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 p8=Object.create,mh=Object.defineProperty,f8=Object.getPrototypeOf,m8=Object.prototype.hasOwnProperty,A8=Object.getOwnPropertyNames,y8=Object.getOwnPropertyDescriptor;var K1=e=>mh(e,"__esModule",{value:!0});var Tg=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),fr=(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(K1(mh(e!=null?p8(f8(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e);var Cg=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)},ve=(e,t,n)=>(Cg(e,t,"read from private field"),n?n.call(e):t.get(e)),ea=(e,t,n,r)=>(Cg(e,t,"write to private field"),r?r.call(e,n):t.set(e,n),n);var x6=Tg(g6=>{K1(g6);fr(g6,{MediaPipeFaceMesh:()=>w2,load:()=>Sae});var w2=class{constructor(t,n,r,a){this.facePipeline=new x2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Gr))l[h]=Gr[h].map(d=>i[d]);let u=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-s.box.startPoint[0],Math.min(t.shape[1],s.box.endPoint[1])-s.box.startPoint[1]]:0,c=s.box?[Math.max(0,s.box.startPoint[0]/t.shape[2]),Math.max(0,s.box.startPoint[1]/t.shape[1]),Math.min(t.shape[2],s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],Math.min(t.shape[1],s.box.endPoint[1]-s.box.startPoint[1])/t.shape[2]]:[];a.push({confidence:s.faceConfidence||s.boxConfidence||0,boxConfidence:s.boxConfidence,faceConfidence:s.faceConfidence,box:u,mesh:i,boxRaw:c,meshRaw:o,annotations:l,image:s.image?rr(s.image):null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Mi=[null,null,null];async function Sae(e){Mi=await Promise.all([!Mi[0]&&e.face.enabled?h6(e):null,!Mi[1]&&e.face.mesh.enabled?Ft(e.face.mesh.modelPath,{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Mi[2]&&e.face.iris.enabled?Ft(e.face.iris.modelPath,{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]);let t=new w2(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: ${e.face.iris.modelPath.match(/\/(.*)\./)[1]}`),t}g6.triangulation=Fi});var g0=Tg(V2=>{K1(V2);fr(V2,{NUM_KEYPOINTS:()=>Fae,connectedPartIndices:()=>$ae,partChannels:()=>Oae,partIds:()=>U2,partNames:()=>Rae,poseChain:()=>Dae});var Rae=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Fae=V2.partNames.length,U2=V2.partNames.reduce((e,t,n)=>(e[t]=n,e),{}),Mae=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],$ae=Mae.map(([e,t])=>[U2[e],U2[t]]),Dae=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]],Oae=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});function Ce(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function Eg(){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 ${process.version}`);return{platform:e,agent:t}}var yh={};fr(yh,{Abs:()=>Zi,Acos:()=>Yi,Acosh:()=>Ji,AdadeltaOptimizer:()=>Ud,AdagradOptimizer:()=>Hd,AdamOptimizer:()=>jd,AdamaxOptimizer:()=>Gd,Add:()=>Ia,AddN:()=>os,All:()=>vh,Any:()=>kh,ArgMax:()=>ls,ArgMin:()=>lu,Asin:()=>Qi,Asinh:()=>eo,Atan:()=>to,Atan2:()=>ro,Atanh:()=>no,AvgPool:()=>us,AvgPool3D:()=>uu,AvgPool3DGrad:()=>Nh,AvgPoolGrad:()=>Ih,BackendWasm:()=>y3,BatchMatMul:()=>cs,BatchToSpaceND:()=>cu,Bincount:()=>Sh,BroadcastTo:()=>jg,Callback:()=>lv,CallbackList:()=>i7,Cast:()=>hs,Ceil:()=>ds,ClipByValue:()=>Na,Complex:()=>Th,ComplexAbs:()=>hu,Concat:()=>ao,Conv2D:()=>ps,Conv2DBackpropFilter:()=>Ch,Conv2DBackpropInput:()=>fs,Conv3D:()=>du,Conv3DBackpropFilterV2:()=>Eh,Conv3DBackpropInputV2:()=>Rh,Cos:()=>ms,Cosh:()=>so,CropAndResize:()=>io,Cumsum:()=>As,CustomCallback:()=>l7,DataStorage:()=>xh,DenseBincount:()=>Fh,DepthToSpace:()=>oo,DepthwiseConv2dNative:()=>ys,DepthwiseConv2dNativeBackpropFilter:()=>Mh,DepthwiseConv2dNativeBackpropInput:()=>$h,Diag:()=>Dh,Dilation2D:()=>pu,Dilation2DBackpropFilter:()=>zh,Dilation2DBackpropInput:()=>Oh,ENV:()=>mr,EarlyStopping:()=>cv,Elu:()=>lo,EluGrad:()=>Ph,Environment:()=>Ug,Equal:()=>co,Erf:()=>uo,Exp:()=>xs,ExpandDims:()=>ho,Expm1:()=>po,FFT:()=>Lh,Fill:()=>fu,FlipLeftRight:()=>fo,Floor:()=>ws,FloorDiv:()=>_s,FromPixels:()=>ed,FusedBatchNorm:()=>bs,FusedConv2D:()=>ei,FusedDepthwiseConv2D:()=>ti,GPGPUContext:()=>up,GatherNd:()=>Ao,GatherV2:()=>mo,GraphModel:()=>Wv,Greater:()=>yo,GreaterEqual:()=>vs,History:()=>o7,IFFT:()=>Wh,Identity:()=>ks,Imag:()=>Bh,InputSpec:()=>Kt,IsFinite:()=>go,IsInf:()=>xo,IsNan:()=>wo,KernelBackend:()=>su,LRN:()=>yu,LRNGrad:()=>Uh,LayerVariable:()=>t7,LayersModel:()=>ha,LeakyRelu:()=>Is,Less:()=>_o,LessEqual:()=>bo,LinSpace:()=>Vh,Log:()=>Ns,Log1p:()=>vo,LogSoftmax:()=>Gg,LogicalAnd:()=>ko,LogicalNot:()=>mu,LogicalOr:()=>Au,MathBackendCPU:()=>Zd,MathBackendWebGL:()=>El,Max:()=>Ss,MaxPool:()=>Cs,MaxPool3D:()=>gu,MaxPool3DGrad:()=>jh,MaxPoolGrad:()=>Hh,MaxPoolWithArgmax:()=>Gh,Maximum:()=>Ts,Mean:()=>Es,Min:()=>Rs,Minimum:()=>Fs,MirrorPad:()=>xu,Mod:()=>Io,MomentumOptimizer:()=>qd,Multinomial:()=>qh,Multiply:()=>Ms,Neg:()=>No,NonMaxSuppressionV3:()=>To,NonMaxSuppressionV4:()=>Co,NonMaxSuppressionV5:()=>Eo,NotEqual:()=>So,OP_SCOPE_SUFFIX:()=>r5,OneHot:()=>$s,OnesLike:()=>Ro,Optimizer:()=>oa,Pack:()=>Fo,PadV2:()=>Ds,Pool:()=>Tk,Pow:()=>Os,Prelu:()=>zs,Prod:()=>Mo,RMSPropOptimizer:()=>Xd,RNN:()=>Hr,Range:()=>wu,Rank:()=>lf,Real:()=>Xh,RealDiv:()=>gs,Reciprocal:()=>$o,Reduction:()=>dn,Relu:()=>Ps,Relu6:()=>Ws,Reshape:()=>Do,ResizeBilinear:()=>Ls,ResizeBilinearGrad:()=>Zh,ResizeNearestNeighbor:()=>_u,ResizeNearestNeighborGrad:()=>Kh,Reverse:()=>Bs,RotateWithOffset:()=>Ko,Round:()=>Vs,Rsqrt:()=>Us,SGDOptimizer:()=>ec,ScatterNd:()=>Oo,Select:()=>zo,Selu:()=>Po,Sequential:()=>Wl,Sigmoid:()=>js,Sign:()=>Bo,Sin:()=>Hs,Sinh:()=>Wo,Slice:()=>Lo,Softmax:()=>Xs,Softplus:()=>Vo,SpaceToBatchND:()=>bu,SparseToDense:()=>Yh,SplitV:()=>Uo,Sqrt:()=>Gs,Square:()=>vu,SquaredDifference:()=>Ks,Step:()=>Ta,StridedSlice:()=>Ho,Sub:()=>Zs,Sum:()=>qs,SymbolicTensor:()=>Ir,Tan:()=>jo,Tanh:()=>Ys,Tensor:()=>Ye,TensorBuffer:()=>Ot,Tile:()=>Sa,TopK:()=>Go,Transform:()=>Jh,Transpose:()=>Js,Unique:()=>Qh,Unpack:()=>qo,UnsortedSegmentSum:()=>ku,Variable:()=>Ru,ZerosLike:()=>Xo,_FusedMatMul:()=>Qs,abs:()=>zt,acos:()=>$f,acosh:()=>Df,add:()=>se,addN:()=>Ma,all:()=>dd,any:()=>Ou,argMax:()=>zu,argMin:()=>Of,asin:()=>zf,asinh:()=>Pf,atan:()=>Lf,atan2:()=>Wf,atanh:()=>Bf,avgPool:()=>Lu,avgPool3d:()=>Hf,backend:()=>P5,backend_util:()=>R,basicLSTMCell:()=>iN,batchNorm:()=>li,batchNorm2d:()=>V5,batchNorm3d:()=>U5,batchNorm4d:()=>H5,batchToSpaceND:()=>Wu,bincount:()=>j5,booleanMaskAsync:()=>hC,broadcastTo:()=>Bu,browser:()=>rl,buffer:()=>We,callbacks:()=>Rne,cast:()=>me,ceil:()=>jf,clipByValue:()=>bn,clone:()=>rr,complex:()=>Ca,concat:()=>st,concat1d:()=>G5,concat2d:()=>ll,concat3d:()=>q5,concat4d:()=>X5,constraints:()=>T3,conv1d:()=>fd,conv2d:()=>ra,conv2dTranspose:()=>md,conv3d:()=>qf,conv3dTranspose:()=>TN,copyRegisteredKernels:()=>Rk,cos:()=>Vu,cosh:()=>Ad,cosineWindow:()=>wm,cumsum:()=>yd,customGrad:()=>$r,data:()=>Bv,denseBincount:()=>Z5,deprecationWarn:()=>Ff,depthToSpace:()=>Xf,depthwiseConv2d:()=>ul,deregisterOp:()=>Mne,device_util:()=>Mu,diag:()=>ON,dilation2d:()=>Kf,disableDeprecationWarnings:()=>wI,dispose:()=>Ee,disposeVariables:()=>_I,div:()=>ge,divNoNan:()=>Zf,dot:()=>Y5,dropout:()=>gx,elu:()=>cl,enableDebugMode:()=>xI,enableProdMode:()=>gI,enclosingPowerOfTwo:()=>xx,engine:()=>Fr,env:()=>J,equal:()=>Da,erf:()=>Yf,exp:()=>qn,expandDims:()=>Fn,expm1:()=>Jf,eye:()=>Qf,fft:()=>Ju,fill:()=>Uu,findBackend:()=>Mf,findBackendFactory:()=>SI,floor:()=>hl,floorDiv:()=>hd,forceHalfFloat:()=>E_,fused:()=>La,gather:()=>ui,gatherND:()=>yx,gather_util:()=>If,getBackend:()=>II,getGradient:()=>af,getKernel:()=>td,getKernelsForBackend:()=>Yo,gpgpu_util:()=>e_,grad:()=>hS,grads:()=>dS,greater:()=>ar,greaterEqual:()=>za,ifft:()=>Al,imag:()=>gd,image:()=>Je,inTopKAsync:()=>bC,initializers:()=>D3,input:()=>X3,io:()=>_n,irfft:()=>$d,isFinite:()=>J5,isInf:()=>Q5,isNaN:()=>ex,keep:()=>Gt,kernel_impls:()=>Pr,layers:()=>q3,leakyRelu:()=>Hu,less:()=>xd,lessEqual:()=>ci,linalg:()=>Rx,linspace:()=>tx,loadGraphModel:()=>Ft,loadLayersModel:()=>Yte,localResponseNormalization:()=>em,log:()=>Mn,log1p:()=>wd,logSigmoid:()=>rx,logSoftmax:()=>bd,logSumExp:()=>rm,logicalAnd:()=>sr,logicalNot:()=>ju,logicalOr:()=>vd,logicalXor:()=>ox,losses:()=>LE,matMul:()=>je,math:()=>x5,max:()=>Xn,maxPool:()=>Gu,maxPool3d:()=>am,maxPoolWithArgmax:()=>lx,maximum:()=>Dr,mean:()=>kt,memory:()=>cd,metrics:()=>sv,min:()=>pl,minimum:()=>fl,mirrorPad:()=>sm,mod:()=>im,model:()=>Kte,models:()=>iv,moments:()=>kd,movingAverage:()=>fC,mul:()=>P,multiRNNCell:()=>BS,multinomial:()=>ux,neg:()=>vt,nextFrame:()=>Kd,norm:()=>Pd,notEqual:()=>di,oneHot:()=>nl,ones:()=>Or,onesLike:()=>$n,op:()=>O,outerProduct:()=>GS,pad:()=>aa,pad1d:()=>KS,pad2d:()=>YS,pad3d:()=>QS,pad4d:()=>tT,pool:()=>cx,pow:()=>sa,prelu:()=>Xu,print:()=>p5,prod:()=>Id,profile:()=>xr,rand:()=>cT,randomGamma:()=>fT,randomNormal:()=>hx,randomUniform:()=>ml,range:()=>Nd,ready:()=>kI,real:()=>Ku,reciprocal:()=>um,registerBackend:()=>sl,registerCallbackConstructor:()=>Jte,registerGradient:()=>qg,registerKernel:()=>ni,registerOp:()=>Fne,regularizers:()=>ov,relu:()=>zr,relu6:()=>Sd,removeBackend:()=>NI,reshape:()=>G,reverse:()=>Dn,reverse1d:()=>vT,reverse2d:()=>IT,reverse3d:()=>ST,reverse4d:()=>CT,rfft:()=>Qu,round:()=>cm,rsqrt:()=>Td,scalar:()=>be,scatterND:()=>Ax,scatter_util:()=>Nf,selu:()=>Cd,separableConv2d:()=>hm,sequential:()=>Zte,serialization:()=>re,setBackend:()=>vI,setPlatform:()=>TI,setWasmPath:()=>GZ,setWasmPaths:()=>qZ,setWebGLContext:()=>sp,setdiff1dAsync:()=>dx,shared:()=>km,sigmoid:()=>Rn,sign:()=>dm,signal:()=>PE,sin:()=>Ed,sinh:()=>Rd,slice:()=>Re,slice1d:()=>Fd,slice2d:()=>pm,slice3d:()=>Md,slice4d:()=>Zu,slice_util:()=>cn,softmax:()=>Yu,softplus:()=>dl,spaceToBatchND:()=>qu,sparseToDense:()=>xm,spectral:()=>zE,split:()=>Lt,sqrt:()=>en,square:()=>lt,squaredDifference:()=>Dd,squeeze:()=>Pa,stack:()=>hn,step:()=>yl,stridedSlice:()=>fm,sub:()=>ye,sum:()=>Te,sumOutType:()=>sd,tan:()=>mm,tanh:()=>ol,tensor:()=>gr,tensor1d:()=>on,tensor2d:()=>kn,tensor3d:()=>ld,tensor4d:()=>tC,tensor5d:()=>nC,tensor6d:()=>rC,tensor_util:()=>Ar,test_util:()=>D5,tidy:()=>B,tile:()=>Oa,time:()=>bI,topk:()=>Am,train:()=>fi,transpose:()=>at,truncatedNormal:()=>Od,unique:()=>zd,unregisterGradient:()=>Ek,unregisterKernel:()=>Ck,unsortedSegmentSum:()=>ym,unstack:()=>ir,upcastType:()=>nr,util:()=>v,valueAndGrad:()=>pS,valueAndGrads:()=>fS,variable:()=>px,variableGrads:()=>nx,version:()=>gae,version_converter:()=>Fre,version_core:()=>yI,version_cpu:()=>ow,version_layers:()=>UA,version_wasm:()=>x3,version_webgl:()=>C_,webgl:()=>cL,webgl_util:()=>Tw,where:()=>vn,whereAsync:()=>gm,zeros:()=>Et,zerosLike:()=>Ue});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}),et=(e,t)=>()=>(t||(t={exports:{}},e(t.exports,t)),t.exports),ze=(e,t)=>{for(var n in t)gh(e,n,{get:t[n],enumerable:!0})},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=et(()=>{}),S8=et((e,t)=>{(function(n,r,a){function s(u){var c=this,h=l();c.next=function(){var d=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=d-(c.c=d|0)},c.c=1,c.s0=h(" "),c.s1=h(" "),c.s2=h(" "),c.s0-=h(u),c.s0<0&&(c.s0+=1),c.s1-=h(u),c.s1<0&&(c.s1+=1),c.s2-=h(u),c.s2<0&&(c.s2+=1),h=null}function i(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function o(u,c){var h=new s(u),d=c&&c.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var u=4022871197,c=function(h){h=h.toString();for(var d=0;d<h.length;d++){u+=h.charCodeAt(d);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),T8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var d=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^d^d>>>8},l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),C8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(d^d<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,h==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),E8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.x,d=u.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,u.i=d+1&7,f};function c(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,h.i=0,p=256;p>0;--p)h.next()}c(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.x&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),R8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.w,d=u.X,p=u.i,f,m;return u.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,u.i=p,m+(h^h>>>16)|0};function c(h,d){var p,f,m,A,y,g=[],_=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,_=Math.max(_,d.length)),m=0,A=-32;A<_;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}c(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.X&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),F8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.b,p=u.c,f=u.d,m=u.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,u.b=d=d<<20^d>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-d|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var h=0;h<c.length+20;h++)u.b^=c.charCodeAt(h)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Z1=et(()=>{}),M8=et((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",u=r.pow(s,i),c=r.pow(2,o),h=c*2,d=s-1,p;function f(w,x,N){var S=[];x=x==!0?{entropy:!0}:x||{};var T=g(y(x.entropy?[w,b(n)]:w==null?_():w,3),S),M=new m(S),D=function(){for(var z=M.g(i),W=u,U=0;z<c;)z=(z+U)*s,W*=s,U=M.g(1);for(;z>=h;)z/=2,W/=2,U>>>=1;return(z+U)/W};return D.int32=function(){return M.g(4)|0},D.quick=function(){return M.g(4)/4294967296},D.double=D,g(b(M.S),n),(x.pass||N||function(z,W,U,H){return H&&(H.S&&A(H,M),z.state=function(){return A(M,{})}),U?(r[l]=z,W):z})(D,T,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(w){var x,N=w.length,S=this,T=0,M=S.i=S.j=0,D=S.S=[];for(N||(w=[N++]);T<s;)D[T]=T++;for(T=0;T<s;T++)D[T]=D[M=d&M+w[T%N]+(x=D[T])],D[M]=x;(S.g=function(z){for(var W,U=0,H=S.i,X=S.j,j=S.S;z--;)W=j[H=d&H+1],U=U*s+j[d&(j[H]=j[X=d&X+W])+(j[X]=W)];return S.i=H,S.j=X,U})(s)}function A(w,x){return x.i=w.i,x.j=w.j,x.S=w.S.slice(),x}function y(w,x){var N=[],S=typeof w,T;if(x&&S=="object")for(T in w)try{N.push(y(w[T],x-1))}catch(M){}return N.length?N:S=="string"?w:w+"\0"}function g(w,x){for(var N=w+"",S,T=0;T<N.length;)x[d&T]=d&(S^=x[d&T]*19)+N.charCodeAt(T++);return b(x)}function _(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),b(w)}catch(S){var x=a.navigator,N=x&&x.plugins;return[+new Date,a,N,a.screen,b(n)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=Z1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),$8=et((e,t)=>{var n=S8(),r=T8(),a=C8(),s=E8(),i=R8(),o=F8(),l=M8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),D8=et((e,t)=>{(function(n,r,a){function s(u){var c=this,h=l();c.next=function(){var d=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=d-(c.c=d|0)},c.c=1,c.s0=h(" "),c.s1=h(" "),c.s2=h(" "),c.s0-=h(u),c.s0<0&&(c.s0+=1),c.s1-=h(u),c.s1<0&&(c.s1+=1),c.s2-=h(u),c.s2<0&&(c.s2+=1),h=null}function i(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function o(u,c){var h=new s(u),d=c&&c.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var u=4022871197,c=function(h){h=h.toString();for(var d=0;d<h.length;d++){u+=h.charCodeAt(d);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),O8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var d=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^d^d>>>8},l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),z8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(d^d<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,h==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),P8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.x,d=u.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,u.i=d+1&7,f};function c(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,h.i=0,p=256;p>0;--p)h.next()}c(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.x&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),L8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.w,d=u.X,p=u.i,f,m;return u.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,u.i=p,m+(h^h>>>16)|0};function c(h,d){var p,f,m,A,y,g=[],_=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,_=Math.max(_,d.length)),m=0,A=-32;A<_;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}c(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.X&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),W8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.b,p=u.c,f=u.d,m=u.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,u.b=d=d<<20^d>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-d|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var h=0;h<c.length+20;h++)u.b^=c.charCodeAt(h)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),B8=et((e,t)=>{(function(n,r){var a=this,s=256,i=6,o=52,l="random",u=r.pow(s,i),c=r.pow(2,o),h=c*2,d=s-1,p;function f(w,x,N){var S=[];x=x==!0?{entropy:!0}:x||{};var T=g(y(x.entropy?[w,b(n)]:w==null?_():w,3),S),M=new m(S),D=function(){for(var z=M.g(i),W=u,U=0;z<c;)z=(z+U)*s,W*=s,U=M.g(1);for(;z>=h;)z/=2,W/=2,U>>>=1;return(z+U)/W};return D.int32=function(){return M.g(4)|0},D.quick=function(){return M.g(4)/4294967296},D.double=D,g(b(M.S),n),(x.pass||N||function(z,W,U,H){return H&&(H.S&&A(H,M),z.state=function(){return A(M,{})}),U?(r[l]=z,W):z})(D,T,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(w){var x,N=w.length,S=this,T=0,M=S.i=S.j=0,D=S.S=[];for(N||(w=[N++]);T<s;)D[T]=T++;for(T=0;T<s;T++)D[T]=D[M=d&M+w[T%N]+(x=D[T])],D[M]=x;(S.g=function(z){for(var W,U=0,H=S.i,X=S.j,j=S.S;z--;)W=j[H=d&H+1],U=U*s+j[d&(j[H]=j[X=d&X+W])+(j[X]=W)];return S.i=H,S.j=X,U})(s)}function A(w,x){return x.i=w.i,x.j=w.j,x.S=w.S.slice(),x}function y(w,x){var N=[],S=typeof w,T;if(x&&S=="object")for(T in w)try{N.push(y(w[T],x-1))}catch(M){}return N.length?N:S=="string"?w:w+"\0"}function g(w,x){for(var N=w+"",S,T=0;T<N.length;)x[d&T]=d&(S^=x[d&T]*19)+N.charCodeAt(T++);return b(x)}function _(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),b(w)}catch(S){var x=a.navigator,N=x&&x.plugins;return[+new Date,a,N,a.screen,b(n)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=Z1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),V8=et((e,t)=>{var n=D8(),r=O8(),a=z8(),s=P8(),i=L8(),o=W8(),l=B8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),au=et(()=>{}),U8=et(()=>{}),H8=et(()=>{}),j8=et((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};function s(){return Q.buffer!=Be&&Jt(Q.buffer),An}function i(){return Q.buffer!=Be&&Jt(Q.buffer),_t}function o(){return Q.buffer!=Be&&Jt(Q.buffer),yn}function l(){return Q.buffer!=Be&&Jt(Q.buffer),Hn}function u(){return Q.buffer!=Be&&Jt(Q.buffer),un}var c=typeof a!="undefined"?a:{},h,d;c.ready=new Promise(function(I,C){h=I,d=C});var p={},f;for(f in c)c.hasOwnProperty(f)&&(p[f]=c[f]);var m=[],A="./this.program",y=function(I,C){throw C},g=!1,_=!1,b=!1,w=!1;g=typeof window=="object",_=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!g&&!b&&!_;var x=c.ENVIRONMENT_IS_PTHREAD||!1;x&&(Be=c.buffer);var N="";function S(I){return c.locateFile?c.locateFile(I,N):N+I}var T,M,D,z,W,U;if(b){_?N=au().dirname(N)+"/":N=__dirname+"/",T=function(I,C){return W||(W=require("fs")),U||(U=au()),I=U.normalize(I),W.readFileSync(I,C?null:"utf8")},D=function(I){var C=T(I,!0);return C.buffer||(C=new Uint8Array(C)),pe(C.buffer),C},process.argv.length>1&&(A=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(I){if(!(I instanceof ru))throw I}),process.on("unhandledRejection",Yr),y=function(I){process.exit(I)},c.inspect=function(){return"[Emscripten Module object]"};var H;try{H=U8()}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=H.Worker}else w?(typeof read!="undefined"&&(T=function(I){return read(I)}),D=function(I){var C;return typeof readbuffer=="function"?new Uint8Array(readbuffer(I)):(C=read(I,"binary"),pe(typeof C=="object"),C)},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||_)&&(_?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="",b?(T=function(I,C){return W||(W=require("fs")),U||(U=au()),I=U.normalize(I),W.readFileSync(I,C?null:"utf8")},D=function(I){var C=T(I,!0);return C.buffer||(C=new Uint8Array(C)),pe(C.buffer),C}):(T=function(I){var C=new XMLHttpRequest;return C.open("GET",I,!1),C.send(null),C.responseText},_&&(D=function(I){var C=new XMLHttpRequest;return C.open("GET",I,!1),C.responseType="arraybuffer",C.send(null),new Uint8Array(C.response)}),M=function(I,C,L){var q=new XMLHttpRequest;q.open("GET",I,!0),q.responseType="arraybuffer",q.onload=function(){if(q.status==200||q.status==0&&q.response){C(q.response);return}L()},q.onerror=L,q.send(null)}),z=function(I){document.title=I});b&&typeof performance=="undefined"&&(global.performance=H8().performance);var X=c.print||console.log.bind(console),j=c.printErr||console.warn.bind(console);for(f in p)p.hasOwnProperty(f)&&(c[f]=p[f]);p=null,c.arguments&&(m=c.arguments),c.thisProgram&&(A=c.thisProgram),c.quit&&(y=c.quit);var ee=Atomics.load,Y=Atomics.store,ae=Atomics.compareExchange,te;c.wasmBinary&&(te=c.wasmBinary);var ie=c.noExitRuntime||!0;typeof WebAssembly!="object"&&Yr("no native wasm support detected");var Q,he,oe=!1,fe;function pe(I,C){I||Yr("Assertion failed: "+C)}function ke(I){var C=c["_"+I];return pe(C,"Cannot call unknown function "+I+", make sure it is exported"),C}function Ne(I,C,L,q,de){var le={string:function(wn){var Gi=0;if(wn!=null&&wn!==0){var Sg=(wn.length<<2)+1;Gi=Ui(Sg),nt(wn,Gi,Sg)}return Gi},array:function(wn){var Gi=Ui(wn.length);return Ke(wn,Gi),Gi}};function ue(wn){return C==="string"?$e(wn):C==="boolean"?Boolean(wn):wn}var we=ke(I),rt=[],Ht=0;if(q)for(var $t=0;$t<q.length;$t++){var ba=le[L[$t]];ba?(Ht===0&&(Ht=nu()),rt[$t]=ba(q[$t])):rt[$t]=q[$t]}var ji=we.apply(null,rt);return ji=ue(ji),Ht!==0&&Vi(Ht),ji}function Me(I,C,L,q){L=L||[];var de=L.every(function(ue){return ue==="number"}),le=C!=="string";return le&&de&&!q?ke(I):function(){return Ne(I,C,L,arguments,q)}}function Oe(I,C,L){for(var q=C+L,de="";!(C>=q);){var le=I[C++];if(!le)return de;if(!(le&128)){de+=String.fromCharCode(le);continue}var ue=I[C++]&63;if((le&224)==192){de+=String.fromCharCode((le&31)<<6|ue);continue}var we=I[C++]&63;if((le&240)==224?le=(le&15)<<12|ue<<6|we:le=(le&7)<<18|ue<<12|we<<6|I[C++]&63,le<65536)de+=String.fromCharCode(le);else{var rt=le-65536;de+=String.fromCharCode(55296|rt>>10,56320|rt&1023)}}return de}function $e(I,C){return I?Oe(i(),I,C):""}function tt(I,C,L,q){if(!(q>0))return 0;for(var de=L,le=L+q-1,ue=0;ue<I.length;++ue){var we=I.charCodeAt(ue);if(we>=55296&&we<=57343){var rt=I.charCodeAt(++ue);we=65536+((we&1023)<<10)|rt&1023}if(we<=127){if(L>=le)break;C[L++]=we}else if(we<=2047){if(L+1>=le)break;C[L++]=192|we>>6,C[L++]=128|we&63}else if(we<=65535){if(L+2>=le)break;C[L++]=224|we>>12,C[L++]=128|we>>6&63,C[L++]=128|we&63}else{if(L+3>=le)break;C[L++]=240|we>>18,C[L++]=128|we>>12&63,C[L++]=128|we>>6&63,C[L++]=128|we&63}}return C[L]=0,L-de}function nt(I,C,L){return tt(I,i(),C,L)}function ot(I){for(var C=0,L=0;L<I.length;++L){var q=I.charCodeAt(L);q>=55296&&q<=57343&&(q=65536+((q&1023)<<10)|I.charCodeAt(++L)&1023),q<=127?++C:q<=2047?C+=2:q<=65535?C+=3:C+=4}return C}function Ke(I,C){s().set(I,C)}function pt(I,C){return I%C>0&&(I+=C-I%C),I}var Be,An,_t,Un,Yt,yn,Hn,En,un;function Jt(I){Be=I,c.HEAP8=An=new Int8Array(I),c.HEAP16=Un=new Int16Array(I),c.HEAP32=yn=new Int32Array(I),c.HEAPU8=_t=new Uint8Array(I),c.HEAPU16=Yt=new Uint16Array(I),c.HEAPU32=Hn=new Uint32Array(I),c.HEAPF32=En=new Float32Array(I),c.HEAPF64=un=new Float64Array(I)}var Cr=c.INITIAL_MEMORY||16777216;if(x)Q=c.wasmMemory,Be=c.buffer;else if(c.wasmMemory)Q=c.wasmMemory;else if(Q=new WebAssembly.Memory({initial:Cr/65536,maximum:2147483648/65536,shared:!0}),!(Q.buffer instanceof SharedArrayBuffer))throw j("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),b&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");Q&&(Be=Q.buffer),Cr=Be.byteLength,Jt(Be);var Jn,Qn=[],Aa=[],Kr=[],ya=[],Di=[],pr=!1,Gc=!1;x||Aa.push({func:function(){lh()}}),x&&(pr=!0);function $0(){if(!x){if(c.preRun)for(typeof c.preRun=="function"&&(c.preRun=[c.preRun]);c.preRun.length;)Kc(c.preRun.shift());zi(Qn)}}function qc(){pr=!0,zi(Aa)}function D0(){x||zi(Kr)}function Xc(){x||(Gc=!0)}function gn(){if(!x){if(c.postRun)for(typeof c.postRun=="function"&&(c.postRun=[c.postRun]);c.postRun.length;)O0(c.postRun.shift());zi(Di)}}function Kc(I){Qn.unshift(I)}function O0(I){Di.unshift(I)}var Zr=0,ga=null,ns=null;function z0(I){pe(!x,"addRunDependency cannot be used in a pthread worker"),Zr++,c.monitorRunDependencies&&c.monitorRunDependencies(Zr)}function P0(I){if(Zr--,c.monitorRunDependencies&&c.monitorRunDependencies(Zr),Zr==0&&(ga!==null&&(clearInterval(ga),ga=null),ns)){var C=ns;ns=null,C()}}c.preloadedImages={},c.preloadedAudios={};function Yr(I){c.onAbort&&c.onAbort(I),x&&console.error("Pthread aborting at "+new Error().stack),I+="",j(I),oe=!0,fe=1,I="abort("+I+"). Build with -s ASSERTIONS=1 for more info.";var C=new WebAssembly.RuntimeError(I);throw d(C),C}function Zc(I,C){return String.prototype.startsWith?I.startsWith(C):I.indexOf(C)===0}var Oi="data:application/octet-stream;base64,";function Yc(I){return Zc(I,Oi)}var L0="file://";function Jc(I){return Zc(I,L0)}var xn="tfjs-backend-wasm-threaded-simd.wasm";Yc(xn)||(xn=S(xn));function W0(I){try{if(I==xn&&te)return new Uint8Array(te);if(D)return D(I);throw"both async and sync fetching of the wasm failed"}catch(C){Yr(C)}}function Qc(){if(!te&&(g||_)){if(typeof fetch=="function"&&!Jc(xn))return fetch(xn,{credentials:"same-origin"}).then(function(I){if(!I.ok)throw"failed to load wasm binary file at '"+xn+"'";return I.arrayBuffer()}).catch(function(){return W0(xn)});if(M)return new Promise(function(I,C){M(xn,function(L){I(new Uint8Array(L))},C)})}return Promise.resolve().then(function(){return W0(xn)})}function B0(){var I={a:R1};function C(ue,we){var rt=ue.exports;if(c.asm=rt,Jn=c.asm.F,he=we,!x){var Ht=Ie.unusedWorkers.length;Ie.unusedWorkers.forEach(function($t){Ie.loadWasmModuleToWorker($t,function(){--Ht||P0("wasm-instantiate")})})}}x||z0("wasm-instantiate");function L(ue){C(ue.instance,ue.module)}function q(ue){return Qc().then(function(we){return WebAssembly.instantiate(we,I)}).then(ue,function(we){j("failed to asynchronously prepare wasm: "+we),Yr(we)})}function de(){return!te&&typeof WebAssembly.instantiateStreaming=="function"&&!Yc(xn)&&!Jc(xn)&&typeof fetch=="function"?fetch(xn,{credentials:"same-origin"}).then(function(ue){var we=WebAssembly.instantiateStreaming(ue,I);return we.then(L,function(rt){return j("wasm streaming compile failed: "+rt),j("falling back to ArrayBuffer instantiation"),q(L)})}):q(L)}if(c.instantiateWasm)try{var le=c.instantiateWasm(I,C);return le}catch(ue){return j("Module.instantiateWasm callback failed with error: "+ue),!1}return de().catch(d),{}}var eh={8991:function(I,C){setTimeout(function(){_g(I,C)},0)}};function V0(){Ie.initRuntime()}function zi(I){for(;I.length>0;){var C=I.shift();if(typeof C=="function"){C(c);continue}var L=C.func;typeof L=="number"?C.arg===void 0?Jn.get(L)():Jn.get(L)(C.arg):L(C.arg===void 0?null:C.arg)}}function Pi(I,C){if(I<=0||I>s().length||I&!0||C<0)return-28;if(C==0)return 0;C>=2147483647&&(C=Infinity);var L=Atomics.load(o(),Hi>>2),q=0;if(L==I){var de=Atomics.compareExchange(o(),Hi>>2,L,0);if(de==L&&(--C,q=1,C<=0))return 1}var le=Atomics.notify(o(),I>>2,C);if(le>=0)return le+q;throw"Atomics.notify returned an unexpected value "+le}c._emscripten_futex_wake=Pi;function U0(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 C=Ie.pthreads[I];C.worker.terminate(),Ie.freeThreadData(C),Ie.runningWorkers.splice(Ie.runningWorkers.indexOf(C.worker),1),C.worker.pthread=void 0}function H0(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 C=Ie.pthreads[I];C.worker.postMessage({cmd:"cancel"})}function j0(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 C=Ie.pthreads[I];if(C){var L=C.worker;Ie.returnWorkerToPool(L)}}var Ie={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var I=8,C=0;C<I;++C)Ie.allocateUnusedWorker()},initRuntime:function(){for(var I=as(228),C=0;C<228/4;++C)l()[I/4+C]=0;o()[I+12>>2]=I;var L=I+152;o()[L>>2]=L;for(var q=as(512),C=0;C<128;++C)l()[q/4+C]=0;Atomics.store(l(),I+100>>2,q),Atomics.store(l(),I+40>>2,I),ph(I,!_,1),wg(I)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Ie.threadExitHandlers.length>0;)Ie.threadExitHandlers.pop()();x&&Bi()&&xg()},threadExit:function(I){var C=Bi();C&&(Atomics.store(l(),C+4>>2,I),Atomics.store(l(),C+0>>2,1),Atomics.store(l(),C+56>>2,1),Atomics.store(l(),C+60>>2,0),Ie.runExitHandlers(),Pi(C+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 C=Ie.pthreads[I];C&&C.worker&&Ie.returnWorkerToPool(C.worker)}Ie.pthreads={};for(var L=0;L<Ie.unusedWorkers.length;++L){var q=Ie.unusedWorkers[L];q.terminate()}Ie.unusedWorkers=[];for(var L=0;L<Ie.runningWorkers.length;++L){var q=Ie.runningWorkers[L],C=q.pthread;Ie.freeThreadData(C),q.terminate()}Ie.runningWorkers=[]},freeThreadData:function(I){if(I){if(I.threadInfoStruct){var C=o()[I.threadInfoStruct+100>>2];o()[I.threadInfoStruct+100>>2]=0,tu(C),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()[Ng>>2]=0;try{I()}finally{o()[Ng>>2]=1}},receiveObjectTransfer:function(I){},loadWasmModuleToWorker:function(I,C){I.onmessage=function(L){var q=L.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(L.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")G1();else if(de==="spawnThread")ih(L.data);else if(de==="cleanupThread")j0(q.thread);else if(de==="killThread")U0(q.thread);else if(de==="cancelThread")H0(q.thread);else if(de==="loaded")I.loaded=!0,C&&C(I),I.runPthread&&(I.runPthread(),delete I.runPthread);else if(de==="print")X("Thread "+q.threadId+": "+q.text);else if(de==="printErr")j("Thread "+q.threadId+": "+q.text);else if(de==="alert")alert("Thread "+q.threadId+": "+q.text);else if(de==="exit"){var ue=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(L.data):L.data.target==="setimmediate"?I.postMessage(L.data):j("worker sent an unknown command "+de);Ie.currentProxiedOperationCallerThread=void 0},I.onerror=function(L){j("pthread sent an error! "+L.filename+":"+L.lineno+": "+L.message)},b&&(I.on("message",function(L){I.onmessage({data:L})}),I.on("error",function(L){I.onerror(L)}),I.on("exit",function(L){})),I.postMessage({cmd:"load",urlOrBlob:c.mainScriptUrlOrBlob||r,wasmMemory:Q,wasmModule:he})},allocateUnusedWorker:function(){var I=S("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 C=performance.now()+I;performance.now()<C;);}};function G0(I,C){kg(I,C),Vi(I)}c.establishStackSpace=G0;function q0(){return ie}c.getNoExitRuntime=q0;function X0(I,C){return Jn.get(I)(C)}c.invokeEntryPoint=X0;function K0(I,C,L,q){Yr("Assertion failed: "+$e(I)+", at: "+[C?$e(C):"unknown filename",L,q?$e(q):"unknown function"])}function Z0(I,C){var L=_main(I,C)}var rs;b?rs=function(){var I=process.hrtime();return I[0]*1e3+I[1]/1e6}:x?rs=function(){return performance.now()-c.__performance_now_clock_drift}:typeof dateNow!="undefined"?rs=dateNow:rs=function(){return performance.now()};function Y0(I){return o()[yg()>>2]=I,I}function J0(I,C){if(x)return xa(1,1,I,C)}function Q0(I,C){if(I==C)postMessage({cmd:"processQueuedMainThreadWork"});else if(x)postMessage({targetThread:I,cmd:"processThreadQueue"});else{var L=Ie.pthreads[I],q=L&&L.worker;if(!q)return;q.postMessage({cmd:"processThreadQueue"})}return 1}function e1(){Yr()}function t1(I,C,L){var q=i1(C,L);return eh[I].apply(null,q)}function n1(I,C){}function r1(I,C,L){if(I<=0||I>s().length||I&!0)return-28;if(g){if(Atomics.load(o(),I>>2)!=C)return-6;for(var q=performance.now(),de=q+L,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(G1(),Atomics.load(o(),I>>2)!=C)return-6;le=Atomics.exchange(o(),Hi>>2,I)}return 0}else{var ue=Atomics.wait(o(),I>>2,C,L);if(ue==="timed-out")return-73;if(ue==="not-equal")return-6;if(ue==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ue}}function a1(I,C,L){i().copyWithin(I,C,C+L)}function s1(){return b?require("os").cpus().length:navigator.hardwareConcurrency}function xa(I,C){for(var L=arguments.length-2,q=nu(),de=L,le=Ui(de*8),ue=le>>3,we=0;we<L;we++){var rt=arguments[2+we];u()[ue+we]=rt}var Ht=vg(I,de,le,C);return Vi(q),Ht}var Kl=[],Zl=[];function i1(I,C){Zl.length=0;var L;for(C>>=2;L=i()[I++];){var q=L<105;q&&C&1&&C++,Zl.push(q?u()[C++>>1]:o()[C]),++C}return Zl}function o1(I,C,L){Kl.length=C;for(var q=L>>3,de=0;de<C;de++)Kl[de]=u()[q+de];var le=I<0,ue=le?eh[-I-1]:E1[I];return ue.apply(null,Kl)}function l1(){return i().length}function u1(I){try{return Q.grow(I-Be.byteLength+65535>>>16),Jt(Q.buffer),1}catch(C){}}function c1(I){var C=l1();if(I<=C)return!1;var L=2147483648;if(I>L)return!1;for(var q=1;q<=4;q*=2){var de=C*(1+.2/q);de=Math.min(de,I+100663296);var le=Math.min(L,pt(Math.max(I,de),65536)),ue=u1(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,C,L){function q(ue,we){if(ue.length!=we.length)return!1;for(var rt in ue)if(ue[rt]!=we[rt])return!1;return!0}for(var de in Le.deferredCalls){var le=Le.deferredCalls[de];if(le.targetFunction==I&&q(le.argsList,L))return}Le.deferredCalls.push({targetFunction:I,precedence:C,argsList:L}),Le.deferredCalls.sort(function(ue,we){return ue.precedence<we.precedence})},removeDeferredCalls:function(I){for(var C=0;C<Le.deferredCalls.length;++C)Le.deferredCalls[C].targetFunction==I&&(Le.deferredCalls.splice(C,1),--C)},canPerformEventHandlerRequests:function(){return Le.inEventHandler&&Le.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(Le.canPerformEventHandlerRequests())for(var I=0;I<Le.deferredCalls.length;++I){var C=Le.deferredCalls[I];Le.deferredCalls.splice(I,1),--I,C.targetFunction.apply(null,C.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(I,C){for(var L=0;L<Le.eventHandlers.length;++L)Le.eventHandlers[L].target==I&&(!C||C==Le.eventHandlers[L].eventTypeString)&&Le._removeHandler(L--)},_removeHandler:function(I){var C=Le.eventHandlers[I];C.target.removeEventListener(C.eventTypeString,C.eventListenerFunc,C.useCapture),Le.eventHandlers.splice(I,1)},registerOrRemoveHandler:function(I){var C=function(q){++Le.inEventHandler,Le.currentEventHandler=I,Le.runDeferredCalls(),I.handlerFunc(q),Le.runDeferredCalls(),--Le.inEventHandler};if(I.callbackfunc)I.eventListenerFunc=C,I.target.addEventListener(I.eventTypeString,C,I.useCapture),Le.eventHandlers.push(I),Le.registerRemoveEventListeners();else for(var L=0;L<Le.eventHandlers.length;++L)Le.eventHandlers[L].target==I.target&&Le.eventHandlers[L].eventTypeString==I.eventTypeString&&Le._removeHandler(L--)},queueEventHandlerOnThread_iiii:function(I,C,L,q,de){var le=nu(),ue=Ui(12);o()[ue>>2]=L,o()[ue+4>>2]=q,o()[ue+8>>2]=de,q1(0,I,637534208,C,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 h1(I){var C=ot(I)+1,L=as(C);return nt(I,L,C),L}function d1(I,C,L,q){var de=nu(),le=Ui(12),ue=0;C&&(ue=h1(C)),o()[le>>2]=ue,o()[le+4>>2]=L,o()[le+8>>2]=q,q1(0,I,657457152,0,ue,le),Vi(de)}function p1(I,C,L,q){C=C?$e(C):"",d1(I,C,L,q)}function f1(I){return I>2?$e(I):I}var m1=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function A1(I){I=f1(I);var C=m1[I]||(typeof document!="undefined"?document.querySelector(I):void 0);return C}function Yl(I){return A1(I)}function th(I,C,L){var q=Yl(I);if(!q)return-4;if(q.canvasSharedPtr&&(o()[q.canvasSharedPtr>>2]=C,o()[q.canvasSharedPtr+4>>2]=L),q.offscreenCanvas||!q.controlTransferredOffscreen){q.offscreenCanvas&&(q=q.offscreenCanvas);var de=!1;if(q.GLctxObject&&q.GLctxObject.GLctx){var le=q.GLctxObject.GLctx.getParameter(2978);de=le[0]===0&&le[1]===0&&le[2]===q.width&&le[3]===q.height}q.width=C,q.height=L,de&&q.GLctxObject.GLctx.viewport(0,0,C,L)}else if(q.canvasSharedPtr){var ue=o()[q.canvasSharedPtr+8>>2];return p1(ue,I,C,L),1}else return-4;return 0}function nh(I,C,L){return x?xa(2,1,I,C,L):th(I,C,L)}function y1(I,C,L){var q=Yl(I);return q?th(I,C,L):nh(I,C,L)}function g1(I){}function x1(I,C){}function w1(I){var C=I.getExtension("ANGLE_instanced_arrays");if(C)return I.vertexAttribDivisor=function(L,q){C.vertexAttribDivisorANGLE(L,q)},I.drawArraysInstanced=function(L,q,de,le){C.drawArraysInstancedANGLE(L,q,de,le)},I.drawElementsInstanced=function(L,q,de,le,ue){C.drawElementsInstancedANGLE(L,q,de,le,ue)},1}function _1(I){var C=I.getExtension("OES_vertex_array_object");if(C)return I.createVertexArray=function(){return C.createVertexArrayOES()},I.deleteVertexArray=function(L){C.deleteVertexArrayOES(L)},I.bindVertexArray=function(L){C.bindVertexArrayOES(L)},I.isVertexArray=function(L){return C.isVertexArrayOES(L)},1}function b1(I){var C=I.getExtension("WEBGL_draw_buffers");if(C)return I.drawBuffers=function(L,q){C.drawBuffersWEBGL(L,q)},1}function v1(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 C=Qe.counter++,L=I.length;L<C;L++)I[L]=null;return C},getSource:function(I,C,L,q){for(var de="",le=0;le<C;++le){var ue=q?o()[q+le*4>>2]:-1;de+=$e(o()[L+le*4>>2],ue<0?void 0:ue)}return de},createContext:function(I,C){var L=I.getContext("webgl",C);if(!L)return 0;var q=Qe.registerContext(L,C);return q},registerContext:function(I,C){var L=as(8);o()[L+4>>2]=Bi();var q={handle:L,attributes:C,version:C.majorVersion,GLctx:I};return I.canvas&&(I.canvas.GLctxObject=q),Qe.contexts[L]=q,(typeof C.enableExtensionsByDefault=="undefined"||C.enableExtensionsByDefault)&&Qe.initExtensions(q),L},makeContextCurrent:function(I){return Qe.currentContext=Qe.contexts[I],c.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 C=I.GLctx;w1(C),_1(C),b1(C),C.disjointTimerQueryExt=C.getExtension("EXT_disjoint_timer_query"),v1(C);var L=C.getSupportedExtensions()||[];L.forEach(function(q){q.indexOf("lose_context")<0&&q.indexOf("debug")<0&&C.getExtension(q)})}},populateUniformTable:function(I){for(var C=Qe.programs[I],L=Qe.programInfos[I]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},q=L.uniforms,de=wa.getProgramParameter(C,35718),le=0;le<de;++le){var ue=wa.getActiveUniform(C,le),we=ue.name;L.maxUniformLength=Math.max(L.maxUniformLength,we.length+1),we.slice(-1)=="]"&&(we=we.slice(0,we.lastIndexOf("[")));var rt=wa.getUniformLocation(C,we);if(rt){var Ht=Qe.getNewId(Qe.uniforms);q[we]=[ue.size,Ht],Qe.uniforms[Ht]=rt;for(var $t=1;$t<ue.size;++$t){var ba=we+"["+$t+"]";rt=wa.getUniformLocation(C,ba),Ht=Qe.getNewId(Qe.uniforms),Qe.uniforms[Ht]=rt}}}}},k1=["default","low-power","high-performance"];function I1(I,C){var L=C>>2,q=o()[L+(24>>2)],de={alpha:!!o()[L+(0>>2)],depth:!!o()[L+(4>>2)],stencil:!!o()[L+(8>>2)],antialias:!!o()[L+(12>>2)],premultipliedAlpha:!!o()[L+(16>>2)],preserveDrawingBuffer:!!o()[L+(20>>2)],powerPreference:k1[q],failIfMajorPerformanceCaveat:!!o()[L+(28>>2)],majorVersion:o()[L+(32>>2)],minorVersion:o()[L+(36>>2)],enableExtensionsByDefault:o()[L+(40>>2)],explicitSwapControl:o()[L+(44>>2)],proxyContextToMainThread:o()[L+(48>>2)],renderViaOffscreenBackBuffer:o()[L+(52>>2)]},le=Yl(I);if(!le||de.explicitSwapControl)return 0;var ue=Qe.createContext(le,de);return ue}function N1(I,C){return I1(I,C)}var Li={mappings:{},buffers:[null,[],[]],printChar:function(I,C){var L=Li.buffers[I];C===0||C===10?((I===1?X:j)(Oe(L,0)),L.length=0):L.push(C)},varargs:void 0,get:function(){Li.varargs+=4;var I=o()[Li.varargs-4>>2];return I},getStr:function(I){var C=$e(I);return C},get64:function(I,C){return I}};function rh(I){return x?xa(3,1,I):0}function ah(I,C,L,q,de){if(x)return xa(4,1,I,C,L,q,de)}function sh(I,C,L,q){if(x)return xa(5,1,I,C,L,q);for(var de=0,le=0;le<L;le++){for(var ue=o()[C+le*8>>2],we=o()[C+(le*8+4)>>2],rt=0;rt<we;rt++)Li.printChar(I,i()[ue+rt]);de+=we}return o()[q>>2]=de,0}function S1(I){var C=Ie.threadExitHandlers.pop();I&&C()}function T1(I,C){Ie.threadExitHandlers.push(function(){Jn.get(I)(C)})}function ih(I){if(x)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var C=Ie.getNewWorker();if(C.pthread!==void 0)throw"Internal error!";if(!I.pthread_ptr)throw"Internal error, no pthread ptr!";Ie.runningWorkers.push(C);for(var L=as(128*4),q=0;q<128;++q)o()[L+q*4>>2]=0;var de=I.stackBase+I.stackSize,le=Ie.pthreads[I.pthread_ptr]={worker:C,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),L),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=gg(),rt=we+40;Atomics.store(l(),ue+(172>>2),rt),C.pthread=le;var Ht={cmd:"run",start_routine:I.startRoutine,arg:I.arg,threadInfoStruct:I.pthread_ptr,stackBase:I.stackBase,stackSize:I.stackSize};C.runPthread=function(){Ht.time=performance.now(),C.postMessage(Ht,I.transferList)},C.loaded&&(C.runPthread(),delete C.runPthread)}function C1(I,C,L,q){if(typeof SharedArrayBuffer=="undefined")return j("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!I)return j("pthread_create called with a null thread pointer!"),28;var de=[],le=0;if(x&&(de.length===0||le))return bg(687865856,I,C,L,q);if(le)return le;var ue=0,we=0,rt=0;C&&C!=-1?(ue=o()[C>>2],ue+=81920,we=o()[C+8>>2],rt=o()[C+12>>2]!==0):ue=2097152;var Ht=we==0;Ht?we=Ig(16,ue):(we-=ue,pe(we>0));for(var $t=as(228),ba=0;ba<228>>2;++ba)l()[($t>>2)+ba]=0;o()[I>>2]=$t,o()[$t+12>>2]=$t;var ji=$t+152;o()[ji>>2]=ji;var wn={stackBase:we,stackSize:ue,allocatedOwnStack:Ht,detached:rt,startRoutine:L,pthread_ptr:$t,arg:q,transferList:de};return x?(wn.cmd="spawnThread",postMessage(wn,de)):ih(wn),0}function oh(I){if(x)return xa(6,1,I);switch(I){case 30:return 16384;case 85:var C=2147483648;return C/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return Y0(28),-1}x||Ie.initMainThreadBlock();var wa,E1=[null,J0,nh,rh,ah,sh,oh],R1={e:K0,r:Z0,x:Q0,b:e1,y:t1,j:n1,c:r1,d:Pi,f:rs,p:a1,z:s1,u:o1,q:c1,v:y1,i:g1,t:x1,w:N1,m:rh,n:ah,g:sh,o:V0,a:Q||c.wasmMemory,k:S1,l:T1,h:C1,s:oh},Ag=B0(),lh=c.___wasm_call_ctors=function(){return(lh=c.___wasm_call_ctors=c.asm.A).apply(null,arguments)},F1=c._init=function(){return(F1=c._init=c.asm.B).apply(null,arguments)},M1=c._register_tensor=function(){return(M1=c._register_tensor=c.asm.C).apply(null,arguments)},$1=c._dispose_data=function(){return($1=c._dispose_data=c.asm.D).apply(null,arguments)},D1=c._dispose=function(){return(D1=c._dispose=c.asm.E).apply(null,arguments)},O1=c._Abs=function(){return(O1=c._Abs=c.asm.G).apply(null,arguments)},z1=c._Add=function(){return(z1=c._Add=c.asm.H).apply(null,arguments)},P1=c._AddN=function(){return(P1=c._AddN=c.asm.I).apply(null,arguments)},L1=c._ArgMax=function(){return(L1=c._ArgMax=c.asm.J).apply(null,arguments)},W1=c._AvgPool=function(){return(W1=c._AvgPool=c.asm.K).apply(null,arguments)},B1=c._BatchMatMul=function(){return(B1=c._BatchMatMul=c.asm.L).apply(null,arguments)},V1=c._Ceil=function(){return(V1=c._Ceil=c.asm.M).apply(null,arguments)},U1=c._ClipByValue=function(){return(U1=c._ClipByValue=c.asm.N).apply(null,arguments)},H1=c._Conv2D=function(){return(H1=c._Conv2D=c.asm.O).apply(null,arguments)},uh=c._Conv2DBackpropInput=function(){return(uh=c._Conv2DBackpropInput=c.asm.P).apply(null,arguments)},ch=c._Cos=function(){return(ch=c._Cos=c.asm.Q).apply(null,arguments)},Jl=c._CropAndResize=function(){return(Jl=c._CropAndResize=c.asm.R).apply(null,arguments)},Wi=c._Cumsum=function(){return(Wi=c._Cumsum=c.asm.S).apply(null,arguments)},j1=c._DepthToSpace=function(){return(j1=c._DepthToSpace=c.asm.T).apply(null,arguments)},Ql=c._DepthwiseConv2dNative=function(){return(Ql=c._DepthwiseConv2dNative=c.asm.U).apply(null,arguments)},K=c._Equal=function(){return(K=c._Equal=c.asm.V).apply(null,arguments)},ne=c._Exp=function(){return(ne=c._Exp=c.asm.W).apply(null,arguments)},Se=c._FlipLeftRight=function(){return(Se=c._FlipLeftRight=c.asm.X).apply(null,arguments)},Ze=c._Floor=function(){return(Ze=c._Floor=c.asm.Y).apply(null,arguments)},Nt=c._FloorDiv=function(){return(Nt=c._FloorDiv=c.asm.Z).apply(null,arguments)},At=c._FusedBatchNorm=function(){return(At=c._FusedBatchNorm=c.asm._).apply(null,arguments)},Ve=c._FusedConv2D=function(){return(Ve=c._FusedConv2D=c.asm.$).apply(null,arguments)},He=c._FusedDepthwiseConv2D=function(){return(He=c._FusedDepthwiseConv2D=c.asm.aa).apply(null,arguments)},Qt=c._Gather=function(){return(Qt=c._Gather=c.asm.ba).apply(null,arguments)},Jr=c._GatherNd=function(){return(Jr=c._GatherNd=c.asm.ca).apply(null,arguments)},Qr=c._Greater=function(){return(Qr=c._Greater=c.asm.da).apply(null,arguments)},hh=c._GreaterEqual=function(){return(hh=c._GreaterEqual=c.asm.ea).apply(null,arguments)},eu=c._LeakyRelu=function(){return(eu=c._LeakyRelu=c.asm.fa).apply(null,arguments)},jn=c._Less=function(){return(jn=c._Less=c.asm.ga).apply(null,arguments)},_a=c._LessEqual=function(){return(_a=c._LessEqual=c.asm.ha).apply(null,arguments)},dh=c._Log=function(){return(dh=c._Log=c.asm.ia).apply(null,arguments)},_4=c._LogicalAnd=function(){return(_4=c._LogicalAnd=c.asm.ja).apply(null,arguments)},b4=c._Max=function(){return(b4=c._Max=c.asm.ka).apply(null,arguments)},v4=c._MaxPool=function(){return(v4=c._MaxPool=c.asm.la).apply(null,arguments)},k4=c._Maximum=function(){return(k4=c._Maximum=c.asm.ma).apply(null,arguments)},I4=c._Mean=function(){return(I4=c._Mean=c.asm.na).apply(null,arguments)},N4=c._Min=function(){return(N4=c._Min=c.asm.oa).apply(null,arguments)},S4=c._Minimum=function(){return(S4=c._Minimum=c.asm.pa).apply(null,arguments)},T4=c._Multiply=function(){return(T4=c._Multiply=c.asm.qa).apply(null,arguments)},C4=c._Neg=function(){return(C4=c._Neg=c.asm.ra).apply(null,arguments)},E4=c._NonMaxSuppressionV3=function(){return(E4=c._NonMaxSuppressionV3=c.asm.sa).apply(null,arguments)},R4=c._NonMaxSuppressionV4=function(){return(R4=c._NonMaxSuppressionV4=c.asm.ta).apply(null,arguments)},F4=c._NonMaxSuppressionV5=function(){return(F4=c._NonMaxSuppressionV5=c.asm.ua).apply(null,arguments)},M4=c._NotEqual=function(){return(M4=c._NotEqual=c.asm.va).apply(null,arguments)},$4=c._OneHot=function(){return($4=c._OneHot=c.asm.wa).apply(null,arguments)},D4=c._PadV2=function(){return(D4=c._PadV2=c.asm.xa).apply(null,arguments)},O4=c._Pow=function(){return(O4=c._Pow=c.asm.ya).apply(null,arguments)},z4=c._Prelu=function(){return(z4=c._Prelu=c.asm.za).apply(null,arguments)},P4=c._Prod=function(){return(P4=c._Prod=c.asm.Aa).apply(null,arguments)},L4=c._RealDiv=function(){return(L4=c._RealDiv=c.asm.Ba).apply(null,arguments)},W4=c._Relu=function(){return(W4=c._Relu=c.asm.Ca).apply(null,arguments)},B4=c._Relu6=function(){return(B4=c._Relu6=c.asm.Da).apply(null,arguments)},V4=c._ResizeBilinear=function(){return(V4=c._ResizeBilinear=c.asm.Ea).apply(null,arguments)},U4=c._Reverse=function(){return(U4=c._Reverse=c.asm.Fa).apply(null,arguments)},H4=c._RotateWithOffset=function(){return(H4=c._RotateWithOffset=c.asm.Ga).apply(null,arguments)},j4=c._Round=function(){return(j4=c._Round=c.asm.Ha).apply(null,arguments)},G4=c._Rsqrt=function(){return(G4=c._Rsqrt=c.asm.Ia).apply(null,arguments)},q4=c._ScatterNd=function(){return(q4=c._ScatterNd=c.asm.Ja).apply(null,arguments)},X4=c._SelectV2=function(){return(X4=c._SelectV2=c.asm.Ka).apply(null,arguments)},K4=c._Sigmoid=function(){return(K4=c._Sigmoid=c.asm.La).apply(null,arguments)},Z4=c._Sin=function(){return(Z4=c._Sin=c.asm.Ma).apply(null,arguments)},Y4=c._Softmax=function(){return(Y4=c._Softmax=c.asm.Na).apply(null,arguments)},J4=c._Sqrt=function(){return(J4=c._Sqrt=c.asm.Oa).apply(null,arguments)},Q4=c._Square=function(){return(Q4=c._Square=c.asm.Pa).apply(null,arguments)},e8=c._SquaredDifference=function(){return(e8=c._SquaredDifference=c.asm.Qa).apply(null,arguments)},t8=c._Step=function(){return(t8=c._Step=c.asm.Ra).apply(null,arguments)},n8=c._StridedSlice=function(){return(n8=c._StridedSlice=c.asm.Sa).apply(null,arguments)},r8=c._Sub=function(){return(r8=c._Sub=c.asm.Ta).apply(null,arguments)},a8=c._Sum=function(){return(a8=c._Sum=c.asm.Ua).apply(null,arguments)},s8=c._Tanh=function(){return(s8=c._Tanh=c.asm.Va).apply(null,arguments)},i8=c._Tile=function(){return(i8=c._Tile=c.asm.Wa).apply(null,arguments)},o8=c._TopK=function(){return(o8=c._TopK=c.asm.Xa).apply(null,arguments)},l8=c._Transpose=function(){return(l8=c._Transpose=c.asm.Ya).apply(null,arguments)},u8=c.__FusedMatMul=function(){return(u8=c.__FusedMatMul=c.asm.Za).apply(null,arguments)},as=c._malloc=function(){return(as=c._malloc=c.asm._a).apply(null,arguments)},tu=c._free=function(){return(tu=c._free=c.asm.$a).apply(null,arguments)},yg=c.___errno_location=function(){return(yg=c.___errno_location=c.asm.ab).apply(null,arguments)},gg=c._emscripten_get_global_libc=function(){return(gg=c._emscripten_get_global_libc=c.asm.bb).apply(null,arguments)},Bi=c._pthread_self=function(){return(Bi=c._pthread_self=c.asm.cb).apply(null,arguments)},xg=c.___pthread_tsd_run_dtors=function(){return(xg=c.___pthread_tsd_run_dtors=c.asm.db).apply(null,arguments)},G1=c._emscripten_main_thread_process_queued_calls=function(){return(G1=c._emscripten_main_thread_process_queued_calls=c.asm.eb).apply(null,arguments)},c8=c._emscripten_current_thread_process_queued_calls=function(){return(c8=c._emscripten_current_thread_process_queued_calls=c.asm.fb).apply(null,arguments)},wg=c._emscripten_register_main_browser_thread_id=function(){return(wg=c._emscripten_register_main_browser_thread_id=c.asm.gb).apply(null,arguments)},_g=c.__emscripten_do_dispatch_to_thread=function(){return(_g=c.__emscripten_do_dispatch_to_thread=c.asm.hb).apply(null,arguments)},bg=c._emscripten_sync_run_in_main_thread_4=function(){return(bg=c._emscripten_sync_run_in_main_thread_4=c.asm.ib).apply(null,arguments)},vg=c._emscripten_run_in_main_runtime_thread_js=function(){return(vg=c._emscripten_run_in_main_runtime_thread_js=c.asm.jb).apply(null,arguments)},q1=c.__emscripten_call_on_thread=function(){return(q1=c.__emscripten_call_on_thread=c.asm.kb).apply(null,arguments)},h8=c._emscripten_tls_init=function(){return(h8=c._emscripten_tls_init=c.asm.lb).apply(null,arguments)},ph=c.__emscripten_thread_init=function(){return(ph=c.__emscripten_thread_init=c.asm.mb).apply(null,arguments)},nu=c.stackSave=function(){return(nu=c.stackSave=c.asm.nb).apply(null,arguments)},Vi=c.stackRestore=function(){return(Vi=c.stackRestore=c.asm.ob).apply(null,arguments)},Ui=c.stackAlloc=function(){return(Ui=c.stackAlloc=c.asm.pb).apply(null,arguments)},kg=c._emscripten_stack_set_limits=function(){return(kg=c._emscripten_stack_set_limits=c.asm.qb).apply(null,arguments)},Ig=c._memalign=function(){return(Ig=c._memalign=c.asm.rb).apply(null,arguments)},Ng=c.__emscripten_allow_main_runtime_queued_calls=9880,Hi=c.__emscripten_main_thread_futex=11368;c.cwrap=Me,c.PThread=Ie,c.PThread=Ie,c.wasmMemory=Q,c.ExitStatus=ru;var fh;function ru(I){this.name="ExitStatus",this.message="Program terminated with exit("+I+")",this.status=I}ns=function I(){fh||X1(),fh||(ns=I)};function X1(I){if(I=I||m,Zr>0)return;if(x){h(c),postMessage({cmd:"loaded"});return}if($0(),Zr>0)return;function C(){fh||(fh=!0,c.calledRun=!0,!oe&&(qc(),D0(),h(c),c.onRuntimeInitialized&&c.onRuntimeInitialized(),gn()))}c.setStatus?(c.setStatus("Running..."),setTimeout(function(){setTimeout(function(){c.setStatus("")},1),C()},1)):C()}c.run=X1;function d8(I,C){if(!(C&&ie&&I===0)){if(!C&&x)throw postMessage({cmd:"exitProcess",returnCode:I}),new ru(I);ie||(Ie.terminateAllThreads(),fe=I,Xc(),c.onExit&&c.onExit(I),oe=!0),y(I,new ru(I))}}if(c.preInit)for(typeof c.preInit=="function"&&(c.preInit=[c.preInit]);c.preInit.length>0;)c.preInit.pop()();return x&&(ie=!1,Ie.initWorker()),X1(),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)}),G8=et((e,t)=>{var n=function(){var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(a){a=a||{};var s=typeof a!="undefined"?a:{},i,o;s.ready=new Promise(function(K,ne){i=K,o=ne});var l={},u;for(u in s)s.hasOwnProperty(u)&&(l[u]=s[u]);var c=[],h="./this.program",d=function(K,ne){throw ne},p=!1,f=!1,m=!1,A=!1;p=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",A=!p&&!m&&!f;var y="";function g(K){return s.locateFile?s.locateFile(K,y):y+K}var _,b,w,x,N,S;m?(f?y=au().dirname(y)+"/":y=__dirname+"/",_=function(K,ne){return N||(N=require("fs")),S||(S=au()),K=S.normalize(K),N.readFileSync(K,ne?null:"utf8")},w=function(K){var ne=_(K,!0);return ne.buffer||(ne=new Uint8Array(ne)),X(ne.buffer),ne},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),c=process.argv.slice(2),process.on("uncaughtException",function(K){if(!(K instanceof j1))throw K}),process.on("unhandledRejection",pr),d=function(K){process.exit(K)},s.inspect=function(){return"[Emscripten Module object]"}):A?(typeof read!="undefined"&&(_=function(K){return read(K)}),w=function(K){var ne;return typeof readbuffer=="function"?new Uint8Array(readbuffer(K)):(ne=read(K,"binary"),X(typeof ne=="object"),ne)},typeof scriptArgs!="undefined"?c=scriptArgs:typeof arguments!="undefined"&&(c=arguments),typeof quit=="function"&&(d=function(K){quit(K)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(p||f)&&(f?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),r&&(y=r),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",_=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.send(null),ne.responseText},f&&(w=function(K){var ne=new XMLHttpRequest;return ne.open("GET",K,!1),ne.responseType="arraybuffer",ne.send(null),new Uint8Array(ne.response)}),b=function(K,ne,Se){var Ze=new XMLHttpRequest;Ze.open("GET",K,!0),Ze.responseType="arraybuffer",Ze.onload=function(){if(Ze.status==200||Ze.status==0&&Ze.response){ne(Ze.response);return}Se()},Ze.onerror=Se,Ze.send(null)},x=function(K){document.title=K});var T=s.print||console.log.bind(console),M=s.printErr||console.warn.bind(console);for(u in l)l.hasOwnProperty(u)&&(s[u]=l[u]);l=null,s.arguments&&(c=s.arguments),s.thisProgram&&(h=s.thisProgram),s.quit&&(d=s.quit);var D;s.wasmBinary&&(D=s.wasmBinary);var z=s.noExitRuntime||!0;typeof WebAssembly!="object"&&pr("no native wasm support detected");var W,U=!1,H;function X(K,ne){K||pr("Assertion failed: "+ne)}function j(K){var ne=s["_"+K];return X(ne,"Cannot call unknown function "+K+", make sure it is exported"),ne}function ee(K,ne,Se,Ze,Nt){var At={string:function(jn){var _a=0;if(jn!=null&&jn!==0){var dh=(jn.length<<2)+1;_a=Jl(dh),he(jn,_a,dh)}return _a},array:function(jn){var _a=Jl(jn.length);return oe(jn,_a),_a}};function Ve(jn){return ne==="string"?ie(jn):ne==="boolean"?Boolean(jn):jn}var He=j(K),Qt=[],Jr=0;if(Ze)for(var Qr=0;Qr<Ze.length;Qr++){var hh=At[Se[Qr]];hh?(Jr===0&&(Jr=uh()),Qt[Qr]=hh(Ze[Qr])):Qt[Qr]=Ze[Qr]}var eu=He.apply(null,Qt);return eu=Ve(eu),Jr!==0&&ch(Jr),eu}function Y(K,ne,Se,Ze){Se=Se||[];var Nt=Se.every(function(Ve){return Ve==="number"}),At=ne!=="string";return At&&Nt&&!Ze?j(K):function(){return ee(K,ne,Se,arguments,Ze)}}var ae=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function te(K,ne,Se){for(var Ze=ne+Se,Nt=ne;K[Nt]&&!(Nt>=Ze);)++Nt;if(Nt-ne>16&&K.subarray&&ae)return ae.decode(K.subarray(ne,Nt));for(var At="";ne<Nt;){var Ve=K[ne++];if(!(Ve&128)){At+=String.fromCharCode(Ve);continue}var He=K[ne++]&63;if((Ve&224)==192){At+=String.fromCharCode((Ve&31)<<6|He);continue}var Qt=K[ne++]&63;if((Ve&240)==224?Ve=(Ve&15)<<12|He<<6|Qt:Ve=(Ve&7)<<18|He<<12|Qt<<6|K[ne++]&63,Ve<65536)At+=String.fromCharCode(Ve);else{var Jr=Ve-65536;At+=String.fromCharCode(55296|Jr>>10,56320|Jr&1023)}}return At}function ie(K,ne){return K?te(Ne,K,ne):""}function Q(K,ne,Se,Ze){if(!(Ze>0))return 0;for(var Nt=Se,At=Se+Ze-1,Ve=0;Ve<K.length;++Ve){var He=K.charCodeAt(Ve);if(He>=55296&&He<=57343){var Qt=K.charCodeAt(++Ve);He=65536+((He&1023)<<10)|Qt&1023}if(He<=127){if(Se>=At)break;ne[Se++]=He}else if(He<=2047){if(Se+1>=At)break;ne[Se++]=192|He>>6,ne[Se++]=128|He&63}else if(He<=65535){if(Se+2>=At)break;ne[Se++]=224|He>>12,ne[Se++]=128|He>>6&63,ne[Se++]=128|He&63}else{if(Se+3>=At)break;ne[Se++]=240|He>>18,ne[Se++]=128|He>>12&63,ne[Se++]=128|He>>6&63,ne[Se++]=128|He&63}}return ne[Se]=0,Se-Nt}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,tt,nt,ot;function Ke(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=tt=new Uint32Array(K),s.HEAPF32=nt=new Float32Array(K),s.HEAPF64=ot=new Float64Array(K)}var pt=s.INITIAL_MEMORY||16777216,Be,An=[],_t=[],Un=[],Yt=[],yn=!1;_t.push({func:function(){Qc()}});function Hn(){if(s.preRun)for(typeof s.preRun=="function"&&(s.preRun=[s.preRun]);s.preRun.length;)Cr(s.preRun.shift());ga(An)}function En(){yn=!0,ga(_t)}function un(){ga(Un)}function Jt(){if(s.postRun)for(typeof s.postRun=="function"&&(s.postRun=[s.postRun]);s.postRun.length;)Jn(s.postRun.shift());ga(Yt)}function Cr(K){An.unshift(K)}function Jn(K){Yt.unshift(K)}var Qn=0,Aa=null,Kr=null;function ya(K){Qn++,s.monitorRunDependencies&&s.monitorRunDependencies(Qn)}function Di(K){if(Qn--,s.monitorRunDependencies&&s.monitorRunDependencies(Qn),Qn==0&&(Aa!==null&&(clearInterval(Aa),Aa=null),Kr)){var ne=Kr;Kr=null,ne()}}s.preloadedImages={},s.preloadedAudios={};function pr(K){s.onAbort&&s.onAbort(K),K+="",M(K),U=!0,H=1,K="abort("+K+"). Build with -s ASSERTIONS=1 for more info.";var ne=new WebAssembly.RuntimeError(K);throw o(ne),ne}function Gc(K,ne){return String.prototype.startsWith?K.startsWith(ne):K.indexOf(ne)===0}var $0="data:application/octet-stream;base64,";function qc(K){return Gc(K,$0)}var D0="file://";function Xc(K){return Gc(K,D0)}var gn="tfjs-backend-wasm.wasm";qc(gn)||(gn=g(gn));function Kc(K){try{if(K==gn&&D)return new Uint8Array(D);if(w)return w(K);throw"both async and sync fetching of the wasm failed"}catch(ne){pr(ne)}}function O0(){if(!D&&(p||f)){if(typeof fetch=="function"&&!Xc(gn))return fetch(gn,{credentials:"same-origin"}).then(function(K){if(!K.ok)throw"failed to load wasm binary file at '"+gn+"'";return K.arrayBuffer()}).catch(function(){return Kc(gn)});if(b)return new Promise(function(K,ne){b(gn,function(Se){K(new Uint8Array(Se))},ne)})}return Promise.resolve().then(function(){return Kc(gn)})}function Zr(){var K={a:xn};function ne(Ve,He){var Qt=Ve.exports;s.asm=Qt,W=s.asm.g,Ke(W.buffer),Be=s.asm.m,Di("wasm-instantiate")}ya("wasm-instantiate");function Se(Ve){ne(Ve.instance)}function Ze(Ve){return O0().then(function(He){return WebAssembly.instantiate(He,K)}).then(Ve,function(He){M("failed to asynchronously prepare wasm: "+He),pr(He)})}function Nt(){return!D&&typeof WebAssembly.instantiateStreaming=="function"&&!qc(gn)&&!Xc(gn)&&typeof fetch=="function"?fetch(gn,{credentials:"same-origin"}).then(function(Ve){var He=WebAssembly.instantiateStreaming(Ve,K);return He.then(Se,function(Qt){return M("wasm streaming compile failed: "+Qt),M("falling back to ArrayBuffer instantiation"),Ze(Se)})}):Ze(Se)}if(s.instantiateWasm)try{var At=s.instantiateWasm(K,ne);return At}catch(Ve){return M("Module.instantiateWasm callback failed with error: "+Ve),!1}return Nt().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 z0(K,ne,Se){Ne.copyWithin(K,ne,ne+Se)}function P0(){return Ne.length}function Yr(K){try{return W.grow(K-pe.byteLength+65535>>>16),Ke(W.buffer),1}catch(ne){}}function Zc(K){var ne=P0(),Se=2147483648;if(K>Se)return!1;for(var Ze=1;Ze<=4;Ze*=2){var Nt=ne*(1+.2/Ze);Nt=Math.min(Nt,K+100663296);var At=Math.min(Se,fe(Math.max(K,Nt),65536)),Ve=Yr(At);if(Ve)return!0}return!1}var Oi={mappings:{},buffers:[null,[],[]],printChar:function(K,ne){var Se=Oi.buffers[K];ne===0||ne===10?((K===1?T:M)(te(Se,0)),Se.length=0):Se.push(ne)},varargs:void 0,get:function(){Oi.varargs+=4;var K=$e[Oi.varargs-4>>2];return K},getStr:function(K){var ne=ie(K);return ne},get64:function(K,ne){return K}};function Yc(K){return 0}function L0(K,ne,Se,Ze,Nt){}function Jc(K,ne,Se,Ze){for(var Nt=0,At=0;At<Se;At++){for(var Ve=$e[ne+At*8>>2],He=$e[ne+(At*8+4)>>2],Qt=0;Qt<He;Qt++)Oi.printChar(K,Ne[Ve+Qt]);Nt+=He}return $e[Ze>>2]=Nt,0}var xn={a:ns,d:z0,e:Zc,f:Yc,c:L0,b:Jc},W0=Zr(),Qc=s.___wasm_call_ctors=function(){return(Qc=s.___wasm_call_ctors=s.asm.h).apply(null,arguments)},B0=s._init=function(){return(B0=s._init=s.asm.i).apply(null,arguments)},eh=s._register_tensor=function(){return(eh=s._register_tensor=s.asm.j).apply(null,arguments)},V0=s._dispose_data=function(){return(V0=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)},U0=s._Add=function(){return(U0=s._Add=s.asm.o).apply(null,arguments)},H0=s._AddN=function(){return(H0=s._AddN=s.asm.p).apply(null,arguments)},j0=s._ArgMax=function(){return(j0=s._ArgMax=s.asm.q).apply(null,arguments)},Ie=s._AvgPool=function(){return(Ie=s._AvgPool=s.asm.r).apply(null,arguments)},G0=s._BatchMatMul=function(){return(G0=s._BatchMatMul=s.asm.s).apply(null,arguments)},q0=s._Ceil=function(){return(q0=s._Ceil=s.asm.t).apply(null,arguments)},X0=s._ClipByValue=function(){return(X0=s._ClipByValue=s.asm.u).apply(null,arguments)},K0=s._Conv2D=function(){return(K0=s._Conv2D=s.asm.v).apply(null,arguments)},Z0=s._Conv2DBackpropInput=function(){return(Z0=s._Conv2DBackpropInput=s.asm.w).apply(null,arguments)},rs=s._Cos=function(){return(rs=s._Cos=s.asm.x).apply(null,arguments)},Y0=s._CropAndResize=function(){return(Y0=s._CropAndResize=s.asm.y).apply(null,arguments)},J0=s._Cumsum=function(){return(J0=s._Cumsum=s.asm.z).apply(null,arguments)},Q0=s._DepthToSpace=function(){return(Q0=s._DepthToSpace=s.asm.A).apply(null,arguments)},e1=s._DepthwiseConv2dNative=function(){return(e1=s._DepthwiseConv2dNative=s.asm.B).apply(null,arguments)},t1=s._Equal=function(){return(t1=s._Equal=s.asm.C).apply(null,arguments)},n1=s._Exp=function(){return(n1=s._Exp=s.asm.D).apply(null,arguments)},r1=s._FlipLeftRight=function(){return(r1=s._FlipLeftRight=s.asm.E).apply(null,arguments)},a1=s._Floor=function(){return(a1=s._Floor=s.asm.F).apply(null,arguments)},s1=s._FloorDiv=function(){return(s1=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)},i1=s._Gather=function(){return(i1=s._Gather=s.asm.K).apply(null,arguments)},o1=s._GatherNd=function(){return(o1=s._GatherNd=s.asm.L).apply(null,arguments)},l1=s._Greater=function(){return(l1=s._Greater=s.asm.M).apply(null,arguments)},u1=s._GreaterEqual=function(){return(u1=s._GreaterEqual=s.asm.N).apply(null,arguments)},c1=s._LeakyRelu=function(){return(c1=s._LeakyRelu=s.asm.O).apply(null,arguments)},Le=s._Less=function(){return(Le=s._Less=s.asm.P).apply(null,arguments)},h1=s._LessEqual=function(){return(h1=s._LessEqual=s.asm.Q).apply(null,arguments)},d1=s._Log=function(){return(d1=s._Log=s.asm.R).apply(null,arguments)},p1=s._LogicalAnd=function(){return(p1=s._LogicalAnd=s.asm.S).apply(null,arguments)},f1=s._Max=function(){return(f1=s._Max=s.asm.T).apply(null,arguments)},m1=s._MaxPool=function(){return(m1=s._MaxPool=s.asm.U).apply(null,arguments)},A1=s._Maximum=function(){return(A1=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)},y1=s._Multiply=function(){return(y1=s._Multiply=s.asm.Z).apply(null,arguments)},g1=s._Neg=function(){return(g1=s._Neg=s.asm._).apply(null,arguments)},x1=s._NonMaxSuppressionV3=function(){return(x1=s._NonMaxSuppressionV3=s.asm.$).apply(null,arguments)},w1=s._NonMaxSuppressionV4=function(){return(w1=s._NonMaxSuppressionV4=s.asm.aa).apply(null,arguments)},_1=s._NonMaxSuppressionV5=function(){return(_1=s._NonMaxSuppressionV5=s.asm.ba).apply(null,arguments)},b1=s._NotEqual=function(){return(b1=s._NotEqual=s.asm.ca).apply(null,arguments)},v1=s._OneHot=function(){return(v1=s._OneHot=s.asm.da).apply(null,arguments)},Qe=s._PadV2=function(){return(Qe=s._PadV2=s.asm.ea).apply(null,arguments)},k1=s._Pow=function(){return(k1=s._Pow=s.asm.fa).apply(null,arguments)},I1=s._Prelu=function(){return(I1=s._Prelu=s.asm.ga).apply(null,arguments)},N1=s._Prod=function(){return(N1=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)},S1=s._Reverse=function(){return(S1=s._Reverse=s.asm.ma).apply(null,arguments)},T1=s._RotateWithOffset=function(){return(T1=s._RotateWithOffset=s.asm.na).apply(null,arguments)},ih=s._Round=function(){return(ih=s._Round=s.asm.oa).apply(null,arguments)},C1=s._Rsqrt=function(){return(C1=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)},E1=s._Sigmoid=function(){return(E1=s._Sigmoid=s.asm.sa).apply(null,arguments)},R1=s._Sin=function(){return(R1=s._Sin=s.asm.ta).apply(null,arguments)},Ag=s._Softmax=function(){return(Ag=s._Softmax=s.asm.ua).apply(null,arguments)},lh=s._Sqrt=function(){return(lh=s._Sqrt=s.asm.va).apply(null,arguments)},F1=s._Square=function(){return(F1=s._Square=s.asm.wa).apply(null,arguments)},M1=s._SquaredDifference=function(){return(M1=s._SquaredDifference=s.asm.xa).apply(null,arguments)},$1=s._Step=function(){return($1=s._Step=s.asm.ya).apply(null,arguments)},D1=s._StridedSlice=function(){return(D1=s._StridedSlice=s.asm.za).apply(null,arguments)},O1=s._Sub=function(){return(O1=s._Sub=s.asm.Aa).apply(null,arguments)},z1=s._Sum=function(){return(z1=s._Sum=s.asm.Ba).apply(null,arguments)},P1=s._Tanh=function(){return(P1=s._Tanh=s.asm.Ca).apply(null,arguments)},L1=s._Tile=function(){return(L1=s._Tile=s.asm.Da).apply(null,arguments)},W1=s._TopK=function(){return(W1=s._TopK=s.asm.Ea).apply(null,arguments)},B1=s._Transpose=function(){return(B1=s._Transpose=s.asm.Fa).apply(null,arguments)},V1=s.__FusedMatMul=function(){return(V1=s.__FusedMatMul=s.asm.Ga).apply(null,arguments)},U1=s._malloc=function(){return(U1=s._malloc=s.asm.Ha).apply(null,arguments)},H1=s._free=function(){return(H1=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 j1(K){this.name="ExitStatus",this.message="Program terminated with exit("+K+")",this.status=K}Kr=function K(){Wi||Ql(),Wi||(Kr=K)};function Ql(K){if(K=K||c,Qn>0||(Hn(),Qn>0))return;function ne(){Wi||(Wi=!0,s.calledRun=!0,!U&&(En(),un(),i(s),s.onRuntimeInitialized&&s.onRuntimeInitialized(),Jt()))}s.setStatus?(s.setStatus("Running..."),setTimeout(function(){setTimeout(function(){s.setStatus("")},1),ne()},1)):ne()}if(s.run=Ql,s.preInit)for(typeof s.preInit=="function"&&(s.preInit=[s.preInit]);s.preInit.length>0;)s.preInit.pop()();return Ql(),a.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}),q8=et((e,t)=>{(function(n,r,a){function s(u){var c=this,h=l();c.next=function(){var d=2091639*c.s0+c.c*23283064365386963e-26;return c.s0=c.s1,c.s1=c.s2,c.s2=d-(c.c=d|0)},c.c=1,c.s0=h(" "),c.s1=h(" "),c.s2=h(" "),c.s0-=h(u),c.s0<0&&(c.s0+=1),c.s1-=h(u),c.s1<0&&(c.s1+=1),c.s2-=h(u),c.s2<0&&(c.s2+=1),h=null}function i(u,c){return c.c=u.c,c.s0=u.s0,c.s1=u.s1,c.s2=u.s2,c}function o(u,c){var h=new s(u),d=c&&c.state,p=h.next;return p.int32=function(){return h.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,d&&(typeof d=="object"&&i(d,h),p.state=function(){return i(h,{})}),p}function l(){var u=4022871197,c=function(h){h=h.toString();for(var d=0;d<h.length;d++){u+=h.charCodeAt(d);var p=.02519603282416938*u;u=p>>>0,p-=u,p*=u,u=p>>>0,p-=u,u+=p*4294967296}return(u>>>0)*23283064365386963e-26};return c}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.alea=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),X8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.x=0,u.y=0,u.z=0,u.w=0,u.next=function(){var d=u.x^u.x<<11;return u.x=u.y,u.y=u.z,u.z=u.w,u.w^=u.w>>>19^d^d>>>8},l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor128=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),K8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.x^u.x>>>2;return u.x=u.y,u.y=u.z,u.z=u.w,u.w=u.v,(u.d=u.d+362437|0)+(u.v=u.v^u.v<<4^(d^d<<1))|0},u.x=0,u.y=0,u.z=0,u.w=0,u.v=0,l===(l|0)?u.x=l:c+=l;for(var h=0;h<c.length+64;h++)u.x^=c.charCodeAt(h)|0,h==c.length&&(u.d=u.x<<10^u.x>>>4),u.next()}function i(l,u){return u.x=l.x,u.y=l.y,u.z=l.z,u.w=l.w,u.v=l.v,u.d=l.d,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorwow=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Z8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.x,d=u.i,p,f,m;return p=h[d],p^=p>>>7,f=p^p<<24,p=h[d+1&7],f^=p^p>>>10,p=h[d+3&7],f^=p^p>>>3,p=h[d+4&7],f^=p^p<<7,p=h[d+7&7],p=p^p<<13,f^=p^p<<9,h[d]=f,u.i=d+1&7,f};function c(h,d){var p,f,m=[];if(d===(d|0))f=m[0]=d;else for(d=""+d,p=0;p<d.length;++p)m[p&7]=m[p&7]<<15^d.charCodeAt(p)+m[p+1&7]<<13;for(;m.length<8;)m.push(0);for(p=0;p<8&&m[p]===0;++p);for(p==8?f=m[7]=-1:f=m[p],h.x=m,h.i=0,p=256;p>0;--p)h.next()}c(u,l)}function i(l,u){return u.x=l.x.slice(),u.i=l.i,u}function o(l,u){l==null&&(l=+new Date);var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.x&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xorshift7=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Y8=et((e,t)=>{(function(n,r,a){function s(l){var u=this;u.next=function(){var h=u.w,d=u.X,p=u.i,f,m;return u.w=h=h+1640531527|0,m=d[p+34&127],f=d[p=p+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=d[p]=m^f,u.i=p,m+(h^h>>>16)|0};function c(h,d){var p,f,m,A,y,g=[],_=128;for(d===(d|0)?(f=d,d=null):(d=d+"\0",f=0,_=Math.max(_,d.length)),m=0,A=-32;A<_;++A)d&&(f^=d.charCodeAt((A+32)%d.length)),A===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,A>=0&&(y=y+1640531527|0,p=g[A&127]^=f+y,m=p==0?m+1:0);for(m>=128&&(g[(d&&d.length||0)&127]=-1),m=127,A=4*128;A>0;--A)f=g[m+34&127],p=g[m=m+1&127],f^=f<<13,p^=p<<17,f^=f>>>15,p^=p>>>12,g[m]=f^p;h.w=y,h.X=g,h.i=m}c(u,l)}function i(l,u){return u.i=l.i,u.w=l.w,u.X=l.X.slice(),u}function o(l,u){l==null&&(l=+new Date);var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(h.X&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.xor4096=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),J8=et((e,t)=>{(function(n,r,a){function s(l){var u=this,c="";u.next=function(){var d=u.b,p=u.c,f=u.d,m=u.a;return d=d<<25^d>>>7^p,p=p-f|0,f=f<<24^f>>>8^m,m=m-d|0,u.b=d=d<<20^d>>>12^p,u.c=p=p-f|0,u.d=f<<16^p>>>16^m,u.a=m-d|0},u.a=0,u.b=0,u.c=2654435769|0,u.d=1367130551,l===Math.floor(l)?(u.a=l/4294967296|0,u.b=l|0):c+=l;for(var h=0;h<c.length+20;h++)u.b^=c.charCodeAt(h)|0,u.next()}function i(l,u){return u.a=l.a,u.b=l.b,u.c=l.c,u.d=l.d,u}function o(l,u){var c=new s(l),h=u&&u.state,d=function(){return(c.next()>>>0)/4294967296};return d.double=function(){do var p=c.next()>>>11,f=(c.next()>>>0)/4294967296,m=(p+f)/(1<<21);while(m===0);return m},d.int32=c.next,d.quick=d,h&&(typeof h=="object"&&i(h,c),d.state=function(){return i(c,{})}),d}r&&r.exports?r.exports=o:a&&a.amd?a(function(){return o}):this.tychei=o})(e,typeof t=="object"&&t,typeof define=="function"&&define)}),Q8=et((e,t)=>{(function(n,r){var a=(0,eval)("this"),s=256,i=6,o=52,l="random",u=r.pow(s,i),c=r.pow(2,o),h=c*2,d=s-1,p;function f(w,x,N){var S=[];x=x==!0?{entropy:!0}:x||{};var T=g(y(x.entropy?[w,b(n)]:w==null?_():w,3),S),M=new m(S),D=function(){for(var z=M.g(i),W=u,U=0;z<c;)z=(z+U)*s,W*=s,U=M.g(1);for(;z>=h;)z/=2,W/=2,U>>>=1;return(z+U)/W};return D.int32=function(){return M.g(4)|0},D.quick=function(){return M.g(4)/4294967296},D.double=D,g(b(M.S),n),(x.pass||N||function(z,W,U,H){return H&&(H.S&&A(H,M),z.state=function(){return A(M,{})}),U?(r[l]=z,W):z})(D,T,"global"in x?x.global:this==r,x.state)}r["seed"+l]=f;function m(w){var x,N=w.length,S=this,T=0,M=S.i=S.j=0,D=S.S=[];for(N||(w=[N++]);T<s;)D[T]=T++;for(T=0;T<s;T++)D[T]=D[M=d&M+w[T%N]+(x=D[T])],D[M]=x;(S.g=function(z){for(var W,U=0,H=S.i,X=S.j,j=S.S;z--;)W=j[H=d&H+1],U=U*s+j[d&(j[H]=j[X=d&X+W])+(j[X]=W)];return S.i=H,S.j=X,U})(s)}function A(w,x){return x.i=w.i,x.j=w.j,x.S=w.S.slice(),x}function y(w,x){var N=[],S=typeof w,T;if(x&&S=="object")for(T in w)try{N.push(y(w[T],x-1))}catch(M){}return N.length?N:S=="string"?w:w+"\0"}function g(w,x){for(var N=w+"",S,T=0;T<N.length;)x[d&T]=d&(S^=x[d&T]*19)+N.charCodeAt(T++);return b(x)}function _(){try{var w;return p&&(w=p.randomBytes)?w=w(s):(w=new Uint8Array(s),(a.crypto||a.msCrypto).getRandomValues(w)),b(w)}catch(S){var x=a.navigator,N=x&&x.plugins;return[+new Date,a,N,a.screen,b(n)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(r.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{p=Z1()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}),Rg=et((e,t)=>{var n=q8(),r=X8(),a=K8(),s=Z8(),i=Y8(),o=J8(),l=Q8();l.alea=n,l.xor128=r,l.xorwow=a,l.xorshift7=s,l.xor4096=i,l.tychei=o,t.exports=l}),ek=et(()=>{}),tk="3.3.0",nk="3.3.0",rk="3.3.0",ak="3.3.0",sk="3.3.0",ik=1e-7,ok=1e-4,xh=class{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}},su=class{refCount(e){return er("refCount")}incRef(e){return er("incRef")}timerAvailable(){return!0}time(e){return er("time")}read(e){return er("read")}readSync(e){return er("readSync")}numDataIds(){return er("numDataIds")}disposeData(e,t){return er("disposeData")}write(e,t,n){return er("write")}move(e,t,n,r,a){return er("move")}memory(){return er("memory")}floatPrecision(){return er("floatPrecision")}epsilon(){return this.floatPrecision()===32?ik:ok}dispose(){return er("dispose")}};function er(e){throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`)}function Fg(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 lk(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 uk(e){return e%2==0?e:e+1}function ck(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function hk(e,t){let n=Math.random();return t*n+(1-n)*e}function dk(e,t){let n=0;for(let r=0;r<e.length;r++){let a=Number(e[r])-Number(t[r]);n+=a*a}return n}function F(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function an(e,t,n=""){F(ta(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function ss(e){F(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function is(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||sn(e)&&!n)for(let r=0;r<e.length;++r)is(e[r],t,n);else t.push(e);return t}function Dt(e){if(e.length===0)return 1;let t=e[0];for(let n=1;n<e.length;n++)t*=e[n];return t}function pk(e){return e.length===0}function ta(e,t){if(e===t)return!0;if(e==null||t==null||e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function jt(e){return e%1==0}function fk(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 mk(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function Ak(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return Fg(t),t}function ou(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function yk(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 gk(e,t){let n=1,r=-1;for(let s=0;s<e.length;++s)if(e[s]>=0)n*=e[s];else if(e[s]===-1){if(r!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${r} and dim ${s}`);r=s}else if(e[s]<0)throw Error(`Shapes can not be < 0. Found ${e[s]} at dim ${s}`);if(r===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let a=e.slice();return a[r]=t/n,a}function tr(e,t){let n=t.length;return e=e==null?t.map((r,a)=>a):[].concat(e),F(e.every(r=>r>=-n&&r<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),F(e.every(r=>jt(r)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(r=>r<0?n+r:r)}function Mg(e,t){let n=[],r=[],a=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||a?null:tr(t,e).sort(),i=0;for(let o=0;o<e.length;++o){if(s!=null){if(s[i]===o&&e[o]!==1)throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);(s[i]==null||s[i]>o)&&e[o]===1&&(n.push(e[o]),r.push(o)),s[i]<=o&&i++}e[o]!==1&&(n.push(e[o]),r.push(o))}return{newShape:n,keptDims:r}}function $g(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 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 if(e==="string")n=new Array(t);else throw new Error(`Unknown data type ${e}`);return n}function Og(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 zg(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function xk(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function sn(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function Y1(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function Pg(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function va(e){return typeof e=="string"||e instanceof String}function Lg(e){return typeof e=="boolean"}function Wg(e){return typeof e=="number"}function wh(e){return Array.isArray(e)?wh(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":Wg(e)?"float32":va(e)?"string":Lg(e)?"bool":"float32"}function ka(e){return!!(e&&e.constructor&&e.call&&e.apply)}function _h(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Xi(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function Bg(e,t,n){let r=new Array;if(t.length===1){let a=t[0];for(let s=0;s<a;s++)r[s]=n[e+s]}else{let a=t[0],s=t.slice(1),i=s.reduce((o,l)=>o*l);for(let o=0;o<a;o++)r[o]=Bg(e+o*i,s,n)}return r}function Ki(e,t){if(e.length===0)return t[0];let n=e.reduce((r,a)=>r*a);if(n===0)return[];if(n!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}.`);return Bg(0,e,t)}function J1(e,t){let n=bh(e,t);for(let r=0;r<n.length;r++)n[r]=1;return n}function bh(e,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool")return new Uint8Array(e);throw new Error(`Unknown data type ${t}`)}function wk(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 ${t}`)}function Q1(e){e.forEach(t=>{F(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function _k(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 bk(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 ef(e){return e&&e.then&&typeof e.then=="function"}var Vg="tfjsflags",Ug=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(ef(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=vk(this.global.location.search);Vg in e&&e[Vg].split(",").forEach(t=>{let[n,r]=t.split(":");this.urlFlags[n]=kk(n,r)})}};function vk(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...r)=>(Ik(t,r[0],r[1]),r.join("="))),t}function Ik(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function kk(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 ${e}.`)}function J(){return mr}var mr=null;function Nk(e){mr=e}var tf;function Hg(){if(tf==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");tf=e}return tf}function Sk(){let e=Hg();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function nf(e,t){let n=Sk();if(n.has(e))return n.get(e);{let r=t();return n.set(e,r),n.get(e)}}var Zi="Abs",Yi="Acos",Ji="Acosh",Ia="Add",os="AddN",vh="All",kh="Any",ls="ArgMax",lu="ArgMin",Qi="Asin",eo="Asinh",to="Atan",no="Atanh",ro="Atan2",us="AvgPool",Ih="AvgPoolGrad",uu="AvgPool3D",Nh="AvgPool3DGrad",cs="BatchMatMul",cu="BatchToSpaceND",Sh="Bincount",jg="BroadcastTo",hs="Cast",ds="Ceil",Na="ClipByValue",Th="Complex",hu="ComplexAbs",ao="Concat",ps="Conv2D",Ch="Conv2DBackpropFilter",fs="Conv2DBackpropInput",du="Conv3D",Eh="Conv3DBackpropFilterV2",Rh="Conv3DBackpropInputV2",ms="Cos",so="Cosh",As="Cumsum",io="CropAndResize",Fh="DenseBincount",oo="DepthToSpace",ys="DepthwiseConv2dNative",Mh="DepthwiseConv2dNativeBackpropFilter",$h="DepthwiseConv2dNativeBackpropInput",Dh="Diag",pu="Dilation2D",Oh="Dilation2DBackpropInput",zh="Dilation2DBackpropFilter",gs="RealDiv",lo="Elu",Ph="EluGrad",uo="Erf",co="Equal",xs="Exp",ho="ExpandDims",po="Expm1",Lh="FFT",fu="Fill",fo="FlipLeftRight",ws="Floor",_s="FloorDiv",bs="FusedBatchNorm",mo="GatherV2",Ao="GatherNd",yo="Greater",vs="GreaterEqual",ks="Identity",Wh="IFFT",Bh="Imag",go="IsFinite",xo="IsInf",wo="IsNan",Is="LeakyRelu",_o="Less",bo="LessEqual",Vh="LinSpace",Ns="Log",vo="Log1p",ko="LogicalAnd",mu="LogicalNot",Au="LogicalOr",Gg="LogSoftmax",yu="LRN",Uh="LRNGrad",Ss="Max",Ts="Maximum",Cs="MaxPool",Hh="MaxPoolGrad",gu="MaxPool3D",jh="MaxPool3DGrad",Gh="MaxPoolWithArgmax",Es="Mean",Rs="Min",Fs="Minimum",xu="MirrorPad",Io="Mod",qh="Multinomial",Ms="Multiply",No="Neg",So="NotEqual",To="NonMaxSuppressionV3",Co="NonMaxSuppressionV4",Eo="NonMaxSuppressionV5",Ro="OnesLike",$s="OneHot",Fo="Pack",Ds="PadV2",Tk="Pool",Os="Pow",zs="Prelu",Mo="Prod",wu="Range",Xh="Real",$o="Reciprocal",Ps="Relu",Do="Reshape",_u="ResizeNearestNeighbor",Kh="ResizeNearestNeighborGrad",Ls="ResizeBilinear",Zh="ResizeBilinearGrad",Ws="Relu6",Bs="Reverse",Vs="Round",Us="Rsqrt",Oo="ScatterNd",zo="Select",Po="Selu",Lo="Slice",Hs="Sin",Wo="Sinh",Bo="Sign",js="Sigmoid",Vo="Softplus",Gs="Sqrt",qs="Sum",bu="SpaceToBatchND",Uo="SplitV",Xs="Softmax",Ks="SquaredDifference",vu="Square",Zs="Sub",Yh="SparseToDense",Ho="StridedSlice",jo="Tan",Ys="Tanh",Sa="Tile",Go="TopK",Jh="Transform",Js="Transpose",Qh="Unique",qo="Unpack",ku="UnsortedSegmentSum",Xo="ZerosLike",Ta="Step",ed="FromPixels",Ko="RotateWithOffset",Qs="_FusedMatMul",ei="FusedConv2D",ti="FusedDepthwiseConv2D",Zo=nf("kernelRegistry",()=>new Map),Iu=nf("gradRegistry",()=>new Map);function td(e,t){let n=rf(e,t);return Zo.get(n)}function af(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=rf(t,n);Zo.has(r)&&console.warn(`The kernel '${t}' for backend '${n}' is already registered`),Zo.set(r,e)}function qg(e){let{kernelName:t}=e;Iu.has(t)&&J().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${t}'`),Iu.set(t,e)}function Ck(e,t){let n=rf(e,t);if(!Zo.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);Zo.delete(n)}function Ek(e){if(!Iu.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);Iu.delete(e)}function Rk(e,t){Yo(e).forEach(n=>{let r=Object.assign({},n,{backendName:t});ni(r)})}function rf(e,t){return`${t}_${e}`}var v={};ze(v,{arraysEqual:()=>ta,assert:()=>F,assertNonNegativeIntegerDimensions:()=>Q1,assertNonNull:()=>ss,assertShapesMatch:()=>an,bytesFromStringArray:()=>Pg,bytesPerElement:()=>Y1,checkConversionForErrors:()=>Og,clamp:()=>iu,computeStrides:()=>Xi,createScalarValue:()=>Fk,createShuffledIndices:()=>Ak,decodeString:()=>rd,distSquared:()=>dk,encodeString:()=>Su,fetch:()=>Mk,flatten:()=>is,getArrayFromDType:()=>Dg,getTypedArrayFromDType:()=>$g,hasEncodingLoss:()=>xk,indexToLoc:()=>bk,inferDtype:()=>wh,inferFromImplicitShape:()=>gk,isBoolean:()=>Lg,isFunction:()=>ka,isInt:()=>jt,isNumber:()=>Wg,isPromise:()=>ef,isScalarShape:()=>pk,isString:()=>va,isTypedArray:()=>sn,isValidDtype:()=>zg,locToIndex:()=>_k,makeOnesTypedArray:()=>J1,makeZerosNestedTypedArray:()=>wk,makeZerosTypedArray:()=>bh,nearestDivisor:()=>_h,nearestLargerEven:()=>uk,now:()=>Nu,parseAxisParam:()=>tr,randUniform:()=>hk,repeatedTry:()=>yk,rightPad:()=>ou,shuffle:()=>Fg,shuffleCombo:()=>lk,sizeFromShape:()=>Dt,sizeToSquarishShape:()=>mk,squeezeShape:()=>Mg,sum:()=>ck,tanh:()=>fk,toNestedArray:()=>Ki,toTypedArray:()=>nd});function Fk(e,t){return t==="string"?Su(e):nd([e],t)}function $k(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function nd(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=is(e)),J().getBool("DEBUG")&&Og(e,t),$k(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 Mk(e,t){return J().platform.fetch(e,t)}function Su(e,t="utf-8"){return t=t||"utf-8",J().platform.encode(e,t)}function rd(e,t="utf-8"){return t=t||"utf-8",J().platform.decode(e,t)}var zk=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new Ok)}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(u=>{Dk(u,l.dtype,e)})}return{kernelName:e,outputs:r,inputs:t,timeMs:s.then(o=>o.kernelMs),extraInfo:s.then(o=>o.getExtraProfileInfo!=null?o.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:r,inputs:a,extraInfo:s}=e;n.forEach(i=>{Promise.all([i.data(),r,s]).then(o=>{this.logger.logKernelProfile(t,i,o[0],o[1],a,o[2])})})}};function Dk(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 Ok=class{logKernelProfile(e,t,n,r,a,s){let i=typeof r=="number"?ou(`${r}ms`,9):r.error,o=ou(e,25),l=t.rank,u=t.size,c=ou(t.shape.toString(),14),h="";for(let d in a){let p=a[d];if(p!=null){let f=p.shape||t.shape,m=f.length;h+=`${d}: ${m}D ${m>0?f:""} `}}console.log(`%c${o} %c${i} %c${l}D ${c} %c${u} %c${h} %c${s}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function Pk(e,t,n){let r={},a={};for(let l=0;l<t.length;l++)r[t[l].id]=!0;for(let l=0;l<e.length;l++){let u=e[l],c=u.inputs;for(let h in c){let d=c[h],p=!1;for(let f=0;f<t.length;f++)if(r[d.id]){u.outputs.forEach(m=>r[m.id]=!0),p=!0,a[u.id]=!0;break}if(p)break}}let s={};s[n.id]=!0;let i={};for(let l=e.length-1;l>=0;l--){let u=e[l],c=u.inputs;for(let h=0;h<u.outputs.length;h++)if(s[u.outputs[h].id]){for(let d in c)s[c[d].id]=!0,i[u.id]=!0;break}}let o=[];for(let l=0;l<e.length;l++){let u=e[l];if(a[u.id]&&i[u.id]){let c={};for(let d in u.inputs){let p=u.inputs[d];r[p.id]&&(c[d]=p)}let h=Object.assign({},u);h.inputs=c,h.outputs=u.outputs,o.push(h)}}return o}function Lk(e,t,n,r){for(let a=t.length-1;a>=0;a--){let s=t[a],i=[];if(s.outputs.forEach(l=>{let u=e[l.id];u!=null?i.push(u):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let o=s.gradient(i);for(let l in s.inputs){if(!(l in o))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(o)}.`);let u=n(()=>o[l]());if(u.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${u.dtype}'`);let c=s.inputs[l];if(!ta(u.shape,c.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${l}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=u;else{let h=e[c.id];e[c.id]=r(h,u),h.dispose()}}}}var Xg=20,Tu=3,sf=7;function Bk(e,t,n,r){let a=Xi(t),s=Wk(e,t,n,a),i=t.length,o=ad(e,t,n,a,s),l=["Tensor"];return r&&(l.push(` dtype: ${n}`),l.push(` rank: ${i}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(o.map(u=>" "+u).join(`
|
|
`)),l.join(`
|
|
`)}function Wk(e,t,n,r){let a=Dt(t),s=r[r.length-1],i=new Array(s).fill(0),o=t.length,l=n==="complex64"?Eu(e):e;if(o>1)for(let u=0;u<a/s;u++){let c=u*s;for(let h=0;h<s;h++)i[h]=Math.max(i[h],Cu(l[c+h],0,n).length)}return i}function Cu(e,t,n){let r;return Array.isArray(e)?r=`${parseFloat(e[0].toFixed(sf))} + ${parseFloat(e[1].toFixed(sf))}j`:va(e)?r=`'${e}'`:n==="bool"?r=Kg(e):r=parseFloat(e.toFixed(sf)).toString(),ou(r,t)}function Kg(e){return e===0?"false":"true"}function ad(e,t,n,r,a,s=!0){let i=n==="complex64"?2:1,o=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=Eu(e);return[Cu(m[0],0,n)]}return n==="bool"?[Kg(e[0])]:[e[0].toString()]}if(l===1){if(o>Xg){let A=Tu*i,y=Array.from(e.slice(0,A)),g=Array.from(e.slice((o-Tu)*i,o*i));return n==="complex64"&&(y=Eu(y),g=Eu(g)),["["+y.map((_,b)=>Cu(_,a[b],n)).join(", ")+", ..., "+g.map((_,b)=>Cu(_,a[o-Tu+b],n)).join(", ")+"]"]}let m=n==="complex64"?Eu(e):Array.from(e);return["["+m.map((A,y)=>Cu(A,a[y],n)).join(", ")+"]"]}let u=t.slice(1),c=r.slice(1),h=r[0]*i,d=[];if(o>Xg){for(let m=0;m<Tu;m++){let A=m*h,y=A+h;d.push(...ad(e.slice(A,y),u,n,c,a,!1))}d.push("...");for(let m=o-Tu;m<o;m++){let A=m*h,y=A+h;d.push(...ad(e.slice(A,y),u,n,c,a,m===o-1))}}else for(let m=0;m<o;m++){let A=m*h,y=A+h;d.push(...ad(e.slice(A,y),u,n,c,a,m===o-1))}let p=l===2?",":"";d[0]="["+d[0]+p;for(let m=1;m<d.length-1;m++)d[m]=" "+d[m]+p;let f=`,
|
|
`;for(let m=2;m<l;m++)f+=`
|
|
`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function Eu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Ot=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Dt(e),n!=null){let r=n.length;F(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Dg(t,this.size),this.strides=Xi(e)}set(e,...t){t.length===0&&(t=[0]),F(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Er().makeTensor(this.values,this.shape,this.dtype)}},Er=null,Jo=null,Vk=null;function Uk(e){Er=e}function Hk(e){Jo=e}function jk(e){Vk=e}var Ye=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Dt(e),this.strides=Xi(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Jo.buffer(this.shape,this.dtype,e)}bufferSync(){return Jo.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Ki(this.shape,e)}arraySync(){return Ki(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Er().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>rd(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Er().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>rd(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Er().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Er().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 Bk(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Jo.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Er().makeVariable(this,e,t,n)}};Object.defineProperty(Ye,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return nf("Tensor",()=>Ye)}Z();var Ru=class extends Ye{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`);Er().disposeTensor(this),this.dataId=e.dataId,Er().incRef(this,null)}dispose(){Er().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Ru,Symbol.hasInstance,{value:e=>e instanceof Ye&&e.assign!=null&&e.assign instanceof Function});var Ar={};ze(Ar,{assertTypesMatch:()=>Zg,getTensorsInContainer:()=>of,isTensorInList:()=>Gk,makeTypesMatch:()=>bt});var lf;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(lf||(lf={}));var uf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(uf||(uf={}));var cf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(cf||(cf={}));var hf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(hf||(hf={}));var df;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(df||(df={}));var qk={float32:hf,int32:uf,bool:cf,complex64:df};function nr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return qk[e][t]}function sd(e){return nr(e,"int32")}function bt(e,t){if(e.dtype===t.dtype)return[e,t];let n=nr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function Zg(e,t){F(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function Gk(e,t){return t.some(n=>n.id===e.id)}function of(e){let t=[],n=new Set;return Yg(e,t,n),t}function Yg(e,t,n){if(e==null)return;if(e instanceof Ye){t.push(e);return}if(!Xk(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),Yg(s,t,n))}}function Xk(e){return Array.isArray(e)||typeof e=="object"}function pf(e){return e.kernelName!=null}var Jg=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Fu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Jg}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 zk(this.backendInstance),!0}setupRegisteredKernels(){Yo(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Yo(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof su)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Fu.nextTensorId++}nextVariableId(){return Fu.nextVariableId++}clone(e){let t=$.runKernel(ks,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(hs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(td(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=pf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(pf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=td(p,this.backendName);F(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let _=g.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:x,dtype:N}=b;return this.makeTensorFromDataId(w,x,N)});if(r){let b=this.getTensorsForGradient(p,f,_);n=this.saveTensorsForBackwardMode(b)}return _}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:u,attrs:c}=e,h=pf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(p=>u[p]!=null?u[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=af(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(F(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&va(e[0])&&(a=e.map(o=>Su(o)));let s=r.write(a,t,n),i=new Ye(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Pg(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new Ye(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*Y1(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*Y1(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=af(e);o!=null&&(r=o.gradFunc),r!=null&&(i.gradient=l=>(l=l.map((u,c)=>{if(u==null){let h=n[c],d=bh(h.size,h.dtype);return this.makeTensor(d,h.shape,h.dtype)}return u}),r(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=of(e),n=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!n.has(s.id)&&s.dispose()}let r=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===r.id&&this.track(a)})}gradients(e,t,n,r=!1){if(F(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));F(a instanceof Ye,()=>"The result y returned by f() must be a tensor.");let s=Pk(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?Kk(a.shape):n,Lk(i,s,l=>this.tidy(l),Zk);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return F(ka(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{F(t.every(i=>i instanceof Ye),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,r={};t.forEach((i,o)=>{r[o]=i});let a=(i,o)=>(n=e(...t,o),F(n.value instanceof Ye,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),F(ka(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),s=(i,o)=>{let l=n.gradFunc(i,o),u=Array.isArray(l)?l:[l];F(u.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),F(u.every(h=>h instanceof Ye),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return u.forEach((h,d)=>{c[d]=()=>h}),c};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=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 Jg;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 Kk(e){let t=J1(Dt(e),"float32");return $.makeTensor(t,e,"float32")}function Qg(){let e=Hg();if(e._tfengine==null){let t=new Ug(e);e._tfengine=new Fu(t)}return Nk(e._tfengine.ENV),Uk(()=>e._tfengine),e._tfengine}var $=Qg();function Zk(e,t){let n={a:e,b:t};return $.runKernel(Ia,n)}var Mu={};ze(Mu,{isBrowser:()=>e5,isMobile:()=>Yk});function Jk(){return typeof navigator!="undefined"&&navigator!=null}function Yk(){if(Jk()){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 e5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var yr=J();yr.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.")});yr.registerFlag("IS_BROWSER",()=>e5());yr.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");yr.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));yr.registerFlag("PROD",()=>!1);yr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>yr.getBool("DEBUG"));yr.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);yr.registerFlag("IS_TEST",()=>!1);yr.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);yr.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Rr(e,t){let n=e;if(sn(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let r=[];for(;Array.isArray(n)||sn(n)&&t!=="string";)r.push(n.length),n=n[0];return Array.isArray(e)&&J().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&t5(e,r,[]),r}function t5(e,t,n){if(n=n||[],!Array.isArray(e)&&!sn(e)){F(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}F(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),F(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let r=t.slice(1);for(let a=0;a<e.length;++a)t5(e[a],r,n.concat(a))}function n5(e,t,n,r){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function E(e,t,n,r="numeric"){if(e instanceof Ye)return n5(r,e.dtype,t,n),e;let a=wh(e);if(a!=="string"&&["bool","int32","float32"].indexOf(r)>=0&&(a=r),n5(r,a,t,n),e==null||!sn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let o=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${o}'`)}let s=Rr(e,a);!sn(e)&&!Array.isArray(e)&&(e=[e]);let i=a!=="string"?nd(e,a):is(e,[],!0);return $.makeTensor(i,s,a)}function $u(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,s)=>E(a,`${t}[${s}]`,n,r))}var r5="__op";function O(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+r5;let a=(...s)=>{$.startScope(n);try{let i=r(...s);return ef(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 Qk(e,t){let n=E(e,"real","complex"),r=E(t,"imag","complex");an(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);let a={real:n,imag:r};return $.runKernel(Th,a)}var Ca=O({complex_:Qk});function Ea(e,t,n,r){if(r==null&&(r=wh(e)),r==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!sn(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Q1(t);let a=Dt(t),s=Dt(n);F(a===s,()=>`Based on the provided shape, [${t}], the tensor should have ${a} values but has ${s}`);for(let i=0;i<n.length;++i){let o=n[i],l=i===n.length-1?o!==Dt(t.slice(i)):!0;F(n[i]===t[i]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!sn(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=r!=="string"?nd(e,r):is(e,[],!0),$.makeTensor(e,t,r)}function gr(e,t,n){let r=Rr(e,n);return Ea(e,t,r,n)}var ff={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},id=4;async function t9(e,t){let n=[],r=[],a=Array.isArray(e)?e.map(i=>i.name):Object.keys(e);for(let i=0;i<a.length;++i){let o=a[i],l=Array.isArray(e)?e[i].tensor:e[o];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${o}': ${l.dtype}`);let u={name:o,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let c=new Promise(async h=>{let d=await l.bytes(),p=d.reduce((A,y)=>A+y.length,0)+id*d.length,f=new Uint8Array(p),m=0;for(let A=0;A<d.length;A++){let y=d[A],g=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(g,m),m+=id,f.set(y,m),m+=y.length}h(f)});r.push(c)}else r.push(l.data());t!=null&&(u.group=t),n.push(u)}let s=await Promise.all(r);return{data:e9(s),specs:n}}function a5(e,t){let n={},r,a=0;for(let s of t){let i=s.name,o=s.dtype,l=s.shape,u=Dt(l),c;if("quantization"in s){let h=s.quantization;if(h.dtype==="uint8"||h.dtype==="uint16"){if(!("min"in h&&"scale"in h))throw new Error(`Weight ${s.name} with quantization ${h.dtype} doesn't have corresponding metadata min and scale.`)}else if(h.dtype==="float16"){if(o!=="float32")throw new Error(`Weight ${s.name} is quantized with ${h.dtype} which only supports weights of type float32 not ${o}.`)}else throw new Error(`Weight ${s.name} has unknown quantization dtype ${h.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let d=ff[h.dtype],p=e.slice(a,a+u*d),f=h.dtype==="uint8"?new Uint8Array(p):new Uint16Array(p);if(o==="float32")if(h.dtype==="uint8"||h.dtype==="uint16"){c=new Float32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];c[m]=A*h.scale+h.min}}else if(h.dtype==="float16")r===void 0&&(r=n9()),c=r(f);else throw new Error(`Unsupported quantization type ${h.dtype} for weight type float32.`);else if(o==="int32"){if(h.dtype!=="uint8"&&h.dtype!=="uint16")throw new Error(`Unsupported quantization type ${h.dtype} for weight type int32.`);c=new Int32Array(f.length);for(let m=0;m<f.length;m++){let A=f[m];c[m]=Math.round(A*h.scale+h.min)}}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=u*d}else if(o==="string"){let h=Dt(s.shape);c=[];for(let d=0;d<h;d++){let p=new Uint32Array(e.slice(a,a+id))[0];a+=id;let f=new Uint8Array(e.slice(a,a+p));c.push(f),a+=p}}else{let h=ff[o],d=e.slice(a,a+u*h);if(o==="float32")c=new Float32Array(d);else if(o==="int32")c=new Int32Array(d);else if(o==="bool")c=new Uint8Array(d);else if(o==="complex64"){c=new Float32Array(d);let p=new Float32Array(c.length/2),f=new Float32Array(c.length/2);for(let y=0;y<p.length;y++)p[y]=c[y*2],f[y]=c[y*2+1];let m=gr(p,l,"float32"),A=gr(f,l,"float32");n[i]=Ca(m,A),m.dispose(),A.dispose()}else throw new Error(`Unsupported dtype in weight '${i}': ${o}`);a+=u*h}o!=="complex64"&&(n[i]=gr(c,l,o))}return n}function e9(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 mf=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function s5(e){return mf?Buffer.byteLength(e):new Blob([e]).size}function r9(e){if(mf)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 a9(e){if(mf){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 Af(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 i5(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:s5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:s5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function s9(){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 i9(){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 o9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function n9(){let e=s9(),t=i9(),n=o9();return r=>{let a=new ArrayBuffer(4*r.length),s=new Uint32Array(a);for(let i=0;i<r.length;i++){let o=r[i],l=e[n[o>>10]+(o&1023)]+t[o>>10];s[i]=l}return new Float32Array(a)}}var St=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return St.instance==null&&(St.instance=new St),St.instance}static registerSaveRouter(e){St.getInstance().saveRouters.push(e)}static registerLoadRouter(e){St.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return St.getHandlers(e,"save")}static getLoadHandlers(e,t){return St.getHandlers(e,"load",t)}static getHandlers(e,t,n){let r=[];return(t==="load"?St.getInstance().loadRouters:St.getInstance().saveRouters).forEach(a=>{let s=a(e,n);s!==null&&r.push(s)}),r}},l9=e=>St.registerSaveRouter(e),u9=e=>St.registerLoadRouter(e),c9=e=>St.getSaveHandlers(e),h9=(e,t)=>St.getLoadHandlers(e,t),yf="tensorflowjs",gf=1,ri="models_store",Ra="model_info_store";function o5(){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 xf(e){let t=e.result;t.createObjectStore(ri,{keyPath:"modelPath"}),t.createObjectStore(Ra,{keyPath:"modelPath"})}var ai=class{constructor(e){if(this.indexedDB=o5(),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(yf,gf);a.onupgradeneeded=()=>xf(a),a.onsuccess=()=>{let s=a.result;if(t==null){let i=s.transaction(ri,"readonly"),o=i.objectStore(ri).get(this.modelPath);o.onsuccess=()=>{if(o.result==null)return s.close(),r(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(o.result.modelArtifacts)},o.onerror=l=>(s.close(),r(o.error)),i.oncomplete=()=>s.close()}else{let i=Du(t),o=s.transaction(Ra,"readwrite"),l=o.objectStore(Ra),u=l.put({modelPath:this.modelPath,modelArtifactsInfo:i}),c;u.onsuccess=()=>{c=s.transaction(ri,"readwrite");let h=c.objectStore(ri).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:i});h.onsuccess=()=>n({modelArtifactsInfo:i}),h.onerror=d=>{l=o.objectStore(Ra);let p=l.delete(this.modelPath);p.onsuccess=()=>(s.close(),r(h.error)),p.onerror=f=>(s.close(),r(h.error))}},u.onerror=h=>(s.close(),r(u.error)),o.oncomplete=()=>{c==null?s.close():c.oncomplete=()=>s.close()}}},a.onerror=s=>r(a.error)})}};ai.URL_SCHEME="indexeddb://";var l5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(ai.URL_SCHEME)?d9(e.slice(ai.URL_SCHEME.length)):null;St.registerSaveRouter(l5);St.registerLoadRouter(l5);function d9(e){return new ai(e)}function p9(e){return e.startsWith(ai.URL_SCHEME)?e.slice(ai.URL_SCHEME.length):e}var f9=class{constructor(){this.indexedDB=o5()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(yf,gf);n.onupgradeneeded=()=>xf(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=p9(e),new Promise((t,n)=>{let r=this.indexedDB.open(yf,gf);r.onupgradeneeded=()=>xf(r),r.onsuccess=()=>{let a=r.result,s=a.transaction(Ra,"readwrite"),i=s.objectStore(Ra),o=i.get(e),l;o.onsuccess=()=>{if(o.result==null)return a.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let u=i.delete(e),c=()=>{l=a.transaction(ri,"readwrite");let h=l.objectStore(ri).delete(e);h.onsuccess=()=>t(o.result.modelArtifactsInfo),h.onerror=d=>n(o.error)};u.onsuccess=c,u.onerror=h=>(c(),a.close(),n(o.error))}},o.onerror=u=>(a.close(),n(o.error)),s.oncomplete=()=>{l==null?a.close():l.oncomplete=()=>a.close()}},r.onerror=a=>n(r.error)})}},na="/",Qo="tensorflowjs_models",u5="info",m9="model_topology",A9="weight_specs",y9="weight_data",g9="model_metadata";function c5(e){return{info:[Qo,e,u5].join(na),topology:[Qo,e,m9].join(na),weightSpecs:[Qo,e,A9].join(na),weightData:[Qo,e,y9].join(na),modelMetadata:[Qo,e,g9].join(na)}}function x9(e){let t=e.split(na);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(na)}function w9(e){return e.startsWith(si.URL_SCHEME)?e.slice(si.URL_SCHEME.length):e}var si=class{constructor(e){if(!J().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=c5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=Du(e);try{this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,r9(e.weightData));let a={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};return e.signature!=null&&(a.signature=e.signature),e.userDefinedMetadata!=null&&(a.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(a.modelInitializer=e.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(a)),{modelArtifactsInfo:r}}catch(a){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);return t.weightData=a9(s),t}};si.URL_SCHEME="localstorage://";var h5=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(si.URL_SCHEME)?_9(e.slice(si.URL_SCHEME.length)):null;St.registerSaveRouter(h5);St.registerLoadRouter(h5);function _9(e){return new si(e)}var b9=class{constructor(){F(J().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),F(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=Qo+na,n=na+u5;for(let r=0;r<this.LS.length;++r){let a=this.LS.key(r);if(a.startsWith(t)&&a.endsWith(n)){let s=x9(a);e[s]=JSON.parse(this.LS.getItem(a))}}return e}async removeModel(e){e=w9(e);let t=c5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),n}},el="://",Gn=class{constructor(){this.managers={}}static getInstance(){return Gn.instance==null&&(Gn.instance=new Gn),Gn.instance}static registerManager(e,t){F(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(el)&&(e=e.slice(0,e.indexOf(el))),F(e.length>0,()=>"scheme must not be an empty string.");let n=Gn.getInstance();F(n.managers[e]==null,()=>`A model store manager is already registered for scheme '${e}'.`),n.managers[e]=t}static getManager(e){let t=this.getInstance().managers[e];if(t==null)throw new Error(`Cannot find model manager for scheme '${e}'`);return t}static getSchemes(){return Object.keys(this.getInstance().managers)}};function od(e){if(e.indexOf(el)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Gn.getSchemes().join(",")}`);return{scheme:e.split(el)[0],path:e.split(el)[1]}}async function d5(e,t,n=!1){F(e!==t,()=>`Old path and new path are the same: '${e}'`);let r=St.getLoadHandlers(e);F(r.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),F(r.length<2,()=>`Copying failed because more than one (${r.length}) load handlers for source URL ${e}.`);let a=r[0],s=St.getSaveHandlers(t);F(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),F(s.length<2,()=>`Copying failed because more than one (${r.length}) save handlers for destination URL ${t}.`);let i=s[0],o=od(e).scheme,l=od(e).path,u=o===od(e).scheme,c=await a.load();n&&u&&await Gn.getManager(o).removeModel(l);let h=await i.save(c);return n&&!u&&await Gn.getManager(o).removeModel(l),h.modelArtifactsInfo}async function v9(){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 k9(e){let t=od(e);return Gn.getManager(t.scheme).removeModel(t.path)}async function I9(e,t){return d5(e,t,!1)}async function N9(e,t){return d5(e,t,!0)}var S9=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 S9);try{Gn.registerManager(si.URL_SCHEME,new b9)}catch(e){}try{Gn.registerManager(ai.URL_SCHEME,new f9)}catch(e){}}var T9={importFetch:()=>N8()},wf,C9=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):(wf==null&&(wf=T9.importFetch()),wf(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 C9);function We(e,t="float32",n){return t=t||"float32",Q1(e),new Ot(e,t,n)}function E9(e,t){let n=E(e,"x","cast");if(!zg(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let r={x:n},a={dtype:t};return $.runKernel(hs,r,a)}var me=O({cast_:E9});function R9(e){let t={x:E(e,"x","clone","string_or_numeric")};return $.runKernel(ks,t)}var rr=O({clone_:R9});function p5(e,t=!1){console.log(e.toString(t))}Qg();var F9={buffer:We,cast:me,clone:rr,print:p5};Hk(F9);var _n={};ze(_n,{browserFiles:()=>M9,browserHTTPRequest:()=>D9,concatenateArrayBuffers:()=>Af,copyModel:()=>I9,decodeWeights:()=>a5,encodeWeights:()=>t9,fromMemory:()=>O9,getLoadHandlers:()=>h9,getModelArtifactsInfoForJSON:()=>Du,getSaveHandlers:()=>c9,http:()=>bf,isHTTPScheme:()=>_f,listModels:()=>v9,loadWeights:()=>$9,moveModel:()=>N9,registerLoadRouter:()=>u9,registerSaveRouter:()=>l9,removeModel:()=>k9,weightsLoaderFactory:()=>f5,withSaveHandler:()=>z9});var P9="model",L9=".json",W9=".weights.bin";function m5(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=P9),this.modelTopologyFileName=e+L9,this.weightDataFileName=e+W9}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 m5(()=>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 m5(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Du(e)}}}};tl.URL_SCHEME="downloads://";var B9=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,r)=>{let a=new FileReader;a.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){r(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let l=i.weightsManifest;if(l==null){r(new Error(`weightManifest field is missing from file ${e.name}`));return}let u;try{u=this.checkManifestAndWeightFiles(l,t)}catch(p){r(p);return}let c=[],h=[],d=[];l.forEach(p=>{p.paths.forEach(f=>{h.push(f),d.push(null)}),c.push(...p.weights)}),l.forEach(p=>{p.paths.forEach(f=>{let m=new FileReader;m.onload=A=>{let y=A.target.result,g=h.indexOf(f);if(d[g]=y,d.indexOf(null)===-1){let _={modelTopology:o,weightSpecs:c,weightData:Af(d),format:i.format,generatedBy:i.generatedBy,convertedBy:i.convertedBy};i.signature!=null&&(_.signature=i.signature),i.userDefinedMetadata!=null&&(_.userDefinedMetadata=i.userDefinedMetadata),i.modelInitializer!=null&&(_.modelInitializer=i.modelInitializer),n(_)}},m.onerror=A=>r(`Failed to weights data from file of path '${f}'.`),m.readAsArrayBuffer(u[f])})})},a.onerror=s=>r(`Failed to read model topology and weights manifest JSON from file '${e.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),a.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],r=t.map(s=>i5(s.name)),a={};for(let s of e)s.paths.forEach(i=>{let o=i5(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${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}},U9=e=>J().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(tl.URL_SCHEME)?V9(e.slice(tl.URL_SCHEME.length)):null;St.registerSaveRouter(U9);function V9(e="model"){return new tl(e)}function M9(e){return new B9(e)}function A5(e,t,n,r){i(e),n=n==null?0:n,r=r==null?1:r,o(n,r);let a=0,s=l=>(l.then(u=>{let c=n+ ++a/e.length*(r-n);return t(c),u}),l);function i(l){F(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function o(l,u){F(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),F(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${u}`),F(u>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${u}`)}return Promise.all(e.map(s))}async function y5(e,t){t==null&&(t={});let n=t.fetchFunc==null?J().platform.fetch:t.fetchFunc,r=e.map(u=>n(u,t.requestInit,{isBinary:!0})),a=0,s=.5,i=(t.onProgress==null?await Promise.all(r):await A5(r,t.onProgress,a,s)).map(u=>u.arrayBuffer()),o=.5,l=1;return t.onProgress==null?await Promise.all(i):await A5(i,t.onProgress,o,l)}async function $9(e,t="",n,r){return f5(a=>y5(a,{requestInit:r}))(e,t,n)}function f5(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=ff[y]*Dt(A.shape),_=()=>{a[f]=!0,s[f]==null&&(s[f]=[]),s[f].push({manifestEntry:A,groupOffset:m,sizeBytes:g})};r!=null?r.forEach((b,w)=>{b===A.name&&(_(),i[w]=!0)}):_(),o.push(A.name),m+=g})}),!i.every(p=>p)){let p=r.filter((f,m)=>!i[m]);throw new Error(`Could not find weights in manifest with names: ${p.join(", ")}.
|
|
Manifest JSON has weights with names: ${o.join(", ")}.`)}let l=a.reduce((p,f,m)=>(f&&p.push(m),p),[]),u=[];l.forEach(p=>{t[p].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;u.push(m)})});let c=await e(u),h={},d=0;return l.forEach(p=>{let f=t[p].paths.length,m=0;for(let _=0;_<f;_++)m+=c[d+_].byteLength;let A=new ArrayBuffer(m),y=new Uint8Array(A),g=0;for(let _=0;_<f;_++){let b=new Uint8Array(c[d+_]);y.set(b,g),g+=b.byteLength}s[p].forEach(_=>{let b=A.slice(_.groupOffset,_.groupOffset+_.sizeBytes),w=a5(b,[_.manifestEntry]);for(let x in w)h[x]=w[x]}),d+=f}),h}}var H9="application/octet-stream",j9="application/json",vf=class{constructor(e,t){if(this.DEFAULT_METHOD="POST",t==null&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.onProgress=t.onProgress,this.weightUrlConverter=t.weightUrlConverter,t.fetchFunc!=null?(F(typeof t.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=J().platform.fetch,F(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&F(e.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,t.requestInit!=null&&t.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{}}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);t.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:n};e.signature!=null&&(r.signature=e.signature),e.userDefinedMetadata!=null&&(r.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(r.modelInitializer=e.modelInitializer),t.body.append("model.json",new Blob([JSON.stringify(r)],{type:j9}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:H9}),"model.weights.bin");let a=await this.fetch(this.path,t);if(a.ok)return{modelArtifactsInfo:Du(e),responses:[a]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${a.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(p){let f=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?f+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":f+=" Please make sure the server is serving valid JSON for this request.",new Error(f)}let n=t.modelTopology,r=t.weightsManifest,a=t.generatedBy,s=t.convertedBy,i=t.format,o=t.signature,l=t.userDefinedMetadata;if(n==null&&r==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let u,c;r!=null&&([u,c]=await this.loadWeights(r));let h={modelTopology:n,weightSpecs:u,weightData:c,generatedBy:a,convertedBy:s,format:i};o!=null&&(h.signature=o),l!=null&&(h.userDefinedMetadata=l);let d=t.modelInitializer;return d&&(h.modelInitializer=d),h}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,r]=G9(t),a=this.weightPathPrefix||n,s=[];for(let u of e)s.push(...u.weights);let i=[],o=[];for(let u of e)for(let c of u.paths)this.weightUrlConverter!=null?o.push(this.weightUrlConverter(c)):i.push(a+c+r);this.weightUrlConverter&&i.push(...await Promise.all(o));let l=await y5(i,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[s,Af(l)]}};vf.URL_SCHEME_REGEX=/^https?:\/\//;function G9(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),r=e.substring(0,t),a=n>t?e.substring(n):"";return[r+"/",a]}function _f(e){return e.match(vf.URL_SCHEME_REGEX)!=null}var g5=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(r=>_f(r)):n=_f(e),n)return bf(e,t)}return null};St.registerSaveRouter(g5);St.registerLoadRouter(g5);function bf(e,t){return new vf(e,t)}function D9(e,t){return bf(e,t)}var kf=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},q9=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function O9(e,t,n,r){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new kf(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 kf({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 kf({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:r}))}function z9(e){return new q9(e)}var x5={};ze(x5,{confusionMatrix:()=>X9});function K9(e,t,n=!1,r=!1){let a=E(e,"a","matMul"),s=E(t,"b","matMul");[a,s]=bt(a,s);let i={a,b:s},o={transposeA:n,transposeB:r};return $.runKernel(cs,i,o)}var je=O({matMul_:K9});function Z9(e,t,n=1,r=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:E(e,"indices","oneHot","int32")},s={depth:t,onValue:n,offValue:r};return $.runKernel($s,a,s)}var nl=O({oneHot_:Z9});function Y9(e,t){let n=E(e,"x","transpose");if(t==null&&(t=n.shape.map((s,i)=>i).reverse()),F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(s=>{F(s>=0&&s<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let r={x:n},a={perm:t};return $.runKernel(Js,r,a)}var at=O({transpose_:Y9});function J9(e,t,n){let r=E(e,"labels","confusionMatrix"),a=E(t,"predictions","confusionMatrix");F(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),F(r.rank===1,()=>`Expected the rank of labels to be 1, but got ${r.rank}`),F(a.rank===1,()=>`Expected the rank of predictions to be 1, but got ${a.rank}`),F(r.shape[0]===a.shape[0],()=>`Mismatch in the number of examples: ${r.shape[0]} vs. ${a.shape[0]}. Labels and predictions should have the same number of elements.`),F(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let s=nl(me(r,"int32"),n),i=nl(me(a,"int32"),n),o=at(s),l=je(o,i);return me(l,"int32")}var X9=O({confusionMatrix_:J9}),rl={};ze(rl,{fromPixels:()=>tI,fromPixelsAsync:()=>Q9,toPixels:()=>eI});function ld(e,t,n){if(ss(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let r=Rr(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 w5(e,t=3){if(t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(e==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let n=!1,r=!1,a=!1,s=!1,i=!1,o=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)r=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)a=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)s=!0;else if(e.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)o=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(a){let d=2;if(a&&e.readyState<d)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(td(ed,$.backendName)!=null){let d={pixels:e},p={numChannels:t};return $.runKernel(ed,d,p)}let[l,u]=a?[e.videoWidth,e.videoHeight]:[e.width,e.height],c;i?c=e.getContext("2d").getImageData(0,0,l,u).data:r||n?c=e.data:(s||a||o)&&(al==null&&(al=document.createElement("canvas").getContext("2d")),al.canvas.width=l,al.canvas.height=u,al.drawImage(e,0,0,l,u),c=al.getImageData(0,0,l,u).data);let h;if(t===4)h=new Int32Array(c);else{let d=l*u;h=new Int32Array(d*t);for(let p=0;p<d;p++)for(let f=0;f<t;++f)h[p*t+f]=c[p*4+f]}return ld(h,[u,l,t],"int32")}function nI(e){return e!=null&&e.data instanceof Uint8Array}function rI(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function aI(e){return e!=null&&e.width!==0&&e.height!==0}function sI(e){return rI()&&!(e instanceof ImageBitmap)&&aI(e)&&!nI(e)}async function Q9(e,t=3){let n=null;if(J().getBool("WRAP_TO_IMAGEBITMAP")&&sI(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 w5(n,t)}async function eI(e,t){let n=E(e,"img","toPixels");if(!(e instanceof Ye)){let u=n;n=me(u,"int32"),u.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[r,a]=n.shape.slice(0,2),s=n.rank===2?1:n.shape[2];if(s>4||s===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let i=await n.data(),o=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(a*r*4);for(let u=0;u<r*a;++u){let c=[0,0,0,255];for(let d=0;d<s;d++){let p=i[u*s+d];if(n.dtype==="float32"){if(p<0||p>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${p}.`)}else if(n.dtype==="int32"&&(p<0||p>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${p}.`);s===1?(c[0]=p*o,c[1]=p*o,c[2]=p*o):c[d]=p*o}let h=u*4;l[h+0]=Math.round(c[0]),l[h+1]=Math.round(c[1]),l[h+2]=Math.round(c[2]),l[h+3]=Math.round(c[3])}if(t!=null){t.width=a,t.height=r;let u=t.getContext("2d"),c=new ImageData(l,a,r);u.putImageData(c,0,0)}return n!==e&&n.dispose(),l}var tI=O({fromPixels_:w5}),If={};ze(If,{prepareAndValidate:()=>_5});function _5(e,t){let n=e.shape.length,r=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(Dt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let a=t.shape,s=a[a.length-1],i=1;for(let h=0;h<a.length-1;++h)i*=a[h];let o=e.shape,l=a.slice();l.pop();let u=1;for(let h=s;h<n;++h)u*=o[h],l.push(o[h]);let c=[...Xi(e.shape).map(h=>h/u),1].slice(0,s);return[l,i,u,c]}var Nf={};ze(Nf,{calculateShapes:()=>b5,validateInput:()=>Tf,validateUpdateShape:()=>Sf});function Sf(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 Tf(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: ${e.shape}`)}Sf(n,t,e)}function b5(e,t,n){let r=t.shape.length,a=r>1?t.shape[r-1]:1,s=n.length,i=1;for(let h=a;h<s;++h)i*=n[h];let o=a<1?1:a,l=Dt(t.shape)/o,u=[...Xi(n.slice(0,a)),1],c=Dt(n);return{sliceRank:a,numUpdates:l,sliceSize:i,strides:u,outputSize:c}}var cn={};ze(cn,{assertParamsValid:()=>iI,computeFlatOffset:()=>lI,computeOutShape:()=>v5,getNormalizedAxes:()=>I5,isSliceContinous:()=>oI,maskToAxes:()=>ud,parseSliceParams:()=>R5,sliceInfo:()=>uI,startForAxis:()=>C5,startIndicesWithElidedDims:()=>N5,stopForAxis:()=>E5,stopIndicesWithElidedDims:()=>S5,stridesForAxis:()=>T5,stridesWithElidedDims:()=>k5});function iI(e,t,n){let r=e.shape.length;F(r===t.length,()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`),F(r===n.length,()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`);for(let a=0;a<r;++a)F(t[a]+n[a]<=e.shape[a],()=>`Error in slice${r}D: begin[${a}] + size[${a}] (${t[a]+n[a]}) would overflow input.shape[${a}] (${e.shape[a]})`)}function ud(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function v5(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 k5(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 F5(e,t,n){return n<=e?n:n-(t-1)}function M5(e,t){let n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function I5(e,t,n,r,a,s,i,o,l){let u=e.length,c=new Array(u),h=new Array(u),d=new Array(u);if(t.length&&n>0){let p=t[0],f=n+1;c=N5(i,p,f,r,e),h=S5(o,p,f,a,e),d=k5(s,p,f,e)}else for(let p=0;p<u;p++)c[p]=C5(i,r,s,e,p,l),h[p]=E5(o,a,s,e,p,l),d[p]=T5(s,p,l);return{begin:c,end:h,strides:d}}function N5(e,t,n,r,a){let s=[...a],i=M5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=0;else{let l=F5(t,n,o),u=r[l];e&1<<l&&(u=0),s[o]=u}return s}function S5(e,t,n,r,a){let s=[...a],i=M5(n,t);for(let o=0;o<s.length;o++)if(i.indexOf(o)>-1)s[o]=Number.MAX_SAFE_INTEGER;else{let l=F5(t,n,o),u=r[l];e&1<<l&&(u=Number.MAX_SAFE_INTEGER),s[o]=u}for(let o=0;o<s.length;o++){let l=a[o];s[o]<0&&(s[o]+=l),s[o]=iu(0,s[o],a[o])}return s}function T5(e,t,n){let r=e[t];return(n&1<<t||r==null)&&(r=1),r}function C5(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 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.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 oI(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 lI(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 R5(e,t,n){let r,a=e.shape.length;typeof t=="number"?r=[t,...new Array(a-1).fill(0)]:t.length<a?r=t.concat(new Array(a-t.length).fill(0)):r=t.slice(),r.forEach(i=>{F(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return n==null?s=new Array(a).fill(-1):typeof n=="number"?s=[n,...new Array(a-1).fill(-1)]:n.length<a?s=n.concat(new Array(a-n.length).fill(-1)):s=n,s=s.map((i,o)=>i>=0?i:(F(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`),e.shape[o]-r[o])),[r,s]}function uI(e,t,n,r,a,s,i,o,l){let u=t.slice(),c=n.slice(),h=r;r==null&&(h=new Array(u.length));let d=ud(i);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(i!==0&&o!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(i!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let p=e.length-u.length,f=ud(o),m=e.slice();f.forEach(x=>{u[x]=0,c[x]=1,m.splice(x,0,1)});let{begin:A,end:y,strides:g}=I5(m,d,p,u,c,h,a,s,i);u=A,c=y,h=g;let _=ud(l);_.forEach(x=>{c[x]=u[x]+1,h[x]=1});let b=v5(u,c,h),w=b.filter((x,N)=>_.indexOf(N)===-1);return{nonStrided:h.every(x=>x===1),$begin:u,$end:c,$strides:h,size:b,newShape:m,outShape:w}}var re={};ze(re,{Serializable:()=>$5,SerializationMap:()=>ii,registerClass:()=>Fa});var $5=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ii=class{constructor(){this.classNameMap={}}static getMap(){return ii.instance==null&&(ii.instance=new ii),ii.instance}static register(e){ii.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function Fa(e){F(e.className!=null,()=>"Class being registered does not have the static className property defined."),F(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),F(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ii.register(e)}var D5={};ze(D5,{TEST_EPSILON_FLOAT16:()=>O5,encodeStrings:()=>z5,expectArrayBuffersEqual:()=>mI,expectArraysClose:()=>cI,expectArraysEqual:()=>dI,expectNumbersClose:()=>pI,expectPromiseToFail:()=>hI,expectValuesInRange:()=>fI,testEpsilon:()=>Cf});var AI=.001,O5=.1;function cI(e,t,n){return n==null&&(n=Cf()),Ef(e,t,(r,a)=>Rf(r,a,n))}function Cf(){return $.backend.floatPrecision()===32?AI:O5}function Ef(e,t,n){let r=!0;if((sn(e)||sn(t))&&(r=!1),sn(e)&&sn(t)&&(r=!0),r){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`)}if(Array.isArray(e)&&Array.isArray(t)){let i=Rr(e),o=Rr(t);if(!ta(i,o))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`)}let a=sn(e)?e:is(e),s=sn(t)?t:is(t);if(a.length!==s.length)throw new Error(`Arrays have different lengths actual: ${a.length} vs expected: ${s.length}.
|
|
Actual: ${a}.
|
|
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=a[i],l=s[i];if(!n(o,l))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${l}.
|
|
Actual: ${a}.
|
|
Expected: ${s}.`)}}function hI(e,t){e().then(()=>t.fail(),()=>t())}function dI(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])?Ef(e,n,(r,a)=>r==a):Ef(e,t,(r,a)=>Rf(r,a,0))}function pI(e,t,n){if(n==null&&(n=Cf()),!Rf(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Rf(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function fI(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function mI(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function z5(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?z5(n):e[t]=Su(n)}return e}var yI="3.3.0";function gI(){J().set("PROD",!0)}function xI(){J().set("DEBUG",!0)}function wI(){J().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function Ff(e){J().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}jk(Ff);function _I(){$.disposeVariables()}function Fr(){return $}function cd(){return $.memory()}function xr(e){return $.profile(e)}function B(e,t){return $.tidy(e,t)}function Ee(e){of(e).forEach(t=>t.dispose())}function Gt(e){return $.keep(e)}function bI(e){return $.time(e)}function vI(e){return $.setBackend(e)}function kI(){return $.ready()}function II(){return $.backendName}function NI(e){$.removeBackend(e)}function Mf(e){return $.findBackend(e)}function SI(e){return $.findBackendFactory(e)}function sl(e,t,n=1){return $.registerBackend(e,t,n)}function P5(){return $.backend}function TI(e,t){J().setPlatform(e,t)}function CI(e,t){let n=E(e,"a","add"),r=E(t,"b","add");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Ia,a)}var se=O({add_:CI});function EI(e,t){let n=E(e,"a","floorDiv"),r=E(t,"b","floorDiv");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(_s,a)}var hd=O({floorDiv_:EI});function RI(e,t){let n=E(e,"a","div"),r=E(t,"b","div");if([n,r]=bt(n,r),n.dtype==="int32"&&r.dtype==="int32")return hd(n,r);let a={a:n,b:r},s={};return $.runKernel(gs,a,s)}var ge=O({div_:RI});function FI(e,t){let n=E(e,"a","mul"),r=E(t,"b","mul");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Ms,a)}var P=O({mul_:FI});function MI(e){let t=E(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return $.runKernel(hu,n)}else{let n={x:t};return $.runKernel(Zi,n)}}var zt=O({abs_:MI});function $I(e){let t={x:E(e,"x","acos")};return $.runKernel(Yi,t)}var $f=O({acos_:$I});function DI(e){let t={x:E(e,"x","acosh")};return $.runKernel(Ji,t)}var Df=O({acosh_:DI});function OI(e){F(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),F(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((a,s)=>E(a,`tensors${s}`,"addN")),n=t[0];t.forEach(a=>{if(a.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(a=>{if(!ta(a.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let r=t;return $.runKernel(os,r)}var Ma=O({addN_:OI});function zI(e,t=null,n=!1){let r={x:E(e,"x","all","bool")},a={axis:t,keepDims:n};return $.runKernel(vh,r,a)}var dd=O({all_:zI});function PI(e,t=null,n=!1){let r={x:E(e,"x","any","bool")},a={axis:t,keepDims:n};return $.runKernel(kh,r,a)}var Ou=O({any_:PI});function LI(e,t=0){let n={x:E(e,"x","argMax")},r={axis:t};return $.runKernel(ls,n,r)}var zu=O({argMax_:LI});function WI(e,t=0){let n={x:E(e,"x","argMin")},r={axis:t};return $.runKernel(lu,n,r)}var Of=O({argMin_:WI});function BI(e){let t={x:E(e,"x","asin")};return $.runKernel(Qi,t)}var zf=O({asin_:BI});function VI(e){let t={x:E(e,"x","asinh")};return $.runKernel(eo,t)}var Pf=O({asinh_:VI});function UI(e){let t={x:E(e,"x","atan")};return $.runKernel(to,t)}var Lf=O({atan_:UI});function HI(e,t){let n=E(e,"a","atan2"),r=E(t,"b","atan2");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(ro,a)}var Wf=O({atan2_:HI});function jI(e){let t={x:E(e,"x","atanh")};return $.runKernel(no,t)}var Bf=O({atanh_:jI});function GI(e,t,n,r,a="NHWC",s){let i=e[3],o=[...t,i],l=L5(a);return Pu(e,o,n,s,r,null,null,l)}function W5(e,t,n,r,a,s,i="channelsLast"){let[o,l]=pd(t),u;if(i==="channelsLast")u=[o,l,e[3],e[3]];else if(i==="channelsFirst")u=[o,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return Pu(e,u,n,r,a,s,!1,i)}function qI(e,t,n,r,a,s,i="NDHWC"){let[o,l,u]=Vf(t),c,h;if(i==="NDHWC")h="channelsLast",c=[o,l,u,e[4],e[4]];else if(i==="NCDHW")h="channelsFirst",c=[o,l,u,e[1],e[1]];else throw new Error(`Unknown dataFormat ${i}`);return B5(e,c,n,r,a,!1,h,s)}function Pu(e,t,n,r,a,s,i=!1,o="channelsLast"){let[l,u,c,h]=[-1,-1,-1,-1];if(o==="channelsLast")[l,u,c,h]=e;else if(o==="channelsFirst")[l,h,u,c]=e;else throw new Error(`Unknown dataFormat ${o}`);let[d,p,,f]=t,[m,A]=pd(n),[y,g]=pd(r),_=il(d,y),b=il(p,g),{padInfo:w,outHeight:x,outWidth:N}=XI(a,u,c,m,A,_,b,s,o),S=i?f*h:f,T;return o==="channelsFirst"?T=[l,S,x,N]:o==="channelsLast"&&(T=[l,x,N,S]),{batchSize:l,dataFormat:o,inHeight:u,inWidth:c,inChannels:h,outHeight:x,outWidth:N,outChannels:S,padInfo:w,strideHeight:m,strideWidth:A,filterHeight:d,filterWidth:p,effectiveFilterHeight:_,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:g,inShape:e,outShape:T,filterShape:t}}function B5(e,t,n,r,a,s=!1,i="channelsLast",o){let[l,u,c,h,d]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[l,u,c,h,d]=e;else if(i==="channelsFirst")[l,d,u,c,h]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,f,m,,A]=t,[y,g,_]=Vf(n),[b,w,x]=Vf(r),N=il(p,b),S=il(f,w),T=il(m,x),{padInfo:M,outDepth:D,outHeight:z,outWidth:W}=KI(a,u,c,h,y,g,_,N,S,T,o),U=s?A*d:A,H;return i==="channelsFirst"?H=[l,U,D,z,W]:i==="channelsLast"&&(H=[l,D,z,W,U]),{batchSize:l,dataFormat:i,inDepth:u,inHeight:c,inWidth:h,inChannels:d,outDepth:D,outHeight:z,outWidth:W,outChannels:U,padInfo:M,strideDepth:y,strideHeight:g,strideWidth:_,filterDepth:p,filterHeight:f,filterWidth:m,effectiveFilterDepth:N,effectiveFilterHeight:S,effectiveFilterWidth:T,dilationDepth:b,dilationHeight:w,dilationWidth:x,inShape:e,outShape:H,filterShape:t}}function ZI(e,t,n,r,a){r==null&&(r=Uf(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 YI(e,t,n,r,a,s){a==null&&(a=Uf(e,t,r));let i=e[0],o=e[1],l=e[2],u=oi((i-t+2*a)/r+1,s),c=oi((o-t+2*a)/r+1,s),h=oi((l-t+2*a)/r+1,s);return[u,c,h,n]}function Uf(e,t,n,r=1){let a=il(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function pd(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Vf(e){return typeof e=="number"?[e,e,e]:e}function il(e,t){return t<=1?e:e+(e-1)*(t-1)}function XI(e,t,n,r,a,s,i,o,l){let u,c,h;if(typeof e=="number"){u={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let d=ZI([t,n],s,r,e,o);c=d[0],h=d[1]}else if(e==="same"){c=Math.ceil(t/r),h=Math.ceil(n/a);let d=Math.max(0,(c-1)*r+s-t),p=Math.max(0,(h-1)*a+i-n),f=Math.floor(d/2),m=d-f,A=Math.floor(p/2),y=p-A;u={top:f,bottom:m,left:A,right:y,type:"SAME"}}else if(e==="valid")u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-s+1)/r),h=Math.ceil((n-i+1)/a);else if(typeof e=="object"){let d=l==="channelsLast"?e[1][0]:e[2][0],p=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];u={top:d,bottom:p,left:f,right:m,type:d===0&&p===0&&f===0&&m===0?"VALID":"EXPLICIT"},c=oi((t-s+d+p)/r+1,o),h=oi((n-i+f+m)/a+1,o)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:u,outHeight:c,outWidth:h}}function KI(e,t,n,r,a,s,i,o,l,u,c){let h,d,p,f;if(typeof e=="number"){h={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let m=YI([t,n,r,1],o,1,a,e,c);d=m[0],p=m[1],f=m[2]}else if(e==="same"){d=Math.ceil(t/a),p=Math.ceil(n/s),f=Math.ceil(r/i);let m=(d-1)*a+o-t,A=(p-1)*s+l-n,y=(f-1)*i+u-r,g=Math.floor(m/2),_=m-g,b=Math.floor(A/2),w=A-b,x=Math.floor(y/2),N=y-x;h={top:b,bottom:w,left:x,right:N,front:g,back:_,type:"SAME"}}else if(e==="valid")h={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},d=Math.ceil((t-o+1)/a),p=Math.ceil((n-l+1)/s),f=Math.ceil((r-u+1)/i);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:h,outDepth:d,outHeight:p,outWidth:f}}function oi(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error(`Unknown roundingMode ${t}`)}}function $a(e){let[t,n,r]=pd(e);return t===1&&n===1&&r===1}function Mr(e,t){return $a(e)||$a(t)}function L5(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function JI(e,t){let n={x:E(e,"x","reshape","string_or_numeric")},r={shape:t};return $.runKernel(Do,n,r)}var G=O({reshape_:JI});function QI(e,t,n,r,a){let s=E(e,"x","avgPool","float32"),i=1;F(Mr(n,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&F(jt(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(us,u,c);return h=me(h,s.dtype),l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Lu=O({avgPool_:QI});function eN(e,t,n,r,a,s="NDHWC"){let i=E(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(jt(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(uu,u,c);return h=me(h,o.dtype),l?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var Hf=O({avgPool3d_:eN});function tN(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=$u(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
|
|
with dtype ${s.dtype}. `)}),n.length===1)return rr(n[0]);let r=n,a={axis:t};return $.runKernel(ao,r,a)}var st=O({concat_:tN});function nN(e){let t={x:E(e,"x","sigmoid")};return $.runKernel(js,t)}var Rn=O({sigmoid_:nN});function rN(e,t,n){let r=E(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return $.runKernel(Lo,a,s)}var Re=O({slice_:rN});function aN(e){let t={x:E(e,"x","tanh")};return $.runKernel(Ys,t)}var ol=O({tanh_:aN});function sN(e,t,n,r,a,s){let i=E(e,"forgetBias","basicLSTMCell"),o=E(t,"lstmKernel","basicLSTMCell"),l=E(n,"lstmBias","basicLSTMCell"),u=E(r,"data","basicLSTMCell"),c=E(a,"c","basicLSTMCell"),h=E(s,"h","basicLSTMCell"),d=st([u,h],1),p=je(d,o),f=se(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Re(f,[0,0],y),_=Re(f,[0,A],y),b=Re(f,[0,A*2],y),w=Re(f,[0,A*3],y),x=se(P(Rn(g),ol(_)),P(c,Rn(se(i,b)))),N=P(ol(x),Rn(w));return[x,N]}var iN=O({basicLSTMCell_:sN});function oN(e,t,n){let r=E(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(cu,s,i)}var Wu=O({batchToSpaceND_:oN});function lN(e){let t;return e.rank===0||e.rank===1?t=G(e,[1,1,1,e.size]):e.rank===2?t=G(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function uN(e,t,n,r,a,s){s==null&&(s=.001);let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;r!=null&&(c=E(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:lN(i),scale:u,offset:c,mean:o,variance:l},d={varianceEpsilon:s},p=$.runKernel(bs,h,d);return G(p,i.shape)}var li=O({batchNorm_:uN});function cN(e,t,n,r,a,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;return r!=null&&(c=E(r,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),li(i,o,l,c,u,s)}var V5=O({batchNorm2d_:cN});function hN(e,t,n,r,a,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;return r!=null&&(c=E(r,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),li(i,o,l,c,u,s)}var U5=O({batchNorm3d_:hN});function dN(e,t,n,r,a,s){let i=E(e,"x","batchNorm"),o=E(t,"mean","batchNorm"),l=E(n,"variance","batchNorm"),u;a!=null&&(u=E(a,"scale","batchNorm"));let c;return r!=null&&(c=E(r,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),li(i,o,l,c,u,s)}var H5=O({batchNorm4d_:dN});function pN(e,t,n){let r=E(e,"x","bincount"),a=E(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return $.runKernel(Sh,s,i)}var j5=O({bincount_:pN});function fN(e,t){let n=E(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let l=n.shape.slice();for(;l.length<t.length;)l.unshift(1);n=G(n,l)}let a=n.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,u)=>l>1?u:-1).filter(l=>l>=0).length===0)return rr(n);let i={x:n},o={reps:s};return $.runKernel(Sa,i,o)}var Bu=O({broadcastTo_:fN});function mN(e){let t={x:E(e,"x","ceil")};return $.runKernel(ds,t)}var jf=O({ceil_:mN});function AN(e,t,n){let r=E(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Na,a,s)}var bn=O({clipByValue_:AN});function yN(e){return st(e,0)}var G5=O({concat1d_:yN});function gN(e,t){return st(e,t)}var ll=O({concat2d_:gN});function xN(e,t){return st(e,t)}var q5=O({concat3d_:xN});function wN(e,t){return st(e,t)}var X5=O({concat4d_:wN});function _N(e,t,n,r,a="NHWC",s=[1,1],i){let o=E(e,"x","conv2d"),l=E(t,"filter","conv2d"),u=o,c=!1;o.rank===3&&(c=!0,u=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(jt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?u.shape[3]:u.shape[1];F(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),F(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:u,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=$.runKernel(ps,d,p);return c?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ra=O({conv2d_:_N});function bN(e,t,n,r,a="NWC",s=1,i){let o=E(e,"x","conv1d"),l=E(t,"filter","conv1d"),u=o,c=!1;o.rank===2&&(c=!0,u=G(o,[1,o.shape[0],o.shape[1]])),F(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(jt(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(Mr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=G(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=G(u,[u.shape[0],1,u.shape[1],u.shape[2]]),p=ra(d,h,[1,n],r,"NHWC",[1,s],i);return c?G(p,[p.shape[2],p.shape[3]]):G(p,[p.shape[0],p.shape[2],p.shape[3]])}var fd=O({conv1d_:bN});function vN(e,t,n,r,a,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,u=!1;t.rank===3&&(u=!0,l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];F(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),F(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(jt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=$.runKernel(fs,d,p);return u?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Gf=O({conv2DBackpropInput_:vN});function kN(e,t,n,r,a,s){let i=E(e,"x","conv2dTranspose"),o=E(t,"filter","conv2dTranspose");return Gf(n,i,o,r,a,"NHWC",s)}var md=O({conv2dTranspose_:kN});function IN(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=E(e,"x","conv3d"),o=E(t,"filter","conv3d"),l=i,u=!1;i.rank===4&&(u=!0,l=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(Mr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let c={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=$.runKernel(du,c,h);return u?G(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var qf=O({conv3d_:IN});function NN(e,t,n,r,a){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=G(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],u=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=$.runKernel(Rh,c,h);return o?G(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var K5=O({conv3DBackpropInput_:NN});function SN(e,t,n,r,a){let s=E(e,"x","conv3dTranspose"),i=E(t,"filter","conv3dTranspose");return K5(n,s,i,r,a)}var TN=O({conv3dTranspose_:SN});function CN(e){let t={x:E(e,"x","cos")};return $.runKernel(ms,t)}var Vu=O({cos_:CN});function EN(e){let t={x:E(e,"x","cosh")};return $.runKernel(so,t)}var Ad=O({cosh_:EN});function RN(e,t=0,n=!1,r=!1){let a={x:E(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return $.runKernel(As,a,s)}var yd=O({cumsum_:RN});function FN(e,t,n,r=!1){let a=E(e,"x","denseBincount"),s=E(t,"weights","denseBincount");F(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),F(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return $.runKernel(Fh,i,o)}var Z5=O({denseBincount_:FN});function MN(e,t,n="NHWC"){let r=E(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${a} and ${t} for depthToSpace with input shape
|
|
${r.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
|
|
${s} and ${t} for depthToSpace with input shape
|
|
${r.shape}`),F(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${r.shape}`);let o={x:r},l={blockSize:t,dataFormat:n};return $.runKernel(oo,o,l)}var Xf=O({depthToSpace_:MN});function $N(e,t,n,r,a="NHWC",s=[1,1],i){let o=E(e,"x","depthwiseConv2d"),l=E(t,"filter","depthwiseConv2d"),u=o,c=!1;o.rank===3&&(c=!0,u=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),F(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),F(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),i!=null&&F(jt(r),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h={x:u,filter:l},d={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},p=$.runKernel(ys,h,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ul=O({depthwiseConv2d_:$N});function DN(e){let t={x:E(e,"x","diag")};return $.runKernel(Dh,t)}var ON=O({diag_:DN});function zN(e,t,n,r,a=[1,1],s="NHWC"){let i=E(e,"x","dilation2d"),o=E(t,"filter","dilation2d");F(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),F(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),F(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=i,u=!1;i.rank===3&&(l=G(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=!0);let c={x:l,filter:o},h={strides:n,pad:r,dilations:a},d=$.runKernel(pu,c,h);return u?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Kf=O({dilation2d_:zN});function PN(e,t){let n=e.length,r=[];for(let a=0;a<n;a++){let s=n-1-a,i=e[s]||1;(t[t.length-1-a]||1)>1&&i===1&&r.unshift(s)}return r}function Pt(e,t){let n=[];for(let r=0;r<t.length;r++){let a=e[e.length-r-1],s=t.length-r-1,i=t[s];(a==null||a===1&&i>1)&&n.unshift(s)}return n}function yt(e,t){let n=[],r=Math.max(e.length,t.length);for(let a=0;a<r;a++){let s=e[e.length-a-1];s==null&&(s=1);let i=t[t.length-a-1];if(i==null&&(i=1),s===1)n.unshift(i);else if(i===1)n.unshift(s);else if(s!==i){let o=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(o)}else n.unshift(s)}return n}function LN(e,t){let n=E(e,"a","equal"),r=E(t,"b","equal");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(co,a)}var Da=O({equal_:LN});function WN(e,t,n){let r=E(t,"a","where"),a=E(n,"b","where"),s=E(e,"condition","where","bool"),i=yt(r.shape,a.shape),o=Bu(r,i),l=Bu(a,i);s.rank===1&&F(s.shape[0]===r.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&an(s.shape,l.shape,"Error in where: ");let u={condition:s,t:o,e:l};return $.runKernel(zo,u)}var vn=O({where_:WN});function BN(e){let t={x:E(e,"x","zerosLike")};return $.runKernel(Xo,t)}var Ue=O({zerosLike_:BN});function VN(e,t){let n=E(e,"a","div"),r=E(t,"b","div");[n,r]=bt(n,r);let a=ge(n,r),s=Ue(a),i=Da(r,s);return vn(i,s,a)}var Zf=O({divNoNan_:VN});function UN(e,t){let n=E(e,"t1","dot"),r=E(t,"t2","dot");F((n.rank===1||n.rank===2)&&(r.rank===1||r.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);let a=n.rank===1?n.size:n.shape[1],s=r.rank===1?r.size:r.shape[0];if(F(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),n.rank===1&&r.rank===1){let i=G(n,[1,-1]),o=G(r,[-1,1]),l=je(i,o);return G(l,[])}else if(n.rank===1&&r.rank===2){let i=G(n,[1,-1]),o=G(r,[r.shape[0],r.shape[1]]),l=je(i,o);return G(l,[l.size])}else if(n.rank===2&&r.rank===1){let i=G(r,[-1,1]),o=je(n,i);return G(o,[o.size])}else{let i=G(r,[r.shape[0],r.shape[1]]);return je(n,i)}}var Y5=O({dot_:UN});function HN(e){let t={x:E(e,"x","elu")};return $.runKernel(lo,t)}var cl=O({elu_:HN});function jN(e){let t=E(e,"x","erf");F(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return $.runKernel(uo,n)}var Yf=O({erf_:jN});function GN(e){let t={x:E(e,"x","exp")};return $.runKernel(xs,t)}var qn=O({exp_:GN});function qN(e,t=0){let n=E(e,"x","expandDims","string_or_numeric");F(t<=n.rank,()=>"Axis must be <= rank of the tensor");let r={input:n},a={dim:t};return $.runKernel(ho,r,a)}var Fn=O({expandDims_:qN});function XN(e){let t={x:E(e,"x","expm1")};return $.runKernel(po,t)}var Jf=O({expm1_:XN});function KN(e,t){let n=E(e,"x","tile","string_or_numeric");F(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let r={x:n},a={reps:t};return $.runKernel(Sa,r,a)}var Oa=O({tile_:KN});function ZN(e,t,n,r="float32"){t==null&&(t=e);let a=We([e,t],r),s=e<=t?e:t;for(let o=0;o<s;++o)a.set(1,o,o);let i=G(a.toTensor(),[e,t]);if(n==null)return i;if(n.length===1)return Oa(Fn(i,0),[n[0],1,1]);if(n.length===2)return Oa(Fn(Fn(i,0),0),[n[0],n[1],1,1]);if(n.length===3)return Oa(Fn(Fn(Fn(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 Qf=O({eye_:ZN});function Uu(e,t,n){let r={shape:e,value:t,dtype:n};return $.runKernel(fu,{},r)}function YN(e){let t={x:E(e,"x","floor")};return $.runKernel(ws,t)}var hl=O({floor_:YN});function JN(e,t,n=0,r=0){let a=E(e,"x","gather"),s=E(t,"indices","gather","int32"),i={x:a,indices:s},o={axis:n,batchDims:r};return $.runKernel(mo,i,o)}var ui=O({gather_:JN});function QN(e,t){let n=E(e,"a","greater"),r=E(t,"b","greater");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(yo,a)}var ar=O({greater_:QN});function eS(e,t){let n=E(e,"a","greaterEqual"),r=E(t,"b","greaterEqual");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(vs,a)}var za=O({greaterEqual_:eS});function tS(e){let t={input:E(e,"input","imag")};return $.runKernel(Bh,t)}var gd=O({imag_:tS});function nS(e){let t={x:E(e,"x","isFinite")};return $.runKernel(go,t)}var J5=O({isFinite_:nS});function rS(e){let t={x:E(e,"x","isInf")};return $.runKernel(xo,t)}var Q5=O({isInf_:rS});function aS(e){let t={x:E(e,"x","isNaN")};return $.runKernel(wo,t)}var ex=O({isNaN_:aS});function sS(e,t=.2){let n={x:E(e,"x","leakyRelu")},r={alpha:t};return $.runKernel(Is,n,r)}var Hu=O({leakyRelu_:sS});function iS(e,t){let n=E(e,"a","less"),r=E(t,"b","less");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(_o,a)}var xd=O({less_:iS});function oS(e,t){let n=E(e,"a","lessEqual"),r=E(t,"b","lessEqual");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(bo,a)}var ci=O({lessEqual_:oS});function tx(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let r={start:e,stop:t,num:n};return $.runKernel(Vh,{},r)}function lS(e,t=5,n=1,r=1,a=.5){let s=E(e,"x","localResponseNormalization");F(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
|
|
rank ${s.rank}.`),F(jt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=G(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:i},u={depthRadius:t,bias:n,alpha:r,beta:a},c=$.runKernel(yu,l,u);return o?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var em=O({localResponseNormalization_:lS});function uS(e){let t={x:E(e,"x","log")};return $.runKernel(Ns,t)}var Mn=O({log_:uS});function cS(e){let t={x:E(e,"x","log1p")};return $.runKernel(vo,t)}var wd=O({log1p_:cS});function hS(e){return F(ka(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let r=E(t,"x","tf.grad","string_or_numeric"),a=n!=null?E(n,"dy","tf.grad"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(r),[r],a);return a!=null&&an(s.shape,a.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),_d(i),i[0]})}}function dS(e){return F(ka(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{F(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let r=$u(t,"args","tf.grads","string_or_numeric"),a=n!=null?E(n,"dy","tf.grads"):null;return $.tidy(()=>{let{value:s,grads:i}=$.gradients(()=>e(...r),r,a);return a!=null&&an(s.shape,a.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),_d(i),i})}}function pS(e){return F(ka(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{F(t instanceof Ye,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),F(n==null||n instanceof Ye,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:r,value:a}=$.gradients(()=>e(t),[t],n);return _d(r),{grad:r[0],value:a}}}function fS(e){return F(ka(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{F(Array.isArray(t)&&t.every(a=>a instanceof Ye),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),F(n==null||n instanceof Ye,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let r=$.gradients(()=>e(...t),t,n);return n!=null&&an(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),_d(r.grads),r}}function nx(e,t){F(ka(e),()=>"The f passed in variableGrads(f) must be a function"),F(t==null||Array.isArray(t)&&t.every(u=>u instanceof Ru),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let u in $.registeredVariables)t.push($.registeredVariables[u])}let r=n?t.filter(u=>!u.trainable):null,a=t.length;t=t.filter(u=>u.trainable),F(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);let s=!0,{value:i,grads:o}=$.gradients(e,t,null,s);F(o.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),F(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let l={};return t.forEach((u,c)=>{o[c]!=null&&(l[u.name]=o[c])}),r!=null&&r.forEach(u=>l[u.name]=null),{value:i,grads:l}}function $r(e){return $.customGrad(e)}function _d(e){if(e.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
|
|
the f you passed encloses all operations that lead from x to y.`)}function mS(e){let t={x:E(e,"x","neg")};return $.runKernel(No,t)}var vt=O({neg_:mS});function AS(e){let t={x:E(e,"x","softplus")};return $.runKernel(Vo,t)}var dl=O({softplus_:AS});function yS(e){let t=E(e,"x","logSigmoid");return $r(n=>({value:vt(dl(vt(n))),gradFunc:r=>P(r,Rn(vt(n)))}))(t)}var rx=O({logSigmoid_:yS});function gS(e,t=null,n=!1){let r={x:E(e,"x","max")},a={reductionIndices:t,keepDims:n};return $.runKernel(Ss,r,a)}var Xn=O({max_:gS});function xS(e,t){let n=E(e,"a","sub"),r=E(t,"b","sub");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Zs,a)}var ye=O({sub_:xS});function wS(e,t=null,n=!1){let r=E(e,"x","sum");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(qs,a,s)}var Te=O({sum_:wS});function _S(e,t=-1){let n=E(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);return $r((r,a)=>{let s=!0,i=Xn(r,t,!0),o=ye(r,i),l=ye(me(o,"float32"),Mn(Te(qn(o),t,s)));return a([l]),{value:l,gradFunc:(u,c)=>{let[h]=c,d=!0,p=qn(h);return ye(u,P(Te(u,t,d),p))}}})(n)}var bd=O({logSoftmax_:_S});function tm(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function ax(e,t,n){let r=e.length+t.length,a=[],s=0,i=0;for(let o=0;o<r;o++)n.indexOf(o)===-1?a.push(e[s++]):a.push(t[i++]);return a}function sx(e,t){let n=[],r=e.length;for(let s=0;s<r;s++)t.indexOf(s)===-1&&n.push(e[s]);let a=t.map(s=>e[s]);return[n,a]}function hi(e,t){let n=t.map(r=>1);return ax(e,n,t)}function bS(e,t,n){F(tm(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function ix(e,t){if(tm(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 nm(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function vS(e,t){let n=[];for(let r=t-e;r<t;++r)n.push(r);return n}function kS(e,t=null,n=!1){let r=E(e,"x","logSumExp"),a=tr(t,r.shape),s=Xn(r,a,!0),i=ye(r,s),o=qn(i),l=Te(o,a),u=Mn(l),c=se(G(s,u.shape),u);if(n){let h=hi(c.shape,a);return G(c,h)}return c}var rm=O({logSumExp_:kS});function IS(e,t){let n=E(e,"a","logicalAnd","bool"),r=E(t,"b","logicalAnd","bool");yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(ko,a)}var sr=O({logicalAnd_:IS});function NS(e){let t={x:E(e,"x","logicalNot","bool")};return $.runKernel(mu,t)}var ju=O({logicalNot_:NS});function SS(e,t){let n=E(e,"a","logicalOr","bool"),r=E(t,"b","logicalOr","bool");yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Au,a)}var vd=O({logicalOr_:SS});function TS(e,t){let n=E(e,"a","logicalXor","bool"),r=E(t,"b","logicalXor","bool");return yt(n.shape,r.shape),sr(vd(e,t),ju(sr(e,t)))}var ox=O({logicalXor_:TS});function CS(e,t,n,r,a){let s=E(e,"x","maxPool"),i=1,o=s,l=!1;s.rank===3&&(l=!0,o=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`),F(Mr(n,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`),a!=null&&F(jt(r),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=$.runKernel(Cs,u,c);return l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Gu=O({maxPool_:CS});function ES(e,t=[1,1,1],n,r,a,s="NDHWC"){let i=E(e,"x","maxPool3d"),o=i,l=!1;i.rank===4&&(l=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(jt(r),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=$.runKernel(gu,u,c);return l?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var am=O({maxPool3d_:ES});function RS(e,t,n,r,a=!1){let s={x:E(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:r,includeBatchInIndex:a},o=$.runKernel(Gh,s,i);return{result:o[0],indexes:o[1]}}var lx=O({maxPoolWithArgmax_:RS});function FS(e,t){let n=E(e,"a","maximum"),r=E(t,"b","maximum");[n,r]=bt(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Ts,a)}var Dr=O({maximum_:FS});function MS(e,t=null,n=!1){let r={x:E(e,"x","mean")},a={axis:t,keepDims:n};return $.runKernel(Es,r,a)}var kt=O({mean_:MS});function $S(e,t=null,n=!1){let r={x:E(e,"x","min")},a={axis:t,keepDims:n};return $.runKernel(Rs,r,a)}var pl=O({min_:$S});function DS(e,t){let n=E(e,"a","minimum"),r=E(t,"b","minimum");[n,r]=bt(n,r),n.dtype==="bool"&&(n=me(n,"int32"),r=me(r,"int32")),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(Fs,a)}var fl=O({minimum_:DS});function OS(e,t,n){F(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let r=E(e,"x","mirrorPad");if(r.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");F(t.length===r.rank,()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`);let a=n==="reflect"?1:0;for(let o=0;o<r.rank;o++)F(t[o].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),F(t[o][0]>=0&&t[o][0]<=r.shape[o]-a&&t[o][1]>=0&&t[o][1]<=r.shape[o]-a,()=>`Padding in dimension ${o} cannot be greater than or equal to ${r.shape[o]-a} or less than 0 for input of shape ${r.shape}`);let s={paddings:t,mode:n},i={x:r};return $.runKernel(xu,i,s)}var sm=O({mirrorPad_:OS});function zS(e,t){let n=E(e,"a","mod"),r=E(t,"b","mod");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Io,a)}var im=O({mod_:zS});function PS(e){let t=E(e,"x","square"),n={};return $.runKernel("Square",{x:t},n)}var lt=O({square_:PS});function LS(e,t=null,n=!1){e=E(e,"x","moments");let r=tr(t,e.shape),a=kt(e,r,n),s=a.shape;n||(s=hi(a.shape,r));let i=lt(ye(me(e,"float32"),G(a,s))),o=kt(i,r,n);return{mean:a,variance:o}}var kd=O({moments_:LS});function WS(e,t,n,r){let a=E(t,"data","multiRNNCell"),s=$u(n,"c","multiRNNCell"),i=$u(r,"h","multiRNNCell"),o=a,l=[];for(let h=0;h<e.length;h++){let d=e[h](o,s[h],i[h]);l.push(d[0]),l.push(d[1]),o=d[1]}let u=[],c=[];for(let h=0;h<l.length;h+=2)u.push(l[h]),c.push(l[h+1]);return[u,c]}var BS=O({multiRNNCell_:WS});function VS(e,t,n,r=!1){let a=E(e,"logits","multinomial"),s=a.size,i=a.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);n=n||Math.random();let o={logits:i===1?G(a,[1,-1]):a},l={numSamples:t,seed:n,normalized:r},u=$.runKernel(qh,o,l);return i===1?G(u,[u.size]):u}var ux=O({multinomial_:VS});function US(e,t){let n=E(e,"a","notEqual"),r=E(t,"b","notEqual");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r};return $.runKernel(So,a)}var di=O({notEqual_:US});function Et(e,t="float32"){if(t==="complex64"){let r=Et(e,"float32"),a=Et(e,"float32");return Ca(r,a)}let n=bh(Dt(e),t);return $.makeTensor(n,e,t)}function Or(e,t="float32"){if(t==="complex64"){let r=Or(e,"float32"),a=Et(e,"float32");return Ca(r,a)}let n=J1(Dt(e),t);return $.makeTensor(n,e,t)}function HS(e){let t={x:E(e,"x","onesLike")};return $.runKernel(Ro,t)}var $n=O({onesLike_:HS});function jS(e,t){let n=E(e,"v1","outerProduct"),r=E(t,"v2","outerProduct");F(n.rank===1&&r.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`);let a=G(n,[-1,1]),s=G(r,[1,-1]);return je(a,s)}var GS=O({outerProduct_:jS});function qS(e,t,n=0){let r=E(e,"x","pad");if(r.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let a={paddings:t,constantValue:n},s={x:r};return $.runKernel(Ds,s,a)}var aa=O({pad_:qS});function XS(e,t,n=0){return F(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),aa(e,[t],n)}var KS=O({pad1d_:XS});function ZS(e,t,n=0){return F(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),aa(e,t,n)}var YS=O({pad2d_:ZS});function JS(e,t,n=0){return F(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),aa(e,t,n)}var QS=O({pad3d_:JS});function eT(e,t,n=0){return F(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),aa(e,t,n)}var tT=O({pad4d_:eT});function nT(e,t,n){let r=E(e,"x","spaceToBatchND");F(r.rank>=1+t.length,()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`),F(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),F(r.shape.reduce((i,o,l)=>l>0&&l<=t.length?i&&(o+n[l-1][0]+n[l-1][1])%t[l-1]==0:i,!0),()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let a={x:r},s={blockShape:t,paddings:n};return $.runKernel(bu,a,s)}var qu=O({spaceToBatchND_:nT});function sT(e,t,n,r,a,s){a==null&&(a=[1,1]),s==null&&(s=1),r===0&&(r="valid");let i=E(e,"x","maxPool"),o=i,l=!1;i.rank===3&&(l=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2]])),F(Mr(s,a),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${a}'`);let u=W5(o.shape,t,s,a,r),c=[u.dilationHeight,u.dilationWidth],h;r==="same"?h=aT([u.filterHeight,u.filterWidth],c):h=[[0,0],[0,0]];let d=c[0]===1&&c[1]===1,[p,f]=rT([u.inHeight,u.inWidth],c,h),m=d?r:"valid",A=d?o:qu(o,c,p),y=(n==="avg"?()=>Lu(A,t,s,m):()=>Gu(A,t,s,m))(),g=d?y:Wu(y,c,f);return l?G(g,[g.shape[1],g.shape[2],g.shape[3]]):g}function rT(e,t,n){let r=n.map(c=>c[0]),a=n.map(c=>c[1]),s=e.concat(r,a),i=t.map((c,h)=>(c-s[h]%c)%c),o=a.map((c,h)=>c+i[h]),l=t.map((c,h)=>[r[h],o[h]]),u=t.map((c,h)=>[0,i[h]]);return[l,u]}function aT(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 cx=O({pool_:sT});function iT(e,t){let n=E(e,"base","pow"),r=E(t,"exp","pow");[n,r]=bt(n,r);let a={a:n,b:r};return $.runKernel(Os,a)}var sa=O({pow_:iT});function oT(e,t){let n=E(e,"x","prelu"),r=E(t,"alpha","prelu"),a={x:n,alpha:r};return $.runKernel(zs,a)}var Xu=O({prelu_:oT});function lT(e,t=null,n=!1){let r=E(e,"x","prod");r.dtype==="bool"&&(r=me(r,"int32"));let a={x:r},s={axis:t,keepDims:n};return $.runKernel(Mo,a,s)}var Id=O({prod_:lT});function uT(e,t,n){let r=Dt(e),a=null;if(n==null||n==="float32")a=new Float32Array(r);else if(n==="int32")a=new Int32Array(r);else if(n==="bool")a=new Uint8Array(r);else throw new Error(`Unknown data type ${n}`);for(let s=0;s<r;s++)a[s]=t();return $.makeTensor(a,e,n)}var cT=O({rand_:uT}),om=qi($8()),lm=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=om.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}},hT=class{constructor(e,t,n,r){this.alpha=e,this.beta=1/t,this.dtype=n;let a=r||Math.random();this.randu=om.alea(a.toString()),this.randn=new lm(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)}},dT=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=om.alea(r)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function pT(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 hT(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 fT=O({randomGamma_:pT});function mT(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error(`Unsupported data type ${r}`);let s=new lm(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 hx=O({randomNormal_:mT});function AT(e,t=0,n=1,r="float32",a){let s=We(e,r),i=new dT(t,n,null,a);for(let o=0;o<s.values.length;o++)s.values[o]=i.nextValue();return s.toTensor()}var ml=O({randomUniform_:AT});function Nd(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 yT(e){let t={input:E(e,"input","real")};return $.runKernel(Xh,t)}var Ku=O({real_:yT});function gT(e){let t={x:E(e,"x","reciprocal")};return $.runKernel($o,t)}var um=O({reciprocal_:gT});function xT(e){let t={x:E(e,"x","relu")};return $.runKernel(Ps,t)}var zr=O({relu_:xT});function wT(e){let t={x:E(e,"x","relu6")};return $.runKernel(Ws,t)}var Sd=O({relu6_:wT});function _T(e,t){let n={x:E(e,"x","reverse")},r={dims:t};return $.runKernel(Bs,n,r)}var Dn=O({reverse_:_T});function bT(e){let t=E(e,"x","reverse");return F(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),Dn(t,0)}var vT=O({reverse1d_:bT});function kT(e,t){let n=E(e,"x","reverse");return F(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),Dn(n,t)}var IT=O({reverse2d_:kT});function NT(e,t){let n=E(e,"x","reverse");return F(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),Dn(n,t)}var ST=O({reverse3d_:NT});function TT(e,t){let n=E(e,"x","reverse");return F(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),Dn(n,t)}var CT=O({reverse4d_:TT});function ET(e){let t={x:E(e,"x","round")};return $.runKernel(Vs,t)}var cm=O({round_:ET});function RT(e){let t={x:E(e,"x","rsqrt")};return $.runKernel(Us,t)}var Td=O({rsqrt_:RT});function be(e,t){if((sn(e)&&t!=="string"||Array.isArray(e))&&t!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(t==="string"&&sn(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Ea(e,[],[],t)}function FT(e){let t={x:E(e,"x","selu")};return $.runKernel(Po,t)}var Cd=O({selu_:FT});function MT(e,t,n,r,a,s=[1,1],i="NHWC"){let o=E(e,"x","separableConv2d"),l=E(t,"depthwiseFilter","separableConv2d"),u=E(n,"pointwiseFilter","separableConv2d"),c=o,h=!1;if(o.rank===3&&(h=!0,c=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");F(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),F(u.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),F(u.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${u.shape[0]}.`),F(u.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);let d=l.shape[2],p=l.shape[3];F(u.shape[2]===d*p,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${d*p}, but got ${u.shape[2]}.`);let f=ul(c,l,r,a,i,s),m=ra(f,u,1,"valid",i);return h?G(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var hm=O({separableConv2d_:MT});async function $T(e,t){let n=E(e,"x","setdiff1d"),r=E(t,"y","setdiff1d");F(n.dtype===r.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${r.dtype}).`),F(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),F(r.rank===1,()=>`y should be 1D tensor, but got y (${r.shape}).`);let a=await n.data(),s=await r.data(),i=new Set(s),o=0;for(let c=0;c<a.length;c++)i.has(a[c])||o++;let l=new Ot([o],n.dtype),u=new Ot([o],"int32");for(let c=0,h=0;c<a.length;c++)i.has(a[c])||(l.values[h]=a[c],u.values[h]=c,h++);return[l.toTensor(),u.toTensor()]}var dx=$T;function DT(e){let t={x:E(e,"x","sign")};return $.runKernel(Bo,t)}var dm=O({sign_:DT});function OT(e){let t={x:E(e,"x","sin")};return $.runKernel(Hs,t)}var Ed=O({sin_:OT});function zT(e){let t={x:E(e,"x","sinh")};return $.runKernel(Wo,t)}var Rd=O({sinh_:zT});function PT(e,t,n){let r=E(e,"x","slice1d");return F(r.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${r.rank} tensor`),Re(r,[t],[n])}var Fd=O({slice1d_:PT});function LT(e,t,n){let r=E(e,"x","slice2d");return F(r.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var pm=O({slice2d_:LT});function WT(e,t,n){let r=E(e,"x","slice3d");return F(r.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Md=O({slice3d_:WT});function BT(e,t,n){let r=E(e,"x","slice4d");return F(r.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`),Re(r,t,n)}var Zu=O({slice4d_:BT});function VT(e,t=-1){let n=E(e,"logits","softmax","float32");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);let r={logits:n},a={dim:t};return $.runKernel(Xs,r,a)}var Yu=O({softmax_:VT});function UT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Lh,t)}var Ju=O({fft_:UT});function HT(e){F(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return $.runKernel(Wh,t)}var Al=O({ifft_:HT});function jT(e){let t=e.shape[e.shape.length-1],n=e.size/t,r;if(t<=2){let a=G(e,[n,t]);r=Al(a)}else{let a=[n,2*(t-1)],s=G(Ku(e),[n,t]),i=G(gd(e),[n,t]),o=Dn(Re(s,[0,1],[n,t-2]),1),l=P(Dn(Re(i,[0,1],[n,t-2]),1),be(-1)),u=st([s,o],1),c=st([i,l],1),h=G(Ca(u,c),[a[0],a[1]]);r=Al(h)}if(r=Ku(r),e.rank===3&&e.shape[0]!==0){let a=r,s=e.shape[0];r=G(r,[s,r.shape[0]/s,r.shape[1]]),a.dispose()}return r}var $d=O({irfft_:jT});function GT(e,t,n=0){let r={x:E(e,"x","split")},a={numOrSizeSplits:t,axis:n};return $.runKernel(Uo,r,a)}var Lt=O({split_:GT});function qT(e,t){F(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],r=e.size/n,a;if(t!=null&&t<n){let f=e.shape.map(A=>0),m=e.shape.map(A=>A);m[e.shape.length-1]=t,a=Re(e,f,m),n=t}else if(t!=null&&t>n){let f=e.shape.map(m=>m);f[e.shape.length-1]=t-n,a=st([e,Et(f)],e.shape.length-1),n=t}else a=e;let s=Ue(a),i=G(Ca(a,s),[r,n]),o=Ju(i),l=Math.floor(n/2)+1,u=Ku(o),c=gd(o),h=Lt(u,[l,n-l],u.shape.length-1),d=Lt(c,[l,n-l],c.shape.length-1),p=a.shape.slice();return p[a.shape.length-1]=l,G(Ca(h[0],d[0]),p)}var Qu=O({rfft_:qT});function XT(e){let t={x:E(e,"x","sqrt")};return $.runKernel(Gs,t)}var en=O({sqrt_:XT});function KT(e,t){let n=E(e,"a","squaredDifference"),r=E(t,"b","squaredDifference");[n,r]=bt(n,r),yt(n.shape,r.shape);let a={a:n,b:r},s={};return $.runKernel(Ks,a,s)}var Dd=O({squaredDifference_:KT});function ZT(e,t){let n=E(e,"x","squeeze");return G(n,Mg(n.shape,t).newShape)}var Pa=O({squeeze_:ZT});function YT(e,t=0){let n=$u(e,"tensors","stack","string_or_numeric");F(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&F(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let r=n,a={axis:t};return $.runKernel(Fo,r,a)}var hn=O({stack_:YT});function JT(e,t=0){let n={x:E(e,"x","step")},r={alpha:t};return $.runKernel(Ta,n,r)}var yl=O({step_:JT});function QT(e,t,n,r,a=0,s=0,i=0,o=0,l=0){let u={x:E(e,"x","stridedSlice")},c={begin:t,end:n,strides:r,beginMask:a,endMask:s,ellipsisMask:i,newAxisMask:o,shrinkAxisMask:l};return $.runKernel(Ho,u,c)}var fm=O({stridedSlice_:QT});function eC(e){let t={x:E(e,"x","tan")};return $.runKernel(jo,t)}var mm=O({tan_:eC});function on(e,t){ss(e);let n=Rr(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Ea(e,null,n,t)}function kn(e,t,n){if(ss(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let r=Rr(e,n);if(r.length!==2&&r.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Ea(e,t,r,n)}function tC(e,t,n){if(ss(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let r=Rr(e,n);if(r.length!==4&&r.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}function nC(e,t,n){if(ss(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let r=Rr(e,n);if(r.length!==5&&r.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Ea(e,t,r,n)}function rC(e,t,n){if(ss(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let r=Rr(e,n);if(r.length!==6&&r.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(r.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||r,Ea(e,t,r,n)}function aC(e,t=1,n=!0){let r=E(e,"x","topk");if(r.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let a=r.shape[r.shape.length-1];if(t>a)throw new Error(`'k' passed to topk() must be <= the last dimension (${a}) but got ${t}`);let s={x:r},i={k:t,sorted:n},[o,l]=$.runKernel(Go,s,i);return{values:o,indices:l}}var Am=O({topk_:aC});function sC(e,t=0,n=1,r,a){if(r!=null&&r==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new lm(t,n,r,!0,a),i=We(e,r);for(let o=0;o<i.values.length;o++)i.values[o]=s.nextValue();return i.toTensor()}var Od=O({truncatedNormal_:sC});function iC(e,t=0){let n=E(e,"x","unique","string_or_numeric");F(n.rank>0,()=>"The input tensor must be at least 1D");let r={x:n},a={axis:t},[s,i]=$.runKernel(Qh,r,a);return{values:s,indices:i}}var zd=O({unique_:iC});function oC(e,t,n){let r=E(e,"x","unsortedSegmentSum"),a=E(t,"segmentIds","unsortedSegmentSum","int32");F(jt(n),()=>"numSegments must be of dtype int");let s={x:r,segmentIds:a},i={numSegments:n};return $.runKernel(ku,s,i)}var ym=O({unsortedSegmentSum_:oC});function lC(e,t=0){let n=E(e,"x","unstack","string_or_numeric");F(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let r={value:n},a={axis:t};return $.runKernel(qo,r,a)}var ir=O({unstack_:lC});function px(e,t=!0,n,r){return $.makeVariable(e,t,n,r)}function fx(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 uC(e){let t=E(e,"condition","whereAsync","bool"),n=await t.data(),r=fx(t.shape,n);return e!==t&&t.dispose(),r}var gm=uC;async function cC(e,t,n){let r=E(e,"tensor","boolMask"),a=E(t,"mask","boolMask","bool"),s=n==null?0:n,i=a.rank,o=r.shape;F(i>0,()=>"mask cannot be scalar"),an(o.slice(s,s+i),a.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=s;m<s+i;m++)l*=o[m];let u=o.slice(0,s).concat([l],o.slice(s+i)),c=G(r,u),h=G(a,[-1]),d=await gm(h),p=Pa(d,[1]),f=ui(c,p,s);return e!==r&&r.dispose(),t!==a&&a.dispose(),p.dispose(),c.dispose(),h.dispose(),d.dispose(),f}var hC=cC;function dC(e,t="euclidean",n=null,r=!1){e=E(e,"x","norm");let a=mx(e,t,n),s=a.shape;if(r){let i=tr(n,e.shape);s=hi(a.shape,i)}return G(a,s)}function mx(e,t,n=null){if(e.rank===0)return zt(e);if(e.rank!==1&&n===null)return mx(G(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return Te(zt(e),n);if(t===Infinity)return Xn(zt(e),n);if(t===-Infinity)return pl(zt(e),n);if(t==="euclidean"||t===2)return en(Te(sa(zt(e),be(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Xn(Te(zt(e),n[0]),n[1]-1);if(t===Infinity)return Xn(Te(zt(e),n[1]),n[0]);if(t===-Infinity)return pl(Te(zt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return en(Te(lt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Pd=O({norm_:dC});function pC(e,t,n,r,a=!0){let s=E(e,"v","movingAverage"),i=E(t,"x","movingAverage"),o=E(n,"decay","movingAverage");Zg(s,i),F(ta(s.shape,i.shape),()=>"Shape mismatch in v and x");let l=be(1),u=ye(l,o),c=P(ye(i,s),u);if(a){F(r!=null,()=>"When using zeroDebias: true, step is required.");let h=E(r,"step","movingAverage");c=ge(c,ye(l,sa(o,h)))}return se(s,c)}var fC=O({movingAverage_:pC});function mC(e,t,n){let r=E(e,"indices","scatterND","int32"),a=E(t,"updates","scatterND");Tf(a,r,n);let s={indices:r,updates:a},i={shape:n};return $.runKernel(Oo,s,i)}var Ax=O({scatterND_:mC});function AC(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 yC(e,t,n,r=0){let a=E(e,"sparseIndices","sparseToDense","int32"),s=E(t,"sparseValues","sparseToDense"),i=E(r,"defaultValue","sparseToDense",s.dtype);AC(a,s,n,i);let o={sparseIndices:a,sparseValues:s,defaultValue:i},l={outputShape:n};return $.runKernel(Yh,o,l)}var xm=O({sparseToDense_:yC});function gC(e,t){let n=E(t,"indices","gatherND","int32"),r={params:E(e,"x","gatherND"),indices:n};return $.runKernel(Ao,r)}var yx=O({gatherND_:gC});function xC(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 wC(e,t,n,r){let a=E(e,"x","dropout");if(F(a.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${a.dtype} tensor instead.`),F(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ye?a.clone():a;let s=xC(a,n),i=1-t,o=ge(hl(se(ml(s,0,1,"float32",r),i)),i);return P(a,o)}var gx=O({dropout_:wC});function xx(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function wm(e,t,n){let r=1-e%2,a=new Float32Array(e);for(let s=0;s<e;++s){let i=2*Math.PI*s/(e+r-1);a[s]=t-n*Math.cos(i)}return on(a,"float32")}async function _C(e,t,n=1){let r=E(e,"predictions","inTopK"),a=E(t,"targets","inTopK");F(r.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),F(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),an(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=r.shape[r.shape.length-1];F(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);let i=await r.data(),o=await a.data(),[l,u]=[i.length/s,s],c=$g("bool",l);for(let h=0;h<l;h++){let d=h*u,p=i.subarray(d,d+u),f=[];for(let m=0;m<p.length;m++)f.push({value:p[m],index:m});f.sort((m,A)=>A.value-m.value),c[h]=0;for(let m=0;m<n;m++)if(f[m].index===o[h]){c[h]=1;break}}return e!==r&&r.dispose(),t!==a&&a.dispose(),gr(c,a.shape,"bool")}var bC=_C,La={};ze(La,{conv2d:()=>vC,depthwiseConv2d:()=>kC,matMul:()=>IC});function NC(e,t,n,r,a,s="NHWC",i){let o=e;e.rank===3&&(o=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]])),F(o.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`),F(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),F(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let u=s==="NHWC"?o.shape[3]:o.shape[1],c=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n[2],()=>`Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${n[2]}.`),F(c===n[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`),i!=null&&F(jt(a),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let h={x:o,dy:l},d={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,filterShape:n};return $.runKernel(Ch,h,d)}var _m=O({conv2DBackpropFilter_:NC});function Ld(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return P(e,yl(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Wd(e,t){let n=t,r=Pt(e.shape,t.shape);return r.length>0&&(n=Te(n,r)),G(n,e.shape)}function Bd(e,t,n,r){if(t==="linear")return e;if(t==="relu")return zr(e);if(t==="elu")return cl(e);if(t==="relu6")return Sd(e);if(t==="prelu")return Xu(e,n);if(t==="leakyrelu")return Hu(e,r);throw new Error(`Unknown fused activation ${t}.`)}var Vd=(e,t)=>!(e>0)||t==="linear";function SC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(l=l||"linear",Vd($.state.gradientDepth,l)===!1){let w=ra(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Bd(w,l,u,c)}let h=E(e,"x","conv2d"),d=E(t,"filter","conv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=G(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),F(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&F(jt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),F(Mr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NHWC",()=>`Error in conv2d: got dataFormat of ${a} but only NHWC is currently supported.`);let m=Pu(p.shape,d.shape,n,s,r,i),A;o!=null&&(A=E(o,"bias","fused conv2d"),[A]=bt(A,h),yt(m.outShape,A.shape));let y;u!=null&&(y=E(u,"prelu weights","fused conv2d"));let g=(w,x)=>{let[N,S,T,M]=x,D=Ld(w,T,l);F($a(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let z=Gf(S.shape,D,N,n,r),W=_m(S,D,N.shape,n,r),U=[z,W];if(M!=null){let H=Wd(M,D);U.push(H)}return U},_={x:p,filter:d,bias:A,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:c};return o==null?$r((w,x,N)=>{let S=$.runKernel(ei,_,b);return N([x,w,S]),f&&(S=G(S,[S.shape[1],S.shape[2],S.shape[3]])),{value:S,gradFunc:g}})(p,d):$r((w,x,N,S)=>{let T=$.runKernel(ei,_,b);return S([x,w,T,N]),f&&(T=G(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(p,d,A)}var vC=O({fusedConv2d_:SC});function TC(e,t,n,r,a,s=[1,1],i){let o=e;e.rank===3&&(o=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={x:o,dy:l},c={strides:r,pad:a,dimRoundingMode:i,dilations:s,filterShape:n};return $.runKernel(Mh,u,c)}var wx=O({depthwiseConv2dNativeBackpropFilter_:TC});function CC(e,t,n,r,a,s=[1,1],i){let o=t,l=!1;t.rank===3&&(l=!0,o=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let u={dy:o,filter:n},c={strides:r,pad:a,dimRoundingMode:i,dilations:s,inputShape:e},h=$.runKernel($h,u,c);return l?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var _x=O({depthwiseConv2dNativeBackpropInput_:CC});function EC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:u,leakyreluAlpha:c}){if(Vd($.state.gradientDepth,l)===!1){let w=ul(e,t,n,r,a,s,i);return o!=null&&(w=se(w,o)),Bd(w,l,u,c)}let h=E(e,"x","depthwiseConv2d"),d=E(t,"filter","depthwiseConv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=G(h,[1,h.shape[0],h.shape[1],h.shape[2]])),F(p.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`),F(d.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`),F(p.shape[3]===d.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${p.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`),s==null&&(s=[1,1]),F(Mr(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i!=null&&F(jt(r),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${i} but got pad ${r}.`);let m=Pu(p.shape,d.shape,n,s,r,i,!0),A;o!=null&&(A=E(o,"bias","fused conv2d"),[A]=bt(A,h),yt(m.outShape,A.shape));let y;u!=null&&(y=E(u,"prelu weights","fused depthwiseConv2d"));let g=(w,x)=>{F($a(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[N,S,T,M]=x,D=Ld(w,T,l),z=_x(S.shape,D,N,n,r,s,i),W=wx(S,D,N.shape,n,r,s,i);if(M!=null){let U=Wd(A,D);return[z,W,U]}return[z,W]},_={x:p,filter:d,bias:A,preluActivationWeights:y},b={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i,activation:l,leakyreluAlpha:c};return o==null?$r((w,x,N)=>{let S=$.runKernel(ti,_,b);return N([x,w,S]),f&&(S=G(S,[S.shape[1],S.shape[2],S.shape[3]])),{value:S,gradFunc:g}})(p,d):$r((w,x,N,S)=>{let T=$.runKernel(ti,_,b);return S([x,w,T,N]),f&&(T=G(T,[T.shape[1],T.shape[2],T.shape[3]])),{value:T,gradFunc:g}})(p,d,A)}var kC=O({fusedDepthwiseConv2d_:EC});function RC({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:o}){if(Vd($.state.gradientDepth,s)===!1){let M=je(e,t,n,r);return a!=null&&(M=se(M,a)),Bd(M,s,i,o)}let l=E(e,"a","fused matMul"),u=E(t,"b","fused matMul");[l,u]=bt(l,u);let c=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?u.shape[u.rank-1]:u.shape[u.rank-2],d=n?l.shape[l.rank-1]:l.shape[l.rank-2],p=r?u.shape[u.rank-2]:u.shape[u.rank-1],f=l.shape.slice(0,-2),m=u.shape.slice(0,-2),A=Dt(f),y=Dt(m);F(l.rank>=2&&u.rank>=2&&l.rank===u.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${u.rank}.`),F(ta(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${u.shape} must match.`),F(c===h,()=>`Error in fused matMul: inner shapes (${c}) and (${h}) of Tensors with shapes ${l.shape} and ${u.shape} and transposeA=${n} and transposeB=${r} must match.`);let g=l.shape.slice(0,-2).concat([d,p]),_=n?G(l,[A,c,d]):G(l,[A,d,c]),b=r?G(u,[y,p,h]):G(u,[y,h,p]),w;a!=null&&(w=E(a,"bias","fused matMul"),[w]=bt(w,l),yt(g,w.shape));let x;i!=null&&(x=E(i,"prelu weights","fused matMul"));let N=(M,D)=>{let[z,W,U,H]=D,X=Ld(G(M,U.shape),U,s),j,ee;if(!n&&!r?(j=je(X,W,!1,!0),ee=je(z,X,!0,!1)):!n&&r?(j=je(X,W,!1,!1),ee=je(X,z,!0,!1)):n&&!r?(j=je(W,X,!1,!0),ee=je(z,X,!1,!1)):(j=je(W,X,!0,!0),ee=je(X,z,!0,!0)),a!=null){let Y=Wd(H,X);return[j,ee,Y]}else return[j,ee]},S={a:_,b,bias:w,preluActivationWeights:x},T={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:o};return a==null?$r((M,D,z)=>{let W=$.runKernel(Qs,S,T);return z([M,D,W]),{value:G(W,g),gradFunc:N}})(_,b):$r((M,D,z,W)=>{let U=$.runKernel(Qs,S,T);return W([M,D,U,z]),{value:G(U,g),gradFunc:N}})(_,b,w)}var IC=O({fusedMatMul_:RC});function FC(e){return wm(e,.54,.46)}var MC=O({hammingWindow_:FC});function $C(e){return wm(e,.5,.5)}var bx=O({hannWindow_:$C});function DC(e,t,n,r=!1,a=0){let s=0,i=[];for(;s+t<=e.size;)i.push(Re(e,s,t)),s+=n;if(r)for(;s<e.size;){let o=s+t-e.size,l=st([Re(e,s,t-o),Uu([o],a)]);i.push(l),s+=n}return i.length===0?kn([],[0,t]):G(st(i),[i.length,t])}var vx=O({frame_:DC});function OC(e,t,n,r,a=bx){r==null&&(r=xx(t));let s=vx(e,t,n),i=P(s,a(t)),o=[];for(let l=0;l<s.shape[0];l++)o.push(Qu(Re(i,[l,0],[1,t]),r));return st(o)}var zC=O({stft_:OC});function PC(e,t,n,r,a="bilinear",s=0){let i=E(e,"image","cropAndResize"),o=E(t,"boxes","cropAndResize","float32"),l=E(n,"boxInd","cropAndResize","int32"),u=o.shape[0];F(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&o.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${u},4] but had shape ${o.shape}.`),F(l.rank===1&&l.shape[0]===u,()=>`Error in cropAndResize: boxInd must be have size [${u}] but had shape ${o.shape}.`),F(r.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),F(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),F(a==="bilinear"||a==="nearest",()=>`method must be bilinear or nearest, but was ${a}`);let c={image:i,boxes:o,boxInd:l},h={method:a,extrapolationValue:s,cropSize:r};return $.runKernel(io,c,h)}var LC=O({cropAndResize_:PC});function WC(e){let t=E(e,"image","flipLeftRight","float32");F(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return $.runKernel(fo,n,{})}var BC=O({flipLeftRight_:WC});function VC(e,t,n=0,r=.5){let a=E(e,"image","rotateWithOffset","float32");F(a.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);let s={image:a},i={radians:t,fillValue:n,center:r};return $.runKernel(Ko,s,i)}var UC=O({rotateWithOffset_:VC});function gl(e,t,n,r,a,s){r==null&&(r=.5),a==null&&(a=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=e.shape[0];return n=Math.min(n,i),F(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),F(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),F(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),F(t.rank===1,()=>"scores must be a 1D tensor"),F(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function HC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=E(e,"boxes","nonMaxSuppression"),i=E(t,"scores","nonMaxSuppression"),o=gl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:a};return $.runKernel(To,{boxes:s,scores:i},l)}var jC=O({nonMaxSuppression_:HC});function qC(e,t,n){let r=GC(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function GC(e,t,n){return KC(e,t,n||XC)}function XC(e,t){return e>t?1:e<t?-1:0}function KC(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 kx(e,t,n,r,a){return bm(e,t,n,r,a,0)}function Ix(e,t,n,r,a,s){return bm(e,t,n,r,a,0,!1,s,!0)}function Nx(e,t,n,r,a,s){return bm(e,t,n,r,a,s,!0)}function bm(e,t,n,r,a,s,i=!1,o=!1,l=!1){let u=[];for(let A=0;A<t.length;A++)t[A]>a&&u.push({score:t[A],boxIndex:A,suppressBeginIndex:0});u.sort(Sx);let c=s>0?-.5/s:0,h=[],d=[];for(;h.length<n&&u.length>0;){let A=u.pop(),{score:y,boxIndex:g,suppressBeginIndex:_}=A;if(y<a)break;let b=!1;for(let w=h.length-1;w>=_;--w){let x=ZC(e,g,h[w]);if(x>=r){b=!0;break}if(A.score=A.score*YC(r,c,x),A.score<=a)break}A.suppressBeginIndex=h.length,b||(A.score===y?(h.push(g),d.push(A.score)):A.score>a&&qC(u,A,Sx))}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 ZC(e,t,n){let r=e.subarray(t*4,t*4+4),a=e.subarray(n*4,n*4+4),s=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),o=Math.max(r[0],r[2]),l=Math.max(r[1],r[3]),u=Math.min(a[0],a[2]),c=Math.min(a[1],a[3]),h=Math.max(a[0],a[2]),d=Math.max(a[1],a[3]),p=(o-s)*(l-i),f=(h-u)*(d-c);if(p<=0||f<=0)return 0;let m=Math.max(s,u),A=Math.max(i,c),y=Math.min(o,h),g=Math.min(l,d),_=Math.max(y-m,0)*Math.max(g-A,0);return _/(p+f-_)}function YC(e,t,n){let r=Math.exp(t*n*n);return n<=e?r:0}function Sx(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function JC(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=E(e,"boxes","nonMaxSuppressionAsync"),i=E(t,"scores","nonMaxSuppressionAsync"),o=gl(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l=await Promise.all([s.data(),i.data()]),u=l[0],c=l[1],{selectedIndices:h}=kx(u,c,n,r,a);return s!==e&&s.dispose(),i!==t&&i.dispose(),on(h,"int32")}var QC=JC;function eE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u={boxes:i,scores:o},c={maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s},h=$.runKernel(Eo,u,c);return{selectedIndices:h[0],selectedScores:h[1]}}var tE=O({nonMaxSuppressionWithScore_:eE});async function nE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){let i=E(e,"boxes","nonMaxSuppressionAsync"),o=E(t,"scores","nonMaxSuppressionAsync"),l=gl(i,o,n,r,a,s);n=l.maxOutputSize,r=l.iouThreshold,a=l.scoreThreshold,s=l.softNmsSigma;let u=await Promise.all([i.data(),o.data()]),c=u[0],h=u[1],{selectedIndices:d,selectedScores:p}=Nx(c,h,n,r,a,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:on(d,"int32"),selectedScores:on(p)}}var rE=nE;function aE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=E(e,"boxes","nonMaxSuppression"),o=E(t,"scores","nonMaxSuppression"),l=gl(i,o,n,r,a,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,d={boxes:i,scores:o},p={maxOutputSize:u,iouThreshold:c,scoreThreshold:h,padToMaxOutputSize:s},f=$.runKernel(Co,d,p);return{selectedIndices:f[0],validOutputs:f[1]}}var sE=O({nonMaxSuppressionPadded_:aE});async function iE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=E(e,"boxes","nonMaxSuppressionAsync"),o=E(t,"scores","nonMaxSuppressionAsync"),l=gl(i,o,n,r,a,null),u=l.maxOutputSize,c=l.iouThreshold,h=l.scoreThreshold,[d,p]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=Ix(d,p,u,c,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:on(f,"int32"),validOutputs:be(m,"int32")}}var oE=iE;function lE(e,t,n=!1,r=!1){let a=E(e,"images","resizeBilinear");F(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),F(r===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},u=$.runKernel(Ls,o,l);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Tx=O({resizeBilinear_:lE});function uE(e,t,n=!1,r=!1){let a=E(e,"images","resizeNearestNeighbor");F(a.rank===3||a.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),F(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),F(a.dtype==="float32"||a.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),F(r===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,i=!1;a.rank===3&&(i=!0,s=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let[]=t,o={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},u=$.runKernel(_u,o,l);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Cx=O({resizeNearestNeighbor_:uE});function cE(e,t,n="nearest",r="constant",a=0,s){let i=E(e,"image","transform","float32"),o=E(t,"transforms","transform","float32");F(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),F(o.rank===2&&(o.shape[0]===i.shape[0]||o.shape[0]===1)&&o.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),F(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let l={image:i,transforms:o},u={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return $.runKernel(Jh,l,u)}var hE=O({transform_:cE});function dE(e,t,n){F(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),F(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let r=E(e,"a","bandPart");F(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);let a=r.shape,[s,i]=r.shape.slice(-2);if(!(t<=s))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`);if(!(n<=i))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);t<0&&(t=s),n<0&&(n=i);let o=G(Nd(0,s,1,"int32"),[-1,1]),l=Nd(0,i,1,"int32"),u=ye(o,l),c=sr(ci(u,be(+t,"int32")),za(u,be(-n,"int32"))),h=Et([s,i],r.dtype);return G(hn(ir(G(r,[-1,s,i])).map(d=>vn(c,d,h))),a)}var pE=O({bandPart_:dE});function fE(e){let t;if(Array.isArray(e)){t=!1,F(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let a=e[0].shape[0];for(let s=1;s<e.length;++s)F(e[s].shape[0]===a,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[s].shape[0]} vs. ${a})`)}else t=!0,e=Lt(e,e.shape[0],0).map(a=>Pa(a,[0]));F(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],r=e;for(let a=0;a<e.length;++a)n.push($.tidy(()=>{let s=r[a];if(a>0)for(let i=0;i<a;++i){let o=P(Te(P(n[i],s)),n[i]);s=ye(s,o)}return ge(s,Pd(s,"euclidean"))}));return t?hn(n,0):n}var mE=O({gramSchmidt_:fE});function AE(e,t=!1){if(F(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return Ex(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,u)=>l*u),r=ir(G(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(l=>{let[u,c]=Ex(l,t);a.push(u),s.push(c)});let i=G(hn(a,0),e.shape),o=G(hn(s,0),e.shape);return[i,o]}}function Ex(e,t=!1){return $.tidy(()=>{F(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],r=e.shape[1],a=Qf(n),s=rr(e),i=kn([[1]],[1,1]),o=rr(i),l=n>=r?r:n;for(let u=0;u<l;++u){let c=s,h=o,d=a;[o,s,a]=$.tidy(()=>{let p=Re(s,[u,u],[n-u,1]),f=Pd(p),m=Re(s,[u,u],[1,1]),A=vn(ar(m,0),kn([[-1]]),kn([[1]])),y=ye(m,P(A,f)),g=ge(p,y);g.shape[0]===1?o=rr(i):o=st([i,Re(g,[1,0],[g.shape[0]-1,g.shape[1]])],0);let _=vt(ge(je(A,y),f)),b=Re(s,[u,0],[n-u,r]),w=P(_,o),x=at(o);if(u===0)s=ye(b,je(w,je(x,b)));else{let T=ye(b,je(w,je(x,b)));s=st([Re(s,[0,0],[u,r]),T],0)}let N=at(w),S=Re(a,[0,u],[n,a.shape[1]-u]);if(u===0)a=ye(S,je(je(S,o),N));else{let T=ye(S,je(je(S,o),N));a=st([Re(a,[0,0],[n,u]),T],1)}return[o,s,a]}),Ee([c,h,d])}return!t&&n>r&&(a=Re(a,[0,0],[n,r]),s=Re(s,[0,0],[r,r])),[a,s]})}var yE=O({qr_:AE}),dn;(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"})(dn||(dn={}));function gE(e,t,n=dn.SUM_BY_NONZERO_WEIGHTS){let r=E(e,"losses","computeWeightedLoss"),a=null;t!=null&&(a=E(t,"weights","computeWeightedLoss"));let s=a==null?r:P(r,a);if(n===dn.NONE)return s;if(n===dn.SUM)return Te(s);if(n===dn.MEAN){if(a==null)return kt(s);{let i=r.size/a.size,o=ge(Te(s),Te(a));return i>1?ge(o,be(i)):o}}if(n===dn.SUM_BY_NONZERO_WEIGHTS){if(a==null)return ge(Te(s),be(r.size));{let i=P(a,Or(r.shape)),o=me(Te(di(i,be(0))),"float32");return ge(Te(s),o)}}throw Error(`Unknown reduction: ${n}`)}var ia=O({computeWeightedLoss_:gE});function xE(e,t,n,r=dn.SUM_BY_NONZERO_WEIGHTS){let a=E(e,"labels","absoluteDifference"),s=E(t,"predictions","absoluteDifference"),i=null;n!=null&&(i=E(n,"weights","absoluteDifference")),an(a.shape,s.shape,"Error in absoluteDifference: ");let o=zt(ye(a,s));return ia(o,i,r)}var wE=O({absoluteDifference_:xE});function _E(e,t,n,r,a=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"labels","cosineDistance"),i=E(t,"predictions","cosineDistance"),o=null;r!=null&&(o=E(r,"weights","cosineDistance")),an(s.shape,i.shape,"Error in cosineDistance: ");let l=be(1),u=ye(l,Te(P(s,i),n,!0));return ia(u,o,a)}var bE=O({cosineDistance_:_E});function vE(e,t,n,r=dn.SUM_BY_NONZERO_WEIGHTS){let a=E(e,"labels","hingeLoss"),s=E(t,"predictions","hingeLoss"),i=null;n!=null&&(i=E(n,"weights","hingeLoss")),an(a.shape,s.shape,"Error in hingeLoss: ");let o=be(1);a=ye(P(be(2),a),o);let l=zr(ye(o,P(a,s)));return ia(l,i,r)}var kE=O({hingeLoss_:vE});function IE(e,t,n,r=1,a=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"labels","huberLoss"),i=E(t,"predictions","huberLoss"),o=null;n!=null&&(o=E(n,"weights","huberLoss")),an(s.shape,i.shape,"Error in huberLoss: ");let l=be(r),u=zt(ye(i,s)),c=fl(u,l),h=ye(u,c),d=se(P(be(.5),lt(c)),P(l,h));return ia(d,o,a)}var NE=O({huberLoss_:IE});function SE(e,t,n,r=1e-7,a=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"labels","logLoss"),i=E(t,"predictions","logLoss"),o=null;n!=null&&(o=E(n,"weights","logLoss")),an(s.shape,i.shape,"Error in logLoss: ");let l=be(1),u=be(r),c=vt(P(s,Mn(se(i,u)))),h=P(ye(l,s),Mn(se(ye(l,i),u))),d=ye(c,h);return ia(d,o,a)}var TE=O({logLoss_:SE});function CE(e,t,n,r=dn.SUM_BY_NONZERO_WEIGHTS){let a=E(e,"labels","meanSquaredError"),s=E(t,"predictions","meanSquaredError"),i=null;n!=null&&(i=E(n,"weights","meanSquaredError")),an(a.shape,s.shape,"Error in meanSquaredError: ");let o=Dd(a,s);return ia(o,i,r)}var EE=O({meanSquaredError_:CE});function RE(e,t){let n=E(e,"labels","sigmoidCrossEntropyWithLogits"),r=E(t,"logits","sigmoidCrossEntropyWithLogits");an(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");let a=zr(r),s=P(r,n),i=wd(qn(vt(zt(r))));return se(ye(a,s),i)}function FE(e,t,n,r=0,a=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"multiClassLabels","sigmoidCrossEntropy"),i=E(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=E(n,"weights","sigmoidCrossEntropy")),an(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){let u=be(r),c=be(1),h=be(.5);s=se(P(s,ye(c,u)),P(h,u))}let l=RE(s,i);return ia(l,o,a)}var ME=O({sigmoidCrossEntropy_:FE});function $E(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 $r((r,a,s)=>{let i=rm(a,[n],!0),o=ye(me(a,"float32"),i);s([r,o]);let l=vt(P(o,r));return{value:Te(l,[n]),gradFunc:(u,c)=>{let[h,d]=c,p=hi(u.shape,[n]);return[P(G(u,p),ye(me(h,"float32"),qn(d))),P(G(u,p),ye(qn(d),me(h,"float32")))]}}})(e,t)}function DE(e,t,n,r=0,a=dn.SUM_BY_NONZERO_WEIGHTS){let s=E(e,"onehotLabels","softmaxCrossEntropy"),i=E(t,"logits","softmaxCrossEntropy"),o=null;if(n!=null&&(o=E(n,"weights","softmaxCrossEntropy")),an(s.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){let u=be(r),c=be(1),h=be(s.shape[1]);s=se(P(s,ye(c,u)),ge(u,h))}let l=$E(s,i);return ia(l,o,a)}var OE=O({softmaxCrossEntropy_:DE}),zE={fft:Ju,ifft:Al,rfft:Qu,irfft:$d},PE={hammingWindow:MC,hannWindow:bx,frame:vx,stft:zC},Je={flipLeftRight:BC,resizeNearestNeighbor:Cx,resizeBilinear:Tx,rotateWithOffset:UC,cropAndResize:LC,nonMaxSuppression:jC,nonMaxSuppressionAsync:QC,nonMaxSuppressionWithScore:tE,nonMaxSuppressionWithScoreAsync:rE,nonMaxSuppressionPadded:sE,nonMaxSuppressionPaddedAsync:oE,transform:hE},Rx={bandPart:pE,gramSchmidt:mE,qr:yE},LE={absoluteDifference:wE,computeWeightedLoss:ia,cosineDistance:bE,hingeLoss:kE,huberLoss:NE,logLoss:TE,meanSquaredError:EE,sigmoidCrossEntropy:ME,softmaxCrossEntropy:OE},oa=class extends $5{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 nx(e,t)}dispose(){this.iterations_!=null&&Ee(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:be(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(oa,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Ud=class extends oa{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:B(()=>Ue(r).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:B(()=>Ue(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;B(()=>{let l=se(P(i,this.rho),P(lt(s),1-this.rho)),u=P(ge(en(se(o,this.epsilon)),en(se(i,this.epsilon))),s),c=se(P(o,this.rho),P(lt(u),1-this.rho));i.assign(l),o.assign(c);let h=se(P(u,-this.learningRate),r);r.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ee(this.accumulatedGrads.map(e=>e.variable)),Ee(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ud.className="Adadelta";Fa(Ud);var Hd=class extends oa{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulatedGrads[n]==null){let i=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:B(()=>Uu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[n].variable;B(()=>{let i=se(s,lt(a));s.assign(i);let o=se(P(ge(a,en(se(i,$.backend.epsilon()))),-this.learningRate),r);r.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ee(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Hd.className="Adagrad";Fa(Hd);var jd=class extends oa{constructor(e,t,n,r=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=be(t).variable(),this.accBeta2=be(n).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=ye(1,this.accBeta1),r=ye(1,this.accBeta2);t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:B(()=>Ue(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:B(()=>Ue(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,c=this.accumulatedSecondMoment[s].variable,h=se(P(u,this.beta1),P(l,1-this.beta1)),d=se(P(c,this.beta2),P(lt(l),1-this.beta2)),p=ge(h,n),f=ge(d,r);u.assign(h),c.assign(d);let m=se(P(ge(p,se(en(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ee(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),B(()=>{this.accBeta1.assign(sa(this.beta1,this.iterations_+1)),this.accBeta2.assign(sa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};jd.className="Adam";Fa(jd);var Gd=class extends oa{constructor(e,t,n,r=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=be(0).variable(),this.accBeta1=be(t).variable()}),r==null&&(this.epsilon=$.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);B(()=>{let n=ye(1,this.accBeta1),r=ge(-this.learningRate,se(P(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=$.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:Ue(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:Ue(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,c=this.accumulatedWeightedInfNorm[s].variable,h=se(P(u,this.beta1),P(l,1-this.beta1)),d=P(c,this.beta2),p=zt(l),f=Dr(d,p);u.assign(h),c.assign(f);let m=se(P(ge(r,n),ge(h,se(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(se(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ee(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ee(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Gd.className="Adamax";Fa(Gd);var ec=class extends oa{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=$.registeredVariables[t];B(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Gt(be(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ec.className="SGD";Fa(ec);var qd=class extends ec{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=be(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t];if(this.accumulations[n]==null){let i=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:B(()=>Ue(r).variable(i))}}let a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];s!=null&&B(()=>{let i,o=se(P(this.m,a),s);this.useNesterov?i=se(P(this.c,se(s,P(o,this.m))),r):i=se(P(this.c,o),r),a.assign(o),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ee(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};qd.className="Momentum";Fa(qd);var Xd=class extends oa{constructor(e,t=.9,n=0,r=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,r==null&&(this.epsilon=$.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=$.registeredVariables[t],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:B(()=>Ue(r).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:B(()=>Ue(r).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:B(()=>Ue(r).variable(a))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;B(()=>{let l=se(P(i,this.decay),P(lt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[n].variable,c=se(P(u,this.decay),P(s,1-this.decay)),h=ge(P(s,this.learningRate),en(ye(l,se(lt(c),this.epsilon)))),d=se(P(o,this.momentum),h);i.assign(l),u.assign(c),o.assign(d);let p=ye(r,d);r.assign(p)}else{let u=se(P(i,this.decay),P(lt(s),1-this.decay)),c=se(P(o,this.momentum),ge(P(s,this.learningRate),en(se(u,this.epsilon))));i.assign(u),o.assign(c);let h=ye(r,c);r.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ee(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ee(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ee(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(r=>({originalName:r.name,variable:r.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Xd.className="RMSProp";Fa(Xd);var pi=class{static sgd(e){return new ec(e)}static momentum(e,t,n=!1){return new qd(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new Xd(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new jd(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new Ud(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new Gd(e,t,n,r,a)}static adagrad(e,t=.1){return new Hd(e,t)}},fi={sgd:pi.sgd,momentum:pi.momentum,adadelta:pi.adadelta,adagrad:pi.adagrad,rmsprop:pi.rmsprop,adamax:pi.adamax,adam:pi.adam},WE=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Kd(){return new Promise(e=>WE(()=>e()))}var R={};ze(R,{ERF_A1:()=>YE,ERF_A2:()=>JE,ERF_A3:()=>QE,ERF_A4:()=>eR,ERF_A5:()=>tR,ERF_P:()=>ZE,PARALLELIZE_THRESHOLD:()=>vm,SELU_SCALE:()=>Mx,SELU_SCALEALPHA:()=>Fx,applyActivation:()=>Bd,assertAndGetBroadcastShape:()=>yt,assertAxesAreInnerMostDims:()=>bS,assertParamsConsistent:()=>BE,assignToTypedArray:()=>uR,axesAreInnerMostDims:()=>tm,calculateShapes:()=>b5,combineLocations:()=>ax,complexWithEvenIndex:()=>iR,complexWithOddIndex:()=>oR,computeConv2DInfo:()=>Pu,computeConv3DInfo:()=>B5,computeDefaultPad:()=>Uf,computeDilation2DInfo:()=>GI,computeOptimalWindowSize:()=>UE,computeOutAndReduceShapes:()=>sx,computeOutShape:()=>VE,computePool2DInfo:()=>W5,computePool3DInfo:()=>qI,convertConv2DDataFormat:()=>L5,eitherStridesOrDilationsAreOne:()=>Mr,expandShapeToKeepDim:()=>hi,exponent:()=>hR,exponents:()=>cR,fromStringArrayToUint8:()=>fR,fromUint8ToStringArray:()=>pR,getAxesPermutation:()=>ix,getBroadcastDims:()=>PN,getComplexWithIndex:()=>lR,getFusedBiasGradient:()=>Wd,getFusedDyActivation:()=>Ld,getImageCenter:()=>HE,getInnerMostAxes:()=>vS,getPermuted:()=>GE,getReductionAxes:()=>Pt,getReshaped:()=>jE,getReshapedPermuted:()=>qE,getSliceBeginCoords:()=>XE,getSliceSize:()=>KE,getUndoAxesPermutation:()=>nm,log:()=>rR,mergeRealAndImagArrays:()=>aR,prepareAndValidate:()=>_5,prepareSplitSize:()=>dR,segment_util:()=>$x,shouldFuse:()=>Vd,slice_util:()=>cn,splitRealAndImagArrays:()=>sR,tupleValuesAreOne:()=>$a,upcastType:()=>nr,validateInput:()=>Tf,validateUpdateShape:()=>Sf,warn:()=>nR});function BE(e,t){let n=e[0].length;e.forEach((a,s)=>{F(a.length===n,()=>`Error in concat${n}D: rank of tensors[${s}] must be the same as the rank of the rest (${n})`)}),F(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let r=e[0];e.forEach((a,s)=>{for(let i=0;i<n;i++)F(i===t||a[i]===r[i],()=>`Error in concat${n}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${r}) along the non-concatenated axis ${s}.`)})}function VE(e,t){let n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var vm=30;function UE(e){return e<=vm?e:_h(e,Math.floor(Math.sqrt(e)))}function HE(e,t,n){let r=n*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[r,a]}function jE(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function GE(e,t,n=!0){let r=[];if(n){r.push(t);for(let a=t+1;a<e;++a)a<=2*t?(r.push(a),r.push(a-(t+1))):r.push(a)}else{let a=[],s=[];for(let i=1;i<e;++i)i>=t*2+1||i%2==1?s.push(i):a.push(i);r.push(...a),r.push(0),r.push(...s)}return r}function qE(e,t,n,r=!0){let a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let s=1;s<e.length;++s)s<=t.length?r?a.push(t[s-1]*e[s]):a.push(e[s]/t[s-1]):a.push(e[s]);return a}function XE(e,t){let n=[0];for(let r=0;r<t;++r)n.push(e[r][0]);return n}function KE(e,t,n){let r=e.slice(0,1);for(let a=0;a<n;++a)r.push(e[a+1]-t[a][0]-t[a][1]);return r}var Fx=1.7580993408473768,Mx=1.0507009873554805,ZE=.3275911,YE=.254829592,JE=-.284496736,QE=1.421413741,eR=-1.453152027,tR=1.061405429;function nR(...e){J().getBool("IS_TEST")||console.warn(...e)}function rR(...e){J().getBool("IS_TEST")||console.log(...e)}function aR(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${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 sR(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 iR(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 oR(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 lR(e,t){let n=e[t*2],r=e[t*2+1];return{real:n,imag:r}}function uR(e,t,n,r){e[r*2]=t,e[r*2+1]=n}function cR(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 hR(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 dR(e,t,n=0){let r=[];if(typeof t=="number")F(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),r=new Array(t).fill(e.shape[n]/t);else{let a=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);F(a<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((o,l)=>l>0?o+l:o);t[s]=e.shape[n]-i}F(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),r=t}return r}var $x={};ze($x,{collectGatherOpShapeInfo:()=>yR,computeOutShape:()=>AR,segOpComputeOptimalWindowSize:()=>mR});function mR(e,t){let n=!1,r;for(e<=vm?(r=e,n=!0):r=_h(e,Math.floor(Math.sqrt(e)));!n;)r>t||r===e?n=!0:r=_h(e,r+1);return r}function AR(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 yR(e,t,n,r){let a=t.shape.length,s=e.shape.length;if(r!==0&&(r<-a||r>a))throw new Error(`Expect batchDims in the range of [-${a}, ${a}], but got ${r}`);if(r<0&&(r+=a),r>s)throw new Error(`batchDims (${r}) must be less than rank(x) (
|
|
${s}).`);if(n<r)throw new Error(`batchDims (${r}) must be less than or equal to axis (${n}).`);for(let h=0;h<r;++h)if(e.shape[h]!==t.shape[h])throw new Error(`x.shape[${h}]: ${e.shape[h]} should be equal to indices.shape[${h}]: ${t.shape[h]}.`);let i=e.shape[n],o=[],l=1,u=1,c=1;for(let h=0;h<r;++h)o.push(e.shape[h]),l*=e.shape[h];for(let h=r;h<n;h++)o.push(e.shape[h]),u*=e.shape[h];for(let h=r;h<a;h++)o.push(t.shape[h]);for(let h=n+1;h<s;h++)o.push(e.shape[h]),c*=e.shape[h];return{batchSize:l,sliceSize:c,outerSize:u,dimSize:i,outputShape:o}}function pR(e){try{return e.map(t=>rd(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function fR(e){return e.map(t=>Su(t))}var Pr={};ze(Pr,{nonMaxSuppressionV3Impl:()=>kx,nonMaxSuppressionV4Impl:()=>Ix,nonMaxSuppressionV5Impl:()=>Nx,whereImpl:()=>fx});function _e(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var gR=Pr.whereImpl,Zd=class extends su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new xh(this,Fr())}nextDataId(){return Zd.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&R.warn(`
|
|
============================
|
|
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
|
|
============================`));let r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return Fr().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){_e([e],"where");let t=this.readSync(e.dataId);return gR(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Zd.nextDataId=0;var km={};ze(km,{addImpl:()=>Ox,bincountImpl:()=>Im,bincountReduceImpl:()=>zx,ceilImpl:()=>Px,concatImpl:()=>Nm,expImpl:()=>Lx,expm1Impl:()=>Wx,floorImpl:()=>Bx,gatherV2Impl:()=>Vx,greaterImpl:()=>Ux,lessImpl:()=>Hx,linSpaceImpl:()=>jx,logImpl:()=>Gx,maxImpl:()=>qx,maximumImpl:()=>Xx,minimumImpl:()=>Kx,multiplyImpl:()=>Sm,negImpl:()=>Zx,notEqualImpl:()=>Yx,prodImpl:()=>Jx,rangeImpl:()=>Cm,rsqrtImpl:()=>Qx,simpleAbsImpl:()=>Dx,sliceImpl:()=>Yd,squaredDifferenceImpl:()=>ew,stridedSliceImpl:()=>tw,subImpl:()=>nw,tileImpl:()=>rw,topKImpl:()=>aw,transposeImpl:()=>Tm,uniqueImpl:()=>sw});function Dx(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var xR=e=>{let{x:t}=e.inputs,n=e.backend;_e(t,"abs");let r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId).values;return r=Dx(a),n.makeOutput(r,t.shape,"float32")},wR={kernelName:Zi,backendName:"cpu",kernelFunc:xR};function Rt(e){return(t,n,r,a,s)=>{let i=R.assertAndGetBroadcastShape(t,n),o=i.length,l=v.computeStrides(i),u=v.sizeFromShape(i),c=v.getTypedArrayFromDType(s,u),h=t.length,d=n.length,p=v.computeStrides(t),f=v.computeStrides(n),m=R.getBroadcastDims(t,i),A=R.getBroadcastDims(n,i);if(m.length+A.length===0)for(let y=0;y<c.length;++y)c[y]=e(r[y%r.length],a[y%a.length]);else for(let y=0;y<c.length;++y){let g=v.indexToLoc(y,o,l),_=g.slice(-h);m.forEach(N=>_[N]=0);let b=v.locToIndex(_,h,p),w=g.slice(-d);A.forEach(N=>w[N]=0);let x=v.locToIndex(w,d,f);c[y]=e(r[b],a[x])}return[c,i]}}function On(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64"),l=n.data.get(o.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",s),imag:n.makeTensorInfo(a.shape,"float32",i)},o}var _R={kernelName:Th,backendName:"cpu",kernelFunc:On};function Jd(e,t,n="float32"){if(n==="complex64"){let a=Jd(e,t,"float32"),s=Jd(e,t,"float32");return On({inputs:{real:a,imag:s},backend:e})}let r=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,r)}function Lr(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var bR={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 vR={kernelName:Xh,backendName:"cpu",kernelFunc:mi};function Wa(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Lr({inputs:{x:a},backend:n});let i=Jd(n,a.shape,a.dtype),o=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=On({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=mi({inputs:{input:a},backend:n}),o=Wa({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Lr({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(a.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(a.shape,"int32",o)}if(s==="bool"){let i=n.data.get(a.dataId).values,o=v.toTypedArray([0],a.dtype),[l,u]=Rt((c,h)=>c!==h?1:0)(a.shape,[],i,o,"bool");return n.makeTensorInfo(u,"bool",l)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var kR={kernelName:hs,backendName:"cpu",kernelFunc:Wa};function qt(e,t,n,r){return n==null?({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;_e([i,o],e);let u=l.data.get(i.dataId).values,c=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,u,c,h);return l.makeTensorInfo(p,h,d)}:({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(i.dtype==="complex64"||o.dtype==="complex64"){let u=Wa({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),c=l.data.get(u.dataId),h=c.complexTensorInfos.real,d=c.complexTensorInfos.imag,p=l.data.get(h.dataId).values,f=l.data.get(d.dataId).values,m=Wa({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),A=l.data.get(m.dataId),y=A.complexTensorInfos.real,g=A.complexTensorInfos.imag,_=l.data.get(y.dataId).values,b=l.data.get(g.dataId).values,[w,x,N]=n(i.shape,o.shape,p,f,_,b),S=l.makeTensorInfo(N,"float32",w),T=l.makeTensorInfo(N,"float32",x),M=On({inputs:{real:S,imag:T},backend:l});return l.disposeIntermediateTensorInfo(u),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(S),l.disposeIntermediateTensorInfo(T),M}else{let u=l.data.get(i.dataId).values,c=l.data.get(o.dataId).values,h=r||i.dtype,[d,p]=t(i.shape,o.shape,u,c,h);return l.makeTensorInfo(p,h,d)}}}function Em(e){return(t,n,r,a,s,i)=>{let o=R.assertAndGetBroadcastShape(t,n),l=v.sizeFromShape(o),u=o.length,c=v.computeStrides(o),h=v.getTypedArrayFromDType("float32",l),d=v.getTypedArrayFromDType("float32",l),p=R.getBroadcastDims(t,o),f=R.getBroadcastDims(n,o),m=R.mergeRealAndImagArrays(r,a),A=R.mergeRealAndImagArrays(s,i),y=t.length,g=v.computeStrides(t),_=n.length,b=v.computeStrides(n);if(p.length+f.length===0)for(let w=0;w<h.length;w++){let x=w%m.length,N=w%A.length,S=e(m[x*2],m[x*2+1],A[N*2],A[N*2+1]);h[w]=S.real,d[w]=S.imag}else for(let w=0;w<h.length;w++){let x=v.indexToLoc(w,u,c),N=x.slice(-y);p.forEach(z=>N[z]=0);let S=v.locToIndex(N,y,g),T=x.slice(-_);f.forEach(z=>T[z]=0);let M=v.locToIndex(T,_,b),D=e(m[S*2],m[S*2+1],A[M*2],A[M*2+1]);h[w]=D.real,d[w]=D.imag}return[h,d,o]}}var Ox=Rt((e,t)=>e+t),IR=Em((e,t,n,r)=>({real:e+n,imag:t+r})),tc=qt(Ia,Ox,IR),NR={kernelName:Ia,backendName:"cpu",kernelFunc:tc};function Im(e,t,n,r,a){let s=v.sizeFromShape(r),i=v.makeZerosTypedArray(a,n);for(let o=0;o<e.length;o++){let l=e[o];if(l<0)throw new Error("Input x must be non-negative!");l>=a||(s>0?i[l]+=t[o]:i[l]+=1)}return i}function zx(e,t,n,r=!1){let a=e.shape[0],s=e.shape[1],i=We([a,n],t.dtype);for(let o=0;o<a;o++)for(let l=0;l<s;l++){let u=e.get(o,l);if(u<0)throw new Error("Input x must be non-negative!");u>=n||(r?i.set(1,o,u):t.size>0?i.set(i.get(o,u)+t.get(o,l),o,u):i.set(i.get(o,u)+1,o,u))}return i}function xl(e){return(t,n,r)=>{let a=v.getTypedArrayFromDType(n,t.length);for(let s=0;s<t.length;++s)a[s]=e(t[s],r);return a}}function it(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(_e(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=v.sizeFromShape(i.shape),c=n||i.dtype,h=v.getArrayFromDType(c,u);for(let d=0;d<u;++d)h[d]=t(l[d],a);return o.makeTensorInfo(i.shape,c,h)}}function wl(e,t,n){return({inputs:r,attrs:a,backend:s})=>{let{x:i}=r;if(_e(i,e),i.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let o=s,l=o.data.get(i.dataId).values,u=n||i.dtype,c=t(l,u,a);return o.makeTensorInfo(i.shape,u,c)}}var Px=xl(e=>Math.ceil(e)),SR=wl(ds,Px),TR={kernelName:ds,backendName:"cpu",kernelFunc:SR};function Nm(e,t,n,r){let a=v.getArrayFromDType(n,v.sizeFromShape(t));if(r&&n!=="string"){let s=0;e.forEach(i=>{let o=v.sizeFromShape(i.shape);a.set(i.vals,s),s+=o})}else{let s=0;e.forEach(i=>{let o=n==="string"?R.fromUint8ToStringArray(i.vals):i.vals,l=0;for(let u=0;u<i.shape[0];++u){let c=u*t[1]+s;for(let h=0;h<i.shape[1];++h)a[c+h]=o[l++]}s+=i.shape[1]})}return a}var Lx=xl(e=>Math.exp(e)),iw=wl(xs,Lx),CR={kernelName:xs,backendName:"cpu",kernelFunc:iw},Wx=xl(e=>Math.expm1(e)),ER=wl(po,Wx),RR={kernelName:po,backendName:"cpu",kernelFunc:ER},Bx=xl(e=>Math.floor(e)),FR=wl(ws,Bx),MR={kernelName:ws,backendName:"cpu",kernelFunc:FR};function Vx(e,t,n){let r=We(n,e.dtype);for(let a=0;a<r.size;++a){let s=r.indexToLoc(a).slice(),i=s[0],o=s[2],l=t.locToIndex([i,o]);s[2]=t.values[l];let u=e.locToIndex(s);r.values[a]=e.values[u]}return r}var Ux=Rt((e,t)=>e>t?1:0),$R=qt(yo,Ux,null,"bool"),DR={kernelName:yo,backendName:"cpu",kernelFunc:$R},Hx=Rt((e,t)=>e<t?1:0),OR=qt(_o,Hx,null,"bool"),zR={kernelName:_o,backendName:"cpu",kernelFunc:OR};function jx(e,t,n){let r=(t-e)/(n-1),a=v.makeZerosTypedArray(n,"float32");a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+r;return a}var Gx=xl(e=>Math.log(e)),PR=wl(Ns,Gx),LR={kernelName:Ns,backendName:"cpu",kernelFunc:PR};function qx(e,t,n,r){let a=v.getTypedArrayFromDType(r,v.sizeFromShape(n));for(let s=0;s<a.length;++s){let i=s*t,o=e[i];for(let l=0;l<t;++l){let u=e[i+l];u>o&&(o=u)}a[s]=o}return a}var Xx=Rt((e,t)=>Math.max(e,t)),WR=qt(Ts,Xx),BR={kernelName:Ts,backendName:"cpu",kernelFunc:WR},Kx=Rt((e,t)=>Math.min(e,t)),VR=qt(Fs,Kx),UR={kernelName:Fs,backendName:"cpu",kernelFunc:VR},Sm=Rt((e,t)=>e*t),HR=Em((e,t,n,r)=>({real:e*n-t*r,imag:e*r+t*n})),Rm=qt(Ms,Sm,HR),jR={kernelName:Ms,backendName:"cpu",kernelFunc:Rm};function Zx(e,t,n){let r=v.createScalarValue(-1,n);return Sm([],t,r,e,n)}function GR(e){let{inputs:t,backend:n}=e,{x:r}=t;_e(r,"neg");let a=n.data.get(r.dataId).values,[s,i]=Zx(a,r.shape,r.dtype);return n.makeTensorInfo(i,r.dtype,s)}var qR={kernelName:No,backendName:"cpu",kernelFunc:GR},Yx=Rt((e,t)=>e!==t?1:0),XR=qt(So,Yx,null,"bool"),KR={kernelName:So,backendName:"cpu",kernelFunc:XR};function Tm(e,t,n,r,a){let s=t.length,i=v.sizeFromShape(t),o=v.computeStrides(t),l=v.computeStrides(a),u=v.getTypedArrayFromDType(n,v.sizeFromShape(a));for(let c=0;c<i;++c){let h=v.indexToLoc(c,s,o),d=new Array(h.length);for(let f=0;f<d.length;f++)d[f]=h[r[f]];let p=v.locToIndex(d,s,l);u[p]=e[c]}return u}function 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 c=0;c<o.length;c++)o[c]=a.shape[s[c]];let l=r.data.get(a.dataId).values,u=Tm(l,a.shape,a.dtype,s,o);return{dataId:r.write(u,o,a.dtype),shape:o,dtype:a.dtype}}var ZR={kernelName:Js,backendName:"cpu",kernelFunc:or};function Jx(e,t,n,r){let[a,s]=R.computeOutAndReduceShapes(e,r),i=nr(t,"int32"),o=v.makeZerosTypedArray(v.sizeFromShape(a),i),l=v.sizeFromShape(s);for(let u=0;u<o.length;++u){let c=u*l,h=1;for(let d=0;d<l;++d)h*=n[c+d];o[u]=h}return{outVals:o,outShape:a,outDtype:i}}function YR(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"prod");let o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=R.getAxesPermutation(l,o),c=l,h=a,d=[];u!=null&&(h=or({inputs:{x:a},backend:n,attrs:{perm:u}}),d.push(h),c=R.getInnerMostAxes(c.length,o));let p=n.data.get(h.dataId).values,{outVals:f,outShape:m,outDtype:A}=Jx(h.shape,h.dtype,p,c),y=m;return i&&(y=R.expandShapeToKeepDim(m,l)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,f)}var JR={kernelName:Mo,backendName:"cpu",kernelFunc:YR};function Cm(e,t,n,r){let a=e===t,s=e<t&&n<0,i=t<e&&n>1;if(a||s||i)return v.makeZerosTypedArray(0,r);let o=Math.abs(Math.ceil((t-e)/n)),l=v.makeZerosTypedArray(o,r);t<e&&n===1&&(n=-1),l[0]=e;for(let u=1;u<l.length;u++)l[u]=l[u-1]+n;return l}var Qx=xl(e=>1/Math.sqrt(e)),QR=wl(Us,Qx),eF={kernelName:Us,backendName:"cpu",kernelFunc:QR};function Yd(e,t,n,r,a){let s=cn.isSliceContinous(r,t,n),i=v.sizeFromShape(n),o=v.computeStrides(r);if(s){let h=cn.computeFlatOffset(t,o);return a==="string"?e.slice(h,h+i):e.subarray(h,h+i)}let l=a==="string"?R.fromUint8ToStringArray(e):e,u=We(r,a,l),c=We(n,a);for(let h=0;h<c.size;++h){let d=c.indexToLoc(h),p=d.map((f,m)=>f+t[m]);c.set(u.get(...p),...d)}return a==="string"?R.fromStringArrayToUint8(c.values):c.values}function Ai(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r;_e(a,"slice");let[o,l]=cn.parseSliceParams(a,s,i);cn.assertParamsValid(a,o,l);let u=n.data.get(a.dataId).values,c=Yd(u,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,c)}var tF={kernelName:Lo,backendName:"cpu",kernelFunc:Ai},ew=Rt((e,t)=>{let n=e-t;return n*n}),nF=qt(Ks,ew),rF={kernelName:Ks,backendName:"cpu",kernelFunc:nF};function tw(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 nw=Rt((e,t)=>e-t),aF=Em((e,t,n,r)=>({real:e-n,imag:t-r})),Fm=qt(Zs,nw,aF),sF={kernelName:Zs,backendName:"cpu",kernelFunc:Fm};function rw(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 aw(e,t,n,r,a){let s=t[t.length-1],[i,o]=[e.length/s,s],l=v.getTypedArrayFromDType(n,i*r),u=v.getTypedArrayFromDType("int32",i*r);for(let h=0;h<i;h++){let d=h*o,p=e.subarray(d,d+o),f=[];for(let g=0;g<p.length;g++)f.push({value:p[g],index:g});f.sort((g,_)=>_.value-g.value);let m=h*r,A=l.subarray(m,m+r),y=u.subarray(m,m+r);for(let g=0;g<r;g++)A[g]=f[g].value,y[g]=f[g].index}let c=t.slice();return c[c.length-1]=r,[We(c,n,l),We(c,"int32",u)]}function sw(e,t,n,r){let a=v.parseAxisParam(t,n)[0],s=[1,n[0],1];for(let f=0;f<a;f++)s[0]*=n[f];s[1]=n[a];for(let f=a+1;f<n.length;f++)s[2]*=n[f];let i={},o=new Int32Array(n[a]),l=new Ot(s,r,e),u=[],c=s[0]===1&&s[2]===1;for(let f=0;f<n[a];f++){let m;if(c)m=e[f].toString();else{let A=[];for(let y=0;y<s[0];y++)for(let g=0;g<s[2];g++)A.push(l.get(y,f,g));m=A.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let A=Object.keys(i).length;i[m]=A,o[f]=A,u.push(f)}}let h=s.slice();h[1]=Object.keys(i).length;let d=new Ot(h,r);u.forEach((f,m)=>{for(let A=0;A<s[0];A++)for(let y=0;y<s[2];y++)d.set(l.get(A,f,y),A,m,y)});let p=n.slice();return p[a]=h[1],{outputValues:d.values,outputShape:p,indices:o}}var ow="3.3.0";sl("cpu",()=>new Zd,1);var lw=it(lo,e=>e>=0?e:Math.exp(e)-1),iF={kernelName:lo,backendName:"cpu",kernelFunc:lw};function uw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;_e([a],"leakyRelu");let i=v.sizeFromShape(a.shape),o=n.data.get(a.dataId).values,l=v.getTypedArrayFromDType("float32",i);for(let u=0;u<o.length;u++)l[u]=o[u]<0?s*o[u]:o[u];return n.makeTensorInfo(a.shape,"float32",l)}var oF={kernelName:Is,backendName:"cpu",kernelFunc:uw},lF=Rt((e,t)=>e<0?t*e:e);function cw(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]=lF(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}var uF={kernelName:zs,backendName:"cpu",kernelFunc:cw},hw=it(Ps,e=>Math.max(0,e)),cF={kernelName:Ps,backendName:"cpu",kernelFunc:hw},dw=it(Ws,e=>Math.min(Math.max(0,e),6)),hF={kernelName:Ws,backendName:"cpu",kernelFunc:dw};function Mm(e,t,n,r,a){if(n==="linear")return Lr({inputs:{x:t},backend:e});if(n==="relu")return hw({inputs:{x:t},backend:e});if(n==="elu")return lw({inputs:{x:t},backend:e});if(n==="relu6")return dw({inputs:{x:t},backend:e});if(n==="prelu")return cw({inputs:{x:t,alpha:r},backend:e});if(n==="leakyrelu")return uw({inputs:{x:t},backend:e,attrs:{alpha:a}});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function gt(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=v.sizeFromShape(a.shape),o=v.inferFromImplicitShape(s,i),l=v.sizeFromShape(o);v.assert(i===l,()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),n.incRef(a.dataId);let u=n.data.get(a.dataId);if(u.complexTensorInfos!=null){let c=u.complexTensorInfos.real,h=u.complexTensorInfos.imag;c.shape=o,h.shape=o}return{dataId:a.dataId,shape:o,dtype:a.dtype}}var dF={kernelName:Do,backendName:"cpu",kernelFunc:gt};function pw(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;_e([a,s],"matMul");let l=a.shape.length,u=s.shape.length,c=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=v.sizeFromShape(f),y=v.sizeFromShape(m),g=A===y||A===1||y===1;v.assert(l>=2&&u>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let _=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);v.assert(c===h,()=>`Error in matMul: inner shapes (${c}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[A,c,d]:[A,d,c],w=o?[y,p,h]:[y,h,p],x=gt({inputs:{x:a},backend:n,attrs:{shape:b}}),N=gt({inputs:{x:s},backend:n,attrs:{shape:w}}),S=i?x.shape[1]:x.shape[2],T=i?x.shape[2]:x.shape[1],M=o?N.shape[1]:N.shape[2],D=Math.max(A,y),z=n.data.get(x.dataId).values,W=n.data.get(N.dataId).values,U=v.computeStrides(x.shape),H=v.computeStrides(N.shape),[X,j,ee]=i?[U[0],1,U[1]]:[U[0],U[1],1],[Y,ae,te]=o?[1,H[1],H[0]]:[H[1],1,H[0]],ie=T*M,Q=We([D,T,M],x.dtype),he=Q.values,oe=n.blockSize;for(let fe=0;fe<D;fe++)for(let pe=0;pe<T;pe+=oe)for(let ke=0;ke<M;ke+=oe)for(let Ne=0;Ne<S;Ne+=oe){let Me=Math.min(pe+oe,T),Oe=Math.min(ke+oe,M),$e=Math.min(Ne+oe,S);for(let tt=pe;tt<Me;tt++)for(let nt=ke;nt<Oe;nt++){let ot=0;for(let Ke=Ne;Ke<$e;Ke++){let pt=Math.min(fe,A-1)*X,Be=Math.min(fe,y-1)*te,An=z[pt+tt*j+Ke*ee],_t=W[Ke*Y+nt*ae+Be];ot+=An*_t}he[fe*ie+(tt*M+nt)]+=ot}}return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(_,Q.dtype,Q.values)}var pF={kernelName:cs,backendName:"cpu",kernelFunc:pw};function fF(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r,d,p,f,m=[];d=pw({inputs:{a,b:s},attrs:{transposeA:l,transposeB:u},backend:n}),i&&(p=tc({inputs:{a:d,b:i},backend:n}),m.push(d),d=p),c&&(f=Mm(n,d,c,o,h),m.push(d),d=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return d}var mF={kernelName:Qs,backendName:"cpu",kernelFunc:fF},AF=it(Yi,e=>Math.acos(e)),yF={kernelName:Yi,backendName:"cpu",kernelFunc:AF},gF=it(Ji,e=>Math.acosh(e)),xF={kernelName:Ji,backendName:"cpu",kernelFunc:gF};function wF(e){let{inputs:t,backend:n}=e,r=t;_e(t,"addN");let a=r.map(o=>n.data.get(o.dataId).values),s=We(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let u=0;u<i.length;u++)i[u]+=l[u]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var _F={kernelName:os,backendName:"cpu",kernelFunc:wF};function bF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"all");let o=v.parseAxisParam(s,a.shape),l=o,u=R.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=or({inputs:{x:a},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("all",l,c.shape.length);let[h,d]=R.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,_=m[g];for(let b=0;b<p;++b){let w=m[g+b];_=_&&w}f[y]=_}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(h,c.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var vF={kernelName:vh,backendName:"cpu",kernelFunc:bF};function kF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"any");let o=v.parseAxisParam(s,a.shape),l=o,u=R.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=or({inputs:{x:a},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("any",l,c.shape.length);let[h,d]=R.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,_=m[g];for(let b=0;b<p;++b){let w=m[g+b];_=_||w}f[y]=_}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(h,c.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var IF={kernelName:kh,backendName:"cpu",kernelFunc:kF};function NF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;_e(a,"argMax");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=or({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[c,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(c),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],_=0;for(let b=0;b<f;++b){let w=m[y+b];w>g&&(g=w,_=b)}p[A]=_}return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(c,"int32",p)}var SF={kernelName:ls,backendName:"cpu",kernelFunc:NF};function TF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;_e(a,"argMin");let i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=or({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],R.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[c,h]=R.computeOutAndReduceShapes(l.shape,i),d=v.sizeFromShape(c),p=v.makeZerosTypedArray(d,"int32"),f=v.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let A=0;A<p.length;++A){let y=A*f,g=m[y],_=0;for(let b=0;b<f;++b){let w=m[y+b];w<g&&(g=w,_=b)}p[A]=_}return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(c,"int32",p)}var CF={kernelName:lu,backendName:"cpu",kernelFunc:TF},EF=it(Qi,e=>Math.asin(e)),RF={kernelName:Qi,backendName:"cpu",kernelFunc:EF},FF=it(eo,e=>Math.asinh(e)),MF={kernelName:eo,backendName:"cpu",kernelFunc:FF},$F=it(to,e=>Math.atan(e)),DF={kernelName:to,backendName:"cpu",kernelFunc:$F},OF=Rt((e,t)=>Math.atan2(e,t)),zF=qt(ro,OF),PF={kernelName:ro,backendName:"cpu",kernelFunc:zF},LF=it(no,e=>Math.atanh(e)),WF={kernelName:no,backendName:"cpu",kernelFunc:LF};function $m(e,t,n,r,a,s){let i=a.strideHeight,o=a.strideWidth,l=a.dilationHeight,u=a.dilationWidth,c=a.effectiveFilterHeight,h=a.effectiveFilterWidth,d=a.padInfo.top,p=a.padInfo.left,f=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=We(a.outShape,n),A=m.values,y=a.outShape[1]*a.outShape[2]*a.outShape[3],g=a.outShape[2]*a.outShape[3],_=a.outShape[3];for(let b=0;b<a.batchSize;++b){let w=b*y,x=b*r[0];for(let N=0;N<a.inChannels;++N)for(let S=0;S<a.outHeight;++S){let T=S*i-d,M=Math.max(0,T),D=Math.min(a.inHeight,c+T),z=w+S*g;for(let W=0;W<a.outWidth;++W){let U=W*o-p,H=Math.max(0,U),X=Math.min(a.inWidth,h+U),j=f,ee=0,Y=0;for(let te=M;te<D;te+=l){let ie=x+te*r[1];for(let Q=H;Q<X;Q+=u){let he=ie+Q*r[2],oe=e[he+N];s==="max"&&oe>j?j=oe:s==="avg"&&(ee+=oe,Y++)}if(isNaN(j))break}let ae=z+W*_+N;A[ae]=s==="avg"?ee/Y:j}}}return m}function fw(e,t,n,r,a=!1,s=!1){let i=We(r.outShape,"int32"),o=r.strideHeight,l=r.strideWidth,u=r.dilationHeight,c=r.dilationWidth,h=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,f=r.padInfo.left,m=We(t,n,e);for(let A=0;A<r.batchSize;++A)for(let y=0;y<r.inChannels;++y)for(let g=0;g<r.outHeight;++g){let _=g*o-p,b=_;for(;b<0;)b+=u;let w=Math.min(r.inHeight,h+_);for(let x=0;x<r.outWidth;++x){let N=x*l-f,S=N;for(;S<0;)S+=c;let T=Math.min(r.inWidth,d+N),M=Number.NEGATIVE_INFINITY,D=-1;for(let z=b;z<w;z+=u){let W=z-_;for(let U=S;U<T;U+=c){let H=U-N,X=m.get(A,z,U,y);X>M&&(M=X,a?D=s?((A*r.inHeight+z)*r.inWidth+U)*r.inChannels+y:(z*r.inWidth+U)*r.inChannels+y:D=W*d+H)}}i.set(D,A,g,x,y)}}return i}function mw(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,u=a.dilationDepth,c=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,A=a.padInfo.top,y=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,_=We(a.outShape,n),b=_.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],x=a.outShape[2]*a.outShape[3]*a.outShape[4],N=a.outShape[3]*a.outShape[4],S=a.outShape[4];for(let T=0;T<a.batchSize;++T){let M=T*w,D=T*r[0];for(let z=0;z<a.inChannels;++z)for(let W=0;W<a.outDepth;++W){let U=W*i-m,H=U;for(;H<0;)H+=u;let X=Math.min(a.inDepth,d+U),j=M+W*x;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-A,ae=Y;for(;ae<0;)ae+=c;let te=Math.min(a.inHeight,p+Y),ie=j+ee*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*S,ke=g,Ne=0,Me=0;for(let $e=H;$e<X;$e+=u){let tt=D+$e*r[1];for(let nt=ae;nt<te;nt+=c){let ot=tt+nt*r[2];for(let Ke=oe;Ke<fe;Ke+=h){let pt=ot+Ke*r[3],Be=e[pt+z];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+z;b[Oe]=s==="avg"?Ne/Me:ke}}}}return _}function BF(e,t){let n=We(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,u=t.effectiveFilterDepth,c=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let A=0;A<t.inChannels;++A)for(let y=0;y<t.outDepth;++y){let g=y*r-d,_=g;for(;_<0;)_+=i;let b=Math.min(t.inDepth,u+g);for(let w=0;w<t.outHeight;++w){let x=w*a-p,N=x;for(;N<0;)N+=o;let S=Math.min(t.inHeight,c+x);for(let T=0;T<t.outWidth;++T){let M=T*s-f,D=M;for(;D<0;)D+=l;let z=Math.min(t.inWidth,h+M),W=Number.NEGATIVE_INFINITY,U=-1;for(let H=_;H<b;H+=i){let X=H-g;for(let j=N;j<S;j+=o){let ee=j-x;for(let Y=D;Y<z;Y+=l){let ae=Y-M,te=e.get(m,H,j,Y,A);te>=W&&(W=te,U=X*c*h+ee*c+ae)}}}n.set(U,m,y,w,T,A)}}}return n}function VF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;_e(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=$m(d,a.shape,a.dtype,p,c,"avg");h=n.makeTensorInfo(c.outShape,a.dtype,f.values)}return h}var UF={kernelName:us,backendName:"cpu",kernelFunc:VF};function HF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r;_e(a,"avgPool3d");let c=R.computePool3DInfo(a.shape,s,i,1,o,l,u),h=n.data.get(a.dataId).values,d=mw(h,a.shape,a.dtype,v.computeStrides(a.shape),c,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var jF={kernelName:uu,backendName:"cpu",kernelFunc:HF};function GF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r;_e([a,s],"avgPool3DGrad");let c=R.computePool3DInfo(s.shape,i,o,1,l,u),h=c.strideDepth,d=c.strideHeight,p=c.strideWidth,f=c.filterDepth,m=c.filterHeight,A=c.filterWidth,y=c.dilationDepth,g=c.dilationHeight,_=c.dilationWidth,b=c.effectiveFilterDepth,w=c.effectiveFilterHeight,x=c.effectiveFilterWidth,N=b-1-c.padInfo.front,S=x-1-c.padInfo.left,T=w-1-c.padInfo.top,M=We(s.shape,"float32"),D=1/(f*m*A),z=n.bufferSync(a);for(let W=0;W<c.batchSize;++W)for(let U=0;U<c.inChannels;++U)for(let H=0;H<c.inDepth;++H)for(let X=0;X<c.inHeight;++X)for(let j=0;j<c.inWidth;++j){let ee=H-N,Y=X-T,ae=j-S,te=0;for(let ie=0;ie<b;ie+=y){let Q=(ee+ie)/h;if(!(Q<0||Q>=c.outDepth||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(Y+he)/d;if(!(oe<0||oe>=c.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe<x;fe+=_){let pe=(ae+fe)/p;pe<0||pe>=c.outWidth||Math.floor(pe)!==pe||(te+=z.get(W,Q,oe,pe,U))}}}M.set(te*D,W,H,X,j,U)}return n.makeTensorInfo(M.shape,M.dtype,M.values)}var qF={kernelName:Nh,backendName:"cpu",kernelFunc:GF};function XF(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;_e([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=c.strideHeight,d=c.strideWidth,p=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,A=c.dilationWidth,y=c.effectiveFilterHeight,g=c.effectiveFilterWidth,_=g-1-c.padInfo.left,b=y-1-c.padInfo.top,w=We(i.shape,"float32"),x=1/(p*f),N=n.data.get(a.dataId).values,S=We(a.shape,"float32",N);for(let T=0;T<c.batchSize;++T)for(let M=0;M<c.inChannels;++M)for(let D=0;D<c.inHeight;++D)for(let z=0;z<c.inWidth;++z){let W=D-b,U=z-_,H=0;for(let X=0;X<y;X+=m){let j=(W+X)/h;if(!(j<0||j>=c.outHeight||Math.floor(j)!==j))for(let ee=0;ee<g;ee+=A){let Y=(U+ee)/d;Y<0||Y>=c.outWidth||Math.floor(Y)!==Y||(H+=S.get(T,j,Y,M))}}w.set(H*x,T,D,z,M)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var KF={kernelName:Ih,backendName:"cpu",kernelFunc:XF};function ZF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;v.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),_e([a,o,l,s,i],"batchNorm");let{varianceEpsilon:u}=r;u==null&&(u=.001);let c=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(c.length),A=f.length,y=p.length,g=d.length,_=h.length,b=0,w=0,x=0,N=0;for(let S=0;S<c.length;++S)m[S]=f[b++]+(c[S]-h[w++])*p[x++]/Math.sqrt(d[N++]+u),b>=A&&(b=0),w>=_&&(w=0),x>=y&&(x=0),N>=g&&(N=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var YF={kernelName:bs,backendName:"cpu",kernelFunc:ZF};function JF(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_e([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=gt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=or({inputs:{x:p},backend:n,attrs:{perm:u}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Ai({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var QF={kernelName:cu,backendName:"cpu",kernelFunc:JF};function eM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,u=Im(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var tM={kernelName:Sh,backendName:"cpu",kernelFunc:eM},nM=it(Na,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),rM={kernelName:Na,backendName:"cpu",kernelFunc:nM},aM=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(v.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let c=o[u],h=l[u];r[u]=Math.hypot(c,h)}return n.makeOutput(r,t.shape,"float32")},sM={kernelName:hu,backendName:"cpu",kernelFunc:aM};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 iM={kernelName:Bh,backendName:"cpu",kernelFunc:_l};function bl(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>v.sizeFromShape(m.shape)>0);if(o.length===1)return Lr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(R.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(b=>mi({inputs:{input:b},backend:n})),A=o.map(b=>_l({inputs:{input:b},backend:n})),y=bl({inputs:m,backend:n,attrs:{axis:s}}),g=bl({inputs:A,backend:n,attrs:{axis:s}}),_=On({inputs:{real:y,imag:g},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),_}let u=o.map(m=>{let A=v.sizeFromShape(m.shape.slice(s));return gt({inputs:{x:m},backend:n,attrs:{shape:[-1,A]}})}),c=u.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=R.computeOutShape(u.map(m=>m.shape),1);let h=u[0].shape[0]===1,d=Nm(c,i,t[0].dtype,h),p=R.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var oM={kernelName:ao,backendName:"cpu",kernelFunc:bl};function Aw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r;_e([a,s],"conv2d");let h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,A=d.dilationWidth,y=d.padInfo.left,g=d.padInfo.top,_=d.dataFormat==="channelsLast",b=new Ot(d.outShape,a.dtype),w=v.computeStrides(a.shape),x=v.computeStrides(s.shape),N=w[0],S=_?w[1]:w[2],T=_?w[2]:1,M=_?1:w[1],D=b.strides[0],z=_?b.strides[1]:b.strides[2],W=_?b.strides[2]:1,U=_?1:b.strides[1],H=n.data.get(a.dataId).values,X=n.data.get(s.dataId).values,j=b.values;for(let ee=0;ee<d.batchSize;++ee){let Y=ee*N,ae=ee*D;for(let te=0;te<d.outHeight;++te){let ie=ae+te*z,Q=te*d.strideHeight-g;for(let he=0;he<p;++he){let oe=Q+he*m;if(oe<0||oe>=d.inHeight)continue;let fe=he*x[0],pe=Y+oe*S;for(let ke=0;ke<d.outWidth;++ke){let Ne=ie+ke*W,Me=ke*d.strideWidth-y;for(let Oe=0;Oe<f;++Oe){let $e=Me+Oe*A;if($e<0||$e>=d.inWidth)continue;let tt=fe+Oe*x[1],nt=pe+$e*T,ot=tt;for(let Ke=0;Ke<d.inChannels;++Ke){let pt=H[nt+Ke*M];for(let Be=0;Be<d.outChannels;++Be)j[Ne+Be*U]+=pt*X[ot+Be];ot+=d.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,j)}var lM={kernelName:ps,backendName:"cpu",kernelFunc:Aw};function uM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r;_e([a,s],"conv2dBackpropFilter");let h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),{strideHeight:p,strideWidth:f,filterHeight:m,filterWidth:A}=d,y=d.dataFormat==="channelsLast",g=new Ot(d.filterShape,"float32"),_=d.padInfo.left,b=d.padInfo.top,w=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=new Ot(a.shape,a.dtype,w),S=new Ot(s.shape,s.dtype,x);for(let T=0;T<m;++T){let M=Math.max(0,Math.ceil((b-T)/p)),D=Math.min(d.outHeight,(d.inHeight+b-T)/p);for(let z=0;z<A;++z){let W=Math.max(0,Math.ceil((_-z)/f)),U=Math.min(d.outWidth,(d.inWidth+_-z)/f);for(let H=0;H<d.inChannels;++H)for(let X=0;X<d.outChannels;++X){let j=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=M;Y<D;++Y){let ae=T+Y*p-b;for(let te=W;te<U;++te){let ie=z+te*f-_;y?j+=N.get(ee,ae,ie,H)*S.get(ee,Y,te,X):j+=N.get(ee,H,ae,ie)*S.get(ee,X,Y,te)}}g.set(j,T,z,H,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var cM={kernelName:Ch,backendName:"cpu",kernelFunc:uM};function hM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r;_e([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),d=v.computeStrides(a.shape),p=R.convertConv2DDataFormat(u),f=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,p),m=new Ot(f.inShape,"float32"),A=m.values,y=n.data.get(a.dataId).values,g=n.data.get(s.dataId).values,[_,b,w]=h,{batchSize:x,filterHeight:N,filterWidth:S,inChannels:T,inHeight:M,inWidth:D,outChannels:z,outHeight:W,outWidth:U,strideHeight:H,strideWidth:X}=f;p=f.dataFormat;let j=N-1-f.padInfo.top,ee=S-1-f.padInfo.left,Y=p==="channelsLast",ae=m.strides[0],te=Y?m.strides[1]:m.strides[2],ie=Y?m.strides[2]:1,Q=Y?1:m.strides[1],he=d[0],oe=Y?d[1]:d[2],fe=Y?d[2]:1,pe=Y?1:d[1];for(let ke=0;ke<x;++ke)for(let Ne=0;Ne<T;++Ne)for(let Me=0;Me<M;++Me){let Oe=Me-j,$e=Math.max(0,Math.ceil(Oe/H)),tt=Math.min(W,(N+Oe)/H);for(let nt=0;nt<D;++nt){let ot=nt-ee,Ke=Math.max(0,Math.ceil(ot/X)),pt=Math.min(U,(S+ot)/X),Be=0;for(let _t=$e;_t<tt;++_t){let Un=_t*H-Oe;for(let Yt=Ke;Yt<pt;++Yt){let yn=Yt*X-ot,Hn=he*ke+oe*_t+fe*Yt,En=_*(N-1-Un)+b*(S-1-yn)+w*Ne;for(let un=0;un<z;++un){let Jt=y[Hn+pe*un],Cr=g[En+un];Be+=Jt*Cr}}}let An=ae*ke+te*Me+ie*nt+Q*Ne;A[An]=Be}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var dM={kernelName:fs,backendName:"cpu",kernelFunc:hM};function pM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;_e([a,s],"conv3d");let u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:c,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:A}=u,y=A.front,g=A.left,_=A.top,b=new Ot(u.outShape,a.dtype),w=n.data.get(a.dataId).values,x=n.data.get(s.dataId).values,N=b.values,S=v.computeStrides(a.shape),T=v.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let D=M*S[0],z=M*b.strides[0];for(let W=0;W<u.outDepth;++W){let U=z+W*b.strides[1],H=W*u.strideDepth-y;for(let X=0;X<c;++X){let j=H+X*p;if(j<0||j>=u.inDepth)continue;let ee=X*T[0],Y=D+j*S[1];for(let ae=0;ae<u.outHeight;++ae){let te=U+ae*b.strides[2],ie=ae*u.strideHeight-_;for(let Q=0;Q<h;++Q){let he=ie+Q*f;if(he<0||he>=u.inHeight)continue;let oe=ee+Q*T[1],fe=Y+he*S[2];for(let pe=0;pe<u.outWidth;++pe){let ke=te+pe*u.outChannels,Ne=pe*u.strideWidth-g;for(let Me=0;Me<d;++Me){let Oe=Ne+Me*m;if(Oe<0||Oe>=u.inWidth)continue;let $e=oe+Me*T[2],tt=fe+Oe*u.inChannels,nt=$e;for(let ot=0;ot<u.inChannels;++ot){let Ke=w[tt+ot];for(let pt=0;pt<u.outChannels;++pt)N[ke+pt]+=Ke*x[nt+pt];nt+=u.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var fM={kernelName:du,backendName:"cpu",kernelFunc:pM};function mM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;_e([a,s],"conv3dBackpropFilterV2");let u=v.computeStrides(a.shape),c=v.computeStrides(s.shape),h=R.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,A=h.filterHeight,y=h.filterWidth,g=new Ot(h.filterShape,"float32"),_=g.values,[b,w,x,N]=g.strides,S=n.data.get(s.dataId).values,[T,M,D,z]=c,W=n.data.get(a.dataId).values,[U,H,X,j]=u,ee=h.padInfo.front,Y=h.padInfo.left,ae=h.padInfo.top;for(let te=0;te<m;++te){let ie=Math.max(0,Math.ceil((ee-te)/d)),Q=Math.min(h.outDepth,(h.inDepth+ee-te)/d),he=te*b;for(let oe=0;oe<A;++oe){let fe=Math.max(0,Math.ceil((ae-oe)/p)),pe=Math.min(h.outHeight,(h.inHeight+ae-oe)/p),ke=oe*w+he;for(let 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),$e=Ne*x+ke;for(let tt=0;tt<h.inChannels;++tt){let nt=tt*N+$e;for(let ot=0;ot<h.outChannels;++ot){let Ke=0;for(let pt=0;pt<h.batchSize;++pt){let Be=pt*U,An=pt*T;for(let _t=ie;_t<Q;++_t){let Un=(te+_t*d-ee)*H+Be,Yt=_t*M+An;for(let yn=fe;yn<pe;++yn){let Hn=(oe+yn*p-ae)*X+Un,En=yn*D+Yt;for(let un=Me;un<Oe;++un){let Jt=(Ne+un*f-Y)*j+Hn,Cr=un*z+En;Ke+=W[Jt+tt]*S[Cr+ot]}}}}_[nt+ot]=Ke}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var AM={kernelName:Eh,backendName:"cpu",kernelFunc:mM};function yM(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;_e([a],"conv3dBackpropInputV2");let u=v.computeStrides(a.shape),c=v.computeStrides(s.shape),h=R.computeConv3DInfo(l,s.shape,o,1,i),d=new Ot(h.inShape,"float32"),p=d.values,[f,m,A,y]=d.strides,g=n.data.get(a.dataId).values,[_,b,w,x]=u,N=n.data.get(s.dataId).values,[S,T,M,D]=c,{batchSize:z,filterDepth:W,filterHeight:U,filterWidth:H,inChannels:X,inDepth:j,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:he,strideHeight:oe,strideWidth:fe}=h,pe=W-1-h.padInfo.front,ke=U-1-h.padInfo.top,Ne=H-1-h.padInfo.left;for(let Me=0;Me<z;++Me)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<j;++$e){let tt=$e-pe,nt=Math.max(0,Math.ceil(tt/he)),ot=Math.min(te,(W+tt)/he);for(let Ke=0;Ke<ee;++Ke){let pt=Ke-ke,Be=Math.max(0,Math.ceil(pt/oe)),An=Math.min(ie,(U+pt)/oe);for(let _t=0;_t<Y;++_t){let Un=_t-Ne,Yt=Math.max(0,Math.ceil(Un/fe)),yn=Math.min(Q,(H+Un)/fe),Hn=0;for(let En=nt;En<ot;++En){let un=En*he-tt;for(let Jt=Be;Jt<An;++Jt){let Cr=Jt*oe-pt;for(let Jn=Yt;Jn<yn;++Jn){let Qn=Jn*fe-Un,Aa=_*Me+b*En+w*Jt+x*Jn,Kr=S*(W-1-un)+T*(U-1-Cr)+M*(H-1-Qn)+D*Oe;for(let ya=0;ya<ae;++ya){let Di=g[Aa+ya],pr=N[Kr+ya];Hn+=Di*pr}}}}p[f*Me+m*$e+A*Ke+y*_t+Oe]=Hn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var gM={kernelName:Rh,backendName:"cpu",kernelFunc:yM},xM=it(ms,e=>Math.cos(e)),wM={kernelName:ms,backendName:"cpu",kernelFunc:xM},_M=it(so,e=>Math.cosh(e)),bM={kernelName:so,backendName:"cpu",kernelFunc:_M};function vM(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,[c,h,d,p]=a.shape,f=s.shape[0],[m,A]=o,y=We([f,m,A,p],"float32"),g=n.data.get(s.dataId).values,_=n.data.get(i.dataId).values,b=n.data.get(a.dataId).values,w=v.computeStrides(a.shape),x=v.computeStrides(y.shape);for(let N=0;N<f;N++){let S=N*4,T=g[S],M=g[S+1],D=g[S+2],z=g[S+3],W=_[N];if(W>=c)continue;let U=m>1?(D-T)*(h-1)/(m-1):0,H=A>1?(z-M)*(d-1)/(A-1):0;for(let X=0;X<m;X++){let j=m>1?T*(h-1)+X*U:.5*(T+D)*(h-1);if(j<0||j>h-1){for(let ee=0;ee<A;ee++)for(let Y=0;Y<p;Y++){let ae=Y+ee*x[2]+X*x[1]+N*x[0];y.values[ae]=u}continue}if(l==="bilinear"){let ee=Math.floor(j),Y=Math.ceil(j),ae=j-ee;for(let te=0;te<A;te++){let ie=A>1?M*(d-1)+te*H:.5*(M+z)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<p;fe++){let pe=fe+te*x[2]+X*x[1]+N*x[0];y.values[pe]=u}continue}let Q=Math.floor(ie),he=Math.ceil(ie),oe=ie-Q;for(let fe=0;fe<p;fe++){let pe=fe+Q*w[2]+ee*w[1]+W*w[0],ke=b[pe];pe=fe+he*w[2]+ee*w[1]+W*w[0];let Ne=b[pe];pe=fe+Q*w[2]+Y*w[1]+W*w[0];let Me=b[pe];pe=fe+he*w[2]+Y*w[1]+W*w[0];let Oe=b[pe],$e=ke+(Ne-ke)*oe,tt=Me+(Oe-Me)*oe;pe=fe+te*x[2]+X*x[1]+N*x[0],y.values[pe]=$e+(tt-$e)*ae}}}else for(let ee=0;ee<A;++ee){let Y=A>1?M*(d-1)+ee*H:.5*(M+z)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*x[2]+X*x[1]+N*x[0];y.values[Q]=u}continue}let ae=Math.round(Y),te=Math.round(j);for(let ie=0;ie<p;ie++){let Q=ie+ae*w[2]+te*w[1]+W*w[0],he=ie+ee*x[2]+X*x[1]+N*x[0];y.values[he]=b[Q]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var kM={kernelName:io,backendName:"cpu",kernelFunc:vM};function IM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;_e(a,"cumsum");let l=R.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=or({inputs:{x:a},backend:n,attrs:{perm:l}}));let c=R.getInnerMostAxes(1,a.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let h=nr(u.dtype,"int32"),d=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),p=n.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,g)=>y+f-g-1:(y,g)=>y+g;for(let y=0;y<p.length;y+=f)for(let g=0;g<f;g++){let _=m(y,g);if(g===0)d[_]=i?0:p[_];else{let b=m(y,g-1);d[_]=i?p[b]+d[b]:p[_]+d[b]}}let A=n.makeTensorInfo(u.shape,h,d);if(l!=null){let y=R.getUndoAxesPermutation(l),g=or({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(u),g}return A}var NM={kernelName:As,backendName:"cpu",kernelFunc:IM};function SM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,c=Im(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=zx(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var TM={kernelName:Fh,backendName:"cpu",kernelFunc:SM};function CM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],u=a.shape[2],c=a.shape[3],h=l*s,d=u*s,p=c/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),A=0;for(let y=0;y<o;++y)for(let g=0;g<h;++g){let _=Math.floor(g/s),b=g%s;for(let w=0;w<d;++w){let x=Math.floor(w/s),N=w%s,S=(b*s+N)*p;for(let T=0;T<p;++T){let M=T+S+c*(x+u*(_+l*y));m[A++]=f[M]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var EM={kernelName:oo,backendName:"cpu",kernelFunc:CM};function yw(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r;_e([a,s],"depthwiseConv2DNative");let c=v.computeStrides(a.shape),h=v.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=R.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:A,dilationWidth:y,padInfo:g}=p,_=g.left,b=g.top,w=p.outChannels/p.inChannels,x=new Ot(p.outShape,a.dtype),N=n.data.get(a.dataId).values,S=n.data.get(s.dataId).values,T=x.values;for(let M=0;M<p.batchSize;++M){let D=M*c[0],z=M*x.strides[0];for(let W=0;W<p.outHeight;++W){let U=z+W*x.strides[1],H=W*p.strideHeight-_;for(let X=0;X<f;++X){let j=H+X*A;if(j<0||j>=p.inHeight)continue;let ee=X*h[0],Y=D+j*c[1];for(let ae=0;ae<p.outWidth;++ae){let te=U+ae*x.strides[2],ie=ae*p.strideWidth-b;for(let Q=0;Q<m;++Q){let he=ie+Q*y;if(he<0||he>=p.inWidth)continue;let oe=ee+Q*h[1],fe=Y+he*p.inChannels,pe=te,ke=oe;for(let Ne=0;Ne<p.inChannels;++Ne){let Me=N[fe+Ne];for(let Oe=0;Oe<w;++Oe)T[pe+Oe]+=Me*S[ke+Oe];pe+=w,ke+=w}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var RM={kernelName:ys,backendName:"cpu",kernelFunc:yw};function FM(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r;_e([a,s],"depthwiseConv2dNativeBackpropFilter");let h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,A=new Ot(h.filterShape,"float32"),y=h.padInfo.left,g=h.padInfo.top,_=h.outChannels/h.inChannels,b=n.data.get(a.dataId).values,w=new Ot(a.shape,a.dtype,b),x=n.data.get(s.dataId).values,N=new Ot(s.shape,s.dtype,x);for(let S=0;S<f;++S){let T=Math.max(0,Math.ceil((g-S)/d)),M=Math.min(h.outHeight,(h.inHeight+g-S)/d);for(let D=0;D<m;++D){let z=Math.max(0,Math.ceil((y-D)/p)),W=Math.min(h.outWidth,(h.inWidth+y-D)/p);for(let U=0;U<h.outChannels;++U){let H=Math.trunc(U/_),X=U%_,j=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=T;Y<M;++Y){let ae=S+Y*d-g;for(let te=z;te<W;++te){let ie=D+te*p-y;j+=w.get(ee,ae,ie,H)*N.get(ee,Y,te,U)}}A.set(j,S,D,H,X)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var MM={kernelName:Mh,backendName:"cpu",kernelFunc:FM};function $M(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r;_e([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),d=v.computeStrides(s.shape),p=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),f=new Ot(p.inShape,"float32"),m=f.values,[A,y,g]=f.strides,_=n.data.get(a.dataId).values,[b,w,x]=h,N=n.data.get(s.dataId).values,[S,T,M]=d,{batchSize:D,filterHeight:z,filterWidth:W,inChannels:U,inHeight:H,inWidth:X,outChannels:j,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=z-1-p.padInfo.top,Q=W-1-p.padInfo.left,he=j/U;for(let oe=0;oe<D;++oe)for(let fe=0;fe<U;++fe)for(let pe=0;pe<H;++pe){let ke=pe-ie,Ne=Math.max(0,Math.ceil(ke/ae)),Me=Math.min(ee,(z+ke)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,tt=Math.max(0,Math.ceil($e/te)),nt=Math.min(Y,(W+$e)/te),ot=0;for(let Ke=Ne;Ke<Me;++Ke){let pt=Ke*ae-ke;for(let Be=tt;Be<nt;++Be){let An=Be*te-$e,_t=b*oe+w*Ke+x*Be,Un=S*(z-1-pt)+T*(W-1-An)+M*fe;for(let Yt=0;Yt<he;++Yt){let yn=fe*he+Yt,Hn=_[_t+yn],En=N[Un+Yt];ot+=Hn*En}}}m[A*oe+y*pe+g*Oe+fe]=ot}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var DM={kernelName:$h,backendName:"cpu",kernelFunc:$M};function OM(e){let{inputs:t,backend:n}=e,{x:r}=t,a=v.sizeFromShape(r.shape),s=n.data.get(r.dataId).values,i=We([a,a],r.dtype),o=i.values;for(let u=0;u<s.length;u++)o[u*a+u]=s[u];let l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}var zM={kernelName:Dh,backendName:"cpu",kernelFunc:OM},PM={kernelName:pu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,u=l.data.get(r.dataId).values,c=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:A,outHeight:y,outWidth:g,padInfo:_,strideHeight:b,strideWidth:w,filterHeight:x,filterWidth:N,dilationHeight:S,dilationWidth:T,outShape:M}=R.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),D=v.sizeFromShape(M),z=M.length,W=v.getArrayFromDType(r.dtype,D);for(let U=0;U<p;++U)for(let H=0;H<y;++H){let X=H*b-_.top;for(let j=0;j<g;++j){let ee=j*w-_.left;for(let Y=0;Y<A;++Y){let ae=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<x;++ie){let Q=X+ie*S;if(Q>=0&&Q<f)for(let he=0;he<N;++he){let oe=ee+he*T;if(oe>=0&&oe<m){let fe=v.locToIndex([U,Q,oe,Y],c,v.computeStrides(r.shape)),pe=v.locToIndex([ie,he,Y],d,v.computeStrides(a.shape)),ke=u[fe]+h[pe];ke>ae&&(ae=ke)}}}let te=v.locToIndex([U,H,j,Y],z,v.computeStrides(M));W[te]=ae}}}return{dataId:l.write(v.toTypedArray(W,r.dtype),M,r.dtype),shape:M,dtype:r.dtype}}},LM={kernelName:zh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:N,dilationWidth:S,outShape:T}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===T.length,()=>`Error in ${zh}, dy must have the same rank as output ${T.length}, but got ${s.rank}`);let M=v.toNestedArray(T,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(a.shape,a.dtype);for(let z=0;z<d;++z)for(let W=0;W<A;++W){let U=W*_-g.top;for(let H=0;H<y;++H){let X=H*b-g.left;for(let j=0;j<m;++j){let ee=Number.MIN_SAFE_INTEGER,Y=0,ae=0;for(let te=0;te<w;++te){let ie=U+te*N;if(ie>=0&&ie<p)for(let Q=0;Q<x;++Q){let he=X+Q*S;if(he>=0&&he<f){let oe=c[z][ie][he][j]+h[te][Q][j];oe>ee&&(ee=oe,Y=te,ae=Q)}}}D[Y][ae][j]+=M[z][W][H][j]}}}return{dataId:u.write(v.toTypedArray(D,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},WM={kernelName:Oh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,u=t,c=v.toNestedArray(r.shape,u.data.get(r.dataId).values),h=v.toNestedArray(a.shape,u.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:A,outWidth:y,padInfo:g,strideHeight:_,strideWidth:b,filterHeight:w,filterWidth:x,dilationHeight:N,dilationWidth:S,outShape:T}=R.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);v.assert(s.rank===T.length,()=>`Error in ${Oh}, dy must have the same rank as output ${T.length}, but got ${s.rank}`);let M=v.toNestedArray(T,u.data.get(s.dataId).values),D=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let z=0;z<d;++z)for(let W=0;W<A;++W){let U=W*_-g.top;for(let H=0;H<y;++H){let X=H*b-g.left;for(let j=0;j<m;++j){let ee=Number.MIN_SAFE_INTEGER,Y=U<0?0:U,ae=X<0?0:X;for(let te=0;te<w;++te){let ie=U+te*N;if(ie>=0&&ie<p)for(let Q=0;Q<x;++Q){let he=X+Q*S;if(he>=0&&he<f){let oe=c[z][ie][he][j]+h[te][Q][j];oe>ee&&(ee=oe,Y=ie,ae=he)}}}D[z][Y][ae][j]+=M[z][W][H][j]}}}return{dataId:u.write(v.toTypedArray(D,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function BM(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;_e([r,a],"eluGrad");let s=new Float32Array(v.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let u=i[l];u>=1?s[l]=o[l]:s[l]=o[l]*(u+1)}return n.makeTensorInfo(a.shape,"float32",s)}var VM={kernelName:Ph,backendName:"cpu",kernelFunc:BM},UM=Rt((e,t)=>e===t?1:0),gw=qt(co,UM,null,"bool"),HM={kernelName:co,backendName:"cpu",kernelFunc:gw},jM=R.ERF_P,GM=R.ERF_A1,qM=R.ERF_A2,XM=R.ERF_A3,KM=R.ERF_A4,ZM=R.ERF_A5,YM=it(uo,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+jM*n);return t*(1-((((ZM*r+KM)*r+XM)*r+qM)*r+GM)*r*Math.exp(-n*n))}),JM={kernelName:uo,backendName:"cpu",kernelFunc:YM};function Qd(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),gt({inputs:{x:a},backend:n,attrs:{shape:o}})}var QM={kernelName:ho,backendName:"cpu",kernelFunc:Qd},e$=Rt((e,t)=>e/t),Dm=qt(gs,e$),Om={kernelName:gs,backendName:"cpu",kernelFunc:Dm};function xw(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,u=[a,s],c=v.sizeFromShape(u),h=v.getTypedArrayFromDType("float32",c),d=v.getTypedArrayFromDType("float32",c);for(let A=0;A<a;A++){let y=Ai({inputs:{x:o},backend:n,attrs:{begin:[A,0],size:[1,s]}}),g=Ai({inputs:{x:l},backend:n,attrs:{begin:[A,0],size:[1,s]}}),_=On({inputs:{real:y,imag:g},backend:n}),{real:b,imag:w}=t$(_,t,n),x=R.mergeRealAndImagArrays(b,w);for(let N=0;N<s;N++){let S=R.getComplexWithIndex(x,N);h[A*s+N]=S.real,d[A*s+N]=S.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(_)}let p=n.makeTensorInfo(u,"float32",h),f=n.makeTensorInfo(u,"float32",d),m=On({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function t$(e,t,n){let r=v.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(n$(r)){let o=zm(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let u=n.makeTensorInfo(l,"float32",o.real),c=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",v.createScalarValue(r,"float32")),d=Lr({inputs:{x:h},backend:n}),p=Om.kernelFunc({inputs:{a:u,b:h},backend:n}),f=Om.kernelFunc({inputs:{a:c,b:d},backend:n}),m=n.data.get(p.dataId).values,A=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:A}}return o}else{let o=R.mergeRealAndImagArrays(s,i),l=r$(o,r,t);return R.splitRealAndImagArrays(l)}}function n$(e){return(e&e-1)==0}function zm(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=R.mergeRealAndImagArrays(e,t),i=n/2,o=R.complexWithEvenIndex(s),l=o.real,u=o.imag,c=[l.length],h=a.makeTensorInfo(c,"float32",l),d=a.makeTensorInfo(c,"float32",u),p=On({inputs:{real:h,imag:d},backend:a}),f=R.complexWithOddIndex(s),m=f.real,A=f.imag,y=[m.length],g=a.makeTensorInfo(y,"float32",m),_=a.makeTensorInfo(y,"float32",A),b=On({inputs:{real:g,imag:_},backend:a}),w=zm(l,u,i,r,a),x=w.real,N=w.imag,S=[x.length],T=a.makeTensorInfo(S,"float32",x),M=a.makeTensorInfo(S,"float32",N),D=On({inputs:{real:T,imag:M},backend:a}),z=zm(m,A,i,r,a),W=z.real,U=z.imag,H=[W.length],X=a.makeTensorInfo(H,"float32",W),j=a.makeTensorInfo(H,"float32",U),ee=On({inputs:{real:X,imag:j},backend:a}),Y=R.exponents(n,r),ae=[Y.real.length],te=a.makeTensorInfo(ae,"float32",Y.real),ie=a.makeTensorInfo(ae,"float32",Y.imag),Q=On({inputs:{real:te,imag:ie},backend:a}),he=Rm({inputs:{a:Q,b:ee},backend:a}),oe=tc({inputs:{a:D,b:he},backend:a}),fe=Fm({inputs:{a:D,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}}),$e=bl({inputs:[Ne,Me],backend:a,attrs:{axis:0}}),tt=a.data.get(Oe.dataId).values,nt=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(_),a.disposeIntermediateTensorInfo(b),a.disposeIntermediateTensorInfo(T),a.disposeIntermediateTensorInfo(M),a.disposeIntermediateTensorInfo(D),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(j),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(he),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(Ne),a.disposeIntermediateTensorInfo(ke),a.disposeIntermediateTensorInfo(Me),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:tt,imag:nt}}function r$(e,t,n){let r=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let l=R.exponent(a*o,t,n),u=R.getComplexWithIndex(e,o);s+=u.real*l.real-u.imag*l.imag,i+=u.real*l.imag+u.imag*l.real}n&&(s/=t,i/=t),R.assignToTypedArray(r,s,i,a)}return r}function a$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=gt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=xw(o,!1,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var s$={kernelName:Lh,backendName:"cpu",kernelFunc:a$};function Pm(e){let{backend:t,attrs:n}=e,{shape:r,value:a,dtype:s}=n,i=s||v.inferDtype(a),o=v.getArrayFromDType(i,v.sizeFromShape(r));return i$(o,a,i),t.makeTensorInfo(r,i,o)}var o$={kernelName:fu,backendName:"cpu",kernelFunc:Pm};function i$(e,t,n){e.fill(t)}var l$={kernelName:fo,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,a=n,s=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[i,o,l,u]=r.shape,c=a.data.get(r.dataId).values;for(let h=0;h<i;h++){let d=h*l*o*u;for(let p=0;p<o;p++){let f=p*(l*u);for(let m=0;m<l;m++){let A=m*u;for(let y=0;y<u;y++){let g=[i,p,m,y][2],_=Math.round(l-g),b=d+f+A+y,w=c[b];if(_>=0&&_<l){let x=_*u,N=d+f+x+y;w=c[N]}s[b]=w}}}}return{dataId:a.write(s,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},u$=Rt((e,t)=>Math.floor(e/t)),c$=qt(_s,u$,null,"int32"),h$={kernelName:_s,backendName:"cpu",kernelFunc:c$};function d$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=Aw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=tc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=Mm(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var p$={kernelName:ei,backendName:"cpu",kernelFunc:d$};function f$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=yw({inputs:{x:a,filter:s},backend:n,attrs:{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d}});if(i){let A=m;m=tc({inputs:{a:m,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(p){let A=m;m=Mm(n,m,p,o,f),n.disposeIntermediateTensorInfo(A)}return m}var m$={kernelName:ti,backendName:"cpu",kernelFunc:f$};function A$(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=v.sizeFromShape(r.shape),i=a.shape,o=i[i.length-1],[l,u,c,h]=R.prepareAndValidate(r,a);if(u===0)return n.makeTensorInfo(l,r.dtype,[]);let d=We([u,c],r.dtype),p=n.data.get(a.dataId).values,f=n.data.get(r.dataId).values;for(let m=0;m<u;m++){let A=[],y=0;for(let g=0;g<o;g++){let _=p[m*o+g];y+=_*h[g],A.push(_)}if(y<0||y>=s/c)throw new Error(`Invalid indices: ${A} does not index into ${r.shape}`);for(let g=0;g<c;g++)d.values[m*c+g]=f[y*c+g]}return n.makeTensorInfo(l,d.dtype,d.values)}var y$={kernelName:Ao,backendName:"cpu",kernelFunc:A$};function g$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r;_e([a,s],"gatherV2");let l=o;o==null&&(l=0);let u=v.sizeFromShape(s.shape),c=v.parseAxisParam(i,a.shape)[0],h=R.segment_util.collectGatherOpShapeInfo(a,s,c,l),d=gt({inputs:{x:a},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),p=gt({inputs:{x:s},backend:n,attrs:{shape:[h.batchSize,u/h.batchSize]}}),f=[h.batchSize,h.outerSize,u/h.batchSize,h.sliceSize],m=n.bufferSync(p),A=n.bufferSync(d),y=Vx(A,m,f);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.makeTensorInfo(h.outputShape,y.dtype,y.values)}var x$={kernelName:mo,backendName:"cpu",kernelFunc:g$},w$=Rt((e,t)=>e>=t?1:0),_$=qt(vs,w$,null,"bool"),b$={kernelName:vs,backendName:"cpu",kernelFunc:_$};function v$(e){let{inputs:t,backend:n}=e,{input:r}=t,a=v.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=a/s,o=gt({inputs:{x:r},backend:n,attrs:{shape:[i,s]}}),l=xw(o,!0,n),u=gt({inputs:{x:l},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var k$={kernelName:Wh,backendName:"cpu",kernelFunc:v$},I$=it(go,e=>Number.isFinite(e)?1:0,"bool"),N$={kernelName:go,backendName:"cpu",kernelFunc:I$},S$=it(xo,e=>Math.abs(e)===Infinity?1:0,"bool"),T$={kernelName:xo,backendName:"cpu",kernelFunc:S$},C$=it(wo,e=>Number.isNaN(e)?1:0,"bool"),E$={kernelName:wo,backendName:"cpu",kernelFunc:C$},R$=Rt((e,t)=>e<=t?1:0),F$=qt(bo,R$,null,"bool"),M$={kernelName:bo,backendName:"cpu",kernelFunc:F$};function $$(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=jx(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var D$={kernelName:Vh,backendName:"cpu",kernelFunc:$$},O$=it(vo,e=>Math.log1p(e)),z$={kernelName:vo,backendName:"cpu",kernelFunc:O$},P$=Rt((e,t)=>e&&t),L$=qt(ko,P$,null,"bool"),W$={kernelName:ko,backendName:"cpu",kernelFunc:L$},B$=it(mu,e=>e?0:1,"bool"),V$={kernelName:mu,backendName:"cpu",kernelFunc:B$},U$=Rt((e,t)=>e||t),H$=qt(Au,U$,null,"bool"),j$={kernelName:Au,backendName:"cpu",kernelFunc:H$};function G$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r;_e(a,"LRN");let u=a.shape[3],c=u-1,h=n.data.get(a.dataId).values,d=v.sizeFromShape(a.shape),p=new Float32Array(d);function f(m){let A=m%u,y=m-A+Math.max(0,A-s),g=m-A+Math.min(A+s,c),_=0;for(;y<=g;y++){let b=h[y];_+=b*b}return _}for(let m=0;m<d;m++){let A=f(m),y=h[m]*Math.pow(i+o*A,-l);p[m]=y}return n.makeTensorInfo(a.shape,a.dtype,p)}var q$={kernelName:yu,backendName:"cpu",kernelFunc:G$};function X$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r;_e(i,"LRNGrad");let h=v.sizeFromShape(i.shape),d=i.shape[3],p=n.data.get(i.dataId).values,f=n.data.get(a.dataId).values,m=n.data.get(s.dataId).values,A=new Float32Array(h),y=h;for(let g=0;g<y;g++){let _=g%d,b=g-_+Math.max(0,_-o),w=g-_+Math.min(d,_+o+1),x=0;for(let N=b;N<w;N++)x+=Math.pow(f[N],2);x=u*x+l;for(let N=b;N<w;N++){let S=-2*u*c*f[N]*m[g]/x;g===N&&(S+=Math.pow(x,-c)),S*=p[g],A[N]+=S}}return n.makeTensorInfo(i.shape,a.dtype,A)}var K$={kernelName:Uh,backendName:"cpu",kernelFunc:X$};function ww(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=n,l=a.shape,u=l.length,c=v.parseAxisParam(s,l),h=c,d=R.getAxesPermutation(h,u),p=o.data.get(a.dataId).values;if(d!=null){let b=new Array(u);for(let w=0;w<b.length;w++)b[w]=l[d[w]];p=Tm(p,l,a.dtype,d,b),h=R.getInnerMostAxes(h.length,u),l=b}_e(a,"max"),R.assertAxesAreInnerMostDims("max",h,u);let[f,m]=R.computeOutAndReduceShapes(l,h),A=v.sizeFromShape(m),y=qx(p,A,f,a.dtype),g=o.write(y,f,a.dtype),_=f;return i&&(_=R.expandShapeToKeepDim(f,c)),{dataId:g,shape:_,dtype:a.dtype}}var Z$={kernelName:Ss,backendName:"cpu",kernelFunc:ww};function Y$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;_e(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l),h;if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))h=Lr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=v.computeStrides(a.shape),f=$m(d,a.shape,a.dtype,p,c,"max");h=n.makeTensorInfo(c.outShape,a.dtype,f.values)}return h}var J$={kernelName:Cs,backendName:"cpu",kernelFunc:Y$};function Q$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r;_e(a,"maxPool3d");let c=R.computePool3DInfo(a.shape,s,i,1,o,l,u),h=n.data.get(a.dataId).values,d=mw(h,a.shape,a.dtype,v.computeStrides(a.shape),c,"max");return n.makeTensorInfo(d.shape,"float32",d.values)}var eD={kernelName:gu,backendName:"cpu",kernelFunc:Q$};function tD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r;_e([a,s],"maxPool3DGrad");let c=R.computePool3DInfo(s.shape,i,o,1,l,u),h=n.bufferSync(s),d=BF(h,c),p=c.strideDepth,f=c.strideHeight,m=c.strideWidth,A=c.dilationDepth,y=c.dilationHeight,g=c.dilationWidth,_=c.effectiveFilterDepth,b=c.effectiveFilterHeight,w=c.effectiveFilterWidth,x=_-1-c.padInfo.front,N=w-1-c.padInfo.left,S=b-1-c.padInfo.top,T=We(s.shape,"float32"),M=n.bufferSync(a);for(let D=0;D<c.batchSize;++D)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let U=0;U<c.inHeight;++U)for(let H=0;H<c.inWidth;++H){let X=W-x,j=U-S,ee=H-N,Y=0;for(let ae=0;ae<_;ae+=A){let te=(X+ae)/p;if(!(te<0||te>=c.outDepth||Math.floor(te)!==te))for(let ie=0;ie<b;ie+=y){let Q=(j+ie)/f;if(!(Q<0||Q>=c.outHeight||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(ee+he)/m;if(oe<0||oe>=c.outWidth||Math.floor(oe)!==oe)continue;let fe=_*b*w-1-d.get(D,te,Q,oe,z),pe=ae*b*w+ie*w+he,ke=fe===pe?1:0;ke!==0&&(Y+=M.get(D,te,Q,oe,z)*ke)}}}T.set(Y,D,W,U,H,z)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var nD={kernelName:jh,backendName:"cpu",kernelFunc:tD};function rD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;_e([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=n.data.get(o.dataId).values,f=We(d.outShape,o.dtype,fw(p,o.shape,o.dtype,d).values),m=d.strideHeight,A=d.strideWidth,y=d.dilationHeight,g=d.dilationWidth,_=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=b-1-d.padInfo.left,x=_-1-d.padInfo.top,N=We(o.shape,"float32"),S=n.data.get(a.dataId).values,T=We(a.shape,"float32",S);for(let M=0;M<d.batchSize;++M)for(let D=0;D<d.inChannels;++D)for(let z=0;z<d.inHeight;++z)for(let W=0;W<d.inWidth;++W){let U=z-x,H=W-w,X=0;for(let j=0;j<_;j+=y){let ee=(U+j)/m;if(!(ee<0||ee>=d.outHeight||Math.floor(ee)!==ee))for(let Y=0;Y<b;Y+=g){let ae=(H+Y)/A;if(ae<0||ae>=d.outWidth||Math.floor(ae)!==ae)continue;let te=_*b-1-f.get(M,ee,ae,D),ie=j*b+Y,Q=te===ie?1:0;Q!==0&&(X+=T.get(M,ee,ae,D)*Q)}}N.set(X,M,z,W,D)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var aD={kernelName:Hh,backendName:"cpu",kernelFunc:rD};function sD(e,t,n,r,a){let s=v.computeStrides(t),i=$m(e,t,n,s,a,"max"),o=fw(e,t,n,a,!0,r);return[i.values,o.values]}var iD={kernelName:Gh,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_e(r,"MaxPoolWithArgmax");let u=l.data.get(r.dataId).values,c=R.computePool2DInfo(r.shape,a,s,[1,1],i),[h,d]=sD(u,r.shape,r.dtype,o,c),p=l.write(h,c.outShape,r.dtype),f=l.write(d,c.outShape,r.dtype);return[{dataId:p,shape:c.outShape,dtype:r.dtype},{dataId:f,shape:c.outShape,dtype:"int32"}]}};function ep(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"sum");let o;a.dtype==="bool"?o=Wa({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Lr({inputs:{x:a},backend:n});let l=o.shape.length,u=v.parseAxisParam(s,o.shape),c=R.getAxesPermutation(u,l),h=u,d=o;c!=null&&(d=or({inputs:{x:o},backend:n,attrs:{perm:c}}),h=R.getInnerMostAxes(h.length,l)),R.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=R.computeOutAndReduceShapes(d.shape,h),m=R.upcastType(d.dtype,"int32"),A=Jd(n,p,m),y=v.sizeFromShape(f),g=n.data.get(A.dataId).values,_=n.data.get(d.dataId).values;for(let b=0;b<g.length;++b){let w=b*y,x=0;for(let N=0;N<y;++N)x+=_[w+N];g[b]=x}if(i){let b=R.expandShapeToKeepDim(A.shape,u),w=A;A=gt({inputs:{x:A},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),c!=null&&n.disposeIntermediateTensorInfo(d),A}var oD={kernelName:qs,backendName:"cpu",kernelFunc:ep};function lD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=v.parseAxisParam(s,a.shape),l=R.computeOutAndReduceShapes(a.shape,o)[1],u=v.sizeFromShape(l),c=[],h=n.makeTensorInfo([],"float32",new Float32Array([u]));c.push(h);let d=Wa({inputs:{x:a},backend:n,attrs:{dtype:"float32"}});c.push(d);let p=Dm({inputs:{a:d,b:h},backend:n});c.push(p);let f=ep({inputs:{x:p},backend:n,attrs:{axis:s,keepDims:i}});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var uD={kernelName:Es,backendName:"cpu",kernelFunc:lD};function cD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;_e(a,"min");let o=v.parseAxisParam(s,a.shape),l=o,u=R.getAxesPermutation(l,a.shape.length),c=a;u!=null&&(c=or({inputs:{x:a},backend:n,attrs:{perm:u}}),l=R.getInnerMostAxes(l.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",l,c.shape.length);let[h,d]=R.computeOutAndReduceShapes(c.shape,l),p=v.sizeFromShape(d),f=v.makeZerosTypedArray(v.sizeFromShape(h),c.dtype),m=n.data.get(c.dataId).values;for(let y=0;y<f.length;++y){let g=y*p,_=m[g];for(let b=0;b<p;++b){let w=m[g+b];w<_&&(_=w)}f[y]=_}u!=null&&n.disposeIntermediateTensorInfo(c);let A=n.makeTensorInfo(h,c.dtype,f);if(i){let y=R.expandShapeToKeepDim(h,o),g=gt({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var hD={kernelName:Rs,backendName:"cpu",kernelFunc:cD};function dD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,mode:i}=r;_e(a,"mirrorPad");let o=s.map((g,_)=>g[0]+a.shape[_]+g[1]),l=s.map(g=>g[0]),u=s.map((g,_)=>g[0]+a.shape[_]),c=i==="reflect"?0:1,h=n.data.get(a.dataId).values,d=a.shape.length,p=v.computeStrides(a.shape),f=v.sizeFromShape(o),m=o.length,A=v.computeStrides(o),y=v.getTypedArrayFromDType(a.dtype,f);for(let g=0;g<f;g++){let _=v.indexToLoc(g,m,A);for(let w=0;w<m;w++)_[w]<l[w]?_[w]=l[w]*2-_[w]-c:_[w]>=u[w]&&(_[w]=(u[w]-1)*2-_[w]+c);_=_.map((w,x)=>w-l[x]);let b=v.locToIndex(_,d,p);y[g]=h[b]}return{dataId:n.write(y,o,a.dtype),shape:o,dtype:a.dtype}}var pD={kernelName:xu,backendName:"cpu",kernelFunc:dD},fD=Rt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),mD=qt(Io,fD),AD={kernelName:Io,backendName:"cpu",kernelFunc:mD},yD=qi(V8());function _w(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=a.shape.length,o=s;if(o===-1&&(o=i-1),o!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);let l=v.parseAxisParam([o],a.shape),u=ww({inputs:{x:a},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),c=R.expandShapeToKeepDim(u.shape,l),h=gt({inputs:{x:u},backend:n,attrs:{shape:c}}),d=Fm({inputs:{a,b:h},backend:n}),p=iw({inputs:{x:d},backend:n}),f=ep({inputs:{x:p},backend:n,attrs:{axis:l,keepDims:!1}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:c}}),A=Dm({inputs:{a:p,b:m},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var gD={kernelName:Xs,backendName:"cpu",kernelFunc:_w};function xD(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:_w({inputs:{logits:a},backend:n,attrs:{dim:-1}}),u=l.shape[0],c=l.shape[1],h=n.data.get(l.dataId).values,d=[u,s],p=v.makeZerosTypedArray(v.sizeFromShape(d),"int32");for(let f=0;f<u;++f){let m=f*c,A=new Float32Array(c-1);A[0]=h[m];for(let _=1;_<A.length;++_)A[_]=A[_-1]+h[m+_];let y=yD.alea(i.toString()),g=f*s;for(let _=0;_<s;++_){let b=y();p[g+_]=A.length;for(let w=0;w<A.length;w++)if(b<A[w]){p[g+_]=w;break}}}return o||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(d,"int32",p)}var wD={kernelName:qh,backendName:"cpu",kernelFunc:xD},_D=Pr.nonMaxSuppressionV3Impl;function bD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r;_e(a,"NonMaxSuppression");let u=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,{selectedIndices:h}=_D(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var vD={kernelName:To,backendName:"cpu",kernelFunc:bD},kD=Pr.nonMaxSuppressionV4Impl;function ID(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r;_e(a,"NonMaxSuppressionPadded");let c=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,{selectedIndices:d,validOutputs:p}=kD(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var ND={kernelName:Co,backendName:"cpu",kernelFunc:ID},SD=Pr.nonMaxSuppressionV5Impl;function TD(e){let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r;_e(a,"NonMaxSuppressionWithScore");let c=n.data.get(a.dataId).values,h=n.data.get(s.dataId).values,d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=SD(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var CD={kernelName:Eo,backendName:"cpu",kernelFunc:TD};function ED(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r;_e(a,"oneHot");let l=v.sizeFromShape(a.shape),u=new Float32Array(l*s);u.fill(o);let c=n.data.get(a.dataId).values;for(let h=0;h<l;++h)c[h]>=0&&c[h]<s&&(u[h*s+c[h]]=i);return n.makeTensorInfo([...a.shape,s],"int32",u)}var RD={kernelName:$s,backendName:"cpu",kernelFunc:ED};function tp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(r.dtype==="complex64"){let a=mi({inputs:{input:r},backend:n}),s=tp({inputs:{x:a},backend:n}),i=_l({inputs:{input:r},backend:n}),o=tp({inputs:{x:i},backend:n}),l=On({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Pm({backend:n,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var FD={kernelName:Xo,backendName:"cpu",kernelFunc:tp};function bw(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=bw({inputs:{x:a},backend:n}),i=_l({inputs:{input:r},backend:n}),o=tp({inputs:{x:i},backend:n}),l=On({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Pm({backend:n,attrs:{shape:r.shape,value:1,dtype:r.dtype}})}var MD={kernelName:Ro,backendName:"cpu",kernelFunc:bw};function vw(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return Qd({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=Qd({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=bl({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var $D={kernelName:Fo,backendName:"cpu",kernelFunc:vw};function DD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r;_e(a,"pad");let o=s.map((y,g)=>y[0]+a.shape[g]+y[1]),l=s.map(y=>y[0]),u=n.data.get(a.dataId).values,c=v.sizeFromShape(a.shape),h=a.shape.length,d=v.computeStrides(a.shape),p=v.sizeFromShape(o),f=o.length,m=v.computeStrides(o),A=v.getTypedArrayFromDType(a.dtype,p);i!==0&&A.fill(i);for(let y=0;y<c;y++){let g=v.indexToLoc(y,h,d).map((b,w)=>b+l[w]),_=v.locToIndex(g,f,m);A[_]=u[y]}return{dataId:n.write(A,o,a.dtype),shape:o,dtype:a.dtype}}var kw={kernelName:Ds,backendName:"cpu",kernelFunc:DD},OD=Rt((e,t)=>Math.pow(e,t)),zD=qt(Os,OD),PD={kernelName:Os,backendName:"cpu",kernelFunc:zD};function LD(e){let{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:i}=n,o=Cm(r,a,i,s);return t.makeTensorInfo([o.length],s,o)}var WD={kernelName:wu,backendName:"cpu",kernelFunc:LD},BD=it($o,e=>1/e),VD={kernelName:$o,backendName:"cpu",kernelFunc:BD};function UD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;_e(a,"resizeBilinear");let l=v.computeStrides(a.shape),[u,c]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(v.sizeFromShape([h,u,c,f])),y=[s&&u>1?d-1:d,s&&c>1?p-1:p],g=[s&&u>1?u-1:u,s&&c>1?c-1:c],_=0,b=y[0]/g[0],w=y[1]/g[1];for(let x=0;x<h;x++)for(let N=0;N<u;N++){let S;i?S=b*(N+.5)-.5:S=b*N;let T=Math.max(0,Math.floor(S)),M=S-T,D=Math.min(d-1,Math.ceil(S)),z=x*l[0]+T*l[1],W=x*l[0]+D*l[1];for(let U=0;U<c;U++){let H;i?H=w*(U+.5)-.5:H=w*U;let X=Math.max(0,Math.floor(H)),j=H-X,ee=Math.min(p-1,Math.ceil(H)),Y=z+X*l[2],ae=W+X*l[2],te=z+ee*l[2],ie=W+ee*l[2];for(let Q=0;Q<f;Q++){let he=m[Y+Q],oe=m[ae+Q],fe=m[te+Q],pe=m[ie+Q],ke=he+(fe-he)*j,Ne=oe+(pe-oe)*j,Me=ke+(Ne-ke)*M;A[_++]=Me}}}return n.makeTensorInfo([h,u,c,f],"float32",A)}var HD={kernelName:Ls,backendName:"cpu",kernelFunc:UD};function jD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;_e([s,a],"resizeBilinearGrad");let o=v.computeStrides(a.shape),[l,u,c,h]=a.shape,[,d,p]=s.shape,f=new Float32Array(l*u*c*h),m=[i&&d>1?u-1:u,i&&p>1?c-1:c],A=[i&&d>1?d-1:d,i&&p>1?p-1:p],y=m[0]/A[0],g=m[1]/A[1],_=n.data.get(s.dataId).values,b=0;for(let w=0;w<l;w++){let x=w*o[0];for(let N=0;N<d;N++){let S=N*y,T=Math.floor(S),M=Math.min(Math.ceil(S),u-1),D=x+T*o[1],z=x+M*o[1],W=S-T,U=1-W;for(let H=0;H<p;H++){let X=H*g,j=Math.floor(X),ee=Math.min(Math.ceil(X),c-1),Y=X-j,ae=1-Y,te=D+j*o[2],ie=D+ee*o[2],Q=z+j*o[2],he=z+ee*o[2],oe=U*ae,fe=U*Y,pe=W*ae,ke=W*Y;for(let Ne=0;Ne<h;Ne++){let Me=_[b++];f[te+Ne]+=Me*oe,f[ie+Ne]+=Me*fe,f[Q+Ne]+=Me*pe,f[he+Ne]+=Me*ke}}}}return n.makeTensorInfo([l,c,u,h],"float32",f)}var GD={kernelName:Zh,backendName:"cpu",kernelFunc:jD};function qD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r;_e(a,"resizeNearestNeighbor");let l=v.computeStrides(a.shape),[u,c]=o,[h,d,p,f]=a.shape,m=n.data.get(a.dataId).values,A=new Float32Array(h*u*c*f),y=[s&&u>1?d-1:d,s&&c>1?p-1:p],g=[s&&u>1?u-1:u,s&&c>1?c-1:c],_=y[0]/g[0],b=y[1]/g[1],w=0;for(let x=0;x<h;x++){let N=x*l[0];for(let S=0;S<u;S++){let T=i?_*(S+.5):_*S,M=Math.min(d-1,s?Math.round(T):Math.floor(T));i&&(M=Math.max(0,M));let D=N+M*l[1];for(let z=0;z<c;z++){let W=i?b*(z+.5):b*z,U=Math.min(p-1,s?Math.round(W):Math.floor(W));i&&(U=Math.max(0,U));let H=D+U*l[2];for(let X=0;X<f;X++){let j=m[H+X];A[w++]=j}}}}return n.makeTensorInfo([h,u,c,f],a.dtype,A)}var XD={kernelName:_u,backendName:"cpu",kernelFunc:qD};function KD(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r;_e([s,a],"resizeNearestNeighborGrad");let o=v.computeStrides(a.shape),l=v.computeStrides(s.shape),[u,c,h,d]=a.shape,[,p,f]=s.shape,m=new Float32Array(u*c*h*d),A=n.data.get(s.dataId).values,y=[i&&p>1?c-1:c,i&&f>1?h-1:h],g=[i&&p>1?p-1:p,i&&f>1?f-1:f],_=y[0]/g[0],b=y[1]/g[1],w=1/_,x=1/b,N=Math.ceil(w)*2+2,S=Math.ceil(x)*2+2;for(let T=0;T<u;T++){let M=T*o[0];for(let D=0;D<c;D++){let z=M+D*o[1],W=Math.floor(D*w),U=Math.floor(W-N/2);for(let H=0;H<h;H++){let X=z+H*o[2],j=Math.floor(H*x),ee=Math.floor(j-S/2);for(let Y=0;Y<d;Y++){let ae=0;for(let te=0;te<N;te++){let ie=te+U;if(ie<0||ie>=p)continue;let Q=M+ie*l[1],he=ie*_,oe=Math.min(c-1,i?Math.round(he):Math.floor(he));if(D===oe)for(let fe=0;fe<S;fe++){let pe=fe+ee;if(pe<0||pe>=f)continue;let ke=Q+pe*l[2],Ne=pe*b,Me=Math.min(h-1,i?Math.round(Ne):Math.floor(Ne));H===Me&&(ae+=A[ke+Y])}}m[X+Y]=ae}}}}return n.makeTensorInfo(a.shape,a.dtype,m)}var ZD={kernelName:Kh,backendName:"cpu",kernelFunc:KD};function YD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r;_e(a,"reverse");let i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Lr({inputs:{x:a},backend:n});let l=new Ot(a.shape,a.dtype),u=n.bufferSync(a);for(let c=0;c<l.size;c++){let h=l.indexToLoc(c),d=h.slice();o.forEach(p=>d[p]=a.shape[p]-1-d[p]),l.set(u.get(...d),...h)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var JD={kernelName:Bs,backendName:"cpu",kernelFunc:YD},QD={kernelName:Ko,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(r.shape)),[u,c,h,d]=r.shape,[p,f]=R.getImageCenter(i,c,h),m=255,A=Math.sin(a),y=Math.cos(a),g=o.data.get(r.dataId).values;for(let _=0;_<u;_++){let b=_*h*c*d;for(let w=0;w<c;w++){let x=w*(h*d);for(let N=0;N<h;N++){let S=N*d;for(let T=0;T<d;T++){let M=[u,w,N,T],D=M[2],z=M[1],W=(D-p)*y-(z-f)*A,U=(D-p)*A+(z-f)*y;W=Math.round(W+p),U=Math.round(U+f);let H=s;if(typeof s!="number"&&(T===3?H=m:H=s[T]),W>=0&&W<h&&U>=0&&U<c){let j=U*(h*d),ee=W*d,Y=b+j+ee+T;H=g[Y]}let X=b+x+S+T;l[X]=H}}}}return{dataId:o.write(l,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},eO=it(Vs,e=>{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),tO={kernelName:Vs,backendName:"cpu",kernelFunc:eO};function Iw(e,t,n,r,a,s,i,o,l,u){let c=[r/a,a],h=e.values,d=t.values;if(r===0)return We(n,t.dtype);let p=We(c,t.dtype);p.values.fill(l);for(let f=0;f<s;f++){let m=[],A=0;for(let y=0;y<i;y++){let g=h[f*i+y];m.push(g),A+=g*o[y]}if(A<0||A>=r/a)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<a;y++)u?p.values[A*a+y]+=d[f*a+y]:p.values[A*a+y]=t.rank===0?d[0]:d[f*a+y]}return p}function nO(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=!0,p=n.bufferSync(a),f=n.bufferSync(s),m=Iw(p,f,i,h,u,l,o,c,0,d);return n.makeTensorInfo(i,m.dtype,m.values)}var rO={kernelName:Oo,backendName:"cpu",kernelFunc:nO};function aO(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t;_e([r,a,s],"select");let i=r.shape.length,o=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,u=n.data.get(s.dataId).values,c=nr(a.dtype,s.dtype),h=v.makeZerosTypedArray(v.sizeFromShape(a.shape),c),d=0,p=i===0||i>1||a.shape.length===1?1:v.sizeFromShape(a.shape.slice(1));for(let f=0;f<o.length;f++)for(let m=0;m<p;m++)o[f]===1?h[d++]=l[f]:h[d++]=u[f];return n.makeTensorInfo(a.shape,c,h)}var sO={kernelName:zo,backendName:"cpu",kernelFunc:aO},iO=R.SELU_SCALEALPHA,oO=R.SELU_SCALE,lO=it(Po,e=>e>=0?oO*e:iO*(Math.exp(e)-1)),uO={kernelName:Po,backendName:"cpu",kernelFunc:lO},cO=it(js,e=>1/(1+Math.exp(-e))),hO={kernelName:js,backendName:"cpu",kernelFunc:cO},dO=it(Bo,e=>e<0?-1:e>0?1:0),pO={kernelName:Bo,backendName:"cpu",kernelFunc:dO},fO=it(Hs,e=>Math.sin(e)),mO={kernelName:Hs,backendName:"cpu",kernelFunc:fO},AO=it(Wo,e=>Math.sinh(e)),yO={kernelName:Wo,backendName:"cpu",kernelFunc:AO},gO=11920928955078125e-23,Nw=Math.log(gO)+2,xO=it(Vo,e=>{let t=e>-Nw,n=e<Nw,r=Math.exp(e),a;return n?a=r:t?a=e:a=Math.log(1+r),a}),wO={kernelName:Vo,backendName:"cpu",kernelFunc:xO};function _O(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_e([a],"spaceToBatchND");let o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let u=kw.kernelFunc({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),c=R.getReshaped(u.shape,s,o,!1),h=R.getPermuted(c.length,s.length,!1),d=R.getReshapedPermuted(u.shape,s,o,!1),p=gt({inputs:{x:u},backend:n,attrs:{shape:c}}),f=or({inputs:{x:p},backend:n,attrs:{perm:h}}),m=gt({inputs:{x:f},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}var bO={kernelName:bu,backendName:"cpu",kernelFunc:_O};function vO(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,sliceSize:c,strides:h,outputSize:d}=R.calculateShapes(s,a,o),p=!1,f=n.bufferSync(a),m=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=Iw(f,m,o,d,c,u,l,h,A,p);return n.makeTensorInfo(o,y.dtype,y.values)}var kO={kernelName:Yh,backendName:"cpu",kernelFunc:vO};function IO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),c=a.shape.slice();return l.map(h=>{let d=[...c];d[o]=h;let p=Ai({inputs:{x:a},backend:n,attrs:{begin:u,size:d}});return u[o]+=h,p})}var NO={kernelName:Uo,backendName:"cpu",kernelFunc:IO},SO=it(Gs,e=>Math.sqrt(e)),TO={kernelName:Gs,backendName:"cpu",kernelFunc:SO},CO={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}}},EO=it(Ta,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),RO={kernelName:Ta,backendName:"cpu",kernelFunc:EO};function FO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r;_e(a,"stridedSlice");let{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=cn.sliceInfo(a.shape,s,i,o,l,u,c,h,d),_=gt({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=Ai({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});b=gt({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else{let x=n.bufferSync(_),N=tw(g,x,m,f);b=n.makeTensorInfo(N.shape,N.dtype,N.values)}let w=gt({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(b),w}var MO={kernelName:Ho,backendName:"cpu",kernelFunc:FO},$O=it(jo,e=>Math.tan(e)),DO={kernelName:jo,backendName:"cpu",kernelFunc:$O},OO=it(Ys,e=>Math.tanh(e)),zO={kernelName:Ys,backendName:"cpu",kernelFunc:OO};function PO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;_e(a,"tile");let i=rw(n.bufferSync(a),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var LO={kernelName:Sa,backendName:"cpu",kernelFunc:PO};function WO(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r;_e(a,"topk");let o=n.data.get(a.dataId).values,[l,u]=aw(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var BO={kernelName:Go,backendName:"cpu",kernelFunc:WO};function HO(e){let{inputs:t,attrs:n,backend:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=v.computeStrides(a.shape),g=y[0],_=y[1],b=y[2],w=v.getTypedArrayFromDType(a.dtype,v.sizeFromShape(A));w.fill(l);let x=r.data.get(a.dataId).values,N=r.data.get(s.dataId).values;for(let S=0;S<c;++S){let T=s.shape[0]===1?N:N.subarray(S*8,S*8+8);for(let M=0;M<f;++M)for(let D=0;D<m;++D)for(let z=0;z<p;++z){let W,U=T[6]*D+T[7]*M+1;if(U===0)continue;let H=(T[0]*D+T[1]*M+T[2])/U,X=(T[3]*D+T[4]*M+T[5])/U,j=Sw(H,d,o),ee=Sw(X,h,o);switch(i){case"nearest":W=VO(x,h,d,g,_,b,S,ee,j,z,l);break;case"bilinear":W=UO(x,h,d,g,_,b,S,ee,j,z,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let Y=S*g+M*_+D*b+z;w[Y]=W}return r.makeTensorInfo(A,a.dtype,w)}return{dataId:r.write(w,A,a.dtype),shape:a.shape,dtype:a.dtype}}var jO={kernelName:Jh,backendName:"cpu",kernelFunc:HO};function Sw(e,t,n){switch(n){case"reflect":return GO(e,t);case"wrap":return qO(e,t);case"nearest":return KO(e,t);case"constant":default:return XO(e,t)}}function GO(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=2*t;n<r&&(n=r*Math.trunc(-n/r)+n),n=n<-t?n+r:-n-1}else if(n>t-1)if(t<=1)n=0;else{let r=2*t;n-=r*Math.trunc(n/r),n>=t&&(n=r-n-1)}return v.clamp(0,n,t-1)}function qO(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let r=t-1;n+=t*(Math.trunc(-n/r)+1)}else if(n>t-1)if(t<=1)n=0;else{let r=t-1;n-=t*Math.trunc(n/r)}return v.clamp(0,n,t-1)}function XO(e,t){return e}function KO(e,t){return v.clamp(0,e,t-1)}function nc(e,t,n,r,a,s,i,o,l,u,c){let h=i*r+o*a+l*s+u;return 0<=o&&o<t&&0<=l&&l<n?e[h]:c}function VO(e,t,n,r,a,s,i,o,l,u,c){let h=Math.round(o),d=Math.round(l);return nc(e,t,n,r,a,s,i,h,d,u,c)}function UO(e,t,n,r,a,s,i,o,l,u,c){let h=Math.floor(o),d=Math.floor(l),p=h+1,f=d+1,m=(f-l)*nc(e,t,n,r,a,s,i,h,d,u,c)+(l-d)*nc(e,t,n,r,a,s,i,h,f,u,c),A=(f-l)*nc(e,t,n,r,a,s,i,p,d,u,c)+(l-d)*nc(e,t,n,r,a,s,i,p,f,u,c);return(p-o)*m+(o-h)*A}function ZO(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;_e(s,"unique");let i=r.data.get(s.dataId).values,{outputValues:o,outputShape:l,indices:u}=sw(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var YO={kernelName:Qh,backendName:"cpu",kernelFunc:ZO};function JO(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape.length,o=a.shape[s],l=new Array(i-1),u=0;for(let p=0;p<i;p++)p!==s&&(l[u++]=a.shape[p]);let c=new Array(i).fill(0),h=a.shape.slice();h[s]=1;let d=new Array(o);for(let p=0;p<d.length;p++){c[s]=p;let f=Ai({inputs:{x:a},backend:n,attrs:{begin:c,size:h}});d[p]=gt({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return d}var QO={kernelName:qo,backendName:"cpu",kernelFunc:JO};function ez(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r;_e(a,"unsortedSegmentSum");let o=a.shape.length,l=s.shape.length,u=[],c=[],h=o-l,d=s;for(let f=0;f<h;++f){let m=Qd({inputs:{input:d},backend:n,attrs:{dim:f+1}});d=m,c.push(m)}for(let f=0;f<i;++f){let m=v.createScalarValue(f,"int32"),A=n.makeTensorInfo([],"int32",m),y=gw({inputs:{a:A,b:d},backend:n}),g=Wa({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),_=Rm({inputs:{a:g,b:a},backend:n}),b=ep({inputs:{x:_},backend:n,attrs:{axis:0,keepDims:!1}});u.push(b),c.push(A),c.push(y),c.push(g),c.push(_),c.push(b)}let p=vw({inputs:u,backend:n,attrs:{axis:0}});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var tz={kernelName:ku,backendName:"cpu",kernelFunc:ez},nz=[mF,wR,yF,xF,NR,_F,vF,IF,SF,CF,RF,MF,DF,PF,WF,UF,jF,qF,KF,pF,YF,QF,tM,kR,TR,rM,_R,sM,oM,cM,dM,lM,AM,gM,fM,wM,bM,kM,NM,TM,EM,RM,MM,DM,zM,PM,WM,LM,Om,iF,VM,HM,JM,CR,QM,RR,s$,o$,l$,MR,h$,p$,m$,y$,x$,DR,b$,bR,k$,iM,N$,T$,E$,oF,zR,M$,D$,LR,z$,W$,V$,j$,q$,K$,BR,J$,eD,nD,aD,iD,Z$,uD,hD,UR,pD,AD,wD,jR,qR,vD,ND,CD,KR,RD,MD,$D,kw,PD,uF,JR,WD,vR,VD,cF,hF,dF,HD,GD,XD,ZD,JD,QD,tO,eF,rO,sO,uO,hO,pO,mO,yO,tF,gD,wO,bO,kO,NO,TO,CO,rF,RO,MO,sF,oD,DO,zO,LO,BO,ZR,jO,YO,QO,tz,FD];for(let e of nz)ni(e);var Tw={};ze(Tw,{assertNotComplex:()=>vl,bindCanvasToFramebuffer:()=>sz,bindColorTextureToFramebuffer:()=>rp,bindTextureToProgramUniformSampler:()=>Uw,bindTextureUnit:()=>Ww,bindVertexBufferToProgramAttribute:()=>Lm,callAndCheck:()=>xe,canBeRepresented:()=>Cw,createFragmentShader:()=>Fw,createFramebuffer:()=>Lw,createProgram:()=>Mw,createStaticIndexBuffer:()=>Ow,createStaticVertexBuffer:()=>Dw,createTexture:()=>zw,createVertexShader:()=>Rw,getBatchDim:()=>yi,getExtensionOrThrow:()=>rc,getFramebufferErrorMessage:()=>Hw,getMaxTexturesInShader:()=>qw,getNumChannels:()=>rz,getProgramUniformLocation:()=>Vw,getProgramUniformLocationOrThrow:()=>Bw,getRowsCols:()=>gi,getShapeAs3D:()=>ap,getTextureShapeFromLogicalShape:()=>jw,getWebGLDisjointQueryTimerVersion:()=>Xw,getWebGLErrorMessage:()=>Ew,getWebGLMaxTextureSize:()=>Gw,hasExtension:()=>Kn,isCapableOfRenderingToFloatTexture:()=>Kw,isDownloadFloatTextureEnabled:()=>Zw,isReshapeFree:()=>sc,isWebGLFenceEnabled:()=>Yw,isWebGLVersionEnabled:()=>Bm,linkProgram:()=>$w,resetMaxTextureSize:()=>iz,resetMaxTexturesInShader:()=>oz,unbindColorTextureFromFramebuffer:()=>Wm,unbindTextureUnit:()=>az,validateFramebuffer:()=>ac,validateProgram:()=>np,validateTextureSize:()=>Pw});var xi={},Vm={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function sp(e,t){xi[e]=t}function Wr(e){if(!(e in xi)){let n=lz(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 uz(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 lz(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=uz(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete xi[e]},!1),e===1?t.getContext("webgl",Vm)||t.getContext("experimental-webgl",Vm):t.getContext("webgl2",Vm)}var ic;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(ic||(ic={}));var Zn;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Zn||(Zn={}));var tn;(function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(tn||(tn={}));function oc(e,t){return[t,e]}function cz(e,t){return e*t}function lc(e){let t=v.sizeFromShape(e),n=Math.ceil(t/4);return v.sizeToSquarishShape(n)}function kl(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function hz(e,t){let[n,r]=kl(e,t);return n*r*4}function Um(e,t){let n=e,r,a,s,i,o,l,u,c,h,d;return J().getNumber("WEBGL_VERSION")===2?(r=n.R32F,a=n.R16F,s=n.RGBA16F,i=n.RGBA32F,o=n.RED,u=4,c=1,h=n.HALF_FLOAT,d=n.FLOAT):(r=e.RGBA,a=e.RGBA,s=e.RGBA,i=n.RGBA,o=e.RGBA,u=4,c=4,h=t!=null?t.HALF_FLOAT_OES:null,d=e.FLOAT),l=e.RGBA,{internalFormatFloat:r,internalFormatHalfFloat:a,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:o,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:h,textureTypeFloat:d}}function xe(e,t){let n=t();return J().getBool("DEBUG")&&dz(e),n}function dz(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+Ew(e,t))}var pz=596e-10,fz=65504;function Cw(e){return!!(J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||pz<Math.abs(e)&&Math.abs(e)<fz)}function Ew(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 Rw(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 Fw(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 mz(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var Az=/ERROR: [0-9]+:([0-9]+):/g;function mz(e,t){let n=Az.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let r=+n[1],a=e.split(`
|
|
`),s=a.length.toString().length+2,i=a.map((h,d)=>v.rightPad((d+1).toString(),s)+h),o=0;for(let h=0;h<i.length;h++)o=Math.max(i[h].length,o);let l=i.slice(0,r-1),u=i.slice(r-1,r),c=i.slice(r);console.log(l.join(`
|
|
`)),console.log(t.split(`
|
|
`)[0]),console.log(`%c ${v.rightPad(u[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
|
|
`))}function Mw(e){return la(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function $w(e,t){if(xe(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function np(e,t){if(xe(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function Dw(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function Ow(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),xe(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function rz(){return J().getNumber("WEBGL_VERSION")===2?1:4}function zw(e){return la(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Pw(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${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 Lw(e){return la(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function Lm(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 Ww(e,t,n){Jw(e,n),xe(e,()=>e.activeTexture(e.TEXTURE0+n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function az(e,t){Jw(e,t),xe(e,()=>e.activeTexture(e.TEXTURE0+t)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function Bw(e,t,n){return la(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function Vw(e,t,n){return e.getUniformLocation(t,n)}function Uw(e,t,n,r){xe(e,()=>Ww(e,t,r)),xe(e,()=>e.uniform1i(n,r))}function sz(e){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),xe(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function rp(e,t,n){xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),xe(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function Wm(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: "+Hw(e,t))}function Hw(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 ${t}`}}function la(e,t,n){let r=xe(e,()=>t());if(r==null)throw new Error(n);return r}function Jw(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function yi(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function gi(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function ap(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[yi(e),...gi(e)]),t}function jw(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?v.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let r=v.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=yi(e),s=2,i=2;return e.length&&([s,i]=gi(e)),r=a*(s/2)*(i/2),v.sizeToSquarishShape(r).map(o=>o*2)}return v.sizeToSquarishShape(r)}function ip(e){return e%2==0}function sc(e,t){if(e=e.slice(-2),t=t.slice(-2),v.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||ip(n)&&ip(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&ip(e[0])&&ip(t[0])}var op,lp;function Gw(e){if(op==null){let t=Wr(e);op=t.getParameter(t.MAX_TEXTURE_SIZE)}return op}function iz(){op=null}function oz(){lp=null}function qw(e){if(lp==null){let t=Wr(e);lp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,lp)}function Xw(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 Bm(e){try{if(Wr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function Kw(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 Hm(t)}function Zw(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 Hm(t);let n="EXT_color_buffer_half_float";if(Kn(t,n)){let r=t.getExtension(n);return yz(t,r)}return!1}return Hm(t)}function Hm(e){let t=Um(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 yz(e,t){let n=Um(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 Yw(e){return e!==2?!1:Wr(e).fenceSync!=null}function vl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=J();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>Bm(2)?2:Bm(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",()=>Gw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>qw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Fe.getNumber("WEBGL_VERSION");return e===0?0:Xw(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",()=>Kw(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",()=>Zw(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Yw(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 pn(){let e,t,n,r,a,s,i,o,l,u;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,l="",u=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",r="varying",a="texture2D",s="gl_FragColor",i="",o=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,u=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function wi(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function jm(e){let t=v.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}var Qw=`
|
|
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;
|
|
}
|
|
`,gz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ic.DENSE;let t=lc(e),n=pn();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;
|
|
}
|
|
`}},xz=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ic.DENSE;let t=lc(e),n=pn();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;
|
|
}
|
|
`}},wz=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Zn.DOWNLOAD;let t=pn();this.outputShape=e,this.userCode=`
|
|
${Qw}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},_z=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Zn.DOWNLOAD;let t=pn();this.outputShape=e,this.userCode=`
|
|
${Qw}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},bz=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=pn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${jm(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.);
|
|
}
|
|
`}},vz=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=pn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let u=0;u<=1;u++){let c=l*2+u;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${u} < ${e[2]}) {
|
|
localCoords[2] += ${u};
|
|
if(localCoords[1] + ${l} < ${e[1]}) {
|
|
localCoords[1] += ${l};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${s};
|
|
c = imod(flatIndex, ${s});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${a}.0);
|
|
values = ${r.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${c}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${c}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${c}] = values[2];
|
|
} else {
|
|
result[${c}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${jm(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${r.output} = ${o};
|
|
}
|
|
`}},e_={};ze(e_,{bindVertexProgramAttributeStreams:()=>u_,createBufferFromOutputTexture:()=>d_,createFloat16MatrixTexture:()=>s_,createFloat16PackedMatrixTexture:()=>l_,createFloat32MatrixTexture:()=>a_,createIndexBuffer:()=>r_,createPackedMatrixTexture:()=>o_,createUnsignedBytesMatrixTexture:()=>i_,createVertexBuffer:()=>n_,createVertexShader:()=>t_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>f_,downloadFloat32MatrixFromBuffer:()=>p_,downloadMatrixFromPackedOutputTexture:()=>A_,downloadPackedMatrixFromBuffer:()=>m_,getInternalFormatForFloat16MatrixTexture:()=>qm,getInternalFormatForFloat16PackedMatrixTexture:()=>Zm,getInternalFormatForFloat32MatrixTexture:()=>Gm,getInternalFormatForPackedMatrixTexture:()=>Km,getInternalFormatForUnsignedBytesMatrixTexture:()=>Xm,uploadDenseMatrixToTexture:()=>c_,uploadPixelDataToTexture:()=>h_});function t_(e){let t=pn(),n=`${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 Rw(e,n)}function n_(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 Dw(e,t)}function r_(e){let t=new Uint16Array([0,1,2,2,1,3]);return Ow(e,t)}function uc(e,t,n,r,a,s){Pw(t,n);let i=zw(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 Gm(e){return e.internalFormatFloat}function a_(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,Gm(r),r.textureFormatFloat,e.FLOAT)}function qm(e){return e.internalFormatHalfFloat}function s_(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,qm(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function Xm(e){return e.downloadTextureFormat}function i_(e,t,n,r){let[a,s]=oc(t,n);return uc(e,a,s,Xm(r),e.RGBA,e.UNSIGNED_BYTE)}function Km(e){return e.internalFormatPackedFloat}function o_(e,t,n,r){let[a,s]=kl(t,n);return uc(e,a,s,Km(r),e.RGBA,e.FLOAT)}function Zm(e){return e.internalFormatPackedHalfFloat}function l_(e,t,n,r){let[a,s]=kl(t,n);return uc(e,a,s,Zm(r),e.RGBA,r.textureTypeHalfFloat)}function u_(e,t,n){let r=0,a=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Lm(e,t,"clipSpacePos",n,3,s,r)&&Lm(e,t,"uv",n,2,s,a)}function c_(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 h_(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 d_(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 p_(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 f_(e,t,n,r){let[a,s]=oc(t,n),i=4,o=new Uint8Array(cz(t*n,i));return xe(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function m_(e,t,n,r,a,s,i,o){let l=e,u=new Float32Array(hz(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function A_(e,t,n){let r=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var up=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,sp(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=rc(this.gl,a),Kn(this.gl,s))this.textureHalfFloatExtension=rc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Kn(this.gl,r))this.colorBufferHalfFloatExtension=rc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=n_(this.gl),this.indexBuffer=r_(this.gl),this.framebuffer=Lw(this.gl),this.textureConfig=Um(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(),a_(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),s_(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),i_(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),h_(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),c_(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),l_(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),o_(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Wm(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>f_(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return m_(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return p_(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=d_(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,()=>A_(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Fw(t,e),r=t_(t),a=Mw(t);return xe(t,()=>t.attachShader(a,r)),xe(t,()=>t.attachShader(a,n)),$w(t,a),this.debug&&np(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=u_(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&np(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?Bw(this.gl,e,t):Vw(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(),Uw(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=kl(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&np(this.gl,this.program),ac(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rc(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=kz(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),rp(this.gl,e,this.framebuffer),this.debug&&ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rp(this.gl,this.outputTexture,this.framebuffer),this.debug&&ac(this.gl)):Wm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;rp(r,e,this.framebuffer),this.debug&&ac(r),this.outputTexture=e,xe(r,()=>r.viewport(0,0,t,n)),xe(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function kz(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:y_}=R;function Mz(e,t,n,r){let a=[];e.forEach(p=>{let f=v.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
|
|
`),i=e.map(p=>Iz(p,t,r)).join(`
|
|
`),o=t.texShape,l=pn(),u=Tz(l),c,h,d=Rz(l);return t.isPacked?(c=Nz(t.logicalShape,o),h=Ez(l)):(c=Sz(t.logicalShape,o),h=Cz(l)),r&&(d+=Fz),[d,u,h,s,c,i,n].join(`
|
|
`)}function Il(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return $z(e);case 1:return Dz(e);case 2:return Oz(e);case 3:return zz(e);case 4:return Pz(e);case 5:return Lz(e);case 6:return Wz(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function g_(e){switch(e.shapeInfo.logicalShape.length){case 0:return Bz(e);case 1:return Vz(e);case 2:return Uz(e);case 3:return Hz(e);default:return jz(e)}}function Iz(e,t,n=!1){let r="";n?r+=g_(e):r+=Il(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=Gz(e,t):r+=qz(e,t)),r}function Nz(e,t){switch(e.length){case 0:return x_();case 1:return Xz(e,t);case 2:return Yz(e,t);case 3:return Kz(e,t);default:return Zz(e,t)}}function Sz(e,t){switch(e.length){case 0:return x_();case 1:return Jz(e,t);case 2:return rP(e,t);case 3:return Qz(e,t);case 4:return eP(e,t);case 5:return tP(e,t);case 6:return nP(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Tz(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function Cz(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function Ez(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function Rz(e){return`${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);
|
|
}
|
|
|
|
${aP}
|
|
${sP}
|
|
${iP}
|
|
`}var aP=`
|
|
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);
|
|
}
|
|
`,sP=`
|
|
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);
|
|
}
|
|
`,iP=`
|
|
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);
|
|
}
|
|
`,Fz=`
|
|
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 x_(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function Xz(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 Jz(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function Kz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function Qz(e,t){let n=wi(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function Zz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
|
|
int b${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}(${o});
|
|
}
|
|
`}function eP(e,t){let n=wi(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function tP(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function nP(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function Yz(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function rP(e,t){return v.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function _i(e){return`offset${e}`}function Bz(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=pn();return`
|
|
vec4 ${n}() {
|
|
return ${r.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function $z(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 Vz(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=pn();return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${a[0]}, ${a[1]}, index);
|
|
return ${s.texture2D}(${t}, uv);
|
|
}
|
|
`}function Dz(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 Uz(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=pn();if(a!=null&&v.arraysEqual(t,a))return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
|
|
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(t[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function Oz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:i}=v.squeezeShape(t),o=s;if(o.length<t.length){let h=Sl(e,o),d=["row","col"];return`
|
|
${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],u=a[1],c=_i(n);return u===1?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:l===1?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${t[1]} + col + ${c};
|
|
vec2 uv = uvFromFlat(${l}, ${u}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Hz(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`
|
|
${g_(p)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${Tl(f,d)});
|
|
}
|
|
`}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),u=l*Math.ceil(t[1]/2),c=pn();return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${u}, ${l}, b, row, col);
|
|
return ${c.texture2D}(${n}, uv);
|
|
}
|
|
`}function zz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Sl(e,l),m=["row","col","depth"];return`
|
|
${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 u=e.shapeInfo.texShape,c=u[0],h=u[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===s&&d==null)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let p=_i(n);return`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${s} + depth + ${p};
|
|
vec2 uv = uvFromFlat(${c}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function jz(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],u=Math.ceil(t[n-1]/2),c=u*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,c*=t[n-f-1],d=`b${f} * ${c} + `+d;let p=pn();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 Pz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Sl(e,o),m=["row","col","depth","depth2"];return`
|
|
${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 u=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],d=c[1];if(d===i&&u==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(d===a&&u==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${t[1]*t[2]}, ${t[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let p=_i(n);return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${s} +
|
|
depth * ${a} + depth2;
|
|
vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Lz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=Sl(e,l),A=["row","col","depth","depth2","depth3"];return`
|
|
${Il(m)}
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${r}(${Tl(A,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${a})) +
|
|
depth3;
|
|
${Nl(e)}
|
|
}
|
|
`;let c=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&c==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(p===a&&c==null)return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=_i(n);return`
|
|
float ${r}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} + depth * ${s} +
|
|
depth2 * ${a} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Wz(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Sl(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${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,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Nl(e)}
|
|
}
|
|
`;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===c&&h==null)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===i&&h==null)return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=_i(n);return`
|
|
float ${r}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${c} + col * ${u} + depth * ${l} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${p}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Nl(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function Gz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=y_(e.shapeInfo.logicalShape,t.logicalShape),l=ut(i),u=i-s,c,h=["x","y","z","w","u","v"];s===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(A=>`coords.${h[A+u]} = 0;`).join(`
|
|
`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+u]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(f&&!m)i===1?p=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:p=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${a}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${r}(${d});
|
|
${p}
|
|
}
|
|
`}function qz(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
|
|
float ${a}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let u=ut(l),c=y_(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${p[m+h]} = 0;`).join(`
|
|
`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
|
|
float ${a}() {
|
|
${u} coords = getOutputCoords();
|
|
${d}
|
|
return get${r}(${f});
|
|
}
|
|
`}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Sl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Tl(e,t){return t.map(n=>e[n]).join(", ")}function oP(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=Mz(s,o,a,t.packedInputs),u=e.createProgram(l),c=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(u,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(u,f,m),d[`offset${f}`]=e.getUniformLocation(u,`offset${f}`,m)}return{program:t,source:l,webGLProgram:u,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:c,nanLoc:h}}function w_(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function lP(e,t,n,r,a){w_(t.inShapeInfos,n),w_([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let u=t.program.variableNames[l],c=t.uniformLocations[u],h=t.uniformLocations[`offset${u}`];if(c!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(c,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(c,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,c,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function uP(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}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:cP,bincountImpl:__,bincountReduceImpl:hP,ceilImpl:dP,concatImpl:pP,expImpl:fP,expm1Impl:mP,floorImpl:AP,gatherV2Impl:yP,greaterImpl:gP,lessImpl:xP,linSpaceImpl:wP,logImpl:_P,maxImpl:bP,maximumImpl:vP,minimumImpl:kP,multiplyImpl:IP,negImpl:NP,prodImpl:SP,rangeImpl:TP,rsqrtImpl:CP,simpleAbsImpl:b_,sliceImpl:EP,stridedSliceImpl:RP,subImpl:FP,tileImpl:MP,topKImpl:$P,transposeImpl:Ym,uniqueImpl:DP}=km;function v_(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function fn(e,t){return t===1?[e]:v_(e,t)}function OP(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 WP=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=fn("rc",t),r=ut(t),a=zP(t,e,n),s=PP(t,e[e.length-1],e[e.length-2],n),i=LP(e,n);this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
|
|
if(${a}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function BP(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 zP(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 PP(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 LP(e,t){let n=e.length,r=BP(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 k_=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=`
|
|
${VP(t)}
|
|
${jm(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function VP(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${wi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var UP=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=N_(t,n),a=S_(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=I_(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=N_(n,r),s=S_(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=I_(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function HP(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 I_(e,t,n,r,a){let s=jP(t,r),i;if(a){let[l,u]=kl(e[0],e[1]);i=l*u}else{let[l,u]=oc(e[0],e[1]);i=l*u}let o=HP(n,s);return i*o}function jP(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return Km(t);case tn.PACKED_2X2_FLOAT16:return Zm(t);case tn.UNPACKED_FLOAT32:return Gm(t);case tn.UNPACKED_FLOAT16:return qm(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return Xm(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function GP(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function N_(e,t){if(e===Zn.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===Zn.RENDER||e==null)return GP(t);if(e===Zn.DOWNLOAD||e===Zn.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function S_(e,t,n){return`${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);
|
|
}
|
|
`}},wr="if (isnan(x)) return x;",qP="return x;",T_="return abs(x);",XP="return (x >= 0.0) ? x : (exp(x) - 1.0);",KP=wr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,ZP=wr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,cp="return x;",YP="return x;",JP=`
|
|
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;
|
|
`,QP=`
|
|
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;
|
|
`,eL=`
|
|
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) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},tL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=fn("rc",t),r=ut(t),a=OP(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${a});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},nL=Pr.whereImpl,rL=1e-7,aL=1e-4,Jm={};function sL(e){return e in Jm||(Jm[e]={}),Jm[e]}var iL=128,oL=600;function lL(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*oL/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=sL(J().getNumber("WEBGL_VERSION")),this.gpgpu=new up(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new UP(this.gpgpu),this.numMBBeforeWarning=lL(),this.texData=new xh(this,Fr())}nextDataId(){return El.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:Zn.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:Zn.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Cl(i,cp):h=new Ba(i,cp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);c=R.mergeRealAndImagArrays(h,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Cl(r,cp):p=new Ba(r,cp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let p=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...lc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];c=R.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}u!=null&&this.disposeIntermediateTensorInfo(u);let h=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Fr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!Cw(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...lc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?ap(t):t,o=s?new _z(i):new wz(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Fr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=iL){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return nL(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=b_(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,T_,e.dtype);let t=new Ba(e.shape,T_),n=this.compileAndRun(t,[e]);return Fr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Fr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new tL(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new WP(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 k_(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=ap(r),i;n?i=new xz(s):i=new gz(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===ic.DENSE){let m=lc(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!sc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let u={shape:s.shape,texData:i,isUniform:!1},c=uP(e,l,u),h=this.getAndSaveBinary(c,()=>oP(this.gpgpu,e,l,u)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),lP(this.gpgpu,h,l,u,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(be(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?rL:aL}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=jw(n,o),t.texShape=c),a!=null){let h=ap(n),d,p=c[1],f=c[0],m=a instanceof Uint8Array;o?([p,f]=kl(c[0],c[1]),d=new vz(h,[f,p],m)):d=new bz(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=Zn.PIXELS:this.texData.get(A.dataId).usage=Zn.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),_=this.texData.get(g.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(c,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=uL(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};El.nextDataId=0;function uL(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 C_="3.3.0";function E_(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Mu.isBrowser()&&sl("webgl",()=>new El,2);var cL={forceHalfFloat:E_},R_=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Rl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},hp=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`,cc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${ut(a)} coords = getOutputCoords();
|
|
`,a===1)s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=fn("coords",a);s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function zn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var hL={kernelName:ks,backendName:"webgl",kernelFunc:zn};function Va(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=zn({inputs:{x:r},backend:n}),l=zn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var dL={kernelName:Th,backendName:"webgl",kernelFunc:Va},F_="return (a < 0.) ? b * a : a;",M_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function pL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(M_,a.shape,i.shape):new Rl(F_,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var fL={kernelName:Is,backendName:"webgl",kernelFunc:pL},$_="return (a < 0.) ? b * a : a;",D_=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function mL(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(D_,r.shape,a.shape):new Rl($_,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var AL={kernelName:zs,backendName:"webgl",kernelFunc:mL},O_="if (isnan(x)) return x;",yL=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,gL=`
|
|
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 qe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Cl(i.shape,t):c=new Ba(i.shape,e),o.runWebGLProgram(c,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,c=o;if(r&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(_=>{let[b,w]=_,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:w.dataId,dtype:w.dtype,shape:u.shape},S=new Rl(e,l.shape,u.shape);return c.runWebGLProgram(S,[x,N],nr(b.dtype,w.dtype))}),g=Va({inputs:{real:A,imag:y},backend:c});return c.disposeIntermediateTensorInfo(A),c.disposeIntermediateTensorInfo(y),g}let h=s||nr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&a!=null){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[A,y]=a(l.shape,u.shape,f.values,m.values,h),g=c.makeTensorInfo(y,h),_=c.texData.get(g.dataId);return _.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new cc(t,l.shape,u.shape,n):p=new Rl(e,l.shape,u.shape),c.runWebGLProgram(p,[l,u],h)}}function dp(e,t=!1){if(e==="linear")return t?YP:qP;if(e==="relu")return t?QP:KP;if(e==="elu")return t?JP:XP;if(e==="relu6")return t?eL:ZP;if(e==="prelu")return t?D_:$_;if(e==="leakyrelu")return t?M_:F_;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var z_=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",_="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
|
|
const float sharedDimension = ${c}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${c}; i++) {
|
|
int batchA = ${g};
|
|
int batchB = ${_};
|
|
vec4 a = getMatrixA(batchA, ${h});
|
|
vec4 b = getMatrixB(batchB, ${d});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${p[0]} * ${f[0]});
|
|
result += (${p[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${A}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},P_={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},L_=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},W_="return a * b;";function B_(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),u=new L_(P_.REAL,r.shape,a.shape),c=new L_(P_.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Va({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[u,c]=IP(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(c,s),d=n.texData.get(h.dataId);return d.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new cc(W_,r.shape,a.shape):i=new Rl(W_,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var xL={kernelName:Ms,backendName:"webgl",kernelFunc:B_};function wL(e,t,n){let r=[yi(e.shape),...gi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[yi(t),...gi(t)],i=new k_(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(a.dataId);return c.isPacked&&!sc(a.shape,l)&&!(c.texture!==null&&sc(c.shape,l))?wL(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var _L={kernelName:Do,backendName:"webgl",kernelFunc:Ae},V_=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";a%n>0&&(u=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${u}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},bL=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,h=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
}
|
|
`,d="vec4";t==="all"?(i="1.0",h=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,d="bvec4"):t==="any"&&(i="0.0",h=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,d="bvec4");let p="";a%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${p}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${h}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${c===1}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
} else if (${c===2}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
} else if (${c===3}) {
|
|
${d} values = ${d}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${h}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function vL(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function bi(e,t,n,r){let a=vL(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],c,h;n==="mean"?c=i===0?new V_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new V_({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):c=new bL({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},n),h=s,s=r.runWebGLProgram(c,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var IL=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=kL(t);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function kL(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 NL=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ut(this.rank),a=v_("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${r} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${o}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${a[this.rank-1]};
|
|
if(++${a[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${o}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function pp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new NL(e.shape,t):new IL(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function SL(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),u=l!=null,c=e;u&&(c=pp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(c.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=Ae({inputs:{x:c},attrs:{shape:[m,f]},backend:r}),y=sd(e.dtype),g=bi(A,y,"sum",r),_=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),u&&r.disposeIntermediateTensorInfo(c),_}function Qm(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return SL(a,s,i,n)}var TL={kernelName:qs,backendName:"webgl",kernelFunc:Qm};function In(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let c=0;c<l.length;c++)l[c]=a.shape[s[c]];let u;if(i.shouldExecuteOnCPU([a])){let c=i.texData.get(a.dataId).values,h=Ym(c,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(u.dataId);d.values=h}else u=pp(a,s,i);return u}var CL={kernelName:Js,backendName:"webgl",kernelFunc:In},U_=1e3;function fp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,h=n?e.shape[u-2]:e.shape[u-1],d=r?t.shape[c-1]:t.shape[c-2],p=n?e.shape[u-1]:e.shape[u-2],f=r?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),_=y===g||y===1||g===1;v.assert(u>=2&&c>=2&&_,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),S=Ae({inputs:{x:t},backend:a,attrs:{shape:x}}),T=[N,S],M=Math.max(y,g),D=n?N.shape[1]:N.shape[2],z=s!=null,W=i!=null,U=l==="leakyrelu",H=l!=null?dp(l,!0):null,X=z||W||U||H!=null,j;if((p===1||f===1)&&D>U_&&X===!1){let Y=N,ae=S;n&&(Y=In({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),T.push(Y)),r&&(ae=In({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),T.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[M,D,1]}}),T.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[M,1,D]}}),T.push(oe));let fe=B_({inputs:{a:Q,b:oe},backend:a});j=Qm({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),T.push(fe)}else{let Y=nr(e.dtype,t.dtype),ae=new z_(w,x,[M,p,f],n,r,z,H,W,U),te=[N,S];if(s!=null&&te.push(s),W&&te.push(i),U){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),T.push(ie)}j=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:j},backend:a,attrs:{shape:b}});T.push(j);for(let Y of T)a.disposeIntermediateTensorInfo(Y);return ee}function EL(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return fp({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}var RL={kernelName:Qs,backendName:"webgl",kernelFunc:EL},H_="return abs(x);";function FL(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=b_(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Cl(r.shape,H_):a=new Ba(r.shape,H_),n.runWebGLProgram(a,[r],r.dtype)}var ML={kernelName:Zi,backendName:"webgl",kernelFunc:FL},$L=wr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,DL=qe({opSnippet:$L}),OL={kernelName:Yi,backendName:"webgl",kernelFunc:DL},zL=wr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,PL=qe({opSnippet:zL}),LL={kernelName:Ji,backendName:"webgl",kernelFunc:PL},j_="return a + b;",WL=nn({opSnippet:j_,packedOpSnippet:j_,supportsComplex:!0,cpuKernelImpl:cP}),BL={kernelName:Ia,backendName:"webgl",kernelFunc:WL},VL=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${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 = ${r};
|
|
setOutput(result);
|
|
}
|
|
`}},UL=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${r};
|
|
setOutput(result);
|
|
}
|
|
`}};function mp(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return zn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=mp({inputs:r.slice(0,o),backend:n}),u=mp({inputs:r.slice(o),backend:n});return mp({inputs:[l,u],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>nr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new UL(r[0].shape,s):new VL(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var HL={kernelName:os,backendName:"webgl",kernelFunc:mp};function jL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("all",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=bi(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var GL={kernelName:vh,backendName:"webgl",kernelFunc:jL};function qL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,o)),R.assertAxesAreInnerMostDims("any",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=bi(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var XL={kernelName:kh,backendName:"webgl",kernelFunc:qL},KL=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 ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},ZL=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ut(o),u=fn("coords",o),c,h;if(s===1){h=o+1;let N=ut(h);c=`
|
|
${N} sourceLocR = ${N}(${u.join()}, 0);
|
|
++${u[o-1]};
|
|
${N} sourceLocG = ${N}(${u.join()}, 0);
|
|
++${u[o-2]};
|
|
${N} sourceLocA = ${N}(${u.join()}, 0);
|
|
--${u[o-1]};
|
|
${N} sourceLocB = ${N}(${u.join()}, 0);
|
|
--${u[o-2]};`}else h=o,c=`
|
|
${l} sourceLocR = coords;
|
|
++${u[o-1]};
|
|
${l} sourceLocG = coords;
|
|
++${u[o-2]};
|
|
${l} sourceLocA = coords;
|
|
--${u[o-1]};
|
|
${l} sourceLocB = coords;
|
|
--${u[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=fn("sourceLocR",h-1).concat("inIdx.r"),A=fn("sourceLocG",h-1).concat("inIdx.g"),y=fn("sourceLocB",h-1).concat("inIdx.b"),g=fn("sourceLocA",h-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",b=r?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${g.join()})));`,w=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,x=r?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${d.join()}),
|
|
vec2(${d.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${d.join()}),
|
|
vec2(${d.slice(-2).join()}));
|
|
}
|
|
${x}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};
|
|
${c}
|
|
ivec4 srcIdx = ivec4(sourceLocR${p}, sourceLocG${p},
|
|
sourceLocB${p}, sourceLocA${p}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${w};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${w};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${_}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function G_(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new KL(o,n,r==null),u=[t];r!=null&&u.push(r);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let h=G_(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function q_(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new ZL(a,i,n,r==null),l=r==null?[t]:[t,r],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let c=q_(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function X_(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),u=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(u);let c=G_(e,u,r);s.push(c);let h=Ae({inputs:{x:c},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return q_(e,t,r)}function YL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let c=X_(n,l,i[0],"max");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var JL={kernelName:ls,backendName:"webgl",kernelFunc:YL};function QL(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=In({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let c=X_(n,l,i[0],"min");return u.forEach(h=>n.disposeIntermediateTensorInfo(h)),c}var eW={kernelName:lu,backendName:"webgl",kernelFunc:QL},tW=wr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,nW=qe({opSnippet:tW}),rW={kernelName:Qi,backendName:"webgl",kernelFunc:nW},aW=wr+"return log(x + sqrt(x * x + 1.0));",sW=qe({opSnippet:aW}),iW={kernelName:eo,backendName:"webgl",kernelFunc:sW},oW=wr+`
|
|
return atan(x);
|
|
`,lW=qe({opSnippet:oW}),uW={kernelName:to,backendName:"webgl",kernelFunc:lW},cW=yL+`
|
|
return atan(a, b);
|
|
`,hW=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+gL+`
|
|
return result;
|
|
`,dW=nn({opSnippet:cW,packedOpSnippet:hW}),pW={kernelName:ro,backendName:"webgl",kernelFunc:dW},fW=wr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,mW=qe({opSnippet:fW}),AW={kernelName:no,backendName:"webgl",kernelFunc:mW},hc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${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 < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h};
|
|
wC += ${u}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${N} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let g="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let b=Math.floor(s/4)*4,w=s%4,x=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${g}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${d}, ${p});
|
|
const float initializationValue = ${y};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${c};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
getValue(batch, xR, xC + 3 * ${u}, d)
|
|
);
|
|
|
|
${x}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${w===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${x}
|
|
} else if (${w===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${x}
|
|
} else if (${w===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${u}, d),
|
|
getValue(batch, xR, xC + 2 * ${u}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${x}
|
|
}
|
|
}
|
|
setOutput(${_});
|
|
}
|
|
`}},eA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",_="0.0";if(g||(_="-1.0 / 1e-20"),n){let T=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${A}, ${y});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${d};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${c}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${h}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xD, xR, xC, ch);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${T} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${p} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,S=`
|
|
if (${g}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${A}, ${y});
|
|
const float initializationValue = ${_};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${_});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${d};
|
|
wD += ${u}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${c}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${x}; wC += 4) {
|
|
int xC = xCCorner + wC * ${h};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${h}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${h}, ch)
|
|
);
|
|
|
|
${S}
|
|
}
|
|
|
|
int xC = xCCorner + ${x};
|
|
if (${N===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${S}
|
|
} else if (${N===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${h}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${S}
|
|
} else if (${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
|
|
);
|
|
|
|
${S}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
}
|
|
`}};function yW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;vl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return zn({inputs:{x:a},backend:n});let h=new hc(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var gW={kernelName:us,backendName:"webgl",kernelFunc:yW};function xW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,l,u),d=new eA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var wW={kernelName:uu,backendName:"webgl",kernelFunc:xW},_W=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${u}, ${c});
|
|
const float avgMultiplier = float(${h});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${o};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${r}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},bW=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=c-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${f}, ${m});
|
|
const float avgMultiplier = float(${A});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${o}) {
|
|
float dyD = float(dyDCorner + wD) / ${a}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
wC += ${u}) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function vW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new bW(d);return n.runWebGLProgram(p,[a],i.dtype)}var kW={kernelName:Nh,backendName:"webgl",kernelFunc:vW};function IW(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;vl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=r,c=R.computePool2DInfo(i.shape,o,l,1,u),h=new _W(c);return n.runWebGLProgram(h,[a],i.dtype)}var NW={kernelName:Ih,backendName:"webgl",kernelFunc:IW};function SW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return fp({a,b:s,transposeA:i,transposeB:o,backend:n})}var TW={kernelName:cs,backendName:"webgl",kernelFunc:SW},CW=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${o};
|
|
float inv = scale * inversesqrt(variance + float(${s}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},EW=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${o};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},RW=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[r,a,s],c=null;i!=null&&(c=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new EW(r.shape,a.shape,s.shape,c,h,l):new CW(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(d,u,u[0].dtype)},FW={kernelName:bs,backendName:"webgl",kernelFunc:RW},$W=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,r=MW(this.rank),a,s=e.map((i,o)=>`sourceLoc.${tA[o]} = start[${o}] + coords.${tA[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)}}},tA=["x","y","z","w","u","v"];function MW(e){if(e===1)return"sourceLoc";if(e<=6)return tA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var DW=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=fn("coords",this.rank),r=fn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
|
|
result.x = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${r[this.rank-1]};
|
|
result.y = ${s};
|
|
--${r[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${r[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${r[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${r[c]} = ${n[c]} + start[${c}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function OW(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=cn.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function dc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=cn.parseSliceParams(a,s,i);if(cn.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=EP(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:u}=n.texData.get(a.dataId),c=cn.isSliceContinous(a.shape,o,l);if(u||!c){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new DW(l):new $W(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),OW(a,o,l,n)}var zW={kernelName:Lo,backendName:"webgl",kernelFunc:dc},PW=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,_)=>g*_),l=R.getReshaped(a.shape,s,o),u=R.getPermuted(l.length,s.length),c=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(c,i,s.length),p=[],f=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),m=In({inputs:{x:f},backend:n,attrs:{perm:u}}),A=Ae({inputs:{x:m},backend:n,attrs:{shape:c}}),y=dc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},LW={kernelName:cu,backendName:"webgl",kernelFunc:PW};function WW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=__(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}var BW={kernelName:Sh,backendName:"webgl",kernelFunc:WW},VW="return float(a != b);",K_=nn({opSnippet:VW,dtype:"bool"}),UW={kernelName:So,backendName:"webgl",kernelFunc:K_};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 HW={kernelName:Xh,backendName:"webgl",kernelFunc:pc},jW="return float(int(x));";function GW(e,t){let n=new Ba(e.shape,jW),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function nA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return zn({inputs:{x:a},backend:n});let i=Et(a.shape),o=nA({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=nA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=zn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return GW(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=K_({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 qW={kernelName:hs,backendName:"webgl",kernelFunc:nA},Z_="return ceil(x);",XW=qe({opSnippet:Z_,packedOpSnippet:Z_,cpuKernelImpl:dP}),KW={kernelName:ds,backendName:"webgl",kernelFunc:XW},ZW=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)}}},YW=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 JW(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 YW(a.shape):o=new ZW(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var QW={kernelName:Na,backendName:"webgl",kernelFunc:JW},eB=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 Y_(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function tB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new eB(r.shape),i=[Y_(r,a.complexTensorInfos.real),Y_(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var nB={kernelName:hu,backendName:"webgl",kernelFunc:tB},rB=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},aB=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ut(r),s=fn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),c=i.join(),h=`if (${l} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${c}), vec2(${u.join()}));
|
|
}`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
|
|
if (${l} < ${o[f]} && ${l} >= ${o[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${Ap(i,l,m)}),
|
|
vec2(${Ap(u,l,m)}));
|
|
}`}let d=o.length,p=o[o.length-1];h+=`
|
|
return getChannel(
|
|
getT${d}(${Ap(i,l,p)}),
|
|
vec2(${Ap(u,l,p)}));`,this.userCode=`
|
|
float getValue(${i.map(f=>"int "+f)}) {
|
|
${h}
|
|
}
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[r-1]} = ${s[r-1]} + 1;
|
|
if (${s[r-1]} < ${n[r-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[r-2]} = ${s[r-2]} + 1;
|
|
if (${s[r-2]} < ${n[r-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[r-1]} = ${s[r-1]} - 1;
|
|
if (${s[r-2]} < ${n[r-2]} &&
|
|
${s[r-1]} < ${n[r-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Ap(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function yp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return zn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var sB={kernelName:Bh,backendName:"webgl",kernelFunc:yp};function Fl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let u=e.map(f=>pc({inputs:{input:f},backend:n})),c=e.map(f=>yp({inputs:{input:f},backend:n})),h=Fl(u,t,n),d=Fl(c,t,n),p=Va({inputs:{real:h,imag:d},backend:n});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),c.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:u,outShape:c}=J_(e,t,n),h=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=u[0].shape[0]===1,p=pP(h,c,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),c=Fl(e.slice(0,u),t,n),h=Fl(e.slice(u),t,n),d=Fl([c,h],t,n);return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new aB(e.map(c=>c.shape),t);return n.runWebGLProgram(u,e,r)}let{tensors2D:a,outShape:s}=J_(e,t,n),i=new rB(a.map(u=>u.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function J_(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function Q_(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return zn({inputs:{x:o[0]},backend:n});let l=o.map(u=>u.shape);return R.assertParamsConsistent(l,s),Fl(o,s,n)}var iB={kernelName:ao,backendName:"webgl",kernelFunc:Q_},eb=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,_="",b="";n&&(r?_=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?_=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:_=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${_}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${l});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${g}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${A}], coords[${y}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${p}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${p}) *
|
|
getW(wR, wC, ${p}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${p}, xR, xC) *
|
|
getW(wR, wC, ${p}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${p}, d2),
|
|
getW(wR, wC, ${p} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${p}),
|
|
getX(batch, xR, xC, ${p} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${p}, xR, xC),
|
|
getX(batch, ${p} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${p}, d2),
|
|
getW(wR, wC, ${p} + 1, d2),
|
|
getW(wR, wC, ${p} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${p}),
|
|
getX(batch, xR, xC, ${p} + 1),
|
|
getX(batch, xR, xC, ${p} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${p}, xR, xC),
|
|
getX(batch, ${p} + 1, xR, xC),
|
|
getX(batch, ${p} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${w}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},oB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${a}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${r});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d2 = coords.u;
|
|
|
|
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xFCorner = xFRCCorner.x;
|
|
int xRCorner = xFRCCorner.y;
|
|
int xCCorner = xFRCCorner.z;
|
|
|
|
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
|
|
// y(yF, yR, yC, d2). ? = to be determined. : = across all
|
|
// values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${c}; wF++) {
|
|
int xF = xFCorner + wF * ${o};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${p}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${p}) *
|
|
getW(wF, wR, wC, ${p}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${p}),
|
|
getX(batch, xF, xR, xC, ${p} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${p}, d2),
|
|
getW(wF, wR, wC, ${p} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${p}),
|
|
getX(batch, xF, xR, xC, ${p} + 1),
|
|
getX(batch, xF, xR, xC, ${p} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${p}, d2),
|
|
getW(wF, wR, wC, ${p} + 1, d2),
|
|
getW(wF, wR, wC, ${p} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},lB=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=pn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,_="";for(let b=0;b<=1;b++)for(let w=0;w<=1;w++)_+=`
|
|
blockIndex = rc.y + ${w};
|
|
pos = rc.x + ${b};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${l})) * ${i} - ${p};
|
|
d0 = offsetY + ${c} * (pos / ${f});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
|
|
d1 = offsetX + ${u} * (int(mod(float(pos), ${f}.) / ${a}.));
|
|
|
|
if(d1 < ${t[g]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${a}.));
|
|
|
|
if (${A}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${b*2+w}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${b*2+w}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${_}
|
|
|
|
${m.output} = result;
|
|
}
|
|
`}};function tb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&c>U_,_=l[2]%2!=0&&!!u.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!_){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=fp({a:w,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(w),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(sc(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let N=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let S=fp({a:w,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=r.texData.get(S.dataId);v.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=x,T.shape=n.outShape,A=zn({inputs:{x:S},backend:r}),A.shape=n.outShape,y.push(S)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function nb({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*u*c,A=d*h,y=[m,A],g=!0,_=!1,b=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(x);let N=new lB(y,w.shape,n),S=r.runWebGLProgram(N,[w],"float32"),T=Ae({inputs:{x:S},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(S),b.push(T);let M=a!=null,D=s!=null,z=o==="leakyrelu",W=o?dp(o,!0):null,U=new z_(T.shape,x.shape,[1,A,n.outChannels],g,_,M,W,D,z),H=[T,x];if(a&&H.push(a),D&&H.push(s),z){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));H.push(Y),b.push(Y)}let X=r.runWebGLProgram(U,H,"float32"),j=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:j}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function uB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=tb({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=nb({x:a,filter:s,convInfo:d,backend:n});else{let m=new eb(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 cB={kernelName:ps,backendName:"webgl",kernelFunc:uB},hB=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);
|
|
}
|
|
`}},dB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${c}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${r}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${s}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},pB=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},fB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${l}, ${u});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${a}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${r}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${r} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function mB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,c,i,1,o,u,!1,h),p=new hB(d);return n.runWebGLProgram(p,[a,s],"float32")}var AB={kernelName:Ch,backendName:"webgl",kernelFunc:mB};function yB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=R.convertConv2DDataFormat(u),d=R.computeConv2DInfo(i,s.shape,o,1,l,c,!1,h),p=new dB(d);return n.runWebGLProgram(p,[a,s],"float32")}var gB={kernelName:fs,backendName:"webgl",kernelFunc:yB};function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeConv3DInfo(a.shape,s.shape,i,l,o),c=new oB(u);return n.runWebGLProgram(c,[a,s],"float32")}var wB={kernelName:du,backendName:"webgl",kernelFunc:xB};function _B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=R.computeConv3DInfo(a.shape,l,i,1,o),c=new pB(u);return n.runWebGLProgram(c,[a,s],"float32")}var bB={kernelName:Eh,backendName:"webgl",kernelFunc:_B};function vB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=R.computeConv3DInfo(l,s.shape,o,1,i),c=new fB(u);return n.runWebGLProgram(c,[a,s],"float32")}var kB={kernelName:Rh,backendName:"webgl",kernelFunc:vB},IB=O_+`
|
|
return cos(x);
|
|
`,NB=qe({opSnippet:IB}),SB={kernelName:ms,backendName:"webgl",kernelFunc:NB},TB=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,CB=qe({opSnippet:TB}),EB={kernelName:so,backendName:"webgl",kernelFunc:CB},RB=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,_,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${g});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${s}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${A};
|
|
float width_scale = ${_};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${p} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${a}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${d} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},FB=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new RB(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")},MB={kernelName:io,backendName:"webgl",kernelFunc:FB},sb=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${rb(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${ut(r)} coords = getOutputCoords();
|
|
int end = ${ab(r,"coords")};
|
|
float val = ${a};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${ab(r,"coords")} = idx;
|
|
val += getX(${rb(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 rb(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 ${e} is not yet supported`)}function ab(e,t){if(e===1)return`${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 $B(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=R.getAxesPermutation([s],l),c=a;u!=null&&(c=In({inputs:{x:a},backend:n,attrs:{perm:u}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=c.shape[h],p=zn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new sb(c.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new sb(c.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(u!=null){let f=R.getUndoAxesPermutation(u),m=In({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),m}return p}var DB={kernelName:As,backendName:"webgl",kernelFunc:$B};function OB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=__(l,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}else if(a.shape.length===2){let l=n.bufferSync(a),u=n.bufferSync(s),c=hP(l,u,i,o);return n.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var zB={kernelName:Fh,backendName:"webgl",kernelFunc:OB},PB=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 LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new PB(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var WB={kernelName:oo,backendName:"webgl",kernelFunc:LB},ib=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?A=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:A=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${u}, ${c});
|
|
const ivec2 pads = ivec2(${o}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${m};
|
|
int q = d2 - d1 * ${m};
|
|
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${h};
|
|
|
|
if (xR < 0 || xR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
if (xC < 0 || xC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${g}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},ob=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let w=0;w<f;w++)A+=`
|
|
vec4 xTexelR${b}C${w*2} = vec4(0.);
|
|
vec4 wR${b}C${w} = vec4(0.);
|
|
vec4 xR${b}C${w} = vec4(0.);`;for(let b=0;b<p;b++)for(let w=0;w<m;w++){let x=w*2;if(A+=`
|
|
xR = xRCorner + ${b*h};
|
|
xC = xCCorner + ${x*d};
|
|
`,c===1){if(x<f&&(l%2==1?A+=`
|
|
xCOffset = xC + 1;
|
|
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
xTexelR${b}C${x}.zw = vec2(0.);
|
|
}
|
|
} else {
|
|
xTexelR${b}C${x} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + 1 - 2;
|
|
if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
vec4 previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
previous.zw = vec2(0.);
|
|
}
|
|
|
|
xR${b}C${x} = vec4(previous.zw, xTexelR${b}C${x}.xy);
|
|
} else {
|
|
xR${b}C${x} = vec4(0, 0, xTexelR${b}C${x}.xy);
|
|
}
|
|
`:A+=`
|
|
if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
|
|
xTexelR${b}C${x} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${b}C${x} = vec4(0.);
|
|
}
|
|
|
|
xR${b}C${x} = xTexelR${b}C${x};
|
|
`,x+1<f)){let N=l%2==0?v.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 < ${i}) {
|
|
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`,d>1&&(A+=`
|
|
xCOffset -= 2;
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${b}C${x} = vec4(0.);
|
|
}
|
|
`),A+=`
|
|
xR${b}C${x+1} = vec4(
|
|
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.xy);
|
|
`):A+=`
|
|
xCOffset = xC + ${N};
|
|
|
|
if(xR >= 0 && xR < ${s} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
|
|
xR${b}C${x+1} = xTexelR${b}C${x+2};
|
|
`}}else x<f&&(A+=`
|
|
if(xR >= 0 && xR < ${s}) {
|
|
`,l%2==1?(A+=`
|
|
xCOffset = xC + 1 - ${c};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${b}C${x} = vec4(0.);
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${i}) {
|
|
xTexelR${b}C${x+2} = getX(batch, xR, xC + 1, d1);
|
|
} else {
|
|
xTexelR${b}C${x+2} = vec4(0.);
|
|
}
|
|
|
|
xR${b}C${x} = vec4(
|
|
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
|
|
`,x+1<f&&(A+=`
|
|
vec4 final = vec4(0.);
|
|
xCOffset = xC + 1 + ${c};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xR${b}C${x+1} = vec4(xTexelR${b}C${x+2}.xy, final.xy);
|
|
`)):(A+=`
|
|
if(xC >= 0 && xC < ${i}) {
|
|
xTexelR${b}C${x} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${b}C${x} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + ${c};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${b}C${x+2} = vec4(0.);
|
|
}
|
|
|
|
xR${b}C${x} = vec4(
|
|
xTexelR${b}C${x}.xy, xTexelR${b}C${x+2}.xy);
|
|
`,x+1<f&&(A+=`
|
|
xR${b}C${x+1} = vec4(
|
|
xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
|
|
`)),A+="}");x<f&&(A+=`
|
|
vec4 wTexelR${b}C${x} = getW(${b}, ${x}, d1, q);
|
|
wR${b}C${x} = vec4(wTexelR${b}C${x}.xz, wTexelR${b}C${x}.xz);
|
|
`,x+1<f&&(A+=`
|
|
vec4 wTexelR${b}C${x+1} = getW(${b}, ${x+1}, d1, q);
|
|
wR${b}C${x+1} =
|
|
vec4(wTexelR${b}C${x+1}.xz, wTexelR${b}C${x+1}.xz);`))}for(let b=0;b<p;b++)for(let w=0;w<f;w++)A+=`dotProd += xR${b}C${w} * wR${b}C${w};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:a?y=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:y=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,g="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${y}
|
|
|
|
const ivec2 strides = ivec2(${u}, ${c});
|
|
const ivec2 pads = ivec2(${o}, ${l});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2;
|
|
int q = 0;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
vec4 dotProd = vec4(0.);
|
|
|
|
${A}
|
|
|
|
vec4 result = dotProd;
|
|
${_}
|
|
${g}
|
|
setOutput(result);
|
|
}
|
|
`}};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=r,c=l;c==null&&(c=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new ob(h):d=new ib(h),n.runWebGLProgram(d,[a,s],"float32")}var VB={kernelName:ys,backendName:"webgl",kernelFunc:BB},UB=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},HB=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function jB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=R.computeConv2DInfo(a.shape,c,i,o,l,u,!0),d=new UB(h);return n.runWebGLProgram(d,[a,s],"float32")}var GB={kernelName:Mh,backendName:"webgl",kernelFunc:jB};function qB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=R.computeConv2DInfo(c,s.shape,i,o,l,u,!0),d=new HB(h);return n.runWebGLProgram(d,[a,s],"float32")}var XB={kernelName:$h,backendName:"webgl",kernelFunc:qB},KB=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 ZB(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new KB(s),l=n.runWebGLProgram(o,[i],i.dtype),u=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var YB={kernelName:Dh,backendName:"webgl",kernelFunc:ZB},JB=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=`
|
|
const ivec2 strides = ivec2(${a}, ${s});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${i}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${o}; w++) {
|
|
int wIn = wBeg + w * ${u};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function QB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),c,h=new JB(u);c=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var eV={kernelName:pu,backendName:"webgl",kernelFunc:QB},tV="return (x >= 0.0) ? x : (exp(x) - 1.0);",nV=`
|
|
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;
|
|
`,rV=qe({opSnippet:tV,packedOpSnippet:nV}),aV={kernelName:lo,backendName:"webgl",kernelFunc:rV},sV="return (b >= 1.0) ? a : a * (b + 1.0);",iV=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,oV=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new cc(iV,r.shape,a.shape):new Rl(sV,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},lV={kernelName:Ph,backendName:"webgl",kernelFunc:oV},uV=`
|
|
return vec4(equal(a, b));
|
|
`,cV="return float(a == b);",hV=nn({opSnippet:cV,packedOpSnippet:uV,dtype:"bool"}),dV={kernelName:co,backendName:"webgl",kernelFunc:hV},pV=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${R.ERF_P};
|
|
float a1 = ${R.ERF_A1};
|
|
float a2 = ${R.ERF_A2};
|
|
float a3 = ${R.ERF_A3};
|
|
float a4 = ${R.ERF_A4};
|
|
float a5 = ${R.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,fV=qe({opSnippet:pV}),mV={kernelName:uo,backendName:"webgl",kernelFunc:fV},lb="return exp(x);",ub=qe({opSnippet:lb,packedOpSnippet:lb,cpuKernelImpl:fP}),AV={kernelName:xs,backendName:"webgl",kernelFunc:ub};function rA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var yV={kernelName:ho,backendName:"webgl",kernelFunc:rA},cb="return exp(x) - 1.0;",gV=qe({opSnippet:cb,packedOpSnippet:cb,cpuKernelImpl:mP}),xV={kernelName:po,backendName:"webgl",kernelFunc:gV},hb=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 db(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,u=new hb("real",l,t),c=new hb("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,h,"float32"),p=n.runWebGLProgram(c,h,"float32"),f=Va({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=Ae({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function wV(e){let{inputs:t,backend:n}=e,{input:r}=t;return db(r,!1,n)}var _V={kernelName:Lh,backendName:"webgl",kernelFunc:wV},bV=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 aA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new bV(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var vV={kernelName:fu,backendName:"webgl",kernelFunc:aA},kV=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},IV={kernelName:fo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new kV(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},pb="return floor(x);",NV=qe({opSnippet:pb,packedOpSnippet:pb,cpuKernelImpl:AP}),SV={kernelName:ws,backendName:"webgl",kernelFunc:NV},TV=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,CV=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,EV=nn({opSnippet:TV,packedOpSnippet:CV,dtype:"int32"}),RV={kernelName:_s,backendName:"webgl",kernelFunc:EV},FV=class{constructor(e){this.variableNames=["A"];let t=pn(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},MV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=pn(),[n,r]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},DV={kernelName:ed,backendName:"webgl",kernelFunc:$V},Ml;function $V(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[u,l],h=[u,l,s];(o||i)&&(Ml==null&&(Ml=document.createElement("canvas").getContext("2d")),Ml.canvas.width=l,Ml.canvas.height=u,Ml.drawImage(a,0,0,l,u),a=Ml.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Zn.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new MV(h):new FV(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function OV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(c),A=R.computeConv2DInfo(a.shape,s.shape,l,h,u,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=tb({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=nb({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,w=o!=null,x=p==="leakyrelu",N=p?dp(p,!1):null,S=new eb(A,b,N,w,x),T=[a,s];if(i&&T.push(i),o&&T.push(o),x){let M=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));T.push(M),g.push(M)}y=n.runWebGLProgram(S,T,"float32")}let _=Ae({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var zV={kernelName:ei,backendName:"webgl",kernelFunc:OV};function PV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=c;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?dp(d,y):null,_=[a,s],b=i!=null,w=o!=null,x=d==="leakyrelu";if(b&&_.push(i),w&&_.push(o),x){let T=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));_.push(T),f.push(T)}let N;y?N=new ob(A,b,g,w,x):N=new ib(A,b,g,w,x);let S=n.runWebGLProgram(N,_,"float32");return f.forEach(T=>n.disposeIntermediateTensorInfo(T)),S}var LV={kernelName:ti,backendName:"webgl",kernelFunc:PV},WV=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ut(t.length),a=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${r} strides = ${r}(${this.strides});
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${s};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function BV(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=R.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/u,u]}}),p=new WV(i,c,[l,u]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var VV={kernelName:Ao,backendName:"webgl",kernelFunc:BV},HV=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),r=UV(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function UV(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 jV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=v.sizeFromShape(s.shape),h=[],d=Ae({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),p=Ae({inputs:{x:s},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});h.push(d),h.push(p);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),_=n.bufferSync(d),b=yP(_,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new HV(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=Ae({inputs:{x:A},backend:n,attrs:{shape:u.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var GV={kernelName:mo,backendName:"webgl",kernelFunc:jV},qV="return float(a > b);",XV=`
|
|
return vec4(greaterThan(a, b));
|
|
`,KV=nn({opSnippet:qV,packedOpSnippet:XV,cpuKernelImpl:gP,dtype:"bool"}),ZV={kernelName:yo,backendName:"webgl",kernelFunc:KV},YV="return float(a >= b);",JV=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,QV=nn({opSnippet:YV,packedOpSnippet:JV,dtype:"bool"}),eU={kernelName:vs,backendName:"webgl",kernelFunc:QV};function tU(e){let{inputs:t,backend:n}=e,{input:r}=t;return db(r,!0,n)}var nU={kernelName:Wh,backendName:"webgl",kernelFunc:tU},rU="return float(!isnan(x) && !isinf(x));",aU=qe({opSnippet:rU,dtype:"bool"}),sU={kernelName:go,backendName:"webgl",kernelFunc:aU},iU="return float(isinf(x));",oU=qe({opSnippet:iU,dtype:"bool"}),lU={kernelName:xo,backendName:"webgl",kernelFunc:oU},uU="return float(isnan(x));",cU=qe({opSnippet:uU,dtype:"bool"}),hU={kernelName:wo,backendName:"webgl",kernelFunc:cU},dU="return float(a < b);",pU=`
|
|
return vec4(lessThan(a, b));
|
|
`,fU=nn({opSnippet:dU,packedOpSnippet:pU,cpuKernelImpl:xP,dtype:"bool"}),mU={kernelName:_o,backendName:"webgl",kernelFunc:fU},AU="return float(a <= b);",yU=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,gU=nn({opSnippet:AU,packedOpSnippet:yU,dtype:"bool"}),xU={kernelName:bo,backendName:"webgl",kernelFunc:gU};function wU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=wP(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var _U={kernelName:Vh,backendName:"webgl",kernelFunc:wU},bU=`if (x < 0.0) return NAN;
|
|
return log(x);`,vU=`
|
|
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;
|
|
`,kU=qe({opSnippet:bU,packedOpSnippet:vU,cpuKernelImpl:_P}),IU={kernelName:Ns,backendName:"webgl",kernelFunc:kU},NU="return log(1.0 + x);",SU=qe({opSnippet:NU}),TU={kernelName:vo,backendName:"webgl",kernelFunc:SU},CU="return float(a >= 1.0 && b >= 1.0);",EU=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,RU=nn({opSnippet:CU,packedOpSnippet:EU,dtype:"bool"}),FU={kernelName:ko,backendName:"webgl",kernelFunc:RU},MU="return float(!(x >= 1.0));",$U=qe({opSnippet:MU}),DU={kernelName:mu,backendName:"webgl",kernelFunc:$U},OU="return float(a >= 1.0 || b >= 1.0);",zU=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,PU=nn({opSnippet:OU,packedOpSnippet:zU,dtype:"bool"}),LU={kernelName:Au,backendName:"webgl",kernelFunc:PU},WU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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 * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},BU=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${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);
|
|
}
|
|
`}},VU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new BU(a.shape,s,i,o,l):new WU(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)},UU={kernelName:yu,backendName:"webgl",kernelFunc:VU},HU=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);
|
|
}
|
|
`}},jU=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new HU(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)},GU={kernelName:Uh,backendName:"webgl",kernelFunc:jU};function qU(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=bi(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function fb(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,_=new Array(o);for(let x=0;x<_.length;x++)_[x]=a.shape[c[x]];let b=Ym(g,a.shape,a.dtype,c,_);p=n.makeTensorInfo(_,a.dtype);let w=n.texData.get(p.dataId);w.values=b}else p=pp(a,c,n);u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("max",u,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,u),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,_=bP(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=_}else y=qU(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var XU={kernelName:Ss,backendName:"webgl",kernelFunc:fb},KU=R_+`
|
|
return max(a, b);
|
|
`,ZU=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+hp+`
|
|
return result;
|
|
`,YU=nn({opSnippet:KU,packedOpSnippet:ZU,cpuKernelImpl:vP}),JU={kernelName:Ts,backendName:"webgl",kernelFunc:YU};function QU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;vl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,u=1;v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let c=R.computePool2DInfo(a.shape,s,i,u,o,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return zn({inputs:{x:a},backend:n});let h=new hc(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var eH={kernelName:Cs,backendName:"webgl",kernelFunc:QU};function tH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,c,o,u,l),d=new eA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var nH={kernelName:gu,backendName:"webgl",kernelFunc:tH},rH=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 * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},aH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=u-1-e.padInfo.left,p=o*l*u-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${c}, ${h}, ${d});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${o};
|
|
wD += ${a}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${u};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${p} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${u} +
|
|
wR * ${u} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function sH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,u,c),p=new eA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new aH(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var iH={kernelName:jh,backendName:"webgl",kernelFunc:sH};function oH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;vl([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,u,1,c,h),p=!0,f=new hc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new rH(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var lH={kernelName:Hh,backendName:"webgl",kernelFunc:oH};function uH(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 cH={kernelName:Gh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let u=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=R.computePool2DInfo(r.shape,a,s,u,i),[h,d]=uH(r,o,c,l);return[h,d]}};function hH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=bi(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var dH={kernelName:Es,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),u=l,c=R.getAxesPermutation(u,o),h=c!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let _=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[c[N]];let w=Ym(_,r.shape,r.dtype,c,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=w}else f=pp(r,c,i);p.push(f),u=R.getInnerMostAxes(u.length,o)}R.assertAxesAreInnerMostDims("sum",u,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=hH(f,A,y,i);for(let _ of p)i.disposeIntermediateTensorInfo(_);return g}};function pH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,c=R.getAxesPermutation(u,o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),u=R.getInnerMostAxes(u.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",u,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=bi(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=Ae({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=Ae({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),c!=null&&n.disposeIntermediateTensorInfo(h),y}var fH={kernelName:Rs,backendName:"webgl",kernelFunc:pH},mH=R_+`
|
|
return min(a, b);
|
|
`,AH=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+hp+`
|
|
return result;
|
|
`,yH=nn({opSnippet:mH,packedOpSnippet:AH,cpuKernelImpl:kP}),gH={kernelName:Fs,backendName:"webgl",kernelFunc:yH},xH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let r=e.length,a=ut(r),s=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${a} start = ${a}(${s});
|
|
${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outC = getOutputCoords();
|
|
for (int i = 0; i < ${r}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${a} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},wH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ut(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=fn("rc",r),l=fn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
|
|
${a} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${h};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${h};
|
|
}
|
|
source -= start;
|
|
`;d=`
|
|
${a} rc = outputLoc;
|
|
${p}
|
|
result[0] = getChannel(getX(${l.join()}), ${c});
|
|
${o[r-1]} += 1;
|
|
if(${u}) {
|
|
${p}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`}else{let p=`
|
|
${a} source = rc;
|
|
${a} lt = ${a}(lessThan(source, start));
|
|
${a} gte = ${a}(greaterThanEqual(source, end));
|
|
${a} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${h}) +
|
|
gte * ((end - 1) * 2 - source + ${h});
|
|
source -= start;
|
|
`;d=`
|
|
${a} rc = outputLoc;
|
|
${p}
|
|
result[0] = getChannel(getX(${l.join()}), ${c});
|
|
${o[r-1]} += 1;
|
|
if(${u}) {
|
|
${p}
|
|
result[1] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
rc = outputLoc;
|
|
${o[r-2]} += 1;
|
|
if(${o[r-2]} < ${this.outputShape[r-2]}) {
|
|
${p}
|
|
result[2] = getChannel(getX(${l.join()}), ${c});
|
|
${o[r-1]} += 1;
|
|
if(${u}) {
|
|
${p}
|
|
result[3] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${a} start = ${a}(${s});
|
|
const ${a} end = ${a}(${i});
|
|
|
|
void main() {
|
|
${a} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}},_H=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new wH(r.shape,a,s):new xH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},bH={kernelName:xu,backendName:"webgl",kernelFunc:_H},vH=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,kH=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+hp+`
|
|
return result;
|
|
`,IH=nn({opSnippet:vH,packedOpSnippet:kH}),NH={kernelName:Io,backendName:"webgl",kernelFunc:IH},SH=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)}}},TH=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,CH=`
|
|
// 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;
|
|
`,mb=nn({opSnippet:TH,packedOpSnippet:CH,checkOutOfBounds:!0}),EH={kernelName:gs,backendName:"webgl",kernelFunc:mb},Ab="return a - b;",yb=nn({opSnippet:Ab,packedOpSnippet:Ab,supportsComplex:!0,cpuKernelImpl:FP}),RH={kernelName:Zs,backendName:"webgl",kernelFunc:yb};function gb(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=fb({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),u=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),c=yb({inputs:{a,b:u},backend:n}),h=ub({inputs:{x:c},backend:n}),d=Qm({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),f=mb({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var FH={kernelName:Xs,backendName:"webgl",kernelFunc:gb};function MH(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:gb({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new SH(u,c,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var $H={kernelName:qh,backendName:"webgl",kernelFunc:MH},xb="return -x;";function DH(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=NP(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,xb):a=new Ba(r.shape,xb),n.runWebGLProgram(a,[r],r.dtype)}var OH={kernelName:No,backendName:"webgl",kernelFunc:DH},zH=Pr.nonMaxSuppressionV3Impl;function PH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=zH(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var LH={kernelName:To,backendName:"webgl",kernelFunc:PH},WH=Pr.nonMaxSuppressionV4Impl;function BH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=WH(c,h,i,o,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var VH={kernelName:Co,backendName:"webgl",kernelFunc:BH},UH=Pr.nonMaxSuppressionV5Impl;function HH(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=u,{selectedIndices:A,selectedScores:y}=UH(c,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var jH={kernelName:Eo,backendName:"webgl",kernelFunc:HH},GH=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(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},qH=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),u=new GH(l,s,i,o),c=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},XH={kernelName:$s,backendName:"webgl",kernelFunc:qH};function gp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=pc({inputs:{input:r},backend:n}),s=gp({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Va({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return aA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var KH={kernelName:Xo,backendName:"webgl",kernelFunc:gp};function wb(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=wb({inputs:{x:a},backend:n}),i=yp({inputs:{input:r},backend:n}),o=gp({inputs:{x:i},backend:n}),l=Va({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return aA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var ZH={kernelName:Ro,backendName:"webgl",kernelFunc:wb};function YH(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return rA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=rA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=Q_({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var JH={kernelName:Fo,backendName:"webgl",kernelFunc:YH},QH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let r=e.length,a=ut(r),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
uniform float value;
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${a} start = ${a}(${s});
|
|
${a} end = ${a}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${a} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${a} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},ej=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ut(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=fn("rc",r),l=fn("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
|
|
if(${u}) {
|
|
`,r===1?"":`}
|
|
rc = outputLoc;
|
|
${o[r-2]} += 1;
|
|
if(${o[r-2]} < ${this.outputShape[r-2]}) {`,r===1?"":` ${o[r-1]} += 1;
|
|
if(${u}) {`],d=r===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",p="";for(let f=0,m=r===1?2:4;f<m;f++)p+=`
|
|
${h[f]}
|
|
if (${d}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${a} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${c});
|
|
}
|
|
`;p+=r===1?"} ":"}}",this.userCode=`
|
|
const ${a} start = ${a}(${s});
|
|
const ${a} end = ${a}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${a} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},_b=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 ej(a.shape,s,i):new QH(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},tj={kernelName:Ds,backendName:"webgl",kernelFunc:_b},nj=`
|
|
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);
|
|
`,rj=`
|
|
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
|
|
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
|
|
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
|
|
vec4 result = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
bvec4 isExpZero = equal(b, vec4(0.0));
|
|
result.r = isExpZero.r ? 1.0 : result.r;
|
|
result.g = isExpZero.g ? 1.0 : result.g;
|
|
result.b = isExpZero.b ? 1.0 : result.b;
|
|
result.a = isExpZero.a ? 1.0 : result.a;
|
|
|
|
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
|
|
`+hp+`
|
|
return result;
|
|
`,aj=nn({opSnippet:nj,packedOpSnippet:rj}),sj={kernelName:Os,backendName:"webgl",kernelFunc:aj};function ij(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),c=u,h=R.getAxesPermutation(c,o),d=a;h!=null&&(d=In({inputs:{x:a},backend:n,attrs:{perm:h}}),c=R.getInnerMostAxes(c.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",c,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=SP(d.shape,d.dtype,f,c);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,c),A=v.sizeFromShape(m),y=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=sd(a.dtype),_=bi(y,g,"prod",n);p=Ae({inputs:{x:_},backend:n,attrs:{shape:f}}),l.push(y),l.push(_)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,u);p=Ae({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var oj={kernelName:Mo,backendName:"webgl",kernelFunc:ij},bb=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=TP(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},lj={kernelName:wu,backendName:"webgl",kernelFunc:bb},uj="return 1.0 / x;",cj=qe({opSnippet:uj}),hj={kernelName:$o,backendName:"webgl",kernelFunc:cj},dj=wr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,pj=`
|
|
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;
|
|
`,fj=qe({opSnippet:dj,packedOpSnippet:pj}),mj={kernelName:Ps,backendName:"webgl",kernelFunc:fj},Aj=wr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,yj=`
|
|
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;
|
|
`,gj=qe({opSnippet:Aj,packedOpSnippet:yj}),xj={kernelName:Ws,backendName:"webgl",kernelFunc:gj},wj=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${h};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},_j=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]},
|
|
${u[1]/c[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${h};
|
|
|
|
// Compute the four integer indices.
|
|
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
|
|
ivec3 sourceCeilRC = ivec3(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function bj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new _j(a.shape,l,u,s,i):new wj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}var vj={kernelName:Ls,backendName:"webgl",kernelFunc:bj},kj=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${h});
|
|
const float invWidthScale = float(${d});
|
|
|
|
const int winHeight = int(${p});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Ij(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new kj(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Nj={kernelName:Zh,backendName:"webgl",kernelFunc:Ij},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 u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${u[0]/c[0]},
|
|
${u[1]/c[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${d};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Tj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=new Sj(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}var Cj={kernelName:_u,backendName:"webgl",kernelFunc:Tj},Ej=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,d=1/c,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${u});
|
|
const float widthScale = float(${c});
|
|
|
|
const float invHeightScale = float(${h});
|
|
const float invWidthScale = float(${d});
|
|
|
|
const int winHeight = int(${p});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${o[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Ej(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Fj={kernelName:Kh,backendName:"webgl",kernelFunc:Rj},Mj=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ut(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${a}));
|
|
}
|
|
`}},$j=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=fn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${a}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${o(r.slice())};
|
|
if(${a}){
|
|
result.g = ${l(r.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${u(r.slice())};
|
|
if(${a}) {
|
|
result.a = ${c(r.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function u(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function c(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return zn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $j(a.shape,o):new Mj(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Oj={kernelName:Bs,backendName:"webgl",kernelFunc:Dj},zj=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Pj={kernelName:Ko,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new zj(r.shape,s),[u,c]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Lj=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Wj=qe({opSnippet:Lj}),Bj={kernelName:Vs,backendName:"webgl",kernelFunc:Wj},Vj="return inversesqrt(x);",Uj=qe({opSnippet:Vj,cpuKernelImpl:CP}),Hj={kernelName:Us,backendName:"webgl",kernelFunc:Uj},vb=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(a.length),l=ut(s.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${a});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${c});
|
|
flattenedIndex += index * ${p};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${d};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function jj(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=R.calculateShapes(s,a,i),d=[h/u,u];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new vb(l,o,p.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=Ae({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var Gj={kernelName:Oo,backendName:"webgl",kernelFunc:jj},qj=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);r=o.join(),a=l.join()}let s=ut(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${r});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${a}));
|
|
} else {
|
|
setOutput(getB(${a}));
|
|
}
|
|
}
|
|
`}};function Xj(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new qj(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],nr(a.dtype,s.dtype))}var Kj={kernelName:zo,backendName:"webgl",kernelFunc:Xj},Zj=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${R.SELU_SCALEALPHA};
|
|
float scale = ${R.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,Yj=qe({opSnippet:Zj}),Jj={kernelName:Po,backendName:"webgl",kernelFunc:Yj},Qj="return 1.0 / (1.0 + exp(-1.0 * x));",eG=qe({opSnippet:Qj}),tG={kernelName:js,backendName:"webgl",kernelFunc:eG},nG=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,rG=qe({opSnippet:nG}),aG={kernelName:Bo,backendName:"webgl",kernelFunc:rG},sG=O_+`
|
|
return sin(x);
|
|
`,iG=qe({opSnippet:sG}),oG={kernelName:Hs,backendName:"webgl",kernelFunc:iG},lG=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,uG=qe({opSnippet:lG}),cG={kernelName:Wo,backendName:"webgl",kernelFunc:uG},hG=`
|
|
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;
|
|
`,dG=qe({opSnippet:hG}),pG={kernelName:Vo,backendName:"webgl",kernelFunc:dG},fG=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],c=_b({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(c.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(c.shape,s,o,!1),f=Ae({inputs:{x:c},backend:n,attrs:{shape:h}}),m=In({inputs:{x:f},backend:n,attrs:{perm:d}}),A=Ae({inputs:{x:m},backend:n,attrs:{shape:p}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},mG={kernelName:bu,backendName:"webgl",kernelFunc:fG};function AG(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new vb(u,l,a.shape.length,s.shape.length,c,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var yG={kernelName:Yh,backendName:"webgl",kernelFunc:AG};function gG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=dc({inputs:{x:a},backend:n,attrs:{begin:c,size:p}});return c[o]+=d,f})}var xG={kernelName:Uo,backendName:"webgl",kernelFunc:gG},wG="return sqrt(x);",_G=qe({opSnippet:wG}),bG={kernelName:Gs,backendName:"webgl",kernelFunc:_G},vG="return x * x;",kG=qe({opSnippet:vG}),IG={kernelName:vu,backendName:"webgl",kernelFunc:kG},kb="return (a - b) * (a - b);",NG=nn({opSnippet:kb,packedOpSnippet:kb}),SG={kernelName:Ks,backendName:"webgl",kernelFunc:NG};function TG({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=wr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Ba(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var CG={kernelName:Ta,backendName:"webgl",kernelFunc:TG},EG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ut(n.length),s=ut(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,u)=>(o++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
|
|
${a} begin = ${a}(${e});
|
|
${a} strides = ${a}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function RG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=cn.sliceInfo(a.shape,s,i,o,l,u,c,h,d),_=Ae({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=dc({inputs:{x:_},backend:n,attrs:{begin:f,size:A}});b=Ae({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([_])){let x=n.texData.get(_.dataId).values,N=We(_.shape,_.dtype,x),S=RP(g,N,m,f);b=n.makeTensorInfo(g,_.dtype,S.values)}else{let x=new EG(f,m,g);b=n.runWebGLProgram(x,[_],_.dtype)}let w=Ae({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(_),n.disposeIntermediateTensorInfo(b),w}var FG={kernelName:Ho,backendName:"webgl",kernelFunc:RG},MG="return tan(x);",$G=qe({opSnippet:MG}),DG={kernelName:jo,backendName:"webgl",kernelFunc:$G},OG=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,zG=qe({opSnippet:OG}),PG={kernelName:Ys,backendName:"webgl",kernelFunc:zG},WG=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let r=ut(this.rank),a=LG(e);this.userCode=`
|
|
void main() {
|
|
${r} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function LG(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]}, ${e[a]})`);return r.join()}function Ib(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if(a.dtype==="string"){let o=n.readSync(a.dataId).map(c=>v.decodeString(c)),l=We(a.shape,a.dtype,o),u=MP(l,s);return n.makeTensorInfo(u.shape,u.dtype,u.values)}let i=new WG(a.shape,s);return n.runWebGLProgram(i,[a],a.dtype)}var BG={kernelName:Sa,backendName:"webgl",kernelFunc:Ib};function VG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:i}=r,o=n.readSync(a.dataId),[l,u]=$P(o,a.shape,a.dtype,s,i);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(u.shape,u.dtype,u.values)]}var UG={kernelName:Go,backendName:"webgl",kernelFunc:VG},HG=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 jG(e){let{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=r,[c,h,d,p]=a.shape,[f,m]=u!=null?u:[h,d],A=[c,f,m,p],y=new HG(h,d,i,o,l,A);return n.runWebGLProgram(y,[a,s],"float32")}var GG={kernelName:Jh,backendName:"webgl",kernelFunc:jG};function qG(e){let{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;vl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=r.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=DP(i,a,s.shape,s.dtype);return[r.makeTensorInfo(l,s.dtype,o),r.makeTensorInfo([u.length],"int32",u)]}var XG={kernelName:Qh,backendName:"webgl",kernelFunc:qG};function KG(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==s&&(u[c++]=i.shape[m]);let h=[],d=new Array(o).fill(0),p=i.shape.slice();p[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[s]=m;let A=dc({inputs:{x:i},backend:n,attrs:{begin:d,size:p}}),y=Ae({inputs:{x:A},backend:n,attrs:{shape:u}});f[m]=y,h.push(A)}return h.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ZG={kernelName:qo,backendName:"webgl",kernelFunc:KG},YG=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/n);this.outputShape=[r,i];let o="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,h=`
|
|
sumValue += dot(values, segFilter);
|
|
`,d="";a%n>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return initializationValue;
|
|
}
|
|
`);let p="";a%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${a}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${p}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${s})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${s})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${u}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${h}
|
|
}
|
|
|
|
int inIdx = inOffset + ${u};
|
|
if (${c===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${h}
|
|
} else if (${c===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${h}
|
|
} else if (${c===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
0
|
|
);
|
|
|
|
${h}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function JG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,segmentIds:s}=t,{numSegments:i}=r,o=a.shape.length,l=[],u=0,c=R.getAxesPermutation([u],o),h=a;c!=null&&(h=In({inputs:{x:a},backend:n,attrs:{perm:c}}),l.push(h),u=R.getInnerMostAxes(1,o)[0]);let d=R.segment_util.computeOutShape(h.shape,u,i),p=v.sizeFromShape([h.shape[u]]),f=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,p]}});l.push(f);let m=sd(a.dtype),A=(b,w,x,N,S)=>{let T=b.shape[0],M=b.shape[1],D=R.segment_util.segOpComputeOptimalWindowSize(M,S),z={windowSize:D,inSize:M,batchSize:T,numSegments:S},W=new YG(z,w),U=n.compileAndRun(W,[b,x],N);if(l.push(U),U.shape[1]===S)return U;let H=bb({backend:n,attrs:{start:0,stop:S,step:1,dtype:"float32"}}),X=Ib({inputs:{x:H},backend:n,attrs:{reps:[M/D]}});return l.push(H),l.push(X),A(U,w,X,N,S)},y=A(f,"unsortedSegmentSum",s,m,i),g=Ae({inputs:{x:y},backend:n,attrs:{shape:d}}),_=g;if(c!=null){l.push(g);let b=R.getUndoAxesPermutation(c);_=In({inputs:{x:_},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),_}var QG={kernelName:ku,backendName:"webgl",kernelFunc:JG},eq=[UU,GU,RL,ML,OL,LL,BL,HL,GL,XL,JL,eW,rW,iW,pW,uW,AW,wW,gW,kW,NW,TW,FW,LW,BW,qW,KW,QW,nB,dL,iB,AB,gB,cB,bB,kB,wB,SB,EB,MB,DB,zB,WB,GB,XB,VB,YB,eV,aV,lV,dV,mV,AV,yV,xV,_V,vV,IV,SV,RV,DV,zV,LV,VV,GV,ZV,eU,hL,nU,sB,sU,lU,hU,fL,mU,xU,_U,TU,IU,FU,DU,LU,XU,nH,eH,iH,lH,cH,JU,dH,fH,gH,bH,NH,$H,xL,OH,LH,VH,jH,UW,XH,ZH,JH,tj,sj,AL,oj,lj,HW,EH,hj,xj,mj,_L,vj,Nj,Cj,Fj,Oj,Pj,Bj,Hj,Gj,Kj,Jj,tG,aG,oG,cG,zW,FH,pG,mG,yG,xG,bG,IG,SG,CG,FG,RH,TL,DG,PG,BG,UG,GG,CL,XG,ZG,QG,KH];for(let e of eq)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 Nb;function tq(e){Nb=e.wasm.cwrap(Qs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function nq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r,d=n.dataIdMap.get(a.dataId).id,p=n.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let S=n.dataIdMap.get(i.dataId);if(S.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${S.shape.length}.`);f=S.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,A=fc[c];if(A==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],g=u?s.shape[1]:s.shape[2],_=a.shape[0],b=n.makeOutput([_,y,g],a.dtype),w=n.dataIdMap.get(b.dataId).id,x=new Uint8Array(new Int32Array(a.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return Nb(d,x,a.shape.length,p,N,s.shape.length,l,u,A,f,m,h||0,w),b}var rq={kernelName:Qs,backendName:"wasm",setupFunc:tq,kernelFunc:nq};function Nn(e){let t;function n(a){t=a.wasm.cwrap(e,null,["number","number"])}function r(a){let{backend:s,inputs:{x:i}}=a,o=s.dataIdMap.get(i.dataId).id,l=s.makeOutput(i.shape,i.dtype),u=s.dataIdMap.get(l.dataId).id;return v.sizeFromShape(l.shape)===0||t(o,u),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:r}}var aq=Nn(Zi);function mn(e,t,n){let r;function a(i){r=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:c}=l,h=o.dataIdMap.get(u.dataId).id,d=o.dataIdMap.get(c.dataId).id,p=n!=null?n:u.dtype,f=R.assertAndGetBroadcastShape(u.shape,c.shape),m=o.makeOutput(f,p);if(v.sizeFromShape(f)===0)return m;let A=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),g=o.dataIdMap.get(m.dataId).id,_=()=>r(h,A,u.shape.length,d,y,c.shape.length,Pn[u.dtype],g);if(t&&u.dtype==="float32")return _(),m;let b=R.getBroadcastDims(u.shape,f),w=R.getBroadcastDims(c.shape,f),x=b.every((S,T)=>S===T),N=w.every((S,T)=>S===T);if(x&&N)return _(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var sq=!0,iq=mn(Ia,sq),Sb;function oq(e){Sb=e.wasm.cwrap(os,null,["array","number","number","number"])}function lq(e){let{inputs:t,backend:n}=e,r=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(r.shape)===0)return r;let a=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=n.dataIdMap.get(r.dataId).id;return Sb(s,a.length,Pn[r.dtype],i),r}var uq={kernelName:os,backendName:"wasm",setupFunc:oq,kernelFunc:lq};function xp(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype),a=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(r).set(a),r}var cq={kernelName:ks,backendName:"wasm",kernelFunc:xp},Tb;function hq(e){Tb=e.wasm.cwrap(Js,null,["number","array","number","number","number","array","number"])}function wp(e){let{inputs:t,backend:n,attrs:r}=e,[a,s]=pq(t.x.shape,r.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=dq(t.x.shape,r.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=xp({inputs:t,backend:n});return f.shape=o,f}let u=n.makeOutput(o,l.dtype),c=n.dataIdMap.get(l.dataId).id,h=n.dataIdMap.get(u.dataId).id,d=new Uint8Array(new Int32Array(s).buffer),p=new Uint8Array(new Int32Array(l.shape).buffer);return Tb(c,p,l.shape.length,Pn[l.dtype],h,d,s.length),u}function dq(e,t){let n=new Array(e.length);for(let r=0;r<n.length;r++)n[r]=e[t[r]];return n}function pq(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 fq={kernelName:Js,backendName:"wasm",kernelFunc:wp,setupFunc:hq};function $l(e,t,n){let r=e.shape,a=e.shape.length,s=v.parseAxisParam(t,r),i=s,o=R.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let c=new Array(a);for(let d=0;d<c.length;d++)c[d]=r[o[d]];i=R.getInnerMostAxes(i.length,a),l=wp({inputs:{x:e},attrs:{perm:o},backend:n});let h=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var Cb;function mq(e){Cb=e.wasm.cwrap(ls,null,["number","number","number","number","number"])}function Aq(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a}=r,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:c,inputWasTransposed:h}=$l(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let d=l.shape.slice(0,-1),p=t.makeOutput(d,"int32"),f=t.dataIdMap.get(p.dataId).id,m=v.sizeFromShape(p.shape),A=l.shape[c[0]];return Cb(o,Pn[l.dtype],m,A,f),h&&t.disposeData(u.dataId),p}var yq={kernelName:ls,backendName:"wasm",kernelFunc:Aq,setupFunc:mq},Eb;function gq(e){Eb=e.wasm.cwrap(us,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function xq(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=R.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,y=c.strideHeight,g=c.strideWidth,_=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=r.makeOutput(c.outShape,"float32"),w=r.dataIdMap.get(b.dataId).id;return Eb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,_,w),b}var wq={kernelName:us,backendName:"wasm",setupFunc:gq,kernelFunc:xq};function _r(e){let{inputs:t,attrs:n}=e,{x:r}=t,{shape:a}=n,s=v.sizeFromShape(r.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${r.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(r.dataId),{dataId:r.dataId,shape:i,dtype:r.dtype}}var _q={kernelName:Do,backendName:"wasm",kernelFunc:_r},Rb;function bq(e){Rb=e.wasm.cwrap(cs,null,["number","array","number","number","array","number","number","number","number"])}function vq(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,c=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],d=i?a.shape[l-1]:a.shape[l-2],p=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),A=v.sizeFromShape(f),y=v.sizeFromShape(m),g=A===y||A===1||y===1;v.assert(l>=2&&u>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let _=(A>y?a.shape.slice(0,-2):s.shape.slice(0,-2)).concat([d,p]);v.assert(c===h,()=>`Error in matMul: inner shapes (${c}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let b=i?[A,c,d]:[A,d,c],w=o?[y,p,h]:[y,h,p],x=_r({inputs:{x:a},backend:n,attrs:{shape:b}}),N=_r({inputs:{x:s},backend:n,attrs:{shape:w}}),S=n.dataIdMap.get(x.dataId).id,T=n.dataIdMap.get(N.dataId).id,M=i?x.shape[2]:x.shape[1],D=o?N.shape[1]:N.shape[2],z=Math.max(A,y),W=n.makeOutput([z,M,D],x.dtype),U=n.dataIdMap.get(W.dataId).id,H=new Uint8Array(new Int32Array(x.shape).buffer),X=new Uint8Array(new Int32Array(N.shape).buffer);return Rb(S,H,x.shape.length,T,X,N.shape.length,i,o,U),n.disposeData(x.dataId),n.disposeData(N.dataId),W.shape=_,W}var kq={kernelName:cs,backendName:"wasm",setupFunc:bq,kernelFunc:vq};function _p(e){let{inputs:{x:t},attrs:{dtype:n},backend:r}=e,a=r.makeOutput(t.shape,n),s=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(a).set(s),a}var Iq={kernelName:hs,backendName:"wasm",kernelFunc:_p},Nq=Nn(ds),Fb;function Sq(e){Fb=e.wasm.cwrap(Na,null,["number","number","number","number"])}function Tq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(l.dataId).id;return Fb(o,s,i,u),l}var Cq={kernelName:Na,backendName:"wasm",setupFunc:Sq,kernelFunc:Tq};function Mb(e){let{inputs:t,backend:n}=e,r=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],a=R.computeOutShape(t.map(p=>p.shape),r),s=t.filter(p=>v.sizeFromShape(p.shape)>0);if(s.length===1)return xp({inputs:{x:s[0]},backend:n});let i=n.makeOutput(a,t[0].dtype);if(v.sizeFromShape(a)===0)return i;let o=s.map(p=>p.shape);if(R.assertParamsConsistent(o,r),s[0].dtype==="string"){let p=s.map(_=>{let b=v.sizeFromShape(_.shape.slice(r));return _r({inputs:{x:_},backend:n,attrs:{shape:[-1,b]}})}),f=p.map(_=>({vals:n.readSync(_.dataId),shape:_.shape}));a=R.computeOutShape(p.map(_=>_.shape),1);let m=p[0].shape[0]===1,A=Nm(f,a,t[0].dtype,m),y=R.computeOutShape(s.map(_=>_.shape),r);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=R.fromStringArrayToUint8(A),p.forEach(_=>n.disposeData(_.dataId)),i}let l=v.sizeFromShape(s[0].shape.slice(0,r)),u=0,c=s.map(p=>{let f=v.sizeFromShape(p.shape.slice(r));return u+=f,f}),h=s.map(p=>n.typedArrayFromHeap(p)),d=n.typedArrayFromHeap(i);for(let p=0;p<l;p++){let f=p*u;for(let m=0;m<h.length;m++){let A=c[m],y=p*A,g=h[m].subarray(y,y+A);d.set(g,f),f+=A}}return i}var Eq={kernelName:ao,backendName:"wasm",kernelFunc:Mb},$b;function Rq(e){$b=e.wasm.cwrap(ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Fq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:h,dataFormat:d}=n,p=R.convertConv2DDataFormat(d),f=R.computeConv2DInfo(a.shape,s.shape,l,u,c,h,!1,p),m=f.filterHeight,A=f.filterWidth,y=f.padInfo.top,g=f.padInfo.right,_=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,x=f.dilationWidth,N=f.strideHeight,S=f.strideWidth,T=f.inChannels,M=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let z=r.makeOutput(f.outShape,"float32"),W=r.dataIdMap.get(z.dataId).id;return $b(i,a.shape[0],a.shape[1],a.shape[2],o,m,A,y,g,_,b,D,w,x,N,S,T,M,W),z}var Mq={kernelName:ps,backendName:"wasm",setupFunc:Rq,kernelFunc:Fq},Db;function $q(e){Db=e.wasm.cwrap(fs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Dq(e){let{backend:t,inputs:n,attrs:r}=e,{dy:a,filter:s}=n,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,inputShape:c}=r,h=1,d=R.convertConv2DDataFormat(l),p=R.computeConv2DInfo(c,s.shape,i,h,o,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:A,inChannels:y,inHeight:g,inWidth:_,outChannels:b,outHeight:w,outWidth:x,strideHeight:N,strideWidth:S}=p,T=m-1-p.padInfo.top,M=A-1-p.padInfo.left,D=p.dataFormat==="channelsLast",z=v.computeStrides(p.inShape),W=v.computeStrides(a.shape),[U,H,X]=v.computeStrides(s.shape),j=z[0],ee=D?z[1]:z[2],Y=D?z[2]:1,ae=D?1:z[1],te=W[0],ie=D?W[1]:W[2],Q=D?W[2]:1,he=D?1:W[1],oe=t.makeOutput(p.inShape,"float32"),fe=t.dataIdMap.get(oe.dataId).id,pe=t.dataIdMap.get(a.dataId).id,ke=t.dataIdMap.get(s.dataId).id;return Db(pe,ke,f,m,A,g,_,y,w,x,b,N,S,T,M,U,H,X,j,ee,Y,ae,te,ie,Q,he,fe),oe}var Oq={kernelName:fs,backendName:"wasm",setupFunc:$q,kernelFunc:Dq},zq=Nn(ms),sA;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(sA||(sA={}));var Ob;function Pq(e){Ob=e.wasm.cwrap(io,null,["number","number","number","number","array","number","number","number","number","number"])}function Lq(e){let{backend:t,inputs:n,attrs:r}=e,{method:a,extrapolationValue:s,cropSize:i}=r,{image:o,boxes:l,boxInd:u}=n,c=l.shape[0],[h,d]=i,p=[c,h,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=_p({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let A=f.id,y=t.dataIdMap.get(l.dataId).id,g=t.dataIdMap.get(u.dataId).id,_=t.makeOutput(p,"float32"),b=t.dataIdMap.get(_.dataId).id,w=new Uint8Array(new Int32Array(o.shape).buffer);return Ob(A,y,g,c,w,h,d,sA[a],s,b),m!=null&&t.disposeData(m.dataId),_}var Wq={kernelName:io,backendName:"wasm",setupFunc:Pq,kernelFunc:Lq},zb;function Bq(e){zb=e.wasm.cwrap(As,null,["number","number","number","number","number","number"])}function Vq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length;v.assert(a.dtype==="float32"||a.dtype==="int32",()=>`cumsum does not support ${a.dtype} tensors in the WASM backend`);let u=R.getAxesPermutation([s],l),c=a;u!==null&&(c=wp({inputs:{x:a},attrs:{perm:u},backend:n}));let h=R.getInnerMostAxes(1,l)[0];R.assertAxesAreInnerMostDims("cumsum",[h],l);let d=n.makeOutput(c.shape,c.dtype),p=c.shape[h],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;zb(f,i?1:0,o?1:0,p,m,Pn[a.dtype]);let A=d;if(u!==null){let y=R.getUndoAxesPermutation(u);A=wp({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return A}var Uq={kernelName:As,backendName:"wasm",setupFunc:Bq,kernelFunc:Vq},Pb;function Hq(e){Pb=e.wasm.cwrap(oo,null,["number","number","number","array","number","array","array","number","number"])}function jq(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],c=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=u*s,p=c/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=t.makeOutput(f,"float32"),A=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(v.computeStrides(a.shape)).buffer),g=new Uint8Array(new Int32Array(f).buffer),_=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return Pb(A,s,i==="NHWC"?1:0,y,a.shape.length-1,g,_,f.length,b),m}var Gq={kernelName:oo,backendName:"wasm",setupFunc:Hq,kernelFunc:jq},Lb;function qq(e){Lb=e.wasm.cwrap(ys,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Xq(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s}=t,i=r.dataIdMap.get(a.dataId).id,o=r.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:h}=n,d=u==null?[1,1]:u,p=R.computeConv2DInfo(a.shape,s.shape,l,d,c,h,!0),f=p.filterHeight,m=p.filterWidth,A=p.padInfo.top,y=p.padInfo.right,g=p.padInfo.bottom,_=p.padInfo.left,b=p.dilationHeight,w=p.dilationWidth,x=p.strideHeight,N=p.strideWidth,S=p.inChannels,T=p.outChannels,M=p.padInfo.type==="SAME"?1:0;if(p.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${p.dataFormat}'. Please use 'channelsLast'.`);let D=r.makeOutput(p.outShape,"float32"),z=r.dataIdMap.get(D.dataId).id;return Lb(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,A,y,g,_,M,b,w,x,N,S,T,z),D}var Kq={kernelName:ys,backendName:"wasm",setupFunc:qq,kernelFunc:Xq},Zq=!1,Yq=mn(co,Zq,"bool"),Jq=Nn(xs);function iA(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),_r({inputs:{x:a},backend:r,attrs:{shape:o}})}var Qq={kernelName:ho,backendName:"wasm",kernelFunc:iA};function eX(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var tX={kernelName:fu,backendName:"wasm",kernelFunc:eX},Wb;function nX(e){Wb=e.wasm.cwrap(fo,null,["number","number","number","number","number","number"])}function rX(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,u,c]=r.shape;return Wb(s,o,l,u,c,i),a}var aX={kernelName:fo,backendName:"wasm",kernelFunc:rX,setupFunc:nX},sX=Nn(ws),iX=!1,oX=mn(_s,iX),Bb;function lX(e){Bb=e.wasm.cwrap(bs,null,["number","number","number","number","number","number","number"])}function uX(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:u}=n,c=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let A=t.dataIdMap.get(m.dataId).id;return Bb(c,h,d,p,f,a,A),m}var cX={kernelName:bs,backendName:"wasm",setupFunc:lX,kernelFunc:uX},Vb;function hX(e){Vb=e.wasm.cwrap(ei,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(a.shape,s.shape,l,c,u,d),A=fc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,S=m.padInfo.right,T=m.padInfo.bottom,M=m.padInfo.left,D=m.dilationHeight,z=m.dilationWidth,W=m.strideHeight,U=m.strideWidth,H=m.inChannels,X=m.padInfo.type==="SAME"?1:0,j=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return Vb(y,j,ee,Y,g,w,x,b,N,S,T,M,X,D,z,W,U,H,_,A,ie,f||0,te),ae}var pX={kernelName:ei,backendName:"wasm",setupFunc:hX,kernelFunc:dX},Ub;function fX(e){Ub=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 mX(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=R.computeConv2DInfo(a.shape,s.shape,l,c,u,d,!0),A=fc[p];if(A==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,_=m.outChannels,b=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==_)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${_})`);b=Q.id}let w=m.filterHeight,x=m.filterWidth,N=m.padInfo.top,S=m.padInfo.right,T=m.padInfo.bottom,M=m.padInfo.left,D=m.dilationHeight,z=m.dilationWidth,W=m.strideHeight,U=m.strideWidth,H=m.inChannels,X=m.padInfo.type==="SAME"?1:0,j=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return Ub(y,j,ee,Y,g,w,x,b,N,S,T,M,X,D,z,W,U,H,_,A,ie,f||0,te),ae}var AX={kernelName:ti,backendName:"wasm",setupFunc:fX,kernelFunc:mX},Hb;function yX(e){Hb=e.wasm.cwrap(Ao,null,["number","number","number","number","number","number","array","number"])}function gX(e){let{backend:t,inputs:n}=e,{params:r,indices:a}=n,[s,i,o,l]=If.prepareAndValidate(r,a),u=t.makeOutput(s,r.dtype);if(i===0)return u;let c=a.shape,h=c[c.length-1],d=t.dataIdMap.get(r.dataId).id,p=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return Hb(d,Pn[r.dtype],p,i,h,o,f,m),u}var xX={kernelName:Ao,backendName:"wasm",setupFunc:yX,kernelFunc:gX},jb;function wX(e){jb=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function _X(e){let{backend:t,inputs:n,attrs:r}=e,{x:a,indices:s}=n,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],u=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),c=_r({inputs:{x:a},attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]},backend:t}),h=v.sizeFromShape(s.shape),d=_r({inputs:{x:s},attrs:{shape:[u.batchSize,h/u.batchSize]},backend:t}),p=[u.batchSize,u.outerSize,h/u.batchSize,u.sliceSize],f=t.makeOutput(p,a.dtype);if(v.sizeFromShape(a.shape)===0)return f;let m=c.shape.length-1,A=t.dataIdMap.get(c.dataId).id,y=t.dataIdMap.get(d.dataId).id,g=t.dataIdMap.get(f.dataId).id,_=new Uint8Array(new Int32Array(v.computeStrides(c.shape)).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(p)).buffer);return jb(A,Pn[a.dtype],_,m,y,u.batchSize,b,g),t.disposeData(c.dataId),t.disposeData(d.dataId),f.shape=u.outputShape,f}var bX={kernelName:mo,backendName:"wasm",setupFunc:wX,kernelFunc:_X},vX=!1,kX=mn(yo,vX,"bool"),IX=!1,NX=mn(vs,IX,"bool"),Gb;function SX(e){Gb=e.wasm.cwrap(Is,null,["number","number","number"])}function TX(e){let{inputs:{x:t},attrs:{alpha:n},backend:r}=e,a=r.dataIdMap.get(t.dataId).id,s=r.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let i=r.dataIdMap.get(s.dataId).id;Gb(a,n,i)}return s}var CX={kernelName:Is,backendName:"wasm",setupFunc:SX,kernelFunc:TX},EX=!1,RX=mn(_o,EX,"bool"),FX=!1,MX=mn(bo,FX,"bool"),$X=Nn(Ns),DX=!1,OX=mn(ko,DX,"bool"),qb;function zX(e){qb=e.wasm.cwrap(Ss,null,["number, number, number"])}function PX(e){let{backend:t,inputs:n,attrs:r}=e,{reductionIndices:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:c,originalAxes:h,inputWasTransposed:d}=$l(i,a,t);if(d){let g=t.dataIdMap.get(u.dataId).id;l=u,o=g}let p=l.shape.length;R.assertAxesAreInnerMostDims("max",c,p);let[f,m]=R.computeOutAndReduceShapes(l.shape,c),A=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;qb(o,A,g)}if(d&&t.disposeData(u.dataId),s){let g=R.expandShapeToKeepDim(y.shape,h);y.shape=g}return y}var LX={kernelName:Ss,backendName:"wasm",setupFunc:zX,kernelFunc:PX},WX=!1,BX=mn(Ts,WX),Xb;function VX(e){Xb=e.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function UX(e){let{inputs:t,attrs:n,backend:r}=e,a=t.x,s=r.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=n,c=R.computePool2DInfo(a.shape,i,o,1,l,u),h=c.filterHeight,d=c.filterWidth,p=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,A=c.padInfo.left,y=c.dilationHeight,g=c.dilationWidth,_=c.strideHeight,b=c.strideWidth,w=c.inChannels,x=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let N=r.makeOutput(c.outShape,"float32"),S=r.dataIdMap.get(N.dataId).id;return Xb(s,a.shape[0],a.shape[1],a.shape[2],h,d,p,f,m,A,y,g,_,b,w,x,S),N}var HX={kernelName:Cs,backendName:"wasm",setupFunc:VX,kernelFunc:UX},Kb;function jX(e){Kb=e.wasm.cwrap(Es,null,["number, number, number"])}function GX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let b=t.dataIdMap.get(c.dataId).id;b!==o&&(u=c,l=b,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,A]=R.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=u;u.dtype!=="float32"&&(g=_p({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(g.dataId).id);let _=t.makeOutput(m,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(_.dataId).id;Kb(l,y,b)}if(p&&t.disposeData(c.dataId),s){let b=R.expandShapeToKeepDim(_.shape,d);_.shape=b}return u.dtype!=="float32"&&t.disposeData(g.dataId),_}var qX={kernelName:Es,backendName:"wasm",setupFunc:jX,kernelFunc:GX},Zb;function XX(e){Zb=e.wasm.cwrap(Rs,null,["number, number, number"])}function KX(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t);if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_)}let f=u.shape.length;R.assertAxesAreInnerMostDims("min",h,f);let[m,A]=R.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;Zb(l,y,_)}if(p&&t.disposeData(c.dataId),s){let _=R.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var ZX={kernelName:Rs,backendName:"wasm",setupFunc:XX,kernelFunc:KX},YX=!1,JX=mn(Fs,YX),QX=!0,eK=mn(Ms,QX),tK=Nn(No);function oA(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 Yb;function nK(e){Yb=e.wasm.cwrap(To,"number",["number","number","number","number","number"])}function rK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=r,{boxes:o,scores:l}=n,u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,h=Yb(u,c,s,a,i),{pSelectedIndices:d,selectedSize:p,pSelectedScores:f,pValidOutputs:m}=oA(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([p],"int32",d)}var aK={kernelName:To,backendName:"wasm",setupFunc:nK,kernelFunc:rK},Jb;function sK(e){Jb=e.wasm.cwrap(Co,"number",["number","number","number","number","number","bool"])}function iK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,d=Jb(c,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=oA(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([],"int32",A);return[y,g]}var oK={kernelName:Co,backendName:"wasm",setupFunc:sK,kernelFunc:iK},Qb;function lK(e){Qb=e.wasm.cwrap(Eo,"number",["number","number","number","number","number","number"])}function uK(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,d=Qb(c,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:A}=oA(t,d);t.wasm._free(A);let y=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[y,g]}var cK={kernelName:Eo,backendName:"wasm",setupFunc:lK,kernelFunc:uK},hK=!1,dK=mn(So,hK,"bool"),e3;function pK(e){e3=e.wasm.cwrap($s,null,["number","number","number","number","number"])}function fK(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),u=n.dataIdMap.get(l.dataId).id,c=n.dataIdMap.get(a.dataId).id;return e3(c,s,i,o,u),l}var mK={kernelName:$s,backendName:"wasm",setupFunc:pK,kernelFunc:fK};function AK(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(1),r}var yK={kernelName:Ro,backendName:"wasm",kernelFunc:AK};function gK(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return iA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(c=>{let h=iA({inputs:{input:c},backend:n,attrs:{dim:a}});return o.push(h),h}),u=Mb({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(c=>n.disposeData(c.dataId)),u}var xK={kernelName:Fo,backendName:"wasm",kernelFunc:gK},t3;function wK(e){t3=e.wasm.cwrap(Ds,null,["number","array","number","number","array","array","number","number"])}function _K(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),p=new Uint8Array(new Int32Array(h).buffer);return t3(i,u,t.shape.length,Pn[t.dtype],d,p,a,l),o}var bK={kernelName:Ds,backendName:"wasm",kernelFunc:_K,setupFunc:wK},vK=!1,kK=mn(Os,vK),n3;function IK(e){n3=e.wasm.cwrap(zs,null,["number","number","number"])}function NK(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 n3(s,i,l),o}var SK={kernelName:zs,backendName:"wasm",setupFunc:IK,kernelFunc:NK},r3;function TK(e){r3=e.wasm.cwrap(Mo,null,["number","number","number","number"])}function CK(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,A]=R.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;r3(l,y,Pn[g.dtype],_)}if(p&&t.disposeData(c.dataId),s){let _=R.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var EK={kernelName:Mo,backendName:"wasm",setupFunc:TK,kernelFunc:CK},RK=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=Cm(r,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},FK={kernelName:wu,backendName:"wasm",kernelFunc:RK},MK=!0,$K=mn(gs,MK),DK=Nn(Ps),OK=Nn(Ws),a3;function zK(e){a3=e.wasm.cwrap(Ls,null,["number","number","number","number","number","number","number","number","number","number"])}function PK(e){let{backend:t,inputs:n,attrs:r}=e,{images:a}=n,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,[c,h,d,p]=a.shape,f=[c,l,u,p],m=t.dataIdMap.get(a.dataId),A;m.dtype!=="float32"&&(A=_p({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(A.dataId));let y=m.id,g=t.makeOutput(f,"float32");if(v.sizeFromShape(a.shape)===0)return g;let _=t.dataIdMap.get(g.dataId).id;return a3(y,c,h,d,p,l,u,s?1:0,i?1:0,_),A!=null&&t.disposeData(A.dataId),g}var LK={kernelName:Ls,backendName:"wasm",setupFunc:zK,kernelFunc:PK},s3;function WK(e){s3=e.wasm.cwrap(Bs,null,["number","array","number","array","number","number"])}function BK(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=v.parseAxisParam(s,a.shape);if(a.shape.length===0)return xp({inputs:{x:a},backend:n});let o=n.makeOutput(a.shape,a.dtype),l=n.dataIdMap.get(a.dataId).id,u=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);s3(l,c,i.length,h,a.shape.length,u);let d=_r({inputs:{x:o},attrs:{shape:a.shape},backend:n});return n.disposeData(o.dataId),d}var VK={kernelName:Bs,backendName:"wasm",kernelFunc:BK,setupFunc:WK},i3;function UK(e){i3=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number","number","number","array","number","number"])}function HK(e){let{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=r,l=n.makeOutput(a.shape,a.dtype),u=n.dataIdMap.get(a.dataId).id,c=n.dataIdMap.get(l.dataId).id,[h,d,p,f]=a.shape,[m,A]=R.getImageCenter(o,d,p),y=i===0,g=255,_=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],b=new Uint8Array(new Int32Array(_).buffer);return i3(u,h,d,p,f,s,m,A,b,_.length,c),l}var jK={kernelName:Ko,backendName:"wasm",kernelFunc:HK,setupFunc:UK},GK=Nn(Vs),qK=Nn(Us),o3;function XK(e){o3=e.wasm.cwrap(Oo,null,["number","number","number","number","number","number","array","number","number"])}function KK(e){let{backend:t,inputs:n,attrs:r}=e,{indices:a,updates:s}=n,{shape:i}=r,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:h,outputSize:d}=Nf.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 o3(p,f,Pn[s.dtype],l,u,c,m,d,A),o}var ZK={kernelName:Oo,backendName:"wasm",setupFunc:XK,kernelFunc:KK},l3;function YK(e){l3=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function JK(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=n.dataIdMap.get(r.dataId).id,o=n.dataIdMap.get(a.dataId).id,l=n.dataIdMap.get(s.dataId).id,u=n.makeOutput(a.shape,a.dtype),c=n.dataIdMap.get(u.dataId).id,h=r.shape.length,d=a.shape.length,p=h===0||h>1||d===1?1:v.sizeFromShape(a.shape.slice(1));return l3(i,o,l,p,c),u}var QK={kernelName:zo,backendName:"wasm",kernelFunc:JK,setupFunc:YK},u3;function eZ(e){u3=e.wasm.cwrap(js,null,["number","number"])}function tZ(e){let{backend:t,inputs:{x:n}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),s=t.dataIdMap.get(a.dataId).id;return v.sizeFromShape(a.shape)===0||u3(r,s),a}var nZ={kernelName:"Sigmoid",backendName:"wasm",setupFunc:eZ,kernelFunc:tZ},rZ=Nn(Hs);function bp(e){let{inputs:{x:t},attrs:{begin:n,size:r},backend:a}=e,[s,i]=cn.parseSliceParams(t,n,r),o=cn.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),c=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=cn.computeFlatOffset(s,c);return t.dtype==="string"?h.stringBytes=l.slice(f,f+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+v.sizeFromShape(i))),u}if(t.dtype==="string"){let f=Yd(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let d=a.typedArrayFromHeap(u),p=t.shape.length;if(p===2)aZ(l,c[0],d,s,i);else if(p===3)sZ(l,c[0],c[1],d,s,i);else if(p===4)iZ(l,c[0],c[1],c[2],d,s,i);else{let f=Yd(l,s,i,t.shape,t.dtype);d.set(f)}return u}function aZ(e,t,n,r,a){let s=0,i=r[0],o=r[1],l=i+a[0];for(let u=i;u<l;u++){let c=u*t+o;n.set(e.subarray(c,c+a[1]),s),s+=a[1]}}function sZ(e,t,n,r,a,s){let i=0,o=a[0],l=a[1],u=a[2],c=o+s[0],h=l+s[1];for(let d=o;d<c;d++)for(let p=l;p<h;p++){let f=d*t+p*n+u;r.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function iZ(e,t,n,r,a,s,i){let o=0,l=s[0],u=s[1],c=s[2],h=l+i[0],d=u+i[1],p=c+i[2],f=s[3];for(let m=l;m<h;m++)for(let A=u;A<d;A++)for(let y=c;y<p;y++){let g=m*t+A*n+y*r+f;a.set(e.subarray(g,g+i[3]),o),o+=i[3]}}var oZ={kernelName:Lo,backendName:"wasm",kernelFunc:bp},c3;function lZ(e){c3=e.wasm.cwrap(Xs,null,["number","number","number","number"])}function uZ(e){let{backend:t,inputs:{logits:n},attrs:{dim:r}}=e,a=t.dataIdMap.get(n.dataId).id,s=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(s.dataId).id,o=n.shape[r],l=v.sizeFromShape(n.shape)/o;return v.sizeFromShape(s.shape)===0||c3(a,i,o,l),s}var cZ={kernelName:Xs,backendName:"wasm",setupFunc:lZ,kernelFunc:uZ};function hZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),u=new Array(a.shape.length).fill(0),c=a.shape.slice();return l.map(h=>{let d=[...c];d[o]=h;let p=bp({inputs:{x:a},attrs:{begin:u,size:d},backend:r});return u[o]+=h,p})}var dZ={kernelName:Uo,backendName:"wasm",kernelFunc:hZ},pZ=Nn(Gs),fZ=Nn(vu),mZ=!0,AZ=mn(Ks,mZ),h3;function yZ(e){h3=e.wasm.cwrap(Ta,null,["number","number","number"])}function gZ(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 h3(i,a,l),o}var xZ={kernelName:Ta,backendName:"wasm",setupFunc:yZ,kernelFunc:gZ},d3;function wZ(e){d3=e.wasm.cwrap(Ho,null,["number","array","number","array","array","array","array","array","number","number"])}function _Z(e){let{backend:t,inputs:n,attrs:r}=e,{x:a}=n,{begin:s,end:i,strides:o}=r;o==null&&(o=new Array(s.length));let{beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:d}=r,p=R.slice_util.maskToAxes(c);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(c!==0&&h!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(c!==0&&d!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=a.shape.length-s.length,m=R.slice_util.maskToAxes(h),A=a.shape.slice();m.forEach(M=>{s[M]=0,i[M]=1,A.splice(M,0,1)});let y=_r({inputs:{x:a},attrs:{shape:A},backend:t}),{begin:g,end:_,strides:b}=R.slice_util.getNormalizedAxes(y.shape,p,f,s,i,o,l,u,c);s=g,i=_,o=b;let w=R.slice_util.maskToAxes(d);w.forEach(M=>{i[M]=s[M]+1,o[M]=1});let x=R.slice_util.computeOutShape(s,i,o),N=x.filter((M,D)=>w.indexOf(D)===-1);if(o.every(M=>M===1)){let M=bp({inputs:{x:y},attrs:{begin:s,size:x},backend:t});t.disposeData(y.dataId);let D=_r({inputs:{x:M},attrs:{shape:N},backend:t});return t.disposeData(M.dataId),D}let S=t.makeOutput(N,"float32");if(!N.some(M=>M===0)){let M=t.dataIdMap.get(y.dataId).id,D=new Uint8Array(new Int32Array(v.computeStrides(y.shape)).buffer),z=new Uint8Array(new Int32Array(s).buffer),W=new Uint8Array(new Int32Array(i).buffer),U=new Uint8Array(new Int32Array(o).buffer),H=new Uint8Array(new Int32Array(N).buffer),X=new Uint8Array(new Int32Array(v.computeStrides(N)).buffer),j=t.dataIdMap.get(S.dataId).id;d3(M,D,y.shape.length,z,W,U,H,X,N.length,j)}t.disposeData(y.dataId);let T=_r({inputs:{x:S},attrs:{shape:N},backend:t});return t.disposeData(S.dataId),T}var bZ={kernelName:Ho,backendName:"wasm",setupFunc:wZ,kernelFunc:_Z},vZ=!0,kZ=mn(Zs,vZ),p3;function IZ(e){p3=e.wasm.cwrap(qs,null,["number, number, number"])}function NZ(e){let{backend:t,inputs:n,attrs:r}=e,{axis:a,keepDims:s}=r,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:c,axes:h,originalAxes:d,inputWasTransposed:p}=$l(i,a,t),f=h;if(p){let _=t.dataIdMap.get(c.dataId).id;_!==o&&(u=c,l=_,f=R.getInnerMostAxes(f.length,u.shape.length))}R.assertAxesAreInnerMostDims("sum",f,u.shape.length);let[m,A]=R.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(A),g=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let _=t.dataIdMap.get(g.dataId).id;p3(l,y,_)}if(p&&t.disposeData(c.dataId),s){let _=R.expandShapeToKeepDim(g.shape,d);g.shape=_}return g}var SZ={kernelName:qs,backendName:"wasm",setupFunc:IZ,kernelFunc:NZ},TZ=Nn(Ys),f3;function CZ(e){f3=e.wasm.cwrap(Sa,null,["number","array","number","array","number","number"])}function EZ(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,s=n.dataIdMap.get(a.dataId).id,{reps:i}=r,o=new Array(a.shape.length);for(let d=0;d<o.length;d++)o[d]=a.shape[d]*i[d];let l=new Uint8Array(new Int32Array(a.shape).buffer),u=new Uint8Array(new Int32Array(o).buffer),c=n.makeOutput(o,a.dtype),h=n.dataIdMap.get(c.dataId).id;return f3(s,l,a.shape.length,u,o.length,Pn[c.dtype],h),c}var RZ={kernelName:Sa,backendName:"wasm",setupFunc:CZ,kernelFunc:EZ},m3;function FZ(e){m3=e.wasm.cwrap(Go,null,["number","array","number","number","number","bool","number","number"])}var MZ=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{k:a,sorted:s}=n,i=t.dataIdMap.get(r.dataId).id,o=new Uint8Array(new Int32Array(r.shape).buffer),l=r.shape.slice();l[l.length-1]=a;let u=t.makeOutput(l,r.dtype),c=t.dataIdMap.get(u.dataId).id,h=t.makeOutput(l,"int32"),d=t.dataIdMap.get(h.dataId).id;return m3(i,o,r.shape.length,Pn[r.dtype],a,s,c,d),[u,h]},$Z={kernelName:Go,backendName:"wasm",setupFunc:FZ,kernelFunc:MZ};function DZ(e){let{inputs:t,backend:n,attrs:r}=e,{value:a}=t,{axis:s}=r;s<0&&(s+=a.shape.length);let i=a.shape[s],o=a.shape.length,l=new Array(o-1),u=0;for(let p=0;p<o;p++)p!==s&&(l[u++]=a.shape[p]);let c=new Array(i),h=new Array(o).fill(0),d=a.shape.slice();d[s]=1;for(let p=0;p<c.length;p++)h[s]=p,c[p]=bp({inputs:{x:a},attrs:{begin:h,size:d},backend:n});return c.map(({dataId:p,dtype:f})=>({dataId:p,dtype:f,shape:l}))}var OZ={kernelName:qo,backendName:"wasm",kernelFunc:DZ};function zZ(e){let{inputs:{x:t},backend:n}=e,r=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(r).fill(0),r}var PZ={kernelName:Xo,backendName:"wasm",kernelFunc:zZ},LZ=[aq,iq,uq,yq,wq,kq,Iq,Nq,Cq,Eq,Mq,Oq,zq,Wq,Uq,Gq,Kq,Yq,Jq,Qq,tX,aX,sX,oX,rq,cX,pX,AX,xX,bX,kX,NX,cq,CX,RX,MX,$X,OX,LX,BX,HX,qX,ZX,JX,eK,tK,aK,oK,cK,dK,mK,yK,xK,bK,kK,SK,EK,FK,$K,DK,OK,_q,LK,VK,jK,qK,GK,ZK,QK,nZ,rZ,oZ,cZ,dZ,pZ,fZ,AZ,xZ,bZ,kZ,SZ,TZ,RZ,$Z,fq,OZ,PZ];for(let e of LZ)ni(e);var lA=J();lA.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])));lA.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(lA.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 A3=qi(j8()),WZ='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()}}}}',BZ=qi(G8()),y3=class extends su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new xh(this,Fr())}write(e,t,n){let r={id:this.dataIdNextNumber++};return this.move(r,e,t,n,1),r}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,r,a){let s=this.dataIdNextNumber++;if(r==="string"){let u=t;this.dataIdMap.set(e,{id:s,stringBytes:u,shape:n,dtype:r,memoryOffset:null,refCount:a});return}let i=v.sizeFromShape(n),o=i*v.bytesPerElement(r),l=this.wasm._malloc(o);this.dataIdMap.set(e,{id:s,memoryOffset:l,shape:n,dtype:r,refCount:a}),this.wasm.tfjs.registerTensor(s,i,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:r,stringBytes:a}=this.dataIdMap.get(e);if(n==="string")return a;let s=this.wasm.HEAPU8.slice(t,t+v.sizeFromShape(r)*v.bytesPerElement(n));return VZ(s.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let r;if(n==null)r=this.write(null,e,t);else{let a=this.dataIdNextNumber++;r={id:a},this.dataIdMap.set(r,{id:a,memoryOffset:n,shape:e,dtype:t,refCount:1});let s=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(a,s,n)}return{dataId:r,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let r=this.wasm.HEAPU8.buffer,{memoryOffset:a}=this.dataIdMap.get(n),s=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(r,a,s);case"int32":return new Int32Array(r,a,s);case"bool":return new Uint8Array(r,a,s);default:throw new Error(`Unknown dtype ${t}`)}}};function UZ(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(r=>{r.ok||t.env.a(`failed to load wasm binary file at '${e}'`),r.arrayBuffer().then(a=>{WebAssembly.instantiate(a,t).then(s=>{n(s.instance)})})}),{})}function g3(e,t,n){if(vp!=null)return vp;let r="tfjs-backend-wasm.wasm";return e&&t?r="tfjs-backend-wasm-threaded-simd.wasm":e&&(r="tfjs-backend-wasm-simd.wasm"),mc!=null&&mc[r]!=null?mc[r]:n+r}async function HZ(){let[e,t]=await Promise.all([J().getAsync("WASM_HAS_SIMD_SUPPORT"),J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,r)=>{let a={};a.locateFile=(o,l)=>{if(o.endsWith(".worker.js")){let u=WZ,c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return o.endsWith(".wasm")?g3(e,t,Ac!=null?Ac:l):l+o},uA&&(a.instantiateWasm=UZ(g3(e,t,Ac!=null?Ac:"")));let s=!1;a.onAbort=()=>{s||yc||(yc=!0,r({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))};let i;t&&e&&vp==null?(a.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+A3.default.toString()],{type:"text/javascript"}),i=(0,A3.default)(a)):i=(0,BZ.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 VZ(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 jZ=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],vp=null,Ac=null,mc={},yc=!1,uA=!1;function GZ(e,t=!1){if(Ff("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),yc)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");vp=e,uA=t}function qZ(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=jZ.filter(r=>mc[r]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}uA=t}var x3="3.3.0",XZ=2;sl("wasm",async()=>{let{wasm:e}=await HZ();return new y3(e)},XZ);Z().prototype.abs=function(){return this.throwIfDisposed(),zt(this)};Z().prototype.acos=function(){return this.throwIfDisposed(),$f(this)};Z().prototype.acosh=function(){return this.throwIfDisposed(),Df(this)};Z().prototype.add=function(e){return this.throwIfDisposed(),se(this,e)};Z().prototype.all=function(e,t){return this.throwIfDisposed(),dd(this,e,t)};Z().prototype.any=function(e,t){return this.throwIfDisposed(),Ou(this,e,t)};Z().prototype.argMax=function(e){return this.throwIfDisposed(),zu(this,e)};Z().prototype.argMin=function(e){return this.throwIfDisposed(),Of(this,e)};Z().prototype.asScalar=function(){return this.throwIfDisposed(),F(this.size===1,()=>"The array must have only 1 element."),G(this,[])};Z().prototype.asType=function(e){return this.throwIfDisposed(),me(this,e)};Z().prototype.as1D=function(){return this.throwIfDisposed(),G(this,[this.size])};Z().prototype.as2D=function(e,t){return this.throwIfDisposed(),G(this,[e,t])};Z().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),G(this,[e,t,n])};Z().prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),G(this,[e,t,n,r])};Z().prototype.as5D=function(e,t,n,r,a){return this.throwIfDisposed(),G(this,[e,t,n,r,a])};Z().prototype.asin=function(){return this.throwIfDisposed(),zf(this)};Z().prototype.asinh=function(){return this.throwIfDisposed(),Pf(this)};Z().prototype.atan=function(){return this.throwIfDisposed(),Lf(this)};Z().prototype.atan2=function(e){return this.throwIfDisposed(),Wf(this,e)};Z().prototype.atanh=function(){return this.throwIfDisposed(),Bf(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(),jf(this)};Z().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),bn(this,e,t)};Z().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ye&&(e=[e]),st([this,...e],t)};Z().prototype.conv1d=function(e,t,n,r,a,s){return this.throwIfDisposed(),fd(this,e,t,n,r,a,s)};Z().prototype.conv2dTranspose=function(e,t,n,r,a){return this.throwIfDisposed(),md(this,e,t,n,r,a)};Z().prototype.conv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),ra(this,e,t,n,r,a,s)};Z().prototype.cos=function(){return this.throwIfDisposed(),Vu(this)};Z().prototype.cosh=function(){return this.throwIfDisposed(),Ad(this)};Z().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),yd(this,e,t,n)};Z().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),Xf(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(),Kf(this,e,t,n,r,a)};Z().prototype.divNoNan=function(e){return this.throwIfDisposed(),Zf(this,e)};Z().prototype.div=function(e){return this.throwIfDisposed(),ge(this,e)};Z().prototype.dot=function(e){return this.throwIfDisposed(),Y5(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(),Yf(this)};Z().prototype.exp=function(){return this.throwIfDisposed(),qn(this)};Z().prototype.expandDims=function(e){return this.throwIfDisposed(),Fn(this,e)};Z().prototype.expm1=function(){return this.throwIfDisposed(),Jf(this)};Z().prototype.fft=function(){return this.throwIfDisposed(),Ju(this)};Z().prototype.flatten=function(){return this.throwIfDisposed(),G(this,[this.size])};Z().prototype.floor=function(){return this.throwIfDisposed(),hl(this)};Z().prototype.floorDiv=function(e){return this.throwIfDisposed(),hd(this,e)};Z().prototype.gather=function(e,t){return this.throwIfDisposed(),ui(this,e,t)};Z().prototype.greaterEqual=function(e){return this.throwIfDisposed(),za(this,e)};Z().prototype.greater=function(e){return this.throwIfDisposed(),ar(this,e)};Z().prototype.ifft=function(){return this.throwIfDisposed(),Al(this)};Z().prototype.irfft=function(){return this.throwIfDisposed(),$d(this)};Z().prototype.isFinite=function(){return this.throwIfDisposed(),J5(this)};Z().prototype.isInf=function(){return this.throwIfDisposed(),Q5(this)};Z().prototype.isNaN=function(){return this.throwIfDisposed(),ex(this)};Z().prototype.leakyRelu=function(e){return this.throwIfDisposed(),Hu(this,e)};Z().prototype.lessEqual=function(e){return this.throwIfDisposed(),ci(this,e)};Z().prototype.less=function(e){return this.throwIfDisposed(),xd(this,e)};Z().prototype.localResponseNormalization=function(e,t,n,r){return this.throwIfDisposed(),em(this,e,t,n,r)};Z().prototype.logSigmoid=function(){return this.throwIfDisposed(),rx(this)};Z().prototype.logSoftmax=function(e){return this.throwIfDisposed(),bd(this,e)};Z().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),rm(this,e,t)};Z().prototype.log=function(){return this.throwIfDisposed(),Mn(this)};Z().prototype.log1p=function(){return this.throwIfDisposed(),wd(this)};Z().prototype.logicalAnd=function(e){return this.throwIfDisposed(),sr(this,e)};Z().prototype.logicalNot=function(){return this.throwIfDisposed(),ju(this)};Z().prototype.logicalOr=function(e){return this.throwIfDisposed(),vd(this,e)};Z().prototype.logicalXor=function(e){return this.throwIfDisposed(),ox(this,e)};Z().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),je(this,e,t,n)};Z().prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),Gu(this,e,t,n,r)};Z().prototype.max=function(e,t){return this.throwIfDisposed(),Xn(this,e,t)};Z().prototype.maximum=function(e){return this.throwIfDisposed(),Dr(this,e)};Z().prototype.mean=function(e,t){return this.throwIfDisposed(),kt(this,e,t)};Z().prototype.min=function(e,t){return this.throwIfDisposed(),pl(this,e,t)};Z().prototype.minimum=function(e){return this.throwIfDisposed(),fl(this,e)};Z().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),sm(this,e,t)};Z().prototype.mod=function(e){return this.throwIfDisposed(),im(this,e)};Z().prototype.mul=function(e){return this.throwIfDisposed(),P(this,e)};Z().prototype.neg=function(){return this.throwIfDisposed(),vt(this)};Z().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Pd(this,e,t,n)};Z().prototype.notEqual=function(e){return this.throwIfDisposed(),di(this,e)};Z().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),nl(this,e,t,n)};Z().prototype.onesLike=function(){return this.throwIfDisposed(),$n(this)};Z().prototype.pad=function(e,t){return this.throwIfDisposed(),aa(this,e,t)};Z().prototype.pool=function(e,t,n,r,a){return this.throwIfDisposed(),cx(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(),Id(this,e,t)};Z().prototype.reciprocal=function(){return this.throwIfDisposed(),um(this)};Z().prototype.relu=function(){return this.throwIfDisposed(),zr(this)};Z().prototype.relu6=function(){return this.throwIfDisposed(),Sd(this)};Z().prototype.reshapeAs=function(e){return this.throwIfDisposed(),G(this,e.shape)};Z().prototype.reshape=function(e){return this.throwIfDisposed(),G(this,e)};Z().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),Tx(this,e,t,n)};Z().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),Cx(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(),cm(this)};Z().prototype.rsqrt=function(){return this.throwIfDisposed(),Td(this)};Z().prototype.selu=function(){return this.throwIfDisposed(),Cd(this)};Z().prototype.separableConv2d=function(e,t,n,r,a,s){return this.throwIfDisposed(),hm(this,e,t,n,r,a,s)};Z().prototype.sigmoid=function(){return this.throwIfDisposed(),Rn(this)};Z().prototype.sign=function(){return this.throwIfDisposed(),dm(this)};Z().prototype.sin=function(){return this.throwIfDisposed(),Ed(this)};Z().prototype.sinh=function(){return this.throwIfDisposed(),Rd(this)};Z().prototype.slice=function(e,t){return this.throwIfDisposed(),Re(this,e,t)};Z().prototype.softmax=function(e){return this.throwIfDisposed(),Yu(this,e)};Z().prototype.softplus=function(){return this.throwIfDisposed(),dl(this)};Z().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),qu(this,e,t)};Z().prototype.split=function(e,t){return this.throwIfDisposed(),Lt(this,e,t)};Z().prototype.sqrt=function(){return this.throwIfDisposed(),en(this)};Z().prototype.square=function(){return this.throwIfDisposed(),lt(this)};Z().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Dd(this,e)};Z().prototype.squeeze=function(e){return this.throwIfDisposed(),Pa(this,e)};Z().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ye?[this,e]:[this,...e];return hn(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(),fm(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(),mm(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(),Am(this,e,t)};Z().prototype.transpose=function(e){return this.throwIfDisposed(),at(this,e)};Z().prototype.unique=function(e){return this.throwIfDisposed(),zd(this,e)};Z().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),ym(this,e,t)};Z().prototype.unstack=function(e){return this.throwIfDisposed(),ir(this,e)};Z().prototype.where=function(e,t){return this.throwIfDisposed(),vn(e,this,t)};Z().prototype.zerosLike=function(){return this.throwIfDisposed(),Ue(this)};var w3={kernelName:Zi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,yl(me(n,"float32"),-1))}}},KZ={kernelName:Yi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=lt(me(n,"float32")),a=en(ye(be(1),r));return vt(ge(e,a))}}}},ZZ={kernelName:Ji,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=en(ye(lt(me(n,"float32")),1));return ge(e,r)}}}},YZ={kernelName:Ia,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=e,i=Pt(n.shape,a);return i.length>0&&(s=Te(s,i)),G(s,n.shape)},b:()=>{let s=e,i=Pt(r.shape,a);return i.length>0&&(s=Te(s,i)),G(s,r.shape)}}}},JZ={kernelName:os,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((r,a)=>{n[a]=()=>e.clone()}),n}},QZ={kernelName:ls,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ue(n)}}},eY={kernelName:lu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>Ue(n)}}},tY={kernelName:Qi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,en(ye(be(1),lt(me(n,"float32")))))}}},nY={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=en(se(be(1),lt(me(n,"float32"))));return ge(e,r)}}}},rY={kernelName:ro,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=se(lt(n),lt(r)),i=P(e,ge(r,s)),o=Pt(n.shape,a);return o.length>0&&(i=Te(i,o)),G(i,n.shape)},b:()=>{let s=se(lt(n),lt(r)),i=vt(P(e,ge(n,s))),o=Pt(r.shape,a);return o.length>0&&(i=Te(i,o)),G(i,r.shape)}}}},aY={kernelName:to,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(lt(me(n,"float32")),1))}}},sY={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,ye(be(1),lt(me(n,"float32"))))}}};function iY(e,t,n,r,a,s){let i=E(e,"dy","avgPool3dGrad"),o=E(t,"input","avgPool3dGrad"),l=i,u=o,c=!1;o.rank===4&&(c=!0,l=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),u=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),F(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),F(u.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${u.rank}.`),s!=null&&F(jt(a),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${s} but got pad ${a}.`);let h={dy:l,input:u},d={filterSize:n,strides:r,pad:a,dimRoundingMode:s},p=$.runKernel(Nh,h,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var oY=O({avgPool3dGrad_:iY}),lY={kernelName:uu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i,dimRoundingMode:o}=n;return{x:()=>oY(e,r,a,s,i,o)}}};function uY(e,t,n,r,a){let s=E(e,"dy","avgPoolGrad"),i=E(t,"input","avgPoolGrad");F(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let o=i,l=s,u=!1;i.rank===3&&(u=!0,o=G(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=G(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),F(o.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);let c={dy:l,input:o},h={filterSize:n,strides:r,pad:a},d=$.runKernel(Ih,c,h);return u?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var cY=O({avgPoolGrad_:uY}),hY={kernelName:us,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{filterSize:a,strides:s,pad:i}=n;return{x:()=>cY(e,r,a,s,i)}}},dY={kernelName:cs,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[r,a]=t,{transposeA:s,transposeB:i}=n;return!s&&!i?{a:()=>je(e,a,!1,!0),b:()=>je(r,e,!0,!1)}:!s&&i?{a:()=>je(e,a,!1,!1),b:()=>je(e,r,!0,!1)}:s&&!i?{a:()=>je(a,e,!1,!0),b:()=>je(r,e,!1,!1)}:{a:()=>je(a,e,!0,!0),b:()=>je(e,r,!0,!0)}}},pY={kernelName:cu,gradFunc:(e,t,n)=>{let{blockShape:r,crops:a}=n;return{x:()=>qu(e,r,a)}}},fY={kernelName:jg,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)}}},mY={kernelName:hs,gradFunc:e=>({x:()=>e.clone()})},AY={kernelName:ds,gradFunc:e=>({x:()=>Ue(e)})},yY={kernelName:Na,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{clipValueMin:a,clipValueMax:s}=n;return{x:()=>vn(sr(za(r,a),ci(r,s)),e,Ue(e))}}},gY={kernelName:hu,inputsToSave:["x"],gradFunc:w3.gradFunc},xY={kernelName:ao,saveAllInputs:!0,gradFunc:(e,t,n)=>{let r=t.map(o=>o.shape),{axis:a}=n,s=tr(a,t[0].shape)[0],i=r.map(o=>o[s]);return Lt(e,i,s).map(o=>()=>o)}},wY={kernelName:ps,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,{dilations:s,strides:i,pad:o,dataFormat:l}=n;return F($a(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>Gf(r.shape,e,a,i,o,l),filter:()=>_m(r,e,a.shape,i,o,l)}}},_Y={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:()=>_m(e,r,a.shape,s,i,o,l)}}};function bY(e,t,n,r,a){let s=e;e.rank===4&&(s=G(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let i=t;i.rank===4&&(i=G(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),F(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),F(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),F(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),F(s.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${n[3]}.`),F(i.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);let o={x:s,dy:i},l={strides:r,pad:a,filterShape:n};return $.runKernel(Eh,o,l)}var vY=O({conv3DBackpropFilter_:bY}),kY={kernelName:du,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s}=n;F($a(r),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${r}'`);let[i,o]=t;return{x:()=>K5(i.shape,e,o,a,s),filter:()=>vY(i,e,o.shape,a,s)}}},IY={kernelName:ms,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(vt(Ed(me(n,"float32"))),e)}}},NY={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Rd(me(n,"float32")),e)}}},SY={kernelName:As,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a,exclusive:s,reverse:i}=n;return{x:()=>{let o=ix([a],r.rank),l=yd(e,a,s,!i);return o!=null&&(l=at(l,o)),l}}}},TY={kernelName:ys,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:r,strides:a,pad:s,dimRoundingMode:i}=n,o=r==null?[1,1]:r;F($a(o),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);let[l,u]=t;return F(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),F(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${u.rank}.`),F(l.shape[3]===u.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),F(Mr(a,o),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${o}'.`),i!=null&&F(jt(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`),{x:()=>_x(l.shape,e,u,a,s,r,i),filter:()=>wx(l,e,u.shape,a,s,r,i)}}},CY={kernelName:pu,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[r,a]=t,s={x:r,filter:a,dy:e},i={x:r,filter:a,dy:e};return{x:()=>$.runKernel(Oh,s,n),filter:()=>$.runKernel(zh,i,n)}}},EY={kernelName:lo,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,r={dy:e,y:n};return{x:()=>$.runKernel(Ph,r)}}},RY={kernelName:uo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(qn(vt(lt(n))),2/Math.sqrt(Math.PI));return{x:()=>P(e,r)}}},FY={kernelName:xs,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,n)}}},MY={kernelName:ho,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>G(e,n.shape)}}},$Y={kernelName:po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,qn(n))}}},DY={kernelName:ws,gradFunc:e=>({x:()=>Ue(e)})},OY={kernelName:_s,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=ge(e,me(r,"float32")),i=Pt(n.shape,a);return i.length>0?G(Te(s,i),n.shape):s},b:()=>{let s=P(e,me(n,"float32")),i=Pt(r.shape,a);i.length>0&&(s=G(Te(s,i),r.shape));let o=lt(r);return vt(ge(s,me(o,"float32")))}}}},zY={kernelName:bs,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:r}=n,[a,s,i,o]=t,l=o==null?be(1):o,u=Pt(s.shape,a.shape),c=[];if(s.rank===1){for(let m=0;m<a.shape.length-1;++m)c.push(a.shape[m]);c.push(1)}let h=ye(a,s),d=P(e,l),p=Td(se(i,be(r))),f=P(P(P(p,p),p),be(-.5));return{x:()=>s.rank===1?G(P(P(e,Oa(G(p,[1,1,1,s.shape[0]]),c)),l),a.shape):G(P(P(e,p),l),a.shape),mean:()=>{let m=P(P(p,be(-1)),d);return s.rank===1&&(m=Te(m,u)),G(m,s.shape)},variance:()=>{let m=P(P(f,h),d);return s.rank===1&&(m=Te(m,u)),G(m,s.shape)},scale:()=>{let m=P(h,p),A=P(e,m);return s.rank===1&&(A=Te(A,u)),G(A,s.shape)},offset:()=>{let m=e;return s.rank===1&&(m=Te(m,u)),G(m,s.shape)}}}},PY={kernelName:mo,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[r,a]=t,{axis:s}=n,i=tr(s,r.shape)[0];return{x:()=>{let o=r.shape,l=a.size,u=o.slice(0,i),c=u.length,h=o.slice(s,o.length).slice(1),d=h.length,p=_3(0,c),f=_3(c+1,c+1+d),m=b3([u,[l],h]),A=G(e,m),y=G(a,[l]),g=b3([[c],p,f]),_=at(A,g),b=ym(_,y,r.shape[i]),w=nm(g);return b=at(b,w),b},indices:()=>a}}};function _3(e,t){let n=[];for(let r=e;r<t;++r)n.push(r);return n}function b3(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 LY={kernelName:vs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>Ue(n),b:()=>Ue(r)}}},WY={kernelName:ks,gradFunc:e=>({x:()=>me(e,"float32")})},BY={kernelName:go,gradFunc:e=>({x:()=>Ue(e)})},VY={kernelName:xo,gradFunc:e=>({x:()=>Ue(e)})},UY={kernelName:wo,gradFunc:e=>({x:()=>Ue(e)})},HY={kernelName:Is,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{alpha:a}=n,s=ar(r,0);return{x:()=>vn(s,e,P(e,a))}}},jY={kernelName:vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,se(n,1))}}},GY={kernelName:Ns,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,me(n,"float32"))}}},qY={kernelName:Gg,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n;return{logits:()=>{let s=!0,i=qn(r);return ye(e,P(Te(e,a,s),i))}}}};function XY(e,t,n,r=5,a=1,s=1,i=.5){let o={x:e,y:t,dy:n},l={depthRadius:r,bias:a,alpha:s,beta:i};return $.runKernel(Uh,o,l)}var KY=O({localResponseNormalizationBackprop_:XY}),ZY={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:()=>KY(r,a,e,s,i,o,l)}}};function v3(e,t,n,r){return t.rank<n.rank&&(t=G(t,hi(t.shape,r))),e.rank<n.rank&&(e=G(e,hi(e.shape,r))),{x:()=>P(e,me(Da(n,t),e.dtype))}}var k3={kernelName:Ss,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{reductionIndices:a}=r,s=t[0],i=t[1],o=tr(a,s.shape),l=v3(e,i,s,o);return{x:()=>l.x()}}},YY={kernelName:Ts,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,me(za(n,r),"float32")),b:()=>P(e,me(xd(n,r),"float32"))}}};function JY(e,t,n,r,a,s,i){let o=E(e,"dy","maxPool3dGrad"),l=E(t,"input","maxPool3dGrad"),u=E(n,"output","maxPool3dGrad"),c=o,h=l,d=u,p=!1;l.rank===4&&(p=!0,c=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),h=G(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),d=G(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]])),F(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),F(h.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${h.rank}.`),F(d.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`),i!=null&&F(jt(s),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let f={dy:c,input:h,output:d},m={filterSize:r,strides:a,pad:s,dimRoundingMode:i},A=$.runKernel(jh,f,m);return p?G(A,[A.shape[1],A.shape[2],A.shape[3],A.shape[4]]):A}var QY=O({maxPool3dGrad_:JY}),eJ={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:()=>QY(e,r,a,s,i,o,l)}}};function tJ(e,t,n,r,a,s,i){let o=E(e,"dy","maxPoolGrad"),l=E(t,"input","maxPoolGrad"),u=E(n,"output","maxPoolGrad");F(l.rank===o.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${o.rank})`),F(o.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`),F(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),i!=null&&F(jt(s),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${i} but got pad ${s}.`);let c={dy:o,input:l,output:u},h={filterSize:r,strides:a,pad:s,dimRoundingMode:i};return $.runKernel(Hh,c,h)}var nJ=O({maxPoolGrad_:tJ}),rJ={kernelName:Cs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r,a]=t,{filterSize:s,strides:i,pad:o}=n;return{x:()=>nJ(e,r,a,s,i,o)}}},aJ={kernelName:Es,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{axis:a}=n,s=tr(a,r.shape),i=sx(r.shape,s)[1],o=Dt(i);return{x:()=>{let l=r.shape.slice();s.forEach(c=>{l[c]=1});let u=G(e,l);return ge(P(u,Or(r.shape,"float32")),o)}}}},sJ={kernelName:Rs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let r=n,{axis:a}=r,[s,i]=t,o=tr(a,s.shape),l=v3(e,i,s,o);return{x:()=>l.x()}}},iJ={kernelName:Fs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t;return{a:()=>P(e,me(ci(n,r),"float32")),b:()=>P(e,me(ar(n,r),"float32"))}}},oJ={kernelName:xu,inputsToSave:["x"],gradFunc:(e,t,n)=>{let r=t[0],{paddings:a}=n,s=a.map(i=>i[0]);return{x:()=>Re(e,s,r.shape)}}},lJ={kernelName:Io,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=Pt(n.shape,a);return s.length>0?G(Te(e,s),n.shape):e},b:()=>{let s=P(e,vt(hl(ge(n,r)))),i=Pt(r.shape,a);return i.length>0?G(Te(s,i),r.shape):s}}}},uJ={kernelName:Ms,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=P(e,me(r,"float32")),i=Pt(n.shape,a);return i.length>0?G(Te(s,i),n.shape):s},b:()=>{let s=P(e,me(n,"float32")),i=Pt(r.shape,a);return i.length>0?G(Te(s,i),r.shape):s}}}},cJ={kernelName:No,gradFunc:e=>({x:()=>vt(e)})},hJ={kernelName:$s,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>Et(n.shape,"float32")}}},dJ={kernelName:Ro,gradFunc:e=>({x:()=>Ue(e)})},pJ={kernelName:Fo,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:r}=n;return ir(e,r).map(a=>()=>a)}},I3={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)}}},fJ={kernelName:Os,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,r,a]=t,s=n,i=r,o=yt(s.shape,i.shape);return{a:()=>{let l=me(i,"float32"),u=P(e,P(l,sa(s,ye(l,be(1))))),c=Pt(s.shape,o);return c.length>0&&(u=Te(u,c)),G(u,s.shape)},b:()=>{let l=ar(s,0),u=vn(l,Mn(s),Ue(s)),c=P(e,P(a,u)),h=Pt(i.shape,o);return h.length>0&&(c=Te(c,h)),G(c,i.shape)}}}},mJ={kernelName:zs,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,r]=t,a=ar(n,0);return{x:()=>vn(a,e,P(e,r)),alpha:()=>{let s=vn(a,Ue(e),P(e,n)),i=Pt(r.shape,e.shape);return i.length>0&&(s=Te(s,i)),G(s,r.shape)}}}},AJ={kernelName:gs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=ge(e,me(r,"float32")),i=Pt(n.shape,a);return i.length>0?G(Te(s,i),n.shape):s},b:()=>{let s=P(e,me(n,"float32")),i=Pt(r.shape,a);i.length>0&&(s=G(Te(s,i),r.shape));let o=lt(r);return vt(ge(s,me(o,"float32")))}}}},yJ={kernelName:$o,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,vt(lt(n)))}}},gJ={kernelName:Ws,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,r=P(ci(n,6),yl(n));return{x:()=>P(e,me(r,"float32"))}}},xJ={kernelName:Ps,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,me(yl(n),"float32"))}}},wJ={kernelName:Do,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>G(e,n.shape)}}},_J={kernelName:Ls,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Zh,a,n)}}},bJ={kernelName:_u,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[r]=t,a={dy:e,images:r};return{images:()=>$.runKernel(Kh,a,n)}}},vJ={kernelName:Bs,gradFunc:(e,t,n)=>{let{dims:r}=n,a=tr(r,e.shape);return{x:()=>Dn(e,a)}}},kJ={kernelName:Vs,gradFunc:e=>({x:()=>Ue(e)})},IJ={kernelName:Us,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>vt(ge(e,P(sa(n,1.5),2)))}}},NJ={kernelName:zo,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>me(Ue(n),"float32"),t:()=>P(e,me(n,e.dtype)),e:()=>P(e,me(ju(n),e.dtype))}}},SJ={kernelName:Po,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let r=ar(n,be(0)),a=be(Fx),s=be(Mx),i=P(e,s),o=P(P(e,a),qn(me(n,"float32")));return vn(r,i,o)}}}},TJ={kernelName:js,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(n,ye(be(1),n)))}}},CJ={kernelName:Bo,gradFunc:e=>({x:()=>Ue(e)})},EJ={kernelName:Hs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Vu(me(n,"float32")),e)}}},RJ={kernelName:Wo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(Ad(me(n,"float32")),e)}}},FJ={kernelName:Lo,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{begin:a,size:s}=n,i=r.shape,[o,l]=R5(r,a,s),u=[];for(let c=0;c<e.rank;c++)u.push([o[c],i[c]-o[c]-l[c]]);return{x:()=>aa(e,u)}}},MJ={kernelName:Xs,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[r]=t,{dim:a}=n,s=!0,i=P(e,r);return{logits:()=>ye(i,P(Te(i,[a],s),r))}}},$J={kernelName:Vo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,Rn(n))}}},N3={kernelName:bu,gradFunc:(e,t,n)=>{let{blockShape:r,paddings:a}=n;return{x:()=>Wu(e,r,a)}}},S3={kernelName:Uo,gradFunc:(e,t,n)=>{let{axis:r}=n;return{x:()=>st(e,r)}}},DJ={kernelName:Gs,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,P(en(me(n,"float32")),2))}}},OJ={kernelName:vu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(e,P(me(n,"float32"),2))}}},zJ={kernelName:Ks,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=be(2);return{a:()=>P(e,P(a,ye(n,r))),b:()=>P(e,P(a,ye(r,n)))}}},PJ={kernelName:Ta,gradFunc:e=>({x:()=>Ue(e)})},LJ={kernelName:Zs,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,r]=t,a=yt(n.shape,r.shape);return{a:()=>{let s=e,i=Pt(n.shape,a);return i.length>0&&(s=Te(s,i)),G(s,n.shape)},b:()=>{let s=e,i=Pt(r.shape,a);return i.length>0&&(s=Te(s,i)),G(vt(s),r.shape)}}}},WJ={kernelName:qs,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,a=r.shape.slice(),{axis:s}=n;tr(s,r.shape).forEach(l=>{a[l]=1});let i=G(e,a),o=P(i,Or(r.shape,"float32"));return{x:()=>o}}},BJ={kernelName:jo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>ge(e,lt(Vu(n)))}}},VJ={kernelName:Ys,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>P(ye(be(1),lt(n)),e)}}},UJ={kernelName:Sa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[r]=t,{reps:a}=n;return{x:()=>{let s=Ue(r);if(r.rank===1)for(let i=0;i<a[0];++i)s=se(s,Re(e,[i*r.shape[0]],[r.shape[0]]));else if(r.rank===2)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1]],[r.shape[0],r.shape[1]]));else if(r.rank===3)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2]],[r.shape[0],r.shape[1],r.shape[2]]));else if(r.rank===4)for(let i=0;i<a[0];++i)for(let o=0;o<a[1];++o)for(let l=0;l<a[2];++l)for(let u=0;u<a[3];++u)s=se(s,Re(e,[i*r.shape[0],o*r.shape[1],l*r.shape[2],u*r.shape[3]],[r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${r.rank} tensors yet.`);return s}}}},HJ={kernelName:Js,gradFunc:(e,t,n)=>{let r=n,{perm:a}=r,s=nm(a);return{x:()=>at(e,s)}}},jJ={kernelName:qo,gradFunc:(e,t,n)=>{let r=n,{axis:a}=r;return{value:()=>hn(e,a)}}},qJ={kernelName:ku,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>GJ(e,n)}}};function GJ(e,t){let n=Dr(t,Ue(t)),r=ui(e,n),a=za(t,be(0,"int32")),s=r.rank-a.rank;for(let o=0;o<s;++o)a=Fn(a,o+1);a=sr(a,Or(r.shape,"bool"));let i=Ue(r);return vn(a,r,i)}var XJ={kernelName:Xo,gradFunc:e=>({x:()=>Ue(e)})},KJ=[w3,KZ,ZZ,YZ,JZ,QZ,eY,tY,nY,rY,aY,sY,lY,hY,dY,pY,fY,mY,AY,yY,gY,xY,_Y,wY,kY,IY,NY,SY,TY,CY,AJ,EY,RY,FY,MY,$Y,OY,DY,zY,PY,LY,WY,BY,VY,UY,HY,jY,GY,qY,ZY,k3,k3,YY,eJ,rJ,aJ,sJ,iJ,oJ,lJ,uJ,cJ,hJ,dJ,pJ,I3,I3,fJ,mJ,yJ,gJ,xJ,wJ,_J,bJ,vJ,kJ,IJ,NJ,SJ,TJ,CJ,EJ,RJ,FJ,MJ,$J,N3,N3,S3,S3,DJ,zJ,OJ,PJ,LJ,WJ,BJ,VJ,UJ,HJ,jJ,qJ,XJ];for(let e of KJ)qg(e);var T3={};ze(T3,{maxNorm:()=>ZJ,minMaxNorm:()=>QJ,nonNeg:()=>JJ,unitNorm:()=>YJ});var cA;function Wt(){return cA==null&&(cA=P5().epsilon()),cA}function br(){return"channelsLast"}var ua=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,ua.prototype)}},vr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,vr.prototype)}},V=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,V.prototype)}},De=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,De.prototype)}},C3=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,C3.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 C3(t)}function E3(e,t){let n=0;for(let r of e)r===t&&n++;return n}function Sn(e){return e.length===1?e[0]:e}function ft(e){return Array.isArray(e)?e:[e]}function ca(e){let t=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return t[0]!=="_"?t:"private"+t}function ki(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var lr={};function hA(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function dA(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>dA(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:dA(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 V(`Unknown ${r}: ${e}. This may be due to one of the following reasons:
|
|
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
|
|
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=e;if(s.className==null||s.config==null)throw new V(`${r}: Improper config format: ${JSON.stringify(s)}.
|
|
'className' and 'config' must set.`);let i=s.className,o,l;if(i in n?[o,l]=n[i]:i in lr?[o,l]=lr.className:i in t&&([o,l]=t[i]),o==null)throw new V(`Unknown ${r}: ${i}. This may be due to one of the following reasons:
|
|
1. The ${r} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
|
|
2. The custom ${r} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let u={};for(let p of Object.keys(lr))u[p]=lr[p];for(let p of Object.keys(n))u[p]=n[p];let c=s.config;c.customObjects=u;let h=Object.assign({},lr);for(let p of Object.keys(n))lr[p]=n[p];dA(s.config);let d=l(o,s.config,n,a);return lr=Object.assign({},h),d}else{let u=Object.assign({},lr);for(let h of Object.keys(n))lr[h]=n[h];let c=new o(s.config);return lr=Object.assign({},u),c}}}function eQ(e,t){return e<t?-1:e>t?1:0}function kp(e,t){return-1*eQ(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 tQ(e){if(e==null)throw new V(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function Ii(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new V(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function pA(e,t,n=0,r=Infinity){return Br(n>=0),Br(r>=n),Array.isArray(e)&&e.length>=n&&e.length<=r&&e.every(a=>typeof a===t)}function Xt(e,t){Array.isArray(e)?(v.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,r)=>Xt(n,`element ${r+1} of ${t}`))):v.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${R3(e)}.`)}function R3(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>R3(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function nQ(e,t){let n=v.now(),r;return(...a)=>{let s=v.now();return s-n<t||(n=s,r=e(...a)),r}}function F3(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function fA(e,t){return B(()=>en(Te(P(e,e),t,!0)))}var xc=class extends re.Serializable{getConfig(){return{}}},mA=class extends xc{constructor(e){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return B(()=>{let t=fA(e,this.axis),n=bn(t,0,this.maxValue);return P(e,ge(n,se(Wt(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};mA.className="MaxNorm";re.registerClass(mA);var AA=class extends xc{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return B(()=>ge(e,se(Wt(),fA(e,this.axis))))}getConfig(){return{axis:this.axis}}};AA.className="UnitNorm";re.registerClass(AA);var yA=class extends xc{apply(e){return zr(e)}};yA.className="NonNeg";re.registerClass(yA);var gA=class extends xc{constructor(e){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=e.minValue!=null?e.minValue:this.defaultMinValue,this.maxValue=e.maxValue!=null?e.maxValue:this.defaultMaxValue,this.rate=e.rate!=null?e.rate:this.defaultRate,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return B(()=>{let t=fA(e,this.axis),n=se(P(this.rate,bn(t,this.minValue,this.maxValue)),P(1-this.rate,t));return P(e,ge(n,se(Wt(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};gA.className="MinMaxNorm";re.registerClass(gA);var M3={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Bt(e){return hA(e)}function $3(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"constraint")}function Vt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in M3?M3[e]:e,config:{}};return $3(t)}else return e instanceof xc?e:$3(e)}function ZJ(e){return new mA(e)}function YJ(e){return new AA(e)}function JJ(){return new yA}function QJ(e){return new gA(e)}var D3={};ze(D3,{constant:()=>sQ,glorotNormal:()=>dQ,glorotUniform:()=>hQ,heNormal:()=>pQ,heUniform:()=>fQ,identity:()=>uQ,leCunNormal:()=>mQ,leCunUniform:()=>AQ,ones:()=>aQ,orthogonal:()=>yQ,randomNormal:()=>oQ,randomUniform:()=>iQ,truncatedNormal:()=>lQ,varianceScaling:()=>cQ,zeros:()=>rQ});var gQ=["channelsFirst","channelsLast"],xQ=["nearest","bilinear"],wQ=["valid","same","causal"],_Q=["max","avg"],bQ=["sum","mul","concat","ave"],Dl=new Map;function Tt(e){Ii(gQ,"DataFormat",e)}function vQ(e){Ii(xQ,"InterpolationFormat",e)}function Yn(e){Ii(wQ,"PaddingMode",e)}function O3(e){Ii(_Q,"PoolMode",e)}var wc=[],z3="/";function Ni(e,t){wc.push(e);try{let n=t();return wc.pop(),n}catch(n){throw wc.pop(),n}}function kQ(){return wc.length===0?"":wc.join(z3)+z3}function L3(e){if(!P3(e))throw new Error("Not a valid tensor name: '"+e+"'");return kQ()+e}function W3(e){if(!P3(e))throw new Error("Not a valid tensor name: '"+e+"'");Dl.has(e)||Dl.set(e,0);let t=Dl.get(e);if(Dl.set(e,Dl.get(e)+1),t>0){let n=`${e}_${t}`;return Dl.set(n,1),n}else return e}var IQ=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function P3(e){return!!e.match(IQ)}function NQ(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 B3(e){return e=Array.isArray(e)?new Float32Array(e):e,on(e)}function Ol(e){return pl(B3(e)).dataSync()[0]}function ja(e){return Xn(B3(e)).dataSync()[0]}function kr(e,t){if(t<e)throw new V(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let r=e;r<t;++r)n.push(r);return n}function _c(e,t){return e.asType(t)}function bc(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),e.reshape(n)}function SQ(e,t){return B(()=>{if(e.shape.length!==2)throw new V(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=bc(e,1);return xA(n,[1,t,1])})}function TQ(e){let t=[Ha(e.shape)];return e.reshape(t)}function CQ(e){if(e.rank<=1)throw new V(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],Ha(e.shape,1)];return e.reshape(t)}function Si(e,t,n){return B(()=>{switch(e.rank){case 1:return Fd(e,t,n);case 2:return pm(e,[t,0],[n,e.shape[1]]);case 3:return Md(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return Zu(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Re(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Re(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new V(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function wA(e,t,n){return B(()=>{switch(e.rank){case 1:return Fd(e,t,n);case 2:return pm(e,[0,t],[e.shape[0],n]);case 3:return Md(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return Zu(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new V(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Ip(e,t,n,r){return B(()=>{switch(e.rank){case 1:return Fd(e,t,n);case 2:switch(r){case 1:return Si(e,t,n);case 2:return wA(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return Si(e,t,n);case 2:return Md(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return wA(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return Si(e,t,n);case 2:return Zu(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return Zu(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return wA(e,t,n);default:throw new V(`The axis is not within the rank of the tensor ${r}`)}default:throw new V(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function _A(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),st(e,t)}function V3(e,t){switch(e.rank){case 1:return G5([e,t]);case 2:return ll([e,t],0);case 3:return q5([e,t],0);case 4:return X5([e,t],0);default:throw new V(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function xA(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new V(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Oa(e,t)}function Np(e,t=0,n=1,r,a){return hx(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?bA(e.rank,r,br()):null,activation:n})}else{let a=e.shape.slice(),s=a.pop();e=e.reshape([-1,s]);let i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],c=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=t.transpose(c).reshape([l,-1]);let h=[...a,...u],d=!1,p=!1;return La.matMul({a:e,b:t,transposeA:d,transposeB:p,bias:r?bA(e.rank,r,br()):null,activation:n}).reshape(h)}}function U3(e,t,n){return B(()=>(Array.isArray(t)?t=on(t,"int32"):t=t.toInt(),ui(e,t,n)))}function vc(e){return P(e,e)}function bA(e,t,n){let r=t.shape;if(t.rank!==1&&t.rank!==e)throw new V(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1,1]):t.reshape([1,r[3],r[0],r[1],r[2]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===4){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1,1]):t.reshape([1,r[2],r[0],r[1]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,1,r[0]]):t.reshape([1].concat(r))}else if(e===3){if(n==="channelsFirst")return r.length===1?t.reshape([1,r[0],1]):t.reshape([1,r[1],r[0]]);if(n==="channelsLast")return r.length===1?t.reshape([1,1,r[0]]):t.reshape([1].concat(r))}else if(e<3)return t;throw new V(`Unsupported input rank by biasAdd: ${t.rank}`)}function Ur(e,t,n){return B(()=>(n==null&&(n=br()),Tt(n),e.add(bA(e.rank,t,n))))}function EQ(e,t=1){if(t!==1)throw new De(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return cl(e)}function RQ(e){return B(()=>ge(e,zt(e).add(1)))}function H3(e,t,n,r){return B(()=>gx(e,t,n,r))}function FQ(e){return B(()=>{let t=se(.5,P(.2,e));return bn(t,0,1)})}function kc(e,t,n=!1){return n?e():t()}var MQ=["fanIn","fanOut","fanAvg"],$Q=["normal","uniform","truncatedNormal"];function DQ(e){Ii(MQ,"FanMode",e)}function OQ(e){Ii($Q,"Distribution",e)}var ur=class extends re.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},vA=class extends ur{apply(e,t){return Et(e,t)}};vA.className="Zeros";re.registerClass(vA);var Sp=class extends ur{apply(e,t){return Or(e,t)}};Sp.className="Ones";re.registerClass(Sp);var kA=class extends ur{constructor(e){super();if(typeof e!="object")throw new V(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new V(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return B(()=>P(be(this.value),Or(e,t)))}getConfig(){return{value:this.value}}};kA.className="Constant";re.registerClass(kA);var IA=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}}};IA.className="RandomUniform";re.registerClass(IA);var NA=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 Np(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};NA.className="RandomNormal";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(`truncatedNormal does not support dType ${t}.`);return Od(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};SA.className="TruncatedNormal";re.registerClass(SA);var TA=class extends ur{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return B(()=>{if(e.length!==2||e[0]!==e[1])throw new V("Identity matrix initializer can only be used for 2D square matrices.");return P(this.gain,Qf(e[0]))})}getConfig(){return{gain:this.gain}}};TA.className="Identity";re.registerClass(TA);function zQ(e,t="channelsLast"){let n,r;if(Tt(t),e.length===2)n=e[0],r=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let a=Ha(e,2);n=e[1]*a,r=e[0]*a}else if(t==="channelsLast"){let a=Ha(e,0,e.length-2);n=e[e.length-2]*a,r=e[e.length-1]*a}}else{let a=Ha(e);n=Math.sqrt(a),r=Math.sqrt(a)}return[n,r]}var Tn=class extends ur{constructor(e){super();if(e.scale<0)throw new V(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,DQ(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,OQ(this.distribution),this.seed=e.seed}apply(e,t){let n=zQ(e),r=n[0],a=n[1],s=this.scale;if(this.mode==="fanIn"?s/=Math.max(1,r):this.mode==="fanOut"?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),this.distribution==="normal"){let i=Math.sqrt(s);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new De(`${this.getClassName()} does not support dType ${t}.`);return Od(e,0,i,t,this.seed)}else{let i=Math.sqrt(3*s);return ml(e,-i,i,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Tn.className="VarianceScaling";re.registerClass(Tn);var Tp=class extends Tn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Tp.className="GlorotUniform";re.registerClass(Tp);var Cp=class extends Tn{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Cp.className="GlorotNormal";re.registerClass(Cp);var Ep=class extends Tn{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Ep.className="HeNormal";re.registerClass(Ep);var Rp=class extends Tn{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Rp.className="HeUniform";re.registerClass(Rp);var Fp=class extends Tn{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Fp.className="LeCunNormal";re.registerClass(Fp);var Mp=class extends Tn{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return Tn.className}};Mp.className="LeCunNormal";re.registerClass(Mp);var CA=class extends ur{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new De("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return B(()=>{if(e.length<2)throw new De("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,r=Np(n,0,1,"float32"),a=Rx.gramSchmidt(r);return e[0]>e[1]&&(a=a.transpose()),P(this.gain,a)})}getConfig(){return{gain:this.gain,seed:this.seed}}};CA.className="Orthogonal";re.registerClass(CA);var j3={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 G3(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"initializer")}function It(e){return hA(e)}function xt(e){if(typeof e=="string"){let t=e in j3?j3[e]:e;if(t==="GlorotNormal")return new Cp;if(t==="GlorotUniform")return new Tp;if(t==="HeNormal")return new Ep;if(t==="HeUniform")return new Rp;if(t==="LeCunNormal")return new Fp;if(t==="LeCunUniform")return new Mp;{let n={};return n.className=t,n.config={},G3(n)}}else return e instanceof ur?e:G3(e)}function rQ(){return new vA}function aQ(){return new Sp}function sQ(e){return new kA(e)}function iQ(e){return new IA(e)}function oQ(e){return new NA(e)}function lQ(e){return new SA(e)}function uQ(e){return new TA(e)}function cQ(e){return new Tn(e)}function hQ(e){return new Tp(e)}function dQ(e){return new Cp(e)}function pQ(e){return new Ep(e)}function fQ(e){return new Rp(e)}function mQ(e){return new Fp(e)}function AQ(e){return new Mp(e)}function yQ(e){return new CA(e)}var q3={};ze(q3,{Layer:()=>Ge,RNN:()=>Hr,RNNCell:()=>Ic,activation:()=>QQ,add:()=>lee,alphaDropout:()=>jee,average:()=>uee,averagePooling1d:()=>EA,averagePooling2d:()=>RA,averagePooling3d:()=>FA,avgPool1d:()=>gee,avgPool2d:()=>wee,avgPool3d:()=>bee,avgPooling1d:()=>xee,avgPooling2d:()=>_ee,avgPooling3d:()=>vee,batchNormalization:()=>mee,bidirectional:()=>zee,concatenate:()=>cee,conv1d:()=>jQ,conv2d:()=>GQ,conv2dTranspose:()=>qQ,conv3d:()=>XQ,convLstm2d:()=>Mee,convLstm2dCell:()=>$ee,cropping2D:()=>ZQ,dense:()=>eee,depthwiseConv2d:()=>JQ,dot:()=>fee,dropout:()=>tee,elu:()=>LQ,embedding:()=>oee,flatten:()=>ree,gaussianDropout:()=>Hee,gaussianNoise:()=>Uee,globalAveragePooling1d:()=>kee,globalAveragePooling2d:()=>Iee,globalMaxPool1d:()=>Lee,globalMaxPool2d:()=>Wee,globalMaxPooling1d:()=>K3,globalMaxPooling2d:()=>Z3,gru:()=>See,gruCell:()=>Tee,input:()=>X3,inputLayer:()=>PQ,layerNormalization:()=>Aee,leakyReLU:()=>BQ,lstm:()=>Cee,lstmCell:()=>Eee,masking:()=>Gee,maxPool1d:()=>Bee,maxPool2d:()=>Vee,maxPooling1d:()=>Y3,maxPooling2d:()=>J3,maxPooling3d:()=>Nee,maximum:()=>hee,minimum:()=>dee,multiply:()=>pee,permute:()=>iee,prelu:()=>VQ,reLU:()=>WQ,repeatVector:()=>aee,reshape:()=>see,rnn:()=>Dee,separableConv2d:()=>KQ,simpleRNN:()=>Ree,simpleRNNCell:()=>Fee,softmax:()=>UQ,spatialDropout1d:()=>nee,stackedRNNCells:()=>Oee,thresholdedReLU:()=>HQ,timeDistributed:()=>Pee,upSampling2d:()=>YQ,zeroPadding2d:()=>yee});var qee=0;function Q3(){return qee++}var $p={};function Dp(e=""){return e in $p||($p[e]=0),$p[e]+=1,e+$p[e].toString()}function MA(e){return Array.isArray(e)&&Array.isArray(e[0])}function Op(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Pe(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new V(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ct(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new V(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function zp(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((r,a)=>r*a);return t}var e7="Variable",t7=class{constructor(e,t="float32",n=e7,r=!0,a=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=Q3(),n=n==null?e7:n,this.originalName=L3(n),this.name=W3(this.originalName),this.trainable_=r,this.constraint=a,this.val=px(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Xee(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 Xee(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function $A(e){return e.map(t=>t.read())}function DA(e){e.forEach(t=>{t[0].write(t[1])})}var Kt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Ir=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=Q3(),s!=null&&(this.originalName=L3(s),this.name=W3(this.originalName)),this.rank=t.length}},Kee=0,Pp=class{constructor(e,t){this.callArgs=t,this.id=Kee++,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}}},Zee=0,Ge=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Zee++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=ca(n)+"_"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),n=[a].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new vr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new V(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Sn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Sn(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 Sn(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 Sn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=ft(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=ft(this.inputSpec);if(e.length!==t.length)throw new V(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let r=e[n],a=t[n];if(a==null)continue;let s=r.rank;if(a.ndim!=null&&s!==a.ndim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&r.dtype!==a.dtype)throw new V(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${r.dtype}.`);if(a.axes){let i=r.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],c=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=r.shape[i];if(o!=null&&l!=null&&o!==l)throw new V(`Input ${n} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${r.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=ft(e),r=!0;for(let s of n)if(!(s instanceof Ir)){r=!1;break}let a=!0;for(let s of n)if(s instanceof Ir){a=!1;break}if(r===a)throw new V("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 ft(e))s.push(i.shape);this.build(Sn(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=ft(s),o=[];for(let l of i)n.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Sn(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=Yee(e),i=this.computeOutputShape(s),o,l=Jee(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,c)=>new Ir(l,u,this,ft(e),t,this.name,c)):o=new Ir(l,i,this,ft(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new De("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,r)=>{n!=null&&e[r]!=null&&e[r]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ua(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ua(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new vr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return zp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return $A(e?this.trainableWeights:this.weights)}setWeights(e){B(()=>{let t=this.weights;if(t.length!==e.length)throw new V(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],r=$A(t);for(let a=0;a<r.length;++a){let s=r[a],i=t[a],o=e[a];if(!v.arraysEqual(s.shape,o.shape))throw new V(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}DA(n)})}addWeight(e,t,n,r,a,s,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new V(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(r=xt("zeros"));let o=r.apply(t,n),l=new t7(o,n,e,s,i);return o.dispose(),a!=null&&this.addLoss(()=>a.apply(l.read())),s==null&&(s=!0),s?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=ft(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,r,a,s,i=null){let o=ft(e);t=ft(t),n=ft(n),r=ft(r),a=Op(a),s=Op(s);let l=[],u=[],c=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),c.push(h.tensorIndex);new Pp({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function Yee(e){e=ft(e);let t=[];for(let n of e)t.push(n.shape);return Sn(t)}function Jee(e){return"float32"}function n7(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let r=t.inboundNodes[n];if(r.inboundLayers.length===0)return r.inputTensors;{let a=[];for(let s=0;s<r.inboundLayers.length;s++){let i=r.inputTensors[s],o=r.inboundLayers[s],l=r.nodeIndices[s],u=n7(i,o,l);for(let c of u)a.indexOf(c)===-1&&a.push(c)}return a}}}var zl=class extends Ge{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Dp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new V("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new V("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new V("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let r=new Ir(this.dtype,this.batchInputShape,this,[],{},this.name);r.nodeIndex=0,r.tensorIndex=0,new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[r],outputTensors:[r],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new V(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};zl.className="InputLayer";re.registerClass(zl);function r7(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new V("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new zl({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Ga(e){if(e==null)return;let t=[],n=[],r=[];for(let a in e){let s=e[a];if(typeof s!="number"){let i=s;t.push(i.data()),n.push(a),r.push(i)}}if(t.length>0){let a=await Promise.all(t);for(let s=0;s<a.length;++s)e[n[s]]=a[s][0];Ee(r)}}function a7(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var s7;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(s7||(s7={}));var Qee=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){}},i7=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)}},ete=class extends Pl{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let r in t){let a=t[r];if(typeof a=="number")this.totals.hasOwnProperty(r)||(this.totals[r]=0),this.totals[r]=this.totals[r]+a*n;else{let s;r in this.totals?s=this.totals[r]:this.totals[r]=0;let i=B(()=>se(this.totals[r],P(a,n)));this.totals[r]=i,s!=null&&s.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:B(()=>{let r=P(ge(1,this.seen),this.totals[n]);t[n]=r,this.totals[n].dispose(),Gt(t[n])}))}},o7=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]}},l7=class extends Pl{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Qee),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");v.isNumber(this.yieldEvery)&&(this.maybeWait=nQ(this.maybeWait.bind(this),this.yieldEvery)),this.trainBegin=e.onTrainBegin,this.trainEnd=e.onTrainEnd,this.epochBegin=e.onEpochBegin,this.epochEnd=e.onEpochEnd,this.batchBegin=e.onBatchBegin,this.batchEnd=e.onBatchEnd,this.yield=e.onYield}async maybeWait(e,t,n){let r=[];this.yield!=null&&(await Ga(n),r.push(this.yield(e,t,n))),r.push(Kd()),await Promise.all(r)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Ga(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Ga(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(Kd()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Ga(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Ga(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(Kd()):v.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Ga(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Ga(e),await this.trainEnd(e))}};function u7(e,t){return e==null&&(e={}),e instanceof Pl?[e]:Array.isArray(e)&&e[0]instanceof Pl?e:ft(e).map(n=>new l7(n,t))}var cr=class{constructor(){}static registerCallbackConstructor(e,t){v.assert(e>=0&&Number.isInteger(e),()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`),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 V("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 c7(e,t,n,r,a,s,i,o,l){let u=new o7,c=[new ete,...cr.createCallbacks(t)];e!=null&&c.push(...e),c.push(u);let h=new i7(c);return h.setParams({epochs:n,initialEpoch:r,samples:a,steps:s,batchSize:i,verbose:t,doValidation:o,metrics:l}),{callbackList:h,history:u}}function Nr(e,t={},n=!1){return gc(e,re.SerializationMap.getMap().classNameMap,t,"layer",n)}function Lp(e,t){return B(()=>{e.dtype!=="float32"&&(e=e.asType("float32"));let n=Te(vc(e),t,!0),r=Uu(n.shape,Wt()),a=en(Dr(n,r));return ge(e,a)})}function Ti(e,t){return B(()=>kt(vc(ye(t,e)),-1))}function Wp(e,t){return B(()=>kt(zt(ye(t,e)),-1))}function Ll(e,t){return B(()=>{let n=ye(e,t),r=bn(zt(e),Wt(),Number.MAX_VALUE),a=zt(ge(n,r));return P(100,kt(a,-1))})}function tte(e,t){return B(()=>{let n=bn(t,Wt(),Number.MAX_VALUE),r=Mn(se(1,n)),a=bn(e,Wt(),Number.MAX_VALUE),s=Mn(se(1,a));return kt(vc(ye(r,s)),-1)})}function nte(e,t){return B(()=>{let n=Dr(0,ye(1,P(e,t)));return kt(vc(n),-1)})}function rte(e,t){return B(()=>{let n=Dr(0,ye(1,P(e,t)));return kt(n,-1)})}function ate(e,t){return B(()=>{let n=Te(P(e,t),-1),r=Xn(P(ye(1,e),t),-1);return Dr(0,se(1,ye(r,n)))})}function ste(e,t){return B(()=>{let n=Math.log(2),r=ye(t,e),a=ye(se(r,dl(P(-2,r))),n);return kt(a,-1)})}function Nc(e,t,n=!1){return B(()=>{if(n)t=Yu(t);else{let r=Te(t,t.shape.length-1,!0);t=ge(t,r)}return t=bn(t,Wt(),1-Wt()),vt(Te(P(e.toFloat(),Mn(t)),t.shape.length-1))})}function Bp(e,t,n=!1){return B(()=>{let r=hl(TQ(e)).toInt();t=bn(t,Wt(),1-Wt());let a=t.shape,s=nl(r,a[a.length-1]).reshape(a);return Nc(s,t,n)})}function ite(e,t){if(!v.arraysEqual(e.shape,t.shape))throw new V(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return B(()=>{let n=t.relu(),r=t.abs().neg();return n.sub(t.mul(e)).add(r.exp().log1p())})}function Vp(e,t){return B(()=>{let n;return n=bn(t,Wt(),1-Wt()),n=Mn(ge(n,ye(1,n))),kt(ite(e,n),-1)})}function ote(e,t){return B(()=>{let n=bn(e,Wt(),1),r=bn(t,Wt(),1);return Te(P(e,Mn(ge(n,r))),-1)})}function lte(e,t){return B(()=>{let n=Mn(se(Wt(),t));return kt(ye(t,P(e,n)),-1)})}function OA(e,t){return B(()=>{let n=Lp(e,-1),r=Lp(t,-1),a=P(n,r);return vt(Te(a,-1))})}var Up={meanSquaredError:Ti,meanAbsoluteError:Wp,meanAbsolutePercentageError:Ll,meanSquaredLogarithmicError:tte,squaredHinge:nte,hinge:rte,categoricalHinge:ate,logcosh:ste,categoricalCrossentropy:Nc,sparseCategoricalCrossentropy:Bp,binaryCrossentropy:Vp,kullbackLeiblerDivergence:ote,poisson:lte,cosineProximity:OA};function zA(e){if(typeof e=="string"){if(e in Up)return Up[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new V(t)}else return e}function PA(e,t){return B(()=>{let n=P(.5,$n(t)),r=_c(ar(t,n),e.dtype);return kt(Da(e,r),-1)})}function LA(e,t){return B(()=>_c(Da(zu(e,-1),zu(t,-1)),"float32"))}function h7(e,t){return B(()=>sr(e.equal(1),t.equal(1)).sum().cast("float32"))}function ute(e,t){return B(()=>sr(e.equal(1),t.equal(0)).sum().cast("float32"))}function cte(e,t){return B(()=>sr(e.equal(0),t.equal(1)).sum().cast("float32"))}function d7(e,t){return B(()=>{let n=h7(e,t),r=cte(e,t),a=n.add(r);return vn(ar(a,0),n.div(a),0).cast("float32")})}function hte(e,t){return B(()=>{let n=h7(e,t),r=ute(e,t),a=n.add(r);return vn(ar(a,0),n.div(a),0).cast("float32")})}function p7(e,t){return Vp(e,t)}function f7(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 dte=Ti,pte=Ti,fte=Wp,mte=Wp,Ate=Ll,yte=Ll,WA=Nc,gte=OA,m7=Bp,Hp={binaryAccuracy:PA,categoricalAccuracy:LA,precision:d7,categoricalCrossentropy:WA,sparseCategoricalCrossentropy:m7,mse:dte,MSE:pte,mae:fte,MAE:mte,mape:Ate,MAPE:yte,cosine:gte};function xte(e){if(typeof e=="string"&&e in Hp)return Hp[e];if(typeof e!="string"&&e!=null)return e;throw new V(`Unknown metric ${e}`)}function jp(e){if(Br(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(Up))if(Up[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(Hp))if(Hp[n]===e){t=n;break}return t!==void 0?t:e.name}}function wte(e){let t={Adagrad:()=>fi.adagrad(.01),Adadelta:()=>fi.adadelta(1,.95,Wt()),Adam:()=>fi.adam(.001,.9,.999,Wt()),Adamax:()=>fi.adamax(.002,.9,.999,Wt(),0),RMSProp:()=>fi.rmsprop(.001,.9,0,Wt()),SGD:()=>fi.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new V(`Unknown Optimizer ${e}`)}var A7=1*1024*1024;function y7(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!BA(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let r=JSON.stringify(e);r.length>A7&&console.warn(`User-defined metadata of model "${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 <= ${A7}.`)}}function BA(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"||!BA(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!BA(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function Ite(e,t,n,r=console.log){let a=bte(e),s=["Layer (type)","Output shape","Param #"];a?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(c=>Math.floor(t*c)));let i;if(!a){s.push("Receives inputs"),i=[];for(let c in e.nodesByDepth)i.push(...e.nodesByDepth[c])}r("_".repeat(t)),Gp(s,n,r),r("=".repeat(t));let o=e.layers;for(let c=0;c<o.length;++c)a?vte(o[c],n,r):kte(o[c],n,i,r),r((c===o.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=_te(e),u=zp(e.nonTrainableWeights);r(`Total params: ${l+u}`),r(`Trainable params: ${l}`),r(`Non-trainable params: ${u}`),r("_".repeat(t))}function _te(e){let t;return e.collectedTrainableWeights!=null?t=zp(e.collectedTrainableWeights):t=zp(e.trainableWeights),t}function bte(e){let t=!0,n=[],r=[];for(let a in e.nodesByDepth)n.push(e.nodesByDepth[a]);for(let a of n){if(a.length>1||a.length===1&&a[0].inboundLayers.length>1){t=!1;break}r.push(...a)}if(t)for(let a of e.layers){let s=!1;for(let i of a.inboundNodes)if(r.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function Gp(e,t,n=console.log){let r="";for(let a=0;a<e.length;++a)a>0&&(r=r.slice(0,r.length-1)+" "),r+=e[a],r=r.slice(0,t[a]),r+=" ".repeat(t[a]-r.length);n(r)}function vte(e,t,n){let r;try{r=JSON.stringify(e.outputShape)}catch(o){r="multiple"}let a=e.name,s=e.getClassName(),i=[`${a} (${s})`,r,e.countParams().toString()];Gp(i,t,n)}function kte(e,t,n,r){let a;try{a=JSON.stringify(e.outputShape)}catch(c){a="multiple"}let s=[];for(let c of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(c)===-1))for(let h=0;h<c.inboundLayers.length;++h){let d=c.inboundLayers[h].name,p=c.nodeIndices[h],f=c.tensorIndices[h];s.push(`${d}[${p}][${f}]`)}let i=e.name,o=e.getClassName(),l=s.length===0?"":s[0],u=[`${i} (${o})`,a,e.countParams().toString(),l];Gp(u,t,r);for(let c=1;c<s.length;++c)Gp(["","","",s[c]],t,r)}function g7(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];g7(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 VA(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];g7(t,a,s)?n.push(s):n.push(VA(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]=VA(a,r)}return n}}var UA="3.3.0";function Nte(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return me(t,e.dtype)}catch(n){throw new V(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var Ci=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof Ci)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=Nte(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new V(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof Ir){if(this.id2Value[e.id]==null)throw new V(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new V(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof Ir){if(this.id2Value[e.id]==null)throw new V(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new V(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&Ee(this.id2Mask)}},HA={},x7={};function Tc(e,t,n,r){let a=n==null?!1:n.training,s=Array.isArray(e),i=s?e:[e],o=i.map(f=>f.name),l=[],u=t.names();for(let f of o)u.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);r!=null&&(r.maxNumTensors=-Infinity,r.minNumTensors=Infinity);let c=o.join(",")+"|"+t.names().join(","),h,d;if(HA[c]==null){let f=Ste(i,t);h=f.sorted,d=f.recipientCounts,HA[c]=h,x7[c]=d}h=HA[c],d={},a||Object.assign(d,x7[c]);let p=new Ci(t);for(let f=0;f<h.length;++f){if(r!=null){let T=cd().numTensors;T>r.maxNumTensors&&(r.maxNumTensors=T),T<r.minNumTensors&&(r.minNumTensors=T)}let m=h[f],A=m.sourceLayer;if(A instanceof zl)continue;let y=[],g=[],_=[],b=!1;for(let T of m.inputs){let M=p.getValue(T),D=p.getMask(T);y.push(M),g.push(D),D!=null&&(b=!0),a||(d[T.name]--,d[T.name]===0&&!t.hasKey(T)&&o.indexOf(T.name)===-1&&!M.isDisposed&&T.sourceLayer.stateful!==!0&&_.push(M))}b&&(n=n||{},n.mask=g[0]);let w=ft(A.apply(y,n)),x=null;A.supportsMasking&&(x=A.computeMask(y,g));let N=Tte(m),S=Array.isArray(N)?N:[N];for(let T=0;T<S.length;++T){p.hasKey(S[T])||p.add(S[T],w[T],Array.isArray(x)?x[0]:x);let M=o.indexOf(S[T].name);M!==-1&&(l[M]=w[T])}a||Ee(_)}return p.disposeMasks(),s?l:l[0]}function Ste(e,t){v.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],r={};if(e.length===1){let a=w7(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=w7(s,t);for(let l of i)a.has(l.name)||(n.push(l),a.add(l.name));for(let l in o)r[l]==null&&(r[l]=new Set),o[l].forEach(u=>r[l].add(u))}}return{sorted:n,recipientCounts:Cte(r)}}function Cte(e){let t={};for(let n in e)t[n]=e[n].size;return t}function w7(e,t){let n=new Set,r=[],a={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),r.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)a[u.name]==null&&(a[u.name]=new Set),a[u.name].add(o.name),!n.has(u.name)&&s.push(u)}}return{sorted:r,recipientMap:a}}function Tte(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 Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Dp(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Ua(this.inputs).length!==this.inputs.length)throw new V(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Ua(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,_=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let g=y.sourceLayer,_=y.nodeIndex,b=y.tensorIndex;Br(_===0,"input layer has >1 nodes"),Br(b===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof zl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},r={},a={},s={},i=[],o=(y,g,_,b,w,x)=>{(b==null||w==null||x==null)&&(b=y.sourceLayer,w=y.nodeIndex,x=y.tensorIndex);let N=b.inboundNodes[w];if(_.indexOf(N)!==-1)throw new vr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(jr.nodeKey(b,w)),b.id in s||(s[b.id]=Object.keys(s).length),_.indexOf(N)===-1&&_.push(N);let S=N.inboundLayers.length;for(let T=0;T<S;T++){let M=N.inputTensors[T],D=N.inboundLayers[T],z=N.nodeIndices[T],W=N.tensorIndices[T];o(M,g,_,D,z,W)}for(g.push(N);_.indexOf(N)>=0;)_.splice(_.indexOf(N),1);i.push(N)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let c=i.slice().reverse();for(let y of c){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],_=r[y.outboundLayer.id]==null?0:r[y.outboundLayer.id];g=Math.max(g,_),r[y.outboundLayer.id]=g,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],x=y.nodeIndices[b],N=w.inboundNodes[x],S=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,S),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(kp);this.layers=[];for(let y of p){let g=d[y];g.sort((_,b)=>{let w=s[_.id],x=s[b.id];return w<x?-1:w>x?1:0});for(let _ of g)_ instanceof jr&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=d,p=Object.keys(h).map(y=>parseInt(y,10)).sort(kp);let f=this.inputs.slice(),m=[];for(let y of p)for(let g of h[y]){let _=g.outboundLayer;if(_!=null){for(let b of g.inputTensors)if(f.indexOf(b)===-1)throw new vr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${_.name}". The following previous layers were accessed without issue: ${m}`);for(let b of g.outputTensors)f.push(b);m.push(_.name)}}this.nodesByDepth=h;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(_=>_===y).length;if(g!==1)throw new vr(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new V("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},r=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new V(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,r++}let a=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)a.push([n[i],e[s]]);else if(t)throw new V(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new V(`${s.length} of ${r} weights are not set: ${s}`)}DA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${UA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=VA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return B(()=>{e=ft(e);let n=new Ci;for(let r=0;r<this.inputs.length;++r)n.add(this.inputs[r],e[r]);return Tc(this.outputs,n,t)})}computeMask(e,t){return B(()=>{e=ft(e);let n;return t==null?n=vi(null,e.length):n=ft(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Op(e);if(t.length!==this.inputLayers.length)throw new V(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";n[u]=l}let r=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(kp);if(r.length>1)for(let i of r){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],A=l.nodeIndices[f],y=l.tensorIndices[f],g=`${m.name}_${A}_${y}`,_=n[g];c.push(_)}let h=u.computeOutputShape(Sn(c)),d=Op(h),p=u.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${u.name}_${p}_${f}`;n[m]=d[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],c=`${o.name}_${l}_${u}`;s.push(c)}for(let i=0;i<s.length;i++){let o=s[i];Br(o in n),a.push(n[o])}return Sn(a)}runInternalGraph(e,t){t==null&&(t=vi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],c=t[o];n[l.id]=[u,c]}let r=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(kp);for(let o of r){let l=this.nodesByDepth[o];for(let u of l){let c=u.outboundLayer,h=u.inputTensors,d=u.outputTensors,p=new Array;for(let f of h)f.id in n&&p.push(n[f.id]);if(p.length===h.length){let f={},m,A,y,g;if(u.callArgs!=null&&(f=u.callArgs),p.length===1){let[_,b]=p[0];f.mask==null&&(f.mask=b),y=ft(c.call(_,f)),g=ft(c.computeMask(_,b)),m=[_],A=[b]}else m=p.map(_=>_[0]),A=p.map(_=>_[1]),f.mask==null&&(f.mask=A),y=ft(c.call(m,f)),g=ft(c.computeMask(m,A));if(c.activityRegularizer)throw new De("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<d.length;++_){let b=d[_],w=y[_],x=g[_];n[b.id]=[w,x]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Br(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=n[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},n;for(let r of this.layers){n=r instanceof jr?1:0;for(let a=0;a<r.inboundNodes.length;a++){let s=jr.nodeKey(r,a);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new V(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new V("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new V(`No such layer: ${e}`)}calculateLosses(){return B(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let r=jr.nodeKey(t,n);this.containerNodes.has(r)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let c=0;c<s.inboundNodes.length;c++){let h=s.inboundNodes[c],d=jr.nodeKey(s,c),p={};if(this.containerNodes.has(d)){if(h.callArgs)try{JSON.stringify(h.callArgs),p=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),p={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let A=h.inboundLayers[m],y=h.nodeIndices[m],g=h.tensorIndices[m],_=jr.nodeKey(A,y),b=t[_];b==null&&(b=0),f.push([A.name,b,g,p])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,n.push(u)}e.layers=n;let r=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[s];r.push([i.name,u,c])}e.inputLayers=r;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=jr.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[s];a.push([i.name,u,c])}return e.outputLayers=a,e}static fromConfig(e,t,n={},r=!1){let a={},s={};function i(m,A){m.name in s?s[m.name].push(A):s[m.name]=[A]}function o(m,A){let y=[],g;for(let _ of A){let b=_[0],w=_[1],x=_[2];if(g=_[3]==null?{}:_[3],!(b in a)){i(m,A);return}let N=a[b];if(N.inboundNodes.length<=w){i(m,A);return}let S=N.inboundNodes[w];y.push(S.outputTensors[x])}y.length>0&&m.apply(Sn(y),g)}function l(m){let A=m.name,y=Nr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(r),a[A]=y,m.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new V(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!tQ(s);)for(let m of c){let A=a[m.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let h=[],d=[],p=t.inputLayers;for(let m of p){let A=m[0],y=m[1],g=m[2];Br(A in a);let _=a[A].inboundNodes[y].outputTensors;h.push(_[g])}let f=t.outputLayers;for(let m of f){let A=m[0],y=m[1],g=m[2];Br(A in a);let _=a[A].inboundNodes[y].outputTensors;d.push(_[g])}return new e({inputs:h,outputs:d,name:u})}get stateful(){if(this._stateful)throw new V("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){B(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function Ete(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: ${JSON.stringify(e)}`)}function _7(e,t){return Ete(e,t,"classWeight")}async function b7(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=B(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());Ee(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),on(i,"float32")}else return null}function Rte(e,t){return P(e,t)}var Fte=32;function k7(e,t){let n,r,a=t;n=a.xs,r=a.ys,v.assert(n!=null&&r!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=v7("input",e.inputNames,n),i=v7("output",e.outputNames,r),o=s[0].shape[0];v.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)v.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)v.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function v7(e,t,n){if(n instanceof Ye)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let r=[];for(let a of t){if(n[a]==null)throw new V(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);r.push(n[a])}return r}}function Mte(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 Dte(e,t,n){let r=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=n.validationData!=null,s,i;if(a)if(I7(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let A=Mte(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(A=>"val_"+A)):u=l.slice();let c=u7(n.callbacks,n.yieldEvery),h=n.verbose==null?1:n.verbose,{callbackList:d,history:p}=c7(c,h,n.epochs,null,null,$te(t,n),null,a,u);d.setModel(e),e.history=p,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let A={};await d.onEpochBegin(f);let y=0,g=0;for(r||(m=await t.iterator());r?y<n.batchesPerEpoch:!0;){let _=await m.next();if(r&&_.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(_.value!=null){let{xs:b,ys:w}=k7(e,_.value),x={};x.batch=g,x.size=b[0].shape[0],await d.onBatchBegin(g,x);let N=[];if(n.classWeight!=null){let M=_7(n.classWeight,e.outputNames);for(let D=0;D<M.length;++D)N.push(await b7(w[D],null,M[D]))}let S=b.concat(w).concat(N),T=o(S);Ee(S);for(let M=0;M<l.length;++M){let D=l[M],z=T[M];x[D]=z,Gt(z)}await d.onBatchEnd(g,x),a7(x),g++,y++}if(r?y>=n.batchesPerEpoch:_.done){if(a){let b;I7(n.validationData)?b=ft(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=ft(e.evaluate(s,i,{batchSize:n.validationBatchSize==null?Fte:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)A[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(f,A),f++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function $te(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function I7(e){return typeof e.iterator=="function"}function Ote(e){return typeof e.next=="function"}async function zte(e,t,n){n=n||{};let r=n.batches!=null,a=e.testFunction,s=[];if(n.verbose>0)throw new De("Verbose mode is not implemented yet.");v.assert(!r||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let i=Ote(t)?t:await t.iterator(),o=0,l=0;for(;r?l<n.batches:!0;){let u=await i.next();if(s=B(()=>{if(u.value){let{xs:c,ys:h}=k7(e,u.value),d=c.concat(h),p=B(()=>a(d));if(Ee(d),l===0)for(let m=0;m<p.length;++m)s.push(be(0));let f=d[0].shape[0];for(let m=0;m<p.length;++m){let A=p[m],y=s[m];s[m]=B(()=>se(s[m],P(f,A))),l>0&&Ee(y)}Ee(p),o+=f,++l}return s}),u.done){r&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let u=0;u<s.length;++u){let c=s[u];s[u]=ge(s[u],o),Ee(c)}return Sn(s)}function jA(e){v.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Cc(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(r=>Si(r,t,n-t)):Si(e,t,n-t)}function GA(e,t){return B(()=>e==null?null:Array.isArray(e)?e.map(n=>GA(n,t)):U3(e,t.dtype==="int32"?t:t.toInt()))}function qA(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 Pte(e,t,n,r,a,s,i,o,l,u,c,h,d,p,f){a==null&&(a=32),s==null&&(s=1),c==null&&(c=!0),d==null&&(d=0);let m=!1;if(l!=null&&u!=null&&(m=!0),f!=null&&(m=!0,p==null))throw new V("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let A=e.checkNumSamples(n,a,p,"steps_per_epoch"),y;A!=null&&(y=kr(0,A)),i==null&&(i=1);let{callbackList:g,history:_}=c7(o,i,s,d,A,p,a,m,h);g.setModel(e),e.history=_,await g.onTrainBegin(),e.stopTraining_=!1;for(let b=d;b<s;++b){await g.onEpochBegin(b);let w={};if(p!=null)throw new De("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new De("batch shuffling is not implemneted yet");c&&v.shuffle(y);let x=on(y),N=qA(A,a);for(let S=0;S<N.length;++S){let T={};if(await g.onBatchBegin(S,T),B(()=>{let M=N[S][0],D=N[S][1],z=Si(x,M,D-M);T.batch=S,T.size=D-M;let W=GA(n,z),U=t(W);for(let H=0;H<r.length;++H){let X=r[H],j=U[H];T[X]=j,Gt(j)}if(S===N.length-1&&m){let H=e.testLoop(l,u,a);for(let X=0;X<r.length;++X){let j=r[X],ee=H[X];Gt(ee),w["val_"+j]=ee}}}),await g.onBatchEnd(S,T),a7(T),e.stopTraining_)break}x.dispose()}if(await g.onEpochEnd(b,w),e.stopTraining_)break}return await g.onTrainEnd(),await e.history.syncData(),e.history}async function Lte(e,t,n,r={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let a,s,i,o,l,u,c;try{let h=r.batchSize==null?32:r.batchSize;jA(h);let d=!1,p=await e.standardizeUserData(t,n,r.sampleWeight,r.classWeight,d,h);a=p[0],s=p[1],c=p[2];let f=!1,m;if(r.validationData!=null&&r.validationData.length>0){if(f=!0,r.validationData.length===2)i=r.validationData[0],o=r.validationData[1];else throw r.validationData.length===3?new De("validationData including sample weights is not supported yet."):new V(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${r.validationData} is invalid.`);let x=!0,N=await e.standardizeUserData(i,o,null,null,x,h);l=N[0],u=N[1],m=l.concat(u)}else if(r.validationSplit!=null&&r.validationSplit>0&&r.validationSplit<1){f=!0;let x=Math.floor(a[0].shape[0]*(1-r.validationSplit)),N=a[0].shape[0];l=Cc(a,x,N),a=Cc(a,0,x),u=Cc(s,x,N),s=Cc(s,0,x),m=l.concat(u)}else r.validationSteps!=null&&(f=!0);let A=a.concat(s).concat(c);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),g=e.getDedupedMetricsNames(),_,b;f?(e.makeTestFunction(),_=e.testFunction,b=g.slice().concat(g.map(x=>"val_"+x))):(_=null,m=[],b=g.slice());let w=u7(r.callbacks,r.yieldEvery);return await Pte(e,y,A,g,h,r.epochs,r.verbose,w,_,m,r.shuffle,b,r.initialEpoch,null,null)}finally{e.isTraining=!1,Ei(a,t),Ei(s,n),Ei(l,i),Ei(u,o),c!=null&&Ee(c)}}function N7(e){let t=[];e instanceof Ye&&(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 Ye)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 Ye)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 Wte(e){return e instanceof Ye}function XA(e){return Array.isArray(e)}function S7(e){return!Wte(e)&&!XA(e)}function T7(e,t,n,r=!0,a=""){if(t==null||t.length===0){if(e!=null){let i=!1;if(XA(e)&&e.length>0)i=!0;else if(S7(e)){for(let o in e)if(e.hasOwnProperty(o)){i=!0;break}}else i=!0;if(i)throw new V(`Error when checking model ${a} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(i=>null);let s;if(S7(e)){e=e,s=[];for(let i of t){if(e[i]==null)throw new V(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(e[i])}}else if(XA(e)){if(e=e,e.length!==t.length)throw new V(`Error when checking model ${a}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);s=e}else{if(e=e,t.length>1)throw new V(`The model ${a} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);s=[e]}if(s=N7(s),n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new V(`Error when checking ${a}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);for(let l=0;l<n[i].length;++l){if(l===0&&!r)continue;let u=o.shape[l],c=n[i][l];if(c!=null&&c>=0&&u!==c)throw new V(`Error when checking ${a}: expected ${t[i]} to have shape [${n[i]}], but got array with shape [${o.shape}].`)}}return s}function Bte(e,t,n){let r=Ua(e.map(s=>s.shape[0]));r.sort();let a=Ua(t.map(s=>s.shape[0]));if(a.sort(),r.length>1)throw new V(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(s=>s.shape))}`);if(a.length>1)throw new V(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(r.length>0&&a.length>0&&!v.arraysEqual(r,a))throw new V(`Input Tensors should have the same number of samples as target Tensors. Found ${r[0]} input sample(s) and ${a[0]} target sample(s).`)}function Vte(e,t,n){let r=[Ti,Vp,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 V(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(r.indexOf(i)!==-1){let l=s.shape.slice(1),u=o.slice(1);for(let c=0;c<l.length;++c){let h=l[c],d=u[c];if(d!=null&&h!==d)throw new V(`A target Tensor with shape ${s.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function C7(e,t,n,r=!0,a=""){let s;if(Array.isArray(e)){if(e.length!==t.length)throw new V(`Error when checking model ${a}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);s=e}else{if(t.length>1)throw new V(`The model expects ${t.length} ${a} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);s=[e]}if(n!=null)for(let i=0;i<t.length;++i){if(n[i]==null)continue;let o=s[i];if(o.shape.length!==n[i].length)throw new V(`Error when checking ${a}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);for(let l=0;l<n[i].length;++l){if(l===0&&!r)continue;let u=o.shape[l],c=n[i][l];if(c!=null&&c!==u)throw new V(`Error when checking ${a}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`)}}}function Ute(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: ${e}`);if(Array.isArray(n))return t.map(r=>n);{let r=[];for(let a of t){let s=n.hasOwnProperty(a)?n[a]:[];Array.isArray(s)||(s=[s]),r.push(s)}return r}}var Hte="layers-model",ha=class extends jr{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new V("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");Ite(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=wte(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof oa))throw new V("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let s in e.loss)if(this.outputNames.indexOf(s)===-1)throw new V(`Unknown entry in loss dictionary: "${s}". Only expected the following keys: ${this.outputNames}`);for(let s of this.outputNames)e.loss[s]==null&&console.warn(`Output "${s}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${s} during training`),t.push(zA(e.loss[s]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new V(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(s=>zA(s))}else{let s=zA(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=Ute(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="",u,c,h;for(let d of o){if(typeof d=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(d)!==-1){let f=this.internalOutputShapes[s];f[f.length-1]===1||this.lossFunctions[s]===Vp?["accuracy","acc"].indexOf(d)!==-1?c=PA:["crossentropy","ce"].indexOf(d)!==-1&&(c=p7):this.lossFunctions[s]===Bp?["accuracy","acc"].indexOf(d)!==-1?c=f7:["crossentropy","ce"].indexOf(d)!==-1&&(c=m7):["accuracy","acc"].indexOf(d)!==-1?c=LA:["crossentropy","ce"].indexOf(d)!==-1&&(c=WA);let m;["accuracy","acc"].indexOf(d)!==-1?m="acc":["crossentropy","ce"].indexOf(d)!==-1&&(m="ce"),h=c,u=l+m}else h=xte(d),u=l+jp(d);let p;Ni(u,()=>{p=h}),a(s,u,p)}})(i)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(e,t,n={}){let r=n.batchSize==null?32:n.batchSize;jA(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 Sn(l)}finally{Ei(s[0],e),Ei(s[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),zte(this,e,t)}checkNumSamples(e,t,n,r="steps"){let a;if(n!=null){if(a=null,t!=null)throw new V(`If ${r} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?a=e[0].shape[0]:a=e.shape[0];else throw new V(`Either the input data should have a defined shape, or ${r} shoud be specified.`);return a}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new V("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),r=n?t:[t],a=this.retrieveSymbolicTensors(r),s=new Ci;if(e instanceof Ye&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new V(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let o=0;o<this.inputs.length;++o)s.add(this.inputs[o],e[o])}else for(let o of this.inputs){let l=e[o.name];if(l==null)throw new V(`No value is provided for the model's input ${o.name}`);s.add(o,l)}let i=Tc(a,s);return n?i:i[0]}retrieveSymbolicTensors(e){let t=vi(null,e.length),n=e.length;for(let r of this.layers){let a=Array.isArray(r.output)?r.output:[r.output],s=a.map(i=>i.name);for(let i=0;i<e.length;++i){let o=s.indexOf(e[i]);if(o!==-1&&(t[i]=a[o],n--),n===0)break}if(n===0)break}if(n>0){let r=[];throw t.forEach((a,s)=>{a==null&&r.push(e[s])}),new V(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(r)}`)}return t}predictLoop(e,t=32,n=!1){return B(()=>{let r=this.checkNumSamples(e);if(n)throw new De("Verbose predictLoop() is not implemented yet.");let a=qA(r,t),s=this.outputs.map(i=>[]);for(let i=0;i<a.length;++i)B(()=>{let o=a[i][0],l=a[i][1],u=Cc(e,o,l),c=[];if(Array.isArray(u))for(let d=0;d<u.length;++d)c.push({key:this.inputs[d],value:u[d]});else c.push({key:this.inputs[0],value:u});let h=new Ci(c);return Tc(this.outputs,h)}).forEach((o,l)=>s[l].push(o));return Sn(s.map(i=>st(i,0)))})}predict(e,t={}){let n=N7(e);C7(n,this.inputNames,this.feedInputShapes,!1);try{let r=t.batchSize==null?32:t.batchSize;return jA(r),this.predictLoop(n,r)}finally{Ei(n,e)}}predictOnBatch(e){C7(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 vr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===Bp?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=T7(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=T7(t,this.feedOutputNames,a,!1,"target"),Bte(e,t,null),Vte(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&r!=null&&r>0&&e[0].shape[0]%r!=0)throw new V(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${r}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,r,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(r!=null){let u=_7(r,this.outputNames);l=[];for(let c=0;c<u.length;++c)l.push(await b7(o[c],null,u[c]))}return[i,o,l]}testLoop(e,t,n,r=0,a){return B(()=>{let s=this.checkNumSamples(t,n,a,"steps"),i=[];if(r>0)throw new De("Verbose mode is not implemented yet.");if(a!=null)throw new De("steps mode in testLoop() is not implemented yet");{let o=qA(s,n),l=on(kr(0,s));for(let u=0;u<o.length;++u){let c=o[u][0],h=o[u][1],d=Si(l,c,h-c),p=GA(t,d),f=e(p);if(u===0)for(let m=0;m<f.length;++m)i.push(be(0));for(let m=0;m<f.length;++m){let A=f[m];i[m]=se(i[m],P(h-c,A))}}for(let u=0;u<i.length;++u)i[u]=ge(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let r=e[n],a=r;E3(e,r)>1&&(a+=`_${E3(e.slice(0,n),r)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let p=0;p<this.inputs.length;++p)u.push({key:this.inputs[p],value:n[p]});let c=new Ci(u),h=Tc(this.outputs,c,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Rte(f,a[p]));let m=kt(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=kt(m(r[A],h[A]))}Gt(f),s.push(f)}return d=kt(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>B(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Ci(s),o=Tc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=kt(u(a[l],o[l]));l===0?n=c:n=se(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],h=kt(u(a[c],o[c]));t.push(h)}return t})}async fit(e,t,n={}){return Lte(this,e,t,n)}async fitDataset(e,t){return Dte(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),Sn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ca(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>ca(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=ca(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[ca(jp(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ca(jp(e)));{let e={};for(let t in this.metrics)e[t]=ca(jp(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Sc(e.optimizer_config),n=Nr(t),r;if(typeof e.loss=="string")r=ki(e.loss);else if(Array.isArray(e.loss))r=e.loss.map(s=>ki(s));else if(e.loss!=null){r={};for(let s in e.loss)r[s]=ki(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>ki(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=ki(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=_n.getSaveHandlers(e);if(i.length===0)throw new V(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new V(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new V("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await _n.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:Hte,generatedBy:`TensorFlow.js tfjs-layers v${UA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await _n.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=_n.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;y7(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){y7(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ha.className="Model";re.registerClass(ha);var E7=class extends ha{};E7.className="Functional";re.registerClass(E7);async function jte(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=Sc(n),a=Nr(r,t);if(e.weightsManifest!=null){let s=await _n.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 qte(e,t){if(t==null&&(t={}),typeof e=="string"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new V(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return Gte(e,void 0,t)}async function Gte(e,t,n){if(n==null&&(n={}),e.load==null)throw new V("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let r=await e.load(),a=r.modelTopology;a.model_config!=null&&(a=a.model_config);let s=n.strict==null?!0:n.strict,i=r.weightData!=null&&r.weightSpecs!=null&&s,o=Nr(Sc(a),t,i),l=r.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),r.userDefinedMetadata!=null&&o.setUserDefinedMetadata(r.userDefinedMetadata),r.weightData!=null){if(r.weightSpecs==null)throw new V("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=Xte(r.weightData,r.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Ee(u),Ee(c.map(h=>h.tensor))}return o}function Xte(e,t){let n=_n.decodeWeights(e,t),r={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:n[s.name]}):r[s.name]=n[s.name]}),{modelWeights:r,optimizerWeights:a}}var Wl=class extends ha{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new V(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Wl||e instanceof ha,n;if(t){if(n=e,n.outputs.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new V("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new V("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let r=r7({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(r)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new V(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new V("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=n7(this.outputs[0])}this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:vi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(r=>r.shape),outputShapes:this.outputs[0].shape})}else{let r=e.apply(this.outputs[0]);if(Array.isArray(r))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[r],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ct(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ha({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new vr("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 vr("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 vr("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 vr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},r=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new V("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof Wl))throw new De(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=Nr(o,void 0,r);r&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new V("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new V("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};Wl.className="Sequential";re.registerClass(Wl);function Kte(e){return new ha(e)}function Zte(e){return new Wl(e)}function Yte(e,t){return t==null&&(t={}),qte(e,t)}function X3(e){return r7(e)}function Jte(e,t){cr.registerCallbackConstructor(e,t)}var Ln=class extends re.Serializable{getConfig(){return{}}},R7=class extends Ln{apply(e,t=1){return EQ(e,t)}};R7.className="elu";re.registerClass(R7);var F7=class extends Ln{apply(e){return Cd(e)}};F7.className="selu";re.registerClass(F7);var M7=class extends Ln{apply(e){return zr(e)}};M7.className="relu";re.registerClass(M7);var $7=class extends Ln{apply(e){return B(()=>fl(6,zr(e)))}};$7.className="relu6";re.registerClass($7);var D7=class extends Ln{apply(e){return e}};D7.className="linear";re.registerClass(D7);var O7=class extends Ln{apply(e){return Rn(e)}};O7.className="sigmoid";re.registerClass(O7);var z7=class extends Ln{apply(e){return FQ(e)}};z7.className="hardSigmoid";re.registerClass(z7);var P7=class extends Ln{apply(e){return dl(e)}};P7.className="softplus";re.registerClass(P7);var L7=class extends Ln{apply(e){return RQ(e)}};L7.className="softsign";re.registerClass(L7);var W7=class extends Ln{apply(e){return ol(e)}};W7.className="tanh";re.registerClass(W7);var KA=class extends Ln{apply(e,t=-1){return Yu(e,t)}};KA.className="softmax";re.registerClass(KA);var B7=class extends Ln{apply(e,t=-1){return bd(e,t)}};B7.className="logSoftmax";re.registerClass(B7);var V7=class extends Ln{apply(e,t=1){return B(()=>Rn(e.mul(t)).mul(e))}};V7.className="swish";re.registerClass(V7);function qa(e){return e.getClassName()}function ZA(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={},ZA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},ZA(t)}else return e instanceof Ln?e:ZA(e)}function YA(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 U7=class extends re.Serializable{},Ec=class extends U7{constructor(e){super();YA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return B(()=>{let t=Et([1]);return this.hasL1&&(t=se(t,Te(P(this.l1,zt(e))))),this.hasL2&&(t=se(t,Te(P(this.l2,vc(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ec.className="L1L2";re.registerClass(Ec);function Qte(e){return YA(e),new Ec({l1:e!=null?e.l1:null,l2:0})}function ene(e){return YA(e),new Ec({l2:e!=null?e.l2:null,l1:0})}var H7={l1l2:"L1L2"};function ht(e){return hA(e)}function j7(e,t={}){return gc(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function wt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in H7?H7[e]:e,config:{}};return j7(t)}else return e instanceof U7?e:j7(e)}var JA=class extends Ge{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=bn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};JA.className="ReLU";re.registerClass(JA);var QA=class extends Ge{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}};QA.className="LeakyReLU";re.registerClass(QA);var ey=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=xt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=wt(e.alphaRegularizer),this.alphaConstraint=Vt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new V(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ct(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let r of this.sharedAxes)t[r-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let r=1;r<e.length;++r)n[r]=e[r];this.inputSpec=[new Kt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Pe(e),Xu(e,this.alpha.read())}getConfig(){let e={alphaInitializer:It(this.alphaInitializer),alphaRegularizer:ht(this.alphaRegularizer),alphaConstraint:Bt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};ey.className="PReLU";re.registerClass(ey);var ty=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${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}};ty.className="ELU";re.registerClass(ty);var ny=class extends Ge{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}};ny.className="ThresholdedReLU";re.registerClass(ny);var ry=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new KA().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}};ry.className="Softmax";re.registerClass(ry);function Bl(e,t,n){if(typeof e=="number")return vi(e,t);if(e.length!==t)throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let r=0;r<t;++r){let a=e[r];if(!NQ(a))throw new V(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Sr(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function qp(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+ja([n-t,0]);else if(r==="same")e=e*t;else throw new V(`Unsupport padding mode: ${r}.`);return e}function ay(e,t){return B(()=>(Tt(t),t==="channelsFirst"?at(e,[0,2,3,1]):e))}function G7(e,t){return B(()=>(Tt(t),t==="channelsFirst"?at(e,[0,2,3,4,1]):e))}function tne(e,t,n,r=1,a="valid",s,i=1){return B(()=>{if(s==null&&(s=br()),Tt(s),e.shape.length!==3)throw new V(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new V(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new V(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=at(e,[0,2,1])),a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=fd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ur(o,n)),o})}function q7(e,t,n,r=[1,1],a="valid",s,i,o=null){return B(()=>{if(s==null&&(s=br()),Tt(s),e.rank!==3&&e.rank!==4)throw new V(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new V(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=ay(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=La.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=at(l,[0,3,1,2])),l})}function nne(e,t,n,r=[1,1,1],a="valid",s,i){return B(()=>{if(s==null&&(s=br()),Tt(s),e.rank!==4&&e.rank!==5)throw new V(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new V(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=G7(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=qf(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ur(o,n)),s==="channelsFirst"&&(o=at(o,[0,4,1,2,3])),o})}var sy=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",sy.verifyArgs(t),this.rank=e,Xt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Bl(t.kernelSize,e,"kernelSize"),this.strides=Bl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Yn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Tt(this.dataFormat),this.activation=Xa(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=xt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Vt(t.biasConstraint),this.biasRegularizer=wt(t.biasRegularizer),this.activityRegularizer=wt(t.activityRegularizer),this.dilationRate=Bl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new V(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new V(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new V(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Br("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!pA(e.kernelSize,"number",1,3))throw new V(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:qa(this.activation),useBias:this.useBias,biasInitializer:It(this.biasInitializer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Rc=class extends sy{constructor(e,t){super(e,t);this.kernel=null,Rc.verifyArgs(t),this.filters=t.filters,Xt(this.filters,"filters"),this.kernelInitializer=xt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Vt(t.kernelConstraint),this.kernelRegularizer=wt(t.kernelRegularizer)}build(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return B(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=F3(this.activation.getClassName());if(a!=null&&this.rank===2)n=q7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=tne(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=q7(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=nne(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new De("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ct(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Sr(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:It(this.kernelInitializer),kernelRegularizer:ht(this.kernelRegularizer),kernelConstraint:Bt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new V(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Fc=class extends Rc{constructor(e){super(2,e);Fc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!pA(e.kernelSize,"number",1,2))throw new V(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Fc.className="Conv2D";re.registerClass(Fc);var Xp=class extends Rc{constructor(e){super(3,e);Xp.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new V(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Xp.className="Conv3D";re.registerClass(Xp);var iy=class extends Fc{constructor(e){super(e);if(this.inputSpec=[new Kt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new V(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ct(e),e.length!==4)throw new V("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new V("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{let n=Pe(e);if(n.shape.length!==4)throw new V(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],u=this.kernelSize[0],c=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=qp(o,h,u,this.padding),f=qp(l,d,c,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=at(n,[0,2,3,1]));let A=md(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=at(A,[0,3,1,2])),this.bias!=null&&(A=Ur(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=ct(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=qp(t[r],o,s,this.padding),t[a]=qp(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};iy.className="Conv2DTranspose";re.registerClass(iy);var X7=class extends Rc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new V("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new V("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new V(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=wt(t.depthwiseRegularizer),this.depthwiseConstraint=Vt(t.depthwiseConstraint),this.pointwiseInitializer=xt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=wt(t.pointwiseRegularizer),this.pointwiseConstraint=Vt(t.pointwiseConstraint)}build(e){if(e=ct(e),e.length<this.rank+2)throw new V(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Kt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return B(()=>{e=Pe(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=at(e,[0,2,3,1])),n=hm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=at(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=It(this.depthwiseInitializer),e.pointwiseInitializer=It(this.pointwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.pointwiseRegularizer=ht(this.pointwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseConstraint),e.pointwiseConstraint=Bt(this.pointwiseConstraint),e}};X7.className="SeparableConv";var oy=class extends X7{constructor(e){super(2,e)}};oy.className="SeparableConv2D";re.registerClass(oy);var Kp=class extends Rc{constructor(e){super(1,e);Kp.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!pA(e.kernelSize,"number",1,1))throw new V(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Kp.className="Conv1D";re.registerClass(Kp);var ly=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return B(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Ip(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Ip(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Ip(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Ip(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}};ly.className="Cropping2D";re.registerClass(ly);var uy=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,vQ(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return B(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=at(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return at(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};uy.className="UpSampling2D";re.registerClass(uy);function rne(e,t,n=[1,1],r="valid",a,s){return B(()=>{a==null&&(a=br()),Tt(a);let i=ay(e,a);if(e.rank!==4)throw new V(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new V(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ul(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=at(i,[0,3,1,2])),i})}var cy=class extends sy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Vt(e.depthwiseConstraint),this.depthwiseRegularizer=wt(e.depthwiseRegularizer)}build(e){if(e=ct(e),e.length<4)throw new V(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new V(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{e=Pe(e);let n=rne(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Sr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Sr(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=It(this.depthwiseInitializer),e.depthwiseRegularizer=ht(this.depthwiseRegularizer),e.depthwiseConstraint=Bt(this.depthwiseRegularizer),e}};cy.className="DepthwiseConv2D";re.registerClass(cy);function K7(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new V("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function Z7(e,t,n,r=!1,a,s,i=!1,o=!1){return B(()=>{let l=t.shape.length;if(l<3)throw new V(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(kr(2,l));if(t=at(t,u),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Fn(a,-1)),a=at(a,u)),r&&(t=Dn(t,0),a!=null&&(a=Dn(a,0)));let c=[],h,d=n,p=t.shape[0],f=ir(t),m;a!=null&&(m=ir(a));for(let y=0;y<p;++y){let g=f[y],_=B(()=>e(g,d));if(a==null)h=_[0],d=_[1];else{let b=B(()=>{let w=m[y],x=$n(w).sub(w),N=_[0].mul(w).add(d[0].mul(x)),S=d.map((T,M)=>_[1][M].mul(w).add(T.mul(x)));return{output:N,newStates:S}});h=b.output,d=b.newStates}o&&c.push(h)}let A;return o&&(A=hn(c,1)),[h,A,d]})}var Hr=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new V("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Zp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new V("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Kt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return kr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){MA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return B(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");MA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Kt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new V(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Kt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ua("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Et([n,r])):this.states_=[Et([n,this.cell.stateSize])];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Et([n,r])):this.states_[0]=Et([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new V(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Gt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=K7(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Kt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Ir){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let c=super.apply(o,t);return this.inputSpec=u,c}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new V(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=Z7((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);let h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h})}getInitialState(e){return B(()=>{let t=Et(e.shape);return t=Te(t,[1,2]),t=bc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?xA(t,[1,n]):t):this.cell.stateSize>1?[xA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Hr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Nr(r,n);return new e(Object.assign(t,{cell:a}))}};Hr.className="RNN";re.registerClass(Hr);var Ic=class extends Ge{},Yp=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,Xt(this.units,"units"),this.activation=Xa(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Ol([1,ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ol([1,ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Vr(P(e,s),this.kernel.read()):a=Vr(e,this.kernel.read()),this.bias!=null&&(a=Ur(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Vr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";re.registerClass(Yp);var hy=class extends Hr{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};hy.className="SimpleRNN";re.registerClass(hy);var Jp=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 V("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Xt(this.units,"units"),this.activation=Xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Ol([1,ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ol([1,ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ct(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return B(()=>{if(e=e,e.length!==2)throw new V(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let u=Vr(e,this.kernel.read());this.useBias&&(u=Ur(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let c=this.recurrentKernel.read(),[h,d]=Lt(c,[2*this.units,this.units],c.rank-1),p=Vr(r,h),[f,m,A]=Lt(u,3,u.rank-1),[y,g]=Lt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let _=Vr(P(o,r),d);l=this.activation.apply(se(A,_));let b=se(P(i,r),P(se(1,vt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),recurrentActivation:qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Jp.className="GRUCell";re.registerClass(Jp);var dy=class extends Hr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jp(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};dy.className="GRU";re.registerClass(dy);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,Xt(this.units,"units"),this.activation=Xa(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Xa(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Vt(e.kernelConstraint),this.recurrentConstraint=Vt(e.recurrentConstraint),this.biasConstraint=Vt(e.biasConstraint),this.dropout=Ol([1,ja([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ol([1,ja([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ct(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends ur{apply(i,o){let l=a.apply([s]),u=new Sp().apply([s]),c=a.apply([s*2]);return V3(V3(l,u),c)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new V(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,c;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let h=Vr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Vr(r,this.recurrentKernel.read())),this.useBias&&(h=Ur(h,this.bias.read()));let[d,p,f,m]=Lt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),u=se(P(l,a),P(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let A=P(c,this.activation.apply(u));return[A,A,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:qa(this.activation),recurrentActivation:qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:It(this.kernelInitializer),recurrentInitializer:It(this.recurrentInitializer),biasInitializer:It(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ht(this.kernelRegularizer),recurrentRegularizer:ht(this.recurrentRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),recurrentConstraint:Bt(this.recurrentConstraint),biasConstraint:Bt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Mc.className="LSTMCell";re.registerClass(Mc);var 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 Mc(e),super(e)}call(e,t){return B(()=>{this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};py.className="LSTM";re.registerClass(py);var Zp=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 B(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){MA(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(Nr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return $A(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]])}DA(t)}};Zp.className="StackedRNNCells";re.registerClass(Zp);function Ka(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>H3(t(),n),i=()=>kc(s,t,r);return!a||a<=1?Gt(i().clone()):Array(a).fill(void 0).map(i).map(o=>Gt(o.clone()))}var ane=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},Y7=class extends Hr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Kt({ndim:5})]}call(e,t){return B(()=>{if(this.cell.dropoutMask!=null&&(Ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new V("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return B(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Et(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){B(()=>{if(!this.stateful)throw new ua("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new V("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Et(a)):this.states_=[Et(a)];else if(e==null)Ee(this.states_),this.keptStates!=null&&(Ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Et(a)):this.states_[0]=Et(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new V(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ee(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new V(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Gt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],c=Sr(l,r[0],a,s[0],i[0]),h=Sr(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}};Y7.className="ConvRNN2D";var Qp=class extends Mc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Xt(this.filters,"filters"),this.kernelSize=Bl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Xt(o,"kernelSize")),this.strides=Bl(r||1,2,"strides"),this.strides.forEach(o=>Xt(o,"strides")),this.padding=a||"valid",Yn(this.padding),this.dataFormat=s||"channelsLast",Tt(this.dataFormat),this.dilationRate=Bl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Xt(o,"dilationRate"))}build(e){var t;e=ct(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new V(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends ur{apply(c,h){let d=l.apply([u]),p=Or([u]),f=l.apply([u*2]);return _A([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return B(()=>{if(e.length!==3)throw new V(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ka({ones:()=>$n(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),u=l(r,o,0),c=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ka({ones:()=>$n(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[_,b,w,x]=Lt(this.kernel.read(),i,g),[N,S,T,M]=this.useBias?Lt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,_,N,this.padding),c=this.inputConv(c,b,S,this.padding),h=this.inputConv(h,w,T,this.padding),d=this.inputConv(d,x,M,this.padding);let[D,z,W,U]=Lt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,D),m=this.recurrentConv(m,z),A=this.recurrentConv(A,W),y=this.recurrentConv(y,U);let H=this.recurrentActivation.apply(se(u,f)),X=this.recurrentActivation.apply(se(c,m)),j=se(P(X,s),P(H,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(j));return[ee,ee,j]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=ane(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ra(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(a,n,this.dataFormat):a}recurrentConv(e,t){return ra(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qp.className="ConvLSTM2DCell";re.registerClass(Qp);var fy=class extends Y7{constructor(e){let t=new Qp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};fy.className="ConvLSTM2D";re.registerClass(fy);var e0=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return kc(()=>H3(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};e0.className="Dropout";re.registerClass(e0);var my=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};my.className="SpatialDropout1D";re.registerClass(my);var Ay=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Xt(this.units,"units"),this.activation=Xa(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=xt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Vt(e.kernelConstraint),this.biasConstraint=Vt(e.biasConstraint),this.kernelRegularizer=wt(e.kernelRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ct(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ct(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=F3(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:It(this.kernelInitializer),biasInitializer:It(this.biasInitializer),kernelRegularizer:ht(this.kernelRegularizer),biasRegularizer:ht(this.biasRegularizer),activityRegularizer:ht(this.activityRegularizer),kernelConstraint:Bt(this.kernelConstraint),biasConstraint:Bt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="Dense";re.registerClass(Ay);var yy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ct(e);for(let t of e.slice(1))if(t==null)throw new V(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ha(e,1)]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return CQ(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};yy.className="Flatten";re.registerClass(yy);var gy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=Xa(e.activation)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:qa(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Activation";re.registerClass(gy);var xy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return B(()=>(e=Pe(e),SQ(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};xy.className="RepeatVector";re.registerClass(xy);var wy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new V("Can only specifiy one unknown dimension.");else a*=l}let i=Ha(e);if(s!==null){if(a===0||i%a!=0)throw new V(n);r[s]=i/a}else if(i!==a)throw new V(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};wy.className="Reshape";re.registerClass(wy);var _y=class extends Ge{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=kr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Kt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ct(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return at(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};_y.className="Permute";re.registerClass(_y);var by=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Ou(di(n,this.maskValue),r)}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Ou(di(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};by.className="Masking";re.registerClass(by);var vy=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Xt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Xt(this.outputDim,"outputDim"),this.embeddingsInitializer=xt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=wt(e.embeddingsRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.embeddingsConstraint=Vt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return B(()=>this.maskZero?(e=Pe(e),di(e,Ue(e))):null)}computeOutputShape(e){if(e=ct(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new V(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=_c(n,"int32")),U3(this.embeddings.read(),n.as1D()).reshape(ct(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:It(this.embeddingsInitializer),embeddingsRegularizer:ht(this.embeddingsRegularizer),activityRegularizer:ht(this.activityRegularizer),embeddingsConstraint:Bt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Embedding";re.registerClass(vy);var Ri=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new V("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ct(e)]),e=e,e.length<2)throw new V(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ua(t),t.length>1)throw new V(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let r=e.map(a=>a.length);e.indexOf(null)===-1&&Ua(r).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return B(()=>{if(e=e,this.reshapeRequired){let n=[],r=e.map(a=>a.rank);if(r.indexOf(null)===-1){let a=ja(r);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=bc(s,1);n.push(s)}return this.mergeFunction(n)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,c=u[0],h=u.slice(1).concat([c]),d=o.reshape([c].concat(Ha(u.slice(1))));d=at(d,[1,0]),d=d.reshape(h),n.push(d),a=!0}else if(l>1){let u=kr(1,l).concat([0]);n.push(at(o,u)),a=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],c=[u].concat(o.slice(0,o.length-1));s=at(s.reshape([-1,u]),[1,0]).reshape(c)}else if(i>1){let o=[i-1].concat(kr(0,i-1));s=at(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let n=[];for(let r of e)r!=null&&r[0]!==null&&n.push(r[0]);return n=Ua(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return B(()=>{if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an Array");if(!Array.isArray(e))throw new V("`inputs` should be an Array");if(t.length!==e.length)throw new V(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(r=>r==null))return null;t=t.map(r=>r==null?r:Fn(r,0));let n=t[0];for(let r=1;r<t.length-1;++r)n=sr(n,t[r]);return n})}},ky=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return t})}};ky.className="Add";re.registerClass(ky);var Iy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=P(t,e[n]);return t})}};Iy.className="Multiply";re.registerClass(Iy);var Ny=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=se(t,e[n]);return P(1/e.length,t)})}};Ny.className="Average";re.registerClass(Ny);var Sy=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Dr(t,e[n]);return t})}};Sy.className="Maximum";re.registerClass(Sy);var Ty=class extends Ri{constructor(e){super(e)}mergeFunction(e){return B(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=fl(t,e[n]);return t})}};Ty.className="Minimum";re.registerClass(Ty);var Cy=class extends Ri{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new V("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let r of e)if(r!=null){t=!1;break}if(t)return;let n=[];for(let r=0;r<e.length;++r){let a=e[r].slice();a.splice(this.axis,1);let s=!1;for(let i of n)if(v.arraysEqual(i,a)){s=!0;break}s||n.push(a)}if(n.length>1)throw new V("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return B(()=>_A(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new V("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(let a of t.slice(1)){if(n[r]==null||a[r]==null){n[r]=null;break}n[r]+=a[r]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new V("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new V("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new V(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return B(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let r=[];for(let s=0;s<e.length;++s)t[s]==null?r.push($n(e[s]).asType("bool")):t[s].rank<e[s].rank?r.push(Fn(t[s],-1)):r.push(t[s]);let a=st(r,this.axis);return dd(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Concatenate";re.registerClass(Cy);function $c(e,t){for(;e<0;)e+=t;return e}function sne(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return B(()=>{let i;if(r>a){i=r-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let u=0;u<i;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=e.matMul(t,l,u)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let u=[];for(let c=l;c<l+i;++c)u.push(c);o=o.squeeze(u)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var Ey=class extends Ri{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new V(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new V(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],r;return Array.isArray(this.axes)?r=this.axes.map((a,s)=>$c(a,e[s].shape.length)):r=[$c(this.axes,t.shape.length),$c(this.axes,n.shape.length)],this.normalize&&(t=Lp(t,r[0]),n=Lp(n,r[1])),sne(t,n,r)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[$c(this.axes,e.length),$c(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);let a=t.concat(n);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="Dot";re.registerClass(Ey);var Ry=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return kc(()=>Np(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Ry.className="GaussianNoise";re.registerClass(Ry);var Fy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?kc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Np(n.shape,1,r))},()=>n,t.training||!1):n})}};Fy.className="GaussianDropout";re.registerClass(Fy);var My=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return kc(()=>{let r=Pe(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=za(ml(n),this.rate);o=_c(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate;return r.mul(o).add(o.add(-1).mul(i)).mul(l).add(u)},()=>Pe(e),t.training||!1)}return e})}};My.className="AlphaDropout";re.registerClass(My);function Dc(e,t,n,r,a,s=.001){let i;if(e.rank===2)i=V5(e,t,n,r,a,s);else if(e.rank===3)i=U5(e,t,n,r,a,s);else if(e.rank===4)i=H5(e,t,n,r,a,s);else throw new De(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function ine(e,t,n,r,a=.001){return B(()=>{let s=kd(e,r),i=s.mean,o=s.variance;return[Dc(e,i,o,n,t,a),i,o]})}function one(e,t,n,r,a=.001){return B(()=>{let s=kd(e,r),i=s.mean,o=s.variance,l=[];for(let p of kr(0,e.rank))r.indexOf(p)!==-1?l.push(1):l.push(e.shape[p]);let u=i.reshape(l),c=o.reshape(l),h=t==null?null:t.reshape(l),d=n==null?null:n.reshape(l);return[Dc(e,u,c,d,h,a),i,o]})}function lne(e,t,n,r,a=.001){return v.arraysEqual(r.slice().sort(),kr(0,e.rank-1))?ine(e,t,n,r,a):one(e,t,n,r,a)}var $y=class extends Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.movingMeanInitializer=xt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=xt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Vt(e.betaConstraint),this.gammaConstraint=Vt(e.gammaConstraint),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer)}build(e){e=ct(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new V(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Kt({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return B(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=kr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=vi(1,s);l[o]=a[o];let u=i.slice();u.sort();let c=!v.arraysEqual(u,kr(0,s).slice(0,s-1)),h=()=>{if(c){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,_=this.scale?this.gamma.read().reshape(l):null;return Dc(r,A,y,g,_,this.epsilon)}else return Dc(r,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return h();let[d,p,f]=lne(r,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(A,y,g)=>{B(()=>{let _=1-g,b=A.read(),w=b.sub(y).mul(_);A.write(b.sub(w))})};return(()=>{m(this.movingMean,p,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),movingMeanInitializer:It(this.movingMeanInitializer),movingVarianceInitializer:It(this.movingVarianceInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer),betaConstraint:Bt(this.betaConstraint),gammaConstraint:Bt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$y.className="BatchNormalization";re.registerClass($y);var Dy=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=xt(e.betaInitializer||"zeros"),this.gammaInitializer=xt(e.gammaInitializer||"ones"),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ct(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ua(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return B(()=>{let s=!0,{mean:i,variance:o}=kd(n,this.axis,s),l=vi(1,a);for(let f of this.axis)l[f]=r[f];let u=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,c=u(this.gamma.read()),h=u(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),c=c.tile(p),h=h.tile(p),Dc(n,i,o,h,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:It(this.betaInitializer),gammaInitializer:It(this.gammaInitializer),betaRegularizer:ht(this.betaRegularizer),gammaRegularizer:ht(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Dy.className="LayerNormalization";re.registerClass(Dy);function une(e,t,n){return B(()=>{if(e.rank!==4)throw new V(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new V("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=br()),n!=="channelsLast"&&n!=="channelsFirst")throw new V(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let r;return n==="channelsFirst"?r=[[0,0],[0,0],t[0],t[1]]:r=[[0,0],t[0],t[1],[0,0]],aa(e,r)})}var Oy=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?br():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new V(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new V(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new V(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ct(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return B(()=>une(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="ZeroPadding2D";re.registerClass(Oy);function t0(e,t,n,r,a,s){return B(()=>{Tt(a),O3(s),Yn(r),n==null&&(n=[1,1]),r==null&&(r="valid"),a==null&&(a=br()),s==null&&(s="max"),e=ay(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Gu(e,t,n,o):i=Lu(e,t,n,o),a==="channelsFirst"&&(i=at(i,[0,3,1,2])),i})}function J7(e,t,n,r,a,s){return B(()=>{Tt(a),O3(s),Yn(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=br()),s==null&&(s="max"),e=G7(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=am(e,t,n,o):i=Hf(e,t,n,o),a==="channelsFirst"&&(i=at(i,[0,4,1,2,3])),i})}var Q7=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new V(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Xt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new V(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Yn(this.padding),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){e=ct(e);let t=Sr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return B(()=>{this.invokeCallHook(e,t),e=bc(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Pa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends Q7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"max")}};zy.className="MaxPooling1D";re.registerClass(zy);var Py=class extends Q7{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"avg")}};Py.className="AveragePooling1D";re.registerClass(Py);var ev=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new V(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Yn(this.padding),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Sr(t,this.poolSize[0],this.padding,this.strides[0]),n=Sr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ly=class extends ev{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"max")}};Ly.className="MaxPooling2D";re.registerClass(Ly);var Wy=class extends ev{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),t0(e,t,n,r,a,"avg")}};Wy.className="AveragePooling2D";re.registerClass(Wy);var tv=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new V(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Xt(this.poolSize,"poolSize"),Xt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),Yn(this.padding),this.inputSpec=[new Kt({ndim:5})]}computeOutputShape(e){e=ct(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Sr(t,this.poolSize[0],this.padding,this.strides[0]),n=Sr(n,this.poolSize[1],this.padding,this.strides[1]),r=Sr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return B(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},By=class extends tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),J7(e,t,n,r,a,"max")}};By.className="MaxPooling3D";re.registerClass(By);var Vy=class extends tv{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Tt(a),Yn(r),J7(e,t,n,r,a,"avg")}};Vy.className="AveragePooling3D";re.registerClass(Vy);var nv=class extends Ge{constructor(e){super(e);this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},Uy=class extends nv{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=Pe(e);return kt(n,1)})}};Uy.className="GlobalAveragePooling1D";re.registerClass(Uy);var Hy=class extends nv{constructor(e){super(e||{})}call(e,t){return B(()=>{let n=Pe(e);return Xn(n,1)})}};Hy.className="GlobalMaxPooling1D";re.registerClass(Hy);var rv=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Tt(this.dataFormat),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},jy=class extends rv{call(e,t){return B(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?kt(n,[1,2]):kt(n,[2,3])})}};jy.className="GlobalAveragePooling2D";re.registerClass(jy);var Gy=class extends rv{call(e,t){return B(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Xn(n,[1,2]):Xn(n,[2,3])})}};Gy.className="GlobalMaxPooling2D";re.registerClass(Gy);var av=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let r=t.layer,a=Nr(r,n);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},qy=class extends av{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ct(e),e.length<3)throw new V(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ct(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return B(()=>(e=Pe(e),Z7((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};qy.className="TimeDistributed";re.registerClass(qy);function cne(e){Ii(bQ,"BidirectionalMergeMode",e)}var hne="concat",Xy=class extends av{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Nr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Nr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?hne:e.mergeMode,cne(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()):Sn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=K7(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new V("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let u=n.map(c=>new Kt({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Ir;for(let l of s)if(l instanceof Ir!==o)throw new V("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=c,h}else return super.apply(e,t)}call(e,t){return B(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Dn(a,1));let i;return this.mergeMode==="concat"?i=_A([r,a]):this.mergeMode==="sum"?i=se(r,a):this.mergeMode==="ave"?i=P(.5,se(r,a)):this.mergeMode==="mul"?i=P(r,a):this.mergeMode==null&&(i=[r,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){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=Nr(t.layer);if(delete t.layer,t.numConstants!=null)throw new De("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let r=t;return r.layer=n,new e(r)}};Xy.className="Bidirectional";re.registerClass(Xy);function PQ(e){return new zl(e)}function LQ(e){return new ty(e)}function WQ(e){return new JA(e)}function BQ(e){return new QA(e)}function VQ(e){return new ey(e)}function UQ(e){return new ry(e)}function HQ(e){return new ny(e)}function jQ(e){return new Kp(e)}function GQ(e){return new Fc(e)}function qQ(e){return new iy(e)}function XQ(e){return new Xp(e)}function KQ(e){return new oy(e)}function ZQ(e){return new ly(e)}function YQ(e){return new uy(e)}function JQ(e){return new cy(e)}function QQ(e){return new gy(e)}function eee(e){return new Ay(e)}function tee(e){return new e0(e)}function nee(e){return new my(e)}function ree(e){return new yy(e)}function aee(e){return new xy(e)}function see(e){return new wy(e)}function iee(e){return new _y(e)}function oee(e){return new vy(e)}function lee(e){return new ky(e)}function uee(e){return new Ny(e)}function cee(e){return new Cy(e)}function hee(e){return new Sy(e)}function dee(e){return new Ty(e)}function pee(e){return new Iy(e)}function fee(e){return new Ey(e)}function mee(e){return new $y(e)}function Aee(e){return new Dy(e)}function yee(e){return new Oy(e)}function EA(e){return new Py(e)}function gee(e){return EA(e)}function xee(e){return EA(e)}function RA(e){return new Wy(e)}function wee(e){return RA(e)}function _ee(e){return RA(e)}function FA(e){return new Vy(e)}function bee(e){return FA(e)}function vee(e){return FA(e)}function kee(e){return new Uy(e)}function Iee(e){return new jy(e)}function K3(e){return new Hy(e)}function Z3(e){return new Gy(e)}function Y3(e){return new zy(e)}function J3(e){return new Ly(e)}function Nee(e){return new By(e)}function See(e){return new dy(e)}function Tee(e){return new Jp(e)}function Cee(e){return new py(e)}function Eee(e){return new Mc(e)}function Ree(e){return new hy(e)}function Fee(e){return new Yp(e)}function Mee(e){return new fy(e)}function $ee(e){return new Qp(e)}function Dee(e){return new Hr(e)}function Oee(e){return new Zp(e)}function zee(e){return new Xy(e)}function Pee(e){return new qy(e)}var Lee=K3,Wee=Z3,Bee=Y3,Vee=J3;function Uee(e){return new Ry(e)}function Hee(e){return new Fy(e)}function jee(e){return new My(e)}function Gee(e){return new by(e)}var sv={};ze(sv,{MAPE:()=>bne,MSE:()=>Ine,binaryAccuracy:()=>dne,binaryCrossentropy:()=>pne,categoricalAccuracy:()=>mne,categoricalCrossentropy:()=>Ane,cosineProximity:()=>xne,mape:()=>vne,meanAbsoluteError:()=>wne,meanAbsolutePercentageError:()=>_ne,meanSquaredError:()=>kne,mse:()=>Nne,precision:()=>yne,recall:()=>gne,sparseCategoricalAccuracy:()=>fne});function dne(e,t){return PA(e,t)}function pne(e,t){return p7(e,t)}function fne(e,t){return f7(e,t)}function mne(e,t){return LA(e,t)}function Ane(e,t){return WA(e,t)}function yne(e,t){return d7(e,t)}function gne(e,t){return hte(e,t)}function xne(e,t){return OA(e,t)}function wne(e,t){return Wp(e,t)}function _ne(e,t){return Ll(e,t)}function bne(e,t){return Ll(e,t)}function vne(e,t){return Ll(e,t)}function kne(e,t){return Ti(e,t)}function Ine(e,t){return Ti(e,t)}function Nne(e,t){return Ti(e,t)}var iv={};ze(iv,{modelFromJSON:()=>jte});var ov={};ze(ov,{l1:()=>Tne,l1l2:()=>Sne,l2:()=>Cne});function Sne(e){return new Ec(e)}function Tne(e){return Qte(e)}function Cne(e){return ene(e)}var lv=class extends Pl{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof ha))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function n0(e,t){return e<t}function uv(e,t){return e>t}var cv=class extends lv{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new De("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=n0:this.mode==="max"?this.monitorFunc=uv:this.monitor.indexOf("acc")!==-1?this.monitorFunc=uv:this.monitorFunc=n0,this.monitorFunc===n0&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===n0?Infinity:-Infinity}async onEpochEnd(e,t){await Ga(t);let n=this.getMonitorValue(t);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=e,this.model.stopTraining=!0)))}async onTrainEnd(e){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(e){e==null&&(e={});let t=e[this.monitor];return t==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`),t}};function Ene(e){return new cv(e)}var Rne={earlyStopping:Ene},Tr;(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"})(Tr||(Tr={}));var hv;(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={}))})(hv||(hv={}));var Ky={};function Fne(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};Ky[e]=n}function dv(e){return Ky[e]}function Mne(e){delete Ky[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 Cn(t.inputNames[s.inputIndexStart],n,r,a);if(s.type==="tensors")return t.inputNames.slice(o,l).map(h=>Cn(h,n,r,a));let u=Cn(t.inputNames.slice(o)[0],n,r,a),c=u.dataSync();return s.type==="number"?c[0]:v.toNestedArray(u.shape,c)}let i=t.attrParams[e];return i&&i.value}function Cn(e,t,n,r){let[a,s]=Wn(e);if(r!=null){let o=r.getHashTableHandleByName(a);if(o!=null)return o}let i=n.currentContextIds.find(o=>!!t[r0(a,o)]);return i!==void 0?t[r0(a,i)][s]:void 0}function $ne(e,t,n){return t[r0(e,n.currentContextId)]}function da(e,t){let[n,r]=Wn(e);return[r0(n,t&&t.currentContextId),r]}function r0(e,t){return t?`${e}-${t}`:e}function Wn(e){let t=e.split(":");return t.length===1?[e,0]:[t[0],Number(t[t.length-1])]}function a0(e,t,n){let r=k("pad",e,t,n);if(r==="explicit"){r=k("explicitPaddings",e,t,n);let a=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)a[s][0]=r[s*2],a[s][1]=r[s*2+1];return a}return r}function pa(e){return e.kept?e:rr(e)}var pv={};ze(pv,{json:()=>Dne});var Dne=[{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}]}],fv={};ze(fv,{json:()=>One});var One=[{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}]}],mv={};ze(mv,{json:()=>zne});var zne=[{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"}]}],Av={};ze(Av,{json:()=>Pne});var Pne=[{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"}]}],yv={};ze(yv,{json:()=>Lne});var Lne=[{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"}]}],gv={};ze(gv,{json:()=>Wne});var Wne=[{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}]}],xv={};ze(xv,{json:()=>Bne});var Bne=[{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"}]}],wv={};ze(wv,{json:()=>Vne});var Vne=[{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"}]}],_v={};ze(_v,{json:()=>Une});var Une=[{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"}]}],bv={};ze(bv,{json:()=>Hne});var Hne=[{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"}]}],vv={};ze(vv,{json:()=>jne});var jne=[{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}]}],kv={};ze(kv,{json:()=>Gne});var Gne=[{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}]}],Iv={};ze(Iv,{json:()=>qne});var qne=[{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}]}],Nv={};ze(Nv,{json:()=>Xne});var Xne=[{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"}]}],Sv={};ze(Sv,{json:()=>Kne});var Kne=[{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}]}],Tv={};ze(Tv,{json:()=>Zne});var Zne=[{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}]}],Cv={};ze(Cv,{json:()=>Yne});var Yne=[{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:[]}],Rv=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[pv,fv,mv,Av,yv,gv,xv,vv,bv,wv,kv,Iv,Nv,Sv,Tv,Cv,_v],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,r)=>(n[r.tfOpName]=r,n),{})}transformGraph(e,t={}){let n=e.node,r=[],a=[],s=[],i=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?r.push(f[m.name]):m.op==="Const"?a.push(f[m.name]):(m.input==null||m.input.length===0)&&s.push(f[m.name]),f),{}),o=[],l=[],u={},c={};t!=null&&(u=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));let h=Object.keys(i);h.forEach(f=>{let m=i[f];m.inputNames.forEach(A=>{let[y]=da(A);m.inputs.push(i[y]),i[y].children.push(m)})}),Object.keys(c).length===0?h.forEach(f=>{let m=i[f];m.children.length===0&&l.push(m)}):Object.keys(c).forEach(f=>{let[m]=da(f),A=i[m];A!=null&&(A.signatureKey=c[f],l.push(A))}),Object.keys(u).length>0?Object.keys(u).forEach(f=>{let[m]=da(f),A=i[m];A&&(A.signatureKey=u[f],o.push(A))}):o=r;let d={};e.library!=null&&e.library.function!=null&&(d=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let p={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:d};return s.length>0&&(p.initNodes=s),p}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=dv(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=Zy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Zy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"string[]":i=a2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=a2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number":i=Jy(e.attr,a.tfName,a.defaultValue||0),i===void 0&&!!a.tfDeprecatedName&&(i=Jy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"number[]":i=r2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=r2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool":i=Yy(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Yy(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"bool[]":i=i2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=i2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape":i=n2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=n2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"shape[]":i=s2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=s2(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"dtype":i=e2(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=e2(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"func":i=Ev(e.attr,a.tfName,a.defaultValue),i===void 0&&!!a.tfDeprecatedName&&(i=Ev(e.attr,a.tfDeprecatedName,a.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${a.type} for op: ${e.op}`)}return r[a.name]={value:i,type:s},r},{})),n}mapFunction(e){let t=e.nodeDef,n=[],r=[],a={};t!=null&&(a=t.reduce((u,c)=>(u[c.name]=this.mapNode(c),c.op==="Const"&&r.push(u[c.name]),u),{}));let s=[],i=[];e.signature.inputArg.forEach(u=>{let[c]=da(u.name),h={name:c,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:Qy(u.type),type:"dtype"}},children:[]};h.signatureKey=u.name,s.push(h),a[c]=h}),Object.keys(a).forEach(u=>{let c=a[u];c.inputNames.forEach(h=>{let[d]=da(h);c.inputs.push(a[d]),a[d].children.push(c)})});let o=e.ret;e.signature.outputArg.forEach(u=>{let[c,h]=da(o[u.name]),d=a[c];d!=null&&(d.defaultOutput=h,i.push(d))});let l=this.mapArgsToSignature(e);return{nodes:a,inputs:s,outputs:i,weights:r,placeholders:n,signature:l}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n),t),{}),outputs:e.signature.outputArg.reduce((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t),{})}}mapArgToTensorInfo(e,t){let n=e.name;return t!=null&&(n=t[n]),{name:n,dtype:e.type}}};function Jne(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 Fv(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):Jne(e);return t?n:n.toLowerCase()}function Zy(e,t,n,r=!1){let a=e[t];return a!=null?Fv(a.s,r):n}function Yy(e,t,n){let r=e[t];return r?r.b:n}function Jy(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 Qy(e){switch(typeof e=="string"&&(e=Tr[e]),e){case Tr.DT_FLOAT:return"float32";case Tr.DT_INT32:case Tr.DT_INT64:case Tr.DT_INT8:case Tr.DT_UINT8:return"int32";case Tr.DT_BOOL:return"bool";case Tr.DT_DOUBLE:return"float32";case Tr.DT_STRING:return"string";default:return null}}function Ev(e,t,n){let r=e[t];return r&&r.func?r.func.name:n}function e2(e,t,n){let r=e[t];return r&&r.type?Qy(r.type):n}function t2(e,t,n){let r=e[t];return r&&r.list&&r.list.type?r.list.type.map(a=>Qy(a)):n}function Mv(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function n2(e,t,n){let r=e[t];return r&&r.shape?Mv(r.shape):n}function r2(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 a2(e,t,n,r=!1){let a=e[t];return a&&a.list&&a.list.s?a.list.s.map(s=>Fv(s,r)):n}function s2(e,t,n){let r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map(a=>Mv(a)):n}function i2(e,t,n){let r=e[t];return r&&r.list&&r.list.b?r.list.b:n}var Qne=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 Cn(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Cn(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Jy(this.node.rawAttrs,e,t);if(n.s!=null)return Zy(this.node.rawAttrs,e,t);if(n.b!=null)return Yy(this.node.rawAttrs,e,t);if(n.shape!=null)return n2(this.node.rawAttrs,e,t);if(n.type!=null)return e2(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return r2(this.node.rawAttrs,e,t);if(n.list.s!=null)return a2(this.node.rawAttrs,e,t);if(n.list.shape!=null)return s2(this.node.rawAttrs,e,t);if(n.list.b!=null)return i2(this.node.rawAttrs,e,t);if(n.list.type!=null)return t2(this.node.rawAttrs,e,t)}return t}},ere=(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[im(k("a",e,t,n),k("b",e,t,n))];case"Mul":return[P(k("a",e,t,n),k("b",e,t,n))];case"RealDiv":case"Div":return[ge(k("a",e,t,n),k("b",e,t,n))];case"DivNoNan":return[Zf(k("a",e,t,n),k("b",e,t,n))];case"FloorDiv":return[hd(k("a",e,t,n),k("b",e,t,n))];case"Sub":return[ye(k("a",e,t,n),k("b",e,t,n))];case"Minimum":return[fl(k("a",e,t,n),k("b",e,t,n))];case"Maximum":return[Dr(k("a",e,t,n),k("b",e,t,n))];case"Pow":return[sa(k("a",e,t,n),k("b",e,t,n))];case"SquaredDifference":return[Dd(k("a",e,t,n),k("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tre=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[zt(k("x",e,t,n))];case"Acos":return[$f(k("x",e,t,n))];case"Acosh":return[Df(k("x",e,t,n))];case"Asin":return[zf(k("x",e,t,n))];case"Asinh":return[Pf(k("x",e,t,n))];case"Atan":return[Lf(k("x",e,t,n))];case"Atan2":return[Wf(k("x",e,t,n),k("y",e,t,n))];case"Atanh":return[Bf(k("x",e,t,n))];case"Ceil":return[jf(k("x",e,t,n))];case"Complex":return[Ca(k("real",e,t,n),k("imag",e,t,n))];case"Cos":return[Vu(k("x",e,t,n))];case"Cosh":return[Ad(k("x",e,t,n))];case"Elu":return[cl(k("x",e,t,n))];case"Erf":return[Yf(k("x",e,t,n))];case"Exp":return[qn(k("x",e,t,n))];case"Expm1":return[Jf(k("x",e,t,n))];case"Floor":return[hl(k("x",e,t,n))];case"Log":return[Mn(k("x",e,t,n))];case"Log1p":return[wd(k("x",e,t,n))];case"Imag":return[gd(k("x",e,t,n))];case"Neg":return[vt(k("x",e,t,n))];case"Reciprocal":return[um(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[cm(k("x",e,t,n))];case"Selu":return[Cd(k("x",e,t,n))];case"Sigmoid":return[Rn(k("x",e,t,n))];case"Sin":return[Ed(k("x",e,t,n))];case"Sign":return[dm(k("x",e,t,n))];case"Sinh":return[Rd(k("x",e,t,n))];case"Softplus":return[dl(k("x",e,t,n))];case"Sqrt":return[en(k("x",e,t,n))];case"Square":return[lt(k("x",e,t,n))];case"Tanh":return[ol(k("x",e,t,n))];case"Tan":return[mm(k("x",e,t,n))];case"ClipByValue":return[bn(k("x",e,t,n),k("clipValueMin",e,t,n),k("clipValueMax",e,t,n))];case"Relu6":return[Sd(k("x",e,t,n))];case"Rsqrt":return[Td(Cn(e.inputNames[0],t,n))];case"Prod":return[Id(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")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let r=0;r<e.length;r++){let a=e[r],s=t[r];v.assert(a<0||s<0||a===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function $v(e){return!(typeof e=="number"||e.some(t=>t<0))}function Oc(e,t,n){let r=o2(e,n),a=!$v(r);if(a&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(a&&t.forEach(s=>{r=o2(s.shape,r)}),!$v(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function o2(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 nre=class{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=be(0),Gt(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
|
because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),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,Gt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,r)=>this.write(n,t[r]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let r=0;r<this.size();r++)e.push(r)}if(e.length===0)return gr([],[0].concat(this.elementShape));let n=this.readMany(e);return hr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),hn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return gr([],[0].concat(this.elementShape));let t=[];for(let r=0;r<this.size();r++)t.push(r);let n=this.readMany(t);return hr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),st(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,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=[];B(()=>{t=G(t,[1,n,a]);for(let o=0;o<e.length;++o){let l=o===0?0:r[o-1],u=[0,l,0],c=[1,e[o],a];s[o]=G(Re(t,u,c),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},zc=class{constructor(e,t,n,r=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(a=>{if(n!==a.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${a.dtype}`);hr(t,a.shape,"TensorList shape mismatch: "),Gt(a)}),this.idTensor=be(0),this.maxNumElements=r,Gt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new zc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);hr(e,this.elementShape,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,e);return B(()=>{let a=this.tensors.map(s=>G(s,r));return hn(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: "),G(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(hr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Gt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);hr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let r=Oc(this.elementShape,this.tensors,t);return G(this.tensors[e],r)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);hr(this.elementShape,t.shape,"TensorList shape mismatch: "),Gt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);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?gr([],[0].concat(r)):B(()=>{let a=e.map(s=>G(this.tensors[s],r));return hn(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?gr([],[0].concat(n)):B(()=>{let r=this.tensors.map(a=>G(a,n));return st(r,0)})}};function rre(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 are(e,t,n){return new zc([],e,t,n)}function sre(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 ire(e,t,n){let r=0,a=t.map(c=>(r+=c,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${r}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=o2(s,n),o=r===0?0:e.size/r,l=B(()=>{let c=[];e=G(e,[1,r,o]);for(let h=0;h<t.length;++h){let d=h===0?0:a[h-1],p=[0,d,0],f=[1,t[h],o];c[h]=G(Re(e,p,f),i)}return e.dispose(),c}),u=new zc([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var ore=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let r=k("thenBranch",e,t,n),a=k("elseBranch",e,t,n),s=k("cond",e,t,n),i=k("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let r=k("body",e,t,n),a=k("cond",e,t,n),s=k("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(c=>c.id),l=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;l[0];){let c=u;u=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);let h=u.map(p=>p.id);c.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()});let d=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await d[0].data(),d.forEach(p=>{!p.kept&&o.indexOf(p.id)===-1&&h.indexOf(p.id)===-1&&p.dispose()})}return u}case"LoopCond":{let r=k("pred",e,t,n);return[pa(r)]}case"Switch":{let r=k("pred",e,t,n),a=k("data",e,t,n);return a.kept||(a=pa(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{let r=e.inputNames.find(a=>Cn(a,t,n)!==void 0);if(r){let a=Cn(r,t,n);return[pa(a)]}return}case"Enter":{let r=k("frameName",e,t,n),a=k("tensor",e,t,n);return n.enterFrame(r),[pa(a)]}case"Exit":{let r=k("tensor",e,t,n);return n.exitFrame(),[pa(r)]}case"NextIteration":{let r=k("tensor",e,t,n);return n.nextIteration(),[pa(r)]}case"TensorArrayV3":{let r=k("size",e,t,n),a=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),u=k("name",e,t,n),c=new nre(u,a,r,s,l,i,o);return n.addTensorArray(c),[c.idTensor,be(1)]}case"TensorArrayWriteV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=k("tensorArrayId",e,t,n),a=k("tensor",e,t,n),s=k("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[be(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=k("indices",e,t,n),a=k("tensor",e,t,n),s=k("elementShape",e,t,n),i=k("numElements",e,t,n),o=sre(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=are(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=rre(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=ire(r,s,a);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Dv(e,t,n){let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=r==="fusedbatchnorm",l=k("numArgs",e,t,n);if(s){if(i&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=k("strides",e,t,n),c=a0(e,t,n),h=k("dataFormat",e,t,n).toUpperCase(),d=k("dilations",e,t,n),[p,f]=k("args",e,t,n),m=k("leakyreluAlpha",e,t,n);return{stride:u,pad:c,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var lre=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=k("stride",e,t,n),a=k("pad",e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilation",e,t,n);return[fd(k("x",e,t,n),k("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let r=k("strides",e,t,n),a=a0(e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilations",e,t,n);return[ra(k("x",e,t,n),k("filter",e,t,n),[r[1],r[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:c}=Dv(e,t,n);return[La.conv2d({x:k("x",e,t,n),filter:k("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:r,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:c}=Dv(e,t,n);return[La.depthwiseConv2d({x:k("x",e,t,n),filter:k("filter",e,t,n),strides:[r[1],r[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let r=k("outputShape",e,t,n),a=k("strides",e,t,n),s=a0(e,t,n);return[md(k("x",e,t,n),k("filter",e,t,n),r,[a[1],a[2]],s)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let r=k("strides",e,t,n),a=a0(e,t,n),s=k("dilations",e,t,n),i=k("dataFormat",e,t,n).toUpperCase();return[ul(k("input",e,t,n),k("filter",e,t,n),[r[1],r[2]],a,i,[s[1],s[2]])]}case"Conv3D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("dataFormat",e,t,n).toUpperCase(),i=k("dilations",e,t,n);return[qf(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}=lx(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[Hf(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[am(k("x",e,t,n),[s[1],s[2],s[3]],[r[1],r[2],r[3]],a)]}case"Dilation2D":{let r=k("strides",e,t,n),a=k("pad",e,t,n),s=k("dilations",e,t,n),i=r[1],o=r[2],l=s[1],u=s[2];return[Kf(k("x",e,t,n),k("filter",e,t,n),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},ure=(e,t,n)=>{switch(e.op){case"Fill":{let r=k("shape",e,t,n),a=k("dtype",e,t,n),s=k("value",e,t,n);return[Uu(r,s,a)]}case"LinSpace":{let r=k("start",e,t,n),a=k("stop",e,t,n),s=k("num",e,t,n);return[tx(r,a,s)]}case"Multinomial":{let r=k("logits",e,t,n),a=k("numSamples",e,t,n),s=k("seed",e,t,n);return[ux(r,a,s)]}case"OneHot":{let r=k("indices",e,t,n),a=k("depth",e,t,n),s=k("onValue",e,t,n),i=k("offValue",e,t,n);return[nl(r,a,s,i)]}case"Ones":return[Or(k("shape",e,t,n),k("dtype",e,t,n))];case"OnesLike":return[$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[Nd(r,a,s,k("dtype",e,t,n))]}case"TruncatedNormal":{let r=k("shape",e,t,n),a=k("mean",e,t,n),s=k("stdDev",e,t,n),i=k("seed",e,t,n);return[Od(r,a,s,k("dtype",e,t,n),i)]}case"Zeros":return[Et(k("shape",e,t,n),k("dtype",e,t,n))];case"ZerosLike":return[Ue(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function l2(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 cre=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=l2(e,t,n),u=await Je.nonMaxSuppressionWithScoreAsync(r,a,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=l2(e,t,n),l=k("padToMaxOutputSize",e,t,n),u=await Je.nonMaxSuppressionPaddedAsync(r,a,s,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:r,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=l2(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 gm(r)];return r.dispose(),a}case"ListDiff":return dx(k("x",e,t,n),k("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},hre=(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=Am(r,a,s);return[i.values,i.indices]}case"Unique":{let r=k("x",e,t,n),a=zd(r);return[a.values,a.indices]}case"UniqueV2":{let r=k("x",e,t,n),a=k("axis",e,t,n),s=zd(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},dre=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let r=k("default",e,t,n);return[Cn(e.name,t,n)||r];case"Placeholder":return[Cn(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let u=k("x",e,t,n);return[pa(u)]}case"IdentityN":return k("x",e,t,n).map(u=>pa(u));case"Snapshot":let a=k("x",e,t,n);return[pa(a)];case"Shape":return[on(k("x",e,t,n).shape,"int32")];case"ShapeN":return k("x",e,t,n).map(u=>on(u.shape));case"Size":return[be(k("x",e,t,n).size,"int32")];case"Rank":return[be(k("x",e,t,n).rank,"int32")];case"NoOp":return[be(1)];case"Print":let s=k("x",e,t,n),i=k("data",e,t,n),o=k("message",e,t,n),l=k("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},pre=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=be(0),this.tensorMap=new Map,Gt(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return be(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(r=>r.dispose()),this.tensorMap.clear(),B(()=>{let r=ir(t),a=n.length,s=r.length;v.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=n[i],l=r[i];Gt(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return B(()=>{let r=[];for(let a=0;a<n.length;a++){let s=n[a],i=this.findWithDefault(s,t);r.push(i)}return hn(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}`)}},fre=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 pre(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`)}},mre=(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`)}},Are=(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[xd(k("a",e,t,n),k("b",e,t,n))];case"LessEqual":return[ci(k("a",e,t,n),k("b",e,t,n))];case"LogicalAnd":return[sr(k("a",e,t,n),k("b",e,t,n))];case"LogicalNot":return[ju(k("a",e,t,n))];case"LogicalOr":return[vd(k("a",e,t,n),k("b",e,t,n))];case"Select":case"SelectV2":return[vn(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`)}},yre=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[je(k("a",e,t,n),k("b",e,t,n),k("transposeA",e,t,n),k("transposeB",e,t,n))];case"Transpose":return[at(k("x",e,t,n),k("perm",e,t,n))];case"_FusedMatMul":let[r,a]=k("fusedOps",e,t,n),s=r==="biasadd",i=a==="prelu",o=k("numArgs",e,t,n),l=k("leakyreluAlpha",e,t,n);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=k("args",e,t,n);return[La.matMul({a:k("a",e,t,n),b:k("b",e,t,n),transposeA:k("transposeA",e,t,n),transposeB:k("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gre=(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[em(k("x",e,t,n),k("radius",e,t,n),k("bias",e,t,n),k("alpha",e,t,n),k("beta",e,t,n))];case"Softmax":return[Yu(k("x",e,t,n))];case"LogSoftmax":return[bd(k("x",e,t,n))];case"SparseToDense":return[xm(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`)}},xre=(e,t,n)=>{switch(e.op){case"Max":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Xn(k("x",e,t,n),i,o)]}case"Mean":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[kt(k("x",e,t,n),i,o)]}case"Min":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[pl(k("x",e,t,n),i,o)]}case"Sum":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Te(k("x",e,t,n),i,o)]}case"All":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[dd(k("x",e,t,n),i,o)]}case"Any":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Ou(k("x",e,t,n),i,o)]}case"ArgMax":{let i=k("axis",e,t,n);return[zu(k("x",e,t,n),i)]}case"ArgMin":{let i=k("axis",e,t,n);return[Of(k("x",e,t,n),i)]}case"Prod":{let i=k("axis",e,t,n),o=k("keepDims",e,t,n);return[Id(k("x",e,t,n),i,o)]}case"Cumsum":{let i=k("axis",e,t,n),o=k("exclusive",e,t,n),l=k("reverse",e,t,n);return[yd(k("x",e,t,n),i,o,l)]}case"Bincount":let r=k("x",e,t,n),a=k("weights",e,t,n),s=k("size",e,t,n);return[j5(r,a,s)];case"DenseBincount":{let i=k("x",e,t,n),o=k("weights",e,t,n),l=k("size",e,t,n),u=k("binaryOutput",e,t,n);return[Z5(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},wre=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=k("n",e,t,n),a=k("axis",e,t,n),s=k("tensors",e,t,n);return s=s.slice(0,r),[st(s,a)]}case"Gather":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[ui(r,me(a,"int32"),0)]}case"GatherV2":{let r=k("axis",e,t,n),a=k("batchDims",e,t,n),s=k("x",e,t,n),i=k("indices",e,t,n);return[ui(s,me(i,"int32"),r,a)]}case"Reverse":{let r=k("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let s=k("x",e,t,n);return[Dn(s,a)]}case"ReverseV2":{let r=k("axis",e,t,n),a=k("x",e,t,n);return[Dn(a,r)]}case"Slice":{let r=k("begin",e,t,n),a=k("size",e,t,n);return[Re(k("x",e,t,n),r,a)]}case"StridedSlice":{let r=k("begin",e,t,n),a=k("end",e,t,n),s=k("strides",e,t,n),i=k("beginMask",e,t,n),o=k("endMask",e,t,n),l=k("ellipsisMask",e,t,n),u=k("newAxisMask",e,t,n),c=k("shrinkAxisMask",e,t,n),h=k("x",e,t,n);return[fm(h,r,a,s,i,o,l,u,c)]}case"Pack":return B(()=>{let r=k("axis",e,t,n),a=k("tensors",e,t,n),s=a[0].shape,i=Pa(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(Pa(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[hn(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 Lt(s,a,r)}case"ScatterNd":{let r=k("indices",e,t,n),a=k("values",e,t,n),s=k("shape",e,t,n);return[Ax(r,a,s)]}case"GatherNd":{let r=k("x",e,t,n),a=k("indices",e,t,n);return[yx(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[xm(r,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_re=(e,t,n)=>{switch(e.op){case"FFT":return[Ju(k("x",e,t,n))];case"IFFT":return[Al(k("x",e,t,n))];case"RFFT":return[Qu(k("x",e,t,n))];case"IRFFT":return[$d(k("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bre=(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[Fn(k("x",e,t,n),r)]}case"Squeeze":{let r=k("axis",e,t,n);return[Pa(k("x",e,t,n),r)]}case"Reshape":return[G(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[sm(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[Xf(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 Ov(e,t,n,r){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return B(()=>ere(s,i,o));case"basic_math":return B(()=>tre(s,i,o));case"control":return ore(s,i,o);case"convolution":return B(()=>lre(s,i,o));case"creation":return B(()=>ure(s,i,o));case"dynamic":return cre(s,i,o);case"evaluation":return B(()=>hre(s,i,o));case"image":return B(()=>mre(s,i,o));case"graph":return B(()=>dre(s,i,o));case"logical":return B(()=>Are(s,i,o));case"matrices":return B(()=>yre(s,i,o));case"normalization":return B(()=>gre(s,i,o));case"reduction":return B(()=>xre(s,i,o));case"slice_join":return B(()=>wre(s,i,o));case"spectral":return B(()=>_re(s,i,o));case"transformation":return B(()=>bre(s,i,o));case"hash_table":return fre(s,i,o,r);case"custom":let l=dv(s.op);if(l&&l.customExecutor)return l.customExecutor(new Qne(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var zv=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 Lv(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(d=>Wn(d)[0]),c=[];r!=null&&(c=r.map(d=>Wn(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((Pv(d)||vre(d)||kre(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Ire(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(c=>Wn(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{r.has(c.name)&&s.push(c)}),e.weights.forEach(c=>{r.has(c.name)&&s.push(c)}),o!=null&&o.forEach(c=>{r.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return u}var Nre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Sre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Tre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Pv(e){return Nre.indexOf(e.op)>=0}function vre(e){return Sre.indexOf(e.op)>=0}function kre(e){return Tre.indexOf(e.op)>=0}var u2=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 u2(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=Lv(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 Ire(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(c=>this.graph.nodes[Wn(c)[0]]),a=t.map(c=>Wn(c)[0]),s=a.map(c=>this.graph.nodes[c]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return B(()=>{let c=new zv(this.weightMap,l,u,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,A]=Wn(f),y=[];y[A]=e[f],h[m]=y});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let A=Ov(m,h,c,this._resourceManager);if(v.isPromise(A))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=A,this.checkTensorForDisposal(m.name,m,h,c,d,a,p)}}return this.parent==null&&c.dispose(d),t.map(f=>Cn(f,h,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(r=>r.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=$ne(o.name,n,r);l!=null&&l.forEach(u=>{if(u&&!a.has(u.id)){let c=i[u.id];c===1?(u.dispose(),delete i[u.id]):c!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new zv(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(h=>Cn(h,i,s)),l=o.map(h=>h.id),u=Object.keys(e).map(h=>e[h].id),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach(h=>{i[h].forEach(d=>{d&&!d.isDisposed&&!c.has(d.id)&&d.dispose()})}),this.parent==null&&s.dispose(c),o}async executeFunctionAsync(e,t,n){let r=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){let a=Object.keys(e),s=a.map(g=>this.graph.nodes[Wn(g)[0]]),i=n.map(g=>Wn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=Lv(e,o,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),p=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[_,b]=Wn(g),w=[];w[b]=e[g],p[_]=w});let f={},m=this.getFrozenTensorIds(p),A={};for(;d.length>0;){let g=this.processStack(s,d,t,p,A,m,i,f,l);await Promise.all(g)}c==null&&!r&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!Pv(g)&&!Cn(g.name,p,t)).map(g=>g.name);if(y.length>0){let g="";throw c!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let h="";if(c.node.op==="Enter"&&k("isConstant",c.node,r,n)&&([h]=da(c.node.name,n)),r[c.node.name]==null){let d=Ov(c.node,r,n,this._resourceManager);h||([h]=da(c.node.name,n));let p=n.currentContext;v.isPromise(d)?u.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,c.node,r,n,s,i,o),this.processChildNodes(c.node,t,n,r,a,l))}else this.processChildNodes(c.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=da(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Cn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Wn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Wn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Wn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Cre=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Ere="?tfjs-format=file",Rre="model.json",Wv=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Cre}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=_n.browserHTTPRequest(e,this.loadOptions);else{let t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let r=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new u2(Rv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Rv.Instance.transformGraph(e.modelInitializer);this.initializer=new u2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Ye)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Ft(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Rre}${Ere}`);let n=new Wv(e,t);return await n.load(),n}var Fre="3.3.0",Bv={};ze(Bv,{CSVDataset:()=>Uv,Dataset:()=>Vl,FileDataSource:()=>Hv,TextLineDataset:()=>Vv,URLDataSource:()=>jv,array:()=>Mre,csv:()=>Dre,func:()=>Ore,generator:()=>zre,microphone:()=>Lre,version_data:()=>Wre,webcam:()=>Pre,zip:()=>$re});var Bre=qi(Rg()),Vre=qi(Rg());function Ure(e,t){return s0(e,t)}function s0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Ul(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=s0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Hre(e,t=qv){return Gv(e,t)}function Gv(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Ul(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(u=>u[i]),l=Gv(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 qv(e){return e===null?null:Ul(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Xv(e,t){let n=new Map;s0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return s0(e,t,n)}function Ul(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ye))}function Gre(e){return e==null||jre(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ye||v.isTypedArray(e)}function jre(e){return e===null||typeof e!="object"&&typeof e!="function"}function Xre(e){return Ure(e,qre)}function qre(e){return e instanceof Ye?{value:e.clone(),recurse:!1}:Ul(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Kv=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}},c2=class extends Kv{constructor(){super(c2.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}};c2.INITIAL_CAPACITY=32;function Zv(e){return new Kre(e)}function h2(e){return new Zre(e)}function Yre(e,t){return new Yv(e,t)}function Qre(e,t=Za.FAIL){return new Jre(e,t)}var Zt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new iae(this,e)}filter(e){return new aae(this,e)}map(e){return new sae(this,e)}mapAsync(e){return new Jv(this,e)}serialMapAsync(e){return new Jv(this,e).serial()}flatmap(e){return new oae(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 rae(this,e,t)}columnMajorBatch(e,t=!0,n=qv){return this.rowMajorBatch(e,t).map(r=>Hre(r,n))}concatenate(e,t){return new Yv(Zv([this,e]),t)}take(e){return e<0||e==null?this:new nae(this,e)}skip(e){return e<0||e==null?this:new tae(this,e)}prefetch(e){return new Qv(this,e)}shuffle(e,t){return new lae(this,e,t)}serial(){return new eae(this)}},Kre=class extends Zt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Xre(e),done:!1}}},Zre=class extends Zt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},eae=class extends Zt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},tae=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ee(e.value)}return this.upstream.next()}},nae=class extends Zt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},rae=class extends Zt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},aae=class extends Zt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ee(e.value)}}},sae=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ar.getTensorsInContainer(e.value),n=this.transform(e.value),r=Ar.getTensorsInContainer(n);for(let a of t)Ar.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},iae=class extends Zt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Jv=class extends Zt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Ar.getTensorsInContainer(e.value),n=await this.transform(e.value),r=Ar.getTensorsInContainer(n);for(let a of t)Ar.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},d2=class extends Zt{constructor(){super();this.outputQueue=new c2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},oae=class extends d2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Ar.getTensorsInContainer(e.value),n=this.transform(e.value),r=Ar.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)Ar.isTensorInList(a,r)||a.dispose();return!0}},Yv=class extends Zt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Za;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Za||(Za={}));var Jre=class extends Zt{constructor(e,t=Za.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Zt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await Xv(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 ${this.count}.`);case Za.SHORTEST:return{value:null,done:!0};case Za.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Qv=class extends Zt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Kv(t)}summary(){return`${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()}},lae=class extends Qv{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Vre.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Vl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
|
|
${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Bn(async()=>(await n.iterator()).columnMajorBatch(e,t,uae),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Bn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Bn(async()=>(await t.iterator()).filter(r=>B(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Bn(async()=>(await t.iterator()).map(n=>B(()=>e(n))),this.size)}mapAsync(e){let t=this;return Bn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Bn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Bn(async()=>{let r=h2(async()=>({value:await t.iterator(),done:!1}));return Yre(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Bn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Bre.alea(t||v.now().toString());return Bn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Bn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Vl.MAX_BUFFER_SIZE=1e4;function Bn(e,t=null){return new class extends Vl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Mre(e){return Bn(async()=>Zv(e),e.length)}function $re(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 Xv(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 Qre(n,Za.SHORTEST)},t)}function uae(e){if(e===null)return null;let t=e[0];return Gre(t)?{value:cae(e),recurse:!1}:{value:null,recurse:!0}}function cae(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ye?hn(e):gr(e)}var Vv=class extends Vl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
|
|
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},i0='"',Pc=Symbol("out"),e6=Symbol("field"),o0=Symbol("quote"),p2=Symbol("quoteafterquote"),t6=Symbol("quoteinquote"),Uv=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 Vv(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Pc;for(let i=0;i<a;i++)switch(s){case Pc:switch(e.charAt(i)){case i0:r=i+1,s=o0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Pc;break;default:s=e6,r=i;break}break;case e6:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Pc,r=i+1;break;default:}break;case o0:switch(e.charAt(i)){case i0:s=p2;break;default:}break;case p2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Pc,r=i+1;break;case i0:s=o0;break;default:s=t6;break}break;case t6:switch(e.charAt(i)){case i0:s=o0;break;default:}break;default:}if(s===p2?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}},n6=class extends Zt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new n6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),gr(n,t)}},r6=class extends Zt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=on([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=kn([s,a,o,i],[1,4])}else this.cropBox=kn([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 r6(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=rl.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return B(()=>{let t=Fn(me(e,"float32"),0),n;n=Je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return G(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},a6=class{},s6=class extends Zt{split(e){return new hae(this,e)}},hae=class extends s6{constructor(e,t){super();this.upstream=e,this.impl=new dae(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dae=class extends d2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},fae=class extends Zt{decodeUTF8(){return new pae(this)}},pae=class extends s6{constructor(e){super();this.upstream=e,this.impl=new mae(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mae=class extends d2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ek();this.decoder=new t("utf8")}}summary(){return`${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}},i6=class extends fae{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function yae(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=Aae(e));let a=await v.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new i6(s,t)}else throw new Error(a.statusText)}var Aae=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 o6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var Hv=class extends a6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(o6(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new i6(this.input,this.options)}},jv=class extends a6{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return o6(this.url)?new Hv(this.url,this.fileOptions).iterator():yae(this.url,this.fileOptions)}};function Dre(e,t={}){return new Uv(new jv(e),t)}function Ore(e){let t=h2(e);return Bn(async()=>t)}function zre(e){return Bn(async()=>{let t=await e();return h2(()=>t.next())})}async function Pre(e,t){return r6.create(e,t)}async function Lre(e){return n6.create(e)}var Wre="3.3.0",gae={tfjs:tk,"tfjs-core":nk,"tfjs-data":rk,"tfjs-layers":ak,"tfjs-converter":sk,"tfjs-backend-cpu":ow,"tfjs-backend-webgl":C_,"tfjs-backend-wasm":x3};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 l6(){if(!Mf(Vn.name)){Ce("backend registration:",Vn.name);try{Vn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Vn.width,Vn.height):document.createElement("canvas")}catch(e){Ce("error: cannot create canvas:",e);return}try{Vn.gl=Vn.canvas.getContext("webgl2",Vn.webGLattr)}catch(e){Ce("error: cannot get WebGL2 context:",e);return}try{sp(2,Vn.gl)}catch(e){Ce("error: cannot set WebGL2 context:",e);return}try{let e=new up(Vn.gl);sl(Vn.name,()=>new El(e),Vn.priority)}catch(e){Ce("error: cannot register WebGL backend:",e);return}try{Yo("webgl").forEach(t=>{let n={...t,backendName:Vn.name};ni(n)})}catch(e){Ce("error: cannot update WebGL backend registration:",e);return}try{mr.set("WEBGL_VERSION",2)}catch(e){Ce("error: cannot set WebGL backend flags:",e);return}Ce("backend registered:",Vn.name)}}var u6=6;function xae(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let r=0;r<t.strides.length;r++){let a=t.strides[r],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[r];for(let l=0;l<s;l++){let u=a*(l+.5);for(let c=0;c<i;c++){let h=a*(c+.5);for(let d=0;d<o;d++)n.push([h,u])}}}return n}var wae=e=>({startEndTensor:e,startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});function _ae(e,t,n){let r=Re(e,[0,1],[-1,2]),a=se(r,t),s=Re(e,[0,3],[-1,2]),i=ge(s,n),o=ge(a,n),l=ge(i,2),u=ye(o,l),c=se(o,l),h=P(u,n),d=P(c,n);return ll([h,d],1)}var c6=class{constructor(t,n){this.model=t,this.anchorsData=xae(t.inputs[0].shape[1]),this.anchors=kn(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,r,a]=B(()=>{let d=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),p=this.model.predict(d),f;if(Array.isArray(p)){let g=p.sort((x,N)=>x.size-N.size),_=st([g[0],g[2]],2),b=st([g[1],g[3]],2);f=st([b,_],1).squeeze(0)}else f=p.squeeze();let m=_ae(f,this.anchors,[this.inputSize,this.inputSize]),A=Re(f,[0,0],[-1,1]),y=Rn(A).squeeze();return[f,m,y]}),s=await Je.nonMaxSuppressionAsync(r,a,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),i=s.arraySync();s.dispose();let l=i.map(h=>Re(r,[h,0],[1,-1])).map(h=>{let d=h.arraySync();return h.dispose(),d}),u=a.dataSync(),c=[];for(let h=0;h<l.length;h++){let d=i[h],p=u[d];if(p>this.config.face.detector.minConfidence){let f=wae(l[h]),m=this.anchorsData[d],A=B(()=>Re(n,[d,u6-1],[1,-1]).squeeze().reshape([u6,-1]));c.push({box:f,landmarks:A,anchor:m,confidence:p})}}return n.dispose(),r.dispose(),a.dispose(),{boxes:c,scaleFactor:[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]}}};async function h6(e){let t=await Ft(e.face.detector.modelPath,{fromTFHub:e.face.detector.modelPath.includes("tfhub.dev")}),n=new c6(t,e);return e.debug&&Ce(`load model: ${e.face.detector.modelPath.match(/\/(.*)\./)[1]}`),n}function d6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],r=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:r}}function Lc(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Hl(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function jl(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return Je.cropAndResize(t,s,[0],n)}function l0(e,t=1.5){let n=Hl(e),r=Lc(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function u0(e){let t=Hl(e),n=Lc(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}var c0=[[1,0,0],[0,1,0],[0,0,1]];function bae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function f2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return bae(n)}function p6(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 vae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function f6(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],vae(t,s)))}return n}function h0(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=p6(t[0],t[1]),i=f6(s,a),o=p6(-t[0],-t[1]);return f6(i,o)}function m6(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 A6(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]},m2=[{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]}],A2=[[.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 kae=[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],Iae=[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],Nae=[33,133,362,263,1,78,308],Kce=kae.map(e=>A2[e]),Zce=Iae.map(e=>A2[e]),Yce=Nae.map(e=>A2[e]);var y2=Gr.leftEyeLower0,g2=Gr.rightEyeLower0,Gl={leftBounds:[y2[0],y2[y2.length-1]],rightBounds:[g2[0],g2[g2.length-1]]},d0={count:468,mouth:13,symmetryLine:[13,Gr.midwayBetweenEyes[0]]},y6={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ql={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function p0(e,t,n,r){for(let a=0;a<m2.length;a++){let{key:s,indices:i}=m2[a],o=Gr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var x2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Lc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?h0(r,[0,0]):c0,l=r!==0?i.map(h=>[...A6(h,o),h[2]]):i,u=r!==0?m6(a):c0,c=[...Hl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+Ya(c,u[0]),h[1]+Ya(c,u[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[Gl.leftBounds[0]][2],r=t[Gl.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=u0(l0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Lc(i),l=Je.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&mr.flags.IS_BROWSER&&(l=Je.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<ql.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],u=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],u])}return{rawCoords:s,iris:s.slice(ql.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Gr[`${r}EyeUpper0`][ql.upperCenter]][2],s=t[Gr[`${r}EyeLower0`][ql.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let u=i;return l===2?u=a:l===4&&(u=s),[o[0],o[1],u]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=d6({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=l0(o),u=u0(l),c=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...u,confidence:h,landmarks:c}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=B(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,u,c=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&mr.flags.IS_BROWSER){let[w,x]=i.landmarks.length>=d0.count?d0.symmetryLine:y6.symmetryLine;c=f2(i.landmarks[w],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),S=[N[0]/t.shape[2],N[1]/t.shape[1]],T=Je.rotateWithOffset(t,c,0,S);h=h0(-c,N),n.face.mesh.enabled?u=jl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.meshSize,this.meshSize]).div(255):u=jl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.boxSize,this.boxSize]).div(255)}else{h=c0;let w=t.clone();n.face.mesh.enabled?u=jl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):u=jl({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:u};let[,d,p]=this.meshDetector.predict(u),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=G(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:x,crop:N}=this.getEyeBox(A,u,Gl.leftBounds[0],Gl.leftBounds[1],!0),{box:S,boxSize:T,crop:M}=this.getEyeBox(A,u,Gl.rightBounds[0],Gl.rightBounds[1]),z=this.irisModel.predict(st([N,M])).dataSync(),W=z.slice(0,ql.numCoordinates*3),{rawCoords:U,iris:H}=this.getEyeCoords(W,w,x,!0),X=z.slice(ql.numCoordinates*3),{rawCoords:j,iris:ee}=this.getEyeCoords(X,S,T),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(p0(A,U,"left",null),p0(A,j,"right",null)):Y<1?p0(A,U,"left",["EyeUpper0","EyeLower0"]):p0(A,j,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,H,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,c,h);i=l0(this.calculateLandmarksBoundingBox(y),1.5);let g=kn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&n.face.detector.return&&mr.flags.IS_BROWSER){let[w,x]=i.landmarks.length>=d0.count?d0.symmetryLine:y6.symmetryLine;c=f2(i.landmarks[w],i.landmarks[x]);let N=Hl({startPoint:i.startPoint,endPoint:i.endPoint}),S=[N[0]/t.shape[2],N[1]/t.shape[1]],T=Je.rotateWithOffset(t,c,0,S);h=h0(-c,N),u=jl({startPoint:i.startPoint,endPoint:i.endPoint},T,[this.meshSize,this.meshSize]).div(255)}let _={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:u,rawCoords:A},b=u0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},_}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var mg=Ah(x6());var b2={};fr(b2,{load:()=>v2,predict:()=>k2});var _2={};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};_2[e]=i,Ce("Human profiler",e,i)}var Ja,f0={age:0},m0=Number.MAX_SAFE_INTEGER;async function v2(e){return Ja||(Ja=await Ft(e.face.age.modelPath),e.debug&&Ce(`load model: ${e.face.age.modelPath.match(/\/(.*)\./)[1]}`)),Ja}async function k2(e,t){return Ja?m0<t.face.age.skipFrames&&t.videoOptimized&&f0.age&&f0.age>0?(m0++,f0):(t.videoOptimized?m0=0:m0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Je.resizeBilinear(e,[Ja.inputs[0].shape[2],Ja.inputs[0].shape[1]],!1),a=P(r,[255]);Ee(r);let s,i={age:0};if(!t.profile)t.face.age.enabled&&(s=await Ja.predict(a));else{let o=t.face.age.enabled?await xr(()=>Ja.predict(a)):{};s=o.result.clone(),o.result.dispose(),qr("age",o)}if(a.dispose(),s){let o=s.dataSync();i.age=Math.trunc(10*o[0])/10}s.dispose(),f0=i,n(i)})):null}var I2={};fr(I2,{load:()=>C2,predict:()=>E2});var fa,N2={gender:""},A0=Number.MAX_SAFE_INTEGER,S2=!1,T2=[.2989,.587,.114];async function C2(e){return fa||(fa=await Ft(e.face.gender.modelPath),S2=fa.inputs[0].shape[3]===1,e.debug&&Ce(`load model: ${e.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),fa}async function E2(e,t){return fa?A0<t.face.gender.skipFrames&&t.videoOptimized&&N2.gender!==""?(A0++,N2):(t.videoOptimized?A0=0:A0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Je.resizeBilinear(e,[fa.inputs[0].shape[2],fa.inputs[0].shape[1]],!1),a;S2?a=B(()=>{let[o,l,u]=Lt(r,3,3),c=P(o,T2[0]),h=P(l,T2[1]),d=P(u,T2[2]);return Ma([c,h,d]).sub(.5).mul(2)}):a=P(r,[255]),Ee(r);let s,i={gender:"",confidence:0};if(!t.profile)t.face.gender.enabled&&(s=await fa.predict(a));else{let o=t.face.gender.enabled?await xr(()=>fa.predict(a)):{};s=o.result.clone(),o.result.dispose(),qr("gender",o)}if(a.dispose(),s){let o=s.dataSync();if(S2)(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(),N2=i,n(i)})):null}var R2={};fr(R2,{load:()=>$2,predict:()=>D2});var Tae=["angry","disgust","fear","happy","sad","surprise","neutral"],Qa,F2=[],y0=Number.MAX_SAFE_INTEGER,M2=[.2989,.587,.114];async function $2(e){return Qa||(Qa=await Ft(e.face.emotion.modelPath),e.debug&&Ce(`load model: ${e.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),Qa}async function D2(e,t){return Qa?y0<t.face.emotion.skipFrames&&t.videoOptimized&&F2.length>0?(y0++,F2):(t.videoOptimized?y0=0:y0=Number.MAX_SAFE_INTEGER,new Promise(async n=>{let r=Je.resizeBilinear(e,[Qa.inputs[0].shape[2],Qa.inputs[0].shape[1]],!1),[a,s,i]=Lt(r,3,3);r.dispose();let o=P(a,M2[0]),l=P(s,M2[1]),u=P(i,M2[2]);a.dispose(),s.dispose(),i.dispose();let c=Ma([o,l,u]);o.dispose(),l.dispose(),u.dispose();let h=B(()=>c.sub(.5).mul(2));c.dispose();let d=[];if(t.face.emotion.enabled){let p;if(t.profile){let f=await xr(()=>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:Tae[f]});d.sort((f,m)=>m.score-f.score)}h.dispose(),F2=d,n(d)})):null}var Xr;async function O2(e){return Xr||(Xr=await Ft(e.face.embedding.modelPath),e.debug&&Ce(`load model: ${e.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),Xr}function w6(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*(1-1*r))/1e3,0)}function z2(e){return B(()=>{let n=e.offsetRaw?[e.offsetRaw]:[[.05,.15,.85,.85]],r=e.image||e.tensor,a=r.shape.length===3?Je.cropAndResize(r.expandDims(0),n,[0],[Xr.inputs[0].shape[2],Xr.inputs[0].shape[1]]):Je.cropAndResize(r,n,[0],[Xr.inputs[0].shape[2],Xr.inputs[0].shape[1]]),s=[.2989,.587,.114],[i,o,l]=Lt(a,3,3),u=P(i,s[0]),c=P(o,s[1]),h=P(l,s[2]),d=Ma([u,c,h]),p=hn([d,d,d],3).squeeze(4),f=p.sub(p.min());return f.div(f.max())})}async function P2(e,t){return Xr?new Promise(async n=>{let r=z2(e),a=[];if(t.face.embedding.enabled)if(t.profile){let s=await xr(()=>Xr.predict({img_inputs:r}));a=[...s.result.dataSync()],s.result.dispose(),qr("emotion",s)}else{let s=await Xr.predict(r);a=s.dataSync(),Ee(s)}r.dispose(),n(a)}):null}var Z2={};fr(Z2,{PoseNet:()=>Y2,load:()=>J2});function Cae(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var L2=class{constructor(t){this.model=t}predict(t){return B(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Cae(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function W2(e){return Math.floor(e/2)}var B2=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(W2(t),t);)this.exchange(t,W2(t)),t=W2(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 Eae(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,u=Math.max(n-a,0),c=Math.min(n+a+1,i);for(let h=u;h<c;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function _6(e,t,n){let[r,a,s]=n.shape,i=new B2(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let u=0;u<s;++u){let c=n.get(o,l,u);c<e||Eae(u,c,o,l,t,n)&&i.enqueue({score:c,part:{heatmapY:o,heatmapX:l,id:u}})}return i}var ma=Ah(g0());var b6=Ah(g0());function H2(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+b6.NUM_KEYPOINTS)}}function x0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=H2(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function j2(e,t,n){return e<t?t:e>n?n:e}function v6(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function G2(e,t){return{x:e.x+t.x,y:e.y+t.y}}var w0=Ah(g0());function k6(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 zae(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+w0.NUM_KEYPOINTS)}}function Pae(e,t){let n=[];for(let r=0;r<w0.NUM_KEYPOINTS;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=zae(a,s,r,t);n.push(o),n.push(i)}return kn(n,[w0.NUM_KEYPOINTS,2])}function I6(e,t,n){return B(()=>e.toTensor().mul(be(t,"int32")).toFloat().add(Pae(e,n)))}function Lae(e,t){return B(()=>{let n=e.div(be(t,"int32"));return e.sub(n.mul(be(t,"int32")))})}function N6(e){let[t,n,r]=e.shape;return B(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(be(n,"int32")).expandDims(1),o=Lae(s,n).expandDims(1);return st([i,o],1)})}var S6=ma.poseChain.map(([e,t])=>[ma.partIds[e],ma.partIds[t]]),q2=S6.map(([,e])=>e),T6=S6.map(([e])=>e),Wae=16;function Bae(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 X2(e,t,n,r){return{y:j2(Math.round(e.y/t),0,n-1),x:j2(Math.round(e.x/t),0,r-1)}}function C6(e,t,n,r,a,s,i,o=2){let[l,u]=r.shape,c=X2(t.position,s,l,u),h=Bae(e,c,i),p=G2(t.position,h);for(let A=0;A<o;A++){let y=X2(p,s,l,u),g=H2(y.y,y.x,n,a);p=G2({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=X2(p,s,l,u),m=r.get(f.y,f.x,n);return{position:p,part:ma.partNames[n],score:m}}function E6(e,t,n,r,a,s){let i=t.shape[2],o=q2.length,l=new Array(i),{part:u,score:c}=e,h=x0(u,r,n);l[u.id]={score:c,part:ma.partNames[u.id],position:h};for(let d=o-1;d>=0;--d){let p=q2[d],f=T6[d];l[p]&&!l[f]&&(l[f]=C6(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=T6[d],f=q2[d];l[p]&&!l[f]&&(l[f]=C6(d,l[p],f,t,n,r,a))}return l}async function R6(e,t,n){let r=0,a=N6(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],u=I6(l,Wae,o),c=await u.buffer(),d=Array.from(k6(i,l)).map((f,m)=>(r+=f,{position:{y:c.get(m,0),x:c.get(m,1)},part:ma.partNames[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),u.dispose(),{keypoints:p,score:r/d.length}}var Vae=1,F6=16;function M6(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return v6(r,n,i.y,i.x)<=t})}function Uae(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(M6(e,t,s,o)||(a+=i),a),0)/n.length}function $6(e,t,n,r,a,s,i){let o=[],l=_6(i,Vae,e),u=a^2;for(;o.length<s&&!l.empty();){let c=l.dequeue(),h=x0(c.part,F6,t);if(M6(o,u,h,c.part.id))continue;let d=E6(c,e,t,F6,n,r),p=Uae(o,u,d);p>i&&o.push({keypoints:d,score:p})}return o}async function D6(e){return Promise.all(e.map(t=>t.buffer()))}function Hae(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 O6(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function K2(e,[t,n],[r,a]){return e.map(i=>Hae(i,t/r,n/a))}async function jae(e,t,n,r){return new Promise(async a=>{let s=await D6([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],u=s[3],c=await $6(i,o,l,u,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=K2(c,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function Gae(e,t,n,r){return new Promise(async a=>{let s=await R6(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=K2([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var Y2=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=O6(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await Gae(t,a,n,this.inputSize):await jae(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 J2(e){let t=await Ft(e.body.modelPath),n=new L2(t);return e.debug&&Ce(`load model: ${e.body.modelPath.match(/\/(.*)\./)[1]}`),new Y2(n)}var rg={};fr(rg,{HandPose:()=>sg,load:()=>ig});function _0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Wc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function z6(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 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]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:r,palmLandmarks:a,confidence:e.confidence}}function b0(e,t=1.5){let n=Wc(e),r=_0(e),a=[t*r[0]/2,t*r[1]/2],s=[n[0]-a[0],n[1]-a[1]],i=[n[0]+a[0],n[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function v0(e){let t=Wc(e),n=_0(e),a=Math.max(...n)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}var Q2=class{constructor(t,n,r){this.model=t,this.anchors=r.map(a=>[a.x_center,a.y_center]),this.anchorsTensor=kn(this.anchors),this.inputSize=n,this.inputSizeTensor=on([n,n]),this.doubleInputSizeTensor=on([n*2,n*2])}normalizeBoxes(t){return B(()=>{let n=Re(t,[0,0],[-1,2]),r=Re(t,[0,2],[-1,2]),a=se(ge(n,this.inputSizeTensor),this.anchorsTensor),s=ge(r,this.doubleInputSizeTensor),i=P(ye(a,s),this.inputSizeTensor),o=P(se(a,s),this.inputSizeTensor);return ll([i,o],1)})}normalizeLandmarks(t,n){return B(()=>{let r=se(ge(t.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[n]);return P(r,this.inputSizeTensor)})}async getBoxes(t,n){let r=this.model.predict(t),a=r.squeeze();r.dispose();let s=B(()=>Rn(Re(a,[0,0],[-1,1])).squeeze()),i=s.dataSync(),o=Re(a,[0,1],[-1,4]),l=this.normalizeBoxes(o);o.dispose();let u=await Je.nonMaxSuppressionAsync(l,i,n.hand.maxHands,n.hand.iouThreshold,n.hand.scoreThreshold),c=u.arraySync();s.dispose(),u.dispose();let h=[];for(let d of c)if(i[d]>=n.hand.minConfidence){let p=Re(l,[d,0],[1,-1]),f=Re(a,[d,5],[1,14]),m=B(()=>this.normalizeLandmarks(f,d).reshape([-1,2]));f.dispose(),h.push({box:p,palmLandmarks:m,confidence:i[d]})}return a.dispose(),l.dispose(),h}async estimateHandBounds(t,n){let r=t.shape[1],a=t.shape[2],s=B(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let l of i){let u=l.box.dataSync(),c=u.slice(0,2),h=u.slice(2,4),d=l.palmLandmarks.arraySync();l.box.dispose(),l.palmLandmarks.dispose(),o.push(P6({startPoint:c,endPoint:h,palmLandmarks:d,confidence:l.confidence},[a/this.inputSize,r/this.inputSize]))}return o}};function qae(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function L6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return qae(n)}var W6=(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 Xae(e,t){let n=[];for(let r=0;r<e.length;r++)n.push(e[r][t]);return n}function B6(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],Xae(t,s)))}return n}function eg(e,t){let n=Math.cos(e),r=Math.sin(e),a=[[n,-r,0],[r,n,0],[0,0,1]],s=W6(t[0],t[1]),i=B6(s,a),o=W6(-t[0],-t[1]);return B6(i,o)}function V6(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 tg(e,t){return[es(e,t[0]),es(e,t[1])]}var Kae=5,U6=1.65,H6=[0,5,9,13,17,1,2],Zae=0,Yae=2,ng=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=>tg([...s,1],n)),a=this.calculateLandmarksBoundingBox(r);return b0(v0(a),Kae)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),r=b0(v0(n),U6);r.palmLandmarks=[];for(let a=0;a<H6.length;a++)r.palmLandmarks.push(t[H6[a]].slice(0,2));return r}transformRawCoords(t,n,r,a){let s=_0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(p=>[i[0]*(p[0]-this.inputSize/2),i[1]*(p[1]-this.inputSize/2),i[2]*p[2]]),l=eg(r,[0,0]),u=o.map(p=>[...tg(p,l),p[2]]),c=V6(a),h=[...Wc(n),1],d=[es(h,c[0]),es(h,c[1])];return u.map(p=>[p[0]+d[0],p[1]+d[1],p[2]])}async estimateHands(t,n){let r=!1,a;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.videoOptimized)&&(a=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.videoOptimized&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==n.hand.maxHands||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(r=!0));let s=[];n.hand.skipInitial&&this.detectedHands===0&&(this.skipped=0);for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let l=n.hand.rotation?L6(o.palmLandmarks[Zae],o.palmLandmarks[Yae]):0,u=Wc(o),c=[u[0]/t.shape[2],u[1]/t.shape[1]],h=n.hand.rotation?Je.rotateWithOffset(t,l,0,c):t.clone(),d=eg(-l,u),p=r?this.getBoxForPalmLandmarks(o.palmLandmarks,d):o,f=z6(p,h,[this.inputSize,this.inputSize]),m=f.div(255);f.dispose(),h.dispose();let[A,y]=await this.landmarkDetector.predict(m);m.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let _=G(y,[-1,3]),b=_.arraySync();y.dispose(),_.dispose();let w=this.transformRawCoords(b,p,l,d),x=this.getBoxForHandLandmarks(w);this.storedBoxes[i]=x;let N={landmarks:w,confidence:g,box:{topLeft:x.startPoint,bottomRight:x.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let l=b0(v0(o),U6),u={confidence:o.confidence,box:{topLeft:l.startPoint,bottomRight:l.endPoint}};s.push(u)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var j6=[{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 ag={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]},sg=class{constructor(t){this.handPipeline=t}static getAnnotations(){return ag}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(ag))i[l]=ag[l].map(u=>s.landmarks[u]);let o=s.box?[Math.max(0,s.box.topLeft[0]),Math.max(0,s.box.topLeft[1]),Math.min(t.shape[2],s.box.bottomRight[0])-s.box.topLeft[0],Math.min(t.shape[1],s.box.bottomRight[1])-s.box.topLeft[1]]:0;a.push({confidence:s.confidence,box:o,landmarks:s.landmarks,annotations:i})}return a}};async function ig(e){let[t,n]=await Promise.all([e.hand.enabled?Ft(e.hand.detector.modelPath,{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?Ft(e.hand.skeleton.modelPath,{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),r=new Q2(t,t==null?void 0:t.inputs[0].shape[2],j6),a=new ng(r,n,n==null?void 0:n.inputs[0].shape[2]),s=new sg(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 og={};fr(og,{load:()=>lg,predict:()=>ug});var G6=["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"],q6=["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 lg(e){return dr||(dr=await Ft(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 ug(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 u=await xr(()=>dr.predict(a));s=u.result.find(c=>c.size===195||c.size===155).dataSync(),u.result.forEach(c=>c.dispose()),qr("blazepose",u)}else{let u=await dr.predict(a);s=u.find(c=>c.size===195||c.size===155).dataSync(),u.forEach(c=>c.dispose())}a.dispose();let i=[],o=s.length===195?G6:q6,l=5;for(let u=0;u<s.length/l;u++)i.push({id:u,part:o[u],position:{x:Math.trunc(n.width*s[l*u+0]/255),y:Math.trunc(n.height*s[l*u+1]/255),z:Math.trunc(s[l*u+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*u+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*u+4]))))/100});return[{keypoints:i}]}var X6=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},K6=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 ${o<0?"up":"down"}`})}return t},Z6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.rightEyeIris)continue;let r=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],a=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],s=Math.abs(r*a),i=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],o=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(i*o);Math.abs(s-l)/Math.max(s,l)<.25&&t.push({iris:n,gesture:"looking at camera"})}return t},Y6=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let r=[];for(let[a,s]of Object.entries(e[n].annotations))a!=="palmBase"&&r.push({name:a.toLowerCase(),position:s[0]});if(r&&r.length>0){let a=r.reduce((i,o)=>i.position[2]<o.position[2]?i:o),s=r.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:n,gesture:`${a.name} forward ${s.name} up`})}}return t};function Jae(e,t,n){let r=function(o,l,u){let c=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");o.replace(c,(h,d)=>(u[d]=0,h))},a=function(o,l){let u=e.createShader(l);if(e.shaderSource(u,o),e.compileShader(u),!e.getShaderParameter(u,e.COMPILE_STATUS))throw new Error("Filter: GL compile failed",e.getShaderInfoLog(u));return u};this.uniform={},this.attribute={};let s=a(t,e.VERTEX_SHADER),i=a(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,s),e.attachShader(this.id,i),e.linkProgram(this.id),!e.getProgramParameter(this.id,e.LINK_STATUS))throw new Error("Filter: GL link failed",e.getProgramInfoLog(this.id));e.useProgram(this.id),r(t,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=e.getAttribLocation(this.id,o);r(t,"uniform",this.uniform),r(n,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=e.getUniformLocation(this.id,o)}function J6(e){e||(e={});let t=0,n=null,r=!1,a=-1,s=[null,null],i=[],o=-1,l=-1,u=null,c=null,h={},d=e.canvas||document.createElement("canvas"),p={},f={INTERMEDIATE:1},m=d.getContext("webgl");if(!m)throw new Error("Filter: getContext() failed");this.addFilter=function(w){let x=Array.prototype.slice.call(arguments,1),N=h[w];i.push({func:N,args:x})},this.reset=function(){i=[]};let A=function(w,x){if(!(w===o&&x===l)){if(d.width=w,o=w,d.height=x,l=x,!u){let N=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);u=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,u),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(w,x){let N=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,N);let S=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,S);let T=m.createTexture();return m.bindTexture(m.TEXTURE_2D,T),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,w,x,0,m.RGBA,m.UNSIGNED_BYTE,null),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.LINEAR),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.framebufferTexture2D(m.FRAMEBUFFER,m.COLOR_ATTACHMENT0,m.TEXTURE_2D,T,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:N,texture:T}},g=function(w){return s[w]=s[w]||y(o,l),s[w]},_=function(w=null){var T,M;let x=null,N=null,S=!1;t===0?x=n:x=(T=g(a))==null?void 0:T.texture,t++,r&&!(w&f.INTERMEDIATE)?(N=null,S=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(c.uniform.flipY,S?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(w){if(A(w.width,w.height),t=0,n||(n=m.createTexture()),m.bindTexture(m.TEXTURE_2D,n),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_S,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_WRAP_T,m.CLAMP_TO_EDGE),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MIN_FILTER,m.NEAREST),m.texParameteri(m.TEXTURE_2D,m.TEXTURE_MAG_FILTER,m.NEAREST),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,m.RGBA,m.UNSIGNED_BYTE,w),i.length===0)return _(),d;for(let x=0;x<i.length;x++){r=x===i.length-1;let N=i[x];N.func.apply(this,N.args||[])}return d};let b=function(w){if(p[w])return c=p[w],m.useProgram(c.id),c;let x={};x.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(`
|
|
`),x.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
|
|
`),c=new Jae(m,x.VERTEX_IDENTITY,w);let N=Float32Array.BYTES_PER_ELEMENT,S=4*N;return m.enableVertexAttribArray(c.attribute.pos),m.vertexAttribPointer(c.attribute.pos,2,m.FLOAT,!1,S,0*N),m.enableVertexAttribArray(c.attribute.uv),m.vertexAttribPointer(c.attribute.uv,2,m.FLOAT,!1,S,2*N),p[w]=c,c};h.colorMatrix=function(w){let x=new Float32Array(w);x[4]/=255,x[9]/=255,x[14]/=255,x[19]/=255;let 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,S=b(N);m.uniform1fv(S.uniform.m,x),_()},h.colorMatrix.SHADER={},h.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(`
|
|
`),h.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(`
|
|
`),h.brightness=function(w){let x=(w||0)+1;h.colorMatrix([x,0,0,0,0,0,x,0,0,0,0,0,x,0,0,0,0,0,1,0])},h.saturation=function(w){let x=(w||0)*2/3+1,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(w){let x=(w||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(w){w=(w||0)/180*Math.PI;let x=Math.cos(w),N=Math.sin(w),S=.213,T=.715,M=.072;h.colorMatrix([S+x*(1-S)+N*-S,T+x*-T+N*-T,M+x*-M+N*(1-M),0,0,S+x*-S+N*.143,T+x*(1-T)+N*.14,M+x*-M+N*-.283,0,0,S+x*-S+N*-(1-S),T+x*-T+N*T,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(w){let x=new Float32Array(w),N=1/o,S=1/l,T=b(h.convolution.SHADER);m.uniform1fv(T.uniform.m,x),m.uniform2f(T.uniform.px,N,S),_()},h.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
|
|
`),h.detectEdges=function(){h.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},h.sobelX=function(){h.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},h.sobelY=function(){h.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},h.sharpen=function(w){let x=w||1;h.convolution.call(this,[0,-1*x,0,-1*x,1+4*x,-1*x,0,-1*x,0])},h.emboss=function(w){let x=w||1;h.convolution.call(this,[-2*x,-1*x,0,-1*x,1,1*x,0,1*x,2*x])},h.blur=function(w){let x=w/7/o,N=w/7/l,S=b(h.blur.SHADER);m.uniform2f(S.uniform.px,0,N),_(f.INTERMEDIATE),m.uniform2f(S.uniform.px,x,0),_()},h.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(`
|
|
`),h.pixelate=function(w){let x=w/o,N=w/l,S=b(h.pixelate.SHADER);m.uniform2f(S.uniform.size,x,N),_()},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 Ct=null,rn=null,Mt=null;function cg(e,t){let n;if(e instanceof Ye)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(t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)return Ce("Human: invalid input",e),{tensor:null,canvas:null};(!Ct||Ct.width!==i||Ct.height!==o)&&(Ct=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),Ct.width!==i&&(Ct.width=i),Ct.height!==o&&(Ct.height=o));let l=Ct.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Ct.width,Ct.height),t.filter.enabled){if((!Mt||!rn||Ct.width!==rn.width||Ct.height!==rn.height)&&(rn=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ct.width,Ct.height):document.createElement("canvas"),rn.width!==Ct.width&&(rn.width=Ct.width),rn.height!==Ct.height&&(rn.height=Ct.height),Mt=mr.flags.IS_BROWSER?new J6({canvas:rn}):null),!Mt)return{tensor:null,canvas:Ct};Mt.reset(),Mt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Mt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Mt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Mt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Mt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Mt.addFilter("hue",t.filter.hue),t.filter.negative&&Mt.addFilter("negative"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.vintage&&Mt.addFilter("brownie"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.kodachrome&&Mt.addFilter("kodachrome"),t.filter.technicolor&&Mt.addFilter("technicolor"),t.filter.polaroid&&Mt.addFilter("polaroid"),t.filter.pixelate!==0&&Mt.addFilter("pixelate",t.filter.pixelate),Mt.apply(Ct)}else rn=Ct,Mt&&(Mt=null);let u;if(rn.data){let h=[rn.height,rn.width,3];u=ld(rn.data,h,"int32")}else if(t.backend==="webgl"||rn instanceof ImageData)u=rl.fromPixels(rn);else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(rn,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);u=rl.fromPixels(p)}let c=u.toFloat();n=c.expandDims(0),u.dispose(),c.dispose()}let r=t.filter.return?rn:null;return{tensor:n,canvas:r}}var mt={backend:"webgl",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"../models/facemesh.json"},iris:{enabled:!0,modelPath:"../models/iris.json"},age:{enabled:!0,modelPath:"../models/age.json",skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender.json",skipFrames:32},emotion:{enabled:!0,minConfidence:.1,skipFrames:33,modelPath:"../models/emotion.json"},embedding:{enabled:!1,modelPath:"../models/mobileface.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var k0=`
|
|
/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==`,I0=`
|
|
/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
|
|
JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
|
|
RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
|
|
AhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAEDAgQFBgf/xABDEAEAAgECBAMECQIDBgUFAQAA
|
|
AQIDBBEFEiExE0FRBiJhcRQjMkJSgZGhsWLBJDNyFSVTY3OSNEPR4fAHFjWCokT/xAAYAQEAAwEA
|
|
AAAAAAAAAAAAAAAAAQIDBP/EACARAQEBAQADAQEBAQEBAAAAAAABAhEDITFBEjJRIhP/2gAMAwEA
|
|
AhEDEQA/APqYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAKNTq8OkxzfNkisQC8eb1XtRNbzXT4q7eU2nu0MntRq/D8StMccvW29ZmdvgjsTyvZjxOLj
|
|
+s8WLxn8TFPXs6Oj9oct7c14rkxz22nrB2I49KOdTjelmszfmpMeUxv/AA28OqwZ4icWWtt/SUi4
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAmdo3nsPNe0Pt
|
|
Fh09Z0+DNWL7+9O/7A3eJcZppsV5raI27esvH6jX5ddM25p79Ilo59VbUZOe2Tm/PeGvfPfT2iKR
|
|
PLv1+DO678XmW/a97U6TtOyzTbTF538/T9WjTNecm9a7126tqk3rSYxY5ta1plRZqZNXGjyZcPXl
|
|
mZmsx+qjBrsuO16xM7eXRt04JrdTltk5OWJnfaWf0a2lty5MdZnfzSn+WOHiOutFpjHa9e8bQ2fp
|
|
+alYy462pk7zXbuxjPesbRS0f6ZZV1ET1tErzXFLHo+A+1ddZf6NrI8PJHa1vN6iJi0bxMTHwfOa
|
|
zhzd61v1846utwniM6DUdb3nBaNrVmd9vjC/ZVePYirBqMWppz4rxaPgtEAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAItaK1m09ojcHnvarjM8P0vh49+a/eY8ng9D
|
|
h1fGM1rxjtGPfvbzdbjuTJxHX48cTPNltM/KsS9Dw7S49Jp6UpHaGe2vjz1y9J7LYK13vHWe7bj2
|
|
ex1tvM80ekuxW3RnW3Vm6P5jRx8H0+OYmMcb+bapo8GKPdpC6bQwtdHU8JpWkdJ/JweL6e23iU67
|
|
d4dubSqyVi9Zi0bwIs68XGp36TtEq7ZJmZmevzdbifCKWtbJinkt6eTgZPFw32t+sRurbWVzxs1y
|
|
Rv6T8V1NZNPtfq0seTm+Kevr+SZuxXjvaPiV8N4viycto9HseG6+uu08W6Rkj7UPmFck1tE1nlmP
|
|
Ld3eA8V8HVVi1pjq6Ma/pnqce/ERMTETHaUrKgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAADW19+TQ5p/p2bLS4v04Zmt5VjeQeJ4bjnLqsupv+Ka1+ERLv4reTmcNxcuC
|
|
vy3l0qdI2hlr66sT02ot0ZV7qqrInruzrVZLGSZ37JjqgYTG0K5lbaFVhDT1Ub456RPweY4hixWi
|
|
eSdpjvD1eWejz3FNHWYtkpvFo9EIseb3tS3SerOms22rfpPqZKzvvHSYUz70TExG6Gdbs2rljeJ/
|
|
Mx5L0vEzPaelnOi98c9J2bFNTFpit47+a+PVUvx9T9nOIfT+GV5p3yY/ds67wvsXqpxau+G09Lx+
|
|
r3TqrEAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADV4ljnLw3U0jvO
|
|
O0fs2lWqyUw6XLkyfYrWZkHldBEV09eveG3Fq1mI3jd4vPrOIaid8G9MP3Y38k6fNrt/rMk9Ou8s
|
|
tfXXn49rGWInuy8SO/k5Gl1E3rG/fzbOe94wTy99mbRvTrMOOvNfJWsesywniukrG/jU6fF43WYN
|
|
TmtEeJtEQ06aSmK2+bNtEd+qfSO17unF9Hmvy1y13XWyVmN4tExLxVK8PmNq5NrT58zawam+m/yc
|
|
0Xj8NpRYSvQZ7xEOdqI3rPozxayNRXe0ct/ON03jmrKB5nV4q1yTO20Obmv4c+cx8HoeI6WZpNoj
|
|
q83niYmYscU0r8aJ6T1n49zeJ+Meqm1drb9J+Kd5p136StGVem9l9TbHxLDFp7W7+sS+q1nesT6w
|
|
+PcAzVjiGHftzQ+v4f8AJpv6On8jH9ZgIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAABp8VrW/C9TW0ztOO3b5Nxp8VmI4bn37TWYB8f1HFtTfUfR9FWJmsdZ9I7MtJxDX5s
|
|
d8ta1y0xzteaR2277rcuhycP12SceLxMeWNpjttHwlu8I0mfQ1y+D7k5YmJmY36T36Ka43z/AF1t
|
|
cI1ds+qxVj7/AEej19PCw9HJ4NoK4OIU5Y35YmZdzVTGebVZabx5jJS+Tmns81rNLm1Wrzc9rVw4
|
|
Yibbem72mXTTS0w0M3BvEta1bWrM95ie5EanY87wXgNOL6XPfxraXLhra/W28bR/dzYzarBqJxRe
|
|
bzE7Rt5vWU9n8mPHOGmS0Ypnea1naJb+k9ncNLR7u2y/WcxXO4TOoyUrN6zD0FaW5Y3hu49FiwUi
|
|
KxCvLMR0hlW0jn6ukWw3iXjOJzbDlneOj3GaN6zDzfFOH+LE7SRGo83XNSZ2lbG2/WfdlvaT2cy6
|
|
rNFInlrv1mfJ37cK4PwTTxOoidRm2+/2/KFuyMp47XB4LivXiunrH2b2iH2qn2K/J8x4fGDNxTSZ
|
|
9Nh8OviRvTyfT6xtWI+DeXs9MNZubypASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAOZx6/LoOWPvWiHTcf2hiZ0e8fc2mf1E5+vP/AEeuSd7RC2uKtI6QjHfeINTfwtPf
|
|
Jvty9WPfbt/lucP03gxfJf7d/wBoReYpm97zaNeLb4Ims9Nt94auDjem1Wo5PFi1onylS+1o7l8V
|
|
bxvtupjDMdNkYtXS1+Stt+m63xImEJ4xjHER2ZxMUjeUTO3VRmydBbjLJqPi08mbeVOXJPq1sl5Q
|
|
Vbkz9+rRy35rxHqzmZlVEe/Ez5LRlW5iyfR6zffaIjq1OSNZps2a21rZInafSPJhxGMl9LStLRWM
|
|
lorM/A4dkrWbYfLZC2W/7K6eubX6b4RzT+W76K8b7G6X62cu3Sten59nsm3j+OXz3/0ANGIAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0OIYfpOHPijvNNo+fdvtXJO18k/
|
|
/OwPFYbz2ls3jx8VqW6xMdWPEdP9D4lkx/dt79flLLHbkxTPwY6nt2512ORTRzE2x4/dpE7cvkme
|
|
E4IrW3hRMxO8THRtU1FKWtvtvK2upx22rzRCtXkqzh2jtF7ZbT122b01ndnpuWuP3Z3+Ky20qDVv
|
|
fauzVy3mejZzNK8dVjqi87KLRLYtXruqvXzkQp7Qoid88R6rcl+WGlW0/Sa22mfhCZOq2x082ix6
|
|
jkm822pO8VrPdr4dNObVeDo8XW3uzMbzK+mvxT7szE27cvnu9j7PcNjSaXx8mOIzZevbrEeic5tN
|
|
+SZnpt8J4fHD9HXHO3PPW0x/DeBtJxx29vaAJQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAKNRim9Z5e89Nl4DzXtVh5babURHrSf7f3ec1+qnDorWrvvt5Pccb0n0zhmWk
|
|
Rvevv1+cPE2rGTFNZU26PFfxwa5dVkjelI2772nZnX6bbrEUq3o0d678u8wmuDL2ittvVjXdneeK
|
|
cGv4jpJ6U56+kS7+j118+GLXpakzHaWlp9NNY3tv+bbiYiNoQy1y30uyZJlrWmZnuym6q1iIJnop
|
|
yW2Te8bdWnnypQqzZOadokiIpSZntWN5lrxki19vNRxrUeBwnNNd+fJEY6/OejXLn3Xe/wDp9wyn
|
|
E8uo4lqqxblv7lJ26T6vpD5X7G8QycKzeBMbzMRM1/FH/wA/h9QwZ6ajDXLitvWzRgsAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAeL45w+dDrZvWv1OWd4+E+j2jX
|
|
12jx67TWw5Y6T2nzifU+rZ1y9eHwzDYxxEy18+DJodXfT5o96vafWPVbjyxDn1OOzHudbM0rt2UW
|
|
iI69mVtRXZq5tREb9VUoy2iIlRbJ0UX1VZ6btTLrI7V6yk62M2oisT1c7JmtkttVMUyZp6x0beDS
|
|
RWOvdKijDimvWd3G9pNRMfRcNfvZOb9Hpb0itJeP47k/3hgjaZnbaP1XxWW3T0movbNS0W645nbf
|
|
0nrMPpXs3xamoxdJiLbe/X1n8Uf3fKsOTw4jbaXo+EarJhtGTHMxeJ6xH7Sti9Zaj6x3HM4NxXFx
|
|
DS1mtoi8dJrv2l011QAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AGjxLhODieOIye7kr9m8d4eM4to9RwjPXFa0ZIvG9bR0fQXmPbDFvTTZPOJmEWS/V8bs9R43NxLL
|
|
G8eFbePg1bajU5/s0l1ceKLx1hbjwRE9mOpx0y2uRTSZsm3PMw2aaKtIjo6kYo9EXpET0hVLXxYK
|
|
xC6MZvyx1lFs0RHfaPiCnU12pLyHGNDbUajBekWma2npWN3p8+opa20e9LSyZLxExTlpM+vdOdcZ
|
|
a9tPS8MyUvFrzWlI6727u1pYxYrbVmb7x+TQx6au3Nqcl7/0rcmW9axGnwZJj1novmxnZXV0fFp4
|
|
ZxLBPgTGK8xzXr5fOH0bFlpmxVyY7Rato3iYfNuG2x56Wrqa8s2jz+7Lu8O12bS6jkwzN6THNNI6
|
|
tvrN68Y4rxlx1vHa0bskAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAA4XtTTm0OKfTJ/aXdcL2pyRGjwU362yb7fkJz9eTxxyZJjyltRXzUZK7TFtl9Lbwy06YzrHwa+
|
|
fJFd/wCVt8m0bQ0eS2qzcm+1K/an+zNZFL5M1pjFXeI72ky48eGnPkvNp27+TPU6nHpMfLXaIjpE
|
|
erk5dRMxOfN1mPeisfshW1ne1a1577Y6x5R3U0zze31FOWI6ze0byU098kRlzbxM9qrMlPDpyRMR
|
|
Md5Vt/Ihp5898mWZm1pjftE91uCt7fCI7dWeHDEW3t723l6rslqxWZnasR+SYhFbzhnfxJ2jyeq9
|
|
lcGXWZcmW0zWKxHLaI7794eJx5fpfEKabT8t8l5isddo3l9S4VjrwrRUwzSJt3tav3pdOL6Y6dXD
|
|
j8HFWm+/KsU4NRXPvtWazHquWVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAa+fXYNP9u8b+kdZBsDkZOO135cWOZn4y5Wu4xqctbe9y19Kp4njt6vi+PDm8DFMWybbzPlV
|
|
5PiGtz67UxbNbeKTtWIjaIXYpnwuaftT5tXJT3vmi1pMsrU5qIrG1V1a+5DCa7b9GFbRr5J6Wnbt
|
|
Cu+Wmk0m8956z8ZWZNorbfzcbX5rZslazPux3hUt41NTntktObJ13+zX1bek01r4/HzVm0bxPXy/
|
|
+bNfDgjVa2uOY92kdfg6ufJOKvLXtttVVSqbcta2vM7zXtHpLQy5ZtMd+vWd+7Zy3mdJHXra3f0c
|
|
vUarw7zFY5rT2hH1Lavnrgx81p3U49Pk4nE5L35MO/StfNRXR5tXnrS8W67WvfyiPSPi7uLHFK1p
|
|
jrtSsbR5Lc4RzsXBaYreP4l45esRD2HD9fnw6evvWvO3Tfr0aGk0U55ra0TFInv6uzgrXFXlx0i0
|
|
77RPlC83Yj+JW7oddqr6vHzTTw9/f6dod+L1t9m0T8pcbFSmPHER3892W0zPuz+jSbVvidkcqmfP
|
|
Sel7bekrI4n4dZnPWIrHeYnZee2Wpy8dEaml4npNZblw5qzb8M9JbYgAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAABEzFYmZnaI7yCXL1XGa0jJXT0571nbee27DiXEprp8nhbxG20W8
|
|
5cbD0ikfnKO+urTPvjoZdXqctdsmTaPSvRpWmsdZ6yztfaGplvv3lWW1tyRlz1x0vkn7Vo5atTNe
|
|
Y0+1o79V2KsZsvX7Ne5mwxnyTNvsx2iGneM/rCdRSuOsTasTt5kRFtpjqmOH4t4nk7estiMNa97R
|
|
Hwhna0iuKTEdmGWa4672nZtRele1N59Zlq6vLOSsYorEc07qcW65euzRvtXvPZy52naZ7ujr6fXV
|
|
rWdukREK8+njHgmZmPc67bq6ivVWhxxgxZLztNrT1mZ/SP4VZs0zaOvfp84WUtNsXLvtv3699+rU
|
|
z7+Jtt5qURqMnPpctaR1rMSw4ZoK57eNk6xHaJRh97Ltt7lo5Z+L1HAPZvVauZ2nFTSzMTzeJEz8
|
|
to6xPfvsZntPZ9rXxabmxzefdrv0j1dXh/BcmstW1qxTHHasR3+b0GPhGl+kWmd64dNEVjf73T7X
|
|
y8vy+Ddx6O3iRakxTH5RXrMw1/lX+3Itw2MFIraN48qRHdZi0cUjmmPen9noox1iO0fNzdXEYrTt
|
|
stcmd9aX0bJ+HePmiKTitO8TMLZ1cVjrMfqpz6ys4pjfrPRWZ9rXXptUit6zO+23VyaRHEc05L1/
|
|
w9J9ys/en1ljqdVbwYw452tlnl3jyjzbmmiMeKtYjpEbLeTXPUU8ee/+qjJpsV5rbkrFqzE1tEbT
|
|
DpYNbW21Mnu29fKWna0KbqTdjXXjld0cvQ63ltGHNPSfs2n+HUbS9c2s2UASqAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAOVxPWe99HpP8ArmP4b+r1EabT3yT3iOkesvMVtN7za07zad5l
|
|
XV5GmM9vVfEstvDx0jtaVVMlq+UJ18b5cMRvPeSuK87bUt+i2Z3PtG7zXpjkzXt6R+TXyTMzvM7t
|
|
ydHqZ+zhv1+Cv/ZuqvPTHMfOYaTMil1a1K2vHSLTELq2v+KWzThGo84rH5rq8JzedqR+ZeI7WnOS
|
|
34pYTafWXR/2Pln/AMyrKOCWnvmiPyR6O1y9585lhWJvl557Q6eo4T4dYiMvW3b3UanhldHpJtGX
|
|
e09unmjsT7eb1l4trI2t0hsZfrdNO0bzy+nzU20/+NmkzO9esz+TZxWis9dttvPv+Tn21jjaW8zn
|
|
26bTG3mp1M/Wzv3t0jyWXiKZJmsTERaZhXXDbNl8WaztWenxZLstPp5pau8frDtVrNMM5cfTfpMf
|
|
3aunxxbes9d/R09Dp8ebJi09ptFr3jtt2WyrW9wy1Jx132mK+Xq9PotT0iIU19ntLtExa3T47T+q
|
|
6nBaYvsZstZ+cT/LeMnUi0TXffo1s2m8Ws2/OIMWk5Jib5L328rS2t94Sh5TV4ppklpW6PT6rh+P
|
|
NbebTHyas8E081mZy5P2W6OFhjxNTE/hr/LoRO0Kvo9dPqctKzMxEx1la5t3tdnjnMs4noievcrO
|
|
yZjeFF1OSnNV0OG62cn1GWffj7Mz5w05joovzY7xes7TE7w0xrjPeex6Ua+j1UarBFu1o6Wj0lsN
|
|
3JfQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrU5o0+nvlt92P3BxuM6nxNRGCs+7Tv8
|
|
2hToxm1r3m9utrTvMsonqyt7XTmcja0u3O6FMfi5t/u0/lzdJM81p9O3zdvHTwsUR5+bfPqOfX1h
|
|
dqV+3O7bs1+T31oqmI3TEM4rvCdkDGIIhlFd2daboS0NXG2bD6bufxXU1vlmu/u4us/N0+L1tTSx
|
|
kr9qk7w89j1FNZMV3jxLzvaJ8mer+LSOZqK2xZotbvljfr/89U453rXt9lse081xZtNjx7TGKu0t
|
|
DHlrevSevaN5Y6+tJ8c7VRNMt63n3ub+6/R54rERMztDYy4a5omclYmfxKcenrjtHLvtPrCnVmdb
|
|
eFe3JXmjy6eS/DrMuLVYsta9Mdt++6qLxO+0dEc8UmInr18iUfReHcXrqccb9Z27Q61Lb13eJ9nc
|
|
1Z35rTvE9avY4bTkpG8xEfB05vYxqybc07R281naGMREdoT5JQqy9mply7Q3bV3iXG1eXw7TWSka
|
|
c258t7+tpT5/BjT7MfHqndz12Z+M4lMMKyziUJJiN1WSu9fku23RaOgKNJqbaTU1t9yelo+D0cTE
|
|
xEx1iXmM1Nt3W4PqvFweDaffx9vjDbGvxz+TP66QDRiAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAOJxzU73rp6z296zsZMkYsdr2naKxvLyObNOfNfJbvad1dXkaeOdpvsc2yuZVzfbfqybutwu
|
|
s5s8R92J3dvJb3tnO4HSMegtmt3nfZvYp8SZl0z45NfSK7onH1bNcfRFqnUKJr0Y7dVtq7prjEsK
|
|
0XVpEM6028mW20IHK41aPo3J6zs4ODhdcvPnvExFevNXpMOrxi/PlrTee7PLX6Pwa09uaNlKtHg9
|
|
dM3z5d7ReOu02nu0JzZMfblrv5R5uvrcdImZ26T1mYhxs1Os7RH93PZ7axuafNfLitvbaYU3yZYt
|
|
PXs9NwHhui1HBa5LVicsb81onrEuVqNNSuS8Y67dZ6xPZa59Il9uX41vEitImZme3q2Kxbxora0T
|
|
Md/ROSa4Ztkj7c9OafL5LuGYubmyX3iu/TfbdSfVnpvZLT/XZK233+Mbbva1xRXyiPk8pwbH4N6T
|
|
adq5a71n0tD1WDL4tPe6Xr0tDpz8YVnJHWEXYxbqlBedoef4tW0XraO09HdyztSZcbUz43C+ee9b
|
|
SVMaeOfqq7+jGckQ1Yz7+7v2RN/WXPXZPjci2+2yyJaVMuy+uSJlA2d+pNoVRbeDcSxyTE+TDDlt
|
|
pdRXLTynrHrDOyiyZeVFnY9TjvXJjres71tG8MnJ4Nqt4tp7T1jrV1nRL1x2cvABKAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAHJ49qfD09cNZ97JPX5PPw2uI6j6Vrsl/ux7tfk1mWr7dOM8iLdm
|
|
vfebREefRsWldw7SxqNbWbR7lPesrn3Vteo7dYjDpMGCvfbeXQ0uLlxRLRxROfUc34p6fCHYrXlr
|
|
EejqrjY8uzCYW7MZjdVKqK9VlaxCYrsnYExBMRMJRPZA8/xPHtmpP9W2xx76vhWOInvt/C7ike7N
|
|
vwzE9kcapGfhlevTaFbFo8RqJ5vy8/RoW09ek0msxHfp3dzNoLzp4zUmZpMbT8HJyYJi20X2n0lh
|
|
ZY1li/RaidBF4w2mK3jrHaFGp1lN+tptPp5IjBkid5mIp16TKu0abBPv33vPlM7z+iPdFNcWXU5I
|
|
tkrNce/b1W5db1nTaf3ax9q0fxDW1ebNk2phty1mOu09VOm8W19orEz23j1TwfSeERFuEYMddptW
|
|
d43dvBn21eKJ75KbW+cf/JcTgMxXTb3nbljz+TpcPmc2uyZO1KRtVtGVdi0bx07qJnllsRO6rNTe
|
|
N4XVamsy8mnvPwc3R2jPwe8TPbdlxXNOPSZfhWWpwO85OFzv57qrODkzeHntSe8Sn6Rv0a3EZ218
|
|
8nXekfr1a0ZLVnqx19dWb6demXybOO7lYMvNMdW9S/VVLo0us7tPHdtUtEwJiZU3jq2Jhham8CVG
|
|
PNODNTJXvWd3qcWSubFXJWd4tG8PK3pPd1OB6veLaa89Y61/u2xfxh5c/rsgNHOAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAANLimq+i6O0xPv392rdeZ4rq/pOqnlnelOkIt5F8Z7Wj27I2I6sb25YY
|
|
V1ImY3dbQ08LRc23vZp2j5OJG+XJWle9p2h6HHtbJXFT7OOIpX+7TxT31j5rycdTh+Dpz+XaG/sw
|
|
w18PHWseULN2trBE9UcrJKBhFU7JAQi0dEomegNDUYovM7x3jb5tO1ZvpbaTLtzRExWfWPJ08kbT
|
|
Ex5NXWYYyV5omYtHWJieyeDzuizfRs19Jn6TM7Ru1uMcJxZqTkw+5f4ebqa7SV1MR4tdrx2vEfy1
|
|
axqsNOTLjnLXytVXi3Xj8+nmsxTLM16d5npPyUzpekTtSK+U7vS6vQ/SYmK1vWPS1HOn2dvvvvE/
|
|
tDO5XlcO+LbfHSd/W3o6/BdDOXPTnj3Kz38rS6Wm4FNrRyRzTH3p6RH/AKvR8L4dXSzE3jmtHn5I
|
|
mbfqLV+m4dbLSsZInHjr3iI6zLpYaxS01rHuxHRHiT9mv6s67Vj1aqL6326MrWiYa+/Q54BxPaGe
|
|
XRZpj8MquB4+Xg8zPnB7SX30to379GxpK1xcHiKz5IS8xr8PLPixH2bftLTy05o6dHYyVjLhy0t1
|
|
izjZa3pMVv3iO/qz1G2L+NbSajbNyW7xLsY8kTDz+fJXFqKZN4iZnafi6WHL0iYlStI7OO+7axW2
|
|
crFl7dW9jvE9ULN+J3ZbdFGOy+AYWpEqN7afNXLj+1Wd23KrJVMvCzseh0+auow1yU7WhY4fCdV4
|
|
OadPefcvPuz6S7jol649Tl4AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAV581NPhtkvO0R+4NPi2
|
|
r8DB4dJ9+/7Q83Po2NTqLanNbLfvPaPSFDHV66sZ5ET0hRknyW2lTtMyouz0c8usx2n7s7vScKwx
|
|
zc1vu/y85p+maJh6Th+SOWeveXR4/wDLm8v+nX5mUWa9bbrInolmu5jdTNkxYFk2Isr3TuCzeGMz
|
|
+THdEyDDJO9Ja823rt2XWnya946pGvktDXta0ztWu/ybvLE9dkcoOf4GbJPWK1j49VmLh9JtE33v
|
|
Mevb9G7WsW8l1ccREISophiJ2jpDYpijbaOjOuOJ8ujOdqxsgVcsUjaETYvbaFFrgu5lVsm0yUtu
|
|
ryg43H5m+GIj1XcJzePoL4pnrWGtxmfchr8JvfHS1622if3QljzTTLes+qrNjrkiYtCzPMxnm095
|
|
YZJ6boS5teB49Tqscza97VtvWvlv8V/FOF34RrIxTM2xXjelp/eHoeA6XnzReY3ivX/0dfivDcfE
|
|
9HbDbaLx1pb0lOs+jO7K8Lis3cN+0NKcd9PmthzV5clJ2mF9J9GHHVL108dm1SznYr/Ft0tuhLb8
|
|
mNohFbMhLWy0mJ3rPXvDvcO1karBG8/WV6Wj+7kWrvDDBlvpdRGSnbzj1hpjX4z8mOx6UYYstc2O
|
|
uSk71tG7Ns5AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeXneJ62dVl5KT9VTt8Z9W9xbWclPo+O
|
|
fft9qfSHEU1pv48ftYST23ZTDC/p0YtlVuvVjMbM5+LCZjYGWGdrTPxiHY4ffaf3cjTxz1v6xMS6
|
|
Olty2iXVj/Dk8n+ndrkhnGRo1v8AFdW3RCrZ5uiYsqrboncSu508yjmZRYQt50TfowYTbYGVrKrT
|
|
uTZjvukQnYhMIGVY2ZxPVWyrHVCWzXpVXkt3TE7Va+W4K7X3jv1auTNy3jdba0RZpamfroQN7Hk3
|
|
6wr1GTaN2OOJiu6Mu98NvgDi8Wy74d/yZ8PiPAiO2zU4nb6qIn1bugjfFE/ASp1ke9u15mbbRDZ1
|
|
Mb823kx0Ontn1OOkedoJCvT8I03gaKsz9q/WW+isRWsVjtHRKyrhe0XCfpWL6Vgr9fjjrEfeh5fF
|
|
feH0V5Dj3DPoOo+k4a/U5J6xH3ZZ7z3228evytOk7NvFbo0cdols47bSybt7HbddHVqUs2aW3Qnq
|
|
xVeu8LILR3SlZw3V/R8nhXn6u0/pLuPMXjeHT4Zruf6jLPvR9mZ8/g1xrvpz+TH7HUAaMAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAABRq9VXSYJyW79qx6yvmdo3l5viGs+maqYrO+OnSvx+KLeLZz2te1rZL2v
|
|
ed7WneZYWnZl5K72YV1xEyxmeqJljzIEWlVkszvbZp5soN3h2SJz3pP3odCnuWmPRxuERfJrZmtZ
|
|
mtY96fR28kbX3dXj/wAuTyf6bmK+9YX1s0cNtm3Sd4LFY2K23W1s16StiUJW7bp22RW3RluBuruz
|
|
mWEgrmCGWyNkoExKE1QlPmsqRDKeyBjaejWy2W3ttDUyz1QKslvehVqKTNosyyTvELabXptIJpaP
|
|
B39Ia2mz+JGpr51jdZefDx2hzuHZObNq58poJaGtjxJ2+LoaKP8ADRPo5+T3skx5OhpOmC0fBNQ0
|
|
5yTbn+bt8A0u9raiY6RHLVwY62mI6zMvaaHBGn0mPHt1iN5+aYVsACBXqMFNTgviyxvW0bSsAeE1
|
|
mkvw7V2w5Ote9besJx2er4rw2nEdNNekZa9aW9JeQjnxZLYskTW9Z2mJY7zz26fHrrdpbZsY7NGt
|
|
mxjvso1b9NmUwpx33XRO4K7VUTE1nmrvEx1bVo2VWiJE/XY4frY1WPlt0y17x6/FuPM0m+HJGTHO
|
|
1qu9pNVXVYt46Xj7VfRtnXXL5MfzexsALsgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHM4jxOMFJphmJv529Dq
|
|
ZLfjDjPEIx450+K3v2+1MeUOHSOWFc3nJkmZnf4yujpVlqunOeFpV2nctLCZUXRM7MJtsWlRkv3Q
|
|
ky5NmpWt9RnrixVm17TtEQnJabXisRMzPSIew9n+CRoccajURvqLx5/chfOest642OGcIpoOG2w7
|
|
ROW9d72+LQvXevyejcPUU5M+SvpLeOataraw2a0dLbLqTtK1G3Es4lVWWUSoldFtmcXUbpidgXzK
|
|
GEW3TuCUSncnsDFMMLSms9EC6J6FpVzbZE5ALy0809ZbFr9GtfrEoFMzuuwz0Ueey3HbaBLDXe7i
|
|
tMOfwWnP9I+NZbuttvhs1uBRtXPb4SDm3iIvf57N7Dbl0VrS5+XrltEd+Z1Jx7cNms9N4TURRw3T
|
|
+PrcO3WszEvZOD7P6aYiMlvu16S7y1QAIAABxOPcLnUY/pWCv1tI96I+9DtgmXl68Biy7/NtUu3+
|
|
O8HnFa2s0tfd75KR5fFyMWTdhrPHVnX9R0cd21S3Rzsdm1iuqs256wrmGcT0RYSx5d047X02SMmO
|
|
esd49YRE9WcdSXhZ2O1p89NRji9J+cei1xMc3wXi+KZj1j1dTTaqmor06WjvWW+ddcu8XK8BZmAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAMMmWmKu952UZ9XFZmuP3revlDTtzWnmvO8q3XGmfHb9ZanV3yxtWeWn7y4es
|
|
vPNtDqZJ6Ts5mppvdl/XXRMyfGvSNlu/RVvtOzLfoipLT1VTKbSpvfogRkvtDVyZOhkyvQcA4Dzz
|
|
XV6yvTvTHMfvK+c9U3rkW+zvA/D21urr789cdZ8vi9KDb45rejl8Rry6iJ/FV1HP4vXbBTJEfYt1
|
|
+UpiHM295bXsqrO9l8QkZ0lZEqqLeyBZHZLGvZkhIndADKJ3TMoqWQMZ6pjsxll2jsCLSrmU2lFY
|
|
36gieyu0LJk3jbsga0wdqzK20QpyztQGprL/AFMrOE05NLkt6qdVWZxNrSe5o9vWBLiUjnzXn0vL
|
|
q555dHt8HOwV928/1z/LpzXxbYccRvzTB+jucOwxh0dI22mY3ltIrHLWIjyjZKyoAAAAACJiJjaY
|
|
3iXleM8InR5J1GniZw2n3oj7s/8Ao9Wi9a3rNbRE1mNpifNFnVs65XhcWTdt47bnFuF24dm8TFEz
|
|
p7T0/pn0a+HJux1OOrOux08d1ndqY7tillVkzExLOk7yd4YxGwluViJhE45raL0na0dtlWO0+bZr
|
|
1TKi+2zptZGTamT3b/tLacvJjiY3XaTWdYxZZ6/dtPm1zrv1z78fPcbwC7EAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABhkyV
|
|
xUm152iAZWtFazNp2iGhm1Vss8uP3aevnKrNntqLdelI7VRHRnrX/HRjx/tZREVjZXeybW6KbWZt
|
|
pCZ6S08tN7Nmbb7zCrJtyoS5145bSx5mWafelr3tsKmS/o08uXyhlly7RPV2+AcBnPNdZrK+53pS
|
|
fP4ytnPVda4y4BwHxOXV6uvu96Unz+MvVxG0bQRG0bR2G0nHLb2gCUDX12LxtFmpHeazt82wT1gH
|
|
mMN4tWs+rcr2aEV8DU5sM/cvO3yb+O0csLUTSdrLphRE8tlkZI7Atr2ZMazDJVKTYSCawi7Ksq7z
|
|
1QERvLK3ZGPrKbyCrbdnMcsbeaa18/RhvvM7oGEwTG0JmYYTIML22a2e28xELM19oURPNO4lOem+
|
|
n3ZY5+prVnMc2GYU4/L4A0a15cNf6rz/AC6fC6+NxCPOuOu/5tHJTbHj+F5/l1+BYumXJMd9o3/d
|
|
MRXYASgAAAAAAABhlxUz4rY8lYtS0bTEvH8R4ffhmo6bzhtPu29Pg9mq1Gnx6rDbFmrzVsizq2df
|
|
zXkMWTeIbNL7tbXaHLwzUctvexWn3bmPL8WFnHVL326VZ91MfFVjvvVlz79kLrcf2m7j7bNHH3bl
|
|
J2SirLQoy4t1++7G0dBC/RanxI8PJPv18/WG241+alovSdrV6w6mDNGfFF4/OPSW2b1zeTPL1aAs
|
|
zAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAVZ9RXBTe3WZ7R6iZOpzZq4ac1p+UermZMl89+a/byj0Ra9815ted59PQ32hlrXXRjH
|
|
DpCLX6ML5NlNsm/ZRqstfdXzbsZt06sLZNvNB1Za8RDWyZdo7q8udq5Mu/mIMt4md2lmy7JzZuWJ
|
|
dHgfBL8RvGo1MTXTxPSPx/8AstJ1XWpIs4BwSdbeNVqq/URPu0n73/s9hEREbRG0QUpWlYrWIisR
|
|
tER5JbSccur2gCUAAAAPM8Sry8Uyz67fwuxbzVPGsE49XGbvF42V4M0TEL33ERnktsxpk3sumK2j
|
|
admFdPFZ33VS2Mdui2J3UU6LYlFSsN2O5NkCyJ6K7T1TEsbAsxdpReerKkTFGMxvYEz0rsqtbbpC
|
|
b2VT1QEzuwtbaGUxspuJU3neWdKoiu8rq12gCI92YatLcublnzbEz1aOptyZqTuDHLfxN6R0+t5X
|
|
qdJhjBp6UiPLeXl9NSMnEKxHa1+bb8nrlvxUAAAAAAAAAAABTqtNj1eC2LLXeto/R43VabJw/VTh
|
|
ydY+7b1h7ho8V4dXiGlmvbJXrS3xRZ1fGv5rzeHN02bEW3cys3xZJx5ImtqztMS3MeTeGFjqlb2O
|
|
8btql3NpbZtYsnSBLeiWfdTjtutid+ghherHS5p0+f3vsX6T8Fkw181d4lMvEWdnHaGnw/UeNh5L
|
|
T7+PpPxbjdyWcvAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAo1Oprgr63ntAmTqdRqK4K9etp7Q5d7Wy2m953lNrWyWm953mVd77R0
|
|
Za1104xxlN9lV8qnJl2a9s3xUXX2ybsJyRDWtl3YWydEC+2VRkzeW6q+T4tbJm+KRdfK1cmWZnlr
|
|
vNp7RC/R6HU8SycmCk7ed57Q9ZwvgOn4fEXtHi5/O9o7fJaZ6z1uRyOEezVstq6jiEbV71xevzer
|
|
rWtKxWsRFY6REeSRrJxz22gCUAAAAAANbX6aNVpL0npMRvWfSXlKamsRMVvXm+EvZXjmpaPWHzfL
|
|
oNRjzXicfWJ8phfPxFejx72x7xMzK+sXiNoiXlq+Pi6fWV/VfTNqfLJl/WTg9Pji8R70LqvMV1Gq
|
|
j/zcv6yz+lanzzZP1lWpelTET6S81Gp1P/Gyf90s412rjtnyfqql6asREdWM9+jz9eJ6yP8Az7uh
|
|
odZqMt458tpB1JvEViI3/RhzRt13/R1MNaziiZiJn5K9ZNceKZiIiQcu/WekT+iYrWI3lzdTrs+8
|
|
8uW0fJzcur1Np/zsn6g79phVaIeetqNR/wAXJ/3SwnUaj/i5P+6UD0ldonum161h5mNRqP8Ai5P1
|
|
lNtRqJjacuT9Qd22WN5aGeZyZd/KHJy59RHbLf8AVq31Gp/4uT9ZEvS8Lr/vSs2npzRtL1z53wK+
|
|
oza/HW2XJNd99pmX0Rb8VAAAAAAAAAAAAAAcHj/C5yV+l4I9+v24jzj1cLFk8nu5jeNpeW41wmdL
|
|
knU6ev1Vp96sfdn/ANFdTrXG+eq1q5F2LLtbZoY8m8d11bbSydErsYsm+zZrO/zcnBm226uhiyRK
|
|
EtrvCrJDOJTeu8A1MWX6Lqq5N/dnpb5O5ExMbx2cPNTeJb/DM/iYPDtPvY+nzhri/jDy5/W6AuwA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAa2p1UYo5adbz+xbxMlvqJ1OqjDHLXree0ejmzNrWm953tPmTPWbWneZ7yoy5YhjrXXTjH8s75N
|
|
mtkyxt0VZM2/m175N1V03yTKubMLXVXybeYLLX2VXy7eam+b0bOg4VquJW+rry4/O9uyZOq3UjVm
|
|
9r25axMzPaIdvhns1kzbZddM0p5Y47z8/R2+HcF03Doi1a8+Xzvbv+TotJnjDXkt+K8ODHp8cY8N
|
|
IpSO0RCwF2YAAAAAAAAACvUZYw6fJkntWN3k8dfHz2vLucdz8mkjFE9bz1+UOZosX1UzPm0nqI/W
|
|
MYo9FlcPNklfFGeH/NshLGun+Cz6PtHZtVZWlRLS+jxPkRpIn7rdoupHTdA5s6SI+7H6Mfo+32Y2
|
|
+To3neSIiZ7A0IjPXpXLePlMotGW3272t85datKzHZjbTVnsDj+FG/2Y/RlGP4R+jo20u7H6N1Ql
|
|
o+H8I/REY957R+jpfReiK6eOYHLtj2tttH6KrY/6Y/R2c+kjeJiFVtLG24hxpw7/AHY/RRkw9O37
|
|
O99Hrt1YX0tfOBLjcGp4XF8c+u8fs9c4dcVcGemSI61nd3IneN1orQAAAAAAAAAAAAABFqxes1tE
|
|
TE9JiUgPKcX4RbRXnNgiZwWnrH4XPi28PdXpW9JraImsxtMS8pxXhF9DecuGJtgmf+1TWW2N/la1
|
|
L7N7T5e3Vy6W3hsYcvLbqzbO9jvvCzvDR0+XeO7crO6FmGSvRThy/RtVXJ92elvk2rRvDUzU7pl4
|
|
izsd2J3jeBpcNz+Lg5LT7+Pp+Xk3W7js5eAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADs0NTrN96Yp6edkW8Wzm6+LNTq4pvTHO9vOfRoWtt
|
|
1mes95YWvs1s2fZldddOczLPLn2ju0MmebT3YZc2/mpm3qqllN1drsbZIhr3yzvtHf4AsvlYYseb
|
|
V5Yx4KTe0+UQ6nDvZ3UazbJqd8OKeu33peq0eh0+hxcmnxxWPOfOfm0mP+steT/ji8N9mKY9suum
|
|
L37+HHaPm9DSlaVitKxWsdohI0Y22gAgAAAAAAAAAABXnyRhw3yT92Nwef4xm8bVzET0rPJH5d12
|
|
CvLhho3rN9RWs9Z23n5y6O21YhrVYbdGOCfrrLPJRpv863zVS6FS09SvZj3lVZZRdPSqmnSWdrIE
|
|
ebOkK4ldTsgW1WKqd1oMZhEVZyRAImOjGI6rJ7IiATNd46qL02bHkiaxaoNGY2n4ImPgtyV2n0Vo
|
|
Gvlx7x2beiyTk08RPevSVUxux00+Fn2n7N+n5rRFb4AAAAAAAAAAAAAAACLVres1tETWekxKQHlu
|
|
L8InR2nPp43wz3j8P/s5dLveWrFqzW0bxPeJeV4xwmdFec+CJnDM9Y/CrY1xv8qvTZ+WYdbDk5oh
|
|
5zHk283U0eo3jaZZ2N5XYjrCnLSJhOK+8d1kxvCqzSwZvousrb7k9LfJ3nB1OLeJdLhufx9LEWn3
|
|
6e7LXN9Ofy5/W4AuxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAETaKxMzO0Qi9646Ta07RDmZ9VbPbaOlI7Qi3i+c3TPUaqcu9adKfy0722ZXvFa9
|
|
XO1OrjrESxt66ZJmcjPUanlidmhkzTZVfLN5VWvsC2b7R3U3yqrZZtO1esz2h2+F+zWTUcuXXTNM
|
|
feKR3n5+iZLVbqRzNJo9TxHLyaekz62ntD1fDOA6fQbZL7Zc/wCKY6R8odLBgxabFGPDSKUjyiFj
|
|
SZkYa3aALKAAAAAAAAAAAAAADQ4pl2pTFH3p3n5Q33E12Tn1eSfKscsLZ+orS00eJqbW+Lfnu1tF
|
|
XaJnZsz3WpCfsyp00fWSvmPdVYOmSUDd8kR3InoQosy7JmUX7MdwZ17ro7KKT1XRPRAsrO0rYndr
|
|
79V1ZBaQiJ6JgCSIJASwrO07MpV2nqBlrv1a1o2bf2qtfLXaQUTO0sb05o3jv3ZXhjS20xEphW5h
|
|
yeJjjf7UdJWNKLziyRePsz0lux1SgAQAAAAAAAAAAAAAADG9K5KTS8Rato2mJZAPIcU4ZbQZuekT
|
|
OC3afT4NXFkmlntc2GmoxWx5K71tG0vHa/RX0GpmlutJ61t6wrY2xr8dXS5uesN+tt4ef0eaa223
|
|
2dnHk3juyreM81OaFGiy/RtZET9jJ7s/2bdutd2jqKeic3iNTsd8a2h1H0jTVtP2o6W+bZbOO+gA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABje9cdJt
|
|
adohGTLXFTmvO0fy52bJfU23t0pHaqLeL5xdK9Rnvqb+cUjtCi94xxvK3JetKuHrdZvaa1ljb10y
|
|
cnIs1Wt3naJc++TmVWvMz1YWybfMGdsm3eWek0mo4jm8PT0mfW3lDf4V7P5tdMZdRviwfvZ6/TaX
|
|
DpMMYsFIpWPTzXmf+steT8jn8L4Dp+HxF77Zc/4pjpHydYGjC3oAAAAAAAAAAAAAAAAADG9opS1p
|
|
7RG7zszN6WtPe0zLua+3Joss/wBOzhzG2OsL5+IrY09dsSyYRijbHEMvOChb7KjF0yS2LQ169Mso
|
|
S24noyrPVXWejNVKbTuw3T3REdQWU6LYlVvsyiUDPfqupPRr79VuOQX1lZEqoZxIMksd0gT2VT0l
|
|
bPZVbuCaW8i8bwr32WxbcGnkjaZa9p2ndv5qbw5+aNugLItF6TEtvTX5sMb969HMpfazc0d9stqe
|
|
vVZDdAQAAAAAAAAAAAAAAAADV1+iprtPOO/2u9bektoB4TJTJpNRbHkja1Z6uto8viVht+0HDvpG
|
|
H6Tjj6zHHvbecONw7Ltfkmeqmo6Ma69DXbbZTkr1mGWO3RneOaGbZRoM30fVzSelMnT83aef1FZ7
|
|
x3h1tBqfpGnjmn369LNc3sc3kzy9bQCzIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAa+q1dNNXr7157VhGp1Xh70x+9f9ocy283m1p5rz3mVbrjXHjt91lz
|
|
5c9+fJ1nyjyhdM8lZlOOIiqrUXikd+kMreunnI5XEdX4dZiZcG+XmtNl/F83PeeWWHDOGanieSKY
|
|
q+5H2rz2hMzWd1Iqx1yajJXHhrNrW6REeb1nCPZumn2z62Ivl7xTyr/6uhwzhGn4Zj2xxzZJ+1kn
|
|
vLoNJnjHW7TbbsAszAAAAAAAAAAAAAAAAAAAAaPFrbaSK/itEOXt0rDf4xb/ACa/GZacRvaF58Q2
|
|
IjasQnzPIhCU92tMbZGzHmotG10C6nZkwpPRmipIllEbMIZIE7solgmJBnCyk9VMM6z1BtVllEqK
|
|
z0WRILYlluriWcSDJVbusV27gwInaSWM9ECyZ3hqamnSWxFmOSOaqRx725bNnSZNs9J+OynVY+WZ
|
|
YYr7TE+nVaIr0Ais81Yn1hKAAAAAAAAAAAAAAAAAABExvG09peU4nov9n66L0j6q/WPg9Y1OJaON
|
|
ZpL0+9HWs/EWzeVz9PbmrEtnyc3h9reHy26TWdnSr2YX6657ijLXpLX0+onSamL/AHJ6W+Tbv2aW
|
|
ekTv16JzeI1Ox6KJiYiY7Slz+E6jxdN4dp3vj6fl5Og2clnKACAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACZ2jeQRMxEbzO0Q08uqtkma4ulfO3r8lefUePMxWf
|
|
cjy9WvlzVxV6T1Z61/x0Y8f7Wc7Ur1lqVy+LqOWJ2hp6rXddon5rOF1tfmz5OkT0qzb8dWbxjp1c
|
|
biuuilJ5Z6r+IcQrixzEy8zl1E6rNt1tMztFY81sztU1eRucN4ffi2p5esRM72n0h7rS6XFo8FcO
|
|
CkVpX082nwXh3+z9FWLxHi36328vg6TZyW9ABAAAAAAAAAAAAAAAAAAAAAADj8Unm1tK/hqppHvw
|
|
y1k8/EMk+m0GOPeafiFpCZYwolnXspvHvLa9mF46gmnZmwozRUiUCBKYYsoBLOFbKAX0llEqqyzi
|
|
QXRLOJVRLOOwLIljZMEgrlhKyYYTAK5nZPN0RZjugUanHzVlz6xtLq361c+9eXItPpXX0dubTU+E
|
|
bL2lw2++O1fSW6m/VYAISAAAAAAAAAAAAAAAAAp1GbwcfTreelYEydcuMcRrM/L9nnlsV6wqpi2r
|
|
tv133mfWVkRyRtEdGFva7MzkYZNoamWN4bV4mYa9qztKIujhVppxGI8r1mJegeZpknBqKZY+7L0t
|
|
LRekWrO8TG8Ns/HJ5ZypAWZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAADS12fp4VJ6z9qVuq1HgUiI+3bpDl589cOKZmevqprXPTbx477rDJlrhr1nq4+s182tMRP
|
|
RqaziXiZJrWekNG17ZbxWJ336M5LXRbI3dLTJrs07RMY6fan1dHLrowY+X7MVjt6N3R6Kul0EbWm
|
|
s7bz8Z+LnabQX43r7Y53php/mXj+Dnv0f1JO1x/8ZxbUzj02O15mfLtD13AvZqnDds+pmMmo26el
|
|
XX0Wh0/D8EYtNjilY7+s/NstpOOTW7QBKgAAAAAAAAAAAAAAAAAAAAAADG88tLW9I3BwJtz6nNf1
|
|
vK/DHVqYJ3pzT5y3MPZeojOWMQylEKpTVjZnDCwkqzYQyRRICATCITAJZQxhMAshnEq4ZQC2srKq
|
|
qrIBZCWNZZgwswmFloVyCu0dFcx1WyrtCBhv5NTPHXds2U5o3hIz4ffbPt+KHUcTSW5c9Jme0u2v
|
|
VYAKpAAAAAAAAAAAAAAAAYZctcVOa35R6tLrltN795/YvknNqrfhpPLH92V5isd9mWq6fHjk6rn0
|
|
ZxG8KK5Jm/wbVZiYZtqrmkqL023bkxvCiY3lJHNyRG81mHS4Rn5sNsNp64+3yaWaNrzOzHBl+i6q
|
|
mT7s9J+S+ay8mex6EIneN47SNXKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAImYiJme0JafEs3h6fkidrZOn5eaLeJk7eOdm1Hi2vmtPTry/CHmOJcUvmvOPF1n09Pm
|
|
6HF9ZGm01qxO3R5vSY7XwzmzTy47zzTEd7en5Mfvt2/PURWdo3tvPrPlKymbktFqTtMTvHzbOLDG
|
|
f63JXbFX7FdnoODcDprZpq9TjiMMTvSn4vj8l5fxnrk91saPSa7i2hpOfbTVt5x1m0fLydzR6PDo
|
|
dPGHBXasd585n1lsRERG0dIF5OOe6tAEqgAAAAAAAAAAAAAAAAAAAAAAADX11+TRZrf0y2Gjxe22
|
|
gtH4piP3TPpXKwxtjhuYo9xq442iIblI2pC1RET2ILd9kxCqRjZmwlCSEohIJAQAAJZISDKGUd2M
|
|
MoBnVbVVCyAWVWeSuqyOwIlXZZKue4MJV2WWYT2QKbKL9YlfdRdIo35b7/Hd3KTzUrPrDh27uxpb
|
|
c2mpPwX/ABX9XAKpAAAAAAAAAAAAAACekTIp1eTwtJmv+GkyJn1oafeazbfpMzLR4jq/o8b823zX
|
|
6XNF8ERCvTcNpxLV5LauvPhx9Irv3lhztdtv8TtaWLicXrt03jzjzb2k1nid56ty3s/w+a7Uwzjn
|
|
1raejlarhmbhl/FpbxMO/fzj5p/ixSeXOvTtRfeI280ZI26tfDm3pWe63LaZx7qtGvniJ6tPLvOK
|
|
fOa9WzbJvTbza02jl3n5SSljscK1MajSxWZ96nSW88xw/VfQ9XMT9nfa3yemid43jtLeXsce88qQ
|
|
EqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADia3UTm1l4j7OP3Y/u
|
|
7Vp2rM+kPJW1PhYcmS0+9MzKm/jbwz31weMzbV8UppazPL9q0/BF4rk1GLDSNqxPWPhCnHmnNrtT
|
|
qPKteWPm6U6OdHaZvO+SaRNvhv12Ub/q3FhtrNVj0uKOt56z6R5y9zix1w4qY6RtWsREOJ7L6OKa
|
|
S2rvX6zNM7T6Vh3mmZyOfya7eACzIAAAAAAAAAAAAAAAAAAAAAAAAAAczjVvqMVfW/8AZ03I41bf
|
|
Lp6/OVs/UVrY47NyOzUxd4bUJpEbb3Z7IiOrKIVSjZhMLJYyhKIgmGUQSDESIEbJEgQmCITEAmGU
|
|
IiGUAyhZVhDOoM4Wx2VQtqBKuyyWEgqlhKyyuyBVaGtkbNmvk7A15l1eH2300R6TMORPSXT4ZO+O
|
|
8fFefEX63gEAAAAAAAAAAAAAAAq1WPxdLlp+Kkx+y1Fvsz8gjhaDauGK8sx07y3OE3m1tT6RaP4c
|
|
vU6yMNKUx73zT0ilY3l2eF6a+m0kRl/zbzz3+Ez5M8z26fJruW6wzYq5sV8d43raNpZjRzPPaTmx
|
|
5b6bJ9rHO3zb2WJ8GWPEscY9bgzxH2t62n19GWW0eHOzHU5XbjXZ1x8WTnz2iZ7S2M1IjH2+LX0V
|
|
KTqs8zO9ot0j8nUthi1J3UaOFMTfLFo6xMbS9BwHWTqdHOO8+/hnln5eTjYMFo1WTH5VnePzXcIm
|
|
2k4zlpPSmXy/hfF5eMfJns69OA2cgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAADG/2LfJ874rW845mubliY7bPoto5qzHrDz0+yePNF41OotaJ7RWNtpV1OtfHqZ715fhu
|
|
j8adNpcVfeyzE2/vLuanhOu1nEctIxTTFa/+ZPbZ3eHcF0vDbTfFE2yzG03t32+DokynXl9+leDB
|
|
TTYKYccbUpWIhYCzEAAAAAAAAAAAAAAAAAAAAAAAAAAAAcXjE/4zDH9M/wAu04XF5/3jj/0f3Wz9
|
|
RUYmzDWxS2I7FSyjuzY1ZKpRKEygEwiWUIkGIk2QJNhKQhMIhkCYZQxhlAMoZwwZwgWQshVCyATL
|
|
CWc9ldpBhZXLOVdpQK7NfJPRdaWvknoDVvPvOnwuel4+TlXn3nS4VPvXj4QtEV0wAAAAAAAAAAAA
|
|
AAAAAVV02CmTxK4qRf8AFFeq0AAAanEsfPpZmO9Ji0NDLfkwdOsulrumiyzHlVzJrz4Ovoy26vB8
|
|
cTBa9NffLtMY77Rv8Yegx5ImkKdJoY1HC81Y+3OSbVn0mGGkmbY45u6tnrrTOu2xGO0RxCd+nNVj
|
|
qKxTV1vH2pjaGtnyzXXYdo96ZmGXEMk15b7/AGZiVerWPTYckZcNbx5wzc7hGbnxXxzPWk7x8pdF
|
|
0S9jh1OXgAlUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAcPjEf4/FP9H93ccXjMf4vDP9Mx+62fqKrx+S+GvibEFSsqyYwlVK
|
|
ZYsmIMoRKYJQIPIEiQ2ATCUQygCGUIhMAyhnDCGUIFkLIV1ZxIMpVWWSrsCuyqyyyq09ECq8tfJK
|
|
66jJ2Bp5J6upwn7dv9Lk5J951uE/av8AJaIrqAAAAAAAAAAAAAAAAAAAAAAq1Mc2myxPnWf4cmtu
|
|
XT9fR0tffk0WSe28bfq5Wbamm3326MtunwfK6PCv/AxPraZ/dz9PO97/AOqf5dHhdZrw7Dv3mOb9
|
|
XOxRFM+avpe38mvkPHf/AFWlrKba7Tzt99ZxKkfR7euyNXMTrtPHfa0z+zPiM/UR8Zj+Wbdu8HpN
|
|
M2bfzrV13M4dO2pyR61dNvj44/J/oAWZgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADj8bj63BPzdhyeNx0wz8ZWz9RWri7Nmv
|
|
VrYu0NmqaRZHZlDGGSiwxZSgCEkCBCQSCQBMJRCYgEsoYx3Z17AlMIhlCBnDOGEM4AlhZZKq4KrK
|
|
7LLKrIFN2vdfZReAaObu6/CO9vk5OePR1uEd7fJeIrqAIAAAAAAAAAAAAAAAAAAAAGtxCk5NFliI
|
|
3mI32+XVyNTyZOHTee946PQKPoeDffw4777eW/yVs60xv+ZxOnr4Okx1t05KRv8Ao41Z5q3yed5m
|
|
XY1szXRZ5jvFJ/hxItP0aOSN9q7yrtr4f2tHFM5+KT16Yq/vK/iGSbXw4vO14UcPx5MGfNbPG18m
|
|
1oj4THRsTw7VanPXVYpi3gzMcnrvCnG11JOupwuN8+a3pEQ6jT4divjxWnJExa09pbjbM5HHu90A
|
|
JUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAHM41H1GOf6nTc/jEf4Ws+lls/UX45uGekNujTwdm5RNIthKIZKLDFlsiQIShIC
|
|
EgCUJ7AmGTGO7IDzZQhMSDJMMYZQgZwzhhDOATuqssmVdgVWVWWyqtCBTeVF19lF+wNLNG7q8I+9
|
|
8nLyupwnt+S8RXUAQAAAAAAAAAAAAAAAAAAAAAAItWL1mto3iY2lyrcLyUxzix2ia2nvPeK+jrCL
|
|
OrTVnxpanhuPPemSs8l6RtE7dJj0ldpNP9GwRSZ3neZmV4cR/Vs4AJQAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAANHi1d9H
|
|
M+kt5ra+vPoskfDdOfqK4mn7Q3aNHBPZu0W0RdDOGFWcKLCJZeTGQQlCQSgASBsCYZQxhlAJTAmA
|
|
TsmAgGcM4YQyjsgRLC3VnaVcgwsrt3Z2V2QK7tbJ1bN5a9waeWO7p8Knt8nNyebpcK8vkvlFdQBA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK9RXmwZI+ErEWjesx6wQeZwejeo0cccuW8
|
|
elpblJaaRGxVnCuss4ZrMvJEgCAASISCQIBlCYYpieoM0wx8k7gzIRueYM4Z79FcSy3QEsLJmWFp
|
|
BjaVVpZWlXMoGNmvkXXlr3kGtknu6XCf7OXkl1OEdl8orqgIAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAHmskcmtzV/rls0U62OXiWX4zErcc9GmkRfWVkSqqziWayxCPIANwBIhIJSxS
|
|
CRG6dwZwlhEs4BluMdzfqgZxLLdXuy3AmVdpZTKuZBjaVVpWWV2QlhZRdfZRcGpl7urwfrzfJy8r
|
|
rcH61vPyWitdMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHA4nHLxKZ9awnH2ZcY
|
|
jbW459aq8fZpfiI2IZwrqzhmsz3Ebm4JN0AMhCQSIASndiAziWUSriWcAyRujc80DM3RCfIETLCW
|
|
UsZEsJYSslXZAwlTddPZTkBp5e7r8Gj6rJPxhx8k9Xa4PG2C8/FaK10QAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAcfjcbZMFvnDWx9m5x2PqcNvS+zSxT7sNPxH62YZQwqzhRZO6UCB
|
|
KUAJTux3SDIRuAncQAmJZRLBMSgZ7iIAZRKd2DICUSlAljLCYWMLIFVukNfI2bNbIDTyT7zu8Ijb
|
|
Sz/qcG/2nf4T/wCE/wD2WnxWt4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHL9oL
|
|
+Hw2cm28VvEuPptfgyVj6yIn0no7/FtJfW8NzYMe3PaPd39d3iMug1WktNc2C9dvPbeP1aZ9xF+v
|
|
T471tHu2iflK2HkqWmvaZj5Surqc9Ps5bx+alTHqYHm68S1Vf/NmfnC2vGNTXvyT84Ql6A3cSvHM
|
|
sfaxVn5Ssrxyv3sM/lKB1xza8bwT3pePyWV4tpZ+/MfOEjfGrXiGlt2zV/PotrqcN/s5aT/+wLRj
|
|
FontMSlAlKEgndO6IAZQljDIEgeQljLCzOVdkCu/SGrkbF56NPNeKxMzMRHxENe0+89DwuNtHHzl
|
|
5PJr8NcnLW3Pbf7r1nCZm2gpae8zMrz4i/W6AgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAETETG0xukB4HVaeMHEtRi26RedvkyjBSfX9W77QYvC4xz7dMlYlrU7M929dWJLFc6aPK0q
|
|
7YLxPS0S22FlP6q38Zac0yR92s/KVc3tHfFf8tpbcsLRvB/dR/8ALLVnU0r9uL1+dZI1mnmdvGpv
|
|
6TOy6ym+Oto2tWJ+cJ/tW+KLK5KW+zes/KU7tG+h01p64qx8Y6NXNo6Y+uPJlp8rLf0rfG7MXtHa
|
|
0x8pZxqs9e2a8f8A7Oj7HaTHn0+f6RWM23LETfr6vRW4PoL99NT8ui7F4+vEdXXtnt+fVbXjGsr/
|
|
AOZE/OsPS29nuH27YrV+VpeV9pdPXhOtw49NG9Mld55+vXcTPd42I47qo7xSfyWV9oM8d8VJ/VxM
|
|
d8l46xWF9cV7en6o/qLfxp2I9ob+eCv/AHMo9op89P8A/wBORGmyT5R+qfo2X8P7n9Q/jTsx7RR5
|
|
6ef+4/8AuHftg/8A6cWcOSO9J/WEbWr3pY7Efzp2Lcfv5YK/9zWy8d1E/ZpSv5Oba1/+Hb9lc+LP
|
|
bFt87I7E/wAabWbiurvEx4nL/pjZzc2bJkn372t85ZXx55/BX85lucC0vPxnTxlnnjm32mOiZqUu
|
|
LJ2p4TwnVavNWaYbRTfre0bQ99pcH0bT0xb78vmtiIiNojaErMwAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAHnfarF7umzRHaZrLjYrdIen9ocPi8JyTt1xzF4eUw23rCm3R4r6bMy
|
|
wt6kdTaWLdjswmNoZontsCm0K5XWjopnuDC0dGpqG5bs08/daKV672MjbSaif6oh6Z5f2LtvptRX
|
|
0tEvUN3Jfo8f7cYve0eX4zV7B5z20xc/C8eSPuZIRficfXlcPaG7ino08HWIbePpLF2NuiyOyrHK
|
|
3fZFSwuovHVfaVF4QK5YWTM9UT0EKry6Ps1Tn4zjn8NZn9nOtLseydObiWW34cf918fWfk+PYANn
|
|
KAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq1WKM+ly4p+/WYeBxTNd6zG0xO0
|
|
vobw3FcP0bi2em20Tbmj5Srr418V9sa2Z7qKyzi07MXUylhaU7yjqhLCeiq3ddaFNxFYW7NLNG8t
|
|
zya+WO6Va9J7FW66mvwidnrXiPY3Ny8RyUn71Jj9Ht3RPjk19HK9pMHj8D1ER3rHN+jqqtTjjNps
|
|
uOe16zAifXzfTz7kNyndpYazS9qT0mszDdoxrsi6m8LazMq6zDOsq1ZEyrt1WWlXaUCqyq0rbKbi
|
|
Fdp6PReyFd8uqv8ACsfy83aXrPZHHto89/xX2/SP/dpj6y8vx6EBq5gAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAB5n2q03LfDqqx39y39npmlxbS/TOG5se29tuavzgWzeV4mtui2
|
|
O3RRSY2hdVhqO2MvI36iu9lUsrSrvDHn6spnmSiq5jooyV6tq1VV69RC32byTh43h8otMx+r6I+Z
|
|
aK/g8TwX7bXh9Mid4iW+fjl8n1ICWb57xLBOm4zqse20Tbmj8+qKdnS9q8PhcTw5tumSm0/OHMxz
|
|
0Za+uzx3sX1t0Zxurr1ZxvspWiZYWZbsbT0QK7KLrZVZJFaqt5vbezNOTg9J/FaZeJns93wCvLwb
|
|
T/GJn92uGHldIBowAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADuAPA67F9H4l
|
|
qMW20VvO3yRWW97T4fC4rXJHSMtI/WGhVlue3b473K2KzMML4+62tujG9pnozXaOSOVFMnVbmq1t
|
|
trJRW5E7wwvUxTvCyY6CHOt7moxz6Wh9PxTzYaT61h8x1MbZK/OH0zTf+Fxf6I/htj45vL9WgLMn
|
|
mvbPFvocGWO9L7fq85p5maw9d7VYvE4JkmPu2if3eW0+PasdFNOnxfF1Y2hlykRsmY+LJ0MZjZXa
|
|
eq2eyi8oQTO0KLdZWzPRjWu6VaqtHR73g0bcI0sf0Q8Nkq93wqNuFaWP+XDTDDytwBowAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAef9q8HNpcGaI60vtPyl56k9Iew49j8ThGe
|
|
PwxFv0l4zH2U26fDfTYiyJljvsjf4sm6vJ1hrXjq2MkqLdZEVbgbMx0auGdmzNt6iHN1Ub5af6of
|
|
TdPG2nxx6Vj+HzaaTm1+nx/iyVj930ysbViPRrj45vL9SAuyc7j1efguqj+jd4/T33rD3HEcPj8O
|
|
1GP8WOY/Z4TTT7sKadHhbcsZnaCJ3TPZk6VdrKbTutmP0U2nqgrGOsr8deiuI2X09EqKM1dt3uuG
|
|
f/jdN/06/wAPE546S9rwud+Gaaf+XH8NMMPK2wGjAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAABrcRp4nDtRWPPHP8PCYusPoWSvNjtX1iYfPuWaXtX8MzCuvjfw32siu8ptXoxi
|
|
0wy5t4YulReqmazu2skbquURWFInddM7VYRGyL291KFnCcfj8e0le/Lbmn8n0N4b2Ur4nHLWmPsY
|
|
5e5a5+OXyXugBZmiY3iY9Xz7NjnTa3Ph/BeYj5PoTxftFg8Hjk2iOmWkW/Psrr418V5WrWd2faFc
|
|
V2jdnEMXWxntupmN7NiYU27iWML6dVMVnddjgVqMsdHr+CW5uE6f4Rt+7yuSsTDv+zWXn0WTHP3L
|
|
/tK+GHl+O0A1c4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA8Dn93W56/wDM
|
|
t/L3z59qp24jn+OS38lnpr4r7ZxHQ2TEstt3PXUrt27K57rr1VT0BjKnJPRbMqMs7QlV2fYvHvrd
|
|
VknyrEfu9m8f7FZI8fVU85iJewbT45NfQBKo817W4eulzxHaZrL0rje09ItwqbfhtBVs3leai8RD
|
|
KLw1sduesL606dWFdsZT1jdhNeq6K9DlhCVUU6s4jZnt1YzAhnM71dH2bycmszY/K1d/0c6OzY4R
|
|
fwuK4p8rTstn6z8k7HrwGzkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHz3
|
|
Vxvr80/8y38voTwGpj/F5/8AqT/JfjTx/WVeyY6FPspc9dZPVXaOq2WEwIUTVRmjo2rNfLHRI3vZ
|
|
DJycXtX8dZh7t879nsnhcbwz23tt+r6I2nxyb+gCVBzuPY/E4PqI9K7ui19fTxNBnp60n+Aj5/pJ
|
|
3jZu1aOnnltMNussdfXbm+l3ZM9URHREdZVXTuT1Nk7boQiOkJw28PU47/htEp5eivJPLMTCZ9Vv
|
|
x7mJ3iJ9UqNHk8XR4b+tIXuhxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD
|
|
weqjbWZ4/wCZP8vePCaz/wDIaiP+Zb+UX408f0r9lOxWOifJhXWjfyYWllPRXYQxnrCrJHRd3YZI
|
|
6A1NJecHEsN/S0T+76bE7xE+r5dk93LW3pL6ZpMni6PDf8VIn9m2fjm8s9rgFmQxvHNS0esbMiew
|
|
PnHLyai9fS0w2aNfUTtrs3+uf5bGPqy068fF227KtSsdFlKqNGMV6myyY6sbdIQI8tlOWOi6Jhhk
|
|
j3RD0vA8nicMx9etZmHRcT2Zyb6XNT8N9/2dt0T449T2AJVAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAHhdfG3E9TH9cvdPEcXjk4zqI/q3L8aeP6xr2TsxpLOekMK6mFo6qpXSrm
|
|
OqBixvHSVmzC4OfqK7S9/wAByeLwbTW9K7fo8Fqo6Paeyl+fglI/Da0NcMPK7QC7AAB8313TiOf/
|
|
AKk/y2MHWrX4jG3E9R/1Lfyv0/aFNOrHxuU7LI7MMayGTVlHWUXhNe6Z6wIUsb9d1m20q7dkDpez
|
|
N9tRqKT5xEvRvKez9+Xis1/FSYerb5+OTyf6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAB43j9eXjN/jWJ/Z7J5L2mry8Upb8VIF8f6aGOey2eynHvOy7bowrrYSxZSwQJ2YXZ
|
|
92N4BoanrEvVexmTm4blr+HJ/aHltRHSXofYm/1Wrp5RaJaYY+X49WA0c4AD51xONuKan/qW/lbp
|
|
+0MOLRtxbU/9SU4J7KadWPjep2WQrr2WRPRk1TvsndXMpiRCb9FNu0rbTuqvKBscCjfi9PhWZeue
|
|
V9n434rafTHL1TfPxy+T/QAszAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHmv
|
|
avHtfTZfnV6VxPajHzcNrf8ABeJFs/XnMcr4no18c+6vr2YadkY2YM57sEDLyY37Mo7MMnYGlqO0
|
|
vQ+xNfqNVb1tEfs87qZ2rL0/sVX/AHdnt65P7Q0wx8vx6UBo5wAHz/jUbcX1PT78qtO2vaCnJxjP
|
|
8Zif2amnnspp04+OjWejKJ6MKdmcMmyJn4m5ZHzEVPMwtJv0VZLbQDqezcb8RzT6Y/7vUPM+ytZt
|
|
n1OTyiIh6Ztn45N/6AFlAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABocbxeLw
|
|
nUR5xXm/Rvq8+OMuDJjntaswEeBxT0bNZ6NatZpNqz3rO0rqsdO3PxlaWEMpY+aqWXkryT0ZT2V3
|
|
7A0dVPuy9f7G124NM/iyT/Z4zWT7sw957MYfB4Fp4/FE2/WWmGHldcBowAAeM9qKcvFeb8VIly9P
|
|
0nq7ntbTbVYL+tJj93CwT76unR4/jo0nozhhTsy3Y1sWljM9Ce7HyQIm3RRlttVbaWrnt0Sh6n2U
|
|
x8vD8mSfv3/h3XN4Bi8Lg2nj8Uc36y6TeOPXugCUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAPD8RxeBxXUU26Tbmj8+quro+02Lw+I4ssdslNvzhzazvDPbq8d7GW7Dfqz2VzG
|
|
0s2qd+iu/Zn5Ksk9BVztX1mI8930zh2LwOHabH+HHWP2fNYp4+vwYvxXiP3fUqxtWIjyjZtj45/L
|
|
faQFmQADzftfj3w6fJ6WmHmsP23rvaqnNwqLfhvEvIYZ+sV038bo0noy36MK9oZQxrdMyrlnMbMZ
|
|
QKrS1M07zEestq/RRjr4utwY/wAV4j91p9V18fQdJj8LR4ccfdpEfsuREbREJbuMAAAAAAAAAAAA
|
|
BAJAAAAEAJEAJQAJQAJEAJQAJQAJEACUJAQlAJEAJQAJQJAAAEAJEAJBAAAJAABAJEJAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABwvanDzaPFmjvjv8A
|
|
tLztJ3h7HjGHx+FainnFeaPnHV4vFbeIU038VbHeGF+kso7Mb9mTdhKnLK3dRm7SIrHhGPxeP6Sv
|
|
9cT/AHfSnz72Zx+J7Q45/BWZ/Z9BbZ+OXyfQBZQABzeP4/E4NqI9Ii36S8Ng/wAx9C4jTxOH6ivr
|
|
jn+Hz3B/mQi/GvjdCnWNlsdI2V07LIlg6USrt2ZzZXMoFV+zPhGLxeOaavpbm/RVltEN72Yx+Jxm
|
|
b7dKUmf7L5+s9/HtRA2cqRACRACRACRACUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAACQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQCQQCRACRACRCQBCQBCQB
|
|
ACRACRACRACRACL1i9LVntMbPATTwdRkxT3pea/u+gPE8Xx+DxrPHlaYt+qNfGvjvtXXsi0dOrKk
|
|
dEXjZg6VMtbP2bMtXUdpEV0/Y2nNxbNf8OP+727xvsXH+N1U/wBEfy9k3nxyb+gCVQAGOWvNivX1
|
|
rMPnGGOXNNfOJ2fSZ6w+dZKeHxDPX8N7R+6L8a+L63KdoZ7q6zvEMpnowdKJ6ywmWUyqvIKM0vQ+
|
|
x+D6rU55+9aKx+TzWa36vbezmDwODYenW+95/Nphj5L6dQBo5wAAAAAAAAAAAAAAAAAAAAAAAAAA
|
|
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAEiAAAEoA
|
|
AAAAAAAAAAAAAEAkEAkRuAkQbgkQAkQAkQAkQAl5T2nx8nEMOT8dNv0l6pwfarHvpcGWPu32/WCr
|
|
YvK4mOem6b9mGKd4Z3idmFdka0y1c892zfpMtLPaNpEV6D2Kj/Eauf6YeweQ9ieuTVz8K/3evbT4
|
|
5NfQBKoAA8FxCvJxrUx/XMvevD8Zry8fz/Haf2RfjTx/6RSOnRMyypHu9kXjowrqVSrvPRnZVl6V
|
|
kK0775MsUjvadn0nT4ow6bFijtSsVfPuFYvpPGtNTy54mfy6vorXDm8l9pEC7JIgBIgBIgBIgBIg
|
|
BIgBIhIAgBIhIAgBIgBIIBIAAhIAhIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJAAAAAAAAAAAAAAA
|
|
AAAAAAAAABAJQkAEAAAAAAAAAAjc3BIjdG4Mkbo5kcwMjdhzHMDPc3V8xzAs3N1fMjmBZubq+Y5g
|
|
Wbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmOYFm5ur5jmBZubq+Y5gWbm6vmTzAz3N2HMnmBlu5ftFTx
|
|
OEZJ/DMW/d0t2rxKni8N1FPWkiZ9eS08e7Cy8dGGn6UhZaJljXZGnmc3UT3dPP2cnUT78xCIV6j2
|
|
H/8A9c/6f7vXPI+w8bU1U+vL/d63du5NfUiDcVSIAS8b7RV5eOb/AIqRL2TyXtNX/e2KfXH/AHlF
|
|
+NPH/pr4+2xcxx0hFpY11K7R16KM32ZWz3UaidqSgrc9kcPicWyZJjfw6T+727y3sXh2xarN+K0V
|
|
h6lvPjj3e0ASqAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJQAAAAAkQAkQAkAAAAAAAAAAAAAAA
|
|
EgAAAAAAAAAAAAAAAAAAAAAgAAABKDcAN0bgkY8xzAyRux5kcwM9zdXNkTcFm6OZXzMeYFvMibKu
|
|
ZHMC2bo51U2RuC2bom6rc3BZzom6sBZzI52ADPnOdggFnMc6skFnMc6rc3BbznOp3RzAv50c6nml
|
|
HMC/nOf4qOY5wX85zqOc5wbHOc7X5znBsc6edr85zg2ec52vzpi4NjmY5bROG+/bllVzsNTk5dLl
|
|
n0pP8BHmMHWNmzt0aum8obm08vVjfrtnxztR0mXHzTvaZdjVRMTLkZo6yiFen9iZ2pqY/wBP93rN
|
|
3kPY+/LfPX1rE/u9XzN3HfqzdO6vmTuIZ7m7Hc3Bnu8t7TR/vHBP9E/y9Pu837SV31umn+if5Rfi
|
|
/j/01MMb1hjkrtKzBG0bMsmOZY11tOYamr6Und0LUc7XT7u3rJPqL8er9lcPhcFpbzyWm39v7O00
|
|
+FYvA4Zpsc94xxu227jv1IAgAAAAAAAAABKAAAASgASgBIgBIgBIgBIhIAAAAAAAAAAAAAAAAAAC
|
|
UACUJAAAAAAAAAAAABIAAAAAAAAAAAAAAAAAAAAg3AEbomQZbo3YzLGbAz3RNlc3YzcFs2YzdVN2
|
|
M2Bdzom6nmNwW86JurTAMuY3REJ2BB1ZRVMVBhsbSsiqeUFXLucq3lTygp5TlXcpygp5TlXcpygp
|
|
5TlXcqOUFXKjlXcrGYBXysdlswiYBVMdUTCyY6sZBWxlnMMZgGLGZZSwkDdHMiWO4MuY5mEyjcFn
|
|
N1OdVzHMC3nTzqeY5gX85zqOZPMC+Lqdbk20eb/RKOZr8QybaK/XvtH7iZ9aGlp2luzT3fg19NHS
|
|
OjbmPcYX67XH1XSZ9XIzRvMuzrK7zLkZYmYnciunb9lZ5dTk+OP+71cXeP8AZnJ/ip2nf3J/l6iL
|
|
/Fu5L9bMWZczXi6YuIbEWTzKIuyiwLt3nuO25uI4a/hx7/rLuczg8TicvFLbfdpEK6+NPH/phhjo
|
|
stLGkctUWnoxrrU3j1cnWTzZq1jzl1clo5Zcu8c+txR63iP3Tn6pv4+g4o5cVI9IiGe7CJ2iE7t3
|
|
GyN2O6dwSINwSISAlAAlACRAAlAAlACRACRCQAAAAAAAAAASgASISAAAAAAAAAAAAACQAAAAAAAA
|
|
AAAAAASAAAAAAAAAAAAAAAAIAAAQCAJljuljsCJlhMs9mOwMJYys5TkBVsjZdyHICrZPKt5E8oK4
|
|
qmKrOVOwMIqyirPY2Bjyp2ZbAI2NmSARsbMgEbI2ZAMdjZICNkbMkSCNmOzJEgx2YyzljMAwlhKy
|
|
WEwCuWErJhhMArlhLOWEgxljMpljIImWMyTKJA3N0IBO5vux3NwZbnMx3NwZczT4jf3MdPW27a3a
|
|
fJOq1XNP2KdIRfi+J2trSYfcjeF+Wm1OicVeWIiN9kai8xjY12ORqultnI1Ecsujq79XP1FovWYI
|
|
rTgeq+j8QrWZ+3Mx+r2UXeC0WG2Ti2kiN5mL807eUREvbzbaejefHJv62Iv8WUXa0WTFhVtRdlF2
|
|
rz9WUXBtc7jR9dqc2T1ttHyhvZMvJitb0jdq6XHNcNenWVN3028U99WRj6Kb02be3Tq18/SN2Lpc
|
|
3UdN9nOmZrqKX/DaJ/d0svvTLRzV3jomK6+Pd1vvWJj0ZczT0mXxNJht60hfFnQ4qu3N1cWTEgs3
|
|
Tur5k7gz3N2O5uDM3Y7m4MtxBuCQASIASIASAAAAAAACRCQAAAAAAAAEoSAAAAAAAAAAAlAAlCQA
|
|
AAAAAAAAAAASAAAAAAAAAAAAIASgAAAEJAQJQCNkbMgGOyOVnsAw5TlZ7GwMOVPKy2NgY7GzIBGx
|
|
skA2AAAAAAAAAAQkBAEghEskAxYzDPZGwK5hjMLJhjMAqmGEwumrCagomFcw2JqqtUFEsLLrV82F
|
|
o7gqljKyYYTGwMZRKUSCAQAboJnaN5Bjkneu0d5W4ccViIiOzHFWbTzNumP1Zarr8eeRMbxDW1Mx
|
|
NO67NbkhzNVnmInqzaOZrL93JyZeV0M1++7S02jvxDWxhxx033tPpC8Z6rrezWjmZyazJG2/u03h
|
|
2vFibTHoqvamiwVwY+nLGzV0+SZ1Mx8G0/45tOhzJ5lXMc3UVXRdlF1HP+iYsDPLPPy49/tz1+Te
|
|
pSIr0ho6ak5Ms5J8o2q6NImOrHV7XX488ypzTtHXo0s9t6zG7c1G1qz6ubeZiZ3UatXJG3yauSO7
|
|
cvMTEx5tPLb3prPRMVr0HB8vicNxf0+7+kt+LOJwTJyY/Bnz3tH93X36N58cWvq6LSyiyndMSlC7
|
|
mZcymLJiwLosmJVRLKLAtiU7q4lMSCzc3YxJuDMRuAlKAEgAAAlAkAAAAAABKAEgAAAAAJAAAAAA
|
|
AAAAAAAEgAAAAAAAAAAAAAkAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAhIAAACAAAASgAAAAAAEAAAA
|
|
hGzJAImGMwzQDDZjNVuyNgUTVhNGxysZqDVmiu1G5NN2M4waM0+DCaN2cbGcQNGaMZq3JxMJxA1J
|
|
qx2bU4kU09slorWNwa20z02RXHbJbl26QvtFovbHWkxEdJt5y2MOHlr2U1W3jx+1hiw8vSO63lmI
|
|
XRTaEWmtY6snRHO1VpmJ+DjavpSZl2s8b7y4HFcnh0n0gha5ebJN55KRM2mdoiPN6fh+kpwXh0Wy
|
|
RHj5Otp/s5Ps1p62y31+em9aTMYt/OfVfxTiPjZ52naI7fBrI5t66xz5+a1rW7yx0eSL6iZjtEOX
|
|
qNbSletom3lENjh2fbHzbbWt3iVozruc+5ztWubf4M4ybpQ2Oboyrva0Vjza8WdDR4OkXt3n9ldX
|
|
kaePP9VtYqctYhdvt5oivTeCZ2YOxXk6ubqMfV0b9mrljfqlFcq88k7z2U5axeItDa1OPessuC8P
|
|
ya7XRWYnwqdbT/ZMilvIu4dpslNdixXja8Y5tt85djZdbDWnGOesRtXFtuw6T27No5Kx2OrKYQlC
|
|
ExKJgBnEpiyvdlEgsizKLKollFgWxLKJVRLKJBbEp3VxLKJBnuMWQJEbpBIAAAJAAAABIAAAAAAA
|
|
lAJAAAAAAAAAAAAAASAAAAAAAAAAAAAJAAAABAJABAlAAAAAAAAAAAAAAAAAAAAAAAAIAAAAAAAA
|
|
AAABAJQAAAAgAABAAI2EoBGyJhkgGPKxmqxAKpownHC+YRMdN5BrTj67R3bOn01o7p01Iv71u89o
|
|
b9a7LfBTfS1vWI2jf12VfQPSW8KX2mas+NC2iv6xMNfJpMnLtEbuuxtMRCtzF55NR5rPps1N/ctP
|
|
y6uHreE6nXZ4pak48X3rT06fB7fNeI33cbX6mI32R/MWu7XF116aDSRhxbRERs8f499bkyZeeKae
|
|
kzE2mdon81/tfxDLGOunwbzlzbx08oaHBvZHJlx48mrvaa94pu04y617576rNGLRRM0397JEd/lu
|
|
9Dw/S3x4qxffo6mm4NjwUiKY4iI9Ib1dHFY6QIaNabbrYrLfrpJtaK1rMzPZb/s+05IpP59OyLeJ
|
|
k7eNfRaOc1ue32I7fGXYpi5Y77M8OGMeOKxHSFsU3Y29deZMzirl6dlVvhLatCjJHeYQv1rXnps1
|
|
8k9/VsW6qLVmZIi1rzitlvFKRvaZ2h6TSaenC9FFY+3brM+sqeG8Prp4+kZ+lvuxPkr1mqm95nfp
|
|
DXM459676a2q1dsV7XietvNno78+CJn1cjX6mOeIm0bR33dfRU5NJjidt9t5afjG/V6JZ7I2QMNh
|
|
nyo2BhsMuVG3wAhMSbbQRAMolnE+iuGUSCyJZRKuGUSCyJZK4llEgyZMYTuCUsYSCQASISAAAlCQ
|
|
AAAAAAEoASCASAAAAAAAAAAAAlACRACQAAAAAAAAAEgCEoASCAAAAAAAAAAAAAAAAAAAAAAABAAA
|
|
AAAAAAAISAIAAAAAAQAAACASgAAAQJAQAAhIDHZhln3do7z0WS18mWsajHjmes7pg3dNi5aRMNqO
|
|
yvDHTpPRaigHZhN4hHRlaVN59JY3zRENLUavaO+yq0iNVlitJ6vNcR1MVi0zO0era1/Ea0rPvbz5
|
|
PM5MWp45qvo2GZrhmfrsnpHpHzTCseEcM/2vrr8Q1Eb4qzy44nziPN63HpYiIiI7LNHoqabBTFii
|
|
IpSNohuVxrKtWMEejPwY9G1FFmHB4mWJn7MdfnIM9JpIx15to5pbUaas/a6rqViI7MxPxqX0UT1r
|
|
O3wVzpbR2hviP5i03Y5s6a879FNtHljydhExCv8AMTPJXBnRZbz0iG5ptFjwe/l96zctMVamTJtE
|
|
yTMibu1VrdTzRMR0j0ed4lr64MVpm0RERvMz5NvX62uOJ69XhOKX1HH9bHDtFvNYnfJeOy0Z2ojX
|
|
6jjnEq6fRUmccTvN/J9H0eKcOnx45neaxEbubwHgOHg+milI3vP2resu3Wu0JQmITsmISDHZHKz2
|
|
JgFc1RMLJhGwK9iIZ7MZgEdgmAEwyiWCdwWRLKJVxKYsC2JTuriWUSDNlEsIlMAySx3SCRCQSIAS
|
|
AAACRACQAAAAAAASIASAAAAAAAAAAAAAAACRACRACQASIAAAAAAAAAAAAAAAAAAAAAAAAQCUAAAA
|
|
AAAAAAIAAAAAAAAQAAAAAACBICBICAAEJAQJQCJcLjuS2ny6fPG/LWdpd1o8T0X07SXx/e7wCdJx
|
|
Wa0jmneHQpxPDMdZmJfNtZm49weZrh0/j4o7VtSZ2+Uw0/8A7o49k92vBLc/ntFohFW9PqGXimOI
|
|
6Tu1L8T3eCx6r2t1O3JwvHjifO99v7t/Bwf2l1PXU6rS6eJ8qUm8x+so5TsekzcSjbvs4mt4rzW5
|
|
K2mbT0itesy2cHsvbvqtbmyz5xERWP2jd1tJwrTaONsOKtZ8585+cnDrzmn4Rq+IZObUROHD32n7
|
|
Vv8A0ej0uhxaXFGPFSK1j0bkY4jyZRVZVXFGUVWbGwKsk8mObekNrSW3pWf1a2aYjHbm7bNnQ1id
|
|
PW0TvuDdhJEbQABMsLW2R0ZTMQrvfbz2YWzVhpanUxEd0dWkW5c8R5uXxDX1w4pnfr5Q19XxKuOJ
|
|
2neXltVqtVxbV/RdJ715+1bypANfiOu1HENV9C0MTfNeesx2rD1PAeBYuE6aKx72W3W9/WVnBuB4
|
|
eF4dqRzZbdb5J72l160WVK02ZxCYhOwI23TsnY2BGxsnYBjsiYZsZBjMMZZSgGEolMsQDdG6NwZ7
|
|
piVe6YkFsSziVMWZRILolMSriWUSCyJTuwhMSDMRCQSI3SAlACRCQAAEoAEoASAAAAAAAAACUACR
|
|
ACQAAAAAAAAAAAAASAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAABAAAAAAAAAAAAACBKAAAAAAAQ
|
|
JQAAAhICEbJAYTWJ7wx8KvpC0BV4ceieWGewDHlNmWwCNjZICNhIDmcZredBecdpiY69FXCOLW+i
|
|
UiZidukulmxxlx2paN4mNng+K4+I8Hy2yaTfl37TXetoCPfRxfp1qi3F48ofKMvtvxak8s6LDv61
|
|
rZji9rPaLUf5PC+bfttS0q8q3p9W/wBrRMdpUZuKdN99nzvFqPbTVz7nD8OKs+do2/mW3h4D7Xaq
|
|
ZnPrtNpqz35aRaYOHY9Zk4pNt9rR+rl6zi+OnS+WN57Rv1lXp/YrNaYtruL6zNPnGO3hxP6O5w/2
|
|
f0HDuun09Yv55Le9afznqcOvO4tBreMTHu30unnva0bWt8on+70nDuE4OHYYx4Kbesz3tPrMuhGO
|
|
IjpDOKrK9YVpsyiGUQnYGOyUgI2SlAIEmwMWMs9kTAMJYzDOYRMArmGErZhhMArlHmzmGMwDE3Ts
|
|
bAbs4swj5pgFkSziVcM4BZEsolXDKAZwyhjCYBkACQhIAAAAAAAJAAAAAAAAAAAAAAAAAAAShIAA
|
|
AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
|
|
BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
|
|
SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
|
|
lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
|
|
2Q==`;var hg={};fr(hg,{author:()=>i4,browser:()=>a4,bugs:()=>o4,default:()=>Qae,description:()=>e4,devDependencies:()=>f4,engines:()=>c4,homepage:()=>l4,keywords:()=>p4,license:()=>u4,main:()=>n4,module:()=>r4,name:()=>Q6,repository:()=>h4,scripts:()=>d4,sideEffects:()=>t4,types:()=>s4,version:()=>dg});var Q6="@vladmandic/human",dg="1.1.0",e4="Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",t4=!1,n4="dist/human.node.js",r4="dist/human.esm.js",a4="dist/human.esm.js",s4="types/human.d.ts",i4="Vladimir Mandic <mandic00@live.com>",o4={url:"https://github.com/vladmandic/human/issues"},l4="https://vladmandic.github.io/human/demo/index.html",u4="MIT",c4={node:">=12.0.0"},h4={type:"git",url:"git+https://github.com/vladmandic/human.git"},d4={start:"node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",dev:"npm install && node server/serve.js",build:"rimraf dist/* && rimraf types/* && node server/build.js && node server/changelog.js",lint:"eslint src demo server",test:"eslint src demo server"},p4=["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"],f4={"@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:"=0.9.0",eslint:"^7.21.0","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",typescript:"^4.2.3"},Qae={name:Q6,version:dg,description:e4,sideEffects:t4,main:n4,module:r4,browser:a4,types:s4,author:i4,bugs:o4,homepage:l4,license:u4,engines:c4,repository:h4,scripts:d4,keywords:p4,devDependencies:f4};var pg={};fr(pg,{all:()=>tse,body:()=>g4,canvas:()=>ese,face:()=>y4,gesture:()=>A4,hand:()=>x4,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 N0(e,t,n){e.fillStyle=ce.color,e.beginPath(),e.arc(t,n,ce.pointSize,0,2*Math.PI),e.fill()}function m4(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 fg(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.lineTo(n[0],parseInt(n[1]));e.stroke(),ce.fillPolygons&&(e.closePath(),e.fill())}}function S0(e,t=[]){if(!(t===void 0||t.length===0)){if(!ce.useCurves||t.length<=2){fg(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 A4(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 y4(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&&m4(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:${Math.trunc(100*r.angle.pitch)/100}`),a.length===0&&a.push("face"),n.fillStyle=ce.color;for(let s=a.length-1;s>=0;s--){let i=Math.max(r.box[0],0),o=s*ce.lineHeight+r.box[1];ce.shadowColor&&ce.shadowColor!==""&&(n.fillStyle=ce.shadowColor,n.fillText(a[s],i+5,o+16)),n.fillStyle=ce.labelColor,n.fillText(a[s],i+4,o+15)}if(n.lineWidth=1,r.mesh){if(ce.drawPoints)for(let s of r.mesh)n.fillStyle=ce.useDepth?`rgba(${127.5+2*s[2]}, ${127.5-2*s[2]}, 255, 0.5)`:ce.color,N0(n,s[0],s[1]);if(ce.drawPolygons){for(let s=0;s<Fi.length/3;s++){let i=[Fi[s*3+0],Fi[s*3+1],Fi[s*3+2]].map(o=>r.mesh[o]);n.strokeStyle=ce.useDepth?`rgba(${127.5+2*i[0][2]}, ${127.5-2*i[0][2]}, 255, 0.3)`:ce.color,n.fillStyle=ce.useDepth?`rgba(${127.5+2*i[0][2]}, ${127.5-2*i[0][2]}, 255, 0.3)`:ce.color,n.lineWidth=1,fg(n,i)}if(r.annotations&&r.annotations.leftEyeIris){n.strokeStyle=ce.useDepth?"rgba(255, 200, 255, 0.3)":ce.color,n.beginPath();let s=Math.abs(r.annotations.leftEyeIris[3][0]-r.annotations.leftEyeIris[1][0])/2,i=Math.abs(r.annotations.leftEyeIris[4][1]-r.annotations.leftEyeIris[2][1])/2;n.ellipse(r.annotations.leftEyeIris[0][0],r.annotations.leftEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ce.fillPolygons&&(n.fillStyle=ce.useDepth?"rgba(255, 255, 200, 0.3)":ce.color,n.fill())}if(r.annotations&&r.annotations.rightEyeIris){n.strokeStyle=ce.useDepth?"rgba(255, 200, 255, 0.3)":ce.color,n.beginPath();let s=Math.abs(r.annotations.rightEyeIris[3][0]-r.annotations.rightEyeIris[1][0])/2,i=Math.abs(r.annotations.rightEyeIris[4][1]-r.annotations.rightEyeIris[2][1])/2;n.ellipse(r.annotations.rightEyeIris[0][0],r.annotations.rightEyeIris[0][1],s,i,0,0,2*Math.PI),n.stroke(),ce.fillPolygons&&(n.fillStyle=ce.useDepth?"rgba(255, 255, 200, 0.3)":ce.color,n.fill())}}}}}var ts=[];async function g4(e,t){if(!t||!e||!(e instanceof HTMLCanvasElement))return;let n=e.getContext("2d");if(!!n){n.lineJoin="round";for(let r=0;r<t.length;r++){if(!ts[r]&&ce.bufferedOutput&&(ts[r]={...t[r]}),n.strokeStyle=ce.color,n.lineWidth=ce.lineWidth,ce.drawPoints)for(let a=0;a<t[r].keypoints.length;a++)n.fillStyle=ce.useDepth&&t[r].keypoints[a].position.z?`rgba(${127.5+2*t[r].keypoints[a].position.z}, ${127.5-2*t[r].keypoints[a].position.z}, 255, 0.5)`:ce.color,ce.bufferedOutput?(ts[r].keypoints[a][0]=(ts[r].keypoints[a][0]+t[r].keypoints[a].position.x)/2,ts[r].keypoints[a][1]=(ts[r].keypoints[a][1]+t[r].keypoints[a].position.y)/2,N0(n,ts[r].keypoints[a][0],ts[r].keypoints[a][1])):N0(n,t[r].keypoints[a].position.x,t[r].keypoints[a].position.y);if(ce.drawLabels){n.font=ce.font;for(let a of t[r].keypoints)n.fillStyle=ce.useDepth&&a.position.z?`rgba(${127.5+2*a.position.z}, ${127.5-2*a.position.z}, 255, 0.5)`:ce.color,n.fillText(`${a.part}`,a.position.x+4,a.position.y+4)}if(ce.drawPolygons){let a,s=[];s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),s.length===5&&fg(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftKnee"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftAnkle"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftHeel"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftFoot"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),S0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightHip"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightKnee"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightAnkle"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightHeel"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightFoot"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),S0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="leftShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftElbow"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftWrist"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="leftPalm"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),S0(n,s),s.length=0,a=t[r].keypoints.find(i=>i.part==="rightShoulder"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightElbow"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightWrist"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),a=t[r].keypoints.find(i=>i.part==="rightPalm"),a&&a.score>mt.body.scoreThreshold&&s.push([a.position.x,a.position.y]),S0(n,s)}}}}async function x4(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,m4(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]}, ${127.5-2*a[2]}, 255, 0.5)`:ce.color,N0(n,a[0],a[1]);if(ce.drawPolygons){let a=s=>{if(!!s)for(let i=0;i<s.length;i++)n.lineWidth=ce.lineWidth,n.beginPath(),n.strokeStyle=ce.useDepth?`rgba(${127.5+2*s[i][2]}, ${127.5-2*s[i][2]}, 255, 0.5)`:ce.color,n.moveTo(s[i>0?i-1:0][0],s[i>0?i-1:0][1]),n.lineTo(s[i][0],s[i][1]),n.stroke()};a(r.annotations.indexFinger),a(r.annotations.middleFinger),a(r.annotations.ringFinger),a(r.annotations.pinky),a(r.annotations.thumb)}}}}async function ese(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function tse(e,t){!t||!e||e instanceof HTMLCanvasElement&&(y4(e,t.face),g4(e,t.body),x4(e,t.hand),A4(e,t.gesture))}var dt=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Bc(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,r)=>(Object.keys(r||{}).forEach(a=>{let s=n[a],i=r[a];Array.isArray(s)&&Array.isArray(i)?n[a]=s.concat(...i):t(s)&&t(i)?n[a]=Bc(s,i):n[a]=i}),n),{})}var T0,Xe,Xl,Vc,Uc,$i,Ut,C0,Hc,E0,jc,R0,F0,M0,w4=class{constructor(t={}){T0.set(this,void 0);Xe.set(this,void 0);Xl.set(this,void 0);Vc.set(this,void 0);Uc.set(this,void 0);$i.set(this,void 0);Ut.set(this,(...t)=>{if(!ve(this,Vc))return;let n=this.tf.engine().state.numTensors,r=ve(this,Xl);ea(this,Xl,n);let a=n-r;a!==0&&Ce(...t,a)});C0.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 this.tf.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Hc.set(this,async(t=!1)=>{if(this.config.backend&&this.config.backend!==""&&t||this.tf.getBackend()!==this.config.backend){let n=dt();if(this.state="backend",this.config.backend&&this.config.backend!==""){if(this.config.debug&&Ce("setting backend:",this.config.backend),this.config.backend==="wasm"){this.config.debug&&Ce("wasm path:",this.config.wasmPath),this.tf.setWasmPaths(this.config.wasmPath);let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&Ce(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),r||Ce("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&l6();try{await this.tf.setBackend(this.config.backend)}catch(r){Ce("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"){this.config.deallocate&&(Ce("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&Ce(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),ve(this,Xe).backend=Math.trunc(dt()-n)}});E0.set(this,t=>{if(!t||t.length<300)return{};let n=(s,i,o,l)=>Math.atan2(l-i,o-s),r=s=>Math.abs(s*180/Math.PI%360);return{roll:n(t[33][0],t[33][1],t[263][0],t[263][1]),yaw:n(t[33][0],t[33][2],t[263][0],t[263][2]),pitch:n(t[10][1],t[10][2],t[152][1],t[152][2])}});jc.set(this,async t=>{var u,c,h,d,p,f;let n,r,a,s,i,o=[];this.state="run:face",n=dt();let l=await((u=this.models.face)==null?void 0:u.estimateFaces(t,this.config));ve(this,Xe).face=Math.trunc(dt()-n);for(let m of l){if(ve(this,Ut).call(this,"Get Face"),!m.image||m.image.isDisposedInternal){Ce("Face object is disposed:",m.image);continue}let A=ve(this,E0).call(this,m.mesh);ve(this,Ut).call(this,"Start Age:"),this.config.async?r=this.config.face.age.enabled?k2(m.image,this.config):{}:(this.state="run:age",n=dt(),r=this.config.face.age.enabled?await k2(m.image,this.config):{},ve(this,Xe).age=Math.trunc(dt()-n)),ve(this,Ut).call(this,"Start Gender:"),this.config.async?a=this.config.face.gender.enabled?E2(m.image,this.config):{}:(this.state="run:gender",n=dt(),a=this.config.face.gender.enabled?await E2(m.image,this.config):{},ve(this,Xe).gender=Math.trunc(dt()-n)),ve(this,Ut).call(this,"Start Emotion:"),this.config.async?s=this.config.face.emotion.enabled?D2(m.image,this.config):{}:(this.state="run:emotion",n=dt(),s=this.config.face.emotion.enabled?await D2(m.image,this.config):{},ve(this,Xe).emotion=Math.trunc(dt()-n)),ve(this,Ut).call(this,"End Emotion:"),ve(this,Ut).call(this,"Start Embedding:"),this.config.async?i=this.config.face.embedding.enabled?P2(m,this.config):[]:(this.state="run:embedding",n=dt(),i=this.config.face.embedding.enabled?await P2(m,this.config):[],ve(this,Xe).embedding=Math.trunc(dt()-n)),ve(this,Ut).call(this,"End Emotion:"),this.config.async&&([r,a,s,i]=await Promise.all([r,a,s,i])),ve(this,Ut).call(this,"Finish Face:"),!this.config.face.iris.enabled&&((c=m==null?void 0:m.annotations)==null?void 0:c.leftEyeIris)&&((h=m==null?void 0:m.annotations)==null?void 0:h.rightEyeIris)&&(delete m.annotations.leftEyeIris,delete m.annotations.rightEyeIris);let y=((d=m.annotations)==null?void 0:d.leftEyeIris)&&((p=m.annotations)==null?void 0:p.rightEyeIris)?11.7*Math.max(Math.abs(m.annotations.leftEyeIris[3][0]-m.annotations.leftEyeIris[1][0]),Math.abs(m.annotations.rightEyeIris[4][1]-m.annotations.rightEyeIris[2][1])):0;o.push({...m,age:r.age,gender:a.gender,genderConfidence:a.confidence,emotion:s,embedding:i,iris:y!==0?Math.trunc(y)/100:0,angle:A,tensor:this.config.face.detector.return?m.image.squeeze():null}),(f=m.image)==null||f.dispose(),ve(this,Ut).call(this,"End Face")}return ve(this,Ut).call(this,"End FaceMesh:"),this.config.async&&(ve(this,Xe).face&&delete ve(this,Xe).face,ve(this,Xe).age&&delete ve(this,Xe).age,ve(this,Xe).gender&&delete ve(this,Xe).gender,ve(this,Xe).emotion&&delete ve(this,Xe).emotion),o});R0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(k0);break;case"full":n=await t(I0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});F0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+k0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+I0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));M0.set(this,async()=>{let t=i=>Buffer.from(i,"base64"),n=this.config.warmup==="face"?t(k0):t(I0),r=(void 0).decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=yh,this.draw=pg,ea(this,T0,hg),this.version=dg,this.config=Bc(mt,t),this.state="idle",ea(this,Xl,0),ea(this,Vc,!1),ea(this,Uc,!1),ea(this,$i,!0),ea(this,Xe,{}),this.models={face:null,posenet:null,blazepose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null},this.image=n=>cg(n,this.config),this.classes={facemesh:mg,age:b2,gender:I2,emotion:R2,body:this.config.body.modelPath.includes("posenet")?Z2:og,hand:rg},this.sysinfo=Eg()}profileData(){return this.config.profile?_2:{}}simmilarity(t,n){return this.config.face.embedding.enabled?w6(t,n):0}enhance(t){return this.config.face.embedding.enabled?z2(t):null}async load(t=null){this.state="load";let n=dt();t&&(this.config=Bc(this.config,t)),ve(this,$i)&&(this.config.debug&&Ce(`version: ${this.version}`),this.config.debug&&Ce(`tfjs version: ${this.tf.version_core}`),this.config.debug&&Ce("platform:",this.sysinfo.platform),this.config.debug&&Ce("agent:",this.sysinfo.agent),await ve(this,Hc).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&Ce("configuration:",this.config),this.config.debug&&Ce("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose]=await Promise.all([this.models.face||(this.config.face.enabled?mg.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?v2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?C2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?$2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?O2(this.config):null),this.models.handpose||(this.config.hand.enabled?ig(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?J2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?lg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await mg.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await v2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await C2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await $2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await O2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await ig(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await J2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await lg(this.config))),ve(this,$i)&&(this.config.debug&&Ce("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ea(this,$i,!1));let r=Math.trunc(dt()-n);r>(ve(this,Xe).load||0)&&(ve(this,Xe).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,C0).call(this,t);s&&(Ce(s,t),r({error:s}));let i=dt();await ve(this,Hc).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),ve(this,Ut).call(this,"Start Scope:"),a=dt();let o=cg(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,Xe).image=Math.trunc(dt()-a),ve(this,Ut).call(this,"Get Image:");let l,u,c;this.config.async?(c=this.config.face.enabled?ve(this,jc).call(this,o.tensor):[],ve(this,Xe).face&&delete ve(this,Xe).face):(this.state="run:face",a=dt(),c=this.config.face.enabled?await ve(this,jc).call(this,o.tensor):[],ve(this,Xe).face=Math.trunc(dt()-a)),ve(this,Ut).call(this,"Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?(d=this.models.posenet)==null?void 0:d.estimatePoses(o.tensor,this.config):[]:l=this.config.body.enabled?ug(o.tensor,this.config):[],ve(this,Xe).body&&delete ve(this,Xe).body):(this.state="run:body",a=dt(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await((p=this.models.posenet)==null?void 0:p.estimatePoses(o.tensor,this.config)):[]:l=this.config.body.enabled?await ug(o.tensor,this.config):[],ve(this,Xe).body=Math.trunc(dt()-a)),ve(this,Ut).call(this,"End Body:"),ve(this,Ut).call(this,"Start Hand:"),this.config.async?(u=this.config.hand.enabled?(f=this.models.handpose)==null?void 0:f.estimateHands(o.tensor,this.config):[],ve(this,Xe).hand&&delete ve(this,Xe).hand):(this.state="run:hand",a=dt(),u=this.config.hand.enabled?await((m=this.models.handpose)==null?void 0:m.estimateHands(o.tensor,this.config)):[],ve(this,Xe).hand=Math.trunc(dt()-a)),ve(this,Ut).call(this,"End Hand:"),this.config.async&&([c,l,u]=await Promise.all([c,l,u])),o.tensor.dispose(),this.config.scoped&&this.tf.engine().endScope(),ve(this,Ut).call(this,"End Scope:");let h=[];this.config.gesture.enabled&&(a=dt(),h=[...K6(c),...X6(l),...Y6(u),...Z6(c)],this.config.async?ve(this,Xe).gesture&&delete ve(this,Xe).gesture:ve(this,Xe).gesture=Math.trunc(dt()-a)),ve(this,Xe).total=Math.trunc(dt()-i),this.state="idle",r({face:c,body:l,hand:u,gesture:h,performance:ve(this,Xe),canvas:o.canvas})})}async warmup(t){let n=dt();t&&(this.config=Bc(this.config,t));let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ve(this,R0).call(this):typeof Image!="undefined"?a=await ve(this,F0).call(this):a=await ve(this,M0).call(this),this.config.videoOptimized=r;let s=dt();return this.config.debug&&Ce("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};T0=new WeakMap,Xe=new WeakMap,Xl=new WeakMap,Vc=new WeakMap,Uc=new WeakMap,$i=new WeakMap,Ut=new WeakMap,C0=new WeakMap,Hc=new WeakMap,E0=new WeakMap,jc=new WeakMap,R0=new WeakMap,F0=new WeakMap,M0=new WeakMap;export{w4 as default};
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|
|
//# sourceMappingURL=human.esm.js.map
|