(c)m=e[f].toString();else{let A=[];for(let y=0;y<s[0];y++)for(let g=0;g<s[2];g++)A.push(l.get(y,f,g));m=A.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let A=Object.keys(i).length;i[m]=A,o[f]=A,u.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let d=new Ot(h,r);u.forEach((f,m)=>{for(let A=0;A<s[0];A++)for(let y=0;y<s[2];y++)d.set(l.get(A,f,y),A,m,y)});let p=n.slice();return p[a]=h[1],{outputValues:d.values,outputShape:p,indices:o}}var uw="3.3.0";sl("cpu",()=>new Zd,1);var cw=it(lo,e=>e>=0?e:Math.exp(e)-1),lF={kernelName:lo,backendName:"cpu",kernelFunc:cw};function hw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;_e([a],"leakyRelu");let i=v.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=v.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return n.makeTensorInfo(a.shape,"float32",l)}var uF={kernelName:Ns,backendName:"cpu",kernelFunc:hw},cF=Rt((e,t)=>e<0?t*e:e);function dw(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t;_e([r,a],"prelu");let s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=cF(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var hF={kernelName:zs,backendName:"cpu",kernelFunc:dw},pw=it(Ps,e=>Math.max(0,e)),dF={kernelName:Ps,backendName:"cpu",kernelFunc:pw},fw=it(Ws,e=>Math.min(Math.max(0,e),6)),pF={kernelName:Ws,backendName:"cpu",kernelFunc:fw};function $m(e,t,n,r,a){if(n==="linear")return Lr({inputs:{x:t},backend:e});if(n==="relu")return pw({inputs:{x:t},backend:e});if(n==="elu")return cw({inputs:{x:t},backend:e});if(n==="relu6")return fw({inputs:{x:t},backend:e});if(n==="prelu")return dw({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return hw({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function gt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=v.sizeFromShape(a.shape),o=v.inferFromImplicitShape(s,i),l=v.sizeFromShape(o);v.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let u=n.data.get(a.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,h=u.complexTensorInfos.imag;c.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var fF={kernelName:Do,backendName:"cpu",kernelFunc:gt};function mw(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;_e([a,s],"matMul");let l=a.shape.length,u=s.shape.length,c=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=v.sizeFromShape(f),y=v.sizeFromShape(m),g=A===y||A===1||y===1;v.assert(l>=2&&u>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let _=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);v.assert(c===h,()=>`Error in matMul: inner shapes (${c}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[A,c,d]:[A,d,c],w=o?[y,p,h]:[y,h,p],x=gt({inputs:{x:a},backend:n,attrs:{shape:b}}),I=gt({inputs:{x:s},backend:n,attrs:{shape:w}}),S=i?x.shape[1]:x.shape[2],T=i?x.shape[2]:x.shape[1],M=o?I.shape[1]:I.shape[2],D=Math.max(A,y),z=n.data.get(x.dataId).values,W=n.data.get(I.dataId).values,U=v.computeStrides(x.shape),H=v.computeStrides(I.shape),[X,j,ee]=i?[U[0],1,U[1]]:[U[0],U[1],1],[Y,ae,te]=o?[1,H[1],H[0]]:[H[1],1,H[0]],ie=T*M,Q=We([D,T,M],x.dtype),he=Q.values,oe=n.blockSize;for(let fe=0;fe<D;fe++)for(let pe=0;pe<T;pe+=oe)for(let ke=0;ke<M;ke+=oe)for(let Ie=0;Ie<S;Ie+=oe){let Me=Math.min(pe+oe,T),Oe=Math.min(ke+oe,M),$e=Math.min(Ie+oe,S);for(let tt=pe;tt<Me;tt++)for(let nt=ke;nt<Oe;nt++){let ot=0;for(let Ze=Ie;Ze<$e;Ze++){let pt=Math.min(fe,A-1)*X,Be=Math.min(fe,y-1)*te,yn=z[pt+tt*j+Ze*ee],_t=W[Ze*Y+nt*ae+Be];ot+=yn*_t}he[fe*ie+(tt*M+nt)]+=ot}}return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(I),n.makeTensorInfo(_,Q.dtype,Q.values)}var mF={kernelName:cs,backendName:"cpu",kernelFunc:mw};function AF(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r,d,p,f,m=[];d=mw({inputs:{a,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(p=tc({inputs:{a:d,b:i},backend:n}),m.push(d),d=p),c&&(f=$m(n,d,c,o,h),m.push(d),d=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return d}var yF={kernelName:Qs,backendName:"cpu",kernelFunc:AF},gF=it(Yi,e=>Math.acos(e)),xF={kernelName:Yi,backendName:"cpu",kernelFunc:gF},wF=it(Ji,e=>Math.acosh(e)),_F={kernelName:Ji,backendName:"cpu",kernelFunc:wF};function bF(e){let{inputs:t,backend:n}=e,r=t;_e(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=We(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var vF={kernelName:os,backendName:"cpu",kernelFunc:bF};function kF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"all");let o=v.parseAxisParam(s,a.shape),l=o,u=R.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=ir({inputs:{x:a},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("all",l,c.shape.length);let[h,d]=R.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,_=m[g];for(let b=0;b<p;++b){let w=m[g+b];_=_&&w}f[y]=_}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(h,c.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var NF={kernelName:vh,backendName:"cpu",kernelFunc:kF};function IF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"any");let o=v.parseAxisParam(s,a.shape),l=o,u=R.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=ir({inputs:{x:a},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("any",l,c.shape.length);let[h,d]=R.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,_=m[g];for(let b=0;b<p;++b){let w=m[g+b];_=_||w}f[y]=_}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(h,c.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var SF={kernelName:kh,backendName:"cpu",kernelFunc:IF};function TF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;_e(a,"argMax");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=ir({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[c,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(c),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],_=0;for(let b=0;b<f;++b){let w=m[y+b];w>g&&(g=w,_=b)}p[A]=_}return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(c,"int32",p)}var CF={kernelName:ls,backendName:"cpu",kernelFunc:TF};function EF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;_e(a,"argMin");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=ir({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[c,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(c),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],_=0;for(let b=0;b<f;++b){let w=m[y+b];w<g&&(g=w,_=b)}p[A]=_}return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(c,"int32",p)}var RF={kernelName:lu,backendName:"cpu",kernelFunc:EF},FF=it(Qi,e=>Math.asin(e)),MF={kernelName:Qi,backendName:"cpu",kernelFunc:FF},$F=it(eo,e=>Math.asinh(e)),DF={kernelName:eo,backendName:"cpu",kernelFunc:$F},OF=it(to,e=>Math.atan(e)),zF={kernelName:to,backendName:"cpu",kernelFunc:OF},PF=Rt((e,t)=>Math.atan2(e,t)),LF=qt(ro,PF),WF={kernelName:ro,backendName:"cpu",kernelFunc:LF},BF=it(no,e=>Math.atanh(e)),VF={kernelName:no,backendName:"cpu",kernelFunc:BF};function Dm(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,u=a.dilationWidth,c=a.effectiveFilterHeight,h=a.effectiveFilterWidth,d=a.padInfo.top,p=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=We(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],_=a.outShape[3];for(let b=0;b<a.batchSize;++b){let w=b*y,x=b*r[0];for(let I=0;I<a.inChannels;++I)for(let S=0;S<a.outHeight;++S){let T=S*i-d,M=Math.max(0,T),D=Math.min(a.inHeight,c+T),z=w+S*g;for(let W=0;W<a.outWidth;++W){let U=W*o-p,H=Math.max(0,U),X=Math.min(a.inWidth,h+U),j=f,ee=0,Y=0;for(let te=M;te<D;te+=l){let ie=x+te*r[1];for(let Q=H;Q<X;Q+=u){let he=ie+Q*r[2],oe=e[he+I];s==="max"&&oe>j?j=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(j))break}let ae=z+W*_+I;A[ae]=s==="avg"?ee/Y:j}}}return m}function Aw(e,t,n,r,a=!1,s=!1){let i=We(r.outShape,"int32"),o=r.strideHeight,l=r.strideWidth,u=r.dilationHeight,c=r.dilationWidth,h=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,f=r.padInfo.left,m=We(t,n,e);for(let A=0;A<r.batchSize;++A)for(let y=0;y<r.inChannels;++y)for(let g=0;g<r.outHeight;++g){let _=g*o-p,b=_;for(;b<0;)b+=u;let w=Math.min(r.inHeight,h+_);for(let x=0;x<r.outWidth;++x){let I=x*l-f,S=I;for(;S<0;)S+=c;let T=Math.min(r.inWidth,d+I),M=Number.NEGATIVE_INFINITY,D=-1;for(let z=b;z<w;z+=u){let W=z-_;for(let U=S;U<T;U+=c){let H=U-I,X=m.get(A,z,U,y);X>M&&(M=X,a?D=s?((A*r.inHeight+z)*r.inWidth+U)*r.inChannels+y:(z*r.inWidth+U)*r.inChannels+y:D=W*d+H)}}i.set(D,A,g,x,y)}}return i}function yw(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,u=a.dilationDepth,c=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,_=We(a.outShape,n),b=_.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],x=a.outShape[2]*a.outShape[3]*a.outShape[4],I=a.outShape[3]*a.outShape[4],S=a.outShape[4];for(let T=0;T<a.batchSize;++T){let M=T*w,D=T*r[0];for(let z=0;z<a.inChannels;++z)for(let W=0;W<a.outDepth;++W){let U=W*i-m,H=U;for(;H<0;)H+=u;let X=Math.min(a.inDepth,d+U),j=M+W*x;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-A,ae=Y;for(;ae<0;)ae+=c;let te=Math.min(a.inHeight,p+Y),ie=j+ee*I;for(let Q=0;Q<a.outWidth;++Q){let he=Q*l-y,oe=he;for(;oe<0;)oe+=h;let fe=Math.min(a.inWidth,f+he),pe=ie+Q*S,ke=g,Ie=0,Me=0;for(let $e=H;$e<X;$e+=u){let tt=D+$e*r[1];for(let nt=ae;nt<te;nt+=c){let ot=tt+nt*r[2];for(let Ze=oe;Ze<fe;Ze+=h){let pt=ot+Ze*r[3],Be=e[pt+z];if(s==="max"&&Be>ke?ke=Be:s==="avg"&&(Ie+=Be,Me++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Oe=pe+z;b[Oe]=s==="avg"?Ie/Me:ke}}}}return _}function UF(e,t){let n=We(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,_=g;for(;_<0;)_+=i;let b=Math.min(t.inDepth,u+g);for(let w=0;w<t.outHeight;++w){let x=w*a-p,I=x;for(;I<0;)I+=o;let S=Math.min(t.inHeight,c+x);for(let T=0;T<t.outWidth;++T){let M=T*s-f,D=M;for(;D<0;)D+=l;let z=Math.min(t.inWidth,h+M),W=Number.NEGATIVE_INFINITY,U=-1;for(let H=_;H<b;H+=i){let X=H-g;for(let j=I;j<S;j+=o){let ee=j-x;for(let Y=D;Y<z;Y+=l){let ae=Y-M,te=e.get(m,H,j,Y,A);te>=W&&(W=te,U=X*c*h+ee*c+ae)}}}n.set(U,m,y,w,T,A)}}}return n}function HF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;_e(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=Dm(d,a.shape,a.dtype,p,c,"avg");h=n.makeTensorInfo(c.outShape,a.dtype,f.values)}return h}var jF={kernelName:us,backendName:"cpu",kernelFunc:HF};function GF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r;_e(a,"avgPool3d");let c=R.computePool3DInfo(a.shape,s,i,1,o,l,u),h=n.data.get(a.dataId).values,d=yw(h,a.shape,a.dtype,v.computeStrides(a.shape),c,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var qF={kernelName:uu,backendName:"cpu",kernelFunc:GF};function XF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r;_e([a,s],"avgPool3DGrad");let c=R.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,_=c.dilationWidth,b=c.effectiveFilterDepth,w=c.effectiveFilterHeight,x=c.effectiveFilterWidth,I=b-1-c.padInfo.front,S=x-1-c.padInfo.left,T=w-1-c.padInfo.top,M=We(s.shape,"float32"),D=1/(f*m*A),z=n.bufferSync(a);for(let W=0;W<c.batchSize;++W)for(let U=0;U<c.inChannels;++U)for(let H=0;H<c.inDepth;++H)for(let X=0;X<c.inHeight;++X)for(let j=0;j<c.inWidth;++j){let ee=H-I,Y=X-T,ae=j-S,te=0;for(let ie=0;ie<b;ie+=y){let Q=(ee+ie)/h;if(!(Q<0||Q>=c.outDepth||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(Y+he)/d;if(!(oe<0||oe>=c.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe<x;fe+=_){let pe=(ae+fe)/p;pe<0||pe>=c.outWidth||Math.floor(pe)!==pe||(te+=z.get(W,Q,oe,pe,U))}}}M.set(te*D,W,H,X,j,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var KF={kernelName:Ih,backendName:"cpu",kernelFunc:XF};function ZF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;_e([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,A=c.dilationWidth,y=c.effectiveFilterHeight,g=c.effectiveFilterWidth,_=g-1-c.padInfo.left,b=y-1-c.padInfo.top,w=We(i.shape,"float32"),x=1/(p*f),I=n.data.get(a.dataId).values,S=We(a.shape,"float32",I);for(let T=0;T<c.batchSize;++T)for(let M=0;M<c.inChannels;++M)for(let D=0;D<c.inHeight;++D)for(let z=0;z<c.inWidth;++z){let W=D-b,U=z-_,H=0;for(let X=0;X<y;X+=m){let j=(W+X)/h;if(!(j<0||j>=c.outHeight||Math.floor(j)!==j))for(let ee=0;ee<g;ee+=A){let Y=(U+ee)/d;Y<0||Y>=c.outWidth||Math.floor(Y)!==Y||(H+=S.get(T,j,Y,M))}}w.set(H*x,T,D,z,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var YF={kernelName:Nh,backendName:"cpu",kernelFunc:ZF};function JF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),_e([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=r;u==null&&(u=.001);let c=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),A=f.length,y=p.length,g=d.length,_=h.length,b=0,w=0,x=0,I=0;for(let S=0;S<c.length;++S)m[S]=f[b++]+(c[S]-h[w++])*p[x++]/Math.sqrt(d[I++]+u),b>=A&&(b=0),w>=_&&(w=0),x>=y&&(x=0),I>=g&&(I=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var QF={kernelName:bs,backendName:"cpu",kernelFunc:JF};function eM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_e([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=gt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=ir({inputs:{x:p},backend:n,attrs:{perm:u}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var tM={kernelName:cu,backendName:"cpu",kernelFunc:eM};function nM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Im(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var rM={kernelName:Sh,backendName:"cpu",kernelFunc:nM},aM=it(Ia,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),sM={kernelName:Ia,backendName:"cpu",kernelFunc:aM},iM=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let c=o[u],h=l[u];r[u]=Math.hypot(c,h)}return n.makeOutput(r,t.shape,"float32")},oM={kernelName:hu,backendName:"cpu",kernelFunc:iM};function _l(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var lM={kernelName:Bh,backendName:"cpu",kernelFunc:_l};function bl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>v.sizeFromShape(m.shape)>0);if(o.length===1)return Lr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(R.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(b=>mi({inputs:{input:b},backend:n})),A=o.map(b=>_l({inputs:{input:b},backend:n})),y=bl({inputs:m,backend:n,attrs:{axis:s}}),g=bl({inputs:A,backend:n,attrs:{axis:s}}),_=On({inputs:{real:y,imag:g},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),_}let u=o.map(m=>{let A=v.sizeFromShape(m.shape.slice(s));return gt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=R.computeOutShape(u.map(m=>m.shape),1);let h=u[0].shape[0]===1,d=Sm(c,i,t[0].dtype,h),p=R.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var uM={kernelName:ao,backendName:"cpu",kernelFunc:bl};function gw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;_e([a,s],"conv2d");let h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new Ot(d.outShape,a.dtype),w=v.computeStrides(a.shape),x=v.computeStrides(s.shape),I=w[0],S=_?w[1]:w[2],T=_?w[2]:1,M=_?1:w[1],D=b.strides[0],z=_?b.strides[1]:b.strides[2],W=_?b.strides[2]:1,U=_?1:b.strides[1],H=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,j=b.values;for(let ee=0;ee<d.batchSize;++ee){let Y=ee*I,ae=ee*D;for(let te=0;te<d.outHeight;++te){let ie=ae+te*z,Q=te*d.strideHeight-g;for(let he=0;he<p;++he){let oe=Q+he*m;if(oe<0||oe>=d.inHeight)continue;let fe=he*x[0],pe=Y+oe*S;for(let ke=0;ke<d.outWidth;++ke){let Ie=ie+ke*W,Me=ke*d.strideWidth-y;for(let Oe=0;Oe<f;++Oe){let $e=Me+Oe*A;if($e<0||$e>=d.inWidth)continue;let tt=fe+Oe*x[1],nt=pe+$e*T,ot=tt;for(let Ze=0;Ze<d.inChannels;++Ze){let pt=H[nt+Ze*M];for(let Be=0;Be<d.outChannels;++Be)j[Ie+Be*U]+=pt*X[ot+Be];ot+=d.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,j)}var cM={kernelName:ps,backendName:"cpu",kernelFunc:gw};function hM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r;_e([a,s],"conv2dBackpropFilter");let h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new Ot(d.filterShape,"float32"),_=d.padInfo.left,b=d.padInfo.top,w=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,I=new Ot(a.shape,a.dtype,w),S=new Ot(s.shape,s.dtype,x);for(let T=0;T<m;++T){let M=Math.max(0,Math.ceil((b-T)/p)),D=Math.min(d.outHeight,(d.inHeight+b-T)/p);for(let z=0;z<A;++z){let W=Math.max(0,Math.ceil((_-z)/f)),U=Math.min(d.outWidth,(d.inWidth+_-z)/f);for(let H=0;H<d.inChannels;++H)for(let X=0;X<d.outChannels;++X){let j=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=M;Y<D;++Y){let ae=T+Y*p-b;for(let te=W;te<U;++te){let ie=z+te*f-_;y?j+=I.get(ee,ae,ie,H)*S.get(ee,Y,te,X):j+=I.get(ee,H,ae,ie)*S.get(ee,X,Y,te)}}g.set(j,T,z,H,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var dM={kernelName:Ch,backendName:"cpu",kernelFunc:hM};function pM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r;_e([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),d=v.computeStrides(a.shape),p=R.convertConv2DDataFormat(u),f=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,p),m=new Ot(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[_,b,w]=h,{batchSize:x,filterHeight:I,filterWidth:S,inChannels:T,inHeight:M,inWidth:D,outChannels:z,outHeight:W,outWidth:U,strideHeight:H,strideWidth:X}=f;p=f.dataFormat;let j=I-1-f.padInfo.top,ee=S-1-f.padInfo.left,Y=p==="channelsLast",ae=m.strides[0],te=Y?m.strides[1]:m.strides[2],ie=Y?m.strides[2]:1,Q=Y?1:m.strides[1],he=d[0],oe=Y?d[1]:d[2],fe=Y?d[2]:1,pe=Y?1:d[1];for(let ke=0;ke<x;++ke)for(let Ie=0;Ie<T;++Ie)for(let Me=0;Me<M;++Me){let Oe=Me-j,$e=Math.max(0,Math.ceil(Oe/H)),tt=Math.min(W,(I+Oe)/H);for(let nt=0;nt<D;++nt){let ot=nt-ee,Ze=Math.max(0,Math.ceil(ot/X)),pt=Math.min(U,(S+ot)/X),Be=0;for(let _t=$e;_t<tt;++_t){let Un=_t*H-Oe;for(let Yt=Ze;Yt<pt;++Yt){let gn=Yt*X-ot,Hn=he*ke+oe*_t+fe*Yt,Rn=_*(I-1-Un)+b*(S-1-gn)+w*Ie;for(let un=0;un<z;++un){let Jt=y[Hn+pe*un],Tr=g[Rn+un];Be+=Jt*Tr}}}let yn=ae*ke+te*Me+ie*nt+Q*Ie;A[yn]=Be}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var fM={kernelName:fs,backendName:"cpu",kernelFunc:pM};function mM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;_e([a,s],"conv3d");let u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:c,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=u,y=A.front,g=A.left,_=A.top,b=new Ot(u.outShape,a.dtype),w=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,I=b.values,S=v.computeStrides(a.shape),T=v.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let D=M*S[0],z=M*b.strides[0];for(let W=0;W<u.outDepth;++W){let U=z+W*b.strides[1],H=W*u.strideDepth-y;for(let X=0;X<c;++X){let j=H+X*p;if(j<0||j>=u.inDepth)continue;let ee=X*T[0],Y=D+j*S[1];for(let ae=0;ae<u.outHeight;++ae){let te=U+ae*b.strides[2],ie=ae*u.strideHeight-_;for(let Q=0;Q<h;++Q){let he=ie+Q*f;if(he<0||he>=u.inHeight)continue;let oe=ee+Q*T[1],fe=Y+he*S[2];for(let pe=0;pe<u.outWidth;++pe){let ke=te+pe*u.outChannels,Ie=pe*u.strideWidth-g;for(let Me=0;Me<d;++Me){let Oe=Ie+Me*m;if(Oe<0||Oe>=u.inWidth)continue;let $e=oe+Me*T[2],tt=fe+Oe*u.inChannels,nt=$e;for(let ot=0;ot<u.inChannels;++ot){let Ze=w[tt+ot];for(let pt=0;pt<u.outChannels;++pt)I[ke+pt]+=Ze*x[nt+pt];nt+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var AM={kernelName:du,backendName:"cpu",kernelFunc:mM};function yM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;_e([a,s],"conv3dBackpropFilterV2");let u=v.computeStrides(a.shape),c=v.computeStrides(s.shape),h=R.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new Ot(h.filterShape,"float32"),_=g.values,[b,w,x,I]=g.strides,S=n.data.get(s.dataId).values,[T,M,D,z]=c,W=n.data.get(a.dataId).values,[U,H,X,j]=u,ee=h.padInfo.front,Y=h.padInfo.left,ae=h.padInfo.top;for(let te=0;te<m;++te){let ie=Math.max(0,Math.ceil((ee-te)/d)),Q=Math.min(h.outDepth,(h.inDepth+ee-te)/d),he=te*b;for(let oe=0;oe<A;++oe){let fe=Math.max(0,Math.ceil((ae-oe)/p)),pe=Math.min(h.outHeight,(h.inHeight+ae-oe)/p),ke=oe*w+he;for(let Ie=0;Ie<y;++Ie){let Me=Math.max(0,Math.ceil((Y-Ie)/f)),Oe=Math.min(h.outWidth,(h.inWidth+Y-Ie)/f),$e=Ie*x+ke;for(let tt=0;tt<h.inChannels;++tt){let nt=tt*I+$e;for(let ot=0;ot<h.outChannels;++ot){let Ze=0;for(let pt=0;pt<h.batchSize;++pt){let Be=pt*U,yn=pt*T;for(let _t=ie;_t<Q;++_t){let Un=(te+_t*d-ee)*H+Be,Yt=_t*M+yn;for(let gn=fe;gn<pe;++gn){let Hn=(oe+gn*p-ae)*X+Un,Rn=gn*D+Yt;for(let un=Me;un<Oe;++un){let Jt=(Ie+un*f-Y)*j+Hn,Tr=un*z+Rn;Ze+=W[Jt+tt]*S[Tr+ot]}}}}_[nt+ot]=Ze}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var gM={kernelName:Eh,backendName:"cpu",kernelFunc:yM};function xM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;_e([a],"conv3dBackpropInputV2");let u=v.computeStrides(a.shape),c=v.computeStrides(s.shape),h=R.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(h.inShape,"float32"),p=d.values,[f,m,A,y]=d.strides,g=n.data.get(a.dataId).values,[_,b,w,x]=u,I=n.data.get(s.dataId).values,[S,T,M,D]=c,{batchSize:z,filterDepth:W,filterHeight:U,filterWidth:H,inChannels:X,inDepth:j,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:he,strideHeight:oe,strideWidth:fe}=h,pe=W-1-h.padInfo.front,ke=U-1-h.padInfo.top,Ie=H-1-h.padInfo.left;for(let Me=0;Me<z;++Me)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<j;++$e){let tt=$e-pe,nt=Math.max(0,Math.ceil(tt/he)),ot=Math.min(te,(W+tt)/he);for(let Ze=0;Ze<ee;++Ze){let pt=Ze-ke,Be=Math.max(0,Math.ceil(pt/oe)),yn=Math.min(ie,(U+pt)/oe);for(let _t=0;_t<Y;++_t){let Un=_t-Ie,Yt=Math.max(0,Math.ceil(Un/fe)),gn=Math.min(Q,(H+Un)/fe),Hn=0;for(let Rn=nt;Rn<ot;++Rn){let un=Rn*he-tt;for(let Jt=Be;Jt<yn;++Jt){let Tr=Jt*oe-pt;for(let Jn=Yt;Jn<gn;++Jn){let Qn=Jn*fe-Un,Aa=_*Me+b*Rn+w*Jt+x*Jn,Kr=S*(W-1-un)+T*(U-1-Tr)+M*(H-1-Qn)+D*Oe;for(let ya=0;ya<ae;++ya){let Di=g[Aa+ya],dr=I[Kr+ya];Hn+=Di*dr}}}}p[f*Me+m*$e+A*Ze+y*_t+Oe]=Hn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var wM={kernelName:Rh,backendName:"cpu",kernelFunc:xM},_M=it(ms,e=>Math.cos(e)),bM={kernelName:ms,backendName:"cpu",kernelFunc:_M},vM=it(so,e=>Math.cosh(e)),kM={kernelName:so,backendName:"cpu",kernelFunc:vM};function NM(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=We([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let I=0;I<f;I++){let S=I*4,T=g[S],M=g[S+1],D=g[S+2],z=g[S+3],W=_[I];if(W>=c)continue;let U=m>1?(D-T)*(h-1)/(m-1):0,H=A>1?(z-M)*(d-1)/(A-1):0;for(let X=0;X<m;X++){let j=m>1?T*(h-1)+X*U:.5*(T+D)*(h-1);if(j<0||j>h-1){for(let ee=0;ee<A;ee++)for(let Y=0;Y<p;Y++){let ae=Y+ee*x[2]+X*x[1]+I*x[0];y.values[ae]=u}continue}if(l==="bilinear"){let ee=Math.floor(j),Y=Math.ceil(j),ae=j-ee;for(let te=0;te<A;te++){let ie=A>1?M*(d-1)+te*H:.5*(M+z)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<p;fe++){let pe=fe+te*x[2]+X*x[1]+I*x[0];y.values[pe]=u}continue}let Q=Math.floor(ie),he=Math.ceil(ie),oe=ie-Q;for(let fe=0;fe<p;fe++){let pe=fe+Q*w[2]+ee*w[1]+W*w[0],ke=b[pe];pe=fe+he*w[2]+ee*w[1]+W*w[0];let Ie=b[pe];pe=fe+Q*w[2]+Y*w[1]+W*w[0];let Me=b[pe];pe=fe+he*w[2]+Y*w[1]+W*w[0];let Oe=b[pe],$e=ke+(Ie-ke)*oe,tt=Me+(Oe-Me)*oe;pe=fe+te*x[2]+X*x[1]+I*x[0],y.values[pe]=$e+(tt-$e)*ae}}}else for(let ee=0;ee<A;++ee){let Y=A>1?M*(d-1)+ee*H:.5*(M+z)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*x[2]+X*x[1]+I*x[0];y.values[Q]=u}continue}let ae=Math.round(Y),te=Math.round(j);for(let ie=0;ie<p;ie++){let Q=ie+ae*w[2]+te*w[1]+W*w[0],he=ie+ee*x[2]+X*x[1]+I*x[0];y.values[he]=b[Q]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var IM={kernelName:io,backendName:"cpu",kernelFunc:NM};function SM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;_e(a,"cumsum");let l=R.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=ir({inputs:{x:a},backend:n,attrs:{perm:l}}));let c=R.getInnerMostAxes(1,a.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let h=nr(u.dtype,"int32"),d=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),p=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,g)=>y+f-g-1:(y,g)=>y+g;for(let y=0;y<p.length;y+=f)for(let g=0;g<f;g++){let _=m(y,g);if(g===0)d[_]=i?0:p[_];else{let b=m(y,g-1);d[_]=i?p[b]+d[b]:p[_]+d[b]}}let A=n.makeTensorInfo(u.shape,h,d);if(l!=null){let y=R.getUndoAxesPermutation(l),g=ir({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(u),g}return A}var TM={kernelName:As,backendName:"cpu",kernelFunc:SM};function CM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,c=Im(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=Lx(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var EM={kernelName:Fh,backendName:"cpu",kernelFunc:CM};function RM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],c=a.shape[3],h=l*s,d=u*s,p=c/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),A=0;for(let y=0;y<o;++y)for(let g=0;g<h;++g){let _=Math.floor(g/s),b=g%s;for(let w=0;w<d;++w){let x=Math.floor(w/s),I=w%s,S=(b*s+I)*p;for(let T=0;T<p;++T){let M=T+S+c*(x+u*(_+l*y));m[A++]=f[M]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var FM={kernelName:oo,backendName:"cpu",kernelFunc:RM};function xw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r;_e([a,s],"depthwiseConv2DNative");let c=v.computeStrides(a.shape),h=v.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=p,_=g.left,b=g.top,w=p.outChannels/p.inChannels,x=new Ot(p.outShape,a.dtype),I=n.data.get(a.dataId).values,S=n.data.get(s.dataId).values,T=x.values;for(let M=0;M<p.batchSize;++M){let D=M*c[0],z=M*x.strides[0];for(let W=0;W<p.outHeight;++W){let U=z+W*x.strides[1],H=W*p.strideHeight-_;for(let X=0;X<f;++X){let j=H+X*A;if(j<0||j>=p.inHeight)continue;let ee=X*h[0],Y=D+j*c[1];for(let ae=0;ae<p.outWidth;++ae){let te=U+ae*x.strides[2],ie=ae*p.strideWidth-b;for(let Q=0;Q<m;++Q){let he=ie+Q*y;if(he<0||he>=p.inWidth)continue;let oe=ee+Q*h[1],fe=Y+he*p.inChannels,pe=te,ke=oe;for(let Ie=0;Ie<p.inChannels;++Ie){let Me=I[fe+Ie];for(let Oe=0;Oe<w;++Oe)T[pe+Oe]+=Me*S[ke+Oe];pe+=w,ke+=w}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var MM={kernelName:ys,backendName:"cpu",kernelFunc:xw};function $M(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r;_e([a,s],"depthwiseConv2dNativeBackpropFilter");let h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,A=new Ot(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,_=h.outChannels/h.inChannels,b=n.data.get(a.dataId).values,w=new Ot(a.shape,a.dtype,b),x=n.data.get(s.dataId).values,I=new Ot(s.shape,s.dtype,x);for(let S=0;S<f;++S){let T=Math.max(0,Math.ceil((g-S)/d)),M=Math.min(h.outHeight,(h.inHeight+g-S)/d);for(let D=0;D<m;++D){let z=Math.max(0,Math.ceil((y-D)/p)),W=Math.min(h.outWidth,(h.inWidth+y-D)/p);for(let U=0;U<h.outChannels;++U){let H=Math.trunc(U/_),X=U%_,j=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=T;Y<M;++Y){let ae=S+Y*d-g;for(let te=z;te<W;++te){let ie=D+te*p-y;j+=w.get(ee,ae,ie,H)*I.get(ee,Y,te,U)}}A.set(j,S,D,H,X)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var DM={kernelName:Mh,backendName:"cpu",kernelFunc:$M};function OM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r;_e([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),f=new Ot(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,_=n.data.get(a.dataId).values,[b,w,x]=h,I=n.data.get(s.dataId).values,[S,T,M]=d,{batchSize:D,filterHeight:z,filterWidth:W,inChannels:U,inHeight:H,inWidth:X,outChannels:j,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=z-1-p.padInfo.top,Q=W-1-p.padInfo.left,he=j/U;for(let oe=0;oe<D;++oe)for(let fe=0;fe<U;++fe)for(let pe=0;pe<H;++pe){let ke=pe-ie,Ie=Math.max(0,Math.ceil(ke/ae)),Me=Math.min(ee,(z+ke)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,tt=Math.max(0,Math.ceil($e/te)),nt=Math.min(Y,(W+$e)/te),ot=0;for(let Ze=Ie;Ze<Me;++Ze){let pt=Ze*ae-ke;for(let Be=tt;Be<nt;++Be){let yn=Be*te-$e,_t=b*oe+w*Ze+x*Be,Un=S*(z-1-pt)+T*(W-1-yn)+M*fe;for(let Yt=0;Yt<he;++Yt){let gn=fe*he+Yt,Hn=_[_t+gn],Rn=I[Un+Yt];ot+=Hn*Rn}}}m[A*oe+y*pe+g*Oe+fe]=ot}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var zM={kernelName:$h,backendName:"cpu",kernelFunc:OM};function PM(e){let{inputs:t,backend:n}=e,{x:r}=t,a=v.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=We([a,a],r.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*a+u]=s[u];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var LM={kernelName:Dh,backendName:"cpu",kernelFunc:PM},WM={kernelName:pu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(r.dataId).values,c=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:A,outHeight:y,outWidth:g,padInfo:_,strideHeight:b,strideWidth:w,filterHeight:x,filterWidth:I,dilationHeight:S,dilationWidth:T,outShape:M}=R.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),D=v.sizeFromShape(M),z=M.length,W=v.getArrayFromDType(r.dtype,D);for(let U=0;U<p;++U)for(let H=0;H<y;++H){let X=H*b-_.top;for(let j=0;j<g;++j){let ee=j*w-_.left;for(let Y=0;Y<A;++Y){let ae=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<x;++ie){let Q=X+ie*S;if(Q>=0&&Q<f)for(let he=0;he<I;++he){let oe=ee+he*T;if(oe>=0&&oe<m){let fe=v.locToIndex([U,Q,oe,Y],c,v.computeStrides(r.shape)),pe=v.locToIndex([ie,he,Y],d,v.computeStrides(a.shape)),ke=u[fe]+h[pe];ke>ae&&(ae=ke)}}}let te=v.locToIndex([U,H,j,Y],z,v.computeStrides(M));W[te]=ae}}}return{dataId:l.write(v.toTypedArray(W,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},BM={kernelName:zh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:I,dilationWidth:S,outShape:T}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===T.length,()=>`Error in ${zh}, dy must have the same rank as output ${T.length}, but got ${s.rank}`);let M=v.toNestedArray(T,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let z=0;z<d;++z)for(let W=0;W<A;++W){let U=W*_-g.top;for(let H=0;H<y;++H){let X=H*b-g.left;for(let j=0;j<m;++j){let ee=Number.MIN_SAFE_INTEGER,Y=0,ae=0;for(let te=0;te<w;++te){let ie=U+te*I;if(ie>=0&&ie<p)for(let Q=0;Q<x;++Q){let he=X+Q*S;if(he>=0&&he<f){let oe=c[z][ie][he][j]+h[te][Q][j];oe>ee&&(ee=oe,Y=te,ae=Q)}}}D[Y][ae][j]+=M[z][W][H][j]}}}return{dataId:u.write(v.toTypedArray(D,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},VM={kernelName:Oh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:I,dilationWidth:S,outShape:T}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===T.length,()=>`Error in ${Oh}, dy must have the same rank as output ${T.length}, but got ${s.rank}`);let M=v.toNestedArray(T,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let z=0;z<d;++z)for(let W=0;W<A;++W){let U=W*_-g.top;for(let H=0;H<y;++H){let X=H*b-g.left;for(let j=0;j<m;++j){let ee=Number.MIN_SAFE_INTEGER,Y=U<0?0:U,ae=X<0?0:X;for(let te=0;te<w;++te){let ie=U+te*I;if(ie>=0&&ie<p)for(let Q=0;Q<x;++Q){let he=X+Q*S;if(he>=0&&he<f){let oe=c[z][ie][he][j]+h[te][Q][j];oe>ee&&(ee=oe,Y=ie,ae=he)}}}D[z][Y][ae][j]+=M[z][W][H][j]}}}return{dataId:u.write(v.toTypedArray(D,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function UM(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;_e([r,a],"eluGrad");let s=new Float32Array(v.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let u=i[l];u>=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(a.shape,"float32",s)}var HM={kernelName:Ph,backendName:"cpu",kernelFunc:UM},jM=Rt((e,t)=>e===t?1:0),ww=qt(co,jM,null,"bool"),GM={kernelName:co,backendName:"cpu",kernelFunc:ww},qM=R.ERF_P,XM=R.ERF_A1,KM=R.ERF_A2,ZM=R.ERF_A3,YM=R.ERF_A4,JM=R.ERF_A5,QM=it(uo,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+qM*n);return t*(1-((((JM*r+YM)*r+ZM)*r+KM)*r+XM)*r*Math.exp(-n*n))}),e$={kernelName:uo,backendName:"cpu",kernelFunc:QM};function Qd(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),gt({inputs:{x:a},backend:n,attrs:{shape:o}})}var t$={kernelName:ho,backendName:"cpu",kernelFunc:Qd},n$=Rt((e,t)=>e/t),Om=qt(gs,n$),zm={kernelName:gs,backendName:"cpu",kernelFunc:Om};function _w(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[a,s],c=v.sizeFromShape(u),h=v.getTypedArrayFromDType("float32",c),d=v.getTypedArrayFromDType("float32",c);for(let A=0;A<a;A++){let y=Ai({inputs:{x:o},backend:n,attrs:{begin:[A,0],size:[1,s]}}),g=Ai({inputs:{x:l},backend:n,attrs:{begin:[A,0],size:[1,s]}}),_=On({inputs:{real:y,imag:g},backend:n}),{real:b,imag:w}=r$(_,t,n),x=R.mergeRealAndImagArrays(b,w);for(let I=0;I<s;I++){let S=R.getComplexWithIndex(x,I);h[A*s+I]=S.real,d[A*s+I]=S.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(_)}let p=n.makeTensorInfo(u,"float32",h),f=n.makeTensorInfo(u,"float32",d),m=On({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function r$(e,t,n){let r=v.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(a$(r)){let o=Pm(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",o.real),c=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",v.createScalarValue(r,"float32")),d=Lr({inputs:{x:h},backend:n}),p=zm.kernelFunc({inputs:{a:u,b:h},backend:n}),f=zm.kernelFunc({inputs:{a:c,b:d},backend:n}),m=n.data.get(p.dataId).values,A=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:A}}return o}else{let o=R.mergeRealAndImagArrays(s,i),l=s$(o,r,t);return R.splitRealAndImagArrays(l)}}function a$(e){return(e&e-1)==0}function Pm(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=R.mergeRealAndImagArrays(e,t),i=n/2,o=R.complexWithEvenIndex(s),l=o.real,u=o.imag,c=[l.length],h=a.makeTensorInfo(c,"float32",l),d=a.makeTensorInfo(c,"float32",u),p=On({inputs:{real:h,imag:d},backend:a}),f=R.complexWithOddIndex(s),m=f.real,A=f.imag,y=[m.length],g=a.makeTensorInfo(y,"float32",m),_=a.makeTensorInfo(y,"float32",A),b=On({inputs:{real:g,imag:_},backend:a}),w=Pm(l,u,i,r,a),x=w.real,I=w.imag,S=[x.length],T=a.makeTensorInfo(S,"float32",x),M=a.makeTensorInfo(S,"float32",I),D=On({inputs:{real:T,imag:M},backend:a}),z=Pm(m,A,i,r,a),W=z.real,U=z.imag,H=[W.length],X=a.makeTensorInfo(H,"float32",W),j=a.makeTensorInfo(H,"float32",U),ee=On({inputs:{real:X,imag:j},backend:a}),Y=R.exponents(n,r),ae=[Y.real.length],te=a.makeTensorInfo(ae,"float32",Y.real),ie=a.makeTensorInfo(ae,"float32",Y.imag),Q=On({inputs:{real:te,imag:ie},backend:a}),he=Fm({inputs:{a:Q,b:ee},backend:a}),oe=tc({inputs:{a:D,b:he},backend:a}),fe=Mm({inputs:{a:D,b:he},backend:a}),pe=mi({inputs:{input:oe},backend:a}),ke=mi({inputs:{input:fe},backend:a}),Ie=_l({inputs:{input:oe},backend:a}),Me=_l({inputs:{input:fe},backend:a}),Oe=bl({inputs:[pe,ke],backend:a,attrs:{axis:0}}),$e=bl({inputs:[Ie,Me],backend:a,attrs:{axis:0}}),tt=a.data.get(Oe.dataId).values,nt=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(_),a.disposeIntermediateTensorInfo(b),a.disposeIntermediateTensorInfo(T),a.disposeIntermediateTensorInfo(M),a.disposeIntermediateTensorInfo(D),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(j),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(he),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(Ie),a.disposeIntermediateTensorInfo(ke),a.disposeIntermediateTensorInfo(Me),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:tt,imag:nt}}function s$(e,t,n){let r=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let l=R.exponent(a*o,t,n),u=R.getComplexWithIndex(e,o);s+=u.real*l.real-u.imag*l.imag,i+=u.real*l.imag+u.imag*l.real}n&&(s/=t,i/=t),R.assignToTypedArray(r,s,i,a)}return r}function i$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=gt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=_w(o,!1,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var o$={kernelName:Lh,backendName:"cpu",kernelFunc:i$};function Lm(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||v.inferDtype(a),o=v.getArrayFromDType(i,v.sizeFromShape(r));return l$(o,a,i),t.makeTensorInfo(r,i,o)}var u$={kernelName:fu,backendName:"cpu",kernelFunc:Lm};function l$(e,t,n){e.fill(t)}var c$={kernelName:fo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[i,o,l,u]=r.shape,c=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*u;for(let p=0;p<o;p++){let f=p*(l*u);for(let m=0;m<l;m++){let A=m*u;for(let y=0;y<u;y++){let g=[i,p,m,y][2],_=Math.round(l-g),b=d+f+A+y,w=c[b];if(_>=0&&_<l){let x=_*u,I=d+f+x+y;w=c[I]}s[b]=w}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},h$=Rt((e,t)=>Math.floor(e/t)),d$=qt(_s,h$,null,"int32"),p$={kernelName:_s,backendName:"cpu",kernelFunc:d$};function f$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=gw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=tc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=$m(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var m$={kernelName:ei,backendName:"cpu",kernelFunc:f$};function A$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=xw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=tc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=$m(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var y$={kernelName:ti,backendName:"cpu",kernelFunc:A$};function g$(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=R.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=We([u,c],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m<u;m++){let A=[],y=0;for(let g=0;g<o;g++){let _=p[m*o+g];y+=_*h[g],A.push(_)}if(y<0||y>=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<c;g++)d.values[m*c+g]=f[y*c+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var x$={kernelName:Ao,backendName:"cpu",kernelFunc:g$};function w$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;_e([a,s],"gatherV2");let l=o;o==null&&(l=0);let u=v.sizeFromShape(s.shape),c=v.parseAxisParam(i,a.shape)[0],h=R.segment_util.collectGatherOpShapeInfo(a,s,c,l),d=gt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=gt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,u/h.batchSize]}}),f=[h.batchSize,h.outerSize,u/h.batchSize,h.sliceSize],m=n.bufferSync(p),A=n.bufferSync(d),y=Hx(A,m,f);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var _$={kernelName:mo,backendName:"cpu",kernelFunc:w$},b$=Rt((e,t)=>e>=t?1:0),v$=qt(vs,b$,null,"bool"),k$={kernelName:vs,backendName:"cpu",kernelFunc:v$};function N$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=gt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=_w(o,!0,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var I$={kernelName:Wh,backendName:"cpu",kernelFunc:N$},S$=it(go,e=>Number.isFinite(e)?1:0,"bool"),T$={kernelName:go,backendName:"cpu",kernelFunc:S$},C$=it(xo,e=>Math.abs(e)===Infinity?1:0,"bool"),E$={kernelName:xo,backendName:"cpu",kernelFunc:C$},R$=it(wo,e=>Number.isNaN(e)?1:0,"bool"),F$={kernelName:wo,backendName:"cpu",kernelFunc:R$},M$=Rt((e,t)=>e<=t?1:0),$$=qt(bo,M$,null,"bool"),D$={kernelName:bo,backendName:"cpu",kernelFunc:$$};function O$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=qx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var z$={kernelName:Vh,backendName:"cpu",kernelFunc:O$},P$=it(vo,e=>Math.log1p(e)),L$={kernelName:vo,backendName:"cpu",kernelFunc:P$},W$=Rt((e,t)=>e&&t),B$=qt(ko,W$,null,"bool"),V$={kernelName:ko,backendName:"cpu",kernelFunc:B$},U$=it(mu,e=>e?0:1,"bool"),H$={kernelName:mu,backendName:"cpu",kernelFunc:U$},j$=Rt((e,t)=>e||t),G$=qt(Au,j$,null,"bool"),q$={kernelName:Au,backendName:"cpu",kernelFunc:G$};function X$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;_e(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let A=m%u,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,c),_=0;for(;y<=g;y++){let b=h[y];_+=b*b}return _}for(let m=0;m<d;m++){let A=f(m),y=h[m]*Math.pow(i+o*A,-l);p[m]=y}return n.makeTensorInfo(a.shape,a.dtype,p)}var K$={kernelName:yu,backendName:"cpu",kernelFunc:X$};function Z$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r;_e(i,"LRNGrad");let h=v.sizeFromShape(i.shape),d=i.shape[3],p=n.data.get(i.dataId).values,f=n.data.get(a.dataId).values,m=n.data.get(s.dataId).values,A=new Float32Array(h),y=h;for(let g=0;g<y;g++){let _=g%d,b=g-_+Math.max(0,_-o),w=g-_+Math.min(d,_+o+1),x=0;for(let I=b;I<w;I++)x+=Math.pow(f[I],2);x=u*x+l;for(let I=b;I<w;I++){let S=-2*u*c*f[I]*m[g]/x;g===I&&(S+=Math.pow(x,-c)),S*=p[g],A[I]+=S}}return n.makeTensorInfo(i.shape,a.dtype,A)}var Y$={kernelName:Uh,backendName:"cpu",kernelFunc:Z$};function bw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,u=l.length,c=v.parseAxisParam(s,l),h=c,d=R.getAxesPermutation(h,u),p=o.data.get(a.dataId).values;if(d!=null){let b=new Array(u);for(let w=0;w<b.length;w++)b[w]=l[d[w]];p=Cm(p,l,a.dtype,d,b),h=R.getInnerMostAxes(h.length,u),l=b}_e(a,"max"),R.assertAxesAreInnerMostDims("max",h,u);let[f,m]=R.computeOutAndReduceShapes(l,h),A=v.sizeFromShape(m),y=Kx(p,A,f,a.dtype),g=o.write(y,f,a.dtype),_=f;return i&&(_=R.expandShapeToKeepDim(f,c)),{dataId:g,shape:_,dtype:a.dtype}}var J$={kernelName:Ss,backendName:"cpu",kernelFunc:bw};function Q$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;_e(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=Dm(d,a.shape,a.dtype,p,c,"max");h=n.makeTensorInfo(c.outShape,a.dtype,f.values)}return h}var eD={kernelName:Cs,backendName:"cpu",kernelFunc:Q$};function tD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r;_e(a,"maxPool3d");let c=R.computePool3DInfo(a.shape,s,i,1,o,l,u),h=n.data.get(a.dataId).values,d=yw(h,a.shape,a.dtype,v.computeStrides(a.shape),c,"max");return n.makeTensorInfo(d.shape,"float32",d.values)}var nD={kernelName:gu,backendName:"cpu",kernelFunc:tD};function rD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r;_e([a,s],"maxPool3DGrad");let c=R.computePool3DInfo(s.shape,i,o,1,l,u),h=n.bufferSync(s),d=UF(h,c),p=c.strideDepth,f=c.strideHeight,m=c.strideWidth,A=c.dilationDepth,y=c.dilationHeight,g=c.dilationWidth,_=c.effectiveFilterDepth,b=c.effectiveFilterHeight,w=c.effectiveFilterWidth,x=_-1-c.padInfo.front,I=w-1-c.padInfo.left,S=b-1-c.padInfo.top,T=We(s.shape,"float32"),M=n.bufferSync(a);for(let D=0;D<c.batchSize;++D)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let U=0;U<c.inHeight;++U)for(let H=0;H<c.inWidth;++H){let X=W-x,j=U-S,ee=H-I,Y=0;for(let ae=0;ae<_;ae+=A){let te=(X+ae)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie<b;ie+=y){let Q=(j+ie)/f;if(!(Q<0||Q>=c.outHeight||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(ee+he)/m;if(oe<0||oe>=c.outWidth||Math.floor(oe)!==oe)continue;let fe=_*b*w-1-d.get(D,te,Q,oe,z),pe=ae*b*w+ie*w+he,ke=fe===pe?1:0;ke!==0&&(Y+=M.get(D,te,Q,oe,z)*ke)}}}T.set(Y,D,W,U,H,z)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var aD={kernelName:jh,backendName:"cpu",kernelFunc:rD};function sD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;_e([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=n.data.get(o.dataId).values,f=We(d.outShape,o.dtype,Aw(p,o.shape,o.dtype,d).values),m=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,_=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,x=_-1-d.padInfo.top,I=We(o.shape,"float32"),S=n.data.get(a.dataId).values,T=We(a.shape,"float32",S);for(let M=0;M<d.batchSize;++M)for(let D=0;D<d.inChannels;++D)for(let z=0;z<d.inHeight;++z)for(let W=0;W<d.inWidth;++W){let U=z-x,H=W-w,X=0;for(let j=0;j<_;j+=y){let ee=(U+j)/m;if(!(ee<0||ee>=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y<b;Y+=g){let ae=(H+Y)/A;if(ae<0||ae>=d.outWidth||Math.floor(ae)!==ae)continue;let te=_*b-1-f.get(M,ee,ae,D),ie=j*b+Y,Q=te===ie?1:0;Q!==0&&(X+=T.get(M,ee,ae,D)*Q)}}I.set(X,M,z,W,D)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}var iD={kernelName:Hh,backendName:"cpu",kernelFunc:sD};function oD(e,t,n,r,a){let s=v.computeStrides(t),i=Dm(e,t,n,s,a,"max"),o=Aw(e,t,n,a,!0,r);return[i.values,o.values]}var lD={kernelName:Gh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_e(r,"MaxPoolWithArgmax");let u=l.data.get(r.dataId).values,c=R.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=oD(u,r.shape,r.dtype,o,c),p=l.write(h,c.outShape,r.dtype),f=l.write(d,c.outShape,r.dtype);return[{dataId:p,shape:c.outShape,dtype:r.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function ep(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"sum");let o;a.dtype==="bool"?o=Wa({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Lr({inputs:{x:a},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),c=R.getAxesPermutation(u,l),h=u,d=o;c!=null&&(d=ir({inputs:{x:o},backend:n,attrs:{perm:c}}),h=R.getInnerMostAxes(h.length,l)),R.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=R.computeOutAndReduceShapes(d.shape,h),m=R.upcastType(d.dtype,"int32"),A=Jd(n,p,m),y=v.sizeFromShape(f),g=n.data.get(A.dataId).values,_=n.data.get(d.dataId).values;for(let b=0;b<g.length;++b){let w=b*y,x=0;for(let I=0;I<y;++I)x+=_[w+I];g[b]=x}if(i){let b=R.expandShapeToKeepDim(A.shape,u),w=A;A=gt({inputs:{x:A},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),c!=null&&n.disposeIntermediateTensorInfo(d),A}var uD={kernelName:qs,backendName:"cpu",kernelFunc:ep};function cD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=v.parseAxisParam(s,a.shape),l=R.computeOutAndReduceShapes(a.shape,o)[1],u=v.sizeFromShape(l),c=[],h=n.makeTensorInfo([],"float32",new Float32Array([u]));c.push(h);let d=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});c.push(d);let p=Om({inputs:{a:d,b:h},backend:n});c.push(p);let f=ep({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var hD={kernelName:Es,backendName:"cpu",kernelFunc:cD};function dD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"min");let o=v.parseAxisParam(s,a.shape),l=o,u=R.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=ir({inputs:{x:a},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",l,c.shape.length);let[h,d]=R.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,_=m[g];for(let b=0;b<p;++b){let w=m[g+b];w<_&&(_=w)}f[y]=_}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(h,c.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var pD={kernelName:Rs,backendName:"cpu",kernelFunc:dD};function fD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;_e(a,"mirrorPad");let o=s.map((g,_)=>g[0]+a.shape[_]+g[1]),l=s.map(g=>g[0]),u=s.map((g,_)=>g[0]+a.shape[_]),c=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=v.computeStrides(a.shape),f=v.sizeFromShape(o),m=o.length,A=v.computeStrides(o),y=v.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let _=v.indexToLoc(g,m,A);for(let w=0;w<m;w++)_[w]<l[w]?_[w]=l[w]*2-_[w]-c:_[w]>=u[w]&&(_[w]=(u[w]-1)*2-_[w]+c);_=_.map((w,x)=>w-l[x]);let b=v.locToIndex(_,d,p);y[g]=h[b]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var mD={kernelName:xu,backendName:"cpu",kernelFunc:fD},AD=Rt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),yD=qt(No,AD),gD={kernelName:No,backendName:"cpu",kernelFunc:yD},xD=qi(H8());function vw(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,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=v.parseAxisParam([o],a.shape),u=bw({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),c=R.expandShapeToKeepDim(u.shape,l),h=gt({inputs:{x:u},backend:n,attrs:{shape:c}}),d=Mm({inputs:{a,b:h},backend:n}),p=lw({inputs:{x:d},backend:n}),f=ep({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Om({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var wD={kernelName:Xs,backendName:"cpu",kernelFunc:vw};function _D(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r;_e(a,"multinomial");let l=o?a:vw({inputs:{logits:a},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],h=n.data.get(l.dataId).values,d=[u,s],p=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let f=0;f<u;++f){let m=f*c,A=new Float32Array(c-1);A[0]=h[m];for(let _=1;_<A.length;++_)A[_]=A[_-1]+h[m+_];let y=xD.alea(i.toString()),g=f*s;for(let _=0;_<s;++_){let b=y();p[g+_]=A.length;for(let w=0;w<A.length;w++)if(b<A[w]){p[g+_]=w;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var bD={kernelName:qh,backendName:"cpu",kernelFunc:_D},vD=Pr.nonMaxSuppressionV3Impl;function kD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;_e(a,"NonMaxSuppression");let u=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,{selectedIndices:h}=vD(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var ND={kernelName:To,backendName:"cpu",kernelFunc:kD},ID=Pr.nonMaxSuppressionV4Impl;function SD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r;_e(a,"NonMaxSuppressionPadded");let c=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=ID(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var TD={kernelName:Co,backendName:"cpu",kernelFunc:SD},CD=Pr.nonMaxSuppressionV5Impl;function ED(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r;_e(a,"NonMaxSuppressionWithScore");let c=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=CD(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var RD={kernelName:Eo,backendName:"cpu",kernelFunc:ED};function FD(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;_e(a,"oneHot");let l=v.sizeFromShape(a.shape),u=new Float32Array(l*s);u.fill(o);let c=n.data.get(a.dataId).values;for(let h=0;h<l;++h)c[h]>=0&&c[h]<s&&(u[h*s+c[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",u)}var MD={kernelName:$s,backendName:"cpu",kernelFunc:FD};function tp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let a=mi({inputs:{input:r},backend:n}),s=tp({inputs:{x:a},backend:n}),i=_l({inputs:{input:r},backend:n}),o=tp({inputs:{x:i},backend:n}),l=On({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Lm({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var $D={kernelName:Xo,backendName:"cpu",kernelFunc:tp};function kw(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(r.dtype==="complex64"){let a=mi({inputs:{input:r},backend:n}),s=kw({inputs:{x:a},backend:n}),i=_l({inputs:{input:r},backend:n}),o=tp({inputs:{x:i},backend:n}),l=On({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Lm({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var DD={kernelName:Ro,backendName:"cpu",kernelFunc:kw};function Nw(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Qd({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=Qd({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=bl({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var OD={kernelName:Fo,backendName:"cpu",kernelFunc:Nw};function zD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;_e(a,"pad");let o=s.map((y,g)=>y[0]+a.shape[g]+y[1]),l=s.map(y=>y[0]),u=n.data.get(a.dataId).values,c=v.sizeFromShape(a.shape),h=a.shape.length,d=v.computeStrides(a.shape),p=v.sizeFromShape(o),f=o.length,m=v.computeStrides(o),A=v.getTypedArrayFromDType(a.dtype,p);i!==0&&A.fill(i);for(let y=0;y<c;y++){let g=v.indexToLoc(y,h,d).map((b,w)=>b+l[w]),_=v.locToIndex(g,f,m);A[_]=u[y]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var Iw={kernelName:Ds,backendName:"cpu",kernelFunc:zD},PD=Rt((e,t)=>Math.pow(e,t)),LD=qt(Os,PD),WD={kernelName:Os,backendName:"cpu",kernelFunc:LD};function BD(e){let{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:i}=n,o=Em(r,a,i,s);return t.makeTensorInfo([o.length],s,o)}var VD={kernelName:wu,backendName:"cpu",kernelFunc:BD},UD=it($o,e=>1/e),HD={kernelName:$o,backendName:"cpu",kernelFunc:UD};function jD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;_e(a,"resizeBilinear");let l=v.computeStrides(a.shape),[u,c]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(v.sizeFromShape([h,u,c,f])),y=[s&&u>1?d-1:d,s&&c>1?p-1:p],g=[s&&u>1?u-1:u,s&&c>1?c-1:c],_=0,b=y[0]/g[0],w=y[1]/g[1];for(let x=0;x<h;x++)for(let I=0;I<u;I++){let S;i?S=b*(I+.5)-.5:S=b*I;let T=Math.max(0,Math.floor(S)),M=S-T,D=Math.min(d-1,Math.ceil(S)),z=x*l[0]+T*l[1],W=x*l[0]+D*l[1];for(let U=0;U<c;U++){let H;i?H=w*(U+.5)-.5:H=w*U;let X=Math.max(0,Math.floor(H)),j=H-X,ee=Math.min(p-1,Math.ceil(H)),Y=z+X*l[2],ae=W+X*l[2],te=z+ee*l[2],ie=W+ee*l[2];for(let Q=0;Q<f;Q++){let he=m[Y+Q],oe=m[ae+Q],fe=m[te+Q],pe=m[ie+Q],ke=he+(fe-he)*j,Ie=oe+(pe-oe)*j,Me=ke+(Ie-ke)*M;A[_++]=Me}}}return n.makeTensorInfo([h,u,c,f],"float32",A)}var GD={kernelName:Ls,backendName:"cpu",kernelFunc:jD};function qD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;_e([s,a],"resizeBilinearGrad");let o=v.computeStrides(a.shape),[l,u,c,h]=a.shape,[,d,p]=s.shape,f=new Float32Array(l*u*c*h),m=[i&&d>1?u-1:u,i&&p>1?c-1:c],A=[i&&d>1?d-1:d,i&&p>1?p-1:p],y=m[0]/A[0],g=m[1]/A[1],_=n.data.get(s.dataId).values,b=0;for(let w=0;w<l;w++){let x=w*o[0];for(let I=0;I<d;I++){let S=I*y,T=Math.floor(S),M=Math.min(Math.ceil(S),u-1),D=x+T*o[1],z=x+M*o[1],W=S-T,U=1-W;for(let H=0;H<p;H++){let X=H*g,j=Math.floor(X),ee=Math.min(Math.ceil(X),c-1),Y=X-j,ae=1-Y,te=D+j*o[2],ie=D+ee*o[2],Q=z+j*o[2],he=z+ee*o[2],oe=U*ae,fe=U*Y,pe=W*ae,ke=W*Y;for(let Ie=0;Ie<h;Ie++){let Me=_[b++];f[te+Ie]+=Me*oe,f[ie+Ie]+=Me*fe,f[Q+Ie]+=Me*pe,f[he+Ie]+=Me*ke}}}}return n.makeTensorInfo([l,c,u,h],"float32",f)}var XD={kernelName:Zh,backendName:"cpu",kernelFunc:qD};function KD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;_e(a,"resizeNearestNeighbor");let l=v.computeStrides(a.shape),[u,c]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(h*u*c*f),y=[s&&u>1?d-1:d,s&&c>1?p-1:p],g=[s&&u>1?u-1:u,s&&c>1?c-1:c],_=y[0]/g[0],b=y[1]/g[1],w=0;for(let x=0;x<h;x++){let I=x*l[0];for(let S=0;S<u;S++){let T=i?_*(S+.5):_*S,M=Math.min(d-1,s?Math.round(T):Math.floor(T));i&&(M=Math.max(0,M));let D=I+M*l[1];for(let z=0;z<c;z++){let W=i?b*(z+.5):b*z,U=Math.min(p-1,s?Math.round(W):Math.floor(W));i&&(U=Math.max(0,U));let H=D+U*l[2];for(let X=0;X<f;X++){let j=m[H+X];A[w++]=j}}}}return n.makeTensorInfo([h,u,c,f],a.dtype,A)}var ZD={kernelName:_u,backendName:"cpu",kernelFunc:KD};function YD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;_e([s,a],"resizeNearestNeighborGrad");let o=v.computeStrides(a.shape),l=v.computeStrides(s.shape),[u,c,h,d]=a.shape,[,p,f]=s.shape,m=new Float32Array(u*c*h*d),A=n.data.get(s.dataId).values,y=[i&&p>1?c-1:c,i&&f>1?h-1:h],g=[i&&p>1?p-1:p,i&&f>1?f-1:f],_=y[0]/g[0],b=y[1]/g[1],w=1/_,x=1/b,I=Math.ceil(w)*2+2,S=Math.ceil(x)*2+2;for(let T=0;T<u;T++){let M=T*o[0];for(let D=0;D<c;D++){let z=M+D*o[1],W=Math.floor(D*w),U=Math.floor(W-I/2);for(let H=0;H<h;H++){let X=z+H*o[2],j=Math.floor(H*x),ee=Math.floor(j-S/2);for(let Y=0;Y<d;Y++){let ae=0;for(let te=0;te<I;te++){let ie=te+U;if(ie<0||ie>=p)continue;let Q=M+ie*l[1],he=ie*_,oe=Math.min(c-1,i?Math.round(he):Math.floor(he));if(D===oe)for(let fe=0;fe<S;fe++){let pe=fe+ee;if(pe<0||pe>=f)continue;let ke=Q+pe*l[2],Ie=pe*b,Me=Math.min(h-1,i?Math.round(Ie):Math.floor(Ie));H===Me&&(ae+=A[ke+Y])}}m[X+Y]=ae}}}}return n.makeTensorInfo(a.shape,a.dtype,m)}var JD={kernelName:Kh,backendName:"cpu",kernelFunc:YD};function QD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;_e(a,"reverse");let i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Lr({inputs:{x:a},backend:n});let l=new Ot(a.shape,a.dtype),u=n.bufferSync(a);for(let c=0;c<l.size;c++){let h=l.indexToLoc(c),d=h.slice();o.forEach(p=>d[p]=a.shape[p]-1-d[p]),l.set(u.get(...d),...h)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var eO={kernelName:Bs,backendName:"cpu",kernelFunc:QD},tO={kernelName:Ko,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[u,c,h,d]=r.shape,[p,f]=R.getImageCenter(i,c,h),m=255,A=Math.sin(a),y=Math.cos(a),g=o.data.get(r.dataId).values;for(let _=0;_<u;_++){let b=_*h*c*d;for(let w=0;w<c;w++){let x=w*(h*d);for(let I=0;I<h;I++){let S=I*d;for(let T=0;T<d;T++){let M=[u,w,I,T],D=M[2],z=M[1],W=(D-p)*y-(z-f)*A,U=(D-p)*A+(z-f)*y;W=Math.round(W+p),U=Math.round(U+f);let H=s;if(typeof s!="number"&&(T===3?H=m:H=s[T]),W>=0&&W<h&&U>=0&&U<c){let j=U*(h*d),ee=W*d,Y=b+j+ee+T;H=g[Y]}let X=b+x+S+T;l[X]=H}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},nO=it(Vs,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}),rO={kernelName:Vs,backendName:"cpu",kernelFunc:nO};function Sw(e,t,n,r,a,s,i,o,l,u){let c=[r/a,a],h=e.values,d=t.values;if(r===0)return We(n,t.dtype);let p=We(c,t.dtype);p.values.fill(l);for(let f=0;f<s;f++){let m=[],A=0;for(let y=0;y<i;y++){let g=h[f*i+y];m.push(g),A+=g*o[y]}if(A<0||A>=r/a)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<a;y++)u?p.values[A*a+y]+=d[f*a+y]:p.values[A*a+y]=t.rank===0?d[0]:d[f*a+y]}return p}function aO(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=!0,p=n.bufferSync(a),f=n.bufferSync(s),m=Sw(p,f,i,h,u,l,o,c,0,d);return n.makeTensorInfo(i,m.dtype,m.values)}var sO={kernelName:Oo,backendName:"cpu",kernelFunc:aO};function iO(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t;_e([r,a,s],"select");let i=r.shape.length,o=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,c=nr(a.dtype,s.dtype),h=v.makeZerosTypedArray(v.sizeFromShape(a.shape),c),d=0,p=i===0||i>1||a.shape.length===1?1:v.sizeFromShape(a.shape.slice(1));for(let f=0;f<o.length;f++)for(let m=0;m<p;m++)o[f]===1?h[d++]=l[f]:h[d++]=u[f];return n.makeTensorInfo(a.shape,c,h)}var oO={kernelName:zo,backendName:"cpu",kernelFunc:iO},lO=R.SELU_SCALEALPHA,uO=R.SELU_SCALE,cO=it(Po,e=>e>=0?uO*e:lO*(Math.exp(e)-1)),hO={kernelName:Po,backendName:"cpu",kernelFunc:cO},dO=it(js,e=>1/(1+Math.exp(-e))),pO={kernelName:js,backendName:"cpu",kernelFunc:dO},fO=it(Bo,e=>e<0?-1:e>0?1:0),mO={kernelName:Bo,backendName:"cpu",kernelFunc:fO},AO=it(Hs,e=>Math.sin(e)),yO={kernelName:Hs,backendName:"cpu",kernelFunc:AO},gO=it(Wo,e=>Math.sinh(e)),xO={kernelName:Wo,backendName:"cpu",kernelFunc:gO},wO=11920928955078125e-23,Tw=Math.log(wO)+2,_O=it(Vo,e=>{let t=e>-Tw,n=e<Tw,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),bO={kernelName:Vo,backendName:"cpu",kernelFunc:_O};function vO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_e([a],"spaceToBatchND");let o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let u=Iw.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),c=R.getReshaped(u.shape,s,o,!1),h=R.getPermuted(c.length,s.length,!1),d=R.getReshapedPermuted(u.shape,s,o,!1),p=gt({inputs:{x:u},backend:n,attrs:{shape:c}}),f=ir({inputs:{x:p},backend:n,attrs:{perm:h}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var kO={kernelName:bu,backendName:"cpu",kernelFunc:vO};function NO(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,sliceSize:c,strides:h,outputSize:d}=R.calculateShapes(s,a,o),p=!1,f=n.bufferSync(a),m=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=Sw(f,m,o,d,c,u,l,h,A,p);return n.makeTensorInfo(o,y.dtype,y.values)}var IO={kernelName:Yh,backendName:"cpu",kernelFunc:NO};function SO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),c=a.shape.slice();return l.map(h=>{let d=[...c];d[o]=h;let p=Ai({inputs:{x:a},backend:n,attrs:{begin:u,size:d}});return u[o]+=h,p})}var TO={kernelName:Uo,backendName:"cpu",kernelFunc:SO},CO=it(Gs,e=>Math.sqrt(e)),EO={kernelName:Gs,backendName:"cpu",kernelFunc:CO},RO={kernelName:vu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,r=t;_e(n,"square");let a=r.data.get(n.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:r.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},FO=it(Ta,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),MO={kernelName:Ta,backendName:"cpu",kernelFunc:FO};function $O(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r;_e(a,"stridedSlice");let{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=cn.sliceInfo(a.shape,s,i,o,l,u,c,h,d),_=gt({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=Ai({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});b=gt({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else{let x=n.bufferSync(_),I=rw(g,x,m,f);b=n.makeTensorInfo(I.shape,I.dtype,I.values)}let w=gt({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(b),w}var DO={kernelName:Ho,backendName:"cpu",kernelFunc:$O},OO=it(jo,e=>Math.tan(e)),zO={kernelName:jo,backendName:"cpu",kernelFunc:OO},PO=it(Ys,e=>Math.tanh(e)),LO={kernelName:Ys,backendName:"cpu",kernelFunc:PO};function WO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;_e(a,"tile");let i=sw(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var BO={kernelName:Sa,backendName:"cpu",kernelFunc:WO};function VO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r;_e(a,"topk");let o=n.data.get(a.dataId).values,[l,u]=iw(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var UO={kernelName:Go,backendName:"cpu",kernelFunc:VO};function GO(e){let{inputs:t,attrs:n,backend:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=v.computeStrides(a.shape),g=y[0],_=y[1],b=y[2],w=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(A));w.fill(l);let x=r.data.get(a.dataId).values,I=r.data.get(s.dataId).values;for(let S=0;S<c;++S){let T=s.shape[0]===1?I:I.subarray(S*8,S*8+8);for(let M=0;M<f;++M)for(let D=0;D<m;++D)for(let z=0;z<p;++z){let W,U=T[6]*D+T[7]*M+1;if(U===0)continue;let H=(T[0]*D+T[1]*M+T[2])/U,X=(T[3]*D+T[4]*M+T[5])/U,j=Cw(H,d,o),ee=Cw(X,h,o);switch(i){case"nearest":W=HO(x,h,d,g,_,b,S,ee,j,z,l);break;case"bilinear":W=jO(x,h,d,g,_,b,S,ee,j,z,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let Y=S*g+M*_+D*b+z;w[Y]=W}return r.makeTensorInfo(A,a.dtype,w)}return{dataId:r.write(w,A,a.dtype),shape:a.shape,dtype:a.dtype}}var qO={kernelName:Jh,backendName:"cpu",kernelFunc:GO};function Cw(e,t,n){switch(n){case"reflect":return XO(e,t);case"wrap":return KO(e,t);case"nearest":return YO(e,t);case"constant":default:return ZO(e,t)}}function XO(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=2*t;n<r&&(n=r*Math.trunc(-n/r)+n),n=n<-t?n+r:-n-1}else if(n>t-1)if(t<=1)n=0;else{let r=2*t;n-=r*Math.trunc(n/r),n>=t&&(n=r-n-1)}return v.clamp(0,n,t-1)}function KO(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=t-1;n+=t*(Math.trunc(-n/r)+1)}else if(n>t-1)if(t<=1)n=0;else{let r=t-1;n-=t*Math.trunc(n/r)}return v.clamp(0,n,t-1)}function ZO(e,t){return e}function YO(e,t){return v.clamp(0,e,t-1)}function nc(e,t,n,r,a,s,i,o,l,u,c){let h=i*r+o*a+l*s+u;return 0<=o&&o<t&&0<=l&&l<n?e[h]:c}function HO(e,t,n,r,a,s,i,o,l,u,c){let h=Math.round(o),d=Math.round(l);return nc(e,t,n,r,a,s,i,h,d,u,c)}function jO(e,t,n,r,a,s,i,o,l,u,c){let h=Math.floor(o),d=Math.floor(l),p=h+1,f=d+1,m=(f-l)*nc(e,t,n,r,a,s,i,h,d,u,c)+(l-d)*nc(e,t,n,r,a,s,i,h,f,u,c),A=(f-l)*nc(e,t,n,r,a,s,i,p,d,u,c)+(l-d)*nc(e,t,n,r,a,s,i,p,f,u,c);return(p-o)*m+(o-h)*A}function JO(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;_e(s,"unique");let i=r.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:u}=ow(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var QO={kernelName:Qh,backendName:"cpu",kernelFunc:JO};function ez(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape.length,o=a.shape[s],l=new Array(i-1),u=0;for(let p=0;p<i;p++)p!==s&&(l[u++]=a.shape[p]);let c=new Array(i).fill(0),h=a.shape.slice();h[s]=1;let d=new Array(o);for(let p=0;p<d.length;p++){c[s]=p;let f=Ai({inputs:{x:a},backend:n,attrs:{begin:c,size:h}});d[p]=gt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return d}var tz={kernelName:qo,backendName:"cpu",kernelFunc:ez};function nz(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r;_e(a,"unsortedSegmentSum");let o=a.shape.length,l=s.shape.length,u=[],c=[],h=o-l,d=s;for(let f=0;f<h;++f){let m=Qd({inputs:{input:d},backend:n,attrs:{dim:f+1}});d=m,c.push(m)}for(let f=0;f<i;++f){let m=v.createScalarValue(f,"int32"),A=n.makeTensorInfo([],"int32",m),y=ww({inputs:{a:A,b:d},backend:n}),g=Wa({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),_=Fm({inputs:{a:g,b:a},backend:n}),b=ep({inputs:{x:_},backend:n,attrs:{axis:0,keepDims:!1}});u.push(b),c.push(A),c.push(y),c.push(g),c.push(_),c.push(b)}let p=Nw({inputs:u,backend:n,attrs:{axis:0}});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var rz={kernelName:ku,backendName:"cpu",kernelFunc:nz},az=[yF,bR,xF,_F,TR,vF,NF,SF,CF,RF,MF,DF,zF,WF,VF,jF,qF,KF,YF,mF,QF,tM,rM,IR,ER,sM,vR,oM,uM,dM,fM,cM,gM,wM,AM,bM,kM,IM,TM,EM,FM,MM,DM,zM,LM,WM,VM,BM,zm,lF,HM,GM,e$,RR,t$,MR,o$,u$,c$,DR,p$,m$,y$,x$,_$,zR,k$,kR,I$,lM,T$,E$,F$,uF,LR,D$,z$,BR,L$,V$,H$,q$,K$,Y$,UR,eD,nD,aD,iD,lD,J$,hD,pD,jR,mD,gD,bD,qR,KR,ND,TD,RD,YR,MD,DD,OD,Iw,WD,hF,eF,VD,NR,HD,dF,pF,fF,GD,XD,ZD,JD,eO,tO,rO,nF,sO,oO,hO,pO,mO,yO,xO,rF,wD,bO,kO,IO,TO,EO,RO,sF,MO,DO,oF,uD,zO,LO,BO,UO,JR,qO,QO,tz,rz,$D];for(let e of az)ni(e);var Ew={};ze(Ew,{assertNotComplex:()=>vl,bindCanvasToFramebuffer:()=>oz,bindColorTextureToFramebuffer:()=>rp,bindTextureToProgramUniformSampler:()=>jw,bindTextureUnit:()=>Vw,bindVertexBufferToProgramAttribute:()=>Wm,callAndCheck:()=>xe,canBeRepresented:()=>Rw,createFragmentShader:()=>$w,createFramebuffer:()=>Bw,createProgram:()=>Dw,createStaticIndexBuffer:()=>Pw,createStaticVertexBuffer:()=>zw,createTexture:()=>Lw,createVertexShader:()=>Mw,getBatchDim:()=>yi,getExtensionOrThrow:()=>rc,getFramebufferErrorMessage:()=>Gw,getMaxTexturesInShader:()=>Kw,getNumChannels:()=>sz,getProgramUniformLocation:()=>Hw,getProgramUniformLocationOrThrow:()=>Uw,getRowsCols:()=>gi,getShapeAs3D:()=>ap,getTextureShapeFromLogicalShape:()=>qw,getWebGLDisjointQueryTimerVersion:()=>Zw,getWebGLErrorMessage:()=>Fw,getWebGLMaxTextureSize:()=>Xw,hasExtension:()=>Kn,isCapableOfRenderingToFloatTexture:()=>Yw,isDownloadFloatTextureEnabled:()=>Jw,isReshapeFree:()=>sc,isWebGLFenceEnabled:()=>Qw,isWebGLVersionEnabled:()=>Vm,linkProgram:()=>Ow,resetMaxTextureSize:()=>lz,resetMaxTexturesInShader:()=>uz,unbindColorTextureFromFramebuffer:()=>Bm,unbindTextureUnit:()=>iz,validateFramebuffer:()=>ac,validateProgram:()=>np,validateTextureSize:()=>Ww});var xi={},Um={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function sp(e,t){xi[e]=t}function Wr(e){if(!(e in xi)){let n=cz(e);if(n!==null)xi[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=xi[e];return t.isContextLost()?(delete xi[e],Wr(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),xi[e])}function hz(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 cz(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=hz(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete xi[e]},!1),e===1?t.getContext("webgl",Um)||t.getContext("experimental-webgl",Um):t.getContext("webgl2",Um)}var ic;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(ic||(ic={}));var Zn;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Zn||(Zn={}));var tn;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(tn||(tn={}));function oc(e,t){return[t,e]}function dz(e,t){return e*t}function lc(e){let t=v.sizeFromShape(e),n=Math.ceil(t/4);return v.sizeToSquarishShape(n)}function kl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function pz(e,t){let[n,r]=kl(e,t);return n*r*4}function Hm(e,t){let n=e,r,a,s,i,o,l,u,c,h,d;return J().getNumber("WEBGL_VERSION")===2?(r=n.R32F,a=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,u=4,c=1,h=n.HALF_FLOAT,d=n.FLOAT):(r=e.RGBA,a=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,u=4,c=4,h=t!=null?t.HALF_FLOAT_OES:null,d=e.FLOAT),l=e.RGBA,{internalFormatFloat:r,internalFormatHalfFloat:a,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:h,textureTypeFloat:d}}function xe(e,t){let n=t();return J().getBool("DEBUG")&&fz(e),n}function fz(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+Fw(e,t))}var mz=596e-10,Az=65504;function Rw(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||mz<Math.abs(e)&&Math.abs(e)<Az)}function Fw(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 rc(e,t){return la(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function Mw(e,t){let n=la(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function $w(e,t){let n=la(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(xe(e,()=>e.shaderSource(n,t)),xe(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw yz(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var gz=/ERROR: [0-9]+:([0-9]+):/g;function yz(e,t){let n=gz.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],a=e.split(`
`),s=a.length.toString().length+2,i=a.map((h,d)=>v.rightPad((d+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,r-1),u=i.slice(r-1,r),c=i.slice(r);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${v.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
`))}function Dw(e){return la(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function Ow(e,t){if(xe(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function np(e,t){if(xe(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function zw(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function Pw(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function sz(){return J().getNumber("WEBGL_VERSION")===2?1:4}function Lw(e){return la(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Ww(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function Bw(e){return la(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Wm(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),xe(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),xe(e,()=>e.enableVertexAttribArray(o)),!0)}function Vw(e,t,n){e_(e,n),xe(e,()=>e.activeTexture(e.TEXTURE0+n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function iz(e,t){e_(e,t),xe(e,()=>e.activeTexture(e.TEXTURE0+t)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Uw(e,t,n){return la(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Hw(e,t,n){return e.getUniformLocation(t,n)}function jw(e,t,n,r){xe(e,()=>Vw(e,t,r)),xe(e,()=>e.uniform1i(n,r))}function oz(e){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),xe(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function rp(e,t,n){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function Bm(e,t){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function ac(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+Gw(e,t))}function Gw(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function la(e,t,n){let r=xe(e,()=>t());if(r==null)throw new Error(n);return r}function e_(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function yi(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function gi(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function ap(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[yi(e),...gi(e)]),t}function qw(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?v.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let r=v.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=yi(e),s=2,i=2;return e.length&&([s,i]=gi(e)),r=a*(s/2)*(i/2),v.sizeToSquarishShape(r).map(o=>o*2)}return v.sizeToSquarishShape(r)}function ip(e){return e%2==0}function sc(e,t){if(e=e.slice(-2),t=t.slice(-2),v.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||ip(n)&&ip(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&ip(e[0])&&ip(t[0])}var op,lp;function Xw(e){if(op==null){let t=Wr(e);op=t.getParameter(t.MAX_TEXTURE_SIZE)}return op}function lz(){op=null}function uz(){lp=null}function Kw(e){if(lp==null){let t=Wr(e);lp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,lp)}function Zw(e){if(e===0)return 0;let t,n=Wr(e);return Kn(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Kn(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Kn(e,t){return e.getExtension(t)!=null}function Vm(e){try{if(Wr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function Yw(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!Kn(t,"OES_texture_float"))return!1}else if(!Kn(t,"EXT_color_buffer_float"))return!1;return jm(t)}function Jw(e){if(e===0)return!1;let t=Wr(e);if(e===1){if(!Kn(t,"OES_texture_float")||!Kn(t,"WEBGL_color_buffer_float"))return!1}else{if(Kn(t,"EXT_color_buffer_float"))return jm(t);let n="EXT_color_buffer_half_float";if(Kn(t,n)){let r=t.getExtension(n);return xz(t,r)}return!1}return jm(t)}function jm(e){let t=Hm(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,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,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function xz(e,t){let n=Hm(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function Qw(e){return e!==2?!1:Wr(e).fenceSync!=null}function vl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=J();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>Vm(2)?2:Vm(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",()=>!1);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",()=>Xw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Kw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:Zw(e)});Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Fe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Mu.isMobile());Fe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Yw(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",()=>Jw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Qw(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",()=>Mu.isMobile()&&Fe.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function fn(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",u=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",r="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:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function wi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function Gm(e){let t=v.computeStrides(e).map(n=>n.toString());return`
int getFlatIndex(ivec3 coords) {
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
}
`}var t_=`
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;
}
`,wz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ic.DENSE;let t=lc(e),n=fn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${wi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${n.output} = result;
}
`}},_z=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ic.DENSE;let t=lc(e),n=fn();this.outputShape=e,this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${wi(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${n.output} = result;
}
`}},bz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Zn.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
${t_}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},vz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zn.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
${t_}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},kz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=fn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
${Gm(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / ${s};
int c = imod(flatIndex, ${s});
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.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];
}
${r.output} = vec4(${i}, 0., 0., 0.);
}
`}},Nz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=fn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=`
localCoords = coords;
if(localCoords[2] + ${u} < ${e[2]}) {
localCoords[2] += ${u};
if(localCoords[1] + ${l} < ${e[1]}) {
localCoords[1] += ${l};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
r = flatIndex / ${s};
c = imod(flatIndex, ${s});
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
values = ${r.texture2D}(A, uv);
if(offset == 0) {
result[${c}] = values[0];
} else if(offset == 1) {
result[${c}] = values[1];
} else if(offset == 2) {
result[${c}] = values[2];
} else {
result[${c}] = values[3];
}
}
}
`}this.userCode=`
${Gm(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${i}
${r.output} = ${o};
}
`}},n_={};ze(n_,{bindVertexProgramAttributeStreams:()=>h_,createBufferFromOutputTexture:()=>f_,createFloat16MatrixTexture:()=>o_,createFloat16PackedMatrixTexture:()=>c_,createFloat32MatrixTexture:()=>i_,createIndexBuffer:()=>s_,createPackedMatrixTexture:()=>u_,createUnsignedBytesMatrixTexture:()=>l_,createVertexBuffer:()=>a_,createVertexShader:()=>r_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>A_,downloadFloat32MatrixFromBuffer:()=>m_,downloadMatrixFromPackedOutputTexture:()=>g_,downloadPackedMatrixFromBuffer:()=>y_,getInternalFormatForFloat16MatrixTexture:()=>Xm,getInternalFormatForFloat16PackedMatrixTexture:()=>Ym,getInternalFormatForFloat32MatrixTexture:()=>qm,getInternalFormatForPackedMatrixTexture:()=>Zm,getInternalFormatForUnsignedBytesMatrixTexture:()=>Km,uploadDenseMatrixToTexture:()=>d_,uploadPixelDataToTexture:()=>p_});function r_(e){let t=fn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return Mw(e,n)}function a_(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 zw(e,t)}function s_(e){let t=new Uint16Array([0,1,2,2,1,3]);return Pw(e,t)}function uc(e,t,n,r,a,s){Ww(t,n);let i=Lw(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function qm(e){return e.internalFormatFloat}function i_(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,qm(r),r.textureFormatFloat,e.FLOAT)}function Xm(e){return e.internalFormatHalfFloat}function o_(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,Xm(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function Km(e){return e.downloadTextureFormat}function l_(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,Km(r),e.RGBA,e.UNSIGNED_BYTE)}function Zm(e){return e.internalFormatPackedFloat}function u_(e,t,n,r){let[a,s]=kl(t,n);return uc(e,a,s,Zm(r),e.RGBA,e.FLOAT)}function Ym(e){return e.internalFormatPackedHalfFloat}function c_(e,t,n,r){let[a,s]=kl(t,n);return uc(e,a,s,Ym(r),e.RGBA,r.textureTypeHalfFloat)}function h_(e,t,n){let r=0,a=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Wm(e,t,"clipSpacePos",n,3,s,r)&&Wm(e,t,"uv",n,2,s,a)}function d_(e,t,n,r,a,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function p_(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function f_(e,t,n,r){let a=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function m_(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function A_(e,t,n,r){let[a,s]=oc(t,n),i=4,o=new Uint8Array(dz(t*n,i));return xe(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function y_(e,t,n,r,a,s,i,o){let l=e,u=new Float32Array(pz(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 g_(e,t,n){let r=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var up=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,sp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=rc(this.gl,a),Kn(this.gl,s))this.textureHalfFloatExtension=rc(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(n),Kn(this.gl,r))this.colorBufferHalfFloatExtension=rc(this.gl,r);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(n="EXT_color_buffer_float",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=a_(this.gl),this.indexBuffer=s_(this.gl),this.framebuffer=Bw(this.gl),this.textureConfig=Hm(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;xe(e,()=>e.finish()),xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.deleteFramebuffer(this.framebuffer)),xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),xe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),i_(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),o_(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),l_(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),p_(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),d_(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),c_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),u_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Bm(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>A_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return y_(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return m_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=f_(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>g_(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=$w(t,e),r=r_(t),a=Dw(t);return xe(t,()=>t.attachShader(a,r)),xe(t,()=>t.attachShader(a,n)),Ow(t,a),this.debug&&np(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=h_(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&np(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Uw(this.gl,e,t):Hw(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),jw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=kl(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&np(this.gl,this.program),ac(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rc(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 n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.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,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,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 n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Iz(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),rp(this.gl,e,this.framebuffer),this.debug&&ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rp(this.gl,this.outputTexture,this.framebuffer),this.debug&&ac(this.gl)):Bm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;rp(r,e,this.framebuffer),this.debug&&ac(r),this.outputTexture=e,xe(r,()=>r.viewport(0,0,t,n)),xe(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,r))}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 Iz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:x_}=R;function Dz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
`),i=e.map(p=>Sz(p,t,r)).join(`
`),o=t.texShape,l=fn(),u=Ez(l),c,h,d=Mz(l);return t.isPacked?(c=Tz(t.logicalShape,o),h=Fz(l)):(c=Cz(t.logicalShape,o),h=Rz(l)),r&&(d+=$z),[d,u,h,s,c,i,n].join(`
`)}function Nl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return Oz(e);case 1:return zz(e);case 2:return Pz(e);case 3:return Lz(e);case 4:return Wz(e);case 5:return Bz(e);case 6:return Vz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function w_(e){switch(e.shapeInfo.logicalShape.length){case 0:return Uz(e);case 1:return Hz(e);case 2:return jz(e);case 3:return Gz(e);default:return qz(e)}}function Sz(e,t,n=!1){let r="";n?r+=w_(e):r+=Nl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Xz(e,t):r+=Kz(e,t)),r}function Tz(e,t){switch(e.length){case 0:return __();case 1:return Zz(e,t);case 2:return Qz(e,t);case 3:return Yz(e,t);default:return Jz(e,t)}}function Cz(e,t){switch(e.length){case 0:return __();case 1:return eP(e,t);case 2:return sP(e,t);case 3:return tP(e,t);case 4:return nP(e,t);case 5:return rP(e,t);case 6:return aP(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Ez(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function Rz(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function Fz(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function Mz(e){return`${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);
}
${iP}
${oP}
${lP}
`}var iP=`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,oP=`
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,lP=`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`,$z=`
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 __(){return`
int getOutputCoords() {
return 0;
}
`}function Zz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
int getOutputCoords() {
return 2 * int(resultUV.x * ${n[1]}.0);
}
`:n[1]===1?`
int getOutputCoords() {
return 2 * int(resultUV.y * ${n[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
}
`}function eP(e,t){return t[0]===1?`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function Yz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec3(b, r, c);
}
`}function tP(e,t){let n=wi(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec3(r, c, d);
}
`}function Jz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
int b${l} = index / ${s};
index -= b${l} * ${s};
`+i,o=`b${l}, `+o;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
${i}
int b = index / ${a};
index -= b * ${a};
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec${e.length}(${o});
}
`}function nP(e,t){let n=wi(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
return ivec4(r, c, d, d2);
}
`}function rP(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function aP(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function Qz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
}
`;let r=Math.ceil(e[1]/2);return`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${n[0]}, ${n[1]}));
int index = resTexRC.x * ${n[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function sP(e,t){return v.arraysEqual(e,t)?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function _i(e){return`offset${e}`}function Uz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=fn();return`
vec4 ${n}() {
return ${r.texture2D}(${t}, halfCR);
}
`}function Oz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${n}() {
return sampleTexture(${t}, halfCR);
}
`;let[s,i]=e.shapeInfo.texShape,o=_i(t);return`
float ${n}() {
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
return sampleTexture(${t}, uv);
}
`}function Hz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=fn();return`
vec4 ${n}(int index) {
vec2 uv = packedUVfrom1D(
${a[0]}, ${a[1]}, index);
return ${s.texture2D}(${t}, uv);
}
`}function zz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
float ${n}(int index) {
${Il(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
float ${n}(int index) {
return sampleTexture(${t}, halfCR);
}
`;let i=_i(t);return s===1?`
float ${n}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${t}, uv);
}
`:a===1?`
float ${n}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
return sampleTexture(${t}, uv);
}
`:`
float ${n}(int index) {
vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
return sampleTexture(${t}, uv);
}
`}function jz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=fn();if(a!=null&&v.arraysEqual(t,a))return`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
return ${o.texture2D}(${n}, uv);
}
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(t[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
return ${o.texture2D}(${n}, uv);
}
`}function Pz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`}let{newShape:s,keptDims:i}=v.squeezeShape(t),o=s;if(o.length<t.length){let h=Sl(e,o),d=["row","col"];return`
${Nl(h)}
float ${r}(int row, int col) {
return ${r}(${Tl(d,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
${Il(e)}
}
`;let l=a[0],u=a[1],c=_i(n);return u===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
return sampleTexture(${n}, uv);
}
`:l===1?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${t[1]} + col + ${c};
vec2 uv = uvFromFlat(${l}, ${u}, index);
return sampleTexture(${n}, uv);
}
`}function Gz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Sl(e,h),f=["b","row","col"];return`
${w_(p)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${Tl(f,d)});
}
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=fn();return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${i}, ${o}, ${u}, ${l}, b, row, col);
return ${c.texture2D}(${n}, uv);
}
`}function Lz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Sl(e,l),m=["row","col","depth"];return`
${Nl(f)}
float ${r}(int row, int col, int depth) {
return ${r}(${Tl(m,o)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${s}, 1)));
${Il(e)}
}
`;let u=e.shapeInfo.texShape,c=u[0],h=u[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${s}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;if(h===s&&d==null)return`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${c}.0);
return sampleTexture(${n}, uv);
}
`;let p=_i(n);return`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${s} + depth + ${p};
vec2 uv = uvFromFlat(${c}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function qz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,c*=t[n-f-1],d=`b${f} * ${c} + `+d;let p=fn();return`
vec4 ${a}(${h}) {
int index = ${d};
int texR = index / ${l};
int texC = index - texR * ${l};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
return ${p.texture2D}(${r}, uv);
}
`}function Wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Sl(e,o),m=["row","col","depth","depth2"];return`
${Nl(f)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${Tl(m,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${i}, ${s}, ${a}, 1)));
${Il(e)}
}
`;let u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],d=c[1];if(d===i&&u==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(d===a&&u==null)return`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${t[1]*t[2]}, ${t[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${d}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let p=_i(n);return`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${s} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
return sampleTexture(${n}, uv);
}
`}function Bz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=Sl(e,l),A=["row","col","depth","depth2","depth3"];return`
${Nl(m)}
float ${r}(int row, int col, int depth, int depth2, int depth3) {
return ${r}(${Tl(A,u)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${o}, ${i}, ${s}, ${a})) +
depth3;
${Il(e)}
}
`;let c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&c==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${i}, ${s}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;if(p===a&&c==null)return`
float ${r}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${n}, uv);
}
`;let f=_i(n);return`
float ${r}(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 + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${n}, uv);
}
`}function Vz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Sl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
${Nl(A)}
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${r}(${Tl(y,s)});
}
`}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${c}, ${u}, ${l}, ${o})) +
dot(
vec2(depth3, depth4),
vec2(${i}, 1)));
${Il(e)}
}
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===c&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${u}, ${l}, ${o}, ${i})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(f===i&&h==null)return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${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, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let m=_i(n);return`
float ${r}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${c} + col * ${u} + depth * ${l} +
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
vec2 uv = uvFromFlat(${p}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function Il(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
for (int i = 0; i < ${n}; i++) {
if (i == index) {
return ${t}[i];
}
}
`}function Xz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=x_(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(`
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(f&&!m)i===1?p=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:p=`
return vec4(outputValue.x);
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${a}() {
${l} coords = getOutputCoords();
${c}
vec4 outputValue = get${r}(${d});
${p}
}
`}function Kz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"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&&v.arraysEqual(i,s))return`
float ${a}() {
return sampleTexture(${n}, resultUV);
}
`;let u=ut(l),c=x_(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${p[m+h]} = 0;`).join(`
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
float ${a}() {
${u} coords = getOutputCoords();
${d}
return get${r}(${f});
}
`}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Sl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Tl(e,t){return t.map(n=>e[n]).join(", ")}function uP(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=Dz(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(u,f,m),d[`offset${f}`]=e.getUniformLocation(u,`offset${f}`,m)}return{program:t,source:l,webGLProgram:u,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:c,nanLoc:h}}function b_(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function cP(e,t,n,r,a){b_(t.inShapeInfos,n),b_([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],c=t.uniformLocations[u],h=t.uniformLocations[`offset${u}`];if(c!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(c,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(c,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,c,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function hP(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:dP,bincountImpl:v_,bincountReduceImpl:pP,ceilImpl:fP,concatImpl:mP,expImpl:AP,expm1Impl:yP,floorImpl:gP,gatherV2Impl:xP,greaterImpl:wP,lessImpl:_P,linSpaceImpl:bP,logImpl:vP,maxImpl:kP,maximumImpl:NP,minimumImpl:IP,multiplyImpl:SP,negImpl:TP,prodImpl:CP,rangeImpl:EP,rsqrtImpl:RP,simpleAbsImpl:k_,sliceImpl:FP,stridedSliceImpl:MP,subImpl:$P,tileImpl:DP,topKImpl:OP,transposeImpl:Jm,uniqueImpl:zP}=Nm;function N_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function mn(e,t){return t===1?[e]:N_(e,t)}function PP(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var VP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;else{let n=mn("rc",t),r=ut(t),a=LP(t,e,n),s=WP(t,e[e.length-1],e[e.length-2],n),i=BP(e,n);this.userCode=`
void main() {
${r} rc = getOutputCoords();
if(${a}) {
setOutput(vec4(0));
} else {
${s}
setOutput(vec4(${i}));
}
}
`}}};function UP(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function LP(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function WP(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
int r = ${a[0]};
int c = ${a[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function BP(e,t){let n=e.length,r=UP(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${r[0]}),
cEdge ? 0. : getA(${r[1]}),
rEdge ? 0. : getA(${r[2]}),
rEdge || cEdge ? 0. : getA(${r[3]})`}var I_=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
${a}
${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${r}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${r>0?"}":""}
`}this.userCode=`
${HP(t)}
${Gm(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${e[1]};
int cols = ${e[2]};
${n}
setOutput(result);
}
`}};function HP(e){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${wi(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var jP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=T_(t,n),a=C_(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=S_(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=T_(n,r),s=C_(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=S_(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function GP(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function S_(e,t,n,r,a){let s=qP(t,r),i;if(a){let[l,u]=kl(e[0],e[1]);i=l*u}else{let[l,u]=oc(e[0],e[1]);i=l*u}let o=GP(n,s);return i*o}function qP(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return Zm(t);case tn.PACKED_2X2_FLOAT16:return Ym(t);case tn.UNPACKED_FLOAT32:return qm(t);case tn.UNPACKED_FLOAT16:return Xm(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return Km(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function XP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function T_(e,t){if(e===Zn.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===Zn.RENDER||e==null)return XP(t);if(e===Zn.DOWNLOAD||e===Zn.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function C_(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ba=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},xr="if (isnan(x)) return x;",KP="return x;",E_="return abs(x);",ZP="return (x >= 0.0) ? x : (exp(x) - 1.0);",YP=xr+`
return (x < 0.0) ? 0.0 : x;
`,JP=xr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,cp="return x;",QP="return x;",eL=`
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;
`,tL=`
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;
`,nL=`
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;
`,Cl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},rL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=mn("rc",t),r=ut(t),a=PP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 packedInput = getA(${a});
setOutput(getChannel(packedInput, ${i}));
}
`}},aL=Pr.whereImpl,sL=1e-7,iL=1e-4,Qm={};function oL(e){return e in Qm||(Qm[e]={}),Qm[e]}var lL=128,uL=600;function cL(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*uL/1024/1024}var El=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.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(J().getNumber("WEBGL_VERSION"));this.binaryCache=oL(J().getNumber("WEBGL_VERSION")),this.gpgpu=new up(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new jP(this.gpgpu),this.numMBBeforeWarning=cL(),this.texData=new xh(this,Fr())}nextDataId(){return El.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Zn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Zn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Cl(i,cp):h=new Ba(i,cp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=R.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Cl(r,cp):p=new Ba(r,cp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!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 p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...lc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Fr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Rw(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...lc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?ap(t):t,o=s?new vz(i):new bz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${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:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.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:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,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(r,n),this.textureManager.releaseTexture(t,r,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}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=lL){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return aL(e.shape,t)}packedUnaryOp(e,t,n){let r=new Cl(e.shape,t),a=this.compileAndRun(r,[e],n);return Fr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=k_(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,E_,e.dtype);let t=new Ba(e.shape,E_),n=this.compileAndRun(t,[e]);return Fr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Fr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new rL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new VP(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[yi(e.shape),...gi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[yi(t),...gi(t)],s=new I_(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=ap(r),i;n?i=new _z(s):i=new wz(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===ic.DENSE){let m=lc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!sc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=hP(e,l,u),h=this.getAndSaveBinary(c,()=>uP(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),cP(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,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=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?sL:iL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=qw(n,o),t.texShape=c),a!=null){let h=ap(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=kl(c[0],c[1]),d=new Nz(h,[f,p],m)):d=new kz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Zn.PIXELS:this.texData.get(A.dataId).usage=Zn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=hL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!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,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};El.nextDataId=0;function hL(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var R_="3.3.0";function F_(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Mu.isBrowser()&&sl("webgl",()=>new El,2);var dL={forceHalfFloat:F_},M_=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,Rl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},hp=`
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;
`,cc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(s=`
${ut(a)} coords = getOutputCoords();
`,a===1)s+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=mn("coords",a);s+=`
bool nextRowOutOfBounds =
(${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);
${s}
setOutput(result);
}
`}};function zn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var pL={kernelName:ks,backendName:"webgl",kernelFunc:zn};function Va(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=zn({inputs:{x:r},backend:n}),l=zn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var fL={kernelName:Th,backendName:"webgl",kernelFunc:Va},$_="return (a < 0.) ? b * a : a;",D_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function mL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(D_,a.shape,i.shape):new Rl($_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var AL={kernelName:Ns,backendName:"webgl",kernelFunc:mL},O_="return (a < 0.) ? b * a : a;",z_=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function yL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(z_,r.shape,a.shape):new Rl(O_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var gL={kernelName:zs,backendName:"webgl",kernelFunc:yL},P_="if (isnan(x)) return x;",xL=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,wL=`
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 Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Cl(i.shape,t):c=new Ba(i.shape,e),o.runWebGLProgram(c,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},I={dataId:w.dataId,dtype:w.dtype,shape:u.shape},S=new Rl(e,l.shape,u.shape);return c.runWebGLProgram(S,[x,I],nr(b.dtype,w.dtype))}),g=Va({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||nr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new cc(t,l.shape,u.shape,n):p=new Rl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function dp(e,t=!1){if(e==="linear")return t?QP:KP;if(e==="relu")return t?tL:YP;if(e==="elu")return t?eL:ZP;if(e==="relu6")return t?nL:JP;if(e==="prelu")return t?z_:O_;if(e==="leakyrelu")return t?D_:$_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var L_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";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}
}`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",_="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
const float sharedDimension = ${c}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${c}; i++) {
int batchA = ${g};
int batchB = ${_};
vec4 a = getMatrixA(batchA, ${h});
vec4 b = getMatrixB(batchB, ${d});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${p[0]} * ${f[0]});
result += (${p[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${A}
setOutput(result);
}
`}},W_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},B_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},V_="return a * b;";function U_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new B_(W_.REAL,r.shape,a.shape),c=new B_(W_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Va({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=SP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new cc(V_,r.shape,a.shape):i=new Rl(V_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var _L={kernelName:Ms,backendName:"webgl",kernelFunc:U_};function bL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new I_(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${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 c=i.texData.get(a.dataId);return c.isPacked&&!sc(a.shape,l)&&!(c.texture!==null&&sc(c.shape,l))?bL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var vL={kernelName:Do,backendName:"webgl",kernelFunc:Ae},H_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>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 * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${i}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${l}
}
int inIdx = inOffset + ${i};
if (${o===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${o===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${o===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},kL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%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);
}
`,d="vec4";t==="all"?(i="1.0",h=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,d="bvec4"):t==="any"&&(i="0.0",h=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,d="bvec4");let p="";a%n>0&&(p=`
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) {
${p}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${i});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${h}
}
int inIdx = inOffset + ${u};
if (${c===1}) {
${d} values = ${d}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${h}
} else if (${c===2}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${h}
} else if (${c===3}) {
${d} values = ${d}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${h}
}
setOutput(${l});
}
`}};function NL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function bi(e,t,n,r){let a=NL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],c,h;n==="mean"?c=i===0?new H_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new H_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):c=new kL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=s,s=r.runWebGLProgram(c,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var SL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=IL(t);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function IL(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var TL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ut(this.rank),a=N_("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
void main() {
${r} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${o}) {
result[1] = ${l};
}
--${a[this.rank-1]};
if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${o}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function pp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new TL(e.shape,t):new SL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function CL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=pp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=Ae({inputs:{x:c},attrs:{shape:[m,f]},backend:r}),y=sd(e.dtype),g=bi(A,y,"sum",r),_=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),_}function eA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return CL(a,s,i,n)}var EL={kernelName:qs,backendName:"webgl",kernelFunc:eA};function In(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c<l.length;c++)l[c]=a.shape[s[c]];let u;if(i.shouldExecuteOnCPU([a])){let c=i.texData.get(a.dataId).values,h=Jm(c,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(u.dataId);d.values=h}else u=pp(a,s,i);return u}var RL={kernelName:Js,backendName:"webgl",kernelFunc:In},j_=1e3;function fp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),_=y===g||y===1||g===1;v.assert(u>=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],I=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),S=Ae({inputs:{x:t},backend:a,attrs:{shape:x}}),T=[I,S],M=Math.max(y,g),D=n?I.shape[1]:I.shape[2],z=s!=null,W=i!=null,U=l==="leakyrelu",H=l!=null?dp(l,!0):null,X=z||W||U||H!=null,j;if((p===1||f===1)&&D>j_&&X===!1){let Y=I,ae=S;n&&(Y=In({inputs:{x:I},backend:a,attrs:{perm:[0,2,1]}}),T.push(Y)),r&&(ae=In({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),T.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[M,D,1]}}),T.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[M,1,D]}}),T.push(oe));let fe=U_({inputs:{a:Q,b:oe},backend:a});j=eA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),T.push(fe)}else{let Y=nr(e.dtype,t.dtype),ae=new L_(w,x,[M,p,f],n,r,z,H,W,U),te=[I,S];if(s!=null&&te.push(s),W&&te.push(i),U){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),T.push(ie)}j=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:j},backend:a,attrs:{shape:b}});T.push(j);for(let Y of T)a.disposeIntermediateTensorInfo(Y);return ee}function FL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return fp({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var ML={kernelName:Qs,backendName:"webgl",kernelFunc:FL},G_="return abs(x);";function $L(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=k_(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Cl(r.shape,G_):a=new Ba(r.shape,G_),n.runWebGLProgram(a,[r],r.dtype)}var DL={kernelName:Zi,backendName:"webgl",kernelFunc:$L},OL=xr+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,zL=Xe({opSnippet:OL}),PL={kernelName:Yi,backendName:"webgl",kernelFunc:zL},LL=xr+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,WL=Xe({opSnippet:LL}),BL={kernelName:Ji,backendName:"webgl",kernelFunc:WL},q_="return a + b;",VL=nn({opSnippet:q_,packedOpSnippet:q_,supportsComplex:!0,cpuKernelImpl:dP}),UL={kernelName:Na,backendName:"webgl",kernelFunc:VL},HL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${r};
setOutput(result);
}
`}},jL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${r};
setOutput(result);
}
`}};function mp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return zn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=mp({inputs:r.slice(0,o),backend:n}),u=mp({inputs:r.slice(o),backend:n});return mp({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>nr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new jL(r[0].shape,s):new HL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var GL={kernelName:os,backendName:"webgl",kernelFunc:mp};function qL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=bi(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var XL={kernelName:vh,backendName:"webgl",kernelFunc:qL};function KL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=bi(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var ZL={kernelName:kh,backendName:"webgl",kernelFunc:KL},YL=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${r};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${r}; i++) {
int inIdx = ${o};
float candidate = getA(batch, inIdx);
if (candidate ${i} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},JL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),u=mn("coords",o),c,h;if(s===1){h=o+1;let I=ut(h);c=`
${I} sourceLocR = ${I}(${u.join()}, 0);
++${u[o-1]};
${I} sourceLocG = ${I}(${u.join()}, 0);
++${u[o-2]};
${I} sourceLocA = ${I}(${u.join()}, 0);
--${u[o-1]};
${I} sourceLocB = ${I}(${u.join()}, 0);
--${u[o-2]};`}else h=o,c=`
${l} sourceLocR = coords;
++${u[o-1]};
${l} sourceLocG = coords;
++${u[o-2]};
${l} sourceLocA = coords;
--${u[o-1]};
${l} sourceLocB = coords;
--${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(I=>"int "+I),m=mn("sourceLocR",h-1).concat("inIdx.r"),A=mn("sourceLocG",h-1).concat("inIdx.g"),y=mn("sourceLocB",h-1).concat("inIdx.b"),g=mn("sourceLocA",h-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",b=r?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${A.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${g.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${A.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,x=r?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${d.join()}),
vec2(${d.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${d.join()}),
vec2(${d.slice(-2).join()}));
}
${x}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
${c}
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
sourceLocB${p}, sourceLocA${p}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${_}(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 X_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new YL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=X_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function K_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new JL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=K_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function Z_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=X_(e,u,r);s.push(c);let h=Ae({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return K_(e,t,r)}function QL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=Z_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var eW={kernelName:ls,backendName:"webgl",kernelFunc:QL};function tW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=Z_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var nW={kernelName:lu,backendName:"webgl",kernelFunc:tW},rW=xr+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,aW=Xe({opSnippet:rW}),sW={kernelName:Qi,backendName:"webgl",kernelFunc:aW},iW=xr+"return log(x + sqrt(x * x + 1.0));",oW=Xe({opSnippet:iW}),lW={kernelName:eo,backendName:"webgl",kernelFunc:oW},uW=xr+`
return atan(x);
`,cW=Xe({opSnippet:uW}),hW={kernelName:to,backendName:"webgl",kernelFunc:cW},dW=xL+`
return atan(a, b);
`,pW=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+wL+`
return result;
`,fW=nn({opSnippet:dW,packedOpSnippet:pW}),mW={kernelName:ro,backendName:"webgl",kernelFunc:fW},AW=xr+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,yW=Xe({opSnippet:AW}),gW={kernelName:no,backendName:"webgl",kernelFunc:yW},hc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let I=">=";this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${p});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${c};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${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 ${I} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let g="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let b=Math.floor(s/4)*4,w=s%4,x=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${g}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${i}, ${o});
const ivec2 pads = ivec2(${d}, ${p});
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 < ${c};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${b}; wC += 4) {
int xC = xCCorner + wC * ${u};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
getValue(batch, xR, xC + 3 * ${u}, d)
);
${x}
}
int xC = xCCorner + ${b};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${x}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
initializationValue,
initializationValue
);
${x}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${u}, d),
getValue(batch, xR, xC + 2 * ${u}, d),
initializationValue
);
${x}
}
}
setOutput(${_});
}
`}},tA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let T=">=";this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${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 < ${d};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p};
wR += ${c}) {
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 ${T} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${r?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 * ${p} * ${f} +
wR * ${f} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let x=Math.floor(s/4)*4,I=s%4,S=`
if (${g}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${i}, ${o}, ${l});
const ivec3 pads = ivec3(${m}, ${A}, ${y});
const float initializationValue = ${_};
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 >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${_});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${d};
wD += ${u}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${p};
wR += ${c}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${x}; 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)
);
${S}
}
int xC = xCCorner + ${x};
if (${I===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${S}
} else if (${I===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${h}, ch),
initializationValue,
initializationValue
);
${S}
} else if (${I===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
);
${S}
}
}
setOutput(${w});
}
}
`}};function xW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;vl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return zn({inputs:{x:a},backend:n});let h=new hc(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var wW={kernelName:us,backendName:"webgl",kernelFunc:xW};function _W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new tA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var bW={kernelName:uu,backendName:"webgl",kernelFunc:_W},vW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${u}, ${c});
const float avgMultiplier = float(${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) / ${r}.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 >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},kW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
const ivec3 pads = ivec3(${p}, ${f}, ${m});
const float avgMultiplier = float(${A});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${c};
wD += ${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 < ${d};
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 NW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new kW(d);return n.runWebGLProgram(p,[a],i.dtype)}var IW={kernelName:Ih,backendName:"webgl",kernelFunc:NW};function SW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;vl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=new vW(c);return n.runWebGLProgram(h,[a],i.dtype)}var TW={kernelName:Nh,backendName:"webgl",kernelFunc:SW};function CW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return fp({a,b:s,transposeA:i,transposeB:o,backend:n})}var EW={kernelName:cs,backendName:"webgl",kernelFunc:CW},RW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.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)));
}
`}},FW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.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);
}
`}},MW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new FW(r.shape,a.shape,s.shape,c,h,l):new RW(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},$W={kernelName:bs,backendName:"webgl",kernelFunc:MW},OW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,r=DW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${nA[o]} = start[${o}] + coords.${nA[o]};`);a=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${s.join(`
`)}
`,this.userCode=`
${n}
void main() {
${a}
setOutput(getSource(${r}));
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},nA=["x","y","z","w","u","v"];function DW(e){if(e===1)return"sourceLoc";if(e<=6)return nA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var zW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=mn("coords",this.rank),r=mn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
result.x = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.y = ${s};
--${r[this.rank-1]};
}
`,o=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${r[this.rank-2]};
result.z = ${s};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${r[this.rank-1]};
result.w = ${s};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
`);this.userCode=`
uniform int start[${this.rank}];
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${i}
${o}
setOutput(result);
}
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function PW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=cn.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function dc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=cn.parseSliceParams(a,s,i);if(cn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=FP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=cn.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zW(l):new OW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),PW(a,o,l,n)}var LW={kernelName:Lo,backendName:"webgl",kernelFunc:dc},WW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=[],f=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),m=In({inputs:{x:f},backend:n,attrs:{perm:u}}),A=Ae({inputs:{x:m},backend:n,attrs:{shape:c}}),y=dc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},BW={kernelName:cu,backendName:"webgl",kernelFunc:WW};function VW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=v_(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var UW={kernelName:Sh,backendName:"webgl",kernelFunc:VW},HW="return float(a != b);",Y_=nn({opSnippet:HW,dtype:"bool"}),jW={kernelName:So,backendName:"webgl",kernelFunc:Y_};function pc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return zn({inputs:{x:a.complexTensorInfos.real},backend:n})}var GW={kernelName:Xh,backendName:"webgl",kernelFunc:pc},qW="return float(int(x));";function XW(e,t){let n=new Ba(e.shape,qW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function rA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return zn({inputs:{x:a},backend:n});let i=Et(a.shape),o=rA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Va({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=pc({inputs:{input:a},backend:n}),o=rA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=zn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return XW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=Y_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var KW={kernelName:hs,backendName:"webgl",kernelFunc:rA},J_="return ceil(x);",ZW=Xe({opSnippet:J_,packedOpSnippet:J_,cpuKernelImpl:fP}),YW={kernelName:ds,backendName:"webgl",kernelFunc:ZW},JW=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},QW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
uniform float minVal;
uniform float maxVal;
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function eB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new QW(a.shape):o=new JW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var tB={kernelName:Ia,backendName:"webgl",kernelFunc:eB},nB=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 Q_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function rB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new nB(r.shape),i=[Q_(r,a.complexTensorInfos.real),Q_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var aB={kernelName:hu,backendName:"webgl",kernelFunc:rB},sB=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},iB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ut(r),s=mn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${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),c=i.join(),h=`if (${l} < ${o[0]}) {
return getChannel(
getT0(${c}), 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}(${Ap(i,l,m)}),
vec2(${Ap(u,l,m)}));
}`}let d=o.length,p=o[o.length-1];h+=`
return getChannel(
getT${d}(${Ap(i,l,p)}),
vec2(${Ap(u,l,p)}));`,this.userCode=`
float getValue(${i.map(f=>"int "+f)}) {
${h}
}
void main() {
${a} coords = getOutputCoords();
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
${s[r-1]} = ${s[r-1]} + 1;
if (${s[r-1]} < ${n[r-1]}) {
result.g = getValue(${s});
}
${s[r-2]} = ${s[r-2]} + 1;
if (${s[r-2]} < ${n[r-2]}) {
result.a = getValue(${s});
}
${s[r-1]} = ${s[r-1]} - 1;
if (${s[r-2]} < ${n[r-2]} &&
${s[r-1]} < ${n[r-1]}) {
result.b = getValue(${s});
}
setOutput(result);
}
`}};function Ap(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function yp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return zn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var oB={kernelName:Bh,backendName:"webgl",kernelFunc:yp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>pc({inputs:{input:f},backend:n})),c=e.map(f=>yp({inputs:{input:f},backend:n})),h=Fl(u,t,n),d=Fl(c,t,n),p=Va({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=eb(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=mP(h,c,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=Fl(e.slice(0,u),t,n),h=Fl(e.slice(u),t,n),d=Fl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new iB(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=eb(e,t,n),i=new sB(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function eb(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function tb(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),Fl(o,s,n)}var lB={kernelName:ao,backendName:"webgl",kernelFunc:tb},nb=class{constructor(e,t=!1,n=null,r=!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,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,_="",b="";n&&(r?_=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?_=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:_=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${_}
const ivec2 strides = ivec2(${o}, ${l});
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${g}];
ivec2 xRCCorner =
ivec2(coords[${A}], coords[${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 < ${d}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${p}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${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, ${p}) *
getW(wR, wC, ${p}, d2);
} else {
dotProd +=
getX(batch, ${p}, xR, xC) *
getW(wR, wC, ${p}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${p}, d2),
getW(wR, wC, ${p} + 1, d2),
getW(wR, wC, ${p} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${p}),
getX(batch, xR, xC, ${p} + 1),
getX(batch, xR, xC, ${p} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${p}, xR, xC),
getX(batch, ${p} + 1, xR, xC),
getX(batch, ${p} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${b}
setOutput(result);
}
`}},uB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${a}, ${s}, ${i});
const ivec3 pads = ivec3(${t}, ${n}, ${r});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${c}; wF++) {
int xF = xFCorner + wF * ${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 < ${d}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${p}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${p}) *
getW(wF, wR, wC, ${p}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${p}),
getX(batch, xF, xR, xC, ${p} + 1),
getX(batch, xF, xR, xC, ${p} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${p}, d2),
getW(wF, wR, wC, ${p} + 1, d2),
getW(wF, wR, wC, ${p} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},cB=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=fn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=`
blockIndex = rc.y + ${w};
pos = rc.x + ${b};
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
d0 = offsetY + ${c} * (pos / ${f});
if(d0 < ${t[y]} && d0 >= 0) {
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.));
if(d1 < ${t[g]} && d1 >= 0) {
ch = int(mod(float(pos), ${a}.));
if (${A}) {
innerDims = vec2(d1, ch);
result[${b*2+w}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${b*2+w}] = 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;
${_}
${m.output} = result;
}
`}};function rb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>j_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=fp({a:w,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:I},backend:r,attrs:{shape:n.outShape}}),y.push(w),y.push(x),y.push(I)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(sc(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let I=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let S=fp({a:w,b:I,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=r.texData.get(S.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,T.shape=n.outShape,A=zn({inputs:{x:S},backend:r}),A.shape=n.outShape,y.push(S)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function ab({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,_=!1,b=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(x);let I=new cB(y,w.shape,n),S=r.runWebGLProgram(I,[w],"float32"),T=Ae({inputs:{x:S},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(S),b.push(T);let M=a!=null,D=s!=null,z=o==="leakyrelu",W=o?dp(o,!0):null,U=new L_(T.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,z),H=[T,x];if(a&&H.push(a),D&&H.push(s),z){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),j=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:j}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function hB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=rb({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=ab({x:a,filter:s,convInfo:d,backend:n});else{let m=new nb(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var dB={kernelName:ps,backendName:"webgl",kernelFunc:hB},pB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${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);
}
`}},fB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`
const ivec2 pads = ivec2(${i}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${c}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${s}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}},mB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=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 * ${n} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${r} - ${i};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},AB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${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 < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${n} - 1 - wR;
for (int wC = 0; wC < ${r}; 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 = ${r} - 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 yB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new pB(d);return n.runWebGLProgram(p,[a,s],"float32")}var gB={kernelName:Ch,backendName:"webgl",kernelFunc:yB};function xB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(u),d=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new fB(d);return n.runWebGLProgram(p,[a,s],"float32")}var wB={kernelName:fs,backendName:"webgl",kernelFunc:xB};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new uB(u);return n.runWebGLProgram(c,[a,s],"float32")}var bB={kernelName:du,backendName:"webgl",kernelFunc:_B};function vB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=R.computeConv3DInfo(a.shape,l,i,1,o),c=new mB(u);return n.runWebGLProgram(c,[a,s],"float32")}var kB={kernelName:Eh,backendName:"webgl",kernelFunc:vB};function NB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=R.computeConv3DInfo(l,s.shape,o,1,i),c=new AB(u);return n.runWebGLProgram(c,[a,s],"float32")}var IB={kernelName:Rh,backendName:"webgl",kernelFunc:NB},SB=P_+`
return cos(x);
`,TB=Xe({opSnippet:SB}),CB={kernelName:ms,backendName:"webgl",kernelFunc:TB},EB=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,RB=Xe({opSnippet:EB}),FB={kernelName:so,backendName:"webgl",kernelFunc:RB},MB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(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(${g});
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 = ${A};
float width_scale = ${_};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${p} ) {
setOutput(float(${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(${d} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},$B=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new MB(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},DB={kernelName:io,backendName:"webgl",kernelFunc:$B},ob=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${sb(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
uniform float index;
void main() {
${ut(r)} coords = getOutputCoords();
int end = ${ib(r,"coords")};
float val = ${a};
int pow2 = int(pow(2.0, index));
if (${i}) {
int idx = ${o};
${ib(r,"coords")} = idx;
val += getX(${sb(r,"coords")});
}
setOutput(val);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function sb(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 ${e} is not yet supported`)}function ib(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function OB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=R.getAxesPermutation([s],l),c=a;u!=null&&(c=In({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=zn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new ob(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new ob(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=In({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var zB={kernelName:As,backendName:"webgl",kernelFunc:OB};function PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=v_(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=pP(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var LB={kernelName:Fh,backendName:"webgl",kernelFunc:PB},WB=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${t};
int offset_h = imod(h, ${t});
int in_w = w / ${t};
int offset_w = imod(w, ${t});
int offset_d = (offset_h * ${t} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new WB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var VB={kernelName:oo,backendName:"webgl",kernelFunc:BB},lb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?A=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:A=`
float activation(float x) {
${n}
}
`,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${A}
const ivec2 strides = ivec2(${u}, ${c});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${m};
int q = d2 - d1 * ${m};
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 < ${p}; wR++) {
int xR = xRCorner + wR * ${h};
if (xR < 0 || xR >= ${s}) {
continue;
}
for (int wC = 0; wC < ${f}; wC++) {
int xC = xCCorner + wC * ${d};
if (xC < 0 || xC >= ${i}) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${g}
${y}
setOutput(result);
}
`}},ub=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let w=0;w<f;w++)A+=`
vec4 xTexelR${b}C${w*2} = vec4(0.);
vec4 wR${b}C${w} = vec4(0.);
vec4 xR${b}C${w} = vec4(0.);`;for(let b=0;b<p;b++)for(let w=0;w<m;w++){let x=w*2;if(A+=`
xR = xRCorner + ${b*h};
xC = xCCorner + ${x*d};
`,c===1){if(x<f&&(l%2==1?A+=`
xCOffset = xC + 1;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
xTexelR${b}C${x}.zw = vec2(0.);
}
} else {
xTexelR${b}C${x} = vec4(0.);
}
xCOffset = xC + 1 - 2;
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
vec4 previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if(xCOffset + 1 >= ${i}) {
previous.zw = vec2(0.);
}
xR${b}C${x} = vec4(previous.zw, xTexelR${b}C${x}.xy);
} else {
xR${b}C${x} = vec4(0, 0, xTexelR${b}C${x}.xy);
}
`:A+=`
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xC, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
xR${b}C${x} = xTexelR${b}C${x};
`,x+1<f)){let I=l%2==0?v.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
xCOffset = xC + ${l%2} + ${I};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
}
`,d>1&&(A+=`
xCOffset -= 2;
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
`),A+=`
xR${b}C${x+1} = vec4(
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.xy);
`):A+=`
xCOffset = xC + ${I};
if(xR >= 0 && xR < ${s} &&
xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
}
xR${b}C${x+1} = xTexelR${b}C${x+2};
`}}else x<f&&(A+=`
if(xR >= 0 && xR < ${s}) {
`,l%2==1?(A+=`
xCOffset = xC + 1 - ${c};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
if(xC + 1 >= 0 && xC + 1 < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xC + 1, d1);
} else {
xTexelR${b}C${x+2} = vec4(0.);
}
xR${b}C${x} = vec4(
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
`,x+1<f&&(A+=`
vec4 final = vec4(0.);
xCOffset = xC + 1 + ${c};
if(xCOffset >= 0 && xCOffset < ${i}) {
final = getX(batch, xR, xCOffset, d1);
}
xR${b}C${x+1} = vec4(xTexelR${b}C${x+2}.xy, final.xy);
`)):(A+=`
if(xC >= 0 && xC < ${i}) {
xTexelR${b}C${x} = getX(batch, xR, xC, d1);
} else {
xTexelR${b}C${x} = vec4(0.);
}
xCOffset = xC + ${c};
if(xCOffset >= 0 && xCOffset < ${i}) {
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
} else {
xTexelR${b}C${x+2} = vec4(0.);
}
xR${b}C${x} = vec4(
xTexelR${b}C${x}.xy, xTexelR${b}C${x+2}.xy);
`,x+1<f&&(A+=`
xR${b}C${x+1} = vec4(
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
`)),A+="}");x<f&&(A+=`
vec4 wTexelR${b}C${x} = getW(${b}, ${x}, d1, q);
wR${b}C${x} = vec4(wTexelR${b}C${x}.xz, wTexelR${b}C${x}.xz);
`,x+1<f&&(A+=`
vec4 wTexelR${b}C${x+1} = getW(${b}, ${x+1}, d1, q);
wR${b}C${x+1} =
vec4(wTexelR${b}C${x+1}.xz, wTexelR${b}C${x+1}.xz);`))}for(let b=0;b<p;b++)for(let w=0;w<f;w++)A+=`dotProd += xR${b}C${w} * wR${b}C${w};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:a?y=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:y=`vec4 activation(vec4 x) {
${n}
}`,g="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${y}
const ivec2 strides = ivec2(${u}, ${c});
const ivec2 pads = ivec2(${o}, ${l});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2;
int q = 0;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
vec4 dotProd = vec4(0.);
${A}
vec4 result = dotProd;
${_}
${g}
setOutput(result);
}
`}};function UB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new ub(h):d=new lb(h),n.runWebGLProgram(d,[a,s],"float32")}var HB={kernelName:ys,backendName:"webgl",kernelFunc:UB},jB=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=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} - ${r};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${a};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},GB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${s}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${r}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${a}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${o}; dm++) {
int d2 = d1 * ${o} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function qB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new jB(h);return n.runWebGLProgram(d,[a,s],"float32")}var XB={kernelName:Mh,backendName:"webgl",kernelFunc:qB};function KB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new GB(h);return n.runWebGLProgram(d,[a,s],"float32")}var ZB={kernelName:$h,backendName:"webgl",kernelFunc:KB},YB=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 JB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new YB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var QB={kernelName:Dh,backendName:"webgl",kernelFunc:JB},eV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=`
const ivec2 strides = ivec2(${a}, ${s});
const ivec2 pads = ivec2(${c}, ${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 < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function tV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new eV(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var nV={kernelName:pu,backendName:"webgl",kernelFunc:tV},rV="return (x >= 0.0) ? x : (exp(x) - 1.0);",aV=`
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;
`,sV=Xe({opSnippet:rV,packedOpSnippet:aV}),iV={kernelName:lo,backendName:"webgl",kernelFunc:sV},oV="return (b >= 1.0) ? a : a * (b + 1.0);",lV=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,uV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(lV,r.shape,a.shape):new Rl(oV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},cV={kernelName:Ph,backendName:"webgl",kernelFunc:uV},hV=`
return vec4(equal(a, b));
`,dV="return float(a == b);",pV=nn({opSnippet:dV,packedOpSnippet:hV,dtype:"bool"}),fV={kernelName:co,backendName:"webgl",kernelFunc:pV},mV=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${R.ERF_P};
float a1 = ${R.ERF_A1};
float a2 = ${R.ERF_A2};
float a3 = ${R.ERF_A3};
float a4 = ${R.ERF_A4};
float a5 = ${R.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,AV=Xe({opSnippet:mV}),yV={kernelName:uo,backendName:"webgl",kernelFunc:AV},cb="return exp(x);",hb=Xe({opSnippet:cb,packedOpSnippet:cb,cpuKernelImpl:AP}),gV={kernelName:xs,backendName:"webgl",kernelFunc:hb};function aA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var xV={kernelName:ho,backendName:"webgl",kernelFunc:aA},db="return exp(x) - 1.0;",wV=Xe({opSnippet:db,packedOpSnippet:db,cpuKernelImpl:yP}),_V={kernelName:po,backendName:"webgl",kernelFunc:wV},pb=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.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) {
${i}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${r});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${r}; 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 fb(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new pb("real",l,t),c=new pb("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Va({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=Ae({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function bV(e){let{inputs:t,backend:n}=e,{input:r}=t;return fb(r,!1,n)}var vV={kernelName:Lh,backendName:"webgl",kernelFunc:bV},kV=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
uniform float value;
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function sA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new kV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var NV={kernelName:fu,backendName:"webgl",kernelFunc:sA},IV=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},SV={kernelName:fo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new IV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},mb="return floor(x);",TV=Xe({opSnippet:mb,packedOpSnippet:mb,cpuKernelImpl:gP}),CV={kernelName:ws,backendName:"webgl",kernelFunc:TV},EV=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,RV=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,FV=nn({opSnippet:EV,packedOpSnippet:RV,dtype:"int32"}),MV={kernelName:_s,backendName:"webgl",kernelFunc:FV},$V=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,r]=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(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},DV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,r]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${r}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${t.output} = result;
}
`}},zV={kernelName:ed,backendName:"webgl",kernelFunc:OV},Ml;function OV(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,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],c=[u,l],h=[u,l,s];(o||i)&&(Ml==null&&(Ml=document.createElement("canvas").getContext("2d")),Ml.canvas.width=l,Ml.canvas.height=u,Ml.drawImage(a,0,0,l,u),a=Ml.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Zn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new DV(h):new $V(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function PV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(c),A=R.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=rb({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=ab({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",I=p?dp(p,!1):null,S=new nb(A,b,I,w,x),T=[a,s];if(i&&T.push(i),o&&T.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));T.push(M),g.push(M)}y=n.runWebGLProgram(S,T,"float32")}let _=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var LV={kernelName:ei,backendName:"webgl",kernelFunc:PV};function WV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?dp(d,y):null,_=[a,s],b=i!=null,w=o!=null,x=d==="leakyrelu";if(b&&_.push(i),w&&_.push(o),x){let T=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));_.push(T),f.push(T)}let I;y?I=new ub(A,b,g,w,x):I=new lb(A,b,g,w,x);let S=n.runWebGLProgram(I,_,"float32");return f.forEach(T=>n.disposeIntermediateTensorInfo(T)),S}var BV={kernelName:ti,backendName:"webgl",kernelFunc:WV},VV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ut(t.length),a=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${r} strides = ${r}(${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 UV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=R.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new VV(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var HV={kernelName:Ao,backendName:"webgl",kernelFunc:UV},GV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),r=jV(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function jV(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function qV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=v.sizeFromShape(s.shape),h=[],d=Ae({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=Ae({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});h.push(d),h.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),_=n.bufferSync(d),b=xP(_,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new GV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var XV={kernelName:mo,backendName:"webgl",kernelFunc:qV},KV="return float(a > b);",ZV=`
return vec4(greaterThan(a, b));
`,YV=nn({opSnippet:KV,packedOpSnippet:ZV,cpuKernelImpl:wP,dtype:"bool"}),JV={kernelName:yo,backendName:"webgl",kernelFunc:YV},QV="return float(a >= b);",eU=`
return vec4(greaterThanEqual(a, b));
`,tU=nn({opSnippet:QV,packedOpSnippet:eU,dtype:"bool"}),nU={kernelName:vs,backendName:"webgl",kernelFunc:tU};function rU(e){let{inputs:t,backend:n}=e,{input:r}=t;return fb(r,!0,n)}var aU={kernelName:Wh,backendName:"webgl",kernelFunc:rU},sU="return float(!isnan(x) && !isinf(x));",iU=Xe({opSnippet:sU,dtype:"bool"}),oU={kernelName:go,backendName:"webgl",kernelFunc:iU},lU="return float(isinf(x));",uU=Xe({opSnippet:lU,dtype:"bool"}),cU={kernelName:xo,backendName:"webgl",kernelFunc:uU},hU="return float(isnan(x));",dU=Xe({opSnippet:hU,dtype:"bool"}),pU={kernelName:wo,backendName:"webgl",kernelFunc:dU},fU="return float(a < b);",mU=`
return vec4(lessThan(a, b));
`,AU=nn({opSnippet:fU,packedOpSnippet:mU,cpuKernelImpl:_P,dtype:"bool"}),yU={kernelName:_o,backendName:"webgl",kernelFunc:AU},gU="return float(a <= b);",xU=`
return vec4(lessThanEqual(a, b));
`,wU=nn({opSnippet:gU,packedOpSnippet:xU,dtype:"bool"}),_U={kernelName:bo,backendName:"webgl",kernelFunc:wU};function bU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=bP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var vU={kernelName:Vh,backendName:"webgl",kernelFunc:bU},kU=`if (x < 0.0) return NAN;
return log(x);`,NU=`
vec4 result = log(x);
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
result.r = isNaN.r == 1.0 ? NAN : result.r;
result.g = isNaN.g == 1.0 ? NAN : result.g;
result.b = isNaN.b == 1.0 ? NAN : result.b;
result.a = isNaN.a == 1.0 ? NAN : result.a;
return result;
`,IU=Xe({opSnippet:kU,packedOpSnippet:NU,cpuKernelImpl:vP}),SU={kernelName:Is,backendName:"webgl",kernelFunc:IU},TU="return log(1.0 + x);",CU=Xe({opSnippet:TU}),EU={kernelName:vo,backendName:"webgl",kernelFunc:CU},RU="return float(a >= 1.0 && b >= 1.0);",FU=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,MU=nn({opSnippet:RU,packedOpSnippet:FU,dtype:"bool"}),$U={kernelName:ko,backendName:"webgl",kernelFunc:MU},DU="return float(!(x >= 1.0));",OU=Xe({opSnippet:DU}),zU={kernelName:mu,backendName:"webgl",kernelFunc:OU},PU="return float(a >= 1.0 || b >= 1.0);",LU=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,WU=nn({opSnippet:PU,packedOpSnippet:LU,dtype:"bool"}),BU={kernelName:Au,backendName:"webgl",kernelFunc:WU},VU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * 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 * ${o};
setOutput(val);
}
`}},UU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${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);
}
`}},HU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new UU(a.shape,s,i,o,l):new VU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},jU={kernelName:yu,backendName:"webgl",kernelFunc:HU},GU=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,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 = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${r}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${r})
* 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);
}
`}},qU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new GU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},XU={kernelName:Uh,backendName:"webgl",kernelFunc:qU};function KU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=bi(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function Ab(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=Jm(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=pp(a,c,n);u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("max",u,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,u),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=kP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=KU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var ZU={kernelName:Ss,backendName:"webgl",kernelFunc:Ab},YU=M_+`
return max(a, b);
`,JU=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+hp+`
return result;
`,QU=nn({opSnippet:YU,packedOpSnippet:JU,cpuKernelImpl:NP}),eH={kernelName:Ts,backendName:"webgl",kernelFunc:QU};function tH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;vl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return zn({inputs:{x:a},backend:n});let h=new hc(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var nH={kernelName:Cs,backendName:"webgl",kernelFunc:tH};function rH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new tA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var aH={kernelName:gu,backendName:"webgl",kernelFunc:rH},sH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=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 += ${r}) {
float dyR = float(dyRCorner + wR) / ${t}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${n}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${s} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},iH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=`
const ivec3 pads = ivec3(${c}, ${h}, ${d});
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) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${u};
wC += ${i}) {
float dyC = float(dyCCorner + wC) / ${r}.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 = ${p} -
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 oH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new tA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new iH(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var lH={kernelName:jh,backendName:"webgl",kernelFunc:oH};function uH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;vl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new hc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new sH(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var cH={kernelName:Hh,backendName:"webgl",kernelFunc:uH};function hH(e,t,n,r){let a=new hc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new hc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var dH={kernelName:Gh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,a,s,u,i),[h,d]=hH(r,o,c,l);return[h,d]}};function pH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=bi(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var fH={kernelName:Es,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let _=i.texData.get(f.dataId).values,b=new Array(o);for(let I=0;I<b.length;I++)b[I]=r.shape[c[I]];let w=Jm(_,r.shape,r.dtype,c,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=w}else f=pp(r,c,i);p.push(f),u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("sum",u,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=pH(f,A,y,i);for(let _ of p)i.disposeIntermediateTensorInfo(_);return g}};function mH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=bi(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var AH={kernelName:Rs,backendName:"webgl",kernelFunc:mH},yH=M_+`
return min(a, b);
`,gH=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+hp+`
return result;
`,xH=nn({opSnippet:yH,packedOpSnippet:gH,cpuKernelImpl:IP}),wH={kernelName:Fs,backendName:"webgl",kernelFunc:xH},_H=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=ut(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===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 < ${r}; 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}));
}
`}},bH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ut(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=mn("rc",r),l=mn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
${a} source = rc;
if (source < start) {
source = start * 2 - source - ${h};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${h};
}
source -= start;
`;d=`
${a} rc = outputLoc;
${p}
result[0] = getChannel(getX(${l.join()}), ${c});
${o[r-1]} += 1;
if(${u}) {
${p}
result[1] = getChannel(getX(${l.join()}), ${c});
}
`}else{let p=`
${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;
`;d=`
${a} rc = outputLoc;
${p}
result[0] = getChannel(getX(${l.join()}), ${c});
${o[r-1]} += 1;
if(${u}) {
${p}
result[1] = getChannel(getX(${l.join()}), ${c});
}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {
${p}
result[2] = getChannel(getX(${l.join()}), ${c});
${o[r-1]} += 1;
if(${u}) {
${p}
result[3] = getChannel(getX(${l.join()}), ${c});
}
}
`}this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${d}
setOutput(result);
}
`}},vH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bH(r.shape,a,s):new _H(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},kH={kernelName:xu,backendName:"webgl",kernelFunc:vH},NH=`if (b == 0.0) return NAN;
return mod(a, b);`,IH=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+hp+`
return result;
`,SH=nn({opSnippet:NH,packedOpSnippet:IH}),TH={kernelName:No,backendName:"webgl",kernelFunc:SH},CH=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
uniform float seed;
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},EH=`
if (a == b) {
return 1.0;
};
return a / b;`,RH=`
// 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;
`,yb=nn({opSnippet:EH,packedOpSnippet:RH,checkOutOfBounds:!0}),FH={kernelName:gs,backendName:"webgl",kernelFunc:yb},gb="return a - b;",xb=nn({opSnippet:gb,packedOpSnippet:gb,supportsComplex:!0,cpuKernelImpl:$P}),MH={kernelName:Zs,backendName:"webgl",kernelFunc:xb};function wb(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=Ab({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),c=xb({inputs:{a,b:u},backend:n}),h=hb({inputs:{x:c},backend:n}),d=eA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),f=yb({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var $H={kernelName:Xs,backendName:"webgl",kernelFunc:wb};function DH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:wb({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new CH(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var OH={kernelName:qh,backendName:"webgl",kernelFunc:DH},_b="return -x;";function zH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=TP(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Cl(r.shape,_b):a=new Ba(r.shape,_b),n.runWebGLProgram(a,[r],r.dtype)}var PH={kernelName:Io,backendName:"webgl",kernelFunc:zH},LH=Pr.nonMaxSuppressionV3Impl;function WH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=LH(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var BH={kernelName:To,backendName:"webgl",kernelFunc:WH},VH=Pr.nonMaxSuppressionV4Impl;function UH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=VH(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var HH={kernelName:Co,backendName:"webgl",kernelFunc:UH},jH=Pr.nonMaxSuppressionV5Impl;function GH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=jH(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var qH={kernelName:Eo,backendName:"webgl",kernelFunc:GH},XH=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${r}), float(${n}),
float(index == coords.y)));
}
`}},KH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new XH(l,s,i,o),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},ZH={kernelName:$s,backendName:"webgl",kernelFunc:KH};function gp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=pc({inputs:{input:r},backend:n}),s=gp({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Va({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return sA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var YH={kernelName:Xo,backendName:"webgl",kernelFunc:gp};function bb(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=pc({inputs:{input:r},backend:n}),s=bb({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Va({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return sA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var JH={kernelName:Ro,backendName:"webgl",kernelFunc:bb};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return aA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=aA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=tb({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var ej={kernelName:Fo,backendName:"webgl",kernelFunc:QH},tj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=ut(r),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,r);if(r===1){this.userCode=`
int start = ${s};
int end = ${i};
uniform float value;
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${a} start = ${a}(${s});
${a} end = ${a}(${i});
uniform float value;
void main() {
${a} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${a} coords = outC - start;
setOutput(getX(${o}));
}
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},nj=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ut(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=mn("rc",r),l=mn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
if(${u}) {
`,r===1?"":`}
rc = outputLoc;
${o[r-2]} += 1;
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
${h[f]}
if (${d}) {
result[${f}] = float(value);
} else {
${a} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${c});
}
`;p+=r===1?"} ":"}}",this.userCode=`
const ${a} start = ${a}(${s});
const ${a} end = ${a}(${i});
uniform float value;
void main() {
${a} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},vb=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nj(a.shape,s,i):new tj(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},rj={kernelName:Ds,backendName:"webgl",kernelFunc:vb},aj=`
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);
`,sj=`
// 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));
`+hp+`
return result;
`,ij=nn({opSnippet:aj,packedOpSnippet:sj}),oj={kernelName:Os,backendName:"webgl",kernelFunc:ij};function lj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=R.getAxesPermutation(c,o),d=a;h!=null&&(d=In({inputs:{x:a},backend:n,attrs:{perm:h}}),c=R.getInnerMostAxes(c.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=CP(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=sd(a.dtype),_=bi(y,g,"prod",n);p=Ae({inputs:{x:_},backend:n,attrs:{shape:f}}),l.push(y),l.push(_)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=Ae({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var uj={kernelName:Mo,backendName:"webgl",kernelFunc:lj},kb=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=EP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},cj={kernelName:wu,backendName:"webgl",kernelFunc:kb},hj="return 1.0 / x;",dj=Xe({opSnippet:hj}),pj={kernelName:$o,backendName:"webgl",kernelFunc:dj},fj=xr+`
return (x < 0.0) ? 0.0 : x;
`,mj=`
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;
`,Aj=Xe({opSnippet:fj,packedOpSnippet:mj}),yj={kernelName:Ps,backendName:"webgl",kernelFunc:Aj},gj=xr+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,xj=`
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;
`,wj=Xe({opSnippet:gj,packedOpSnippet:xj}),_j={kernelName:Ws,backendName:"webgl",kernelFunc:wj},bj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/c[0]},
${u[1]/c[1]});
const vec2 inputShapeRC = vec2(${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);
}
`}},vj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${u[0]/c[0]},
${u[1]/c[1]},
${u[1]/c[1]});
const vec3 inputShapeRC = vec3(${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 < ${n-1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`}};function kj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new vj(a.shape,l,u,s,i):new bj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var Nj={kernelName:Ls,backendName:"webgl",kernelFunc:kj},Ij=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${c});
const float invHeightScale = float(${h});
const float invWidthScale = float(${d});
const int winHeight = int(${p});
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), ${r-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 Sj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Ij(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Tj={kernelName:Zh,backendName:"webgl",kernelFunc:Sj},Cj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${u[0]/c[0]},
${u[1]/c[1]});
const vec2 inputShapeRC = vec2(${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 = ${d};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}};function Ej(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new Cj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var Rj={kernelName:_u,backendName:"webgl",kernelFunc:Ej},Fj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${u});
const float widthScale = float(${c});
const float invHeightScale = float(${h});
const float invWidthScale = float(${d});
const int winHeight = int(${p});
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(${l[1]}));
int sourceNearestRow = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${a}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Fj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var $j={kernelName:Kh,backendName:"webgl",kernelFunc:Mj},Dj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ut(n);this.userCode=`
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${a}));
}
`}},Oj=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=mn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${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 = ${o(r.slice())};
if(${a}){
result.g = ${l(r.slice())};
}
if(${s}) {
result.b = ${u(r.slice())};
if(${a}) {
result.a = ${c(r.slice())};
}
}
setOutput(result);
}
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function zj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return zn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Oj(a.shape,o):new Dj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Pj={kernelName:Bs,backendName:"webgl",kernelFunc:zj},Lj=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=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=`
uniform vec4 params;
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${a}
if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Wj={kernelName:Ko,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Lj(r.shape,s),[u,c]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Bj=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Vj=Xe({opSnippet:Bj}),Uj={kernelName:Vs,backendName:"webgl",kernelFunc:Vj},Hj="return inversesqrt(x);",jj=Xe({opSnippet:Hj,cpuKernelImpl:RP}),Gj={kernelName:Us,backendName:"webgl",kernelFunc:jj},Nb=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(a.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=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 < ${t}; j++) {
int index = round(${c});
flattenedIndex += index * ${p};
}
if (flattenedIndex == coords[0]) {
sum += ${d};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function qj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new Nb(l,o,p.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=Ae({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var Xj={kernelName:Oo,backendName:"webgl",kernelFunc:qj},Kj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);r=o.join(),a=l.join()}let s=ut(n);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
float cVal = getC(${r});
if (cVal >= 1.0) {
setOutput(getA(${a}));
} else {
setOutput(getB(${a}));
}
}
`}};function Zj(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Kj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],nr(a.dtype,s.dtype))}var Yj={kernelName:zo,backendName:"webgl",kernelFunc:Zj},Jj=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${R.SELU_SCALEALPHA};
float scale = ${R.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Qj=Xe({opSnippet:Jj}),eG={kernelName:Po,backendName:"webgl",kernelFunc:Qj},tG="return 1.0 / (1.0 + exp(-1.0 * x));",nG=Xe({opSnippet:tG}),rG={kernelName:js,backendName:"webgl",kernelFunc:nG},aG=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,sG=Xe({opSnippet:aG}),iG={kernelName:Bo,backendName:"webgl",kernelFunc:sG},oG=P_+`
return sin(x);
`,lG=Xe({opSnippet:oG}),uG={kernelName:Hs,backendName:"webgl",kernelFunc:lG},cG=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,hG=Xe({opSnippet:cG}),dG={kernelName:Wo,backendName:"webgl",kernelFunc:hG},pG=`
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;
`,fG=Xe({opSnippet:pG}),mG={kernelName:Vo,backendName:"webgl",kernelFunc:fG},AG=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],c=vb({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(c.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(c.shape,s,o,!1),f=Ae({inputs:{x:c},backend:n,attrs:{shape:h}}),m=In({inputs:{x:f},backend:n,attrs:{perm:d}}),A=Ae({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},yG={kernelName:bu,backendName:"webgl",kernelFunc:AG};function gG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new Nb(u,l,a.shape.length,s.shape.length,c,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var xG={kernelName:Yh,backendName:"webgl",kernelFunc:gG};function wG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=dc({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,f})}var _G={kernelName:Uo,backendName:"webgl",kernelFunc:wG},bG="return sqrt(x);",vG=Xe({opSnippet:bG}),kG={kernelName:Gs,backendName:"webgl",kernelFunc:vG},NG="return x * x;",IG=Xe({opSnippet:NG}),SG={kernelName:vu,backendName:"webgl",kernelFunc:IG},Ib="return (a - b) * (a - b);",TG=nn({opSnippet:Ib,packedOpSnippet:Ib}),CG={kernelName:Ks,backendName:"webgl",kernelFunc:TG};function EG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=xr+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,s=new Ba(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var RG={kernelName:Ta,backendName:"webgl",kernelFunc:EG},FG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ut(n.length),s=ut(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
${a} begin = ${a}(${e});
${a} strides = ${a}(${t});
void main() {
${s} coords = getOutputCoords();
setOutput(getX(${i}));
}
`}};function MG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=cn.sliceInfo(a.shape,s,i,o,l,u,c,h,d),_=Ae({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=dc({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});b=Ae({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([_])){let x=n.texData.get(_.dataId).values,I=We(_.shape,_.dtype,x),S=MP(g,I,m,f);b=n.makeTensorInfo(g,_.dtype,S.values)}else{let x=new FG(f,m,g);b=n.runWebGLProgram(x,[_],_.dtype)}let w=Ae({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(b),w}var $G={kernelName:Ho,backendName:"webgl",kernelFunc:MG},DG="return tan(x);",OG=Xe({opSnippet:DG}),zG={kernelName:jo,backendName:"webgl",kernelFunc:OG},PG=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,LG=Xe({opSnippet:PG}),WG={kernelName:Ys,backendName:"webgl",kernelFunc:LG},VG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=BG(e);this.userCode=`
void main() {
${r} resRC = getOutputCoords();
setOutput(getA(${a}));
}
`}};function BG(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}function Sb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(c=>v.decodeString(c)),l=We(a.shape,a.dtype,o),u=DP(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new VG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var UG={kernelName:Sa,backendName:"webgl",kernelFunc:Sb};function HG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=OP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var jG={kernelName:Go,backendName:"webgl",kernelFunc:HG},GG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${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 qG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=new GG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var XG={kernelName:Jh,backendName:"webgl",kernelFunc:qG};function KG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;vl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=zP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var ZG={kernelName:Qh,backendName:"webgl",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==s&&(u[c++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=dc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=Ae({inputs:{x:A},backend:n,attrs:{shape:u}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var JG={kernelName:qo,backendName:"webgl",kernelFunc:YG},QG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,h=`
sumValue += dot(values, segFilter);
`,d="";a%n>0&&(d=`
if (inIdx < 0 || inIdx >= ${a}) {
return initializationValue;
}
`);let p="";a%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${a}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${o};
float getValue(int batch, int inIdx) {
${d}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${p}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${s})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${s})));
float sumValue = 0.0;
for (int i = 0; i < ${u}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${h}
}
int inIdx = inOffset + ${u};
if (${c===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${h}
} else if (${c===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${h}
} else if (${c===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${h}
}
setOutput(${l});
}
`}};function eq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],u=0,c=R.getAxesPermutation([u],o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),l.push(h),u=R.getInnerMostAxes(1,o)[0]);let d=R.segment_util.computeOutShape(h.shape,u,i),p=v.sizeFromShape([h.shape[u]]),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=sd(a.dtype),A=(b,w,x,I,S)=>{let T=b.shape[0],M=b.shape[1],D=R.segment_util.segOpComputeOptimalWindowSize(M,S),z={windowSize:D,inSize:M,batchSize:T,numSegments:S},W=new QG(z,w),U=n.compileAndRun(W,[b,x],I);if(l.push(U),U.shape[1]===S)return U;let H=kb({backend:n,attrs:{start:0,stop:S,step:1,dtype:"float32"}}),X=Sb({inputs:{x:H},backend:n,attrs:{reps:[M/D]}});return l.push(H),l.push(X),A(U,w,X,I,S)},y=A(f,"unsortedSegmentSum",s,m,i),g=Ae({inputs:{x:y},backend:n,attrs:{shape:d}}),_=g;if(c!=null){l.push(g);let b=R.getUndoAxesPermutation(c);_=In({inputs:{x:_},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var tq={kernelName:ku,backendName:"webgl",kernelFunc:eq},nq=[jU,XU,ML,DL,PL,BL,UL,GL,XL,ZL,eW,nW,sW,lW,mW,hW,gW,bW,wW,IW,TW,EW,$W,BW,UW,KW,YW,tB,aB,fL,lB,gB,wB,dB,kB,IB,bB,CB,FB,DB,zB,LB,VB,XB,ZB,HB,QB,nV,iV,cV,fV,yV,gV,xV,_V,vV,NV,SV,CV,MV,zV,LV,BV,HV,XV,JV,nU,pL,aU,oB,oU,cU,pU,AL,yU,_U,vU,EU,SU,$U,zU,BU,ZU,aH,nH,lH,cH,dH,eH,fH,AH,wH,kH,TH,OH,_L,PH,BH,HH,qH,jW,ZH,JH,ej,rj,oj,gL,uj,cj,GW,FH,pj,_j,yj,vL,Nj,Tj,Rj,$j,Pj,Wj,Uj,Gj,Xj,Yj,eG,rG,iG,uG,dG,LW,$H,mG,yG,xG,_G,kG,SG,CG,RG,$G,MH,EL,zG,WG,UG,jG,XG,RL,ZG,JG,tq,YH];for(let e of nq)ni(e);var Pn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Pn||(Pn={}));var fc;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu"})(fc||(fc={}));var Tb;function rq(e){Tb=e.wasm.cwrap(Qs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function aq(e){let{inputs:t,backend:n,attrs:r}=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:c,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let S=n.dataIdMap.get(i.dataId);if(S.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${S.shape.length}.`);f=S.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=fc[c];if(A==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=u?s.shape[1]:s.shape[2],_=a.shape[0],b=n.makeOutput([_,y,g],a.dtype),w=n.dataIdMap.get(b.dataId).id,x=new Uint8Array(new Int32Array(a.shape).buffer),I=new Uint8Array(new Int32Array(s.shape).buffer);return Tb(d,x,a.shape.length,p,I,s.shape.length,l,u,A,f,m,h||0,w),b}var sq={kernelName:Qs,backendName:"wasm",setupFunc:rq,kernelFunc:aq};function Sn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return v.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var iq=Sn(Zi);function An(e,t,n){let r;function a(i){r=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:c}=l,h=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,p=n!=null?n:u.dtype,f=R.assertAndGetBroadcastShape(u.shape,c.shape),m=o.makeOutput(f,p);if(v.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),g=o.dataIdMap.get(m.dataId).id,_=()=>r(h,A,u.shape.length,d,y,c.shape.length,Pn[u.dtype],g);if(t&&u.dtype==="float32")return _(),m;let b=R.getBroadcastDims(u.shape,f),w=R.getBroadcastDims(c.shape,f),x=b.every((S,T)=>S===T),I=w.every((S,T)=>S===T);if(x&&I)return _(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var oq=!0,lq=An(Na,oq),Cb;function uq(e){Cb=e.wasm.cwrap(os,null,["array","number","number","number"])}function cq(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return Cb(s,a.length,Pn[r.dtype],i),r}var hq={kernelName:os,backendName:"wasm",setupFunc:uq,kernelFunc:cq};function xp(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var dq={kernelName:ks,backendName:"wasm",kernelFunc:xp},Eb;function pq(e){Eb=e.wasm.cwrap(Js,null,["number","array","number","number","number","array","number"])}function wp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=mq(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=fq(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=xp({inputs:t,backend:n});return f.shape=o,f}let u=n.makeOutput(o,l.dtype),c=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(u.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return Eb(c,p,l.shape.length,Pn[l.dtype],h,d,s.length),u}function fq(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function mq(e,t){let n=[],r=[];for(let a=0;a<e.length;++a)e[a]!==1&&n.push(e[a]),e[t[a]]!==1&&r.push(t[a]);for(let a=0;a<r.length;++a){let s=-1;for(let i=0;i<r.length;++i)r[i]>=a&&(s===-1||r[s]>r[i])&&(s=i);r[s]=a}return[n,r]}var Aq={kernelName:Js,backendName:"wasm",kernelFunc:wp,setupFunc:pq};function $l(e,t,n){let r=e.shape,a=e.shape.length,s=v.parseAxisParam(t,r),i=s,o=R.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let c=new Array(a);for(let d=0;d<c.length;d++)c[d]=r[o[d]];i=R.getInnerMostAxes(i.length,a),l=wp({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var Rb;function yq(e){Rb=e.wasm.cwrap(ls,null,["number","number","number","number","number"])}function gq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:c,inputWasTransposed:h}=$l(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),f=t.dataIdMap.get(p.dataId).id,m=v.sizeFromShape(p.shape),A=l.shape[c[0]];return Rb(o,Pn[l.dtype],m,A,f),h&&t.disposeData(u.dataId),p}var xq={kernelName:ls,backendName:"wasm",kernelFunc:gq,setupFunc:yq},Fb;function wq(e){Fb=e.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _q(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=R.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,y=c.strideHeight,g=c.strideWidth,_=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=r.makeOutput(c.outShape,"float32"),w=r.dataIdMap.get(b.dataId).id;return Fb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,_,w),b}var bq={kernelName:us,backendName:"wasm",setupFunc:wq,kernelFunc:_q};function wr(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=v.sizeFromShape(r.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var vq={kernelName:Do,backendName:"wasm",kernelFunc:wr},Mb;function kq(e){Mb=e.wasm.cwrap(cs,null,["number","array","number","number","array","number","number","number","number"])}function Nq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,c=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=v.sizeFromShape(f),y=v.sizeFromShape(m),g=A===y||A===1||y===1;v.assert(l>=2&&u>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let _=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);v.assert(c===h,()=>`Error in matMul: inner shapes (${c}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[A,c,d]:[A,d,c],w=o?[y,p,h]:[y,h,p],x=wr({inputs:{x:a},backend:n,attrs:{shape:b}}),I=wr({inputs:{x:s},backend:n,attrs:{shape:w}}),S=n.dataIdMap.get(x.dataId).id,T=n.dataIdMap.get(I.dataId).id,M=i?x.shape[2]:x.shape[1],D=o?I.shape[1]:I.shape[2],z=Math.max(A,y),W=n.makeOutput([z,M,D],x.dtype),U=n.dataIdMap.get(W.dataId).id,H=new Uint8Array(new Int32Array(x.shape).buffer),X=new Uint8Array(new Int32Array(I.shape).buffer);return Mb(S,H,x.shape.length,T,X,I.shape.length,i,o,U),n.disposeData(x.dataId),n.disposeData(I.dataId),W.shape=_,W}var Iq={kernelName:cs,backendName:"wasm",setupFunc:kq,kernelFunc:Nq};function _p(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,a=r.makeOutput(t.shape,n),s=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(s),a}var Sq={kernelName:hs,backendName:"wasm",kernelFunc:_p},Tq=Sn(ds),$b;function Cq(e){$b=e.wasm.cwrap(Ia,null,["number","number","number","number"])}function Eq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(l.dataId).id;return $b(o,s,i,u),l}var Rq={kernelName:Ia,backendName:"wasm",setupFunc:Cq,kernelFunc:Eq};function Db(e){let{inputs:t,backend:n}=e,r=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=R.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>v.sizeFromShape(p.shape)>0);if(s.length===1)return xp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(v.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(R.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(_=>{let b=v.sizeFromShape(_.shape.slice(r));return wr({inputs:{x:_},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(_=>({vals:n.readSync(_.dataId),shape:_.shape}));a=R.computeOutShape(p.map(_=>_.shape),1);let m=p[0].shape[0]===1,A=Sm(f,a,t[0].dtype,m),y=R.computeOutShape(s.map(_=>_.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=R.fromStringArrayToUint8(A),p.forEach(_=>n.disposeData(_.dataId)),i}let l=v.sizeFromShape(s[0].shape.slice(0,r)),u=0,c=s.map(p=>{let f=v.sizeFromShape(p.shape.slice(r));return u+=f,f}),h=s.map(p=>n.typedArrayFromHeap(p)),d=n.typedArrayFromHeap(i);for(let p=0;p<l;p++){let f=p*u;for(let m=0;m<h.length;m++){let A=c[m],y=p*A,g=h[m].subarray(y,y+A);d.set(g,f),f+=A}}return i}var Fq={kernelName:ao,backendName:"wasm",kernelFunc:Db},Ob;function Mq(e){Ob=e.wasm.cwrap(ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $q(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:h,dataFormat:d}=n,p=R.convertConv2DDataFormat(d),f=R.computeConv2DInfo(a.shape,s.shape,l,u,c,h,!1,p),m=f.filterHeight,A=f.filterWidth,y=f.padInfo.top,g=f.padInfo.right,_=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,x=f.dilationWidth,I=f.strideHeight,S=f.strideWidth,T=f.inChannels,M=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let z=r.makeOutput(f.outShape,"float32"),W=r.dataIdMap.get(z.dataId).id;return Ob(i,a.shape[0],a.shape[1],a.shape[2],o,m,A,y,g,_,b,D,w,x,I,S,T,M,W),z}var Dq={kernelName:ps,backendName:"wasm",setupFunc:Mq,kernelFunc:$q},zb;function Oq(e){zb=e.wasm.cwrap(fs,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 zq(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:c}=r,h=1,d=R.convertConv2DDataFormat(l),p=R.computeConv2DInfo(c,s.shape,i,h,o,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:A,inChannels:y,inHeight:g,inWidth:_,outChannels:b,outHeight:w,outWidth:x,strideHeight:I,strideWidth:S}=p,T=m-1-p.padInfo.top,M=A-1-p.padInfo.left,D=p.dataFormat==="channelsLast",z=v.computeStrides(p.inShape),W=v.computeStrides(a.shape),[U,H,X]=v.computeStrides(s.shape),j=z[0],ee=D?z[1]:z[2],Y=D?z[2]:1,ae=D?1:z[1],te=W[0],ie=D?W[1]:W[2],Q=D?W[2]:1,he=D?1:W[1],oe=t.makeOutput(p.inShape,"float32"),fe=t.dataIdMap.get(oe.dataId).id,pe=t.dataIdMap.get(a.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return zb(pe,ke,f,m,A,g,_,y,w,x,b,I,S,T,M,U,H,X,j,ee,Y,ae,te,ie,Q,he,fe),oe}var Pq={kernelName:fs,backendName:"wasm",setupFunc:Oq,kernelFunc:zq},Lq=Sn(ms),iA;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(iA||(iA={}));var Pb;function Wq(e){Pb=e.wasm.cwrap(io,null,["number","number","number","number","array","number","number","number","number","number"])}function Bq(e){let{backend:t,inputs:n,attrs:r}=e,{method:a,extrapolationValue:s,cropSize:i}=r,{image:o,boxes:l,boxInd:u}=n,c=l.shape[0],[h,d]=i,p=[c,h,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=_p({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let A=f.id,y=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(u.dataId).id,_=t.makeOutput(p,"float32"),b=t.dataIdMap.get(_.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return Pb(A,y,g,c,w,h,d,iA[a],s,b),m!=null&&t.disposeData(m.dataId),_}var Vq={kernelName:io,backendName:"wasm",setupFunc:Wq,kernelFunc:Bq},Lb;function Uq(e){Lb=e.wasm.cwrap(As,null,["number","number","number","number","number","number"])}function Hq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=R.getAxesPermutation([s],l),c=a;u!==null&&(c=wp({inputs:{x:a},attrs:{perm:u},backend:n}));let h=R.getInnerMostAxes(1,l)[0];R.assertAxesAreInnerMostDims("cumsum",[h],l);let d=n.makeOutput(c.shape,c.dtype),p=c.shape[h],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;Lb(f,i?1:0,o?1:0,p,m,Pn[a.dtype]);let A=d;if(u!==null){let y=R.getUndoAxesPermutation(u);A=wp({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return A}var jq={kernelName:As,backendName:"wasm",setupFunc:Uq,kernelFunc:Hq},Wb;function Gq(e){Wb=e.wasm.cwrap(oo,null,["number","number","number","array","number","array","array","number","number"])}function qq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(f).buffer),_=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return Wb(A,s,i==="NHWC"?1:0,y,a.shape.length-1,g,_,f.length,b),m}var Xq={kernelName:oo,backendName:"wasm",setupFunc:Gq,kernelFunc:qq},Bb;function Kq(e){Bb=e.wasm.cwrap(ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:h}=n,d=u==null?[1,1]:u,p=R.computeConv2DInfo(a.shape,s.shape,l,d,c,h,!0),f=p.filterHeight,m=p.filterWidth,A=p.padInfo.top,y=p.padInfo.right,g=p.padInfo.bottom,_=p.padInfo.left,b=p.dilationHeight,w=p.dilationWidth,x=p.strideHeight,I=p.strideWidth,S=p.inChannels,T=p.outChannels,M=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),z=r.dataIdMap.get(D.dataId).id;return Bb(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,_,M,b,w,x,I,S,T,z),D}var Yq={kernelName:ys,backendName:"wasm",setupFunc:Kq,kernelFunc:Zq},Jq=!1,Qq=An(co,Jq,"bool"),eX=Sn(xs);function oA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),wr({inputs:{x:a},backend:r,attrs:{shape:o}})}var tX={kernelName:ho,backendName:"wasm",kernelFunc:oA};function nX(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var rX={kernelName:fu,backendName:"wasm",kernelFunc:nX},Vb;function aX(e){Vb=e.wasm.cwrap(fo,null,["number","number","number","number","number","number"])}function sX(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return Vb(s,o,l,u,c,i),a}var iX={kernelName:fo,backendName:"wasm",kernelFunc:sX,setupFunc:aX},oX=Sn(ws),lX=!1,uX=An(_s,lX),Ub;function cX(e){Ub=e.wasm.cwrap(bs,null,["number","number","number","number","number","number","number"])}function hX(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return Ub(c,h,d,p,f,a,A),m}var dX={kernelName:bs,backendName:"wasm",setupFunc:cX,kernelFunc:hX},Hb;function pX(e){Hb=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=fc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=m.filterHeight,x=m.filterWidth,I=m.padInfo.top,S=m.padInfo.right,T=m.padInfo.bottom,M=m.padInfo.left,D=m.dilationHeight,z=m.dilationWidth,W=m.strideHeight,U=m.strideWidth,H=m.inChannels,X=m.padInfo.type==="SAME"?1:0,j=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return Hb(y,j,ee,Y,g,w,x,b,I,S,T,M,X,D,z,W,U,H,_,A,ie,f||0,te),ae}var mX={kernelName:ei,backendName:"wasm",setupFunc:pX,kernelFunc:fX},jb;function AX(e){jb=e.wasm.cwrap(ti,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 yX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=fc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=m.filterHeight,x=m.filterWidth,I=m.padInfo.top,S=m.padInfo.right,T=m.padInfo.bottom,M=m.padInfo.left,D=m.dilationHeight,z=m.dilationWidth,W=m.strideHeight,U=m.strideWidth,H=m.inChannels,X=m.padInfo.type==="SAME"?1:0,j=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return jb(y,j,ee,Y,g,w,x,b,I,S,T,M,X,D,z,W,U,H,_,A,ie,f||0,te),ae}var gX={kernelName:ti,backendName:"wasm",setupFunc:AX,kernelFunc:yX},Gb;function xX(e){Gb=e.wasm.cwrap(Ao,null,["number","number","number","number","number","number","array","number"])}function wX(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=If.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(i===0)return u;let c=a.shape,h=c[c.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return Gb(d,Pn[r.dtype],p,i,h,o,f,m),u}var _X={kernelName:Ao,backendName:"wasm",setupFunc:xX,kernelFunc:wX},qb;function bX(e){qb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function vX(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=wr({inputs:{x:a},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=wr({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],f=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return f;let m=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,_=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return qb(A,Pn[a.dtype],_,m,y,u.batchSize,b,g),t.disposeData(c.dataId),t.disposeData(d.dataId),f.shape=u.outputShape,f}var kX={kernelName:mo,backendName:"wasm",setupFunc:bX,kernelFunc:vX},NX=!1,IX=An(yo,NX,"bool"),SX=!1,TX=An(vs,SX,"bool"),Xb;function CX(e){Xb=e.wasm.cwrap(Ns,null,["number","number","number"])}function EX(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;Xb(a,n,i)}return s}var RX={kernelName:Ns,backendName:"wasm",setupFunc:CX,kernelFunc:EX},FX=!1,MX=An(_o,FX,"bool"),$X=!1,DX=An(bo,$X,"bool"),OX=Sn(Is),zX=!1,PX=An(ko,zX,"bool"),Kb;function LX(e){Kb=e.wasm.cwrap(Ss,null,["number, number, number"])}function WX(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:h,inputWasTransposed:d}=$l(i,a,t);if(d){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let p=l.shape.length;R.assertAxesAreInnerMostDims("max",c,p);let[f,m]=R.computeOutAndReduceShapes(l.shape,c),A=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;Kb(o,A,g)}if(d&&t.disposeData(u.dataId),s){let g=R.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var BX={kernelName:Ss,backendName:"wasm",setupFunc:LX,kernelFunc:WX},VX=!1,UX=An(Ts,VX),Zb;function HX(e){Zb=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jX(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=R.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,y=c.dilationHeight,g=c.dilationWidth,_=c.strideHeight,b=c.strideWidth,w=c.inChannels,x=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let I=r.makeOutput(c.outShape,"float32"),S=r.dataIdMap.get(I.dataId).id;return Zb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,_,b,w,x,S),I}var GX={kernelName:Cs,backendName:"wasm",setupFunc:HX,kernelFunc:jX},Yb;function qX(e){Yb=e.wasm.cwrap(Es,null,["number, number, number"])}function XX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let b=t.dataIdMap.get(c.dataId).id;b!==o&&(u=c,l=b,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,A]=R.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=u;u.dtype!=="float32"&&(g=_p({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let _=t.makeOutput(m,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(_.dataId).id;Yb(l,y,b)}if(p&&t.disposeData(c.dataId),s){let b=R.expandShapeToKeepDim(_.shape,d);_.shape=b}return u.dtype!=="float32"&&t.disposeData(g.dataId),_}var KX={kernelName:Es,backendName:"wasm",setupFunc:qX,kernelFunc:XX},Jb;function ZX(e){Jb=e.wasm.cwrap(Rs,null,["number, number, number"])}function YX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t);if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_)}let f=u.shape.length;R.assertAxesAreInnerMostDims("min",h,f);let[m,A]=R.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;Jb(l,y,_)}if(p&&t.disposeData(c.dataId),s){let _=R.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var JX={kernelName:Rs,backendName:"wasm",setupFunc:ZX,kernelFunc:YX},QX=!1,eK=An(Fs,QX),tK=!0,nK=An(Ms,tK),rK=Sn(Io);function lA(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var Qb;function aK(e){Qb=e.wasm.cwrap(To,"number",["number","number","number","number","number"])}function sK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=r,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,h=Qb(u,c,s,a,i),{pSelectedIndices:d,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=lA(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",d)}var iK={kernelName:To,backendName:"wasm",setupFunc:aK,kernelFunc:sK},e3;function oK(e){e3=e.wasm.cwrap(Co,"number",["number","number","number","number","number","bool"])}function lK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,d=e3(c,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=lA(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var uK={kernelName:Co,backendName:"wasm",setupFunc:oK,kernelFunc:lK},t3;function cK(e){t3=e.wasm.cwrap(Eo,"number",["number","number","number","number","number","number"])}function hK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,d=t3(c,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=lA(t,d);t.wasm._free(A);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[y,g]}var dK={kernelName:Eo,backendName:"wasm",setupFunc:cK,kernelFunc:hK},pK=!1,fK=An(So,pK,"bool"),n3;function mK(e){n3=e.wasm.cwrap($s,null,["number","number","number","number","number"])}function AK(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(a.dataId).id;return n3(c,s,i,o,u),l}var yK={kernelName:$s,backendName:"wasm",setupFunc:mK,kernelFunc:AK};function gK(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var xK={kernelName:Ro,backendName:"wasm",kernelFunc:gK};function wK(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return oA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=oA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=Db({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeData(c.dataId)),u}var _K={kernelName:Fo,backendName:"wasm",kernelFunc:wK},r3;function bK(e){r3=e.wasm.cwrap(Ds,null,["number","array","number","number","array","array","number","number"])}function vK(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(h).buffer);return r3(i,u,t.shape.length,Pn[t.dtype],d,p,a,l),o}var kK={kernelName:Ds,backendName:"wasm",kernelFunc:vK,setupFunc:bK},NK=!1,IK=An(Os,NK),a3;function SK(e){a3=e.wasm.cwrap(zs,null,["number","number","number"])}function TK(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return a3(s,i,l),o}var CK={kernelName:zs,backendName:"wasm",setupFunc:SK,kernelFunc:TK},s3;function EK(e){s3=e.wasm.cwrap(Mo,null,["number","number","number","number"])}function RK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,A]=R.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;s3(l,y,Pn[g.dtype],_)}if(p&&t.disposeData(c.dataId),s){let _=R.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var FK={kernelName:Mo,backendName:"wasm",setupFunc:EK,kernelFunc:RK},MK=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Em(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},$K={kernelName:wu,backendName:"wasm",kernelFunc:MK},DK=!0,OK=An(gs,DK),zK=Sn(Ps),PK=Sn(Ws),i3;function LK(e){i3=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number"])}function WK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,[c,h,d,p]=a.shape,f=[c,l,u,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=_p({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(v.sizeFromShape(a.shape)===0)return g;let _=t.dataIdMap.get(g.dataId).id;return i3(y,c,h,d,p,l,u,s?1:0,i?1:0,_),A!=null&&t.disposeData(A.dataId),g}var BK={kernelName:Ls,backendName:"wasm",setupFunc:LK,kernelFunc:WK},o3;function VK(e){o3=e.wasm.cwrap(Bs,null,["number","array","number","array","number","number"])}function UK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=v.parseAxisParam(s,a.shape);if(a.shape.length===0)return xp({inputs:{x:a},backend:n});let o=n.makeOutput(a.shape,a.dtype),l=n.dataIdMap.get(a.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);o3(l,c,i.length,h,a.shape.length,u);let d=wr({inputs:{x:o},attrs:{shape:a.shape},backend:n});return n.disposeData(o.dataId),d}var HK={kernelName:Bs,backendName:"wasm",kernelFunc:UK,setupFunc:VK},l3;function jK(e){l3=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","number","number","array","number","number"])}function GK(e){let{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=r,l=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(l.dataId).id,[h,d,p,f]=a.shape,[m,A]=R.getImageCenter(o,d,p),y=i===0,g=255,_=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],b=new Uint8Array(new Int32Array(_).buffer);return l3(u,h,d,p,f,s,m,A,b,_.length,c),l}var qK={kernelName:Ko,backendName:"wasm",kernelFunc:GK,setupFunc:jK},XK=Sn(Vs),KK=Sn(Us),u3;function ZK(e){u3=e.wasm.cwrap(Oo,null,["number","number","number","number","number","number","array","number","number"])}function YK(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:h,outputSize:d}=Sf.calculateShapes(s,a,i),p=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),A=t.dataIdMap.get(o.dataId).id;return u3(p,f,Pn[s.dtype],l,u,c,m,d,A),o}var JK={kernelName:Oo,backendName:"wasm",setupFunc:ZK,kernelFunc:YK},c3;function QK(e){c3=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function eZ(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(a.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(u.dataId).id,h=r.shape.length,d=a.shape.length,p=h===0||h>1||d===1?1:v.sizeFromShape(a.shape.slice(1));return c3(i,o,l,p,c),u}var tZ={kernelName:zo,backendName:"wasm",kernelFunc:eZ,setupFunc:QK},h3;function nZ(e){h3=e.wasm.cwrap(js,null,["number","number"])}function rZ(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(a.dataId).id;return v.sizeFromShape(a.shape)===0||h3(r,s),a}var aZ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:nZ,kernelFunc:rZ},sZ=Sn(Hs);function bp(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=cn.parseSliceParams(t,n,r),o=cn.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),c=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=cn.computeFlatOffset(s,c);return t.dtype==="string"?h.stringBytes=l.slice(f,f+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+v.sizeFromShape(i))),u}if(t.dtype==="string"){let f=Yd(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let d=a.typedArrayFromHeap(u),p=t.shape.length;if(p===2)iZ(l,c[0],d,s,i);else if(p===3)oZ(l,c[0],c[1],d,s,i);else if(p===4)lZ(l,c[0],c[1],c[2],d,s,i);else{let f=Yd(l,s,i,t.shape,t.dtype);d.set(f)}return u}function iZ(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let u=i;u<l;u++){let c=u*t+o;n.set(e.subarray(c,c+a[1]),s),s+=a[1]}}function oZ(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],u=a[2],c=o+s[0],h=l+s[1];for(let d=o;d<c;d++)for(let p=l;p<h;p++){let f=d*t+p*n+u;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function lZ(e,t,n,r,a,s,i){let o=0,l=s[0],u=s[1],c=s[2],h=l+i[0],d=u+i[1],p=c+i[2],f=s[3];for(let m=l;m<h;m++)for(let A=u;A<d;A++)for(let y=c;y<p;y++){let g=m*t+A*n+y*r+f;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var uZ={kernelName:Lo,backendName:"wasm",kernelFunc:bp},d3;function cZ(e){d3=e.wasm.cwrap(Xs,null,["number","number","number","number"])}function hZ(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,a=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[r],l=v.sizeFromShape(n.shape)/o;return v.sizeFromShape(s.shape)===0||d3(a,i,o,l),s}var dZ={kernelName:Xs,backendName:"wasm",setupFunc:cZ,kernelFunc:hZ};function pZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),c=a.shape.slice();return l.map(h=>{let d=[...c];d[o]=h;let p=bp({inputs:{x:a},attrs:{begin:u,size:d},backend:r});return u[o]+=h,p})}var fZ={kernelName:Uo,backendName:"wasm",kernelFunc:pZ},mZ=Sn(Gs),AZ=Sn(vu),yZ=!0,gZ=An(Ks,yZ),p3;function xZ(e){p3=e.wasm.cwrap(Ta,null,["number","number","number"])}function wZ(e){let{backend:t,inputs:n,attrs:r}=e,{alpha:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=t.makeOutput(s.shape,s.dtype),l=t.dataIdMap.get(o.dataId).id;return p3(i,a,l),o}var _Z={kernelName:Ta,backendName:"wasm",setupFunc:xZ,kernelFunc:wZ},f3;function bZ(e){f3=e.wasm.cwrap(Ho,null,["number","array","number","array","array","array","array","array","number","number"])}function vZ(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{begin:s,end:i,strides:o}=r;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,p=R.slice_util.maskToAxes(c);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&h!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=a.shape.length-s.length,m=R.slice_util.maskToAxes(h),A=a.shape.slice();m.forEach(M=>{s[M]=0,i[M]=1,A.splice(M,0,1)});let y=wr({inputs:{x:a},attrs:{shape:A},backend:t}),{begin:g,end:_,strides:b}=R.slice_util.getNormalizedAxes(y.shape,p,f,s,i,o,l,u,c);s=g,i=_,o=b;let w=R.slice_util.maskToAxes(d);w.forEach(M=>{i[M]=s[M]+1,o[M]=1});let x=R.slice_util.computeOutShape(s,i,o),I=x.filter((M,D)=>w.indexOf(D)===-1);if(o.every(M=>M===1)){let M=bp({inputs:{x:y},attrs:{begin:s,size:x},backend:t});t.disposeData(y.dataId);let D=wr({inputs:{x:M},attrs:{shape:I},backend:t});return t.disposeData(M.dataId),D}let S=t.makeOutput(I,"float32");if(!I.some(M=>M===0)){let M=t.dataIdMap.get(y.dataId).id,D=new Uint8Array(new Int32Array(v.computeStrides(y.shape)).buffer),z=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),H=new Uint8Array(new Int32Array(I).buffer),X=new Uint8Array(new Int32Array(v.computeStrides(I)).buffer),j=t.dataIdMap.get(S.dataId).id;f3(M,D,y.shape.length,z,W,U,H,X,I.length,j)}t.disposeData(y.dataId);let T=wr({inputs:{x:S},attrs:{shape:I},backend:t});return t.disposeData(S.dataId),T}var kZ={kernelName:Ho,backendName:"wasm",setupFunc:bZ,kernelFunc:vZ},NZ=!0,IZ=An(Zs,NZ),m3;function SZ(e){m3=e.wasm.cwrap(qs,null,["number, number, number"])}function TZ(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,A]=R.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;m3(l,y,_)}if(p&&t.disposeData(c.dataId),s){let _=R.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var CZ={kernelName:qs,backendName:"wasm",setupFunc:SZ,kernelFunc:TZ},EZ=Sn(Ys),A3;function RZ(e){A3=e.wasm.cwrap(Sa,null,["number","array","number","array","number","number"])}function FZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,s=n.dataIdMap.get(a.dataId).id,{reps:i}=r,o=new Array(a.shape.length);for(let d=0;d<o.length;d++)o[d]=a.shape[d]*i[d];let l=new Uint8Array(new Int32Array(a.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),c=n.makeOutput(o,a.dtype),h=n.dataIdMap.get(c.dataId).id;return A3(s,l,a.shape.length,u,o.length,Pn[c.dtype],h),c}var MZ={kernelName:Sa,backendName:"wasm",setupFunc:RZ,kernelFunc:FZ},y3;function $Z(e){y3=e.wasm.cwrap(Go,null,["number","array","number","number","number","bool","number","number"])}var DZ=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{k:a,sorted:s}=n,i=t.dataIdMap.get(r.dataId).id,o=new Uint8Array(new Int32Array(r.shape).buffer),l=r.shape.slice();l[l.length-1]=a;let u=t.makeOutput(l,r.dtype),c=t.dataIdMap.get(u.dataId).id,h=t.makeOutput(l,"int32"),d=t.dataIdMap.get(h.dataId).id;return y3(i,o,r.shape.length,Pn[r.dtype],a,s,c,d),[u,h]},OZ={kernelName:Go,backendName:"wasm",setupFunc:$Z,kernelFunc:DZ};function zZ(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape[s],o=a.shape.length,l=new Array(o-1),u=0;for(let p=0;p<o;p++)p!==s&&(l[u++]=a.shape[p]);let c=new Array(i),h=new Array(o).fill(0),d=a.shape.slice();d[s]=1;for(let p=0;p<c.length;p++)h[s]=p,c[p]=bp({inputs:{x:a},attrs:{begin:h,size:d},backend:n});return c.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var PZ={kernelName:qo,backendName:"wasm",kernelFunc:zZ};function LZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var WZ={kernelName:Xo,backendName:"wasm",kernelFunc:LZ},BZ=[iq,lq,hq,xq,bq,Iq,Sq,Tq,Rq,Fq,Dq,Pq,Lq,Vq,jq,Xq,Yq,Qq,eX,tX,rX,iX,oX,uX,sq,dX,mX,gX,_X,kX,IX,TX,dq,RX,MX,DX,OX,PX,BX,UX,GX,KX,JX,eK,nK,rK,iK,uK,dK,fK,yK,xK,_K,kK,IK,CK,FK,$K,OK,zK,PK,vq,BK,HK,qK,KK,XK,JK,tZ,aZ,sZ,uZ,dZ,fZ,mZ,AZ,gZ,_Z,kZ,IZ,CZ,EZ,MZ,OZ,Aq,PZ,WZ];for(let e of BZ)ni(e);var uA=J();uA.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])));uA.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(uA.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 g3=qi(q8()),VZ='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',UZ=qi(X8()),x3=class extends su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new xh(this,Fr())}write(e,t,n){let r={id:this.dataIdNextNumber++};return this.move(r,e,t,n,1),r}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,r,a){let s=this.dataIdNextNumber++;if(r==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:r,memoryOffset:null,refCount:a});return}let i=v.sizeFromShape(n),o=i*v.bytesPerElement(r),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:r,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){let{memoryOffset:t,dtype:n,shape:r,stringBytes:a}=this.dataIdMap.get(e);if(n==="string")return a;let s=this.wasm.HEAPU8.slice(t,t+v.sizeFromShape(r)*v.bytesPerElement(n));return HZ(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let r;if(n==null)r=this.write(null,e,t);else{let a=this.dataIdNextNumber++;r={id:a},this.dataIdMap.set(r,{id:a,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,s,n)}return{dataId:r,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let r=this.wasm.HEAPU8.buffer,{memoryOffset:a}=this.dataIdMap.get(n),s=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(r,a,s);case"int32":return new Int32Array(r,a,s);case"bool":return new Uint8Array(r,a,s);default:throw new Error(`Unknown dtype ${t}`)}}};function jZ(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance)})})}),{})}function w3(e,t,n){if(vp!=null)return vp;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),mc!=null&&mc[r]!=null?mc[r]:n+r}async function GZ(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let a={};a.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=VZ,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return o.endsWith(".wasm")?w3(e,t,Ac!=null?Ac:l):l+o},cA&&(a.instantiateWasm=jZ(w3(e,t,Ac!=null?Ac:"")));let s=!1;a.onAbort=()=>{s||yc||(yc=!0,r({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&&vp==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+g3.default.toString()],{type:"text/javascript"}),i=(0,g3.default)(a)):i=(0,UZ.default)(a),i.then(o=>{s=!0,yc=!1;let l=null;o.tfjs={init:o.cwrap("init",null,[]),registerTensor:o.cwrap("register_tensor",null,["number","number","number"]),disposeData:o.cwrap("dispose_data",l,["number"]),dispose:o.cwrap("dispose",l,[])},n({wasm:o})})})}function HZ(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 qZ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],vp=null,Ac=null,mc={},yc=!1,cA=!1;function XZ(e,t=!1){if(Mf("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),yc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");vp=e,cA=t}function KZ(e,t=!1){if(yc)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")Ac=e;else{mc=e;let n=qZ.filter(r=>mc[r]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}cA=t}var _3="3.3.0",ZZ=2;sl("wasm",async()=>{let{wasm:e}=await GZ();return new x3(e)},ZZ);Z().prototype.abs=function(){return this.throwIfDisposed(),zt(this)};Z().prototype.acos=function(){return this.throwIfDisposed(),Df(this)};Z().prototype.acosh=function(){return this.throwIfDisposed(),Of(this)};Z().prototype.add=function(e){return this.throwIfDisposed(),se(this,e)};Z().prototype.all=function(e,t){return this.throwIfDisposed(),dd(this,e,t)};Z().prototype.any=function(e,t){return this.throwIfDisposed(),Ou(this,e,t)};Z().prototype.argMax=function(e){return this.throwIfDisposed(),zu(this,e)};Z().prototype.argMin=function(e){return this.throwIfDisposed(),zf(this,e)};Z().prototype.asScalar=function(){return this.throwIfDisposed(),F(this.size===1,()=>"The array must have only 1 element."),G(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.as1D=function(){return this.throwIfDisposed(),G(this,[this.size])};Z().prototype.as2D=function(e,t){return this.throwIfDisposed(),G(this,[e,t])};Z().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),G(this,[e,t,n])};Z().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),G(this,[e,t,n,r])};Z().prototype.as5D=function(e,t,n,r,a){return this.throwIfDisposed(),G(this,[e,t,n,r,a])};Z().prototype.asin=function(){return this.throwIfDisposed(),Pf(this)};Z().prototype.asinh=function(){return this.throwIfDisposed(),Lf(this)};Z().prototype.atan=function(){return this.throwIfDisposed(),Wf(this)};Z().prototype.atan2=function(e){return this.throwIfDisposed(),Bf(this,e)};Z().prototype.atanh=function(){return this.throwIfDisposed(),Vf(this)};Z().prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),Lu(this,e,t,n,r)};Z().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),Wu(this,e,t)};Z().prototype.batchNorm=function(e,t,n,r,a){return this.throwIfDisposed(),li(this,e,t,n,r,a)};Z().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Bu(this,e)};Z().prototype.cast=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.ceil=function(){return this.throwIfDisposed(),Gf(this)};Z().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),vn(this,e,t)};Z().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ue&&(e=[e]),st([this,...e],t)};Z().prototype.conv1d=function(e,t,n,r,a,s){return this.throwIfDisposed(),fd(this,e,t,n,r,a,s)};Z().prototype.conv2dTranspose=function(e,t,n,r,a){return this.throwIfDisposed(),md(this,e,t,n,r,a)};Z().prototype.conv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),ra(this,e,t,n,r,a,s)};Z().prototype.cos=function(){return this.throwIfDisposed(),Vu(this)};Z().prototype.cosh=function(){return this.throwIfDisposed(),Ad(this)};Z().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),yd(this,e,t,n)};Z().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),Kf(this,e,t)};Z().prototype.depthwiseConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),ul(this,e,t,n,r,a,s)};Z().prototype.dilation2d=function(e,t,n,r,a){return this.throwIfDisposed(),Zf(this,e,t,n,r,a)};Z().prototype.divNoNan=function(e){return this.throwIfDisposed(),Yf(this,e)};Z().prototype.div=function(e){return this.throwIfDisposed(),ge(this,e)};Z().prototype.dot=function(e){return this.throwIfDisposed(),Q5(this,e)};Z().prototype.elu=function(){return this.throwIfDisposed(),cl(this)};Z().prototype.equal=function(e){return this.throwIfDisposed(),Da(this,e)};Z().prototype.erf=function(){return this.throwIfDisposed(),Jf(this)};Z().prototype.exp=function(){return this.throwIfDisposed(),qn(this)};Z().prototype.expandDims=function(e){return this.throwIfDisposed(),hn(this,e)};Z().prototype.expm1=function(){return this.throwIfDisposed(),Qf(this)};Z().prototype.fft=function(){return this.throwIfDisposed(),Ju(this)};Z().prototype.flatten=function(){return this.throwIfDisposed(),G(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),hl(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),hd(this,e)};Z().prototype.gather=function(e,t){return this.throwIfDisposed(),ui(this,e,t)};Z().prototype.greaterEqual=function(e){return this.throwIfDisposed(),za(this,e)};Z().prototype.greater=function(e){return this.throwIfDisposed(),rr(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),Al(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),$d(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),ex(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),tx(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),nx(this)};Z().prototype.leakyRelu=function(e){return this.throwIfDisposed(),Hu(this,e)};Z().prototype.lessEqual=function(e){return this.throwIfDisposed(),ci(this,e)};Z().prototype.less=function(e){return this.throwIfDisposed(),xd(this,e)};Z().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),tm(this,e,t,n,r)};Z().prototype.logSigmoid=function(){return this.throwIfDisposed(),sx(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),bd(this,e)};Z().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),am(this,e,t)};Z().prototype.log=function(){return this.throwIfDisposed(),Mn(this)};Z().prototype.log1p=function(){return this.throwIfDisposed(),wd(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),ar(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),ju(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),vd(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),ux(this,e)};Z().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Ge(this,e,t,n)};Z().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),Gu(this,e,t,n,r)};Z().prototype.max=function(e,t){return this.throwIfDisposed(),Xn(this,e,t)};Z().prototype.maximum=function(e){return this.throwIfDisposed(),Dr(this,e)};Z().prototype.mean=function(e,t){return this.throwIfDisposed(),kt(this,e,t)};Z().prototype.min=function(e,t){return this.throwIfDisposed(),pl(this,e,t)};Z().prototype.minimum=function(e){return this.throwIfDisposed(),fl(this,e)};Z().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),im(this,e,t)};Z().prototype.mod=function(e){return this.throwIfDisposed(),om(this,e)};Z().prototype.mul=function(e){return this.throwIfDisposed(),P(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),vt(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Pd(this,e,t,n)};Z().prototype.notEqual=function(e){return this.throwIfDisposed(),di(this,e)};Z().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),nl(this,e,t,n)};Z().prototype.onesLike=function(){return this.throwIfDisposed(),$n(this)};Z().prototype.pad=function(e,t){return this.throwIfDisposed(),aa(this,e,t)};Z().prototype.pool=function(e,t,n,r,a){return this.throwIfDisposed(),dx(this,e,t,n,r,a)};Z().prototype.pow=function(e){return this.throwIfDisposed(),sa(this,e)};Z().prototype.prelu=function(e){return this.throwIfDisposed(),Xu(this,e)};Z().prototype.prod=function(e,t){return this.throwIfDisposed(),Nd(this,e,t)};Z().prototype.reciprocal=function(){return this.throwIfDisposed(),cm(this)};Z().prototype.relu=function(){return this.throwIfDisposed(),zr(this)};Z().prototype.relu6=function(){return this.throwIfDisposed(),Sd(this)};Z().prototype.reshapeAs=function(e){return this.throwIfDisposed(),G(this,e.shape)};Z().prototype.reshape=function(e){return this.throwIfDisposed(),G(this,e)};Z().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),Ex(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),Rx(this,e,t,n)};Z().prototype.reverse=function(e){return this.throwIfDisposed(),Dn(this,e)};Z().prototype.rfft=function(){return this.throwIfDisposed(),Qu(this)};Z().prototype.round=function(){return this.throwIfDisposed(),hm(this)};Z().prototype.rsqrt=function(){return this.throwIfDisposed(),Td(this)};Z().prototype.selu=function(){return this.throwIfDisposed(),Cd(this)};Z().prototype.separableConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),dm(this,e,t,n,r,a,s)};Z().prototype.sigmoid=function(){return this.throwIfDisposed(),Fn(this)};Z().prototype.sign=function(){return this.throwIfDisposed(),pm(this)};Z().prototype.sin=function(){return this.throwIfDisposed(),Ed(this)};Z().prototype.sinh=function(){return this.throwIfDisposed(),Rd(this)};Z().prototype.slice=function(e,t){return this.throwIfDisposed(),Re(this,e,t)};Z().prototype.softmax=function(e){return this.throwIfDisposed(),Yu(this,e)};Z().prototype.softplus=function(){return this.throwIfDisposed(),dl(this)};Z().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),qu(this,e,t)};Z().prototype.split=function(e,t){return this.throwIfDisposed(),Lt(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),en(this)};Z().prototype.square=function(){return this.throwIfDisposed(),lt(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Dd(this,e)};Z().prototype.squeeze=function(e){return this.throwIfDisposed(),Pa(this,e)};Z().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ue?[this,e]:[this,...e];return dn(n,t)};Z().prototype.step=function(e){return this.throwIfDisposed(),yl(this,e)};Z().prototype.stridedSlice=function(e,t,n,r,a,s,i,o){return this.throwIfDisposed(),mm(this,e,t,n,r,a,s,i,o)};Z().prototype.sub=function(e){return this.throwIfDisposed(),ye(this,e)};Z().prototype.sum=function(e,t){return this.throwIfDisposed(),Te(this,e,t)};Z().prototype.tan=function(){return this.throwIfDisposed(),Am(this)};Z().prototype.tanh=function(){return this.throwIfDisposed(),ol(this)};Z().prototype.tile=function(e){return this.throwIfDisposed(),Oa(this,e)};Z().prototype.toBool=function(){return this.throwIfDisposed(),me(this,"bool")};Z().prototype.toFloat=function(){return this.throwIfDisposed(),me(this,"float32")};Z().prototype.toInt=function(){return this.throwIfDisposed(),me(this,"int32")};Z().prototype.topk=function(e,t){return this.throwIfDisposed(),ym(this,e,t)};Z().prototype.transpose=function(e){return this.throwIfDisposed(),at(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),zd(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),gm(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),sr(this,e)};Z().prototype.where=function(e,t){return this.throwIfDisposed(),kn(e,this,t)};Z().prototype.zerosLike=function(){return this.throwIfDisposed(),He(this)};var b3={kernelName:Zi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,yl(me(n,"float32"),-1))}}},YZ={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=lt(me(n,"float32")),a=en(ye(be(1),r));return vt(ge(e,a))}}}},JZ={kernelName:Ji,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=en(ye(lt(me(n,"float32")),1));return ge(e,r)}}}},QZ={kernelName:Na,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=e,i=Pt(n.shape,a);return i.length>0&&(s=Te(s,i)),G(s,n.shape)},b:()=>{let s=e,i=Pt(r.shape,a);return i.length>0&&(s=Te(s,i)),G(s,r.shape)}}}},eY={kernelName:os,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},tY={kernelName:ls,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},nY={kernelName:lu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},rY={kernelName:Qi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,en(ye(be(1),lt(me(n,"float32")))))}}},aY={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=en(se(be(1),lt(me(n,"float32"))));return ge(e,r)}}}},sY={kernelName:ro,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=se(lt(n),lt(r)),i=P(e,ge(r,s)),o=Pt(n.shape,a);return o.length>0&&(i=Te(i,o)),G(i,n.shape)},b:()=>{let s=se(lt(n),lt(r)),i=vt(P(e,ge(n,s))),o=Pt(r.shape,a);return o.length>0&&(i=Te(i,o)),G(i,r.shape)}}}},iY={kernelName:to,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(lt(me(n,"float32")),1))}}},oY={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ye(be(1),lt(me(n,"float32"))))}}};function lY(e,t,n,r,a,s){let i=E(e,"dy","avgPool3dGrad"),o=E(t,"input","avgPool3dGrad"),l=i,u=o,c=!1;o.rank===4&&(c=!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]])),F(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),F(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),s!=null&&F(jt(a),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${a}.`);let h={dy:l,input:u},d={filterSize:n,strides:r,pad:a,dimRoundingMode:s},p=$.runKernel(Ih,h,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var uY=O({avgPool3dGrad_:lY}),cY={kernelName:uu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>uY(e,r,a,s,i,o)}}};function hY(e,t,n,r,a){let s=E(e,"dy","avgPoolGrad"),i=E(t,"input","avgPoolGrad");F(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]])),F(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),F(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let c={dy:l,input:o},h={filterSize:n,strides:r,pad:a},d=$.runKernel(Nh,c,h);return u?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var dY=O({avgPoolGrad_:hY}),pY={kernelName:us,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>dY(e,r,a,s,i)}}},fY={kernelName:cs,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,a]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>Ge(e,a,!1,!0),b:()=>Ge(r,e,!0,!1)}:!s&&i?{a:()=>Ge(e,a,!1,!1),b:()=>Ge(e,r,!0,!1)}:s&&!i?{a:()=>Ge(a,e,!1,!0),b:()=>Ge(r,e,!1,!1)}:{a:()=>Ge(a,e,!0,!0),b:()=>Ge(e,r,!0,!0)}}},mY={kernelName:cu,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>qu(e,r,a)}}},AY={kernelName:qg,gradFunc:(e,t,n)=>{let r=n,a=r.inputShape,s=r.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:()=>Te(e,o,!0)}}},yY={kernelName:hs,gradFunc:e=>({x:()=>e.clone()})},gY={kernelName:ds,gradFunc:e=>({x:()=>He(e)})},xY={kernelName:Ia,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>kn(ar(za(r,a),ci(r,s)),e,He(e))}}},wY={kernelName:hu,inputsToSave:["x"],gradFunc:b3.gradFunc},_Y={kernelName:ao,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=tr(a,t[0].shape)[0],i=r.map(o=>o[s]);return Lt(e,i,s).map(o=>()=>o)}},bY={kernelName:ps,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return F($a(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>qf(r.shape,e,a,i,o,l),filter:()=>bm(r,e,a.shape,i,o,l)}}},vY={kernelName:fs,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{strides:s,pad:i,dataFormat:o,dimRoundingMode:l}=n;return{dy:()=>ra(e,a,s,i,o,1,l),filter:()=>bm(e,r,a.shape,s,i,o,l)}}};function kY(e,t,n,r,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]])),F(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),F(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),F(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),F(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),F(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:r,pad:a,filterShape:n};return $.runKernel(Eh,o,l)}var NY=O({conv3DBackpropFilter_:kY}),IY={kernelName:du,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;F($a(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>Y5(i.shape,e,o,a,s),filter:()=>NY(i,e,o.shape,a,s)}}},SY={kernelName:ms,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(vt(Ed(me(n,"float32"))),e)}}},TY={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Rd(me(n,"float32")),e)}}},CY={kernelName:As,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=lx([a],r.rank),l=yd(e,a,s,!i);return o!=null&&(l=at(l,o)),l}}}},EY={kernelName:ys,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;F($a(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,u]=t;return F(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),F(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),F(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),F(Mr(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${o}'.`),i!=null&&F(jt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>vx(l.shape,e,u,a,s,r,i),filter:()=>bx(l,e,u.shape,a,s,r,i)}}},RY={kernelName:pu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>$.runKernel(Oh,s,n),filter:()=>$.runKernel(zh,i,n)}}},FY={kernelName:lo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>$.runKernel(Ph,r)}}},MY={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(qn(vt(lt(n))),2/Math.sqrt(Math.PI));return{x:()=>P(e,r)}}},$Y={kernelName:xs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,n)}}},DY={kernelName:ho,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>G(e,n.shape)}}},OY={kernelName:po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,qn(n))}}},zY={kernelName:ws,gradFunc:e=>({x:()=>He(e)})},PY={kernelName:_s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=ge(e,me(r,"float32")),i=Pt(n.shape,a);return i.length>0?G(Te(s,i),n.shape):s},b:()=>{let s=P(e,me(n,"float32")),i=Pt(r.shape,a);i.length>0&&(s=G(Te(s,i),r.shape));let o=lt(r);return vt(ge(s,me(o,"float32")))}}}},LY={kernelName:bs,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[a,s,i,o]=t,l=o==null?be(1):o,u=Pt(s.shape,a.shape),c=[];if(s.rank===1){for(let m=0;m<a.shape.length-1;++m)c.push(a.shape[m]);c.push(1)}let h=ye(a,s),d=P(e,l),p=Td(se(i,be(r))),f=P(P(P(p,p),p),be(-.5));return{x:()=>s.rank===1?G(P(P(e,Oa(G(p,[1,1,1,s.shape[0]]),c)),l),a.shape):G(P(P(e,p),l),a.shape),mean:()=>{let m=P(P(p,be(-1)),d);return s.rank===1&&(m=Te(m,u)),G(m,s.shape)},variance:()=>{let m=P(P(f,h),d);return s.rank===1&&(m=Te(m,u)),G(m,s.shape)},scale:()=>{let m=P(h,p),A=P(e,m);return s.rank===1&&(A=Te(A,u)),G(A,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=Te(m,u)),G(m,s.shape)}}}},WY={kernelName:mo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=tr(s,r.shape)[0];return{x:()=>{let o=r.shape,l=a.size,u=o.slice(0,i),c=u.length,h=o.slice(s,o.length).slice(1),d=h.length,p=v3(0,c),f=v3(c+1,c+1+d),m=k3([u,[l],h]),A=G(e,m),y=G(a,[l]),g=k3([[c],p,f]),_=at(A,g),b=gm(_,y,r.shape[i]),w=rm(g);return b=at(b,w),b},indices:()=>a}}};function v3(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function k3(e){let t=[];for(let n=0;n<e.length;++n)for(let r=0;r<e[n].length;++r)t.push(e[n][r]);return t}var BY={kernelName:vs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>He(n),b:()=>He(r)}}},VY={kernelName:ks,gradFunc:e=>({x:()=>me(e,"float32")})},UY={kernelName:go,gradFunc:e=>({x:()=>He(e)})},HY={kernelName:xo,gradFunc:e=>({x:()=>He(e)})},jY={kernelName:wo,gradFunc:e=>({x:()=>He(e)})},GY={kernelName:Ns,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=rr(r,0);return{x:()=>kn(s,e,P(e,a))}}},qY={kernelName:vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(n,1))}}},XY={kernelName:Is,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,me(n,"float32"))}}},KY={kernelName:Xg,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=qn(r);return ye(e,P(Te(e,a,s),i))}}}};function ZY(e,t,n,r=5,a=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:r,bias:a,alpha:s,beta:i};return $.runKernel(Uh,o,l)}var YY=O({localResponseNormalizationBackprop_:ZY}),JY={kernelName:yu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>YY(r,a,e,s,i,o,l)}}};function N3(e,t,n,r){return t.rank<n.rank&&(t=G(t,hi(t.shape,r))),e.rank<n.rank&&(e=G(e,hi(e.shape,r))),{x:()=>P(e,me(Da(n,t),e.dtype))}}var I3={kernelName:Ss,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:a}=r,s=t[0],i=t[1],o=tr(a,s.shape),l=N3(e,i,s,o);return{x:()=>l.x()}}},QY={kernelName:Ts,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,me(za(n,r),"float32")),b:()=>P(e,me(xd(n,r),"float32"))}}};function eJ(e,t,n,r,a,s,i){let o=E(e,"dy","maxPool3dGrad"),l=E(t,"input","maxPool3dGrad"),u=E(n,"output","maxPool3dGrad"),c=o,h=l,d=u,p=!1;l.rank===4&&(p=!0,c=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]]),d=G(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),F(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),F(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),F(d.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`),i!=null&&F(jt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let f={dy:c,input:h,output:d},m={filterSize:r,strides:a,pad:s,dimRoundingMode:i},A=$.runKernel(jh,f,m);return p?G(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var tJ=O({maxPool3dGrad_:eJ}),nJ={kernelName:gu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n;return{x:()=>tJ(e,r,a,s,i,o,l)}}};function rJ(e,t,n,r,a,s,i){let o=E(e,"dy","maxPoolGrad"),l=E(t,"input","maxPoolGrad"),u=E(n,"output","maxPoolGrad");F(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),F(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),F(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&F(jt(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let c={dy:o,input:l,output:u},h={filterSize:r,strides:a,pad:s,dimRoundingMode:i};return $.runKernel(Hh,c,h)}var aJ=O({maxPoolGrad_:rJ}),sJ={kernelName:Cs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>aJ(e,r,a,s,i,o)}}},iJ={kernelName:Es,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=tr(a,r.shape),i=ox(r.shape,s)[1],o=Dt(i);return{x:()=>{let l=r.shape.slice();s.forEach(c=>{l[c]=1});let u=G(e,l);return ge(P(u,Or(r.shape,"float32")),o)}}}},oJ={kernelName:Rs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=tr(a,s.shape),l=N3(e,i,s,o);return{x:()=>l.x()}}},lJ={kernelName:Fs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,me(ci(n,r),"float32")),b:()=>P(e,me(rr(n,r),"float32"))}}},uJ={kernelName:xu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Re(e,s,r.shape)}}},cJ={kernelName:No,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=Pt(n.shape,a);return s.length>0?G(Te(e,s),n.shape):e},b:()=>{let s=P(e,vt(hl(ge(n,r)))),i=Pt(r.shape,a);return i.length>0?G(Te(s,i),r.shape):s}}}},hJ={kernelName:Ms,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=P(e,me(r,"float32")),i=Pt(n.shape,a);return i.length>0?G(Te(s,i),n.shape):s},b:()=>{let s=P(e,me(n,"float32")),i=Pt(r.shape,a);return i.length>0?G(Te(s,i),r.shape):s}}}},dJ={kernelName:Io,gradFunc:e=>({x:()=>vt(e)})},pJ={kernelName:$s,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Et(n.shape,"float32")}}},fJ={kernelName:Ro,gradFunc:e=>({x:()=>He(e)})},mJ={kernelName:Fo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return sr(e,r).map(a=>()=>a)}},S3={kernelName:Ds,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Re(e,s,r.shape)}}},AJ={kernelName:Os,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=yt(s.shape,i.shape);return{a:()=>{let l=me(i,"float32"),u=P(e,P(l,sa(s,ye(l,be(1))))),c=Pt(s.shape,o);return c.length>0&&(u=Te(u,c)),G(u,s.shape)},b:()=>{let l=rr(s,0),u=kn(l,Mn(s),He(s)),c=P(e,P(a,u)),h=Pt(i.shape,o);return h.length>0&&(c=Te(c,h)),G(c,i.shape)}}}},yJ={kernelName:zs,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=rr(n,0);return{x:()=>kn(a,e,P(e,r)),alpha:()=>{let s=kn(a,He(e),P(e,n)),i=Pt(r.shape,e.shape);return i.length>0&&(s=Te(s,i)),G(s,r.shape)}}}},gJ={kernelName:gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=ge(e,me(r,"float32")),i=Pt(n.shape,a);return i.length>0?G(Te(s,i),n.shape):s},b:()=>{let s=P(e,me(n,"float32")),i=Pt(r.shape,a);i.length>0&&(s=G(Te(s,i),r.shape));let o=lt(r);return vt(ge(s,me(o,"float32")))}}}},xJ={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,vt(lt(n)))}}},wJ={kernelName:Ws,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(ci(n,6),yl(n));return{x:()=>P(e,me(r,"float32"))}}},_J={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,me(yl(n),"float32"))}}},bJ={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>G(e,n.shape)}}},vJ={kernelName:Ls,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Zh,a,n)}}},kJ={kernelName:_u,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Kh,a,n)}}},NJ={kernelName:Bs,gradFunc:(e,t,n)=>{let{dims:r}=n,a=tr(r,e.shape);return{x:()=>Dn(e,a)}}},IJ={kernelName:Vs,gradFunc:e=>({x:()=>He(e)})},SJ={kernelName:Us,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>vt(ge(e,P(sa(n,1.5),2)))}}},TJ={kernelName:zo,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>me(He(n),"float32"),t:()=>P(e,me(n,e.dtype)),e:()=>P(e,me(ju(n),e.dtype))}}},CJ={kernelName:Po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=rr(n,be(0)),a=be($x),s=be(Dx),i=P(e,s),o=P(P(e,a),qn(me(n,"float32")));return kn(r,i,o)}}}},EJ={kernelName:js,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(n,ye(be(1),n)))}}},RJ={kernelName:Bo,gradFunc:e=>({x:()=>He(e)})},FJ={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Vu(me(n,"float32")),e)}}},MJ={kernelName:Wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Ad(me(n,"float32")),e)}}},$J={kernelName:Lo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=M5(r,a,s),u=[];for(let c=0;c<e.rank;c++)u.push([o[c],i[c]-o[c]-l[c]]);return{x:()=>aa(e,u)}}},DJ={kernelName:Xs,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=P(e,r);return{logits:()=>ye(i,P(Te(i,[a],s),r))}}},OJ={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,Fn(n))}}},T3={kernelName:bu,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Wu(e,r,a)}}},C3={kernelName:Uo,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>st(e,r)}}},zJ={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,P(en(me(n,"float32")),2))}}},PJ={kernelName:vu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(me(n,"float32"),2))}}},LJ={kernelName:Ks,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=be(2);return{a:()=>P(e,P(a,ye(n,r))),b:()=>P(e,P(a,ye(r,n)))}}},WJ={kernelName:Ta,gradFunc:e=>({x:()=>He(e)})},BJ={kernelName:Zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=e,i=Pt(n.shape,a);return i.length>0&&(s=Te(s,i)),G(s,n.shape)},b:()=>{let s=e,i=Pt(r.shape,a);return i.length>0&&(s=Te(s,i)),G(vt(s),r.shape)}}}},VJ={kernelName:qs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;tr(s,r.shape).forEach(l=>{a[l]=1});let i=G(e,a),o=P(i,Or(r.shape,"float32"));return{x:()=>o}}},UJ={kernelName:jo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,lt(Vu(n)))}}},HJ={kernelName:Ys,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(ye(be(1),lt(n)),e)}}},jJ={kernelName:Sa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:a}=n;return{x:()=>{let s=He(r);if(r.rank===1)for(let i=0;i<a[0];++i)s=se(s,Re(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.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=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.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=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2],u*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${r.rank} tensors yet.`);return s}}}},GJ={kernelName:Js,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=rm(a);return{x:()=>at(e,s)}}},qJ={kernelName:qo,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>dn(e,a)}}},KJ={kernelName:ku,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>XJ(e,n)}}};function XJ(e,t){let n=Dr(t,He(t)),r=ui(e,n),a=za(t,be(0,"int32")),s=r.rank-a.rank;for(let o=0;o<s;++o)a=hn(a,o+1);a=ar(a,Or(r.shape,"bool"));let i=He(r);return kn(a,r,i)}var ZJ={kernelName:Xo,gradFunc:e=>({x:()=>He(e)})},YJ=[b3,YZ,JZ,QZ,eY,tY,nY,rY,aY,sY,iY,oY,cY,pY,fY,mY,AY,yY,gY,xY,wY,_Y,vY,bY,IY,SY,TY,CY,EY,RY,gJ,FY,MY,$Y,DY,OY,PY,zY,LY,WY,BY,VY,UY,HY,jY,GY,qY,XY,KY,JY,I3,I3,QY,nJ,sJ,iJ,oJ,lJ,uJ,cJ,hJ,dJ,pJ,fJ,mJ,S3,S3,AJ,yJ,xJ,wJ,_J,bJ,vJ,kJ,NJ,IJ,SJ,TJ,CJ,EJ,RJ,FJ,MJ,$J,DJ,OJ,T3,T3,C3,C3,zJ,LJ,PJ,WJ,BJ,VJ,UJ,HJ,jJ,GJ,qJ,KJ,ZJ];for(let e of YJ)Kg(e);var E3={};ze(E3,{maxNorm:()=>JJ,minMaxNorm:()=>tQ,nonNeg:()=>eQ,unitNorm:()=>QJ});var hA;function Wt(){return hA==null&&(hA=W5().epsilon()),hA}function _r(){return"channelsLast"}var ua=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ua.prototype)}},br=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,br.prototype)}},V=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,V.prototype)}},De=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,De.prototype)}},R3=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,R3.prototype)}};function vi(e,t){if(Array.isArray(e)){let n=[];for(let r=0;r<t;r++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function Br(e,t){if(!e)throw new R3(t)}function F3(e,t){let n=0;for(let r of e)r===t&&n++;return n}function Tn(e){return e.length===1?e[0]:e}function ft(e){return Array.isArray(e)?e:[e]}function ca(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function ki(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var or={};function dA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function pA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>pA(t));else{let t=Object.keys(e);for(let n of t){let r=e[n];r!=null&&typeof r=="object"&&(!Array.isArray(r)&&r.type==="ndarray"&&typeof r.value=="number"?e[n]=r.value:pA(r))}}}function gc(e,t={},n={},r="object",a=!1){if(typeof e=="string"){let s=e,i;if(s in n)i=n[s];else if(s in or)i=or[s];else if(i=t[s],i==null)throw new V(`Unknown ${r}: ${e}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new V(`${r}: Improper config format: ${JSON.stringify(s)}.
'className' and 'config' must set.`);let i=s.className,o,l;if(i in n?[o,l]=n[i]:i in or?[o,l]=or.className:i in t&&([o,l]=t[i]),o==null)throw new V(`Unknown ${r}: ${i}. This may be due to one of the following reasons:
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let p of Object.keys(or))u[p]=or[p];for(let p of Object.keys(n))u[p]=n[p];let c=s.config;c.customObjects=u;let h=Object.assign({},or);for(let p of Object.keys(n))or[p]=n[p];pA(s.config);let d=l(o,s.config,n,a);return or=Object.assign({},h),d}else{let u=Object.assign({},or);for(let h of Object.keys(n))or[h]=n[h];let c=new o(s.config);return or=Object.assign({},u),c}}}function nQ(e,t){return e<t?-1:e>t?1:0}function kp(e,t){return-1*nQ(e,t)}function Ua(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function rQ(e){if(e==null)throw new V(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Ni(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new V(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function fA(e,t,n=0,r=Infinity){return Br(n>=0),Br(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function Xt(e,t){Array.isArray(e)?(v.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>Xt(n,`element ${r+1} of ${t}`))):v.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${M3(e)}.`)}function M3(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>M3(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function aQ(e,t){let n=v.now(),r;return(...a)=>{let s=v.now();return s-n<t||(n=s,r=e(...a)),r}}function $3(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function mA(e,t){return B(()=>en(Te(P(e,e),t,!0)))}var xc=class extends re.Serializable{getConfig(){return{}}},AA=class extends xc{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 B(()=>{let t=mA(e,this.axis),n=vn(t,0,this.maxValue);return P(e,ge(n,se(Wt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};AA.className="MaxNorm";re.registerClass(AA);var yA=class extends xc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return B(()=>ge(e,se(Wt(),mA(e,this.axis))))}getConfig(){return{axis:this.axis}}};yA.className="UnitNorm";re.registerClass(yA);var gA=class extends xc{apply(e){return zr(e)}};gA.className="NonNeg";re.registerClass(gA);var xA=class extends xc{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 B(()=>{let t=mA(e,this.axis),n=se(P(this.rate,vn(t,this.minValue,this.maxValue)),P(1-this.rate,t));return P(e,ge(n,se(Wt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};xA.className="MinMaxNorm";re.registerClass(xA);var D3={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Bt(e){return dA(e)}function O3(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Vt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in D3?D3[e]:e,config:{}};return O3(t)}else return e instanceof xc?e:O3(e)}function JJ(e){return new AA(e)}function QJ(e){return new yA(e)}function eQ(){return new gA}function tQ(e){return new xA(e)}var z3={};ze(z3,{constant:()=>oQ,glorotNormal:()=>fQ,glorotUniform:()=>pQ,heNormal:()=>mQ,heUniform:()=>AQ,identity:()=>hQ,leCunNormal:()=>yQ,leCunUniform:()=>gQ,ones:()=>iQ,orthogonal:()=>xQ,randomNormal:()=>uQ,randomUniform:()=>lQ,truncatedNormal:()=>cQ,varianceScaling:()=>dQ,zeros:()=>sQ});var wQ=["channelsFirst","channelsLast"],_Q=["nearest","bilinear"],bQ=["valid","same","causal"],vQ=["max","avg"],kQ=["sum","mul","concat","ave"],Dl=new Map;function Tt(e){Ni(wQ,"DataFormat",e)}function NQ(e){Ni(_Q,"InterpolationFormat",e)}function Yn(e){Ni(bQ,"PaddingMode",e)}function P3(e){Ni(vQ,"PoolMode",e)}var wc=[],L3="/";function Ii(e,t){wc.push(e);try{let n=t();return wc.pop(),n}catch(n){throw wc.pop(),n}}function IQ(){return wc.length===0?"":wc.join(L3)+L3}function B3(e){if(!W3(e))throw new Error("Not a valid tensor name: '"+e+"'");return IQ()+e}function V3(e){if(!W3(e))throw new Error("Not a valid tensor name: '"+e+"'");Dl.has(e)||Dl.set(e,0);let t=Dl.get(e);if(Dl.set(e,Dl.get(e)+1),t>0){let n=`${e}_${t}`;return Dl.set(n,1),n}else return e}var SQ=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function W3(e){return!!e.match(SQ)}function TQ(e){return e===parseInt(e.toString(),10)}function Ha(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let r=1;for(let a=t;a<n;++a)r*=e[a];return r}function U3(e){return e=Array.isArray(e)?new Float32Array(e):e,on(e)}function Ol(e){return pl(U3(e)).dataSync()[0]}function ja(e){return Xn(U3(e)).dataSync()[0]}function vr(e,t){if(t<e)throw new V(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let r=e;r<t;++r)n.push(r);return n}function _c(e,t){return e.asType(t)}function bc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function CQ(e,t){return B(()=>{if(e.shape.length!==2)throw new V(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=bc(e,1);return wA(n,[1,t,1])})}function EQ(e){let t=[Ha(e.shape)];return e.reshape(t)}function RQ(e){if(e.rank<=1)throw new V(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Ha(e.shape,1)];return e.reshape(t)}function Si(e,t,n){return B(()=>{switch(e.rank){case 1:return Fd(e,t,n);case 2:return fm(e,[t,0],[n,e.shape[1]]);case 3:return Md(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Zu(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Re(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Re(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new V(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function _A(e,t,n){return B(()=>{switch(e.rank){case 1:return Fd(e,t,n);case 2:return fm(e,[0,t],[e.shape[0],n]);case 3:return Md(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Zu(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new V(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Np(e,t,n,r){return B(()=>{switch(e.rank){case 1:return Fd(e,t,n);case 2:switch(r){case 1:return Si(e,t,n);case 2:return _A(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return Si(e,t,n);case 2:return Md(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return _A(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return Si(e,t,n);case 2:return Zu(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Zu(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return _A(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${r}`)}default:throw new V(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function bA(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),st(e,t)}function H3(e,t){switch(e.rank){case 1:return X5([e,t]);case 2:return ll([e,t],0);case 3:return K5([e,t],0);case 4:return Z5([e,t],0);default:throw new V(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function wA(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new V(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Oa(e,t)}function Ip(e,t=0,n=1,r,a){return px(e,t,n,r,a)}function Vr(e,t,n,r){if(e.rank<2||t.rank<2)throw new De(`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 De(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let a=!1,s=!1;return La.matMul({a:e,b:t,transposeA:a,transposeB:s,bias:r?vA(e.rank,r,_r()):null,activation:n})}else{let a=e.shape.slice(),s=a.pop();e=e.reshape([-1,s]);let i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],c=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=t.transpose(c).reshape([l,-1]);let h=[...a,...u],d=!1,p=!1;return La.matMul({a:e,b:t,transposeA:d,transposeB:p,bias:r?vA(e.rank,r,_r()):null,activation:n}).reshape(h)}}function j3(e,t,n){return B(()=>(Array.isArray(t)?t=on(t,"int32"):t=t.toInt(),ui(e,t,n)))}function vc(e){return P(e,e)}function vA(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new V(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1,1]):t.reshape([1,r[3],r[0],r[1],r[2]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===4){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1]):t.reshape([1,r[2],r[0],r[1]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===3){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1]):t.reshape([1,r[1],r[0]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,r[0]]):t.reshape([1].concat(r))}else if(e<3)return t;throw new V(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ur(e,t,n){return B(()=>(n==null&&(n=_r()),Tt(n),e.add(vA(e.rank,t,n))))}function FQ(e,t=1){if(t!==1)throw new De(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return cl(e)}function MQ(e){return B(()=>ge(e,zt(e).add(1)))}function G3(e,t,n,r){return B(()=>wx(e,t,n,r))}function $Q(e){return B(()=>{let t=se(.5,P(.2,e));return vn(t,0,1)})}function kc(e,t,n=!1){return n?e():t()}var DQ=["fanIn","fanOut","fanAvg"],OQ=["normal","uniform","truncatedNormal"];function zQ(e){Ni(DQ,"FanMode",e)}function PQ(e){Ni(OQ,"Distribution",e)}var lr=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},kA=class extends lr{apply(e,t){return Et(e,t)}};kA.className="Zeros";re.registerClass(kA);var Sp=class extends lr{apply(e,t){return Or(e,t)}};Sp.className="Ones";re.registerClass(Sp);var NA=class extends lr{constructor(e){super();if(typeof e!="object")throw new V(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new V(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return B(()=>P(be(this.value),Or(e,t)))}getConfig(){return{value:this.value}}};NA.className="Constant";re.registerClass(NA);var IA=class extends lr{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 ml(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};IA.className="RandomUniform";re.registerClass(IA);var SA=class extends lr{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 De(`randomNormal does not support dType ${t}.`);return Ip(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};SA.className="RandomNormal";re.registerClass(SA);var TA=class extends lr{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 De(`truncatedNormal does not support dType ${t}.`);return Od(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TA.className="TruncatedNormal";re.registerClass(TA);var CA=class extends lr{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return B(()=>{if(e.length!==2||e[0]!==e[1])throw new V("Identity matrix initializer can only be used for 2D square matrices.");return P(this.gain,em(e[0]))})}getConfig(){return{gain:this.gain}}};CA.className="Identity";re.registerClass(CA);function LQ(e,t="channelsLast"){let n,r;if(Tt(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Ha(e,2);n=e[1]*a,r=e[0]*a}else if(t==="channelsLast"){let a=Ha(e,0,e.length-2);n=e[e.length-2]*a,r=e[e.length-1]*a}}else{let a=Ha(e);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}var Cn=class extends lr{constructor(e){super();if(e.scale<0)throw new V(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,zQ(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,PQ(this.distribution),this.seed=e.seed}apply(e,t){let n=LQ(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`${this.getClassName()} does not support dType ${t}.`);return Od(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return ml(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Cn.className="VarianceScaling";re.registerClass(Cn);var Tp=class extends Cn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Tp.className="GlorotUniform";re.registerClass(Tp);var Cp=class extends Cn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Cp.className="GlorotNormal";re.registerClass(Cp);var Ep=class extends Cn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Ep.className="HeNormal";re.registerClass(Ep);var Rp=class extends Cn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Rp.className="HeUniform";re.registerClass(Rp);var Fp=class extends Cn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Fp.className="LeCunNormal";re.registerClass(Fp);var Mp=class extends Cn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Mp.className="LeCunNormal";re.registerClass(Mp);var EA=class extends lr{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new De("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return B(()=>{if(e.length<2)throw new De("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=Ip(n,0,1,"float32"),a=Mx.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),P(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};EA.className="Orthogonal";re.registerClass(EA);var q3={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 X3(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function Nt(e){return dA(e)}function xt(e){if(typeof e=="string"){let t=e in q3?q3[e]:e;if(t==="GlorotNormal")return new Cp;if(t==="GlorotUniform")return new Tp;if(t==="HeNormal")return new Ep;if(t==="HeUniform")return new Rp;if(t==="LeCunNormal")return new Fp;if(t==="LeCunUniform")return new Mp;{let n={};return n.className=t,n.config={},X3(n)}}else return e instanceof lr?e:X3(e)}function sQ(){return new kA}function iQ(){return new Sp}function oQ(e){return new NA(e)}function lQ(e){return new IA(e)}function uQ(e){return new SA(e)}function cQ(e){return new TA(e)}function hQ(e){return new CA(e)}function dQ(e){return new Cn(e)}function pQ(e){return new Tp(e)}function fQ(e){return new Cp(e)}function mQ(e){return new Ep(e)}function AQ(e){return new Rp(e)}function yQ(e){return new Fp(e)}function gQ(e){return new Mp(e)}function xQ(e){return new EA(e)}var K3={};ze(K3,{Layer:()=>qe,RNN:()=>Hr,RNNCell:()=>Nc,activation:()=>tee,add:()=>cee,alphaDropout:()=>qee,average:()=>hee,averagePooling1d:()=>RA,averagePooling2d:()=>FA,averagePooling3d:()=>MA,avgPool1d:()=>wee,avgPool2d:()=>bee,avgPool3d:()=>kee,avgPooling1d:()=>_ee,avgPooling2d:()=>vee,avgPooling3d:()=>Nee,batchNormalization:()=>yee,bidirectional:()=>Lee,concatenate:()=>dee,conv1d:()=>qQ,conv2d:()=>XQ,conv2dTranspose:()=>KQ,conv3d:()=>ZQ,convLstm2d:()=>Dee,convLstm2dCell:()=>Oee,cropping2D:()=>JQ,dense:()=>nee,depthwiseConv2d:()=>eee,dot:()=>Aee,dropout:()=>ree,elu:()=>BQ,embedding:()=>uee,flatten:()=>see,gaussianDropout:()=>Gee,gaussianNoise:()=>jee,globalAveragePooling1d:()=>Iee,globalAveragePooling2d:()=>See,globalMaxPool1d:()=>Bee,globalMaxPool2d:()=>Vee,globalMaxPooling1d:()=>Y3,globalMaxPooling2d:()=>J3,gru:()=>Cee,gruCell:()=>Eee,input:()=>Z3,inputLayer:()=>WQ,layerNormalization:()=>gee,leakyReLU:()=>UQ,lstm:()=>Ree,lstmCell:()=>Fee,masking:()=>Xee,maxPool1d:()=>Uee,maxPool2d:()=>Hee,maxPooling1d:()=>Q3,maxPooling2d:()=>e7,maxPooling3d:()=>Tee,maximum:()=>pee,minimum:()=>fee,multiply:()=>mee,permute:()=>lee,prelu:()=>HQ,reLU:()=>VQ,repeatVector:()=>iee,reshape:()=>oee,rnn:()=>zee,separableConv2d:()=>YQ,simpleRNN:()=>Mee,simpleRNNCell:()=>$ee,softmax:()=>jQ,spatialDropout1d:()=>aee,stackedRNNCells:()=>Pee,thresholdedReLU:()=>GQ,timeDistributed:()=>Wee,upSampling2d:()=>QQ,zeroPadding2d:()=>xee});var Kee=0;function t7(){return Kee++}var $p={};function Dp(e=""){return e in $p||($p[e]=0),$p[e]+=1,e+$p[e].toString()}function $A(e){return Array.isArray(e)&&Array.isArray(e[0])}function Op(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Pe(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new V(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ct(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new V(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function zp(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((r,a)=>r*a);return t}var n7="Variable",r7=class{constructor(e,t="float32",n=n7,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=t7(),n=n==null?n7:n,this.originalName=B3(n),this.name=V3(this.originalName),this.trainable_=r,this.constraint=a,this.val=mx(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Zee(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 Zee(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function DA(e){return e.map(t=>t.read())}function OA(e){e.forEach(t=>{t[0].write(t[1])})}var Kt=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||{}}},kr=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=t7(),s!=null&&(this.originalName=B3(s),this.name=V3(this.originalName)),this.rank=t.length}},Yee=0,Pp=class{constructor(e,t){this.callArgs=t,this.id=Yee++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},Jee=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Jee++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=ca(n)+"_"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),n=[a].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}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 br(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new V(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Tn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Tn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ua(`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 ua(`Layer ${this.name} is not connected, no input to return.`);return Tn(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new ua(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new ua(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return Tn(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=ft(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=ft(this.inputSpec);if(e.length!==t.length)throw new V(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],a=t[n];if(a==null)continue;let s=r.rank;if(a.ndim!=null&&s!==a.ndim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&r.dtype!==a.dtype)throw new V(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${r.dtype}.`);if(a.axes){let i=r.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],c=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new V(`Input ${n} 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=r.shape[i];if(o!=null&&l!=null&&o!==l)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ft(e),r=!0;for(let s of n)if(!(s instanceof kr)){r=!1;break}let a=!0;for(let s of n)if(s instanceof kr){a=!1;break}if(r===a)throw new V("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ii(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of ft(e))s.push(i.shape);this.build(Tn(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=ft(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Tn(o),this.activityRegularizer!=null)throw new De("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=Qee(e),i=this.computeOutputShape(s),o,l=ete(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,c)=>new kr(l,u,this,ft(e),t,this.name,c)):o=new kr(l,i,this,ft(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new De("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ua(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ua(`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 br(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return zp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return DA(e?this.trainableWeights:this.weights)}setWeights(e){B(()=>{let t=this.weights;if(t.length!==e.length)throw new V(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],r=DA(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!v.arraysEqual(s.shape,o.shape))throw new V(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}OA(n)})}addWeight(e,t,n,r,a,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new V(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=xt("zeros"));let o=r.apply(t,n),l=new r7(o,n,e,s,i);return o.dispose(),a!=null&&this.addLoss(()=>a.apply(l.read())),s==null&&(s=!0),s?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=ft(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=ft(e);t=ft(t),n=ft(n),r=ft(r),a=Op(a),s=Op(s);let l=[],u=[],c=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),c.push(h.tensorIndex);new Pp({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Qee(e){e=ft(e);let t=[];for(let n of e)t.push(n.shape);return Tn(t)}function ete(e){return"float32"}function a7(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let r=t.inboundNodes[n];if(r.inboundLayers.length===0)return r.inputTensors;{let a=[];for(let s=0;s<r.inboundLayers.length;s++){let i=r.inputTensors[s],o=r.inboundLayers[s],l=r.nodeIndices[s],u=a7(i,o,l);for(let c of u)a.indexOf(c)===-1&&a.push(c)}return a}}}var zl=class extends qe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Dp("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 V("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 V("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new V("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let r=new kr(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new V(`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}}};zl.className="InputLayer";re.registerClass(zl);function s7(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 V("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new zl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Ga(e){if(e==null)return;let t=[],n=[],r=[];for(let a in e){let s=e[a];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(a),r.push(i)}}if(t.length>0){let a=await Promise.all(t);for(let s=0;s<a.length;++s)e[n[s]]=a[s][0];Ee(r)}}function i7(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var o7;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(o7||(o7={}));var tte=125,Pl=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){}},l7=class{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(let t of this.callbacks)t.setParams(e)}setModel(e){for(let t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainEnd(e)}},nte=class extends Pl{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let r in t){let a=t[r];if(typeof a=="number")this.totals.hasOwnProperty(r)||(this.totals[r]=0),this.totals[r]=this.totals[r]+a*n;else{let s;r in this.totals?s=this.totals[r]:this.totals[r]=0;let i=B(()=>se(this.totals[r],P(a,n)));this.totals[r]=i,s!=null&&s.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:B(()=>{let r=P(ge(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),Gt(t[n])}))}},u7=class extends Pl{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let 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),n.push(i)}}let r=await Promise.all(e);for(let a=0;a<r.length;++a)this.history[t[a]][n[a]].dispose(),this.history[t[a]][n[a]]=r[a][0]}},c7=class extends Pl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=tte),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");v.isNumber(this.yieldEvery)&&(this.maybeWait=aQ(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let r=[];this.yield!=null&&(await Ga(n),r.push(this.yield(e,t,n))),r.push(Kd()),await Promise.all(r)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Ga(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Ga(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(Kd()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Ga(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Ga(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(Kd()):v.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Ga(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Ga(e),await this.trainEnd(e))}};function h7(e,t){return e==null&&(e={}),e instanceof Pl?[e]:Array.isArray(e)&&e[0]instanceof Pl?e:ft(e).map(n=>new c7(n,t))}var ur=class{constructor(){}static registerCallbackConstructor(e,t){v.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),ur.checkForDuplicate(t),ur.constructors[e]==null&&(ur.constructors[e]=[]),ur.constructors[e].push(t)}static checkForDuplicate(e){for(let t in ur.constructors)ur.constructors[+t].forEach(n=>{if(n===e)throw new V("Duplicate callback constructor.")})}static clear(){ur.constructors={}}static createCallbacks(e){let t=[];for(let n in ur.constructors){let r=+n;e>=r&&t.push(...ur.constructors[r])}return t.map(n=>new n)}};ur.constructors={};function d7(e,t,n,r,a,s,i,o,l){let u=new u7,c=[new nte,...ur.createCallbacks(t)];e!=null&&c.push(...e),c.push(u);let h=new l7(c);return h.setParams({epochs:n,initialEpoch:r,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:u}}function Nr(e,t={},n=!1){return gc(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function Lp(e,t){return B(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Te(vc(e),t,!0),r=Uu(n.shape,Wt()),a=en(Dr(n,r));return ge(e,a)})}function Ti(e,t){return B(()=>kt(vc(ye(t,e)),-1))}function Wp(e,t){return B(()=>kt(zt(ye(t,e)),-1))}function Ll(e,t){return B(()=>{let n=ye(e,t),r=vn(zt(e),Wt(),Number.MAX_VALUE),a=zt(ge(n,r));return P(100,kt(a,-1))})}function rte(e,t){return B(()=>{let n=vn(t,Wt(),Number.MAX_VALUE),r=Mn(se(1,n)),a=vn(e,Wt(),Number.MAX_VALUE),s=Mn(se(1,a));return kt(vc(ye(r,s)),-1)})}function ate(e,t){return B(()=>{let n=Dr(0,ye(1,P(e,t)));return kt(vc(n),-1)})}function ste(e,t){return B(()=>{let n=Dr(0,ye(1,P(e,t)));return kt(n,-1)})}function ite(e,t){return B(()=>{let n=Te(P(e,t),-1),r=Xn(P(ye(1,e),t),-1);return Dr(0,se(1,ye(r,n)))})}function ote(e,t){return B(()=>{let n=Math.log(2),r=ye(t,e),a=ye(se(r,dl(P(-2,r))),n);return kt(a,-1)})}function Ic(e,t,n=!1){return B(()=>{if(n)t=Yu(t);else{let r=Te(t,t.shape.length-1,!0);t=ge(t,r)}return t=vn(t,Wt(),1-Wt()),vt(Te(P(e.toFloat(),Mn(t)),t.shape.length-1))})}function Bp(e,t,n=!1){return B(()=>{let r=hl(EQ(e)).toInt();t=vn(t,Wt(),1-Wt());let a=t.shape,s=nl(r,a[a.length-1]).reshape(a);return Ic(s,t,n)})}function lte(e,t){if(!v.arraysEqual(e.shape,t.shape))throw new V(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return B(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function Vp(e,t){return B(()=>{let n;return n=vn(t,Wt(),1-Wt()),n=Mn(ge(n,ye(1,n))),kt(lte(e,n),-1)})}function ute(e,t){return B(()=>{let n=vn(e,Wt(),1),r=vn(t,Wt(),1);return Te(P(e,Mn(ge(n,r))),-1)})}function cte(e,t){return B(()=>{let n=Mn(se(Wt(),t));return kt(ye(t,P(e,n)),-1)})}function zA(e,t){return B(()=>{let n=Lp(e,-1),r=Lp(t,-1),a=P(n,r);return vt(Te(a,-1))})}var Up={meanSquaredError:Ti,meanAbsoluteError:Wp,meanAbsolutePercentageError:Ll,meanSquaredLogarithmicError:rte,squaredHinge:ate,hinge:ste,categoricalHinge:ite,logcosh:ote,categoricalCrossentropy:Ic,sparseCategoricalCrossentropy:Bp,binaryCrossentropy:Vp,kullbackLeiblerDivergence:ute,poisson:cte,cosineProximity:zA};function PA(e){if(typeof e=="string"){if(e in Up)return Up[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new V(t)}else return e}function LA(e,t){return B(()=>{let n=P(.5,$n(t)),r=_c(rr(t,n),e.dtype);return kt(Da(e,r),-1)})}function WA(e,t){return B(()=>_c(Da(zu(e,-1),zu(t,-1)),"float32"))}function p7(e,t){return B(()=>ar(e.equal(1),t.equal(1)).sum().cast("float32"))}function hte(e,t){return B(()=>ar(e.equal(1),t.equal(0)).sum().cast("float32"))}function dte(e,t){return B(()=>ar(e.equal(0),t.equal(1)).sum().cast("float32"))}function f7(e,t){return B(()=>{let n=p7(e,t),r=dte(e,t),a=n.add(r);return kn(rr(a,0),n.div(a),0).cast("float32")})}function pte(e,t){return B(()=>{let n=p7(e,t),r=hte(e,t),a=n.add(r);return kn(rr(a,0),n.div(a),0).cast("float32")})}function m7(e,t){return Vp(e,t)}function A7(e,t){return e.rank===t.rank&&(e=e.squeeze([e.rank-1])),t=t.argMax(-1),t.dtype!==e.dtype&&(t=t.asType(e.dtype)),Da(e,t).asType("float32")}var fte=Ti,mte=Ti,Ate=Wp,yte=Wp,gte=Ll,xte=Ll,BA=Ic,wte=zA,y7=Bp,Hp={binaryAccuracy:LA,categoricalAccuracy:WA,precision:f7,categoricalCrossentropy:BA,sparseCategoricalCrossentropy:y7,mse:fte,MSE:mte,mae:Ate,MAE:yte,mape:gte,MAPE:xte,cosine:wte};function _te(e){if(typeof e=="string"&&e in Hp)return Hp[e];if(typeof e!="string"&&e!=null)return e;throw new V(`Unknown metric ${e}`)}function jp(e){if(Br(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Up))if(Up[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Hp))if(Hp[n]===e){t=n;break}return t!==void 0?t:e.name}}function bte(e){let t={Adagrad:()=>fi.adagrad(.01),Adadelta:()=>fi.adadelta(1,.95,Wt()),Adam:()=>fi.adam(.001,.9,.999,Wt()),Adamax:()=>fi.adamax(.002,.9,.999,Wt(),0),RMSProp:()=>fi.rmsprop(.001,.9,0,Wt()),SGD:()=>fi.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new V(`Unknown Optimizer ${e}`)}var g7=1*1024*1024;function x7(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!VA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>g7&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${r.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${g7}.`)}}function VA(e){if(e===null)return!0;if(typeof e=="object")if(Object.getPrototypeOf(e)===Object.prototype){let t=Object.keys(e);for(let n of t)if(typeof n!="string"||!VA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!VA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function Ste(e,t,n,r=console.log){let a=kte(e),s=["Layer (type)","Output shape","Param #"];a?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(c=>Math.floor(t*c)));let i;if(!a){s.push("Receives inputs"),i=[];for(let c in e.nodesByDepth)i.push(...e.nodesByDepth[c])}r("_".repeat(t)),Gp(s,n,r),r("=".repeat(t));let o=e.layers;for(let c=0;c<o.length;++c)a?Nte(o[c],n,r):Ite(o[c],n,i,r),r((c===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=vte(e),u=zp(e.nonTrainableWeights);r(`Total params: ${l+u}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${u}`),r("_".repeat(t))}function vte(e){let t;return e.collectedTrainableWeights!=null?t=zp(e.collectedTrainableWeights):t=zp(e.trainableWeights),t}function kte(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function Gp(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function Nte(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];Gp(i,t,n)}function Ite(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(c){a="multiple"}let s=[];for(let c of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(c)===-1))for(let h=0;h<c.inboundLayers.length;++h){let d=c.inboundLayers[h].name,p=c.nodeIndices[h],f=c.tensorIndices[h];s.push(`${d}[${p}][${f}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],u=[`${i} (${o})`,a,e.countParams().toString(),l];Gp(u,t,r);for(let c=1;c<s.length;++c)Gp(["","","",s[c]],t,r)}function w7(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Sc(e,t){if(e===null)return null;if(typeof e=="string")return ki(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let a=0;a<r;++a){let s=e[a];w7(t,a,s)?n.push(s):n.push(Sc(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r];if(r==="name"&&typeof a=="string")n[r]=a;else{let s=ki(r);n[s]=Sc(a,s)}}return n}}function UA(e,t){if(e==null)return null;if(typeof e=="string")return ca(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],r=e.length;for(let a=0;a<r;++a){let s=e[a];w7(t,a,s)?n.push(s):n.push(UA(s,t))}return n}else{let n={};for(let r of Object.keys(e)){let a=e[r],s=ca(r);(r==="name"||r==="className")&&typeof a=="string"?n[s]=a:n[s]=UA(a,r)}return n}}var HA="3.3.0";function Tte(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return me(t,e.dtype)}catch(n){throw new V(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Ci=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Ci)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=Tte(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new V(`Duplicate key: name=${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 kr){if(this.id2Value[e.id]==null)throw new V(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new V(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof kr){if(this.id2Value[e.id]==null)throw new V(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new V(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Ee(this.id2Mask)}},jA={},_7={};function Tc(e,t,n,r){let a=n==null?!1:n.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);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let c=o.join(",")+"|"+t.names().join(","),h,d;if(jA[c]==null){let f=Cte(i,t);h=f.sorted,d=f.recipientCounts,jA[c]=h,_7[c]=d}h=jA[c],d={},a||Object.assign(d,_7[c]);let p=new Ci(t);for(let f=0;f<h.length;++f){if(r!=null){let T=cd().numTensors;T>r.maxNumTensors&&(r.maxNumTensors=T),T<r.minNumTensors&&(r.minNumTensors=T)}let m=h[f],A=m.sourceLayer;if(A instanceof zl)continue;let y=[],g=[],_=[],b=!1;for(let T of m.inputs){let M=p.getValue(T),D=p.getMask(T);y.push(M),g.push(D),D!=null&&(b=!0),a||(d[T.name]--,d[T.name]===0&&!t.hasKey(T)&&o.indexOf(T.name)===-1&&!M.isDisposed&&T.sourceLayer.stateful!==!0&&_.push(M))}b&&(n=n||{},n.mask=g[0]);let w=ft(A.apply(y,n)),x=null;A.supportsMasking&&(x=A.computeMask(y,g));let I=Ete(m),S=Array.isArray(I)?I:[I];for(let T=0;T<S.length;++T){p.hasKey(S[T])||p.add(S[T],w[T],Array.isArray(x)?x[0]:x);let M=o.indexOf(S[T].name);M!==-1&&(l[M]=w[T])}a||Ee(_)}return p.disposeMasks(),s?l:l[0]}function Cte(e,t){v.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=b7(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=b7(s,t);for(let l of i)a.has(l.name)||(n.push(l),a.add(l.name));for(let l in o)r[l]==null&&(r[l]=new Set),o[l].forEach(u=>r[l].add(u))}}return{sorted:n,recipientCounts:Rte(r)}}function Rte(e){let t={};for(let n in e)t[n]=e[n].size;return t}function b7(e,t){let n=new Set,r=[],a={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),r.push(o),n.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),!n.has(u.name)&&s.push(u)}}return{sorted:r,recipientMap:a}}function Ete(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let r=0;r<e.sourceLayer.inboundNodes.length;++r)for(let a of e.sourceLayer.inboundNodes[r].outputTensors)if(a.id===e.id){n=r;break}t=e.sourceLayer.getOutputAt(n)}return t}var jr=class extends qe{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Dp(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],Ua(this.inputs).length!==this.inputs.length)throw new V(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Ua(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 g=y.sourceLayer,_=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let g=y.sourceLayer,_=y.nodeIndex,b=y.tensorIndex;Br(_===0,"input layer has >1 nodes"),Br(b===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof zl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,_,b,w,x)=>{(b==null||w==null||x==null)&&(b=y.sourceLayer,w=y.nodeIndex,x=y.tensorIndex);let I=b.inboundNodes[w];if(_.indexOf(I)!==-1)throw new br(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(g.indexOf(I)!==-1)return;this.containerNodes.add(jr.nodeKey(b,w)),b.id in s||(s[b.id]=Object.keys(s).length),_.indexOf(I)===-1&&_.push(I);let S=I.inboundLayers.length;for(let T=0;T<S;T++){let M=I.inputTensors[T],D=I.inboundLayers[T],z=I.nodeIndices[T],W=I.tensorIndices[T];o(M,g,_,D,z,W)}for(g.push(I);_.indexOf(I)>=0;)_.splice(_.indexOf(I),1);i.push(I)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let c=i.slice().reverse();for(let y of c){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],_=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,_),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],x=y.nodeIndices[b],I=w.inboundNodes[x],S=t[I.id]==null?0:t[I.id];t[I.id]=Math.max(g+1,S),n[I.id]=I}}let h={};for(let y in t){let g=t[y];g in h||(h[g]=[]),h[g].push(n[y])}let d={};for(let y in r){let g=r[y];g in d||(d[g]=[]),d[g].push(a[y])}let p=Object.keys(d).map(y=>parseInt(y,10)).sort(kp);this.layers=[];for(let y of p){let g=d[y];g.sort((_,b)=>{let w=s[_.id],x=s[b.id];return w<x?-1:w>x?1:0});for(let _ of g)_ instanceof jr&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(kp);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let _=g.outboundLayer;if(_!=null){for(let b of g.inputTensors)if(f.indexOf(b)===-1)throw new br(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${_.name}". The following previous layers were accessed without issue: ${m}`);for(let b of g.outputTensors)f.push(b);m.push(_.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(_=>_===y).length;if(g!==1)throw new br(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new V("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new V(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)a.push([n[i],e[s]]);else if(t)throw new V(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new V(`${s.length} of ${r} weights are not set: ${s}`)}OA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${HA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=UA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return B(()=>{e=ft(e);let n=new Ci;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Tc(this.outputs,n,t)})}computeMask(e,t){return B(()=>{e=ft(e);let n;return t==null?n=vi(null,e.length):n=ft(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Op(e);if(t.length!==this.inputLayers.length)throw new V(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(kp);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,_=n[g];c.push(_)}let h=u.computeOutputShape(Tn(c)),d=Op(h),p=u.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${u.name}_${p}_${f}`;n[m]=d[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],c=`${o.name}_${l}_${u}`;s.push(c)}for(let i=0;i<s.length;i++){let o=s[i];Br(o in n),a.push(n[o])}return Tn(a)}runInternalGraph(e,t){t==null&&(t=vi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],c=t[o];n[l.id]=[u,c]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(kp);for(let o of r){let l=this.nodesByDepth[o];for(let u of l){let c=u.outboundLayer,h=u.inputTensors,d=u.outputTensors,p=new Array;for(let f of h)f.id in n&&p.push(n[f.id]);if(p.length===h.length){let f={},m,A,y,g;if(u.callArgs!=null&&(f=u.callArgs),p.length===1){let[_,b]=p[0];f.mask==null&&(f.mask=b),y=ft(c.call(_,f)),g=ft(c.computeMask(_,b)),m=[_],A=[b]}else m=p.map(_=>_[0]),A=p.map(_=>_[1]),f.mask==null&&(f.mask=A),y=ft(c.call(m,f)),g=ft(c.computeMask(m,A));if(c.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<d.length;++_){let b=d[_],w=y[_],x=g[_];n[b.id]=[w,x]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Br(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof jr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=jr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new V(`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 V("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new V(`No such layer: ${e}`)}calculateLosses(){return B(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=jr.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let c=0;c<s.inboundNodes.length;c++){let h=s.inboundNodes[c],d=jr.nodeKey(s,c),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=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).`),p={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let A=h.inboundLayers[m],y=h.nodeIndices[m],g=h.tensorIndices[m],_=jr.nodeKey(A,y),b=t[_];b==null&&(b=0),f.push([A.name,b,g,p])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[s];r.push([i.name,u,c])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[s];a.push([i.name,u,c])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(m,A){m.name in s?s[m.name].push(A):s[m.name]=[A]}function o(m,A){let y=[],g;for(let _ of A){let b=_[0],w=_[1],x=_[2];if(g=_[3]==null?{}:_[3],!(b in a)){i(m,A);return}let I=a[b];if(I.inboundNodes.length<=w){i(m,A);return}let S=I.inboundNodes[w];y.push(S.outputTensors[x])}y.length>0&&m.apply(Tn(y),g)}function l(m){let A=m.name,y=Nr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new V(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!rQ(s);)for(let m of c){let A=a[m.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],d=[],p=t.inputLayers;for(let m of p){let A=m[0],y=m[1],g=m[2];Br(A in a);let _=a[A].inboundNodes[y].outputTensors;h.push(_[g])}let f=t.outputLayers;for(let m of f){let A=m[0],y=m[1],g=m[2];Br(A in a);let _=a[A].inboundNodes[y].outputTensors;d.push(_[g])}return new e({inputs:h,outputs:d,name:u})}get stateful(){if(this._stateful)throw new V("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(){B(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function Fte(e,t,n){let r=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(r===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!==r)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${r} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${r}) outputs, so ${n} must be either an array with ${r} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function v7(e,t){return Fte(e,t,"classWeight")}async function k7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=B(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());Ee(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),on(i,"float32")}else return null}function Mte(e,t){return P(e,t)}var $te=32;function I7(e,t){let n,r,a=t;n=a.xs,r=a.ys,v.assert(n!=null&&r!=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=N7("input",e.inputNames,n),i=N7("output",e.outputNames,r),o=s[0].shape[0];v.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)})`),v.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++)v.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++)v.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 N7(e,t,n){if(n instanceof Ue)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new V(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Dte(e){if(e.length===3)throw new De("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function zte(e,t,n){let r=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=n.validationData!=null,s,i;if(a)if(S7(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let A=Dte(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(A=>"val_"+A)):u=l.slice();let c=h7(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=d7(c,h,n.epochs,null,null,Ote(t,n),null,a,u);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await d.onEpochBegin(f);let y=0,g=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let _=await m.next();if(r&&_.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(_.value!=null){let{xs:b,ys:w}=I7(e,_.value),x={};x.batch=g,x.size=b[0].shape[0],await d.onBatchBegin(g,x);let I=[];if(n.classWeight!=null){let M=v7(n.classWeight,e.outputNames);for(let D=0;D<M.length;++D)I.push(await k7(w[D],null,M[D]))}let S=b.concat(w).concat(I),T=o(S);Ee(S);for(let M=0;M<l.length;++M){let D=l[M],z=T[M];x[D]=z,Gt(z)}await d.onBatchEnd(g,x),i7(x),g++,y++}if(r?y>=n.batchesPerEpoch:_.done){if(a){let b;S7(n.validationData)?b=ft(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=ft(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?$te:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)A[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(f,A),f++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function Ote(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function S7(e){return typeof e.iterator=="function"}function Pte(e){return typeof e.next=="function"}async function Lte(e,t,n){n=n||{};let r=n.batches!=null,a=e.testFunction,s=[];if(n.verbose>0)throw new De("Verbose mode is not implemented yet.");v.assert(!r||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=Pte(t)?t:await t.iterator(),o=0,l=0;for(;r?l<n.batches:!0;){let u=await i.next();if(s=B(()=>{if(u.value){let{xs:c,ys:h}=I7(e,u.value),d=c.concat(h),p=B(()=>a(d));if(Ee(d),l===0)for(let m=0;m<p.length;++m)s.push(be(0));let f=d[0].shape[0];for(let m=0;m<p.length;++m){let A=p[m],y=s[m];s[m]=B(()=>se(s[m],P(f,A))),l>0&&Ee(y)}Ee(p),o+=f,++l}return s}),u.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let u=0;u<s.length;++u){let c=s[u];s[u]=ge(s[u],o),Ee(c)}return Tn(s)}function GA(e){v.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Cc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>Si(r,t,n-t)):Si(e,t,n-t)}function qA(e,t){return B(()=>e==null?null:Array.isArray(e)?e.map(n=>qA(n,t)):j3(e,t.dtype==="int32"?t:t.toInt()))}function XA(e,t){let n=[],r=0,a=null;for(;r<e;)a=r+t,a>=e&&(a=e),n.push([r,a]),r=a;return n}async function Wte(e,t,n,r,a,s,i,o,l,u,c,h,d,p,f){a==null&&(a=32),s==null&&(s=1),c==null&&(c=!0),d==null&&(d=0);let m=!1;if(l!=null&&u!=null&&(m=!0),f!=null&&(m=!0,p==null))throw new V("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let A=e.checkNumSamples(n,a,p,"steps_per_epoch"),y;A!=null&&(y=vr(0,A)),i==null&&(i=1);let{callbackList:g,history:_}=d7(o,i,s,d,A,p,a,m,h);g.setModel(e),e.history=_,await g.onTrainBegin(),e.stopTraining_=!1;for(let b=d;b<s;++b){await g.onEpochBegin(b);let w={};if(p!=null)throw new De("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new De("batch shuffling is not implemneted yet");c&&v.shuffle(y);let x=on(y),I=XA(A,a);for(let S=0;S<I.length;++S){let T={};if(await g.onBatchBegin(S,T),B(()=>{let M=I[S][0],D=I[S][1],z=Si(x,M,D-M);T.batch=S,T.size=D-M;let W=qA(n,z),U=t(W);for(let H=0;H<r.length;++H){let X=r[H],j=U[H];T[X]=j,Gt(j)}if(S===I.length-1&&m){let H=e.testLoop(l,u,a);for(let X=0;X<r.length;++X){let j=r[X],ee=H[X];Gt(ee),w["val_"+j]=ee}}}),await g.onBatchEnd(S,T),i7(T),e.stopTraining_)break}x.dispose()}if(await g.onEpochEnd(b,w),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function Bte(e,t,n,r={}){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,c;try{let h=r.batchSize==null?32:r.batchSize;GA(h);let d=!1,p=await e.standardizeUserData(t,n,r.sampleWeight,r.classWeight,d,h);a=p[0],s=p[1],c=p[2];let f=!1,m;if(r.validationData!=null&&r.validationData.length>0){if(f=!0,r.validationData.length===2)i=r.validationData[0],o=r.validationData[1];else throw r.validationData.length===3?new De("validationData including sample weights is not supported yet."):new V(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let x=!0,I=await e.standardizeUserData(i,o,null,null,x,h);l=I[0],u=I[1],m=l.concat(u)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let x=Math.floor(a[0].shape[0]*(1-r.validationSplit)),I=a[0].shape[0];l=Cc(a,x,I),a=Cc(a,0,x),u=Cc(s,x,I),s=Cc(s,0,x),m=l.concat(u)}else r.validationSteps!=null&&(f=!0);let A=a.concat(s).concat(c);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),_,b;f?(e.makeTestFunction(),_=e.testFunction,b=g.slice().concat(g.map(x=>"val_"+x))):(_=null,m=[],b=g.slice());let w=h7(r.callbacks,r.yieldEvery);return await Wte(e,y,A,g,h,r.epochs,r.verbose,w,_,m,r.shuffle,b,r.initialEpoch,null,null)}finally{e.isTraining=!1,Ei(a,t),Ei(s,n),Ei(l,i),Ei(u,o),c!=null&&Ee(c)}}function T7(e){let t=[];e instanceof Ue&&(e=[e]);for(let n=0;n<e.length;++n){let r=e[n];if(r.rank===1)t.push(bc(r,1));else{if(r.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(r)}}return t}function Ei(e,t){if(e==null)return;let n=[];if(t instanceof Ue)n.push(t.id);else if(Array.isArray(t))t.forEach(a=>n.push(a.id));else if(t!=null)for(let a in t){let s=t[a];n.push(s.id)}let r=[];if(e instanceof Ue)n.indexOf(e.id)===-1&&r.push(e);else if(Array.isArray(e))e.forEach(a=>{n.indexOf(a.id)===-1&&r.push(a)});else if(e!=null)for(let a in e){let s=e[a];n.indexOf(s.id)===-1&&r.push(s)}r.forEach(a=>{a.isDisposed||a.dispose()})}function Vte(e){return e instanceof Ue}function KA(e){return Array.isArray(e)}function C7(e){return!Vte(e)&&!KA(e)}function E7(e,t,n,r=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(KA(e)&&e.length>0)i=!0;else if(C7(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new V(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(C7(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new V(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(KA(e)){if(e=e,e.length!==t.length)throw new V(`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 V(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=T7(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new V(`Error when checking ${a}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);for(let l=0;l<n[i].length;++l){if(l===0&&!r)continue;let u=o.shape[l],c=n[i][l];if(c!=null&&c>=0&&u!==c)throw new V(`Error when checking ${a}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Ute(e,t,n){let r=Ua(e.map(s=>s.shape[0]));r.sort();let a=Ua(t.map(s=>s.shape[0]));if(a.sort(),r.length>1)throw new V(`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 V(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(r.length>0&&a.length>0&&!v.arraysEqual(r,a))throw new V(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${a[0]} target sample(s).`)}function Hte(e,t,n){let r=[Ti,Vp,Ic];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(i!=null){if(i===Ic&&s.shape[s.shape.length-1]===1)throw new V(`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(r.indexOf(i)!==-1){let l=s.shape.slice(1),u=o.slice(1);for(let c=0;c<l.length;++c){let h=l[c],d=u[c];if(d!=null&&h!==d)throw new V(`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 R7(e,t,n,r=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new V(`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 V(`The model expects ${t.length} ${a} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new V(`Error when checking ${a}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let l=0;l<n[i].length;++l){if(l===0&&!r)continue;let u=o.shape[l],c=n[i][l];if(c!=null&&c!==u)throw new V(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function jte(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>[]);let n;if(typeof e=="string"||typeof e=="function")n=[e];else if(Array.isArray(e)||typeof e=="object")n=e;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var Gte="layers-model",ha=class extends jr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new V("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");Ste(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=bte(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof oa))throw new V("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new V(`Unknown entry in loss dictionary: "${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(PA(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new V(`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=>PA(s))}else{let s=PA(e.loss);this.outputs.forEach(i=>{t.push(s)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let s=0;s<this.outputs.length;++s){let i=this.internalOutputShapes[s],o=this.outputNames[s];this.feedOutputNames.push(o),this.feedOutputShapes.push(i),this.feedLossFns.push(this.lossFunctions[s])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],Ii("loss",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=this.lossFunctions[s];this.outputs.length>1&&(this.metricsTensors.push([i,s]),this.metricsNames.push(this.outputNames[s]+"_loss"))}});let r=jte(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])};Ii("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=r[s];(o=>{let l="",u,c,h;for(let d of o){if(typeof d=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(d)!==-1){let f=this.internalOutputShapes[s];f[f.length-1]===1||this.lossFunctions[s]===Vp?["accuracy","acc"].indexOf(d)!==-1?c=LA:["crossentropy","ce"].indexOf(d)!==-1&&(c=m7):this.lossFunctions[s]===Bp?["accuracy","acc"].indexOf(d)!==-1?c=A7:["crossentropy","ce"].indexOf(d)!==-1&&(c=y7):["accuracy","acc"].indexOf(d)!==-1?c=WA:["crossentropy","ce"].indexOf(d)!==-1&&(c=BA);let m;["accuracy","acc"].indexOf(d)!==-1?m="acc":["crossentropy","ce"].indexOf(d)!==-1&&(m="ce"),h=c,u=l+m}else h=_te(d),u=l+jp(d);let p;Ii(u,()=>{p=h}),a(s,u,p)}})(i)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let r=n.batchSize==null?32:n.batchSize;GA(r);let a=!0,s=this.standardizeUserDataXY(e,t,a,r);try{let i=s[0].concat(s[1]);this.makeTestFunction();let o=this.testFunction,l=this.testLoop(o,i,r,n.verbose,n.steps);return Tn(l)}finally{Ei(s[0],e),Ei(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),Lte(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(n!=null){if(a=null,t!=null)throw new V(`If ${r} 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 V(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new V("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Ci;if(e instanceof Ue&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new V(`The number of inputs provided (${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 V(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=Tc(a,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=vi(null,e.length),n=e.length;for(let r of this.layers){let a=Array.isArray(r.output)?r.output:[r.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],n--),n===0)break}if(n===0)break}if(n>0){let r=[];throw t.forEach((a,s)=>{a==null&&r.push(e[s])}),new V(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return B(()=>{let r=this.checkNumSamples(e);if(n)throw new De("Verbose predictLoop() is not implemented yet.");let a=XA(r,t),s=this.outputs.map(i=>[]);for(let i=0;i<a.length;++i)B(()=>{let o=a[i][0],l=a[i][1],u=Cc(e,o,l),c=[];if(Array.isArray(u))for(let d=0;d<u.length;++d)c.push({key:this.inputs[d],value:u[d]});else c.push({key:this.inputs[0],value:u});let h=new Ci(c);return Tc(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return Tn(s.map(i=>st(i,0)))})}predict(e,t={}){let n=T7(e);R7(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return GA(r),this.predictLoop(n,r)}finally{Ei(n,e)}}predictOnBatch(e){R7(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,r){if(this.optimizer_==null)throw new br("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]===Bp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=E7(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=E7(t,this.feedOutputNames,a,!1,"target"),Ute(e,t,null),Hte(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new V(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let u=v7(r,this.outputNames);l=[];for(let c=0;c<u.length;++c)l.push(await k7(o[c],null,u[c]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return B(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new De("Verbose mode is not implemented yet.");if(a!=null)throw new De("steps mode in testLoop() is not implemented yet");{let o=XA(s,n),l=on(vr(0,s));for(let u=0;u<o.length;++u){let c=o[u][0],h=o[u][1],d=Si(l,c,h-c),p=qA(t,d),f=e(p);if(u===0)for(let m=0;m<f.length;++m)i.push(be(0));for(let m=0;m<f.length;++m){let A=f[m];i[m]=se(i[m],P(h-c,A))}}for(let u=0;u<i.length;++u)i[u]=ge(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;F3(e,r)>1&&(a+=`_${F3(e.slice(0,n),r)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=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 p=0;p<this.inputs.length;++p)u.push({key:this.inputs[p],value:n[p]});let c=new Ci(u),h=Tc(this.outputs,c,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Mte(f,a[p]));let m=kt(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=kt(m(r[A],h[A]))}Gt(f),s.push(f)}return d=kt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>B(()=>{let t=[],n,r=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:r[l]});let i=new Ci(s),o=Tc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=kt(u(a[l],o[l]));l===0?n=c:n=se(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],h=kt(u(a[c],o[c]));t.push(h)}return t})}async fit(e,t,n={}){return Bte(this,e,t,n)}async fitDataset(e,t){return zte(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return Ee(s),Tn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[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=cd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ca(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=>ca(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ca(n[r]);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[ca(jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ca(jp(e)));{let e={};for(let t in this.metrics)e[t]=ca(jp(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=Sc(e.optimizer_config),n=Nr(t),r;if(typeof e.loss=="string")r=ki(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>ki(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=ki(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>ki(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=ki(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=bn.getSaveHandlers(e);if(i.length===0)throw new V(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new V(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new V("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await bn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Gte,generatedBy:`TensorFlow.js tfjs-layers v${HA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await bn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=bn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;x7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){x7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ha.className="Model";re.registerClass(ha);var F7=class extends ha{};F7.className="Functional";re.registerClass(F7);async function qte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Sc(n),a=Nr(r,t);if(e.weightsManifest!=null){let s=await bn.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),Ee(s)}return a}async function Kte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=bn.getLoadHandlers(e,t);if(n.length===0)n.push(bn.browserHTTPRequest(e,t));else if(n.length>1)throw new V(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Xte(e,void 0,t)}async function Xte(e,t,n){if(n==null&&(n={}),e.load==null)throw new V("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=Nr(Sc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new V("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=Zte(r.weightData,r.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Ee(u),Ee(c.map(h=>h.tensor))}return o}function Zte(e,t){let n=bn.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Wl=class extends ha{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp("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 V(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Wl||e instanceof ha,n;if(t){if(n=e,n.outputs.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new V("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 V("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=s7({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new V(`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 V("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=a7(this.outputs[0])}this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:vi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))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=[r],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(ct(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 ha({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new br("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Wl))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Nr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("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 V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Wl.className="Sequential";re.registerClass(Wl);function Yte(e){return new ha(e)}function Jte(e){return new Wl(e)}function Qte(e,t){return t==null&&(t={}),Kte(e,t)}function Z3(e){return s7(e)}function ene(e,t){ur.registerCallbackConstructor(e,t)}var Ln=class extends re.Serializable{getConfig(){return{}}},M7=class extends Ln{apply(e,t=1){return FQ(e,t)}};M7.className="elu";re.registerClass(M7);var $7=class extends Ln{apply(e){return Cd(e)}};$7.className="selu";re.registerClass($7);var D7=class extends Ln{apply(e){return zr(e)}};D7.className="relu";re.registerClass(D7);var O7=class extends Ln{apply(e){return B(()=>fl(6,zr(e)))}};O7.className="relu6";re.registerClass(O7);var z7=class extends Ln{apply(e){return e}};z7.className="linear";re.registerClass(z7);var P7=class extends Ln{apply(e){return Fn(e)}};P7.className="sigmoid";re.registerClass(P7);var L7=class extends Ln{apply(e){return $Q(e)}};L7.className="hardSigmoid";re.registerClass(L7);var W7=class extends Ln{apply(e){return dl(e)}};W7.className="softplus";re.registerClass(W7);var B7=class extends Ln{apply(e){return MQ(e)}};B7.className="softsign";re.registerClass(B7);var V7=class extends Ln{apply(e){return ol(e)}};V7.className="tanh";re.registerClass(V7);var ZA=class extends Ln{apply(e,t=-1){return Yu(e,t)}};ZA.className="softmax";re.registerClass(ZA);var U7=class extends Ln{apply(e,t=-1){return bd(e,t)}};U7.className="logSoftmax";re.registerClass(U7);var H7=class extends Ln{apply(e,t=1){return B(()=>Fn(e.mul(t)).mul(e))}};H7.className="swish";re.registerClass(H7);function qa(e){return e.getClassName()}function YA(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function Xa(e){if(e==null){let t={};return t.className="linear",t.config={},YA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},YA(t)}else return e instanceof Ln?e:YA(e)}function JA(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 j7=class extends re.Serializable{},Ec=class extends j7{constructor(e){super();JA(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 B(()=>{let t=Et([1]);return this.hasL1&&(t=se(t,Te(P(this.l1,zt(e))))),this.hasL2&&(t=se(t,Te(P(this.l2,vc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ec.className="L1L2";re.registerClass(Ec);function tne(e){return JA(e),new Ec({l1:e!=null?e.l1:null,l2:0})}function nne(e){return JA(e),new Ec({l2:e!=null?e.l2:null,l1:0})}var G7={l1l2:"L1L2"};function ht(e){return dA(e)}function q7(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function wt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in G7?G7[e]:e,config:{}};return q7(t)}else return e instanceof j7?e:q7(e)}var QA=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Pe(e);let n=zr(e);return this.maxValue!=null&&(n=vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};QA.className="ReLU";re.registerClass(QA);var ey=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return Hu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};ey.className="LeakyReLU";re.registerClass(ey);var ty=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=xt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=wt(e.alphaRegularizer),this.alphaConstraint=Vt(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 V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ct(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Kt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Pe(e),Xu(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Nt(this.alphaInitializer),alphaRegularizer:ht(this.alphaRegularizer),alphaConstraint:Bt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};ty.className="PReLU";re.registerClass(ty);var ny=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return cl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};ny.className="ELU";re.registerClass(ny);var ry=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Pe(e);return n.mul(_c(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ry.className="ThresholdedReLU";re.registerClass(ry);var ay=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new ZA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ay.className="Softmax";re.registerClass(ay);function Bl(e,t,n){if(typeof e=="number")return vi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!TQ(a))throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Ir(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+ja([n-t,0]);else if(r==="same")e=e*t;else throw new V(`Unsupport padding mode: ${r}.`);return e}function sy(e,t){return B(()=>(Tt(t),t==="channelsFirst"?at(e,[0,2,3,1]):e))}function X7(e,t){return B(()=>(Tt(t),t==="channelsFirst"?at(e,[0,2,3,4,1]):e))}function rne(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=_r()),Tt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=at(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=fd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function K7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=_r()),Tt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=sy(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=La.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=at(l,[0,3,1,2])),l})}function ane(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=_r()),Tt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=X7(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Xf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=at(o,[0,4,1,2,3])),o})}var iy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",iy.verifyArgs(t),this.rank=e,Xt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Bl(t.kernelSize,e,"kernelSize"),this.strides=Bl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Yn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Tt(this.dataFormat),this.activation=Xa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=xt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=wt(t.biasRegularizer),this.activityRegularizer=wt(t.activityRegularizer),this.dilationRate=Bl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`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 V(`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 V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!fA(e.kernelSize,"number",1,3))throw new V(`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:qa(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Rc=class extends iy{constructor(e,t){super(e,t);this.kernel=null,Rc.verifyArgs(t),this.filters=t.filters,Xt(this.filters,"filters"),this.kernelInitializer=xt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=wt(t.kernelRegularizer)}build(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return B(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=$3(this.activation.getClassName());if(a!=null&&this.rank===2)n=K7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=rne(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=K7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=ane(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ct(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Ir(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:ht(this.kernelRegularizer),kernelConstraint:Bt(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 V(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Fc=class extends Rc{constructor(e){super(2,e);Fc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!fA(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Fc.className="Conv2D";re.registerClass(Fc);var Xp=class extends Rc{constructor(e){super(3,e);Xp.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 V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xp.className="Conv3D";re.registerClass(Xp);var oy=class extends Fc{constructor(e){super(e);if(this.inputSpec=[new Kt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ct(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=Pe(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qp(o,h,u,this.padding),f=qp(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=at(n,[0,2,3,1]));let A=md(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=at(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ct(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qp(t[r],o,s,this.padding),t[a]=qp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};oy.className="Conv2DTranspose";re.registerClass(oy);var Z7=class extends Rc{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 V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("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 V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=wt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=wt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=ct(e),e.length<this.rank+2)throw new V(`Inputs to SeparableConv${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 V(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"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 Kt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{e=Pe(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=at(e,[0,2,3,1])),n=dm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=at(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseConstraint),e.pointwiseConstraint=Bt(this.pointwiseConstraint),e}};Z7.className="SeparableConv";var ly=class extends Z7{constructor(e){super(2,e)}};ly.className="SeparableConv2D";re.registerClass(ly);var Kp=class extends Rc{constructor(e){super(1,e);Kp.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"&&!fA(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv1D";re.registerClass(Kp);var uy=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return B(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Np(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Np(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Np(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Np(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};uy.className="Cropping2D";re.registerClass(uy);var cy=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,NQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return B(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=at(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return at(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};cy.className="UpSampling2D";re.registerClass(cy);function sne(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=_r()),Tt(a);let i=sy(e,a);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ul(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=at(i,[0,3,1,2])),i})}var hy=class extends iy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=wt(e.depthwiseRegularizer)}build(e){if(e=ct(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=Pe(e);let n=sne(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Ir(t,this.kernelSize[0],this.padding,this.strides[0]),s=Ir(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseRegularizer),e}};hy.className="DepthwiseConv2D";re.registerClass(hy);function Y7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),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),n=a(n),{inputs:e,initialState:t,constants:n}}function J7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(vr(2,l));if(t=at(t,u),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=hn(a,-1)),a=at(a,u)),r&&(t=Dn(t,0),a!=null&&(a=Dn(a,0)));let c=[],h,d=n,p=t.shape[0],f=sr(t),m;a!=null&&(m=sr(a));for(let y=0;y<p;++y){let g=f[y],_=B(()=>e(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=m[y],x=$n(w).sub(w),I=_[0].mul(w).add(d[0].mul(x)),S=d.map((T,M)=>_[1][M].mul(w).add(T.mul(x)));return{output:I,newStates:S}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=dn(c,1)),[h,A,d]})}var Hr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("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 Kt({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 vr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){$A(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");$A(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Kt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Kt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ua("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("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(r=>Et([n,r])):this.states_=[Et([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Et([n,r])):this.states_[0]=Et([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`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()):Ee(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new V(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Gt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=Y7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Kt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof kr){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(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 V(`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:r},o=J7((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=Et(e.shape);return t=Te(t,[1,2]),t=bc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?wA(t,[1,n]):t):this.cell.stateSize>1?[wA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Hr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Nr(r,n);return new e(Object.assign(t,{cell:a}))}};Hr.className="RNN";re.registerClass(Hr);var Nc=class extends qe{},Yp=class extends Nc{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,Xt(this.units,"units"),this.activation=Xa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Ol([1,ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ol([1,ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(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 B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(P(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";re.registerClass(Yp);var dy=class extends Hr{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};dy.className="SimpleRNN";re.registerClass(dy);var Jp=class extends Nc{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 V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Xt(this.units,"units"),this.activation=Xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Ol([1,ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ol([1,ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(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 B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let u=Vr(e,this.kernel.read());this.useBias&&(u=Ur(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let c=this.recurrentKernel.read(),[h,d]=Lt(c,[2*this.units,this.units],c.rank-1),p=Vr(r,h),[f,m,A]=Lt(u,3,u.rank-1),[y,g]=Lt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let _=Vr(P(o,r),d);l=this.activation.apply(se(A,_));let b=se(P(i,r),P(se(1,vt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),recurrentActivation:qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Jp.className="GRUCell";re.registerClass(Jp);var py=class extends Hr{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 Jp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};py.className="GRU";re.registerClass(py);var Mc=class extends Nc{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,Xt(this.units,"units"),this.activation=Xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Ol([1,ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ol([1,ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ct(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends lr{apply(i,o){let l=a.apply([s]),u=new Sp().apply([s]),c=a.apply([s*2]);return H3(H3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=Lt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),u=se(P(l,a),P(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let A=P(c,this.activation.apply(u));return[A,A,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),recurrentActivation:qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Mc.className="LSTMCell";re.registerClass(Mc);var fy=class extends Hr{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 Mc(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};fy.className="LSTM";re.registerClass(fy);var Zp=class extends Nc{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 B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){$A(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ii(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Nr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return DA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}OA(t)}};Zp.className="StackedRNNCells";re.registerClass(Zp);function Ka(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>G3(t(),n),i=()=>kc(s,t,r);return!a||a<=1?Gt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Gt(o.clone()))}var ine=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},Q7=class extends Hr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Kt({ndim:5})]}call(e,t){return B(()=>{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,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 B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Et(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ua("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new V("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(()=>Et(a)):this.states_=[Et(a)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Et(a)):this.states_[0]=Et(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`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()):Ee(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new V(`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=>Gt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=Ir(l,r[0],a,s[0],i[0]),h=Ir(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};Q7.className="ConvRNN2D";var Qp=class extends Mc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Xt(this.filters,"filters"),this.kernelSize=Bl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Xt(o,"kernelSize")),this.strides=Bl(r||1,2,"strides"),this.strides.forEach(o=>Xt(o,"strides")),this.padding=a||"valid",Yn(this.padding),this.dataFormat=s||"channelsLast",Tt(this.dataFormat),this.dilationRate=Bl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Xt(o,"dilationRate"))}build(e){var t;e=ct(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,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 lr{apply(c,h){let d=l.apply([u]),p=Or([u]),f=l.apply([u*2]);return bA([d,p,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 B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Lt(this.kernel.read(),i,g),[I,S,T,M]=this.useBias?Lt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,I,this.padding),c=this.inputConv(c,b,S,this.padding),h=this.inputConv(h,w,T,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,z,W,U]=Lt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,D),m=this.recurrentConv(m,z),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(u,f)),X=this.recurrentActivation.apply(se(c,m)),j=se(P(X,s),P(H,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(j));return[ee,ee,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ine(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ra(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return ra(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var my=class extends Q7{constructor(e){let t=new Qp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};my.className="ConvLSTM2D";re.registerClass(my);var e0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return kc(()=>G3(n,this.rate,a,this.seed),()=>n,r)}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()}};e0.className="Dropout";re.registerClass(e0);var Ay=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ay.className="SpatialDropout1D";re.registerClass(Ay);var yy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Xt(this.units,"units"),this.activation=Xa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=wt(e.kernelRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ct(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=ct(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=$3(this.activation.getClassName()),a;return r!=null?a=Vr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Vr(n,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:qa(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};yy.className="Dense";re.registerClass(yy);var gy=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ct(e);for(let t of e.slice(1))if(t==null)throw new V(`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],Ha(e,1)]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return RQ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};gy.className="Flatten";re.registerClass(gy);var xy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Xa(e.activation)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:qa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};xy.className="Activation";re.registerClass(xy);var wy=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return B(()=>(e=Pe(e),CQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};wy.className="RepeatVector";re.registerClass(wy);var _y=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new V("Can only specifiy one unknown dimension.");else a*=l}let i=Ha(e);if(s!==null){if(a===0||i%a!=0)throw new V(n);r[s]=i/a}else if(i!==a)throw new V(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};_y.className="Reshape";re.registerClass(_y);var by=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=vr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Kt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ct(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return at(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className="Permute";re.registerClass(by);var vy=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Ou(di(n,this.maskValue),r)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Ou(di(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};vy.className="Masking";re.registerClass(vy);var ky=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Xt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Xt(this.outputDim,"outputDim"),this.embeddingsInitializer=xt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=wt(e.embeddingsRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.embeddingsConstraint=Vt(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 B(()=>this.maskZero?(e=Pe(e),di(e,He(e))):null)}computeOutputShape(e){if(e=ct(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=_c(n,"int32")),j3(this.embeddings.read(),n.as1D()).reshape(ct(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:ht(this.embeddingsRegularizer),activityRegularizer:ht(this.activityRegularizer),embeddingsConstraint:Bt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};ky.className="Embedding";re.registerClass(ky);var Ri=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new V("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ct(e)]),e=e,e.length<2)throw new V(`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=Ua(t),t.length>1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ua(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=ja(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=bc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,c=u[0],h=u.slice(1).concat([c]),d=o.reshape([c].concat(Ha(u.slice(1))));d=at(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let u=vr(1,l).concat([0]);n.push(at(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=at(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(vr(0,i-1));s=at(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ua(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return B(()=>{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:hn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=ar(n,t[r]);return n})}},Ny=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};Ny.className="Add";re.registerClass(Ny);var Iy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=P(t,e[n]);return t})}};Iy.className="Multiply";re.registerClass(Iy);var Sy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return P(1/e.length,t)})}};Sy.className="Average";re.registerClass(Sy);var Ty=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Dr(t,e[n]);return t})}};Ty.className="Maximum";re.registerClass(Ty);var Cy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=fl(t,e[n]);return t})}};Cy.className="Minimum";re.registerClass(Cy);var Ey=class extends Ri{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 V("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(v.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return B(()=>bA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new V("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new V("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new V(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return B(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push($n(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(hn(t[s],-1)):r.push(t[s]);let a=st(r,this.axis);return dd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Concatenate";re.registerClass(Ey);function $c(e,t){for(;e<0;)e+=t;return e}function one(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return B(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c<l+i;++c)u.push(c);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Ry=class extends Ri{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>$c(a,e[s].shape.length)):r=[$c(this.axes,t.shape.length),$c(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,r[0]),n=Lp(n,r[1])),one(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[$c(this.axes,e.length),$c(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);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}};Ry.className="Dot";re.registerClass(Ry);var Fy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return kc(()=>Ip(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Fy.className="GaussianNoise";re.registerClass(Fy);var My=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?kc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Ip(n.shape,1,r))},()=>n,t.training||!1):n})}};My.className="GaussianDropout";re.registerClass(My);var $y=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return kc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=za(ml(n),this.rate);o=_c(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Pe(e),t.training||!1)}return e})}};$y.className="AlphaDropout";re.registerClass($y);function Dc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=H5(e,t,n,r,a,s);else if(e.rank===3)i=j5(e,t,n,r,a,s);else if(e.rank===4)i=G5(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function lne(e,t,n,r,a=.001){return B(()=>{let s=kd(e,r),i=s.mean,o=s.variance;return[Dc(e,i,o,n,t,a),i,o]})}function une(e,t,n,r,a=.001){return B(()=>{let s=kd(e,r),i=s.mean,o=s.variance,l=[];for(let p of vr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Dc(e,u,c,d,h,a),i,o]})}function cne(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),vr(0,e.rank-1))?lne(e,t,n,r,a):une(e,t,n,r,a)}var Dy=class extends qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.movingMeanInitializer=xt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=xt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer)}build(e){e=ct(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Kt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=vr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=vi(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,vr(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return Dc(r,A,y,g,_,this.epsilon)}else return Dc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=cne(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{B(()=>{let _=1-g,b=A.read(),w=b.sub(y).mul(_);A.write(b.sub(w))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),movingMeanInitializer:Nt(this.movingMeanInitializer),movingVarianceInitializer:Nt(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:Bt(this.betaConstraint),gammaConstraint:Bt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Dy.className="BatchNormalization";re.registerClass(Dy);var Oy=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ct(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!==Ua(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return B(()=>{let s=!0,{mean:i,variance:o}=kd(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),c=c.tile(p),h=h.tile(p),Dc(n,i,o,h,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="LayerNormalization";re.registerClass(Oy);function hne(e,t,n){return B(()=>{if(e.rank!==4)throw new V(`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 V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=_r()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],aa(e,r)})}var zy=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?_r():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ct(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return B(()=>hne(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};zy.className="ZeroPadding2D";re.registerClass(zy);function t0(e,t,n,r,a,s){return B(()=>{Tt(a),P3(s),Yn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=_r()),s==null&&(s="max"),e=sy(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Gu(e,t,n,o):i=Lu(e,t,n,o),a==="channelsFirst"&&(i=at(i,[0,3,1,2])),i})}function ev(e,t,n,r,a,s){return B(()=>{Tt(a),P3(s),Yn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=_r()),s==null&&(s="max"),e=X7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=sm(e,t,n,o):i=jf(e,t,n,o),a==="channelsFirst"&&(i=at(i,[0,4,1,2,3])),i})}var tv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Xt(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 V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Yn(this.padding),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){e=ct(e);let t=Ir(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=bc(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Pa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Py=class extends tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"max")}};Py.className="MaxPooling1D";re.registerClass(Py);var Ly=class extends tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"avg")}};Ly.className="AveragePooling1D";re.registerClass(Ly);var nv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`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];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Yn(this.padding),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},Wy=class extends nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"max")}};Wy.className="MaxPooling2D";re.registerClass(Wy);var By=class extends nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"avg")}};By.className="AveragePooling2D";re.registerClass(By);var rv=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`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];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Yn(this.padding),this.inputSpec=[new Kt({ndim:5})]}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ir(t,this.poolSize[0],this.padding,this.strides[0]),n=Ir(n,this.poolSize[1],this.padding,this.strides[1]),r=Ir(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},Vy=class extends rv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),ev(e,t,n,r,a,"max")}};Vy.className="MaxPooling3D";re.registerClass(Vy);var Uy=class extends rv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),ev(e,t,n,r,a,"avg")}};Uy.className="AveragePooling3D";re.registerClass(Uy);var av=class extends qe{constructor(e){super(e);this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},Hy=class extends av{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=Pe(e);return kt(n,1)})}};Hy.className="GlobalAveragePooling1D";re.registerClass(Hy);var jy=class extends av{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=Pe(e);return Xn(n,1)})}};jy.className="GlobalMaxPooling1D";re.registerClass(jy);var sv=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Gy=class extends sv{call(e,t){return B(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?kt(n,[1,2]):kt(n,[2,3])})}};Gy.className="GlobalAveragePooling2D";re.registerClass(Gy);var qy=class extends sv{call(e,t){return B(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Xn(n,[1,2]):Xn(n,[2,3])})}};qy.className="GlobalMaxPooling2D";re.registerClass(qy);var iv=class extends qe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=Nr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Xy=class extends iv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ct(e),e.length<3)throw new V(`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=ct(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return B(()=>(e=Pe(e),J7((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Xy.className="TimeDistributed";re.registerClass(Xy);function dne(e){Ni(kQ,"BidirectionalMergeMode",e)}var pne="concat",Ky=class extends iv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Nr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Nr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?pne:e.mergeMode,dne(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Tn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=Y7(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new Kt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof kr;for(let l of s)if(l instanceof kr!==o)throw new V("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),c=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=c,h}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=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(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Dn(a,1));let i;return this.mergeMode==="concat"?i=bA([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=P(.5,se(r,a)):this.mergeMode==="mul"?i=P(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ii(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ii(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Nr(t.layer);if(delete t.layer,t.numConstants!=null)throw new De("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};Ky.className="Bidirectional";re.registerClass(Ky);function WQ(e){return new zl(e)}function BQ(e){return new ny(e)}function VQ(e){return new QA(e)}function UQ(e){return new ey(e)}function HQ(e){return new ty(e)}function jQ(e){return new ay(e)}function GQ(e){return new ry(e)}function qQ(e){return new Kp(e)}function XQ(e){return new Fc(e)}function KQ(e){return new oy(e)}function ZQ(e){return new Xp(e)}function YQ(e){return new ly(e)}function JQ(e){return new uy(e)}function QQ(e){return new cy(e)}function eee(e){return new hy(e)}function tee(e){return new xy(e)}function nee(e){return new yy(e)}function ree(e){return new e0(e)}function aee(e){return new Ay(e)}function see(e){return new gy(e)}function iee(e){return new wy(e)}function oee(e){return new _y(e)}function lee(e){return new by(e)}function uee(e){return new ky(e)}function cee(e){return new Ny(e)}function hee(e){return new Sy(e)}function dee(e){return new Ey(e)}function pee(e){return new Ty(e)}function fee(e){return new Cy(e)}function mee(e){return new Iy(e)}function Aee(e){return new Ry(e)}function yee(e){return new Dy(e)}function gee(e){return new Oy(e)}function xee(e){return new zy(e)}function RA(e){return new Ly(e)}function wee(e){return RA(e)}function _ee(e){return RA(e)}function FA(e){return new By(e)}function bee(e){return FA(e)}function vee(e){return FA(e)}function MA(e){return new Uy(e)}function kee(e){return MA(e)}function Nee(e){return MA(e)}function Iee(e){return new Hy(e)}function See(e){return new Gy(e)}function Y3(e){return new jy(e)}function J3(e){return new qy(e)}function Q3(e){return new Py(e)}function e7(e){return new Wy(e)}function Tee(e){return new Vy(e)}function Cee(e){return new py(e)}function Eee(e){return new Jp(e)}function Ree(e){return new fy(e)}function Fee(e){return new Mc(e)}function Mee(e){return new dy(e)}function $ee(e){return new Yp(e)}function Dee(e){return new my(e)}function Oee(e){return new Qp(e)}function zee(e){return new Hr(e)}function Pee(e){return new Zp(e)}function Lee(e){return new Ky(e)}function Wee(e){return new Xy(e)}var Bee=Y3,Vee=J3,Uee=Q3,Hee=e7;function jee(e){return new Fy(e)}function Gee(e){return new My(e)}function qee(e){return new $y(e)}function Xee(e){return new vy(e)}var ov={};ze(ov,{MAPE:()=>kne,MSE:()=>Sne,binaryAccuracy:()=>fne,binaryCrossentropy:()=>mne,categoricalAccuracy:()=>yne,categoricalCrossentropy:()=>gne,cosineProximity:()=>_ne,mape:()=>Nne,meanAbsoluteError:()=>bne,meanAbsolutePercentageError:()=>vne,meanSquaredError:()=>Ine,mse:()=>Tne,precision:()=>xne,recall:()=>wne,sparseCategoricalAccuracy:()=>Ane});function fne(e,t){return LA(e,t)}function mne(e,t){return m7(e,t)}function Ane(e,t){return A7(e,t)}function yne(e,t){return WA(e,t)}function gne(e,t){return BA(e,t)}function xne(e,t){return f7(e,t)}function wne(e,t){return pte(e,t)}function _ne(e,t){return zA(e,t)}function bne(e,t){return Wp(e,t)}function vne(e,t){return Ll(e,t)}function kne(e,t){return Ll(e,t)}function Nne(e,t){return Ll(e,t)}function Ine(e,t){return Ti(e,t)}function Sne(e,t){return Ti(e,t)}function Tne(e,t){return Ti(e,t)}var lv={};ze(lv,{modelFromJSON:()=>qte});var uv={};ze(uv,{l1:()=>Ene,l1l2:()=>Cne,l2:()=>Rne});function Cne(e){return new Ec(e)}function Ene(e){return tne(e)}function Rne(e){return nne(e)}var cv=class extends Pl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ha))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function n0(e,t){return e<t}function hv(e,t){return e>t}var dv=class extends cv{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new De("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=n0:this.mode==="max"?this.monitorFunc=hv:this.monitor.indexOf("acc")!==-1?this.monitorFunc=hv:this.monitorFunc=n0,this.monitorFunc===n0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===n0?Infinity:-Infinity}async onEpochEnd(e,t){await Ga(t);let n=this.getMonitorValue(t);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){e==null&&(e={});let t=e[this.monitor];return t==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`),t}};function Fne(e){return new dv(e)}var Mne={earlyStopping:Fne},Sr;(function(e){e[e.DT_INVALID=0]="DT_INVALID",e[e.DT_FLOAT=1]="DT_FLOAT",e[e.DT_DOUBLE=2]="DT_DOUBLE",e[e.DT_INT32=3]="DT_INT32",e[e.DT_UINT8=4]="DT_UINT8",e[e.DT_INT16=5]="DT_INT16",e[e.DT_INT8=6]="DT_INT8",e[e.DT_STRING=7]="DT_STRING",e[e.DT_COMPLEX64=8]="DT_COMPLEX64",e[e.DT_INT64=9]="DT_INT64",e[e.DT_BOOL=10]="DT_BOOL",e[e.DT_QINT8=11]="DT_QINT8",e[e.DT_QUINT8=12]="DT_QUINT8",e[e.DT_QINT32=13]="DT_QINT32",e[e.DT_BFLOAT16=14]="DT_BFLOAT16",e[e.DT_FLOAT_REF=101]="DT_FLOAT_REF",e[e.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",e[e.DT_INT32_REF=103]="DT_INT32_REF",e[e.DT_UINT8_REF=104]="DT_UINT8_REF",e[e.DT_INT16_REF=105]="DT_INT16_REF",e[e.DT_INT8_REF=106]="DT_INT8_REF",e[e.DT_STRING_REF=107]="DT_STRING_REF",e[e.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",e[e.DT_INT64_REF=109]="DT_INT64_REF",e[e.DT_BOOL_REF=110]="DT_BOOL_REF",e[e.DT_QINT8_REF=111]="DT_QINT8_REF",e[e.DT_QUINT8_REF=112]="DT_QUINT8_REF",e[e.DT_QINT32_REF=113]="DT_QINT32_REF",e[e.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(Sr||(Sr={}));var pv;(function(e){let t;(function(n){n[n.LEGACY=0]="LEGACY",n[n.V1=1]="V1",n[n.V2=2]="V2"})(t=e.CheckpointFormatVersion||(e.CheckpointFormatVersion={}))})(pv||(pv={}));var Zy={};function $ne(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Zy[e]=n}function fv(e){return Zy[e]}function Dne(e){delete Zy[e]}function k(e,t,n,r,a){let s=t.inputParams[e];if(s&&s.inputIndexStart!==void 0){let o=s.inputIndexStart,l=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?o+1:s.inputIndexEnd;if(s.type==="tensor")return En(t.inputNames[s.inputIndexStart],n,r,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>En(h,n,r,a));let u=En(t.inputNames.slice(o)[0],n,r,a),c=u.dataSync();return s.type==="number"?c[0]:v.toNestedArray(u.shape,c)}let i=t.attrParams[e];return i&&i.value}function En(e,t,n,r){let[a,s]=Wn(e);if(r!=null){let o=r.getHashTableHandleByName(a);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[r0(a,o)]);return i!==void 0?t[r0(a,i)][s]:void 0}function One(e,t,n){return t[r0(e,n.currentContextId)]}function da(e,t){let[n,r]=Wn(e);return[r0(n,t&&t.currentContextId),r]}function r0(e,t){return t?`${e}-${t}`:e}function Wn(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function a0(e,t,n){let r=k("pad",e,t,n);if(r==="explicit"){r=k("explicitPaddings",e,t,n);let a=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)a[s][0]=r[s*2],a[s][1]=r[s*2+1];return a}return r}function pa(e){return e.kept?e:Rr(e)}var mv={};ze(mv,{json:()=>zne});var zne=[{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}]}],Av={};ze(Av,{json:()=>Pne});var Pne=[{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}]}],yv={};ze(yv,{json:()=>Lne});var Lne=[{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"}]}],gv={};ze(gv,{json:()=>Wne});var Wne=[{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"}]}],xv={};ze(xv,{json:()=>Bne});var Bne=[{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"}]}],wv={};ze(wv,{json:()=>Vne});var Vne=[{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}]}],_v={};ze(_v,{json:()=>Une});var Une=[{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"}]}],bv={};ze(bv,{json:()=>Hne});var Hne=[{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"}]}],vv={};ze(vv,{json:()=>jne});var jne=[{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"}]}],kv={};ze(kv,{json:()=>Gne});var Gne=[{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"}]}],Nv={};ze(Nv,{json:()=>qne});var qne=[{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}]}],Iv={};ze(Iv,{json:()=>Xne});var Xne=[{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}]}],Sv={};ze(Sv,{json:()=>Kne});var Kne=[{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}]}],Tv={};ze(Tv,{json:()=>Zne});var Zne=[{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"}]}],Cv={};ze(Cv,{json:()=>Yne});var Yne=[{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}]}],Ev={};ze(Ev,{json:()=>Jne});var Jne=[{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}]}],Rv={};ze(Rv,{json:()=>Qne});var Qne=[{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:[]}],Mv=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[mv,Av,yv,gv,xv,wv,_v,Nv,kv,bv,Iv,Sv,Tv,Cv,Ev,Rv,vv],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,r)=>(n[r.tfOpName]=r,n),{})}transformGraph(e,t={}){let n=e.node,r=[],a=[],s=[],i=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?r.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={},c={};t!=null&&(u=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));let h=Object.keys(i);h.forEach(f=>{let m=i[f];m.inputNames.forEach(A=>{let[y]=da(A);m.inputs.push(i[y]),i[y].children.push(m)})}),Object.keys(c).length===0?h.forEach(f=>{let m=i[f];m.children.length===0&&l.push(m)}):Object.keys(c).forEach(f=>{let[m]=da(f),A=i[m];A!=null&&(A.signatureKey=c[f],l.push(A))}),Object.keys(u).length>0?Object.keys(u).forEach(f=>{let[m]=da(f),A=i[m];A&&(A.signatureKey=u[f],o.push(A))}):o=r;let d={};e.library!=null&&e.library.function!=null&&(d=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let p={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:d};return s.length>0&&(p.initNodes=s),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=fv(e.op)||this.opMappers[e.op]||{};e.attr==null&&(e.attr={});let n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map(r=>r.startsWith("^")?r.substr(1):r),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((r,a)=>(r[a.name]={type:a.type,inputIndexStart:a.start,inputIndexEnd:a.end},r),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((r,a)=>{let s=a.type,i;switch(a.type){case"string":i=Yy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Yy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=s2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=s2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=Qy(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=Qy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=a2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=a2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=Jy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Jy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=o2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=o2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=r2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=r2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=i2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=i2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=t2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=t2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype[]":i=n2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=n2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"func":i=Fv(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Fv(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 r[a.name]={value:i,type:s},r},{})),n}mapFunction(e){let t=e.nodeDef,n=[],r=[],a={};t!=null&&(a=t.reduce((u,c)=>(u[c.name]=this.mapNode(c),c.op==="Const"&&r.push(u[c.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[c]=da(u.name),h={name:c,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:e2(u.type),type:"dtype"}},children:[]};h.signatureKey=u.name,s.push(h),a[c]=h}),Object.keys(a).forEach(u=>{let c=a[u];c.inputNames.forEach(h=>{let[d]=da(h);c.inputs.push(a[d]),a[d].children.push(c)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[c,h]=da(o[u.name]),d=a[c];d!=null&&(d.defaultOutput=h,i.push(d))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:r,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function ere(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 $v(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):ere(e);return t?n:n.toLowerCase()}function Yy(e,t,n,r=!1){let a=e[t];return a!=null?$v(a.s,r):n}function Jy(e,t,n){let r=e[t];return r?r.b:n}function Qy(e,t,n){let r=e[t]||{},a=r.i!=null?r.i:r.f!=null?r.f:n;return typeof a=="number"?a:parseInt(a,10)}function e2(e){switch(typeof e=="string"&&(e=Sr[e]),e){case Sr.DT_FLOAT:return"float32";case Sr.DT_INT32:case Sr.DT_INT64:case Sr.DT_INT8:case Sr.DT_UINT8:return"int32";case Sr.DT_BOOL:return"bool";case Sr.DT_DOUBLE:return"float32";case Sr.DT_STRING:return"string";default:return null}}function Fv(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function t2(e,t,n){let r=e[t];return r&&r.type?e2(r.type):n}function n2(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>e2(a)):n}function Dv(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function r2(e,t,n){let r=e[t];return r&&r.shape?Dv(r.shape):n}function a2(e,t,n){let r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map(a=>typeof a=="number"?a:parseInt(a,10)):n}function s2(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>$v(s,r)):n}function i2(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>Dv(a)):n}function o2(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var tre=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(r=>this.getInput(r)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((r,a)=>(r[a]=this.getAttr(a),r),{}))}getInput(e){return En(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return En(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Qy(this.node.rawAttrs,e,t);if(n.s!=null)return Yy(this.node.rawAttrs,e,t);if(n.b!=null)return Jy(this.node.rawAttrs,e,t);if(n.shape!=null)return r2(this.node.rawAttrs,e,t);if(n.type!=null)return t2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return a2(this.node.rawAttrs,e,t);if(n.list.s!=null)return s2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return i2(this.node.rawAttrs,e,t);if(n.list.b!=null)return o2(this.node.rawAttrs,e,t);if(n.list.type!=null)return n2(this.node.rawAttrs,e,t)}return t}},nre=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[se(k("a",e,t,n),k("b",e,t,n))];case"AddN":return[Ma(k("tensors",e,t,n))];case"FloorMod":case"Mod":return[om(k("a",e,t,n),k("b",e,t,n))];case"Mul":return[P(k("a",e,t,n),k("b",e,t,n))];case"RealDiv":case"Div":return[ge(k("a",e,t,n),k("b",e,t,n))];case"DivNoNan":return[Yf(k("a",e,t,n),k("b",e,t,n))];case"FloorDiv":return[hd(k("a",e,t,n),k("b",e,t,n))];case"Sub":return[ye(k("a",e,t,n),k("b",e,t,n))];case"Minimum":return[fl(k("a",e,t,n),k("b",e,t,n))];case"Maximum":return[Dr(k("a",e,t,n),k("b",e,t,n))];case"Pow":return[sa(k("a",e,t,n),k("b",e,t,n))];case"SquaredDifference":return[Dd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},rre=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[zt(k("x",e,t,n))];case"Acos":return[Df(k("x",e,t,n))];case"Acosh":return[Of(k("x",e,t,n))];case"Asin":return[Pf(k("x",e,t,n))];case"Asinh":return[Lf(k("x",e,t,n))];case"Atan":return[Wf(k("x",e,t,n))];case"Atan2":return[Bf(k("x",e,t,n),k("y",e,t,n))];case"Atanh":return[Vf(k("x",e,t,n))];case"Ceil":return[Gf(k("x",e,t,n))];case"Complex":return[Ca(k("real",e,t,n),k("imag",e,t,n))];case"Cos":return[Vu(k("x",e,t,n))];case"Cosh":return[Ad(k("x",e,t,n))];case"Elu":return[cl(k("x",e,t,n))];case"Erf":return[Jf(k("x",e,t,n))];case"Exp":return[qn(k("x",e,t,n))];case"Expm1":return[Qf(k("x",e,t,n))];case"Floor":return[hl(k("x",e,t,n))];case"Log":return[Mn(k("x",e,t,n))];case"Log1p":return[wd(k("x",e,t,n))];case"Imag":return[gd(k("x",e,t,n))];case"Neg":return[vt(k("x",e,t,n))];case"Reciprocal":return[cm(k("x",e,t,n))];case"Real":return[Ku(k("x",e,t,n))];case"Relu":return[zr(k("x",e,t,n))];case"Round":return[hm(k("x",e,t,n))];case"Selu":return[Cd(k("x",e,t,n))];case"Sigmoid":return[Fn(k("x",e,t,n))];case"Sin":return[Ed(k("x",e,t,n))];case"Sign":return[pm(k("x",e,t,n))];case"Sinh":return[Rd(k("x",e,t,n))];case"Softplus":return[dl(k("x",e,t,n))];case"Sqrt":return[en(k("x",e,t,n))];case"Square":return[lt(k("x",e,t,n))];case"Tanh":return[ol(k("x",e,t,n))];case"Tan":return[Am(k("x",e,t,n))];case"ClipByValue":return[vn(k("x",e,t,n),k("clipValueMin",e,t,n),k("clipValueMax",e,t,n))];case"Relu6":return[Sd(k("x",e,t,n))];case"Rsqrt":return[Td(En(e.inputNames[0],t,n))];case"Prod":return[Nd(k("x",e,t,n),k("axes",e,t,n))];case"LeakyRelu":return[Hu(k("x",e,t,n),k("alpha",e,t,n))];case"Prelu":return[Xu(k("x",e,t,n),k("alpha",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function cr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let r=0;r<e.length;r++){let a=e[r],s=t[r];v.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Ov(e){return!(typeof e=="number"||e.some(t=>t<0))}function Oc(e,t,n){let r=l2(e,n),a=!Ov(r);if(a&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(a&&t.forEach(s=>{r=l2(s.shape,r)}),!Ov(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function l2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let r=0;r<e.length;++r){let a=e[r],s=t[r];if(a>=0&&s>=0&&a!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=a>=0?a:s}return n}var are=class{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=be(0),Gt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),cr(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Gt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,r)=>this.write(n,t[r]))}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 r=0;r<this.size();r++)e.push(r)}if(e.length===0)return yr([],[0].concat(this.elementShape));let n=this.readMany(e);return cr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),dn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return yr([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return cr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),st(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,sr(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,r=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let a=n===0?0:t.size/n,s=[];B(()=>{t=G(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],u=[0,l,0],c=[1,e[o],a];s[o]=G(Re(t,u,c),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},zc=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(a=>{if(n!==a.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${a.dtype}`);cr(t,a.shape,"TensorList shape mismatch: "),Gt(a)}),this.idTensor=be(0),this.maxNumElements=r,Gt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new zc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);cr(e,this.elementShape,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,e);return B(()=>{let a=this.tensors.map(s=>G(s,r));return dn(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 n=Oc(this.elementShape,this.tensors,e),r=this.tensors.pop();return cr(r.shape,e,"TensorList shape mismatch: "),G(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(cr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Gt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);cr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,t);return G(this.tensors[e],r)}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.`);cr(this.elementShape,t.shape,"TensorList shape mismatch: "),Gt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);cr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let r=Oc(this.elementShape,this.tensors,n);return e.length===0?yr([],[0].concat(r)):B(()=>{let a=e.map(s=>G(this.tensors[s],r));return dn(a,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);cr(this.elementShape,t,"TensorList shape mismatch: ");let n=Oc(this.elementShape,this.tensors,t);return this.size()===0?yr([],[0].concat(n)):B(()=>{let r=this.tensors.map(a=>G(a,n));return st(r,0)})}};function sre(e,t,n){let r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let a=e.shape.slice(1);cr(a,t,"TensorList shape mismatch: ");let s=sr(e);return new zc(s,t,r)}function ire(e,t,n){return new zc([],e,t,n)}function ore(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let a=Math.max(...t);if(r!=null&&r!==-1&&a>=r)throw new Error(`Max index must be < array size (${a} vs. ${r})`);let s=new zc([],n,e.dtype,r),i=sr(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function lre(e,t,n){let r=0,a=t.map(c=>(r+=c,r));if(r!==e.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: ${e.shape}`);let s=e.shape.slice(1),i=l2(s,n),o=r===0?0:e.size/r,l=B(()=>{let c=[];e=G(e,[1,r,o]);for(let h=0;h<t.length;++h){let d=h===0?0:a[h-1],p=[0,d,0],f=[1,t[h],o];c[h]=G(Re(e,p,f),i)}return e.dispose(),c}),u=new zc([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var ure=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=k("thenBranch",e,t,n),a=k("elseBranch",e,t,n),s=k("cond",e,t,n),i=k("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=k("body",e,t,n),a=k("cond",e,t,n),s=k("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(c=>c.id),l=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;l[0];){let c=u;u=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let h=u.map(p=>p.id);c.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()})}return u}case"LoopCond":{let r=k("pred",e,t,n);return[pa(r)]}case"Switch":{let r=k("pred",e,t,n),a=k("data",e,t,n);return a.kept||(a=pa(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>En(a,t,n)!==void 0);if(r){let a=En(r,t,n);return[pa(a)]}return}case"Enter":{let r=k("frameName",e,t,n),a=k("tensor",e,t,n);return n.enterFrame(r),[pa(a)]}case"Exit":{let r=k("tensor",e,t,n);return n.exitFrame(),[pa(r)]}case"NextIteration":{let r=k("tensor",e,t,n);return n.nextIteration(),[pa(r)]}case"TensorArrayV3":{let r=k("size",e,t,n),a=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),u=k("name",e,t,n),c=new are(u,a,r,s,l,i,o);return n.addTensorArray(c),[c.idTensor,be(1)]}case"TensorArrayWriteV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=k("tensorArrayId",e,t,n),a=k("tensor",e,t,n),s=k("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[be(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=k("indices",e,t,n),a=k("tensor",e,t,n),s=k("elementShape",e,t,n),i=k("numElements",e,t,n),o=ore(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=ire(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=k("tensorListId",e,t,n),a=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=sre(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=k("tensorListId",e,t,n),a=n.getTensorList(r.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=k("tensorListId",e,t,n),a=k("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("lengths",e,t,n),i=lre(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function zv(e,t,n){let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="fusedbatchnorm",l=k("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=k("strides",e,t,n),c=a0(e,t,n),h=k("dataFormat",e,t,n).toUpperCase(),d=k("dilations",e,t,n),[p,f]=k("args",e,t,n),m=k("leakyreluAlpha",e,t,n);return{stride:u,pad:c,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var cre=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=k("stride",e,t,n),a=k("pad",e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilation",e,t,n);return[fd(k("x",e,t,n),k("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=k("strides",e,t,n),a=a0(e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilations",e,t,n);return[ra(k("x",e,t,n),k("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:c}=zv(e,t,n);return[La.conv2d({x:k("x",e,t,n),filter:k("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:c}=zv(e,t,n);return[La.depthwiseConv2d({x:k("x",e,t,n),filter:k("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=k("outputShape",e,t,n),a=k("strides",e,t,n),s=a0(e,t,n);return[md(k("x",e,t,n),k("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=k("strides",e,t,n),a=a0(e,t,n),s=k("dilations",e,t,n),i=k("dataFormat",e,t,n).toUpperCase();return[ul(k("input",e,t,n),k("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilations",e,t,n);return[Xf(k("x",e,t,n),k("filter",e,t,n),[r[1],r[2],r[3]],a,s,[i[1],i[2],i[3]])]}case"AvgPool":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[Lu(k("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPool":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[Gu(k("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a)]}case"MaxPoolWithArgmax":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n),i=k("includeBatchInIndex",e,t,n),{result:o,indexes:l}=cx(k("x",e,t,n),[s[1],s[2]],[r[1],r[2]],a,i);return[o,l]}case"AvgPool3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[jf(k("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"MaxPool3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("kernelSize",e,t,n);return[sm(k("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("dilations",e,t,n),i=r[1],o=r[2],l=s[1],u=s[2];return[Zf(k("x",e,t,n),k("filter",e,t,n),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hre=(e,t,n)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),a=k("dtype",e,t,n),s=k("value",e,t,n);return[Uu(r,s,a)]}case"LinSpace":{let r=k("start",e,t,n),a=k("stop",e,t,n),s=k("num",e,t,n);return[rx(r,a,s)]}case"Multinomial":{let r=k("logits",e,t,n),a=k("numSamples",e,t,n),s=k("seed",e,t,n);return[hx(r,a,s)]}case"OneHot":{let r=k("indices",e,t,n),a=k("depth",e,t,n),s=k("onValue",e,t,n),i=k("offValue",e,t,n);return[nl(r,a,s,i)]}case"Ones":return[Or(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[$n(k("x",e,t,n))];case"RandomUniform":return[ml(k("shape",e,t,n),k("minval",e,t,n),k("maxval",e,t,n),k("dtype",e,t,n))];case"Range":{let r=k("start",e,t,n),a=k("stop",e,t,n),s=k("step",e,t,n);return[Id(r,a,s,k("dtype",e,t,n))]}case"TruncatedNormal":{let r=k("shape",e,t,n),a=k("mean",e,t,n),s=k("stdDev",e,t,n),i=k("seed",e,t,n);return[Od(r,a,s,k("dtype",e,t,n),i)]}case"Zeros":return[Et(k("shape",e,t,n),k("dtype",e,t,n))];case"ZerosLike":return[He(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function u2(e,t,n){let r=k("boxes",e,t,n),a=k("scores",e,t,n),s=k("maxOutputSize",e,t,n),i=k("iouThreshold",e,t,n),o=k("scoreThreshold",e,t,n),l=k("softNmsSigma",e,t,n);return{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var dre=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=u2(e,t,n),u=await Je.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=u2(e,t,n),l=k("padToMaxOutputSize",e,t,n),u=await Je.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=u2(e,t,n);return[await Je.nonMaxSuppressionAsync(r,a,s,i,o)]}case"Where":{let r=me(k("condition",e,t,n),"bool"),a=[await xm(r)];return r.dispose(),a}case"ListDiff":return fx(k("x",e,t,n),k("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},pre=(e,t,n)=>{switch(e.op){case"TopKV2":{let r=k("x",e,t,n),a=k("k",e,t,n),s=k("sorted",e,t,n),i=ym(r,a,s);return[i.values,i.indices]}case"Unique":{let r=k("x",e,t,n),a=zd(r);return[a.values,a.indices]}case"UniqueV2":{let r=k("x",e,t,n),a=k("axis",e,t,n),s=zd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fre=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=k("default",e,t,n);return[En(e.name,t,n)||r];case"Placeholder":return[En(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=k("x",e,t,n);return[pa(u)]}case"IdentityN":return k("x",e,t,n).map(u=>pa(u));case"Snapshot":let a=k("x",e,t,n);return[pa(a)];case"Shape":return[on(k("x",e,t,n).shape,"int32")];case"ShapeN":return k("x",e,t,n).map(u=>on(u.shape));case"Size":return[be(k("x",e,t,n).size,"int32")];case"Rank":return[be(k("x",e,t,n).rank,"int32")];case"NoOp":return[be(1)];case"Print":let s=k("x",e,t,n),i=k("data",e,t,n),o=k("message",e,t,n),l=k("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mre=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=be(0),this.tensorMap=new Map,Gt(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 be(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),B(()=>{let r=sr(t),a=n.length,s=r.length;v.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=n[i],l=r[i];Gt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return B(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return dn(r)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Are=async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,n),s=k("valueDType",e,t,n),i=new mre(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("values",e,t,n);return[await r.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=k("tableHandle",e,t,n,r),s=k("keys",e,t,n),i=k("defaultValue",e,t,n);return[await r.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=k("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},yre=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[Je.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let r=k("images",e,t,n),a=k("size",e,t,n),s=k("alignCorners",e,t,n),i=k("halfPixelCenters",e,t,n);return[Je.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{let r=k("image",e,t,n),a=k("boxes",e,t,n),s=k("boxInd",e,t,n),i=k("cropSize",e,t,n),o=k("method",e,t,n),l=k("extrapolationValue",e,t,n);return[Je.cropAndResize(r,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},gre=(e,t,n)=>{switch(e.op){case"Equal":return[Da(k("a",e,t,n),k("b",e,t,n))];case"NotEqual":return[di(k("a",e,t,n),k("b",e,t,n))];case"Greater":return[rr(k("a",e,t,n),k("b",e,t,n))];case"GreaterEqual":return[za(k("a",e,t,n),k("b",e,t,n))];case"Less":return[xd(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[ci(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[ar(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[ju(k("a",e,t,n))];case"LogicalOr":return[vd(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[kn(k("condition",e,t,n),k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},xre=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Ge(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Transpose":return[at(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=k("numArgs",e,t,n),l=k("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=k("args",e,t,n);return[La.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wre=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[li(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"FusedBatchNormV3":return[li(k("x",e,t,n),k("mean",e,t,n),k("variance",e,t,n),k("offset",e,t,n),k("scale",e,t,n),k("epsilon",e,t,n))];case"LRN":return[tm(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[Yu(k("x",e,t,n))];case"LogSoftmax":return[bd(k("x",e,t,n))];case"SparseToDense":return[wm(k("sparseIndices",e,t,n),k("outputShape",e,t,n),k("sparseValues",e,t,n),k("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_re=(e,t,n)=>{switch(e.op){case"Max":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Xn(k("x",e,t,n),i,o)]}case"Mean":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[kt(k("x",e,t,n),i,o)]}case"Min":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[pl(k("x",e,t,n),i,o)]}case"Sum":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Te(k("x",e,t,n),i,o)]}case"All":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[dd(k("x",e,t,n),i,o)]}case"Any":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Ou(k("x",e,t,n),i,o)]}case"ArgMax":{let i=k("axis",e,t,n);return[zu(k("x",e,t,n),i)]}case"ArgMin":{let i=k("axis",e,t,n);return[zf(k("x",e,t,n),i)]}case"Prod":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Nd(k("x",e,t,n),i,o)]}case"Cumsum":{let i=k("axis",e,t,n),o=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[yd(k("x",e,t,n),i,o,l)]}case"Bincount":let r=k("x",e,t,n),a=k("weights",e,t,n),s=k("size",e,t,n);return[q5(r,a,s)];case"DenseBincount":{let i=k("x",e,t,n),o=k("weights",e,t,n),l=k("size",e,t,n),u=k("binaryOutput",e,t,n);return[J5(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},bre=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[st(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[ui(r,me(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[ui(s,me(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=k("x",e,t,n);return[Dn(s,a)]}case"ReverseV2":{let r=k("axis",e,t,n),a=k("x",e,t,n);return[Dn(a,r)]}case"Slice":{let r=k("begin",e,t,n),a=k("size",e,t,n);return[Re(k("x",e,t,n),r,a)]}case"StridedSlice":{let r=k("begin",e,t,n),a=k("end",e,t,n),s=k("strides",e,t,n),i=k("beginMask",e,t,n),o=k("endMask",e,t,n),l=k("ellipsisMask",e,t,n),u=k("newAxisMask",e,t,n),c=k("shrinkAxisMask",e,t,n),h=k("x",e,t,n);return[mm(h,r,a,s,i,o,l,u,c)]}case"Pack":return B(()=>{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Pa(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(Pa(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[dn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return sr(a,r)}case"Tile":{let r=k("reps",e,t,n);return[Oa(k("x",e,t,n),r)]}case"Split":case"SplitV":{let r=k("axis",e,t,n),a=k("numOrSizeSplits",e,t,n),s=k("x",e,t,n);return Lt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[gx(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[xx(r,a)]}case"SparseToDense":{let r=k("sparseIndices",e,t,n),a=k("outputShape",e,t,n),s=k("sparseValues",e,t,n),i=k("defaultValue",e,t,n);return[wm(r,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},vre=(e,t,n)=>{switch(e.op){case"FFT":return[Ju(k("x",e,t,n))];case"IFFT":return[Al(k("x",e,t,n))];case"RFFT":return[Qu(k("x",e,t,n))];case"IRFFT":return[$d(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},kre=(e,t,n)=>{switch(e.op){case"Cast":return[me(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let r=k("axis",e,t,n);return[hn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Pa(k("x",e,t,n),r)]}case"Reshape":return[G(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[im(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[aa(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let r=k("blockShape",e,t,n),a=k("paddings",e,t,n);return[qu(k("x",e,t,n),r,a)]}case"BatchToSpaceND":{let r=k("blockShape",e,t,n),a=k("crops",e,t,n);return[Wu(k("x",e,t,n),r,a)]}case"DepthToSpace":{let r=k("blockSize",e,t,n),a=k("dataFormat",e,t,n).toUpperCase();return[Kf(k("x",e,t,n),r,a)]}case"BroadcastTo":return[Bu(k("x",e,t,n),k("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Pv(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>nre(s,i,o));case"basic_math":return B(()=>rre(s,i,o));case"control":return ure(s,i,o);case"convolution":return B(()=>cre(s,i,o));case"creation":return B(()=>hre(s,i,o));case"dynamic":return dre(s,i,o);case"evaluation":return B(()=>pre(s,i,o));case"image":return B(()=>yre(s,i,o));case"graph":return B(()=>fre(s,i,o));case"logical":return B(()=>gre(s,i,o));case"matrices":return B(()=>xre(s,i,o));case"normalization":return B(()=>wre(s,i,o));case"reduction":return B(()=>_re(s,i,o));case"slice_join":return B(()=>bre(s,i,o));case"spectral":return B(()=>vre(s,i,o));case"transformation":return B(()=>kre(s,i,o));case"hash_table":return Are(s,i,o,r);case"custom":let l=fv(s.op);if(l&&l.customExecutor)return l.customExecutor(new tre(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var Lv=class{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Bv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>Wn(d)[0]),c=[];r!=null&&(c=r.map(d=>Wn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((Wv(d)||Nre(d)||Ire(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Sre(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>Wn(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Tre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Cre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Ere=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Wv(e){return Tre.indexOf(e.op)>=0}function Nre(e){return Cre.indexOf(e.op)>=0}function Ire(e){return Ere.indexOf(e.op)>=0}var c2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new c2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=Bv(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;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 [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Sre(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[Wn(c)[0]]),a=t.map(c=>Wn(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let c=new Lv(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Wn(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let A=Pv(m,h,c,this._resourceManager);if(v.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=A,this.checkTensorForDisposal(m.name,m,h,c,d,a,p)}}return this.parent==null&&c.dispose(d),t.map(f=>En(f,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=One(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new Lv(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>En(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Wn(g)[0]]),i=n.map(g=>Wn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=Bv(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,b]=Wn(g),w=[];w[b]=e[g],p[_]=w});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}c==null&&!r&&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(g=>!Wv(g)&&!En(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`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}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=da(c.node.name,n)),r[c.node.name]==null){let d=Pv(c.node,r,n,this._resourceManager);h||([h]=da(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=da(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!En(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!En(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Wn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Wn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Wn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Rre=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Fre="?tfjs-format=file",Mre="model.json",Vv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Rre}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=bn.browserHTTPRequest(e,this.loadOptions);else{let t=bn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(bn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=bn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new c2(Mv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Mv.Instance.transformGraph(e.modelInitializer);this.initializer=new c2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=bn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof Ue)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Ft(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Mre}${Fre}`);let n=new Vv(e,t);return await n.load(),n}var $re="3.3.0",Uv={};ze(Uv,{CSVDataset:()=>jv,Dataset:()=>Vl,FileDataSource:()=>Gv,TextLineDataset:()=>Hv,URLDataSource:()=>qv,array:()=>Dre,csv:()=>zre,func:()=>Pre,generator:()=>Lre,microphone:()=>Bre,version_data:()=>Vre,webcam:()=>Wre,zip:()=>Ore});var Ure=qi(Mg()),Hre=qi(Mg());function jre(e,t){return s0(e,t)}function s0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.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(Ul(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=s0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Gre(e,t=Kv){return Xv(e,t)}function Xv(e,t,n=new Set){let r=e[0];if(n.has(r))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(Ul(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(u=>u[i]),l=Xv(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function Kv(e){return e===null?null:Ul(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Zv(e,t){let n=new Map;s0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return s0(e,t,n)}function Ul(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ue))}function Xre(e){return e==null||qre(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ue||v.isTypedArray(e)}function qre(e){return e===null||typeof e!="object"&&typeof e!="function"}function Zre(e){return jre(e,Kre)}function Kre(e){return e instanceof Ue?{value:e.clone(),recurse:!1}:Ul(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Yv=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},h2=class extends Yv{constructor(){super(h2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};h2.INITIAL_CAPACITY=32;function Jv(e){return new Yre(e)}function d2(e){return new Jre(e)}function Qre(e,t){return new Qv(e,t)}function tae(e,t=Za.FAIL){return new eae(e,t)}var Zt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new lae(this,e)}filter(e){return new iae(this,e)}map(e){return new oae(this,e)}mapAsync(e){return new e6(this,e)}serialMapAsync(e){return new e6(this,e).serial()}flatmap(e){return new uae(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 sae(this,e,t)}columnMajorBatch(e,t=!0,n=Kv){return this.rowMajorBatch(e,t).map(r=>Gre(r,n))}concatenate(e,t){return new Qv(Jv([this,e]),t)}take(e){return e<0||e==null?this:new aae(this,e)}skip(e){return e<0||e==null?this:new rae(this,e)}prefetch(e){return new t6(this,e)}shuffle(e,t){return new cae(this,e,t)}serial(){return new nae(this)}},Yre=class extends Zt{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:Zre(e),done:!1}}},Jre=class extends Zt{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: ${e.message}`,e}}},nae=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},rae=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ee(e.value)}return this.upstream.next()}},aae=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},sae=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},iae=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;Ee(e.value)}}},oae=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=mr.getTensorsInContainer(e.value),n=this.transform(e.value),r=mr.getTensorsInContainer(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},lae=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},e6=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=mr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=mr.getTensorsInContainer(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},p2=class extends Zt{constructor(){super();this.outputQueue=new h2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},uae=class extends p2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=mr.getTensorsInContainer(e.value),n=this.transform(e.value),r=mr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return!0}},Qv=class extends Zt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Za;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Za||(Za={}));var eae=class extends Zt{constructor(e,t=Za.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Zt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await Zv(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Za.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Za.SHORTEST:return{value:null,done:!0};case Za.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},t6=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Yv(t)}summary(){return`${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()}},cae=class extends t6{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Hre.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Vl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Bn(async()=>(await n.iterator()).columnMajorBatch(e,t,hae),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Bn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Bn(async()=>(await t.iterator()).filter(r=>B(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Bn(async()=>(await t.iterator()).map(n=>B(()=>e(n))),this.size)}mapAsync(e){let t=this;return Bn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Bn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Bn(async()=>{let r=d2(async()=>({value:await t.iterator(),done:!1}));return Qre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Bn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Ure.alea(t||v.now().toString());return Bn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Bn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Vl.MAX_BUFFER_SIZE=1e4;function Bn(e,t=null){return new class extends Vl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Dre(e){return Bn(async()=>Jv(e),e.length)}function Ore(e){if(!Ul(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Bn(async()=>{let n=await Zv(e,r=>{if(r instanceof Vl)return{value:r.iterator(),recurse:!1};if(Ul(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return tae(n,Za.SHORTEST)},t)}function hae(e){if(e===null)return null;let t=e[0];return Xre(t)?{value:dae(e),recurse:!1}:{value:null,recurse:!0}}function dae(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ue?dn(e):yr(e)}var Hv=class extends Vl{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))}},i0='"',Pc=Symbol("out"),n6=Symbol("field"),o0=Symbol("quote"),f2=Symbol("quoteafterquote"),r6=Symbol("quoteinquote"),jv=class extends Vl{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 Hv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};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?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Pc;for(let i=0;i<a;i++)switch(s){case Pc:switch(e.charAt(i)){case i0:r=i+1,s=o0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Pc;break;default:s=n6,r=i;break}break;case n6:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Pc,r=i+1;break;default:}break;case o0:switch(e.charAt(i)){case i0:s=f2;break;default:}break;case f2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Pc,r=i+1;break;case i0:s=o0;break;default:s=r6;break}break;case r6:switch(e.charAt(i)){case i0:s=o0;break;default:}break;default:}if(s===f2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},a6=class extends Zt{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 a6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),yr(n,t)}},s6=class extends Zt{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=on([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=Nn([s,a,o,i],[1,4])}else this.cropBox=Nn([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 n=new s6(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=rl.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=hn(me(e,"float32"),0),n;n=Je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return G(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},i6=class{},o6=class extends Zt{split(e){return new pae(this,e)}},pae=class extends o6{constructor(e,t){super();this.upstream=e,this.impl=new fae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},fae=class extends p2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Aae=class extends Zt{decodeUTF8(){return new mae(this)}},mae=class extends o6{constructor(e){super();this.upstream=e,this.impl=new yae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yae=class extends p2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=nk();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},l6=class extends Aae{constructor(e,t={}){super();this.file=e,this.options=t,v.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 n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function xae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=gae(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new l6(s,t)}else throw new Error(a.statusText)}var gae=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 u6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var Gv=class extends i6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(u6(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new l6(this.input,this.options)}},qv=class extends i6{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return u6(this.url)?new Gv(this.url,this.fileOptions).iterator():xae(this.url,this.fileOptions)}};function zre(e,t={}){return new jv(new qv(e),t)}function Pre(e){let t=d2(e);return Bn(async()=>t)}function Lre(e){return Bn(async()=>{let t=await e();return d2(()=>t.next())})}async function Wre(e,t){return s6.create(e,t)}async function Bre(e){return a6.create(e)}var Vre="3.3.0",wae={tfjs:rk,"tfjs-core":ak,"tfjs-data":sk,"tfjs-layers":ik,"tfjs-converter":ok,"tfjs-backend-cpu":uw,"tfjs-backend-webgl":R_,"tfjs-backend-wasm":_3};var Vn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function c6(){if(!$f(Vn.name)){Ce("backend registration:",Vn.name);try{Vn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Vn.width,Vn.height):document.createElement("canvas")}catch(e){Ce("error: cannot create canvas:",e);return}try{Vn.gl=Vn.canvas.getContext("webgl2",Vn.webGLattr)}catch(e){Ce("error: cannot get WebGL2 context:",e);return}try{sp(2,Vn.gl)}catch(e){Ce("error: cannot set WebGL2 context:",e);return}try{let e=new up(Vn.gl);sl(Vn.name,()=>new El(e),Vn.priority)}catch(e){Ce("error: cannot register WebGL backend:",e);return}try{Yo("webgl").forEach(t=>{let n={...t,backendName:Vn.name};ni(n)})}catch(e){Ce("error: cannot update WebGL backend registration:",e);return}try{fr.set("WEBGL_VERSION",2)}catch(e){Ce("error: cannot set WebGL backend flags:",e);return}Ce("backend registered:",Vn.name)}}var h6=6;function _ae(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r<t.strides.length;r++){let a=t.strides[r],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[r];for(let l=0;l<s;l++){let u=a*(l+.5);for(let c=0;c<i;c++){let h=a*(c+.5);for(let d=0;d<o;d++)n.push([h,u])}}}return n}var bae=e=>({startEndTensor:e,startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});function vae(e,t,n){let r=Re(e,[0,1],[-1,2]),a=se(r,t),s=Re(e,[0,3],[-1,2]),i=ge(s,n),o=ge(a,n),l=ge(i,2),u=ye(o,l),c=se(o,l),h=P(u,n),d=P(c,n);return ll([h,d],1)}var d6=class{constructor(t,n){this.model=t,this.anchorsData=_ae(t.inputs[0].shape[1]),this.anchors=Nn(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=B(()=>{let d=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),p=this.model.predict(d),f;if(Array.isArray(p)){let g=p.sort((x,I)=>x.size-I.size),_=st([g[0],g[2]],2),b=st([g[1],g[3]],2);f=st([b,_],1).squeeze(0)}else f=p.squeeze();let m=vae(f,this.anchors,[this.inputSize,this.inputSize]),A=Re(f,[0,0],[-1,1]),y=Fn(A).squeeze();return[f,m,y]}),s=await Je.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Re(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;h<l.length;h++){let d=i[h],p=u[d];if(p>this.config.face.detector.minConfidence){let f=bae(l[h]),m=this.anchorsData[d],A=B(()=>Re(n,[d,h6-1],[1,-1]).squeeze().reshape([h6,-1]));c.push({box:f,landmarks:A,anchor:m,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function p6(e){let t=await Ft(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new d6(t,e);return e.debug&&Ce(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function f6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function Lc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Hl(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function jl(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Je.cropAndResize(t,s,[0],n)}function l0(e,t=1.5){let n=Hl(e),r=Lc(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function u0(e){let t=Hl(e),n=Lc(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}var c0=[[1,0,0],[0,1,0],[0,0,1]];function kae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function m2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return kae(n)}function m6(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function Ya(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Nae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function A6(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(Ya(e[a],Nae(t,s)))}return n}function h0(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=m6(t[0],t[1]),i=A6(s,a),o=m6(-t[0],-t[1]);return A6(i,o)}function y6(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-Ya(t[0],n),-Ya(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function g6(e,t){return[Ya(e,t[0]),Ya(e,t[1])]}var Gr={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]},A2=[{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]}],y2=[[.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]],Fi=[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 Iae=[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],Sae=[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],Tae=[33,133,362,263,1,78,308],Yce=Iae.map(e=>y2[e]),Jce=Sae.map(e=>y2[e]),Qce=Tae.map(e=>y2[e]);var g2=Gr.leftEyeLower0,x2=Gr.rightEyeLower0,Gl={leftBounds:[g2[0],g2[g2.length-1]],rightBounds:[x2[0],x2[x2.length-1]]},d0={count:468,mouth:13,symmetryLine:[13,Gr.midwayBetweenEyes[0]]},x6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ql={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function p0(e,t,n,r){for(let a=0;a<A2.length;a++){let{key:s,indices:i}=A2[a],o=Gr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var w2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Lc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?h0(r,[0,0]):c0,l=r!==0?i.map(h=>[...g6(h,o),h[2]]):i,u=r!==0?y6(a):c0,c=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(c,u[0]),h[1]+Ya(c,u[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[Gl.leftBounds[0]][2],r=t[Gl.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=u0(l0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Lc(i),l=Je.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&fr.flags.IS_BROWSER&&(l=Je.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<ql.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(ql.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Gr[`${r}EyeUpper0`][ql.upperCenter]][2],s=t[Gr[`${r}EyeLower0`][ql.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=a:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=f6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=l0(o),u=u0(l),c=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:h,landmarks:c}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=B(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,u,c=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&fr.flags.IS_BROWSER){let[w,x]=i.landmarks.length>=d0.count?d0.symmetryLine:x6.symmetryLine;c=m2(i.landmarks[w],i.landmarks[x]);let I=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),S=[I[0]/t.shape[2],I[1]/t.shape[1]],T=Je.rotateWithOffset(t,c,0,S);h=h0(-c,I),n.face.mesh.enabled?u=jl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.meshSize,this.meshSize]).div(255):u=jl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.boxSize,this.boxSize]).div(255)}else{h=c0;let w=t.clone();n.face.mesh.enabled?u=jl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):u=jl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:u};let[,d,p]=this.meshDetector.predict(u),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=G(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:x,crop:I}=this.getEyeBox(A,u,Gl.leftBounds[0],Gl.leftBounds[1],!0),{box:S,boxSize:T,crop:M}=this.getEyeBox(A,u,Gl.rightBounds[0],Gl.rightBounds[1]),z=this.irisModel.predict(st([I,M])).dataSync(),W=z.slice(0,ql.numCoordinates*3),{rawCoords:U,iris:H}=this.getEyeCoords(W,w,x,!0),X=z.slice(ql.numCoordinates*3),{rawCoords:j,iris:ee}=this.getEyeCoords(X,S,T),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(p0(A,U,"left",null),p0(A,j,"right",null)):Y<1?p0(A,U,"left",["EyeUpper0","EyeLower0"]):p0(A,j,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,H,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,c,h);i=l0(this.calculateLandmarksBoundingBox(y),1.5);let g=Nn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&n.face.detector.return&&fr.flags.IS_BROWSER){let[w,x]=i.landmarks.length>=d0.count?d0.symmetryLine:x6.symmetryLine;c=m2(i.landmarks[w],i.landmarks[x]);let I=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),S=[I[0]/t.shape[2],I[1]/t.shape[1]],T=Je.rotateWithOffset(t,c,0,S);h=h0(-c,I),u=jl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.meshSize,this.meshSize]).div(255)}let _={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:u,rawCoords:A},b=u0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},_}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var yg=Ah(_6());var v2={};pr(v2,{load:()=>k2,predict:()=>N2});var b2={};function qr(e,t){if(!t||!t.kernels)return;let n=5,r=t.kernels.filter(o=>o.kernelTimeMs>0).reduce((o,l)=>o+=l.kernelTimeMs,0),a=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.kernelTimeMs>0).sort((o,l)=>l.kernelTimeMs-o.kernelTimeMs),s=t.kernels.map((o,l)=>(o.id=l,o)).filter(o=>o.totalBytesSnapshot>0).sort((o,l)=>l.totalBytesSnapshot-o.totalBytesSnapshot);a.length>n&&(a.length=n),s.length>n&&(s.length=n);let i={newBytes:t.newBytes,newTensors:t.newTensors,peakBytes:t.peakBytes,numKernelOps:t.kernels.length,timeKernelOps:r,slowestKernelOps:a,largestKernelOps:s};b2[e]=i,Ce("Human profiler",e,i)}var Ja,f0={age:0},m0=Number.MAX_SAFE_INTEGER;async function k2(e){return Ja||(Ja=await Ft(e.face.age.modelPath),e.debug&&Ce(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),Ja}async function N2(e,t){return Ja?m0<t.face.age.skipFrames&&t.videoOptimized&&f0.age&&f0.age>0?(m0++,f0):(t.videoOptimized?m0=0:m0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Je.resizeBilinear(e,[Ja.inputs[0].shape[2],Ja.inputs[0].shape[1]],!1),a=P(r,[255]);Ee(r);let s,i={age:0};if(!t.profile)t.face.age.enabled&&(s=await Ja.predict(a));else{let o=t.face.age.enabled?await gr(()=>Ja.predict(a)):{};s=o.result.clone(),o.result.dispose(),qr("age",o)}if(a.dispose(),s){let o=s.dataSync();i.age=Math.trunc(10*o[0])/10}s.dispose(),f0=i,n(i)})):null}var I2={};pr(I2,{load:()=>E2,predict:()=>R2});var fa,S2={gender:""},A0=Number.MAX_SAFE_INTEGER,T2=!1,C2=[.2989,.587,.114];async function E2(e){return fa||(fa=await Ft(e.face.gender.modelPath),T2=fa.inputs[0].shape[3]===1,e.debug&&Ce(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),fa}async function R2(e,t){return fa?A0<t.face.gender.skipFrames&&t.videoOptimized&&S2.gender!==""?(A0++,S2):(t.videoOptimized?A0=0:A0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Je.resizeBilinear(e,[fa.inputs[0].shape[2],fa.inputs[0].shape[1]],!1),a;T2?a=B(()=>{let[o,l,u]=Lt(r,3,3),c=P(o,C2[0]),h=P(l,C2[1]),d=P(u,C2[2]);return Ma([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Ee(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await fa.predict(a));else{let o=t.face.gender.enabled?await gr(()=>fa.predict(a)):{};s=o.result.clone(),o.result.dispose(),qr("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(T2)(o[0]>t.face.gender.minConfidence||o[1]>t.face.gender.minConfidence)&&(i.gender=o[0]>o[1]?"female":"male",i.confidence=o[0]>o[1]?Math.trunc(100*o[0])/100:Math.trunc(100*o[1])/100);else{let l=Math.trunc(200*Math.abs(o[0]-.5))/100;l>t.face.gender.minConfidence&&(i.gender=o[0]<=.5?"female":"male",i.confidence=Math.min(.99,l))}}s.dispose(),S2=i,n(i)})):null}var F2={};pr(F2,{load:()=>D2,predict:()=>O2});var Eae=["angry","disgust","fear","happy","sad","surprise","neutral"],Qa,M2=[],y0=Number.MAX_SAFE_INTEGER,$2=[.2989,.587,.114];async function D2(e){return Qa||(Qa=await Ft(e.face.emotion.modelPath),e.debug&&Ce(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),Qa}async function O2(e,t){return Qa?y0<t.face.emotion.skipFrames&&t.videoOptimized&&M2.length>0?(y0++,M2):(t.videoOptimized?y0=0:y0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Je.resizeBilinear(e,[Qa.inputs[0].shape[2],Qa.inputs[0].shape[1]],!1),[a,s,i]=Lt(r,3,3);r.dispose();let o=P(a,$2[0]),l=P(s,$2[1]),u=P(i,$2[2]);a.dispose(),s.dispose(),i.dispose();let c=Ma([o,l,u]);o.dispose(),l.dispose(),u.dispose();let h=B(()=>c.sub(.5).mul(2));c.dispose();let d=[];if(t.face.emotion.enabled){let p;if(t.profile){let f=await gr(()=>Qa.predict(h));p=f.result.dataSync(),f.result.dispose(),qr("emotion",f)}else{let f=await Qa.predict(h);p=f.dataSync(),Ee(f)}for(let f=0;f<p.length;f++)p[f]>t.face.emotion.minConfidence&&d.push({score:Math.min(.99,Math.trunc(100*p[f])/100),emotion:Eae[f]});d.sort((f,m)=>m.score-f.score)}h.dispose(),M2=d,n(d)})):null}var Xr;async function z2(e){return Xr||(Xr=await Ft(e.face.embedding.modelPath),e.debug&&Ce(`load model: ${e.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),Xr}function P2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let r=e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(Math.trunc(1e3*(1-r))/1e3,0)}function b6(e,t,n=0){let r={simmilarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return r;for(let a of t)if(a.embedding&&a.name){let s=P2(e,a.embedding);s>n&&s>r.simmilarity&&(r={...a,simmilarity:s})}return r}function L2(e){return B(()=>{let n=[[.05,.15,.85,.85]],r=e.image||e.tensor;if(!(r instanceof Ue))return null;let a=r.shape.length===3?Je.cropAndResize(hn(r,0),n,[0],[Xr.inputs[0].shape[2],Xr.inputs[0].shape[1]]):Je.cropAndResize(r,n,[0],[Xr.inputs[0].shape[2],Xr.inputs[0].shape[1]]),s=[.2989,.587,.114],[i,o,l]=Lt(a,3,3),u=P(i,s[0]),c=P(o,s[1]),h=P(l,s[2]),d=Ma([u,c,h]),p=dn([d,d,d],3).squeeze(4),f=p.sub(p.min());return f.div(f.max())})}async function W2(e,t){return Xr?new Promise(async n=>{let r=[];if(t.face.embedding.enabled){let a=L2(e);if(!t.profile)r=B(()=>[...Xr.predict(a).reshape([128,2]).logSumExp(1).dataSync()]);else{let s=await gr(()=>Xr.predict({img_inputs:a}));r=[...s.result.dataSync()],s.result.dispose(),qr("emotion",s)}Ee(a)}n(r)}):[]}var J2={};pr(J2,{PoseNet:()=>Q2,load:()=>eg});function Rae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var B2=class{constructor(t){this.model=t}predict(t){return B(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Rae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function V2(e){return Math.floor(e/2)}var U2=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(V2(t),t);)this.exchange(t,V2(t)),t=V2(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function Fae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,u=Math.max(n-a,0),c=Math.min(n+a+1,i);for(let h=u;h<c;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function v6(e,t,n){let[r,a,s]=n.shape,i=new U2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let u=0;u<s;++u){let c=n.get(o,l,u);c<e||Fae(u,c,o,l,t,n)&&i.enqueue({score:c,part:{heatmapY:o,heatmapX:l,id:u}})}return i}var ma=Ah(g0());var k6=Ah(g0());function G2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+k6.NUM_KEYPOINTS)}}function x0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=G2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function q2(e,t,n){return e<t?t:e>n?n:e}function N6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function X2(e,t){return{x:e.x+t.x,y:e.y+t.y}}var w0=Ah(g0());function I6(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function Lae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+w0.NUM_KEYPOINTS)}}function Wae(e,t){let n=[];for(let r=0;r<w0.NUM_KEYPOINTS;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=Lae(a,s,r,t);n.push(o),n.push(i)}return Nn(n,[w0.NUM_KEYPOINTS,2])}function S6(e,t,n){return B(()=>e.toTensor().mul(be(t,"int32")).toFloat().add(Wae(e,n)))}function Bae(e,t){return B(()=>{let n=e.div(be(t,"int32"));return e.sub(n.mul(be(t,"int32")))})}function T6(e){let[t,n,r]=e.shape;return B(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(be(n,"int32")).expandDims(1),o=Bae(s,n).expandDims(1);return st([i,o],1)})}var C6=ma.poseChain.map(([e,t])=>[ma.partIds[e],ma.partIds[t]]),K2=C6.map(([,e])=>e),E6=C6.map(([e])=>e),Vae=16;function Uae(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function Z2(e,t,n,r){return{y:q2(Math.round(e.y/t),0,n-1),x:q2(Math.round(e.x/t),0,r-1)}}function R6(e,t,n,r,a,s,i,o=2){let[l,u]=r.shape,c=Z2(t.position,s,l,u),h=Uae(e,c,i),p=X2(t.position,h);for(let A=0;A<o;A++){let y=Z2(p,s,l,u),g=G2(y.y,y.x,n,a);p=X2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=Z2(p,s,l,u),m=r.get(f.y,f.x,n);return{position:p,part:ma.partNames[n],score:m}}function F6(e,t,n,r,a,s){let i=t.shape[2],o=K2.length,l=new Array(i),{part:u,score:c}=e,h=x0(u,r,n);l[u.id]={score:c,part:ma.partNames[u.id],position:h};for(let d=o-1;d>=0;--d){let p=K2[d],f=E6[d];l[p]&&!l[f]&&(l[f]=R6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=E6[d],f=K2[d];l[p]&&!l[f]&&(l[f]=R6(d,l[p],f,t,n,r,a))}return l}async function M6(e,t,n){let r=0,a=T6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],u=S6(l,Vae,o),c=await u.buffer(),d=Array.from(I6(i,l)).map((f,m)=>(r+=f,{position:{y:c.get(m,0),x:c.get(m,1)},part:ma.partNames[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),u.dispose(),{keypoints:p,score:r/d.length}}var Hae=1,$6=16;function D6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return N6(r,n,i.y,i.x)<=t})}function jae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(D6(e,t,s,o)||(a+=i),a),0)/n.length}function O6(e,t,n,r,a,s,i){let o=[],l=v6(i,Hae,e),u=a^2;for(;o.length<s&&!l.empty();){let c=l.dequeue(),h=x0(c.part,$6,t);if(D6(o,u,h,c.part.id))continue;let d=F6(c,e,t,$6,n,r),p=jae(o,u,d);p>i&&o.push({keypoints:d,score:p})}return o}async function z6(e){return Promise.all(e.map(t=>t.buffer()))}function Gae(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:r,part:a,position:s})=>({score:r,part:a,position:{x:Math.trunc(s.x*n),y:Math.trunc(s.y*t)}}))}}function P6(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function Y2(e,[t,n],[r,a]){return e.map(i=>Gae(i,t/r,n/a))}async function qae(e,t,n,r){return new Promise(async a=>{let s=await z6([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],u=s[3],c=await O6(i,o,l,u,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=Y2(c,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function Xae(e,t,n,r){return new Promise(async a=>{let s=await M6(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=Y2([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var Q2=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let r=P6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Xae(t,a,n,this.inputSize):await qae(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function eg(e){let t=await Ft(e.body.modelPath),n=new B2(t);return e.debug&&Ce(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new Q2(n)}var sg={};pr(sg,{HandPose:()=>og,load:()=>lg});function _0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Wc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function L6(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Je.cropAndResize(t,s,[0],n)}function W6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[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:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function b0(e,t=1.5){let n=Wc(e),r=_0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function v0(e){let t=Wc(e),n=_0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var tg=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=Nn(this.anchors),this.inputSize=n,this.inputSizeTensor=on([n,n]),this.doubleInputSizeTensor=on([n*2,n*2])}normalizeBoxes(t){return B(()=>{let n=Re(t,[0,0],[-1,2]),r=Re(t,[0,2],[-1,2]),a=se(ge(n,this.inputSizeTensor),this.anchorsTensor),s=ge(r,this.doubleInputSizeTensor),i=P(ye(a,s),this.inputSizeTensor),o=P(se(a,s),this.inputSizeTensor);return ll([i,o],1)})}normalizeLandmarks(t,n){return B(()=>{let r=se(ge(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return P(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=B(()=>Fn(Re(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let u=await Je.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),c=u.arraySync();s.dispose(),u.dispose();let h=[];for(let d of c)if(i[d]>=n.hand.minConfidence){let p=Re(l,[d,0],[1,-1]),f=Re(a,[d,5],[1,14]),m=B(()=>this.normalizeLandmarks(f,d).reshape([-1,2]));f.dispose(),h.push({box:p,palmLandmarks:m,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=B(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),c=u.slice(0,2),h=u.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(W6({startPoint:c,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function Kae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function B6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Kae(n)}var V6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function es(e,t){let n=0;for(let r=0;r<e.length;r++)n+=e[r]*t[r];return n}function Zae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function U6(e,t){let n=[],r=e.length;for(let a=0;a<r;a++){n.push([]);for(let s=0;s<r;s++)n[a].push(es(e[a],Zae(t,s)))}return n}function ng(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=V6(t[0],t[1]),i=U6(s,a),o=V6(-t[0],-t[1]);return U6(i,o)}function H6(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],r=[-es(t[0],n),-es(t[1],n)];return[t[0].concat(r[0]),t[1].concat(r[1]),[0,0,1]]}function rg(e,t){return[es(e,t[0]),es(e,t[1])]}var Yae=5,j6=1.65,G6=[0,5,9,13,17,1,2],Jae=0,Qae=2,ag=class{constructor(t,n,r){this.handDetector=t,this.landmarkDetector=n,this.inputSize=r,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(t,n){let r=t.map(s=>rg([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return b0(v0(a),Yae)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=b0(v0(n),j6);r.palmLandmarks=[];for(let a=0;a<G6.length;a++)r.palmLandmarks.push(t[G6[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=_0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(p=>[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=ng(r,[0,0]),u=o.map(p=>[...rg(p,l),p[2]]),c=H6(a),h=[...Wc(n),1],d=[es(h,c[0]),es(h,c[1])];return u.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let l=n.hand.rotation?B6(o.palmLandmarks[Jae],o.palmLandmarks[Qae]):0,u=Wc(o),c=[u[0]/t.shape[2],u[1]/t.shape[1]],h=n.hand.rotation?Je.rotateWithOffset(t,l,0,c):t.clone(),d=ng(-l,u),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,f=L6(p,h,[this.inputSize,this.inputSize]),m=f.div(255);f.dispose(),h.dispose();let[A,y]=await this.landmarkDetector.predict(m);m.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let _=G(y,[-1,3]),b=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=x;let I={landmarks:w,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(I)}else this.storedBoxes[i]=null;y.dispose()}else{let l=b0(v0(o),j6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var q6=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.703125,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.734375,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.765625,y_center:.046875},{w:1,h:1,x_center:.796875,y_center:.046875},{w:1,h:1,x_center:.796875,y_center:.046875},{w:1,h:1,x_center:.828125,y_center:.046875},{w:1,h:1,x_center:.828125,y_center:.046875},{w:1,h:1,x_center:.859375,y_center:.046875},{w:1,h:1,x_center:.859375,y_center:.046875},{w:1,h:1,x_center:.890625,y_center:.046875},{w:1,h:1,x_center:.890625,y_center:.046875},{w:1,h:1,x_center:.921875,y_center:.046875},{w:1,h:1,x_center:.921875,y_center:.046875},{w:1,h:1,x_center:.953125,y_center:.046875},{w:1,h:1,x_center:.953125,y_center:.046875},{w:1,h:1,x_center:.984375,y_center:.046875},{w:1,h:1,x_center:.984375,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.078125},{w:1,h:1,x_center:.015625,y_center:.078125},{w:1,h:1,x_center:.046875,y_center:.078125},{w:1,h:1,x_center:.046875,y_center:.078125},{w:1,h:1,x_center:.078125,y_center:.078125},{w:1,h:1,x_center:.078125,y_center:.078125},{w:1,h:1,x_center:.109375,y_center:.078125},{w:1,h:1,x_center:.109375,y_center:.078125},{w:1,h:1,x_center:.140625,y_center:.078125},{w:1,h:1,x_center:.140625,y_center:.078125},{w:1,h:1,x_center:.171875,y_center:.078125},{w:1,h:1,x_center:.171875,y_center:.078125},{w:1,h:1,x_center:.203125,y_center:.078125},{w:1,h:1,x_center:.203125,y_center:.078125},{w:1,h:1,x_center:.234375,y_center:.078125},{w:1,h:1,x_center:.234375,y_center:.078125},{w:1,h:1,x_center:.265625,y_center:.078125},{w:1,h:1,x_center:.265625,y_center:.078125},{w:1,h:1,x_center:.296875,y_center:.078125},{w:1,h:1,x_center:.296875,y_center:.078125},{w:1,h:1,x_center:.328125,y_center:.078125},{w:1,h:1,x_center:.328125,y_center:.078125},{w:1,h:1,x_center:.359375,y_center:.078125},{w:1,h:1,x_center:.359375,y_center:.078125},{w:1,h:1,x_center:.390625,y_center:.078125},{w:1,h:1,x_center:.390625,y_center:.078125},{w:1,h:1,x_center:.421875,y_center:.078125},{w:1,h:1,x_center:.421875,y_center:.078125},{w:1,h:1,x_center:.453125,y_center:.078125},{w:1,h:1,x_center:.453125,y_center:.078125},{w:1,h:1,x_center:.484375,y_center:.078125},{w:1,h:1,x_center:.484375,y_center:.078125},{w:1,h:1,x_center:.515625,y_center:.078125},{w:1,h:1,x_center:.515625,y_center:.078125},{w:1,h:1,x_center:.546875,y_center:.078125},{w:1,h:1,x_center:.546875,y_center:.078125},{w:1,h:1,x_center:.578125,y_center:.078125},{w:1,h:1,x_center:.578125,y_center:.078125},{w:1,h:1,x_center:.609375,y_center:.078125},{w:1,h:1,x_center:.609375,y_center:.078125},{w:1,h:1,x_center:.640625,y_center:.078125},{w:1,h:1,x_center:.640625,y_center:.078125},{w:1,h:1,x_center:.671875,y_center:.078125},{w:1,h:1,x_center:.671875,y_center:.078125},{w:1,h:1,x_center:.703125,y_center:.078125},{w:1,h:1,x_center:.703125,y_center:.078125},{w:1,h:1,x_center:.734375,y_center:.078125},{w:1,h:1,x_center:.734375,y_center:.078125},{w:1,h:1,x_center:.765625,y_center:.078125},{w:1,h:1,x_center:.765625,y_center:.078125},{w:1,h:1,x_center:.796875,y_center:.078125},{w:1,h:1,x_center:.796875,y_center:.078125},{w:1,h:1,x_center:.828125,y_center:.078125},{w:1,h:1,x_center:.828125,y_center:.078125},{w:1,h:1,x_center:.859375,y_center:.078125},{w:1,h:1,x_center:.859375,y_center:.078125},{w:1,h:1,x_center:.890625,y_center:.078125},{w:1,h:1,x_center:.890625,y_center:.078125},{w:1,h:1,x_center:.921875,y_center:.078125},{w:1,h:1,x_center:.921875,y_center:.078125},{w:1,h:1,x_center:.953125,y_center:.078125},{w:1,h:1,x_center:.953125,y_center:.078125},{w:1,h:1,x_center:.984375,y_center:.078125},{w:1,h:1,x_center:.984375,y_center:.078125},{w:1,h:1,x_center:.015625,y_center:.109375},{w:1,h:1,x_center:.015625,y_center:.109375},{w:1,h:1,x_center:.046875,y_center:.109375},{w:1,h:1,x_center:.046875,y_center:.109375},{w:1,h:1,x_center:.078125,y_center:.109375},{w:1,h:1,x_center:.078125,y_center:.109375},{w:1,h:1,x_center:.109375,y_center:.109375},{w:1,h:1,x_center:.109375,y_center:.109375},{w:1,h:1,x_center:.140625,y_center:.109375},{w:1,h:1,x_center:.140625,y_center:.109375},{w:1,h:1,x_center:.171875,y_center:.109375},{w:1,h:1,x_center:.171875,y_center:.109375},{w:1,h:1,x_center:.203125,y_center:.109375},{w:1,h:1,x_center:.203125,y_center:.109375},{w:1,h:1,x_center:.234375,y_center:.109375},{w:1,h:1,x_center:.234375,y_center:.109375},{w:1,h:1,x_center:.265625,y_center:.109375},{w:1,h:1,x_center:.265625,y_center:.109375},{w:1,h:1,x_center:.296875,y_center:.109375},{w:1,h:1,x_center:.296875,y_center:.109375},{w:1,h:1,x_center:.328125,y_center:.109375},{w:1,h:1,x_center:.328125,y_center:.109375},{w:1,h:1,x_center:.359375,y_center:.109375},{w:1,h:1,x_center:.359375,y_center:.109375},{w:1,h:1,x_center:.390625,y_center:.109375},{w:1,h:1,x_center:.390625,y_center:.109375},{w:1,h:1,x_center:.421875,y_center:.109375},{w:1,h:1,x_center:.421875,y_center:.109375},{w:1,h:1,x_center:.453125,y_center:.109375},{w:1,h:1,x_center:.453125,y_center:.109375},{w:1,h:1,x_center:.484375,y_center:.109375},{w:1,h:1,x_center:.484375,y_center:.109375},{w:1,h:1,x_center:.515625,y_center:.109375},{w:1,h:1,x_center:.515625,y_center:.109375},{w:1,h:1,x_center:.546875,y_center:.109375},{w:1,h:1,x_center:.546875,y_center:.109375},{w:1,h:1,x_center:.578125,y_center:.109375},{w:1,h:1,x_center:.578125,y_center:.109375},{w:1,h:1,x_center:.609375,y_center:.109375},{w:1,h:1,x_center:.609375,y_center:.109375},{w:1,h:1,x_center:.640625,y_center:.109375},{w:1,h:1,x_center:.640625,y_center:.109375},{w:1,h:1,x_center:.671875,y_center:.109375},{w:1,h:1,x_center:.671875,y_center:.109375},{w:1,h:1,x_center:.703125,y_center:.109375},{w:1,h:1,x_center:.703125,y_center:.109375},{w:1,h:1,x_center:.734375,y_center:.109375},{w:1,h:1,x_center:.734375,y_center:.109375},{w:1,h:1,x_center:.765625,y_center:.109375},{w:1,h:1,x_center:.765625,y_center:.109375},{w:1,h:1,x_center:.796875,y_center:.109375},{w:1,h:1,x_center:.796875,y_center:.109375},{w:1,h:1,x_center:.828125,y_center:.109375},{w:1,h:1,x_center:.828125,y_center:.109375},{w:1,h:1,x_center:.859375,y_center:.109375},{w:1,h:1,x_center:.859375,y_center:.109375},{w:1,h:1,x_center:.890625,y_center:.109375},{w:1,h:1,x_center:.890625,y_center:.109375},{w:1,h:1,x_center:.921875,y_center:.109375},{w:1,h:1,x_center:.921875,y_center:.109375},{w:1,h:1,x_center:.953125,y_center:.109375},{w:1,h:1,x_center:.953125,y_center:.109375},{w:1,h:1,x_center:.984375,y_center:.109375},{w:1,h:1,x_center:.984375,y_center:.109375},{w:1,h:1,x_center:.015625,y_center:.140625},{w:1,h:1,x_center:.015625,y_center:.140625},{w:1,h:1,x_center:.046875,y_center:.140625},{w:1,h:1,x_center:.046875,y_center:.140625},{w:1,h:1,x_center:.078125,y_center:.140625},{w:1,h:1,x_center:.078125,y_center:.140625},{w:1,h:1,x_center:.109375,y_center:.140625},{w:1,h:1,x_center:.109375,y_center:.140625},{w:1,h:1,x_center:.140625,y_center:.140625},{w:1,h:1,x_center:.140625,y_center:.140625},{w:1,h:1,x_center:.171875,y_center:.140625},{w:1,h:1,x_center:.171875,y_center:.140625},{w:1,h:1,x_center:.203125,y_center:.140625},{w:1,h:1,x_center:.203125,y_center:.140625},{w:1,h:1,x_center:.234375,y_center:.140625},{w:1,h:1,x_center:.234375,y_center:.140625},{w:1,h:1,x_center:.265625,y_center:.140625},{w:1,h:1,x_center:.265625,y_center:.140625},{w:1,h:1,x_center:.296875,y_center:.140625},{w:1,h:1,x_center:.296875,y_center:.140625},{w:1,h:1,x_center:.328125,y_center:.140625},{w:1,h:1,x_center:.328125,y_center:.140625},{w:1,h:1,x_center:.359375,y_center:.140625},{w:1,h:1,x_center:.359375,y_center:.140625},{w:1,h:1,x_center:.390625,y_center:.140625},{w:1,h:1,x_center:.390625,y_center:.140625},{w:1,h:1,x_center:.421875,y_center:.140625},{w:1,h:1,x_center:.421875,y_center:.140625},{w:1,h:1,x_center:.453125,y_center:.140625},{w:1,h:1,x_center:.453125,y_center:.140625},{w:1,h:1,x_center:.484375,y_center:.140625},{w:1,h:1,x_center:.484375,y_center:.140625},{w:1,h:1,x_center:.515625,y_center:.140625},{w:1,h:1,x_center:.515625,y_center:.140625},{w:1,h:1,x_center:.546875,y_center:.140625},{w:1,h:1,x_center:.546875,y_center:.140625},{w:1,h:1,x_center:.578125,y_center:.140625},{w:1,h:1,x_center:.578125,y_center:.140625},{w:1,h:1,x_center:.609375,y_center:.140625},{w:1,h:1,x_center:.609375,y_center:.140625},{w:1,h:1,x_center:.640625,y_center:.140625},{w:1,h:1,x_center:.640625,y_center:.140625},{w:1,h:1,x_center:.671875,y_center:.140625},{w:1,h:1,x_center:.671875,y_center:.140625},{w:1,h:1,x_center:.703125,y_center:.140625},{w:1,h:1,x_center:.703125,y_center:.140625},{w:1,h:1,x_center:.734375,y_center:.140625},{w:1,h:1,x_center:.734375,y_center:.140625},{w:1,h:1,x_center:.765625,y_center:.140625},{w:1,h:1,x_center:.765625,y_center:.140625},{w:1,h:1,x_center:.796875,y_center:.140625},{w:1,h:1,x_center:.796875,y_center:.140625},{w:1,h:1,x_center:.828125,y_center:.140625},{w:1,h:1,x_center:.828125,y_center:.140625},{w:1,h:1,x_center:.859375,y_center:.140625},{w:1,h:1,x_center:.859375,y_center:.140625},{w:1,h:1,x_center:.890625,y_center:.140625},{w:1,h:1,x_center:.890625,y_center:.140625},{w:1,h:1,x_center:.921875,y_center:.140625},{w:1,h:1,x_center:.921875,y_center:.140625},{w:1,h:1,x_center:.953125,y_center:.140625},{w:1,h:1,x_center:.953125,y_center:.140625},{w:1,h:1,x_center:.984375,y_center:.140625},{w:1,h:1,x_center:.984375,y_center:.140625},{w:1,h:1,x_center:.015625,y_center:.171875},{w:1,h:1,x_center:.015625,y_center:.171875},{w:1,h:1,x_center:.046875,y_center:.171875},{w:1,h:1,x_center:.046875,y_center:.171875},{w:1,h:1,x_center:.078125,y_center:.171875},{w:1,h:1,x_center:.078125,y_center:.171875},{w:1,h:1,x_center:.109375,y_center:.171875},{w:1,h:1,x_center:.109375,y_center:.171875},{w:1,h:1,x_center:.140625,y_center:.171875},{w:1,h:1,x_center:.140625,y_center:.171875},{w:1,h:1,x_center:.171875,y_center:.171875},{w:1,h:1,x_center:.171875,y_center:.171875},{w:1,h:1,x_center:.203125,y_center:.171875},{w:1,h:1,x_center:.203125,y_center:.171875},{w:1,h:1,x_center:.234375,y_center:.171875},{w:1,h:1,x_center:.234375,y_center:.171875},{w:1,h:1,x_center:.265625,y_center:.171875},{w:1,h:1,x_center:.265625,y_center:.171875},{w:1,h:1,x_center:.296875,y_center:.171875},{w:1,h:1,x_center:.296875,y_center:.171875},{w:1,h:1,x_center:.328125,y_center:.171875},{w:1,h:1,x_center:.328125,y_center:.171875},{w:1,h:1,x_center:.359375,y_center:.171875},{w:1,h:1,x_center:.359375,y_center:.171875},{w:1,h:1,x_center:.390625,y_center:.171875},{w:1,h:1,x_center:.390625,y_center:.171875},{w:1,h:1,x_center:.421875,y_center:.171875},{w:1,h:1,x_center:.421875,y_center:.171875},{w:1,h:1,x_center:.453125,y_center:.171875},{w:1,h:1,x_center:.453125,y_center:.171875},{w:1,h:1,x_center:.484375,y_center:.171875},{w:1,h:1,x_center:.484375,y_center:.171875},{w:1,h:1,x_center:.515625,y_center:.171875},{w:1,h:1,x_center:.515625,y_center:.171875},{w:1,h:1,x_center:.546875,y_center:.171875},{w:1,h:1,x_center:.546875,y_center:.171875},{w:1,h:1,x_center:.578125,y_center:.171875},{w:1,h:1,x_center:.578125,y_center:.171875},{w:1,h:1,x_center:.609375,y_center:.171875},{w:1,h:1,x_center:.609375,y_center:.171875},{w:1,h:1,x_center:.640625,y_center:.171875},{w:1,h:1,x_center:.640625,y_center:.171875},{w:1,h:1,x_center:.671875,y_center:.171875},{w:1,h:1,x_center:.671875,y_center:.171875},{w:1,h:1,x_center:.703125,y_center:.171875},{w:1,h:1,x_center:.703125,y_center:.171875},{w:1,h:1,x_center:.734375,y_center:.171875},{w:1,h:1,x_center:.734375,y_center:.171875},{w:1,h:1,x_center:.765625,y_center:.171875},{w:1,h:1,x_center:.765625,y_center:.171875},{w:1,h:1,x_center:.796875,y_center:.171875},{w:1,h:1,x_center:.796875,y_center:.171875},{w:1,h:1,x_center:.828125,y_center:.171875},{w:1,h:1,x_center:.828125,y_center:.171875},{w:1,h:1,x_center:.859375,y_center:.171875},{w:1,h:1,x_center:.859375,y_center:.171875},{w:1,h:1,x_center:.890625,y_center:.171875},{w:1,h:1,x_center:.890625,y_center:.171875},{w:1,h:1,x_center:.921875,y_center:.171875},{w:1,h:1,x_center:.921875,y_center:.171875},{w:1,h:1,x_center:.953125,y_center:.171875},{w:1,h:1,x_center:.953125,y_center:.171875},{w:1,h:1,x_center:.984375,y_center:.171875},{w:1,h:1,x_center:.984375,y_center:.171875},{w:1,h:1,x_center:.015625,y_center:.203125},{w:1,h:1,x_center:.015625,y_center:.203125},{w:1,h:1,x_center:.046875,y_center:.203125},{w:1,h:1,x_center:.046875,y_center:.203125},{w:1,h:1,x_center:.078125,y_center:.203125},{w:1,h:1,x_center:.078125,y_center:.203125},{w:1,h:1,x_center:.109375,y_center:.203125},{w:1,h:1,x_center:.109375,y_center:.203125},{w:1,h:1,x_center:.140625,y_center:.203125},{w:1,h:1,x_center:.140625,y_center:.203125},{w:1,h:1,x_center:.171875,y_center:.203125},{w:1,h:1,x_center:.171875,y_center:.203125},{w:1,h:1,x_center:.203125,y_center:.203125},{w:1,h:1,x_center:.203125,y_center:.203125},{w:1,h:1,x_center:.234375,y_center:.203125},{w:1,h:1,x_center:.234375,y_center:.203125},{w:1,h:1,x_center:.265625,y_center:.203125},{w:1,h:1,x_center:.265625,y_center:.203125},{w:1,h:1,x_center:.296875,y_center:.203125},{w:1,h:1,x_center:.296875,y_center:.203125},{w:1,h:1,x_center:.328125,y_center:.203125},{w:1,h:1,x_center:.328125,y_center:.203125},{w:1,h:1,x_center:.359375,y_center:.203125},{w:1,h:1,x_center:.359375,y_center:.203125},{w:1,h:1,x_center:.390625,y_center:.203125},{w:1,h:1,x_center:.390625,y_center:.203125},{w:1,h:1,x_center:.421875,y_center:.203125},{w:1,h:1,x_center:.421875,y_center:.203125},{w:1,h:1,x_center:.453125,y_center:.203125},{w:1,h:1,x_center:.453125,y_center:.203125},{w:1,h:1,x_center:.484375,y_center:.203125},{w:1,h:1,x_center:.484375,y_center:.203125},{w:1,h:1,x_center:.515625,y_center:.203125},{w:1,h:1,x_center:.515625,y_center:.203125},{w:1,h:1,x_center:.546875,y_center:.203125},{w:1,h:1,x_center:.546875,y_center:.203125},{w:1,h:1,x_center:.578125,y_center:.203125},{w:1,h:1,x_center:.578125,y_center:.203125},{w:1,h:1,x_center:.609375,y_center:.203125},{w:1,h:1,x_center:.609375,y_center:.203125},{w:1,h:1,x_center:.640625,y_center:.203125},{w:1,h:1,x_center:.640625,y_center:.203125},{w:1,h:1,x_center:.671875,y_center:.203125},{w:1,h:1,x_center:.671875,y_center:.203125},{w:1,h:1,x_center:.703125,y_center:.203125},{w:1,h:1,x_center:.703125,y_center:.203125},{w:1,h:1,x_center:.734375,y_center:.203125},{w:1,h:1,x_center:.734375,y_center:.203125},{w:1,h:1,x_center:.765625,y_center:.203125},{w:1,h:1,x_center:.765625,y_center:.203125},{w:1,h:1,x_center:.796875,y_center:.203125},{w:1,h:1,x_center:.796875,y_center:.203125},{w:1,h:1,x_center:.828125,y_center:.203125},{w:1,h:1,x_center:.828125,y_center:.203125},{w:1,h:1,x_center:.859375,y_center:.203125},{w:1,h:1,x_center:.859375,y_center:.203125},{w:1,h:1,x_center:.890625,y_center:.203125},{w:1,h:1,x_center:.890625,y_center:.203125},{w:1,h:1,x_center:.921875,y_center:.203125},{w:1,h:1,x_center:.921875,y_center:.203125},{w:1,h:1,x_center:.953125,y_center:.203125},{w:1,h:1,x_center:.953125,y_center:.203125},{w:1,h:1,x_center:.984375,y_center:.203125},{w:1,h:1,x_center:.984375,y_center:.203125},{w:1,h:1,x_center:.015625,y_center:.234375},{w:1,h:1,x_center:.015625,y_center:.234375},{w:1,h:1,x_center:.046875,y_center:.234375},{w:1,h:1,x_center:.046875,y_center:.234375},{w:1,h:1,x_center:.078125,y_center:.234375},{w:1,h:1,x_center:.078125,y_center:.234375},{w:1,h:1,x_center:.109375,y_center:.234375},{w:1,h:1,x_center:.109375,y_center:.234375},{w:1,h:1,x_center:.140625,y_center:.234375},{w:1,h:1,x_center:.140625,y_center:.234375},{w:1,h:1,x_center:.171875,y_center:.234375},{w:1,h:1,x_center:.171875,y_center:.234375},{w:1,h:1,x_center:.203125,y_center:.234375},{w:1,h:1,x_center:.203125,y_center:.234375},{w:1,h:1,x_center:.234375,y_center:.234375},{w:1,h:1,x_center:.234375,y_center:.234375},{w:1,h:1,x_center:.265625,y_center:.234375},{w:1,h:1,x_center:.265625,y_center:.234375},{w:1,h:1,x_center:.296875,y_center:.234375},{w:1,h:1,x_center:.296875,y_center:.234375},{w:1,h:1,x_center:.328125,y_center:.234375},{w:1,h:1,x_center:.328125,y_center:.234375},{w:1,h:1,x_center:.359375,y_center:.234375},{w:1,h:1,x_center:.359375,y_center:.234375},{w:1,h:1,x_center:.390625,y_center:.234375},{w:1,h:1,x_center:.390625,y_center:.234375},{w:1,h:1,x_center:.421875,y_center:.234375},{w:1,h:1,x_center:.421875,y_center:.234375},{w:1,h:1,x_center:.453125,y_center:.234375},{w:1,h:1,x_center:.453125,y_center:.234375},{w:1,h:1,x_center:.484375,y_center:.234375},{w:1,h:1,x_center:.484375,y_center:.234375},{w:1,h:1,x_center:.515625,y_center:.234375},{w:1,h:1,x_center:.515625,y_center:.234375},{w:1,h:1,x_center:.546875,y_center:.234375},{w:1,h:1,x_center:.546875,y_center:.234375},{w:1,h:1,x_center:.578125,y_center:.234375},{w:1,h:1,x_center:.578125,y_center:.234375},{w:1,h:1,x_center:.609375,y_center:.234375},{w:1,h:1,x_center:.609375,y_center:.234375},{w:1,h:1,x_center:.640625,y_center:.234375},{w:1,h:1,x_center:.640625,y_center:.234375},{w:1,h:1,x_center:.671875,y_center:.234375},{w:1,h:1,x_center:.671875,y_center:.234375},{w:1,h:1,x_center:.703125,y_center:.234375},{w:1,h:1,x_center:.703125,y_center:.234375},{w:1,h:1,x_center:.734375,y_center:.234375},{w:1,h:1,x_center:.734375,y_center:.234375},{w:1,h:1,x_center:.765625,y_center:.234375},{w:1,h:1,x_center:.765625,y_center:.234375},{w:1,h:1,x_center:.796875,y_center:.234375},{w:1,h:1,x_center:.796875,y_center:.234375},{w:1,h:1,x_center:.828125,y_center:.234375},{w:1,h:1,x_center:.828125,y_center:.234375},{w:1,h:1,x_center:.859375,y_center:.234375},{w:1,h:1,x_center:.859375,y_center:.234375},{w:1,h:1,x_center:.890625,y_center:.234375},{w:1,h:1,x_center:.890625,y_center:.234375},{w:1,h:1,x_center:.921875,y_center:.234375},{w:1,h:1,x_center:.921875,y_center:.234375},{w:1,h:1,x_center:.953125,y_center:.234375},{w:1,h:1,x_center:.953125,y_center:.234375},{w:1,h:1,x_center:.984375,y_center:.234375},{w:1,h:1,x_center:.984375,y_center:.234375},{w:1,h:1,x_center:.015625,y_center:.265625},{w:1,h:1,x_center:.015625,y_center:.265625},{w:1,h:1,x_center:.046875,y_center:.265625},{w:1,h:1,x_center:.046875,y_center:.265625},{w:1,h:1,x_center:.078125,y_center:.265625},{w:1,h:1,x_center:.078125,y_center:.265625},{w:1,h:1,x_center:.109375,y_center:.265625},{w:1,h:1,x_center:.109375,y_center:.265625},{w:1,h:1,x_center:.140625,y_center:.265625},{w:1,h:1,x_center:.140625,y_center:.265625},{w:1,h:1,x_center:.171875,y_center:.265625},{w:1,h:1,x_center:.171875,y_center:.265625},{w:1,h:1,x_center:.203125,y_center:.265625},{w:1,h:1,x_center:.203125,y_center:.265625},{w:1,h:1,x_center:.234375,y_center:.265625},{w:1,h:1,x_center:.234375,y_center:.265625},{w:1,h:1,x_center:.265625,y_center:.265625},{w:1,h:1,x_center:.265625,y_center:.265625},{w:1,h:1,x_center:.296875,y_center:.265625},{w:1,h:1,x_center:.296875,y_center:.265625},{w:1,h:1,x_center:.328125,y_center:.265625},{w:1,h:1,x_center:.328125,y_center:.265625},{w:1,h:1,x_center:.359375,y_center:.265625},{w:1,h:1,x_center:.359375,y_center:.265625},{w:1,h:1,x_center:.390625,y_center:.265625},{w:1,h:1,x_center:.390625,y_center:.265625},{w:1,h:1,x_center:.421875,y_center:.265625},{w:1,h:1,x_center:.421875,y_center:.265625},{w:1,h:1,x_center:.453125,y_center:.265625},{w:1,h:1,x_center:.453125,y_center:.265625},{w:1,h:1,x_center:.484375,y_center:.265625},{w:1,h:1,x_center:.484375,y_center:.265625},{w:1,h:1,x_center:.515625,y_center:.265625},{w:1,h:1,x_center:.515625,y_center:.265625},{w:1,h:1,x_center:.546875,y_center:.265625},{w:1,h:1,x_center:.546875,y_center:.265625},{w:1,h:1,x_center:.578125,y_center:.265625},{w:1,h:1,x_center:.578125,y_center:.265625},{w:1,h:1,x_center:.609375,y_center:.265625},{w:1,h:1,x_center:.609375,y_center:.265625},{w:1,h:1,x_center:.640625,y_center:.265625},{w:1,h:1,x_center:.640625,y_center:.265625},{w:1,h:1,x_center:.671875,y_center:.265625},{w:1,h:1,x_center:.671875,y_center:.265625},{w:1,h:1,x_center:.703125,y_center:.265625},{w:1,h:1,x_center:.703125,y_center:.265625},{w:1,h:1,x_center:.734375,y_center:.265625},{w:1,h:1,x_center:.734375,y_center:.265625},{w:1,h:1,x_center:.765625,y_center:.265625},{w:1,h:1,x_center:.765625,y_center:.265625},{w:1,h:1,x_center:.796875,y_center:.265625},{w:1,h:1,x_center:.796875,y_center:.265625},{w:1,h:1,x_center:.828125,y_center:.265625},{w:1,h:1,x_center:.828125,y_center:.265625},{w:1,h:1,x_center:.859375,y_center:.265625},{w:1,h:1,x_center:.859375,y_center:.265625},{w:1,h:1,x_center:.890625,y_center:.265625},{w:1,h:1,x_center:.890625,y_center:.265625},{w:1,h:1,x_center:.921875,y_center:.265625},{w:1,h:1,x_center:.921875,y_center:.265625},{w:1,h:1,x_center:.953125,y_center:.265625},{w:1,h:1,x_center:.953125,y_center:.265625},{w:1,h:1,x_center:.984375,y_center:.265625},{w:1,h:1,x_center:.984375,y_center:.265625},{w:1,h:1,x_center:.015625,y_center:.296875},{w:1,h:1,x_center:.015625,y_center:.296875},{w:1,h:1,x_center:.046875,y_center:.296875},{w:1,h:1,x_center:.046875,y_center:.296875},{w:1,h:1,x_center:.078125,y_center:.296875},{w:1,h:1,x_center:.078125,y_center:.296875},{w:1,h:1,x_center:.109375,y_center:.296875},{w:1,h:1,x_center:.109375,y_center:.296875},{w:1,h:1,x_center:.140625,y_center:.296875},{w:1,h:1,x_center:.140625,y_center:.296875},{w:1,h:1,x_center:.171875,y_center:.296875},{w:1,h:1,x_center:.171875,y_center:.296875},{w:1,h:1,x_center:.203125,y_center:.296875},{w:1,h:1,x_center:.203125,y_center:.296875},{w:1,h:1,x_center:.234375,y_center:.296875},{w:1,h:1,x_center:.234375,y_center:.296875},{w:1,h:1,x_center:.265625,y_center:.296875},{w:1,h:1,x_center:.265625,y_center:.296875},{w:1,h:1,x_center:.296875,y_center:.296875},{w:1,h:1,x_center:.296875,y_center:.296875},{w:1,h:1,x_center:.328125,y_center:.296875},{w:1,h:1,x_center:.328125,y_center:.296875},{w:1,h:1,x_center:.359375,y_center:.296875},{w:1,h:1,x_center:.359375,y_center:.296875},{w:1,h:1,x_center:.390625,y_center:.296875},{w:1,h:1,x_center:.390625,y_center:.296875},{w:1,h:1,x_center:.421875,y_center:.296875},{w:1,h:1,x_center:.421875,y_center:.296875},{w:1,h:1,x_center:.453125,y_center:.296875},{w:1,h:1,x_center:.453125,y_center:.296875},{w:1,h:1,x_center:.484375,y_center:.296875},{w:1,h:1,x_center:.484375,y_center:.296875},{w:1,h:1,x_center:.515625,y_center:.296875},{w:1,h:1,x_center:.515625,y_center:.296875},{w:1,h:1,x_center:.546875,y_center:.296875},{w:1,h:1,x_center:.546875,y_center:.296875},{w:1,h:1,x_center:.578125,y_center:.296875},{w:1,h:1,x_center:.578125,y_center:.296875},{w:1,h:1,x_center:.609375,y_center:.296875},{w:1,h:1,x_center:.609375,y_center:.296875},{w:1,h:1,x_center:.640625,y_center:.296875},{w:1,h:1,x_center:.640625,y_center:.296875},{w:1,h:1,x_center:.671875,y_center:.296875},{w:1,h:1,x_center:.671875,y_center:.296875},{w:1,h:1,x_center:.703125,y_center:.296875},{w:1,h:1,x_center:.703125,y_center:.296875},{w:1,h:1,x_center:.734375,y_center:.296875},{w:1,h:1,x_center:.734375,y_center:.296875},{w:1,h:1,x_center:.765625,y_center:.296875},{w:1,h:1,x_center:.765625,y_center:.296875},{w:1,h:1,x_center:.796875,y_center:.296875},{w:1,h:1,x_center:.796875,y_center:.296875},{w:1,h:1,x_center:.828125,y_center:.296875},{w:1,h:1,x_center:.828125,y_center:.296875},{w:1,h:1,x_center:.859375,y_center:.296875},{w:1,h:1,x_center:.859375,y_center:.296875},{w:1,h:1,x_center:.890625,y_center:.296875},{w:1,h:1,x_center:.890625,y_center:.296875},{w:1,h:1,x_center:.921875,y_center:.296875},{w:1,h:1,x_center:.921875,y_center:.296875},{w:1,h:1,x_center:.953125,y_center:.296875},{w:1,h:1,x_center:.953125,y_center:.296875},{w:1,h:1,x_center:.984375,y_center:.296875},{w:1,h:1,x_center:.984375,y_center:.296875},{w:1,h:1,x_center:.015625,y_center:.328125},{w:1,h:1,x_center:.015625,y_center:.328125},{w:1,h:1,x_center:.046875,y_center:.328125},{w:1,h:1,x_center:.046875,y_center:.328125},{w:1,h:1,x_center:.078125,y_center:.328125},{w:1,h:1,x_center:.078125,y_center:.328125},{w:1,h:1,x_center:.109375,y_center:.328125},{w:1,h:1,x_center:.109375,y_center:.328125},{w:1,h:1,x_center:.140625,y_center:.328125},{w:1,h:1,x_center:.140625,y_center:.328125},{w:1,h:1,x_center:.171875,y_center:.328125},{w:1,h:1,x_center:.171875,y_center:.328125},{w:1,h:1,x_center:.203125,y_center:.328125},{w:1,h:1,x_center:.203125,y_center:.328125},{w:1,h:1,x_center:.234375,y_center:.328125},{w:1,h:1,x_center:.234375,y_center:.328125},{w:1,h:1,x_center:.265625,y_center:.328125},{w:1,h:1,x_center:.265625,y_center:.328125},{w:1,h:1,x_center:.296875,y_center:.328125},{w:1,h:1,x_center:.296875,y_center:.328125},{w:1,h:1,x_center:.328125,y_center:.328125},{w:1,h:1,x_center:.328125,y_center:.328125},{w:1,h:1,x_center:.359375,y_center:.328125},{w:1,h:1,x_center:.359375,y_center:.328125},{w:1,h:1,x_center:.390625,y_center:.328125},{w:1,h:1,x_center:.390625,y_center:.328125},{w:1,h:1,x_center:.421875,y_center:.328125},{w:1,h:1,x_center:.421875,y_center:.328125},{w:1,h:1,x_center:.453125,y_center:.328125},{w:1,h:1,x_center:.453125,y_center:.328125},{w:1,h:1,x_center:.484375,y_center:.328125},{w:1,h:1,x_center:.484375,y_center:.328125},{w:1,h:1,x_center:.515625,y_center:.328125},{w:1,h:1,x_center:.515625,y_center:.328125},{w:1,h:1,x_center:.546875,y_center:.328125},{w:1,h:1,x_center:.546875,y_center:.328125},{w:1,h:1,x_center:.578125,y_center:.328125},{w:1,h:1,x_center:.578125,y_center:.328125},{w:1,h:1,x_center:.609375,y_center:.328125},{w:1,h:1,x_center:.609375,y_center:.328125},{w:1,h:1,x_center:.640625,y_center:.328125},{w:1,h:1,x_center:.640625,y_center:.328125},{w:1,h:1,x_center:.671875,y_center:.328125},{w:1,h:1,x_center:.671875,y_center:.328125},{w:1,h:1,x_center:.703125,y_center:.328125},{w:1,h:1,x_center:.703125,y_center:.328125},{w:1,h:1,x_center:.734375,y_center:.328125},{w:1,h:1,x_center:.734375,y_center:.328125},{w:1,h:1,x_center:.765625,y_center:.328125},{w:1,h:1,x_center:.765625,y_center:.328125},{w:1,h:1,x_center:.796875,y_center:.328125},{w:1,h:1,x_center:.796875,y_center:.328125},{w:1,h:1,x_center:.828125,y_center:.328125},{w:1,h:1,x_center:.828125,y_center:.328125},{w:1,h:1,x_center:.859375,y_center:.328125},{w:1,h:1,x_center:.859375,y_center:.328125},{w:1,h:1,x_center:.890625,y_center:.328125},{w:1,h:1,x_center:.890625,y_center:.328125},{w:1,h:1,x_center:.921875,y_center:.328125},{w:1,h:1,x_center:.921875,y_center:.328125},{w:1,h:1,x_center:.953125,y_center:.328125},{w:1,h:1,x_center:.953125,y_center:.328125},{w:1,h:1,x_center:.984375,y_center:.328125},{w:1,h:1,x_center:.984375,y_center:.328125},{w:1,h:1,x_center:.015625,y_center:.359375},{w:1,h:1,x_center:.015625,y_center:.359375},{w:1,h:1,x_center:.046875,y_center:.359375},{w:1,h:1,x_center:.046875,y_center:.359375},{w:1,h:1,x_center:.078125,y_center:.359375},{w:1,h:1,x_center:.078125,y_center:.359375},{w:1,h:1,x_center:.109375,y_center:.359375},{w:1,h:1,x_center:.109375,y_center:.359375},{w:1,h:1,x_center:.140625,y_center:.359375},{w:1,h:1,x_center:.140625,y_center:.359375},{w:1,h:1,x_center:.171875,y_center:.359375},{w:1,h:1,x_center:.171875,y_center:.359375},{w:1,h:1,x_center:.203125,y_center:.359375},{w:1,h:1,x_center:.203125,y_center:.359375},{w:1,h:1,x_center:.234375,y_center:.359375},{w:1,h:1,x_center:.234375,y_center:.359375},{w:1,h:1,x_center:.265625,y_center:.359375},{w:1,h:1,x_center:.265625,y_center:.359375},{w:1,h:1,x_center:.296875,y_center:.359375},{w:1,h:1,x_center:.296875,y_center:.359375},{w:1,h:1,x_center:.328125,y_center:.359375},{w:1,h:1,x_center:.328125,y_center:.359375},{w:1,h:1,x_center:.359375,y_center:.359375},{w:1,h:1,x_center:.359375,y_center:.359375},{w:1,h:1,x_center:.390625,y_center:.359375},{w:1,h:1,x_center:.390625,y_center:.359375},{w:1,h:1,x_center:.421875,y_center:.359375},{w:1,h:1,x_center:.421875,y_center:.359375},{w:1,h:1,x_center:.453125,y_center:.359375},{w:1,h:1,x_center:.453125,y_center:.359375},{w:1,h:1,x_center:.484375,y_center:.359375},{w:1,h:1,x_center:.484375,y_center:.359375},{w:1,h:1,x_center:.515625,y_center:.359375},{w:1,h:1,x_center:.515625,y_center:.359375},{w:1,h:1,x_center:.546875,y_center:.359375},{w:1,h:1,x_center:.546875,y_center:.359375},{w:1,h:1,x_center:.578125,y_center:.359375},{w:1,h:1,x_center:.578125,y_center:.359375},{w:1,h:1,x_center:.609375,y_center:.359375},{w:1,h:1,x_center:.609375,y_center:.359375},{w:1,h:1,x_center:.640625,y_center:.359375},{w:1,h:1,x_center:.640625,y_center:.359375},{w:1,h:1,x_center:.671875,y_center:.359375},{w:1,h:1,x_center:.671875,y_center:.359375},{w:1,h:1,x_center:.703125,y_center:.359375},{w:1,h:1,x_center:.703125,y_center:.359375},{w:1,h:1,x_center:.734375,y_center:.359375},{w:1,h:1,x_center:.734375,y_center:.359375},{w:1,h:1,x_center:.765625,y_center:.359375},{w:1,h:1,x_center:.765625,y_center:.359375},{w:1,h:1,x_center:.796875,y_center:.359375},{w:1,h:1,x_center:.796875,y_center:.359375},{w:1,h:1,x_center:.828125,y_center:.359375},{w:1,h:1,x_center:.828125,y_center:.359375},{w:1,h:1,x_center:.859375,y_center:.359375},{w:1,h:1,x_center:.859375,y_center:.359375},{w:1,h:1,x_center:.890625,y_center:.359375},{w:1,h:1,x_center:.890625,y_center:.359375},{w:1,h:1,x_center:.921875,y_center:.359375},{w:1,h:1,x_center:.921875,y_center:.359375},{w:1,h:1,x_center:.953125,y_center:.359375},{w:1,h:1,x_center:.953125,y_center:.359375},{w:1,h:1,x_center:.984375,y_center:.359375},{w:1,h:1,x_center:.984375,y_center:.359375},{w:1,h:1,x_center:.015625,y_center:.390625},{w:1,h:1,x_center:.015625,y_center:.390625},{w:1,h:1,x_center:.046875,y_center:.390625},{w:1,h:1,x_center:.046875,y_center:.390625},{w:1,h:1,x_center:.078125,y_center:.390625},{w:1,h:1,x_center:.078125,y_center:.390625},{w:1,h:1,x_center:.109375,y_center:.390625},{w:1,h:1,x_center:.109375,y_center:.390625},{w:1,h:1,x_center:.140625,y_center:.390625},{w:1,h:1,x_center:.140625,y_center:.390625},{w:1,h:1,x_center:.171875,y_center:.390625},{w:1,h:1,x_center:.171875,y_center:.390625},{w:1,h:1,x_center:.203125,y_center:.390625},{w:1,h:1,x_center:.203125,y_center:.390625},{w:1,h:1,x_center:.234375,y_center:.390625},{w:1,h:1,x_center:.234375,y_center:.390625},{w:1,h:1,x_center:.265625,y_center:.390625},{w:1,h:1,x_center:.265625,y_center:.390625},{w:1,h:1,x_center:.296875,y_center:.390625},{w:1,h:1,x_center:.296875,y_center:.390625},{w:1,h:1,x_center:.328125,y_center:.390625},{w:1,h:1,x_center:.328125,y_center:.390625},{w:1,h:1,x_center:.359375,y_center:.390625},{w:1,h:1,x_center:.359375,y_center:.390625},{w:1,h:1,x_center:.390625,y_center:.390625},{w:1,h:1,x_center:.390625,y_center:.390625},{w:1,h:1,x_center:.421875,y_center:.390625},{w:1,h:1,x_center:.421875,y_center:.390625},{w:1,h:1,x_center:.453125,y_center:.390625},{w:1,h:1,x_center:.453125,y_center:.390625},{w:1,h:1,x_center:.484375,y_center:.390625},{w:1,h:1,x_center:.484375,y_center:.390625},{w:1,h:1,x_center:.515625,y_center:.390625},{w:1,h:1,x_center:.515625,y_center:.390625},{w:1,h:1,x_center:.546875,y_center:.390625},{w:1,h:1,x_center:.546875,y_center:.390625},{w:1,h:1,x_center:.578125,y_center:.390625},{w:1,h:1,x_center:.578125,y_center:.390625},{w:1,h:1,x_center:.609375,y_center:.390625},{w:1,h:1,x_center:.609375,y_center:.390625},{w:1,h:1,x_center:.640625,y_center:.390625},{w:1,h:1,x_center:.640625,y_center:.390625},{w:1,h:1,x_center:.671875,y_center:.390625},{w:1,h:1,x_center:.671875,y_center:.390625},{w:1,h:1,x_center:.703125,y_center:.390625},{w:1,h:1,x_center:.703125,y_center:.390625},{w:1,h:1,x_center:.734375,y_center:.390625},{w:1,h:1,x_center:.734375,y_center:.390625},{w:1,h:1,x_center:.765625,y_center:.390625},{w:1,h:1,x_center:.765625,y_center:.390625},{w:1,h:1,x_center:.796875,y_center:.390625},{w:1,h:1,x_center:.796875,y_center:.390625},{w:1,h:1,x_center:.828125,y_center:.390625},{w:1,h:1,x_center:.828125,y_center:.390625},{w:1,h:1,x_center:.859375,y_center:.390625},{w:1,h:1,x_center:.859375,y_center:.390625},{w:1,h:1,x_center:.890625,y_center:.390625},{w:1,h:1,x_center:.890625,y_center:.390625},{w:1,h:1,x_center:.921875,y_center:.390625},{w:1,h:1,x_center:.921875,y_center:.390625},{w:1,h:1,x_center:.953125,y_center:.390625},{w:1,h:1,x_center:.953125,y_center:.390625},{w:1,h:1,x_center:.984375,y_center:.390625},{w:1,h:1,x_center:.984375,y_center:.390625},{w:1,h:1,x_center:.015625,y_center:.421875},{w:1,h:1,x_center:.015625,y_center:.421875},{w:1,h:1,x_center:.046875,y_center:.421875},{w:1,h:1,x_center:.046875,y_center:.421875},{w:1,h:1,x_center:.078125,y_center:.421875},{w:1,h:1,x_center:.078125,y_center:.421875},{w:1,h:1,x_center:.109375,y_center:.421875},{w:1,h:1,x_center:.109375,y_center:.421875},{w:1,h:1,x_center:.140625,y_center:.421875},{w:1,h:1,x_center:.140625,y_center:.421875},{w:1,h:1,x_center:.171875,y_center:.421875},{w:1,h:1,x_center:.171875,y_center:.421875},{w:1,h:1,x_center:.203125,y_center:.421875},{w:1,h:1,x_center:.203125,y_center:.421875},{w:1,h:1,x_center:.234375,y_center:.421875},{w:1,h:1,x_center:.234375,y_center:.421875},{w:1,h:1,x_center:.265625,y_center:.421875},{w:1,h:1,x_center:.265625,y_center:.421875},{w:1,h:1,x_center:.296875,y_center:.421875},{w:1,h:1,x_center:.296875,y_center:.421875},{w:1,h:1,x_center:.328125,y_center:.421875},{w:1,h:1,x_center:.328125,y_center:.421875},{w:1,h:1,x_center:.359375,y_center:.421875},{w:1,h:1,x_center:.359375,y_center:.421875},{w:1,h:1,x_center:.390625,y_center:.421875},{w:1,h:1,x_center:.390625,y_center:.421875},{w:1,h:1,x_center:.421875,y_center:.421875},{w:1,h:1,x_center:.421875,y_center:.421875},{w:1,h:1,x_center:.453125,y_center:.421875},{w:1,h:1,x_center:.453125,y_center:.421875},{w:1,h:1,x_center:.484375,y_center:.421875},{w:1,h:1,x_center:.484375,y_center:.421875},{w:1,h:1,x_center:.515625,y_center:.421875},{w:1,h:1,x_center:.515625,y_center:.421875},{w:1,h:1,x_center:.546875,y_center:.421875},{w:1,h:1,x_center:.546875,y_center:.421875},{w:1,h:1,x_center:.578125,y_center:.421875},{w:1,h:1,x_center:.578125,y_center:.421875},{w:1,h:1,x_center:.609375,y_center:.421875},{w:1,h:1,x_center:.609375,y_center:.421875},{w:1,h:1,x_center:.640625,y_center:.421875},{w:1,h:1,x_center:.640625,y_center:.421875},{w:1,h:1,x_center:.671875,y_center:.421875},{w:1,h:1,x_center:.671875,y_center:.421875},{w:1,h:1,x_center:.703125,y_center:.421875},{w:1,h:1,x_center:.703125,y_center:.421875},{w:1,h:1,x_center:.734375,y_center:.421875},{w:1,h:1,x_center:.734375,y_center:.421875},{w:1,h:1,x_center:.765625,y_center:.421875},{w:1,h:1,x_center:.765625,y_center:.421875},{w:1,h:1,x_center:.796875,y_center:.421875},{w:1,h:1,x_center:.796875,y_center:.421875},{w:1,h:1,x_center:.828125,y_center:.421875},{w:1,h:1,x_center:.828125,y_center:.421875},{w:1,h:1,x_center:.859375,y_center:.421875},{w:1,h:1,x_center:.859375,y_center:.421875},{w:1,h:1,x_center:.890625,y_center:.421875},{w:1,h:1,x_center:.890625,y_center:.421875},{w:1,h:1,x_center:.921875,y_center:.421875},{w:1,h:1,x_center:.921875,y_center:.421875},{w:1,h:1,x_center:.953125,y_center:.421875},{w:1,h:1,x_center:.953125,y_center:.421875},{w:1,h:1,x_center:.984375,y_center:.421875},{w:1,h:1,x_center:.984375,y_center:.421875},{w:1,h:1,x_center:.015625,y_center:.453125},{w:1,h:1,x_center:.015625,y_center:.453125},{w:1,h:1,x_center:.046875,y_center:.453125},{w:1,h:1,x_center:.046875,y_center:.453125},{w:1,h:1,x_center:.078125,y_center:.453125},{w:1,h:1,x_center:.078125,y_center:.453125},{w:1,h:1,x_center:.109375,y_center:.453125},{w:1,h:1,x_center:.109375,y_center:.453125},{w:1,h:1,x_center:.140625,y_center:.453125},{w:1,h:1,x_center:.140625,y_center:.453125},{w:1,h:1,x_center:.171875,y_center:.453125},{w:1,h:1,x_center:.171875,y_center:.453125},{w:1,h:1,x_center:.203125,y_center:.453125},{w:1,h:1,x_center:.203125,y_center:.453125},{w:1,h:1,x_center:.234375,y_center:.453125},{w:1,h:1,x_center:.234375,y_center:.453125},{w:1,h:1,x_center:.265625,y_center:.453125},{w:1,h:1,x_center:.265625,y_center:.453125},{w:1,h:1,x_center:.296875,y_center:.453125},{w:1,h:1,x_center:.296875,y_center:.453125},{w:1,h:1,x_center:.328125,y_center:.453125},{w:1,h:1,x_center:.328125,y_center:.453125},{w:1,h:1,x_center:.359375,y_center:.453125},{w:1,h:1,x_center:.359375,y_center:.453125},{w:1,h:1,x_center:.390625,y_center:.453125},{w:1,h:1,x_center:.390625,y_center:.453125},{w:1,h:1,x_center:.421875,y_center:.453125},{w:1,h:1,x_center:.421875,y_center:.453125},{w:1,h:1,x_center:.453125,y_center:.453125},{w:1,h:1,x_center:.453125,y_center:.453125},{w:1,h:1,x_center:.484375,y_center:.453125},{w:1,h:1,x_center:.484375,y_center:.453125},{w:1,h:1,x_center:.515625,y_center:.453125},{w:1,h:1,x_center:.515625,y_center:.453125},{w:1,h:1,x_center:.546875,y_center:.453125},{w:1,h:1,x_center:.546875,y_center:.453125},{w:1,h:1,x_center:.578125,y_center:.453125},{w:1,h:1,x_center:.578125,y_center:.453125},{w:1,h:1,x_center:.609375,y_center:.453125},{w:1,h:1,x_center:.609375,y_center:.453125},{w:1,h:1,x_center:.640625,y_center:.453125},{w:1,h:1,x_center:.640625,y_center:.453125},{w:1,h:1,x_center:.671875,y_center:.453125},{w:1,h:1,x_center:.671875,y_center:.453125},{w:1,h:1,x_center:.703125,y_center:.453125},{w:1,h:1,x_center:.703125,y_center:.453125},{w:1,h:1,x_center:.734375,y_center:.453125},{w:1,h:1,x_center:.734375,y_center:.453125},{w:1,h:1,x_center:.765625,y_center:.453125},{w:1,h:1,x_center:.765625,y_center:.453125},{w:1,h:1,x_center:.796875,y_center:.453125},{w:1,h:1,x_center:.796875,y_center:.453125},{w:1,h:1,x_center:.828125,y_center:.453125},{w:1,h:1,x_center:.828125,y_center:.453125},{w:1,h:1,x_center:.859375,y_center:.453125},{w:1,h:1,x_center:.859375,y_center:.453125},{w:1,h:1,x_center:.890625,y_center:.453125},{w:1,h:1,x_center:.890625,y_center:.453125},{w:1,h:1,x_center:.921875,y_center:.453125},{w:1,h:1,x_center:.921875,y_center:.453125},{w:1,h:1,x_center:.953125,y_center:.453125},{w:1,h:1,x_center:.953125,y_center:.453125},{w:1,h:1,x_center:.984375,y_center:.453125},{w:1,h:1,x_center:.984375,y_center:.453125},{w:1,h:1,x_center:.015625,y_center:.484375},{w:1,h:1,x_center:.015625,y_center:.484375},{w:1,h:1,x_center:.046875,y_center:.484375},{w:1,h:1,x_center:.046875,y_center:.484375},{w:1,h:1,x_center:.078125,y_center:.484375},{w:1,h:1,x_center:.078125,y_center:.484375},{w:1,h:1,x_center:.109375,y_center:.484375},{w:1,h:1,x_center:.109375,y_center:.484375},{w:1,h:1,x_center:.140625,y_center:.484375},{w:1,h:1,x_center:.140625,y_center:.484375},{w:1,h:1,x_center:.171875,y_center:.484375},{w:1,h:1,x_center:.171875,y_center:.484375},{w:1,h:1,x_center:.203125,y_center:.484375},{w:1,h:1,x_center:.203125,y_center:.484375},{w:1,h:1,x_center:.234375,y_center:.484375},{w:1,h:1,x_center:.234375,y_center:.484375},{w:1,h:1,x_center:.265625,y_center:.484375},{w:1,h:1,x_center:.265625,y_center:.484375},{w:1,h:1,x_center:.296875,y_center:.484375},{w:1,h:1,x_center:.296875,y_center:.484375},{w:1,h:1,x_center:.328125,y_center:.484375},{w:1,h:1,x_center:.328125,y_center:.484375},{w:1,h:1,x_center:.359375,y_center:.484375},{w:1,h:1,x_center:.359375,y_center:.484375},{w:1,h:1,x_center:.390625,y_center:.484375},{w:1,h:1,x_center:.390625,y_center:.484375},{w:1,h:1,x_center:.421875,y_center:.484375},{w:1,h:1,x_center:.421875,y_center:.484375},{w:1,h:1,x_center:.453125,y_center:.484375},{w:1,h:1,x_center:.453125,y_center:.484375},{w:1,h:1,x_center:.484375,y_center:.484375},{w:1,h:1,x_center:.484375,y_center:.484375},{w:1,h:1,x_center:.515625,y_center:.484375},{w:1,h:1,x_center:.515625,y_center:.484375},{w:1,h:1,x_center:.546875,y_center:.484375},{w:1,h:1,x_center:.546875,y_center:.484375},{w:1,h:1,x_center:.578125,y_center:.484375},{w:1,h:1,x_center:.578125,y_center:.484375},{w:1,h:1,x_center:.609375,y_center:.484375},{w:1,h:1,x_center:.609375,y_center:.484375},{w:1,h:1,x_center:.640625,y_center:.484375},{w:1,h:1,x_center:.640625,y_center:.484375},{w:1,h:1,x_center:.671875,y_center:.484375},{w:1,h:1,x_center:.671875,y_center:.484375},{w:1,h:1,x_center:.703125,y_center:.484375},{w:1,h:1,x_center:.703125,y_center:.484375},{w:1,h:1,x_center:.734375,y_center:.484375},{w:1,h:1,x_center:.734375,y_center:.484375},{w:1,h:1,x_center:.765625,y_center:.484375},{w:1,h:1,x_center:.765625,y_center:.484375},{w:1,h:1,x_center:.796875,y_center:.484375},{w:1,h:1,x_center:.796875,y_center:.484375},{w:1,h:1,x_center:.828125,y_center:.484375},{w:1,h:1,x_center:.828125,y_center:.484375},{w:1,h:1,x_center:.859375,y_center:.484375},{w:1,h:1,x_center:.859375,y_center:.484375},{w:1,h:1,x_center:.890625,y_center:.484375},{w:1,h:1,x_center:.890625,y_center:.484375},{w:1,h:1,x_center:.921875,y_center:.484375},{w:1,h:1,x_center:.921875,y_center:.484375},{w:1,h:1,x_center:.953125,y_center:.484375},{w:1,h:1,x_center:.953125,y_center:.484375},{w:1,h:1,x_center:.984375,y_center:.484375},{w:1,h:1,x_center:.984375,y_center:.484375},{w:1,h:1,x_center:.015625,y_center:.515625},{w:1,h:1,x_center:.015625,y_center:.515625},{w:1,h:1,x_center:.046875,y_center:.515625},{w:1,h:1,x_center:.046875,y_center:.515625},{w:1,h:1,x_center:.078125,y_center:.515625},{w:1,h:1,x_center:.078125,y_center:.515625},{w:1,h:1,x_center:.109375,y_center:.515625},{w:1,h:1,x_center:.109375,y_center:.515625},{w:1,h:1,x_center:.140625,y_center:.515625},{w:1,h:1,x_center:.140625,y_center:.515625},{w:1,h:1,x_center:.171875,y_center:.515625},{w:1,h:1,x_center:.171875,y_center:.515625},{w:1,h:1,x_center:.203125,y_center:.515625},{w:1,h:1,x_center:.203125,y_center:.515625},{w:1,h:1,x_center:.234375,y_center:.515625},{w:1,h:1,x_center:.234375,y_center:.515625},{w:1,h:1,x_center:.265625,y_center:.515625},{w:1,h:1,x_center:.265625,y_center:.515625},{w:1,h:1,x_center:.296875,y_center:.515625},{w:1,h:1,x_center:.296875,y_center:.515625},{w:1,h:1,x_center:.328125,y_center:.515625},{w:1,h:1,x_center:.328125,y_center:.515625},{w:1,h:1,x_center:.359375,y_center:.515625},{w:1,h:1,x_center:.359375,y_center:.515625},{w:1,h:1,x_center:.390625,y_center:.515625},{w:1,h:1,x_center:.390625,y_center:.515625},{w:1,h:1,x_center:.421875,y_center:.515625},{w:1,h:1,x_center:.421875,y_center:.515625},{w:1,h:1,x_center:.453125,y_center:.515625},{w:1,h:1,x_center:.453125,y_center:.515625},{w:1,h:1,x_center:.484375,y_center:.515625},{w:1,h:1,x_center:.484375,y_center:.515625},{w:1,h:1,x_center:.515625,y_center:.515625},{w:1,h:1,x_center:.515625,y_center:.515625},{w:1,h:1,x_center:.546875,y_center:.515625},{w:1,h:1,x_center:.546875,y_center:.515625},{w:1,h:1,x_center:.578125,y_center:.515625},{w:1,h:1,x_center:.578125,y_center:.515625},{w:1,h:1,x_center:.609375,y_center:.515625},{w:1,h:1,x_center:.609375,y_center:.515625},{w:1,h:1,x_center:.640625,y_center:.515625},{w:1,h:1,x_center:.640625,y_center:.515625},{w:1,h:1,x_center:.671875,y_center:.515625},{w:1,h:1,x_center:.671875,y_center:.515625},{w:1,h:1,x_center:.703125,y_center:.515625},{w:1,h:1,x_center:.703125,y_center:.515625},{w:1,h:1,x_center:.734375,y_center:.515625},{w:1,h:1,x_center:.734375,y_center:.515625},{w:1,h:1,x_center:.765625,y_center:.515625},{w:1,h:1,x_center:.765625,y_center:.515625},{w:1,h:1,x_center:.796875,y_center:.515625},{w:1,h:1,x_center:.796875,y_center:.515625},{w:1,h:1,x_center:.828125,y_center:.515625},{w:1,h:1,x_center:.828125,y_center:.515625},{w:1,h:1,x_center:.859375,y_center:.515625},{w:1,h:1,x_center:.859375,y_center:.515625},{w:1,h:1,x_center:.890625,y_center:.515625},{w:1,h:1,x_center:.890625,y_center:.515625},{w:1,h:1,x_center:.921875,y_center:.515625},{w:1,h:1,x_center:.921875,y_center:.515625},{w:1,h:1,x_center:.953125,y_center:.515625},{w:1,h:1,x_center:.953125,y_center:.515625},{w:1,h:1,x_center:.984375,y_center:.515625},{w:1,h:1,x_center:.984375,y_center:.515625},{w:1,h:1,x_center:.015625,y_center:.546875},{w:1,h:1,x_center:.015625,y_center:.546875},{w:1,h:1,x_center:.046875,y_center:.546875},{w:1,h:1,x_center:.046875,y_center:.546875},{w:1,h:1,x_center:.078125,y_center:.546875},{w:1,h:1,x_center:.078125,y_center:.546875},{w:1,h:1,x_center:.109375,y_center:.546875},{w:1,h:1,x_center:.109375,y_center:.546875},{w:1,h:1,x_center:.140625,y_center:.546875},{w:1,h:1,x_center:.140625,y_center:.546875},{w:1,h:1,x_center:.171875,y_center:.546875},{w:1,h:1,x_center:.171875,y_center:.546875},{w:1,h:1,x_center:.203125,y_center:.546875},{w:1,h:1,x_center:.203125,y_center:.546875},{w:1,h:1,x_center:.234375,y_center:.546875},{w:1,h:1,x_center:.234375,y_center:.546875},{w:1,h:1,x_center:.265625,y_center:.546875},{w:1,h:1,x_center:.265625,y_center:.546875},{w:1,h:1,x_center:.296875,y_center:.546875},{w:1,h:1,x_center:.296875,y_center:.546875},{w:1,h:1,x_center:.328125,y_center:.546875},{w:1,h:1,x_center:.328125,y_center:.546875},{w:1,h:1,x_center:.359375,y_center:.546875},{w:1,h:1,x_center:.359375,y_center:.546875},{w:1,h:1,x_center:.390625,y_center:.546875},{w:1,h:1,x_center:.390625,y_center:.546875},{w:1,h:1,x_center:.421875,y_center:.546875},{w:1,h:1,x_center:.421875,y_center:.546875},{w:1,h:1,x_center:.453125,y_center:.546875},{w:1,h:1,x_center:.453125,y_center:.546875},{w:1,h:1,x_center:.484375,y_center:.546875},{w:1,h:1,x_center:.484375,y_center:.546875},{w:1,h:1,x_center:.515625,y_center:.546875},{w:1,h:1,x_center:.515625,y_center:.546875},{w:1,h:1,x_center:.546875,y_center:.546875},{w:1,h:1,x_center:.546875,y_center:.546875},{w:1,h:1,x_center:.578125,y_center:.546875},{w:1,h:1,x_center:.578125,y_center:.546875},{w:1,h:1,x_center:.609375,y_center:.546875},{w:1,h:1,x_center:.609375,y_center:.546875},{w:1,h:1,x_center:.640625,y_center:.546875},{w:1,h:1,x_center:.640625,y_center:.546875},{w:1,h:1,x_center:.671875,y_center:.546875},{w:1,h:1,x_center:.671875,y_center:.546875},{w:1,h:1,x_center:.703125,y_center:.546875},{w:1,h:1,x_center:.703125,y_center:.546875},{w:1,h:1,x_center:.734375,y_center:.546875},{w:1,h:1,x_center:.734375,y_center:.546875},{w:1,h:1,x_center:.765625,y_center:.546875},{w:1,h:1,x_center:.765625,y_center:.546875},{w:1,h:1,x_center:.796875,y_center:.546875},{w:1,h:1,x_center:.796875,y_center:.546875},{w:1,h:1,x_center:.828125,y_center:.546875},{w:1,h:1,x_center:.828125,y_center:.546875},{w:1,h:1,x_center:.859375,y_center:.546875},{w:1,h:1,x_center:.859375,y_center:.546875},{w:1,h:1,x_center:.890625,y_center:.546875},{w:1,h:1,x_center:.890625,y_center:.546875},{w:1,h:1,x_center:.921875,y_center:.546875},{w:1,h:1,x_center:.921875,y_center:.546875},{w:1,h:1,x_center:.953125,y_center:.546875},{w:1,h:1,x_center:.953125,y_center:.546875},{w:1,h:1,x_center:.984375,y_center:.546875},{w:1,h:1,x_center:.984375,y_center:.546875},{w:1,h:1,x_center:.015625,y_center:.578125},{w:1,h:1,x_center:.015625,y_center:.578125},{w:1,h:1,x_center:.046875,y_center:.578125},{w:1,h:1,x_center:.046875,y_center:.578125},{w:1,h:1,x_center:.078125,y_center:.578125},{w:1,h:1,x_center:.078125,y_center:.578125},{w:1,h:1,x_center:.109375,y_center:.578125},{w:1,h:1,x_center:.109375,y_center:.578125},{w:1,h:1,x_center:.140625,y_center:.578125},{w:1,h:1,x_center:.140625,y_center:.578125},{w:1,h:1,x_center:.171875,y_center:.578125},{w:1,h:1,x_center:.171875,y_center:.578125},{w:1,h:1,x_center:.203125,y_center:.578125},{w:1,h:1,x_center:.203125,y_center:.578125},{w:1,h:1,x_center:.234375,y_center:.578125},{w:1,h:1,x_center:.234375,y_center:.578125},{w:1,h:1,x_center:.265625,y_center:.578125},{w:1,h:1,x_center:.265625,y_center:.578125},{w:1,h:1,x_center:.296875,y_center:.578125},{w:1,h:1,x_center:.296875,y_center:.578125},{w:1,h:1,x_center:.328125,y_center:.578125},{w:1,h:1,x_center:.328125,y_center:.578125},{w:1,h:1,x_center:.359375,y_center:.578125},{w:1,h:1,x_center:.359375,y_center:.578125},{w:1,h:1,x_center:.390625,y_center:.578125},{w:1,h:1,x_center:.390625,y_center:.578125},{w:1,h:1,x_center:.421875,y_center:.578125},{w:1,h:1,x_center:.421875,y_center:.578125},{w:1,h:1,x_center:.453125,y_center:.578125},{w:1,h:1,x_center:.453125,y_center:.578125},{w:1,h:1,x_center:.484375,y_center:.578125},{w:1,h:1,x_center:.484375,y_center:.578125},{w:1,h:1,x_center:.515625,y_center:.578125},{w:1,h:1,x_center:.515625,y_center:.578125},{w:1,h:1,x_center:.546875,y_center:.578125},{w:1,h:1,x_center:.546875,y_center:.578125},{w:1,h:1,x_center:.578125,y_center:.578125},{w:1,h:1,x_center:.578125,y_center:.578125},{w:1,h:1,x_center:.609375,y_center:.578125},{w:1,h:1,x_center:.609375,y_center:.578125},{w:1,h:1,x_center:.640625,y_center:.578125},{w:1,h:1,x_center:.640625,y_center:.578125},{w:1,h:1,x_center:.671875,y_center:.578125},{w:1,h:1,x_center:.671875,y_center:.578125},{w:1,h:1,x_center:.703125,y_center:.578125},{w:1,h:1,x_center:.703125,y_center:.578125},{w:1,h:1,x_center:.734375,y_center:.578125},{w:1,h:1,x_center:.734375,y_center:.578125},{w:1,h:1,x_center:.765625,y_center:.578125},{w:1,h:1,x_center:.765625,y_center:.578125},{w:1,h:1,x_center:.796875,y_center:.578125},{w:1,h:1,x_center:.796875,y_center:.578125},{w:1,h:1,x_center:.828125,y_center:.578125},{w:1,h:1,x_center:.828125,y_center:.578125},{w:1,h:1,x_center:.859375,y_center:.578125},{w:1,h:1,x_center:.859375,y_center:.578125},{w:1,h:1,x_center:.890625,y_center:.578125},{w:1,h:1,x_center:.890625,y_center:.578125},{w:1,h:1,x_center:.921875,y_center:.578125},{w:1,h:1,x_center:.921875,y_center:.578125},{w:1,h:1,x_center:.953125,y_center:.578125},{w:1,h:1,x_center:.953125,y_center:.578125},{w:1,h:1,x_center:.984375,y_center:.578125},{w:1,h:1,x_center:.984375,y_center:.578125},{w:1,h:1,x_center:.015625,y_center:.609375},{w:1,h:1,x_center:.015625,y_center:.609375},{w:1,h:1,x_center:.046875,y_center:.609375},{w:1,h:1,x_center:.046875,y_center:.609375},{w:1,h:1,x_center:.078125,y_center:.609375},{w:1,h:1,x_center:.078125,y_center:.609375},{w:1,h:1,x_center:.109375,y_center:.609375},{w:1,h:1,x_center:.109375,y_center:.609375},{w:1,h:1,x_center:.140625,y_center:.609375},{w:1,h:1,x_center:.140625,y_center:.609375},{w:1,h:1,x_center:.171875,y_center:.609375},{w:1,h:1,x_center:.171875,y_center:.609375},{w:1,h:1,x_center:.203125,y_center:.609375},{w:1,h:1,x_center:.203125,y_center:.609375},{w:1,h:1,x_center:.234375,y_center:.609375},{w:1,h:1,x_center:.234375,y_center:.609375},{w:1,h:1,x_center:.265625,y_center:.609375},{w:1,h:1,x_center:.265625,y_center:.609375},{w:1,h:1,x_center:.296875,y_center:.609375},{w:1,h:1,x_center:.296875,y_center:.609375},{w:1,h:1,x_center:.328125,y_center:.609375},{w:1,h:1,x_center:.328125,y_center:.609375},{w:1,h:1,x_center:.359375,y_center:.609375},{w:1,h:1,x_center:.359375,y_center:.609375},{w:1,h:1,x_center:.390625,y_center:.609375},{w:1,h:1,x_center:.390625,y_center:.609375},{w:1,h:1,x_center:.421875,y_center:.609375},{w:1,h:1,x_center:.421875,y_center:.609375},{w:1,h:1,x_center:.453125,y_center:.609375},{w:1,h:1,x_center:.453125,y_center:.609375},{w:1,h:1,x_center:.484375,y_center:.609375},{w:1,h:1,x_center:.484375,y_center:.609375},{w:1,h:1,x_center:.515625,y_center:.609375},{w:1,h:1,x_center:.515625,y_center:.609375},{w:1,h:1,x_center:.546875,y_center:.609375},{w:1,h:1,x_center:.546875,y_center:.609375},{w:1,h:1,x_center:.578125,y_center:.609375},{w:1,h:1,x_center:.578125,y_center:.609375},{w:1,h:1,x_center:.609375,y_center:.609375},{w:1,h:1,x_center:.609375,y_center:.609375},{w:1,h:1,x_center:.640625,y_center:.609375},{w:1,h:1,x_center:.640625,y_center:.609375},{w:1,h:1,x_center:.671875,y_center:.609375},{w:1,h:1,x_center:.671875,y_center:.609375},{w:1,h:1,x_center:.703125,y_center:.609375},{w:1,h:1,x_center:.703125,y_center:.609375},{w:1,h:1,x_center:.734375,y_center:.609375},{w:1,h:1,x_center:.734375,y_center:.609375},{w:1,h:1,x_center:.765625,y_center:.609375},{w:1,h:1,x_center:.765625,y_center:.609375},{w:1,h:1,x_center:.796875,y_center:.609375},{w:1,h:1,x_center:.796875,y_center:.609375},{w:1,h:1,x_center:.828125,y_center:.609375},{w:1,h:1,x_center:.828125,y_center:.609375},{w:1,h:1,x_center:.859375,y_center:.609375},{w:1,h:1,x_center:.859375,y_center:.609375},{w:1,h:1,x_center:.890625,y_center:.609375},{w:1,h:1,x_center:.890625,y_center:.609375},{w:1,h:1,x_center:.921875,y_center:.609375},{w:1,h:1,x_center:.921875,y_center:.609375},{w:1,h:1,x_center:.953125,y_center:.609375},{w:1,h:1,x_center:.953125,y_center:.609375},{w:1,h:1,x_center:.984375,y_center:.609375},{w:1,h:1,x_center:.984375,y_center:.609375},{w:1,h:1,x_center:.015625,y_center:.640625},{w:1,h:1,x_center:.015625,y_center:.640625},{w:1,h:1,x_center:.046875,y_center:.640625},{w:1,h:1,x_center:.046875,y_center:.640625},{w:1,h:1,x_center:.078125,y_center:.640625},{w:1,h:1,x_center:.078125,y_center:.640625},{w:1,h:1,x_center:.109375,y_center:.640625},{w:1,h:1,x_center:.109375,y_center:.640625},{w:1,h:1,x_center:.140625,y_center:.640625},{w:1,h:1,x_center:.140625,y_center:.640625},{w:1,h:1,x_center:.171875,y_center:.640625},{w:1,h:1,x_center:.171875,y_center:.640625},{w:1,h:1,x_center:.203125,y_center:.640625},{w:1,h:1,x_center:.203125,y_center:.640625},{w:1,h:1,x_center:.234375,y_center:.640625},{w:1,h:1,x_center:.234375,y_center:.640625},{w:1,h:1,x_center:.265625,y_center:.640625},{w:1,h:1,x_center:.265625,y_center:.640625},{w:1,h:1,x_center:.296875,y_center:.640625},{w:1,h:1,x_center:.296875,y_center:.640625},{w:1,h:1,x_center:.328125,y_center:.640625},{w:1,h:1,x_center:.328125,y_center:.640625},{w:1,h:1,x_center:.359375,y_center:.640625},{w:1,h:1,x_center:.359375,y_center:.640625},{w:1,h:1,x_center:.390625,y_center:.640625},{w:1,h:1,x_center:.390625,y_center:.640625},{w:1,h:1,x_center:.421875,y_center:.640625},{w:1,h:1,x_center:.421875,y_center:.640625},{w:1,h:1,x_center:.453125,y_center:.640625},{w:1,h:1,x_center:.453125,y_center:.640625},{w:1,h:1,x_center:.484375,y_center:.640625},{w:1,h:1,x_center:.484375,y_center:.640625},{w:1,h:1,x_center:.515625,y_center:.640625},{w:1,h:1,x_center:.515625,y_center:.640625},{w:1,h:1,x_center:.546875,y_center:.640625},{w:1,h:1,x_center:.546875,y_center:.640625},{w:1,h:1,x_center:.578125,y_center:.640625},{w:1,h:1,x_center:.578125,y_center:.640625},{w:1,h:1,x_center:.609375,y_center:.640625},{w:1,h:1,x_center:.609375,y_center:.640625},{w:1,h:1,x_center:.640625,y_center:.640625},{w:1,h:1,x_center:.640625,y_center:.640625},{w:1,h:1,x_center:.671875,y_center:.640625},{w:1,h:1,x_center:.671875,y_center:.640625},{w:1,h:1,x_center:.703125,y_center:.640625},{w:1,h:1,x_center:.703125,y_center:.640625},{w:1,h:1,x_center:.734375,y_center:.640625},{w:1,h:1,x_center:.734375,y_center:.640625},{w:1,h:1,x_center:.765625,y_center:.640625},{w:1,h:1,x_center:.765625,y_center:.640625},{w:1,h:1,x_center:.796875,y_center:.640625},{w:1,h:1,x_center:.796875,y_center:.640625},{w:1,h:1,x_center:.828125,y_center:.640625},{w:1,h:1,x_center:.828125,y_center:.640625},{w:1,h:1,x_center:.859375,y_center:.640625},{w:1,h:1,x_center:.859375,y_center:.640625},{w:1,h:1,x_center:.890625,y_center:.640625},{w:1,h:1,x_center:.890625,y_center:.640625},{w:1,h:1,x_center:.921875,y_center:.640625},{w:1,h:1,x_center:.921875,y_center:.640625},{w:1,h:1,x_center:.953125,y_center:.640625},{w:1,h:1,x_center:.953125,y_center:.640625},{w:1,h:1,x_center:.984375,y_center:.640625},{w:1,h:1,x_center:.984375,y_center:.640625},{w:1,h:1,x_center:.015625,y_center:.671875},{w:1,h:1,x_center:.015625,y_center:.671875},{w:1,h:1,x_center:.046875,y_center:.671875},{w:1,h:1,x_center:.046875,y_center:.671875},{w:1,h:1,x_center:.078125,y_center:.671875},{w:1,h:1,x_center:.078125,y_center:.671875},{w:1,h:1,x_center:.109375,y_center:.671875},{w:1,h:1,x_center:.109375,y_center:.671875},{w:1,h:1,x_center:.140625,y_center:.671875},{w:1,h:1,x_center:.140625,y_center:.671875},{w:1,h:1,x_center:.171875,y_center:.671875},{w:1,h:1,x_center:.171875,y_center:.671875},{w:1,h:1,x_center:.203125,y_center:.671875},{w:1,h:1,x_center:.203125,y_center:.671875},{w:1,h:1,x_center:.234375,y_center:.671875},{w:1,h:1,x_center:.234375,y_center:.671875},{w:1,h:1,x_center:.265625,y_center:.671875},{w:1,h:1,x_center:.265625,y_center:.671875},{w:1,h:1,x_center:.296875,y_center:.671875},{w:1,h:1,x_center:.296875,y_center:.671875},{w:1,h:1,x_center:.328125,y_center:.671875},{w:1,h:1,x_center:.328125,y_center:.671875},{w:1,h:1,x_center:.359375,y_center:.671875},{w:1,h:1,x_center:.359375,y_center:.671875},{w:1,h:1,x_center:.390625,y_center:.671875},{w:1,h:1,x_center:.390625,y_center:.671875},{w:1,h:1,x_center:.421875,y_center:.671875},{w:1,h:1,x_center:.421875,y_center:.671875},{w:1,h:1,x_center:.453125,y_center:.671875},{w:1,h:1,x_center:.453125,y_center:.671875},{w:1,h:1,x_center:.484375,y_center:.671875},{w:1,h:1,x_center:.484375,y_center:.671875},{w:1,h:1,x_center:.515625,y_center:.671875},{w:1,h:1,x_center:.515625,y_center:.671875},{w:1,h:1,x_center:.546875,y_center:.671875},{w:1,h:1,x_center:.546875,y_center:.671875},{w:1,h:1,x_center:.578125,y_center:.671875},{w:1,h:1,x_center:.578125,y_center:.671875},{w:1,h:1,x_center:.609375,y_center:.671875},{w:1,h:1,x_center:.609375,y_center:.671875},{w:1,h:1,x_center:.640625,y_center:.671875},{w:1,h:1,x_center:.640625,y_center:.671875},{w:1,h:1,x_center:.671875,y_center:.671875},{w:1,h:1,x_center:.671875,y_center:.671875},{w:1,h:1,x_center:.703125,y_center:.671875},{w:1,h:1,x_center:.703125,y_center:.671875},{w:1,h:1,x_center:.734375,y_center:.671875},{w:1,h:1,x_center:.734375,y_center:.671875},{w:1,h:1,x_center:.765625,y_center:.671875},{w:1,h:1,x_center:.765625,y_center:.671875},{w:1,h:1,x_center:.796875,y_center:.671875},{w:1,h:1,x_center:.796875,y_center:.671875},{w:1,h:1,x_center:.828125,y_center:.671875},{w:1,h:1,x_center:.828125,y_center:.671875},{w:1,h:1,x_center:.859375,y_center:.671875},{w:1,h:1,x_center:.859375,y_center:.671875},{w:1,h:1,x_center:.890625,y_center:.671875},{w:1,h:1,x_center:.890625,y_center:.671875},{w:1,h:1,x_center:.921875,y_center:.671875},{w:1,h:1,x_center:.921875,y_center:.671875},{w:1,h:1,x_center:.953125,y_center:.671875},{w:1,h:1,x_center:.953125,y_center:.671875},{w:1,h:1,x_center:.984375,y_center:.671875},{w:1,h:1,x_center:.984375,y_center:.671875},{w:1,h:1,x_center:.015625,y_center:.703125},{w:1,h:1,x_center:.015625,y_center:.703125},{w:1,h:1,x_center:.046875,y_center:.703125},{w:1,h:1,x_center:.046875,y_center:.703125},{w:1,h:1,x_center:.078125,y_center:.703125},{w:1,h:1,x_center:.078125,y_center:.703125},{w:1,h:1,x_center:.109375,y_center:.703125},{w:1,h:1,x_center:.109375,y_center:.703125},{w:1,h:1,x_center:.140625,y_center:.703125},{w:1,h:1,x_center:.140625,y_center:.703125},{w:1,h:1,x_center:.171875,y_center:.703125},{w:1,h:1,x_center:.171875,y_center:.703125},{w:1,h:1,x_center:.203125,y_center:.703125},{w:1,h:1,x_center:.203125,y_center:.703125},{w:1,h:1,x_center:.234375,y_center:.703125},{w:1,h:1,x_center:.234375,y_center:.703125},{w:1,h:1,x_center:.265625,y_center:.703125},{w:1,h:1,x_center:.265625,y_center:.703125},{w:1,h:1,x_center:.296875,y_center:.703125},{w:1,h:1,x_center:.296875,y_center:.703125},{w:1,h:1,x_center:.328125,y_center:.703125},{w:1,h:1,x_center:.328125,y_center:.703125},{w:1,h:1,x_center:.359375,y_center:.703125},{w:1,h:1,x_center:.359375,y_center:.703125},{w:1,h:1,x_center:.390625,y_center:.703125},{w:1,h:1,x_center:.390625,y_center:.703125},{w:1,h:1,x_center:.421875,y_center:.703125},{w:1,h:1,x_center:.421875,y_center:.703125},{w:1,h:1,x_center:.453125,y_center:.703125},{w:1,h:1,x_center:.453125,y_center:.703125},{w:1,h:1,x_center:.484375,y_center:.703125},{w:1,h:1,x_center:.484375,y_center:.703125},{w:1,h:1,x_center:.515625,y_center:.703125},{w:1,h:1,x_center:.515625,y_center:.703125},{w:1,h:1,x_center:.546875,y_center:.703125},{w:1,h:1,x_center:.546875,y_center:.703125},{w:1,h:1,x_center:.578125,y_center:.703125},{w:1,h:1,x_center:.578125,y_center:.703125},{w:1,h:1,x_center:.609375,y_center:.703125},{w:1,h:1,x_center:.609375,y_center:.703125},{w:1,h:1,x_center:.640625,y_center:.703125},{w:1,h:1,x_center:.640625,y_center:.703125},{w:1,h:1,x_center:.671875,y_center:.703125},{w:1,h:1,x_center:.671875,y_center:.703125},{w:1,h:1,x_center:.703125,y_center:.703125},{w:1,h:1,x_center:.703125,y_center:.703125},{w:1,h:1,x_center:.734375,y_center:.703125},{w:1,h:1,x_center:.734375,y_center:.703125},{w:1,h:1,x_center:.765625,y_center:.703125},{w:1,h:1,x_center:.765625,y_center:.703125},{w:1,h:1,x_center:.796875,y_center:.703125},{w:1,h:1,x_center:.796875,y_center:.703125},{w:1,h:1,x_center:.828125,y_center:.703125},{w:1,h:1,x_center:.828125,y_center:.703125},{w:1,h:1,x_center:.859375,y_center:.703125},{w:1,h:1,x_center:.859375,y_center:.703125},{w:1,h:1,x_center:.890625,y_center:.703125},{w:1,h:1,x_center:.890625,y_center:.703125},{w:1,h:1,x_center:.921875,y_center:.703125},{w:1,h:1,x_center:.921875,y_center:.703125},{w:1,h:1,x_center:.953125,y_center:.703125},{w:1,h:1,x_center:.953125,y_center:.703125},{w:1,h:1,x_center:.984375,y_center:.703125},{w:1,h:1,x_center:.984375,y_center:.703125},{w:1,h:1,x_center:.015625,y_center:.734375},{w:1,h:1,x_center:.015625,y_center:.734375},{w:1,h:1,x_center:.046875,y_center:.734375},{w:1,h:1,x_center:.046875,y_center:.734375},{w:1,h:1,x_center:.078125,y_center:.734375},{w:1,h:1,x_center:.078125,y_center:.734375},{w:1,h:1,x_center:.109375,y_center:.734375},{w:1,h:1,x_center:.109375,y_center:.734375},{w:1,h:1,x_center:.140625,y_center:.734375},{w:1,h:1,x_center:.140625,y_center:.734375},{w:1,h:1,x_center:.171875,y_center:.734375},{w:1,h:1,x_center:.171875,y_center:.734375},{w:1,h:1,x_center:.203125,y_center:.734375},{w:1,h:1,x_center:.203125,y_center:.734375},{w:1,h:1,x_center:.234375,y_center:.734375},{w:1,h:1,x_center:.234375,y_center:.734375},{w:1,h:1,x_center:.265625,y_center:.734375},{w:1,h:1,x_center:.265625,y_center:.734375},{w:1,h:1,x_center:.296875,y_center:.734375},{w:1,h:1,x_center:.296875,y_center:.734375},{w:1,h:1,x_center:.328125,y_center:.734375},{w:1,h:1,x_center:.328125,y_center:.734375},{w:1,h:1,x_center:.359375,y_center:.734375},{w:1,h:1,x_center:.359375,y_center:.734375},{w:1,h:1,x_center:.390625,y_center:.734375},{w:1,h:1,x_center:.390625,y_center:.734375},{w:1,h:1,x_center:.421875,y_center:.734375},{w:1,h:1,x_center:.421875,y_center:.734375},{w:1,h:1,x_center:.453125,y_center:.734375},{w:1,h:1,x_center:.453125,y_center:.734375},{w:1,h:1,x_center:.484375,y_center:.734375},{w:1,h:1,x_center:.484375,y_center:.734375},{w:1,h:1,x_center:.515625,y_center:.734375},{w:1,h:1,x_center:.515625,y_center:.734375},{w:1,h:1,x_center:.546875,y_center:.734375},{w:1,h:1,x_center:.546875,y_center:.734375},{w:1,h:1,x_center:.578125,y_center:.734375},{w:1,h:1,x_center:.578125,y_center:.734375},{w:1,h:1,x_center:.609375,y_center:.734375},{w:1,h:1,x_center:.609375,y_center:.734375},{w:1,h:1,x_center:.640625,y_center:.734375},{w:1,h:1,x_center:.640625,y_center:.734375},{w:1,h:1,x_center:.671875,y_center:.734375},{w:1,h:1,x_center:.671875,y_center:.734375},{w:1,h:1,x_center:.703125,y_center:.734375},{w:1,h:1,x_center:.703125,y_center:.734375},{w:1,h:1,x_center:.734375,y_center:.734375},{w:1,h:1,x_center:.734375,y_center:.734375},{w:1,h:1,x_center:.765625,y_center:.734375},{w:1,h:1,x_center:.765625,y_center:.734375},{w:1,h:1,x_center:.796875,y_center:.734375},{w:1,h:1,x_center:.796875,y_center:.734375},{w:1,h:1,x_center:.828125,y_center:.734375},{w:1,h:1,x_center:.828125,y_center:.734375},{w:1,h:1,x_center:.859375,y_center:.734375},{w:1,h:1,x_center:.859375,y_center:.734375},{w:1,h:1,x_center:.890625,y_center:.734375},{w:1,h:1,x_center:.890625,y_center:.734375},{w:1,h:1,x_center:.921875,y_center:.734375},{w:1,h:1,x_center:.921875,y_center:.734375},{w:1,h:1,x_center:.953125,y_center:.734375},{w:1,h:1,x_center:.953125,y_center:.734375},{w:1,h:1,x_center:.984375,y_center:.734375},{w:1,h:1,x_center:.984375,y_center:.734375},{w:1,h:1,x_center:.015625,y_center:.765625},{w:1,h:1,x_center:.015625,y_center:.765625},{w:1,h:1,x_center:.046875,y_center:.765625},{w:1,h:1,x_center:.046875,y_center:.765625},{w:1,h:1,x_center:.078125,y_center:.765625},{w:1,h:1,x_center:.078125,y_center:.765625},{w:1,h:1,x_center:.109375,y_center:.765625},{w:1,h:1,x_center:.109375,y_center:.765625},{w:1,h:1,x_center:.140625,y_center:.765625},{w:1,h:1,x_center:.140625,y_center:.765625},{w:1,h:1,x_center:.171875,y_center:.765625},{w:1,h:1,x_center:.171875,y_center:.765625},{w:1,h:1,x_center:.203125,y_center:.765625},{w:1,h:1,x_center:.203125,y_center:.765625},{w:1,h:1,x_center:.234375,y_center:.765625},{w:1,h:1,x_center:.234375,y_center:.765625},{w:1,h:1,x_center:.265625,y_center:.765625},{w:1,h:1,x_center:.265625,y_center:.765625},{w:1,h:1,x_center:.296875,y_center:.765625},{w:1,h:1,x_center:.296875,y_center:.765625},{w:1,h:1,x_center:.328125,y_center:.765625},{w:1,h:1,x_center:.328125,y_center:.765625},{w:1,h:1,x_center:.359375,y_center:.765625},{w:1,h:1,x_center:.359375,y_center:.765625},{w:1,h:1,x_center:.390625,y_center:.765625},{w:1,h:1,x_center:.390625,y_center:.765625},{w:1,h:1,x_center:.421875,y_center:.765625},{w:1,h:1,x_center:.421875,y_center:.765625},{w:1,h:1,x_center:.453125,y_center:.765625},{w:1,h:1,x_center:.453125,y_center:.765625},{w:1,h:1,x_center:.484375,y_center:.765625},{w:1,h:1,x_center:.484375,y_center:.765625},{w:1,h:1,x_center:.515625,y_center:.765625},{w:1,h:1,x_center:.515625,y_center:.765625},{w:1,h:1,x_center:.546875,y_center:.765625},{w:1,h:1,x_center:.546875,y_center:.765625},{w:1,h:1,x_center:.578125,y_center:.765625},{w:1,h:1,x_center:.578125,y_center:.765625},{w:1,h:1,x_center:.609375,y_center:.765625},{w:1,h:1,x_center:.609375,y_center:.765625},{w:1,h:1,x_center:.640625,y_center:.765625},{w:1,h:1,x_center:.640625,y_center:.765625},{w:1,h:1,x_center:.671875,y_center:.765625},{w:1,h:1,x_center:.671875,y_center:.765625},{w:1,h:1,x_center:.703125,y_center:.765625},{w:1,h:1,x_center:.703125,y_center:.765625},{w:1,h:1,x_center:.734375,y_center:.765625},{w:1,h:1,x_center:.734375,y_center:.765625},{w:1,h:1,x_center:.765625,y_center:.765625},{w:1,h:1,x_center:.765625,y_center:.765625},{w:1,h:1,x_center:.796875,y_center:.765625},{w:1,h:1,x_center:.796875,y_center:.765625},{w:1,h:1,x_center:.828125,y_center:.765625},{w:1,h:1,x_center:.828125,y_center:.765625},{w:1,h:1,x_center:.859375,y_center:.765625},{w:1,h:1,x_center:.859375,y_center:.765625},{w:1,h:1,x_center:.890625,y_center:.765625},{w:1,h:1,x_center:.890625,y_center:.765625},{w:1,h:1,x_center:.921875,y_center:.765625},{w:1,h:1,x_center:.921875,y_center:.765625},{w:1,h:1,x_center:.953125,y_center:.765625},{w:1,h:1,x_center:.953125,y_center:.765625},{w:1,h:1,x_center:.984375,y_center:.765625},{w:1,h:1,x_center:.984375,y_center:.765625},{w:1,h:1,x_center:.015625,y_center:.796875},{w:1,h:1,x_center:.015625,y_center:.796875},{w:1,h:1,x_center:.046875,y_center:.796875},{w:1,h:1,x_center:.046875,y_center:.796875},{w:1,h:1,x_center:.078125,y_center:.796875},{w:1,h:1,x_center:.078125,y_center:.796875},{w:1,h:1,x_center:.109375,y_center:.796875},{w:1,h:1,x_center:.109375,y_center:.796875},{w:1,h:1,x_center:.140625,y_center:.796875},{w:1,h:1,x_center:.140625,y_center:.796875},{w:1,h:1,x_center:.171875,y_center:.796875},{w:1,h:1,x_center:.171875,y_center:.796875},{w:1,h:1,x_center:.203125,y_center:.796875},{w:1,h:1,x_center:.203125,y_center:.796875},{w:1,h:1,x_center:.234375,y_center:.796875},{w:1,h:1,x_center:.234375,y_center:.796875},{w:1,h:1,x_center:.265625,y_center:.796875},{w:1,h:1,x_center:.265625,y_center:.796875},{w:1,h:1,x_center:.296875,y_center:.796875},{w:1,h:1,x_center:.296875,y_center:.796875},{w:1,h:1,x_center:.328125,y_center:.796875},{w:1,h:1,x_center:.328125,y_center:.796875},{w:1,h:1,x_center:.359375,y_center:.796875},{w:1,h:1,x_center:.359375,y_center:.796875},{w:1,h:1,x_center:.390625,y_center:.796875},{w:1,h:1,x_center:.390625,y_center:.796875},{w:1,h:1,x_center:.421875,y_center:.796875},{w:1,h:1,x_center:.421875,y_center:.796875},{w:1,h:1,x_center:.453125,y_center:.796875},{w:1,h:1,x_center:.453125,y_center:.796875},{w:1,h:1,x_center:.484375,y_center:.796875},{w:1,h:1,x_center:.484375,y_center:.796875},{w:1,h:1,x_center:.515625,y_center:.796875},{w:1,h:1,x_center:.515625,y_center:.796875},{w:1,h:1,x_center:.546875,y_center:.796875},{w:1,h:1,x_center:.546875,y_center:.796875},{w:1,h:1,x_center:.578125,y_center:.796875},{w:1,h:1,x_center:.578125,y_center:.796875},{w:1,h:1,x_center:.609375,y_center:.796875},{w:1,h:1,x_center:.609375,y_center:.796875},{w:1,h:1,x_center:.640625,y_center:.796875},{w:1,h:1,x_center:.640625,y_center:.796875},{w:1,h:1,x_center:.671875,y_center:.796875},{w:1,h:1,x_center:.671875,y_center:.796875},{w:1,h:1,x_center:.703125,y_center:.796875},{w:1,h:1,x_center:.703125,y_center:.796875},{w:1,h:1,x_center:.734375,y_center:.796875},{w:1,h:1,x_center:.734375,y_center:.796875},{w:1,h:1,x_center:.765625,y_center:.796875},{w:1,h:1,x_center:.765625,y_center:.796875},{w:1,h:1,x_center:.796875,y_center:.796875},{w:1,h:1,x_center:.796875,y_center:.796875},{w:1,h:1,x_center:.828125,y_center:.796875},{w:1,h:1,x_center:.828125,y_center:.796875},{w:1,h:1,x_center:.859375,y_center:.796875},{w:1,h:1,x_center:.859375,y_center:.796875},{w:1,h:1,x_center:.890625,y_center:.796875},{w:1,h:1,x_center:.890625,y_center:.796875},{w:1,h:1,x_center:.921875,y_center:.796875},{w:1,h:1,x_center:.921875,y_center:.796875},{w:1,h:1,x_center:.953125,y_center:.796875},{w:1,h:1,x_center:.953125,y_center:.796875},{w:1,h:1,x_center:.984375,y_center:.796875},{w:1,h:1,x_center:.984375,y_center:.796875},{w:1,h:1,x_center:.015625,y_center:.828125},{w:1,h:1,x_center:.015625,y_center:.828125},{w:1,h:1,x_center:.046875,y_center:.828125},{w:1,h:1,x_center:.046875,y_center:.828125},{w:1,h:1,x_center:.078125,y_center:.828125},{w:1,h:1,x_center:.078125,y_center:.828125},{w:1,h:1,x_center:.109375,y_center:.828125},{w:1,h:1,x_center:.109375,y_center:.828125},{w:1,h:1,x_center:.140625,y_center:.828125},{w:1,h:1,x_center:.140625,y_center:.828125},{w:1,h:1,x_center:.171875,y_center:.828125},{w:1,h:1,x_center:.171875,y_center:.828125},{w:1,h:1,x_center:.203125,y_center:.828125},{w:1,h:1,x_center:.203125,y_center:.828125},{w:1,h:1,x_center:.234375,y_center:.828125},{w:1,h:1,x_center:.234375,y_center:.828125},{w:1,h:1,x_center:.265625,y_center:.828125},{w:1,h:1,x_center:.265625,y_center:.828125},{w:1,h:1,x_center:.296875,y_center:.828125},{w:1,h:1,x_center:.296875,y_center:.828125},{w:1,h:1,x_center:.328125,y_center:.828125},{w:1,h:1,x_center:.328125,y_center:.828125},{w:1,h:1,x_center:.359375,y_center:.828125},{w:1,h:1,x_center:.359375,y_center:.828125},{w:1,h:1,x_center:.390625,y_center:.828125},{w:1,h:1,x_center:.390625,y_center:.828125},{w:1,h:1,x_center:.421875,y_center:.828125},{w:1,h:1,x_center:.421875,y_center:.828125},{w:1,h:1,x_center:.453125,y_center:.828125},{w:1,h:1,x_center:.453125,y_center:.828125},{w:1,h:1,x_center:.484375,y_center:.828125},{w:1,h:1,x_center:.484375,y_center:.828125},{w:1,h:1,x_center:.515625,y_center:.828125},{w:1,h:1,x_center:.515625,y_center:.828125},{w:1,h:1,x_center:.546875,y_center:.828125},{w:1,h:1,x_center:.546875,y_center:.828125},{w:1,h:1,x_center:.578125,y_center:.828125},{w:1,h:1,x_center:.578125,y_center:.828125},{w:1,h:1,x_center:.609375,y_center:.828125},{w:1,h:1,x_center:.609375,y_center:.828125},{w:1,h:1,x_center:.640625,y_center:.828125},{w:1,h:1,x_center:.640625,y_center:.828125},{w:1,h:1,x_center:.671875,y_center:.828125},{w:1,h:1,x_center:.671875,y_center:.828125},{w:1,h:1,x_center:.703125,y_center:.828125},{w:1,h:1,x_center:.703125,y_center:.828125},{w:1,h:1,x_center:.734375,y_center:.828125},{w:1,h:1,x_center:.734375,y_center:.828125},{w:1,h:1,x_center:.765625,y_center:.828125},{w:1,h:1,x_center:.765625,y_center:.828125},{w:1,h:1,x_center:.796875,y_center:.828125},{w:1,h:1,x_center:.796875,y_center:.828125},{w:1,h:1,x_center:.828125,y_center:.828125},{w:1,h:1,x_center:.828125,y_center:.828125},{w:1,h:1,x_center:.859375,y_center:.828125},{w:1,h:1,x_center:.859375,y_center:.828125},{w:1,h:1,x_center:.890625,y_center:.828125},{w:1,h:1,x_center:.890625,y_center:.828125},{w:1,h:1,x_center:.921875,y_center:.828125},{w:1,h:1,x_center:.921875,y_center:.828125},{w:1,h:1,x_center:.953125,y_center:.828125},{w:1,h:1,x_center:.953125,y_center:.828125},{w:1,h:1,x_center:.984375,y_center:.828125},{w:1,h:1,x_center:.984375,y_center:.828125},{w:1,h:1,x_center:.015625,y_center:.859375},{w:1,h:1,x_center:.015625,y_center:.859375},{w:1,h:1,x_center:.046875,y_center:.859375},{w:1,h:1,x_center:.046875,y_center:.859375},{w:1,h:1,x_center:.078125,y_center:.859375},{w:1,h:1,x_center:.078125,y_center:.859375},{w:1,h:1,x_center:.109375,y_center:.859375},{w:1,h:1,x_center:.109375,y_center:.859375},{w:1,h:1,x_center:.140625,y_center:.859375},{w:1,h:1,x_center:.140625,y_center:.859375},{w:1,h:1,x_center:.171875,y_center:.859375},{w:1,h:1,x_center:.171875,y_center:.859375},{w:1,h:1,x_center:.203125,y_center:.859375},{w:1,h:1,x_center:.203125,y_center:.859375},{w:1,h:1,x_center:.234375,y_center:.859375},{w:1,h:1,x_center:.234375,y_center:.859375},{w:1,h:1,x_center:.265625,y_center:.859375},{w:1,h:1,x_center:.265625,y_center:.859375},{w:1,h:1,x_center:.296875,y_center:.859375},{w:1,h:1,x_center:.296875,y_center:.859375},{w:1,h:1,x_center:.328125,y_center:.859375},{w:1,h:1,x_center:.328125,y_center:.859375},{w:1,h:1,x_center:.359375,y_center:.859375},{w:1,h:1,x_center:.359375,y_center:.859375},{w:1,h:1,x_center:.390625,y_center:.859375},{w:1,h:1,x_center:.390625,y_center:.859375},{w:1,h:1,x_center:.421875,y_center:.859375},{w:1,h:1,x_center:.421875,y_center:.859375},{w:1,h:1,x_center:.453125,y_center:.859375},{w:1,h:1,x_center:.453125,y_center:.859375},{w:1,h:1,x_center:.484375,y_center:.859375},{w:1,h:1,x_center:.484375,y_center:.859375},{w:1,h:1,x_center:.515625,y_center:.859375},{w:1,h:1,x_center:.515625,y_center:.859375},{w:1,h:1,x_center:.546875,y_center:.859375},{w:1,h:1,x_center:.546875,y_center:.859375},{w:1,h:1,x_center:.578125,y_center:.859375},{w:1,h:1,x_center:.578125,y_center:.859375},{w:1,h:1,x_center:.609375,y_center:.859375},{w:1,h:1,x_center:.609375,y_center:.859375},{w:1,h:1,x_center:.640625,y_center:.859375},{w:1,h:1,x_center:.640625,y_center:.859375},{w:1,h:1,x_center:.671875,y_center:.859375},{w:1,h:1,x_center:.671875,y_center:.859375},{w:1,h:1,x_center:.703125,y_center:.859375},{w:1,h:1,x_center:.703125,y_center:.859375},{w:1,h:1,x_center:.734375,y_center:.859375},{w:1,h:1,x_center:.734375,y_center:.859375},{w:1,h:1,x_center:.765625,y_center:.859375},{w:1,h:1,x_center:.765625,y_center:.859375},{w:1,h:1,x_center:.796875,y_center:.859375},{w:1,h:1,x_center:.796875,y_center:.859375},{w:1,h:1,x_center:.828125,y_center:.859375},{w:1,h:1,x_center:.828125,y_center:.859375},{w:1,h:1,x_center:.859375,y_center:.859375},{w:1,h:1,x_center:.859375,y_center:.859375},{w:1,h:1,x_center:.890625,y_center:.859375},{w:1,h:1,x_center:.890625,y_center:.859375},{w:1,h:1,x_center:.921875,y_center:.859375},{w:1,h:1,x_center:.921875,y_center:.859375},{w:1,h:1,x_center:.953125,y_center:.859375},{w:1,h:1,x_center:.953125,y_center:.859375},{w:1,h:1,x_center:.984375,y_center:.859375},{w:1,h:1,x_center:.984375,y_center:.859375},{w:1,h:1,x_center:.015625,y_center:.890625},{w:1,h:1,x_center:.015625,y_center:.890625},{w:1,h:1,x_center:.046875,y_center:.890625},{w:1,h:1,x_center:.046875,y_center:.890625},{w:1,h:1,x_center:.078125,y_center:.890625},{w:1,h:1,x_center:.078125,y_center:.890625},{w:1,h:1,x_center:.109375,y_center:.890625},{w:1,h:1,x_center:.109375,y_center:.890625},{w:1,h:1,x_center:.140625,y_center:.890625},{w:1,h:1,x_center:.140625,y_center:.890625},{w:1,h:1,x_center:.171875,y_center:.890625},{w:1,h:1,x_center:.171875,y_center:.890625},{w:1,h:1,x_center:.203125,y_center:.890625},{w:1,h:1,x_center:.203125,y_center:.890625},{w:1,h:1,x_center:.234375,y_center:.890625},{w:1,h:1,x_center:.234375,y_center:.890625},{w:1,h:1,x_center:.265625,y_center:.890625},{w:1,h:1,x_center:.265625,y_center:.890625},{w:1,h:1,x_center:.296875,y_center:.890625},{w:1,h:1,x_center:.296875,y_center:.890625},{w:1,h:1,x_center:.328125,y_center:.890625},{w:1,h:1,x_center:.328125,y_center:.890625},{w:1,h:1,x_center:.359375,y_center:.890625},{w:1,h:1,x_center:.359375,y_center:.890625},{w:1,h:1,x_center:.390625,y_center:.890625},{w:1,h:1,x_center:.390625,y_center:.890625},{w:1,h:1,x_center:.421875,y_center:.890625},{w:1,h:1,x_center:.421875,y_center:.890625},{w:1,h:1,x_center:.453125,y_center:.890625},{w:1,h:1,x_center:.453125,y_center:.890625},{w:1,h:1,x_center:.484375,y_center:.890625},{w:1,h:1,x_center:.484375,y_center:.890625},{w:1,h:1,x_center:.515625,y_center:.890625},{w:1,h:1,x_center:.515625,y_center:.890625},{w:1,h:1,x_center:.546875,y_center:.890625},{w:1,h:1,x_center:.546875,y_center:.890625},{w:1,h:1,x_center:.578125,y_center:.890625},{w:1,h:1,x_center:.578125,y_center:.890625},{w:1,h:1,x_center:.609375,y_center:.890625},{w:1,h:1,x_center:.609375,y_center:.890625},{w:1,h:1,x_center:.640625,y_center:.890625},{w:1,h:1,x_center:.640625,y_center:.890625},{w:1,h:1,x_center:.671875,y_center:.890625},{w:1,h:1,x_center:.671875,y_center:.890625},{w:1,h:1,x_center:.703125,y_center:.890625},{w:1,h:1,x_center:.703125,y_center:.890625},{w:1,h:1,x_center:.734375,y_center:.890625},{w:1,h:1,x_center:.734375,y_center:.890625},{w:1,h:1,x_center:.765625,y_center:.890625},{w:1,h:1,x_center:.765625,y_center:.890625},{w:1,h:1,x_center:.796875,y_center:.890625},{w:1,h:1,x_center:.796875,y_center:.890625},{w:1,h:1,x_center:.828125,y_center:.890625},{w:1,h:1,x_center:.828125,y_center:.890625},{w:1,h:1,x_center:.859375,y_center:.890625},{w:1,h:1,x_center:.859375,y_center:.890625},{w:1,h:1,x_center:.890625,y_center:.890625},{w:1,h:1,x_center:.890625,y_center:.890625},{w:1,h:1,x_center:.921875,y_center:.890625},{w:1,h:1,x_center:.921875,y_center:.890625},{w:1,h:1,x_center:.953125,y_center:.890625},{w:1,h:1,x_center:.953125,y_center:.890625},{w:1,h:1,x_center:.984375,y_center:.890625},{w:1,h:1,x_center:.984375,y_center:.890625},{w:1,h:1,x_center:.015625,y_center:.921875},{w:1,h:1,x_center:.015625,y_center:.921875},{w:1,h:1,x_center:.046875,y_center:.921875},{w:1,h:1,x_center:.046875,y_center:.921875},{w:1,h:1,x_center:.078125,y_center:.921875},{w:1,h:1,x_center:.078125,y_center:.921875},{w:1,h:1,x_center:.109375,y_center:.921875},{w:1,h:1,x_center:.109375,y_center:.921875},{w:1,h:1,x_center:.140625,y_center:.921875},{w:1,h:1,x_center:.140625,y_center:.921875},{w:1,h:1,x_center:.171875,y_center:.921875},{w:1,h:1,x_center:.171875,y_center:.921875},{w:1,h:1,x_center:.203125,y_center:.921875},{w:1,h:1,x_center:.203125,y_center:.921875},{w:1,h:1,x_center:.234375,y_center:.921875},{w:1,h:1,x_center:.234375,y_center:.921875},{w:1,h:1,x_center:.265625,y_center:.921875},{w:1,h:1,x_center:.265625,y_center:.921875},{w:1,h:1,x_center:.296875,y_center:.921875},{w:1,h:1,x_center:.296875,y_center:.921875},{w:1,h:1,x_center:.328125,y_center:.921875},{w:1,h:1,x_center:.328125,y_center:.921875},{w:1,h:1,x_center:.359375,y_center:.921875},{w:1,h:1,x_center:.359375,y_center:.921875},{w:1,h:1,x_center:.390625,y_center:.921875},{w:1,h:1,x_center:.390625,y_center:.921875},{w:1,h:1,x_center:.421875,y_center:.921875},{w:1,h:1,x_center:.421875,y_center:.921875},{w:1,h:1,x_center:.453125,y_center:.921875},{w:1,h:1,x_center:.453125,y_center:.921875},{w:1,h:1,x_center:.484375,y_center:.921875},{w:1,h:1,x_center:.484375,y_center:.921875},{w:1,h:1,x_center:.515625,y_center:.921875},{w:1,h:1,x_center:.515625,y_center:.921875},{w:1,h:1,x_center:.546875,y_center:.921875},{w:1,h:1,x_center:.546875,y_center:.921875},{w:1,h:1,x_center:.578125,y_center:.921875},{w:1,h:1,x_center:.578125,y_center:.921875},{w:1,h:1,x_center:.609375,y_center:.921875},{w:1,h:1,x_center:.609375,y_center:.921875},{w:1,h:1,x_center:.640625,y_center:.921875},{w:1,h:1,x_center:.640625,y_center:.921875},{w:1,h:1,x_center:.671875,y_center:.921875},{w:1,h:1,x_center:.671875,y_center:.921875},{w:1,h:1,x_center:.703125,y_center:.921875},{w:1,h:1,x_center:.703125,y_center:.921875},{w:1,h:1,x_center:.734375,y_center:.921875},{w:1,h:1,x_center:.734375,y_center:.921875},{w:1,h:1,x_center:.765625,y_center:.921875},{w:1,h:1,x_center:.765625,y_center:.921875},{w:1,h:1,x_center:.796875,y_center:.921875},{w:1,h:1,x_center:.796875,y_center:.921875},{w:1,h:1,x_center:.828125,y_center:.921875},{w:1,h:1,x_center:.828125,y_center:.921875},{w:1,h:1,x_center:.859375,y_center:.921875},{w:1,h:1,x_center:.859375,y_center:.921875},{w:1,h:1,x_center:.890625,y_center:.921875},{w:1,h:1,x_center:.890625,y_center:.921875},{w:1,h:1,x_center:.921875,y_center:.921875},{w:1,h:1,x_center:.921875,y_center:.921875},{w:1,h:1,x_center:.953125,y_center:.921875},{w:1,h:1,x_center:.953125,y_center:.921875},{w:1,h:1,x_center:.984375,y_center:.921875},{w:1,h:1,x_center:.984375,y_center:.921875},{w:1,h:1,x_center:.015625,y_center:.953125},{w:1,h:1,x_center:.015625,y_center:.953125},{w:1,h:1,x_center:.046875,y_center:.953125},{w:1,h:1,x_center:.046875,y_center:.953125},{w:1,h:1,x_center:.078125,y_center:.953125},{w:1,h:1,x_center:.078125,y_center:.953125},{w:1,h:1,x_center:.109375,y_center:.953125},{w:1,h:1,x_center:.109375,y_center:.953125},{w:1,h:1,x_center:.140625,y_center:.953125},{w:1,h:1,x_center:.140625,y_center:.953125},{w:1,h:1,x_center:.171875,y_center:.953125},{w:1,h:1,x_center:.171875,y_center:.953125},{w:1,h:1,x_center:.203125,y_center:.953125},{w:1,h:1,x_center:.203125,y_center:.953125},{w:1,h:1,x_center:.234375,y_center:.953125},{w:1,h:1,x_center:.234375,y_center:.953125},{w:1,h:1,x_center:.265625,y_center:.953125},{w:1,h:1,x_center:.265625,y_center:.953125},{w:1,h:1,x_center:.296875,y_center:.953125},{w:1,h:1,x_center:.296875,y_center:.953125},{w:1,h:1,x_center:.328125,y_center:.953125},{w:1,h:1,x_center:.328125,y_center:.953125},{w:1,h:1,x_center:.359375,y_center:.953125},{w:1,h:1,x_center:.359375,y_center:.953125},{w:1,h:1,x_center:.390625,y_center:.953125},{w:1,h:1,x_center:.390625,y_center:.953125},{w:1,h:1,x_center:.421875,y_center:.953125},{w:1,h:1,x_center:.421875,y_center:.953125},{w:1,h:1,x_center:.453125,y_center:.953125},{w:1,h:1,x_center:.453125,y_center:.953125},{w:1,h:1,x_center:.484375,y_center:.953125},{w:1,h:1,x_center:.484375,y_center:.953125},{w:1,h:1,x_center:.515625,y_center:.953125},{w:1,h:1,x_center:.515625,y_center:.953125},{w:1,h:1,x_center:.546875,y_center:.953125},{w:1,h:1,x_center:.546875,y_center:.953125},{w:1,h:1,x_center:.578125,y_center:.953125},{w:1,h:1,x_center:.578125,y_center:.953125},{w:1,h:1,x_center:.609375,y_center:.953125},{w:1,h:1,x_center:.609375,y_center:.953125},{w:1,h:1,x_center:.640625,y_center:.953125},{w:1,h:1,x_center:.640625,y_center:.953125},{w:1,h:1,x_center:.671875,y_center:.953125},{w:1,h:1,x_center:.671875,y_center:.953125},{w:1,h:1,x_center:.703125,y_center:.953125},{w:1,h:1,x_center:.703125,y_center:.953125},{w:1,h:1,x_center:.734375,y_center:.953125},{w:1,h:1,x_center:.734375,y_center:.953125},{w:1,h:1,x_center:.765625,y_center:.953125},{w:1,h:1,x_center:.765625,y_center:.953125},{w:1,h:1,x_center:.796875,y_center:.953125},{w:1,h:1,x_center:.796875,y_center:.953125},{w:1,h:1,x_center:.828125,y_center:.953125},{w:1,h:1,x_center:.828125,y_center:.953125},{w:1,h:1,x_center:.859375,y_center:.953125},{w:1,h:1,x_center:.859375,y_center:.953125},{w:1,h:1,x_center:.890625,y_center:.953125},{w:1,h:1,x_center:.890625,y_center:.953125},{w:1,h:1,x_center:.921875,y_center:.953125},{w:1,h:1,x_center:.921875,y_center:.953125},{w:1,h:1,x_center:.953125,y_center:.953125},{w:1,h:1,x_center:.953125,y_center:.953125},{w:1,h:1,x_center:.984375,y_center:.953125},{w:1,h:1,x_center:.984375,y_center:.953125},{w:1,h:1,x_center:.015625,y_center:.984375},{w:1,h:1,x_center:.015625,y_center:.984375},{w:1,h:1,x_center:.046875,y_center:.984375},{w:1,h:1,x_center:.046875,y_center:.984375},{w:1,h:1,x_center:.078125,y_center:.984375},{w:1,h:1,x_center:.078125,y_center:.984375},{w:1,h:1,x_center:.109375,y_center:.984375},{w:1,h:1,x_center:.109375,y_center:.984375},{w:1,h:1,x_center:.140625,y_center:.984375},{w:1,h:1,x_center:.140625,y_center:.984375},{w:1,h:1,x_center:.171875,y_center:.984375},{w:1,h:1,x_center:.171875,y_center:.984375},{w:1,h:1,x_center:.203125,y_center:.984375},{w:1,h:1,x_center:.203125,y_center:.984375},{w:1,h:1,x_center:.234375,y_center:.984375},{w:1,h:1,x_center:.234375,y_center:.984375},{w:1,h:1,x_center:.265625,y_center:.984375},{w:1,h:1,x_center:.265625,y_center:.984375},{w:1,h:1,x_center:.296875,y_center:.984375},{w:1,h:1,x_center:.296875,y_center:.984375},{w:1,h:1,x_center:.328125,y_center:.984375},{w:1,h:1,x_center:.328125,y_center:.984375},{w:1,h:1,x_center:.359375,y_center:.984375},{w:1,h:1,x_center:.359375,y_center:.984375},{w:1,h:1,x_center:.390625,y_center:.984375},{w:1,h:1,x_center:.390625,y_center:.984375},{w:1,h:1,x_center:.421875,y_center:.984375},{w:1,h:1,x_center:.421875,y_center:.984375},{w:1,h:1,x_center:.453125,y_center:.984375},{w:1,h:1,x_center:.453125,y_center:.984375},{w:1,h:1,x_center:.484375,y_center:.984375},{w:1,h:1,x_center:.484375,y_center:.984375},{w:1,h:1,x_center:.515625,y_center:.984375},{w:1,h:1,x_center:.515625,y_center:.984375},{w:1,h:1,x_center:.546875,y_center:.984375},{w:1,h:1,x_center:.546875,y_center:.984375},{w:1,h:1,x_center:.578125,y_center:.984375},{w:1,h:1,x_center:.578125,y_center:.984375},{w:1,h:1,x_center:.609375,y_center:.984375},{w:1,h:1,x_center:.609375,y_center:.984375},{w:1,h:1,x_center:.640625,y_center:.984375},{w:1,h:1,x_center:.640625,y_center:.984375},{w:1,h:1,x_center:.671875,y_center:.984375},{w:1,h:1,x_center:.671875,y_center:.984375},{w:1,h:1,x_center:.703125,y_center:.984375},{w:1,h:1,x_center:.703125,y_center:.984375},{w:1,h:1,x_center:.734375,y_center:.984375},{w:1,h:1,x_center:.734375,y_center:.984375},{w:1,h:1,x_center:.765625,y_center:.984375},{w:1,h:1,x_center:.765625,y_center:.984375},{w:1,h:1,x_center:.796875,y_center:.984375},{w:1,h:1,x_center:.796875,y_center:.984375},{w:1,h:1,x_center:.828125,y_center:.984375},{w:1,h:1,x_center:.828125,y_center:.984375},{w:1,h:1,x_center:.859375,y_center:.984375},{w:1,h:1,x_center:.859375,y_center:.984375},{w:1,h:1,x_center:.890625,y_center:.984375},{w:1,h:1,x_center:.890625,y_center:.984375},{w:1,h:1,x_center:.921875,y_center:.984375},{w:1,h:1,x_center:.921875,y_center:.984375},{w:1,h:1,x_center:.953125,y_center:.984375},{w:1,h:1,x_center:.953125,y_center:.984375},{w:1,h:1,x_center:.984375,y_center:.984375},{w:1,h:1,x_center:.984375,y_center:.984375},{w:1,h:1,x_center:.03125,y_center:.03125},{w:1,h:1,x_center:.03125,y_center:.03125},{w:1,h:1,x_center:.09375,y_center:.03125},{w:1,h:1,x_center:.09375,y_center:.03125},{w:1,h:1,x_center:.15625,y_center:.03125},{w:1,h:1,x_center:.15625,y_center:.03125},{w:1,h:1,x_center:.21875,y_center:.03125},{w:1,h:1,x_center:.21875,y_center:.03125},{w:1,h:1,x_center:.28125,y_center:.03125},{w:1,h:1,x_center:.28125,y_center:.03125},{w:1,h:1,x_center:.34375,y_center:.03125},{w:1,h:1,x_center:.34375,y_center:.03125},{w:1,h:1,x_center:.40625,y_center:.03125},{w:1,h:1,x_center:.40625,y_center:.03125},{w:1,h:1,x_center:.46875,y_center:.03125},{w:1,h:1,x_center:.46875,y_center:.03125},{w:1,h:1,x_center:.53125,y_center:.03125},{w:1,h:1,x_center:.53125,y_center:.03125},{w:1,h:1,x_center:.59375,y_center:.03125},{w:1,h:1,x_center:.59375,y_center:.03125},{w:1,h:1,x_center:.65625,y_center:.03125},{w:1,h:1,x_center:.65625,y_center:.03125},{w:1,h:1,x_center:.71875,y_center:.03125},{w:1,h:1,x_center:.71875,y_center:.03125},{w:1,h:1,x_center:.78125,y_center:.03125},{w:1,h:1,x_center:.78125,y_center:.03125},{w:1,h:1,x_center:.84375,y_center:.03125},{w:1,h:1,x_center:.84375,y_center:.03125},{w:1,h:1,x_center:.90625,y_center:.03125},{w:1,h:1,x_center:.90625,y_center:.03125},{w:1,h:1,x_center:.96875,y_center:.03125},{w:1,h:1,x_center:.96875,y_center:.03125},{w:1,h:1,x_center:.03125,y_center:.09375},{w:1,h:1,x_center:.03125,y_center:.09375},{w:1,h:1,x_center:.09375,y_center:.09375},{w:1,h:1,x_center:.09375,y_center:.09375},{w:1,h:1,x_center:.15625,y_center:.09375},{w:1,h:1,x_center:.15625,y_center:.09375},{w:1,h:1,x_center:.21875,y_center:.09375},{w:1,h:1,x_center:.21875,y_center:.09375},{w:1,h:1,x_center:.28125,y_center:.09375},{w:1,h:1,x_center:.28125,y_center:.09375},{w:1,h:1,x_center:.34375,y_center:.09375},{w:1,h:1,x_center:.34375,y_center:.09375},{w:1,h:1,x_center:.40625,y_center:.09375},{w:1,h:1,x_center:.40625,y_center:.09375},{w:1,h:1,x_center:.46875,y_center:.09375},{w:1,h:1,x_center:.46875,y_center:.09375},{w:1,h:1,x_center:.53125,y_center:.09375},{w:1,h:1,x_center:.53125,y_center:.09375},{w:1,h:1,x_center:.59375,y_center:.09375},{w:1,h:1,x_center:.59375,y_center:.09375},{w:1,h:1,x_center:.65625,y_center:.09375},{w:1,h:1,x_center:.65625,y_center:.09375},{w:1,h:1,x_center:.71875,y_center:.09375},{w:1,h:1,x_center:.71875,y_center:.09375},{w:1,h:1,x_center:.78125,y_center:.09375},{w:1,h:1,x_center:.78125,y_center:.09375},{w:1,h:1,x_center:.84375,y_center:.09375},{w:1,h:1,x_center:.84375,y_center:.09375},{w:1,h:1,x_center:.90625,y_center:.09375},{w:1,h:1,x_center:.90625,y_center:.09375},{w:1,h:1,x_center:.96875,y_center:.09375},{w:1,h:1,x_center:.96875,y_center:.09375},{w:1,h:1,x_center:.03125,y_center:.15625},{w:1,h:1,x_center:.03125,y_center:.15625},{w:1,h:1,x_center:.09375,y_center:.15625},{w:1,h:1,x_center:.09375,y_center:.15625},{w:1,h:1,x_center:.15625,y_center:.15625},{w:1,h:1,x_center:.15625,y_center:.15625},{w:1,h:1,x_center:.21875,y_center:.15625},{w:1,h:1,x_center:.21875,y_center:.15625},{w:1,h:1,x_center:.28125,y_center:.15625},{w:1,h:1,x_center:.28125,y_center:.15625},{w:1,h:1,x_center:.34375,y_center:.15625},{w:1,h:1,x_center:.34375,y_center:.15625},{w:1,h:1,x_center:.40625,y_center:.15625},{w:1,h:1,x_center:.40625,y_center:.15625},{w:1,h:1,x_center:.46875,y_center:.15625},{w:1,h:1,x_center:.46875,y_center:.15625},{w:1,h:1,x_center:.53125,y_center:.15625},{w:1,h:1,x_center:.53125,y_center:.15625},{w:1,h:1,x_center:.59375,y_center:.15625},{w:1,h:1,x_center:.59375,y_center:.15625},{w:1,h:1,x_center:.65625,y_center:.15625},{w:1,h:1,x_center:.65625,y_center:.15625},{w:1,h:1,x_center:.71875,y_center:.15625},{w:1,h:1,x_center:.71875,y_center:.15625},{w:1,h:1,x_center:.78125,y_center:.15625},{w:1,h:1,x_center:.78125,y_center:.15625},{w:1,h:1,x_center:.84375,y_center:.15625},{w:1,h:1,x_center:.84375,y_center:.15625},{w:1,h:1,x_center:.90625,y_center:.15625},{w:1,h:1,x_center:.90625,y_center:.15625},{w:1,h:1,x_center:.96875,y_center:.15625},{w:1,h:1,x_center:.96875,y_center:.15625},{w:1,h:1,x_center:.03125,y_center:.21875},{w:1,h:1,x_center:.03125,y_center:.21875},{w:1,h:1,x_center:.09375,y_center:.21875},{w:1,h:1,x_center:.09375,y_center:.21875},{w:1,h:1,x_center:.15625,y_center:.21875},{w:1,h:1,x_center:.15625,y_center:.21875},{w:1,h:1,x_center:.21875,y_center:.21875},{w:1,h:1,x_center:.21875,y_center:.21875},{w:1,h:1,x_center:.28125,y_center:.21875},{w:1,h:1,x_center:.28125,y_center:.21875},{w:1,h:1,x_center:.34375,y_center:.21875},{w:1,h:1,x_center:.34375,y_center:.21875},{w:1,h:1,x_center:.40625,y_center:.21875},{w:1,h:1,x_center:.40625,y_center:.21875},{w:1,h:1,x_center:.46875,y_center:.21875},{w:1,h:1,x_center:.46875,y_center:.21875},{w:1,h:1,x_center:.53125,y_center:.21875},{w:1,h:1,x_center:.53125,y_center:.21875},{w:1,h:1,x_center:.59375,y_center:.21875},{w:1,h:1,x_center:.59375,y_center:.21875},{w:1,h:1,x_center:.65625,y_center:.21875},{w:1,h:1,x_center:.65625,y_center:.21875},{w:1,h:1,x_center:.71875,y_center:.21875},{w:1,h:1,x_center:.71875,y_center:.21875},{w:1,h:1,x_center:.78125,y_center:.21875},{w:1,h:1,x_center:.78125,y_center:.21875},{w:1,h:1,x_center:.84375,y_center:.21875},{w:1,h:1,x_center:.84375,y_center:.21875},{w:1,h:1,x_center:.90625,y_center:.21875},{w:1,h:1,x_center:.90625,y_center:.21875},{w:1,h:1,x_center:.96875,y_center:.21875},{w:1,h:1,x_center:.96875,y_center:.21875},{w:1,h:1,x_center:.03125,y_center:.28125},{w:1,h:1,x_center:.03125,y_center:.28125},{w:1,h:1,x_center:.09375,y_center:.28125},{w:1,h:1,x_center:.09375,y_center:.28125},{w:1,h:1,x_center:.15625,y_center:.28125},{w:1,h:1,x_center:.15625,y_center:.28125},{w:1,h:1,x_center:.21875,y_center:.28125},{w:1,h:1,x_center:.21875,y_center:.28125},{w:1,h:1,x_center:.28125,y_center:.28125},{w:1,h:1,x_center:.28125,y_center:.28125},{w:1,h:1,x_center:.34375,y_center:.28125},{w:1,h:1,x_center:.34375,y_center:.28125},{w:1,h:1,x_center:.40625,y_center:.28125},{w:1,h:1,x_center:.40625,y_center:.28125},{w:1,h:1,x_center:.46875,y_center:.28125},{w:1,h:1,x_center:.46875,y_center:.28125},{w:1,h:1,x_center:.53125,y_center:.28125},{w:1,h:1,x_center:.53125,y_center:.28125},{w:1,h:1,x_center:.59375,y_center:.28125},{w:1,h:1,x_center:.59375,y_center:.28125},{w:1,h:1,x_center:.65625,y_center:.28125},{w:1,h:1,x_center:.65625,y_center:.28125},{w:1,h:1,x_center:.71875,y_center:.28125},{w:1,h:1,x_center:.71875,y_center:.28125},{w:1,h:1,x_center:.78125,y_center:.28125},{w:1,h:1,x_center:.78125,y_center:.28125},{w:1,h:1,x_center:.84375,y_center:.28125},{w:1,h:1,x_center:.84375,y_center:.28125},{w:1,h:1,x_center:.90625,y_center:.28125},{w:1,h:1,x_center:.90625,y_center:.28125},{w:1,h:1,x_center:.96875,y_center:.28125},{w:1,h:1,x_center:.96875,y_center:.28125},{w:1,h:1,x_center:.03125,y_center:.34375},{w:1,h:1,x_center:.03125,y_center:.34375},{w:1,h:1,x_center:.09375,y_center:.34375},{w:1,h:1,x_center:.09375,y_center:.34375},{w:1,h:1,x_center:.15625,y_center:.34375},{w:1,h:1,x_center:.15625,y_center:.34375},{w:1,h:1,x_center:.21875,y_center:.34375},{w:1,h:1,x_center:.21875,y_center:.34375},{w:1,h:1,x_center:.28125,y_center:.34375},{w:1,h:1,x_center:.28125,y_center:.34375},{w:1,h:1,x_center:.34375,y_center:.34375},{w:1,h:1,x_center:.34375,y_center:.34375},{w:1,h:1,x_center:.40625,y_center:.34375},{w:1,h:1,x_center:.40625,y_center:.34375},{w:1,h:1,x_center:.46875,y_center:.34375},{w:1,h:1,x_center:.46875,y_center:.34375},{w:1,h:1,x_center:.53125,y_center:.34375},{w:1,h:1,x_center:.53125,y_center:.34375},{w:1,h:1,x_center:.59375,y_center:.34375},{w:1,h:1,x_center:.59375,y_center:.34375},{w:1,h:1,x_center:.65625,y_center:.34375},{w:1,h:1,x_center:.65625,y_center:.34375},{w:1,h:1,x_center:.71875,y_center:.34375},{w:1,h:1,x_center:.71875,y_center:.34375},{w:1,h:1,x_center:.78125,y_center:.34375},{w:1,h:1,x_center:.78125,y_center:.34375},{w:1,h:1,x_center:.84375,y_center:.34375},{w:1,h:1,x_center:.84375,y_center:.34375},{w:1,h:1,x_center:.90625,y_center:.34375},{w:1,h:1,x_center:.90625,y_center:.34375},{w:1,h:1,x_center:.96875,y_center:.34375},{w:1,h:1,x_center:.96875,y_center:.34375},{w:1,h:1,x_center:.03125,y_center:.40625},{w:1,h:1,x_center:.03125,y_center:.40625},{w:1,h:1,x_center:.09375,y_center:.40625},{w:1,h:1,x_center:.09375,y_center:.40625},{w:1,h:1,x_center:.15625,y_center:.40625},{w:1,h:1,x_center:.15625,y_center:.40625},{w:1,h:1,x_center:.21875,y_center:.40625},{w:1,h:1,x_center:.21875,y_center:.40625},{w:1,h:1,x_center:.28125,y_center:.40625},{w:1,h:1,x_center:.28125,y_center:.40625},{w:1,h:1,x_center:.34375,y_center:.40625},{w:1,h:1,x_center:.34375,y_center:.40625},{w:1,h:1,x_center:.40625,y_center:.40625},{w:1,h:1,x_center:.40625,y_center:.40625},{w:1,h:1,x_center:.46875,y_center:.40625},{w:1,h:1,x_center:.46875,y_center:.40625},{w:1,h:1,x_center:.53125,y_center:.40625},{w:1,h:1,x_center:.53125,y_center:.40625},{w:1,h:1,x_center:.59375,y_center:.40625},{w:1,h:1,x_center:.59375,y_center:.40625},{w:1,h:1,x_center:.65625,y_center:.40625},{w:1,h:1,x_center:.65625,y_center:.40625},{w:1,h:1,x_center:.71875,y_center:.40625},{w:1,h:1,x_center:.71875,y_center:.40625},{w:1,h:1,x_center:.78125,y_center:.40625},{w:1,h:1,x_center:.78125,y_center:.40625},{w:1,h:1,x_center:.84375,y_center:.40625},{w:1,h:1,x_center:.84375,y_center:.40625},{w:1,h:1,x_center:.90625,y_center:.40625},{w:1,h:1,x_center:.90625,y_center:.40625},{w:1,h:1,x_center:.96875,y_center:.40625},{w:1,h:1,x_center:.96875,y_center:.40625},{w:1,h:1,x_center:.03125,y_center:.46875},{w:1,h:1,x_center:.03125,y_center:.46875},{w:1,h:1,x_center:.09375,y_center:.46875},{w:1,h:1,x_center:.09375,y_center:.46875},{w:1,h:1,x_center:.15625,y_center:.46875},{w:1,h:1,x_center:.15625,y_center:.46875},{w:1,h:1,x_center:.21875,y_center:.46875},{w:1,h:1,x_center:.21875,y_center:.46875},{w:1,h:1,x_center:.28125,y_center:.46875},{w:1,h:1,x_center:.28125,y_center:.46875},{w:1,h:1,x_center:.34375,y_center:.46875},{w:1,h:1,x_center:.34375,y_center:.46875},{w:1,h:1,x_center:.40625,y_center:.46875},{w:1,h:1,x_center:.40625,y_center:.46875},{w:1,h:1,x_center:.46875,y_center:.46875},{w:1,h:1,x_center:.46875,y_center:.46875},{w:1,h:1,x_center:.53125,y_center:.46875},{w:1,h:1,x_center:.53125,y_center:.46875},{w:1,h:1,x_center:.59375,y_center:.46875},{w:1,h:1,x_center:.59375,y_center:.46875},{w:1,h:1,x_center:.65625,y_center:.46875},{w:1,h:1,x_center:.65625,y_center:.46875},{w:1,h:1,x_center:.71875,y_center:.46875},{w:1,h:1,x_center:.71875,y_center:.46875},{w:1,h:1,x_center:.78125,y_center:.46875},{w:1,h:1,x_center:.78125,y_center:.46875},{w:1,h:1,x_center:.84375,y_center:.46875},{w:1,h:1,x_center:.84375,y_center:.46875},{w:1,h:1,x_center:.90625,y_center:.46875},{w:1,h:1,x_center:.90625,y_center:.46875},{w:1,h:1,x_center:.96875,y_center:.46875},{w:1,h:1,x_center:.96875,y_center:.46875},{w:1,h:1,x_center:.03125,y_center:.53125},{w:1,h:1,x_center:.03125,y_center:.53125},{w:1,h:1,x_center:.09375,y_center:.53125},{w:1,h:1,x_center:.09375,y_center:.53125},{w:1,h:1,x_center:.15625,y_center:.53125},{w:1,h:1,x_center:.15625,y_center:.53125},{w:1,h:1,x_center:.21875,y_center:.53125},{w:1,h:1,x_center:.21875,y_center:.53125},{w:1,h:1,x_center:.28125,y_center:.53125},{w:1,h:1,x_center:.28125,y_center:.53125},{w:1,h:1,x_center:.34375,y_center:.53125},{w:1,h:1,x_center:.34375,y_center:.53125},{w:1,h:1,x_center:.40625,y_center:.53125},{w:1,h:1,x_center:.40625,y_center:.53125},{w:1,h:1,x_center:.46875,y_center:.53125},{w:1,h:1,x_center:.46875,y_center:.53125},{w:1,h:1,x_center:.53125,y_center:.53125},{w:1,h:1,x_center:.53125,y_center:.53125},{w:1,h:1,x_center:.59375,y_center:.53125},{w:1,h:1,x_center:.59375,y_center:.53125},{w:1,h:1,x_center:.65625,y_center:.53125},{w:1,h:1,x_center:.65625,y_center:.53125},{w:1,h:1,x_center:.71875,y_center:.53125},{w:1,h:1,x_center:.71875,y_center:.53125},{w:1,h:1,x_center:.78125,y_center:.53125},{w:1,h:1,x_center:.78125,y_center:.53125},{w:1,h:1,x_center:.84375,y_center:.53125},{w:1,h:1,x_center:.84375,y_center:.53125},{w:1,h:1,x_center:.90625,y_center:.53125},{w:1,h:1,x_center:.90625,y_center:.53125},{w:1,h:1,x_center:.96875,y_center:.53125},{w:1,h:1,x_center:.96875,y_center:.53125},{w:1,h:1,x_center:.03125,y_center:.59375},{w:1,h:1,x_center:.03125,y_center:.59375},{w:1,h:1,x_center:.09375,y_center:.59375},{w:1,h:1,x_center:.09375,y_center:.59375},{w:1,h:1,x_center:.15625,y_center:.59375},{w:1,h:1,x_center:.15625,y_center:.59375},{w:1,h:1,x_center:.21875,y_center:.59375},{w:1,h:1,x_center:.21875,y_center:.59375},{w:1,h:1,x_center:.28125,y_center:.59375},{w:1,h:1,x_center:.28125,y_center:.59375},{w:1,h:1,x_center:.34375,y_center:.59375},{w:1,h:1,x_center:.34375,y_center:.59375},{w:1,h:1,x_center:.40625,y_center:.59375},{w:1,h:1,x_center:.40625,y_center:.59375},{w:1,h:1,x_center:.46875,y_center:.59375},{w:1,h:1,x_center:.46875,y_center:.59375},{w:1,h:1,x_center:.53125,y_center:.59375},{w:1,h:1,x_center:.53125,y_center:.59375},{w:1,h:1,x_center:.59375,y_center:.59375},{w:1,h:1,x_center:.59375,y_center:.59375},{w:1,h:1,x_center:.65625,y_center:.59375},{w:1,h:1,x_center:.65625,y_center:.59375},{w:1,h:1,x_center:.71875,y_center:.59375},{w:1,h:1,x_center:.71875,y_center:.59375},{w:1,h:1,x_center:.78125,y_center:.59375},{w:1,h:1,x_center:.78125,y_center:.59375},{w:1,h:1,x_center:.84375,y_center:.59375},{w:1,h:1,x_center:.84375,y_center:.59375},{w:1,h:1,x_center:.90625,y_center:.59375},{w:1,h:1,x_center:.90625,y_center:.59375},{w:1,h:1,x_center:.96875,y_center:.59375},{w:1,h:1,x_center:.96875,y_center:.59375},{w:1,h:1,x_center:.03125,y_center:.65625},{w:1,h:1,x_center:.03125,y_center:.65625},{w:1,h:1,x_center:.09375,y_center:.65625},{w:1,h:1,x_center:.09375,y_center:.65625},{w:1,h:1,x_center:.15625,y_center:.65625},{w:1,h:1,x_center:.15625,y_center:.65625},{w:1,h:1,x_center:.21875,y_center:.65625},{w:1,h:1,x_center:.21875,y_center:.65625},{w:1,h:1,x_center:.28125,y_center:.65625},{w:1,h:1,x_center:.28125,y_center:.65625},{w:1,h:1,x_center:.34375,y_center:.65625},{w:1,h:1,x_center:.34375,y_center:.65625},{w:1,h:1,x_center:.40625,y_center:.65625},{w:1,h:1,x_center:.40625,y_center:.65625},{w:1,h:1,x_center:.46875,y_center:.65625},{w:1,h:1,x_center:.46875,y_center:.65625},{w:1,h:1,x_center:.53125,y_center:.65625},{w:1,h:1,x_center:.53125,y_center:.65625},{w:1,h:1,x_center:.59375,y_center:.65625},{w:1,h:1,x_center:.59375,y_center:.65625},{w:1,h:1,x_center:.65625,y_center:.65625},{w:1,h:1,x_center:.65625,y_center:.65625},{w:1,h:1,x_center:.71875,y_center:.65625},{w:1,h:1,x_center:.71875,y_center:.65625},{w:1,h:1,x_center:.78125,y_center:.65625},{w:1,h:1,x_center:.78125,y_center:.65625},{w:1,h:1,x_center:.84375,y_center:.65625},{w:1,h:1,x_center:.84375,y_center:.65625},{w:1,h:1,x_center:.90625,y_center:.65625},{w:1,h:1,x_center:.90625,y_center:.65625},{w:1,h:1,x_center:.96875,y_center:.65625},{w:1,h:1,x_center:.96875,y_center:.65625},{w:1,h:1,x_center:.03125,y_center:.71875},{w:1,h:1,x_center:.03125,y_center:.71875},{w:1,h:1,x_center:.09375,y_center:.71875},{w:1,h:1,x_center:.09375,y_center:.71875},{w:1,h:1,x_center:.15625,y_center:.71875},{w:1,h:1,x_center:.15625,y_center:.71875},{w:1,h:1,x_center:.21875,y_center:.71875},{w:1,h:1,x_center:.21875,y_center:.71875},{w:1,h:1,x_center:.28125,y_center:.71875},{w:1,h:1,x_center:.28125,y_center:.71875},{w:1,h:1,x_center:.34375,y_center:.71875},{w:1,h:1,x_center:.34375,y_center:.71875},{w:1,h:1,x_center:.40625,y_center:.71875},{w:1,h:1,x_center:.40625,y_center:.71875},{w:1,h:1,x_center:.46875,y_center:.71875},{w:1,h:1,x_center:.46875,y_center:.71875},{w:1,h:1,x_center:.53125,y_center:.71875},{w:1,h:1,x_center:.53125,y_center:.71875},{w:1,h:1,x_center:.59375,y_center:.71875},{w:1,h:1,x_center:.59375,y_center:.71875},{w:1,h:1,x_center:.65625,y_center:.71875},{w:1,h:1,x_center:.65625,y_center:.71875},{w:1,h:1,x_center:.71875,y_center:.71875},{w:1,h:1,x_center:.71875,y_center:.71875},{w:1,h:1,x_center:.78125,y_center:.71875},{w:1,h:1,x_center:.78125,y_center:.71875},{w:1,h:1,x_center:.84375,y_center:.71875},{w:1,h:1,x_center:.84375,y_center:.71875},{w:1,h:1,x_center:.90625,y_center:.71875},{w:1,h:1,x_center:.90625,y_center:.71875},{w:1,h:1,x_center:.96875,y_center:.71875},{w:1,h:1,x_center:.96875,y_center:.71875},{w:1,h:1,x_center:.03125,y_center:.78125},{w:1,h:1,x_center:.03125,y_center:.78125},{w:1,h:1,x_center:.09375,y_center:.78125},{w:1,h:1,x_center:.09375,y_center:.78125},{w:1,h:1,x_center:.15625,y_center:.78125},{w:1,h:1,x_center:.15625,y_center:.78125},{w:1,h:1,x_center:.21875,y_center:.78125},{w:1,h:1,x_center:.21875,y_center:.78125},{w:1,h:1,x_center:.28125,y_center:.78125},{w:1,h:1,x_center:.28125,y_center:.78125},{w:1,h:1,x_center:.34375,y_center:.78125},{w:1,h:1,x_center:.34375,y_center:.78125},{w:1,h:1,x_center:.40625,y_center:.78125},{w:1,h:1,x_center:.40625,y_center:.78125},{w:1,h:1,x_center:.46875,y_center:.78125},{w:1,h:1,x_center:.46875,y_center:.78125},{w:1,h:1,x_center:.53125,y_center:.78125},{w:1,h:1,x_center:.53125,y_center:.78125},{w:1,h:1,x_center:.59375,y_center:.78125},{w:1,h:1,x_center:.59375,y_center:.78125},{w:1,h:1,x_center:.65625,y_center:.78125},{w:1,h:1,x_center:.65625,y_center:.78125},{w:1,h:1,x_center:.71875,y_center:.78125},{w:1,h:1,x_center:.71875,y_center:.78125},{w:1,h:1,x_center:.78125,y_center:.78125},{w:1,h:1,x_center:.78125,y_center:.78125},{w:1,h:1,x_center:.84375,y_center:.78125},{w:1,h:1,x_center:.84375,y_center:.78125},{w:1,h:1,x_center:.90625,y_center:.78125},{w:1,h:1,x_center:.90625,y_center:.78125},{w:1,h:1,x_center:.96875,y_center:.78125},{w:1,h:1,x_center:.96875,y_center:.78125},{w:1,h:1,x_center:.03125,y_center:.84375},{w:1,h:1,x_center:.03125,y_center:.84375},{w:1,h:1,x_center:.09375,y_center:.84375},{w:1,h:1,x_center:.09375,y_center:.84375},{w:1,h:1,x_center:.15625,y_center:.84375},{w:1,h:1,x_center:.15625,y_center:.84375},{w:1,h:1,x_center:.21875,y_center:.84375},{w:1,h:1,x_center:.21875,y_center:.84375},{w:1,h:1,x_center:.28125,y_center:.84375},{w:1,h:1,x_center:.28125,y_center:.84375},{w:1,h:1,x_center:.34375,y_center:.84375},{w:1,h:1,x_center:.34375,y_center:.84375},{w:1,h:1,x_center:.40625,y_center:.84375},{w:1,h:1,x_center:.40625,y_center:.84375},{w:1,h:1,x_center:.46875,y_center:.84375},{w:1,h:1,x_center:.46875,y_center:.84375},{w:1,h:1,x_center:.53125,y_center:.84375},{w:1,h:1,x_center:.53125,y_center:.84375},{w:1,h:1,x_center:.59375,y_center:.84375},{w:1,h:1,x_center:.59375,y_center:.84375},{w:1,h:1,x_center:.65625,y_center:.84375},{w:1,h:1,x_center:.65625,y_center:.84375},{w:1,h:1,x_center:.71875,y_center:.84375},{w:1,h:1,x_center:.71875,y_center:.84375},{w:1,h:1,x_center:.78125,y_center:.84375},{w:1,h:1,x_center:.78125,y_center:.84375},{w:1,h:1,x_center:.84375,y_center:.84375},{w:1,h:1,x_center:.84375,y_center:.84375},{w:1,h:1,x_center:.90625,y_center:.84375},{w:1,h:1,x_center:.90625,y_center:.84375},{w:1,h:1,x_center:.96875,y_center:.84375},{w:1,h:1,x_center:.96875,y_center:.84375},{w:1,h:1,x_center:.03125,y_center:.90625},{w:1,h:1,x_center:.03125,y_center:.90625},{w:1,h:1,x_center:.09375,y_center:.90625},{w:1,h:1,x_center:.09375,y_center:.90625},{w:1,h:1,x_center:.15625,y_center:.90625},{w:1,h:1,x_center:.15625,y_center:.90625},{w:1,h:1,x_center:.21875,y_center:.90625},{w:1,h:1,x_center:.21875,y_center:.90625},{w:1,h:1,x_center:.28125,y_center:.90625},{w:1,h:1,x_center:.28125,y_center:.90625},{w:1,h:1,x_center:.34375,y_center:.90625},{w:1,h:1,x_center:.34375,y_center:.90625},{w:1,h:1,x_center:.40625,y_center:.90625},{w:1,h:1,x_center:.40625,y_center:.90625},{w:1,h:1,x_center:.46875,y_center:.90625},{w:1,h:1,x_center:.46875,y_center:.90625},{w:1,h:1,x_center:.53125,y_center:.90625},{w:1,h:1,x_center:.53125,y_center:.90625},{w:1,h:1,x_center:.59375,y_center:.90625},{w:1,h:1,x_center:.59375,y_center:.90625},{w:1,h:1,x_center:.65625,y_center:.90625},{w:1,h:1,x_center:.65625,y_center:.90625},{w:1,h:1,x_center:.71875,y_center:.90625},{w:1,h:1,x_center:.71875,y_center:.90625},{w:1,h:1,x_center:.78125,y_center:.90625},{w:1,h:1,x_center:.78125,y_center:.90625},{w:1,h:1,x_center:.84375,y_center:.90625},{w:1,h:1,x_center:.84375,y_center:.90625},{w:1,h:1,x_center:.90625,y_center:.90625},{w:1,h:1,x_center:.90625,y_center:.90625},{w:1,h:1,x_center:.96875,y_center:.90625},{w:1,h:1,x_center:.96875,y_center:.90625},{w:1,h:1,x_center:.03125,y_center:.96875},{w:1,h:1,x_center:.03125,y_center:.96875},{w:1,h:1,x_center:.09375,y_center:.96875},{w:1,h:1,x_center:.09375,y_center:.96875},{w:1,h:1,x_center:.15625,y_center:.96875},{w:1,h:1,x_center:.15625,y_center:.96875},{w:1,h:1,x_center:.21875,y_center:.96875},{w:1,h:1,x_center:.21875,y_center:.96875},{w:1,h:1,x_center:.28125,y_center:.96875},{w:1,h:1,x_center:.28125,y_center:.96875},{w:1,h:1,x_center:.34375,y_center:.96875},{w:1,h:1,x_center:.34375,y_center:.96875},{w:1,h:1,x_center:.40625,y_center:.96875},{w:1,h:1,x_center:.40625,y_center:.96875},{w:1,h:1,x_center:.46875,y_center:.96875},{w:1,h:1,x_center:.46875,y_center:.96875},{w:1,h:1,x_center:.53125,y_center:.96875},{w:1,h:1,x_center:.53125,y_center:.96875},{w:1,h:1,x_center:.59375,y_center:.96875},{w:1,h:1,x_center:.59375,y_center:.96875},{w:1,h:1,x_center:.65625,y_center:.96875},{w:1,h:1,x_center:.65625,y_center:.96875},{w:1,h:1,x_center:.71875,y_center:.96875},{w:1,h:1,x_center:.71875,y_center:.96875},{w:1,h:1,x_center:.78125,y_center:.96875},{w:1,h:1,x_center:.78125,y_center:.96875},{w:1,h:1,x_center:.84375,y_center:.96875},{w:1,h:1,x_center:.84375,y_center:.96875},{w:1,h:1,x_center:.90625,y_center:.96875},{w:1,h:1,x_center:.90625,y_center:.96875},{w:1,h:1,x_center:.96875,y_center:.96875},{w:1,h:1,x_center:.96875,y_center:.96875},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.1875,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.3125,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.4375,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.5625,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.6875,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.8125,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.9375,y_center:.0625},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.1875,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.3125,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.4375,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.5625,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.6875,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.8125,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.9375,y_center:.1875},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.1875,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.3125,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.4375,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.5625,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.6875,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.8125,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.9375,y_center:.3125},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.1875,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.3125,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.4375,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.5625,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.6875,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.8125,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.9375,y_center:.4375},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.1875,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.3125,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.4375,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.5625,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.6875,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.8125,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.9375,y_center:.5625},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.1875,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.3125,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.4375,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.5625,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.6875,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.8125,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.9375,y_center:.6875},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.1875,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.3125,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.4375,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.5625,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.6875,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.8125,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.9375,y_center:.8125},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.0625,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.1875,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.3125,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.4375,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.5625,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.6875,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.8125,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375},{w:1,h:1,x_center:.9375,y_center:.9375}];var ig={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},og=class{constructor(t){this.handPipeline=t}static getAnnotations(){return ig}async estimateHands(t,n){let r=await this.handPipeline.estimateHands(t,n);if(!r)return[];let a=[];for(let s of r){let i={};if(s.landmarks)for(let l of Object.keys(ig))i[l]=ig[l].map(u=>s.landmarks[u]);let o=s.box?[Math.max(0,s.box.topLeft[0]),Math.max(0,s.box.topLeft[1]),Math.min(t.shape[2],s.box.bottomRight[0])-s.box.topLeft[0],Math.min(t.shape[1],s.box.bottomRight[1])-s.box.topLeft[1]]:0;a.push({confidence:s.confidence,box:o,landmarks:s.landmarks,annotations:i})}return a}};async function lg(e){let[t,n]=await Promise.all([e.hand.enabled?Ft(e.hand.detector.modelPath,{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Ft(e.hand.skeleton.modelPath,{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),r=new tg(t,t==null?void 0:t.inputs[0].shape[2],q6),a=new ag(r,n,n==null?void 0:n.inputs[0].shape[2]),s=new og(a);return e.hand.enabled&&e.debug&&Ce(`load model: ${e.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),e.hand.landmarks&&e.debug&&Ce(`load model: ${e.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),s}var ug={};pr(ug,{load:()=>cg,predict:()=>hg});var X6=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],K6=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var hr;async function cg(e){return hr||(hr=await Ft(e.body.modelPath),hr.width=parseInt(hr.signature.inputs["input_1:0"].tensorShape.dim[2].size),hr.height=parseInt(hr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ce(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),hr}async function hg(e,t){if(!hr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Je.resizeBilinear(e,[hr.width,hr.height],!1),a=ge(r,[255]);r.dispose();let s;if(t.profile){let u=await gr(()=>hr.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),qr("blazepose",u)}else{let u=await hr.predict(a);s=u.find(c=>c.size===195||c.size===155).dataSync(),u.forEach(c=>c.dispose())}a.dispose();let i=[],o=s.length===195?X6:K6,l=5;for(let u=0;u<s.length/l;u++)i.push({id:u,part:o[u],position:{x:Math.trunc(n.width*s[l*u+0]/255),y:Math.trunc(n.height*s[l*u+1]/255),z:Math.trunc(s[l*u+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*u+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*u+4]))))/100});return[{keypoints:i}]}var Z6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=e[n].keypoints.find(l=>l.part==="leftWrist"),a=e[n].keypoints.find(l=>l.part==="rightWrist"),s=e[n].keypoints.find(l=>l.part==="nose");s&&r&&a&&r.position.y<s.position.y&&a.position.y<s.position.y?t.push({body:n,gesture:"i give up"}):s&&r&&r.position.y<s.position.y?t.push({body:n,gesture:"raise left hand"}):s&&a&&a.position.y<s.position.y&&t.push({body:n,gesture:"raise right hand"});let i=e[n].keypoints.find(l=>l.part==="leftShoulder"),o=e[n].keypoints.find(l=>l.part==="rightShoulder");i&&o&&t.push({body:n,gesture:`leaning ${i.position.y>o.position.y?"left":"right"}`})}return t},Y6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>0){let r=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(r)<10?t.push({face:n,gesture:"facing camera"}):t.push({face:n,gesture:`facing ${r<0?"right":"left"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let o=e[n].mesh[152][2];Math.abs(o)>10&&t.push({face:n,gesture:`head ${o<0?"up":"down"}`})}return t},J6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o);Math.abs(s-l)/Math.max(s,l)<.25&&t.push({iris:n,gesture:"looking at camera"})}return t},Q6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t};function ese(e,t,n){let r=function(o,l,u){let c=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");o.replace(c,(h,d)=>(u[d]=0,h))},a=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let s=a(t,e.VERTEX_SHADER),i=a(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);r(t,"uniform",this.uniform),r(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)}function e4(e){e||(e={});let t=0,n=null,r=!1,a=-1,s=[null,null],i=[],o=-1,l=-1,u=null,c=null,h={},d=e.canvas||document.createElement("canvas"),p={},f={INTERMEDIATE:1},m=d.getContext("webgl");if(!m)throw new Error("Filter: getContext() failed");this.addFilter=function(w){let x=Array.prototype.slice.call(arguments,1),I=h[w];i.push({func:I,args:x})},this.reset=function(){i=[]};let A=function(w,x){if(!(w===o&&x===l)){if(d.width=w,o=w,d.height=x,l=x,!u){let I=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);u=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,u),m.bufferData(m.ARRAY_BUFFER,I,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,o,l),s=[null,null]}},y=function(w,x){let I=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,I);let S=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,S);let T=m.createTexture();return m.bindTexture(m.TEXTURE_2D,T),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,w,x,0,m.RGBA,m.UNSIGNED_BYTE,null),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.framebufferTexture2D(m.FRAMEBUFFER,m.COLOR_ATTACHMENT0,m.TEXTURE_2D,T,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:I,texture:T}},g=function(w){return s[w]=s[w]||y(o,l),s[w]},_=function(w=null){var T,M;let x=null,I=null,S=!1;t===0?x=n:x=(T=g(a))==null?void 0:T.texture,t++,r&&!(w&f.INTERMEDIATE)?(I=null,S=t%2==0):(a=(a+1)%2,I=(M=g(a))==null?void 0:M.fbo),m.bindTexture(m.TEXTURE_2D,x),m.bindFramebuffer(m.FRAMEBUFFER,I),m.uniform1f(c.uniform.flipY,S?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(w){if(A(w.width,w.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,w),i.length===0)return _(),d;for(let x=0;x<i.length;x++){r=x===i.length-1;let I=i[x];I.func.apply(this,I.args||[])}return d};let b=function(w){if(p[w])return c=p[w],m.useProgram(c.id),c;let x={};x.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
`),x.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`),c=new ese(m,x.VERTEX_IDENTITY,w);let I=Float32Array.BYTES_PER_ELEMENT,S=4*I;return m.enableVertexAttribArray(c.attribute.pos),m.vertexAttribPointer(c.attribute.pos,2,m.FLOAT,!1,S,0*I),m.enableVertexAttribArray(c.attribute.uv),m.vertexAttribPointer(c.attribute.uv,2,m.FLOAT,!1,S,2*I),p[w]=c,c};h.colorMatrix=function(w){let x=new Float32Array(w);x[4]/=255,x[9]/=255,x[14]/=255,x[19]/=255;let I=x[18]===1&&x[3]===0&&x[8]===0&&x[13]===0&&x[15]===0&&x[16]===0&&x[17]===0&&x[19]===0?h.colorMatrix.SHADER.WITHOUT_ALPHA:h.colorMatrix.SHADER.WITH_ALPHA,S=b(I);m.uniform1fv(S.uniform.m,x),_()},h.colorMatrix.SHADER={},h.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
`),h.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
`),h.brightness=function(w){let x=(w||0)+1;h.colorMatrix([x,0,0,0,0,0,x,0,0,0,0,0,x,0,0,0,0,0,1,0])},h.saturation=function(w){let x=(w||0)*2/3+1,I=(x-1)*-.5;h.colorMatrix([x,I,I,0,0,I,x,I,0,0,I,I,x,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(w){let x=(w||0)+1,I=-128*(x-1);h.colorMatrix([x,0,0,0,I,0,x,0,0,I,0,0,x,0,I,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(w){w=(w||0)/180*Math.PI;let x=Math.cos(w),I=Math.sin(w),S=.213,T=.715,M=.072;h.colorMatrix([S+x*(1-S)+I*-S,T+x*-T+I*-T,M+x*-M+I*(1-M),0,0,S+x*-S+I*.143,T+x*(1-T)+I*.14,M+x*-M+I*-.283,0,0,S+x*-S+I*-(1-S),T+x*-T+I*T,M+x*(1-M)+I*M,0,0,0,0,0,1,0])},h.desaturateLuminance=function(){h.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},h.sepia=function(){h.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},h.brownie=function(){h.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},h.vintagePinhole=function(){h.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},h.kodachrome=function(){h.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])},h.technicolor=function(){h.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])},h.polaroid=function(){h.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},h.shiftToBGR=function(){h.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},h.convolution=function(w){let x=new Float32Array(w),I=1/o,S=1/l,T=b(h.convolution.SHADER);m.uniform1fv(T.uniform.m,x),m.uniform2f(T.uniform.px,I,S),_()},h.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
`),h.detectEdges=function(){h.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},h.sobelX=function(){h.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},h.sobelY=function(){h.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},h.sharpen=function(w){let x=w||1;h.convolution.call(this,[0,-1*x,0,-1*x,1+4*x,-1*x,0,-1*x,0])},h.emboss=function(w){let x=w||1;h.convolution.call(this,[-2*x,-1*x,0,-1*x,1,1*x,0,1*x,2*x])},h.blur=function(w){let x=w/7/o,I=w/7/l,S=b(h.blur.SHADER);m.uniform2f(S.uniform.px,0,I),_(f.INTERMEDIATE),m.uniform2f(S.uniform.px,x,0),_()},h.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
`),h.pixelate=function(w){let x=w/o,I=w/l,S=b(h.pixelate.SHADER);m.uniform2f(S.uniform.size,x,I),_()},h.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
`)}var k0=2048,Ct=null,rn=null,Mt=null;function dg(e,t){let n;if(e instanceof Ue)n=Rr(e);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>k0&&(i=k0,o=i*s/a),o>k0&&(o=k0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)return Ce("Human: invalid input",e),{tensor:null,canvas:null};(!Ct||Ct.width!==i||Ct.height!==o)&&(Ct=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Ct.width!==i&&(Ct.width=i),Ct.height!==o&&(Ct.height=o));let l=Ct.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Ct.width,Ct.height),t.filter.enabled){if((!Mt||!rn||Ct.width!==rn.width||Ct.height!==rn.height)&&(rn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ct.width,Ct.height):document.createElement("canvas"),rn.width!==Ct.width&&(rn.width=Ct.width),rn.height!==Ct.height&&(rn.height=Ct.height),Mt=fr.flags.IS_BROWSER?new e4({canvas:rn}):null),!Mt)return{tensor:null,canvas:Ct};Mt.reset(),Mt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Mt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Mt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Mt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Mt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Mt.addFilter("hue",t.filter.hue),t.filter.negative&&Mt.addFilter("negative"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.vintage&&Mt.addFilter("brownie"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.kodachrome&&Mt.addFilter("kodachrome"),t.filter.technicolor&&Mt.addFilter("technicolor"),t.filter.polaroid&&Mt.addFilter("polaroid"),t.filter.pixelate!==0&&Mt.addFilter("pixelate",t.filter.pixelate),Mt.apply(Ct)}else rn=Ct,Mt&&(Mt=null);let u;if(rn.data){let h=[rn.height,rn.width,3];u=ld(rn.data,h,"int32")}else if(t.backend==="webgl"||rn instanceof ImageData)u=rl.fromPixels(rn);else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(rn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);u=rl.fromPixels(p)}let c=u.toFloat();n=c.expandDims(0),u.dispose(),c.dispose()}let r=t.filter.return?rn:null;return{tensor:n,canvas:r}}var mt={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,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:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},age:{enabled:!0,modelPath:"../models/age.json",skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:32},emotion:{enabled:!0,minConfidence:.1,skipFrames:33,modelPath:"../models/emotion.json"},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var N0=`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==`,I0=`
/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
AhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAEDAgQFBgf/xABDEAEAAgECBAMECQIDBgUFAQAA
AQIDBBEFEiExE0FRBiJhcRQjMkJSgZGhsWLBJDNyFSVTY3OSNEPR4fAHFjWCokT/xAAYAQEAAwEA
AAAAAAAAAAAAAAAAAQIDBP/EACARAQEBAQADAQEBAQEBAAAAAAABAhEDITFBEjJRIhP/2gAMAwEA
AhEDEQA/APqYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAKNTq8OkxzfNkisQC8eb1XtRNbzXT4q7eU2nu0MntRq/D8StMccvW29ZmdvgjsTyvZjxOLj
+s8WLxn8TFPXs6Oj9oct7c14rkxz22nrB2I49KOdTjelmszfmpMeUxv/AA28OqwZ4icWWtt/SUi4
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAmdo3nsPNe0Pt
Fh09Z0+DNWL7+9O/7A3eJcZppsV5raI27esvH6jX5ddM25p79Ilo59VbUZOe2Tm/PeGvfPfT2iKR
PLv1+DO678XmW/a97U6TtOyzTbTF538/T9WjTNecm9a7126tqk3rSYxY5ta1plRZqZNXGjyZcPXl
mZmsx+qjBrsuO16xM7eXRt04JrdTltk5OWJnfaWf0a2lty5MdZnfzSn+WOHiOutFpjHa9e8bQ2fp
+alYy462pk7zXbuxjPesbRS0f6ZZV1ET1tErzXFLHo+A+1ddZf6NrI8PJHa1vN6iJi0bxMTHwfOa
zhzd61v1846utwniM6DUdb3nBaNrVmd9vjC/ZVePYirBqMWppz4rxaPgtEAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAItaK1m09ojcHnvarjM8P0vh49+a/eY8ng9D
h1fGM1rxjtGPfvbzdbjuTJxHX48cTPNltM/KsS9Dw7S49Jp6UpHaGe2vjz1y9J7LYK13vHWe7bj2
ex1tvM80ekuxW3RnW3Vm6P5jRx8H0+OYmMcb+bapo8GKPdpC6bQwtdHU8JpWkdJ/JweL6e23iU67
d4dubSqyVi9Zi0bwIs68XGp36TtEq7ZJmZmevzdbifCKWtbJinkt6eTgZPFw32t+sRurbWVzxs1y
Rv6T8V1NZNPtfq0seTm+Kevr+SZuxXjvaPiV8N4viycto9HseG6+uu08W6Rkj7UPmFck1tE1nlmP
Ld3eA8V8HVVi1pjq6Ma/pnqce/ERMTETHaUrKgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAADW19+TQ5p/p2bLS4v04Zmt5VjeQeJ4bjnLqsupv+Ka1+ERLv4reTmcNxcuC
vy3l0qdI2hlr66sT02ot0ZV7qqrInruzrVZLGSZ37JjqgYTG0K5lbaFVhDT1Ub456RPweY4hixWi
eSdpjvD1eWejz3FNHWYtkpvFo9EIseb3tS3SerOms22rfpPqZKzvvHSYUz70TExG6Gdbs2rljeJ/
Mx5L0vEzPaelnOi98c9J2bFNTFpit47+a+PVUvx9T9nOIfT+GV5p3yY/ds67wvsXqpxau+G09Lx+
r3TqrEAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADV4ljnLw3U0jvO
O0fs2lWqyUw6XLkyfYrWZkHldBEV09eveG3Fq1mI3jd4vPrOIaid8G9MP3Y38k6fNrt/rMk9Ou8s
tfXXn49rGWInuy8SO/k5Gl1E3rG/fzbOe94wTy99mbRvTrMOOvNfJWsesywniukrG/jU6fF43WYN
TmtEeJtEQ06aSmK2+bNtEd+qfSO17unF9Hmvy1y13XWyVmN4tExLxVK8PmNq5NrT58zawam+m/yc
0Xj8NpRYSvQZ7xEOdqI3rPozxayNRXe0ct/ON03jmrKB5nV4q1yTO20Obmv4c+cx8HoeI6WZpNoj
q83niYmYscU0r8aJ6T1n49zeJ+Meqm1drb9J+Kd5p136StGVem9l9TbHxLDFp7W7+sS+q1nesT6w
+PcAzVjiGHftzQ+v4f8AJpv6On8jH9ZgIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAABp8VrW/C9TW0ztOO3b5Nxp8VmI4bn37TWYB8f1HFtTfUfR9FWJmsdZ9I7MtJxDX5s
d8ta1y0xzteaR2277rcuhycP12SceLxMeWNpjttHwlu8I0mfQ1y+D7k5YmJmY36T36Ka43z/AF1t
cI1ds+qxVj7/AEej19PCw9HJ4NoK4OIU5Y35YmZdzVTGebVZabx5jJS+Tmns81rNLm1Wrzc9rVw4
Yibbem72mXTTS0w0M3BvEta1bWrM95ie5EanY87wXgNOL6XPfxraXLhra/W28bR/dzYzarBqJxRe
bzE7Rt5vWU9n8mPHOGmS0Ypnea1naJb+k9ncNLR7u2y/WcxXO4TOoyUrN6zD0FaW5Y3hu49FiwUi
KxCvLMR0hlW0jn6ukWw3iXjOJzbDlneOj3GaN6zDzfFOH+LE7SRGo83XNSZ2lbG2/WfdlvaT2cy6
rNFInlrv1mfJ37cK4PwTTxOoidRm2+/2/KFuyMp47XB4LivXiunrH2b2iH2qn2K/J8x4fGDNxTSZ
9Nh8OviRvTyfT6xtWI+DeXs9MNZubypASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAOZx6/LoOWPvWiHTcf2hiZ0e8fc2mf1E5+vP/AEeuSd7RC2uKtI6QjHfeINTfwtPf
Jvty9WPfbt/lucP03gxfJf7d/wBoReYpm97zaNeLb4Ims9Nt94auDjem1Wo5PFi1onylS+1o7l8V
bxvtupjDMdNkYtXS1+Stt+m63xImEJ4xjHER2ZxMUjeUTO3VRmydBbjLJqPi08mbeVOXJPq1sl5Q
Vbkz9+rRy35rxHqzmZlVEe/Ez5LRlW5iyfR6zffaIjq1OSNZps2a21rZInafSPJhxGMl9LStLRWM
lorM/A4dkrWbYfLZC2W/7K6eubX6b4RzT+W76K8b7G6X62cu3Sten59nsm3j+OXz3/0ANGIAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0OIYfpOHPijvNNo+fdvtXJO18k/
/OwPFYbz2ls3jx8VqW6xMdWPEdP9D4lkx/dt79flLLHbkxTPwY6nt2512ORTRzE2x4/dpE7cvkme
E4IrW3hRMxO8THRtU1FKWtvtvK2upx22rzRCtXkqzh2jtF7ZbT122b01ndnpuWuP3Z3+Ky20qDVv
fauzVy3mejZzNK8dVjqi87KLRLYtXruqvXzkQp7Qoid88R6rcl+WGlW0/Sa22mfhCZOq2x082ix6
jkm822pO8VrPdr4dNObVeDo8XW3uzMbzK+mvxT7szE27cvnu9j7PcNjSaXx8mOIzZevbrEeic5tN
+SZnpt8J4fHD9HXHO3PPW0x/DeBtJxx29vaAJQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAKNRim9Z5e89Nl4DzXtVh5babURHrSf7f3ec1+qnDorWrvvt5Pccb0n0zhmWk
Rvevv1+cPE2rGTFNZU26PFfxwa5dVkjelI2772nZnX6bbrEUq3o0d678u8wmuDL2ittvVjXdneeK
cGv4jpJ6U56+kS7+j118+GLXpakzHaWlp9NNY3tv+bbiYiNoQy1y30uyZJlrWmZnuym6q1iIJnop
yW2Te8bdWnnypQqzZOadokiIpSZntWN5lrxki19vNRxrUeBwnNNd+fJEY6/OejXLn3Xe/wDp9wyn
E8uo4lqqxblv7lJ26T6vpD5X7G8QycKzeBMbzMRM1/FH/wA/h9QwZ6ajDXLitvWzRgsAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeL45w+dDrZvWv1OWd4+E+j2jX
12jx67TWw5Y6T2nzifU+rZ1y9eHwzDYxxEy18+DJodXfT5o96vafWPVbjyxDn1OOzHudbM0rt2UW
iI69mVtRXZq5tREb9VUoy2iIlRbJ0UX1VZ6btTLrI7V6yk62M2oisT1c7JmtkttVMUyZp6x0beDS
RWOvdKijDimvWd3G9pNRMfRcNfvZOb9Hpb0itJeP47k/3hgjaZnbaP1XxWW3T0movbNS0W645nbf
0nrMPpXs3xamoxdJiLbe/X1n8Uf3fKsOTw4jbaXo+EarJhtGTHMxeJ6xH7Sti9Zaj6x3HM4NxXFx
DS1mtoi8dJrv2l011QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AGjxLhODieOIye7kr9m8d4eM4to9RwjPXFa0ZIvG9bR0fQXmPbDFvTTZPOJmEWS/V8bs9R43NxLL
G8eFbePg1bajU5/s0l1ceKLx1hbjwRE9mOpx0y2uRTSZsm3PMw2aaKtIjo6kYo9EXpET0hVLXxYK
xC6MZvyx1lFs0RHfaPiCnU12pLyHGNDbUajBekWma2npWN3p8+opa20e9LSyZLxExTlpM+vdOdcZ
a9tPS8MyUvFrzWlI6727u1pYxYrbVmb7x+TQx6au3Nqcl7/0rcmW9axGnwZJj1novmxnZXV0fFp4
ZxLBPgTGK8xzXr5fOH0bFlpmxVyY7Rato3iYfNuG2x56Wrqa8s2jz+7Lu8O12bS6jkwzN6THNNI6
tvrN68Y4rxlx1vHa0bskAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAA4XtTTm0OKfTJ/aXdcL2pyRGjwU362yb7fkJz9eTxxyZJjyltRXzUZK7TFtl9Lbwy06YzrHwa+
fJFd/wCVt8m0bQ0eS2qzcm+1K/an+zNZFL5M1pjFXeI72ky48eGnPkvNp27+TPU6nHpMfLXaIjpE
erk5dRMxOfN1mPeisfshW1ne1a1577Y6x5R3U0zze31FOWI6ze0byU098kRlzbxM9qrMlPDpyRMR
Md5Vt/Ihp5898mWZm1pjftE91uCt7fCI7dWeHDEW3t723l6rslqxWZnasR+SYhFbzhnfxJ2jyeq9
lcGXWZcmW0zWKxHLaI7794eJx5fpfEKabT8t8l5isddo3l9S4VjrwrRUwzSJt3tav3pdOL6Y6dXD
j8HFWm+/KsU4NRXPvtWazHquWVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAa+fXYNP9u8b+kdZBsDkZOO135cWOZn4y5Wu4xqctbe9y19Kp4njt6vi+PDm8DFMWybbzPlV
5PiGtz67UxbNbeKTtWIjaIXYpnwuaftT5tXJT3vmi1pMsrU5qIrG1V1a+5DCa7b9GFbRr5J6Wnbt
Cu+Wmk0m8956z8ZWZNorbfzcbX5rZslazPux3hUt41NTntktObJ13+zX1bek01r4/HzVm0bxPXy/
+bNfDgjVa2uOY92kdfg6ufJOKvLXtttVVSqbcta2vM7zXtHpLQy5ZtMd+vWd+7Zy3mdJHXra3f0c
vUarw7zFY5rT2hH1Lavnrgx81p3U49Pk4nE5L35MO/StfNRXR5tXnrS8W67WvfyiPSPi7uLHFK1p
jrtSsbR5Lc4RzsXBaYreP4l45esRD2HD9fnw6evvWvO3Tfr0aGk0U55ra0TFInv6uzgrXFXlx0i0
77RPlC83Yj+JW7oddqr6vHzTTw9/f6dod+L1t9m0T8pcbFSmPHER3892W0zPuz+jSbVvidkcqmfP
Sel7bekrI4n4dZnPWIrHeYnZee2Wpy8dEaml4npNZblw5qzb8M9JbYgAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAABEzFYmZnaI7yCXL1XGa0jJXT0571nbee27DiXEprp8nhbxG20W8
5cbD0ikfnKO+urTPvjoZdXqctdsmTaPSvRpWmsdZ6yztfaGplvv3lWW1tyRlz1x0vkn7Vo5atTNe
Y0+1o79V2KsZsvX7Ne5mwxnyTNvsx2iGneM/rCdRSuOsTasTt5kRFtpjqmOH4t4nk7estiMNa97R
Hwhna0iuKTEdmGWa4672nZtRele1N59Zlq6vLOSsYorEc07qcW65euzRvtXvPZy52naZ7ujr6fXV
rWdukREK8+njHgmZmPc67bq6ivVWhxxgxZLztNrT1mZ/SP4VZs0zaOvfp84WUtNsXLvtv3699+rU
z7+Jtt5qURqMnPpctaR1rMSw4ZoK57eNk6xHaJRh97Ltt7lo5Z+L1HAPZvVauZ2nFTSzMTzeJEz8
to6xPfvsZntPZ9rXxabmxzefdrv0j1dXh/BcmstW1qxTHHasR3+b0GPhGl+kWmd64dNEVjf73T7X
y8vy+Ddx6O3iRakxTH5RXrMw1/lX+3Itw2MFIraN48qRHdZi0cUjmmPen9noox1iO0fNzdXEYrTt
stcmd9aX0bJ+HePmiKTitO8TMLZ1cVjrMfqpz6ys4pjfrPRWZ9rXXptUit6zO+23VyaRHEc05L1/
w9J9ys/en1ljqdVbwYw452tlnl3jyjzbmmiMeKtYjpEbLeTXPUU8ee/+qjJpsV5rbkrFqzE1tEbT
DpYNbW21Mnu29fKWna0KbqTdjXXjld0cvQ63ltGHNPSfs2n+HUbS9c2s2UASqAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAOVxPWe99HpP8ArmP4b+r1EabT3yT3iOkesvMVtN7za07zad5l
XV5GmM9vVfEstvDx0jtaVVMlq+UJ18b5cMRvPeSuK87bUt+i2Z3PtG7zXpjkzXt6R+TXyTMzvM7t
ydHqZ+zhv1+Cv/ZuqvPTHMfOYaTMil1a1K2vHSLTELq2v+KWzThGo84rH5rq8JzedqR+ZeI7WnOS
34pYTafWXR/2Pln/AMyrKOCWnvmiPyR6O1y9585lhWJvl557Q6eo4T4dYiMvW3b3UanhldHpJtGX
e09unmjsT7eb1l4trI2t0hsZfrdNO0bzy+nzU20/+NmkzO9esz+TZxWis9dttvPv+Tn21jjaW8zn
26bTG3mp1M/Wzv3t0jyWXiKZJmsTERaZhXXDbNl8WaztWenxZLstPp5pau8frDtVrNMM5cfTfpMf
3aunxxbes9d/R09Dp8ebJi09ptFr3jtt2WyrW9wy1Jx132mK+Xq9PotT0iIU19ntLtExa3T47T+q
6nBaYvsZstZ+cT/LeMnUi0TXffo1s2m8Ws2/OIMWk5Jib5L328rS2t94Sh5TV4ppklpW6PT6rh+P
NbebTHyas8E081mZy5P2W6OFhjxNTE/hr/LoRO0Kvo9dPqctKzMxEx1la5t3tdnjnMs4noievcrO
yZjeFF1OSnNV0OG62cn1GWffj7Mz5w05joovzY7xes7TE7w0xrjPeex6Ua+j1UarBFu1o6Wj0lsN
3JfQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrU5o0+nvlt92P3BxuM6nxNRGCs+7Tv8
2hToxm1r3m9utrTvMsonqyt7XTmcja0u3O6FMfi5t/u0/lzdJM81p9O3zdvHTwsUR5+bfPqOfX1h
dqV+3O7bs1+T31oqmI3TEM4rvCdkDGIIhlFd2daboS0NXG2bD6bufxXU1vlmu/u4us/N0+L1tTSx
kr9qk7w89j1FNZMV3jxLzvaJ8mer+LSOZqK2xZotbvljfr/89U453rXt9lse081xZtNjx7TGKu0t
DHlrevSevaN5Y6+tJ8c7VRNMt63n3ub+6/R54rERMztDYy4a5omclYmfxKcenrjtHLvtPrCnVmdb
eFe3JXmjy6eS/DrMuLVYsta9Mdt++6qLxO+0dEc8UmInr18iUfReHcXrqccb9Z27Q61Lb13eJ9nc
1Z35rTvE9avY4bTkpG8xEfB05vYxqybc07R281naGMREdoT5JQqy9mply7Q3bV3iXG1eXw7TWSka
c258t7+tpT5/BjT7MfHqndz12Z+M4lMMKyziUJJiN1WSu9fku23RaOgKNJqbaTU1t9yelo+D0cTE
xEx1iXmM1Nt3W4PqvFweDaffx9vjDbGvxz+TP66QDRiAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAOJxzU73rp6z296zsZMkYsdr2naKxvLyObNOfNfJbvad1dXkaeOdpvsc2yuZVzfbfqybutwu
s5s8R92J3dvJb3tnO4HSMegtmt3nfZvYp8SZl0z45NfSK7onH1bNcfRFqnUKJr0Y7dVtq7prjEsK
0XVpEM6028mW20IHK41aPo3J6zs4ODhdcvPnvExFevNXpMOrxi/PlrTee7PLX6Pwa09uaNlKtHg9
dM3z5d7ReOu02nu0JzZMfblrv5R5uvrcdImZ26T1mYhxs1Os7RH93PZ7axuafNfLitvbaYU3yZYt
PXs9NwHhui1HBa5LVicsb81onrEuVqNNSuS8Y67dZ6xPZa59Il9uX41vEitImZme3q2Kxbxora0T
Md/ROSa4Ztkj7c9OafL5LuGYubmyX3iu/TfbdSfVnpvZLT/XZK233+Mbbva1xRXyiPk8pwbH4N6T
adq5a71n0tD1WDL4tPe6Xr0tDpz8YVnJHWEXYxbqlBedoef4tW0XraO09HdyztSZcbUz43C+ee9b
SVMaeOfqq7+jGckQ1Yz7+7v2RN/WXPXZPjci2+2yyJaVMuy+uSJlA2d+pNoVRbeDcSxyTE+TDDlt
pdRXLTynrHrDOyiyZeVFnY9TjvXJjres71tG8MnJ4Nqt4tp7T1jrV1nRL1x2cvABKAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAHJ49qfD09cNZ97JPX5PPw2uI6j6Vrsl/ux7tfk1mWr7dOM8iLdm
vfebREefRsWldw7SxqNbWbR7lPesrn3Vteo7dYjDpMGCvfbeXQ0uLlxRLRxROfUc34p6fCHYrXlr
EejqrjY8uzCYW7MZjdVKqK9VlaxCYrsnYExBMRMJRPZA8/xPHtmpP9W2xx76vhWOInvt/C7ike7N
vwzE9kcapGfhlevTaFbFo8RqJ5vy8/RoW09ek0msxHfp3dzNoLzp4zUmZpMbT8HJyYJi20X2n0lh
ZY1li/RaidBF4w2mK3jrHaFGp1lN+tptPp5IjBkid5mIp16TKu0abBPv33vPlM7z+iPdFNcWXU5I
tkrNce/b1W5db1nTaf3ax9q0fxDW1ebNk2phty1mOu09VOm8W19orEz23j1TwfSeERFuEYMddptW
d43dvBn21eKJ75KbW+cf/JcTgMxXTb3nbljz+TpcPmc2uyZO1KRtVtGVdi0bx07qJnllsRO6rNTe
N4XVamsy8mnvPwc3R2jPwe8TPbdlxXNOPSZfhWWpwO85OFzv57qrODkzeHntSe8Sn6Rv0a3EZ218
8nXekfr1a0ZLVnqx19dWb6demXybOO7lYMvNMdW9S/VVLo0us7tPHdtUtEwJiZU3jq2Jhham8CVG
PNODNTJXvWd3qcWSubFXJWd4tG8PK3pPd1OB6veLaa89Y61/u2xfxh5c/rsgNHOAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAANLimq+i6O0xPv392rdeZ4rq/pOqnlnelOkIt5F8Z7Wj27I2I6sb25YY
V1ImY3dbQ08LRc23vZp2j5OJG+XJWle9p2h6HHtbJXFT7OOIpX+7TxT31j5rycdTh+Dpz+XaG/sw
w18PHWseULN2trBE9UcrJKBhFU7JAQi0dEomegNDUYovM7x3jb5tO1ZvpbaTLtzRExWfWPJ08kbT
Ex5NXWYYyV5omYtHWJieyeDzuizfRs19Jn6TM7Ru1uMcJxZqTkw+5f4ebqa7SV1MR4tdrx2vEfy1
axqsNOTLjnLXytVXi3Xj8+nmsxTLM16d5npPyUzpekTtSK+U7vS6vQ/SYmK1vWPS1HOn2dvvvvE/
tDO5XlcO+LbfHSd/W3o6/BdDOXPTnj3Kz38rS6Wm4FNrRyRzTH3p6RH/AKvR8L4dXSzE3jmtHn5I
mbfqLV+m4dbLSsZInHjr3iI6zLpYaxS01rHuxHRHiT9mv6s67Vj1aqL6326MrWiYa+/Q54BxPaGe
XRZpj8MquB4+Xg8zPnB7SX30to379GxpK1xcHiKz5IS8xr8PLPixH2bftLTy05o6dHYyVjLhy0t1
izjZa3pMVv3iO/qz1G2L+NbSajbNyW7xLsY8kTDz+fJXFqKZN4iZnafi6WHL0iYlStI7OO+7axW2
crFl7dW9jvE9ULN+J3ZbdFGOy+AYWpEqN7afNXLj+1Wd23KrJVMvCzseh0+auow1yU7WhY4fCdV4
OadPefcvPuz6S7jol649Tl4AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAV581NPhtkvO0R+4NPi2
r8DB4dJ9+/7Q83Po2NTqLanNbLfvPaPSFDHV66sZ5ET0hRknyW2lTtMyouz0c8usx2n7s7vScKwx
zc1vu/y85p+maJh6Th+SOWeveXR4/wDLm8v+nX5mUWa9bbrInolmu5jdTNkxYFk2Isr3TuCzeGMz
+THdEyDDJO9Ja823rt2XWnya946pGvktDXta0ztWu/ybvLE9dkcoOf4GbJPWK1j49VmLh9JtE33v
Mevb9G7WsW8l1ccREISophiJ2jpDYpijbaOjOuOJ8ujOdqxsgVcsUjaETYvbaFFrgu5lVsm0yUtu
ryg43H5m+GIj1XcJzePoL4pnrWGtxmfchr8JvfHS1622if3QljzTTLes+qrNjrkiYtCzPMxnm095
YZJ6boS5teB49Tqscza97VtvWvlv8V/FOF34RrIxTM2xXjelp/eHoeA6XnzReY3ivX/0dfivDcfE
9HbDbaLx1pb0lOs+jO7K8Lis3cN+0NKcd9PmthzV5clJ2mF9J9GHHVL108dm1SznYr/Ft0tuhLb8
mNohFbMhLWy0mJ3rPXvDvcO1karBG8/WV6Wj+7kWrvDDBlvpdRGSnbzj1hpjX4z8mOx6UYYstc2O
uSk71tG7Ns5AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeXneJ62dVl5KT9VTt8Z9W9xbWclPo+O
fft9qfSHEU1pv48ftYST23ZTDC/p0YtlVuvVjMbM5+LCZjYGWGdrTPxiHY4ffaf3cjTxz1v6xMS6
Olty2iXVj/Dk8n+ndrkhnGRo1v8AFdW3RCrZ5uiYsqrboncSu508yjmZRYQt50TfowYTbYGVrKrT
uTZjvukQnYhMIGVY2ZxPVWyrHVCWzXpVXkt3TE7Va+W4K7X3jv1auTNy3jdba0RZpamfroQN7Hk3
6wr1GTaN2OOJiu6Mu98NvgDi8Wy74d/yZ8PiPAiO2zU4nb6qIn1bugjfFE/ASp1ke9u15mbbRDZ1
Mb823kx0Ontn1OOkedoJCvT8I03gaKsz9q/WW+isRWsVjtHRKyrhe0XCfpWL6Vgr9fjjrEfeh5fF
feH0V5Dj3DPoOo+k4a/U5J6xH3ZZ7z3228evytOk7NvFbo0cdols47bSybt7HbddHVqUs2aW3Qnq
xVeu8LILR3SlZw3V/R8nhXn6u0/pLuPMXjeHT4Zruf6jLPvR9mZ8/g1xrvpz+TH7HUAaMAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAABRq9VXSYJyW79qx6yvmdo3l5viGs+maqYrO+OnSvx+KLeLZz2te1rZL2v
ed7WneZYWnZl5K72YV1xEyxmeqJljzIEWlVkszvbZp5soN3h2SJz3pP3odCnuWmPRxuERfJrZmtZ
mtY96fR28kbX3dXj/wAuTyf6bmK+9YX1s0cNtm3Sd4LFY2K23W1s16StiUJW7bp22RW3RluBuruz
mWEgrmCGWyNkoExKE1QlPmsqRDKeyBjaejWy2W3ttDUyz1QKslvehVqKTNosyyTvELabXptIJpaP
B39Ia2mz+JGpr51jdZefDx2hzuHZObNq58poJaGtjxJ2+LoaKP8ADRPo5+T3skx5OhpOmC0fBNQ0
5yTbn+bt8A0u9raiY6RHLVwY62mI6zMvaaHBGn0mPHt1iN5+aYVsACBXqMFNTgviyxvW0bSsAeE1
mkvw7V2w5Ote9besJx2er4rw2nEdNNekZa9aW9JeQjnxZLYskTW9Z2mJY7zz26fHrrdpbZsY7NGt
mxjvso1b9NmUwpx33XRO4K7VUTE1nmrvEx1bVo2VWiJE/XY4frY1WPlt0y17x6/FuPM0m+HJGTHO
1qu9pNVXVYt46Xj7VfRtnXXL5MfzexsALsgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHM4jxOMFJphmJv529Dq
ZLfjDjPEIx450+K3v2+1MeUOHSOWFc3nJkmZnf4yujpVlqunOeFpV2nctLCZUXRM7MJtsWlRkv3Q
ky5NmpWt9RnrixVm17TtEQnJabXisRMzPSIew9n+CRoccajURvqLx5/chfOest642OGcIpoOG2w7
ROW9d72+LQvXevyejcPUU5M+SvpLeOataraw2a0dLbLqTtK1G3Es4lVWWUSoldFtmcXUbpidgXzK
GEW3TuCUSncnsDFMMLSms9EC6J6FpVzbZE5ALy0809ZbFr9GtfrEoFMzuuwz0Ueey3HbaBLDXe7i
tMOfwWnP9I+NZbuttvhs1uBRtXPb4SDm3iIvf57N7Dbl0VrS5+XrltEd+Z1Jx7cNms9N4TURRw3T
+PrcO3WszEvZOD7P6aYiMlvu16S7y1QAIAABxOPcLnUY/pWCv1tI96I+9DtgmXl68Biy7/NtUu3+
O8HnFa2s0tfd75KR5fFyMWTdhrPHVnX9R0cd21S3Rzsdm1iuqs256wrmGcT0RYSx5d047X02SMmO
esd49YRE9WcdSXhZ2O1p89NRji9J+cei1xMc3wXi+KZj1j1dTTaqmor06WjvWW+ddcu8XK8BZmAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAMMmWmKu952UZ9XFZmuP3revlDTtzWnmvO8q3XGmfHb9ZanV3yxtWeWn7y4es
vPNtDqZJ6Ts5mppvdl/XXRMyfGvSNlu/RVvtOzLfoipLT1VTKbSpvfogRkvtDVyZOhkyvQcA4Dzz
XV6yvTvTHMfvK+c9U3rkW+zvA/D21urr789cdZ8vi9KDb45rejl8Rry6iJ/FV1HP4vXbBTJEfYt1
+UpiHM295bXsqrO9l8QkZ0lZEqqLeyBZHZLGvZkhIndADKJ3TMoqWQMZ6pjsxll2jsCLSrmU2lFY
36gieyu0LJk3jbsga0wdqzK20QpyztQGprL/AFMrOE05NLkt6qdVWZxNrSe5o9vWBLiUjnzXn0vL
q555dHt8HOwV928/1z/LpzXxbYccRvzTB+jucOwxh0dI22mY3ltIrHLWIjyjZKyoAAAAACJiJjaY
3iXleM8InR5J1GniZw2n3oj7s/8Ao9Wi9a3rNbRE1mNpifNFnVs65XhcWTdt47bnFuF24dm8TFEz
p7T0/pn0a+HJux1OOrOux08d1ndqY7tillVkzExLOk7yd4YxGwluViJhE45raL0na0dtlWO0+bZr
1TKi+2zptZGTamT3b/tLacvJjiY3XaTWdYxZZ6/dtPm1zrv1z78fPcbwC7EAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABhkyV
xUm152iAZWtFazNp2iGhm1Vss8uP3aevnKrNntqLdelI7VRHRnrX/HRjx/tZREVjZXeybW6KbWZt
pCZ6S08tN7Nmbb7zCrJtyoS5145bSx5mWafelr3tsKmS/o08uXyhlly7RPV2+AcBnPNdZrK+53pS
fP4ytnPVda4y4BwHxOXV6uvu96Unz+MvVxG0bQRG0bR2G0nHLb2gCUDX12LxtFmpHeazt82wT1gH
mMN4tWs+rcr2aEV8DU5sM/cvO3yb+O0csLUTSdrLphRE8tlkZI7Atr2ZMazDJVKTYSCawi7Ksq7z
1QERvLK3ZGPrKbyCrbdnMcsbeaa18/RhvvM7oGEwTG0JmYYTIML22a2e28xELM19oURPNO4lOem+
n3ZY5+prVnMc2GYU4/L4A0a15cNf6rz/AC6fC6+NxCPOuOu/5tHJTbHj+F5/l1+BYumXJMd9o3/d
MRXYASgAAAAAAABhlxUz4rY8lYtS0bTEvH8R4ffhmo6bzhtPu29Pg9mq1Gnx6rDbFmrzVsizq2df
zXkMWTeIbNL7tbXaHLwzUctvexWn3bmPL8WFnHVL326VZ91MfFVjvvVlz79kLrcf2m7j7bNHH3bl
J2SirLQoy4t1++7G0dBC/RanxI8PJPv18/WG241+alovSdrV6w6mDNGfFF4/OPSW2b1zeTPL1aAs
zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAVZ9RXBTe3WZ7R6iZOpzZq4ac1p+UermZMl89+a/byj0Ra9815ted59PQ32hlrXXRjH
DpCLX6ML5NlNsm/ZRqstfdXzbsZt06sLZNvNB1Za8RDWyZdo7q8udq5Mu/mIMt4md2lmy7JzZuWJ
dHgfBL8RvGo1MTXTxPSPx/8AstJ1XWpIs4BwSdbeNVqq/URPu0n73/s9hEREbRG0QUpWlYrWIisR
tER5JbSccur2gCUAAAAPM8Sry8Uyz67fwuxbzVPGsE49XGbvF42V4M0TEL33ERnktsxpk3sumK2j
admFdPFZ33VS2Mdui2J3UU6LYlFSsN2O5NkCyJ6K7T1TEsbAsxdpReerKkTFGMxvYEz0rsqtbbpC
b2VT1QEzuwtbaGUxspuJU3neWdKoiu8rq12gCI92YatLcublnzbEz1aOptyZqTuDHLfxN6R0+t5X
qdJhjBp6UiPLeXl9NSMnEKxHa1+bb8nrlvxUAAAAAAAAAAABTqtNj1eC2LLXeto/R43VabJw/VTh
ydY+7b1h7ho8V4dXiGlmvbJXrS3xRZ1fGv5rzeHN02bEW3cys3xZJx5ImtqztMS3MeTeGFjqlb2O
8btql3NpbZtYsnSBLeiWfdTjtutid+ghherHS5p0+f3vsX6T8Fkw181d4lMvEWdnHaGnw/UeNh5L
T7+PpPxbjdyWcvAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAo1Oprgr63ntAmTqdRqK4K9etp7Q5d7Wy2m953lNrWyWm953mVd77R0
Za1104xxlN9lV8qnJl2a9s3xUXX2ybsJyRDWtl3YWydEC+2VRkzeW6q+T4tbJm+KRdfK1cmWZnlr
vNp7RC/R6HU8SycmCk7ed57Q9ZwvgOn4fEXtHi5/O9o7fJaZ6z1uRyOEezVstq6jiEbV71xevzer
rWtKxWsRFY6REeSRrJxz22gCUAAAAAANbX6aNVpL0npMRvWfSXlKamsRMVvXm+EvZXjmpaPWHzfL
oNRjzXicfWJ8phfPxFejx72x7xMzK+sXiNoiXlq+Pi6fWV/VfTNqfLJl/WTg9Pji8R70LqvMV1Gq
j/zcv6yz+lanzzZP1lWpelTET6S81Gp1P/Gyf90s412rjtnyfqql6asREdWM9+jz9eJ6yP8Az7uh
odZqMt458tpB1JvEViI3/RhzRt13/R1MNaziiZiJn5K9ZNceKZiIiQcu/WekT+iYrWI3lzdTrs+8
8uW0fJzcur1Np/zsn6g79phVaIeetqNR/wAXJ/3SwnUaj/i5P+6UD0ldonum161h5mNRqP8Ai5P1
lNtRqJjacuT9Qd22WN5aGeZyZd/KHJy59RHbLf8AVq31Gp/4uT9ZEvS8Lr/vSs2npzRtL1z53wK+
oza/HW2XJNd99pmX0Rb8VAAAAAAAAAAAAAAcHj/C5yV+l4I9+v24jzj1cLFk8nu5jeNpeW41wmdL
knU6ev1Vp96sfdn/ANFdTrXG+eq1q5F2LLtbZoY8m8d11bbSydErsYsm+zZrO/zcnBm226uhiyRK
EtrvCrJDOJTeu8A1MWX6Lqq5N/dnpb5O5ExMbx2cPNTeJb/DM/iYPDtPvY+nzhri/jDy5/W6AuwA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAa2p1UYo5adbz+xbxMlvqJ1OqjDHLXree0ejmzNrWm953tPmTPWbWneZ7yoy5YhjrXXTjH8s75N
mtkyxt0VZM2/m175N1V03yTKubMLXVXybeYLLX2VXy7eam+b0bOg4VquJW+rry4/O9uyZOq3UjVm
9r25axMzPaIdvhns1kzbZddM0p5Y47z8/R2+HcF03Doi1a8+Xzvbv+TotJnjDXkt+K8ODHp8cY8N
IpSO0RCwF2YAAAAAAAAACvUZYw6fJkntWN3k8dfHz2vLucdz8mkjFE9bz1+UOZosX1UzPm0nqI/W
MYo9FlcPNklfFGeH/NshLGun+Cz6PtHZtVZWlRLS+jxPkRpIn7rdoupHTdA5s6SI+7H6Mfo+32Y2
+To3neSIiZ7A0IjPXpXLePlMotGW3272t85datKzHZjbTVnsDj+FG/2Y/RlGP4R+jo20u7H6N1Ql
o+H8I/REY957R+jpfReiK6eOYHLtj2tttH6KrY/6Y/R2c+kjeJiFVtLG24hxpw7/AHY/RRkw9O37
O99Hrt1YX0tfOBLjcGp4XF8c+u8fs9c4dcVcGemSI61nd3IneN1orQAAAAAAAAAAAAABFqxes1tE
TE9JiUgPKcX4RbRXnNgiZwWnrH4XPi28PdXpW9JraImsxtMS8pxXhF9DecuGJtgmf+1TWW2N/la1
L7N7T5e3Vy6W3hsYcvLbqzbO9jvvCzvDR0+XeO7crO6FmGSvRThy/RtVXJ92elvk2rRvDUzU7pl4
izsd2J3jeBpcNz+Lg5LT7+Pp+Xk3W7js5eAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADs0NTrN96Yp6edkW8Wzm6+LNTq4pvTHO9vOfRoWtt
1mes95YWvs1s2fZldddOczLPLn2ju0MmebT3YZc2/mpm3qqllN1drsbZIhr3yzvtHf4AsvlYYseb
V5Yx4KTe0+UQ6nDvZ3UazbJqd8OKeu33peq0eh0+hxcmnxxWPOfOfm0mP+steT/ji8N9mKY9suum
L37+HHaPm9DSlaVitKxWsdohI0Y22gAgAAAAAAAAAABXnyRhw3yT92Nwef4xm8bVzET0rPJH5d12
CvLhho3rN9RWs9Z23n5y6O21YhrVYbdGOCfrrLPJRpv863zVS6FS09SvZj3lVZZRdPSqmnSWdrIE
ebOkK4ldTsgW1WKqd1oMZhEVZyRAImOjGI6rJ7IiATNd46qL02bHkiaxaoNGY2n4ImPgtyV2n0Vo
Gvlx7x2beiyTk08RPevSVUxux00+Fn2n7N+n5rRFb4AAAAAAAAAAAAAAACLVres1tETWekxKQHlu
L8InR2nPp43wz3j8P/s5dLveWrFqzW0bxPeJeV4xwmdFec+CJnDM9Y/CrY1xv8qvTZ+WYdbDk5oh
5zHk283U0eo3jaZZ2N5XYjrCnLSJhOK+8d1kxvCqzSwZvousrb7k9LfJ3nB1OLeJdLhufx9LEWn3
6e7LXN9Ofy5/W4AuxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAETaKxMzO0Qi9646Ta07RDmZ9VbPbaOlI7Qi3i+c3TPUaqcu9adKfy0722ZXvFa9
XO1OrjrESxt66ZJmcjPUanlidmhkzTZVfLN5VWvsC2b7R3U3yqrZZtO1esz2h2+F+zWTUcuXXTNM
feKR3n5+iZLVbqRzNJo9TxHLyaekz62ntD1fDOA6fQbZL7Zc/wCKY6R8odLBgxabFGPDSKUjyiFj
SZkYa3aALKAAAAAAAAAAAAAADQ4pl2pTFH3p3n5Q33E12Tn1eSfKscsLZ+orS00eJqbW+Lfnu1tF
XaJnZsz3WpCfsyp00fWSvmPdVYOmSUDd8kR3InoQosy7JmUX7MdwZ17ro7KKT1XRPRAsrO0rYndr
79V1ZBaQiJ6JgCSIJASwrO07MpV2nqBlrv1a1o2bf2qtfLXaQUTO0sb05o3jv3ZXhjS20xEphW5h
yeJjjf7UdJWNKLziyRePsz0lux1SgAQAAAAAAAAAAAAAADG9K5KTS8Rato2mJZAPIcU4ZbQZuekT
OC3afT4NXFkmlntc2GmoxWx5K71tG0vHa/RX0GpmlutJ61t6wrY2xr8dXS5uesN+tt4ef0eaa223
2dnHk3juyreM81OaFGiy/RtZET9jJ7s/2bdutd2jqKeic3iNTsd8a2h1H0jTVtP2o6W+bZbOO+gA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABje9cdJt
adohGTLXFTmvO0fy52bJfU23t0pHaqLeL5xdK9Rnvqb+cUjtCi94xxvK3JetKuHrdZvaa1ljb10y
cnIs1Wt3naJc++TmVWvMz1YWybfMGdsm3eWek0mo4jm8PT0mfW3lDf4V7P5tdMZdRviwfvZ6/TaX
DpMMYsFIpWPTzXmf+steT8jn8L4Dp+HxF77Zc/4pjpHydYGjC3oAAAAAAAAAAAAAAAAADG9opS1p
7RG7zszN6WtPe0zLua+3Joss/wBOzhzG2OsL5+IrY09dsSyYRijbHEMvOChb7KjF0yS2LQ169Mso
S24noyrPVXWejNVKbTuw3T3REdQWU6LYlVvsyiUDPfqupPRr79VuOQX1lZEqoZxIMksd0gT2VT0l
bPZVbuCaW8i8bwr32WxbcGnkjaZa9p2ndv5qbw5+aNugLItF6TEtvTX5sMb969HMpfazc0d9stqe
vVZDdAQAAAAAAAAAAAAAAAADV1+iprtPOO/2u9bektoB4TJTJpNRbHkja1Z6uto8viVht+0HDvpG
H6Tjj6zHHvbecONw7Ltfkmeqmo6Ma69DXbbZTkr1mGWO3RneOaGbZRoM30fVzSelMnT83aef1FZ7
x3h1tBqfpGnjmn369LNc3sc3kzy9bQCzIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAa+q1dNNXr7157VhGp1Xh70x+9f9ocy283m1p5rz3mVbrjXHjt91lz
5c9+fJ1nyjyhdM8lZlOOIiqrUXikd+kMreunnI5XEdX4dZiZcG+XmtNl/F83PeeWWHDOGanieSKY
q+5H2rz2hMzWd1Iqx1yajJXHhrNrW6REeb1nCPZumn2z62Ivl7xTyr/6uhwzhGn4Zj2xxzZJ+1kn
vLoNJnjHW7TbbsAszAAAAAAAAAAAAAAAAAAAAaPFrbaSK/itEOXt0rDf4xb/ACa/GZacRvaF58Q2
IjasQnzPIhCU92tMbZGzHmotG10C6nZkwpPRmipIllEbMIZIE7solgmJBnCyk9VMM6z1BtVllEqK
z0WRILYlluriWcSDJVbusV27gwInaSWM9ECyZ3hqamnSWxFmOSOaqRx725bNnSZNs9J+OynVY+WZ
YYr7TE+nVaIr0Ais81Yn1hKAAAAAAAAAAAAAAAAAABExvG09peU4nov9n66L0j6q/WPg9Y1OJaON
ZpL0+9HWs/EWzeVz9PbmrEtnyc3h9reHy26TWdnSr2YX6657ijLXpLX0+onSamL/AHJ6W+Tbv2aW
ekTv16JzeI1Ox6KJiYiY7Slz+E6jxdN4dp3vj6fl5Og2clnKACAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeQRMxEbzO0Q08uqtkma4ulfO3r8lefUePMxWf
cjy9WvlzVxV6T1Z61/x0Y8f7Wc7Ur1lqVy+LqOWJ2hp6rXddon5rOF1tfmz5OkT0qzb8dWbxjp1c
biuuilJ5Z6r+IcQrixzEy8zl1E6rNt1tMztFY81sztU1eRucN4ffi2p5esRM72n0h7rS6XFo8FcO
CkVpX082nwXh3+z9FWLxHi36328vg6TZyW9ABAAAAAAAAAAAAAAAAAAAAAADj8Unm1tK/hqppHvw
y1k8/EMk+m0GOPeafiFpCZYwolnXspvHvLa9mF46gmnZmwozRUiUCBKYYsoBLOFbKAX0llEqqyzi
QXRLOJVRLOOwLIljZMEgrlhKyYYTAK5nZPN0RZjugUanHzVlz6xtLq361c+9eXItPpXX0dubTU+E
bL2lw2++O1fSW6m/VYAISAAAAAAAAAAAAAAAAAp1GbwcfTreelYEydcuMcRrM/L9nnlsV6wqpi2r
tv133mfWVkRyRtEdGFva7MzkYZNoamWN4bV4mYa9qztKIujhVppxGI8r1mJegeZpknBqKZY+7L0t
LRekWrO8TG8Ns/HJ5ZypAWZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAADS12fp4VJ6z9qVuq1HgUiI+3bpDl589cOKZmevqprXPTbx477rDJlrhr1nq4+s182tMRP
RqaziXiZJrWekNG17ZbxWJ336M5LXRbI3dLTJrs07RMY6fan1dHLrowY+X7MVjt6N3R6Kul0EbWm
s7bz8Z+LnabQX43r7Y53php/mXj+Dnv0f1JO1x/8ZxbUzj02O15mfLtD13AvZqnDds+pmMmo26el
XX0Wh0/D8EYtNjilY7+s/NstpOOTW7QBKgAAAAAAAAAAAAAAAAAAAAAADG88tLW9I3BwJtz6nNf1
vK/DHVqYJ3pzT5y3MPZeojOWMQylEKpTVjZnDCwkqzYQyRRICATCITAJZQxhMAshnEq4ZQC2srKq
qrIBZCWNZZgwswmFloVyCu0dFcx1WyrtCBhv5NTPHXds2U5o3hIz4ffbPt+KHUcTSW5c9Jme0u2v
VYAKpAAAAAAAAAAAAAAAAYZctcVOa35R6tLrltN795/YvknNqrfhpPLH92V5isd9mWq6fHjk6rn0
ZxG8KK5Jm/wbVZiYZtqrmkqL023bkxvCiY3lJHNyRG81mHS4Rn5sNsNp64+3yaWaNrzOzHBl+i6q
mT7s9J+S+ay8mex6EIneN47SNXKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAImYiJme0JafEs3h6fkidrZOn5eaLeJk7eOdm1Hi2vmtPTry/CHmOJcUvmvOPF1n09Pm
6HF9ZGm01qxO3R5vSY7XwzmzTy47zzTEd7en5Mfvt2/PURWdo3tvPrPlKymbktFqTtMTvHzbOLDG
f63JXbFX7FdnoODcDprZpq9TjiMMTvSn4vj8l5fxnrk91saPSa7i2hpOfbTVt5x1m0fLydzR6PDo
dPGHBXasd585n1lsRERG0dIF5OOe6tAEqgAAAAAAAAAAAAAAAAAAAAAAADX11+TRZrf0y2Gjxe22
gtH4piP3TPpXKwxtjhuYo9xq442iIblI2pC1RET2ILd9kxCqRjZmwlCSEohIJAQAAJZISDKGUd2M
MoBnVbVVCyAWVWeSuqyOwIlXZZKue4MJV2WWYT2QKbKL9YlfdRdIo35b7/Hd3KTzUrPrDh27uxpb
c2mpPwX/ABX9XAKpAAAAAAAAAAAAAACekTIp1eTwtJmv+GkyJn1oafeazbfpMzLR4jq/o8b823zX
6XNF8ERCvTcNpxLV5LauvPhx9Irv3lhztdtv8TtaWLicXrt03jzjzb2k1nid56ty3s/w+a7Uwzjn
1raejlarhmbhl/FpbxMO/fzj5p/ixSeXOvTtRfeI280ZI26tfDm3pWe63LaZx7qtGvniJ6tPLvOK
fOa9WzbJvTbza02jl3n5SSljscK1MajSxWZ96nSW88xw/VfQ9XMT9nfa3yemid43jtLeXsce88qQ
EqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADia3UTm1l4j7OP3Y/u
7Vp2rM+kPJW1PhYcmS0+9MzKm/jbwz31weMzbV8UppazPL9q0/BF4rk1GLDSNqxPWPhCnHmnNrtT
qPKteWPm6U6OdHaZvO+SaRNvhv12Ub/q3FhtrNVj0uKOt56z6R5y9zix1w4qY6RtWsREOJ7L6OKa
S2rvX6zNM7T6Vh3mmZyOfya7eACzIAAAAAAAAAAAAAAAAAAAAAAAAAAczjVvqMVfW/8AZ03I41bf
Lp6/OVs/UVrY47NyOzUxd4bUJpEbb3Z7IiOrKIVSjZhMLJYyhKIgmGUQSDESIEbJEgQmCITEAmGU
IiGUAyhZVhDOoM4Wx2VQtqBKuyyWEgqlhKyyuyBVaGtkbNmvk7A15l1eH2300R6TMORPSXT4ZO+O
8fFefEX63gEAAAAAAAAAAAAAAAq1WPxdLlp+Kkx+y1Fvsz8gjhaDauGK8sx07y3OE3m1tT6RaP4c
vU6yMNKUx73zT0ilY3l2eF6a+m0kRl/zbzz3+Ez5M8z26fJruW6wzYq5sV8d43raNpZjRzPPaTmx
5b6bJ9rHO3zb2WJ8GWPEscY9bgzxH2t62n19GWW0eHOzHU5XbjXZ1x8WTnz2iZ7S2M1IjH2+LX0V
KTqs8zO9ot0j8nUthi1J3UaOFMTfLFo6xMbS9BwHWTqdHOO8+/hnln5eTjYMFo1WTH5VnePzXcIm
2k4zlpPSmXy/hfF5eMfJns69OA2cgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAADG/2LfJ874rW845mubliY7bPoto5qzHrDz0+yePNF41OotaJ7RWNtpV1OtfHqZ715fhu
j8adNpcVfeyzE2/vLuanhOu1nEctIxTTFa/+ZPbZ3eHcF0vDbTfFE2yzG03t32+DokynXl9+leDB
TTYKYccbUpWIhYCzEAAAAAAAAAAAAAAAAAAAAAAAAAAAAcXjE/4zDH9M/wAu04XF5/3jj/0f3Wz9
RUYmzDWxS2I7FSyjuzY1ZKpRKEygEwiWUIkGIk2QJNhKQhMIhkCYZQxhlAMoZwwZwgWQshVCyATL
CWc9ldpBhZXLOVdpQK7NfJPRdaWvknoDVvPvOnwuel4+TlXn3nS4VPvXj4QtEV0wAAAAAAAAAAAA
AAAAAVV02CmTxK4qRf8AFFeq0AAAanEsfPpZmO9Ji0NDLfkwdOsulrumiyzHlVzJrz4Ovoy26vB8
cTBa9NffLtMY77Rv8Yegx5ImkKdJoY1HC81Y+3OSbVn0mGGkmbY45u6tnrrTOu2xGO0RxCd+nNVj
qKxTV1vH2pjaGtnyzXXYdo96ZmGXEMk15b7/AGZiVerWPTYckZcNbx5wzc7hGbnxXxzPWk7x8pdF
0S9jh1OXgAlUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAcPjEf4/FP9H93ccXjMf4vDP9Mx+62fqKrx+S+GvibEFSsqyYwlVK
ZYsmIMoRKYJQIPIEiQ2ATCUQygCGUIhMAyhnDCGUIFkLIV1ZxIMpVWWSrsCuyqyyyq09ECq8tfJK
66jJ2Bp5J6upwn7dv9Lk5J951uE/av8AJaIrqAAAAAAAAAAAAAAAAAAAAAAq1Mc2myxPnWf4cmtu
XT9fR0tffk0WSe28bfq5Wbamm3326MtunwfK6PCv/AxPraZ/dz9PO97/AOqf5dHhdZrw7Dv3mOb9
XOxRFM+avpe38mvkPHf/AFWlrKba7Tzt99ZxKkfR7euyNXMTrtPHfa0z+zPiM/UR8Zj+Wbdu8HpN
M2bfzrV13M4dO2pyR61dNvj44/J/oAWZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADj8bj63BPzdhyeNx0wz8ZWz9RWri7Nmv
VrYu0NmqaRZHZlDGGSiwxZSgCEkCBCQSCQBMJRCYgEsoYx3Z17AlMIhlCBnDOGEM4AlhZZKq4KrK
7LLKrIFN2vdfZReAaObu6/CO9vk5OePR1uEd7fJeIrqAIAAAAAAAAAAAAAAAAAAAAGtxCk5NFliI
3mI32+XVyNTyZOHTee946PQKPoeDffw4777eW/yVs60xv+ZxOnr4Okx1t05KRv8Ao41Z5q3yed5m
XY1szXRZ5jvFJ/hxItP0aOSN9q7yrtr4f2tHFM5+KT16Yq/vK/iGSbXw4vO14UcPx5MGfNbPG18m
1oj4THRsTw7VanPXVYpi3gzMcnrvCnG11JOupwuN8+a3pEQ6jT4divjxWnJExa09pbjbM5HHu90A
JUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAHM41H1GOf6nTc/jEf4Ws+lls/UX45uGekNujTwdm5RNIthKIZKLDFlsiQIShIC
EgCUJ7AmGTGO7IDzZQhMSDJMMYZQgZwzhhDOATuqssmVdgVWVWWyqtCBTeVF19lF+wNLNG7q8I+9
8nLyupwnt+S8RXUAQAAAAAAAAAAAAAAAAAAAAAAItWL1mto3iY2lyrcLyUxzix2ia2nvPeK+jrCL
OrTVnxpanhuPPemSs8l6RtE7dJj0ldpNP9GwRSZ3neZmV4cR/Vs4AJQAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANHi1d9H
M+kt5ra+vPoskfDdOfqK4mn7Q3aNHBPZu0W0RdDOGFWcKLCJZeTGQQlCQSgASBsCYZQxhlAJTAmA
TsmAgGcM4YQyjsgRLC3VnaVcgwsrt3Z2V2QK7tbJ1bN5a9waeWO7p8Knt8nNyebpcK8vkvlFdQBA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK9RXmwZI+ErEWjesx6wQeZwejeo0cccuW8
elpblJaaRGxVnCuss4ZrMvJEgCAASISCQIBlCYYpieoM0wx8k7gzIRueYM4Z79FcSy3QEsLJmWFp
BjaVVpZWlXMoGNmvkXXlr3kGtknu6XCf7OXkl1OEdl8orqgIAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAHmskcmtzV/rls0U62OXiWX4zErcc9GmkRfWVkSqqziWayxCPIANwBIhIJSxS
CRG6dwZwlhEs4BluMdzfqgZxLLdXuy3AmVdpZTKuZBjaVVpWWV2QlhZRdfZRcGpl7urwfrzfJy8r
rcH61vPyWitdMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHA4nHLxKZ9awnH2ZcY
jbW459aq8fZpfiI2IZwrqzhmsz3Ebm4JN0AMhCQSIASndiAziWUSriWcAyRujc80DM3RCfIETLCW
UsZEsJYSslXZAwlTddPZTkBp5e7r8Gj6rJPxhx8k9Xa4PG2C8/FaK10QAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAcfjcbZMFvnDWx9m5x2PqcNvS+zSxT7sNPxH62YZQwqzhRZO6UCB
KUAJTux3SDIRuAncQAmJZRLBMSgZ7iIAZRKd2DICUSlAljLCYWMLIFVukNfI2bNbIDTyT7zu8Ijb
Sz/qcG/2nf4T/wCE/wD2WnxWt4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHL9oL
+Hw2cm28VvEuPptfgyVj6yIn0no7/FtJfW8NzYMe3PaPd39d3iMug1WktNc2C9dvPbeP1aZ9xF+v
T471tHu2iflK2HkqWmvaZj5Surqc9Ps5bx+alTHqYHm68S1Vf/NmfnC2vGNTXvyT84Ql6A3cSvHM
sfaxVn5Ssrxyv3sM/lKB1xza8bwT3pePyWV4tpZ+/MfOEjfGrXiGlt2zV/PotrqcN/s5aT/+wLRj
FontMSlAlKEgndO6IAZQljDIEgeQljLCzOVdkCu/SGrkbF56NPNeKxMzMRHxENe0+89DwuNtHHzl
5PJr8NcnLW3Pbf7r1nCZm2gpae8zMrz4i/W6AgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAETETG0xukB4HVaeMHEtRi26RedvkyjBSfX9W77QYvC4xz7dMlYlrU7M929dWJLFc6aPK0q
7YLxPS0S22FlP6q38Zac0yR92s/KVc3tHfFf8tpbcsLRvB/dR/8ALLVnU0r9uL1+dZI1mnmdvGpv
6TOy6ym+Oto2tWJ+cJ/tW+KLK5KW+zes/KU7tG+h01p64qx8Y6NXNo6Y+uPJlp8rLf0rfG7MXtHa
0x8pZxqs9e2a8f8A7Oj7HaTHn0+f6RWM23LETfr6vRW4PoL99NT8ui7F4+vEdXXtnt+fVbXjGsr/
AOZE/OsPS29nuH27YrV+VpeV9pdPXhOtw49NG9Mld55+vXcTPd42I47qo7xSfyWV9oM8d8VJ/VxM
d8l46xWF9cV7en6o/qLfxp2I9ob+eCv/AHMo9op89P8A/wBORGmyT5R+qfo2X8P7n9Q/jTsx7RR5
6ef+4/8AuHftg/8A6cWcOSO9J/WEbWr3pY7Efzp2Lcfv5YK/9zWy8d1E/ZpSv5Oba1/+Hb9lc+LP
bFt87I7E/wAabWbiurvEx4nL/pjZzc2bJkn372t85ZXx55/BX85lucC0vPxnTxlnnjm32mOiZqUu
LJ2p4TwnVavNWaYbRTfre0bQ99pcH0bT0xb78vmtiIiNojaErMwAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAHnfarF7umzRHaZrLjYrdIen9ocPi8JyTt1xzF4eUw23rCm3R4r6bMy
wt6kdTaWLdjswmNoZontsCm0K5XWjopnuDC0dGpqG5bs08/daKV672MjbSaif6oh6Z5f2LtvptRX
0tEvUN3Jfo8f7cYve0eX4zV7B5z20xc/C8eSPuZIRficfXlcPaG7ino08HWIbePpLF2NuiyOyrHK
3fZFSwuovHVfaVF4QK5YWTM9UT0EKry6Ps1Tn4zjn8NZn9nOtLseydObiWW34cf918fWfk+PYANn
KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq1WKM+ly4p+/WYeBxTNd6zG0xO0
vobw3FcP0bi2em20Tbmj5Srr418V9sa2Z7qKyzi07MXUylhaU7yjqhLCeiq3ddaFNxFYW7NLNG8t
zya+WO6Va9J7FW66mvwidnrXiPY3Ny8RyUn71Jj9Ht3RPjk19HK9pMHj8D1ER3rHN+jqqtTjjNps
uOe16zAifXzfTz7kNyndpYazS9qT0mszDdoxrsi6m8LazMq6zDOsq1ZEyrt1WWlXaUCqyq0rbKbi
Fdp6PReyFd8uqv8ACsfy83aXrPZHHto89/xX2/SP/dpj6y8vx6EBq5gAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAB5n2q03LfDqqx39y39npmlxbS/TOG5se29tuavzgWzeV4mtui2
O3RRSY2hdVhqO2MvI36iu9lUsrSrvDHn6spnmSiq5jooyV6tq1VV69RC32byTh43h8otMx+r6I+Z
aK/g8TwX7bXh9Mid4iW+fjl8n1ICWb57xLBOm4zqse20Tbmj8+qKdnS9q8PhcTw5tumSm0/OHMxz
0Za+uzx3sX1t0Zxurr1ZxvspWiZYWZbsbT0QK7KLrZVZJFaqt5vbezNOTg9J/FaZeJns93wCvLwb
T/GJn92uGHldIBowAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADuAPA67F9H4l
qMW20VvO3yRWW97T4fC4rXJHSMtI/WGhVlue3b473K2KzMML4+62tujG9pnozXaOSOVFMnVbmq1t
trJRW5E7wwvUxTvCyY6CHOt7moxz6Wh9PxTzYaT61h8x1MbZK/OH0zTf+Fxf6I/htj45vL9WgLMn
mvbPFvocGWO9L7fq85p5maw9d7VYvE4JkmPu2if3eW0+PasdFNOnxfF1Y2hlykRsmY+LJ0MZjZXa
eq2eyi8oQTO0KLdZWzPRjWu6VaqtHR73g0bcI0sf0Q8Nkq93wqNuFaWP+XDTDDytwBowAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAef9q8HNpcGaI60vtPyl56k9Iew49j8ThGe
PwxFv0l4zH2U26fDfTYiyJljvsjf4sm6vJ1hrXjq2MkqLdZEVbgbMx0auGdmzNt6iHN1Ub5af6of
TdPG2nxx6Vj+HzaaTm1+nx/iyVj930ysbViPRrj45vL9SAuyc7j1efguqj+jd4/T33rD3HEcPj8O
1GP8WOY/Z4TTT7sKadHhbcsZnaCJ3TPZk6VdrKbTutmP0U2nqgrGOsr8deiuI2X09EqKM1dt3uuG
f/jdN/06/wAPE546S9rwud+Gaaf+XH8NMMPK2wGjAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAABrcRp4nDtRWPPHP8PCYusPoWSvNjtX1iYfPuWaXtX8MzCuvjfw32siu8ptXoxi
0wy5t4YulReqmazu2skbquURWFInddM7VYRGyL291KFnCcfj8e0le/Lbmn8n0N4b2Ur4nHLWmPsY
5e5a5+OXyXugBZmiY3iY9Xz7NjnTa3Ph/BeYj5PoTxftFg8Hjk2iOmWkW/Psrr418V5WrWd2faFc
V2jdnEMXWxntupmN7NiYU27iWML6dVMVnddjgVqMsdHr+CW5uE6f4Rt+7yuSsTDv+zWXn0WTHP3L
/tK+GHl+O0A1c4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8Dn93W56/wDM
t/L3z59qp24jn+OS38lnpr4r7ZxHQ2TEstt3PXUrt27K57rr1VT0BjKnJPRbMqMs7QlV2fYvHvrd
VknyrEfu9m8f7FZI8fVU85iJewbT45NfQBKo817W4eulzxHaZrL0rje09ItwqbfhtBVs3leai8RD
KLw1sduesL606dWFdsZT1jdhNeq6K9DlhCVUU6s4jZnt1YzAhnM71dH2bycmszY/K1d/0c6OzY4R
fwuK4p8rTstn6z8k7HrwGzkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHz3
Vxvr80/8y38voTwGpj/F5/8AqT/JfjTx/WVeyY6FPspc9dZPVXaOq2WEwIUTVRmjo2rNfLHRI3vZ
DJycXtX8dZh7t879nsnhcbwz23tt+r6I2nxyb+gCVBzuPY/E4PqI9K7ui19fTxNBnp60n+Aj5/pJ
3jZu1aOnnltMNussdfXbm+l3ZM9URHREdZVXTuT1Nk7boQiOkJw28PU47/htEp5eivJPLMTCZ9Vv
x7mJ3iJ9UqNHk8XR4b+tIXuhxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD
weqjbWZ4/wCZP8vePCaz/wDIaiP+Zb+UX408f0r9lOxWOifJhXWjfyYWllPRXYQxnrCrJHRd3YZI
6A1NJecHEsN/S0T+76bE7xE+r5dk93LW3pL6ZpMni6PDf8VIn9m2fjm8s9rgFmQxvHNS0esbMiew
PnHLyai9fS0w2aNfUTtrs3+uf5bGPqy068fF227KtSsdFlKqNGMV6myyY6sbdIQI8tlOWOi6Jhhk
j3RD0vA8nicMx9etZmHRcT2Zyb6XNT8N9/2dt0T449T2AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAHhdfG3E9TH9cvdPEcXjk4zqI/q3L8aeP6xr2TsxpLOekMK6mFo6qpXSrm
OqBixvHSVmzC4OfqK7S9/wAByeLwbTW9K7fo8Fqo6Paeyl+fglI/Da0NcMPK7QC7AAB8313TiOf/
AKk/y2MHWrX4jG3E9R/1Lfyv0/aFNOrHxuU7LI7MMayGTVlHWUXhNe6Z6wIUsb9d1m20q7dkDpez
N9tRqKT5xEvRvKez9+Xis1/FSYerb5+OTyf6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAB43j9eXjN/jWJ/Z7J5L2mry8Upb8VIF8f6aGOey2eynHvOy7bowrrYSxZSwQJ2YXZ
92N4BoanrEvVexmTm4blr+HJ/aHltRHSXofYm/1Wrp5RaJaYY+X49WA0c4AD51xONuKan/qW/lbp
+0MOLRtxbU/9SU4J7KadWPjep2WQrr2WRPRk1TvsndXMpiRCb9FNu0rbTuqvKBscCjfi9PhWZeue
V9n434rafTHL1TfPxy+T/QAszAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHmv
avHtfTZfnV6VxPajHzcNrf8ABeJFs/XnMcr4no18c+6vr2YadkY2YM57sEDLyY37Mo7MMnYGlqO0
vQ+xNfqNVb1tEfs87qZ2rL0/sVX/AHdnt65P7Q0wx8vx6UBo5wAHz/jUbcX1PT78qtO2vaCnJxjP
8Zif2amnnspp04+OjWejKJ6MKdmcMmyJn4m5ZHzEVPMwtJv0VZLbQDqezcb8RzT6Y/7vUPM+ytZt
n1OTyiIh6Ztn45N/6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABocbxeLw
nUR5xXm/Rvq8+OMuDJjntaswEeBxT0bNZ6NatZpNqz3rO0rqsdO3PxlaWEMpY+aqWXkryT0ZT2V3
7A0dVPuy9f7G124NM/iyT/Z4zWT7sw957MYfB4Fp4/FE2/WWmGHldcBowAAeM9qKcvFeb8VIly9P
0nq7ntbTbVYL+tJj93CwT76unR4/jo0nozhhTsy3Y1sWljM9Ce7HyQIm3RRlttVbaWrnt0Sh6n2U
x8vD8mSfv3/h3XN4Bi8Lg2nj8Uc36y6TeOPXugCUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAPD8RxeBxXUU26Tbmj8+quro+02Lw+I4ssdslNvzhzazvDPbq8d7GW7Dfqz2VzG
0s2qd+iu/Zn5Ksk9BVztX1mI8930zh2LwOHabH+HHWP2fNYp4+vwYvxXiP3fUqxtWIjyjZtj45/L
faQFmQADzftfj3w6fJ6WmHmsP23rvaqnNwqLfhvEvIYZ+sV038bo0noy36MK9oZQxrdMyrlnMbMZ
QKrS1M07zEestq/RRjr4utwY/wAV4j91p9V18fQdJj8LR4ccfdpEfsuREbREJbuMAAAAAAAAAAAA
BAJAAAAEAJEAJQAJQAJEAJQAJQAJEACUJAQlAJEAJQAJQJAAAEAJEAJBAAAJAABAJEJAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABwvanDzaPFmjvjv8A
tLztJ3h7HjGHx+FainnFeaPnHV4vFbeIU038VbHeGF+kso7Mb9mTdhKnLK3dRm7SIrHhGPxeP6Sv
9cT/AHfSnz72Zx+J7Q45/BWZ/Z9BbZ+OXyfQBZQABzeP4/E4NqI9Ii36S8Ng/wAx9C4jTxOH6ivr
jn+Hz3B/mQi/GvjdCnWNlsdI2V07LIlg6USrt2ZzZXMoFV+zPhGLxeOaavpbm/RVltEN72Yx+Jxm
b7dKUmf7L5+s9/HtRA2cqRACRACRACRACUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAACQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQCQQCRACRACRCQBCQBCQB
ACRACRACRACRACL1i9LVntMbPATTwdRkxT3pea/u+gPE8Xx+DxrPHlaYt+qNfGvjvtXXsi0dOrKk
dEXjZg6VMtbP2bMtXUdpEV0/Y2nNxbNf8OP+727xvsXH+N1U/wBEfy9k3nxyb+gCVQAGOWvNivX1
rMPnGGOXNNfOJ2fSZ6w+dZKeHxDPX8N7R+6L8a+L63KdoZ7q6zvEMpnowdKJ6ywmWUyqvIKM0vQ+
x+D6rU55+9aKx+TzWa36vbezmDwODYenW+95/Nphj5L6dQBo5wAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAEiAAAEoA
AAAAAAAAAAAAAEAkEAkRuAkQbgkQAkQAkQAkQAl5T2nx8nEMOT8dNv0l6pwfarHvpcGWPu32/WCr
YvK4mOem6b9mGKd4Z3idmFdka0y1c892zfpMtLPaNpEV6D2Kj/Eauf6YeweQ9ieuTVz8K/3evbT4
5NfQBKoAA8FxCvJxrUx/XMvevD8Zry8fz/Haf2RfjTx/6RSOnRMyypHu9kXjowrqVSrvPRnZVl6V
kK0775MsUjvadn0nT4ow6bFijtSsVfPuFYvpPGtNTy54mfy6vorXDm8l9pEC7JIgBIgBIgBIgBIg
BIgBIhIAgBIhIAgBIgBIIBIAAhIAhIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJAAAAAAAAAAAAAAA
AAAAAAAAABAJQkAEAAAAAAAAAAjc3BIjdG4Mkbo5kcwMjdhzHMDPc3V8xzAs3N1fMjmBZubq+Y5g
Wbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmTzAz3N2HMnmBlu5ftFTx
OEZJ/DMW/d0t2rxKni8N1FPWkiZ9eS08e7Cy8dGGn6UhZaJljXZGnmc3UT3dPP2cnUT78xCIV6j2
H/8A9c/6f7vXPI+w8bU1U+vL/d63du5NfUiDcVSIAS8b7RV5eOb/AIqRL2TyXtNX/e2KfXH/AHlF
+NPH/pr4+2xcxx0hFpY11K7R16KM32ZWz3UaidqSgrc9kcPicWyZJjfw6T+727y3sXh2xarN+K0V
h6lvPjj3e0ASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJQAAAAAkQAkQAkAAAAAAAAAAAAAAA
EgAAAAAAAAAAAAAAAAAAAAAgAAABKDcAN0bgkY8xzAyRux5kcwM9zdXNkTcFm6OZXzMeYFvMibKu
ZHMC2bo51U2RuC2bom6rc3BZzom6sBZzI52ADPnOdggFnMc6skFnMc6rc3BbznOp3RzAv50c6nml
HMC/nOf4qOY5wX85zqOc5wbHOc7X5znBsc6edr85zg2ec52vzpi4NjmY5bROG+/bllVzsNTk5dLl
n0pP8BHmMHWNmzt0aum8obm08vVjfrtnxztR0mXHzTvaZdjVRMTLkZo6yiFen9iZ2pqY/wBP93rN
3kPY+/LfPX1rE/u9XzN3HfqzdO6vmTuIZ7m7Hc3Bnu8t7TR/vHBP9E/y9Pu837SV31umn+if5Rfi
/j/01MMb1hjkrtKzBG0bMsmOZY11tOYamr6Und0LUc7XT7u3rJPqL8er9lcPhcFpbzyWm39v7O00
+FYvA4Zpsc94xxu227jv1IAgAAAAAAAAABKAAAASgASgBIgBIgBIgBIhIAAAAAAAAAAAAAAAAAAC
UACUJAAAAAAAAAAAABIAAAAAAAAAAAAAAAAAAAAg3AEbomQZbo3YzLGbAz3RNlc3YzcFs2YzdVN2
M2Bdzom6nmNwW86JurTAMuY3REJ2BB1ZRVMVBhsbSsiqeUFXLucq3lTygp5TlXcpygp5TlXcpygp
5TlXcqOUFXKjlXcrGYBXysdlswiYBVMdUTCyY6sZBWxlnMMZgGLGZZSwkDdHMiWO4MuY5mEyjcFn
N1OdVzHMC3nTzqeY5gX85zqOZPMC+Lqdbk20eb/RKOZr8QybaK/XvtH7iZ9aGlp2luzT3fg19NHS
OjbmPcYX67XH1XSZ9XIzRvMuzrK7zLkZYmYnciunb9lZ5dTk+OP+71cXeP8AZnJ/ip2nf3J/l6iL
/Fu5L9bMWZczXi6YuIbEWTzKIuyiwLt3nuO25uI4a/hx7/rLuczg8TicvFLbfdpEK6+NPH/phhjo
stLGkctUWnoxrrU3j1cnWTzZq1jzl1clo5Zcu8c+txR63iP3Tn6pv4+g4o5cVI9IiGe7CJ2iE7t3
GyN2O6dwSINwSISAlAAlACRAAlAAlACRACRCQAAAAAAAAAASgASISAAAAAAAAAAAAACQAAAAAAAA
AAAAAASAAAAAAAAAAAAAAAAIAAAQCAJljuljsCJlhMs9mOwMJYys5TkBVsjZdyHICrZPKt5E8oK4
qmKrOVOwMIqyirPY2Bjyp2ZbAI2NmSARsbMgEbI2ZAMdjZICNkbMkSCNmOzJEgx2YyzljMAwlhKy
WEwCuWErJhhMArlhLOWEgxljMpljIImWMyTKJA3N0IBO5vux3NwZbnMx3NwZczT4jf3MdPW27a3a
fJOq1XNP2KdIRfi+J2trSYfcjeF+Wm1OicVeWIiN9kai8xjY12ORqultnI1Ecsujq79XP1FovWYI
rTgeq+j8QrWZ+3Mx+r2UXeC0WG2Ti2kiN5mL807eUREvbzbaejefHJv62Iv8WUXa0WTFhVtRdlF2
rz9WUXBtc7jR9dqc2T1ttHyhvZMvJitb0jdq6XHNcNenWVN3028U99WRj6Kb02be3Tq18/SN2Lpc
3UdN9nOmZrqKX/DaJ/d0svvTLRzV3jomK6+Pd1vvWJj0ZczT0mXxNJht60hfFnQ4qu3N1cWTEgs3
Tur5k7gz3N2O5uDM3Y7m4MtxBuCQASIASIASAAAAAAACRCQAAAAAAAAEoSAAAAAAAAAAAlAAlCQA
AAAAAAAAAAASAAAAAAAAAAAAIASgAAAEJAQJQCNkbMgGOyOVnsAw5TlZ7GwMOVPKy2NgY7GzIBGx
skA2AAAAAAAAAAQkBAEghEskAxYzDPZGwK5hjMLJhjMAqmGEwumrCagomFcw2JqqtUFEsLLrV82F
o7gqljKyYYTGwMZRKUSCAQAboJnaN5Bjkneu0d5W4ccViIiOzHFWbTzNumP1Zarr8eeRMbxDW1Mx
NO67NbkhzNVnmInqzaOZrL93JyZeV0M1++7S02jvxDWxhxx033tPpC8Z6rrezWjmZyazJG2/u03h
2vFibTHoqvamiwVwY+nLGzV0+SZ1Mx8G0/45tOhzJ5lXMc3UVXRdlF1HP+iYsDPLPPy49/tz1+Te
pSIr0ho6ak5Ms5J8o2q6NImOrHV7XX488ypzTtHXo0s9t6zG7c1G1qz6ubeZiZ3UatXJG3yauSO7
cvMTEx5tPLb3prPRMVr0HB8vicNxf0+7+kt+LOJwTJyY/Bnz3tH93X36N58cWvq6LSyiyndMSlC7
mZcymLJiwLosmJVRLKLAtiU7q4lMSCzc3YxJuDMRuAlKAEgAAAlAkAAAAAABKAEgAAAAAJAAAAAA
AAAAAAAEgAAAAAAAAAAAAAkAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAhIAAACAAAASgAAAAAAEAAAA
hGzJAImGMwzQDDZjNVuyNgUTVhNGxysZqDVmiu1G5NN2M4waM0+DCaN2cbGcQNGaMZq3JxMJxA1J
qx2bU4kU09slorWNwa20z02RXHbJbl26QvtFovbHWkxEdJt5y2MOHlr2U1W3jx+1hiw8vSO63lmI
XRTaEWmtY6snRHO1VpmJ+DjavpSZl2s8b7y4HFcnh0n0gha5ebJN55KRM2mdoiPN6fh+kpwXh0Wy
RHj5Otp/s5Ps1p62y31+em9aTMYt/OfVfxTiPjZ52naI7fBrI5t66xz5+a1rW7yx0eSL6iZjtEOX
qNbSletom3lENjh2fbHzbbWt3iVozruc+5ztWubf4M4ybpQ2Oboyrva0Vjza8WdDR4OkXt3n9ldX
kaePP9VtYqctYhdvt5oivTeCZ2YOxXk6ubqMfV0b9mrljfqlFcq88k7z2U5axeItDa1OPessuC8P
ya7XRWYnwqdbT/ZMilvIu4dpslNdixXja8Y5tt85djZdbDWnGOesRtXFtuw6T27No5Kx2OrKYQlC
ExKJgBnEpiyvdlEgsizKLKollFgWxLKJVRLKJBbEp3VxLKJBnuMWQJEbpBIAAAJAAAABIAAAAAAA
lAJAAAAAAAAAAAAAASAAAAAAAAAAAAAJAAAABAJABAlAAAAAAAAAAAAAAAAAAAAAAAAIAAAAAAAA
AAABAJQAAAAgAABAAI2EoBGyJhkgGPKxmqxAKpownHC+YRMdN5BrTj67R3bOn01o7p01Iv71u89o
b9a7LfBTfS1vWI2jf12VfQPSW8KX2mas+NC2iv6xMNfJpMnLtEbuuxtMRCtzF55NR5rPps1N/ctP
y6uHreE6nXZ4pak48X3rT06fB7fNeI33cbX6mI32R/MWu7XF116aDSRhxbRERs8f499bkyZeeKae
kzE2mdon81/tfxDLGOunwbzlzbx08oaHBvZHJlx48mrvaa94pu04y617576rNGLRRM0397JEd/lu
9Dw/S3x4qxffo6mm4NjwUiKY4iI9Ib1dHFY6QIaNabbrYrLfrpJtaK1rMzPZb/s+05IpP59OyLeJ
k7eNfRaOc1ue32I7fGXYpi5Y77M8OGMeOKxHSFsU3Y29deZMzirl6dlVvhLatCjJHeYQv1rXnps1
8k9/VsW6qLVmZIi1rzitlvFKRvaZ2h6TSaenC9FFY+3brM+sqeG8Prp4+kZ+lvuxPkr1mqm95nfp
DXM459676a2q1dsV7XietvNno78+CJn1cjX6mOeIm0bR33dfRU5NJjidt9t5afjG/V6JZ7I2QMNh
nyo2BhsMuVG3wAhMSbbQRAMolnE+iuGUSCyJZRKuGUSCyJZK4llEgyZMYTuCUsYSCQASISAAAlCQ
AAAAAAEoASCASAAAAAAAAAAAAlACRACQAAAAAAAAAEgCEoASCAAAAAAAAAAAAAAAAAAAAAAABAAA
AAAAAAAISAIAAAAAAQAAACASgAAAQJAQAAhIDHZhln3do7z0WS18mWsajHjmes7pg3dNi5aRMNqO
yvDHTpPRaigHZhN4hHRlaVN59JY3zRENLUavaO+yq0iNVlitJ6vNcR1MVi0zO0era1/Ea0rPvbz5
PM5MWp45qvo2GZrhmfrsnpHpHzTCseEcM/2vrr8Q1Eb4qzy44nziPN63HpYiIiI7LNHoqabBTFii
IpSNohuVxrKtWMEejPwY9G1FFmHB4mWJn7MdfnIM9JpIx15to5pbUaas/a6rqViI7MxPxqX0UT1r
O3wVzpbR2hviP5i03Y5s6a879FNtHljydhExCv8AMTPJXBnRZbz0iG5ptFjwe/l96zctMVamTJtE
yTMibu1VrdTzRMR0j0ed4lr64MVpm0RERvMz5NvX62uOJ69XhOKX1HH9bHDtFvNYnfJeOy0Z2ojX
6jjnEq6fRUmccTvN/J9H0eKcOnx45neaxEbubwHgOHg+milI3vP2resu3Wu0JQmITsmISDHZHKz2
JgFc1RMLJhGwK9iIZ7MZgEdgmAEwyiWCdwWRLKJVxKYsC2JTuriWUSDNlEsIlMAySx3SCRCQSIAS
AAACRACQAAAAAAASIASAAAAAAAAAAAAAAACRACRACQASIAAAAAAAAAAAAAAAAAAAAAAAAQCUAAAA
AAAAAAIAAAAAAAAQAAAAAACBICBICAAEJAQJQCJcLjuS2ny6fPG/LWdpd1o8T0X07SXx/e7wCdJx
Wa0jmneHQpxPDMdZmJfNtZm49weZrh0/j4o7VtSZ2+Uw0/8A7o49k92vBLc/ntFohFW9PqGXimOI
6Tu1L8T3eCx6r2t1O3JwvHjifO99v7t/Bwf2l1PXU6rS6eJ8qUm8x+so5TsekzcSjbvs4mt4rzW5
K2mbT0itesy2cHsvbvqtbmyz5xERWP2jd1tJwrTaONsOKtZ8585+cnDrzmn4Rq+IZObUROHD32n7
Vv8A0ej0uhxaXFGPFSK1j0bkY4jyZRVZVXFGUVWbGwKsk8mObekNrSW3pWf1a2aYjHbm7bNnQ1id
PW0TvuDdhJEbQABMsLW2R0ZTMQrvfbz2YWzVhpanUxEd0dWkW5c8R5uXxDX1w4pnfr5Q19XxKuOJ
2neXltVqtVxbV/RdJ715+1bypANfiOu1HENV9C0MTfNeesx2rD1PAeBYuE6aKx72W3W9/WVnBuB4
eF4dqRzZbdb5J72l160WVK02ZxCYhOwI23TsnY2BGxsnYBjsiYZsZBjMMZZSgGEolMsQDdG6NwZ7
piVe6YkFsSziVMWZRILolMSriWUSCyJTuwhMSDMRCQSI3SAlACRCQAAEoAEoASAAAAAAAAACUACR
ACQAAAAAAAAAAAAASAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAABAAAAAAAAAAAAACBKAAAAAAAQ
JQAAAhICEbJAYTWJ7wx8KvpC0BV4ceieWGewDHlNmWwCNjZICNhIDmcZredBecdpiY69FXCOLW+i
UiZidukulmxxlx2paN4mNng+K4+I8Hy2yaTfl37TXetoCPfRxfp1qi3F48ofKMvtvxak8s6LDv61
rZji9rPaLUf5PC+bfttS0q8q3p9W/wBrRMdpUZuKdN99nzvFqPbTVz7nD8OKs+do2/mW3h4D7Xaq
ZnPrtNpqz35aRaYOHY9Zk4pNt9rR+rl6zi+OnS+WN57Rv1lXp/YrNaYtruL6zNPnGO3hxP6O5w/2
f0HDuun09Yv55Le9afznqcOvO4tBreMTHu30unnva0bWt8on+70nDuE4OHYYx4Kbesz3tPrMuhGO
IjpDOKrK9YVpsyiGUQnYGOyUgI2SlAIEmwMWMs9kTAMJYzDOYRMArmGErZhhMArlHmzmGMwDE3Ts
bAbs4swj5pgFkSziVcM4BZEsolXDKAZwyhjCYBkACQhIAAAAAAAJAAAAAAAAAAAAAAAAAAAShIAA
AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
2Q==`;var pg={};pr(pg,{author:()=>l4,browser:()=>i4,bugs:()=>u4,default:()=>tse,description:()=>n4,devDependencies:()=>A4,engines:()=>d4,homepage:()=>c4,keywords:()=>m4,license:()=>h4,main:()=>a4,module:()=>s4,name:()=>t4,repository:()=>p4,scripts:()=>f4,sideEffects:()=>r4,types:()=>o4,version:()=>fg});var t4="@vladmandic/human",fg="1.1.6",n4="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",r4=!1,a4="dist/human.node.js",s4="dist/human.esm.js",i4="dist/human.esm.js",o4="types/src/human.d.ts",l4="Vladimir Mandic <mandic00@live.com>",u4={url:"https://github.com/vladmandic/human/issues"},c4="https://vladmandic.github.io/human/demo/index.html",h4="MIT",d4={node:">=12.0.0"},p4={type:"git",url:"git+https://github.com/vladmandic/human.git"},f4={start:"node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation demo/node.js",dev:"node --trace-warnings --unhandled-rejections=strict --trace-uncaught server/serve.js",build:"rimraf dist/* types/* typedoc/* && node --trace-warnings --unhandled-rejections=strict --trace-uncaught server/build.js",lint:"eslint src server demo",test:"npm run lint && npm run start"},m4=["tensorflowjs","face-detection","face-geometry","face-embedding","face-recognition","body-tracking","hand-tracking","iris-tracking","age-estimation","emotion-detection","gender-prediction","gesture-recognition","blazeface","blazepose"],A4={"@microsoft/api-extractor":"^7.13.2","@tensorflow/tfjs":"^3.3.0","@tensorflow/tfjs-backend-cpu":"^3.3.0","@tensorflow/tfjs-backend-wasm":"^3.3.0","@tensorflow/tfjs-backend-webgl":"^3.3.0","@tensorflow/tfjs-converter":"^3.3.0","@tensorflow/tfjs-core":"^3.3.0","@tensorflow/tfjs-data":"^3.3.0","@tensorflow/tfjs-layers":"^3.3.0","@tensorflow/tfjs-node":"^3.3.0","@tensorflow/tfjs-node-gpu":"^3.3.0","@types/node":"^14.14.35","@typescript-eslint/eslint-plugin":"^4.18.0","@typescript-eslint/parser":"^4.18.0","@vladmandic/pilogger":"^0.2.14",chokidar:"^3.5.1",dayjs:"^1.10.4",esbuild:"^0.9.2",eslint:"^7.22.0","eslint-config-airbnb-base":"^14.2.1","eslint-plugin-import":"^2.22.1","eslint-plugin-json":"^2.1.2","eslint-plugin-node":"^11.1.0","eslint-plugin-promise":"^4.3.1",rimraf:"^3.0.2",seedrandom:"^3.0.5","simple-git":"^2.37.0",tslib:"^2.1.0",typedoc:"^0.20.32",typescript:"^4.2.3"},tse={name:t4,version:fg,description:n4,sideEffects:r4,main:a4,module:s4,browser:i4,types:o4,author:l4,bugs:u4,homepage:c4,license:h4,engines:d4,repository:p4,scripts:f4,keywords:m4,devDependencies:A4};var mg={};pr(mg,{all:()=>rse,body:()=>w4,canvas:()=>nse,drawOptions:()=>ce,face:()=>x4,gesture:()=>g4,hand:()=>_4});var ce={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1};function S0(e,t,n,r=null){e.fillStyle=ce.useDepth&&r?`rgba(${127.5+2*(r||0)}, ${127.5-2*(r||0)}, 255, 0.3)`:ce.color,e.beginPath(),e.arc(t,n,ce.pointSize,0,2*Math.PI),e.fill()}function y4(e,t,n,r,a){if(e.beginPath(),ce.useCurves){let s=(t+t+r)/2,i=(n+n+a)/2;e.ellipse(s,i,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=ce.lineWidth,e.moveTo(t+ce.roundRect,n),e.lineTo(t+r-ce.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+ce.roundRect),e.lineTo(t+r,n+a-ce.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-ce.roundRect,n+a),e.lineTo(t+ce.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-ce.roundRect),e.lineTo(t,n+ce.roundRect),e.quadraticCurveTo(t,n,t+ce.roundRect,n),e.closePath();e.stroke()}function Ag(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=ce.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:ce.color,e.fillStyle=ce.useDepth&&n[2]?`rgba(${127.5+2*n[2]}, ${127.5-2*n[2]}, 255, 0.3)`:ce.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),ce.fillPolygons&&(e.closePath(),e.fill())}}function T0(e,t=[]){if(!(t===void 0||t.length===0)){if(!ce.useCurves||t.length<=2){Ag(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,a=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),ce.fillPolygons&&(e.closePath(),e.fill())}}async function g4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=ce.font,n.fillStyle=ce.color;let r=1;for(let a=0;a<t.length;a++){let s=[],i=[];if([s,i]=Object.entries(t[a]),i.length>1&&i[1].length>0){let o=s[1]>0?`#${s[1]}`:"",l=`${s[0]} ${o}: ${i[1]}`;ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText(l,8,2+r*ce.lineHeight)),n.fillStyle=ce.labelColor,n.fillText(l,6,0+r*ce.lineHeight),r+=1}}}async function x4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n)for(let r of t){n.font=ce.font,n.strokeStyle=ce.color,n.fillStyle=ce.color,ce.drawBoxes&&y4(n,r.box[0],r.box[1],r.box[2],r.box[3]);let a=[];if(a.push(`face confidence: ${Math.trunc(100*r.confidence)}%`),r.genderConfidence&&a.push(`${r.gender||""} ${Math.trunc(100*r.genderConfidence)}% confident`),r.age&&a.push(`age: ${r.age||""}`),r.iris&&a.push(`iris distance: ${r.iris}`),r.emotion&&r.emotion.length>0){let s=r.emotion.map(i=>`${Math.trunc(100*i.score)}% ${i.emotion}`);a.push(s.join(" "))}r.angle&&r.angle.roll&&a.push(`roll: ${Math.trunc(100*r.angle.roll)/100} yaw:${Math.trunc(100*r.angle.yaw)/100} pitch:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=ce.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*ce.lineHeight+r.box[1];ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=ce.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh){if(ce.drawPoints)for(let s of r.mesh)S0(n,s[0],s[1],s[2]);if(ce.drawPolygons){n.lineWidth=1;for(let s=0;s<Fi.length/3;s++){let i=[Fi[s*3+0],Fi[s*3+1],Fi[s*3+2]].map(o=>r.mesh[o]);Ag(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=ce.useDepth?"rgba(255, 200, 255, 0.3)":ce.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ce.fillPolygons&&(n.fillStyle=ce.useDepth?"rgba(255, 255, 200, 0.3)":ce.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=ce.useDepth?"rgba(255, 200, 255, 0.3)":ce.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ce.fillPolygons&&(n.fillStyle=ce.useDepth?"rgba(255, 255, 200, 0.3)":ce.color,n.fill())}}}}}var ts=[];async function w4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!ts[r]&&ce.bufferedOutput&&(ts[r]={...t[r]}),n.strokeStyle=ce.color,n.lineWidth=ce.lineWidth,ce.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=ce.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:ce.color,ce.bufferedOutput?(ts[r].keypoints[a][0]=(ts[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,ts[r].keypoints[a][1]=(ts[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,S0(n,ts[r].keypoints[a][0],ts[r].keypoints[a][1])):S0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(ce.drawLabels){n.font=ce.font;for(let a of t[r].keypoints)n.fillStyle=ce.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:ce.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4)}if(ce.drawPolygons){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&Ag(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),T0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),T0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),T0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),T0(n,s)}}}}async function _4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round",n.font=ce.font;for(let r of t){if(ce.drawBoxes&&(n.strokeStyle=ce.color,n.fillStyle=ce.color,y4(n,r.box[0],r.box[1],r.box[2],r.box[3]),ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText("hand",r.box[0]+3,1+r.box[1]+ce.lineHeight,r.box[2])),n.fillStyle=ce.labelColor,n.fillText("hand",r.box[0]+2,0+r.box[1]+ce.lineHeight,r.box[2]),n.stroke()),ce.drawPoints&&r.landmarks&&r.landmarks.length>0)for(let a of r.landmarks)n.fillStyle=ce.useDepth?`rgba(${127.5+2*a[2]}, ${127.5-2*a[2]}, 255, 0.5)`:ce.color,S0(n,a[0],a[1]);if(ce.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=ce.lineWidth,n.beginPath(),n.strokeStyle=ce.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:ce.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function nse(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function rse(e,t){!t||!e||e instanceof HTMLCanvasElement&&(x4(e,t.face),w4(e,t.body),_4(e,t.hand),g4(e,t.gesture))}var dt=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Bc(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Bc(s,i):n[a]=i}),n),{})}var C0,Ke,Xl,Vc,Uc,$i,Ut,E0,Hc,R0,jc,F0,M0,$0,b4=class{constructor(t={}){C0.set(this,void 0);Ke.set(this,void 0);Xl.set(this,void 0);Vc.set(this,void 0);Uc.set(this,void 0);$i.set(this,void 0);Ut.set(this,(...t)=>{if(!ve(this,Vc))return;let n=this.tf.engine().state.numTensors,r=ve(this,Xl);ea(this,Xl,n);let a=n-r;a!==0&&Ce(...t,a)});E0.set(this,t=>{if(!ve(this,Uc))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof Ue))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Hc.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=dt();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.config.debug&&Ce("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ce("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ce(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ce("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&c6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ce("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ce("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ce(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),ve(this,Ke).backend=Math.trunc(dt()-n)}});R0.set(this,t=>{if(!t||t.length<300)return{roll:null,yaw:null,pitch:null};let n=(s,i,o,l)=>Math.atan2(l-i,o-s),r=s=>Math.abs(s*180/Math.PI%360);return{roll:n(t[33][0],t[33][1],t[263][0],t[263][1]),yaw:n(t[33][0],t[33][2],t[263][0],t[263][2]),pitch:n(t[10][1],t[10][2],t[152][1],t[152][2])}});jc.set(this,async t=>{var u,c,h,d,p,f,m;let n,r,a,s,i,o=[];this.state="run:face",n=dt();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));if(ve(this,Ke).face=Math.trunc(dt()-n),!l)return[];for(let A of l){if(ve(this,Ut).call(this,"Get Face"),!A.image||A.image.isDisposedInternal){Ce("Face object is disposed:",A.image);continue}let y=ve(this,R0).call(this,A.mesh);ve(this,Ut).call(this,"Start Age:"),this.config.async?r=this.config.face.age.enabled?N2(A.image,this.config):{}:(this.state="run:age",n=dt(),r=this.config.face.age.enabled?await N2(A.image,this.config):{},ve(this,Ke).age=Math.trunc(dt()-n)),ve(this,Ut).call(this,"Start Gender:"),this.config.async?a=this.config.face.gender.enabled?R2(A.image,this.config):{}:(this.state="run:gender",n=dt(),a=this.config.face.gender.enabled?await R2(A.image,this.config):{},ve(this,Ke).gender=Math.trunc(dt()-n)),ve(this,Ut).call(this,"Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?O2(A.image,this.config):{}:(this.state="run:emotion",n=dt(),s=this.config.face.emotion.enabled?await O2(A.image,this.config):{},ve(this,Ke).emotion=Math.trunc(dt()-n)),ve(this,Ut).call(this,"End Emotion:"),ve(this,Ut).call(this,"Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?W2(A,this.config):[]:(this.state="run:embedding",n=dt(),i=this.config.face.embedding.enabled?await W2(A,this.config):[],ve(this,Ke).embedding=Math.trunc(dt()-n)),ve(this,Ut).call(this,"End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),ve(this,Ut).call(this,"Finish Face:"),!this.config.face.iris.enabled&&((c=A==null?void 0:A.annotations)==null?void 0:c.leftEyeIris)&&((h=A==null?void 0:A.annotations)==null?void 0:h.rightEyeIris)&&(delete A.annotations.leftEyeIris,delete A.annotations.rightEyeIris);let g=((d=A.annotations)==null?void 0:d.leftEyeIris)&&((p=A.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(A.annotations.leftEyeIris[3][0]-A.annotations.leftEyeIris[1][0]),Math.abs(A.annotations.rightEyeIris[4][1]-A.annotations.rightEyeIris[2][1])):0;o.push({...A,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:g!==0?Math.trunc(g)/100:0,angle:y,tensor:this.config.face.detector.return?(f=A.image)==null?void 0:f.squeeze():null}),(m=A.image)==null||m.dispose(),ve(this,Ut).call(this,"End Face")}return ve(this,Ut).call(this,"End FaceMesh:"),this.config.async&&(ve(this,Ke).face&&delete ve(this,Ke).face,ve(this,Ke).age&&delete ve(this,Ke).age,ve(this,Ke).gender&&delete ve(this,Ke).gender,ve(this,Ke).emotion&&delete ve(this,Ke).emotion),o});F0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(N0);break;case"full":n=await t(I0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});M0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+N0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+I0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));$0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(N0):t(I0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=mg,ea(this,C0,pg),this.version=fg,this.config=Bc(mt,t),this.state="idle",ea(this,Xl,0),ea(this,Vc,!1),ea(this,Uc,!1),ea(this,$i,!0),ea(this,Ke,{}),this.models={face:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null},this.image=n=>dg(n,this.config),this.classes={facemesh:yg,age:v2,gender:I2,emotion:F2,body:this.config.body.modelPath.includes("posenet")?J2:ug,hand:sg},this.sysinfo=Fg()}profileData(){return this.config.profile?b2:{}}simmilarity(t,n){return this.config.face.embedding.enabled?P2(t,n):0}enhance(t){return L2(t)}match(t,n,r=0){return b6(t,n,r)}async load(t={}){this.state="load";let n=dt();t&&(this.config=Bc(this.config,t)),ve(this,$i)&&(this.config.debug&&Ce(`version: ${this.version}`),this.config.debug&&Ce(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ce("platform:",this.sysinfo.platform),this.config.debug&&Ce("agent:",this.sysinfo.agent),await ve(this,Hc).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ce("configuration:",this.config),this.config.debug&&Ce("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?yg.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?k2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?E2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?D2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?z2(this.config):null),this.models.handpose||(this.config.hand.enabled?lg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?eg(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?cg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await yg.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await k2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await E2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await D2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await z2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await lg(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await eg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await cg(this.config))),ve(this,$i)&&(this.config.debug&&Ce("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ea(this,$i,!1));let r=Math.trunc(dt()-n);r>(ve(this,Ke).load||0)&&(ve(this,Ke).load=r)}async detect(t,n={}){return new Promise(async r=>{var d,p,f,m;this.state="config";let a;this.config=Bc(this.config,n),this.state="check";let s=ve(this,E0).call(this,t);s&&(Ce(s,t),r({error:s}));let i=dt();await ve(this,Hc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),ve(this,Ut).call(this,"Start Scope:"),a=dt();let o=dg(t,this.config);if(!o||!o.tensor){Ce("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}ve(this,Ke).image=Math.trunc(dt()-a),ve(this,Ut).call(this,"Get Image:");let l,u,c;this.config.async?(c=this.config.face.enabled?ve(this,jc).call(this,o.tensor):[],ve(this,Ke).face&&delete ve(this,Ke).face):(this.state="run:face",a=dt(),c=this.config.face.enabled?await ve(this,jc).call(this,o.tensor):[],ve(this,Ke).face=Math.trunc(dt()-a)),ve(this,Ut).call(this,"Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(o.tensor,this.config):[]:l=this.config.body.enabled?hg(o.tensor,this.config):[],ve(this,Ke).body&&delete ve(this,Ke).body):(this.state="run:body",a=dt(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(o.tensor,this.config)):[]:l=this.config.body.enabled?await hg(o.tensor,this.config):[],ve(this,Ke).body=Math.trunc(dt()-a)),ve(this,Ut).call(this,"End Body:"),ve(this,Ut).call(this,"Start Hand:"),this.config.async?(u=this.config.hand.enabled?(f=this.models.handpose)==null?void 0:f.estimateHands(o.tensor,this.config):[],ve(this,Ke).hand&&delete ve(this,Ke).hand):(this.state="run:hand",a=dt(),u=this.config.hand.enabled?await((m=this.models.handpose)==null?void 0:m.estimateHands(o.tensor,this.config)):[],ve(this,Ke).hand=Math.trunc(dt()-a)),ve(this,Ut).call(this,"End Hand:"),this.config.async&&([c,l,u]=await Promise.all([c,l,u])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),ve(this,Ut).call(this,"End Scope:");let h=[];this.config.gesture.enabled&&(a=dt(),h=[...Y6(c),...Z6(l),...Q6(u),...J6(c)],this.config.async?ve(this,Ke).gesture&&delete ve(this,Ke).gesture:ve(this,Ke).gesture=Math.trunc(dt()-a)),ve(this,Ke).total=Math.trunc(dt()-i),this.state="idle",r({face:c,body:l,hand:u,gesture:h,performance:ve(this,Ke),canvas:o.canvas})})}async warmup(t={}){let n=dt();t&&(this.config=Bc(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ve(this,F0).call(this):typeof Image!="undefined"?a=await ve(this,M0).call(this):a=await ve(this,$0).call(this),this.config.videoOptimized=r;let s=dt();return this.config.debug&&Ce("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};C0=new WeakMap,Ke=new WeakMap,Xl=new WeakMap,Vc=new WeakMap,Uc=new WeakMap,$i=new WeakMap,Ut=new WeakMap,E0=new WeakMap,Hc=new WeakMap,R0=new WeakMap,jc=new WeakMap,F0=new WeakMap,M0=new WeakMap,$0=new WeakMap;export{b4 as Human,b4 as default};
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=human.esm.js.map
Reference in New Issue View Git Blame Copy Permalink