mirror of https://github.com/vladmandic/human
5068 lines
1.3 MiB
5068 lines
1.3 MiB
|
|
/*
|
|
Human library
|
|
homepage: <https://github.com/vladmandic/human>
|
|
author: <https://github.com/vladmandic>'
|
|
*/
|
|
|
|
var Human=(()=>{var p8=Object.create,mh=Object.defineProperty,f8=Object.getPrototypeOf,m8=Object.prototype.hasOwnProperty,A8=Object.getOwnPropertyNames,y8=Object.getOwnPropertyDescriptor;var Y1=e=>mh(e,"__esModule",{value:!0});var Eg=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),er=(e,t)=>{for(var n in t)mh(e,n,{get:t[n],enumerable:!0})},g8=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of A8(t))!m8.call(e,r)&&r!=="default"&&mh(e,r,{get:()=>t[r],enumerable:!(n=y8(t,r))||n.enumerable});return e},Ah=e=>g8(Y1(mh(e!=null?p8(f8(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 w6=Eg(x6=>{Y1(x6);er(x6,{MediaPipeFaceMesh:()=>_2,load:()=>dae});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 c=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,u=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:c,mesh:i,boxRaw:u,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 dae(e){Mi=await Promise.all([!Mi[0]&&e.face.enabled?d6(e):null,!Mi[1]&&e.face.mesh.enabled?Rt(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Mi[2]&&e.face.iris.enabled?Rt(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: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}x6.triangulation=Fi});var x0=Eg(U2=>{Y1(U2);er(U2,{NUM_KEYPOINTS:()=>yae,connectedPartIndices:()=>xae,partChannels:()=>_ae,partIds:()=>H2,partNames:()=>Aae,poseChain:()=>wae});var Aae=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],yae=U2.partNames.length,H2=U2.partNames.reduce((e,t,n)=>(e[t]=n,e),{}),gae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],xae=gae.map(([e,t])=>[H2[e],H2[t]]),wae=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]],_ae=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var Bae={};er(Bae,{Human:()=>yg,default:()=>yg});function Ce(...e){let t=new Date,n=`${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 ${process.version}`);return{platform:e,agent:t}}var yh={};er(yh,{Abs:()=>Zi,Acos:()=>Yi,Acosh:()=>Ji,AdadeltaOptimizer:()=>Hd,AdagradOptimizer:()=>jd,AdamOptimizer:()=>Gd,AdamaxOptimizer:()=>qd,Add:()=>Ia,AddN:()=>os,All:()=>kh,Any:()=>Ih,ArgMax:()=>ls,ArgMin:()=>lu,Asin:()=>Qi,Asinh:()=>eo,Atan:()=>to,Atan2:()=>ro,Atanh:()=>no,AvgPool:()=>us,AvgPool3D:()=>uu,AvgPool3DGrad:()=>Sh,AvgPoolGrad:()=>Nh,BackendWasm:()=>g3,BatchMatMul:()=>cs,BatchToSpaceND:()=>cu,Bincount:()=>Th,BroadcastTo:()=>Gg,Callback:()=>uv,CallbackList:()=>o7,Cast:()=>hs,Ceil:()=>ds,ClipByValue:()=>Na,Complex:()=>Ch,ComplexAbs:()=>hu,Concat:()=>ao,Conv2D:()=>ps,Conv2DBackpropFilter:()=>Eh,Conv2DBackpropInput:()=>fs,Conv3D:()=>du,Conv3DBackpropFilterV2:()=>Rh,Conv3DBackpropInputV2:()=>Fh,Cos:()=>ms,Cosh:()=>so,CropAndResize:()=>io,Cumsum:()=>As,CustomCallback:()=>u7,DataStorage:()=>wh,DenseBincount:()=>Mh,DepthToSpace:()=>oo,DepthwiseConv2dNative:()=>ys,DepthwiseConv2dNativeBackpropFilter:()=>$h,DepthwiseConv2dNativeBackpropInput:()=>Dh,Diag:()=>Oh,Dilation2D:()=>pu,Dilation2DBackpropFilter:()=>Ph,Dilation2DBackpropInput:()=>zh,ENV:()=>fr,EarlyStopping:()=>hv,Elu:()=>lo,EluGrad:()=>Lh,Environment:()=>Hg,Equal:()=>co,Erf:()=>uo,Exp:()=>xs,ExpandDims:()=>ho,Expm1:()=>po,FFT:()=>Wh,Fill:()=>fu,FlipLeftRight:()=>fo,Floor:()=>ws,FloorDiv:()=>_s,FromPixels:()=>td,FusedBatchNorm:()=>bs,FusedConv2D:()=>ei,FusedDepthwiseConv2D:()=>ti,GPGPUContext:()=>cp,GatherNd:()=>Ao,GatherV2:()=>mo,GraphModel:()=>Bv,Greater:()=>yo,GreaterEqual:()=>vs,History:()=>l7,IFFT:()=>Bh,Identity:()=>ks,Imag:()=>Vh,InputSpec:()=>Xt,IsFinite:()=>go,IsInf:()=>xo,IsNan:()=>wo,KernelBackend:()=>su,LRN:()=>yu,LRNGrad:()=>Hh,LayerVariable:()=>n7,LayersModel:()=>ha,LeakyRelu:()=>Is,Less:()=>_o,LessEqual:()=>bo,LinSpace:()=>Uh,Log:()=>Ns,Log1p:()=>vo,LogSoftmax:()=>qg,LogicalAnd:()=>ko,LogicalNot:()=>mu,LogicalOr:()=>Au,MathBackendCPU:()=>Yd,MathBackendWebGL:()=>El,Max:()=>Ss,MaxPool:()=>Cs,MaxPool3D:()=>gu,MaxPool3DGrad:()=>Gh,MaxPoolGrad:()=>jh,MaxPoolWithArgmax:()=>qh,Maximum:()=>Ts,Mean:()=>Es,Min:()=>Rs,Minimum:()=>Fs,MirrorPad:()=>xu,Mod:()=>Io,MomentumOptimizer:()=>Xd,Multinomial:()=>Xh,Multiply:()=>Ms,Neg:()=>No,NonMaxSuppressionV3:()=>To,NonMaxSuppressionV4:()=>Co,NonMaxSuppressionV5:()=>Eo,NotEqual:()=>So,OP_SCOPE_SUFFIX:()=>a5,OneHot:()=>$s,OnesLike:()=>Ro,Optimizer:()=>oa,Pack:()=>Fo,PadV2:()=>Ds,Pool:()=>pk,Pow:()=>Os,Prelu:()=>zs,Prod:()=>Mo,RMSPropOptimizer:()=>Kd,RNN:()=>Hr,Range:()=>wu,Rank:()=>uf,Real:()=>Kh,RealDiv:()=>gs,Reciprocal:()=>$o,Reduction:()=>pn,Relu:()=>Ps,Relu6:()=>Ws,Reshape:()=>Do,ResizeBilinear:()=>Ls,ResizeBilinearGrad:()=>Yh,ResizeNearestNeighbor:()=>_u,ResizeNearestNeighborGrad:()=>Zh,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:()=>Jh,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:()=>Dt,Tile:()=>Sa,TopK:()=>Go,Transform:()=>Qh,Transpose:()=>Js,Unique:()=>ed,Unpack:()=>qo,UnsortedSegmentSum:()=>ku,Variable:()=>Ru,ZerosLike:()=>Xo,_FusedMatMul:()=>Qs,abs:()=>Ot,acos:()=>Df,acosh:()=>Of,add:()=>se,addN:()=>Ma,all:()=>pd,any:()=>Ou,argMax:()=>zu,argMin:()=>zf,asin:()=>Pf,asinh:()=>Lf,atan:()=>Wf,atan2:()=>Bf,atanh:()=>Vf,avgPool:()=>Lu,avgPool3d:()=>jf,backend:()=>L5,backend_util:()=>C,basicLSTMCell:()=>jI,batchNorm:()=>li,batchNorm2d:()=>U5,batchNorm3d:()=>H5,batchNorm4d:()=>j5,batchToSpaceND:()=>Wu,bincount:()=>G5,booleanMaskAsync:()=>ZT,broadcastTo:()=>Bu,browser:()=>rl,buffer:()=>We,callbacks:()=>Ane,cast:()=>me,ceil:()=>Gf,clipByValue:()=>vn,clone:()=>Rr,complex:()=>Ca,concat:()=>at,concat1d:()=>q5,concat2d:()=>ll,concat3d:()=>X5,concat4d:()=>K5,constraints:()=>C3,conv1d:()=>md,conv2d:()=>ra,conv2dTranspose:()=>Ad,conv3d:()=>Xf,conv3dTranspose:()=>pN,copyRegisteredKernels:()=>Ak,cos:()=>Vu,cosh:()=>yd,cosineWindow:()=>_m,cumsum:()=>gd,customGrad:()=>$r,data:()=>Vv,denseBincount:()=>Y5,deprecationWarn:()=>Mf,depthToSpace:()=>Kf,depthwiseConv2d:()=>ul,deregisterOp:()=>gne,device_util:()=>Mu,diag:()=>_N,dilation2d:()=>Zf,disableDeprecationWarnings:()=>sI,dispose:()=>Ee,disposeVariables:()=>iI,div:()=>ge,divNoNan:()=>Yf,dot:()=>J5,dropout:()=>xx,elu:()=>cl,enableDebugMode:()=>aI,enableProdMode:()=>rI,enclosingPowerOfTwo:()=>wx,engine:()=>Fr,env:()=>J,equal:()=>Da,erf:()=>Jf,exp:()=>qn,expandDims:()=>hn,expm1:()=>Qf,eye:()=>em,fft:()=>Ju,fill:()=>Uu,findBackend:()=>$f,findBackendFactory:()=>dI,floor:()=>hl,floorDiv:()=>dd,forceHalfFloat:()=>R_,fused:()=>La,gather:()=>ui,gatherND:()=>gx,gather_util:()=>Nf,getBackend:()=>cI,getGradient:()=>sf,getKernel:()=>nd,getKernelsForBackend:()=>Yo,gpgpu_util:()=>t_,grad:()=>ZN,grads:()=>YN,greater:()=>ar,greaterEqual:()=>za,ifft:()=>Al,imag:()=>xd,image:()=>Je,inTopKAsync:()=>oC,initializers:()=>O3,input:()=>K3,io:()=>bn,irfft:()=>Dd,isFinite:()=>Q5,isInf:()=>ex,isNaN:()=>tx,keep:()=>jt,kernel_impls:()=>Pr,layers:()=>X3,leakyRelu:()=>Hu,less:()=>wd,lessEqual:()=>ci,linalg:()=>Fx,linspace:()=>nx,loadGraphModel:()=>Rt,loadLayersModel:()=>Ote,localResponseNormalization:()=>tm,log:()=>Mn,log1p:()=>_d,logSigmoid:()=>ax,logSoftmax:()=>vd,logSumExp:()=>am,logicalAnd:()=>sr,logicalNot:()=>ju,logicalOr:()=>kd,logicalXor:()=>lx,losses:()=>kE,matMul:()=>Ge,math:()=>w5,max:()=>Xn,maxPool:()=>Gu,maxPool3d:()=>sm,maxPoolWithArgmax:()=>ux,maximum:()=>Dr,mean:()=>vt,memory:()=>hd,metrics:()=>iv,min:()=>pl,minimum:()=>fl,mirrorPad:()=>im,mod:()=>om,model:()=>$te,models:()=>ov,moments:()=>Id,movingAverage:()=>QT,mul:()=>O,multiRNNCell:()=>NS,multinomial:()=>cx,neg:()=>bt,nextFrame:()=>Zd,norm:()=>Ld,notEqual:()=>di,oneHot:()=>nl,ones:()=>Or,onesLike:()=>$n,op:()=>D,outerProduct:()=>RS,pad:()=>aa,pad1d:()=>$S,pad2d:()=>OS,pad3d:()=>PS,pad4d:()=>WS,pool:()=>hx,pow:()=>sa,prelu:()=>Xu,print:()=>f5,prod:()=>Nd,profile:()=>gr,rand:()=>KS,randomGamma:()=>QS,randomNormal:()=>dx,randomUniform:()=>ml,range:()=>Sd,ready:()=>uI,real:()=>Ku,reciprocal:()=>cm,registerBackend:()=>sl,registerCallbackConstructor:()=>zte,registerGradient:()=>Xg,registerKernel:()=>ni,registerOp:()=>yne,regularizers:()=>lv,relu:()=>zr,relu6:()=>Td,removeBackend:()=>hI,reshape:()=>U,reverse:()=>Dn,reverse1d:()=>lT,reverse2d:()=>cT,reverse3d:()=>dT,reverse4d:()=>fT,rfft:()=>Qu,round:()=>hm,rsqrt:()=>Cd,scalar:()=>be,scatterND:()=>yx,scatter_util:()=>Sf,selu:()=>Ed,separableConv2d:()=>dm,sequential:()=>Dte,serialization:()=>re,setBackend:()=>lI,setPlatform:()=>pI,setWasmPath:()=>RZ,setWasmPaths:()=>FZ,setWebGLContext:()=>ip,setdiff1dAsync:()=>px,shared:()=>Im,sigmoid:()=>Fn,sign:()=>pm,signal:()=>vE,sin:()=>Rd,sinh:()=>Fd,slice:()=>Re,slice1d:()=>Md,slice2d:()=>fm,slice3d:()=>$d,slice4d:()=>Zu,slice_util:()=>cn,softmax:()=>Yu,softplus:()=>dl,spaceToBatchND:()=>qu,sparseToDense:()=>wm,spectral:()=>bE,split:()=>Pt,sqrt:()=>Qt,square:()=>ot,squaredDifference:()=>Od,squeeze:()=>Pa,stack:()=>dn,step:()=>yl,stridedSlice:()=>mm,sub:()=>ye,sum:()=>Te,sumOutType:()=>id,tan:()=>Am,tanh:()=>ol,tensor:()=>yr,tensor1d:()=>sn,tensor2d:()=>In,tensor3d:()=>ud,tensor4d:()=>WT,tensor5d:()=>BT,tensor6d:()=>VT,tensor_util:()=>mr,test_util:()=>O5,tidy:()=>P,tile:()=>Oa,time:()=>oI,topk:()=>ym,train:()=>fi,transpose:()=>rt,truncatedNormal:()=>zd,unique:()=>Pd,unregisterGradient:()=>mk,unregisterKernel:()=>fk,unsortedSegmentSum:()=>gm,unstack:()=>ir,upcastType:()=>rr,util:()=>b,valueAndGrad:()=>JN,valueAndGrads:()=>QN,variable:()=>fx,variableGrads:()=>rx,version:()=>rae,version_converter:()=>yre,version_core:()=>nI,version_cpu:()=>lw,version_layers:()=>HA,version_wasm:()=>w3,version_webgl:()=>E_,webgl:()=>KP,webgl_util:()=>Cw,where:()=>kn,whereAsync:()=>xm,zeros:()=>Ct,zerosLike:()=>He});var x8=Object.create,gh=Object.defineProperty,w8=Object.getPrototypeOf,_8=Object.prototype.hasOwnProperty,b8=Object.getOwnPropertyNames,v8=Object.getOwnPropertyDescriptor,k8=e=>gh(e,"__esModule",{value:!0}),un=(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})},I8=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of b8(t))!_8.call(e,r)&&r!=="default"&&gh(e,r,{get:()=>t[r],enumerable:!(n=v8(t,r))||n.enumerable});return e},qi=e=>I8(k8(gh(e!=null?x8(w8(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),N8=un(()=>{}),S8=un((e,t)=>{(function(n,r,a){function s(c){var u=this,h=l();u.next=function(){var d=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=d-(u.c=d|0)},u.c=1,u.s0=h(" "),u.s1=h(" "),u.s2=h(" "),u.s0-=h(c),u.s0<0&&(u.s0+=1),u.s1-=h(c),u.s1<0&&(u.s1+=1),u.s2-=h(c),u.s2<0&&(u.s2+=1),h=null}function i(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function o(c,u){var h=new s(c),d=u&&u.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 c=4022871197,u=function(h){h=h.toString();for(var d=0;d<h.length;d++){c+=h.charCodeAt(d);var p=.02519603282416938*c;c=p>>>0,p-=c,p*=c,c=p>>>0,p-=c,c+=p*4294967296}return(c>>>0)*23283064365386963e-26};return u}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)}),T8=un((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var d=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^d^d>>>8},l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function o(l,c){var u=new s(l),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),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)}),C8=un((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var d=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(d^d<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var h=0;h<u.length+64;h++)c.x^=u.charCodeAt(h)|0,h==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function i(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),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)}),E8=un((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.x,d=c.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,c.i=d+1&7,f};function u(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()}u(c,l)}function i(l,c){return c.x=l.x.slice(),c.i=l.i,c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=u.next,d.quick=d,h&&(h.x&&i(h,u),d.state=function(){return i(u,{})}),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)}),R8=un((e,t)=>{(function(n,r,a){function s(l){var c=this;c.next=function(){var h=c.w,d=c.X,p=c.i,f,m;return c.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,c.i=p,m+(h^h>>>16)|0};function u(h,d){var p,f,m,A,y,g=[],w=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,w=Math.max(w,d.length)),m=0,A=-32;A<w;++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}u(c,l)}function i(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function o(l,c){l==null&&(l=+new Date);var u=new s(l),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=u.next,d.quick=d,h&&(h.X&&i(h,u),d.state=function(){return i(u,{})}),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)}),F8=un((e,t)=>{(function(n,r,a){function s(l){var c=this,u="";c.next=function(){var d=c.b,p=c.c,f=c.d,m=c.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,c.b=d=d<<20^d>>>12^p,c.c=p=p-f|0,c.d=f<<16^p>>>16^m,c.a=m-d|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var h=0;h<u.length+20;h++)c.b^=u.charCodeAt(h)|0,c.next()}function i(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function o(l,c){var u=new s(l),h=c&&c.state,d=function(){return(u.next()>>>0)/4294967296};return d.double=function(){do var p=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=u.next,d.quick=d,h&&(typeof h=="object"&&i(h,u),d.state=function(){return i(u,{})}),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)}),M8=un(()=>{}),$8=un((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",c=r.pow(s,i),u=r.pow(2,o),h=u*2,d=s-1,p;function f(v,x,N){var E=[];x=x==!0?{entropy:!0}:x||{};var F=g(y(x.entropy?[v,_(n)]:v==null?w():v,3),E),M=new m(E),W=function(){for(var V=M.g(i),B=c,H=0;V<u;)V=(V+H)*s,B*=s,H=M.g(1);for(;V>=h;)V/=2,B/=2,H>>>=1;return(V+H)/B};return W.int32=function(){return M.g(4)|0},W.quick=function(){return M.g(4)/4294967296},W.double=W,g(_(M.S),n),(x.pass||N||function(V,B,H,j){return j&&(j.S&&A(j,M),V.state=function(){return A(M,{})}),H?(r[l]=V,B):V})(W,F,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(v){var x,N=v.length,E=this,F=0,M=E.i=E.j=0,W=E.S=[];for(N||(v=[N++]);F<s;)W[F]=F++;for(F=0;F<s;F++)W[F]=W[M=d&M+v[F%N]+(x=W[F])],W[M]=x;(E.g=function(V){for(var B,H=0,j=E.i,X=E.j,G=E.S;V--;)B=G[j=d&j+1],H=H*s+G[d&(G[j]=G[X=d&X+B])+(G[X]=B)];return E.i=j,E.j=X,H})(s)}function A(v,x){return x.i=v.i,x.j=v.j,x.S=v.S.slice(),x}function y(v,x){var N=[],E=typeof v,F;if(x&&E=="object")for(F in v)try{N.push(y(v[F],x-1))}catch(M){}return N.length?N:E=="string"?v:v+"\0"}function g(v,x){for(var N=v+"",E,F=0;F<N.length;)x[d&F]=d&(E^=x[d&F]*19)+N.charCodeAt(F++);return _(x)}function w(){try{var v;return p&&(v=p.randomBytes)?v=v(s):(v=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(v)),_(v)}catch(E){var x=a.navigator,N=x&&x.plugins;return[+new Date,a,N,a.screen,_(n)]}}function _(v){return String.fromCharCode.apply(0,v)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=M8()}catch(v){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),xh=un((e,t)=>{var n=S8(),r=T8(),a=C8(),s=E8(),i=R8(),o=F8(),l=$8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),au=un(()=>{}),D8=un(()=>{}),O8=un(()=>{}),z8=un((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&&Yt(Q.buffer),yn}function i(){return Q.buffer!=Be&&Yt(Q.buffer),wt}function o(){return Q.buffer!=Be&&Yt(Q.buffer),gn}function l(){return Q.buffer!=Be&&Yt(Q.buffer),Hn}function c(){return Q.buffer!=Be&&Yt(Q.buffer),ln}var u=typeof a!="undefined"?a:{},h,d;u.ready=new Promise(function(I,S){h=I,d=S});var p={},f;for(f in u)u.hasOwnProperty(f)&&(p[f]=u[f]);var m=[],A="./this.program",y=function(I,S){throw S},g=!1,w=!1,_=!1,v=!1;g=typeof window=="object",w=typeof importScripts=="function",_=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",v=!g&&!_&&!w;var x=u.ENVIRONMENT_IS_PTHREAD||!1;x&&(Be=u.buffer);var N="";function E(I){return u.locateFile?u.locateFile(I,N):N+I}var F,M,W,V,B,H;if(_){w?N=au().dirname(N)+"/":N=__dirname+"/",F=function(I,S){return B||(B=require("fs")),H||(H=au()),I=H.normalize(I),B.readFileSync(I,S?null:"utf8")},W=function(I){var S=F(I,!0);return S.buffer||(S=new Uint8Array(S)),pe(S.buffer),S},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(I){if(!(I instanceof ru))throw I}),process.on("unhandledRejection",Yr),y=function(I){process.exit(I)},u.inspect=function(){return"[Emscripten Module object]"};var j;try{j=D8()}catch(I){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),I}global.Worker=j.Worker}else v?(typeof read!="undefined"&&(F=function(I){return read(I)}),W=function(I){var S;return typeof readbuffer=="function"?new Uint8Array(readbuffer(I)):(S=read(I,"binary"),pe(typeof S=="object"),S)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(y=function(I){quit(I)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(g||w)&&(w?N=self.location.href:typeof document!="undefined"&&document.currentScript&&(N=document.currentScript.src),typeof r!="undefined"&&r&&(N=r),N.indexOf("blob:")!==0?N=N.substr(0,N.lastIndexOf("/")+1):N="",_?(F=function(I,S){return B||(B=require("fs")),H||(H=au()),I=H.normalize(I),B.readFileSync(I,S?null:"utf8")},W=function(I){var S=F(I,!0);return S.buffer||(S=new Uint8Array(S)),pe(S.buffer),S}):(F=function(I){var S=new XMLHttpRequest;return S.open("GET",I,!1),S.send(null),S.responseText},w&&(W=function(I){var S=new XMLHttpRequest;return S.open("GET",I,!1),S.responseType="arraybuffer",S.send(null),new Uint8Array(S.response)}),M=function(I,S,z){var q=new XMLHttpRequest;q.open("GET",I,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){S(q.response);return}z()},q.onerror=z,q.send(null)}),V=function(I){document.title=I});_&&typeof performance=="undefined"&&(global.performance=O8().performance);var X=u.print||console.log.bind(console),G=u.printErr||console.warn.bind(console);for(f in p)p.hasOwnProperty(f)&&(u[f]=p[f]);p=null,u.arguments&&(m=u.arguments),u.thisProgram&&(A=u.thisProgram),u.quit&&(y=u.quit);var ee=Atomics.load,Y=Atomics.store,ae=Atomics.compareExchange,te;u.wasmBinary&&(te=u.wasmBinary);var ie=u.noExitRuntime||!0;typeof WebAssembly!="object"&&Yr("no native wasm support detected");var Q,he,oe=!1,fe;function pe(I,S){I||Yr("Assertion failed: "+S)}function ke(I){var S=u["_"+I];return pe(S,"Cannot call unknown function "+I+", make sure it is exported"),S}function Ne(I,S,z,q,de){var le={string:function(_n){var Gi=0;if(_n!=null&&_n!==0){var Cg=(_n.length<<2)+1;Gi=Ui(Cg),tt(_n,Gi,Cg)}return Gi},array:function(_n){var Gi=Ui(_n.length);return Ze(_n,Gi),Gi}};function ue(_n){return S==="string"?$e(_n):S==="boolean"?Boolean(_n):_n}var we=ke(I),nt=[],Ut=0;if(q)for(var Mt=0;Mt<q.length;Mt++){var ba=le[z[Mt]];ba?(Ut===0&&(Ut=nu()),nt[Mt]=ba(q[Mt])):nt[Mt]=q[Mt]}var ji=we.apply(null,nt);return ji=ue(ji),Ut!==0&&Vi(Ut),ji}function Me(I,S,z,q){z=z||[];var de=z.every(function(ue){return ue==="number"}),le=S!=="string";return le&&de&&!q?ke(I):function(){return Ne(I,S,z,arguments,q)}}function Oe(I,S,z){for(var q=S+z,de="";!(S>=q);){var le=I[S++];if(!le)return de;if(!(le&128)){de+=String.fromCharCode(le);continue}var ue=I[S++]&63;if((le&224)==192){de+=String.fromCharCode((le&31)<<6|ue);continue}var we=I[S++]&63;if((le&240)==224?le=(le&15)<<12|ue<<6|we:le=(le&7)<<18|ue<<12|we<<6|I[S++]&63,le<65536)de+=String.fromCharCode(le);else{var nt=le-65536;de+=String.fromCharCode(55296|nt>>10,56320|nt&1023)}}return de}function $e(I,S){return I?Oe(i(),I,S):""}function et(I,S,z,q){if(!(q>0))return 0;for(var de=z,le=z+q-1,ue=0;ue<I.length;++ue){var we=I.charCodeAt(ue);if(we>=55296&&we<=57343){var nt=I.charCodeAt(++ue);we=65536+((we&1023)<<10)|nt&1023}if(we<=127){if(z>=le)break;S[z++]=we}else if(we<=2047){if(z+1>=le)break;S[z++]=192|we>>6,S[z++]=128|we&63}else if(we<=65535){if(z+2>=le)break;S[z++]=224|we>>12,S[z++]=128|we>>6&63,S[z++]=128|we&63}else{if(z+3>=le)break;S[z++]=240|we>>18,S[z++]=128|we>>12&63,S[z++]=128|we>>6&63,S[z++]=128|we&63}}return S[z]=0,z-de}function tt(I,S,z){return et(I,i(),S,z)}function it(I){for(var S=0,z=0;z<I.length;++z){var q=I.charCodeAt(z);q>=55296&&q<=57343&&(q=65536+((q&1023)<<10)|I.charCodeAt(++z)&1023),q<=127?++S:q<=2047?S+=2:q<=65535?S+=3:S+=4}return S}function Ze(I,S){s().set(I,S)}function dt(I,S){return I%S>0&&(I+=S-I%S),I}var Be,yn,wt,Un,Zt,gn,Hn,Rn,ln;function Yt(I){Be=I,u.HEAP8=yn=new Int8Array(I),u.HEAP16=Un=new Int16Array(I),u.HEAP32=gn=new Int32Array(I),u.HEAPU8=wt=new Uint8Array(I),u.HEAPU16=Zt=new Uint16Array(I),u.HEAPU32=Hn=new Uint32Array(I),u.HEAPF32=Rn=new Float32Array(I),u.HEAPF64=ln=new Float64Array(I)}var Tr=u.INITIAL_MEMORY||16777216;if(x)Q=u.wasmMemory,Be=u.buffer;else if(u.wasmMemory)Q=u.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Tr/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw G("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"),_&&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,Yt(Be);var Jn,Qn=[],Aa=[],Kr=[],ya=[],Di=[],pr=!1,Gc=!1;x||Aa.push({func:function(){lh()}}),x&&(pr=!0);function O0(){if(!x){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)Kc(u.preRun.shift());zi(Qn)}}function qc(){pr=!0,zi(Aa)}function z0(){x||zi(Kr)}function Xc(){x||(Gc=!0)}function xn(){if(!x){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)P0(u.postRun.shift());zi(Di)}}function Kc(I){Qn.unshift(I)}function P0(I){Di.unshift(I)}var Zr=0,ga=null,ns=null;function L0(I){pe(!x,"addRunDependency cannot be used in a pthread worker"),Zr++,u.monitorRunDependencies&&u.monitorRunDependencies(Zr)}function W0(I){if(Zr--,u.monitorRunDependencies&&u.monitorRunDependencies(Zr),Zr==0&&(ga!==null&&(clearInterval(ga),ga=null),ns)){var S=ns;ns=null,S()}}u.preloadedImages={},u.preloadedAudios={};function Yr(I){u.onAbort&&u.onAbort(I),x&&console.error("Pthread aborting at "+new Error().stack),I+="",G(I),oe=!0,fe=1,I="abort("+I+"). Build with -s ASSERTIONS=1 for more info.";var S=new WebAssembly.RuntimeError(I);throw d(S),S}function Zc(I,S){return String.prototype.startsWith?I.startsWith(S):I.indexOf(S)===0}var Oi="data:application/octet-stream;base64,";function Yc(I){return Zc(I,Oi)}var B0="file://";function Jc(I){return Zc(I,B0)}var wn="tfjs-backend-wasm-threaded-simd.wasm";Yc(wn)||(wn=E(wn));function V0(I){try{if(I==wn&&te)return new Uint8Array(te);if(W)return W(I);throw"both async and sync fetching of the wasm failed"}catch(S){Yr(S)}}function Qc(){if(!te&&(g||w)){if(typeof fetch=="function"&&!Jc(wn))return fetch(wn,{credentials:"same-origin"}).then(function(I){if(!I.ok)throw"failed to load wasm binary file at '"+wn+"'";return I.arrayBuffer()}).catch(function(){return V0(wn)});if(M)return new Promise(function(I,S){M(wn,function(z){I(new Uint8Array(z))},S)})}return Promise.resolve().then(function(){return V0(wn)})}function U0(){var I={a:M1};function S(ue,we){var nt=ue.exports;if(u.asm=nt,Jn=u.asm.F,he=we,!x){var Ut=Ie.unusedWorkers.length;Ie.unusedWorkers.forEach(function(Mt){Ie.loadWasmModuleToWorker(Mt,function(){--Ut||W0("wasm-instantiate")})})}}x||L0("wasm-instantiate");function z(ue){S(ue.instance,ue.module)}function q(ue){return Qc().then(function(we){return WebAssembly.instantiate(we,I)}).then(ue,function(we){G("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,I);return we.then(z,function(nt){return G("wasm streaming compile failed: "+nt),G("falling back to ArrayBuffer instantiation"),q(z)})}):q(z)}if(u.instantiateWasm)try{var le=u.instantiateWasm(I,S);return le}catch(ue){return G("Module.instantiateWasm callback failed with error: "+ue),!1}return de().catch(d),{}}var eh={8991:function(I,S){setTimeout(function(){vg(I,S)},0)}};function H0(){Ie.initRuntime()}function zi(I){for(;I.length>0;){var S=I.shift();if(typeof S=="function"){S(u);continue}var z=S.func;typeof z=="number"?S.arg===void 0?Jn.get(z)():Jn.get(z)(S.arg):z(S.arg===void 0?null:S.arg)}}function Pi(I,S){if(I<=0||I>s().length||I&!0||S<0)return-28;if(S==0)return 0;S>=2147483647&&(S=Infinity);var z=Atomics.load(o(),Hi>>2),q=0;if(z==I){var de=Atomics.compareExchange(o(),Hi>>2,z,0);if(de==z&&(--S,q=1,S<=0))return 1}var le=Atomics.notify(o(),I>>2,S);if(le>=0)return le+q;throw"Atomics.notify returned an unexpected value "+le}u._emscripten_futex_wake=Pi;function j0(I){if(x)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in killThread!";o()[I+12>>2]=0;var S=Ie.pthreads[I];S.worker.terminate(),Ie.freeThreadData(S),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(S.worker),1),S.worker.pthread=void 0}function G0(I){if(x)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in cancelThread!";var S=Ie.pthreads[I];S.worker.postMessage({cmd:"cancel"})}function q0(I){if(x)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!I)throw"Internal Error! Null pthread_ptr in cleanupThread!";o()[I+12>>2]=0;var S=Ie.pthreads[I];if(S){var z=S.worker;Ie.returnWorkerToPool(z)}}var Ie={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var I=8,S=0;S<I;++S)Ie.allocateUnusedWorker()},initRuntime:function(){for(var I=as(228),S=0;S<228/4;++S)l()[I/4+S]=0;o()[I+12>>2]=I;var z=I+152;o()[z>>2]=z;for(var q=as(512),S=0;S<128;++S)l()[q/4+S]=0;Atomics.store(l(),I+100>>2,q),Atomics.store(l(),I+40>>2,I),ph(I,!w,1),bg(I)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ie.threadExitHandlers.length>0;)Ie.threadExitHandlers.pop()();x&&Bi()&&_g()},threadExit:function(I){var S=Bi();S&&(Atomics.store(l(),S+4>>2,I),Atomics.store(l(),S+0>>2,1),Atomics.store(l(),S+56>>2,1),Atomics.store(l(),S+60>>2,0),Ie.runExitHandlers(),Pi(S+0,2147483647),ph(0,0,0),x&&postMessage({cmd:"exit"}))},threadCancel:function(){Ie.runExitHandlers();var I=Bi();Atomics.store(l(),I+4>>2,-1),Atomics.store(l(),I+0>>2,1),Pi(I+0,2147483647),ph(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var I in Ie.pthreads){var S=Ie.pthreads[I];S&&S.worker&&Ie.returnWorkerToPool(S.worker)}Ie.pthreads={};for(var z=0;z<Ie.unusedWorkers.length;++z){var q=Ie.unusedWorkers[z];q.terminate()}Ie.unusedWorkers=[];for(var z=0;z<Ie.runningWorkers.length;++z){var q=Ie.runningWorkers[z],S=q.pthread;Ie.freeThreadData(S),q.terminate()}Ie.runningWorkers=[]},freeThreadData:function(I){if(I){if(I.threadInfoStruct){var S=o()[I.threadInfoStruct+100>>2];o()[I.threadInfoStruct+100>>2]=0,tu(S),tu(I.threadInfoStruct)}I.threadInfoStruct=0,I.allocatedOwnStack&&I.stackBase&&tu(I.stackBase),I.stackBase=0,I.worker&&(I.worker.pthread=null)}},returnWorkerToPool:function(I){Ie.runWithoutMainThreadQueuedCalls(function(){delete Ie.pthreads[I.pthread.threadInfoStruct],Ie.unusedWorkers.push(I),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(I),1),Ie.freeThreadData(I.pthread),I.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(I){o()[Tg>>2]=0;try{I()}finally{o()[Tg>>2]=1}},receiveObjectTransfer:function(I){},loadWasmModuleToWorker:function(I,S){I.onmessage=function(z){var q=z.data,de=q.cmd;if(I.pthread&&(Ie.currentProxiedOperationCallerThread=I.pthread.threadInfoStruct),q.targetThread&&q.targetThread!=Bi()){var le=Ie.pthreads[q.targetThread];le?le.worker.postMessage(z.data,q.transferList):console.error('Internal error! Worker sent a message "'+de+'" to target pthread '+q.targetThread+", but that thread no longer exists!"),Ie.currentProxiedOperationCallerThread=void 0;return}if(de==="processQueuedMainThreadWork")X1();else if(de==="spawnThread")ih(z.data);else if(de==="cleanupThread")q0(q.thread);else if(de==="killThread")j0(q.thread);else if(de==="cancelThread")G0(q.thread);else if(de==="loaded")I.loaded=!0,S&&S(I),I.runPthread&&(I.runPthread(),delete I.runPthread);else if(de==="print")X("Thread "+q.threadId+": "+q.text);else if(de==="printErr")G("Thread "+q.threadId+": "+q.text);else if(de==="alert")alert("Thread "+q.threadId+": "+q.text);else if(de==="exit"){var ue=I.pthread&&Atomics.load(l(),I.pthread.threadInfoStruct+64>>2);ue&&Ie.returnWorkerToPool(I)}else if(de==="exitProcess")try{d8(q.returnCode)}catch(we){if(we instanceof ru)return;throw we}else de==="cancelDone"?Ie.returnWorkerToPool(I):de==="objectTransfer"?Ie.receiveObjectTransfer(z.data):z.data.target==="setimmediate"?I.postMessage(z.data):G("worker sent an unknown command "+de);Ie.currentProxiedOperationCallerThread=void 0},I.onerror=function(z){G("pthread sent an error! "+z.filename+":"+z.lineno+": "+z.message)},_&&(I.on("message",function(z){I.onmessage({data:z})}),I.on("error",function(z){I.onerror(z)}),I.on("exit",function(z){})),I.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||r,wasmMemory:Q,wasmModule:he})},allocateUnusedWorker:function(){var I=E("tfjs-backend-wasm-threaded-simd.worker.js");Ie.unusedWorkers.push(new Worker(I))},getNewWorker:function(){return Ie.unusedWorkers.length==0&&(Ie.allocateUnusedWorker(),Ie.loadWasmModuleToWorker(Ie.unusedWorkers[0])),Ie.unusedWorkers.length>0?Ie.unusedWorkers.pop():null},busySpinWait:function(I){for(var S=performance.now()+I;performance.now()<S;);}};function X0(I,S){Ng(I,S),Vi(I)}u.establishStackSpace=X0;function K0(){return ie}u.getNoExitRuntime=K0;function Z0(I,S){return Jn.get(I)(S)}u.invokeEntryPoint=Z0;function Y0(I,S,z,q){Yr("Assertion failed: "+$e(I)+", at: "+[S?$e(S):"unknown filename",z,q?$e(q):"unknown function"])}function J0(I,S){var z=_main(I,S)}var rs;_?rs=function(){var I=process.hrtime();return I[0]*1e3+I[1]/1e6}:x?rs=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?rs=dateNow:rs=function(){return performance.now()};function Q0(I){return o()[xg()>>2]=I,I}function e1(I,S){if(x)return xa(1,1,I,S)}function t1(I,S){if(I==S)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:I,cmd:"processThreadQueue"});else{var z=Ie.pthreads[I],q=z&&z.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function n1(){Yr()}function r1(I,S,z){var q=l1(S,z);return eh[I].apply(null,q)}function a1(I,S){}function s1(I,S,z){if(I<=0||I>s().length||I&!0)return-28;if(g){if(Atomics.load(o(),I>>2)!=S)return-6;for(var q=performance.now(),de=q+z,le=Atomics.exchange(o(),Hi>>2,I);;){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(X1(),Atomics.load(o(),I>>2)!=S)return-6;le=Atomics.exchange(o(),Hi>>2,I)}return 0}else{var ue=Atomics.wait(o(),I>>2,S,z);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 i1(I,S,z){i().copyWithin(I,S,S+z)}function o1(){return _?require("os").cpus().length:navigator.hardwareConcurrency}function xa(I,S){for(var z=arguments.length-2,q=nu(),de=z,le=Ui(de*8),ue=le>>3,we=0;we<z;we++){var nt=arguments[2+we];c()[ue+we]=nt}var Ut=Ig(I,de,le,S);return Vi(q),Ut}var Kl=[],Zl=[];function l1(I,S){Zl.length=0;var z;for(S>>=2;z=i()[I++];){var q=z<105;q&&S&1&&S++,Zl.push(q?c()[S++>>1]:o()[S]),++S}return Zl}function u1(I,S,z){Kl.length=S;for(var q=z>>3,de=0;de<S;de++)Kl[de]=c()[q+de];var le=I<0,ue=le?eh[-I-1]:F1[I];return ue.apply(null,Kl)}function c1(){return i().length}function h1(I){try{return Q.grow(I-Be.byteLength+65535>>>16),Yt(Q.buffer),1}catch(S){}}function d1(I){var S=c1();if(I<=S)return!1;var z=2147483648;if(I>z)return!1;for(var q=1;q<=4;q*=2){var de=S*(1+.2/q);de=Math.min(de,I+100663296);var le=Math.min(z,dt(Math.max(I,de),65536)),ue=h1(le);if(ue)return!0}return!1}var Le={inEventHandler:0,removeAllEventListeners:function(){for(var I=Le.eventHandlers.length-1;I>=0;--I)Le._removeHandler(I);Le.eventHandlers=[],Le.deferredCalls=[]},registerRemoveEventListeners:function(){Le.removeEventListenersRegistered||(ya.push(Le.removeAllEventListeners),Le.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(I,S,z){function q(ue,we){if(ue.length!=we.length)return!1;for(var nt in ue)if(ue[nt]!=we[nt])return!1;return!0}for(var de in Le.deferredCalls){var le=Le.deferredCalls[de];if(le.targetFunction==I&&q(le.argsList,z))return}Le.deferredCalls.push({targetFunction:I,precedence:S,argsList:z}),Le.deferredCalls.sort(function(ue,we){return ue.precedence<we.precedence})},removeDeferredCalls:function(I){for(var S=0;S<Le.deferredCalls.length;++S)Le.deferredCalls[S].targetFunction==I&&(Le.deferredCalls.splice(S,1),--S)},canPerformEventHandlerRequests:function(){return Le.inEventHandler&&Le.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Le.canPerformEventHandlerRequests())for(var I=0;I<Le.deferredCalls.length;++I){var S=Le.deferredCalls[I];Le.deferredCalls.splice(I,1),--I,S.targetFunction.apply(null,S.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(I,S){for(var z=0;z<Le.eventHandlers.length;++z)Le.eventHandlers[z].target==I&&(!S||S==Le.eventHandlers[z].eventTypeString)&&Le._removeHandler(z--)},_removeHandler:function(I){var S=Le.eventHandlers[I];S.target.removeEventListener(S.eventTypeString,S.eventListenerFunc,S.useCapture),Le.eventHandlers.splice(I,1)},registerOrRemoveHandler:function(I){var S=function(q){++Le.inEventHandler,Le.currentEventHandler=I,Le.runDeferredCalls(),I.handlerFunc(q),Le.runDeferredCalls(),--Le.inEventHandler};if(I.callbackfunc)I.eventListenerFunc=S,I.target.addEventListener(I.eventTypeString,S,I.useCapture),Le.eventHandlers.push(I),Le.registerRemoveEventListeners();else for(var z=0;z<Le.eventHandlers.length;++z)Le.eventHandlers[z].target==I.target&&Le.eventHandlers[z].eventTypeString==I.eventTypeString&&Le._removeHandler(z--)},queueEventHandlerOnThread_iiii:function(I,S,z,q,de){var le=nu(),ue=Ui(12);o()[ue>>2]=z,o()[ue+4>>2]=q,o()[ue+8>>2]=de,K1(0,I,637534208,S,q,ue),Vi(le)},getTargetThreadForEventCallback:function(I){switch(I){case 1:return 0;case 2:return Ie.currentProxiedOperationCallerThread;default:return I}},getNodeNameForTarget:function(I){return I?I==window?"#window":I==screen?"#screen":I&&I.nodeName?I.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function p1(I){var S=it(I)+1,z=as(S);return tt(I,z,S),z}function f1(I,S,z,q){var de=nu(),le=Ui(12),ue=0;S&&(ue=p1(S)),o()[le>>2]=ue,o()[le+4>>2]=z,o()[le+8>>2]=q,K1(0,I,657457152,0,ue,le),Vi(de)}function m1(I,S,z,q){S=S?$e(S):"",f1(I,S,z,q)}function A1(I){return I>2?$e(I):I}var y1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function g1(I){I=A1(I);var S=y1[I]||(typeof document!="undefined"?document.querySelector(I):void 0);return S}function Yl(I){return g1(I)}function th(I,S,z){var q=Yl(I);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=S,o()[q.canvasSharedPtr+4>>2]=z),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=S,q.height=z,de&&q.GLctxObject.GLctx.viewport(0,0,S,z)}else if(q.canvasSharedPtr){var ue=o()[q.canvasSharedPtr+8>>2];return m1(ue,I,S,z),1}else return-4;return 0}function nh(I,S,z){return x?xa(2,1,I,S,z):th(I,S,z)}function x1(I,S,z){var q=Yl(I);return q?th(I,S,z):nh(I,S,z)}function w1(I){}function _1(I,S){}function b1(I){var S=I.getExtension("ANGLE_instanced_arrays");if(S)return I.vertexAttribDivisor=function(z,q){S.vertexAttribDivisorANGLE(z,q)},I.drawArraysInstanced=function(z,q,de,le){S.drawArraysInstancedANGLE(z,q,de,le)},I.drawElementsInstanced=function(z,q,de,le,ue){S.drawElementsInstancedANGLE(z,q,de,le,ue)},1}function v1(I){var S=I.getExtension("OES_vertex_array_object");if(S)return I.createVertexArray=function(){return S.createVertexArrayOES()},I.deleteVertexArray=function(z){S.deleteVertexArrayOES(z)},I.bindVertexArray=function(z){S.bindVertexArrayOES(z)},I.isVertexArray=function(z){return S.isVertexArrayOES(z)},1}function k1(I){var S=I.getExtension("WEBGL_draw_buffers");if(S)return I.drawBuffers=function(z,q){S.drawBuffersWEBGL(z,q)},1}function I1(I){return!!(I.multiDrawWebgl=I.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(I){Qe.lastError||(Qe.lastError=I)},getNewId:function(I){for(var S=Qe.counter++,z=I.length;z<S;z++)I[z]=null;return S},getSource:function(I,S,z,q){for(var de="",le=0;le<S;++le){var ue=q?o()[q+le*4>>2]:-1;de+=$e(o()[z+le*4>>2],ue<0?void 0:ue)}return de},createContext:function(I,S){var z=I.getContext("webgl",S);if(!z)return 0;var q=Qe.registerContext(z,S);return q},registerContext:function(I,S){var z=as(8);o()[z+4>>2]=Bi();var q={handle:z,attributes:S,version:S.majorVersion,GLctx:I};return I.canvas&&(I.canvas.GLctxObject=q),Qe.contexts[z]=q,(typeof S.enableExtensionsByDefault=="undefined"||S.enableExtensionsByDefault)&&Qe.initExtensions(q),z},makeContextCurrent:function(I){return Qe.currentContext=Qe.contexts[I],u.ctx=wa=Qe.currentContext&&Qe.currentContext.GLctx,!(I&&!wa)},getContext:function(I){return Qe.contexts[I]},deleteContext:function(I){Qe.currentContext===Qe.contexts[I]&&(Qe.currentContext=null),typeof Le=="object"&&Le.removeAllHandlersOnTarget(Qe.contexts[I].GLctx.canvas),Qe.contexts[I]&&Qe.contexts[I].GLctx.canvas&&(Qe.contexts[I].GLctx.canvas.GLctxObject=void 0),tu(Qe.contexts[I].handle),Qe.contexts[I]=null},initExtensions:function(I){if(I||(I=Qe.currentContext),!I.initExtensionsDone){I.initExtensionsDone=!0;var S=I.GLctx;b1(S),v1(S),k1(S),S.disjointTimerQueryExt=S.getExtension("EXT_disjoint_timer_query"),I1(S);var z=S.getSupportedExtensions()||[];z.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&S.getExtension(q)})}},populateUniformTable:function(I){for(var S=Qe.programs[I],z=Qe.programInfos[I]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=z.uniforms,de=wa.getProgramParameter(S,35718),le=0;le<de;++le){var ue=wa.getActiveUniform(S,le),we=ue.name;z.maxUniformLength=Math.max(z.maxUniformLength,we.length+1),we.slice(-1)=="]"&&(we=we.slice(0,we.lastIndexOf("[")));var nt=wa.getUniformLocation(S,we);if(nt){var Ut=Qe.getNewId(Qe.uniforms);q[we]=[ue.size,Ut],Qe.uniforms[Ut]=nt;for(var Mt=1;Mt<ue.size;++Mt){var ba=we+"["+Mt+"]";nt=wa.getUniformLocation(S,ba),Ut=Qe.getNewId(Qe.uniforms),Qe.uniforms[Ut]=nt}}}}},N1=["default","low-power","high-performance"];function S1(I,S){var z=S>>2,q=o()[z+(24>>2)],de={alpha:!!o()[z+(0>>2)],depth:!!o()[z+(4>>2)],stencil:!!o()[z+(8>>2)],antialias:!!o()[z+(12>>2)],premultipliedAlpha:!!o()[z+(16>>2)],preserveDrawingBuffer:!!o()[z+(20>>2)],powerPreference:N1[q],failIfMajorPerformanceCaveat:!!o()[z+(28>>2)],majorVersion:o()[z+(32>>2)],minorVersion:o()[z+(36>>2)],enableExtensionsByDefault:o()[z+(40>>2)],explicitSwapControl:o()[z+(44>>2)],proxyContextToMainThread:o()[z+(48>>2)],renderViaOffscreenBackBuffer:o()[z+(52>>2)]},le=Yl(I);if(!le||de.explicitSwapControl)return 0;var ue=Qe.createContext(le,de);return ue}function T1(I,S){return S1(I,S)}var Li={mappings:{},buffers:[null,[],[]],printChar:function(I,S){var z=Li.buffers[I];S===0||S===10?((I===1?X:G)(Oe(z,0)),z.length=0):z.push(S)},varargs:void 0,get:function(){Li.varargs+=4;var I=o()[Li.varargs-4>>2];return I},getStr:function(I){var S=$e(I);return S},get64:function(I,S){return I}};function rh(I){return x?xa(3,1,I):0}function ah(I,S,z,q,de){if(x)return xa(4,1,I,S,z,q,de)}function sh(I,S,z,q){if(x)return xa(5,1,I,S,z,q);for(var de=0,le=0;le<z;le++){for(var ue=o()[S+le*8>>2],we=o()[S+(le*8+4)>>2],nt=0;nt<we;nt++)Li.printChar(I,i()[ue+nt]);de+=we}return o()[q>>2]=de,0}function C1(I){var S=Ie.threadExitHandlers.pop();I&&S()}function E1(I,S){Ie.threadExitHandlers.push(function(){Jn.get(I)(S)})}function ih(I){if(x)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var S=Ie.getNewWorker();if(S.pthread!==void 0)throw"Internal error!";if(!I.pthread_ptr)throw"Internal error, no pthread ptr!";Ie.runningWorkers.push(S);for(var z=as(128*4),q=0;q<128;++q)o()[z+q*4>>2]=0;var de=I.stackBase+I.stackSize,le=Ie.pthreads[I.pthread_ptr]={worker:S,stackBase:I.stackBase,stackSize:I.stackSize,allocatedOwnStack:I.allocatedOwnStack,threadInfoStruct:I.pthread_ptr},ue=le.threadInfoStruct>>2;Atomics.store(l(),ue+(64>>2),I.detached),Atomics.store(l(),ue+(100>>2),z),Atomics.store(l(),ue+(40>>2),le.threadInfoStruct),Atomics.store(l(),ue+(80>>2),I.stackSize),Atomics.store(l(),ue+(76>>2),de),Atomics.store(l(),ue+(104>>2),I.stackSize),Atomics.store(l(),ue+(104+8>>2),de),Atomics.store(l(),ue+(104+12>>2),I.detached);var we=wg(),nt=we+40;Atomics.store(l(),ue+(172>>2),nt),S.pthread=le;var Ut={cmd:"run",start_routine:I.startRoutine,arg:I.arg,threadInfoStruct:I.pthread_ptr,stackBase:I.stackBase,stackSize:I.stackSize};S.runPthread=function(){Ut.time=performance.now(),S.postMessage(Ut,I.transferList)},S.loaded&&(S.runPthread(),delete S.runPthread)}function R1(I,S,z,q){if(typeof SharedArrayBuffer=="undefined")return G("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!I)return G("pthread_create called with a null thread pointer!"),28;var de=[],le=0;if(x&&(de.length===0||le))return kg(687865856,I,S,z,q);if(le)return le;var ue=0,we=0,nt=0;S&&S!=-1?(ue=o()[S>>2],ue+=81920,we=o()[S+8>>2],nt=o()[S+12>>2]!==0):ue=2097152;var Ut=we==0;Ut?we=Sg(16,ue):(we-=ue,pe(we>0));for(var Mt=as(228),ba=0;ba<228>>2;++ba)l()[(Mt>>2)+ba]=0;o()[I>>2]=Mt,o()[Mt+12>>2]=Mt;var ji=Mt+152;o()[ji>>2]=ji;var _n={stackBase:we,stackSize:ue,allocatedOwnStack:Ut,detached:nt,startRoutine:z,pthread_ptr:Mt,arg:q,transferList:de};return x?(_n.cmd="spawnThread",postMessage(_n,de)):ih(_n),0}function oh(I){if(x)return xa(6,1,I);switch(I){case 30:return 16384;case 85:var S=2147483648;return S/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 Q0(28),-1}x||Ie.initMainThreadBlock();var wa,F1=[null,e1,nh,rh,ah,sh,oh],M1={e:Y0,r:J0,x:t1,b:n1,y:r1,j:a1,c:s1,d:Pi,f:rs,p:i1,z:o1,u:u1,q:d1,v:x1,i:w1,t:_1,w:T1,m:rh,n:ah,g:sh,o:H0,a:Q||u.wasmMemory,k:C1,l:E1,h:R1,s:oh},gg=U0(),lh=u.___wasm_call_ctors=function(){return(lh=u.___wasm_call_ctors=u.asm.A).apply(null,arguments)},$1=u._init=function(){return($1=u._init=u.asm.B).apply(null,arguments)},D1=u._register_tensor=function(){return(D1=u._register_tensor=u.asm.C).apply(null,arguments)},O1=u._dispose_data=function(){return(O1=u._dispose_data=u.asm.D).apply(null,arguments)},z1=u._dispose=function(){return(z1=u._dispose=u.asm.E).apply(null,arguments)},P1=u._Abs=function(){return(P1=u._Abs=u.asm.G).apply(null,arguments)},L1=u._Add=function(){return(L1=u._Add=u.asm.H).apply(null,arguments)},W1=u._AddN=function(){return(W1=u._AddN=u.asm.I).apply(null,arguments)},B1=u._ArgMax=function(){return(B1=u._ArgMax=u.asm.J).apply(null,arguments)},V1=u._AvgPool=function(){return(V1=u._AvgPool=u.asm.K).apply(null,arguments)},U1=u._BatchMatMul=function(){return(U1=u._BatchMatMul=u.asm.L).apply(null,arguments)},H1=u._Ceil=function(){return(H1=u._Ceil=u.asm.M).apply(null,arguments)},j1=u._ClipByValue=function(){return(j1=u._ClipByValue=u.asm.N).apply(null,arguments)},G1=u._Conv2D=function(){return(G1=u._Conv2D=u.asm.O).apply(null,arguments)},uh=u._Conv2DBackpropInput=function(){return(uh=u._Conv2DBackpropInput=u.asm.P).apply(null,arguments)},ch=u._Cos=function(){return(ch=u._Cos=u.asm.Q).apply(null,arguments)},Jl=u._CropAndResize=function(){return(Jl=u._CropAndResize=u.asm.R).apply(null,arguments)},Wi=u._Cumsum=function(){return(Wi=u._Cumsum=u.asm.S).apply(null,arguments)},q1=u._DepthToSpace=function(){return(q1=u._DepthToSpace=u.asm.T).apply(null,arguments)},Ql=u._DepthwiseConv2dNative=function(){return(Ql=u._DepthwiseConv2dNative=u.asm.U).apply(null,arguments)},K=u._Equal=function(){return(K=u._Equal=u.asm.V).apply(null,arguments)},ne=u._Exp=function(){return(ne=u._Exp=u.asm.W).apply(null,arguments)},Se=u._FlipLeftRight=function(){return(Se=u._FlipLeftRight=u.asm.X).apply(null,arguments)},Ye=u._Floor=function(){return(Ye=u._Floor=u.asm.Y).apply(null,arguments)},It=u._FloorDiv=function(){return(It=u._FloorDiv=u.asm.Z).apply(null,arguments)},mt=u._FusedBatchNorm=function(){return(mt=u._FusedBatchNorm=u.asm._).apply(null,arguments)},Ve=u._FusedConv2D=function(){return(Ve=u._FusedConv2D=u.asm.$).apply(null,arguments)},je=u._FusedDepthwiseConv2D=function(){return(je=u._FusedDepthwiseConv2D=u.asm.aa).apply(null,arguments)},Jt=u._Gather=function(){return(Jt=u._Gather=u.asm.ba).apply(null,arguments)},Jr=u._GatherNd=function(){return(Jr=u._GatherNd=u.asm.ca).apply(null,arguments)},Qr=u._Greater=function(){return(Qr=u._Greater=u.asm.da).apply(null,arguments)},hh=u._GreaterEqual=function(){return(hh=u._GreaterEqual=u.asm.ea).apply(null,arguments)},eu=u._LeakyRelu=function(){return(eu=u._LeakyRelu=u.asm.fa).apply(null,arguments)},jn=u._Less=function(){return(jn=u._Less=u.asm.ga).apply(null,arguments)},_a=u._LessEqual=function(){return(_a=u._LessEqual=u.asm.ha).apply(null,arguments)},dh=u._Log=function(){return(dh=u._Log=u.asm.ia).apply(null,arguments)},_4=u._LogicalAnd=function(){return(_4=u._LogicalAnd=u.asm.ja).apply(null,arguments)},b4=u._Max=function(){return(b4=u._Max=u.asm.ka).apply(null,arguments)},v4=u._MaxPool=function(){return(v4=u._MaxPool=u.asm.la).apply(null,arguments)},k4=u._Maximum=function(){return(k4=u._Maximum=u.asm.ma).apply(null,arguments)},I4=u._Mean=function(){return(I4=u._Mean=u.asm.na).apply(null,arguments)},N4=u._Min=function(){return(N4=u._Min=u.asm.oa).apply(null,arguments)},S4=u._Minimum=function(){return(S4=u._Minimum=u.asm.pa).apply(null,arguments)},T4=u._Multiply=function(){return(T4=u._Multiply=u.asm.qa).apply(null,arguments)},C4=u._Neg=function(){return(C4=u._Neg=u.asm.ra).apply(null,arguments)},E4=u._NonMaxSuppressionV3=function(){return(E4=u._NonMaxSuppressionV3=u.asm.sa).apply(null,arguments)},R4=u._NonMaxSuppressionV4=function(){return(R4=u._NonMaxSuppressionV4=u.asm.ta).apply(null,arguments)},F4=u._NonMaxSuppressionV5=function(){return(F4=u._NonMaxSuppressionV5=u.asm.ua).apply(null,arguments)},M4=u._NotEqual=function(){return(M4=u._NotEqual=u.asm.va).apply(null,arguments)},$4=u._OneHot=function(){return($4=u._OneHot=u.asm.wa).apply(null,arguments)},D4=u._PadV2=function(){return(D4=u._PadV2=u.asm.xa).apply(null,arguments)},O4=u._Pow=function(){return(O4=u._Pow=u.asm.ya).apply(null,arguments)},z4=u._Prelu=function(){return(z4=u._Prelu=u.asm.za).apply(null,arguments)},P4=u._Prod=function(){return(P4=u._Prod=u.asm.Aa).apply(null,arguments)},L4=u._RealDiv=function(){return(L4=u._RealDiv=u.asm.Ba).apply(null,arguments)},W4=u._Relu=function(){return(W4=u._Relu=u.asm.Ca).apply(null,arguments)},B4=u._Relu6=function(){return(B4=u._Relu6=u.asm.Da).apply(null,arguments)},V4=u._ResizeBilinear=function(){return(V4=u._ResizeBilinear=u.asm.Ea).apply(null,arguments)},U4=u._Reverse=function(){return(U4=u._Reverse=u.asm.Fa).apply(null,arguments)},H4=u._RotateWithOffset=function(){return(H4=u._RotateWithOffset=u.asm.Ga).apply(null,arguments)},j4=u._Round=function(){return(j4=u._Round=u.asm.Ha).apply(null,arguments)},G4=u._Rsqrt=function(){return(G4=u._Rsqrt=u.asm.Ia).apply(null,arguments)},q4=u._ScatterNd=function(){return(q4=u._ScatterNd=u.asm.Ja).apply(null,arguments)},X4=u._SelectV2=function(){return(X4=u._SelectV2=u.asm.Ka).apply(null,arguments)},K4=u._Sigmoid=function(){return(K4=u._Sigmoid=u.asm.La).apply(null,arguments)},Z4=u._Sin=function(){return(Z4=u._Sin=u.asm.Ma).apply(null,arguments)},Y4=u._Softmax=function(){return(Y4=u._Softmax=u.asm.Na).apply(null,arguments)},J4=u._Sqrt=function(){return(J4=u._Sqrt=u.asm.Oa).apply(null,arguments)},Q4=u._Square=function(){return(Q4=u._Square=u.asm.Pa).apply(null,arguments)},e8=u._SquaredDifference=function(){return(e8=u._SquaredDifference=u.asm.Qa).apply(null,arguments)},t8=u._Step=function(){return(t8=u._Step=u.asm.Ra).apply(null,arguments)},n8=u._StridedSlice=function(){return(n8=u._StridedSlice=u.asm.Sa).apply(null,arguments)},r8=u._Sub=function(){return(r8=u._Sub=u.asm.Ta).apply(null,arguments)},a8=u._Sum=function(){return(a8=u._Sum=u.asm.Ua).apply(null,arguments)},s8=u._Tanh=function(){return(s8=u._Tanh=u.asm.Va).apply(null,arguments)},i8=u._Tile=function(){return(i8=u._Tile=u.asm.Wa).apply(null,arguments)},o8=u._TopK=function(){return(o8=u._TopK=u.asm.Xa).apply(null,arguments)},l8=u._Transpose=function(){return(l8=u._Transpose=u.asm.Ya).apply(null,arguments)},u8=u.__FusedMatMul=function(){return(u8=u.__FusedMatMul=u.asm.Za).apply(null,arguments)},as=u._malloc=function(){return(as=u._malloc=u.asm._a).apply(null,arguments)},tu=u._free=function(){return(tu=u._free=u.asm.$a).apply(null,arguments)},xg=u.___errno_location=function(){return(xg=u.___errno_location=u.asm.ab).apply(null,arguments)},wg=u._emscripten_get_global_libc=function(){return(wg=u._emscripten_get_global_libc=u.asm.bb).apply(null,arguments)},Bi=u._pthread_self=function(){return(Bi=u._pthread_self=u.asm.cb).apply(null,arguments)},_g=u.___pthread_tsd_run_dtors=function(){return(_g=u.___pthread_tsd_run_dtors=u.asm.db).apply(null,arguments)},X1=u._emscripten_main_thread_process_queued_calls=function(){return(X1=u._emscripten_main_thread_process_queued_calls=u.asm.eb).apply(null,arguments)},c8=u._emscripten_current_thread_process_queued_calls=function(){return(c8=u._emscripten_current_thread_process_queued_calls=u.asm.fb).apply(null,arguments)},bg=u._emscripten_register_main_browser_thread_id=function(){return(bg=u._emscripten_register_main_browser_thread_id=u.asm.gb).apply(null,arguments)},vg=u.__emscripten_do_dispatch_to_thread=function(){return(vg=u.__emscripten_do_dispatch_to_thread=u.asm.hb).apply(null,arguments)},kg=u._emscripten_sync_run_in_main_thread_4=function(){return(kg=u._emscripten_sync_run_in_main_thread_4=u.asm.ib).apply(null,arguments)},Ig=u._emscripten_run_in_main_runtime_thread_js=function(){return(Ig=u._emscripten_run_in_main_runtime_thread_js=u.asm.jb).apply(null,arguments)},K1=u.__emscripten_call_on_thread=function(){return(K1=u.__emscripten_call_on_thread=u.asm.kb).apply(null,arguments)},h8=u._emscripten_tls_init=function(){return(h8=u._emscripten_tls_init=u.asm.lb).apply(null,arguments)},ph=u.__emscripten_thread_init=function(){return(ph=u.__emscripten_thread_init=u.asm.mb).apply(null,arguments)},nu=u.stackSave=function(){return(nu=u.stackSave=u.asm.nb).apply(null,arguments)},Vi=u.stackRestore=function(){return(Vi=u.stackRestore=u.asm.ob).apply(null,arguments)},Ui=u.stackAlloc=function(){return(Ui=u.stackAlloc=u.asm.pb).apply(null,arguments)},Ng=u._emscripten_stack_set_limits=function(){return(Ng=u._emscripten_stack_set_limits=u.asm.qb).apply(null,arguments)},Sg=u._memalign=function(){return(Sg=u._memalign=u.asm.rb).apply(null,arguments)},Tg=u.__emscripten_allow_main_runtime_queued_calls=9880,Hi=u.__emscripten_main_thread_futex=11368;u.cwrap=Me,u.PThread=Ie,u.PThread=Ie,u.wasmMemory=Q,u.ExitStatus=ru;var fh;function ru(I){this.name="ExitStatus",this.message="Program terminated with exit("+I+")",this.status=I}ns=function I(){fh||Z1(),fh||(ns=I)};function Z1(I){if(I=I||m,Zr>0)return;if(x){h(u),postMessage({cmd:"loaded"});return}if(O0(),Zr>0)return;function S(){fh||(fh=!0,u.calledRun=!0,!oe&&(qc(),z0(),h(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),xn()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),S()},1)):S()}u.run=Z1;function d8(I,S){if(!(S&&ie&&I===0)){if(!S&&x)throw postMessage({cmd:"exitProcess",returnCode:I}),new ru(I);ie||(Ie.terminateAllThreads(),fe=I,Xc(),u.onExit&&u.onExit(I),oe=!0),y(I,new ru(I))}}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return x&&(ie=!1,Ie.initWorker()),Z1(),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)}),P8=un((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={},c;for(c in s)s.hasOwnProperty(c)&&(l[c]=s[c]);var u=[],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 w,_,v,x,N,E;m?(f?y=au().dirname(y)+"/":y=__dirname+"/",w=function(K,ne){return N||(N=require("fs")),E||(E=au()),K=E.normalize(K),N.readFileSync(K,ne?null:"utf8")},v=function(K){var ne=w(K,!0);return ne.buffer||(ne=new Uint8Array(ne)),X(ne.buffer),ne},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),u=process.argv.slice(2),process.on("uncaughtException",function(K){if(!(K instanceof q1))throw K}),process.on("unhandledRejection",pr),d=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):A?(typeof read!="undefined"&&(w=function(K){return read(K)}),v=function(K){var ne;return typeof readbuffer=="function"?new Uint8Array(readbuffer(K)):(ne=read(K,"binary"),X(typeof ne=="object"),ne)},typeof scriptArgs!="undefined"?u=scriptArgs:typeof arguments!="undefined"&&(u=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="",w=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.send(null),ne.responseText},f&&(v=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),_=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 F=s.print||console.log.bind(console),M=s.printErr||console.warn.bind(console);for(c in l)l.hasOwnProperty(c)&&(s[c]=l[c]);l=null,s.arguments&&(u=s.arguments),s.thisProgram&&(h=s.thisProgram),s.quit&&(d=s.quit);var W;s.wasmBinary&&(W=s.wasmBinary);var V=s.noExitRuntime||!0;typeof WebAssembly!="object"&&pr("no native wasm support detected");var B,H=!1,j;function X(K,ne){K||pr("Assertion failed: "+ne)}function G(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 mt={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=G(K),Jt=[],Jr=0;if(Ye)for(var Qr=0;Qr<Ye.length;Qr++){var hh=mt[Se[Qr]];hh?(Jr===0&&(Jr=uh()),Jt[Qr]=hh(Ye[Qr])):Jt[Qr]=Ye[Qr]}var eu=je.apply(null,Jt);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"}),mt=ne!=="string";return mt&&It&&!Ye?G(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 mt="";ne<It;){var Ve=K[ne++];if(!(Ve&128)){mt+=String.fromCharCode(Ve);continue}var je=K[ne++]&63;if((Ve&224)==192){mt+=String.fromCharCode((Ve&31)<<6|je);continue}var Jt=K[ne++]&63;if((Ve&240)==224?Ve=(Ve&15)<<12|je<<6|Jt:Ve=(Ve&7)<<18|je<<12|Jt<<6|K[ne++]&63,Ve<65536)mt+=String.fromCharCode(Ve);else{var Jr=Ve-65536;mt+=String.fromCharCode(55296|Jr>>10,56320|Jr&1023)}}return mt}function ie(K,ne){return K?te(Ne,K,ne):""}function Q(K,ne,Se,Ye){if(!(Ye>0))return 0;for(var It=Se,mt=Se+Ye-1,Ve=0;Ve<K.length;++Ve){var je=K.charCodeAt(Ve);if(je>=55296&&je<=57343){var Jt=K.charCodeAt(++Ve);je=65536+((je&1023)<<10)|Jt&1023}if(je<=127){if(Se>=mt)break;ne[Se++]=je}else if(je<=2047){if(Se+1>=mt)break;ne[Se++]=192|je>>6,ne[Se++]=128|je&63}else if(je<=65535){if(Se+2>=mt)break;ne[Se++]=224|je>>12,ne[Se++]=128|je>>6&63,ne[Se++]=128|je&63}else{if(Se+3>=mt)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,Ne,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,Ne,Me,Oe,$e,et,tt,it;function Ze(K){pe=K,s.HEAP8=ke=new Int8Array(K),s.HEAP16=Me=new Int16Array(K),s.HEAP32=$e=new Int32Array(K),s.HEAPU8=Ne=new Uint8Array(K),s.HEAPU16=Oe=new Uint16Array(K),s.HEAPU32=et=new Uint32Array(K),s.HEAPF32=tt=new Float32Array(K),s.HEAPF64=it=new Float64Array(K)}var dt=s.INITIAL_MEMORY||16777216,Be,yn=[],wt=[],Un=[],Zt=[],gn=!1;wt.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(wt)}function ln(){ga(Un)}function Yt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)Jn(s.postRun.shift());ga(Zt)}function Tr(K){yn.unshift(K)}function Jn(K){Zt.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 pr(K){s.onAbort&&s.onAbort(K),K+="",M(K),H=!0,j=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 O0="data:application/octet-stream;base64,";function qc(K){return Gc(K,O0)}var z0="file://";function Xc(K){return Gc(K,z0)}var xn="tfjs-backend-wasm.wasm";qc(xn)||(xn=g(xn));function Kc(K){try{if(K==xn&&W)return new Uint8Array(W);if(v)return v(K);throw"both async and sync fetching of the wasm failed"}catch(ne){pr(ne)}}function P0(){if(!W&&(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(_)return new Promise(function(K,ne){_(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 Jt=Ve.exports;s.asm=Jt,B=s.asm.g,Ze(B.buffer),Be=s.asm.m,Di("wasm-instantiate")}ya("wasm-instantiate");function Se(Ve){ne(Ve.instance)}function Ye(Ve){return P0().then(function(je){return WebAssembly.instantiate(je,K)}).then(Ve,function(je){M("failed to asynchronously prepare wasm: "+je),pr(je)})}function It(){return!W&&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(Jt){return M("wasm streaming compile failed: "+Jt),M("falling back to ArrayBuffer instantiation"),Ye(Se)})}):Ye(Se)}if(s.instantiateWasm)try{var mt=s.instantiateWasm(K,ne);return mt}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(){pr()}function L0(K,ne,Se){Ne.copyWithin(K,ne,ne+Se)}function W0(){return Ne.length}function Yr(K){try{return B.grow(K-pe.byteLength+65535>>>16),Ze(B.buffer),1}catch(ne){}}function Zc(K){var ne=W0(),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 mt=Math.min(Se,fe(Math.max(K,It),65536)),Ve=Yr(mt);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?F:M)(te(Se,0)),Se.length=0):Se.push(ne)},varargs:void 0,get:function(){Oi.varargs+=4;var K=$e[Oi.varargs-4>>2];return K},getStr:function(K){var ne=ie(K);return ne},get64:function(K,ne){return K}};function Yc(K){return 0}function B0(K,ne,Se,Ye,It){}function Jc(K,ne,Se,Ye){for(var It=0,mt=0;mt<Se;mt++){for(var Ve=$e[ne+mt*8>>2],je=$e[ne+(mt*8+4)>>2],Jt=0;Jt<je;Jt++)Oi.printChar(K,Ne[Ve+Jt]);It+=je}return $e[Ye>>2]=It,0}var wn={a:ns,d:L0,e:Zc,f:Yc,c:B0,b:Jc},V0=Zr(),Qc=s.___wasm_call_ctors=function(){return(Qc=s.___wasm_call_ctors=s.asm.h).apply(null,arguments)},U0=s._init=function(){return(U0=s._init=s.asm.i).apply(null,arguments)},eh=s._register_tensor=function(){return(eh=s._register_tensor=s.asm.j).apply(null,arguments)},H0=s._dispose_data=function(){return(H0=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)},j0=s._Add=function(){return(j0=s._Add=s.asm.o).apply(null,arguments)},G0=s._AddN=function(){return(G0=s._AddN=s.asm.p).apply(null,arguments)},q0=s._ArgMax=function(){return(q0=s._ArgMax=s.asm.q).apply(null,arguments)},Ie=s._AvgPool=function(){return(Ie=s._AvgPool=s.asm.r).apply(null,arguments)},X0=s._BatchMatMul=function(){return(X0=s._BatchMatMul=s.asm.s).apply(null,arguments)},K0=s._Ceil=function(){return(K0=s._Ceil=s.asm.t).apply(null,arguments)},Z0=s._ClipByValue=function(){return(Z0=s._ClipByValue=s.asm.u).apply(null,arguments)},Y0=s._Conv2D=function(){return(Y0=s._Conv2D=s.asm.v).apply(null,arguments)},J0=s._Conv2DBackpropInput=function(){return(J0=s._Conv2DBackpropInput=s.asm.w).apply(null,arguments)},rs=s._Cos=function(){return(rs=s._Cos=s.asm.x).apply(null,arguments)},Q0=s._CropAndResize=function(){return(Q0=s._CropAndResize=s.asm.y).apply(null,arguments)},e1=s._Cumsum=function(){return(e1=s._Cumsum=s.asm.z).apply(null,arguments)},t1=s._DepthToSpace=function(){return(t1=s._DepthToSpace=s.asm.A).apply(null,arguments)},n1=s._DepthwiseConv2dNative=function(){return(n1=s._DepthwiseConv2dNative=s.asm.B).apply(null,arguments)},r1=s._Equal=function(){return(r1=s._Equal=s.asm.C).apply(null,arguments)},a1=s._Exp=function(){return(a1=s._Exp=s.asm.D).apply(null,arguments)},s1=s._FlipLeftRight=function(){return(s1=s._FlipLeftRight=s.asm.E).apply(null,arguments)},i1=s._Floor=function(){return(i1=s._Floor=s.asm.F).apply(null,arguments)},o1=s._FloorDiv=function(){return(o1=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)},l1=s._Gather=function(){return(l1=s._Gather=s.asm.K).apply(null,arguments)},u1=s._GatherNd=function(){return(u1=s._GatherNd=s.asm.L).apply(null,arguments)},c1=s._Greater=function(){return(c1=s._Greater=s.asm.M).apply(null,arguments)},h1=s._GreaterEqual=function(){return(h1=s._GreaterEqual=s.asm.N).apply(null,arguments)},d1=s._LeakyRelu=function(){return(d1=s._LeakyRelu=s.asm.O).apply(null,arguments)},Le=s._Less=function(){return(Le=s._Less=s.asm.P).apply(null,arguments)},p1=s._LessEqual=function(){return(p1=s._LessEqual=s.asm.Q).apply(null,arguments)},f1=s._Log=function(){return(f1=s._Log=s.asm.R).apply(null,arguments)},m1=s._LogicalAnd=function(){return(m1=s._LogicalAnd=s.asm.S).apply(null,arguments)},A1=s._Max=function(){return(A1=s._Max=s.asm.T).apply(null,arguments)},y1=s._MaxPool=function(){return(y1=s._MaxPool=s.asm.U).apply(null,arguments)},g1=s._Maximum=function(){return(g1=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)},x1=s._Multiply=function(){return(x1=s._Multiply=s.asm.Z).apply(null,arguments)},w1=s._Neg=function(){return(w1=s._Neg=s.asm._).apply(null,arguments)},_1=s._NonMaxSuppressionV3=function(){return(_1=s._NonMaxSuppressionV3=s.asm.$).apply(null,arguments)},b1=s._NonMaxSuppressionV4=function(){return(b1=s._NonMaxSuppressionV4=s.asm.aa).apply(null,arguments)},v1=s._NonMaxSuppressionV5=function(){return(v1=s._NonMaxSuppressionV5=s.asm.ba).apply(null,arguments)},k1=s._NotEqual=function(){return(k1=s._NotEqual=s.asm.ca).apply(null,arguments)},I1=s._OneHot=function(){return(I1=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)},S1=s._Prelu=function(){return(S1=s._Prelu=s.asm.ga).apply(null,arguments)},T1=s._Prod=function(){return(T1=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)},C1=s._Reverse=function(){return(C1=s._Reverse=s.asm.ma).apply(null,arguments)},E1=s._RotateWithOffset=function(){return(E1=s._RotateWithOffset=s.asm.na).apply(null,arguments)},ih=s._Round=function(){return(ih=s._Round=s.asm.oa).apply(null,arguments)},R1=s._Rsqrt=function(){return(R1=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)},F1=s._Sigmoid=function(){return(F1=s._Sigmoid=s.asm.sa).apply(null,arguments)},M1=s._Sin=function(){return(M1=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)},$1=s._Square=function(){return($1=s._Square=s.asm.wa).apply(null,arguments)},D1=s._SquaredDifference=function(){return(D1=s._SquaredDifference=s.asm.xa).apply(null,arguments)},O1=s._Step=function(){return(O1=s._Step=s.asm.ya).apply(null,arguments)},z1=s._StridedSlice=function(){return(z1=s._StridedSlice=s.asm.za).apply(null,arguments)},P1=s._Sub=function(){return(P1=s._Sub=s.asm.Aa).apply(null,arguments)},L1=s._Sum=function(){return(L1=s._Sum=s.asm.Ba).apply(null,arguments)},W1=s._Tanh=function(){return(W1=s._Tanh=s.asm.Ca).apply(null,arguments)},B1=s._Tile=function(){return(B1=s._Tile=s.asm.Da).apply(null,arguments)},V1=s._TopK=function(){return(V1=s._TopK=s.asm.Ea).apply(null,arguments)},U1=s._Transpose=function(){return(U1=s._Transpose=s.asm.Fa).apply(null,arguments)},H1=s.__FusedMatMul=function(){return(H1=s.__FusedMatMul=s.asm.Ga).apply(null,arguments)},j1=s._malloc=function(){return(j1=s._malloc=s.asm.Ha).apply(null,arguments)},G1=s._free=function(){return(G1=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 q1(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||u,Qn>0||(Hn(),Qn>0))return;function ne(){Wi||(Wi=!0,s.calledRun=!0,!H&&(Rn(),ln(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Yt()))}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)}),L8=un(()=>{}),W8="3.3.0",B8="3.3.0",V8="3.3.0",U8="3.3.0",H8="3.3.0",j8=1e-7,G8=1e-4,wh=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 tr("refCount")}incRef(e){return tr("incRef")}timerAvailable(){return!0}time(e){return tr("time")}read(e){return tr("read")}readSync(e){return tr("readSync")}numDataIds(){return tr("numDataIds")}disposeData(e,t){return tr("disposeData")}write(e,t,n){return tr("write")}move(e,t,n,r,a){return tr("move")}memory(){return tr("memory")}floatPrecision(){return tr("floatPrecision")}epsilon(){return this.floatPrecision()===32?j8:G8}dispose(){return tr("dispose")}};function tr(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 Mg(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 q8(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 X8(e){return e%2==0?e:e+1}function K8(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function Z8(e,t){let n=Math.random();return t*n+(1-n)*e}function Y8(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 R(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function rn(e,t,n=""){R(ta(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ss(e){R(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)||an(e)&&!n)for(let r=0;r<e.length;++r)is(e[r],t,n);else t.push(e);return t}function $t(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 J8(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 Ht(e){return e%1==0}function Q8(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 ek(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function tk(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Mg(t),t}function ou(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function nk(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 rk(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} / ${n}`);let a=e.slice();return a[r]=t/n,a}function nr(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),R(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),R(e.every(r=>Ht(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function $g(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:nr(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 Dg(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 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 if(e==="string")n=new Array(t);else throw new Error(`Unknown data type ${e}`);return n}function zg(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 Pg(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function ak(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function an(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 ${e}`)}function Lg(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function va(e){return typeof e=="string"||e instanceof String}function Wg(e){return typeof e=="boolean"}function Bg(e){return typeof e=="number"}function _h(e){return Array.isArray(e)?_h(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Bg(e)?"float32":va(e)?"string":Wg(e)?"bool":"float32"}function ka(e){return!!(e&&e.constructor&&e.call&&e.apply)}function bh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Xi(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function Vg(e,t,n){let r=new Array;if(t.length===1){let a=t[0];for(let s=0;s<a;s++)r[s]=n[e+s]}else{let a=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<a;o++)r[o]=Vg(e+o*i,s,n)}return r}function Ki(e,t){if(e.length===0)return t[0];let n=e.reduce((r,a)=>r*a);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return Vg(0,e,t)}function Q1(e,t){let n=vh(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function vh(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 sk(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 ${t}`)}function ef(e){e.forEach(t=>{R(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function ik(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 ok(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 Ug="tfjsflags",Hg=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=lk(this.global.location.search);Ug in e&&e[Ug].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=uk(n,r)})}};function lk(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(ck(t,r[0],r[1]),r.join("="))),t}function ck(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function uk(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 ${e}.`)}function J(){return fr}var fr=null;function hk(e){fr=e}var nf;function jg(){if(nf==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");nf=e}return nf}function dk(){let e=jg();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function rf(e,t){let n=dk();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var Zi="Abs",Yi="Acos",Ji="Acosh",Ia="Add",os="AddN",kh="All",Ih="Any",ls="ArgMax",lu="ArgMin",Qi="Asin",eo="Asinh",to="Atan",no="Atanh",ro="Atan2",us="AvgPool",Nh="AvgPoolGrad",uu="AvgPool3D",Sh="AvgPool3DGrad",cs="BatchMatMul",cu="BatchToSpaceND",Th="Bincount",Gg="BroadcastTo",hs="Cast",ds="Ceil",Na="ClipByValue",Ch="Complex",hu="ComplexAbs",ao="Concat",ps="Conv2D",Eh="Conv2DBackpropFilter",fs="Conv2DBackpropInput",du="Conv3D",Rh="Conv3DBackpropFilterV2",Fh="Conv3DBackpropInputV2",ms="Cos",so="Cosh",As="Cumsum",io="CropAndResize",Mh="DenseBincount",oo="DepthToSpace",ys="DepthwiseConv2dNative",$h="DepthwiseConv2dNativeBackpropFilter",Dh="DepthwiseConv2dNativeBackpropInput",Oh="Diag",pu="Dilation2D",zh="Dilation2DBackpropInput",Ph="Dilation2DBackpropFilter",gs="RealDiv",lo="Elu",Lh="EluGrad",uo="Erf",co="Equal",xs="Exp",ho="ExpandDims",po="Expm1",Wh="FFT",fu="Fill",fo="FlipLeftRight",ws="Floor",_s="FloorDiv",bs="FusedBatchNorm",mo="GatherV2",Ao="GatherNd",yo="Greater",vs="GreaterEqual",ks="Identity",Bh="IFFT",Vh="Imag",go="IsFinite",xo="IsInf",wo="IsNan",Is="LeakyRelu",_o="Less",bo="LessEqual",Uh="LinSpace",Ns="Log",vo="Log1p",ko="LogicalAnd",mu="LogicalNot",Au="LogicalOr",qg="LogSoftmax",yu="LRN",Hh="LRNGrad",Ss="Max",Ts="Maximum",Cs="MaxPool",jh="MaxPoolGrad",gu="MaxPool3D",Gh="MaxPool3DGrad",qh="MaxPoolWithArgmax",Es="Mean",Rs="Min",Fs="Minimum",xu="MirrorPad",Io="Mod",Xh="Multinomial",Ms="Multiply",No="Neg",So="NotEqual",To="NonMaxSuppressionV3",Co="NonMaxSuppressionV4",Eo="NonMaxSuppressionV5",Ro="OnesLike",$s="OneHot",Fo="Pack",Ds="PadV2",pk="Pool",Os="Pow",zs="Prelu",Mo="Prod",wu="Range",Kh="Real",$o="Reciprocal",Ps="Relu",Do="Reshape",_u="ResizeNearestNeighbor",Zh="ResizeNearestNeighborGrad",Ls="ResizeBilinear",Yh="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",Jh="SparseToDense",Ho="StridedSlice",jo="Tan",Ys="Tanh",Sa="Tile",Go="TopK",Qh="Transform",Js="Transpose",ed="Unique",qo="Unpack",ku="UnsortedSegmentSum",Xo="ZerosLike",Ta="Step",td="FromPixels",Ko="RotateWithOffset",Qs="_FusedMatMul",ei="FusedConv2D",ti="FusedDepthwiseConv2D",Zo=rf("kernelRegistry",()=>new Map),Iu=rf("gradRegistry",()=>new Map);function nd(e,t){let n=af(e,t);return Zo.get(n)}function sf(e){return Iu.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 Xg(e){let{kernelName:t}=e;Iu.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Iu.set(t,e)}function fk(e,t){let n=af(e,t);if(!Zo.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);Zo.delete(n)}function mk(e){if(!Iu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Iu.delete(e)}function Ak(e,t){Yo(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});ni(r)})}function af(e,t){return`${t}_${e}`}var b={};ze(b,{arraysEqual:()=>ta,assert:()=>R,assertNonNegativeIntegerDimensions:()=>ef,assertNonNull:()=>ss,assertShapesMatch:()=>rn,bytesFromStringArray:()=>Lg,bytesPerElement:()=>J1,checkConversionForErrors:()=>zg,clamp:()=>iu,computeStrides:()=>Xi,createScalarValue:()=>yk,createShuffledIndices:()=>tk,decodeString:()=>ad,distSquared:()=>Y8,encodeString:()=>Su,fetch:()=>gk,flatten:()=>is,getArrayFromDType:()=>Og,getTypedArrayFromDType:()=>Dg,hasEncodingLoss:()=>ak,indexToLoc:()=>ok,inferDtype:()=>_h,inferFromImplicitShape:()=>rk,isBoolean:()=>Wg,isFunction:()=>ka,isInt:()=>Ht,isNumber:()=>Bg,isPromise:()=>tf,isScalarShape:()=>J8,isString:()=>va,isTypedArray:()=>an,isValidDtype:()=>Pg,locToIndex:()=>ik,makeOnesTypedArray:()=>Q1,makeZerosNestedTypedArray:()=>sk,makeZerosTypedArray:()=>vh,nearestDivisor:()=>bh,nearestLargerEven:()=>X8,now:()=>Nu,parseAxisParam:()=>nr,randUniform:()=>Z8,repeatedTry:()=>nk,rightPad:()=>ou,shuffle:()=>Mg,shuffleCombo:()=>q8,sizeFromShape:()=>$t,sizeToSquarishShape:()=>ek,squeezeShape:()=>$g,sum:()=>K8,tanh:()=>Q8,toNestedArray:()=>Ki,toTypedArray:()=>rd});function yk(e,t){return t==="string"?Su(e):rd([e],t)}function xk(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function rd(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")&&zg(e,t),xk(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 Nu(){return J().platform.now()}function gk(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 ad(e,t="utf-8"){return t=t||"utf-8",J().platform.decode(e,t)}var bk=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new _k)}profileKernel(e,t,n){let r,a=()=>{r=n()},s,i=Nu();if(this.backendTimer.timerAvailable())s=this.backendTimer.time(a);else{a();for(let o of r)o.dataSync();s=Promise.resolve({kernelMs:Nu()-i})}if(J().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let o=0;o<r.length;o++){let l=r[o];l.data().then(c=>{wk(c,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 wk(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 _k=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?ou(`${r}ms`,9):r.error,o=ou(e,25),l=t.rank,c=t.size,u=ou(t.shape.toString(),14),h="";for(let d in a){let p=a[d];if(p!=null){let f=p.shape||t.shape,m=f.length;h+=`${d}: ${m}D ${m>0?f:""} `}}console.log(`%c${o} %c${i} %c${l}D ${u} %c${c} %c${h} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function vk(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 c=e[l],u=c.inputs;for(let h in u){let d=u[h],p=!1;for(let f=0;f<t.length;f++)if(r[d.id]){c.outputs.forEach(m=>r[m.id]=!0),p=!0,a[c.id]=!0;break}if(p)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let c=e[l],u=c.inputs;for(let h=0;h<c.outputs.length;h++)if(s[c.outputs[h].id]){for(let d in u)s[u[d].id]=!0,i[c.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let c=e[l];if(a[c.id]&&i[c.id]){let u={};for(let d in c.inputs){let p=c.inputs[d];r[p.id]&&(u[d]=p)}let h=Object.assign({},c);h.inputs=u,h.outputs=c.outputs,o.push(h)}}return o}function kk(e,t,n,r){for(let a=t.length-1;a>=0;a--){let s=t[a],i=[];if(s.outputs.forEach(l=>{let c=e[l.id];c!=null?i.push(c):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 c=n(()=>o[l]());if(c.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${c.dtype}'`);let u=s.inputs[l];if(!ta(c.shape,u.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${c.shape}', which does not match the shape of the input '${u.shape}'`);if(e[u.id]==null)e[u.id]=c;else{let h=e[u.id];e[u.id]=r(h,c),h.dispose()}}}}var Kg=20,Tu=3,of=7;function Nk(e,t,n,r){let a=Xi(t),s=Ik(e,t,n,a),i=t.length,o=sd(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(c=>" "+c).join(`
|
|
`)),l.join(`
|
|
`)}function Ik(e,t,n,r){let a=$t(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Eu(e):e;if(o>1)for(let c=0;c<a/s;c++){let u=c*s;for(let h=0;h<s;h++)i[h]=Math.max(i[h],Cu(l[u+h],0,n).length)}return i}function Cu(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(of))} + ${parseFloat(e[1].toFixed(of))}j`:va(e)?r=`'${e}'`:n==="bool"?r=Zg(e):r=parseFloat(e.toFixed(of)).toString(),ou(r,t)}function Zg(e){return e===0?"false":"true"}function sd(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"?[Zg(e[0])]:[e[0].toString()]}if(l===1){if(o>Kg){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((w,_)=>Cu(w,a[_],n)).join(", ")+", ..., "+g.map((w,_)=>Cu(w,a[o-Tu+_],n)).join(", ")+"]"]}let m=n==="complex64"?Eu(e):Array.from(e);return["["+m.map((A,y)=>Cu(A,a[y],n)).join(", ")+"]"]}let c=t.slice(1),u=r.slice(1),h=r[0]*i,d=[];if(o>Kg){for(let m=0;m<Tu;m++){let A=m*h,y=A+h;d.push(...sd(e.slice(A,y),c,n,u,a,!1))}d.push("...");for(let m=o-Tu;m<o;m++){let A=m*h,y=A+h;d.push(...sd(e.slice(A,y),c,n,u,a,m===o-1))}}else for(let m=0;m<o;m++){let A=m*h,y=A+h;d.push(...sd(e.slice(A,y),c,n,u,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 Dt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=$t(e),n!=null){let r=n.length;R(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Og(t,this.size),this.strides=Xi(e)}set(e,...t){t.length===0&&(t=[0]),R(t.length===this.rank,()=>`The number of provided coordinates (${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,Sk=null;function Tk(e){Cr=e}function Ck(e){Jo=e}function Ek(e){Sk=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=$t(e),this.strides=Xi(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Jo.buffer(this.shape,this.dtype,e)}bufferSync(){return Jo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Ki(this.shape,e)}arraySync(){return Ki(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Cr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>ad(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Cr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>ad(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Cr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Cr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Jo.print(this,e)}clone(){return this.throwIfDisposed(),Jo.clone(this)}toString(e=!1){let t=this.dataSync();return Nk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Jo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Cr().makeVariable(this,e,t,n)}};Object.defineProperty(Ue,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return rf("Tensor",()=>Ue)}Z();var Ru=class extends Ue{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${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:()=>Yg,getTensorsInContainer:()=>lf,isTensorInList:()=>Rk,makeTypesMatch:()=>_t});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 Fk={float32:df,int32:cf,bool:hf,complex64:pf};function rr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Fk[e][t]}function id(e){return rr(e,"int32")}function _t(e,t){if(e.dtype===t.dtype)return[e,t];let n=rr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Yg(e,t){R(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Rk(e,t){return t.some(n=>n.id===e.id)}function lf(e){let t=[],n=new Set;return Jg(e,t,n),t}function Jg(e,t,n){if(e==null)return;if(e instanceof Ue){t.push(e);return}if(!Mk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),Jg(s,t,n))}}function Mk(e){return Array.isArray(e)||typeof e=="object"}function ff(e){return e.kernelName!=null}var Qg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Fu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Qg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 bk(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 $.runKernel(hs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(nd(e,this.backendName)==null)throw new Error(`Kernel '${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=nd(p,this.backendName);R(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 w=g.map(_=>{if(_.rank!=null)return _;let{dataId:v,shape:x,dtype:N}=_;return this.makeTensorFromDataId(v,x,N)});if(r){let _=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(_)}return w}}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:c,attrs:u}=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,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),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(c).map(p=>c[p]!=null?c[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?(R(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,c)=>s[c]);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=Lg(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((c,u)=>{if(c==null){let h=n[u],d=vh(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return c}),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(R(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));R(a instanceof Ue,()=>"The result y returned by f() must be a tensor.");let s=vk(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?$k(a.shape):n,kk(i,s,l=>this.tidy(l),Dk);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return R(ka(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{R(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),R(n.value instanceof Ue,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),R(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),c=Array.isArray(l)?l:[l];R(c.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(...)."),R(c.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 u={};return c.forEach((h,d)=>{u[d]=()=>h}),u};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=Nu(),n=await this.backend.time(e);return n.wallMs=Nu()-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 Qg;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 $k(e){let t=Q1($t(e),"float32");return $.makeTensor(t,e,"float32")}function e5(){let e=jg();if(e._tfengine==null){let t=new Hg(e);e._tfengine=new Fu(t)}return hk(e._tfengine.ENV),Tk(()=>e._tfengine),e._tfengine}var $=e5();function Dk(e,t){let n={a:e,b:t};return $.runKernel(Ia,n)}var Mu={};ze(Mu,{isBrowser:()=>t5,isMobile:()=>Ok});function zk(){return typeof navigator!="undefined"&&navigator!=null}function Ok(){if(zk()){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 t5(){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",()=>t5());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(an(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||an(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&n5(e,r,[]),r}function n5(e,t,n){if(n=n||[],!Array.isArray(e)&&!an(e)){R(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}R(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),R(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)n5(e[a],r,n.concat(a))}function r5(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function T(e,t,n,r="numeric"){if(e instanceof Ue)return r5(r,e.dtype,t,n),e;let a=_h(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),r5(r,a,t,n),e==null||!an(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Er(e,a);!an(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?rd(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 ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>T(a,`${t}[${s}]`,n,r))}var a5="__op";function D(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 ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+a5;let a=(...s)=>{$.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 Pk(e,t){let n=T(e,"real","complex"),r=T(t,"imag","complex");rn(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 $.runKernel(Ch,a)}var Ca=D({complex_:Pk});function Ea(e,t,n,r){if(r==null&&(r=_h(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!an(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){ef(t);let a=$t(t),s=$t(n);R(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==$t(t.slice(i)):!0;R(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!an(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?rd(e,r):is(e,[],!0),$.makeTensor(e,t,r)}function yr(e,t,n){let r=Er(e,n);return Ea(e,t,r,n)}var mf={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},od=4;async function Wk(e,t){let n=[],r=[],a=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<a.length;++i){let o=a[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let c={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async h=>{let d=await l.bytes(),p=d.reduce((A,y)=>A+y.length,0)+od*d.length,f=new Uint8Array(p),m=0;for(let A=0;A<d.length;A++){let y=d[A],g=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(g,m),m+=od,f.set(y,m),m+=y.length}h(f)});r.push(u)}else r.push(l.data());t!=null&&(c.group=t),n.push(c)}let s=await Promise.all(r);return{data:Lk(s),specs:n}}function s5(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,c=$t(l),u;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+c*d),f=h.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){u=new Float32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];u[m]=A*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=Bk()),u=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 ${h.dtype} for weight type int32.`);u=new Int32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];u[m]=Math.round(A*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=c*d}else if(o==="string"){let h=$t(s.shape);u=[];for(let d=0;d<h;d++){let p=new Uint32Array(e.slice(a,a+od))[0];a+=od;let f=new Uint8Array(e.slice(a,a+p));u.push(f),a+=p}}else{let h=mf[o],d=e.slice(a,a+c*h);if(o==="float32")u=new Float32Array(d);else if(o==="int32")u=new Int32Array(d);else if(o==="bool")u=new Uint8Array(d);else if(o==="complex64"){u=new Float32Array(d);let p=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let y=0;y<p.length;y++)p[y]=u[y*2],f[y]=u[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+=c*h}o!=="complex64"&&(n[i]=yr(u,l,o))}return n}function Lk(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 i5(e){return Af?Buffer.byteLength(e):new Blob([e]).size}function Vk(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 Uk(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 o5(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:i5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:i5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function Hk(){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 jk(){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 Gk(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function Bk(){let e=Hk(),t=jk(),n=Gk();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 Nt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Nt.instance==null&&(Nt.instance=new Nt),Nt.instance}static registerSaveRouter(e){Nt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Nt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Nt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Nt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?Nt.getInstance().loadRouters:Nt.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},qk=e=>Nt.registerSaveRouter(e),Xk=e=>Nt.registerLoadRouter(e),Kk=e=>Nt.getSaveHandlers(e),Zk=(e,t)=>Nt.getLoadHandlers(e,t),gf="tensorflowjs",xf=1,ri="models_store",Ra="model_info_store";function l5(){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=l5(),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),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),u;c.onsuccess=()=>{u=s.transaction(ri,"readwrite");let h=u.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))}},c.onerror=h=>(s.close(),r(c.error)),o.oncomplete=()=>{u==null?s.close():u.oncomplete=()=>s.close()}}},a.onerror=s=>r(a.error)})}};ai.URL_SCHEME="indexeddb://";var u5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ai.URL_SCHEME)?Yk(e.slice(ai.URL_SCHEME.length)):null;Nt.registerSaveRouter(u5);Nt.registerLoadRouter(u5);function Yk(e){return new ai(e)}function Jk(e){return e.startsWith(ai.URL_SCHEME)?e.slice(ai.URL_SCHEME.length):e}var Qk=class{constructor(){this.indexedDB=l5()}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=Jk(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 c=i.delete(e),u=()=>{l=a.transaction(ri,"readwrite");let h=l.objectStore(ri).delete(e);h.onsuccess=()=>t(o.result.modelArtifactsInfo),h.onerror=d=>n(o.error)};c.onsuccess=u,c.onerror=h=>(u(),a.close(),n(o.error))}},o.onerror=c=>(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",c5="info",e9="model_topology",t9="weight_specs",n9="weight_data",r9="model_metadata";function h5(e){return{info:[Qo,e,c5].join(na),topology:[Qo,e,e9].join(na),weightSpecs:[Qo,e,t9].join(na),weightData:[Qo,e,n9].join(na),modelMetadata:[Qo,e,r9].join(na)}}function a9(e){let t=e.split(na);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(na)}function s9(e){return e.startsWith(si.URL_SCHEME)?e.slice(si.URL_SCHEME.length):e}var si=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=h5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=Du(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,Vk(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=Uk(s),t}};si.URL_SCHEME="localstorage://";var d5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(si.URL_SCHEME)?i9(e.slice(si.URL_SCHEME.length)):null;Nt.registerSaveRouter(d5);Nt.registerLoadRouter(d5);function i9(e){return new si(e)}var o9=class{constructor(){R(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),R(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Qo+na,n=na+c5;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=a9(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=s9(e);let t=h5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${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){R(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(el)&&(e=e.slice(0,e.indexOf(el))),R(e.length>0,()=>"scheme must not be an empty string.");let n=Gn.getInstance();R(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 ld(e){if(e.indexOf(el)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Gn.getSchemes().join(",")}`);return{scheme:e.split(el)[0],path:e.split(el)[1]}}async function p5(e,t,n=!1){R(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=Nt.getLoadHandlers(e);R(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),R(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=Nt.getSaveHandlers(t);R(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),R(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=ld(e).scheme,l=ld(e).path,c=o===ld(e).scheme,u=await a.load();n&&c&&await Gn.getManager(o).removeModel(l);let h=await i.save(u);return n&&!c&&await Gn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function l9(){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 u9(e){let t=ld(e);return Gn.getManager(t.scheme).removeModel(t.path)}async function c9(e,t){return p5(e,t,!1)}async function h9(e,t){return p5(e,t,!0)}var d9=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 d9);try{Gn.registerManager(si.URL_SCHEME,new o9)}catch(e){}try{Gn.registerManager(ai.URL_SCHEME,new Qk)}catch(e){}}var p9={importFetch:()=>N8()},_f,f9=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=p9.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 f9);function We(e,t="float32",n){return t=t||"float32",ef(e),new Dt(e,t,n)}function m9(e,t){let n=T(e,"x","cast");if(!Pg(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=D({cast_:m9});function A9(e){let t={x:T(e,"x","clone","string_or_numeric")};return $.runKernel(ks,t)}var Rr=D({clone_:A9});function f5(e,t=!1){console.log(e.toString(t))}e5();var y9={buffer:We,cast:me,clone:Rr,print:f5};Ck(y9);var bn={};ze(bn,{browserFiles:()=>g9,browserHTTPRequest:()=>w9,concatenateArrayBuffers:()=>yf,copyModel:()=>c9,decodeWeights:()=>s5,encodeWeights:()=>Wk,fromMemory:()=>_9,getLoadHandlers:()=>Zk,getModelArtifactsInfoForJSON:()=>Du,getSaveHandlers:()=>Kk,http:()=>vf,isHTTPScheme:()=>bf,listModels:()=>l9,loadWeights:()=>x9,moveModel:()=>h9,registerLoadRouter:()=>Xk,registerSaveRouter:()=>qk,removeModel:()=>u9,weightsLoaderFactory:()=>m5,withSaveHandler:()=>b9});var v9="model",k9=".json",I9=".weights.bin";function A5(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=v9),this.modelTopologyFileName=e+k9,this.weightDataFileName=e+I9}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 A5(()=>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 A5(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Du(e)}}}};tl.URL_SCHEME="downloads://";var N9=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 ${e.name}`));return}let c;try{c=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let u=[],h=[],d=[];l.forEach(p=>{p.paths.forEach(f=>{h.push(f),d.push(null)}),u.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=A=>{let y=A.target.result,g=h.indexOf(f);if(d[g]=y,d.indexOf(null)===-1){let w={modelTopology:o,weightSpecs:u,weightData:yf(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(w.signature=i.signature),i.userDefinedMetadata!=null&&(w.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(w.modelInitializer=i.modelInitializer),n(w)}},m.onerror=A=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(c[f])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>o5(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=o5(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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}},T9=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(tl.URL_SCHEME)?S9(e.slice(tl.URL_SCHEME.length)):null;Nt.registerSaveRouter(T9);function S9(e="model"){return new tl(e)}function g9(e){return new N9(e)}function y5(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(c=>{let u=n+ ++a/e.length*(r-n);return t(u),c}),l);function i(l){R(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,c){R(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),R(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),R(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(s))}async function g5(e,t){t==null&&(t={});let n=t.fetchFunc==null?J().platform.fetch:t.fetchFunc,r=e.map(c=>n(c,t.requestInit,{isBinary:!0})),a=0,s=.5,i=(t.onProgress==null?await Promise.all(r):await y5(r,t.onProgress,a,s)).map(c=>c.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await y5(i,t.onProgress,o,l)}async function x9(e,t="",n,r){return m5(a=>g5(a,{requestInit:r}))(e,t,n)}function m5(e){return async(t,n="",r)=>{let a=t.map(()=>!1),s={},i=r!=null?r.map(()=>!1):[],o=[];if(t.forEach((p,f)=>{let m=0;p.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=mf[y]*$t(A.shape),w=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:A,groupOffset:m,sizeBytes:g})};r!=null?r.forEach((_,v)=>{_===A.name&&(w(),i[v]=!0)}):w(),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),[]),c=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;c.push(m)})});let u=await e(c),h={},d=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let w=0;w<f;w++)m+=u[d+w].byteLength;let A=new ArrayBuffer(m),y=new Uint8Array(A),g=0;for(let w=0;w<f;w++){let _=new Uint8Array(u[d+w]);y.set(_,g),g+=_.byteLength}s[p].forEach(w=>{let _=A.slice(w.groupOffset,w.groupOffset+w.sizeBytes),v=s5(_,[w.manifestEntry]);for(let x in v)h[x]=v[x]}),d+=f}),h}}var C9="application/octet-stream",E9="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?(R(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,R(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&R(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:E9}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:C9}),"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 c,u;r!=null&&([c,u]=await this.loadWeights(r));let h={modelTopology:n,weightSpecs:c,weightData:u,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]=R9(t),a=this.weightPathPrefix||n,s=[];for(let c of e)s.push(...c.weights);let i=[],o=[];for(let c of e)for(let u of c.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(u)):i.push(a+u+r);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await g5(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,yf(l)]}};kf.URL_SCHEME_REGEX=/^https?:\/\//;function R9(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 x5=(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};Nt.registerSaveRouter(x5);Nt.registerLoadRouter(x5);function vf(e,t){return new kf(e,t)}function w9(e,t){return vf(e,t)}var If=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},F9=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function _9(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new If(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 If({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 If({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function b9(e){return new F9(e)}var w5={};ze(w5,{confusionMatrix:()=>M9});function $9(e,t,n=!1,r=!1){let a=T(e,"a","matMul"),s=T(t,"b","matMul");[a,s]=_t(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return $.runKernel(cs,i,o)}var Ge=D({matMul_:$9});function D9(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:T(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return $.runKernel($s,a,s)}var nl=D({oneHot_:D9});function O9(e,t){let n=T(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),R(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{R(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 rt=D({transpose_:O9});function z9(e,t,n){let r=T(e,"labels","confusionMatrix"),a=T(t,"predictions","confusionMatrix");R(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),R(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),R(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),R(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.`),R(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=rt(s),l=Ge(o,i);return me(l,"int32")}var M9=D({confusionMatrix_:z9}),rl={};ze(rl,{fromPixels:()=>W9,fromPixelsAsync:()=>P9,toPixels:()=>L9});function ud(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 _5(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 ${e.constructor.name}`);if(a){let d=2;if(a&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(nd(td,$.backendName)!=null){let d={pixels:e},p={numChannels:t};return $.runKernel(td,d,p)}let[l,c]=a?[e.videoWidth,e.videoHeight]:[e.width,e.height],u;i?u=e.getContext("2d").getImageData(0,0,l,c).data:r||n?u=e.data:(s||a||o)&&(al==null&&(al=document.createElement("canvas").getContext("2d")),al.canvas.width=l,al.canvas.height=c,al.drawImage(e,0,0,l,c),u=al.getImageData(0,0,l,c).data);let h;if(t===4)h=new Int32Array(u);else{let d=l*c;h=new Int32Array(d*t);for(let p=0;p<d;p++)for(let f=0;f<t;++f)h[p*t+f]=u[p*4+f]}return ud(h,[c,l,t],"int32")}function B9(e){return e!=null&&e.data instanceof Uint8Array}function V9(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function U9(e){return e!=null&&e.width!==0&&e.height!==0}function H9(e){return V9()&&!(e instanceof ImageBitmap)&&U9(e)&&!B9(e)}async function P9(e,t=3){let n=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&H9(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 _5(n,t)}async function L9(e,t){let n=T(e,"img","toPixels");if(!(e instanceof Ue)){let c=n;n=me(c,"int32"),c.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 c=0;c<r*a;++c){let u=[0,0,0,255];for(let d=0;d<s;d++){let p=i[c*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?(u[0]=p*o,u[1]=p*o,u[2]=p*o):u[d]=p*o}let h=c*4;l[h+0]=Math.round(u[0]),l[h+1]=Math.round(u[1]),l[h+2]=Math.round(u[2]),l[h+3]=Math.round(u[3])}if(t!=null){t.width=a,t.height=r;let c=t.getContext("2d"),u=new ImageData(l,a,r);c.putImageData(u,0,0)}return n!==e&&n.dispose(),l}var W9=D({fromPixels_:_5}),Nf={};ze(Nf,{prepareAndValidate:()=>b5});function b5(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. ${n}`);if($t(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 c=1;for(let h=s;h<n;++h)c*=o[h],l.push(o[h]);let u=[...Xi(e.shape).map(h=>h/c),1].slice(0,s);return[l,i,c,u]}var Sf={};ze(Sf,{calculateShapes:()=>v5,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: ${e.shape}`)}Tf(n,t,e)}function v5(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=$t(t.shape)/o,c=[...Xi(n.slice(0,a)),1],u=$t(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:c,outputSize:u}}var cn={};ze(cn,{assertParamsValid:()=>j9,computeFlatOffset:()=>q9,computeOutShape:()=>k5,getNormalizedAxes:()=>N5,isSliceContinous:()=>G9,maskToAxes:()=>cd,parseSliceParams:()=>F5,sliceInfo:()=>X9,startForAxis:()=>E5,startIndicesWithElidedDims:()=>S5,stopForAxis:()=>R5,stopIndicesWithElidedDims:()=>T5,stridesForAxis:()=>C5,stridesWithElidedDims:()=>I5});function j9(e,t,n){let r=e.shape.length;R(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),R(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)R(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 cd(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function k5(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 M5(e,t,n){return n<=e?n:n-(t-1)}function $5(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function N5(e,t,n,r,a,s,i,o,l){let c=e.length,u=new Array(c),h=new Array(c),d=new Array(c);if(t.length&&n>0){let p=t[0],f=n+1;u=S5(i,p,f,r,e),h=T5(o,p,f,a,e),d=I5(s,p,f,e)}else for(let p=0;p<c;p++)u[p]=E5(i,r,s,e,p,l),h[p]=R5(o,a,s,e,p,l),d[p]=C5(s,p,l);return{begin:u,end:h,strides:d}}function S5(e,t,n,r,a){let s=[...a],i=$5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=M5(t,n,o),c=r[l];e&1<<l&&(c=0),s[o]=c}return s}function T5(e,t,n,r,a){let s=[...a],i=$5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=M5(t,n,o),c=r[l];e&1<<l&&(c=Number.MAX_SAFE_INTEGER),s[o]=c}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 C5(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function E5(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 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.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 G9(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 q9(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 F5(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=>{R(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:(R(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function X9(e,t,n,r,a,s,i,o,l){let c=t.slice(),u=n.slice(),h=r;r==null&&(h=new Array(c.length));let d=cd(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-c.length,f=cd(o),m=e.slice();f.forEach(x=>{c[x]=0,u[x]=1,m.splice(x,0,1)});let{begin:A,end:y,strides:g}=N5(m,d,p,c,u,h,a,s,i);c=A,u=y,h=g;let w=cd(l);w.forEach(x=>{u[x]=c[x]+1,h[x]=1});let _=k5(c,u,h),v=_.filter((x,N)=>w.indexOf(N)===-1);return{nonStrided:h.every(x=>x===1),$begin:c,$end:u,$strides:h,size:_,newShape:m,outShape:v}}var re={};ze(re,{Serializable:()=>D5,SerializationMap:()=>ii,registerClass:()=>Fa});var D5=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){R(e.className!=null,()=>"Class being registered does not have the static className property defined."),R(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),R(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ii.register(e)}var O5={};ze(O5,{TEST_EPSILON_FLOAT16:()=>z5,encodeStrings:()=>P5,expectArrayBuffersEqual:()=>eI,expectArraysClose:()=>K9,expectArraysEqual:()=>Y9,expectNumbersClose:()=>J9,expectPromiseToFail:()=>Z9,expectValuesInRange:()=>Q9,testEpsilon:()=>Ef});var tI=.001,z5=.1;function K9(e,t,n){return n==null&&(n=Ef()),Rf(e,t,(r,a)=>Ff(r,a,n))}function Ef(){return $.backend.floatPrecision()===32?tI:z5}function Rf(e,t,n){let r=!0;if((an(e)||an(t))&&(r=!1),an(e)&&an(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=an(e)?e:is(e),s=an(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 Z9(e,t){e().then(()=>t.fail(),()=>t())}function Y9(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 J9(e,t,n){if(n==null&&(n=Ef()),!Ff(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Ff(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function Q9(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function eI(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function P5(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?P5(n):e[t]=Su(n)}return e}var nI="3.3.0";function rI(){J().set("PROD",!0)}function aI(){J().set("DEBUG",!0)}function sI(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Mf(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}Ek(Mf);function iI(){$.disposeVariables()}function Fr(){return $}function hd(){return $.memory()}function gr(e){return $.profile(e)}function P(e,t){return $.tidy(e,t)}function Ee(e){lf(e).forEach(t=>t.dispose())}function jt(e){return $.keep(e)}function oI(e){return $.time(e)}function lI(e){return $.setBackend(e)}function uI(){return $.ready()}function cI(){return $.backendName}function hI(e){$.removeBackend(e)}function $f(e){return $.findBackend(e)}function dI(e){return $.findBackendFactory(e)}function sl(e,t,n=1){return $.registerBackend(e,t,n)}function L5(){return $.backend}function pI(e,t){J().setPlatform(e,t)}function fI(e,t){let n=T(e,"a","add"),r=T(t,"b","add");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(Ia,a)}var se=D({add_:fI});function mI(e,t){let n=T(e,"a","floorDiv"),r=T(t,"b","floorDiv");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(_s,a)}var dd=D({floorDiv_:mI});function AI(e,t){let n=T(e,"a","div"),r=T(t,"b","div");if([n,r]=_t(n,r),n.dtype==="int32"&&r.dtype==="int32")return dd(n,r);let a={a:n,b:r},s={};return $.runKernel(gs,a,s)}var ge=D({div_:AI});function yI(e,t){let n=T(e,"a","mul"),r=T(t,"b","mul");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(Ms,a)}var O=D({mul_:yI});function gI(e){let t=T(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 Ot=D({abs_:gI});function xI(e){let t={x:T(e,"x","acos")};return $.runKernel(Yi,t)}var Df=D({acos_:xI});function wI(e){let t={x:T(e,"x","acosh")};return $.runKernel(Ji,t)}var Of=D({acosh_:wI});function _I(e){R(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),R(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>T(a,`tensors${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=D({addN_:_I});function bI(e,t=null,n=!1){let r={x:T(e,"x","all","bool")},a={axis:t,keepDims:n};return $.runKernel(kh,r,a)}var pd=D({all_:bI});function vI(e,t=null,n=!1){let r={x:T(e,"x","any","bool")},a={axis:t,keepDims:n};return $.runKernel(Ih,r,a)}var Ou=D({any_:vI});function kI(e,t=0){let n={x:T(e,"x","argMax")},r={axis:t};return $.runKernel(ls,n,r)}var zu=D({argMax_:kI});function II(e,t=0){let n={x:T(e,"x","argMin")},r={axis:t};return $.runKernel(lu,n,r)}var zf=D({argMin_:II});function NI(e){let t={x:T(e,"x","asin")};return $.runKernel(Qi,t)}var Pf=D({asin_:NI});function SI(e){let t={x:T(e,"x","asinh")};return $.runKernel(eo,t)}var Lf=D({asinh_:SI});function TI(e){let t={x:T(e,"x","atan")};return $.runKernel(to,t)}var Wf=D({atan_:TI});function CI(e,t){let n=T(e,"a","atan2"),r=T(t,"b","atan2");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(ro,a)}var Bf=D({atan2_:CI});function EI(e){let t={x:T(e,"x","atanh")};return $.runKernel(no,t)}var Vf=D({atanh_:EI});function RI(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=W5(a);return Pu(e,o,n,s,r,null,null,l)}function B5(e,t,n,r,a,s,i="channelsLast"){let[o,l]=fd(t),c;if(i==="channelsLast")c=[o,l,e[3],e[3]];else if(i==="channelsFirst")c=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Pu(e,c,n,r,a,s,!1,i)}function FI(e,t,n,r,a,s,i="NDHWC"){let[o,l,c]=Uf(t),u,h;if(i==="NDHWC")h="channelsLast",u=[o,l,c,e[4],e[4]];else if(i==="NCDHW")h="channelsFirst",u=[o,l,c,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return V5(e,u,n,r,a,!1,h,s)}function Pu(e,t,n,r,a,s,i=!1,o="channelsLast"){let[l,c,u,h]=[-1,-1,-1,-1];if(o==="channelsLast")[l,c,u,h]=e;else if(o==="channelsFirst")[l,h,c,u]=e;else throw new Error(`Unknown dataFormat ${o}`);let[d,p,,f]=t,[m,A]=fd(n),[y,g]=fd(r),w=il(d,y),_=il(p,g),{padInfo:v,outHeight:x,outWidth:N}=MI(a,c,u,m,A,w,_,s,o),E=i?f*h:f,F;return o==="channelsFirst"?F=[l,E,x,N]:o==="channelsLast"&&(F=[l,x,N,E]),{batchSize:l,dataFormat:o,inHeight:c,inWidth:u,inChannels:h,outHeight:x,outWidth:N,outChannels:E,padInfo:v,strideHeight:m,strideWidth:A,filterHeight:d,filterWidth:p,effectiveFilterHeight:w,effectiveFilterWidth:_,dilationHeight:y,dilationWidth:g,inShape:e,outShape:F,filterShape:t}}function V5(e,t,n,r,a,s=!1,i="channelsLast",o){let[l,c,u,h,d]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,c,u,h,d]=e;else if(i==="channelsFirst")[l,d,c,u,h]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,f,m,,A]=t,[y,g,w]=Uf(n),[_,v,x]=Uf(r),N=il(p,_),E=il(f,v),F=il(m,x),{padInfo:M,outDepth:W,outHeight:V,outWidth:B}=$I(a,c,u,h,y,g,w,N,E,F,o),H=s?A*d:A,j;return i==="channelsFirst"?j=[l,H,W,V,B]:i==="channelsLast"&&(j=[l,W,V,B,H]),{batchSize:l,dataFormat:i,inDepth:c,inHeight:u,inWidth:h,inChannels:d,outDepth:W,outHeight:V,outWidth:B,outChannels:H,padInfo:M,strideDepth:y,strideHeight:g,strideWidth:w,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:N,effectiveFilterHeight:E,effectiveFilterWidth:F,dilationDepth:_,dilationHeight:v,dilationWidth:x,inShape:e,outShape:j,filterShape:t}}function DI(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 OI(e,t,n,r,a,s){a==null&&(a=Hf(e,t,r));let i=e[0],o=e[1],l=e[2],c=oi((i-t+2*a)/r+1,s),u=oi((o-t+2*a)/r+1,s),h=oi((l-t+2*a)/r+1,s);return[c,u,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 fd(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 MI(e,t,n,r,a,s,i,o,l){let c,u,h;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let d=DI([t,n],s,r,e,o);u=d[0],h=d[1]}else if(e==="same"){u=Math.ceil(t/r),h=Math.ceil(n/a);let d=Math.max(0,(u-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;c={top:f,bottom:m,left:A,right:y,type:"SAME"}}else if(e==="valid")c={top:0,bottom:0,left:0,right:0,type:"VALID"},u=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];c={top:d,bottom:p,left:f,right:m,type:d===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},u=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:c,outHeight:u,outWidth:h}}function $I(e,t,n,r,a,s,i,o,l,c,u){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=OI([t,n,r,1],o,1,a,e,u);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+c-r,g=Math.floor(m/2),w=m-g,_=Math.floor(A/2),v=A-_,x=Math.floor(y/2),N=y-x;h={top:_,bottom:v,left:x,right:N,front:g,back:w,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-c+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]=fd(e);return t===1&&n===1&&r===1}function Mr(e,t){return $a(e)||$a(t)}function W5(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function zI(e,t){let n={x:T(e,"x","reshape","string_or_numeric")},r={shape:t};return $.runKernel(Do,n,r)}var U=D({reshape_:zI});function PI(e,t,n,r,a){let s=T(e,"x","avgPool","float32"),i=1;R(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=U(s,[1,s.shape[0],s.shape[1],s.shape[2]])),R(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&R(Ht(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(us,c,u);return h=me(h,s.dtype),l?U(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Lu=D({avgPool_:PI});function LI(e,t,n,r,a,s="NDHWC"){let i=T(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=U(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),R(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),R(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&R(Ht(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(uu,c,u);return h=me(h,o.dtype),l?U(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var jf=D({avgPool3d_:LI});function WI(e,t=0){R(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 at=D({concat_:WI});function BI(e){let t={x:T(e,"x","sigmoid")};return $.runKernel(js,t)}var Fn=D({sigmoid_:BI});function VI(e,t,n){let r=T(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=D({slice_:VI});function UI(e){let t={x:T(e,"x","tanh")};return $.runKernel(Ys,t)}var ol=D({tanh_:UI});function HI(e,t,n,r,a,s){let i=T(e,"forgetBias","basicLSTMCell"),o=T(t,"lstmKernel","basicLSTMCell"),l=T(n,"lstmBias","basicLSTMCell"),c=T(r,"data","basicLSTMCell"),u=T(a,"c","basicLSTMCell"),h=T(s,"h","basicLSTMCell"),d=at([c,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),w=Re(f,[0,A],y),_=Re(f,[0,A*2],y),v=Re(f,[0,A*3],y),x=se(O(Fn(g),ol(w)),O(u,Fn(se(i,_)))),N=O(ol(x),Fn(v));return[x,N]}var jI=D({basicLSTMCell_:HI});function GI(e,t,n){let r=T(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);R(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),R(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),R(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 $.runKernel(cu,s,i)}var Wu=D({batchToSpaceND_:GI});function qI(e){let t;return e.rank===0||e.rank===1?t=U(e,[1,1,1,e.size]):e.rank===2?t=U(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function XI(e,t,n,r,a,s){s==null&&(s=.001);let i=T(e,"x","batchNorm"),o=T(t,"mean","batchNorm"),l=T(n,"variance","batchNorm"),c;a!=null&&(c=T(a,"scale","batchNorm"));let u;r!=null&&(u=T(r,"offset","batchNorm")),R(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),R(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),R(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:qI(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(bs,h,d);return U(p,i.shape)}var li=D({batchNorm_:XI});function KI(e,t,n,r,a,s){let i=T(e,"x","batchNorm"),o=T(t,"mean","batchNorm"),l=T(n,"variance","batchNorm"),c;a!=null&&(c=T(a,"scale","batchNorm"));let u;return r!=null&&(u=T(r,"offset","batchNorm")),R(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),R(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),R(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&R(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&R(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),li(i,o,l,u,c,s)}var U5=D({batchNorm2d_:KI});function ZI(e,t,n,r,a,s){let i=T(e,"x","batchNorm"),o=T(t,"mean","batchNorm"),l=T(n,"variance","batchNorm"),c;a!=null&&(c=T(a,"scale","batchNorm"));let u;return r!=null&&(u=T(r,"offset","batchNorm")),R(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),R(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),R(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&R(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&R(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),li(i,o,l,u,c,s)}var H5=D({batchNorm3d_:ZI});function YI(e,t,n,r,a,s){let i=T(e,"x","batchNorm"),o=T(t,"mean","batchNorm"),l=T(n,"variance","batchNorm"),c;a!=null&&(c=T(a,"scale","batchNorm"));let u;return r!=null&&(u=T(r,"offset","batchNorm")),R(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),R(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),R(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&R(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&R(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),li(i,o,l,u,c,s)}var j5=D({batchNorm4d_:YI});function JI(e,t,n){let r=T(e,"x","bincount"),a=T(t,"weights","bincount");R(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),R(n>=0,()=>`size must be non-negative, but got ${n}.`),R(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 $.runKernel(Th,s,i)}var G5=D({bincount_:JI});function QI(e,t){let n=T(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${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=U(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,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return Rr(n);let i={x:n},o={reps:s};return $.runKernel(Sa,i,o)}var Bu=D({broadcastTo_:QI});function eN(e){let t={x:T(e,"x","ceil")};return $.runKernel(ds,t)}var Gf=D({ceil_:eN});function tN(e,t,n){let r=T(e,"x","clipByValue");R(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Na,a,s)}var vn=D({clipByValue_:tN});function nN(e){return at(e,0)}var q5=D({concat1d_:nN});function rN(e,t){return at(e,t)}var ll=D({concat2d_:rN});function aN(e,t){return at(e,t)}var X5=D({concat3d_:aN});function sN(e,t){return at(e,t)}var K5=D({concat4d_:sN});function iN(e,t,n,r,a="NHWC",s=[1,1],i){let o=T(e,"x","conv2d"),l=T(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=U(o,[1,o.shape[0],o.shape[1],o.shape[2]])),R(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),R(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&R(Ht(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];R(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),R(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:c,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=$.runKernel(ps,d,p);return u?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ra=D({conv2d_:iN});function oN(e,t,n,r,a="NWC",s=1,i){let o=T(e,"x","conv1d"),l=T(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=U(o,[1,o.shape[0],o.shape[1]])),R(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),R(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&R(Ht(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),R(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),R(Mr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),R(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=U(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=U(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=ra(d,h,[1,n],r,"NHWC",[1,s],i);return u?U(p,[p.shape[2],p.shape[3]]):U(p,[p.shape[0],p.shape[2],p.shape[3]])}var md=D({conv1d_:oN});function lN(e,t,n,r,a,s="NHWC",i){R(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),R(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),R(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),R(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];R(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),R(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&R(Ht(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 c?U(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var qf=D({conv2DBackpropInput_:lN});function uN(e,t,n,r,a,s){let i=T(e,"x","conv2dTranspose"),o=T(t,"filter","conv2dTranspose");return qf(n,i,o,r,a,"NHWC",s)}var Ad=D({conv2dTranspose_:uN});function cN(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=T(e,"x","conv3d"),o=T(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=U(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),R(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),R(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),R(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),R(Mr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),R(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=$.runKernel(du,u,h);return c?U(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Xf=D({conv3d_:cN});function hN(e,t,n,r,a){R(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=U(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],c=i.shape[4];R(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),R(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),R(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),R(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),R(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel(Fh,u,h);return o?U(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Z5=D({conv3DBackpropInput_:hN});function dN(e,t,n,r,a){let s=T(e,"x","conv3dTranspose"),i=T(t,"filter","conv3dTranspose");return Z5(n,s,i,r,a)}var pN=D({conv3dTranspose_:dN});function fN(e){let t={x:T(e,"x","cos")};return $.runKernel(ms,t)}var Vu=D({cos_:fN});function mN(e){let t={x:T(e,"x","cosh")};return $.runKernel(so,t)}var yd=D({cosh_:mN});function AN(e,t=0,n=!1,r=!1){let a={x:T(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(As,a,s)}var gd=D({cumsum_:AN});function yN(e,t,n,r=!1){let a=T(e,"x","denseBincount"),s=T(t,"weights","denseBincount");R(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),R(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),R(n>=0,()=>`size must be non-negative, but got ${n}.`),R(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(Mh,i,o)}var Y5=D({denseBincount_:yN});function gN(e,t,n="NHWC"){let r=T(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];R(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${a} and ${t} for depthToSpace with input shape
|
|
${r.shape}`),R(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${t} for depthToSpace with input shape
|
|
${r.shape}`),R(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=D({depthToSpace_:gN});function xN(e,t,n,r,a="NHWC",s=[1,1],i){let o=T(e,"x","depthwiseConv2d"),l=T(t,"filter","depthwiseConv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=U(o,[1,o.shape[0],o.shape[1],o.shape[2]])),R(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),R(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),R(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&R(Ht(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h={x:c,filter:l},d={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},p=$.runKernel(ys,h,d);return u?U(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ul=D({depthwiseConv2d_:xN});function wN(e){let t={x:T(e,"x","diag")};return $.runKernel(Oh,t)}var _N=D({diag_:wN});function bN(e,t,n,r,a=[1,1],s="NHWC"){let i=T(e,"x","dilation2d"),o=T(t,"filter","dilation2d");R(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),R(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),R(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,c=!1;i.rank===3&&(l=U(i,[1,i.shape[0],i.shape[1],i.shape[2]]),c=!0);let u={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=$.runKernel(pu,u,h);return c?U(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Zf=D({dilation2d_:bN});function vN(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 zt(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 At(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 kN(e,t){let n=T(e,"a","equal"),r=T(t,"b","equal");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(co,a)}var Da=D({equal_:kN});function IN(e,t,n){let r=T(t,"a","where"),a=T(n,"b","where"),s=T(e,"condition","where","bool"),i=At(r.shape,a.shape),o=Bu(r,i),l=Bu(a,i);s.rank===1&&R(s.shape[0]===r.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&rn(s.shape,l.shape,"Error in where: ");let c={condition:s,t:o,e:l};return $.runKernel(zo,c)}var kn=D({where_:IN});function NN(e){let t={x:T(e,"x","zerosLike")};return $.runKernel(Xo,t)}var He=D({zerosLike_:NN});function SN(e,t){let n=T(e,"a","div"),r=T(t,"b","div");[n,r]=_t(n,r);let a=ge(n,r),s=He(a),i=Da(r,s);return kn(i,s,a)}var Yf=D({divNoNan_:SN});function TN(e,t){let n=T(e,"t1","dot"),r=T(t,"t2","dot");R((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(R(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=U(n,[1,-1]),o=U(r,[-1,1]),l=Ge(i,o);return U(l,[])}else if(n.rank===1&&r.rank===2){let i=U(n,[1,-1]),o=U(r,[r.shape[0],r.shape[1]]),l=Ge(i,o);return U(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=U(r,[-1,1]),o=Ge(n,i);return U(o,[o.size])}else{let i=U(r,[r.shape[0],r.shape[1]]);return Ge(n,i)}}var J5=D({dot_:TN});function CN(e){let t={x:T(e,"x","elu")};return $.runKernel(lo,t)}var cl=D({elu_:CN});function EN(e){let t=T(e,"x","erf");R(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=D({erf_:EN});function RN(e){let t={x:T(e,"x","exp")};return $.runKernel(xs,t)}var qn=D({exp_:RN});function FN(e,t=0){let n=T(e,"x","expandDims","string_or_numeric");R(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return $.runKernel(ho,r,a)}var hn=D({expandDims_:FN});function MN(e){let t={x:T(e,"x","expm1")};return $.runKernel(po,t)}var Qf=D({expm1_:MN});function $N(e,t){let n=T(e,"x","tile","string_or_numeric");R(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 $.runKernel(Sa,r,a)}var Oa=D({tile_:$N});function DN(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=U(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=D({eye_:DN});function Uu(e,t,n){let r={shape:e,value:t,dtype:n};return $.runKernel(fu,{},r)}function ON(e){let t={x:T(e,"x","floor")};return $.runKernel(ws,t)}var hl=D({floor_:ON});function zN(e,t,n=0,r=0){let a=T(e,"x","gather"),s=T(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return $.runKernel(mo,i,o)}var ui=D({gather_:zN});function PN(e,t){let n=T(e,"a","greater"),r=T(t,"b","greater");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(yo,a)}var ar=D({greater_:PN});function LN(e,t){let n=T(e,"a","greaterEqual"),r=T(t,"b","greaterEqual");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(vs,a)}var za=D({greaterEqual_:LN});function WN(e){let t={input:T(e,"input","imag")};return $.runKernel(Vh,t)}var xd=D({imag_:WN});function BN(e){let t={x:T(e,"x","isFinite")};return $.runKernel(go,t)}var Q5=D({isFinite_:BN});function VN(e){let t={x:T(e,"x","isInf")};return $.runKernel(xo,t)}var ex=D({isInf_:VN});function UN(e){let t={x:T(e,"x","isNaN")};return $.runKernel(wo,t)}var tx=D({isNaN_:UN});function HN(e,t=.2){let n={x:T(e,"x","leakyRelu")},r={alpha:t};return $.runKernel(Is,n,r)}var Hu=D({leakyRelu_:HN});function jN(e,t){let n=T(e,"a","less"),r=T(t,"b","less");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(_o,a)}var wd=D({less_:jN});function GN(e,t){let n=T(e,"a","lessEqual"),r=T(t,"b","lessEqual");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(bo,a)}var ci=D({lessEqual_:GN});function nx(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(Uh,{},r)}function qN(e,t=5,n=1,r=1,a=.5){let s=T(e,"x","localResponseNormalization");R(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
|
|
rank ${s.rank}.`),R(Ht(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=U(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},c={depthRadius:t,bias:n,alpha:r,beta:a},u=$.runKernel(yu,l,c);return o?U(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var tm=D({localResponseNormalization_:qN});function XN(e){let t={x:T(e,"x","log")};return $.runKernel(Ns,t)}var Mn=D({log_:XN});function KN(e){let t={x:T(e,"x","log1p")};return $.runKernel(vo,t)}var _d=D({log1p_:KN});function ZN(e){return R(ka(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=T(t,"x","tf.grad","string_or_numeric"),a=n!=null?T(n,"dy","tf.grad"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(r),[r],a);return a!=null&&rn(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),bd(i),i[0]})}}function YN(e){return R(ka(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{R(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?T(n,"dy","tf.grads"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(...r),r,a);return a!=null&&rn(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),bd(i),i})}}function JN(e){return R(ka(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{R(t instanceof Ue,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),R(n==null||n instanceof Ue,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=$.gradients(()=>e(t),[t],n);return bd(r),{grad:r[0],value:a}}}function QN(e){return R(ka(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{R(Array.isArray(t)&&t.every(a=>a instanceof Ue),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),R(n==null||n instanceof Ue,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=$.gradients(()=>e(...t),t,n);return n!=null&&rn(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),bd(r.grads),r}}function rx(e,t){R(ka(e),()=>"The f passed in variableGrads(f) must be a function"),R(t==null||Array.isArray(t)&&t.every(c=>c instanceof Ru),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in $.registeredVariables)t.push($.registeredVariables[c])}let r=n?t.filter(c=>!c.trainable):null,a=t.length;t=t.filter(c=>c.trainable),R(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);R(o.some(c=>c!=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()."),R(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((c,u)=>{o[u]!=null&&(l[c.name]=o[u])}),r!=null&&r.forEach(c=>l[c.name]=null),{value:i,grads:l}}function $r(e){return $.customGrad(e)}function bd(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 eS(e){let t={x:T(e,"x","neg")};return $.runKernel(No,t)}var bt=D({neg_:eS});function tS(e){let t={x:T(e,"x","softplus")};return $.runKernel(Vo,t)}var dl=D({softplus_:tS});function nS(e){let t=T(e,"x","logSigmoid");return $r(n=>({value:bt(dl(bt(n))),gradFunc:r=>O(r,Fn(bt(n)))}))(t)}var ax=D({logSigmoid_:nS});function rS(e,t=null,n=!1){let r={x:T(e,"x","max")},a={reductionIndices:t,keepDims:n};return $.runKernel(Ss,r,a)}var Xn=D({max_:rS});function aS(e,t){let n=T(e,"a","sub"),r=T(t,"b","sub");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(Zs,a)}var ye=D({sub_:aS});function sS(e,t=null,n=!1){let r=T(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=D({sum_:sS});function iS(e,t=-1){let n=T(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 $r((r,a)=>{let s=!0,i=Xn(r,t,!0),o=ye(r,i),l=ye(me(o,"float32"),Mn(Te(qn(o),t,s)));return a([l]),{value:l,gradFunc:(c,u)=>{let[h]=u,d=!0,p=qn(h);return ye(c,O(Te(c,t,d),p))}}})(n)}var vd=D({logSoftmax_:iS});function nm(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function sx(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function ix(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function hi(e,t){let n=t.map(r=>1);return sx(e,n,t)}function oS(e,t,n){R(nm(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function ox(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 lS(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function uS(e,t=null,n=!1){let r=T(e,"x","logSumExp"),a=nr(t,r.shape),s=Xn(r,a,!0),i=ye(r,s),o=qn(i),l=Te(o,a),c=Mn(l),u=se(U(s,c.shape),c);if(n){let h=hi(u.shape,a);return U(u,h)}return u}var am=D({logSumExp_:uS});function cS(e,t){let n=T(e,"a","logicalAnd","bool"),r=T(t,"b","logicalAnd","bool");At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(ko,a)}var sr=D({logicalAnd_:cS});function hS(e){let t={x:T(e,"x","logicalNot","bool")};return $.runKernel(mu,t)}var ju=D({logicalNot_:hS});function dS(e,t){let n=T(e,"a","logicalOr","bool"),r=T(t,"b","logicalOr","bool");At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Au,a)}var kd=D({logicalOr_:dS});function pS(e,t){let n=T(e,"a","logicalXor","bool"),r=T(t,"b","logicalXor","bool");return At(n.shape,r.shape),sr(kd(e,t),ju(sr(e,t)))}var lx=D({logicalXor_:pS});function fS(e,t,n,r,a){let s=T(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=U(s,[1,s.shape[0],s.shape[1],s.shape[2]])),R(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),R(Mr(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&R(Ht(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(Cs,c,u);return l?U(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Gu=D({maxPool_:fS});function mS(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=T(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=U(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),R(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),R(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&R(Ht(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(gu,c,u);return l?U(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var sm=D({maxPool3d_:mS});function AS(e,t,n,r,a=!1){let s={x:T(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:a},o=$.runKernel(qh,s,i);return{result:o[0],indexes:o[1]}}var ux=D({maxPoolWithArgmax_:AS});function yS(e,t){let n=T(e,"a","maximum"),r=T(t,"b","maximum");[n,r]=_t(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Ts,a)}var Dr=D({maximum_:yS});function gS(e,t=null,n=!1){let r={x:T(e,"x","mean")},a={axis:t,keepDims:n};return $.runKernel(Es,r,a)}var vt=D({mean_:gS});function xS(e,t=null,n=!1){let r={x:T(e,"x","min")},a={axis:t,keepDims:n};return $.runKernel(Rs,r,a)}var pl=D({min_:xS});function wS(e,t){let n=T(e,"a","minimum"),r=T(t,"b","minimum");[n,r]=_t(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fs,a)}var fl=D({minimum_:wS});function _S(e,t,n){R(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=T(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");R(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++)R(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),R(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=D({mirrorPad_:_S});function bS(e,t){let n=T(e,"a","mod"),r=T(t,"b","mod");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(Io,a)}var om=D({mod_:bS});function vS(e){let t=T(e,"x","square"),n={};return $.runKernel("Square",{x:t},n)}var ot=D({square_:vS});function kS(e,t=null,n=!1){e=T(e,"x","moments");let r=nr(t,e.shape),a=vt(e,r,n),s=a.shape;n||(s=hi(a.shape,r));let i=ot(ye(me(e,"float32"),U(a,s))),o=vt(i,r,n);return{mean:a,variance:o}}var Id=D({moments_:kS});function IS(e,t,n,r){let a=T(t,"data","multiRNNCell"),s=$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 c=[],u=[];for(let h=0;h<l.length;h+=2)c.push(l[h]),u.push(l[h+1]);return[c,u]}var NS=D({multiRNNCell_:IS});function SS(e,t,n,r=!1){let a=T(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 ${i}`);n=n||Math.random();let o={logits:i===1?U(a,[1,-1]):a},l={numSamples:t,seed:n,normalized:r},c=$.runKernel(Xh,o,l);return i===1?U(c,[c.size]):c}var cx=D({multinomial_:SS});function TS(e,t){let n=T(e,"a","notEqual"),r=T(t,"b","notEqual");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(So,a)}var di=D({notEqual_:TS});function Ct(e,t="float32"){if(t==="complex64"){let r=Ct(e,"float32"),a=Ct(e,"float32");return Ca(r,a)}let n=vh($t(e),t);return $.makeTensor(n,e,t)}function Or(e,t="float32"){if(t==="complex64"){let r=Or(e,"float32"),a=Ct(e,"float32");return Ca(r,a)}let n=Q1($t(e),t);return $.makeTensor(n,e,t)}function CS(e){let t={x:T(e,"x","onesLike")};return $.runKernel(Ro,t)}var $n=D({onesLike_:CS});function ES(e,t){let n=T(e,"v1","outerProduct"),r=T(t,"v2","outerProduct");R(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let a=U(n,[-1,1]),s=U(r,[1,-1]);return Ge(a,s)}var RS=D({outerProduct_:ES});function FS(e,t,n=0){let r=T(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=D({pad_:FS});function MS(e,t,n=0){return R(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),aa(e,[t],n)}var $S=D({pad1d_:MS});function DS(e,t,n=0){return R(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 OS=D({pad2d_:DS});function zS(e,t,n=0){return R(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 PS=D({pad3d_:zS});function LS(e,t,n=0){return R(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 WS=D({pad4d_:LS});function BS(e,t,n){let r=T(e,"x","spaceToBatchND");R(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),R(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),R(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=D({spaceToBatchND_:BS});function HS(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=T(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=U(i,[1,i.shape[0],i.shape[1],i.shape[2]])),R(Mr(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let c=B5(o.shape,t,s,a,r),u=[c.dilationHeight,c.dilationWidth],h;r==="same"?h=US([c.filterHeight,c.filterWidth],u):h=[[0,0],[0,0]];let d=u[0]===1&&u[1]===1,[p,f]=VS([c.inHeight,c.inWidth],u,h),m=d?r:"valid",A=d?o:qu(o,u,p),y=(n==="avg"?()=>Lu(A,t,s,m):()=>Gu(A,t,s,m))(),g=d?y:Wu(y,u,f);return l?U(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function VS(e,t,n){let r=n.map(u=>u[0]),a=n.map(u=>u[1]),s=e.concat(r,a),i=t.map((u,h)=>(u-s[h]%u)%u),o=a.map((u,h)=>u+i[h]),l=t.map((u,h)=>[r[h],o[h]]),c=t.map((u,h)=>[0,i[h]]);return[l,c]}function US(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 hx=D({pool_:HS});function jS(e,t){let n=T(e,"base","pow"),r=T(t,"exp","pow");[n,r]=_t(n,r);let a={a:n,b:r};return $.runKernel(Os,a)}var sa=D({pow_:jS});function GS(e,t){let n=T(e,"x","prelu"),r=T(t,"alpha","prelu"),a={x:n,alpha:r};return $.runKernel(zs,a)}var Xu=D({prelu_:GS});function qS(e,t=null,n=!1){let r=T(e,"x","prod");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Mo,a,s)}var Nd=D({prod_:qS});function XS(e,t,n){let r=$t(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 KS=D({rand_:XS}),lm=qi(xh()),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}},ZS=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)}},YS=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 JS(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 ZS(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 QS=D({randomGamma_:JS});function eT(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 dx=D({randomNormal_:eT});function tT(e,t=0,n=1,r="float32",a){let s=We(e,r),i=new YS(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var ml=D({randomUniform_:tT});function Sd(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 nT(e){let t={input:T(e,"input","real")};return $.runKernel(Kh,t)}var Ku=D({real_:nT});function rT(e){let t={x:T(e,"x","reciprocal")};return $.runKernel($o,t)}var cm=D({reciprocal_:rT});function aT(e){let t={x:T(e,"x","relu")};return $.runKernel(Ps,t)}var zr=D({relu_:aT});function sT(e){let t={x:T(e,"x","relu6")};return $.runKernel(Ws,t)}var Td=D({relu6_:sT});function iT(e,t){let n={x:T(e,"x","reverse")},r={dims:t};return $.runKernel(Bs,n,r)}var Dn=D({reverse_:iT});function oT(e){let t=T(e,"x","reverse");return R(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Dn(t,0)}var lT=D({reverse1d_:oT});function uT(e,t){let n=T(e,"x","reverse");return R(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Dn(n,t)}var cT=D({reverse2d_:uT});function hT(e,t){let n=T(e,"x","reverse");return R(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Dn(n,t)}var dT=D({reverse3d_:hT});function pT(e,t){let n=T(e,"x","reverse");return R(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Dn(n,t)}var fT=D({reverse4d_:pT});function mT(e){let t={x:T(e,"x","round")};return $.runKernel(Vs,t)}var hm=D({round_:mT});function AT(e){let t={x:T(e,"x","rsqrt")};return $.runKernel(Us,t)}var Cd=D({rsqrt_:AT});function be(e,t){if((an(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"&&an(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Ea(e,[],[],t)}function yT(e){let t={x:T(e,"x","selu")};return $.runKernel(Po,t)}var Ed=D({selu_:yT});function gT(e,t,n,r,a,s=[1,1],i="NHWC"){let o=T(e,"x","separableConv2d"),l=T(t,"depthwiseFilter","separableConv2d"),c=T(n,"pointwiseFilter","separableConv2d"),u=o,h=!1;if(o.rank===3&&(h=!0,u=U(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");R(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),R(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),R(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),R(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${c.shape[0]}.`),R(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`);let d=l.shape[2],p=l.shape[3];R(c.shape[2]===d*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*p}, but got ${c.shape[2]}.`);let f=ul(u,l,r,a,i,s),m=ra(f,c,1,"valid",i);return h?U(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var dm=D({separableConv2d_:gT});async function xT(e,t){let n=T(e,"x","setdiff1d"),r=T(t,"y","setdiff1d");R(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),R(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),R(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 u=0;u<a.length;u++)i.has(a[u])||o++;let l=new Dt([o],n.dtype),c=new Dt([o],"int32");for(let u=0,h=0;u<a.length;u++)i.has(a[u])||(l.values[h]=a[u],c.values[h]=u,h++);return[l.toTensor(),c.toTensor()]}var px=xT;function wT(e){let t={x:T(e,"x","sign")};return $.runKernel(Bo,t)}var pm=D({sign_:wT});function _T(e){let t={x:T(e,"x","sin")};return $.runKernel(Hs,t)}var Rd=D({sin_:_T});function bT(e){let t={x:T(e,"x","sinh")};return $.runKernel(Wo,t)}var Fd=D({sinh_:bT});function vT(e,t,n){let r=T(e,"x","slice1d");return R(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Re(r,[t],[n])}var Md=D({slice1d_:vT});function kT(e,t,n){let r=T(e,"x","slice2d");return R(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var fm=D({slice2d_:kT});function IT(e,t,n){let r=T(e,"x","slice3d");return R(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var $d=D({slice3d_:IT});function NT(e,t,n){let r=T(e,"x","slice4d");return R(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Zu=D({slice4d_:NT});function ST(e,t=-1){let n=T(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=D({softmax_:ST});function TT(e){R(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Wh,t)}var Ju=D({fft_:TT});function CT(e){R(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Bh,t)}var Al=D({ifft_:CT});function ET(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=U(e,[n,t]);r=Al(a)}else{let a=[n,2*(t-1)],s=U(Ku(e),[n,t]),i=U(xd(e),[n,t]),o=Dn(Re(s,[0,1],[n,t-2]),1),l=O(Dn(Re(i,[0,1],[n,t-2]),1),be(-1)),c=at([s,o],1),u=at([i,l],1),h=U(Ca(c,u),[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=U(r,[s,r.shape[0]/s,r.shape[1]]),a.dispose()}return r}var Dd=D({irfft_:ET});function RT(e,t,n=0){let r={x:T(e,"x","split")},a={numOrSizeSplits:t,axis:n};return $.runKernel(Uo,r,a)}var Pt=D({split_:RT});function FT(e,t){R(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=at([e,Ct(f)],e.shape.length-1),n=t}else a=e;let s=He(a),i=U(Ca(a,s),[r,n]),o=Ju(i),l=Math.floor(n/2)+1,c=Ku(o),u=xd(o),h=Pt(c,[l,n-l],c.shape.length-1),d=Pt(u,[l,n-l],u.shape.length-1),p=a.shape.slice();return p[a.shape.length-1]=l,U(Ca(h[0],d[0]),p)}var Qu=D({rfft_:FT});function MT(e){let t={x:T(e,"x","sqrt")};return $.runKernel(Gs,t)}var Qt=D({sqrt_:MT});function $T(e,t){let n=T(e,"a","squaredDifference"),r=T(t,"b","squaredDifference");[n,r]=_t(n,r),At(n.shape,r.shape);let a={a:n,b:r},s={};return $.runKernel(Ks,a,s)}var Od=D({squaredDifference_:$T});function DT(e,t){let n=T(e,"x","squeeze");return U(n,$g(n.shape,t).newShape)}var Pa=D({squeeze_:DT});function OT(e,t=0){let n=$u(e,"tensors","stack","string_or_numeric");R(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&R(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return $.runKernel(Fo,r,a)}var dn=D({stack_:OT});function zT(e,t=0){let n={x:T(e,"x","step")},r={alpha:t};return $.runKernel(Ta,n,r)}var yl=D({step_:zT});function PT(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let c={x:T(e,"x","stridedSlice")},u={begin:t,end:n,strides:r,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return $.runKernel(Ho,c,u)}var mm=D({stridedSlice_:PT});function LT(e){let t={x:T(e,"x","tan")};return $.runKernel(jo,t)}var Am=D({tan_:LT});function sn(e,t){ss(e);let n=Er(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Ea(e,null,n,t)}function In(e,t,n){if(ss(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Er(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Ea(e,t,r,n)}function WT(e,t,n){if(ss(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Er(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}function BT(e,t,n){if(ss(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Er(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}function VT(e,t,n){if(ss(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Er(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Ea(e,t,r,n)}function UT(e,t=1,n=!0){let r=T(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=r.shape[r.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${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=D({topk_:UT});function HT(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 zd=D({truncatedNormal_:HT});function jT(e,t=0){let n=T(e,"x","unique","string_or_numeric");R(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=$.runKernel(ed,r,a);return{values:s,indices:i}}var Pd=D({unique_:jT});function GT(e,t,n){let r=T(e,"x","unsortedSegmentSum"),a=T(t,"segmentIds","unsortedSegmentSum","int32");R(Ht(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return $.runKernel(ku,s,i)}var gm=D({unsortedSegmentSum_:GT});function qT(e,t=0){let n=T(e,"x","unstack","string_or_numeric");R(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 ir=D({unstack_:qT});function fx(e,t=!0,n,r){return $.makeVariable(e,t,n,r)}function mx(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 XT(e){let t=T(e,"condition","whereAsync","bool"),n=await t.data(),r=mx(t.shape,n);return e!==t&&t.dispose(),r}var xm=XT;async function KT(e,t,n){let r=T(e,"tensor","boolMask"),a=T(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;R(i>0,()=>"mask cannot be scalar"),rn(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 c=o.slice(0,s).concat([l],o.slice(s+i)),u=U(r,c),h=U(a,[-1]),d=await xm(h),p=Pa(d,[1]),f=ui(u,p,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),p.dispose(),u.dispose(),h.dispose(),d.dispose(),f}var ZT=KT;function YT(e,t="euclidean",n=null,r=!1){e=T(e,"x","norm");let a=Ax(e,t,n),s=a.shape;if(r){let i=nr(n,e.shape);s=hi(a.shape,i)}return U(a,s)}function Ax(e,t,n=null){if(e.rank===0)return Ot(e);if(e.rank!==1&&n===null)return Ax(U(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Te(Ot(e),n);if(t===Infinity)return Xn(Ot(e),n);if(t===-Infinity)return pl(Ot(e),n);if(t==="euclidean"||t===2)return Qt(Te(sa(Ot(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(Ot(e),n[0]),n[1]-1);if(t===Infinity)return Xn(Te(Ot(e),n[1]),n[0]);if(t===-Infinity)return pl(Te(Ot(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Qt(Te(ot(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Ld=D({norm_:YT});function JT(e,t,n,r,a=!0){let s=T(e,"v","movingAverage"),i=T(t,"x","movingAverage"),o=T(n,"decay","movingAverage");Yg(s,i),R(ta(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=be(1),c=ye(l,o),u=O(ye(i,s),c);if(a){R(r!=null,()=>"When using zeroDebias: true, step is required.");let h=T(r,"step","movingAverage");u=ge(u,ye(l,sa(o,h)))}return se(s,u)}var QT=D({movingAverage_:JT});function eC(e,t,n){let r=T(e,"indices","scatterND","int32"),a=T(t,"updates","scatterND");Cf(a,r,n);let s={indices:r,updates:a},i={shape:n};return $.runKernel(Oo,s,i)}var yx=D({scatterND_:eC});function tC(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 nC(e,t,n,r=0){let a=T(e,"sparseIndices","sparseToDense","int32"),s=T(t,"sparseValues","sparseToDense"),i=T(r,"defaultValue","sparseToDense",s.dtype);tC(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return $.runKernel(Jh,o,l)}var wm=D({sparseToDense_:nC});function rC(e,t){let n=T(t,"indices","gatherND","int32"),r={params:T(e,"x","gatherND"),indices:n};return $.runKernel(Ao,r)}var gx=D({gatherND_:rC});function aC(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 sC(e,t,n,r){let a=T(e,"x","dropout");if(R(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.`),R(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=aC(a,n),i=1-t,o=ge(hl(se(ml(s,0,1,"float32",r),i)),i);return O(a,o)}var xx=D({dropout_:sC});function wx(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 sn(a,"float32")}async function iC(e,t,n=1){let r=T(e,"predictions","inTopK"),a=T(t,"targets","inTopK");R(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),R(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),rn(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];R(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,c]=[i.length/s,s],u=Dg("bool",l);for(let h=0;h<l;h++){let d=h*c,p=i.subarray(d,d+c),f=[];for(let m=0;m<p.length;m++)f.push({value:p[m],index:m});f.sort((m,A)=>A.value-m.value),u[h]=0;for(let m=0;m<n;m++)if(f[m].index===o[h]){u[h]=1;break}}return e!==r&&r.dispose(),t!==a&&a.dispose(),yr(u,a.shape,"bool")}var oC=iC,La={};ze(La,{conv2d:()=>lC,depthwiseConv2d:()=>uC,matMul:()=>cC});function hC(e,t,n,r,a,s="NHWC",i){let o=e;e.rank===3&&(o=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]])),R(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),R(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),R(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=s==="NHWC"?o.shape[3]:o.shape[1],u=s==="NHWC"?l.shape[3]:l.shape[1];R(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),R(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),i!=null&&R(Ht(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(Eh,h,d)}var bm=D({conv2DBackpropFilter_:hC});function Wd(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return O(e,yl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Bd(e,t){let n=t,r=zt(e.shape,t.shape);return r.length>0&&(n=Te(n,r)),U(n,e.shape)}function Vd(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 Td(e);if(t==="prelu")return Xu(e,n);if(t==="leakyrelu")return Hu(e,r);throw new Error(`Unknown fused activation ${t}.`)}var Ud=(e,t)=>!(e>0)||t==="linear";function dC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Ud($.state.gradientDepth,l)===!1){let v=ra(e,t,n,r,a,s,i);return o!=null&&(v=se(v,o)),Vd(v,l,c,u)}let h=T(e,"x","conv2d"),d=T(t,"filter","conv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=U(h,[1,h.shape[0],h.shape[1],h.shape[2]])),R(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),R(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&R(Ht(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),R(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),R(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),R(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=Pu(p.shape,d.shape,n,s,r,i),A;o!=null&&(A=T(o,"bias","fused conv2d"),[A]=_t(A,h),At(m.outShape,A.shape));let y;c!=null&&(y=T(c,"prelu weights","fused conv2d"));let g=(v,x)=>{let[N,E,F,M]=x,W=Wd(v,F,l);R($a(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let V=qf(E.shape,W,N,n,r),B=bm(E,W,N.shape,n,r),H=[V,B];if(M!=null){let j=Bd(M,W);H.push(j)}return H},w={x:p,filter:d,bias:A,preluActivationWeights:y},_={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?$r((v,x,N)=>{let E=$.runKernel(ei,w,_);return N([x,v,E]),f&&(E=U(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(p,d):$r((v,x,N,E)=>{let F=$.runKernel(ei,w,_);return E([x,v,F,N]),f&&(F=U(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:g}})(p,d,A)}var lC=D({fusedConv2d_:dC});function pC(e,t,n,r,a,s=[1,1],i){let o=e;e.rank===3&&(o=U(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:o,dy:l},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,filterShape:n};return $.runKernel($h,c,u)}var _x=D({depthwiseConv2dNativeBackpropFilter_:pC});function fC(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=U(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:o,filter:n},u={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=$.runKernel(Dh,c,u);return l?U(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var bx=D({depthwiseConv2dNativeBackpropInput_:fC});function mC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(Ud($.state.gradientDepth,l)===!1){let v=ul(e,t,n,r,a,s,i);return o!=null&&(v=se(v,o)),Vd(v,l,c,u)}let h=T(e,"x","depthwiseConv2d"),d=T(t,"filter","depthwiseConv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=U(h,[1,h.shape[0],h.shape[1],h.shape[2]])),R(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),R(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),R(p.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),R(Mr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&R(Ht(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let m=Pu(p.shape,d.shape,n,s,r,i,!0),A;o!=null&&(A=T(o,"bias","fused conv2d"),[A]=_t(A,h),At(m.outShape,A.shape));let y;c!=null&&(y=T(c,"prelu weights","fused depthwiseConv2d"));let g=(v,x)=>{R($a(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[N,E,F,M]=x,W=Wd(v,F,l),V=bx(E.shape,W,N,n,r,s,i),B=_x(E,W,N.shape,n,r,s,i);if(M!=null){let H=Bd(A,W);return[V,B,H]}return[V,B]},w={x:p,filter:d,bias:A,preluActivationWeights:y},_={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:u};return o==null?$r((v,x,N)=>{let E=$.runKernel(ti,w,_);return N([x,v,E]),f&&(E=U(E,[E.shape[1],E.shape[2],E.shape[3]])),{value:E,gradFunc:g}})(p,d):$r((v,x,N,E)=>{let F=$.runKernel(ti,w,_);return E([x,v,F,N]),f&&(F=U(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:g}})(p,d,A)}var uC=D({fusedDepthwiseConv2d_:mC});function AC({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Ud($.state.gradientDepth,s)===!1){let M=Ge(e,t,n,r);return a!=null&&(M=se(M,a)),Vd(M,s,i,o)}let l=T(e,"a","fused matMul"),c=T(t,"b","fused matMul");[l,c]=_t(l,c);let u=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?c.shape[c.rank-1]:c.shape[c.rank-2],d=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=r?c.shape[c.rank-2]:c.shape[c.rank-1],f=l.shape.slice(0,-2),m=c.shape.slice(0,-2),A=$t(f),y=$t(m);R(l.rank>=2&&c.rank>=2&&l.rank===c.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${c.rank}.`),R(ta(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),R(u===h,()=>`Error in fused matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([d,p]),w=n?U(l,[A,u,d]):U(l,[A,d,u]),_=r?U(c,[y,p,h]):U(c,[y,h,p]),v;a!=null&&(v=T(a,"bias","fused matMul"),[v]=_t(v,l),At(g,v.shape));let x;i!=null&&(x=T(i,"prelu weights","fused matMul"));let N=(M,W)=>{let[V,B,H,j]=W,X=Wd(U(M,H.shape),H,s),G,ee;if(!n&&!r?(G=Ge(X,B,!1,!0),ee=Ge(V,X,!0,!1)):!n&&r?(G=Ge(X,B,!1,!1),ee=Ge(X,V,!0,!1)):n&&!r?(G=Ge(B,X,!1,!0),ee=Ge(V,X,!1,!1)):(G=Ge(B,X,!0,!0),ee=Ge(X,V,!0,!0)),a!=null){let Y=Bd(j,X);return[G,ee,Y]}else return[G,ee]},E={a:w,b:_,bias:v,preluActivationWeights:x},F={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?$r((M,W,V)=>{let B=$.runKernel(Qs,E,F);return V([M,W,B]),{value:U(B,g),gradFunc:N}})(w,_):$r((M,W,V,B)=>{let H=$.runKernel(Qs,E,F);return B([M,W,H,V]),{value:U(H,g),gradFunc:N}})(w,_,v)}var cC=D({fusedMatMul_:AC});function yC(e){return _m(e,.54,.46)}var gC=D({hammingWindow_:yC});function xC(e){return _m(e,.5,.5)}var vx=D({hannWindow_:xC});function wC(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=at([Re(e,s,t-o),Uu([o],a)]);i.push(l),s+=n}return i.length===0?In([],[0,t]):U(at(i),[i.length,t])}var kx=D({frame_:wC});function _C(e,t,n,r,a=vx){r==null&&(r=wx(t));let s=kx(e,t,n),i=O(s,a(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(Qu(Re(i,[l,0],[1,t]),r));return at(o)}var bC=D({stft_:_C});function vC(e,t,n,r,a="bilinear",s=0){let i=T(e,"image","cropAndResize"),o=T(t,"boxes","cropAndResize","float32"),l=T(n,"boxInd","cropAndResize","int32"),c=o.shape[0];R(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),R(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${o.shape}.`),R(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${o.shape}.`),R(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),R(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),R(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let u={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:r};return $.runKernel(io,u,h)}var kC=D({cropAndResize_:vC});function IC(e){let t=T(e,"image","flipLeftRight","float32");R(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 NC=D({flipLeftRight_:IC});function SC(e,t,n=0,r=.5){let a=T(e,"image","rotateWithOffset","float32");R(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 $.runKernel(Ko,s,i)}var TC=D({rotateWithOffset_:SC});function gl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),R(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),R(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),R(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),R(t.rank===1,()=>"scores must be a 1D tensor"),R(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),R(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function CC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=T(e,"boxes","nonMaxSuppression"),i=T(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 EC=D({nonMaxSuppression_:CC});function FC(e,t,n){let r=RC(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function RC(e,t,n){return $C(e,t,n||MC)}function MC(e,t){return e>t?1:e<t?-1:0}function $C(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 Nx(e,t,n,r,a,s){return vm(e,t,n,r,a,0,!1,s,!0)}function Sx(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 c=[];for(let A=0;A<t.length;A++)t[A]>a&&c.push({score:t[A],boxIndex:A,suppressBeginIndex:0});c.sort(Tx);let u=s>0?-.5/s:0,h=[],d=[];for(;h.length<n&&c.length>0;){let A=c.pop(),{score:y,boxIndex:g,suppressBeginIndex:w}=A;if(y<a)break;let _=!1;for(let v=h.length-1;v>=w;--v){let x=DC(e,g,h[v]);if(x>=r){_=!0;break}if(A.score=A.score*OC(r,u,x),A.score<=a)break}A.suppressBeginIndex=h.length,_||(A.score===y?(h.push(g),d.push(A.score)):A.score>a&&FC(c,A,Tx))}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 DC(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]),c=Math.min(a[0],a[2]),u=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-c)*(d-u);if(p<=0||f<=0)return 0;let m=Math.max(s,c),A=Math.max(i,u),y=Math.min(o,h),g=Math.min(l,d),w=Math.max(y-m,0)*Math.max(g-A,0);return w/(p+f-w)}function OC(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function Tx(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function zC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=T(e,"boxes","nonMaxSuppressionAsync"),i=T(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()]),c=l[0],u=l[1],{selectedIndices:h}=Ix(c,u,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),sn(h,"int32")}var PC=zC;function LC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=T(e,"boxes","nonMaxSuppression"),o=T(t,"scores","nonMaxSuppression"),l=gl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c={boxes:i,scores:o},u={maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s},h=$.runKernel(Eo,c,u);return{selectedIndices:h[0],selectedScores:h[1]}}var WC=D({nonMaxSuppressionWithScore_:LC});async function BC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=T(e,"boxes","nonMaxSuppressionAsync"),o=T(t,"scores","nonMaxSuppressionAsync"),l=gl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let c=await Promise.all([i.data(),o.data()]),u=c[0],h=c[1],{selectedIndices:d,selectedScores:p}=Sx(u,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:sn(d,"int32"),selectedScores:sn(p)}}var VC=BC;function UC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=T(e,"boxes","nonMaxSuppression"),o=T(t,"scores","nonMaxSuppression"),l=gl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,d={boxes:i,scores:o},p={maxOutputSize:c,iouThreshold:u,scoreThreshold:h,padToMaxOutputSize:s},f=$.runKernel(Co,d,p);return{selectedIndices:f[0],validOutputs:f[1]}}var HC=D({nonMaxSuppressionPadded_:UC});async function jC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=T(e,"boxes","nonMaxSuppressionAsync"),o=T(t,"scores","nonMaxSuppressionAsync"),l=gl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,[d,p]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=Nx(d,p,c,u,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:sn(f,"int32"),validOutputs:be(m,"int32")}}var GC=jC;function qC(e,t,n=!1,r=!1){let a=T(e,"images","resizeBilinear");R(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),R(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),R(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=U(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(Ls,o,l);return i?U(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Cx=D({resizeBilinear_:qC});function XC(e,t,n=!1,r=!1){let a=T(e,"images","resizeNearestNeighbor");R(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),R(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),R(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),R(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=U(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=$.runKernel(_u,o,l);return i?U(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Ex=D({resizeNearestNeighbor_:XC});function KC(e,t,n="nearest",r="constant",a=0,s){let i=T(e,"image","transform","float32"),o=T(t,"transforms","transform","float32");R(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),R(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"),R(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},c={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return $.runKernel(Qh,l,c)}var ZC=D({transform_:KC});function YC(e,t,n){R(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),R(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=T(e,"a","bandPart");R(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 (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=U(Sd(0,s,1,"int32"),[-1,1]),l=Sd(0,i,1,"int32"),c=ye(o,l),u=sr(ci(c,be(+t,"int32")),za(c,be(-n,"int32"))),h=Ct([s,i],r.dtype);return U(dn(ir(U(r,[-1,s,i])).map(d=>kn(u,d,h))),a)}var JC=D({bandPart_:YC});function QC(e){let t;if(Array.isArray(e)){t=!1,R(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)R(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Pt(e,e.shape[0],0).map(a=>Pa(a,[0]));R(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${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=O(Te(O(n[i],s)),n[i]);s=ye(s,o)}return ge(s,Ld(s,"euclidean"))}));return t?dn(n,0):n}var eE=D({gramSchmidt_:QC});function tE(e,t=!1){if(R(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return Rx(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),r=ir(U(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[c,u]=Rx(l,t);a.push(c),s.push(u)});let i=U(dn(a,0),e.shape),o=U(dn(s,0),e.shape);return[i,o]}}function Rx(e,t=!1){return $.tidy(()=>{R(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=em(n),s=Rr(e),i=In([[1]],[1,1]),o=Rr(i),l=n>=r?r:n;for(let c=0;c<l;++c){let u=s,h=o,d=a;[o,s,a]=$.tidy(()=>{let p=Re(s,[c,c],[n-c,1]),f=Ld(p),m=Re(s,[c,c],[1,1]),A=kn(ar(m,0),In([[-1]]),In([[1]])),y=ye(m,O(A,f)),g=ge(p,y);g.shape[0]===1?o=Rr(i):o=at([i,Re(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let w=bt(ge(Ge(A,y),f)),_=Re(s,[c,0],[n-c,r]),v=O(w,o),x=rt(o);if(c===0)s=ye(_,Ge(v,Ge(x,_)));else{let F=ye(_,Ge(v,Ge(x,_)));s=at([Re(s,[0,0],[c,r]),F],0)}let N=rt(v),E=Re(a,[0,c],[n,a.shape[1]-c]);if(c===0)a=ye(E,Ge(Ge(E,o),N));else{let F=ye(E,Ge(Ge(E,o),N));a=at([Re(a,[0,0],[n,c]),F],1)}return[o,s,a]}),Ee([u,h,d])}return!t&&n>r&&(a=Re(a,[0,0],[n,r]),s=Re(s,[0,0],[r,r])),[a,s]})}var nE=D({qr_:tE}),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 rE(e,t,n=pn.SUM_BY_NONZERO_WEIGHTS){let r=T(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=T(t,"weights","computeWeightedLoss"));let s=a==null?r:O(r,a);if(n===pn.NONE)return s;if(n===pn.SUM)return Te(s);if(n===pn.MEAN){if(a==null)return vt(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=O(a,Or(r.shape)),o=me(Te(di(i,be(0))),"float32");return ge(Te(s),o)}}throw Error(`Unknown reduction: ${n}`)}var ia=D({computeWeightedLoss_:rE});function aE(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=T(e,"labels","absoluteDifference"),s=T(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=T(n,"weights","absoluteDifference")),rn(a.shape,s.shape,"Error in absoluteDifference: ");let o=Ot(ye(a,s));return ia(o,i,r)}var sE=D({absoluteDifference_:aE});function iE(e,t,n,r,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=T(e,"labels","cosineDistance"),i=T(t,"predictions","cosineDistance"),o=null;r!=null&&(o=T(r,"weights","cosineDistance")),rn(s.shape,i.shape,"Error in cosineDistance: ");let l=be(1),c=ye(l,Te(O(s,i),n,!0));return ia(c,o,a)}var oE=D({cosineDistance_:iE});function lE(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=T(e,"labels","hingeLoss"),s=T(t,"predictions","hingeLoss"),i=null;n!=null&&(i=T(n,"weights","hingeLoss")),rn(a.shape,s.shape,"Error in hingeLoss: ");let o=be(1);a=ye(O(be(2),a),o);let l=zr(ye(o,O(a,s)));return ia(l,i,r)}var uE=D({hingeLoss_:lE});function cE(e,t,n,r=1,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=T(e,"labels","huberLoss"),i=T(t,"predictions","huberLoss"),o=null;n!=null&&(o=T(n,"weights","huberLoss")),rn(s.shape,i.shape,"Error in huberLoss: ");let l=be(r),c=Ot(ye(i,s)),u=fl(c,l),h=ye(c,u),d=se(O(be(.5),ot(u)),O(l,h));return ia(d,o,a)}var hE=D({huberLoss_:cE});function dE(e,t,n,r=1e-7,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=T(e,"labels","logLoss"),i=T(t,"predictions","logLoss"),o=null;n!=null&&(o=T(n,"weights","logLoss")),rn(s.shape,i.shape,"Error in logLoss: ");let l=be(1),c=be(r),u=bt(O(s,Mn(se(i,c)))),h=O(ye(l,s),Mn(se(ye(l,i),c))),d=ye(u,h);return ia(d,o,a)}var pE=D({logLoss_:dE});function fE(e,t,n,r=pn.SUM_BY_NONZERO_WEIGHTS){let a=T(e,"labels","meanSquaredError"),s=T(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=T(n,"weights","meanSquaredError")),rn(a.shape,s.shape,"Error in meanSquaredError: ");let o=Od(a,s);return ia(o,i,r)}var mE=D({meanSquaredError_:fE});function AE(e,t){let n=T(e,"labels","sigmoidCrossEntropyWithLogits"),r=T(t,"logits","sigmoidCrossEntropyWithLogits");rn(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=zr(r),s=O(r,n),i=_d(qn(bt(Ot(r))));return se(ye(a,s),i)}function yE(e,t,n,r=0,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=T(e,"multiClassLabels","sigmoidCrossEntropy"),i=T(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=T(n,"weights","sigmoidCrossEntropy")),rn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let c=be(r),u=be(1),h=be(.5);s=se(O(s,ye(u,c)),O(h,c))}let l=AE(s,i);return ia(l,o,a)}var gE=D({sigmoidCrossEntropy_:yE});function xE(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=bt(O(o,r));return{value:Te(l,[n]),gradFunc:(c,u)=>{let[h,d]=u,p=hi(c.shape,[n]);return[O(U(c,p),ye(me(h,"float32"),qn(d))),O(U(c,p),ye(qn(d),me(h,"float32")))]}}})(e,t)}function wE(e,t,n,r=0,a=pn.SUM_BY_NONZERO_WEIGHTS){let s=T(e,"onehotLabels","softmaxCrossEntropy"),i=T(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=T(n,"weights","softmaxCrossEntropy")),rn(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let c=be(r),u=be(1),h=be(s.shape[1]);s=se(O(s,ye(u,c)),ge(c,h))}let l=xE(s,i);return ia(l,o,a)}var _E=D({softmaxCrossEntropy_:wE}),bE={fft:Ju,ifft:Al,rfft:Qu,irfft:Dd},vE={hammingWindow:gC,hannWindow:vx,frame:kx,stft:bC},Je={flipLeftRight:NC,resizeNearestNeighbor:Ex,resizeBilinear:Cx,rotateWithOffset:TC,cropAndResize:kC,nonMaxSuppression:EC,nonMaxSuppressionAsync:PC,nonMaxSuppressionWithScore:WC,nonMaxSuppressionWithScoreAsync:VC,nonMaxSuppressionPadded:HC,nonMaxSuppressionPaddedAsync:GC,transform:ZC},Fx={bandPart:JC,gramSchmidt:eE,qr:nE},kE={absoluteDifference:sE,computeWeightedLoss:ia,cosineDistance:oE,hingeLoss:uE,huberLoss:hE,logLoss:pE,meanSquaredError:mE,sigmoidCrossEntropy:gE,softmaxCrossEntropy:_E},oa=class extends D5{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 rx(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(oa,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Hd=class extends oa{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.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:P(()=>He(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:P(()=>He(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;P(()=>{let l=se(O(i,this.rho),O(ot(s),1-this.rho)),c=O(ge(Qt(se(o,this.epsilon)),Qt(se(i,this.epsilon))),s),u=se(O(o,this.rho),O(ot(c),1-this.rho));i.assign(l),o.assign(u);let h=se(O(c,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Hd.className="Adadelta";Fa(Hd);var jd=class extends oa{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:P(()=>Uu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;P(()=>{let i=se(s,ot(a));s.assign(i);let o=se(O(ge(a,Qt(se(i,$.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)}};jd.className="Adagrad";Fa(jd);var Gd=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=[],P(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=ye(1,this.accBeta1),r=ye(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:P(()=>He(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:P(()=>He(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedSecondMoment[s].variable,h=se(O(c,this.beta1),O(l,1-this.beta1)),d=se(O(u,this.beta2),O(ot(l),1-this.beta2)),p=ge(h,n),f=ge(d,r);c.assign(h),u.assign(d);let m=se(O(ge(p,se(Qt(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(O(this.accBeta1,this.beta1)),this.accBeta2.assign(O(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),P(()=>{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)}};Gd.className="Adam";Fa(Gd);var qd=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=[],P(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);P(()=>{let n=ye(1,this.accBeta1),r=ge(-this.learningRate,se(O(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=$.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 c=this.accumulatedFirstMoment[s].variable,u=this.accumulatedWeightedInfNorm[s].variable,h=se(O(c,this.beta1),O(l,1-this.beta1)),d=O(u,this.beta2),p=Ot(l),f=Dr(d,p);c.assign(h),u.assign(f);let m=se(O(ge(r,n),ge(h,se(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(O(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)}};qd.className="Adamax";Fa(qd);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=$.registeredVariables[t];P(()=>{let s=se(O(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=jt(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ec.className="SGD";Fa(ec);var Xd=class extends ec{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:P(()=>He(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&P(()=>{let i,o=se(O(this.m,a),s);this.useNesterov?i=se(O(this.c,se(s,O(o,this.m))),r):i=se(O(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ee(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Xd.className="Momentum";Fa(Xd);var Kd=class extends oa{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.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:P(()=>He(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:P(()=>He(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:P(()=>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;P(()=>{let l=se(O(i,this.decay),O(ot(s),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[n].variable,u=se(O(c,this.decay),O(s,1-this.decay)),h=ge(O(s,this.learningRate),Qt(ye(l,se(ot(u),this.epsilon)))),d=se(O(o,this.momentum),h);i.assign(l),c.assign(u),o.assign(d);let p=ye(r,d);r.assign(p)}else{let c=se(O(i,this.decay),O(ot(s),1-this.decay)),u=se(O(o,this.momentum),ge(O(s,this.learningRate),Qt(se(c,this.epsilon))));i.assign(c),o.assign(u);let h=ye(r,u);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)}};Kd.className="RMSProp";Fa(Kd);var pi=class{static sgd(e){return new ec(e)}static momentum(e,t,n=!1){return new Xd(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new Kd(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new Gd(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Hd(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new qd(e,t,n,r,a)}static adagrad(e,t=.1){return new jd(e,t)}},fi={sgd:pi.sgd,momentum:pi.momentum,adadelta:pi.adadelta,adagrad:pi.adagrad,rmsprop:pi.rmsprop,adamax:pi.adamax,adam:pi.adam},IE=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Zd(){return new Promise(e=>IE(()=>e()))}var C={};ze(C,{ERF_A1:()=>OE,ERF_A2:()=>zE,ERF_A3:()=>PE,ERF_A4:()=>LE,ERF_A5:()=>WE,ERF_P:()=>DE,PARALLELIZE_THRESHOLD:()=>km,SELU_SCALE:()=>$x,SELU_SCALEALPHA:()=>Mx,applyActivation:()=>Vd,assertAndGetBroadcastShape:()=>At,assertAxesAreInnerMostDims:()=>oS,assertParamsConsistent:()=>NE,assignToTypedArray:()=>XE,axesAreInnerMostDims:()=>nm,calculateShapes:()=>v5,combineLocations:()=>sx,complexWithEvenIndex:()=>jE,complexWithOddIndex:()=>GE,computeConv2DInfo:()=>Pu,computeConv3DInfo:()=>V5,computeDefaultPad:()=>Hf,computeDilation2DInfo:()=>RI,computeOptimalWindowSize:()=>TE,computeOutAndReduceShapes:()=>ix,computeOutShape:()=>SE,computePool2DInfo:()=>B5,computePool3DInfo:()=>FI,convertConv2DDataFormat:()=>W5,eitherStridesOrDilationsAreOne:()=>Mr,expandShapeToKeepDim:()=>hi,exponent:()=>ZE,exponents:()=>KE,fromStringArrayToUint8:()=>QE,fromUint8ToStringArray:()=>JE,getAxesPermutation:()=>ox,getBroadcastDims:()=>vN,getComplexWithIndex:()=>qE,getFusedBiasGradient:()=>Bd,getFusedDyActivation:()=>Wd,getImageCenter:()=>CE,getInnerMostAxes:()=>lS,getPermuted:()=>RE,getReductionAxes:()=>zt,getReshaped:()=>EE,getReshapedPermuted:()=>FE,getSliceBeginCoords:()=>ME,getSliceSize:()=>$E,getUndoAxesPermutation:()=>rm,log:()=>VE,mergeRealAndImagArrays:()=>UE,prepareAndValidate:()=>b5,prepareSplitSize:()=>YE,segment_util:()=>Dx,shouldFuse:()=>Ud,slice_util:()=>cn,splitRealAndImagArrays:()=>HE,tupleValuesAreOne:()=>$a,upcastType:()=>rr,validateInput:()=>Cf,validateUpdateShape:()=>Tf,warn:()=>BE});function NE(e,t){let n=e[0].length;e.forEach((a,s)=>{R(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),R(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)R(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function SE(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 TE(e){return e<=km?e:bh(e,Math.floor(Math.sqrt(e)))}function CE(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function EE(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 RE(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 FE(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 ME(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function $E(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var Mx=1.7580993408473768,$x=1.0507009873554805,DE=.3275911,OE=.254829592,zE=-.284496736,PE=1.421413741,LE=-1.453152027,WE=1.061405429;function BE(...e){J().getBool("IS_TEST")||console.warn(...e)}function VE(...e){J().getBool("IS_TEST")||console.log(...e)}function UE(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function HE(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function jE(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=0;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function GE(e){let t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t);for(let a=2;a<e.length;a+=4)n[Math.floor(a/4)]=e[a],r[Math.floor(a/4)]=e[a+1];return{real:n,imag:r}}function qE(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function XE(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function KE(e,t){let n=new Float32Array(e/2),r=new Float32Array(e/2);for(let a=0;a<Math.ceil(e/2);a++){let s=(t?2:-2)*Math.PI*(a/e);n[a]=Math.cos(s),r[a]=Math.sin(s)}return{real:n,imag:r}}function ZE(e,t,n){let r=(n?2:-2)*Math.PI*(e/t),a=Math.cos(r),s=Math.sin(r);return{real:a,imag:s}}function YE(e,t,n=0){let r=[];if(typeof t=="number")R(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);R(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}R(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var Dx={};ze(Dx,{collectGatherOpShapeInfo:()=>nR,computeOutShape:()=>tR,segOpComputeOptimalWindowSize:()=>eR});function eR(e,t){let n=!1,r;for(e<=km?(r=e,n=!0):r=bh(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=bh(e,r+1);return r}function tR(e,t,n){let r=[],a=e.length;for(let s=0;s<a;s++)s!==t?r.push(e[s]):r.push(n);return r}function nR(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${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,c=1,u=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]),c*=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]),u*=e.shape[h];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:i,outputShape:o}}function JE(e){try{return e.map(t=>ad(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function QE(e){return e.map(t=>Su(t))}var Pr={};ze(Pr,{nonMaxSuppressionV3Impl:()=>Ix,nonMaxSuppressionV4Impl:()=>Nx,nonMaxSuppressionV5Impl:()=>Sx,whereImpl:()=>mx});function _e(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&b.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var rR=Pr.whereImpl,Yd=class extends su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new wh(this,Fr())}nextDataId(){return Yd.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&C.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&&b.isString(n[0])){let a=n.map(s=>b.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 C.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=>b.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=b.now();return e(),{kernelMs:b.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 rR(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Yd.nextDataId=0;var Im={};ze(Im,{addImpl:()=>zx,bincountImpl:()=>Nm,bincountReduceImpl:()=>Px,ceilImpl:()=>Lx,concatImpl:()=>Sm,expImpl:()=>Wx,expm1Impl:()=>Bx,floorImpl:()=>Vx,gatherV2Impl:()=>Ux,greaterImpl:()=>Hx,lessImpl:()=>jx,linSpaceImpl:()=>Gx,logImpl:()=>qx,maxImpl:()=>Xx,maximumImpl:()=>Kx,minimumImpl:()=>Zx,multiplyImpl:()=>Tm,negImpl:()=>Yx,notEqualImpl:()=>Jx,prodImpl:()=>Qx,rangeImpl:()=>Em,rsqrtImpl:()=>ew,simpleAbsImpl:()=>Ox,sliceImpl:()=>Jd,squaredDifferenceImpl:()=>tw,stridedSliceImpl:()=>nw,subImpl:()=>rw,tileImpl:()=>aw,topKImpl:()=>sw,transposeImpl:()=>Cm,uniqueImpl:()=>iw});function Ox(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var aR=e=>{let{x:t}=e.inputs,n=e.backend;_e(t,"abs");let r=new Float32Array(b.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=Ox(a),n.makeOutput(r,t.shape,"float32")},sR={kernelName:Zi,backendName:"cpu",kernelFunc:aR};function Et(e){return(t,n,r,a,s)=>{let i=C.assertAndGetBroadcastShape(t,n),o=i.length,l=b.computeStrides(i),c=b.sizeFromShape(i),u=b.getTypedArrayFromDType(s,c),h=t.length,d=n.length,p=b.computeStrides(t),f=b.computeStrides(n),m=C.getBroadcastDims(t,i),A=C.getBroadcastDims(n,i);if(m.length+A.length===0)for(let y=0;y<u.length;++y)u[y]=e(r[y%r.length],a[y%a.length]);else for(let y=0;y<u.length;++y){let g=b.indexToLoc(y,o,l),w=g.slice(-h);m.forEach(N=>w[N]=0);let _=b.locToIndex(w,h,p),v=g.slice(-d);A.forEach(N=>v[N]=0);let x=b.locToIndex(v,d,f);u[y]=e(r[_],a[x])}return[u,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 iR={kernelName:Ch,backendName:"cpu",kernelFunc:On};function Qd(e,t,n="float32"){if(n==="complex64"){let a=Qd(e,t,"float32"),s=Qd(e,t,"float32");return On({inputs:{real:a,imag:s},backend:e})}let r=b.makeZerosTypedArray(b.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 oR={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 lR={kernelName:Kh,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=Qd(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(!b.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=b.toTypedArray([0],a.dtype),[l,c]=Et((u,h)=>u!==h?1:0)(a.shape,[],i,o,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var uR={kernelName:hs,backendName:"cpu",kernelFunc:Wa};function Gt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;_e([i,o],e);let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,c,u,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 c=Wa({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),h=u.complexTensorInfos.real,d=u.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,w=l.data.get(y.dataId).values,_=l.data.get(g.dataId).values,[v,x,N]=n(i.shape,o.shape,p,f,w,_),E=l.makeTensorInfo(N,"float32",v),F=l.makeTensorInfo(N,"float32",x),M=On({inputs:{real:E,imag:F},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(E),l.disposeIntermediateTensorInfo(F),M}else{let c=l.data.get(i.dataId).values,u=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,c,u,h);return l.makeTensorInfo(p,h,d)}}}function Rm(e){return(t,n,r,a,s,i)=>{let o=C.assertAndGetBroadcastShape(t,n),l=b.sizeFromShape(o),c=o.length,u=b.computeStrides(o),h=b.getTypedArrayFromDType("float32",l),d=b.getTypedArrayFromDType("float32",l),p=C.getBroadcastDims(t,o),f=C.getBroadcastDims(n,o),m=C.mergeRealAndImagArrays(r,a),A=C.mergeRealAndImagArrays(s,i),y=t.length,g=b.computeStrides(t),w=n.length,_=b.computeStrides(n);if(p.length+f.length===0)for(let v=0;v<h.length;v++){let x=v%m.length,N=v%A.length,E=e(m[x*2],m[x*2+1],A[N*2],A[N*2+1]);h[v]=E.real,d[v]=E.imag}else for(let v=0;v<h.length;v++){let x=b.indexToLoc(v,c,u),N=x.slice(-y);p.forEach(V=>N[V]=0);let E=b.locToIndex(N,y,g),F=x.slice(-w);f.forEach(V=>F[V]=0);let M=b.locToIndex(F,w,_),W=e(m[E*2],m[E*2+1],A[M*2],A[M*2+1]);h[v]=W.real,d[v]=W.imag}return[h,d,o]}}var zx=Et((e,t)=>e+t),cR=Rm((e,t,n,r)=>({real:e+n,imag:t+r})),tc=Gt(Ia,zx,cR),hR={kernelName:Ia,backendName:"cpu",kernelFunc:tc};function Nm(e,t,n,r,a){let s=b.sizeFromShape(r),i=b.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 Px(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 c=e.get(o,l);if(c<0)throw new Error("Input x must be non-negative!");c>=n||(r?i.set(1,o,c):t.size>0?i.set(i.get(o,c)+t.get(o,l),o,c):i.set(i.get(o,c)+1,o,c))}return i}function xl(e){return(t,n,r)=>{let a=b.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function st(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,c=b.sizeFromShape(i.shape),u=n||i.dtype,h=b.getArrayFromDType(u,c);for(let d=0;d<c;++d)h[d]=t(l[d],a);return o.makeTensorInfo(i.shape,u,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,c=n||i.dtype,u=t(l,c,a);return o.makeTensorInfo(i.shape,c,u)}}var Lx=xl(e=>Math.ceil(e)),dR=wl(ds,Lx),pR={kernelName:ds,backendName:"cpu",kernelFunc:dR};function Sm(e,t,n,r){let a=b.getArrayFromDType(n,b.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=b.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?C.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let c=0;c<i.shape[0];++c){let u=c*t[1]+s;for(let h=0;h<i.shape[1];++h)a[u+h]=o[l++]}s+=i.shape[1]})}return a}var Wx=xl(e=>Math.exp(e)),ow=wl(xs,Wx),fR={kernelName:xs,backendName:"cpu",kernelFunc:ow},Bx=xl(e=>Math.expm1(e)),mR=wl(po,Bx),AR={kernelName:po,backendName:"cpu",kernelFunc:mR},Vx=xl(e=>Math.floor(e)),yR=wl(ws,Vx),gR={kernelName:ws,backendName:"cpu",kernelFunc:yR};function Ux(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 c=e.locToIndex(s);r.values[a]=e.values[c]}return r}var Hx=Et((e,t)=>e>t?1:0),xR=Gt(yo,Hx,null,"bool"),wR={kernelName:yo,backendName:"cpu",kernelFunc:xR},jx=Et((e,t)=>e<t?1:0),_R=Gt(_o,jx,null,"bool"),bR={kernelName:_o,backendName:"cpu",kernelFunc:_R};function Gx(e,t,n){let r=(t-e)/(n-1),a=b.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var qx=xl(e=>Math.log(e)),vR=wl(Ns,qx),kR={kernelName:Ns,backendName:"cpu",kernelFunc:vR};function Xx(e,t,n,r){let a=b.getTypedArrayFromDType(r,b.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 c=e[i+l];c>o&&(o=c)}a[s]=o}return a}var Kx=Et((e,t)=>Math.max(e,t)),IR=Gt(Ts,Kx),NR={kernelName:Ts,backendName:"cpu",kernelFunc:IR},Zx=Et((e,t)=>Math.min(e,t)),SR=Gt(Fs,Zx),TR={kernelName:Fs,backendName:"cpu",kernelFunc:SR},Tm=Et((e,t)=>e*t),CR=Rm((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),Fm=Gt(Ms,Tm,CR),ER={kernelName:Ms,backendName:"cpu",kernelFunc:Fm};function Yx(e,t,n){let r=b.createScalarValue(-1,n);return Tm([],t,r,e,n)}function RR(e){let{inputs:t,backend:n}=e,{x:r}=t;_e(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=Yx(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var FR={kernelName:No,backendName:"cpu",kernelFunc:RR},Jx=Et((e,t)=>e!==t?1:0),MR=Gt(So,Jx,null,"bool"),$R={kernelName:So,backendName:"cpu",kernelFunc:MR};function Cm(e,t,n,r,a){let s=t.length,i=b.sizeFromShape(t),o=b.computeStrides(t),l=b.computeStrides(a),c=b.getTypedArrayFromDType(n,b.sizeFromShape(a));for(let u=0;u<i;++u){let h=b.indexToLoc(u,s,o),d=new Array(h.length);for(let f=0;f<d.length;f++)d[f]=h[r[f]];let p=b.locToIndex(d,s,l);c[p]=e[u]}return c}function or(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 u=0;u<o.length;u++)o[u]=a.shape[s[u]];let l=r.data.get(a.dataId).values,c=Cm(l,a.shape,a.dtype,s,o);return{dataId:r.write(c,o,a.dtype),shape:o,dtype:a.dtype}}var DR={kernelName:Js,backendName:"cpu",kernelFunc:or};function Qx(e,t,n,r){let[a,s]=C.computeOutAndReduceShapes(e,r),i=rr(t,"int32"),o=b.makeZerosTypedArray(b.sizeFromShape(a),i),l=b.sizeFromShape(s);for(let c=0;c<o.length;++c){let u=c*l,h=1;for(let d=0;d<l;++d)h*=n[u+d];o[c]=h}return{outVals:o,outShape:a,outDtype:i}}function OR(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=b.parseAxisParam(s,a.shape),c=C.getAxesPermutation(l,o),u=l,h=a,d=[];c!=null&&(h=or({inputs:{x:a},backend:n,attrs:{perm:c}}),d.push(h),u=C.getInnerMostAxes(u.length,o));let p=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:A}=Qx(h.shape,h.dtype,p,u),y=m;return i&&(y=C.expandShapeToKeepDim(m,l)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,f)}var zR={kernelName:Mo,backendName:"cpu",kernelFunc:OR};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 b.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=b.makeZerosTypedArray(o,r);t<e&&n===1&&(n=-1),l[0]=e;for(let c=1;c<l.length;c++)l[c]=l[c-1]+n;return l}var ew=xl(e=>1/Math.sqrt(e)),PR=wl(Us,ew),LR={kernelName:Us,backendName:"cpu",kernelFunc:PR};function Jd(e,t,n,r,a){let s=cn.isSliceContinous(r,t,n),i=b.sizeFromShape(n),o=b.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"?C.fromUint8ToStringArray(e):e,c=We(r,a,l),u=We(n,a);for(let h=0;h<u.size;++h){let d=u.indexToLoc(h),p=d.map((f,m)=>f+t[m]);u.set(c.get(...p),...d)}return a==="string"?C.fromStringArrayToUint8(u.values):u.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 c=n.data.get(a.dataId).values,u=Jd(c,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,u)}var WR={kernelName:Lo,backendName:"cpu",kernelFunc:Ai},tw=Et((e,t)=>{let n=e-t;return n*n}),BR=Gt(Ks,tw),VR={kernelName:Ks,backendName:"cpu",kernelFunc:BR};function nw(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 rw=Et((e,t)=>e-t),UR=Rm((e,t,n,r)=>({real:e-n,imag:t-r})),Mm=Gt(Zs,rw,UR),HR={kernelName:Zs,backendName:"cpu",kernelFunc:Mm};function aw(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 sw(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=b.getTypedArrayFromDType(n,i*r),c=b.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,w)=>w.value-g.value);let m=h*r,A=l.subarray(m,m+r),y=c.subarray(m,m+r);for(let g=0;g<r;g++)A[g]=f[g].value,y[g]=f[g].index}let u=t.slice();return u[u.length-1]=r,[We(u,n,l),We(u,"int32",c)]}function iw(e,t,n,r){let a=b.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 Dt(s,r,e),c=[],u=s[0]===1&&s[2]===1;for(let f=0;f<n[a];f++){let m;if(u)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,c.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let d=new Dt(h,r);c.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 lw="3.3.0";sl("cpu",()=>new Yd,1);var uw=st(lo,e=>e>=0?e:Math.exp(e)-1),jR={kernelName:lo,backendName:"cpu",kernelFunc:uw};function cw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;_e([a],"leakyRelu");let i=b.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=b.getTypedArrayFromDType("float32",i);for(let c=0;c<o.length;c++)l[c]=o[c]<0?s*o[c]:o[c];return n.makeTensorInfo(a.shape,"float32",l)}var GR={kernelName:Is,backendName:"cpu",kernelFunc:cw},qR=Et((e,t)=>e<0?t*e:e);function hw(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]=qR(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var XR={kernelName:zs,backendName:"cpu",kernelFunc:hw},dw=st(Ps,e=>Math.max(0,e)),KR={kernelName:Ps,backendName:"cpu",kernelFunc:dw},pw=st(Ws,e=>Math.min(Math.max(0,e),6)),ZR={kernelName:Ws,backendName:"cpu",kernelFunc:pw};function $m(e,t,n,r,a){if(n==="linear")return Lr({inputs:{x:t},backend:e});if(n==="relu")return dw({inputs:{x:t},backend:e});if(n==="elu")return uw({inputs:{x:t},backend:e});if(n==="relu6")return pw({inputs:{x:t},backend:e});if(n==="prelu")return hw({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return cw({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function yt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=b.sizeFromShape(a.shape),o=b.inferFromImplicitShape(s,i),l=b.sizeFromShape(o);b.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let c=n.data.get(a.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,h=c.complexTensorInfos.imag;u.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var YR={kernelName:Do,backendName:"cpu",kernelFunc:yt};function fw(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;_e([a,s],"matMul");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=b.sizeFromShape(f),y=b.sizeFromShape(m),g=A===y||A===1||y===1;b.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let w=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);b.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let _=i?[A,u,d]:[A,d,u],v=o?[y,p,h]:[y,h,p],x=yt({inputs:{x:a},backend:n,attrs:{shape:_}}),N=yt({inputs:{x:s},backend:n,attrs:{shape:v}}),E=i?x.shape[1]:x.shape[2],F=i?x.shape[2]:x.shape[1],M=o?N.shape[1]:N.shape[2],W=Math.max(A,y),V=n.data.get(x.dataId).values,B=n.data.get(N.dataId).values,H=b.computeStrides(x.shape),j=b.computeStrides(N.shape),[X,G,ee]=i?[H[0],1,H[1]]:[H[0],H[1],1],[Y,ae,te]=o?[1,j[1],j[0]]:[j[1],1,j[0]],ie=F*M,Q=We([W,F,M],x.dtype),he=Q.values,oe=n.blockSize;for(let fe=0;fe<W;fe++)for(let pe=0;pe<F;pe+=oe)for(let ke=0;ke<M;ke+=oe)for(let Ne=0;Ne<E;Ne+=oe){let Me=Math.min(pe+oe,F),Oe=Math.min(ke+oe,M),$e=Math.min(Ne+oe,E);for(let et=pe;et<Me;et++)for(let tt=ke;tt<Oe;tt++){let it=0;for(let Ze=Ne;Ze<$e;Ze++){let dt=Math.min(fe,A-1)*X,Be=Math.min(fe,y-1)*te,yn=V[dt+et*G+Ze*ee],wt=B[Ze*Y+tt*ae+Be];it+=yn*wt}he[fe*ie+(et*M+tt)]+=it}}return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(w,Q.dtype,Q.values)}var JR={kernelName:cs,backendName:"cpu",kernelFunc:fw};function QR(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r,d,p,f,m=[];d=fw({inputs:{a,b:s},attrs:{transposeA:l,transposeB:c},backend:n}),i&&(p=tc({inputs:{a:d,b:i},backend:n}),m.push(d),d=p),u&&(f=$m(n,d,u,o,h),m.push(d),d=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return d}var eF={kernelName:Qs,backendName:"cpu",kernelFunc:QR},tF=st(Yi,e=>Math.acos(e)),nF={kernelName:Yi,backendName:"cpu",kernelFunc:tF},rF=st(Ji,e=>Math.acosh(e)),aF={kernelName:Ji,backendName:"cpu",kernelFunc:rF};function sF(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 c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var iF={kernelName:os,backendName:"cpu",kernelFunc:sF};function oF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"all");let o=b.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=or({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=b.sizeFromShape(d),f=b.makeZerosTypedArray(b.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let _=0;_<p;++_){let v=m[g+_];w=w&&v}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var lF={kernelName:kh,backendName:"cpu",kernelFunc:oF};function uF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"any");let o=b.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=or({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=b.sizeFromShape(d),f=b.makeZerosTypedArray(b.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let _=0;_<p;++_){let v=m[g+_];w=w||v}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var cF={kernelName:Ih,backendName:"cpu",kernelFunc:uF};function hF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;_e(a,"argMax");let i=b.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=or({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=b.sizeFromShape(u),p=b.makeZerosTypedArray(d,"int32"),f=b.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let _=0;_<f;++_){let v=m[y+_];v>g&&(g=v,w=_)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var dF={kernelName:ls,backendName:"cpu",kernelFunc:hF};function pF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;_e(a,"argMin");let i=b.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=or({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=b.sizeFromShape(u),p=b.makeZerosTypedArray(d,"int32"),f=b.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],w=0;for(let _=0;_<f;++_){let v=m[y+_];v<g&&(g=v,w=_)}p[A]=w}return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(u,"int32",p)}var fF={kernelName:lu,backendName:"cpu",kernelFunc:pF},mF=st(Qi,e=>Math.asin(e)),AF={kernelName:Qi,backendName:"cpu",kernelFunc:mF},yF=st(eo,e=>Math.asinh(e)),gF={kernelName:eo,backendName:"cpu",kernelFunc:yF},xF=st(to,e=>Math.atan(e)),wF={kernelName:to,backendName:"cpu",kernelFunc:xF},_F=Et((e,t)=>Math.atan2(e,t)),bF=Gt(ro,_F),vF={kernelName:ro,backendName:"cpu",kernelFunc:bF},kF=st(no,e=>Math.atanh(e)),IF={kernelName:no,backendName:"cpu",kernelFunc:kF};function Dm(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,c=a.dilationWidth,u=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],w=a.outShape[3];for(let _=0;_<a.batchSize;++_){let v=_*y,x=_*r[0];for(let N=0;N<a.inChannels;++N)for(let E=0;E<a.outHeight;++E){let F=E*i-d,M=Math.max(0,F),W=Math.min(a.inHeight,u+F),V=v+E*g;for(let B=0;B<a.outWidth;++B){let H=B*o-p,j=Math.max(0,H),X=Math.min(a.inWidth,h+H),G=f,ee=0,Y=0;for(let te=M;te<W;te+=l){let ie=x+te*r[1];for(let Q=j;Q<X;Q+=c){let he=ie+Q*r[2],oe=e[he+N];s==="max"&&oe>G?G=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(G))break}let ae=V+B*w+N;A[ae]=s==="avg"?ee/Y:G}}}return m}function mw(e,t,n,r,a=!1,s=!1){let i=We(r.outShape,"int32"),o=r.strideHeight,l=r.strideWidth,c=r.dilationHeight,u=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 w=g*o-p,_=w;for(;_<0;)_+=c;let v=Math.min(r.inHeight,h+w);for(let x=0;x<r.outWidth;++x){let N=x*l-f,E=N;for(;E<0;)E+=u;let F=Math.min(r.inWidth,d+N),M=Number.NEGATIVE_INFINITY,W=-1;for(let V=_;V<v;V+=c){let B=V-w;for(let H=E;H<F;H+=u){let j=H-N,X=m.get(A,V,H,y);X>M&&(M=X,a?W=s?((A*r.inHeight+V)*r.inWidth+H)*r.inChannels+y:(V*r.inWidth+H)*r.inChannels+y:W=B*d+j)}}i.set(W,A,g,x,y)}}return i}function Aw(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=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,w=We(a.outShape,n),_=w.values,v=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],x=a.outShape[2]*a.outShape[3]*a.outShape[4],N=a.outShape[3]*a.outShape[4],E=a.outShape[4];for(let F=0;F<a.batchSize;++F){let M=F*v,W=F*r[0];for(let V=0;V<a.inChannels;++V)for(let B=0;B<a.outDepth;++B){let H=B*i-m,j=H;for(;j<0;)j+=c;let X=Math.min(a.inDepth,d+H),G=M+B*x;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-A,ae=Y;for(;ae<0;)ae+=u;let te=Math.min(a.inHeight,p+Y),ie=G+ee*N;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*E,ke=g,Ne=0,Me=0;for(let $e=j;$e<X;$e+=c){let et=W+$e*r[1];for(let tt=ae;tt<te;tt+=u){let it=et+tt*r[2];for(let Ze=oe;Ze<fe;Ze+=h){let dt=it+Ze*r[3],Be=e[dt+V];if(s==="max"&&Be>ke?ke=Be:s==="avg"&&(Ne+=Be,Me++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Oe=pe+V;_[Oe]=s==="avg"?Ne/Me:ke}}}}return w}function NF(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,c=t.effectiveFilterDepth,u=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,w=g;for(;w<0;)w+=i;let _=Math.min(t.inDepth,c+g);for(let v=0;v<t.outHeight;++v){let x=v*a-p,N=x;for(;N<0;)N+=o;let E=Math.min(t.inHeight,u+x);for(let F=0;F<t.outWidth;++F){let M=F*s-f,W=M;for(;W<0;)W+=l;let V=Math.min(t.inWidth,h+M),B=Number.NEGATIVE_INFINITY,H=-1;for(let j=w;j<_;j+=i){let X=j-g;for(let G=N;G<E;G+=o){let ee=G-x;for(let Y=W;Y<V;Y+=l){let ae=Y-M,te=e.get(m,j,G,Y,A);te>=B&&(B=te,H=X*u*h+ee*u+ae)}}}n.set(H,m,y,v,F,A)}}}return n}function SF(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,c=1;b.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&b.arraysEqual(u.inShape,u.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=b.computeStrides(a.shape),f=Dm(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var TF={kernelName:us,backendName:"cpu",kernelFunc:SF};function CF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;_e(a,"avgPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=Aw(h,a.shape,a.dtype,b.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var EF={kernelName:uu,backendName:"cpu",kernelFunc:CF};function RF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;_e([a,s],"avgPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,A=u.filterWidth,y=u.dilationDepth,g=u.dilationHeight,w=u.dilationWidth,_=u.effectiveFilterDepth,v=u.effectiveFilterHeight,x=u.effectiveFilterWidth,N=_-1-u.padInfo.front,E=x-1-u.padInfo.left,F=v-1-u.padInfo.top,M=We(s.shape,"float32"),W=1/(f*m*A),V=n.bufferSync(a);for(let B=0;B<u.batchSize;++B)for(let H=0;H<u.inChannels;++H)for(let j=0;j<u.inDepth;++j)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=j-N,Y=X-F,ae=G-E,te=0;for(let ie=0;ie<_;ie+=y){let Q=(ee+ie)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let he=0;he<v;he+=g){let oe=(Y+he)/d;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe<x;fe+=w){let pe=(ae+fe)/p;pe<0||pe>=u.outWidth||Math.floor(pe)!==pe||(te+=V.get(B,Q,oe,pe,H))}}}M.set(te*W,B,j,X,G,H)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var FF={kernelName:Sh,backendName:"cpu",kernelFunc:RF};function MF(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:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,A=u.dilationWidth,y=u.effectiveFilterHeight,g=u.effectiveFilterWidth,w=g-1-u.padInfo.left,_=y-1-u.padInfo.top,v=We(i.shape,"float32"),x=1/(p*f),N=n.data.get(a.dataId).values,E=We(a.shape,"float32",N);for(let F=0;F<u.batchSize;++F)for(let M=0;M<u.inChannels;++M)for(let W=0;W<u.inHeight;++W)for(let V=0;V<u.inWidth;++V){let B=W-_,H=V-w,j=0;for(let X=0;X<y;X+=m){let G=(B+X)/h;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=A){let Y=(H+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(j+=E.get(F,G,Y,M))}}v.set(j*x,F,W,V,M)}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var $F={kernelName:Nh,backendName:"cpu",kernelFunc:MF};function DF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;b.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),b.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),b.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:c}=r;c==null&&(c=.001);let u=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(u.length),A=f.length,y=p.length,g=d.length,w=h.length,_=0,v=0,x=0,N=0;for(let E=0;E<u.length;++E)m[E]=f[_++]+(u[E]-h[v++])*p[x++]/Math.sqrt(d[N++]+c),_>=A&&(_=0),v>=w&&(v=0),x>=y&&(x=0),N>=g&&(N=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var OF={kernelName:bs,backendName:"cpu",kernelFunc:DF};function zF(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=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=yt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=or({inputs:{x:p},backend:n,attrs:{perm:c}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var PF={kernelName:cu,backendName:"cpu",kernelFunc:zF};function LF(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,c=Nm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var WF={kernelName:Th,backendName:"cpu",kernelFunc:LF},BF=st(Na,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),VF={kernelName:Na,backendName:"cpu",kernelFunc:BF},UF=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(b.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 c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},HF={kernelName:hu,backendName:"cpu",kernelFunc:UF};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 jF={kernelName:Vh,backendName:"cpu",kernelFunc:_l};function bl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=b.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),s);if(b.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>b.sizeFromShape(m.shape)>0);if(o.length===1)return Lr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(_=>mi({inputs:{input:_},backend:n})),A=o.map(_=>_l({inputs:{input:_},backend:n})),y=bl({inputs:m,backend:n,attrs:{axis:s}}),g=bl({inputs:A,backend:n,attrs:{axis:s}}),w=On({inputs:{real:y,imag:g},backend:n});return m.forEach(_=>n.disposeIntermediateTensorInfo(_)),A.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),w}let c=o.map(m=>{let A=b.sizeFromShape(m.shape.slice(s));return yt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=Sm(u,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var GF={kernelName:ao,backendName:"cpu",kernelFunc:bl};function yw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r;_e([a,s],"conv2d");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,w=d.dataFormat==="channelsLast",_=new Dt(d.outShape,a.dtype),v=b.computeStrides(a.shape),x=b.computeStrides(s.shape),N=v[0],E=w?v[1]:v[2],F=w?v[2]:1,M=w?1:v[1],W=_.strides[0],V=w?_.strides[1]:_.strides[2],B=w?_.strides[2]:1,H=w?1:_.strides[1],j=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,G=_.values;for(let ee=0;ee<d.batchSize;++ee){let Y=ee*N,ae=ee*W;for(let te=0;te<d.outHeight;++te){let ie=ae+te*V,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*E;for(let ke=0;ke<d.outWidth;++ke){let Ne=ie+ke*B,Me=ke*d.strideWidth-y;for(let Oe=0;Oe<f;++Oe){let $e=Me+Oe*A;if($e<0||$e>=d.inWidth)continue;let et=fe+Oe*x[1],tt=pe+$e*F,it=et;for(let Ze=0;Ze<d.inChannels;++Ze){let dt=j[tt+Ze*M];for(let Be=0;Be<d.outChannels;++Be)G[Ne+Be*H]+=dt*X[it+Be];it+=d.outChannels}}}}}}return n.makeTensorInfo(_.shape,_.dtype,G)}var qF={kernelName:ps,backendName:"cpu",kernelFunc:yw};function XF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r;_e([a,s],"conv2dBackpropFilter");let h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new Dt(d.filterShape,"float32"),w=d.padInfo.left,_=d.padInfo.top,v=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=new Dt(a.shape,a.dtype,v),E=new Dt(s.shape,s.dtype,x);for(let F=0;F<m;++F){let M=Math.max(0,Math.ceil((_-F)/p)),W=Math.min(d.outHeight,(d.inHeight+_-F)/p);for(let V=0;V<A;++V){let B=Math.max(0,Math.ceil((w-V)/f)),H=Math.min(d.outWidth,(d.inWidth+w-V)/f);for(let j=0;j<d.inChannels;++j)for(let X=0;X<d.outChannels;++X){let G=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=M;Y<W;++Y){let ae=F+Y*p-_;for(let te=B;te<H;++te){let ie=V+te*f-w;y?G+=N.get(ee,ae,ie,j)*E.get(ee,Y,te,X):G+=N.get(ee,j,ae,ie)*E.get(ee,X,Y,te)}}g.set(G,F,V,j,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var KF={kernelName:Eh,backendName:"cpu",kernelFunc:XF};function ZF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;_e([a,s],"conv2dBackpropInput");let h=b.computeStrides(s.shape),d=b.computeStrides(a.shape),p=C.convertConv2DDataFormat(c),f=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new Dt(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[w,_,v]=h,{batchSize:x,filterHeight:N,filterWidth:E,inChannels:F,inHeight:M,inWidth:W,outChannels:V,outHeight:B,outWidth:H,strideHeight:j,strideWidth:X}=f;p=f.dataFormat;let G=N-1-f.padInfo.top,ee=E-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 Ne=0;Ne<F;++Ne)for(let Me=0;Me<M;++Me){let Oe=Me-G,$e=Math.max(0,Math.ceil(Oe/j)),et=Math.min(B,(N+Oe)/j);for(let tt=0;tt<W;++tt){let it=tt-ee,Ze=Math.max(0,Math.ceil(it/X)),dt=Math.min(H,(E+it)/X),Be=0;for(let wt=$e;wt<et;++wt){let Un=wt*j-Oe;for(let Zt=Ze;Zt<dt;++Zt){let gn=Zt*X-it,Hn=he*ke+oe*wt+fe*Zt,Rn=w*(N-1-Un)+_*(E-1-gn)+v*Ne;for(let ln=0;ln<V;++ln){let Yt=y[Hn+pe*ln],Tr=g[Rn+ln];Be+=Yt*Tr}}}let yn=ae*ke+te*Me+ie*tt+Q*Ne;A[yn]=Be}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var YF={kernelName:fs,backendName:"cpu",kernelFunc:ZF};function JF(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 c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=c,y=A.front,g=A.left,w=A.top,_=new Dt(c.outShape,a.dtype),v=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=_.values,E=b.computeStrides(a.shape),F=b.computeStrides(s.shape);for(let M=0;M<c.batchSize;++M){let W=M*E[0],V=M*_.strides[0];for(let B=0;B<c.outDepth;++B){let H=V+B*_.strides[1],j=B*c.strideDepth-y;for(let X=0;X<u;++X){let G=j+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*F[0],Y=W+G*E[1];for(let ae=0;ae<c.outHeight;++ae){let te=H+ae*_.strides[2],ie=ae*c.strideHeight-w;for(let Q=0;Q<h;++Q){let he=ie+Q*f;if(he<0||he>=c.inHeight)continue;let oe=ee+Q*F[1],fe=Y+he*E[2];for(let pe=0;pe<c.outWidth;++pe){let ke=te+pe*c.outChannels,Ne=pe*c.strideWidth-g;for(let Me=0;Me<d;++Me){let Oe=Ne+Me*m;if(Oe<0||Oe>=c.inWidth)continue;let $e=oe+Me*F[2],et=fe+Oe*c.inChannels,tt=$e;for(let it=0;it<c.inChannels;++it){let Ze=v[et+it];for(let dt=0;dt<c.outChannels;++dt)N[ke+dt]+=Ze*x[tt+dt];tt+=c.outChannels}}}}}}}}return n.makeTensorInfo(_.shape,_.dtype,_.values)}var QF={kernelName:du,backendName:"cpu",kernelFunc:JF};function eM(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 c=b.computeStrides(a.shape),u=b.computeStrides(s.shape),h=C.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 Dt(h.filterShape,"float32"),w=g.values,[_,v,x,N]=g.strides,E=n.data.get(s.dataId).values,[F,M,W,V]=u,B=n.data.get(a.dataId).values,[H,j,X,G]=c,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*_;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*v+he;for(let Ne=0;Ne<y;++Ne){let Me=Math.max(0,Math.ceil((Y-Ne)/f)),Oe=Math.min(h.outWidth,(h.inWidth+Y-Ne)/f),$e=Ne*x+ke;for(let et=0;et<h.inChannels;++et){let tt=et*N+$e;for(let it=0;it<h.outChannels;++it){let Ze=0;for(let dt=0;dt<h.batchSize;++dt){let Be=dt*H,yn=dt*F;for(let wt=ie;wt<Q;++wt){let Un=(te+wt*d-ee)*j+Be,Zt=wt*M+yn;for(let gn=fe;gn<pe;++gn){let Hn=(oe+gn*p-ae)*X+Un,Rn=gn*W+Zt;for(let ln=Me;ln<Oe;++ln){let Yt=(Ne+ln*f-Y)*G+Hn,Tr=ln*V+Rn;Ze+=B[Yt+et]*E[Tr+it]}}}}w[tt+it]=Ze}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var tM={kernelName:Rh,backendName:"cpu",kernelFunc:eM};function nM(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 c=b.computeStrides(a.shape),u=b.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),d=new Dt(h.inShape,"float32"),p=d.values,[f,m,A,y]=d.strides,g=n.data.get(a.dataId).values,[w,_,v,x]=c,N=n.data.get(s.dataId).values,[E,F,M,W]=u,{batchSize:V,filterDepth:B,filterHeight:H,filterWidth:j,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:he,strideHeight:oe,strideWidth:fe}=h,pe=B-1-h.padInfo.front,ke=H-1-h.padInfo.top,Ne=j-1-h.padInfo.left;for(let Me=0;Me<V;++Me)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<G;++$e){let et=$e-pe,tt=Math.max(0,Math.ceil(et/he)),it=Math.min(te,(B+et)/he);for(let Ze=0;Ze<ee;++Ze){let dt=Ze-ke,Be=Math.max(0,Math.ceil(dt/oe)),yn=Math.min(ie,(H+dt)/oe);for(let wt=0;wt<Y;++wt){let Un=wt-Ne,Zt=Math.max(0,Math.ceil(Un/fe)),gn=Math.min(Q,(j+Un)/fe),Hn=0;for(let Rn=tt;Rn<it;++Rn){let ln=Rn*he-et;for(let Yt=Be;Yt<yn;++Yt){let Tr=Yt*oe-dt;for(let Jn=Zt;Jn<gn;++Jn){let Qn=Jn*fe-Un,Aa=w*Me+_*Rn+v*Yt+x*Jn,Kr=E*(B-1-ln)+F*(H-1-Tr)+M*(j-1-Qn)+W*Oe;for(let ya=0;ya<ae;++ya){let Di=g[Aa+ya],pr=N[Kr+ya];Hn+=Di*pr}}}}p[f*Me+m*$e+A*Ze+y*wt+Oe]=Hn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var rM={kernelName:Fh,backendName:"cpu",kernelFunc:nM},aM=st(ms,e=>Math.cos(e)),sM={kernelName:ms,backendName:"cpu",kernelFunc:aM},iM=st(so,e=>Math.cosh(e)),oM={kernelName:so,backendName:"cpu",kernelFunc:iM};function lM(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,[u,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=We([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,w=n.data.get(i.dataId).values,_=n.data.get(a.dataId).values,v=b.computeStrides(a.shape),x=b.computeStrides(y.shape);for(let N=0;N<f;N++){let E=N*4,F=g[E],M=g[E+1],W=g[E+2],V=g[E+3],B=w[N];if(B>=u)continue;let H=m>1?(W-F)*(h-1)/(m-1):0,j=A>1?(V-M)*(d-1)/(A-1):0;for(let X=0;X<m;X++){let G=m>1?F*(h-1)+X*H:.5*(F+W)*(h-1);if(G<0||G>h-1){for(let ee=0;ee<A;ee++)for(let Y=0;Y<p;Y++){let ae=Y+ee*x[2]+X*x[1]+N*x[0];y.values[ae]=c}continue}if(l==="bilinear"){let ee=Math.floor(G),Y=Math.ceil(G),ae=G-ee;for(let te=0;te<A;te++){let ie=A>1?M*(d-1)+te*j:.5*(M+V)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<p;fe++){let pe=fe+te*x[2]+X*x[1]+N*x[0];y.values[pe]=c}continue}let Q=Math.floor(ie),he=Math.ceil(ie),oe=ie-Q;for(let fe=0;fe<p;fe++){let pe=fe+Q*v[2]+ee*v[1]+B*v[0],ke=_[pe];pe=fe+he*v[2]+ee*v[1]+B*v[0];let Ne=_[pe];pe=fe+Q*v[2]+Y*v[1]+B*v[0];let Me=_[pe];pe=fe+he*v[2]+Y*v[1]+B*v[0];let Oe=_[pe],$e=ke+(Ne-ke)*oe,et=Me+(Oe-Me)*oe;pe=fe+te*x[2]+X*x[1]+N*x[0],y.values[pe]=$e+(et-$e)*ae}}}else for(let ee=0;ee<A;++ee){let Y=A>1?M*(d-1)+ee*j:.5*(M+V)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*x[2]+X*x[1]+N*x[0];y.values[Q]=c}continue}let ae=Math.round(Y),te=Math.round(G);for(let ie=0;ie<p;ie++){let Q=ie+ae*v[2]+te*v[1]+B*v[0],he=ie+ee*x[2]+X*x[1]+N*x[0];y.values[he]=_[Q]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var uM={kernelName:io,backendName:"cpu",kernelFunc:lM};function cM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;_e(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=or({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=C.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=rr(c.dtype,"int32"),d=b.makeZerosTypedArray(b.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.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 w=m(y,g);if(g===0)d[w]=i?0:p[w];else{let _=m(y,g-1);d[w]=i?p[_]+d[_]:p[w]+d[_]}}let A=n.makeTensorInfo(c.shape,h,d);if(l!=null){let y=C.getUndoAxesPermutation(l),g=or({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(c),g}return A}var hM={kernelName:As,backendName:"cpu",kernelFunc:cM};function dM(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,c=n.data.get(s.dataId).values,u=Nm(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=Px(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var pM={kernelName:Mh,backendName:"cpu",kernelFunc:dM};function fM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;b.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),b.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,d=c*s,p=u/(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 w=Math.floor(g/s),_=g%s;for(let v=0;v<d;++v){let x=Math.floor(v/s),N=v%s,E=(_*s+N)*p;for(let F=0;F<p;++F){let M=F+E+u*(x+c*(w+l*y));m[A++]=f[M]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var mM={kernelName:oo,backendName:"cpu",kernelFunc:fM};function gw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;_e([a,s],"depthwiseConv2DNative");let u=b.computeStrides(a.shape),h=b.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),b.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=C.computeConv2DInfo(a.shape,s.shape,i,d,o,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=p,w=g.left,_=g.top,v=p.outChannels/p.inChannels,x=new Dt(p.outShape,a.dtype),N=n.data.get(a.dataId).values,E=n.data.get(s.dataId).values,F=x.values;for(let M=0;M<p.batchSize;++M){let W=M*u[0],V=M*x.strides[0];for(let B=0;B<p.outHeight;++B){let H=V+B*x.strides[1],j=B*p.strideHeight-w;for(let X=0;X<f;++X){let G=j+X*A;if(G<0||G>=p.inHeight)continue;let ee=X*h[0],Y=W+G*u[1];for(let ae=0;ae<p.outWidth;++ae){let te=H+ae*x.strides[2],ie=ae*p.strideWidth-_;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 Ne=0;Ne<p.inChannels;++Ne){let Me=N[fe+Ne];for(let Oe=0;Oe<v;++Oe)F[pe+Oe]+=Me*E[ke+Oe];pe+=v,ke+=v}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var AM={kernelName:ys,backendName:"cpu",kernelFunc:gw};function yM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r;_e([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,A=new Dt(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,w=h.outChannels/h.inChannels,_=n.data.get(a.dataId).values,v=new Dt(a.shape,a.dtype,_),x=n.data.get(s.dataId).values,N=new Dt(s.shape,s.dtype,x);for(let E=0;E<f;++E){let F=Math.max(0,Math.ceil((g-E)/d)),M=Math.min(h.outHeight,(h.inHeight+g-E)/d);for(let W=0;W<m;++W){let V=Math.max(0,Math.ceil((y-W)/p)),B=Math.min(h.outWidth,(h.inWidth+y-W)/p);for(let H=0;H<h.outChannels;++H){let j=Math.trunc(H/w),X=H%w,G=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=F;Y<M;++Y){let ae=E+Y*d-g;for(let te=V;te<B;++te){let ie=W+te*p-y;G+=v.get(ee,ae,ie,j)*N.get(ee,Y,te,H)}}A.set(G,E,W,j,X)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var gM={kernelName:$h,backendName:"cpu",kernelFunc:yM};function xM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;_e([a,s],"depthwiseConv2DNativeBackpropInput");let h=b.computeStrides(a.shape),d=b.computeStrides(s.shape),p=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new Dt(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,w=n.data.get(a.dataId).values,[_,v,x]=h,N=n.data.get(s.dataId).values,[E,F,M]=d,{batchSize:W,filterHeight:V,filterWidth:B,inChannels:H,inHeight:j,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=V-1-p.padInfo.top,Q=B-1-p.padInfo.left,he=G/H;for(let oe=0;oe<W;++oe)for(let fe=0;fe<H;++fe)for(let pe=0;pe<j;++pe){let ke=pe-ie,Ne=Math.max(0,Math.ceil(ke/ae)),Me=Math.min(ee,(V+ke)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,et=Math.max(0,Math.ceil($e/te)),tt=Math.min(Y,(B+$e)/te),it=0;for(let Ze=Ne;Ze<Me;++Ze){let dt=Ze*ae-ke;for(let Be=et;Be<tt;++Be){let yn=Be*te-$e,wt=_*oe+v*Ze+x*Be,Un=E*(V-1-dt)+F*(B-1-yn)+M*fe;for(let Zt=0;Zt<he;++Zt){let gn=fe*he+Zt,Hn=w[wt+gn],Rn=N[Un+Zt];it+=Hn*Rn}}}m[A*oe+y*pe+g*Oe+fe]=it}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var wM={kernelName:Dh,backendName:"cpu",kernelFunc:xM};function _M(e){let{inputs:t,backend:n}=e,{x:r}=t,a=b.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=We([a,a],r.dtype),o=i.values;for(let c=0;c<s.length;c++)o[c*a+c]=s[c];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var bM={kernelName:Oh,backendName:"cpu",kernelFunc:_M},vM={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,c=l.data.get(r.dataId).values,u=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:w,strideHeight:_,strideWidth:v,filterHeight:x,filterWidth:N,dilationHeight:E,dilationWidth:F,outShape:M}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),W=b.sizeFromShape(M),V=M.length,B=b.getArrayFromDType(r.dtype,W);for(let H=0;H<p;++H)for(let j=0;j<y;++j){let X=j*_-w.top;for(let G=0;G<g;++G){let ee=G*v-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*E;if(Q>=0&&Q<f)for(let he=0;he<N;++he){let oe=ee+he*F;if(oe>=0&&oe<m){let fe=b.locToIndex([H,Q,oe,Y],u,b.computeStrides(r.shape)),pe=b.locToIndex([ie,he,Y],d,b.computeStrides(a.shape)),ke=c[fe]+h[pe];ke>ae&&(ae=ke)}}}let te=b.locToIndex([H,j,G,Y],V,b.computeStrides(M));B[te]=ae}}}return{dataId:l.write(b.toTypedArray(B,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},kM={kernelName:Ph,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=b.toNestedArray(r.shape,c.data.get(r.dataId).values),h=b.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:w,strideWidth:_,filterHeight:v,filterWidth:x,dilationHeight:N,dilationWidth:E,outShape:F}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);b.assert(s.rank===F.length,()=>`Error in ${Ph}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=b.toNestedArray(F,c.data.get(s.dataId).values),W=b.makeZerosNestedTypedArray(a.shape,a.dtype);for(let V=0;V<d;++V)for(let B=0;B<A;++B){let H=B*w-g.top;for(let j=0;j<y;++j){let X=j*_-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=0,ae=0;for(let te=0;te<v;++te){let ie=H+te*N;if(ie>=0&&ie<p)for(let Q=0;Q<x;++Q){let he=X+Q*E;if(he>=0&&he<f){let oe=u[V][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=te,ae=Q)}}}W[Y][ae][G]+=M[V][B][j][G]}}}return{dataId:c.write(b.toTypedArray(W,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},IM={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,c=t,u=b.toNestedArray(r.shape,c.data.get(r.dataId).values),h=b.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:w,strideWidth:_,filterHeight:v,filterWidth:x,dilationHeight:N,dilationWidth:E,outShape:F}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);b.assert(s.rank===F.length,()=>`Error in ${zh}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=b.toNestedArray(F,c.data.get(s.dataId).values),W=b.makeZerosNestedTypedArray(r.shape,r.dtype);for(let V=0;V<d;++V)for(let B=0;B<A;++B){let H=B*w-g.top;for(let j=0;j<y;++j){let X=j*_-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=H<0?0:H,ae=X<0?0:X;for(let te=0;te<v;++te){let ie=H+te*N;if(ie>=0&&ie<p)for(let Q=0;Q<x;++Q){let he=X+Q*E;if(he>=0&&he<f){let oe=u[V][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=ie,ae=he)}}}W[V][Y][ae][G]+=M[V][B][j][G]}}}return{dataId:c.write(b.toTypedArray(W,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function NM(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;_e([r,a],"eluGrad");let s=new Float32Array(b.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(a.shape,"float32",s)}var SM={kernelName:Lh,backendName:"cpu",kernelFunc:NM},TM=Et((e,t)=>e===t?1:0),xw=Gt(co,TM,null,"bool"),CM={kernelName:co,backendName:"cpu",kernelFunc:xw},EM=C.ERF_P,RM=C.ERF_A1,FM=C.ERF_A2,MM=C.ERF_A3,$M=C.ERF_A4,DM=C.ERF_A5,OM=st(uo,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+EM*n);return t*(1-((((DM*r+$M)*r+MM)*r+FM)*r+RM)*r*Math.exp(-n*n))}),zM={kernelName:uo,backendName:"cpu",kernelFunc:OM};function ep(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(b.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),yt({inputs:{x:a},backend:n,attrs:{shape:o}})}var PM={kernelName:ho,backendName:"cpu",kernelFunc:ep},LM=Et((e,t)=>e/t),Om=Gt(gs,LM),zm={kernelName:gs,backendName:"cpu",kernelFunc:Om};function ww(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,c=[a,s],u=b.sizeFromShape(c),h=b.getTypedArrayFromDType("float32",u),d=b.getTypedArrayFromDType("float32",u);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]}}),w=On({inputs:{real:y,imag:g},backend:n}),{real:_,imag:v}=WM(w,t,n),x=C.mergeRealAndImagArrays(_,v);for(let N=0;N<s;N++){let E=C.getComplexWithIndex(x,N);h[A*s+N]=E.real,d[A*s+N]=E.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(w)}let p=n.makeTensorInfo(c,"float32",h),f=n.makeTensorInfo(c,"float32",d),m=On({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function WM(e,t,n){let r=b.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(BM(r)){let o=Pm(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",b.createScalarValue(r,"float32")),d=Lr({inputs:{x:h},backend:n}),p=zm.kernelFunc({inputs:{a:c,b:h},backend:n}),f=zm.kernelFunc({inputs:{a:u,b:d},backend:n}),m=n.data.get(p.dataId).values,A=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:A}}return o}else{let o=C.mergeRealAndImagArrays(s,i),l=VM(o,r,t);return C.splitRealAndImagArrays(l)}}function BM(e){return(e&e-1)==0}function Pm(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=C.mergeRealAndImagArrays(e,t),i=n/2,o=C.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),d=a.makeTensorInfo(u,"float32",c),p=On({inputs:{real:h,imag:d},backend:a}),f=C.complexWithOddIndex(s),m=f.real,A=f.imag,y=[m.length],g=a.makeTensorInfo(y,"float32",m),w=a.makeTensorInfo(y,"float32",A),_=On({inputs:{real:g,imag:w},backend:a}),v=Pm(l,c,i,r,a),x=v.real,N=v.imag,E=[x.length],F=a.makeTensorInfo(E,"float32",x),M=a.makeTensorInfo(E,"float32",N),W=On({inputs:{real:F,imag:M},backend:a}),V=Pm(m,A,i,r,a),B=V.real,H=V.imag,j=[B.length],X=a.makeTensorInfo(j,"float32",B),G=a.makeTensorInfo(j,"float32",H),ee=On({inputs:{real:X,imag:G},backend:a}),Y=C.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:W,b:he},backend:a}),fe=Mm({inputs:{a:W,b:he},backend:a}),pe=mi({inputs:{input:oe},backend:a}),ke=mi({inputs:{input:fe},backend:a}),Ne=_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:[Ne,Me],backend:a,attrs:{axis:0}}),et=a.data.get(Oe.dataId).values,tt=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(w),a.disposeIntermediateTensorInfo(_),a.disposeIntermediateTensorInfo(F),a.disposeIntermediateTensorInfo(M),a.disposeIntermediateTensorInfo(W),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(G),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(he),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(Ne),a.disposeIntermediateTensorInfo(ke),a.disposeIntermediateTensorInfo(Me),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:et,imag:tt}}function VM(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=C.exponent(a*o,t,n),c=C.getComplexWithIndex(e,o);s+=c.real*l.real-c.imag*l.imag,i+=c.real*l.imag+c.imag*l.real}n&&(s/=t,i/=t),C.assignToTypedArray(r,s,i,a)}return r}function UM(e){let{inputs:t,backend:n}=e,{input:r}=t,a=b.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=yt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=ww(o,!1,n),c=yt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var HM={kernelName:Wh,backendName:"cpu",kernelFunc:UM};function Lm(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||b.inferDtype(a),o=b.getArrayFromDType(i,b.sizeFromShape(r));return jM(o,a,i),t.makeTensorInfo(r,i,o)}var GM={kernelName:fu,backendName:"cpu",kernelFunc:Lm};function jM(e,t,n){e.fill(t)}var qM={kernelName:fo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=b.getTypedArrayFromDType(r.dtype,b.sizeFromShape(r.shape)),[i,o,l,c]=r.shape,u=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*c;for(let p=0;p<o;p++){let f=p*(l*c);for(let m=0;m<l;m++){let A=m*c;for(let y=0;y<c;y++){let g=[i,p,m,y][2],w=Math.round(l-g),_=d+f+A+y,v=u[_];if(w>=0&&w<l){let x=w*c,N=d+f+x+y;v=u[N]}s[_]=v}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},XM=Et((e,t)=>Math.floor(e/t)),KM=Gt(_s,XM,null,"int32"),ZM={kernelName:_s,backendName:"cpu",kernelFunc:KM};function YM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=yw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,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=$m(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var JM={kernelName:ei,backendName:"cpu",kernelFunc:YM};function QM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=gw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=tc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=$m(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var e$={kernelName:ti,backendName:"cpu",kernelFunc:QM};function t$(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=b.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,c,u,h]=C.prepareAndValidate(r,a);if(c===0)return n.makeTensorInfo(l,r.dtype,[]);let d=We([c,u],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m<c;m++){let A=[],y=0;for(let g=0;g<o;g++){let w=p[m*o+g];y+=w*h[g],A.push(w)}if(y<0||y>=s/u)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<u;g++)d.values[m*u+g]=f[y*u+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var n$={kernelName:Ao,backendName:"cpu",kernelFunc:t$};function r$(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 c=b.sizeFromShape(s.shape),u=b.parseAxisParam(i,a.shape)[0],h=C.segment_util.collectGatherOpShapeInfo(a,s,u,l),d=yt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=yt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,c/h.batchSize]}}),f=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],m=n.bufferSync(p),A=n.bufferSync(d),y=Ux(A,m,f);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var a$={kernelName:mo,backendName:"cpu",kernelFunc:r$},s$=Et((e,t)=>e>=t?1:0),i$=Gt(vs,s$,null,"bool"),o$={kernelName:vs,backendName:"cpu",kernelFunc:i$};function l$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=b.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=yt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=ww(o,!0,n),c=yt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var u$={kernelName:Bh,backendName:"cpu",kernelFunc:l$},c$=st(go,e=>Number.isFinite(e)?1:0,"bool"),h$={kernelName:go,backendName:"cpu",kernelFunc:c$},d$=st(xo,e=>Math.abs(e)===Infinity?1:0,"bool"),p$={kernelName:xo,backendName:"cpu",kernelFunc:d$},f$=st(wo,e=>Number.isNaN(e)?1:0,"bool"),m$={kernelName:wo,backendName:"cpu",kernelFunc:f$},A$=Et((e,t)=>e<=t?1:0),y$=Gt(bo,A$,null,"bool"),g$={kernelName:bo,backendName:"cpu",kernelFunc:y$};function x$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=Gx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var w$={kernelName:Uh,backendName:"cpu",kernelFunc:x$},_$=st(vo,e=>Math.log1p(e)),b$={kernelName:vo,backendName:"cpu",kernelFunc:_$},v$=Et((e,t)=>e&&t),k$=Gt(ko,v$,null,"bool"),I$={kernelName:ko,backendName:"cpu",kernelFunc:k$},N$=st(mu,e=>e?0:1,"bool"),S$={kernelName:mu,backendName:"cpu",kernelFunc:N$},T$=Et((e,t)=>e||t),C$=Gt(Au,T$,null,"bool"),E$={kernelName:Au,backendName:"cpu",kernelFunc:C$};function R$(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 c=a.shape[3],u=c-1,h=n.data.get(a.dataId).values,d=b.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let A=m%c,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,u),w=0;for(;y<=g;y++){let _=h[y];w+=_*_}return w}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 F$={kernelName:yu,backendName:"cpu",kernelFunc:R$};function M$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r;_e(i,"LRNGrad");let h=b.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 w=g%d,_=g-w+Math.max(0,w-o),v=g-w+Math.min(d,w+o+1),x=0;for(let N=_;N<v;N++)x+=Math.pow(f[N],2);x=c*x+l;for(let N=_;N<v;N++){let E=-2*c*u*f[N]*m[g]/x;g===N&&(E+=Math.pow(x,-u)),E*=p[g],A[N]+=E}}return n.makeTensorInfo(i.shape,a.dtype,A)}var $$={kernelName:Hh,backendName:"cpu",kernelFunc:M$};function _w(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,c=l.length,u=b.parseAxisParam(s,l),h=u,d=C.getAxesPermutation(h,c),p=o.data.get(a.dataId).values;if(d!=null){let _=new Array(c);for(let v=0;v<_.length;v++)_[v]=l[d[v]];p=Cm(p,l,a.dtype,d,_),h=C.getInnerMostAxes(h.length,c),l=_}_e(a,"max"),C.assertAxesAreInnerMostDims("max",h,c);let[f,m]=C.computeOutAndReduceShapes(l,h),A=b.sizeFromShape(m),y=Xx(p,A,f,a.dtype),g=o.write(y,f,a.dtype),w=f;return i&&(w=C.expandShapeToKeepDim(f,u)),{dataId:g,shape:w,dtype:a.dtype}}var D$={kernelName:Ss,backendName:"cpu",kernelFunc:_w};function O$(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,c=1;b.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&b.arraysEqual(u.inShape,u.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=b.computeStrides(a.shape),f=Dm(d,a.shape,a.dtype,p,u,"max");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var z$={kernelName:Cs,backendName:"cpu",kernelFunc:O$};function P$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;_e(a,"maxPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=Aw(h,a.shape,a.dtype,b.computeStrides(a.shape),u,"max");return n.makeTensorInfo(d.shape,"float32",d.values)}var L$={kernelName:gu,backendName:"cpu",kernelFunc:P$};function W$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;_e([a,s],"maxPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=n.bufferSync(s),d=NF(h,u),p=u.strideDepth,f=u.strideHeight,m=u.strideWidth,A=u.dilationDepth,y=u.dilationHeight,g=u.dilationWidth,w=u.effectiveFilterDepth,_=u.effectiveFilterHeight,v=u.effectiveFilterWidth,x=w-1-u.padInfo.front,N=v-1-u.padInfo.left,E=_-1-u.padInfo.top,F=We(s.shape,"float32"),M=n.bufferSync(a);for(let W=0;W<u.batchSize;++W)for(let V=0;V<u.inChannels;++V)for(let B=0;B<u.inDepth;++B)for(let H=0;H<u.inHeight;++H)for(let j=0;j<u.inWidth;++j){let X=B-x,G=H-E,ee=j-N,Y=0;for(let ae=0;ae<w;ae+=A){let te=(X+ae)/p;if(!(te<0||te>=u.outDepth||Math.floor(te)!==te))for(let ie=0;ie<_;ie+=y){let Q=(G+ie)/f;if(!(Q<0||Q>=u.outHeight||Math.floor(Q)!==Q))for(let he=0;he<v;he+=g){let oe=(ee+he)/m;if(oe<0||oe>=u.outWidth||Math.floor(oe)!==oe)continue;let fe=w*_*v-1-d.get(W,te,Q,oe,V),pe=ae*_*v+ie*v+he,ke=fe===pe?1:0;ke!==0&&(Y+=M.get(W,te,Q,oe,V)*ke)}}}F.set(Y,W,B,H,j,V)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var B$={kernelName:Gh,backendName:"cpu",kernelFunc:W$};function V$(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:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=n.data.get(o.dataId).values,f=We(d.outShape,o.dtype,mw(p,o.shape,o.dtype,d).values),m=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,w=d.effectiveFilterHeight,_=d.effectiveFilterWidth,v=_-1-d.padInfo.left,x=w-1-d.padInfo.top,N=We(o.shape,"float32"),E=n.data.get(a.dataId).values,F=We(a.shape,"float32",E);for(let M=0;M<d.batchSize;++M)for(let W=0;W<d.inChannels;++W)for(let V=0;V<d.inHeight;++V)for(let B=0;B<d.inWidth;++B){let H=V-x,j=B-v,X=0;for(let G=0;G<w;G+=y){let ee=(H+G)/m;if(!(ee<0||ee>=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y<_;Y+=g){let ae=(j+Y)/A;if(ae<0||ae>=d.outWidth||Math.floor(ae)!==ae)continue;let te=w*_-1-f.get(M,ee,ae,W),ie=G*_+Y,Q=te===ie?1:0;Q!==0&&(X+=F.get(M,ee,ae,W)*Q)}}N.set(X,M,V,B,W)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var U$={kernelName:jh,backendName:"cpu",kernelFunc:V$};function H$(e,t,n,r,a){let s=b.computeStrides(t),i=Dm(e,t,n,s,a,"max"),o=mw(e,t,n,a,!0,r);return[i.values,o.values]}var j$={kernelName:qh,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 c=l.data.get(r.dataId).values,u=C.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=H$(c,r.shape,r.dtype,o,u),p=l.write(h,u.outShape,r.dtype),f=l.write(d,u.outShape,r.dtype);return[{dataId:p,shape:u.outShape,dtype:r.dtype},{dataId:f,shape:u.outShape,dtype:"int32"}]}};function tp(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,c=b.parseAxisParam(s,o.shape),u=C.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=or({inputs:{x:o},backend:n,attrs:{perm:u}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=C.computeOutAndReduceShapes(d.shape,h),m=C.upcastType(d.dtype,"int32"),A=Qd(n,p,m),y=b.sizeFromShape(f),g=n.data.get(A.dataId).values,w=n.data.get(d.dataId).values;for(let _=0;_<g.length;++_){let v=_*y,x=0;for(let N=0;N<y;++N)x+=w[v+N];g[_]=x}if(i){let _=C.expandShapeToKeepDim(A.shape,c),v=A;A=yt({inputs:{x:A},backend:n,attrs:{shape:_}}),n.disposeIntermediateTensorInfo(v)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),A}var G$={kernelName:qs,backendName:"cpu",kernelFunc:tp};function q$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=b.parseAxisParam(s,a.shape),l=C.computeOutAndReduceShapes(a.shape,o)[1],c=b.sizeFromShape(l),u=[],h=n.makeTensorInfo([],"float32",new Float32Array([c]));u.push(h);let d=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});u.push(d);let p=Om({inputs:{a:d,b:h},backend:n});u.push(p);let f=tp({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var X$={kernelName:Es,backendName:"cpu",kernelFunc:q$};function K$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"min");let o=b.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=or({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=b.sizeFromShape(d),f=b.makeZerosTypedArray(b.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,w=m[g];for(let _=0;_<p;++_){let v=m[g+_];v<w&&(w=v)}f[y]=w}c!=null&&n.disposeIntermediateTensorInfo(u);let A=n.makeTensorInfo(h,u.dtype,f);if(i){let y=C.expandShapeToKeepDim(h,o),g=yt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var Z$={kernelName:Rs,backendName:"cpu",kernelFunc:K$};function Y$(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,w)=>g[0]+a.shape[w]+g[1]),l=s.map(g=>g[0]),c=s.map((g,w)=>g[0]+a.shape[w]),u=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=b.computeStrides(a.shape),f=b.sizeFromShape(o),m=o.length,A=b.computeStrides(o),y=b.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let w=b.indexToLoc(g,m,A);for(let v=0;v<m;v++)w[v]<l[v]?w[v]=l[v]*2-w[v]-u:w[v]>=c[v]&&(w[v]=(c[v]-1)*2-w[v]+u);w=w.map((v,x)=>v-l[x]);let _=b.locToIndex(w,d,p);y[g]=h[_]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var J$={kernelName:xu,backendName:"cpu",kernelFunc:Y$},Q$=Et((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),eD=Gt(Io,Q$),tD={kernelName:Io,backendName:"cpu",kernelFunc:eD},nD=qi(xh());function bw(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=b.parseAxisParam([o],a.shape),c=_w({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=C.expandShapeToKeepDim(c.shape,l),h=yt({inputs:{x:c},backend:n,attrs:{shape:u}}),d=Mm({inputs:{a,b:h},backend:n}),p=ow({inputs:{x:d},backend:n}),f=tp({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=Om({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var rD={kernelName:Xs,backendName:"cpu",kernelFunc:bw};function aD(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:bw({inputs:{logits:a},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],h=n.data.get(l.dataId).values,d=[c,s],p=b.makeZerosTypedArray(b.sizeFromShape(d),"int32");for(let f=0;f<c;++f){let m=f*u,A=new Float32Array(u-1);A[0]=h[m];for(let w=1;w<A.length;++w)A[w]=A[w-1]+h[m+w];let y=nD.alea(i.toString()),g=f*s;for(let w=0;w<s;++w){let _=y();p[g+w]=A.length;for(let v=0;v<A.length;v++)if(_<A[v]){p[g+w]=v;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var sD={kernelName:Xh,backendName:"cpu",kernelFunc:aD},iD=Pr.nonMaxSuppressionV3Impl;function oD(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 c=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,{selectedIndices:h}=iD(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var lD={kernelName:To,backendName:"cpu",kernelFunc:oD},uD=Pr.nonMaxSuppressionV4Impl;function cD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r;_e(a,"NonMaxSuppressionPadded");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=uD(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var hD={kernelName:Co,backendName:"cpu",kernelFunc:cD},dD=Pr.nonMaxSuppressionV5Impl;function pD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r;_e(a,"NonMaxSuppressionWithScore");let u=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=dD(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var fD={kernelName:Eo,backendName:"cpu",kernelFunc:pD};function mD(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;_e(a,"oneHot");let l=b.sizeFromShape(a.shape),c=new Float32Array(l*s);c.fill(o);let u=n.data.get(a.dataId).values;for(let h=0;h<l;++h)u[h]>=0&&u[h]<s&&(c[h*s+u[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",c)}var AD={kernelName:$s,backendName:"cpu",kernelFunc:mD};function np(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=np({inputs:{x:a},backend:n}),i=_l({inputs:{input:r},backend:n}),o=np({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 yD={kernelName:Xo,backendName:"cpu",kernelFunc:np};function vw(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=vw({inputs:{x:a},backend:n}),i=_l({inputs:{input:r},backend:n}),o=np({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 gD={kernelName:Ro,backendName:"cpu",kernelFunc:vw};function kw(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return ep({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{b.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),b.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=ep({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=bl({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var xD={kernelName:Fo,backendName:"cpu",kernelFunc:kw};function wD(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]),c=n.data.get(a.dataId).values,u=b.sizeFromShape(a.shape),h=a.shape.length,d=b.computeStrides(a.shape),p=b.sizeFromShape(o),f=o.length,m=b.computeStrides(o),A=b.getTypedArrayFromDType(a.dtype,p);i!==0&&A.fill(i);for(let y=0;y<u;y++){let g=b.indexToLoc(y,h,d).map((_,v)=>_+l[v]),w=b.locToIndex(g,f,m);A[w]=c[y]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var Iw={kernelName:Ds,backendName:"cpu",kernelFunc:wD},_D=Et((e,t)=>Math.pow(e,t)),bD=Gt(Os,_D),vD={kernelName:Os,backendName:"cpu",kernelFunc:bD};function kD(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 ID={kernelName:wu,backendName:"cpu",kernelFunc:kD},ND=st($o,e=>1/e),SD={kernelName:$o,backendName:"cpu",kernelFunc:ND};function TD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;_e(a,"resizeBilinear");let l=b.computeStrides(a.shape),[c,u]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(b.sizeFromShape([h,c,u,f])),y=[s&&c>1?d-1:d,s&&u>1?p-1:p],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],w=0,_=y[0]/g[0],v=y[1]/g[1];for(let x=0;x<h;x++)for(let N=0;N<c;N++){let E;i?E=_*(N+.5)-.5:E=_*N;let F=Math.max(0,Math.floor(E)),M=E-F,W=Math.min(d-1,Math.ceil(E)),V=x*l[0]+F*l[1],B=x*l[0]+W*l[1];for(let H=0;H<u;H++){let j;i?j=v*(H+.5)-.5:j=v*H;let X=Math.max(0,Math.floor(j)),G=j-X,ee=Math.min(p-1,Math.ceil(j)),Y=V+X*l[2],ae=B+X*l[2],te=V+ee*l[2],ie=B+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)*G,Ne=oe+(pe-oe)*G,Me=ke+(Ne-ke)*M;A[w++]=Me}}}return n.makeTensorInfo([h,c,u,f],"float32",A)}var CD={kernelName:Ls,backendName:"cpu",kernelFunc:TD};function ED(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;_e([s,a],"resizeBilinearGrad");let o=b.computeStrides(a.shape),[l,c,u,h]=a.shape,[,d,p]=s.shape,f=new Float32Array(l*c*u*h),m=[i&&d>1?c-1:c,i&&p>1?u-1:u],A=[i&&d>1?d-1:d,i&&p>1?p-1:p],y=m[0]/A[0],g=m[1]/A[1],w=n.data.get(s.dataId).values,_=0;for(let v=0;v<l;v++){let x=v*o[0];for(let N=0;N<d;N++){let E=N*y,F=Math.floor(E),M=Math.min(Math.ceil(E),c-1),W=x+F*o[1],V=x+M*o[1],B=E-F,H=1-B;for(let j=0;j<p;j++){let X=j*g,G=Math.floor(X),ee=Math.min(Math.ceil(X),u-1),Y=X-G,ae=1-Y,te=W+G*o[2],ie=W+ee*o[2],Q=V+G*o[2],he=V+ee*o[2],oe=H*ae,fe=H*Y,pe=B*ae,ke=B*Y;for(let Ne=0;Ne<h;Ne++){let Me=w[_++];f[te+Ne]+=Me*oe,f[ie+Ne]+=Me*fe,f[Q+Ne]+=Me*pe,f[he+Ne]+=Me*ke}}}}return n.makeTensorInfo([l,u,c,h],"float32",f)}var RD={kernelName:Yh,backendName:"cpu",kernelFunc:ED};function FD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;_e(a,"resizeNearestNeighbor");let l=b.computeStrides(a.shape),[c,u]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(h*c*u*f),y=[s&&c>1?d-1:d,s&&u>1?p-1:p],g=[s&&c>1?c-1:c,s&&u>1?u-1:u],w=y[0]/g[0],_=y[1]/g[1],v=0;for(let x=0;x<h;x++){let N=x*l[0];for(let E=0;E<c;E++){let F=i?w*(E+.5):w*E,M=Math.min(d-1,s?Math.round(F):Math.floor(F));i&&(M=Math.max(0,M));let W=N+M*l[1];for(let V=0;V<u;V++){let B=i?_*(V+.5):_*V,H=Math.min(p-1,s?Math.round(B):Math.floor(B));i&&(H=Math.max(0,H));let j=W+H*l[2];for(let X=0;X<f;X++){let G=m[j+X];A[v++]=G}}}}return n.makeTensorInfo([h,c,u,f],a.dtype,A)}var MD={kernelName:_u,backendName:"cpu",kernelFunc:FD};function $D(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;_e([s,a],"resizeNearestNeighborGrad");let o=b.computeStrides(a.shape),l=b.computeStrides(s.shape),[c,u,h,d]=a.shape,[,p,f]=s.shape,m=new Float32Array(c*u*h*d),A=n.data.get(s.dataId).values,y=[i&&p>1?u-1:u,i&&f>1?h-1:h],g=[i&&p>1?p-1:p,i&&f>1?f-1:f],w=y[0]/g[0],_=y[1]/g[1],v=1/w,x=1/_,N=Math.ceil(v)*2+2,E=Math.ceil(x)*2+2;for(let F=0;F<c;F++){let M=F*o[0];for(let W=0;W<u;W++){let V=M+W*o[1],B=Math.floor(W*v),H=Math.floor(B-N/2);for(let j=0;j<h;j++){let X=V+j*o[2],G=Math.floor(j*x),ee=Math.floor(G-E/2);for(let Y=0;Y<d;Y++){let ae=0;for(let te=0;te<N;te++){let ie=te+H;if(ie<0||ie>=p)continue;let Q=M+ie*l[1],he=ie*w,oe=Math.min(u-1,i?Math.round(he):Math.floor(he));if(W===oe)for(let fe=0;fe<E;fe++){let pe=fe+ee;if(pe<0||pe>=f)continue;let ke=Q+pe*l[2],Ne=pe*_,Me=Math.min(h-1,i?Math.round(Ne):Math.floor(Ne));j===Me&&(ae+=A[ke+Y])}}m[X+Y]=ae}}}}return n.makeTensorInfo(a.shape,a.dtype,m)}var DD={kernelName:Zh,backendName:"cpu",kernelFunc:$D};function OD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;_e(a,"reverse");let i=a.shape.length,o=b.parseAxisParam(s,a.shape);if(i===0)return Lr({inputs:{x:a},backend:n});let l=new Dt(a.shape,a.dtype),c=n.bufferSync(a);for(let u=0;u<l.size;u++){let h=l.indexToLoc(u),d=h.slice();o.forEach(p=>d[p]=a.shape[p]-1-d[p]),l.set(c.get(...d),...h)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var zD={kernelName:Bs,backendName:"cpu",kernelFunc:OD},PD={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=b.getTypedArrayFromDType(r.dtype,b.sizeFromShape(r.shape)),[c,u,h,d]=r.shape,[p,f]=C.getImageCenter(i,u,h),m=255,A=Math.sin(a),y=Math.cos(a),g=o.data.get(r.dataId).values;for(let w=0;w<c;w++){let _=w*h*u*d;for(let v=0;v<u;v++){let x=v*(h*d);for(let N=0;N<h;N++){let E=N*d;for(let F=0;F<d;F++){let M=[c,v,N,F],W=M[2],V=M[1],B=(W-p)*y-(V-f)*A,H=(W-p)*A+(V-f)*y;B=Math.round(B+p),H=Math.round(H+f);let j=s;if(typeof s!="number"&&(F===3?j=m:j=s[F]),B>=0&&B<h&&H>=0&&H<u){let G=H*(h*d),ee=B*d,Y=_+G+ee+F;j=g[Y]}let X=_+x+E+F;l[X]=j}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},LD=st(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}),WD={kernelName:Vs,backendName:"cpu",kernelFunc:LD};function Nw(e,t,n,r,a,s,i,o,l,c){let u=[r/a,a],h=e.values,d=t.values;if(r===0)return We(n,t.dtype);let p=We(u,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++)c?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 BD(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),d=!0,p=n.bufferSync(a),f=n.bufferSync(s),m=Nw(p,f,i,h,c,l,o,u,0,d);return n.makeTensorInfo(i,m.dtype,m.values)}var VD={kernelName:Oo,backendName:"cpu",kernelFunc:BD};function UD(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,c=n.data.get(s.dataId).values,u=rr(a.dtype,s.dtype),h=b.makeZerosTypedArray(b.sizeFromShape(a.shape),u),d=0,p=i===0||i>1||a.shape.length===1?1:b.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++]=c[f];return n.makeTensorInfo(a.shape,u,h)}var HD={kernelName:zo,backendName:"cpu",kernelFunc:UD},jD=C.SELU_SCALEALPHA,GD=C.SELU_SCALE,qD=st(Po,e=>e>=0?GD*e:jD*(Math.exp(e)-1)),XD={kernelName:Po,backendName:"cpu",kernelFunc:qD},KD=st(js,e=>1/(1+Math.exp(-e))),ZD={kernelName:js,backendName:"cpu",kernelFunc:KD},YD=st(Bo,e=>e<0?-1:e>0?1:0),JD={kernelName:Bo,backendName:"cpu",kernelFunc:YD},QD=st(Hs,e=>Math.sin(e)),eO={kernelName:Hs,backendName:"cpu",kernelFunc:QD},tO=st(Wo,e=>Math.sinh(e)),nO={kernelName:Wo,backendName:"cpu",kernelFunc:tO},rO=11920928955078125e-23,Sw=Math.log(rO)+2,aO=st(Vo,e=>{let t=e>-Sw,n=e<Sw,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),sO={kernelName:Vo,backendName:"cpu",kernelFunc:aO};function iO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_e([a],"spaceToBatchND");let o=b.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let c=Iw.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),u=C.getReshaped(c.shape,s,o,!1),h=C.getPermuted(u.length,s.length,!1),d=C.getReshapedPermuted(c.shape,s,o,!1),p=yt({inputs:{x:c},backend:n,attrs:{shape:u}}),f=or({inputs:{x:p},backend:n,attrs:{perm:h}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var oO={kernelName:bu,backendName:"cpu",kernelFunc:iO};function lO(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:d}=C.calculateShapes(s,a,o),p=!1,f=n.bufferSync(a),m=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=Nw(f,m,o,d,u,c,l,h,A,p);return n.makeTensorInfo(o,y.dtype,y.values)}var uO={kernelName:Jh,backendName:"cpu",kernelFunc:lO};function cO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=b.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=new Array(a.shape.length).fill(0),u=a.shape.slice();return l.map(h=>{let d=[...u];d[o]=h;let p=Ai({inputs:{x:a},backend:n,attrs:{begin:c,size:d}});return c[o]+=h,p})}var hO={kernelName:Uo,backendName:"cpu",kernelFunc:cO},dO=st(Gs,e=>Math.sqrt(e)),pO={kernelName:Gs,backendName:"cpu",kernelFunc:dO},fO={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}}},mO=st(Ta,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),AO={kernelName:Ta,backendName:"cpu",kernelFunc:mO};function yO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,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,c,u,h,d),w=yt({inputs:{x:a},backend:n,attrs:{shape:y}}),_;if(p){let x=Ai({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});_=yt({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))_=n.makeTensorInfo(g,a.dtype,[]);else{let x=n.bufferSync(w),N=nw(g,x,m,f);_=n.makeTensorInfo(N.shape,N.dtype,N.values)}let v=yt({inputs:{x:_},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(_),v}var gO={kernelName:Ho,backendName:"cpu",kernelFunc:yO},xO=st(jo,e=>Math.tan(e)),wO={kernelName:jo,backendName:"cpu",kernelFunc:xO},_O=st(Ys,e=>Math.tanh(e)),bO={kernelName:Ys,backendName:"cpu",kernelFunc:_O};function vO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;_e(a,"tile");let i=aw(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var kO={kernelName:Sa,backendName:"cpu",kernelFunc:vO};function IO(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,c]=sw(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var NO={kernelName:Go,backendName:"cpu",kernelFunc:IO};function CO(e){let{inputs:t,attrs:n,backend:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=n,[u,h,d,p]=a.shape,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=b.computeStrides(a.shape),g=y[0],w=y[1],_=y[2],v=b.getTypedArrayFromDType(a.dtype,b.sizeFromShape(A));v.fill(l);let x=r.data.get(a.dataId).values,N=r.data.get(s.dataId).values;for(let E=0;E<u;++E){let F=s.shape[0]===1?N:N.subarray(E*8,E*8+8);for(let M=0;M<f;++M)for(let W=0;W<m;++W)for(let V=0;V<p;++V){let B,H=F[6]*W+F[7]*M+1;if(H===0)continue;let j=(F[0]*W+F[1]*M+F[2])/H,X=(F[3]*W+F[4]*M+F[5])/H,G=Tw(j,d,o),ee=Tw(X,h,o);switch(i){case"nearest":B=SO(x,h,d,g,w,_,E,ee,G,V,l);break;case"bilinear":B=TO(x,h,d,g,w,_,E,ee,G,V,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let Y=E*g+M*w+W*_+V;v[Y]=B}return r.makeTensorInfo(A,a.dtype,v)}return{dataId:r.write(v,A,a.dtype),shape:a.shape,dtype:a.dtype}}var EO={kernelName:Qh,backendName:"cpu",kernelFunc:CO};function Tw(e,t,n){switch(n){case"reflect":return RO(e,t);case"wrap":return FO(e,t);case"nearest":return $O(e,t);case"constant":default:return MO(e,t)}}function RO(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 b.clamp(0,n,t-1)}function FO(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 b.clamp(0,n,t-1)}function MO(e,t){return e}function $O(e,t){return b.clamp(0,e,t-1)}function nc(e,t,n,r,a,s,i,o,l,c,u){let h=i*r+o*a+l*s+c;return 0<=o&&o<t&&0<=l&&l<n?e[h]:u}function SO(e,t,n,r,a,s,i,o,l,c,u){let h=Math.round(o),d=Math.round(l);return nc(e,t,n,r,a,s,i,h,d,c,u)}function TO(e,t,n,r,a,s,i,o,l,c,u){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,c,u)+(l-d)*nc(e,t,n,r,a,s,i,h,f,c,u),A=(f-l)*nc(e,t,n,r,a,s,i,p,d,c,u)+(l-d)*nc(e,t,n,r,a,s,i,p,f,c,u);return(p-o)*m+(o-h)*A}function DO(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:c}=iw(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var OO={kernelName:ed,backendName:"cpu",kernelFunc:DO};function zO(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),c=0;for(let p=0;p<i;p++)p!==s&&(l[c++]=a.shape[p]);let u=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++){u[s]=p;let f=Ai({inputs:{x:a},backend:n,attrs:{begin:u,size:h}});d[p]=yt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return d}var PO={kernelName:qo,backendName:"cpu",kernelFunc:zO};function LO(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,c=[],u=[],h=o-l,d=s;for(let f=0;f<h;++f){let m=ep({inputs:{input:d},backend:n,attrs:{dim:f+1}});d=m,u.push(m)}for(let f=0;f<i;++f){let m=b.createScalarValue(f,"int32"),A=n.makeTensorInfo([],"int32",m),y=xw({inputs:{a:A,b:d},backend:n}),g=Wa({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),w=Fm({inputs:{a:g,b:a},backend:n}),_=tp({inputs:{x:w},backend:n,attrs:{axis:0,keepDims:!1}});c.push(_),u.push(A),u.push(y),u.push(g),u.push(w),u.push(_)}let p=kw({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var WO={kernelName:ku,backendName:"cpu",kernelFunc:LO},BO=[eF,sR,nF,aF,hR,iF,lF,cF,dF,fF,AF,gF,wF,vF,IF,TF,EF,FF,$F,JR,OF,PF,WF,uR,pR,VF,iR,HF,GF,KF,YF,qF,tM,rM,QF,sM,oM,uM,hM,pM,mM,AM,gM,wM,bM,vM,IM,kM,zm,jR,SM,CM,zM,fR,PM,AR,HM,GM,qM,gR,ZM,JM,e$,n$,a$,wR,o$,oR,u$,jF,h$,p$,m$,GR,bR,g$,w$,kR,b$,I$,S$,E$,F$,$$,NR,z$,L$,B$,U$,j$,D$,X$,Z$,TR,J$,tD,sD,ER,FR,lD,hD,fD,$R,AD,gD,xD,Iw,vD,XR,zR,ID,lR,SD,KR,ZR,YR,CD,RD,MD,DD,zD,PD,WD,LR,VD,HD,XD,ZD,JD,eO,nO,WR,rD,sO,oO,uO,hO,pO,fO,VR,AO,gO,HR,G$,wO,bO,kO,NO,DR,EO,OO,PO,WO,yD];for(let e of BO)ni(e);var Cw={};ze(Cw,{assertNotComplex:()=>vl,bindCanvasToFramebuffer:()=>HO,bindColorTextureToFramebuffer:()=>ap,bindTextureToProgramUniformSampler:()=>Hw,bindTextureUnit:()=>Bw,bindVertexBufferToProgramAttribute:()=>Wm,callAndCheck:()=>xe,canBeRepresented:()=>Ew,createFragmentShader:()=>Mw,createFramebuffer:()=>Ww,createProgram:()=>$w,createStaticIndexBuffer:()=>zw,createStaticVertexBuffer:()=>Ow,createTexture:()=>Pw,createVertexShader:()=>Fw,getBatchDim:()=>yi,getExtensionOrThrow:()=>rc,getFramebufferErrorMessage:()=>jw,getMaxTexturesInShader:()=>Xw,getNumChannels:()=>VO,getProgramUniformLocation:()=>Uw,getProgramUniformLocationOrThrow:()=>Vw,getRowsCols:()=>gi,getShapeAs3D:()=>sp,getTextureShapeFromLogicalShape:()=>Gw,getWebGLDisjointQueryTimerVersion:()=>Kw,getWebGLErrorMessage:()=>Rw,getWebGLMaxTextureSize:()=>qw,hasExtension:()=>Kn,isCapableOfRenderingToFloatTexture:()=>Zw,isDownloadFloatTextureEnabled:()=>Yw,isReshapeFree:()=>sc,isWebGLFenceEnabled:()=>Jw,isWebGLVersionEnabled:()=>Vm,linkProgram:()=>Dw,resetMaxTextureSize:()=>jO,resetMaxTexturesInShader:()=>GO,unbindColorTextureFromFramebuffer:()=>Bm,unbindTextureUnit:()=>UO,validateFramebuffer:()=>ac,validateProgram:()=>rp,validateTextureSize:()=>Lw});var xi={},Um={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function ip(e,t){xi[e]=t}function Wr(e){if(!(e in xi)){let n=qO(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 XO(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 qO(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=XO(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 en;(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"})(en||(en={}));function oc(e,t){return[t,e]}function KO(e,t){return e*t}function lc(e){let t=b.sizeFromShape(e),n=Math.ceil(t/4);return b.sizeToSquarishShape(n)}function kl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function ZO(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,c,u,h,d;return J().getNumber("WEBGL_VERSION")===2?(r=n.R32F,a=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,c=4,u=1,h=n.HALF_FLOAT,d=n.FLOAT):(r=e.RGBA,a=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,c=4,u=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:c,defaultNumChannels:u,textureTypeHalfFloat:h,textureTypeFloat:d}}function xe(e,t){let n=t();return J().getBool("DEBUG")&&YO(e),n}function YO(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+Rw(e,t))}var JO=596e-10,QO=65504;function Ew(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||JO<Math.abs(e)&&Math.abs(e)<QO)}function Rw(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 Fw(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 Mw(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 ez(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var tz=/ERROR: [0-9]+:([0-9]+):/g;function ez(e,t){let n=tz.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)=>b.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),c=i.slice(r-1,r),u=i.slice(r);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${b.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
|
|
`))}function $w(e){return la(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function Dw(e,t){if(xe(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function rp(e,t){if(xe(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function Ow(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function zw(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function VO(){return J().getNumber("WEBGL_VERSION")===2?1:4}function Pw(e){return la(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Lw(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${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 Ww(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 Bw(e,t,n){Qw(e,n),xe(e,()=>e.activeTexture(e.TEXTURE0+n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function UO(e,t){Qw(e,t),xe(e,()=>e.activeTexture(e.TEXTURE0+t)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Vw(e,t,n){return la(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Uw(e,t,n){return e.getUniformLocation(t,n)}function Hw(e,t,n,r){xe(e,()=>Bw(e,t,r)),xe(e,()=>e.uniform1i(n,r))}function HO(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 ap(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: "+jw(e,t))}function jw(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 ${t}`}}function la(e,t,n){let r=xe(e,()=>t());if(r==null)throw new Error(n);return r}function Qw(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function yi(e,t=2){return b.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 sp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[yi(e),...gi(e)]),t}function Gw(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?b.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=b.squeezeShape(e).newShape);let r=b.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),b.sizeToSquarishShape(r).map(o=>o*2)}return b.sizeToSquarishShape(r)}function op(e){return e%2==0}function sc(e,t){if(e=e.slice(-2),t=t.slice(-2),b.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||op(n)&&op(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&op(e[0])&&op(t[0])}var lp,up;function qw(e){if(lp==null){let t=Wr(e);lp=t.getParameter(t.MAX_TEXTURE_SIZE)}return lp}function jO(){lp=null}function GO(){up=null}function Xw(e){if(up==null){let t=Wr(e);up=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,up)}function Kw(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 Zw(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 Yw(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 nz(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 nz(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 Jw(e){return e!==2?!1:Wr(e).fenceSync!=null}function vl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&b.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",()=>qw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Xw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:Kw(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",()=>Zw(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",()=>Yw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Jw(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,c;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="",c=`
|
|
#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));
|
|
}
|
|
`,c=`
|
|
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:c}}function wi(e,t,n="index"){let r=b.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=b.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}var e_=`
|
|
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;
|
|
}
|
|
`,rz=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;
|
|
}
|
|
`}},az=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;
|
|
}
|
|
`}},sz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Zn.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
|
|
${e_}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},iz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zn.DOWNLOAD;let t=fn();this.outputShape=e,this.userCode=`
|
|
${e_}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},oz=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.);
|
|
}
|
|
`}},lz=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 c=0;c<=1;c++){let u=l*2+c;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${c} < ${e[2]}) {
|
|
localCoords[2] += ${c};
|
|
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 = ${r.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${u}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${u}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${u}] = values[2];
|
|
} else {
|
|
result[${u}] = 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} = ${o};
|
|
}
|
|
`}},t_={};ze(t_,{bindVertexProgramAttributeStreams:()=>c_,createBufferFromOutputTexture:()=>p_,createFloat16MatrixTexture:()=>i_,createFloat16PackedMatrixTexture:()=>u_,createFloat32MatrixTexture:()=>s_,createIndexBuffer:()=>a_,createPackedMatrixTexture:()=>l_,createUnsignedBytesMatrixTexture:()=>o_,createVertexBuffer:()=>r_,createVertexShader:()=>n_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>m_,downloadFloat32MatrixFromBuffer:()=>f_,downloadMatrixFromPackedOutputTexture:()=>y_,downloadPackedMatrixFromBuffer:()=>A_,getInternalFormatForFloat16MatrixTexture:()=>Xm,getInternalFormatForFloat16PackedMatrixTexture:()=>Ym,getInternalFormatForFloat32MatrixTexture:()=>qm,getInternalFormatForPackedMatrixTexture:()=>Zm,getInternalFormatForUnsignedBytesMatrixTexture:()=>Km,uploadDenseMatrixToTexture:()=>h_,uploadPixelDataToTexture:()=>d_});function n_(e){let t=fn(),n=`${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 Fw(e,n)}function r_(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 Ow(e,t)}function a_(e){let t=new Uint16Array([0,1,2,2,1,3]);return zw(e,t)}function uc(e,t,n,r,a,s){Lw(t,n);let i=Pw(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 s_(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 i_(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 o_(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 l_(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 u_(e,t,n,r){let[a,s]=kl(t,n);return uc(e,a,s,Ym(r),e.RGBA,r.textureTypeHalfFloat)}function c_(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 h_(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 d_(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 p_(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 f_(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 m_(e,t,n,r){let[a,s]=oc(t,n),i=4,o=new Uint8Array(KO(t*n,i));return xe(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function A_(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(ZO(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function y_(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 cp=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,ip(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=r_(this.gl),this.indexBuffer=a_(this.gl),this.framebuffer=Ww(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(),s_(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),i_(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),o_(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),d_(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),h_(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),u_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),l_(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,()=>m_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return A_(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return f_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=p_(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,()=>y_(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Mw(t,e),r=n_(t),a=$w(t);return xe(t,()=>t.attachShader(a,r)),xe(t,()=>t.attachShader(a,n)),Dw(t,a),this.debug&&rp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=c_(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&&rp(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Vw(this.gl,e,t):Uw(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(),Hw(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&&rp(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 b.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=uz(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)&&b.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ap(this.gl,e,this.framebuffer),this.debug&&ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ap(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;ap(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 uz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:g_}=C;function gz(e,t,n,r){let a=[];e.forEach(p=>{let f=b.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=>cz(p,t,r)).join(`
|
|
`),o=t.texShape,l=fn(),c=pz(l),u,h,d=Az(l);return t.isPacked?(u=hz(t.logicalShape,o),h=mz(l)):(u=dz(t.logicalShape,o),h=fz(l)),r&&(d+=yz),[d,c,h,s,u,i,n].join(`
|
|
`)}function Il(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return xz(e);case 1:return wz(e);case 2:return _z(e);case 3:return bz(e);case 4:return vz(e);case 5:return kz(e);case 6:return Iz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function x_(e){switch(e.shapeInfo.logicalShape.length){case 0:return Nz(e);case 1:return Sz(e);case 2:return Tz(e);case 3:return Cz(e);default:return Ez(e)}}function cz(e,t,n=!1){let r="";n?r+=x_(e):r+=Il(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Rz(e,t):r+=Fz(e,t)),r}function hz(e,t){switch(e.length){case 0:return w_();case 1:return Mz(e,t);case 2:return Oz(e,t);case 3:return $z(e,t);default:return Dz(e,t)}}function dz(e,t){switch(e.length){case 0:return w_();case 1:return zz(e,t);case 2:return Vz(e,t);case 3:return Pz(e,t);case 4:return Lz(e,t);case 5:return Wz(e,t);case 6:return Bz(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function pz(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function fz(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function mz(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function Az(e){return`${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);
|
|
}
|
|
|
|
${Uz}
|
|
${Hz}
|
|
${jz}
|
|
`}var Uz=`
|
|
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);
|
|
}
|
|
`,Hz=`
|
|
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);
|
|
}
|
|
`,jz=`
|
|
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);
|
|
}
|
|
`,yz=`
|
|
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 w_(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function Mz(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 zz(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 $z(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function Pz(e,t){let n=wi(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function Dz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
|
|
int b${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}(${o});
|
|
}
|
|
`}function Lz(e,t){let n=wi(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function Wz(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function Bz(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function Oz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(b.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Vz(e,t){return b.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 Nz(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 xz(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 Sz(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 wz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Nl(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 Tz(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&&b.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)],c=Math.ceil(t[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function _z(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&&b.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}=b.squeezeShape(t),o=s;if(o.length<t.length){let h=Sl(e,o),d=["row","col"];return`
|
|
${Il(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)));
|
|
${Nl(e)}
|
|
}
|
|
`;let l=a[0],c=a[1],u=_i(n);return c===1?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${u}), vec3(${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, ${u}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${t[1]} + col + ${u};
|
|
vec2 uv = uvFromFlat(${l}, ${c}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Cz(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`
|
|
${x_(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),c=l*Math.ceil(t[1]/2),u=fn();return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${c}, ${l}, b, row, col);
|
|
return ${u.texture2D}(${n}, uv);
|
|
}
|
|
`}function bz(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}=b.squeezeShape(t),l=i;if(l.length<t.length){let f=Sl(e,l),m=["row","col","depth"];return`
|
|
${Il(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)));
|
|
${Nl(e)}
|
|
}
|
|
`;let c=e.shapeInfo.texShape,u=c[0],h=c[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, ${u}.0);
|
|
return sampleTexture(${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, ${u}.0);
|
|
return sampleTexture(${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 + ${p};
|
|
vec2 uv = uvFromFlat(${u}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Ez(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],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+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 vz(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}=b.squeezeShape(t);if(o.length<t.length){let f=Sl(e,o),m=["row","col","depth","depth2"];return`
|
|
${Il(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)));
|
|
${Nl(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==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&&c==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 kz(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:c}=b.squeezeShape(t);if(l.length<t.length){let m=Sl(e,l),A=["row","col","depth","depth2","depth3"];return`
|
|
${Il(m)}
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${r}(${Tl(A,c)});
|
|
}
|
|
`}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;
|
|
${Nl(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==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&&u==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 Iz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=b.squeezeShape(t);if(a.length<t.length){let A=Sl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Il(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,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${c}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Nl(e)}
|
|
}
|
|
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${c}, ${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 * ${u} + col * ${c} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${p}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Nl(e){let t=e.name,n=b.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function Rz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=g_(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
|
|
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=b.sizeFromShape(e.shapeInfo.logicalShape)===1,m=b.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();
|
|
${u}
|
|
vec4 outputValue = get${r}(${d});
|
|
${p}
|
|
}
|
|
`}function Fz(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&&b.arraysEqual(i,s))return`
|
|
float ${a}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let c=lt(l),u=g_(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.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}() {
|
|
${c} coords = getOutputCoords();
|
|
${d}
|
|
return get${r}(${f});
|
|
}
|
|
`}function lt(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 ${e} is not yet supported`)}function Sl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Tl(e,t){return t.map(n=>e[n]).join(", ")}function Gz(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=gz(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function __(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(!b.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(!b.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function qz(e,t,n,r,a){__(t.inShapeInfos,n),__([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(b.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function Xz(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}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:Kz,bincountImpl:b_,bincountReduceImpl:Zz,ceilImpl:Yz,concatImpl:Jz,expImpl:Qz,expm1Impl:eP,floorImpl:tP,gatherV2Impl:nP,greaterImpl:rP,lessImpl:aP,linSpaceImpl:sP,logImpl:iP,maxImpl:oP,maximumImpl:lP,minimumImpl:uP,multiplyImpl:cP,negImpl:hP,prodImpl:dP,rangeImpl:pP,rsqrtImpl:fP,simpleAbsImpl:v_,sliceImpl:mP,stridedSliceImpl:AP,subImpl:yP,tileImpl:gP,topKImpl:xP,transposeImpl:Jm,uniqueImpl:wP}=Im;function k_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function mn(e,t){return t===1?[e]:k_(e,t)}function _P(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 IP=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=lt(t),a=bP(t,e,n),s=vP(t,e[e.length-1],e[e.length-2],n),i=kP(e,n);this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
|
|
if(${a}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function NP(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 bP(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 vP(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 kP(e,t){let n=e.length,r=NP(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=`
|
|
${SP(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 SP(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${wi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var TP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=S_(t,n),a=T_(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=N_(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===en.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===en.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===en.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=S_(n,r),s=T_(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=N_(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 CP(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 N_(e,t,n,r,a){let s=EP(t,r),i;if(a){let[l,c]=kl(e[0],e[1]);i=l*c}else{let[l,c]=oc(e[0],e[1]);i=l*c}let o=CP(n,s);return i*o}function EP(e,t){switch(e){case en.PACKED_2X2_FLOAT32:return Zm(t);case en.PACKED_2X2_FLOAT16:return Ym(t);case en.UNPACKED_FLOAT32:return qm(t);case en.UNPACKED_FLOAT16:return Xm(t);case en.PACKED_4X1_UNSIGNED_BYTE:return Km(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function RP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?en.PACKED_2X2_FLOAT32:en.UNPACKED_FLOAT32:e?en.PACKED_2X2_FLOAT16:en.UNPACKED_FLOAT16}function S_(e,t){if(e===Zn.UPLOAD)return en.PACKED_2X2_FLOAT32;if(e===Zn.RENDER||e==null)return RP(t);if(e===Zn.DOWNLOAD||e===Zn.PIXELS)return en.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function T_(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ba=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},xr="if (isnan(x)) return x;",FP="return x;",C_="return abs(x);",MP="return (x >= 0.0) ? x : (exp(x) - 1.0);",$P=xr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,DP=xr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,hp="return x;",OP="return x;",zP=`
|
|
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;
|
|
`,PP=`
|
|
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;
|
|
`,LP=`
|
|
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) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},WP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=mn("rc",t),r=lt(t),a=_P(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${a});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},BP=Pr.whereImpl,VP=1e-7,UP=1e-4,Qm={};function HP(e){return e in Qm||(Qm[e]={}),Qm[e]}var jP=128,GP=600;function qP(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*GP/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=HP(J().getNumber("WEBGL_VERSION")),this.gpgpu=new cp(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 TP(this.gpgpu),this.numMBBeforeWarning=qP(),this.texData=new wh(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,hp):h=new Ba(i,hp);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,c;l&&(c=b.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=C.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=b.now()-c),this.convertAndCacheOnCPU(e,u)}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,hp):p=new Ba(r,hp);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,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...lc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=b.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),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=>b.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(!Ew(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=b.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...lc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?sp(t):t,o=s?new iz(i):new sz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=b.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=b.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=b.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${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:b.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=b.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 c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.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=jP){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&&b.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return BP(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=v_(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,C_,e.dtype);let t=new Ba(e.shape,C_),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&&b.isString(n[0])){let a=n.map(s=>b.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 WP(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new IP(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=sp(r),i;n?i=new az(s):i=new rz(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),b.sizeFromShape(s.shape)===0)return i.values=b.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&&b.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 c={shape:s.shape,texData:i,isUniform:!1},u=Xz(e,l,c),h=this.getAndSaveBinary(u,()=>Gz(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),qz(this.gpgpu,h,l,c,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=b.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=P(()=>{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?VP:UP}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,c;l&&(c=b.now());let u=t.texShape;if(u==null&&(u=Gw(n,o),t.texShape=u),a!=null){let h=sp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=kl(u[0],u[1]),d=new lz(h,[f,p],m)):d=new oz(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),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=b.now()-c)}else{let h=this.acquireTexture(u,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=XP(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]*b.bytesPerElement(t)}};El.nextDataId=0;function XP(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 E_="3.3.0";function R_(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Mu.isBrowser()&&sl("webgl",()=>new El,2);var KP={forceHalfFloat:R_},F_=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Rl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},dp=`
|
|
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=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||b.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${lt(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);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function zn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var ZP={kernelName:ks,backendName:"webgl",kernelFunc:zn};function Va(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=zn({inputs:{x:r},backend:n}),l=zn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var YP={kernelName:Ch,backendName:"webgl",kernelFunc:Va},M_="return (a < 0.) ? b * a : a;",$_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function JP(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",b.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc($_,a.shape,i.shape):new Rl(M_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var QP={kernelName:Is,backendName:"webgl",kernelFunc:JP},D_="return (a < 0.) ? b * a : a;",O_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function eL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(O_,r.shape,a.shape):new Rl(D_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var tL={kernelName:zs,backendName:"webgl",kernelFunc:eL},z_="if (isnan(x)) return x;",nL=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,rL=`
|
|
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 c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Cl(i.shape,t):u=new Ba(i.shape,e),o.runWebGLProgram(u,[i],l)}}function tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[_,v]=w,x={dataId:_.dataId,dtype:_.dtype,shape:l.shape},N={dataId:v.dataId,dtype:v.dtype,shape:c.shape},E=new Rl(e,l.shape,c.shape);return u.runWebGLProgram(E,[x,N],rr(_.dtype,v.dtype))}),g=Va({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||rr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new cc(t,l.shape,c.shape,n):p=new Rl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function pp(e,t=!1){if(e==="linear")return t?OP:FP;if(e==="relu")return t?PP:$P;if(e==="elu")return t?zP:MP;if(e==="relu6")return t?LP:DP;if(e==="prelu")return t?O_:D_;if(e==="leakyrelu")return t?$_:M_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P_=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 c=r?e[1]:e[2],u=Math.ceil(c/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",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
|
|
const float sharedDimension = ${u}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${u}; i++) {
|
|
int batchA = ${g};
|
|
int batchB = ${w};
|
|
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);
|
|
}
|
|
`}},L_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},W_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},B_="return a * b;";function V_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new W_(L_.REAL,r.shape,a.shape),u=new W_(L_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Va({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=cP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new cc(B_,r.shape,a.shape):i=new Rl(B_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var aL={kernelName:Ms,backendName:"webgl",kernelFunc:V_};function sL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new I_(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=b.sizeFromShape(a.shape),l=b.inferFromImplicitShape(s,o),c=b.sizeFromShape(l);b.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!sc(a.shape,l)&&!(u.texture!==null&&sc(u.shape,l))?sL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var iL={kernelName:Do,backendName:"webgl",kernelFunc:Ae},U_=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 u=1/t;l=`sumValue += dot(values * ${b.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
|
|
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) {
|
|
${c}
|
|
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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},oL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${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 c=Math.floor(n/4)*4,u=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 < ${c}; 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 + ${c};
|
|
if (${u===1}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
} else if (${u===2}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
} else if (${u===3}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function lL(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=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function bi(e,t,n,r){let a=lL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new U_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new U_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new oL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var cL=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=lt(this.rank),a=uL(t);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function uL(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 hL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=k_("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${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] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function fp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hL(e.shape,t):new cL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function dL(e,t,n,r){let a=t,s=e.shape.length,i=b.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=fp(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let f=b.sizeFromShape(d),m=b.sizeFromShape(e.shape)/f,A=Ae({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=id(e.dtype),g=bi(A,y,"sum",r),w=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function eA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return dL(a,s,i,n)}var pL={kernelName:qs,backendName:"webgl",kernelFunc:eA};function Nn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=Jm(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=fp(a,s,i);return c}var fL={kernelName:Js,backendName:"webgl",kernelFunc:Nn},H_=1e3;function mp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=b.sizeFromShape(m),g=b.sizeFromShape(A),w=y===g||y===1||g===1;b.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let _=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);b.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let v=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=Ae({inputs:{x:e},backend:a,attrs:{shape:v}}),E=Ae({inputs:{x:t},backend:a,attrs:{shape:x}}),F=[N,E],M=Math.max(y,g),W=n?N.shape[1]:N.shape[2],V=s!=null,B=i!=null,H=l==="leakyrelu",j=l!=null?pp(l,!0):null,X=V||B||H||j!=null,G;if((p===1||f===1)&&W>H_&&X===!1){let Y=N,ae=E;n&&(Y=Nn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),F.push(Y)),r&&(ae=Nn({inputs:{x:E},backend:a,attrs:{perm:[0,2,1]}}),F.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[M,W,1]}}),F.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[M,1,W]}}),F.push(oe));let fe=V_({inputs:{a:Q,b:oe},backend:a});G=eA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),F.push(fe)}else{let Y=rr(e.dtype,t.dtype),ae=new P_(v,x,[M,p,f],n,r,V,j,B,H),te=[N,E];if(s!=null&&te.push(s),B&&te.push(i),H){let ie=a.makeTensorInfo([],"float32",b.createScalarValue(o,"float32"));te.push(ie),F.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:_}});F.push(G);for(let Y of F)a.disposeIntermediateTensorInfo(Y);return ee}function mL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return mp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var AL={kernelName:Qs,backendName:"webgl",kernelFunc:mL},j_="return abs(x);";function yL(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=v_(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Cl(r.shape,j_):a=new Ba(r.shape,j_),n.runWebGLProgram(a,[r],r.dtype)}var gL={kernelName:Zi,backendName:"webgl",kernelFunc:yL},xL=xr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,wL=Xe({opSnippet:xL}),_L={kernelName:Yi,backendName:"webgl",kernelFunc:wL},bL=xr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,vL=Xe({opSnippet:bL}),kL={kernelName:Ji,backendName:"webgl",kernelFunc:vL},G_="return a + b;",IL=tn({opSnippet:G_,packedOpSnippet:G_,supportsComplex:!0,cpuKernelImpl:Kz}),NL={kernelName:Ia,backendName:"webgl",kernelFunc:IL},SL=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 = ${r};
|
|
setOutput(result);
|
|
}
|
|
`}},TL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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 Ap(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=Ap({inputs:r.slice(0,o),backend:n}),c=Ap({inputs:r.slice(o),backend:n});return Ap({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>rr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new TL(r[0].shape,s):new SL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var CL={kernelName:os,backendName:"webgl",kernelFunc:Ap};function EL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=b.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=Nn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=b.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=C.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),u!=null&&n.disposeIntermediateTensorInfo(h),y}var RL={kernelName:kh,backendName:"webgl",kernelFunc:EL};function FL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=b.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=Nn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=b.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=C.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),u!=null&&n.disposeIntermediateTensorInfo(h),y}var ML={kernelName:Ih,backendName:"webgl",kernelFunc:FL},$L=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 ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},DL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,b.assert(e.length>2,()=>`Packed arg${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=lt(o),c=mn("coords",o),u,h;if(s===1){h=o+1;let N=lt(h);u=`
|
|
${N} sourceLocR = ${N}(${c.join()}, 0);
|
|
++${c[o-1]};
|
|
${N} sourceLocG = ${N}(${c.join()}, 0);
|
|
++${c[o-2]};
|
|
${N} sourceLocA = ${N}(${c.join()}, 0);
|
|
--${c[o-1]};
|
|
${N} sourceLocB = ${N}(${c.join()}, 0);
|
|
--${c[o-2]};`}else h=o,u=`
|
|
${l} sourceLocR = coords;
|
|
++${c[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${c[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${c[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),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"),w=n==="max"?"greaterThan":"lessThan",_=r?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${g.join()})));`,v=`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 = ${c[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${c[o-2]} < ${i[o-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
|
|
sourceLocB${p}, sourceLocA${p}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${v};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${_}
|
|
vec4 candidate = ${v};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${w}(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 q_(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=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new $L(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=q_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function X_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new DL(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=X_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function K_(e,t,n,r){let a=[n];if(C.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]=C.computeOutAndReduceShapes(t.shape,a),l=b.sizeFromShape(o),c=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=q_(e,c,r);s.push(u);let h=Ae({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return X_(e,t,r)}function OL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=b.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=Nn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=K_(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var zL={kernelName:ls,backendName:"webgl",kernelFunc:OL};function PL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=b.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=Nn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=K_(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var LL={kernelName:lu,backendName:"webgl",kernelFunc:PL},WL=xr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,BL=Xe({opSnippet:WL}),VL={kernelName:Qi,backendName:"webgl",kernelFunc:BL},UL=xr+"return log(x + sqrt(x * x + 1.0));",HL=Xe({opSnippet:UL}),jL={kernelName:eo,backendName:"webgl",kernelFunc:HL},GL=xr+`
|
|
return atan(x);
|
|
`,qL=Xe({opSnippet:GL}),XL={kernelName:to,backendName:"webgl",kernelFunc:qL},KL=nL+`
|
|
return atan(a, b);
|
|
`,ZL=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+rL+`
|
|
return result;
|
|
`,YL=tn({opSnippet:KL,packedOpSnippet:ZL}),JL={kernelName:ro,backendName:"webgl",kernelFunc:YL},QL=xr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,eW=Xe({opSnippet:QL}),tW={kernelName:no,backendName:"webgl",kernelFunc:eW},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,c=e.dilationWidth,u=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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${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 < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h};
|
|
wC += ${c}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${N} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let g="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let _=Math.floor(s/4)*4,v=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 < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${_}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${x}
|
|
}
|
|
|
|
int xC = xCCorner + ${_};
|
|
if (${v===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${x}
|
|
} else if (${v===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${x}
|
|
} else if (${v===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${x}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
`}},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,c=e.dilationDepth,u=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",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let F=">=";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 += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
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 ${F} 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 * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let _="max",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(v="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,E=`
|
|
if (${g}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${_}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${A}, ${y});
|
|
const float initializationValue = ${w};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${w});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${d};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
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)
|
|
);
|
|
|
|
${E}
|
|
}
|
|
|
|
int xC = xCCorner + ${x};
|
|
if (${N===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
} else if (${N===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
} else if (${N===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
}
|
|
}
|
|
setOutput(${v});
|
|
}
|
|
}
|
|
`}};function nW(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,c=1;b.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&b.arraysEqual(u.inShape,u.outShape))return zn({inputs:{x:a},backend:n});let h=new hc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var rW={kernelName:us,backendName:"webgl",kernelFunc:nW};function aW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new tA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var sW={kernelName:uu,backendName:"webgl",kernelFunc:aW},iW=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,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${u});
|
|
const float avgMultiplier = float(${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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},oW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 < ${u};
|
|
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 += ${c}) {
|
|
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 lW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new oW(d);return n.runWebGLProgram(p,[a],i.dtype)}var uW={kernelName:Sh,backendName:"webgl",kernelFunc:lW};function cW(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:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new iW(u);return n.runWebGLProgram(h,[a],i.dtype)}var hW={kernelName:Nh,backendName:"webgl",kernelFunc:cW};function dW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return mp({a,b:s,transposeA:i,transposeB:o,backend:n})}var pW={kernelName:cs,backendName:"webgl",kernelFunc:dW},fW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.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)));
|
|
}
|
|
`}},mW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.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);
|
|
}
|
|
`}},AW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;b.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),b.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),b.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 c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new mW(r.shape,a.shape,s.shape,u,h,l):new fW(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},yW={kernelName:bs,backendName:"webgl",kernelFunc:AW},xW=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=gW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${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 gW(e){if(e===1)return"sourceLoc";if(e<=6)return nA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var wW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=mn("coords",this.rank),r=mn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${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((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${r[u]} = ${n[u]} + start[${u}];`).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 _W(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,b.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),b.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=mP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=cn.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wW(l):new xW(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),_W(a,o,l,n)}var bW={kernelName:Lo,backendName:"webgl",kernelFunc:dc},vW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;b.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=[],f=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),m=Nn({inputs:{x:f},backend:n,attrs:{perm:c}}),A=Ae({inputs:{x:m},backend:n,attrs:{shape:u}}),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},kW={kernelName:cu,backendName:"webgl",kernelFunc:vW};function IW(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),c=b_(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var NW={kernelName:Th,backendName:"webgl",kernelFunc:IW},SW="return float(a != b);",Z_=tn({opSnippet:SW,dtype:"bool"}),TW={kernelName:So,backendName:"webgl",kernelFunc:Z_};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 CW={kernelName:Kh,backendName:"webgl",kernelFunc:pc},EW="return float(int(x));";function RW(e,t){let n=new Ba(e.shape,EW),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=Ct(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(!b.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 RW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",b.getTypedArrayFromDType("bool",1)),o=Z_({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var FW={kernelName:hs,backendName:"webgl",kernelFunc:rA},Y_="return ceil(x);",MW=Xe({opSnippet:Y_,packedOpSnippet:Y_,cpuKernelImpl:Yz}),$W={kernelName:ds,backendName:"webgl",kernelFunc:MW},DW=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)}}},OW=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 zW(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 OW(a.shape):o=new DW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var PW={kernelName:Na,backendName:"webgl",kernelFunc:zW},LW=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 J_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function WW(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new LW(r.shape),i=[J_(r,a.complexTensorInfos.real),J_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var BW={kernelName:hu,backendName:"webgl",kernelFunc:WW},VW=class{constructor(e){this.outputShape=[],this.outputShape=C.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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},UW=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=lt(r),s=mn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${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],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${c.join()}));
|
|
}`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
|
|
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${yp(i,l,m)}),
|
|
vec2(${yp(c,l,m)}));
|
|
}`}let d=o.length,p=o[o.length-1];h+=`
|
|
return getChannel(
|
|
getT${d}(${yp(i,l,p)}),
|
|
vec2(${yp(c,l,p)}));`,this.userCode=`
|
|
float getValue(${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 yp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function gp(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 HW={kernelName:Vh,backendName:"webgl",kernelFunc:gp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>pc({inputs:{input:f},backend:n})),u=e.map(f=>gp({inputs:{input:f},backend:n})),h=Fl(c,t,n),d=Fl(u,t,n),p=Va({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=Q_(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=Jz(h,u,r,d),f=C.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Fl(e.slice(0,c),t,n),h=Fl(e.slice(c),t,n),d=Fl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new UW(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=Q_(e,t,n),i=new VW(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function Q_(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,b.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function eb(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=b.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(b.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>b.sizeFromShape(c.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),Fl(o,s,n)}var jW={kernelName:ao,backendName:"webgl",kernelFunc:eb},tb=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,c=e.dilationHeight,u=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,w="",_="";n&&(r?w=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?w=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:w=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,_="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${w}
|
|
|
|
const ivec2 strides = ivec2(${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 * ${c};
|
|
|
|
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 < ${p}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${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;
|
|
${v}
|
|
${_}
|
|
setOutput(result);
|
|
}
|
|
`}},GW=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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 < ${u}; 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 * ${c};
|
|
|
|
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);
|
|
}
|
|
`}},qW=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:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=fn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let _=0;_<=1;_++)for(let v=0;v<=1;v++)w+=`
|
|
blockIndex = rc.y + ${v};
|
|
pos = rc.x + ${_};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
|
|
d0 = offsetY + ${u} * (pos / ${f});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
|
|
d1 = offsetX + ${c} * (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[${_*2+v}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${_*2+v}] = 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;
|
|
|
|
${w}
|
|
|
|
${m.output} = result;
|
|
}
|
|
`}};function nb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=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)&&u>H_,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],v=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,_,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=mp({a:v,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=Ae({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(v),y.push(x),y.push(N)}else{let _=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),v={dataId:e.dataId,shape:[1,_,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,b.assert(sc(c.shape,v.shape),()=>`packed reshape ${c.shape} to ${v.shape} isn't free`);let N=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let E=mp({a:v,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),F=r.texData.get(E.dataId);b.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,F.shape=n.outShape,A=zn({inputs:{x:E},backend:r}),A.shape=n.outShape,y.push(E)}for(let _ of y)r.disposeIntermediateTensorInfo(_);return A}function rb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,_=[],v=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,m,b.sizeFromShape(t.shape)/m]}});_.push(v),_.push(x);let N=new qW(y,v.shape,n),E=r.runWebGLProgram(N,[v],"float32"),F=Ae({inputs:{x:E},backend:r,attrs:{shape:[1,y[0],y[1]]}});_.push(E),_.push(F);let M=a!=null,W=s!=null,V=o==="leakyrelu",B=o?pp(o,!0):null,H=new P_(F.shape,x.shape,[1,A,n.outChannels],g,w,M,B,W,V),j=[F,x];if(a&&j.push(a),W&&j.push(s),V){let Y=r.makeTensorInfo([],"float32",b.createScalarValue(i,"float32"));j.push(Y),_.push(Y)}let X=r.runWebGLProgram(H,j,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});_.push(X);for(let Y of _)r.disposeIntermediateTensorInfo(Y);return ee}function XW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!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=nb({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=rb({x:a,filter:s,convInfo:d,backend:n});else{let m=new tb(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 KW={kernelName:ps,backendName:"webgl",kernelFunc:XW},ZW=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);
|
|
}
|
|
`}},YW=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,c=s?2:3,u=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${u}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - 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);
|
|
}
|
|
`}},JW=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},QW=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${c});
|
|
|
|
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 eB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new ZW(d);return n.runWebGLProgram(p,[a,s],"float32")}var tB={kernelName:Eh,backendName:"webgl",kernelFunc:eB};function nB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(c),d=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new YW(d);return n.runWebGLProgram(p,[a,s],"float32")}var rB={kernelName:fs,backendName:"webgl",kernelFunc:nB};function aB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new GW(c);return n.runWebGLProgram(u,[a,s],"float32")}var sB={kernelName:du,backendName:"webgl",kernelFunc:aB};function iB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new JW(c);return n.runWebGLProgram(u,[a,s],"float32")}var oB={kernelName:Rh,backendName:"webgl",kernelFunc:iB};function lB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new QW(c);return n.runWebGLProgram(u,[a,s],"float32")}var uB={kernelName:Fh,backendName:"webgl",kernelFunc:lB},cB=z_+`
|
|
return cos(x);
|
|
`,hB=Xe({opSnippet:cB}),dB={kernelName:ms,backendName:"webgl",kernelFunc:hB},pB=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,fB=Xe({opSnippet:pB}),mB={kernelName:so,backendName:"webgl",kernelFunc:fB},AB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,_]=h>1?[`${(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 = ${w};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${p} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
float in_x = ${_};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${d} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},yB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new AB(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},gB={kernelName:io,backendName:"webgl",kernelFunc:yB},ib=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${ab(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() {
|
|
${lt(r)} coords = getOutputCoords();
|
|
int end = ${sb(r,"coords")};
|
|
float val = ${a};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${sb(r,"coords")} = idx;
|
|
val += getX(${ab(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 ab(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 ${e} is not yet supported`)}function sb(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=Nn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=zn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new ib(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new ib(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=Nn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var wB={kernelName:As,backendName:"webgl",kernelFunc:xB};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=b_(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=Zz(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var bB={kernelName:Mh,backendName:"webgl",kernelFunc:_B},vB=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 kB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;b.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],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new vB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var IB={kernelName:oo,backendName:"webgl",kernelFunc:kB},ob=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,c=e.strideHeight,u=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=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${u});
|
|
const ivec2 pads = ivec2(${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}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},lb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let _=0;_<p;_++)for(let v=0;v<f;v++)A+=`
|
|
vec4 xTexelR${_}C${v*2} = vec4(0.);
|
|
vec4 wR${_}C${v} = vec4(0.);
|
|
vec4 xR${_}C${v} = vec4(0.);`;for(let _=0;_<p;_++)for(let v=0;v<m;v++){let x=v*2;if(A+=`
|
|
xR = xRCorner + ${_*h};
|
|
xC = xCCorner + ${x*d};
|
|
`,u===1){if(x<f&&(l%2==1?A+=`
|
|
xCOffset = xC + 1;
|
|
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${x} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
xTexelR${_}C${x}.zw = vec2(0.);
|
|
}
|
|
} else {
|
|
xTexelR${_}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 >= ${i}) {
|
|
previous.zw = vec2(0.);
|
|
}
|
|
|
|
xR${_}C${x} = vec4(previous.zw, xTexelR${_}C${x}.xy);
|
|
} else {
|
|
xR${_}C${x} = vec4(0, 0, xTexelR${_}C${x}.xy);
|
|
}
|
|
`:A+=`
|
|
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
|
|
xTexelR${_}C${x} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${_}C${x} = vec4(0.);
|
|
}
|
|
|
|
xR${_}C${x} = xTexelR${_}C${x};
|
|
`,x+1<f)){let N=l%2==0?b.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
|
|
xCOffset = xC + ${l%2} + ${N};
|
|
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${x+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`,d>1&&(A+=`
|
|
xCOffset -= 2;
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${x} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${_}C${x} = vec4(0.);
|
|
}
|
|
`),A+=`
|
|
xR${_}C${x+1} = vec4(
|
|
xTexelR${_}C${x}.zw, xTexelR${_}C${x+2}.xy);
|
|
`):A+=`
|
|
xCOffset = xC + ${N};
|
|
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${x+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
|
|
xR${_}C${x+1} = xTexelR${_}C${x+2};
|
|
`}}else x<f&&(A+=`
|
|
if(xR >= 0 && xR < ${s}) {
|
|
`,l%2==1?(A+=`
|
|
xCOffset = xC + 1 - ${u};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${x} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${_}C${x} = vec4(0.);
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${i}) {
|
|
xTexelR${_}C${x+2} = getX(batch, xR, xC + 1, d1);
|
|
} else {
|
|
xTexelR${_}C${x+2} = vec4(0.);
|
|
}
|
|
|
|
xR${_}C${x} = vec4(
|
|
xTexelR${_}C${x}.zw, xTexelR${_}C${x+2}.zw);
|
|
`,x+1<f&&(A+=`
|
|
vec4 final = vec4(0.);
|
|
xCOffset = xC + 1 + ${u};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xR${_}C${x+1} = vec4(xTexelR${_}C${x+2}.xy, final.xy);
|
|
`)):(A+=`
|
|
if(xC >= 0 && xC < ${i}) {
|
|
xTexelR${_}C${x} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${_}C${x} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + ${u};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${_}C${x+2} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${_}C${x+2} = vec4(0.);
|
|
}
|
|
|
|
xR${_}C${x} = vec4(
|
|
xTexelR${_}C${x}.xy, xTexelR${_}C${x+2}.xy);
|
|
`,x+1<f&&(A+=`
|
|
xR${_}C${x+1} = vec4(
|
|
xTexelR${_}C${x}.zw, xTexelR${_}C${x+2}.zw);
|
|
`)),A+="}");x<f&&(A+=`
|
|
vec4 wTexelR${_}C${x} = getW(${_}, ${x}, d1, q);
|
|
wR${_}C${x} = vec4(wTexelR${_}C${x}.xz, wTexelR${_}C${x}.xz);
|
|
`,x+1<f&&(A+=`
|
|
vec4 wTexelR${_}C${x+1} = getW(${_}, ${x+1}, d1, q);
|
|
wR${_}C${x+1} =
|
|
vec4(wTexelR${_}C${x+1}.xz, wTexelR${_}C${x+1}.xz);`))}for(let _=0;_<p;_++)for(let v=0;v<f;v++)A+=`dotProd += xR${_}C${v} * wR${_}C${v};`;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 w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${y}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${u});
|
|
const ivec2 pads = ivec2(${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;
|
|
${w}
|
|
${g}
|
|
setOutput(result);
|
|
}
|
|
`}};function NB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),b.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new lb(h):d=new ob(h),n.runWebGLProgram(d,[a,s],"float32")}var SB={kernelName:ys,backendName:"webgl",kernelFunc:NB},TB=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},CB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function EB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new TB(h);return n.runWebGLProgram(d,[a,s],"float32")}var RB={kernelName:$h,backendName:"webgl",kernelFunc:EB};function FB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new CB(h);return n.runWebGLProgram(d,[a,s],"float32")}var MB={kernelName:Dh,backendName:"webgl",kernelFunc:FB},$B=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 DB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=b.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new $B(s),l=n.runWebGLProgram(o,[i],i.dtype),c=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var OB={kernelName:Oh,backendName:"webgl",kernelFunc:DB},zB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
|
|
const ivec2 strides = ivec2(${a}, ${s});
|
|
const ivec2 pads = ivec2(${u}, ${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 * ${c};
|
|
|
|
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 PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new zB(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var LB={kernelName:pu,backendName:"webgl",kernelFunc:PB},WB="return (x >= 0.0) ? x : (exp(x) - 1.0);",BB=`
|
|
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;
|
|
`,VB=Xe({opSnippet:WB,packedOpSnippet:BB}),UB={kernelName:lo,backendName:"webgl",kernelFunc:VB},HB="return (b >= 1.0) ? a : a * (b + 1.0);",jB=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,GB=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(jB,r.shape,a.shape):new Rl(HB,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},qB={kernelName:Lh,backendName:"webgl",kernelFunc:GB},XB=`
|
|
return vec4(equal(a, b));
|
|
`,KB="return float(a == b);",ZB=tn({opSnippet:KB,packedOpSnippet:XB,dtype:"bool"}),YB={kernelName:co,backendName:"webgl",kernelFunc:ZB},JB=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${C.ERF_P};
|
|
float a1 = ${C.ERF_A1};
|
|
float a2 = ${C.ERF_A2};
|
|
float a3 = ${C.ERF_A3};
|
|
float a4 = ${C.ERF_A4};
|
|
float a5 = ${C.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,QB=Xe({opSnippet:JB}),eV={kernelName:uo,backendName:"webgl",kernelFunc:QB},ub="return exp(x);",cb=Xe({opSnippet:ub,packedOpSnippet:ub,cpuKernelImpl:Qz}),tV={kernelName:xs,backendName:"webgl",kernelFunc:cb};function aA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(b.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var nV={kernelName:ho,backendName:"webgl",kernelFunc:aA},hb="return exp(x) - 1.0;",rV=Xe({opSnippet:hb,packedOpSnippet:hb,cpuKernelImpl:eP}),aV={kernelName:po,backendName:"webgl",kernelFunc:rV},db=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 pb(e,t,n){let r=n.texData.get(e.dataId),a=b.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,c=new db("real",l,t),u=new db("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(c,h,"float32"),p=n.runWebGLProgram(u,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 sV(e){let{inputs:t,backend:n}=e,{input:r}=t;return pb(r,!1,n)}var iV={kernelName:Wh,backendName:"webgl",kernelFunc:sV},oV=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||b.inferDtype(a),s==="string"){let i=b.getArrayFromDType(s,b.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new oV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var lV={kernelName:fu,backendName:"webgl",kernelFunc:sA},uV=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},cV={kernelName:fo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new uV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},fb="return floor(x);",hV=Xe({opSnippet:fb,packedOpSnippet:fb,cpuKernelImpl:tP}),dV={kernelName:ws,backendName:"webgl",kernelFunc:hV},pV=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,fV=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,mV=tn({opSnippet:pV,packedOpSnippet:fV,dtype:"int32"}),AV={kernelName:_s,backendName:"webgl",kernelFunc:mV},yV=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},gV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},wV={kernelName:td,backendName:"webgl",kernelFunc:xV},Ml;function xV(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,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(Ml==null&&(Ml=document.createElement("canvas").getContext("2d")),Ml.canvas.width=l,Ml.canvas.height=c,Ml.drawImage(a,0,0,l,c),a=Ml.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=Zn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new gV(h):new yV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function _V(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(u),A=C.computeConv2DInfo(a.shape,s.shape,l,h,c,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=nb({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=rb({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let _=i!=null,v=o!=null,x=p==="leakyrelu",N=p?pp(p,!1):null,E=new tb(A,_,N,v,x),F=[a,s];if(i&&F.push(i),o&&F.push(o),x){let M=n.makeTensorInfo([],"float32",b.createScalarValue(f,"float32"));F.push(M),g.push(M)}y=n.runWebGLProgram(E,F,"float32")}let w=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(_=>n.disposeIntermediateTensorInfo(_)),w}var bV={kernelName:ei,backendName:"webgl",kernelFunc:_V};function vV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),b.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?pp(d,y):null,w=[a,s],_=i!=null,v=o!=null,x=d==="leakyrelu";if(_&&w.push(i),v&&w.push(o),x){let F=n.makeTensorInfo([],"float32",b.createScalarValue(p,"float32"));w.push(F),f.push(F)}let N;y?N=new lb(A,_,g,v,x):N=new ob(A,_,g,v,x);let E=n.runWebGLProgram(N,w,"float32");return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),E}var kV={kernelName:ti,backendName:"webgl",kernelFunc:vV},IV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(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 NV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[b.sizeFromShape(r.shape)/c,c]}}),p=new IV(i,u,[l,c]),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 SV={kernelName:Ao,backendName:"webgl",kernelFunc:NV},CV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=TV(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function TV(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(`${n[a]}`);return r.join()}function EV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=b.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=b.sizeFromShape(s.shape),h=[],d=Ae({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=Ae({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),_=nP(w,g,f);return h.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.makeTensorInfo(c.outputShape,_.dtype,_.values)}let m=new CV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var RV={kernelName:mo,backendName:"webgl",kernelFunc:EV},FV="return float(a > b);",MV=`
|
|
return vec4(greaterThan(a, b));
|
|
`,$V=tn({opSnippet:FV,packedOpSnippet:MV,cpuKernelImpl:rP,dtype:"bool"}),DV={kernelName:yo,backendName:"webgl",kernelFunc:$V},OV="return float(a >= b);",zV=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,PV=tn({opSnippet:OV,packedOpSnippet:zV,dtype:"bool"}),LV={kernelName:vs,backendName:"webgl",kernelFunc:PV};function WV(e){let{inputs:t,backend:n}=e,{input:r}=t;return pb(r,!0,n)}var BV={kernelName:Bh,backendName:"webgl",kernelFunc:WV},VV="return float(!isnan(x) && !isinf(x));",UV=Xe({opSnippet:VV,dtype:"bool"}),HV={kernelName:go,backendName:"webgl",kernelFunc:UV},jV="return float(isinf(x));",GV=Xe({opSnippet:jV,dtype:"bool"}),qV={kernelName:xo,backendName:"webgl",kernelFunc:GV},XV="return float(isnan(x));",KV=Xe({opSnippet:XV,dtype:"bool"}),ZV={kernelName:wo,backendName:"webgl",kernelFunc:KV},YV="return float(a < b);",JV=`
|
|
return vec4(lessThan(a, b));
|
|
`,QV=tn({opSnippet:YV,packedOpSnippet:JV,cpuKernelImpl:aP,dtype:"bool"}),eU={kernelName:_o,backendName:"webgl",kernelFunc:QV},tU="return float(a <= b);",nU=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,rU=tn({opSnippet:tU,packedOpSnippet:nU,dtype:"bool"}),aU={kernelName:bo,backendName:"webgl",kernelFunc:rU};function sU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=sP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var iU={kernelName:Uh,backendName:"webgl",kernelFunc:sU},oU=`if (x < 0.0) return NAN;
|
|
return log(x);`,lU=`
|
|
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;
|
|
`,uU=Xe({opSnippet:oU,packedOpSnippet:lU,cpuKernelImpl:iP}),cU={kernelName:Ns,backendName:"webgl",kernelFunc:uU},hU="return log(1.0 + x);",dU=Xe({opSnippet:hU}),pU={kernelName:vo,backendName:"webgl",kernelFunc:dU},fU="return float(a >= 1.0 && b >= 1.0);",mU=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,AU=tn({opSnippet:fU,packedOpSnippet:mU,dtype:"bool"}),yU={kernelName:ko,backendName:"webgl",kernelFunc:AU},gU="return float(!(x >= 1.0));",xU=Xe({opSnippet:gU}),wU={kernelName:mu,backendName:"webgl",kernelFunc:xU},_U="return float(a >= 1.0 || b >= 1.0);",bU=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,vU=tn({opSnippet:_U,packedOpSnippet:bU,dtype:"bool"}),kU={kernelName:Au,backendName:"webgl",kernelFunc:vU},IU=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 * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},NU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
|
|
}
|
|
`}},SU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new NU(a.shape,s,i,o,l):new IU(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},TU={kernelName:yu,backendName:"webgl",kernelFunc:SU},CU=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 * ${a});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},EU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new CU(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},RU={kernelName:Hh,backendName:"webgl",kernelFunc:EU};function FU(e,t,n,r){let a=b.sizeFromShape(t),s=b.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=bi(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function mb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=b.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let _=Jm(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let v=n.texData.get(p.dataId);v.values=_}else p=fp(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=C.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=oP(g,b.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let _=n.texData.get(y.dataId);_.values=w}else y=FU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var MU={kernelName:Ss,backendName:"webgl",kernelFunc:mb},$U=F_+`
|
|
return max(a, b);
|
|
`,DU=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+dp+`
|
|
return result;
|
|
`,OU=tn({opSnippet:$U,packedOpSnippet:DU,cpuKernelImpl:lP}),zU={kernelName:Ts,backendName:"webgl",kernelFunc:OU};function PU(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,c=1;b.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&b.arraysEqual(u.inShape,u.outShape))return zn({inputs:{x:a},backend:n});let h=new hc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var LU={kernelName:Cs,backendName:"webgl",kernelFunc:PU};function WU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new tA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var BU={kernelName:gu,backendName:"webgl",kernelFunc:WU},VU=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 * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},UU=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${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 < ${c};
|
|
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} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function HU(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new tA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new UU(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var jU={kernelName:Gh,backendName:"webgl",kernelFunc:HU};function GU(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:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new hc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new VU(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var qU={kernelName:jh,backendName:"webgl",kernelFunc:GU};function XU(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 KU={kernelName:qh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;b.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];b.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,d]=XU(r,o,u,l);return[h,d]}};function ZU(e,t,n,r){let a=b.sizeFromShape(t),s=b.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 YU={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=b.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,_=new Array(o);for(let N=0;N<_.length;N++)_[N]=r.shape[u[N]];let v=Jm(w,r.shape,r.dtype,u,_);f=i.makeTensorInfo(_,r.dtype);let x=i.texData.get(f.dataId);x.values=v}else f=fp(r,u,i);p.push(f),c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=C.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=C.expandShapeToKeepDim(m,l));let g=ZU(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function JU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=b.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=Nn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=b.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=C.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),u!=null&&n.disposeIntermediateTensorInfo(h),y}var QU={kernelName:Rs,backendName:"webgl",kernelFunc:JU},eH=F_+`
|
|
return min(a, b);
|
|
`,tH=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+dp+`
|
|
return result;
|
|
`,nH=tn({opSnippet:eH,packedOpSnippet:tH,cpuKernelImpl:uP}),rH={kernelName:Fs,backendName:"webgl",kernelFunc:nH},aH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=lt(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).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}));
|
|
}
|
|
`}},sH=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=lt(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),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${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()}), ${u});
|
|
${o[r-1]} += 1;
|
|
if(${c}) {
|
|
${p}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}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()}), ${u});
|
|
${o[r-1]} += 1;
|
|
if(${c}) {
|
|
${p}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${o[r-2]} += 1;
|
|
if(${o[r-2]} < ${this.outputShape[r-2]}) {
|
|
${p}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${o[r-1]} += 1;
|
|
if(${c}) {
|
|
${p}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${a} start = ${a}(${s});
|
|
const ${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}},iH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sH(r.shape,a,s):new aH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},oH={kernelName:xu,backendName:"webgl",kernelFunc:iH},lH=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,uH=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+dp+`
|
|
return result;
|
|
`,cH=tn({opSnippet:lH,packedOpSnippet:uH}),hH={kernelName:Io,backendName:"webgl",kernelFunc:cH},dH=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)}}},pH=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,fH=`
|
|
// 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;
|
|
`,Ab=tn({opSnippet:pH,packedOpSnippet:fH,checkOutOfBounds:!0}),mH={kernelName:gs,backendName:"webgl",kernelFunc:Ab},yb="return a - b;",gb=tn({opSnippet:yb,packedOpSnippet:yb,supportsComplex:!0,cpuKernelImpl:yP}),AH={kernelName:Zs,backendName:"webgl",kernelFunc:gb};function xb(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=b.parseAxisParam([s],a.shape),o=mb({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),u=gb({inputs:{a,b:c},backend:n}),h=cb({inputs:{x:u},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=Ab({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var yH={kernelName:Xs,backendName:"webgl",kernelFunc:xb};function gH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:xb({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new dH(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var xH={kernelName:Xh,backendName:"webgl",kernelFunc:gH},wb="return -x;";function wH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=hP(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,wb):a=new Ba(r.shape,wb),n.runWebGLProgram(a,[r],r.dtype)}var _H={kernelName:No,backendName:"webgl",kernelFunc:wH},bH=Pr.nonMaxSuppressionV3Impl;function vH(e){C.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,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=bH(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var kH={kernelName:To,backendName:"webgl",kernelFunc:vH},IH=Pr.nonMaxSuppressionV4Impl;function NH(e){C.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:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=IH(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var SH={kernelName:Co,backendName:"webgl",kernelFunc:NH},TH=Pr.nonMaxSuppressionV5Impl;function CH(e){C.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:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=TH(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var EH={kernelName:Eo,backendName:"webgl",kernelFunc:CH},RH=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)));
|
|
}
|
|
`}},FH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=b.sizeFromShape(a.shape),c=new RH(l,s,i,o),u=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},MH={kernelName:$s,backendName:"webgl",kernelFunc:FH};function xp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=pc({inputs:{input:r},backend:n}),s=xp({inputs:{x:a},backend:n}),i=gp({inputs:{input:r},backend:n}),o=xp({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 $H={kernelName:Xo,backendName:"webgl",kernelFunc:xp};function _b(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=_b({inputs:{x:a},backend:n}),i=gp({inputs:{input:r},backend:n}),o=xp({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 DH={kernelName:Ro,backendName:"webgl",kernelFunc:_b};function OH(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(u=>{b.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),b.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=aA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=eb({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var zH={kernelName:Fo,backendName:"webgl",kernelFunc:OH},PH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).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)}}},LH=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=lt(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),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
|
|
if(${c}) {
|
|
`,r===1?"":`}
|
|
rc = outputLoc;
|
|
${o[r-2]} += 1;
|
|
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
|
|
if(${c}) {`],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()}), ${u});
|
|
}
|
|
`;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)}}},bb=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 LH(a.shape,s,i):new PH(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},WH={kernelName:Ds,backendName:"webgl",kernelFunc:bb},BH=`
|
|
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);
|
|
`,VH=`
|
|
// 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));
|
|
`+dp+`
|
|
return result;
|
|
`,UH=tn({opSnippet:BH,packedOpSnippet:VH}),HH={kernelName:Os,backendName:"webgl",kernelFunc:UH};function jH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=b.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),d=a;h!=null&&(d=Nn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=dP(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,u),A=b.sizeFromShape(m),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=id(a.dtype),w=bi(y,g,"prod",n);p=Ae({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,c);p=Ae({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var GH={kernelName:Mo,backendName:"webgl",kernelFunc:jH},vb=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=pP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},qH={kernelName:wu,backendName:"webgl",kernelFunc:vb},XH="return 1.0 / x;",KH=Xe({opSnippet:XH}),ZH={kernelName:$o,backendName:"webgl",kernelFunc:KH},YH=xr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,JH=`
|
|
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;
|
|
`,QH=Xe({opSnippet:YH,packedOpSnippet:JH}),ej={kernelName:Ps,backendName:"webgl",kernelFunc:QH},tj=xr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,nj=`
|
|
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;
|
|
`,rj=Xe({opSnippet:tj,packedOpSnippet:nj}),aj={kernelName:Ws,backendName:"webgl",kernelFunc:rj},sj=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 c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[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(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${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);
|
|
}
|
|
`}},ij=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 c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[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(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]},
|
|
${c[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${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 oj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ij(a.shape,l,c,s,i):new sj(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var lj={kernelName:Ls,backendName:"webgl",kernelFunc:oj},uj=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],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,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(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${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 cj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new uj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var hj={kernelName:Yh,backendName:"webgl",kernelFunc:cj},dj=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 c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[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(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${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 + ${h})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new dj(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var fj={kernelName:_u,backendName:"webgl",kernelFunc:pj},mj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Aj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new mj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var yj={kernelName:Zh,backendName:"webgl",kernelFunc:Aj},gj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=lt(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${a}));
|
|
}
|
|
`}},xj=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 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${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 = ${c(r.slice())};
|
|
if(${a}) {
|
|
result.a = ${u(r.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function wj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=b.parseAxisParam(s,a.shape);if(i===0)return zn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xj(a.shape,o):new gj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var _j={kernelName:Bs,backendName:"webgl",kernelFunc:wj},bj=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},vj={kernelName:Ko,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new bj(r.shape,s),[c,u]=C.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},kj=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Ij=Xe({opSnippet:kj}),Nj={kernelName:Vs,backendName:"webgl",kernelFunc:Ij},Sj="return inversesqrt(x);",Tj=Xe({opSnippet:Sj,cpuKernelImpl:fP}),Cj={kernelName:Us,backendName:"webgl",kernelFunc:Tj},kb=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,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 < ${t}; j++) {
|
|
int index = round(${u});
|
|
flattenedIndex += index * ${p};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${d};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function Ej(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),d=[h/c,c];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,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new kb(l,o,p.shape.length,f.shape.length,u,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 Rj={kernelName:Oo,backendName:"webgl",kernelFunc:Ej},Fj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=lt(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${r});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${a}));
|
|
} else {
|
|
setOutput(getB(${a}));
|
|
}
|
|
}
|
|
`}};function Mj(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Fj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],rr(a.dtype,s.dtype))}var $j={kernelName:zo,backendName:"webgl",kernelFunc:Mj},Dj=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${C.SELU_SCALEALPHA};
|
|
float scale = ${C.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Oj=Xe({opSnippet:Dj}),zj={kernelName:Po,backendName:"webgl",kernelFunc:Oj},Pj="return 1.0 / (1.0 + exp(-1.0 * x));",Lj=Xe({opSnippet:Pj}),Wj={kernelName:js,backendName:"webgl",kernelFunc:Lj},Bj=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,Vj=Xe({opSnippet:Bj}),Uj={kernelName:Bo,backendName:"webgl",kernelFunc:Vj},Hj=z_+`
|
|
return sin(x);
|
|
`,jj=Xe({opSnippet:Hj}),Gj={kernelName:Hs,backendName:"webgl",kernelFunc:jj},qj=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Xj=Xe({opSnippet:qj}),Kj={kernelName:Wo,backendName:"webgl",kernelFunc:Xj},Zj=`
|
|
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;
|
|
`,Yj=Xe({opSnippet:Zj}),Jj={kernelName:Vo,backendName:"webgl",kernelFunc:Yj},Qj=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;b.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 c=[],u=bb({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(u.shape,s,o,!1),d=C.getPermuted(h.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),f=Ae({inputs:{x:u},backend:n,attrs:{shape:h}}),m=Nn({inputs:{x:f},backend:n,attrs:{perm:d}}),A=Ae({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},eG={kernelName:bu,backendName:"webgl",kernelFunc:Qj};function tG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new kb(c,l,a.shape.length,s.shape.length,u,[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 nG={kernelName:Jh,backendName:"webgl",kernelFunc:tG};function rG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=b.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).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:u,size:p}});return u[o]+=d,f})}var aG={kernelName:Uo,backendName:"webgl",kernelFunc:rG},sG="return sqrt(x);",iG=Xe({opSnippet:sG}),oG={kernelName:Gs,backendName:"webgl",kernelFunc:iG},lG="return x * x;",uG=Xe({opSnippet:lG}),cG={kernelName:vu,backendName:"webgl",kernelFunc:uG},Ib="return (a - b) * (a - b);",hG=tn({opSnippet:Ib,packedOpSnippet:Ib}),dG={kernelName:Ks,backendName:"webgl",kernelFunc:hG};function pG({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 fG={kernelName:Ta,backendName:"webgl",kernelFunc:pG},mG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${a} begin = ${a}(${e});
|
|
${a} strides = ${a}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function AG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,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,c,u,h,d),w=Ae({inputs:{x:a},backend:n,attrs:{shape:y}}),_;if(p){let x=dc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});_=Ae({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))_=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=We(w.shape,w.dtype,x),E=AP(g,N,m,f);_=n.makeTensorInfo(g,w.dtype,E.values)}else{let x=new mG(f,m,g);_=n.runWebGLProgram(x,[w],w.dtype)}let v=Ae({inputs:{x:_},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(_),v}var yG={kernelName:Ho,backendName:"webgl",kernelFunc:AG},gG="return tan(x);",xG=Xe({opSnippet:gG}),wG={kernelName:jo,backendName:"webgl",kernelFunc:xG},_G=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,bG=Xe({opSnippet:_G}),vG={kernelName:Ys,backendName:"webgl",kernelFunc:bG},IG=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=lt(this.rank),a=kG(e);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function kG(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]}, ${e[a]})`);return r.join()}function Nb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(u=>b.decodeString(u)),l=We(a.shape,a.dtype,o),c=gP(l,s);return n.makeTensorInfo(c.shape,c.dtype,c.values)}let i=new IG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var NG={kernelName:Sa,backendName:"webgl",kernelFunc:Nb};function SG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,c]=xP(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var TG={kernelName:Go,backendName:"webgl",kernelFunc:SG},CG=class{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(r){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${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 EG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:c}=r,[u,h,d,p]=a.shape,[f,m]=c!=null?c:[h,d],A=[u,f,m,p],y=new CG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var RG={kernelName:Qh,backendName:"webgl",kernelFunc:EG};function FG(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:c}=wP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([c.length],"int32",c)]}var MG={kernelName:ed,backendName:"webgl",kernelFunc:FG};function $G(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],c=new Array(o-1),u=0;for(let m=0;m<o;m++)m!==s&&(c[u++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=dc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=Ae({inputs:{x:A},backend:n,attrs:{shape:c}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var DG={kernelName:qo,backendName:"webgl",kernelFunc:$G},OG=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",c=Math.floor(n/4)*4,u=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 < ${c}; 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 + ${c};
|
|
if (${u===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
|
|
);
|
|
|
|
${h}
|
|
} else if (${u===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
|
|
);
|
|
|
|
${h}
|
|
} else if (${u===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 zG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],c=0,u=C.getAxesPermutation([c],o),h=a;u!=null&&(h=Nn({inputs:{x:a},backend:n,attrs:{perm:u}}),l.push(h),c=C.getInnerMostAxes(1,o)[0]);let d=C.segment_util.computeOutShape(h.shape,c,i),p=b.sizeFromShape([h.shape[c]]),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=id(a.dtype),A=(_,v,x,N,E)=>{let F=_.shape[0],M=_.shape[1],W=C.segment_util.segOpComputeOptimalWindowSize(M,E),V={windowSize:W,inSize:M,batchSize:F,numSegments:E},B=new OG(V,v),H=n.compileAndRun(B,[_,x],N);if(l.push(H),H.shape[1]===E)return H;let j=vb({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=Nb({inputs:{x:j},backend:n,attrs:{reps:[M/W]}});return l.push(j),l.push(X),A(H,v,X,N,E)},y=A(f,"unsortedSegmentSum",s,m,i),g=Ae({inputs:{x:y},backend:n,attrs:{shape:d}}),w=g;if(u!=null){l.push(g);let _=C.getUndoAxesPermutation(u);w=Nn({inputs:{x:w},backend:n,attrs:{perm:_}})}return l.forEach(_=>n.disposeIntermediateTensorInfo(_)),w}var PG={kernelName:ku,backendName:"webgl",kernelFunc:zG},LG=[TU,RU,AL,gL,_L,kL,NL,CL,RL,ML,zL,LL,VL,jL,JL,XL,tW,sW,rW,uW,hW,pW,yW,kW,NW,FW,$W,PW,BW,YP,jW,tB,rB,KW,oB,uB,sB,dB,mB,gB,wB,bB,IB,RB,MB,SB,OB,LB,UB,qB,YB,eV,tV,nV,aV,iV,lV,cV,dV,AV,wV,bV,kV,SV,RV,DV,LV,ZP,BV,HW,HV,qV,ZV,QP,eU,aU,iU,pU,cU,yU,wU,kU,MU,BU,LU,jU,qU,KU,zU,YU,QU,rH,oH,hH,xH,aL,_H,kH,SH,EH,TW,MH,DH,zH,WH,HH,tL,GH,qH,CW,mH,ZH,aj,ej,iL,lj,hj,fj,yj,_j,vj,Nj,Cj,Rj,$j,zj,Wj,Uj,Gj,Kj,bW,yH,Jj,eG,nG,aG,oG,cG,dG,fG,yG,AH,pL,wG,vG,NG,TG,RG,fL,MG,DG,PG,$H];for(let e of LG)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 Sb;function WG(e){Sb=e.wasm.cwrap(Qs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function BG(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:c,activation:u,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=fc[u];if(A==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=c?s.shape[1]:s.shape[2],w=a.shape[0],_=n.makeOutput([w,y,g],a.dtype),v=n.dataIdMap.get(_.dataId).id,x=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return Sb(d,x,a.shape.length,p,N,s.shape.length,l,c,A,f,m,h||0,v),_}var VG={kernelName:Qs,backendName:"wasm",setupFunc:WG,kernelFunc:BG};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),c=s.dataIdMap.get(l.dataId).id;return b.sizeFromShape(l.shape)===0||t(o,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var UG=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:c,b:u}=l,h=o.dataIdMap.get(c.dataId).id,d=o.dataIdMap.get(u.dataId).id,p=n!=null?n:c.dtype,f=C.assertAndGetBroadcastShape(c.shape,u.shape),m=o.makeOutput(f,p);if(b.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),g=o.dataIdMap.get(m.dataId).id,w=()=>r(h,A,c.shape.length,d,y,u.shape.length,Pn[c.dtype],g);if(t&&c.dtype==="float32")return w(),m;let _=C.getBroadcastDims(c.shape,f),v=C.getBroadcastDims(u.shape,f),x=_.every((E,F)=>E===F),N=v.every((E,F)=>E===F);if(x&&N)return w(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var HG=!0,jG=An(Ia,HG),Tb;function GG(e){Tb=e.wasm.cwrap(os,null,["array","number","number","number"])}function qG(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(b.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 Tb(s,a.length,Pn[r.dtype],i),r}var XG={kernelName:os,backendName:"wasm",setupFunc:GG,kernelFunc:qG};function wp(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 KG={kernelName:ks,backendName:"wasm",kernelFunc:wp},Cb;function ZG(e){Cb=e.wasm.cwrap(Js,null,["number","array","number","number","number","array","number"])}function _p(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=JG(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=YG(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=wp({inputs:t,backend:n});return f.shape=o,f}let c=n.makeOutput(o,l.dtype),u=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(c.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return Cb(u,p,l.shape.length,Pn[l.dtype],h,d,s.length),c}function YG(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function JG(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 QG={kernelName:Js,backendName:"wasm",kernelFunc:_p,setupFunc:ZG};function $l(e,t,n){let r=e.shape,a=e.shape.length,s=b.parseAxisParam(t,r),i=s,o=C.getAxesPermutation(i,a),l=null,c=!1;if(o!=null){let u=new Array(a);for(let d=0;d<u.length;d++)u[d]=r[o[d]];i=C.getInnerMostAxes(i.length,a),l=_p({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(c=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:c}}var Eb;function eq(e){Eb=e.wasm.cwrap(ls,null,["number","number","number","number","number"])}function tq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:c,axes:u,inputWasTransposed:h}=$l(s,a,t);if(h){let y=t.dataIdMap.get(c.dataId).id;y!==i&&(l=c,o=y)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),f=t.dataIdMap.get(p.dataId).id,m=b.sizeFromShape(p.shape),A=l.shape[u[0]];return Eb(o,Pn[l.dtype],m,A,f),h&&t.disposeData(c.dataId),p}var nq={kernelName:ls,backendName:"wasm",kernelFunc:tq,setupFunc:eq},Rb;function rq(e){Rb=e.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function aq(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:c}=n,u=C.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.strideHeight,g=u.strideWidth,w=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let _=r.makeOutput(u.outShape,"float32"),v=r.dataIdMap.get(_.dataId).id;return Rb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,w,v),_}var sq={kernelName:us,backendName:"wasm",setupFunc:rq,kernelFunc:aq};function wr(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=b.sizeFromShape(r.shape),i=b.inferFromImplicitShape(a,s);return b.assert(s===b.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var iq={kernelName:Do,backendName:"wasm",kernelFunc:wr},Fb;function oq(e){Fb=e.wasm.cwrap(cs,null,["number","array","number","number","array","number","number","number","number"])}function lq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,c=s.shape.length,u=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[c-1]:s.shape[c-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[c-2]:s.shape[c-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=b.sizeFromShape(f),y=b.sizeFromShape(m),g=A===y||A===1||y===1;b.assert(l>=2&&c>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let w=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);b.assert(u===h,()=>`Error in matMul: inner shapes (${u}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let _=i?[A,u,d]:[A,d,u],v=o?[y,p,h]:[y,h,p],x=wr({inputs:{x:a},backend:n,attrs:{shape:_}}),N=wr({inputs:{x:s},backend:n,attrs:{shape:v}}),E=n.dataIdMap.get(x.dataId).id,F=n.dataIdMap.get(N.dataId).id,M=i?x.shape[2]:x.shape[1],W=o?N.shape[1]:N.shape[2],V=Math.max(A,y),B=n.makeOutput([V,M,W],x.dtype),H=n.dataIdMap.get(B.dataId).id,j=new Uint8Array(new Int32Array(x.shape).buffer),X=new Uint8Array(new Int32Array(N.shape).buffer);return Fb(E,j,x.shape.length,F,X,N.shape.length,i,o,H),n.disposeData(x.dataId),n.disposeData(N.dataId),B.shape=w,B}var uq={kernelName:cs,backendName:"wasm",setupFunc:oq,kernelFunc:lq};function bp(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 cq={kernelName:hs,backendName:"wasm",kernelFunc:bp},hq=Sn(ds),Mb;function dq(e){Mb=e.wasm.cwrap(Na,null,["number","number","number","number"])}function pq(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),c=n.dataIdMap.get(l.dataId).id;return Mb(o,s,i,c),l}var fq={kernelName:Na,backendName:"wasm",setupFunc:dq,kernelFunc:pq};function $b(e){let{inputs:t,backend:n}=e,r=b.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=C.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>b.sizeFromShape(p.shape)>0);if(s.length===1)return wp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(b.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(C.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(w=>{let _=b.sizeFromShape(w.shape.slice(r));return wr({inputs:{x:w},backend:n,attrs:{shape:[-1,_]}})}),f=p.map(w=>({vals:n.readSync(w.dataId),shape:w.shape}));a=C.computeOutShape(p.map(w=>w.shape),1);let m=p[0].shape[0]===1,A=Sm(f,a,t[0].dtype,m),y=C.computeOutShape(s.map(w=>w.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=C.fromStringArrayToUint8(A),p.forEach(w=>n.disposeData(w.dataId)),i}let l=b.sizeFromShape(s[0].shape.slice(0,r)),c=0,u=s.map(p=>{let f=b.sizeFromShape(p.shape.slice(r));return c+=f,f}),h=s.map(p=>n.typedArrayFromHeap(p)),d=n.typedArrayFromHeap(i);for(let p=0;p<l;p++){let f=p*c;for(let m=0;m<h.length;m++){let A=u[m],y=p*A,g=h[m].subarray(y,y+A);d.set(g,f),f+=A}}return i}var mq={kernelName:ao,backendName:"wasm",kernelFunc:$b},Db;function Aq(e){Db=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 yq(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:c,pad:u,dimRoundingMode:h,dataFormat:d}=n,p=C.convertConv2DDataFormat(d),f=C.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!1,p),m=f.filterHeight,A=f.filterWidth,y=f.padInfo.top,g=f.padInfo.right,w=f.padInfo.bottom,_=f.padInfo.left,v=f.dilationHeight,x=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,F=f.inChannels,M=f.outChannels,W=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 V=r.makeOutput(f.outShape,"float32"),B=r.dataIdMap.get(V.dataId).id;return Db(i,a.shape[0],a.shape[1],a.shape[2],o,m,A,y,g,w,_,W,v,x,N,E,F,M,B),V}var gq={kernelName:ps,backendName:"wasm",setupFunc:Aq,kernelFunc:yq},Ob;function xq(e){Ob=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 wq(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,inputShape:u}=r,h=1,d=C.convertConv2DDataFormat(l),p=C.computeConv2DInfo(u,s.shape,i,h,o,c,!1,d),{batchSize:f,filterHeight:m,filterWidth:A,inChannels:y,inHeight:g,inWidth:w,outChannels:_,outHeight:v,outWidth:x,strideHeight:N,strideWidth:E}=p,F=m-1-p.padInfo.top,M=A-1-p.padInfo.left,W=p.dataFormat==="channelsLast",V=b.computeStrides(p.inShape),B=b.computeStrides(a.shape),[H,j,X]=b.computeStrides(s.shape),G=V[0],ee=W?V[1]:V[2],Y=W?V[2]:1,ae=W?1:V[1],te=B[0],ie=W?B[1]:B[2],Q=W?B[2]:1,he=W?1:B[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 Ob(pe,ke,f,m,A,g,w,y,v,x,_,N,E,F,M,H,j,X,G,ee,Y,ae,te,ie,Q,he,fe),oe}var _q={kernelName:fs,backendName:"wasm",setupFunc:xq,kernelFunc:wq},bq=Sn(ms),iA;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(iA||(iA={}));var zb;function vq(e){zb=e.wasm.cwrap(io,null,["number","number","number","number","array","number","number","number","number","number"])}function kq(e){let{backend:t,inputs:n,attrs:r}=e,{method:a,extrapolationValue:s,cropSize:i}=r,{image:o,boxes:l,boxInd:c}=n,u=l.shape[0],[h,d]=i,p=[u,h,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=bp({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(c.dataId).id,w=t.makeOutput(p,"float32"),_=t.dataIdMap.get(w.dataId).id,v=new Uint8Array(new Int32Array(o.shape).buffer);return zb(A,y,g,u,v,h,d,iA[a],s,_),m!=null&&t.disposeData(m.dataId),w}var Iq={kernelName:io,backendName:"wasm",setupFunc:vq,kernelFunc:kq},Pb;function Nq(e){Pb=e.wasm.cwrap(As,null,["number","number","number","number","number","number"])}function Sq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;b.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let c=C.getAxesPermutation([s],l),u=a;c!==null&&(u=_p({inputs:{x:a},attrs:{perm:c},backend:n}));let h=C.getInnerMostAxes(1,l)[0];C.assertAxesAreInnerMostDims("cumsum",[h],l);let d=n.makeOutput(u.shape,u.dtype),p=u.shape[h],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(d.dataId).id;Pb(f,i?1:0,o?1:0,p,m,Pn[a.dtype]);let A=d;if(c!==null){let y=C.getUndoAxesPermutation(c);A=_p({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(d.dataId)}return A}var Tq={kernelName:As,backendName:"wasm",setupFunc:Nq,kernelFunc:Sq},Lb;function Cq(e){Lb=e.wasm.cwrap(oo,null,["number","number","number","array","number","array","array","number","number"])}function Eq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;b.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(b.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(f).buffer),w=new Uint8Array(new Int32Array(b.computeStrides(f)).buffer),_=t.dataIdMap.get(m.dataId).id;return Lb(A,s,i==="NHWC"?1:0,y,a.shape.length-1,g,w,f.length,_),m}var Rq={kernelName:oo,backendName:"wasm",setupFunc:Cq,kernelFunc:Eq},Wb;function Fq(e){Wb=e.wasm.cwrap(ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Mq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:h}=n,d=c==null?[1,1]:c,p=C.computeConv2DInfo(a.shape,s.shape,l,d,u,h,!0),f=p.filterHeight,m=p.filterWidth,A=p.padInfo.top,y=p.padInfo.right,g=p.padInfo.bottom,w=p.padInfo.left,_=p.dilationHeight,v=p.dilationWidth,x=p.strideHeight,N=p.strideWidth,E=p.inChannels,F=p.outChannels,M=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let W=r.makeOutput(p.outShape,"float32"),V=r.dataIdMap.get(W.dataId).id;return Wb(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,w,M,_,v,x,N,E,F,V),W}var $q={kernelName:ys,backendName:"wasm",setupFunc:Fq,kernelFunc:Mq},Dq=!1,Oq=An(co,Dq,"bool"),zq=Sn(xs);function oA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(b.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),wr({inputs:{x:a},backend:r,attrs:{shape:o}})}var Pq={kernelName:ho,backendName:"wasm",kernelFunc:oA};function Lq(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var Wq={kernelName:fu,backendName:"wasm",kernelFunc:Lq},Bb;function Bq(e){Bb=e.wasm.cwrap(fo,null,["number","number","number","number","number","number"])}function Vq(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return Bb(s,o,l,c,u,i),a}var Uq={kernelName:fo,backendName:"wasm",kernelFunc:Vq,setupFunc:Bq},Hq=Sn(ws),jq=!1,Gq=An(_s,jq),Vb;function qq(e){Vb=e.wasm.cwrap(bs,null,["number","number","number","number","number","number","number"])}function Xq(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(b.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return Vb(u,h,d,p,f,a,A),m}var Kq={kernelName:bs,backendName:"wasm",setupFunc:qq,kernelFunc:Xq},Ub;function Zq(e){Ub=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d),A=fc[p];if(A==null)throw new Error(`${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,w=m.outChannels,_=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 ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);_=Q.id}let v=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,F=m.padInfo.bottom,M=m.padInfo.left,W=m.dilationHeight,V=m.dilationWidth,B=m.strideHeight,H=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${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 Ub(y,G,ee,Y,g,v,x,_,N,E,F,M,X,W,V,B,H,j,w,A,ie,f||0,te),ae}var Jq={kernelName:ei,backendName:"wasm",setupFunc:Zq,kernelFunc:Yq},Hb;function Qq(e){Hb=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 eX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,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,w=m.outChannels,_=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 ${Q.shape.length}.`);if(Q.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${w})`);_=Q.id}let v=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,F=m.padInfo.bottom,M=m.padInfo.left,W=m.dilationHeight,V=m.dilationWidth,B=m.strideHeight,H=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${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 Hb(y,G,ee,Y,g,v,x,_,N,E,F,M,X,W,V,B,H,j,w,A,ie,f||0,te),ae}var tX={kernelName:ti,backendName:"wasm",setupFunc:Qq,kernelFunc:eX},jb;function nX(e){jb=e.wasm.cwrap(Ao,null,["number","number","number","number","number","number","array","number"])}function rX(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=Nf.prepareAndValidate(r,a),c=t.makeOutput(s,r.dtype);if(i===0)return c;let u=a.shape,h=u[u.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(c.dataId).id;return jb(d,Pn[r.dtype],p,i,h,o,f,m),c}var aX={kernelName:Ao,backendName:"wasm",setupFunc:nX,kernelFunc:rX},Gb;function sX(e){Gb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function iX(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=b.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=wr({inputs:{x:a},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),h=b.sizeFromShape(s.shape),d=wr({inputs:{x:s},attrs:{shape:[c.batchSize,h/c.batchSize]},backend:t}),p=[c.batchSize,c.outerSize,h/c.batchSize,c.sliceSize],f=t.makeOutput(p,a.dtype);if(b.sizeFromShape(a.shape)===0)return f;let m=u.shape.length-1,A=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,w=new Uint8Array(new Int32Array(b.computeStrides(u.shape)).buffer),_=new Uint8Array(new Int32Array(b.computeStrides(p)).buffer);return Gb(A,Pn[a.dtype],w,m,y,c.batchSize,_,g),t.disposeData(u.dataId),t.disposeData(d.dataId),f.shape=c.outputShape,f}var oX={kernelName:mo,backendName:"wasm",setupFunc:sX,kernelFunc:iX},lX=!1,uX=An(yo,lX,"bool"),cX=!1,hX=An(vs,cX,"bool"),qb;function dX(e){qb=e.wasm.cwrap(Is,null,["number","number","number"])}function pX(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(b.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;qb(a,n,i)}return s}var fX={kernelName:Is,backendName:"wasm",setupFunc:dX,kernelFunc:pX},mX=!1,AX=An(_o,mX,"bool"),yX=!1,gX=An(bo,yX,"bool"),xX=Sn(Ns),wX=!1,_X=An(ko,wX,"bool"),Xb;function bX(e){Xb=e.wasm.cwrap(Ss,null,["number, number, number"])}function vX(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:c,axes:u,originalAxes:h,inputWasTransposed:d}=$l(i,a,t);if(d){let g=t.dataIdMap.get(c.dataId).id;l=c,o=g}let p=l.shape.length;C.assertAxesAreInnerMostDims("max",u,p);let[f,m]=C.computeOutAndReduceShapes(l.shape,u),A=b.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(b.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;Xb(o,A,g)}if(d&&t.disposeData(c.dataId),s){let g=C.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var kX={kernelName:Ss,backendName:"wasm",setupFunc:bX,kernelFunc:vX},IX=!1,NX=An(Ts,IX),Kb;function SX(e){Kb=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function TX(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=n,u=C.computePool2DInfo(a.shape,i,o,1,l,c),h=u.filterHeight,d=u.filterWidth,p=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,A=u.padInfo.left,y=u.dilationHeight,g=u.dilationWidth,w=u.strideHeight,_=u.strideWidth,v=u.inChannels,x=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let N=r.makeOutput(u.outShape,"float32"),E=r.dataIdMap.get(N.dataId).id;return Kb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,w,_,v,x,E),N}var CX={kernelName:Cs,backendName:"wasm",setupFunc:SX,kernelFunc:TX},Zb;function EX(e){Zb=e.wasm.cwrap(Es,null,["number, number, number"])}function RX(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,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let _=t.dataIdMap.get(u.dataId).id;_!==o&&(c=u,l=_,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=b.sizeFromShape(A),g=c;c.dtype!=="float32"&&(g=bp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let w=t.makeOutput(m,"float32");if(b.sizeFromShape(c.shape)!==0){let _=t.dataIdMap.get(w.dataId).id;Zb(l,y,_)}if(p&&t.disposeData(u.dataId),s){let _=C.expandShapeToKeepDim(w.shape,d);w.shape=_}return c.dtype!=="float32"&&t.disposeData(g.dataId),w}var FX={kernelName:Es,backendName:"wasm",setupFunc:EX,kernelFunc:RX},Yb;function MX(e){Yb=e.wasm.cwrap(Rs,null,["number, number, number"])}function $X(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t);if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w)}let f=c.shape.length;C.assertAxesAreInnerMostDims("min",h,f);let[m,A]=C.computeOutAndReduceShapes(c.shape,h),y=b.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(b.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;Yb(l,y,w)}if(p&&t.disposeData(u.dataId),s){let w=C.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var DX={kernelName:Rs,backendName:"wasm",setupFunc:MX,kernelFunc:$X},OX=!1,zX=An(Fs,OX),PX=!0,LX=An(Ms,PX),WX=Sn(No);function lA(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var Jb;function BX(e){Jb=e.wasm.cwrap(To,"number",["number","number","number","number","number"])}function VX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=r,{boxes:o,scores:l}=n,c=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(l.dataId).id,h=Jb(c,u,s,a,i),{pSelectedIndices:d,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=lA(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",d)}var UX={kernelName:To,backendName:"wasm",setupFunc:BX,kernelFunc:VX},Qb;function HX(e){Qb=e.wasm.cwrap(Co,"number",["number","number","number","number","number","bool"])}function jX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=Qb(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=lA(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var GX={kernelName:Co,backendName:"wasm",setupFunc:HX,kernelFunc:jX},e3;function qX(e){e3=e.wasm.cwrap(Eo,"number",["number","number","number","number","number","number"])}function XX(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=e3(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=lA(t,d);t.wasm._free(A);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[y,g]}var KX={kernelName:Eo,backendName:"wasm",setupFunc:qX,kernelFunc:XX},ZX=!1,YX=An(So,ZX,"bool"),t3;function JX(e){t3=e.wasm.cwrap($s,null,["number","number","number","number","number"])}function QX(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"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(a.dataId).id;return t3(u,s,i,o,c),l}var eK={kernelName:$s,backendName:"wasm",setupFunc:JX,kernelFunc:QX};function tK(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var nK={kernelName:Ro,backendName:"wasm",kernelFunc:tK};function rK(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return oA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{b.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),b.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=oA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=$b({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeData(u.dataId)),c}var aK={kernelName:Fo,backendName:"wasm",kernelFunc:rK},n3;function sK(e){n3=e.wasm.cwrap(Ds,null,["number","array","number","number","array","array","number","number"])}function iK(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,c=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return n3(i,c,t.shape.length,Pn[t.dtype],d,p,a,l),o}var oK={kernelName:Ds,backendName:"wasm",kernelFunc:iK,setupFunc:sK},lK=!1,uK=An(Os,lK),r3;function cK(e){r3=e.wasm.cwrap(zs,null,["number","number","number"])}function hK(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 r3(s,i,l),o}var dK={kernelName:zs,backendName:"wasm",setupFunc:cK,kernelFunc:hK},a3;function pK(e){a3=e.wasm.cwrap(Mo,null,["number","number","number","number"])}function fK(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,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=b.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(b.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;a3(l,y,Pn[g.dtype],w)}if(p&&t.disposeData(u.dataId),s){let w=C.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var mK={kernelName:Mo,backendName:"wasm",setupFunc:pK,kernelFunc:fK},AK=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},yK={kernelName:wu,backendName:"wasm",kernelFunc:AK},gK=!0,xK=An(gs,gK),wK=Sn(Ps),_K=Sn(Ws),s3;function bK(e){s3=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number"])}function vK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,[u,h,d,p]=a.shape,f=[u,l,c,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=bp({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(b.sizeFromShape(a.shape)===0)return g;let w=t.dataIdMap.get(g.dataId).id;return s3(y,u,h,d,p,l,c,s?1:0,i?1:0,w),A!=null&&t.disposeData(A.dataId),g}var kK={kernelName:Ls,backendName:"wasm",setupFunc:bK,kernelFunc:vK},i3;function IK(e){i3=e.wasm.cwrap(Bs,null,["number","array","number","array","number","number"])}function NK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=b.parseAxisParam(s,a.shape);if(a.shape.length===0)return wp({inputs:{x:a},backend:n});let o=n.makeOutput(a.shape,a.dtype),l=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);i3(l,u,i.length,h,a.shape.length,c);let d=wr({inputs:{x:o},attrs:{shape:a.shape},backend:n});return n.disposeData(o.dataId),d}var SK={kernelName:Bs,backendName:"wasm",kernelFunc:NK,setupFunc:IK},o3;function TK(e){o3=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","number","number","array","number","number"])}function CK(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),c=n.dataIdMap.get(a.dataId).id,u=n.dataIdMap.get(l.dataId).id,[h,d,p,f]=a.shape,[m,A]=C.getImageCenter(o,d,p),y=i===0,g=255,w=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],_=new Uint8Array(new Int32Array(w).buffer);return o3(c,h,d,p,f,s,m,A,_,w.length,u),l}var EK={kernelName:Ko,backendName:"wasm",kernelFunc:CK,setupFunc:TK},RK=Sn(Vs),FK=Sn(Us),l3;function MK(e){l3=e.wasm.cwrap(Oo,null,["number","number","number","number","number","number","array","number","number"])}function $K(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(b.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:h,outputSize:d}=Sf.calculateShapes(s,a,i),p=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),A=t.dataIdMap.get(o.dataId).id;return l3(p,f,Pn[s.dtype],l,c,u,m,d,A),o}var DK={kernelName:Oo,backendName:"wasm",setupFunc:MK,kernelFunc:$K},u3;function OK(e){u3=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function zK(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,c=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(c.dataId).id,h=r.shape.length,d=a.shape.length,p=h===0||h>1||d===1?1:b.sizeFromShape(a.shape.slice(1));return u3(i,o,l,p,u),c}var PK={kernelName:zo,backendName:"wasm",kernelFunc:zK,setupFunc:OK},c3;function LK(e){c3=e.wasm.cwrap(js,null,["number","number"])}function WK(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 b.sizeFromShape(a.shape)===0||c3(r,s),a}var BK={kernelName:"Sigmoid",backendName:"wasm",setupFunc:LK,kernelFunc:WK},VK=Sn(Hs);function vp(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),c=a.makeOutput(i,t.dtype),u=b.computeStrides(t.shape),h=a.dataIdMap.get(c.dataId);if(o){let f=cn.computeFlatOffset(s,u);return t.dtype==="string"?h.stringBytes=l.slice(f,f+b.sizeFromShape(i)):a.typedArrayFromHeap(c).set(l.subarray(f,f+b.sizeFromShape(i))),c}if(t.dtype==="string"){let f=Jd(l,s,i,t.shape,t.dtype);return h.stringBytes=f,c}let d=a.typedArrayFromHeap(c),p=t.shape.length;if(p===2)UK(l,u[0],d,s,i);else if(p===3)HK(l,u[0],u[1],d,s,i);else if(p===4)jK(l,u[0],u[1],u[2],d,s,i);else{let f=Jd(l,s,i,t.shape,t.dtype);d.set(f)}return c}function UK(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let c=i;c<l;c++){let u=c*t+o;n.set(e.subarray(u,u+a[1]),s),s+=a[1]}}function HK(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],c=a[2],u=o+s[0],h=l+s[1];for(let d=o;d<u;d++)for(let p=l;p<h;p++){let f=d*t+p*n+c;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function jK(e,t,n,r,a,s,i){let o=0,l=s[0],c=s[1],u=s[2],h=l+i[0],d=c+i[1],p=u+i[2],f=s[3];for(let m=l;m<h;m++)for(let A=c;A<d;A++)for(let y=u;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 GK={kernelName:Lo,backendName:"wasm",kernelFunc:vp},h3;function qK(e){h3=e.wasm.cwrap(Xs,null,["number","number","number","number"])}function XK(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=b.sizeFromShape(n.shape)/o;return b.sizeFromShape(s.shape)===0||h3(a,i,o,l),s}var KK={kernelName:Xs,backendName:"wasm",setupFunc:qK,kernelFunc:XK};function ZK(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=b.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=new Array(a.shape.length).fill(0),u=a.shape.slice();return l.map(h=>{let d=[...u];d[o]=h;let p=vp({inputs:{x:a},attrs:{begin:c,size:d},backend:r});return c[o]+=h,p})}var YK={kernelName:Uo,backendName:"wasm",kernelFunc:ZK},JK=Sn(Gs),QK=Sn(vu),eZ=!0,tZ=An(Ks,eZ),d3;function nZ(e){d3=e.wasm.cwrap(Ta,null,["number","number","number"])}function rZ(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 d3(i,a,l),o}var aZ={kernelName:Ta,backendName:"wasm",setupFunc:nZ,kernelFunc:rZ},p3;function sZ(e){p3=e.wasm.cwrap(Ho,null,["number","array","number","array","array","array","array","array","number","number"])}function iZ(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:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,p=C.slice_util.maskToAxes(u);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&h!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=a.shape.length-s.length,m=C.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:w,strides:_}=C.slice_util.getNormalizedAxes(y.shape,p,f,s,i,o,l,c,u);s=g,i=w,o=_;let v=C.slice_util.maskToAxes(d);v.forEach(M=>{i[M]=s[M]+1,o[M]=1});let x=C.slice_util.computeOutShape(s,i,o),N=x.filter((M,W)=>v.indexOf(W)===-1);if(o.every(M=>M===1)){let M=vp({inputs:{x:y},attrs:{begin:s,size:x},backend:t});t.disposeData(y.dataId);let W=wr({inputs:{x:M},attrs:{shape:N},backend:t});return t.disposeData(M.dataId),W}let E=t.makeOutput(N,"float32");if(!N.some(M=>M===0)){let M=t.dataIdMap.get(y.dataId).id,W=new Uint8Array(new Int32Array(b.computeStrides(y.shape)).buffer),V=new Uint8Array(new Int32Array(s).buffer),B=new Uint8Array(new Int32Array(i).buffer),H=new Uint8Array(new Int32Array(o).buffer),j=new Uint8Array(new Int32Array(N).buffer),X=new Uint8Array(new Int32Array(b.computeStrides(N)).buffer),G=t.dataIdMap.get(E.dataId).id;p3(M,W,y.shape.length,V,B,H,j,X,N.length,G)}t.disposeData(y.dataId);let F=wr({inputs:{x:E},attrs:{shape:N},backend:t});return t.disposeData(E.dataId),F}var oZ={kernelName:Ho,backendName:"wasm",setupFunc:sZ,kernelFunc:iZ},lZ=!0,uZ=An(Zs,lZ),f3;function cZ(e){f3=e.wasm.cwrap(qs,null,["number, number, number"])}function hZ(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,c=i,{transposed:u,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let w=t.dataIdMap.get(u.dataId).id;w!==o&&(c=u,l=w,f=C.getInnerMostAxes(f.length,c.shape.length))}C.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,A]=C.computeOutAndReduceShapes(c.shape,f),y=b.sizeFromShape(A),g=t.makeOutput(m,c.dtype);if(b.sizeFromShape(c.shape)!==0){let w=t.dataIdMap.get(g.dataId).id;f3(l,y,w)}if(p&&t.disposeData(u.dataId),s){let w=C.expandShapeToKeepDim(g.shape,d);g.shape=w}return g}var dZ={kernelName:qs,backendName:"wasm",setupFunc:cZ,kernelFunc:hZ},pZ=Sn(Ys),m3;function fZ(e){m3=e.wasm.cwrap(Sa,null,["number","array","number","array","number","number"])}function mZ(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),c=new Uint8Array(new Int32Array(o).buffer),u=n.makeOutput(o,a.dtype),h=n.dataIdMap.get(u.dataId).id;return m3(s,l,a.shape.length,c,o.length,Pn[u.dtype],h),u}var AZ={kernelName:Sa,backendName:"wasm",setupFunc:fZ,kernelFunc:mZ},A3;function yZ(e){A3=e.wasm.cwrap(Go,null,["number","array","number","number","number","bool","number","number"])}var gZ=({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 c=t.makeOutput(l,r.dtype),u=t.dataIdMap.get(c.dataId).id,h=t.makeOutput(l,"int32"),d=t.dataIdMap.get(h.dataId).id;return A3(i,o,r.shape.length,Pn[r.dtype],a,s,u,d),[c,h]},xZ={kernelName:Go,backendName:"wasm",setupFunc:yZ,kernelFunc:gZ};function wZ(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),c=0;for(let p=0;p<o;p++)p!==s&&(l[c++]=a.shape[p]);let u=new Array(i),h=new Array(o).fill(0),d=a.shape.slice();d[s]=1;for(let p=0;p<u.length;p++)h[s]=p,u[p]=vp({inputs:{x:a},attrs:{begin:h,size:d},backend:n});return u.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var _Z={kernelName:qo,backendName:"wasm",kernelFunc:wZ};function bZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var vZ={kernelName:Xo,backendName:"wasm",kernelFunc:bZ},kZ=[UG,jG,XG,nq,sq,uq,cq,hq,fq,mq,gq,_q,bq,Iq,Tq,Rq,$q,Oq,zq,Pq,Wq,Uq,Hq,Gq,VG,Kq,Jq,tX,aX,oX,uX,hX,KG,fX,AX,gX,xX,_X,kX,NX,CX,FX,DX,zX,LX,WX,UX,GX,KX,YX,eK,nK,aK,oK,uK,dK,mK,yK,xK,wK,_K,iq,kK,SK,EK,FK,RK,DK,PK,BK,VK,GK,KK,YK,JK,QK,tZ,aZ,oZ,uZ,dZ,pZ,AZ,xZ,QG,_Z,vZ];for(let e of kZ)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 y3=qi(z8()),IZ='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()}}}}',NZ=qi(P8()),g3=class extends su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new wh(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=b.now();return e(),{kernelMs:b.now()-t}}move(e,t,n,r,a){let s=this.dataIdNextNumber++;if(r==="string"){let c=t;this.dataIdMap.set(e,{id:s,stringBytes:c,shape:n,dtype:r,memoryOffset:null,refCount:a});return}let i=b.sizeFromShape(n),o=i*b.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+b.sizeFromShape(r)*b.bytesPerElement(n));return SZ(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=b.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=b.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 ${t}`)}}};function TZ(e){return(t,n)=>(b.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance)})})}),{})}function x3(e,t,n){if(kp!=null)return kp;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 CZ(){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 c=IZ,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return o.endsWith(".wasm")?x3(e,t,Ac!=null?Ac:l):l+o},cA&&(a.instantiateWasm=TZ(x3(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&&kp==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+y3.default.toString()],{type:"text/javascript"}),i=(0,y3.default)(a)):i=(0,NZ.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 SZ(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 EZ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],kp=null,Ac=null,mc={},yc=!1,cA=!1;function RZ(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()`");kp=e,cA=t}function FZ(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=EZ.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 w3="3.3.0",MZ=2;sl("wasm",async()=>{let{wasm:e}=await CZ();return new g3(e)},MZ);Z().prototype.abs=function(){return this.throwIfDisposed(),Ot(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(),pd(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(),R(this.size===1,()=>"The array must have only 1 element."),U(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.as1D=function(){return this.throwIfDisposed(),U(this,[this.size])};Z().prototype.as2D=function(e,t){return this.throwIfDisposed(),U(this,[e,t])};Z().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),U(this,[e,t,n])};Z().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),U(this,[e,t,n,r])};Z().prototype.as5D=function(e,t,n,r,a){return this.throwIfDisposed(),U(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]),at([this,...e],t)};Z().prototype.conv1d=function(e,t,n,r,a,s){return this.throwIfDisposed(),md(this,e,t,n,r,a,s)};Z().prototype.conv2dTranspose=function(e,t,n,r,a){return this.throwIfDisposed(),Ad(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(),yd(this)};Z().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),gd(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(),J5(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(),U(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),hl(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),dd(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(),ar(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),Al(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),Dd(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),Q5(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),ex(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),tx(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(),wd(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(),ax(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),vd(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(),_d(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),sr(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),ju(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),kd(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),lx(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(),vt(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(),O(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),bt(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Ld(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(),hx(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(),Td(this)};Z().prototype.reshapeAs=function(e){return this.throwIfDisposed(),U(this,e.shape)};Z().prototype.reshape=function(e){return this.throwIfDisposed(),U(this,e)};Z().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),Cx(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),Ex(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(),Cd(this)};Z().prototype.selu=function(){return this.throwIfDisposed(),Ed(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(),Rd(this)};Z().prototype.sinh=function(){return this.throwIfDisposed(),Fd(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(),Pt(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),Qt(this)};Z().prototype.square=function(){return this.throwIfDisposed(),ot(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Od(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(),rt(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),Pd(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),gm(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),ir(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 _3={kernelName:Zi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,yl(me(n,"float32"),-1))}}},$Z={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=ot(me(n,"float32")),a=Qt(ye(be(1),r));return bt(ge(e,a))}}}},DZ={kernelName:Ji,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Qt(ye(ot(me(n,"float32")),1));return ge(e,r)}}}},OZ={kernelName:Ia,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=e,i=zt(n.shape,a);return i.length>0&&(s=Te(s,i)),U(s,n.shape)},b:()=>{let s=e,i=zt(r.shape,a);return i.length>0&&(s=Te(s,i)),U(s,r.shape)}}}},zZ={kernelName:os,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},PZ={kernelName:ls,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},LZ={kernelName:lu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>He(n)}}},WZ={kernelName:Qi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,Qt(ye(be(1),ot(me(n,"float32")))))}}},BZ={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=Qt(se(be(1),ot(me(n,"float32"))));return ge(e,r)}}}},VZ={kernelName:ro,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=se(ot(n),ot(r)),i=O(e,ge(r,s)),o=zt(n.shape,a);return o.length>0&&(i=Te(i,o)),U(i,n.shape)},b:()=>{let s=se(ot(n),ot(r)),i=bt(O(e,ge(n,s))),o=zt(r.shape,a);return o.length>0&&(i=Te(i,o)),U(i,r.shape)}}}},UZ={kernelName:to,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(ot(me(n,"float32")),1))}}},HZ={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ye(be(1),ot(me(n,"float32"))))}}};function jZ(e,t,n,r,a,s){let i=T(e,"dy","avgPool3dGrad"),o=T(t,"input","avgPool3dGrad"),l=i,c=o,u=!1;o.rank===4&&(u=!0,l=U(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),c=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),R(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),R(c.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${c.rank}.`),s!=null&&R(Ht(a),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${a}.`);let h={dy:l,input:c},d={filterSize:n,strides:r,pad:a,dimRoundingMode:s},p=$.runKernel(Sh,h,d);return u?U(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var GZ=D({avgPool3dGrad_:jZ}),qZ={kernelName:uu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>GZ(e,r,a,s,i,o)}}};function XZ(e,t,n,r,a){let s=T(e,"dy","avgPoolGrad"),i=T(t,"input","avgPoolGrad");R(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,c=!1;i.rank===3&&(c=!0,o=U(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=U(s,[1,s.shape[0],s.shape[1],s.shape[2]])),R(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),R(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let u={dy:l,input:o},h={filterSize:n,strides:r,pad:a},d=$.runKernel(Nh,u,h);return c?U(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var KZ=D({avgPoolGrad_:XZ}),ZZ={kernelName:us,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>KZ(e,r,a,s,i)}}},YZ={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)}}},JZ={kernelName:cu,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>qu(e,r,a)}}},QZ={kernelName:Gg,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)}}},eY={kernelName:hs,gradFunc:e=>({x:()=>e.clone()})},tY={kernelName:ds,gradFunc:e=>({x:()=>He(e)})},nY={kernelName:Na,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>kn(sr(za(r,a),ci(r,s)),e,He(e))}}},rY={kernelName:hu,inputsToSave:["x"],gradFunc:_3.gradFunc},aY={kernelName:ao,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=nr(a,t[0].shape)[0],i=r.map(o=>o[s]);return Pt(e,i,s).map(o=>()=>o)}},sY={kernelName:ps,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return R($a(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>qf(r.shape,e,a,i,o,l),filter:()=>bm(r,e,a.shape,i,o,l)}}},iY={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 oY(e,t,n,r,a){let s=e;e.rank===4&&(s=U(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=U(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),R(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),R(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),R(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),R(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),R(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 $.runKernel(Rh,o,l)}var lY=D({conv3DBackpropFilter_:oY}),uY={kernelName:du,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;R($a(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>Z5(i.shape,e,o,a,s),filter:()=>lY(i,e,o.shape,a,s)}}},cY={kernelName:ms,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(bt(Rd(me(n,"float32"))),e)}}},hY={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(Fd(me(n,"float32")),e)}}},dY={kernelName:As,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=ox([a],r.rank),l=gd(e,a,s,!i);return o!=null&&(l=rt(l,o)),l}}}},pY={kernelName:ys,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;R($a(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,c]=t;return R(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),R(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),R(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),R(Mr(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${o}'.`),i!=null&&R(Ht(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>bx(l.shape,e,c,a,s,r,i),filter:()=>_x(l,e,c.shape,a,s,r,i)}}},fY={kernelName:pu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>$.runKernel(zh,s,n),filter:()=>$.runKernel(Ph,i,n)}}},mY={kernelName:lo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>$.runKernel(Lh,r)}}},AY={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=O(qn(bt(ot(n))),2/Math.sqrt(Math.PI));return{x:()=>O(e,r)}}},yY={kernelName:xs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,n)}}},gY={kernelName:ho,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>U(e,n.shape)}}},xY={kernelName:po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,qn(n))}}},wY={kernelName:ws,gradFunc:e=>({x:()=>He(e)})},_Y={kernelName:_s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=ge(e,me(r,"float32")),i=zt(n.shape,a);return i.length>0?U(Te(s,i),n.shape):s},b:()=>{let s=O(e,me(n,"float32")),i=zt(r.shape,a);i.length>0&&(s=U(Te(s,i),r.shape));let o=ot(r);return bt(ge(s,me(o,"float32")))}}}},bY={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,c=zt(s.shape,a.shape),u=[];if(s.rank===1){for(let m=0;m<a.shape.length-1;++m)u.push(a.shape[m]);u.push(1)}let h=ye(a,s),d=O(e,l),p=Cd(se(i,be(r))),f=O(O(O(p,p),p),be(-.5));return{x:()=>s.rank===1?U(O(O(e,Oa(U(p,[1,1,1,s.shape[0]]),u)),l),a.shape):U(O(O(e,p),l),a.shape),mean:()=>{let m=O(O(p,be(-1)),d);return s.rank===1&&(m=Te(m,c)),U(m,s.shape)},variance:()=>{let m=O(O(f,h),d);return s.rank===1&&(m=Te(m,c)),U(m,s.shape)},scale:()=>{let m=O(h,p),A=O(e,m);return s.rank===1&&(A=Te(A,c)),U(A,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=Te(m,c)),U(m,s.shape)}}}},vY={kernelName:mo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=nr(s,r.shape)[0];return{x:()=>{let o=r.shape,l=a.size,c=o.slice(0,i),u=c.length,h=o.slice(s,o.length).slice(1),d=h.length,p=b3(0,u),f=b3(u+1,u+1+d),m=v3([c,[l],h]),A=U(e,m),y=U(a,[l]),g=v3([[u],p,f]),w=rt(A,g),_=gm(w,y,r.shape[i]),v=rm(g);return _=rt(_,v),_},indices:()=>a}}};function b3(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function v3(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 kY={kernelName:vs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>He(n),b:()=>He(r)}}},IY={kernelName:ks,gradFunc:e=>({x:()=>me(e,"float32")})},NY={kernelName:go,gradFunc:e=>({x:()=>He(e)})},SY={kernelName:xo,gradFunc:e=>({x:()=>He(e)})},TY={kernelName:wo,gradFunc:e=>({x:()=>He(e)})},CY={kernelName:Is,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=ar(r,0);return{x:()=>kn(s,e,O(e,a))}}},EY={kernelName:vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(n,1))}}},RY={kernelName:Ns,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,me(n,"float32"))}}},FY={kernelName:qg,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=qn(r);return ye(e,O(Te(e,a,s),i))}}}};function MY(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(Hh,o,l)}var $Y=D({localResponseNormalizationBackprop_:MY}),DY={kernelName:yu,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n;return{x:()=>$Y(r,a,e,s,i,o,l)}}};function k3(e,t,n,r){return t.rank<n.rank&&(t=U(t,hi(t.shape,r))),e.rank<n.rank&&(e=U(e,hi(e.shape,r))),{x:()=>O(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=nr(a,s.shape),l=k3(e,i,s,o);return{x:()=>l.x()}}},OY={kernelName:Ts,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>O(e,me(za(n,r),"float32")),b:()=>O(e,me(wd(n,r),"float32"))}}};function zY(e,t,n,r,a,s,i){let o=T(e,"dy","maxPool3dGrad"),l=T(t,"input","maxPool3dGrad"),c=T(n,"output","maxPool3dGrad"),u=o,h=l,d=c,p=!1;l.rank===4&&(p=!0,u=U(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),h=U(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),d=U(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]])),R(u.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),R(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),R(d.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`),i!=null&&R(Ht(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let f={dy:u,input:h,output:d},m={filterSize:r,strides:a,pad:s,dimRoundingMode:i},A=$.runKernel(Gh,f,m);return p?U(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var PY=D({maxPool3dGrad_:zY}),LY={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:()=>PY(e,r,a,s,i,o,l)}}};function WY(e,t,n,r,a,s,i){let o=T(e,"dy","maxPoolGrad"),l=T(t,"input","maxPoolGrad"),c=T(n,"output","maxPoolGrad");R(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),R(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),R(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&R(Ht(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let u={dy:o,input:l,output:c},h={filterSize:r,strides:a,pad:s,dimRoundingMode:i};return $.runKernel(jh,u,h)}var BY=D({maxPoolGrad_:WY}),VY={kernelName:Cs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>BY(e,r,a,s,i,o)}}},UY={kernelName:Es,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=nr(a,r.shape),i=ix(r.shape,s)[1],o=$t(i);return{x:()=>{let l=r.shape.slice();s.forEach(u=>{l[u]=1});let c=U(e,l);return ge(O(c,Or(r.shape,"float32")),o)}}}},HY={kernelName:Rs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=nr(a,s.shape),l=k3(e,i,s,o);return{x:()=>l.x()}}},jY={kernelName:Fs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>O(e,me(ci(n,r),"float32")),b:()=>O(e,me(ar(n,r),"float32"))}}},GY={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)}}},qY={kernelName:Io,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=zt(n.shape,a);return s.length>0?U(Te(e,s),n.shape):e},b:()=>{let s=O(e,bt(hl(ge(n,r)))),i=zt(r.shape,a);return i.length>0?U(Te(s,i),r.shape):s}}}},XY={kernelName:Ms,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=O(e,me(r,"float32")),i=zt(n.shape,a);return i.length>0?U(Te(s,i),n.shape):s},b:()=>{let s=O(e,me(n,"float32")),i=zt(r.shape,a);return i.length>0?U(Te(s,i),r.shape):s}}}},KY={kernelName:No,gradFunc:e=>({x:()=>bt(e)})},ZY={kernelName:$s,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Ct(n.shape,"float32")}}},YY={kernelName:Ro,gradFunc:e=>({x:()=>He(e)})},JY={kernelName:Fo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return ir(e,r).map(a=>()=>a)}},N3={kernelName:Ds,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Re(e,s,r.shape)}}},QY={kernelName:Os,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=At(s.shape,i.shape);return{a:()=>{let l=me(i,"float32"),c=O(e,O(l,sa(s,ye(l,be(1))))),u=zt(s.shape,o);return u.length>0&&(c=Te(c,u)),U(c,s.shape)},b:()=>{let l=ar(s,0),c=kn(l,Mn(s),He(s)),u=O(e,O(a,c)),h=zt(i.shape,o);return h.length>0&&(u=Te(u,h)),U(u,i.shape)}}}},eJ={kernelName:zs,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=ar(n,0);return{x:()=>kn(a,e,O(e,r)),alpha:()=>{let s=kn(a,He(e),O(e,n)),i=zt(r.shape,e.shape);return i.length>0&&(s=Te(s,i)),U(s,r.shape)}}}},tJ={kernelName:gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=ge(e,me(r,"float32")),i=zt(n.shape,a);return i.length>0?U(Te(s,i),n.shape):s},b:()=>{let s=O(e,me(n,"float32")),i=zt(r.shape,a);i.length>0&&(s=U(Te(s,i),r.shape));let o=ot(r);return bt(ge(s,me(o,"float32")))}}}},nJ={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,bt(ot(n)))}}},rJ={kernelName:Ws,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=O(ci(n,6),yl(n));return{x:()=>O(e,me(r,"float32"))}}},aJ={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,me(yl(n),"float32"))}}},sJ={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>U(e,n.shape)}}},iJ={kernelName:Ls,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Yh,a,n)}}},oJ={kernelName:_u,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Zh,a,n)}}},lJ={kernelName:Bs,gradFunc:(e,t,n)=>{let{dims:r}=n,a=nr(r,e.shape);return{x:()=>Dn(e,a)}}},uJ={kernelName:Vs,gradFunc:e=>({x:()=>He(e)})},cJ={kernelName:Us,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>bt(ge(e,O(sa(n,1.5),2)))}}},hJ={kernelName:zo,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>me(He(n),"float32"),t:()=>O(e,me(n,e.dtype)),e:()=>O(e,me(ju(n),e.dtype))}}},dJ={kernelName:Po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=ar(n,be(0)),a=be(Mx),s=be($x),i=O(e,s),o=O(O(e,a),qn(me(n,"float32")));return kn(r,i,o)}}}},pJ={kernelName:js,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,O(n,ye(be(1),n)))}}},fJ={kernelName:Bo,gradFunc:e=>({x:()=>He(e)})},mJ={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(Vu(me(n,"float32")),e)}}},AJ={kernelName:Wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(yd(me(n,"float32")),e)}}},yJ={kernelName:Lo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=F5(r,a,s),c=[];for(let u=0;u<e.rank;u++)c.push([o[u],i[u]-o[u]-l[u]]);return{x:()=>aa(e,c)}}},gJ={kernelName:Xs,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=O(e,r);return{logits:()=>ye(i,O(Te(i,[a],s),r))}}},xJ={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,Fn(n))}}},S3={kernelName:bu,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Wu(e,r,a)}}},T3={kernelName:Uo,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>at(e,r)}}},wJ={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,O(Qt(me(n,"float32")),2))}}},_J={kernelName:vu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(e,O(me(n,"float32"),2))}}},bJ={kernelName:Ks,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=be(2);return{a:()=>O(e,O(a,ye(n,r))),b:()=>O(e,O(a,ye(r,n)))}}},vJ={kernelName:Ta,gradFunc:e=>({x:()=>He(e)})},kJ={kernelName:Zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=At(n.shape,r.shape);return{a:()=>{let s=e,i=zt(n.shape,a);return i.length>0&&(s=Te(s,i)),U(s,n.shape)},b:()=>{let s=e,i=zt(r.shape,a);return i.length>0&&(s=Te(s,i)),U(bt(s),r.shape)}}}},IJ={kernelName:qs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;nr(s,r.shape).forEach(l=>{a[l]=1});let i=U(e,a),o=O(i,Or(r.shape,"float32"));return{x:()=>o}}},NJ={kernelName:jo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ot(Vu(n)))}}},SJ={kernelName:Ys,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>O(ye(be(1),ot(n)),e)}}},TJ={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 c=0;c<a[3];++c)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2],c*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}}}},CJ={kernelName:Js,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=rm(a);return{x:()=>rt(e,s)}}},EJ={kernelName:qo,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>dn(e,a)}}},FJ={kernelName:ku,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>RJ(e,n)}}};function RJ(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=sr(a,Or(r.shape,"bool"));let i=He(r);return kn(a,r,i)}var MJ={kernelName:Xo,gradFunc:e=>({x:()=>He(e)})},$J=[_3,$Z,DZ,OZ,zZ,PZ,LZ,WZ,BZ,VZ,UZ,HZ,qZ,ZZ,YZ,JZ,QZ,eY,tY,nY,rY,aY,iY,sY,uY,cY,hY,dY,pY,fY,tJ,mY,AY,yY,gY,xY,_Y,wY,bY,vY,kY,IY,NY,SY,TY,CY,EY,RY,FY,DY,I3,I3,OY,LY,VY,UY,HY,jY,GY,qY,XY,KY,ZY,YY,JY,N3,N3,QY,eJ,nJ,rJ,aJ,sJ,iJ,oJ,lJ,uJ,cJ,hJ,dJ,pJ,fJ,mJ,AJ,yJ,gJ,xJ,S3,S3,T3,T3,wJ,bJ,_J,vJ,kJ,IJ,NJ,SJ,TJ,CJ,EJ,FJ,MJ];for(let e of $J)Xg(e);var C3={};ze(C3,{maxNorm:()=>DJ,minMaxNorm:()=>PJ,nonNeg:()=>zJ,unitNorm:()=>OJ});var hA;function Lt(){return hA==null&&(hA=L5().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)}},L=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,L.prototype)}},De=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,De.prototype)}},E3=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,E3.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 E3(t)}function R3(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 pt(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 lr={};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 lr)i=lr[s];else if(i=t[s],i==null)throw new L(`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 ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new L(`${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 lr?[o,l]=lr.className:i in t&&([o,l]=t[i]),o==null)throw new L(`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 c={};for(let p of Object.keys(lr))c[p]=lr[p];for(let p of Object.keys(n))c[p]=n[p];let u=s.config;u.customObjects=c;let h=Object.assign({},lr);for(let p of Object.keys(n))lr[p]=n[p];pA(s.config);let d=l(o,s.config,n,a);return lr=Object.assign({},h),d}else{let c=Object.assign({},lr);for(let h of Object.keys(n))lr[h]=n[h];let u=new o(s.config);return lr=Object.assign({},c),u}}}function LJ(e,t){return e<t?-1:e>t?1:0}function Ip(e,t){return-1*LJ(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 WJ(e){if(e==null)throw new L(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Ii(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new L(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function fA(e,t,n=0,r=Infinity){return Br(n>=0),Br(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function qt(e,t){Array.isArray(e)?(b.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>qt(n,`element ${r+1} of ${t}`))):b.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${F3(e)}.`)}function F3(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>F3(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function BJ(e,t){let n=b.now(),r;return(...a)=>{let s=b.now();return s-n<t||(n=s,r=e(...a)),r}}function M3(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function mA(e,t){return P(()=>Qt(Te(O(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 P(()=>{let t=mA(e,this.axis),n=vn(t,0,this.maxValue);return O(e,ge(n,se(Lt(),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 P(()=>ge(e,se(Lt(),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 P(()=>{let t=mA(e,this.axis),n=se(O(this.rate,vn(t,this.minValue,this.maxValue)),O(1-this.rate,t));return O(e,ge(n,se(Lt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};xA.className="MinMaxNorm";re.registerClass(xA);var $3={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Wt(e){return dA(e)}function D3(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Bt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in $3?$3[e]:e,config:{}};return D3(t)}else return e instanceof xc?e:D3(e)}function DJ(e){return new AA(e)}function OJ(e){return new yA(e)}function zJ(){return new gA}function PJ(e){return new xA(e)}var O3={};ze(O3,{constant:()=>HJ,glorotNormal:()=>YJ,glorotUniform:()=>ZJ,heNormal:()=>JJ,heUniform:()=>QJ,identity:()=>XJ,leCunNormal:()=>eQ,leCunUniform:()=>tQ,ones:()=>UJ,orthogonal:()=>nQ,randomNormal:()=>GJ,randomUniform:()=>jJ,truncatedNormal:()=>qJ,varianceScaling:()=>KJ,zeros:()=>VJ});var rQ=["channelsFirst","channelsLast"],aQ=["nearest","bilinear"],sQ=["valid","same","causal"],iQ=["max","avg"],oQ=["sum","mul","concat","ave"],Dl=new Map;function St(e){Ii(rQ,"DataFormat",e)}function lQ(e){Ii(aQ,"InterpolationFormat",e)}function Yn(e){Ii(sQ,"PaddingMode",e)}function z3(e){Ii(iQ,"PoolMode",e)}var wc=[],P3="/";function Ni(e,t){wc.push(e);try{let n=t();return wc.pop(),n}catch(n){throw wc.pop(),n}}function uQ(){return wc.length===0?"":wc.join(P3)+P3}function W3(e){if(!L3(e))throw new Error("Not a valid tensor name: '"+e+"'");return uQ()+e}function B3(e){if(!L3(e))throw new Error("Not a valid tensor name: '"+e+"'");Dl.has(e)||Dl.set(e,0);let t=Dl.get(e);if(Dl.set(e,Dl.get(e)+1),t>0){let n=`${e}_${t}`;return Dl.set(n,1),n}else return e}var cQ=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function L3(e){return!!e.match(cQ)}function hQ(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 V3(e){return e=Array.isArray(e)?new Float32Array(e):e,sn(e)}function Ol(e){return pl(V3(e)).dataSync()[0]}function ja(e){return Xn(V3(e)).dataSync()[0]}function vr(e,t){if(t<e)throw new L(`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 dQ(e,t){return P(()=>{if(e.shape.length!==2)throw new L(`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 pQ(e){let t=[Ha(e.shape)];return e.reshape(t)}function fQ(e){if(e.rank<=1)throw new L(`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 P(()=>{switch(e.rank){case 1:return Md(e,t,n);case 2:return fm(e,[t,0],[n,e.shape[1]]);case 3:return $d(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 L(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function _A(e,t,n){return P(()=>{switch(e.rank){case 1:return Md(e,t,n);case 2:return fm(e,[0,t],[e.shape[0],n]);case 3:return $d(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 L(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Np(e,t,n,r){return P(()=>{switch(e.rank){case 1:return Md(e,t,n);case 2:switch(r){case 1:return Si(e,t,n);case 2:return _A(e,t,n);default:throw new L(`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 $d(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return _A(e,t,n);default:throw new L(`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 L(`The axis is not within the rank of the tensor ${r}`)}default:throw new L(`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),at(e,t)}function U3(e,t){switch(e.rank){case 1:return q5([e,t]);case 2:return ll([e,t],0);case 3:return X5([e,t],0);case 4:return K5([e,t],0);default:throw new L(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function wA(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new L(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Oa(e,t)}function Sp(e,t=0,n=1,r,a){return dx(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(),c=[...i,o],u=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=t.transpose(u).reshape([l,-1]);let h=[...a,...c],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 H3(e,t,n){return P(()=>(Array.isArray(t)?t=sn(t,"int32"):t=t.toInt(),ui(e,t,n)))}function vc(e){return O(e,e)}function vA(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new L(`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 L(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ur(e,t,n){return P(()=>(n==null&&(n=_r()),St(n),e.add(vA(e.rank,t,n))))}function mQ(e,t=1){if(t!==1)throw new De(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return cl(e)}function AQ(e){return P(()=>ge(e,Ot(e).add(1)))}function j3(e,t,n,r){return P(()=>xx(e,t,n,r))}function yQ(e){return P(()=>{let t=se(.5,O(.2,e));return vn(t,0,1)})}function kc(e,t,n=!1){return n?e():t()}var gQ=["fanIn","fanOut","fanAvg"],xQ=["normal","uniform","truncatedNormal"];function wQ(e){Ii(gQ,"FanMode",e)}function _Q(e){Ii(xQ,"Distribution",e)}var ur=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},kA=class extends ur{apply(e,t){return Ct(e,t)}};kA.className="Zeros";re.registerClass(kA);var Tp=class extends ur{apply(e,t){return Or(e,t)}};Tp.className="Ones";re.registerClass(Tp);var IA=class extends ur{constructor(e){super();if(typeof e!="object")throw new L(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new L(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return P(()=>O(be(this.value),Or(e,t)))}getConfig(){return{value:this.value}}};IA.className="Constant";re.registerClass(IA);var NA=class extends ur{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}}};NA.className="RandomUniform";re.registerClass(NA);var SA=class extends ur{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 Sp(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 ur{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 zd(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 ur{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return P(()=>{if(e.length!==2||e[0]!==e[1])throw new L("Identity matrix initializer can only be used for 2D square matrices.");return O(this.gain,em(e[0]))})}getConfig(){return{gain:this.gain}}};CA.className="Identity";re.registerClass(CA);function bQ(e,t="channelsLast"){let n,r;if(St(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 ur{constructor(e){super();if(e.scale<0)throw new L(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,wQ(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,_Q(this.distribution),this.seed=e.seed}apply(e,t){let n=bQ(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`${this.getClassName()} does not support dType ${t}.`);return zd(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 Cp=class extends Cn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Cp.className="GlorotUniform";re.registerClass(Cp);var Ep=class extends Cn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Ep.className="GlorotNormal";re.registerClass(Ep);var Rp=class extends Cn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Rp.className="HeNormal";re.registerClass(Rp);var Fp=class extends Cn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Fp.className="HeUniform";re.registerClass(Fp);var Mp=class extends Cn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};Mp.className="LeCunNormal";re.registerClass(Mp);var $p=class extends Cn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Cn.className}};$p.className="LeCunNormal";re.registerClass($p);var EA=class extends ur{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new De("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return P(()=>{if(e.length<2)throw new De("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=Sp(n,0,1,"float32"),a=Fx.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),O(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};EA.className="Orthogonal";re.registerClass(EA);var G3={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 q3(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function kt(e){return dA(e)}function gt(e){if(typeof e=="string"){let t=e in G3?G3[e]:e;if(t==="GlorotNormal")return new Ep;if(t==="GlorotUniform")return new Cp;if(t==="HeNormal")return new Rp;if(t==="HeUniform")return new Fp;if(t==="LeCunNormal")return new Mp;if(t==="LeCunUniform")return new $p;{let n={};return n.className=t,n.config={},q3(n)}}else return e instanceof ur?e:q3(e)}function VJ(){return new kA}function UJ(){return new Tp}function HJ(e){return new IA(e)}function jJ(e){return new NA(e)}function GJ(e){return new SA(e)}function qJ(e){return new TA(e)}function XJ(e){return new CA(e)}function KJ(e){return new Cn(e)}function ZJ(e){return new Cp(e)}function YJ(e){return new Ep(e)}function JJ(e){return new Rp(e)}function QJ(e){return new Fp(e)}function eQ(e){return new Mp(e)}function tQ(e){return new $p(e)}function nQ(e){return new EA(e)}var X3={};ze(X3,{Layer:()=>qe,RNN:()=>Hr,RNNCell:()=>Ic,activation:()=>PQ,add:()=>qQ,alphaDropout:()=>Eee,average:()=>XQ,averagePooling1d:()=>RA,averagePooling2d:()=>FA,averagePooling3d:()=>MA,avgPool1d:()=>ree,avgPool2d:()=>see,avgPool3d:()=>oee,avgPooling1d:()=>aee,avgPooling2d:()=>iee,avgPooling3d:()=>lee,batchNormalization:()=>eee,bidirectional:()=>bee,concatenate:()=>KQ,conv1d:()=>EQ,conv2d:()=>RQ,conv2dTranspose:()=>FQ,conv3d:()=>MQ,convLstm2d:()=>gee,convLstm2dCell:()=>xee,cropping2D:()=>DQ,dense:()=>LQ,depthwiseConv2d:()=>zQ,dot:()=>QQ,dropout:()=>WQ,elu:()=>kQ,embedding:()=>GQ,flatten:()=>VQ,gaussianDropout:()=>Cee,gaussianNoise:()=>Tee,globalAveragePooling1d:()=>uee,globalAveragePooling2d:()=>cee,globalMaxPool1d:()=>kee,globalMaxPool2d:()=>Iee,globalMaxPooling1d:()=>Z3,globalMaxPooling2d:()=>Y3,gru:()=>dee,gruCell:()=>pee,input:()=>K3,inputLayer:()=>vQ,layerNormalization:()=>tee,leakyReLU:()=>NQ,lstm:()=>fee,lstmCell:()=>mee,masking:()=>Ree,maxPool1d:()=>Nee,maxPool2d:()=>See,maxPooling1d:()=>J3,maxPooling2d:()=>Q3,maxPooling3d:()=>hee,maximum:()=>ZQ,minimum:()=>YQ,multiply:()=>JQ,permute:()=>jQ,prelu:()=>SQ,reLU:()=>IQ,repeatVector:()=>UQ,reshape:()=>HQ,rnn:()=>wee,separableConv2d:()=>$Q,simpleRNN:()=>Aee,simpleRNNCell:()=>yee,softmax:()=>TQ,spatialDropout1d:()=>BQ,stackedRNNCells:()=>_ee,thresholdedReLU:()=>CQ,timeDistributed:()=>vee,upSampling2d:()=>OQ,zeroPadding2d:()=>nee});var Fee=0;function e7(){return Fee++}var Dp={};function Op(e=""){return e in Dp||(Dp[e]=0),Dp[e]+=1,e+Dp[e].toString()}function $A(e){return Array.isArray(e)&&Array.isArray(e[0])}function zp(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 L(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ut(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new L(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function Pp(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 t7="Variable",n7=class{constructor(e,t="float32",n=t7,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=e7(),n=n==null?t7:n,this.originalName=W3(n),this.name=B3(this.originalName),this.trainable_=r,this.constraint=a,this.val=fx(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Mee(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 ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function Mee(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function DA(e){return e.map(t=>t.read())}function OA(e){e.forEach(t=>{t[0].write(t[1])})}var Xt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},kr=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=e7(),s!=null&&(this.originalName=W3(s),this.name=B3(this.originalName)),this.rank=t.length}},$ee=0,Lp=class{constructor(e,t){this.callArgs=t,this.id=$ee++,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}}},Dee=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Dee++,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)+"_"+Op(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 L(`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=pt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=pt(this.inputSpec);if(e.length!==t.length)throw new L(`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 L(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new L(`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 L(`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 L(`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),c=a.axes[o],u=l>=0?i[l]:i[i.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new L(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} 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 L(`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=pt(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 L("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ni(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of pt(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=pt(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=Oee(e),i=this.computeOutputShape(s),o,l=zee(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((c,u)=>new kr(l,c,this,pt(e),t,this.name,u)):o=new kr(l,i,this,pt(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 ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Pp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return DA(e?this.trainableWeights:this.weights)}setWeights(e){P(()=>{let t=this.weights;if(t.length!==e.length)throw new L(`You called setWeights(weights) on layer "${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(!b.arraysEqual(s.shape,o.shape))throw new L(`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 L(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=gt("zeros"));let o=r.apply(t,n),l=new n7(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=pt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=pt(e);t=pt(t),n=pt(n),r=pt(r),a=zp(a),s=zp(s);let l=[],c=[],u=[];for(let h of o)l.push(h.sourceLayer),c.push(h.nodeIndex),u.push(h.tensorIndex);new Lp({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Oee(e){e=pt(e);let t=[];for(let n of e)t.push(n.shape);return Tn(t)}function zee(e){return"float32"}function r7(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],c=r7(i,o,l);for(let u of c)a.indexOf(u)===-1&&a.push(u)}return a}}}var zl=class extends qe{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Op("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 L("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 L("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new L("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 Lp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new L(`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 a7(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 L("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 s7(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var i7;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(i7||(i7={}));var Pee=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){}},o7=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)}},Lee=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=P(()=>se(this.totals[r],O(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:P(()=>{let r=O(ge(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),jt(t[n])}))}},l7=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]}},u7=class extends Pl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Pee),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");b.isNumber(this.yieldEvery)&&(this.maybeWait=BJ(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(Zd()),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(Zd()),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(Zd()):b.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 c7(e,t){return e==null&&(e={}),e instanceof Pl?[e]:Array.isArray(e)&&e[0]instanceof Pl?e:pt(e).map(n=>new u7(n,t))}var cr=class{constructor(){}static registerCallbackConstructor(e,t){b.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),cr.checkForDuplicate(t),cr.constructors[e]==null&&(cr.constructors[e]=[]),cr.constructors[e].push(t)}static checkForDuplicate(e){for(let t in cr.constructors)cr.constructors[+t].forEach(n=>{if(n===e)throw new L("Duplicate callback constructor.")})}static clear(){cr.constructors={}}static createCallbacks(e){let t=[];for(let n in cr.constructors){let r=+n;e>=r&&t.push(...cr.constructors[r])}return t.map(n=>new n)}};cr.constructors={};function h7(e,t,n,r,a,s,i,o,l){let c=new l7,u=[new Lee,...cr.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let h=new o7(u);return h.setParams({epochs:n,initialEpoch:r,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:c}}function Ir(e,t={},n=!1){return gc(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function Wp(e,t){return P(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Te(vc(e),t,!0),r=Uu(n.shape,Lt()),a=Qt(Dr(n,r));return ge(e,a)})}function Ti(e,t){return P(()=>vt(vc(ye(t,e)),-1))}function Bp(e,t){return P(()=>vt(Ot(ye(t,e)),-1))}function Ll(e,t){return P(()=>{let n=ye(e,t),r=vn(Ot(e),Lt(),Number.MAX_VALUE),a=Ot(ge(n,r));return O(100,vt(a,-1))})}function Wee(e,t){return P(()=>{let n=vn(t,Lt(),Number.MAX_VALUE),r=Mn(se(1,n)),a=vn(e,Lt(),Number.MAX_VALUE),s=Mn(se(1,a));return vt(vc(ye(r,s)),-1)})}function Bee(e,t){return P(()=>{let n=Dr(0,ye(1,O(e,t)));return vt(vc(n),-1)})}function Vee(e,t){return P(()=>{let n=Dr(0,ye(1,O(e,t)));return vt(n,-1)})}function Uee(e,t){return P(()=>{let n=Te(O(e,t),-1),r=Xn(O(ye(1,e),t),-1);return Dr(0,se(1,ye(r,n)))})}function Hee(e,t){return P(()=>{let n=Math.log(2),r=ye(t,e),a=ye(se(r,dl(O(-2,r))),n);return vt(a,-1)})}function Nc(e,t,n=!1){return P(()=>{if(n)t=Yu(t);else{let r=Te(t,t.shape.length-1,!0);t=ge(t,r)}return t=vn(t,Lt(),1-Lt()),bt(Te(O(e.toFloat(),Mn(t)),t.shape.length-1))})}function Vp(e,t,n=!1){return P(()=>{let r=hl(pQ(e)).toInt();t=vn(t,Lt(),1-Lt());let a=t.shape,s=nl(r,a[a.length-1]).reshape(a);return Nc(s,t,n)})}function jee(e,t){if(!b.arraysEqual(e.shape,t.shape))throw new L(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return P(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function Up(e,t){return P(()=>{let n;return n=vn(t,Lt(),1-Lt()),n=Mn(ge(n,ye(1,n))),vt(jee(e,n),-1)})}function Gee(e,t){return P(()=>{let n=vn(e,Lt(),1),r=vn(t,Lt(),1);return Te(O(e,Mn(ge(n,r))),-1)})}function qee(e,t){return P(()=>{let n=Mn(se(Lt(),t));return vt(ye(t,O(e,n)),-1)})}function zA(e,t){return P(()=>{let n=Wp(e,-1),r=Wp(t,-1),a=O(n,r);return bt(Te(a,-1))})}var Hp={meanSquaredError:Ti,meanAbsoluteError:Bp,meanAbsolutePercentageError:Ll,meanSquaredLogarithmicError:Wee,squaredHinge:Bee,hinge:Vee,categoricalHinge:Uee,logcosh:Hee,categoricalCrossentropy:Nc,sparseCategoricalCrossentropy:Vp,binaryCrossentropy:Up,kullbackLeiblerDivergence:Gee,poisson:qee,cosineProximity:zA};function PA(e){if(typeof e=="string"){if(e in Hp)return Hp[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new L(t)}else return e}function LA(e,t){return P(()=>{let n=O(.5,$n(t)),r=_c(ar(t,n),e.dtype);return vt(Da(e,r),-1)})}function WA(e,t){return P(()=>_c(Da(zu(e,-1),zu(t,-1)),"float32"))}function d7(e,t){return P(()=>sr(e.equal(1),t.equal(1)).sum().cast("float32"))}function Xee(e,t){return P(()=>sr(e.equal(1),t.equal(0)).sum().cast("float32"))}function Kee(e,t){return P(()=>sr(e.equal(0),t.equal(1)).sum().cast("float32"))}function p7(e,t){return P(()=>{let n=d7(e,t),r=Kee(e,t),a=n.add(r);return kn(ar(a,0),n.div(a),0).cast("float32")})}function Zee(e,t){return P(()=>{let n=d7(e,t),r=Xee(e,t),a=n.add(r);return kn(ar(a,0),n.div(a),0).cast("float32")})}function f7(e,t){return Up(e,t)}function m7(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 Yee=Ti,Jee=Ti,Qee=Bp,ete=Bp,tte=Ll,nte=Ll,BA=Nc,rte=zA,A7=Vp,jp={binaryAccuracy:LA,categoricalAccuracy:WA,precision:p7,categoricalCrossentropy:BA,sparseCategoricalCrossentropy:A7,mse:Yee,MSE:Jee,mae:Qee,MAE:ete,mape:tte,MAPE:nte,cosine:rte};function ate(e){if(typeof e=="string"&&e in jp)return jp[e];if(typeof e!="string"&&e!=null)return e;throw new L(`Unknown metric ${e}`)}function Gp(e){if(Br(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Hp))if(Hp[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(jp))if(jp[n]===e){t=n;break}return t!==void 0?t:e.name}}function ste(e){let t={Adagrad:()=>fi.adagrad(.01),Adadelta:()=>fi.adadelta(1,.95,Lt()),Adam:()=>fi.adam(.001,.9,.999,Lt()),Adamax:()=>fi.adamax(.002,.9,.999,Lt(),0),RMSProp:()=>fi.rmsprop(.001,.9,0,Lt()),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 L(`Unknown Optimizer ${e}`)}var y7=1*1024*1024;function g7(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!VA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>y7&&console.warn(`User-defined metadata of model "${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 <= ${y7}.`)}}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 cte(e,t,n,r=console.log){let a=ote(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(u=>Math.floor(t*u)));let i;if(!a){s.push("Receives inputs"),i=[];for(let u in e.nodesByDepth)i.push(...e.nodesByDepth[u])}r("_".repeat(t)),qp(s,n,r),r("=".repeat(t));let o=e.layers;for(let u=0;u<o.length;++u)a?lte(o[u],n,r):ute(o[u],n,i,r),r((u===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=ite(e),c=Pp(e.nonTrainableWeights);r(`Total params: ${l+c}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${c}`),r("_".repeat(t))}function ite(e){let t;return e.collectedTrainableWeights!=null?t=Pp(e.collectedTrainableWeights):t=Pp(e.trainableWeights),t}function ote(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function qp(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function lte(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];qp(i,t,n)}function ute(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(u){a="multiple"}let s=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let h=0;h<u.inboundLayers.length;++h){let d=u.inboundLayers[h].name,p=u.nodeIndices[h],f=u.tensorIndices[h];s.push(`${d}[${p}][${f}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],c=[`${i} (${o})`,a,e.countParams().toString(),l];qp(c,t,r);for(let u=1;u<s.length;++u)qp(["","","",s[u]],t,r)}function x7(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];x7(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];x7(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 hte(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return me(t,e.dtype)}catch(n){throw new L(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Ci=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Ci)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=hte(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new L(`Duplicate key: name=${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 L(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new L(`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 L(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new L(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Ee(this.id2Mask)}},jA={},w7={};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=[],c=t.names();for(let f of o)c.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let u=o.join(",")+"|"+t.names().join(","),h,d;if(jA[u]==null){let f=dte(i,t);h=f.sorted,d=f.recipientCounts,jA[u]=h,w7[u]=d}h=jA[u],d={},a||Object.assign(d,w7[u]);let p=new Ci(t);for(let f=0;f<h.length;++f){if(r!=null){let F=hd().numTensors;F>r.maxNumTensors&&(r.maxNumTensors=F),F<r.minNumTensors&&(r.minNumTensors=F)}let m=h[f],A=m.sourceLayer;if(A instanceof zl)continue;let y=[],g=[],w=[],_=!1;for(let F of m.inputs){let M=p.getValue(F),W=p.getMask(F);y.push(M),g.push(W),W!=null&&(_=!0),a||(d[F.name]--,d[F.name]===0&&!t.hasKey(F)&&o.indexOf(F.name)===-1&&!M.isDisposed&&F.sourceLayer.stateful!==!0&&w.push(M))}_&&(n=n||{},n.mask=g[0]);let v=pt(A.apply(y,n)),x=null;A.supportsMasking&&(x=A.computeMask(y,g));let N=pte(m),E=Array.isArray(N)?N:[N];for(let F=0;F<E.length;++F){p.hasKey(E[F])||p.add(E[F],v[F],Array.isArray(x)?x[0]:x);let M=o.indexOf(E[F].name);M!==-1&&(l[M]=v[F])}a||Ee(w)}return p.disposeMasks(),s?l:l[0]}function dte(e,t){b.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=_7(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=_7(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(c=>r[l].add(c))}}return{sorted:n,recipientCounts:fte(r)}}function fte(e){let t={};for(let n in e)t[n]=e[n].size;return t}function _7(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 c of o.inputs)a[c.name]==null&&(a[c.name]=new Set),a[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:r,recipientMap:a}}function pte(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=Op(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 L(`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,w=y.nodeIndex,_=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(_)}for(let y of this.inputs){let g=y.sourceLayer,w=y.nodeIndex,_=y.tensorIndex;Br(w===0,"input layer has >1 nodes"),Br(_===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(_)}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,w,_,v,x)=>{(_==null||v==null||x==null)&&(_=y.sourceLayer,v=y.nodeIndex,x=y.tensorIndex);let N=_.inboundNodes[v];if(w.indexOf(N)!==-1)throw new br(`The tensor ${y.name} at layer "${_.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(jr.nodeKey(_,v)),_.id in s||(s[_.id]=Object.keys(s).length),w.indexOf(N)===-1&&w.push(N);let E=N.inboundLayers.length;for(let F=0;F<E;F++){let M=N.inputTensors[F],W=N.inboundLayers[F],V=N.nodeIndices[F],B=N.tensorIndices[F];o(M,g,w,W,V,B)}for(g.push(N);w.indexOf(N)>=0;)w.splice(w.indexOf(N),1);i.push(N)},l=[],c=[];for(let y of this.outputs)o(y,l,c);let u=i.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],w=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,w),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let _=0;_<y.inboundLayers.length;_++){let v=y.inboundLayers[_],x=y.nodeIndices[_],N=v.inboundNodes[x],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,E),n[N.id]=N}}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(Ip);this.layers=[];for(let y of p){let g=d[y];g.sort((w,_)=>{let v=s[w.id],x=s[_.id];return v<x?-1:v>x?1:0});for(let w of g)w instanceof jr&&this.internalContainerRefs.push(w),this.layers.push(w)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(Ip);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let w=g.outboundLayer;if(w!=null){for(let _ of g.inputTensors)if(f.indexOf(_)===-1)throw new br(`Graph disconnected: cannot obtain value for tensor ${_} at layer "${w.name}". The following previous layers were accessed without issue: ${m}`);for(let _ of g.outputTensors)f.push(_);m.push(w.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(w=>w===y).length;if(g!==1)throw new br(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Lp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 L("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 L(`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 L(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new L(`${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 P(()=>{e=pt(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 P(()=>{e=pt(e);let n;return t==null?n=vi(null,e.length):n=pt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=zp(e);if(t.length!==this.inputLayers.length)throw new L(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],c=o.name+"_0_0";n[c]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Ip);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,w=n[g];u.push(w)}let h=c.computeOutputShape(Tn(u)),d=zp(h),p=c.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${c.name}_${p}_${f}`;n[m]=d[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],u=`${o.name}_${l}_${c}`;s.push(u)}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],c=e[o],u=t[o];n[l.id]=[c,u]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Ip);for(let o of r){let l=this.nodesByDepth[o];for(let c of l){let u=c.outboundLayer,h=c.inputTensors,d=c.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(c.callArgs!=null&&(f=c.callArgs),p.length===1){let[w,_]=p[0];f.mask==null&&(f.mask=_),y=pt(u.call(w,f)),g=pt(u.computeMask(w,_)),m=[w],A=[_]}else m=p.map(w=>w[0]),A=p.map(w=>w[1]),f.mask==null&&(f.mask=A),y=pt(u.call(m,f)),g=pt(u.computeMask(m,A));if(u.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let w=0;w<d.length;++w){let _=d[w],v=y[w],x=g[w];n[_.id]=[v,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,c]=n[o.id];i.push(l.shape),a.push(l),s.push(c)}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 L(`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 L("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new L(`No such layer: ${e}`)}calculateLosses(){return P(()=>{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 u=0;u<s.inboundNodes.length;u++){let h=s.inboundNodes[u],d=jr.nodeKey(s,u),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],w=jr.nodeKey(A,y),_=t[w];_==null&&(_=0),f.push([A.name,_,g,p])}l.push(f)}}}let c={};c.name=s.name,c.className=i,c.config=o,c.inboundNodes=l,n.push(c)}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 c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[s];r.push([i.name,c,u])}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 c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[s];a.push([i.name,c,u])}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 w of A){let _=w[0],v=w[1],x=w[2];if(g=w[3]==null?{}:w[3],!(_ in a)){i(m,A);return}let N=a[_];if(N.inboundNodes.length<=v){i(m,A);return}let E=N.inboundNodes[v];y.push(E.outputTensors[x])}y.length>0&&m.apply(Tn(y),g)}function l(m){let A=m.name,y=Ir(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new L(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!WJ(s);)for(let m of u){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 w=a[A].inboundNodes[y].outputTensors;h.push(w[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 w=a[A].inboundNodes[y].outputTensors;d.push(w[g])}return new e({inputs:h,outputs:d,name:c})}get stateful(){if(this._stateful)throw new L("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(){P(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function mte(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: ${JSON.stringify(e)}`)}function b7(e,t){return mte(e,t,"classWeight")}async function v7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=P(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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])}),sn(i,"float32")}else return null}function Ate(e,t){return O(e,t)}var yte=32;function I7(e,t){let n,r,a=t;n=a.xs,r=a.ys,b.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=k7("input",e.inputNames,n),i=k7("output",e.outputNames,r),o=s[0].shape[0];b.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)})`),b.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++)b.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++)b.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 k7(e,t,n){if(n instanceof Ue)return[n];if(Array.isArray(n))return b.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 L(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function gte(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 wte(e,t,n){let r=n.batchesPerEpoch!=null;if(b.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),b.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),b.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}`),b.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}`),b.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(N7(n.validationData))b.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=gte(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;a?c=l.slice().concat(l.map(A=>"val_"+A)):c=l.slice();let u=c7(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=h7(u,h,n.epochs,null,null,xte(t,n),null,a,c);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 w=await m.next();if(r&&w.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, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(w.value!=null){let{xs:_,ys:v}=I7(e,w.value),x={};x.batch=g,x.size=_[0].shape[0],await d.onBatchBegin(g,x);let N=[];if(n.classWeight!=null){let M=b7(n.classWeight,e.outputNames);for(let W=0;W<M.length;++W)N.push(await v7(v[W],null,M[W]))}let E=_.concat(v).concat(N),F=o(E);Ee(E);for(let M=0;M<l.length;++M){let W=l[M],V=F[M];x[W]=V,jt(V)}await d.onBatchEnd(g,x),s7(x),g++,y++}if(r?y>=n.batchesPerEpoch:w.done){if(a){let _;N7(n.validationData)?_=pt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):_=pt(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?yte:n.validationBatchSize,verbose:0}));for(let v=0;v<e.metricsNames.length;++v)A[`val_${e.metricsNames[v]}`]=_[v]}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 xte(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function N7(e){return typeof e.iterator=="function"}function _te(e){return typeof e.next=="function"}async function bte(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.");b.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=_te(t)?t:await t.iterator(),o=0,l=0;for(;r?l<n.batches:!0;){let c=await i.next();if(s=P(()=>{if(c.value){let{xs:u,ys:h}=I7(e,c.value),d=u.concat(h),p=P(()=>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]=P(()=>se(s[m],O(f,A))),l>0&&Ee(y)}Ee(p),o+=f,++l}return s}),c.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 c=0;c<s.length;++c){let u=s[c];s[c]=ge(s[c],o),Ee(u)}return Tn(s)}function GA(e){b.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 P(()=>e==null?null:Array.isArray(e)?e.map(n=>qA(n,t)):H3(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 vte(e,t,n,r,a,s,i,o,l,c,u,h,d,p,f){a==null&&(a=32),s==null&&(s=1),u==null&&(u=!0),d==null&&(d=0);let m=!1;if(l!=null&&c!=null&&(m=!0),f!=null&&(m=!0,p==null))throw new L("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:w}=h7(o,i,s,d,A,p,a,m,h);g.setModel(e),e.history=w,await g.onTrainBegin(),e.stopTraining_=!1;for(let _=d;_<s;++_){await g.onEpochBegin(_);let v={};if(p!=null)throw new De("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new De("batch shuffling is not implemneted yet");u&&b.shuffle(y);let x=sn(y),N=XA(A,a);for(let E=0;E<N.length;++E){let F={};if(await g.onBatchBegin(E,F),P(()=>{let M=N[E][0],W=N[E][1],V=Si(x,M,W-M);F.batch=E,F.size=W-M;let B=qA(n,V),H=t(B);for(let j=0;j<r.length;++j){let X=r[j],G=H[j];F[X]=G,jt(G)}if(E===N.length-1&&m){let j=e.testLoop(l,c,a);for(let X=0;X<r.length;++X){let G=r[X],ee=j[X];jt(ee),v["val_"+G]=ee}}}),await g.onBatchEnd(E,F),s7(F),e.stopTraining_)break}x.dispose()}if(await g.onEpochEnd(_,v),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function kte(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,c,u;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],u=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 L(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let x=!0,N=await e.standardizeUserData(i,o,null,null,x,h);l=N[0],c=N[1],m=l.concat(c)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let x=Math.floor(a[0].shape[0]*(1-r.validationSplit)),N=a[0].shape[0];l=Cc(a,x,N),a=Cc(a,0,x),c=Cc(s,x,N),s=Cc(s,0,x),m=l.concat(c)}else r.validationSteps!=null&&(f=!0);let A=a.concat(s).concat(u);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),w,_;f?(e.makeTestFunction(),w=e.testFunction,_=g.slice().concat(g.map(x=>"val_"+x))):(w=null,m=[],_=g.slice());let v=c7(r.callbacks,r.yieldEvery);return await vte(e,y,A,g,h,r.epochs,r.verbose,v,w,m,r.shuffle,_,r.initialEpoch,null,null)}finally{e.isTraining=!1,Ei(a,t),Ei(s,n),Ei(l,i),Ei(c,o),u!=null&&Ee(u)}}function S7(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 Ite(e){return e instanceof Ue}function KA(e){return Array.isArray(e)}function T7(e){return!Ite(e)&&!KA(e)}function C7(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(T7(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new L(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(T7(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new L(`No data provided for "${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 L(`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 L(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=S7(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 L(`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 c=o.shape[l],u=n[i][l];if(u!=null&&u>=0&&c!==u)throw new L(`Error when checking ${a}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Nte(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 L(`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 L(`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&&!b.arraysEqual(r,a))throw new L(`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 Ste(e,t,n){let r=[Ti,Up,Nc];for(let a=0;a<e.length;++a){let s=e[a],i=t[a],o=n[a];if(i!=null){if(i===Nc&&s.shape[s.shape.length-1]===1)throw new L(`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),c=o.slice(1);for(let u=0;u<l.length;++u){let h=l[u],d=c[u];if(d!=null&&h!==d)throw new L(`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 E7(e,t,n,r=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new L(`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 L(`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 L(`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 c=o.shape[l],u=n[i][l];if(u!=null&&u!==c)throw new L(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function Tte(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: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var Cte="layers-model",ha=class extends jr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new L("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");cte(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=ste(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof oa))throw new L("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new L(`Unknown entry in loss dictionary: "${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 L(`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=[],Ni("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=Tte(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])};Ni("metric",()=>{for(let s=0;s<this.outputs.length;++s){if(n.indexOf(s)!==-1)continue;let i=r[s];(o=>{let l="",c,u,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]===Up?["accuracy","acc"].indexOf(d)!==-1?u=LA:["crossentropy","ce"].indexOf(d)!==-1&&(u=f7):this.lossFunctions[s]===Vp?["accuracy","acc"].indexOf(d)!==-1?u=m7:["crossentropy","ce"].indexOf(d)!==-1&&(u=A7):["accuracy","acc"].indexOf(d)!==-1?u=WA:["crossentropy","ce"].indexOf(d)!==-1&&(u=BA);let m;["accuracy","acc"].indexOf(d)!==-1?m="acc":["crossentropy","ce"].indexOf(d)!==-1&&(m="ce"),h=u,c=l+m}else h=ate(d),c=l+Gp(d);let p;Ni(c,()=>{p=h}),a(s,c,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(),bte(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(n!=null){if(a=null,t!=null)throw new L(`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 L(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new L("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Ci;if(e instanceof Ue&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new L(`The number of inputs provided (${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 L(`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 L(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return P(()=>{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)P(()=>{let o=a[i][0],l=a[i][1],c=Cc(e,o,l),u=[];if(Array.isArray(c))for(let d=0;d<c.length;++d)u.push({key:this.inputs[d],value:c[d]});else u.push({key:this.inputs[0],value:c});let h=new Ci(u);return Tc(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return Tn(s.map(i=>at(i,0)))})}predict(e,t={}){let n=S7(e);E7(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){E7(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]===Vp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=C7(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=C7(t,this.feedOutputNames,a,!1,"target"),Nte(e,t,null),Ste(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new L(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let c=b7(r,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await v7(o[u],null,c[u]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return P(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new De("Verbose mode is not implemented yet.");if(a!=null)throw new De("steps mode in testLoop() is not implemented yet");{let o=XA(s,n),l=sn(vr(0,s));for(let c=0;c<o.length;++c){let u=o[c][0],h=o[c][1],d=Si(l,u,h-u),p=qA(t,d),f=e(p);if(c===0)for(let m=0;m<f.length;++m)i.push(be(0));for(let m=0;m<f.length;++m){let A=f[m];i[m]=se(i[m],O(h-u,A))}}for(let c=0;c<i.length;++c)i[c]=ge(i[c],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;R3(e,r)>1&&(a+=`_${R3(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 c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Ci(c),h=Tc(this.outputs,u,{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=Ate(f,a[p]));let m=vt(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=vt(m(r[A],h[A]))}jt(f),s.push(f)}return d=vt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>P(()=>{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 c=this.lossFunctions[l],u=vt(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=vt(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return kte(this,e,t,n)}async fitDataset(e,t){return wte(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=hd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-hd().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(Gp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ca(Gp(e)));{let e={};for(let t in this.metrics)e[t]=ca(Gp(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=Ir(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 L(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new L(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new L("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await bn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Cte,generatedBy:`TensorFlow.js tfjs-layers v${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;g7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){g7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ha.className="Model";re.registerClass(ha);var R7=class extends ha{};R7.className="Functional";re.registerClass(R7);async function Ete(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=Ir(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 Fte(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 L(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Rte(e,void 0,t)}async function Rte(e,t,n){if(n==null&&(n={}),e.load==null)throw new L("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=Ir(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 L("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=Mte(r.weightData,r.weightSpecs);o.loadWeights(c,s),o.optimizer!=null&&u.length>0&&await o.optimizer.setWeights(u),Ee(c),Ee(u.map(h=>h.tensor))}return o}function Mte(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:Op("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 L(`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 L("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 L("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 L("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=a7({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 L(`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 L("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=r7(this.outputs[0])}this.inboundNodes=[],new Lp({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(ut(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 L("Legacy serialization format not supported yet.");a=t}else b.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=Ir(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new L("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 L("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 $te(e){return new ha(e)}function Dte(e){return new Wl(e)}function Ote(e,t){return t==null&&(t={}),Fte(e,t)}function K3(e){return a7(e)}function zte(e,t){cr.registerCallbackConstructor(e,t)}var Ln=class extends re.Serializable{getConfig(){return{}}},F7=class extends Ln{apply(e,t=1){return mQ(e,t)}};F7.className="elu";re.registerClass(F7);var M7=class extends Ln{apply(e){return Ed(e)}};M7.className="selu";re.registerClass(M7);var $7=class extends Ln{apply(e){return zr(e)}};$7.className="relu";re.registerClass($7);var D7=class extends Ln{apply(e){return P(()=>fl(6,zr(e)))}};D7.className="relu6";re.registerClass(D7);var O7=class extends Ln{apply(e){return e}};O7.className="linear";re.registerClass(O7);var z7=class extends Ln{apply(e){return Fn(e)}};z7.className="sigmoid";re.registerClass(z7);var P7=class extends Ln{apply(e){return yQ(e)}};P7.className="hardSigmoid";re.registerClass(P7);var L7=class extends Ln{apply(e){return dl(e)}};L7.className="softplus";re.registerClass(L7);var W7=class extends Ln{apply(e){return AQ(e)}};W7.className="softsign";re.registerClass(W7);var B7=class extends Ln{apply(e){return ol(e)}};B7.className="tanh";re.registerClass(B7);var ZA=class extends Ln{apply(e,t=-1){return Yu(e,t)}};ZA.className="softmax";re.registerClass(ZA);var V7=class extends Ln{apply(e,t=-1){return vd(e,t)}};V7.className="logSoftmax";re.registerClass(V7);var U7=class extends Ln{apply(e,t=1){return P(()=>Fn(e.mul(t)).mul(e))}};U7.className="swish";re.registerClass(U7);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 H7=class extends re.Serializable{},Ec=class extends H7{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 P(()=>{let t=Ct([1]);return this.hasL1&&(t=se(t,Te(O(this.l1,Ot(e))))),this.hasL2&&(t=se(t,Te(O(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 Pte(e){return JA(e),new Ec({l1:e!=null?e.l1:null,l2:0})}function Lte(e){return JA(e),new Ec({l2:e!=null?e.l2:null,l1:0})}var j7={l1l2:"L1L2"};function ct(e){return dA(e)}function G7(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in j7?j7[e]:e,config:{}};return G7(t)}else return e instanceof H7?e:G7(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=gt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=xt(e.alphaRegularizer),this.alphaConstraint=Bt(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 L(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ut(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Xt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Pe(e),Xu(e,this.alpha.read())}getConfig(){let e={alphaInitializer:kt(this.alphaInitializer),alphaRegularizer:ct(this.alphaRegularizer),alphaConstraint:Wt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};ty.className="PReLU";re.registerClass(ty);var ny=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${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 L(`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(!hQ(a))throw new L(`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 Nr(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 Xp(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 L(`Unsupport padding mode: ${r}.`);return e}function sy(e,t){return P(()=>(St(t),t==="channelsFirst"?rt(e,[0,2,3,1]):e))}function q7(e,t){return P(()=>(St(t),t==="channelsFirst"?rt(e,[0,2,3,4,1]):e))}function Wte(e,t,n,r=1,a="valid",s,i=1){return P(()=>{if(s==null&&(s=_r()),St(s),e.shape.length!==3)throw new L(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new L(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new L(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=rt(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=md(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function X7(e,t,n,r=[1,1],a="valid",s,i,o=null){return P(()=>{if(s==null&&(s=_r()),St(s),e.rank!==3&&e.rank!==4)throw new L(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new L(`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=rt(l,[0,3,1,2])),l})}function Bte(e,t,n,r=[1,1,1],a="valid",s,i){return P(()=>{if(s==null&&(s=_r()),St(s),e.rank!==4&&e.rank!==5)throw new L(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new L(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=q7(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Xf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=rt(o,[0,4,1,2,3])),o})}var iy=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",iy.verifyArgs(t),this.rank=e,qt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${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,St(this.dataFormat),this.activation=Xa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(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 L(`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 L(`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 L(`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 L(`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:kt(this.biasInitializer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),biasConstraint:Wt(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,qt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L(`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 P(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=M3(this.activation.getClassName());if(a!=null&&this.rank===2)n=X7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=Wte(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=X7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Bte(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=ut(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=Nr(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:kt(this.kernelInitializer),kernelRegularizer:ct(this.kernelRegularizer),kernelConstraint:Wt(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 L(`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 L(`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 Kp=class extends Rc{constructor(e){super(3,e);Kp.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 L(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv3D";re.registerClass(Kp);var oy=class extends Fc{constructor(e){super(e);if(this.inputSpec=[new Xt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new L(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ut(e),e.length!==4)throw new L("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Xt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{let n=Pe(e);if(n.shape.length!==4)throw new L(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=Xp(o,h,c,this.padding),f=Xp(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=rt(n,[0,2,3,1]));let A=Ad(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=rt(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=ut(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]=Xp(t[r],o,s,this.padding),t[a]=Xp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};oy.className="Conv2DTranspose";re.registerClass(oy);var K7=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 L("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new L("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 L(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=ut(e),e.length<this.rank+2)throw new L(`Inputs to SeparableConv${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 L(`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 Xt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return P(()=>{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=rt(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=rt(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=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.pointwiseRegularizer=ct(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};K7.className="SeparableConv";var ly=class extends K7{constructor(e){super(2,e)}};ly.className="SeparableConv2D";re.registerClass(ly);var Zp=class extends Rc{constructor(e){super(1,e);Zp.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 L(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Zp.className="Conv1D";re.registerClass(Zp);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 P(()=>{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,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,lQ(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 P(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=rt(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 rt(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 Vte(e,t,n=[1,1],r="valid",a,s){return P(()=>{a==null&&(a=_r()),St(a);let i=sy(e,a);if(e.rank!==4)throw new L(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new L(`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=rt(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=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=ut(e),e.length<4)throw new L(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new L(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 P(()=>{e=Pe(e);let n=Vte(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=ut(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=Nr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Nr(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=kt(this.depthwiseInitializer),e.depthwiseRegularizer=ct(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};hy.className="DepthwiseConv2D";re.registerClass(hy);function Z7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new L("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 Y7(e,t,n,r=!1,a,s,i=!1,o=!1){return P(()=>{let l=t.shape.length;if(l<3)throw new L(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(vr(2,l));if(t=rt(t,c),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=hn(a,-1)),a=rt(a,c)),r&&(t=Dn(t,0),a!=null&&(a=Dn(a,0)));let u=[],h,d=n,p=t.shape[0],f=ir(t),m;a!=null&&(m=ir(a));for(let y=0;y<p;++y){let g=f[y],w=P(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let _=P(()=>{let v=m[y],x=$n(v).sub(v),N=w[0].mul(v).add(d[0].mul(x)),E=d.map((F,M)=>w[1][M].mul(v).add(F.mul(x)));return{output:N,newStates:E}});h=_.output,d=_.newStates}o&&u.push(h)}let A;return o&&(A=dn(u,1)),[h,A,d]})}var Hr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new L("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Yp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new L("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 Xt({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){$A(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return P(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");$A(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Xt({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(!b.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new L(`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 Xt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){P(()=>{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 L("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=>Ct([n,r])):this.states_=[Ct([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=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new L(`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(!b.arraysEqual(a.shape,i))throw new L(`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=>jt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=Z7(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 Xt({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),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return P(()=>{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 L(`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=Y7((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],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return P(()=>{let t=Ct(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=Ir(r,n);return new e(Object.assign(t,{cell:a}))}};Hr.className="RNN";re.registerClass(Hr);var Ic=class extends qe{},Jp=class extends Ic{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,qt(this.units,"units"),this.activation=Xa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(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=ut(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 P(()=>{if(e=e,e.length!==2)throw new L(`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(O(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=O(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:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Jp.className="SimpleRNNCell";re.registerClass(Jp);var dy=class extends Hr{constructor(e){e.cell=new Jp(e),super(e)}call(e,t){return P(()=>{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 Qp=class extends Ic{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 L("GRUCell does not support reset_after parameter set to true.");this.units=e.units,qt(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=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(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=ut(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 P(()=>{if(e=e,e.length!==2)throw new L(`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=O(e,a[0]));let c=Vr(e,this.kernel.read());this.useBias&&(c=Ur(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Pt(u,[2*this.units,this.units],u.rank-1),p=Vr(r,h),[f,m,A]=Pt(c,3,c.rank-1),[y,g]=Pt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Vr(O(o,r),d);l=this.activation.apply(se(A,w));let _=se(O(i,r),O(se(1,bt(i)),l));return[_,_]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),recurrentActivation:qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Qp.className="GRUCell";re.registerClass(Qp);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 Qp(e),super(e)}call(e,t){return P(()=>{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 Ic{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,qt(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=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(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=ut(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 ur{apply(i,o){let l=a.apply([s]),c=new Tp().apply([s]),u=a.apply([s*2]);return U3(U3(l,c),u)}},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 P(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new L(`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,c,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=O(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=Pt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(O(l,a),O(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=O(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),recurrentActivation:qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ct(this.kernelRegularizer),recurrentRegularizer:ct(this.recurrentRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(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 P(()=>{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 Yp=class extends Ic{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 P(()=>{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)=>{Ni(`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(Ir(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)}};Yp.className="StackedRNNCells";re.registerClass(Yp);function Ka(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>j3(t(),n),i=()=>kc(s,t,r);return!a||a<=1?jt(i().clone()):Array(a).fill(void 0).map(i).map(o=>jt(o.clone()))}var Ute=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},J7=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 Xt({ndim:5})]}call(e,t){return P(()=>{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 L("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 P(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){P(()=>{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 L("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(()=>Ct(a)):this.states_=[Ct(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(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new L(`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(!b.arraysEqual(i.shape,o))throw new L(`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=>jt(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],c=e[o?4:3],u=Nr(l,r[0],a,s[0],i[0]),h=Nr(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};J7.className="ConvRNN2D";var e0=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,qt(this.filters,"filters"),this.kernelSize=Bl(n,2,"kernelSize"),this.kernelSize.forEach(o=>qt(o,"kernelSize")),this.strides=Bl(r||1,2,"strides"),this.strides.forEach(o=>qt(o,"strides")),this.padding=a||"valid",Yn(this.padding),this.dataFormat=s||"channelsLast",St(this.dataFormat),this.dilationRate=Bl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>qt(o,"dilationRate"))}build(e){var t;e=ut(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new L(`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,c=this.filters;o=new(t=class extends ur{apply(u,h){let d=l.apply([c]),p=Or([c]),f=l.apply([c*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 P(()=>{if(e.length!==3)throw new L(`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:O(ae[te],Y),c=l(r,o,0),u=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,[w,_,v,x]=Pt(this.kernel.read(),i,g),[N,E,F,M]=this.useBias?Pt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,N,this.padding),u=this.inputConv(u,_,E,this.padding),h=this.inputConv(h,v,F,this.padding),d=this.inputConv(d,x,M,this.padding);let[W,V,B,H]=Pt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,W),m=this.recurrentConv(m,V),A=this.recurrentConv(A,B),y=this.recurrentConv(y,H);let j=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(O(X,s),O(j,this.activation.apply(se(h,A)))),ee=O(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Ute(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")}};e0.className="ConvLSTM2DCell";re.registerClass(e0);var my=class extends J7{constructor(e){let t=new e0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};my.className="ConvLSTM2D";re.registerClass(my);var t0=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 P(()=>{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(()=>j3(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()}};t0.className="Dropout";re.registerClass(t0);var Ay=class extends t0{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,qt(this.units,"units"),this.activation=Xa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ut(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=ut(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return P(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=M3(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:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:ct(this.kernelRegularizer),biasRegularizer:ct(this.biasRegularizer),activityRegularizer:ct(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(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=ut(e);for(let t of e.slice(1))if(t==null)throw new L(`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 P(()=>{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 fQ(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 P(()=>{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 P(()=>(e=Pe(e),dQ(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 L("Can only specifiy one unknown dimension.");else a*=l}let i=Ha(e);if(s!==null){if(a===0||i%a!=0)throw new L(n);r[s]=i/a}else if(i!==a)throw new L(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 P(()=>{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(!b.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 Xt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ut(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return rt(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 P(()=>{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(pt(e.inputLength))}this.inputDim=e.inputDim,qt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,qt(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Bt(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 P(()=>this.maskZero?(e=Pe(e),di(e,He(e))):null)}computeOutputShape(e){if(e=ut(e),this.inputLength==null)return[...e,this.outputDim];let t=pt(this.inputLength);if(t.length!==e.length-1)throw new L(`"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 L(`"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 P(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=_c(n,"int32")),H3(this.embeddings.read(),n.as1D()).reshape(ut(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:ct(this.embeddingsRegularizer),activityRegularizer:ct(this.activityRegularizer),embeddingsConstraint:Wt(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 L("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=[ut(e)]),e=e,e.length<2)throw new L(`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 L(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ua(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return P(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=ja(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=bc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let c=o.shape,u=c[0],h=c.slice(1).concat([u]),d=o.reshape([u].concat(Ha(c.slice(1))));d=rt(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let c=vr(1,l).concat([0]);n.push(rt(o,c)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,c=o[l-1],u=[c].concat(o.slice(0,o.length-1));s=rt(s.reshape([-1,c]),[1,0]).reshape(u)}else if(i>1){let o=[i-1].concat(vr(0,i-1));s=rt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ua(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return P(()=>{if(t==null)return null;if(!Array.isArray(t))throw new L("`mask` should be an Array");if(!Array.isArray(e))throw new L("`inputs` should be an Array");if(t.length!==e.length)throw new L(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${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=sr(n,t[r]);return n})}},Iy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};Iy.className="Add";re.registerClass(Iy);var Ny=class extends Ri{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=O(t,e[n]);return t})}};Ny.className="Multiply";re.registerClass(Ny);var Sy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return P(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return O(1/e.length,t)})}};Sy.className="Average";re.registerClass(Sy);var Ty=class extends Ri{constructor(e){super(e)}mergeFunction(e){return P(()=>{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 P(()=>{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 L("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(b.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new L("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return P(()=>bA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new L("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 L("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new L("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new L(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return P(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push($n(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(hn(t[s],-1)):r.push(t[s]);let a=at(r,this.axis);return pd(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 Hte(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(b.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),b.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 P(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)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,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}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){b.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 L(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new L(`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=Wp(t,r[0]),n=Wp(n,r[1])),Hte(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){b.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 P(()=>{this.invokeCallHook(e,t);let n=Pe(e);return kc(()=>Sp(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 P(()=>{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(Sp(n.shape,1,r))},()=>n,t.training||!1):n})}};My.className="GaussianDropout";re.registerClass(My);var $y=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return P(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return kc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=za(ml(n),this.rate);o=_c(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(c)},()=>Pe(e),t.training||!1)}return e})}};$y.className="AlphaDropout";re.registerClass($y);function Dc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=U5(e,t,n,r,a,s);else if(e.rank===3)i=H5(e,t,n,r,a,s);else if(e.rank===4)i=j5(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function jte(e,t,n,r,a=.001){return P(()=>{let s=Id(e,r),i=s.mean,o=s.variance;return[Dc(e,i,o,n,t,a),i,o]})}function Gte(e,t,n,r,a=.001){return P(()=>{let s=Id(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 c=i.reshape(l),u=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Dc(e,c,u,d,h,a),i,o]})}function qte(e,t,n,r,a=.001){return b.arraysEqual(r.slice().sort(),vr(0,e.rank-1))?jte(e,t,n,r,a):Gte(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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Bt(e.betaConstraint),this.gammaConstraint=Bt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=ut(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new L(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Xt({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 P(()=>{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 c=i.slice();c.sort();let u=!b.arraysEqual(c,vr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return Dc(r,A,y,g,w,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]=qte(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{P(()=>{let w=1-g,_=A.read(),v=_.sub(y).mul(w);A.write(_.sub(v))})};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:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer),betaConstraint:Wt(this.betaConstraint),gammaConstraint:Wt(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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ut(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 P(()=>{let s=!0,{mean:i,variance:o}=Id(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(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),u=u.tile(p),h=h.tile(p),Dc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:ct(this.betaRegularizer),gammaRegularizer:ct(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="LayerNormalization";re.registerClass(Oy);function Xte(e,t,n){return P(()=>{if(e.rank!==4)throw new L(`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 L("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 L(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],aa(e,r)})}var zy=class extends qe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?_r():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new L(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 L(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new L(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ut(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 P(()=>Xte(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 n0(e,t,n,r,a,s){return P(()=>{St(a),z3(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=rt(i,[0,3,1,2])),i})}function Q7(e,t,n,r,a,s){return P(()=>{St(a),z3(s),Yn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=_r()),s==null&&(s="max"),e=q7(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=rt(i,[0,4,1,2,3])),i})}var ev=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 L(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(qt(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 L(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Yn(this.padding),this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){e=ut(e);let t=Nr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return P(()=>{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 ev{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Yn(r),n0(e,t,n,r,a,"max")}};Py.className="MaxPooling1D";re.registerClass(Py);var Ly=class extends ev{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Yn(r),n0(e,t,n,r,a,"avg")}};Ly.className="AveragePooling1D";re.registerClass(Ly);var tv=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 L(`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];qt(this.poolSize,"poolSize"),qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Yn(this.padding),this.inputSpec=[new Xt({ndim:4})]}computeOutputShape(e){e=ut(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Nr(t,this.poolSize[0],this.padding,this.strides[0]),n=Nr(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 P(()=>(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 tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Yn(r),n0(e,t,n,r,a,"max")}};Wy.className="MaxPooling2D";re.registerClass(Wy);var By=class extends tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Yn(r),n0(e,t,n,r,a,"avg")}};By.className="AveragePooling2D";re.registerClass(By);var nv=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 L(`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];qt(this.poolSize,"poolSize"),qt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Yn(this.padding),this.inputSpec=[new Xt({ndim:5})]}computeOutputShape(e){e=ut(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=Nr(t,this.poolSize[0],this.padding,this.strides[0]),n=Nr(n,this.poolSize[1],this.padding,this.strides[1]),r=Nr(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 P(()=>(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 nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Yn(r),Q7(e,t,n,r,a,"max")}};Vy.className="MaxPooling3D";re.registerClass(Vy);var Uy=class extends nv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return St(a),Yn(r),Q7(e,t,n,r,a,"avg")}};Uy.className="AveragePooling3D";re.registerClass(Uy);var rv=class extends qe{constructor(e){super(e);this.inputSpec=[new Xt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},Hy=class extends rv{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Pe(e);return vt(n,1)})}};Hy.className="GlobalAveragePooling1D";re.registerClass(Hy);var jy=class extends rv{constructor(e){super(e||{})}call(e,t){return P(()=>{let n=Pe(e);return Xn(n,1)})}};jy.className="GlobalMaxPooling1D";re.registerClass(jy);var av=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Xt({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 av{call(e,t){return P(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?vt(n,[1,2]):vt(n,[2,3])})}};Gy.className="GlobalAveragePooling2D";re.registerClass(Gy);var qy=class extends av{call(e,t){return P(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Xn(n,[1,2]):Xn(n,[2,3])})}};qy.className="GlobalMaxPooling2D";re.registerClass(qy);var sv=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=Ir(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Xy=class extends sv{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ut(e),e.length<3)throw new L(`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=ut(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 P(()=>(e=Pe(e),Y7((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Xy.className="TimeDistributed";re.registerClass(Xy);function Kte(e){Ii(oQ,"BidirectionalMergeMode",e)}var Zte="concat",Ky=class extends sv{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ir(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Ir(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Zte:e.mergeMode,Kte(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=Z7(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 L("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 c=n.map(u=>new Xt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}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 L("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),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return P(()=>{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=O(.5,se(r,a)):this.mergeMode==="mul"?i=O(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){Ni(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ni(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=Ir(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 vQ(e){return new zl(e)}function kQ(e){return new ny(e)}function IQ(e){return new QA(e)}function NQ(e){return new ey(e)}function SQ(e){return new ty(e)}function TQ(e){return new ay(e)}function CQ(e){return new ry(e)}function EQ(e){return new Zp(e)}function RQ(e){return new Fc(e)}function FQ(e){return new oy(e)}function MQ(e){return new Kp(e)}function $Q(e){return new ly(e)}function DQ(e){return new uy(e)}function OQ(e){return new cy(e)}function zQ(e){return new hy(e)}function PQ(e){return new xy(e)}function LQ(e){return new yy(e)}function WQ(e){return new t0(e)}function BQ(e){return new Ay(e)}function VQ(e){return new gy(e)}function UQ(e){return new wy(e)}function HQ(e){return new _y(e)}function jQ(e){return new by(e)}function GQ(e){return new ky(e)}function qQ(e){return new Iy(e)}function XQ(e){return new Sy(e)}function KQ(e){return new Ey(e)}function ZQ(e){return new Ty(e)}function YQ(e){return new Cy(e)}function JQ(e){return new Ny(e)}function QQ(e){return new Ry(e)}function eee(e){return new Dy(e)}function tee(e){return new Oy(e)}function nee(e){return new zy(e)}function RA(e){return new Ly(e)}function ree(e){return RA(e)}function aee(e){return RA(e)}function FA(e){return new By(e)}function see(e){return FA(e)}function iee(e){return FA(e)}function MA(e){return new Uy(e)}function oee(e){return MA(e)}function lee(e){return MA(e)}function uee(e){return new Hy(e)}function cee(e){return new Gy(e)}function Z3(e){return new jy(e)}function Y3(e){return new qy(e)}function J3(e){return new Py(e)}function Q3(e){return new Wy(e)}function hee(e){return new Vy(e)}function dee(e){return new py(e)}function pee(e){return new Qp(e)}function fee(e){return new fy(e)}function mee(e){return new Mc(e)}function Aee(e){return new dy(e)}function yee(e){return new Jp(e)}function gee(e){return new my(e)}function xee(e){return new e0(e)}function wee(e){return new Hr(e)}function _ee(e){return new Yp(e)}function bee(e){return new Ky(e)}function vee(e){return new Xy(e)}var kee=Z3,Iee=Y3,Nee=J3,See=Q3;function Tee(e){return new Fy(e)}function Cee(e){return new My(e)}function Eee(e){return new $y(e)}function Ree(e){return new vy(e)}var iv={};ze(iv,{MAPE:()=>one,MSE:()=>cne,binaryAccuracy:()=>Yte,binaryCrossentropy:()=>Jte,categoricalAccuracy:()=>ene,categoricalCrossentropy:()=>tne,cosineProximity:()=>ane,mape:()=>lne,meanAbsoluteError:()=>sne,meanAbsolutePercentageError:()=>ine,meanSquaredError:()=>une,mse:()=>hne,precision:()=>nne,recall:()=>rne,sparseCategoricalAccuracy:()=>Qte});function Yte(e,t){return LA(e,t)}function Jte(e,t){return f7(e,t)}function Qte(e,t){return m7(e,t)}function ene(e,t){return WA(e,t)}function tne(e,t){return BA(e,t)}function nne(e,t){return p7(e,t)}function rne(e,t){return Zee(e,t)}function ane(e,t){return zA(e,t)}function sne(e,t){return Bp(e,t)}function ine(e,t){return Ll(e,t)}function one(e,t){return Ll(e,t)}function lne(e,t){return Ll(e,t)}function une(e,t){return Ti(e,t)}function cne(e,t){return Ti(e,t)}function hne(e,t){return Ti(e,t)}var ov={};ze(ov,{modelFromJSON:()=>Ete});var lv={};ze(lv,{l1:()=>pne,l1l2:()=>dne,l2:()=>fne});function dne(e){return new Ec(e)}function pne(e){return Pte(e)}function fne(e){return Lte(e)}var uv=class extends Pl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ha))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function r0(e,t){return e<t}function cv(e,t){return e>t}var hv=class extends uv{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new De("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=r0:this.mode==="max"?this.monitorFunc=cv:this.monitor.indexOf("acc")!==-1?this.monitorFunc=cv:this.monitorFunc=r0,this.monitorFunc===r0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===r0?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 mne(e){return new hv(e)}var Ane={earlyStopping:mne},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 dv;(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={}))})(dv||(dv={}));var Zy={};function yne(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Zy[e]=n}function pv(e){return Zy[e]}function gne(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 c=En(t.inputNames.slice(o)[0],n,r,a),u=c.dataSync();return s.type==="number"?u[0]:b.toNestedArray(c.shape,u)}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[a0(a,o)]);return i!==void 0?t[a0(a,i)][s]:void 0}function xne(e,t,n){return t[a0(e,n.currentContextId)]}function da(e,t){let[n,r]=Wn(e);return[a0(n,t&&t.currentContextId),r]}function a0(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 s0(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 fv={};ze(fv,{json:()=>wne});var wne=[{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}]}],mv={};ze(mv,{json:()=>_ne});var _ne=[{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}]}],Av={};ze(Av,{json:()=>bne});var bne=[{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"}]}],yv={};ze(yv,{json:()=>vne});var vne=[{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"}]}],gv={};ze(gv,{json:()=>kne});var kne=[{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"}]}],xv={};ze(xv,{json:()=>Ine});var Ine=[{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}]}],wv={};ze(wv,{json:()=>Nne});var Nne=[{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"}]}],_v={};ze(_v,{json:()=>Sne});var Sne=[{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"}]}],bv={};ze(bv,{json:()=>Tne});var Tne=[{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"}]}],vv={};ze(vv,{json:()=>Cne});var Cne=[{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"}]}],kv={};ze(kv,{json:()=>Ene});var Ene=[{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:()=>Rne});var Rne=[{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}]}],Nv={};ze(Nv,{json:()=>Fne});var Fne=[{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}]}],Sv={};ze(Sv,{json:()=>Mne});var Mne=[{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"}]}],Tv={};ze(Tv,{json:()=>$ne});var $ne=[{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}]}],Cv={};ze(Cv,{json:()=>Dne});var Dne=[{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}]}],Ev={};ze(Ev,{json:()=>One});var One=[{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:[]}],Fv=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[fv,mv,Av,yv,gv,xv,wv,kv,vv,_v,Iv,Nv,Sv,Tv,Cv,Ev,bv],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=[],c={},u={};t!=null&&(c=this.mapSignatureEntries(t.inputs),u=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(u).length===0?h.forEach(f=>{let m=i[f];m.children.length===0&&l.push(m)}):Object.keys(u).forEach(f=>{let[m]=da(f),A=i[m];A!=null&&(A.signatureKey=u[f],l.push(A))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=da(f),A=i[m];A&&(A.signatureKey=c[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=pv(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=Rv(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Rv(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((c,u)=>(c[u.name]=this.mapNode(u),u.op==="Const"&&r.push(c[u.name]),c),{}));let s=[],i=[];e.signature.inputArg.forEach(c=>{let[u]=da(c.name),h={name:u,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:e2(c.type),type:"dtype"}},children:[]};h.signatureKey=c.name,s.push(h),a[u]=h}),Object.keys(a).forEach(c=>{let u=a[c];u.inputNames.forEach(h=>{let[d]=da(h);u.inputs.push(a[d]),a[d].children.push(u)})});let o=e.ret;e.signature.outputArg.forEach(c=>{let[u,h]=da(o[c.name]),d=a[u];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 zne(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 Mv(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):zne(e);return t?n:n.toLowerCase()}function Yy(e,t,n,r=!1){let a=e[t];return a!=null?Mv(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 Rv(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 $v(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function r2(e,t,n){let r=e[t];return r&&r.shape?$v(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=>Mv(s,r)):n}function i2(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>$v(a)):n}function o2(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var Pne=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}},Lne=(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[O(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[dd(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[Od(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Wne=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Ot(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[yd(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[_d(k("x",e,t,n))];case"Imag":return[xd(k("x",e,t,n))];case"Neg":return[bt(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[Ed(k("x",e,t,n))];case"Sigmoid":return[Fn(k("x",e,t,n))];case"Sin":return[Rd(k("x",e,t,n))];case"Sign":return[pm(k("x",e,t,n))];case"Sinh":return[Fd(k("x",e,t,n))];case"Softplus":return[dl(k("x",e,t,n))];case"Sqrt":return[Qt(k("x",e,t,n))];case"Square":return[ot(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[Td(k("x",e,t,n))];case"Rsqrt":return[Cd(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 hr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){b.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];b.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Dv(e){return!(typeof e=="number"||e.some(t=>t<0))}function Oc(e,t,n){let r=l2(e,n),a=!Dv(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)}),!Dv(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 Bne=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),jt(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),hr(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,jt(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 hr(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 ${e}`);if(this.size()===0)return yr([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return hr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),at(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,ir(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=[];P(()=>{t=U(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],c=[0,l,0],u=[1,e[o],a];s[o]=U(Re(t,c,u),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}`);hr(t,a.shape,"TensorList shape mismatch: "),jt(a)}),this.idTensor=be(0),this.maxNumElements=r,jt(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.`);hr(e,this.elementShape,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,e);return P(()=>{let a=this.tensors.map(s=>U(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 hr(r.shape,e,"TensorList shape mismatch: "),U(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(hr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");jt(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.`);hr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,t);return U(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.`);hr(this.elementShape,t.shape,"TensorList shape mismatch: "),jt(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}`);hr(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)):P(()=>{let a=e.map(s=>U(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}`);hr(this.elementShape,t,"TensorList shape mismatch: ");let n=Oc(this.elementShape,this.tensors,t);return this.size()===0?yr([],[0].concat(n)):P(()=>{let r=this.tensors.map(a=>U(a,n));return at(r,0)})}};function Vne(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);hr(a,t,"TensorList shape mismatch: ");let s=ir(e);return new zc(s,t,r)}function Une(e,t,n){return new zc([],e,t,n)}function Hne(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=ir(e,0);return t.forEach((o,l)=>{s.setItem(o,i[l])}),s}function jne(e,t,n){let r=0,a=t.map(u=>(r+=u,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=P(()=>{let u=[];e=U(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];u[h]=U(Re(e,p,f),i)}return e.dispose(),u}),c=new zc([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var Gne=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(u=>u.id),l=await i[0].data();i.forEach(u=>{!u.kept&&o.indexOf(u.id)===-1&&u.dispose()});let c=s;for(;l[0];){let u=c;c=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let h=c.map(p=>p.id);u.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(c,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 c}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),c=k("name",e,t,n),u=new Bne(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.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=Hne(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=Une(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=Vne(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=jne(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Ov(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 c=k("strides",e,t,n),u=s0(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:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var qne=(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[md(k("x",e,t,n),k("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=k("strides",e,t,n),a=s0(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:c,leakyreluAlpha:u}=Ov(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:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:c,leakyreluAlpha:u}=Ov(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:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=k("outputShape",e,t,n),a=k("strides",e,t,n),s=s0(e,t,n);return[Ad(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=s0(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}=ux(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],c=s[2];return[Zf(k("x",e,t,n),k("filter",e,t,n),[i,o],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Xne=(e,t,n)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),a=k("dtype",e,t,n),s=k("value",e,t,n);return[Uu(r,s,a)]}case"LinSpace":{let r=k("start",e,t,n),a=k("stop",e,t,n),s=k("num",e,t,n);return[nx(r,a,s)]}case"Multinomial":{let r=k("logits",e,t,n),a=k("numSamples",e,t,n),s=k("seed",e,t,n);return[cx(r,a,s)]}case"OneHot":{let r=k("indices",e,t,n),a=k("depth",e,t,n),s=k("onValue",e,t,n),i=k("offValue",e,t,n);return[nl(r,a,s,i)]}case"Ones":return[Or(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[$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[Sd(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[zd(r,a,s,k("dtype",e,t,n),i)]}case"Zeros":return[Ct(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 Kne=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),c=await Je.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=u2(e,t,n),l=k("padToMaxOutputSize",e,t,n),c=await Je.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[c.selectedIndices,c.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 px(k("x",e,t,n),k("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},Zne=(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=Pd(r);return[a.values,a.indices]}case"UniqueV2":{let r=k("x",e,t,n),a=k("axis",e,t,n),s=Pd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yne=(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 c=k("x",e,t,n);return[pa(c)]}case"IdentityN":return k("x",e,t,n).map(c=>pa(c));case"Snapshot":let a=k("x",e,t,n);return[pa(a)];case"Shape":return[sn(k("x",e,t,n).shape,"int32")];case"ShapeN":return k("x",e,t,n).map(c=>sn(c.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 c=0;c<i.length;c++)console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jne=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=be(0),this.tensorMap=new Map,jt(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(),P(()=>{let r=ir(t),a=n.length,s=r.length;b.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];jt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return P(()=>{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}`)}},Qne=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 Jne(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`)}},ere=(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`)}},tre=(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[ar(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[wd(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[sr(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[ju(k("a",e,t,n))];case"LogicalOr":return[kd(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`)}},nre=(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[rt(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[c,u]=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:c,activation:a,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},rre=(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[vd(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`)}},are=(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[vt(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[pd(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[gd(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[G5(r,a,s)];case"DenseBincount":{let i=k("x",e,t,n),o=k("weights",e,t,n),l=k("size",e,t,n),c=k("binaryOutput",e,t,n);return[Y5(i,o,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sre=(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),[at(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),c=k("newAxisMask",e,t,n),u=k("shrinkAxisMask",e,t,n),h=k("x",e,t,n);return[mm(h,r,a,s,i,o,l,c,u)]}case"Pack":return P(()=>{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 c=b.arraysEqual(l.shape,s);if(!c&&!b.arraysEqual(Pa(l).shape,i))throw new Error("the input tensors shape does not match");return c?l:U(l,s)});return[dn(o,r)]});case"Unpack":{let r=k("axis",e,t,n),a=k("tensor",e,t,n);return ir(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 Pt(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[yx(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[gx(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`)}},ire=(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[Dd(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ore=(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[U(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 zv(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return P(()=>Lne(s,i,o));case"basic_math":return P(()=>Wne(s,i,o));case"control":return Gne(s,i,o);case"convolution":return P(()=>qne(s,i,o));case"creation":return P(()=>Xne(s,i,o));case"dynamic":return Kne(s,i,o);case"evaluation":return P(()=>Zne(s,i,o));case"image":return P(()=>ere(s,i,o));case"graph":return P(()=>Yne(s,i,o));case"logical":return P(()=>tre(s,i,o));case"matrices":return P(()=>nre(s,i,o));case"normalization":return P(()=>rre(s,i,o));case"reduction":return P(()=>are(s,i,o));case"slice_join":return P(()=>sre(s,i,o));case"spectral":return P(()=>ire(s,i,o));case"transformation":return P(()=>ore(s,i,o));case"hash_table":return Qne(s,i,o,r);case"custom":let l=pv(s.op);if(l&&l.customExecutor)return l.customExecutor(new Pne(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 b.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var Pv=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 Wv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Wn(d)[0]),u=[];r!=null&&(u=r.map(d=>Wn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((Lv(d)||lre(d)||ure(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&&c.indexOf(d.name)===-1&&u.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 cre(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Wn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var hre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],dre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],pre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Lv(e){return hre.indexOf(e.op)>=0}function lre(e){return dre.indexOf(e.op)>=0}function ure(e){return pre.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=Wv(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 cre(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(u=>this.graph.nodes[Wn(u)[0]]),a=t.map(u=>Wn(u)[0]),s=a.map(u=>this.graph.nodes[u]);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={},c={};return P(()=>{let u=new Pv(this.weightMap,l,c,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=zv(m,h,u,this._resourceManager);if(b.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,u,d,a,p)}}return this.parent==null&&u.dispose(d),t.map(f=>En(f,h,u))})}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=xne(o.name,n,r);l!=null&&l.forEach(c=>{if(c&&!a.has(c.id)){let u=i[c.id];u===1?(c.dispose(),delete i[c.id]):u!=null&&i[c.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 Pv(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),c=Object.keys(e).map(h=>e[h].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!u.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(u),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:c,dynamicNode:u,syncInputs:h}=Wv(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[w,_]=Wn(g),v=[];v[_]=e[g],p[w]=v});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)}u==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=>!Lv(g)&&!En(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw u!=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: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&k("isConstant",u.node,r,n)&&([h]=da(u.node.name,n)),r[u.node.name]==null){let d=zv(u.node,r,n,this._resourceManager);h||([h]=da(u.node.name,n));let p=n.currentContext;b.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=da(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!En(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!En(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Wn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);b.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&b.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 '${t}' is not found in the graph`)})}},fre=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},mre="?tfjs-format=file",Are="model.json",Bv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new fre}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=bn.browserHTTPRequest(e,this.loadOptions);else{let t=bn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(bn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=bn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new c2(Fv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Fv.Instance.transformGraph(e.modelInitializer);this.initializer=new c2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=bn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Rt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Are}${mre}`);let n=new Bv(e,t);return await n.load(),n}var yre="3.3.0",Vv={};ze(Vv,{CSVDataset:()=>Hv,Dataset:()=>Vl,FileDataSource:()=>jv,TextLineDataset:()=>Uv,URLDataSource:()=>Gv,array:()=>gre,csv:()=>wre,func:()=>_re,generator:()=>bre,microphone:()=>kre,version_data:()=>Ire,webcam:()=>vre,zip:()=>xre});var Nre=qi(xh()),Sre=qi(xh());function Tre(e,t){return i0(e,t)}function i0(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=i0(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 Cre(e,t=Xv){return qv(e,t)}function qv(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(c=>c[i]),l=qv(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 Xv(e){return e===null?null:Ul(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Kv(e,t){let n=new Map;i0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(b.isPromise(a)){let s=await a;n.set(r,s)}}return i0(e,t,n)}function Ul(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ue))}function Rre(e){return e==null||Ere(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ue||b.isTypedArray(e)}function Ere(e){return e===null||typeof e!="object"&&typeof e!="function"}function Mre(e){return Tre(e,Fre)}function Fre(e){return e instanceof Ue?{value:e.clone(),recurse:!1}:Ul(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Zv=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 Zv{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 Yv(e){return new $re(e)}function d2(e){return new Dre(e)}function Ore(e,t){return new Jv(e,t)}function Pre(e,t=Za.FAIL){return new zre(e,t)}var Kt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new jre(this,e)}filter(e){return new Ure(this,e)}map(e){return new Hre(this,e)}mapAsync(e){return new Qv(this,e)}serialMapAsync(e){return new Qv(this,e).serial()}flatmap(e){return new Gre(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Vre(this,e,t)}columnMajorBatch(e,t=!0,n=Xv){return this.rowMajorBatch(e,t).map(r=>Cre(r,n))}concatenate(e,t){return new Jv(Yv([this,e]),t)}take(e){return e<0||e==null?this:new Bre(this,e)}skip(e){return e<0||e==null?this:new Wre(this,e)}prefetch(e){return new e6(this,e)}shuffle(e,t){return new qre(this,e,t)}serial(){return new Lre(this)}},$re=class extends Kt{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:Mre(e),done:!1}}},Dre=class extends Kt{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: ${e.message}`,e}}},Lre=class extends Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Wre=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ee(e.value)}return this.upstream.next()}},Bre=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Vre=class extends Kt{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Ure=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},Hre=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=mr.getTensorsInContainer(e.value),n=this.transform(e.value),r=mr.getTensorsInContainer(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},jre=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Qv=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=mr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=mr.getTensorsInContainer(n);for(let a of t)mr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},p2=class extends Kt{constructor(){super();this.outputQueue=new h2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Gre=class extends p2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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}},Jv=class extends Kt{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 zre=class extends Kt{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 Kt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await Kv(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 ${this.count}.`);case Za.SHORTEST:return{value:null,done:!0};case Za.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},e6=class extends Kt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Zv(t)}summary(){return`${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()}},qre=class extends e6{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Sre.alea(n||b.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;b.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Bn(async()=>(await n.iterator()).columnMajorBatch(e,t,Xre),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Bn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Bn(async()=>(await t.iterator()).filter(r=>P(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Bn(async()=>(await t.iterator()).map(n=>P(()=>e(n))),this.size)}mapAsync(e){let t=this;return Bn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Bn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Bn(async()=>{let r=d2(async()=>({value:await t.iterator(),done:!1}));return Ore(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Bn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Nre.alea(t||b.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 gre(e){return Bn(async()=>Yv(e),e.length)}function xre(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 Kv(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 Pre(n,Za.SHORTEST)},t)}function Xre(e){if(e===null)return null;let t=e[0];return Rre(t)?{value:Kre(e),recurse:!1}:{value:null,recurse:!0}}function Kre(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 Uv=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))}},o0='"',Pc=Symbol("out"),t6=Symbol("field"),l0=Symbol("quote"),f2=Symbol("quoteafterquote"),n6=Symbol("quoteinquote"),Hv=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 Uv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(b.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&&b.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(b.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 c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}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 o0:r=i+1,s=l0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Pc;break;default:s=t6,r=i;break}break;case t6:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Pc,r=i+1;break;default:}break;case l0:switch(e.charAt(i)){case o0: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 o0:s=l0;break;default:s=n6;break}break;case n6:switch(e.charAt(i)){case o0:s=l0;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}},r6=class extends Kt{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 r6(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(b.sizeFromShape(t));return n.set(e,n.length-e.length),yr(n,t)}},a6=class extends Kt{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=sn([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=In([s,a,o,i],[1,4])}else this.cropBox=In([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 a6(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&b.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return P(()=>{let t=hn(me(e,"float32"),0),n;n=Je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return U(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},s6=class{},i6=class extends Kt{split(e){return new Zre(this,e)}},Zre=class extends i6{constructor(e,t){super();this.upstream=e,this.impl=new Yre(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Yre=class extends p2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Qre=class extends Kt{decodeUTF8(){return new Jre(this)}},Jre=class extends i6{constructor(e){super();this.upstream=e,this.impl=new eae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},eae=class extends p2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=L8();this.decoder=new t("utf8")}}summary(){return`${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}},o6=class extends Qre{constructor(e,t={}){super();this.file=e,this.options=t,b.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 nae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=tae(e));let a=await b.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new o6(s,t)}else throw new Error(a.statusText)}var tae=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 l6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var jv=class extends s6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(l6(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new o6(this.input,this.options)}},Gv=class extends s6{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return l6(this.url)?new jv(this.url,this.fileOptions).iterator():nae(this.url,this.fileOptions)}};function wre(e,t={}){return new Hv(new Gv(e),t)}function _re(e){let t=d2(e);return Bn(async()=>t)}function bre(e){return Bn(async()=>{let t=await e();return d2(()=>t.next())})}async function vre(e,t){return a6.create(e,t)}async function kre(e){return r6.create(e)}var Ire="3.3.0",rae={tfjs:W8,"tfjs-core":B8,"tfjs-data":V8,"tfjs-layers":U8,"tfjs-converter":H8,"tfjs-backend-cpu":lw,"tfjs-backend-webgl":E_,"tfjs-backend-wasm":w3};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 u6(){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{ip(2,Vn.gl)}catch(e){Ce("error: cannot set WebGL2 context:",e);return}try{let e=new cp(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 c6=6;function aae(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 c=a*(l+.5);for(let u=0;u<i;u++){let h=a*(u+.5);for(let d=0;d<o;d++)n.push([h,c])}}}return n}var sae=e=>({startEndTensor:e,startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});function iae(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),c=ye(o,l),u=se(o,l),h=O(c,n),d=O(u,n);return ll([h,d],1)}var h6=class{constructor(t,n){this.model=t,this.anchorsData=aae(t.inputs[0].shape[1]),this.anchors=In(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]=P(()=>{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,N)=>x.size-N.size),w=at([g[0],g[2]],2),_=at([g[1],g[3]],2);f=at([_,w],1).squeeze(0)}else f=p.squeeze();let m=iae(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}),c=a.dataSync(),u=[];for(let h=0;h<l.length;h++){let d=i[h],p=c[d];if(p>this.config.face.detector.minConfidence){let f=sae(l[h]),m=this.anchorsData[d],A=P(()=>Re(n,[d,c6-1],[1,-1]).squeeze().reshape([c6,-1]));u.push({box:f,landmarks:A,anchor:m,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:u,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function d6(e){let t=await Rt(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new h6(t,e);return e.debug&&Ce(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function p6(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 u0(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 c0(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 h0=[[1,0,0],[0,1,0],[0,0,1]];function oae(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 oae(n)}function f6(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 lae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function m6(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],lae(t,s)))}return n}function d0(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=f6(t[0],t[1]),i=m6(s,a),o=f6(-t[0],-t[1]);return m6(i,o)}function A6(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 y6(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 uae=[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],cae=[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],hae=[33,133,362,263,1,78,308],Dce=uae.map(e=>y2[e]),Oce=cae.map(e=>y2[e]),zce=hae.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]]},p0={count:468,mouth:13,symmetryLine:[13,Gr.midwayBetweenEyes[0]]},g6={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 f0(e,t,n,r){for(let a=0;a<A2.length;a++){let{key:s,indices:i}=A2[a],o=Gr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var w2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Lc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?d0(r,[0,0]):h0,l=r!==0?i.map(h=>[...y6(h,o),h[2]]):i,c=r!==0?A6(a):h0,u=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(u,c[0]),h[1]+Ya(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[Gl.leftBounds[0]][2],r=t[Gl.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=c0(u0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Lc(i),l=Je.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&fr.flags.IS_BROWSER&&(l=Je.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<ql.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(ql.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Gr[`${r}EyeUpper0`][ql.upperCenter]][2],s=t[Gr[`${r}EyeLower0`][ql.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}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=p6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=u0(o),c=c0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=P(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&fr.flags.IS_BROWSER){let[v,x]=i.landmarks.length>=p0.count?p0.symmetryLine:g6.symmetryLine;u=m2(i.landmarks[v],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),E=[N[0]/t.shape[2],N[1]/t.shape[1]],F=Je.rotateWithOffset(t,u,0,E);h=d0(-u,N),n.face.mesh.enabled?c=jl({startPoint:i.startPoint,endPoint:i.endPoint},F,[this.meshSize,this.meshSize]).div(255):c=jl({startPoint:i.startPoint,endPoint:i.endPoint},F,[this.boxSize,this.boxSize]).div(255)}else{h=h0;let v=t.clone();n.face.mesh.enabled?c=jl({startPoint:i.startPoint,endPoint:i.endPoint},v,[this.meshSize,this.meshSize]).div(255):c=jl({startPoint:i.startPoint,endPoint:i.endPoint},v,[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:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=U(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:v,boxSize:x,crop:N}=this.getEyeBox(A,c,Gl.leftBounds[0],Gl.leftBounds[1],!0),{box:E,boxSize:F,crop:M}=this.getEyeBox(A,c,Gl.rightBounds[0],Gl.rightBounds[1]),V=this.irisModel.predict(at([N,M])).dataSync(),B=V.slice(0,ql.numCoordinates*3),{rawCoords:H,iris:j}=this.getEyeCoords(B,v,x,!0),X=V.slice(ql.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,E,F),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(f0(A,H,"left",null),f0(A,G,"right",null)):Y<1?f0(A,H,"left",["EyeUpper0","EyeLower0"]):f0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,j,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=u0(this.calculateLandmarksBoundingBox(y),1.5);let g=In(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&n.face.detector.return&&fr.flags.IS_BROWSER){let[v,x]=i.landmarks.length>=p0.count?p0.symmetryLine:g6.symmetryLine;u=m2(i.landmarks[v],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),E=[N[0]/t.shape[2],N[1]/t.shape[1]],F=Je.rotateWithOffset(t,u,0,E);h=d0(-u,N),c=jl({startPoint:i.startPoint,endPoint:i.endPoint},F,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},_=c0(i);return this.storedBoxes[o]={..._,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));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 Ag=Ah(w6());var v2={};er(v2,{load:()=>k2,predict:()=>I2});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,m0={age:0},A0=Number.MAX_SAFE_INTEGER;async function k2(e){return Ja||(Ja=await Rt(e.face.age.modelPath),e.debug&&Ce(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),Ja}async function I2(e,t){return Ja?A0<t.face.age.skipFrames&&t.videoOptimized&&m0.age&&m0.age>0?(A0++,m0):(t.videoOptimized?A0=0:A0=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=O(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(),m0=i,n(i)})):null}var N2={};er(N2,{load:()=>E2,predict:()=>R2});var fa,S2={gender:""},y0=Number.MAX_SAFE_INTEGER,T2=!1,C2=[.2989,.587,.114];async function E2(e){return fa||(fa=await Rt(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?y0<t.face.gender.skipFrames&&t.videoOptimized&&S2.gender!==""?(y0++,S2):(t.videoOptimized?y0=0:y0=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=P(()=>{let[o,l,c]=Pt(r,3,3),u=O(o,C2[0]),h=O(l,C2[1]),d=O(c,C2[2]);return Ma([u,h,d]).sub(.5).mul(2)}):a=O(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={};er(F2,{load:()=>D2,predict:()=>O2});var pae=["angry","disgust","fear","happy","sad","surprise","neutral"],Qa,M2=[],g0=Number.MAX_SAFE_INTEGER,$2=[.2989,.587,.114];async function D2(e){return Qa||(Qa=await Rt(e.face.emotion.modelPath),e.debug&&Ce(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),Qa}async function O2(e,t){return Qa?g0<t.face.emotion.skipFrames&&t.videoOptimized&&M2.length>0?(g0++,M2):(t.videoOptimized?g0=0:g0=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]=Pt(r,3,3);r.dispose();let o=O(a,$2[0]),l=O(s,$2[1]),c=O(i,$2[2]);a.dispose(),s.dispose(),i.dispose();let u=Ma([o,l,c]);o.dispose(),l.dispose(),c.dispose();let h=P(()=>u.sub(.5).mul(2));u.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:pae[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 Rt(e.face.embedding.modelPath),e.debug&&Ce(`load model: ${e.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),Xr}function _6(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 P2(e){return P(()=>{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]=Pt(a,3,3),c=O(i,s[0]),u=O(o,s[1]),h=O(l,s[2]),d=Ma([c,u,h]),p=dn([d,d,d],3).squeeze(4),f=p.sub(p.min());return f.div(f.max())})}async function L2(e,t){return Xr?new Promise(async n=>{let r=[];if(t.face.embedding.enabled){let a=P2(e);if(!t.profile)r=P(()=>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)}a.dispose()}n(r)}):null}var Y2={};er(Y2,{PoseNet:()=>J2,load:()=>Q2});function fae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var W2=class{constructor(t){this.model=t}predict(t){return P(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=fae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function B2(e){return Math.floor(e/2)}var V2=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(B2(t),t);)this.exchange(t,B2(t)),t=B2(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 mae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++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 b6(e,t,n){let[r,a,s]=n.shape,i=new V2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||mae(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var ma=Ah(x0());var v6=Ah(x0());function j2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+v6.NUM_KEYPOINTS)}}function w0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=j2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function G2(e,t,n){return e<t?t:e>n?n:e}function k6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function q2(e,t){return{x:e.x+t.x,y:e.y+t.y}}var _0=Ah(x0());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 bae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+_0.NUM_KEYPOINTS)}}function vae(e,t){let n=[];for(let r=0;r<_0.NUM_KEYPOINTS;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=bae(a,s,r,t);n.push(o),n.push(i)}return In(n,[_0.NUM_KEYPOINTS,2])}function N6(e,t,n){return P(()=>e.toTensor().mul(be(t,"int32")).toFloat().add(vae(e,n)))}function kae(e,t){return P(()=>{let n=e.div(be(t,"int32"));return e.sub(n.mul(be(t,"int32")))})}function S6(e){let[t,n,r]=e.shape;return P(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(be(n,"int32")).expandDims(1),o=kae(s,n).expandDims(1);return at([i,o],1)})}var T6=ma.poseChain.map(([e,t])=>[ma.partIds[e],ma.partIds[t]]),X2=T6.map(([,e])=>e),C6=T6.map(([e])=>e),Iae=16;function Nae(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 K2(e,t,n,r){return{y:G2(Math.round(e.y/t),0,n-1),x:G2(Math.round(e.x/t),0,r-1)}}function E6(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=K2(t.position,s,l,c),h=Nae(e,u,i),p=q2(t.position,h);for(let A=0;A<o;A++){let y=K2(p,s,l,c),g=j2(y.y,y.x,n,a);p=q2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=K2(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:ma.partNames[n],score:m}}function R6(e,t,n,r,a,s){let i=t.shape[2],o=X2.length,l=new Array(i),{part:c,score:u}=e,h=w0(c,r,n);l[c.id]={score:u,part:ma.partNames[c.id],position:h};for(let d=o-1;d>=0;--d){let p=X2[d],f=C6[d];l[p]&&!l[f]&&(l[f]=E6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=C6[d],f=X2[d];l[p]&&!l[f]&&(l[f]=E6(d,l[p],f,t,n,r,a))}return l}async function F6(e,t,n){let r=0,a=S6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=N6(l,Iae,o),u=await c.buffer(),d=Array.from(I6(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:ma.partNames[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var Sae=1,M6=16;function $6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return k6(r,n,i.y,i.x)<=t})}function Tae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>($6(e,t,s,o)||(a+=i),a),0)/n.length}function D6(e,t,n,r,a,s,i){let o=[],l=b6(i,Sae,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=w0(u.part,M6,t);if($6(o,c,h,u.part.id))continue;let d=R6(u,e,t,M6,n,r),p=Tae(o,c,d);p>i&&o.push({keypoints:d,score:p})}return o}async function O6(e){return Promise.all(e.map(t=>t.buffer()))}function Cae(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 z6(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function Z2(e,[t,n],[r,a]){return e.map(i=>Cae(i,t/r,n/a))}async function Eae(e,t,n,r){return new Promise(async a=>{let s=await O6([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],c=s[3],u=await D6(i,o,l,c,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=Z2(u,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function Rae(e,t,n,r){return new Promise(async a=>{let s=await F6(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=Z2([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var J2=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=z6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Rae(t,a,n,this.inputSize):await Eae(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 Q2(e){let t=await Rt(e.body.modelPath),n=new W2(t);return e.debug&&Ce(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new J2(n)}var ag={};er(ag,{HandPose:()=>ig,load:()=>og});function b0(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 P6(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 L6(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 v0(e,t=1.5){let n=Wc(e),r=b0(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 k0(e){let t=Wc(e),n=b0(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 eg=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=In(this.anchors),this.inputSize=n,this.inputSizeTensor=sn([n,n]),this.doubleInputSizeTensor=sn([n*2,n*2])}normalizeBoxes(t){return P(()=>{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=O(ye(a,s),this.inputSizeTensor),o=O(se(a,s),this.inputSizeTensor);return ll([i,o],1)})}normalizeLandmarks(t,n){return P(()=>{let r=se(ge(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return O(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=P(()=>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 c=await Je.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),u=c.arraySync();s.dispose(),c.dispose();let h=[];for(let d of u)if(i[d]>=n.hand.minConfidence){let p=Re(l,[d,0],[1,-1]),f=Re(a,[d,5],[1,14]),m=P(()=>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=P(()=>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 c=l.box.dataSync(),u=c.slice(0,2),h=c.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(L6({startPoint:u,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function Fae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function W6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Fae(n)}var B6=(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 Mae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function V6(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],Mae(t,s)))}return n}function tg(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=B6(t[0],t[1]),i=V6(s,a),o=B6(-t[0],-t[1]);return V6(i,o)}function U6(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 ng(e,t){return[es(e,t[0]),es(e,t[1])]}var $ae=5,H6=1.65,j6=[0,5,9,13,17,1,2],Dae=0,Oae=2,rg=class{constructor(t,n,r){this.handDetector=t,this.landmarkDetector=n,this.inputSize=r,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(t,n){let r=t.map(s=>ng([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return v0(k0(a),$ae)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=v0(k0(n),H6);r.palmLandmarks=[];for(let a=0;a<j6.length;a++)r.palmLandmarks.push(t[j6[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=b0(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=tg(r,[0,0]),c=o.map(p=>[...ng(p,l),p[2]]),u=U6(a),h=[...Wc(n),1],d=[es(h,u[0]),es(h,u[1])];return c.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?W6(o.palmLandmarks[Dae],o.palmLandmarks[Oae]):0,c=Wc(o),u=[c[0]/t.shape[2],c[1]/t.shape[1]],h=n.hand.rotation?Je.rotateWithOffset(t,l,0,u):t.clone(),d=tg(-l,c),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,f=P6(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 w=U(y,[-1,3]),_=w.arraySync();y.dispose(),w.dispose();let v=this.transformRawCoords(_,p,l,d),x=this.getBoxForHandLandmarks(v);this.storedBoxes[i]=x;let N={landmarks:v,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=v0(k0(o),H6),c={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(c)}}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 G6=[{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 sg={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]},ig=class{constructor(t){this.handPipeline=t}static getAnnotations(){return sg}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(sg))i[l]=sg[l].map(c=>s.landmarks[c]);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 og(e){let[t,n]=await Promise.all([e.hand.enabled?Rt(e.hand.detector.modelPath,{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Rt(e.hand.skeleton.modelPath,{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),r=new eg(t,t==null?void 0:t.inputs[0].shape[2],G6),a=new rg(r,n,n==null?void 0:n.inputs[0].shape[2]),s=new ig(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 lg={};er(lg,{load:()=>ug,predict:()=>cg});var q6=["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"],X6=["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 dr;async function ug(e){return dr||(dr=await Rt(e.body.modelPath),dr.width=parseInt(dr.signature.inputs["input_1:0"].tensorShape.dim[2].size),dr.height=parseInt(dr.signature.inputs["input_1:0"].tensorShape.dim[1].size),e.debug&&Ce(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`)),dr}async function cg(e,t){if(!dr||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Je.resizeBilinear(e,[dr.width,dr.height],!1),a=ge(r,[255]);r.dispose();let s;if(t.profile){let c=await gr(()=>dr.predict(a));s=c.result.find(u=>u.size===195||u.size===155).dataSync(),c.result.forEach(u=>u.dispose()),qr("blazepose",c)}else{let c=await dr.predict(a);s=c.find(u=>u.size===195||u.size===155).dataSync(),c.forEach(u=>u.dispose())}a.dispose();let i=[],o=s.length===195?q6:X6,l=5;for(let c=0;c<s.length/l;c++)i.push({id:c,part:o[c],position:{x:Math.trunc(n.width*s[l*c+0]/255),y:Math.trunc(n.height*s[l*c+1]/255),z:Math.trunc(s[l*c+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*c+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*c+4]))))/100});return[{keypoints:i}]}var K6=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},Z6=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 ${o<0?"up":"down"}`})}return t},Y6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o);Math.abs(s-l)/Math.max(s,l)<.25&&t.push({iris:n,gesture:"looking at camera"})}return t},J6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t};function zae(e,t,n){let r=function(o,l,c){let u=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");o.replace(u,(h,d)=>(c[d]=0,h))},a=function(o,l){let c=e.createShader(l);if(e.shaderSource(c,o),e.compileShader(c),!e.getShaderParameter(c,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(c));return c};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 Q6(e){e||(e={});let t=0,n=null,r=!1,a=-1,s=[null,null],i=[],o=-1,l=-1,c=null,u=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(v){let x=Array.prototype.slice.call(arguments,1),N=h[v];i.push({func:N,args:x})},this.reset=function(){i=[]};let A=function(v,x){if(!(v===o&&x===l)){if(d.width=v,o=v,d.height=x,l=x,!c){let N=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);c=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,c),m.bufferData(m.ARRAY_BUFFER,N,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,o,l),s=[null,null]}},y=function(v,x){let N=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,N);let E=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,E);let F=m.createTexture();return m.bindTexture(m.TEXTURE_2D,F),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,v,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,F,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:N,texture:F}},g=function(v){return s[v]=s[v]||y(o,l),s[v]},w=function(v=null){var F,M;let x=null,N=null,E=!1;t===0?x=n:x=(F=g(a))==null?void 0:F.texture,t++,r&&!(v&f.INTERMEDIATE)?(N=null,E=t%2==0):(a=(a+1)%2,N=(M=g(a))==null?void 0:M.fbo),m.bindTexture(m.TEXTURE_2D,x),m.bindFramebuffer(m.FRAMEBUFFER,N),m.uniform1f(u.uniform.flipY,E?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(v){if(A(v.width,v.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,v),i.length===0)return w(),d;for(let x=0;x<i.length;x++){r=x===i.length-1;let N=i[x];N.func.apply(this,N.args||[])}return d};let _=function(v){if(p[v])return u=p[v],m.useProgram(u.id),u;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(`
|
|
`),u=new zae(m,x.VERTEX_IDENTITY,v);let N=Float32Array.BYTES_PER_ELEMENT,E=4*N;return m.enableVertexAttribArray(u.attribute.pos),m.vertexAttribPointer(u.attribute.pos,2,m.FLOAT,!1,E,0*N),m.enableVertexAttribArray(u.attribute.uv),m.vertexAttribPointer(u.attribute.uv,2,m.FLOAT,!1,E,2*N),p[v]=u,u};h.colorMatrix=function(v){let x=new Float32Array(v);x[4]/=255,x[9]/=255,x[14]/=255,x[19]/=255;let N=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,E=_(N);m.uniform1fv(E.uniform.m,x),w()},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(v){let x=(v||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(v){let x=(v||0)*2/3+1,N=(x-1)*-.5;h.colorMatrix([x,N,N,0,0,N,x,N,0,0,N,N,x,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(v){let x=(v||0)+1,N=-128*(x-1);h.colorMatrix([x,0,0,0,N,0,x,0,0,N,0,0,x,0,N,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(v){v=(v||0)/180*Math.PI;let x=Math.cos(v),N=Math.sin(v),E=.213,F=.715,M=.072;h.colorMatrix([E+x*(1-E)+N*-E,F+x*-F+N*-F,M+x*-M+N*(1-M),0,0,E+x*-E+N*.143,F+x*(1-F)+N*.14,M+x*-M+N*-.283,0,0,E+x*-E+N*-(1-E),F+x*-F+N*F,M+x*(1-M)+N*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(v){let x=new Float32Array(v),N=1/o,E=1/l,F=_(h.convolution.SHADER);m.uniform1fv(F.uniform.m,x),m.uniform2f(F.uniform.px,N,E),w()},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(v){let x=v||1;h.convolution.call(this,[0,-1*x,0,-1*x,1+4*x,-1*x,0,-1*x,0])},h.emboss=function(v){let x=v||1;h.convolution.call(this,[-2*x,-1*x,0,-1*x,1,1*x,0,1*x,2*x])},h.blur=function(v){let x=v/7/o,N=v/7/l,E=_(h.blur.SHADER);m.uniform2f(E.uniform.px,0,N),w(f.INTERMEDIATE),m.uniform2f(E.uniform.px,x,0),w()},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(v){let x=v/o,N=v/l,E=_(h.pixelate.SHADER);m.uniform2f(E.uniform.size,x,N),w()},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 I0=2048,Tt=null,nn=null,Ft=null;function hg(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>I0&&(i=I0,o=i*s/a),o>I0&&(o=I0,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};(!Tt||Tt.width!==i||Tt.height!==o)&&(Tt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Tt.width!==i&&(Tt.width=i),Tt.height!==o&&(Tt.height=o));let l=Tt.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Tt.width,Tt.height),t.filter.enabled){if((!Ft||!nn||Tt.width!==nn.width||Tt.height!==nn.height)&&(nn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Tt.width,Tt.height):document.createElement("canvas"),nn.width!==Tt.width&&(nn.width=Tt.width),nn.height!==Tt.height&&(nn.height=Tt.height),Ft=fr.flags.IS_BROWSER?new Q6({canvas:nn}):null),!Ft)return{tensor:null,canvas:Tt};Ft.reset(),Ft.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Ft.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ft.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ft.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Ft.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Ft.addFilter("hue",t.filter.hue),t.filter.negative&&Ft.addFilter("negative"),t.filter.sepia&&Ft.addFilter("sepia"),t.filter.vintage&&Ft.addFilter("brownie"),t.filter.sepia&&Ft.addFilter("sepia"),t.filter.kodachrome&&Ft.addFilter("kodachrome"),t.filter.technicolor&&Ft.addFilter("technicolor"),t.filter.polaroid&&Ft.addFilter("polaroid"),t.filter.pixelate!==0&&Ft.addFilter("pixelate",t.filter.pixelate),Ft.apply(Tt)}else nn=Tt,Ft&&(Ft=null);let c;if(nn.data){let h=[nn.height,nn.width,3];c=ud(nn.data,h,"int32")}else if(t.backend==="webgl"||nn instanceof ImageData)c=rl.fromPixels(nn);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(nn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=rl.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?nn:null;return{tensor:n,canvas:r}}var ft={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=`
|
|
/9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
|
|
AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
|
|
bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
|
|
IxwWFiAsICMmJykqKRkfLTAtKDAlKCko/9sAQwEHBwcKCAoTCgoTKBoWGigoKCgoKCgoKCgoKCgo
|
|
KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgBAAEAAwEhAAIRAQMRAf/E
|
|
AB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAE
|
|
EQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZH
|
|
SElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1
|
|
tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEB
|
|
AQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXET
|
|
IjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFla
|
|
Y2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG
|
|
x8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A+qaKACigApGOKAML
|
|
Xp8xlF5A7V4X8RtYs7PzfNImnx8sa8Kp9z3q2tEgp6angWs62ZZ5CTGoJ6DArGNz5p+UrID6EUrF
|
|
PUlW1EuN0XNW7PQ2L5j3JnoKXN0KijqNP0eYoqXBdgPuuo+ZPeupisWn2Jd4+0r924XgsQOCff3/
|
|
AJ1FzRKxDqGii6m3siiQ8F1XGfXI6YNWLfRbiRQMkcZI9fpTDluT2/h6Qy8gDPbtmtG38JeY480Z
|
|
5zSLUTZg8M28YwYxjAArXtdPt402qgHbpSaLWhma3o0Uqk7Nx9DWLaaVblgPs6qRyds2M/gRSQp9
|
|
zZOni2iWS2hlQ+kjYz9OMGrdjq89vIPPVhj+8M/lQyDq9P1WOYBlMZz1AOD+VdDaTiReOKulK0jO
|
|
tHmi0WDTlr0TyxRVhT8tJjIX+9SUxHXUV553BRQAVBcPhSBTSuxPY86+IGti0s5I7dsORy9fM3i6
|
|
8e8mfDO5P90ZrWWiJicNPpZZtxV/xrW0jQt4DOv6Vk2dEEdTY6BHuB25rpbPSo0QARjP0qTRI17W
|
|
wA/hFaMWmoQMgflQXYsDS142rU9tpqqenfNA7GgtihxkdKuRW6qMY/GkDZY8sY4Ap4hXbyB+VArk
|
|
EtuH4wPyrk/EGkOm+a3jw3suRQLc5i38SX9hJ9nnY+XnBUdPyNdFY6pa3KkkAE9l6f8AfJ/pSJT6
|
|
GhDmI+Zb4ZRycdv6ium0nUhKFydrelTsNnS2829RnrVgV6NKXNG55lWPLIM81Op+WrZkRMfmNNzT
|
|
A7GivPO4KKAEY4XNYWt3vkwPg4OK0giJdjw/xrqhm87Zs8tc7pX5A+leSajf6aHYJ50kn4AZpTep
|
|
rBWRm2Vobm4BXfyehPFdnpmnBFUY5rI2SN63tlToK0YI+KZpFF+3QdavwoKTLtoW0Toaswpk5pCb
|
|
LCxipAhoIuP2dKevHXoaYDylRyxhlwRQI4nxVoCXWZI1GfpXGtbSWjYPGP73+NIGupt6TqMsLruZ
|
|
ih4xnP5V09mQ+JLd8gn0xSYJnVaVdkook69K34zuUGunDS3Rx4qOzHVIp4rrOMY3NJQI7GivPO8K
|
|
KAILt9kZrz3xlebYiu8KCCWb0XvW0NFch6ysfO3jLVjfXLIn+pQkKorl7WxNxIPl71g2dUUdpo+l
|
|
pBGvHPet23iC8ihFosrxirkHQUFo0IF4FXI1O726CpKLacCrMJoJLYHAPpTwucHpSRJJ5e4AZI9x
|
|
UqpxzVpCuOC8cUpQUMRnXttuB4rjNdsYyeVwfXpmpGmcvcQyafMCFJjPY10eg34BUg4DcZP8jUO4
|
|
HaRq3lLNF+IHet7R7jz7c56rwa2wz9+xhiVeFy/T1PFegeaNPWigDsc0ZrzzvDNIaAM7VpNqdegr
|
|
xL4l6kywyRhseZ19lrdfAZL4jxYg3Fw20d63tJsdrDI5rm3Z3R0R0Mce1eKnQYAplIkWrMJ45oZS
|
|
NO3PHbNXIyfpSGWowSOasxLUiZdjFSqtNEMkUemKlAGKsRJjAppFAiORMjmsTVrNZEO4cfSoZSOD
|
|
1eJ7WXBUzQZ+7nkfSo7e2Ei+ZaMzxntjBX2NSU1Y6/wxqojiEFzkA8KTXYaUoWRyv3W5rSjpNHPX
|
|
+BmpSg8V6J5gUUAdhRXnneFFAGHrTfu5PpXzj8S70/aZtxzztXFbv4DKHxHI+H4GZiz9zxXXW8G3
|
|
GBXMjvLRXAx0oPGPSmMVeOnWrMTYpFI0bcg1fh54xmgovRcD3qxETSIZcRvzp+/BpEkqsBUqsM9K
|
|
q4Em4Gkxk0yRGXrVW6i8yFhkg+tJjRxGsWrxllkUMh9eK5uMz6bcebbnfG33kPcVkay2OntPKuo0
|
|
nhXI67c8qa7Lw3c+adjcEDGK1paSRhVV4s6A0or0jyRRQ1AHX0V553hRQBz+vNtt5z3xXzX8Qbdm
|
|
uic5YnOMdK3l8JnTXvlbwpYl+WySOgrp5YfLOOB9O1c62O7qQkc+9RsKChFPWp4DluOlSykaNruH
|
|
ArUgHShFNF2NT1qxGO3NBmyxGcE1N2560CFzjrUysO9JAPDDjFOVuKoQuSRTWouBkazbCa3cd8cV
|
|
wF7IISQccHBzUSWpV9C3o1x5b5GAjdQD1rs9DjC3kckbEhqKfxIzn8LOupRXqnkPccBSkUAzraK8
|
|
87wooA5rxMSI3HqK8B8bQl9Q8sffY5b/AAraXwkUviNrw9pH2W1ViMMRTdRjw4HpWNtDti9TPc4P
|
|
FQs2M5qdyyMHLcfjV63HTAoBGtap0wK0YxigpsuRDtVhVYd6GQydVwwIqdRnqKCR23I5pCMUW6gD
|
|
YNKuetAEise9KTxQBWuFyhrznxNZkXjFeN3I+tTIZg2OqmzmxNF0PO3vXp/g2+hukVl4zyPanTXv
|
|
JmVR+60dpThXpnlPceopWFAbnV0V553hSGgRynjC5FujOey14Ssp1HxNmTnc+a3kvcIpv37HoEYQ
|
|
QmMdVHSsnVbYJF5jVk0dsNzlruVIsl2wKxbjWrVHILjg1CRbZJb+ILHPzyhfStODWLQgFJFYd+el
|
|
UJM27HUIXxhga1Y5lLVLKLkMnoauxnPPrSEx7ShF+Y/n2qrc6xBbhizDAqkK1zJuvG9nbg8ZA681
|
|
ly/Ei052RO3uKAsZlx8QGd8xxvt9Aa1NH8dK7AXMcip64zigdkdrZX8F7EJLdwwNXMkrz1qRMRly
|
|
CK4TxmpidWI49felPYSOMmi80NIoOV6qRzXYeA5SskYPfirpfEjGr8LPWVHyD6U4CvQPL3ZItOYc
|
|
UDOoNFeed4Uhpks4H4iE/Z5MeleMeGULeLgjds10S+BGdL+Jc9OSBU2Huc5Nc74yvUtrcDBrJnZF
|
|
63PJdXvLy/lKWw46bvQVz82jXhkLO5Y+9ZlsYthcRnbIjY9R3q3awTRkEM3WmJI6C0ea3dGRsr1x
|
|
XY6TqW9FLHnjrUs0izpLK5DDjofSta3ckH09KRUkZuuTvFGdvPauE1Y3U6Mqbssf/rUxHPTaJPK2
|
|
ZmJPbBqzY6DCZh5xJC9s9aBJHU6dpemJjfEmfetJtI0+VPkUr/unFOxdiextHs33W07YHQHk11mk
|
|
Xb3KbZ1xIvcd6LEyWho4Nct41sTPYb16ipexCPPZN+wYGCvH1rrPAEJmvkPoc1VL4kZVvgZ6yFwK
|
|
cBXoHkkqinFaVyzo80GuE7WJRQSziPiGdthK5HQV4x4J/wBI8WPIewNdEvgRNL42emO/yj1UHNef
|
|
eNpRczbC+I17DvWT2OqJxc0sMK4TCisy41q0hfEkqj8aixdwTXNOlwvmqD9anS9tXH7uVG+hosO4
|
|
/wC0oOhrR0+6G4YNIEzsNEuCxAPNdjZruA4xxUmjINSjURksOlcbqFykbnjFA1sYGoassaknCqO5
|
|
rl7rxhGm7yBnBxuJq0rkSlYpw+NLlsfd5P8AerVsvHEqSBHwPVgcgVpyMyVXU3rXxcHYETAk+hru
|
|
/DWti6ZSTyOKzZqndHaxvvUGq2rQ+dYyqR24qWI8dvbr7LqDxyDAzXpvw6FvIxePGSM06Xxoyr/A
|
|
zviKFHNegeX1J41zUhXioGbuaSuM6wpCaBHG/EcA6HN/exxXjXw2jL67cv8A3Qa6H8CFR+NnoWpO
|
|
I4XI44rxLxrqjQzSEsQM1gdSPM9U1uR1YbmWIdXHf2rmpIb67YS28UrRlsLI3c/jW0VZGUpO5pW1
|
|
jfLNOjahawzwReYI5cjzMkDavHJ5/SrVv9uhtPtVxCPLBwzxnlT9KGghLU3tKvvPjHzbl7EGuisJ
|
|
GRxWLOg7nRXJEbDjmvSNK+aFSfSoZr0KutRkphc4NcRrdkVjL9aVio7Hk3iqS8ubhrWzUlsZY9kG
|
|
cZNc5D4aee5MclzJIFTzHAO0MfatqSOWu7bFS1srDUZEis0vIZoUxPvfcC+4/dx2xjr712XiTwXb
|
|
WmlQ6hol3cRhoFd4rlg3zY5wR0GelavQwjq7GD4etdVvSnk2wAB+9v8A8mvcfA2kXiRo0/UdcDis
|
|
ZnTTulqeoWqbUAJqWUb42X1FZlnjfjSwlGrr5S/eNdD4RkvLAAQ4yRyaUZcruVKl7TQ9I0G+mnzH
|
|
ckFwM8VuIK7ac3KF2eXiKapz5UWYxipNtMyNejNch0jSar3cjR27uoyQCRVRWom9DxTx54gu5fMi
|
|
lbKdMVjfCZPNlv5v9rFbVHpYqjGzbOn8SzFI9o715L4u0r7arYzk+lYdTqSujy7U/C0u4vHk+WwO
|
|
xuh9q3J9dgvbdVukMV1EwbDDgn04rZMwlHoZ+orZ6hfQ3RWVnQYCgZAq+8U0ln5NtBsV2yxYcfgK
|
|
JtW0CnB31LlroVwJ1nQLGDjeP7w+lb0dsFxjrWB0tHS6NuWPJ6A16ToUm63T3Gallr4S7cxiTjrX
|
|
PaxaF7dlVeSMUhxZ5jd+H7qCa4eF3DSE5x3zXN3Wk6jbyeaiFWUY6ZyPStYS5SalPmVipFbX0E4c
|
|
W0alvmPHJrag0rVvEE6LdljGpG2NRtQD+tW5XMI0uU9M8NeFo9PiQhecDIIrtrOMIoG3H4VlJm9t
|
|
C6CB06VPGM1IHLeItGS6uw+ORT7e3jsbQvj7gzUNam0JaWE+HN7NqOqX80n3FO1RXo8YzXdS+BHk
|
|
4z+KyzGPapcU2YIv7qQtiuaxvcaWqG4O6FwfSrS1JbPnrxoxkv7qIfejcitj4V2f2exumI+8+aKn
|
|
xHTT+G5d8Txlm4rjLxMsQwzWT3OiK0Mm6sEkVsAcjFc1d+FEmlGwEDPQVopaEuOpr6f4ZWNAu3tW
|
|
vHpAj5ZQcUFIWaDjGMVUMQ3cVDBmvbhY7QAV2nh+T/R1yeKhlrY31+b61FcQK6nIoJMi401WblRi
|
|
qr6PCw5UYq9y+YgOgWzNkRrx3xWjp+nx2v3FQcelAbmko9anQ4GBUNisPHWr1qMrQhS2K11HvmYV
|
|
hamcxSRZ5xRIqluS/DKAQQXZxyXrvo2FdlL4EeZjH+/ZbjNSZpswLNBrE1Gt7VE4ODVIlnh/j61F
|
|
j4lmeTGyUbq6LwdEqWbeX0YbhSqfEddP4Bddj4JIrhL5d8h7VjI6oLQqKNzelWre3yc4/ClFjaL6
|
|
wqBxxUUxwCKu5BmXRA6c+9ZjP83FSBoQuPs4BrsNBlUW659KmRrDY6G1lyQtW3Hy0lqQ1qVJnAbm
|
|
oy3b9KYJCqRj3o4zRctIlhjLHmpSuOBRbQOpLGpPFaES7UqkZzKN1KsEc87/AHUUmvPLTVGv72aQ
|
|
k7WJwKmRrQ3ud74Ltilgz4++2a6iNDXdS0gjyMU71my7GpqTbxSbMki3SViajTTHqkSeR/GeyZmg
|
|
nQHkEE1S+F+oPPavBL96I4/Cia1udVF+4dVrkW+Fq8+v4tjMDWUkdVJ6WM0cNV+F+MVmjUcZgqnP
|
|
1qpNNnkcVRLiZtxIS1UzzIF7mghlxUZpVQdq6nTVdAoAOKzkbQWhvwM6gMM1twOJYx3NOJE11Kt1
|
|
H1/pVVlwBkk+9NocXoOQ45FPj+fkUJFF2NSB700v/hTEty5ZpkjvVyUgcCq6GM9zC14/8Se6GcZQ
|
|
1574Xs5WkI2HBPHFQ1dm1KSSZ7Rotn9l0+KPHIHNacae1dy0Vjxaj5ptlhVp+2s2CJ9ppCKzuWNx
|
|
zSFc1SYrHNeNdIGpaYw25ZeRXmvheyk0jVpEdcLJ0q3ZxNKTa0O3vQHg/DNcHrsJDmsmjspnNzNt
|
|
fFIJ24GazOhC+azDmgZIOOKBsp3J2qSaZodubq58yQ4QAnmhGT3NO18pb7BORmu205LfYpyKVkWp
|
|
Oxr5gKYWoIZWgfGfloFq1qTPLubnGO1RPtxg4P0oBAkY/hBz6VNDDkZ6AU0W2WSdqkdKr9ZOaGSj
|
|
VtcLHmnOcgmmYvcz7mBLy3MbdD1q9ouiRK6bUAVeelOC1InPlidSsWMDFOCEdq3uefykqrinYqGy
|
|
rFvApMVka2DAowKAsMkRXQqwyDXn/iWyitNQ3qPl6itIvRoF8RXinW4tQ6HI6GuW8SIVBPalc6qe
|
|
5x9x97r3qruwTjrWZ0ksZ9TUmcDNAmZ9/wAoao63rR0+w22MLPtAzt6mghmfofiB76LdJBJBIp5D
|
|
d/oa7bSdWLIPnpDi9TM8TeKdas51XTbIyxd3J/pXS+E/EFxqNoFu7do5OmD60maHWrnZyDRkn/69
|
|
MlEyOR0xntVoNx+FUgYjPxg4FLCuWDZyKQr2RoRnP0qO+nEFpJITgAUzLqZnhu6+0rknOTXpOmwJ
|
|
Fbrt5yMmnHYyr6Oxb2ijaKLnPYMClwKQWK3n0hn+lachHOJ9pNNN0apQFzsY10a4v4hXQh0xpieQ
|
|
MA1XLZNjhK80cT8OdV+3Wl3A7ZZJCw+hrR1qLcjZ/CsbnfHRnFXseHJArOYYbrUs1uPhYbuatqFP
|
|
ByfSkMq3UIINYkto+87Tx6GkSxfsDbflGD7CtTw/pk4nzITtPIFMFudsukh4Rxz71paTpKwP5jcn
|
|
0qTRy0NORMDgVCqewoJTJgAoxjntTiTu7fWmFxAcnn1q3EPl+X8KZMi4gKqB1Peob/Tv7Us5bfeU
|
|
yOoq4R5nYxqT5I8xieH9J1DTbvyJELRg8ODwa9Ms5mSFV9BWiptbnNVrKdmif7Q1KLg96XIZc5Is
|
|
pNL5pqeUrmMtZs0jzV08phchaY00zH1p2ZNxjS1g+LdJOt6U9ssmxjyGp2urDjLlaZzng/wUPDqz
|
|
TSTmWeTrjpVjVk3Rvjr2rnqQ5dDvo1XUd2cTqSNk9OKxXGCeKxZ1DAxHTr2q5C/y8GokUhsz54qu
|
|
uCxzSQjQ0+FZblR2ro4bZYiMVQ0dBb7Qi5x0qzuG5QOh71LYErDufpSeWrHnimIXbjkUjLkH1Hem
|
|
gGxryc+tXI19KYmWegq9YLiLJ7mtqS945cS7QsWehqxA9dEjz4krPSxyZqbFFhGxUm6smjRM55Lk
|
|
HvSvNxXTY57kLT+9MNwKdhXGm5FIbkU7Bca1wMEVhaiuQcVhXWiZ14R6tHGanGBI2OtYkqEHjgVy
|
|
s9ErEeo6UBsHipKEZs5qpPdRxcbhx70NCSuybTNWihc5brW9Fq6vjMnFSdEIdDRi8RRKygZbHFbu
|
|
m6nb3RA3gMegNJhOm0jbXGOoxTuCc1Rz3FyoGKawz9KaAVcZqeMgCmIkB4FaUTbYwB6V00Fuzixb
|
|
0SFMuDU8Mlbs4UPeXHeiOXkUrDuXYnyKk3cVk0ap6HMxxketSMhrcwRC0dMMZFMQ3yzSeVQAeUaz
|
|
9Vj8uPd271nVV4m+GdpnHX67pCeKyLtBtNcR6xlk9RVeWTb3qRnO6trgttyIfm71z7ai8j7/AJmN
|
|
DNqUVa5Yi1AnjynHuBV+11YJhWWXcP8AZNSzqgmaEerSsf3NtIQP4mGKtRavdRgMIpVI9KjU0a7n
|
|
R6T43uYQI7qN2Tpkqciu503VVuQGAYZHQjFVc4alPlZrpKGAznpTwxOc9+lWjIlUACnM4XApiLNk
|
|
nmvnsK0NvpXZRVonmYqV52GsmanhXitTmFkSiJTSAvwrxUxXIrJ7miOfjf1pzNWxkRlqYWpgJupu
|
|
6gQbuahvIxPA6eo4pNXVioS5WmefakGhndH4INZs5DJXA10PaTurmLO21uKpSZqGMoXGnRzBiyjd
|
|
9Kx5rcQS428fSkjanLoaOliHGZFB56VswW+mtPufcBsGOAfmxz+tFkd8HpoaUx09FAtFY8DO71qb
|
|
Sms/Nb7RbecG6AEjFLS5c78t+p0djpVs9wsyQiJAdyr1rW+zqjErzSe559Sbk9S3C+MA1bjbgE1S
|
|
MSXzMVG0vNUI2tPKrAuCMnrVzNd0PhR49W/O2xrHmp4TxVMzQshpIzzQBehqesnuaI5VGzT2bitz
|
|
FEbNTC1ADS1JupgG6l3UAc14s04yR/aYRll+8BXCtLncDXFWjys9TCz5oW7GddH5qqNzWDOgQnC8
|
|
VSuo1kHzAGkPYopEY2+RWxV23Vzj5G/Kg3jWaNazhZuqNXS6TaKhB2c0jR1nJWOlhOxRxU4YkCgx
|
|
Y0OQatQyDbyaaFYe8uF4NY3iC9ltbVGj43NTIL3h7WzMihjzXVQXYYDdW9Cf2WcOJpfaRZ3g9KsQ
|
|
mupnCLIabGeaAL0LcVY3cVmzRHIxtUhetzEjZqjLUAIWpN1ArhupwagAfDKQ3Q1594v0c2bm6tx+
|
|
5Y8j+6ayrR5onThp8s7dzkZjuqAAmuBnqC7c0iwgtzSA0rWzjfGRW3ZadDu4AoNYo2rfS4v7orSh
|
|
05UA2r0pDbsTm29KRottBNyJ0wpJ9KhD7f6U0ikNWffIFBz60zVUW52ow4UcUN6EPcx44WsbgOmd
|
|
ua7TT5Bd24KHnFKnLlZFSN4koluLdueRWvp14swweG9DXoxldHlTjYtzGoo25qzEvwtUxas2jRPQ
|
|
5CNqkLVsYoYzUzdQA3dSFqBBmnqaBhuqhriCXTpVIzxUz+Fl03aSPI9QTypW2/dz0qKNw3SvOPZR
|
|
Mqin8VLKRcs3O4Cuk0w/MDjt1NBtHY6O2IIHY1pxgFaETIRwMkjtVSUEk4570MlFW5bap6dKzWm8
|
|
1tqH8aY+hp2FvGoGayNevVt7/ap4xzUvYjqTLtvLPcvJxSaVcyWsxTnFZlnT2t15xHmCtOBYwQy4
|
|
B9q7cPO+jPPxFO2qLEj5HWo42+aus4HpoX4W4FTF+KlotbHII9SFuK0MUNZqiLUDE3UbqBBupwag
|
|
Bc1DefPbyD/ZND2KjujyPWlKzuPesRZjHJXms9lMuw3StjnmphKDSLTJ7OfE3JrpbO4GQc9qlnRA
|
|
3LO82k5NbFvdADkjBoCSHyXIIIzgVQvdRigT7wzjgUzO1jHknlvG7qnp61etYFQDIpCZoqVijzXn
|
|
3iC8EmsOuaCGb/heR/s0ijkVv6fbxy3QMg5xmsnuX0Ldzut3+UYTPWk+2GJSe+M1pFtamcldalmx
|
|
1eO4XaThhWnC+TXqR2PHqL3maUJ4qRjxSEjj42qXdxVmaGs1MJoATfSbqBAG5p6mgAzTJTmNvpQU
|
|
tzzHXY83D/U1zF5FhjgV5r3Pa6FMsV5HWnLe7RhqBRdmTwagN2d2K2rPU1C5LAnPrUs6Iysbdrq6
|
|
f3gK0BrUKj/WClY05iM6xLOcQAj3NT29uznfKSzHuadzNu7NSBFjHNSm5VO9IRnajqoWMhTzXFtA
|
|
bvUfMduSeg702Qz0rS7FbTToQFwzjJqaGTFyfK5PQViyzUuFmuIdgGABya5u/vTaN5cnUHFUmLoZ
|
|
zyskwlgJweSK6zQdUEwVJeGr0aUrxPLxEfe0OrhPAqVjxWhznGRtUwatDK4jNxURbmkAm6jNABup
|
|
6tQAFqhupNtu59qUnZFwV5JHnWsHdIx96w5lz15rzT2uhRmt85xWbcxMnUGmZlB0bdxmrNvFIcfM
|
|
350mWjbs7YkDJY/jW5ZWW4jikWkdNp9mqYJFaJdEHHakUULu/VB1rLn1Ld/FgetMGYd/qWSQmSa0
|
|
/AemS32pfa7piLeLkg9z6UmQtz0W7uQ2cZx0A9BVzR7cAea6j2rPqX0L99KRat5A6Dk1wOoKZ52a
|
|
YfMORTYRLujiGWEq6/NWza2yKQVHNdOHerRy4laJo6TTnbbtb8KuM3Fdh5z3OJjbmpt3FaMxAtUZ
|
|
agBN1GaQBzTwaAAms3VbjERUGsa07RsdeFpuUuY4jUjljWTKK4j02RE4IpJYFk6imQkVl0xWarsO
|
|
mAEcUi0bNnZBR0rWtoguMCkUi21wI161mXuocEKaYXMS4u+pY/hVCSWSY4HT0pEmlouiSahdpEBl
|
|
mOceleiwWcNjClvHgJH97Hc1EmVFFi3Czy7mwIl/WtJbjP7uLgd/apQ2VNVvtsBhiPzdK5S4nAuR
|
|
nqOCaTGi9pcytPlU+XpmumtWII44rah8ZjiNIXRuWeNvvViQ/LXpJWPJbu7nCRvVkNxVsxBmqJmo
|
|
EPiXca0YLMuOlJsuKuPlsSi5IrNuG8s4HWs5VEkbwoOTKsk+FJY4rC1K53k1xTk5O7PSpwVNWRzt
|
|
4cms+WpKICtSLTETQj5q0YeBSGiys23pUguGxQMq3E59ayrm4x3yaAKiRtO2WPHcmhruKFxFajzZ
|
|
ScA44qRHoXhuMaLpxaUg6hcDLMf4F9KlhuDeXGASIl+8azZslYma68y48m1+7nFW5rtbRNhb5z1p
|
|
iMKbUg0zuW4A4rPgb7VdKXOMmpA7HRbMS7nUYiUda0lkQOBngVrS+JGdbWLRt2bAx5BqeQ/LXpnj
|
|
PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
|
|
c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
|
|
8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
|
|
ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
|
|
euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,S0=`
|
|
/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 dg={};er(dg,{author:()=>o4,browser:()=>s4,bugs:()=>l4,default:()=>Pae,description:()=>t4,devDependencies:()=>m4,engines:()=>h4,homepage:()=>u4,keywords:()=>f4,license:()=>c4,main:()=>r4,module:()=>a4,name:()=>e4,repository:()=>d4,scripts:()=>p4,sideEffects:()=>n4,types:()=>i4,version:()=>pg});var e4="@vladmandic/human",pg="1.1.2",t4="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",n4=!1,r4="dist/human.node.js",a4="dist/human.esm.js",s4="dist/human.esm.js",i4="types/src/human.d.ts",o4="Vladimir Mandic <mandic00@live.com>",l4={url:"https://github.com/vladmandic/human/issues"},u4="https://vladmandic.github.io/human/demo/index.html",c4="MIT",h4={node:">=12.0.0"},d4={type:"git",url:"git+https://github.com/vladmandic/human.git"},p4={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"},f4=["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"],m4={"@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":"^14.14.34","@typescript-eslint/eslint-plugin":"^4.17.0","@typescript-eslint/parser":"^4.17.0","@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","simple-git":"^2.36.2",tslib:"^2.1.0",typedoc:"^0.20.30",typescript:"^4.2.3"},Pae={name:e4,version:pg,description:t4,sideEffects:n4,main:r4,module:a4,browser:s4,types:i4,author:o4,bugs:l4,homepage:u4,license:c4,engines:h4,repository:d4,scripts:p4,keywords:f4,devDependencies:m4};var fg={};er(fg,{all:()=>Wae,body:()=>x4,canvas:()=>Lae,face:()=>g4,gesture:()=>y4,hand:()=>w4,options:()=>ce});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 T0(e,t,n,r=null){e.fillStyle=ce.useDepth&&r?`rgba(${127.5+2*(r||0)}, ${127.5-2*(r||0)}, 255, 0.3)`:ce.color,e.beginPath(),e.arc(t,n,ce.pointSize,0,2*Math.PI),e.fill()}function A4(e,t,n,r,a){if(e.beginPath(),ce.useCurves){let s=(t+t+r)/2,i=(n+n+a)/2;e.ellipse(s,i,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=ce.lineWidth,e.moveTo(t+ce.roundRect,n),e.lineTo(t+r-ce.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+ce.roundRect),e.lineTo(t+r,n+a-ce.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-ce.roundRect,n+a),e.lineTo(t+ce.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-ce.roundRect),e.lineTo(t,n+ce.roundRect),e.quadraticCurveTo(t,n,t+ce.roundRect,n),e.closePath();e.stroke()}function mg(e,t=[]){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=ce.useDepth&&n[2]?`rgba(${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]}, ${127.5-2*n[2]}, 255, 0.3)`:ce.color,e.lineTo(n[0],parseInt(n[1]));e.stroke(),ce.fillPolygons&&(e.closePath(),e.fill())}}function C0(e,t=[]){if(!(t===void 0||t.length===0)){if(!ce.useCurves||t.length<=2){mg(e,t);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,a=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),ce.fillPolygons&&(e.closePath(),e.fill())}}async function y4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!n)return;n.font=ce.font,n.fillStyle=ce.color;let r=1;for(let a=0;a<t.length;a++){let s=[],i=[];if([s,i]=Object.entries(t[a]),i.length>1&&i[1].length>0){let o=s[1]>0?`#${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 g4(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&&A4(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:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=ce.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*ce.lineHeight+r.box[1];ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=ce.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh){if(ce.drawPoints)for(let s of r.mesh)T0(n,s[0],s[1],s[2]);if(ce.drawPolygons){n.lineWidth=1;for(let s=0;s<Fi.length/3;s++){let i=[Fi[s*3+0],Fi[s*3+1],Fi[s*3+2]].map(o=>r.mesh[o]);mg(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=ce.useDepth?"rgba(255, 200, 255, 0.3)":ce.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ce.fillPolygons&&(n.fillStyle=ce.useDepth?"rgba(255, 255, 200, 0.3)":ce.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=ce.useDepth?"rgba(255, 200, 255, 0.3)":ce.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ce.fillPolygons&&(n.fillStyle=ce.useDepth?"rgba(255, 255, 200, 0.3)":ce.color,n.fill())}}}}}var ts=[];async function x4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!ts[r]&&ce.bufferedOutput&&(ts[r]={...t[r]}),n.strokeStyle=ce.color,n.lineWidth=ce.lineWidth,ce.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=ce.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:ce.color,ce.bufferedOutput?(ts[r].keypoints[a][0]=(ts[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,ts[r].keypoints[a][1]=(ts[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,T0(n,ts[r].keypoints[a][0],ts[r].keypoints[a][1])):T0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(ce.drawLabels){n.font=ce.font;for(let a of t[r].keypoints)n.fillStyle=ce.useDepth&&a.position.z?`rgba(${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>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&mg(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),C0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),C0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),C0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>ft.body.scoreThreshold&&s.push([a.position.x,a.position.y]),C0(n,s)}}}}async function w4(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,A4(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]}, ${127.5-2*a[2]}, 255, 0.5)`:ce.color,T0(n,a[0],a[1]);if(ce.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=ce.lineWidth,n.beginPath(),n.strokeStyle=ce.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:ce.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function Lae(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Wae(e,t){!t||!e||e instanceof HTMLCanvasElement&&(g4(e,t.face),x4(e,t.body),w4(e,t.hand),y4(e,t.gesture))}var ht=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Bc(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Bc(s,i):n[a]=i}),n),{})}var E0,Ke,Xl,Vc,Uc,$i,Vt,R0,Hc,F0,jc,M0,$0,D0,yg=class{constructor(t={}){E0.set(this,void 0);Ke.set(this,void 0);Xl.set(this,void 0);Vc.set(this,void 0);Uc.set(this,void 0);$i.set(this,void 0);Vt.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)});R0.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=ht();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"} ${a?"multithreaded":"singlethreaded"}`),r||Ce("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&u6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ce("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ce("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ce(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),ve(this,Ke).backend=Math.trunc(ht()-n)}});F0.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 c,u,h,d,p,f,m;let n,r,a,s,i,o=[];this.state="run:face",n=ht();let l=await((c=this.models.face)==null?void 0:c.estimateFaces(t,this.config));if(ve(this,Ke).face=Math.trunc(ht()-n),!l)return[];for(let A of l){if(ve(this,Vt).call(this,"Get Face"),!A.image||A.image.isDisposedInternal){Ce("Face object is disposed:",A.image);continue}let y=ve(this,F0).call(this,A.mesh);ve(this,Vt).call(this,"Start Age:"),this.config.async?r=this.config.face.age.enabled?I2(A.image,this.config):{}:(this.state="run:age",n=ht(),r=this.config.face.age.enabled?await I2(A.image,this.config):{},ve(this,Ke).age=Math.trunc(ht()-n)),ve(this,Vt).call(this,"Start Gender:"),this.config.async?a=this.config.face.gender.enabled?R2(A.image,this.config):{}:(this.state="run:gender",n=ht(),a=this.config.face.gender.enabled?await R2(A.image,this.config):{},ve(this,Ke).gender=Math.trunc(ht()-n)),ve(this,Vt).call(this,"Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?O2(A.image,this.config):{}:(this.state="run:emotion",n=ht(),s=this.config.face.emotion.enabled?await O2(A.image,this.config):{},ve(this,Ke).emotion=Math.trunc(ht()-n)),ve(this,Vt).call(this,"End Emotion:"),ve(this,Vt).call(this,"Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?L2(A,this.config):[]:(this.state="run:embedding",n=ht(),i=this.config.face.embedding.enabled?await L2(A,this.config):[],ve(this,Ke).embedding=Math.trunc(ht()-n)),ve(this,Vt).call(this,"End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),ve(this,Vt).call(this,"Finish Face:"),!this.config.face.iris.enabled&&((u=A==null?void 0:A.annotations)==null?void 0:u.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,Vt).call(this,"End Face")}return ve(this,Vt).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});M0.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(S0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});$0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+N0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+S0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));D0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(N0):t(S0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=fg,ea(this,E0,dg),this.version=pg,this.config=Bc(ft,t),this.state="idle",ea(this,Xl,0),ea(this,Vc,!1),ea(this,Uc,!1),ea(this,$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=>hg(n,this.config),this.classes={facemesh:Ag,age:v2,gender:N2,emotion:F2,body:this.config.body.modelPath.includes("posenet")?Y2:lg,hand:ag},this.sysinfo=Fg()}profileData(){return this.config.profile?b2:{}}simmilarity(t,n){return this.config.face.embedding.enabled?_6(t,n):0}enhance(t){return this.config.face.embedding.enabled?P2(t):null}async load(t={}){this.state="load";let n=ht();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?Ag.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?og(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?Q2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?ug(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await Ag.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 og(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await Q2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await ug(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(ht()-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,R0).call(this,t);s&&(Ce(s,t),r({error:s}));let i=ht();await ve(this,Hc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),ve(this,Vt).call(this,"Start Scope:"),a=ht();let o=hg(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(ht()-a),ve(this,Vt).call(this,"Get Image:");let l,c,u;this.config.async?(u=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=ht(),u=this.config.face.enabled?await ve(this,jc).call(this,o.tensor):[],ve(this,Ke).face=Math.trunc(ht()-a)),ve(this,Vt).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?cg(o.tensor,this.config):[],ve(this,Ke).body&&delete ve(this,Ke).body):(this.state="run:body",a=ht(),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 cg(o.tensor,this.config):[],ve(this,Ke).body=Math.trunc(ht()-a)),ve(this,Vt).call(this,"End Body:"),ve(this,Vt).call(this,"Start Hand:"),this.config.async?(c=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=ht(),c=this.config.hand.enabled?await((m=this.models.handpose)==null?void 0:m.estimateHands(o.tensor,this.config)):[],ve(this,Ke).hand=Math.trunc(ht()-a)),ve(this,Vt).call(this,"End Hand:"),this.config.async&&([u,l,c]=await Promise.all([u,l,c])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),ve(this,Vt).call(this,"End Scope:");let h=[];this.config.gesture.enabled&&(a=ht(),h=[...Z6(u),...K6(l),...J6(c),...Y6(u)],this.config.async?ve(this,Ke).gesture&&delete ve(this,Ke).gesture:ve(this,Ke).gesture=Math.trunc(ht()-a)),ve(this,Ke).total=Math.trunc(ht()-i),this.state="idle",r({face:u,body:l,hand:c,gesture:h,performance:ve(this,Ke),canvas:o.canvas})})}async warmup(t={}){let n=ht();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,M0).call(this):typeof Image!="undefined"?a=await ve(this,$0).call(this):a=await ve(this,D0).call(this),this.config.videoOptimized=r;let s=ht();return this.config.debug&&Ce("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};E0=new WeakMap,Ke=new WeakMap,Xl=new WeakMap,Vc=new WeakMap,Uc=new WeakMap,$i=new WeakMap,Vt=new WeakMap,R0=new WeakMap,Hc=new WeakMap,F0=new WeakMap,jc=new WeakMap,M0=new WeakMap,$0=new WeakMap,D0=new WeakMap;return Bae;})();
|
|
/**
|
|
* @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.js.map
|