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

7788 lines
1.5 MiB
Raw Blame History

/*
Human
homepage: <https://github.com/vladmandic/human>
author: <https://github.com/vladmandic>'
*/
var Human=(()=>{var t2=Object.defineProperty;var RT=(e,t,n)=>t in e?t2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var $T=e=>t2(e,"__esModule",{value:!0});var Yo=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof require!="undefined"?require:t)[n]}):e)(function(e){if(typeof require!="undefined")return require.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')});var Mc=(e,t)=>{$T(e);for(var n in t)t2(e,n,{get:t[n],enumerable:!0})};var Te=(e,t,n)=>(RT(e,typeof t!="symbol"?t+"":t,n),n),u5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var zc=(e,t,n)=>(u5(e,t,"read from private field"),n?n.call(e):t.get(e)),Lc=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Bc=(e,t,n,s)=>(u5(e,t,"write to private field"),s?s.call(e,n):t.set(e,n),n);var Q0e={};Mc(Q0e,{Human:()=>W8,Models:()=>zp,default:()=>W8,defaults:()=>ba,env:()=>ie});function ct(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: ${r} expecting json file`);return r}function ae(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}var ot=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function n2(e,t,n="config",s=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")n2(e[r],t[r],r,s);else{let a=e&&typeof e[r]!="undefined";a||s.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let o=e&&typeof e[r]==typeof t[r];a&&!o&&s.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&s.length>0&&ae("invalid configuration",s),s}function fn(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,s)=>(Object.keys(s||{}).forEach(r=>{let a=n[r],o=s[r];Array.isArray(a)&&Array.isArray(o)?n[r]=a.concat(...o):t(a)&&t(o)?n[r]=fn(a,o):n[r]=o}),n),{})}var ba={backend:"",modelBasePath:"",wasmPath:"",debug:!0,async:!0,warmup:"full",cacheSensitivity:.75,skipFrame:!1,filter:{enabled:!0,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:11,minConfidence:.2,iouThreshold:.1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:12,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:13,minConfidence:.1}},body:{enabled:!0,modelPath:"movenet-lightning.json",detector:{modelPath:""},maxDetected:-1,minConfidence:.2,skipFrames:1},hand:{enabled:!0,rotation:!0,skipFrames:1,minConfidence:.55,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:15},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var Il={};Mc(Il,{Abs:()=>ti,Acos:()=>ql,Acosh:()=>Xl,AdadeltaOptimizer:()=>Ff,AdagradOptimizer:()=>Of,AdamOptimizer:()=>Mf,AdamaxOptimizer:()=>zf,Add:()=>qr,AddN:()=>ka,All:()=>Kl,Any:()=>Zl,ArgMax:()=>Ia,ArgMin:()=>Yl,Asin:()=>Jl,Asinh:()=>Ql,Atan:()=>eu,Atan2:()=>nu,Atanh:()=>tu,AvgPool:()=>Sa,AvgPool3D:()=>Hc,AvgPool3DGrad:()=>gh,AvgPoolGrad:()=>mh,BackendWasm:()=>xC,BatchMatMul:()=>Ca,BatchToSpaceND:()=>ni,Bincount:()=>yh,BroadcastArgs:()=>u2,BroadcastTo:()=>N5,Callback:()=>yk,CallbackList:()=>uw,Cast:()=>Ta,Ceil:()=>Na,ClipByValue:()=>Xr,Complex:()=>jc,ComplexAbs:()=>qc,Concat:()=>si,Conv2D:()=>Ea,Conv2DBackpropFilter:()=>Ah,Conv2DBackpropInput:()=>Ra,Conv3D:()=>Xc,Conv3DBackpropFilterV2:()=>xh,Conv3DBackpropInputV2:()=>bh,Cos:()=>$a,Cosh:()=>Da,CropAndResize:()=>ai,Cumsum:()=>ri,CustomCallback:()=>dw,DataStorage:()=>Vc,DenseBincount:()=>vh,DepthToSpace:()=>oi,DepthwiseConv2dNative:()=>_a,DepthwiseConv2dNativeBackpropFilter:()=>wh,DepthwiseConv2dNativeBackpropInput:()=>kh,Diag:()=>Ih,Dilation2D:()=>Kc,Dilation2DBackpropFilter:()=>Ch,Dilation2DBackpropInput:()=>Sh,ENV:()=>qs,EarlyStopping:()=>xk,Einsum:()=>Zc,Elu:()=>Fa,EluGrad:()=>Th,Environment:()=>C5,Equal:()=>ii,Erf:()=>su,Exp:()=>Oa,ExpandDims:()=>li,Expm1:()=>ui,FFT:()=>Nh,Fill:()=>ru,FlipLeftRight:()=>ci,Floor:()=>Ma,FloorDiv:()=>za,FromPixels:()=>ad,FusedBatchNorm:()=>La,FusedConv2D:()=>go,FusedDepthwiseConv2D:()=>yo,GPGPUContext:()=>Lm,GatherNd:()=>pi,GatherV2:()=>di,GraphModel:()=>Yk,Greater:()=>hi,GreaterEqual:()=>Ba,History:()=>cw,IFFT:()=>Eh,Identity:()=>Wa,Imag:()=>Yc,InputSpec:()=>Yt,IsFinite:()=>au,IsInf:()=>ou,IsNan:()=>iu,KernelBackend:()=>Gl,LRN:()=>Qc,LRNGrad:()=>$h,LayerVariable:()=>rw,LayersModel:()=>aa,LeakyRelu:()=>fi,Less:()=>mi,LessEqual:()=>gi,LinSpace:()=>Rh,Log:()=>Va,Log1p:()=>lu,LogSoftmax:()=>E5,LogicalAnd:()=>yi,LogicalNot:()=>uu,LogicalOr:()=>Jc,MathBackendWebGL:()=>ic,Max:()=>Ua,MaxPool:()=>Ha,MaxPool3D:()=>ed,MaxPool3DGrad:()=>_h,MaxPoolGrad:()=>Dh,MaxPoolWithArgmax:()=>Ph,Maximum:()=>Ga,Mean:()=>ja,Min:()=>qa,Minimum:()=>Xa,MirrorPad:()=>Ka,Mod:()=>cu,MomentumOptimizer:()=>Lf,Multinomial:()=>Fh,Multiply:()=>Za,Neg:()=>Ai,NonMaxSuppressionV3:()=>bi,NonMaxSuppressionV4:()=>du,NonMaxSuppressionV5:()=>vi,NotEqual:()=>xi,OP_SCOPE_SUFFIX:()=>G5,OneHot:()=>ki,OnesLike:()=>wi,Optimizer:()=>na,Pack:()=>Ii,PadV2:()=>Ya,Pool:()=>TN,Pow:()=>Ja,Prelu:()=>Qa,Prod:()=>Si,RMSPropOptimizer:()=>Bf,RNN:()=>Fr,Range:()=>pu,Rank:()=>f2,Real:()=>td,RealDiv:()=>Pa,Reciprocal:()=>hu,Reduction:()=>Vn,Relu:()=>eo,Relu6:()=>no,Reshape:()=>Ci,ResizeBilinear:()=>to,ResizeBilinearGrad:()=>Mh,ResizeNearestNeighbor:()=>fu,ResizeNearestNeighborGrad:()=>Oh,Reverse:()=>Ti,RotateWithOffset:()=>Bi,Round:()=>Ni,Rsqrt:()=>so,SGDOptimizer:()=>Fd,ScatterNd:()=>Ei,Select:()=>Ri,Selu:()=>mu,Sequential:()=>qu,Sigmoid:()=>ao,Sign:()=>gu,Sin:()=>ro,Sinh:()=>Di,Slice:()=>$i,Softmax:()=>lo,Softplus:()=>yu,SpaceToBatchND:()=>_i,SparseFillEmptyRows:()=>zh,SparseReshape:()=>Lh,SparseSegmentMean:()=>Bh,SparseSegmentSum:()=>Wh,SparseToDense:()=>nd,SplitV:()=>Pi,Sqrt:()=>oo,Square:()=>Au,SquaredDifference:()=>uo,Step:()=>fo,StridedSlice:()=>Fi,StringNGrams:()=>sd,StringSplit:()=>Vh,StringToHashBucketFast:()=>Uh,Sub:()=>co,Sum:()=>io,SymbolicTensor:()=>gr,Tan:()=>Oi,Tanh:()=>po,Tensor:()=>Ke,TensorBuffer:()=>tn,Tile:()=>Kr,TopK:()=>xu,Transform:()=>Mi,Transpose:()=>ho,Unique:()=>Gh,Unpack:()=>zi,UnsortedSegmentSum:()=>rd,Variable:()=>hd,ZerosLike:()=>Li,_FusedMatMul:()=>mo,abs:()=>Kt,acos:()=>V2,acosh:()=>U2,add:()=>ue,addN:()=>sf,all:()=>rf,any:()=>Ad,argMax:()=>_s,argMin:()=>G2,asin:()=>H2,asinh:()=>j2,atan:()=>q2,atan2:()=>X2,atanh:()=>K2,avgPool:()=>bd,avgPool3d:()=>J2,backend:()=>Tr,backend_util:()=>E,basicLSTMCell:()=>oR,batchNorm:()=>Zi,batchNorm2d:()=>_3,batchNorm3d:()=>P3,batchNorm4d:()=>F3,batchToSpaceND:()=>vd,bincount:()=>Q2,booleanMaskAsync:()=>h_,broadcastArgs:()=>O3,broadcastTo:()=>Eu,browser:()=>Xs,buffer:()=>We,callbacks:()=>TW,cast:()=>pe,ceil:()=>e1,clipByValue:()=>ss,clone:()=>ir,complex:()=>xo,concat:()=>kt,concat1d:()=>M3,concat2d:()=>Ru,concat3d:()=>z3,concat4d:()=>L3,constraints:()=>Mv,conv1d:()=>of,conv2d:()=>Qr,conv2dTranspose:()=>lf,conv3d:()=>n1,conv3dTranspose:()=>W3,copyRegisteredKernels:()=>RN,cos:()=>wd,cosh:()=>uf,cosineWindow:()=>E1,cumsum:()=>cf,customGrad:()=>Er,data:()=>Jk,denseBincount:()=>V3,deprecationWarn:()=>B2,depthToSpace:()=>s1,depthwiseConv2d:()=>$u,deregisterOp:()=>EW,device_util:()=>wu,diag:()=>OR,dilation2d:()=>r1,disableDeprecationWarnings:()=>wE,dispose:()=>ne,disposeVariables:()=>kE,div:()=>fe,divNoNan:()=>a1,dot:()=>U3,dropout:()=>cv,einsum:()=>G3,elu:()=>Du,enableDebugMode:()=>vE,enableProdMode:()=>N3,enclosingPowerOfTwo:()=>dv,engine:()=>ts,env:()=>Z,equal:()=>ms,erf:()=>o1,exp:()=>gs,expandDims:()=>Ht,expm1:()=>i1,eye:()=>l1,fft:()=>Dd,fill:()=>_u,findBackend:()=>W2,findBackendFactory:()=>TE,floor:()=>Pu,floorDiv:()=>nf,forceHalfFloat:()=>I4,fused:()=>Co,gather:()=>Yi,gatherND:()=>uv,gather_util:()=>_2,getBackend:()=>lr,getGradient:()=>c2,getKernel:()=>Hh,getKernelsForBackend:()=>Zr,gpgpu_util:()=>JI,grad:()=>p$,grads:()=>h$,greater:()=>rs,greaterEqual:()=>Io,ifft:()=>Lu,imag:()=>df,image:()=>$e,inTopKAsync:()=>I_,initializers:()=>Gv,input:()=>Pw,io:()=>es,irfft:()=>Sf,isFinite:()=>H3,isInf:()=>j3,isNaN:()=>u1,keep:()=>An,kernel_impls:()=>Zs,layers:()=>tw,leakyRelu:()=>kd,less:()=>pf,lessEqual:()=>So,linalg:()=>kv,linspace:()=>q3,loadGraphModel:()=>ut,loadLayersModel:()=>ML,localResponseNormalization:()=>c1,log:()=>ys,log1p:()=>Id,logSigmoid:()=>K3,logSoftmax:()=>ff,logSumExp:()=>h1,logicalAnd:()=>Ks,logicalNot:()=>Sd,logicalOr:()=>mf,logicalXor:()=>Q3,losses:()=>oF,matMul:()=>Xe,math:()=>l3,max:()=>Bn,maxPool:()=>Cd,maxPool3d:()=>f1,maxPoolWithArgmax:()=>ev,maximum:()=>Rr,mean:()=>zt,memory:()=>ef,meshgrid:()=>F$,metrics:()=>fk,min:()=>Td,minimum:()=>Fu,mirrorPad:()=>m1,mod:()=>g1,model:()=>FL,models:()=>mk,moments:()=>gf,movingAverage:()=>g_,mul:()=>L,multiRNNCell:()=>U$,multinomial:()=>tv,neg:()=>_t,nextFrame:()=>Wf,norm:()=>Ef,notEqual:()=>el,oneHot:()=>Cu,ones:()=>As,onesLike:()=>xs,op:()=>U,outerProduct:()=>X$,pad:()=>ur,pad1d:()=>Y$,pad2d:()=>Q$,pad3d:()=>tD,pad4d:()=>sD,pool:()=>nv,pow:()=>ea,prelu:()=>Ed,print:()=>n3,prod:()=>yf,profile:()=>IE,rand:()=>pD,randomGamma:()=>gD,randomNormal:()=>sv,randomUniform:()=>Ou,range:()=>Mu,ready:()=>tf,real:()=>Rd,reciprocal:()=>x1,registerBackend:()=>qi,registerCallbackConstructor:()=>zL,registerGradient:()=>R5,registerKernel:()=>Yr,registerOp:()=>NW,regularizers:()=>gk,relu:()=>cr,relu6:()=>Af,removeBackend:()=>CE,reshape:()=>G,reverse:()=>bs,reverse1d:()=>SD,reverse2d:()=>TD,reverse3d:()=>ED,reverse4d:()=>$D,rfft:()=>_d,round:()=>xf,rsqrt:()=>bf,scalar:()=>Ee,scatterND:()=>lv,scatter_util:()=>P2,selu:()=>vf,separableConv2d:()=>b1,sequential:()=>OL,serialization:()=>de,setBackend:()=>E3,setPlatform:()=>NE,setWasmPath:()=>ice,setWasmPaths:()=>vC,setWebGLContext:()=>Rm,setdiff1dAsync:()=>rv,sigmoid:()=>ns,sign:()=>v1,signal:()=>aF,sin:()=>wf,sinh:()=>kf,slice:()=>_e,slice1d:()=>If,slice2d:()=>w1,slice3d:()=>zu,slice4d:()=>$d,slice_util:()=>yn,softmax:()=>tl,softplus:()=>Ji,spaceToBatchND:()=>Nd,sparse:()=>Pd,sparseToDense:()=>N1,spectral:()=>rF,split:()=>xn,sqrt:()=>Cn,square:()=>vt,squaredDifference:()=>Cf,squeeze:()=>dt,stack:()=>Tn,step:()=>Bu,stridedSlice:()=>k1,string:()=>Pf,sub:()=>xe,sum:()=>we,sumOutType:()=>fd,tan:()=>I1,tanh:()=>Ki,tensor:()=>nn,tensor1d:()=>Zt,tensor2d:()=>dr,tensor3d:()=>u3,tensor4d:()=>s_,tensor5d:()=>r_,tensor6d:()=>a_,tensor_util:()=>ar,test_util:()=>S3,tidy:()=>j,tile:()=>Ps,time:()=>SE,topk:()=>S1,train:()=>sl,transpose:()=>et,truncatedNormal:()=>Tf,unique:()=>Nf,unregisterGradient:()=>EN,unregisterKernel:()=>NN,unsortedSegmentSum:()=>C1,unstack:()=>Wn,upcastType:()=>Ln,util:()=>v,valueAndGrad:()=>f$,valueAndGrads:()=>m$,variable:()=>av,variableGrads:()=>X3,version:()=>Qme,version_converter:()=>DV,version_core:()=>Qh,version_layers:()=>cy,version_wasm:()=>lce,version_webgl:()=>GY,webgl:()=>HY,webgl_util:()=>wI,webgpu:()=>r6,where:()=>Pn,whereAsync:()=>T1,zeros:()=>jt,zerosLike:()=>tt});var DT=Object.create,ch=Object.defineProperty,_T=Object.getOwnPropertyDescriptor,PT=Object.getOwnPropertyNames,FT=Object.getPrototypeOf,OT=Object.prototype.hasOwnProperty,c5=e=>ch(e,"__esModule",{value:!0}),Ul=(e=>typeof Yo!="undefined"?Yo:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof Yo!="undefined"?Yo:t)[n]}):e)(function(e){if(typeof Yo!="undefined")return Yo.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')}),Dt=(e,t)=>function(){return t||(0,e[Object.keys(e)[0]])((t={exports:{}}).exports,t),t.exports},Le=(e,t)=>{c5(e);for(var n in t)ch(e,n,{get:t[n],enumerable:!0})},MT=(e,t,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of PT(t))!OT.call(e,s)&&s!=="default"&&ch(e,s,{get:()=>t[s],enumerable:!(n=_T(t,s))||n.enumerable});return e},Jo=e=>MT(c5(ch(e!=null?DT(FT(e)):{},"default",e&&e.__esModule&&"default"in e?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),zT=Dt({"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(e,t){t.exports=s;var n=null;try{n=new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0,97,115,109,1,0,0,0,1,13,2,96,0,1,127,96,4,127,127,127,127,1,127,3,7,6,0,1,1,1,1,1,6,6,1,127,1,65,0,11,7,50,6,3,109,117,108,0,1,5,100,105,118,95,115,0,2,5,100,105,118,95,117,0,3,5,114,101,109,95,115,0,4,5,114,101,109,95,117,0,5,8,103,101,116,95,104,105,103,104,0,0,10,191,1,6,4,0,35,0,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,126,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,127,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,128,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,129,34,4,66,32,135,167,36,0,32,4,167,11,36,1,1,126,32,0,173,32,1,173,66,32,134,132,32,2,173,32,3,173,66,32,134,132,130,34,4,66,32,135,167,36,0,32,4,167,11])),{}).exports}catch(D){}function s(D,T,O){this.low=D|0,this.high=T|0,this.unsigned=!!O}s.prototype.__isLong__,Object.defineProperty(s.prototype,"__isLong__",{value:!0});function r(D){return(D&&D.__isLong__)===!0}s.isLong=r;var a={},o={};function i(D,T){var O,B,H;return T?(D>>>=0,(H=0<=D&&D<256)&&(B=o[D],B)?B:(O=c(D,(D|0)<0?-1:0,!0),H&&(o[D]=O),O)):(D|=0,(H=-128<=D&&D<128)&&(B=a[D],B)?B:(O=c(D,D<0?-1:0,!1),H&&(a[D]=O),O))}s.fromInt=i;function l(D,T){if(isNaN(D))return T?b:x;if(T){if(D<0)return b;if(D>=g)return R}else{if(D<=-y)return P;if(D+1>=y)return N}return D<0?l(-D,T).neg():c(D%m|0,D/m|0,T)}s.fromNumber=l;function c(D,T,O){return new s(D,T,O)}s.fromBits=c;var u=Math.pow;function d(D,T,O){if(D.length===0)throw Error("empty string");if(D==="NaN"||D==="Infinity"||D==="+Infinity"||D==="-Infinity")return x;if(typeof T=="number"?(O=T,T=!1):T=!!T,O=O||10,O<2||36<O)throw RangeError("radix");var B;if((B=D.indexOf("-"))>0)throw Error("interior hyphen");if(B===0)return d(D.substring(1),T,O).neg();for(var H=l(u(O,8)),z=x,X=0;X<D.length;X+=8){var ee=Math.min(8,D.length-X),J=parseInt(D.substring(X,X+ee),O);if(ee<8){var Q=l(u(O,ee));z=z.mul(Q).add(l(J))}else z=z.mul(H),z=z.add(l(J))}return z.unsigned=T,z}s.fromString=d;function p(D,T){return typeof D=="number"?l(D,T):typeof D=="string"?d(D,T):c(D.low,D.high,typeof T=="boolean"?T:D.unsigned)}s.fromValue=p;var h=1<<16,f=1<<24,m=h*h,g=m*m,y=g/2,A=i(f),x=i(0);s.ZERO=x;var b=i(0,!0);s.UZERO=b;var w=i(1);s.ONE=w;var k=i(1,!0);s.UONE=k;var S=i(-1);s.NEG_ONE=S;var N=c(4294967295|0,2147483647|0,!1);s.MAX_VALUE=N;var R=c(4294967295|0,4294967295|0,!0);s.MAX_UNSIGNED_VALUE=R;var P=c(0,2147483648|0,!1);s.MIN_VALUE=P;var $=s.prototype;$.toInt=function(){return this.unsigned?this.low>>>0:this.low},$.toNumber=function(){return this.unsigned?(this.high>>>0)*m+(this.low>>>0):this.high*m+(this.low>>>0)},$.toString=function(T){if(T=T||10,T<2||36<T)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(P)){var O=l(T),B=this.div(O),H=B.mul(O).sub(this);return B.toString(T)+H.toInt().toString(T)}else return"-"+this.neg().toString(T);for(var z=l(u(T,6),this.unsigned),X=this,ee="";;){var J=X.div(z),Q=X.sub(J.mul(z)).toInt()>>>0,te=Q.toString(T);if(X=J,X.isZero())return te+ee;for(;te.length<6;)te="0"+te;ee=""+te+ee}},$.getHighBits=function(){return this.high},$.getHighBitsUnsigned=function(){return this.high>>>0},$.getLowBits=function(){return this.low},$.getLowBitsUnsigned=function(){return this.low>>>0},$.getNumBitsAbs=function(){if(this.isNegative())return this.eq(P)?64:this.neg().getNumBitsAbs();for(var T=this.high!=0?this.high:this.low,O=31;O>0&&(T&1<<O)==0;O--);return this.high!=0?O+33:O+1},$.isZero=function(){return this.high===0&&this.low===0},$.eqz=$.isZero,$.isNegative=function(){return!this.unsigned&&this.high<0},$.isPositive=function(){return this.unsigned||this.high>=0},$.isOdd=function(){return(this.low&1)==1},$.isEven=function(){return(this.low&1)==0},$.equals=function(T){return r(T)||(T=p(T)),this.unsigned!==T.unsigned&&this.high>>>31==1&&T.high>>>31==1?!1:this.high===T.high&&this.low===T.low},$.eq=$.equals,$.notEquals=function(T){return!this.eq(T)},$.neq=$.notEquals,$.ne=$.notEquals,$.lessThan=function(T){return this.comp(T)<0},$.lt=$.lessThan,$.lessThanOrEqual=function(T){return this.comp(T)<=0},$.lte=$.lessThanOrEqual,$.le=$.lessThanOrEqual,$.greaterThan=function(T){return this.comp(T)>0},$.gt=$.greaterThan,$.greaterThanOrEqual=function(T){return this.comp(T)>=0},$.gte=$.greaterThanOrEqual,$.ge=$.greaterThanOrEqual,$.compare=function(T){if(r(T)||(T=p(T)),this.eq(T))return 0;var O=this.isNegative(),B=T.isNegative();return O&&!B?-1:!O&&B?1:this.unsigned?T.high>>>0>this.high>>>0||T.high===this.high&&T.low>>>0>this.low>>>0?-1:1:this.sub(T).isNegative()?-1:1},$.comp=$.compare,$.negate=function(){return!this.unsigned&&this.eq(P)?P:this.not().add(w)},$.neg=$.negate,$.add=function(T){r(T)||(T=p(T));var O=this.high>>>16,B=this.high&65535,H=this.low>>>16,z=this.low&65535,X=T.high>>>16,ee=T.high&65535,J=T.low>>>16,Q=T.low&65535,te=0,K=0,oe=0,ce=0;return ce+=z+Q,oe+=ce>>>16,ce&=65535,oe+=H+J,K+=oe>>>16,oe&=65535,K+=B+ee,te+=K>>>16,K&=65535,te+=O+X,te&=65535,c(oe<<16|ce,te<<16|K,this.unsigned)},$.subtract=function(T){return r(T)||(T=p(T)),this.add(T.neg())},$.sub=$.subtract,$.multiply=function(T){if(this.isZero())return x;if(r(T)||(T=p(T)),n){var O=n.mul(this.low,this.high,T.low,T.high);return c(O,n.get_high(),this.unsigned)}if(T.isZero())return x;if(this.eq(P))return T.isOdd()?P:x;if(T.eq(P))return this.isOdd()?P:x;if(this.isNegative())return T.isNegative()?this.neg().mul(T.neg()):this.neg().mul(T).neg();if(T.isNegative())return this.mul(T.neg()).neg();if(this.lt(A)&&T.lt(A))return l(this.toNumber()*T.toNumber(),this.unsigned);var B=this.high>>>16,H=this.high&65535,z=this.low>>>16,X=this.low&65535,ee=T.high>>>16,J=T.high&65535,Q=T.low>>>16,te=T.low&65535,K=0,oe=0,ce=0,he=0;return he+=X*te,ce+=he>>>16,he&=65535,ce+=z*te,oe+=ce>>>16,ce&=65535,ce+=X*Q,oe+=ce>>>16,ce&=65535,oe+=H*te,K+=oe>>>16,oe&=65535,oe+=z*Q,K+=oe>>>16,oe&=65535,oe+=X*J,K+=oe>>>16,oe&=65535,K+=B*te+H*Q+z*J+X*ee,K&=65535,c(ce<<16|he,K<<16|oe,this.unsigned)},$.mul=$.multiply,$.divide=function(T){if(r(T)||(T=p(T)),T.isZero())throw Error("division by zero");if(n){if(!this.unsigned&&this.high===-2147483648&&T.low===-1&&T.high===-1)return this;var O=(this.unsigned?n.div_u:n.div_s)(this.low,this.high,T.low,T.high);return c(O,n.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?b:x;var B,H,z;if(this.unsigned){if(T.unsigned||(T=T.toUnsigned()),T.gt(this))return b;if(T.gt(this.shru(1)))return k;z=b}else{if(this.eq(P)){if(T.eq(w)||T.eq(S))return P;if(T.eq(P))return w;var X=this.shr(1);return B=X.div(T).shl(1),B.eq(x)?T.isNegative()?w:S:(H=this.sub(T.mul(B)),z=B.add(H.div(T)),z)}else if(T.eq(P))return this.unsigned?b:x;if(this.isNegative())return T.isNegative()?this.neg().div(T.neg()):this.neg().div(T).neg();if(T.isNegative())return this.div(T.neg()).neg();z=x}for(H=this;H.gte(T);){B=Math.max(1,Math.floor(H.toNumber()/T.toNumber()));for(var ee=Math.ceil(Math.log(B)/Math.LN2),J=ee<=48?1:u(2,ee-48),Q=l(B),te=Q.mul(T);te.isNegative()||te.gt(H);)B-=J,Q=l(B,this.unsigned),te=Q.mul(T);Q.isZero()&&(Q=w),z=z.add(Q),H=H.sub(te)}return z},$.div=$.divide,$.modulo=function(T){if(r(T)||(T=p(T)),n){var O=(this.unsigned?n.rem_u:n.rem_s)(this.low,this.high,T.low,T.high);return c(O,n.get_high(),this.unsigned)}return this.sub(this.div(T).mul(T))},$.mod=$.modulo,$.rem=$.modulo,$.not=function(){return c(~this.low,~this.high,this.unsigned)},$.and=function(T){return r(T)||(T=p(T)),c(this.low&T.low,this.high&T.high,this.unsigned)},$.or=function(T){return r(T)||(T=p(T)),c(this.low|T.low,this.high|T.high,this.unsigned)},$.xor=function(T){return r(T)||(T=p(T)),c(this.low^T.low,this.high^T.high,this.unsigned)},$.shiftLeft=function(T){return r(T)&&(T=T.toInt()),(T&=63)==0?this:T<32?c(this.low<<T,this.high<<T|this.low>>>32-T,this.unsigned):c(0,this.low<<T-32,this.unsigned)},$.shl=$.shiftLeft,$.shiftRight=function(T){return r(T)&&(T=T.toInt()),(T&=63)==0?this:T<32?c(this.low>>>T|this.high<<32-T,this.high>>T,this.unsigned):c(this.high>>T-32,this.high>=0?0:-1,this.unsigned)},$.shr=$.shiftRight,$.shiftRightUnsigned=function(T){if(r(T)&&(T=T.toInt()),T&=63,T===0)return this;var O=this.high;if(T<32){var B=this.low;return c(B>>>T|O<<32-T,O>>>T,this.unsigned)}else return T===32?c(O,0,this.unsigned):c(O>>>T-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.toSigned=function(){return this.unsigned?c(this.low,this.high,!1):this},$.toUnsigned=function(){return this.unsigned?this:c(this.low,this.high,!0)},$.toBytes=function(T){return T?this.toBytesLE():this.toBytesBE()},$.toBytesLE=function(){var T=this.high,O=this.low;return[O&255,O>>>8&255,O>>>16&255,O>>>24,T&255,T>>>8&255,T>>>16&255,T>>>24]},$.toBytesBE=function(){var T=this.high,O=this.low;return[T>>>24,T>>>16&255,T>>>8&255,T&255,O>>>24,O>>>16&255,O>>>8&255,O&255]},s.fromBytes=function(T,O,B){return B?s.fromBytesLE(T,O):s.fromBytesBE(T,O)},s.fromBytesLE=function(T,O){return new s(T[0]|T[1]<<8|T[2]<<16|T[3]<<24,T[4]|T[5]<<8|T[6]<<16|T[7]<<24,O)},s.fromBytesBE=function(T,O){return new s(T[4]<<24|T[5]<<16|T[6]<<8|T[7],T[0]<<24|T[1]<<16|T[2]<<8|T[3],O)}}}),LT=Dt({"(disabled):node_modules/.pnpm/node-fetch@2.6.5/node_modules/node-fetch/browser.js"(){}}),BT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(c){var u=this,d=l();u.next=function(){var p=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=p-(u.c=p|0)},u.c=1,u.s0=d(" "),u.s1=d(" "),u.s2=d(" "),u.s0-=d(c),u.s0<0&&(u.s0+=1),u.s1-=d(c),u.s1<0&&(u.s1+=1),u.s2-=d(c),u.s2<0&&(u.s2+=1),d=null}function o(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function i(c,u){var d=new a(c),p=u&&u.state,h=d.next;return h.int32=function(){return d.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,p&&(typeof p=="object"&&o(p,d),h.state=function(){return o(d,{})}),h}function l(){var c=4022871197,u=function(d){d=d.toString();for(var p=0;p<d.length;p++){c+=d.charCodeAt(p);var h=.02519603282416938*c;c=h>>>0,h-=c,h*=c,c=h>>>0,h-=c,c+=h*4294967296}return(c>>>0)*23283064365386963e-26};return u}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),WT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var p=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^p^p>>>8},l===(l|0)?c.x=l:u+=l;for(var d=0;d<u.length+64;d++)c.x^=u.charCodeAt(d)|0,c.next()}function o(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),VT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.next=function(){var p=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(p^p<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var d=0;d<u.length+64;d++)c.x^=u.charCodeAt(d)|0,d==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function o(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),UT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var c=this;c.next=function(){var d=c.x,p=c.i,h,f,m;return h=d[p],h^=h>>>7,f=h^h<<24,h=d[p+1&7],f^=h^h>>>10,h=d[p+3&7],f^=h^h>>>3,h=d[p+4&7],f^=h^h<<7,h=d[p+7&7],h=h^h<<13,f^=h^h<<9,d[p]=f,c.i=p+1&7,f};function u(d,p){var h,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,h=0;h<p.length;++h)m[h&7]=m[h&7]<<15^p.charCodeAt(h)+m[h+1&7]<<13;for(;m.length<8;)m.push(0);for(h=0;h<8&&m[h]===0;++h);for(h==8?f=m[7]=-1:f=m[h],d.x=m,d.i=0,h=256;h>0;--h)d.next()}u(c,l)}function o(l,c){return c.x=l.x.slice(),c.i=l.i,c}function i(l,c){l==null&&(l=+new Date);var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(d.x&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),GT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var c=this;c.next=function(){var d=c.w,p=c.X,h=c.i,f,m;return c.w=d=d+1640531527|0,m=p[h+34&127],f=p[h=h+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[h]=m^f,c.i=h,m+(d^d>>>16)|0};function u(d,p){var h,f,m,g,y,A=[],x=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,x=Math.max(x,p.length)),m=0,g=-32;g<x;++g)p&&(f^=p.charCodeAt((g+32)%p.length)),g===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,g>=0&&(y=y+1640531527|0,h=A[g&127]^=f+y,m=h==0?m+1:0);for(m>=128&&(A[(p&&p.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=A[m+34&127],h=A[m=m+1&127],f^=f<<13,h^=h<<17,f^=f>>>15,h^=h>>>12,A[m]=f^h;d.w=y,d.X=A,d.i=m}u(c,l)}function o(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function i(l,c){l==null&&(l=+new Date);var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(d.X&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),HT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.next=function(){var p=c.b,h=c.c,f=c.d,m=c.a;return p=p<<25^p>>>7^h,h=h-f|0,f=f<<24^f>>>8^m,m=m-p|0,c.b=p=p<<20^p>>>12^h,c.c=h=h-f|0,c.d=f<<16^h>>>16^m,c.a=m-p|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var d=0;d<u.length+20;d++)c.b^=u.charCodeAt(d)|0,c.next()}function o(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),d5=Dt({"(disabled):crypto"(){}}),jT=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,s){var r=this,a=256,o=6,i=52,l="random",c=s.pow(a,o),u=s.pow(2,i),d=u*2,p=a-1,h;function f(w,k,S){var N=[];k=k==!0?{entropy:!0}:k||{};var R=A(y(k.entropy?[w,b(n)]:w==null?x():w,3),N),P=new m(N),$=function(){for(var D=P.g(o),T=c,O=0;D<u;)D=(D+O)*a,T*=a,O=P.g(1);for(;D>=d;)D/=2,T/=2,O>>>=1;return(D+O)/T};return $.int32=function(){return P.g(4)|0},$.quick=function(){return P.g(4)/4294967296},$.double=$,A(b(P.S),n),(k.pass||S||function(D,T,O,B){return B&&(B.S&&g(B,P),D.state=function(){return g(P,{})}),O?(s[l]=D,T):D})($,R,"global"in k?k.global:this==s,k.state)}s["seed"+l]=f;function m(w){var k,S=w.length,N=this,R=0,P=N.i=N.j=0,$=N.S=[];for(S||(w=[S++]);R<a;)$[R]=R++;for(R=0;R<a;R++)$[R]=$[P=p&P+w[R%S]+(k=$[R])],$[P]=k;(N.g=function(D){for(var T,O=0,B=N.i,H=N.j,z=N.S;D--;)T=z[B=p&B+1],O=O*a+z[p&(z[B]=z[H=p&H+T])+(z[H]=T)];return N.i=B,N.j=H,O})(a)}function g(w,k){return k.i=w.i,k.j=w.j,k.S=w.S.slice(),k}function y(w,k){var S=[],N=typeof w,R;if(k&&N=="object")for(R in w)try{S.push(y(w[R],k-1))}catch(P){}return S.length?S:N=="string"?w:w+"\0"}function A(w,k){for(var S=w+"",N,R=0;R<S.length;)k[p&R]=p&(N^=k[p&R]*19)+S.charCodeAt(R++);return b(k)}function x(){try{var w;return h&&(w=h.randomBytes)?w=w(a):(w=new Uint8Array(a),(r.crypto||r.msCrypto).getRandomValues(w)),b(w)}catch(N){var k=r.navigator,S=k&&k.plugins;return[+new Date,r,S,r.screen,b(n)]}}function b(w){return String.fromCharCode.apply(0,w)}if(A(s.random(),n),typeof t=="object"&&t.exports){t.exports=f;try{h=d5()}catch(w){}}else typeof define=="function"&&define.amd&&define(function(){return f})})([],Math)}}),p5=Dt({"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(e,t){var n=BT(),s=WT(),r=VT(),a=UT(),o=GT(),i=HT(),l=jT();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),qT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(e,t){(function(n,s,r){function a(c){var u=this,d=l();u.next=function(){var p=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=p-(u.c=p|0)},u.c=1,u.s0=d(" "),u.s1=d(" "),u.s2=d(" "),u.s0-=d(c),u.s0<0&&(u.s0+=1),u.s1-=d(c),u.s1<0&&(u.s1+=1),u.s2-=d(c),u.s2<0&&(u.s2+=1),d=null}function o(c,u){return u.c=c.c,u.s0=c.s0,u.s1=c.s1,u.s2=c.s2,u}function i(c,u){var d=new a(c),p=u&&u.state,h=d.next;return h.int32=function(){return d.next()*4294967296|0},h.double=function(){return h()+(h()*2097152|0)*11102230246251565e-32},h.quick=h,p&&(typeof p=="object"&&o(p,d),h.state=function(){return o(d,{})}),h}function l(){var c=4022871197,u=function(d){d=String(d);for(var p=0;p<d.length;p++){c+=d.charCodeAt(p);var h=.02519603282416938*c;c=h>>>0,h-=c,h*=c,c=h>>>0,h-=c,c+=h*4294967296}return(c>>>0)*23283064365386963e-26};return u}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.alea=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),XT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.x=0,c.y=0,c.z=0,c.w=0,c.next=function(){var p=c.x^c.x<<11;return c.x=c.y,c.y=c.z,c.z=c.w,c.w^=c.w>>>19^p^p>>>8},l===(l|0)?c.x=l:u+=l;for(var d=0;d<u.length+64;d++)c.x^=u.charCodeAt(d)|0,c.next()}function o(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor128=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),KT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.next=function(){var p=c.x^c.x>>>2;return c.x=c.y,c.y=c.z,c.z=c.w,c.w=c.v,(c.d=c.d+362437|0)+(c.v=c.v^c.v<<4^(p^p<<1))|0},c.x=0,c.y=0,c.z=0,c.w=0,c.v=0,l===(l|0)?c.x=l:u+=l;for(var d=0;d<u.length+64;d++)c.x^=u.charCodeAt(d)|0,d==u.length&&(c.d=c.x<<10^c.x>>>4),c.next()}function o(l,c){return c.x=l.x,c.y=l.y,c.z=l.z,c.w=l.w,c.v=l.v,c.d=l.d,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorwow=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),ZT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(e,t){(function(n,s,r){function a(l){var c=this;c.next=function(){var d=c.x,p=c.i,h,f,m;return h=d[p],h^=h>>>7,f=h^h<<24,h=d[p+1&7],f^=h^h>>>10,h=d[p+3&7],f^=h^h>>>3,h=d[p+4&7],f^=h^h<<7,h=d[p+7&7],h=h^h<<13,f^=h^h<<9,d[p]=f,c.i=p+1&7,f};function u(d,p){var h,f,m=[];if(p===(p|0))f=m[0]=p;else for(p=""+p,h=0;h<p.length;++h)m[h&7]=m[h&7]<<15^p.charCodeAt(h)+m[h+1&7]<<13;for(;m.length<8;)m.push(0);for(h=0;h<8&&m[h]===0;++h);for(h==8?f=m[7]=-1:f=m[h],d.x=m,d.i=0,h=256;h>0;--h)d.next()}u(c,l)}function o(l,c){return c.x=l.x.slice(),c.i=l.i,c}function i(l,c){l==null&&(l=+new Date);var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(d.x&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xorshift7=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),YT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(e,t){(function(n,s,r){function a(l){var c=this;c.next=function(){var d=c.w,p=c.X,h=c.i,f,m;return c.w=d=d+1640531527|0,m=p[h+34&127],f=p[h=h+1&127],m^=m<<13,f^=f<<17,m^=m>>>15,f^=f>>>12,m=p[h]=m^f,c.i=h,m+(d^d>>>16)|0};function u(d,p){var h,f,m,g,y,A=[],x=128;for(p===(p|0)?(f=p,p=null):(p=p+"\0",f=0,x=Math.max(x,p.length)),m=0,g=-32;g<x;++g)p&&(f^=p.charCodeAt((g+32)%p.length)),g===0&&(y=f),f^=f<<10,f^=f>>>15,f^=f<<4,f^=f>>>13,g>=0&&(y=y+1640531527|0,h=A[g&127]^=f+y,m=h==0?m+1:0);for(m>=128&&(A[(p&&p.length||0)&127]=-1),m=127,g=4*128;g>0;--g)f=A[m+34&127],h=A[m=m+1&127],f^=f<<13,h^=h<<17,f^=f>>>15,h^=h>>>12,A[m]=f^h;d.w=y,d.X=A,d.i=m}u(c,l)}function o(l,c){return c.i=l.i,c.w=l.w,c.X=l.X.slice(),c}function i(l,c){l==null&&(l=+new Date);var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(d.X&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.xor4096=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),JT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(e,t){(function(n,s,r){function a(l){var c=this,u="";c.next=function(){var p=c.b,h=c.c,f=c.d,m=c.a;return p=p<<25^p>>>7^h,h=h-f|0,f=f<<24^f>>>8^m,m=m-p|0,c.b=p=p<<20^p>>>12^h,c.c=h=h-f|0,c.d=f<<16^h>>>16^m,c.a=m-p|0},c.a=0,c.b=0,c.c=2654435769|0,c.d=1367130551,l===Math.floor(l)?(c.a=l/4294967296|0,c.b=l|0):u+=l;for(var d=0;d<u.length+20;d++)c.b^=u.charCodeAt(d)|0,c.next()}function o(l,c){return c.a=l.a,c.b=l.b,c.c=l.c,c.d=l.d,c}function i(l,c){var u=new a(l),d=c&&c.state,p=function(){return(u.next()>>>0)/4294967296};return p.double=function(){do var h=u.next()>>>11,f=(u.next()>>>0)/4294967296,m=(h+f)/(1<<21);while(m===0);return m},p.int32=u.next,p.quick=p,d&&(typeof d=="object"&&o(d,u),p.state=function(){return o(u,{})}),p}s&&s.exports?s.exports=i:r&&r.amd?r(function(){return i}):this.tychei=i})(e,typeof t=="object"&&t,typeof define=="function"&&define)}}),QT=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(e,t){(function(n,s,r){var a=256,o=6,i=52,l="random",c=r.pow(a,o),u=r.pow(2,i),d=u*2,p=a-1,h;function f(w,k,S){var N=[];k=k==!0?{entropy:!0}:k||{};var R=A(y(k.entropy?[w,b(s)]:w==null?x():w,3),N),P=new m(N),$=function(){for(var D=P.g(o),T=c,O=0;D<u;)D=(D+O)*a,T*=a,O=P.g(1);for(;D>=d;)D/=2,T/=2,O>>>=1;return(D+O)/T};return $.int32=function(){return P.g(4)|0},$.quick=function(){return P.g(4)/4294967296},$.double=$,A(b(P.S),s),(k.pass||S||function(D,T,O,B){return B&&(B.S&&g(B,P),D.state=function(){return g(P,{})}),O?(r[l]=D,T):D})($,R,"global"in k?k.global:this==r,k.state)}function m(w){var k,S=w.length,N=this,R=0,P=N.i=N.j=0,$=N.S=[];for(S||(w=[S++]);R<a;)$[R]=R++;for(R=0;R<a;R++)$[R]=$[P=p&P+w[R%S]+(k=$[R])],$[P]=k;(N.g=function(D){for(var T,O=0,B=N.i,H=N.j,z=N.S;D--;)T=z[B=p&B+1],O=O*a+z[p&(z[B]=z[H=p&H+T])+(z[H]=T)];return N.i=B,N.j=H,O})(a)}function g(w,k){return k.i=w.i,k.j=w.j,k.S=w.S.slice(),k}function y(w,k){var S=[],N=typeof w,R;if(k&&N=="object")for(R in w)try{S.push(y(w[R],k-1))}catch(P){}return S.length?S:N=="string"?w:w+"\0"}function A(w,k){for(var S=w+"",N,R=0;R<S.length;)k[p&R]=p&(N^=k[p&R]*19)+S.charCodeAt(R++);return b(k)}function x(){try{var w;return h&&(w=h.randomBytes)?w=w(a):(w=new Uint8Array(a),(n.crypto||n.msCrypto).getRandomValues(w)),b(w)}catch(N){var k=n.navigator,S=k&&k.plugins;return[+new Date,n,S,n.screen,b(s)]}}function b(w){return String.fromCharCode.apply(0,w)}if(A(r.random(),s),typeof t=="object"&&t.exports){t.exports=f;try{h=d5()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return f}):r["seed"+l]=f})(typeof self!="undefined"?self:e,[],Math)}}),h5=Dt({"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(e,t){var n=qT(),s=XT(),r=KT(),a=ZT(),o=YT(),i=JT(),l=QT();l.alea=n,l.xor128=s,l.xorwow=r,l.xorshift7=a,l.xor4096=o,l.tychei=i,t.exports=l}}),f5=Dt({"(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"(){}}),Wc=Dt({"(disabled):path"(){}}),eN=Dt({"(disabled):worker_threads"(){}}),tN=Dt({"(disabled):perf_hooks"(){}}),nN=Dt({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.9.0_@tensorflow+tfjs-core@3.9.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e,t){var n=function(){var s=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(s=s||__filename),function(r){r=r||{};function a(){return K.buffer!=Ye&&In(K.buffer),Yn}function o(){return K.buffer!=Ye&&In(K.buffer),Ot}function i(){return K.buffer!=Ye&&In(K.buffer),Gs}function l(){return K.buffer!=Ye&&In(K.buffer),Fn}function c(){return K.buffer!=Ye&&In(K.buffer),Es}var u=typeof r!="undefined"?r:{},d,p;u.ready=new Promise(function(C,F){d=C,p=F});var h={},f;for(f in u)u.hasOwnProperty(f)&&(h[f]=u[f]);var m=[],g="./this.program",y=function(C,F){throw F},A=!1,x=!1,b=!1,w=!1;A=typeof window=="object",x=typeof importScripts=="function",b=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",w=!A&&!b&&!x;var k=u.ENVIRONMENT_IS_PTHREAD||!1;k&&(Ye=u.buffer);var S="";function N(C){return u.locateFile?u.locateFile(C,S):S+C}var R,P,$,D,T,O;if(b){x?S=Wc().dirname(S)+"/":S=__dirname+"/",R=function(F,V){return T||(T=Ul("fs")),O||(O=Wc()),F=O.normalize(F),T.readFileSync(F,V?null:"utf8")},$=function(F){var V=R(F,!0);return V.buffer||(V=new Uint8Array(V)),Ae(V.buffer),V},process.argv.length>1&&(g=process.argv[1].replace(/\\/g,"/")),m=process.argv.slice(2),process.on("uncaughtException",function(C){if(!(C instanceof Oc))throw C}),process.on("unhandledRejection",Ur),y=function(C){process.exit(C)},u.inspect=function(){return"[Emscripten Module object]"};var B;try{B=eN()}catch(C){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),C}global.Worker=B.Worker}else w?(typeof read!="undefined"&&(R=function(F){return read(F)}),$=function(F){var V;return typeof readbuffer=="function"?new Uint8Array(readbuffer(F)):(V=read(F,"binary"),Ae(typeof V=="object"),V)},typeof scriptArgs!="undefined"?m=scriptArgs:typeof arguments!="undefined"&&(m=arguments),typeof quit=="function"&&(y=function(C){quit(C)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(A||x)&&(x?S=self.location.href:typeof document!="undefined"&&document.currentScript&&(S=document.currentScript.src),typeof s!="undefined"&&s&&(S=s),S.indexOf("blob:")!==0?S=S.substr(0,S.lastIndexOf("/")+1):S="",b?(R=function(F,V){return T||(T=Ul("fs")),O||(O=Wc()),F=O.normalize(F),T.readFileSync(F,V?null:"utf8")},$=function(F){var V=R(F,!0);return V.buffer||(V=new Uint8Array(V)),Ae(V.buffer),V}):(R=function(C){var F=new XMLHttpRequest;return F.open("GET",C,!1),F.send(null),F.responseText},x&&($=function(C){var F=new XMLHttpRequest;return F.open("GET",C,!1),F.responseType="arraybuffer",F.send(null),new Uint8Array(F.response)}),P=function(C,F,V){var Y=new XMLHttpRequest;Y.open("GET",C,!0),Y.responseType="arraybuffer",Y.onload=function(){if(Y.status==200||Y.status==0&&Y.response){F(Y.response);return}V()},Y.onerror=V,Y.send(null)}),D=function(C){document.title=C});b&&typeof performance=="undefined"&&(global.performance=tN().performance);var H=u.print||console.log.bind(console),z=u.printErr||console.warn.bind(console);for(f in h)h.hasOwnProperty(f)&&(u[f]=h[f]);h=null,u.arguments&&(m=u.arguments),u.thisProgram&&(g=u.thisProgram),u.quit&&(y=u.quit);var X=Atomics.load,ee=Atomics.store,J=Atomics.compareExchange,Q;u.wasmBinary&&(Q=u.wasmBinary);var te=u.noExitRuntime||!0;typeof WebAssembly!="object"&&Ur("no native wasm support detected");var K,oe,ce=!1,he;function Ae(C,F){C||Ur("Assertion failed: "+F)}function Ie(C){var F=u["_"+C];return Ae(F,"Cannot call unknown function "+C+", make sure it is exported"),F}function Se(C,F,V,Y,ye){var me={string:function(On){var Vl=0;if(On!=null&&On!==0){var l5=(On.length<<2)+1;Vl=Ll(l5),mt(On,Vl,l5)}return Vl},array:function(On){var Vl=Ll(On.length);return pt(On,Vl),Vl}};function ge(On){return F==="string"?ze(On):F==="boolean"?Boolean(On):On}var Ce=Ie(C),yt=[],hn=0;if(Y)for(var en=0;en<Y.length;en++){var xa=me[V[en]];xa?(hn===0&&(hn=Fc()),yt[en]=xa(Y[en])):yt[en]=Y[en]}var Wl=Ce.apply(null,yt);return Wl=ge(Wl),hn!==0&&zl(hn),Wl}function Oe(C,F,V,Y){V=V||[];var ye=V.every(function(ge){return ge==="number"}),me=F!=="string";return me&&ye&&!Y?Ie(C):function(){return Se(C,F,V,arguments,Y)}}function Ue(C,F,V){for(var Y=F+V,ye="";!(F>=Y);){var me=C[F++];if(!me)return ye;if(!(me&128)){ye+=String.fromCharCode(me);continue}var ge=C[F++]&63;if((me&224)==192){ye+=String.fromCharCode((me&31)<<6|ge);continue}var Ce=C[F++]&63;if((me&240)==224?me=(me&15)<<12|ge<<6|Ce:me=(me&7)<<18|ge<<12|Ce<<6|C[F++]&63,me<65536)ye+=String.fromCharCode(me);else{var yt=me-65536;ye+=String.fromCharCode(55296|yt>>10,56320|yt&1023)}}return ye}function ze(C,F){return C?Ue(o(),C,F):""}function wt(C,F,V,Y){if(!(Y>0))return 0;for(var ye=V,me=V+Y-1,ge=0;ge<C.length;++ge){var Ce=C.charCodeAt(ge);if(Ce>=55296&&Ce<=57343){var yt=C.charCodeAt(++ge);Ce=65536+((Ce&1023)<<10)|yt&1023}if(Ce<=127){if(V>=me)break;F[V++]=Ce}else if(Ce<=2047){if(V+1>=me)break;F[V++]=192|Ce>>6,F[V++]=128|Ce&63}else if(Ce<=65535){if(V+2>=me)break;F[V++]=224|Ce>>12,F[V++]=128|Ce>>6&63,F[V++]=128|Ce&63}else{if(V+3>=me)break;F[V++]=240|Ce>>18,F[V++]=128|Ce>>12&63,F[V++]=128|Ce>>6&63,F[V++]=128|Ce&63}}return F[V]=0,V-ye}function mt(C,F,V){return wt(C,o(),F,V)}function gt(C){for(var F=0,V=0;V<C.length;++V){var Y=C.charCodeAt(V);Y>=55296&&Y<=57343&&(Y=65536+((Y&1023)<<10)|C.charCodeAt(++V)&1023),Y<=127?++F:Y<=2047?F+=2:Y<=65535?F+=3:F+=4}return F}function pt(C,F){a().set(C,F)}function bt(C,F){return C%F>0&&(C+=F-C%F),C}var Ye,Yn,Ot,ps,kn,Gs,Fn,Ns,Es;function In(C){Ye=C,u.HEAP8=Yn=new Int8Array(C),u.HEAP16=ps=new Int16Array(C),u.HEAP32=Gs=new Int32Array(C),u.HEAPU8=Ot=new Uint8Array(C),u.HEAPU16=kn=new Uint16Array(C),u.HEAPU32=Fn=new Uint32Array(C),u.HEAPF32=Ns=new Float32Array(C),u.HEAPF64=Es=new Float64Array(C)}var Rs=u.INITIAL_MEMORY||16777216;if(k)K=u.wasmMemory,Ye=u.buffer;else if(u.wasmMemory)K=u.wasmMemory;else if(K=new WebAssembly.Memory({initial:Rs/65536,maximum:2147483648/65536,shared:!0}),!(K.buffer instanceof SharedArrayBuffer))throw z("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");K&&(Ye=K.buffer),Rs=Ye.byteLength,In(Ye);var $s,hs=[],wr=[],Wr=[],fa=[],Dl=[],kr=!1,Vp=!1;k||wr.push({func:function(){sh()}});function E0(){if(!k){if(u.preRun)for(typeof u.preRun=="function"&&(u.preRun=[u.preRun]);u.preRun.length;)Gp(u.preRun.shift());Pl(hs)}}function Cc(){kr=!0,!k&&Pl(wr)}function R0(){k||Pl(Wr)}function Up(){k||(Vp=!0)}function Jn(){if(!k){if(u.postRun)for(typeof u.postRun=="function"&&(u.postRun=[u.postRun]);u.postRun.length;)$0(u.postRun.shift());Pl(Dl)}}function Gp(C){hs.unshift(C)}function $0(C){Dl.unshift(C)}var Vr=0,ma=null,Xo=null;function D0(C){Ae(!k,"addRunDependency cannot be used in a pthread worker"),Vr++,u.monitorRunDependencies&&u.monitorRunDependencies(Vr)}function _0(C){if(Vr--,u.monitorRunDependencies&&u.monitorRunDependencies(Vr),Vr==0&&(ma!==null&&(clearInterval(ma),ma=null),Xo)){var F=Xo;Xo=null,F()}}u.preloadedImages={},u.preloadedAudios={};function Ur(C){u.onAbort&&u.onAbort(C),k&&console.error("Pthread aborting at "+new Error().stack),C+="",z(C),ce=!0,he=1,C="abort("+C+"). Build with -s ASSERTIONS=1 for more info.";var F=new WebAssembly.RuntimeError(C);throw p(F),F}function Hp(C,F){return String.prototype.startsWith?C.startsWith(F):C.indexOf(F)===0}var _l="data:application/octet-stream;base64,";function jp(C){return Hp(C,_l)}var P0="file://";function qp(C){return Hp(C,P0)}var Qn="tfjs-backend-wasm-threaded-simd.wasm";jp(Qn)||(Qn=N(Qn));function Xp(C){try{if(C==Qn&&Q)return new Uint8Array(Q);if($)return $(C);throw"both async and sync fetching of the wasm failed"}catch(F){Ur(F)}}function F0(){if(!Q&&(A||x)){if(typeof fetch=="function"&&!qp(Qn))return fetch(Qn,{credentials:"same-origin"}).then(function(C){if(!C.ok)throw"failed to load wasm binary file at '"+Qn+"'";return C.arrayBuffer()}).catch(function(){return Xp(Qn)});if(P)return new Promise(function(C,F){P(Qn,function(V){C(new Uint8Array(V))},F)})}return Promise.resolve().then(function(){return Xp(Qn)})}function O0(){var C={a:Cg};function F(ge,Ce){var yt=ge.exports;if(u.asm=yt,$s=u.asm.F,oe=Ce,!k){var hn=Re.unusedWorkers.length;Re.unusedWorkers.forEach(function(en){Re.loadWasmModuleToWorker(en,function(){--hn||_0("wasm-instantiate")})})}}k||D0("wasm-instantiate");function V(ge){F(ge.instance,ge.module)}function Y(ge){return F0().then(function(Ce){return WebAssembly.instantiate(Ce,C)}).then(ge,function(Ce){z("failed to asynchronously prepare wasm: "+Ce),Ur(Ce)})}function ye(){return!Q&&typeof WebAssembly.instantiateStreaming=="function"&&!jp(Qn)&&!qp(Qn)&&typeof fetch=="function"?fetch(Qn,{credentials:"same-origin"}).then(function(ge){var Ce=WebAssembly.instantiateStreaming(ge,C);return Ce.then(V,function(yt){return z("wasm streaming compile failed: "+yt),z("falling back to ArrayBuffer instantiation"),Y(V)})}):Y(V)}if(u.instantiateWasm)try{var me=u.instantiateWasm(C,F);return me}catch(ge){return z("Module.instantiateWasm callback failed with error: "+ge),!1}return ye().catch(p),{}}var M0={10024:function(){throw"Canceled!"},10042:function(C,F){setTimeout(function(){n5(C,F)},0)}};function Kp(){Re.initRuntime()}function Pl(C){for(;C.length>0;){var F=C.shift();if(typeof F=="function"){F(u);continue}var V=F.func;typeof V=="number"?F.arg===void 0?$s.get(V)():$s.get(V)(F.arg):V(F.arg===void 0?null:F.arg)}}function Tc(C,F){if(C<=0||C>a().length||C&!0||F<0)return-28;if(F==0)return 0;F>=2147483647&&(F=1/0);var V=Atomics.load(i(),Bl>>2),Y=0;if(V==C){var ye=Atomics.compareExchange(i(),Bl>>2,V,0);if(ye==V&&(--F,Y=1,F<=0))return 1}var me=Atomics.notify(i(),C>>2,F);if(me>=0)return me+Y;throw"Atomics.notify returned an unexpected value "+me}u._emscripten_futex_wake=Tc;function z0(C){if(k)throw"Internal Error! killThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in killThread!";i()[C+12>>2]=0;var F=Re.pthreads[C];F.worker.terminate(),Re.freeThreadData(F),Re.runningWorkers.splice(Re.runningWorkers.indexOf(F.worker),1),F.worker.pthread=void 0}function L0(C){if(k)throw"Internal Error! cancelThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in cancelThread!";var F=Re.pthreads[C];F.worker.postMessage({cmd:"cancel"})}function B0(C){if(k)throw"Internal Error! cleanupThread() can only ever be called from main application thread!";if(!C)throw"Internal Error! Null pthread_ptr in cleanupThread!";var F=Re.pthreads[C];if(F){i()[C+12>>2]=0;var V=F.worker;Re.returnWorkerToPool(V)}}var Re={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){for(var C=Math.min(4,Math.max(1,(navigator.hardwareConcurrency||1)/2)),F=0;F<C;++F)Re.allocateUnusedWorker()},initRuntime:function(){for(var C=Zo(228),F=0;F<228/4;++F)l()[C/4+F]=0;i()[C+12>>2]=C;var V=C+152;i()[V>>2]=V;for(var Y=Zo(512),F=0;F<128;++F)l()[Y/4+F]=0;Atomics.store(l(),C+100>>2,Y),Atomics.store(l(),C+40>>2,C),Qg(C,!x,1),t5(C)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){for(;Re.threadExitHandlers.length>0;)Re.threadExitHandlers.pop()();k&&Ml()&&e5()},runExitHandlersAndDeinitThread:function(C,F){Atomics.store(l(),C+56>>2,1),Atomics.store(l(),C+60>>2,0),Re.runExitHandlers(),Atomics.store(l(),C+4>>2,F),Atomics.store(l(),C+0>>2,1),Tc(C+0,2147483647),Qg(0,0,0)},threadExit:function(C){var F=Ml();F&&(Re.runExitHandlersAndDeinitThread(F,C),k&&postMessage({cmd:"exit"}))},threadCancel:function(){Re.runExitHandlersAndDeinitThread(Ml(),-1),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var C in Re.pthreads){var F=Re.pthreads[C];F&&F.worker&&Re.returnWorkerToPool(F.worker)}Re.pthreads={};for(var V=0;V<Re.unusedWorkers.length;++V){var Y=Re.unusedWorkers[V];Y.terminate()}Re.unusedWorkers=[];for(var V=0;V<Re.runningWorkers.length;++V){var Y=Re.runningWorkers[V],F=Y.pthread;Re.freeThreadData(F),Y.terminate()}Re.runningWorkers=[]},freeThreadData:function(C){if(!!C){if(C.threadInfoStruct){var F=i()[C.threadInfoStruct+100>>2];i()[C.threadInfoStruct+100>>2]=0,Pc(F),Pc(C.threadInfoStruct)}C.threadInfoStruct=0,C.allocatedOwnStack&&C.stackBase&&Pc(C.stackBase),C.stackBase=0,C.worker&&(C.worker.pthread=null)}},returnWorkerToPool:function(C){Re.runWithoutMainThreadQueuedCalls(function(){delete Re.pthreads[C.pthread.threadInfoStruct],Re.unusedWorkers.push(C),Re.runningWorkers.splice(Re.runningWorkers.indexOf(C),1),Re.freeThreadData(C.pthread),C.pthread=void 0})},runWithoutMainThreadQueuedCalls:function(C){i()[i5>>2]=0;try{C()}finally{i()[i5>>2]=1}},receiveObjectTransfer:function(C){},loadWasmModuleToWorker:function(C,F){C.onmessage=function(V){var Y=V.data,ye=Y.cmd;if(C.pthread&&(Re.currentProxiedOperationCallerThread=C.pthread.threadInfoStruct),Y.targetThread&&Y.targetThread!=Ml()){var me=Re.pthreads[Y.targetThread];me?me.worker.postMessage(V.data,Y.transferList):console.error('Internal error! Worker sent a message "'+ye+'" to target pthread '+Y.targetThread+", but that thread no longer exists!"),Re.currentProxiedOperationCallerThread=void 0;return}if(ye==="processQueuedMainThreadWork")Yg();else if(ye==="spawnThread")th(V.data);else if(ye==="cleanupThread")B0(Y.thread);else if(ye==="killThread")z0(Y.thread);else if(ye==="cancelThread")L0(Y.thread);else if(ye==="loaded")C.loaded=!0,F&&F(C),C.runPthread&&(C.runPthread(),delete C.runPthread);else if(ye==="print")H("Thread "+Y.threadId+": "+Y.text);else if(ye==="printErr")z("Thread "+Y.threadId+": "+Y.text);else if(ye==="alert")alert("Thread "+Y.threadId+": "+Y.text);else if(ye==="exit"){var ge=C.pthread&&Atomics.load(l(),C.pthread.threadInfoStruct+64>>2);ge&&Re.returnWorkerToPool(C)}else if(ye==="exitProcess")try{ET(Y.returnCode)}catch(Ce){if(Ce instanceof Oc)return;throw Ce}else ye==="cancelDone"?Re.returnWorkerToPool(C):ye==="objectTransfer"?Re.receiveObjectTransfer(V.data):V.data.target==="setimmediate"?C.postMessage(V.data):z("worker sent an unknown command "+ye);Re.currentProxiedOperationCallerThread=void 0},C.onerror=function(V){z("pthread sent an error! "+V.filename+":"+V.lineno+": "+V.message)},b&&(C.on("message",function(V){C.onmessage({data:V})}),C.on("error",function(V){C.onerror(V)}),C.on("exit",function(V){})),C.postMessage({cmd:"load",urlOrBlob:u.mainScriptUrlOrBlob||s,wasmMemory:K,wasmModule:oe})},allocateUnusedWorker:function(){var C=N("tfjs-backend-wasm-threaded-simd.worker.js");Re.unusedWorkers.push(new Worker(C))},getNewWorker:function(){return Re.unusedWorkers.length==0&&(Re.allocateUnusedWorker(),Re.loadWasmModuleToWorker(Re.unusedWorkers[0])),Re.unusedWorkers.length>0?Re.unusedWorkers.pop():null},busySpinWait:function(C){for(var F=performance.now()+C;performance.now()<F;);}};function W0(C,F){a5(C,F),zl(C)}u.establishStackSpace=W0;function V0(){return te}u.getNoExitRuntime=V0;function U0(C,F){return $s.get(C)(F)}u.invokeEntryPoint=U0;function G0(C,F,V,Y){Ur("Assertion failed: "+ze(C)+", at: "+[F?ze(F):"unknown filename",V,Y?ze(Y):"unknown function"])}function H0(C,F){var V=_main(C,F)}var Ko;b?Ko=function(){var C=process.hrtime();return C[0]*1e3+C[1]/1e6}:k?Ko=function(){return performance.now()-u.__performance_now_clock_drift}:typeof dateNow!="undefined"?Ko=dateNow:Ko=function(){return performance.now()};function j0(C){return i()[Jb()>>2]=C,C}function q0(C,F){if(k)return ga(1,1,C,F)}function X0(C,F){if(C==F)postMessage({cmd:"processQueuedMainThreadWork"});else if(k)postMessage({targetThread:C,cmd:"processThreadQueue"});else{var V=Re.pthreads[C],Y=V&&V.worker;if(!Y)return;Y.postMessage({cmd:"processThreadQueue"})}return 1}function K0(){Ur()}function Z0(C,F,V){var Y=tg(F,V);return M0[C].apply(null,Y)}function Y0(C,F){}function J0(C,F,V){if(C<=0||C>a().length||C&!0)return-28;if(A){if(Atomics.load(i(),C>>2)!=F)return-6;for(var ye=performance.now(),me=ye+V,ge=Atomics.exchange(i(),Bl>>2,C);;){if(ye=performance.now(),ye>me)return ge=Atomics.exchange(i(),Bl>>2,0),-73;if(ge=Atomics.exchange(i(),Bl>>2,0),ge==0)break;if(Yg(),Atomics.load(i(),C>>2)!=F)return-6;ge=Atomics.exchange(i(),Bl>>2,C)}return 0}else{var Y=Atomics.wait(i(),C>>2,F,V);if(Y==="timed-out")return-73;if(Y==="not-equal")return-6;if(Y==="ok")return 0;throw"Atomics.wait returned an unexpected value "+Y}}function Q0(C,F,V){o().copyWithin(C,F,F+V)}function eg(){return b?Ul("os").cpus().length:navigator.hardwareConcurrency}function ga(C,F){for(var V=arguments.length-2,Y=Fc(),ye=V,me=Ll(ye*8),ge=me>>3,Ce=0;Ce<V;Ce++){var yt=arguments[2+Ce];c()[ge+Ce]=yt}var hn=r5(C,ye,me,F);return zl(Y),hn}var Nc=[],Ec=[];function tg(C,F){Ec.length=0;var V;for(F>>=2;V=o()[C++];){var Y=V<105;Y&&F&1&&F++,Ec.push(Y?c()[F++>>1]:i()[F]),++F}return Ec}function ng(C,F,V){Nc.length=F;for(var Y=V>>3,ye=0;ye<F;ye++)Nc[ye]=c()[Y+ye];var me=C<0,ge=me?M0[-C-1]:Sg[C];return ge.apply(null,Nc)}function sg(){return o().length}function rg(C){try{return K.grow(C-Ye.byteLength+65535>>>16),In(K.buffer),1}catch(F){}}function ag(C){var F=sg();if(C<=F)return!1;var V=2147483648;if(C>V)return!1;for(var Y=1;Y<=4;Y*=2){var ye=F*(1+.2/Y);ye=Math.min(ye,C+100663296);var me=Math.min(V,bt(Math.max(C,ye),65536)),ge=rg(me);if(ge)return!0}return!1}var qe={inEventHandler:0,removeAllEventListeners:function(){for(var C=qe.eventHandlers.length-1;C>=0;--C)qe._removeHandler(C);qe.eventHandlers=[],qe.deferredCalls=[]},registerRemoveEventListeners:function(){qe.removeEventListenersRegistered||(fa.push(qe.removeAllEventListeners),qe.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(C,F,V){function Y(ge,Ce){if(ge.length!=Ce.length)return!1;for(var yt in ge)if(ge[yt]!=Ce[yt])return!1;return!0}for(var ye in qe.deferredCalls){var me=qe.deferredCalls[ye];if(me.targetFunction==C&&Y(me.argsList,V))return}qe.deferredCalls.push({targetFunction:C,precedence:F,argsList:V}),qe.deferredCalls.sort(function(ge,Ce){return ge.precedence<Ce.precedence})},removeDeferredCalls:function(C){for(var F=0;F<qe.deferredCalls.length;++F)qe.deferredCalls[F].targetFunction==C&&(qe.deferredCalls.splice(F,1),--F)},canPerformEventHandlerRequests:function(){return qe.inEventHandler&&qe.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!!qe.canPerformEventHandlerRequests())for(var C=0;C<qe.deferredCalls.length;++C){var F=qe.deferredCalls[C];qe.deferredCalls.splice(C,1),--C,F.targetFunction.apply(null,F.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(C,F){for(var V=0;V<qe.eventHandlers.length;++V)qe.eventHandlers[V].target==C&&(!F||F==qe.eventHandlers[V].eventTypeString)&&qe._removeHandler(V--)},_removeHandler:function(C){var F=qe.eventHandlers[C];F.target.removeEventListener(F.eventTypeString,F.eventListenerFunc,F.useCapture),qe.eventHandlers.splice(C,1)},registerOrRemoveHandler:function(C){var F=function(ye){++qe.inEventHandler,qe.currentEventHandler=C,qe.runDeferredCalls(),C.handlerFunc(ye),qe.runDeferredCalls(),--qe.inEventHandler};if(C.callbackfunc)C.eventListenerFunc=F,C.target.addEventListener(C.eventTypeString,F,C.useCapture),qe.eventHandlers.push(C),qe.registerRemoveEventListeners();else for(var V=0;V<qe.eventHandlers.length;++V)qe.eventHandlers[V].target==C.target&&qe.eventHandlers[V].eventTypeString==C.eventTypeString&&qe._removeHandler(V--)},queueEventHandlerOnThread_iiii:function(C,F,V,Y,ye){var me=Fc(),ge=Ll(12);i()[ge>>2]=V,i()[ge+4>>2]=Y,i()[ge+8>>2]=ye,Jg(0,C,637534208,F,Y,ge),zl(me)},getTargetThreadForEventCallback:function(C){switch(C){case 1:return 0;case 2:return Re.currentProxiedOperationCallerThread;default:return C}},getNodeNameForTarget:function(C){return C?C==window?"#window":C==screen?"#screen":C&&C.nodeName?C.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function og(C){var F=gt(C)+1,V=Zo(F);return mt(C,V,F),V}function ig(C,F,V,Y){var ye=Fc(),me=Ll(12),ge=0;F&&(ge=og(F)),i()[me>>2]=ge,i()[me+4>>2]=V,i()[me+8>>2]=Y,Jg(0,C,657457152,0,ge,me),zl(ye)}function lg(C,F,V,Y){F=F?ze(F):"",ig(C,F,V,Y)}function ug(C){return C>2?ze(C):C}var cg=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function dg(C){C=ug(C);var F=cg[C]||(typeof document!="undefined"?document.querySelector(C):void 0);return F}function Rc(C){return dg(C)}function Zp(C,F,V){var Y=Rc(C);if(!Y)return-4;if(Y.canvasSharedPtr&&(i()[Y.canvasSharedPtr>>2]=F,i()[Y.canvasSharedPtr+4>>2]=V),Y.offscreenCanvas||!Y.controlTransferredOffscreen){Y.offscreenCanvas&&(Y=Y.offscreenCanvas);var ye=!1;if(Y.GLctxObject&&Y.GLctxObject.GLctx){var me=Y.GLctxObject.GLctx.getParameter(2978);ye=me[0]===0&&me[1]===0&&me[2]===Y.width&&me[3]===Y.height}Y.width=F,Y.height=V,ye&&Y.GLctxObject.GLctx.viewport(0,0,F,V)}else if(Y.canvasSharedPtr){var ge=i()[Y.canvasSharedPtr+8>>2];return lg(ge,C,F,V),1}else return-4;return 0}function Yp(C,F,V){return k?ga(2,1,C,F,V):Zp(C,F,V)}function pg(C,F,V){var Y=Rc(C);return Y?Zp(C,F,V):Yp(C,F,V)}function hg(C){}function fg(C,F){}function mg(C){var F=C.getExtension("ANGLE_instanced_arrays");if(F)return C.vertexAttribDivisor=function(V,Y){F.vertexAttribDivisorANGLE(V,Y)},C.drawArraysInstanced=function(V,Y,ye,me){F.drawArraysInstancedANGLE(V,Y,ye,me)},C.drawElementsInstanced=function(V,Y,ye,me,ge){F.drawElementsInstancedANGLE(V,Y,ye,me,ge)},1}function gg(C){var F=C.getExtension("OES_vertex_array_object");if(F)return C.createVertexArray=function(){return F.createVertexArrayOES()},C.deleteVertexArray=function(V){F.deleteVertexArrayOES(V)},C.bindVertexArray=function(V){F.bindVertexArrayOES(V)},C.isVertexArray=function(V){return F.isVertexArrayOES(V)},1}function yg(C){var F=C.getExtension("WEBGL_draw_buffers");if(F)return C.drawBuffers=function(V,Y){F.drawBuffersWEBGL(V,Y)},1}function Ag(C){return!!(C.multiDrawWebgl=C.getExtension("WEBGL_multi_draw"))}var ht={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function(F){ht.lastError||(ht.lastError=F)},getNewId:function(C){for(var F=ht.counter++,V=C.length;V<F;V++)C[V]=null;return F},getSource:function(C,F,V,Y){for(var ye="",me=0;me<F;++me){var ge=Y?i()[Y+me*4>>2]:-1;ye+=ze(i()[V+me*4>>2],ge<0?void 0:ge)}return ye},createContext:function(C,F){var V=C.getContext("webgl",F);if(!V)return 0;var Y=ht.registerContext(V,F);return Y},registerContext:function(C,F){var V=Zo(8);i()[V+4>>2]=Ml();var Y={handle:V,attributes:F,version:F.majorVersion,GLctx:C};return C.canvas&&(C.canvas.GLctxObject=Y),ht.contexts[V]=Y,(typeof F.enableExtensionsByDefault=="undefined"||F.enableExtensionsByDefault)&&ht.initExtensions(Y),V},makeContextCurrent:function(C){return ht.currentContext=ht.contexts[C],u.ctx=ya=ht.currentContext&&ht.currentContext.GLctx,!(C&&!ya)},getContext:function(C){return ht.contexts[C]},deleteContext:function(C){ht.currentContext===ht.contexts[C]&&(ht.currentContext=null),typeof qe=="object"&&qe.removeAllHandlersOnTarget(ht.contexts[C].GLctx.canvas),ht.contexts[C]&&ht.contexts[C].GLctx.canvas&&(ht.contexts[C].GLctx.canvas.GLctxObject=void 0),Pc(ht.contexts[C].handle),ht.contexts[C]=null},initExtensions:function(C){if(C||(C=ht.currentContext),!C.initExtensionsDone){C.initExtensionsDone=!0;var F=C.GLctx;mg(F),gg(F),yg(F),F.disjointTimerQueryExt=F.getExtension("EXT_disjoint_timer_query"),Ag(F);var V=F.getSupportedExtensions()||[];V.forEach(function(Y){Y.indexOf("lose_context")<0&&Y.indexOf("debug")<0&&F.getExtension(Y)})}},populateUniformTable:function(C){for(var F=ht.programs[C],V=ht.programInfos[C]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},Y=V.uniforms,ye=ya.getProgramParameter(F,35718),me=0;me<ye;++me){var ge=ya.getActiveUniform(F,me),Ce=ge.name;V.maxUniformLength=Math.max(V.maxUniformLength,Ce.length+1),Ce.slice(-1)=="]"&&(Ce=Ce.slice(0,Ce.lastIndexOf("[")));var yt=ya.getUniformLocation(F,Ce);if(yt){var hn=ht.getNewId(ht.uniforms);Y[Ce]=[ge.size,hn],ht.uniforms[hn]=yt;for(var en=1;en<ge.size;++en){var xa=Ce+"["+en+"]";yt=ya.getUniformLocation(F,xa),hn=ht.getNewId(ht.uniforms),ht.uniforms[hn]=yt}}}}},xg=["default","low-power","high-performance"];function bg(C,F){var V=F>>2,Y=i()[V+(24>>2)],ye={alpha:!!i()[V+(0>>2)],depth:!!i()[V+(4>>2)],stencil:!!i()[V+(8>>2)],antialias:!!i()[V+(12>>2)],premultipliedAlpha:!!i()[V+(16>>2)],preserveDrawingBuffer:!!i()[V+(20>>2)],powerPreference:xg[Y],failIfMajorPerformanceCaveat:!!i()[V+(28>>2)],majorVersion:i()[V+(32>>2)],minorVersion:i()[V+(36>>2)],enableExtensionsByDefault:i()[V+(40>>2)],explicitSwapControl:i()[V+(44>>2)],proxyContextToMainThread:i()[V+(48>>2)],renderViaOffscreenBackBuffer:i()[V+(52>>2)]},me=Rc(C);if(!me||ye.explicitSwapControl)return 0;var ge=ht.createContext(me,ye);return ge}function vg(C,F){return bg(C,F)}var Fl={mappings:{},buffers:[null,[],[]],printChar:function(C,F){var V=Fl.buffers[C];F===0||F===10?((C===1?H:z)(Ue(V,0)),V.length=0):V.push(F)},varargs:void 0,get:function(){Fl.varargs+=4;var C=i()[Fl.varargs-4>>2];return C},getStr:function(C){var F=ze(C);return F},get64:function(C,F){return C}};function Jp(C){return k?ga(3,1,C):0}function Qp(C,F,V,Y,ye){if(k)return ga(4,1,C,F,V,Y,ye)}function eh(C,F,V,Y){if(k)return ga(5,1,C,F,V,Y);for(var ye=0,me=0;me<V;me++){for(var ge=i()[F+me*8>>2],Ce=i()[F+(me*8+4)>>2],yt=0;yt<Ce;yt++)Fl.printChar(C,o()[ge+yt]);ye+=Ce}return i()[Y>>2]=ye,0}function wg(C){var F=Re.threadExitHandlers.pop();C&&F()}function kg(C,F){Re.threadExitHandlers.push(function(){$s.get(C)(F)})}function th(C){if(k)throw"Internal Error! spawnThread() can only ever be called from main application thread!";var F=Re.getNewWorker();if(F.pthread!==void 0)throw"Internal error!";if(!C.pthread_ptr)throw"Internal error, no pthread ptr!";Re.runningWorkers.push(F);for(var V=Zo(128*4),Y=0;Y<128;++Y)i()[V+Y*4>>2]=0;var ye=C.stackBase+C.stackSize,me=Re.pthreads[C.pthread_ptr]={worker:F,stackBase:C.stackBase,stackSize:C.stackSize,allocatedOwnStack:C.allocatedOwnStack,threadInfoStruct:C.pthread_ptr},ge=me.threadInfoStruct>>2;Atomics.store(l(),ge+(64>>2),C.detached),Atomics.store(l(),ge+(100>>2),V),Atomics.store(l(),ge+(40>>2),me.threadInfoStruct),Atomics.store(l(),ge+(80>>2),C.stackSize),Atomics.store(l(),ge+(76>>2),ye),Atomics.store(l(),ge+(104>>2),C.stackSize),Atomics.store(l(),ge+(104+8>>2),ye),Atomics.store(l(),ge+(104+12>>2),C.detached);var Ce=Qb(),yt=Ce+40;Atomics.store(l(),ge+(172>>2),yt),F.pthread=me;var hn={cmd:"run",start_routine:C.startRoutine,arg:C.arg,threadInfoStruct:C.pthread_ptr,stackBase:C.stackBase,stackSize:C.stackSize};F.runPthread=function(){hn.time=performance.now(),F.postMessage(hn,C.transferList)},F.loaded&&(F.runPthread(),delete F.runPthread)}function Ig(C,F,V,Y){if(typeof SharedArrayBuffer=="undefined")return z("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!C)return z("pthread_create called with a null thread pointer!"),28;var ye=[],me=0;if(k&&(ye.length===0||me))return s5(687865856,C,F,V,Y);if(me)return me;var ge=0,Ce=0,yt=0;F&&F!=-1?(ge=i()[F>>2],ge+=81920,Ce=i()[F+8>>2],yt=i()[F+12>>2]!==0):ge=2097152;var hn=Ce==0;hn?Ce=o5(16,ge):(Ce-=ge,Ae(Ce>0));for(var en=Zo(228),xa=0;xa<228>>2;++xa)l()[(en>>2)+xa]=0;i()[C>>2]=en,i()[en+12>>2]=en;var Wl=en+152;i()[Wl>>2]=Wl;var On={stackBase:Ce,stackSize:ge,allocatedOwnStack:hn,detached:yt,startRoutine:V,pthread_ptr:en,arg:Y,transferList:ye};return k?(On.cmd="spawnThread",postMessage(On,ye)):th(On),0}function nh(C){if(k)return ga(6,1,C);switch(C){case 30:return 16384;case 85:var F=2147483648;return F/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return j0(28),-1}k||Re.initMainThreadBlock();var ya,Sg=[null,q0,Yp,Jp,Qp,eh,nh],Cg={e:G0,r:H0,x:X0,b:K0,y:Z0,j:Y0,c:J0,d:Tc,f:Ko,p:Q0,z:eg,u:ng,q:ag,v:pg,i:hg,t:fg,w:vg,m:Jp,n:Qp,g:eh,o:Kp,a:K||u.wasmMemory,k:wg,l:kg,h:Ig,s:nh},Yb=O0(),sh=u.___wasm_call_ctors=function(){return(sh=u.___wasm_call_ctors=u.asm.A).apply(null,arguments)},Tg=u._init=function(){return(Tg=u._init=u.asm.B).apply(null,arguments)},Ng=u._register_tensor=function(){return(Ng=u._register_tensor=u.asm.C).apply(null,arguments)},Eg=u._dispose_data=function(){return(Eg=u._dispose_data=u.asm.D).apply(null,arguments)},Rg=u._dispose=function(){return(Rg=u._dispose=u.asm.E).apply(null,arguments)},$g=u._Abs=function(){return($g=u._Abs=u.asm.G).apply(null,arguments)},Dg=u._Add=function(){return(Dg=u._Add=u.asm.H).apply(null,arguments)},_g=u._AddN=function(){return(_g=u._AddN=u.asm.I).apply(null,arguments)},Pg=u._All=function(){return(Pg=u._All=u.asm.J).apply(null,arguments)},Fg=u._Any=function(){return(Fg=u._Any=u.asm.K).apply(null,arguments)},Og=u._ArgMax=function(){return(Og=u._ArgMax=u.asm.L).apply(null,arguments)},Mg=u._AvgPool=function(){return(Mg=u._AvgPool=u.asm.M).apply(null,arguments)},zg=u._BatchMatMul=function(){return(zg=u._BatchMatMul=u.asm.N).apply(null,arguments)},Lg=u._Ceil=function(){return(Lg=u._Ceil=u.asm.O).apply(null,arguments)},Bg=u._ClipByValue=function(){return(Bg=u._ClipByValue=u.asm.P).apply(null,arguments)},Wg=u._Conv2D=function(){return(Wg=u._Conv2D=u.asm.Q).apply(null,arguments)},Vg=u._Conv2DBackpropInput=function(){return(Vg=u._Conv2DBackpropInput=u.asm.R).apply(null,arguments)},Ug=u._Cos=function(){return(Ug=u._Cos=u.asm.S).apply(null,arguments)},Gg=u._Cosh=function(){return(Gg=u._Cosh=u.asm.T).apply(null,arguments)},Hg=u._CropAndResize=function(){return(Hg=u._CropAndResize=u.asm.U).apply(null,arguments)},jg=u._Cumsum=function(){return(jg=u._Cumsum=u.asm.V).apply(null,arguments)},qg=u._DepthToSpace=function(){return(qg=u._DepthToSpace=u.asm.W).apply(null,arguments)},Xg=u._DepthwiseConv2dNative=function(){return(Xg=u._DepthwiseConv2dNative=u.asm.X).apply(null,arguments)},Kg=u._Elu=function(){return(Kg=u._Elu=u.asm.Y).apply(null,arguments)},rh=u._Equal=function(){return(rh=u._Equal=u.asm.Z).apply(null,arguments)},ah=u._Exp=function(){return(ah=u._Exp=u.asm._).apply(null,arguments)},oh=u._FlipLeftRight=function(){return(oh=u._FlipLeftRight=u.asm.$).apply(null,arguments)},$c=u._Floor=function(){return($c=u._Floor=u.asm.aa).apply(null,arguments)},Ol=u._FloorDiv=function(){return(Ol=u._FloorDiv=u.asm.ba).apply(null,arguments)},Zg=u._FusedBatchNorm=function(){return(Zg=u._FusedBatchNorm=u.asm.ca).apply(null,arguments)},Dc=u._FusedConv2D=function(){return(Dc=u._FusedConv2D=u.asm.da).apply(null,arguments)},se=u._FusedDepthwiseConv2D=function(){return(se=u._FusedDepthwiseConv2D=u.asm.ea).apply(null,arguments)},le=u._Gather=function(){return(le=u._Gather=u.asm.fa).apply(null,arguments)},ve=u._GatherNd=function(){return(ve=u._GatherNd=u.asm.ga).apply(null,arguments)},lt=u._Greater=function(){return(lt=u._Greater=u.asm.ha).apply(null,arguments)},Wt=u._GreaterEqual=function(){return(Wt=u._GreaterEqual=u.asm.ia).apply(null,arguments)},$t=u._LeakyRelu=function(){return($t=u._LeakyRelu=u.asm.ja).apply(null,arguments)},Qe=u._Less=function(){return(Qe=u._Less=u.asm.ka).apply(null,arguments)},st=u._LessEqual=function(){return(st=u._LessEqual=u.asm.la).apply(null,arguments)},Sn=u._Log=function(){return(Sn=u._Log=u.asm.ma).apply(null,arguments)},Gr=u._LogicalAnd=function(){return(Gr=u._LogicalAnd=u.asm.na).apply(null,arguments)},Hr=u._Max=function(){return(Hr=u._Max=u.asm.oa).apply(null,arguments)},ih=u._MaxPool=function(){return(ih=u._MaxPool=u.asm.pa).apply(null,arguments)},_c=u._Maximum=function(){return(_c=u._Maximum=u.asm.qa).apply(null,arguments)},fs=u._Mean=function(){return(fs=u._Mean=u.asm.ra).apply(null,arguments)},Aa=u._Min=function(){return(Aa=u._Min=u.asm.sa).apply(null,arguments)},lh=u._Minimum=function(){return(lh=u._Minimum=u.asm.ta).apply(null,arguments)},V8=u._MirrorPad=function(){return(V8=u._MirrorPad=u.asm.ua).apply(null,arguments)},U8=u._Multiply=function(){return(U8=u._Multiply=u.asm.va).apply(null,arguments)},G8=u._Neg=function(){return(G8=u._Neg=u.asm.wa).apply(null,arguments)},H8=u._NonMaxSuppressionV3=function(){return(H8=u._NonMaxSuppressionV3=u.asm.xa).apply(null,arguments)},j8=u._NonMaxSuppressionV4=function(){return(j8=u._NonMaxSuppressionV4=u.asm.ya).apply(null,arguments)},q8=u._NonMaxSuppressionV5=function(){return(q8=u._NonMaxSuppressionV5=u.asm.za).apply(null,arguments)},X8=u._NotEqual=function(){return(X8=u._NotEqual=u.asm.Aa).apply(null,arguments)},K8=u._OneHot=function(){return(K8=u._OneHot=u.asm.Ba).apply(null,arguments)},Z8=u._PadV2=function(){return(Z8=u._PadV2=u.asm.Ca).apply(null,arguments)},Y8=u._Pow=function(){return(Y8=u._Pow=u.asm.Da).apply(null,arguments)},J8=u._Prelu=function(){return(J8=u._Prelu=u.asm.Ea).apply(null,arguments)},Q8=u._Prod=function(){return(Q8=u._Prod=u.asm.Fa).apply(null,arguments)},eT=u._RealDiv=function(){return(eT=u._RealDiv=u.asm.Ga).apply(null,arguments)},tT=u._Relu=function(){return(tT=u._Relu=u.asm.Ha).apply(null,arguments)},nT=u._Relu6=function(){return(nT=u._Relu6=u.asm.Ia).apply(null,arguments)},sT=u._ResizeBilinear=function(){return(sT=u._ResizeBilinear=u.asm.Ja).apply(null,arguments)},rT=u._Reverse=function(){return(rT=u._Reverse=u.asm.Ka).apply(null,arguments)},aT=u._RotateWithOffset=function(){return(aT=u._RotateWithOffset=u.asm.La).apply(null,arguments)},oT=u._Round=function(){return(oT=u._Round=u.asm.Ma).apply(null,arguments)},iT=u._Rsqrt=function(){return(iT=u._Rsqrt=u.asm.Na).apply(null,arguments)},lT=u._ScatterNd=function(){return(lT=u._ScatterNd=u.asm.Oa).apply(null,arguments)},uT=u._SelectV2=function(){return(uT=u._SelectV2=u.asm.Pa).apply(null,arguments)},cT=u._Sigmoid=function(){return(cT=u._Sigmoid=u.asm.Qa).apply(null,arguments)},dT=u._Sin=function(){return(dT=u._Sin=u.asm.Ra).apply(null,arguments)},pT=u._Softmax=function(){return(pT=u._Softmax=u.asm.Sa).apply(null,arguments)},hT=u._Sqrt=function(){return(hT=u._Sqrt=u.asm.Ta).apply(null,arguments)},fT=u._Square=function(){return(fT=u._Square=u.asm.Ua).apply(null,arguments)},mT=u._SquaredDifference=function(){return(mT=u._SquaredDifference=u.asm.Va).apply(null,arguments)},gT=u._Step=function(){return(gT=u._Step=u.asm.Wa).apply(null,arguments)},yT=u._StridedSlice=function(){return(yT=u._StridedSlice=u.asm.Xa).apply(null,arguments)},AT=u._Sub=function(){return(AT=u._Sub=u.asm.Ya).apply(null,arguments)},xT=u._Sum=function(){return(xT=u._Sum=u.asm.Za).apply(null,arguments)},bT=u._Tan=function(){return(bT=u._Tan=u.asm._a).apply(null,arguments)},vT=u._Tanh=function(){return(vT=u._Tanh=u.asm.$a).apply(null,arguments)},wT=u._Tile=function(){return(wT=u._Tile=u.asm.ab).apply(null,arguments)},kT=u._TopK=function(){return(kT=u._TopK=u.asm.bb).apply(null,arguments)},IT=u._Transform=function(){return(IT=u._Transform=u.asm.cb).apply(null,arguments)},ST=u._Transpose=function(){return(ST=u._Transpose=u.asm.db).apply(null,arguments)},CT=u.__FusedMatMul=function(){return(CT=u.__FusedMatMul=u.asm.eb).apply(null,arguments)},Zo=u._malloc=function(){return(Zo=u._malloc=u.asm.fb).apply(null,arguments)},Pc=u._free=function(){return(Pc=u._free=u.asm.gb).apply(null,arguments)},Jb=u.___errno_location=function(){return(Jb=u.___errno_location=u.asm.hb).apply(null,arguments)},Qb=u._emscripten_get_global_libc=function(){return(Qb=u._emscripten_get_global_libc=u.asm.ib).apply(null,arguments)},Ml=u._pthread_self=function(){return(Ml=u._pthread_self=u.asm.jb).apply(null,arguments)},e5=u.___pthread_tsd_run_dtors=function(){return(e5=u.___pthread_tsd_run_dtors=u.asm.kb).apply(null,arguments)},Yg=u._emscripten_main_thread_process_queued_calls=function(){return(Yg=u._emscripten_main_thread_process_queued_calls=u.asm.lb).apply(null,arguments)},TT=u._emscripten_current_thread_process_queued_calls=function(){return(TT=u._emscripten_current_thread_process_queued_calls=u.asm.mb).apply(null,arguments)},t5=u._emscripten_register_main_browser_thread_id=function(){return(t5=u._emscripten_register_main_browser_thread_id=u.asm.nb).apply(null,arguments)},n5=u.__emscripten_do_dispatch_to_thread=function(){return(n5=u.__emscripten_do_dispatch_to_thread=u.asm.ob).apply(null,arguments)},s5=u._emscripten_sync_run_in_main_thread_4=function(){return(s5=u._emscripten_sync_run_in_main_thread_4=u.asm.pb).apply(null,arguments)},r5=u._emscripten_run_in_main_runtime_thread_js=function(){return(r5=u._emscripten_run_in_main_runtime_thread_js=u.asm.qb).apply(null,arguments)},Jg=u.__emscripten_call_on_thread=function(){return(Jg=u.__emscripten_call_on_thread=u.asm.rb).apply(null,arguments)},NT=u._emscripten_tls_init=function(){return(NT=u._emscripten_tls_init=u.asm.sb).apply(null,arguments)},Qg=u.__emscripten_thread_init=function(){return(Qg=u.__emscripten_thread_init=u.asm.tb).apply(null,arguments)},Fc=u.stackSave=function(){return(Fc=u.stackSave=u.asm.ub).apply(null,arguments)},zl=u.stackRestore=function(){return(zl=u.stackRestore=u.asm.vb).apply(null,arguments)},Ll=u.stackAlloc=function(){return(Ll=u.stackAlloc=u.asm.wb).apply(null,arguments)},a5=u._emscripten_stack_set_limits=function(){return(a5=u._emscripten_stack_set_limits=u.asm.xb).apply(null,arguments)},o5=u._memalign=function(){return(o5=u._memalign=u.asm.yb).apply(null,arguments)},i5=u.__emscripten_allow_main_runtime_queued_calls=10016,Bl=u.__emscripten_main_thread_futex=11652;u.cwrap=Oe,u.PThread=Re,u.PThread=Re,u.wasmMemory=K,u.ExitStatus=Oc;var uh;function Oc(C){this.name="ExitStatus",this.message="Program terminated with exit("+C+")",this.status=C}Xo=function C(){uh||e2(),uh||(Xo=C)};function e2(C){if(C=C||m,Vr>0)return;if(k){d(u),Cc(),postMessage({cmd:"loaded"});return}if(E0(),Vr>0)return;function F(){uh||(uh=!0,u.calledRun=!0,!ce&&(Cc(),R0(),d(u),u.onRuntimeInitialized&&u.onRuntimeInitialized(),Jn()))}u.setStatus?(u.setStatus("Running..."),setTimeout(function(){setTimeout(function(){u.setStatus("")},1),F()},1)):F()}u.run=e2;function ET(C,F){if(!(F&&te&&C===0)){if(!F&&k)throw postMessage({cmd:"exitProcess",returnCode:C}),new Oc(C);te||(Re.terminateAllThreads(),he=C,Up(),u.onExit&&u.onExit(C),ce=!0),y(C,new Oc(C))}}if(u.preInit)for(typeof u.preInit=="function"&&(u.preInit=[u.preInit]);u.preInit.length>0;)u.preInit.pop()();return k&&(te=!1,Re.initWorker()),e2(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModuleThreadedSimd=n)}}),sN=Dt({"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.9.0_@tensorflow+tfjs-core@3.9.0/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e,t){var n=function(){var s=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(s=s||__filename),function(r){r=r||{};var a=typeof r!="undefined"?r:{},o,i;a.ready=new Promise(function(se,le){o=se,i=le});var l={},c;for(c in a)a.hasOwnProperty(c)&&(l[c]=a[c]);var u=[],d="./this.program",p=function(se,le){throw le},h=!1,f=!1,m=!1,g=!1;h=typeof window=="object",f=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",g=!h&&!m&&!f;var y="";function A(se){return a.locateFile?a.locateFile(se,y):y+se}var x,b,w,k,S,N;m?(f?y=Wc().dirname(y)+"/":y=__dirname+"/",x=function(le,ve){return S||(S=Ul("fs")),N||(N=Wc()),le=N.normalize(le),S.readFileSync(le,ve?null:"utf8")},w=function(le){var ve=x(le,!0);return ve.buffer||(ve=new Uint8Array(ve)),H(ve.buffer),ve},process.argv.length>1&&(d=process.argv[1].replace(/\\/g,"/")),u=process.argv.slice(2),process.on("uncaughtException",function(se){if(!(se instanceof Zg))throw se}),process.on("unhandledRejection",kr),p=function(se){process.exit(se)},a.inspect=function(){return"[Emscripten Module object]"}):g?(typeof read!="undefined"&&(x=function(le){return read(le)}),w=function(le){var ve;return typeof readbuffer=="function"?new Uint8Array(readbuffer(le)):(ve=read(le,"binary"),H(typeof ve=="object"),ve)},typeof scriptArgs!="undefined"?u=scriptArgs:typeof arguments!="undefined"&&(u=arguments),typeof quit=="function"&&(p=function(se){quit(se)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(h||f)&&(f?y=self.location.href:typeof document!="undefined"&&document.currentScript&&(y=document.currentScript.src),s&&(y=s),y.indexOf("blob:")!==0?y=y.substr(0,y.lastIndexOf("/")+1):y="",x=function(se){var le=new XMLHttpRequest;return le.open("GET",se,!1),le.send(null),le.responseText},f&&(w=function(se){var le=new XMLHttpRequest;return le.open("GET",se,!1),le.responseType="arraybuffer",le.send(null),new Uint8Array(le.response)}),b=function(se,le,ve){var lt=new XMLHttpRequest;lt.open("GET",se,!0),lt.responseType="arraybuffer",lt.onload=function(){if(lt.status==200||lt.status==0&&lt.response){le(lt.response);return}ve()},lt.onerror=ve,lt.send(null)},k=function(se){document.title=se});var R=a.print||console.log.bind(console),P=a.printErr||console.warn.bind(console);for(c in l)l.hasOwnProperty(c)&&(a[c]=l[c]);l=null,a.arguments&&(u=a.arguments),a.thisProgram&&(d=a.thisProgram),a.quit&&(p=a.quit);var $;a.wasmBinary&&($=a.wasmBinary);var D=a.noExitRuntime||!0;typeof WebAssembly!="object"&&kr("no native wasm support detected");var T,O=!1,B;function H(se,le){se||kr("Assertion failed: "+le)}function z(se){var le=a["_"+se];return H(le,"Cannot call unknown function "+se+", make sure it is exported"),le}function X(se,le,ve,lt,Wt){var $t={string:function(fs){var Aa=0;if(fs!=null&&fs!==0){var lh=(fs.length<<2)+1;Aa=$c(lh),oe(fs,Aa,lh)}return Aa},array:function(fs){var Aa=$c(fs.length);return ce(fs,Aa),Aa}};function Qe(fs){return le==="string"?te(fs):le==="boolean"?Boolean(fs):fs}var st=z(se),Sn=[],Gr=0;if(lt)for(var Hr=0;Hr<lt.length;Hr++){var ih=$t[ve[Hr]];ih?(Gr===0&&(Gr=ah()),Sn[Hr]=ih(lt[Hr])):Sn[Hr]=lt[Hr]}var _c=st.apply(null,Sn);return _c=Qe(_c),Gr!==0&&oh(Gr),_c}function ee(se,le,ve,lt){ve=ve||[];var Wt=ve.every(function(Qe){return Qe==="number"}),$t=le!=="string";return $t&&Wt&&!lt?z(se):function(){return X(se,le,ve,arguments,lt)}}var J=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function Q(se,le,ve){for(var lt=le+ve,Wt=le;se[Wt]&&!(Wt>=lt);)++Wt;if(Wt-le>16&&se.subarray&&J)return J.decode(se.subarray(le,Wt));for(var $t="";le<Wt;){var Qe=se[le++];if(!(Qe&128)){$t+=String.fromCharCode(Qe);continue}var st=se[le++]&63;if((Qe&224)==192){$t+=String.fromCharCode((Qe&31)<<6|st);continue}var Sn=se[le++]&63;if((Qe&240)==224?Qe=(Qe&15)<<12|st<<6|Sn:Qe=(Qe&7)<<18|st<<12|Sn<<6|se[le++]&63,Qe<65536)$t+=String.fromCharCode(Qe);else{var Gr=Qe-65536;$t+=String.fromCharCode(55296|Gr>>10,56320|Gr&1023)}}return $t}function te(se,le){return se?Q(Se,se,le):""}function K(se,le,ve,lt){if(!(lt>0))return 0;for(var Wt=ve,$t=ve+lt-1,Qe=0;Qe<se.length;++Qe){var st=se.charCodeAt(Qe);if(st>=55296&&st<=57343){var Sn=se.charCodeAt(++Qe);st=65536+((st&1023)<<10)|Sn&1023}if(st<=127){if(ve>=$t)break;le[ve++]=st}else if(st<=2047){if(ve+1>=$t)break;le[ve++]=192|st>>6,le[ve++]=128|st&63}else if(st<=65535){if(ve+2>=$t)break;le[ve++]=224|st>>12,le[ve++]=128|st>>6&63,le[ve++]=128|st&63}else{if(ve+3>=$t)break;le[ve++]=240|st>>18,le[ve++]=128|st>>12&63,le[ve++]=128|st>>6&63,le[ve++]=128|st&63}}return le[ve]=0,ve-Wt}function oe(se,le,ve){return K(se,Se,le,ve)}function ce(se,le){Ie.set(se,le)}function he(se,le){return se%le>0&&(se+=le-se%le),se}var Ae,Ie,Se,Oe,Ue,ze,wt,mt,gt;function pt(se){Ae=se,a.HEAP8=Ie=new Int8Array(se),a.HEAP16=Oe=new Int16Array(se),a.HEAP32=ze=new Int32Array(se),a.HEAPU8=Se=new Uint8Array(se),a.HEAPU16=Ue=new Uint16Array(se),a.HEAPU32=wt=new Uint32Array(se),a.HEAPF32=mt=new Float32Array(se),a.HEAPF64=gt=new Float64Array(se)}var bt=a.INITIAL_MEMORY||16777216,Ye,Yn=[],Ot=[],ps=[],kn=[],Gs=!1;Ot.push({func:function(){Kp()}});function Fn(){if(a.preRun)for(typeof a.preRun=="function"&&(a.preRun=[a.preRun]);a.preRun.length;)Rs(a.preRun.shift());ma(Yn)}function Ns(){Gs=!0,ma(Ot)}function Es(){ma(ps)}function In(){if(a.postRun)for(typeof a.postRun=="function"&&(a.postRun=[a.postRun]);a.postRun.length;)$s(a.postRun.shift());ma(kn)}function Rs(se){Yn.unshift(se)}function $s(se){kn.unshift(se)}var hs=0,wr=null,Wr=null;function fa(se){hs++,a.monitorRunDependencies&&a.monitorRunDependencies(hs)}function Dl(se){if(hs--,a.monitorRunDependencies&&a.monitorRunDependencies(hs),hs==0&&(wr!==null&&(clearInterval(wr),wr=null),Wr)){var le=Wr;Wr=null,le()}}a.preloadedImages={},a.preloadedAudios={};function kr(se){a.onAbort&&a.onAbort(se),se+="",P(se),O=!0,B=1,se="abort("+se+"). Build with -s ASSERTIONS=1 for more info.";var le=new WebAssembly.RuntimeError(se);throw i(le),le}function Vp(se,le){return String.prototype.startsWith?se.startsWith(le):se.indexOf(le)===0}var E0="data:application/octet-stream;base64,";function Cc(se){return Vp(se,E0)}var R0="file://";function Up(se){return Vp(se,R0)}var Jn="tfjs-backend-wasm.wasm";Cc(Jn)||(Jn=A(Jn));function Gp(se){try{if(se==Jn&&$)return new Uint8Array($);if(w)return w(se);throw"both async and sync fetching of the wasm failed"}catch(le){kr(le)}}function $0(){if(!$&&(h||f)){if(typeof fetch=="function"&&!Up(Jn))return fetch(Jn,{credentials:"same-origin"}).then(function(se){if(!se.ok)throw"failed to load wasm binary file at '"+Jn+"'";return se.arrayBuffer()}).catch(function(){return Gp(Jn)});if(b)return new Promise(function(se,le){b(Jn,function(ve){se(new Uint8Array(ve))},le)})}return Promise.resolve().then(function(){return Gp(Jn)})}function Vr(){var se={a:O0};function le(Qe,st){var Sn=Qe.exports;a.asm=Sn,T=a.asm.i,pt(T.buffer),Ye=a.asm.o,Dl("wasm-instantiate")}fa("wasm-instantiate");function ve(Qe){le(Qe.instance)}function lt(Qe){return $0().then(function(st){return WebAssembly.instantiate(st,se)}).then(Qe,function(st){P("failed to asynchronously prepare wasm: "+st),kr(st)})}function Wt(){return!$&&typeof WebAssembly.instantiateStreaming=="function"&&!Cc(Jn)&&!Up(Jn)&&typeof fetch=="function"?fetch(Jn,{credentials:"same-origin"}).then(function(Qe){var st=WebAssembly.instantiateStreaming(Qe,se);return st.then(ve,function(Sn){return P("wasm streaming compile failed: "+Sn),P("falling back to ArrayBuffer instantiation"),lt(ve)})}):lt(ve)}if(a.instantiateWasm)try{var $t=a.instantiateWasm(se,le);return $t}catch(Qe){return P("Module.instantiateWasm callback failed with error: "+Qe),!1}return Wt().catch(i),{}}function ma(se){for(;se.length>0;){var le=se.shift();if(typeof le=="function"){le(a);continue}var ve=le.func;typeof ve=="number"?le.arg===void 0?Ye.get(ve)():Ye.get(ve)(le.arg):ve(le.arg===void 0?null:le.arg)}}function Xo(){kr()}function D0(se,le,ve){Se.copyWithin(se,le,le+ve)}function _0(){return Se.length}function Ur(se){try{return T.grow(se-Ae.byteLength+65535>>>16),pt(T.buffer),1}catch(le){}}function Hp(se){var le=_0(),ve=2147483648;if(se>ve)return!1;for(var lt=1;lt<=4;lt*=2){var Wt=le*(1+.2/lt);Wt=Math.min(Wt,se+100663296);var $t=Math.min(ve,he(Math.max(se,Wt),65536)),Qe=Ur($t);if(Qe)return!0}return!1}var _l={mappings:{},buffers:[null,[],[]],printChar:function(se,le){var ve=_l.buffers[se];le===0||le===10?((se===1?R:P)(Q(ve,0)),ve.length=0):ve.push(le)},varargs:void 0,get:function(){_l.varargs+=4;var se=ze[_l.varargs-4>>2];return se},getStr:function(se){var le=te(se);return le},get64:function(se,le){return se}};function jp(se){return 0}function P0(se,le,ve,lt,Wt){}function qp(se,le,ve,lt){for(var Wt=0,$t=0;$t<ve;$t++){for(var Qe=ze[le+$t*8>>2],st=ze[le+($t*8+4)>>2],Sn=0;Sn<st;Sn++)_l.printChar(se,Se[Qe+Sn]);Wt+=st}return ze[lt>>2]=Wt,0}function Qn(){return 6}function Xp(se){return ze[rh()>>2]=se,se}function F0(se){switch(se){case 30:return 16384;case 85:var le=2147483648;return le/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 Xp(28),-1}var O0={a:Xo,d:D0,e:Hp,f:jp,c:P0,b:qp,g:Qn,h:F0},M0=Vr(),Kp=a.___wasm_call_ctors=function(){return(Kp=a.___wasm_call_ctors=a.asm.j).apply(null,arguments)},Pl=a._init=function(){return(Pl=a._init=a.asm.k).apply(null,arguments)},Tc=a._register_tensor=function(){return(Tc=a._register_tensor=a.asm.l).apply(null,arguments)},z0=a._dispose_data=function(){return(z0=a._dispose_data=a.asm.m).apply(null,arguments)},L0=a._dispose=function(){return(L0=a._dispose=a.asm.n).apply(null,arguments)},B0=a._Abs=function(){return(B0=a._Abs=a.asm.p).apply(null,arguments)},Re=a._Add=function(){return(Re=a._Add=a.asm.q).apply(null,arguments)},W0=a._AddN=function(){return(W0=a._AddN=a.asm.r).apply(null,arguments)},V0=a._All=function(){return(V0=a._All=a.asm.s).apply(null,arguments)},U0=a._Any=function(){return(U0=a._Any=a.asm.t).apply(null,arguments)},G0=a._ArgMax=function(){return(G0=a._ArgMax=a.asm.u).apply(null,arguments)},H0=a._AvgPool=function(){return(H0=a._AvgPool=a.asm.v).apply(null,arguments)},Ko=a._BatchMatMul=function(){return(Ko=a._BatchMatMul=a.asm.w).apply(null,arguments)},j0=a._Ceil=function(){return(j0=a._Ceil=a.asm.x).apply(null,arguments)},q0=a._ClipByValue=function(){return(q0=a._ClipByValue=a.asm.y).apply(null,arguments)},X0=a._Conv2D=function(){return(X0=a._Conv2D=a.asm.z).apply(null,arguments)},K0=a._Conv2DBackpropInput=function(){return(K0=a._Conv2DBackpropInput=a.asm.A).apply(null,arguments)},Z0=a._Cos=function(){return(Z0=a._Cos=a.asm.B).apply(null,arguments)},Y0=a._Cosh=function(){return(Y0=a._Cosh=a.asm.C).apply(null,arguments)},J0=a._CropAndResize=function(){return(J0=a._CropAndResize=a.asm.D).apply(null,arguments)},Q0=a._Cumsum=function(){return(Q0=a._Cumsum=a.asm.E).apply(null,arguments)},eg=a._DepthToSpace=function(){return(eg=a._DepthToSpace=a.asm.F).apply(null,arguments)},ga=a._DepthwiseConv2dNative=function(){return(ga=a._DepthwiseConv2dNative=a.asm.G).apply(null,arguments)},Nc=a._Elu=function(){return(Nc=a._Elu=a.asm.H).apply(null,arguments)},Ec=a._Equal=function(){return(Ec=a._Equal=a.asm.I).apply(null,arguments)},tg=a._Exp=function(){return(tg=a._Exp=a.asm.J).apply(null,arguments)},ng=a._FlipLeftRight=function(){return(ng=a._FlipLeftRight=a.asm.K).apply(null,arguments)},sg=a._Floor=function(){return(sg=a._Floor=a.asm.L).apply(null,arguments)},rg=a._FloorDiv=function(){return(rg=a._FloorDiv=a.asm.M).apply(null,arguments)},ag=a._FusedBatchNorm=function(){return(ag=a._FusedBatchNorm=a.asm.N).apply(null,arguments)},qe=a._FusedConv2D=function(){return(qe=a._FusedConv2D=a.asm.O).apply(null,arguments)},og=a._FusedDepthwiseConv2D=function(){return(og=a._FusedDepthwiseConv2D=a.asm.P).apply(null,arguments)},ig=a._Gather=function(){return(ig=a._Gather=a.asm.Q).apply(null,arguments)},lg=a._GatherNd=function(){return(lg=a._GatherNd=a.asm.R).apply(null,arguments)},ug=a._Greater=function(){return(ug=a._Greater=a.asm.S).apply(null,arguments)},cg=a._GreaterEqual=function(){return(cg=a._GreaterEqual=a.asm.T).apply(null,arguments)},dg=a._LeakyRelu=function(){return(dg=a._LeakyRelu=a.asm.U).apply(null,arguments)},Rc=a._Less=function(){return(Rc=a._Less=a.asm.V).apply(null,arguments)},Zp=a._LessEqual=function(){return(Zp=a._LessEqual=a.asm.W).apply(null,arguments)},Yp=a._Log=function(){return(Yp=a._Log=a.asm.X).apply(null,arguments)},pg=a._LogicalAnd=function(){return(pg=a._LogicalAnd=a.asm.Y).apply(null,arguments)},hg=a._Max=function(){return(hg=a._Max=a.asm.Z).apply(null,arguments)},fg=a._MaxPool=function(){return(fg=a._MaxPool=a.asm._).apply(null,arguments)},mg=a._Maximum=function(){return(mg=a._Maximum=a.asm.$).apply(null,arguments)},gg=a._Mean=function(){return(gg=a._Mean=a.asm.aa).apply(null,arguments)},yg=a._Min=function(){return(yg=a._Min=a.asm.ba).apply(null,arguments)},Ag=a._Minimum=function(){return(Ag=a._Minimum=a.asm.ca).apply(null,arguments)},ht=a._MirrorPad=function(){return(ht=a._MirrorPad=a.asm.da).apply(null,arguments)},xg=a._Multiply=function(){return(xg=a._Multiply=a.asm.ea).apply(null,arguments)},bg=a._Neg=function(){return(bg=a._Neg=a.asm.fa).apply(null,arguments)},vg=a._NonMaxSuppressionV3=function(){return(vg=a._NonMaxSuppressionV3=a.asm.ga).apply(null,arguments)},Fl=a._NonMaxSuppressionV4=function(){return(Fl=a._NonMaxSuppressionV4=a.asm.ha).apply(null,arguments)},Jp=a._NonMaxSuppressionV5=function(){return(Jp=a._NonMaxSuppressionV5=a.asm.ia).apply(null,arguments)},Qp=a._NotEqual=function(){return(Qp=a._NotEqual=a.asm.ja).apply(null,arguments)},eh=a._OneHot=function(){return(eh=a._OneHot=a.asm.ka).apply(null,arguments)},wg=a._PadV2=function(){return(wg=a._PadV2=a.asm.la).apply(null,arguments)},kg=a._Pow=function(){return(kg=a._Pow=a.asm.ma).apply(null,arguments)},th=a._Prelu=function(){return(th=a._Prelu=a.asm.na).apply(null,arguments)},Ig=a._Prod=function(){return(Ig=a._Prod=a.asm.oa).apply(null,arguments)},nh=a._RealDiv=function(){return(nh=a._RealDiv=a.asm.pa).apply(null,arguments)},ya=a._Relu=function(){return(ya=a._Relu=a.asm.qa).apply(null,arguments)},Sg=a._Relu6=function(){return(Sg=a._Relu6=a.asm.ra).apply(null,arguments)},Cg=a._ResizeBilinear=function(){return(Cg=a._ResizeBilinear=a.asm.sa).apply(null,arguments)},Yb=a._Reverse=function(){return(Yb=a._Reverse=a.asm.ta).apply(null,arguments)},sh=a._RotateWithOffset=function(){return(sh=a._RotateWithOffset=a.asm.ua).apply(null,arguments)},Tg=a._Round=function(){return(Tg=a._Round=a.asm.va).apply(null,arguments)},Ng=a._Rsqrt=function(){return(Ng=a._Rsqrt=a.asm.wa).apply(null,arguments)},Eg=a._ScatterNd=function(){return(Eg=a._ScatterNd=a.asm.xa).apply(null,arguments)},Rg=a._SelectV2=function(){return(Rg=a._SelectV2=a.asm.ya).apply(null,arguments)},$g=a._Sigmoid=function(){return($g=a._Sigmoid=a.asm.za).apply(null,arguments)},Dg=a._Sin=function(){return(Dg=a._Sin=a.asm.Aa).apply(null,arguments)},_g=a._Softmax=function(){return(_g=a._Softmax=a.asm.Ba).apply(null,arguments)},Pg=a._Sqrt=function(){return(Pg=a._Sqrt=a.asm.Ca).apply(null,arguments)},Fg=a._Square=function(){return(Fg=a._Square=a.asm.Da).apply(null,arguments)},Og=a._SquaredDifference=function(){return(Og=a._SquaredDifference=a.asm.Ea).apply(null,arguments)},Mg=a._Step=function(){return(Mg=a._Step=a.asm.Fa).apply(null,arguments)},zg=a._StridedSlice=function(){return(zg=a._StridedSlice=a.asm.Ga).apply(null,arguments)},Lg=a._Sub=function(){return(Lg=a._Sub=a.asm.Ha).apply(null,arguments)},Bg=a._Sum=function(){return(Bg=a._Sum=a.asm.Ia).apply(null,arguments)},Wg=a._Tan=function(){return(Wg=a._Tan=a.asm.Ja).apply(null,arguments)},Vg=a._Tanh=function(){return(Vg=a._Tanh=a.asm.Ka).apply(null,arguments)},Ug=a._Tile=function(){return(Ug=a._Tile=a.asm.La).apply(null,arguments)},Gg=a._TopK=function(){return(Gg=a._TopK=a.asm.Ma).apply(null,arguments)},Hg=a._Transform=function(){return(Hg=a._Transform=a.asm.Na).apply(null,arguments)},jg=a._Transpose=function(){return(jg=a._Transpose=a.asm.Oa).apply(null,arguments)},qg=a.__FusedMatMul=function(){return(qg=a.__FusedMatMul=a.asm.Pa).apply(null,arguments)},Xg=a._malloc=function(){return(Xg=a._malloc=a.asm.Qa).apply(null,arguments)},Kg=a._free=function(){return(Kg=a._free=a.asm.Ra).apply(null,arguments)},rh=a.___errno_location=function(){return(rh=a.___errno_location=a.asm.Sa).apply(null,arguments)},ah=a.stackSave=function(){return(ah=a.stackSave=a.asm.Ta).apply(null,arguments)},oh=a.stackRestore=function(){return(oh=a.stackRestore=a.asm.Ua).apply(null,arguments)},$c=a.stackAlloc=function(){return($c=a.stackAlloc=a.asm.Va).apply(null,arguments)};a.cwrap=ee;var Ol;function Zg(se){this.name="ExitStatus",this.message="Program terminated with exit("+se+")",this.status=se}Wr=function se(){Ol||Dc(),Ol||(Wr=se)};function Dc(se){if(se=se||u,hs>0||(Fn(),hs>0))return;function le(){Ol||(Ol=!0,a.calledRun=!0,!O&&(Ns(),Es(),o(a),a.onRuntimeInitialized&&a.onRuntimeInitialized(),In()))}a.setStatus?(a.setStatus("Running..."),setTimeout(function(){setTimeout(function(){a.setStatus("")},1),le()},1)):le()}if(a.run=Dc,a.preInit)for(typeof a.preInit=="function"&&(a.preInit=[a.preInit]);a.preInit.length>0;)a.preInit.pop()();return Dc(),r.ready}}();typeof e=="object"&&typeof t=="object"?t.exports=n:typeof define=="function"&&define.amd?define([],function(){return n}):typeof e=="object"&&(e.WasmBackendModule=n)}}),rN=1e-7,aN=1e-4,Vc=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}},Gl=class{refCount(e){return Hs("refCount")}incRef(e){return Hs("incRef")}timerAvailable(){return!0}time(e){return Hs("time")}read(e){return Hs("read")}readSync(e){return Hs("readSync")}numDataIds(){return Hs("numDataIds")}disposeData(e,t){return Hs("disposeData")}write(e,t,n){return Hs("write")}move(e,t,n,s,r){return Hs("move")}memory(){return Hs("memory")}floatPrecision(){return Hs("floatPrecision")}epsilon(){return this.floatPrecision()===32?rN:aN}dispose(){return Hs("dispose")}};function Hs(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 m5(e){let t=e.length,n=0;for(;t>0;)n=Math.random()*t|0,t--,dh(e,t,n)}function oN(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,s=0;for(;n>0;)s=Math.random()*n|0,n--,dh(e,n,s),dh(t,n,s)}function Uc(e,t,n){return Math.max(e,Math.min(t,n))}function iN(e){return e%2==0?e:e+1}function dh(e,t,n){let s=e[t];e[t]=e[n],e[n]=s}function lN(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}function uN(e,t){let n=Math.random();return t*n+(1-n)*e}function cN(e,t){let n=0;for(let s=0;s<e.length;s++){let r=Number(e[s])-Number(t[s]);n+=r*r}return n}function M(e,t){if(!e)throw new Error(typeof t=="string"?t:t())}function Mn(e,t,n=""){M(jr(e,t),()=>n+` Shapes ${e} and ${t} must match`)}function Qo(e){M(e!=null,()=>"The input to the tensor constructor must be a non-null value.")}function ei(e,t=[],n=!1){if(t==null&&(t=[]),Array.isArray(e)||_n(e)&&!n)for(let s=0;s<e.length;++s)ei(e[s],t,n);else t.push(e);return t}function Gt(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 dN(e){return e.length===0}function jr(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 mn(e){return e%1==0}function pN(e){if(Math.tanh!=null)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return-1;{let t=Math.exp(2*e);return(t-1)/(t+1)}}function hN(e){let t=Math.ceil(Math.sqrt(e));return[t,Math.ceil(e/t)]}function fN(e){let t=new Uint32Array(e);for(let n=0;n<e;++n)t[n]=n;return m5(t),t}function Gc(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function mN(e,t=s=>0,n){return new Promise((s,r)=>{let a=0,o=()=>{if(e()){s();return}a++;let i=t(a);if(n!=null&&a>=n){r();return}setTimeout(o,i)};o()})}function gN(e,t){let n=1,s=-1;for(let a=0;a<e.length;++a)if(e[a]>=0)n*=e[a];else if(e[a]===-1){if(s!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${s} and dim ${a}`);s=a}else if(e[a]<0)throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);if(s===-1){if(t>0&&t!==n)throw Error(`Size(${t}) must match the product of shape ${e}`);return e}if(n===0)throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);if(t%n!=0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);let r=e.slice();return r[s]=t/n,r}function js(e,t){let n=t.length;return e=e==null?t.map((s,r)=>r):[].concat(e),M(e.every(s=>s>=-n&&s<n),()=>`All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`),M(e.every(s=>mn(s)),()=>`All values in axis param must be integers but got axis ${e}`),e.map(s=>s<0?n+s:s)}function g5(e,t){let n=[],s=[],r=t!=null&&Array.isArray(t)&&t.length===0,a=t==null||r?null:js(t,e).sort(),o=0;for(let i=0;i<e.length;++i){if(a!=null){if(a[o]===i&&e[i]!==1)throw new Error(`Can't squeeze axis ${i} since its dim '${e[i]}' is not 1`);(a[o]==null||a[o]>i)&&e[i]===1&&(n.push(e[i]),s.push(i)),a[o]<=i&&o++}e[i]!==1&&(n.push(e[i]),s.push(i))}return{newShape:n,keptDims:s}}function y5(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 A5(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 x5(e,t){for(let n=0;n<e.length;n++){let s=e[n];if(isNaN(s)||!isFinite(s))throw Error(`A tensor of type ${t} being uploaded contains ${s}.`)}}function b5(e){return e==="bool"||e==="complex64"||e==="float32"||e==="int32"||e==="string"}function yN(e,t){return!(t==="complex64"||t==="float32"&&e!=="complex64"||t==="int32"&&e!=="float32"&&e!=="complex64"||t==="bool"&&e==="bool")}function _n(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function s2(e){if(e==="float32"||e==="int32")return 4;if(e==="complex64")return 8;if(e==="bool")return 1;throw new Error(`Unknown dtype ${e}`)}function v5(e){if(e==null)return 0;let t=0;return e.forEach(n=>t+=n.length),t}function va(e){return typeof e=="string"||e instanceof String}function w5(e){return typeof e=="boolean"}function k5(e){return typeof e=="number"}function ph(e){return Array.isArray(e)?ph(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":k5(e)?"float32":va(e)?"string":w5(e)?"bool":"float32"}function wa(e){return!!(e&&e.constructor&&e.call&&e.apply)}function hh(e,t){for(let n=t;n<e;++n)if(e%n==0)return n;return e}function Hl(e){let t=e.length;if(t<2)return[];let n=new Array(t-1);n[t-2]=e[t-1];for(let s=t-3;s>=0;--s)n[s]=n[s+1]*e[s+1];return n}function I5(e,t,n,s=!1){let r=new Array;if(t.length===1){let a=t[0]*(s?2:1);for(let o=0;o<a;o++)r[o]=n[e+o]}else{let a=t[0],o=t.slice(1),i=o.reduce((l,c)=>l*c)*(s?2:1);for(let l=0;l<a;l++)r[l]=I5(e+l*i,o,n,s)}return r}function jl(e,t,n=!1){if(e.length===0)return t[0];let s=e.reduce((r,a)=>r*a)*(n?2:1);if(s===0)return[];if(s!==t.length)throw new Error(`[${e}] does not match the input size ${t.length}${n?" for a complex tensor":""}.`);return I5(0,e,t,n)}function r2(e,t){let n=fh(e,t);for(let s=0;s<n.length;s++)n[s]=1;return n}function fh(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 AN(e,t){let n=e.reduce((s,r)=>s*r,1);if(t==null||t==="float32")return jl(e,new Float32Array(n));if(t==="int32")return jl(e,new Int32Array(n));if(t==="bool")return jl(e,new Uint8Array(n));throw new Error(`Unknown data type ${t}`)}function a2(e){e.forEach(t=>{M(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${e}].`)})}function xN(e,t,n){if(t===0)return 0;if(t===1)return e[0];let s=e[e.length-1];for(let r=0;r<e.length-1;++r)s+=n[r]*e[r];return s}function bN(e,t,n){if(t===0)return[];if(t===1)return[e];let s=new Array(t);for(let r=0;r<s.length-1;++r)s[r]=Math.floor(e/n[r]),e-=s[r]*n[r];return s[s.length-1]=e,s}function o2(e){return e&&e.then&&typeof e.then=="function"}function Ir(...e){Z().getBool("IS_TEST")||Z().getBool("PROD")||console.warn(...e)}function vN(...e){Z().getBool("IS_TEST")||Z().getBool("PROD")||console.log(...e)}var S5="tfjsflags",C5=class{constructor(e){this.global=e,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=wN,this.populateURLFlags()}setPlatform(e,t){this.platform!=null&&Ir(`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 s=this.urlFlags[e];Ir(`Setting feature override from URL ${e}: ${s}.`),this.set(e,s)}}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(o2(t))throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[e]=t,this.flags[e]}getNumber(e){return this.get(e)}getBool(e){return this.get(e)}getFlags(){return this.flags}get features(){return this.flags}set(e,t){if(this.flagRegistry[e]==null)throw new Error(`Cannot set flag ${e} as it has not been registered.`);this.flags[e]=t,this.flagRegistry[e].setHook!=null&&this.flagRegistry[e].setHook(t)}evaluateFlag(e){if(this.flagRegistry[e]==null)throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);return this.flagRegistry[e].evaluationFn()}setFlags(e){this.flags=Object.assign({},e)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let e=this.getQueryParams(this.global.location.search);S5 in e&&e[S5].split(",").forEach(n=>{let[s,r]=n.split(":");this.urlFlags[s]=IN(s,r)})}};function wN(e){let t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(n,...s)=>(kN(t,s[0],s[1]),s.join("="))),t}function kN(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}function IN(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 ${e}.`)}function Z(){return qs}var qs=null;function SN(e){qs=e}var i2;function T5(){if(i2==null){let e;if(typeof window!="undefined")e=window;else if(typeof global!="undefined")e=global;else if(typeof process!="undefined")e=process;else if(typeof self!="undefined")e=self;else throw new Error("Could not find a global object");i2=e}return i2}function CN(){let e=T5();return e._tfGlobals==null&&(e._tfGlobals=new Map),e._tfGlobals}function l2(e,t){let n=CN();if(n.has(e))return n.get(e);{let s=t();return n.set(e,s),n.get(e)}}var ti="Abs",ql="Acos",Xl="Acosh",qr="Add",ka="AddN",Kl="All",Zl="Any",Ia="ArgMax",Yl="ArgMin",Jl="Asin",Ql="Asinh",eu="Atan",tu="Atanh",nu="Atan2",Sa="AvgPool",mh="AvgPoolGrad",Hc="AvgPool3D",gh="AvgPool3DGrad",Ca="BatchMatMul",ni="BatchToSpaceND",yh="Bincount",N5="BroadcastTo",u2="BroadcastArgs",Ta="Cast",Na="Ceil",Xr="ClipByValue",jc="Complex",qc="ComplexAbs",si="Concat",Ea="Conv2D",Ah="Conv2DBackpropFilter",Ra="Conv2DBackpropInput",Xc="Conv3D",xh="Conv3DBackpropFilterV2",bh="Conv3DBackpropInputV2",$a="Cos",Da="Cosh",ri="Cumsum",ai="CropAndResize",vh="DenseBincount",oi="DepthToSpace",_a="DepthwiseConv2dNative",wh="DepthwiseConv2dNativeBackpropFilter",kh="DepthwiseConv2dNativeBackpropInput",Ih="Diag",Kc="Dilation2D",Sh="Dilation2DBackpropInput",Ch="Dilation2DBackpropFilter",Pa="RealDiv",Zc="Einsum",Fa="Elu",Th="EluGrad",su="Erf",ii="Equal",Oa="Exp",li="ExpandDims",ui="Expm1",Nh="FFT",ru="Fill",ci="FlipLeftRight",Ma="Floor",za="FloorDiv",La="FusedBatchNorm",di="GatherV2",pi="GatherNd",hi="Greater",Ba="GreaterEqual",Wa="Identity",Eh="IFFT",Yc="Imag",au="IsFinite",ou="IsInf",iu="IsNan",fi="LeakyRelu",mi="Less",gi="LessEqual",Rh="LinSpace",Va="Log",lu="Log1p",yi="LogicalAnd",uu="LogicalNot",Jc="LogicalOr",E5="LogSoftmax",Qc="LRN",$h="LRNGrad",Ua="Max",Ga="Maximum",Ha="MaxPool",Dh="MaxPoolGrad",ed="MaxPool3D",_h="MaxPool3DGrad",Ph="MaxPoolWithArgmax",ja="Mean",qa="Min",Xa="Minimum",Ka="MirrorPad",cu="Mod",Fh="Multinomial",Za="Multiply",Ai="Neg",xi="NotEqual",bi="NonMaxSuppressionV3",du="NonMaxSuppressionV4",vi="NonMaxSuppressionV5",wi="OnesLike",ki="OneHot",Ii="Pack",Ya="PadV2",TN="Pool",Ja="Pow",Qa="Prelu",Si="Prod",pu="Range",td="Real",hu="Reciprocal",eo="Relu",Ci="Reshape",fu="ResizeNearestNeighbor",Oh="ResizeNearestNeighborGrad",to="ResizeBilinear",Mh="ResizeBilinearGrad",no="Relu6",Ti="Reverse",Ni="Round",so="Rsqrt",Ei="ScatterNd",Ri="Select",mu="Selu",$i="Slice",ro="Sin",Di="Sinh",gu="Sign",ao="Sigmoid",yu="Softplus",oo="Sqrt",io="Sum",_i="SpaceToBatchND",Pi="SplitV",lo="Softmax",zh="SparseFillEmptyRows",Lh="SparseReshape",Bh="SparseSegmentMean",Wh="SparseSegmentSum",nd="SparseToDense",uo="SquaredDifference",Au="Square",Fi="StridedSlice",sd="StringNGrams",Vh="StringSplit",Uh="StringToHashBucketFast",co="Sub",Oi="Tan",po="Tanh",Kr="Tile",xu="TopK",Mi="Transform",ho="Transpose",Gh="Unique",zi="Unpack",rd="UnsortedSegmentSum",Li="ZerosLike",fo="Step",ad="FromPixels",Bi="RotateWithOffset",mo="_FusedMatMul",go="FusedConv2D",yo="FusedDepthwiseConv2D",bu=l2("kernelRegistry",()=>new Map),od=l2("gradRegistry",()=>new Map);function Hh(e,t){let n=d2(e,t);return bu.get(n)}function c2(e){return od.get(e)}function Zr(e){let t=bu.entries(),n=[];for(;;){let{done:s,value:r}=t.next();if(s)break;let[a,o]=r,[i]=a.split("_");i===e&&n.push(o)}return n}function Yr(e){let{kernelName:t,backendName:n}=e,s=d2(t,n);bu.has(s)&&Ir(`The kernel '${t}' for backend '${n}' is already registered`),bu.set(s,e)}function R5(e){let{kernelName:t}=e;od.has(t)&&Z().getBool("DEBUG")&&Ir(`Overriding the gradient for '${t}'`),od.set(t,e)}function NN(e,t){let n=d2(e,t);if(!bu.has(n))throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);bu.delete(n)}function EN(e){if(!od.has(e))throw new Error(`The gradient '${e}' for backend is not registered`);od.delete(e)}function RN(e,t){Zr(e).forEach(s=>{let r=Object.assign({},s,{backendName:t});Yr(r)})}function d2(e,t){return`${t}_${e}`}var v={};Le(v,{arraysEqual:()=>jr,assert:()=>M,assertNonNegativeIntegerDimensions:()=>a2,assertNonNull:()=>Qo,assertShapesMatch:()=>Mn,bytesFromStringArray:()=>v5,bytesPerElement:()=>s2,checkConversionForErrors:()=>x5,clamp:()=>Uc,computeStrides:()=>Hl,createScalarValue:()=>ON,createShuffledIndices:()=>fN,decodeString:()=>Xh,distSquared:()=>cN,encodeString:()=>ud,fetch:()=>zN,fingerPrint64:()=>FN,flatten:()=>ei,getArrayFromDType:()=>A5,getTypedArrayFromDType:()=>y5,hasEncodingLoss:()=>yN,hexToLong:()=>id,indexToLoc:()=>bN,inferDtype:()=>ph,inferFromImplicitShape:()=>gN,isBoolean:()=>w5,isFunction:()=>wa,isInt:()=>mn,isNumber:()=>k5,isPromise:()=>o2,isScalarShape:()=>dN,isString:()=>va,isTypedArray:()=>_n,isValidDtype:()=>b5,locToIndex:()=>xN,makeOnesTypedArray:()=>r2,makeZerosNestedTypedArray:()=>AN,makeZerosTypedArray:()=>fh,nearestDivisor:()=>hh,nearestLargerEven:()=>iN,now:()=>ld,parseAxisParam:()=>js,randUniform:()=>uN,repeatedTry:()=>mN,rightPad:()=>Gc,shuffle:()=>m5,shuffleCombo:()=>oN,sizeFromShape:()=>Gt,sizeToSquarishShape:()=>hN,squeezeShape:()=>g5,sum:()=>lN,swap:()=>dh,tanh:()=>pN,toNestedArray:()=>jl,toTypedArray:()=>qh});var $5=Jo(zT()),Wi=$5.default||$5;function id(e){return Wi.fromString(e,!0,16)}var D5=id("c3a5c85c97cb3127"),Vi=id("b492b66fbe98f273"),zn=id("9ae16a3b2f90404f");function p2(e){return e.xor(e.shru(47))}function _5(e,t,n){let s=e.slice(t,t+n);return Wi.fromBytes(Array.from(s),!0,!0)}function Ct(e,t){return _5(e,t,8)}function P5(e,t){return _5(e,t,4)}function gn(e,t){return t===0?e:e.shru(t).or(e.shl(64-t))}function Ao(e,t,n=id("9ddfea08eb382d69")){let s=e.xor(t).mul(n);s=s.xor(s.shru(47));let r=t.xor(s).mul(n);return r=r.xor(r.shru(47)),r=r.mul(n),r}function $N(e,t,n,s,r,a){r=r.add(e),a=gn(a.add(r).add(s),21);let o=r;return r=r.add(t),r=r.add(n),a=a.add(gn(r,44)),[r.add(s),a.add(o)]}function jh(e,t,n,s){return $N(Ct(e,t),Ct(e,t+8),Ct(e,t+16),Ct(e,t+24),n,s)}function DN(e,t=e.length){if(t>=8){let n=zn.add(t*2),s=Ct(e,0).add(zn),r=Ct(e,t-8),a=gn(r,37).mul(n).add(s),o=gn(s,25).add(r).mul(n);return Ao(a,o,n)}if(t>=4){let n=zn.add(t*2),s=P5(e,0);return Ao(s.shl(3).add(t),P5(e,t-4),n)}if(t>0){let n=e[0],s=e[t>>1],r=e[t-1],a=n+(s<<8),o=t+(r<<2);return p2(zn.mul(a).xor(D5.mul(o))).mul(zn)}return zn}function _N(e,t=e.length){let n=zn.add(t*2),s=Ct(e,0).mul(Vi),r=Ct(e,8),a=Ct(e,t-8).mul(n),o=Ct(e,t-16).mul(zn);return Ao(gn(s.add(r),43).add(gn(a,30)).add(o),s.add(gn(r.add(zn),18)).add(a),n)}function PN(e,t=e.length){let n=zn.add(t*2),s=Ct(e,0).mul(zn),r=Ct(e,8),a=Ct(e,t-8).mul(n),o=Ct(e,t-16).mul(zn),i=gn(s.add(r),43).add(gn(a,30)).add(o),l=Ao(i,s.add(gn(r.add(zn),18)).add(a),n),c=Ct(e,16).mul(n),u=Ct(e,24),d=i.add(Ct(e,t-32)).mul(n),p=l.add(Ct(e,t-24)).mul(n);return Ao(gn(c.add(u),43).add(gn(d,30)).add(p),c.add(gn(u.add(s),18)).add(d),n)}function FN(e,t=e.length){let n=Wi.fromNumber(81,!0);if(t<=32)return t<=16?DN(e,t):_N(e,t);if(t<=64)return PN(e,t);let s=n,r=n.mul(Vi).add(113),a=p2(r.mul(zn).add(113)).mul(zn),o=[Wi.UZERO,Wi.UZERO],i=[Wi.UZERO,Wi.UZERO];s=s.mul(zn).add(Ct(e,0));let l=0,c=(t-1>>6)*64,u=c+(t-1&63)-63;do s=gn(s.add(r).add(o[0]).add(Ct(e,l+8)),37).mul(Vi),r=gn(r.add(o[1]).add(Ct(e,l+48)),42).mul(Vi),s=s.xor(i[1]),r=r.add(o[0]).add(Ct(e,l+40)),a=gn(a.add(i[0]),33).mul(Vi),o=jh(e,l,o[1].mul(Vi),s.add(i[0])),i=jh(e,l+32,a.add(i[1]),r.add(Ct(e,l+16))),[a,s]=[s,a],l+=64;while(l!==c);let d=Vi.add(a.and(255).shl(1));return l=u,i[0]=i[0].add(t-1&63),o[0]=o[0].add(i[0]),i[0]=i[0].add(o[0]),s=gn(s.add(r).add(o[0]).add(Ct(e,l+8)),37).mul(d),r=gn(r.add(o[1]).add(Ct(e,l+48)),42).mul(d),s=s.xor(i[1].mul(9)),r=r.add(o[0].mul(9).add(Ct(e,l+40))),a=gn(a.add(i[0]),33).mul(d),o=jh(e,l,o[1].mul(d),s.add(i[0])),i=jh(e,l+32,a.add(i[1]),r.add(Ct(e,l+16))),[a,s]=[s,a],Ao(Ao(o[0],i[0],d).add(p2(r).mul(D5)).add(a),Ao(o[1],i[1],d).add(s),d)}function ON(e,t){return t==="string"?ud(e):qh([e],t)}function MN(e,t){return e instanceof Float32Array&&t==="float32"||e instanceof Int32Array&&t==="int32"||e instanceof Uint8Array&&t==="bool"}function qh(e,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(e)&&(e=ei(e)),Z().getBool("DEBUG")&&x5(e,t),MN(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 s=0;s<n.length;++s)Math.round(e[s])!==0&&(n[s]=1);return n}else throw new Error(`Unknown data type ${t}`)}function ld(){return Z().platform.now()}function zN(e,t){return Z().platform.fetch(e,t)}function ud(e,t="utf-8"){return t=t||"utf-8",Z().platform.encode(e,t)}function Xh(e,t="utf-8"){return t=t||"utf-8",Z().platform.decode(e,t)}var LN=class{constructor(e,t){this.backendTimer=e,this.logger=t,t==null&&(this.logger=new WN)}profileKernel(e,t,n){let s,r=()=>{s=n()},a,o=ld();if(this.backendTimer.timerAvailable())a=this.backendTimer.time(r);else{r();for(let l of s)l.dataSync();a=Promise.resolve({kernelMs:ld()-o})}if(Z().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<s.length;l++){let c=s[l];c.data().then(u=>{BN(u,c.dtype,e)})}return{kernelName:e,outputs:s,inputs:t,timeMs:a.then(l=>l.kernelMs),extraInfo:a.then(l=>l.getExtraProfileInfo!=null?l.getExtraProfileInfo():"")}}logKernelProfile(e){let{kernelName:t,outputs:n,timeMs:s,inputs:r,extraInfo:a}=e;n.forEach(o=>{Promise.all([o.data(),s,a]).then(i=>{this.logger.logKernelProfile(t,o,i[0],i[1],r,i[2])})})}};function BN(e,t,n){if(t!=="float32")return!1;for(let s=0;s<e.length;s++){let r=e[s];if(isNaN(r)||!isFinite(r))return console.warn(`Found ${r} in the result of '${n}'`),!0}return!1}var WN=class{logKernelProfile(e,t,n,s,r,a){let o=typeof s=="number"?Gc(`${s}ms`,9):s.error,i=Gc(e,25),l=t.rank,c=t.size,u=Gc(t.shape.toString(),14),d="";for(let p in r){let h=r[p];if(h!=null){let f=h.shape||t.shape,m=f.length;d+=`${p}: ${m}D ${m>0?f:""} `}}console.log(`%c${i} %c${o} %c${l}D ${u} %c${c} %c${d} %c${a}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function VN(e,t,n){let s={},r={};for(let l=0;l<t.length;l++)s[t[l].id]=!0;for(let l=0;l<e.length;l++){let c=e[l],u=c.inputs;for(let d in u){let p=u[d],h=!1;for(let f=0;f<t.length;f++)if(s[p.id]){c.outputs.forEach(m=>s[m.id]=!0),h=!0,r[c.id]=!0;break}if(h)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let c=e[l],u=c.inputs;for(let d=0;d<c.outputs.length;d++)if(a[c.outputs[d].id]){for(let p in u)a[u[p].id]=!0,o[c.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let c=e[l];if(r[c.id]&&o[c.id]){let u={};for(let p in c.inputs){let h=c.inputs[p];s[h.id]&&(u[p]=h)}let d=Object.assign({},c);d.inputs=u,d.outputs=c.outputs,i.push(d)}}return i}function UN(e,t,n,s){for(let r=t.length-1;r>=0;r--){let a=t[r],o=[];if(a.outputs.forEach(l=>{let c=e[l.id];c!=null?o.push(c):o.push(null)}),a.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);let i=a.gradient(o);for(let l in a.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. Available gradients found: ${Object.keys(i)}.`);let c=n(()=>i[l]());if(c.dtype!=="float32")throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${l} must have 'float32' dtype, but has '${c.dtype}'`);let u=a.inputs[l];if(!jr(c.shape,u.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${l}' has shape '${c.shape}', which does not match the shape of the input '${u.shape}'`);if(e[u.id]==null)e[u.id]=c;else{let d=e[u.id];e[u.id]=s(d,c),d.dispose()}}}}var F5=20,cd=3,h2=7;function GN(e,t,n,s){let r=Hl(t),a=HN(e,t,n,r),o=t.length,i=Kh(e,t,n,r,a),l=["Tensor"];return s&&(l.push(` dtype: ${n}`),l.push(` rank: ${o}`),l.push(` shape: [${t}]`),l.push(" values:")),l.push(i.map(c=>" "+c).join(`
`)),l.join(`
`)}function HN(e,t,n,s){let r=Gt(t),a=s[s.length-1],o=new Array(a).fill(0),i=t.length,l=n==="complex64"?pd(e):e;if(i>1)for(let c=0;c<r/a;c++){let u=c*a;for(let d=0;d<a;d++)o[d]=Math.max(o[d],dd(l[u+d],0,n).length)}return o}function dd(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(h2))} + ${parseFloat(e[1].toFixed(h2))}j`:va(e)?s=`'${e}'`:n==="bool"?s=O5(e):s=parseFloat(e.toFixed(h2)).toString(),Gc(s,t)}function O5(e){return e===0?"false":"true"}function Kh(e,t,n,s,r,a=!0){let o=n==="complex64"?2:1,i=t[0],l=t.length;if(l===0){if(n==="complex64"){let m=pd(e);return[dd(m[0],0,n)]}return n==="bool"?[O5(e[0])]:[e[0].toString()]}if(l===1){if(i>F5){let g=cd*o,y=Array.from(e.slice(0,g)),A=Array.from(e.slice((i-cd)*o,i*o));return n==="complex64"&&(y=pd(y),A=pd(A)),["["+y.map((x,b)=>dd(x,r[b],n)).join(", ")+", ..., "+A.map((x,b)=>dd(x,r[i-cd+b],n)).join(", ")+"]"]}let m=n==="complex64"?pd(e):Array.from(e);return["["+m.map((g,y)=>dd(g,r[y],n)).join(", ")+"]"]}let c=t.slice(1),u=s.slice(1),d=s[0]*o,p=[];if(i>F5){for(let m=0;m<cd;m++){let g=m*d,y=g+d;p.push(...Kh(e.slice(g,y),c,n,u,r,!1))}p.push("...");for(let m=i-cd;m<i;m++){let g=m*d,y=g+d;p.push(...Kh(e.slice(g,y),c,n,u,r,m===i-1))}}else for(let m=0;m<i;m++){let g=m*d,y=g+d;p.push(...Kh(e.slice(g,y),c,n,u,r,m===i-1))}let h=l===2?",":"";p[0]="["+p[0]+h;for(let m=1;m<p.length-1;m++)p[m]=" "+p[m]+h;let f=`,
`;for(let m=2;m<l;m++)f+=`
`;return p[p.length-1]=" "+p[p.length-1]+"]"+(a?"":f),p}function pd(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var tn=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Gt(e),n!=null){let s=n.length;M(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${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||A5(t,this.size),this.strides=Hl(e)}set(e,...t){t.length===0&&(t=[0]),M(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 s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];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 Sr().makeTensor(this.values,this.shape,this.dtype)}},Sr=null,vu=null,jN=null;function qN(e){Sr=e}function XN(e){vu=e}function KN(e){jN=e}var Ke=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Gt(e),this.strides=Hl(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return vu.buffer(this.shape,this.dtype,e)}bufferSync(){return vu.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return jl(this.shape,e,this.dtype==="complex64")}arraySync(){return jl(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Sr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Xh(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=Sr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Xh(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 Sr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Sr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return vu.print(this,e)}clone(){return this.throwIfDisposed(),vu.clone(this)}toString(e=!1){let t=this.dataSync();return GN(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),vu.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Sr().makeVariable(this,e,t,n)}};Object.defineProperty(Ke,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function re(){return l2("Tensor",()=>Ke)}re();var hd=class extends Ke{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s);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(!jr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Sr().disposeTensor(this),this.dataId=e.dataId,Sr().incRef(this,null)}dispose(){Sr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(hd,Symbol.hasInstance,{value:e=>e instanceof Ke&&e.assign!=null&&e.assign instanceof Function});var ar={};Le(ar,{assertTypesMatch:()=>M5,getTensorsInContainer:()=>x2,isTensorInList:()=>YN,makeTypesMatch:()=>Mt});var f2;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(f2||(f2={}));var m2;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(m2||(m2={}));var g2;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(g2||(g2={}));var y2;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(y2||(y2={}));var A2;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(A2||(A2={}));var ZN={float32:y2,int32:m2,bool:g2,complex64:A2};function Ln(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return ZN[e][t]}function fd(e){return Ln(e,"int32")}function Mt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Ln(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function M5(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function YN(e,t){return t.some(n=>n.id===e.id)}function x2(e){let t=[],n=new Set;return z5(e,t,n),t}function z5(e,t,n){if(e==null)return;if(e instanceof Ke){t.push(e);return}if(!JN(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),z5(a,t,n))}}function JN(e){return Array.isArray(e)||typeof e=="object"}function b2(e){return e.kernelName!=null}var L5=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},md=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new L5}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Ir(`${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 LN(this.backendInstance),!0}setupRegisteredKernels(){Zr(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Zr(e).forEach(n=>{n.disposeFunc!=null&&n.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 Gl)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,Ir(`Initialization of backend ${e} failed`),Ir(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Ir(`Initialization of backend ${e} failed`),Ir(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),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 s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return md.nextTensorId++}nextVariableId(){return md.nextVariableId++}clone(e){let t=W.runKernel(Wa,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return W.runKernel(Ta,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Hh(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 s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=b2(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(b2(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Hh(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:k,dtype:S}=b;return this.makeTensorFromDataId(w,k,S)});if(s){let b=this.getTensorsForGradient(h,f,x);n=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:c,attrs:u}=e,d=b2(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,c,t,d,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=c2(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,c)=>a[c]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&va(e[0])&&(r=e.map(i=>ud(i)));let a=s.write(r,t,n),o=new Ke(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=v5(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new Ke(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new hd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*s2(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 hd||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*s2(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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=c2(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((c,u)=>{if(c==null){let d=n[u],p=fh(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return c}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}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=x2(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(M(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));M(r instanceof Ke,()=>"The result y returned by f() must be a tensor.");let a=VN(this.state.activeTape,t,r);if(!s&&a.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 o={};o[r.id]=n==null?QN(r.shape):n,UN(o,a,l=>this.tidy(l),e9);let i=t.map(l=>o[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:r,grads:i}})}customGrad(e){return M(wa(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{M(t.every(o=>o instanceof Ke),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((o,i)=>{s[i]=o});let r=(o,i)=>(n=e(...t,i),M(n.value instanceof Ke,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),M(wa(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(o,i)=>{let l=n.gradFunc(o,i),c=Array.isArray(l)?l:[l];M(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),M(c.every(d=>d instanceof Ke),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let u={};return c.forEach((d,p)=>{u[p]=()=>d}),u};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}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=ld(),n=await this.backend.time(e);return n.wallMs=ld()-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 L5;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}};md.nextTensorId=0;md.nextVariableId=0;function QN(e){let t=r2(Gt(e),"float32");return W.makeTensor(t,e,"float32")}function B5(){let e=T5();if(e._tfengine==null){let t=new C5(e);e._tfengine=new md(t)}return SN(e._tfengine.ENV),qN(()=>e._tfengine),e._tfengine}var W=B5();function e9(e,t){let n={a:e,b:t};return W.runKernel(qr,n)}var wu={};Le(wu,{isBrowser:()=>W5,isMobile:()=>n9});function t9(){return typeof navigator!="undefined"&&navigator!=null}function n9(e){if(e||t9()){if(e||(e=navigator),e.product==="ReactNative")return!0;let t=e.userAgent||e.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let n=e;return n.userAgentData&&n.userAgentData.mobile}return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function W5(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var or=Z();or.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.")});or.registerFlag("IS_BROWSER",()=>W5());or.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");or.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));or.registerFlag("PROD",()=>!1);or.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>or.getBool("DEBUG"));or.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);or.registerFlag("IS_TEST",()=>!1);or.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);or.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);function Cr(e,t){let n=e;if(_n(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let s=[];for(;Array.isArray(n)||_n(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&Z().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&V5(e,s,[]),s}function V5(e,t,n){if(n=n||[],!Array.isArray(e)&&!_n(e)){M(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}M(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),M(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let s=t.slice(1);for(let r=0;r<e.length;++r)V5(e[r],s,n.concat(r))}function U5(e,t,n,s){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}}function _(e,t,n,s="numeric"){if(e instanceof Ke)return U5(s,e.dtype,t,n),e;let r=ph(e);if(r!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(r=s),U5(s,r,t,n),e==null||!_n(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let l=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${l}'`)}let a=Cr(e,r);!_n(e)&&!Array.isArray(e)&&(e=[e]);let i=r!=="string"?qh(e,r):ei(e,[],!0);return W.makeTensor(i,a,r)}function gd(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,o)=>_(a,`${t}[${o}]`,n,s))}var G5="__op";function U(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],s=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1)),n=n+G5;let r=(...a)=>{W.startScope(n);try{let o=s(...a);return o2(o)&&console.error("Cannot return a Promise inside of tidy."),W.endScope(o),o}catch(o){throw W.endScope(null),o}};return Object.defineProperty(r,"name",{value:n,configurable:!0}),r}function s9(e,t){let n=_(e,"real","complex"),s=_(t,"imag","complex");Mn(n.shape,s.shape,`real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);let r={real:n,imag:s};return W.runKernel(jc,r)}var xo=U({complex_:s9});function bo(e,t,n,s){if(s==null&&(s=ph(e)),s==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!_n(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){a2(t);let r=Gt(t),a=Gt(n);M(r===a,()=>`Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);for(let o=0;o<n.length;++o){let i=n[o],l=o===n.length-1?i!==Gt(t.slice(o)):!0;M(n[o]===t[o]||!l,()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `)}}return!_n(e)&&!Array.isArray(e)&&(e=[e]),t=t||n,e=s!=="string"?qh(e,s):ei(e,[],!0),W.makeTensor(e,t,s)}function nn(e,t,n){let s=Cr(e,n);return bo(e,t,s,n)}var v2={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},Zh=4;async function r9(e,t){let n=[],s=[],r=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);for(let o=0;o<r.length;++o){let i=r[o],l=Array.isArray(e)?e[o].tensor:e[i];if(l.dtype!=="float32"&&l.dtype!=="int32"&&l.dtype!=="bool"&&l.dtype!=="string"&&l.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${i}': ${l.dtype}`);let c={name:i,shape:l.shape,dtype:l.dtype};if(l.dtype==="string"){let u=new Promise(async d=>{let p=await l.bytes(),h=p.reduce((g,y)=>g+y.length,0)+Zh*p.length,f=new Uint8Array(h),m=0;for(let g=0;g<p.length;g++){let y=p[g],A=new Uint8Array(new Uint32Array([y.length]).buffer);f.set(A,m),m+=Zh,f.set(y,m),m+=y.length}d(f)});s.push(u)}else s.push(l.data());t!=null&&(c.group=t),n.push(c)}let a=await Promise.all(s);return{data:a9(a),specs:n}}function H5(e,t){let n={},s,r=0;for(let a of t){let o=a.name,i=a.dtype,l=a.shape,c=Gt(l),u;if("quantization"in a){let d=a.quantization;if(d.dtype==="uint8"||d.dtype==="uint16"){if(!("min"in d&&"scale"in d))throw new Error(`Weight ${a.name} with quantization ${d.dtype} doesn't have corresponding metadata min and scale.`)}else if(d.dtype==="float16"){if(i!=="float32")throw new Error(`Weight ${a.name} is quantized with ${d.dtype} which only supports weights of type float32 not ${i}.`)}else throw new Error(`Weight ${a.name} has unknown quantization dtype ${d.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let p=v2[d.dtype],h=e.slice(r,r+c*p),f=d.dtype==="uint8"?new Uint8Array(h):new Uint16Array(h);if(i==="float32")if(d.dtype==="uint8"||d.dtype==="uint16"){u=new Float32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];u[m]=g*d.scale+d.min}}else if(d.dtype==="float16")s===void 0&&(s=d9()),u=s(f);else throw new Error(`Unsupported quantization type ${d.dtype} for weight type float32.`);else if(i==="int32"){if(d.dtype!=="uint8"&&d.dtype!=="uint16")throw new Error(`Unsupported quantization type ${d.dtype} for weight type int32.`);u=new Int32Array(f.length);for(let m=0;m<f.length;m++){let g=f[m];u[m]=Math.round(g*d.scale+d.min)}}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=c*p}else if(i==="string"){let d=Gt(a.shape);u=[];for(let p=0;p<d;p++){let h=new Uint32Array(e.slice(r,r+Zh))[0];r+=Zh;let f=new Uint8Array(e.slice(r,r+h));u.push(f),r+=h}}else{let d=v2[i],p=e.slice(r,r+c*d);if(i==="float32")u=new Float32Array(p);else if(i==="int32")u=new Int32Array(p);else if(i==="bool")u=new Uint8Array(p);else if(i==="complex64"){u=new Float32Array(p);let h=new Float32Array(u.length/2),f=new Float32Array(u.length/2);for(let y=0;y<h.length;y++)h[y]=u[y*2],f[y]=u[y*2+1];let m=nn(h,l,"float32"),g=nn(f,l,"float32");n[o]=xo(m,g),m.dispose(),g.dispose()}else throw new Error(`Unsupported dtype in weight '${o}': ${i}`);r+=c*d}i!=="complex64"&&(n[o]=nn(u,l,i))}return n}function a9(e){if(e===null)throw new Error(`Invalid input value: ${JSON.stringify(e)}`);let t=0,n=[];e.forEach(a=>{if(t+=a.byteLength,n.push(a.byteLength===a.buffer.byteLength?a:new a.constructor(a)),!(a instanceof Float32Array||a instanceof Int32Array||a instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`)});let s=new Uint8Array(t),r=0;return n.forEach(a=>{s.set(new Uint8Array(a.buffer),r),r+=a.byteLength}),s.buffer}var w2=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function j5(e){return w2?Buffer.byteLength(e):new Blob([e]).size}function o9(e){if(w2)return Buffer.from(e).toString("base64");let t=new Uint8Array(e),n="";for(let s=0,r=t.length;s<r;s++)n+=String.fromCharCode(t[s]);return btoa(n)}function i9(e){if(w2){let s=Buffer.from(e,"base64");return s.buffer.slice(s.byteOffset,s.byteOffset+s.byteLength)}let t=atob(e),n=new Uint8Array(t.length);for(let s=0;s<t.length;++s)n.set([t.charCodeAt(s)],s);return n.buffer}function k2(e){if(e.length===1)return e[0];let t=0;e.forEach(r=>{t+=r.byteLength});let n=new Uint8Array(t),s=0;return e.forEach(r=>{n.set(new Uint8Array(r),s),s+=r.byteLength}),n.buffer}function q5(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 X5(e,t){let n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,weightsManifest:t};return e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),e.trainingConfig!=null&&(n.trainingConfig=e.trainingConfig),n}async function I2(e,t){let n={modelTopology:e.modelTopology,format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy};if(e.trainingConfig!=null&&(n.trainingConfig=e.trainingConfig),e.weightsManifest!=null){let[s,r]=await t(e.weightsManifest);n.weightSpecs=s,n.weightData=r}return e.signature!=null&&(n.signature=e.signature),e.userDefinedMetadata!=null&&(n.userDefinedMetadata=e.userDefinedMetadata),e.modelInitializer!=null&&(n.modelInitializer=e.modelInitializer),n}function yd(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:j5(JSON.stringify(e.modelTopology)),weightSpecsBytes:e.weightSpecs==null?0:j5(JSON.stringify(e.weightSpecs)),weightDataBytes:e.weightData==null?0:e.weightData.byteLength}}function l9(){let e=n=>{let s=n<<13,r=0;for(;(s&8388608)==0;)r-=8388608,s<<=1;return s&=~8388608,r+=947912704,s|r},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let n=1024;n<2048;n++)t[n]=939524096+(n-1024<<13);return t}function u9(){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 c9(){let e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}function d9(){let e=l9(),t=u9(),n=c9();return s=>{let r=new ArrayBuffer(4*s.length),a=new Uint32Array(r);for(let o=0;o<s.length;o++){let i=s[o],l=e[n[i>>10]+(i&1023)]+t[i>>10];a[o]=l}return new Float32Array(r)}}var Vt=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return Vt.instance==null&&(Vt.instance=new Vt),Vt.instance}static registerSaveRouter(e){Vt.getInstance().saveRouters.push(e)}static registerLoadRouter(e){Vt.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return Vt.getHandlers(e,"save")}static getLoadHandlers(e,t){return Vt.getHandlers(e,"load",t)}static getHandlers(e,t,n){let s=[];return(t==="load"?Vt.getInstance().loadRouters:Vt.getInstance().saveRouters).forEach(a=>{let o=a(e,n);o!==null&&s.push(o)}),s}},p9=e=>Vt.registerSaveRouter(e),h9=e=>Vt.registerLoadRouter(e),f9=e=>Vt.getSaveHandlers(e),m9=(e,t)=>Vt.getLoadHandlers(e,t),S2="tensorflowjs",C2=1,Ui="models_store",vo="model_info_store";function K5(){if(!Z().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 T2(e){let t=e.result;t.createObjectStore(Ui,{keyPath:"modelPath"}),t.createObjectStore(vo,{keyPath:"modelPath"})}var Gi=class{constructor(e){if(this.indexedDB=K5(),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,s)=>{let r=this.indexedDB.open(S2,C2);r.onupgradeneeded=()=>T2(r),r.onsuccess=()=>{let a=r.result;if(t==null){let o=a.transaction(Ui,"readonly"),l=o.objectStore(Ui).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return a.close(),s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=c=>(a.close(),s(l.error)),o.oncomplete=()=>a.close()}else{let o=yd(t),i=a.transaction(vo,"readwrite"),l=i.objectStore(vo),c=l.put({modelPath:this.modelPath,modelArtifactsInfo:o}),u;c.onsuccess=()=>{u=a.transaction(Ui,"readwrite");let p=u.objectStore(Ui).put({modelPath:this.modelPath,modelArtifacts:t,modelArtifactsInfo:o});p.onsuccess=()=>n({modelArtifactsInfo:o}),p.onerror=h=>{l=i.objectStore(vo);let f=l.delete(this.modelPath);f.onsuccess=()=>(a.close(),s(p.error)),f.onerror=m=>(a.close(),s(p.error))}},c.onerror=d=>(a.close(),s(c.error)),i.oncomplete=()=>{u==null?a.close():u.oncomplete=()=>a.close()}}},r.onerror=a=>s(r.error)})}};Gi.URL_SCHEME="indexeddb://";var Z5=e=>Z().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Gi.URL_SCHEME)?g9(e.slice(Gi.URL_SCHEME.length)):null;Vt.registerSaveRouter(Z5);Vt.registerLoadRouter(Z5);function g9(e){return new Gi(e)}function y9(e){return e.startsWith(Gi.URL_SCHEME)?e.slice(Gi.URL_SCHEME.length):e}var A9=class{constructor(){this.indexedDB=K5()}async listModels(){return new Promise((e,t)=>{let n=this.indexedDB.open(S2,C2);n.onupgradeneeded=()=>T2(n),n.onsuccess=()=>{let s=n.result,r=s.transaction(vo,"readonly"),o=r.objectStore(vo).getAll();o.onsuccess=()=>{let i={};for(let l of o.result)i[l.modelPath]=l.modelArtifactsInfo;e(i)},o.onerror=i=>(s.close(),t(o.error)),r.oncomplete=()=>s.close()},n.onerror=s=>t(n.error)})}async removeModel(e){return e=y9(e),new Promise((t,n)=>{let s=this.indexedDB.open(S2,C2);s.onupgradeneeded=()=>T2(s),s.onsuccess=()=>{let r=s.result,a=r.transaction(vo,"readwrite"),o=a.objectStore(vo),i=o.get(e),l;i.onsuccess=()=>{if(i.result==null)return r.close(),n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));{let c=o.delete(e),u=()=>{l=r.transaction(Ui,"readwrite");let p=l.objectStore(Ui).delete(e);p.onsuccess=()=>t(i.result.modelArtifactsInfo),p.onerror=h=>n(i.error)};c.onsuccess=u,c.onerror=d=>(u(),r.close(),n(i.error))}},i.onerror=c=>(r.close(),n(i.error)),a.oncomplete=()=>{l==null?r.close():l.oncomplete=()=>r.close()}},s.onerror=r=>n(s.error)})}},Jr="/",ku="tensorflowjs_models",Y5="info",x9="model_topology",b9="weight_specs",v9="weight_data",w9="model_metadata";function J5(e){return{info:[ku,e,Y5].join(Jr),topology:[ku,e,x9].join(Jr),weightSpecs:[ku,e,b9].join(Jr),weightData:[ku,e,v9].join(Jr),modelMetadata:[ku,e,w9].join(Jr)}}function Q5(e){for(let t of Object.values(e))window.localStorage.removeItem(t)}function k9(e){let t=e.split(Jr);if(t.length<3)throw new Error(`Invalid key format: ${e}`);return t.slice(1,t.length-1).join(Jr)}function I9(e){return e.startsWith(Hi.URL_SCHEME)?e.slice(Hi.URL_SCHEME.length):e}var Hi=class{constructor(e){if(!Z().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,e==null||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=J5(this.modelPath)}async save(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),s=yd(e);try{this.LS.setItem(this.keys.info,JSON.stringify(s)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,o9(e.weightData));let r={format:e.format,generatedBy:e.generatedBy,convertedBy:e.convertedBy,signature:e.signature!=null?e.signature:void 0,userDefinedMetadata:e.userDefinedMetadata!=null?e.userDefinedMetadata:void 0,modelInitializer:e.modelInitializer!=null?e.modelInitializer:void 0,trainingConfig:e.trainingConfig!=null?e.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(r)),{modelArtifactsInfo:s}}catch(r){throw Q5(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`)}}}async load(){let e=JSON.parse(this.LS.getItem(this.keys.info));if(e==null)throw new Error(`In local storage, there is no model with name '${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 '${this.modelPath}' is missing.`);t.modelTopology=n;let s=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(s==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);t.weightSpecs=s;let r=this.LS.getItem(this.keys.modelMetadata);if(r!=null){let o=JSON.parse(r);t.format=o.format,t.generatedBy=o.generatedBy,t.convertedBy=o.convertedBy,o.signature!=null&&(t.signature=o.signature),o.userDefinedMetadata!=null&&(t.userDefinedMetadata=o.userDefinedMetadata),o.modelInitializer!=null&&(t.modelInitializer=o.modelInitializer),o.trainingConfig!=null&&(t.trainingConfig=o.trainingConfig)}let a=this.LS.getItem(this.keys.weightData);if(a==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return t.weightData=i9(a),t}};Hi.URL_SCHEME="localstorage://";var e3=e=>Z().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Hi.URL_SCHEME)?S9(e.slice(Hi.URL_SCHEME.length)):null;Vt.registerSaveRouter(e3);Vt.registerLoadRouter(e3);function S9(e){return new Hi(e)}var C9=class{constructor(){M(Z().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),M(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let e={},t=ku+Jr,n=Jr+Y5;for(let s=0;s<this.LS.length;++s){let r=this.LS.key(s);if(r.startsWith(t)&&r.endsWith(n)){let a=k9(r);e[a]=JSON.parse(this.LS.getItem(r))}}return e}async removeModel(e){e=I9(e);let t=J5(e);if(this.LS.getItem(t.info)==null)throw new Error(`Cannot find model at path '${e}'`);let n=JSON.parse(this.LS.getItem(t.info));return Q5(t),n}},Iu="://",Ds=class{constructor(){this.managers={}}static getInstance(){return Ds.instance==null&&(Ds.instance=new Ds),Ds.instance}static registerManager(e,t){M(e!=null,()=>"scheme must not be undefined or null."),e.endsWith(Iu)&&(e=e.slice(0,e.indexOf(Iu))),M(e.length>0,()=>"scheme must not be an empty string.");let n=Ds.getInstance();M(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 Yh(e){if(e.indexOf(Iu)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Ds.getSchemes().join(",")}`);return{scheme:e.split(Iu)[0],path:e.split(Iu)[1]}}async function t3(e,t,n=!1){M(e!==t,()=>`Old path and new path are the same: '${e}'`);let s=Vt.getLoadHandlers(e);M(s.length>0,()=>`Copying failed because no load handler is found for source URL ${e}.`),M(s.length<2,()=>`Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);let r=s[0],a=Vt.getSaveHandlers(t);M(a.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),M(a.length<2,()=>`Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);let o=a[0],i=Yh(e).scheme,l=Yh(e).path,c=i===Yh(e).scheme,u=await r.load();n&&c&&await Ds.getManager(i).removeModel(l);let d=await o.save(u);return n&&!c&&await Ds.getManager(i).removeModel(l),d.modelArtifactsInfo}async function T9(){let e=Ds.getSchemes(),t={};for(let n of e){let s=await Ds.getManager(n).listModels();for(let r in s){let a=n+Iu+r;t[a]=s[r]}}return t}async function N9(e){let t=Yh(e);return Ds.getManager(t.scheme).removeModel(t.path)}async function E9(e,t){return t3(e,t,!1)}async function R9(e,t){return t3(e,t,!0)}var $9=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(Z().get("IS_BROWSER")){Z().setPlatform("browser",new $9);try{Ds.registerManager(Hi.URL_SCHEME,new C9)}catch(e){}try{Ds.registerManager(Gi.URL_SCHEME,new A9)}catch(e){}}var D9={importFetch:()=>LT()},N2,_9=class{constructor(){this.util=Ul("util"),this.textEncoder=new this.util.TextEncoder}fetch(e,t){return Z().global.fetch!=null?Z().global.fetch(e,t):(N2==null&&(N2=D9.importFetch()),N2(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)}};Z().get("IS_NODE")&&Z().setPlatform("node",new _9);function We(e,t="float32",n){return t=t||"float32",a2(e),new tn(e,t,n)}function P9(e,t){let n=_(e,"x","cast");if(!b5(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 s={x:n},r={dtype:t};return W.runKernel(Ta,s,r)}var pe=U({cast_:P9});function F9(e){let n={x:_(e,"x","clone","string_or_numeric")};return W.runKernel(Wa,n)}var ir=U({clone_:F9});function n3(e,t=!1){console.log(e.toString(t))}B5();var O9={buffer:We,cast:pe,clone:ir,print:n3};XN(O9);var es={};Le(es,{browserFiles:()=>U9,browserHTTPRequest:()=>X9,concatenateArrayBuffers:()=>k2,copyModel:()=>E9,decodeWeights:()=>H5,encodeWeights:()=>r9,fromMemory:()=>Z9,getLoadHandlers:()=>m9,getModelArtifactsForJSON:()=>I2,getModelArtifactsInfoForJSON:()=>yd,getSaveHandlers:()=>f9,http:()=>$2,isHTTPScheme:()=>R2,listModels:()=>T9,loadWeights:()=>G9,moveModel:()=>R9,registerLoadRouter:()=>h9,registerSaveRouter:()=>p9,removeModel:()=>N9,weightsLoaderFactory:()=>o3,withSaveHandler:()=>Y9});var M9="model",z9=".json",L9=".weights.bin";function s3(e){return new Promise(t=>setTimeout(t)).then(e)}var Su=class{constructor(e){if(!Z().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(Su.URL_SCHEME)&&(e=e.slice(Su.URL_SCHEME.length)),(e==null||e.length===0)&&(e=M9),this.modelJsonFileName=e+z9,this.weightDataFileName=e+L9}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}],s=X5(e,n),r=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=r,await s3(()=>a.dispatchEvent(new MouseEvent("click"))),e.weightData!=null){let o=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;o.download=this.weightDataFileName,o.href=t,await s3(()=>o.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:yd(e)}}}};Su.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.jsonFile=e[0],this.weightsFiles=e.slice(1)}async load(){return new Promise((e,t)=>{let n=new FileReader;n.onload=s=>{let r=JSON.parse(s.target.result),a=r.modelTopology;if(a==null){t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(r.weightsManifest==null){t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){e({modelTopology:a});return}let i=I2(r,l=>this.loadWeights(l));e(i)},n.onerror=s=>t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})}loadWeights(e){let t=[],n=[];for(let a of e)t.push(...a.weights),n.push(...a.paths);let s=this.checkManifestAndWeightFiles(e),r=n.map(a=>this.loadWeightsFile(a,s[a]));return Promise.all(r).then(a=>[t,k2(a)])}loadWeightsFile(e,t){return new Promise((n,s)=>{let r=new FileReader;r.onload=a=>{let o=a.target.result;n(o)},r.onerror=a=>s(`Failed to weights data from file of path '${e}'.`),r.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){let t=[],n=this.weightsFiles.map(r=>q5(r.name)),s={};for(let r of e)r.paths.forEach(a=>{let o=q5(a);if(t.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(t.push(o),n.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);s[a]=this.weightsFiles[n.indexOf(o)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return s}},W9=e=>Z().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(Su.URL_SCHEME)?V9(e.slice(Su.URL_SCHEME.length)):null;Vt.registerSaveRouter(W9);function V9(e="model"){return new Su(e)}function U9(e){return new B9(e)}function r3(e,t,n,s){o(e),n=n==null?0:n,s=s==null?1:s,i(n,s);let r=0,a=l=>(l.then(c=>{let u=n+ ++r/e.length*(s-n);return t(u),c}),l);function o(l){M(l!=null&&Array.isArray(l)&&l.length>0,()=>"promises must be a none empty array")}function i(l,c){M(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${l}`),M(c>=0&&c<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${c}`),M(c>=l,()=>`startFraction must be no more than endFraction, but got startFraction ${l} and endFraction ${c}`)}return Promise.all(e.map(a))}async function a3(e,t){t==null&&(t={});let n=t.fetchFunc==null?Z().platform.fetch:t.fetchFunc,s=e.map(d=>n(d,t.requestInit,{isBinary:!0})),r=0,a=.5,i=(t.onProgress==null?await Promise.all(s):await r3(s,t.onProgress,r,a)).map(d=>d.arrayBuffer()),l=.5,c=1;return t.onProgress==null?await Promise.all(i):await r3(i,t.onProgress,l,c)}async function G9(e,t="",n,s){return o3(o=>a3(o,{requestInit:s}))(e,t,n)}function o3(e){return async(t,n="",s)=>{let r=t.map(()=>!1),a={},o=s!=null?s.map(()=>!1):[],i=[];if(t.forEach((h,f)=>{let m=0;h.weights.forEach(g=>{let y="quantization"in g?g.quantization.dtype:g.dtype,A=v2[y]*Gt(g.shape),x=()=>{r[f]=!0,a[f]==null&&(a[f]=[]),a[f].push({manifestEntry:g,groupOffset:m,sizeBytes:A})};s!=null?s.forEach((b,w)=>{b===g.name&&(x(),o[w]=!0)}):x(),i.push(g.name),m+=A})}),!o.every(h=>h)){let h=s.filter((f,m)=>!o[m]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}.
Manifest JSON has weights with names: ${i.join(", ")}.`)}let l=r.reduce((h,f,m)=>(f&&h.push(m),h),[]),c=[];l.forEach(h=>{t[h].paths.forEach(f=>{let m=n+(n.endsWith("/")?"":"/")+f;c.push(m)})});let u=await e(c),d={},p=0;return l.forEach(h=>{let f=t[h].paths.length,m=0;for(let b=0;b<f;b++)m+=u[p+b].byteLength;let g=new ArrayBuffer(m),y=new Uint8Array(g),A=0;for(let b=0;b<f;b++){let w=new Uint8Array(u[p+b]);y.set(w,A),A+=w.byteLength}a[h].forEach(b=>{let w=g.slice(b.groupOffset,b.groupOffset+b.sizeBytes),k=H5(w,[b.manifestEntry]);for(let S in k)d[S]=k[S]}),p+=f}),d}}var H9="application/octet-stream",j9="application/json",E2=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?(M(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=Z().platform.fetch,M(e!=null&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&M(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}],s=X5(e,n);t.body.append("model.json",new Blob([JSON.stringify(s)],{type:j9}),"model.json"),e.weightData!=null&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:H9}),"model.weights.bin");let r=await this.fetch(this.path,t);if(r.ok)return{modelArtifactsInfo:yd(e),responses:[r]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`)}async load(){let e=await this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=await e.json()}catch(r){let a=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?a+=" 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.":a+=" Please make sure the server is serving valid JSON for this request.",new Error(a)}let n=t.modelTopology,s=t.weightsManifest;if(n==null&&s==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return I2(t,r=>this.loadWeights(r))}async loadWeights(e){let t=Array.isArray(this.path)?this.path[1]:this.path,[n,s]=q9(t),r=this.weightPathPrefix||n,a=[];for(let c of e)a.push(...c.weights);let o=[],i=[];for(let c of e)for(let u of c.paths)this.weightUrlConverter!=null?i.push(this.weightUrlConverter(u)):o.push(r+u+s);this.weightUrlConverter&&o.push(...await Promise.all(i));let l=await a3(o,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[a,k2(l)]}};E2.URL_SCHEME_REGEX=/^https?:\/\//;function q9(e){let t=e.lastIndexOf("/"),n=e.lastIndexOf("?"),s=e.substring(0,t),r=n>t?e.substring(n):"";return[s+"/",r]}function R2(e){return e.match(E2.URL_SCHEME_REGEX)!=null}var i3=(e,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let n=!0;if(Array.isArray(e)?n=e.every(s=>R2(s)):n=R2(e),n)return $2(e,t)}return null};Vt.registerSaveRouter(i3);Vt.registerLoadRouter(i3);function $2(e,t){return new E2(e,t)}function X9(e,t){return $2(e,t)}var D2=class{constructor(e){this.modelArtifacts=e}async load(){return this.modelArtifacts}},K9=class{constructor(e){this.saveHandler=e}async save(e){return this.saveHandler(e)}};function Z9(e,t,n,s){return arguments.length===1?e.modelTopology!=null||e.weightSpecs!=null?new D2(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 D2({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 D2({modelTopology:e,weightSpecs:t,weightData:n,trainingConfig:s}))}function Y9(e){return new K9(e)}var l3={};Le(l3,{confusionMatrix:()=>nE});function J9(e,t,n=!1,s=!1){let r=_(e,"a","matMul"),a=_(t,"b","matMul");[r,a]=Mt(r,a);let o={a:r,b:a},i={transposeA:n,transposeB:s};return W.runKernel(Ca,o,i)}var Xe=U({matMul_:J9});function Q9(e,t,n=1,s=0){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let a={indices:_(e,"indices","oneHot","int32")},o={depth:t,onValue:n,offValue:s};return W.runKernel(ki,a,o)}var Cu=U({oneHot_:Q9});function eE(e,t){let n=_(e,"x","transpose");if(t==null&&(t=n.shape.map((a,o)=>o).reverse()),M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(a=>{M(a>=0&&a<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let s={x:n},r={perm:t};return W.runKernel(ho,s,r)}var et=U({transpose_:eE});function tE(e,t,n){let s=_(e,"labels","confusionMatrix"),r=_(t,"predictions","confusionMatrix");M(n==null||n>0&&Number.isInteger(n),()=>`If provided, numClasses must be a positive integer, but got ${n}`),M(s.rank===1,()=>`Expected the rank of labels to be 1, but got ${s.rank}`),M(r.rank===1,()=>`Expected the rank of predictions to be 1, but got ${r.rank}`),M(s.shape[0]===r.shape[0],()=>`Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`),M(n>0&&Number.isInteger(n),()=>`numClasses is required to be a positive integer, but got ${n}`);let a=Cu(pe(s,"int32"),n),o=Cu(pe(r,"int32"),n),i=et(a),l=Xe(i,o);return pe(l,"int32")}var nE=U({confusionMatrix_:tE}),Xs={};Le(Xs,{fromPixels:()=>uE,fromPixelsAsync:()=>iE,toPixels:()=>lE});function u3(e,t,n){if(Qo(e),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let s=Cr(e,n);if(s.length!==3&&s.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return bo(e,t,s,n)}var Tu;function c3(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,s=!1,r=!1,a=!1,o=!1,i=!1;if(e.data instanceof Uint8Array)n=!0;else if(typeof ImageData!="undefined"&&e instanceof ImageData)s=!0;else if(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)r=!0;else if(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)a=!0;else if(e.getContext!=null)o=!0;else if(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)i=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);if(r){let f=2;if(r&&e.readyState<f)throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.")}if(Hh(ad,W.backendName)!=null){let f={pixels:e},m={numChannels:t};return W.runKernel(ad,f,m)}let[c,u]=r?[e.videoWidth,e.videoHeight]:[e.width,e.height],d;o?d=e.getContext("2d").getImageData(0,0,c,u).data:s||n?d=e.data:(a||r||i)&&(Tu==null&&(Tu=document.createElement("canvas").getContext("2d")),Tu.canvas.width=c,Tu.canvas.height=u,Tu.drawImage(e,0,0,c,u),d=Tu.getImageData(0,0,c,u).data);let p;if(t===4)p=new Int32Array(d);else{let f=c*u;p=new Int32Array(f*t);for(let m=0;m<f;m++)for(let g=0;g<t;++g)p[m*t+g]=d[m*4+g]}return u3(p,[u,c,t],"int32")}function sE(e){return e!=null&&e.data instanceof Uint8Array}function rE(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function aE(e){return e!=null&&e.width!==0&&e.height!==0}function oE(e){return rE()&&!(e instanceof ImageBitmap)&&aE(e)&&!sE(e)}async function iE(e,t=3){let n=null;if(Z().getBool("WRAP_TO_IMAGEBITMAP")&&oE(e)){let s;try{s=await createImageBitmap(e,{premultiplyAlpha:"none"})}catch(r){s=null}s!=null&&s.width===e.width&&s.height===e.height?n=s:n=e}else n=e;return c3(n,t)}async function lE(e,t){let n=_(e,"img","toPixels");if(!(e instanceof Ke)){let c=n;n=pe(c,"int32"),c.dispose()}if(n.rank!==2&&n.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);let[s,r]=n.shape.slice(0,2),a=n.rank===2?1:n.shape[2];if(a>4||a===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);if(n.dtype!=="float32"&&n.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);let o=await n.data(),i=n.dtype==="float32"?255:1,l=new Uint8ClampedArray(r*s*4);for(let c=0;c<s*r;++c){let u=[0,0,0,255];for(let p=0;p<a;p++){let h=o[c*a+p];if(n.dtype==="float32"){if(h<0||h>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`)}else if(n.dtype==="int32"&&(h<0||h>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);a===1?(u[0]=h*i,u[1]=h*i,u[2]=h*i):u[p]=h*i}let d=c*4;l[d+0]=Math.round(u[0]),l[d+1]=Math.round(u[1]),l[d+2]=Math.round(u[2]),l[d+3]=Math.round(u[3])}if(t!=null){t.width=r,t.height=s;let c=t.getContext("2d"),u=new ImageData(l,r,s);c.putImageData(u,0,0)}return n!==e&&n.dispose(),l}var uE=U({fromPixels_:c3}),_2={};Le(_2,{prepareAndValidate:()=>d3});function d3(e,t){let n=e.shape.length,s=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(s<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${s}.`);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[s-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[s-1]} vs. ${n}`);if(Gt(e.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);let r=t.shape,a=r[r.length-1],o=1;for(let d=0;d<r.length-1;++d)o*=r[d];let i=e.shape,l=r.slice();l.pop();let c=1;for(let d=a;d<n;++d)c*=i[d],l.push(i[d]);let u=[...Hl(e.shape).map(d=>d/c),1].slice(0,a);return[l,o,c,u]}var P2={};Le(P2,{calculateShapes:()=>p3,validateInput:()=>O2,validateUpdateShape:()=>F2});function F2(e,t,n){let s=t.rank>1?t.shape[t.rank-1]:1,r=t.rank>1?t.rank-1:1,a=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;if(n.rank<r)throw new Error(a+` update.rank < ${r}. `);if(e.length<s+(n.rank-r))throw new Error(a+` Output shape length < ${s+(n.rank-r)}`);if(n.rank!==r+e.length-s)throw new Error(a+` update.rank != ${r+e.length-s}`);for(let o=0;o<r;++o)if(n.shape[o]!==t.shape[o])throw new Error(a+` updates.shape[${o}] (${n.shape[o]}) != indices.shape[${o}] (${t.shape[o]}).`);for(let o=0;o<n.rank-r;++o)if(n.shape[o+r]!==e[o+s])throw new Error(a+` updates.shape[${o+r}] (${n.shape[o+r]}) != shape[${o+r}] (${e[o+r]})`)}function O2(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: ${e.shape}`)}F2(n,t,e)}function p3(e,t,n){let s=t.shape.length,r=s>1?t.shape[s-1]:1,a=n.length,o=1;for(let d=r;d<a;++d)o*=n[d];let i=r<1?1:r,l=Gt(t.shape)/i,c=[...Hl(n.slice(0,r)),1],u=Gt(n);return{sliceRank:r,numUpdates:l,sliceSize:o,strides:c,outputSize:u}}var yn={};Le(yn,{assertParamsValid:()=>cE,computeFlatOffset:()=>pE,computeOutShape:()=>h3,getNormalizedAxes:()=>y3,isSliceContinous:()=>dE,maskToAxes:()=>Jh,parseSliceParams:()=>k3,sliceInfo:()=>hE,startForAxis:()=>v3,startIndicesWithElidedDims:()=>A3,stopForAxis:()=>w3,stopIndicesWithElidedDims:()=>x3,stridesForAxis:()=>b3,stridesWithElidedDims:()=>f3});function cE(e,t,n){let s=e.shape.length;M(s===t.length,()=>`Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`),M(s===n.length,()=>`Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);for(let r=0;r<s;++r)M(t[r]+n[r]<=e.shape[r],()=>`Error in slice${s}D: begin[${r}] + size[${r}] (${t[r]+n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`)}function Jh(e){let t=[],n=0;for(;e>0;)e&1&&t.push(n),e/=2,n++;return t}function h3(e,t,n){let s=[];for(let r=0;r<e.length;r++)s[r]=Math.ceil((t[r]-e[r])/n[r]);return s}function f3(e,t,n,s){let r=[...e];for(let a=r.length;a<s.length;a++)r.push(1);for(let a=0;a<n;a++)a===0?r[t]=1:(r.splice(t,0,1),r.pop());return r}function m3(e,t,n){return n<=e?n:n-(t-1)}function g3(e,t){let n=[];for(let s=0;s<e;s++)n.push(t+s);return n}function y3(e,t,n,s,r,a,o,i,l){let c=e.length,u=new Array(c),d=new Array(c),p=new Array(c);if(t.length&&n>0){let h=t[0],f=n+1;u=A3(o,h,f,s,e),d=x3(i,h,f,r,e),p=f3(a,h,f,e)}else for(let h=0;h<c;h++)u[h]=v3(o,s,a,e,h,l),d[h]=w3(i,r,a,e,h,l),p[h]=b3(a,h,l);return{begin:u,end:d,strides:p}}function A3(e,t,n,s,r){let a=[...r],o=g3(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=0;else{let l=m3(t,n,i),c=s[l];e&1<<l&&(c=0),a[i]=c}return a}function x3(e,t,n,s,r){let a=[...r],o=g3(n,t);for(let i=0;i<a.length;i++)if(o.indexOf(i)>-1)a[i]=Number.MAX_SAFE_INTEGER;else{let l=m3(t,n,i),c=s[l];e&1<<l&&(c=Number.MAX_SAFE_INTEGER),a[i]=c}for(let i=0;i<a.length;i++){let l=r[i];a[i]<0&&(a[i]+=l),a[i]=Uc(0,a[i],r[i])}return a}function b3(e,t,n){let s=e[t];return(n&1<<t||s==null)&&(s=1),s}function v3(e,t,n,s,r,a){let o=t[r],i=n[r]||1;(e&1<<r||a&1<<r||o==null)&&(i>0?o=Number.MIN_SAFE_INTEGER:o=Number.MAX_SAFE_INTEGER);let l=s[r];return o<0&&(o+=l),o=Uc(0,o,l-1),o}function w3(e,t,n,s,r,a){let o=t[r],i=n[r]||1;(e&1<<r||a&1<<r||o==null)&&(i>0?o=Number.MAX_SAFE_INTEGER:o=Number.MIN_SAFE_INTEGER);let l=s[r];return o<0&&(o+=l),i>0?o=Uc(0,o,l):o=Uc(-1,o,l-1),o}function dE(e,t,n){let s=n.length;for(let r=0;r<n.length;r++)if(n[r]>1){s=r;break}for(let r=s+1;r<n.length;r++)if(t[r]>0||n[r]!==e[r])return!1;return!0}function pE(e,t){let n=e.length>0?e[e.length-1]:1;for(let s=0;s<e.length-1;s++)n+=e[s]*t[s];return n}function k3(e,t,n){let s,r=e.shape.length;typeof t=="number"?s=[t,...new Array(r-1).fill(0)]:t.length<r?s=t.concat(new Array(r-t.length).fill(0)):s=t.slice(),s.forEach(o=>{M(o!==-1,()=>"slice() does not support negative begin indexing.")});let a;return n==null?a=new Array(r).fill(-1):typeof n=="number"?a=[n,...new Array(r-1).fill(-1)]:n.length<r?a=n.concat(new Array(r-n.length).fill(-1)):a=n,a=a.map((o,i)=>o>=0?o:(M(o===-1,()=>`Negative size values should be exactly -1 but got ${o} for the slice() size at index ${i}.`),e.shape[i]-s[i])),[s,a]}function hE(e,t,n,s,r,a,o,i,l){let c=t.slice(),u=n.slice(),d=s;s==null&&(d=new Array(c.length));let p=Jh(o);if(p.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(o!==0&&i!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(o!==0&&l!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let h=e.length-c.length,f=Jh(i),m=e.slice();f.forEach(S=>{c[S]=0,u[S]=1,m.splice(S,0,1)});let{begin:g,end:y,strides:A}=y3(m,p,h,c,u,d,r,a,o);c=g,u=y,d=A;let x=Jh(l);x.forEach(S=>{u[S]=c[S]+1,d[S]=1});let b=h3(c,u,d),w=b.filter((S,N)=>x.indexOf(N)===-1);return{nonStrided:d.every(S=>S===1),$begin:c,$end:u,$strides:d,size:b,newShape:m,outShape:w}}var de={};Le(de,{Serializable:()=>I3,SerializationMap:()=>ji,registerClass:()=>wo});var I3=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},ji=class{constructor(){this.classNameMap={}}static getMap(){return ji.instance==null&&(ji.instance=new ji),ji.instance}static register(e){ji.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function wo(e){M(e.className!=null,()=>"Class being registered does not have the static className property defined."),M(typeof e.className=="string",()=>"className is required to be a string, but got type "+typeof e.className),M(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),ji.register(e)}var S3={};Le(S3,{TEST_EPSILON_FLOAT16:()=>C3,encodeStrings:()=>T3,expectArrayBuffersEqual:()=>bE,expectArraysClose:()=>mE,expectArraysEqual:()=>yE,expectNumbersClose:()=>AE,expectPromiseToFail:()=>gE,expectValuesInRange:()=>xE,testEpsilon:()=>M2});var fE=.001,C3=.1;function mE(e,t,n){return n==null&&(n=M2()),z2(e,t,(s,r)=>L2(s,r,n))}function M2(){return W.backend.floatPrecision()===32?fE:C3}function z2(e,t,n){let s=!0;if((_n(e)||_n(t))&&(s=!1),_n(e)&&_n(t)&&(s=!0),s){let o=e.constructor.name,i=t.constructor.name;if(o!==i)throw new Error(`Arrays are of different type. Actual: ${o}. Expected: ${i}`)}if(Array.isArray(e)&&Array.isArray(t)){let o=Cr(e),i=Cr(t);if(!jr(o,i))throw new Error(`Arrays have different shapes. Actual: [${o}]. Expected: [${i}]`)}let r=_n(e)?e:ei(e),a=_n(t)?t:ei(t);if(r.length!==a.length)throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
Actual: ${r}.
Expected: ${a}.`);for(let o=0;o<a.length;++o){let i=r[o],l=a[o];if(!n(i,l))throw new Error(`Arrays differ: actual[${o}] = ${i}, expected[${o}] = ${l}.
Actual: ${r}.
Expected: ${a}.`)}}function gE(e,t){e().then(()=>t.fail(),()=>t())}function yE(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])?z2(e,n,(s,r)=>s==r):z2(e,t,(s,r)=>L2(s,r,0))}function AE(e,t,n){if(n==null&&(n=M2()),!L2(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function L2(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function xE(e,t,n){for(let s=0;s<e.length;s++)if(e[s]<t||e[s]>n)throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`)}function bE(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function T3(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?T3(n):e[t]=ud(n)}return e}var Qh="3.9.0";function N3(){Z().set("PROD",!0)}function vE(){Z().set("DEBUG",!0)}function wE(){Z().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function B2(e){Z().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(e+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}KN(B2);function kE(){W.disposeVariables()}function ts(){return W}function ef(){return W.memory()}function IE(e){return W.profile(e)}function j(e,t){return W.tidy(e,t)}function ne(e){x2(e).forEach(n=>n.dispose())}function An(e){return W.keep(e)}function SE(e){return W.time(e)}function E3(e){return W.setBackend(e)}function tf(){return W.ready()}function lr(){return W.backendName}function CE(e){W.removeBackend(e)}function W2(e){return W.findBackend(e)}function TE(e){return W.findBackendFactory(e)}function qi(e,t,n=1){return W.registerBackend(e,t,n)}function Tr(){return W.backend}function NE(e,t){Z().setPlatform(e,t)}function EE(e,t){let n=_(e,"a","add"),s=_(t,"b","add");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(qr,r)}var ue=U({add_:EE});function RE(e,t){let n=_(e,"a","floorDiv"),s=_(t,"b","floorDiv");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(za,r)}var nf=U({floorDiv_:RE});function $E(e,t){let n=_(e,"a","div"),s=_(t,"b","div");if([n,s]=Mt(n,s),n.dtype==="int32"&&s.dtype==="int32")return nf(n,s);let r={a:n,b:s},a={};return W.runKernel(Pa,r,a)}var fe=U({div_:$E});function DE(e,t){let n=_(e,"a","mul"),s=_(t,"b","mul");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(Za,r)}var L=U({mul_:DE});function _E(e){let t=_(e,"x","abs");if(t.dtype==="complex64"){let n={x:t};return W.runKernel(qc,n)}else{let n={x:t};return W.runKernel(ti,n)}}var Kt=U({abs_:_E});function PE(e){let n={x:_(e,"x","acos")};return W.runKernel(ql,n)}var V2=U({acos_:PE});function FE(e){let n={x:_(e,"x","acosh")};return W.runKernel(Xl,n)}var U2=U({acosh_:FE});function OE(e){M(Array.isArray(e),()=>"The argument passed to tf.addN() must be a list of tensors"),M(e.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${e.length}`);let t=e.map((r,a)=>_(r,`tensors${a}`,"addN")),n=t[0];t.forEach(r=>{if(r.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(r=>{if(!jr(r.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let s=t;return W.runKernel(ka,s)}var sf=U({addN_:OE});function ME(e,t=null,n=!1){let r={x:_(e,"x","all","bool")},a={axis:t,keepDims:n};return W.runKernel(Kl,r,a)}var rf=U({all_:ME});function zE(e,t=null,n=!1){let r={x:_(e,"x","any","bool")},a={axis:t,keepDims:n};return W.runKernel(Zl,r,a)}var Ad=U({any_:zE});function LE(e,t=0){let s={x:_(e,"x","argMax")},r={axis:t};return W.runKernel(Ia,s,r)}var _s=U({argMax_:LE});function BE(e,t=0){let s={x:_(e,"x","argMin")},r={axis:t};return W.runKernel(Yl,s,r)}var G2=U({argMin_:BE});function WE(e){let n={x:_(e,"x","asin")};return W.runKernel(Jl,n)}var H2=U({asin_:WE});function VE(e){let n={x:_(e,"x","asinh")};return W.runKernel(Ql,n)}var j2=U({asinh_:VE});function UE(e){let n={x:_(e,"x","atan")};return W.runKernel(eu,n)}var q2=U({atan_:UE});function GE(e,t){let n=_(e,"a","atan2"),s=_(t,"b","atan2");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(nu,r)}var X2=U({atan2_:GE});function HE(e){let n={x:_(e,"x","atanh")};return W.runKernel(tu,n)}var K2=U({atanh_:HE});function jE(e,t,n,s,r="NHWC",a){let o=e[3],i=[...t,o],l=D3(r);return xd(e,i,n,a,s,null,null,l)}function R3(e,t,n,s,r,a,o="channelsLast"){let[i,l]=af(t),c;if(o==="channelsLast")c=[i,l,e[3],e[3]];else if(o==="channelsFirst")c=[i,l,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return xd(e,c,n,s,r,a,!1,o)}function qE(e,t,n,s,r,a,o="NDHWC"){let[i,l,c]=Y2(t),u,d;if(o==="NDHWC")d="channelsLast",u=[i,l,c,e[4],e[4]];else if(o==="NCDHW")d="channelsFirst",u=[i,l,c,e[1],e[1]];else throw new Error(`Unknown dataFormat ${o}`);return $3(e,u,n,s,r,!1,d,a)}function xd(e,t,n,s,r,a,o=!1,i="channelsLast"){let[l,c,u,d]=[-1,-1,-1,-1];if(i==="channelsLast")[l,c,u,d]=e;else if(i==="channelsFirst")[l,d,c,u]=e;else throw new Error(`Unknown dataFormat ${i}`);let[p,h,,f]=t,[m,g]=af(n),[y,A]=af(s),x=Nu(p,y),b=Nu(h,A),{padInfo:w,outHeight:k,outWidth:S}=ZE(r,c,u,m,g,x,b,a,i),N=o?f*d:f,R;return i==="channelsFirst"?R=[l,N,k,S]:i==="channelsLast"&&(R=[l,k,S,N]),{batchSize:l,dataFormat:i,inHeight:c,inWidth:u,inChannels:d,outHeight:k,outWidth:S,outChannels:N,padInfo:w,strideHeight:m,strideWidth:g,filterHeight:p,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:b,dilationHeight:y,dilationWidth:A,inShape:e,outShape:R,filterShape:t}}function $3(e,t,n,s,r,a=!1,o="channelsLast",i){let[l,c,u,d,p]=[-1,-1,-1,-1,-1];if(o==="channelsLast")[l,c,u,d,p]=e;else if(o==="channelsFirst")[l,p,c,u,d]=e;else throw new Error(`Unknown dataFormat ${o}`);let[h,f,m,,g]=t,[y,A,x]=Y2(n),[b,w,k]=Y2(s),S=Nu(h,b),N=Nu(f,w),R=Nu(m,k),{padInfo:P,outDepth:$,outHeight:D,outWidth:T}=YE(r,c,u,d,y,A,x,S,N,R,i),O=a?g*p:g,B;return o==="channelsFirst"?B=[l,O,$,D,T]:o==="channelsLast"&&(B=[l,$,D,T,O]),{batchSize:l,dataFormat:o,inDepth:c,inHeight:u,inWidth:d,inChannels:p,outDepth:$,outHeight:D,outWidth:T,outChannels:O,padInfo:P,strideDepth:y,strideHeight:A,strideWidth:x,filterDepth:h,filterHeight:f,filterWidth:m,effectiveFilterDepth:S,effectiveFilterHeight:N,effectiveFilterWidth:R,dilationDepth:b,dilationHeight:w,dilationWidth:k,inShape:e,outShape:B,filterShape:t}}function XE(e,t,n,s,r){s==null&&(s=Z2(e,t,n));let a=e[0],o=e[1],i=Xi((a-t+2*s)/n+1,r),l=Xi((o-t+2*s)/n+1,r);return[i,l]}function KE(e,t,n,s,r,a){r==null&&(r=Z2(e,t,s));let o=e[0],i=e[1],l=e[2],c=Xi((o-t+2*r)/s+1,a),u=Xi((i-t+2*r)/s+1,a),d=Xi((l-t+2*r)/s+1,a);return[c,u,d,n]}function Z2(e,t,n,s=1){let r=Nu(t,s);return Math.floor((e[0]*(n-1)-n+r)/2)}function af(e){return typeof e=="number"?[e,e,e]:e.length===2?[e[0],e[1],1]:e}function Y2(e){return typeof e=="number"?[e,e,e]:e}function Nu(e,t){return t<=1?e:e+(e-1)*(t-1)}function ZE(e,t,n,s,r,a,o,i,l){let c,u,d;if(typeof e=="number"){c={top:e,bottom:e,left:e,right:e,type:e===0?"VALID":"NUMBER"};let h=XE([t,n],a,s,e,i);u=h[0],d=h[1]}else if(e==="same"){u=Math.ceil(t/s),d=Math.ceil(n/r);let p=Math.max(0,(u-1)*s+a-t),h=Math.max(0,(d-1)*r+o-n),f=Math.floor(p/2),m=p-f,g=Math.floor(h/2),y=h-g;c={top:f,bottom:m,left:g,right:y,type:"SAME"}}else if(e==="valid")c={top:0,bottom:0,left:0,right:0,type:"VALID"},u=Math.ceil((t-a+1)/s),d=Math.ceil((n-o+1)/r);else if(typeof e=="object"){let p=l==="channelsLast"?e[1][0]:e[2][0],h=l==="channelsLast"?e[1][1]:e[2][1],f=l==="channelsLast"?e[2][0]:e[3][0],m=l==="channelsLast"?e[2][1]:e[3][1];c={top:p,bottom:h,left:f,right:m,type:p===0&&h===0&&f===0&&m===0?"VALID":"EXPLICIT"},u=Xi((t-a+p+h)/s+1,i),d=Xi((n-o+f+m)/r+1,i)}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:c,outHeight:u,outWidth:d}}function YE(e,t,n,s,r,a,o,i,l,c,u){let d,p,h,f;if(typeof e=="number"){d={top:e,bottom:e,left:e,right:e,front:e,back:e,type:e===0?"VALID":"NUMBER"};let g=KE([t,n,s,1],i,1,r,e,u);p=g[0],h=g[1],f=g[2]}else if(e==="same"){p=Math.ceil(t/r),h=Math.ceil(n/a),f=Math.ceil(s/o);let m=(p-1)*r+i-t,g=(h-1)*a+l-n,y=(f-1)*o+c-s,A=Math.floor(m/2),x=m-A,b=Math.floor(g/2),w=g-b,k=Math.floor(y/2),S=y-k;d={top:b,bottom:w,left:k,right:S,front:A,back:x,type:"SAME"}}else if(e==="valid")d={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-i+1)/r),h=Math.ceil((n-l+1)/a),f=Math.ceil((s-c+1)/o);else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:d,outDepth:p,outHeight:h,outWidth:f}}function Xi(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 ko(e){let[t,n,s]=af(e);return t===1&&n===1&&s===1}function Nr(e,t){return ko(e)||ko(t)}function D3(e){if(e==="NHWC")return"channelsLast";if(e==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${e}`)}function JE(e,t){let s={x:_(e,"x","reshape","string_or_numeric")},r={shape:t};return W.runKernel(Ci,s,r)}var G=U({reshape_:JE});function QE(e,t,n,s,r){let a=_(e,"x","avgPool","float32"),o=1;M(Nr(n,o),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`);let i=a,l=!1;a.rank===3&&(l=!0,i=G(a,[1,a.shape[0],a.shape[1],a.shape[2]])),M(i.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${i.rank}.`),r!=null&&M(mn(s),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=W.runKernel(Sa,c,u);return d=pe(d,a.dtype),l?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var bd=U({avgPool_:QE});function eR(e,t,n,s,r,a="NDHWC"){let o=_(e,"x","avgPool3d","float32"),i=o,l=!1;o.rank===4&&(l=!0,i=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(i.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`),M(a==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),r!=null&&M(mn(s),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=W.runKernel(Hc,c,u);return d=pe(d,i.dtype),l?G(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var J2=U({avgPool3d_:eR});function tR(e,t=0){M(e.length>=1,()=>"Pass at least one tensor to concat");let n=gd(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(a=>{if(a.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${a.dtype}. `)}),n.length===1)return ir(n[0]);let s=n,r={axis:t};return W.runKernel(si,s,r)}var kt=U({concat_:tR});function nR(e){let n={x:_(e,"x","sigmoid")};return W.runKernel(ao,n)}var ns=U({sigmoid_:nR});function sR(e,t,n){let s=_(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return W.runKernel($i,r,a)}var _e=U({slice_:sR});function rR(e){let n={x:_(e,"x","tanh")};return W.runKernel(po,n)}var Ki=U({tanh_:rR});function aR(e,t,n,s,r,a){let o=_(e,"forgetBias","basicLSTMCell"),i=_(t,"lstmKernel","basicLSTMCell"),l=_(n,"lstmBias","basicLSTMCell"),c=_(s,"data","basicLSTMCell"),u=_(r,"c","basicLSTMCell"),d=_(a,"h","basicLSTMCell"),p=kt([c,d],1),h=Xe(p,i),f=ue(h,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],A=_e(f,[0,0],y),x=_e(f,[0,g],y),b=_e(f,[0,g*2],y),w=_e(f,[0,g*3],y),k=ue(L(ns(A),Ki(x)),L(u,ns(ue(o,b)))),S=L(Ki(k),ns(w));return[k,S]}var oR=U({basicLSTMCell_:aR});function iR(e,t,n){let s=_(e,"x","batchToSpaceND"),r=t.reduce((i,l)=>i*l);M(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),M(s.shape[0]%r==0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return W.runKernel(ni,a,o)}var vd=U({batchToSpaceND_:iR});function lR(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 uR(e,t,n,s,r,a){a==null&&(a=.001);let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;s!=null&&(u=_(s,"offset","batchNorm")),M(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),M(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),M(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let p={x:lR(o),scale:c,offset:u,mean:i,variance:l},h={varianceEpsilon:a},f=W.runKernel(La,p,h);return G(f,o.shape)}var Zi=U({batchNorm_:uR});function cR(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return s!=null&&(u=_(s,"offset","batchNorm")),M(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),M(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),M(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),Zi(o,i,l,u,c,a)}var _3=U({batchNorm2d_:cR});function dR(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return s!=null&&(u=_(s,"offset","batchNorm")),M(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),M(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),Zi(o,i,l,u,c,a)}var P3=U({batchNorm3d_:dR});function pR(e,t,n,s,r,a){let o=_(e,"x","batchNorm"),i=_(t,"mean","batchNorm"),l=_(n,"variance","batchNorm"),c;r!=null&&(c=_(r,"scale","batchNorm"));let u;return s!=null&&(u=_(s,"offset","batchNorm")),M(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),M(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Zi(o,i,l,u,c,a)}var F3=U({batchNorm4d_:pR});function hR(e,t,n){let s=_(e,"x","bincount"),r=_(t,"weights","bincount");M(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return W.runKernel(yh,a,o)}var Q2=U({bincount_:hR});function fR(e,t){let n=_(e,"s0","broadcastArgs","int32"),s=_(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);let r={s0:n,s1:s};return W.runKernel(u2,r)}var O3=U({broadcastArgs_:fR});function mR(e,t){let n=_(e,"broadcastTo","x"),s=n.shape;if(t.some(c=>!(c>0)||c%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let c=n.shape.slice();for(;c.length<t.length;)c.unshift(1);n=G(n,c)}let r=n.shape,a=Array.from(t);for(let c=t.length-1;c>=0;c--)if(r[c]===t[c])a[c]=1;else if(n.shape[c]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((c,u)=>c>1?u:-1).filter(c=>c>=0).length===0)return ir(n);let i={x:n},l={reps:a};return W.runKernel(Kr,i,l)}var Eu=U({broadcastTo_:mR});function gR(e){let n={x:_(e,"x","ceil")};return W.runKernel(Na,n)}var e1=U({ceil_:gR});function yR(e,t,n){let s=_(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return W.runKernel(Xr,r,a)}var ss=U({clipByValue_:yR});function AR(e){return kt(e,0)}var M3=U({concat1d_:AR});function xR(e,t){return kt(e,t)}var Ru=U({concat2d_:xR});function bR(e,t){return kt(e,t)}var z3=U({concat3d_:bR});function vR(e,t){return kt(e,t)}var L3=U({concat4d_:vR});function wR(e,t,n,s,r="NHWC",a=[1,1],o){let i=_(e,"x","conv2d"),l=_(t,"filter","conv2d"),c=i,u=!1;i.rank===3&&(u=!0,c=G(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),o!=null&&M(mn(s),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d=r==="NHWC"?c.shape[3]:c.shape[1];M(d===l.shape[2],()=>`Error in conv2d: depth of input (${d}) must match input depth for filter ${l.shape[2]}.`),M(Nr(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let p={x:c,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=W.runKernel(Ea,p,h);return u?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Qr=U({conv2d_:wR});function kR(e,t,n,s,r="NWC",a=1,o){let i=_(e,"x","conv1d"),l=_(t,"filter","conv1d"),c=i,u=!1;i.rank===2&&(u=!0,c=G(i,[1,i.shape[0],i.shape[1]])),M(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),o!=null&&M(mn(s),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(Nr(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),M(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=G(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=G(c,[c.shape[0],1,c.shape[1],c.shape[2]]),g=Qr(p,d,[1,n],s,"NHWC",[1,a],o);return u?G(g,[g.shape[2],g.shape[3]]):G(g,[g.shape[0],g.shape[2],g.shape[3]])}var of=U({conv1d_:kR});function IR(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,c=!1;t.rank===3&&(c=!0,l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),M(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),o!=null&&M(mn(r),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let p={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=W.runKernel(Ra,p,h);return c?G(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var t1=U({conv2DBackpropInput_:IR});function SR(e,t,n,s,r,a){let o=_(e,"x","conv2dTranspose"),i=_(t,"filter","conv2dTranspose");return t1(n,o,i,s,r,"NHWC",a)}var lf=U({conv2dTranspose_:SR});function CR(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=_(e,"x","conv3d"),i=_(t,"filter","conv3d"),l=o,c=!1;o.rank===4&&(c=!0,l=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),M(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),M(Nr(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=W.runKernel(Xc,u,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var n1=U({conv3d_:CR});function TR(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=G(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],c=o.shape[4];M(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),M(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=W.runKernel(bh,u,d);return i?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var B3=U({conv3DBackpropInput_:TR});function NR(e,t,n,s,r){let a=_(e,"x","conv3dTranspose"),o=_(t,"filter","conv3dTranspose");return B3(n,a,o,s,r)}var W3=U({conv3dTranspose_:NR});function ER(e){let n={x:_(e,"x","cos")};return W.runKernel($a,n)}var wd=U({cos_:ER});function RR(e){let n={x:_(e,"x","cosh")};return W.runKernel(Da,n)}var uf=U({cosh_:RR});function $R(e,t=0,n=!1,s=!1){let a={x:_(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return W.runKernel(ri,a,o)}var cf=U({cumsum_:$R});function DR(e,t,n,s=!1){let r=_(e,"x","denseBincount"),a=_(t,"weights","denseBincount");M(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),M(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return W.runKernel(vh,o,i)}var V3=U({denseBincount_:DR});function _R(e,t,n="NHWC"){let s=_(e,"x","depthToSpace"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];M(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${r} and ${t} for depthToSpace with input shape
${s.shape}`),M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${a} and ${t} for depthToSpace with input shape
${s.shape}`),M(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${s.shape}`);let i={x:s},l={blockSize:t,dataFormat:n};return W.runKernel(oi,i,l)}var s1=U({depthToSpace_:_R});function PR(e,t,n,s,r="NHWC",a=[1,1],o){let i=_(e,"x","depthwiseConv2d"),l=_(t,"filter","depthwiseConv2d"),c=i,u=!1;i.rank===3&&(u=!0,c=G(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&M(mn(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=W.runKernel(_a,d,p);return u?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var $u=U({depthwiseConv2d_:PR});function FR(e){let n={x:_(e,"x","diag")};return W.runKernel(Ih,n)}var OR=U({diag_:FR});function MR(e,t,n,s,r=[1,1],a="NHWC"){let o=_(e,"x","dilation2d"),i=_(t,"filter","dilation2d");M(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),M(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),M(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,c=!1;o.rank===3&&(l=G(o,[1,o.shape[0],o.shape[1],o.shape[2]]),c=!0);let u={x:l,filter:i},d={strides:n,pad:s,dilations:r},p=W.runKernel(Kc,u,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var r1=U({dilation2d_:MR});function zR(e,t){let n=e.length,s=[];for(let r=0;r<n;r++){let a=n-1-r,o=e[a]||1;(t[t.length-1-r]||1)>1&&o===1&&s.unshift(a)}return s}function sn(e,t){let n=[];for(let s=0;s<t.length;s++){let r=e[e.length-s-1],a=t.length-s-1,o=t[a];(r==null||r===1&&o>1)&&n.unshift(a)}return n}function Tt(e,t){let n=[],s=Math.max(e.length,t.length);for(let r=0;r<s;r++){let a=e[e.length-r-1];a==null&&(a=1);let o=t[t.length-r-1];if(o==null&&(o=1),a===1)n.unshift(o);else if(o===1)n.unshift(a);else if(a!==o){let i=`Operands could not be broadcast together with shapes ${e} and ${t}.`;throw Error(i)}else n.unshift(a)}return n}function LR(e,t){let n=_(e,"a","equal","string_or_numeric"),s=_(t,"b","equal","string_or_numeric");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(ii,r)}var ms=U({equal_:LR});function BR(e,t,n){let s=_(t,"a","where"),r=_(n,"b","where"),a=_(e,"condition","where","bool"),o=Tt(Tt(a.shape,s.shape),r.shape),i=Eu(a,o),l=Eu(s,o),c=Eu(r,o),u={condition:i,t:l,e:c};return W.runKernel(Ri,u)}var Pn=U({where_:BR});function WR(e){let n={x:_(e,"x","zerosLike")};return W.runKernel(Li,n)}var tt=U({zerosLike_:WR});function VR(e,t){let n=_(e,"a","div"),s=_(t,"b","div");[n,s]=Mt(n,s);let r=fe(n,s),a=tt(r),o=ms(s,a);return Pn(o,a,r)}var a1=U({divNoNan_:VR});function UR(e,t){let n=_(e,"t1","dot"),s=_(t,"t2","dot");M((n.rank===1||n.rank===2)&&(s.rank===1||s.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);let r=n.rank===1?n.size:n.shape[1],a=s.rank===1?s.size:s.shape[0];if(M(r===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`),n.rank===1&&s.rank===1){let o=G(n,[1,-1]),i=G(s,[-1,1]),l=Xe(o,i);return G(l,[])}else if(n.rank===1&&s.rank===2){let o=G(n,[1,-1]),i=G(s,[s.shape[0],s.shape[1]]),l=Xe(o,i);return G(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=G(s,[-1,1]),i=Xe(n,o);return G(i,[i.size])}else{let o=G(s,[s.shape[0],s.shape[1]]);return Xe(n,o)}}var U3=U({dot_:UR});function GR(e,...t){let n=t.map((r,a)=>_(r,`tensors${a}`,"einsum")),s={equation:e};return W.runKernel(Zc,n,s)}var G3=U({einsum_:GR});function HR(e){let n={x:_(e,"x","elu")};return W.runKernel(Fa,n)}var Du=U({elu_:HR});function jR(e){let t=_(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=pe(t,"float32"));let n={x:t};return W.runKernel(su,n)}var o1=U({erf_:jR});function qR(e){let n={x:_(e,"x","exp")};return W.runKernel(Oa,n)}var gs=U({exp_:qR});function XR(e,t=0){let n=_(e,"x","expandDims","string_or_numeric");M(t<=n.rank,()=>"Axis must be <= rank of the tensor");let s={input:n},r={dim:t};return W.runKernel(li,s,r)}var Ht=U({expandDims_:XR});function KR(e){let n={x:_(e,"x","expm1")};return W.runKernel(ui,n)}var i1=U({expm1_:KR});function ZR(e,t){let n=_(e,"x","tile","string_or_numeric");M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let s={x:n},r={reps:t};return W.runKernel(Kr,s,r)}var Ps=U({tile_:ZR});function YR(e,t,n,s="float32"){t==null&&(t=e);let r=We([e,t],s),a=e<=t?e:t;for(let i=0;i<a;++i)r.set(1,i,i);let o=G(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return Ps(Ht(o,0),[n[0],1,1]);if(n.length===2)return Ps(Ht(Ht(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Ps(Ht(Ht(Ht(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var l1=U({eye_:YR});function _u(e,t,n){let s={shape:e,value:t,dtype:n};return W.runKernel(ru,{},s)}function JR(e){let n={x:_(e,"x","floor")};return W.runKernel(Ma,n)}var Pu=U({floor_:JR});function QR(e,t,n=0,s=0){let r=_(e,"x","gather"),a=_(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return W.runKernel(di,o,i)}var Yi=U({gather_:QR});function e$(e,t){let n=_(e,"a","greater","string_or_numeric"),s=_(t,"b","greater","string_or_numeric");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(hi,r)}var rs=U({greater_:e$});function t$(e,t){let n=_(e,"a","greaterEqual","string_or_numeric"),s=_(t,"b","greaterEqual","string_or_numeric");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ba,r)}var Io=U({greaterEqual_:t$});function n$(e){let n={input:_(e,"input","imag")};return W.runKernel(Yc,n)}var df=U({imag_:n$});function s$(e){let n={x:_(e,"x","isFinite")};return W.runKernel(au,n)}var H3=U({isFinite_:s$});function r$(e){let n={x:_(e,"x","isInf")};return W.runKernel(ou,n)}var j3=U({isInf_:r$});function a$(e){let n={x:_(e,"x","isNaN")};return W.runKernel(iu,n)}var u1=U({isNaN_:a$});function o$(e,t=.2){let s={x:_(e,"x","leakyRelu")},r={alpha:t};return W.runKernel(fi,s,r)}var kd=U({leakyRelu_:o$});function i$(e,t){let n=_(e,"a","less","string_or_numeric"),s=_(t,"b","less","string_or_numeric");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(mi,r)}var pf=U({less_:i$});function l$(e,t){let n=_(e,"a","lessEqual","string_or_numeric"),s=_(t,"b","lessEqual","string_or_numeric");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(gi,r)}var So=U({lessEqual_:l$});function q3(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let s={start:e,stop:t,num:n};return W.runKernel(Rh,{},s)}function u$(e,t=5,n=1,s=1,r=.5){let a=_(e,"x","localResponseNormalization");M(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
rank ${a.rank}.`),M(mn(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=G(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},c={depthRadius:t,bias:n,alpha:s,beta:r},u=W.runKernel(Qc,l,c);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var c1=U({localResponseNormalization_:u$});function c$(e){let n={x:_(e,"x","log")};return W.runKernel(Va,n)}var ys=U({log_:c$});function d$(e){let n={x:_(e,"x","log1p")};return W.runKernel(lu,n)}var Id=U({log1p_:d$});function p$(e){return M(wa(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=_(t,"x","tf.grad","string_or_numeric"),r=n!=null?_(n,"dy","tf.grad"):null;return W.tidy(()=>{let{value:a,grads:o}=W.gradients(()=>e(s),[s],r);return r!=null&&Mn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),hf(o),o[0]})}}function h$(e){return M(wa(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{M(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let s=gd(t,"args","tf.grads","string_or_numeric"),r=n!=null?_(n,"dy","tf.grads"):null;return W.tidy(()=>{let{value:a,grads:o}=W.gradients(()=>e(...s),s,r);return r!=null&&Mn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),hf(o),o})}}function f$(e){return M(wa(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof Ke,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof Ke,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=W.gradients(()=>e(t),[t],n);return hf(s),{grad:s[0],value:r}}}function m$(e){return M(wa(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof Ke),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof Ke,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=W.gradients(()=>e(...t),t,n);return n!=null&&Mn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),hf(s.grads),s}}function X3(e,t){M(wa(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(c=>c instanceof hd),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in W.registeredVariables)t.push(W.registeredVariables[c])}let s=n?t.filter(c=>!c.trainable):null,r=t.length;t=t.filter(c=>c.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=W.gradients(e,t,null,a);M(i.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((c,u)=>{i[u]!=null&&(l[c.name]=i[u])}),s!=null&&s.forEach(c=>l[c.name]=null),{value:o,grads:l}}function Er(e){return W.customGrad(e)}function hf(e){if(e.filter(n=>n==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 g$(e){let n={x:_(e,"x","neg")};return W.runKernel(Ai,n)}var _t=U({neg_:g$});function y$(e){let n={x:_(e,"x","softplus")};return W.runKernel(yu,n)}var Ji=U({softplus_:y$});function A$(e){let t=_(e,"x","logSigmoid");return Er(s=>({value:_t(Ji(_t(s))),gradFunc:o=>L(o,ns(_t(s)))}))(t)}var K3=U({logSigmoid_:A$});function x$(e,t=null,n=!1){let r={x:_(e,"x","max")},a={reductionIndices:t,keepDims:n};return W.runKernel(Ua,r,a)}var Bn=U({max_:x$});function b$(e,t){let n=_(e,"a","sub"),s=_(t,"b","sub");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(co,r)}var xe=U({sub_:b$});function v$(e,t=null,n=!1){let s=_(e,"x","sum");s.dtype==="bool"&&(s=pe(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return W.runKernel(io,r,a)}var we=U({sum_:v$});function w$(e,t=-1){let n=_(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 ${t}`);return Er((r,a)=>{let o=!0,i=Bn(r,t,!0),l=xe(r,i),c=xe(pe(l,"float32"),ys(we(gs(l),t,o)));return a([c]),{value:c,gradFunc:(d,p)=>{let[h]=p,f=!0,m=gs(h);return xe(d,L(we(d,t,f),m))}}})(n)}var ff=U({logSoftmax_:w$});function d1(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function Z3(e,t,n){let s=e.length+t.length,r=[],a=0,o=0;for(let i=0;i<s;i++)n.indexOf(i)===-1?r.push(e[a++]):r.push(t[o++]);return r}function Y3(e,t){let n=[],s=e.length;for(let a=0;a<s;a++)t.indexOf(a)===-1&&n.push(e[a]);let r=t.map(a=>e[a]);return[n,r]}function Qi(e,t){let n=t.map(s=>1);return Z3(e,n,t)}function k$(e,t,n){M(d1(t,n),()=>`${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`)}function J3(e,t){if(d1(e,t))return null;let n=[];for(let s=0;s<t;++s)e.indexOf(s)===-1&&n.push(s);return e.forEach(s=>n.push(s)),n}function p1(e){return e.map((t,n)=>[n,t]).sort((t,n)=>t[1]-n[1]).map(t=>t[0])}function I$(e,t){let n=[];for(let s=t-e;s<t;++s)n.push(s);return n}function S$(e,t=null,n=!1){let s=_(e,"x","logSumExp"),r=js(t,s.shape),a=Bn(s,r,!0),o=xe(s,a),i=gs(o),l=we(i,r),c=ys(l),u=ue(G(a,c.shape),c);if(n){let d=Qi(u.shape,r);return G(u,d)}return u}var h1=U({logSumExp_:S$});function C$(e,t){let n=_(e,"a","logicalAnd","bool"),s=_(t,"b","logicalAnd","bool");Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(yi,r)}var Ks=U({logicalAnd_:C$});function T$(e){let n={x:_(e,"x","logicalNot","bool")};return W.runKernel(uu,n)}var Sd=U({logicalNot_:T$});function N$(e,t){let n=_(e,"a","logicalOr","bool"),s=_(t,"b","logicalOr","bool");Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Jc,r)}var mf=U({logicalOr_:N$});function E$(e,t){let n=_(e,"a","logicalXor","bool"),s=_(t,"b","logicalXor","bool");return Tt(n.shape,s.shape),Ks(mf(e,t),Sd(Ks(e,t)))}var Q3=U({logicalXor_:E$});function R$(e,t,n,s,r){let a=_(e,"x","maxPool"),o=1,i=a,l=!1;a.rank===3&&(l=!0,i=G(a,[1,a.shape[0],a.shape[1],a.shape[2]])),M(i.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${i.rank}.`),M(Nr(n,o),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${o}'`),r!=null&&M(mn(s),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r},d=W.runKernel(Ha,c,u);return l?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Cd=U({maxPool_:R$});function $$(e,t=[1,1,1],n,s,r,a="NDHWC"){let o=_(e,"x","maxPool3d"),i=o,l=!1;o.rank===4&&(l=!0,i=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(i.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`),M(a==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`),r!=null&&M(mn(s),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${r} but got pad ${s}.`);let c={x:i},u={filterSize:t,strides:n,pad:s,dimRoundingMode:r,dataFormat:a},d=W.runKernel(ed,c,u);return l?G(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var f1=U({maxPool3d_:$$});function D$(e,t,n,s,r=!1){let o={x:_(e,"x","maxPoolWithArgmax")},i={filterSize:t,strides:n,pad:s,includeBatchInIndex:r},l=W.runKernel(Ph,o,i);return{result:l[0],indexes:l[1]}}var ev=U({maxPoolWithArgmax_:D$});function _$(e,t){let n=_(e,"a","maximum"),s=_(t,"b","maximum");[n,s]=Mt(n,s),n.dtype==="bool"&&(n=pe(n,"int32"),s=pe(s,"int32")),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ga,r)}var Rr=U({maximum_:_$});function P$(e,t=null,n=!1){let r={x:_(e,"x","mean")},a={axis:t,keepDims:n};return W.runKernel(ja,r,a)}var zt=U({mean_:P$});function jt(e,t="float32"){if(t==="complex64"){let s=jt(e,"float32"),r=jt(e,"float32");return xo(s,r)}let n=fh(Gt(e),t);return W.makeTensor(n,e,t)}function As(e,t="float32"){if(t==="complex64"){let s=As(e,"float32"),r=jt(e,"float32");return xo(s,r)}let n=r2(Gt(e),t);return W.makeTensor(n,e,t)}function F$(e,t,{indexing:n="xy"}={}){if(n!=="xy"&&n!=="ij")throw new TypeError(`${n} is not a valid third argument to meshgrid`);if(e===void 0)return[];let s=_(e,"x","meshgrid",e instanceof Ke?e.dtype:"float32");if(t===void 0)return[s];let r=_(t,"y","meshgrid",t instanceof Ke?t.dtype:"float32"),a=Gt(s.shape),o=Gt(r.shape);return n==="xy"?(s=G(s,[1,-1]),r=G(r,[-1,1]),[Xe(As([o,1],s.dtype),s),Xe(r,As([1,a],r.dtype))]):(s=G(s,[-1,1]),r=G(r,[1,-1]),[Xe(s,As([1,o],s.dtype)),Xe(As([a,1],r.dtype),r)])}function O$(e,t=null,n=!1){let r={x:_(e,"x","min")},a={axis:t,keepDims:n};return W.runKernel(qa,r,a)}var Td=U({min_:O$});function M$(e,t){let n=_(e,"a","minimum"),s=_(t,"b","minimum");[n,s]=Mt(n,s),n.dtype==="bool"&&(n=pe(n,"int32"),s=pe(s,"int32")),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Xa,r)}var Fu=U({minimum_:M$});function z$(e,t,n){M(n==="reflect"||n==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);let s=_(e,"x","mirrorPad");if(s.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");M(t.length===s.rank,()=>`Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);let r=n==="reflect"?1:0;for(let i=0;i<s.rank;i++)M(t[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),M(t[i][0]>=0&&t[i][0]<=s.shape[i]-r&&t[i][1]>=0&&t[i][1]<=s.shape[i]-r,()=>`Padding in dimension ${i} cannot be greater than or equal to ${s.shape[i]-r} or less than 0 for input of shape ${s.shape}`);let a={paddings:t,mode:n},o={x:s};return W.runKernel(Ka,o,a)}var m1=U({mirrorPad_:z$});function L$(e,t){let n=_(e,"a","mod"),s=_(t,"b","mod");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(cu,r)}var g1=U({mod_:L$});function B$(e){let t=_(e,"x","square"),n={};return W.runKernel("Square",{x:t},n)}var vt=U({square_:B$});function W$(e,t=null,n=!1){e=_(e,"x","moments");let s=js(t,e.shape),r=zt(e,s,n),a=r.shape;n||(a=Qi(r.shape,s));let o=vt(xe(pe(e,"float32"),G(r,a))),i=zt(o,s,n);return{mean:r,variance:i}}var gf=U({moments_:W$});function V$(e,t,n,s){let r=_(t,"data","multiRNNCell"),a=gd(n,"c","multiRNNCell"),o=gd(s,"h","multiRNNCell"),i=r,l=[];for(let d=0;d<e.length;d++){let p=e[d](i,a[d],o[d]);l.push(p[0]),l.push(p[1]),i=p[1]}let c=[],u=[];for(let d=0;d<l.length;d+=2)c.push(l[d]),u.push(l[d+1]);return[c,u]}var U$=U({multiRNNCell_:V$});function G$(e,t,n,s=!1){let r=_(e,"logits","multinomial"),a=r.size,o=r.rank;if(a<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);if(o>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);n=n||Math.random();let l={logits:o===1?G(r,[1,-1]):r},c={numSamples:t,seed:n,normalized:s},u=W.runKernel(Fh,l,c);return o===1?G(u,[u.size]):u}var tv=U({multinomial_:G$});function H$(e,t){let n=_(e,"a","notEqual","string_or_numeric"),s=_(t,"b","notEqual","string_or_numeric");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(xi,r)}var el=U({notEqual_:H$});function j$(e){let n={x:_(e,"x","onesLike")};return W.runKernel(wi,n)}var xs=U({onesLike_:j$});function q$(e,t){let n=_(e,"v1","outerProduct"),s=_(t,"v2","outerProduct");M(n.rank===1&&s.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);let r=G(n,[-1,1]),a=G(s,[1,-1]);return Xe(r,a)}var X$=U({outerProduct_:q$});function K$(e,t,n=0){let s=_(e,"x","pad");if(s.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let r={paddings:t,constantValue:n},a={x:s};return W.runKernel(Ya,a,r)}var ur=U({pad_:K$});function Z$(e,t,n=0){return M(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),ur(e,[t],n)}var Y$=U({pad1d_:Z$});function J$(e,t,n=0){return M(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),ur(e,t,n)}var Q$=U({pad2d_:J$});function eD(e,t,n=0){return M(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."),ur(e,t,n)}var tD=U({pad3d_:eD});function nD(e,t,n=0){return M(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."),ur(e,t,n)}var sD=U({pad4d_:nD});function rD(e,t,n){let s=_(e,"x","spaceToBatchND");M(s.rank>=1+t.length,()=>`input rank ${s.rank} should be > than [blockShape] ${t.length}`),M(n.length===t.length,()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`),M(s.shape.reduce((o,i,l)=>l>0&&l<=t.length?o&&(i+n[l-1][0]+n[l-1][1])%t[l-1]==0:o,!0),()=>`input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);let r={x:s},a={blockShape:t,paddings:n};return W.runKernel(_i,r,a)}var Nd=U({spaceToBatchND_:rD});function aD(e,t,n,s,r,a){r==null&&(r=[1,1]),a==null&&(a=1),s===0&&(s="valid");let o=_(e,"x","maxPool"),i=o,l=!1;o.rank===3&&(l=!0,i=G(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(Nr(a,r),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);let c=R3(i.shape,t,a,r,s),u=[c.dilationHeight,c.dilationWidth],d;s==="same"?d=iD([c.filterHeight,c.filterWidth],u):d=[[0,0],[0,0]];let p=u[0]===1&&u[1]===1,[h,f]=oD([c.inHeight,c.inWidth],u,d),m=p?s:"valid",g=p?i:Nd(i,u,h),A=(n==="avg"?()=>bd(g,t,a,m):()=>Cd(g,t,a,m))(),x=p?A:vd(A,u,f);return l?G(x,[x.shape[1],x.shape[2],x.shape[3]]):x}function oD(e,t,n){let s=n.map(u=>u[0]),r=n.map(u=>u[1]),a=e.concat(s,r),o=t.map((u,d)=>(u-a[d]%u)%u),i=r.map((u,d)=>u+o[d]),l=t.map((u,d)=>[s[d],i[d]]),c=t.map((u,d)=>[0,o[d]]);return[l,c]}function iD(e,t){let s=e.map((o,i)=>o+(o-1)*(t[i]-1)).map(o=>o-1),r=s.map(o=>Math.floor(o/2)),a=s.map((o,i)=>o-r[i]);return s.map((o,i)=>[r[i],a[i]])}var nv=U({pool_:aD});function lD(e,t){let n=_(e,"base","pow"),s=_(t,"exp","pow");[n,s]=Mt(n,s);let r={a:n,b:s};return W.runKernel(Ja,r)}var ea=U({pow_:lD});function uD(e,t){let n=_(e,"x","prelu"),s=_(t,"alpha","prelu"),r={x:n,alpha:s};return W.runKernel(Qa,r)}var Ed=U({prelu_:uD});function cD(e,t=null,n=!1){let s=_(e,"x","prod");s.dtype==="bool"&&(s=pe(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return W.runKernel(Si,r,a)}var yf=U({prod_:cD});function dD(e,t,n){let s=Gt(e),r=null;if(n==null||n==="float32")r=new Float32Array(s);else if(n==="int32")r=new Int32Array(s);else if(n==="bool")r=new Uint8Array(s);else throw new Error(`Unknown data type ${n}`);for(let a=0;a<s;a++)r[a]=t();return W.makeTensor(r,e,n)}var pD=U({rand_:dD}),y1=Jo(p5()),A1=class{constructor(e,t,n,s,r){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=s,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let a=r||Math.random();this.random=y1.alea(a.toString())}nextValue(){if(!isNaN(this.nextVal)){let s=this.nextVal;return this.nextVal=NaN,s}let e,t,n=!1;for(;!n;){let s,r,a;do s=2*this.random()-1,r=2*this.random()-1,a=s*s+r*r;while(a>=1||a===0);let o=Math.sqrt(-2*Math.log(a)/a);e=this.mean+this.stdDev*s*o,t=this.mean+this.stdDev*r*o,(!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}},hD=class{constructor(e,t,n,s){this.alpha=e,this.beta=1/t,this.dtype=n;let r=s||Math.random();this.randu=y1.alea(r.toString()),this.randn=new A1(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,s,r,a;for(;;){do s=this.randn.nextValue(),a=1+this.c*s;while(a<=0);if(a*=a*a,e=s*s,t=1-.331*e*e,n=.5*e+this.d*(1-a+Math.log(a)),r=this.randu(),r<t||Math.log(r)<n)break}return a=1/this.beta*this.d*a,this.alpha<1&&(a*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(a)}convertValue(e){return this.dtype==="float32"?e:Math.round(e)}},fD=class{constructor(e=0,t=1,n,s){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=e,this.range=t-e,this.dtype=n,s==null&&(s=Math.random()),typeof s=="number"&&(s=s.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);this.random=y1.alea(s)}convertValue(e){return this.canReturnFloat()?e:Math.round(e)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function mD(e,t,n=1,s="float32",r){if(n==null&&(n=1),s==null&&(s="float32"),s!=="float32"&&s!=="int32")throw new Error(`Unsupported data type ${s}`);let a=new hD(t,n,s,r),o=We(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var gD=U({randomGamma_:mD});function yD(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error(`Unsupported data type ${s}`);let a=new A1(t,n,s,!1,r),o=We(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var sv=U({randomNormal_:yD});function AD(e,t=0,n=1,s="float32",r){let a=We(e,s),o=new fD(t,n,null,r);for(let i=0;i<a.values.length;i++)a.values[i]=o.nextValue();return a.toTensor()}var Ou=U({randomUniform_:AD});function Mu(e,t,n=1,s="float32"){if(n===0)throw new Error("Cannot have a step of zero");let r={start:e,stop:t,step:n,dtype:s};return W.runKernel(pu,{},r)}function xD(e){let n={input:_(e,"input","real")};return W.runKernel(td,n)}var Rd=U({real_:xD});function bD(e){let n={x:_(e,"x","reciprocal")};return W.runKernel(hu,n)}var x1=U({reciprocal_:bD});function vD(e){let n={x:_(e,"x","relu")};return W.runKernel(eo,n)}var cr=U({relu_:vD});function wD(e){let n={x:_(e,"x","relu6")};return W.runKernel(no,n)}var Af=U({relu6_:wD});function kD(e,t){let s={x:_(e,"x","reverse")},r={dims:t};return W.runKernel(Ti,s,r)}var bs=U({reverse_:kD});function ID(e){let t=_(e,"x","reverse");return M(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),bs(t,0)}var SD=U({reverse1d_:ID});function CD(e,t){let n=_(e,"x","reverse");return M(n.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`),bs(n,t)}var TD=U({reverse2d_:CD});function ND(e,t){let n=_(e,"x","reverse");return M(n.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`),bs(n,t)}var ED=U({reverse3d_:ND});function RD(e,t){let n=_(e,"x","reverse");return M(n.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),bs(n,t)}var $D=U({reverse4d_:RD});function DD(e){let n={x:_(e,"x","round")};return W.runKernel(Ni,n)}var xf=U({round_:DD});function _D(e){let n={x:_(e,"x","rsqrt")};return W.runKernel(so,n)}var bf=U({rsqrt_:_D});function Ee(e,t){if((_n(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"&&_n(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return bo(e,[],[],t)}function PD(e){let n={x:_(e,"x","selu")};return W.runKernel(mu,n)}var vf=U({selu_:PD});function FD(e,t,n,s,r,a=[1,1],o="NHWC"){let i=_(e,"x","separableConv2d"),l=_(t,"depthwiseFilter","separableConv2d"),c=_(n,"pointwiseFilter","separableConv2d"),u=i,d=!1;if(i.rank===3&&(d=!0,u=G(i,[1,i.shape[0],i.shape[1],i.shape[2]])),o==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");M(u.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${u.rank}.`),M(l.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),M(c.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${c.shape[0]}.`),M(c.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${c.shape[1]}.`);let p=l.shape[2],h=l.shape[3];M(c.shape[2]===p*h,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${p*h}, but got ${c.shape[2]}.`);let f=$u(u,l,s,r,o,a),g=Qr(f,c,1,"valid",o);return d?G(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var b1=U({separableConv2d_:FD});async function OD(e,t){let n=_(e,"x","setdiff1d"),s=_(t,"y","setdiff1d");M(n.dtype===s.dtype,()=>`x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`),M(n.rank===1,()=>`x should be 1D tensor, but got x (${n.shape}).`),M(s.rank===1,()=>`y should be 1D tensor, but got y (${s.shape}).`);let r=await n.data(),a=await s.data(),o=new Set(a),i=0;for(let u=0;u<r.length;u++)o.has(r[u])||i++;let l=new tn([i],n.dtype),c=new tn([i],"int32");for(let u=0,d=0;u<r.length;u++)o.has(r[u])||(l.values[d]=r[u],c.values[d]=u,d++);return[l.toTensor(),c.toTensor()]}var rv=OD;function MD(e){let n={x:_(e,"x","sign")};return W.runKernel(gu,n)}var v1=U({sign_:MD});function zD(e){let n={x:_(e,"x","sin")};return W.runKernel(ro,n)}var wf=U({sin_:zD});function LD(e){let n={x:_(e,"x","sinh")};return W.runKernel(Di,n)}var kf=U({sinh_:LD});function BD(e,t,n){let s=_(e,"x","slice1d");return M(s.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`),_e(s,[t],[n])}var If=U({slice1d_:BD});function WD(e,t,n){let s=_(e,"x","slice2d");return M(s.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`),_e(s,t,n)}var w1=U({slice2d_:WD});function VD(e,t,n){let s=_(e,"x","slice3d");return M(s.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`),_e(s,t,n)}var zu=U({slice3d_:VD});function UD(e,t,n){let s=_(e,"x","slice4d");return M(s.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`),_e(s,t,n)}var $d=U({slice4d_:UD});function GD(e,t=-1){let n=_(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 s={logits:n},r={dim:t};return W.runKernel(lo,s,r)}var tl=U({softmax_:GD});function HD(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);let t={input:e};return W.runKernel(Nh,t)}var Dd=U({fft_:HD});function jD(e){M(e.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);let t={input:e};return W.runKernel(Eh,t)}var Lu=U({ifft_:jD});function qD(e){let t=e.shape[e.shape.length-1],n=e.size/t,s;if(t<=2){let r=G(e,[n,t]);s=Lu(r)}else{let r=[n,2*(t-1)],a=G(Rd(e),[n,t]),o=G(df(e),[n,t]),i=bs(_e(a,[0,1],[n,t-2]),1),l=L(bs(_e(o,[0,1],[n,t-2]),1),Ee(-1)),c=kt([a,i],1),u=kt([o,l],1),d=G(xo(c,u),[r[0],r[1]]);s=Lu(d)}if(s=Rd(s),e.rank===3&&e.shape[0]!==0){let r=s,a=e.shape[0];s=G(s,[a,s.shape[0]/a,s.shape[1]]),r.dispose()}return s}var Sf=U({irfft_:qD});function XD(e,t,n=0){let r={x:_(e,"x","split")},a={numOrSizeSplits:t,axis:n};return W.runKernel(Pi,r,a)}var xn=U({split_:XD});function KD(e,t){M(e.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${e.dtype}`);let n=e.shape[e.shape.length-1],s=e.size/n,r;if(t!=null&&t<n){let f=e.shape.map(g=>0),m=e.shape.map(g=>g);m[e.shape.length-1]=t,r=_e(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,r=kt([e,jt(f)],e.shape.length-1),n=t}else r=e;let a=tt(r),o=G(xo(r,a),[s,n]),i=Dd(o),l=Math.floor(n/2)+1,c=Rd(i),u=df(i),d=xn(c,[l,n-l],c.shape.length-1),p=xn(u,[l,n-l],u.shape.length-1),h=r.shape.slice();return h[r.shape.length-1]=l,G(xo(d[0],p[0]),h)}var _d=U({rfft_:KD});function ZD(e){let n={x:_(e,"x","sqrt")};return W.runKernel(oo,n)}var Cn=U({sqrt_:ZD});function YD(e,t){let n=_(e,"a","squaredDifference"),s=_(t,"b","squaredDifference");[n,s]=Mt(n,s),Tt(n.shape,s.shape);let r={a:n,b:s},a={};return W.runKernel(uo,r,a)}var Cf=U({squaredDifference_:YD});function JD(e,t){let n=_(e,"x","squeeze");return G(n,g5(n.shape,t).newShape)}var dt=U({squeeze_:JD});function QD(e,t=0){let n=gd(e,"tensors","stack","string_or_numeric");M(n.length>=1,()=>"Pass at least one tensor to tf.stack"),n.length>0&&M(t<=n[0].rank,()=>"Axis must be <= rank of the tensor");let s=n,r={axis:t};return W.runKernel(Ii,s,r)}var Tn=U({stack_:QD});function e_(e,t=0){let s={x:_(e,"x","step")},r={alpha:t};return W.runKernel(fo,s,r)}var Bu=U({step_:e_});function t_(e,t,n,s,r=0,a=0,o=0,i=0,l=0){let u={x:_(e,"x","stridedSlice","string_or_numeric")},d={begin:t,end:n,strides:s,beginMask:r,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:l};return W.runKernel(Fi,u,d)}var k1=U({stridedSlice_:t_});function n_(e){let n={x:_(e,"x","tan")};return W.runKernel(Oi,n)}var I1=U({tan_:n_});function Zt(e,t){Qo(e);let n=Cr(e,t);if(n.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return bo(e,null,n,t)}function dr(e,t,n){if(Qo(e),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let s=Cr(e,n);if(s.length!==2&&s.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return bo(e,t,s,n)}function s_(e,t,n){if(Qo(e),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let s=Cr(e,n);if(s.length!==4&&s.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return bo(e,t,s,n)}function r_(e,t,n){if(Qo(e),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let s=Cr(e,n);if(s.length!==5&&s.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return bo(e,t,s,n)}function a_(e,t,n){if(Qo(e),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let s=Cr(e,n);if(s.length!==6&&s.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(s.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||s,bo(e,t,s,n)}function o_(e,t=1,n=!0){let s=_(e,"x","topk");if(s.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let r=s.shape[s.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>r)throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);let a={x:s},o={k:t,sorted:n},[i,l]=W.runKernel(xu,a,o);return{values:i,indices:l}}var S1=U({topk_:o_});function i_(e,t=0,n=1,s,r){if(s!=null&&s==="bool")throw new Error("Unsupported data type $ { dtype }");let a=new A1(t,n,s,!0,r),o=We(e,s);for(let i=0;i<o.values.length;i++)o.values[i]=a.nextValue();return o.toTensor()}var Tf=U({truncatedNormal_:i_});function l_(e,t=0){let n=_(e,"x","unique","string_or_numeric");M(n.rank>0,()=>"The input tensor must be at least 1D");let s={x:n},r={axis:t},[a,o]=W.runKernel(Gh,s,r);return{values:a,indices:o}}var Nf=U({unique_:l_});function u_(e,t,n){let s=_(e,"x","unsortedSegmentSum"),r=_(t,"segmentIds","unsortedSegmentSum","int32");M(mn(n),()=>"numSegments must be of dtype int");let a={x:s,segmentIds:r},o={numSegments:n};return W.runKernel(rd,a,o)}var C1=U({unsortedSegmentSum_:u_});function c_(e,t=0){let n=_(e,"x","unstack","string_or_numeric");M(t>=-n.shape.length&&t<n.shape.length,()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);let s={value:n},r={axis:t};return W.runKernel(zi,s,r)}var Wn=U({unstack_:c_});function av(e,t=!0,n,s){return W.makeVariable(e,t,n,s)}function ov(e,t){let n=[];for(let a=0;a<t.length;a++)t[a]&&n.push(a);let s=We(e,"int32"),r=We([n.length,e.length],"int32");for(let a=0;a<n.length;a++){let o=s.indexToLoc(n[a]),i=a*e.length;r.values.set(o,i)}return r.toTensor()}async function d_(e){let t=_(e,"condition","whereAsync","bool"),n=await t.data(),s=ov(t.shape,n);return e!==t&&t.dispose(),s}var T1=d_;async function p_(e,t,n){let s=_(e,"tensor","boolMask"),r=_(t,"mask","boolMask","bool"),a=n==null?0:n,o=r.rank,i=s.shape;M(o>0,()=>"mask cannot be scalar"),Mn(i.slice(a,a+o),r.shape,"mask's shape must match the first K dimensions of tensor's shape,");let l=1;for(let m=a;m<a+o;m++)l*=i[m];let c=i.slice(0,a).concat([l],i.slice(a+o)),u=G(s,c),d=G(r,[-1]),p=await T1(d),h=dt(p,[1]),f=Yi(u,h,a);return e!==s&&s.dispose(),t!==r&&r.dispose(),h.dispose(),u.dispose(),d.dispose(),p.dispose(),f}var h_=p_;function f_(e,t="euclidean",n=null,s=!1){e=_(e,"x","norm");let r=iv(e,t,n),a=r.shape;if(s){let o=js(n,e.shape);a=Qi(r.shape,o)}return G(r,a)}function iv(e,t,n=null){if(e.rank===0)return Kt(e);if(e.rank!==1&&n===null)return iv(G(e,[-1]),t,n);if(e.rank===1||typeof n=="number"||Array.isArray(n)&&n.length===1){if(t===1)return we(Kt(e),n);if(t===1/0)return Bn(Kt(e),n);if(t===-1/0)return Td(Kt(e),n);if(t==="euclidean"||t===2)return Cn(we(ea(Kt(e),Ee(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&n.length===2){if(t===1)return Bn(we(Kt(e),n[0]),n[1]-1);if(t===1/0)return Bn(we(Kt(e),n[1]),n[0]);if(t===-1/0)return Td(we(Kt(e),n[1]),n[0]);if(t==="fro"||t==="euclidean")return Cn(we(vt(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}var Ef=U({norm_:f_});function m_(e,t,n,s,r=!0){let a=_(e,"v","movingAverage"),o=_(t,"x","movingAverage"),i=_(n,"decay","movingAverage");M5(a,o),M(jr(a.shape,o.shape),()=>"Shape mismatch in v and x");let l=Ee(1),c=xe(l,i),u=L(xe(o,a),c);if(r){M(s!=null,()=>"When using zeroDebias: true, step is required.");let d=_(s,"step","movingAverage");u=fe(u,xe(l,ea(i,d)))}return ue(a,u)}var g_=U({movingAverage_:m_});function y_(e,t,n){let s=_(e,"indices","scatterND","int32"),r=_(t,"updates","scatterND");O2(r,s,n);let a={indices:s,updates:r},o={shape:n};return W.runKernel(Ei,a,o)}var lv=U({scatterND_:y_});function A_(e,t,n,s){if(e.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);let r=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);let o=t.size;if(!(t.rank===0||t.rank===1&&o===r))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);if(t.dtype!==s.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function x_(e,t,n,s=0){let r=_(e,"sparseIndices","sparseToDense","int32"),a=_(t,"sparseValues","sparseToDense"),o=_(s,"defaultValue","sparseToDense",a.dtype);A_(r,a,n,o);let i={sparseIndices:r,sparseValues:a,defaultValue:o},l={outputShape:n};return W.runKernel(nd,i,l)}var N1=U({sparseToDense_:x_});function b_(e,t){let n=_(t,"indices","gatherND","int32"),r={params:_(e,"x","gatherND","string_or_numeric"),indices:n};return W.runKernel(pi,r)}var uv=U({gatherND_:b_});function v_(e,t){if(t==null)return e.shape.slice();if(jr(e.shape,t))return t;if(e.shape.length===t.length){let n=[];for(let s=0;s<e.shape.length;s++)t[s]==null&&e.shape[s]!=null?n.push(e.shape[s]):n.push(t[s]);return n}return t}function w_(e,t,n,s){let r=_(e,"x","dropout");if(M(r.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`),M(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return e instanceof Ke?r.clone():r;let a=v_(r,n),o=1-t,i=fe(Pu(ue(Ou(a,0,1,"float32",s),o)),o);return L(r,i)}var cv=U({dropout_:w_});function dv(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function E1(e,t,n){let s=1-e%2,r=new Float32Array(e);for(let a=0;a<e;++a){let o=2*Math.PI*a/(e+s-1);r[a]=t-n*Math.cos(o)}return Zt(r,"float32")}async function k_(e,t,n=1){let s=_(e,"predictions","inTopK"),r=_(t,"targets","inTopK");M(s.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`),M(s.rank-1===r.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`),Mn(s.shape.slice(0,s.shape.length-1),r.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let a=s.shape[s.shape.length-1];M(n>0&&n<=a,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);let o=await s.data(),i=await r.data(),[l,c]=[o.length/a,a],u=y5("bool",l);for(let d=0;d<l;d++){let p=d*c,h=o.subarray(p,p+c),f=[];for(let m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort((m,g)=>g.value-m.value),u[d]=0;for(let m=0;m<n;m++)if(f[m].index===i[d]){u[d]=1;break}}return e!==s&&s.dispose(),t!==r&&r.dispose(),nn(u,r.shape,"bool")}var I_=k_,Co={};Le(Co,{conv2d:()=>T_,depthwiseConv2d:()=>$_,matMul:()=>__});function S_(e,t,n,s,r,a="NHWC",o){let i=e;e.rank===3&&(i=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]])),M(i.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`),M(l.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),M(n.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);let c=a==="NHWC"?i.shape[3]:i.shape[1],u=a==="NHWC"?l.shape[3]:l.shape[1];M(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),M(u===n[3],()=>`Error in conv2dDerFilter: depth of dy (${u}) must match output depth for filter (${n[3]}).`),o!=null&&M(mn(r),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${o} but got pad ${r}.`);let d={x:i,dy:l},p={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,filterShape:n};return W.runKernel(Ah,d,p)}var R1=U({conv2DBackpropFilter_:S_});function Rf(e,t,n){if(n==null||n==="linear")return e;if(n==="relu")return L(e,Bu(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function $f(e,t){let n=t,s=sn(e.shape,t.shape);return s.length>0&&(n=we(n,s)),G(n,e.shape)}function Df(e,t,n,s){if(t==="linear")return e;if(t==="relu")return cr(e);if(t==="elu")return Du(e);if(t==="relu6")return Af(e);if(t==="prelu")return Ed(e,n);if(t==="leakyrelu")return kd(e,s);if(t==="sigmoid")return ns(e);throw new Error(`Unknown fused activation ${t}.`)}var _f=(e,t)=>!(e>0)||t==="linear";function C_({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",_f(W.state.gradientDepth,l)===!1){let w=Qr(e,t,n,s,r,a,o);return i!=null&&(w=ue(w,i)),Df(w,l,c,u)}let d=_(e,"x","conv2d"),p=_(t,"filter","conv2d"),h=d,f=!1;d.rank===3&&(f=!0,h=G(d,[1,d.shape[0],d.shape[1],d.shape[2]])),M(h.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`),M(p.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${p.rank}.`),o!=null&&M(mn(s),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`),M(h.shape[3]===p.shape[2],()=>`Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${p.shape[2]}.`),M(Nr(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NHWC",()=>`Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);let m=xd(h.shape,p.shape,n,a,s,o),g;i!=null&&(g=_(i,"bias","fused conv2d"),[g]=Mt(g,d),Tt(m.outShape,g.shape));let y;c!=null&&(y=_(c,"prelu weights","fused conv2d"));let A=(w,k)=>{let[S,N,R,P]=k,$=Rf(w,R,l);M(ko(a),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);let D=t1(N.shape,$,S,n,s),T=R1(N,$,S.shape,n,s),O=[D,T];if(P!=null){let B=$f(P,$);O.push(B)}return O},x={x:h,filter:p,bias:g,preluActivationWeights:y},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:u};return i==null?Er((k,S,N)=>{let R=W.runKernel(go,x,b);return N([S,k,R]),f&&(R=G(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:A}})(h,p):Er((k,S,N,R)=>{let P=W.runKernel(go,x,b);return R([S,k,P,N]),f&&(P=G(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:A}})(h,p,g)}var T_=U({fusedConv2d_:C_});function N_(e,t,n,s,r,a=[1,1],o){let i=e;e.rank===3&&(i=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let l=t;l.rank===3&&(l=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={x:i,dy:l},u={strides:s,pad:r,dimRoundingMode:o,dilations:a,filterShape:n};return W.runKernel(wh,c,u)}var pv=U({depthwiseConv2dNativeBackpropFilter_:N_});function E_(e,t,n,s,r,a=[1,1],o){let i=t,l=!1;t.rank===3&&(l=!0,i=G(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let c={dy:i,filter:n},u={strides:s,pad:r,dimRoundingMode:o,dilations:a,inputShape:e},d=W.runKernel(kh,c,u);return l?G(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var hv=U({depthwiseConv2dNativeBackpropInput_:E_});function R_({x:e,filter:t,strides:n,pad:s,dataFormat:r="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(_f(W.state.gradientDepth,l)===!1){let w=$u(e,t,n,s,r,a,o);return i!=null&&(w=ue(w,i)),Df(w,l,c,u)}let d=_(e,"x","depthwiseConv2d"),p=_(t,"filter","depthwiseConv2d"),h=d,f=!1;d.rank===3&&(f=!0,h=G(d,[1,d.shape[0],d.shape[1],d.shape[2]])),M(h.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`),M(p.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`),M(h.shape[3]===p.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${p.shape[2]}.`),a==null&&(a=[1,1]),M(Nr(n,a),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),o!=null&&M(mn(s),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${o} but got pad ${s}.`);let m=xd(h.shape,p.shape,n,a,s,o,!0),g;i!=null&&(g=_(i,"bias","fused conv2d"),[g]=Mt(g,d),Tt(m.outShape,g.shape));let y;c!=null&&(y=_(c,"prelu weights","fused depthwiseConv2d"));let A=(w,k)=>{M(ko(a),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[S,N,R,P]=k,$=Rf(w,R,l),D=hv(N.shape,$,S,n,s,a,o),T=pv(N,$,S.shape,n,s,a,o);if(P!=null){let O=$f(g,$);return[D,T,O]}return[D,T]},x={x:h,filter:p,bias:g,preluActivationWeights:y},b={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o,activation:l,leakyreluAlpha:u};return i==null?Er((k,S,N)=>{let R=W.runKernel(yo,x,b);return N([S,k,R]),f&&(R=G(R,[R.shape[1],R.shape[2],R.shape[3]])),{value:R,gradFunc:A}})(h,p):Er((k,S,N,R)=>{let P=W.runKernel(yo,x,b);return R([S,k,P,N]),f&&(P=G(P,[P.shape[1],P.shape[2],P.shape[3]])),{value:P,gradFunc:A}})(h,p,g)}var $_=U({fusedDepthwiseConv2d_:R_});function D_({a:e,b:t,transposeA:n=!1,transposeB:s=!1,bias:r,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i}){if(_f(W.state.gradientDepth,a)===!1){let P=Xe(e,t,n,s);return r!=null&&(P=ue(P,r)),Df(P,a,o,i)}let l=_(e,"a","fused matMul"),c=_(t,"b","fused matMul");[l,c]=Mt(l,c);let u=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=s?c.shape[c.rank-1]:c.shape[c.rank-2],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],h=s?c.shape[c.rank-2]:c.shape[c.rank-1],f=l.shape.slice(0,-2),m=c.shape.slice(0,-2),g=Gt(f),y=Gt(m);M(l.rank>=2&&c.rank>=2&&l.rank===c.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${l.rank} and ${c.rank}.`),M(jr(f,m),()=>`Error in fused matMul: outer dimensions (${f}) and (${m}) of Tensors with shapes ${l.shape} and ${c.shape} must match.`),M(u===d,()=>`Error in fused matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${s} must match.`);let A=l.shape.slice(0,-2).concat([p,h]),x=n?G(l,[g,u,p]):G(l,[g,p,u]),b=s?G(c,[y,h,d]):G(c,[y,d,h]),w;r!=null&&(w=_(r,"bias","fused matMul"),[w]=Mt(w,l),Tt(A,w.shape));let k;o!=null&&(k=_(o,"prelu weights","fused matMul"));let S=(P,$)=>{let[D,T,O,B]=$,H=Rf(G(P,O.shape),O,a),z,X;if(!n&&!s?(z=Xe(H,T,!1,!0),X=Xe(D,H,!0,!1)):!n&&s?(z=Xe(H,T,!1,!1),X=Xe(H,D,!0,!1)):n&&!s?(z=Xe(T,H,!1,!0),X=Xe(D,H,!1,!1)):(z=Xe(T,H,!0,!0),X=Xe(H,D,!0,!0)),r!=null){let ee=$f(B,H);return[z,X,ee]}else return[z,X]},N={a:x,b,bias:w,preluActivationWeights:k},R={transposeA:n,transposeB:s,activation:a,leakyreluAlpha:i};return r==null?Er(($,D,T)=>{let O=W.runKernel(mo,N,R);return T([$,D,O]),{value:G(O,A),gradFunc:S}})(x,b):Er(($,D,T,O)=>{let B=W.runKernel(mo,N,R);return O([$,D,B,T]),{value:G(B,A),gradFunc:S}})(x,b,w)}var __=U({fusedMatMul_:D_});function P_(e){return E1(e,.54,.46)}var F_=U({hammingWindow_:P_});function O_(e){return E1(e,.5,.5)}var fv=U({hannWindow_:O_});function M_(e,t,n,s=!1,r=0){let a=0,o=[];for(;a+t<=e.size;)o.push(_e(e,a,t)),a+=n;if(s)for(;a<e.size;){let i=a+t-e.size,l=kt([_e(e,a,t-i),_u([i],r)]);o.push(l),a+=n}return o.length===0?dr([],[0,t]):G(kt(o),[o.length,t])}var mv=U({frame_:M_});function z_(e,t,n,s,r=fv){s==null&&(s=dv(t));let a=mv(e,t,n),o=L(a,r(t));return _d(o,s)}var L_=U({stft_:z_});function B_(e,t,n,s,r="bilinear",a=0){let o=_(e,"image","cropAndResize"),i=_(t,"boxes","cropAndResize","float32"),l=_(n,"boxInd","cropAndResize","int32"),c=i.shape[0];M(o.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),M(i.rank===2&&i.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${i.shape}.`),M(l.rank===1&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${i.shape}.`),M(s.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`),M(s[0]>=1&&s[1]>=1,()=>`cropSize must be atleast [1,1], but was ${s}`),M(r==="bilinear"||r==="nearest",()=>`method must be bilinear or nearest, but was ${r}`);let u={image:o,boxes:i,boxInd:l},d={method:r,extrapolationValue:a,cropSize:s};return W.runKernel(ai,u,d)}var W_=U({cropAndResize_:B_});function V_(e){let t=_(e,"image","flipLeftRight","float32");M(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let n={image:t};return W.runKernel(ci,n,{})}var U_=U({flipLeftRight_:V_});function G_(e){let t=_(e,"image","grayscaleToRGB"),n=t.rank-1,s=t.shape[n];M(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),M(s===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);let r=new Array(t.rank);return r.fill(1,0,n),r[n]=3,Ps(t,r)}var H_=U({grayscaleToRGB_:G_});function j_(e,t,n=0,s=.5){let r=_(e,"image","rotateWithOffset","float32");M(r.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);let a={image:r},o={radians:t,fillValue:n,center:s};return W.runKernel(Bi,a,o)}var q_=U({rotateWithOffset_:j_});function Wu(e,t,n,s,r,a){s==null&&(s=.5),r==null&&(r=Number.NEGATIVE_INFINITY),a==null&&(a=0);let o=e.shape[0];return n=Math.min(n,o),M(0<=s&&s<=1,()=>`iouThreshold must be in [0, 1], but was '${s}'`),M(e.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),M(e.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),M(t.rank===1,()=>"scores must be a 1D tensor"),M(t.shape[0]===o,()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`),M(0<=a&&a<=1,()=>`softNmsSigma must be in [0, 1], but was '${a}'`),{maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a}}function X_(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=_(e,"boxes","nonMaxSuppression"),o=_(t,"scores","nonMaxSuppression"),i=Wu(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l={maxOutputSize:n,iouThreshold:s,scoreThreshold:r};return W.runKernel(bi,{boxes:a,scores:o},l)}var K_=U({nonMaxSuppression_:X_});function Z_(e,t,n){let s=Y_(e,t,n),r=s<0?-(s+1):s;e.splice(r,0,t)}function Y_(e,t,n){return Q_(e,t,n||J_)}function J_(e,t){return e>t?1:e<t?-1:0}function Q_(e,t,n){let s=0,r=e.length,a=0,o=!1;for(;s<r;){a=s+(r-s>>>1);let i=n(t,e[a]);i>0?s=a+1:(r=a,o=!i)}return o?s:-s-1}function gv(e,t,n,s,r){return $1(e,t,n,s,r,0)}function yv(e,t,n,s,r,a){return $1(e,t,n,s,r,0,!1,a,!0)}function Av(e,t,n,s,r,a){return $1(e,t,n,s,r,a,!0)}function $1(e,t,n,s,r,a,o=!1,i=!1,l=!1){let c=[];for(let g=0;g<t.length;g++)t[g]>r&&c.push({score:t[g],boxIndex:g,suppressBeginIndex:0});c.sort(xv);let u=a>0?-.5/a:0,d=[],p=[];for(;d.length<n&&c.length>0;){let g=c.pop(),{score:y,boxIndex:A,suppressBeginIndex:x}=g;if(y<r)break;let b=!1;for(let w=d.length-1;w>=x;--w){let k=eP(e,A,d[w]);if(k>=s){b=!0;break}if(g.score=g.score*tP(s,u,k),g.score<=r)break}g.suppressBeginIndex=d.length,b||(g.score===y?(d.push(A),p.push(g.score)):g.score>r&&Z_(c,g,xv))}let h=d.length,f=n-h;i&&f>0&&(d.push(...new Array(f).fill(0)),p.push(...new Array(f).fill(0)));let m={selectedIndices:d};return o&&(m.selectedScores=p),l&&(m.validOutputs=h),m}function eP(e,t,n){let s=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),a=Math.min(s[0],s[2]),o=Math.min(s[1],s[3]),i=Math.max(s[0],s[2]),l=Math.max(s[1],s[3]),c=Math.min(r[0],r[2]),u=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),p=Math.max(r[1],r[3]),h=(i-a)*(l-o),f=(d-c)*(p-u);if(h<=0||f<=0)return 0;let m=Math.max(a,c),g=Math.max(o,u),y=Math.min(i,d),A=Math.min(l,p),x=Math.max(y-m,0)*Math.max(A-g,0);return x/(h+f-x)}function tP(e,t,n){let s=Math.exp(t*n*n);return n<=e?s:0}function xv(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function nP(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=_(e,"boxes","nonMaxSuppressionAsync"),o=_(t,"scores","nonMaxSuppressionAsync"),i=Wu(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),c=l[0],u=l[1],{selectedIndices:d}=gv(c,u,n,s,r);return a!==e&&a.dispose(),o!==t&&o.dispose(),Zt(d,"int32")}var sP=nP;function rP(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=_(e,"boxes","nonMaxSuppression"),i=_(t,"scores","nonMaxSuppression"),l=Wu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c={boxes:o,scores:i},u={maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a},d=W.runKernel(vi,c,u);return{selectedIndices:d[0],selectedScores:d[1]}}var aP=U({nonMaxSuppressionWithScore_:rP});async function oP(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=_(e,"boxes","nonMaxSuppressionAsync"),i=_(t,"scores","nonMaxSuppressionAsync"),l=Wu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c=await Promise.all([o.data(),i.data()]),u=c[0],d=c[1],{selectedIndices:p,selectedScores:h}=Av(u,d,n,s,r,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Zt(p,"int32"),selectedScores:Zt(h)}}var iP=oP;function lP(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=_(e,"boxes","nonMaxSuppression"),i=_(t,"scores","nonMaxSuppression"),l=Wu(o,i,n,s,r,null),c=l.maxOutputSize,u=l.iouThreshold,d=l.scoreThreshold,p={boxes:o,scores:i},h={maxOutputSize:c,iouThreshold:u,scoreThreshold:d,padToMaxOutputSize:a},f=W.runKernel(du,p,h);return{selectedIndices:f[0],validOutputs:f[1]}}var uP=U({nonMaxSuppressionPadded_:lP});async function cP(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=_(e,"boxes","nonMaxSuppressionAsync"),i=_(t,"scores","nonMaxSuppressionAsync"),l=Wu(o,i,n,s,r,null),c=l.maxOutputSize,u=l.iouThreshold,d=l.scoreThreshold,[p,h]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=yv(p,h,c,u,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Zt(f,"int32"),validOutputs:Ee(m,"int32")}}var dP=cP;function pP(e,t,n=!1,s=!1){let r=_(e,"images","resizeBilinear");M(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),M(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),M(s===!1||n===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=G(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=W.runKernel(to,i,l);return o?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var bv=U({resizeBilinear_:pP});function hP(e,t,n=!1,s=!1){let r=_(e,"images","resizeNearestNeighbor");M(r.rank===3||r.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`),M(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),M(r.dtype==="float32"||r.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),M(s===!1||n===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let a=r,o=!1;r.rank===3&&(o=!0,a=G(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=W.runKernel(fu,i,l);return o?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var vv=U({resizeNearestNeighbor_:hP});function fP(e,t="binary",n=!1,s=.5){let r=_(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],c=L(Zt([s]),255),u,d,p,h;if(M(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),M(r.shape[2]===3||r.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`),M(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),M(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[u,d,p]=xn(r,[1,1,1],-1);let g=L(u,a),y=L(d,o),A=L(p,i);h=ue(ue(g,y),A)}else h=e;if(t==="otsu"){let g=Q2(pe(xf(h),"int32"),nn([]),256);c=mP(g,l)}let f=n?So(h,c):rs(h,c);return pe(L(f,255),"int32")}function mP(e,t){let n=Zt([-1]),s=Zt([0]),r=Zt([0]),a,o,i,l,c,u;for(let d=0;d<e.size-1;d++){a=_e(e,0,d+1),o=_e(e,d+1),c=fe(we(a),t),u=fe(we(o),t);let p=we(L(a,Mu(0,a.size)));i=fe(p,we(a));let h=_u(o.shape,a.size),f=ue(Mu(0,o.size),h),m=L(o,f);l=fe(we(m),we(o));let g=xe(i,l),y=xe(i,l),A=L(c,u);r=L(L(A,g),y);let x=rs(r,s);s=Pn(x,r,s),n=Pn(x,Zt([d]),n)}return n}var gP=U({threshold_:fP});function yP(e,t,n="nearest",s="constant",r=0,a){let o=_(e,"image","transform","float32"),i=_(t,"transforms","transform","float32");M(o.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${o.rank}.`),M(i.rank===2&&(i.shape[0]===o.shape[0]||i.shape[0]===1)&&i.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),M(a==null||a.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${a}.`);let l={image:o,transforms:i},c={interpolation:n,fillMode:s,fillValue:r,outputShape:a};return W.runKernel(Mi,l,c)}var AP=U({transform_:yP});function xP(e,t,n){M(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),M(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let s=_(e,"a","bandPart");M(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);let r=s.shape,[a,o]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=G(Mu(0,a,1,"int32"),[-1,1]),l=Mu(0,o,1,"int32"),c=xe(i,l),u=Ks(So(c,Ee(+t,"int32")),Io(c,Ee(-n,"int32"))),d=jt([a,o],s.dtype);return G(Tn(Wn(G(s,[-1,a,o])).map(p=>Pn(u,p,d))),r)}var bP=U({bandPart_:xP});function vP(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=xn(e,e.shape[0],0).map(r=>dt(r,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],s=e;for(let r=0;r<e.length;++r)n.push(W.tidy(()=>{let a=s[r];if(r>0)for(let o=0;o<r;++o){let i=L(we(L(n[o],a)),n[o]);a=xe(a,i)}return fe(a,Ef(a,"euclidean"))}));return t?Tn(n,0):n}var wP=U({gramSchmidt_:vP});function kP(e,t=!1){if(M(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return wv(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),s=Wn(G(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach(l=>{let[c,u]=wv(l,t);r.push(c),a.push(u)});let o=G(Tn(r,0),e.shape),i=G(Tn(a,0),e.shape);return[o,i]}}function wv(e,t=!1){return W.tidy(()=>{M(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],s=e.shape[1],r=l1(n),a=ir(e),o=dr([[1]],[1,1]),i=ir(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=W.tidy(()=>{let h=_e(a,[c,c],[n-c,1]),f=Ef(h),m=_e(a,[c,c],[1,1]),g=Pn(rs(m,0),dr([[-1]]),dr([[1]])),y=xe(m,L(g,f)),A=fe(h,y);A.shape[0]===1?i=ir(o):i=kt([o,_e(A,[1,0],[A.shape[0]-1,A.shape[1]])],0);let x=_t(fe(Xe(g,y),f)),b=_e(a,[c,0],[n-c,s]),w=L(x,i),k=et(i);if(c===0)a=xe(b,Xe(w,Xe(k,b)));else{let R=xe(b,Xe(w,Xe(k,b)));a=kt([_e(a,[0,0],[c,s]),R],0)}let S=et(w),N=_e(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=xe(N,Xe(Xe(N,i),S));else{let R=xe(N,Xe(Xe(N,i),S));r=kt([_e(r,[0,0],[n,c]),R],1)}return[i,a,r]}),ne([u,d,p])}return!t&&n>s&&(r=_e(r,[0,0],[n,s]),a=_e(a,[0,0],[s,s])),[r,a]})}var IP=U({qr_:kP}),Vn;(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"})(Vn||(Vn={}));function SP(e,t,n=Vn.SUM_BY_NONZERO_WEIGHTS){let s=_(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=_(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===Vn.NONE)return a;if(n===Vn.SUM)return we(a);if(n===Vn.MEAN){if(r==null)return zt(a);{let o=s.size/r.size,i=fe(we(a),we(r));return o>1?fe(i,Ee(o)):i}}if(n===Vn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return fe(we(a),Ee(s.size));{let o=L(r,As(s.shape)),i=pe(we(el(o,Ee(0))),"float32");return fe(we(a),i)}}throw Error(`Unknown reduction: ${n}`)}var ta=U({computeWeightedLoss_:SP});function CP(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","absoluteDifference"),a=_(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=_(n,"weights","absoluteDifference")),Mn(r.shape,a.shape,"Error in absoluteDifference: ");let i=Kt(xe(r,a));return ta(i,o,s)}var TP=U({absoluteDifference_:CP});function NP(e,t,n,s,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"labels","cosineDistance"),o=_(t,"predictions","cosineDistance"),i=null;s!=null&&(i=_(s,"weights","cosineDistance")),Mn(a.shape,o.shape,"Error in cosineDistance: ");let l=Ee(1),c=xe(l,we(L(a,o),n,!0));return ta(c,i,r)}var EP=U({cosineDistance_:NP});function RP(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","hingeLoss"),a=_(t,"predictions","hingeLoss"),o=null;n!=null&&(o=_(n,"weights","hingeLoss")),Mn(r.shape,a.shape,"Error in hingeLoss: ");let i=Ee(1);r=xe(L(Ee(2),r),i);let l=cr(xe(i,L(r,a)));return ta(l,o,s)}var $P=U({hingeLoss_:RP});function DP(e,t,n,s=1,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"labels","huberLoss"),o=_(t,"predictions","huberLoss"),i=null;n!=null&&(i=_(n,"weights","huberLoss")),Mn(a.shape,o.shape,"Error in huberLoss: ");let l=Ee(s),c=Kt(xe(o,a)),u=Fu(c,l),d=xe(c,u),p=ue(L(Ee(.5),vt(u)),L(l,d));return ta(p,i,r)}var _P=U({huberLoss_:DP});function PP(e,t,n,s=1e-7,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"labels","logLoss"),o=_(t,"predictions","logLoss"),i=null;n!=null&&(i=_(n,"weights","logLoss")),Mn(a.shape,o.shape,"Error in logLoss: ");let l=Ee(1),c=Ee(s),u=_t(L(a,ys(ue(o,c)))),d=L(xe(l,a),ys(ue(xe(l,o),c))),p=xe(u,d);return ta(p,i,r)}var FP=U({logLoss_:PP});function OP(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=_(e,"labels","meanSquaredError"),a=_(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=_(n,"weights","meanSquaredError")),Mn(r.shape,a.shape,"Error in meanSquaredError: ");let i=Cf(r,a);return ta(i,o,s)}var MP=U({meanSquaredError_:OP});function zP(e,t){let n=_(e,"labels","sigmoidCrossEntropyWithLogits"),s=_(t,"logits","sigmoidCrossEntropyWithLogits");Mn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=cr(s),a=L(s,n),o=Id(gs(_t(Kt(s))));return ue(xe(r,a),o)}function LP(e,t,n,s=0,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"multiClassLabels","sigmoidCrossEntropy"),o=_(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=_(n,"weights","sigmoidCrossEntropy")),Mn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=Ee(s),u=Ee(1),d=Ee(.5);a=ue(L(a,xe(u,c)),L(d,c))}let l=zP(a,o);return ta(l,i,r)}var BP=U({sigmoidCrossEntropy_:LP});function WP(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 Er((r,a,o)=>{let l=h1(a,[n],!0),c=xe(pe(a,"float32"),l);o([r,c]);let u=_t(L(c,r));return{value:we(u,[n]),gradFunc:(h,f)=>{let[m,g]=f,y=Qi(h.shape,[n]);return[L(G(h,y),xe(pe(m,"float32"),gs(g))),L(G(h,y),xe(gs(g),pe(m,"float32")))]}}})(e,t)}function VP(e,t,n,s=0,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=_(e,"onehotLabels","softmaxCrossEntropy"),o=_(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=_(n,"weights","softmaxCrossEntropy")),Mn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let c=Ee(s),u=Ee(1),d=Ee(a.shape[1]);a=ue(L(a,xe(u,c)),fe(c,d))}let l=WP(a,o);return ta(l,i,r)}var UP=U({softmaxCrossEntropy_:VP});function GP(e,t,n,s){let r=_(e,"indices","sparseFillEmptyRows"),a=_(t,"values","sparseFillEmptyRows"),o=_(n,"denseShape","sparseFillEmptyRows"),i=_(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},c=W.runKernel(zh,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var HP=U({sparseFillEmptyRows_:GP});function jP(e,t,n){let s=_(e,"inputIndices","sparseReshape"),r=_(t,"inputShape","sparseReshape"),a=_(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=W.runKernel(Lh,o);return{outputIndices:i[0],outputShape:i[1]}}var qP=U({sparseReshape_:jP});function XP(e,t,n){let s=_(e,"data","sparseSegmentMean"),r=_(t,"indices","sparseSegmentMean"),a=_(n,"segmentIds","sparseSegmentMean");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Bh,o)}var KP=U({sparseSegmentMean_:XP});function ZP(e,t,n){let s=_(e,"data","sparseSegmentSum"),r=_(t,"indices","sparseSegmentSum"),a=_(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Wh,o)}var YP=U({sparseSegmentSum_:ZP});function JP(e,t,n,s,r,a,o,i){let l=_(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let c=_(t,"dataSplits","stringNGrams");if(c.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let u={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:c},p=W.runKernel(sd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var QP=U({stringNGrams_:JP});function eF(e,t,n=!0){let s=_(e,"input","stringSplit","string"),r=_(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=W.runKernel(Vh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var tF=U({stringSplit_:eF});function nF(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return W.runKernel(Uh,r,s)}var sF=U({stringToHashBucketFast_:nF}),rF={fft:Dd,ifft:Lu,rfft:_d,irfft:Sf},aF={hammingWindow:F_,hannWindow:fv,frame:mv,stft:L_},$e={flipLeftRight:U_,grayscaleToRGB:H_,resizeNearestNeighbor:vv,resizeBilinear:bv,rotateWithOffset:q_,cropAndResize:W_,nonMaxSuppression:K_,nonMaxSuppressionAsync:sP,nonMaxSuppressionWithScore:aP,nonMaxSuppressionWithScoreAsync:iP,nonMaxSuppressionPadded:uP,nonMaxSuppressionPaddedAsync:dP,threshold:gP,transform:AP},kv={bandPart:bP,gramSchmidt:wP,qr:IP},oF={absoluteDifference:TP,computeWeightedLoss:ta,cosineDistance:EP,hingeLoss:$P,huberLoss:_P,logLoss:FP,meanSquaredError:MP,sigmoidCrossEntropy:BP,softmaxCrossEntropy:UP},Pd={sparseFillEmptyRows:HP,sparseReshape:qP,sparseSegmentMean:KP,sparseSegmentSum:YP},Pf={stringNGrams:QP,stringSplit:tF,stringToHashBucketFast:sF},na=class extends I3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else this.applyGradients(r);return ne(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return X3(e,t)}dispose(){this.iterations_!=null&&ne(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ee(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(na,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Ff=class extends na{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>tt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;j(()=>{let c=ue(L(i,this.rho),L(vt(o),1-this.rho)),u=L(fe(Cn(ue(l,this.epsilon)),Cn(ue(i,this.epsilon))),o),d=ue(L(l,this.rho),L(vt(u),1-this.rho));i.assign(c),l.assign(d);let p=ue(L(u,-this.learningRate),r);r.assign(p)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ne(this.accumulatedGrads.map(e=>e.variable)),ne(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Ff.className="Adadelta";wo(Ff);var Of=class extends na{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>_u(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;j(()=>{let i=ue(o,vt(a));o.assign(i);let l=ue(L(fe(a,Cn(ue(i,W.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ne(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)}};Of.className="Adagrad";wo(Of);var Mf=class extends na{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=Ee(t).variable(),this.accBeta2=Ee(n).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=xe(1,this.accBeta1),s=xe(1,this.accBeta2);t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>tt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>tt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=ue(L(u,this.beta2),L(vt(l),1-this.beta2)),h=fe(d,n),f=fe(p,s);c.assign(d),u.assign(p);let m=ue(L(fe(h,ue(Cn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ne(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),j(()=>{this.accBeta1.assign(ea(this.beta1,this.iterations_+1)),this.accBeta2.assign(ea(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};Mf.className="Adam";wo(Mf);var zf=class extends na{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ee(0).variable(),this.accBeta1=Ee(t).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=xe(1,this.accBeta1),s=fe(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:tt(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:tt(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=L(u,this.beta2),h=Kt(l),f=Rr(p,h);c.assign(d),u.assign(f);let m=ue(L(fe(s,n),fe(d,ue(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ue(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ne(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)}};zf.className="Adamax";wo(zf);var Fd=class extends na{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=W.registeredVariables[n];j(()=>{let o=ue(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=An(Ee(-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)}};Fd.className="SGD";wo(Fd);var Lf=class extends Fd{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ee(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulations[s]==null){let i=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>tt(r).variable(i))}}let a=this.accumulations[s].variable,o=Array.isArray(e)?e[s].tensor:e[n];o!=null&&j(()=>{let i,l=ue(L(this.m,a),o);this.useNesterov?i=ue(L(this.c,ue(o,L(l,this.m))),r):i=ue(L(this.c,l),r),a.assign(l),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ne(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Lf.className="Momentum";wo(Lf);var Bf=class extends na{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>tt(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>tt(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>tt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;j(()=>{let c=ue(L(i,this.decay),L(vt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=ue(L(u,this.decay),L(o,1-this.decay)),p=fe(L(o,this.learningRate),Cn(xe(c,ue(vt(d),this.epsilon)))),h=ue(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=xe(r,h);r.assign(f)}else{let u=ue(L(i,this.decay),L(vt(o),1-this.decay)),d=ue(L(l,this.momentum),fe(L(o,this.learningRate),Cn(ue(u,this.epsilon))));i.assign(u),l.assign(d);let p=xe(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ne(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(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.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)}};Bf.className="RMSProp";wo(Bf);var nl=class{static sgd(e){return new Fd(e)}static momentum(e,t,n=!1){return new Lf(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new Bf(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new Mf(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Ff(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new zf(e,t,n,s,r)}static adagrad(e,t=.1){return new Of(e,t)}},sl={sgd:nl.sgd,momentum:nl.momentum,adadelta:nl.adadelta,adagrad:nl.adagrad,rmsprop:nl.rmsprop,adamax:nl.adamax,adam:nl.adam},iF=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function Wf(){return new Promise(e=>iF(()=>e()))}var E={};Le(E,{ERF_A1:()=>AF,ERF_A2:()=>xF,ERF_A3:()=>bF,ERF_A4:()=>vF,ERF_A5:()=>wF,ERF_P:()=>yF,PARALLELIZE_THRESHOLD:()=>D1,SELU_SCALE:()=>Sv,SELU_SCALEALPHA:()=>Iv,applyActivation:()=>Df,assertAndGetBroadcastShape:()=>Tt,assertAxesAreInnerMostDims:()=>k$,assertParamsConsistent:()=>lF,assignToTypedArray:()=>NF,axesAreInnerMostDims:()=>d1,calculateShapes:()=>p3,checkEinsumDimSizes:()=>PF,combineLocations:()=>Z3,complexWithEvenIndex:()=>SF,complexWithOddIndex:()=>CF,computeConv2DInfo:()=>xd,computeConv3DInfo:()=>$3,computeDefaultPad:()=>Z2,computeDilation2DInfo:()=>jE,computeOptimalWindowSize:()=>cF,computeOutAndReduceShapes:()=>Y3,computeOutShape:()=>uF,computePool2DInfo:()=>R3,computePool3DInfo:()=>qE,convertConv2DDataFormat:()=>D3,decodeEinsumEquation:()=>DF,eitherStridesOrDilationsAreOne:()=>Nr,expandShapeToKeepDim:()=>Qi,exponent:()=>RF,exponents:()=>EF,fromStringArrayToUint8:()=>UF,fromUint8ToStringArray:()=>VF,getAxesPermutation:()=>J3,getBroadcastDims:()=>zR,getComplexWithIndex:()=>TF,getEinsumComputePath:()=>FF,getEinsumPermutation:()=>_F,getFusedBiasGradient:()=>$f,getFusedDyActivation:()=>Rf,getImageCenter:()=>dF,getInnerMostAxes:()=>I$,getPermuted:()=>hF,getReductionAxes:()=>sn,getReshaped:()=>pF,getReshapedPermuted:()=>fF,getSliceBeginCoords:()=>mF,getSliceSize:()=>gF,getUndoAxesPermutation:()=>p1,isIdentityPermutation:()=>OF,log:()=>vN,mergeRealAndImagArrays:()=>kF,prepareAndValidate:()=>d3,prepareSplitSize:()=>zF,segment_util:()=>Nv,shouldFuse:()=>_f,slice_util:()=>yn,splitRealAndImagArrays:()=>IF,tupleValuesAreOne:()=>ko,upcastType:()=>Ln,validateInput:()=>O2,validateUpdateShape:()=>F2,warn:()=>Ir});function lF(e,t){let n=e[0].length;e.forEach((r,a)=>{M(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),M(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let o=0;o<n;o++)M(o===t||r[o]===s[o],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function uF(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var D1=30;function cF(e){return e<=D1?e:hh(e,Math.floor(Math.sqrt(e)))}function dF(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function pF(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let o=0;o<a;++o)r=r.concat([e[o+1]/t[o],t[o]]);r=r.concat(e.slice(a+1))}return r}function hF(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let o=1;o<e;++o)o>=t*2+1||o%2==1?a.push(o):r.push(o);s.push(...r),s.push(0),s.push(...a)}return s}function fF(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function mF(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function gF(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var Iv=1.7580993408473768,Sv=1.0507009873554805,yF=.3275911,AF=.254829592,xF=-.284496736,bF=1.421413741,vF=-1.453152027,wF=1.061405429;function kF(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function IF(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let s=0;s<e.length;s+=2)t[s/2]=e[s],n[s/2]=e[s+1];return{real:t,imag:n}}function SF(e){let t=Math.ceil(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=0;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function CF(e){let t=Math.floor(e.length/4),n=new Float32Array(t),s=new Float32Array(t);for(let r=2;r<e.length;r+=4)n[Math.floor(r/4)]=e[r],s[Math.floor(r/4)]=e[r+1];return{real:n,imag:s}}function TF(e,t){let n=e[t*2],s=e[t*2+1];return{real:n,imag:s}}function NF(e,t,n,s){e[s*2]=t,e[s*2+1]=n}function EF(e,t){let n=new Float32Array(e/2),s=new Float32Array(e/2);for(let r=0;r<Math.ceil(e/2);r++){let a=(t?2:-2)*Math.PI*(r/e);n[r]=Math.cos(a),s[r]=Math.sin(a)}return{real:n,imag:s}}function RF(e,t,n){let s=(n?2:-2)*Math.PI*(e/t),r=Math.cos(s),a=Math.sin(s);return{real:r,imag:a}}var _1="->",$F=/->/g,Cv=",",Tv="...";function DF(e,t){e=e.replace(/\s/g,"");let n=(e.length-e.replace($F,"").length)/_1.length;if(n<1)throw new Error("Equations without an arrow are not supported.");if(n>1)throw new Error(`Equation must contain exactly one arrow ("${_1}").`);let[s,r]=e.split(_1);M(s.indexOf(Tv)===-1,()=>`The ellipsis notation ("${Tv}") is not supported yet.`);let a=s.split(Cv),o=a.length;if(t!==o)throw new Error(`Expected ${o} input tensors, received ${t}`);if(o>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let i=[];for(let p=0;p<r.length;++p){let h=r[p];if(!a.some(f=>f.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);i.indexOf(h)===-1&&i.push(h)}for(let p=0;p<s.length;++p){let h=s[p];i.indexOf(h)===-1&&h!==Cv&&i.push(h)}let l=new Array(a.length);for(let p=0;p<o;++p){if(new Set(a[p].split("")).size!==a[p].length)throw new Error(`Found duplicate axes in input component ${a[p]}. Support for duplicate axes in input is not implemented yet.`);l[p]=[];for(let h=0;h<a[p].length;++h)l[p].push(i.indexOf(a[p][h]))}let c=i.length,u=r.length,d=[];for(let p=u;p<c;++p)d.push(p);return{allDims:i,summedDims:d,idDims:l}}function _F(e,t){let n=new Array(e);n.fill(-1);for(let r=0;r<t.length;++r)n[t[r]]=r;let s=[];for(let r=0;r<e;++r)n[r]===-1&&s.push(r);return n=n.filter(r=>r!==-1),{permutationIndices:n,expandDims:s}}function PF(e,t,n){let s=new Array(e);for(let r=0;r<n.length;++r){let a=n[r].shape;for(let o=0;o<t[r].length;++o)s[t[r][o]]===void 0?s[t[r][o]]=a[o]:M(s[t[r][o]]===a[o],()=>`Expected dimension ${s[t[r][o]]} at axis ${o} of input shaped ${JSON.stringify(a)}, but got dimension ${a[o]}`)}}function FF(e,t){let n=e,s=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let o=0;o<r;++o)s.push([]);let a=[];for(let o=0;o<n.length;++o){let i=n[o],l=MF(t,i);for(let c of l)a.indexOf(c)===-1&&(s[o].push(c),a.push(c))}return{path:n,steps:s}}function OF(e){return e.every((t,n)=>t===n)}function MF(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function zF(e,t,n=0){let s=[];if(typeof t=="number")M(e.shape[n]%t==0,()=>"Number of splits must evenly divide the axis."),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((o,i)=>(i===-1&&(o+=1),o),0);M(r<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let o=t.reduce((i,l)=>l>0?i+l:i);t[a]=e.shape[n]-o}M(e.shape[n]===t.reduce((o,i)=>o+i),()=>"The sum of sizes must match the size of the axis dimension."),s=t}return s}var Nv={};Le(Nv,{collectGatherOpShapeInfo:()=>WF,computeOutShape:()=>BF,segOpComputeOptimalWindowSize:()=>LF});function LF(e,t){let n=!1,s;for(e<=D1?(s=e,n=!0):s=hh(e,Math.floor(Math.sqrt(e)));!n;)s>t||s===e?n=!0:s=hh(e,s+1);return s}function BF(e,t,n){let s=[],r=e.length;for(let a=0;a<r;a++)a!==t?s.push(e[a]):s.push(n);return s}function WF(e,t,n,s){let r=t.shape.length,a=e.shape.length;if(s!==0&&(s<-r||s>r))throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${s}`);if(s<0&&(s+=r),s>a)throw new Error(`batchDims (${s}) must be less than rank(x) (
${a}).`);if(n<s)throw new Error(`batchDims (${s}) must be less than or equal to axis (${n}).`);for(let d=0;d<s;++d)if(e.shape[d]!==t.shape[d])throw new Error(`x.shape[${d}]: ${e.shape[d]} should be equal to indices.shape[${d}]: ${t.shape[d]}.`);let o=e.shape[n],i=[],l=1,c=1,u=1;for(let d=0;d<s;++d)i.push(e.shape[d]),l*=e.shape[d];for(let d=s;d<n;d++)i.push(e.shape[d]),c*=e.shape[d];for(let d=s;d<r;d++)i.push(t.shape[d]);for(let d=n+1;d<a;d++)i.push(e.shape[d]),u*=e.shape[d];return{batchSize:l,sliceSize:u,outerSize:c,dimSize:o,outputShape:i}}function VF(e){try{return e.map(t=>Xh(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function UF(e){return e.map(t=>ud(t))}var Zs={};Le(Zs,{nonMaxSuppressionV3Impl:()=>gv,nonMaxSuppressionV4Impl:()=>yv,nonMaxSuppressionV5Impl:()=>Av,whereImpl:()=>ov});var Ev={kernelName:ti,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,Bu(pe(n,"float32"),-1))}}},GF={kernelName:ql,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=vt(pe(n,"float32")),r=Cn(xe(Ee(1),s));return _t(fe(e,r))}}}},HF={kernelName:Xl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Cn(xe(vt(pe(n,"float32")),1));return fe(e,s)}}}},jF={kernelName:qr,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=e,l=sn(n.shape,r);return l.length>0&&(i=we(i,l)),G(i,n.shape)},b:()=>{let i=e,l=sn(s.shape,r);return l.length>0&&(i=we(i,l)),G(i,s.shape)}}}},qF={kernelName:ka,saveAllInputs:!0,gradFunc:(e,t)=>{let n={};return t.forEach((s,r)=>{n[r]=()=>e.clone()}),n}},XF={kernelName:Ia,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>tt(n)}}},KF={kernelName:Yl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>tt(n)}}},ZF={kernelName:Jl,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,Cn(xe(Ee(1),vt(pe(n,"float32")))))}}},YF={kernelName:Ql,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=Cn(ue(Ee(1),vt(pe(n,"float32"))));return fe(e,s)}}}},JF={kernelName:nu,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=ue(vt(n),vt(s)),l=L(e,fe(s,i)),c=sn(n.shape,r);return c.length>0&&(l=we(l,c)),G(l,n.shape)},b:()=>{let i=ue(vt(n),vt(s)),l=_t(L(e,fe(n,i))),c=sn(s.shape,r);return c.length>0&&(l=we(l,c)),G(l,s.shape)}}}},QF={kernelName:eu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ue(vt(pe(n,"float32")),1))}}},eO={kernelName:tu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,xe(Ee(1),vt(pe(n,"float32"))))}}};function tO(e,t,n,s,r,a){let o=_(e,"dy","avgPool3dGrad"),i=_(t,"input","avgPool3dGrad"),l=o,c=i,u=!1;i.rank===4&&(u=!0,l=G(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]]),c=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(l.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${l.rank}.`),M(c.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${c.rank}.`),a!=null&&M(mn(r),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let d={dy:l,input:c},p={filterSize:n,strides:s,pad:r,dimRoundingMode:a},h=W.runKernel(gh,d,p);return u?G(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var nO=U({avgPool3dGrad_:tO}),sO={kernelName:Hc,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o,dimRoundingMode:i}=n;return{x:()=>nO(e,s,r,a,o,i)}}};function rO(e,t,n,s,r){let a=_(e,"dy","avgPoolGrad"),o=_(t,"input","avgPoolGrad");M(o.rank===a.rank,()=>`Rank of input (${o.rank}) does not match rank of dy (${a.rank})`);let i=o,l=a,c=!1;o.rank===3&&(c=!0,i=G(o,[1,o.shape[0],o.shape[1],o.shape[2]]),l=G(a,[1,a.shape[0],a.shape[1],a.shape[2]])),M(l.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${l.rank}.`),M(i.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${i.rank}.`);let u={dy:l,input:i},d={filterSize:n,strides:s,pad:r},p=W.runKernel(mh,u,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var aO=U({avgPoolGrad_:rO}),oO={kernelName:Sa,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{filterSize:r,strides:a,pad:o}=n;return{x:()=>aO(e,s,r,a,o)}}},iO={kernelName:Ca,inputsToSave:["a","b"],gradFunc:(e,t,n)=>{let[s,r]=t,{transposeA:a,transposeB:o}=n;return!a&&!o?{a:()=>Xe(e,r,!1,!0),b:()=>Xe(s,e,!0,!1)}:!a&&o?{a:()=>Xe(e,r,!1,!1),b:()=>Xe(e,s,!0,!1)}:a&&!o?{a:()=>Xe(r,e,!1,!0),b:()=>Xe(s,e,!1,!1)}:{a:()=>Xe(r,e,!0,!0),b:()=>Xe(e,s,!0,!0)}}},lO={kernelName:ni,gradFunc:(e,t,n)=>{let{blockShape:s,crops:r}=n;return{x:()=>Nd(e,s,r)}}},uO={kernelName:N5,gradFunc:(e,t,n)=>{let s=n,r=s.inputShape,a=s.shape,o=Array.from(a);for(let l=r.length-1;l>=0;l--)if(r[l]===a[l])o[l]=1;else if(r[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);let i=[];for(let l=0;l<o.length;l++)o[l]>1&&i.push(l);return{x:()=>we(e,i,!0)}}},cO={kernelName:Ta,gradFunc:e=>({x:()=>e.clone()})},dO={kernelName:Na,gradFunc:e=>({x:()=>tt(e)})},pO={kernelName:Xr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{clipValueMin:r,clipValueMax:a}=n;return{x:()=>Pn(Ks(Io(s,r),So(s,a)),e,tt(e))}}},hO={kernelName:qc,inputsToSave:["x"],gradFunc:Ev.gradFunc},fO={kernelName:si,saveAllInputs:!0,gradFunc:(e,t,n)=>{let s=t.map(l=>l.shape),{axis:r}=n,a=js(r,t[0].shape)[0],o=s.map(l=>l[a]);return xn(e,o,a).map(l=>()=>l)}},mO={kernelName:Ea,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{dilations:a,strides:o,pad:i,dataFormat:l}=n;return M(ko(a),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`),{x:()=>t1(s.shape,e,r,o,i,l),filter:()=>R1(s,e,r.shape,o,i,l)}}},gO={kernelName:Ra,inputsToSave:["dy","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,{strides:a,pad:o,dataFormat:i,dimRoundingMode:l}=n;return{dy:()=>Qr(e,r,a,o,i,1,l),filter:()=>R1(e,s,r.shape,a,o,i,l)}}};function yO(e,t,n,s,r){let a=e;e.rank===4&&(a=G(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]));let o=t;o.rank===4&&(o=G(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),M(a.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`),M(o.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${o.shape}.`),M(n.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`),M(a.shape[4]===n[3],()=>`Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`),M(o.shape[4]===n[4],()=>`Error in conv3dDerFilter: depth of dy (${o.shape[4]}) must match output depth for filter (${n[4]}).`);let i={x:a,dy:o},l={strides:s,pad:r,filterShape:n};return W.runKernel(xh,i,l)}var AO=U({conv3DBackpropFilter_:yO}),xO={kernelName:Xc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a}=n;M(ko(s),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let[o,i]=t;return{x:()=>B3(o.shape,e,i,r,a),filter:()=>AO(o,e,i.shape,r,a)}}},bO={kernelName:$a,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(_t(wf(pe(n,"float32"))),e)}}},vO={kernelName:Da,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(kf(pe(n,"float32")),e)}}},wO={kernelName:ri,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r,exclusive:a,reverse:o}=n;return{x:()=>{let i=J3([r],s.rank),l=cf(e,r,a,!o);return i!=null&&(l=et(l,i)),l}}}},kO={kernelName:_a,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let{dilations:s,strides:r,pad:a,dimRoundingMode:o}=n,i=s==null?[1,1]:s;M(ko(i),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${i}'`);let[l,c]=t;return M(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${l.rank}.`),M(c.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${c.rank}.`),M(l.shape[3]===c.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${c.shape[2]}.`),M(Nr(r,i),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${r} and dilations '${i}'.`),o!=null&&M(mn(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`),{x:()=>hv(l.shape,e,c,r,a,i,o),filter:()=>pv(l,e,c.shape,r,a,i,o)}}},IO={kernelName:Kc,inputsToSave:["x","filter"],gradFunc:(e,t,n)=>{let[s,r]=t,a={x:s,filter:r,dy:e},o={x:s,filter:r,dy:e};return{x:()=>W.runKernel(Sh,a,n),filter:()=>W.runKernel(Ch,o,n)}}},SO={kernelName:Fa,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t,s={dy:e,y:n};return{x:()=>W.runKernel(Th,s)}}},CO={kernelName:su,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=L(gs(_t(vt(n))),2/Math.sqrt(Math.PI));return{x:()=>L(e,s)}}},TO={kernelName:Oa,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,n)}}},NO={kernelName:li,inputsToSave:["input"],gradFunc:(e,t)=>{let[n]=t;return{input:()=>G(e,n.shape)}}},EO={kernelName:ui,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,gs(n))}}},RO={kernelName:Ma,gradFunc:e=>({x:()=>tt(e)})},$O={kernelName:za,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=fe(e,pe(s,"float32")),l=sn(n.shape,r);return l.length>0?G(we(i,l),n.shape):i},b:()=>{let i=L(e,pe(n,"float32")),l=sn(s.shape,r);l.length>0&&(i=G(we(i,l),s.shape));let c=vt(s);return _t(fe(i,pe(c,"float32")))}}}},DO={kernelName:La,inputsToSave:["x","mean","variance","scale"],gradFunc:(e,t,n)=>{let{varianceEpsilon:s}=n,[r,a,o,i]=t,l=i==null?Ee(1):i,c=sn(a.shape,r.shape),u=[];if(a.rank===1){for(let b=0;b<r.shape.length-1;++b)u.push(r.shape[b]);u.push(1)}let d=xe(r,a),p=L(e,l),h=bf(ue(o,Ee(s))),f=L(L(L(h,h),h),Ee(-.5));return{x:()=>a.rank===1?G(L(L(e,Ps(G(h,[1,1,1,a.shape[0]]),u)),l),r.shape):G(L(L(e,h),l),r.shape),mean:()=>{let b=L(L(h,Ee(-1)),p);return a.rank===1&&(b=we(b,c)),G(b,a.shape)},variance:()=>{let b=L(L(f,d),p);return a.rank===1&&(b=we(b,c)),G(b,a.shape)},scale:()=>{let b=L(d,h),w=L(e,b);return a.rank===1&&(w=we(w,c)),G(w,a.shape)},offset:()=>{let b=e;return a.rank===1&&(b=we(b,c)),G(b,a.shape)}}}},_O={kernelName:di,inputsToSave:["x","indices"],gradFunc:(e,t,n)=>{let[s,r]=t,{axis:a}=n,o=js(a,s.shape)[0];return{x:()=>{let l=s.shape,c=r.size,u=l.slice(0,o),d=u.length,p=l.slice(a,l.length).slice(1),h=p.length,f=Rv(0,d),m=Rv(d+1,d+1+h),g=$v([u,[c],p]),y=G(e,g),A=G(r,[c]),x=$v([[d],f,m]),b=et(y,x),w=C1(b,A,s.shape[o]),k=p1(x);return w=et(w,k),w},indices:()=>r}}};function Rv(e,t){let n=[];for(let s=e;s<t;++s)n.push(s);return n}function $v(e){let t=[];for(let n=0;n<e.length;++n)for(let s=0;s<e[n].length;++s)t.push(e[n][s]);return t}var PO={kernelName:Ba,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>tt(n),b:()=>tt(s)}}},FO={kernelName:Wa,gradFunc:e=>({x:()=>pe(e,"float32")})},OO={kernelName:au,gradFunc:e=>({x:()=>tt(e)})},MO={kernelName:ou,gradFunc:e=>({x:()=>tt(e)})},zO={kernelName:iu,gradFunc:e=>({x:()=>tt(e)})},LO={kernelName:fi,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{alpha:r}=n,a=rs(s,0);return{x:()=>Pn(a,e,L(e,r))}}},BO={kernelName:lu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,ue(n,1))}}},WO={kernelName:Va,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,pe(n,"float32"))}}},VO={kernelName:E5,inputsToSave:[],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n;return{logits:()=>{let a=!0,o=gs(s);return xe(e,L(we(e,r,a),o))}}}};function UO(e,t,n,s=5,r=1,a=1,o=.5){let i={x:e,y:t,dy:n},l={depthRadius:s,bias:r,alpha:a,beta:o};return W.runKernel($h,i,l)}var GO=U({localResponseNormalizationBackprop_:UO}),HO={kernelName:Qc,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{depthRadius:a,bias:o,alpha:i,beta:l}=n;return{x:()=>GO(s,r,e,a,o,i,l)}}};function Dv(e,t,n,s){return t.rank<n.rank&&(t=G(t,Qi(t.shape,s))),e.rank<n.rank&&(e=G(e,Qi(e.shape,s))),{x:()=>L(e,pe(ms(n,t),e.dtype))}}var _v={kernelName:Ua,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{reductionIndices:r}=s,a=t[0],o=t[1],i=js(r,a.shape),l=Dv(e,o,a,i);return{x:()=>l.x()}}},jO={kernelName:Ga,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>L(e,pe(Io(n,s),"float32")),b:()=>L(e,pe(pf(n,s),"float32"))}}};function qO(e,t,n,s,r,a,o){let i=_(e,"dy","maxPool3dGrad"),l=_(t,"input","maxPool3dGrad"),c=_(n,"output","maxPool3dGrad"),u=i,d=l,p=c,h=!1;l.rank===4&&(h=!0,u=G(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),d=G(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]]),p=G(c,[1,c.shape[0],c.shape[1],c.shape[2],c.shape[3]])),M(u.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),M(d.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${d.rank}.`),M(p.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${p.rank}.`),o!=null&&M(mn(a),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let f={dy:u,input:d,output:p},m={filterSize:s,strides:r,pad:a,dimRoundingMode:o},g=W.runKernel(_h,f,m);return h?G(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var XO=U({maxPool3dGrad_:qO}),KO={kernelName:ed,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=n;return{x:()=>XO(e,s,r,a,o,i,l)}}};function ZO(e,t,n,s,r,a,o){let i=_(e,"dy","maxPoolGrad"),l=_(t,"input","maxPoolGrad"),c=_(n,"output","maxPoolGrad");M(l.rank===i.rank,()=>`Rank of input (${l.rank}) does not match rank of dy (${i.rank})`),M(i.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${i.rank}.`),M(l.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${l.rank}.`),o!=null&&M(mn(a),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${o} but got pad ${a}.`);let u={dy:i,input:l,output:c},d={filterSize:s,strides:r,pad:a,dimRoundingMode:o};return W.runKernel(Dh,u,d)}var YO=U({maxPoolGrad_:ZO}),JO={kernelName:Ha,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s,r]=t,{filterSize:a,strides:o,pad:i}=n;return{x:()=>YO(e,s,r,a,o,i)}}},QO={kernelName:ja,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{axis:r}=n,a=js(r,s.shape),i=Y3(s.shape,a)[1],l=Gt(i);return{x:()=>{let u=s.shape.slice();a.forEach(h=>{u[h]=1});let d=G(e,u);return fe(L(d,As(s.shape,"float32")),l)}}}},eM={kernelName:qa,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(e,t,n)=>{let s=n,{axis:r}=s,[a,o]=t,i=js(r,a.shape),l=Dv(e,o,a,i);return{x:()=>l.x()}}},tM={kernelName:Xa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t;return{a:()=>L(e,pe(So(n,s),"float32")),b:()=>L(e,pe(rs(n,s),"float32"))}}},nM={kernelName:Ka,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>_e(e,a,s.shape)}}},sM={kernelName:cu,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=sn(n.shape,r);return i.length>0?G(we(e,i),n.shape):e},b:()=>{let i=L(e,_t(Pu(fe(n,s)))),l=sn(s.shape,r);return l.length>0?G(we(i,l),s.shape):i}}}},rM={kernelName:Za,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=L(e,pe(s,"float32")),l=sn(n.shape,r);return l.length>0?G(we(i,l),n.shape):i},b:()=>{let i=L(e,pe(n,"float32")),l=sn(s.shape,r);return l.length>0?G(we(i,l),s.shape):i}}}},aM={kernelName:Ai,gradFunc:e=>({x:()=>_t(e)})},oM={kernelName:ki,inputsToSave:["indices"],gradFunc:(e,t)=>{let n=t[0];return{indices:()=>jt(n.shape,"float32")}}},iM={kernelName:wi,gradFunc:e=>({x:()=>tt(e)})},lM={kernelName:Ii,saveAllInputs:!0,gradFunc:(e,t,n)=>{let{axis:s}=n;return Wn(e,s).map(a=>()=>a)}},Pv={kernelName:Ya,inputsToSave:["x"],gradFunc:(e,t,n)=>{let s=t[0],{paddings:r}=n,a=r.map(o=>o[0]);return{x:()=>_e(e,a,s.shape)}}},uM={kernelName:Ja,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(e,t)=>{let[n,s,r]=t,a=n,o=s,i=Tt(a.shape,o.shape);return{a:()=>{let u=pe(o,"float32"),d=L(e,L(u,ea(a,xe(u,Ee(1))))),p=sn(a.shape,i);return p.length>0&&(d=we(d,p)),G(d,a.shape)},b:()=>{let u=rs(a,0),d=Pn(u,ys(a),tt(a)),p=L(e,L(r,d)),h=sn(o.shape,i);return h.length>0&&(p=we(p,h)),G(p,o.shape)}}}},cM={kernelName:Qa,inputsToSave:["x","alpha"],gradFunc:(e,t)=>{let[n,s]=t,r=rs(n,0);return{x:()=>Pn(r,e,L(e,s)),alpha:()=>{let a=Pn(r,tt(e),L(e,n)),o=sn(s.shape,e.shape);return o.length>0&&(a=we(a,o)),G(a,s.shape)}}}},dM={kernelName:Pa,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=fe(e,pe(s,"float32")),l=sn(n.shape,r);return l.length>0?G(we(i,l),n.shape):i},b:()=>{let i=L(e,pe(n,"float32")),l=sn(s.shape,r);l.length>0&&(i=G(we(i,l),s.shape));let c=vt(s);return _t(fe(i,pe(c,"float32")))}}}},pM={kernelName:hu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,_t(vt(n)))}}},hM={kernelName:no,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t,s=L(So(n,6),Bu(n));return{x:()=>L(e,pe(s,"float32"))}}},fM={kernelName:eo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,pe(Bu(n),"float32"))}}},mM={kernelName:Ci,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>G(e,n.shape)}}},gM={kernelName:to,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>W.runKernel(Mh,r,n)}}},yM={kernelName:fu,inputsToSave:["images"],gradFunc:(e,t,n)=>{let[s]=t,r={dy:e,images:s};return{images:()=>W.runKernel(Oh,r,n)}}},AM={kernelName:Ti,gradFunc:(e,t,n)=>{let{dims:s}=n,r=js(s,e.shape);return{x:()=>bs(e,r)}}},xM={kernelName:Ni,gradFunc:e=>({x:()=>tt(e)})},bM={kernelName:so,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>_t(fe(e,L(ea(n,1.5),2)))}}},vM={kernelName:Ri,inputsToSave:["condition"],gradFunc:(e,t)=>{let[n]=t;return{condition:()=>pe(tt(n),"float32"),t:()=>L(e,pe(n,e.dtype)),e:()=>L(e,pe(Sd(n),e.dtype))}}},wM={kernelName:mu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>{let s=rs(n,Ee(0)),r=Ee(Iv),a=Ee(Sv),o=L(e,a),i=L(L(e,r),gs(pe(n,"float32")));return Pn(s,o,i)}}}},kM={kernelName:ao,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(n,xe(Ee(1),n)))}}},IM={kernelName:gu,gradFunc:e=>({x:()=>tt(e)})},SM={kernelName:ro,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(wd(pe(n,"float32")),e)}}},CM={kernelName:Di,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(uf(pe(n,"float32")),e)}}},TM={kernelName:$i,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{begin:r,size:a}=n,o=s.shape,[i,l]=k3(s,r,a),c=[];for(let u=0;u<e.rank;u++)c.push([i[u],o[u]-i[u]-l[u]]);return{x:()=>ur(e,c)}}},NM={kernelName:lo,outputsToSave:[!0],gradFunc:(e,t,n)=>{let[s]=t,{dim:r}=n,a=!0,o=L(e,s);return{logits:()=>xe(o,L(we(o,[r],a),s))}}},EM={kernelName:yu,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,ns(n))}}},Fv={kernelName:_i,gradFunc:(e,t,n)=>{let{blockShape:s,paddings:r}=n;return{x:()=>vd(e,s,r)}}},Ov={kernelName:Pi,gradFunc:(e,t,n)=>{let{axis:s}=n;return{x:()=>kt(e,s)}}},RM={kernelName:oo,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,L(Cn(pe(n,"float32")),2))}}},$M={kernelName:Au,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(e,L(pe(n,"float32"),2))}}},DM={kernelName:uo,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Ee(2);return{a:()=>L(e,L(r,xe(n,s))),b:()=>L(e,L(r,xe(s,n)))}}},_M={kernelName:fo,gradFunc:e=>({x:()=>tt(e)})},PM={kernelName:co,inputsToSave:["a","b"],gradFunc:(e,t)=>{let[n,s]=t,r=Tt(n.shape,s.shape);return{a:()=>{let i=e,l=sn(n.shape,r);return l.length>0&&(i=we(i,l)),G(i,n.shape)},b:()=>{let i=e,l=sn(s.shape,r);return l.length>0&&(i=we(i,l)),G(_t(i),s.shape)}}}},FM={kernelName:io,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,r=s.shape.slice(),{axis:a}=n;js(a,s.shape).forEach(c=>{r[c]=1});let i=G(e,r),l=L(i,As(s.shape,"float32"));return{x:()=>l}}},OM={kernelName:Oi,inputsToSave:["x"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>fe(e,vt(wd(n)))}}},MM={kernelName:po,outputsToSave:[!0],gradFunc:(e,t)=>{let[n]=t;return{x:()=>L(xe(Ee(1),vt(n)),e)}}},zM={kernelName:Kr,inputsToSave:["x"],gradFunc:(e,t,n)=>{let[s]=t,{reps:r}=n;return{x:()=>{let o=tt(s);if(s.rank===1)for(let i=0;i<r[0];++i)o=ue(o,_e(e,[i*s.shape[0]],[s.shape[0]]));else if(s.rank===2)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)o=ue(o,_e(e,[i*s.shape[0],l*s.shape[1]],[s.shape[0],s.shape[1]]));else if(s.rank===3)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let c=0;c<r[2];++c)o=ue(o,_e(e,[i*s.shape[0],l*s.shape[1],c*s.shape[2]],[s.shape[0],s.shape[1],s.shape[2]]));else if(s.rank===4)for(let i=0;i<r[0];++i)for(let l=0;l<r[1];++l)for(let c=0;c<r[2];++c)for(let u=0;u<r[3];++u)o=ue(o,_e(e,[i*s.shape[0],l*s.shape[1],c*s.shape[2],u*s.shape[3]],[s.shape[0],s.shape[1],s.shape[2],s.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);return o}}}},LM={kernelName:ho,gradFunc:(e,t,n)=>{let s=n,{perm:r}=s,a=p1(r);return{x:()=>et(e,a)}}},BM={kernelName:zi,gradFunc:(e,t,n)=>{let s=n,{axis:r}=s;return{value:()=>Tn(e,r)}}},WM={kernelName:rd,inputsToSave:["segmentIds"],gradFunc:(e,t)=>{let[n]=t;return{x:()=>VM(e,n)}}};function VM(e,t){let n=Rr(t,tt(t)),s=Yi(e,n),r=Io(t,Ee(0,"int32")),a=s.rank-r.rank;for(let i=0;i<a;++i)r=Ht(r,i+1);r=Ks(r,As(s.shape,"bool"));let o=tt(s);return Pn(r,s,o)}var UM={kernelName:Li,gradFunc:e=>({x:()=>tt(e)})},GM=[Ev,GF,HF,jF,qF,XF,KF,ZF,YF,JF,QF,eO,sO,oO,iO,lO,uO,cO,dO,pO,hO,fO,gO,mO,xO,bO,vO,wO,kO,IO,dM,SO,CO,TO,NO,EO,$O,RO,DO,_O,PO,FO,OO,MO,zO,LO,BO,WO,VO,HO,_v,_v,jO,KO,JO,QO,eM,tM,nM,sM,rM,aM,oM,iM,lM,Pv,Pv,uM,cM,pM,hM,fM,mM,gM,yM,AM,xM,bM,vM,wM,kM,IM,SM,CM,TM,NM,EM,Fv,Fv,Ov,Ov,RM,DM,$M,_M,PM,FM,OM,MM,zM,LM,BM,WM,UM];for(let e of GM)R5(e);re().prototype.abs=function(){return this.throwIfDisposed(),Kt(this)};re().prototype.acos=function(){return this.throwIfDisposed(),V2(this)};re().prototype.acosh=function(){return this.throwIfDisposed(),U2(this)};re().prototype.add=function(e){return this.throwIfDisposed(),ue(this,e)};re().prototype.all=function(e,t){return this.throwIfDisposed(),rf(this,e,t)};re().prototype.any=function(e,t){return this.throwIfDisposed(),Ad(this,e,t)};re().prototype.argMax=function(e){return this.throwIfDisposed(),_s(this,e)};re().prototype.argMin=function(e){return this.throwIfDisposed(),G2(this,e)};re().prototype.asScalar=function(){return this.throwIfDisposed(),M(this.size===1,()=>"The array must have only 1 element."),G(this,[])};re().prototype.asType=function(e){return this.throwIfDisposed(),pe(this,e)};re().prototype.as1D=function(){return this.throwIfDisposed(),G(this,[this.size])};re().prototype.as2D=function(e,t){return this.throwIfDisposed(),G(this,[e,t])};re().prototype.as3D=function(e,t,n){return this.throwIfDisposed(),G(this,[e,t,n])};re().prototype.as4D=function(e,t,n,s){return this.throwIfDisposed(),G(this,[e,t,n,s])};re().prototype.as5D=function(e,t,n,s,r){return this.throwIfDisposed(),G(this,[e,t,n,s,r])};re().prototype.asin=function(){return this.throwIfDisposed(),H2(this)};re().prototype.asinh=function(){return this.throwIfDisposed(),j2(this)};re().prototype.atan=function(){return this.throwIfDisposed(),q2(this)};re().prototype.atan2=function(e){return this.throwIfDisposed(),X2(this,e)};re().prototype.atanh=function(){return this.throwIfDisposed(),K2(this)};re().prototype.avgPool=function(e,t,n,s){return this.throwIfDisposed(),bd(this,e,t,n,s)};re().prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),vd(this,e,t)};re().prototype.batchNorm=function(e,t,n,s,r){return this.throwIfDisposed(),Zi(this,e,t,n,s,r)};re().prototype.broadcastTo=function(e){return this.throwIfDisposed(),Eu(this,e)};re().prototype.cast=function(e){return this.throwIfDisposed(),pe(this,e)};re().prototype.ceil=function(){return this.throwIfDisposed(),e1(this)};re().prototype.clipByValue=function(e,t){return this.throwIfDisposed(),ss(this,e,t)};re().prototype.concat=function(e,t){return this.throwIfDisposed(),e instanceof Ke&&(e=[e]),kt([this,...e],t)};re().prototype.conv1d=function(e,t,n,s,r,a){return this.throwIfDisposed(),of(this,e,t,n,s,r,a)};re().prototype.conv2dTranspose=function(e,t,n,s,r){return this.throwIfDisposed(),lf(this,e,t,n,s,r)};re().prototype.conv2d=function(e,t,n,s,r,a){return this.throwIfDisposed(),Qr(this,e,t,n,s,r,a)};re().prototype.cos=function(){return this.throwIfDisposed(),wd(this)};re().prototype.cosh=function(){return this.throwIfDisposed(),uf(this)};re().prototype.cumsum=function(e,t,n){return this.throwIfDisposed(),cf(this,e,t,n)};re().prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),s1(this,e,t)};re().prototype.depthwiseConv2d=function(e,t,n,s,r,a){return this.throwIfDisposed(),$u(this,e,t,n,s,r,a)};re().prototype.dilation2d=function(e,t,n,s,r){return this.throwIfDisposed(),r1(this,e,t,n,s,r)};re().prototype.divNoNan=function(e){return this.throwIfDisposed(),a1(this,e)};re().prototype.div=function(e){return this.throwIfDisposed(),fe(this,e)};re().prototype.dot=function(e){return this.throwIfDisposed(),U3(this,e)};re().prototype.elu=function(){return this.throwIfDisposed(),Du(this)};re().prototype.equal=function(e){return this.throwIfDisposed(),ms(this,e)};re().prototype.erf=function(){return this.throwIfDisposed(),o1(this)};re().prototype.exp=function(){return this.throwIfDisposed(),gs(this)};re().prototype.expandDims=function(e){return this.throwIfDisposed(),Ht(this,e)};re().prototype.expm1=function(){return this.throwIfDisposed(),i1(this)};re().prototype.fft=function(){return this.throwIfDisposed(),Dd(this)};re().prototype.flatten=function(){return this.throwIfDisposed(),G(this,[this.size])};re().prototype.floor=function(){return this.throwIfDisposed(),Pu(this)};re().prototype.floorDiv=function(e){return this.throwIfDisposed(),nf(this,e)};re().prototype.gather=function(e,t){return this.throwIfDisposed(),Yi(this,e,t)};re().prototype.greaterEqual=function(e){return this.throwIfDisposed(),Io(this,e)};re().prototype.greater=function(e){return this.throwIfDisposed(),rs(this,e)};re().prototype.ifft=function(){return this.throwIfDisposed(),Lu(this)};re().prototype.irfft=function(){return this.throwIfDisposed(),Sf(this)};re().prototype.isFinite=function(){return this.throwIfDisposed(),H3(this)};re().prototype.isInf=function(){return this.throwIfDisposed(),j3(this)};re().prototype.isNaN=function(){return this.throwIfDisposed(),u1(this)};re().prototype.leakyRelu=function(e){return this.throwIfDisposed(),kd(this,e)};re().prototype.lessEqual=function(e){return this.throwIfDisposed(),So(this,e)};re().prototype.less=function(e){return this.throwIfDisposed(),pf(this,e)};re().prototype.localResponseNormalization=function(e,t,n,s){return this.throwIfDisposed(),c1(this,e,t,n,s)};re().prototype.logSigmoid=function(){return this.throwIfDisposed(),K3(this)};re().prototype.logSoftmax=function(e){return this.throwIfDisposed(),ff(this,e)};re().prototype.logSumExp=function(e,t){return this.throwIfDisposed(),h1(this,e,t)};re().prototype.log=function(){return this.throwIfDisposed(),ys(this)};re().prototype.log1p=function(){return this.throwIfDisposed(),Id(this)};re().prototype.logicalAnd=function(e){return this.throwIfDisposed(),Ks(this,e)};re().prototype.logicalNot=function(){return this.throwIfDisposed(),Sd(this)};re().prototype.logicalOr=function(e){return this.throwIfDisposed(),mf(this,e)};re().prototype.logicalXor=function(e){return this.throwIfDisposed(),Q3(this,e)};re().prototype.matMul=function(e,t,n){return this.throwIfDisposed(),Xe(this,e,t,n)};re().prototype.maxPool=function(e,t,n,s){return this.throwIfDisposed(),Cd(this,e,t,n,s)};re().prototype.max=function(e,t){return this.throwIfDisposed(),Bn(this,e,t)};re().prototype.maximum=function(e){return this.throwIfDisposed(),Rr(this,e)};re().prototype.mean=function(e,t){return this.throwIfDisposed(),zt(this,e,t)};re().prototype.min=function(e,t){return this.throwIfDisposed(),Td(this,e,t)};re().prototype.minimum=function(e){return this.throwIfDisposed(),Fu(this,e)};re().prototype.mirrorPad=function(e,t){return this.throwIfDisposed(),m1(this,e,t)};re().prototype.mod=function(e){return this.throwIfDisposed(),g1(this,e)};re().prototype.mul=function(e){return this.throwIfDisposed(),L(this,e)};re().prototype.neg=function(){return this.throwIfDisposed(),_t(this)};re().prototype.norm=function(e,t,n){return this.throwIfDisposed(),Ef(this,e,t,n)};re().prototype.notEqual=function(e){return this.throwIfDisposed(),el(this,e)};re().prototype.oneHot=function(e,t=1,n=0){return this.throwIfDisposed(),Cu(this,e,t,n)};re().prototype.onesLike=function(){return this.throwIfDisposed(),xs(this)};re().prototype.pad=function(e,t){return this.throwIfDisposed(),ur(this,e,t)};re().prototype.pool=function(e,t,n,s,r){return this.throwIfDisposed(),nv(this,e,t,n,s,r)};re().prototype.pow=function(e){return this.throwIfDisposed(),ea(this,e)};re().prototype.prelu=function(e){return this.throwIfDisposed(),Ed(this,e)};re().prototype.prod=function(e,t){return this.throwIfDisposed(),yf(this,e,t)};re().prototype.reciprocal=function(){return this.throwIfDisposed(),x1(this)};re().prototype.relu=function(){return this.throwIfDisposed(),cr(this)};re().prototype.relu6=function(){return this.throwIfDisposed(),Af(this)};re().prototype.reshapeAs=function(e){return this.throwIfDisposed(),G(this,e.shape)};re().prototype.reshape=function(e){return this.throwIfDisposed(),G(this,e)};re().prototype.resizeBilinear=function(e,t,n){return this.throwIfDisposed(),bv(this,e,t,n)};re().prototype.resizeNearestNeighbor=function(e,t,n){return this.throwIfDisposed(),vv(this,e,t,n)};re().prototype.reverse=function(e){return this.throwIfDisposed(),bs(this,e)};re().prototype.rfft=function(){return this.throwIfDisposed(),_d(this)};re().prototype.round=function(){return this.throwIfDisposed(),xf(this)};re().prototype.rsqrt=function(){return this.throwIfDisposed(),bf(this)};re().prototype.selu=function(){return this.throwIfDisposed(),vf(this)};re().prototype.separableConv2d=function(e,t,n,s,r,a){return this.throwIfDisposed(),b1(this,e,t,n,s,r,a)};re().prototype.sigmoid=function(){return this.throwIfDisposed(),ns(this)};re().prototype.sign=function(){return this.throwIfDisposed(),v1(this)};re().prototype.sin=function(){return this.throwIfDisposed(),wf(this)};re().prototype.sinh=function(){return this.throwIfDisposed(),kf(this)};re().prototype.slice=function(e,t){return this.throwIfDisposed(),_e(this,e,t)};re().prototype.softmax=function(e){return this.throwIfDisposed(),tl(this,e)};re().prototype.softplus=function(){return this.throwIfDisposed(),Ji(this)};re().prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),Nd(this,e,t)};re().prototype.split=function(e,t){return this.throwIfDisposed(),xn(this,e,t)};re().prototype.sqrt=function(){return this.throwIfDisposed(),Cn(this)};re().prototype.square=function(){return this.throwIfDisposed(),vt(this)};re().prototype.squaredDifference=function(e){return this.throwIfDisposed(),Cf(this,e)};re().prototype.squeeze=function(e){return this.throwIfDisposed(),dt(this,e)};re().prototype.stack=function(e,t){this.throwIfDisposed();let n=e instanceof Ke?[this,e]:[this,...e];return Tn(n,t)};re().prototype.step=function(e){return this.throwIfDisposed(),Bu(this,e)};re().prototype.stridedSlice=function(e,t,n,s,r,a,o,i){return this.throwIfDisposed(),k1(this,e,t,n,s,r,a,o,i)};re().prototype.sub=function(e){return this.throwIfDisposed(),xe(this,e)};re().prototype.sum=function(e,t){return this.throwIfDisposed(),we(this,e,t)};re().prototype.tan=function(){return this.throwIfDisposed(),I1(this)};re().prototype.tanh=function(){return this.throwIfDisposed(),Ki(this)};re().prototype.tile=function(e){return this.throwIfDisposed(),Ps(this,e)};re().prototype.toBool=function(){return this.throwIfDisposed(),pe(this,"bool")};re().prototype.toFloat=function(){return this.throwIfDisposed(),pe(this,"float32")};re().prototype.toInt=function(){return this.throwIfDisposed(),pe(this,"int32")};re().prototype.topk=function(e,t){return this.throwIfDisposed(),S1(this,e,t)};re().prototype.transpose=function(e){return this.throwIfDisposed(),et(this,e)};re().prototype.unique=function(e){return this.throwIfDisposed(),Nf(this,e)};re().prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),C1(this,e,t)};re().prototype.unstack=function(e){return this.throwIfDisposed(),Wn(this,e)};re().prototype.where=function(e,t){return this.throwIfDisposed(),Pn(e,this,t)};re().prototype.zerosLike=function(){return this.throwIfDisposed(),tt(this)};var Mv={};Le(Mv,{maxNorm:()=>XM,minMaxNorm:()=>YM,nonNeg:()=>ZM,unitNorm:()=>KM});var P1;function rn(){return P1==null&&(P1=Tr().epsilon()),P1}function pr(){return"channelsLast"}var sa=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,sa.prototype)}},hr=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,hr.prototype)}},q=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,q.prototype)}},Ve=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,Ve.prototype)}},zv=class extends Error{constructor(e){super(e);Object.setPrototypeOf(this,zv.prototype)}};function rl(e,t){if(Array.isArray(e)){let n=[];for(let s=0;s<t;s++)n=n.concat(e);return n}else{let n=new Array(t);return n.fill(e),n}}function $r(e,t){if(!e)throw new zv(t)}function Lv(e,t){let n=0;for(let s of e)s===t&&n++;return n}function as(e){return e.length===1?e[0]:e}function Nt(e){return Array.isArray(e)?e:[e]}function ra(e){let n=e.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return n[0]!=="_"?n:"private"+n}function al(e){return e.length<=1||e.indexOf("_")===-1?e:e.replace(/[_]+(\w|$)/g,(t,n)=>n.toUpperCase())}var Ys={};function F1(e){if(e==null)return null;let t={};return t.className=e.getClassName(),t.config=e.getConfig(),t}function O1(e){if(!(e==null||typeof e!="object"))if(Array.isArray(e))e.forEach(t=>O1(t));else{let t=Object.keys(e);for(let n of t){let s=e[n];s!=null&&typeof s=="object"&&(!Array.isArray(s)&&s.type==="ndarray"&&typeof s.value=="number"?e[n]=s.value:O1(s))}}}function Od(e,t={},n={},s="object",r=!1){if(typeof e=="string"){let a=e,o;if(a in n)o=n[a];else if(a in Ys)o=Ys[a];else if(o=t[a],o==null)throw new q(`Unknown ${s}: ${e}. This may be due to one of the following reasons:
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return o}else{let a=e;if(a.className==null||a.config==null)throw new q(`${s}: Improper config format: ${JSON.stringify(a)}.
'className' and 'config' must set.`);let o=a.className,i,l;if(o in n?[i,l]=n[o]:o in Ys?[i,l]=Ys.className:o in t&&([i,l]=t[o]),i==null)throw new q(`Unknown ${s}: ${o}. This may be due to one of the following reasons:
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(l!=null){let c={};for(let h of Object.keys(Ys))c[h]=Ys[h];for(let h of Object.keys(n))c[h]=n[h];let u=a.config;u.customObjects=c;let d=Object.assign({},Ys);for(let h of Object.keys(n))Ys[h]=n[h];O1(a.config);let p=l(i,a.config,n,r);return Ys=Object.assign({},d),p}else{let c=Object.assign({},Ys);for(let d of Object.keys(n))Ys[d]=n[d];let u=new i(a.config);return Ys=Object.assign({},c),u}}}function HM(e,t){return e<t?-1:e>t?1:0}function Vf(e,t){return-1*HM(e,t)}function To(e){if(e==null)return e;let t=[];for(let n of e)t.indexOf(n)===-1&&t.push(n);return t}function jM(e){if(e==null)throw new q(`Invalid value in obj: ${JSON.stringify(e)}`);for(let t in e)if(e.hasOwnProperty(t))return!1;return!0}function ol(e,t,n){if(n!=null&&e.indexOf(n)<0)throw new q(`${n} is not a valid ${t}. Valid values are ${e} or null/undefined.`)}function M1(e,t,n=0,s=1/0){return $r(n>=0),$r(s>=n),Array.isArray(e)&&e.length>=n&&e.length<=s&&e.every(r=>typeof r===t)}function bn(e,t){Array.isArray(e)?(v.assert(e.length>0,()=>`${t} is unexpectedly an empty array.`),e.forEach((n,s)=>bn(n,`element ${s+1} of ${t}`))):v.assert(Number.isInteger(e)&&e>0,()=>`Expected ${t} to be a positive integer, but got ${Bv(e)}.`)}function Bv(e){return e===null?"null":Array.isArray(e)?"["+e.map(t=>Bv(t)).join(",")+"]":typeof e=="string"?`"${e}"`:`${e}`}function qM(e,t){let n=v.now(),s;return(...a)=>{let o=v.now();return o-n<t||(n=o,s=e(...a)),s}}function Wv(e){return e==="relu"?"relu":e==="linear"?"linear":e==="elu"?"elu":null}function z1(e,t){return j(()=>Cn(we(L(e,e),t,!0)))}var Md=class extends de.Serializable{getConfig(){return{}}},L1=class extends Md{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 j(()=>{let t=z1(e,this.axis),n=ss(t,0,this.maxValue);return L(e,fe(n,ue(rn(),t)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};L1.className="MaxNorm";de.registerClass(L1);var B1=class extends Md{constructor(e){super();this.defaultAxis=0,this.axis=e.axis!=null?e.axis:this.defaultAxis}apply(e){return j(()=>fe(e,ue(rn(),z1(e,this.axis))))}getConfig(){return{axis:this.axis}}};B1.className="UnitNorm";de.registerClass(B1);var W1=class extends Md{apply(e){return cr(e)}};W1.className="NonNeg";de.registerClass(W1);var V1=class extends Md{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 j(()=>{let t=z1(e,this.axis),n=ue(L(this.rate,ss(t,this.minValue,this.maxValue)),L(1-this.rate,t));return L(e,fe(n,ue(rn(),t)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};V1.className="MinMaxNorm";de.registerClass(V1);var Vv={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function an(e){return F1(e)}function Uv(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"constraint")}function on(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Vv?Vv[e]:e,config:{}};return Uv(n)}else return e instanceof Md?e:Uv(e)}function XM(e){return new L1(e)}function KM(e){return new B1(e)}function ZM(){return new W1}function YM(e){return new V1(e)}var Gv={};Le(Gv,{constant:()=>bz,glorotNormal:()=>Tz,glorotUniform:()=>Cz,heNormal:()=>Nz,heUniform:()=>Ez,identity:()=>Iz,leCunNormal:()=>Rz,leCunUniform:()=>$z,ones:()=>xz,orthogonal:()=>Dz,randomNormal:()=>wz,randomUniform:()=>vz,truncatedNormal:()=>kz,varianceScaling:()=>Sz,zeros:()=>Az});var JM=["channelsFirst","channelsLast"],QM=["nearest","bilinear"],ez=["valid","same","causal"],tz=["max","avg"],nz=["sum","mul","concat","ave"],Vu=new Map;function qt(e){ol(JM,"DataFormat",e)}function sz(e){ol(QM,"InterpolationFormat",e)}function Fs(e){ol(ez,"PaddingMode",e)}function Hv(e){ol(tz,"PoolMode",e)}var zd=[],jv="/";function il(e,t){zd.push(e);try{let n=t();return zd.pop(),n}catch(n){throw zd.pop(),n}}function rz(){return zd.length===0?"":zd.join(jv)+jv}function qv(e){if(!Kv(e))throw new Error("Not a valid tensor name: '"+e+"'");return rz()+e}function Xv(e){if(!Kv(e))throw new Error("Not a valid tensor name: '"+e+"'");Vu.has(e)||Vu.set(e,0);let t=Vu.get(e);if(Vu.set(e,Vu.get(e)+1),t>0){let n=`${e}_${t}`;return Vu.set(n,1),n}else return e}var az=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Kv(e){return!!e.match(az)}function oz(e){return e===parseInt(e.toString(),10)}function No(e,t,n){t==null&&(t=0),n==null&&(n=e.length);let s=1;for(let r=t;r<n;++r)s*=e[r];return s}function Uu(e){if(e.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let n=0;n<e.length;n++){let s=e[n];s<t&&(t=s)}return t}function Eo(e){if(e.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let n=0;n<e.length;n++){let s=e[n];s>t&&(t=s)}return t}function fr(e,t){if(t<e)throw new q(`end (${t}) < begin (${e}) is forbidden.`);let n=[];for(let s=e;s<t;++s)n.push(s);return n}function Uf(e,t){return pe(e,t)}function Ld(e,t=-1){let n=e.shape.slice();return t<0&&(t=n.length+t+1),n.splice(t,0,1),G(e,n)}function iz(e,t){return j(()=>{if(e.shape.length!==2)throw new q(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);let n=Ld(e,1);return H1(n,[1,t,1])})}function lz(e){let t=[No(e.shape)];return G(e,t)}function uz(e){if(e.rank<=1)throw new q(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);let t=[e.shape[0],No(e.shape,1)];return G(e,t)}function ll(e,t,n){return j(()=>{switch(e.rank){case 1:return If(e,t,n);case 2:return w1(e,[t,0],[n,e.shape[1]]);case 3:return zu(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return $d(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return _e(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return _e(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 q(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}})}function U1(e,t,n){return j(()=>{switch(e.rank){case 1:return If(e,t,n);case 2:return w1(e,[0,t],[e.shape[0],n]);case 3:return zu(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return $d(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new q(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function Gf(e,t,n,s){return j(()=>{switch(e.rank){case 1:return If(e,t,n);case 2:switch(s){case 1:return ll(e,t,n);case 2:return U1(e,t,n);default:throw new q(`The axis is not within the rank of the tensor ${s}`)}case 3:switch(s){case 1:return ll(e,t,n);case 2:return zu(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return U1(e,t,n);default:throw new q(`The axis is not within the rank of the tensor ${s}`)}case 4:switch(s){case 1:return ll(e,t,n);case 2:return $d(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return $d(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return U1(e,t,n);default:throw new q(`The axis is not within the rank of the tensor ${s}`)}default:throw new q(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}})}function G1(e,t=-1){let n;return t<0&&(n=e[0].rank,n!==0?t=n:t=0),t===e[0].rank&&(t=-1),kt(e,t)}function Zv(e,t){switch(e.rank){case 1:return M3([e,t]);case 2:return Ru([e,t],0);case 3:return z3([e,t],0);case 4:return L3([e,t],0);default:throw new q(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function H1(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new q(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return Ps(e,t)}function Hf(e,t=0,n=1,s,r){return sv(e,t,n,s,r)}function Dr(e,t,n,s){if(e.rank<2||t.rank<2)throw new Ve(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3){let r=e.shape.slice(-1)[0],a=t.shape.slice(-2)[0];if(r!==a)throw new Ve(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`)}if(e.rank===2&&t.rank===2){let r=!1,a=!1;return Co.matMul({a:e,b:t,transposeA:r,transposeB:a,bias:s?j1(e.rank,s,pr()):null,activation:n})}else{let r=e.shape.slice(),a=r.pop();e=G(e,[-1,a]);let o=t.shape.slice(),i=o.pop(),l=o.pop(),c=[...o,i],u=Array.from({length:t.rank},(f,m)=>m===0?t.rank-2:m<=t.rank-2?m-1:m);t=G(et(t,u),[l,-1]);let d=[...r,...c],p=!1,h=!1;return G(Co.matMul({a:e,b:t,transposeA:p,transposeB:h,bias:s?j1(e.rank,s,pr()):null,activation:n}),d)}}function Yv(e,t,n){return j(()=>(Array.isArray(t)?t=Zt(t,"int32"):t=pe(t,"int32"),Yi(e,t,n)))}function Bd(e){return L(e,e)}function j1(e,t,n){let s=t.shape;if(t.rank!==1&&t.rank!==e)throw new q(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(e===5){if(n==="channelsFirst")return s.length===1?G(t,[1,s[0],1,1,1]):G(t,[1,s[3],s[0],s[1],s[2]]);if(n==="channelsLast")return s.length===1?G(t,[1,1,1,1,s[0]]):G(t,[1].concat(s))}else if(e===4){if(n==="channelsFirst")return s.length===1?G(t,[1,s[0],1,1]):G(t,[1,s[2],s[0],s[1]]);if(n==="channelsLast")return s.length===1?G(t,[1,1,1,s[0]]):G(t,[1].concat(s))}else if(e===3){if(n==="channelsFirst")return s.length===1?G(t,[1,s[0],1]):G(t,[1,s[1],s[0]]);if(n==="channelsLast")return s.length===1?G(t,[1,1,s[0]]):G(t,[1].concat(s))}else if(e<3)return t;throw new q(`Unsupported input rank by biasAdd: ${t.rank}`)}function mr(e,t,n){return j(()=>(n==null&&(n=pr()),qt(n),ue(e,j1(e.rank,t,n))))}function cz(e,t=1){if(t!==1)throw new Ve(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return Du(e)}function dz(e){return j(()=>fe(e,ue(Kt(e),1)))}function Jv(e,t,n,s){return j(()=>cv(e,t,n,s))}function pz(e){return j(()=>{let t=ue(.5,L(.2,e));return ss(t,0,1)})}function Wd(e,t,n=!1){return n?e():t()}var hz=["fanIn","fanOut","fanAvg"],fz=["normal","uniform","truncatedNormal"];function mz(e){ol(hz,"FanMode",e)}function gz(e){ol(fz,"Distribution",e)}var Js=class extends de.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},q1=class extends Js{apply(e,t){return jt(e,t)}};q1.className="Zeros";de.registerClass(q1);var jf=class extends Js{apply(e,t){return As(e,t)}};jf.className="Ones";de.registerClass(jf);var X1=class extends Js{constructor(e){super();if(typeof e!="object")throw new q(`Expected argument of type ConstantConfig but got ${e}`);if(e.value===void 0)throw new q(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return j(()=>L(Ee(this.value),As(e,t)))}getConfig(){return{value:this.value}}};X1.className="Constant";de.registerClass(X1);var K1=class extends Js{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 Ou(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};K1.className="RandomUniform";de.registerClass(K1);var Z1=class extends Js{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Ve(`randomNormal does not support dType ${t}.`);return Hf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Z1.className="RandomNormal";de.registerClass(Z1);var Y1=class extends Js{constructor(e){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Ve(`truncatedNormal does not support dType ${t}.`);return Tf(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Y1.className="TruncatedNormal";de.registerClass(Y1);var J1=class extends Js{constructor(e){super();this.gain=e.gain!=null?e.gain:1}apply(e,t){return j(()=>{if(e.length!==2||e[0]!==e[1])throw new q("Identity matrix initializer can only be used for 2D square matrices.");return L(this.gain,l1(e[0]))})}getConfig(){return{gain:this.gain}}};J1.className="Identity";de.registerClass(J1);function yz(e,t="channelsLast"){let n,s;if(qt(t),e.length===2)n=e[0],s=e[1];else if([3,4,5].indexOf(e.length)!==-1){if(t==="channelsFirst"){let r=No(e,2);n=e[1]*r,s=e[0]*r}else if(t==="channelsLast"){let r=No(e,0,e.length-2);n=e[e.length-2]*r,s=e[e.length-1]*r}}else{let r=No(e);n=Math.sqrt(r),s=Math.sqrt(r)}return[n,s]}var os=class extends Js{constructor(e){super();if(e.scale<0)throw new q(`scale must be a positive float. Got: ${e.scale}`);this.scale=e.scale==null?1:e.scale,this.mode=e.mode==null?"fanIn":e.mode,mz(this.mode),this.distribution=e.distribution==null?"normal":e.distribution,gz(this.distribution),this.seed=e.seed}apply(e,t){let n=yz(e),s=n[0],r=n[1],a=this.scale;if(this.mode==="fanIn"?a/=Math.max(1,s):this.mode==="fanOut"?a/=Math.max(1,r):a/=Math.max(1,(s+r)/2),this.distribution==="normal"){let o=Math.sqrt(a);if(t=t||"float32",t!=="float32"&&t!=="int32")throw new Ve(`${this.getClassName()} does not support dType ${t}.`);return Tf(e,0,o,t,this.seed)}else{let o=Math.sqrt(3*a);return Ou(e,-o,o,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};os.className="VarianceScaling";de.registerClass(os);var qf=class extends os{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return os.className}};qf.className="GlorotUniform";de.registerClass(qf);var Xf=class extends os{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return os.className}};Xf.className="GlorotNormal";de.registerClass(Xf);var Kf=class extends os{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return os.className}};Kf.className="HeNormal";de.registerClass(Kf);var Zf=class extends os{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return os.className}};Zf.className="HeUniform";de.registerClass(Zf);var Yf=class extends os{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:e==null?null:e.seed})}getClassName(){return os.className}};Yf.className="LeCunNormal";de.registerClass(Yf);var Jf=class extends os{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:e==null?null:e.seed})}getClassName(){return os.className}};Jf.className="LeCunNormal";de.registerClass(Jf);var Q1=class extends Js{constructor(e){super();if(this.DEFAULT_GAIN=1,this.gain=e.gain==null?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,this.seed!=null)throw new Ve("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return j(()=>{if(e.length<2)throw new Ve("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);let n=e[0]>e[1]?[e[1],e[0]]:e,s=Hf(n,0,1,"float32"),r=kv.gramSchmidt(s);return e[0]>e[1]&&(r=et(r)),L(this.gain,r)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Q1.className="Orthogonal";de.registerClass(Q1);var Qv={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 ew(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"initializer")}function Lt(e){return F1(e)}function Pt(e){if(typeof e=="string"){let t=e in Qv?Qv[e]:e;if(t==="GlorotNormal")return new Xf;if(t==="GlorotUniform")return new qf;if(t==="HeNormal")return new Kf;if(t==="HeUniform")return new Zf;if(t==="LeCunNormal")return new Yf;if(t==="LeCunUniform")return new Jf;{let n={};return n.className=t,n.config={},ew(n)}}else return e instanceof Js?e:ew(e)}function Az(){return new q1}function xz(){return new jf}function bz(e){return new X1(e)}function vz(e){return new K1(e)}function wz(e){return new Z1(e)}function kz(e){return new Y1(e)}function Iz(e){return new J1(e)}function Sz(e){return new os(e)}function Cz(e){return new qf(e)}function Tz(e){return new Xf(e)}function Nz(e){return new Kf(e)}function Ez(e){return new Zf(e)}function Rz(e){return new Yf(e)}function $z(e){return new Jf(e)}function Dz(e){return new Q1(e)}var tw={};Le(tw,{Layer:()=>rt,RNN:()=>Fr,RNNCell:()=>Zd,activation:()=>fB,add:()=>kB,alphaDropout:()=>oW,average:()=>IB,averagePooling1d:()=>yA,averagePooling2d:()=>AA,averagePooling3d:()=>xA,avgPool1d:()=>_B,avgPool2d:()=>FB,avgPool3d:()=>MB,avgPooling1d:()=>PB,avgPooling2d:()=>OB,avgPooling3d:()=>zB,batchNormalization:()=>RB,bidirectional:()=>JB,concatenate:()=>SB,conv1d:()=>aB,conv2d:()=>oB,conv2dTranspose:()=>iB,conv3d:()=>lB,conv3dTranspose:()=>uB,convLstm2d:()=>XB,convLstm2dCell:()=>KB,cropping2D:()=>dB,dense:()=>mB,depthwiseConv2d:()=>hB,dot:()=>EB,dropout:()=>gB,elu:()=>QL,embedding:()=>wB,flatten:()=>AB,gaussianDropout:()=>aW,gaussianNoise:()=>rW,globalAveragePooling1d:()=>LB,globalAveragePooling2d:()=>BB,globalMaxPool1d:()=>eW,globalMaxPool2d:()=>tW,globalMaxPooling1d:()=>ck,globalMaxPooling2d:()=>dk,gru:()=>VB,gruCell:()=>UB,input:()=>Pw,inputLayer:()=>JL,layerNormalization:()=>$B,leakyReLU:()=>tB,lstm:()=>GB,lstmCell:()=>HB,masking:()=>iW,maxPool1d:()=>nW,maxPool2d:()=>sW,maxPooling1d:()=>pk,maxPooling2d:()=>hk,maxPooling3d:()=>WB,maximum:()=>CB,minimum:()=>TB,multiply:()=>NB,permute:()=>vB,prelu:()=>nB,reLU:()=>eB,repeatVector:()=>xB,reshape:()=>bB,rnn:()=>ZB,separableConv2d:()=>cB,simpleRNN:()=>jB,simpleRNNCell:()=>qB,softmax:()=>sB,spatialDropout1d:()=>yB,stackedRNNCells:()=>YB,thresholdedReLU:()=>rB,timeDistributed:()=>QB,upSampling2d:()=>pB,zeroPadding2d:()=>DB});var _z=0;function nw(){return _z++}var Qf={};function em(e=""){return e in Qf||(Qf[e]=0),Qf[e]+=1,e+Qf[e].toString()}function ey(e){return Array.isArray(e)&&Array.isArray(e[0])}function tm(e){return e.length===0?[]:Array.isArray(e[0])?e:[e]}function Ge(e){let t;if(Array.isArray(e)){if(e.length!==1)throw new q(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function At(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(e.length===1)return e=e,e[0];throw new q(`Expected exactly 1 Shape; got ${e.length}`)}else return e}function nm(e){let t=0;for(let n of e)n.shape.length===0?t+=1:t+=n.shape.reduce((s,r)=>s*r);return t}var sw="Variable",rw=class{constructor(e,t="float32",n=sw,s=!0,r=null){this.dtype=t==null?"float32":t,this.shape=e.shape,this.id=nw(),n=n==null?sw:n,this.originalName=qv(n),this.name=Xv(this.originalName),this.trainable_=s,this.constraint=r,this.val=av(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),Pz(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 Pz(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function ty(e){return e.map(t=>t.read())}function ny(e){e.forEach(t=>{t[0].write(t[1])})}var Yt=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||{}}},gr=class{constructor(e,t,n,s,r,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=o,this.id=nw(),a!=null&&(this.originalName=qv(a),this.name=Xv(this.originalName)),this.rank=t.length}},Fz=0,sm=class{constructor(e,t){this.callArgs=t,this.id=Fz++,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}}},Oz=0,rt=class extends de.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=Oz++,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=ra(n)+"_"+em(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}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 hr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return as(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return as(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new sa(`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 sa(`Layer ${this.name} is not connected, no input to return.`);return as(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new sa(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new sa(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return as(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=Nt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=Nt(this.inputSpec);if(e.length!==t.length)throw new q(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new q(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),c=r.axes[i],u=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.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=Nt(e),s=!0;for(let a of n)if(!(a instanceof gr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof gr){r=!1;break}if(s===r)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return il(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of Nt(e))a.push(o.shape);this.build(as(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=Nt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=as(i),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=Mz(e),o=this.computeOutputShape(a),i,l=zz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((c,u)=>new gr(l,c,this,Nt(e),t,this.name,u)):i=new gr(l,o,this,Nt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}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 ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${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 sa(`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 sa(`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 hr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return nm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return ty(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=ty(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!v.arraysEqual(a.shape,i.shape))throw new q(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}ny(n)})}addWeight(e,t,n,s,r,a,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=Pt("zeros"));let i=s.apply(t,n),l=new rw(i,n,e,a,o);return i.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?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=Nt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=Nt(e);t=Nt(t),n=Nt(n),s=Nt(s),r=tm(r),a=tm(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new sm({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 Mz(e){e=Nt(e);let t=[];for(let n of e)t.push(n.shape);return as(t)}function zz(e){return"float32"}function aw(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let o=s.inputTensors[a],i=s.inboundLayers[a],l=s.nodeIndices[a],c=aw(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Gu=class extends rt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:em("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new gr(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Gu.className="InputLayer";de.registerClass(Gu);function ow(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new q("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Gu({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Ro(e){if(e==null)return;let t=[],n=[],s=[];for(let r in e){let a=e[r];if(typeof a!="number"){let o=a;t.push(o.data()),n.push(r),s.push(o)}}if(t.length>0){let r=await Promise.all(t);for(let a=0;a<r.length;++a)e[n[a]]=r[a][0];ne(s)}}function iw(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var lw;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(lw||(lw={}));var Lz=125,Hu=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){}},uw=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)}},Bz=class extends Hu{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 s in t){let r=t[s];if(typeof r=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+r*n;else{let a;s in this.totals?a=this.totals[s]:this.totals[s]=0;let o=j(()=>ue(this.totals[s],L(r,n)));this.totals[s]=o,a!=null&&a.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:j(()=>{let s=L(fe(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),An(t[n])}))}},cw=class extends Hu{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let r in this.history){let a=this.history[r];for(let o=0;o<a.length;++o)if(typeof a[o]!="number"){let i=a[o];e.push(i.data()),t.push(r),n.push(o)}}let s=await Promise.all(e);for(let r=0;r<s.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=s[r][0]}},dw=class extends Hu{constructor(e,t){super();if(this.currentEpoch=0,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=Lz),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=qM(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 s=[];this.yield!=null&&(await Ro(n),s.push(this.yield(e,t,n))),s.push(Wf()),await Promise.all(s)}async onEpochBegin(e,t){this.currentEpoch=e,this.epochBegin!=null&&(await Ro(t),await this.epochBegin(e,t))}async onEpochEnd(e,t){let n=[];this.epochEnd!=null&&(await Ro(t),n.push(this.epochEnd(e,t))),this.yieldEvery==="epoch"&&n.push(Wf()),await Promise.all(n)}async onBatchBegin(e,t){this.batchBegin!=null&&(await Ro(t),await this.batchBegin(e,t))}async onBatchEnd(e,t){let n=[];this.batchEnd!=null&&(await Ro(t),n.push(this.batchEnd(e,t))),this.yieldEvery==="batch"?n.push(Wf()):v.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,e,t)),await Promise.all(n)}async onTrainBegin(e){this.trainBegin!=null&&(await Ro(e),await this.trainBegin(e))}async onTrainEnd(e){this.trainEnd!=null&&(await Ro(e),await this.trainEnd(e))}};function pw(e,t){return e==null&&(e={}),e instanceof Hu?[e]:Array.isArray(e)&&e[0]instanceof Hu?e:Nt(e).map(s=>new dw(s,t))}var Qs=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}`),Qs.checkForDuplicate(t),Qs.constructors[e]==null&&(Qs.constructors[e]=[]),Qs.constructors[e].push(t)}static checkForDuplicate(e){for(let t in Qs.constructors)Qs.constructors[+t].forEach(s=>{if(s===e)throw new q("Duplicate callback constructor.")})}static clear(){Qs.constructors={}}static createCallbacks(e){let t=[];for(let n in Qs.constructors){let s=+n;e>=s&&t.push(...Qs.constructors[s])}return t.map(n=>new n)}};Qs.constructors={};function hw(e,t,n,s,r,a,o,i,l){let c=new cw,u=[new Bz,...Qs.createCallbacks(t)];e!=null&&u.push(...e),u.push(c);let d=new uw(u);return d.setParams({epochs:n,initialEpoch:s,samples:r,steps:a,batchSize:o,verbose:t,doValidation:i,metrics:l}),{callbackList:d,history:c}}function yr(e,t={},n=!1){return Od(e,de.SerializationMap.getMap().classNameMap,t,"layer",n)}function rm(e,t){return j(()=>{e.dtype!=="float32"&&(e=pe(e,"float32"));let n=we(Bd(e),t,!0),s=_u(n.shape,rn()),r=Cn(Rr(n,s));return fe(e,r)})}function ul(e,t){return j(()=>zt(Bd(xe(t,e)),-1))}function am(e,t){return j(()=>zt(Kt(xe(t,e)),-1))}function ju(e,t){return j(()=>{let n=xe(e,t),s=ss(Kt(e),rn(),Number.MAX_VALUE),r=Kt(fe(n,s));return L(100,zt(r,-1))})}function Wz(e,t){return j(()=>{let n=ss(t,rn(),Number.MAX_VALUE),s=ys(ue(1,n)),r=ss(e,rn(),Number.MAX_VALUE),a=ys(ue(1,r));return zt(Bd(xe(s,a)),-1)})}function Vz(e,t){return j(()=>{let n=Rr(0,xe(1,L(e,t)));return zt(Bd(n),-1)})}function Uz(e,t){return j(()=>{let n=Rr(0,xe(1,L(e,t)));return zt(n,-1)})}function Gz(e,t){return j(()=>{let n=we(L(e,t),-1),s=Bn(L(xe(1,e),t),-1);return Rr(0,ue(1,xe(s,n)))})}function Hz(e,t){return j(()=>{let n=Math.log(2),s=xe(t,e),r=xe(ue(s,Ji(L(-2,s))),n);return zt(r,-1)})}function Vd(e,t,n=!1){return j(()=>{if(n)t=tl(t);else{let s=we(t,t.shape.length-1,!0);t=fe(t,s)}return t=ss(t,rn(),1-rn()),_t(we(L(pe(e,"float32"),ys(t)),t.shape.length-1))})}function om(e,t,n=!1){return j(()=>{let s=pe(Pu(lz(e)),"int32");t=ss(t,rn(),1-rn());let r=t.shape,a=G(Cu(s,r[r.length-1]),r);return Vd(a,t,n)})}function jz(e,t){if(!v.arraysEqual(e.shape,t.shape))throw new q(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);return j(()=>{let n=cr(t),s=_t(Kt(t));return ue(xe(n,L(t,e)),Id(gs(s)))})}function im(e,t){return j(()=>{let n;return n=ss(t,rn(),1-rn()),n=ys(fe(n,xe(1,n))),zt(jz(e,n),-1)})}function qz(e,t){return j(()=>{let n=ss(e,rn(),1),s=ss(t,rn(),1);return we(L(e,ys(fe(n,s))),-1)})}function Xz(e,t){return j(()=>{let n=ys(ue(rn(),t));return zt(xe(t,L(e,n)),-1)})}function sy(e,t){return j(()=>{let n=rm(e,-1),s=rm(t,-1),r=L(n,s);return _t(we(r,-1))})}var lm={meanSquaredError:ul,meanAbsoluteError:am,meanAbsolutePercentageError:ju,meanSquaredLogarithmicError:Wz,squaredHinge:Vz,hinge:Uz,categoricalHinge:Gz,logcosh:Hz,categoricalCrossentropy:Vd,sparseCategoricalCrossentropy:om,binaryCrossentropy:im,kullbackLeiblerDivergence:qz,poisson:Xz,cosineProximity:sy};function ry(e){if(typeof e=="string"){if(e in lm)return lm[e];let t=`Unknown loss ${e}`;throw e.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new q(t)}else return e}function ay(e,t){return j(()=>{let n=L(.5,xs(t)),s=Uf(rs(t,n),e.dtype);return zt(ms(e,s),-1)})}function oy(e,t){return j(()=>Uf(ms(_s(e,-1),_s(t,-1)),"float32"))}function fw(e,t){return j(()=>pe(we(Ks(ms(e,1),ms(t,1))),"float32"))}function Kz(e,t){return j(()=>pe(we(Ks(ms(e,1),ms(t,0))),"float32"))}function Zz(e,t){return j(()=>pe(we(Ks(ms(e,0),ms(t,1))),"float32"))}function mw(e,t){return j(()=>{let n=fw(e,t),s=Zz(e,t),r=ue(n,s);return pe(Pn(rs(r,0),fe(n,r),0),"float32")})}function Yz(e,t){return j(()=>{let n=fw(e,t),s=Kz(e,t),r=ue(n,s);return pe(Pn(rs(r,0),fe(n,r),0),"float32")})}function gw(e,t){return im(e,t)}function yw(e,t){return e.rank===t.rank&&(e=dt(e,[e.rank-1])),t=_s(t,-1),t.dtype!==e.dtype&&(t=pe(t,e.dtype)),pe(ms(e,t),"float32")}var Jz=ul,Qz=ul,eL=am,tL=am,nL=ju,sL=ju,iy=Vd,rL=sy,Aw=om,um={binaryAccuracy:ay,categoricalAccuracy:oy,precision:mw,categoricalCrossentropy:iy,sparseCategoricalCrossentropy:Aw,mse:Jz,MSE:Qz,mae:eL,MAE:tL,mape:nL,MAPE:sL,cosine:rL};function aL(e){if(typeof e=="string"&&e in um)return um[e];if(typeof e!="string"&&e!=null)return e;throw new q(`Unknown metric ${e}`)}function cm(e){if($r(e!==null,`Unknown LossOrMetricFn ${e}`),typeof e=="string")return e;{let t;for(let n of Object.keys(lm))if(lm[n]===e){t=n;break}if(t!==void 0)return t;for(let n of Object.keys(um))if(um[n]===e){t=n;break}return t!==void 0?t:e.name}}function oL(e){let t={Adagrad:()=>sl.adagrad(.01),Adadelta:()=>sl.adadelta(1,.95,rn()),Adam:()=>sl.adam(.001,.9,.999,rn()),Adamax:()=>sl.adamax(.002,.9,.999,rn(),0),RMSProp:()=>sl.rmsprop(.001,.9,0,rn()),SGD:()=>sl.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,e in t)return t[e]();throw new q(`Unknown Optimizer ${e}`)}var xw=1*1024*1024;function bw(e,t,n=!1){if(e==null||typeof e!="object"||Object.getPrototypeOf(e)!==Object.prototype||!ly(e))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(n){let s=JSON.stringify(e);s.length>xw&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${xw}.`)}}function ly(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"||!ly(e[n]))return!1;return!0}else if(Array.isArray(e)){for(let t of e)if(!ly(t))return!1;return!0}else return!1;else{let t=typeof e;return t==="string"||t==="number"||t==="boolean"}}function iL(e,t,n,s=console.log){let r=uL(e),a=["Layer (type)","Output shape","Param #"];r?(t=t||65,n=n||[.45,.85,1]):(t=t||98,n=n||[.33,.55,.67,1]),n[n.length-1]<=1&&(n=n.map(u=>Math.floor(t*u)));let o;if(!r){a.push("Receives inputs"),o=[];for(let u in e.nodesByDepth)o.push(...e.nodesByDepth[u])}s("_".repeat(t)),dm(a,n,s),s("=".repeat(t));let i=e.layers;for(let u=0;u<i.length;++u)r?cL(i[u],n,s):dL(i[u],n,o,s),s((u===i.length-1?"=":"_").repeat(t));e.checkTrainableWeightsConsistency();let l=lL(e),c=nm(e.nonTrainableWeights);s(`Total params: ${l+c}`),s(`Trainable params: ${l}`),s(`Non-trainable params: ${c}`),s("_".repeat(t))}function lL(e){let t;return e.collectedTrainableWeights!=null?t=nm(e.collectedTrainableWeights):t=nm(e.trainableWeights),t}function uL(e){let t=!0,n=[],s=[];for(let r in e.nodesByDepth)n.push(e.nodesByDepth[r]);for(let r of n){if(r.length>1||r.length===1&&r[0].inboundLayers.length>1){t=!1;break}s.push(...r)}if(t)for(let r of e.layers){let a=!1;for(let o of r.inboundNodes)if(s.indexOf(o)!==-1)if(a){t=!1;break}else a=!0;if(!t)break}return t}function dm(e,t,n=console.log){let s="";for(let r=0;r<e.length;++r)r>0&&(s=s.slice(0,s.length-1)+" "),s+=e[r],s=s.slice(0,t[r]),s+=" ".repeat(t[r]-s.length);n(s)}function cL(e,t,n){let s;try{s=JSON.stringify(e.outputShape)}catch(i){s="multiple"}let r=e.name,a=e.getClassName(),o=[`${r} (${a})`,s,e.countParams().toString()];dm(o,t,n)}function dL(e,t,n,s){let r;try{r=JSON.stringify(e.outputShape)}catch(u){r="multiple"}let a=[];for(let u of e.inboundNodes)if(!(n!=null&&n.length>0&&n.indexOf(u)===-1))for(let d=0;d<u.inboundLayers.length;++d){let p=u.inboundLayers[d].name,h=u.nodeIndices[d],f=u.tensorIndices[d];a.push(`${p}[${h}][${f}]`)}let o=e.name,i=e.getClassName(),l=a.length===0?"":a[0],c=[`${o} (${i})`,r,e.countParams().toString(),l];dm(c,t,s);for(let u=1;u<a.length;++u)dm(["","","",a[u]],t,s)}function vw(e,t,n){return(e==="inboundNodes"||e==="outputLayers"||e==="inputLayers")&&t===0&&typeof n=="string"}function Ud(e,t){if(e===null)return null;if(typeof e=="string")return al(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],s=e.length;for(let r=0;r<s;++r){let a=e[r];vw(t,r,a)?n.push(a):n.push(Ud(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s];if(s==="name"&&typeof r=="string")n[s]=r;else{let a=al(s);n[a]=Ud(r,a)}}return n}}function uy(e,t){if(e==null)return null;if(typeof e=="string")return ra(e);if(typeof e=="number"||typeof e=="boolean")return e;if(e instanceof Array){let n=[],s=e.length;for(let r=0;r<s;++r){let a=e[r];vw(t,r,a)?n.push(a):n.push(uy(a,t))}return n}else{let n={};for(let s of Object.keys(e)){let r=e[s],a=ra(s);(s==="name"||s==="className")&&typeof r=="string"?n[a]=r:n[a]=uy(r,s)}return n}}var cy="3.9.0";function pL(e,t){if(e.dtype==null||e.dtype===t.dtype)return t;try{return pe(t,e.dtype)}catch(n){throw new q(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`)}}var cl=class{constructor(e){if(this.id2Value={},this.id2Mask={},this.name2Id={},e instanceof cl)for(let t in e.id2Value)this.id2Value[t]=e.id2Value[t],t in e.id2Mask&&(this.id2Mask[t]=e.id2Mask[t]);else{if(e==null)return;for(let t of e)this.add(t.key,t.value)}}add(e,t,n){if(this.id2Value[e.id]==null)this.id2Value[e.id]=pL(e,t),this.name2Id[e.name]=e.id,n!=null&&(this.id2Mask[e.id]=n);else throw new q(`Duplicate key: name=${e.name}, id=${e.id}`);return this}addFeed(e){this.add(e.key,e.value)}hasKey(e){return this.id2Value[e.id]!=null}names(){return Object.keys(this.name2Id)}getValue(e){if(e instanceof gr){if(this.id2Value[e.id]==null)throw new q(`Nonexistent key: ${e.name}`);return this.id2Value[e.id]}else{let t=this.name2Id[e];if(t==null)throw new q(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Value[t]}}getMask(e){if(e instanceof gr){if(this.id2Value[e.id]==null)throw new q(`Nonexistent key: ${e.name}`);return this.id2Mask[e.id]}else{let t=this.name2Id[e];if(t==null)throw new q(`Feed dict has no SymbolicTensor name: ${e}`);return this.id2Mask[t]}}disposeMasks(){this.id2Mask!=null&&ne(this.id2Mask)}},dy={},ww={};function Gd(e,t,n,s){let r=n==null?!1:n.training,a=Array.isArray(e),o=a?e:[e],i=o.map(f=>f.name),l=[],c=t.names();for(let f of i)c.indexOf(f)!==-1?l.push(t.getValue(f)):l.push(null);s!=null&&(s.maxNumTensors=-1/0,s.minNumTensors=1/0);let u=i.join(",")+"|"+t.names().join(","),d,p;if(dy[u]==null){let f=hL(o,t);d=f.sorted,p=f.recipientCounts,dy[u]=d,ww[u]=p}d=dy[u],p={},r||Object.assign(p,ww[u]);let h=new cl(t);for(let f=0;f<d.length;++f){if(s!=null){let R=ef().numTensors;R>s.maxNumTensors&&(s.maxNumTensors=R),R<s.minNumTensors&&(s.minNumTensors=R)}let m=d[f],g=m.sourceLayer;if(g instanceof Gu)continue;let y=[],A=[],x=[],b=!1;for(let R of m.inputs){let P=h.getValue(R),$=h.getMask(R);y.push(P),A.push($),$!=null&&(b=!0),r||(p[R.name]--,p[R.name]===0&&!t.hasKey(R)&&i.indexOf(R.name)===-1&&!P.isDisposed&&R.sourceLayer.stateful!==!0&&x.push(P))}b&&(n=n||{},n.mask=A[0]);let w=Nt(g.apply(y,n)),k=null;g.supportsMasking&&(k=g.computeMask(y,A));let S=mL(m),N=Array.isArray(S)?S:[S];for(let R=0;R<N.length;++R){h.hasKey(N[R])||h.add(N[R],w[R],Array.isArray(k)?k[0]:k);let P=i.indexOf(N[R].name);P!==-1&&(l[P]=w[R])}r||ne(x)}return h.disposeMasks(),a?l:l[0]}function hL(e,t){v.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let n=[],s={};if(e.length===1){let r=kw(e[0],t);n=r.sorted,s=r.recipientMap}else{let r=new Set;for(let a of e){let{sorted:o,recipientMap:i}=kw(a,t);for(let l of o)r.has(l.name)||(n.push(l),r.add(l.name));for(let l in i)s[l]==null&&(s[l]=new Set),i[l].forEach(c=>s[l].add(c))}}return{sorted:n,recipientCounts:fL(s)}}function fL(e){let t={};for(let n in e)t[n]=e[n].size;return t}function kw(e,t){let n=new Set,s=[],r={};for(let i of t.names())n.add(i);let a=[],o=[];for(a.push(e);a.length>0;){let i=a[a.length-1];if(n.has(i.name)){a.pop();continue}let l=o[o.length-1]===a.length-1;if(i.inputs.length===0||l)a.pop(),s.push(i),n.add(i.name),l&&o.pop();else{o.push(a.length-1);for(let c of i.inputs)r[c.name]==null&&(r[c.name]=new Set),r[c.name].add(i.name),!n.has(c.name)&&a.push(c)}}return{sorted:s,recipientMap:r}}function mL(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var _r=class extends rt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=em(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],To(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);To(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;$r(x===0,"input layer has >1 nodes"),$r(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof Gu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},s={},r={},a={},o=[],i=(y,A,x,b,w,k)=>{(b==null||w==null||k==null)&&(b=y.sourceLayer,w=y.nodeIndex,k=y.tensorIndex);let S=b.inboundNodes[w];if(x.indexOf(S)!==-1)throw new hr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(_r.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),x.indexOf(S)===-1&&x.push(S);let N=S.inboundLayers.length;for(let R=0;R<N;R++){let P=S.inputTensors[R],$=S.inboundLayers[R],D=S.nodeIndices[R],T=S.tensorIndices[R];i(P,A,x,$,D,T)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);o.push(S)},l=[],c=[];for(let y of this.outputs)i(y,l,c);let u=o.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=s[y.outboundLayer.id]==null?0:s[y.outboundLayer.id];A=Math.max(A,x),s[y.outboundLayer.id]=A,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],k=y.nodeIndices[b],S=w.inboundNodes[k],N=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,N),n[S.id]=S}}let d={};for(let y in t){let A=t[y];A in d||(d[A]=[]),d[A].push(n[y])}let p={};for(let y in s){let A=s[y];A in p||(p[A]=[]),p[A].push(r[y])}let h=Object.keys(p).map(y=>parseInt(y,10)).sort(Vf);this.layers=[];for(let y of h){let A=p[y];A.sort((x,b)=>{let w=a[x.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let x of A)x instanceof _r&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,h=Object.keys(d).map(y=>parseInt(y,10)).sort(Vf);let f=this.inputs.slice(),m=[];for(let y of h)for(let A of d[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new hr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new hr(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let 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={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new q(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new q(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new q(`${a.length} of ${s} weights are not set: ${a}`)}ny(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${cy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=uy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=Nt(e);let n=new cl;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Gd(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=Nt(e);let n;return t==null?n=rl(null,e.length):n=Nt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=tm(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Vf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=n[A];u.push(x)}let d=c.computeOutputShape(as(u)),p=tm(d),h=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],u=`${i.name}_${l}_${c}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];$r(i in n),r.push(n[i])}return as(r)}runInternalGraph(e,t){t==null&&(t=rl(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Vf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,y,A;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[x,b]=h[0];f.mask==null&&(f.mask=b),y=Nt(u.call(x,f)),A=Nt(u.computeMask(x,b)),m=[x],g=[b]}else m=h.map(x=>x[0]),g=h.map(x=>x[1]),f.mask==null&&(f.mask=g),y=Nt(u.call(m,f)),A=Nt(u.computeMask(m,g));if(u.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],w=y[x],k=A[x];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){$r(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,c]=n[i.id];o.push(l.shape),r.push(l),a.push(c)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof _r?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=_r.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=_r.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=_r.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],y=d.nodeIndices[m],A=d.tensorIndices[m],x=_r.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,h])}l.push(f)}}}let c={};c.name=a.name,c.className=o,c.config=i,c.inboundNodes=l,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=_r.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[a];s.push([o.name,c,u])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=_r.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[a];r.push([o.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let y=[],A;for(let x of g){let b=x[0],w=x[1],k=x[2];if(A=x[3]==null?{}:x[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=w){o(m,g);return}let N=S.inboundNodes[w];y.push(N.outputTensors[k])}y.length>0&&m.apply(as(y),A)}function l(m){let g=m.name,y=yr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(s),r[g]=y,m.inboundNodes.forEach(x=>{if(!(x instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${x}`);o(y,x)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!jM(a);)for(let m of u){let g=r[m.name];if(g.name in a){let y=a[g.name];delete a[g.name];for(let A of y)i(g,A)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],y=m[1],A=m[2];$r(g in r);let b=r[g].inboundNodes[y].outputTensors;d.push(b[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];$r(g in r);let b=r[g].inboundNodes[y].outputTensors;p.push(b[A])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gL(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===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!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function Iw(e,t){return gL(e,t,"classWeight")}async function Sw(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return ir(e);if(e.shape.length===2){if(e.shape[1]>1)return _s(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());ne(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Zt(o,"float32")}else return null}function yL(e,t){return L(e,t)}var AL=32;function Cw(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=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 a=Tw("input",e.inputNames,n),o=Tw("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)v.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function Tw(e,t,n){if(n instanceof Ke)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 s=[];for(let r of t){if(n[r]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function xL(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function bL(e,t,n){let s=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(!s||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 r=n.validationData!=null,a,o;if(r)if(Nw(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 g=xL(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=pw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=hw(u,d,n.epochs,null,null,vL(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(s||(m=await t.iterator());s?y<n.batchesPerEpoch:!0;){let x=await m.next();if(s&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(x.value!=null){let{xs:b,ys:w}=Cw(e,x.value),k={};k.batch=A,k.size=b[0].shape[0],await p.onBatchBegin(A,k);let S=[];if(n.classWeight!=null){let P=Iw(n.classWeight,e.outputNames);for(let $=0;$<P.length;++$)S.push(await Sw(w[$],null,P[$]))}let N=b.concat(w).concat(S),R=i(N);ne(N);for(let P=0;P<l.length;++P){let $=l[P],D=R[P];k[$]=D,An(D)}await p.onBatchEnd(A,k),iw(k),A++,y++}if(s?y>=n.batchesPerEpoch:x.done){if(r){let b;Nw(n.validationData)?b=Nt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=Nt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?AL:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,g),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function vL(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function Nw(e){return typeof e.iterator=="function"}function wL(e){return typeof e.next=="function"}async function kL(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new Ve("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=wL(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let c=await o.next();if(a=j(()=>{if(c.value){let{xs:u,ys:d}=Cw(e,c.value),p=u.concat(d),h=j(()=>r(p));if(ne(p),l===0)for(let m=0;m<h.length;++m)a.push(Ee(0));let f=p[0].shape[0];for(let m=0;m<h.length;++m){let g=h[m],y=a[m];a[m]=j(()=>ue(a[m],L(f,g))),l>0&&ne(y)}ne(h),i+=f,++l}return a}),c.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let c=0;c<a.length;++c){let u=a[c];a[c]=fe(a[c],i),ne(u)}return as(a)}function py(e){v.assert(e>0&&Number.isInteger(e),()=>`batchSize is required to be a positive integer, but got ${e}`)}function Hd(e,t,n){return e==null?[null]:Array.isArray(e)?e.map(s=>ll(s,t,n-t)):ll(e,t,n-t)}function hy(e,t){return j(()=>e==null?null:Array.isArray(e)?e.map(n=>hy(n,t)):Yv(e,t.dtype==="int32"?t:pe(t,"int32")))}function fy(e,t){let n=[],s=0,r=null;for(;s<e;)r=s+t,r>=e&&(r=e),n.push([s,r]),s=r;return n}async function IL(e,t,n,s,r,a,o,i,l,c,u,d,p,h,f){r==null&&(r=32),a==null&&(a=1),u==null&&(u=!0),p==null&&(p=0);let m=!1;if(l!=null&&c!=null&&(m=!0),f!=null&&(m=!0,h==null))throw new q("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=e.checkNumSamples(n,r,h,"steps_per_epoch"),y;g!=null&&(y=fr(0,g)),o==null&&(o=1);let{callbackList:A,history:x}=hw(i,o,a,p,g,h,r,m,d);A.setModel(e),e.history=x,await A.onTrainBegin(),e.stopTraining_=!1;for(let b=p;b<a;++b){await A.onEpochBegin(b);let w={};if(h!=null)throw new Ve("stepsPerEpoch mode is not implemented yet.");{if(u==="batch")throw new Ve("batch shuffling is not implemneted yet");u&&v.shuffle(y);let k=Zt(y),S=fy(g,r);for(let N=0;N<S.length;++N){let R={};if(await A.onBatchBegin(N,R),j(()=>{let P=S[N][0],$=S[N][1],D=ll(k,P,$-P);R.batch=N,R.size=$-P;let T=hy(n,D),O=t(T);for(let B=0;B<s.length;++B){let H=s[B],z=O[B];R[H]=z,An(z)}if(N===S.length-1&&m){let B=e.testLoop(l,c,r);for(let H=0;H<s.length;++H){let z=s[H],X=B[H];An(X),w["val_"+z]=X}}}),await A.onBatchEnd(N,R),iw(R),e.stopTraining_)break}k.dispose()}if(await A.onEpochEnd(b,w),e.stopTraining_)break}return await A.onTrainEnd(),await e.history.syncData(),e.history}async function SL(e,t,n,s={}){if(e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;let r,a,o,i,l,c,u;try{let d=s.batchSize==null?32:s.batchSize;py(d);let p=!1,h=await e.standardizeUserData(t,n,s.sampleWeight,s.classWeight,p,d);r=h[0],a=h[1],u=h[2];let f=!1,m;if(s.validationData!=null&&s.validationData.length>0){if(f=!0,s.validationData.length===2)o=s.validationData[0],i=s.validationData[1];else throw s.validationData.length===3?new Ve("validationData including sample weights is not supported yet."):new q(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);let S=!0,N=await e.standardizeUserData(o,i,null,null,S,d);l=N[0],c=N[1],m=l.concat(c)}else if(s.validationSplit!=null&&s.validationSplit>0&&s.validationSplit<1){f=!0;let S=Math.floor(r[0].shape[0]*(1-s.validationSplit)),N=r[0].shape[0];l=Hd(r,S,N),r=Hd(r,0,S),c=Hd(a,S,N),a=Hd(a,0,S),m=l.concat(c)}else s.validationSteps!=null&&(f=!0);let g=r.concat(a).concat(u);e.checkTrainableWeightsConsistency();let y=e.makeTrainFunction(),A=e.getDedupedMetricsNames(),x,b;f?(e.makeTestFunction(),x=e.testFunction,b=A.slice().concat(A.map(S=>"val_"+S))):(x=null,m=[],b=A.slice());let w=pw(s.callbacks,s.yieldEvery);return await IL(e,y,g,A,d,s.epochs,s.verbose,w,x,m,s.shuffle,b,s.initialEpoch,null,null)}finally{e.isTraining=!1,dl(r,t),dl(a,n),dl(l,o),dl(c,i),u!=null&&ne(u)}}function Ew(e){let t=[];e instanceof Ke&&(e=[e]);for(let n=0;n<e.length;++n){let s=e[n];if(s.rank===1)t.push(Ld(s,1));else{if(s.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(s)}}return t}function dl(e,t){if(e==null)return;let n=[];if(t instanceof Ke)n.push(t.id);else if(Array.isArray(t))t.forEach(r=>n.push(r.id));else if(t!=null)for(let r in t){let a=t[r];n.push(a.id)}let s=[];if(e instanceof Ke)n.indexOf(e.id)===-1&&s.push(e);else if(Array.isArray(e))e.forEach(r=>{n.indexOf(r.id)===-1&&s.push(r)});else if(e!=null)for(let r in e){let a=e[r];n.indexOf(a.id)===-1&&s.push(a)}s.forEach(r=>{r.isDisposed||r.dispose()})}function CL(e){return e instanceof Ke}function my(e){return Array.isArray(e)}function Rw(e){return!CL(e)&&!my(e)}function $w(e,t,n,s=!0,r=""){if(t==null||t.length===0){if(e!=null){let o=!1;if(my(e)&&e.length>0)o=!0;else if(Rw(e)){for(let i in e)if(e.hasOwnProperty(i)){o=!0;break}}else o=!0;if(o)throw new q(`Error when checking model ${r} expected no data, but got ${e}`)}return[]}if(e==null)return t.map(o=>null);let a;if(Rw(e)){e=e,a=[];for(let o of t){if(e[o]==null)throw new q(`No data provided for "${o}". Need data for each key in: ${t}`);a.push(e[o])}}else if(my(e)){if(e=e,e.length!==t.length)throw new q(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);a=e}else{if(e=e,t.length>1)throw new q(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);a=[e]}if(a=Ew(a),n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new q(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s). but got array with shape ${i.shape}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let c=i.shape[l],u=n[o][l];if(u!=null&&u>=0&&c!==u)throw new q(`${r} expected a batch of elements where each example has shape [${n[o].slice(1,n[o].length)}] (i.e.,tensor shape [*,${n[o].slice(1,n[o].length)}]) but the ${r} received an input with ${i.shape[0]} examples, each with shape [${i.shape.slice(1,i.shape.length)}] (tensor shape [${i.shape}])`)}}return a}function TL(e,t,n){let s=To(e.map(a=>a.shape[0]));s.sort();let r=To(t.map(a=>a.shape[0]));if(r.sort(),s.length>1)throw new q(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map(a=>a.shape))}`);if(r.length>1)throw new q(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(a=>a.shape))}`);if(s.length>0&&r.length>0&&!v.arraysEqual(s,r))throw new q(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`)}function NL(e,t,n){let s=[ul,im,Vd];for(let r=0;r<e.length;++r){let a=e[r],o=t[r],i=n[r];if(o!=null){if(o===Vd&&a.shape[a.shape.length-1]===1)throw new q(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(s.indexOf(o)!==-1){let l=a.shape.slice(1),c=i.slice(1);for(let u=0;u<l.length;++u){let d=l[u],p=c[u];if(p!=null&&d!==p)throw new q(`A target Tensor with shape ${a.shape} was passed for an output of shape ${i}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function Dw(e,t,n,s=!0,r=""){let a;if(Array.isArray(e)){if(e.length!==t.length)throw new q(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);a=e}else{if(t.length>1)throw new q(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);a=[e]}if(n!=null)for(let o=0;o<t.length;++o){if(n[o]==null)continue;let i=a[o];if(i.shape.length!==n[o].length)throw new q(`Error when checking ${r}: expected ${t[o]} to have ${n[o].length} dimension(s), but got array with shape ${JSON.stringify(i.shape)}`);for(let l=0;l<n[o].length;++l){if(l===0&&!s)continue;let c=i.shape[l],u=n[o][l];if(u!=null&&u!==c)throw new q(`Error when checking ${r}: expected ${t[o]} to have shape ${JSON.stringify(n[o])} but got array with shape ${JSON.stringify(i.shape)}.`)}}}function EL(e,t){if(e==null||Array.isArray(e)&&e.length===0)return t.map(s=>[]);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: ${e}`);if(Array.isArray(n))return t.map(s=>n);{let s=[];for(let r of t){let a=n.hasOwnProperty(r)?n[r]:[];Array.isArray(a)||(a=[a]),s.push(a)}return s}}var RL="layers-model",aa=class extends _r{constructor(e){super(e);this.isTraining=!1}summary(e,t,n=console.log){if(!this.built)throw new q("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");iL(this,e,t,n)}compile(e){if(e.loss==null&&(e.loss=[]),this.loss=e.loss,typeof e.optimizer=="string")this.optimizer_=oL(e.optimizer),this.isOptimizerOwned=!0;else{if(!(e.optimizer instanceof na))throw new q("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=e.optimizer,this.isOptimizerOwned=!1}let t=[];if(!Array.isArray(e.loss)&&typeof e.loss!="string"&&typeof e.loss!="function"){e.loss=e.loss;for(let a in e.loss)if(this.outputNames.indexOf(a)===-1)throw new q(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);for(let a of this.outputNames)e.loss[a]==null&&console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`),t.push(ry(e.loss[a]))}else if(Array.isArray(e.loss)){if(e.loss.length!==this.outputs.length)throw new q(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);t=e.loss.map(o=>ry(o))}else{let a=ry(e.loss);this.outputs.forEach(o=>{t.push(a)})}this.lossFunctions=t,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let a=0;a<this.outputs.length;++a){let o=this.internalOutputShapes[a],i=this.outputNames[a];this.feedOutputNames.push(i),this.feedOutputShapes.push(o),this.feedLossFns.push(this.lossFunctions[a])}let n=[];this.metrics=e.metrics,this.metricsNames=["loss"],this.metricsTensors=[],il("loss",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=this.lossFunctions[a];this.outputs.length>1&&(this.metricsTensors.push([o,a]),this.metricsNames.push(this.outputNames[a]+"_loss"))}});let s=EL(e.metrics,this.outputNames),r=(a,o,i)=>{this.outputNames.length>1&&(o=this.outputNames[a]+"_"+o),this.metricsNames.push(o),this.metricsTensors.push([i,a])};il("metric",()=>{for(let a=0;a<this.outputs.length;++a){if(n.indexOf(a)!==-1)continue;let o=s[a];(l=>{let c="",u,d,p;for(let h of l){if(typeof h=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(h)!==-1){let m=this.internalOutputShapes[a];m[m.length-1]===1||this.lossFunctions[a]===im?["accuracy","acc"].indexOf(h)!==-1?d=ay:["crossentropy","ce"].indexOf(h)!==-1&&(d=gw):this.lossFunctions[a]===om?["accuracy","acc"].indexOf(h)!==-1?d=yw:["crossentropy","ce"].indexOf(h)!==-1&&(d=Aw):["accuracy","acc"].indexOf(h)!==-1?d=oy:["crossentropy","ce"].indexOf(h)!==-1&&(d=iy);let g;["accuracy","acc"].indexOf(h)!==-1?g="acc":["crossentropy","ce"].indexOf(h)!==-1&&(g="ce"),p=d,u=c+g}else p=aL(h),u=c+cm(h);let f;il(u,()=>{f=p}),r(a,u,f)}})(o)}}),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 s=n.batchSize==null?32:n.batchSize;py(s);let r=!0,a=this.standardizeUserDataXY(e,t,r,s);try{let o=a[0].concat(a[1]);this.makeTestFunction();let i=this.testFunction,l=this.testLoop(i,o,s,n.verbose,n.steps);return as(l)}finally{dl(a[0],e),dl(a[1],t)}}async evaluateDataset(e,t){return this.makeTestFunction(),kL(this,e,t)}checkNumSamples(e,t,n,s="steps"){let r;if(n!=null){if(r=null,t!=null)throw new q(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`)}else if(e!=null)Array.isArray(e)?r=e[0].shape[0]:r=e.shape[0];else throw new q(`Either the input data should have a defined shape, or ${s} shoud be specified.`);return r}execute(e,t){if(Array.isArray(t)&&t.length===0)throw new q("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(t),s=n?t:[t],r=this.retrieveSymbolicTensors(s),a=new cl;if(e instanceof Ke&&(e=[e]),Array.isArray(e)){if(e.length!==this.inputs.length)throw new q(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;i<this.inputs.length;++i)a.add(this.inputs[i],e[i])}else for(let i of this.inputs){let l=e[i.name];if(l==null)throw new q(`No value is provided for the model's input ${i.name}`);a.add(i,l)}let o=Gd(r,a);return n?o:o[0]}retrieveSymbolicTensors(e){let t=rl(null,e.length),n=e.length;for(let s of this.layers){let r=Array.isArray(s.output)?s.output:[s.output],a=r.map(o=>o.name);for(let o=0;o<e.length;++o){let i=a.indexOf(e[o]);if(i!==-1&&(t[o]=r[i],n--),n===0)break}if(n===0)break}if(n>0){let s=[];throw t.forEach((r,a)=>{r==null&&s.push(e[a])}),new q(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`)}return t}predictLoop(e,t=32,n=!1){return j(()=>{let s=this.checkNumSamples(e);if(n)throw new Ve("Verbose predictLoop() is not implemented yet.");let r=fy(s,t),a=this.outputs.map(o=>[]);for(let o=0;o<r.length;++o)j(()=>{let l=r[o][0],c=r[o][1],u=Hd(e,l,c),d=[];if(Array.isArray(u))for(let h=0;h<u.length;++h)d.push({key:this.inputs[h],value:u[h]});else d.push({key:this.inputs[0],value:u});let p=new cl(d);return Gd(this.outputs,p)}).forEach((l,c)=>a[c].push(l));return as(a.map(o=>kt(o,0)))})}predict(e,t={}){let n=Ew(e);Dw(n,this.inputNames,this.feedInputShapes,!1);try{let s=t.batchSize==null?32:t.batchSize;return py(s),this.predictLoop(n,s)}finally{dl(n,e)}}predictOnBatch(e){Dw(e,this.inputNames,this.feedInputShapes,!0);let t=(Array.isArray(e)?e[0]:e).shape[0];return this.predictLoop(e,t)}standardizeUserDataXY(e,t,n=!0,s){if(this.optimizer_==null)throw new hr("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let o=this.feedOutputShapes[a];this.feedLossFns[a]===om?r.push(o.slice(0,o.length-1).concat([1])):r.push(o)}if(e=$w(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=$w(t,this.feedOutputNames,r,!1,"target"),TL(e,t,null),NL(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!=0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[o,i]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(s!=null){let c=Iw(s,this.outputNames);l=[];for(let u=0;u<c.length;++u)l.push(await Sw(i[u],null,c[u]))}return[o,i,l]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,"steps"),o=[];if(s>0)throw new Ve("Verbose mode is not implemented yet.");if(r!=null)throw new Ve("steps mode in testLoop() is not implemented yet");{let i=fy(a,n),l=Zt(fr(0,a));for(let c=0;c<i.length;++c){let u=i[c][0],d=i[c][1],p=ll(l,u,d-u),h=hy(t,p),f=e(h);if(c===0)for(let m=0;m<f.length;++m)o.push(Ee(0));for(let m=0;m<f.length;++m){let g=f[m];o[m]=ue(o[m],L(d-u,g))}}for(let c=0;c<o.length;++c)o[c]=fe(o[c],a)}return o})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Lv(e,s)>1&&(r+=`_${Lv(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let u=[];for(let f=0;f<this.inputs.length;++f)u.push({key:this.inputs[f],value:n[f]});let d=new cl(u),p=Gd(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],p[f]);r[f]!=null&&(g=yL(g,r[f]));let y=zt(g);t.push(y),f===0?h=g:h=ue(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],y=this.metricsTensors[f][1];m=zt(g(s[y],p[y]))}An(m),a.push(m)}return h=zt(h),this.calculateLosses().forEach(f=>{h=ue(h,f)}),h},i=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new cl(a),i=Gd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=zt(c(r[l],i[l]));l===0?n=u:n=ue(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],d=zt(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return SL(this,e,t,n)}async fitDataset(e,t){return bL(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let c=await l.data();i.push(c[0])}return ne(o),as(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});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=ef().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-ef().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ra(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=>ra(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=ra(n[s]);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[ra(cm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ra(cm(e)));{let e={};for(let t in this.metrics)e[t]=ra(cm(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=Ud(e.optimizer_config),n=yr(t),s;if(typeof e.loss=="string")s=al(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>al(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=al(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>al(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=al(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=es.getSaveHandlers(e);if(l.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new q(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await es.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:RL,generatedBy:`TensorFlow.js tfjs-layers v${cy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await es.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=es.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;bw(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){bw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};aa.className="Model";de.registerClass(aa);var _w=class extends aa{};_w.className="Functional";de.registerClass(_w);async function $L(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Ud(n),r=yr(s,t);if(e.weightsManifest!=null){let a=await es.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),ne(a)}return r}async function DL(e,t){if(t==null&&(t={}),typeof e=="string"){let n=es.getLoadHandlers(e,t);if(n.length===0)n.push(es.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return _L(e,void 0,t)}async function _L(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=yr(Ud(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=PL(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),ne(c),ne(u.map(d=>d.tensor))}return i}function PL(e,t){let n=es.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var qu=class extends aa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:em("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof qu||e instanceof aa,n;if(t){if(n=e,n.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=ow({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=aw(this.outputs[0])}this.inboundNodes=[],new sm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:rl(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))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=[s],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(At(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 aa({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 hr("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 hr("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 hr("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 hr("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={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");r=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."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof qu))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=yr(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};qu.className="Sequential";de.registerClass(qu);function FL(e){return new aa(e)}function OL(e){return new qu(e)}function ML(e,t){return t==null&&(t={}),DL(e,t)}function Pw(e){return ow(e)}function zL(e,t){Qs.registerCallbackConstructor(e,t)}var is=class extends de.Serializable{getConfig(){return{}}},Fw=class extends is{apply(e,t=1){return cz(e,t)}};Fw.className="elu";de.registerClass(Fw);var Ow=class extends is{apply(e){return vf(e)}};Ow.className="selu";de.registerClass(Ow);var Mw=class extends is{apply(e){return cr(e)}};Mw.className="relu";de.registerClass(Mw);var zw=class extends is{apply(e){return j(()=>Fu(6,cr(e)))}};zw.className="relu6";de.registerClass(zw);var Lw=class extends is{apply(e){return e}};Lw.className="linear";de.registerClass(Lw);var Bw=class extends is{apply(e){return ns(e)}};Bw.className="sigmoid";de.registerClass(Bw);var Ww=class extends is{apply(e){return pz(e)}};Ww.className="hardSigmoid";de.registerClass(Ww);var Vw=class extends is{apply(e){return Ji(e)}};Vw.className="softplus";de.registerClass(Vw);var Uw=class extends is{apply(e){return dz(e)}};Uw.className="softsign";de.registerClass(Uw);var Gw=class extends is{apply(e){return Ki(e)}};Gw.className="tanh";de.registerClass(Gw);var gy=class extends is{apply(e,t=-1){return tl(e,t)}};gy.className="softmax";de.registerClass(gy);var Hw=class extends is{apply(e,t=-1){return ff(e,t)}};Hw.className="logSoftmax";de.registerClass(Hw);var jw=class extends is{apply(e,t=1){return j(()=>L(ns(L(e,t)),e))}};jw.className="swish";de.registerClass(jw);var qw=class extends is{apply(e){return j(()=>L(e,Ki(Ji(e))))}};qw.className="mish";de.registerClass(qw);function $o(e){return e.getClassName()}function yy(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"activation")}function Do(e){if(e==null){let t={};return t.className="linear",t.config={},yy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},yy(t)}else return e instanceof is?e:yy(e)}function Ay(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 Xw=class extends de.Serializable{},jd=class extends Xw{constructor(e){super();Ay(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 j(()=>{let t=jt([1]);return this.hasL1&&(t=ue(t,we(L(this.l1,Kt(e))))),this.hasL2&&(t=ue(t,we(L(this.l2,Bd(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};jd.className="L1L2";de.registerClass(jd);function LL(e){return Ay(e),new jd({l1:e!=null?e.l1:null,l2:0})}function BL(e){return Ay(e),new jd({l2:e!=null?e.l2:null,l1:0})}var Kw={l1l2:"L1L2"};function It(e){return F1(e)}function Zw(e,t={}){return Od(e,de.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Kw?Kw[e]:e,config:{}};return Zw(n)}else return e instanceof Xw?e:Zw(e)}var xy=class extends rt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let n=cr(e);return this.maxValue!=null&&(n=ss(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};xy.className="ReLU";de.registerClass(xy);var by=class extends rt{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=Ge(e);return kd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};by.className="LeakyReLU";de.registerClass(by);var vy=class extends rt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Pt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ft(e.alphaRegularizer),this.alphaConstraint=on(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=At(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Yt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ge(e),Ed(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Lt(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};vy.className="PReLU";de.registerClass(vy);var wy=class extends rt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ge(e);return Du(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wy.className="ELU";de.registerClass(wy);var ky=class extends rt{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=Ge(e);return L(n,pe(rs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};ky.className="ThresholdedReLU";de.registerClass(ky);var Iy=class extends rt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new gy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ge(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}};Iy.className="Softmax";de.registerClass(Iy);function Xu(e,t,n){if(typeof e=="number")return rl(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!oz(r))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Ar(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Pr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Eo([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function Sy(e,t){return j(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,1]):e))}function Yw(e,t){return j(()=>(qt(t),t==="channelsFirst"?et(e,[0,2,3,4,1]):e))}function WL(e,t,n,s=1,r="valid",a,o=1){return j(()=>{if(a==null&&(a=pr()),qt(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=et(e,[0,2,1])),r==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=of(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=mr(i,n)),i})}function Jw(e,t,n,s=[1,1],r="valid",a,o,i=null){return j(()=>{if(a==null&&(a=pr()),qt(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=Sy(e,a);if(r==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Co.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=et(l,[0,3,1,2])),l})}function VL(e,t,n,s=[1,1,1],r="valid",a,o){return j(()=>{if(a==null&&(a=pr()),qt(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Yw(e,a);if(r==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=n1(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=mr(i,n)),a==="channelsFirst"&&(i=et(i,[0,4,1,2,3])),i})}var Cy=class extends rt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Cy.verifyArgs(t),this.rank=e,bn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xu(t.kernelSize,e,"kernelSize"),this.strides=Xu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Fs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,qt(this.dataFormat),this.activation=Do(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=on(t.biasConstraint),this.biasRegularizer=Ft(t.biasRegularizer),this.activityRegularizer=Ft(t.activityRegularizer),this.dilationRate=Xu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if($r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!M1(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:$o(this.activation),useBias:this.useBias,biasInitializer:Lt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},qd=class extends Cy{constructor(e,t){super(e,t);this.kernel=null,qd.verifyArgs(t),this.filters=t.filters,bn(this.filters,"filters"),this.kernelInitializer=Pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=on(t.kernelConstraint),this.kernelRegularizer=Ft(t.kernelRegularizer)}build(e){e=At(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,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 j(()=>{e=Ge(e);let n,s=this.bias==null?null:this.bias.read(),r=Wv(this.activation.getClassName());if(r!=null&&this.rank===2)n=Jw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=WL(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Jw(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=VL(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=At(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=Ar(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Lt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:an(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Xd=class extends qd{constructor(e){super(2,e);Xd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!M1(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Xd.className="Conv2D";de.registerClass(Xd);var Kd=class extends qd{constructor(e){super(3,e);Kd.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Kd.className="Conv3D";de.registerClass(Kd);var Ty=class extends Xd{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ge(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=Pr(i,d,c,this.padding),f=Pr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,1]));let g=lf(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=et(g,[0,3,1,2])),this.bias!=null&&(g=mr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=At(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Pr(t[s],i,a,this.padding),t[r]=Pr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ty.className="Conv2DTranspose";de.registerClass(Ty);var Ny=class extends Kd{constructor(e){super(e);if(this.inputSpec=[new Yt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=At(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Yt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Ge(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Pr(l,f,d,this.padding),A=Pr(c,m,p,this.padding),x=Pr(u,g,h,this.padding),b=[r,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(n=et(n,[0,2,3,4,1]));let w=W3(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=et(w,[0,4,1,2,3])),this.bias!==null&&(w=mr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=At(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=Pr(t[s],c,o,this.padding),t[r]=Pr(t[r],u,i,this.padding),t[a]=Pr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ny.className="Conv3DTranspose";de.registerClass(Ny);var Qw=class extends qd{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=on(t.depthwiseConstraint),this.pointwiseInitializer=Pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=on(t.pointwiseConstraint)}build(e){if(e=At(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Yt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Ge(e);let n;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=et(e,[0,2,3,1])),n=b1(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=et(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=Lt(this.depthwiseInitializer),e.pointwiseInitializer=Lt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}};Qw.className="SeparableConv";var Ey=class extends Qw{constructor(e){super(2,e)}};Ey.className="SeparableConv2D";de.registerClass(Ey);var pm=class extends qd{constructor(e){super(1,e);pm.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"&&!M1(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};pm.className="Conv1D";de.registerClass(pm);var Ry=class extends rt{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 j(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let n=Gf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gf(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}};Ry.className="Cropping2D";de.registerClass(Ry);var $y=class extends rt{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,sz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Ge(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=et(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a]);return et(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$y.className="UpSampling2D";de.registerClass($y);function UL(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=pr()),qt(r);let o=Sy(e,r);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=$u(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}var Dy=class extends Cy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=on(e.depthwiseConstraint),this.depthwiseRegularizer=Ft(e.depthwiseRegularizer)}build(e){if(e=At(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,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 j(()=>{e=Ge(e);let n=UL(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=mr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Ar(t,this.kernelSize[0],this.padding,this.strides[0]),a=Ar(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}};Dy.className="DepthwiseConv2D";de.registerClass(Dy);function ek(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function tk(e,t,n,s=!1,r,a,o=!1,i=!1){return j(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(fr(2,l));if(t=et(t,c),a!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Ht(r,-1)),r=et(r,c)),s&&(t=bs(t,0),r!=null&&(r=bs(r,0)));let u=[],d,p=n,h=t.shape[0],f=Wn(t),m;r!=null&&(m=Wn(r));for(let y=0;y<h;++y){let A=f[y],x=j(()=>e(A,p));if(r==null)d=x[0],p=x[1];else{let b=j(()=>{let w=m[y],k=xe(xs(w),w),S=ue(L(x[0],w),L(p[0],k)),N=p.map((R,P)=>ue(L(x[1][P],w),L(R,k)));return{output:S,newStates:N}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=Tn(u,1)),[d,g,p]})}var Fr=class extends rt{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new mm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Yt({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 fr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ey(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}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 Ve("Constants support is not implemented in RNN yet.");ey(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Yt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Ve("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Yt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>jt([n,s])):this.states_=[jt([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>jt([n,s])):this.states_[0]=jt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>An(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=ek(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Yt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof gr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ge(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new q(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=tk((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return j(()=>{let t=jt(e.shape);return t=we(t,[1,2]),t=Ld(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?H1(t,[1,n]):t):this.cell.stateSize>1?[H1(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()===Fr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=yr(s,n);return new e(Object.assign(t,{cell:r}))}};Fr.className="RNN";de.registerClass(Fr);var Zd=class extends rt{},hm=class extends Zd{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,bn(this.units,"units"),this.activation=Do(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,Eo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Eo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 j(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=_o({ones:()=>xs(e),rate:this.dropout,training:s})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=_o({ones:()=>xs(n),rate:this.recurrentDropout,training:s}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Dr(L(e,a),this.kernel.read()):r=Dr(e,this.kernel.read()),this.bias!=null&&(r=mr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(r,Dr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:$o(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};hm.className="SimpleRNNCell";de.registerClass(hm);var _y=class extends Fr{constructor(e){e.cell=new hm(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};_y.className="SimpleRNN";de.registerClass(_y);var fm=class extends Zd{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,bn(this.units,"units"),this.activation=Do(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Do(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,Eo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Eo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=At(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 j(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=_o({ones:()=>xs(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=_o({ones:()=>xs(s),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=Dr(e,this.kernel.read());this.useBias&&(c=mr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=xn(u,[2*this.units,this.units],u.rank-1),h=Dr(s,d),[f,m,g]=xn(c,3,c.rank-1),[y,A]=xn(h,2,h.rank-1);o=this.recurrentActivation.apply(ue(f,y)),i=this.recurrentActivation.apply(ue(m,A));let x=Dr(L(i,s),p);l=this.activation.apply(ue(g,x));let b=ue(L(o,s),L(ue(1,_t(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:$o(this.activation),recurrentActivation:$o(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};fm.className="GRUCell";de.registerClass(fm);var Py=class extends Fr{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 fm(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Py.className="GRU";de.registerClass(Py);var Yd=class extends Zd{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,bn(this.units,"units"),this.activation=Do(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Do(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=Uu([1,Eo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Eo([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=At(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 s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends Js{apply(i,l){let c=r.apply([a]),u=new jf().apply([a]),d=r.apply([a*2]);return Zv(Zv(c,u),d)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=_o({ones:()=>xs(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=_o({ones:()=>xs(s),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let d=Dr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=ue(d,Dr(s,this.recurrentKernel.read())),this.useBias&&(d=mr(d,this.bias.read()));let[p,h,f,m]=xn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=ue(L(l,r),L(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:$o(this.activation),recurrentActivation:$o(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Yd.className="LSTMCell";de.registerClass(Yd);var Fy=class extends Fr{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 Yd(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Fy.className="LSTM";de.registerClass(Fy);var mm=class extends Zd{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 j(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){ey(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{il(`RNNCell_${s}`,()=>{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()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(yr(r,n));return new e({cells:s})}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 ty(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}ny(t)}};mm.className="StackedRNNCells";de.registerClass(mm);function _o(e){let{ones:t,rate:n,training:s=!1,count:r=1}=e,a=()=>Jv(t(),n),o=()=>Wd(a,t,s);return!r||r<=1?An(o().clone()):Array(r).fill(void 0).map(o).map(l=>An(l.clone()))}var GL=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},nk=class extends Fr{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Yt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=jt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>jt(r)):this.states_=[jt(r)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>jt(r)):this.states_[0]=jt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new q(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>An(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=Ar(l,s[0],r,a[0],o[0]),d=Ar(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};nk.className="ConvRNN2D";var gm=class extends Yd{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t}));this.filters=t,bn(this.filters,"filters"),this.kernelSize=Xu(n,2,"kernelSize"),this.kernelSize.forEach(i=>bn(i,"kernelSize")),this.strides=Xu(s||1,2,"strides"),this.strides.forEach(i=>bn(i,"strides")),this.padding=r||"valid",Fs(this.padding),this.dataFormat=a||"channelsLast",qt(this.dataFormat),this.dilationRate=Xu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>bn(i,"dilationRate"))}build(e){var t;e=At(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends Js{apply(d,p){let h=l.apply([c]),f=As([c]),m=l.apply([c*2]);return G1([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=_o({ones:()=>xs(s),rate:this.dropout,training:n,count:o}));let i=this.dropoutMask,l=(ee,J,Q)=>!J||!J[Q]?ee:L(J[Q],ee),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=_o({ones:()=>xs(r),rate:this.recurrentDropout,training:n,count:o}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),A=3,[x,b,w,k]=xn(this.kernel.read(),o,A),[S,N,R,P]=this.useBias?xn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,x,S,this.padding),u=this.inputConv(u,b,N,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,P,this.padding);let[$,D,T,O]=xn(this.recurrentKernel.read(),o,A);f=this.recurrentConv(f,$),m=this.recurrentConv(m,D),g=this.recurrentConv(g,T),y=this.recurrentConv(y,O);let B=this.recurrentActivation.apply(ue(c,f)),H=this.recurrentActivation.apply(ue(u,m)),z=ue(L(H,a),L(B,this.activation.apply(ue(d,g)))),X=L(this.recurrentActivation.apply(ue(p,y)),this.activation.apply(z));return[X,X,z]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=GL(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Qr(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?mr(r,n,this.dataFormat):r}recurrentConv(e,t){return Qr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};gm.className="ConvLSTM2DCell";de.registerClass(gm);var Oy=class extends nk{constructor(e){let t=new gm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Oy.className="ConvLSTM2D";de.registerClass(Oy);var ym=class extends rt{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 s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wd(()=>Jv(n,this.rate,r,this.seed),()=>n,s)}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()}};ym.className="Dropout";de.registerClass(ym);var My=class extends ym{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};My.className="SpatialDropout1D";de.registerClass(My);var zy=class extends rt{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,bn(this.units,"units"),this.activation=Do(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=on(e.kernelConstraint),this.biasConstraint=on(e.biasConstraint),this.kernelRegularizer=Ft(e.kernelRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=At(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=At(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=Wv(this.activation.getClassName()),r;return s!=null?r=Dr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Dr(n,this.kernel.read()),this.bias!=null&&(r=mr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:$o(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Dense";de.registerClass(zy);var Ly=class extends rt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=At(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],No(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=et(n,s)}return uz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Ly.className="Flatten";de.registerClass(Ly);var By=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Do(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return this.activation.apply(n)})}getConfig(){let e={activation:$o(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};By.className="Activation";de.registerClass(By);var Wy=class extends rt{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 j(()=>(e=Ge(e),iz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="RepeatVector";de.registerClass(Wy);var Vy=class extends rt{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.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new q("Can only specifiy one unknown dimension.");else r*=l}let o=No(e);if(a!==null){if(r===0||o%r!=0)throw new q(n);s[a]=o/r}else if(o!==r)throw new q(n);return s}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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Vy.className="Reshape";de.registerClass(Vy);var Uy=class extends rt{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=fr(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 Yt({ndim:this.dims.length+1})]}computeOutputShape(e){e=At(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return et(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="Permute";de.registerClass(Uy);var Gy=class extends rt{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=Ge(e),s=-1;return Ad(el(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e),s=-1,r=!0,a=Ad(el(n,this.maskValue),s,r);return L(n,pe(a,n.dtype))})}};Gy.className="Masking";de.registerClass(Gy);var Hy=class extends rt{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(Nt(e.inputLength))}this.inputDim=e.inputDim,bn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,bn(this.outputDim,"outputDim"),this.embeddingsInitializer=Pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ft(e.embeddingsRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.embeddingsConstraint=on(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 j(()=>this.maskZero?(e=Ge(e),el(e,tt(e))):null)}computeOutputShape(e){if(e=At(e),this.inputLength==null)return[...e,this.outputDim];let t=Nt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Ge(e);n.dtype!=="int32"&&(n=Uf(n,"int32"));let s=Yv(this.embeddings.read(),G(n,[n.size]));return G(s,At(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Lt(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="Embedding";de.registerClass(Hy);var pl=class extends rt{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[At(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=To(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&To(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Eo(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Ld(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=G(i,[u].concat(No(c.slice(1))));p=et(p,[1,0]),p=G(p,d),n.push(p),r=!0}else if(l>1){let c=fr(1,l).concat([0]);n.push(et(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(et(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(fr(0,o-1));a=et(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=To(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Ht(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ks(n,t[s]);return n})}},jy=class extends pl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return t})}};jy.className="Add";de.registerClass(jy);var qy=class extends pl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};qy.className="Multiply";de.registerClass(qy);var Xy=class extends pl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return L(1/e.length,t)})}};Xy.className="Average";de.registerClass(Xy);var Ky=class extends pl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Rr(t,e[n]);return t})}};Ky.className="Maximum";de.registerClass(Ky);var Zy=class extends pl{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Fu(t,e[n]);return t})}};Zy.className="Minimum";de.registerClass(Zy);var Yy=class extends pl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>G1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(pe(xs(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Ht(t[a],-1)):s.push(t[a]);let r=kt(s,this.axis);return rf(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Yy.className="Concatenate";de.registerClass(Yy);function Jd(e,t){for(;e<0;)e+=t;return e}function HL(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(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 Ve("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;c<o;++c)l.push(1);t=G(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let c=0;c<o;++c)l.push(1);e=G(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=we(L(e,t),a[0]):i=we(L(et(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=Xe(e,t,l,c)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u<l+o;++u)c.push(u);i=dt(i,c)}return i.shape.length===1&&(i=Ht(i,1)),i})}var Jy=class extends pl{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 Ve("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Jd(r,e[a].shape.length)):s=[Jd(this.axes,t.shape.length),Jd(this.axes,n.shape.length)],this.normalize&&(t=rm(t,s[0]),n=rm(n,s[1])),HL(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Jd(this.axes,e.length),Jd(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 Ve("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="Dot";de.registerClass(Jy);var Qy=class extends rt{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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return Wd(()=>ue(Hf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Qy.className="GaussianNoise";de.registerClass(Qy);var eA=class extends rt{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 j(()=>{this.invokeCallHook(e,t);let n=Ge(e);return this.rate>0&&this.rate<1?Wd(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Hf(n.shape,1,r))},()=>n,t.training||!1):n})}};eA.className="GaussianDropout";de.registerClass(eA);var tA=class extends rt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wd(()=>{let r=Ge(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=Io(Ou(n),this.rate);l=Uf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=ue(L(r,l),L(ue(l,-1),i));return ue(L(d,c),u)},()=>Ge(e),t.training||!1)}return e})}};tA.className="AlphaDropout";de.registerClass(tA);function Qd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=_3(e,t,n,s,r,a);else if(e.rank===3)o=P3(e,t,n,s,r,a);else if(e.rank===4)o=F3(e,t,n,s,r,a);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function jL(e,t,n,s,r=.001){return j(()=>{let a=gf(e,s),o=a.mean,i=a.variance;return[Qd(e,o,i,n,t,r),o,i]})}function qL(e,t,n,s,r=.001){return j(()=>{let a=gf(e,s),o=a.mean,i=a.variance,l=[];for(let f of fr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Qd(e,c,u,p,d,r),o,i]})}function XL(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),fr(0,e.rank-1))?jL(e,t,n,s,r):qL(e,t,n,s,r)}var nA=class extends rt{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=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=on(e.betaConstraint),this.gammaConstraint=on(e.gammaConstraint),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer)}build(e){e=At(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Yt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Ge(e),r=s.shape,a=r.length,o=fr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=rl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,fr(0,a).slice(0,a-1)),d=()=>{if(u){let y=G(this.movingMean.read(),l),A=G(this.movingVariance.read(),l),x=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Qd(s,y,A,x,b,this.epsilon)}else return Qd(s,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 d();let[p,h,f]=XL(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,A,x)=>{j(()=>{let b=1-x,w=y.read(),k=L(xe(w,A),b);y.write(xe(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),movingMeanInitializer:Lt(this.movingMeanInitializer),movingVarianceInitializer:Lt(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};nA.className="BatchNormalization";de.registerClass(nA);var sA=class extends rt{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Pt(e.betaInitializer||"zeros"),this.gammaInitializer=Pt(e.gammaInitializer||"ones"),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=At(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==To(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Ge(e),s=n.shape,r=s.length;return j(()=>{let a=!0,{mean:o,variance:i}=gf(n,this.axis,a),l=rl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r&&this.axis!==[r-1]?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=Ps(o,p),i=Ps(i,p),u=Ps(u,h),d=Ps(d,h),Qd(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};sA.className="LayerNormalization";de.registerClass(sA);function KL(e,t,n){return j(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=pr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],ur(e,s)})}var rA=class extends rt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?pr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,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 q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=At(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 j(()=>KL(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};rA.className="ZeroPadding2D";de.registerClass(rA);function Am(e,t,n,s,r,a){return j(()=>{qt(r),Hv(a),Fs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=pr()),a==null&&(a="max"),e=Sy(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Cd(e,t,n,i):o=bd(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,3,1,2])),o})}function sk(e,t,n,s,r,a){return j(()=>{qt(r),Hv(a),Fs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=pr()),a==null&&(a="max"),e=Yw(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=f1(e,t,n,i):o=J2(e,t,n,i),r==="channelsFirst"&&(o=et(o,[0,4,1,2,3])),o})}var rk=class extends rt{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(bn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Fs(this.padding),this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){e=At(e);let t=Ar(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Ld(Ge(e),2);let n=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return dt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},aA=class extends rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"max")}};aA.className="MaxPooling1D";de.registerClass(aA);var oA=class extends rk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"avg")}};oA.className="AveragePooling1D";de.registerClass(oA);var ak=class extends rt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Fs(this.padding),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Ar(t,this.poolSize[0],this.padding,this.strides[0]),n=Ar(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 j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},iA=class extends ak{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"max")}};iA.className="MaxPooling2D";de.registerClass(iA);var lA=class extends ak{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),Am(e,t,n,s,r,"avg")}};lA.className="AveragePooling2D";de.registerClass(lA);var ok=class extends rt{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];bn(this.poolSize,"poolSize"),bn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),Fs(this.padding),this.inputSpec=[new Yt({ndim:5})]}computeOutputShape(e){e=At(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Ar(t,this.poolSize[0],this.padding,this.strides[0]),n=Ar(n,this.poolSize[1],this.padding,this.strides[1]),s=Ar(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},uA=class extends ok{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),sk(e,t,n,s,r,"max")}};uA.className="MaxPooling3D";de.registerClass(uA);var cA=class extends ok{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Fs(s),sk(e,t,n,s,r,"avg")}};cA.className="AveragePooling3D";de.registerClass(cA);var ik=class extends rt{constructor(e){super(e);this.inputSpec=[new Yt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},dA=class extends ik{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Ge(e);return zt(n,1)})}};dA.className="GlobalAveragePooling1D";de.registerClass(dA);var pA=class extends ik{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Ge(e);return Bn(n,1)})}};pA.className="GlobalMaxPooling1D";de.registerClass(pA);var lk=class extends rt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.inputSpec=[new Yt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},hA=class extends lk{call(e,t){return j(()=>{let n=Ge(e);return this.dataFormat==="channelsLast"?zt(n,[1,2]):zt(n,[2,3])})}};hA.className="GlobalAveragePooling2D";de.registerClass(hA);var fA=class extends lk{call(e,t){return j(()=>{let n=Ge(e);return this.dataFormat==="channelsLast"?Bn(n,[1,2]):Bn(n,[2,3])})}};fA.className="GlobalMaxPooling2D";de.registerClass(fA);var uk=class extends rt{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 s=t.layer,r=yr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},mA=class extends uk{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=At(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=At(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Ge(e),tk((a,o)=>[Ge(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};mA.className="TimeDistributed";de.registerClass(mA);function ZL(e){ol(nz,"BidirectionalMergeMode",e)}var YL="concat",gA=class extends uk{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=yr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=yr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?YL:e.mergeMode,ZL(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,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,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):as(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=ek(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Yt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof gr;for(let l of a)if(l instanceof gr!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=bs(r,1));let o;return this.mergeMode==="concat"?o=G1([s,r]):this.mergeMode==="sum"?o=ue(s,r):this.mergeMode==="ave"?o=L(.5,ue(s,r)):this.mergeMode==="mul"?o=L(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){il(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),il(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=yr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ve("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};gA.className="Bidirectional";de.registerClass(gA);function JL(e){return new Gu(e)}function QL(e){return new wy(e)}function eB(e){return new xy(e)}function tB(e){return new by(e)}function nB(e){return new vy(e)}function sB(e){return new Iy(e)}function rB(e){return new ky(e)}function aB(e){return new pm(e)}function oB(e){return new Xd(e)}function iB(e){return new Ty(e)}function lB(e){return new Kd(e)}function uB(e){return new Ny(e)}function cB(e){return new Ey(e)}function dB(e){return new Ry(e)}function pB(e){return new $y(e)}function hB(e){return new Dy(e)}function fB(e){return new By(e)}function mB(e){return new zy(e)}function gB(e){return new ym(e)}function yB(e){return new My(e)}function AB(e){return new Ly(e)}function xB(e){return new Wy(e)}function bB(e){return new Vy(e)}function vB(e){return new Uy(e)}function wB(e){return new Hy(e)}function kB(e){return new jy(e)}function IB(e){return new Xy(e)}function SB(e){return new Yy(e)}function CB(e){return new Ky(e)}function TB(e){return new Zy(e)}function NB(e){return new qy(e)}function EB(e){return new Jy(e)}function RB(e){return new nA(e)}function $B(e){return new sA(e)}function DB(e){return new rA(e)}function yA(e){return new oA(e)}function _B(e){return yA(e)}function PB(e){return yA(e)}function AA(e){return new lA(e)}function FB(e){return AA(e)}function OB(e){return AA(e)}function xA(e){return new cA(e)}function MB(e){return xA(e)}function zB(e){return xA(e)}function LB(e){return new dA(e)}function BB(e){return new hA(e)}function ck(e){return new pA(e)}function dk(e){return new fA(e)}function pk(e){return new aA(e)}function hk(e){return new iA(e)}function WB(e){return new uA(e)}function VB(e){return new Py(e)}function UB(e){return new fm(e)}function GB(e){return new Fy(e)}function HB(e){return new Yd(e)}function jB(e){return new _y(e)}function qB(e){return new hm(e)}function XB(e){return new Oy(e)}function KB(e){return new gm(e)}function ZB(e){return new Fr(e)}function YB(e){return new mm(e)}function JB(e){return new gA(e)}function QB(e){return new mA(e)}var eW=ck,tW=dk,nW=pk,sW=hk;function rW(e){return new Qy(e)}function aW(e){return new eA(e)}function oW(e){return new tA(e)}function iW(e){return new Gy(e)}var fk={};Le(fk,{MAPE:()=>AW,MSE:()=>vW,binaryAccuracy:()=>lW,binaryCrossentropy:()=>uW,categoricalAccuracy:()=>dW,categoricalCrossentropy:()=>pW,cosineProximity:()=>mW,mape:()=>xW,meanAbsoluteError:()=>gW,meanAbsolutePercentageError:()=>yW,meanSquaredError:()=>bW,mse:()=>wW,precision:()=>hW,recall:()=>fW,sparseCategoricalAccuracy:()=>cW});function lW(e,t){return ay(e,t)}function uW(e,t){return gw(e,t)}function cW(e,t){return yw(e,t)}function dW(e,t){return oy(e,t)}function pW(e,t){return iy(e,t)}function hW(e,t){return mw(e,t)}function fW(e,t){return Yz(e,t)}function mW(e,t){return sy(e,t)}function gW(e,t){return am(e,t)}function yW(e,t){return ju(e,t)}function AW(e,t){return ju(e,t)}function xW(e,t){return ju(e,t)}function bW(e,t){return ul(e,t)}function vW(e,t){return ul(e,t)}function wW(e,t){return ul(e,t)}var mk={};Le(mk,{modelFromJSON:()=>$L});var gk={};Le(gk,{l1:()=>IW,l1l2:()=>kW,l2:()=>SW});function kW(e){return new jd(e)}function IW(e){return LL(e)}function SW(e){return BL(e)}var yk=class extends Hu{constructor(){super(...arguments);this.model=null}setModel(e){if(!(e instanceof aa))throw new Error("model must be a LayersModel, not some other Container");this.model=e}};function xm(e,t){return e<t}function Ak(e,t){return e>t}var xk=class extends yk{constructor(e){super();if(e==null&&(e={}),e.restoreBestWeights)throw new Ve("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=e.monitor||"val_loss",this.minDelta=Math.abs(e.minDelta||0),this.patience=e.patience||0,this.verbose=e.verbose||0,this.mode=e.mode||"auto",this.baseline=e.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=xm:this.mode==="max"?this.monitorFunc=Ak:this.monitor.indexOf("acc")!==-1?this.monitorFunc=Ak:this.monitorFunc=xm,this.monitorFunc===xm&&(this.minDelta*=-1)}async onTrainBegin(e){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===xm?1/0:-1/0}async onEpochEnd(e,t){await Ro(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 CW(e){return new xk(e)}var TW={earlyStopping:CW},xr;(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"})(xr||(xr={}));var bk;(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={}))})(bk||(bk={}));var bA={};function NW(e,t){let n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};bA[e]=n}function vk(e){return bA[e]}function EW(e){delete bA[e]}function I(e,t,n,s,r){let a=t.inputParams[e];if(a&&a.inputIndexStart!==void 0){let i=a.inputIndexStart,l=a.inputIndexEnd===0?void 0:a.inputIndexEnd===void 0?i+1:a.inputIndexEnd;if(a.type==="tensor")return Un(t.inputNames[a.inputIndexStart],n,s,r);if(a.type==="tensors")return t.inputNames.slice(i,l).map(p=>Un(p,n,s,r));let c=Un(t.inputNames.slice(i)[0],n,s,r),u=c.dataSync();return a.type==="number"?u[0]:v.toNestedArray(c.shape,u)}let o=t.attrParams[e];return o&&o.value}function Un(e,t,n,s){let[r,a]=vs(e);if(s!=null){let i=s.getHashTableHandleByName(r);if(i!=null)return i}let o=n.currentContextIds.find(i=>!!t[bm(r,i)]);return o!==void 0?t[bm(r,o)][a]:void 0}function RW(e,t,n){return t[bm(e,n.currentContextId)]}function oa(e,t){let[n,s,r]=vs(e);return[bm(n,t&&t.currentContextId),s,r]}function bm(e,t){return t?`${e}-${t}`:e}function vs(e){let t=e.split(":");if(t.length===1)return[e,0,void 0];let n=t[0],s=t.length===3?t[1]:void 0,r=Number(t[t.length-1]);return[n,r,s]}function vm(e,t,n){let s=I("pad",e,t,n);if(s==="explicit"){s=I("explicitPaddings",e,t,n);let r=[[0,0],[0,0],[0,0],[0,0]];for(let a=0;a<4;a++)r[a][0]=s[a*2],r[a][1]=s[a*2+1];return r}return s}function ia(e){return e.kept?e:ir(e)}var wk={};Le(wk,{json:()=>$W});var $W=[{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}]}],kk={};Le(kk,{json:()=>DW});var DW=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"IsNan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],Ik={};Le(Ik,{json:()=>_W});var _W=[{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"}]}],Sk={};Le(Sk,{json:()=>PW});var PW=[{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"}]}],Ck={};Le(Ck,{json:()=>FW});var FW=[{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"}]}],Tk={};Le(Tk,{json:()=>OW});var OW=[{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}]}],Nk={};Le(Nk,{json:()=>MW});var MW=[{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"}]}],Ek={};Le(Ek,{json:()=>zW});var zW=[{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"}]}],Rk={};Le(Rk,{json:()=>LW});var LW=[{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"}]}],$k={};Le($k,{json:()=>BW});var BW=[{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"}]}],Dk={};Le(Dk,{json:()=>WW});var WW=[{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}]}],_k={};Le(_k,{json:()=>VW});var VW=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Einsum",category:"matrices",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"equation",name:"equation",type:"string"},{tfName:"N",name:"n",type:"number",defaultValue:2},{tfName:"T",name:"dtype",type:"dtype"}]}],Pk={};Le(Pk,{json:()=>UW});var UW=[{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}]}],Fk={};Le(Fk,{json:()=>GW});var GW=[{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"}]}],Ok={};Le(Ok,{json:()=>HW});var HW=[{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}]}],Mk={};Le(Mk,{json:()=>jW});var jW=[{tfOpName:"SparseFillEmptyRows",category:"sparse",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"denseShape",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}]},{tfOpName:"SparseReshape",category:"sparse",inputs:[{start:0,name:"inputIndices",type:"tensor"},{start:1,name:"inputShape",type:"tensor"},{start:2,name:"newShape",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SparseSegmentMean",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]},{tfOpName:"SparseSegmentSum",category:"sparse",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"segmentIds",type:"tensor"}]}],zk={};Le(zk,{json:()=>qW});var qW=[{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}]}],Lk={};Le(Lk,{json:()=>XW});var XW=[{tfOpName:"StringNGrams",category:"string",inputs:[{start:0,name:"data",type:"tensor"},{start:1,name:"dataSplits",type:"tensor"}],attrs:[{tfName:"separator",name:"separator",type:"string"},{tfName:"ngram_widths",name:"nGramWidths",type:"number[]"},{tfName:"left_pad",name:"leftPad",type:"string"},{tfName:"right_pad",name:"rightPad",type:"string"},{tfName:"pad_width",name:"padWidth",type:"number"},{tfName:"preserve_short_sequences",name:"preserveShortSequences",type:"bool"}],outputs:["ngrams","ngrams_splits"]},{tfOpName:"StringSplit",category:"string",inputs:[{start:0,name:"input",type:"tensor"},{start:1,name:"delimiter",type:"tensor"}],attrs:[{tfName:"skip_empty",name:"skipEmpty",type:"bool"}],outputs:["indices","values","shape"]},{tfOpName:"StringToHashBucketFast",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"num_buckets",name:"numBuckets",type:"number"}]}],Bk={};Le(Bk,{json:()=>KW});var KW=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]},{tfOpName:"BroadcastArgs",category:"transformation",inputs:[{start:0,name:"s0",type:"tensor"},{start:1,name:"s1",type:"tensor"}],attrs:[]}],Wk=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let e=[wk,kk,Ik,Sk,Ck,Tk,Nk,Ek,Rk,$k,Dk,_k,Pk,Fk,Ok,Mk,zk,Lk,Bk],t=[].concat(...e.map(n=>n.json));this.opMappers=t.reduce((n,s)=>(n[s.tfOpName]=s,n),{})}transformGraph(e,t={}){let n=e.node,s=[],r=[],a=[],o=n.reduce((f,m)=>(f[m.name]=this.mapNode(m),m.op.startsWith("Placeholder")?s.push(f[m.name]):m.op==="Const"?r.push(f[m.name]):(m.input==null||m.input.length===0)&&a.push(f[m.name]),f),{}),i=[],l=[],c={},u={};t!=null&&(c=this.mapSignatureEntries(t.inputs),u=this.mapSignatureEntries(t.outputs));let d=Object.keys(o);d.forEach(f=>{let m=o[f];m.inputNames.forEach((g,y)=>{let[A,,x]=oa(g),b=o[A];if(b.outputs!=null){let w=b.outputs.indexOf(x);if(w!==-1){let k=`${A}:${w}`;m.inputNames[y]=k}}m.inputs.push(b),b.children.push(m)})}),Object.keys(u).length===0?d.forEach(f=>{let m=o[f];m.children.length===0&&l.push(m)}):Object.keys(u).forEach(f=>{let[m]=oa(f),g=o[m];g!=null&&(g.signatureKey=u[f],l.push(g))}),Object.keys(c).length>0?Object.keys(c).forEach(f=>{let[m]=oa(f),g=o[m];g&&(g.signatureKey=c[f],i.push(g))}):i=s;let p={};e.library!=null&&e.library.function!=null&&(p=e.library.function.reduce((f,m)=>(f[m.signature.name]=this.mapFunction(m),f),{}));let h={nodes:o,inputs:i,outputs:l,weights:r,placeholders:s,signature:t,functions:p};return a.length>0&&(h.initNodes=a),h}mapSignatureEntries(e){return Object.keys(e||{}).reduce((t,n)=>(t[e[n].name]=n,t),{})}mapNode(e){let t=vk(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(s=>s.startsWith("^")?s.substr(1):s),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr,outputs:t.outputs};return t.inputs!=null&&(n.inputParams=t.inputs.reduce((s,r)=>(s[r.name]={type:r.type,inputIndexStart:r.start,inputIndexEnd:r.end},s),{})),t.attrs!=null&&(n.attrParams=t.attrs.reduce((s,r)=>{let a=r.type,o;switch(r.type){case"string":o=vA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=vA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"string[]":o=EA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=EA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number":o=kA(e.attr,r.tfName,r.defaultValue||0),o===void 0&&!!r.tfDeprecatedName&&(o=kA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"number[]":o=NA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=NA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool":o=wA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=wA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"bool[]":o=$A(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=$A(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape":o=TA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=TA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"shape[]":o=RA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=RA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype":o=SA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=SA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"dtype[]":o=CA(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=CA(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"func":o=Uk(e.attr,r.tfName,r.defaultValue),o===void 0&&!!r.tfDeprecatedName&&(o=Uk(e.attr,r.tfDeprecatedName,r.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`)}return s[r.name]={value:o,type:a},s},{})),n}mapFunction(e){let t=e.nodeDef,n=[],s=[],r={};t!=null&&(r=t.reduce((u,d)=>(u[d.name]=this.mapNode(d),d.op==="Const"&&s.push(u[d.name]),u),{}));let a=[],o=[];e.signature.inputArg.forEach(u=>{let[d]=oa(u.name),p={name:d,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:IA(u.type),type:"dtype"}},children:[]};p.signatureKey=u.name,a.push(p),r[d]=p}),Object.keys(r).forEach(u=>{let d=r[u];d.inputNames.forEach((p,h)=>{let[f,,m]=oa(p),g=r[f];if(g.outputs!=null){let y=g.outputs.indexOf(m);if(y!==-1){let A=`${f}:${y}`;d.inputNames[h]=A}}d.inputs.push(g),g.children.push(d)})});let l=e.ret;e.signature.outputArg.forEach(u=>{let[d,p]=oa(l[u.name]),h=r[d];h!=null&&(h.defaultOutput=p,o.push(h))});let c=this.mapArgsToSignature(e);return{nodes:r,inputs:a,outputs:o,weights:s,placeholders:n,signature:c}}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 ZW(e){let t=Z().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 Vk(e,t){let n=Array.isArray(e)?String.fromCharCode.apply(null,e):ZW(e);return t?n:n.toLowerCase()}function vA(e,t,n,s=!1){let r=e[t];return r!=null?Vk(r.s,s):n}function wA(e,t,n){let s=e[t];return s?s.b:n}function kA(e,t,n){let s=e[t]||{},r=s.i!=null?s.i:s.f!=null?s.f:n;return typeof r=="number"?r:parseInt(r,10)}function IA(e){switch(typeof e=="string"&&(e=xr[e]),e){case xr.DT_FLOAT:return"float32";case xr.DT_INT32:case xr.DT_INT64:case xr.DT_INT8:case xr.DT_UINT8:return"int32";case xr.DT_BOOL:return"bool";case xr.DT_DOUBLE:return"float32";case xr.DT_STRING:return"string";default:return null}}function Uk(e,t,n){let s=e[t];return s&&s.func?s.func.name:n}function SA(e,t,n){let s=e[t];return s&&s.type?IA(s.type):n}function CA(e,t,n){let s=e[t];return s&&s.list&&s.list.type?s.list.type.map(r=>IA(r)):n}function Gk(e){if(!e.unknownRank)return e.dim!=null?e.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function TA(e,t,n){let s=e[t];return s&&s.shape?Gk(s.shape):n}function NA(e,t,n){let s=e[t];return s?((s.list.f&&s.list.f.length?s.list.f:s.list.i)||[]).map(r=>typeof r=="number"?r:parseInt(r,10)):n}function EA(e,t,n,s=!1){let r=e[t];return r&&r.list&&r.list.s?r.list.s.map(a=>Vk(a,s)):n}function RA(e,t,n){let s=e[t];return s&&s.list&&s.list.shape?s.list.shape.map(r=>Gk(r)):n}function $A(e,t,n){let s=e[t];return s&&s.list&&s.list.b?s.list.b:n}var YW=class{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(s=>this.getInput(s)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((s,r)=>(s[r]=this.getAttr(r),s),{}))}getInput(e){return Un(e,this.tensorMap,this.context)}getAttr(e,t){let n=this.node.rawAttrs[e];if(n.tensor!=null)return Un(e,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return kA(this.node.rawAttrs,e,t);if(n.s!=null)return vA(this.node.rawAttrs,e,t);if(n.b!=null)return wA(this.node.rawAttrs,e,t);if(n.shape!=null)return TA(this.node.rawAttrs,e,t);if(n.type!=null)return SA(this.node.rawAttrs,e,t);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return NA(this.node.rawAttrs,e,t);if(n.list.s!=null)return EA(this.node.rawAttrs,e,t);if(n.list.shape!=null)return RA(this.node.rawAttrs,e,t);if(n.list.b!=null)return $A(this.node.rawAttrs,e,t);if(n.list.type!=null)return CA(this.node.rawAttrs,e,t)}return t}},JW=(e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[ue(I("a",e,t,n),I("b",e,t,n))];case"AddN":return[sf(I("tensors",e,t,n))];case"FloorMod":case"Mod":return[g1(I("a",e,t,n),I("b",e,t,n))];case"Mul":return[L(I("a",e,t,n),I("b",e,t,n))];case"RealDiv":case"Div":return[fe(I("a",e,t,n),I("b",e,t,n))];case"DivNoNan":return[a1(I("a",e,t,n),I("b",e,t,n))];case"FloorDiv":return[nf(I("a",e,t,n),I("b",e,t,n))];case"Sub":return[xe(I("a",e,t,n),I("b",e,t,n))];case"Minimum":return[Fu(I("a",e,t,n),I("b",e,t,n))];case"Maximum":return[Rr(I("a",e,t,n),I("b",e,t,n))];case"Pow":return[ea(I("a",e,t,n),I("b",e,t,n))];case"SquaredDifference":return[Cf(I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},QW=(e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[Kt(I("x",e,t,n))];case"Acos":return[V2(I("x",e,t,n))];case"Acosh":return[U2(I("x",e,t,n))];case"Asin":return[H2(I("x",e,t,n))];case"Asinh":return[j2(I("x",e,t,n))];case"Atan":return[q2(I("x",e,t,n))];case"Atan2":return[X2(I("x",e,t,n),I("y",e,t,n))];case"Atanh":return[K2(I("x",e,t,n))];case"Ceil":return[e1(I("x",e,t,n))];case"Complex":return[xo(I("real",e,t,n),I("imag",e,t,n))];case"Cos":return[wd(I("x",e,t,n))];case"Cosh":return[uf(I("x",e,t,n))];case"Elu":return[Du(I("x",e,t,n))];case"Erf":return[o1(I("x",e,t,n))];case"Exp":return[gs(I("x",e,t,n))];case"Expm1":return[i1(I("x",e,t,n))];case"Floor":return[Pu(I("x",e,t,n))];case"Log":return[ys(I("x",e,t,n))];case"Log1p":return[Id(I("x",e,t,n))];case"Imag":return[df(I("x",e,t,n))];case"Neg":return[_t(I("x",e,t,n))];case"Reciprocal":return[x1(I("x",e,t,n))];case"Real":return[Rd(I("x",e,t,n))];case"Relu":return[cr(I("x",e,t,n))];case"Round":return[xf(I("x",e,t,n))];case"Selu":return[vf(I("x",e,t,n))];case"Sigmoid":return[ns(I("x",e,t,n))];case"Sin":return[wf(I("x",e,t,n))];case"Sign":return[v1(I("x",e,t,n))];case"Sinh":return[kf(I("x",e,t,n))];case"Softplus":return[Ji(I("x",e,t,n))];case"Sqrt":return[Cn(I("x",e,t,n))];case"Square":return[vt(I("x",e,t,n))];case"Tanh":return[Ki(I("x",e,t,n))];case"Tan":return[I1(I("x",e,t,n))];case"ClipByValue":return[ss(I("x",e,t,n),I("clipValueMin",e,t,n),I("clipValueMax",e,t,n))];case"Relu6":return[Af(I("x",e,t,n))];case"Rsqrt":return[bf(Un(e.inputNames[0],t,n))];case"Prod":return[yf(I("x",e,t,n),I("axes",e,t,n))];case"LeakyRelu":return[kd(I("x",e,t,n),I("alpha",e,t,n))];case"Prelu":return[Ed(I("x",e,t,n),I("alpha",e,t,n))];case"IsNan":return[u1(Un(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function er(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 s=0;s<e.length;s++){let r=e[s],a=t[s];v.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function Hk(e){return!(typeof e=="number"||e.some(t=>t<0))}function ep(e,t,n){let s=DA(e,n),r=!Hk(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=DA(a.shape,s)}),!Hk(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function DA(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 s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var eV=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Ee(0),An(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),er(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,An(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,s)=>this.write(n,t[s]))}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 s=0;s<this.size();s++)e.push(s)}if(e.length===0)return nn([],[0].concat(this.elementShape));let n=this.readMany(e);return er(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Tn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return nn([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return er(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),kt(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,Wn(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,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];j(()=>{t=G(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],c=[0,l,0],u=[1,e[i],r];a[i]=G(_e(t,c,u),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},tp=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);er(t,r.shape,"TensorList shape mismatch: "),An(r)}),this.idTensor=Ee(0),this.maxNumElements=s,An(this.idTensor)}get id(){return this.idTensor.id}copy(){return new tp([...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.`);er(e,this.elementShape,"TensorList shape mismatch: ");let s=ep(this.elementShape,this.tensors,e);return j(()=>{let r=this.tensors.map(a=>G(a,s));return Tn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=ep(this.elementShape,this.tensors,e),s=this.tensors.pop();return er(s.shape,e,"TensorList shape mismatch: "),G(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(er(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");An(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.`);er(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=ep(this.elementShape,this.tensors,t);return G(this.tensors[e],s)}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.`);er(this.elementShape,t.shape,"TensorList shape mismatch: "),An(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}`);er(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=ep(this.elementShape,this.tensors,n);return e.length===0?nn([],[0].concat(s)):j(()=>{let r=e.map(a=>G(this.tensors[a],s));return Tn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);er(this.elementShape,t,"TensorList shape mismatch: ");let n=ep(this.elementShape,this.tensors,t);return this.size()===0?nn([],[0].concat(n)):j(()=>{let s=this.tensors.map(r=>G(r,n));return kt(s,0)})}};function tV(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);er(r,t,"TensorList shape mismatch: ");let a=Wn(e);return new tp(a,t,s)}function nV(e,t,n){return new tp([],e,t,n)}function sV(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r} vs. ${s})`);let a=new tp([],n,e.dtype,s),o=Wn(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function rV(e,t,n){let s=0,r=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
tensor.shape[0], but sum of lengths is
${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=DA(a,n),i=s===0?0:e.size/s,l=j(()=>{let u=[];e=G(e,[1,s,i]);for(let d=0;d<t.length;++d){let p=d===0?0:r[d-1],h=[0,p,0],f=[1,t[d],i];u[d]=G(_e(e,h,f),o)}return e.dispose(),u}),c=new tp([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var aV=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),a=I("cond",e,t,n),o=I("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=I("body",e,t,n),r=I("cond",e,t,n),a=I("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(u=>u.id),l=await o[0].data();o.forEach(u=>{!u.kept&&i.indexOf(u.id)===-1&&u.dispose()});let c=a;for(;l[0];){let u=c;c=await n.functionMap[s].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);let d=c.map(h=>h.id);u.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()});let p=await n.functionMap[r].executeFunctionAsync(c,n.tensorArrayMap,n.tensorListMap);l=await p[0].data(),p.forEach(h=>{!h.kept&&i.indexOf(h.id)===-1&&d.indexOf(h.id)===-1&&h.dispose()})}return c}case"LoopCond":{let s=I("pred",e,t,n);return[ia(s)]}case"Switch":{let s=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=ia(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let s=e.inputNames.find(r=>Un(r,t,n)!==void 0);if(s){let r=Un(s,t,n);return[ia(r)]}return}case"Enter":{let s=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(s),[ia(r)]}case"Exit":{let s=I("tensor",e,t,n);return n.exitFrame(),[ia(s)]}case"NextIteration":{let s=I("tensor",e,t,n);return n.nextIteration(),[ia(s)]}case"TensorArrayV3":{let s=I("size",e,t,n),r=I("dtype",e,t,n),a=I("elementShape",e,t,n),o=I("dynamicSize",e,t,n),i=I("clearAfterRead",e,t,n),l=I("identicalElementShapes",e,t,n),c=I("name",e,t,n),u=new eV(c,r,s,a,l,o,i);return n.addTensorArray(u),[u.idTensor,Ee(1)]}case"TensorArrayWriteV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(s.id);return o.write(r,a),[o.idTensor]}case"TensorArrayReadV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("tensor",e,t,n),o=n.getTensorArray(s.id);return o.scatter(r,a),[o.idTensor]}case"TensorArrayConcatV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=I("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),a=I("lengths",e,t,n),o=n.getTensorArray(s.id);return o.split(a,r),[o.idTensor]}case"TensorArraySizeV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[Ee(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),o=n.getTensorList(s.id);return o.setItem(r,a),[o.idTensor]}case"TensorListGetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{let s=I("indices",e,t,n),r=I("tensor",e,t,n),a=I("elementShape",e,t,n),o=I("numElements",e,t,n),i=sV(r,s,a,o);return n.addTensorList(i),[i.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=I(a,e,t,n),i=nV(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),o=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),o=tV(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),o=I("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case"TensorListSplit":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("lengths",e,t,n),o=rV(s,a,r);return n.addTensorList(o),[o.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jk(e,t,n){let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",o=!a,i=r==="prelu",l=s==="fusedbatchnorm",c=I("numArgs",e,t,n);if(a){if(i&&c!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&c!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let u=I("strides",e,t,n),d=vm(e,t,n),p=I("dataFormat",e,t,n).toUpperCase(),h=I("dilations",e,t,n),[f,m]=I("args",e,t,n);o&&(m=f,f=void 0);let g=I("leakyreluAlpha",e,t,n);return{stride:u,pad:d,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var oV=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=I("stride",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilation",e,t,n);return[of(I("x",e,t,n),I("filter",e,t,n),s,r,a,o)]}case"Conv2D":{let s=I("strides",e,t,n),r=vm(e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilations",e,t,n);return[Qr(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,a,[o[1],o[2]])]}case"_FusedConv2D":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:c,leakyreluAlpha:u}=jk(e,t,n);return[Co.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"FusedDepthwiseConv2dNative":{let{stride:s,pad:r,dataFormat:a,dilations:o,biasArg:i,preluArg:l,activationFunc:c,leakyreluAlpha:u}=jk(e,t,n);return[Co.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[o[1],o[2]],bias:i,activation:c,preluActivationWeights:l,leakyreluAlpha:u})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let s=I("outputShape",e,t,n),r=I("strides",e,t,n),a=vm(e,t,n);return[lf(I("x",e,t,n),I("filter",e,t,n),s,[r[1],r[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let s=I("strides",e,t,n),r=vm(e,t,n),a=I("dilations",e,t,n),o=I("dataFormat",e,t,n).toUpperCase();return[$u(I("input",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,o,[a[1],a[2]])]}case"Conv3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),o=I("dilations",e,t,n);return[n1(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2],s[3]],r,a,[o[1],o[2],o[3]])]}case"AvgPool":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[bd(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPool":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[Cd(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPoolWithArgmax":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n),o=I("includeBatchInIndex",e,t,n),{result:i,indexes:l}=ev(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r,o);return[i,l]}case"AvgPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[J2(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"MaxPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[f1(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"Dilation2D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dilations",e,t,n),o=s[1],i=s[2],l=a[1],c=a[2];return[r1(I("x",e,t,n),I("filter",e,t,n),[o,i],r,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},iV=(e,t,n)=>{switch(e.op){case"Fill":{let s=I("shape",e,t,n),r=I("dtype",e,t,n),a=I("value",e,t,n);return[_u(s,a,r)]}case"LinSpace":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("num",e,t,n);return[q3(s,r,a)]}case"Multinomial":{let s=I("logits",e,t,n),r=I("numSamples",e,t,n),a=I("seed",e,t,n);return[tv(s,r,a)]}case"OneHot":{let s=I("indices",e,t,n),r=I("depth",e,t,n),a=I("onValue",e,t,n),o=I("offValue",e,t,n);return[Cu(s,r,a,o)]}case"Ones":return[As(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[xs(I("x",e,t,n))];case"RandomUniform":return[Ou(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("step",e,t,n);return[Mu(s,r,a,I("dtype",e,t,n))]}case"TruncatedNormal":{let s=I("shape",e,t,n),r=I("mean",e,t,n),a=I("stdDev",e,t,n),o=I("seed",e,t,n);return[Tf(s,r,a,I("dtype",e,t,n),o)]}case"Zeros":return[jt(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[tt(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function _A(e,t,n){let s=I("boxes",e,t,n),r=I("scores",e,t,n),a=I("maxOutputSize",e,t,n),o=I("iouThreshold",e,t,n),i=I("scoreThreshold",e,t,n),l=I("softNmsSigma",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}}var lV=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i,softNmsSigma:l}=_A(e,t,n),c=await $e.nonMaxSuppressionWithScoreAsync(s,r,a,o,i,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=_A(e,t,n),l=I("padToMaxOutputSize",e,t,n),c=await $e.nonMaxSuppressionPaddedAsync(s,r,a,o,i,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:o,scoreThreshold:i}=_A(e,t,n);return[await $e.nonMaxSuppressionAsync(s,r,a,o,i)]}case"Where":{let s=pe(I("condition",e,t,n),"bool"),r=[await T1(s)];return s.dispose(),r}case"ListDiff":return rv(I("x",e,t,n),I("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},uV=(e,t,n)=>{switch(e.op){case"TopKV2":{let s=I("x",e,t,n),r=I("k",e,t,n),a=I("sorted",e,t,n),o=S1(s,r,a);return[o.values,o.indices]}case"Unique":{let s=I("x",e,t,n),r=Nf(s);return[r.values,r.indices]}case"UniqueV2":{let s=I("x",e,t,n),r=I("axis",e,t,n),a=Nf(s,r);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},cV=(e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":let s=I("default",e,t,n);return[Un(e.name,t,n)||s];case"Placeholder":return[Un(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=I("x",e,t,n);return[ia(c)]}case"IdentityN":return I("x",e,t,n).map(c=>ia(c));case"Snapshot":let r=I("x",e,t,n);return[ia(r)];case"Shape":return[Zt(I("x",e,t,n).shape,"int32")];case"ShapeN":return I("x",e,t,n).map(c=>Zt(c.shape));case"Size":return[Ee(I("x",e,t,n).size,"int32")];case"Rank":return[Ee(I("x",e,t,n).rank,"int32")];case"NoOp":return[Ee(1)];case"Print":let a=I("x",e,t,n),o=I("data",e,t,n),i=I("message",e,t,n),l=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let c=0;c<o.length;c++)console.log(Array.prototype.slice.call(o[c].dataSync()).slice(0,l));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},dV=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ee(0),this.tensorMap=new Map,An(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 Ee(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),j(()=>{let s=Wn(t),r=n.length,a=s.length;v.assert(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let o=0;o<r;o++){let i=n[o],l=s[o];An(l),this.tensorMap.set(i,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return j(()=>{let s=[];for(let r=0;r<n.length;r++){let a=n[r],o=this.findWithDefault(a,t);s.push(o)}return Tn(s)})}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}`)}},pV=async(e,t,n,s)=>{switch(e.op){case"HashTable":case"HashTableV2":{let r=I("keyDType",e,t,n),a=I("valueDType",e,t,n),o=new dV(r,a);return s.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{let r=I("tableHandle",e,t,n,s),a=I("keys",e,t,n),o=I("values",e,t,n);return[await s.getHashTableById(r.id).import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{let r=I("tableHandle",e,t,n,s),a=I("keys",e,t,n),o=I("defaultValue",e,t,n);return[await s.getHashTableById(r.id).find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{let r=I("tableHandle",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},hV=(e,t,n)=>{switch(e.op){case"ResizeBilinear":{let s=I("images",e,t,n),r=I("size",e,t,n),a=I("alignCorners",e,t,n),o=I("halfPixelCenters",e,t,n);return[$e.resizeBilinear(s,[r[0],r[1]],a,o)]}case"ResizeNearestNeighbor":{let s=I("images",e,t,n),r=I("size",e,t,n),a=I("alignCorners",e,t,n),o=I("halfPixelCenters",e,t,n);return[$e.resizeNearestNeighbor(s,[r[0],r[1]],a,o)]}case"CropAndResize":{let s=I("image",e,t,n),r=I("boxes",e,t,n),a=I("boxInd",e,t,n),o=I("cropSize",e,t,n),i=I("method",e,t,n),l=I("extrapolationValue",e,t,n);return[$e.cropAndResize(s,r,a,o,i,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},fV=(e,t,n)=>{switch(e.op){case"Equal":return[ms(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[el(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[rs(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[Io(I("a",e,t,n),I("b",e,t,n))];case"Less":return[pf(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[So(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[Ks(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[Sd(I("a",e,t,n))];case"LogicalOr":return[mf(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[Pn(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},mV=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Xe(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[G3(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[et(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",o=r==="prelu",i=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(a){if(o&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,u]=I("args",e,t,n);return[Co.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:c,activation:r,preluActivationWeights:u,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},gV=(e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Zi(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[Zi(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[c1(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[tl(I("x",e,t,n))];case"LogSoftmax":return[ff(I("x",e,t,n))];case"SparseToDense":return[N1(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},yV=(e,t,n)=>{switch(e.op){case"Max":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Bn(I("x",e,t,n),o,i)]}case"Mean":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[zt(I("x",e,t,n),o,i)]}case"Min":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Td(I("x",e,t,n),o,i)]}case"Sum":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[we(I("x",e,t,n),o,i)]}case"All":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[rf(I("x",e,t,n),o,i)]}case"Any":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Ad(I("x",e,t,n),o,i)]}case"ArgMax":{let o=I("axis",e,t,n);return[_s(I("x",e,t,n),o)]}case"ArgMin":{let o=I("axis",e,t,n);return[G2(I("x",e,t,n),o)]}case"Prod":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[yf(I("x",e,t,n),o,i)]}case"Cumsum":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[cf(I("x",e,t,n),o,i,l)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[Q2(s,r,a)];case"DenseBincount":{let o=I("x",e,t,n),i=I("weights",e,t,n),l=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[V3(o,i,l,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},AV=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[kt(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Yi(s,pe(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),o=I("indices",e,t,n);return[Yi(a,pe(o,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let o=0;o<s.length;o++)s[o]&&r.push(o);let a=I("x",e,t,n);return[bs(a,r)]}case"ReverseV2":{let s=I("axis",e,t,n),r=I("x",e,t,n);return[bs(r,s)]}case"Slice":{let s=I("begin",e,t,n),r=I("size",e,t,n);return[_e(I("x",e,t,n),s,r)]}case"StridedSlice":{let s=I("begin",e,t,n),r=I("end",e,t,n),a=I("strides",e,t,n),o=I("beginMask",e,t,n),i=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),c=I("newAxisMask",e,t,n),u=I("shrinkAxisMask",e,t,n),d=I("x",e,t,n);return[k1(d,s,r,a,o,i,l,c,u)]}case"Pack":return j(()=>{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,o=dt(r[0]).shape,i=r.map(l=>{let c=v.arraysEqual(l.shape,a);if(!c&&!v.arraysEqual(dt(l).shape,o))throw new Error("the input tensors shape does not match");return c?l:G(l,a)});return[Tn(i,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Wn(r,s)}case"Tile":{let s=I("reps",e,t,n);return[Ps(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return xn(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[lv(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[uv(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[N1(s,a,r,a.dtype===o.dtype?o:pe(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},xV=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=Pd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Pd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Pd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Pd.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},bV=(e,t,n)=>{switch(e.op){case"FFT":return[Dd(I("x",e,t,n))];case"IFFT":return[Lu(I("x",e,t,n))];case"RFFT":return[_d(I("x",e,t,n))];case"IRFFT":return[Sf(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},vV=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Pf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Pf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Pf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wV=(e,t,n)=>{switch(e.op){case"Cast":return[pe(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Ht(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[dt(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[m1(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[ur(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[Nd(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[vd(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[s1(I("x",e,t,n),s,r)]}case"BroadcastTo":return[Eu(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[O3(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function qk(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return j(()=>JW(a,o,i));case"basic_math":return j(()=>QW(a,o,i));case"control":return aV(a,o,i);case"convolution":return j(()=>oV(a,o,i));case"creation":return j(()=>iV(a,o,i));case"dynamic":return lV(a,o,i);case"evaluation":return j(()=>uV(a,o,i));case"image":return j(()=>hV(a,o,i));case"graph":return j(()=>cV(a,o,i));case"logical":return j(()=>fV(a,o,i));case"matrices":return j(()=>mV(a,o,i));case"normalization":return j(()=>gV(a,o,i));case"reduction":return j(()=>yV(a,o,i));case"slice_join":return j(()=>AV(a,o,i));case"sparse":return j(()=>xV(a,o,i));case"spectral":return j(()=>bV(a,o,i));case"string":return j(()=>vV(a,o,i));case"transformation":return j(()=>wV(a,o,i));case"hash_table":return pV(a,o,i,s);case"custom":let l=vk(a.op);if(l&&l.customExecutor)return l.customExecutor(new YW(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.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(r)?r.then(a=>[].concat(a)):[].concat(r)}var Xk=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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 Kk(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>vs(p)[0]),u=[];s!=null&&(u=s.map(p=>vs(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((Zk(p)||TV(p)||NV(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function kV(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>vs(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var IV=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],SV=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],CV=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Zk(e){return IV.indexOf(e.op)>=0}function TV(e){return SV.indexOf(e.op)>=0}function NV(e){return CV.indexOf(e.op)>=0}var PA=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 PA(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(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Kk(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return kV(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 s=n.map(u=>this.graph.nodes[vs(u)[0]]),r=t.map(u=>vs(u)[0]),a=r.map(u=>this.graph.nodes[u]);a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return j(()=>{let u=new Xk(this.weightMap,l,c,this.functionExecutorMap),d=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=vs(f),y=[];y[g]=e[f],d[m]=y});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=qk(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,u,p,r,h)}}return this.parent==null&&u.dispose(p),t.map(f=>Un(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=RW(i.name,n,s);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let u=o[c.id];u===1?(c.dispose(),delete o[c.id]):u!=null&&o[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new Xk(this.weightMap,s,r,this.functionExecutorMap),o=await this.executeWithControlFlow(e,a,t,n),i=t.map(d=>Un(d,o,a)),l=i.map(d=>d.id),c=Object.keys(e).map(d=>e[d].id),u=new Set([...l,...c,...this.weightIds]);return Object.keys(o).forEach(d=>{o[d].forEach(h=>{h&&!h.kept&&!h.isDisposed&&!u.has(h.id)&&h.dispose()})}),this.parent==null&&a.dispose(u),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(A=>this.graph.nodes[vs(A)[0]]),o=n.map(A=>vs(A)[0]),i=o.map(A=>this.graph.nodes[A]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=Kk(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(A=>{let[x,b]=vs(A),w=[];w[b]=e[A],h[x]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let A=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(A)}u==null&&!s&&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=i.filter(A=>!Zk(A)&&!Un(A.name,h,t)).map(A=>A.name);if(y.length>0){let A="";throw u!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${A}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&I("isConstant",u.node,s,n)&&([d]=oa(u.node.name,n)),s[u.node.name]==null){let p=qk(u.node,s,n,this._resourceManager);d||([d]=oa(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=oa(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=vs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=vs(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=vs(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},EV=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},RV="?tfjs-format=file",$V="model.json",Yk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new EV}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=es.browserHTTPRequest(e,this.loadOptions);else{let t=es.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(es.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 s=es.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new PA(Wk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Wk.Instance.transformGraph(e.modelInitializer);this.initializer=new PA(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=es.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 Ke)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ut(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${$V}${RV}`);let n=new Yk(e,t);return await n.load(),n}var DV="3.9.0",Jk={};Le(Jk,{CSVDataset:()=>c7,Dataset:()=>Zu,FileDataSource:()=>y7,TextLineDataset:()=>i7,URLDataSource:()=>A7,array:()=>tU,csv:()=>pU,func:()=>hU,generator:()=>fU,microphone:()=>gU,version_data:()=>yU,webcam:()=>mU,zip:()=>nU});var _V=Jo(h5()),PV=Jo(h5());function FV(e,t){return wm(e,t)}function wm(e,t,n=new Map,s=new Set){if(e==null)return null;if(s.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(Ku(e)){let a=Array.isArray(e)?[]:{};s.add(e);for(let o in e){let i=e[o],l=wm(i,t,n,s);a[o]=l}return s.delete(e),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function OV(e,t=e7){return Qk(e,t)}function Qk(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Ku(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(c=>c[o]),l=Qk(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function e7(e){return e===null?null:Ku(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function t7(e,t){let n=new Map;wm(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let o=await a;n.set(r,o)}}return wm(e,t,n)}function Ku(e){let t=!1;if(Z().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=f5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Ke)&&!(e instanceof Promise)&&!t)}function MV(e){return e==null||zV(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Ke||v.isTypedArray(e)}function zV(e){return e===null||typeof e!="object"&&typeof e!="function"}function LV(e){return FV(e,BV)}function BV(e){return e instanceof Ke?{value:e.clone(),recurse:!1}:Ku(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var n7=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}},FA=class extends n7{constructor(){super(FA.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 s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};FA.INITIAL_CAPACITY=32;function s7(e){return new UV(e)}function OA(e){return new GV(e)}function WV(e,t){return new a7(e,t)}function VV(e,t=Po.FAIL){return new QV(e,t)}var vn=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 YV(this,e)}filter(e){return new KV(this,e)}map(e){return new ZV(this,e)}mapAsync(e){return new r7(this,e)}serialMapAsync(e){return new r7(this,e).serial()}flatmap(e){return new JV(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 XV(this,e,t)}columnMajorBatch(e,t=!0,n=e7){return this.rowMajorBatch(e,t).map(r=>OV(r,n))}concatenate(e,t){return new a7(s7([this,e]),t)}take(e){return e<0||e==null?this:new qV(this,e)}skip(e){return e<0||e==null?this:new jV(this,e)}prefetch(e){return new o7(this,e)}shuffle(e,t){return new eU(this,e,t)}serial(){return new HV(this)}},UV=class extends vn{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:LV(e),done:!1}}},GV=class extends vn{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: ${e.message}`,e}}},HV=class extends vn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},jV=class extends vn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;ne(e.value)}return this.upstream.next()}},qV=class extends vn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},XV=class extends vn{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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},KV=class extends vn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;ne(e.value)}}},ZV=class extends vn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ar.getTensorsInContainer(e.value),n=this.transform(e.value),s=ar.getTensorsInContainer(n);for(let r of t)ar.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},YV=class extends vn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},r7=class extends vn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ar.getTensorsInContainer(e.value),n=await this.transform(e.value),s=ar.getTensorsInContainer(n);for(let r of t)ar.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},MA=class extends vn{constructor(){super();this.outputQueue=new FA,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},JV=class extends MA{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${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),s=ar.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)ar.isTensorInList(r,s)||r.dispose();return!0}},a7=class extends vn{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}},Po;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Po||(Po={}));var QV=class extends vn{constructor(e,t=Po.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 s(a){return a instanceof vn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await t7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Po.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Po.SHORTEST:return{value:null,done:!0};case Po.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},o7=class extends vn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new n7(t)}summary(){return`${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()}},eU=class extends o7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=PV.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}}},Zu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ws(async()=>(await n.iterator()).columnMajorBatch(e,t,sU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ws(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ws(async()=>(await t.iterator()).filter(s=>j(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ws(async()=>(await t.iterator()).map(n=>j(()=>e(n))),this.size)}mapAsync(e){let t=this;return ws(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ws(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ws(async()=>{let s=OA(async()=>({value:await t.iterator(),done:!1}));return WV(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ws(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=_V.alea(t||v.now().toString());return ws(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.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,ws(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Zu.MAX_BUFFER_SIZE=1e4;function ws(e,t=null){return new class extends Zu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function tU(e){return ws(async()=>s7(e),e.length)}function nU(e){if(!Ku(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 ws(async()=>{let n=await t7(e,s=>{if(s instanceof Zu)return{value:s.iterator(),recurse:!1};if(Ku(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return VV(n,Po.SHORTEST)},t)}function sU(e){if(e===null)return null;let t=e[0];return MV(t)?{value:rU(e),recurse:!1}:{value:null,recurse:!0}}function rU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Ke?Tn(e):nn(e)}var i7=class extends Zu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},km='"',np=Symbol("out"),l7=Symbol("field"),Im=Symbol("quote"),zA=Symbol("quoteafterquote"),u7=Symbol("quoteinquote"),c7=class extends Zu{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 i7(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((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!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={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let c=Number(i);if(isNaN(c))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=c;else switch(o.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(i);break;default:l=c}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=np;for(let o=0;o<r;o++)switch(a){case np:switch(e.charAt(o)){case km:s=o+1,a=Im;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=np;break;default:a=l7,s=o;break}break;case l7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=np,s=o+1;break;default:}break;case Im:switch(e.charAt(o)){case km:a=zA;break;default:}break;case zA:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=np,s=o+1;break;case km:a=Im;break;default:a=u7;break}break;case u7:switch(e.charAt(o)){case km:a=Im;break;default:}break;default:}if(a===zA?n.push(e.substring(s,r-1)):n.push(e.substring(s)),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}},d7=class extends vn{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(Z().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new d7(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 s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[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(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({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((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),nn(n,t)}},p7=class extends vn{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=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=dr([a,r,i,o],[1,4])}else this.cropBox=dr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Z().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 p7(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=Xs.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Ht(pe(e,"float32"),0),n;n=$e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},h7=class{},f7=class extends vn{split(e){return new aU(this,e)}},aU=class extends f7{constructor(e,t){super();this.upstream=e,this.impl=new oU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},oU=class extends MA{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}},iU=class extends vn{decodeUTF8(){return new lU(this)}},lU=class extends f7{constructor(e){super();this.upstream=e,this.impl=new uU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},uU=class extends MA{constructor(e){super();if(this.upstream=e,Z().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=f5();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 Z().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},m7=class extends iU{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Z().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function cU(e,t={}){let n,s;typeof e=="string"?n=e:(n=e.url,s=dU(e));let r=await v.fetch(n,s);if(r.ok){let a=new Uint8Array(await r.arrayBuffer());return new m7(a,t)}else throw new Error(r.statusText)}var dU=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function g7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var y7=class extends h7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(g7(this.input)&&Z().get("IS_NODE")){let e=Ul("fs");this.input=e.readFileSync(this.input.substr(7))}return new m7(this.input,this.options)}},A7=class extends h7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return g7(this.url)?new y7(this.url,this.fileOptions).iterator():cU(this.url,this.fileOptions)}};function pU(e,t={}){return new c7(new A7(e),t)}function hU(e){let t=OA(e);return ws(async()=>t)}function fU(e){return ws(async()=>{let t=await e();return OA(()=>t.next())})}async function mU(e,t){return p7.create(e,t)}async function gU(e){return d7.create(e)}var yU="3.9.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var AU=Zs.whereImpl,LA=class extends Gl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Vc(this,ts())}nextDataId(){return LA.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Z().get("IS_NODE")&&E.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 s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,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,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return E.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return ts().makeTensorFromDataId(s,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){Ne([e],"where");let t=this.readSync(e.dataId);return AU(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};LA.nextDataId=0;var BA={};Le(BA,{addImpl:()=>b7,bincountImpl:()=>VA,bincountReduceImpl:()=>v7,ceilImpl:()=>w7,concatImpl:()=>UA,equalImpl:()=>k7,expImpl:()=>S7,expm1Impl:()=>T7,floorImpl:()=>N7,gatherNdImpl:()=>E7,gatherV2Impl:()=>R7,greaterEqualImpl:()=>D7,greaterImpl:()=>$7,lessEqualImpl:()=>P7,lessImpl:()=>_7,linSpaceImpl:()=>F7,logImpl:()=>O7,maxImpl:()=>M7,maximumImpl:()=>z7,minimumImpl:()=>L7,multiplyImpl:()=>GA,negImpl:()=>B7,notEqualImpl:()=>W7,prodImpl:()=>V7,rangeImpl:()=>jA,rsqrtImpl:()=>U7,sigmoidImpl:()=>oG,simpleAbsImpl:()=>x7,sliceImpl:()=>Tm,sparseFillEmptyRowsImpl:()=>H7,sparseReshapeImpl:()=>j7,sparseSegmentReductionImpl:()=>qA,sqrtImpl:()=>uG,squaredDifferenceImpl:()=>q7,stridedSliceImpl:()=>X7,stringNGramsImpl:()=>K7,stringSplitImpl:()=>Z7,stringToHashBucketFastImpl:()=>Y7,subImpl:()=>J7,tileImpl:()=>Q7,topKImpl:()=>tI,transposeImpl:()=>HA,uniqueImpl:()=>nI});function x7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var xU=e=>{let{x:t}=e.inputs,n=e.backend;Ne(t,"abs");let s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=x7(r),n.makeOutput(s,t.shape,"float32")},bU={kernelName:ti,backendName:"cpu",kernelFunc:xU};function Jt(e){return(t,n,s,r,a)=>{let o=E.assertAndGetBroadcastShape(t,n),i=o.length,l=v.computeStrides(o),c=v.sizeFromShape(o),u=v.getTypedArrayFromDType(a,c),d=t.length,p=n.length,h=v.computeStrides(t),f=v.computeStrides(n),m=E.getBroadcastDims(t,o),g=E.getBroadcastDims(n,o);if(m.length+g.length===0)for(let y=0;y<u.length;++y)u[y]=e(s[y%s.length],r[y%r.length]);else for(let y=0;y<u.length;++y){let A=v.indexToLoc(y,i,l),x=A.slice(-d);m.forEach(S=>x[S]=0);let b=v.locToIndex(x,d,h),w=A.slice(-p);g.forEach(S=>w[S]=0);let k=v.locToIndex(w,p,f);u[y]=e(s[b],r[k])}return[u,o]}}function ks(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=n.makeTensorInfo(s.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",a),imag:n.makeTensorInfo(r.shape,"float32",o)},i}var vU={kernelName:jc,backendName:"cpu",kernelFunc:ks};function Sm(e,t,n="float32"){if(n==="complex64"){let r=Sm(e,t,"float32"),a=Sm(e,t,"float32");return ks({inputs:{real:r,imag:a},backend:e})}let s=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function Or(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var wU={kernelName:Wa,backendName:"cpu",kernelFunc:Or};function hl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var kU={kernelName:td,backendName:"cpu",kernelFunc:hl};function Fo(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Or({inputs:{x:r},backend:n});let o=Sm(n,r.shape,r.dtype),i=Fo({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ks({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=hl({inputs:{input:r},backend:n}),i=Fo({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Or({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(r.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(r.shape,"int32",i)}if(a==="bool"){let o=n.data.get(r.dataId).values,i=v.toTypedArray([0],r.dtype),[l,c]=Jt((u,d)=>u!==d?1:0)(r.shape,[],o,i,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var IU={kernelName:Ta,backendName:"cpu",kernelFunc:Fo};function wn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Ne([o,i],e);let c=l.data.get(o.dataId).values,u=l.data.get(i.dataId).values,d=o.dtype==="string"?E.fromUint8ToStringArray(c):c,p=o.dtype==="string"?E.fromUint8ToStringArray(u):u,h=s||o.dtype,[f,m]=t(o.shape,i.shape,d,p,h);return l.makeTensorInfo(m,h,f)}:({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(o.dtype==="complex64"||i.dtype==="complex64"){let c=Fo({inputs:{x:o},backend:l,attrs:{dtype:"complex64"}}),u=l.data.get(c.dataId),d=u.complexTensorInfos.real,p=u.complexTensorInfos.imag,h=l.data.get(d.dataId).values,f=l.data.get(p.dataId).values,m=Fo({inputs:{x:i},backend:l,attrs:{dtype:"complex64"}}),g=l.data.get(m.dataId),y=g.complexTensorInfos.real,A=g.complexTensorInfos.imag,x=l.data.get(y.dataId).values,b=l.data.get(A.dataId).values,[w,k,S]=n(o.shape,i.shape,h,f,x,b),N=l.makeTensorInfo(S,"float32",w),R=l.makeTensorInfo(S,"float32",k),P=ks({inputs:{real:N,imag:R},backend:l});return l.disposeIntermediateTensorInfo(c),l.disposeIntermediateTensorInfo(m),l.disposeIntermediateTensorInfo(N),l.disposeIntermediateTensorInfo(R),P}else{let c=l.data.get(o.dataId).values,u=l.data.get(i.dataId).values,d=s||o.dtype,[p,h]=t(o.shape,i.shape,c,u,d);return l.makeTensorInfo(h,d,p)}}}function WA(e){return(t,n,s,r,a,o)=>{let i=E.assertAndGetBroadcastShape(t,n),l=v.sizeFromShape(i),c=i.length,u=v.computeStrides(i),d=v.getTypedArrayFromDType("float32",l),p=v.getTypedArrayFromDType("float32",l),h=E.getBroadcastDims(t,i),f=E.getBroadcastDims(n,i),m=E.mergeRealAndImagArrays(s,r),g=E.mergeRealAndImagArrays(a,o),y=t.length,A=v.computeStrides(t),x=n.length,b=v.computeStrides(n);if(h.length+f.length===0)for(let w=0;w<d.length;w++){let k=w%m.length,S=w%g.length,N=e(m[k*2],m[k*2+1],g[S*2],g[S*2+1]);d[w]=N.real,p[w]=N.imag}else for(let w=0;w<d.length;w++){let k=v.indexToLoc(w,c,u),S=k.slice(-y);h.forEach(D=>S[D]=0);let N=v.locToIndex(S,y,A),R=k.slice(-x);f.forEach(D=>R[D]=0);let P=v.locToIndex(R,x,b),$=e(m[N*2],m[N*2+1],g[P*2],g[P*2+1]);d[w]=$.real,p[w]=$.imag}return[d,p,i]}}var b7=Jt((e,t)=>e+t),SU=WA((e,t,n,s)=>({real:e+n,imag:t+s})),sp=wn(qr,b7,SU),CU={kernelName:qr,backendName:"cpu",kernelFunc:sp};function VA(e,t,n,s,r){let a=v.sizeFromShape(s),o=v.makeZerosTypedArray(r,n);for(let i=0;i<e.length;i++){let l=e[i];if(l<0)throw new Error("Input x must be non-negative!");l>=r||(a>0?o[l]+=t[i]:o[l]+=1)}return o}function v7(e,t,n,s=!1){let r=e.shape[0],a=e.shape[1],o=We([r,n],t.dtype);for(let i=0;i<r;i++)for(let l=0;l<a;l++){let c=e.get(i,l);if(c<0)throw new Error("Input x must be non-negative!");c>=n||(s?o.set(1,i,c):t.size>0?o.set(o.get(i,c)+t.get(i,l),i,c):o.set(o.get(i,c)+1,i,c))}return o}function Oo(e){return(t,n,s)=>{let r=v.getTypedArrayFromDType(n,t.length);for(let a=0;a<t.length;++a)r[a]=e(t[a],s);return r}}function xt(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Ne(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,c=v.sizeFromShape(o.shape),u=n||o.dtype,d=v.getArrayFromDType(u,c);for(let p=0;p<c;++p)d[p]=t(l[p],r);return i.makeTensorInfo(o.shape,u,d)}}function Yu(e,t,n){return({inputs:s,attrs:r,backend:a})=>{let{x:o}=s;if(Ne(o,e),o.dtype==="string"||n==="string")throw new Error("unaryKernelFunc does not support string input/output");let i=a,l=i.data.get(o.dataId).values,c=n||o.dtype,u=t(l,c,r);return i.makeTensorInfo(o.shape,c,u)}}var w7=Oo(e=>Math.ceil(e)),TU=Yu(Na,w7),NU={kernelName:Na,backendName:"cpu",kernelFunc:TU};function UA(e,t,n,s){let r=v.getArrayFromDType(n,v.sizeFromShape(t));if(s&&n!=="string"){let a=0;e.forEach(o=>{let i=v.sizeFromShape(o.shape);r.set(o.vals,a),a+=i})}else{let a=0;e.forEach(o=>{let i=n==="string"?E.fromUint8ToStringArray(o.vals):o.vals,l=0;for(let c=0;c<o.shape[0];++c){let u=c*t[1]+a;for(let d=0;d<o.shape[1];++d)r[u+d]=i[l++]}a+=o.shape[1]})}return r}var k7=Jt((e,t)=>e===t?1:0),I7=wn(ii,k7,null,"bool"),EU={kernelName:ii,backendName:"cpu",kernelFunc:I7},S7=Oo(e=>Math.exp(e)),C7=Yu(Oa,S7),RU={kernelName:Oa,backendName:"cpu",kernelFunc:C7},T7=Oo(e=>Math.expm1(e)),$U=Yu(ui,T7),DU={kernelName:ui,backendName:"cpu",kernelFunc:$U},N7=Oo(e=>Math.floor(e)),_U=Yu(Ma,N7),PU={kernelName:Ma,backendName:"cpu",kernelFunc:_U};function E7(e,t,n,s,r,a,o,i,l){let c=We([s,a],n);for(let u=0;u<s;u++){let d=[],p=0;for(let h=0;h<r;h++){let f=e[u*r+h];p+=f*o[h],d.push(f)}if(p<0||p>=l/a)throw new Error(`Invalid indices: ${d} does not index into ${i}`);for(let h=0;h<a;h++)c.values[u*a+h]=t.get(...t.indexToLoc(p*a+h))}return c}function R7(e,t,n){let s=We(n,e.dtype);for(let r=0;r<s.size;++r){let o=s.indexToLoc(r).slice(),i=o[0],l=o[2],c=t.locToIndex([i,l]);o[2]=t.values[c];let u=e.locToIndex(o);s.values[r]=e.values[u]}return s}var $7=Jt((e,t)=>e>t?1:0),FU=wn(hi,$7,null,"bool"),OU={kernelName:hi,backendName:"cpu",kernelFunc:FU},D7=Jt((e,t)=>e>=t?1:0),MU=wn(Ba,D7,null,"bool"),zU={kernelName:Ba,backendName:"cpu",kernelFunc:MU},_7=Jt((e,t)=>e<t?1:0),LU=wn(mi,_7,null,"bool"),BU={kernelName:mi,backendName:"cpu",kernelFunc:LU},P7=Jt((e,t)=>e<=t?1:0),WU=wn(gi,P7,null,"bool"),VU={kernelName:gi,backendName:"cpu",kernelFunc:WU};function F7(e,t,n){let s=(t-e)/(n-1),r=v.makeZerosTypedArray(n,"float32");r[0]=e;for(let a=1;a<r.length;a++)r[a]=r[a-1]+s;return r}var O7=Oo(e=>Math.log(e)),UU=Yu(Va,O7),GU={kernelName:Va,backendName:"cpu",kernelFunc:UU};function M7(e,t,n,s){let r=v.getTypedArrayFromDType(s,v.sizeFromShape(n));for(let a=0;a<r.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let c=e[o+l];(Number.isNaN(c)||c>i)&&(i=c)}r[a]=i}return r}var z7=Jt((e,t)=>Math.max(e,t)),HU=wn(Ga,z7),jU={kernelName:Ga,backendName:"cpu",kernelFunc:HU},L7=Jt((e,t)=>Math.min(e,t)),qU=wn(Xa,L7),XU={kernelName:Xa,backendName:"cpu",kernelFunc:qU},GA=Jt((e,t)=>e*t),KU=WA((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),Cm=wn(Za,GA,KU),ZU={kernelName:Za,backendName:"cpu",kernelFunc:Cm};function B7(e,t,n){let s=v.createScalarValue(-1,n);return GA([],t,s,e,n)}function YU(e){let{inputs:t,backend:n}=e,{x:s}=t;Ne(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=B7(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var JU={kernelName:Ai,backendName:"cpu",kernelFunc:YU},W7=Jt((e,t)=>e!==t?1:0),QU=wn(xi,W7,null,"bool"),eG={kernelName:xi,backendName:"cpu",kernelFunc:QU};function HA(e,t,n,s,r){let a=t.length,o=v.sizeFromShape(t),i=v.computeStrides(t),l=v.computeStrides(r),c=v.getTypedArrayFromDType(n,v.sizeFromShape(r));for(let u=0;u<o;++u){let d=v.indexToLoc(u,a,i),p=new Array(d.length);for(let f=0;f<p.length;f++)p[f]=d[s[f]];let h=v.locToIndex(p,a,l);c[h]=e[u]}return c}function Os(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{perm:a}=n;Ne(r,"transpose");let o=r.shape.length,i=new Array(o);for(let d=0;d<i.length;d++)i[d]=r.shape[a[d]];let l=s.data.get(r.dataId).values,c=HA(l,r.shape,r.dtype,a,i);return{dataId:s.write(c,i,r.dtype),shape:i,dtype:r.dtype}}var tG={kernelName:ho,backendName:"cpu",kernelFunc:Os};function V7(e,t,n,s){let[r,a]=E.computeOutAndReduceShapes(e,s),o=Ln(t,"int32"),i=v.makeZerosTypedArray(v.sizeFromShape(r),o),l=v.sizeFromShape(a);for(let c=0;c<i.length;++c){let u=c*l,d=1;for(let p=0;p<l;++p)d*=n[u+p];i[c]=d}return{outVals:i,outShape:r,outDtype:o}}function nG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"prod");let i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=E.getAxesPermutation(l,i),u=l,d=r,p=[];c!=null&&(d=Os({inputs:{x:r},backend:n,attrs:{perm:c}}),p.push(d),u=E.getInnerMostAxes(u.length,i));let h=n.data.get(d.dataId).values,{outVals:f,outShape:m,outDtype:g}=V7(d.shape,d.dtype,h,u),y=m;return o&&(y=E.expandShapeToKeepDim(m,l)),p.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(y,g,f)}var sG={kernelName:Si,backendName:"cpu",kernelFunc:nG};function jA(e,t,n,s){let r=e===t,a=e<t&&n<0,o=t<e&&n>1;if(r||a||o)return v.makeZerosTypedArray(0,s);let i=Math.abs(Math.ceil((t-e)/n)),l=v.makeZerosTypedArray(i,s);t<e&&n===1&&(n=-1),l[0]=e;for(let c=1;c<l.length;c++)l[c]=l[c-1]+n;return l}var U7=Oo(e=>1/Math.sqrt(e)),rG=Yu(so,U7),aG={kernelName:so,backendName:"cpu",kernelFunc:rG},oG=Oo(e=>1/(1+Math.exp(-e))),G7=xt(ao,e=>1/(1+Math.exp(-e))),iG={kernelName:ao,backendName:"cpu",kernelFunc:G7};function Tm(e,t,n,s,r){let a=yn.isSliceContinous(s,t,n),o=v.sizeFromShape(n),i=v.computeStrides(s);if(a){let d=yn.computeFlatOffset(t,i);return r==="string"?e.slice(d,d+o):e.subarray(d,d+o)}let l=r==="string"?E.fromUint8ToStringArray(e):e,c=We(s,r,l),u=We(n,r);for(let d=0;d<u.size;++d){let p=u.indexToLoc(d),h=p.map((f,m)=>f+t[m]);u.set(c.get(...h),...p)}return r==="string"?E.fromStringArrayToUint8(u.values):u.values}function fl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;Ne(r,"slice");let[i,l]=yn.parseSliceParams(r,a,o);yn.assertParamsValid(r,i,l);let c=n.data.get(r.dataId).values,u=Tm(c,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}var lG={kernelName:$i,backendName:"cpu",kernelFunc:fl};function H7(e,t,n,s,r,a,o){let i=t[0],l=a[0],c=new Array(l),u=new Array(i),d=t[1];if(l===0){if(i!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
indices.shape[0] = ${i}`);let g=v.getArrayFromDType(n,0),y=v.getArrayFromDType(r,0);return[g,[0,d],y,c,u]}let p=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let y=e[g*d];if(y<0)throw new Error(`indices(${g}, 0) is invalid: ${y} < 0`);if(y>=l)throw new Error(`indices(${g}, 0) is invalid: ${y} >= ${l}`);++f[y],p=p&&y>=h,h=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;c[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,y=s;for(let A=0;A<i;++A)u[A]=A;return[g,[i,d],y,c,u]}else{let g=f[l-1],y=v.getArrayFromDType(n,g*d),A=v.getArrayFromDType(r,g),x=new Array(l).fill(0);for(let b=0;b<i;++b){let w=e[b*d],k=x[w],S=(w===0?0:f[w-1])+k;x[w]++;for(let N=0;N<d;++N)y[S*d+N]=e[b*d+N];A[S]=s[b],u[b]=S}for(let b=0;b<l;++b)if(x[b]===0){let k=b===0?0:f[b-1];y[k*d+0]=b;for(let S=1;S<d;++S)y[k*d+S]=0;A[k]=o}return[y,[g,d],A,c,u]}}function j7(e,t,n,s,r){let a=v.sizeFromShape(s),o=t[0],i=r.length,l=[],c=1,u=-1;for(let g=0;g<i;++g){let y=r[g];if(y===-1){if(u!==-1)throw new Error(`only one output dimension may be -1, not both ${u} and ${g}`);u=g,l.push(1)}else{if(y<0)throw new Error(`size ${g} must be non-negative, not ${y}`);c*=y,l.push(y)}}if(u!==-1){if(c<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/c);if(c*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
dense values, but the requested shape requires a multiple of ${c}. inputShape=${s} outputShape= ${l}`);l[u]=g}let d=v.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${s} outputShape=${l}`);let p=s.length,h=[];if(p>0){h[p-1]=1;for(let g=p-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=v.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let y=0;for(let A=0;A<p;++A)y+=e[g*p+A]*h[A];for(let A=0;A<i;++A)m[g*i+A]=Math.trunc(y/f[A]),y%=f[A]}return[m,[o,i],l]}function qA(e,t,n,s,r,a=!1,o=0){let i=s.length;if(i!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],c=l[1],d=i>0?r[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let h=p.reduce((x,b)=>x*b,1),f=v.getArrayFromDType(n,h);if(i===0)return d>0&&f.fill(o),[f,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,y=0,A=r[m];for(;;){let x=0;if(g<i){if(x=r[g],A===x){++g;continue}if(A>=x)throw new Error("segment ids are not increasing")}if(A<0||A>=d)throw new Error(`Segment id ${A} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);A>y&&f.fill(o,y*c,A*c);for(let b=m;b<g;++b){let w=s[b];if(w<0||w>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<c;k++)f[A*c+k]+=e[w*c+k]}if(a)for(let b=0;b<c;b++)f[A*c+b]/=g-m;if(m=g,++g,y=A+1,A=x,g>i)break}return y<d&&f.fill(o,y*c,d*c),[f,p]}var uG=Oo(e=>Math.sqrt(e)),cG=xt(oo,e=>Math.sqrt(e)),dG={kernelName:oo,backendName:"cpu",kernelFunc:cG},q7=Jt((e,t)=>{let n=e-t;return n*n}),pG=wn(uo,q7),hG={kernelName:uo,backendName:"cpu",kernelFunc:pG};function X7(e,t,n,s){let r=We(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var fG=class{constructor(e,t,n,s,r,a){this.separator=v.encodeString(e),this.nGramWidths=t,this.leftPad=v.encodeString(n),this.rightPad=v.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),c=Math.max(0,i-(r-(o+1))),u=a-(l+c),d=t+(l>0?0:o-i),p=0;p+=l*this.leftPad.length;for(let y=0;y<u;++y)p+=e[d+y].length;p+=c*this.rightPad.length,p+=(l+c+u-1)*this.separator.length,n[s+o]=new Uint8Array(p);let f=n[s+o],m=0,g=y=>y.forEach(A=>f[m++]=A);for(let y=0;y<l;++y)g(this.leftPad),g(this.separator);for(let y=0;y<u-1;++y)g(e[d+y]),g(this.separator);if(u>0){g(e[d+u-1]);for(let y=0;y<c;++y)g(this.separator),g(this.rightPad)}else{for(let y=0;y<c-1;++y)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let c=t[l]>=i;if(c=c&&t[l]<=n,!c)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${i}`)}let r=s-1,a=v.getArrayFromDType("int32",s);if(n===0||s===0){let i=new Array(n);for(let l=0;l<=r;++l)a[l]=0;return[i,a]}a[0]=0;for(let i=1;i<=r;++i){let l=t[i]-t[i-1],c=0;this.nGramWidths.forEach(u=>{c+=this.getNumNGrams(l,u)}),this.preserveShort&&l>0&&c===0&&(c=1),a[i]=a[i-1]+c}let o=new Array(a[r]);for(let i=0;i<r;++i){let l=t[i],c=a[i];if(this.nGramWidths.forEach(u=>{let d=t[i+1]-t[i],p=this.getNumNGrams(d,u);this.createNGrams(e,l,o,c,p,u),c+=p}),this.preserveShort&&c===a[i]){let u=t[i+1]-t[i];if(u===0)continue;let d=u+2*this.padWidth,p=1;this.createNGrams(e,l,o,c,p,d)}}return[o,a]}};function K7(e,t,n,s,r,a,o,i){return new fG(n,s,r,a,o,i).compute(e,t)}function mG(e,t,n,s){if(!e.length)return;if(t.length===0){for(let a=0;a<e.length;++a)s.push(e.subarray(a,a+1));return}if(t.length===1){let a=t[0],o=e.indexOf(a);for(;o!==-1;){let i=e.subarray(0,o);(!n||i.length!==0)&&s.push(i),e=e.subarray(o+1),o=e.indexOf(a)}(!n||e.length!==0)&&s.push(e);return}let r=0;for(let a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(r,a);(!n||o.length!==0)&&s.push(o),r=a+1}}function Z7(e,t,n){let s=e.length,r=[],a=0,o=0,i=new Array(s);for(let p=0;p<s;++p){let h=r.length;mG(e[p],t,n,r);let f=r.length-h;i[p]=f,a+=f,o=Math.max(o,f)}let l=v.getArrayFromDType("int32",a*2),c=new Array(a),u=[s,o],d=0;for(let p=0;p<s;++p)for(let h=0;h<i[p];++h)l[d*2]=p,l[d*2+1]=h,c[d]=r[d],++d;return[l,c,u]}function Y7(e,t){let n=v.getArrayFromDType("int32",e.length);for(let s=0;s<e.length;++s)n[s]=v.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();return n}var J7=Jt((e,t)=>e-t),gG=WA((e,t,n,s)=>({real:e-n,imag:t-s})),XA=wn(co,J7,gG),yG={kernelName:co,backendName:"cpu",kernelFunc:XA};function Q7(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let s=We(n,e.dtype);for(let r=0;r<s.values.length;++r){let a=s.indexToLoc(r),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);s.values[r]=e.values[i]}return s}var rp=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function eI(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let i=s-n+1,l=t-n+1,c=Math.log(i),u=.5*Math.exp(2*c/3),d=.5*Math.sqrt(c*u*(i-u)/i)*Math.sign(l-i/2),p=Math.max(n,Math.floor(t-l*u/i+d)),h=Math.min(s,Math.floor(t+(i-l)*u/i+d));eI(e,t,p,h)}let r=e[t],a=n,o=s;for(v.swap(e,n,t),rp(e[s],r)>0&&v.swap(e,n,s);a<o;){for(v.swap(e,a,o),a++,o--;rp(e[a],r)<0;)a=a+1;for(;rp(e[o],r)>0;)o=o-1}rp(e[n],r)===0?v.swap(e,n,o):(o=o+1,v.swap(e,o,s)),o<=t&&(n=o+1),t<=o&&(s=o-1)}}function tI(e,t,n,s,r){let a=t[t.length-1],[o,i]=[e.length/a,a],l=v.getTypedArrayFromDType(n,o*s),c=v.getTypedArrayFromDType("int32",o*s);for(let d=0;d<o;d++){let p=d*i,h=e.subarray(p,p+i),f=new Array(h.length);h.forEach((A,x)=>f[x]={value:A,index:x}),s<f.length&&(eI(f,s),f=f.slice(0,s)),r&&f.sort(rp);let m=d*s,g=l.subarray(m,m+s),y=c.subarray(m,m+s);for(let A=0;A<s;A++)g[A]=f[A].value,y[A]=f[A].index}let u=t.slice();return u[u.length-1]=s,[We(u,n,l),We(u,"int32",c)]}function nI(e,t,n,s){let r=v.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[r]),l=new tn(a,s,e),c=[],u=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(u)m=e[f].toString();else{let g=[];for(let y=0;y<a[0];y++)for(let A=0;A<a[2];A++)g.push(l.get(y,f,A));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,c.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new tn(d,s);c.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let y=0;y<a[2];y++)p.set(l.get(g,f,y),g,m,y)});let h=n.slice();return h[r]=d[1],{outputValues:p.values,outputShape:h,indices:i}}qi("cpu",()=>new LA,1);var sI=xt(Fa,e=>e>=0?e:Math.exp(e)-1),AG={kernelName:Fa,backendName:"cpu",kernelFunc:sI};function rI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ne([r],"leakyRelu");let o=v.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=v.getTypedArrayFromDType("float32",o);for(let c=0;c<i.length;c++)l[c]=i[c]<0?a*i[c]:i[c];return n.makeTensorInfo(r.shape,"float32",l)}var xG={kernelName:fi,backendName:"cpu",kernelFunc:rI},bG=Jt((e,t)=>e<0?t*e:e);function aI(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Ne([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=bG(s.shape,r.shape,a,o,s.dtype);return n.makeTensorInfo(l,s.dtype,i)}var vG={kernelName:Qa,backendName:"cpu",kernelFunc:aI},oI=xt(eo,e=>Math.max(0,e)),wG={kernelName:eo,backendName:"cpu",kernelFunc:oI},iI=xt(no,e=>Math.min(Math.max(0,e),6)),kG={kernelName:no,backendName:"cpu",kernelFunc:iI};function KA(e,t,n,s,r){if(n==="linear")return Or({inputs:{x:t},backend:e});if(n==="relu")return oI({inputs:{x:t},backend:e});if(n==="elu")return sI({inputs:{x:t},backend:e});if(n==="relu6")return iI({inputs:{x:t},backend:e});if(n==="prelu")return aI({inputs:{x:t,alpha:s},backend:e});if(n==="leakyrelu")return rI({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n==="sigmoid")return G7({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function Et(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=v.sizeFromShape(r.shape),i=v.inferFromImplicitShape(a,o),l=v.sizeFromShape(i);v.assert(o===l,()=>`The new shape (${i}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let c=n.data.get(r.dataId);if(c.complexTensorInfos!=null){let u=c.complexTensorInfos.real,d=c.complexTensorInfos.imag;u.shape=i,d.shape=i}return{dataId:r.dataId,shape:i,dtype:r.dtype}}var IG={kernelName:Ci,backendName:"cpu",kernelFunc:Et};function lI(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;Ne([r,a],"matMul");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=g===y||g===1||y===1;v.assert(l>=2&&c>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>y?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let w=o?[g,u,p]:[g,p,u],k=i?[y,h,d]:[y,d,h],S=Et({inputs:{x:r},backend:n,attrs:{shape:w}}),N=Et({inputs:{x:a},backend:n,attrs:{shape:k}}),R=o?S.shape[1]:S.shape[2],P=o?S.shape[2]:S.shape[1],$=i?N.shape[1]:N.shape[2],D=Math.max(g,y),T=n.data.get(S.dataId).values,O=n.data.get(N.dataId).values,B=v.computeStrides(S.shape),H=v.computeStrides(N.shape),[z,X,ee]=o?[B[0],1,B[1]]:[B[0],B[1],1],[J,Q,te]=i?[1,H[1],H[0]]:[H[1],1,H[0]],K=P*$,oe=We([D,P,$],S.dtype),ce=oe.values,he=n.blockSize;for(let Ae=0;Ae<D;Ae++)for(let Ie=0;Ie<P;Ie+=he)for(let Se=0;Se<$;Se+=he)for(let Oe=0;Oe<R;Oe+=he){let Ue=Math.min(Ie+he,P),ze=Math.min(Se+he,$),wt=Math.min(Oe+he,R);for(let mt=Ie;mt<Ue;mt++)for(let gt=Se;gt<ze;gt++){let pt=0;for(let bt=Oe;bt<wt;bt++){let Ye=Math.min(Ae,g-1)*z,Yn=Math.min(Ae,y-1)*te,Ot=T[Ye+mt*X+bt*ee],ps=O[bt*J+gt*Q+Yn];pt+=Ot*ps}ce[Ae*K+(mt*$+gt)]+=pt}}return n.disposeIntermediateTensorInfo(S),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(b,oe.dtype,oe.values)}var SG={kernelName:Ca,backendName:"cpu",kernelFunc:lI};function CG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p,h,f,m=[];p=lI({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:c},backend:n}),o&&(h=sp({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=KA(n,p,u,i,d),m.push(p),p=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return p}var TG={kernelName:mo,backendName:"cpu",kernelFunc:CG},NG=xt(ql,e=>Math.acos(e)),EG={kernelName:ql,backendName:"cpu",kernelFunc:NG},RG=xt(Xl,e=>Math.acosh(e)),$G={kernelName:Xl,backendName:"cpu",kernelFunc:RG};function DG(e){let{inputs:t,backend:n}=e,s=t;Ne(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=We(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let c=0;c<o.length;c++)o[c]+=l[c]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var _G={kernelName:ka,backendName:"cpu",kernelFunc:DG};function PG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"all");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Os({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("all",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let A=y*h,x=m[A];for(let b=0;b<h;++b){let w=m[A+b];x=x&&w}f[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let y=E.expandShapeToKeepDim(d,i),A=Et({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var FG={kernelName:Kl,backendName:"cpu",kernelFunc:PG};function OG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"any");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Os({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("any",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let A=y*h,x=m[A];for(let b=0;b<h;++b){let w=m[A+b];x=x||w}f[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let y=E.expandShapeToKeepDim(d,i),A=Et({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var MG={kernelName:Zl,backendName:"cpu",kernelFunc:OG};function zG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ne(r,"argMax");let o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Os({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],E.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[u,d]=E.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let w=m[y+b];w>A&&(A=w,x=b)}h[g]=x}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var LG={kernelName:Ia,backendName:"cpu",kernelFunc:zG};function BG(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Ne(r,"argMin");let o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Os({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],E.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[u,d]=E.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*f,A=m[y],x=0;for(let b=0;b<f;++b){let w=m[y+b];w<A&&(A=w,x=b)}h[g]=x}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var WG={kernelName:Yl,backendName:"cpu",kernelFunc:BG},VG=xt(Jl,e=>Math.asin(e)),UG={kernelName:Jl,backendName:"cpu",kernelFunc:VG},GG=xt(Ql,e=>Math.asinh(e)),HG={kernelName:Ql,backendName:"cpu",kernelFunc:GG},jG=xt(eu,e=>Math.atan(e)),qG={kernelName:eu,backendName:"cpu",kernelFunc:jG},XG=Jt((e,t)=>Math.atan2(e,t)),KG=wn(nu,XG),ZG={kernelName:nu,backendName:"cpu",kernelFunc:KG},YG=xt(tu,e=>Math.atanh(e)),JG={kernelName:tu,backendName:"cpu",kernelFunc:YG};function ZA(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=We(r.outShape,n),g=m.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],A=r.outShape[2]*r.outShape[3],x=r.outShape[3];for(let b=0;b<r.batchSize;++b){let w=b*y,k=b*s[0];for(let S=0;S<r.inChannels;++S)for(let N=0;N<r.outHeight;++N){let R=N*o-p,P=Math.max(0,R),$=Math.min(r.inHeight,u+R),D=w+N*A;for(let T=0;T<r.outWidth;++T){let O=T*i-h,B=Math.max(0,O),H=Math.min(r.inWidth,d+O),z=f,X=0,ee=0;for(let Q=P;Q<$;Q+=l){let te=k+Q*s[1];for(let K=B;K<H;K+=c){let oe=te+K*s[2],ce=e[oe+S];a==="max"&&ce>z?z=ce:a==="avg"&&(X+=ce,ee++)}if(isNaN(z))break}let J=D+T*x+S;g[J]=a==="avg"?X/ee:z}}}return m}function uI(e,t,n,s,r=!1,a=!1){let o=We(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,c=s.dilationHeight,u=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=We(t,n,e);for(let g=0;g<s.batchSize;++g)for(let y=0;y<s.inChannels;++y)for(let A=0;A<s.outHeight;++A){let x=A*i-h,b=x;for(;b<0;)b+=c;let w=Math.min(s.inHeight,d+x);for(let k=0;k<s.outWidth;++k){let S=k*l-f,N=S;for(;N<0;)N+=u;let R=Math.min(s.inWidth,p+S),P=Number.NEGATIVE_INFINITY,$=-1;for(let D=b;D<w;D+=c){let T=D-x;for(let O=N;O<R;O+=u){let B=O-S,H=m.get(g,D,O,y);H>P&&(P=H,r?$=a?((g*s.inHeight+D)*s.inWidth+O)*s.inChannels+y:(D*s.inWidth+O)*s.inChannels+y:$=T*p+B)}}o.set($,g,A,k,y)}}return o}function cI(e,t,n,s,r,a){let o=r.strideDepth,i=r.strideHeight,l=r.strideWidth,c=r.dilationDepth,u=r.dilationHeight,d=r.dilationWidth,p=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,y=r.padInfo.left,A=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(r.outShape,n),b=x.values,w=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],k=r.outShape[2]*r.outShape[3]*r.outShape[4],S=r.outShape[3]*r.outShape[4],N=r.outShape[4];for(let R=0;R<r.batchSize;++R){let P=R*w,$=R*s[0];for(let D=0;D<r.inChannels;++D)for(let T=0;T<r.outDepth;++T){let O=T*o-m,B=O;for(;B<0;)B+=c;let H=Math.min(r.inDepth,p+O),z=P+T*k;for(let X=0;X<r.outHeight;++X){let ee=X*i-g,J=ee;for(;J<0;)J+=u;let Q=Math.min(r.inHeight,h+ee),te=z+X*S;for(let K=0;K<r.outWidth;++K){let oe=K*l-y,ce=oe;for(;ce<0;)ce+=d;let he=Math.min(r.inWidth,f+oe),Ae=te+K*N,Ie=A,Se=0,Oe=0;for(let ze=B;ze<H;ze+=c){let wt=$+ze*s[1];for(let mt=J;mt<Q;mt+=u){let gt=wt+mt*s[2];for(let pt=ce;pt<he;pt+=d){let bt=gt+pt*s[3],Ye=e[bt+D];if(a==="max"&&Ye>Ie?Ie=Ye:a==="avg"&&(Se+=Ye,Oe++),isNaN(Ie))break}if(isNaN(Ie))break}if(isNaN(Ie))break}let Ue=Ae+D;b[Ue]=a==="avg"?Se/Oe:Ie}}}}return x}function QG(e,t){let n=We(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let A=y*s-p,x=A;for(;x<0;)x+=o;let b=Math.min(t.inDepth,c+A);for(let w=0;w<t.outHeight;++w){let k=w*r-h,S=k;for(;S<0;)S+=i;let N=Math.min(t.inHeight,u+k);for(let R=0;R<t.outWidth;++R){let P=R*a-f,$=P;for(;$<0;)$+=l;let D=Math.min(t.inWidth,d+P),T=Number.NEGATIVE_INFINITY,O=-1;for(let B=x;B<b;B+=o){let H=B-A;for(let z=S;z<N;z+=i){let X=z-k;for(let ee=$;ee<D;ee+=l){let J=ee-P,Q=e.get(m,B,z,ee,g);Q>=T&&(T=Q,O=H*u*d+X*u+J)}}}n.set(O,m,y,w,R,g)}}}return n}function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ne(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l),d;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))d=Or({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=v.computeStrides(r.shape),f=ZA(p,r.shape,r.dtype,h,u,"avg");d=n.makeTensorInfo(u.outShape,r.dtype,f.values)}return d}var tH={kernelName:Sa,backendName:"cpu",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s;Ne(r,"avgPool3d");let u=E.computePool3DInfo(r.shape,a,o,1,i,l,c),d=n.data.get(r.dataId).values,p=cI(d,r.shape,r.dtype,v.computeStrides(r.shape),u,"avg");return n.makeTensorInfo(p.shape,"float32",p.values)}var sH={kernelName:Hc,backendName:"cpu",kernelFunc:nH};function rH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=s;Ne([r,a],"avgPool3DGrad");let u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,A=u.dilationHeight,x=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,k=u.effectiveFilterWidth,S=b-1-u.padInfo.front,N=k-1-u.padInfo.left,R=w-1-u.padInfo.top,P=We(a.shape,"float32"),$=1/(f*m*g),D=n.bufferSync(r);for(let T=0;T<u.batchSize;++T)for(let O=0;O<u.inChannels;++O)for(let B=0;B<u.inDepth;++B)for(let H=0;H<u.inHeight;++H)for(let z=0;z<u.inWidth;++z){let X=B-S,ee=H-R,J=z-N,Q=0;for(let te=0;te<b;te+=y){let K=(X+te)/d;if(!(K<0||K>=u.outDepth||Math.floor(K)!==K))for(let oe=0;oe<w;oe+=A){let ce=(ee+oe)/p;if(!(ce<0||ce>=u.outHeight||Math.floor(ce)!==ce))for(let he=0;he<k;he+=x){let Ae=(J+he)/h;if(Ae<0||Ae>=u.outWidth||Math.floor(Ae)!==Ae)continue;Q+=D.get(T,K,ce,Ae,O)}}}P.set(Q*$,T,B,H,z,O)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var aH={kernelName:gh,backendName:"cpu",kernelFunc:rH};function oH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ne([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=u.strideHeight,p=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,A=u.effectiveFilterWidth,x=A-1-u.padInfo.left,b=y-1-u.padInfo.top,w=We(o.shape,"float32"),k=1/(h*f),S=n.data.get(r.dataId).values,N=We(r.shape,"float32",S);for(let R=0;R<u.batchSize;++R)for(let P=0;P<u.inChannels;++P)for(let $=0;$<u.inHeight;++$)for(let D=0;D<u.inWidth;++D){let T=$-b,O=D-x,B=0;for(let H=0;H<y;H+=m){let z=(T+H)/d;if(!(z<0||z>=u.outHeight||Math.floor(z)!==z))for(let X=0;X<A;X+=g){let ee=(O+X)/p;if(ee<0||ee>=u.outWidth||Math.floor(ee)!==ee)continue;B+=N.get(R,z,ee,P)}}w.set(B*k,R,$,D,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var iH={kernelName:mh,backendName:"cpu",kernelFunc:oH};function lH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,scale:a,offset:o,mean:i,variance:l}=t;v.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ne([r,i,l,a,o],"batchNorm");let{varianceEpsilon:c}=s;c==null&&(c=.001);let u=n.data.get(r.dataId).values,d=n.data.get(i.dataId).values,p=n.data.get(l.dataId).values,h=a?n.data.get(a.dataId).values:new Float32Array([1]),f=o?n.data.get(o.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),g=f.length,y=h.length,A=p.length,x=d.length,b=0,w=0,k=0,S=0;for(let N=0;N<u.length;++N)m[N]=f[b++]+(u[N]-d[w++])*h[k++]/Math.sqrt(p[S++]+c),b>=g&&(b=0),w>=x&&(w=0),k>=y&&(k=0),S>=A&&(S=0);return n.makeTensorInfo(r.shape,r.dtype,m)}var uH={kernelName:La,backendName:"cpu",kernelFunc:lH};function cH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ne([r],"batchToSpaceND");let i=a.reduce((y,A)=>y*A),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=Et({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Os({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Et({inputs:{x:f},backend:n,attrs:{shape:u}}),g=fl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var dH={kernelName:ni,backendName:"cpu",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=VA(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var hH={kernelName:yh,backendName:"cpu",kernelFunc:pH};function fH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var mH={kernelName:u2,backendName:"cpu",kernelFunc:fH},gH=xt(Xr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),yH={kernelName:Xr,backendName:"cpu",kernelFunc:gH},AH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;c<i.length;c++){let u=i[c],d=l[c];s[c]=Math.hypot(u,d)}return n.makeOutput(s,t.shape,"float32")},xH={kernelName:qc,backendName:"cpu",kernelFunc:AH};function Ju(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var bH={kernelName:Yc,backendName:"cpu",kernelFunc:Ju};function Qu(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(m=>m.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>v.sizeFromShape(m.shape)>0);if(i.length===1)return Or({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(E.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>hl({inputs:{input:b},backend:n})),g=i.map(b=>Ju({inputs:{input:b},backend:n})),y=Qu({inputs:m,backend:n,attrs:{axis:a}}),A=Qu({inputs:g,backend:n,attrs:{axis:a}}),x=ks({inputs:{real:y,imag:A},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),x}let c=i.map(m=>{let g=v.sizeFromShape(m.shape.slice(a));return Et({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=E.computeOutShape(c.map(m=>m.shape),1);let d=c[0].shape[0]===1,p=UA(u,o,t[0].dtype,d),h=E.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var vH={kernelName:si,backendName:"cpu",kernelFunc:Qu};function dI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s;Ne([r,a],"conv2d");let d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,A=p.padInfo.top,x=p.dataFormat==="channelsLast",b=new tn(p.outShape,r.dtype),w=v.computeStrides(r.shape),k=v.computeStrides(a.shape),S=w[0],N=x?w[1]:w[2],R=x?w[2]:1,P=x?1:w[1],$=b.strides[0],D=x?b.strides[1]:b.strides[2],T=x?b.strides[2]:1,O=x?1:b.strides[1],B=n.data.get(r.dataId).values,H=n.data.get(a.dataId).values,z=b.values;for(let X=0;X<p.batchSize;++X){let ee=X*S,J=X*$;for(let Q=0;Q<p.outHeight;++Q){let te=J+Q*D,K=Q*p.strideHeight-A;for(let oe=0;oe<h;++oe){let ce=K+oe*m;if(ce<0||ce>=p.inHeight)continue;let he=oe*k[0],Ae=ee+ce*N;for(let Ie=0;Ie<p.outWidth;++Ie){let Se=te+Ie*T,Oe=Ie*p.strideWidth-y;for(let Ue=0;Ue<f;++Ue){let ze=Oe+Ue*g;if(ze<0||ze>=p.inWidth)continue;let wt=he+Ue*k[1],mt=Ae+ze*R,gt=wt;for(let pt=0;pt<p.inChannels;++pt){let bt=B[mt+pt*P];for(let Ye=0;Ye<p.outChannels;++Ye)z[Se+Ye*O]+=bt*H[gt+Ye];gt+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,z)}var wH={kernelName:Ea,backendName:"cpu",kernelFunc:dI};function kH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s;Ne([r,a],"conv2dBackpropFilter");let d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new tn(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=new tn(r.shape,r.dtype,w),N=new tn(a.shape,a.dtype,k);for(let R=0;R<m;++R){let P=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outHeight,(p.inHeight+b-R)/h);for(let D=0;D<g;++D){let T=Math.max(0,Math.ceil((x-D)/f)),O=Math.min(p.outWidth,(p.inWidth+x-D)/f);for(let B=0;B<p.inChannels;++B)for(let H=0;H<p.outChannels;++H){let z=0;for(let X=0;X<p.batchSize;++X)for(let ee=P;ee<$;++ee){let J=R+ee*h-b;for(let Q=T;Q<O;++Q){let te=D+Q*f-x;y?z+=S.get(X,J,te,B)*N.get(X,ee,Q,H):z+=S.get(X,B,J,te)*N.get(X,H,ee,Q)}}A.set(z,R,D,B,H)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var IH={kernelName:Ah,backendName:"cpu",kernelFunc:kH};function SH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s;Ne([r,a],"conv2dBackpropInput");let d=v.computeStrides(a.shape),p=v.computeStrides(r.shape),h=E.convertConv2DDataFormat(c),f=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,h),m=new tn(f.inShape,"float32"),g=m.values,y=n.data.get(r.dataId).values,A=n.data.get(a.dataId).values,[x,b,w]=d,{batchSize:k,filterHeight:S,filterWidth:N,inChannels:R,inHeight:P,inWidth:$,outChannels:D,outHeight:T,outWidth:O,strideHeight:B,strideWidth:H}=f;h=f.dataFormat;let z=S-1-f.padInfo.top,X=N-1-f.padInfo.left,ee=h==="channelsLast",J=m.strides[0],Q=ee?m.strides[1]:m.strides[2],te=ee?m.strides[2]:1,K=ee?1:m.strides[1],oe=p[0],ce=ee?p[1]:p[2],he=ee?p[2]:1,Ae=ee?1:p[1];for(let Ie=0;Ie<k;++Ie)for(let Se=0;Se<R;++Se)for(let Oe=0;Oe<P;++Oe){let Ue=Oe-z,ze=Math.max(0,Math.ceil(Ue/B)),wt=Math.min(T,(S+Ue)/B);for(let mt=0;mt<$;++mt){let gt=mt-X,pt=Math.max(0,Math.ceil(gt/H)),bt=Math.min(O,(N+gt)/H),Ye=0;for(let Ot=ze;Ot<wt;++Ot){let ps=Ot*B-Ue;for(let kn=pt;kn<bt;++kn){let Gs=kn*H-gt,Fn=oe*Ie+ce*Ot+he*kn,Ns=x*(S-1-ps)+b*(N-1-Gs)+w*Se;for(let Es=0;Es<D;++Es){let In=y[Fn+Ae*Es],Rs=A[Ns+Es];Ye+=In*Rs}}}let Yn=J*Ie+Q*Oe+te*mt+K*Se;g[Yn]=Ye}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var CH={kernelName:Ra,backendName:"cpu",kernelFunc:SH};function TH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Ne([r,a],"conv3d");let c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:u,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=c,y=g.front,A=g.left,x=g.top,b=new tn(c.outShape,r.dtype),w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,S=b.values,N=v.computeStrides(r.shape),R=v.computeStrides(a.shape);for(let P=0;P<c.batchSize;++P){let $=P*N[0],D=P*b.strides[0];for(let T=0;T<c.outDepth;++T){let O=D+T*b.strides[1],B=T*c.strideDepth-y;for(let H=0;H<u;++H){let z=B+H*h;if(z<0||z>=c.inDepth)continue;let X=H*R[0],ee=$+z*N[1];for(let J=0;J<c.outHeight;++J){let Q=O+J*b.strides[2],te=J*c.strideHeight-x;for(let K=0;K<d;++K){let oe=te+K*f;if(oe<0||oe>=c.inHeight)continue;let ce=X+K*R[1],he=ee+oe*N[2];for(let Ae=0;Ae<c.outWidth;++Ae){let Ie=Q+Ae*c.outChannels,Se=Ae*c.strideWidth-A;for(let Oe=0;Oe<p;++Oe){let Ue=Se+Oe*m;if(Ue<0||Ue>=c.inWidth)continue;let ze=ce+Oe*R[2],wt=he+Ue*c.inChannels,mt=ze;for(let gt=0;gt<c.inChannels;++gt){let pt=w[wt+gt];for(let bt=0;bt<c.outChannels;++bt)S[Ie+bt]+=pt*k[mt+bt];mt+=c.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var NH={kernelName:Xc,backendName:"cpu",kernelFunc:TH};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Ne([r,a],"conv3dBackpropFilterV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,y=d.filterWidth,A=new tn(d.filterShape,"float32"),x=A.values,[b,w,k,S]=A.strides,N=n.data.get(a.dataId).values,[R,P,$,D]=u,T=n.data.get(r.dataId).values,[O,B,H,z]=c,X=d.padInfo.front,ee=d.padInfo.left,J=d.padInfo.top;for(let Q=0;Q<m;++Q){let te=Math.max(0,Math.ceil((X-Q)/p)),K=Math.min(d.outDepth,(d.inDepth+X-Q)/p),oe=Q*b;for(let ce=0;ce<g;++ce){let he=Math.max(0,Math.ceil((J-ce)/h)),Ae=Math.min(d.outHeight,(d.inHeight+J-ce)/h),Ie=ce*w+oe;for(let Se=0;Se<y;++Se){let Oe=Math.max(0,Math.ceil((ee-Se)/f)),Ue=Math.min(d.outWidth,(d.inWidth+ee-Se)/f),ze=Se*k+Ie;for(let wt=0;wt<d.inChannels;++wt){let mt=wt*S+ze;for(let gt=0;gt<d.outChannels;++gt){let pt=0;for(let bt=0;bt<d.batchSize;++bt){let Ye=bt*O,Yn=bt*R;for(let Ot=te;Ot<K;++Ot){let kn=(Q+Ot*p-X)*B+Ye,Gs=Ot*P+Yn;for(let Fn=he;Fn<Ae;++Fn){let Es=(ce+Fn*h-J)*H+kn,In=Fn*$+Gs;for(let Rs=Oe;Rs<Ue;++Rs){let hs=(Se+Rs*f-ee)*z+Es,wr=Rs*D+In;pt+=T[hs+wt]*N[wr+gt]}}}}x[mt+gt]=pt}}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var RH={kernelName:xh,backendName:"cpu",kernelFunc:EH};function $H(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ne([r],"conv3dBackpropInputV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(l,a.shape,i,1,o),p=new tn(d.inShape,"float32"),h=p.values,[f,m,g,y]=p.strides,A=n.data.get(r.dataId).values,[x,b,w,k]=c,S=n.data.get(a.dataId).values,[N,R,P,$]=u,{batchSize:D,filterDepth:T,filterHeight:O,filterWidth:B,inChannels:H,inDepth:z,inHeight:X,inWidth:ee,outChannels:J,outDepth:Q,outHeight:te,outWidth:K,strideDepth:oe,strideHeight:ce,strideWidth:he}=d,Ae=T-1-d.padInfo.front,Ie=O-1-d.padInfo.top,Se=B-1-d.padInfo.left;for(let Oe=0;Oe<D;++Oe)for(let Ue=0;Ue<H;++Ue)for(let ze=0;ze<z;++ze){let wt=ze-Ae,mt=Math.max(0,Math.ceil(wt/oe)),gt=Math.min(Q,(T+wt)/oe);for(let pt=0;pt<X;++pt){let bt=pt-Ie,Ye=Math.max(0,Math.ceil(bt/ce)),Yn=Math.min(te,(O+bt)/ce);for(let Ot=0;Ot<ee;++Ot){let ps=Ot-Se,kn=Math.max(0,Math.ceil(ps/he)),Gs=Math.min(K,(B+ps)/he),Fn=0;for(let Ns=mt;Ns<gt;++Ns){let Es=Ns*oe-wt;for(let In=Ye;In<Yn;++In){let Rs=In*ce-bt;for(let $s=kn;$s<Gs;++$s){let hs=$s*he-ps,wr=x*Oe+b*Ns+w*In+k*$s,Wr=N*(T-1-Es)+R*(O-1-Rs)+P*(B-1-hs)+$*Ue;for(let fa=0;fa<J;++fa){let Dl=A[wr+fa],kr=S[Wr+fa];Fn+=Dl*kr}}}}h[f*Oe+m*ze+g*pt+y*Ot+Ue]=Fn}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var DH={kernelName:bh,backendName:"cpu",kernelFunc:$H},_H=xt($a,e=>Math.cos(e)),PH={kernelName:$a,backendName:"cpu",kernelFunc:_H},FH=xt(Da,e=>Math.cosh(e)),OH={kernelName:Da,backendName:"cpu",kernelFunc:FH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,y=We([f,m,g,h],"float32"),A=n.data.get(a.dataId).values,x=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(y.shape);for(let S=0;S<f;S++){let N=S*4,R=A[N],P=A[N+1],$=A[N+2],D=A[N+3],T=x[S];if(T>=u)continue;let O=m>1?($-R)*(d-1)/(m-1):0,B=g>1?(D-P)*(p-1)/(g-1):0;for(let H=0;H<m;H++){let z=m>1?R*(d-1)+H*O:.5*(R+$)*(d-1);if(z<0||z>d-1){for(let X=0;X<g;X++)for(let ee=0;ee<h;ee++){let J=ee+X*k[2]+H*k[1]+S*k[0];y.values[J]=c}continue}if(l==="bilinear"){let X=Math.floor(z),ee=Math.ceil(z),J=z-X;for(let Q=0;Q<g;Q++){let te=g>1?P*(p-1)+Q*B:.5*(P+D)*(p-1);if(te<0||te>p-1){for(let he=0;he<h;he++){let Ae=he+Q*k[2]+H*k[1]+S*k[0];y.values[Ae]=c}continue}let K=Math.floor(te),oe=Math.ceil(te),ce=te-K;for(let he=0;he<h;he++){let Ae=he+K*w[2]+X*w[1]+T*w[0],Ie=b[Ae];Ae=he+oe*w[2]+X*w[1]+T*w[0];let Se=b[Ae];Ae=he+K*w[2]+ee*w[1]+T*w[0];let Oe=b[Ae];Ae=he+oe*w[2]+ee*w[1]+T*w[0];let Ue=b[Ae],ze=Ie+(Se-Ie)*ce,wt=Oe+(Ue-Oe)*ce;Ae=he+Q*k[2]+H*k[1]+S*k[0],y.values[Ae]=ze+(wt-ze)*J}}}else for(let X=0;X<g;++X){let ee=g>1?P*(p-1)+X*B:.5*(P+D)*(p-1);if(ee<0||ee>p-1){for(let te=0;te<h;te++){let K=te+X*k[2]+H*k[1]+S*k[0];y.values[K]=c}continue}let J=Math.round(ee),Q=Math.round(z);for(let te=0;te<h;te++){let K=te+J*w[2]+Q*w[1]+T*w[0],oe=te+X*k[2]+H*k[1]+S*k[0];y.values[oe]=b[K]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var zH={kernelName:ai,backendName:"cpu",kernelFunc:MH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ne(r,"cumsum");let l=E.getAxesPermutation([a],r.shape.length),c=r;l!=null&&(c=Os({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=E.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let d=Ln(c.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=i?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<h.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=o?0:h[x];else{let b=m(y,A-1);p[x]=o?h[b]+p[b]:h[x]+p[b]}}let g=n.makeTensorInfo(c.shape,d,p);if(l!=null){let y=E.getUndoAxesPermutation(l),A=Os({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),A}return g}var BH={kernelName:ri,backendName:"cpu",kernelFunc:LH};function WH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=VA(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=v7(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var VH={kernelName:vh,backendName:"cpu",kernelFunc:WH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`),v.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],d=l*a,p=c*a,h=u/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let y=0;y<i;++y)for(let A=0;A<d;++A){let x=Math.floor(A/a),b=A%a;for(let w=0;w<p;++w){let k=Math.floor(w/a),S=w%a,N=(b*a+S)*h;for(let R=0;R<h;++R){let $=R+N+u*(k+c*(x+l*y));m[g++]=f[$]}}}return n.makeTensorInfo([i,d,p,h],r.dtype,m)}var GH={kernelName:oi,backendName:"cpu",kernelFunc:UH};function pI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s;Ne([r,a],"depthwiseConv2DNative");let u=v.computeStrides(r.shape),d=v.computeStrides(a.shape),p=l;p==null&&(p=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${p}'`);let h=E.computeConv2DInfo(r.shape,a.shape,o,p,i,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:A}=h,x=A.left,b=A.top,w=h.outChannels/h.inChannels,k=new tn(h.outShape,r.dtype),S=n.data.get(r.dataId).values,N=n.data.get(a.dataId).values,R=k.values;for(let P=0;P<h.batchSize;++P){let $=P*u[0],D=P*k.strides[0];for(let T=0;T<h.outHeight;++T){let O=D+T*k.strides[1],B=T*h.strideHeight-b;for(let H=0;H<f;++H){let z=B+H*g;if(z<0||z>=h.inHeight)continue;let X=H*d[0],ee=$+z*u[1];for(let J=0;J<h.outWidth;++J){let Q=O+J*k.strides[2],te=J*h.strideWidth-x;for(let K=0;K<m;++K){let oe=te+K*y;if(oe<0||oe>=h.inWidth)continue;let ce=X+K*d[1],he=ee+oe*h.inChannels,Ae=Q,Ie=ce;for(let Se=0;Se<h.inChannels;++Se){let Oe=S[he+Se];for(let Ue=0;Ue<w;++Ue)R[Ae+Ue]+=Oe*N[Ie+Ue];Ae+=w,Ie+=w}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var HH={kernelName:_a,backendName:"cpu",kernelFunc:pI};function jH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s;Ne([r,a],"depthwiseConv2dNativeBackpropFilter");let d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new tn(d.filterShape,"float32"),y=d.padInfo.left,A=d.padInfo.top,x=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,w=new tn(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,S=new tn(a.shape,a.dtype,k);for(let N=0;N<f;++N){let R=Math.max(0,Math.ceil((A-N)/p)),P=Math.min(d.outHeight,(d.inHeight+A-N)/p);for(let $=0;$<m;++$){let D=Math.max(0,Math.ceil((y-$)/h)),T=Math.min(d.outWidth,(d.inWidth+y-$)/h);for(let O=0;O<d.outChannels;++O){let B=Math.trunc(O/x),H=O%x,z=0;for(let X=0;X<d.batchSize;++X)for(let ee=R;ee<P;++ee){let J=N+ee*p-A;for(let Q=D;Q<T;++Q){let te=$+Q*h-y;z+=w.get(X,J,te,B)*S.get(X,ee,Q,O)}}g.set(z,N,$,B,H)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var qH={kernelName:wh,backendName:"cpu",kernelFunc:jH};function XH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new tn(h.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=n.data.get(r.dataId).values,[b,w,k]=d,S=n.data.get(a.dataId).values,[N,R,P]=p,{batchSize:$,filterHeight:D,filterWidth:T,inChannels:O,inHeight:B,inWidth:H,outChannels:z,outHeight:X,outWidth:ee,strideHeight:J,strideWidth:Q}=h,te=D-1-h.padInfo.top,K=T-1-h.padInfo.left,oe=z/O;for(let ce=0;ce<$;++ce)for(let he=0;he<O;++he)for(let Ae=0;Ae<B;++Ae){let Ie=Ae-te,Se=Math.max(0,Math.ceil(Ie/J)),Oe=Math.min(X,(D+Ie)/J);for(let Ue=0;Ue<H;++Ue){let ze=Ue-K,wt=Math.max(0,Math.ceil(ze/Q)),mt=Math.min(ee,(T+ze)/Q),gt=0;for(let pt=Se;pt<Oe;++pt){let bt=pt*J-Ie;for(let Ye=wt;Ye<mt;++Ye){let Yn=Ye*Q-ze,Ot=b*ce+w*pt+k*Ye,ps=N*(D-1-bt)+R*(T-1-Yn)+P*he;for(let kn=0;kn<oe;++kn){let Gs=he*oe+kn,Fn=x[Ot+Gs],Ns=S[ps+kn];gt+=Fn*Ns}}}m[g*ce+y*Ae+A*Ue+he]=gt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var KH={kernelName:kh,backendName:"cpu",kernelFunc:XH};function ZH(e){let{inputs:t,backend:n}=e,{x:s}=t,r=v.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=We([r,r],s.dtype),i=o.values;for(let c=0;c<a.length;c++)i[c*r+c]=a[c];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var YH={kernelName:Ih,backendName:"cpu",kernelFunc:ZH},JH={kernelName:Kc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,c=l.data.get(s.dataId).values,u=s.shape.length,d=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:A,padInfo:x,strideHeight:b,strideWidth:w,filterHeight:k,filterWidth:S,dilationHeight:N,dilationWidth:R,outShape:P}=E.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),$=v.sizeFromShape(P),D=P.length,T=v.getArrayFromDType(s.dtype,$);for(let B=0;B<h;++B)for(let H=0;H<y;++H){let z=H*b-x.top;for(let X=0;X<A;++X){let ee=X*w-x.left;for(let J=0;J<g;++J){let Q=Number.MIN_SAFE_INTEGER;for(let K=0;K<k;++K){let oe=z+K*N;if(oe>=0&&oe<f)for(let ce=0;ce<S;++ce){let he=ee+ce*R;if(he>=0&&he<m){let Ae=v.locToIndex([B,oe,he,J],u,v.computeStrides(s.shape)),Ie=v.locToIndex([K,ce,J],p,v.computeStrides(r.shape)),Se=c[Ae]+d[Ie];Se>Q&&(Q=Se)}}}let te=v.locToIndex([B,H,X,J],D,v.computeStrides(P));T[te]=Q}}}return{dataId:l.write(v.toTypedArray(T,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},QH={kernelName:Ch,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:S,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Ch}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let P=v.toNestedArray(R,c.data.get(a.dataId).values),$=v.makeZerosNestedTypedArray(r.shape,r.dtype);for(let T=0;T<p;++T)for(let O=0;O<g;++O){let B=O*x-A.top;for(let H=0;H<y;++H){let z=H*b-A.left;for(let X=0;X<m;++X){let ee=Number.MIN_SAFE_INTEGER,J=0,Q=0;for(let te=0;te<w;++te){let K=B+te*S;if(K>=0&&K<h)for(let oe=0;oe<k;++oe){let ce=z+oe*N;if(ce>=0&&ce<f){let he=u[T][K][ce][X]+d[te][oe][X];he>ee&&(ee=he,J=te,Q=oe)}}}$[J][Q][X]+=P[T][O][H][X]}}}return{dataId:c.write(v.toTypedArray($,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},ej={kernelName:Sh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:o,pad:i,dilations:l}=n,c=t,u=v.toNestedArray(s.shape,c.data.get(s.dataId).values),d=v.toNestedArray(r.shape,c.data.get(r.dataId).values),{batchSize:p,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:y,padInfo:A,strideHeight:x,strideWidth:b,filterHeight:w,filterWidth:k,dilationHeight:S,dilationWidth:N,outShape:R}=E.computeDilation2DInfo(s.shape,r.shape,o,i,"NHWC",l);v.assert(a.rank===R.length,()=>`Error in ${Sh}, dy must have the same rank as output ${R.length}, but got ${a.rank}`);let P=v.toNestedArray(R,c.data.get(a.dataId).values),$=v.makeZerosNestedTypedArray(s.shape,s.dtype);for(let T=0;T<p;++T)for(let O=0;O<g;++O){let B=O*x-A.top;for(let H=0;H<y;++H){let z=H*b-A.left;for(let X=0;X<m;++X){let ee=Number.MIN_SAFE_INTEGER,J=B<0?0:B,Q=z<0?0:z;for(let te=0;te<w;++te){let K=B+te*S;if(K>=0&&K<h)for(let oe=0;oe<k;++oe){let ce=z+oe*N;if(ce>=0&&ce<f){let he=u[T][K][ce][X]+d[te][oe][X];he>ee&&(ee=he,J=K,Q=ce)}}}$[T][J][Q][X]+=P[T][O][H][X]}}}return{dataId:c.write(v.toTypedArray($,s.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}};function ap(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"sum");let i;r.dtype==="bool"?i=Fo({inputs:{x:r},backend:n,attrs:{dtype:"int32"}}):i=Or({inputs:{x:r},backend:n});let l=i.shape.length,c=v.parseAxisParam(a,i.shape),u=E.getAxesPermutation(c,l),d=c,p=i;u!=null&&(p=Os({inputs:{x:i},backend:n,attrs:{perm:u}}),d=E.getInnerMostAxes(d.length,l)),E.assertAxesAreInnerMostDims("sum",d,p.shape.length);let[h,f]=E.computeOutAndReduceShapes(p.shape,d),m=E.upcastType(p.dtype,"int32"),g=Sm(n,h,m),y=v.sizeFromShape(f),A=n.data.get(g.dataId).values,x=n.data.get(p.dataId).values;for(let b=0;b<A.length;++b){let w=b*y,k=0;for(let S=0;S<y;++S)k+=x[w+S];A[b]=k}if(o){let b=E.expandShapeToKeepDim(g.shape,c),w=g;g=Et({inputs:{x:g},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(i),u!=null&&n.disposeIntermediateTensorInfo(p),g}var tj={kernelName:io,backendName:"cpu",kernelFunc:ap};function nj(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=a[g]:(x=Os({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=Et({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=Cm({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=ap({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var sj={kernelName:Zc,backendName:"cpu",kernelFunc:nj};function rj(e){let{inputs:t,backend:n}=e,{dy:s,y:r}=t;Ne([s,r],"eluGrad");let a=new Float32Array(v.sizeFromShape(r.shape)),o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values;for(let l=0;l<o.length;++l){let c=o[l];c>=1?a[l]=i[l]:a[l]=i[l]*(c+1)}return n.makeTensorInfo(r.shape,"float32",a)}var aj={kernelName:Th,backendName:"cpu",kernelFunc:rj},oj=E.ERF_P,ij=E.ERF_A1,lj=E.ERF_A2,uj=E.ERF_A3,cj=E.ERF_A4,dj=E.ERF_A5,pj=xt(su,e=>{let t=Math.sign(e),n=Math.abs(e),s=1/(1+oj*n);return t*(1-((((dj*s+cj)*s+uj)*s+lj)*s+ij)*s*Math.exp(-n*n))}),hj={kernelName:su,backendName:"cpu",kernelFunc:pj};function Nm(e){let{inputs:t,backend:n,attrs:s}=e,{input:r}=t,{dim:a}=s,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),Et({inputs:{x:r},backend:n,attrs:{shape:i}})}var fj={kernelName:li,backendName:"cpu",kernelFunc:Nm},mj=Jt((e,t)=>e/t),YA=wn(Pa,mj),JA={kernelName:Pa,backendName:"cpu",kernelFunc:YA};function hI(e,t,n){let s=e.shape,r=s[0],a=s[1],o=n.data.get(e.dataId),i=o.complexTensorInfos.real,l=o.complexTensorInfos.imag,c=[r,a],u=v.sizeFromShape(c),d=v.getTypedArrayFromDType("float32",u),p=v.getTypedArrayFromDType("float32",u);for(let g=0;g<r;g++){let y=fl({inputs:{x:i},backend:n,attrs:{begin:[g,0],size:[1,a]}}),A=fl({inputs:{x:l},backend:n,attrs:{begin:[g,0],size:[1,a]}}),x=ks({inputs:{real:y,imag:A},backend:n}),{real:b,imag:w}=gj(x,t,n),k=E.mergeRealAndImagArrays(b,w);for(let S=0;S<a;S++){let N=E.getComplexWithIndex(k,S);d[g*a+S]=N.real,p[g*a+S]=N.imag}n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(x)}let h=n.makeTensorInfo(c,"float32",d),f=n.makeTensorInfo(c,"float32",p),m=ks({inputs:{real:h,imag:f},backend:n});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}function gj(e,t,n){let s=v.sizeFromShape(e.shape),r=n.data.get(e.dataId),a=n.data.get(r.complexTensorInfos.real.dataId).values,o=n.data.get(r.complexTensorInfos.imag.dataId).values;if(yj(s)){let i=QA(a,o,s,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",i.real),u=n.makeTensorInfo(l,"float32",i.imag),d=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),p=Or({inputs:{x:d},backend:n}),h=JA.kernelFunc({inputs:{a:c,b:d},backend:n}),f=JA.kernelFunc({inputs:{a:u,b:p},backend:n}),m=n.data.get(h.dataId).values,g=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),{real:m,imag:g}}return i}else{let i=E.mergeRealAndImagArrays(a,o),l=Aj(i,s,t);return E.splitRealAndImagArrays(l)}}function yj(e){return(e&e-1)==0}function QA(e,t,n,s,r){if(n===1)return{real:e,imag:t};let a=E.mergeRealAndImagArrays(e,t),o=n/2,i=E.complexWithEvenIndex(a),l=i.real,c=i.imag,u=[l.length],d=r.makeTensorInfo(u,"float32",l),p=r.makeTensorInfo(u,"float32",c),h=ks({inputs:{real:d,imag:p},backend:r}),f=E.complexWithOddIndex(a),m=f.real,g=f.imag,y=[m.length],A=r.makeTensorInfo(y,"float32",m),x=r.makeTensorInfo(y,"float32",g),b=ks({inputs:{real:A,imag:x},backend:r}),w=QA(l,c,o,s,r),k=w.real,S=w.imag,N=[k.length],R=r.makeTensorInfo(N,"float32",k),P=r.makeTensorInfo(N,"float32",S),$=ks({inputs:{real:R,imag:P},backend:r}),D=QA(m,g,o,s,r),T=D.real,O=D.imag,B=[T.length],H=r.makeTensorInfo(B,"float32",T),z=r.makeTensorInfo(B,"float32",O),X=ks({inputs:{real:H,imag:z},backend:r}),ee=E.exponents(n,s),J=[ee.real.length],Q=r.makeTensorInfo(J,"float32",ee.real),te=r.makeTensorInfo(J,"float32",ee.imag),K=ks({inputs:{real:Q,imag:te},backend:r}),oe=Cm({inputs:{a:K,b:X},backend:r}),ce=sp({inputs:{a:$,b:oe},backend:r}),he=XA({inputs:{a:$,b:oe},backend:r}),Ae=hl({inputs:{input:ce},backend:r}),Ie=hl({inputs:{input:he},backend:r}),Se=Ju({inputs:{input:ce},backend:r}),Oe=Ju({inputs:{input:he},backend:r}),Ue=Qu({inputs:[Ae,Ie],backend:r,attrs:{axis:0}}),ze=Qu({inputs:[Se,Oe],backend:r,attrs:{axis:0}}),wt=r.data.get(Ue.dataId).values,mt=r.data.get(ze.dataId).values;return r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(b),r.disposeIntermediateTensorInfo(R),r.disposeIntermediateTensorInfo(P),r.disposeIntermediateTensorInfo($),r.disposeIntermediateTensorInfo(H),r.disposeIntermediateTensorInfo(z),r.disposeIntermediateTensorInfo(X),r.disposeIntermediateTensorInfo(Q),r.disposeIntermediateTensorInfo(te),r.disposeIntermediateTensorInfo(K),r.disposeIntermediateTensorInfo(oe),r.disposeIntermediateTensorInfo(ce),r.disposeIntermediateTensorInfo(he),r.disposeIntermediateTensorInfo(Ae),r.disposeIntermediateTensorInfo(Se),r.disposeIntermediateTensorInfo(Ie),r.disposeIntermediateTensorInfo(Oe),r.disposeIntermediateTensorInfo(Ue),r.disposeIntermediateTensorInfo(ze),{real:wt,imag:mt}}function Aj(e,t,n){let s=new Float32Array(t*2);for(let r=0;r<t;r++){let a=0,o=0;for(let i=0;i<t;i++){let l=E.exponent(r*i,t,n),c=E.getComplexWithIndex(e,i);a+=c.real*l.real-c.imag*l.imag,o+=c.real*l.imag+c.imag*l.real}n&&(a/=t,o/=t),E.assignToTypedArray(s,a,o,r)}return s}function xj(e){let{inputs:t,backend:n}=e,{input:s}=t,r=v.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=Et({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=hI(i,!1,n),c=Et({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var bj={kernelName:Nh,backendName:"cpu",kernelFunc:xj};function ex(e){let{backend:t,attrs:n}=e,{shape:s,value:r,dtype:a}=n,o=a||v.inferDtype(r),i=v.getArrayFromDType(o,v.sizeFromShape(s));return wj(i,r,o),t.makeTensorInfo(s,o,i)}var vj={kernelName:ru,backendName:"cpu",kernelFunc:ex};function wj(e,t,n){e.fill(t)}var kj={kernelName:ci,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,r=n,a=v.getTypedArrayFromDType(s.dtype,v.sizeFromShape(s.shape)),[o,i,l,c]=s.shape,u=r.data.get(s.dataId).values;for(let p=0;p<o;p++){let h=p*l*i*c;for(let f=0;f<i;f++){let m=f*(l*c);for(let g=0;g<l;g++){let y=g*c;for(let A=0;A<c;A++){let x=Math.round(l-g-1),b=h+m+y+A,w=u[b];if(x>=0&&x<l){let k=x*c,S=h+m+k+A;w=u[S]}a[b]=w}}}}return{dataId:r.write(a,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},Ij=Jt((e,t)=>Math.floor(e/t)),Sj=wn(za,Ij,null,"int32"),Cj={kernelName:za,backendName:"cpu",kernelFunc:Sj};function Tj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=dI({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=sp({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=KA(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Nj={kernelName:go,backendName:"cpu",kernelFunc:Tj};function Ej(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=pI({inputs:{x:r,filter:a},backend:n,attrs:{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p}});if(o){let g=m;m=sp({inputs:{a:m,b:o},backend:n}),n.disposeIntermediateTensorInfo(g)}if(h){let g=m;m=KA(n,m,h,i,f),n.disposeIntermediateTensorInfo(g)}return m}var Rj={kernelName:yo,backendName:"cpu",kernelFunc:Ej};function $j(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=v.sizeFromShape(s.shape),o=r.shape,i=o[o.length-1],[l,c,u,d]=E.prepareAndValidate(s,r);if(c===0)return n.makeTensorInfo(l,s.dtype,[]);let p=n.data.get(r.dataId).values,h=n.bufferSync(s),f=E7(p,h,s.dtype,c,i,u,d,s.shape,a);return n.makeTensorInfo(l,s.dtype,f.values)}var Dj={kernelName:pi,backendName:"cpu",kernelFunc:$j};function _j(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s;Ne([r,a],"gatherV2");let l=i;i==null&&(l=0);let c=v.sizeFromShape(a.shape),u=v.parseAxisParam(o,r.shape)[0],d=E.segment_util.collectGatherOpShapeInfo(r,a,u,l),p=Et({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),h=Et({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,c/d.batchSize]}}),f=[d.batchSize,d.outerSize,c/d.batchSize,d.sliceSize],m=n.bufferSync(h),g=n.bufferSync(p),y=R7(g,m,f);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.makeTensorInfo(d.outputShape,y.dtype,y.values)}var Pj={kernelName:di,backendName:"cpu",kernelFunc:_j};function Fj(e){let{inputs:t,backend:n}=e,{input:s}=t,r=v.sizeFromShape(s.shape),a=s.shape[s.shape.length-1],o=r/a,i=Et({inputs:{x:s},backend:n,attrs:{shape:[o,a]}}),l=hI(i,!0,n),c=Et({inputs:{x:l},backend:n,attrs:{shape:s.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var Oj={kernelName:Eh,backendName:"cpu",kernelFunc:Fj},Mj=xt(au,e=>Number.isFinite(e)?1:0,"bool"),zj={kernelName:au,backendName:"cpu",kernelFunc:Mj},Lj=xt(ou,e=>Math.abs(e)===1/0?1:0,"bool"),Bj={kernelName:ou,backendName:"cpu",kernelFunc:Lj},Wj=xt(iu,e=>Number.isNaN(e)?1:0,"bool"),Vj={kernelName:iu,backendName:"cpu",kernelFunc:Wj};function Uj(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=F7(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var Gj={kernelName:Rh,backendName:"cpu",kernelFunc:Uj},Hj=xt(lu,e=>Math.log1p(e)),jj={kernelName:lu,backendName:"cpu",kernelFunc:Hj},qj=Jt((e,t)=>e&&t),Xj=wn(yi,qj,null,"bool"),Kj={kernelName:yi,backendName:"cpu",kernelFunc:Xj},Zj=xt(uu,e=>e?0:1,"bool"),Yj={kernelName:uu,backendName:"cpu",kernelFunc:Zj},Jj=Jt((e,t)=>e||t),Qj=wn(Jc,Jj,null,"bool"),eq={kernelName:Jc,backendName:"cpu",kernelFunc:Qj};function tq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s;Ne(r,"LRN");let c=r.shape[3],u=c-1,d=n.data.get(r.dataId).values,p=v.sizeFromShape(r.shape),h=new Float32Array(p);function f(m){let g=m%c,y=m-g+Math.max(0,g-a),A=m-g+Math.min(g+a,u),x=0;for(;y<=A;y++){let b=d[y];x+=b*b}return x}for(let m=0;m<p;m++){let g=f(m),y=d[m]*Math.pow(o+i*g,-l);h[m]=y}return n.makeTensorInfo(r.shape,r.dtype,h)}var nq={kernelName:Qc,backendName:"cpu",kernelFunc:tq};function sq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s;Ne(o,"LRNGrad");let d=v.sizeFromShape(o.shape),p=o.shape[3],h=n.data.get(o.dataId).values,f=n.data.get(r.dataId).values,m=n.data.get(a.dataId).values,g=new Float32Array(d),y=d;for(let A=0;A<y;A++){let x=A%p,b=A-x+Math.max(0,x-i),w=A-x+Math.min(p,x+i+1),k=0;for(let S=b;S<w;S++)k+=Math.pow(f[S],2);k=c*k+l;for(let S=b;S<w;S++){let N=-2*c*u*f[S]*m[A]/k;A===S&&(N+=Math.pow(k,-u)),N*=h[A],g[S]+=N}}return n.makeTensorInfo(o.shape,r.dtype,g)}var rq={kernelName:$h,backendName:"cpu",kernelFunc:sq};function fI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=n,l=r.shape,c=l.length,u=v.parseAxisParam(a,l),d=u,p=E.getAxesPermutation(d,c),h=i.data.get(r.dataId).values;if(p!=null){let b=new Array(c);for(let w=0;w<b.length;w++)b[w]=l[p[w]];h=HA(h,l,r.dtype,p,b),d=E.getInnerMostAxes(d.length,c),l=b}Ne(r,"max"),E.assertAxesAreInnerMostDims("max",d,c);let[f,m]=E.computeOutAndReduceShapes(l,d),g=v.sizeFromShape(m),y=M7(h,g,f,r.dtype),A=i.write(y,f,r.dtype),x=f;return o&&(x=E.expandShapeToKeepDim(f,u)),{dataId:A,shape:x,dtype:r.dtype}}var aq={kernelName:Ua,backendName:"cpu",kernelFunc:fI};function oq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Ne(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l),d;if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))d=Or({inputs:{x:r},backend:n});else{let p=n.data.get(r.dataId).values,h=v.computeStrides(r.shape),f=ZA(p,r.shape,r.dtype,h,u,"max");d=n.makeTensorInfo(u.outShape,r.dtype,f.values)}return d}var iq={kernelName:Ha,backendName:"cpu",kernelFunc:oq};function lq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s;Ne(r,"maxPool3d");let u=E.computePool3DInfo(r.shape,a,o,1,i,l,c),d=n.data.get(r.dataId).values,p=cI(d,r.shape,r.dtype,v.computeStrides(r.shape),u,"max");return n.makeTensorInfo(p.shape,"float32",p.values)}var uq={kernelName:ed,backendName:"cpu",kernelFunc:lq};function cq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=s;Ne([r,a],"maxPool3DGrad");let u=E.computePool3DInfo(a.shape,o,i,1,l,c),d=n.bufferSync(a),p=QG(d,u),h=u.strideDepth,f=u.strideHeight,m=u.strideWidth,g=u.dilationDepth,y=u.dilationHeight,A=u.dilationWidth,x=u.effectiveFilterDepth,b=u.effectiveFilterHeight,w=u.effectiveFilterWidth,k=x-1-u.padInfo.front,S=w-1-u.padInfo.left,N=b-1-u.padInfo.top,R=We(a.shape,"float32"),P=n.bufferSync(r);for(let $=0;$<u.batchSize;++$)for(let D=0;D<u.inChannels;++D)for(let T=0;T<u.inDepth;++T)for(let O=0;O<u.inHeight;++O)for(let B=0;B<u.inWidth;++B){let H=T-k,z=O-N,X=B-S,ee=0;for(let J=0;J<x;J+=g){let Q=(H+J)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let te=0;te<b;te+=y){let K=(z+te)/f;if(!(K<0||K>=u.outHeight||Math.floor(K)!==K))for(let oe=0;oe<w;oe+=A){let ce=(X+oe)/m;if(ce<0||ce>=u.outWidth||Math.floor(ce)!==ce)continue;let he=x*b*w-1-p.get($,Q,K,ce,D),Ae=J*b*w+te*w+oe,Ie=he===Ae?1:0;if(Ie===0)continue;ee+=P.get($,Q,K,ce,D)*Ie}}}R.set(ee,$,T,O,B,D)}return n.makeTensorInfo(R.shape,R.dtype,R.values)}var dq={kernelName:_h,backendName:"cpu",kernelFunc:cq};function pq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Ne([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=n.data.get(i.dataId).values,f=We(p.outShape,i.dtype,uI(h,i.shape,i.dtype,p).values),m=p.strideHeight,g=p.strideWidth,y=p.dilationHeight,A=p.dilationWidth,x=p.effectiveFilterHeight,b=p.effectiveFilterWidth,w=b-1-p.padInfo.left,k=x-1-p.padInfo.top,S=We(i.shape,"float32"),N=n.data.get(r.dataId).values,R=We(r.shape,"float32",N);for(let P=0;P<p.batchSize;++P)for(let $=0;$<p.inChannels;++$)for(let D=0;D<p.inHeight;++D)for(let T=0;T<p.inWidth;++T){let O=D-k,B=T-w,H=0;for(let z=0;z<x;z+=y){let X=(O+z)/m;if(!(X<0||X>=p.outHeight||Math.floor(X)!==X))for(let ee=0;ee<b;ee+=A){let J=(B+ee)/g;if(J<0||J>=p.outWidth||Math.floor(J)!==J)continue;let Q=x*b-1-f.get(P,X,J,$),te=z*b+ee,K=Q===te?1:0;if(K===0)continue;H+=R.get(P,X,J,$)*K}}S.set(H,P,D,T,$)}return n.makeTensorInfo(S.shape,S.dtype,S.values)}var hq={kernelName:Dh,backendName:"cpu",kernelFunc:pq};function fq(e,t,n,s,r){let a=v.computeStrides(t),o=ZA(e,t,n,a,r,"max"),i=uI(e,t,n,r,!0,s);return[o.values,i.values]}var mq={kernelName:Ph,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;Ne(s,"MaxPoolWithArgmax");let c=l.data.get(s.dataId).values,u=E.computePool2DInfo(s.shape,r,a,[1,1],o),[d,p]=fq(c,s.shape,s.dtype,i,u),h=l.write(d,u.outShape,s.dtype),f=l.write(p,u.outShape,s.dtype);return[{dataId:h,shape:u.outShape,dtype:s.dtype},{dataId:f,shape:u.outShape,dtype:"int32"}]}};function gq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=v.parseAxisParam(a,r.shape),c=E.computeOutAndReduceShapes(r.shape,i)[1],u=v.sizeFromShape(c),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([u]));d.push(p);let h=Fo({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(h);let f=YA({inputs:{a:h,b:p},backend:n});d.push(f);let m=ap({inputs:{x:f},backend:n,attrs:{axis:a,keepDims:o}});return d.forEach(g=>n.disposeIntermediateTensorInfo(g)),m}var yq={kernelName:ja,backendName:"cpu",kernelFunc:gq};function Aq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"min");let i=v.parseAxisParam(a,r.shape),l=i,c=E.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Os({inputs:{x:r},backend:n,attrs:{perm:c}}),l=E.getInnerMostAxes(l.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",l,u.shape.length);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let A=y*h,x=m[A];for(let b=0;b<h;++b){let w=m[A+b];(Number.isNaN(w)||w<x)&&(x=w)}f[y]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let y=E.expandShapeToKeepDim(d,i),A=Et({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),A}return g}var xq={kernelName:qa,backendName:"cpu",kernelFunc:Aq};function bq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,mode:o}=s;Ne(r,"mirrorPad");let i=a.map((x,b)=>x[0]+r.shape[b]+x[1]),l=a.map(x=>x[0]),c=a.map((x,b)=>x[0]+r.shape[b]),u=o==="reflect"?0:1,d=n.data.get(r.dataId).values,p=r.shape.length,h=v.computeStrides(r.shape),f=v.sizeFromShape(i),m=i.length,g=v.computeStrides(i),y=v.getTypedArrayFromDType(r.dtype,f);for(let x=0;x<f;x++){let b=v.indexToLoc(x,m,g);for(let k=0;k<m;k++)b[k]<l[k]?b[k]=l[k]*2-b[k]-u:b[k]>=c[k]&&(b[k]=(c[k]-1)*2-b[k]+u);b=b.map((k,S)=>k-l[S]);let w=v.locToIndex(b,p,h);y[x]=d[w]}return{dataId:n.write(y,i,r.dtype),shape:i,dtype:r.dtype}}var vq={kernelName:Ka,backendName:"cpu",kernelFunc:bq},wq=Jt((e,t)=>{let n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t}),kq=wn(cu,wq),Iq={kernelName:cu,backendName:"cpu",kernelFunc:kq},Sq=Jo(p5());function mI(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=r.shape.length,i=a;if(i===-1&&(i=o-1),i!==o-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${o} and dim was ${i}`);let l=v.parseAxisParam([i],r.shape),c=fI({inputs:{x:r},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),u=E.expandShapeToKeepDim(c.shape,l),d=Et({inputs:{x:c},backend:n,attrs:{shape:u}}),p=XA({inputs:{a:r,b:d},backend:n}),h=C7({inputs:{x:p},backend:n}),f=ap({inputs:{x:h},backend:n,attrs:{axis:l,keepDims:!1}}),m=Et({inputs:{x:f},backend:n,attrs:{shape:u}}),g=YA({inputs:{a:h,b:m},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Cq={kernelName:lo,backendName:"cpu",kernelFunc:mI};function Tq(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s;Ne(r,"multinomial");let l=i?r:mI({inputs:{logits:r},backend:n,attrs:{dim:-1}}),c=l.shape[0],u=l.shape[1],d=n.data.get(l.dataId).values,p=[c,a],h=v.makeZerosTypedArray(v.sizeFromShape(p),"int32");for(let f=0;f<c;++f){let m=f*u,g=new Float32Array(u-1);g[0]=d[m];for(let x=1;x<g.length;++x)g[x]=g[x-1]+d[m+x];let y=Sq.alea(o.toString()),A=f*a;for(let x=0;x<a;++x){let b=y();h[A+x]=g.length;for(let w=0;w<g.length;w++)if(b<g[w]){h[A+x]=w;break}}}return i||n.disposeIntermediateTensorInfo(l),n.makeTensorInfo(p,"int32",h)}var Nq={kernelName:Fh,backendName:"cpu",kernelFunc:Tq},Eq=Zs.nonMaxSuppressionV3Impl;function Rq(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s;Ne(r,"NonMaxSuppression");let c=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,{selectedIndices:d}=Eq(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var $q={kernelName:bi,backendName:"cpu",kernelFunc:Rq},Dq=Zs.nonMaxSuppressionV4Impl;function _q(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s;Ne(r,"NonMaxSuppressionPadded");let u=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,{selectedIndices:p,validOutputs:h}=Dq(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Pq={kernelName:du,backendName:"cpu",kernelFunc:_q},Fq=Zs.nonMaxSuppressionV5Impl;function Oq(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s;Ne(r,"NonMaxSuppressionWithScore");let u=n.data.get(r.dataId).values,d=n.data.get(a.dataId).values,p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Fq(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Mq={kernelName:vi,backendName:"cpu",kernelFunc:Oq};function zq(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s;Ne(r,"oneHot");let l=v.sizeFromShape(r.shape),c=new Float32Array(l*a);c.fill(i);let u=n.data.get(r.dataId).values;for(let d=0;d<l;++d)u[d]>=0&&u[d]<a&&(c[d*a+u[d]]=o);return n.makeTensorInfo([...r.shape,a],"int32",c)}var Lq={kernelName:ki,backendName:"cpu",kernelFunc:zq};function Em(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(s.dtype==="complex64"){let r=hl({inputs:{input:s},backend:n}),a=Em({inputs:{x:r},backend:n}),o=Ju({inputs:{input:s},backend:n}),i=Em({inputs:{x:o},backend:n}),l=ks({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return ex({backend:n,attrs:{shape:s.shape,value:0,dtype:s.dtype}})}var Bq={kernelName:Li,backendName:"cpu",kernelFunc:Em};function gI(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(s.dtype==="complex64"){let r=hl({inputs:{input:s},backend:n}),a=gI({inputs:{x:r},backend:n}),o=Ju({inputs:{input:s},backend:n}),i=Em({inputs:{x:o},backend:n}),l=ks({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return ex({backend:n,attrs:{shape:s.shape,value:1,dtype:s.dtype}})}var Wq={kernelName:wi,backendName:"cpu",kernelFunc:gI};function yI(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Nm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Nm({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=Qu({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Vq={kernelName:Ii,backendName:"cpu",kernelFunc:yI};function Uq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;Ne(r,"pad");let i=a.map((A,x)=>A[0]+r.shape[x]+A[1]),l=a.map(A=>A[0]),c=n.data.get(r.dataId).values,u=v.sizeFromShape(r.shape),d=r.shape.length,p=v.computeStrides(r.shape),h=v.sizeFromShape(i),f=i.length,m=v.computeStrides(i),g=v.getTypedArrayFromDType(r.dtype,h);o!==0&&g.fill(o);for(let A=0;A<u;A++){let b=v.indexToLoc(A,d,p).map((k,S)=>k+l[S]),w=v.locToIndex(b,f,m);g[w]=c[A]}return{dataId:n.write(g,i,r.dtype),shape:i,dtype:r.dtype}}var AI={kernelName:Ya,backendName:"cpu",kernelFunc:Uq},Gq=Jt((e,t)=>Math.pow(e,t)),Hq=wn(Ja,Gq),jq={kernelName:Ja,backendName:"cpu",kernelFunc:Hq};function qq(e){let{backend:t,attrs:n}=e,{start:s,stop:r,dtype:a,step:o}=n,i=jA(s,r,o,a);return t.makeTensorInfo([i.length],a,i)}var Xq={kernelName:pu,backendName:"cpu",kernelFunc:qq},Kq=xt(hu,e=>1/e),Zq={kernelName:hu,backendName:"cpu",kernelFunc:Kq};function Yq(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ne(r,"resizeBilinear");let l=v.computeStrides(r.shape),[c,u]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(v.sizeFromShape([d,c,u,f])),y=[a&&c>1?p-1:p,a&&u>1?h-1:h],A=[a&&c>1?c-1:c,a&&u>1?u-1:u],x=0,b=y[0]/A[0],w=y[1]/A[1];for(let k=0;k<d;k++)for(let S=0;S<c;S++){let N;o?N=b*(S+.5)-.5:N=b*S;let R=Math.max(0,Math.floor(N)),P=N-R,$=Math.min(p-1,Math.ceil(N)),D=k*l[0]+R*l[1],T=k*l[0]+$*l[1];for(let O=0;O<u;O++){let B;o?B=w*(O+.5)-.5:B=w*O;let H=Math.max(0,Math.floor(B)),z=B-H,X=Math.min(h-1,Math.ceil(B)),ee=D+H*l[2],J=T+H*l[2],Q=D+X*l[2],te=T+X*l[2];for(let K=0;K<f;K++){let oe=m[ee+K],ce=m[J+K],he=m[Q+K],Ae=m[te+K],Ie=oe+(he-oe)*z,Se=ce+(Ae-ce)*z,Oe=Ie+(Se-Ie)*P;g[x++]=Oe}}}return n.makeTensorInfo([d,c,u,f],"float32",g)}var Jq={kernelName:to,backendName:"cpu",kernelFunc:Yq};function Qq(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ne([a,r],"resizeBilinearGrad");let i=v.computeStrides(r.shape),[l,c,u,d]=r.shape,[,p,h]=a.shape,f=new Float32Array(l*c*u*d),m=[o&&p>1?c-1:c,o&&h>1?u-1:u],g=[o&&p>1?p-1:p,o&&h>1?h-1:h],y=m[0]/g[0],A=m[1]/g[1],x=n.data.get(a.dataId).values,b=0;for(let w=0;w<l;w++){let k=w*i[0];for(let S=0;S<p;S++){let N=S*y,R=Math.floor(N),P=Math.min(Math.ceil(N),c-1),$=k+R*i[1],D=k+P*i[1],T=N-R,O=1-T;for(let B=0;B<h;B++){let H=B*A,z=Math.floor(H),X=Math.min(Math.ceil(H),u-1),ee=H-z,J=1-ee,Q=$+z*i[2],te=$+X*i[2],K=D+z*i[2],oe=D+X*i[2],ce=O*J,he=O*ee,Ae=T*J,Ie=T*ee;for(let Se=0;Se<d;Se++){let Oe=x[b++];f[Q+Se]+=Oe*ce,f[te+Se]+=Oe*he,f[K+Se]+=Oe*Ae,f[oe+Se]+=Oe*Ie}}}}return n.makeTensorInfo([l,u,c,d],"float32",f)}var eX={kernelName:Mh,backendName:"cpu",kernelFunc:Qq};function tX(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s;Ne(r,"resizeNearestNeighbor");let l=v.computeStrides(r.shape),[c,u]=i,[d,p,h,f]=r.shape,m=n.data.get(r.dataId).values,g=new Float32Array(d*c*u*f),y=[a&&c>1?p-1:p,a&&u>1?h-1:h],A=[a&&c>1?c-1:c,a&&u>1?u-1:u],x=y[0]/A[0],b=y[1]/A[1],w=0;for(let k=0;k<d;k++){let S=k*l[0];for(let N=0;N<c;N++){let R=o?x*(N+.5):x*N,P=Math.min(p-1,a?Math.round(R):Math.floor(R));o&&(P=Math.max(0,P));let $=S+P*l[1];for(let D=0;D<u;D++){let T=o?b*(D+.5):b*D,O=Math.min(h-1,a?Math.round(T):Math.floor(T));o&&(O=Math.max(0,O));let B=$+O*l[2];for(let H=0;H<f;H++){let z=m[B+H];g[w++]=z}}}}return n.makeTensorInfo([d,c,u,f],r.dtype,g)}var nX={kernelName:fu,backendName:"cpu",kernelFunc:tX};function sX(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s;Ne([a,r],"resizeNearestNeighborGrad");let i=v.computeStrides(r.shape),l=v.computeStrides(a.shape),[c,u,d,p]=r.shape,[,h,f]=a.shape,m=new Float32Array(c*u*d*p),g=n.data.get(a.dataId).values,y=[o&&h>1?u-1:u,o&&f>1?d-1:d],A=[o&&h>1?h-1:h,o&&f>1?f-1:f],x=y[0]/A[0],b=y[1]/A[1],w=1/x,k=1/b,S=Math.ceil(w)*2+2,N=Math.ceil(k)*2+2;for(let R=0;R<c;R++){let P=R*i[0];for(let $=0;$<u;$++){let D=P+$*i[1],T=Math.floor($*w),O=Math.floor(T-S/2);for(let B=0;B<d;B++){let H=D+B*i[2],z=Math.floor(B*k),X=Math.floor(z-N/2);for(let ee=0;ee<p;ee++){let J=0;for(let Q=0;Q<S;Q++){let te=Q+O;if(te<0||te>=h)continue;let K=P+te*l[1],oe=te*x,ce=Math.min(u-1,o?Math.round(oe):Math.floor(oe));if($===ce)for(let he=0;he<N;he++){let Ae=he+X;if(Ae<0||Ae>=f)continue;let Ie=K+Ae*l[2],Se=Ae*b,Oe=Math.min(d-1,o?Math.round(Se):Math.floor(Se));B===Oe&&(J+=g[Ie+ee])}}m[H+ee]=J}}}}return n.makeTensorInfo(r.shape,r.dtype,m)}var rX={kernelName:Oh,backendName:"cpu",kernelFunc:sX};function aX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s;Ne(r,"reverse");let o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Or({inputs:{x:r},backend:n});let l=new tn(r.shape,r.dtype),c=n.bufferSync(r);for(let u=0;u<l.size;u++){let d=l.indexToLoc(u),p=d.slice();i.forEach(h=>p[h]=r.shape[h]-1-p[h]),l.set(c.get(...p),...d)}return n.makeTensorInfo(l.shape,l.dtype,l.values)}var oX={kernelName:Ti,backendName:"cpu",kernelFunc:aX},iX={kernelName:Bi,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=v.getTypedArrayFromDType(s.dtype,v.sizeFromShape(s.shape)),[c,u,d,p]=s.shape,[h,f]=E.getImageCenter(o,u,d),m=255,g=Math.sin(r),y=Math.cos(r),A=i.data.get(s.dataId).values;for(let b=0;b<c;b++){let w=b*d*u*p;for(let k=0;k<u;k++){let S=k*(d*p);for(let N=0;N<d;N++){let R=N*p;for(let P=0;P<p;P++){let $=[c,k,N,P],D=$[2],T=$[1],O=(D-h)*y-(T-f)*g,B=(D-h)*g+(T-f)*y;O=Math.round(O+h),B=Math.round(B+f);let H=a;if(typeof a!="number"&&(P===3?H=m:H=a[P]),O>=0&&O<d&&B>=0&&B<u){let X=B*(d*p),ee=O*p,J=w+X+ee+P;H=A[J]}let z=w+S+R+P;l[z]=H}}}}return{dataId:i.write(l,s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},lX=xt(Ni,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}),uX={kernelName:Ni,backendName:"cpu",kernelFunc:lX};function xI(e,t,n,s,r,a,o,i,l,c){let u=[s/r,r],d=e.values,p=t.values;if(s===0)return We(n,t.dtype);let h=We(u,t.dtype);h.values.fill(l);for(let f=0;f<a;f++){let m=[],g=0;for(let y=0;y<o;y++){let A=d[f*o+y];m.push(A),g+=A*i[y]}if(g<0||g>=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<r;y++)c?h.values[g*r+y]+=p[f*r+y]:h.values[g*r+y]=t.rank===0?p[0]:p[f*r+y]}return h}function cX(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=!0,h=n.bufferSync(r),f=n.bufferSync(a),m=xI(h,f,o,d,c,l,i,u,0,p);return n.makeTensorInfo(o,m.dtype,m.values)}var dX={kernelName:Ei,backendName:"cpu",kernelFunc:cX};function pX(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Ne([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=Ln(r.dtype,a.dtype),d=v.makeZerosTypedArray(v.sizeFromShape(r.shape),u),p=0,h=o===0||o>1||r.shape.length===1?1:v.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=l[f]:d[p++]=c[f];return n.makeTensorInfo(r.shape,u,d)}var hX={kernelName:Ri,backendName:"cpu",kernelFunc:pX},fX=E.SELU_SCALEALPHA,mX=E.SELU_SCALE,gX=xt(mu,e=>e>=0?mX*e:fX*(Math.exp(e)-1)),yX={kernelName:mu,backendName:"cpu",kernelFunc:gX},AX=xt(gu,e=>e<0?-1:e>0?1:0),xX={kernelName:gu,backendName:"cpu",kernelFunc:AX},bX=xt(ro,e=>Math.sin(e)),vX={kernelName:ro,backendName:"cpu",kernelFunc:bX},wX=xt(Di,e=>Math.sinh(e)),kX={kernelName:Di,backendName:"cpu",kernelFunc:wX},IX=11920928955078125e-23,bI=Math.log(IX)+2,SX=xt(yu,e=>{let t=e>-bI,n=e<bI,s=Math.exp(e),r;return n?r=s:t?r=e:r=Math.log(1+s),r}),CX={kernelName:yu,backendName:"cpu",kernelFunc:SX};function TX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Ne([r],"spaceToBatchND");let i=v.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let c=AI.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,a,i,!1),d=E.getPermuted(u.length,a.length,!1),p=E.getReshapedPermuted(c.shape,a,i,!1),m=Et({inputs:{x:c},backend:n,attrs:{shape:u}}),A=Os({inputs:{x:m},backend:n,attrs:{perm:d}}),w=Et({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),w}var NX={kernelName:_i,backendName:"cpu",kernelFunc:TX};function EX(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[d,p,h,f,m]=H7(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var RX={kernelName:zh,backendName:"cpu",kernelFunc:EX};function $X(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[c,u,d]=j7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var DX={kernelName:Lh,backendName:"cpu",kernelFunc:$X};function _X(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=qA(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var PX={kernelName:Bh,backendName:"cpu",kernelFunc:_X};function FX(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=qA(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var OX={kernelName:Wh,backendName:"cpu",kernelFunc:FX};function MX(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,sliceSize:u,strides:d,outputSize:p}=E.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],y=xI(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,y.dtype,y.values)}var zX={kernelName:nd,backendName:"cpu",kernelFunc:MX};function LX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(d=>{let p=[...u];p[i]=d;let h=fl({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=d,h})}var BX={kernelName:Pi,backendName:"cpu",kernelFunc:LX},WX={kernelName:Au,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t;Ne(n,"square");let r=s.data.get(n.dataId).values,a=new Float32Array(r.length);for(let i=0;i<r.length;++i){let l=r[i];a[i]=l*l}return{dataId:s.write(a,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},VX=xt(fo,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),UX={kernelName:fo,backendName:"cpu",kernelFunc:VX};function GX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s;Ne(r,"stridedSlice");let{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=yn.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=Et({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=fl({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=Et({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(k)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else{let k=n.bufferSync(x),S=X7(A,k,m,f);b=n.makeTensorInfo(S.shape,S.dtype,S.values)}let w=Et({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),w}var HX={kernelName:Fi,backendName:"cpu",kernelFunc:GX};function jX(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.data.get(u.dataId).values,h=n.data.get(d.dataId).values,[f,m]=K7(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var qX={kernelName:sd,backendName:"cpu",kernelFunc:jX};function XX(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values[0],[c,u,d]=Z7(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var KX={kernelName:Vh,backendName:"cpu",kernelFunc:XX};function ZX(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.data.get(a.dataId).values,i=Y7(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var YX={kernelName:Uh,backendName:"cpu",kernelFunc:ZX},JX=xt(Oi,e=>Math.tan(e)),QX={kernelName:Oi,backendName:"cpu",kernelFunc:JX},eK=xt(po,e=>Math.tanh(e)),tK={kernelName:po,backendName:"cpu",kernelFunc:eK};function nK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;Ne(r,"tile");let o=Q7(n.bufferSync(r),a);return n.makeTensorInfo(o.shape,o.dtype,o.values)}var sK={kernelName:Kr,backendName:"cpu",kernelFunc:nK};function rK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s;Ne(r,"topk");let i=n.data.get(r.dataId).values,[l,c]=tI(i,r.shape,r.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var aK={kernelName:xu,backendName:"cpu",kernelFunc:rK};function oK(e){let{inputs:t,attrs:n,backend:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=n,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=v.computeStrides(r.shape),A=y[0],x=y[1],b=y[2],w=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(g));w.fill(l);let k=s.data.get(r.dataId).values,S=s.data.get(a.dataId).values;for(let R=0;R<u;++R){let P=a.shape[0]===1?S:S.subarray(R*8,R*8+8);for(let $=0;$<f;++$)for(let D=0;D<m;++D)for(let T=0;T<h;++T){let O,B=P[6]*D+P[7]*$+1;if(B===0)continue;let H=(P[0]*D+P[1]*$+P[2])/B,z=(P[3]*D+P[4]*$+P[5])/B,X=vI(H,p,i),ee=vI(z,d,i);switch(o){case"nearest":O=pK(k,d,p,A,x,b,R,ee,X,T,l);break;case"bilinear":O=hK(k,d,p,A,x,b,R,ee,X,T,l);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${o}`)}let J=R*A+$*x+D*b+T;w[J]=O}return s.makeTensorInfo(g,r.dtype,w)}return{dataId:s.write(w,g,r.dtype),shape:r.shape,dtype:r.dtype}}var iK={kernelName:Mi,backendName:"cpu",kernelFunc:oK};function vI(e,t,n){switch(n){case"reflect":return lK(e,t);case"wrap":return uK(e,t);case"nearest":return dK(e,t);case"constant":default:return cK(e,t)}}function lK(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=2*t;n<s&&(n=s*Math.trunc(-n/s)+n),n=n<-t?n+s:-n-1}else if(n>t-1)if(t<=1)n=0;else{let s=2*t;n-=s*Math.trunc(n/s),n>=t&&(n=s-n-1)}return v.clamp(0,n,t-1)}function uK(e,t){let n=e;if(n<0)if(t<=1)n=0;else{let s=t-1;n+=t*(Math.trunc(-n/s)+1)}else if(n>t-1)if(t<=1)n=0;else{let s=t-1;n-=t*Math.trunc(n/s)}return v.clamp(0,n,t-1)}function cK(e,t){return e}function dK(e,t){return v.clamp(0,e,t-1)}function op(e,t,n,s,r,a,o,i,l,c,u){let d=o*s+i*r+l*a+c;return 0<=i&&i<t&&0<=l&&l<n?e[d]:u}function pK(e,t,n,s,r,a,o,i,l,c,u){let d=Math.round(i),p=Math.round(l);return op(e,t,n,s,r,a,o,d,p,c,u)}function hK(e,t,n,s,r,a,o,i,l,c,u){let d=Math.floor(i),p=Math.floor(l),h=d+1,f=p+1,m=(f-l)*op(e,t,n,s,r,a,o,d,p,c,u)+(l-p)*op(e,t,n,s,r,a,o,d,f,c,u),g=(f-l)*op(e,t,n,s,r,a,o,h,p,c,u)+(l-p)*op(e,t,n,s,r,a,o,h,f,c,u);return(h-i)*m+(i-d)*g}function fK(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Ne(a,"unique");let o=s.data.get(a.dataId).values,{outputValues:i,outputShape:l,indices:c}=nI(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var mK={kernelName:Gh,backendName:"cpu",kernelFunc:fK};function gK(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape.length,i=r.shape[a],l=new Array(o-1),c=0;for(let h=0;h<o;h++)h!==a&&(l[c++]=r.shape[h]);let u=new Array(o).fill(0),d=r.shape.slice();d[a]=1;let p=new Array(i);for(let h=0;h<p.length;h++){u[a]=h;let f=fl({inputs:{x:r},backend:n,attrs:{begin:u,size:d}});p[h]=Et({inputs:{x:f},backend:n,attrs:{shape:l}}),n.disposeIntermediateTensorInfo(f)}return p}var yK={kernelName:zi,backendName:"cpu",kernelFunc:gK};function AK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s;Ne(r,"unsortedSegmentSum");let i=r.shape.length,l=a.shape.length,c=[],u=[],d=i-l,p=a;for(let f=0;f<d;++f){let m=Nm({inputs:{input:p},backend:n,attrs:{dim:f+1}});p=m,u.push(m)}for(let f=0;f<o;++f){let m=v.createScalarValue(f,"int32"),g=n.makeTensorInfo([],"int32",m),y=I7({inputs:{a:g,b:p},backend:n}),A=Fo({inputs:{x:y},backend:n,attrs:{dtype:"float32"}}),x=Cm({inputs:{a:A,b:r},backend:n}),b=ap({inputs:{x},backend:n,attrs:{axis:0,keepDims:!1}});c.push(b),u.push(g),u.push(y),u.push(A),u.push(x),u.push(b)}let h=yI({inputs:c,backend:n,attrs:{axis:0}});return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var xK={kernelName:rd,backendName:"cpu",kernelFunc:AK},bK=[TG,bU,EG,$G,CU,_G,FG,MG,LG,WG,UG,HG,qG,ZG,JG,tH,sH,aH,iH,SG,uH,dH,hH,mH,IU,NU,yH,vU,xH,vH,IH,CH,wH,RH,DH,NH,PH,OH,zH,BH,VH,GH,HH,qH,KH,YH,JH,ej,QH,JA,sj,AG,aj,EU,hj,RU,fj,DU,bj,vj,kj,PU,Cj,Nj,Rj,Dj,Pj,OU,zU,wU,Oj,bH,zj,Bj,Vj,xG,BU,VU,Gj,GU,jj,Kj,Yj,eq,nq,rq,jU,iq,uq,dq,hq,mq,aq,yq,xq,XU,vq,Iq,Nq,ZU,JU,$q,Pq,Mq,eG,Lq,Wq,Vq,AI,jq,vG,sG,Xq,kU,Zq,wG,kG,IG,Jq,eX,nX,rX,oX,iX,uX,aG,dX,hX,yX,iG,xX,vX,kX,lG,Cq,CX,NX,RX,DX,PX,OX,zX,BX,dG,WX,hG,UX,HX,qX,KX,YX,yG,tj,QX,tK,sK,aK,tG,iK,mK,yK,xK,Bq];for(let e of bK)Yr(e);var wI={};Le(wI,{assertNotComplex:()=>tc,bindCanvasToFramebuffer:()=>DK,bindColorTextureToFramebuffer:()=>_m,bindTextureToProgramUniformSampler:()=>MI,bindTextureUnit:()=>PI,bindVertexBufferToProgramAttribute:()=>sx,callAndCheck:()=>ke,canBeRepresented:()=>kI,createFragmentShader:()=>CI,createFramebuffer:()=>_I,createProgram:()=>TI,createStaticIndexBuffer:()=>RI,createStaticVertexBuffer:()=>EI,createTexture:()=>$I,createVertexShader:()=>SI,getBatchDim:()=>gl,getExtensionOrThrow:()=>up,getFramebufferErrorMessage:()=>zI,getMaxTexturesInShader:()=>VI,getNumChannels:()=>RK,getProgramUniformLocation:()=>OI,getProgramUniformLocationOrThrow:()=>FI,getRowsCols:()=>yl,getShapeAs3D:()=>Pm,getTextureShapeFromLogicalShape:()=>BI,getWebGLDisjointQueryTimerVersion:()=>UI,getWebGLErrorMessage:()=>II,getWebGLMaxTextureSize:()=>WI,hasExtension:()=>zs,isCapableOfRenderingToFloatTexture:()=>GI,isDownloadFloatTextureEnabled:()=>HI,isReshapeFree:()=>dp,isWebGLFenceEnabled:()=>jI,isWebGLVersionEnabled:()=>ax,linkProgram:()=>NI,resetMaxTextureSize:()=>_K,resetMaxTexturesInShader:()=>PK,unbindColorTextureFromFramebuffer:()=>rx,unbindTextureUnit:()=>$K,validateFramebuffer:()=>cp,validateProgram:()=>Dm,validateTextureSize:()=>DI});var ml={},tx={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function Rm(e,t){ml[e]=t}function Mr(e){if(!(e in ml)){let n=wK(e);if(n!==null)ml[e]=n;else return console.log("Could not get context for WebGL version",e),null}let t=ml[e];return t.isContextLost()?(delete ml[e],Mr(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),ml[e])}function vK(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 wK(e){if(e!==1&&e!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let t=vK(e);return t.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete ml[e]},!1),e===1?t.getContext("webgl",tx)||t.getContext("experimental-webgl",tx):t.getContext("webgl2",tx)}var ip;(function(e){e[e.DENSE=0]="DENSE",e[e.SHARED_BATCH=1]="SHARED_BATCH"})(ip||(ip={}));var Ms;(function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"})(Ms||(Ms={}));var Nn;(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"})(Nn||(Nn={}));function lp(e,t){return[t,e]}function kK(e,t){return e*t}function $m(e){let t=v.sizeFromShape(e),n=Math.ceil(t/4);return v.sizeToSquarishShape(n)}function ec(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function IK(e,t){let[n,s]=ec(e,t);return n*s*4}function nx(e,t){let n=e,s,r,a,o,i,l,c,u,d,p;return Z().getNumber("WEBGL_VERSION")===2?(s=n.R32F,r=n.R16F,a=n.RGBA16F,o=n.RGBA32F,i=n.RED,c=4,u=1,d=n.HALF_FLOAT,p=n.FLOAT):(s=e.RGBA,r=e.RGBA,a=e.RGBA,o=n.RGBA,i=e.RGBA,c=4,u=4,d=t!=null?t.HALF_FLOAT_OES:null,p=e.FLOAT),l=e.RGBA,{internalFormatFloat:s,internalFormatHalfFloat:r,internalFormatPackedHalfFloat:a,internalFormatPackedFloat:o,textureFormatFloat:i,downloadTextureFormat:l,downloadUnpackNumChannels:c,defaultNumChannels:u,textureTypeHalfFloat:d,textureTypeFloat:p}}function ke(e,t){let n=t();return Z().getBool("DEBUG")&&SK(e),n}function SK(e){let t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+II(e,t))}var CK=596e-10,TK=65504;function kI(e){return!!(Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||e===0||CK<Math.abs(e)&&Math.abs(e)<TK)}function II(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 up(e,t){return la(e,()=>e.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function SI(e,t){let n=la(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(ke(e,()=>e.shaderSource(n,t)),ke(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 CI(e,t){let n=la(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ke(e,()=>e.shaderSource(n,t)),ke(e,()=>e.compileShader(n)),e.getShaderParameter(n,e.COMPILE_STATUS)===!1)throw EK(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var NK=/ERROR: [0-9]+:([0-9]+):/g;function EK(e,t){let n=NK.exec(t);if(n==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(e);return}let s=+n[1],r=e.split(`
`),a=r.length.toString().length+2,o=r.map((d,p)=>v.rightPad((p+1).toString(),a)+d),i=0;for(let d=0;d<o.length;d++)i=Math.max(o[d].length,i);let l=o.slice(0,s-1),c=o.slice(s-1,s),u=o.slice(s);console.log(l.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${v.rightPad(c[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
`))}function TI(e){return la(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function NI(e,t){if(ke(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 Dm(e,t){if(ke(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function EI(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ke(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function RI(e,t){let n=la(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return ke(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),ke(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function RK(){return Z().getNumber("WEBGL_VERSION")===2?1:4}function $I(e){return la(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function DI(e,t){let n=Z().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let s=`[${e}x${t}]`;throw new Error("Requested texture size "+s+" is invalid.")}if(e>n||t>n){let s=`[${e}x${t}]`,r=`[${n}x${n}]`;throw new Error("Requested texture size "+s+" greater than WebGL maximum on this browser / GPU "+r+".")}}function _I(e){return la(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function sx(e,t,n,s,r,a,o){let i=e.getAttribLocation(t,n);return i===-1?!1:(ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,s)),ke(e,()=>e.vertexAttribPointer(i,r,e.FLOAT,!1,a,o)),ke(e,()=>e.enableVertexAttribArray(i)),!0)}function PI(e,t,n){LI(e,n),ke(e,()=>e.activeTexture(e.TEXTURE0+n)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function $K(e,t){LI(e,t),ke(e,()=>e.activeTexture(e.TEXTURE0+t)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function FI(e,t,n){return la(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function OI(e,t,n){return e.getUniformLocation(t,n)}function MI(e,t,n,s){ke(e,()=>PI(e,t,s)),ke(e,()=>e.uniform1i(n,s))}function DK(e){ke(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ke(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),ke(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function _m(e,t,n){ke(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),ke(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function rx(e,t){ke(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),ke(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function cp(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+zI(e,t))}function zI(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 ${t}`}}function la(e,t,n){let s=ke(e,()=>t());if(s==null)throw new Error(n);return s}function LI(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function gl(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function yl(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 Pm(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[gl(e),...yl(e)]),t}function BI(e,t=!1){let n=Z().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?v.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let s=v.sizeFromShape(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=gl(e),a=2,o=2;return e.length&&([a,o]=yl(e)),s=r*(a/2)*(o/2),v.sizeToSquarishShape(s).map(i=>i*2)}return v.sizeToSquarishShape(s)}function Fm(e){return e%2==0}function dp(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],s=t.slice(-1)[0];if(n===s||Fm(n)&&Fm(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Fm(e[0])&&Fm(t[0])}var Om,Mm;function WI(e){if(Om==null){let t=Mr(e);Om=t.getParameter(t.MAX_TEXTURE_SIZE)}return Om}function _K(){Om=null}function PK(){Mm=null}function VI(e){if(Mm==null){let t=Mr(e);Mm=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Mm)}function UI(e){if(e===0)return 0;let t,n=Mr(e);return zs(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:zs(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function zs(e,t){return e.getExtension(t)!=null}function ax(e){try{if(Mr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function GI(e){if(e===0)return!1;let t=Mr(e);if(e===1){if(!zs(t,"OES_texture_float"))return!1}else if(!zs(t,"EXT_color_buffer_float"))return!1;return ox(t)}function HI(e){if(e===0)return!1;let t=Mr(e);if(e===1){if(!zs(t,"OES_texture_float")||!zs(t,"WEBGL_color_buffer_float"))return!1}else{if(zs(t,"EXT_color_buffer_float"))return ox(t);let s="EXT_color_buffer_half_float";if(zs(t,s)){let r=t.getExtension(s);return FK(t,r)}return!1}return ox(t)}function ox(e){let t=nx(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function FK(e,t){let n=nx(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function jI(e){return e!==2?!1:Mr(e).fenceSync!=null}function tc(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 De=Z();De.registerFlag("HAS_WEBGL",()=>De.getNumber("WEBGL_VERSION")>0);De.registerFlag("WEBGL_VERSION",()=>ax(2)?2:ax(1)?1:0);De.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);De.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>De.get("WEBGL_VERSION")===2);De.registerFlag("WEBGL_CPU_FORWARD",()=>!0);De.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);De.registerFlag("WEBGL_PACK",()=>De.getBool("HAS_WEBGL"));De.registerFlag("WEBGL_PACK_NORMALIZATION",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_CLIP",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_REDUCE",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_LAZILY_UNPACK",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_CONV_IM2COL",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>WI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>VI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=De.getNumber("WEBGL_VERSION");return e===0?0:UI(e)});De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>De.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!wu.isMobile());De.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>GI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>De.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:De.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));De.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>HI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_FENCE_API_ENABLED",()=>jI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>De.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);De.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}.`)});De.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>wu.isMobile()&&De.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}.`)});De.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);De.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);De.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);De.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Gn(){let e,t,n,s,r,a,o,i,l,c;return Z().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
bool isnan_custom(float val) {
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`,l="",c=`
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`,l=`
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`,c=`
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function Al(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function zm(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function OK(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function MK(e,t,n="index"){let s=e.map((a,o)=>o),r=OK(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function ix(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;
}
`}function lx(){return`
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`}var qI=`
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`,{getBroadcastDims:XI}=E;function zK(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=ux(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
`),a=e.map(h=>LK(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
`),o=t.texShape,i=Gn(),l=VK(i),c,u,d=HK(i);return t.isPacked?(c=BK(t.logicalShape,o,n.enableShapeUniforms),u=GK(i)):(c=WK(t.logicalShape,o,n.enableShapeUniforms),u=UK(i)),n.packedInputs&&(d+=KK),[d,l,u,r,c,a,n.userCode].join(`
`)}function nc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return iZ(e,t);case 1:return uZ(e,t);case 2:return dZ(e,t);case 3:return hZ(e,t);case 4:return mZ(e,t);case 5:return gZ(e);case 6:return yZ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function KI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return oZ(e);case 1:return lZ(e,t);case 2:return cZ(e,t);case 3:return pZ(e,t);default:return fZ(e,t)}}function LK(e,t,n=!1,s){let r="";n?r+=KI(e,s):r+=nc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=AZ(e,t):r+=xZ(e,t)),r}function BK(e,t,n){switch(e.length){case 0:return ZI();case 1:return ZK(e,t,n);case 2:return rZ(e,t,n);case 3:return JK(e,t,n);default:return eZ(e,t,n)}}function WK(e,t,n){switch(e.length){case 0:return ZI();case 1:return YK(e,t,n);case 2:return aZ(e,t,n);case 3:return QK(e,t,n);case 4:return tZ(e,t,n);case 5:return nZ(e,t);case 6:return sZ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function VK(e){return`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}
`}function UK(e){return`
void setOutput(float val) {
${e.output} = vec4(val, 0, 0, 0);
}
`}function GK(e){return`
void setOutput(vec4 val) {
${e.output} = val;
}
`}function HK(e){return`${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);
}
${jK}
${qK}
${XK}
`}var jK=`
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);
}
`,qK=`
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);
}
`,XK=`
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);
}
`,KK=`
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 ZI(){return`
int getOutputCoords() {
return 0;
}
`}function ZK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.x * ${s[1]}.0);
}
`:s[1]===1?n?`
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`:`
int getOutputCoords() {
return 2 * int(resultUV.y * ${s[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
}
`}function YK(e,t,n){return t[0]===1?n?`
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`:`
int getOutputCoords() {
return int(resultUV.x * ${t[1]}.0);
}
`:t[1]===1?n?`
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`:`
int getOutputCoords() {
return int(resultUV.y * ${t[0]}.0);
}
`:n?`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`:`
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
return resTexRC.x * ${t[1]} + resTexRC.y;
}
`}function JK(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[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 QK(e,t,n){if(n)return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${zm(["r","c","d"],e)}
return ivec3(r, c, d);
}
`;let s=Al(["r","c","d"],e);return`
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec3(r, c, d);
}
`}function eZ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
int b${c} = index / ${o};
index -= b${c} * ${o};
`+i,l=`b${c}, `+l;return`
ivec${e.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[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}(${l});
}
`}function tZ(e,t,n){if(n)return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${zm(["r","c","d","d2"],e)}
return ivec4(r, c, d, d2);
}
`;let s=Al(["r","c","d","d2"],e);return`
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${s}
return ivec4(r, c, d, d2);
}
`}function nZ(e,t){let n=Al(["r","c","d","d2","d3"],e);return`
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`}function sZ(e,t){let n=Al(["r","c","d","d2","d3","d4"],e);return`
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
${n}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`}function rZ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
}
`;let r=Math.ceil(e[1]/2);return n?`
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${s[0]}, ${s[1]}));
int index = resTexRC.x * ${s[1]} + resTexRC.y;
int r = 2 * (index / ${r});
int c = imod(index, ${r}) * 2;
return ivec2(r, c);
}
`}function aZ(e,t,n){return v.arraysEqual(e,t)?n?`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`:`
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
}
`:e[1]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(index, 0);
}
`:e[0]===1?n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
return ivec2(0, index);
}
`:n?`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`:`
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.x * ${t[1]} + resTexRC.y;
int r = index / ${e[1]};
int c = index - r * ${e[1]};
return ivec2(r, c);
}
`}function xl(e){return`offset${e}`}function oZ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Gn();return`
vec4 ${n}() {
return ${s.texture2D}(${t}, halfCR);
}
`}function iZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
float ${s}() {
return sampleTexture(${n}, halfCR);
}
`;let o=xl(n);if(t)return`
float ${s}() {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
return sampleTexture(${n}, uv);
}
`;let[i,l]=e.shapeInfo.texShape;return`
float ${s}() {
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
return sampleTexture(${n}, uv);
}
`}function lZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Gn();if(t)return`
vec4 ${s}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${a.texture2D}(${n}, uv);
}
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
vec4 ${s}(int index) {
vec2 uv = packedUVfrom1D(
${o[0]}, ${o[1]}, index);
return ${a.texture2D}(${n}, uv);
}
`}function uZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
float ${s}(int index) {
${sc(e)}
}
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
float ${s}(int index) {
return sampleTexture(${n}, halfCR);
}
`;let i=xl(n);return o===1?t?`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
return sampleTexture(${n}, uv);
}
`:a===1?t?`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
return sampleTexture(${n}, uv);
}
`:t?`
float ${s}(int index) {
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
return sampleTexture(${n}, uv);
}
`:`
float ${s}(int index) {
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
return sampleTexture(${n}, uv);
}
`}function cZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Gn();if(a!=null&&v.arraysEqual(n,a))return t?`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return ${l.texture2D}(${s}, uv);
}
`:`
vec4 ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
return ${l.texture2D}(${s}, uv);
}
`;if(t)return`
vec4 ${r}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${l.texture2D}(${s}, uv);
}
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
vec4 ${r}(int row, int col) {
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
return ${l.texture2D}(${s}, uv);
}
`}function dZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`;let p=a[0],h=a[1];return`
float ${r}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=rc(e,l),h=["row","col"];return`
${nc(p,t)}
float ${r}(int row, int col) {
return ${r}(${ac(h,i)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
${sc(e)}
}
`;let c=a[0],u=a[1],d=xl(s);return u===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
return sampleTexture(${s}, uv);
}
`:c===1?t?`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
return sampleTexture(${s}, uv);
}
`:t?`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${s}Shape[1] + col + ${d};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${n[1]} + col + ${d};
vec2 uv = uvFromFlat(${c}, ${u}, index);
return sampleTexture(${s}, uv);
}
`}function pZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=rc(e,p),m=["b","row","col"];return`
${KI(f,t)}
vec4 ${r}(int b, int row, int col) {
return ${r}(${ac(m,h)});
}
`}let i=Gn();if(t)return`
vec4 ${r}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
vec4 ${r}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${l}, ${c}, ${d}, ${u}, b, row, col);
return ${i.texture2D}(${s}, uv);
}
`}function hZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=rc(e,c),g=["row","col","depth"];return`
${nc(m,t)}
float ${r}(int row, int col, int depth) {
return ${r}(${ac(g,l)});
}
`}if(e.shapeInfo.isUniform)return`
float ${r}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${a}, ${o}, 1)));
${sc(e)}
}
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
float ${r}(int row, int col, int depth) {
int stride1 = ${s}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${o}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;if(p===o&&h==null)return t?`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
return sampleTexture(${s}, uv);
}
`;let f=xl(s);return t?`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${s}Shape[1] * ${s}Shape[2];
int stride1 = ${s}Shape[2];
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${a} + col * ${o} + depth + ${f};
vec2 uv = uvFromFlat(${d}, ${p}, index);
return sampleTexture(${s}, uv);
}
`}function fZ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Gn();if(t)return`
vec4 ${s}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${n}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
}
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
vec4 ${s}(${h}) {
int index = ${f};
int texR = index / ${u};
int texC = index - texR * ${u};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
return ${r.texture2D}(${n}, uv);
}
`}function mZ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let A=rc(e,l),x=["row","col","depth","depth2"];return`
${nc(A,t)}
float ${r}(int row, int col, int depth, int depth2) {
return ${r}(${ac(x,c)});
}
`}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}, ${o}, ${a}, 1)));
${sc(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
${f}
${m}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${o}, ${a}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;if(h===a&&u==null)return t?`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}TexShape[1], ${s}TexShape[0]);
return sampleTexture(${s}, uv);
}
`:`
float ${r}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${n[1]*n[2]}, ${n[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${s}, uv);
}
`;let y=xl(s);return t?`
float ${r}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${f}
${m}
${g}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${y});
return sampleTexture(${s}, uv);
}
`:`
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 * ${o} +
depth * ${a} + depth2;
vec2 uv = uvFromFlat(${p}, ${h}, index + ${y});
return sampleTexture(${s}, uv);
}
`}function gZ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=rc(e,l),g=["row","col","depth","depth2","depth3"];return`
${nc(m)}
float ${s}(int row, int col, int depth, int depth2, int depth3) {
return ${s}(${ac(g,c)});
}
`}if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${i}, ${o}, ${a}, ${r})) +
depth3;
${sc(e)}
}
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${o}, ${a}, ${r}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;if(h===r&&u==null)return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${t[1]*t[2]*t[3]},
${t[2]*t[3]}, ${t[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${h}.0, ${p}.0);
return sampleTexture(${n}, uv);
}
`;let f=xl(n);return`
float ${s}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${i} + col * ${o} + depth * ${a} +
depth2 * ${r} + depth3 + ${f};
vec2 uv = uvFromFlat(${p}, ${h}, index);
return sampleTexture(${n}, uv);
}
`}function yZ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=rc(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
${nc(g)}
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${s}(${ac(y,a)});
}
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${u}, ${c}, ${l}, ${i})) +
dot(
vec2(depth3, depth4),
vec2(${o}, 1)));
${sc(e)}
}
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${c}, ${l}, ${i}, ${o})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${f}.0, ${h}.0);
return sampleTexture(${n}, uv);
}
`;if(f===o&&d==null)return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${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, ${h}.0);
return sampleTexture(${n}, uv);
}
`;let m=xl(n);return`
float ${s}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${u} + col * ${c} + depth * ${l} +
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
vec2 uv = uvFromFlat(${h}, ${f}, index);
return sampleTexture(${n}, uv);
}
`}function sc(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 ${t}[i];
}
}
`}function AZ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=XI(e.shapeInfo.logicalShape,t.logicalShape),l=St(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(A=>`coords.${d[A+c]} = 0;`).join(`
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((A,x)=>`coords.${d[x+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,y=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)h=`
return vec4(outputValue.xy, outputValue.xy);
`;else if(m&&!y)o===1?h=`
return vec4(outputValue.x, outputValue.x, 0., 0.);
`:h=`
return vec4(outputValue.x);
`;else if(i.length){let A=a-2,x=a-1;i.indexOf(A)>-1&&i.indexOf(x)>-1?h="return vec4(outputValue.x);":i.indexOf(A)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(x)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
vec4 ${r}() {
${l} coords = getOutputCoords();
${u}
vec4 outputValue = get${s}(${p});
${h}
}
`}function xZ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
float ${r}() {
return sampleTexture(${n}, resultUV);
}
`;let c=St(l),u=XI(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
float ${r}() {
${c} coords = getOutputCoords();
${p}
return get${s}(${f});
}
`}function St(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ux(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function rc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ac(e,t){return t.map(n=>e[n]).join(", ")}function bZ(e,t,n,s){let r=n.map((x,b)=>{let w={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(w.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[b],shapeInfo:w}}),a=r.map(x=>x.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=zK(r,o,t),l=e.createProgram(i),c=null,u=e.getUniformLocation(l,"NAN",!1);Z().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let d=!1,p={},h={},f={};for(let x=0;x<t.variableNames.length;x++){let b=t.variableNames[x];p[b]=e.getUniformLocation(l,b,d),p[`offset${b}`]=e.getUniformLocation(l,`offset${b}`,d),t.enableShapeUniforms&&(h[`${b}Shape`]=e.getUniformLocation(l,`${b}Shape`,d),f[`${b}TexShape`]=e.getUniformLocation(l,`${b}TexShape`,d))}let m,g,y;t.enableShapeUniforms&&(m=e.getUniformLocation(l,"outShape",d),y=e.getUniformLocation(l,"outShapeStrides",d),g=e.getUniformLocation(l,"outTexShape",d));let A=[];return t.customUniforms&&t.customUniforms.forEach((x,b)=>{A[b]=e.getUniformLocation(l,x.name,d)}),{program:t,source:i,webGLProgram:l,uniformLocations:p,customUniformLocations:A,inShapeInfos:a,outShapeInfo:o,infLoc:c,nanLoc:u,inShapesLocations:h,inTexShapesLocations:f,outShapeLocation:m,outShapeStridesLocation:y,outTexShapeLocation:g}}function YI(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,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function vZ(e,t,n,s,r){t.program.enableShapeUniforms||(YI(t.inShapeInfos,n),YI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Z().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=ux(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,c)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,c)=>{let u=t.customUniformLocations[c],d=r[c];if(l.type==="float")e.gl.uniform1fv(u,d);else if(l.type==="vec2")e.gl.uniform2fv(u,d);else if(l.type==="vec3")e.gl.uniform3fv(u,d);else if(l.type==="vec4")e.gl.uniform4fv(u,d);else if(l.type==="int")e.gl.uniform1iv(u,d);else if(l.type==="ivec2")e.gl.uniform2iv(u,d);else if(l.type==="ivec3")e.gl.uniform3iv(u,d);else if(l.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function wZ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=ux(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=v.computeStrides(u);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),y=v.sizeFromShape(o.shape)===1,A=E.getBroadcastDims(o.shape,n.shape),x=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${x}_${c?d:""}_${u.length}_${y}_${A}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Z().getNumber("WEBGL_VERSION")}`,a}function Ls(e){return Z().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var kZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ip.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?zm(["r","c","d"],e):Al(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${t.output} = result;
}
`}},IZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ip.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length),this.userCode=`
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms?zm(["r","c","d"],e):Al(["r","c","d"],e)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${t.output} = result;
}
`}},SZ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Ms.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
${qI}
void main() {
float x = getAAtOutCoords();
${t.output} = encode_float(x);
}
`}},CZ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ms.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
${qI}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${t.output} = encode_float(x);
}
`}},TZ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
${this.enableShapeUniforms?lx():ix(e)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${n.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];
}
${n.output} = vec4(${s}, 0., 0., 0.);
}
`}},NZ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
localCoords = coords;
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
localCoords[2] += ${o};
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
localCoords[1] += ${a};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${n.texture2D}(A, uv);
if (offset == 0) {
result[${i}] = values[0];
} else if (offset == 1) {
result[${i}] = values[1];
} else if (offset == 2) {
result[${i}] = values[2];
} else {
result[${i}] = values[3];
}
}
}
`}this.userCode=`
${this.enableShapeUniforms?lx():ix(e)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${s}
${n.output} = ${r};
}
`}},JI={};Le(JI,{bindVertexProgramAttributeStreams:()=>i4,createBufferFromOutputTexture:()=>c4,createFloat16MatrixTexture:()=>s4,createFloat16PackedMatrixTexture:()=>o4,createFloat32MatrixTexture:()=>n4,createIndexBuffer:()=>t4,createPackedMatrixTexture:()=>a4,createUnsignedBytesMatrixTexture:()=>r4,createVertexBuffer:()=>e4,createVertexShader:()=>QI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>p4,downloadFloat32MatrixFromBuffer:()=>d4,downloadMatrixFromPackedOutputTexture:()=>f4,downloadPackedMatrixFromBuffer:()=>h4,getInternalFormatForFloat16MatrixTexture:()=>dx,getInternalFormatForFloat16PackedMatrixTexture:()=>fx,getInternalFormatForFloat32MatrixTexture:()=>cx,getInternalFormatForPackedMatrixTexture:()=>hx,getInternalFormatForUnsignedBytesMatrixTexture:()=>px,uploadDenseMatrixToTexture:()=>l4,uploadPixelDataToTexture:()=>u4});function QI(e){let t=Gn(),n=`${t.version}
precision highp float;
${t.attribute} vec3 clipSpacePos;
${t.attribute} vec2 uv;
${t.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;return SI(e,n)}function e4(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return EI(e,t)}function t4(e){let t=new Uint16Array([0,1,2,2,1,3]);return RI(e,t)}function pp(e,t,n,s,r,a){DI(t,n);let o=$I(e),i=e.TEXTURE_2D;return ke(e,()=>e.bindTexture(i,o)),ke(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ke(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ke(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),ke(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),ke(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function cx(e){return e.internalFormatFloat}function n4(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,cx(s),s.textureFormatFloat,e.FLOAT)}function dx(e){return e.internalFormatHalfFloat}function s4(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,dx(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function px(e){return e.downloadTextureFormat}function r4(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,px(s),e.RGBA,e.UNSIGNED_BYTE)}function hx(e){return e.internalFormatPackedFloat}function a4(e,t,n,s){let[r,a]=ec(t,n);return pp(e,r,a,hx(s),e.RGBA,e.FLOAT)}function fx(e){return e.internalFormatPackedHalfFloat}function o4(e,t,n,s){let[r,a]=ec(t,n);return pp(e,r,a,fx(s),e.RGBA,s.textureTypeHalfFloat)}function i4(e,t,n){let s=0,r=3*4,a=3*4+2*4;return ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),sx(e,t,"clipSpacePos",n,3,a,s)&&sx(e,t,"uv",n,2,a,r)}function l4(e,t,n,s,r,a){ke(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),ke(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function u4(e,t,n){ke(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?ke(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):ke(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function c4(e,t,n,s){let r=e.createBuffer();ke(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return ke(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),ke(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ke(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function d4(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function p4(e,t,n,s){let[r,a]=lp(t,n),o=4,i=new Uint8Array(kK(t*n,o));return ke(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function h4(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(IK(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function f4(e,t,n){let s=new Float32Array(t*n*4);return ke(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Lm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Z().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Rm(t,e)):this.gl=Mr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Z().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=up(this.gl,r),zs(this.gl,a))this.textureHalfFloatExtension=up(this.gl,a);else if(Z().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),zs(this.gl,s))this.colorBufferHalfFloatExtension=up(this.gl,s);else if(Z().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",zs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(zs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=e4(this.gl),this.indexBuffer=t4(this.gl),this.framebuffer=_I(this.gl),this.textureConfig=nx(this.gl,this.textureHalfFloatExtension)}get debug(){return Z().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;ke(e,()=>e.finish()),ke(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ke(e,()=>e.deleteFramebuffer(this.framebuffer)),ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ke(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ke(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),n4(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),s4(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),r4(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),u4(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),l4(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),o4(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),a4(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(rx(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>p4(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return h4(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return d4(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=c4(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Z().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>f4(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=CI(t,e);this.vertexShader==null&&(this.vertexShader=QI(t));let s=TI(t);return ke(t,()=>t.attachShader(s,this.vertexShader)),ke(t,()=>t.attachShader(s,n)),NI(t,s),this.debug&&Dm(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=i4(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ke(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Dm(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?FI(this.gl,e,t):OI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ke(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(),MI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=ec(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Dm(this.gl,this.program),cp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ke(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ke(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=up(this.gl,Z().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(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Z().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,Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Z().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,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=EZ(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(),_m(this.gl,e,this.framebuffer),this.debug&&cp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(_m(this.gl,this.outputTexture,this.framebuffer),this.debug&&cp(this.gl)):rx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;_m(s,e,this.framebuffer),this.debug&&cp(s),this.outputTexture=e,ke(s,()=>s.viewport(0,0,t,n)),ke(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,n,s))}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 EZ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:RZ,bincountImpl:m4,bincountReduceImpl:$Z,ceilImpl:DZ,concatImpl:_Z,equalImpl:PZ,expImpl:FZ,expm1Impl:OZ,floorImpl:MZ,gatherNdImpl:zZ,gatherV2Impl:LZ,greaterImpl:BZ,greaterEqualImpl:WZ,lessImpl:VZ,lessEqualImpl:UZ,linSpaceImpl:GZ,logImpl:HZ,maxImpl:jZ,maximumImpl:qZ,minimumImpl:XZ,multiplyImpl:KZ,negImpl:ZZ,notEqualImpl:YZ,prodImpl:JZ,rangeImpl:QZ,rsqrtImpl:eY,sigmoidImpl:tY,simpleAbsImpl:g4,sliceImpl:nY,sparseFillEmptyRowsImpl:sY,sparseReshapeImpl:rY,sparseSegmentReductionImpl:y4,sqrtImpl:aY,stridedSliceImpl:oY,stringNGramsImpl:iY,stringSplitImpl:lY,stringToHashBucketFastImpl:uY,subImpl:cY,tileImpl:dY,topKImpl:pY,transposeImpl:mx,uniqueImpl:hY}=BA;function A4(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Hn(e,t){return t===1?[e]:A4(e,t)}function fY(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var mY=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=Hn("rc",t),s=St(t),r=yY(t,e,n),a=AY(t,e[e.length-1],e[e.length-2],n),o=xY(e,n);this.userCode=`
void main() {
${s} rc = getOutputCoords();
if(${r}) {
setOutput(vec4(0));
} else {
${a}
setOutput(vec4(${o}));
}
}
`}}};function gY(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function yY(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function AY(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
int r = ${r[0]};
int c = ${r[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${t};
bool rEdge = rp1 >= ${n};
`}function xY(e,t){let n=e.length,s=gY(n,t);return n===1?`getA(rc),
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
0, 0`:`getA(${s[0]}),
cEdge ? 0. : getA(${s[1]}),
rEdge ? 0. : getA(${s[2]}),
rEdge || cEdge ? 0. : getA(${s[3]})`}var x4=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
${r}
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${s}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${s>0?"}":""}
`}this.userCode=`
${bY(t,this.enableShapeUniforms)}
${this.enableShapeUniforms?lx():ix(e)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
${n}
setOutput(result);
}
`}};function bY(e,t){return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${t?MK(["r","c","d"],"inputShape"):Al(["r","c","d"],e)}
return ivec3(r, c, d);
}
`}var vY=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=v4(t,n),r=w4(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=b4(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Nn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Nn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Nn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Nn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Nn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=v4(n,s),a=w4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=b4(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Z().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function wY(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function b4(e,t,n,s,r){let a=kY(t,s),o;if(r){let[l,c]=ec(e[0],e[1]);o=l*c}else{let[l,c]=lp(e[0],e[1]);o=l*c}let i=wY(n,a);return o*i}function kY(e,t){switch(e){case Nn.PACKED_2X2_FLOAT32:return hx(t);case Nn.PACKED_2X2_FLOAT16:return fx(t);case Nn.UNPACKED_FLOAT32:return cx(t);case Nn.UNPACKED_FLOAT16:return dx(t);case Nn.PACKED_4X1_UNSIGNED_BYTE:return px(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function IY(e){return Z().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Nn.PACKED_2X2_FLOAT32:Nn.UNPACKED_FLOAT32:e?Nn.PACKED_2X2_FLOAT16:Nn.UNPACKED_FLOAT16}function v4(e,t){if(e===Ms.UPLOAD)return Nn.PACKED_2X2_FLOAT32;if(e===Ms.RENDER||e==null)return IY(t);if(e===Ms.DOWNLOAD||e===Ms.PIXELS)return Nn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function w4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Mo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length),this.userCode=`
float unaryOperation(float x) {
${t}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`}},br="if (isnan(x)) return x;",SY="return x;",k4="return abs(x);",CY="return (x >= 0.0) ? x : (exp(x) - 1.0);",TY=br+`
return (x < 0.0) ? 0.0 : x;
`,NY=br+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,Bm="return x;",EY="return 1.0 / (1.0 + exp(-1.0 * x));",RY="return x;",$Y=`
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;
`,DY=`
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;
`,_Y=`
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;
`,PY="return 1.0 / (1.0 + exp(-1.0 * x));",oc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length),this.userCode=`
vec4 unaryOperation(vec4 x) {
${t}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`}},FY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Hn("rc",t),s=St(t),r=fY(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 packedInput = getA(${r});
setOutput(getChannel(packedInput, ${o}));
}
`}},OY=Zs.whereImpl,MY=1e-7,zY=1e-4,Wm={};function LY(e){return e in Wm||(Wm[e]={}),Wm[e]}var BY=Z().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),WY=600;function VY(){return Z().global.screen==null?1024:Z().global.screen.height*Z().global.screen.width*window.devicePixelRatio*WY/1024/1024}var ic=class extends Gl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Z().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Mr(Z().getNumber("WEBGL_VERSION"));this.binaryCache=LY(Z().getNumber("WEBGL_VERSION")),this.gpgpu=new Lm(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 vY(this.gpgpu),this.numMBBeforeWarning=VY(),this.texData=new Vc(this,ts())}nextDataId(){return ic.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Z().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Z().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 s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Ms.UPLOAD,refCount:1}),s}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,s,r){if(Z().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Ms.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new oc(o,Bm):d=new Mo(o,Bm);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=E.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new oc(s,Bm):h=new Mo(s,Bm);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Z().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(a!=="complex64"&&Z().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...$m(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=E.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let h=this.gpgpu.gl;ke(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ts().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){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(!kI(n))throw Z().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Z().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...$m(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Z().getBool("WEBGL_PACK")&&s===!0,o=a?Pm(t):t,i=a?new CZ(o):new SZ(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Z().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Z().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:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BY){return Z().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return OY(e.shape,t)}packedUnaryOp(e,t,n){let s=new oc(e.shape,t),r=this.compileAndRun(s,[e],n);return ts().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=g4(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Z().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,k4,e.dtype);let t=new Mo(e.shape,k4),n=this.compileAndRun(t,[e]);return ts().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return ts().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new FY(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new mY(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[gl(e.shape),...yl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[gl(t),...yl(t)],a=new x4(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=Pm(s),o,i=$m(a);n?o=new IZ(a):o=new kZ(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===ip.DENSE){let m=$m(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],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 g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=Z().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}else if(!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!dp(g.shape,m.shape)){let y=m,A=m.shape;m.shape=g.shape,m=this.packedReshape(m,A),i.push(m),g=this.texData.get(m.dataId),y.shape=A}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=wZ(e,l,c),d=this.getAndSaveBinary(u,()=>bZ(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),vZ(this.gpgpu,d,l,c,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Z().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Z().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Z().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),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=j(()=>{if(!Z().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Z().getBool("DEBUG");Z().set("DEBUG",!1);let t=this.abs(Ee(1e-8)).dataSync()[0];if(Z().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?MY:zY}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=BI(n,i),t.texShape=u),r!=null){let d=Pm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array;i?([h,f]=ec(u[0],u[1]),p=new NZ(d,m)):p=new TZ(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=Ms.PIXELS:this.texData.get(g.dataId).usage=Ms.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let y=[[f,h]],A=!0,x=this.runWebGLProgram(p,[g],s,y,A),b=this.texData.get(x.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(x.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let d=this.acquireTexture(u,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UY(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};ic.nextDataId=0;function UY(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 s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var GY="3.9.0";function I4(){Z().set("WEBGL_FORCE_F16_TEXTURES",!0)}wu.isBrowser()&&qi("webgl",()=>new ic,2);var HY={forceHalfFloat:I4},S4=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,lc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Ls(this.outputShape.length),this.userCode=`
float binaryOperation(float a, float b) {
${e}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`}},Vm=`
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;
`,hp=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Ls(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;else if(a=`
${St(r)} coords = getOutputCoords();
`,r===1)this.enableShapeUniforms?a+=`
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`:a+=`
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;else{let i=Hn("coords",r);this.enableShapeUniforms?a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= outShape[${r} - 2];
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= outShape[${r} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`:a+=`
bool nextRowOutOfBounds =
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
bool nextColOutOfBounds =
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`}this.userCode=`
vec4 binaryOperation(vec4 a, vec4 b) {
${e}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${a}
setOutput(result);
}
`}};function Is(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var jY={kernelName:Wa,backendName:"webgl",kernelFunc:Is};function zo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Is({inputs:{x:s},backend:n}),l=Is({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var qY={kernelName:jc,backendName:"webgl",kernelFunc:zo},C4="return (a < 0.) ? b * a : a;",T4=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function XY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(T4,r.shape,o.shape):new lc(C4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],r.dtype);return n.disposeIntermediateTensorInfo(o),l}var KY={kernelName:fi,backendName:"webgl",kernelFunc:XY},N4="return (a < 0.) ? b * a : a;",E4=`
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function ZY(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(E4,s.shape,r.shape):new lc(N4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)}var YY={kernelName:Qa,backendName:"webgl",kernelFunc:ZY},R4="if (isnan(x)) return x;",JY=`
if (isnan(a)) return a;
if (isnan(b)) return b;
`,QY=`
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new oc(o.shape,t):u=new Mo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function En({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,w]=x,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new lc(e,l.shape,c.shape);return u.runWebGLProgram(N,[k,S],Ln(b.dtype,w.dtype))}),A=zo({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),A}let d=a||Ln(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?E.fromUint8ToStringArray(f):f,y=l.dtype==="string"?E.fromUint8ToStringArray(m):m,[A,x]=r(l.shape,c.shape,g,y,d),b=u.makeTensorInfo(x,d),w=u.texData.get(b.dataId);return w.values=A,b}let p=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new hp(t,l.shape,c.shape,n):h=new lc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Um(e,t=!1){if(e==="linear")return t?RY:SY;if(e==="relu")return t?DY:TY;if(e==="elu")return t?$Y:CY;if(e==="relu6")return t?_Y:NY;if(e==="prelu")return t?E4:N4;if(e==="leakyrelu")return t?T4:C4;if(e==="sigmoid")return t?PY:EY;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var $4=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Ls(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${o}
}`:l?m=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${o}
}`:m=`vec4 activation(vec4 x) {
${o}
}`,g="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let A="rc.x",x="rc.x";e[0]<t[0]?A=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
${m}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${u}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${u}; i++) {
int batchA = ${A};
int batchB = ${x};
vec4 a = getMatrixA(batchA, ${d});
vec4 b = getMatrixB(batchB, ${p});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${h[0]} * ${f[0]});
result += (${h[1]} * ${f[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${y}
${g}
setOutput(result);
}
`}},D4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},_4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${e}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`}},P4="return a * b;";function gx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=E.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new _4(D4.REAL,s.shape,r.shape),u=new _4(D4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=zo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=KZ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new hp(P4,s.shape,r.shape):o=new lc(P4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var eJ={kernelName:Za,backendName:"webgl",kernelFunc:gx};function tJ(e,t,n){let s=[gl(e.shape),...yl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[gl(t),...yl(t)],o=new x4(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function be(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!dp(r.shape,l)&&!(u.texture!==null&&dp(u.shape,l))?tJ(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var nJ={kernelName:Ci,backendName:"webgl",kernelFunc:be},F4=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
if (inIdx < 0 || inIdx >= ${r}) {
return 0.0;
}
`),this.userCode=`
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${c}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
float sumValue = 0.0;
for (int i = 0; i < ${o}; 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 + ${o};
if (${i===1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${l}
} else if (${i===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${l}
} else if (${i===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${l}
}
setOutput(sumValue);
}
`}},sJ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,d=`
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 = ${i}(values, minMaxValue);
if (${t==="min"} || ${t==="max"}) {
minMaxValue = ${i}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`,p="vec4";t==="all"?(o="1.0",d=`
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`,p="bvec4"):t==="any"&&(o="0.0",d=`
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`,p="bvec4");let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`),this.userCode=`
const float initializationValue = ${o};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${h}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${n};
vec4 minMaxValue = vec4(${o});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${d}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
${p} values = ${p}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${d}
} else if (${u===2}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${d}
} else if (${u===3}) {
${p} values = ${p}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${d}
}
setOutput(${l});
}
`}};function rJ(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function bl(e,t,n,s){let r=rJ(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:c}=r[o],u,d;n==="mean"?u=o===0?new F4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new F4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new sJ({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var aJ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=St(this.rank),r=oJ(t);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function oJ(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"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var iJ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=St(this.rank),r=A4("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
void main() {
${s} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${l};
if(${i}) {
result[1] = ${l};
}
--${r[this.rank-1]};
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
result[2] = ${l};
if(${i}) {
result[3] = ${l};
}
}
setOutput(result);
}
`}};function Gm(e,t,n){let s=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iJ(e.shape,t):new aJ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function lJ(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=E.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Gm(e,l,s),i=E.getInnerMostAxes(i.length,a)),E.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=E.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,y=be({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),A=fd(e.dtype),x=bl(y,A,"sum",s),b=be({inputs:{x},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(x),c&&s.disposeIntermediateTensorInfo(u),b}function Hm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return lJ(r,a,o,n)}var uJ={kernelName:io,backendName:"webgl",kernelFunc:Hm};function jn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=mx(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Gm(r,a,o);return c}var cJ={kernelName:ho,backendName:"webgl",kernelFunc:jn},O4=1e3;function jm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=y===A||y===1||A===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[y,d,h]:[y,h,d],S=s?[A,f,p]:[A,p,f],N=be({inputs:{x:e},backend:r,attrs:{shape:k}}),R=be({inputs:{x:t},backend:r,attrs:{shape:S}}),P=[N,R],$=Math.max(y,A),D=n?N.shape[1]:N.shape[2],T=a!=null,O=o!=null,B=l==="leakyrelu",H=l!=null?Um(l,!0):null,z=T||O||B||H!=null,X;if((h===1||f===1)&&D>O4&&z===!1){let J=N,Q=R;n&&(J=jn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),P.push(J)),s&&(Q=jn({inputs:{x:R},backend:r,attrs:{perm:[0,2,1]}}),P.push(Q));let te=f!==1,K=f===1,oe=J;te&&(oe=be({inputs:{x:J},backend:r,attrs:{shape:[$,D,1]}}),P.push(oe));let ce=f===1?2:1,he=Q;K&&(he=be({inputs:{x:Q},backend:r,attrs:{shape:[$,1,D]}}),P.push(he));let Ae=gx({inputs:{a:oe,b:he},backend:r});X=Hm({inputs:{x:Ae},backend:r,attrs:{axis:ce,keepDims:!0}}),P.push(Ae)}else{let J=Ln(e.dtype,t.dtype),Q=new $4(k,S,[$,h,f],n,s,T,H,O,B),te=[N,R];if(a!=null&&te.push(a),O&&te.push(o),B){let K=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));te.push(K),P.push(K)}X=r.runWebGLProgram(Q,te,J)}let ee=be({inputs:{x:X},backend:r,attrs:{shape:w}});P.push(X);for(let J of P)r.disposeIntermediateTensorInfo(J);return ee}function dJ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return jm({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var pJ={kernelName:mo,backendName:"webgl",kernelFunc:dJ},M4="return abs(x);";function hJ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=g4(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new oc(s.shape,M4):r=new Mo(s.shape,M4),n.runWebGLProgram(r,[s],s.dtype)}var fJ={kernelName:ti,backendName:"webgl",kernelFunc:hJ},mJ=br+`
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`,gJ=it({opSnippet:mJ}),yJ={kernelName:ql,backendName:"webgl",kernelFunc:gJ},AJ=br+`
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,xJ=it({opSnippet:AJ}),bJ={kernelName:Xl,backendName:"webgl",kernelFunc:xJ},z4="return a + b;",vJ=En({opSnippet:z4,packedOpSnippet:z4,supportsComplex:!0,cpuKernelImpl:RZ}),wJ={kernelName:qr,backendName:"webgl",kernelFunc:vJ},kJ=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
float result = ${s};
setOutput(result);
}
`}},IJ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
void main() {
${n.join(`
`)}
vec4 result = ${s};
setOutput(result);
}
`}};function qm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Is({inputs:{x:s[0]},backend:n});if(s.length>Z().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=qm({inputs:s.slice(0,l),backend:n}),u=qm({inputs:s.slice(l),backend:n});return qm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Ln(l,c)),a=s.map(l=>l.shape),i=Z().getBool("WEBGL_PACK")?new IJ(s[0].shape,a):new kJ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var SJ={kernelName:ka,backendName:"webgl",kernelFunc:qm};function CJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("all",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=bl(m,m.dtype,"all",n),y;if(o){let A=E.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var TJ={kernelName:Kl,backendName:"webgl",kernelFunc:CJ};function NJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("any",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=bl(m,m.dtype,"any",n),y;if(o){let A=E.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var EJ={kernelName:Zl,backendName:"webgl",kernelFunc:NJ},RJ=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=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 * ${s};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${s}; i++) {
int inIdx = ${i};
float candidate = getA(batch, inIdx);
if (candidate ${o} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`}},$J=class{constructor(e,t,n,s){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 r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=St(i),c=Hn("coords",i),u,d;if(a===1){d=i+1;let S=St(d);u=`
${S} sourceLocR = ${S}(${c.join()}, 0);
++${c[i-1]};
${S} sourceLocG = ${S}(${c.join()}, 0);
++${c[i-2]};
${S} sourceLocA = ${S}(${c.join()}, 0);
--${c[i-1]};
${S} sourceLocB = ${S}(${c.join()}, 0);
--${c[i-2]};`}else d=i,u=`
${l} sourceLocR = coords;
++${c[i-1]};
${l} sourceLocG = coords;
++${c[i-2]};
${l} sourceLocA = coords;
--${c[i-1]};
${l} sourceLocB = coords;
--${c[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=Hn("sourceLocR",d-1).concat("inIdx.r"),g=Hn("sourceLocG",d-1).concat("inIdx.g"),y=Hn("sourceLocB",d-1).concat("inIdx.b"),A=Hn("sourceLocA",d-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",b=s?"":`
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
getBestIndicesAChannel(${g.join()}),
getBestIndicesAChannel(${y.join()}),
getBestIndicesAChannel(${A.join()})));`,w=`vec4(
getAChannel(${m.join()}),
hasNextCol ? getAChannel(${g.join()}) : 0.,
hasNextRow ? getAChannel(${y.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${A.join()}) : 0.)`,k=s?"":`
float getBestIndicesAChannel(${f.join()}) {
return getChannel(getBestIndicesA(${p.join()}),
vec2(${p.slice(-2).join()}));
}`;this.userCode=`
float getAChannel(${f.join()}) {
return getChannel(getA(${p.join()}),
vec2(${p.slice(-2).join()}));
}
${k}
void main() {
${l} coords = getOutputCoords();
bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};
bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};
${u}
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
sourceLocB${h}, sourceLocA${h}) * ${t};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${w};
for (int i = 0; i < ${t}; i++) {
inIdx = srcIdx;
${b}
vec4 candidate = ${w};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`}};function L4(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=E.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new RJ(i,n,s==null),c=[t];s!=null&&c.push(s);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let d=L4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function B4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=E.computeOptimalWindowSize(a),i=new $J(r,o,n,s==null),l=s==null?[t]:[t,s],c=e.runWebGLProgram(i,l,"int32");if(c.shape.length===t.shape.length){let u=B4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function W4(e,t,n,s){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Z().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=E.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=be({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=L4(e,p,s);a.push(h);let f=be({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return B4(e,t,s)}function DJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=W4(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var _J={kernelName:Ia,backendName:"webgl",kernelFunc:DJ};function PJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=W4(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var FJ={kernelName:Yl,backendName:"webgl",kernelFunc:PJ},OJ=br+`
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`,MJ=it({opSnippet:OJ}),zJ={kernelName:Jl,backendName:"webgl",kernelFunc:MJ},LJ=br+"return log(x + sqrt(x * x + 1.0));",BJ=it({opSnippet:LJ}),WJ={kernelName:Ql,backendName:"webgl",kernelFunc:BJ},VJ=br+`
return atan(x);
`,UJ=it({opSnippet:VJ}),GJ={kernelName:eu,backendName:"webgl",kernelFunc:UJ},HJ=JY+`
return atan(a, b);
`,jJ=`
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+QY+`
return result;
`,qJ=En({opSnippet:HJ,packedOpSnippet:jJ}),XJ={kernelName:nu,backendName:"webgl",kernelFunc:qJ},KJ=br+`
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ZJ=it({opSnippet:KJ}),YJ={kernelName:tu,backendName:"webgl",kernelFunc:ZJ},fp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${u};
wR += ${l}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${d};
wC += ${c}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${S} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;return}let A="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=`
if (${f}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${A}(values, minMaxValue);
}
`;this.userCode=`
const ivec2 strides = ivec2(${o}, ${i});
const ivec2 pads = ivec2(${p}, ${h});
const float initializationValue = ${y};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${y});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${u};
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 * ${c};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
getValue(batch, xR, xC + 3 * ${c}, d)
);
${k}
}
int xC = xCCorner + ${b};
if (${w===1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${k}
} else if (${w===2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
initializationValue,
initializationValue
);
${k}
} else if (${w===3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${c}, d),
getValue(batch, xR, xC + 2 * ${c}, d),
initializationValue
);
${k}
}
}
setOutput(${x});
}
`}},yx=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${y});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${f};
wC += ${d}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${R} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${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 k=Math.floor(a/4)*4,S=a%4,N=`
if (${A}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${b}(values, minMaxValue);
}
`;this.userCode=`
const ivec3 strides =
ivec3(${o}, ${i}, ${l});
const ivec3 pads = ivec3(${m}, ${g}, ${y});
const float initializationValue = ${x};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${e.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${x});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${p};
wD += ${c}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${h};
wR += ${u}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${k}; wC += 4) {
int xC = xCCorner + wC * ${d};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
);
${N}
}
int xC = xCCorner + ${k};
if (${S===1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${N}
} else if (${S===2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
initializationValue,
initializationValue
);
${N}
} else if (${S===3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${d}, ch),
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
initializationValue
);
${N}
}
}
setOutput(${w});
}
}
`}};function JJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Is({inputs:{x:r},backend:n});let d=new fp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var QJ={kernelName:Sa,backendName:"webgl",kernelFunc:JJ};function eQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new yx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var tQ={kernelName:Hc,backendName:"webgl",kernelFunc:eQ},nQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
const ivec2 pads = ivec2(${c}, ${u});
const float avgMultiplier = float(${d});
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 < ${i};
wR += ${a}) {
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 < ${l};
wC+= ${o}) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`}},sQ=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
const ivec3 pads = ivec3(${h}, ${f}, ${m});
const float avgMultiplier = float(${g});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${u};
wD += ${i}) {
float dyD = float(dyDCorner + wD) / ${r}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${d};
wR += ${l}) {
float dyR = float(dyRCorner + wR) / ${a}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${p};
wC += ${c}) {
float dyC = float(dyCCorner + wC) / ${o}.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 rQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new sQ(p);return n.runWebGLProgram(h,[r],o.dtype)}var aQ={kernelName:gh,backendName:"webgl",kernelFunc:rQ};function oQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;tc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=new nQ(u);return n.runWebGLProgram(d,[r],o.dtype)}var iQ={kernelName:mh,backendName:"webgl",kernelFunc:oQ};function lQ(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return jm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var uQ={kernelName:Ca,backendName:"webgl",kernelFunc:lQ},cQ=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${o};
float scale = ${i};
float inv = scale * inversesqrt(variance + float(${a}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`}},dQ=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
void main() {
vec4 offset = ${o};
vec4 scale = ${i};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
setOutput((x - mean) * inv + offset);
}
`}},pQ=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=Z().getBool("WEBGL_PACK_NORMALIZATION")?new dQ(s.shape,r.shape,a.shape,u,d,l):new cQ(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},hQ={kernelName:La,backendName:"webgl",kernelFunc:pQ},fQ=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=St(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=mQ(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Ax[o]} = start[${o}] + coords.${Ax[o]};`);s=`
${t} sourceLoc;
${t} coords = getOutputCoords();
${r.join(`
`)}
`,this.userCode=`
void main() {
${s}
setOutput(getSource(${n}));
}
`}},Ax=["x","y","z","w","u","v"];function mQ(e){if(e===1)return"sourceLoc";if(e<=6)return Ax.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var gQ=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=St(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
result.x = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.y = ${a};
--${s[this.rank-1]};
}
`,i=this.rank===1?"":`
--${n[this.rank-1]};
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
++${s[this.rank-2]};
result.z = ${a};
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
++${s[this.rank-1]};
result.w = ${a};
}
}
`,l=this.rank<=4?`sourceLoc = coords +
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
`);this.userCode=`
void main() {
${t} coords = getOutputCoords();
${t} sourceLoc;
${l}
vec4 result = vec4(0.);
${o}
${i}
setOutput(result);
}
`}};function yQ(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=yn.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function uc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=yn.parseSliceParams(r,a,o);if(yn.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=nY(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=yn.isSliceContinous(r.shape,i,l);if(c||!u){let d=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gQ(l):new fQ(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),yQ(r,i,l,n)}var AQ={kernelName:$i,backendName:"webgl",kernelFunc:uc},xQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=be({inputs:{x:r},backend:n,attrs:{shape:l}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=be({inputs:{x:m},backend:n,attrs:{shape:u}}),y=uc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},bQ={kernelName:ni,backendName:"webgl",kernelFunc:xQ};function vQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=m4(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var wQ={kernelName:yh,backendName:"webgl",kernelFunc:vQ},kQ="return float(a != b);",V4=En({opSnippet:kQ,cpuKernelImpl:YZ,dtype:"bool"}),IQ={kernelName:xi,backendName:"webgl",kernelFunc:V4};function mp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Is({inputs:{x:r.complexTensorInfos.real},backend:n})}var SQ={kernelName:td,backendName:"webgl",kernelFunc:mp},CQ="return float(int(x));";function TQ(e,t){let n=new Mo(e.shape,CQ),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function xx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Is({inputs:{x:r},backend:n});let o=jt(r.shape),i=xx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=zo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=mp({inputs:{input:r},backend:n}),i=xx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Is({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return TQ(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=V4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var NQ={kernelName:Ta,backendName:"webgl",kernelFunc:xx},U4="return ceil(x);",EQ=it({opSnippet:U4,packedOpSnippet:U4,cpuKernelImpl:DZ}),RQ={kernelName:Na,backendName:"webgl",kernelFunc:EQ},$Q=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`}},DQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`}};function _Q(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Z().getBool("WEBGL_PACK_CLIP")?i=new DQ(r.shape):i=new $Q(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var PQ={kernelName:Xr,backendName:"webgl",kernelFunc:_Q},FQ=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`}};function G4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function OQ(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new FQ(s.shape),o=[G4(s,r.complexTensorInfos.real),G4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var MQ={kernelName:qc,backendName:"webgl",kernelFunc:OQ},zQ=class{constructor(e){this.outputShape=[],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${n.join(`
`)}
}
`}},LQ=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=St(s),a=Hn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
return getChannel(
getT0(${u}), vec2(${c.join()}));
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
return getChannel(
getT${f}(${Xm(o,l,m)}),
vec2(${Xm(c,l,m)}));
}`}let p=i.length,h=i[i.length-1];d+=`
return getChannel(
getT${p}(${Xm(o,l,h)}),
vec2(${Xm(c,l,h)}));`,this.userCode=`
float getValue(${o.map(f=>"int "+f)}) {
${d}
}
void main() {
${r} coords = getOutputCoords();
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
${a[s-1]} = ${a[s-1]} + 1;
if (${a[s-1]} < ${n[s-1]}) {
result.g = getValue(${a});
}
${a[s-2]} = ${a[s-2]} + 1;
if (${a[s-2]} < ${n[s-2]}) {
result.a = getValue(${a});
}
${a[s-1]} = ${a[s-1]} - 1;
if (${a[s-2]} < ${n[s-2]} &&
${a[s-1]} < ${n[s-1]}) {
result.b = getValue(${a});
}
setOutput(result);
}
`}};function Xm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function Km(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Is({inputs:{x:r.complexTensorInfos.imag},backend:n})}var BQ={kernelName:Yc,backendName:"webgl",kernelFunc:Km};function cc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>mp({inputs:{input:m},backend:n})),d=e.map(m=>Km({inputs:{input:m},backend:n})),p=cc(u,t,n),h=cc(d,t,n),f=zo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return be({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=E.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=_Z(d,p,s,h),m=E.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Z().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=cc(e.slice(0,u),t,n),p=cc(e.slice(u),t,n),h=cc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new LQ(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=WQ(e,t,n),i=new zQ(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=be({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function WQ(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>be({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function H4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return Is({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),cc(i,a,n)}var VQ={kernelName:si,backendName:"webgl",kernelFunc:H4},j4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";n&&(s?x=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?x=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:x=`
float activation(float x) {
${n}
}
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${x}
const ivec2 strides = ivec2(${i}, ${l});
const ivec2 pads = ivec2(${a}, ${o});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${A}];
ivec2 xRCCorner =
ivec2(coords[${g}], coords[${y}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${c};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${u};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${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, ${h}) *
getW(wR, wC, ${h}, d2);
} else {
dotProd +=
getX(batch, ${h}, xR, xC) *
getW(wR, wC, ${h}, d2);
}
} else if (${f===2}) {
vec2 wValues = vec2(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2)
);
if (${m}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${f===3}) {
vec3 wValues = vec3(
getW(wR, wC, ${h}, d2),
getW(wR, wC, ${h} + 1, d2),
getW(wR, wC, ${h} + 2, d2)
);
if (${m}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${h}),
getX(batch, xR, xC, ${h} + 1),
getX(batch, xR, xC, ${h} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${h}, xR, xC),
getX(batch, ${h} + 1, xR, xC),
getX(batch, ${h} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${w}
${b}
setOutput(result);
}
`}},UQ=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
const ivec3 strides = ivec3(${r}, ${a}, ${o});
const ivec3 pads = ivec3(${t}, ${n}, ${s});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${u}; wF++) {
int xF = xFCorner + wF * ${i};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int wR = 0; wR < ${d}; wR++) {
int xR = xRCorner + wR * ${l};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int wC = 0; wC < ${p}; wC++) {
int xC = xCCorner + wC * ${c};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${h}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${f===1}) {
dotProd +=
getX(batch, xF, xR, xC, ${h}) *
getW(wF, wR, wC, ${h}, d2);
} else if (${f===2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${f===3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${h}),
getX(batch, xF, xR, xC, ${h} + 1),
getX(batch, xF, xR, xC, ${h} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${h}, d2),
getW(wF, wR, wC, ${h} + 1, d2),
getW(wF, wR, wC, ${h} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`}},GQ=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Ls(this.outputShape.length);let{dataFormat:n}=t,s=Gn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=`
blockIndex = rc.y + ${u};
pos = rc.x + ${c};
${i}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${a}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${o}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${r}) {
innerDims = vec2(d1, ch);
result[${c*2+u}] = getChannel(
getA(d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${c*2+u}] = getChannel(
getA(ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;this.userCode=`
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${l}
${s.output} = result;
}
`}};function q4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((d===1||p===1)&&u>O4)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(dp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let S=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let N=jm({a:w,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(N.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=Is({inputs:{x:N},backend:s}),g.shape=n.outShape,y.push(N)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=be({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=jm({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=be({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(k),y.push(S)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function X4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,y=[m,g],A=!0,x=!1,b=[],w=be({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let S=new GQ(y,n),N=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(S,[w],"float32",N),P=be({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let $=r!=null,D=a!=null,T=i==="leakyrelu",O=i?Um(i,!0):null,B=new $4(P.shape,k.shape,[1,g,n.outChannels],A,x,$,O,D,T),H=[P,k];if(r&&H.push(r),D&&H.push(a),T){let J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));H.push(J),b.push(J)}let z=s.runWebGLProgram(B,H,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],ee=be({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return ee}function HQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=q4({x:r,filter:a,convInfo:p,backend:n});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=X4({x:r,filter:a,convInfo:p,backend:n});else{let m=new j4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=be({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jQ={kernelName:Ea,backendName:"webgl",kernelFunc:HQ},qQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=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} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
if (${a}) {
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);
}
`}},XQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${u}];
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${t}; wR++) {
float dyR = float(dyRCorner + wR) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
if (${a}) {
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);
}
`}},KQ=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${e.batchSize}; b++) {
for (int yF = 0; yF < ${e.outDepth}; yF++) {
int xF = wF + yF * ${t} - ${r};
if (xF < 0 || xF >= ${e.inDepth}) {
continue;
}
for (int yR = 0; yR < ${e.outHeight}; yR++) {
int xR = wR + yR * ${n} - ${a};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${s} - ${o};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`}},ZQ=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
const ivec3 pads = ivec3(${i}, ${l}, ${c});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${t}; wF++) {
float dyF = float(dyFCorner + wF) / ${r}.0;
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${t} - 1 - wF;
for (int wR = 0; wR < ${n}; wR++) {
float dyR = float(dyRCorner + wR) / ${a}.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 < ${s}; wC++) {
float dyC = float(dyCCorner + wC) / ${o}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${s} - 1 - wC;
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`}};function YQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new qQ(p);return n.runWebGLProgram(h,[r,a],"float32")}var JQ={kernelName:Ah,backendName:"webgl",kernelFunc:YQ};function QQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new XQ(p);return n.runWebGLProgram(h,[r,a],"float32")}var eee={kernelName:Ra,backendName:"webgl",kernelFunc:QQ};function tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new UQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var nee={kernelName:Xc,backendName:"webgl",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=E.computeConv3DInfo(r.shape,l,o,1,i),u=new KQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var ree={kernelName:xh,backendName:"webgl",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=E.computeConv3DInfo(l,a.shape,i,1,o),u=new ZQ(c);return n.runWebGLProgram(u,[r,a],"float32")}var oee={kernelName:bh,backendName:"webgl",kernelFunc:aee},iee=R4+`
return cos(x);
`,lee=it({opSnippet:iee}),uee={kernelName:$a,backendName:"webgl",kernelFunc:lee},cee=`
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`,dee=it({opSnippet:cee}),pee={kernelName:Da,backendName:"webgl",kernelFunc:dee},hee=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[A,x,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
const float height_ratio = float(${m});
const float width_ratio = float(${A});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${a}) {
return;
}
float height_scale = ${g};
float width_scale = ${x};
float in_y = ${y};
if( in_y < 0.0 || in_y > ${h} ) {
setOutput(float(${r}));
return;
}
float in_x = ${b};
if( in_x < 0.0 || in_x > ${f} ) {
setOutput(float(${r}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${p} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`}},fee=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new hee(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},mee={kernelName:ai,backendName:"webgl",kernelFunc:fee},K4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${Z4(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
void main() {
${St(s)} coords = getOutputCoords();
int end = ${Y4(s,"coords")};
float val = ${r};
int pow2 = int(pow(2.0, index));
if (${o}) {
int idx = ${i};
${Y4(s,"coords")} = idx;
val += getX(${Z4(s,"coords")});
}
setOutput(val);
}
`}};function Z4(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 ${e} is not yet supported`)}function Y4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function gee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=Is({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new K4(u.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new K4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var yee={kernelName:ri,backendName:"webgl",kernelFunc:gee};function Aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=m4(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=$Z(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var xee={kernelName:vh,backendName:"webgl",kernelFunc:Aee},bee=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 vee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new bee(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var wee={kernelName:oi,backendName:"webgl",kernelFunc:vee},J4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ls(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?l=`float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${n}
}`:l=`
float activation(float x) {
${n}
}
`,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${l}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${i};
int q = d2 - d1 * ${i};
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 < ${a}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${o}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${u}
${c}
setOutput(result);
}
`}},Q4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Ls(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
vec4 xTexelC${g*2};
int xTexelC${g*2}Ready;
vec4 xTexelC${g*2+1};
int xTexelC${g*2+1}Ready;
vec4 xC${g};`;for(let g=0;g<c;g++){for(let y=0;y<u;y++)p+=`
xTexelC${y*2} = vec4(0.0);
xTexelC${y*2}Ready = 0;
xTexelC${y*2+1} = vec4(0.0);
xTexelC${y*2+1}Ready = 0;
xC${y} = vec4(0.0);`;p+=`
xR = xRCorner + ${g} * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;for(let y=0;y<(d+1)/2;y++){let A=y*2;if(p+=`
xC = xCCorner + ${A*l};
`,i===1){if(A<u&&(o%2==1?(p+=`
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
`,l===1&&A>0?p+=`
xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.xy);
`:p+=`
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${A} = vec4(previous.zw, xTexelC${A}.xy);
} else {
xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy);
}
`):p+=`
if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
xC${A} = xTexelC${A};
`,A+1<u)){let x=o%2==0?v.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
xCOffset = xC + imod(pads[1], 2) + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
`,l>1&&(p+=`
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
xTexelC${A}Ready = 1;
}
`),p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy);
`):x===1?p+=`
xC${A+1} = xTexelC${A};
`:p+=`
xCOffset = xC + ${x};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
xC${A+1} = xTexelC${A+1};
`}}else A<u&&(o%2==1?(p+=`
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.0);
}
xTexelC${A+1}Ready = 1;
}
xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`,A+1<u&&(p+=`
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${A+1} = vec4(xTexelC${A+1}.xy, final.xy);
`)):(p+=`
if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
xTexelC${A} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${A}.zw = vec2(0.0);
}
xTexelC${A}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${A+1}.zw = vec2(0.);
}
xTexelC${A+1}Ready = 1;
}
xC${A} = vec4(
xTexelC${A}.xy, xTexelC${A+1}.xy);
`,A+1<u&&(p+=`
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
`)));A<u&&(p+=`
wTexel = getW(${g}, ${A}, d1, q);
dotProd += xC${A} * vec4(wTexel.xz, wTexel.xz);
`,A+1<u&&(p+=`
wTexel = getW(${g}, ${A+1}, d1, q);
dotProd += xC${A+1} * vec4(wTexel.xz, wTexel.xz);
`))}p+=`
}
`}let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${n}
}`:r?h=`vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${n}
}`:h=`vec4 activation(vec4 x) {
${n}
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
${h}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${a};
int q = d2 - d1 * ${a};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${p}
vec4 result = dotProd - vec4(0.000000000000001);
${m}
${f}
setOutput(result);
}
`}};function kee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new Q4(d):p=new J4(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var Iee={kernelName:_a,backendName:"webgl",kernelFunc:kee},See=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=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 * ${a} + 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} - ${s};
if (xR < 0 || xR >= ${e.inHeight}) {
continue;
}
for (int yC = 0; yC < ${e.outWidth}; yC++) {
int xC = wC + yC * ${n} - ${r};
if (xC < 0 || xC >= ${e.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`}},Cee=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
const ivec2 pads = ivec2(${a}, ${o});
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) / ${s}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${t} - 1 - wR;
for (int wC = 0; wC < ${n}; wC++) {
float dyC = float(dyCCorner + wC) / ${r}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${n} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${i}; dm++) {
int d2 = d1 * ${i} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`}};function Tee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new See(d);return n.runWebGLProgram(p,[r,a],"float32")}var Nee={kernelName:wh,backendName:"webgl",kernelFunc:Tee};function Eee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Cee(d);return n.runWebGLProgram(p,[r,a],"float32")}var Ree={kernelName:kh,backendName:"webgl",kernelFunc:Eee},$ee=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 Dee(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=be({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new $ee(a),l=n.runWebGLProgram(i,[o],o.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var _ee={kernelName:Ih,backendName:"webgl",kernelFunc:Dee},Pee=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
const ivec2 strides = ivec2(${r}, ${a});
const ivec2 pads = ivec2(${u}, ${d});
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 < ${o}; h++) {
int hIn = hBeg + h * ${l};
if (hIn >= 0 && hIn < ${t}) {
for (int w = 0; w < ${i}; w++) {
int wIn = wBeg + w * ${c};
if (wIn >= 0 && wIn < ${n}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`}};function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new Pee(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var Oee={kernelName:Kc,backendName:"webgl",kernelFunc:Fee};function Mee(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=a[g]:(x=jn({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=be({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=gx({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Hm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var zee={kernelName:Zc,backendName:"webgl",kernelFunc:Mee},Lee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bee=`
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;
`,Wee=it({opSnippet:Lee,packedOpSnippet:Bee}),Vee={kernelName:Fa,backendName:"webgl",kernelFunc:Wee},Uee="return (b >= 1.0) ? a : a * (b + 1.0);",Gee=`
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,Hee=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Z().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new hp(Gee,s.shape,r.shape):new lc(Uee,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},jee={kernelName:Th,backendName:"webgl",kernelFunc:Hee},qee=`
return vec4(equal(a, b));
`,Xee="return float(a == b);",Kee=En({opSnippet:Xee,packedOpSnippet:qee,dtype:"bool",cpuKernelImpl:PZ}),Zee={kernelName:ii,backendName:"webgl",kernelFunc:Kee},Yee=`
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${E.ERF_P};
float a1 = ${E.ERF_A1};
float a2 = ${E.ERF_A2};
float a3 = ${E.ERF_A3};
float a4 = ${E.ERF_A4};
float a5 = ${E.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`,Jee=it({opSnippet:Yee}),Qee={kernelName:su,backendName:"webgl",kernelFunc:Jee},eS="return exp(x);",tS=it({opSnippet:eS,packedOpSnippet:eS,cpuKernelImpl:FZ}),ete={kernelName:Oa,backendName:"webgl",kernelFunc:tS};function bx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),be({inputs:{x:a},backend:s,attrs:{shape:i}})}var tte={kernelName:li,backendName:"webgl",kernelFunc:bx},nS="return exp(x) - 1.0;",nte=it({opSnippet:nS,packedOpSnippet:nS,cpuKernelImpl:OZ}),ste={kernelName:ui,backendName:"webgl",kernelFunc:nte},sS=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
const float exponentMultiplier = ${r};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${o}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${s});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${s}; 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) / ${a};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`}};function rS(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=be({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new sS("real",l,t),u=new sS("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=zo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=be({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function rte(e){let{inputs:t,backend:n}=e,{input:s}=t;return rS(s,!1,n)}var ate={kernelName:Nh,backendName:"webgl",kernelFunc:rte},ote=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`}};function gp(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new ote(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var ite={kernelName:ru,backendName:"webgl",kernelFunc:gp},lte=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${t} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${t}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`}},ute={kernelName:ci,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new lte(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},aS="return floor(x);",cte=it({opSnippet:aS,packedOpSnippet:aS,cpuKernelImpl:MZ}),dte={kernelName:Ma,backendName:"webgl",kernelFunc:cte},pte=`
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`,hte=`
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`,fte=En({opSnippet:pte,packedOpSnippet:hte,dtype:"int32"}),mte={kernelName:za,backendName:"webgl",kernelFunc:fte},gte=class{constructor(e){this.variableNames=["A"];let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
vec4 values = ${t.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`}},yte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${s}.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;
}
`}},Ate={kernelName:ad,backendName:"webgl",kernelFunc:xte},dc;function xte(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(dc==null&&(dc=document.createElement("canvas").getContext("2d")),dc.canvas.width=l,dc.canvas.height=c,dc.drawImage(r,0,0,l,c),r=dc.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=Ms.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Z().getBool("WEBGL_PACK")?new yte(d):new gte(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function bte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=q4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Z().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=X4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",S=h?Um(h,!1):null,N=new j4(g,b,S,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(P),A.push(P)}y=n.runWebGLProgram(N,R,"float32")}let x=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var vte={kernelName:go,backendName:"webgl",kernelFunc:bte};function wte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=E.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),y=Z().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,A=p?Um(p,y):null,x=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&x.push(o),w&&x.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));x.push(P),f.push(P)}let S;y?S=new Q4(g,b,A,w,k):S=new J4(g,b,A,w,k);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(S,x,"float32",N);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),R}var kte={kernelName:yo,backendName:"webgl",kernelFunc:wte},Ite=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=St(t.length),r=St(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
${s} strides = ${s}(${this.strides});
void main() {
${r} coords = getOutputCoords();
int flattenIndex = 0;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
flattenIndex += index * ${a};
}
setOutput(getX(flattenIndex, coords[1]));
}
`}};function Ste(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),A=n.bufferSync(s),x=zZ(y,A,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,x.values)}let f=new Ite(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=be({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Cte={kernelName:pi,backendName:"webgl",kernelFunc:Ste},Tte=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=St(this.rank),s=Nte(e,2);this.userCode=`
void main() {
${n} resRC = getOutputCoords();
setOutput(getA(${s}));
}
`}};function Nte(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function oS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=be({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=be({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let A=n.bufferSync(h),x=n.bufferSync(p),b=LZ(x,A,f);return d.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new Tte(p.shape,f),g=n.runWebGLProgram(m,[p,h],p.dtype);d.push(g);let y=be({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(A=>n.disposeIntermediateTensorInfo(A)),y}var Ete={kernelName:di,backendName:"webgl",kernelFunc:oS},Rte="return float(a > b);",$te=`
return vec4(greaterThan(a, b));
`,Dte=En({opSnippet:Rte,packedOpSnippet:$te,cpuKernelImpl:BZ,dtype:"bool"}),_te={kernelName:hi,backendName:"webgl",kernelFunc:Dte},Pte="return float(a >= b);",Fte=`
return vec4(greaterThanEqual(a, b));
`,Ote=En({opSnippet:Pte,packedOpSnippet:Fte,dtype:"bool",cpuKernelImpl:WZ}),Mte={kernelName:Ba,backendName:"webgl",kernelFunc:Ote};function zte(e){let{inputs:t,backend:n}=e,{input:s}=t;return rS(s,!0,n)}var Lte={kernelName:Eh,backendName:"webgl",kernelFunc:zte},Bte="return float(!isnan(x) && !isinf(x));",Wte=it({opSnippet:Bte,dtype:"bool"}),Vte={kernelName:au,backendName:"webgl",kernelFunc:Wte},Ute="return float(isinf(x));",Gte=it({opSnippet:Ute,dtype:"bool"}),Hte={kernelName:ou,backendName:"webgl",kernelFunc:Gte},jte="return float(isnan(x));",qte=it({opSnippet:jte,dtype:"bool"}),Xte={kernelName:iu,backendName:"webgl",kernelFunc:qte},Kte="return float(a < b);",Zte=`
return vec4(lessThan(a, b));
`,Yte=En({opSnippet:Kte,packedOpSnippet:Zte,cpuKernelImpl:VZ,dtype:"bool"}),Jte={kernelName:mi,backendName:"webgl",kernelFunc:Yte},Qte="return float(a <= b);",ene=`
return vec4(lessThanEqual(a, b));
`,tne=En({opSnippet:Qte,packedOpSnippet:ene,cpuKernelImpl:UZ,dtype:"bool"}),nne={kernelName:gi,backendName:"webgl",kernelFunc:tne};function sne(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=GZ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var rne={kernelName:Rh,backendName:"webgl",kernelFunc:sne},ane=`if (x < 0.0) return NAN;
return log(x);`,one=`
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;
`,ine=it({opSnippet:ane,packedOpSnippet:one,cpuKernelImpl:HZ}),lne={kernelName:Va,backendName:"webgl",kernelFunc:ine},une="return log(1.0 + x);",cne=it({opSnippet:une}),dne={kernelName:lu,backendName:"webgl",kernelFunc:cne},pne="return float(a >= 1.0 && b >= 1.0);",hne=`
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`,fne=En({opSnippet:pne,packedOpSnippet:hne,dtype:"bool"}),mne={kernelName:yi,backendName:"webgl",kernelFunc:fne},gne="return float(!(x >= 1.0));",yne=it({opSnippet:gne}),Ane={kernelName:uu,backendName:"webgl",kernelFunc:yne},xne="return float(a >= 1.0 || b >= 1.0);",bne=`
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`,vne=En({opSnippet:xne,packedOpSnippet:bne,dtype:"bool"}),wne={kernelName:Jc,backendName:"webgl",kernelFunc:vne},kne=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${a}; j <= ${a}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${o}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${i};
setOutput(val);
}
`}},Ine=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${a};
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 = - ${a}; j <= ${a}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
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 * ${i};
setOutput(result);
}
`}},Sne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=Z().getBool("WEBGL_PACK_NORMALIZATION")?new Ine(r.shape,a,o,i,l):new kne(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Cne={kernelName:Qc,backendName:"webgl",kernelFunc:Sne},Tne=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${t})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${t} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${s}) * norm + float(${n});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${s})
* float(${r})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${r});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`}},Nne=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Tne(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},Ene={kernelName:$h,backendName:"webgl",kernelFunc:Nne};function Rne(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=bl(i,e.dtype,"max",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function iS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let x=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[u[S]];let w=mx(x,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=Gm(r,u,n);c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("max",c,i);let[f,m]=E.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=E.expandShapeToKeepDim(f,l));let y;if(p){let x=n.texData.get(h.dataId).values,b=jZ(x,v.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=b}else y=Rne(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var $ne={kernelName:Ua,backendName:"webgl",kernelFunc:iS},Dne=S4+`
return max(a, b);
`,_ne=`
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Vm+`
return result;
`,Pne=En({opSnippet:Dne,packedOpSnippet:_ne,cpuKernelImpl:qZ}),Fne={kernelName:Ga,backendName:"webgl",kernelFunc:Pne};function One(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Is({inputs:{x:r},backend:n});let d=new fp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var Mne={kernelName:Ha,backendName:"webgl",kernelFunc:One};function zne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=E.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new yx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Lne={kernelName:ed,backendName:"webgl",kernelFunc:zne},Bne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
const ivec2 pads = ivec2(${o}, ${i});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${r};
wR += ${s}) {
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 < ${a}; 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 * ${a} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`}},Wne=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
const ivec3 pads = ivec3(${u}, ${d}, ${p});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${i};
wD += ${r}) {
float dyD = float(dyDCorner + wD) / ${t}.0;
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${l};
wR += ${a}) {
float dyR = float(dyRCorner + wR) / ${n}.0;
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${c};
wC += ${o}) {
float dyC = float(dyCCorner + wC) / ${s}.0;
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${h} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${l} * ${c} +
wR * ${c} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`}};function Vne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new yx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new Wne(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Une={kernelName:_h,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;tc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new fp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new Bne(p),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var Hne={kernelName:Dh,backendName:"webgl",kernelFunc:Gne};function jne(e,t,n,s){let r=new fp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new fp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var qne={kernelName:Ph,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(E.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=E.computePool2DInfo(s.shape,r,a,c,o),[d,p]=jne(s,i,u,l);return[d,p]}};function Xne(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=bl(i,"float32","mean",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var Kne={kernelName:ja,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=E.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let N=0;N<w.length;N++)w[N]=s.shape[u[N]];let k=mx(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=Gm(s,u,o);h.push(f),c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=E.computeOutAndReduceShapes(f.shape,c),y=m;r&&(y=E.expandShapeToKeepDim(m,l));let A=Xne(f,g,y,o);for(let x of h)o.disposeIntermediateTensorInfo(x);return A}};function Zne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=bl(m,m.dtype,"min",n),y;if(o){let A=E.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:A}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var Yne={kernelName:qa,backendName:"webgl",kernelFunc:Zne},Jne=S4+`
return min(a, b);
`,Qne=`
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
`+Vm+`
return result;
`,ese=En({opSnippet:Jne,packedOpSnippet:Qne,cpuKernelImpl:XZ}),tse={kernelName:Xa,backendName:"webgl",kernelFunc:ese},nse=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let s=e.length,r=St(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${l};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${l};
}
setOutput(getX(outC - start));
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
for (int i = 0; i < ${s}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${l};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
}
}
${r} coords = outC - start;
setOutput(getX(${i}));
}
`}},sse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=St(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
${r} source = rc;
if (source < start) {
source = start * 2 - source - ${d};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${d};
}
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
`}else{let h=`
${r} source = rc;
${r} lt = ${r}(lessThan(source, start));
${r} gte = ${r}(greaterThanEqual(source, end));
${r} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${d}) +
gte * ((end - 1) * 2 - source + ${d});
source -= start;
`;p=`
${r} rc = outputLoc;
${h}
result[0] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[1] = getChannel(getX(${l.join()}), ${u});
}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {
${h}
result[2] = getChannel(getX(${l.join()}), ${u});
${i[s-1]} += 1;
if(${c}) {
${h}
result[3] = getChannel(getX(${l.join()}), ${u});
}
}
`}this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${p}
setOutput(result);
}
`}},rse=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sse(s.shape,r,a):new nse(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},ase={kernelName:Ka,backendName:"webgl",kernelFunc:rse},ose=`if (b == 0.0) return NAN;
return mod(a, b);`,ise=`
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
`+Vm+`
return result;
`,lse=En({opSnippet:ose,packedOpSnippet:ise}),use={kernelName:cu,backendName:"webgl",kernelFunc:lse},cse=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${t-1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${t-1}));
}
`}},dse=`
if (a == b) {
return 1.0;
};
return a / b;`,pse=`
// 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;
`,lS=En({opSnippet:dse,packedOpSnippet:pse,checkOutOfBounds:!0}),hse={kernelName:Pa,backendName:"webgl",kernelFunc:lS},uS="return a - b;",cS=En({opSnippet:uS,packedOpSnippet:uS,supportsComplex:!0,cpuKernelImpl:cY}),fse={kernelName:co,backendName:"webgl",kernelFunc:cS};function dS(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=iS({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=be({inputs:{x:i},backend:n,attrs:{shape:l}}),u=cS({inputs:{a:r,b:c},backend:n}),d=tS({inputs:{x:u},backend:n}),p=Hm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=be({inputs:{x:p},backend:n,attrs:{shape:l}}),f=lS({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var mse={kernelName:lo,backendName:"webgl",kernelFunc:dS};function gse(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:dS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new cse(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var yse={kernelName:Fh,backendName:"webgl",kernelFunc:gse},pS="return -x;";function Ase(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=ZZ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Z().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new oc(s.shape,pS):r=new Mo(s.shape,pS),n.runWebGLProgram(r,[s],s.dtype)}var xse={kernelName:Ai,backendName:"webgl",kernelFunc:Ase},bse=Zs.nonMaxSuppressionV3Impl;function vse(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=bse(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var wse={kernelName:bi,backendName:"webgl",kernelFunc:vse},kse=Zs.nonMaxSuppressionV4Impl;function Ise(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=kse(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Sse={kernelName:du,backendName:"webgl",kernelFunc:Ise},Cse=Zs.nonMaxSuppressionV5Impl;function Tse(e){E.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Cse(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Nse={kernelName:vi,backendName:"webgl",kernelFunc:Tse},Ese=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${s}), float(${n}),
float(index == coords.y)));
}
`}},Rse=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new Ese(l,a,o,i),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=be({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},$se={kernelName:ki,backendName:"webgl",kernelFunc:Rse};function Zm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=Zm({inputs:{x:r},backend:n}),o=Km({inputs:{input:s},backend:n}),i=Zm({inputs:{x:o},backend:n}),l=zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Dse={kernelName:Li,backendName:"webgl",kernelFunc:Zm};function hS(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=mp({inputs:{input:s},backend:n}),a=hS({inputs:{x:r},backend:n}),o=Km({inputs:{input:s},backend:n}),i=Zm({inputs:{x:o},backend:n}),l=zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return gp({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var _se={kernelName:wi,backendName:"webgl",kernelFunc:hS};function Pse(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return bx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=bx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=H4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var Fse={kernelName:Ii,backendName:"webgl",kernelFunc:Pse},Ose=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=St(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
int start = ${a};
int end = ${o};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;return}this.userCode=`
${r} start = ${r}(${a});
${r} end = ${r}(${o});
void main() {
${r} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${r} coords = outC - start;
setOutput(getX(${i}));
}
}
`}},Mse=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=St(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
if(${c}) {
`,s===1?"":`}
rc = outputLoc;
${i[s-2]} += 1;
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
${d[f]}
if (${p}) {
result[${f}] = float(value);
} else {
${r} source = rc - start;
result[${f}] = getChannel(getX(${l.join()}), ${u});
}
`;h+=s===1?"} ":"}}",this.userCode=`
const ${r} start = ${r}(${a});
const ${r} end = ${r}(${o});
void main() {
${r} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${h}
setOutput(result);
}
`}},fS=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return gp({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Mse(r.shape,a,o):new Ose(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},zse={kernelName:Ya,backendName:"webgl",kernelFunc:fS},Lse=`
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);
`,Bse=`
// 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));
`+Vm+`
return result;
`,Wse=En({opSnippet:Lse,packedOpSnippet:Bse}),Vse={kernelName:Ja,backendName:"webgl",kernelFunc:Wse};function Use(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=E.getAxesPermutation(u,i),p=r;d!=null&&(p=jn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=E.getInnerMostAxes(u.length,i),l.push(p)),E.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=JZ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=E.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),y=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),A=fd(r.dtype),x=bl(y,A,"prod",n);h=be({inputs:{x},backend:n,attrs:{shape:f}}),l.push(y),l.push(x)}if(o){l.push(h);let f=E.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Gse={kernelName:Si,backendName:"webgl",kernelFunc:Use},mS=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=QZ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Hse={kernelName:pu,backendName:"webgl",kernelFunc:mS},jse="return 1.0 / x;",qse=it({opSnippet:jse}),Xse={kernelName:hu,backendName:"webgl",kernelFunc:qse},Kse=br+`
return (x < 0.0) ? 0.0 : x;
`,Zse=`
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;
`,Yse=it({opSnippet:Kse,packedOpSnippet:Zse}),Jse={kernelName:eo,backendName:"webgl",kernelFunc:Yse},Qse=br+`
return (x < 0.0) ? 0.0 : min(6.0, x);
`,ere=`
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;
`,tre=it({opSnippet:Qse,packedOpSnippet:ere}),nre={kernelName:no,backendName:"webgl",kernelFunc:tre},sre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.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 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);
}
`}},rre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.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 = ${d};
// 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 are(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new rre(r.shape,l,c,a,o):new sre(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var ore={kernelName:to,backendName:"webgl",kernelFunc:are},ire=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
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 >= ${a}) {
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 >= ${o}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function lre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new ire(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var ure={kernelName:Mh,backendName:"webgl",kernelFunc:lre},cre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec2 effectiveInputOverOutputRatioRC = vec2(
${c[0]/u[0]},
${c[1]/u[1]});
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`}},dre=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
const vec3 effectiveInputOverOutputRatioRC = vec3(
${c[0]/u[0]},
${c[1]/u[1]},
${c[1]/u[1]});
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
${i}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${p};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${l-1};
bool hasNextRow = coords.z < ${n-1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`}};function pre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Z().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dre(r.shape,l,c,a,o):new cre(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var hre={kernelName:fu,backendName:"webgl",kernelFunc:pre},fre=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${c});
const float widthScale = float(${u});
const float invHeightScale = float(${d});
const float invWidthScale = float(${p});
const int winHeight = int(${h});
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 >= ${a}) {
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 >= ${o}) {
continue;
}
float sourceFracRow =
float(${i[0]}) *
(float(dyR) / float(${l[0]}));
float sourceFracCol =
float(${i[1]}) *
(float(dyC) / float(${l[1]}));
int sourceNearestRow = int(min(
float(int(${s}) - 1),
${n} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${r}) - 1),
${n} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`}};function mre(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new fre(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var gre={kernelName:Oh,backendName:"webgl",kernelFunc:mre},yre=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
void main() {
int coord = getOutputCoords();
setOutput(getX(${e[0]} - coord - 1));
}
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=St(n);this.userCode=`
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${r}));
}
`}},Are=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-${n} tensor is not yet supported`);this.outputShape=e;let s=Hn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=St(n);n===1?this.userCode=`
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${e[0]} - rc - 1),
${e[0]} - rc - 1);
if(${r}){
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
${e[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`:this.userCode=`
void main() {
${o} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${i(s.slice())};
if(${r}){
result.g = ${l(s.slice())};
}
if(${a}) {
result.b = ${c(s.slice())};
if(${r}) {
result.a = ${u(s.slice())};
}
}
setOutput(result);
}
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((y,A)=>p(A,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Is({inputs:{x:r},backend:n});let l=Z().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Are(r.shape,i):new yre(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var bre={kernelName:Ti,backendName:"webgl",kernelFunc:xre},vre=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
vec3 fill = vec3(${t.join(",")});
float outputValue = fill[coords[3]];`,this.userCode=`
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${r}
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`}},wre={kernelName:Bi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new vre(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},kre=`
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`,Ire=it({opSnippet:kre}),Sre={kernelName:Ni,backendName:"webgl",kernelFunc:Ire},Cre="return inversesqrt(x);",Tre=it({opSnippet:Cre,cpuKernelImpl:eY}),Nre={kernelName:so,backendName:"webgl",kernelFunc:Tre},gS=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=St(r.length),l=St(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
${i} strides = ${i}(${r});
void main() {
${l} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${e}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${t}; j++) {
int index = round(${u});
flattenedIndex += index * ${h};
}
if (flattenedIndex == coords[0]) {
sum += ${p};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`}};function Ere(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=be({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new gS(l,i,h.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(g,[f,h,m],f.dtype),A=be({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),A}var Rre={kernelName:Ei,backendName:"webgl",kernelFunc:Ere},$re=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);s=i.join(),r=l.join()}let a=St(n);this.userCode=`
void main() {
${a} resRC = getOutputCoords();
float cVal = getC(${s});
if (cVal >= 1.0) {
setOutput(getA(${r}));
} else {
setOutput(getB(${r}));
}
}
`}};function Dre(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new $re(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var _re={kernelName:Ri,backendName:"webgl",kernelFunc:Dre},Pre=`
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${E.SELU_SCALEALPHA};
float scale = ${E.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`,Fre=it({opSnippet:Pre}),Ore={kernelName:mu,backendName:"webgl",kernelFunc:Fre},yS="return 1.0 / (1.0 + exp(-1.0 * x));",Mre=it({opSnippet:yS,packedOpSnippet:yS,cpuKernelImpl:tY}),zre={kernelName:ao,backendName:"webgl",kernelFunc:Mre},Lre=`
if (isnan(x)) { return 0.0; }
return sign(x);
`,Bre=it({opSnippet:Lre}),Wre={kernelName:gu,backendName:"webgl",kernelFunc:Bre},Vre=R4+`
return sin(x);
`,Ure=it({opSnippet:Vre}),Gre={kernelName:ro,backendName:"webgl",kernelFunc:Ure},Hre=`
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`,jre=it({opSnippet:Hre}),qre={kernelName:Di,backendName:"webgl",kernelFunc:jre},Xre=`
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;
`,Kre=it({opSnippet:Xre}),Zre={kernelName:yu,backendName:"webgl",kernelFunc:Kre},Yre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,A)=>y*A),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=fS({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=be({inputs:{x:u},backend:n,attrs:{shape:d}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=be({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Jre={kernelName:_i,backendName:"webgl",kernelFunc:Yre};function Qre(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=sY(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var eae={kernelName:zh,backendName:"webgl",kernelFunc:Qre};function tae(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=rY(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var nae={kernelName:Lh,backendName:"webgl",kernelFunc:tae};function sae(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=y4(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var rae={kernelName:Bh,backendName:"webgl",kernelFunc:sae};function aae(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=y4(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var oae={kernelName:Wh,backendName:"webgl",kernelFunc:aae};function iae(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=new gS(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=be({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var lae={kernelName:nd,backendName:"webgl",kernelFunc:iae};function uae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=uc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var cae={kernelName:Pi,backendName:"webgl",kernelFunc:uae},AS="return sqrt(x);",dae=it({opSnippet:AS,packedOpSnippet:AS,cpuKernelImpl:aY}),pae={kernelName:oo,backendName:"webgl",kernelFunc:dae},hae="return x * x;",fae=it({opSnippet:hae}),mae={kernelName:Au,backendName:"webgl",kernelFunc:fae},xS="return (a - b) * (a - b);",gae=En({opSnippet:xS,packedOpSnippet:xS}),yae={kernelName:uo,backendName:"webgl",kernelFunc:gae};function Aae({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=br+`
return x > 0.0 ? 1.0 : float(${t.alpha});
`,a=new Mo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var xae={kernelName:fo,backendName:"webgl",kernelFunc:Aae},bae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=St(n.length),a=St(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
${r} begin = ${r}(${e});
${r} strides = ${r}(${t});
void main() {
${a} coords = getOutputCoords();
setOutput(getX(${o}));
}
`}};function vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=yn.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=be({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=uc({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=be({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeIntermediateTensorInfo(k)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let N=n.texData.get(x.dataId).values,R=We(x.shape,x.dtype,N),P=oY(A,R,m,f);b=n.makeTensorInfo(A,x.dtype,P.values)}else{let S=new bae(f,m,A);b=n.runWebGLProgram(S,[x],x.dtype)}let w=be({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(b),w}var wae={kernelName:Fi,backendName:"webgl",kernelFunc:vae};function kae(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=iY(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Iae={kernelName:sd,backendName:"webgl",kernelFunc:kae};function Sae(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=lY(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Cae={kernelName:Vh,backendName:"webgl",kernelFunc:Sae};function Tae(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=uY(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Nae={kernelName:Uh,backendName:"webgl",kernelFunc:Tae},Eae="return tan(x);",Rae=it({opSnippet:Eae}),$ae={kernelName:Oi,backendName:"webgl",kernelFunc:Rae},Dae=`
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,_ae=it({opSnippet:Dae}),Pae={kernelName:po,backendName:"webgl",kernelFunc:_ae},Fae=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=St(this.rank),r=Oae(e);this.userCode=`
void main() {
${s} resRC = getOutputCoords();
setOutput(getA(${r}));
}
`}};function Oae(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function bS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=We(r.shape,r.dtype,c),d=dY(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Fae(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var Mae={kernelName:Kr,backendName:"webgl",kernelFunc:bS},zae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`}},Lae=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`}};function vl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function vS(e){let t=1;for(;t<e;)t*=2;return t}function Bae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Z().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Z().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let P=n.readSync(r.dataId),[$,D]=pY(P,c,r.dtype,a,o);return[n.makeTensorInfo($.shape,$.dtype,$.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,gp({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=be({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&vl(n,h);let y=vS(a),A=vS(u),x=null,b=()=>x===null?[g,g]:[g,x],w=(P,$,D)=>{let T=b(),O=new zae(D),H=[[u],[x===null?1:0],[Number.NEGATIVE_INFINITY],[P],[$]],z=x;x=n.runWebGLProgram(O,T,"int32",H),vl(n,z)};for(let P=1;P<y;P*=2){let $=P*2;for(let D=P;D>=1;D/=2)w($,D,[m,A])}for(let P=A;P>y;P/=2){let $=b(),D=new Lae([m,P/2]),O=[[u],[x===null?1:0],[y]],B=x;x=n.runWebGLProgram(D,$,"int32",O),vl(n,B);let H=y/2,z=H*2;for(let X=H;X>=1;X/=2)w(z,X,x.shape)}let k=x;x=uc({inputs:{x},backend:n,attrs:{begin:0,size:[m,a]}}),vl(n,k);let S=oS({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});vl(n,g);let N=c.slice(0,-1);N.push(a),k=x,x=be({inputs:{x},attrs:{shape:N},backend:n}),vl(n,k);let R=S;return S=be({inputs:{x:S},attrs:{shape:N},backend:n}),vl(n,R),[S,x]}var Wae={kernelName:xu,backendName:"webgl",kernelFunc:Bae},Vae=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${i} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${i} == 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 (${i} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${r});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${r});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${t}));
float mapY = mapCoord(inY, float(${e}));
if (${o} == 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 Uae(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Vae(d,p,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var Gae={kernelName:Mi,backendName:"webgl",kernelFunc:Uae};function Hae(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=hY(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var jae={kernelName:Gh,backendName:"webgl",kernelFunc:Hae};function qae(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=uc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=be({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Xae={kernelName:zi,backendName:"webgl",kernelFunc:qae},Kae=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=`
sumValue += dot(values, segFilter);
`,p="";r%n>0&&(p=`
if (inIdx < 0 || inIdx >= ${r}) {
return initializationValue;
}
`);let h="";r%n>0&&(h=`
if (inIdx < 0 || inIdx >= ${r}) {
return -1.0;
}
`),this.userCode=`
const float initializationValue = ${i};
float getValue(int batch, int inIdx) {
${p}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${h}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${a})) * float(${n}));
int currentSeg = int(mod(float(outIdx), float(${a})));
float sumValue = 0.0;
for (int i = 0; i < ${c}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${d}
}
int inIdx = inOffset + ${c};
if (${u===1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${d}
} else if (${u===2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${d}
} else if (${u===3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${d}
}
setOutput(${l});
}
`}};function Zae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=E.getAxesPermutation([c],i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=E.getInnerMostAxes(1,i)[0]);let p=E.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=be({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=fd(r.dtype),g=(b,w,k,S,N)=>{let R=b.shape[0],P=b.shape[1],$=E.segment_util.segOpComputeOptimalWindowSize(P,N),D={windowSize:$,inSize:P,batchSize:R,numSegments:N},T=new Kae(D,w),O=n.compileAndRun(T,[b,k],S);if(l.push(O),O.shape[1]===N)return O;let B=mS({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),H=bS({inputs:{x:B},backend:n,attrs:{reps:[P/$]}});return l.push(B),l.push(H),g(O,w,H,S,N)},y=g(f,"unsortedSegmentSum",a,m,o),A=be({inputs:{x:y},backend:n,attrs:{shape:p}}),x=A;if(u!=null){l.push(A);let b=E.getUndoAxesPermutation(u);x=jn({inputs:{x},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),x}var Yae={kernelName:rd,backendName:"webgl",kernelFunc:Zae},Jae=[Cne,Ene,pJ,fJ,yJ,bJ,wJ,SJ,TJ,EJ,_J,FJ,zJ,WJ,XJ,GJ,YJ,tQ,QJ,aQ,iQ,uQ,hQ,bQ,wQ,NQ,RQ,PQ,MQ,qY,VQ,JQ,eee,jQ,ree,oee,nee,uee,pee,mee,yee,xee,wee,Nee,Ree,Iee,_ee,Oee,zee,Vee,jee,Zee,Qee,ete,tte,ste,ate,ite,ute,dte,mte,Ate,vte,kte,Cte,Ete,_te,Mte,jY,Lte,BQ,Vte,Hte,Xte,KY,Jte,nne,rne,dne,lne,mne,Ane,wne,$ne,Lne,Mne,Une,Hne,qne,Fne,Kne,Yne,tse,ase,use,yse,eJ,xse,wse,Sse,Nse,IQ,$se,_se,Fse,zse,Vse,YY,Gse,Hse,SQ,hse,Xse,nre,Jse,nJ,ore,ure,hre,gre,bre,wre,Sre,Nre,Rre,_re,Ore,zre,Wre,Gre,qre,AQ,mse,Zre,Jre,eae,nae,rae,oae,lae,cae,pae,mae,yae,xae,wae,Iae,Cae,Nae,fse,uJ,$ae,Pae,Mae,Wae,Gae,cJ,jae,Xae,Yae,Dse];for(let e of Jae)Yr(e);var ls;(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"})(ls||(ls={}));var yp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(yp||(yp={}));var wS;function Qae(e){wS=e.wasm.cwrap(mo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function eoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let N=n.dataIdMap.get(o.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=yp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],A=c?a.shape[1]:a.shape[2],x=r.shape[0],b=n.makeOutput([x,y,A],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return wS(p,k,r.shape.length,h,S,a.shape.length,l,c,g,f,m,d||0,w),b}var toe={kernelName:mo,backendName:"wasm",setupFunc:Qae,kernelFunc:eoe};function Rn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function s(r){let{backend:a,inputs:{x:o}}=r,i=a.dataIdMap.get(o.dataId).id,l=a.makeOutput(o.shape,o.dtype),c=a.dataIdMap.get(l.dataId).id;return v.sizeFromShape(l.shape)===0||t(i,c),l}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:s}}var noe=Rn(ti);function qn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=E.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),A=i.dataIdMap.get(m.dataId).id,x=()=>s(d,g,c.shape.length,p,y,u.shape.length,ls[c.dtype],A);if(t&&c.dtype==="float32")return x(),m;let b=E.getBroadcastDims(c.shape,f),w=E.getBroadcastDims(u.shape,f),k=b.every((N,R)=>N===R),S=w.every((N,R)=>N===R);if(k&&S)return x(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var soe=!0,roe=qn(qr,soe),kS;function aoe(e){kS=e.wasm.cwrap(ka,null,["array","number","number","number"])}function ooe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return kS(a,r.length,ls[s.dtype],o),s}var ioe={kernelName:ka,backendName:"wasm",setupFunc:aoe,kernelFunc:ooe};function Ym(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var loe={kernelName:Wa,backendName:"wasm",kernelFunc:Ym},IS;function uoe(e){IS=e.wasm.cwrap(ho,null,["number","array","number","number","number","array","number"])}function pc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=doe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=coe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=Ym({inputs:t,backend:n});return f.shape=i,f}let c=n.makeOutput(i,l.dtype),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(c.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return IS(u,h,l.shape.length,ls[l.dtype],d,p,a.length),c}function coe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function doe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var poe={kernelName:ho,backendName:"wasm",kernelFunc:pc,setupFunc:uoe};function Lo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=E.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h<u.length;h++)u[h]=s[i[h]];o=E.getInnerMostAxes(o.length,r),l=pc({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(c=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:c}}var SS;function hoe(e){SS=e.wasm.cwrap(Kl,null,["number, number, number"])}function foe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;E.assertAxesAreInnerMostDims("all",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;SS(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var moe={kernelName:Kl,backendName:"wasm",setupFunc:hoe,kernelFunc:foe},CS;function goe(e){CS=e.wasm.cwrap(Zl,null,["number, number, number"])}function yoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;E.assertAxesAreInnerMostDims("any",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;CS(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Aoe={kernelName:Zl,backendName:"wasm",setupFunc:goe,kernelFunc:yoe},TS;function xoe(e){TS=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function boe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:c,axes:u,inputWasTransposed:d}=Lo(a,r,t);if(d){let y=t.dataIdMap.get(c.dataId).id;y!==o&&(l=c,i=y)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[u[0]];return TS(i,ls[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var voe={kernelName:Ia,backendName:"wasm",kernelFunc:boe,setupFunc:xoe},NS;function woe(e){NS=e.wasm.cwrap(Sa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function koe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,A=u.strideWidth,x=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=s.makeOutput(u.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return NS(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,y,A,x,w),b}var Ioe={kernelName:Sa,backendName:"wasm",setupFunc:woe,kernelFunc:koe};function us(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Soe={kernelName:Ci,backendName:"wasm",kernelFunc:us},ES;function Coe(e){ES=e.wasm.cwrap(Ca,null,["number","array","number","number","array","number","number","number","number"])}function Toe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=g===y||g===1||y===1;v.assert(l>=2&&c>=2&&A,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${m}).`);let b=(g>y?r.shape.slice(0,-2):a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let w=o?[g,u,p]:[g,p,u],k=i?[y,h,d]:[y,d,h],S=us({inputs:{x:r},backend:n,attrs:{shape:w}}),N=us({inputs:{x:a},backend:n,attrs:{shape:k}}),R=n.dataIdMap.get(S.dataId).id,P=n.dataIdMap.get(N.dataId).id,$=o?S.shape[2]:S.shape[1],D=i?N.shape[1]:N.shape[2],T=Math.max(g,y),O=n.makeOutput([T,$,D],S.dtype),B=n.dataIdMap.get(O.dataId).id,H=new Uint8Array(new Int32Array(S.shape).buffer),z=new Uint8Array(new Int32Array(N.shape).buffer);return ES(R,H,S.shape.length,P,z,N.shape.length,o,i,B),n.disposeData(S.dataId),n.disposeData(N.dataId),O.shape=b,O}var Noe={kernelName:Ca,backendName:"wasm",setupFunc:Coe,kernelFunc:Toe};function Ap(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=yn.parseSliceParams(t,n,s),i=yn.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),c=r.makeOutput(o,t.dtype),u=v.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(i){let f=yn.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(o))),c}if(t.dtype==="string"){let f=Tm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Eoe(l,u[0],p,a,o);else if(h===3)Roe(l,u[0],u[1],p,a,o);else if(h===4)$oe(l,u[0],u[1],u[2],p,a,o);else{let f=Tm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function Eoe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;c<l;c++){let u=c*t+i;n.set(e.subarray(u,u+r[1]),a),a+=r[1]}}function Roe(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],c=r[2],u=i+a[0],d=l+a[1];for(let p=i;p<u;p++)for(let h=l;h<d;h++){let f=p*t+h*n+c;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function $oe(e,t,n,s,r,a,o){let i=0,l=a[0],c=a[1],u=a[2],d=l+o[0],p=c+o[1],h=u+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=c;g<p;g++)for(let y=u;y<h;y++){let A=m*t+g*n+y*s+f;r.set(e.subarray(A,A+o[3]),i),i+=o[3]}}var Doe={kernelName:$i,backendName:"wasm",kernelFunc:Ap};function _oe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((y,A)=>y*A),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=us({inputs:{x:r},backend:n,attrs:{shape:l}}),f=pc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=us({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Ap({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Poe={kernelName:ni,backendName:"wasm",kernelFunc:_oe};function Jm(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Foe={kernelName:Ta,backendName:"wasm",kernelFunc:Jm},Ooe=Rn(Na),RS;function Moe(e){RS=e.wasm.cwrap(Xr,null,["number","number","number","number"])}function zoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return RS(i,a,o,c),l}var Loe={kernelName:Xr,backendName:"wasm",setupFunc:Moe,kernelFunc:zoe};function $S(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return Ym({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(E.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(x=>{let b=v.sizeFromShape(x.shape.slice(s));return us({inputs:{x},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=E.computeOutShape(h.map(x=>x.shape),1);let m=h[0].shape[0]===1,g=UA(f,r,t[0].dtype,m),y=E.computeOutShape(a.map(x=>x.shape),s);o.shape=y;let A=n.dataIdMap.get(o.dataId);return A.stringBytes=E.fromStringArrayToUint8(g),h.forEach(x=>n.disposeData(x.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=u[m],y=h*g,A=d[m].subarray(y,y+g);p.set(A,f),f+=g}}return o}var Boe={kernelName:si,backendName:"wasm",kernelFunc:$S},DS;function Woe(e){DS=e.wasm.cwrap(Ea,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Voe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d,dataFormat:p}=n,h=E.convertConv2DDataFormat(p),f=E.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,N=f.strideWidth,R=f.inChannels,P=f.outChannels,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let D=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(D.dataId).id;return DS(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,y,A,x,b,$,w,k,S,N,R,P,T),D}var Uoe={kernelName:Ea,backendName:"wasm",setupFunc:Woe,kernelFunc:Voe},_S;function Goe(e){_S=e.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,inputShape:u}=s,d=1,p=E.convertConv2DDataFormat(l),h=E.computeConv2DInfo(u,a.shape,o,d,i,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:A,inWidth:x,outChannels:b,outHeight:w,outWidth:k,strideHeight:S,strideWidth:N}=h,R=m-1-h.padInfo.top,P=g-1-h.padInfo.left,$=h.dataFormat==="channelsLast",D=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[O,B,H]=v.computeStrides(a.shape),z=D[0],X=$?D[1]:D[2],ee=$?D[2]:1,J=$?1:D[1],Q=T[0],te=$?T[1]:T[2],K=$?T[2]:1,oe=$?1:T[1],ce=t.makeOutput(h.inShape,"float32"),he=t.dataIdMap.get(ce.dataId).id,Ae=t.dataIdMap.get(r.dataId).id,Ie=t.dataIdMap.get(a.dataId).id;return _S(Ae,Ie,f,m,g,A,x,y,w,k,b,S,N,R,P,O,B,H,z,X,ee,J,Q,te,K,oe,he),ce}var joe={kernelName:Ra,backendName:"wasm",setupFunc:Goe,kernelFunc:Hoe},qoe=Rn($a),Xoe=Rn(Da),vx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(vx||(vx={}));var PS;function Koe(e){PS=e.wasm.cwrap(ai,null,["number","number","number","number","array","number","number","number","number","number"])}function Zoe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:c}=n,u=l.shape[0],[d,p]=o,h=[u,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=Jm({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,A=t.dataIdMap.get(c.dataId).id,x=t.makeOutput(h,"float32"),b=t.dataIdMap.get(x.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return PS(g,y,A,u,w,d,p,vx[r],a,b),m!=null&&t.disposeData(m.dataId),x}var Yoe={kernelName:ai,backendName:"wasm",setupFunc:Koe,kernelFunc:Zoe},FS;function Joe(e){FS=e.wasm.cwrap(ri,null,["number","number","number","number","number","number"])}function Qoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=E.getAxesPermutation([a],l),u=r;c!==null&&(u=pc({inputs:{x:r},attrs:{perm:c},backend:n}));let d=E.getInnerMostAxes(1,l)[0];E.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;FS(f,o?1:0,i?1:0,h,m,ls[r.dtype]);let g=p;if(c!==null){let y=E.getUndoAxesPermutation(c);g=pc({inputs:{x:p},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var eie={kernelName:ri,backendName:"wasm",setupFunc:Joe,kernelFunc:Qoe},OS;function tie(e){OS=e.wasm.cwrap(oi,null,["number","number","number","array","number","array","array","number","number"])}function nie(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s;v.assert(a>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${a}`);let i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(r.dataId).id,A=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return OS(y,a,o==="NHWC"?1:0,A,r.shape.length-1,x,b,f.length,w),m}var sie={kernelName:oi,backendName:"wasm",setupFunc:tie,kernelFunc:nie},MS;function rie(e){MS=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function aie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d}=n,p=c==null?[1,1]:c,h=E.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,A=h.padInfo.bottom,x=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,N=h.inChannels,R=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let $=s.makeOutput(h.outShape,"float32"),D=s.dataIdMap.get($.dataId).id;return MS(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,y,A,x,P,b,w,k,S,N,R,D),$}var oie={kernelName:_a,backendName:"wasm",setupFunc:rie,kernelFunc:aie},iie=Rn(Fa),lie=!1,uie=qn(ii,lie,"bool"),cie=Rn(Oa);function wx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),us({inputs:{x:r},backend:s,attrs:{shape:i}})}var die={kernelName:li,backendName:"wasm",kernelFunc:wx};function zS(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var pie={kernelName:ru,backendName:"wasm",kernelFunc:zS},LS;function hie(e){LS=e.wasm.cwrap(ci,null,["number","number","number","number","number","number"])}function fie(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return LS(a,i,l,c,u,o),r}var mie={kernelName:ci,backendName:"wasm",kernelFunc:fie,setupFunc:hie},gie=Rn(Ma),yie=!1,Aie=qn(za,yie),BS;function xie(e){BS=e.wasm.cwrap(La,null,["number","number","number","number","number","number","number"])}function bie(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return BS(u,d,p,h,f,r,g),m}var vie={kernelName:La,backendName:"wasm",setupFunc:xie,kernelFunc:bie},WS;function wie(e){WS=e.wasm.cwrap(go,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=yp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,A=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let K=s.dataIdMap.get(o.dataId);if(K.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${K.shape.length}.`);if(K.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${K.shape}) does not match the number of output channels (${x})`);b=K.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,$=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,B=m.inChannels,H=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),Q=s.dataIdMap.get(J.dataId).id,te=i==null?0:s.dataIdMap.get(i.dataId).id;return WS(y,z,X,ee,A,w,k,b,S,N,R,P,H,$,D,T,O,B,x,g,te,f||0,Q),J}var Iie={kernelName:go,backendName:"wasm",setupFunc:wie,kernelFunc:kie},VS;function Sie(e){VS=e.wasm.cwrap(yo,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 Cie(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=yp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,A=s.dataIdMap.get(a.dataId).id,x=m.outChannels,b=0;if(o!=null){let K=s.dataIdMap.get(o.dataId);if(K.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${K.shape.length}.`);if(K.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${K.shape}) does not match the number of output channels (${x})`);b=K.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,$=m.dilationHeight,D=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,B=m.inChannels,H=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,ee=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),Q=s.dataIdMap.get(J.dataId).id,te=i==null?0:s.dataIdMap.get(i.dataId).id;return VS(y,z,X,ee,A,w,k,b,S,N,R,P,H,$,D,T,O,B,x,g,te,f||0,Q),J}var Tie={kernelName:yo,backendName:"wasm",setupFunc:Sie,kernelFunc:Cie},US;function Nie(e){US=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","array","number"])}function Eie(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=_2.prepareAndValidate(s,r),c=t.makeOutput(a,s.dtype);if(o===0)return c;let u=r.shape,d=u[u.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),y=t.dataIdMap.get(c.dataId).id;return US(h,ls[s.dtype],m,o,d,i,g,y),c}var Rie={kernelName:pi,backendName:"wasm",setupFunc:Nie,kernelFunc:Eie},GS;function $ie(e){GS=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Die(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=us({inputs:{x:r},attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]},backend:t}),d=v.sizeFromShape(a.shape),p=us({inputs:{x:a},attrs:{shape:[c.batchSize,d/c.batchSize]},backend:t}),h=[c.batchSize,c.outerSize,d/c.batchSize,c.sliceSize],f=t.makeOutput(h,r.dtype);if(v.sizeFromShape(r.shape)===0)return f;let m=u.shape.length-1,y=t.dataIdMap.get(u.dataId).id,x=t.dataIdMap.get(p.dataId).id,b=t.dataIdMap.get(f.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(u.shape)).buffer),k=new Uint8Array(new Int32Array(v.computeStrides(h)).buffer);return GS(y,ls[r.dtype],w,m,x,c.batchSize,k,b),t.disposeData(u.dataId),t.disposeData(p.dataId),f.shape=c.outputShape,f}var _ie={kernelName:di,backendName:"wasm",setupFunc:$ie,kernelFunc:Die},Pie=!1,Fie=qn(hi,Pie,"bool"),Oie=!1,Mie=qn(Ba,Oie,"bool"),HS;function zie(e){HS=e.wasm.cwrap(fi,null,["number","number","number"])}function Lie(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,t.dtype);if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;HS(r,n,o)}return a}var Bie={kernelName:fi,backendName:"wasm",setupFunc:zie,kernelFunc:Lie},Wie=!1,Vie=qn(mi,Wie,"bool"),Uie=!1,Gie=qn(gi,Uie,"bool"),Hie=Rn(Va),jie=!1,qie=qn(yi,jie,"bool"),jS;function Xie(e){jS=e.wasm.cwrap(Ua,null,["number, number, number"])}function Kie(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;c=u,l=x}let f=c.shape.length;E.assertAxesAreInnerMostDims("max",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),A=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;jS(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Zie={kernelName:Ua,backendName:"wasm",setupFunc:Xie,kernelFunc:Kie},Yie=!1,Jie=qn(Ga,Yie),qS;function Qie(e){qS=e.wasm.cwrap(Ha,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ele(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.dilationHeight,A=u.dilationWidth,x=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let S=s.makeOutput(u.outShape,"float32"),N=s.dataIdMap.get(S.dataId).id;return qS(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,y,A,x,b,w,k,N),S}var tle={kernelName:Ha,backendName:"wasm",setupFunc:Qie,kernelFunc:ele},XS;function nle(e){XS=e.wasm.cwrap(ja,null,["number, number, number"])}function sle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t),f=d;if(h){let b=t.dataIdMap.get(u.dataId).id;b!==i&&(c=u,l=b,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),y=v.sizeFromShape(g),A=c;c.dtype!=="float32"&&(A=Jm({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;XS(l,y,b)}if(h&&t.disposeData(u.dataId),a){let b=E.expandShapeToKeepDim(x.shape,p);x.shape=b}return c.dtype!=="float32"&&t.disposeData(A.dataId),x}var rle={kernelName:ja,backendName:"wasm",setupFunc:nle,kernelFunc:sle},KS;function ale(e){KS=e.wasm.cwrap(qa,null,["number, number, number"])}function ole(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t);if(h){let x=t.dataIdMap.get(u.dataId).id;x!==i&&(c=u,l=x)}let f=c.shape.length;E.assertAxesAreInnerMostDims("min",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),A=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;KS(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var ile={kernelName:qa,backendName:"wasm",setupFunc:ale,kernelFunc:ole},lle=!1,ule=qn(Xa,lle),kx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(kx||(kx={}));var ZS;function cle(e){ZS=e.wasm.cwrap(Ka,null,["number","array","number","number","array","array","number","number"])}function dle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return ZS(o,c,t.shape.length,ls[t.dtype],p,h,kx[r],l),i}var ple={kernelName:Ka,backendName:"wasm",kernelFunc:dle,setupFunc:cle},hle=!0,fle=qn(Za,hle),mle=Rn(Ai);function Ix(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var YS;function gle(e){YS=e.wasm.cwrap(bi,"number",["number","number","number","number","number"])}function yle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=YS(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Ix(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var Ale={kernelName:bi,backendName:"wasm",setupFunc:gle,kernelFunc:yle},JS;function xle(e){JS=e.wasm.cwrap(du,"number",["number","number","number","number","number","bool"])}function ble(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=JS(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ix(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),A=t.makeOutput([],"int32",g);return[y,A]}var vle={kernelName:du,backendName:"wasm",setupFunc:xle,kernelFunc:ble},QS;function wle(e){QS=e.wasm.cwrap(vi,"number",["number","number","number","number","number","number"])}function kle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=QS(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ix(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),A=t.makeOutput([f],"float32",m);return[y,A]}var Ile={kernelName:vi,backendName:"wasm",setupFunc:wle,kernelFunc:kle},Sle=!1,Cle=qn(xi,Sle,"bool"),eC;function Tle(e){eC=e.wasm.cwrap(ki,null,["number","number","number","number","number"])}function Nle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return eC(d,a,o,i,c),l}var Ele={kernelName:ki,backendName:"wasm",setupFunc:Tle,kernelFunc:Nle};function Rle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var $le={kernelName:wi,backendName:"wasm",kernelFunc:Rle};function Dle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return wx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=wx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=$S({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var _le={kernelName:Ii,backendName:"wasm",kernelFunc:Dle},tC;function Ple(e){tC=e.wasm.cwrap(Ya,null,["number","array","number","number","array","array","number","number"])}function Fle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(v.sizeFromShape(t.shape)===0)return zS({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return tC(o,u,t.shape.length,ls[t.dtype],h,f,r,c),i}var nC={kernelName:Ya,backendName:"wasm",kernelFunc:Fle,setupFunc:Ple},Ole=!1,Mle=qn(Ja,Ole),sC;function zle(e){sC=e.wasm.cwrap(Qa,null,["number","number","number"])}function Lle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,i=n.makeOutput(s.shape,"float32"),l=n.dataIdMap.get(i.dataId).id;return sC(a,o,l),i}var Ble={kernelName:Qa,backendName:"wasm",setupFunc:zle,kernelFunc:Lle},rC;function Wle(e){rC=e.wasm.cwrap(Si,null,["number","number","number","number"])}function Vle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t),f=d;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==i&&(c=u,l=x,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("prod",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),y=v.sizeFromShape(g),A=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;rC(l,y,ls[A.dtype],x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Ule={kernelName:Si,backendName:"wasm",setupFunc:Wle,kernelFunc:Vle},Gle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=jA(s,r,a,o),l=t.makeOutput([i.length],o);return t.typedArrayFromHeap(l).set(i),l},Hle={kernelName:pu,backendName:"wasm",kernelFunc:Gle},jle=!0,qle=qn(Pa,jle),Xle=Rn(eo),Kle=Rn(no),aC;function Zle(e){aC=e.wasm.cwrap(to,null,["number","number","number","number","number","number","number","number","number","number"])}function Yle(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,[u,d,p,h]=r.shape,f=[u,l,c,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=Jm({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(v.sizeFromShape(r.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return aC(y,u,d,p,h,l,c,a?1:0,o?1:0,x),g!=null&&t.disposeData(g.dataId),A}var Jle={kernelName:to,backendName:"wasm",setupFunc:Zle,kernelFunc:Yle},oC;function Qle(e){oC=e.wasm.cwrap(Ti,null,["number","array","number","array","number","number"])}function eue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=v.parseAxisParam(a,r.shape);if(r.shape.length===0)return Ym({inputs:{x:r},backend:n});let i=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(o).buffer),d=new Uint8Array(new Int32Array(r.shape).buffer);oC(l,u,o.length,d,r.shape.length,c);let p=us({inputs:{x:i},attrs:{shape:r.shape},backend:n});return n.disposeData(i.dataId),p}var tue={kernelName:Ti,backendName:"wasm",kernelFunc:eue,setupFunc:Qle},iC;function nue(e){iC=e.wasm.cwrap(Bi,null,["number","number","number","number","number","number","number","number","array","number","number"])}function sue(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:o,center:i}=s,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(l.dataId).id,[d,p,h,f]=r.shape,[m,g]=E.getImageCenter(i,p,h),y=o===0,A=255,x=typeof o=="number"?[o,o,o,y?0:A]:[...o,A],b=new Uint8Array(new Int32Array(x).buffer);return iC(c,d,p,h,f,a,m,g,b,x.length,u),l}var rue={kernelName:Bi,backendName:"wasm",kernelFunc:sue,setupFunc:nue},aue=Rn(Ni),oue=Rn(so),lC;function iue(e){lC=e.wasm.cwrap(Ei,null,["number","number","number","number","number","number","array","number","number"])}function lue(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:o}=s,i=t.makeOutput(o,a.dtype);if(v.sizeFromShape(o)===0)return i;let{sliceRank:l,numUpdates:c,sliceSize:u,strides:d,outputSize:p}=P2.calculateShapes(a,r,o),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(d).buffer),A=t.dataIdMap.get(i.dataId).id;return lC(f,g,ls[a.dtype],l,c,u,y,p,A),i}var uue={kernelName:Ei,backendName:"wasm",setupFunc:iue,kernelFunc:lue},uC;function cue(e){uC=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function due(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(a.dataId).id,c=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(c.dataId).id,d=s.shape.length,p=r.shape.length,h=d===0||d>1||p===1?1:v.sizeFromShape(r.shape.slice(1));return uC(o,i,l,h,u),c}var pue={kernelName:Ri,backendName:"wasm",kernelFunc:due,setupFunc:cue},cC;function hue(e){cC=e.wasm.cwrap(ao,null,["number","number"])}function fue(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return v.sizeFromShape(r.shape)===0||cC(s,a),r}var mue={kernelName:"Sigmoid",backendName:"wasm",setupFunc:hue,kernelFunc:fue},gue=Rn(ro),dC;function yue(e){dC=e.wasm.cwrap(lo,null,["number","number","number","number"])}function Aue(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),o=t.dataIdMap.get(a.dataId).id,i=n.shape[s],l=v.sizeFromShape(n.shape)/i;return v.sizeFromShape(a.shape)===0||dC(r,o,i,l),a}var xue={kernelName:lo,backendName:"wasm",setupFunc:yue,kernelFunc:Aue};function bue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s,i=v.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let c=nC.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=E.getReshaped(c.shape,a,i,!1),d=E.getPermuted(u.length,a.length,!1),p=E.getReshapedPermuted(c.shape,a,i,!1),m=us({inputs:{x:c},backend:n,attrs:{shape:u}}),A=pc({inputs:{x:m},backend:n,attrs:{perm:d}}),w=us({inputs:{x:A},backend:n,attrs:{shape:p}});return n.disposeData(c.dataId),n.disposeData(m.dataId),n.disposeData(A.dataId),w}var vue={kernelName:_i,backendName:"wasm",kernelFunc:bue};function wue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=n,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(d=>{let p=[...u];p[i]=d;let h=Ap({inputs:{x:r},attrs:{begin:c,size:p},backend:s});return c[i]+=d,h})}var kue={kernelName:Pi,backendName:"wasm",kernelFunc:wue},Iue=Rn(oo),Sue=Rn(Au),Cue=!0,Tue=qn(uo,Cue),pC;function Nue(e){pC=e.wasm.cwrap(fo,null,["number","number","number"])}function Eue(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=t.makeOutput(a.shape,a.dtype),l=t.dataIdMap.get(i.dataId).id;return pC(o,r,l),i}var Rue={kernelName:fo,backendName:"wasm",setupFunc:Nue,kernelFunc:Eue},hC;function $ue(e){hC=e.wasm.cwrap(Fi,null,["number","array","number","array","array","array","array","array","number","number"])}function Due(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:o,strides:i}=s;i==null&&(i=new Array(a.length));let{beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,h=E.slice_util.maskToAxes(u);if(h.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(u!==0&&d!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(u!==0&&p!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let f=r.shape.length-a.length,m=E.slice_util.maskToAxes(d),g=r.shape.slice();m.forEach($=>{a[$]=0,o[$]=1,g.splice($,0,1)});let y=us({inputs:{x:r},attrs:{shape:g},backend:t}),{begin:A,end:x,strides:b}=E.slice_util.getNormalizedAxes(y.shape,h,f,a,o,i,l,c,u);a=A,o=x,i=b;let w=E.slice_util.maskToAxes(p);w.forEach($=>{o[$]=a[$]+1,i[$]=1});let k=E.slice_util.computeOutShape(a,o,i),S=k.filter(($,D)=>w.indexOf(D)===-1);if(i.every($=>$===1)){let $=Ap({inputs:{x:y},attrs:{begin:a,size:k},backend:t});t.disposeData(y.dataId);let D=us({inputs:{x:$},attrs:{shape:S},backend:t});return t.disposeData($.dataId),D}let R=t.makeOutput(S,"float32");if(!S.some($=>$===0)){let $=t.dataIdMap.get(y.dataId).id,D=new Uint8Array(new Int32Array(v.computeStrides(y.shape)).buffer),T=new Uint8Array(new Int32Array(a).buffer),O=new Uint8Array(new Int32Array(o).buffer),B=new Uint8Array(new Int32Array(i).buffer),H=new Uint8Array(new Int32Array(S).buffer),z=new Uint8Array(new Int32Array(v.computeStrides(S)).buffer),X=t.dataIdMap.get(R.dataId).id;hC($,D,y.shape.length,T,O,B,H,z,S.length,X)}t.disposeData(y.dataId);let P=us({inputs:{x:R},attrs:{shape:S},backend:t});return t.disposeData(R.dataId),P}var _ue={kernelName:Fi,backendName:"wasm",setupFunc:$ue,kernelFunc:Due},Pue=!0,Fue=qn(co,Pue),fC;function Oue(e){fC=e.wasm.cwrap(io,null,["number, number, number"])}function Mue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Lo(o,r,t),f=d;if(h){let x=t.dataIdMap.get(u.dataId).id;x!==i&&(c=u,l=x,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("sum",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),y=v.sizeFromShape(g),A=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;fC(l,y,x)}if(h&&t.disposeData(u.dataId),a){let x=E.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var zue={kernelName:io,backendName:"wasm",setupFunc:Oue,kernelFunc:Mue},Lue=Rn(Oi),Bue=Rn(po),mC;function Wue(e){mC=e.wasm.cwrap(Kr,null,["number","array","number","array","number","number"])}function Vue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,a=n.dataIdMap.get(r.dataId).id,{reps:o}=s,i=new Array(r.shape.length);for(let p=0;p<i.length;p++)i[p]=r.shape[p]*o[p];let l=new Uint8Array(new Int32Array(r.shape).buffer),c=new Uint8Array(new Int32Array(i).buffer),u=n.makeOutput(i,r.dtype),d=n.dataIdMap.get(u.dataId).id;return mC(a,l,r.shape.length,c,i.length,ls[u.dtype],d),u}var Uue={kernelName:Kr,backendName:"wasm",setupFunc:Wue,kernelFunc:Vue},gC;function Gue(e){gC=e.wasm.cwrap(xu,null,["number","array","number","number","number","bool","number","number"])}var Hue=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{k:r,sorted:a}=n,o=t.dataIdMap.get(s.dataId).id,i=new Uint8Array(new Int32Array(s.shape).buffer),l=s.shape.slice();l[l.length-1]=r;let c=t.makeOutput(l,s.dtype),u=t.dataIdMap.get(c.dataId).id,d=t.makeOutput(l,"int32"),p=t.dataIdMap.get(d.dataId).id;return gC(o,i,s.shape.length,ls[s.dtype],r,a,u,p),[c,d]},jue={kernelName:xu,backendName:"wasm",setupFunc:Gue,kernelFunc:Hue},yC;function que(e){yC=e.wasm.cwrap(Mi,null,["number","number","bool","number","number","number","number","number","number","array","number","number","number","number","number"])}function Xue(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),A=t.makeOutput(g,r.dtype),x=t.dataIdMap.get(A.dataId).id,w=t.dataIdMap.get(r.dataId).id,S=t.dataIdMap.get(a.dataId).id,N=o==="nearest"?1:2,R;switch(i){case"constant":R=1;break;case"reflect":R=2;break;case"wrap":R=3;break;case"nearest":R=4;break;default:R=1;break}return yC(w,S,a.shape[0]>1,u,f,m,h,p,d,y,r.shape.length-1,N,R,l,x),A}var Kue={kernelName:Mi,backendName:"wasm",setupFunc:que,kernelFunc:Xue};function Zue(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape[a],i=r.shape.length,l=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==a&&(l[c++]=r.shape[h]);let u=new Array(o),d=new Array(i).fill(0),p=r.shape.slice();p[a]=1;for(let h=0;h<u.length;h++)d[a]=h,u[h]=Ap({inputs:{x:r},attrs:{begin:d,size:p},backend:n});return u.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Yue={kernelName:zi,backendName:"wasm",kernelFunc:Zue};function Jue(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var Que={kernelName:Li,backendName:"wasm",kernelFunc:Jue},ece=[noe,roe,ioe,moe,Aoe,voe,Ioe,Noe,Poe,Foe,Ooe,Loe,Boe,Uoe,joe,qoe,Xoe,Yoe,eie,sie,oie,iie,uie,cie,die,pie,mie,gie,Aie,toe,vie,Iie,Tie,Rie,_ie,Fie,Mie,loe,Bie,Vie,Gie,Hie,qie,Zie,Jie,tle,rle,ile,ule,ple,fle,mle,Ale,vle,Ile,Cle,Ele,$le,_le,nC,Mle,Ble,Ule,Hle,qle,Xle,Kle,Soe,Jle,tue,rue,oue,aue,uue,pue,mue,gue,Doe,xue,vue,kue,Iue,Sue,Tue,Rue,_ue,Fue,zue,Lue,Bue,Uue,jue,Kue,poe,Yue,Que];for(let e of ece)Yr(e);var Sx=Z();Sx.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])));Sx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Sx.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 AC=Jo(nN()),tce='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()}}}}',nce=Jo(sN()),xC=class extends Gl{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new Vc(this,ts())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let c=t;this.dataIdMap.set(e,{id:a,stringBytes:c,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e){let{memoryOffset:t,dtype:n,shape:s,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let a=this.wasm.HEAPU8.slice(t,t+v.sizeFromShape(s)*v.bytesPerElement(n));return ace(a.buffer,n)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function sce(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function bC(e,t,n){if(Qm!=null)return Qm;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),bp!=null&&bp[s]!=null?bp[s]:n+s}async function rce(){let[e,t]=await Promise.all([Z().getAsync("WASM_HAS_SIMD_SUPPORT"),Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let c=tce,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?bC(e,t,xp!=null?xp:l):l+i},Cx&&(r.instantiateWasm=sce(bC(e,t,xp!=null?xp:"")));let a=!1;r.onAbort=()=>{if(a||vp)return;vp=!0,s({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 o;t&&e&&Qm==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+AC.default.toString()],{type:"text/javascript"}),o=(0,AC.default)(r)):o=(0,nce.default)(r),o.then(i=>{a=!0,vp=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",l,["number"]),dispose:i.cwrap("dispose",l,[])},n({wasm:i})})})}function ace(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 oce=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Qm=null,xp=null,bp={},vp=!1,Cx=!1;function ice(e,t=!1){if(B2("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),vp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Qm=e,Cx=t}function vC(e,t=!1){if(vp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")xp=e;else{bp=e;let n=oce.filter(s=>bp[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Cx=t}var lce="3.9.0",uce=2;qi("wasm",async()=>{let{wasm:e}=await rce();return new xC(e)},uce);var zr=Z();zr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function cce(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function ln(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function e0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function He(){return`
let index = getGlobalIndex(globalId, localId);
`}function Me(){return`
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>, [[builtin(global_invocation_id)]] globalId : vec3<u32>)
`}function dce(e,t,n,s=!1){let r=`
let workGroupSizeX = ${n.workGroupSize[0]}u;
let workGroupSizeY = ${n.workGroupSize[1]}u;
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=IC(t.shape),f=`
[[block]] struct Matrix0 {
numbers: array<${e0(t.dtype,n.isVec4)}>;
};
[[block]] struct Uniform {
size : i32;
numChannels : i32;
outShapeStrides : vec2<i32>;
dispatchSize : vec3<u32>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
`;return[wC,f,r,kC,h,n.getUserCode()].join(`
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${ln(e[f].shape.length)}; `}),o+=`outShape : ${ln(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
outShapeStrides: ${ln(i)}; `,n.size!=null&&(o+="size : i32; "),o+="dispatchSize : vec3<u32>; ",n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),a.push(`
[[block]] struct Matrix0 {
numbers: array<${e0(t.dtype,n.isVec4)}>;
};
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
`),n.variableNames.forEach((h,f)=>{a.push(`
[[block]] struct Matrix${1+f} {
numbers: array<${e0(e[f].dtype,n.isVec4)}>;
};
[[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
`)}),o!==""&&a.push(`
[[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
`),a.push(r);let[l,c]=gce(t.shape,n.dispatchLayout),u=IC(t.shape),d=[wC,a.join(`
`),kC,u,l,pce(t.shape,t.dtype,n.isVec4)];if(c===t.shape.length){let h=e.map(f=>hce(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
`)}var wC=`
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
var res: i32 = a / b;
let mod: i32 = a % b;
if (sign < 0. && mod != 0) {
res = res - 1;
}
return res;
}
fn isNanCustom(val : f32) -> bool {
if (val > 0.0) {
return false;
}
if (val < 0.0) {
return false;
}
if (val == 0.0) {
return false;
}
return true;
}
fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
var res = vec4<f32> (0.0);
for (var i = 0u; i < 4u; i = i + 1u) {
if (isNanCustom(val[i])) {
res[i] = 1.0;
} else {
res[i] = 0.0;
}
}
return res;
}
// Checks whether coordinates lie within the bounds of the shape.
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
return all(coord >= vec4<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
return all(coord >= vec3<i32>(0)) &&
all(coord < shape);
}
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
return all(coord >= vec2<i32>(0)) &&
all(coord < shape);
}
`,kC=`
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
return coord;
}
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(shape.y), 1.0)));
}
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0)));
}
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0)));
}
// Only used when the y/z dimension of workgroup size is 1.
fn getGlobalIndex(globalId : vec3<u32>, localId : vec3<u32>) -> i32 {
if (uniforms.dispatchSize.y == 1u && uniforms.dispatchSize.z == 1u) {
return i32(globalId.x);
}
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
localId.y * workGroupSizeX + localId.x;
let workGroupID = (globalId - localId)/vec3<u32>(
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
return i32((workGroupID.z * uniforms.dispatchSize.x * uniforms.dispatchSize.y +
workGroupID.y * uniforms.dispatchSize.x + workGroupID.x) *
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
localInvocationIndex);
}
`;function pce(e,t,n){let s=e.length,r=e0(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
result.numbers[flatIndex] = ${r}(value);
}`:a=`fn setOutputFlat(flatIndex : i32, value : f32) {
result.numbers[flatIndex] = ${r}(value);
}
fn setOutputFlatI32(flatIndex : i32, value : i32) {
result.numbers[flatIndex] = ${r}(value);
}`,s>=2){switch(s){case 2:a+=`
fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(uniforms.outShapeStrides), 1.0)));
}
`;break;case 3:a+=`
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0)));
}
`;break;case 4:a+=`
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
return i32(dot(vec4<f32>(coords), vec4<f32>(
f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0)));
}
`;break;default:v.assert(!1,()=>`Unsupported ${s}D shape`);break}let o=["d0","d1","d2","d3"].slice(0,s),i=ln(s);n?a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex / 4, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlatI32(flatIndex / 4, value);
}
`:a+=`
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlat(flatIndex, value);
}
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
setOutputFlatI32(flatIndex, value);
}
`}return a}function hce(e,t,n,s){let r=fce(e,n);return e.shape.length<=t.length&&(r+=mce(e,t,n,s)),r}function fce(e,t){let n=e.name,s=e.shape.length,r=ln(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?`
fn ${a}() -> vec4<f32> {
return vec4<f32>(${n}.numbers[0]);
}
`:`
fn ${a}() ->f32 {
return f32(${n}.numbers[0]);
}
`;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?`
fn ${a}(${i}) -> vec4<f32> {
return vec4<f32>(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l}) / 4]);
}
`:`
fn ${a}(${i}) -> f32 {
return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
${l})]);
}
`}function mce(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=ln(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return vec4<f32>(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return vec4<f32>(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
return f32(${r}.numbers[globalIndex]);
}
fn ${o}ByCoords(coords : ${c}) -> f32 {
return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]);
}
`;let u=E.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
return get${a}();
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32{
return get${a}();
}
fn ${o}ByCoords(coords : ${c}) -> f32{
return get${a}();
}
`;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(`
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=ln(i),y=e.shape.map((A,x)=>`coords[${x+d}]`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
fn ${o}ByCoords(coordsIn : ${c}) -> vec4<f32> {
var coords = coordsIn;
${p}
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
}
`:`
fn ${o}ByGlobalId(globalId : vec3<u32>, globalIndex : i32) -> f32 {
var coords = getOutputCoords(globalId, globalIndex);
${p}
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
}
fn ${o}ByCoords(coordsIn : ${c}) -> f32 {
var coords = coordsIn;
${p}
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
}
`}function gce(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${ln(a)}{
return getCoordsFromFlatIndex(i32(globalIndex));
}
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=cce(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=ln(l),d=`fn getOutputCoords(globalId : vec3<u32>, globalIndex : i32) -> ${u} {
${o}
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function IC(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=ln(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
return vec2<i32>(d0, d1);
}`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return`
fn getCoordsFromFlatIndex(index : i32) -> ${s} {
${a}
return ${s}(${r.join(",")});
}
`}var SC={};Le(SC,{ArrayBufferToTypedArray:()=>CC,GPUBytesPerElement:()=>Rx,computeDispatch:()=>Be,computeWorkGroupSizeForConv2d:()=>Tx,computeWorkGroupSizeForMatMul:()=>Nx,computeWorkPerThreadForConv2d:()=>Ex,flatDispatchLayout:()=>at,isWebGPUSupported:()=>$x,tilesFitEvenlyIntoShape:()=>ua});var hc=65535,wl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ua(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]==0)}function Be(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(wl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(wl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(wl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=hc&&a<=hc&&o<=hc)return[r,a,o];v.assert(r>hc&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>hc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=hc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function Tx(e,t){let n=wl(e.x.map(r=>t[r])),s=wl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Nx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Ex(e,t){let n=wl(e.x.map(r=>t[r])),s=wl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function at(e){return{x:e.map((t,n)=>n)}}function Rx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function CC(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function $x(){return!!navigator.gpu}var je;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(je||(je={}));var yce="return a + b;",Ace="return areal * breal - aimag * bimag;",xce="return areal * bimag + aimag * breal;",bce="return a / b;",vce="return a * b;",wce="return (a - b) * (a - b);",kce="return a - b;",Ice="return f32(a == b);",Sce="return vec4<f32>(a == b);",Cce="return f32(a > b);",Tce="return vec4<f32>(a > b);",Nce="return f32(a >= b);",Ece="return vec4<f32>(a >= b);",Rce="return f32(a < b);",$ce="return vec4<f32>(a < b);",Dce="return f32(a <= b);",_ce="return vec4<f32>(a <= b);",Pce="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Fce=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
vec4<f32>(b >= vec4<f32>(1.0)));`,Oce=`
if (isNanCustom(a)) { return a; }
if (isNanCustom(b)) { return b; }
`,TC=`
if (isNaN.r > 0.) {
resultTemp.r = uniforms.NAN;
}
if (isNaN.g > 0.) {
resultTemp.g = uniforms.NAN;
}
if (isNaN.b > 0.) {
resultTemp.b = uniforms.NAN;
}
if (isNaN.a > 0.) {
resultTemp.a = uniforms.NAN;
}
`,Mce=`
let s = sign(a) * sign(b);
let ia = i32(round(a));
let ib = i32(round(b));
return f32(idiv(ia, ib, s));
`,zce=`
let ia = vec4<i32>(round(a));
let ib = vec4<i32>(round(b));
let cond = ib != vec4<i32>(0);
var resultTemp = vec4<i32>(0);
let s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4<f32>(resultTemp);
`,Lce="return f32(a != b);",Bce="return vec4<f32>(a != b);",Wce=`
if(a < 0.0 && floor(b) < b) {
return uniforms.NAN;
}
if (b == 0.0) {
return 1.0;
}
if (round(abs(b) % 2.0) != 1.0) {
return pow(abs(a), b);
}
return sign(a) * pow(abs(a), b);
`,Vce=`
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
let isModRound1 = vec4<f32>(isModRound1Bool);
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
var resultTemp = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
let isExpZero = b == vec4<f32>(0.0);
if (isExpZero.r) {
resultTemp.r = 1.0;
}
if (isExpZero.g) {
resultTemp.g = 1.0;
}
if (isExpZero.b) {
resultTemp.b = 1.0;
}
if (isExpZero.a) {
resultTemp.a = 1.0;
}
let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
${TC}
return resultTemp;
`,Uce="if (a < 0.0) { return b * a; } return a;",Gce=`
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;function NC(e,t){let n=t?TC:Oce;return t?`
var resultTemp = vec4<f32>(${e}(a, b));
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
`+n+`
return resultTemp;
`:n+`
return ${e}(a, b);
`}function wp(e,t){switch(e){case je.MUL:return vce;case je.ADD:return yce;case je.SUB:return kce;case je.DIV:return bce;case je.EQUAL:return t?Sce:Ice;case je.GREATER:return t?Tce:Cce;case je.GREATER_EQUAL:return t?Ece:Nce;case je.LESS:return t?$ce:Rce;case je.LESS_EQUAL:return t?_ce:Dce;case je.LOGICAL_AND:return t?Fce:Pce;case je.NOT_EQUAL:return t?Bce:Lce;case je.SQUARED_DIFFERENCE:return wce;case je.INT_DIV:return t?zce:Mce;case je.PRELU:return t?Gce:Uce;case je.MAX:return NC("max",t);case je.MIN:return NC("min",t);case je.POW:return t?Vce:Wce;case je.COMPLEX_MULTIPLY_REAL:return Ace;case je.COMPLEX_MULTIPLY_IMAG:return xce;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Fe;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(Fe||(Fe={}));var Hce="return abs(a);",jce="return ceil(a);",qce="return cos(a);",Xce=`
let e2x = exp(-a);
return (e2x + 1.0 / e2x) / 2.0;
`,Kce="return exp(a) - 1.0;",Zce="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",Yce=`
var resFloat = exp(a) - vec4<f32>(1.0);
if (a.r >= 0.0) {
resFloat.r = a.r;
}
if (a.g >= 0.0) {
resFloat.g = a.g;
}
if (a.b >= 0.0) {
resFloat.b = a.b;
}
if (a.a >= 0.0) {
resFloat.a = a.a;
}
return resFloat;
`,Jce="return exp(a);",Qce="return floor(a);",ede="return a;",tde=`if (a < 0.0) { return 1.0/0.0; }
return log(a);`,nde="return f32(!(a >= 1.0));",sde="return -a;",rde="return (a < 0.0) ? b * a : a;",ade="return max(a, 0.0);",ode="return clamp(a, 0.0, 6.0);",ide="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",lde=`
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
let isNaN = isNan(a);
if (isNaN.r) {
resFloat.r = a.r;
}
if (isNaN.g) {
resFloat.g = a.g;
}
if (isNaN.b) {
resFloat.b = a.b;
}
if (isNaN.a) {
resFloat.a = a.a;
}
return resFloat;
`,ude="return 1.0/sqrt(a);",cde="return 1.0 / (1.0 + exp(-1.0 * a));",dde="return sin(a);",pde=`
let e2x = exp(a);
return (e2x - 1.0 / e2x) / 2.0;
`,hde="return sqrt(a);",fde="return a * a;",mde=`
let e2x = exp(-2.0 * abs(a));
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`,gde="return f32(i32((a)));";function fc(e,t){switch(e){case Fe.ABS:return Hce;case Fe.COS:return qce;case Fe.COSH:return Xce;case Fe.CEIL:return jce;case Fe.ELU:return t?Yce:Zce;case Fe.EXP:return Jce;case Fe.EXPM1:return Kce;case Fe.FLOOR:return Qce;case Fe.LINEAR:return ede;case Fe.LOG:return tde;case Fe.LOGICAL_NOT:return nde;case Fe.NEG:return sde;case Fe.PRELU:return rde;case Fe.RELU:return t?lde:ade;case Fe.RELU6:return t?ide:ode;case Fe.RSQRT:return ude;case Fe.SIGMOID:return cde;case Fe.SIN:return dde;case Fe.SINH:return pde;case Fe.SQRT:return hde;case Fe.SQUARE:return fde;case Fe.TANH:return mde;case Fe.TO_INT:return gde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Bo(e,t=!1){if(e===null)return null;if(e==="linear")return fc(Fe.LINEAR);if(e==="relu")return fc(Fe.RELU,t);if(e==="elu")return fc(Fe.ELU,t);if(e==="relu6")return fc(Fe.RELU6,t);if(e==="prelu")return wp(je.PRELU,t);if(e==="sigmoid")return fc(Fe.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function EC(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;
let RowPerThread = ${n.RowPerThread};
let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
let TileAOuter = ${n.TileAOuter};
let TileBOuter = ${n.TileBOuter};
let TileInner = ${n.TileInner};
${Me()} {
let tileRow = i32(localId.y) * RowPerThread;
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y) * RowPerThread;
let globalCol = i32(globalId.x);
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
var acc: array<vec4<f32>, ${n.RowPerThread}>;
var ACached : vec4<f32>;
var BCached : array<vec4<f32>, 4>;
// Loop over shared dimension.
var globalColA = tileCol;
let RowPerThreadB = TileInner / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileCol;
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
}
globalColA = globalColA + TileInner / ColPerThread;
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol;
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
for (var i = 0; i < RowPerThread; i = i + 1) {
ACached = mm_Asub[tileRow + i][k];
acc[i] = BCached[0] * ACached.x + acc[i];
acc[i] = BCached[1] * ACached.y + acc[i];
acc[i] = BCached[2] * ACached.z + acc[i];
acc[i] = BCached[3] * ACached.w + acc[i];
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
mm_write(globalRow + innerRow,
globalCol,
acc[innerRow], globalId);
}
}`}function yde(e){return`
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
let tileSize = ${e[0]*4};
${Me()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
// Without this initialization strange values show up in acc.
var acc = vec4<f32>(0.0);
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * tileSize / 4 + tileCol;
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < tileSize / 4; k = k + 1) {
let rowB = t * tileSize + k * 4;
let BCached0 = mm_readB(rowB, globalCol, globalId);
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
let ACached = mm_Asub[k];
acc = acc + BCached0 * ACached.x;
acc = acc + BCached1 * ACached.y;
acc = acc + BCached2 * ACached.z;
acc = acc + BCached3 * ACached.w;
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var Ade=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Nx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ua(o,this.aShape.slice(1)),ua(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
}
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=Bo(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${e};
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
let batch = i32(globalId.z);
${t};
}
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
{
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col * 4);
${r}
${s}
setOutput(outCoord[0], outCoord[1], outCoord[2], value);
}
}
${this.outputShape[1]>1?EC([this.vecSize,this.workPerThread,1],this.workGroupSize):yde(this.workGroupSize)}
`}};function Dx(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
${Me()} {
let tileRow = i32(localId.y) * ${e[1]};
let tileCol = i32(localId.x) * ${e[0]};
let globalRow = i32(globalId.y) * ${e[1]};
let globalCol = i32(globalId.x) * ${e[0]};
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
var ACached : f32;
var BCached : array<f32, ${e[0]}>;
// Without this initialization strange values show up in acc.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = 0.0;
}
}
let ColPerThreadA = ${r} / ${t[0]};
let tileColA = i32(localId.x) * ColPerThreadA;
let RowPerThreadB = ${r} / ${t[1]};
let tileRowB = i32(localId.y) * RowPerThreadB;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
let inputRow = tileRow + innerRow;
let inputCol = tileColA + innerCol;
mm_Asub[inputRow][inputCol] = mm_readA(
globalRow + innerRow,
t * ${r} + inputCol, globalId);
}
}
// Load one tile of B into local memory.
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
let inputRow = tileRowB + innerRow;
let inputCol = tileCol + innerCol;
mm_Bsub[inputRow][inputCol] = mm_readB(
t * ${r} + inputRow,
globalCol + innerCol, globalId);
}
}
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < ${r}; k = k + 1) {
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
BCached[inner] = mm_Bsub[k][tileCol + inner];
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
ACached = mm_Asub[tileRow + innerRow][k];
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
}
}
}
workgroupBarrier();
}
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
if ((globalCol + innerCol) < uniforms.dimBOuter &&
(globalRow + innerRow) < uniforms.dimAOuter) {
mm_write(globalRow + innerRow,
globalCol + innerCol,
acc[innerRow][innerCol], globalId);
}
}
}
}
`}function xde(e){return`
let TileSize = ${e[0]*4};
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
${Me()} {
let tileCol = i32(localId.x);
let globalCol = i32(globalId.x);
let globalRow = i32(globalId.y);
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
// Without this initialization strange values show up in acc.
var acc = 0.0;
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
// Load one tile of A into local memory.
let colA = t * TileSize + tileCol * 4;
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
mm_readA(globalRow, colA + 1, globalId),
mm_readA(globalRow, colA + 2, globalId),
mm_readA(globalRow, colA + 3, globalId));
workgroupBarrier();
// Compute acc values for a single thread.
for (var k = 0; k < TileSize / 4; k = k + 1) {
let rowB = t * TileSize + k * 4;
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
mm_readB(rowB + 1, globalCol, globalId),
mm_readB(rowB + 2, globalCol, globalId),
mm_readB(rowB + 3, globalCol, globalId));
let ACached = mm_Asub[k];
acc = acc + dot(ACached, BCached);
}
workgroupBarrier();
}
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
mm_write(globalRow, globalCol, acc, globalId);
}
}
`}var RC=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=Nx(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ua(r,this.aShape.slice(1)),ua(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
}
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
}
return 0.0;`;let n="",s="";if(this.activation){let o=Bo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
var value = valueIn;
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
${r}
${s}
setOutput(batch, row, col, value);
}
${this.outputShape[1]>1?Dx([this.workPerThread,this.workPerThread,1],this.workGroupSize):xde(this.workGroupSize)}
`}};function bde(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
// If the output size is small for matrix multiplication, avoid to use vec4
// and handle some elements per thread to optimally utilize the ALU.
// Introduces two shared memory buffers, some logical threads could handle
// arithmetic operations and others handle IO operations between barrier api,
// makes ALUs and load/store units work simultaneously, could improves
// the performance.
${Me()} {
let tileRow = i32(localId.y);
let tileCol = i32(localId.x);
let globalRow = i32(globalId.y);
let globalCol = i32(globalId.x);
// uniforms.dimInner should be greater than 0.
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
var acc = 0.0;
var globalColA = tileCol;
var globalRowB = tileRow;
for (var t = 0; t < numTiles; t = t + 1) {
if (t == 0) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
}
} else {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub1[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
}
}
}
workgroupBarrier();
if (t != 0) {
t = t + 1;
}
if (t < numTiles) {
if (tileRow < ${t}) {
// Load one tile of A and B into local memory.
// globalRow is always greater than or equal tileRow.
mm_Asub2[tileRow][tileCol] =
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
globalColA = globalColA + ${s};
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
globalRowB = globalRowB + ${s};
} else {
// Compute acc values for a single thread.
for (var k = 0; k < ${s}; k = k + 1) {
let subRow = tileRow - ${t};
if (subRow < 0) {
continue;
}
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
}
}
}
workgroupBarrier();
}
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
if (tileRow >= ${t} && writeCol >= 0) {
mm_write(writeCol, globalCol, acc, globalId);
}
}
`}var vde=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
}
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
}
return 0.0;`,n="",s="";if(this.activation){let o=Bo(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${o}
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
${o}
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${n}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
let batch = i32(globalId.z);
${e}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let batch = i32(globalId.z);
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
${t}
}
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
let batch = i32(globalId.z);
let outCoord = vec3<i32>(batch, row, col);
var value = valueIn;
${r}
${s}
setOutput(batch, row, col, value);
}
}
${bde(this.workGroupSize)}
`}};function nt(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var wde={kernelName:Ci,backendName:"webgpu",kernelFunc:nt};function _x({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=y===A||y===1||A===1;v.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`);let w=(y>A?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[y,d,h]:[y,h,d],S=s?[A,f,p]:[A,p,f],N=nt({inputs:{x:e},backend:r,attrs:{shape:k}}),R=nt({inputs:{x:t},backend:r,attrs:{shape:S}}),P=[N,R],$=Math.max(y,A),D=d%4==0&&f%4==0&&!n&&!s&&f>=32,T;!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?T=new vde(k,S,[$,h,f],a,l,o):D?T=new Ade(k,[$,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):T=new RC(k,[$,h,f],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let O=[N,R];a&&O.push(a),o&&O.push(o);let B=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],H=r.runWebGPUProgram(T,O,e.dtype,B),z=nt({inputs:{x:H},backend:r,attrs:{shape:w}});P.push(H);for(let X of P)r.disposeData(X.dataId);return z}function kde(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return _x({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Ide={kernelName:mo,backendName:"webgpu",kernelFunc:kde},$C=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
fn binaryOpComplex(
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
${wp(this.op,!1)}
}
${Me()} {
${He()}
if(index < uniforms.size) {
let areal = getARealAtOutCoordsByGlobalId(globalId, index);
let aimag = getAImagAtOutCoordsByGlobalId(globalId, index);
let breal = getBRealAtOutCoordsByGlobalId(globalId, index);
let bimag = getBImagAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
}
}
`}},Sde=class{constructor(e,t,n,s){this.variableNames=["A","B"];let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%(this.workGroupSize[0]*this.workPerThread)==0,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}_${this.sizeFit}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords);
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
let b = getBAtOutCoordsByCoords(coords);`,n=this.sizeFit?`let coords = getCoordsFromFlatIndex(flatIndex);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));`:`if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
${t}
setOutputFlat(flatIndex, binaryOperation(a, b));
}`;return`
fn binaryOperation(a : f32, b : f32) -> f32 {
${wp(this.op,!1)}
}
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
${Me()} {
${He()}
// Fill in the shared memory buffer. Here we need a loop to make sure
// that all data in A|B are uploaded when |sharedMemorySize| is larger
// than work group size.
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
}
workgroupBarrier();
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
${n}
}
}
`}},Cde=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.fitShape=this.size%this.workGroupSize[0]==0,this.shaderKey=`binaryVec4_${e}_${this.fitShape}`,this.size=v.sizeFromShape(this.outputShape)/this.workPerThread}getUserCode(){let e,n=`fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
${wp(this.op,this.isVec4)}
}`;return this.fitShape?e=`
${n}
${Me()} {
${He()}
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Me()} {
${He()}
if (index < uniforms.size) {
let a = vec4<f32>(A.numbers[index]);
let b = vec4<f32>(B.numbers[index]);
setOutputFlat(index, binaryOperation(a, b));
}
}
`,e}},DC=class{constructor(e,t,n){this.variableNames=["A","B"];let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=at(this.outputShape),this.size=v.sizeFromShape(this.outputShape),this.sizeFit=this.size%s==0,this.shapesFit=v.arraysEqual(t,n)&&this.sizeFit,this.workPerThread=this.sizeFit||this.shapesFit?1:2,this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=`binary_${e}_${this.sizeFit}_${this.shapesFit}`,this.op=e}getUserCode(){let e,n=` fn binaryOperation(a : f32, b : f32) -> f32 {
${wp(this.op,!1)}
}`;return this.shapesFit?e=`
${n}
${Me()} {
${He()}
let a = f32(A[index]);
let b = f32(B[index]);
setOutputFlat(index, binaryOperation(a, b));
}
`:this.sizeFit?e=`
${n}
${Me()} {
${He()}
let coords = getCoordsFromFlatIndex(index);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(index, binaryOperation(a, b));
}
`:e=`
${n}
${Me()} {
${He()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1 ) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let a = getAAtOutCoordsByCoords(coords);
let b = getBAtOutCoordsByCoords(coords);
setOutputFlat(flatIndex, binaryOperation(a, b));
}
}
}
`,e}};function _C(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new Cde(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Sde(e,t,n,a):new DC(e,t,n)}function tr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Tde={kernelName:Wa,backendName:"webgpu",kernelFunc:tr};function mc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=tr({inputs:{x:s},backend:n}),l=tr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Nde={kernelName:jc,backendName:"webgpu",kernelFunc:mc},t0=class{constructor(e,t){this.variableNames=["A"];let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.size=v.sizeFromShape(this.outputShape),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
fn unaryOperation(a : f32) -> f32 {
${fc(this.op,!1)}
}
${Me()} {
${He()}
if (index < uniforms.size) {
let a = getAAtOutCoordsByGlobalId(globalId, index);
setOutputFlat(index, unaryOperation(a));
}
}
`}};function $n({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new t0(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function Xn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==je.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:A.dataId,dtype:A.dtype,shape:i.shape},w=_C(e,o.shape,i.shape);return l.runWebGPUProgram(w,[x,b],Ln(y.dtype,A.dtype))});else{let g=new $C(je.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new $C(je.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),A=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=mc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Ln(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?E.fromUint8ToStringArray(d):d,f=o.dtype==="string"?E.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=_C(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Ede,ceilImpl:Rde,concatImpl:$de,equalImpl:Dde,expImpl:_de,expm1Impl:Pde,floorImpl:Fde,gatherNdImpl:Ode,gatherV2Impl:Mde,greaterEqualImpl:zde,greaterImpl:Lde,lessEqualImpl:Bde,lessImpl:Wde,logImpl:Vde,maxImpl:Ude,maximumImpl:Gde,minimumImpl:Hde,multiplyImpl:jde,negImpl:qde,notEqualImpl:Xde,prodImpl:Kde,rangeImpl:Zde,rsqrtImpl:Yde,simpleAbsImpl:Jde,sliceImpl:Qde,stridedSliceImpl:epe,stringNGramsImpl:tpe,subImpl:npe,tileImpl:spe,transposeImpl:rpe,uniqueImpl:age}=BA,ape=$n({opType:Fe.ABS,cpuKernelImpl:Jde}),ope={kernelName:ti,backendName:"webgpu",kernelFunc:ape},ipe=Xn({opSnippet:je.ADD,cpuKernelImpl:Ede,supportsComplex:!0}),lpe={kernelName:qr,backendName:"webgpu",kernelFunc:ipe},upe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
${Me()} {
${He()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
${e.join(`
`)}
setOutputFlat(flatIndex, ${t});
}
}
}
`}};function cpe(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return tr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Ln(i,l)),a=s.map(i=>i.shape),o=new upe(a);return n.runWebGPUProgram(o,s,r)}var dpe={kernelName:ka,backendName:"webgpu",kernelFunc:cpe},PC=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];E.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=E.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=`
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,n=`
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) {
workgroupBarrier();
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
let i = i32(localId.x) * ${this.reductionFactor} + w;
if (i < currentSize) {
let candidateIndex = xBestIndices[i];
let candidate = xBestValues[i];
if(candidate ${this.op} bestValue && !isNanCustom(candidate)) {
bestValue = candidate;
bestIndex = candidateIndex;
}
}
}
xBestIndices[localId.x] = bestIndex;
xBestValues[localId.x] = bestValue;
}
if (localId.x == 0u) {
setOutputFlatI32(flatOutputIndex, i32(bestIndex));
}
`,s=ln(this.outputShape.length),r=(i,l)=>this.outputShape.length===1?i:`${i}[${l}]`,a=i=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${i}]`;return`
fn DIV_CEIL(a : i32, b : i32) -> i32 {
return ((a - 1) / b + 1);
}
let WorkGroupSize = ${this.workGroupSize[0]};
${e?t:""}
// In order to get a flattened index into the input tensor, we need to
// add back the index along the reduced dimension to |outputCoords|.
// This function outputs the offset to the first value along
// |axis| and the stride to get the next value of the input along |axis|.
fn getInputCoordInfo(globalId : vec3<u32>, globalIndex : i32) -> vec2<i32>{
let outputCoords : ${s} = getOutputCoords(globalId, globalIndex);
var i = ${this.outputShape.length-1};
var stride = 1;
var inputStride = 1;
var offset = 0;
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
let length = ${a(`${this.inputShape.length} - r`)};
if (${this.inputShape.length} - r == uniforms.axis) {
inputStride = stride;
} else {
offset = offset + ${r("outputCoords","i")} * stride;
i = i - 1;
}
stride = stride * length;
}
return vec2<i32>(offset, inputStride);
}
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
return coordInfo[0] + coordInfo[1] * index;
}
${Me()} {
${He()}
let coordInfo = getInputCoordInfo(globalId, index);
var bestIndex = 0;
var bestValue = x.numbers[getInputIndex(coordInfo, bestIndex)];
let Length = ${a("uniforms.axis")};
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
for (var w = 0; w < WorkPerThread; w = w + 1) {
let i = i32(globalId.x) * WorkPerThread + w;
if (i < Length) {
let candidate = x.numbers[getInputIndex(coordInfo, i)];
if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) {
bestValue = candidate;
bestIndex = i;
}
}
}
let flatOutputIndex = i32(globalId.y);
${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"}
}
`}},ppe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
let TILE_DIM = ${this.workGroupSize[0]};
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
${Me()} {
${He()}
let workGroupID = (globalId - localId)/vec3<u32>(${this.workGroupSize[0]}u, ${this.workGroupSize[1]}u, ${this.workGroupSize[2]}u);
var x = i32(workGroupID.x) * TILE_DIM + i32(localId.x);
var y = i32(workGroupID.y) * TILE_DIM + i32(localId.y);
let width = uniforms.outShape[0];
let height = uniforms.outShape[1];
if (x < width && y < height) {
tile[localId.y][localId.x] =
A.numbers[y * width + x];
}
workgroupBarrier();
x = i32(workGroupID.y) * TILE_DIM + i32(localId.x);
y = i32(workGroupID.x) * TILE_DIM + i32(localId.y);
if (x < height && y < width) {
setOutputFlat((y * height + x), tile[localId.x]
[localId.y]);
}
}
`}},hpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=ln(this.outputShape.length),t=fpe(this.newDim);return`
${Me()} {
${He()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let resRC = getCoordsFromFlatIndex(flatIndex);
setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
${e}(${t}), uniforms.aShape)]);
}
}
}
`}};function fpe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function kl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=rpe(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new ppe(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new hpe(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var mpe={kernelName:ho,backendName:"webgpu",kernelFunc:kl};function gpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=kl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new PC(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var ype={kernelName:Ia,backendName:"webgpu",kernelFunc:gpe};function Ape(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=kl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new PC(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var xpe={kernelName:Yl,backendName:"webgpu",kernelFunc:Ape},FC=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
var count = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
let xR = xRCorner + wR;
if (xR < 0 || xR >= uniforms.convDims.x) {
continue;
}
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
let xC = xCCorner + wC;
if (xC < 0 || xC >= uniforms.convDims.y) {
continue;
}
let value = getX(batch, xR, xC, coords[3]);
${e}
}
}
setOutput(batch, coords[1], coords[2], coords[3], ${t});
}
}
`}},OC=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let d = coords[3];
if (all(coords < uniforms.outShape)) {
let xRCCorner = coords.yz * uniforms.stride;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
let value = getX(batch, xRCorner, xCCorner, d);
setOutput(batch, coords[1], coords[2], d, value);
}
}
`}};function bpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return tr({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new OC(u):(d=new FC(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var vpe={kernelName:Sa,backendName:"webgpu",kernelFunc:bpe};function wpe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return _x({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var kpe={kernelName:Ca,backendName:"webgpu",kernelFunc:wpe},Ipe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=t,this.rank=t.length,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ln(e.length)}; `,this.shaderKey="slice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=ln(this.rank),t=Spe(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Px[a]} = uniforms.start[${a}] + coords.${Px[a]};`),`
${Me()} {
${He()}
if (index < uniforms.size) {
var sourceLoc : ${e};
let coords = getOutputCoords(globalId, index);
${n.join(`
`)}
setOutputFlat(index, getSource(${t}));
}
}
`}},Px=["x","y","z","w","u","v"];function Spe(e){if(e===1)return"sourceLoc";if(e<=6)return Px.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function kp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=yn.parseSliceParams(r,a,o);if(yn.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=Qde(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new Ipe(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var Cpe={kernelName:$i,backendName:"webgpu",kernelFunc:kp},Tpe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=nt({inputs:{x:r},backend:n,attrs:{shape:l}}),m=kl({inputs:{x:f},backend:n,attrs:{perm:c}}),g=nt({inputs:{x:m},backend:n,attrs:{shape:u}}),y=kp({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(A=>n.disposeData(A.dataId)),y},Npe={kernelName:ni,backendName:"webgpu",kernelFunc:Tpe},MC=Xn({opSnippet:je.NOT_EQUAL,dtype:"bool",cpuKernelImpl:Xde}),Epe={kernelName:xi,backendName:"webgpu",kernelFunc:MC};function Ip(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.real},backend:n})}var Rpe={kernelName:td,backendName:"webgpu",kernelFunc:Ip};function $pe(e,t){let n=new t0(e.shape,Fe.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Fx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return tr({inputs:{x:r},backend:n});let o=jt(r.shape),i=Fx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=mc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Ip({inputs:{input:r},backend:n}),i=Fx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=tr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return $pe(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=MC({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Dpe={kernelName:Ta,backendName:"webgpu",kernelFunc:Fx},_pe=$n({opType:Fe.CEIL,cpuKernelImpl:Rde}),Ppe={kernelName:Na,backendName:"webgpu",kernelFunc:_pe},Fpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4",this.size=v.sizeFromShape(this.outputShape)/4}getUserCode(){return`
${Me()} {
${He()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
var clampedValue : vec4<f32>;
for (var i = 0; i < 4; i = i + 1) {
if (isNanCustom(value[i])) {
clampedValue[i] = value[i];
} else {
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
}
}
setOutputFlat(index, clampedValue);
}
}
`}},Ope=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Me()} {
${He()}
if(index < uniforms.size) {
let value = getAAtOutCoordsByGlobalId(globalId, index);
if (isNanCustom(value)) {
setOutputFlat(index, value);
return;
}
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
}
}
`}};function Mpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new Fpe(r.shape):i=new Ope(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var zpe={kernelName:Xr,backendName:"webgpu",kernelFunc:Mpe},Lpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;a<e.length;a++)e[a]=e[a-1]+this.shapes[a][1];t.push(`if (yC < ${e[0]}){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`);for(let a=1;a<e.length;a++){let o=e[a-1];t.push(`elseif (yC < ${e[a]}){ setOutput(coords.x, coords.y, getT${a}(yR, yC - ${o})); }`)}let s=e.length,r=e[e.length-1];t.push(`else { setOutput(coords.x, coords.y, getT${s}(yR, yC - ${r})); }`)}else t.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
${Me()} {
${He()}
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if(flatIndex < uniforms.size) {
let coords = getCoordsFromFlatIndex(flatIndex);
let yR = coords.x;
let yC = coords.y;
${t.join(`
`)}
}
}
}
`}};function n0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Bpe={kernelName:Yc,backendName:"webgpu",kernelFunc:n0};function Ox(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>Ip({inputs:{input:m},backend:n})),d=e.map(m=>n0({inputs:{input:m},backend:n})),p=Ox(u,t,n),h=Ox(d,t,n),f=mc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return nt({inputs:{x:y},backend:n,attrs:{shape:[-1,A]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=E.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=$de(d,p,s,h),m=E.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeData(y.dataId)),g}let{tensors2D:a,outShape:o}=Wpe(e,t,n),i=new Lpe(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=nt({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function Wpe(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>nt({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function zC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return tr({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),Ox(i,a,n)}var Vpe={kernelName:si,backendName:"webgpu",kernelFunc:zC},Upe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
${Me()} {
${He()}
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
let rc = getCoordsFromFlatIndex(flatIndex);
if(flatIndex < uniforms.size) {
let blockIndex = rc[0];
let pos = rc[1];
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
var value = 0.0;
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
uniforms.pad[0];
let d1 = offsetX + uniforms.dilation[0] * ((pos %
uniforms.itemsPerBlockRow) / uniforms.inChannels);
let ch = pos % uniforms.inChannels;
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
value = getA(d0, d1, ch);
}
}
setOutputFlat(flatIndex, value);
}
}
}
`}};function LC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=nt({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=nt({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=_x({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=nt({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Gpe({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=n,x=A==="channelsLast",b=l*c*u,w=m*f,k=[w,b],S=!1,N=!1,R=[],P=nt({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),$=nt({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(P),R.push($);let D=new Upe(k,x),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],O=s.runWebGPUProgram(D,[P],P.dtype,T),B=nt({inputs:{x:O},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(O),R.push(B);let H=[1,k[0],k[1]],z=new RC(H,[1,w,n.outChannels],Z().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,N),X=H[1],ee=H[2],J=n.outChannels,Q=[{type:"int32",data:[X]},{type:"int32",data:[J]},{type:"int32",data:[ee]}],te=s.runWebGPUProgram(z,[B,$],B.dtype,Q),K=x?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],oe=nt({inputs:{x:te},backend:s,attrs:{shape:K}});R.push(te);for(let ce of R)s.disposeData(ce.dataId);return oe}var BC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(r,[o,l]),ua(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape);
let divBy4Remainder${e} = flatIndex${e} % 4;
let divBy4Index${e} = flatIndex${e} / 4;
let curData${e} = x.numbers[divBy4Index${e}];
if (divBy4Remainder${e} == 0) {
temp = curData${e};
} else {
// TODO: This could end up being a redundant load with another one in
// the same shader invocation. Perhaps there's an opportunity for
// optimization
let nextData${e} = x.numbers[divBy4Index${e} + 1];
if (divBy4Remainder${e} == 1) {
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
} elseif (divBy4Remainder${e} == 2) {
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
} elseif (divBy4Remainder${e} == 3) {
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
}
}
`}getUserCode(){let t=EC([4,4,1],this.workGroupSize),r=`let outRow = r / uniforms.outShape[2];
let outCol = r % uniforms.outShape[2];
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
let inChCoord = c % uniforms.xShape[3];
var coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
inChCoord);
var resData = vec4<f32>(0.0);
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
// the 'same' padding type.
if (coordsInBounds4D(coord, uniforms.xShape)) {
resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
} else {
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
${this.getSampleAWithRemainder(1)}
resData = temp;
if (WCol == (uniforms.filterDims[1] - 1)) {
coord = vec4<i32>(
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
${this.getSampleAWithRemainder(2)}
if (inChCoord == 0) {
resData = vec4<f32>(resData.xyz, temp.x);
} elseif (inChCoord == 1) {
resData = vec4<f32>(resData.xy, temp.xy);
} else {
resData = vec4<f32>(resData.x, temp.xyz);
}
}
`}
return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
${r}
}
return vec4<f32>(0.0);
`,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter / 4 + col];
}
return vec4<f32>(0.0);
`,i="",l="";if(this.activation){let d=Bo(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${d}
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
let b = getLeakyreluAlphaAtOutCoords();
${d}
}`,new Error("Leakyrelu is not supported.");i=`
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
${d}
}`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${i}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
let r = row;
let c = col * 4;
var batch = i32(globalId.z);
${a}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
${o}
}
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
{
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col * 4);
${c}
${l}
setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
value);
}
}
${t}
`}},WC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ex(this.dispatchLayout,this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ua(s,[a,i]),ua(r,[i,o])]}getUserCode(){let e=Dx(this.elementsPerThread,this.workGroupSize),t=`
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
let coord = vec4<i32>(
batch,
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
col % uniforms.xShape[3]);
// The bounds checking is always needed since we use it to pad zero for the
// 'same' padding type.
if(coordsInBounds4D(coord, uniforms.xShape)) {
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
${t}
}
return 0.0;
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
return W.numbers[row * uniforms.dimBOuter + col];
}
return 0.0;
`,r="",a="";if(this.activation){let l=Bo(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${l}
}`:r=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
${l}
}
`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${r}
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
${n}
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
${s}
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
${o}
${a}
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${e}
`}},VC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Bo(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
${r}
}`:e=`
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
${r}
}
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
${e}
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
let coord = vec4<i32>(batch, row, col, chan);
if(coordsInBounds4D(coord, uniforms.xShape)) {
return getX(batch, row, col, chan);
}
return 0.0;
}
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
let coord = vec4<i32>(row, col, xChannel, outChannel);
if(coordsInBounds4D(coord, uniforms.wShape)) {
return getW(row, col, xChannel, outChannel);
}
return 0.0;
}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
${n}
${t}
setOutput(batch, row, col, chan, value);
}
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let outChannel = coords[3];
var acc = 0.0;
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
let v = readInp(batch, coordRow, coordCol, xChannel);
let f = readFilt(row, col, xChannel, outChannel);
acc = acc + v * f;
}
}
}
writeResult(batch, coords[1], coords[2], outChannel, acc);
}
`}};function Hpe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return LC({x:r,filter:a,convInfo:p,backend:s});if(Z().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return Gpe({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=Z().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new VC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new BC(p):h=new WC(p),!g){let y=p.outShape[1]*p.outShape[2],A=p.outShape[3],x=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[y]},{type:"int32",data:[A]},{type:"int32",data:[x]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var jpe={kernelName:Ea,backendName:"webgpu",kernelFunc:Hpe},qpe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ex(this.dispatchLayout,this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
var batch = i32(globalId.z);
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
let outRow = row / uniforms.outShape[2];
let outCol = row % uniforms.outShape[2];
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
return 0.0;
}
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
return 0.0;
}
let coord = vec4<i32>(
batch,
i32(xR),
i32(xC),
col % uniforms.outBackprop[3]);
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
}
return 0.0;
}
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
let coordX = uniforms.filterDims.x - 1 -
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
let coordY = uniforms.filterDims.y - 1 -
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
coordX >= 0 && coordY >= 0) {
let coord = vec4<i32>(coordX, coordY, col,
row % uniforms.outBackprop[3]);
return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
}
return 0.0;
}
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
var batch = i32(globalId.z);
var value = valueInput;
let outCoord = vec4<i32>(
batch,
row / uniforms.outShape[2],
row % uniforms.outShape[2],
col);
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
}
${Dx(this.elementsPerThread,this.workGroupSize)}
`}},Xpe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.outputShape=e.inShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
let batch = coords[0];
let d1 = coords[${n}];
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
let dyRCorner = dyCorner.x;
let dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
let wRPerm = uniforms.filterDims.x - 1 - wR;
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
wRPerm < 0) {
continue;
}
let idyR = dyR;
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
let wCPerm = uniforms.filterDims.y - 1 - wC;
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
fract(dyC) > 0.0 || wCPerm < 0) {
continue;
}
let idyC = dyC;
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
if (${this.isChannelsLast}) {
let xValue = getDy(batch, idyR, idyC, d2);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
} else {
let xValue = getDy(batch, d2, idyR, idyC);
let wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd = dotProd + xValue * wValue;
}
}
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd);
}
}
`}};function Kpe(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Z().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Xpe(p);else{f=new qpe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Zpe={kernelName:Ra,backendName:"webgpu",kernelFunc:Kpe},Ype=$n({opType:Fe.COS}),Jpe={kernelName:$a,backendName:"webgpu",kernelFunc:Ype},Qpe=$n({opType:Fe.COSH}),ehe={kernelName:Da,backendName:"webgpu",kernelFunc:Qpe},the=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1];let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
fn writeResult(coords : vec4<i32>, value : f32) {
if (coordsInBounds4D(coords, uniforms.outShape)) {
setOutput(coords[0], coords[1], coords[2], coords[3], value);
}
}
${Me()} {
${He()}
let height_ratio = f32(${n});
let width_ratio = f32(${a});
let coords = getOutputCoords(globalId, index);
let b = coords[0];
let y = coords[1];
let x = coords[2];
let d = coords[3];
// get box vals
let y1 = getBoxes(b, 0);
let x1 = getBoxes(b, 1);
let y2 = getBoxes(b, 2);
let x2 = getBoxes(b, 3);
// get image in batch index
let bInd = i32(round(getBoxInd(b)));
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
return;
}
let height_scale = ${s};
let width_scale = ${o};
let in_y = ${r};
if( in_y < 0.0 || in_y > ${e} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let in_x = ${i};
if( in_x < 0.0 || in_x > ${t} ) {
writeResult(coords, uniforms.extrapolationValue);
return;
}
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
if(${this.methodId} == 1) {
// Compute the four integer indices.
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
let top = topLeft + (topRight - topLeft) * fracCR.x;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
let newValue = top + (bottom - top) * fracCR.y;
writeResult(coords, newValue);
} else {
// Compute the coordinators of nearest neighbor point.
let sourceNearestCR = vec2<i32>(floor(
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
let newValue = getImage(
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
writeResult(coords,newValue);
}
}
`}},nhe=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new the(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},she={kernelName:ai,backendName:"webgpu",kernelFunc:nhe},rhe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.size=v.sizeFromShape(this.outputShape),this.dataFormat=t}getUserCode(){return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let b = coords[0];
let h = ${this.getHeightCoordString()};
let w = ${this.getWidthCoordString()};
let d = ${this.getDepthCoordString()};
let in_h = h / uniforms.blockSize;
let offset_h = h % uniforms.blockSize;
let in_w = w / uniforms.blockSize;
let offset_w = w % uniforms.blockSize;
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
${this.getOutputDepthSize()};
let in_d = d + offset_d;
let rlt = ${this.getInputSamplingString()};
setOutputFlat(index, rlt);
}
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ahe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new rhe(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var ohe={kernelName:oi,backendName:"webgpu",kernelFunc:ahe},UC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Bo(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, globalIndex);
${r}
}`:e=`
fn activation(a : vec4<f32>, globalId : vec3<u32>, globalIndex : i32) -> vec4<f32> {
${r}
}
`,t="dotProd[i] = activation(dotProd[i], globalId, index);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
${e}
${Me()} {
${He()}
let batch = 0;
let r = i32(globalId.x);
let c = i32(globalId.y) * 4;
let d2 = i32(globalId.z) * 4;
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
let d1 = d2;
let q = 0;
let xRCorner = xRCCorner.x;
let xCCorner = xRCCorner.y;
var wVals : array<vec4<f32>, 9>;
wVals[0] = getW(0, 0, d1, q);
wVals[1] = getW(0, 1, d1, q);
wVals[2] = getW(0, 2, d1, q);
wVals[3] = getW(1, 0, d1, q);
wVals[4] = getW(1, 1, d1, q);
wVals[5] = getW(1, 2, d1, q);
wVals[6] = getW(2, 0, d1, q);
wVals[7] = getW(2, 1, d1, q);
wVals[8] = getW(2, 2, d1, q);
var xVals : array<array<vec4<f32>, 6>, 3>;
for (var wR = 0; wR < 3; wR = wR + 1) {
let xR = xRCorner + wR * uniforms.dilation[0];
for (var wC = 0; wC < 6; wC = wC + 1) {
let xC = xCCorner + wC * uniforms.dilation[1];
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
xVals[wR][wC] = vec4<f32>(0.0);
} else {
xVals[wR][wC] = getX(batch, xR, xC, d1);
}
}
}
var dotProd : array<vec4<f32>, 4>;
dotProd[0] = vec4<f32>(0.0);
dotProd[1] = vec4<f32>(0.0);
dotProd[2] = vec4<f32>(0.0);
dotProd[3] = vec4<f32>(0.0);
for (var wR = 0; wR < 3; wR = wR + 1) {
for (var wC = 0; wC < 3; wC = wC + 1) {
let indexW = wR * 3 + wC;
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
}
}
for (var i = 0; i < 4; i = i + 1) {
let coords = vec4<i32>(batch, r, c + i, d2);
if (coordsInBounds4D(coords, uniforms.outShape)) {
${n}
${t}
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
}
}
}
`}},GC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=Bo(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
let b = getPreluActivationWeightsAtOutCoordsByGlobalId(globalId, index);
${a}
}`:t=`
fn activation(a : f32, globalId : vec3<u32>, index : i32) -> f32 {
${a}
}
`,n="dotProd = activation(dotProd, globalId, index);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByGlobalId(globalId, index);":"";return`
${t}
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
let coord = vec4<i32>(batch, row, col, chan);
if (coordsInBounds4D(coord, uniforms.outShape)) {
setOutput(batch, row, col, chan, value);
}
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let batch = coords[0];
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
let d2 = coords[3];
let d1 = d2 / ${e};
let q = d2 - d1 * ${e};
let inputRowStart = xRCCorner.x;
let inputColStart = xRCCorner.y;
let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0];
let inputColEnd = inputColStart + ${this.convInfo.filterWidth} * uniforms.dilation[1];
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
var dotProd = 0.0;
// Extract if checking out of for loop for performance.
if (inputRowStart >= 0 && inputColStart >= 0 &&
inputRowEnd < uniforms.inDims[0] && inputColEnd < uniforms.inDims[1]) {
// Here using a constant value |this.convInfo.filterHeight| instead
// of uniform value is in order to loop unrolling.
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
} else {
for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
let xR = inputRowStart + wR * uniforms.dilation[0];
if (xR < 0 || xR >= uniforms.inDims[0]) {
continue;
}
for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
let xC = inputColStart + wC * uniforms.dilation[1];
if (xC < 0 || xC >= uniforms.inDims[1]) {
continue;
}
let xVal = getX(batch, xR, xC, d1);
let wVal = getW(wR, wC, d1, q);
dotProd = dotProd + xVal * wVal;
}
}
}
${s}
${n}
writeResult(batch, coords[1], coords[2], d2, dotProd);
}
`}};function ihe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new UC(d):p=new GC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var lhe={kernelName:_a,backendName:"webgpu",kernelFunc:ihe},HC=Xn({opSnippet:je.MUL,cpuKernelImpl:jde,supportsComplex:!0}),uhe={kernelName:Za,backendName:"webgpu",kernelFunc:HC},che=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=`
if (isNanCustom(candidate)) {
bestValue = uniforms.NAN;
} elseif (candidate ${this.reduceType==="min"?"<":">"}
bestValue)
{ bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=`
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
`,a=`
xBestValues[localId.x] = bestValue;
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "}
var currentSize = WorkGroupSize;
for(; currentSize > 1;) {
workgroupBarrier();
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
let i = i32(localId.x) * ${this.reductionFactor} + w;
if (i < currentSize) {
let candidate = xBestValues[i];
${t}
}
}
workgroupBarrier();
xBestValues[localId.x] = bestValue;
currentSize = DIV_CEIL(currentSize, ${this.reductionFactor});
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""}
}
if (localId.x == 0u) {
${s}
}
`;return`
fn DIV_CEIL(a : i32, b : i32) -> i32 {
return ((a - 1) / b + 1);
}
let WorkGroupSize = ${this.workGroupSize[0]};
${e?r:""}
fn getOffset(globalId : vec3<u32>, index : i32) -> i32 {
let outputCoords = getOutputCoords(globalId, index);
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
return offset;
}
${Me()} {
${He()}
let offset= getOffset(globalId, index);
var bestValue = ${n};
let Length = uniforms.reduceSize;
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
for (var w = 0; w < WorkPerThread; w = w + 1) {
let i = i32(globalId.x) * WorkPerThread + w;
if (i < Length) {
let candidate = f32(x.numbers[offset + i]);
${t}
}
}
let flatOutputIndex = i32(globalId.y);
${e?a:s}
}
`}};function Sp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=kl({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=Ude(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=Kde(u.shape,u.dtype,m,l);f=r.makeTensorInfo(A,x,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),y=v.sizeFromShape(u.shape)/m,A={windowSize:m,inSize:m,batchSize:y,outSize:1},x=s==="mean"?"float32":fd(e.dtype),b=[{type:"int32",data:[m]}],w=new che(A,s,x),k=r.runWebGPUProgram(w,[u],x,b);o.push(k),f=nt({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Mx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"sum",n)}var dhe={kernelName:io,backendName:"webgpu",kernelFunc:Mx};function phe(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:A}=E.getEinsumPermutation(h,l[g]),x;E.isIdentityPermutation(y)?x=a[g]:(x=kl({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=nt({inputs:{x},backend:n,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=HC({inputs:{a:x,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Mx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var hhe={kernelName:Zc,backendName:"webgpu",kernelFunc:phe},fhe=$n({opType:Fe.ELU}),mhe={kernelName:Fa,backendName:"webgpu",kernelFunc:fhe},ghe=Xn({opSnippet:je.EQUAL,dtype:"bool",cpuKernelImpl:Dde}),yhe={kernelName:ii,backendName:"webgpu",kernelFunc:ghe},jC=$n({opType:Fe.EXP,cpuKernelImpl:_de,dtype:"float32"}),Ahe={kernelName:Oa,backendName:"webgpu",kernelFunc:jC};function zx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),nt({inputs:{x:a},backend:s,attrs:{shape:i}})}var xhe={kernelName:li,backendName:"webgpu",kernelFunc:zx},bhe=$n({opType:Fe.EXPM1,cpuKernelImpl:Pde}),vhe={kernelName:ui,backendName:"webgpu",kernelFunc:bhe},whe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workPerThread=4,this.workGroupSize=[16,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="fill",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Me()} {
${He()}
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
let flatIndex = index * ${this.workPerThread} + i;
if (flatIndex < uniforms.size) {
setOutputFlat(flatIndex, uniforms.value);
}
}
}
`}};function s0(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new whe(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var khe={kernelName:ru,backendName:"webgpu",kernelFunc:s0},Ihe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let coordX = uniforms.xShape[2] - coords[2] - 1;
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
setOutputFlat(index, outputValue);
}
}
`}},She={kernelName:ci,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Ihe(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Che=$n({opType:Fe.FLOOR,cpuKernelImpl:Fde}),The={kernelName:Ma,backendName:"webgpu",kernelFunc:Che},Nhe=Xn({opSnippet:je.INT_DIV,dtype:"int32"}),Ehe={kernelName:za,backendName:"webgpu",kernelFunc:Nhe},Rhe=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},qC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=dce(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function XC(e,t,n,s="",r=""){return(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r+e.shaderKey}function KC(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=XC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>qC(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let y=[i,o,...l,...u.dispatch];u.setUniform(n.device,y);let A;if(a){let x={source:t};A=n.device.importExternalTexture(x)}else A=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,A,c.dataId),c}var $he={kernelName:ad,backendName:"webgpu",kernelFunc:Dhe},gc;function Dhe(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(Z().getBool("WEBGPU_USE_IMPORT")&&o)return KC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(gc==null&&(gc=document.createElement("canvas").getContext("2d")),gc.canvas.width=u,gc.canvas.height=d,gc.drawImage(r,0,0,u,d),r=gc.canvas),c||l||o||i)return KC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let y=h.length,A=0;for(let x=0;x<y;x++)x%4<a&&(f[A++]=h[x])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var _he=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(E.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalId(globalId, index)");let t="1.0";this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalId(globalId, index)");let n=this.outputShape.length,s=ln(n),r="setOutput(coords[0], coords[1], coords[2], coords[3], value);";return n===2&&(r="setOutput(coords[0], coords[1], value);"),n===3&&(r="setOutput(coords[0], coords[1], coords[2], value);"),`
fn writeResult(coords : ${s}, value : f32) {
if (coordsInBounds${n}D(coords, uniforms.outShape)) {
${r}
}
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
let xValue = getXAtOutCoordsByGlobalId(globalId, index);
let meanValue = getMeanAtOutCoordsByGlobalId(globalId, index);
let varianValue = getVarianceAtOutCoordsByGlobalId(globalId, index);
let offsetValue = ${e};
let scaleValue = ${t};
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
writeResult(coords,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
}
`}},Phe={kernelName:La,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new _he(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function Fhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y=o!=null,A=i!=null,x;if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))return LC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=Z().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],S=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)x=new VC(g,y,h,A);else{w?x=new BC(g,y,h,A):x=new WC(g,y,h,A);let R=g.outShape[1]*g.outShape[2],P=g.outShape[3],$=g.filterHeight*g.filterWidth*g.inShape[3];S.push({type:"int32",data:[R]},{type:"int32",data:[P]},{type:"int32",data:[$]})}let N=[r,a];return y&&N.push(o),A&&N.push(i),n.runWebGPUProgram(x,N,r.dtype,S)}var Ohe={kernelName:go,backendName:"webgpu",kernelFunc:Fhe};function Mhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(E.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=E.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,y=i!=null;g&&m.push(o),y&&m.push(i);let A;f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?A=new UC(f,g,p,y):A=new GC(f,g,p,y);let x=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}];return n.runWebGPUProgram(A,m,"float32",x)}var zhe={kernelName:yo,backendName:"webgpu",kernelFunc:Mhe},Lhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.size=v.sizeFromShape(this.outputShape),this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${ln(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
var flattenIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexTemp = i32(round(getIndices(coords[0], j)));
let strideNum = ${e};
flattenIndex = flattenIndex + indexTemp * strideNum;
}
if (index < uniforms.size) {
setOutputFlat(index, getA(flattenIndex, coords[1]));
}
}
`}};function Bhe(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=E.prepareAndValidate(s,r),p=nt({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=nt({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),b=Ode(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new Lhe(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),y=nt({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var Whe={kernelName:pi,backendName:"webgpu",kernelFunc:Bhe},Vhe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=Uhe(this.aShape,"i32");return`
${Me()} {
${He()}
let resRC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
setOutputFlat(index, getA(${e}));
}
}
`}};function Uhe(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function Ghe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=nt({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=nt({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let w=n.tensorMap.get(m.dataId).values,k=We(m.shape,m.dtype,w),N=n.tensorMap.get(f.dataId).values,R=We(f.shape,f.dtype,N),P=Mde(R,k,g);return h.forEach($=>n.disposeData($.dataId)),n.makeTensorInfo(d.outputShape,P.dtype,P.values)}let y=new Vhe(f.shape,g),A=n.runWebGPUProgram(y,[f,m],f.dtype);h.push(A);let x=nt({inputs:{x:A},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeData(b.dataId)),x}var Hhe={kernelName:di,backendName:"webgpu",kernelFunc:Ghe},jhe=Xn({opSnippet:je.GREATER,cpuKernelImpl:Lde,dtype:"bool"}),qhe={kernelName:hi,backendName:"webgpu",kernelFunc:jhe},Xhe=Xn({opSnippet:je.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:zde}),Khe={kernelName:Ba,backendName:"webgpu",kernelFunc:Xhe},Zhe=Xn({opSnippet:je.LESS,dtype:"bool",cpuKernelImpl:Wde}),Yhe={kernelName:mi,backendName:"webgpu",kernelFunc:Zhe},Jhe=Xn({opSnippet:je.LESS_EQUAL,dtype:"bool",cpuKernelImpl:Bde}),Qhe={kernelName:gi,backendName:"webgpu",kernelFunc:Jhe},efe=$n({opType:Fe.LOG,cpuKernelImpl:Vde}),tfe={kernelName:Va,backendName:"webgpu",kernelFunc:efe},nfe=Xn({opSnippet:je.LOGICAL_AND,dtype:"bool"}),sfe={kernelName:yi,backendName:"webgpu",kernelFunc:nfe},rfe=$n({opType:Fe.LOGICAL_NOT}),afe={kernelName:uu,backendName:"webgpu",kernelFunc:rfe};function ZC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Sp(r,a,o,"max",n)}var ofe={kernelName:Ua,backendName:"webgpu",kernelFunc:ZC},ife=Xn({opSnippet:je.MAX,cpuKernelImpl:Gde}),lfe={kernelName:Ga,backendName:"webgpu",kernelFunc:ife};function ufe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return tr({inputs:{x:r},backend:n});d=new OC(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new FC(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var cfe={kernelName:Ha,backendName:"webgpu",kernelFunc:ufe};function dfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Sp(r,o,a,"mean",n)}var pfe={kernelName:ja,backendName:"webgpu",kernelFunc:dfe};function hfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"min",n)}var ffe={kernelName:qa,backendName:"webgpu",kernelFunc:hfe},mfe=Xn({opSnippet:je.MIN,cpuKernelImpl:Hde}),gfe={kernelName:Xa,backendName:"webgpu",kernelFunc:mfe},yfe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`,this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=ln(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Me()} {
${He()}
let start = ${o}(${t});
let end = ${o}(${n});
var outC = getOutputCoords(globalId, index);
if (index < uniforms.size) {
for (var i = 0; i < ${e}; i = i + 1) {
if (${a} < ${s}) {
${a} = ${s} * 2 - ${a} - ${this.offset};
} elseif(${a} >= ${r}) {
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
}
}
let coords = outC - start;
setOutputFlat(index, getX(${i}));
}
}
`}},Afe={kernelName:Ka,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new yfe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function xfe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=qde(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new t0(s.shape,Fe.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var bfe={kernelName:Ai,backendName:"webgpu",kernelFunc:xfe};function vfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Zs.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var wfe={kernelName:bi,backendName:"webgpu",kernelFunc:vfe};function kfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Zs.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Ife={kernelName:vi,backendName:"webgpu",kernelFunc:kfe};function r0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Ip({inputs:{input:s},backend:n}),a=r0({inputs:{x:r},backend:n}),o=n0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=mc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return s0({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Sfe={kernelName:Li,backendName:"webgpu",kernelFunc:r0};function YC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Ip({inputs:{input:s},backend:n}),a=YC({inputs:{x:r},backend:n}),o=n0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=mc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return s0({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Cfe={kernelName:wi,backendName:"webgpu",kernelFunc:YC};function Tfe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return zx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=zx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=zC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Nfe={kernelName:Ii,backendName:"webgpu",kernelFunc:Tfe},Efe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.xShape.length,t=ln(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
${Me()} {
${He()}
let start = ${r};
let end = ${a};
if (index < uniforms.size) {
let outC = getOutputCoords(globalId, index);
if (${o} || ${i}) {
setOutputFlat(index, uniforms.constantValue);
} else {
let coords = outC - start;
setOutputFlat(index, getX(${l}));
}
}
}
`}},JC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return tr({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return s0({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Efe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Rfe={kernelName:Ya,backendName:"webgpu",kernelFunc:JC},$fe=Xn({opSnippet:je.POW}),Dfe={kernelName:Ja,backendName:"webgpu",kernelFunc:$fe};function _fe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new DC(je.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Pfe={kernelName:Qa,backendName:"webgpu",kernelFunc:_fe};function Ffe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sp(r,a,o,"prod",n)}var Ofe={kernelName:Si,backendName:"webgpu",kernelFunc:Ffe},Mfe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Zde(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},zfe={kernelName:pu,backendName:"webgpu",kernelFunc:Mfe},QC=Xn({opSnippet:je.DIV}),Lfe={kernelName:Pa,backendName:"webgpu",kernelFunc:QC},Bfe=$n({opType:Fe.RELU}),Wfe={kernelName:eo,backendName:"webgpu",kernelFunc:Bfe},Vfe=$n({opType:Fe.RELU6}),Ufe={kernelName:no,backendName:"webgpu",kernelFunc:Vfe},Gfe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (all(coords < uniforms.outShape)) {
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
let effectiveOutSize = vec2<f32>(
${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
// Compute the four integer indices.
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
let sourceCeilRC = vec2<i32>(
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
let top = topLeft + (topRight - topLeft) * fracRC.y;
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
let newValue = top + (bottom - top) * fracRC.x;
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function Hfe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new Gfe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var jfe={kernelName:to,backendName:"webgpu",kernelFunc:Hfe},qfe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":t="vec2<f32>(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return`
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (all(coords < uniforms.outShape)) {
let b = coords[0];
let d = coords[3];
let rc = coords.yz;
let effectiveInSize = vec2<f32>(
${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
let effectiveOutSize = vec2<f32>(
${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
let effectiveInputOverOutputRatioRC =
effectiveInSize / effectiveOutSize;
// Fractional source index
let sourceFracIndexRC = ${t};
// Compute the coordinators of nearest neighbor point.
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
let sourceNearestRC = vec2<i32>(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${e})));
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(b, coords[1], coords[2], d, newValue);
}
}
`}};function Xfe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new qfe(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var Kfe={kernelName:fu,backendName:"webgpu",kernelFunc:Xfe},Zfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
cosRadians : f32;`,this.shaderKey="rotate",this.size=v.sizeFromShape(this.outputShape),this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
uniforms.sinRadians;
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
uniforms.cosRadians;
let coordX = i32(round(coordXFloat + uniforms.centerX));
let coordY = i32(round(coordYFloat + uniforms.centerY));
${this.fillSnippet}
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
coordY < uniforms.xShape[1]) {
outputValue = getX(coords[0], coordY, coordX, coords[3]);
}
setOutputFlat(index, outputValue);
}
}
`}},Yfe={kernelName:Bi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Zfe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},Jfe=$n({opType:Fe.RSQRT,cpuKernelImpl:Yde}),Qfe={kernelName:so,backendName:"webgpu",kernelFunc:Jfe},e6=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.outputShape=a,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`,this.size=v.sizeFromShape(this.outputShape);let l=ln(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
${Me()} {
${He()}
let globalIndex = index * ${this.workPerThread};
if (globalIndex < uniforms.size) {
var sum = vec4<f32>(0.0);
var found = vec4<bool>(false);
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
var flattenedIndex = 0;
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
let indexInside = i32(round(${this.indicesSnippet}));
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
let coords = getCoordsFromFlatIndex(curIndex);
if (flattenedIndex == coords[0]) {
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
found[innerIndex] = true;
}
}
}
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
let curIndex = globalIndex + innerIndex;
if (curIndex < uniforms.size)
{
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
}
}
}
}`}};function eme(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=nt({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=nt({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=[{type:"int32",data:[l]},{type:"int32",data:[i]},{type:"int32",data:u}],y=new e6(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGPUProgram(y,[f,h,m],f.dtype,g),x=nt({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(A.dataId),n.disposeData(m.dataId),x}var tme={kernelName:Ei,backendName:"webgpu",kernelFunc:eme},nme=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.outputShape=t,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
${Me()} {
${He()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
let cVal = getC(${e});
if (cVal >= 1.0) {
setOutputFlat(index, getA(${t}));
} else {
setOutputFlat(index, getB(${t}));
}
}
}
`}};function sme(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new nme(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var rme={kernelName:Ri,backendName:"webgpu",kernelFunc:sme},ame=$n({opType:Fe.SIGMOID}),ome={kernelName:ao,backendName:"webgpu",kernelFunc:ame},ime=$n({opType:Fe.SIN}),lme={kernelName:ro,backendName:"webgpu",kernelFunc:ime},ume=$n({opType:Fe.SINH}),cme={kernelName:Di,backendName:"webgpu",kernelFunc:ume},t6=Xn({opSnippet:je.SUB,cpuKernelImpl:npe,supportsComplex:!0}),dme={kernelName:co,backendName:"webgpu",kernelFunc:t6};function pme(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=ZC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=nt({inputs:{x:i},backend:n,attrs:{shape:l}}),u=t6({inputs:{a:r,b:c},backend:n}),d=jC({inputs:{x:u},backend:n}),p=Mx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=nt({inputs:{x:p},backend:n,attrs:{shape:l}}),f=QC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var hme={kernelName:lo,backendName:"webgpu",kernelFunc:pme},fme=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,A)=>y*A),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=JC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=nt({inputs:{x:u},backend:n,attrs:{shape:d}}),m=kl({inputs:{x:f},backend:n,attrs:{perm:p}}),g=nt({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeData(y.dataId)),g},mme={kernelName:_i,backendName:"webgpu",kernelFunc:fme};function gme(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new e6(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=nt({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var yme={kernelName:nd,backendName:"webgpu",kernelFunc:gme};function Ame(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=kp({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var xme={kernelName:Pi,backendName:"webgpu",kernelFunc:Ame},bme=$n({opType:Fe.SQRT}),vme={kernelName:oo,backendName:"webgpu",kernelFunc:bme},wme={kernelName:Au,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new t0(n.shape,Fe.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},kme=Xn({opSnippet:je.SQUARED_DIFFERENCE}),Ime={kernelName:uo,backendName:"webgpu",kernelFunc:kme},Sme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=ln(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice",this.size=v.sizeFromShape(this.outputShape)}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
${Me()} {
${He()}
if (index < uniforms.size) {
let coords = getOutputCoords(globalId, index);
setOutputFlat(index, getX(${t}));
}
}
`}};function Cme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{nonStrided:h,$begin:f,$strides:m,size:g,newShape:y,outShape:A}=yn.sliceInfo(r.shape,a,o,i,l,c,u,d,p),x=nt({inputs:{x:r},backend:n,attrs:{shape:y}}),b;if(h){let k=kp({inputs:{x},backend:n,attrs:{begin:f,size:g}});b=nt({inputs:{x:k},backend:n,attrs:{shape:A}}),n.disposeData(k.dataId)}else if(A.some(k=>k===0))b=n.makeTensorInfo(A,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let N=n.tensorMap.get(x.dataId).values,R=We(x.shape,x.dtype,N),P=epe(A,R,m,f);b=n.makeTensorInfo(A,x.dtype,P.values)}else{let S=new Sme(A),N=[{type:"int32",data:f},{type:"int32",data:m}];b=n.runWebGPUProgram(S,[x],x.dtype,N)}let w=nt({inputs:{x:b},backend:n,attrs:{shape:A}});return n.disposeData(x.dataId),n.disposeData(b.dataId),w}var Tme={kernelName:Fi,backendName:"webgpu",kernelFunc:Cme};function Nme(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=tpe(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Eme={kernelName:sd,backendName:"webgpu",kernelFunc:Nme},Rme=$n({opType:Fe.TANH}),$me={kernelName:po,backendName:"webgpu",kernelFunc:Rme},Dme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.size=v.sizeFromShape(this.outputShape),this.shaderKey="tile"}getUserCode(){let e=_me(this.rank,"uniforms.");return`
${Me()} {
${He()}
if (index < uniforms.size) {
let resRC = getOutputCoords(globalId, index);
setOutputFlat(index, getA(${e}));
}
}
`}};function _me(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Pme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=We(r.shape,r.dtype,c),d=spe(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Dme(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Fme={kernelName:Kr,backendName:"webgpu",kernelFunc:Pme},Ome=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.outputShape=e,this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
fn mapCoord(outCoord : f32, len : f32) -> f32{
var inCoord = outCoord;
if(uniforms.fillModeId == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
inCoord;
}
if (inCoord < -len) {
inCoord = inCoord + sz2;
} else {
inCoord = -inCoord - 1.0;
}
}
} elseif (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz2 = 2.0 * len;
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} elseif (uniforms.fillModeId == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
}
} elseif (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
let sz = len - 1.0;
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} elseif (uniforms.fillModeId == 4) {
return clamp(outCoord, 0.0, len - 1.0);
}
return outCoord;
}
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
channel : i32) -> f32 {
var outputValue : f32;
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = uniforms.fillValue;
}
return outputValue;
}
${Me()} {
${He()}
let coords = getOutputCoords(globalId, index);
if (coordsInBounds4D(coords, uniforms.outShape)) {
var outputValue : f32;
let batch = coords[0];
let x = coords[2];
let y = coords[1];
let channel = coords[3];
let xf = f32(x);
let yf = f32(y);
let a1 = getTransforms(batch, 0);
let a2 = getTransforms(batch, 1);
let a3 = getTransforms(batch, 2);
let b1 = getTransforms(batch, 3);
let b2 = getTransforms(batch, 4);
let b3 = getTransforms(batch, 5);
let c1 = getTransforms(batch, 6);
let c2 = getTransforms(batch, 7);
let projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = uniforms.fillValue;
} else {
let inX = (a1 * xf + a2 * yf + a3) / projection;
let inY = (b1 * xf + b2 * yf + b3) / projection;
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
if (uniforms.interpolationModeId == 1) {
let coordY = i32(round(mapY));
let coordX = i32(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
let yFloor = floor(mapY);
let xFloor = floor(mapX);
let yCeil = yFloor + 1.0;
let xCeil = xFloor + 1.0;
let valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
let valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(coords[0], coords[1], coords[2], coords[3], outputValue);
}
}
`}};function Mme(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new Ome(g),A=o==="nearest"?1:2,x;switch(i){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var zme={kernelName:Mi,backendName:"webgpu",kernelFunc:Mme};function Lme(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=kp({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=nt({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var Bme={kernelName:zi,backendName:"webgpu",kernelFunc:Lme},Wme=[Ide,ope,lpe,dpe,ype,xpe,vpe,kpe,Npe,Dpe,Ppe,zpe,Nde,Vpe,jpe,Zpe,Jpe,ehe,she,ohe,lhe,hhe,mhe,yhe,xhe,Ahe,vhe,khe,She,$he,The,Ehe,Phe,Ohe,zhe,Whe,Hhe,qhe,Khe,Tde,Bpe,Yhe,Qhe,tfe,sfe,afe,ofe,lfe,cfe,pfe,ffe,gfe,Afe,uhe,bfe,wfe,Ife,Epe,Cfe,Nfe,Rfe,Pfe,Ofe,Dfe,zfe,Rpe,Lfe,Wfe,Ufe,wde,jfe,Kfe,Yfe,Qfe,tme,rme,ome,lme,cme,Cpe,Tme,Eme,hme,mme,xme,yme,vme,wme,Ime,dme,dhe,$me,Fme,zme,mpe,Bme,Sfe];for(let e of Wme)Yr(e);var Vme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=n6(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=n6(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function n6(e,t){return`${e}_${t}`}var s6=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=at(this.outputShape),this.dispatch=Be(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
[[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
${Me()} {
${He()}
let flatIndexBase = index * uniforms.numChannels;
let coords = getCoordsFromFlatIndex(flatIndexBase);
let values = ${e};
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
let flatIndex = flatIndexBase + i;
if (flatIndex < uniforms.size) {
result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
}
}
}
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Ume=class extends s6{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gme=Z().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),a0=class extends Gl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!$x())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Vme(this.device),this.tensorMap=new Vc(this,ts()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return a0.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*Rx(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*Rx(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new s6),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Ume),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Z().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=E.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=CC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;l<n;++l)r.push({type:a.type,data:[0]}),s++;r.push({type:a.type,data:a.data}),s=s+a.data.length,t+=a.data.length+n}),this.arrayToDataView(r,s)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s){let r=this.makeTensorInfo(e.outputShape,n),a=this.tensorMap.get(r.dataId);if(v.sizeFromShape(r.shape)===0)return a.values=v.getTypedArrayFromDType(r.dtype,0),r;let o=[{type:"float32",data:[NaN]}],i=t.concat(r).map(R=>R.shape),l="int32";i.map(R=>{o.push({type:l,data:R})});let c=v.computeStrides(r.shape);o.push({type:l,data:c}),e.size!=null&&o.push({type:l,data:[e.size]}),o.push({type:"uint32",data:e.dispatch}),s&&(o=[...o,...s]);let u=null,d=this.computePadding(o),p=d.byteLength;u=this.makeUniformsDataView(d);let h=t.map((R,P)=>{if(R.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(R.dataId),{dtype:this.tensorMap.get(R.dataId).dtype,shape:R.shape,name:e.variableNames[P]}});this.uploadToGPU(r.dataId);let f=h.map(R=>R.dtype).concat(r.dtype),m=h.map(R=>E.getBroadcastDims(R.shape,r.shape)),g=h.map(R=>v.arraysEqual(R.shape,r.shape)).join("_"),y=m.map(R=>R.join("_")).join(";"),A=XC(e,i,f,y,g),{bindGroupLayout:x,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),w=this.getAndSavePipeline(A,()=>qC(this.device,e,b,h,r)),k=this.activeTimers!=null,S=Rhe(this.device,x,t.map(R=>this.tensorToBinding(R)),this.tensorToBinding(r),u);this.ensureCommandEncoderReady();let N=this.getComputePass();if(k&&this.supportTimeQuery&&N.writeTimestamp(this.querySet,0),N.setPipeline(w),N.setBindGroup(0,S),N.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),k&&this.supportTimeQuery&&N.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(R=>{this.commandQueueOwnedIds.add(R.dataId)}),this.commandQueueOwnedIds.add(r.dataId),u){let R={byteSize:p,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};this.uniformDisposalQueue.push(R)}return Z().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),k&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Gme){return Z().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}};a0.nextDataId=0;var r6={};Le(r6,{WebGPUBackend:()=>a0,webgpu_util:()=>SC});wu.isBrowser()&&$x()&&qi("webgpu",async()=>{Z().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Z().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new a0(r,s)},3);var Hme="3.9.0",jme="3.9.0",qme="3.9.0",Xme="3.9.0",Kme="3.9.0",Zme="3.9.0",Yme="3.9.0",Jme="3.9.0",Qme={tfjs:Hme,"tfjs-core":jme,"tfjs-data":qme,"tfjs-layers":Xme,"tfjs-converter":Kme,"tfjs-backend-cpu":Zme,"tfjs-backend-webgl":Yme,"tfjs-backend-wasm":Jme};var Lx="2.3.1";function t0e(e,t,n){let s=function(i,l,c){let u=new RegExp("\\b"+l+" \\w+ (\\w+)","ig");i.replace(u,(d,p)=>(c[p]=0,d))},r=function(i,l){let c=e.createShader(l);if(e.shaderSource(c,i),e.compileShader(c),!e.getShaderParameter(c,e.COMPILE_STATUS))throw new Error("filter: gl compile failed",e.getShaderInfoLog(c));return c};this.uniform={},this.attribute={};let a=r(t,e.VERTEX_SHADER),o=r(n,e.FRAGMENT_SHADER);if(this.id=e.createProgram(),e.attachShader(this.id,a),e.attachShader(this.id,o),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),s(t,"attribute",this.attribute);for(let i in this.attribute)this.attribute[i]=e.getAttribLocation(this.id,i);s(t,"uniform",this.uniform),s(n,"uniform",this.uniform);for(let i in this.uniform)this.uniform[i]=e.getUniformLocation(this.id,i)}function a6(e){e||(e={});let t=0,n=null,s=!1,r=-1,a=[null,null],o=[],i=-1,l=-1,c=null,u=null,d={},p=e.canvas||document.createElement("canvas"),h={},f={INTERMEDIATE:1},m=p.getContext("webgl");if(!m)throw new Error("filter: context failed");this.addFilter=function(w){let k=Array.prototype.slice.call(arguments,1),S=d[w];o.push({func:S,args:k})},this.reset=function(){o=[]};let g=function(w,k){if(!(w===i&&k===l)){if(p.width=w,i=w,p.height=k,l=k,!c){let S=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);c=m.createBuffer(),m.bindBuffer(m.ARRAY_BUFFER,c),m.bufferData(m.ARRAY_BUFFER,S,m.STATIC_DRAW),m.pixelStorei(m.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}m.viewport(0,0,i,l),a=[null,null]}},y=function(w,k){let S=m.createFramebuffer();m.bindFramebuffer(m.FRAMEBUFFER,S);let N=m.createRenderbuffer();m.bindRenderbuffer(m.RENDERBUFFER,N);let R=m.createTexture();return m.bindTexture(m.TEXTURE_2D,R),m.texImage2D(m.TEXTURE_2D,0,m.RGBA,w,k,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,R,0),m.bindTexture(m.TEXTURE_2D,null),m.bindFramebuffer(m.FRAMEBUFFER,null),{fbo:S,texture:R}},A=function(w){return a[w]=a[w]||y(i,l),a[w]},x=function(w=null){var R,P;let k=null,S=null,N=!1;t===0?k=n:k=(R=A(r))==null?void 0:R.texture,t++,s&&!(w&f.INTERMEDIATE)?(S=null,N=t%2==0):(r=(r+1)%2,S=(P=A(r))==null?void 0:P.fbo),m.bindTexture(m.TEXTURE_2D,k),m.bindFramebuffer(m.FRAMEBUFFER,S),m.uniform1f(u.uniform.flipY,N?-1:1),m.drawArrays(m.TRIANGLES,0,6)};this.apply=function(w){if(g(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),o.length===0)return x(),p;for(let k=0;k<o.length;k++){s=k===o.length-1;let S=o[k];S.func.apply(this,S.args||[])}return p};let b=function(w){if(h[w])return u=h[w],m.useProgram(u.id),u;let k={};k.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(`
`),k.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(`
`),u=new t0e(m,k.VERTEX_IDENTITY,w);let S=Float32Array.BYTES_PER_ELEMENT,N=4*S;return m.enableVertexAttribArray(u.attribute.pos),m.vertexAttribPointer(u.attribute.pos,2,m.FLOAT,!1,N,0*S),m.enableVertexAttribArray(u.attribute.uv),m.vertexAttribPointer(u.attribute.uv,2,m.FLOAT,!1,N,2*S),h[w]=u,u};d.colorMatrix=function(w){let k=new Float32Array(w);k[4]/=255,k[9]/=255,k[14]/=255,k[19]/=255;let S=k[18]===1&&k[3]===0&&k[8]===0&&k[13]===0&&k[15]===0&&k[16]===0&&k[17]===0&&k[19]===0?d.colorMatrix.SHADER.WITHOUT_ALPHA:d.colorMatrix.SHADER.WITH_ALPHA,N=b(S);m.uniform1fv(N.uniform.m,k),x()},d.colorMatrix.SHADER={},d.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(`
`),d.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(`
`),d.brightness=function(w){let k=(w||0)+1;d.colorMatrix([k,0,0,0,0,0,k,0,0,0,0,0,k,0,0,0,0,0,1,0])},d.saturation=function(w){let k=(w||0)*2/3+1,S=(k-1)*-.5;d.colorMatrix([k,S,S,0,0,S,k,S,0,0,S,S,k,0,0,0,0,0,1,0])},d.desaturate=function(){d.saturation(-1)},d.contrast=function(w){let k=(w||0)+1,S=-128*(k-1);d.colorMatrix([k,0,0,0,S,0,k,0,0,S,0,0,k,0,S,0,0,0,1,0])},d.negative=function(){d.contrast(-2)},d.hue=function(w){w=(w||0)/180*Math.PI;let k=Math.cos(w),S=Math.sin(w),N=.213,R=.715,P=.072;d.colorMatrix([N+k*(1-N)+S*-N,R+k*-R+S*-R,P+k*-P+S*(1-P),0,0,N+k*-N+S*.143,R+k*(1-R)+S*.14,P+k*-P+S*-.283,0,0,N+k*-N+S*-(1-N),R+k*-R+S*R,P+k*(1-P)+S*P,0,0,0,0,0,1,0])},d.desaturateLuminance=function(){d.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},d.sepia=function(){d.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},d.brownie=function(){d.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},d.vintagePinhole=function(){d.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},d.kodachrome=function(){d.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])},d.technicolor=function(){d.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])},d.polaroid=function(){d.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},d.shiftToBGR=function(){d.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},d.convolution=function(w){let k=new Float32Array(w),S=1/i,N=1/l,R=b(d.convolution.SHADER);m.uniform1fv(R.uniform.m,k),m.uniform2f(R.uniform.px,S,N),x()},d.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(`
`),d.detectEdges=function(){d.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},d.sobelX=function(){d.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},d.sobelY=function(){d.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},d.sharpen=function(w){let k=w||1;d.convolution.call(this,[0,-1*k,0,-1*k,1+4*k,-1*k,0,-1*k,0])},d.emboss=function(w){let k=w||1;d.convolution.call(this,[-2*k,-1*k,0,-1*k,1,1*k,0,1*k,2*k])},d.blur=function(w){let k=w/7/i,S=w/7/l,N=b(d.blur.SHADER);m.uniform2f(N.uniform.px,0,S),x(f.INTERMEDIATE),m.uniform2f(N.uniform.px,k,0),x()},d.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(`
`),d.pixelate=function(w){let k=w/i,S=w/l,N=b(d.pixelate.SHADER);m.uniform2f(N.uniform.size,k,S),x()},d.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 ie={browser:void 0,node:void 0,worker:void 0,platform:void 0,agent:void 0,initial:!0,backends:[],offscreen:void 0,filter:void 0,tfjs:{version:void 0},wasm:{supported:void 0,backend:void 0,simd:void 0,multithread:void 0},webgl:{supported:void 0,backend:void 0,version:void 0,renderer:void 0},webgpu:{supported:void 0,backend:void 0,adapter:void 0},kernels:[],Canvas:void 0,Image:void 0,ImageData:void 0};async function n0e(){var n;ie.backends=Object.keys(ts().registryFactory),ie.wasm.supported=typeof WebAssembly!="undefined",ie.wasm.backend=ie.backends.includes("wasm"),ie.wasm.supported&&ie.wasm.backend&&lr()==="wasm"&&(ie.wasm.simd=await Z().getAsync("WASM_HAS_SIMD_SUPPORT"),ie.wasm.multithread=await Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let e=Bs(100,100),t=e?e.getContext("webgl2"):void 0;if(ie.webgl.supported=typeof t!="undefined",ie.webgl.backend=ie.backends.includes("webgl"),ie.webgl.supported&&ie.webgl.backend&&(lr()==="webgl"||lr()==="humangl")){let s=Tr().gpgpu!=="undefined"?await Tr().getGPGPUContext().gl:null;s&&(ie.webgl.version=s.getParameter(s.VERSION),ie.webgl.renderer=s.getParameter(s.RENDERER))}ie.webgpu.supported=ie.browser&&typeof navigator.gpu!="undefined",ie.webgpu.backend=ie.backends.includes("webgpu"),ie.webgpu.supported&&(ie.webgpu.adapter=(n=await navigator.gpu.requestAdapter())==null?void 0:n.name),ie.kernels=Zr(lr()).map(s=>s.kernelName.toLowerCase())}async function o0(){if(ie.browser=typeof navigator!="undefined",ie.node=typeof process!="undefined",ie.tfjs.version=Qh,ie.offscreen=typeof ie.offscreen=="undefined"?typeof OffscreenCanvas!="undefined":ie.offscreen,typeof navigator!="undefined"){let e=navigator.userAgent.match(/\(([^()]+)\)/g);if(e&&e[0]){let t=e[0].match(/\(([^()]+)\)/g);ie.platform=t&&t[0]?t[0].replace(/\(|\)/g,""):"",ie.agent=navigator.userAgent.replace(e[0],""),ie.platform[1]&&(ie.agent=ie.agent.replace(e[1],"")),ie.agent=ie.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(ie.platform=`${process.platform} ${process.arch}`,ie.agent=`NodeJS ${process.version}`);ie.worker=ie.browser&&ie.offscreen?typeof WorkerGlobalScope!="undefined":void 0,await n0e()}async function o6(e){ie=fn(ie,e)}var i0=2048,ft=null,Ss=null,Ut;function Bs(e,t){let n;if(ie.browser)if(ie.offscreen)n=new OffscreenCanvas(e,t);else{if(typeof document=="undefined")throw new Error("attempted to run in web worker but offscreenCanvas is not supported");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof ie.Canvas!="undefined"?n=new ie.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Bx(e,t){let n=t||Bs(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}function yc(e,t,n=!0){if(!e)return t.debug&&ae("input is missing"),{tensor:null,canvas:null};if(!(e instanceof Ke)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ie.Canvas!="undefined"&&e instanceof ie.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input type is not recognized");if(e instanceof Ke){if(e.isDisposedInternal)throw new Error("input tensor is disposed");if(!e.shape||e.shape.length!==4||e.shape[0]!==1||e.shape[3]!==3)throw new Error(`input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);return{tensor:ir(e),canvas:t.filter.return?Ss:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&ae("input stream is not ready"),{tensor:null,canvas:ft};let s=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!s||!r)return t.debug&&ae("cannot determine input dimensions"),{tensor:null,canvas:ft};let a=s,o=r;if(a>i0&&(a=i0,o=Math.trunc(a*r/s)),o>i0&&(o=i0,a=Math.trunc(o*s/r)),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("input cannot determine dimension");(!ft||(ft==null?void 0:ft.width)!==a||(ft==null?void 0:ft.height)!==o)&&(ft=Bs(a,o));let i=ft.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,ft==null?void 0:ft.width,ft==null?void 0:ft.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,ft==null?void 0:ft.width,ft==null?void 0:ft.height),(!Ss||ft.width!==Ss.width||(ft==null?void 0:ft.height)!==(Ss==null?void 0:Ss.height))&&(Ss=Bs(ft.width,ft.height)),t.filter.enabled&&ie.webgl.supported){if(Ut||(Ut=ie.browser?new a6({canvas:Ss}):null),ie.filter=!!Ut,!Ut)return{tensor:null,canvas:ft};Ut.reset(),Ut.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Ut.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Ut.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Ut.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Ut.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Ut.addFilter("hue",t.filter.hue),t.filter.negative&&Ut.addFilter("negative"),t.filter.sepia&&Ut.addFilter("sepia"),t.filter.vintage&&Ut.addFilter("brownie"),t.filter.sepia&&Ut.addFilter("sepia"),t.filter.kodachrome&&Ut.addFilter("kodachrome"),t.filter.technicolor&&Ut.addFilter("technicolor"),t.filter.polaroid&&Ut.addFilter("polaroid"),t.filter.pixelate!==0&&Ut.addFilter("pixelate",t.filter.pixelate),Ut.apply(ft)}else Bx(ft,Ss),Ut&&(Ut=null),ie.filter=!!Ut;if(!n)return{tensor:null,canvas:Ss};let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(ie.browser&&Xs)l=Xs?Xs.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=nn(p,[e.height,e.width,c],"float32")}else if(Xs&&ie.browser)if(t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu")l=Xs.fromPixels(Ss);else{let p=Bx(Ss);l=Xs.fromPixels(p)}else{let f=Bx(Ss).getContext("2d").getImageData(0,0,a,o);c=e.data.length/a/o;let m=new Uint8Array(f.data.buffer);l=nn(m,[a,o,c])}if(c===4){let p=zu(l,[0,0,0],[-1,-1,3]);ne(l),l=p}if(!l)throw new Error("cannot create tensor from input");let u=pe(l,"float32"),d=Ht(u,0);return ne([l,u]),{tensor:d,canvas:t.filter.return?Ss:null}}}var Wx=0,Vx=1,Ux=0,s0e=async e=>{let t=48,n=$e.resizeBilinear(e,[Math.trunc((e.shape[1]||1)/t),Math.trunc((e.shape[2]||1)/t)]),s=async()=>{let o=we(n),i=await o.data();return ne(o),i[0]},r=async()=>{let o=await n.data(),i=0;for(let l=0;l<o.length/3;l++)i+=o[3*l+2];return i};if(Ux===0){let o=performance.now();await r();let i=performance.now();await s();let l=performance.now();Ux=i-o<l-i?1:2}let a=Ux===1?await r():await s();return ne(n),a};async function i6(e,t){if(e.cacheSensitivity===0)return!1;let n=await s0e(t),s=100*(Math.max(n,Wx)/Math.min(n,Wx)-1);Wx=n;let r=s<Math.max(e.cacheSensitivity,Vx);return Vx=s>10*e.cacheSensitivity?0:s,r=r&&Vx>0,r}var Lr={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]},Gx={count:468,mouth:13,symmetryLine:[13,Lr.midwayBetweenEyes[0]]},Cp={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Hx=[{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]}],Tp=[[.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]],Sl=[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 r0e=[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],a0e=[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],o0e=[33,133,362,263,1,78,308],yge=r0e.map(e=>Tp[e]),Age=a0e.map(e=>Tp[e]),xge=o0e.map(e=>Tp[e]);var l6=e=>({startPoint:_e(e,[0,0],[-1,2]),endPoint:_e(e,[0,2],[-1,2])});var Np=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],l0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],jx=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],qx=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],u6=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s}},Xx=(e,t,n)=>{let s=t.shape[1],r=t.shape[2];return $e.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n)},Ep=(e,t=1.5)=>{let n=l0(e),s=Np(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks}},Rp=e=>{let t=l0(e),n=Np(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks}},u0=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},c0=[[1,0,0],[0,1,0],[0,0,1]],i0e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),l0e=(e,t)=>i0e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var c6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Cl=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},u0e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},d6=(e,t)=>{let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Cl(e[r],u0e(t,a)))}return n},p6=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=c6(t[0],t[1]),o=d6(a,r),i=c6(-t[0],-t[1]);return d6(o,i)},c0e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Cl(t[0],n),-Cl(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},d0e=(e,t)=>[Cl(e,t[0]),Cl(e,t[1])];function h6(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let c=r*(l+.5);for(let u=0;u<o;u++){let d=r*(u+.5);for(let p=0;p<i;p++)n.push([d,c])}}}return n}function f6(e,t,n,s,r){let a=Np({startPoint:t.startPoint,endPoint:t.endPoint}),o=e.map(d=>[a[0]/r*(d[0]-r/2),a[1]/r*(d[1]-r/2),d[2]||0]),i=n!==0?p6(n,[0,0]):c0,l=n!==0?o.map(d=>[...d0e(d,i),d[2]]):o,c=n!==0?c0e(s):c0,u=[...l0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return l.map(d=>[Math.round(d[0]+Cl(u,c[0])),Math.round(d[1]+Cl(u,c[1])),Math.round(d[2]||0)])}function Kx(e,t,n){let[s,r]=e.landmarks.length>=Gx.count?Gx.symmetryLine:Cp.symmetryLine,a=l0e(e.landmarks[s],e.landmarks[r]),o=l0({startPoint:e.startPoint,endPoint:e.endPoint}),i=[o[0]/t.shape[2],o[1]/t.shape[1]],l=$e.rotateWithOffset(t,a,0,i),c=p6(-a,o),u=Xx({startPoint:e.startPoint,endPoint:e.endPoint},l,[n,n]),d=fe(u,255);return ne(u),ne(l),[a,c,d]}var m6=6,Ws,Zx=[],g6=null,Vs=0,$p=()=>Vs;async function y6(e){var t;return ie.initial&&(Ws=null),Ws?e.debug&&ae("cached model:",Ws.modelUrl):(Ws=await ut(ct(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Ws||!Ws.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",Ws.modelUrl)),Vs=Ws.inputs[0].shape?Ws.inputs[0].shape[2]:0,Vs===-1&&(Vs=64),Zx=h6(Vs),g6=dr(Zx),Ws}function p0e(e){let t=_e(e,[0,1],[-1,2]),n=ue(t,g6),s=_e(e,[0,3],[-1,2]),r=fe(s,Vs),a=fe(n,Vs),o=fe(r,2),i=xe(a,o),l=ue(a,o),c=L(i,Vs),u=L(l,Vs);return Ru([c,u],1)}async function A6(e,t){var c,u,d,p;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let[n,s,r]=j(()=>{let h=$e.resizeBilinear(e,[Vs,Vs]),f=xe(fe(h,127.5),.5),m=Ws==null?void 0:Ws.execute(f),g;if(Array.isArray(m)){let b=m.sort((N,R)=>N.size-R.size),w=kt([b[0],b[2]],2),k=kt([b[1],b[3]],2),S=kt([k,w],1);g=dt(S,0)}else g=dt(m);let y=p0e(g),A=_e(g,[0,0],[-1,1]),x=dt(ns(A));return[g,y,x]}),a=await $e.nonMaxSuppressionAsync(s,r,((c=t.face.detector)==null?void 0:c.maxDetected)||0,((u=t.face.detector)==null?void 0:u.iouThreshold)||0,((d=t.face.detector)==null?void 0:d.minConfidence)||0),o=await a.array();ne(a);let i=[],l=await r.data();for(let h=0;h<o.length;h++){let f=l[o[h]];if(f>(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let m=_e(s,[o[h],0],[1,-1]),g=j(()=>G(dt(_e(n,[o[h],m6-1],[1,-1])),[m6,-1]));i.push({box:l6(m),landmarks:g,anchor:Zx[o[h]],confidence:f}),ne(m)}}return ne(n),ne(s),ne(r),{boxes:i,scaleFactor:[e.shape[2]/Vs,e.shape[1]/Vs]}}var nr,Wo=0,h0e=2.3,Yx=Lr.leftEyeLower0,Jx=Lr.rightEyeLower0,Ac={leftBounds:[Yx[0],Yx[Yx.length-1]],rightBounds:[Jx[0],Jx[Jx.length-1]]},xc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function x6(e){var t;return ie.initial&&(nr=null),nr?e.debug&&ae("cached model:",nr.modelUrl):(nr=await ut(ct(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!nr||!nr.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",nr.modelUrl)),Wo=nr.inputs[0].shape?nr.inputs[0].shape[2]:0,Wo===-1&&(Wo=64),nr}function d0(e,t,n,s){for(let r=0;r<Hx.length;r++){let{key:a,indices:o}=Hx[r],i=Lr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let c=o[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var f0e=e=>{let t=e[Ac.leftBounds[0]][2],n=e[Ac.rightBounds[0]][2];return t-n},b6=(e,t,n,s,r=!1,a)=>{let o=Rp(Ep(u0([e[n],e[s]]),h0e)),i=Np(o),l=$e.cropAndResize(t,[[o.startPoint[1]/a,o.startPoint[0]/a,o.endPoint[1]/a,o.endPoint[0]/a]],[0],[Wo,Wo]);if(r&&ie.kernels.includes("flipleftright")){let c=$e.flipLeftRight(l);ne(l),l=c}return{box:o,boxSize:i,crop:l}},v6=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<xc.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/Wo:o/Wo)*n[0]+t.startPoint[0],i/Wo*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(xc.index)}},w6=(e,t,n)=>{let s=e[Lr[`${n}EyeUpper0`][xc.upperCenter]][2],r=e[Lr[`${n}EyeLower0`][xc.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function k6(e,t,n,s){if(!nr)return n.debug&&ae("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=b6(e,t,Ac.leftBounds[0],Ac.leftBounds[1],!0,s),{box:i,boxSize:l,crop:c}=b6(e,t,Ac.rightBounds[0],Ac.rightBounds[1],!0,s),u=kt([o,c]);ne(o),ne(c);let d=nr.predict(u);ne(u);let p=await d.data();ne(d);let h=p.slice(0,xc.numCoordinates*3),{rawCoords:f,iris:m}=v6(h,r,a,!0),g=p.slice(xc.numCoordinates*3),{rawCoords:y,iris:A}=v6(g,i,l),x=f0e(e);Math.abs(x)<30?(d0(e,f,"left",null),d0(e,y,"right",null)):x<1?d0(e,f,"left",["EyeUpper0","EyeLower0"]):d0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=w6(e,m,"left"),w=w6(e,A,"right");return e.concat(b).concat(w)}var Br=[],sr=null,vr=0,Qx=Number.MAX_SAFE_INTEGER,I6=0;async function S6(e,t){var a,o,i,l,c,u,d,p,h,f,m,g;if(!t.skipFrame||(I6!==((a=t.face.detector)==null?void 0:a.maxDetected)||!((o=t.face.mesh)==null?void 0:o.enabled))&&Qx>(((i=t.face.detector)==null?void 0:i.skipFrames)||0)){let y=await A6(e,t);Br=[];for(let A of y.boxes){let x=await A.box.startPoint.data(),b=await A.box.endPoint.data(),w=await A.landmarks.array();Br.push({startPoint:x,endPoint:b,landmarks:w,confidence:A.confidence})}y.boxes.forEach(A=>ne([A.box.startPoint,A.box.endPoint,A.landmarks]));for(let A=0;A<Br.length;A++){let x=u6({startPoint:Br[A].startPoint,endPoint:Br[A].endPoint},y.scaleFactor),b=Ep(x),w=Rp(b);Br[A]={...w,confidence:Br[A].confidence,landmarks:Br[A].landmarks}}Qx=0}else Qx++;let n=[],s=[],r=0;for(let y of Br){let A=0,x,b={id:r++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if(((l=t.face.detector)==null?void 0:l.rotation)&&((c=t.face.mesh)==null?void 0:c.enabled)&&ie.kernels.includes("rotatewithoffset"))[A,x,b.tensor]=Kx(y,e,vr);else{x=c0;let w=Xx({startPoint:y.startPoint,endPoint:y.endPoint},e,((u=t.face.mesh)==null?void 0:u.enabled)?[vr,vr]:[$p(),$p()]);b.tensor=fe(w,255),ne(w)}if(b.boxScore=Math.round(100*y.confidence)/100,(d=t.face.mesh)==null?void 0:d.enabled)if(!sr)t.debug&&ae("face mesh detection requested, but model is not loaded");else{let[w,k,S]=sr.execute(b.tensor);ne(w);let N=(await k.data())[0];ne(k);let R=G(S,[-1,3]),P=await R.array();if(ne(S),ne(R),N<(((p=t.face.detector)==null?void 0:p.minConfidence)||1))y.confidence=N;else{((h=t.face.iris)==null?void 0:h.enabled)&&(P=await k6(P,b.tensor,t,vr)),b.mesh=f6(P,y,A,x,vr),b.meshRaw=b.mesh.map($=>[$[0]/(e.shape[2]||0),$[1]/(e.shape[1]||0),($[2]||0)/vr]),y={...Ep(u0(b.mesh),1.5),confidence:y.confidence};for(let $ of Object.keys(Lr))b.annotations[$]=Lr[$].map(D=>b.mesh[D]);((f=t.face.detector)==null?void 0:f.rotation)&&t.face.mesh.enabled&&((m=t.face.description)==null?void 0:m.enabled)&&ie.kernels.includes("rotatewithoffset")&&(ne(b.tensor),[A,x,b.tensor]=Kx(y,e,vr)),b.box=jx(y,e),b.boxRaw=qx(y,e),b.score=Math.round(100*N||100*y.confidence||0)/100,b.faceScore=Math.round(100*N)/100,y={...Rp(y),confidence:y.confidence,faceConfidence:N}}}else{b.box=jx(y,e),b.boxRaw=qx(y,e),b.score=Math.round(100*y.confidence||0)/100,b.mesh=y.landmarks.map(w=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*w[0]/$p(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*w[1]/$p()]),b.meshRaw=b.mesh.map(w=>[w[0]/(e.shape[2]||0),w[1]/(e.shape[1]||0),(w[2]||0)/vr]);for(let w of Object.keys(Cp))b.annotations[w]=[b.mesh[Cp[w]]]}n.push(b),s.push(y)}return((g=t.face.mesh)==null?void 0:g.enabled)&&(Br=s.filter(y=>{var A;return y.confidence>(((A=t.face.detector)==null?void 0:A.minConfidence)||0)})),I6=n.length,n}async function C6(e){var t;return ie.initial&&(sr=null),sr?e.debug&&ae("cached model:",sr.modelUrl):(sr=await ut(ct(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!sr||!sr.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",sr.modelUrl)),vr=sr.inputs[0].shape?sr.inputs[0].shape[2]:0,vr===-1&&(vr=64),sr}var T6=Sl,N6=Tp;var Kn,p0=[],E6=0,eb=Number.MAX_SAFE_INTEGER;async function R6(e){var n,s;let t=ct(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return ie.initial&&(Kn=null),Kn?e.debug&&ae("cached model:",t):(Kn=await ut(t),Kn?e.debug&&ae("load model:",t):ae("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),Kn}function tb(e){return j(()=>{let n=e.image||e.tensor||e;if(!(n instanceof Ke))return null;let s=[[.05,.15,.85,.85]];if(!(Kn==null?void 0:Kn.inputs[0].shape))return null;let r=n.shape.length===3?$e.cropAndResize(Ht(n,0),s,[0],[Kn.inputs[0].shape[2],Kn.inputs[0].shape[1]]):$e.cropAndResize(n,s,[0],[Kn.inputs[0].shape[2],Kn.inputs[0].shape[1]]);return L(r,255)})}async function nb(e,t,n,s){var r,a,o;return Kn?eb<(((r=t.face.description)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&E6===s&&((a=p0[n])==null?void 0:a.age)&&((o=p0[n])==null?void 0:o.age)>0?(eb++,p0[n]):(eb=0,new Promise(async i=>{var d,p;let l=tb(e),c,u={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(((d=t.face.description)==null?void 0:d.enabled)&&(c=await(Kn==null?void 0:Kn.predict(l))),ne(l),c){let h=await c.find(b=>b.shape[1]===1).data(),f=Math.trunc(200*Math.abs(h[0]-.5))/100;f>(((p=t.face.description)==null?void 0:p.minConfidence)||0)&&(u.gender=h[0]<=.5?"female":"male",u.genderScore=Math.min(.99,f));let m=_s(c.find(b=>b.shape[1]===100),1),g=(await m.data())[0];ne(m);let y=await c.find(b=>b.shape[1]===100).data();u.age=Math.round(y[g-1]>y[g+1]?10*g-100*y[g-1]:10*g+100*y[g+1])/10;let x=await c.find(b=>b.shape[1]===1024).data();u.descriptor=[...x],c.forEach(b=>ne(b))}p0[n]=u,E6=s,i(u)})):null}var m0e=["angry","disgust","fear","happy","sad","surprise","neutral"],un,h0=[],$6=0,sb=Number.MAX_SAFE_INTEGER,rb=[.2989,.587,.114];async function D6(e){var t;return ie.initial&&(un=null),un?e.debug&&ae("cached model:",un.modelUrl):(un=await ut(ct(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!un||!un.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",un.modelUrl)),un}async function ab(e,t,n,s){var r;return un?sb<(((r=t.face.emotion)==null?void 0:r.skipFrames)||0)&&t.skipFrame&&$6===s&&h0[n]&&h0[n].length>0?(sb++,h0[n]):(sb=0,new Promise(async a=>{var g,y;let o=$e.resizeBilinear(e,[(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[2]:0,(un==null?void 0:un.inputs[0].shape)?un.inputs[0].shape[1]:0],!1),[i,l,c]=xn(o,3,3);ne(o);let u=L(i,rb[0]),d=L(l,rb[1]),p=L(c,rb[2]);ne(i),ne(l),ne(c);let h=sf([u,d,p]);ne(u),ne(d),ne(p);let f=j(()=>L(xe(h,.5),2));ne(h);let m=[];if((g=t.face.emotion)==null?void 0:g.enabled){let A=await(un==null?void 0:un.predict(f)),x=await A.data();ne(A);for(let b=0;b<x.length;b++)x[b]>(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&m.push({score:Math.min(.99,Math.trunc(100*x[b])/100),emotion:m0e[b]});m.sort((b,w)=>w.score-b.score)}ne(f),h0[n]=m,$6=s,a(m)})):null}var Dp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],g0e=Dp.length,_p=Dp.reduce((e,t,n)=>(e[t]=n,e),{}),y0e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Yge=y0e.map(([e,t])=>[_p[e],_p[t]]),_6=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function P6(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function F6(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]}))});return e.map((c,u)=>i(c,u))}var ob=class{constructor(t,n){Te(this,"priorityQueue");Te(this,"numberOfElements");Te(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let s=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=s}};function ib(e,t,n,s){return{y:s.get(e,t,n),x:s.get(e,t,n+g0e)}}function lb(e,t,n){let{heatmapY:s,heatmapX:r,id:a}=e,{y:o,x:i}=ib(s,r,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function ub(e,t,n){return e<t?t:e>n?n:e}function O6(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function cb(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Cs,A0e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],f0=1,bc=16,x0e=50**2;function M6(e,t,n,s,r,a,o=2){let i=y=>({y:a.get(y.y,y.x,e),x:a.get(y.y,y.x,a.shape[2]/2+e)}),l=(y,A,x)=>({y:ub(Math.round(y.y/bc),0,A-1),x:ub(Math.round(y.x/bc),0,x-1)}),[c,u]=s.shape,d=l(t.position,c,u),p=i(d),f=cb(t.position,p);for(let y=0;y<o;y++){let A=l(f,c,u),x=ib(A.y,A.x,n,r);f=cb({x:A.x*bc,y:A.y*bc},{x:x.x,y:x.y})}let m=l(f,c,u),g=s.get(m.y,m.x,n);return{position:f,part:Dp[n],score:g}}function b0e(e,t,n,s,r){let a=_6.map(([p,h])=>[_p[p],_p[h]]),o=a.map(([,p])=>p),i=a.map(([p])=>p),l=t.shape[2],c=o.length,u=new Array(l),d=lb(e.part,bc,n);u[e.part.id]={score:e.score,part:Dp[e.part.id],position:d};for(let p=c-1;p>=0;--p){let h=o[p],f=i[p];u[h]&&!u[f]&&(u[f]=M6(p,u[h],f,t,n,r))}for(let p=0;p<c;++p){let h=i[p],f=o[p];u[h]&&!u[f]&&(u[f]=M6(p,u[h],f,t,n,s))}return u}function v0e(e,t,n,s,r){let[a,o]=r.shape,i=!0,l=Math.max(n-f0,0),c=Math.min(n+f0+1,a);for(let u=l;u<c;++u){let d=Math.max(s-f0,0),p=Math.min(s+f0+1,o);for(let h=d;h<p;++h)if(r.get(u,h,e)>t){i=!1;break}if(!i)break}return i}function w0e(e,t){let[n,s,r]=t.shape,a=new ob(n*s*r,({score:o})=>o);for(let o=0;o<n;++o)for(let i=0;i<s;++i)for(let l=0;l<r;++l){let c=t.get(o,i,l);c<e||v0e(l,c,o,i,t)&&a.enqueue({score:c,part:{heatmapY:o,heatmapX:i,id:l}})}return a}function z6(e,{x:t,y:n},s){return e.some(({keypoints:r})=>{var o;let a=(o=r[s])==null?void 0:o.position;return a?O6(n,t,a.y,a.x)<=x0e:!1})}function k0e(e,t){return t.reduce((s,{position:r,score:a},o)=>(z6(e,r,o)||(s+=a),s),0)/t.length}function I0e(e,t,n,s,r,a){let o=[],i=w0e(a,t);for(;o.length<r&&!i.empty();){let l=i.dequeue(),c=lb(l.part,bc,e);if(z6(o,c,l.part.id))continue;let u=b0e(l,t,e,n,s);u=u.filter(h=>h.score>a);let d=k0e(o,u),p=P6(u);d>a&&o.push({keypoints:u,box:p,score:Math.round(100*d)/100})}return o}async function db(e,t){let n=j(()=>{if(!Cs.inputs[0].shape)return[];let o=$e.resizeBilinear(e,[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]]),i=xe(fe(pe(o,"float32"),127.5),1),c=Cs.execute(i,A0e).map(u=>dt(u,[0]));return c[1]=c[1].sigmoid(),c}),s=await Promise.all(n.map(o=>o.buffer()));for(let o of n)ne(o);let r=await I0e(s[0],s[1],s[2],s[3],t.body.maxDetected,t.body.minConfidence);return Cs.inputs[0].shape?F6(r,[e.shape[1],e.shape[2]],[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]]):[]}async function L6(e){return!Cs||ie.initial?(Cs=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!Cs||!Cs.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",Cs.modelUrl)):e.debug&&ae("cached model:",Cs.modelUrl),Cs}function m0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Pp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function B6(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return $e.cropAndResize(t,a,[0],n)}function W6(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function g0(e,t=1.5){let n=Pp(e),s=m0(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function y0(e){let t=Pp(e),n=m0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}var V6=[{x:.015625,y:.015625},{x:.015625,y:.015625},{x:.046875,y:.015625},{x:.046875,y:.015625},{x:.078125,y:.015625},{x:.078125,y:.015625},{x:.109375,y:.015625},{x:.109375,y:.015625},{x:.140625,y:.015625},{x:.140625,y:.015625},{x:.171875,y:.015625},{x:.171875,y:.015625},{x:.203125,y:.015625},{x:.203125,y:.015625},{x:.234375,y:.015625},{x:.234375,y:.015625},{x:.265625,y:.015625},{x:.265625,y:.015625},{x:.296875,y:.015625},{x:.296875,y:.015625},{x:.328125,y:.015625},{x:.328125,y:.015625},{x:.359375,y:.015625},{x:.359375,y:.015625},{x:.390625,y:.015625},{x:.390625,y:.015625},{x:.421875,y:.015625},{x:.421875,y:.015625},{x:.453125,y:.015625},{x:.453125,y:.015625},{x:.484375,y:.015625},{x:.484375,y:.015625},{x:.515625,y:.015625},{x:.515625,y:.015625},{x:.546875,y:.015625},{x:.546875,y:.015625},{x:.578125,y:.015625},{x:.578125,y:.015625},{x:.609375,y:.015625},{x:.609375,y:.015625},{x:.640625,y:.015625},{x:.640625,y:.015625},{x:.671875,y:.015625},{x:.671875,y:.015625},{x:.703125,y:.015625},{x:.703125,y:.015625},{x:.734375,y:.015625},{x:.734375,y:.015625},{x:.765625,y:.015625},{x:.765625,y:.015625},{x:.796875,y:.015625},{x:.796875,y:.015625},{x:.828125,y:.015625},{x:.828125,y:.015625},{x:.859375,y:.015625},{x:.859375,y:.015625},{x:.890625,y:.015625},{x:.890625,y:.015625},{x:.921875,y:.015625},{x:.921875,y:.015625},{x:.953125,y:.015625},{x:.953125,y:.015625},{x:.984375,y:.015625},{x:.984375,y:.015625},{x:.015625,y:.046875},{x:.015625,y:.046875},{x:.046875,y:.046875},{x:.046875,y:.046875},{x:.078125,y:.046875},{x:.078125,y:.046875},{x:.109375,y:.046875},{x:.109375,y:.046875},{x:.140625,y:.046875},{x:.140625,y:.046875},{x:.171875,y:.046875},{x:.171875,y:.046875},{x:.203125,y:.046875},{x:.203125,y:.046875},{x:.234375,y:.046875},{x:.234375,y:.046875},{x:.265625,y:.046875},{x:.265625,y:.046875},{x:.296875,y:.046875},{x:.296875,y:.046875},{x:.328125,y:.046875},{x:.328125,y:.046875},{x:.359375,y:.046875},{x:.359375,y:.046875},{x:.390625,y:.046875},{x:.390625,y:.046875},{x:.421875,y:.046875},{x:.421875,y:.046875},{x:.453125,y:.046875},{x:.453125,y:.046875},{x:.484375,y:.046875},{x:.484375,y:.046875},{x:.515625,y:.046875},{x:.515625,y:.046875},{x:.546875,y:.046875},{x:.546875,y:.046875},{x:.578125,y:.046875},{x:.578125,y:.046875},{x:.609375,y:.046875},{x:.609375,y:.046875},{x:.640625,y:.046875},{x:.640625,y:.046875},{x:.671875,y:.046875},{x:.671875,y:.046875},{x:.703125,y:.046875},{x:.703125,y:.046875},{x:.734375,y:.046875},{x:.734375,y:.046875},{x:.765625,y:.046875},{x:.765625,y:.046875},{x:.796875,y:.046875},{x:.796875,y:.046875},{x:.828125,y:.046875},{x:.828125,y:.046875},{x:.859375,y:.046875},{x:.859375,y:.046875},{x:.890625,y:.046875},{x:.890625,y:.046875},{x:.921875,y:.046875},{x:.921875,y:.046875},{x:.953125,y:.046875},{x:.953125,y:.046875},{x:.984375,y:.046875},{x:.984375,y:.046875},{x:.015625,y:.078125},{x:.015625,y:.078125},{x:.046875,y:.078125},{x:.046875,y:.078125},{x:.078125,y:.078125},{x:.078125,y:.078125},{x:.109375,y:.078125},{x:.109375,y:.078125},{x:.140625,y:.078125},{x:.140625,y:.078125},{x:.171875,y:.078125},{x:.171875,y:.078125},{x:.203125,y:.078125},{x:.203125,y:.078125},{x:.234375,y:.078125},{x:.234375,y:.078125},{x:.265625,y:.078125},{x:.265625,y:.078125},{x:.296875,y:.078125},{x:.296875,y:.078125},{x:.328125,y:.078125},{x:.328125,y:.078125},{x:.359375,y:.078125},{x:.359375,y:.078125},{x:.390625,y:.078125},{x:.390625,y:.078125},{x:.421875,y:.078125},{x:.421875,y:.078125},{x:.453125,y:.078125},{x:.453125,y:.078125},{x:.484375,y:.078125},{x:.484375,y:.078125},{x:.515625,y:.078125},{x:.515625,y:.078125},{x:.546875,y:.078125},{x:.546875,y:.078125},{x:.578125,y:.078125},{x:.578125,y:.078125},{x:.609375,y:.078125},{x:.609375,y:.078125},{x:.640625,y:.078125},{x:.640625,y:.078125},{x:.671875,y:.078125},{x:.671875,y:.078125},{x:.703125,y:.078125},{x:.703125,y:.078125},{x:.734375,y:.078125},{x:.734375,y:.078125},{x:.765625,y:.078125},{x:.765625,y:.078125},{x:.796875,y:.078125},{x:.796875,y:.078125},{x:.828125,y:.078125},{x:.828125,y:.078125},{x:.859375,y:.078125},{x:.859375,y:.078125},{x:.890625,y:.078125},{x:.890625,y:.078125},{x:.921875,y:.078125},{x:.921875,y:.078125},{x:.953125,y:.078125},{x:.953125,y:.078125},{x:.984375,y:.078125},{x:.984375,y:.078125},{x:.015625,y:.109375},{x:.015625,y:.109375},{x:.046875,y:.109375},{x:.046875,y:.109375},{x:.078125,y:.109375},{x:.078125,y:.109375},{x:.109375,y:.109375},{x:.109375,y:.109375},{x:.140625,y:.109375},{x:.140625,y:.109375},{x:.171875,y:.109375},{x:.171875,y:.109375},{x:.203125,y:.109375},{x:.203125,y:.109375},{x:.234375,y:.109375},{x:.234375,y:.109375},{x:.265625,y:.109375},{x:.265625,y:.109375},{x:.296875,y:.109375},{x:.296875,y:.109375},{x:.328125,y:.109375},{x:.328125,y:.109375},{x:.359375,y:.109375},{x:.359375,y:.109375},{x:.390625,y:.109375},{x:.390625,y:.109375},{x:.421875,y:.109375},{x:.421875,y:.109375},{x:.453125,y:.109375},{x:.453125,y:.109375},{x:.484375,y:.109375},{x:.484375,y:.109375},{x:.515625,y:.109375},{x:.515625,y:.109375},{x:.546875,y:.109375},{x:.546875,y:.109375},{x:.578125,y:.109375},{x:.578125,y:.109375},{x:.609375,y:.109375},{x:.609375,y:.109375},{x:.640625,y:.109375},{x:.640625,y:.109375},{x:.671875,y:.109375},{x:.671875,y:.109375},{x:.703125,y:.109375},{x:.703125,y:.109375},{x:.734375,y:.109375},{x:.734375,y:.109375},{x:.765625,y:.109375},{x:.765625,y:.109375},{x:.796875,y:.109375},{x:.796875,y:.109375},{x:.828125,y:.109375},{x:.828125,y:.109375},{x:.859375,y:.109375},{x:.859375,y:.109375},{x:.890625,y:.109375},{x:.890625,y:.109375},{x:.921875,y:.109375},{x:.921875,y:.109375},{x:.953125,y:.109375},{x:.953125,y:.109375},{x:.984375,y:.109375},{x:.984375,y:.109375},{x:.015625,y:.140625},{x:.015625,y:.140625},{x:.046875,y:.140625},{x:.046875,y:.140625},{x:.078125,y:.140625},{x:.078125,y:.140625},{x:.109375,y:.140625},{x:.109375,y:.140625},{x:.140625,y:.140625},{x:.140625,y:.140625},{x:.171875,y:.140625},{x:.171875,y:.140625},{x:.203125,y:.140625},{x:.203125,y:.140625},{x:.234375,y:.140625},{x:.234375,y:.140625},{x:.265625,y:.140625},{x:.265625,y:.140625},{x:.296875,y:.140625},{x:.296875,y:.140625},{x:.328125,y:.140625},{x:.328125,y:.140625},{x:.359375,y:.140625},{x:.359375,y:.140625},{x:.390625,y:.140625},{x:.390625,y:.140625},{x:.421875,y:.140625},{x:.421875,y:.140625},{x:.453125,y:.140625},{x:.453125,y:.140625},{x:.484375,y:.140625},{x:.484375,y:.140625},{x:.515625,y:.140625},{x:.515625,y:.140625},{x:.546875,y:.140625},{x:.546875,y:.140625},{x:.578125,y:.140625},{x:.578125,y:.140625},{x:.609375,y:.140625},{x:.609375,y:.140625},{x:.640625,y:.140625},{x:.640625,y:.140625},{x:.671875,y:.140625},{x:.671875,y:.140625},{x:.703125,y:.140625},{x:.703125,y:.140625},{x:.734375,y:.140625},{x:.734375,y:.140625},{x:.765625,y:.140625},{x:.765625,y:.140625},{x:.796875,y:.140625},{x:.796875,y:.140625},{x:.828125,y:.140625},{x:.828125,y:.140625},{x:.859375,y:.140625},{x:.859375,y:.140625},{x:.890625,y:.140625},{x:.890625,y:.140625},{x:.921875,y:.140625},{x:.921875,y:.140625},{x:.953125,y:.140625},{x:.953125,y:.140625},{x:.984375,y:.140625},{x:.984375,y:.140625},{x:.015625,y:.171875},{x:.015625,y:.171875},{x:.046875,y:.171875},{x:.046875,y:.171875},{x:.078125,y:.171875},{x:.078125,y:.171875},{x:.109375,y:.171875},{x:.109375,y:.171875},{x:.140625,y:.171875},{x:.140625,y:.171875},{x:.171875,y:.171875},{x:.171875,y:.171875},{x:.203125,y:.171875},{x:.203125,y:.171875},{x:.234375,y:.171875},{x:.234375,y:.171875},{x:.265625,y:.171875},{x:.265625,y:.171875},{x:.296875,y:.171875},{x:.296875,y:.171875},{x:.328125,y:.171875},{x:.328125,y:.171875},{x:.359375,y:.171875},{x:.359375,y:.171875},{x:.390625,y:.171875},{x:.390625,y:.171875},{x:.421875,y:.171875},{x:.421875,y:.171875},{x:.453125,y:.171875},{x:.453125,y:.171875},{x:.484375,y:.171875},{x:.484375,y:.171875},{x:.515625,y:.171875},{x:.515625,y:.171875},{x:.546875,y:.171875},{x:.546875,y:.171875},{x:.578125,y:.171875},{x:.578125,y:.171875},{x:.609375,y:.171875},{x:.609375,y:.171875},{x:.640625,y:.171875},{x:.640625,y:.171875},{x:.671875,y:.171875},{x:.671875,y:.171875},{x:.703125,y:.171875},{x:.703125,y:.171875},{x:.734375,y:.171875},{x:.734375,y:.171875},{x:.765625,y:.171875},{x:.765625,y:.171875},{x:.796875,y:.171875},{x:.796875,y:.171875},{x:.828125,y:.171875},{x:.828125,y:.171875},{x:.859375,y:.171875},{x:.859375,y:.171875},{x:.890625,y:.171875},{x:.890625,y:.171875},{x:.921875,y:.171875},{x:.921875,y:.171875},{x:.953125,y:.171875},{x:.953125,y:.171875},{x:.984375,y:.171875},{x:.984375,y:.171875},{x:.015625,y:.203125},{x:.015625,y:.203125},{x:.046875,y:.203125},{x:.046875,y:.203125},{x:.078125,y:.203125},{x:.078125,y:.203125},{x:.109375,y:.203125},{x:.109375,y:.203125},{x:.140625,y:.203125},{x:.140625,y:.203125},{x:.171875,y:.203125},{x:.171875,y:.203125},{x:.203125,y:.203125},{x:.203125,y:.203125},{x:.234375,y:.203125},{x:.234375,y:.203125},{x:.265625,y:.203125},{x:.265625,y:.203125},{x:.296875,y:.203125},{x:.296875,y:.203125},{x:.328125,y:.203125},{x:.328125,y:.203125},{x:.359375,y:.203125},{x:.359375,y:.203125},{x:.390625,y:.203125},{x:.390625,y:.203125},{x:.421875,y:.203125},{x:.421875,y:.203125},{x:.453125,y:.203125},{x:.453125,y:.203125},{x:.484375,y:.203125},{x:.484375,y:.203125},{x:.515625,y:.203125},{x:.515625,y:.203125},{x:.546875,y:.203125},{x:.546875,y:.203125},{x:.578125,y:.203125},{x:.578125,y:.203125},{x:.609375,y:.203125},{x:.609375,y:.203125},{x:.640625,y:.203125},{x:.640625,y:.203125},{x:.671875,y:.203125},{x:.671875,y:.203125},{x:.703125,y:.203125},{x:.703125,y:.203125},{x:.734375,y:.203125},{x:.734375,y:.203125},{x:.765625,y:.203125},{x:.765625,y:.203125},{x:.796875,y:.203125},{x:.796875,y:.203125},{x:.828125,y:.203125},{x:.828125,y:.203125},{x:.859375,y:.203125},{x:.859375,y:.203125},{x:.890625,y:.203125},{x:.890625,y:.203125},{x:.921875,y:.203125},{x:.921875,y:.203125},{x:.953125,y:.203125},{x:.953125,y:.203125},{x:.984375,y:.203125},{x:.984375,y:.203125},{x:.015625,y:.234375},{x:.015625,y:.234375},{x:.046875,y:.234375},{x:.046875,y:.234375},{x:.078125,y:.234375},{x:.078125,y:.234375},{x:.109375,y:.234375},{x:.109375,y:.234375},{x:.140625,y:.234375},{x:.140625,y:.234375},{x:.171875,y:.234375},{x:.171875,y:.234375},{x:.203125,y:.234375},{x:.203125,y:.234375},{x:.234375,y:.234375},{x:.234375,y:.234375},{x:.265625,y:.234375},{x:.265625,y:.234375},{x:.296875,y:.234375},{x:.296875,y:.234375},{x:.328125,y:.234375},{x:.328125,y:.234375},{x:.359375,y:.234375},{x:.359375,y:.234375},{x:.390625,y:.234375},{x:.390625,y:.234375},{x:.421875,y:.234375},{x:.421875,y:.234375},{x:.453125,y:.234375},{x:.453125,y:.234375},{x:.484375,y:.234375},{x:.484375,y:.234375},{x:.515625,y:.234375},{x:.515625,y:.234375},{x:.546875,y:.234375},{x:.546875,y:.234375},{x:.578125,y:.234375},{x:.578125,y:.234375},{x:.609375,y:.234375},{x:.609375,y:.234375},{x:.640625,y:.234375},{x:.640625,y:.234375},{x:.671875,y:.234375},{x:.671875,y:.234375},{x:.703125,y:.234375},{x:.703125,y:.234375},{x:.734375,y:.234375},{x:.734375,y:.234375},{x:.765625,y:.234375},{x:.765625,y:.234375},{x:.796875,y:.234375},{x:.796875,y:.234375},{x:.828125,y:.234375},{x:.828125,y:.234375},{x:.859375,y:.234375},{x:.859375,y:.234375},{x:.890625,y:.234375},{x:.890625,y:.234375},{x:.921875,y:.234375},{x:.921875,y:.234375},{x:.953125,y:.234375},{x:.953125,y:.234375},{x:.984375,y:.234375},{x:.984375,y:.234375},{x:.015625,y:.265625},{x:.015625,y:.265625},{x:.046875,y:.265625},{x:.046875,y:.265625},{x:.078125,y:.265625},{x:.078125,y:.265625},{x:.109375,y:.265625},{x:.109375,y:.265625},{x:.140625,y:.265625},{x:.140625,y:.265625},{x:.171875,y:.265625},{x:.171875,y:.265625},{x:.203125,y:.265625},{x:.203125,y:.265625},{x:.234375,y:.265625},{x:.234375,y:.265625},{x:.265625,y:.265625},{x:.265625,y:.265625},{x:.296875,y:.265625},{x:.296875,y:.265625},{x:.328125,y:.265625},{x:.328125,y:.265625},{x:.359375,y:.265625},{x:.359375,y:.265625},{x:.390625,y:.265625},{x:.390625,y:.265625},{x:.421875,y:.265625},{x:.421875,y:.265625},{x:.453125,y:.265625},{x:.453125,y:.265625},{x:.484375,y:.265625},{x:.484375,y:.265625},{x:.515625,y:.265625},{x:.515625,y:.265625},{x:.546875,y:.265625},{x:.546875,y:.265625},{x:.578125,y:.265625},{x:.578125,y:.265625},{x:.609375,y:.265625},{x:.609375,y:.265625},{x:.640625,y:.265625},{x:.640625,y:.265625},{x:.671875,y:.265625},{x:.671875,y:.265625},{x:.703125,y:.265625},{x:.703125,y:.265625},{x:.734375,y:.265625},{x:.734375,y:.265625},{x:.765625,y:.265625},{x:.765625,y:.265625},{x:.796875,y:.265625},{x:.796875,y:.265625},{x:.828125,y:.265625},{x:.828125,y:.265625},{x:.859375,y:.265625},{x:.859375,y:.265625},{x:.890625,y:.265625},{x:.890625,y:.265625},{x:.921875,y:.265625},{x:.921875,y:.265625},{x:.953125,y:.265625},{x:.953125,y:.265625},{x:.984375,y:.265625},{x:.984375,y:.265625},{x:.015625,y:.296875},{x:.015625,y:.296875},{x:.046875,y:.296875},{x:.046875,y:.296875},{x:.078125,y:.296875},{x:.078125,y:.296875},{x:.109375,y:.296875},{x:.109375,y:.296875},{x:.140625,y:.296875},{x:.140625,y:.296875},{x:.171875,y:.296875},{x:.171875,y:.296875},{x:.203125,y:.296875},{x:.203125,y:.296875},{x:.234375,y:.296875},{x:.234375,y:.296875},{x:.265625,y:.296875},{x:.265625,y:.296875},{x:.296875,y:.296875},{x:.296875,y:.296875},{x:.328125,y:.296875},{x:.328125,y:.296875},{x:.359375,y:.296875},{x:.359375,y:.296875},{x:.390625,y:.296875},{x:.390625,y:.296875},{x:.421875,y:.296875},{x:.421875,y:.296875},{x:.453125,y:.296875},{x:.453125,y:.296875},{x:.484375,y:.296875},{x:.484375,y:.296875},{x:.515625,y:.296875},{x:.515625,y:.296875},{x:.546875,y:.296875},{x:.546875,y:.296875},{x:.578125,y:.296875},{x:.578125,y:.296875},{x:.609375,y:.296875},{x:.609375,y:.296875},{x:.640625,y:.296875},{x:.640625,y:.296875},{x:.671875,y:.296875},{x:.671875,y:.296875},{x:.703125,y:.296875},{x:.703125,y:.296875},{x:.734375,y:.296875},{x:.734375,y:.296875},{x:.765625,y:.296875},{x:.765625,y:.296875},{x:.796875,y:.296875},{x:.796875,y:.296875},{x:.828125,y:.296875},{x:.828125,y:.296875},{x:.859375,y:.296875},{x:.859375,y:.296875},{x:.890625,y:.296875},{x:.890625,y:.296875},{x:.921875,y:.296875},{x:.921875,y:.296875},{x:.953125,y:.296875},{x:.953125,y:.296875},{x:.984375,y:.296875},{x:.984375,y:.296875},{x:.015625,y:.328125},{x:.015625,y:.328125},{x:.046875,y:.328125},{x:.046875,y:.328125},{x:.078125,y:.328125},{x:.078125,y:.328125},{x:.109375,y:.328125},{x:.109375,y:.328125},{x:.140625,y:.328125},{x:.140625,y:.328125},{x:.171875,y:.328125},{x:.171875,y:.328125},{x:.203125,y:.328125},{x:.203125,y:.328125},{x:.234375,y:.328125},{x:.234375,y:.328125},{x:.265625,y:.328125},{x:.265625,y:.328125},{x:.296875,y:.328125},{x:.296875,y:.328125},{x:.328125,y:.328125},{x:.328125,y:.328125},{x:.359375,y:.328125},{x:.359375,y:.328125},{x:.390625,y:.328125},{x:.390625,y:.328125},{x:.421875,y:.328125},{x:.421875,y:.328125},{x:.453125,y:.328125},{x:.453125,y:.328125},{x:.484375,y:.328125},{x:.484375,y:.328125},{x:.515625,y:.328125},{x:.515625,y:.328125},{x:.546875,y:.328125},{x:.546875,y:.328125},{x:.578125,y:.328125},{x:.578125,y:.328125},{x:.609375,y:.328125},{x:.609375,y:.328125},{x:.640625,y:.328125},{x:.640625,y:.328125},{x:.671875,y:.328125},{x:.671875,y:.328125},{x:.703125,y:.328125},{x:.703125,y:.328125},{x:.734375,y:.328125},{x:.734375,y:.328125},{x:.765625,y:.328125},{x:.765625,y:.328125},{x:.796875,y:.328125},{x:.796875,y:.328125},{x:.828125,y:.328125},{x:.828125,y:.328125},{x:.859375,y:.328125},{x:.859375,y:.328125},{x:.890625,y:.328125},{x:.890625,y:.328125},{x:.921875,y:.328125},{x:.921875,y:.328125},{x:.953125,y:.328125},{x:.953125,y:.328125},{x:.984375,y:.328125},{x:.984375,y:.328125},{x:.015625,y:.359375},{x:.015625,y:.359375},{x:.046875,y:.359375},{x:.046875,y:.359375},{x:.078125,y:.359375},{x:.078125,y:.359375},{x:.109375,y:.359375},{x:.109375,y:.359375},{x:.140625,y:.359375},{x:.140625,y:.359375},{x:.171875,y:.359375},{x:.171875,y:.359375},{x:.203125,y:.359375},{x:.203125,y:.359375},{x:.234375,y:.359375},{x:.234375,y:.359375},{x:.265625,y:.359375},{x:.265625,y:.359375},{x:.296875,y:.359375},{x:.296875,y:.359375},{x:.328125,y:.359375},{x:.328125,y:.359375},{x:.359375,y:.359375},{x:.359375,y:.359375},{x:.390625,y:.359375},{x:.390625,y:.359375},{x:.421875,y:.359375},{x:.421875,y:.359375},{x:.453125,y:.359375},{x:.453125,y:.359375},{x:.484375,y:.359375},{x:.484375,y:.359375},{x:.515625,y:.359375},{x:.515625,y:.359375},{x:.546875,y:.359375},{x:.546875,y:.359375},{x:.578125,y:.359375},{x:.578125,y:.359375},{x:.609375,y:.359375},{x:.609375,y:.359375},{x:.640625,y:.359375},{x:.640625,y:.359375},{x:.671875,y:.359375},{x:.671875,y:.359375},{x:.703125,y:.359375},{x:.703125,y:.359375},{x:.734375,y:.359375},{x:.734375,y:.359375},{x:.765625,y:.359375},{x:.765625,y:.359375},{x:.796875,y:.359375},{x:.796875,y:.359375},{x:.828125,y:.359375},{x:.828125,y:.359375},{x:.859375,y:.359375},{x:.859375,y:.359375},{x:.890625,y:.359375},{x:.890625,y:.359375},{x:.921875,y:.359375},{x:.921875,y:.359375},{x:.953125,y:.359375},{x:.953125,y:.359375},{x:.984375,y:.359375},{x:.984375,y:.359375},{x:.015625,y:.390625},{x:.015625,y:.390625},{x:.046875,y:.390625},{x:.046875,y:.390625},{x:.078125,y:.390625},{x:.078125,y:.390625},{x:.109375,y:.390625},{x:.109375,y:.390625},{x:.140625,y:.390625},{x:.140625,y:.390625},{x:.171875,y:.390625},{x:.171875,y:.390625},{x:.203125,y:.390625},{x:.203125,y:.390625},{x:.234375,y:.390625},{x:.234375,y:.390625},{x:.265625,y:.390625},{x:.265625,y:.390625},{x:.296875,y:.390625},{x:.296875,y:.390625},{x:.328125,y:.390625},{x:.328125,y:.390625},{x:.359375,y:.390625},{x:.359375,y:.390625},{x:.390625,y:.390625},{x:.390625,y:.390625},{x:.421875,y:.390625},{x:.421875,y:.390625},{x:.453125,y:.390625},{x:.453125,y:.390625},{x:.484375,y:.390625},{x:.484375,y:.390625},{x:.515625,y:.390625},{x:.515625,y:.390625},{x:.546875,y:.390625},{x:.546875,y:.390625},{x:.578125,y:.390625},{x:.578125,y:.390625},{x:.609375,y:.390625},{x:.609375,y:.390625},{x:.640625,y:.390625},{x:.640625,y:.390625},{x:.671875,y:.390625},{x:.671875,y:.390625},{x:.703125,y:.390625},{x:.703125,y:.390625},{x:.734375,y:.390625},{x:.734375,y:.390625},{x:.765625,y:.390625},{x:.765625,y:.390625},{x:.796875,y:.390625},{x:.796875,y:.390625},{x:.828125,y:.390625},{x:.828125,y:.390625},{x:.859375,y:.390625},{x:.859375,y:.390625},{x:.890625,y:.390625},{x:.890625,y:.390625},{x:.921875,y:.390625},{x:.921875,y:.390625},{x:.953125,y:.390625},{x:.953125,y:.390625},{x:.984375,y:.390625},{x:.984375,y:.390625},{x:.015625,y:.421875},{x:.015625,y:.421875},{x:.046875,y:.421875},{x:.046875,y:.421875},{x:.078125,y:.421875},{x:.078125,y:.421875},{x:.109375,y:.421875},{x:.109375,y:.421875},{x:.140625,y:.421875},{x:.140625,y:.421875},{x:.171875,y:.421875},{x:.171875,y:.421875},{x:.203125,y:.421875},{x:.203125,y:.421875},{x:.234375,y:.421875},{x:.234375,y:.421875},{x:.265625,y:.421875},{x:.265625,y:.421875},{x:.296875,y:.421875},{x:.296875,y:.421875},{x:.328125,y:.421875},{x:.328125,y:.421875},{x:.359375,y:.421875},{x:.359375,y:.421875},{x:.390625,y:.421875},{x:.390625,y:.421875},{x:.421875,y:.421875},{x:.421875,y:.421875},{x:.453125,y:.421875},{x:.453125,y:.421875},{x:.484375,y:.421875},{x:.484375,y:.421875},{x:.515625,y:.421875},{x:.515625,y:.421875},{x:.546875,y:.421875},{x:.546875,y:.421875},{x:.578125,y:.421875},{x:.578125,y:.421875},{x:.609375,y:.421875},{x:.609375,y:.421875},{x:.640625,y:.421875},{x:.640625,y:.421875},{x:.671875,y:.421875},{x:.671875,y:.421875},{x:.703125,y:.421875},{x:.703125,y:.421875},{x:.734375,y:.421875},{x:.734375,y:.421875},{x:.765625,y:.421875},{x:.765625,y:.421875},{x:.796875,y:.421875},{x:.796875,y:.421875},{x:.828125,y:.421875},{x:.828125,y:.421875},{x:.859375,y:.421875},{x:.859375,y:.421875},{x:.890625,y:.421875},{x:.890625,y:.421875},{x:.921875,y:.421875},{x:.921875,y:.421875},{x:.953125,y:.421875},{x:.953125,y:.421875},{x:.984375,y:.421875},{x:.984375,y:.421875},{x:.015625,y:.453125},{x:.015625,y:.453125},{x:.046875,y:.453125},{x:.046875,y:.453125},{x:.078125,y:.453125},{x:.078125,y:.453125},{x:.109375,y:.453125},{x:.109375,y:.453125},{x:.140625,y:.453125},{x:.140625,y:.453125},{x:.171875,y:.453125},{x:.171875,y:.453125},{x:.203125,y:.453125},{x:.203125,y:.453125},{x:.234375,y:.453125},{x:.234375,y:.453125},{x:.265625,y:.453125},{x:.265625,y:.453125},{x:.296875,y:.453125},{x:.296875,y:.453125},{x:.328125,y:.453125},{x:.328125,y:.453125},{x:.359375,y:.453125},{x:.359375,y:.453125},{x:.390625,y:.453125},{x:.390625,y:.453125},{x:.421875,y:.453125},{x:.421875,y:.453125},{x:.453125,y:.453125},{x:.453125,y:.453125},{x:.484375,y:.453125},{x:.484375,y:.453125},{x:.515625,y:.453125},{x:.515625,y:.453125},{x:.546875,y:.453125},{x:.546875,y:.453125},{x:.578125,y:.453125},{x:.578125,y:.453125},{x:.609375,y:.453125},{x:.609375,y:.453125},{x:.640625,y:.453125},{x:.640625,y:.453125},{x:.671875,y:.453125},{x:.671875,y:.453125},{x:.703125,y:.453125},{x:.703125,y:.453125},{x:.734375,y:.453125},{x:.734375,y:.453125},{x:.765625,y:.453125},{x:.765625,y:.453125},{x:.796875,y:.453125},{x:.796875,y:.453125},{x:.828125,y:.453125},{x:.828125,y:.453125},{x:.859375,y:.453125},{x:.859375,y:.453125},{x:.890625,y:.453125},{x:.890625,y:.453125},{x:.921875,y:.453125},{x:.921875,y:.453125},{x:.953125,y:.453125},{x:.953125,y:.453125},{x:.984375,y:.453125},{x:.984375,y:.453125},{x:.015625,y:.484375},{x:.015625,y:.484375},{x:.046875,y:.484375},{x:.046875,y:.484375},{x:.078125,y:.484375},{x:.078125,y:.484375},{x:.109375,y:.484375},{x:.109375,y:.484375},{x:.140625,y:.484375},{x:.140625,y:.484375},{x:.171875,y:.484375},{x:.171875,y:.484375},{x:.203125,y:.484375},{x:.203125,y:.484375},{x:.234375,y:.484375},{x:.234375,y:.484375},{x:.265625,y:.484375},{x:.265625,y:.484375},{x:.296875,y:.484375},{x:.296875,y:.484375},{x:.328125,y:.484375},{x:.328125,y:.484375},{x:.359375,y:.484375},{x:.359375,y:.484375},{x:.390625,y:.484375},{x:.390625,y:.484375},{x:.421875,y:.484375},{x:.421875,y:.484375},{x:.453125,y:.484375},{x:.453125,y:.484375},{x:.484375,y:.484375},{x:.484375,y:.484375},{x:.515625,y:.484375},{x:.515625,y:.484375},{x:.546875,y:.484375},{x:.546875,y:.484375},{x:.578125,y:.484375},{x:.578125,y:.484375},{x:.609375,y:.484375},{x:.609375,y:.484375},{x:.640625,y:.484375},{x:.640625,y:.484375},{x:.671875,y:.484375},{x:.671875,y:.484375},{x:.703125,y:.484375},{x:.703125,y:.484375},{x:.734375,y:.484375},{x:.734375,y:.484375},{x:.765625,y:.484375},{x:.765625,y:.484375},{x:.796875,y:.484375},{x:.796875,y:.484375},{x:.828125,y:.484375},{x:.828125,y:.484375},{x:.859375,y:.484375},{x:.859375,y:.484375},{x:.890625,y:.484375},{x:.890625,y:.484375},{x:.921875,y:.484375},{x:.921875,y:.484375},{x:.953125,y:.484375},{x:.953125,y:.484375},{x:.984375,y:.484375},{x:.984375,y:.484375},{x:.015625,y:.515625},{x:.015625,y:.515625},{x:.046875,y:.515625},{x:.046875,y:.515625},{x:.078125,y:.515625},{x:.078125,y:.515625},{x:.109375,y:.515625},{x:.109375,y:.515625},{x:.140625,y:.515625},{x:.140625,y:.515625},{x:.171875,y:.515625},{x:.171875,y:.515625},{x:.203125,y:.515625},{x:.203125,y:.515625},{x:.234375,y:.515625},{x:.234375,y:.515625},{x:.265625,y:.515625},{x:.265625,y:.515625},{x:.296875,y:.515625},{x:.296875,y:.515625},{x:.328125,y:.515625},{x:.328125,y:.515625},{x:.359375,y:.515625},{x:.359375,y:.515625},{x:.390625,y:.515625},{x:.390625,y:.515625},{x:.421875,y:.515625},{x:.421875,y:.515625},{x:.453125,y:.515625},{x:.453125,y:.515625},{x:.484375,y:.515625},{x:.484375,y:.515625},{x:.515625,y:.515625},{x:.515625,y:.515625},{x:.546875,y:.515625},{x:.546875,y:.515625},{x:.578125,y:.515625},{x:.578125,y:.515625},{x:.609375,y:.515625},{x:.609375,y:.515625},{x:.640625,y:.515625},{x:.640625,y:.515625},{x:.671875,y:.515625},{x:.671875,y:.515625},{x:.703125,y:.515625},{x:.703125,y:.515625},{x:.734375,y:.515625},{x:.734375,y:.515625},{x:.765625,y:.515625},{x:.765625,y:.515625},{x:.796875,y:.515625},{x:.796875,y:.515625},{x:.828125,y:.515625},{x:.828125,y:.515625},{x:.859375,y:.515625},{x:.859375,y:.515625},{x:.890625,y:.515625},{x:.890625,y:.515625},{x:.921875,y:.515625},{x:.921875,y:.515625},{x:.953125,y:.515625},{x:.953125,y:.515625},{x:.984375,y:.515625},{x:.984375,y:.515625},{x:.015625,y:.546875},{x:.015625,y:.546875},{x:.046875,y:.546875},{x:.046875,y:.546875},{x:.078125,y:.546875},{x:.078125,y:.546875},{x:.109375,y:.546875},{x:.109375,y:.546875},{x:.140625,y:.546875},{x:.140625,y:.546875},{x:.171875,y:.546875},{x:.171875,y:.546875},{x:.203125,y:.546875},{x:.203125,y:.546875},{x:.234375,y:.546875},{x:.234375,y:.546875},{x:.265625,y:.546875},{x:.265625,y:.546875},{x:.296875,y:.546875},{x:.296875,y:.546875},{x:.328125,y:.546875},{x:.328125,y:.546875},{x:.359375,y:.546875},{x:.359375,y:.546875},{x:.390625,y:.546875},{x:.390625,y:.546875},{x:.421875,y:.546875},{x:.421875,y:.546875},{x:.453125,y:.546875},{x:.453125,y:.546875},{x:.484375,y:.546875},{x:.484375,y:.546875},{x:.515625,y:.546875},{x:.515625,y:.546875},{x:.546875,y:.546875},{x:.546875,y:.546875},{x:.578125,y:.546875},{x:.578125,y:.546875},{x:.609375,y:.546875},{x:.609375,y:.546875},{x:.640625,y:.546875},{x:.640625,y:.546875},{x:.671875,y:.546875},{x:.671875,y:.546875},{x:.703125,y:.546875},{x:.703125,y:.546875},{x:.734375,y:.546875},{x:.734375,y:.546875},{x:.765625,y:.546875},{x:.765625,y:.546875},{x:.796875,y:.546875},{x:.796875,y:.546875},{x:.828125,y:.546875},{x:.828125,y:.546875},{x:.859375,y:.546875},{x:.859375,y:.546875},{x:.890625,y:.546875},{x:.890625,y:.546875},{x:.921875,y:.546875},{x:.921875,y:.546875},{x:.953125,y:.546875},{x:.953125,y:.546875},{x:.984375,y:.546875},{x:.984375,y:.546875},{x:.015625,y:.578125},{x:.015625,y:.578125},{x:.046875,y:.578125},{x:.046875,y:.578125},{x:.078125,y:.578125},{x:.078125,y:.578125},{x:.109375,y:.578125},{x:.109375,y:.578125},{x:.140625,y:.578125},{x:.140625,y:.578125},{x:.171875,y:.578125},{x:.171875,y:.578125},{x:.203125,y:.578125},{x:.203125,y:.578125},{x:.234375,y:.578125},{x:.234375,y:.578125},{x:.265625,y:.578125},{x:.265625,y:.578125},{x:.296875,y:.578125},{x:.296875,y:.578125},{x:.328125,y:.578125},{x:.328125,y:.578125},{x:.359375,y:.578125},{x:.359375,y:.578125},{x:.390625,y:.578125},{x:.390625,y:.578125},{x:.421875,y:.578125},{x:.421875,y:.578125},{x:.453125,y:.578125},{x:.453125,y:.578125},{x:.484375,y:.578125},{x:.484375,y:.578125},{x:.515625,y:.578125},{x:.515625,y:.578125},{x:.546875,y:.578125},{x:.546875,y:.578125},{x:.578125,y:.578125},{x:.578125,y:.578125},{x:.609375,y:.578125},{x:.609375,y:.578125},{x:.640625,y:.578125},{x:.640625,y:.578125},{x:.671875,y:.578125},{x:.671875,y:.578125},{x:.703125,y:.578125},{x:.703125,y:.578125},{x:.734375,y:.578125},{x:.734375,y:.578125},{x:.765625,y:.578125},{x:.765625,y:.578125},{x:.796875,y:.578125},{x:.796875,y:.578125},{x:.828125,y:.578125},{x:.828125,y:.578125},{x:.859375,y:.578125},{x:.859375,y:.578125},{x:.890625,y:.578125},{x:.890625,y:.578125},{x:.921875,y:.578125},{x:.921875,y:.578125},{x:.953125,y:.578125},{x:.953125,y:.578125},{x:.984375,y:.578125},{x:.984375,y:.578125},{x:.015625,y:.609375},{x:.015625,y:.609375},{x:.046875,y:.609375},{x:.046875,y:.609375},{x:.078125,y:.609375},{x:.078125,y:.609375},{x:.109375,y:.609375},{x:.109375,y:.609375},{x:.140625,y:.609375},{x:.140625,y:.609375},{x:.171875,y:.609375},{x:.171875,y:.609375},{x:.203125,y:.609375},{x:.203125,y:.609375},{x:.234375,y:.609375},{x:.234375,y:.609375},{x:.265625,y:.609375},{x:.265625,y:.609375},{x:.296875,y:.609375},{x:.296875,y:.609375},{x:.328125,y:.609375},{x:.328125,y:.609375},{x:.359375,y:.609375},{x:.359375,y:.609375},{x:.390625,y:.609375},{x:.390625,y:.609375},{x:.421875,y:.609375},{x:.421875,y:.609375},{x:.453125,y:.609375},{x:.453125,y:.609375},{x:.484375,y:.609375},{x:.484375,y:.609375},{x:.515625,y:.609375},{x:.515625,y:.609375},{x:.546875,y:.609375},{x:.546875,y:.609375},{x:.578125,y:.609375},{x:.578125,y:.609375},{x:.609375,y:.609375},{x:.609375,y:.609375},{x:.640625,y:.609375},{x:.640625,y:.609375},{x:.671875,y:.609375},{x:.671875,y:.609375},{x:.703125,y:.609375},{x:.703125,y:.609375},{x:.734375,y:.609375},{x:.734375,y:.609375},{x:.765625,y:.609375},{x:.765625,y:.609375},{x:.796875,y:.609375},{x:.796875,y:.609375},{x:.828125,y:.609375},{x:.828125,y:.609375},{x:.859375,y:.609375},{x:.859375,y:.609375},{x:.890625,y:.609375},{x:.890625,y:.609375},{x:.921875,y:.609375},{x:.921875,y:.609375},{x:.953125,y:.609375},{x:.953125,y:.609375},{x:.984375,y:.609375},{x:.984375,y:.609375},{x:.015625,y:.640625},{x:.015625,y:.640625},{x:.046875,y:.640625},{x:.046875,y:.640625},{x:.078125,y:.640625},{x:.078125,y:.640625},{x:.109375,y:.640625},{x:.109375,y:.640625},{x:.140625,y:.640625},{x:.140625,y:.640625},{x:.171875,y:.640625},{x:.171875,y:.640625},{x:.203125,y:.640625},{x:.203125,y:.640625},{x:.234375,y:.640625},{x:.234375,y:.640625},{x:.265625,y:.640625},{x:.265625,y:.640625},{x:.296875,y:.640625},{x:.296875,y:.640625},{x:.328125,y:.640625},{x:.328125,y:.640625},{x:.359375,y:.640625},{x:.359375,y:.640625},{x:.390625,y:.640625},{x:.390625,y:.640625},{x:.421875,y:.640625},{x:.421875,y:.640625},{x:.453125,y:.640625},{x:.453125,y:.640625},{x:.484375,y:.640625},{x:.484375,y:.640625},{x:.515625,y:.640625},{x:.515625,y:.640625},{x:.546875,y:.640625},{x:.546875,y:.640625},{x:.578125,y:.640625},{x:.578125,y:.640625},{x:.609375,y:.640625},{x:.609375,y:.640625},{x:.640625,y:.640625},{x:.640625,y:.640625},{x:.671875,y:.640625},{x:.671875,y:.640625},{x:.703125,y:.640625},{x:.703125,y:.640625},{x:.734375,y:.640625},{x:.734375,y:.640625},{x:.765625,y:.640625},{x:.765625,y:.640625},{x:.796875,y:.640625},{x:.796875,y:.640625},{x:.828125,y:.640625},{x:.828125,y:.640625},{x:.859375,y:.640625},{x:.859375,y:.640625},{x:.890625,y:.640625},{x:.890625,y:.640625},{x:.921875,y:.640625},{x:.921875,y:.640625},{x:.953125,y:.640625},{x:.953125,y:.640625},{x:.984375,y:.640625},{x:.984375,y:.640625},{x:.015625,y:.671875},{x:.015625,y:.671875},{x:.046875,y:.671875},{x:.046875,y:.671875},{x:.078125,y:.671875},{x:.078125,y:.671875},{x:.109375,y:.671875},{x:.109375,y:.671875},{x:.140625,y:.671875},{x:.140625,y:.671875},{x:.171875,y:.671875},{x:.171875,y:.671875},{x:.203125,y:.671875},{x:.203125,y:.671875},{x:.234375,y:.671875},{x:.234375,y:.671875},{x:.265625,y:.671875},{x:.265625,y:.671875},{x:.296875,y:.671875},{x:.296875,y:.671875},{x:.328125,y:.671875},{x:.328125,y:.671875},{x:.359375,y:.671875},{x:.359375,y:.671875},{x:.390625,y:.671875},{x:.390625,y:.671875},{x:.421875,y:.671875},{x:.421875,y:.671875},{x:.453125,y:.671875},{x:.453125,y:.671875},{x:.484375,y:.671875},{x:.484375,y:.671875},{x:.515625,y:.671875},{x:.515625,y:.671875},{x:.546875,y:.671875},{x:.546875,y:.671875},{x:.578125,y:.671875},{x:.578125,y:.671875},{x:.609375,y:.671875},{x:.609375,y:.671875},{x:.640625,y:.671875},{x:.640625,y:.671875},{x:.671875,y:.671875},{x:.671875,y:.671875},{x:.703125,y:.671875},{x:.703125,y:.671875},{x:.734375,y:.671875},{x:.734375,y:.671875},{x:.765625,y:.671875},{x:.765625,y:.671875},{x:.796875,y:.671875},{x:.796875,y:.671875},{x:.828125,y:.671875},{x:.828125,y:.671875},{x:.859375,y:.671875},{x:.859375,y:.671875},{x:.890625,y:.671875},{x:.890625,y:.671875},{x:.921875,y:.671875},{x:.921875,y:.671875},{x:.953125,y:.671875},{x:.953125,y:.671875},{x:.984375,y:.671875},{x:.984375,y:.671875},{x:.015625,y:.703125},{x:.015625,y:.703125},{x:.046875,y:.703125},{x:.046875,y:.703125},{x:.078125,y:.703125},{x:.078125,y:.703125},{x:.109375,y:.703125},{x:.109375,y:.703125},{x:.140625,y:.703125},{x:.140625,y:.703125},{x:.171875,y:.703125},{x:.171875,y:.703125},{x:.203125,y:.703125},{x:.203125,y:.703125},{x:.234375,y:.703125},{x:.234375,y:.703125},{x:.265625,y:.703125},{x:.265625,y:.703125},{x:.296875,y:.703125},{x:.296875,y:.703125},{x:.328125,y:.703125},{x:.328125,y:.703125},{x:.359375,y:.703125},{x:.359375,y:.703125},{x:.390625,y:.703125},{x:.390625,y:.703125},{x:.421875,y:.703125},{x:.421875,y:.703125},{x:.453125,y:.703125},{x:.453125,y:.703125},{x:.484375,y:.703125},{x:.484375,y:.703125},{x:.515625,y:.703125},{x:.515625,y:.703125},{x:.546875,y:.703125},{x:.546875,y:.703125},{x:.578125,y:.703125},{x:.578125,y:.703125},{x:.609375,y:.703125},{x:.609375,y:.703125},{x:.640625,y:.703125},{x:.640625,y:.703125},{x:.671875,y:.703125},{x:.671875,y:.703125},{x:.703125,y:.703125},{x:.703125,y:.703125},{x:.734375,y:.703125},{x:.734375,y:.703125},{x:.765625,y:.703125},{x:.765625,y:.703125},{x:.796875,y:.703125},{x:.796875,y:.703125},{x:.828125,y:.703125},{x:.828125,y:.703125},{x:.859375,y:.703125},{x:.859375,y:.703125},{x:.890625,y:.703125},{x:.890625,y:.703125},{x:.921875,y:.703125},{x:.921875,y:.703125},{x:.953125,y:.703125},{x:.953125,y:.703125},{x:.984375,y:.703125},{x:.984375,y:.703125},{x:.015625,y:.734375},{x:.015625,y:.734375},{x:.046875,y:.734375},{x:.046875,y:.734375},{x:.078125,y:.734375},{x:.078125,y:.734375},{x:.109375,y:.734375},{x:.109375,y:.734375},{x:.140625,y:.734375},{x:.140625,y:.734375},{x:.171875,y:.734375},{x:.171875,y:.734375},{x:.203125,y:.734375},{x:.203125,y:.734375},{x:.234375,y:.734375},{x:.234375,y:.734375},{x:.265625,y:.734375},{x:.265625,y:.734375},{x:.296875,y:.734375},{x:.296875,y:.734375},{x:.328125,y:.734375},{x:.328125,y:.734375},{x:.359375,y:.734375},{x:.359375,y:.734375},{x:.390625,y:.734375},{x:.390625,y:.734375},{x:.421875,y:.734375},{x:.421875,y:.734375},{x:.453125,y:.734375},{x:.453125,y:.734375},{x:.484375,y:.734375},{x:.484375,y:.734375},{x:.515625,y:.734375},{x:.515625,y:.734375},{x:.546875,y:.734375},{x:.546875,y:.734375},{x:.578125,y:.734375},{x:.578125,y:.734375},{x:.609375,y:.734375},{x:.609375,y:.734375},{x:.640625,y:.734375},{x:.640625,y:.734375},{x:.671875,y:.734375},{x:.671875,y:.734375},{x:.703125,y:.734375},{x:.703125,y:.734375},{x:.734375,y:.734375},{x:.734375,y:.734375},{x:.765625,y:.734375},{x:.765625,y:.734375},{x:.796875,y:.734375},{x:.796875,y:.734375},{x:.828125,y:.734375},{x:.828125,y:.734375},{x:.859375,y:.734375},{x:.859375,y:.734375},{x:.890625,y:.734375},{x:.890625,y:.734375},{x:.921875,y:.734375},{x:.921875,y:.734375},{x:.953125,y:.734375},{x:.953125,y:.734375},{x:.984375,y:.734375},{x:.984375,y:.734375},{x:.015625,y:.765625},{x:.015625,y:.765625},{x:.046875,y:.765625},{x:.046875,y:.765625},{x:.078125,y:.765625},{x:.078125,y:.765625},{x:.109375,y:.765625},{x:.109375,y:.765625},{x:.140625,y:.765625},{x:.140625,y:.765625},{x:.171875,y:.765625},{x:.171875,y:.765625},{x:.203125,y:.765625},{x:.203125,y:.765625},{x:.234375,y:.765625},{x:.234375,y:.765625},{x:.265625,y:.765625},{x:.265625,y:.765625},{x:.296875,y:.765625},{x:.296875,y:.765625},{x:.328125,y:.765625},{x:.328125,y:.765625},{x:.359375,y:.765625},{x:.359375,y:.765625},{x:.390625,y:.765625},{x:.390625,y:.765625},{x:.421875,y:.765625},{x:.421875,y:.765625},{x:.453125,y:.765625},{x:.453125,y:.765625},{x:.484375,y:.765625},{x:.484375,y:.765625},{x:.515625,y:.765625},{x:.515625,y:.765625},{x:.546875,y:.765625},{x:.546875,y:.765625},{x:.578125,y:.765625},{x:.578125,y:.765625},{x:.609375,y:.765625},{x:.609375,y:.765625},{x:.640625,y:.765625},{x:.640625,y:.765625},{x:.671875,y:.765625},{x:.671875,y:.765625},{x:.703125,y:.765625},{x:.703125,y:.765625},{x:.734375,y:.765625},{x:.734375,y:.765625},{x:.765625,y:.765625},{x:.765625,y:.765625},{x:.796875,y:.765625},{x:.796875,y:.765625},{x:.828125,y:.765625},{x:.828125,y:.765625},{x:.859375,y:.765625},{x:.859375,y:.765625},{x:.890625,y:.765625},{x:.890625,y:.765625},{x:.921875,y:.765625},{x:.921875,y:.765625},{x:.953125,y:.765625},{x:.953125,y:.765625},{x:.984375,y:.765625},{x:.984375,y:.765625},{x:.015625,y:.796875},{x:.015625,y:.796875},{x:.046875,y:.796875},{x:.046875,y:.796875},{x:.078125,y:.796875},{x:.078125,y:.796875},{x:.109375,y:.796875},{x:.109375,y:.796875},{x:.140625,y:.796875},{x:.140625,y:.796875},{x:.171875,y:.796875},{x:.171875,y:.796875},{x:.203125,y:.796875},{x:.203125,y:.796875},{x:.234375,y:.796875},{x:.234375,y:.796875},{x:.265625,y:.796875},{x:.265625,y:.796875},{x:.296875,y:.796875},{x:.296875,y:.796875},{x:.328125,y:.796875},{x:.328125,y:.796875},{x:.359375,y:.796875},{x:.359375,y:.796875},{x:.390625,y:.796875},{x:.390625,y:.796875},{x:.421875,y:.796875},{x:.421875,y:.796875},{x:.453125,y:.796875},{x:.453125,y:.796875},{x:.484375,y:.796875},{x:.484375,y:.796875},{x:.515625,y:.796875},{x:.515625,y:.796875},{x:.546875,y:.796875},{x:.546875,y:.796875},{x:.578125,y:.796875},{x:.578125,y:.796875},{x:.609375,y:.796875},{x:.609375,y:.796875},{x:.640625,y:.796875},{x:.640625,y:.796875},{x:.671875,y:.796875},{x:.671875,y:.796875},{x:.703125,y:.796875},{x:.703125,y:.796875},{x:.734375,y:.796875},{x:.734375,y:.796875},{x:.765625,y:.796875},{x:.765625,y:.796875},{x:.796875,y:.796875},{x:.796875,y:.796875},{x:.828125,y:.796875},{x:.828125,y:.796875},{x:.859375,y:.796875},{x:.859375,y:.796875},{x:.890625,y:.796875},{x:.890625,y:.796875},{x:.921875,y:.796875},{x:.921875,y:.796875},{x:.953125,y:.796875},{x:.953125,y:.796875},{x:.984375,y:.796875},{x:.984375,y:.796875},{x:.015625,y:.828125},{x:.015625,y:.828125},{x:.046875,y:.828125},{x:.046875,y:.828125},{x:.078125,y:.828125},{x:.078125,y:.828125},{x:.109375,y:.828125},{x:.109375,y:.828125},{x:.140625,y:.828125},{x:.140625,y:.828125},{x:.171875,y:.828125},{x:.171875,y:.828125},{x:.203125,y:.828125},{x:.203125,y:.828125},{x:.234375,y:.828125},{x:.234375,y:.828125},{x:.265625,y:.828125},{x:.265625,y:.828125},{x:.296875,y:.828125},{x:.296875,y:.828125},{x:.328125,y:.828125},{x:.328125,y:.828125},{x:.359375,y:.828125},{x:.359375,y:.828125},{x:.390625,y:.828125},{x:.390625,y:.828125},{x:.421875,y:.828125},{x:.421875,y:.828125},{x:.453125,y:.828125},{x:.453125,y:.828125},{x:.484375,y:.828125},{x:.484375,y:.828125},{x:.515625,y:.828125},{x:.515625,y:.828125},{x:.546875,y:.828125},{x:.546875,y:.828125},{x:.578125,y:.828125},{x:.578125,y:.828125},{x:.609375,y:.828125},{x:.609375,y:.828125},{x:.640625,y:.828125},{x:.640625,y:.828125},{x:.671875,y:.828125},{x:.671875,y:.828125},{x:.703125,y:.828125},{x:.703125,y:.828125},{x:.734375,y:.828125},{x:.734375,y:.828125},{x:.765625,y:.828125},{x:.765625,y:.828125},{x:.796875,y:.828125},{x:.796875,y:.828125},{x:.828125,y:.828125},{x:.828125,y:.828125},{x:.859375,y:.828125},{x:.859375,y:.828125},{x:.890625,y:.828125},{x:.890625,y:.828125},{x:.921875,y:.828125},{x:.921875,y:.828125},{x:.953125,y:.828125},{x:.953125,y:.828125},{x:.984375,y:.828125},{x:.984375,y:.828125},{x:.015625,y:.859375},{x:.015625,y:.859375},{x:.046875,y:.859375},{x:.046875,y:.859375},{x:.078125,y:.859375},{x:.078125,y:.859375},{x:.109375,y:.859375},{x:.109375,y:.859375},{x:.140625,y:.859375},{x:.140625,y:.859375},{x:.171875,y:.859375},{x:.171875,y:.859375},{x:.203125,y:.859375},{x:.203125,y:.859375},{x:.234375,y:.859375},{x:.234375,y:.859375},{x:.265625,y:.859375},{x:.265625,y:.859375},{x:.296875,y:.859375},{x:.296875,y:.859375},{x:.328125,y:.859375},{x:.328125,y:.859375},{x:.359375,y:.859375},{x:.359375,y:.859375},{x:.390625,y:.859375},{x:.390625,y:.859375},{x:.421875,y:.859375},{x:.421875,y:.859375},{x:.453125,y:.859375},{x:.453125,y:.859375},{x:.484375,y:.859375},{x:.484375,y:.859375},{x:.515625,y:.859375},{x:.515625,y:.859375},{x:.546875,y:.859375},{x:.546875,y:.859375},{x:.578125,y:.859375},{x:.578125,y:.859375},{x:.609375,y:.859375},{x:.609375,y:.859375},{x:.640625,y:.859375},{x:.640625,y:.859375},{x:.671875,y:.859375},{x:.671875,y:.859375},{x:.703125,y:.859375},{x:.703125,y:.859375},{x:.734375,y:.859375},{x:.734375,y:.859375},{x:.765625,y:.859375},{x:.765625,y:.859375},{x:.796875,y:.859375},{x:.796875,y:.859375},{x:.828125,y:.859375},{x:.828125,y:.859375},{x:.859375,y:.859375},{x:.859375,y:.859375},{x:.890625,y:.859375},{x:.890625,y:.859375},{x:.921875,y:.859375},{x:.921875,y:.859375},{x:.953125,y:.859375},{x:.953125,y:.859375},{x:.984375,y:.859375},{x:.984375,y:.859375},{x:.015625,y:.890625},{x:.015625,y:.890625},{x:.046875,y:.890625},{x:.046875,y:.890625},{x:.078125,y:.890625},{x:.078125,y:.890625},{x:.109375,y:.890625},{x:.109375,y:.890625},{x:.140625,y:.890625},{x:.140625,y:.890625},{x:.171875,y:.890625},{x:.171875,y:.890625},{x:.203125,y:.890625},{x:.203125,y:.890625},{x:.234375,y:.890625},{x:.234375,y:.890625},{x:.265625,y:.890625},{x:.265625,y:.890625},{x:.296875,y:.890625},{x:.296875,y:.890625},{x:.328125,y:.890625},{x:.328125,y:.890625},{x:.359375,y:.890625},{x:.359375,y:.890625},{x:.390625,y:.890625},{x:.390625,y:.890625},{x:.421875,y:.890625},{x:.421875,y:.890625},{x:.453125,y:.890625},{x:.453125,y:.890625},{x:.484375,y:.890625},{x:.484375,y:.890625},{x:.515625,y:.890625},{x:.515625,y:.890625},{x:.546875,y:.890625},{x:.546875,y:.890625},{x:.578125,y:.890625},{x:.578125,y:.890625},{x:.609375,y:.890625},{x:.609375,y:.890625},{x:.640625,y:.890625},{x:.640625,y:.890625},{x:.671875,y:.890625},{x:.671875,y:.890625},{x:.703125,y:.890625},{x:.703125,y:.890625},{x:.734375,y:.890625},{x:.734375,y:.890625},{x:.765625,y:.890625},{x:.765625,y:.890625},{x:.796875,y:.890625},{x:.796875,y:.890625},{x:.828125,y:.890625},{x:.828125,y:.890625},{x:.859375,y:.890625},{x:.859375,y:.890625},{x:.890625,y:.890625},{x:.890625,y:.890625},{x:.921875,y:.890625},{x:.921875,y:.890625},{x:.953125,y:.890625},{x:.953125,y:.890625},{x:.984375,y:.890625},{x:.984375,y:.890625},{x:.015625,y:.921875},{x:.015625,y:.921875},{x:.046875,y:.921875},{x:.046875,y:.921875},{x:.078125,y:.921875},{x:.078125,y:.921875},{x:.109375,y:.921875},{x:.109375,y:.921875},{x:.140625,y:.921875},{x:.140625,y:.921875},{x:.171875,y:.921875},{x:.171875,y:.921875},{x:.203125,y:.921875},{x:.203125,y:.921875},{x:.234375,y:.921875},{x:.234375,y:.921875},{x:.265625,y:.921875},{x:.265625,y:.921875},{x:.296875,y:.921875},{x:.296875,y:.921875},{x:.328125,y:.921875},{x:.328125,y:.921875},{x:.359375,y:.921875},{x:.359375,y:.921875},{x:.390625,y:.921875},{x:.390625,y:.921875},{x:.421875,y:.921875},{x:.421875,y:.921875},{x:.453125,y:.921875},{x:.453125,y:.921875},{x:.484375,y:.921875},{x:.484375,y:.921875},{x:.515625,y:.921875},{x:.515625,y:.921875},{x:.546875,y:.921875},{x:.546875,y:.921875},{x:.578125,y:.921875},{x:.578125,y:.921875},{x:.609375,y:.921875},{x:.609375,y:.921875},{x:.640625,y:.921875},{x:.640625,y:.921875},{x:.671875,y:.921875},{x:.671875,y:.921875},{x:.703125,y:.921875},{x:.703125,y:.921875},{x:.734375,y:.921875},{x:.734375,y:.921875},{x:.765625,y:.921875},{x:.765625,y:.921875},{x:.796875,y:.921875},{x:.796875,y:.921875},{x:.828125,y:.921875},{x:.828125,y:.921875},{x:.859375,y:.921875},{x:.859375,y:.921875},{x:.890625,y:.921875},{x:.890625,y:.921875},{x:.921875,y:.921875},{x:.921875,y:.921875},{x:.953125,y:.921875},{x:.953125,y:.921875},{x:.984375,y:.921875},{x:.984375,y:.921875},{x:.015625,y:.953125},{x:.015625,y:.953125},{x:.046875,y:.953125},{x:.046875,y:.953125},{x:.078125,y:.953125},{x:.078125,y:.953125},{x:.109375,y:.953125},{x:.109375,y:.953125},{x:.140625,y:.953125},{x:.140625,y:.953125},{x:.171875,y:.953125},{x:.171875,y:.953125},{x:.203125,y:.953125},{x:.203125,y:.953125},{x:.234375,y:.953125},{x:.234375,y:.953125},{x:.265625,y:.953125},{x:.265625,y:.953125},{x:.296875,y:.953125},{x:.296875,y:.953125},{x:.328125,y:.953125},{x:.328125,y:.953125},{x:.359375,y:.953125},{x:.359375,y:.953125},{x:.390625,y:.953125},{x:.390625,y:.953125},{x:.421875,y:.953125},{x:.421875,y:.953125},{x:.453125,y:.953125},{x:.453125,y:.953125},{x:.484375,y:.953125},{x:.484375,y:.953125},{x:.515625,y:.953125},{x:.515625,y:.953125},{x:.546875,y:.953125},{x:.546875,y:.953125},{x:.578125,y:.953125},{x:.578125,y:.953125},{x:.609375,y:.953125},{x:.609375,y:.953125},{x:.640625,y:.953125},{x:.640625,y:.953125},{x:.671875,y:.953125},{x:.671875,y:.953125},{x:.703125,y:.953125},{x:.703125,y:.953125},{x:.734375,y:.953125},{x:.734375,y:.953125},{x:.765625,y:.953125},{x:.765625,y:.953125},{x:.796875,y:.953125},{x:.796875,y:.953125},{x:.828125,y:.953125},{x:.828125,y:.953125},{x:.859375,y:.953125},{x:.859375,y:.953125},{x:.890625,y:.953125},{x:.890625,y:.953125},{x:.921875,y:.953125},{x:.921875,y:.953125},{x:.953125,y:.953125},{x:.953125,y:.953125},{x:.984375,y:.953125},{x:.984375,y:.953125},{x:.015625,y:.984375},{x:.015625,y:.984375},{x:.046875,y:.984375},{x:.046875,y:.984375},{x:.078125,y:.984375},{x:.078125,y:.984375},{x:.109375,y:.984375},{x:.109375,y:.984375},{x:.140625,y:.984375},{x:.140625,y:.984375},{x:.171875,y:.984375},{x:.171875,y:.984375},{x:.203125,y:.984375},{x:.203125,y:.984375},{x:.234375,y:.984375},{x:.234375,y:.984375},{x:.265625,y:.984375},{x:.265625,y:.984375},{x:.296875,y:.984375},{x:.296875,y:.984375},{x:.328125,y:.984375},{x:.328125,y:.984375},{x:.359375,y:.984375},{x:.359375,y:.984375},{x:.390625,y:.984375},{x:.390625,y:.984375},{x:.421875,y:.984375},{x:.421875,y:.984375},{x:.453125,y:.984375},{x:.453125,y:.984375},{x:.484375,y:.984375},{x:.484375,y:.984375},{x:.515625,y:.984375},{x:.515625,y:.984375},{x:.546875,y:.984375},{x:.546875,y:.984375},{x:.578125,y:.984375},{x:.578125,y:.984375},{x:.609375,y:.984375},{x:.609375,y:.984375},{x:.640625,y:.984375},{x:.640625,y:.984375},{x:.671875,y:.984375},{x:.671875,y:.984375},{x:.703125,y:.984375},{x:.703125,y:.984375},{x:.734375,y:.984375},{x:.734375,y:.984375},{x:.765625,y:.984375},{x:.765625,y:.984375},{x:.796875,y:.984375},{x:.796875,y:.984375},{x:.828125,y:.984375},{x:.828125,y:.984375},{x:.859375,y:.984375},{x:.859375,y:.984375},{x:.890625,y:.984375},{x:.890625,y:.984375},{x:.921875,y:.984375},{x:.921875,y:.984375},{x:.953125,y:.984375},{x:.953125,y:.984375},{x:.984375,y:.984375},{x:.984375,y:.984375},{x:.03125,y:.03125},{x:.03125,y:.03125},{x:.09375,y:.03125},{x:.09375,y:.03125},{x:.15625,y:.03125},{x:.15625,y:.03125},{x:.21875,y:.03125},{x:.21875,y:.03125},{x:.28125,y:.03125},{x:.28125,y:.03125},{x:.34375,y:.03125},{x:.34375,y:.03125},{x:.40625,y:.03125},{x:.40625,y:.03125},{x:.46875,y:.03125},{x:.46875,y:.03125},{x:.53125,y:.03125},{x:.53125,y:.03125},{x:.59375,y:.03125},{x:.59375,y:.03125},{x:.65625,y:.03125},{x:.65625,y:.03125},{x:.71875,y:.03125},{x:.71875,y:.03125},{x:.78125,y:.03125},{x:.78125,y:.03125},{x:.84375,y:.03125},{x:.84375,y:.03125},{x:.90625,y:.03125},{x:.90625,y:.03125},{x:.96875,y:.03125},{x:.96875,y:.03125},{x:.03125,y:.09375},{x:.03125,y:.09375},{x:.09375,y:.09375},{x:.09375,y:.09375},{x:.15625,y:.09375},{x:.15625,y:.09375},{x:.21875,y:.09375},{x:.21875,y:.09375},{x:.28125,y:.09375},{x:.28125,y:.09375},{x:.34375,y:.09375},{x:.34375,y:.09375},{x:.40625,y:.09375},{x:.40625,y:.09375},{x:.46875,y:.09375},{x:.46875,y:.09375},{x:.53125,y:.09375},{x:.53125,y:.09375},{x:.59375,y:.09375},{x:.59375,y:.09375},{x:.65625,y:.09375},{x:.65625,y:.09375},{x:.71875,y:.09375},{x:.71875,y:.09375},{x:.78125,y:.09375},{x:.78125,y:.09375},{x:.84375,y:.09375},{x:.84375,y:.09375},{x:.90625,y:.09375},{x:.90625,y:.09375},{x:.96875,y:.09375},{x:.96875,y:.09375},{x:.03125,y:.15625},{x:.03125,y:.15625},{x:.09375,y:.15625},{x:.09375,y:.15625},{x:.15625,y:.15625},{x:.15625,y:.15625},{x:.21875,y:.15625},{x:.21875,y:.15625},{x:.28125,y:.15625},{x:.28125,y:.15625},{x:.34375,y:.15625},{x:.34375,y:.15625},{x:.40625,y:.15625},{x:.40625,y:.15625},{x:.46875,y:.15625},{x:.46875,y:.15625},{x:.53125,y:.15625},{x:.53125,y:.15625},{x:.59375,y:.15625},{x:.59375,y:.15625},{x:.65625,y:.15625},{x:.65625,y:.15625},{x:.71875,y:.15625},{x:.71875,y:.15625},{x:.78125,y:.15625},{x:.78125,y:.15625},{x:.84375,y:.15625},{x:.84375,y:.15625},{x:.90625,y:.15625},{x:.90625,y:.15625},{x:.96875,y:.15625},{x:.96875,y:.15625},{x:.03125,y:.21875},{x:.03125,y:.21875},{x:.09375,y:.21875},{x:.09375,y:.21875},{x:.15625,y:.21875},{x:.15625,y:.21875},{x:.21875,y:.21875},{x:.21875,y:.21875},{x:.28125,y:.21875},{x:.28125,y:.21875},{x:.34375,y:.21875},{x:.34375,y:.21875},{x:.40625,y:.21875},{x:.40625,y:.21875},{x:.46875,y:.21875},{x:.46875,y:.21875},{x:.53125,y:.21875},{x:.53125,y:.21875},{x:.59375,y:.21875},{x:.59375,y:.21875},{x:.65625,y:.21875},{x:.65625,y:.21875},{x:.71875,y:.21875},{x:.71875,y:.21875},{x:.78125,y:.21875},{x:.78125,y:.21875},{x:.84375,y:.21875},{x:.84375,y:.21875},{x:.90625,y:.21875},{x:.90625,y:.21875},{x:.96875,y:.21875},{x:.96875,y:.21875},{x:.03125,y:.28125},{x:.03125,y:.28125},{x:.09375,y:.28125},{x:.09375,y:.28125},{x:.15625,y:.28125},{x:.15625,y:.28125},{x:.21875,y:.28125},{x:.21875,y:.28125},{x:.28125,y:.28125},{x:.28125,y:.28125},{x:.34375,y:.28125},{x:.34375,y:.28125},{x:.40625,y:.28125},{x:.40625,y:.28125},{x:.46875,y:.28125},{x:.46875,y:.28125},{x:.53125,y:.28125},{x:.53125,y:.28125},{x:.59375,y:.28125},{x:.59375,y:.28125},{x:.65625,y:.28125},{x:.65625,y:.28125},{x:.71875,y:.28125},{x:.71875,y:.28125},{x:.78125,y:.28125},{x:.78125,y:.28125},{x:.84375,y:.28125},{x:.84375,y:.28125},{x:.90625,y:.28125},{x:.90625,y:.28125},{x:.96875,y:.28125},{x:.96875,y:.28125},{x:.03125,y:.34375},{x:.03125,y:.34375},{x:.09375,y:.34375},{x:.09375,y:.34375},{x:.15625,y:.34375},{x:.15625,y:.34375},{x:.21875,y:.34375},{x:.21875,y:.34375},{x:.28125,y:.34375},{x:.28125,y:.34375},{x:.34375,y:.34375},{x:.34375,y:.34375},{x:.40625,y:.34375},{x:.40625,y:.34375},{x:.46875,y:.34375},{x:.46875,y:.34375},{x:.53125,y:.34375},{x:.53125,y:.34375},{x:.59375,y:.34375},{x:.59375,y:.34375},{x:.65625,y:.34375},{x:.65625,y:.34375},{x:.71875,y:.34375},{x:.71875,y:.34375},{x:.78125,y:.34375},{x:.78125,y:.34375},{x:.84375,y:.34375},{x:.84375,y:.34375},{x:.90625,y:.34375},{x:.90625,y:.34375},{x:.96875,y:.34375},{x:.96875,y:.34375},{x:.03125,y:.40625},{x:.03125,y:.40625},{x:.09375,y:.40625},{x:.09375,y:.40625},{x:.15625,y:.40625},{x:.15625,y:.40625},{x:.21875,y:.40625},{x:.21875,y:.40625},{x:.28125,y:.40625},{x:.28125,y:.40625},{x:.34375,y:.40625},{x:.34375,y:.40625},{x:.40625,y:.40625},{x:.40625,y:.40625},{x:.46875,y:.40625},{x:.46875,y:.40625},{x:.53125,y:.40625},{x:.53125,y:.40625},{x:.59375,y:.40625},{x:.59375,y:.40625},{x:.65625,y:.40625},{x:.65625,y:.40625},{x:.71875,y:.40625},{x:.71875,y:.40625},{x:.78125,y:.40625},{x:.78125,y:.40625},{x:.84375,y:.40625},{x:.84375,y:.40625},{x:.90625,y:.40625},{x:.90625,y:.40625},{x:.96875,y:.40625},{x:.96875,y:.40625},{x:.03125,y:.46875},{x:.03125,y:.46875},{x:.09375,y:.46875},{x:.09375,y:.46875},{x:.15625,y:.46875},{x:.15625,y:.46875},{x:.21875,y:.46875},{x:.21875,y:.46875},{x:.28125,y:.46875},{x:.28125,y:.46875},{x:.34375,y:.46875},{x:.34375,y:.46875},{x:.40625,y:.46875},{x:.40625,y:.46875},{x:.46875,y:.46875},{x:.46875,y:.46875},{x:.53125,y:.46875},{x:.53125,y:.46875},{x:.59375,y:.46875},{x:.59375,y:.46875},{x:.65625,y:.46875},{x:.65625,y:.46875},{x:.71875,y:.46875},{x:.71875,y:.46875},{x:.78125,y:.46875},{x:.78125,y:.46875},{x:.84375,y:.46875},{x:.84375,y:.46875},{x:.90625,y:.46875},{x:.90625,y:.46875},{x:.96875,y:.46875},{x:.96875,y:.46875},{x:.03125,y:.53125},{x:.03125,y:.53125},{x:.09375,y:.53125},{x:.09375,y:.53125},{x:.15625,y:.53125},{x:.15625,y:.53125},{x:.21875,y:.53125},{x:.21875,y:.53125},{x:.28125,y:.53125},{x:.28125,y:.53125},{x:.34375,y:.53125},{x:.34375,y:.53125},{x:.40625,y:.53125},{x:.40625,y:.53125},{x:.46875,y:.53125},{x:.46875,y:.53125},{x:.53125,y:.53125},{x:.53125,y:.53125},{x:.59375,y:.53125},{x:.59375,y:.53125},{x:.65625,y:.53125},{x:.65625,y:.53125},{x:.71875,y:.53125},{x:.71875,y:.53125},{x:.78125,y:.53125},{x:.78125,y:.53125},{x:.84375,y:.53125},{x:.84375,y:.53125},{x:.90625,y:.53125},{x:.90625,y:.53125},{x:.96875,y:.53125},{x:.96875,y:.53125},{x:.03125,y:.59375},{x:.03125,y:.59375},{x:.09375,y:.59375},{x:.09375,y:.59375},{x:.15625,y:.59375},{x:.15625,y:.59375},{x:.21875,y:.59375},{x:.21875,y:.59375},{x:.28125,y:.59375},{x:.28125,y:.59375},{x:.34375,y:.59375},{x:.34375,y:.59375},{x:.40625,y:.59375},{x:.40625,y:.59375},{x:.46875,y:.59375},{x:.46875,y:.59375},{x:.53125,y:.59375},{x:.53125,y:.59375},{x:.59375,y:.59375},{x:.59375,y:.59375},{x:.65625,y:.59375},{x:.65625,y:.59375},{x:.71875,y:.59375},{x:.71875,y:.59375},{x:.78125,y:.59375},{x:.78125,y:.59375},{x:.84375,y:.59375},{x:.84375,y:.59375},{x:.90625,y:.59375},{x:.90625,y:.59375},{x:.96875,y:.59375},{x:.96875,y:.59375},{x:.03125,y:.65625},{x:.03125,y:.65625},{x:.09375,y:.65625},{x:.09375,y:.65625},{x:.15625,y:.65625},{x:.15625,y:.65625},{x:.21875,y:.65625},{x:.21875,y:.65625},{x:.28125,y:.65625},{x:.28125,y:.65625},{x:.34375,y:.65625},{x:.34375,y:.65625},{x:.40625,y:.65625},{x:.40625,y:.65625},{x:.46875,y:.65625},{x:.46875,y:.65625},{x:.53125,y:.65625},{x:.53125,y:.65625},{x:.59375,y:.65625},{x:.59375,y:.65625},{x:.65625,y:.65625},{x:.65625,y:.65625},{x:.71875,y:.65625},{x:.71875,y:.65625},{x:.78125,y:.65625},{x:.78125,y:.65625},{x:.84375,y:.65625},{x:.84375,y:.65625},{x:.90625,y:.65625},{x:.90625,y:.65625},{x:.96875,y:.65625},{x:.96875,y:.65625},{x:.03125,y:.71875},{x:.03125,y:.71875},{x:.09375,y:.71875},{x:.09375,y:.71875},{x:.15625,y:.71875},{x:.15625,y:.71875},{x:.21875,y:.71875},{x:.21875,y:.71875},{x:.28125,y:.71875},{x:.28125,y:.71875},{x:.34375,y:.71875},{x:.34375,y:.71875},{x:.40625,y:.71875},{x:.40625,y:.71875},{x:.46875,y:.71875},{x:.46875,y:.71875},{x:.53125,y:.71875},{x:.53125,y:.71875},{x:.59375,y:.71875},{x:.59375,y:.71875},{x:.65625,y:.71875},{x:.65625,y:.71875},{x:.71875,y:.71875},{x:.71875,y:.71875},{x:.78125,y:.71875},{x:.78125,y:.71875},{x:.84375,y:.71875},{x:.84375,y:.71875},{x:.90625,y:.71875},{x:.90625,y:.71875},{x:.96875,y:.71875},{x:.96875,y:.71875},{x:.03125,y:.78125},{x:.03125,y:.78125},{x:.09375,y:.78125},{x:.09375,y:.78125},{x:.15625,y:.78125},{x:.15625,y:.78125},{x:.21875,y:.78125},{x:.21875,y:.78125},{x:.28125,y:.78125},{x:.28125,y:.78125},{x:.34375,y:.78125},{x:.34375,y:.78125},{x:.40625,y:.78125},{x:.40625,y:.78125},{x:.46875,y:.78125},{x:.46875,y:.78125},{x:.53125,y:.78125},{x:.53125,y:.78125},{x:.59375,y:.78125},{x:.59375,y:.78125},{x:.65625,y:.78125},{x:.65625,y:.78125},{x:.71875,y:.78125},{x:.71875,y:.78125},{x:.78125,y:.78125},{x:.78125,y:.78125},{x:.84375,y:.78125},{x:.84375,y:.78125},{x:.90625,y:.78125},{x:.90625,y:.78125},{x:.96875,y:.78125},{x:.96875,y:.78125},{x:.03125,y:.84375},{x:.03125,y:.84375},{x:.09375,y:.84375},{x:.09375,y:.84375},{x:.15625,y:.84375},{x:.15625,y:.84375},{x:.21875,y:.84375},{x:.21875,y:.84375},{x:.28125,y:.84375},{x:.28125,y:.84375},{x:.34375,y:.84375},{x:.34375,y:.84375},{x:.40625,y:.84375},{x:.40625,y:.84375},{x:.46875,y:.84375},{x:.46875,y:.84375},{x:.53125,y:.84375},{x:.53125,y:.84375},{x:.59375,y:.84375},{x:.59375,y:.84375},{x:.65625,y:.84375},{x:.65625,y:.84375},{x:.71875,y:.84375},{x:.71875,y:.84375},{x:.78125,y:.84375},{x:.78125,y:.84375},{x:.84375,y:.84375},{x:.84375,y:.84375},{x:.90625,y:.84375},{x:.90625,y:.84375},{x:.96875,y:.84375},{x:.96875,y:.84375},{x:.03125,y:.90625},{x:.03125,y:.90625},{x:.09375,y:.90625},{x:.09375,y:.90625},{x:.15625,y:.90625},{x:.15625,y:.90625},{x:.21875,y:.90625},{x:.21875,y:.90625},{x:.28125,y:.90625},{x:.28125,y:.90625},{x:.34375,y:.90625},{x:.34375,y:.90625},{x:.40625,y:.90625},{x:.40625,y:.90625},{x:.46875,y:.90625},{x:.46875,y:.90625},{x:.53125,y:.90625},{x:.53125,y:.90625},{x:.59375,y:.90625},{x:.59375,y:.90625},{x:.65625,y:.90625},{x:.65625,y:.90625},{x:.71875,y:.90625},{x:.71875,y:.90625},{x:.78125,y:.90625},{x:.78125,y:.90625},{x:.84375,y:.90625},{x:.84375,y:.90625},{x:.90625,y:.90625},{x:.90625,y:.90625},{x:.96875,y:.90625},{x:.96875,y:.90625},{x:.03125,y:.96875},{x:.03125,y:.96875},{x:.09375,y:.96875},{x:.09375,y:.96875},{x:.15625,y:.96875},{x:.15625,y:.96875},{x:.21875,y:.96875},{x:.21875,y:.96875},{x:.28125,y:.96875},{x:.28125,y:.96875},{x:.34375,y:.96875},{x:.34375,y:.96875},{x:.40625,y:.96875},{x:.40625,y:.96875},{x:.46875,y:.96875},{x:.46875,y:.96875},{x:.53125,y:.96875},{x:.53125,y:.96875},{x:.59375,y:.96875},{x:.59375,y:.96875},{x:.65625,y:.96875},{x:.65625,y:.96875},{x:.71875,y:.96875},{x:.71875,y:.96875},{x:.78125,y:.96875},{x:.78125,y:.96875},{x:.84375,y:.96875},{x:.84375,y:.96875},{x:.90625,y:.96875},{x:.90625,y:.96875},{x:.96875,y:.96875},{x:.96875,y:.96875},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.0625,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.1875,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.3125,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.4375,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.5625,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.6875,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.8125,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.9375,y:.0625},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.0625,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.1875,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.3125,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.4375,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.5625,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.6875,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.8125,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.9375,y:.1875},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.0625,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.1875,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.3125,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.4375,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.5625,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.6875,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.8125,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.9375,y:.3125},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.0625,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.1875,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.3125,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.4375,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.5625,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.6875,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.8125,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.9375,y:.4375},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.0625,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.1875,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.3125,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.4375,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.5625,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.6875,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.8125,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.9375,y:.5625},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.0625,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.1875,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.3125,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.4375,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.5625,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.6875,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.8125,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.9375,y:.6875},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.0625,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.1875,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.3125,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.4375,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.5625,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.6875,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.8125,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.9375,y:.8125},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.0625,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.1875,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.3125,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.4375,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.5625,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.6875,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.8125,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375},{x:.9375,y:.9375}];var pb=class{constructor(t){Te(this,"model");Te(this,"anchors");Te(this,"anchorsTensor");Te(this,"inputSize");Te(this,"inputSizeTensor");Te(this,"doubleInputSizeTensor");this.model=t,this.anchors=V6.map(n=>[n.x,n.y]),this.anchorsTensor=dr(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=Zt([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=Zt([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){return j(()=>{let n=_e(t,[0,0],[-1,2]),s=_e(t,[0,2],[-1,2]),r=ue(fe(n,this.inputSizeTensor),this.anchorsTensor),a=fe(s,this.doubleInputSizeTensor),o=L(xe(r,a),this.inputSizeTensor),i=L(ue(r,a),this.inputSizeTensor);return Ru([o,i],1)})}normalizeLandmarks(t,n){return j(()=>{let s=ue(fe(G(t,[-1,7,2]),this.inputSizeTensor),this.anchors[n]);return L(s,this.inputSizeTensor)})}async getBoxes(t,n){let s={};s.batched=this.model.predict(t),s.predictions=dt(s.batched),s.scores=j(()=>dt(ns(_e(s.predictions,[0,0],[-1,1]))));let r=await s.scores.data();s.boxes=_e(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await $e.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l=_e(s.norm,[i,0],[1,-1]),c=j(()=>G(this.normalizeLandmarks(_e(s.predictions,[i,5],[1,14]),i),[-1,2]));o.push({box:l,palmLandmarks:c,confidence:r[i]})}for(let i of Object.keys(s))ne(s[i]);return o}async estimateHandBounds(t,n){let s=t.shape[1],r=t.shape[2],a=j(()=>xe(fe($e.resizeBilinear(t,[this.inputSize,this.inputSize]),127.5),1)),o=await this.getBoxes(a,n);ne(a);let i=[];if(!o||o.length===0)return i;for(let l of o){let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array();ne(l.box),ne(l.palmLandmarks),i.push(W6({startPoint:u,endPoint:d,palmLandmarks:p,confidence:l.confidence},[r/this.inputSize,s/this.inputSize]))}return i}};function S0e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function U6(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return S0e(n)}var G6=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Vo(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function C0e(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function H6(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Vo(e[r],C0e(t,a)))}return n}function hb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=G6(t[0],t[1]),o=H6(a,r),i=G6(-t[0],-t[1]);return H6(o,i)}function j6(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Vo(t[0],n),-Vo(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function fb(e,t){return[Vo(e,t[0]),Vo(e,t[1])]}var T0e=5,q6=1.65,X6=[0,5,9,13,17,1,2],N0e=0,E0e=2,mb=class{constructor(t,n){Te(this,"handDetector");Te(this,"handPoseModel");Te(this,"inputSize");Te(this,"storedBoxes");Te(this,"skipped");Te(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>fb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return g0(y0(r),T0e)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=g0(y0(n),q6);s.palmLandmarks=[];for(let r=0;r<X6.length;r++)s.palmLandmarks.push(t[X6[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=m0(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=hb(s,[0,0]),c=i.map(h=>[...fb(h,l),h[2]]),u=j6(r),d=[...Pp(n),1],p=[Vo(d,u[0]),Vo(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let a=[];for(let o=0;o<this.storedBoxes.length;o++){let i=this.storedBoxes[o];if(!!i)if(n.hand.landmarks){let l=n.hand.rotation?U6(i.palmLandmarks[N0e],i.palmLandmarks[E0e]):0,c=Pp(i),u=[c[0]/t.shape[2],c[1]/t.shape[1]],d=n.hand.rotation&&ie.kernels.includes("rotatewithoffset")?$e.rotateWithOffset(t,l,0,u):t.clone(),p=hb(-l,c),h=s?this.getBoxForPalmLandmarks(i.palmLandmarks,p):i,f=B6(h,d,[this.inputSize,this.inputSize]),m=fe(f,255);ne(f),ne(d);let[g,y]=await this.handPoseModel.predict(m);ne(m);let A=(await g.data())[0];if(ne(g),A>=n.hand.minConfidence/4){let x=G(y,[-1,3]),b=await x.array();ne(y),ne(x);let w=this.transformRawCoords(b,h,l,p),k=this.getBoxForHandLandmarks(w);this.storedBoxes[o]={...k,confidence:A};let S={landmarks:w,confidence:A,boxConfidence:i.confidence,fingerConfidence:A,box:{topLeft:k.startPoint,bottomRight:k.endPoint}};a.push(S)}else this.storedBoxes[o]=null;ne(y)}else{let l=g0(y0(i),q6),c={confidence:i.confidence,boxConfidence:i.confidence,fingerConfidence:0,box:{topLeft:l.startPoint,bottomRight:l.endPoint},landmarks:[]};a.push(c)}}return this.storedBoxes=this.storedBoxes.filter(o=>o!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var Je={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Je.nameMapping[e],getPoints:e=>Je.pointsMapping[e]},cs={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>cs.nameMapping[e]},Ze={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Ze.nameMapping[e]},A0=class{constructor(t){Te(this,"name");Te(this,"curls");Te(this,"directions");Te(this,"weights");Te(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}addCurl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}addDirection(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}setWeight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var Uo=new A0("thumbs up");Uo.addCurl(Je.thumb,cs.none,1);Uo.addDirection(Je.thumb,Ze.verticalUp,1);Uo.addDirection(Je.thumb,Ze.diagonalUpLeft,.25);Uo.addDirection(Je.thumb,Ze.diagonalUpRight,.25);for(let e of[Je.index,Je.middle,Je.ring,Je.pinky])Uo.addCurl(e,cs.full,1),Uo.addDirection(e,Ze.horizontalLeft,1),Uo.addDirection(e,Ze.horizontalRight,1);var Qt=new A0("victory");Qt.addCurl(Je.thumb,cs.half,.5);Qt.addCurl(Je.thumb,cs.none,.5);Qt.addDirection(Je.thumb,Ze.verticalUp,1);Qt.addDirection(Je.thumb,Ze.diagonalUpLeft,1);Qt.addCurl(Je.index,cs.none,1);Qt.addDirection(Je.index,Ze.verticalUp,.75);Qt.addDirection(Je.index,Ze.diagonalUpLeft,1);Qt.addCurl(Je.middle,cs.none,1);Qt.addDirection(Je.middle,Ze.verticalUp,1);Qt.addDirection(Je.middle,Ze.diagonalUpLeft,.75);Qt.addCurl(Je.ring,cs.full,1);Qt.addDirection(Je.ring,Ze.verticalUp,.2);Qt.addDirection(Je.ring,Ze.diagonalUpLeft,1);Qt.addDirection(Je.ring,Ze.horizontalLeft,.2);Qt.addCurl(Je.pinky,cs.full,1);Qt.addDirection(Je.pinky,Ze.verticalUp,.2);Qt.addDirection(Je.pinky,Ze.diagonalUpLeft,1);Qt.addDirection(Je.pinky,Ze.horizontalLeft,.2);Qt.setWeight(Je.index,2);Qt.setWeight(Je.middle,2);var K6=[Uo,Qt];var R0e=.7,Tl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function Z6(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function Y6(e,t){if(!e||!t)return[0,0];let n=Z6(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=Z6(e[1],e[2],t[1],t[2]);return[n,s]}function J6(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function $0e(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],c=e[2]-t[2],u=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+c*c),h=Math.sqrt(r*r+i*i+u*u),f=Math.sqrt(a*a+l*l+d*d),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Tl.NO_CURL_START_LIMIT?y=cs.none:g>Tl.HALF_CURL_START_LIMIT?y=cs.half:y=cs.full,y}function Q6(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=Ze.horizontalLeft:r=Ze.horizontalRight:s===Math.abs(t)?t>0?r=Ze.horizontalLeft:r=Ze.horizontalRight:n>0?r=Ze.horizontalLeft:r=Ze.horizontalRight,r}function e8(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=Ze.verticalDown:r=Ze.verticalUp:s===Math.abs(t)?t<0?r=Ze.verticalDown:r=Ze.verticalUp:n<0?r=Ze.verticalDown:r=Ze.verticalUp,r}function D0e(e,t,n,s,r,a,o,i){let l,c=e8(e,t,n,s),u=Q6(r,a,o,i);return c===Ze.verticalUp?u===Ze.horizontalLeft?l=Ze.diagonalUpLeft:l=Ze.diagonalUpRight:u===Ze.horizontalLeft?l=Ze.diagonalDownLeft:l=Ze.diagonalDownRight,l}function _0e(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],c=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),p=0,h=0,f=0,m=d/(u+1e-5);m>1.5?p+=Tl.DISTANCE_VOTE_POWER:m>.66?h+=Tl.DISTANCE_VOTE_POWER:f+=Tl.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),y=Math.sqrt(a*a+l*l),A=Math.sqrt(o*o+c*c),x=Math.max(g,y,A),b=e[0],w=e[1],k=n[0],S=n[1];x===g?(k=n[0],S=n[1]):x===A&&(b=t[0],w=t[1]);let P=Y6([b,w],[k,S]),$=J6(P,Tl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],h+=$[1],f+=$[2];for(let T of s){let O=J6(T,Tl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let D;return p===Math.max(p,h,f)?D=e8(l,i,c,d):f===Math.max(h,f)?D=Q6(a,r,o,u):D=D0e(l,i,c,d,a,r,o,u),D}function t8(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Je.all){let o=Je.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=Y6(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Je.all){let o=a===Je.thumb?1:0,i=Je.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=$0e(l,c,u),p=_0e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function x0(e){if(!e||e.length===0)return null;let t=t8(e),n={};for(let s of Je.all)n[Je.getName(s)]={curl:cs.getName(t.curls[s]),direction:Ze.getName(t.directions[s])};return n}function n8(e){let t=[];if(!e||e.length===0)return t;let n=t8(e);for(let s of K6){let r=s.matchAgainst(n.curls,n.directions);r>=R0e&&t.push({name:s.name,confidence:r})}return t}var s8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ca,da,r8;async function gb(e,t){let n=await r8.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let u of Object.keys(s8))a[u]=s8[u].map(d=>n[r].landmarks[d]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]<i[0]&&(i[0]=u[0]),u[1]<i[1]&&(i[1]=u[1]),u[0]>i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=x0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function yb(e){var n,s,r,a,o,i;ie.initial&&(ca=null,da=null),!ca||!da?([ca,da]=await Promise.all([e.hand.enabled?ut(ct(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 0:s.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?ut(ct(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((a=e.hand.skeleton)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!ca||!ca.modelUrl?ae("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&ae("load model:",ca.modelUrl),!da||!da.modelUrl?ae("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&ae("load model:",da.modelUrl))):(e.debug&&ae("cached model:",ca.modelUrl),e.debug&&ae("cached model:",da.modelUrl));let t=new pb(ca);return r8=new mb(t,da),[ca,da]}function Ab(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function a8(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function vc(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function Fp(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var Rt=[null,null],P0e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Go=[[0,0],[0,0]],F0e=["hand","fist","pinch","point","face","tip","pinchtip"],o8=1.6,O0e=512,M0e=1.2,Op=0,pa=[0,0],Xt={boxes:[],hands:[]},i8={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]};async function l8(e){var t;if(ie.initial&&(Rt[0]=null),Rt[0])e.debug&&ae("cached model:",Rt[0].modelUrl);else{wc(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Rt[0]=await ut(ct(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let n=Object.values(Rt[0].modelSignature.inputs);Go[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Go[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Rt[0]||!Rt[0].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Rt[0].modelUrl)}return Rt[0]}async function u8(e){var t;if(ie.initial&&(Rt[1]=null),Rt[1])e.debug&&ae("cached model:",Rt[1].modelUrl);else{Rt[1]=await ut(ct(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let n=Object.values(Rt[1].modelSignature.inputs);Go[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Go[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,!Rt[1]||!Rt[1].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Rt[1].modelUrl)}return Rt[1]}async function z0e(e,t){let n=[];if(!e||!Rt[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,O0e),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Rt[0].executeAsync(s.cast,P0e),s.boxes=dt(s.rawBoxes,[0,2]),s.scores=dt(s.rawScores,[0]);let i=Wn(s.scores,1);i.splice(4,1),s.filtered=Tn(i,1),ne(...i),s.max=Bn(s.filtered,1),s.argmax=_s(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=_e(s.boxes,p,1),f=await h.data();ne(h);let m=Math.max(f[3]-f[1],f[2]-f[0]),g=vc([f[1],f[0],m,m],M0e),y=Fp(g),A=[Math.trunc(f[1]*pa[0]),Math.trunc(f[0]*pa[1]),Math.trunc((f[3]-f[1])*pa[0]),Math.trunc((f[2]-f[0])*pa[1])],x=u[p],b=F0e[d[p]],w={id:l++,score:x,box:A,boxRaw:g,boxCrop:y,label:b};n.push(w)}return Object.keys(s).forEach(p=>ne(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function c8(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Rt[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[Go[1][0],Go[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=fe(r.cast,255),[r.score,r.keypoints]=Rt[1].execute(r.div);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=G(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/Go[1][1],u[1]/Go[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);console.log(pa,t.box),s.keypoints=c.map(u=>[pa[0]*u[0]+t.box[0],pa[1]*u[1]+t.box[1],u[2]||0]),s.landmarks=x0(s.keypoints);for(let u of Object.keys(i8))s.annotations[u]=i8[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>ne(r[i]))}return s}var Nl=0;async function xb(e,t){var n,s;return Nl++,!Rt[0]||!Rt[1]||!((n=Rt[0])==null?void 0:n.inputs[0].shape)||!((s=Rt[1])==null?void 0:s.inputs[0].shape)?[]:(pa=[e.shape[2]||0,e.shape[1]||0],Op++,t.skipFrame&&Op<=(t.hand.skipFrames||0)?(console.log(Nl,"SKIP",{results:Xt.hands.length}),Xt.hands):new Promise(async r=>{console.log(Nl,"DETECT",{skipped:Op,hands:Xt.hands.length,boxes:Xt.boxes.length}),t.skipFrame&&Op<=10*(t.hand.skipFrames||0)&&Xt.hands.length>0?(Xt.hands=await Promise.all(Xt.boxes.map(o=>c8(e,o,t))),console.log(Nl,"HANDS",{hands:Xt.hands.length})):(Xt.boxes=await z0e(e,t),console.log(Nl,"BOXES",{hands:Xt.boxes.length}),Xt.hands=await Promise.all(Xt.boxes.map(o=>c8(e,o,t))),console.log(Nl,"HANDS",{hands:Xt.hands.length}),Op=0);let a=[...Xt.boxes];if(Xt.boxes.length=0,t.cacheSensitivity>0){for(let o=0;o<Xt.hands.length;o++){let i=a8(Xt.hands[o].keypoints,pa);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&Xt.hands[o].fingerScore&&Xt.hands[o].fingerScore>(t.hand.minConfidence||0)){let l=vc(i.box,o8),c=vc(i.boxRaw,o8),u=Fp(c);Xt.boxes.push({...a[o],box:l,boxRaw:c,boxCrop:u})}}console.log(Nl,"CACHED",{hands:Xt.boxes.length})}r(Xt.hands)}))}var wb={};Mc(wb,{connected:()=>vb,kpt:()=>bb});var bb=["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","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],vb={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var d8={initial:!0},cn=[null,null],Ho=[[0,0],[0,0]],kb=Number.MAX_SAFE_INTEGER,Ib,Sb=null,jo=[[0,0],[0,0],[0,0],[0,0]];async function p8(e){var t,n;if(d8.initial&&(cn[0]=null),!cn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){cn[0]=await ut(ct(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let s=Object.values(cn[0].modelSignature.inputs);Ho[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,Ho[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!cn[0]||!cn[0].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",cn[0].modelUrl)}else e.debug&&cn[0]&&ae("cached model:",cn[0].modelUrl);return cn[0]}async function h8(e){var t;if(d8.initial&&(cn[1]=null),cn[1])e.debug&&ae("cached model:",cn[1].modelUrl);else{cn[1]=await ut(ct(e.modelBasePath,e.body.modelPath||""));let n=Object.values(cn[1].modelSignature.inputs);Ho[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Ho[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 0:t.includes("lite"))?Ib=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:Ib=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!cn[1]||!cn[1].modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",cn[1].modelUrl)}return cn[1]}function L0e(e,t){let n=e.map(o=>o.position[0]),s=e.map(o=>o.position[1]),r=[Math.min(...n),Math.min(...s),Math.max(...n)-Math.min(...n),Math.max(...s)-Math.min(...s)],a=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{keypointsBox:r,keypointsBoxRaw:a}}async function B0e(e){let t={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;jo=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],t.pad=ur(e,jo),t.resize=$e.resizeBilinear(t.pad,[Ho[1][0],Ho[1][1]]);let n=fe(t.resize,255);return Object.keys(t).forEach(s=>ne(t[s])),n}function W0e(e,t){for(let n of e)n.position=[n.position[0]*(t[0]+jo[2][0]+jo[2][1])/t[0]-jo[2][0],n.position[1]*(t[1]+jo[1][0]+jo[1][1])/t[1]-jo[1][0],n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];return e}async function V0e(e,t,n){var d;let s={};s.input=await B0e(e),[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=await((d=cn[1])==null?void 0:d.execute(s.input,Ib));let r=await s.ld.data(),a=[],o=5;for(let p=0;p<r.length/o;p++){let h=(100-Math.trunc(100/(1+Math.exp(r[o*p+3]))))/100,f=[r[o*p+0]/Ho[1][0],r[o*p+1]/Ho[1][1],r[o*p+2]+0],m=[Math.trunc(n[0]*f[0]),Math.trunc(n[1]*f[1]),f[2]];a.push({part:bb[p],positionRaw:f,position:m,score:h})}let i=Math.round(100*a.reduce((p,h)=>p+=h.score,0)/a.length)/100;if(i<(t.body.minConfidence||0))return null;let l=W0e(a,n),c=L0e(l,[n[0],n[1]]);Object.keys(s).forEach(p=>ne(s[p]));let u={};for(let[p,h]of Object.entries(vb)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(A=>A.part===h[m]),y=l.find(A=>A.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}u[p]=f}return{id:0,score:i,box:c.keypointsBox,boxRaw:c.keypointsBoxRaw,keypoints:l,annotations:u}}async function Cb(e,t){let n=[e.shape[2]||0,e.shape[1]||0];return kb<(t.body.skipFrames||0)&&t.skipFrame?kb++:(Sb=await V0e(e,t,n),kb=0),Sb?[Sb]:[]}var Eb={};Mc(Eb,{connected:()=>Nb,kpt:()=>Tb});var Tb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Nb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var dn,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Rb=Number.MAX_SAFE_INTEGER;async function $b(e){return ie.initial&&(dn=null),dn?e.debug&&ae("cached model:",dn.modelUrl):(dn=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!dn||!dn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",dn.modelUrl)),dn}function U0e(e,t){let[n,s]=e.shape;return j(()=>{let r=(i,l)=>xe(i,L(fe(i,Ee(l,"int32")),Ee(l,"int32"))),a=G(e,[s*n]),o=Bn(a,0).dataSync()[0];if(o>t){let i=_s(a,0),l=r(i,n).dataSync()[0],c=fe(i,Ee(n,"int32")).dataSync()[0];return[l,c,o]}return[0,0,o]})}async function Db(e,t){var n;return Rb<(((n=t.body)==null?void 0:n.skipFrames)||0)&&t.skipFrame&&Object.keys(Zn.keypoints).length>0?(Rb++,[Zn]):(Rb=0,new Promise(async s=>{var u;let r=j(()=>{if(!(dn==null?void 0:dn.inputs[0].shape))return null;let d=$e.resizeBilinear(e,[dn.inputs[0].shape[2],dn.inputs[0].shape[1]],!1);return L(d,2).sub(1)}),a;if(t.body.enabled&&(a=await(dn==null?void 0:dn.predict(r))),ne(r),a){Zn.keypoints.length=0;let d=a.squeeze();ne(a);let p=d.unstack(2);ne(d);for(let h=0;h<p.length;h++){let[f,m,g]=U0e(p[h],t.body.minConfidence);g>(((u=t.body)==null?void 0:u.minConfidence)||0)&&Zn.keypoints.push({score:Math.round(100*g)/100,part:Tb[h],positionRaw:[f/dn.inputs[0].shape[2],m/dn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/dn.inputs[0].shape[2]),Math.round(e.shape[1]*m/dn.inputs[0].shape[1])]})}p.forEach(h=>ne(h))}Zn.score=Zn.keypoints.reduce((d,p)=>p.score>d?p.score:d,0);let o=Zn.keypoints.map(d=>d.position[0]),i=Zn.keypoints.map(d=>d.position[1]);Zn.box=[Math.min(...o),Math.min(...i),Math.max(...o)-Math.min(...o),Math.max(...i)-Math.min(...i)];let l=Zn.keypoints.map(d=>d.positionRaw[0]),c=Zn.keypoints.map(d=>d.positionRaw[1]);Zn.boxRaw=[Math.min(...l),Math.min(...c),Math.max(...l)-Math.min(...l),Math.max(...c)-Math.min(...c)];for(let[d,p]of Object.entries(Nb)){let h=[];for(let f=0;f<p.length-1;f++){let m=Zn.keypoints.find(y=>y.part===p[f]),g=Zn.keypoints.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}Zn.annotations[d]=h}s([Zn])}))}var Pb={};Mc(Pb,{connected:()=>b0,kpt:()=>Mp,pairs:()=>_b});var Mp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_b=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],b0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var pn,El=0,G0e=1.5,Dn={boxes:[],bodies:[]},Fb=Number.MAX_SAFE_INTEGER,ds=[];async function f8(e){return ie.initial&&(pn=null),pn?e.debug&&ae("cached model:",pn.modelUrl):(wc(["size"],e),pn=await ut(ct(e.modelBasePath,e.body.modelPath||"")),!pn||!pn.modelUrl?ae("load model failed:",e.body.modelPath):e.debug&&ae("load model:",pn.modelUrl)),El=pn.inputs[0].shape?pn.inputs[0].shape[2]:0,El===-1&&(El=256),pn}function m8(){for(let e of _b){let t=ds.find(s=>s.part===e[0]),n=ds.find(s=>s.part===e[1]);if(t&&n&&t.position[0]>n.position[0]){let s=t;t=n,n=s}}}async function g8(e,t,n,s){let r=e[0][0];ds.length=0;let a=0;for(let c=0;c<r.length;c++)if(a=r[c][2],a>t.body.minConfidence){let u=[(s[3]-s[1])*r[c][1]+s[1],(s[2]-s[0])*r[c][0]+s[0]];ds.push({score:Math.round(100*a)/100,part:Mp[c],positionRaw:u,position:[Math.round((n.shape[2]||0)*u[0]),Math.round((n.shape[1]||0)*u[1])]})}m8(),a=ds.reduce((c,u)=>u.score>c?u.score:c,0);let o=[],i=Ab(ds.map(c=>c.position),[n.shape[2],n.shape[1]]),l={};for(let[c,u]of Object.entries(b0)){let d=[];for(let p=0;p<u.length-1;p++){let h=ds.find(m=>m.part===u[p]),f=ds.find(m=>m.part===u[p+1]);h&&f&&h.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&d.push([h.position,f.position])}l[c]=d}return o.push({id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:ds,annotations:l}),o}async function y8(e,t,n,s){let r=[];for(let a=0;a<e[0].length;a++){let o=e[0][a],i=Math.round(100*o[51+4])/100;if(i>t.body.minConfidence){ds.length=0;for(let u=0;u<17;u++){let d=o[3*u+2];if(d>t.body.minConfidence){let p=[(s[3]-s[1])*o[3*u+1]+s[1],(s[2]-s[0])*o[3*u+0]+s[0]];ds.push({part:Mp[u],score:Math.round(100*d)/100,positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}}m8();let l=Ab(ds.map(u=>u.position),[n.shape[2],n.shape[1]]),c={};for(let[u,d]of Object.entries(b0)){let p=[];for(let h=0;h<d.length-1;h++){let f=ds.find(g=>g.part===d[h]),m=ds.find(g=>g.part===d[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}c[u]=p}r.push({id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...ds],annotations:c})}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function Ob(e,t){return!pn||!(pn==null?void 0:pn.inputs[0].shape)?[]:(t.skipFrame||(Dn.boxes.length=0),Fb++,t.skipFrame&&Fb<=(t.body.skipFrames||0)?Dn.bodies:new Promise(async n=>{let s={};if(Fb=0,Dn.bodies=[],Dn.boxes.length>=(t.body.maxDetected||0))for(let r=0;r<Dn.boxes.length;r++){s.crop=$e.cropAndResize(e,[Dn.boxes[r]],[0],[El,El],"bilinear"),s.cast=pe(s.crop,"int32"),s.res=await(pn==null?void 0:pn.predict(s.cast));let a=await s.res.array(),o=s.res.shape[2]===17?await g8(a,t,e,Dn.boxes[r]):await y8(a,t,e,Dn.boxes[r]);Dn.bodies=Dn.bodies.concat(o),Object.keys(s).forEach(i=>ne(s[i]))}if(Dn.bodies.length!==t.body.maxDetected){s.resized=$e.resizeBilinear(e,[El,El],!1),s.cast=pe(s.resized,"int32"),s.res=await(pn==null?void 0:pn.predict(s.cast));let r=await s.res.array();Dn.bodies=s.res.shape[2]===17?await g8(r,t,e,[0,0,1,1]):await y8(r,t,e,[0,0,1,1]),Object.keys(s).forEach(a=>ne(s[a]))}Dn.boxes.length=0;for(let r=0;r<Dn.bodies.length;r++)if(Dn.bodies[r].keypoints.length>Mp.length/2){let a=vc(Dn.bodies[r].boxRaw,G0e),o=Fp(a);Dn.boxes.push(o)}n(Dn.bodies)}))}var kc=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Ts,v0=[],Mb=Number.MAX_SAFE_INTEGER,w0=2.5;async function A8(e){if(!Ts||ie.initial){Ts=await ut(ct(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Ts.modelSignature.inputs);if(Ts.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Ts.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!Ts||!Ts.modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",Ts.modelUrl)}else e.debug&&ae("cached model:",Ts.modelUrl);return Ts}async function H0e(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])j(async()=>{var g,y;let u=c*13,d=(g=e.find(A=>A.shape[1]===u**2&&A.shape[2]===kc.length))==null?void 0:g.squeeze(),p=(y=e.find(A=>A.shape[1]===u**2&&A.shape[2]<kc.length))==null?void 0:y.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let A=0;A<d.shape[0];A++)for(let x=0;x<d.shape[1];x++){let b=m[A][x];if(b>s.object.minConfidence&&x!==61){let w=(.5+Math.trunc(A%u))/u,k=(.5+Math.trunc(A/u))/u,S=f[A].map(B=>B*(u/c/t)),[N,R]=[w-w0/c*S[0],k-w0/c*S[1]],[P,$]=[w+w0/c*S[2]-N,k+w0/c*S[3]-R],D=[N,R,P,$];D=D.map(B=>Math.max(0,Math.min(B,1)));let T=[D[0]*n[0],D[1]*n[1],D[2]*n[0],D[3]*n[1]],O={id:r++,score:Math.round(100*b)/100,class:x+1,label:kc[x].label,box:T.map(B=>Math.trunc(B)),boxRaw:D};a.push(O)}}});e.forEach(c=>ne(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await $e.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),ne(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function zb(e,t){return Mb<(t.object.skipFrames||0)&&t.skipFrame&&v0.length>0?(Mb++,v0):(Mb=0,!ie.kernels.includes("mod")||!ie.kernels.includes("sparsetodense")?v0:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=$e.resizeBilinear(e,[Ts.inputSize,Ts.inputSize],!1),a=fe(r,255),o=a.transpose([0,3,1,2]);ne(a),ne(r);let i;t.object.enabled&&(i=await Ts.predict(o)),ne(o);let l=await H0e(i,Ts.inputSize,s,t);v0=l,n(l)}))}var rr,Rl=0,k0=[],Lb=Number.MAX_SAFE_INTEGER;async function x8(e){if(ie.initial&&(rr=null),rr)e.debug&&ae("cached model:",rr.modelUrl);else{wc(["floormod"],e),rr=await ut(ct(e.modelBasePath,e.object.modelPath||""));let t=Object.values(rr.modelSignature.inputs);Rl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!rr||!rr.modelUrl?ae("load model failed:",e.object.modelPath):e.debug&&ae("load model:",rr.modelUrl)}return rr}async function j0e(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=dt(e);ne(e);let o=xn(a,6,1);ne(a);let i=Tn([o[1],o[0],o[3],o[2]],1),l=dt(i);ne(i);let c=dt(o[4]),u=dt(o[5]);o.forEach(f=>ne(f));let d=await $e.nonMaxSuppressionAsync(l,c,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);ne(l),ne(c),ne(u);let p=await d.data();ne(d);let h=0;for(let f of p){let m=Math.trunc(100*r[0][f][4])/100,g=r[0][f][5],y=kc[g].label,[A,x]=[r[0][f][0]/Rl,r[0][f][1]/Rl],b=[A,x,r[0][f][2]/Rl-A,r[0][f][3]/Rl-x],w=[Math.trunc(b[0]*t[0]),Math.trunc(b[1]*t[1]),Math.trunc(b[2]*t[0]),Math.trunc(b[3]*t[1])];s.push({id:h++,score:m,class:g,label:y,box:w,boxRaw:b})}return s}async function Bb(e,t){return Lb<(t.object.skipFrames||0)&&t.skipFrame&&k0.length>0?(Lb++,k0):(Lb=0,!ie.kernels.includes("mod")||!ie.kernels.includes("sparsetodense")?k0:new Promise(async n=>{let s=[e.shape[2],e.shape[1]],r=$e.resizeBilinear(e,[Rl,Rl]),a=t.object.enabled?rr==null?void 0:rr.execute(r,["tower_0/detections"]):null;ne(r);let o=await j0e(a,s,t);k0=o,n(o)}))}var Us,Wb=!1;async function Vb(e){return!Us||ie.initial?(Us=await ut(ct(e.modelBasePath,e.segmentation.modelPath||"")),!Us||!Us.modelUrl?ae("load model failed:",e.segmentation.modelPath):e.debug&&ae("load model:",Us.modelUrl)):e.debug&&ae("cached model:",Us.modelUrl),Us}async function b8(e,t,n){var m,g;if(Wb)return{data:[],canvas:null,alpha:null};Wb=!0,Us||await Vb(n);let s=yc(e,n),r=((m=s.canvas)==null?void 0:m.width)||0,a=((g=s.canvas)==null?void 0:g.height)||0;if(!s.tensor)return{data:[],canvas:null,alpha:null};let o={};o.resize=$e.resizeBilinear(s.tensor,[Us.inputs[0].shape?Us.inputs[0].shape[1]:0,Us.inputs[0].shape?Us.inputs[0].shape[2]:0],!1),ne(s.tensor),o.norm=fe(o.resize,255),o.res=Us.predict(o.norm),o.squeeze=dt(o.res,0),o.squeeze.shape[2]===2?(o.softmax=tl(o.squeeze),[o.bg,o.fg]=Wn(o.softmax,2),o.expand=Ht(o.fg,2),o.pad=Ht(o.expand,0),o.crop=$e.cropAndResize(o.pad,[[0,0,.5,.5]],[0],[r,a]),o.data=dt(o.crop,0)):o.data=$e.resizeBilinear(o.squeeze,[a,r]);let i=Array.from(await o.data.data());if(ie.node&&!ie.Canvas&&typeof ImageData=="undefined")return n.debug&&ae("canvas support missing"),Object.keys(o).forEach(y=>ne(o[y])),{data:i,canvas:null,alpha:null};let l=Bs(r,a);await Xs.toPixels(o.data,l);let c=l.getContext("2d");n.segmentation.blur&&n.segmentation.blur>0&&(c.filter=`blur(${n.segmentation.blur}px)`);let u=c.getImageData(0,0,r,a),d=Bs(r,a),p=d.getContext("2d");s.canvas&&p.drawImage(s.canvas,0,0),p.globalCompositeOperation="darken",n.segmentation.blur&&n.segmentation.blur>0&&(p.filter=`blur(${n.segmentation.blur}px)`),p.drawImage(l,0,0),p.globalCompositeOperation="source-over",p.filter="none";let h=p.getImageData(0,0,r,a);for(let y=0;y<r*a;y++)h.data[4*y+3]=u.data[4*y+0];p.putImageData(h,0,0);let f=null;if(t&&d){f=Bs(r,a);let y=yc(t,n);ne(y.tensor);let A=f.getContext("2d");A.drawImage(y.canvas,0,0,f.width,f.height),A.drawImage(d,0,0)}return Object.keys(o).forEach(y=>ne(o[y])),Wb=!1,{data:i,canvas:f||d,alpha:l}}var qo;var b1e=Number.MAX_SAFE_INTEGER;async function v8(e){return ie.initial&&(qo=null),qo?e.debug&&ae("cached model:",qo.modelUrl):(qo=await ut(ct(e.modelBasePath,e.face.agegenderrace.modelPath)),!qo||!qo.modelUrl?ae("load model failed:",e.face.agegenderrace.modelPath):e.debug&&ae("load model:",qo.modelUrl)),qo}var zp=class{constructor(){Te(this,"age",null);Te(this,"agegenderrace",null);Te(this,"blazeposedetect",null);Te(this,"blazepose",null);Te(this,"centernet",null);Te(this,"efficientpose",null);Te(this,"embedding",null);Te(this,"emotion",null);Te(this,"facedetect",null);Te(this,"faceiris",null);Te(this,"facemesh",null);Te(this,"faceres",null);Te(this,"gender",null);Te(this,"handpose",null);Te(this,"handskeleton",null);Te(this,"handtrack",null);Te(this,"movenet",null);Te(this,"nanodet",null);Te(this,"posenet",null);Te(this,"segmentation",null)}};function Ub(e){for(let t of Object.keys(e.models))e.models[t]=null}async function w8(e){var t,n,s,r,a,o,i,l,c,u,d,p,h,f,m,g,y,A,x,b,w,k,S,N,R,P,$,D,T,O;ie.initial&&Ub(e),e.config.hand.enabled&&(!e.models.handpose&&((n=(t=e.config.hand.detector)==null?void 0:t.modelPath)==null?void 0:n.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await yb(e.config)),!e.models.handskeleton&&e.config.hand.landmarks&&((r=(s=e.config.hand.detector)==null?void 0:s.modelPath)==null?void 0:r.includes("handdetect"))&&([e.models.handpose,e.models.handskeleton]=await yb(e.config))),e.config.face.enabled&&!e.models.facedetect&&(e.models.facedetect=y6(e.config)),e.config.face.enabled&&((a=e.config.face.mesh)==null?void 0:a.enabled)&&!e.models.facemesh&&(e.models.facemesh=C6(e.config)),e.config.face.enabled&&((o=e.config.face.iris)==null?void 0:o.enabled)&&!e.models.faceiris&&(e.models.faceiris=x6(e.config)),e.config.hand.enabled&&!e.models.handtrack&&((l=(i=e.config.hand.detector)==null?void 0:i.modelPath)==null?void 0:l.includes("handtrack"))&&(e.models.handtrack=l8(e.config)),e.config.hand.enabled&&e.config.hand.landmarks&&!e.models.handskeleton&&((u=(c=e.config.hand.detector)==null?void 0:c.modelPath)==null?void 0:u.includes("handtrack"))&&(e.models.handskeleton=u8(e.config)),e.config.body.enabled&&!e.models.posenet&&((p=(d=e.config.body)==null?void 0:d.modelPath)==null?void 0:p.includes("posenet"))&&(e.models.posenet=L6(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((f=(h=e.config.body)==null?void 0:h.modelPath)==null?void 0:f.includes("efficientpose"))&&(e.models.efficientpose=$b(e.config)),e.config.body.enabled&&!e.models.blazepose&&((g=(m=e.config.body)==null?void 0:m.modelPath)==null?void 0:g.includes("blazepose"))&&(e.models.blazepose=h8(e.config)),e.config.body.enabled&&!e.models.blazeposedetect&&((y=e.config.body.detector)==null?void 0:y.modelPath)&&((x=(A=e.config.body)==null?void 0:A.modelPath)==null?void 0:x.includes("blazepose"))&&(e.models.blazeposedetect=p8(e.config)),e.config.body.enabled&&!e.models.efficientpose&&((w=(b=e.config.body)==null?void 0:b.modelPath)==null?void 0:w.includes("efficientpose"))&&(e.models.efficientpose=$b(e.config)),e.config.body.enabled&&!e.models.movenet&&((S=(k=e.config.body)==null?void 0:k.modelPath)==null?void 0:S.includes("movenet"))&&(e.models.movenet=f8(e.config)),e.config.object.enabled&&!e.models.nanodet&&((R=(N=e.config.object)==null?void 0:N.modelPath)==null?void 0:R.includes("nanodet"))&&(e.models.nanodet=A8(e.config)),e.config.object.enabled&&!e.models.centernet&&(($=(P=e.config.object)==null?void 0:P.modelPath)==null?void 0:$.includes("centernet"))&&(e.models.centernet=x8(e.config)),e.config.face.enabled&&((D=e.config.face.emotion)==null?void 0:D.enabled)&&!e.models.emotion&&(e.models.emotion=D6(e.config)),e.config.face.enabled&&((T=e.config.face.description)==null?void 0:T.enabled)&&!e.models.faceres&&(e.models.faceres=R6(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=Vb(e.config)),e.config.face.enabled&&((O=e.config.face.agegenderrace)==null?void 0:O.enabled)&&!e.models.agegenderrace&&(e.models.agegenderrace=v8(e.config));for await(let B of Object.keys(e.models))e.models[B]&&typeof e.models[B]!="undefined"&&(e.models[B]=await e.models[B])}async function k8(e){let t=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"];for(let n of Object.keys(e.models))if(e.models[n]){let s=[];Array.isArray(e.models[n])?s=e.models[n].filter(r=>r!==null).map(r=>r&&r.executor?r:r.model):s=[e.models[n]];for(let r of s){if(!r){e.config.debug&&ae("model marked as loaded but not defined:",n);continue}let a=[],o=r==null?void 0:r.executor;if(o&&o.graph.nodes)for(let l of Object.values(o.graph.nodes)){let c=l.op.toLowerCase();a.includes(c)||a.push(c)}else!o&&e.config.debug&&ae("model signature not determined:",n);let i=[];for(let l of a)!t.includes(l)&&!e.env.kernels.includes(l)&&!e.env.kernels.includes(l.replace("_",""))&&!e.env.kernels.includes(l.replace("native",""))&&!e.env.kernels.includes(l.replace("v2",""))&&i.push(l);i.length>0&&e.config.debug&&ae("model validation:",n,i)}}}var Bt={name:"humangl",priority:999,canvas:null,gl:null,extensions:[],webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function q0e(){let e=Bt.gl;!e||(Bt.extensions=e.getSupportedExtensions())}async function I8(e){var t;if(e.config.backend==="humangl"&&(Bt.name in ts().registry&&(!Bt.gl||!Bt.gl.getParameter(Bt.gl.VERSION))&&(ae("error: humangl backend invalid context"),Ub(e)),!W2(Bt.name))){try{Bt.canvas=await Bs(100,100)}catch(s){ae("error: cannot create canvas:",s);return}try{Bt.gl=(t=Bt.canvas)==null?void 0:t.getContext("webgl2",Bt.webGLattr),Bt.canvas&&(Bt.canvas.addEventListener("webglcontextlost",async s=>{throw ae("error: humangl:",s.type),ae("possible browser memory leak using webgl or conflict with multiple backend registrations"),e.emit("error"),new Error("browser webgl error")}),Bt.canvas.addEventListener("webglcontextrestored",s=>{ae("error: humangl context restored:",s)}),Bt.canvas.addEventListener("webglcontextcreationerror",s=>{ae("error: humangl context create:",s)}))}catch(s){ae("error: cannot get WebGL context:",s);return}try{Rm(2,Bt.gl)}catch(s){ae("error: cannot set WebGL context:",s);return}try{let s=new Lm(Bt.gl);qi(Bt.name,()=>new ic(s),Bt.priority)}catch(s){ae("error: cannot register WebGL backend:",s);return}try{Zr("webgl").forEach(r=>{let a={...r,backendName:Bt.name};Yr(a)})}catch(s){ae("error: cannot update WebGL backend registration:",s);return}let n=Tr().getGPGPUContext?Tr().getGPGPUContext().gl:null;if(n)ae(`humangl webgl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`);else{ae("error: no current gl context:",n,Bt.gl);return}try{qs.set("WEBGL_VERSION",2)}catch(s){ae("error: cannot set WebGL backend flags:",s);return}q0e(),ae("backend registered:",Bt.name)}}async function I0(e,t=!1){if(e.state="backend",t||ie.initial||e.config.backend&&e.config.backend.length>0&&lr()!==e.config.backend){let n=ot();if(e.config.backend&&e.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&e.config.debug&&e.config.debug&&ae("running inside web worker"),ie.browser&&e.config.backend==="tensorflow"&&(e.config.debug&&ae("override: backend set to tensorflow while running in browser"),e.config.backend="humangl"),ie.node&&(e.config.backend==="webgl"||e.config.backend==="humangl")&&(e.config.debug&&ae(`override: backend set to ${e.config.backend} while running in nodejs`),e.config.backend="tensorflow"),ie.browser&&e.config.backend==="webgpu")if(typeof navigator=="undefined"||typeof navigator.gpu=="undefined")ae("override: backend set to webgpu but browser does not support webgpu"),e.config.backend="humangl";else{let r=await navigator.gpu.requestAdapter();e.config.debug&&ae("enumerated webgpu adapter:",r)}e.config.backend==="humangl"&&await I8(e);let s=Object.keys(ts().registryFactory);if(e.config.debug&&ae("available backends:",s),s.includes(e.config.backend)||(ae(`error: backend ${e.config.backend} not found in registry`),e.config.backend=ie.node?"tensorflow":"webgl",e.config.debug&&ae(`override: setting backend ${e.config.backend}`)),e.config.debug&&ae("setting backend:",e.config.backend),e.config.backend==="wasm"){if(e.config.debug&&ae("wasm path:",e.config.wasmPath),typeof(Il==null?void 0:Il.setWasmPaths)!="undefined")await vC(e.config.wasmPath);else throw new Error("wasm backend is not loaded");let r=await Z().getAsync("WASM_HAS_SIMD_SUPPORT"),a=await Z().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");e.config.debug&&ae(`wasm execution: ${r?"SIMD":"no SIMD"} ${a?"multithreaded":"singlethreaded"}`),e.config.debug&&!r&&ae("warning: wasm simd support is not enabled")}try{await E3(e.config.backend),await tf()}catch(r){return ae("error: cannot set backend:",e.config.backend,r),!1}}if(lr()==="humangl"&&(qs.set("CHECK_COMPUTATION_FOR_ERRORS",!1),qs.set("WEBGL_CPU_FORWARD",!0),qs.set("WEBGL_PACK_DEPTHWISECONV",!1),qs.set("WEBGL_USE_SHAPES_UNIFORMS",!0),qs.set("CPU_HANDOFF_SIZE_THRESHOLD",256),typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(ae("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),qs.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0)),Tr().getGPGPUContext)){let s=await Tr().getGPGPUContext().gl;e.config.debug&&ae(`gl version:${s.getParameter(s.VERSION)} renderer:${s.getParameter(s.RENDERER)}`)}lr()==="webgpu"&&qs.set("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",256),N3(),await tf(),e.performance.backend=Math.trunc(ot()-n),e.config.backend=lr(),o0(),e.env=ie}return!0}function wc(e,t){for(let n of e){let s={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&ae("kernelFunc",n,t.backend)}};Yr(s)}ie.kernels=Zr(lr()).map(n=>n.kernelName.toLowerCase())}var ha={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},$l=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("invalid canvas")},Ic=e=>Math.round(e*180/Math.PI);function Gb(e,t,n,s=0,r){e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Lp(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+s-a.roundRect,n),e.quadraticCurveTo(t+s,n,t+s,n+a.roundRect),e.lineTo(t+s,n+r-a.roundRect),e.quadraticCurveTo(t+s,n+r,t+s-a.roundRect,n+r),e.lineTo(t+a.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function S8(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let s of t){let r=s[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function X0e(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){S8(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function C8(e,t,n,s=5){let r,a,o;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.moveTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),e.closePath(),e.stroke(),e.fill()}async function Hb(e,t,n){let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let c=i[1]>0?`#${i[1]}`:"",u=`${i[0]} ${c}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(u,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(u,6,0+a*s.lineHeight),a+=1}}}async function jb(e,t,n){var a,o,i,l,c;let s=fn(ha,n);if(!t||!e)return;let r=$l(e);for(let u of t){r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Lp(r,u.box[0],u.box[1],u.box[2],u.box[3],s);let d=[];if(d.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`distance: ${u.iris}`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&d.push(`roll: ${Ic(u.rotation.angle.roll)}\xB0 yaw:${Ic(u.rotation.angle.yaw)}\xB0 pitch:${Ic(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${Ic(u.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=s.color;for(let p=d.length-1;p>=0;p--){let h=Math.max(u.box[0],0),f=p*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(d[p],h+5,f+16)),r.fillStyle=s.labelColor,r.fillText(d[p],h+4,f+15)}if(r.lineWidth=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let p of u.mesh)Gb(r,p[0],p[1],p[2],s);if(s.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let p=0;p<Sl.length/3;p++){let h=[Sl[p*3+0],Sl[p*3+1],Sl[p*3+2]].map(f=>u.mesh[f]);S8(r,h,s)}if(u.annotations&&u.annotations.leftEyeIris&&u.annotations.leftEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let p=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,h=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],p,h,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let p=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,h=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],p,h,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((a=u.rotation)==null?void 0:a.angle)){r.strokeStyle="pink";let p=u.box[0]+u.box[2]/2-u.box[3]*Ic(u.rotation.angle.yaw)/90,h=u.box[1]+u.box[3]/2+u.box[2]*Ic(u.rotation.angle.pitch)/90,f=new Path2D(`
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
C
${p} ${u.box[1]},
${p} ${u.box[1]+u.box[3]},
${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
`),m=new Path2D(`
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
C
${u.box[0]} ${h},
${u.box[0]+u.box[2]} ${h},
${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
`);r.stroke(m),r.stroke(f)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let p=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];C8(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[p[0],p[1]],4);let h=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];C8(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[h[0],h[1]],4)}}}}}async function qb(e,t,n){var a;let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(Lp(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Gb(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)X0e(r,l,s)}}async function Xb(e,t,n){let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Lp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Gb(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+2*i[l][2]}, ${127.5-2*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function Kb(e,t,n){let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Lp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function T8(e,t,n){let s=fn(ha,n);if(!t||!e)return;let r=$l(e);r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Lp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}async function N8(e,t){if(!e||!t)return;$l(t).drawImage(e,0,0)}async function E8(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ot(),r=fn(ha,n),a=Promise.all([jb(e,t.face,r),qb(e,t.body,r),Xb(e,t.hand,r),Kb(e,t.object,r),Hb(e,t.gesture,r)]);return t.performance.draw=Math.trunc(ot()-s),a}var K0e=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},R8=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},s=(g,y)=>{let A=g[0]-y[0],x=g[1]-y[1],b=g[2]-y[2];return[A,x,b]},r=(g,y)=>{let A=g[1]*y[2]-g[2]*y[1],x=g[2]*y[0]-g[0]*y[2],b=g[0]*y[1]-g[1]*y[0];return[A,x,b]},a=g=>{let[y,A,x,b,w,k,S,N,R]=g,P,$,D;return b<1?b>-1?(D=Math.asin(b),$=Math.atan2(-S,y),P=Math.atan2(-k,w)):(D=-Math.PI/2,$=-Math.atan2(N,R),P=0):(D=Math.PI/2,$=Math.atan2(N,R),P=0),isNaN(P)&&(P=0),isNaN($)&&($=0),isNaN(D)&&(D=0),{pitch:2*-P,yaw:2*-$,roll:2*-D}},o=g=>{let y=(x,b,w,k)=>Math.atan2(k-b,w-x);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?K0e(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var Zb=async(e,t)=>{var d,p,h,f;let n,s,r,a,o,i,l,c=[];e.state="run:face",n=ot();let u=await S6(t,e.config);if(e.performance.face=Math.trunc(ot()-n),!t.shape||t.shape.length!==4)return[];if(!u)return[];for(let m=0;m<u.length;m++){if(e.analyze("Get Face"),!u[m].tensor||u[m].tensor.isDisposedInternal){ae("Face object is disposed:",u[m].tensor);continue}let g=R8(u[m],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?ab(u[m].tensor||nn([]),e.config,m,u.length):{}:(e.state="run:emotion",n=ot(),o=e.config.face.emotion.enabled?await ab(u[m].tensor||nn([]),e.config,m,u.length):{},e.performance.emotion=Math.trunc(ot()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?l=e.config.face.description.enabled?nb(u[m].tensor||nn([]),e.config,m,u.length):[]:(e.state="run:description",n=ot(),l=e.config.face.description.enabled?await nb(u[m].tensor||nn([]),e.config,m,u.length):[],e.performance.embedding=Math.trunc(ot()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,r]=await Promise.all([s,a,o,i,l,r])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=u[m])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((f=(h=u[m])==null?void 0:h.annotations)==null?void 0:f.rightEyeIris)&&(delete u[m].annotations.leftEyeIris,delete u[m].annotations.rightEyeIris);let y=u[m].annotations&&u[m].annotations.leftEyeIris&&u[m].annotations.leftEyeIris[0]&&u[m].annotations.rightEyeIris&&u[m].annotations.rightEyeIris[0]&&u[m].annotations.leftEyeIris.length>0&&u[m].annotations.rightEyeIris.length>0&&u[m].annotations.leftEyeIris[0]!==null&&u[m].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(u[m].annotations.leftEyeIris[3][0]-u[m].annotations.leftEyeIris[1][0]),Math.abs(u[m].annotations.rightEyeIris[4][1]-u[m].annotations.rightEyeIris[2][1]))/t.shape[2]:0,A=e.config.face.detector.return?dt(u[m].tensor):null;ne(u[m].tensor),u[m].tensor&&delete u[m].tensor,c.push({...u[m],id:m,age:l.age,gender:l.gender,genderScore:l.genderScore,embedding:l.descriptor,emotion:o,iris:y!==0?Math.trunc(500/y/11.7)/100:0,rotation:g,tensor:A}),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),c};var $8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},D8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=e[n].mesh[33][2]-e[n].mesh[263][2];Math.abs(s)<10?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let o=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]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},_8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(c=!1),p>.06&&t.push({iris:n,gesture:"looking right"}),d>.06&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},P8=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=n8(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Pe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function F8(e,t){var o,i,l,c,u,d,p,h,f,m,g,y,A,x,b,w,k,S,N,R,P,$,D,T,O,B,H;let n=performance.now();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(Pe.canvas=e.canvas,!Pe.body||e.body.length!==Pe.body.length)Pe.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let X=e.body[z].box.map((K,oe)=>((r-1)*Pe.body[z].box[oe]+K)/r),ee=e.body[z].boxRaw.map((K,oe)=>((r-1)*Pe.body[z].boxRaw[oe]+K)/r),J=e.body[z].keypoints.map((K,oe)=>({score:K.score,part:K.part,position:[Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].position[0]+K.position[0])/r:K.position[0],Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].position[1]+K.position[1])/r:K.position[1]],positionRaw:[Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].positionRaw[0]+K.positionRaw[0])/r:K.position[0],Pe.body[z].keypoints[oe]?((r-1)*Pe.body[z].keypoints[oe].positionRaw[1]+K.positionRaw[1])/r:K.position[1]]})),Q={},te={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?te=Eb:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?te=wb:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(te=Pb);for(let[K,oe]of Object.entries(te.connected)){let ce=[];for(let he=0;he<oe.length-1;he++){let Ae=J.find(Se=>Se.part===oe[he]),Ie=J.find(Se=>Se.part===oe[he+1]);Ae&&Ie&&Ae.score>(t.body.minConfidence||0)&&Ie.score>(t.body.minConfidence||0)&&ce.push([Ae.position,Ie.position])}Q[K]=ce}Pe.body[z]={...e.body[z],box:X,boxRaw:ee,keypoints:J,annotations:Q}}if(!Pe.hand||e.hand.length!==Pe.hand.length)Pe.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let X=e.hand[z].box.map((te,K)=>((r-1)*Pe.hand[z].box[K]+te)/r),ee=e.hand[z].boxRaw.map((te,K)=>((r-1)*Pe.hand[z].boxRaw[K]+te)/r);Pe.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Pe.hand[z].keypoints=e.hand[z].keypoints);let J=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((te,K)=>te.map((oe,ce)=>((r-1)*(Pe.hand[z].keypoints[K][ce]||1)+(oe||0))/r)):[],Q={};if(Object.keys(Pe.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Pe.hand[z].annotations=e.hand[z].annotations,Q=Pe.hand[z].annotations;else if(e.hand[z].annotations)for(let te of Object.keys(e.hand[z].annotations))Q[te]=e.hand[z].annotations[te]&&e.hand[z].annotations[te][0]?e.hand[z].annotations[te].map((K,oe)=>K.map((ce,he)=>((r-1)*Pe.hand[z].annotations[te][oe][he]+ce)/r)):null;Pe.hand[z]={...e.hand[z],box:X,boxRaw:ee,keypoints:J,annotations:Q}}if(!Pe.face||e.face.length!==Pe.face.length)Pe.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let X=e.face[z].box.map((Q,te)=>((r-1)*Pe.face[z].box[te]+Q)/r),ee=e.face[z].boxRaw.map((Q,te)=>((r-1)*Pe.face[z].boxRaw[te]+Q)/r),J={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};J.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,J.angle={roll:((r-1)*(((f=(h=Pe.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((A=(y=Pe.face[z].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[z].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(w=Pe.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((N=(S=e.face[z].rotation)==null?void 0:S.angle)==null?void 0:N.pitch)||0))/r},J.gaze={bearing:((r-1)*(((P=(R=Pe.face[z].rotation)==null?void 0:R.gaze)==null?void 0:P.bearing)||0)+(((D=($=e.face[z].rotation)==null?void 0:$.gaze)==null?void 0:D.bearing)||0))/r,strength:((r-1)*(((O=(T=Pe.face[z].rotation)==null?void 0:T.gaze)==null?void 0:O.strength)||0)+(((H=(B=e.face[z].rotation)==null?void 0:B.gaze)==null?void 0:H.strength)||0))/r},Pe.face[z]={...e.face[z],rotation:J,box:X,boxRaw:ee}}if(!Pe.object||e.object.length!==Pe.object.length)Pe.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let X=e.object[z].box.map((J,Q)=>((r-1)*Pe.object[z].box[Q]+J)/r),ee=e.object[z].boxRaw.map((J,Q)=>((r-1)*Pe.object[z].boxRaw[Q]+J)/r);Pe.object[z]={...e.object[z],box:X,boxRaw:ee}}if(e.persons){let z=e.persons;if(!Pe.persons||z.length!==Pe.persons.length)Pe.persons=JSON.parse(JSON.stringify(z));else for(let X=0;X<z.length;X++)Pe.persons[X].box=z[X].box.map((ee,J)=>((r-1)*Pe.persons[X].box[J]+ee)/r)}e.gesture&&(Pe.gesture=e.gesture);let a=performance.now();return e.performance&&(Pe.performance={...e.performance,interpolate:Math.round(a-n)}),Pe}function S0(e,t,n={order:2}){let s=0;for(let r=0;r<e.length;r++){let a=n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);s+=n.order===2?a*a:a**n.order}return s}function O8(e,t,n={order:2}){let s=S0(e,t,n),r=n.order===2?Math.sqrt(s):s**(1/n.order);return Math.max(0,100-r)/100}function M8(e,t,n={order:2,threshold:0}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let s=Number.MAX_SAFE_INTEGER,r=-1;for(let a=0;a<t.length;a++){let o=S0(e,t[a],{order:n.order});if(o<s&&(s=o,r=a),s<n.threshold)break}return s=n.order===2?Math.sqrt(s):s**(1/n.order),{index:r,distance:s,similarity:Math.max(0,100-s)/100}}function z8(e,t,n,s,r){var i,l,c,u,d,p,h,f,m,g,y,A,x,b,w,k;let a=0,o=[];for(let S of e){let N={id:a++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of t)S.box[0]>O.box[0]&&S.box[0]<O.box[0]+O.box[2]&&S.box[1]+S.box[3]>O.box[1]&&S.box[1]+S.box[3]<O.box[1]+O.box[3]&&(N.body=O);if(N.body)for(let O of n)O.box[0]+O.box[2]>N.body.box[0]&&O.box[0]+O.box[2]<N.body.box[0]+N.body.box[2]&&O.box[1]+O.box[3]>N.body.box[1]&&O.box[1]+O.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.left=O),O.box[0]<N.body.box[0]+N.body.box[2]&&O.box[0]>N.body.box[0]&&O.box[1]+O.box[3]>N.body.box[1]&&O.box[1]+O.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.right=O);for(let O of s)O.face!==void 0&&O.face===S.id?(i=N.gestures)==null||i.push(O):O.iris!==void 0&&O.iris===S.id?(l=N.gestures)==null||l.push(O):O.body!==void 0&&O.body===((c=N.body)==null?void 0:c.id)?(u=N.gestures)==null||u.push(O):O.hand!==void 0&&O.hand===((p=(d=N.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=N.gestures)==null||h.push(O):O.hand!==void 0&&O.hand===((m=(f=N.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=N.gestures)==null||g.push(O));let R=[],P=[],$=O=>{O&&O.length===4&&(R.push(O[0],O[0]+O[2]),P.push(O[1],O[1]+O[3]))};$((y=N.face)==null?void 0:y.box),$((A=N.body)==null?void 0:A.box),$((b=(x=N.hands)==null?void 0:x.left)==null?void 0:b.box),$((k=(w=N.hands)==null?void 0:w.right)==null?void 0:k.box);let D=Math.min(...R),T=Math.min(...P);N.box=[D,T,Math.max(...R)-D,Math.max(...P)-T],r&&r[1]&&r[2]&&(N.boxRaw=[N.box[0]/r[2],N.box[1]/r[1],N.box[2]/r[2],N.box[3]/r[1]]),o.push(N)}return o}var C0=`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==`,T0=`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`;async function Z0e(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(C0);break;case"body":case"full":n=await t(T0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function Y0e(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+C0;break;case"full":case"body":n="data:image/jpeg;base64,"+T0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:ie.Image&&(s=new ie.Image),s.onload=async()=>{let r=Bs(s.naturalWidth,s.naturalHeight);if(!r)ae("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function J0e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(C0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(T0)),!n)return null;let s;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ae("Warmup tfjs-node not loaded");return s}async function L8(e,t){let n=ot();if(e.state="warmup",t&&(e.config=fn(e.config,t)),!e.config.warmup||e.config.warmup==="none")return{error:"null"};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await Z0e(e):typeof Image!="undefined"||ie.Canvas!==void 0?s=await Y0e(e):s=await J0e(e);let a=ot();e.config.debug&&ae("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Sc,Bp,Wp,N0,W8=class{constructor(t){Te(this,"version");Te(this,"config");Te(this,"result");Te(this,"state");Te(this,"process");Te(this,"tf");Te(this,"env");Te(this,"draw");Te(this,"models");Te(this,"events");Te(this,"faceTriangulation");Te(this,"faceUVMap");Te(this,"performance");Lc(this,Sc,void 0);Lc(this,Bp,void 0);Lc(this,Wp,void 0);Te(this,"gl");Te(this,"analyze",(...t)=>{if(!zc(this,Bp))return;let n=this.tf.engine().state.numTensors,s=zc(this,Sc);Bc(this,Sc,n);let r=n-s;r!==0&&ae(...t,r)});Lc(this,N0,t=>{if(!zc(this,Wp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Ke))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Te(this,"similarity",O8);Te(this,"distance",S0);Te(this,"match",M8);Te(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});o0(),this.env=ie,ba.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Qh}/dist/`,ba.modelBasePath=this.env.browser?"../models/":"file://models/",ba.backend=this.env.browser?"humangl":"tensorflow",this.version=Lx,Object.defineProperty(this,"version",{value:Lx}),this.config=JSON.parse(JSON.stringify(ba)),Object.seal(this.config),t&&(this.config=fn(this.config,t)),this.tf=Il,this.state="idle",Bc(this,Sc,0),Bc(this,Bp,!1),Bc(this,Wp,!1),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new zp,this.draw={options:ha,canvas:(n,s)=>N8(n,s),face:(n,s,r)=>jb(n,s,r),body:(n,s,r)=>qb(n,s,r),hand:(n,s,r)=>Xb(n,s,r),gesture:(n,s,r)=>Hb(n,s,r),object:(n,s,r)=>Kb(n,s,r),person:(n,s,r)=>T8(n,s,r),all:(n,s,r)=>E8(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=T6,this.faceUVMap=N6,this.gl=Bt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ba)),this.config.backend=t}validate(t){return n2(ba,t||this.config)}image(t,n=!0){return yc(t,this.config,n)}async segmentation(t,n){return b8(t,n,this.config)}enhance(t){return tb(t)}async init(){await I0(this,!0),await this.tf.ready(),o6(this.env)}async load(t){this.state="load";let n=ot(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=fn(this.config,t)),ie.initial&&(this.config.debug&&ae(`version: ${this.version}`),this.config.debug&&ae(`tfjs version: ${this.tf.version_core}`),await I0(this)||ae("error: backend check failed"),await tf(),this.env.browser&&(this.config.debug&&ae("configuration:",this.config),this.config.debug&&ae("tf flags:",this.tf.ENV.flags))),await w8(this),ie.initial&&this.config.debug&&ae("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),ie.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await k8(this),this.emit("load"));let a=Math.trunc(ot()-n);a>(this.performance.load||0)&&(this.performance.load=a)}next(t=this.result){return F8(t,this.config)}async warmup(t){return L8(this,t)}async detect(t,n){return this.state="detect",new Promise(async s=>{var y,A,x,b,w,k,S,N,R,P,$,D,T,O,B,H,z,X,ee,J,Q,te;this.state="config";let r,a;this.config=fn(this.config,n),this.state="check";let o=zc(this,N0).call(this,t);o&&(ae(o,t),s({error:o}));let i=ot();await I0(this),await this.load(),r=ot(),this.state="image";let l=yc(t,this.config);if(this.process=l,this.performance.image=Math.trunc(ot()-r),this.analyze("Get Image:"),!l.tensor){this.config.debug&&ae("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=ot(),this.config.skipFrame=await i6(this.config,l.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(ot()-r),this.analyze("Check Changed:");let c=[],u=[],d=[],p=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?Zb(this,l.tensor):[],this.performance.face&&delete this.performance.face):(r=ot(),c=this.config.face.enabled?await Zb(this,l.tensor):[],a=Math.trunc(ot()-r),a>0&&(this.performance.face=a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let h=this.config.body.maxDetected===-1?fn(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?(((y=this.config.body.modelPath)==null?void 0:y.includes("posenet"))?u=this.config.body.enabled?db(l.tensor,h):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?u=this.config.body.enabled?Cb(l.tensor,h):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?u=this.config.body.enabled?Db(l.tensor,h):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("movenet"))&&(u=this.config.body.enabled?Ob(l.tensor,h):[]),this.performance.body&&delete this.performance.body):(r=ot(),((w=this.config.body.modelPath)==null?void 0:w.includes("posenet"))?u=this.config.body.enabled?await db(l.tensor,h):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?u=this.config.body.enabled?await Cb(l.tensor,h):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("efficientpose"))?u=this.config.body.enabled?await Db(l.tensor,h):[]:((N=this.config.body.modelPath)==null?void 0:N.includes("movenet"))&&(u=this.config.body.enabled?await Ob(l.tensor,h):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.body=a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?fn(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?(((P=(R=this.config.hand.detector)==null?void 0:R.modelPath)==null?void 0:P.includes("handdetect"))?d=this.config.hand.enabled?gb(l.tensor,f):[]:((D=($=this.config.hand.detector)==null?void 0:$.modelPath)==null?void 0:D.includes("handtrack"))&&(d=this.config.hand.enabled?xb(l.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=ot(),((O=(T=this.config.hand.detector)==null?void 0:T.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?await gb(l.tensor,f):[]:((H=(B=this.config.hand.detector)==null?void 0:B.modelPath)==null?void 0:H.includes("handtrack"))&&(d=this.config.hand.enabled?await xb(l.tensor,f):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.hand=a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((z=this.config.object.modelPath)==null?void 0:z.includes("nanodet"))?p=this.config.object.enabled?zb(l.tensor,this.config):[]:((X=this.config.object.modelPath)==null?void 0:X.includes("centernet"))&&(p=this.config.object.enabled?Bb(l.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ot(),((ee=this.config.object.modelPath)==null?void 0:ee.includes("nanodet"))?p=this.config.object.enabled?await zb(l.tensor,this.config):[]:((J=this.config.object.modelPath)==null?void 0:J.includes("centernet"))&&(p=this.config.object.enabled?await Bb(l.tensor,this.config):[]),a=Math.trunc(ot()-r),a>0&&(this.performance.object=a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,u,d,p]=await Promise.all([c,u,d,p])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=ot(),m=[...D8(c),...$8(u),...P8(d),..._8(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(ot()-r)),this.performance.total=Math.trunc(ot()-i);let g=((te=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:te.shape)||[];this.result={face:c,body:u,hand:d,gesture:m,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return z8(c,u,d,m,g)}},ne(l.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Sc=new WeakMap,Bp=new WeakMap,Wp=new WeakMap,N0=new WeakMap;return Q0e;})();
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* =============================================================================
*/
/**
* @license
* Copyright 2019 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC
*
* Use of this source code is governed by an MIT-style
* license that can be found in the LICENSE file or at
* https://opensource.org/licenses/MIT.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use backend file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the License);
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
Reference in New Issue View Git Blame Copy Permalink