human/dist/human.esm.js

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

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var __export = (target, all2) => {
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};

// dist/tfjs.esm.js
var tfjs_esm_exports = {};
__export(tfjs_esm_exports, {
  Abs: () => ys,
  Acos: () => aa,
  Acosh: () => ia,
  AdadeltaOptimizer: () => yu,
  AdagradOptimizer: () => bu,
  AdamOptimizer: () => Cu,
  AdamaxOptimizer: () => Su,
  Add: () => eo,
  AddN: () => Po,
  All: () => Mo,
  Any: () => Lo,
  ArgMax: () => Bo,
  ArgMin: () => ei,
  Asin: () => ua,
  Asinh: () => pa,
  Atan: () => ca,
  Atan2: () => ma,
  Atanh: () => la,
  AvgPool: () => Vo,
  AvgPool3D: () => dp,
  AvgPool3DGrad: () => vm,
  AvgPoolGrad: () => mp,
  BackendWasm: () => Ll,
  BatchMatMul: () => zo,
  BatchToSpaceND: () => bs,
  Bincount: () => ti,
  BroadcastArgs: () => fp,
  BroadcastTo: () => ise,
  Cast: () => co,
  Ceil: () => Wo,
  ClipByValue: () => lo,
  Complex: () => ri,
  ComplexAbs: () => hp,
  Concat: () => Cs,
  Conv2D: () => Uo,
  Conv2DBackpropFilter: () => oi,
  Conv2DBackpropInput: () => Go,
  Conv3D: () => gp,
  Conv3DBackpropFilterV2: () => km,
  Conv3DBackpropInputV2: () => xp,
  Cos: () => Ho,
  Cosh: () => Ko,
  CropAndResize: () => Xo,
  Cumprod: () => qo,
  Cumsum: () => jo,
  DataStorage: () => Fo,
  DenseBincount: () => ni,
  DepthToSpace: () => Yo,
  DepthwiseConv2dNative: () => Qo,
  DepthwiseConv2dNativeBackpropFilter: () => yp,
  DepthwiseConv2dNativeBackpropInput: () => bp,
  Diag: () => si,
  Dilation2D: () => ai,
  Dilation2DBackpropFilter: () => $b,
  Dilation2DBackpropInput: () => _b,
  ENV: () => kb,
  Einsum: () => ii,
  Elu: () => Jo,
  EluGrad: () => Nm,
  Environment: () => Uc,
  Equal: () => en,
  Erf: () => da,
  Exp: () => tn,
  ExpandDims: () => Ss,
  Expm1: () => fa,
  FFT: () => ui,
  Fill: () => ws,
  FlipLeftRight: () => rn,
  Floor: () => on,
  FloorDiv: () => nn,
  FromPixels: () => Zi,
  FusedBatchNorm: () => sn,
  FusedConv2D: () => go,
  FusedDepthwiseConv2D: () => xo,
  GPGPUContext: () => Lu,
  GatherNd: () => an,
  GatherV2: () => Is,
  GraphModel: () => fl,
  Greater: () => un,
  GreaterEqual: () => pn,
  IFFT: () => pi,
  Identity: () => mo,
  Imag: () => ci,
  IsFinite: () => ha,
  IsInf: () => ga,
  IsNan: () => cn,
  KernelBackend: () => Zr,
  LRN: () => mi,
  LRNGrad: () => Tm,
  LeakyRelu: () => ln,
  Less: () => mn,
  LessEqual: () => dn,
  LinSpace: () => li,
  Log: () => fn,
  Log1p: () => xa,
  LogSoftmax: () => use,
  LogicalAnd: () => hn,
  LogicalNot: () => gn,
  LogicalOr: () => xn,
  LogicalXor: () => XI,
  LowerBound: () => pse,
  MathBackendCPU: () => Pi,
  MathBackendWebGL: () => Vi,
  Max: () => yn,
  MaxPool: () => Cn,
  MaxPool3D: () => Cp,
  MaxPool3DGrad: () => $m,
  MaxPoolGrad: () => _m,
  MaxPoolWithArgmax: () => Sp,
  Maximum: () => bn,
  Mean: () => Sn,
  Min: () => wn,
  Minimum: () => In,
  MirrorPad: () => vn,
  Mod: () => ya,
  MomentumOptimizer: () => wu,
  Multinomial: () => wp,
  Multiply: () => kn,
  Neg: () => vs,
  NonMaxSuppressionV3: () => Tn,
  NonMaxSuppressionV4: () => ba,
  NonMaxSuppressionV5: () => _n,
  NotEqual: () => Nn,
  OP_SCOPE_SUFFIX: () => jb,
  OneHot: () => $n,
  OnesLike: () => ks,
  Optimizer: () => wr,
  OptimizerConstructors: () => cl,
  Pack: () => Ns,
  PadV2: () => En,
  Pool: () => cse,
  Pow: () => An,
  Prelu: () => Rn,
  Prod: () => Dn,
  RMSPropOptimizer: () => Iu,
  RaggedGather: () => Ip,
  RaggedRange: () => vp,
  RaggedTensorToTensor: () => kp,
  Range: () => Ts,
  Rank: () => Lb,
  Real: () => di,
  RealDiv: () => Zo,
  Reciprocal: () => Fn,
  Reduction: () => $t,
  Relu: () => On,
  Relu6: () => Ln,
  Reshape: () => _s,
  ResizeBilinear: () => Mn,
  ResizeBilinearGrad: () => Am,
  ResizeNearestNeighbor: () => Pn,
  ResizeNearestNeighborGrad: () => Em,
  Reverse: () => Bn,
  RotateWithOffset: () => ts,
  Round: () => Vn,
  Rsqrt: () => zn,
  SGDOptimizer: () => Pa,
  ScatterNd: () => Wn,
  SearchSorted: () => fi,
  Select: () => $s,
  Selu: () => Ca,
  Sigmoid: () => Gn,
  Sign: () => wa,
  Sin: () => Un,
  Sinh: () => Sa,
  Slice: () => Es,
  Softmax: () => qn,
  Softplus: () => Ia,
  SpaceToBatchND: () => As,
  SparseFillEmptyRows: () => hi,
  SparseReshape: () => va,
  SparseSegmentMean: () => gi,
  SparseSegmentSum: () => xi,
  SparseToDense: () => yi,
  SplitV: () => Rs,
  Sqrt: () => Hn,
  Square: () => bi,
  SquaredDifference: () => jn,
  Step: () => fo,
  StridedSlice: () => Xn,
  StringNGrams: () => Ds,
  StringSplit: () => Ci,
  StringToHashBucketFast: () => Si,
  Sub: () => Yn,
  Sum: () => Kn,
  Tan: () => Qn,
  Tanh: () => Zn,
  Tensor: () => it,
  TensorBuffer: () => st,
  Tile: () => to,
  TopK: () => Jn,
  Transform: () => es,
  Transpose: () => ro,
  Unique: () => Np,
  Unpack: () => Fs,
  UnsortedSegmentSum: () => Tp,
  UpperBound: () => lse,
  Variable: () => Na,
  WebGPUBackend: () => Gi,
  ZerosLike: () => Os,
  _FusedMatMul: () => ho,
  abs: () => Yt,
  acos: () => G0,
  acosh: () => H0,
  add: () => be,
  addN: () => K0,
  all: () => q0,
  any: () => j0,
  argMax: () => X0,
  argMin: () => Y0,
  asin: () => Q0,
  asinh: () => Z0,
  atan: () => J0,
  atan2: () => ev,
  atanh: () => tv,
  avgPool: () => qm,
  avgPool3d: () => nv,
  backend: () => Pie,
  backend_util: () => S,
  basicLSTMCell: () => sv,
  batchNorm: () => _i,
  batchNorm2d: () => iv,
  batchNorm3d: () => uv,
  batchNorm4d: () => pv,
  batchToSpaceND: () => jm,
  bincount: () => Xm,
  booleanMaskAsync: () => VG,
  broadcastArgs: () => cv,
  broadcastTo: () => $i,
  broadcast_util: () => yr,
  browser: () => gN,
  buffer: () => le,
  cast: () => je,
  ceil: () => lv,
  clipByValue: () => mv,
  clone: () => Lr,
  complex: () => Tr,
  concat: () => xt,
  concat1d: () => dv,
  concat2d: () => fv,
  concat3d: () => hv,
  concat4d: () => gv,
  conv1d: () => xv,
  conv2d: () => Ei,
  conv2dTranspose: () => yv,
  conv3d: () => bv,
  conv3dTranspose: () => Sv,
  copyRegisteredKernels: () => bse,
  cos: () => wv,
  cosh: () => Iv,
  cosineWindow: () => al,
  cumprod: () => vv,
  cumsum: () => kv,
  customGrad: () => br,
  denseBincount: () => Nv,
  deprecationWarn: () => nC,
  depthToSpace: () => Tv,
  depthwiseConv2d: () => Bp,
  deregisterOp: () => Mq,
  device_util: () => ki,
  diag: () => _v,
  dilation2d: () => $v,
  disableDeprecationWarnings: () => kie,
  dispose: () => Ot,
  disposeVariables: () => Nie,
  div: () => He,
  divNoNan: () => Av,
  dot: () => Rv,
  dropout: () => ZG,
  einsum: () => Dv,
  elu: () => Jm,
  enableDebugMode: () => vie,
  enableProdMode: () => Iie,
  enclosingPowerOfTwo: () => CC,
  engine: () => sr,
  env: () => O,
  equal: () => Zm,
  erf: () => Fv,
  euclideanNorm: () => Mv,
  exp: () => So,
  expandDims: () => Ra,
  expm1: () => Lv,
  eye: () => ed,
  fft: () => zp,
  fill: () => Us,
  findBackend: () => Fie,
  findBackendFactory: () => Oie,
  floor: () => td,
  floorDiv: () => Km,
  forceHalfFloat: () => GE,
  fused: () => SC,
  gather: () => rd,
  gatherND: () => YG,
  gather_util: () => Wd,
  getBackend: () => Rie,
  getGradient: () => Eb,
  getKernel: () => Kc,
  getKernelsForBackend: () => Rm,
  getThreadsCount: () => Kee,
  gpgpu_util: () => $w,
  grad: () => HU,
  grads: () => KU,
  greater: () => uu,
  greaterEqual: () => od,
  ifft: () => du,
  imag: () => Ri,
  image: () => eK,
  inTopKAsync: () => eH,
  io: () => La,
  irfft: () => Td,
  isFinite: () => Bv,
  isInf: () => Vv,
  isNaN: () => zv,
  keep: () => _r,
  kernel_impls: () => Vt,
  leakyRelu: () => nd,
  less: () => Wv,
  lessEqual: () => Vp,
  linalg: () => tK,
  linspace: () => Uv,
  loadGraphModel: () => A6,
  loadGraphModelSync: () => R6,
  localResponseNormalization: () => Gv,
  log: () => Da,
  log1p: () => sd,
  logSigmoid: () => Hv,
  logSoftmax: () => Kv,
  logSumExp: () => ud,
  logicalAnd: () => pu,
  logicalNot: () => pd,
  logicalOr: () => cd,
  logicalXor: () => qv,
  losses: () => rK,
  lowerBound: () => jv,
  matMul: () => Xe,
  math: () => fN,
  max: () => Gs,
  maxPool: () => md,
  maxPool3d: () => Xv,
  maxPoolWithArgmax: () => Yv,
  maximum: () => dd,
  mean: () => cu,
  memory: () => Tie,
  meshgrid: () => Qv,
  min: () => rl,
  minimum: () => fd,
  mirrorPad: () => Zv,
  mod: () => Jv,
  moments: () => ek,
  movingAverage: () => UG,
  mul: () => ne,
  multiRNNCell: () => tk,
  multinomial: () => rk,
  neg: () => Cr,
  nextFrame: () => AC,
  norm: () => iu,
  notEqual: () => hd,
  oneHot: () => nl,
  ones: () => Hs,
  onesLike: () => ok,
  op: () => N,
  outerProduct: () => nk,
  pad: () => Ks,
  pad1d: () => sk,
  pad2d: () => ak,
  pad3d: () => ik,
  pad4d: () => uk,
  pool: () => pk,
  pow: () => Aa,
  prelu: () => xd,
  print: () => Hm,
  prod: () => ck,
  profile: () => _ie,
  raggedGather: () => lk,
  raggedRange: () => mk,
  raggedTensorToTensor: () => dk,
  rand: () => fk,
  randomGamma: () => Ok,
  randomNormal: () => Id,
  randomStandardNormal: () => Pk,
  randomUniform: () => vd,
  range: () => Di,
  ready: () => Aie,
  real: () => Fa,
  reciprocal: () => Mk,
  registerBackend: () => Ti,
  registerGradient: () => gse,
  registerKernel: () => ka,
  registerOp: () => Pq,
  relu: () => Fi,
  relu6: () => kd,
  removeBackend: () => Die,
  reshape: () => W,
  reverse: () => no,
  reverse1d: () => Lk,
  reverse2d: () => Bk,
  reverse3d: () => Vk,
  reverse4d: () => zk,
  rfft: () => Wp,
  round: () => Nd,
  rsqrt: () => Wk,
  scalar: () => Ce,
  scatterND: () => HG,
  scatter_util: () => sl,
  searchSorted: () => ol,
  selu: () => Uk,
  separableConv2d: () => Gk,
  serialization: () => oN,
  setBackend: () => Eie,
  setPlatform: () => Mie,
  setThreadsCount: () => Hee,
  setWasmPath: () => Uee,
  setWasmPaths: () => Gee,
  setWebGLContext: () => US,
  setdiff1dAsync: () => Hk,
  shared: () => Zp,
  sigmoid: () => Ws,
  sign: () => Kk,
  signal: () => JH,
  sin: () => qk,
  sinh: () => jk,
  slice: () => Ke,
  slice1d: () => Xk,
  slice2d: () => Yk,
  slice3d: () => Qk,
  slice4d: () => Zk,
  slice_util: () => ut,
  softmax: () => Jk,
  softplus: () => id,
  spaceToBatchND: () => gd,
  sparse: () => oK,
  sparseToDense: () => jG,
  spectral: () => ZH,
  split: () => Oa,
  sqrt: () => $r,
  square: () => Qt,
  squaredDifference: () => _d,
  squeeze: () => Up,
  stack: () => Sr,
  step: () => $d,
  stridedSlice: () => e1,
  string: () => nK,
  sub: () => ke,
  sum: () => et,
  sumOutType: () => Ta,
  tan: () => t1,
  tanh: () => tl,
  tensor: () => nr,
  tensor1d: () => dr,
  tensor2d: () => Oi,
  tensor3d: () => Ed,
  tensor4d: () => r1,
  tensor5d: () => o1,
  tensor6d: () => n1,
  tensor_util: () => C0,
  test_util: () => Fk,
  tidy: () => Ee,
  tile: () => Ai,
  time: () => $ie,
  topk: () => s1,
  train: () => YMe,
  transpose: () => Gp,
  truncatedNormal: () => a1,
  unique: () => i1,
  unregisterGradient: () => yse,
  unregisterKernel: () => xse,
  unsortedSegmentSum: () => u1,
  unstack: () => so,
  upcastType: () => dt,
  upperBound: () => p1,
  util: () => y,
  valueAndGrad: () => qU,
  valueAndGrads: () => jU,
  variable: () => c1,
  variableGrads: () => pC,
  version: () => tse,
  version_converter: () => F6,
  version_core: () => RK,
  version_cpu: () => pj,
  version_wasm: () => qee,
  version_webgl: () => nY,
  webgl: () => CJe,
  webgl_util: () => nc,
  webgpu_util: () => hI,
  where: () => ss,
  whereAsync: () => Rd,
  zeros: () => Br,
  zerosLike: () => Ut
});
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  It.MIN_VALUE = Pr;
  var de = It.prototype;
  de.toInt = function() {
    return this.unsigned ? this.low >>> 0 : this.low;
  };
  de.toNumber = function() {
    return this.unsigned ? (this.high >>> 0) * Ep + (this.low >>> 0) : this.high * Ep + (this.low >>> 0);
  };
  de.toString = function(e) {
    if (e = e || 10, e < 2 || 36 < e)
      throw RangeError("radix");
    if (this.isZero())
      return "0";
    if (this.isNegative())
      if (this.eq(Pr)) {
        var t10 = bo(e), o = this.div(t10), n = o.mul(t10).sub(this);
        return o.toString(e) + n.toInt().toString(e);
      } else
        return "-" + this.neg().toString(e);
    for (var s = bo(Dm(e, 6), this.unsigned), a = this, i = ""; ; ) {
      var p = a.div(s), u = a.sub(p.mul(s)).toInt() >>> 0, c = u.toString(e);
      if (a = p, a.isZero())
        return c + i;
      for (; c.length < 6; )
        c = "0" + c;
      i = "" + c + i;
    }
  };
  de.getHighBits = function() {
    return this.high;
  };
  de.getHighBitsUnsigned = function() {
    return this.high >>> 0;
  };
  de.getLowBits = function() {
    return this.low;
  };
  de.getLowBitsUnsigned = function() {
    return this.low >>> 0;
  };
  de.getNumBitsAbs = function() {
    if (this.isNegative())
      return this.eq(Pr) ? 64 : this.neg().getNumBitsAbs();
    for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && !(e & 1 << t10); t10--)
      ;
    return this.high != 0 ? t10 + 33 : t10 + 1;
  };
  de.isZero = function() {
    return this.high === 0 && this.low === 0;
  };
  de.eqz = de.isZero;
  de.isNegative = function() {
    return !this.unsigned && this.high < 0;
  };
  de.isPositive = function() {
    return this.unsigned || this.high >= 0;
  };
  de.isOdd = function() {
    return (this.low & 1) === 1;
  };
  de.isEven = function() {
    return (this.low & 1) === 0;
  };
  de.equals = function(e) {
    return Mr(e) || (e = rs(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low;
  };
  de.eq = de.equals;
  de.notEquals = function(e) {
    return !this.eq(e);
  };
  de.neq = de.notEquals;
  de.ne = de.notEquals;
  de.lessThan = function(e) {
    return this.comp(e) < 0;
  };
  de.lt = de.lessThan;
  de.lessThanOrEqual = function(e) {
    return this.comp(e) <= 0;
  };
  de.lte = de.lessThanOrEqual;
  de.le = de.lessThanOrEqual;
  de.greaterThan = function(e) {
    return this.comp(e) > 0;
  };
  de.gt = de.greaterThan;
  de.greaterThanOrEqual = function(e) {
    return this.comp(e) >= 0;
  };
  de.gte = de.greaterThanOrEqual;
  de.ge = de.greaterThanOrEqual;
  de.compare = function(e) {
    if (Mr(e) || (e = rs(e)), this.eq(e))
      return 0;
    var t10 = this.isNegative(), o = e.isNegative();
    return t10 && !o ? -1 : !t10 && o ? 1 : this.unsigned ? e.high >>> 0 > this.high >>> 0 || e.high === this.high && e.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(e).isNegative() ? -1 : 1;
  };
  de.comp = de.compare;
  de.negate = function() {
    return !this.unsigned && this.eq(Pr) ? Pr : this.not().add($p);
  };
  de.neg = de.negate;
  de.add = function(e) {
    Mr(e) || (e = rs(e));
    var t10 = this.high >>> 16, o = this.high & 65535, n = this.low >>> 16, s = this.low & 65535, a = e.high >>> 16, i = e.high & 65535, p = e.low >>> 16, u = e.low & 65535, c = 0, l = 0, m = 0, d = 0;
    return d += s + u, m += d >>> 16, d &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t10 + a, c &= 65535, vt(m << 16 | d, c << 16 | l, this.unsigned);
  };
  de.subtract = function(e) {
    return Mr(e) || (e = rs(e)), this.add(e.neg());
  };
  de.sub = de.subtract;
  de.multiply = function(e) {
    if (this.isZero())
      return Co;
    if (Mr(e) || (e = rs(e)), yo) {
      var t10 = yo.mul(this.low, this.high, e.low, e.high);
      return vt(t10, yo.get_high(), this.unsigned);
    }
    if (e.isZero())
      return Co;
    if (this.eq(Pr))
      return e.isOdd() ? Pr : Co;
    if (e.eq(Pr))
      return this.isOdd() ? Pr : Co;
    if (this.isNegative())
      return e.isNegative() ? this.neg().mul(e.neg()) : this.neg().mul(e).neg();
    if (e.isNegative())
      return this.mul(e.neg()).neg();
    if (this.lt(t0) && e.lt(t0))
      return bo(this.toNumber() * e.toNumber(), this.unsigned);
    var o = this.high >>> 16, n = this.high & 65535, s = this.low >>> 16, a = this.low & 65535, i = e.high >>> 16, p = e.high & 65535, u = e.low >>> 16, c = e.low & 65535, l = 0, m = 0, d = 0, f = 0;
    return f += a * c, d += f >>> 16, f &= 65535, d += s * c, m += d >>> 16, d &= 65535, d += a * u, m += d >>> 16, d &= 65535, m += n * c, l += m >>> 16, m &= 65535, m += s * u, l += m >>> 16, m &= 65535, m += a * p, l += m >>> 16, m &= 65535, l += o * c + n * u + s * p + a * i, l &= 65535, vt(d << 16 | f, l << 16 | m, this.unsigned);
  };
  de.mul = de.multiply;
  de.divide = function(e) {
    if (Mr(e) || (e = rs(e)), e.isZero())
      throw Error("division by zero");
    if (yo) {
      if (!this.unsigned && this.high === -2147483648 && e.low === -1 && e.high === -1)
        return this;
      var t10 = (this.unsigned ? yo.div_u : yo.div_s)(this.low, this.high, e.low, e.high);
      return vt(t10, yo.get_high(), this.unsigned);
    }
    if (this.isZero())
      return this.unsigned ? Ji : Co;
    var o, n, s;
    if (this.unsigned) {
      if (e.unsigned || (e = e.toUnsigned()), e.gt(this))
        return Ji;
      if (e.gt(this.shru(1)))
        return o0;
      s = Ji;
    } else {
      if (this.eq(Pr)) {
        if (e.eq($p) || e.eq(Rb))
          return Pr;
        if (e.eq(Pr))
          return $p;
        var a = this.shr(1);
        return o = a.div(e).shl(1), o.eq(Co) ? e.isNegative() ? $p : Rb : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s);
      } else if (e.eq(Pr))
        return this.unsigned ? Ji : Co;
      if (this.isNegative())
        return e.isNegative() ? this.neg().div(e.neg()) : this.neg().div(e).neg();
      if (e.isNegative())
        return this.div(e.neg()).neg();
      s = Co;
    }
    for (n = this; n.gte(e); ) {
      o = Math.max(1, Math.floor(n.toNumber() / e.toNumber()));
      for (var i = Math.ceil(Math.log(o) / Math.LN2), p = i <= 48 ? 1 : Dm(2, i - 48), u = bo(o), c = u.mul(e); c.isNegative() || c.gt(n); )
        o -= p, u = bo(o, this.unsigned), c = u.mul(e);
      u.isZero() && (u = $p), s = s.add(u), n = n.sub(c);
    }
    return s;
  };
  de.div = de.divide;
  de.modulo = function(e) {
    if (Mr(e) || (e = rs(e)), yo) {
      var t10 = (this.unsigned ? yo.rem_u : yo.rem_s)(this.low, this.high, e.low, e.high);
      return vt(t10, yo.get_high(), this.unsigned);
    }
    return this.sub(this.div(e).mul(e));
  };
  de.mod = de.modulo;
  de.rem = de.modulo;
  de.not = function() {
    return vt(~this.low, ~this.high, this.unsigned);
  };
  de.and = function(e) {
    return Mr(e) || (e = rs(e)), vt(this.low & e.low, this.high & e.high, this.unsigned);
  };
  de.or = function(e) {
    return Mr(e) || (e = rs(e)), vt(this.low | e.low, this.high | e.high, this.unsigned);
  };
  de.xor = function(e) {
    return Mr(e) || (e = rs(e)), vt(this.low ^ e.low, this.high ^ e.high, this.unsigned);
  };
  de.shiftLeft = function(e) {
    return Mr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? vt(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : vt(0, this.low << e - 32, this.unsigned);
  };
  de.shl = de.shiftLeft;
  de.shiftRight = function(e) {
    return Mr(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? vt(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : vt(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned);
  };
  de.shr = de.shiftRight;
  de.shiftRightUnsigned = function(e) {
    if (Mr(e) && (e = e.toInt()), e &= 63, e === 0)
      return this;
    var t10 = this.high;
    if (e < 32) {
      var o = this.low;
      return vt(o >>> e | t10 << 32 - e, t10 >>> e, this.unsigned);
    } else
      return e === 32 ? vt(t10, 0, this.unsigned) : vt(t10 >>> e - 32, 0, this.unsigned);
  };
  de.shru = de.shiftRightUnsigned;
  de.shr_u = de.shiftRightUnsigned;
  de.toSigned = function() {
    return this.unsigned ? vt(this.low, this.high, false) : this;
  };
  de.toUnsigned = function() {
    return this.unsigned ? this : vt(this.low, this.high, true);
  };
  de.toBytes = function(e) {
    return e ? this.toBytesLE() : this.toBytesBE();
  };
  de.toBytesLE = function() {
    var e = this.high, t10 = this.low;
    return [t10 & 255, t10 >>> 8 & 255, t10 >>> 16 & 255, t10 >>> 24, e & 255, e >>> 8 & 255, e >>> 16 & 255, e >>> 24];
  };
  de.toBytesBE = function() {
    var e = this.high, t10 = this.low;
    return [e >>> 24, e >>> 16 & 255, e >>> 8 & 255, e & 255, t10 >>> 24, t10 >>> 16 & 255, t10 >>> 8 & 255, t10 & 255];
  };
  It.fromBytes = function(e, t10, o) {
    return o ? It.fromBytesLE(e, t10) : It.fromBytesBE(e, t10);
  };
  It.fromBytesLE = function(e, t10) {
    return new It(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t10);
  };
  It.fromBytesBE = function(e, t10) {
    return new It(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t10);
  };
});
var W0 = Kt(() => {
});
var U0 = Kt(() => {
});
var gk = Kt((hk, cC) => {
  (function(r, e, t10) {
    function o(i) {
      var p = this, u = a();
      p.next = function() {
        var c = 2091639 * p.s0 + p.c * 23283064365386963e-26;
        return p.s0 = p.s1, p.s1 = p.s2, p.s2 = c - (p.c = c | 0);
      }, p.c = 1, p.s0 = u(" "), p.s1 = u(" "), p.s2 = u(" "), p.s0 -= u(i), p.s0 < 0 && (p.s0 += 1), p.s1 -= u(i), p.s1 < 0 && (p.s1 += 1), p.s2 -= u(i), p.s2 < 0 && (p.s2 += 1), u = null;
    }
    function n(i, p) {
      return p.c = i.c, p.s0 = i.s0, p.s1 = i.s1, p.s2 = i.s2, p;
    }
    function s(i, p) {
      var u = new o(i), c = p && p.state, l = u.next;
      return l.int32 = function() {
        return u.next() * 4294967296 | 0;
      }, l.double = function() {
        return l() + (l() * 2097152 | 0) * 11102230246251565e-32;
      }, l.quick = l, c && (typeof c == "object" && n(c, u), l.state = function() {
        return n(u, {});
      }), l;
    }
    function a() {
      var i = 4022871197, p = function(u) {
        u = String(u);
        for (var c = 0; c < u.length; c++) {
          i += u.charCodeAt(c);
          var l = 0.02519603282416938 * i;
          i = l >>> 0, l -= i, l *= i, i = l >>> 0, l -= i, i += l * 4294967296;
        }
        return (i >>> 0) * 23283064365386963e-26;
      };
      return p;
    }
    e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
      return s;
    }) : this.alea = s;
  })(hk, typeof cC == "object" && cC, typeof define == "function" && define);
});
var yk = Kt((xk, lC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this, p = "";
      i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.next = function() {
        var c = i.x ^ i.x << 11;
        return i.x = i.y, i.y = i.z, i.z = i.w, i.w ^= i.w >>> 19 ^ c ^ c >>> 8;
      }, a === (a | 0) ? i.x = a : p += a;
      for (var u = 0; u < p.length + 64; u++)
        i.x ^= p.charCodeAt(u) | 0, i.next();
    }
    function n(a, i) {
      return i.x = a.x, i.y = a.y, i.z = a.z, i.w = a.w, i;
    }
    function s(a, i) {
      var p = new o(a), u = i && i.state, c = function() {
        return (p.next() >>> 0) / 4294967296;
      };
      return c.double = function() {
        do
          var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
        while (d === 0);
        return d;
      }, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
        return n(p, {});
      }), c;
    }
    e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
      return s;
    }) : this.xor128 = s;
  })(xk, typeof lC == "object" && lC, typeof define == "function" && define);
});
var Ck = Kt((bk, mC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this, p = "";
      i.next = function() {
        var c = i.x ^ i.x >>> 2;
        return i.x = i.y, i.y = i.z, i.z = i.w, i.w = i.v, (i.d = i.d + 362437 | 0) + (i.v = i.v ^ i.v << 4 ^ (c ^ c << 1)) | 0;
      }, i.x = 0, i.y = 0, i.z = 0, i.w = 0, i.v = 0, a === (a | 0) ? i.x = a : p += a;
      for (var u = 0; u < p.length + 64; u++)
        i.x ^= p.charCodeAt(u) | 0, u == p.length && (i.d = i.x << 10 ^ i.x >>> 4), i.next();
    }
    function n(a, i) {
      return i.x = a.x, i.y = a.y, i.z = a.z, i.w = a.w, i.v = a.v, i.d = a.d, i;
    }
    function s(a, i) {
      var p = new o(a), u = i && i.state, c = function() {
        return (p.next() >>> 0) / 4294967296;
      };
      return c.double = function() {
        do
          var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
        while (d === 0);
        return d;
      }, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
        return n(p, {});
      }), c;
    }
    e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
      return s;
    }) : this.xorwow = s;
  })(bk, typeof mC == "object" && mC, typeof define == "function" && define);
});
var wk = Kt((Sk, dC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this;
      i.next = function() {
        var u = i.x, c = i.i, l, m, d;
        return l = u[c], l ^= l >>> 7, m = l ^ l << 24, l = u[c + 1 & 7], m ^= l ^ l >>> 10, l = u[c + 3 & 7], m ^= l ^ l >>> 3, l = u[c + 4 & 7], m ^= l ^ l << 7, l = u[c + 7 & 7], l = l ^ l << 13, m ^= l ^ l << 9, u[c] = m, i.i = c + 1 & 7, m;
      };
      function p(u, c) {
        var l, m, d = [];
        if (c === (c | 0))
          m = d[0] = c;
        else
          for (c = "" + c, l = 0; l < c.length; ++l)
            d[l & 7] = d[l & 7] << 15 ^ c.charCodeAt(l) + d[l + 1 & 7] << 13;
        for (; d.length < 8; )
          d.push(0);
        for (l = 0; l < 8 && d[l] === 0; ++l)
          ;
        for (l == 8 ? m = d[7] = -1 : m = d[l], u.x = d, u.i = 0, l = 256; l > 0; --l)
          u.next();
      }
      p(i, a);
    }
    function n(a, i) {
      return i.x = a.x.slice(), i.i = a.i, i;
    }
    function s(a, i) {
      a == null && (a = +new Date());
      var p = new o(a), u = i && i.state, c = function() {
        return (p.next() >>> 0) / 4294967296;
      };
      return c.double = function() {
        do
          var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
        while (d === 0);
        return d;
      }, c.int32 = p.next, c.quick = c, u && (u.x && n(u, p), c.state = function() {
        return n(p, {});
      }), c;
    }
    e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
      return s;
    }) : this.xorshift7 = s;
  })(Sk, typeof dC == "object" && dC, typeof define == "function" && define);
});
var vk = Kt((Ik, fC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this;
      i.next = function() {
        var u = i.w, c = i.X, l = i.i, m, d;
        return i.w = u = u + 1640531527 | 0, d = c[l + 34 & 127], m = c[l = l + 1 & 127], d ^= d << 13, m ^= m << 17, d ^= d >>> 15, m ^= m >>> 12, d = c[l] = d ^ m, i.i = l, d + (u ^ u >>> 16) | 0;
      };
      function p(u, c) {
        var l, m, d, f, h, g = [], x = 128;
        for (c === (c | 0) ? (m = c, c = null) : (c = c + "\0", m = 0, x = Math.max(x, c.length)), d = 0, f = -32; f < x; ++f)
          c && (m ^= c.charCodeAt((f + 32) % c.length)), f === 0 && (h = m), m ^= m << 10, m ^= m >>> 15, m ^= m << 4, m ^= m >>> 13, f >= 0 && (h = h + 1640531527 | 0, l = g[f & 127] ^= m + h, d = l == 0 ? d + 1 : 0);
        for (d >= 128 && (g[(c && c.length || 0) & 127] = -1), d = 127, f = 4 * 128; f > 0; --f)
          m = g[d + 34 & 127], l = g[d = d + 1 & 127], m ^= m << 13, l ^= l << 17, m ^= m >>> 15, l ^= l >>> 12, g[d] = m ^ l;
        u.w = h, u.X = g, u.i = d;
      }
      p(i, a);
    }
    function n(a, i) {
      return i.i = a.i, i.w = a.w, i.X = a.X.slice(), i;
    }
    function s(a, i) {
      a == null && (a = +new Date());
      var p = new o(a), u = i && i.state, c = function() {
        return (p.next() >>> 0) / 4294967296;
      };
      return c.double = function() {
        do
          var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
        while (d === 0);
        return d;
      }, c.int32 = p.next, c.quick = c, u && (u.X && n(u, p), c.state = function() {
        return n(p, {});
      }), c;
    }
    e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
      return s;
    }) : this.xor4096 = s;
  })(Ik, typeof fC == "object" && fC, typeof define == "function" && define);
});
var Nk = Kt((kk, hC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this, p = "";
      i.next = function() {
        var c = i.b, l = i.c, m = i.d, d = i.a;
        return c = c << 25 ^ c >>> 7 ^ l, l = l - m | 0, m = m << 24 ^ m >>> 8 ^ d, d = d - c | 0, i.b = c = c << 20 ^ c >>> 12 ^ l, i.c = l = l - m | 0, i.d = m << 16 ^ l >>> 16 ^ d, i.a = d - c | 0;
      }, i.a = 0, i.b = 0, i.c = -1640531527, i.d = 1367130551, a === Math.floor(a) ? (i.a = a / 4294967296 | 0, i.b = a | 0) : p += a;
      for (var u = 0; u < p.length + 20; u++)
        i.b ^= p.charCodeAt(u) | 0, i.next();
    }
    function n(a, i) {
      return i.a = a.a, i.b = a.b, i.c = a.c, i.d = a.d, i;
    }
    function s(a, i) {
      var p = new o(a), u = i && i.state, c = function() {
        return (p.next() >>> 0) / 4294967296;
      };
      return c.double = function() {
        do
          var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, d = (l + m) / (1 << 21);
        while (d === 0);
        return d;
      }, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
        return n(p, {});
      }), c;
    }
    e && e.exports ? e.exports = s : t10 && t10.amd ? t10(function() {
      return s;
    }) : this.tychei = s;
  })(kk, typeof hC == "object" && hC, typeof define == "function" && define);
});
var Tk = Kt(() => {
});
var $k = Kt((_k, yd) => {
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      var _ = [];
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          D = (D + M) * o, P *= o, M = A.g(1);
        for (; D >= u; )
          D /= 2, P /= 2, M >>>= 1;
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      return R.int32 = function() {
        return A.g(4) | 0;
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          return f(A, {});
        }), M ? (t10[a] = D, P) : D;
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    function f(C, w) {
      return w.i = C.i, w.j = C.j, w.S = C.S.slice(), w;
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    function g(C, w) {
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        w[c & E] = c & (_ ^= w[c & E] * 19) + k.charCodeAt(E++);
      return b(w);
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    function x() {
      try {
        var C;
        return l && (C = l.randomBytes) ? C = C(o) : (C = new Uint8Array(o), (r.crypto || r.msCrypto).getRandomValues(C)), b(C);
      } catch (_) {
        var w = r.navigator, k = w && w.plugins;
        return [+new Date(), r, k, r.screen, b(e)];
      }
    }
    function b(C) {
      return String.fromCharCode.apply(0, C);
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      yd.exports = m;
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        l = Tk();
      } catch (C) {
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        return m;
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  })(typeof self != "undefined" ? self : _k, [], Math);
});
var gC = Kt((QSe, Ek) => {
  var P4 = gk(), M4 = yk(), L4 = Ck(), B4 = wk(), V4 = vk(), z4 = Nk(), lu = $k();
  lu.alea = P4;
  lu.xor128 = M4;
  lu.xorwow = L4;
  lu.xorshift7 = B4;
  lu.xor4096 = V4;
  lu.tychei = z4;
  Ek.exports = lu;
});
var oI = Kt(() => {
});
var nI = Kt(() => {
});
var x3 = Kt(() => {
});
var y3 = Kt(() => {
});
var b3 = Kt(() => {
});
var C3 = Kt((wg, aI) => {
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        return te.buffer != ze && Nt(te.buffer), Rr;
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        return te.buffer != ze && Nt(te.buffer), Pt;
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        c = F, l = B;
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      var m;
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      var E, A, R, D;
      function P(F) {
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          B = ep(B) ? new URL(B) : L.normalize(B), M.readFile(B, function(Me, Pe) {
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          if (!(B instanceof qi))
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          throw B;
        }), g = (B, ue) => {
          if (Do())
            throw process.exitCode = B, ue;
          P(ue), process.exit(B);
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          return "[Emscripten Module object]";
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        let F;
        try {
          F = x3();
        } catch (B) {
          throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), B;
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        global.Worker = F.Worker;
      } else
        (x || b) && (b ? k = self.location.href : typeof document != "undefined" && document.currentScript && (k = document.currentScript.src), typeof r != "undefined" && r && (k = r), k.indexOf("blob:") !== 0 ? k = k.substr(0, k.replace(/[?#].*/, "").lastIndexOf("/") + 1) : k = "", C || (E = (F) => {
          var B = new XMLHttpRequest();
          return B.open("GET", F, false), B.send(null), B.responseText;
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          var B = new XMLHttpRequest();
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          var _e = new XMLHttpRequest();
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              B(_e.response);
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            ue();
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`), z = (F) => M.writeSync(2, F + `
`));
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      var H = 4, q = Atomics.load, Z = Atomics.store, ee = Atomics.compareExchange, oe;
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      var te, ie, ce = false, ye;
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        F || Ki(B);
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      var Ie = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0;
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        for (var _e = B + ue, Me = B; F[Me] && !(Me >= _e); )
          ++Me;
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          return Ie.decode(F.buffer instanceof SharedArrayBuffer ? F.slice(B, Me) : F.subarray(B, Me));
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          var fe = F[B++];
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            Pe += String.fromCharCode(fe);
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          var ve = F[B++] & 63;
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            Pe += String.fromCharCode((fe & 31) << 6 | ve);
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          var Ft = F[B++] & 63;
          if ((fe & 240) == 224 ? fe = (fe & 15) << 12 | ve << 6 | Ft : fe = (fe & 7) << 18 | ve << 12 | Ft << 6 | F[B++] & 63, fe < 65536)
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          else {
            var Qr = fe - 65536;
            Pe += String.fromCharCode(55296 | Qr >> 10, 56320 | Qr & 1023);
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      function Re(F, B) {
        return F ? Ae(o(), F, B) : "";
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      function ot(F, B, ue, _e) {
        if (!(_e > 0))
          return 0;
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          var ve = F.charCodeAt(fe);
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            var Ft = F.charCodeAt(++fe);
            ve = 65536 + ((ve & 1023) << 10) | Ft & 1023;
          }
          if (ve <= 127) {
            if (ue >= Pe)
              break;
            B[ue++] = ve;
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            if (ue + 1 >= Pe)
              break;
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          } else if (ve <= 65535) {
            if (ue + 2 >= Pe)
              break;
            B[ue++] = 224 | ve >> 12, B[ue++] = 128 | ve >> 6 & 63, B[ue++] = 128 | ve & 63;
          } else {
            if (ue + 3 >= Pe)
              break;
            B[ue++] = 240 | ve >> 18, B[ue++] = 128 | ve >> 12 & 63, B[ue++] = 128 | ve >> 6 & 63, B[ue++] = 128 | ve & 63;
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      function pt(F, B, ue) {
        return ot(F, o(), B, ue);
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      var ze, ct, at, ft, ht, Rr, Pt, qr, er;
      w && (ze = u.buffer);
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      function Do() {
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      function hs() {
        if (u.preRun)
          for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; )
            Dc(u.preRun.shift());
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      function qt() {
        ea = true, !w && Pc(Xr);
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      function ta() {
        if (!w) {
          if (u.postRun)
            for (typeof u.postRun == "function" && (u.postRun = [u.postRun]); u.postRun.length; )
              AI(u.postRun.shift());
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      }
      function Dc(F) {
        jr.unshift(F);
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      function Fc(F) {
        Xr.unshift(F);
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      function AI(F) {
        cr.unshift(F);
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      var Ya = 0, Ju = null, ra = null;
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        Ya++, u.monitorRunDependencies && u.monitorRunDependencies(Ya);
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      function Yl(F) {
        if (Ya--, u.monitorRunDependencies && u.monitorRunDependencies(Ya), Ya == 0 && (Ju !== null && (clearInterval(Ju), Ju = null), ra)) {
          var B = ra;
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      function Ki(F) {
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      }
      var Dx = "data:application/octet-stream;base64,";
      function Ql(F) {
        return F.startsWith(Dx);
      }
      function ep(F) {
        return F.startsWith("file://");
      }
      var lr;
      lr = "tfjs-backend-wasm-threaded-simd.wasm", Ql(lr) || (lr = _(lr));
      function Zl(F) {
        try {
          if (F == lr && oe)
            return new Uint8Array(oe);
          if (R)
            return R(F);
          throw "both async and sync fetching of the wasm failed";
        } catch (B) {
          Ki(B);
        }
      }
      function Fx() {
        if (!oe && (x || b)) {
          if (typeof fetch == "function" && !ep(lr))
            return fetch(lr, { credentials: "same-origin" }).then(function(F) {
              if (!F.ok)
                throw "failed to load wasm binary file at '" + lr + "'";
              return F.arrayBuffer();
            }).catch(function() {
              return Zl(lr);
            });
          if (A)
            return new Promise(function(F, B) {
              A(lr, function(ue) {
                F(new Uint8Array(ue));
              }, B);
            });
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        return Promise.resolve().then(function() {
          return Zl(lr);
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      }
      function Ox() {
        var F = { env: lm, wasi_snapshot_preview1: lm };
        function B(fe, ve) {
          var Ft = fe.exports;
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            var Qr = Oe.unusedWorkers.length;
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                --Qr || Yl("wasm-instantiate");
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        }
        w || Rx("wasm-instantiate");
        function ue(fe) {
          B(fe.instance, fe.module);
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        function _e(fe) {
          return Fx().then(function(ve) {
            return WebAssembly.instantiate(ve, F);
          }).then(function(ve) {
            return ve;
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            K("failed to asynchronously prepare wasm: " + ve), Ki(ve);
          });
        }
        function Me() {
          return !oe && typeof WebAssembly.instantiateStreaming == "function" && !Ql(lr) && !ep(lr) && !C && typeof fetch == "function" ? fetch(lr, { credentials: "same-origin" }).then(function(fe) {
            var ve = WebAssembly.instantiateStreaming(fe, F);
            return ve.then(ue, function(Ft) {
              return K("wasm streaming compile failed: " + Ft), K("falling back to ArrayBuffer instantiation"), _e(ue);
            });
          }) : _e(ue);
        }
        if (u.instantiateWasm)
          try {
            var Pe = u.instantiateWasm(F, B);
            return Pe;
          } catch (fe) {
            K("Module.instantiateWasm callback failed with error: " + fe), l(fe);
          }
        return Me().catch(l), {};
      }
      var RI, DI, Jl = {};
      function qi(F) {
        this.name = "ExitStatus", this.message = "Program terminated with exit(" + F + ")", this.status = F;
      }
      function Px(F) {
        var B = Oe.pthreads[F];
        delete Oe.pthreads[F], B.terminate(), hb(F), Oe.runningWorkers.splice(Oe.runningWorkers.indexOf(B), 1), B.pthread_ptr = 0;
      }
      function Mx(F) {
        var B = Oe.pthreads[F];
        B.postMessage({ cmd: "cancel" });
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      function Oc(F) {
        var B = Oe.pthreads[F];
        Ne(B), Oe.returnWorkerToPool(B);
      }
      function Lx(F) {
        var B = Oe.getNewWorker();
        if (!B)
          return 6;
        Oe.runningWorkers.push(B), Oe.pthreads[F.pthread_ptr] = B, B.pthread_ptr = F.pthread_ptr;
        var ue = { cmd: "run", start_routine: F.startRoutine, arg: F.arg, pthread_ptr: F.pthread_ptr };
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          C && B.ref(), B.postMessage(ue, F.transferList), delete B.runPthread;
        }, B.loaded && B.runPthread(), 0;
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      var em = { varargs: void 0, get: function() {
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        return F;
      }, getStr: function(F) {
        var B = Re(F);
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      } };
      function tm(F) {
        if (w)
          return Qa(1, 1, F);
        ye = F, Do() || (Oe.terminateAllThreads(), u.onExit && u.onExit(F), ce = true), g(F, new qi(F));
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      function Bx(F, B) {
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        if (F instanceof qi || F == "unwind")
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          Oe.allocateUnusedWorker();
      }, initWorker: function() {
        J = false;
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        ye = F;
      }, terminateAllThreads: function() {
        for (var F of Object.values(Oe.pthreads))
          Oe.returnWorkerToPool(F);
        for (var F of Oe.unusedWorkers)
          F.terminate();
        Oe.unusedWorkers = [];
      }, returnWorkerToPool: function(F) {
        var B = F.pthread_ptr;
        delete Oe.pthreads[B], Oe.unusedWorkers.push(F), Oe.runningWorkers.splice(Oe.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, C && F.unref(), hb(B);
      }, receiveObjectTransfer: function(F) {
      }, threadInitTLS: function() {
        Oe.tlsInitFunctions.forEach((F) => F());
      }, loadWasmModuleToWorker: function(F, B) {
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          var fe = Pe.data, ve = fe.cmd;
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            var Ft = Oe.pthreads[fe.targetThread];
            Ft ? Ft.postMessage(fe, fe.transferList) : K('Internal error! Worker sent a message "' + ve + '" to target pthread ' + fe.targetThread + ", but that thread no longer exists!"), Oe.currentProxiedOperationCallerThread = void 0;
            return;
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        }, F.onerror = (Pe) => {
          var fe = "worker sent an error!";
          throw K(fe + " " + Pe.filename + ":" + Pe.lineno + ": " + Pe.message), Pe;
        }, C && (F.on("message", function(Pe) {
          F.onmessage({ data: Pe });
        }), F.on("error", function(Pe) {
          F.onerror(Pe);
        }), F.on("detachedExit", function() {
        }));
        var ue = [], _e = ["onExit", "onAbort", "print", "printErr"];
        for (var Me of _e)
          u.hasOwnProperty(Me) && ue.push(Me);
        F.postMessage({ cmd: "load", handlers: ue, urlOrBlob: u.mainScriptUrlOrBlob || r, wasmMemory: te, wasmModule: ie });
      }, allocateUnusedWorker: function() {
        var F, B = _("tfjs-backend-wasm-threaded-simd.worker.js");
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      }, getNewWorker: function() {
        return Oe.unusedWorkers.length == 0 && (Oe.allocateUnusedWorker(), Oe.loadWasmModuleToWorker(Oe.unusedWorkers[0])), Oe.unusedWorkers.pop();
      } };
      u.PThread = Oe;
      function Pc(F) {
        for (; F.length > 0; )
          F.shift()(u);
      }
      function zx() {
        var F = xm(), B = s()[F + 52 >> 2], ue = s()[F + 56 >> 2], _e = B - ue;
        BI(B, _e), ym(B);
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      u.establishStackSpace = zx;
      function om(F) {
        if (w)
          return Qa(2, 0, F);
        try {
          rm(F);
        } catch (B) {
          Vx(B);
        }
      }
      var tp = [];
      function Wx(F) {
        var B = tp[F];
        return B || (F >= tp.length && (tp.length = F + 1), tp[F] = B = rr.get(F)), B;
      }
      function Ux(F, B) {
        var ue = Wx(F)(B);
        Do() ? Oe.setExitStatus(ue) : LI(ue);
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      u.invokeEntryPoint = Ux;
      function Gx(F) {
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      }
      function Hx(F) {
        OI(F, !b, 1, !x), Oe.threadInitTLS();
      }
      function Kx(F) {
        w ? postMessage({ cmd: "cleanupThread", thread: F }) : Oc(F);
      }
      function nm(F, B, ue, _e) {
        return w ? Qa(3, 1, F, B, ue, _e) : sm(F, B, ue, _e);
      }
      function sm(F, B, ue, _e) {
        if (typeof SharedArrayBuffer == "undefined")
          return K("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
        var Me = [], Pe = 0;
        if (w && (Me.length === 0 || Pe))
          return nm(F, B, ue, _e);
        if (Pe)
          return Pe;
        var fe = { startRoutine: ue, pthread_ptr: F, arg: _e, transferList: Me };
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      }
      function qx() {
        return 65536;
      }
      var jx = true;
      function Xx() {
        return jx;
      }
      function Mc(F) {
        Atomics.store(s(), F >> 2, 1), xm() && MI(F), Atomics.compareExchange(s(), F >> 2, 1, 0);
      }
      u.executeNotifiedProxyingQueue = Mc;
      function Yx(F, B, ue, _e) {
        if (F == B)
          setTimeout(() => Mc(_e));
        else if (w)
          postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: _e });
        else {
          var Me = Oe.pthreads[F];
          if (!Me)
            return;
          Me.postMessage({ cmd: "processProxyingQueue", queue: _e });
        }
        return 1;
      }
      function Qx(F, B, ue) {
        return -1;
      }
      function Zx() {
        Ki("");
      }
      function ji(F) {
        ji.shown || (ji.shown = {}), ji.shown[F] || (ji.shown[F] = 1, C && (F = "warning: " + F), K(F));
      }
      function Jx() {
        C || b || ji("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
      }
      function ey() {
        return Date.now();
      }
      function am() {
        return 2147483648;
      }
      function ty() {
        return am();
      }
      var Lc;
      C ? Lc = () => {
        var F = process.hrtime();
        return F[0] * 1e3 + F[1] / 1e6;
      } : Lc = () => performance.timeOrigin + performance.now();
      function ry(F, B, ue) {
        o().copyWithin(F, B, B + ue);
      }
      function oy() {
        return C ? b3().cpus().length : navigator.hardwareConcurrency;
      }
      function ny(F) {
        var B = gb(), ue = F();
        return ym(B), ue;
      }
      function Qa(F, B) {
        var ue = arguments.length - 2, _e = arguments;
        return ny(() => {
          for (var Me = ue, Pe = bm(Me * 8), fe = Pe >> 3, ve = 0; ve < ue; ve++) {
            var Ft = _e[2 + ve];
            p()[fe + ve] = Ft;
          }
          return PI(F, Me, Pe, B);
        });
      }
      var Bc = [];
      function sy(F, B, ue) {
        Bc.length = B;
        for (var _e = ue >> 3, Me = 0; Me < B; Me++)
          Bc[Me] = p()[_e + Me];
        var Pe = F < 0, fe = Pe ? Jl[-F - 1] : fy[F];
        return fe.apply(null, Bc);
      }
      function ay(F) {
        try {
          return te.grow(F - ze.byteLength + 65535 >>> 16), Nt(te.buffer), 1;
        } catch (B) {
        }
      }
      function iy(F) {
        var B = o().length;
        if (F = F >>> 0, F <= B)
          return false;
        var ue = am();
        if (F > ue)
          return false;
        let _e = (Ft, Qr) => Ft + (Qr - Ft % Qr) % Qr;
        for (var Me = 1; Me <= 4; Me *= 2) {
          var Pe = B * (1 + 0.2 / Me);
          Pe = Math.min(Pe, F + 100663296);
          var fe = Math.min(ue, _e(Math.max(F, Pe), 65536)), ve = ay(fe);
          if (ve)
            return true;
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        return false;
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      function uy() {
        throw "unwind";
      }
      function im(F) {
        return w ? Qa(4, 1, F) : 52;
      }
      function um(F, B, ue, _e, Me) {
        return w ? Qa(5, 1, F, B, ue, _e, Me) : 70;
      }
      var py = [null, [], []];
      function cy(F, B) {
        var ue = py[F];
        B === 0 || B === 10 ? ((F === 1 ? U : K)(Ae(ue, 0)), ue.length = 0) : ue.push(B);
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      function pm(F, B, ue, _e) {
        if (w)
          return Qa(6, 1, F, B, ue, _e);
        for (var Me = 0, Pe = 0; Pe < ue; Pe++) {
          var fe = a()[B >> 2], ve = a()[B + 4 >> 2];
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            cy(F, o()[fe + Ft]);
          Me += ve;
        }
        return a()[_e >> 2] = Me, 0;
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      function cm(F) {
        var B = u["_" + F];
        return B;
      }
      function ly(F, B) {
        t10().set(F, B);
      }
      function my(F, B, ue, _e, Me) {
        var Pe = { string: (Dr) => {
          var sp = 0;
          if (Dr != null && Dr !== 0) {
            var WI = (Dr.length << 2) + 1;
            sp = bm(WI), pt(Dr, sp, WI);
          }
          return sp;
        }, array: (Dr) => {
          var sp = bm(Dr.length);
          return ly(Dr, sp), sp;
        } };
        function fe(Dr) {
          return B === "string" ? Re(Dr) : B === "boolean" ? Boolean(Dr) : Dr;
        }
        var ve = cm(F), Ft = [], Qr = 0;
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          for (var na = 0; na < _e.length; na++) {
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            zI ? (Qr === 0 && (Qr = gb()), Ft[na] = zI(_e[na])) : Ft[na] = _e[na];
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        return xb = OV(xb), xb;
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      function dy(F, B, ue, _e) {
        ue = ue || [];
        var Me = ue.every((fe) => fe === "number" || fe === "boolean"), Pe = B !== "string";
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          return my(F, B, ue, arguments, _e);
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      }
      Oe.init();
      var fy = [null, tm, om, nm, im, um, pm], lm = { __emscripten_init_main_thread_js: Hx, __emscripten_thread_cleanup: Kx, __pthread_create_js: sm, _emscripten_default_pthread_stack_size: qx, _emscripten_get_now_is_monotonic: Xx, _emscripten_notify_task_queue: Yx, _emscripten_set_offscreencanvas_size: Qx, abort: Zx, emscripten_check_blocking_allowed: Jx, emscripten_date_now: ey, emscripten_get_heap_max: ty, emscripten_get_now: Lc, emscripten_memcpy_big: ry, emscripten_num_logical_cores: oy, emscripten_receive_on_main_thread_js: sy, emscripten_resize_heap: iy, emscripten_unwind_to_js_event_loop: uy, exit: rm, fd_close: im, fd_seek: um, fd_write: pm, memory: te || u.wasmMemory }, FI = Ox(), hy = u.___wasm_call_ctors = function() {
        return (hy = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments);
      }, gy = u._init = function() {
        return (gy = u._init = u.asm.init).apply(null, arguments);
      }, xy = u._init_with_threads_count = function() {
        return (xy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments);
      }, yy = u._get_threads_count = function() {
        return (yy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments);
      }, by = u._register_tensor = function() {
        return (by = u._register_tensor = u.asm.register_tensor).apply(null, arguments);
      }, Cy = u._dispose_data = function() {
        return (Cy = u._dispose_data = u.asm.dispose_data).apply(null, arguments);
      }, Sy = u._dispose = function() {
        return (Sy = u._dispose = u.asm.dispose).apply(null, arguments);
      }, wy = u._Abs = function() {
        return (wy = u._Abs = u.asm.Abs).apply(null, arguments);
      }, Iy = u._Add = function() {
        return (Iy = u._Add = u.asm.Add).apply(null, arguments);
      }, vy = u._AddN = function() {
        return (vy = u._AddN = u.asm.AddN).apply(null, arguments);
      }, ky = u._All = function() {
        return (ky = u._All = u.asm.All).apply(null, arguments);
      }, Ny = u._Any = function() {
        return (Ny = u._Any = u.asm.Any).apply(null, arguments);
      }, Ty = u._ArgMax = function() {
        return (Ty = u._ArgMax = u.asm.ArgMax).apply(null, arguments);
      }, _y = u._AvgPool = function() {
        return (_y = u._AvgPool = u.asm.AvgPool).apply(null, arguments);
      }, $y = u._BatchMatMul = function() {
        return ($y = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments);
      }, Ey = u._Ceil = function() {
        return (Ey = u._Ceil = u.asm.Ceil).apply(null, arguments);
      }, Ay = u._ClipByValue = function() {
        return (Ay = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments);
      }, Ry = u._Conv2D = function() {
        return (Ry = u._Conv2D = u.asm.Conv2D).apply(null, arguments);
      }, Dy = u._Conv2DBackpropInput = function() {
        return (Dy = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments);
      }, Fy = u._Cos = function() {
        return (Fy = u._Cos = u.asm.Cos).apply(null, arguments);
      }, Oy = u._Cosh = function() {
        return (Oy = u._Cosh = u.asm.Cosh).apply(null, arguments);
      }, Py = u._CropAndResize = function() {
        return (Py = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments);
      }, My = u._Cumprod = function() {
        return (My = u._Cumprod = u.asm.Cumprod).apply(null, arguments);
      }, Ly = u._Cumsum = function() {
        return (Ly = u._Cumsum = u.asm.Cumsum).apply(null, arguments);
      }, By = u._DepthToSpace = function() {
        return (By = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments);
      }, Vy = u._DepthwiseConv2dNative = function() {
        return (Vy = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, zy = u._Elu = function() {
        return (zy = u._Elu = u.asm.Elu).apply(null, arguments);
      }, Wy = u._Equal = function() {
        return (Wy = u._Equal = u.asm.Equal).apply(null, arguments);
      }, Uy = u._Exp = function() {
        return (Uy = u._Exp = u.asm.Exp).apply(null, arguments);
      }, Gy = u._FlipLeftRight = function() {
        return (Gy = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments);
      }, Hy = u._Floor = function() {
        return (Hy = u._Floor = u.asm.Floor).apply(null, arguments);
      }, Ky = u._FloorDiv = function() {
        return (Ky = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments);
      }, qy = u._FusedBatchNorm = function() {
        return (qy = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments);
      }, jy = u._FusedConv2D = function() {
        return (jy = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments);
      }, Xy = u._FusedDepthwiseConv2D = function() {
        return (Xy = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments);
      }, Yy = u._Gather = function() {
        return (Yy = u._Gather = u.asm.Gather).apply(null, arguments);
      }, Qy = u._GatherNd = function() {
        return (Qy = u._GatherNd = u.asm.GatherNd).apply(null, arguments);
      }, Zy = u._Greater = function() {
        return (Zy = u._Greater = u.asm.Greater).apply(null, arguments);
      }, Jy = u._GreaterEqual = function() {
        return (Jy = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments);
      }, eb = u._IsNan = function() {
        return (eb = u._IsNan = u.asm.IsNan).apply(null, arguments);
      }, tb = u._LeakyRelu = function() {
        return (tb = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments);
      }, rb = u._Less = function() {
        return (rb = u._Less = u.asm.Less).apply(null, arguments);
      }, ob = u._LessEqual = function() {
        return (ob = u._LessEqual = u.asm.LessEqual).apply(null, arguments);
      }, nb = u._Log = function() {
        return (nb = u._Log = u.asm.Log).apply(null, arguments);
      }, sb = u._LogicalAnd = function() {
        return (sb = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments);
      }, ab = u._LogicalNot = function() {
        return (ab = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments);
      }, ib = u._LogicalOr = function() {
        return (ib = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments);
      }, ub = u._LogicalXor = function() {
        return (ub = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments);
      }, pb = u._Max = function() {
        return (pb = u._Max = u.asm.Max).apply(null, arguments);
      }, cb = u._MaxPool = function() {
        return (cb = u._MaxPool = u.asm.MaxPool).apply(null, arguments);
      }, mm = u._Maximum = function() {
        return (mm = u._Maximum = u.asm.Maximum).apply(null, arguments);
      }, dm = u._Mean = function() {
        return (dm = u._Mean = u.asm.Mean).apply(null, arguments);
      }, Vc = u._Min = function() {
        return (Vc = u._Min = u.asm.Min).apply(null, arguments);
      }, lb = u._Minimum = function() {
        return (lb = u._Minimum = u.asm.Minimum).apply(null, arguments);
      }, mb = u._MirrorPad = function() {
        return (mb = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments);
      }, rp = u._Multiply = function() {
        return (rp = u._Multiply = u.asm.Multiply).apply(null, arguments);
      }, fm = u._Neg = function() {
        return (fm = u._Neg = u.asm.Neg).apply(null, arguments);
      }, op = u._NonMaxSuppressionV3 = function() {
        return (op = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments);
      }, np = u._NonMaxSuppressionV4 = function() {
        return (np = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments);
      }, db = u._NonMaxSuppressionV5 = function() {
        return (db = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments);
      }, G = u._NotEqual = function() {
        return (G = u._NotEqual = u.asm.NotEqual).apply(null, arguments);
      }, se = u._OneHot = function() {
        return (se = u._OneHot = u.asm.OneHot).apply(null, arguments);
      }, Te = u._PadV2 = function() {
        return (Te = u._PadV2 = u.asm.PadV2).apply(null, arguments);
      }, nt = u._Pow = function() {
        return (nt = u._Pow = u.asm.Pow).apply(null, arguments);
      }, Tt = u._Prelu = function() {
        return (Tt = u._Prelu = u.asm.Prelu).apply(null, arguments);
      }, _t = u._Prod = function() {
        return (_t = u._Prod = u.asm.Prod).apply(null, arguments);
      }, qe = u._RealDiv = function() {
        return (qe = u._RealDiv = u.asm.RealDiv).apply(null, arguments);
      }, Ue = u._Reciprocal = function() {
        return (Ue = u._Reciprocal = u.asm.Reciprocal).apply(null, arguments);
      }, Wt = u._Relu = function() {
        return (Wt = u._Relu = u.asm.Relu).apply(null, arguments);
      }, Yr = u._Relu6 = function() {
        return (Yr = u._Relu6 = u.asm.Relu6).apply(null, arguments);
      }, oa = u._ResizeBilinear = function() {
        return (oa = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments);
      }, hm = u._ResizeNearestNeighbor = function() {
        return (hm = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments);
      }, zc = u._Reverse = function() {
        return (zc = u._Reverse = u.asm.Reverse).apply(null, arguments);
      }, fb = u._RotateWithOffset = function() {
        return (fb = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments);
      }, hr = u._Round = function() {
        return (hr = u._Round = u.asm.Round).apply(null, arguments);
      }, Za = u._Rsqrt = function() {
        return (Za = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments);
      }, gm = u._ScatterNd = function() {
        return (gm = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments);
      }, oV = u._SelectV2 = function() {
        return (oV = u._SelectV2 = u.asm.SelectV2).apply(null, arguments);
      }, nV = u._Sigmoid = function() {
        return (nV = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments);
      }, sV = u._Sin = function() {
        return (sV = u._Sin = u.asm.Sin).apply(null, arguments);
      }, aV = u._Softmax = function() {
        return (aV = u._Softmax = u.asm.Softmax).apply(null, arguments);
      }, iV = u._SparseFillEmptyRows = function() {
        return (iV = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments);
      }, uV = u._SparseReshape = function() {
        return (uV = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments);
      }, pV = u._SparseSegmentReduction = function() {
        return (pV = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments);
      }, cV = u._Sqrt = function() {
        return (cV = u._Sqrt = u.asm.Sqrt).apply(null, arguments);
      }, lV = u._Square = function() {
        return (lV = u._Square = u.asm.Square).apply(null, arguments);
      }, mV = u._SquaredDifference = function() {
        return (mV = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments);
      }, dV = u._Step = function() {
        return (dV = u._Step = u.asm.Step).apply(null, arguments);
      }, fV = u._StridedSlice = function() {
        return (fV = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments);
      }, hV = u._Sub = function() {
        return (hV = u._Sub = u.asm.Sub).apply(null, arguments);
      }, gV = u._Sum = function() {
        return (gV = u._Sum = u.asm.Sum).apply(null, arguments);
      }, xV = u._Tan = function() {
        return (xV = u._Tan = u.asm.Tan).apply(null, arguments);
      }, yV = u._Tanh = function() {
        return (yV = u._Tanh = u.asm.Tanh).apply(null, arguments);
      }, bV = u._Tile = function() {
        return (bV = u._Tile = u.asm.Tile).apply(null, arguments);
      }, CV = u._TopK = function() {
        return (CV = u._TopK = u.asm.TopK).apply(null, arguments);
      }, SV = u._Transform = function() {
        return (SV = u._Transform = u.asm.Transform).apply(null, arguments);
      }, wV = u._Transpose = function() {
        return (wV = u._Transpose = u.asm.Transpose).apply(null, arguments);
      }, IV = u.__FusedMatMul = function() {
        return (IV = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments);
      }, vV = u._malloc = function() {
        return (vV = u._malloc = u.asm.malloc).apply(null, arguments);
      }, kV = u._free = function() {
        return (kV = u._free = u.asm.free).apply(null, arguments);
      }, NV = u.__emscripten_tls_init = function() {
        return (NV = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments);
      }, xm = u._pthread_self = function() {
        return (xm = u._pthread_self = u.asm.pthread_self).apply(null, arguments);
      }, TV = u.___errno_location = function() {
        return (TV = u.___errno_location = u.asm.__errno_location).apply(null, arguments);
      }, OI = u.__emscripten_thread_init = function() {
        return (OI = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments);
      }, _V = u.__emscripten_thread_crashed = function() {
        return (_V = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments);
      }, $V = u._emscripten_main_thread_process_queued_calls = function() {
        return ($V = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
      }, EV = u._emscripten_main_browser_thread_id = function() {
        return (EV = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments);
      }, PI = u._emscripten_run_in_main_runtime_thread_js = function() {
        return (PI = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
      }, AV = u._emscripten_dispatch_to_thread_ = function() {
        return (AV = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
      }, MI = u.__emscripten_proxy_execute_task_queue = function() {
        return (MI = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments);
      }, hb = u.__emscripten_thread_free_data = function() {
        return (hb = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments);
      }, LI = u.__emscripten_thread_exit = function() {
        return (LI = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments);
      }, BI = u._emscripten_stack_set_limits = function() {
        return (BI = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments);
      }, gb = u.stackSave = function() {
        return (gb = u.stackSave = u.asm.stackSave).apply(null, arguments);
      }, ym = u.stackRestore = function() {
        return (ym = u.stackRestore = u.asm.stackRestore).apply(null, arguments);
      }, bm = u.stackAlloc = function() {
        return (bm = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments);
      }, RV = u.dynCall_iijjiiii = function() {
        return (RV = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments);
      }, DV = u.dynCall_jiji = function() {
        return (DV = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments);
      };
      u.keepRuntimeAlive = Do, u.wasmMemory = te, u.cwrap = dy, u.ExitStatus = qi, u.PThread = Oe;
      var Cm;
      ra = function F() {
        Cm || VI(), Cm || (ra = F);
      };
      function VI(F) {
        if (F = F || f, Ya > 0)
          return;
        if (w) {
          c(u), qt(), startWorker(u);
          return;
        }
        if (hs(), Ya > 0)
          return;
        function B() {
          Cm || (Cm = true, u.calledRun = true, !ce && (qt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), ta()));
        }
        u.setStatus ? (u.setStatus("Running..."), setTimeout(function() {
          setTimeout(function() {
            u.setStatus("");
          }, 1), B();
        }, 1)) : B();
      }
      if (u.preInit)
        for (typeof u.preInit == "function" && (u.preInit = [u.preInit]); u.preInit.length > 0; )
          u.preInit.pop()();
      VI();
      var Sm;
      m && (Sm = { uncaughtException: process.listeners("uncaughtException").filter(function(F) {
        return !m.uncaughtException.indexOf(F) > -1;
      }), unhandledRejection: process.listeners("unhandledRejection").filter(function(F) {
        return !m.unhandledRejection.indexOf(F) > -1;
      }) });
      var wm;
      if (typeof WasmBackendModule != "undefined")
        wm = WasmBackendModule;
      else if (typeof e != "undefined")
        wm = e;
      else
        throw new Error("Could not find wasm module in post.js");
      if (Sm) {
        var FV = wm._dispose;
        wm._dispose = function() {
          FV(), Sm.uncaughtException.forEach(function(F) {
            process.removeListener("uncaughtException", F);
          }), Sm.unhandledRejection.forEach(function(F) {
            process.removeListener("unhandledRejection", F);
          });
        };
      }
      return e.ready;
    };
  })();
  typeof wg == "object" && typeof aI == "object" ? aI.exports = sI : typeof define == "function" && define.amd ? define([], function() {
    return sI;
  }) : typeof wg == "object" && (wg.WasmBackendModuleThreadedSimd = sI);
});
var w3 = Kt((a1t, S3) => {
  S3.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}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)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};`;
});
var I3 = Kt((Ig, uI) => {
  var iI = (() => {
    var r = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
    return typeof __filename != "undefined" && (r = r || __filename), function(e) {
      e = e || {};
      var t10 = typeof e != "undefined" ? e : {}, o, n;
      t10.ready = new Promise(function(G, se) {
        o = G, n = se;
      });
      var s;
      typeof process != "undefined" && process.listeners && (s = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") });
      var a = Object.assign({}, t10), i = [], p = "./this.program", u = (G, se) => {
        throw se;
      }, c = typeof window == "object", l = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", d = "";
      function f(G) {
        return t10.locateFile ? t10.locateFile(G, d) : d + G;
      }
      var h, g, x, b;
      function C(G) {
        if (G instanceof Ju)
          return;
        E("exiting due to exception: " + G);
      }
      if (m) {
        var w = oI(), k = nI();
        l ? d = k.dirname(d) + "/" : d = __dirname + "/", h = (G, se) => (G = hs(G) ? new URL(G) : k.normalize(G), w.readFileSync(G, se ? void 0 : "utf8")), x = (G) => {
          var se = h(G, true);
          return se.buffer || (se = new Uint8Array(se)), se;
        }, g = (G, se, Te) => {
          G = hs(G) ? new URL(G) : k.normalize(G), w.readFile(G, function(nt, Tt) {
            nt ? Te(nt) : se(Tt.buffer);
          });
        }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), i = process.argv.slice(2), process.on("uncaughtException", function(G) {
          if (!(G instanceof Ju))
            throw G;
        }), process.on("unhandledRejection", function(G) {
          throw G;
        }), u = (G, se) => {
          if (at())
            throw process.exitCode = G, se;
          C(se), process.exit(G);
        }, t10.inspect = function() {
          return "[Emscripten Module object]";
        };
      } else
        (c || l) && (l ? d = self.location.href : typeof document != "undefined" && document.currentScript && (d = document.currentScript.src), r && (d = r), d.indexOf("blob:") !== 0 ? d = d.substr(0, d.replace(/[?#].*/, "").lastIndexOf("/") + 1) : d = "", h = (G) => {
          var se = new XMLHttpRequest();
          return se.open("GET", G, false), se.send(null), se.responseText;
        }, l && (x = (G) => {
          var se = new XMLHttpRequest();
          return se.open("GET", G, false), se.responseType = "arraybuffer", se.send(null), new Uint8Array(se.response);
        }), g = (G, se, Te) => {
          var nt = new XMLHttpRequest();
          nt.open("GET", G, true), nt.responseType = "arraybuffer", nt.onload = () => {
            if (nt.status == 200 || nt.status == 0 && nt.response) {
              se(nt.response);
              return;
            }
            Te();
          }, nt.onerror = Te, nt.send(null);
        }, b = (G) => document.title = G);
      var _ = t10.print || console.log.bind(console), E = t10.printErr || console.warn.bind(console);
      Object.assign(t10, a), a = null, t10.arguments && (i = t10.arguments), t10.thisProgram && (p = t10.thisProgram), t10.quit && (u = t10.quit);
      var A = 4, R;
      t10.wasmBinary && (R = t10.wasmBinary);
      var D = t10.noExitRuntime || true;
      typeof WebAssembly != "object" && cr("no native wasm support detected");
      var P, M = false, L;
      function V(G, se) {
        G || cr(se);
      }
      var z = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0;
      function U(G, se, Te) {
        for (var nt = se + Te, Tt = se; G[Tt] && !(Tt >= nt); )
          ++Tt;
        if (Tt - se > 16 && G.buffer && z)
          return z.decode(G.subarray(se, Tt));
        for (var _t = ""; se < Tt; ) {
          var qe = G[se++];
          if (!(qe & 128)) {
            _t += String.fromCharCode(qe);
            continue;
          }
          var Ue = G[se++] & 63;
          if ((qe & 224) == 192) {
            _t += String.fromCharCode((qe & 31) << 6 | Ue);
            continue;
          }
          var Wt = G[se++] & 63;
          if ((qe & 240) == 224 ? qe = (qe & 15) << 12 | Ue << 6 | Wt : qe = (qe & 7) << 18 | Ue << 12 | Wt << 6 | G[se++] & 63, qe < 65536)
            _t += String.fromCharCode(qe);
          else {
            var Yr = qe - 65536;
            _t += String.fromCharCode(55296 | Yr >> 10, 56320 | Yr & 1023);
          }
        }
        return _t;
      }
      function K(G, se) {
        return G ? U(oe, G, se) : "";
      }
      function H(G, se, Te, nt) {
        if (!(nt > 0))
          return 0;
        for (var Tt = Te, _t = Te + nt - 1, qe = 0; qe < G.length; ++qe) {
          var Ue = G.charCodeAt(qe);
          if (Ue >= 55296 && Ue <= 57343) {
            var Wt = G.charCodeAt(++qe);
            Ue = 65536 + ((Ue & 1023) << 10) | Wt & 1023;
          }
          if (Ue <= 127) {
            if (Te >= _t)
              break;
            se[Te++] = Ue;
          } else if (Ue <= 2047) {
            if (Te + 1 >= _t)
              break;
            se[Te++] = 192 | Ue >> 6, se[Te++] = 128 | Ue & 63;
          } else if (Ue <= 65535) {
            if (Te + 2 >= _t)
              break;
            se[Te++] = 224 | Ue >> 12, se[Te++] = 128 | Ue >> 6 & 63, se[Te++] = 128 | Ue & 63;
          } else {
            if (Te + 3 >= _t)
              break;
            se[Te++] = 240 | Ue >> 18, se[Te++] = 128 | Ue >> 12 & 63, se[Te++] = 128 | Ue >> 6 & 63, se[Te++] = 128 | Ue & 63;
          }
        }
        return se[Te] = 0, Te - Tt;
      }
      function q(G, se, Te) {
        return H(G, oe, se, Te);
      }
      var Z, ee, oe, J, te, ie, ce, ye, Ne;
      function Ie(G) {
        Z = G, t10.HEAP8 = ee = new Int8Array(G), t10.HEAP16 = J = new Int16Array(G), t10.HEAP32 = ie = new Int32Array(G), t10.HEAPU8 = oe = new Uint8Array(G), t10.HEAPU16 = te = new Uint16Array(G), t10.HEAPU32 = ce = new Uint32Array(G), t10.HEAPF32 = ye = new Float32Array(G), t10.HEAPF64 = Ne = new Float64Array(G);
      }
      var Ae = t10.INITIAL_MEMORY || 16777216, Re, ot = [], pt = [], ze = [], ct = false;
      function at() {
        return D;
      }
      function ft() {
        if (t10.preRun)
          for (typeof t10.preRun == "function" && (t10.preRun = [t10.preRun]); t10.preRun.length; )
            Pt(t10.preRun.shift());
        ra(ot);
      }
      function ht() {
        ct = true, ra(pt);
      }
      function Rr() {
        if (t10.postRun)
          for (typeof t10.postRun == "function" && (t10.postRun = [t10.postRun]); t10.postRun.length; )
            er(t10.postRun.shift());
        ra(ze);
      }
      function Pt(G) {
        ot.unshift(G);
      }
      function qr(G) {
        pt.unshift(G);
      }
      function er(G) {
        ze.unshift(G);
      }
      var Nt = 0, tr = null, rr = null;
      function jr(G) {
        Nt++, t10.monitorRunDependencies && t10.monitorRunDependencies(Nt);
      }
      function Xr(G) {
        if (Nt--, t10.monitorRunDependencies && t10.monitorRunDependencies(Nt), Nt == 0 && (tr !== null && (clearInterval(tr), tr = null), rr)) {
          var se = rr;
          rr = null, se();
        }
      }
      function cr(G) {
        t10.onAbort && t10.onAbort(G), G = "Aborted(" + G + ")", E(G), M = true, L = 1, G += ". Build with -sASSERTIONS for more info.";
        var se = new WebAssembly.RuntimeError(G);
        throw n(se), se;
      }
      var ea = "data:application/octet-stream;base64,";
      function Do(G) {
        return G.startsWith(ea);
      }
      function hs(G) {
        return G.startsWith("file://");
      }
      var qt;
      qt = "tfjs-backend-wasm.wasm", Do(qt) || (qt = f(qt));
      function ta(G) {
        try {
          if (G == qt && R)
            return new Uint8Array(R);
          if (x)
            return x(G);
          throw "both async and sync fetching of the wasm failed";
        } catch (se) {
          cr(se);
        }
      }
      function Dc() {
        if (!R && (c || l)) {
          if (typeof fetch == "function" && !hs(qt))
            return fetch(qt, { credentials: "same-origin" }).then(function(G) {
              if (!G.ok)
                throw "failed to load wasm binary file at '" + qt + "'";
              return G.arrayBuffer();
            }).catch(function() {
              return ta(qt);
            });
          if (g)
            return new Promise(function(G, se) {
              g(qt, function(Te) {
                G(new Uint8Array(Te));
              }, se);
            });
        }
        return Promise.resolve().then(function() {
          return ta(qt);
        });
      }
      function Fc() {
        var G = { env: Oc, wasi_snapshot_preview1: Oc };
        function se(qe, Ue) {
          var Wt = qe.exports;
          t10.asm = Wt, P = t10.asm.memory, Ie(P.buffer), Re = t10.asm.__indirect_function_table, qr(t10.asm.__wasm_call_ctors), Xr("wasm-instantiate");
        }
        jr("wasm-instantiate");
        function Te(qe) {
          se(qe.instance);
        }
        function nt(qe) {
          return Dc().then(function(Ue) {
            return WebAssembly.instantiate(Ue, G);
          }).then(function(Ue) {
            return Ue;
          }).then(qe, function(Ue) {
            E("failed to asynchronously prepare wasm: " + Ue), cr(Ue);
          });
        }
        function Tt() {
          return !R && typeof WebAssembly.instantiateStreaming == "function" && !Do(qt) && !hs(qt) && !m && typeof fetch == "function" ? fetch(qt, { credentials: "same-origin" }).then(function(qe) {
            var Ue = WebAssembly.instantiateStreaming(qe, G);
            return Ue.then(Te, function(Wt) {
              return E("wasm streaming compile failed: " + Wt), E("falling back to ArrayBuffer instantiation"), nt(Te);
            });
          }) : nt(Te);
        }
        if (t10.instantiateWasm)
          try {
            var _t = t10.instantiateWasm(G, se);
            return _t;
          } catch (qe) {
            E("Module.instantiateWasm callback failed with error: " + qe), n(qe);
          }
        return Tt().catch(n), {};
      }
      var AI, Ya;
      function Ju(G) {
        this.name = "ExitStatus", this.message = "Program terminated with exit(" + G + ")", this.status = G;
      }
      function ra(G) {
        for (; G.length > 0; )
          G.shift()(t10);
      }
      function Rx() {
        cr("");
      }
      function Yl() {
        return 2147483648;
      }
      function Ki() {
        return Yl();
      }
      function Dx(G, se, Te) {
        oe.copyWithin(G, se, se + Te);
      }
      function Ql(G) {
        try {
          return P.grow(G - Z.byteLength + 65535 >>> 16), Ie(P.buffer), 1;
        } catch (se) {
        }
      }
      function ep(G) {
        var se = oe.length;
        G = G >>> 0;
        var Te = Yl();
        if (G > Te)
          return false;
        let nt = (Wt, Yr) => Wt + (Yr - Wt % Yr) % Yr;
        for (var Tt = 1; Tt <= 4; Tt *= 2) {
          var _t = se * (1 + 0.2 / Tt);
          _t = Math.min(_t, G + 100663296);
          var qe = Math.min(Te, nt(Math.max(G, _t), 65536)), Ue = Ql(qe);
          if (Ue)
            return true;
        }
        return false;
      }
      var lr = { varargs: void 0, get: function() {
        lr.varargs += 4;
        var G = ie[lr.varargs - 4 >> 2];
        return G;
      }, getStr: function(G) {
        var se = K(G);
        return se;
      } };
      function Zl(G) {
        return 52;
      }
      function Fx(G, se, Te, nt, Tt) {
        return 70;
      }
      var Ox = [null, [], []];
      function RI(G, se) {
        var Te = Ox[G];
        se === 0 || se === 10 ? ((G === 1 ? _ : E)(U(Te, 0)), Te.length = 0) : Te.push(se);
      }
      function DI(G, se, Te, nt) {
        for (var Tt = 0, _t = 0; _t < Te; _t++) {
          var qe = ce[se >> 2], Ue = ce[se + 4 >> 2];
          se += 8;
          for (var Wt = 0; Wt < Ue; Wt++)
            RI(G, oe[qe + Wt]);
          Tt += Ue;
        }
        return ce[nt >> 2] = Tt, 0;
      }
      function Jl(G) {
        var se = t10["_" + G];
        return se;
      }
      function qi(G, se) {
        ee.set(G, se);
      }
      function Px(G, se, Te, nt, Tt) {
        var _t = { string: (hr) => {
          var Za = 0;
          if (hr != null && hr !== 0) {
            var gm = (hr.length << 2) + 1;
            Za = Vc(gm), q(hr, Za, gm);
          }
          return Za;
        }, array: (hr) => {
          var Za = Vc(hr.length);
          return qi(hr, Za), Za;
        } };
        function qe(hr) {
          return se === "string" ? K(hr) : se === "boolean" ? Boolean(hr) : hr;
        }
        var Ue = Jl(G), Wt = [], Yr = 0;
        if (nt)
          for (var oa = 0; oa < nt.length; oa++) {
            var hm = _t[Te[oa]];
            hm ? (Yr === 0 && (Yr = mm()), Wt[oa] = hm(nt[oa])) : Wt[oa] = nt[oa];
          }
        var zc = Ue.apply(null, Wt);
        function fb(hr) {
          return Yr !== 0 && dm(Yr), qe(hr);
        }
        return zc = fb(zc), zc;
      }
      function Mx(G, se, Te, nt) {
        Te = Te || [];
        var Tt = Te.every((qe) => qe === "number" || qe === "boolean"), _t = se !== "string";
        return _t && Tt && !nt ? Jl(G) : function() {
          return Px(G, se, Te, arguments, nt);
        };
      }
      var Oc = { abort: Rx, emscripten_get_heap_max: Ki, emscripten_memcpy_big: Dx, emscripten_resize_heap: ep, fd_close: Zl, fd_seek: Fx, fd_write: DI }, Lx = Fc(), em = t10.___wasm_call_ctors = function() {
        return (em = t10.___wasm_call_ctors = t10.asm.__wasm_call_ctors).apply(null, arguments);
      }, tm = t10._init = function() {
        return (tm = t10._init = t10.asm.init).apply(null, arguments);
      }, Bx = t10._init_with_threads_count = function() {
        return (Bx = t10._init_with_threads_count = t10.asm.init_with_threads_count).apply(null, arguments);
      }, rm = t10._get_threads_count = function() {
        return (rm = t10._get_threads_count = t10.asm.get_threads_count).apply(null, arguments);
      }, Vx = t10._register_tensor = function() {
        return (Vx = t10._register_tensor = t10.asm.register_tensor).apply(null, arguments);
      }, Oe = t10._dispose_data = function() {
        return (Oe = t10._dispose_data = t10.asm.dispose_data).apply(null, arguments);
      }, Pc = t10._dispose = function() {
        return (Pc = t10._dispose = t10.asm.dispose).apply(null, arguments);
      }, zx = t10._Abs = function() {
        return (zx = t10._Abs = t10.asm.Abs).apply(null, arguments);
      }, om = t10._Add = function() {
        return (om = t10._Add = t10.asm.Add).apply(null, arguments);
      }, tp = t10._AddN = function() {
        return (tp = t10._AddN = t10.asm.AddN).apply(null, arguments);
      }, Wx = t10._All = function() {
        return (Wx = t10._All = t10.asm.All).apply(null, arguments);
      }, Ux = t10._Any = function() {
        return (Ux = t10._Any = t10.asm.Any).apply(null, arguments);
      }, Gx = t10._ArgMax = function() {
        return (Gx = t10._ArgMax = t10.asm.ArgMax).apply(null, arguments);
      }, Hx = t10._AvgPool = function() {
        return (Hx = t10._AvgPool = t10.asm.AvgPool).apply(null, arguments);
      }, Kx = t10._BatchMatMul = function() {
        return (Kx = t10._BatchMatMul = t10.asm.BatchMatMul).apply(null, arguments);
      }, nm = t10._Ceil = function() {
        return (nm = t10._Ceil = t10.asm.Ceil).apply(null, arguments);
      }, sm = t10._ClipByValue = function() {
        return (sm = t10._ClipByValue = t10.asm.ClipByValue).apply(null, arguments);
      }, qx = t10._Conv2D = function() {
        return (qx = t10._Conv2D = t10.asm.Conv2D).apply(null, arguments);
      }, jx = t10._Conv2DBackpropInput = function() {
        return (jx = t10._Conv2DBackpropInput = t10.asm.Conv2DBackpropInput).apply(null, arguments);
      }, Xx = t10._Cos = function() {
        return (Xx = t10._Cos = t10.asm.Cos).apply(null, arguments);
      }, Mc = t10._Cosh = function() {
        return (Mc = t10._Cosh = t10.asm.Cosh).apply(null, arguments);
      }, Yx = t10._CropAndResize = function() {
        return (Yx = t10._CropAndResize = t10.asm.CropAndResize).apply(null, arguments);
      }, Qx = t10._Cumprod = function() {
        return (Qx = t10._Cumprod = t10.asm.Cumprod).apply(null, arguments);
      }, Zx = t10._Cumsum = function() {
        return (Zx = t10._Cumsum = t10.asm.Cumsum).apply(null, arguments);
      }, ji = t10._DepthToSpace = function() {
        return (ji = t10._DepthToSpace = t10.asm.DepthToSpace).apply(null, arguments);
      }, Jx = t10._DepthwiseConv2dNative = function() {
        return (Jx = t10._DepthwiseConv2dNative = t10.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, ey = t10._Elu = function() {
        return (ey = t10._Elu = t10.asm.Elu).apply(null, arguments);
      }, am = t10._Equal = function() {
        return (am = t10._Equal = t10.asm.Equal).apply(null, arguments);
      }, ty = t10._Exp = function() {
        return (ty = t10._Exp = t10.asm.Exp).apply(null, arguments);
      }, Lc = t10._FlipLeftRight = function() {
        return (Lc = t10._FlipLeftRight = t10.asm.FlipLeftRight).apply(null, arguments);
      }, ry = t10._Floor = function() {
        return (ry = t10._Floor = t10.asm.Floor).apply(null, arguments);
      }, oy = t10._FloorDiv = function() {
        return (oy = t10._FloorDiv = t10.asm.FloorDiv).apply(null, arguments);
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function Qi(r) {
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};
function sz(r) {
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function az(r, e, t10) {
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function iz(r, e) {
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function O() {
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var ys = "Abs";
var aa = "Acos";
var ia = "Acosh";
var eo = "Add";
var Po = "AddN";
var Mo = "All";
var Lo = "Any";
var Bo = "ArgMax";
var ei = "ArgMin";
var ua = "Asin";
var pa = "Asinh";
var ca = "Atan";
var la = "Atanh";
var ma = "Atan2";
var Vo = "AvgPool";
var mp = "AvgPoolGrad";
var dp = "AvgPool3D";
var vm = "AvgPool3DGrad";
var zo = "BatchMatMul";
var bs = "BatchToSpaceND";
var ti = "Bincount";
var ise = "BroadcastTo";
var fp = "BroadcastArgs";
var co = "Cast";
var Wo = "Ceil";
var lo = "ClipByValue";
var ri = "Complex";
var hp = "ComplexAbs";
var Cs = "Concat";
var Uo = "Conv2D";
var oi = "Conv2DBackpropFilter";
var Go = "Conv2DBackpropInput";
var gp = "Conv3D";
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var xp = "Conv3DBackpropInputV2";
var Ho = "Cos";
var Ko = "Cosh";
var qo = "Cumprod";
var jo = "Cumsum";
var Xo = "CropAndResize";
var ni = "DenseBincount";
var Yo = "DepthToSpace";
var Qo = "DepthwiseConv2dNative";
var yp = "DepthwiseConv2dNativeBackpropFilter";
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var si = "Diag";
var ai = "Dilation2D";
var _b = "Dilation2DBackpropInput";
var $b = "Dilation2DBackpropFilter";
var Zo = "RealDiv";
var ii = "Einsum";
var Jo = "Elu";
var Nm = "EluGrad";
var da = "Erf";
var en = "Equal";
var tn = "Exp";
var Ss = "ExpandDims";
var fa = "Expm1";
var ui = "FFT";
var ws = "Fill";
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var on = "Floor";
var nn = "FloorDiv";
var sn = "FusedBatchNorm";
var Is = "GatherV2";
var an = "GatherNd";
var un = "Greater";
var pn = "GreaterEqual";
var mo = "Identity";
var pi = "IFFT";
var ci = "Imag";
var ha = "IsFinite";
var ga = "IsInf";
var cn = "IsNan";
var ln = "LeakyRelu";
var mn = "Less";
var dn = "LessEqual";
var li = "LinSpace";
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var Cp = "MaxPool3D";
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var Tn = "NonMaxSuppressionV3";
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var _n = "NonMaxSuppressionV5";
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var On = "Relu";
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var Pn = "ResizeNearestNeighbor";
var Em = "ResizeNearestNeighborGrad";
var Mn = "ResizeBilinear";
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var Bn = "Reverse";
var Vn = "Round";
var zn = "Rsqrt";
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var Ia = "Softplus";
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var Kn = "Sum";
var As = "SpaceToBatchND";
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var hi = "SparseFillEmptyRows";
var va = "SparseReshape";
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var Ds = "StringNGrams";
var Ci = "StringSplit";
var Si = "StringToHashBucketFast";
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var Zn = "Tanh";
var to = "Tile";
var Jn = "TopK";
var es = "Transform";
var ro = "Transpose";
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var Zi = "FromPixels";
var ts = "RotateWithOffset";
var ho = "_FusedMatMul";
var go = "FusedConv2D";
var xo = "FusedDepthwiseConv2D";
function Ps(...r) {
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function pz(...r) {
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var _p = Gc("kernelRegistry", () => /* @__PURE__ */ new Map());
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}
function gse(r) {
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}
function xse(r, e) {
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}
function yse(r) {
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}
function bse(r, e) {
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}
function Ab(r, e) {
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}
var y = {};
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var Ob = ap(i0());
var ru = Ob.default || Ob;
function qc(r) {
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}
var p0 = qc("c3a5c85c97cb3127");
var tu = qc("b492b66fbe98f273");
var gr = qc("9ae16a3b2f90404f");
function Fb(r) {
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}
function c0(r, e, t10) {
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}
function Ct(r, e) {
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}
function u0(r, e) {
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}
function jt(r, e) {
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}
function wi(r, e, t10 = qc("9ddfea08eb382d69")) {
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}
function lz(r, e, t10, o, n, s) {
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}
function Fm(r, e, t10, o) {
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}
function mz(r, e = r.length) {
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  if (e >= 4) {
    let t10 = gr.add(e * 2), o = u0(r, 0);
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  return gr;
}
function dz(r, e = r.length) {
  let t10 = gr.add(e * 2), o = Ct(r, 0).mul(tu), n = Ct(r, 8), s = Ct(r, e - 8).mul(t10), a = Ct(r, e - 16).mul(gr);
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}
function fz(r, e = r.length) {
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  return wi(jt(u.add(c), 43).add(jt(l, 30)).add(m), u.add(jt(c.add(o), 18)).add(l), t10);
}
function hz(r, e = r.length) {
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}
function gz(r, e) {
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}
function xz(r, e) {
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}
function Ap(r, e) {
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    throw new Error("Cannot convert a string[] to a TypedArray");
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  if (e == null || e === "float32" || e === "complex64")
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    return new Int32Array(r);
  if (e === "bool") {
    let t10 = new Uint8Array(r.length);
    for (let o = 0; o < t10.length; ++o)
      Math.round(r[o]) !== 0 && (t10[o] = 1);
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    throw new Error(`Unknown data type ${e}`);
}
function ou() {
  return O().platform.now();
}
function yz(r, e) {
  return O().platform.fetch(r, e);
}
function Ii(r, e = "utf-8") {
  return e = e || "utf-8", O().platform.encode(r, e);
}
function Rp(r, e = "utf-8") {
  return e = e || "utf-8", O().platform.decode(r, e);
}
function Bt(r) {
  return O().platform.isTypedArray(r);
}
function os(r, e = [], t10 = false) {
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      os(r[o], e, t10);
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      os(r[n], e, t10);
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}
var Om = class {
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    this.backendTimer = e, this.logger = t10, t10 == null && (this.logger = new Pb());
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  profileKernel(e, t10, o) {
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      n = o();
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        u.dataSync();
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    if (O().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
      for (let u = 0; u < n.length; u++) {
        let c = n[u];
        c.data().then((l) => {
          bz(l, c.dtype, e);
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  logKernelProfile(e) {
    let { kernelName: t10, outputs: o, timeMs: n, inputs: s, extraInfo: a } = e;
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      Promise.all([i.data(), n, a]).then((p) => {
        this.logger.logKernelProfile(t10, i, p[0], p[1], s, p[2]);
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};
function bz(r, e, t10) {
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    return false;
  for (let o = 0; o < r.length; o++) {
    let n = r[o];
    if (isNaN(n) || !isFinite(n))
      return console.warn(`Found ${n} in the result of '${t10}'`), true;
  }
  return false;
}
var Pb = class {
  logKernelProfile(e, t10, o, n, s, a) {
    let i = typeof n == "number" ? Yi(`${n}ms`, 9) : n.error, p = Yi(e, 25), u = t10.rank, c = t10.size, l = Yi(t10.shape.toString(), 14), m = "";
    for (let d in s) {
      let f = s[d];
      if (f != null) {
        let h = f.shape || t10.shape, g = h.length;
        m += `${d}: ${g}D ${g > 0 ? h : ""} `;
      }
    }
    console.log(`%c${p}	%c${i}	%c${u}D ${l}	%c${c}	%c${m}	%c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
  }
};
function l0(r, e, t10) {
  let o = {}, n = {};
  for (let p = 0; p < e.length; p++)
    o[e[p].id] = true;
  for (let p = 0; p < r.length; p++) {
    let u = r[p], c = u.inputs;
    for (let l in c) {
      let m = c[l], d = false;
      for (let f = 0; f < e.length; f++)
        if (o[m.id]) {
          u.outputs.forEach((h) => o[h.id] = true), d = true, n[u.id] = true;
          break;
        }
      if (d)
        break;
    }
  }
  let s = {};
  s[t10.id] = true;
  let a = {};
  for (let p = r.length - 1; p >= 0; p--) {
    let u = r[p], c = u.inputs;
    for (let l = 0; l < u.outputs.length; l++)
      if (s[u.outputs[l].id]) {
        for (let m in c)
          s[c[m].id] = true, a[u.id] = true;
        break;
      }
  }
  let i = [];
  for (let p = 0; p < r.length; p++) {
    let u = r[p];
    if (n[u.id] && a[u.id]) {
      let c = {};
      for (let m in u.inputs) {
        let d = u.inputs[m];
        o[d.id] && (c[m] = d);
      }
      let l = Object.assign({}, u);
      l.inputs = c, l.outputs = u.outputs, i.push(l);
    }
  }
  return i;
}
function m0(r, e, t10, o) {
  for (let n = e.length - 1; n >= 0; n--) {
    let s = e[n], a = [];
    if (s.outputs.forEach((p) => {
      let u = r[p.id];
      u != null ? a.push(u) : a.push(null);
    }), s.gradient == null)
      throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);
    let i = s.gradient(a);
    for (let p in s.inputs) {
      if (!(p in i))
        throw new Error(`Cannot backprop through input ${p}. Available gradients found: ${Object.keys(i)}.`);
      let u = t10(() => i[p]());
      if (u.dtype !== "float32")
        throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${p} must have 'float32' dtype, but has '${u.dtype}'`);
      let c = s.inputs[p];
      if (!Or(u.shape, c.shape))
        throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${p}' has shape '${u.shape}', which does not match the shape of the input '${c.shape}'`);
      if (r[c.id] == null)
        r[c.id] = u;
      else {
        let l = r[c.id];
        r[c.id] = o(l, u), l.dispose();
      }
    }
  }
}
var d0 = 20;
var jc = 3;
var Mb = 7;
function f0(r, e, t10, o) {
  let n = xs(e), s = Cz(r, e, t10, n), a = e.length, i = Pm(r, e, t10, n, s), p = ["Tensor"];
  return o && (p.push(`  dtype: ${t10}`), p.push(`  rank: ${a}`), p.push(`  shape: [${e}]`), p.push("  values:")), p.push(i.map((u) => "    " + u).join(`
`)), p.join(`
`);
}
function Cz(r, e, t10, o) {
  let n = We(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t10 === "complex64" ? Yc(r) : r;
  if (i > 1)
    for (let u = 0; u < n / s; u++) {
      let c = u * s;
      for (let l = 0; l < s; l++)
        a[l] = Math.max(a[l], Xc(p[c + l], 0, t10).length);
    }
  return a;
}
function Xc(r, e, t10) {
  let o;
  return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(Mb))} + ${parseFloat(r[1].toFixed(Mb))}j` : Oo(r) ? o = `'${r}'` : t10 === "bool" ? o = h0(r) : o = parseFloat(r.toFixed(Mb)).toString(), Yi(o, e);
}
function h0(r) {
  return r === 0 ? "false" : "true";
}
function Pm(r, e, t10, o, n, s = true) {
  let a = t10 === "complex64" ? 2 : 1, i = e[0], p = e.length;
  if (p === 0) {
    if (t10 === "complex64") {
      let h = Yc(r);
      return [Xc(h[0], 0, t10)];
    }
    return t10 === "bool" ? [h0(r[0])] : [r[0].toString()];
  }
  if (p === 1) {
    if (i > d0) {
      let g = jc * a, x = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - jc) * a, i * a));
      return t10 === "complex64" && (x = Yc(x), b = Yc(b)), ["[" + x.map((C, w) => Xc(C, n[w], t10)).join(", ") + ", ..., " + b.map((C, w) => Xc(C, n[i - jc + w], t10)).join(", ") + "]"];
    }
    return ["[" + (t10 === "complex64" ? Yc(r) : Array.from(r)).map((g, x) => Xc(g, n[x], t10)).join(", ") + "]"];
  }
  let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = [];
  if (i > d0) {
    for (let h = 0; h < jc; h++) {
      let g = h * l, x = g + l;
      m.push(...Pm(r.slice(g, x), u, t10, c, n, false));
    }
    m.push("...");
    for (let h = i - jc; h < i; h++) {
      let g = h * l, x = g + l;
      m.push(...Pm(r.slice(g, x), u, t10, c, n, h === i - 1));
    }
  } else
    for (let h = 0; h < i; h++) {
      let g = h * l, x = g + l;
      m.push(...Pm(r.slice(g, x), u, t10, c, n, h === i - 1));
    }
  let d = p === 2 ? "," : "";
  m[0] = "[" + (i > 0 ? m[0] + d : "");
  for (let h = 1; h < m.length - 1; h++)
    m[h] = " " + m[h] + d;
  let f = `,
`;
  for (let h = 2; h < p; h++)
    f += `
`;
  return m[m.length - 1] = " " + m[m.length - 1] + "]" + (s ? "" : f), m;
}
function Yc(r) {
  let e = [];
  for (let t10 = 0; t10 < r.length; t10 += 2)
    e.push([r[t10], r[t10 + 1]]);
  return e;
}
var st = class {
  constructor(e, t10, o) {
    if (this.dtype = t10, this.shape = e.slice(), this.size = We(e), o != null) {
      let n = o.length;
      $(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
    }
    if (t10 === "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 = o || Sb(t10, this.size), this.strides = xs(e);
  }
  set(e, ...t10) {
    t10.length === 0 && (t10 = [0]), $(t10.length === this.rank, () => `The number of provided coordinates (${t10.length}) must match the rank (${this.rank})`);
    let o = this.locToIndex(t10);
    this.values[o] = e;
  }
  get(...e) {
    e.length === 0 && (e = [0]);
    let t10 = 0;
    for (let n of e) {
      if (n < 0 || n >= this.shape[t10]) {
        let s = `Requested out of range element at ${e}.   Buffer shape=${this.shape}`;
        throw new Error(s);
      }
      t10++;
    }
    let o = e[e.length - 1];
    for (let n = 0; n < e.length - 1; ++n)
      o += this.strides[n] * e[n];
    return this.values[o];
  }
  locToIndex(e) {
    if (this.rank === 0)
      return 0;
    if (this.rank === 1)
      return e[0];
    let t10 = e[e.length - 1];
    for (let o = 0; o < e.length - 1; ++o)
      t10 += this.strides[o] * e[o];
    return t10;
  }
  indexToLoc(e) {
    if (this.rank === 0)
      return [];
    if (this.rank === 1)
      return [e];
    let t10 = new Array(this.shape.length);
    for (let o = 0; o < t10.length - 1; ++o)
      t10[o] = Math.floor(e / this.strides[o]), e -= t10[o] * this.strides[o];
    return t10[t10.length - 1] = e, t10;
  }
  get rank() {
    return this.shape.length;
  }
  toTensor() {
    return ns().makeTensor(this.values, this.shape, this.dtype);
  }
};
var ns = null;
var Dp = null;
var Sz = null;
function g0(r) {
  ns = r;
}
function x0(r) {
  Dp = r;
}
function y0(r) {
  Sz = r;
}
var it = class {
  constructor(e, t10, o, n) {
    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t10 || "float32", this.size = We(e), this.strides = xs(e), this.dataId = o, this.id = n, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
  }
  get rank() {
    return this.shape.length;
  }
  async buffer() {
    let e = await this.data();
    return Dp.buffer(this.shape, this.dtype, e);
  }
  bufferSync() {
    return Dp.buffer(this.shape, this.dtype, this.dataSync());
  }
  async array() {
    let e = await this.data();
    return Xi(this.shape, e, this.dtype === "complex64");
  }
  arraySync() {
    return Xi(this.shape, this.dataSync(), this.dtype === "complex64");
  }
  async data() {
    this.throwIfDisposed();
    let e = ns().read(this.dataId);
    if (this.dtype === "string") {
      let t10 = await e;
      try {
        return t10.map((o) => Rp(o));
      } catch (o) {
        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
      }
    }
    return e;
  }
  dataToGPU(e) {
    return this.throwIfDisposed(), ns().readToGPU(this.dataId, e);
  }
  dataSync() {
    this.throwIfDisposed();
    let e = ns().readSync(this.dataId);
    if (this.dtype === "string")
      try {
        return e.map((t10) => Rp(t10));
      } catch (t10) {
        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 ns().read(this.dataId);
    return this.dtype === "string" ? e : new Uint8Array(e.buffer);
  }
  dispose() {
    this.isDisposed || (ns().disposeTensor(this), this.isDisposedInternal = true);
  }
  get isDisposed() {
    return this.isDisposedInternal;
  }
  throwIfDisposed() {
    if (this.isDisposed)
      throw new Error("Tensor is disposed.");
  }
  print(e = false) {
    return Dp.print(this, e);
  }
  clone() {
    return this.throwIfDisposed(), Dp.clone(this);
  }
  toString(e = false) {
    let t10 = this.dataSync();
    return f0(t10, this.shape, this.dtype, e);
  }
  cast(e) {
    return this.throwIfDisposed(), Dp.cast(this, e);
  }
  variable(e = true, t10, o) {
    return this.throwIfDisposed(), ns().makeVariable(this, e, t10, o);
  }
};
Object.defineProperty(it, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null });
function wz() {
  return Gc("Tensor", () => it);
}
wz();
var Na = class extends it {
  constructor(e, t10, o, n) {
    super(e.shape, e.dtype, e.dataId, n), this.trainable = t10, this.name = o;
  }
  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 (!Or(e.shape, this.shape))
      throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
    ns().disposeTensor(this), this.dataId = e.dataId, ns().incRef(this, null);
  }
  dispose() {
    ns().disposeVariable(this), this.isDisposedInternal = true;
  }
};
Object.defineProperty(Na, Symbol.hasInstance, { value: (r) => r instanceof it && r.assign != null && r.assign instanceof Function });
var C0 = {};
Ge(C0, { assertTypesMatch: () => Ub, getTensorsInContainer: () => Qc, isTensorInList: () => vz, makeTypesMatch: () => De });
var Lb;
(function(r) {
  r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6";
})(Lb || (Lb = {}));
var Bb;
(function(r) {
  r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64";
})(Bb || (Bb = {}));
var Vb;
(function(r) {
  r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64";
})(Vb || (Vb = {}));
var zb;
(function(r) {
  r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64";
})(zb || (zb = {}));
var Wb;
(function(r) {
  r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64";
})(Wb || (Wb = {}));
var Iz = { float32: zb, int32: Bb, bool: Vb, complex64: Wb };
function dt(r, e) {
  if (r === "string" || e === "string") {
    if (r === "string" && e === "string")
      return "string";
    throw new Error(`Can not upcast ${r} with ${e}`);
  }
  return Iz[r][e];
}
function Ta(r) {
  return dt(r, "int32");
}
function De(r, e) {
  if (r.dtype === e.dtype)
    return [r, e];
  let t10 = dt(r.dtype, e.dtype);
  return [r.cast(t10), e.cast(t10)];
}
function Ub(r, e) {
  $(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`);
}
function vz(r, e) {
  return e.some((t10) => t10.id === r.id);
}
function Qc(r) {
  let e = [];
  return b0(r, e, /* @__PURE__ */ new Set()), e;
}
function b0(r, e, t10) {
  if (r == null)
    return;
  if (r instanceof it) {
    e.push(r);
    return;
  }
  if (!kz(r))
    return;
  let o = r;
  for (let n in o) {
    let s = o[n];
    t10.has(s) || (t10.add(s), b0(s, e, t10));
  }
}
function kz(r) {
  return Array.isArray(r) || typeof r == "object";
}
function Gb(r) {
  return r.kernelName != null;
}
var Mm = class {
  constructor() {
    this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
      return Array.from(new Set(this.kernels.map((e) => e.name)));
    } };
  }
  dispose() {
    for (let e in this.registeredVariables)
      this.registeredVariables[e].dispose();
  }
};
var vi = class {
  constructor(e) {
    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new Mm();
  }
  async ready() {
    if (this.pendingBackendInit != null)
      return this.pendingBackendInit.then(() => {
      });
    if (this.backendInstance != null)
      return;
    let e = this.getSortedBackends();
    for (let t10 = 0; t10 < e.length; t10++) {
      let o = e[t10];
      if (await this.initializeBackend(o).success) {
        await this.setBackend(o);
        return;
      }
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  get backend() {
    if (this.pendingBackendInit != null)
      throw new Error(`Backend '${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: t10 } = this.initializeBackendsAndReturnBest();
      if (t10)
        throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
      this.setBackend(e);
    }
    return this.backendInstance;
  }
  backendNames() {
    return Object.keys(this.registryFactory);
  }
  findBackend(e) {
    if (!(e in this.registry))
      if (e in this.registryFactory) {
        let { asyncInit: t10 } = this.initializeBackend(e);
        if (t10)
          return null;
      } else
        return null;
    return this.registry[e];
  }
  findBackendFactory(e) {
    return e in this.registryFactory ? this.registryFactory[e].factory : null;
  }
  registerBackend(e, t10, o = 1) {
    return e in this.registryFactory ? (Ps(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t10, priority: o }, true);
  }
  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: t10, asyncInit: o } = this.initializeBackend(e);
      if (!(o ? await t10 : t10))
        return false;
    }
    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Om(this.backendInstance), true;
  }
  setupRegisteredKernels() {
    Rm(this.backendName).forEach((t10) => {
      t10.setupFunc != null && t10.setupFunc(this.backendInstance);
    });
  }
  disposeRegisteredKernels(e) {
    Rm(e).forEach((o) => {
      o.disposeFunc != null && o.disposeFunc(this.registry[e]);
    });
  }
  initializeBackend(e) {
    let t10 = this.registryFactory[e];
    if (t10 == null)
      throw new Error(`Cannot initialize backend ${e}, no registration found.`);
    try {
      let o = t10.factory();
      if (o && !(o instanceof Zr) && typeof o.then == "function") {
        let n = ++this.pendingBackendInitId, s = o.then((a) => n < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (n < this.pendingBackendInitId || (this.pendingBackendInit = null, Ps(`Initialization of backend ${e} failed`), Ps(a.stack || a.message)), false));
        return this.pendingBackendInit = s, { success: s, asyncInit: true };
      } else
        return this.registry[e] = o, { success: true, asyncInit: false };
    } catch (o) {
      return Ps(`Initialization of backend ${e} failed`), Ps(o.stack || o.message), { success: false, asyncInit: false };
    }
  }
  removeBackend(e) {
    if (!(e in this.registryFactory))
      throw new Error(`${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, t10) => this.registryFactory[t10].priority - this.registryFactory[e].priority);
  }
  initializeBackendsAndReturnBest() {
    let e = this.getSortedBackends();
    for (let t10 = 0; t10 < e.length; t10++) {
      let o = e[t10], { success: n, asyncInit: s } = this.initializeBackend(o);
      if (s || n)
        return { name: o, asyncInit: s };
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  moveData(e, t10) {
    let o = this.state.tensorInfo.get(t10), n = o.backend, s = this.readSync(t10), a = n.refCount(t10);
    n.disposeData(t10, true), o.backend = e, e.move(t10, s, o.shape, o.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
  }
  tidy(e, t10) {
    let o = null;
    if (t10 == null) {
      if (typeof e != "function")
        throw new Error("Please provide a function to tidy()");
      t10 = 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 t10 != "function")
        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
      o = e;
    }
    let n;
    return this.scopedRun(() => this.startScope(o), () => this.endScope(n), () => (n = t10(), n instanceof Promise && console.error("Cannot return a Promise inside of tidy."), n));
  }
  scopedRun(e, t10, o) {
    e();
    try {
      let n = o();
      return t10(), n;
    } catch (n) {
      throw t10(), n;
    }
  }
  nextTensorId() {
    return vi.nextTensorId++;
  }
  nextVariableId() {
    return vi.nextVariableId++;
  }
  clone(e) {
    let t10 = T.runKernel(mo, { x: e }), o = { x: e }, n = (a) => ({ x: () => {
      let i = "float32", p = { x: a }, u = { dtype: i };
      return T.runKernel(co, p, u);
    } }), s = [];
    return this.addTapeNode(this.state.activeScope.name, o, [t10], n, s, {}), t10;
  }
  runKernel(e, t10, o) {
    if (this.backendName == null && this.backend, !(Kc(e, this.backendName) != null))
      throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);
    return this.runKernelFunc({ kernelName: e, inputs: t10, attrs: o });
  }
  shouldCheckForMemLeaks() {
    return this.ENV.getBool("IS_TEST");
  }
  checkKernelForMemLeak(e, t10, o) {
    let n = this.backend.numDataIds(), s = 0;
    o.forEach((p) => {
      s += p.dtype === "complex64" ? 3 : 1;
    });
    let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = n - t10 - s - a;
    if (i > 0)
      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`);
  }
  runKernelFunc(e) {
    let t10, o = [], n = this.isTapeOn(), s = this.state.numBytes, a = this.state.numTensors;
    this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
    let i;
    this.backendName == null && this.backend;
    let p, u = Gb(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
    if (Gb(e)) {
      let { kernelName: f, inputs: h, attrs: g } = e;
      this.backendName == null && this.backend;
      let x = Kc(f, this.backendName);
      $(x != null, () => `Cannot find registered kernel '${f}' for backend '${this.backendName}'`), i = () => {
        let b = this.backend.numDataIds();
        p = x.kernelFunc({ inputs: h, attrs: g, backend: this.backend });
        let C = Array.isArray(p) ? p : [p];
        this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(f, b, C);
        let w = C.map((k) => k.rank != null ? k : this.makeTensorFromTensorInfo(k));
        if (n) {
          let k = this.getTensorsForGradient(f, h, w);
          o = this.saveTensorsForBackwardMode(k);
        }
        return w;
      };
    } else {
      let { forwardFunc: f } = e, h = (g) => {
        n && (o = g.map((x) => this.keep(this.clone(x))));
      };
      i = () => {
        let g = this.backend.numDataIds();
        p = this.tidy(() => f(this.backend, h));
        let x = Array.isArray(p) ? p : [p];
        return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, g, x), x;
      };
    }
    let { inputs: c, attrs: l } = e, m = Gb(e) ? null : e.backwardsFunc, d;
    return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
      !this.ENV.getBool("DEBUG") && !this.state.profiling ? t10 = i() : (d = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t10 = d.outputs);
    }), n && this.addTapeNode(u, c, t10, m, o, l), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - s, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(c).map((f) => c[f] != null ? c[f].shape : null), outputShapes: t10.map((f) => f.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(p) ? t10 : t10[0];
  }
  saveTensorsForBackwardMode(e) {
    return e.map((o) => this.keep(this.clone(o)));
  }
  getTensorsForGradient(e, t10, o) {
    let n = Eb(e);
    if (n != null) {
      let s = n.inputsToSave || [], a = n.outputsToSave || [], i;
      n.saveAllInputs ? ($(Array.isArray(t10), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t10).map((u) => t10[u])) : i = s.map((u) => t10[u]);
      let p = o.filter((u, c) => a[c]);
      return i.concat(p);
    }
    return [];
  }
  makeTensor(e, t10, o, n) {
    if (e == null)
      throw new Error("Values passed to engine.makeTensor() are null");
    o = o || "float32", n = n || this.backend;
    let s = e;
    o === "string" && Oo(e[0]) && (s = e.map((p) => Ii(p)));
    let a = n.write(s, t10, o), i = new it(t10, o, a, this.nextTensorId());
    if (this.trackTensor(i, n), o === "string") {
      let p = this.state.tensorInfo.get(a), u = vb(s);
      this.state.numBytes += u - p.bytes, p.bytes = u;
    }
    return i;
  }
  makeTensorFromDataId(e, t10, o, n) {
    o = o || "float32";
    let s = { dataId: e, shape: t10, dtype: o };
    return this.makeTensorFromTensorInfo(s, n);
  }
  makeTensorFromTensorInfo(e, t10) {
    let { dataId: o, shape: n, dtype: s } = e, a = new it(n, s, o, this.nextTensorId());
    return this.trackTensor(a, t10), a;
  }
  makeVariable(e, t10 = true, o, n) {
    o = o || this.nextVariableId().toString(), n != null && n !== e.dtype && (e = e.cast(n));
    let s = new Na(e, t10, o, this.nextTensorId());
    if (this.state.registeredVariables[s.name] != null)
      throw new Error(`Variable with name ${s.name} was already registered`);
    return this.state.registeredVariables[s.name] = s, this.incRef(s, this.backend), s;
  }
  trackTensor(e, t10) {
    this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
    let o = 0;
    e.dtype !== "complex64" && e.dtype !== "string" && (o = e.size * up(e.dtype)), this.state.numBytes += o, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t10 || this.backend, dtype: e.dtype, shape: e.shape, bytes: o })), e instanceof Na || this.track(e);
  }
  incRef(e, t10) {
    this.trackTensor(e, t10), this.backend.incRef(e.dataId);
  }
  removeDataId(e, t10) {
    this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t10 && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--);
  }
  disposeTensor(e) {
    if (!this.state.tensorInfo.has(e.dataId))
      return;
    let t10 = this.state.tensorInfo.get(e.dataId);
    if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t10.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
      let o = e.size * up(e.dtype);
      this.state.numBytes -= o;
    }
    t10.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t10.backend);
  }
  disposeVariables() {
    for (let e in this.state.registeredVariables) {
      let t10 = this.state.registeredVariables[e];
      this.disposeVariable(t10);
    }
  }
  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 = true, 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 = true;
    let t10 = this.state.numBytes, o = this.state.numTensors;
    this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((n) => n.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t10, this.state.activeProfile.newTensors = this.state.numTensors - o;
    for (let n of this.state.activeProfile.kernels)
      n.kernelTimeMs = await n.kernelTimeMs, n.extraInfo = await n.extraInfo;
    return this.state.activeProfile;
  }
  isTapeOn() {
    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
  }
  addTapeNode(e, t10, o, n, s, a) {
    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t10, outputs: o, saved: s }, p = Eb(e);
    p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => {
      if (c == null) {
        let m = o[l], d = lp(m.size, m.dtype);
        return this.makeTensor(d, m.shape, m.dtype);
      }
      return c;
    }), n(u.length > 1 ? u : u[0], s, a))), this.state.activeTape.push(i);
  }
  keep(e) {
    return e.kept = true, e;
  }
  startTape() {
    this.state.gradientDepth === 0 && (this.state.activeTape = []), this.state.gradientDepth++;
  }
  endTape() {
    this.state.gradientDepth--;
  }
  startScope(e) {
    let t10 = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ };
    e && (t10.name = e), this.state.scopeStack.push(t10), this.state.activeScope = t10;
  }
  endScope(e) {
    let t10 = Qc(e), o = new Set(t10.map((s) => s.id));
    for (let s = 0; s < this.state.activeScope.track.length; s++) {
      let a = this.state.activeScope.track[s];
      !a.kept && !o.has(a.id) && a.dispose();
    }
    let n = this.state.scopeStack.pop();
    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t10.forEach((s) => {
      !s.kept && s.scopeId === n.id && this.track(s);
    });
  }
  gradients(e, t10, o, n = false) {
    if ($(t10.length > 0, () => "gradients() received an empty list of xs."), o != null && o.dtype !== "float32")
      throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);
    let s = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
    $(s instanceof it, () => "The result y returned by f() must be a tensor.");
    let a = l0(this.state.activeTape, t10, s);
    if (!n && a.length === 0 && t10.length > 0)
      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
    return this.tidy("backward", () => {
      let i = {};
      i[s.id] = o == null ? Nz(s.shape) : o, m0(i, a, (u) => this.tidy(u), Tz);
      let p = t10.map((u) => i[u.id]);
      return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => {
        for (let c of u.saved)
          c.dispose();
      }), this.state.activeTape = null), { value: s, grads: p };
    });
  }
  customGrad(e) {
    return $(gs(e), () => "The f passed in customGrad(f) must be a function."), (...t10) => {
      $(t10.every((i) => i instanceof it), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
      let o, n = {};
      t10.forEach((i, p) => {
        n[p] = i;
      });
      let s = (i, p) => (o = e(...t10, p), $(o.value instanceof it, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(gs(o.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), o.value), a = (i, p) => {
        let u = o.gradFunc(i, p), c = Array.isArray(u) ? u : [u];
        $(c.length === t10.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(...)."), $(c.every((m) => m instanceof it), () => "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 l = {};
        return c.forEach((m, d) => {
          l[d] = () => m;
        }), l;
      };
      return this.runKernelFunc({ forwardFunc: s, backwardsFunc: a, inputs: n });
    };
  }
  readSync(e) {
    return this.state.tensorInfo.get(e).backend.readSync(e);
  }
  read(e) {
    return this.state.tensorInfo.get(e).backend.read(e);
  }
  readToGPU(e, t10) {
    return this.state.tensorInfo.get(e).backend.readToGPU(e, t10);
  }
  async time(e) {
    let t10 = ou(), o = await this.backend.time(e);
    return o.wallMs = ou() - t10, o;
  }
  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 Mm();
    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;
  }
};
vi.nextTensorId = 0;
vi.nextVariableId = 0;
function Nz(r) {
  let e = Wc(We(r), "float32");
  return T.makeTensor(e, r, "float32");
}
function Hb() {
  let r = Tb();
  if (r._tfengine == null) {
    let e = new Uc(r);
    r._tfengine = new vi(e);
  }
  return jI(r._tfengine.ENV), g0(() => r._tfengine), r._tfengine;
}
var T = Hb();
function Tz(r, e) {
  let t10 = { a: r, b: e };
  return T.runKernel(eo, t10);
}
var ki = {};
Ge(ki, { isBrowser: () => qb, isMobile: () => Ez, mockIsMobile: () => $z });
function _z() {
  return typeof navigator != "undefined" && navigator != null;
}
var Kb;
function $z(r) {
  Kb = r;
}
function Ez(r) {
  if (Kb !== void 0)
    return Kb;
  if (r || _z()) {
    if (r || (r = navigator), r.product === "ReactNative")
      return true;
    let e = r.userAgent || r.vendor || (typeof window != "undefined" ? window.opera : "");
    if (!e) {
      let t10 = r;
      return t10.userAgentData && t10.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(e) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0, 4));
  }
  return false;
}
function qb() {
  return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
}
var oo = O();
oo.registerFlag("DEBUG", () => false, (r) => {
  r && 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.");
});
oo.registerFlag("IS_BROWSER", () => qb());
oo.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
oo.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
oo.registerFlag("PROD", () => false);
oo.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => oo.getBool("DEBUG"));
oo.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
oo.registerFlag("IS_TEST", () => false);
oo.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
oo.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
oo.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false);
oo.registerFlag("USE_SETTIMEOUTCUSTOM", () => false);
function or(r, e) {
  let t10 = r;
  if (Bt(r))
    return e === "string" ? [] : [r.length];
  if (typeof r == "object") {
    if ("texture" in r) {
      let s = r.channels || "RGBA";
      return [r.height, r.width * s.length];
    } else if ("buffer" in r && !(r.buffer instanceof ArrayBuffer))
      return [r.buffer.size / (e == null ? 4 : up(e))];
  }
  if (!Array.isArray(r))
    return [];
  let n = [];
  for (; Array.isArray(t10) || Bt(t10) && e !== "string"; )
    n.push(t10.length), t10 = t10[0];
  return Array.isArray(r) && O().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && w0(r, n, []), n;
}
function w0(r, e, t10) {
  if (t10 = t10 || [], !Array.isArray(r) && !Bt(r)) {
    $(e.length === 0, () => `Element arr[${t10.join("][")}] is a primitive, but should be an array/TypedArray of ${e[0]} elements`);
    return;
  }
  $(e.length > 0, () => `Element arr[${t10.join("][")}] should be a primitive, but is an array of ${r.length} elements`), $(r.length === e[0], () => `Element arr[${t10.join("][")}] should have ${e[0]} elements, but has ${r.length} elements`);
  let o = e.slice(1);
  for (let n = 0; n < r.length; ++n)
    w0(r[n], o, t10.concat(n));
}
function S0(r, e, t10, o) {
  if (r !== "string_or_numeric") {
    if (r == null)
      throw new Error("Expected dtype cannot be null.");
    if (r !== "numeric" && r !== e || r === "numeric" && e === "string")
      throw new Error(`Argument '${t10}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`);
  }
}
function v(r, e, t10, o = "numeric") {
  if (r instanceof it)
    return S0(o, r.dtype, e, t10), r;
  let n = pp(r);
  if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), S0(o, n, e, t10), r == null || !Bt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") {
    let p = r == null ? "null" : r.constructor.name;
    throw new Error(`Argument '${e}' passed to '${t10}' must be a Tensor or TensorLike, but got '${p}'`);
  }
  let s = or(r, n);
  !Bt(r) && !Array.isArray(r) && (r = [r]);
  let i = n !== "string" ? Ap(r, n) : os(r, [], true);
  return T.makeTensor(i, s, n);
}
function _a(r, e, t10, o = "numeric") {
  if (!Array.isArray(r))
    throw new Error(`Argument ${e} passed to ${t10} must be a \`Tensor[]\` or \`TensorLike[]\``);
  return r.map((s, a) => v(s, `${e}[${a}]`, t10, o));
}
var jb = "__op";
function N(r) {
  let e = Object.keys(r);
  if (e.length !== 1)
    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${e.length} keys.`);
  let t10 = e[0], o = r[t10];
  t10.endsWith("_") && (t10 = t10.substring(0, t10.length - 1)), t10 = t10 + jb;
  let n = (...s) => {
    T.startScope(t10);
    try {
      let a = o(...s);
      return Qi(a) && console.error("Cannot return a Promise inside of tidy."), T.endScope(a), a;
    } catch (a) {
      throw T.endScope(null), a;
    }
  };
  return Object.defineProperty(n, "name", { value: t10, configurable: true }), n;
}
function Az(r, e) {
  let t10 = v(r, "real", "complex"), o = v(e, "imag", "complex");
  gt(t10.shape, o.shape, `real and imag shapes, ${t10.shape} and ${o.shape}, must match in call to tf.complex().`);
  let n = { real: t10, imag: o };
  return T.runKernel(ri, n);
}
var Tr = N({ complex_: Az });
function xr(r, e, t10, o) {
  if (o == null)
    o = pp(r);
  else if (o === "complex64")
    throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
  if (typeof r == "object" && ("texture" in r || "buffer" in r && !(r.buffer instanceof ArrayBuffer))) {
    if (o !== "float32" && o !== "int32")
      throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${o}.`);
    return T.backend.createTensorFromGPUData(r, e || t10, o);
  }
  if (!Bt(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "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 (e != null) {
    bt(e);
    let n = We(e), s = We(t10);
    $(n === s, () => `Based on the provided shape, [${e}], the tensor should have ${n} values but has ${s}`);
    for (let a = 0; a < t10.length; ++a) {
      let i = t10[a], p = a === t10.length - 1 ? i !== We(e.slice(a)) : true;
      $(t10[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t10}) does not match the provided shape (${e}). `);
    }
  }
  return !Bt(r) && !Array.isArray(r) && (r = [r]), e = e || t10, r = o !== "string" ? Ap(r, o) : os(r, [], true), T.makeTensor(r, e, o);
}
function nr(r, e, t10) {
  let o = or(r, t10);
  return xr(r, e, o, t10);
}
var Zc = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var Lm = 4;
async function v0(r, e) {
  let t10 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r);
  for (let a = 0; a < n.length; ++a) {
    let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i];
    if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64")
      throw new Error(`Unsupported dtype in weight '${i}': ${p.dtype}`);
    let u = { name: i, shape: p.shape, dtype: p.dtype };
    if (p.dtype === "string") {
      let c = new Promise(async (l) => {
        let m = await p.bytes(), d = m.reduce((g, x) => g + x.length, 0) + Lm * m.length, f = new Uint8Array(d), h = 0;
        for (let g = 0; g < m.length; g++) {
          let x = m[g], b = new Uint8Array(new Uint32Array([x.length]).buffer);
          f.set(b, h), h += Lm, f.set(x, h), h += x.length;
        }
        l(f);
      });
      o.push(c);
    } else
      o.push(p.data());
    e != null && (u.group = e), t10.push(u);
  }
  let s = await Promise.all(o);
  return { data: Rz(s), specs: t10 };
}
function Bm(r, e) {
  let t10 = {}, o, n = 0;
  for (let s of e) {
    let a = s.name, i = s.dtype, p = s.shape, u = We(p), c;
    if ("quantization" in s) {
      let l = s.quantization;
      if (l.dtype === "uint8" || l.dtype === "uint16") {
        if (!("min" in l && "scale" in l))
          throw new Error(`Weight ${s.name} with quantization ${l.dtype} doesn't have corresponding metadata min and scale.`);
      } else if (l.dtype === "float16") {
        if (i !== "float32")
          throw new Error(`Weight ${s.name} is quantized with ${l.dtype} which only supports weights of type float32 not ${i}.`);
      } else
        throw new Error(`Weight ${s.name} has unknown quantization dtype ${l.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
      let m = Zc[l.dtype], d = r.slice(n, n + u * m), f = l.dtype === "uint8" ? new Uint8Array(d) : new Uint16Array(d);
      if (i === "float32")
        if (l.dtype === "uint8" || l.dtype === "uint16") {
          c = new Float32Array(f.length);
          for (let h = 0; h < f.length; h++) {
            let g = f[h];
            c[h] = g * l.scale + l.min;
          }
        } else if (l.dtype === "float16")
          o === void 0 && (o = Pz()), c = o(f);
        else
          throw new Error(`Unsupported quantization type ${l.dtype} for weight type float32.`);
      else if (i === "int32") {
        if (l.dtype !== "uint8" && l.dtype !== "uint16")
          throw new Error(`Unsupported quantization type ${l.dtype} for weight type int32.`);
        c = new Int32Array(f.length);
        for (let h = 0; h < f.length; h++) {
          let g = f[h];
          c[h] = Math.round(g * l.scale + l.min);
        }
      } else
        throw new Error(`Unsupported dtype in weight '${a}': ${i}`);
      n += u * m;
    } else if (i === "string") {
      let l = We(s.shape);
      c = [];
      for (let m = 0; m < l; m++) {
        let d = new Uint32Array(r.slice(n, n + Lm))[0];
        n += Lm;
        let f = new Uint8Array(r.slice(n, n + d));
        c.push(f), n += d;
      }
    } else {
      let l = Zc[i], m = r.slice(n, n + u * l);
      if (i === "float32")
        c = new Float32Array(m);
      else if (i === "int32")
        c = new Int32Array(m);
      else if (i === "bool")
        c = new Uint8Array(m);
      else if (i === "complex64") {
        c = new Float32Array(m);
        let d = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2);
        for (let x = 0; x < d.length; x++)
          d[x] = c[x * 2], f[x] = c[x * 2 + 1];
        let h = nr(d, p, "float32"), g = nr(f, p, "float32");
        t10[a] = Tr(h, g), h.dispose(), g.dispose();
      } else
        throw new Error(`Unsupported dtype in weight '${a}': ${i}`);
      n += u * l;
    }
    i !== "complex64" && (t10[a] = nr(c, p, i));
  }
  return t10;
}
function Rz(r) {
  if (r === null)
    throw new Error(`Invalid input value: ${JSON.stringify(r)}`);
  let e = 0, t10 = [];
  r.forEach((s) => {
    if (e += s.byteLength, t10.push(s.byteLength === s.buffer.byteLength ? s : new s.constructor(s)), !(s instanceof Float32Array || s instanceof Int32Array || s instanceof Uint8Array))
      throw new Error(`Unsupported TypedArray subtype: ${s.constructor.name}`);
  });
  let o = new Uint8Array(e), n = 0;
  return t10.forEach((s) => {
    o.set(new Uint8Array(s.buffer), n), n += s.byteLength;
  }), o.buffer;
}
var Xb = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function I0(r) {
  return Xb ? Buffer.byteLength(r) : new Blob([r]).size;
}
function k0(r) {
  if (Xb)
    return Buffer.from(r).toString("base64");
  let e = new Uint8Array(r), t10 = "";
  for (let o = 0, n = e.length; o < n; o++)
    t10 += String.fromCharCode(e[o]);
  return btoa(t10);
}
function N0(r) {
  if (Xb) {
    let o = Buffer.from(r, "base64");
    return o.buffer.slice(o.byteOffset, o.byteOffset + o.byteLength);
  }
  let e = atob(r), t10 = new Uint8Array(e.length);
  for (let o = 0; o < e.length; ++o)
    t10.set([e.charCodeAt(o)], o);
  return t10.buffer;
}
function Fp(r) {
  if (r.length === 1)
    return r[0];
  let e = 0;
  r.forEach((n) => {
    e += n.byteLength;
  });
  let t10 = new Uint8Array(e), o = 0;
  return r.forEach((n) => {
    t10.set(new Uint8Array(n), o), o += n.byteLength;
  }), t10.buffer;
}
function Yb(r) {
  let e = "/";
  for (r = r.trim(); r.endsWith(e); )
    r = r.slice(0, r.length - 1);
  let t10 = r.split(e);
  return t10[t10.length - 1];
}
function Vm(r, e) {
  let t10 = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy, weightsManifest: e };
  return r.signature != null && (t10.signature = r.signature), r.userDefinedMetadata != null && (t10.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (t10.modelInitializer = r.modelInitializer), r.initializerSignature != null && (t10.initializerSignature = r.initializerSignature), r.trainingConfig != null && (t10.trainingConfig = r.trainingConfig), t10;
}
function Qb(r, e, t10) {
  let o = { modelTopology: r.modelTopology, format: r.format, generatedBy: r.generatedBy, convertedBy: r.convertedBy };
  if (r.trainingConfig != null && (o.trainingConfig = r.trainingConfig), r.weightsManifest != null) {
    if (!e)
      throw new Error("modelJSON has weightsManifest but weightSpecs is null");
    if (!t10)
      throw new Error("modelJSON has weightsManifest but weightData is null");
    o.weightSpecs = e, o.weightData = t10;
  }
  return r.signature != null && (o.signature = r.signature), r.userDefinedMetadata != null && (o.userDefinedMetadata = r.userDefinedMetadata), r.modelInitializer != null && (o.modelInitializer = r.modelInitializer), r.initializerSignature != null && (o.initializerSignature = r.initializerSignature), o;
}
async function Op(r, e) {
  let t10, o;
  return r.weightsManifest != null && ([t10, o] = await e(r.weightsManifest)), Qb(r, t10, o);
}
function Ms(r) {
  if (r.modelTopology instanceof ArrayBuffer)
    throw new Error("Expected JSON model topology, received ArrayBuffer.");
  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: r.modelTopology == null ? 0 : I0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : I0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength };
}
function zm(r) {
  let e = [];
  for (let t10 of r)
    e.push(...t10.weights);
  return e;
}
function Dz() {
  let r = (t10) => {
    let o = t10 << 13, n = 0;
    for (; !(o & 8388608); )
      n -= 8388608, o <<= 1;
    return o &= -8388609, n += 947912704, o | n;
  }, e = new Uint32Array(2048);
  e[0] = 0;
  for (let t10 = 1; t10 < 1024; t10++)
    e[t10] = r(t10);
  for (let t10 = 1024; t10 < 2048; t10++)
    e[t10] = 939524096 + (t10 - 1024 << 13);
  return e;
}
function Fz() {
  let r = new Uint32Array(64);
  r[0] = 0, r[31] = 1199570944, r[32] = 2147483648, r[63] = 3347054592;
  for (let e = 1; e < 31; e++)
    r[e] = e << 23;
  for (let e = 33; e < 63; e++)
    r[e] = 2147483648 + (e - 32 << 23);
  return r;
}
function Oz() {
  let r = new Uint32Array(64);
  for (let e = 0; e < 64; e++)
    r[e] = 1024;
  return r[0] = r[32] = 0, r;
}
function Pz() {
  let r = Dz(), e = Fz(), t10 = Oz();
  return (o) => {
    let n = new ArrayBuffer(4 * o.length), s = new Uint32Array(n);
    for (let a = 0; a < o.length; a++) {
      let i = o[a], p = r[t10[i >> 10] + (i & 1023)] + e[i >> 10];
      s[a] = p;
    }
    return new Float32Array(n);
  };
}
var mt = class {
  constructor() {
    this.saveRouters = [], this.loadRouters = [];
  }
  static getInstance() {
    return mt.instance == null && (mt.instance = new mt()), mt.instance;
  }
  static registerSaveRouter(e) {
    mt.getInstance().saveRouters.push(e);
  }
  static registerLoadRouter(e) {
    mt.getInstance().loadRouters.push(e);
  }
  static getSaveHandlers(e) {
    return mt.getHandlers(e, "save");
  }
  static getLoadHandlers(e, t10) {
    return mt.getHandlers(e, "load", t10);
  }
  static getHandlers(e, t10, o) {
    let n = [];
    return (t10 === "load" ? mt.getInstance().loadRouters : mt.getInstance().saveRouters).forEach((a) => {
      let i = a(e, o);
      i !== null && n.push(i);
    }), n;
  }
};
var T0 = (r) => mt.registerSaveRouter(r);
var _0 = (r) => mt.registerLoadRouter(r);
var $0 = (r) => mt.getSaveHandlers(r);
var E0 = (r, e) => mt.getLoadHandlers(r, e);
var Zb = "tensorflowjs";
var Jb = 1;
var nu = "models_store";
var Ni = "model_info_store";
function A0() {
  if (!O().getBool("IS_BROWSER"))
    throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
  let r = typeof window == "undefined" ? self : window, e = r.indexedDB || r.mozIndexedDB || r.webkitIndexedDB || r.msIndexedDB || r.shimIndexedDB;
  if (e == null)
    throw new Error("The current browser does not appear to support IndexedDB.");
  return e;
}
function eC(r) {
  let e = r.result;
  e.createObjectStore(nu, { keyPath: "modelPath" }), e.createObjectStore(Ni, { keyPath: "modelPath" });
}
var Ls = class {
  constructor(e) {
    if (this.indexedDB = A0(), e == null || !e)
      throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
    this.modelPath = e;
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
    return this.databaseAction(this.modelPath, e);
  }
  async load() {
    return this.databaseAction(this.modelPath);
  }
  databaseAction(e, t10) {
    return new Promise((o, n) => {
      let s = this.indexedDB.open(Zb, Jb);
      s.onupgradeneeded = () => eC(s), s.onsuccess = () => {
        let a = s.result;
        if (t10 == null) {
          let i = a.transaction(nu, "readonly"), u = i.objectStore(nu).get(this.modelPath);
          u.onsuccess = () => {
            if (u.result == null)
              return a.close(), n(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
            o(u.result.modelArtifacts);
          }, u.onerror = (c) => (a.close(), n(u.error)), i.oncomplete = () => a.close();
        } else {
          let i = Ms(t10), p = a.transaction(Ni, "readwrite"), u = p.objectStore(Ni), c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), l;
          c.onsuccess = () => {
            l = a.transaction(nu, "readwrite");
            let d = l.objectStore(nu).put({ modelPath: this.modelPath, modelArtifacts: t10, modelArtifactsInfo: i });
            d.onsuccess = () => o({ modelArtifactsInfo: i }), d.onerror = (f) => {
              u = p.objectStore(Ni);
              let h = u.delete(this.modelPath);
              h.onsuccess = () => (a.close(), n(d.error)), h.onerror = (g) => (a.close(), n(d.error));
            };
          }, c.onerror = (m) => (a.close(), n(c.error)), p.oncomplete = () => {
            l == null ? a.close() : l.oncomplete = () => a.close();
          };
        }
      }, s.onerror = (a) => n(s.error);
    });
  }
};
Ls.URL_SCHEME = "indexeddb://";
var R0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ls.URL_SCHEME) ? Mz(r.slice(Ls.URL_SCHEME.length)) : null;
mt.registerSaveRouter(R0);
mt.registerLoadRouter(R0);
function Mz(r) {
  return new Ls(r);
}
function Lz(r) {
  return r.startsWith(Ls.URL_SCHEME) ? r.slice(Ls.URL_SCHEME.length) : r;
}
var Wm = class {
  constructor() {
    this.indexedDB = A0();
  }
  async listModels() {
    return new Promise((e, t10) => {
      let o = this.indexedDB.open(Zb, Jb);
      o.onupgradeneeded = () => eC(o), o.onsuccess = () => {
        let n = o.result, s = n.transaction(Ni, "readonly"), i = s.objectStore(Ni).getAll();
        i.onsuccess = () => {
          let p = {};
          for (let u of i.result)
            p[u.modelPath] = u.modelArtifactsInfo;
          e(p);
        }, i.onerror = (p) => (n.close(), t10(i.error)), s.oncomplete = () => n.close();
      }, o.onerror = (n) => t10(o.error);
    });
  }
  async removeModel(e) {
    return e = Lz(e), new Promise((t10, o) => {
      let n = this.indexedDB.open(Zb, Jb);
      n.onupgradeneeded = () => eC(n), n.onsuccess = () => {
        let s = n.result, a = s.transaction(Ni, "readwrite"), i = a.objectStore(Ni), p = i.get(e), u;
        p.onsuccess = () => {
          if (p.result == null)
            return s.close(), o(new Error(`Cannot find model with path '${e}' in IndexedDB.`));
          {
            let c = i.delete(e), l = () => {
              u = s.transaction(nu, "readwrite");
              let d = u.objectStore(nu).delete(e);
              d.onsuccess = () => t10(p.result.modelArtifactsInfo), d.onerror = (f) => o(p.error);
            };
            c.onsuccess = l, c.onerror = (m) => (l(), s.close(), o(p.error));
          }
        }, p.onerror = (c) => (s.close(), o(p.error)), a.oncomplete = () => {
          u == null ? s.close() : u.oncomplete = () => s.close();
        };
      }, n.onerror = (s) => o(n.error);
    });
  }
};
var $a = "/";
var Pp = "tensorflowjs_models";
var D0 = "info";
var Bz = "model_topology";
var Vz = "weight_specs";
var zz = "weight_data";
var Wz = "model_metadata";
function F0(r) {
  return { info: [Pp, r, D0].join($a), topology: [Pp, r, Bz].join($a), weightSpecs: [Pp, r, Vz].join($a), weightData: [Pp, r, zz].join($a), modelMetadata: [Pp, r, Wz].join($a) };
}
function O0(r) {
  for (let e of Object.values(r))
    window.localStorage.removeItem(e);
}
function Uz(r) {
  let e = r.split($a);
  if (e.length < 3)
    throw new Error(`Invalid key format: ${r}`);
  return e.slice(1, e.length - 1).join($a);
}
function Gz(r) {
  return r.startsWith(Bs.URL_SCHEME) ? r.slice(Bs.URL_SCHEME.length) : r;
}
var Bs = class {
  constructor(e) {
    if (!O().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 = F0(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 t10 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = Ms(e);
      try {
        this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t10), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, k0(e.weightData));
        let s = { 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, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
        return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(s)), { modelArtifactsInfo: n };
      } catch (s) {
        throw O0(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${n.modelTopologyBytes}, weightSpecsBytes=${n.weightSpecsBytes}, weightDataBytes=${n.weightDataBytes}.`);
      }
    }
  }
  async load() {
    let e = JSON.parse(this.LS.getItem(this.keys.info));
    if (e == null)
      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
    if (e.modelTopologyType !== "JSON")
      throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
    let t10 = {}, o = JSON.parse(this.LS.getItem(this.keys.topology));
    if (o == null)
      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
    t10.modelTopology = o;
    let n = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
    if (n == null)
      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
    t10.weightSpecs = n;
    let s = this.LS.getItem(this.keys.modelMetadata);
    if (s != null) {
      let i = JSON.parse(s);
      t10.format = i.format, t10.generatedBy = i.generatedBy, t10.convertedBy = i.convertedBy, i.signature != null && (t10.signature = i.signature), i.userDefinedMetadata != null && (t10.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t10.modelInitializer = i.modelInitializer), i.initializerSignature != null && (t10.initializerSignature = i.initializerSignature), i.trainingConfig != null && (t10.trainingConfig = i.trainingConfig);
    }
    let a = this.LS.getItem(this.keys.weightData);
    if (a == null)
      throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
    return t10.weightData = N0(a), t10;
  }
};
Bs.URL_SCHEME = "localstorage://";
var P0 = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Bs.URL_SCHEME) ? Hz(r.slice(Bs.URL_SCHEME.length)) : null;
mt.registerSaveRouter(P0);
mt.registerLoadRouter(P0);
function Hz(r) {
  return new Bs(r);
}
var Um = class {
  constructor() {
    $(O().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
  }
  async listModels() {
    let e = {}, t10 = Pp + $a, o = $a + D0;
    for (let n = 0; n < this.LS.length; ++n) {
      let s = this.LS.key(n);
      if (s.startsWith(t10) && s.endsWith(o)) {
        let a = Uz(s);
        e[a] = JSON.parse(this.LS.getItem(s));
      }
    }
    return e;
  }
  async removeModel(e) {
    e = Gz(e);
    let t10 = F0(e);
    if (this.LS.getItem(t10.info) == null)
      throw new Error(`Cannot find model at path '${e}'`);
    let o = JSON.parse(this.LS.getItem(t10.info));
    return O0(t10), o;
  }
};
var Mp = "://";
var Xt = class {
  constructor() {
    this.managers = {};
  }
  static getInstance() {
    return Xt.instance == null && (Xt.instance = new Xt()), Xt.instance;
  }
  static registerManager(e, t10) {
    $(e != null, () => "scheme must not be undefined or null."), e.endsWith(Mp) && (e = e.slice(0, e.indexOf(Mp))), $(e.length > 0, () => "scheme must not be an empty string.");
    let o = Xt.getInstance();
    $(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t10;
  }
  static getManager(e) {
    let t10 = Xt.getInstance().managers[e];
    if (t10 == null)
      throw new Error(`Cannot find model manager for scheme '${e}'`);
    return t10;
  }
  static getSchemes() {
    return Object.keys(Xt.getInstance().managers);
  }
};
function Gm(r) {
  if (r.indexOf(Mp) === -1)
    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Xt.getSchemes().join(",")}`);
  return { scheme: r.split(Mp)[0], path: r.split(Mp)[1] };
}
async function M0(r, e, t10 = false) {
  $(r !== e, () => `Old path and new path are the same: '${r}'`);
  let o = mt.getLoadHandlers(r);
  $(o.length > 0, () => `Copying failed because no load handler is found for source URL ${r}.`), $(o.length < 2, () => `Copying failed because more than one (${o.length}) load handlers for source URL ${r}.`);
  let n = o[0], s = mt.getSaveHandlers(e);
  $(s.length > 0, () => `Copying failed because no save handler is found for destination URL ${e}.`), $(s.length < 2, () => `Copying failed because more than one (${o.length}) save handlers for destination URL ${e}.`);
  let a = s[0], i = Gm(r).scheme, p = Gm(r).path, u = i === Gm(r).scheme, c = await n.load();
  t10 && u && await Xt.getManager(i).removeModel(p);
  let l = await a.save(c);
  return t10 && !u && await Xt.getManager(i).removeModel(p), l.modelArtifactsInfo;
}
async function L0() {
  let r = Xt.getSchemes(), e = {};
  for (let t10 of r) {
    let o = await Xt.getManager(t10).listModels();
    for (let n in o) {
      let s = t10 + Mp + n;
      e[s] = o[n];
    }
  }
  return e;
}
async function B0(r) {
  let e = Gm(r);
  return Xt.getManager(e.scheme).removeModel(e.path);
}
async function V0(r, e) {
  return M0(r, e, false);
}
async function z0(r, e) {
  return M0(r, e, true);
}
var tC = class {
  constructor() {
    this.messageName = "setTimeoutCustom", this.functionRefs = [], this.handledMessageCount = 0, this.hasEventListener = false;
  }
  fetch(e, t10) {
    return fetch(e, t10);
  }
  now() {
    return performance.now();
  }
  encode(e, t10) {
    if (t10 !== "utf-8" && t10 !== "utf8")
      throw new Error(`Browser's encoder only supports utf-8, but got ${t10}`);
    return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e);
  }
  decode(e, t10) {
    return new TextDecoder(t10).decode(e);
  }
  setTimeoutCustom(e, t10) {
    if (typeof window == "undefined" || !O().getBool("USE_SETTIMEOUTCUSTOM")) {
      setTimeout(e, t10);
      return;
    }
    this.functionRefs.push(e), setTimeout(() => {
      window.postMessage({ name: this.messageName, index: this.functionRefs.length - 1 }, "*");
    }, t10), this.hasEventListener || (this.hasEventListener = true, window.addEventListener("message", (o) => {
      if (o.source === window && o.data.name === this.messageName) {
        o.stopPropagation();
        let n = this.functionRefs[o.data.index];
        n(), this.handledMessageCount++, this.handledMessageCount === this.functionRefs.length && (this.functionRefs = [], this.handledMessageCount = 0);
      }
    }, true));
  }
  isTypedArray(e) {
    return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray;
  }
};
if (O().get("IS_BROWSER")) {
  O().setPlatform("browser", new tC());
  try {
    Xt.registerManager(Bs.URL_SCHEME, new Um());
  } catch (r) {
  }
  try {
    Xt.registerManager(Ls.URL_SCHEME, new Wm());
  } catch (r) {
  }
}
var Kz = { importFetch: () => W0() };
var rC;
var oC = class {
  constructor() {
    this.util = U0(), this.textEncoder = new this.util.TextEncoder();
  }
  fetch(e, t10) {
    return O().global.fetch != null ? O().global.fetch(e, t10) : (rC == null && (rC = Kz.importFetch()), rC(e, t10));
  }
  now() {
    let e = process.hrtime();
    return e[0] * 1e3 + e[1] / 1e6;
  }
  encode(e, t10) {
    if (t10 !== "utf-8" && t10 !== "utf8")
      throw new Error(`Node built-in encoder only supports utf-8, but got ${t10}`);
    return this.textEncoder.encode(e);
  }
  decode(e, t10) {
    return e.length === 0 ? "" : new this.util.TextDecoder(t10).decode(e);
  }
  isTypedArray(e) {
    return this.util.types.isFloat32Array(e) || this.util.types.isInt32Array(e) || this.util.types.isUint8Array(e) || this.util.types.isUint8ClampedArray(e);
  }
};
O().get("IS_NODE") && !O().get("IS_BROWSER") && O().setPlatform("node", new oC());
function le(r, e = "float32", t10) {
  return e = e || "float32", bt(r), new st(r, e, t10);
}
function qz(r, e) {
  let t10 = v(r, "x", "cast");
  if (!Ib(e))
    throw new Error(`Failed to cast to unknown dtype ${e}`);
  if (e === "string" && t10.dtype !== "string" || e !== "string" && t10.dtype === "string")
    throw new Error("Only strings can be casted to strings");
  let o = { x: t10 }, n = { dtype: e };
  return T.runKernel(co, o, n);
}
var je = N({ cast_: qz });
function jz(r) {
  let t10 = { x: v(r, "x", "clone", "string_or_numeric") };
  return T.runKernel(mo, t10);
}
var Lr = N({ clone_: jz });
function Hm(r, e = false) {
  console.log(r.toString(e));
}
Hb();
var Xz = { buffer: le, cast: je, clone: Lr, print: Hm };
x0(Xz);
function Iie() {
  O().set("PROD", true);
}
function vie() {
  O().set("DEBUG", true);
}
function kie() {
  O().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function nC(r) {
  O().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
y0(nC);
function Nie() {
  T.disposeVariables();
}
function sr() {
  return T;
}
function Tie() {
  return T.memory();
}
function _ie(r) {
  return T.profile(r);
}
function Ee(r, e) {
  return T.tidy(r, e);
}
function Ot(r) {
  Qc(r).forEach((t10) => t10.dispose());
}
function _r(r) {
  return T.keep(r);
}
function $ie(r) {
  return T.time(r);
}
function Eie(r) {
  return T.setBackend(r);
}
function Aie() {
  return T.ready();
}
function Rie() {
  return T.backendName;
}
function Die(r) {
  T.removeBackend(r);
}
function Fie(r) {
  return T.findBackend(r);
}
function Oie(r) {
  return T.findBackendFactory(r);
}
function Ti(r, e, t10 = 1) {
  return T.registerBackend(r, e, t10);
}
function Pie() {
  return T.backend;
}
function Mie(r, e) {
  O().setPlatform(r, e);
}
function Yz(r, e) {
  let t10 = v(r, "a", "add"), o = v(e, "b", "add");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(eo, n);
}
var be = N({ add_: Yz });
function Qz(r, e) {
  let t10 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(nn, n);
}
var Km = N({ floorDiv_: Qz });
function Zz(r, e) {
  let t10 = v(r, "a", "div"), o = v(e, "b", "div");
  if ([t10, o] = De(t10, o), t10.dtype === "int32" && o.dtype === "int32")
    return Km(t10, o);
  let n = { a: t10, b: o }, s = {};
  return T.runKernel(Zo, n, s);
}
var He = N({ div_: Zz });
function Jz(r, e) {
  let t10 = v(r, "a", "mul"), o = v(e, "b", "mul");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(kn, n);
}
var ne = N({ mul_: Jz });
function eW(r) {
  let e = v(r, "x", "abs");
  if (e.dtype === "complex64") {
    let t10 = { x: e };
    return T.runKernel(hp, t10);
  } else {
    let t10 = { x: e };
    return T.runKernel(ys, t10);
  }
}
var Yt = N({ abs_: eW });
function tW(r) {
  let t10 = { x: v(r, "x", "acos") };
  return T.runKernel(aa, t10);
}
var G0 = N({ acos_: tW });
function rW(r) {
  let t10 = { x: v(r, "x", "acosh") };
  return T.runKernel(ia, t10);
}
var H0 = N({ acosh_: rW });
function oW(r) {
  $(Array.isArray(r), () => "The argument passed to tf.addN() must be a list of tensors"), $(r.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${r.length}`);
  let e = r.map((n, s) => v(n, `tensors${s}`, "addN")), t10 = e[0];
  e.forEach((n) => {
    if (n.dtype !== t10.dtype)
      throw new Error("All tensors passed to tf.addN() must have the same dtype");
  }), e.forEach((n) => {
    if (!Or(n.shape, t10.shape))
      throw new Error("All tensors passed to tf.addN() must have the same shape");
  });
  let o = e;
  return T.runKernel(Po, o);
}
var K0 = N({ addN_: oW });
function nW(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t10 };
  return T.runKernel(Mo, n, s);
}
var q0 = N({ all_: nW });
function sW(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t10 };
  return T.runKernel(Lo, n, s);
}
var j0 = N({ any_: sW });
function aW(r, e = 0) {
  let o = { x: v(r, "x", "argMax") }, n = { axis: e };
  return T.runKernel(Bo, o, n);
}
var X0 = N({ argMax_: aW });
function iW(r, e = 0) {
  let o = { x: v(r, "x", "argMin") }, n = { axis: e };
  return T.runKernel(ei, o, n);
}
var Y0 = N({ argMin_: iW });
function uW(r) {
  let t10 = { x: v(r, "x", "asin") };
  return T.runKernel(ua, t10);
}
var Q0 = N({ asin_: uW });
function pW(r) {
  let t10 = { x: v(r, "x", "asinh") };
  return T.runKernel(pa, t10);
}
var Z0 = N({ asinh_: pW });
function cW(r) {
  let t10 = { x: v(r, "x", "atan") };
  return T.runKernel(ca, t10);
}
var J0 = N({ atan_: cW });
function lW(r, e) {
  let t10 = v(r, "a", "atan2"), o = v(e, "b", "atan2");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(ma, n);
}
var ev = N({ atan2_: lW });
function mW(r) {
  let t10 = { x: v(r, "x", "atanh") };
  return T.runKernel(la, t10);
}
var tv = N({ atanh_: mW });
function dW(r, e, t10, o, n = "NHWC", s) {
  let a = r[3], i = [...e, a], p = ov(n);
  return au(r, i, t10, s, o, null, null, p);
}
function aC(r, e, t10, o, n, s, a = "channelsLast") {
  let [i, p] = Jc(e), u;
  if (a === "channelsLast")
    u = [i, p, r[3], r[3]];
  else if (a === "channelsFirst")
    u = [i, p, r[1], r[1]];
  else
    throw new Error(`Unknown dataFormat ${a}`);
  return au(r, u, t10, o, n, s, false, a);
}
function fW(r, e, t10, o, n, s, a = "NDHWC") {
  let [i, p, u] = sC(e), c, l;
  if (a === "NDHWC")
    l = "channelsLast", c = [i, p, u, r[4], r[4]];
  else if (a === "NCDHW")
    l = "channelsFirst", c = [i, p, u, r[1], r[1]];
  else
    throw new Error(`Unknown dataFormat ${a}`);
  return rv(r, c, t10, o, n, false, l, s);
}
function au(r, e, t10, o, n, s, a = false, i = "channelsLast") {
  let [p, u, c, l] = [-1, -1, -1, -1];
  if (i === "channelsLast")
    [p, u, c, l] = r;
  else if (i === "channelsFirst")
    [p, l, u, c] = r;
  else
    throw new Error(`Unknown dataFormat ${i}`);
  let [m, d, , f] = e, [h, g] = Jc(t10), [x, b] = Jc(o), C = Lp(m, x), w = Lp(d, b), { padInfo: k, outHeight: _, outWidth: E } = xW(n, u, c, h, g, C, w, s, i), A = a ? f * l : f, R;
  return i === "channelsFirst" ? R = [p, A, _, E] : i === "channelsLast" && (R = [p, _, E, A]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: A, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: d, effectiveFilterHeight: C, effectiveFilterWidth: w, dilationHeight: x, dilationWidth: b, inShape: r, outShape: R, filterShape: e };
}
function rv(r, e, t10, o, n, s = false, a = "channelsLast", i) {
  let [p, u, c, l, m] = [-1, -1, -1, -1, -1];
  if (a === "channelsLast")
    [p, u, c, l, m] = r;
  else if (a === "channelsFirst")
    [p, m, u, c, l] = r;
  else
    throw new Error(`Unknown dataFormat ${a}`);
  let [d, f, h, , g] = e, [x, b, C] = sC(t10), [w, k, _] = sC(o), E = Lp(d, w), A = Lp(f, k), R = Lp(h, _), { padInfo: D, outDepth: P, outHeight: M, outWidth: L } = yW(n, u, c, l, x, b, C, E, A, R, i), V = s ? g * m : g, z;
  return a === "channelsFirst" ? z = [p, V, P, M, L] : a === "channelsLast" && (z = [p, P, M, L, V]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: P, outHeight: M, outWidth: L, outChannels: V, padInfo: D, strideDepth: x, strideHeight: b, strideWidth: C, filterDepth: d, filterHeight: f, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: A, effectiveFilterWidth: R, dilationDepth: w, dilationHeight: k, dilationWidth: _, inShape: r, outShape: z, filterShape: e };
}
function hW(r, e, t10, o, n) {
  o == null && (o = iC(r, e, t10));
  let s = r[0], a = r[1], i = el((s - e + 2 * o) / t10 + 1, n), p = el((a - e + 2 * o) / t10 + 1, n);
  return [i, p];
}
function gW(r, e, t10, o, n, s) {
  n == null && (n = iC(r, e[0], o[0]));
  let a = [0, 0, 0, t10];
  for (let i = 0; i < 3; i++)
    r[i] + 2 * n >= e[i] && (a[i] = el((r[i] - e[i] + 2 * n) / o[i] + 1, s));
  return a;
}
function iC(r, e, t10, o = 1) {
  let n = Lp(e, o);
  return Math.floor((r[0] * (t10 - 1) - t10 + n) / 2);
}
function Jc(r) {
  return typeof r == "number" ? [r, r, r] : r.length === 2 ? [r[0], r[1], 1] : r;
}
function sC(r) {
  return typeof r == "number" ? [r, r, r] : r;
}
function Lp(r, e) {
  return e <= 1 ? r : r + (r - 1) * (e - 1);
}
function xW(r, e, t10, o, n, s, a, i, p) {
  let u, c, l;
  if (typeof r == "number") {
    u = { top: r, bottom: r, left: r, right: r, type: r === 0 ? "VALID" : "NUMBER" };
    let d = hW([e, t10], s, o, r, i);
    c = d[0], l = d[1];
  } else if (r === "same") {
    c = Math.ceil(e / o), l = Math.ceil(t10 / n);
    let m = Math.max(0, (c - 1) * o + s - e), d = Math.max(0, (l - 1) * n + a - t10), f = Math.floor(m / 2), h = m - f, g = Math.floor(d / 2), x = d - g;
    u = { top: f, bottom: h, left: g, right: x, type: "SAME" };
  } else if (r === "valid")
    u = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((e - s + 1) / o), l = Math.ceil((t10 - a + 1) / n);
  else if (typeof r == "object") {
    let m = p === "channelsLast" ? r[1][0] : r[2][0], d = p === "channelsLast" ? r[1][1] : r[2][1], f = p === "channelsLast" ? r[2][0] : r[3][0], h = p === "channelsLast" ? r[2][1] : r[3][1];
    u = { top: m, bottom: d, left: f, right: h, type: m === 0 && d === 0 && f === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = el((e - s + m + d) / o + 1, i), l = el((t10 - a + f + h) / n + 1, i);
  } else
    throw Error(`Unknown padding parameter: ${r}`);
  return { padInfo: u, outHeight: c, outWidth: l };
}
function yW(r, e, t10, o, n, s, a, i, p, u, c) {
  let l, m, d, f;
  if (r === "valid" && (r = 0), typeof r == "number") {
    l = { top: r, bottom: r, left: r, right: r, front: r, back: r, type: r === 0 ? "VALID" : "NUMBER" };
    let g = gW([e, t10, o, 1], [i, p, u], 1, [n, s, a], r, c);
    m = g[0], d = g[1], f = g[2];
  } else if (r === "same") {
    m = Math.ceil(e / n), d = Math.ceil(t10 / s), f = Math.ceil(o / a);
    let h = (m - 1) * n + i - e, g = (d - 1) * s + p - t10, x = (f - 1) * a + u - o, b = Math.floor(h / 2), C = h - b, w = Math.floor(g / 2), k = g - w, _ = Math.floor(x / 2), E = x - _;
    l = { top: w, bottom: k, left: _, right: E, front: b, back: C, type: "SAME" };
  } else
    throw Error(`Unknown padding parameter: ${r}`);
  return { padInfo: l, outDepth: m, outHeight: d, outWidth: f };
}
function el(r, e) {
  if (!e)
    return Math.trunc(r);
  switch (e) {
    case "round":
      return Math.round(r);
    case "ceil":
      return Math.ceil(r);
    case "floor":
      return Math.floor(r);
    default:
      throw new Error(`Unknown roundingMode ${e}`);
  }
}
function su(r) {
  let [e, t10, o] = Jc(r);
  return e === 1 && t10 === 1 && o === 1;
}
function mr(r, e) {
  return su(r) || su(e);
}
function Vs(r) {
  return Jc(r).every((e) => e > 0);
}
function ov(r) {
  if (r === "NHWC")
    return "channelsLast";
  if (r === "NCHW")
    return "channelsFirst";
  throw new Error(`Unknown dataFormat ${r}`);
}
function Mt(r, e, t10) {
  if (t10 != null) {
    if (typeof e == "string")
      throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`);
    if (typeof e == "number")
      $(sa(e), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${e}.`);
    else if (typeof e == "object")
      e.forEach((o) => {
        o.forEach((n) => {
          $(sa(n), () => `Error in ${r}: pad must be an integer when using dimRoundingMode ${t10} but got pad ${n}.`);
        });
      });
    else
      throw Error(`Error in ${r}: Unknown padding parameter: ${e}`);
  }
}
function bW(r, e) {
  let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: e };
  return T.runKernel(_s, o, n);
}
var W = N({ reshape_: bW });
function CW(r, e, t10, o, n) {
  let s = v(r, "x", "avgPool", "float32"), a = 1;
  $(mr(t10, a), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`);
  let i = s, p = false;
  s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${i.rank}.`), Mt("avgPool", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(Vo, u, c);
  return l = je(l, s.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var qm = N({ avgPool_: CW });
function SW(r, e, t10, o, n, s = "NDHWC") {
  let a = v(r, "x", "avgPool3d", "float32"), i = a, p = false;
  a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), $(typeof t10 == "number" && t10 > 0 || Array.isArray(t10) && t10[0] > 0 && t10[1] > 0 && t10[2] > 0, () => `Error in avgPool3d: Stride must be > 0, but got '${t10}'`), Mt("avgPool3d", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(dp, u, c);
  return l = je(l, i.dtype), p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var nv = N({ avgPool3d_: SW });
function wW(r, e = 0) {
  $(r.length >= 1, () => "Pass at least one tensor to concat");
  let t10 = _a(r, "tensors", "concat", "string_or_numeric");
  if (t10[0].dtype === "complex64" && t10.forEach((s) => {
    if (s.dtype !== "complex64")
      throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${s.dtype}. `);
  }), t10.length === 1)
    return Lr(t10[0]);
  let o = t10, n = { axis: e };
  return T.runKernel(Cs, o, n);
}
var xt = N({ concat_: wW });
function IW(r, e, t10 = false, o = false) {
  let n = v(r, "a", "matMul"), s = v(e, "b", "matMul");
  [n, s] = De(n, s);
  let a = { a: n, b: s }, i = { transposeA: t10, transposeB: o };
  return T.runKernel(zo, a, i);
}
var Xe = N({ matMul_: IW });
function vW(r) {
  let t10 = { x: v(r, "x", "sigmoid", "float32") };
  return T.runKernel(Gn, t10);
}
var Ws = N({ sigmoid_: vW });
function kW(r, e, t10) {
  let o = v(r, "x", "slice", "string_or_numeric");
  if (o.rank === 0)
    throw new Error("Slicing scalar is not possible");
  let n = { x: o }, s = { begin: e, size: t10 };
  return T.runKernel(Es, n, s);
}
var Ke = N({ slice_: kW });
function NW(r) {
  let t10 = { x: v(r, "x", "tanh", "float32") };
  return T.runKernel(Zn, t10);
}
var tl = N({ tanh_: NW });
function TW(r, e, t10, o, n, s) {
  let a = v(r, "forgetBias", "basicLSTMCell"), i = v(e, "lstmKernel", "basicLSTMCell"), p = v(t10, "lstmBias", "basicLSTMCell"), u = v(o, "data", "basicLSTMCell"), c = v(n, "c", "basicLSTMCell"), l = v(s, "h", "basicLSTMCell"), m = xt([u, l], 1), d = Xe(m, i), f = be(d, p), h = f.shape[0], g = f.shape[1] / 4, x = [h, g], b = Ke(f, [0, 0], x), C = Ke(f, [0, g], x), w = Ke(f, [0, g * 2], x), k = Ke(f, [0, g * 3], x), _ = be(ne(Ws(b), tl(C)), ne(c, Ws(be(a, w)))), E = ne(tl(_), Ws(k));
  return [_, E];
}
var sv = N({ basicLSTMCell_: TW });
function _W(r, e, t10) {
  let o = v(r, "x", "batchToSpaceND"), n = e.reduce((i, p) => i * p);
  $(o.rank >= 1 + e.length, () => `input rank is ${o.rank} but should be > than blockShape.length ${e.length}`), $(t10.length === e.length, () => `crops.length is ${t10.length} but should be equal to blockShape.length  ${e.length}`), $(o.shape[0] % n === 0, () => `input tensor batch is ${o.shape[0]} but is not divisible by the product of the elements of blockShape ${e.join(" * ")} === ${n}`);
  let s = { x: o }, a = { blockShape: e, crops: t10 };
  return T.runKernel(bs, s, a);
}
var jm = N({ batchToSpaceND_: _W });
function av(r) {
  let e;
  return r.rank === 0 || r.rank === 1 ? e = W(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = W(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e;
}
function $W(r, e, t10, o, n, s) {
  s == null && (s = 1e-3);
  let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
  n != null && (u = v(n, "scale", "batchNorm"));
  let c;
  o != null && (c = v(o, "offset", "batchNorm")), $(i.rank === p.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), $(c == null || i.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), $(u == null || i.rank === u.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
  let m = { x: av(a), scale: u, offset: c, mean: i, variance: p }, d = { varianceEpsilon: s }, f = T.runKernel(sn, m, d);
  return W(f, a.shape);
}
var _i = N({ batchNorm_: $W });
function EW(r, e, t10, o, n, s) {
  let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
  n != null && (u = v(n, "scale", "batchNorm"));
  let c;
  return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${a.rank}.`), $(i.rank === 2 || i.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`), $(p.rank === 2 || p.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), _i(a, i, p, c, u, s);
}
var iv = N({ batchNorm2d_: EW });
function AW(r, e, t10, o, n, s) {
  let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
  n != null && (u = v(n, "scale", "batchNorm"));
  let c;
  return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`), $(i.rank === 3 || i.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`), $(p.rank === 3 || p.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), _i(a, i, p, c, u, s);
}
var uv = N({ batchNorm3d_: AW });
function RW(r, e, t10, o, n, s) {
  let a = v(r, "x", "batchNorm"), i = v(e, "mean", "batchNorm"), p = v(t10, "variance", "batchNorm"), u;
  n != null && (u = v(n, "scale", "batchNorm"));
  let c;
  return o != null && (c = v(o, "offset", "batchNorm")), $(a.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`), $(i.rank === 4 || i.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`), $(p.rank === 4 || p.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`), u != null && $(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`), c != null && $(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), _i(a, i, p, c, u, s);
}
var pv = N({ batchNorm4d_: RW });
function DW(r, e, t10) {
  let o = v(r, "x", "bincount"), n = v(e, "weights", "bincount");
  $(o.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${o.dtype}`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(n.size === o.size || n.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);
  let s = { x: o, weights: n }, a = { size: t10 };
  return T.runKernel(ti, s, a);
}
var Xm = N({ bincount_: DW });
function FW(r, e) {
  let t10 = v(r, "s0", "broadcastArgs", "int32"), o = v(e, "s1", "broadcastArgs", "int32");
  if (t10.rank !== 1)
    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t10.rank}`);
  if (o.rank !== 1)
    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);
  let n = { s0: t10, s1: o };
  return T.runKernel(fp, n);
}
var cv = N({ broadcastArgs_: FW });
function OW(r, e) {
  let t10 = v(r, "broadcastTo", "x"), o = t10.shape;
  if (bt(e), e.length < t10.rank)
    throw new Error(`broadcastTo(): shape.length=${e.length} < input.rank=${t10.rank}.`);
  if (e.length > t10.rank) {
    let u = t10.shape.slice();
    for (; u.length < e.length; )
      u.unshift(1);
    t10 = W(t10, u);
  }
  let n = t10.shape, s = Array.from(e);
  for (let u = e.length - 1; u >= 0; u--)
    if (n[u] === e[u])
      s[u] = 1;
    else if (t10.shape[u] !== 1)
      throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);
  if (s.map((u, c) => u > 1 ? c : -1).filter((u) => u >= 0).length === 0)
    return Lr(t10);
  let i = { x: t10 }, p = { reps: s };
  return T.runKernel(to, i, p);
}
var $i = N({ broadcastTo_: OW });
function PW(r) {
  let t10 = { x: v(r, "x", "ceil", "float32") };
  return T.runKernel(Wo, t10);
}
var lv = N({ ceil_: PW });
function Us(r, e, t10) {
  bt(r);
  let o = { shape: r, value: e, dtype: t10 };
  return T.runKernel(ws, {}, o);
}
function MW(r, e, t10) {
  let o = v(r, "x", "clipByValue");
  if ($(e <= t10, () => `Error in clip: min (${e}) must be less than or equal to max (${t10}).`), e === t10)
    return Us(o.shape, e, o.dtype);
  let n = { x: o }, s = { clipValueMin: e, clipValueMax: t10 };
  return T.runKernel(lo, n, s);
}
var mv = N({ clipByValue_: MW });
function LW(r) {
  return xt(r, 0);
}
var dv = N({ concat1d_: LW });
function BW(r, e) {
  return xt(r, e);
}
var fv = N({ concat2d_: BW });
function VW(r, e) {
  return xt(r, e);
}
var hv = N({ concat3d_: VW });
function zW(r, e) {
  return xt(r, e);
}
var gv = N({ concat4d_: zW });
function WW(r, e, t10, o, n = "NHWC", s = [1, 1], a) {
  let i = v(r, "x", "conv2d", "float32"), p = v(e, "filter", "conv2d", "float32"), u = i, c = false;
  i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`), Mt("conv2d", o, a);
  let l = n === "NHWC" ? u.shape[3] : u.shape[1];
  $(l === p.shape[2], () => `Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`), $(mr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(Vs(s), () => "Error in conv2D: Dilated rates should be larger than 0."), $(Vs(t10), () => "Error in conv2D: Strides should be larger than 0.");
  let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(Uo, m, d);
  return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var Ei = N({ conv2d_: WW });
function UW(r, e, t10, o, n = "NWC", s = 1, a) {
  let i = v(r, "x", "conv1d"), p = v(e, "filter", "conv1d"), u = i, c = false;
  i.rank === 2 && (c = true, u = W(i, [1, i.shape[0], i.shape[1]])), $(u.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${u.rank}.`), $(p.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`), Mt("conv1d", o, a), $(u.shape[2] === p.shape[1], () => `Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`), $(mr(t10, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t10} and dilation '${s}'`), $(Vs(s), () => "Error in conv1D: Dilated rates should be larger than 0."), $(Vs(t10), () => "Error in conv1D: Stride should be larger than 0."), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);
  let l = W(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = W(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = Ei(m, l, [1, t10], o, "NHWC", [1, s], a);
  return c ? W(g, [g.shape[2], g.shape[3]]) : W(g, [g.shape[0], g.shape[2], g.shape[3]]);
}
var xv = N({ conv1d_: UW });
function GW(r, e, t10, o, n, s = "NHWC", a) {
  $(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);
  let i = r, p = e, u = false;
  e.rank === 3 && (u = true, p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]), i = [1, r[0], r[1], r[2]]), $(i.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`), $(p.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`), $(t10.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${t10.rank}`);
  let c = s === "NHWC" ? i[3] : i[1], l = s === "NHWC" ? p.shape[3] : p.shape[1];
  $(c === t10.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t10.shape[2]}.`), $(l === t10.shape[3], () => `Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t10.shape[3]}.`), Mt("conv2dDerInput", n, a);
  let m = { dy: p, filter: t10 }, d = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, f = T.runKernel(Go, m, d);
  return u ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var Ym = N({ conv2DBackpropInput_: GW });
function HW(r, e, t10, o, n, s) {
  let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose");
  return Ym(t10, a, i, o, n, "NHWC", s);
}
var yv = N({ conv2dTranspose_: HW });
function KW(r, e, t10, o, n = "NDHWC", s = [1, 1, 1]) {
  let a = v(r, "x", "conv3d"), i = v(e, "filter", "conv3d"), p = a, u = false;
  a.rank === 4 && (u = true, p = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(p.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${p.rank}.`), $(i.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`), $(p.shape[4] === i.shape[3], () => `Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`), $(mr(t10, s), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), $(n === "NDHWC", () => `Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`), $(Vs(s), () => "Error in conv3D: Dilated rates should be larger than 0."), $(Vs(t10), () => "Error in conv3D: Strides should be larger than 0.");
  let c = { x: p, filter: i }, l = { strides: t10, pad: o, dataFormat: n, dilations: s }, m = T.runKernel(gp, c, l);
  return u ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var bv = N({ conv3d_: KW });
function qW(r, e, t10, o, n) {
  $(r.length === e.rank, () => `Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);
  let s = r, a = e, i = false;
  e.rank === 4 && (i = true, a = W(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]), s = [1, r[0], r[1], r[2], r[3]]);
  let p = s[4], u = a.shape[4];
  $(s.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`), $(a.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`), $(t10.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${t10.rank}`), $(p === t10.shape[3], () => `Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t10.shape[3]}.`), $(u === t10.shape[4], () => `Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t10.shape[4]}.`);
  let c = { dy: a, filter: t10 }, l = { pad: n, strides: o, inputShape: s }, m = T.runKernel(xp, c, l);
  return i ? W(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var Cv = N({ conv3DBackpropInput_: qW });
function jW(r, e, t10, o, n) {
  let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose");
  return Cv(t10, s, a, o, n);
}
var Sv = N({ conv3dTranspose_: jW });
function XW(r) {
  let t10 = { x: v(r, "x", "cos", "float32") };
  return T.runKernel(Ho, t10);
}
var wv = N({ cos_: XW });
function YW(r) {
  let t10 = { x: v(r, "x", "cosh", "float32") };
  return T.runKernel(Ko, t10);
}
var Iv = N({ cosh_: YW });
function QW(r, e = 0, t10 = false, o = false) {
  let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t10, reverse: o };
  return T.runKernel(qo, s, a);
}
var vv = N({ cumprod_: QW });
function ZW(r, e = 0, t10 = false, o = false) {
  let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t10, reverse: o };
  return T.runKernel(jo, s, a);
}
var kv = N({ cumsum_: ZW });
function JW(r, e, t10, o = false) {
  let n = v(r, "x", "denseBincount"), s = v(e, "weights", "denseBincount");
  $(n.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${n.dtype}`), $(n.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`), $(t10 >= 0, () => `size must be non-negative, but got ${t10}.`), $(s.size === n.size || s.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);
  let a = { x: n, weights: s }, i = { size: t10, binaryOutput: o };
  return T.runKernel(ni, a, i);
}
var Nv = N({ denseBincount_: JW });
function eU(r, e, t10 = "NHWC") {
  let o = v(r, "x", "depthToSpace", "float32"), n = t10 === "NHWC" ? o.shape[1] : o.shape[2], s = t10 === "NHWC" ? o.shape[2] : o.shape[3], a = t10 === "NHWC" ? o.shape[3] : o.shape[1];
  $(e > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${e}`), $(n * e >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${n} and ${e}  for depthToSpace with input shape
    ${o.shape}`), $(s * e >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${s} and ${e} for depthToSpace with input shape
        ${o.shape}`), $(a % (e * e) === 0, () => `Dimension size must be evenly divisible by ${e * e} but is ${a} for depthToSpace with input shape ${o.shape}`);
  let i = { x: o }, p = { blockSize: e, dataFormat: t10 };
  return T.runKernel(Yo, i, p);
}
var Tv = N({ depthToSpace_: eU });
function tU(r, e, t10, o, n = "NHWC", s = [1, 1], a) {
  let i = v(r, "x", "depthwiseConv2d", "float32"), p = v(e, "filter", "depthwiseConv2d", "float32"), u = i, c = false;
  i.rank === 3 && (c = true, u = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(u.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`), $(p.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);
  let l = n === "NHWC" ? u.shape[3] : u.shape[1];
  $(l === p.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`), Mt("depthwiseConv2d", o, a);
  let m = { x: u, filter: p }, d = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, f = T.runKernel(Qo, m, d);
  return c ? W(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var Bp = N({ depthwiseConv2d_: tU });
function rU(r) {
  let t10 = { x: v(r, "x", "diag") };
  return T.runKernel(si, t10);
}
var _v = N({ diag_: rU });
function oU(r, e, t10, o, n = [1, 1], s = "NHWC") {
  let a = v(r, "x", "dilation2d"), i = v(e, "filter", "dilation2d");
  $(a.rank === 3 || a.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`), $(i.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`), $(s === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);
  let p = a, u = false;
  a.rank === 3 && (p = W(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true), $(p.shape[3] === i.shape[2], () => `Error in dilation2d:  input and filter must have the same depth: ${p.shape[3]} vs ${i.shape[2]}`);
  let c = { x: p, filter: i }, l = { strides: t10, pad: o, dilations: n }, m = T.runKernel(ai, c, l);
  return u ? W(m, [m.shape[1], m.shape[2], m.shape[3]]) : m;
}
var $v = N({ dilation2d_: oU });
var yr = {};
Ge(yr, { assertAndGetBroadcastShape: () => Je, getBroadcastDims: () => Ev, getReductionAxes: () => Qm });
function Ev(r, e) {
  let t10 = r.length, o = [];
  for (let n = 0; n < t10; n++) {
    let s = t10 - 1 - n, a = r[s] || 1;
    (e[e.length - 1 - n] || 1) > 1 && a === 1 && o.unshift(s);
  }
  return o;
}
function Qm(r, e) {
  let t10 = [];
  for (let o = 0; o < e.length; o++) {
    let n = r[r.length - o - 1], s = e.length - o - 1, a = e[s];
    (n == null || n === 1 && a > 1) && t10.unshift(s);
  }
  return t10;
}
function Je(r, e) {
  let t10 = [], o = Math.max(r.length, e.length);
  for (let n = 0; n < o; n++) {
    let s = r[r.length - n - 1];
    s == null && (s = 1);
    let a = e[e.length - n - 1];
    if (a == null && (a = 1), s === 1)
      t10.unshift(a);
    else if (a === 1)
      t10.unshift(s);
    else if (s !== a) {
      let i = `Operands could not be broadcast together with shapes ${r} and ${e}.`;
      throw Error(i);
    } else
      t10.unshift(s);
  }
  return t10;
}
function nU(r, e) {
  let t10 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(en, n);
}
var Zm = N({ equal_: nU });
function sU(r, e, t10) {
  let o = v(e, "a", "where"), n = v(t10, "b", "where"), s = v(r, "condition", "where", "bool"), a = Je(Je(s.shape, o.shape), n.shape), i = $i(s, a), p = $i(o, a), u = $i(n, a), c = { condition: i, t: p, e: u };
  return T.runKernel($s, c);
}
var ss = N({ where_: sU });
function aU(r) {
  let t10 = { x: v(r, "x", "zerosLike") };
  return T.runKernel(Os, t10);
}
var Ut = N({ zerosLike_: aU });
function iU(r, e) {
  let t10 = v(r, "a", "div"), o = v(e, "b", "div");
  [t10, o] = De(t10, o);
  let n = He(t10, o), s = Ut(n), a = Zm(o, s);
  return ss(a, s, n);
}
var Av = N({ divNoNan_: iU });
function uU(r, e) {
  let t10 = v(r, "t1", "dot"), o = v(e, "t2", "dot");
  $((t10.rank === 1 || t10.rank === 2) && (o.rank === 1 || o.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${t10.rank} and ${o.rank}.`);
  let n = t10.rank === 1 ? t10.size : t10.shape[1], s = o.rank === 1 ? o.size : o.shape[0];
  if ($(n === s, () => `Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`), t10.rank === 1 && o.rank === 1) {
    let a = W(t10, [1, -1]), i = W(o, [-1, 1]), p = Xe(a, i);
    return W(p, []);
  } else if (t10.rank === 1 && o.rank === 2) {
    let a = W(t10, [1, -1]), i = W(o, [o.shape[0], o.shape[1]]), p = Xe(a, i);
    return W(p, [p.size]);
  } else if (t10.rank === 2 && o.rank === 1) {
    let a = W(o, [-1, 1]), i = Xe(t10, a);
    return W(i, [i.size]);
  } else {
    let a = W(o, [o.shape[0], o.shape[1]]);
    return Xe(t10, a);
  }
}
var Rv = N({ dot_: uU });
function pU(r, ...e) {
  let t10 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r };
  return T.runKernel(ii, t10, o);
}
var Dv = N({ einsum_: pU });
function cU(r) {
  let t10 = { x: v(r, "x", "elu", "float32") };
  return T.runKernel(Jo, t10);
}
var Jm = N({ elu_: cU });
function lU(r) {
  let e = v(r, "x", "erf");
  $(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = je(e, "float32"));
  let t10 = { x: e };
  return T.runKernel(da, t10);
}
var Fv = N({ erf_: lU });
function uC(r, e) {
  for (let t10 = 0; t10 < r.length; ++t10)
    if (r[r.length - t10 - 1] !== e - 1 - t10)
      return false;
  return true;
}
function Ov(r, e, t10) {
  let o = r.length + e.length, n = [], s = 0, a = 0;
  for (let i = 0; i < o; i++)
    t10.indexOf(i) === -1 ? n.push(r[s++]) : n.push(e[a++]);
  return n;
}
function mU(r, e) {
  let t10 = [], o = r.length;
  for (let s = 0; s < o; s++)
    e.indexOf(s) === -1 && t10.push(r[s]);
  let n = e.map((s) => r[s]);
  return [t10, n];
}
function Ea(r, e) {
  let t10 = e.map((o) => 1);
  return Ov(r, t10, e);
}
function dU(r, e, t10) {
  $(uC(e, t10), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t10} input.`);
}
function fU(r, e) {
  if (uC(r, e))
    return null;
  let t10 = [];
  for (let o = 0; o < e; ++o)
    r.indexOf(o) === -1 && t10.push(o);
  return r.forEach((o) => t10.push(o)), t10;
}
function hU(r) {
  return r.map((e, t10) => [t10, e]).sort((e, t10) => e[1] - t10[1]).map((e) => e[0]);
}
function gU(r, e) {
  let t10 = [];
  for (let o = e - r; o < e; ++o)
    t10.push(o);
  return t10;
}
function yU(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t10 };
  return T.runKernel(yn, n, s);
}
var Gs = N({ max_: yU });
function bU(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t10 };
  return T.runKernel(wn, n, s);
}
var rl = N({ min_: bU });
function CU(r, e) {
  let t10 = v(r, "base", "pow"), o = v(e, "exp", "pow");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(An, n);
}
var Aa = N({ pow_: CU });
function Ce(r, e) {
  if ((Bt(r) && e !== "string" || Array.isArray(r)) && e !== "complex64")
    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
  if (e === "string" && Bt(r) && !(r instanceof Uint8Array))
    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
  return xr(r, [], [], e);
}
function SU(r) {
  let t10 = { x: v(r, "x", "sqrt", "float32") };
  return T.runKernel(Hn, t10);
}
var $r = N({ sqrt_: SU });
function wU(r) {
  let e = v(r, "x", "square"), t10 = {};
  return T.runKernel("Square", { x: e }, t10);
}
var Qt = N({ square_: wU });
function IU(r, e = null, t10 = false) {
  let o = v(r, "x", "sum");
  o.dtype === "bool" && (o = je(o, "int32"));
  let n = { x: o }, s = { axis: e, keepDims: t10 };
  return T.runKernel(Kn, n, s);
}
var et = N({ sum_: IU });
function vU(r, e = "euclidean", t10 = null, o = false) {
  r = v(r, "x", "norm");
  let n = Pv(r, e, t10), s = n.shape;
  if (o) {
    let a = Ja(t10, r.shape);
    s = Ea(n.shape, a);
  }
  return W(n, s);
}
function Pv(r, e, t10 = null) {
  if (r.rank === 0)
    return Yt(r);
  if (r.rank !== 1 && t10 === null)
    return Pv(W(r, [-1]), e, t10);
  if (r.rank === 1 || typeof t10 == "number" || Array.isArray(t10) && t10.length === 1) {
    if (e === 1)
      return et(Yt(r), t10);
    if (e === 1 / 0)
      return Gs(Yt(r), t10);
    if (e === -1 / 0)
      return rl(Yt(r), t10);
    if (e === "euclidean" || e === 2)
      return $r(et(Aa(Yt(r), Ce(2, "int32")), t10));
    throw new Error(`Error in norm: invalid ord value: ${e}`);
  }
  if (Array.isArray(t10) && t10.length === 2) {
    if (e === 1)
      return Gs(et(Yt(r), t10[0]), t10[1] - 1);
    if (e === 1 / 0)
      return Gs(et(Yt(r), t10[1]), t10[0]);
    if (e === -1 / 0)
      return rl(et(Yt(r), t10[1]), t10[0]);
    if (e === "fro" || e === "euclidean")
      return $r(et(Qt(r), t10));
    throw new Error(`Error in norm: invalid ord value: ${e}`);
  }
  throw new Error(`Error in norm: invalid axis: ${t10}`);
}
var iu = N({ norm_: vU });
function kU(r, e = null, t10 = false) {
  return iu(r, "euclidean", e, t10);
}
var Mv = N({ euclideanNorm_: kU });
function NU(r) {
  let t10 = { x: v(r, "x", "exp") };
  return T.runKernel(tn, t10);
}
var So = N({ exp_: NU });
function TU(r, e = 0) {
  let t10 = v(r, "x", "expandDims", "string_or_numeric");
  $(e <= t10.rank, () => "Axis must be <= rank of the tensor");
  let o = { input: t10 }, n = { dim: e };
  return T.runKernel(Ss, o, n);
}
var Ra = N({ expandDims_: TU });
function _U(r) {
  let t10 = { x: v(r, "x", "expm1") };
  return T.runKernel(fa, t10);
}
var Lv = N({ expm1_: _U });
function $U(r, e) {
  let t10 = v(r, "x", "tile", "string_or_numeric");
  $(t10.rank === e.length, () => `Error in transpose: rank of input ${t10.rank} must match length of reps ${e}.`);
  let o = { x: t10 }, n = { reps: e };
  return T.runKernel(to, o, n);
}
var Ai = N({ tile_: $U });
function EU(r, e, t10, o = "float32") {
  e == null && (e = r);
  let n = le([r, e], o), s = r <= e ? r : e;
  for (let i = 0; i < s; ++i)
    n.set(1, i, i);
  let a = W(n.toTensor(), [r, e]);
  if (t10 == null)
    return a;
  if (t10.length === 1)
    return Ai(Ra(a, 0), [t10[0], 1, 1]);
  if (t10.length === 2)
    return Ai(Ra(Ra(a, 0), 0), [t10[0], t10[1], 1, 1]);
  if (t10.length === 3)
    return Ai(Ra(Ra(Ra(a, 0), 0), 0), [t10[0], t10[1], t10[2], 1, 1]);
  throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t10.length}D.`);
}
var ed = N({ eye_: EU });
function AU(r) {
  let t10 = { x: v(r, "x", "floor", "float32") };
  return T.runKernel(on, t10);
}
var td = N({ floor_: AU });
function RU(r, e, t10 = 0, o = 0) {
  let n = v(r, "x", "gather"), s = v(e, "indices", "gather", "int32"), a = { x: n, indices: s }, i = { axis: t10, batchDims: o };
  return T.runKernel(Is, a, i);
}
var rd = N({ gather_: RU });
function DU(r, e) {
  let t10 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(un, n);
}
var uu = N({ greater_: DU });
function FU(r, e) {
  let t10 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(pn, n);
}
var od = N({ greaterEqual_: FU });
function OU(r) {
  let t10 = { input: v(r, "input", "imag") };
  return T.runKernel(ci, t10);
}
var Ri = N({ imag_: OU });
function PU(r) {
  let t10 = { x: v(r, "x", "isFinite") };
  return T.runKernel(ha, t10);
}
var Bv = N({ isFinite_: PU });
function MU(r) {
  let t10 = { x: v(r, "x", "isInf") };
  return T.runKernel(ga, t10);
}
var Vv = N({ isInf_: MU });
function LU(r) {
  let t10 = { x: v(r, "x", "isNaN") };
  return T.runKernel(cn, t10);
}
var zv = N({ isNaN_: LU });
function BU(r, e = 0.2) {
  let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: e };
  return T.runKernel(ln, o, n);
}
var nd = N({ leakyRelu_: BU });
function VU(r, e) {
  let t10 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(mn, n);
}
var Wv = N({ less_: VU });
function zU(r, e) {
  let t10 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(dn, n);
}
var Vp = N({ lessEqual_: zU });
function Uv(r, e, t10) {
  if (t10 <= 0)
    throw new Error("The number of values should be positive.");
  let o = { start: r, stop: e, num: t10 };
  return T.runKernel(li, {}, o);
}
function WU(r, e = 5, t10 = 1, o = 1, n = 0.5) {
  let s = v(r, "x", "localResponseNormalization");
  $(s.rank === 4 || s.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${s.rank}.`), $(sa(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);
  let a = s, i = false;
  s.rank === 3 && (i = true, a = W(s, [1, s.shape[0], s.shape[1], s.shape[2]]));
  let p = { x: a }, u = { depthRadius: e, bias: t10, alpha: o, beta: n }, c = T.runKernel(mi, p, u);
  return i ? W(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var Gv = N({ localResponseNormalization_: WU });
function UU(r) {
  let t10 = { x: v(r, "x", "log", "float32") };
  return T.runKernel(fn, t10);
}
var Da = N({ log_: UU });
function GU(r) {
  let t10 = { x: v(r, "x", "log1p") };
  return T.runKernel(xa, t10);
}
var sd = N({ log1p_: GU });
function HU(r) {
  return $(gs(r), () => "The f passed in grad(f) must be a function"), (e, t10) => {
    let o = v(e, "x", "tf.grad", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grad") : null;
    return T.tidy(() => {
      let { value: s, grads: a } = T.gradients(() => r(o), [o], n);
      return n != null && gt(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), ad(a), a[0];
    });
  };
}
function KU(r) {
  return $(gs(r), () => "The f passed in grads(f) must be a function"), (e, t10) => {
    $(Array.isArray(e), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
    let o = _a(e, "args", "tf.grads", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grads") : null;
    return T.tidy(() => {
      let { value: s, grads: a } = T.gradients(() => r(...o), o, n);
      return n != null && gt(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), ad(a), a;
    });
  };
}
function qU(r) {
  return $(gs(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t10) => {
    $(e instanceof it, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t10 == null || t10 instanceof it, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
    let { grads: o, value: n } = T.gradients(() => r(e), [e], t10);
    return ad(o), { grad: o[0], value: n };
  };
}
function jU(r) {
  return $(gs(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t10) => {
    $(Array.isArray(e) && e.every((n) => n instanceof it), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t10 == null || t10 instanceof it, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
    let o = T.gradients(() => r(...e), e, t10);
    return t10 != null && gt(o.value.shape, t10.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), ad(o.grads), o;
  };
}
function pC(r, e) {
  $(gs(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof Na), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
  let t10 = e != null;
  if (!t10) {
    e = [];
    for (let u in T.registeredVariables)
      e.push(T.registeredVariables[u]);
  }
  let o = t10 ? e.filter((u) => !u.trainable) : null, n = e.length;
  e = e.filter((u) => u.trainable), $(e.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`);
  let s = true, { value: a, grads: i } = T.gradients(r, e, null, s);
  $(i.some((u) => u != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), $(a.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);
  let p = {};
  return e.forEach((u, c) => {
    i[c] != null && (p[u.name] = i[c]);
  }), o != null && o.forEach((u) => p[u.name] = null), { value: a, grads: p };
}
function br(r) {
  return T.customGrad(r);
}
function ad(r) {
  if (r.filter((t10) => t10 == 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 XU(r) {
  let t10 = { x: v(r, "x", "neg") };
  return T.runKernel(vs, t10);
}
var Cr = N({ neg_: XU });
function YU(r) {
  let t10 = { x: v(r, "x", "softplus") };
  return T.runKernel(Ia, t10);
}
var id = N({ softplus_: YU });
function QU(r) {
  let e = v(r, "x", "logSigmoid");
  return br((o) => ({ value: Cr(id(Cr(o))), gradFunc: (a) => ne(a, Ws(Cr(o))) }))(e);
}
var Hv = N({ logSigmoid_: QU });
function ZU(r, e) {
  let t10 = v(r, "a", "sub"), o = v(e, "b", "sub");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(Yn, n);
}
var ke = N({ sub_: ZU });
function JU(r, e = -1) {
  let t10 = v(r, "logits", "logSoftmax");
  if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1)
    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and axis was ${e}`);
  return br((n, s) => {
    let i = Gs(n, e, true), p = ke(n, i), u = ke(je(p, "float32"), Da(et(So(p), e, true)));
    return s([u]), { value: u, gradFunc: (l, m) => {
      let [d] = m, f = true, h = So(d);
      return ke(l, ne(et(l, e, f), h));
    } };
  })(t10);
}
var Kv = N({ logSoftmax_: JU });
function e4(r, e = null, t10 = false) {
  let o = v(r, "x", "logSumExp"), n = Ja(e, o.shape), s = Gs(o, n, true), a = ke(o, s), i = So(a), p = et(i, n), u = Da(p), c = be(W(s, u.shape), u);
  if (t10) {
    let l = Ea(c.shape, n);
    return W(c, l);
  }
  return c;
}
var ud = N({ logSumExp_: e4 });
function t4(r, e) {
  let t10 = v(r, "a", "logicalAnd", "bool"), o = v(e, "b", "logicalAnd", "bool");
  Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(hn, n);
}
var pu = N({ logicalAnd_: t4 });
function r4(r) {
  let t10 = { x: v(r, "x", "logicalNot", "bool") };
  return T.runKernel(gn, t10);
}
var pd = N({ logicalNot_: r4 });
function o4(r, e) {
  let t10 = v(r, "a", "logicalOr", "bool"), o = v(e, "b", "logicalOr", "bool");
  Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(xn, n);
}
var cd = N({ logicalOr_: o4 });
function n4(r, e) {
  let t10 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool");
  return Je(t10.shape, o.shape), pu(cd(r, e), pd(pu(r, e)));
}
var qv = N({ logicalXor_: n4 });
var ld = 2147483648;
function s4(r, e, t10 = "left") {
  let o = v(r, "sortedSequence", "searchSorted"), n = v(e, "values", "searchSorted"), s = o.shape[o.shape.length - 1], a = n.shape[n.shape.length - 1], i = W(o, [-1, s]), p = W(n, [-1, a]);
  if (i.rank < 2)
    throw new Error("Sorted input argument must be at least 2-dimensional");
  if (i.shape[0] !== p.shape[0])
    throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");
  if (We(p.shape) >= ld)
    throw new Error(`values tensor size must less than ${ld}`);
  if (i.shape[1] >= ld)
    throw new Error(`trailing dim_size must less than ${ld} for int32 output type, was ${i.shape[1]}`);
  let u = { sortedSequence: i, values: p }, c = { side: t10 };
  return T.runKernel(fi, u, c);
}
var ol = N({ searchSorted_: s4 });
function jv(r, e) {
  return ol(r, e, "left");
}
function a4(r, e, t10, o, n) {
  let s = v(r, "x", "maxPool"), a = 1, i = s, p = false;
  s.rank === 3 && (p = true, i = W(s, [1, s.shape[0], s.shape[1], s.shape[2]])), $(i.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${i.rank}.`), $(mr(t10, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`), Mt("maxPool", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = T.runKernel(Cn, u, c);
  return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var md = N({ maxPool_: a4 });
function i4(r, e = [1, 1, 1], t10, o, n, s = "NDHWC") {
  let a = v(r, "x", "maxPool3d"), i = a, p = false;
  a.rank === 4 && (p = true, i = W(a, [1, a.shape[0], a.shape[1], a.shape[2], a.shape[3]])), $(i.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`), $(s === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`), Mt("maxPool3d", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = T.runKernel(Cp, u, c);
  return p ? W(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var Xv = N({ maxPool3d_: i4 });
function u4(r, e, t10, o, n = false) {
  let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t10, pad: o, includeBatchInIndex: n }, p = T.runKernel(Sp, a, i);
  return { result: p[0], indexes: p[1] };
}
var Yv = N({ maxPoolWithArgmax_: u4 });
function p4(r, e) {
  let t10 = v(r, "a", "maximum"), o = v(e, "b", "maximum");
  [t10, o] = De(t10, o), t10.dtype === "bool" && (t10 = je(t10, "int32"), o = je(o, "int32")), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(bn, n);
}
var dd = N({ maximum_: p4 });
function c4(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t10 };
  return T.runKernel(Sn, n, s);
}
var cu = N({ mean_: c4 });
function Br(r, e = "float32") {
  if (bt(r), e === "complex64") {
    let o = Br(r, "float32"), n = Br(r, "float32");
    return Tr(o, n);
  }
  let t10 = lp(We(r), e);
  return T.makeTensor(t10, r, e);
}
function Hs(r, e = "float32") {
  if (bt(r), e === "complex64") {
    let o = Hs(r, "float32"), n = Br(r, "float32");
    return Tr(o, n);
  }
  let t10 = Wc(We(r), e);
  return T.makeTensor(t10, r, e);
}
function Qv(r, e, { indexing: t10 = "xy" } = {}) {
  if (t10 !== "xy" && t10 !== "ij")
    throw new TypeError(`${t10} is not a valid third argument to meshgrid`);
  if (r === void 0)
    return [];
  let o = v(r, "x", "meshgrid", r instanceof it ? r.dtype : "float32");
  if (e === void 0)
    return [o];
  let n = v(e, "y", "meshgrid", e instanceof it ? e.dtype : "float32"), s = We(o.shape), a = We(n.shape);
  return t10 === "xy" ? (o = W(o, [1, -1]), n = W(n, [-1, 1]), [Xe(Hs([a, 1], o.dtype), o), Xe(n, Hs([1, s], n.dtype))]) : (o = W(o, [-1, 1]), n = W(n, [1, -1]), [Xe(o, Hs([1, a], o.dtype)), Xe(Hs([s, 1], n.dtype), n)]);
}
function l4(r, e) {
  let t10 = v(r, "a", "minimum"), o = v(e, "b", "minimum");
  [t10, o] = De(t10, o), t10.dtype === "bool" && (t10 = je(t10, "int32"), o = je(o, "int32")), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(In, n);
}
var fd = N({ minimum_: l4 });
function m4(r, e, t10) {
  $(t10 === "reflect" || t10 === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${t10}.`);
  let o = v(r, "x", "mirrorPad");
  if (o.rank === 0)
    throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
  $(e.length === o.rank, () => `Padding doesn't match input. Must be ${o.rank}. Got ${e.length}.`);
  let n = t10 === "reflect" ? 1 : 0;
  for (let i = 0; i < o.rank; i++)
    $(e[i].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), $(e[i][0] >= 0 && e[i][0] <= o.shape[i] - n && e[i][1] >= 0 && e[i][1] <= o.shape[i] - n, () => `Padding in dimension ${i} cannot be greater than or equal to ${o.shape[i] - n} or less than 0 for input of shape ${o.shape}`);
  let s = { paddings: e, mode: t10 }, a = { x: o };
  return T.runKernel(vn, a, s);
}
var Zv = N({ mirrorPad_: m4 });
function d4(r, e) {
  let t10 = v(r, "a", "mod"), o = v(e, "b", "mod");
  [t10, o] = De(t10, o);
  let n = { a: t10, b: o };
  return T.runKernel(ya, n);
}
var Jv = N({ mod_: d4 });
function f4(r, e = null, t10 = false) {
  r = v(r, "x", "moments");
  let o = Ja(e, r.shape), n = cu(r, o, t10), s = n.shape;
  t10 || (s = Ea(n.shape, o));
  let a = Qt(ke(je(r, "float32"), W(n, s))), i = cu(a, o, t10);
  return { mean: n, variance: i };
}
var ek = N({ moments_: f4 });
function h4(r, e, t10, o) {
  let n = v(e, "data", "multiRNNCell"), s = _a(t10, "c", "multiRNNCell"), a = _a(o, "h", "multiRNNCell"), i = n, p = [];
  for (let l = 0; l < r.length; l++) {
    let m = r[l](i, s[l], a[l]);
    p.push(m[0]), p.push(m[1]), i = m[1];
  }
  let u = [], c = [];
  for (let l = 0; l < p.length; l += 2)
    u.push(p[l]), c.push(p[l + 1]);
  return [u, c];
}
var tk = N({ multiRNNCell_: h4 });
function g4(r, e, t10, o = false) {
  let n = v(r, "logits", "multinomial"), s = n.size, a = n.rank;
  if (s < 2)
    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);
  if (a > 2)
    throw new Error(`Rank of probabilities must be 1 or 2, but is ${a}`);
  t10 = t10 || Math.random();
  let p = { logits: a === 1 ? W(n, [1, -1]) : n }, u = { numSamples: e, seed: t10, normalized: o }, c = T.runKernel(wp, p, u);
  return a === 1 ? W(c, [c.size]) : c;
}
var rk = N({ multinomial_: g4 });
function x4(r, e) {
  let t10 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return T.runKernel(Nn, n);
}
var hd = N({ notEqual_: x4 });
function y4(r, e, t10 = 1, o = 0, n = "int32") {
  if (e < 2)
    throw new Error(`Error in oneHot: depth must be >=2, but it is ${e}`);
  let a = { indices: v(r, "indices", "oneHot", "int32") }, i = { dtype: n, depth: e, onValue: t10, offValue: o };
  return T.runKernel($n, a, i);
}
var nl = N({ oneHot_: y4 });
function b4(r) {
  let t10 = { x: v(r, "x", "onesLike") };
  return T.runKernel(ks, t10);
}
var ok = N({ onesLike_: b4 });
function C4(r, e) {
  let t10 = v(r, "v1", "outerProduct"), o = v(e, "v2", "outerProduct");
  $(t10.rank === 1 && o.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${t10.rank} and ${o.rank}.`);
  let n = W(t10, [-1, 1]), s = W(o, [1, -1]);
  return Xe(n, s);
}
var nk = N({ outerProduct_: C4 });
function S4(r, e, t10 = 0) {
  let o = v(r, "x", "pad");
  if (o.rank === 0)
    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
  let n = { paddings: e, constantValue: t10 }, s = { x: o };
  return T.runKernel(En, s, n);
}
var Ks = N({ pad_: S4 });
function w4(r, e, t10 = 0) {
  return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), Ks(r, [e], t10);
}
var sk = N({ pad1d_: w4 });
function I4(r, e, t10 = 0) {
  return $(e.length === 2 && e[0].length === 2 && e[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ks(r, e, t10);
}
var ak = N({ pad2d_: I4 });
function v4(r, e, t10 = 0) {
  return $(e.length === 3 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ks(r, e, t10);
}
var ik = N({ pad3d_: v4 });
function k4(r, e, t10 = 0) {
  return $(e.length === 4 && e[0].length === 2 && e[1].length === 2 && e[2].length === 2 && e[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), Ks(r, e, t10);
}
var uk = N({ pad4d_: k4 });
function N4(r, e, t10) {
  let o = v(r, "x", "spaceToBatchND");
  $(o.rank >= 1 + e.length, () => `input rank ${o.rank} should be > than [blockShape] ${e.length}`), $(t10.length === e.length, () => `paddings.shape[0] ${t10.length} must be equal to [blockShape] ${e.length}`), $(o.shape.reduce((a, i, p) => p > 0 && p <= e.length ? a && (i + t10[p - 1][0] + t10[p - 1][1]) % e[p - 1] === 0 : a, true), () => `input spatial dimensions ${o.shape.slice(1)} with paddings ${t10.toString()} must be divisible by blockShapes ${e.toString()}`);
  let n = { x: o }, s = { blockShape: e, paddings: t10 };
  return T.runKernel(As, n, s);
}
var gd = N({ spaceToBatchND_: N4 });
function T4(r, e, t10, o, n, s, a) {
  n == null && (n = [1, 1]), s == null && (s = 1), o === 0 && (o = "valid");
  let i = v(r, "x", "maxPool"), p = i, u = false;
  i.rank === 3 && (u = true, p = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(mr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`);
  let c = aC(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m;
  o === "same" ? m = $4([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]];
  let d = l[0] === 1 && l[1] === 1, [f, h] = _4([c.inHeight, c.inWidth], l, m), g = d ? o : "valid", x = d ? p : gd(p, l, f), C = (t10 === "avg" ? () => qm(x, e, s, g, a) : () => md(x, e, s, g, a))(), w = d ? C : jm(C, l, h);
  return u ? W(w, [w.shape[1], w.shape[2], w.shape[3]]) : w;
}
function _4(r, e, t10) {
  let o = t10.map((c) => c[0]), n = t10.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]);
  return [p, u];
}
function $4(r, e) {
  let o = r.map((a, i) => a + (a - 1) * (e[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]);
  return o.map((a, i) => [n[i], s[i]]);
}
var pk = N({ pool_: T4 });
function E4(r, e) {
  let t10 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t10, alpha: o };
  return T.runKernel(Rn, n);
}
var xd = N({ prelu_: E4 });
function A4(r, e = null, t10 = false) {
  let o = v(r, "x", "prod");
  o.dtype === "bool" && (o = je(o, "int32"));
  let n = { x: o }, s = { axis: e, keepDims: t10 };
  return T.runKernel(Dn, n, s);
}
var ck = N({ prod_: A4 });
function R4(r, e, t10, o) {
  let n = r.map((c, l) => v(c, `tensors${l}`, "raggedGather", "int32")), s = v(e, "paramsDenseValues", "raggedGather"), a = v(t10, "indices", "raggedGather", "int32"), i = { paramsNestedSplits: n, paramsDenseValues: s, indices: a }, p = { outputRaggedRank: o }, u = T.runKernel(Ip, i, p);
  return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] };
}
var lk = N({ raggedGather_: R4 });
function D4(r, e, t10) {
  let o = v(r, "starts", "raggedRange"), n = v(e, "limits", "raggedRange", o.dtype), s = v(t10, "deltas", "raggedRange", o.dtype), a = { starts: o, limits: n, deltas: s }, i = T.runKernel(vp, a);
  return { rtNestedSplits: i[0], rtDenseValues: i[1] };
}
var mk = N({ raggedRange_: D4 });
function F4(r, e, t10, o, n) {
  let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t10, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n };
  return T.runKernel(kp, u, c);
}
var dk = N({ raggedTensorToTensor_: F4 });
function O4(r, e, t10) {
  bt(r);
  let o = We(r), n = null;
  if (t10 == null || t10 === "float32")
    n = new Float32Array(o);
  else if (t10 === "int32")
    n = new Int32Array(o);
  else if (t10 === "bool")
    n = new Uint8Array(o);
  else
    throw new Error(`Unknown data type ${t10}`);
  for (let s = 0; s < o; s++)
    n[s] = e();
  return T.makeTensor(n, r, t10);
}
var fk = N({ rand_: O4 });
var wd = ap(gC());
var Fk = {};
Ge(Fk, { TEST_EPSILON_FLOAT16: () => Ak, createVideoElement: () => j4, encodeStrings: () => Dk, expectArrayBuffersEqual: () => q4, expectArraysClose: () => U4, expectArraysEqual: () => H4, expectNumbersClose: () => Rk, expectPromiseToFail: () => G4, expectValuesInRange: () => K4, play: () => X4, testEpsilon: () => bd });
var W4 = 1e-3;
var Ak = 0.1;
function U4(r, e, t10) {
  return t10 == null && (t10 = bd()), xC(r, e, (o, n) => yC(o, n, t10));
}
function bd() {
  return T.backend.floatPrecision() === 32 ? W4 : Ak;
}
function xC(r, e, t10) {
  let o = true;
  if ((Bt(r) || Bt(e)) && (o = false), Bt(r) && Bt(e) && (o = true), o) {
    let a = r.constructor.name, i = e.constructor.name;
    if (a !== i)
      throw new Error(`Arrays are of different type. Actual: ${a}. Expected: ${i}`);
  }
  if (Array.isArray(r) && Array.isArray(e)) {
    let a = or(r), i = or(e);
    if (!Or(a, i))
      throw new Error(`Arrays have different shapes. Actual: [${a}]. Expected: [${i}]`);
  }
  let n = Bt(r) ? r : os(r), s = Bt(e) ? e : os(e);
  if (n.length !== s.length)
    throw new Error(`Arrays have different lengths actual: ${n.length} vs expected: ${s.length}.
Actual:   ${n}.
Expected: ${s}.`);
  for (let a = 0; a < s.length; ++a) {
    let i = n[a], p = s[a];
    if (!t10(i, p))
      throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${p}.
Actual:   ${n}.
Expected: ${s}.`);
  }
  typeof expect != "undefined" && expect().nothing();
}
function G4(r, e) {
  r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing();
}
function H4(r, e) {
  let t10 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e;
  return Oo(r) || Oo(r[0]) || Oo(e) || Oo(e[0]) ? xC(r, t10, (o, n) => o == n) : xC(r, e, (o, n) => yC(o, n, 0));
}
function Rk(r, e, t10) {
  if (t10 == null && (t10 = bd()), !yC(r, e, t10))
    throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`);
  typeof expect != "undefined" && expect().nothing();
}
function yC(r, e, t10) {
  return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t10);
}
function K4(r, e, t10) {
  for (let o = 0; o < r.length; o++)
    if (r[o] < e || r[o] > t10)
      throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t10}`);
}
function q4(r, e) {
  let t10 = new Float32Array(r), o = new Float32Array(e);
  if (t10.length !== o.length)
    throw new Error(`Expected ArrayBuffer to be of length ${o.length}, but it was ${t10.length}`);
  for (let n = 0; n < o.length; n++)
    if (t10[n] !== o[n])
      throw new Error(`Expected ArrayBuffer value at ${n} to be ${o[n]} but got ${t10[n]} instead`);
}
function Dk(r) {
  for (let e = 0; e < r.length; e++) {
    let t10 = r[e];
    Array.isArray(t10) ? Dk(t10) : r[e] = Ii(t10);
  }
  return r;
}
function j4(r) {
  let e = document.createElement("video");
  return "playsInline" in e && (e.playsInline = true), e.muted = true, e.loop = true, e.style.position = "fixed", e.style.left = "0px", e.style.top = "0px", e.preload = "auto", e.appendChild(r), new Promise((t10) => {
    e.addEventListener("loadeddata", (o) => t10(e)), e.load();
  });
}
async function X4(r) {
  await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => {
    r.requestVideoFrameCallback(e);
  });
}
var mu = class {
  constructor(e, t10, o, n, s) {
    this.mean = e, this.stdDev = t10, this.dtype = o, this.nextVal = NaN, this.truncated = n, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2);
    let a = s || Math.random();
    this.random = wd.alea(a.toString());
  }
  nextValue() {
    if (!isNaN(this.nextVal)) {
      let n = this.nextVal;
      return this.nextVal = NaN, n;
    }
    let e, t10, o = false;
    for (; !o; ) {
      let n, s, a;
      do
        n = 2 * this.random() - 1, s = 2 * this.random() - 1, a = n * n + s * s;
      while (a >= 1 || a === 0);
      let i = Math.sqrt(-2 * Math.log(a) / a);
      e = this.mean + this.stdDev * n * i, t10 = this.mean + this.stdDev * s * i, (!this.truncated || this.isValidTruncated(e)) && (o = true);
    }
    return (!this.truncated || this.isValidTruncated(t10)) && (this.nextVal = this.convertValue(t10)), 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;
  }
};
var Cd = class {
  constructor(e, t10, o, n) {
    this.alpha = e, this.beta = 1 / t10, this.dtype = o;
    let s = n || Math.random();
    this.randu = wd.alea(s.toString()), this.randn = new mu(0, 1, o, false, 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, t10, o, n, s, a;
    for (; ; ) {
      do
        n = this.randn.nextValue(), a = 1 + this.c * n;
      while (a <= 0);
      if (a *= a * a, e = n * n, t10 = 1 - 0.331 * e * e, o = 0.5 * e + this.d * (1 - a + Math.log(a)), s = this.randu(), s < t10 || Math.log(s) < o)
        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);
  }
};
var Sd = class {
  constructor(e = 0, t10 = 1, o, n) {
    if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t10 - e, this.dtype = o, n == null && (n = Math.random()), typeof n == "number" && (n = n.toString()), !this.canReturnFloat() && this.range <= 1)
      throw new Error(`The difference between ${e} - ${t10} <= 1 and dtype is not float`);
    this.random = wd.alea(n);
  }
  convertValue(e) {
    return this.canReturnFloat() ? e : Math.round(e);
  }
  nextValue() {
    return this.convertValue(this.min + this.range * this.random());
  }
};
function Y4(r, e, t10 = 1, o = "float32", n) {
  if (bt(r), t10 == null && (t10 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32")
    throw new Error(`Unsupported data type ${o}`);
  let s = new Cd(e, t10, o, n), a = le(r, o);
  for (let i = 0; i < a.values.length; i++)
    a.values[i] = s.nextValue();
  return a.toTensor();
}
var Ok = N({ randomGamma_: Y4 });
function Q4(r, e = 0, t10 = 1, o, n) {
  if (bt(r), o != null && o === "bool")
    throw new Error(`Unsupported data type ${o}`);
  let s = new mu(e, t10, o, false, n), a = le(r, o);
  for (let i = 0; i < a.values.length; i++)
    a.values[i] = s.nextValue();
  return a.toTensor();
}
var Id = N({ randomNormal_: Q4 });
function Z4(r, e, t10) {
  if (e != null && e === "bool")
    throw new Error(`Unsupported data type ${e}`);
  return Id(r, 0, 1, e, t10);
}
var Pk = N({ randomStandardNormal_: Z4 });
function J4(r, e = 0, t10 = 1, o = "float32", n) {
  bt(r);
  let s = le(r, o), a = new Sd(e, t10, null, n);
  for (let i = 0; i < s.values.length; i++)
    s.values[i] = a.nextValue();
  return s.toTensor();
}
var vd = N({ randomUniform_: J4 });
function Di(r, e, t10 = 1, o = "float32") {
  if (t10 === 0)
    throw new Error("Cannot have a step of zero");
  let n = { start: r, stop: e, step: t10, dtype: o };
  return T.runKernel(Ts, {}, n);
}
function eG(r) {
  let t10 = { input: v(r, "input", "real") };
  return T.runKernel(di, t10);
}
var Fa = N({ real_: eG });
function tG(r) {
  let t10 = { x: v(r, "x", "reciprocal") };
  return T.runKernel(Fn, t10);
}
var Mk = N({ reciprocal_: tG });
function rG(r) {
  let t10 = { x: v(r, "x", "relu") };
  return T.runKernel(On, t10);
}
var Fi = N({ relu_: rG });
function oG(r) {
  let t10 = { x: v(r, "x", "relu6") };
  return T.runKernel(Ln, t10);
}
var kd = N({ relu6_: oG });
function nG(r, e) {
  let o = { x: v(r, "x", "reverse") }, n = { dims: e };
  return T.runKernel(Bn, o, n);
}
var no = N({ reverse_: nG });
function sG(r) {
  let e = v(r, "x", "reverse");
  return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), no(e, 0);
}
var Lk = N({ reverse1d_: sG });
function aG(r, e) {
  let t10 = v(r, "x", "reverse");
  return $(t10.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${t10.rank}.`), no(t10, e);
}
var Bk = N({ reverse2d_: aG });
function iG(r, e) {
  let t10 = v(r, "x", "reverse");
  return $(t10.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${t10.rank}.`), no(t10, e);
}
var Vk = N({ reverse3d_: iG });
function uG(r, e) {
  let t10 = v(r, "x", "reverse");
  return $(t10.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${t10.rank}.`), no(t10, e);
}
var zk = N({ reverse4d_: uG });
function pG(r) {
  let t10 = { x: v(r, "x", "round") };
  return T.runKernel(Vn, t10);
}
var Nd = N({ round_: pG });
function cG(r) {
  let t10 = { x: v(r, "x", "rsqrt", "float32") };
  return T.runKernel(zn, t10);
}
var Wk = N({ rsqrt_: cG });
function lG(r) {
  let t10 = { x: v(r, "x", "selu") };
  return T.runKernel(Ca, t10);
}
var Uk = N({ selu_: lG });
function mG(r, e, t10, o, n, s = [1, 1], a = "NHWC") {
  let i = v(r, "x", "separableConv2d"), p = v(e, "depthwiseFilter", "separableConv2d"), u = v(t10, "pointwiseFilter", "separableConv2d"), c = i, l = false;
  if (i.rank === 3 && (l = true, c = W(i, [1, i.shape[0], i.shape[1], i.shape[2]])), a === "NCHW")
    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
  $(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), $(p.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${p.rank}.`), $(u.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${p.rank}.`), $(u.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${u.shape[0]}.`), $(u.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${u.shape[1]}.`);
  let m = p.shape[2], d = p.shape[3];
  $(u.shape[2] === m * d, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * d}, but got ${u.shape[2]}.`);
  let f = Bp(c, p, o, n, a, s), g = Ei(f, u, 1, "valid", a);
  return l ? W(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var Gk = N({ separableConv2d_: mG });
async function dG(r, e) {
  let t10 = v(r, "x", "setdiff1d"), o = v(e, "y", "setdiff1d");
  $(t10.dtype === o.dtype, () => `x and y should have the same dtype, but got x (${t10.dtype}) and y (${o.dtype}).`), $(t10.rank === 1, () => `x should be 1D tensor, but got x (${t10.shape}).`), $(o.rank === 1, () => `y should be 1D tensor, but got y (${o.shape}).`);
  let n = await t10.data(), s = await o.data(), a = new Set(s), i = 0;
  for (let c = 0; c < n.length; c++)
    a.has(n[c]) || i++;
  let p = new st([i], t10.dtype), u = new st([i], "int32");
  for (let c = 0, l = 0; c < n.length; c++)
    a.has(n[c]) || (p.values[l] = n[c], u.values[l] = c, l++);
  return [p.toTensor(), u.toTensor()];
}
var Hk = dG;
function fG(r) {
  let t10 = { x: v(r, "x", "sign") };
  return T.runKernel(wa, t10);
}
var Kk = N({ sign_: fG });
function hG(r) {
  let t10 = { x: v(r, "x", "sin", "float32") };
  return T.runKernel(Un, t10);
}
var qk = N({ sin_: hG });
function gG(r) {
  let t10 = { x: v(r, "x", "sinh") };
  return T.runKernel(Sa, t10);
}
var jk = N({ sinh_: gG });
function xG(r, e, t10) {
  let o = v(r, "x", "slice1d");
  return $(o.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${o.rank} tensor`), Ke(o, [e], [t10]);
}
var Xk = N({ slice1d_: xG });
function yG(r, e, t10) {
  let o = v(r, "x", "slice2d");
  return $(o.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${o.rank} tensor`), Ke(o, e, t10);
}
var Yk = N({ slice2d_: yG });
function bG(r, e, t10) {
  let o = v(r, "x", "slice3d");
  return $(o.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${o.rank} tensor`), Ke(o, e, t10);
}
var Qk = N({ slice3d_: bG });
function CG(r, e, t10) {
  let o = v(r, "x", "slice4d");
  return $(o.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${o.rank} tensor`), Ke(o, e, t10);
}
var Zk = N({ slice4d_: CG });
function SG(r, e = -1) {
  let t10 = v(r, "logits", "softmax", "float32");
  if (e === -1 && (e = t10.rank - 1), e !== t10.rank - 1)
    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${t10.rank} and dim was ${e}`);
  let o = { logits: t10 }, n = { dim: e };
  return T.runKernel(qn, o, n);
}
var Jk = N({ softmax_: SG });
function wG(r) {
  $(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);
  let e = { input: r };
  return T.runKernel(ui, e);
}
var zp = N({ fft_: wG });
function IG(r) {
  $(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);
  let e = { input: r };
  return T.runKernel(pi, e);
}
var du = N({ ifft_: IG });
function vG(r) {
  let e = r.shape[r.shape.length - 1], t10 = r.size / e, o;
  if (e <= 2) {
    let n = W(r, [t10, e]);
    o = du(n);
  } else {
    let n = [t10, 2 * (e - 1)], s = W(Fa(r), [t10, e]), a = W(Ri(r), [t10, e]), i = no(Ke(s, [0, 1], [t10, e - 2]), 1), p = ne(no(Ke(a, [0, 1], [t10, e - 2]), 1), Ce(-1)), u = xt([s, i], 1), c = xt([a, p], 1), l = W(Tr(u, c), [n[0], n[1]]);
    o = du(l);
  }
  if (o = Fa(o), r.rank === 3 && r.shape[0] !== 0) {
    let n = o, s = r.shape[0];
    o = W(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose();
  }
  return o;
}
var Td = N({ irfft_: vG });
function kG(r, e, t10 = 0) {
  let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t10 };
  return T.runKernel(Rs, n, s);
}
var Oa = N({ split_: kG });
function NG(r, e) {
  $(r.dtype === "float32", () => `The dtype for rfft() must be real value but got ${r.dtype}`);
  let t10 = r.shape[r.shape.length - 1], o = r.size / t10, n;
  if (e != null && e < t10) {
    let f = r.shape.map((g) => 0), h = r.shape.map((g) => g);
    h[r.shape.length - 1] = e, n = Ke(r, f, h), t10 = e;
  } else if (e != null && e > t10) {
    let f = r.shape.map((h) => h);
    f[r.shape.length - 1] = e - t10, n = xt([r, Br(f)], r.shape.length - 1), t10 = e;
  } else
    n = r;
  let s = Ut(n), a = W(Tr(n, s), [o, t10]), i = zp(a), p = Math.floor(t10 / 2) + 1, u = Fa(i), c = Ri(i), l = Oa(u, [p, t10 - p], u.shape.length - 1), m = Oa(c, [p, t10 - p], c.shape.length - 1), d = n.shape.slice();
  return d[n.shape.length - 1] = p, W(Tr(l[0], m[0]), d);
}
var Wp = N({ rfft_: NG });
function TG(r, e) {
  let t10 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference");
  [t10, o] = De(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o }, s = {};
  return T.runKernel(jn, n, s);
}
var _d = N({ squaredDifference_: TG });
function _G(r, e) {
  let t10 = v(r, "x", "squeeze", "string_or_numeric");
  return W(t10, bb(t10.shape, e).newShape);
}
var Up = N({ squeeze_: _G });
function $G(r, e = 0) {
  let t10 = _a(r, "tensors", "stack", "string_or_numeric");
  $(t10.length >= 1, () => "Pass at least one tensor to tf.stack"), t10.length > 0 && $(e <= t10[0].rank, () => "Axis must be <= rank of the tensor");
  let o = t10, n = { axis: e };
  return T.runKernel(Ns, o, n);
}
var Sr = N({ stack_: $G });
function EG(r, e = 0) {
  let o = { x: v(r, "x", "step") }, n = { alpha: e };
  return T.runKernel(fo, o, n);
}
var $d = N({ step_: EG });
function AG(r, e, t10, o, n = 0, s = 0, a = 0, i = 0, p = 0) {
  let c = { x: v(r, "x", "stridedSlice", "string_or_numeric") }, l = { begin: e, end: t10, strides: o, beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
  return T.runKernel(Xn, c, l);
}
var e1 = N({ stridedSlice_: AG });
function RG(r) {
  let t10 = { x: v(r, "x", "tan", "float32") };
  return T.runKernel(Qn, t10);
}
var t1 = N({ tan_: RG });
function dr(r, e) {
  Jr(r);
  let t10 = or(r, e);
  if (t10.length !== 1)
    throw new Error("tensor1d() requires values to be a flat/TypedArray");
  return xr(r, null, t10, e);
}
function Oi(r, e, t10) {
  if (Jr(r), e != null && e.length !== 2)
    throw new Error("tensor2d() requires shape to have two numbers");
  let o = or(r, t10);
  if (o.length !== 2 && o.length !== 1)
    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
  if (o.length === 1 && e == null)
    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
  return xr(r, e, o, t10);
}
function Ed(r, e, t10) {
  if (Jr(r), e != null && e.length !== 3)
    throw new Error("tensor3d() requires shape to have three numbers");
  let o = or(r, t10);
  if (o.length !== 3 && o.length !== 1)
    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
  if (o.length === 1 && e == null)
    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
  return xr(r, e, o, t10);
}
function r1(r, e, t10) {
  if (Jr(r), e != null && e.length !== 4)
    throw new Error("tensor4d() requires shape to have four numbers");
  let o = or(r, t10);
  if (o.length !== 4 && o.length !== 1)
    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
  if (o.length === 1 && e == null)
    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
  return xr(r, e, o, t10);
}
function o1(r, e, t10) {
  if (Jr(r), e != null && e.length !== 5)
    throw new Error("tensor5d() requires shape to have five numbers");
  let o = or(r, t10);
  if (o.length !== 5 && o.length !== 1)
    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
  if (o.length === 1 && e == null)
    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
  return xr(r, e, o, t10);
}
function n1(r, e, t10) {
  if (Jr(r), e != null && e.length !== 6)
    throw new Error("tensor6d() requires shape to have six numbers");
  let o = or(r, t10);
  if (o.length !== 6 && o.length !== 1)
    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
  if (o.length === 1 && e == null)
    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
  return e = e || o, xr(r, e, o, t10);
}
function DG(r, e = 1, t10 = true) {
  let o = v(r, "x", "topk");
  if (o.rank === 0)
    throw new Error("topk() expects the input to be of rank 1 or higher");
  let n = o.shape[o.shape.length - 1];
  if (e < 0)
    throw new Error(`'k' passed to topk() must be >= 0 but got ${e}`);
  if (e > n)
    throw new Error(`'k' passed to topk() must be <= the last dimension (${n}) but got ${e}`);
  let s = { x: o }, a = { k: e, sorted: t10 }, [i, p] = T.runKernel(Jn, s, a);
  return { values: i, indices: p };
}
var s1 = N({ topk_: DG });
function FG(r, e = 0, t10 = 1, o, n) {
  if (bt(r), o != null && o === "bool")
    throw new Error("Unsupported data type $ { dtype }");
  let s = new mu(e, t10, o, true, n), a = le(r, o);
  for (let i = 0; i < a.values.length; i++)
    a.values[i] = s.nextValue();
  return a.toTensor();
}
var a1 = N({ truncatedNormal_: FG });
function OG(r, e = 0) {
  let t10 = v(r, "x", "unique", "string_or_numeric");
  $(t10.rank > 0, () => "The input tensor must be at least 1D");
  let o = { x: t10 }, n = { axis: e }, [s, a] = T.runKernel(Np, o, n);
  return { values: s, indices: a };
}
var i1 = N({ unique_: OG });
function PG(r, e, t10) {
  let o = v(r, "x", "unsortedSegmentSum"), n = v(e, "segmentIds", "unsortedSegmentSum", "int32");
  $(sa(t10), () => "numSegments must be of dtype int");
  let s = { x: o, segmentIds: n }, a = { numSegments: t10 };
  return T.runKernel(Tp, s, a);
}
var u1 = N({ unsortedSegmentSum_: PG });
function MG(r, e = 0) {
  let t10 = v(r, "x", "unstack", "string_or_numeric");
  $(e >= -t10.shape.length && e < t10.shape.length, () => `Axis = ${e} is not in [-${t10.shape.length}, ${t10.shape.length})`);
  let o = { value: t10 }, n = { axis: e };
  return T.runKernel(Fs, o, n);
}
var so = N({ unstack_: MG });
function p1(r, e) {
  return ol(r, e, "right");
}
function c1(r, e = true, t10, o) {
  return T.makeVariable(r, e, t10, o);
}
function Ad(r, e) {
  let t10 = [];
  for (let s = 0; s < e.length; s++)
    e[s] && t10.push(s);
  let o = le(r, "int32"), n = le([t10.length, r.length], "int32");
  for (let s = 0; s < t10.length; s++) {
    let a = o.indexToLoc(t10[s]), i = s * r.length;
    n.values.set(a, i);
  }
  return n.toTensor();
}
async function LG(r) {
  let e = v(r, "condition", "whereAsync", "bool"), t10 = await e.data(), o = Ad(e.shape, t10);
  return r !== e && e.dispose(), o;
}
var Rd = LG;
async function BG(r, e, t10) {
  let o = v(r, "tensor", "boolMask"), n = v(e, "mask", "boolMask", "bool"), s = t10 == null ? 0 : t10, a = n.rank, i = o.shape;
  $(a > 0, () => "mask cannot be scalar"), gt(i.slice(s, s + a), n.shape, "mask's shape must match the first K dimensions of tensor's shape,");
  let p = 1;
  for (let h = s; h < s + a; h++)
    p *= i[h];
  let u = i.slice(0, s).concat([p], i.slice(s + a)), c = W(o, u), l = W(n, [-1]), m = await Rd(l), d = Up(m, [1]), f = rd(c, d, s);
  return r !== o && o.dispose(), e !== n && n.dispose(), d.dispose(), c.dispose(), l.dispose(), m.dispose(), f;
}
var VG = BG;
function zG(r, e, t10) {
  let o = v(r, "x", "transpose");
  if (e == null && (e = o.shape.map((a, i) => i).reverse()), $(o.rank === e.length, () => `Error in transpose: rank of input ${o.rank} must match length of perm ${e}.`), e.forEach((a) => {
    $(a >= 0 && a < o.rank, () => `All entries in 'perm' must be between 0 and ${o.rank - 1} but got ${e}`);
  }), o.rank <= 1)
    return o.clone();
  let n = { x: o }, s = { perm: e };
  return o.dtype === "complex64" ? Ee(() => {
    let a = Fa(o), i = Ri(o);
    return a = T.runKernel(ro, { x: a }, s), i = T.runKernel(ro, { x: i }, s), t10 && (i = Cr(i)), Tr(a, i);
  }) : T.runKernel(ro, n, s);
}
var Gp = N({ transpose_: zG });
function WG(r, e, t10, o, n = true) {
  let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t10, "decay", "movingAverage");
  Ub(s, a), $(Or(s.shape, a.shape), () => "Shape mismatch in v and x");
  let p = Ce(1), u = ke(p, i), c = ne(ke(a, s), u);
  if (n) {
    $(o != null, () => "When using zeroDebias: true, step is required.");
    let l = v(o, "step", "movingAverage");
    c = He(c, ke(p, Aa(i, l)));
  }
  return be(s, c);
}
var UG = N({ movingAverage_: WG });
var sl = {};
Ge(sl, { calculateShapes: () => l1, validateInput: () => Dd, validateUpdateShape: () => bC });
function bC(r, e, t10) {
  let o = e.rank > 1 ? e.shape[e.rank - 1] : 1, n = e.rank > 1 ? e.rank - 1 : 1, s = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${t10.shape}, indices.shape: ${e.shape}, shape: ${r}, sliceDim: ${o}, and batchDim: ${n}.`;
  if (t10.rank < n)
    throw new Error(s + ` update.rank < ${n}. `);
  if (r.length < o + (t10.rank - n))
    throw new Error(s + ` Output shape length < ${o + (t10.rank - n)}`);
  if (t10.rank !== n + r.length - o)
    throw new Error(s + ` update.rank != ${n + r.length - o}`);
  for (let a = 0; a < n; ++a)
    if (t10.shape[a] !== e.shape[a])
      throw new Error(s + ` updates.shape[${a}] (${t10.shape[a]}) != indices.shape[${a}] (${e.shape[a]}).`);
  for (let a = 0; a < t10.rank - n; ++a)
    if (t10.shape[a + n] !== r[a + o])
      throw new Error(s + ` updates.shape[${a + n}] (${t10.shape[a + n]}) != shape[${a + n}] (${r[a + n]})`);
}
function Dd(r, e, t10) {
  if (e.rank < 1)
    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${e.rank}.`);
  if (r.rank < 1)
    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`);
  if (e.dtype !== "int32")
    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${e.dtype}`);
  if (t10.length < 1)
    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${t10}`);
  if (t10.length === 0) {
    if (e.size === 0)
      throw new Error(`Indices specified for empty output. indices shape: ${e.shape}`);
    if (r.size === 0)
      throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`);
  }
  bC(t10, e, r);
}
function l1(r, e, t10) {
  let o = e.shape.length, n = o > 1 ? e.shape[o - 1] : 1, s = t10.length, a = 1;
  for (let l = n; l < s; ++l)
    a *= t10[l];
  let i = n < 1 ? 1 : n, p = We(e.shape) / i, u = [...xs(t10.slice(0, n)), 1], c = We(t10);
  return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c };
}
function GG(r, e, t10) {
  bt(t10);
  let o = v(r, "indices", "scatterND", "int32"), n = v(e, "updates", "scatterND");
  Dd(n, o, t10);
  let s = { indices: o, updates: n }, a = { shape: t10 };
  return T.runKernel(Wn, s, a);
}
var HG = N({ scatterND_: GG });
function m1(r, e, t10, o) {
  if (r.dtype !== "int32")
    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${r.dtype}.`);
  if (r.rank > 2)
    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${r.shape}.`);
  let n = r.rank > 0 ? r.shape[0] : 1, s = r.rank > 1 ? r.shape[1] : 1;
  if (t10.length !== s)
    throw new Error(`outputShape has incorrect number of elements:, ${t10.length}, should be: ${s}.`);
  let a = e.size;
  if (!(e.rank === 0 || e.rank === 1 && a === n))
    throw new Error(`sparseValues has incorrect shape ${e.shape}, should be [] or [${n}]`);
  if (e.dtype !== o.dtype)
    throw new Error("sparseValues.dtype must match defaultValues.dtype");
}
function qG(r, e, t10, o = 0) {
  bt(t10);
  let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(e, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype);
  m1(n, s, t10, a);
  let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t10 };
  return T.runKernel(yi, i, p);
}
var jG = N({ sparseToDense_: qG });
function XG(r, e) {
  let t10 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t10 };
  return T.runKernel(an, n);
}
var YG = N({ gatherND_: XG });
function d1(r, e) {
  if (e == null)
    return r.shape.slice();
  if (Or(r.shape, e))
    return e;
  if (r.shape.length === e.length) {
    let t10 = [];
    for (let o = 0; o < r.shape.length; o++)
      e[o] == null && r.shape[o] != null ? t10.push(r.shape[o]) : t10.push(e[o]);
    return t10;
  }
  return e;
}
function QG(r, e, t10, o) {
  let n = v(r, "x", "dropout");
  if ($(n.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${n.dtype} tensor instead.`), $(e >= 0 && e < 1, () => `rate must be a float in the range [0, 1), but got ${e}.`), e === 0)
    return r instanceof it ? n.clone() : n;
  let s = d1(n, t10), a = 1 - e, i = He(td(be(vd(s, 0, 1, "float32", o), a)), a);
  return ne(n, i);
}
var ZG = N({ dropout_: QG });
function CC(r) {
  return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2))));
}
function al(r, e, t10) {
  let o = 1 - r % 2, n = new Float32Array(r);
  for (let s = 0; s < r; ++s) {
    let a = 2 * Math.PI * s / (r + o - 1);
    n[s] = e - t10 * Math.cos(a);
  }
  return dr(n, "float32");
}
async function JG(r, e, t10 = 1) {
  let o = v(r, "predictions", "inTopK"), n = v(e, "targets", "inTopK");
  $(o.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${o.rank}`), $(o.rank - 1 === n.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${o.rank} and targets rank ${n.rank}`), gt(o.shape.slice(0, o.shape.length - 1), n.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
  let s = o.shape[o.shape.length - 1];
  $(t10 > 0 && t10 <= s, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${t10}`);
  let a = await o.data(), i = await n.data(), [p, u] = [a.length / s, s], c = Cb("bool", p);
  for (let l = 0; l < p; l++) {
    let m = l * u, d = a.subarray(m, m + u), f = [];
    for (let h = 0; h < d.length; h++)
      f.push({ value: d[h], index: h });
    f.sort((h, g) => g.value - h.value), c[l] = 0;
    for (let h = 0; h < t10; h++)
      if (f[h].index === i[l]) {
        c[l] = 1;
        break;
      }
  }
  return r !== o && o.dispose(), e !== n && n.dispose(), nr(c, n.shape, "bool");
}
var eH = JG;
var SC = {};
Ge(SC, { conv2d: () => h1, depthwiseConv2d: () => y1, matMul: () => b1 });
function tH(r, e, t10, o, n, s = "NHWC", a) {
  let i = r;
  r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let p = e;
  p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]])), $(i.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${i.shape}.`), $(p.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${p.shape}.`), $(t10.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${t10}.`);
  let u = s === "NHWC" ? i.shape[3] : i.shape[1], c = s === "NHWC" ? p.shape[3] : p.shape[1];
  $(u === t10[2], () => `Error in conv2dDerFilter: depth of input ${u}) must match input depth in filter (${t10[2]}.`), $(c === t10[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${t10[3]}).`), Mt("conv2dDerFilter", n, a);
  let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t10 };
  return T.runKernel(oi, l, m);
}
var f1 = N({ conv2DBackpropFilter_: tH });
function fu(r, e, t10) {
  if (t10 == null || t10 === "linear")
    return r;
  if (t10 === "relu")
    return ne(r, $d(e));
  throw new Error(`Cannot compute gradient for fused activation ${t10}.`);
}
function hu(r, e) {
  let t10 = e, o = Qm(r.shape, e.shape);
  return o.length > 0 && (t10 = et(t10, o)), W(t10, r.shape);
}
function gu(r, e, t10, o) {
  if (e === "linear")
    return r;
  if (e === "relu")
    return Fi(r);
  if (e === "elu")
    return Jm(r);
  if (e === "relu6")
    return kd(r);
  if (e === "prelu")
    return xd(r, t10);
  if (e === "leakyrelu")
    return nd(r, o);
  if (e === "sigmoid")
    return Ws(r);
  throw new Error(`Unknown fused activation ${e}.`);
}
var xu = (r, e) => !(r > 0) || e === "linear";
function rH({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) {
  if (p = p || "linear", xu(T.state.gradientDepth, p) === false) {
    $(n === "NHWC", () => `Error in fused conv2d: got dataFormat of ${n} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
    let _ = Ei(r, e, t10, o, n, s, a);
    return i != null && (_ = be(_, i)), gu(_, p, u, c);
  }
  let l = v(r, "x", "conv2d", "float32"), m = v(e, "filter", "conv2d", "float32"), d = l, f = false;
  l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Mt("fused conv2d", o, a);
  let h = n === "NHWC" ? d.shape[3] : d.shape[1];
  $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(mr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`);
  let g = au(d.shape, m.shape, t10, s, o, a), x;
  i != null && (x = v(i, "bias", "fused conv2d"), [x] = De(x, l), n === "NHWC" ? Je(g.outShape, x.shape) : ($(x.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${x.shape.length}.`), $(x.shape.length === 0 || x.shape[0] === g.outChannels || x.shape[0] === 1, () => `Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));
  let b;
  if (u != null) {
    let _ = u.shape;
    if ($(_.length <= 1 || _.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${_.length}.`), _.length === 1)
      $(_[0] === 1 || _[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the number of output channels (${g.outChannels}).`);
    else if (_.length === 3)
      try {
        Je(_, g.outShape);
      } catch (E) {
        let A = `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the output shape of the conv2d (${g.outShape}).`;
        throw Error(A);
      }
    b = v(u, "prelu weights", "fused conv2d");
  }
  let C = (_, E) => {
    $(n === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${n} but only NHWC is currently supported.`);
    let [A, R, D, P] = E, M = fu(_, D, p);
    $(su(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
    let L = Ym(R.shape, M, A, t10, o), V = f1(R, M, A.shape, t10, o), z = [L, V];
    if (P != null) {
      let U = hu(P, M);
      z.push(U);
    }
    return z;
  }, w = { x: d, filter: m, bias: x, preluActivationWeights: b }, k = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
  return i == null ? br((E, A, R) => {
    let D = T.runKernel(go, w, k);
    return R([A, E, D]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: C };
  })(d, m) : br((E, A, R, D) => {
    let P = T.runKernel(go, w, k);
    return D([A, E, P, R]), f && (P = W(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: C };
  })(d, m, x);
}
var h1 = N({ fusedConv2d_: rH });
function oH(r, e, t10, o, n, s = [1, 1], a) {
  let i = r;
  r.rank === 3 && (i = W(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let p = e;
  p.rank === 3 && (p = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
  let u = { x: i, dy: p }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, filterShape: t10 };
  return T.runKernel(yp, u, c);
}
var g1 = N({ depthwiseConv2dNativeBackpropFilter_: oH });
function nH(r, e, t10, o, n, s = [1, 1], a) {
  let i = e, p = false;
  e.rank === 3 && (p = true, i = W(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
  let u = { dy: i, filter: t10 }, c = { strides: o, pad: n, dimRoundingMode: a, dilations: s, inputShape: r }, l = T.runKernel(bp, u, c);
  return p ? W(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var x1 = N({ depthwiseConv2dNativeBackpropInput_: nH });
function sH({ x: r, filter: e, strides: t10, pad: o, dataFormat: n = "NHWC", dilations: s = [1, 1], dimRoundingMode: a, bias: i, activation: p = "linear", preluActivationWeights: u, leakyreluAlpha: c }) {
  if (xu(T.state.gradientDepth, p) === false) {
    let k = Bp(r, e, t10, o, n, s, a);
    return i != null && (k = be(k, i)), gu(k, p, u, c);
  }
  let l = v(r, "x", "depthwiseConv2d", "float32"), m = v(e, "filter", "depthwiseConv2d", "float32"), d = l, f = false;
  l.rank === 3 && (f = true, d = W(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(d.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${d.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(d.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${d.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(mr(t10, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), Mt("fused depthwiseConv2d", o, a);
  let h = au(d.shape, m.shape, t10, s, o, a, true), g;
  i != null && (g = v(i, "bias", "fused conv2d"), [g] = De(g, l), Je(h.outShape, g.shape));
  let x;
  u != null && (x = v(u, "prelu weights", "fused depthwiseConv2d"));
  let b = (k, _) => {
    $(su(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);
    let [E, A, R, D] = _, P = fu(k, R, p), M = x1(A.shape, P, E, t10, o, s, a), L = g1(A, P, E.shape, t10, o, s, a);
    if (D != null) {
      let V = hu(g, P);
      return [M, L, V];
    }
    return [M, L];
  }, C = { x: d, filter: m, bias: g, preluActivationWeights: x }, w = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
  return i == null ? br((_, E, A) => {
    let R = T.runKernel(xo, C, w);
    return A([E, _, R]), f && (R = W(R, [R.shape[1], R.shape[2], R.shape[3]])), { value: R, gradFunc: b };
  })(d, m) : br((_, E, A, R) => {
    let D = T.runKernel(xo, C, w);
    return R([E, _, D, A]), f && (D = W(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b };
  })(d, m, g);
}
var y1 = N({ fusedDepthwiseConv2d_: sH });
function aH({ a: r, b: e, transposeA: t10 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) {
  if (xu(T.state.gradientDepth, s) === false) {
    let P = Xe(r, e, t10, o);
    return n != null && (P = be(P, n)), gu(P, s, a, i);
  }
  let p = v(r, "a", "fused matMul"), u = v(e, "b", "fused matMul");
  [p, u] = De(p, u);
  let c = t10 ? p.shape[p.rank - 2] : p.shape[p.rank - 1], l = o ? u.shape[u.rank - 1] : u.shape[u.rank - 2], m = t10 ? p.shape[p.rank - 1] : p.shape[p.rank - 2], d = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], f = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = We(f), x = We(h);
  $(c === l, () => `Error in fused matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${p.shape} and ${u.shape} and transposeA=${t10} and transposeB=${o} must match.`);
  let C = Je(p.shape.slice(0, -2), u.shape.slice(0, -2)).concat([m, d]), w = t10 ? W(p, [g, c, m]) : W(p, [g, m, c]), k = o ? W(u, [x, d, l]) : W(u, [x, l, d]), _;
  n != null && (_ = v(n, "bias", "fused matMul"), [_] = De(_, p), Je(C, _.shape));
  let E;
  a != null && (E = v(a, "prelu weights", "fused matMul"));
  let A = (P, M) => {
    let [L, V, z, U] = M, K = fu(W(P, z.shape), z, s), H, q;
    if (!t10 && !o ? (H = Xe(K, V, false, true), q = Xe(L, K, true, false)) : !t10 && o ? (H = Xe(K, V, false, false), q = Xe(K, L, true, false)) : t10 && !o ? (H = Xe(V, K, false, true), q = Xe(L, K, false, false)) : (H = Xe(V, K, true, true), q = Xe(K, L, true, true)), n != null) {
      let Z = hu(U, K);
      return [H, q, Z];
    } else
      return [H, q];
  }, R = { a: w, b: k, bias: _, preluActivationWeights: E }, D = { transposeA: t10, transposeB: o, activation: s, leakyreluAlpha: i };
  return n == null ? br((M, L, V) => {
    let z = T.runKernel(ho, R, D);
    return V([M, L, z]), { value: W(z, C), gradFunc: A };
  })(w, k) : br((M, L, V, z) => {
    let U = T.runKernel(ho, R, D);
    return z([M, L, U, V]), { value: W(U, C), gradFunc: A };
  })(w, k, _);
}
var b1 = N({ fusedMatMul_: aH });
function iH(r) {
  return al(r, 0.54, 0.46);
}
var C1 = N({ hammingWindow_: iH });
function uH(r) {
  return al(r, 0.5, 0.5);
}
var Fd = N({ hannWindow_: uH });
function pH(r, e, t10, o = false, n = 0) {
  let s = 0, a = [];
  for (; s + e <= r.size; )
    a.push(Ke(r, s, e)), s += t10;
  if (o)
    for (; s < r.size; ) {
      let i = s + e - r.size, p = xt([Ke(r, s, e - i), Us([i], n)]);
      a.push(p), s += t10;
    }
  return a.length === 0 ? Oi([], [0, e]) : W(xt(a), [a.length, e]);
}
var Od = N({ frame_: pH });
function cH(r, e, t10, o, n = Fd) {
  o == null && (o = CC(e));
  let s = Od(r, e, t10), a = ne(s, n(e));
  return Wp(a, o);
}
var S1 = N({ stft_: cH });
function lH(r, e, t10, o, n = "bilinear", s = 0) {
  let a = v(r, "image", "cropAndResize"), i = v(e, "boxes", "cropAndResize", "float32"), p = v(t10, "boxInd", "cropAndResize", "int32"), u = i.shape[0];
  $(a.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && i.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${u},4] but had shape ${i.shape}.`), $(p.rank === 1 && p.shape[0] === u, () => `Error in cropAndResize: boxInd must be have size [${u}] but had shape ${i.shape}.`), $(o.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${o.length}.`), $(o[0] >= 1 && o[1] >= 1, () => `cropSize must be atleast [1,1], but was ${o}`), $(n === "bilinear" || n === "nearest", () => `method must be bilinear or nearest, but was ${n}`);
  let c = { image: a, boxes: i, boxInd: p }, l = { method: n, extrapolationValue: s, cropSize: o };
  return T.runKernel(Xo, c, l);
}
var w1 = N({ cropAndResize_: lH });
function mH(r) {
  let e = v(r, "image", "flipLeftRight", "float32");
  $(e.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${e.rank}.`);
  let t10 = { image: e };
  return T.runKernel(rn, t10, {});
}
var I1 = N({ flipLeftRight_: mH });
function dH(r) {
  let e = v(r, "image", "grayscaleToRGB"), t10 = e.rank - 1, o = e.shape[t10];
  $(e.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${e.rank}.`), $(o === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${o}.`);
  let n = new Array(e.rank);
  return n.fill(1, 0, t10), n[t10] = 3, Ai(e, n);
}
var v1 = N({ grayscaleToRGB_: dH });
function fH(r, e, t10 = 0, o = 0.5) {
  let n = v(r, "image", "rotateWithOffset", "float32");
  $(n.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${n.rank}.`);
  let s = { image: n }, a = { radians: e, fillValue: t10, center: o };
  return T.runKernel(ts, s, a);
}
var k1 = N({ rotateWithOffset_: fH });
function wo(r, e, t10, o, n, s) {
  o == null && (o = 0.5), n == null && (n = Number.NEGATIVE_INFINITY), s == null && (s = 0);
  let a = r.shape[0];
  return t10 = Math.min(t10, a), $(0 <= o && o <= 1, () => `iouThreshold must be in [0, 1], but was '${o}'`), $(r.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${r.rank}'`), $(r.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`), $(e.rank === 1, () => "scores must be a 1D tensor"), $(e.shape[0] === a, () => `scores has incompatible shape with boxes. Expected ${a}, but was ${e.shape[0]}`), $(0 <= s && s <= 1, () => `softNmsSigma must be in [0, 1], but was '${s}'`), { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s };
}
function hH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) {
  let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = wo(s, a, t10, o, n);
  t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold;
  let p = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n };
  return T.runKernel(Tn, { boxes: s, scores: a }, p);
}
var N1 = N({ nonMaxSuppression_: hH });
function T1(r, e, t10) {
  let o = gH(r, e, t10), n = o < 0 ? -(o + 1) : o;
  r.splice(n, 0, e);
}
function gH(r, e, t10) {
  return yH(r, e, t10 || xH);
}
function xH(r, e) {
  return r > e ? 1 : r < e ? -1 : 0;
}
function yH(r, e, t10) {
  let o = 0, n = r.length, s = 0, a = false;
  for (; o < n; ) {
    s = o + (n - o >>> 1);
    let i = t10(e, r[s]);
    i > 0 ? o = s + 1 : (n = s, a = !i);
  }
  return a ? o : -o - 1;
}
function Pd(r, e, t10, o, n) {
  return wC(r, e, t10, o, n, 0);
}
function Md(r, e, t10, o, n, s) {
  return wC(r, e, t10, o, n, 0, false, s, true);
}
function Ld(r, e, t10, o, n, s) {
  return wC(r, e, t10, o, n, s, true);
}
function wC(r, e, t10, o, n, s, a = false, i = false, p = false) {
  let u = [];
  for (let g = 0; g < e.length; g++)
    e[g] > n && u.push({ score: e[g], boxIndex: g, suppressBeginIndex: 0 });
  u.sort(_1);
  let c = s > 0 ? -0.5 / s : 0, l = [], m = [];
  for (; l.length < t10 && u.length > 0; ) {
    let g = u.pop(), { score: x, boxIndex: b, suppressBeginIndex: C } = g;
    if (x < n)
      break;
    let w = false;
    for (let k = l.length - 1; k >= C; --k) {
      let _ = bH(r, b, l[k]);
      if (_ >= o) {
        w = true;
        break;
      }
      if (g.score = g.score * CH(o, c, _), g.score <= n)
        break;
    }
    g.suppressBeginIndex = l.length, w || (g.score === x ? (l.push(b), m.push(g.score)) : g.score > n && T1(u, g, _1));
  }
  let d = l.length, f = t10 - d;
  i && f > 0 && (l.push(...new Array(f).fill(0)), m.push(...new Array(f).fill(0)));
  let h = { selectedIndices: l };
  return a && (h.selectedScores = m), p && (h.validOutputs = d), h;
}
function bH(r, e, t10) {
  let o = r.subarray(e * 4, e * 4 + 4), n = r.subarray(t10 * 4, t10 * 4 + 4), s = Math.min(o[0], o[2]), a = Math.min(o[1], o[3]), i = Math.max(o[0], o[2]), p = Math.max(o[1], o[3]), u = Math.min(n[0], n[2]), c = Math.min(n[1], n[3]), l = Math.max(n[0], n[2]), m = Math.max(n[1], n[3]), d = (i - s) * (p - a), f = (l - u) * (m - c);
  if (d <= 0 || f <= 0)
    return 0;
  let h = Math.max(s, u), g = Math.max(a, c), x = Math.min(i, l), b = Math.min(p, m), C = Math.max(x - h, 0) * Math.max(b - g, 0);
  return C / (d + f - C);
}
function CH(r, e, t10) {
  let o = Math.exp(e * t10 * t10);
  return t10 <= r ? o : 0;
}
function _1(r, e) {
  return r.score - e.score || r.score === e.score && e.boxIndex - r.boxIndex;
}
async function SH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) {
  let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = wo(s, a, t10, o, n);
  t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold;
  let p = await Promise.all([s.data(), a.data()]), u = p[0], c = p[1], { selectedIndices: l } = Pd(u, c, t10, o, n);
  return s !== r && s.dispose(), a !== e && a.dispose(), dr(l, "int32");
}
var $1 = SH;
function wH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
  let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = wo(a, i, t10, o, n, s);
  t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma;
  let u = { boxes: a, scores: i }, c = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n, softNmsSigma: s }, l = T.runKernel(_n, u, c);
  return { selectedIndices: l[0], selectedScores: l[1] };
}
var E1 = N({ nonMaxSuppressionWithScore_: wH });
async function IH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
  let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = wo(a, i, t10, o, n, s);
  t10 = p.maxOutputSize, o = p.iouThreshold, n = p.scoreThreshold, s = p.softNmsSigma;
  let u = await Promise.all([a.data(), i.data()]), c = u[0], l = u[1], { selectedIndices: m, selectedScores: d } = Ld(c, l, t10, o, n, s);
  return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: dr(m, "int32"), selectedScores: dr(d) };
}
var A1 = IH;
function vH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
  let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = wo(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, d = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, f = T.runKernel(ba, m, d);
  return { selectedIndices: f[0], validOutputs: f[1] };
}
var R1 = N({ nonMaxSuppressionPadded_: vH });
async function kH(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
  let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = wo(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, d] = await Promise.all([a.data(), i.data()]), { selectedIndices: f, validOutputs: h } = Md(m, d, u, c, l, s);
  return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: dr(f, "int32"), validOutputs: Ce(h, "int32") };
}
var D1 = kH;
function NH(r, e, t10 = false, o = false) {
  let n = v(r, "images", "resizeBilinear");
  $(n.rank === 3 || n.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${e}.`), $(o === false || t10 === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
  let s = n, a = false;
  n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
  let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(Mn, i, p);
  return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var F1 = N({ resizeBilinear_: NH });
function TH(r, e, t10 = false, o = false) {
  let n = v(r, "images", "resizeNearestNeighbor");
  $(n.rank === 3 || n.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`), $(e.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`), $(n.dtype === "float32" || n.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), $(o === false || t10 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
  let s = n, a = false;
  n.rank === 3 && (a = true, s = W(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
  let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = T.runKernel(Pn, i, p);
  return a ? W(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var O1 = N({ resizeNearestNeighbor_: TH });
function _H(r, e = "binary", t10 = false, o = 0.5) {
  let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = ne(dr([o]), 255), c, l, m, d;
  if ($(n.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${n.rank}.`), $(n.shape[2] === 3 || n.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${n.shape[2]}.`), $(n.dtype === "int32" || n.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${n.dtype}.`), $(e === "otsu" || e === "binary", () => `Method must be binary or otsu, but was ${e}`), n.shape[2] === 3) {
    [c, l, m] = Oa(n, [1, 1, 1], -1);
    let g = ne(c, s), x = ne(l, a), b = ne(m, i);
    d = be(be(g, x), b);
  } else
    d = r;
  if (e === "otsu") {
    let g = Xm(je(Nd(d), "int32"), nr([]), 256);
    u = $H(g, p);
  }
  let f = t10 ? Vp(d, u) : uu(d, u);
  return je(ne(f, 255), "int32");
}
function $H(r, e) {
  let t10 = dr([-1]), o = dr([0]), n = dr([0]), s, a, i, p, u, c;
  for (let l = 0; l < r.size - 1; l++) {
    s = Ke(r, 0, l + 1), a = Ke(r, l + 1), u = He(et(s), e), c = He(et(a), e);
    let m = et(ne(s, Di(0, s.size)));
    i = He(m, et(s));
    let d = Us(a.shape, s.size), f = be(Di(0, a.size), d), h = ne(a, f);
    p = He(et(h), et(a));
    let g = ke(i, p), x = ke(i, p), b = ne(u, c);
    n = ne(ne(b, g), x);
    let C = uu(n, o);
    o = ss(C, n, o), t10 = ss(C, dr([l]), t10);
  }
  return t10;
}
var P1 = N({ threshold_: _H });
function EH(r, e, t10 = "nearest", o = "constant", n = 0, s) {
  let a = v(r, "image", "transform", "float32"), i = v(e, "transforms", "transform", "float32");
  $(a.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${a.rank}.`), $(i.rank === 2 && (i.shape[0] === a.shape[0] || i.shape[0] === 1) && i.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), $(s == null || s.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${s}.`);
  let p = { image: a, transforms: i }, u = { interpolation: t10, fillMode: o, fillValue: n, outputShape: s };
  return T.runKernel(es, p, u);
}
var M1 = N({ transform_: EH });
function AH(r, e, t10) {
  $(e % 1 === 0, () => `bandPart(): numLower must be an integer, got ${e}.`), $(t10 % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${t10}.`);
  let o = v(r, "a", "bandPart");
  $(o.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${o.rank}.`);
  let n = o.shape, [s, a] = o.shape.slice(-2);
  if (!(e <= s))
    throw new Error(`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`);
  if (!(t10 <= a))
    throw new Error(`bandPart(): numUpper (${t10}) must not be greater than the number of columns (${a}).`);
  e < 0 && (e = s), t10 < 0 && (t10 = a);
  let i = W(Di(0, s, 1, "int32"), [-1, 1]), p = Di(0, a, 1, "int32"), u = ke(i, p), c = pu(Vp(u, Ce(+e, "int32")), od(u, Ce(-t10, "int32"))), l = Br([s, a], o.dtype);
  return W(Sr(so(W(o, [-1, s, a])).map((m) => ss(c, m, l))), n);
}
var L1 = N({ bandPart_: AH });
function RH(r) {
  let e;
  if (Array.isArray(r)) {
    e = false, $(r != null && r.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
    let n = r[0].shape[0];
    for (let s = 1; s < r.length; ++s)
      $(r[s].shape[0] === n, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`);
  } else
    e = true, r = Oa(r, r.shape[0], 0).map((n) => Up(n, [0]));
  $(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);
  let t10 = [], o = r;
  for (let n = 0; n < r.length; ++n)
    t10.push(T.tidy(() => {
      let s = o[n];
      if (n > 0)
        for (let a = 0; a < n; ++a) {
          let i = ne(et(ne(t10[a], s)), t10[a]);
          s = ke(s, i);
        }
      return He(s, iu(s, "euclidean"));
    }));
  return e ? Sr(t10, 0) : t10;
}
var B1 = N({ gramSchmidt_: RH });
function DH(r, e = false) {
  if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2)
    return V1(r, e);
  {
    let t10 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = so(W(r, [t10, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = [];
    o.forEach((p) => {
      let [u, c] = V1(p, e);
      n.push(u), s.push(c);
    });
    let a = W(Sr(n, 0), r.shape), i = W(Sr(s, 0), r.shape);
    return [a, i];
  }
}
function V1(r, e = false) {
  return T.tidy(() => {
    $(r.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);
    let t10 = r.shape[0], o = r.shape[1], n = ed(t10), s = Lr(r), a = Oi([[1]], [1, 1]), i = Lr(a), p = t10 >= o ? o : t10;
    for (let u = 0; u < p; ++u) {
      let c = s, l = i, m = n;
      [i, s, n] = T.tidy(() => {
        let d = Ke(s, [u, u], [t10 - u, 1]), f = iu(d), h = Ke(s, [u, u], [1, 1]), g = ss(uu(h, 0), Oi([[-1]]), Oi([[1]])), x = ke(h, ne(g, f)), b = He(d, x);
        b.shape[0] === 1 ? i = Lr(a) : i = xt([a, Ke(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0);
        let C = Cr(He(Xe(g, x), f)), w = Ke(s, [u, 0], [t10 - u, o]), k = ne(C, i), _ = Gp(i);
        if (u === 0)
          s = ke(w, Xe(k, Xe(_, w)));
        else {
          let R = ke(w, Xe(k, Xe(_, w)));
          s = xt([Ke(s, [0, 0], [u, o]), R], 0);
        }
        let E = Gp(k), A = Ke(n, [0, u], [t10, n.shape[1] - u]);
        if (u === 0)
          n = ke(A, Xe(Xe(A, i), E));
        else {
          let R = ke(A, Xe(Xe(A, i), E));
          n = xt([Ke(n, [0, 0], [t10, u]), R], 1);
        }
        return [i, s, n];
      }), Ot([c, l, m]);
    }
    return !e && t10 > o && (n = Ke(n, [0, 0], [t10, o]), s = Ke(s, [0, 0], [o, o])), [n, s];
  });
}
var z1 = N({ qr_: DH });
var $t;
(function(r) {
  r[r.NONE = 0] = "NONE", r[r.MEAN = 1] = "MEAN", r[r.SUM = 2] = "SUM", r[r.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS";
})($t || ($t = {}));
function FH(r, e, t10 = $t.SUM_BY_NONZERO_WEIGHTS) {
  let o = v(r, "losses", "computeWeightedLoss"), n = null;
  e != null && (n = v(e, "weights", "computeWeightedLoss"));
  let s = n == null ? o : ne(o, n);
  if (t10 === $t.NONE)
    return s;
  if (t10 === $t.SUM)
    return et(s);
  if (t10 === $t.MEAN) {
    if (n == null)
      return cu(s);
    {
      let a = o.size / n.size, i = He(et(s), et(n));
      return a > 1 ? He(i, Ce(a)) : i;
    }
  }
  if (t10 === $t.SUM_BY_NONZERO_WEIGHTS) {
    if (n == null)
      return He(et(s), Ce(o.size));
    {
      let a = ne(n, Hs(o.shape)), i = je(et(hd(a, Ce(0))), "float32");
      return He(et(s), i);
    }
  }
  throw Error(`Unknown reduction: ${t10}`);
}
var ar = N({ computeWeightedLoss_: FH });
function OH(r, e, t10, o = $t.SUM_BY_NONZERO_WEIGHTS) {
  let n = v(r, "labels", "absoluteDifference"), s = v(e, "predictions", "absoluteDifference"), a = null;
  t10 != null && (a = v(t10, "weights", "absoluteDifference")), gt(n.shape, s.shape, "Error in absoluteDifference: ");
  let i = Yt(ke(n, s));
  return ar(i, a, o);
}
var W1 = N({ absoluteDifference_: OH });
function PH(r, e, t10, o, n = $t.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "labels", "cosineDistance"), a = v(e, "predictions", "cosineDistance"), i = null;
  o != null && (i = v(o, "weights", "cosineDistance")), gt(s.shape, a.shape, "Error in cosineDistance: ");
  let p = Ce(1), u = ke(p, et(ne(s, a), t10, true));
  return ar(u, i, n);
}
var U1 = N({ cosineDistance_: PH });
function MH(r, e, t10, o = $t.SUM_BY_NONZERO_WEIGHTS) {
  let n = v(r, "labels", "hingeLoss"), s = v(e, "predictions", "hingeLoss"), a = null;
  t10 != null && (a = v(t10, "weights", "hingeLoss")), gt(n.shape, s.shape, "Error in hingeLoss: ");
  let i = Ce(1);
  n = ke(ne(Ce(2), n), i);
  let p = Fi(ke(i, ne(n, s)));
  return ar(p, a, o);
}
var G1 = N({ hingeLoss_: MH });
function LH(r, e, t10, o = 1, n = $t.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "labels", "huberLoss"), a = v(e, "predictions", "huberLoss"), i = null;
  t10 != null && (i = v(t10, "weights", "huberLoss")), gt(s.shape, a.shape, "Error in huberLoss: ");
  let p = Ce(o), u = Yt(ke(a, s)), c = fd(u, p), l = ke(u, c), m = be(ne(Ce(0.5), Qt(c)), ne(p, l));
  return ar(m, i, n);
}
var H1 = N({ huberLoss_: LH });
function BH(r, e, t10, o = 1e-7, n = $t.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "labels", "logLoss"), a = v(e, "predictions", "logLoss"), i = null;
  t10 != null && (i = v(t10, "weights", "logLoss")), gt(s.shape, a.shape, "Error in logLoss: ");
  let p = Ce(1), u = Ce(o), c = Cr(ne(s, Da(be(a, u)))), l = ne(ke(p, s), Da(be(ke(p, a), u))), m = ke(c, l);
  return ar(m, i, n);
}
var K1 = N({ logLoss_: BH });
function VH(r, e, t10, o = $t.SUM_BY_NONZERO_WEIGHTS) {
  let n = v(r, "labels", "meanSquaredError"), s = v(e, "predictions", "meanSquaredError"), a = null;
  t10 != null && (a = v(t10, "weights", "meanSquaredError")), gt(n.shape, s.shape, "Error in meanSquaredError: ");
  let i = _d(n, s);
  return ar(i, a, o);
}
var q1 = N({ meanSquaredError_: VH });
function zH(r, e) {
  let t10 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits");
  gt(t10.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: ");
  let n = Fi(o), s = ne(o, t10), a = sd(So(Cr(Yt(o))));
  return be(ke(n, s), a);
}
function WH(r, e, t10, o = 0, n = $t.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "multiClassLabels", "sigmoidCrossEntropy"), a = v(e, "logits", "sigmoidCrossEntropy"), i = null;
  if (t10 != null && (i = v(t10, "weights", "sigmoidCrossEntropy")), gt(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) {
    let u = Ce(o), c = Ce(1), l = Ce(0.5);
    s = be(ne(s, ke(c, u)), ne(l, u));
  }
  let p = zH(s, a);
  return ar(p, i, n);
}
var j1 = N({ sigmoidCrossEntropy_: WH });
function UH(r, e, t10 = -1) {
  if (t10 === -1 && (t10 = e.rank - 1), t10 !== e.rank - 1)
    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t10}`);
  return br((n, s, a) => {
    let p = ud(s, [t10], true), u = ke(je(s, "float32"), p);
    a([n, u]);
    let c = Cr(ne(u, n));
    return { value: et(c, [t10]), gradFunc: (d, f) => {
      let [h, g] = f, x = Ea(d.shape, [t10]);
      return [ne(W(d, x), ke(je(h, "float32"), So(g))), ne(W(d, x), ke(So(g), je(h, "float32")))];
    } };
  })(r, e);
}
function GH(r, e, t10, o = 0, n = $t.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "onehotLabels", "softmaxCrossEntropy"), a = v(e, "logits", "softmaxCrossEntropy"), i = null;
  if (t10 != null && (i = v(t10, "weights", "softmaxCrossEntropy")), gt(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) {
    let u = Ce(o), c = Ce(1), l = Ce(s.shape[1]);
    s = be(ne(s, ke(c, u)), He(u, l));
  }
  let p = UH(s, a);
  return ar(p, i, n);
}
var X1 = N({ softmaxCrossEntropy_: GH });
function HH(r, e, t10, o) {
  let n = v(r, "indices", "sparseFillEmptyRows", "int32"), s = v(e, "values", "sparseFillEmptyRows"), a = v(t10, "denseShape", "sparseFillEmptyRows", "int32"), i = v(o, "defaultValue", "sparseFillEmptyRows", s.dtype);
  if (n.rank !== 2)
    throw new Error(`Indices should be Tensor2D but received shape
        ${n.shape}`);
  if (s.rank !== 1)
    throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);
  if (a.rank !== 1)
    throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);
  if (i.rank !== 0)
    throw new Error(`Default value should be a scalar but received shape ${i.shape}`);
  let p = { indices: n, values: s, denseShape: a, defaultValue: i }, u = T.runKernel(hi, p);
  return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] };
}
var Y1 = N({ sparseFillEmptyRows_: HH });
function KH(r, e, t10) {
  let o = v(r, "inputIndices", "sparseReshape", "int32"), n = v(e, "inputShape", "sparseReshape", "int32"), s = v(t10, "newShape", "sparseReshape", "int32");
  if (o.rank !== 2)
    throw new Error(`Input indices should be Tensor2D but received shape
        ${o.shape}`);
  if (n.rank !== 1)
    throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);
  if (s.rank !== 1)
    throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);
  let a = { inputIndices: o, inputShape: n, newShape: s }, i = T.runKernel(va, a);
  return { outputIndices: i[0], outputShape: i[1] };
}
var Q1 = N({ sparseReshape_: KH });
function qH(r, e, t10) {
  let o = v(r, "data", "sparseSegmentMean"), n = v(e, "indices", "sparseSegmentMean", "int32"), s = v(t10, "segmentIds", "sparseSegmentMean", "int32");
  if (o.rank < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (n.rank !== 1)
    throw new Error(`Indices should be Tensor1D but received shape
          ${n.shape}`);
  if (s.rank !== 1)
    throw new Error(`Segment ids should be Tensor1D but received shape
          ${s.shape}`);
  let a = { data: o, indices: n, segmentIds: s };
  return T.runKernel(gi, a);
}
var Z1 = N({ sparseSegmentMean_: qH });
function jH(r, e, t10) {
  let o = v(r, "data", "sparseSegmentSum"), n = v(e, "indices", "sparseSegmentSum", "int32"), s = v(t10, "segmentIds", "sparseSegmentSum", "int32");
  if (o.rank < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (n.rank !== 1)
    throw new Error(`Indices should be Tensor1D but received shape
         ${n.shape}`);
  if (s.rank !== 1)
    throw new Error(`Segment ids should be Tensor1D but received shape
         ${s.shape}`);
  let a = { data: o, indices: n, segmentIds: s };
  return T.runKernel(xi, a);
}
var J1 = N({ sparseSegmentSum_: jH });
function XH(r, e, t10, o, n, s, a, i) {
  let p = v(r, "data", "stringNGrams", "string");
  if (p.dtype !== "string")
    throw new Error("Data must be of datatype string");
  if (p.shape.length !== 1)
    throw new Error(`Data must be a vector, saw: ${p.shape}`);
  let u = v(e, "dataSplits", "stringNGrams");
  if (u.dtype !== "int32")
    throw new Error("Data splits must be of datatype int32");
  let c = { separator: t10, nGramWidths: o, leftPad: n, rightPad: s, padWidth: a, preserveShortSequences: i }, l = { data: p, dataSplits: u }, m = T.runKernel(Ds, l, c);
  return { nGrams: m[0], nGramsSplits: m[1] };
}
var eN = N({ stringNGrams_: XH });
function YH(r, e, t10 = true) {
  let o = v(r, "input", "stringSplit", "string"), n = v(e, "delimiter", "stringSplit", "string");
  if (o.rank !== 1)
    throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);
  if (n.rank !== 0)
    throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);
  let s = { skipEmpty: t10 }, a = { input: o, delimiter: n }, i = T.runKernel(Ci, a, s);
  return { indices: i[0], values: i[1], shape: i[2] };
}
var tN = N({ stringSplit_: YH });
function QH(r, e) {
  let t10 = v(r, "input", "stringToHashBucketFast", "string"), o = { numBuckets: e };
  if (e <= 0)
    throw new Error("Number of buckets must be at least 1");
  let n = { input: t10 };
  return T.runKernel(Si, n, o);
}
var rN = N({ stringToHashBucketFast_: QH });
var ZH = { fft: zp, ifft: du, rfft: Wp, irfft: Td };
var JH = { hammingWindow: C1, hannWindow: Fd, frame: Od, stft: S1 };
var eK = { flipLeftRight: I1, grayscaleToRGB: v1, resizeNearestNeighbor: O1, resizeBilinear: F1, rotateWithOffset: k1, cropAndResize: w1, nonMaxSuppression: N1, nonMaxSuppressionAsync: $1, nonMaxSuppressionWithScore: E1, nonMaxSuppressionWithScoreAsync: A1, nonMaxSuppressionPadded: R1, nonMaxSuppressionPaddedAsync: D1, threshold: P1, transform: M1 };
var tK = { bandPart: L1, gramSchmidt: B1, qr: z1 };
var rK = { absoluteDifference: W1, computeWeightedLoss: ar, cosineDistance: U1, hingeLoss: G1, huberLoss: H1, logLoss: K1, meanSquaredError: q1, sigmoidCrossEntropy: j1, softmaxCrossEntropy: X1 };
var oK = { sparseFillEmptyRows: Y1, sparseReshape: Q1, sparseSegmentMean: Z1, sparseSegmentSum: J1 };
var nK = { stringNGrams: eN, stringSplit: tN, stringToHashBucketFast: rN };
var oN = {};
Ge(oN, { Serializable: () => il, SerializationMap: () => qs, registerClass: () => IC });
var il = class {
  getClassName() {
    return this.constructor.className;
  }
  static fromConfig(e, t10) {
    return new e(t10);
  }
};
var qs = class {
  constructor() {
    this.classNameMap = {};
  }
  static getMap() {
    return qs.instance == null && (qs.instance = new qs()), qs.instance;
  }
  static register(e) {
    qs.getMap().classNameMap[e.className] = [e, e.fromConfig];
  }
};
function IC(r) {
  $(r.className != null, () => "Class being registered does not have the static className property defined."), $(typeof r.className == "string", () => "className is required to be a string, but got type " + typeof r.className), $(r.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), qs.register(r);
}
var wr = class extends il {
  minimize(e, t10 = false, o) {
    let { value: n, grads: s } = this.computeGradients(e, o);
    if (o != null) {
      let a = o.map((i) => ({ name: i.name, tensor: s[i.name] }));
      this.applyGradients(a);
    } else
      this.applyGradients(s);
    return Ot(s), t10 ? n : (n.dispose(), null);
  }
  get iterations() {
    return this.iterations_ == null && (this.iterations_ = 0), this.iterations_;
  }
  incrementIterations() {
    this.iterations_ = this.iterations + 1;
  }
  computeGradients(e, t10) {
    return pC(e, t10);
  }
  dispose() {
    this.iterations_ != null && Ot(this.iterations_);
  }
  async saveIterations() {
    return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: Ce(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 ${this.getClassName()}`);
  }
  async extractIterations(e) {
    return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
  }
};
Object.defineProperty(wr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null });
var yu = class extends wr {
  constructor(e, t10, o = null) {
    super(), this.learningRate = e, this.rho = t10, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = T.backend.epsilon());
  }
  static get className() {
    return "Adadelta";
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = T.registeredVariables[o], a = false;
      this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accum_grad`, variable: Ee(() => Ut(s).variable(a)) }), this.accumulatedUpdates[n] == null && (this.accumulatedUpdates[n] = { originalName: `${o}/accum_var`, variable: Ee(() => Ut(s).variable(a)) });
      let i = Array.isArray(e) ? e[n].tensor : e[o];
      if (i == null)
        return;
      let p = this.accumulatedGrads[n].variable, u = this.accumulatedUpdates[n].variable;
      Ee(() => {
        let c = be(ne(p, this.rho), ne(Qt(i), 1 - this.rho)), l = ne(He($r(be(u, this.epsilon)), $r(be(p, this.epsilon))), i), m = be(ne(u, this.rho), ne(Qt(l), 1 - this.rho));
        p.assign(c), u.assign(m);
        let d = be(ne(l, -this.learningRate), s);
        s.assign(d);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedUpdates != null && (Ot(this.accumulatedGrads.map((e) => e.variable)), Ot(this.accumulatedUpdates.map((e) => e.variable)));
  }
  async getWeights() {
    let e = [...this.accumulatedGrads, ...this.accumulatedUpdates];
    return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t10 = e.length / 2, o = false;
    this.accumulatedGrads = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedUpdates = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon };
  }
  static fromConfig(e, t10) {
    return new e(t10.learningRate, t10.rho, t10.epsilon);
  }
};
var bu = class extends wr {
  constructor(e, t10 = 0.1) {
    super(), this.learningRate = e, this.initialAccumulatorValue = t10, this.accumulatedGrads = [];
  }
  static get className() {
    return "Adagrad";
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = T.registeredVariables[o];
      this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: Ee(() => Us(s.shape, this.initialAccumulatorValue).variable(false)) });
      let a = Array.isArray(e) ? e[n].tensor : e[o];
      if (a == null)
        return;
      let i = this.accumulatedGrads[n].variable;
      Ee(() => {
        let p = be(i, Qt(a));
        i.assign(p);
        let u = be(ne(He(a, $r(be(p, T.backend.epsilon()))), -this.learningRate), s);
        s.assign(u);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedGrads != null && Ot(this.accumulatedGrads.map((e) => e.variable));
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulatedGrads.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t10 = false;
    this.accumulatedGrads = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue };
  }
  static fromConfig(e, t10) {
    return new e(t10.learningRate, t10.initialAccumulatorValue);
  }
};
var Cu = class extends wr {
  constructor(e, t10, o, n = null) {
    super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], Ee(() => {
      this.accBeta1 = Ce(t10).variable(), this.accBeta2 = Ce(o).variable();
    }), n == null && (this.epsilon = T.backend.epsilon());
  }
  static get className() {
    return "Adam";
  }
  applyGradients(e) {
    let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
    Ee(() => {
      let o = ke(1, this.accBeta1), n = ke(1, this.accBeta2);
      t10.forEach((s, a) => {
        let i = T.registeredVariables[s], p = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ee(() => Ut(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: Ee(() => Ut(i).variable(p)) });
        let u = Array.isArray(e) ? e[a].tensor : e[s];
        if (u == null)
          return;
        let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedSecondMoment[a].variable, m = be(ne(c, this.beta1), ne(u, 1 - this.beta1)), d = be(ne(l, this.beta2), ne(Qt(u), 1 - this.beta2)), f = He(m, o), h = He(d, n);
        c.assign(m), l.assign(d);
        let g = be(ne(He(f, be($r(h), this.epsilon)), -this.learningRate), i);
        i.assign(g);
      }), this.accBeta1.assign(ne(this.accBeta1, this.beta1)), this.accBeta2.assign(ne(this.accBeta2, this.beta2));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Ot(this.accumulatedSecondMoment.map((e) => e.variable));
  }
  async getWeights() {
    let e = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
    return [await this.saveIterations()].concat(e.map((t10) => ({ name: t10.originalName, tensor: t10.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e), Ee(() => {
      this.accBeta1.assign(Aa(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(Aa(this.beta2, this.iterations_ + 1));
    });
    let t10 = e.length / 2, o = false;
    this.accumulatedFirstMoment = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedSecondMoment = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon };
  }
  static fromConfig(e, t10) {
    return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon);
  }
};
var Su = class extends wr {
  constructor(e, t10, o, n = null, s = 0) {
    super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], Ee(() => {
      this.iteration = Ce(0).variable(), this.accBeta1 = Ce(t10).variable();
    }), n == null && (this.epsilon = T.backend.epsilon());
  }
  static get className() {
    return "Adamax";
  }
  applyGradients(e) {
    let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
    Ee(() => {
      let o = ke(1, this.accBeta1), n = He(-this.learningRate, be(ne(this.iteration, this.decay), 1));
      t10.forEach((s, a) => {
        let i = T.registeredVariables[s], p = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ut(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Ut(i).variable(p) });
        let u = Array.isArray(e) ? e[a].tensor : e[s];
        if (u == null)
          return;
        let c = this.accumulatedFirstMoment[a].variable, l = this.accumulatedWeightedInfNorm[a].variable, m = be(ne(c, this.beta1), ne(u, 1 - this.beta1)), d = ne(l, this.beta2), f = Yt(u), h = dd(d, f);
        c.assign(m), l.assign(h);
        let g = be(ne(He(n, o), He(m, be(h, this.epsilon))), i);
        i.assign(g);
      }), this.iteration.assign(be(this.iteration, 1)), this.accBeta1.assign(ne(this.accBeta1, this.beta1));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Ot(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Ot(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, t10) {
    return new e(t10.learningRate, t10.beta1, t10.beta2, t10.epsilon, t10.decay);
  }
};
var Pa = class extends wr {
  constructor(e) {
    super(), this.learningRate = e, this.setLearningRate(e);
  }
  static get className() {
    return "SGD";
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = Array.isArray(e) ? e[n].tensor : e[o];
      if (s == null)
        return;
      let a = T.registeredVariables[o];
      Ee(() => {
        let i = be(ne(this.c, s), a);
        a.assign(i);
      });
    }), this.incrementIterations();
  }
  setLearningRate(e) {
    this.learningRate = e, this.c != null && this.c.dispose(), this.c = _r(Ce(-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, t10) {
    return new e(t10.learningRate);
  }
};
var wu = class extends Pa {
  constructor(e, t10, o = false) {
    super(e), this.learningRate = e, this.momentum = t10, this.useNesterov = o, this.accumulations = [], this.m = Ce(this.momentum);
  }
  static get className() {
    return "Momentum";
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = T.registeredVariables[o];
      this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: Ee(() => Ut(s).variable(false)) });
      let a = this.accumulations[n].variable, i = Array.isArray(e) ? e[n].tensor : e[o];
      i != null && Ee(() => {
        let p, u = be(ne(this.m, a), i);
        this.useNesterov ? p = be(ne(this.c, be(i, ne(u, this.m))), s) : p = be(ne(this.c, u), s), a.assign(u), s.assign(p);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.m.dispose(), this.accumulations != null && Ot(this.accumulations.map((e) => e.variable));
  }
  setMomentum(e) {
    this.momentum = e;
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulations.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t10 = false;
    this.accumulations = e.map((o) => ({ originalName: o.name, variable: o.tensor.variable(t10) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov };
  }
  static fromConfig(e, t10) {
    return new e(t10.learningRate, t10.momentum, t10.useNesterov);
  }
};
var Iu = class extends wr {
  constructor(e, t10 = 0.9, o = 0, n = null, s = false) {
    if (super(), this.learningRate = e, this.decay = t10, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = T.backend.epsilon()), e == null)
      throw new Error("learningRate for RMSPropOptimizer must be defined.");
  }
  static get className() {
    return "RMSProp";
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = T.registeredVariables[o], a = false;
      this.accumulatedMeanSquares[n] == null && (this.accumulatedMeanSquares[n] = { originalName: `${o}/rms`, variable: Ee(() => Ut(s).variable(a)) }), this.accumulatedMoments[n] == null && (this.accumulatedMoments[n] = { originalName: `${o}/momentum`, variable: Ee(() => Ut(s).variable(a)) }), this.accumulatedMeanGrads[n] == null && this.centered && (this.accumulatedMeanGrads[n] = { originalName: `${o}/mg`, variable: Ee(() => Ut(s).variable(a)) });
      let i = Array.isArray(e) ? e[n].tensor : e[o];
      if (i == null)
        return;
      let p = this.accumulatedMeanSquares[n].variable, u = this.accumulatedMoments[n].variable;
      Ee(() => {
        let c = be(ne(p, this.decay), ne(Qt(i), 1 - this.decay));
        if (this.centered) {
          let l = this.accumulatedMeanGrads[n].variable, m = be(ne(l, this.decay), ne(i, 1 - this.decay)), d = He(ne(i, this.learningRate), $r(ke(c, be(Qt(m), this.epsilon)))), f = be(ne(u, this.momentum), d);
          p.assign(c), l.assign(m), u.assign(f);
          let h = ke(s, f);
          s.assign(h);
        } else {
          let l = be(ne(p, this.decay), ne(Qt(i), 1 - this.decay)), m = be(ne(u, this.momentum), He(ne(i, this.learningRate), $r(be(l, this.epsilon))));
          p.assign(l), u.assign(m);
          let d = ke(s, m);
          s.assign(d);
        }
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedMeanSquares != null && Ot(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Ot(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Ot(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((t10) => ({ name: t10.originalName, tensor: t10.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t10 = this.centered ? e.length / 3 : e.length / 2, o = false;
    this.accumulatedMeanSquares = e.slice(0, t10).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.accumulatedMoments = e.slice(t10, t10 * 2).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })), this.centered && (this.accumulatedMeanGrads = e.slice(t10 * 2, t10 * 3).map((n) => ({ originalName: n.name, variable: n.tensor.variable(o) })));
  }
  getConfig() {
    return { learningRate: this.learningRate, decay: this.decay, momentum: this.momentum, epsilon: this.epsilon, centered: this.centered };
  }
  static fromConfig(e, t10) {
    return new e(t10.learningRate, t10.decay, t10.momentum, t10.epsilon, t10.centered);
  }
};
var sK = [yu, bu, Cu, Su, wu, Iu, Pa];
function nN() {
  for (let r of sK)
    IC(r);
}
var La = {};
Ge(La, { browserFiles: () => aN, browserHTTPRequest: () => pN, concatenateArrayBuffers: () => Fp, copyModel: () => V0, decodeWeights: () => Bm, encodeWeights: () => v0, fromMemory: () => cN, fromMemorySync: () => $C, getLoadHandlers: () => E0, getModelArtifactsForJSON: () => Op, getModelArtifactsForJSONSync: () => Qb, getModelArtifactsInfoForJSON: () => Ms, getSaveHandlers: () => $0, getWeightSpecs: () => zm, http: () => Vd, isHTTPScheme: () => Bd, listModels: () => L0, loadWeights: () => iN, moveModel: () => z0, registerLoadRouter: () => _0, registerSaveRouter: () => T0, removeModel: () => B0, weightsLoaderFactory: () => TC, withSaveHandler: () => lN, withSaveHandlerSync: () => mN });
var aK = "model";
var iK = ".json";
var uK = ".weights.bin";
function sN(r) {
  return new Promise((e) => setTimeout(e)).then(r);
}
var Ma = class {
  constructor(e) {
    if (!O().getBool("IS_BROWSER"))
      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
    e.startsWith(Ma.URL_SCHEME) && (e = e.slice(Ma.URL_SCHEME.length)), (e == null || e.length === 0) && (e = aK), this.modelJsonFileName = e + iK, this.weightDataFileName = e + uK;
  }
  async save(e) {
    if (typeof document == "undefined")
      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
    let t10 = 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 o = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], n = Vm(e, o), s = window.URL.createObjectURL(new Blob([JSON.stringify(n)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
      if (a.download = this.modelJsonFileName, a.href = s, await sN(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
        let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
        i.download = this.weightDataFileName, i.href = t10, await sN(() => i.dispatchEvent(new MouseEvent("click")));
      }
      return { modelArtifactsInfo: Ms(e) };
    }
  }
};
Ma.URL_SCHEME = "downloads://";
var vC = 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, t10) => {
      let o = new FileReader();
      o.onload = (n) => {
        let s = JSON.parse(n.target.result), a = s.modelTopology;
        if (a == null) {
          t10(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
          return;
        }
        if (s.weightsManifest == null) {
          t10(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
          return;
        }
        if (this.weightsFiles.length === 0) {
          e({ modelTopology: a });
          return;
        }
        let p = Op(s, (u) => this.loadWeights(u));
        e(p);
      }, o.onerror = (n) => t10(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), o.readAsText(this.jsonFile);
    });
  }
  loadWeights(e) {
    let t10 = [], o = [];
    for (let a of e)
      t10.push(...a.weights), o.push(...a.paths);
    let n = this.checkManifestAndWeightFiles(e), s = o.map((a) => this.loadWeightsFile(a, n[a]));
    return Promise.all(s).then((a) => [t10, Fp(a)]);
  }
  loadWeightsFile(e, t10) {
    return new Promise((o, n) => {
      let s = new FileReader();
      s.onload = (a) => {
        let i = a.target.result;
        o(i);
      }, s.onerror = (a) => n(`Failed to weights data from file of path '${e}'.`), s.readAsArrayBuffer(t10);
    });
  }
  checkManifestAndWeightFiles(e) {
    let t10 = [], o = this.weightsFiles.map((s) => Yb(s.name)), n = {};
    for (let s of e)
      s.paths.forEach((a) => {
        let i = Yb(a);
        if (t10.indexOf(i) !== -1)
          throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
        if (t10.push(i), o.indexOf(i) === -1)
          throw new Error(`Weight file with basename '${i}' is not provided.`);
        n[a] = this.weightsFiles[o.indexOf(i)];
      });
    if (t10.length !== this.weightsFiles.length)
      throw new Error(`Mismatch in the number of files in weights manifest (${t10.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
    return n;
  }
};
var pK = (r) => O().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ma.URL_SCHEME) ? cK(r.slice(Ma.URL_SCHEME.length)) : null;
mt.registerSaveRouter(pK);
function cK(r = "model") {
  return new Ma(r);
}
function aN(r) {
  return new vC(r);
}
function kC(r, e, t10, o) {
  a(r), t10 = t10 == null ? 0 : t10, o = o == null ? 1 : o, i(t10, o);
  let n = 0, s = (p) => (p.then((u) => {
    let c = t10 + ++n / r.length * (o - t10);
    return e(c), u;
  }), p);
  function a(p) {
    $(p != null && Array.isArray(p) && p.length > 0, () => "promises must be a none empty array");
  }
  function i(p, u) {
    $(p >= 0 && p <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${p}`), $(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${u}`), $(u >= p, () => `startFraction must be no more than endFraction, but got startFraction ${p} and endFraction ${u}`);
  }
  return Promise.all(r.map(s));
}
async function NC(r, e) {
  e == null && (e = {});
  let t10 = e.fetchFunc == null ? O().platform.fetch : e.fetchFunc, o = r.map((l) => t10(l, e.requestInit, { isBinary: true })), n = 0, s = 0.5, i = (e.onProgress == null ? await Promise.all(o) : await kC(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1;
  return e.onProgress == null ? await Promise.all(i) : await kC(i, e.onProgress, p, u);
}
async function iN(r, e = "", t10, o) {
  return TC((a) => NC(a, { requestInit: o }))(r, e, t10);
}
function TC(r) {
  return async (e, t10 = "", o) => {
    let n = e.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = [];
    if (e.forEach((d, f) => {
      let h = 0;
      d.weights.forEach((g) => {
        let x = "quantization" in g ? g.quantization.dtype : g.dtype, b = Zc[x] * We(g.shape), C = () => {
          n[f] = true, s[f] == null && (s[f] = []), s[f].push({ manifestEntry: g, groupOffset: h, sizeBytes: b });
        };
        o != null ? o.forEach((w, k) => {
          w === g.name && (C(), a[k] = true);
        }) : C(), i.push(g.name), h += b;
      });
    }), !a.every((d) => d)) {
      let d = o.filter((f, h) => !a[h]);
      throw new Error(`Could not find weights in manifest with names: ${d.join(", ")}. 
Manifest JSON has weights with names: ${i.join(", ")}.`);
    }
    let p = n.reduce((d, f, h) => (f && d.push(h), d), []), u = [];
    p.forEach((d) => {
      e[d].paths.forEach((f) => {
        let h = t10 + (t10.endsWith("/") ? "" : "/") + f;
        u.push(h);
      });
    });
    let c = await r(u), l = {}, m = 0;
    return p.forEach((d) => {
      let f = e[d].paths.length, h = 0;
      for (let w = 0; w < f; w++)
        h += c[m + w].byteLength;
      let g = new ArrayBuffer(h), x = new Uint8Array(g), b = 0;
      for (let w = 0; w < f; w++) {
        let k = new Uint8Array(c[m + w]);
        x.set(k, b), b += k.byteLength;
      }
      s[d].forEach((w) => {
        let k = g.slice(w.groupOffset, w.groupOffset + w.sizeBytes), _ = Bm(k, [w.manifestEntry]);
        for (let E in _)
          l[E] = _[E];
      }), m += f;
    }), l;
  };
}
var lK = "application/octet-stream";
var mK = "application/json";
var ul = class {
  constructor(e, t10) {
    if (this.DEFAULT_METHOD = "POST", t10 == null && (t10 = {}), this.weightPathPrefix = t10.weightPathPrefix, this.onProgress = t10.onProgress, this.weightUrlConverter = t10.weightUrlConverter, t10.fetchFunc != null ? ($(typeof t10.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 = t10.fetchFunc) : this.fetch = O().platform.fetch, $(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && $(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t10.requestInit != null && t10.requestInit.body != null)
      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
    this.requestInit = t10.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 t10 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
    t10.body = new FormData();
    let o = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], n = Vm(e, o);
    t10.body.append("model.json", new Blob([JSON.stringify(n)], { type: mK }), "model.json"), e.weightData != null && t10.body.append("model.weights.bin", new Blob([e.weightData], { type: lK }), "model.weights.bin");
    let s = await this.fetch(this.path, t10);
    if (s.ok)
      return { modelArtifactsInfo: Ms(e), responses: [s] };
    throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.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 t10;
    try {
      t10 = await e.json();
    } catch (s) {
      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 o = t10.modelTopology, n = t10.weightsManifest;
    if (o == null && n == null)
      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
    return Op(t10, (s) => this.loadWeights(s));
  }
  async loadWeights(e) {
    let t10 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = dK(t10), s = this.weightPathPrefix || o, a = zm(e), i = [], p = [];
    for (let c of e)
      for (let l of c.paths)
        this.weightUrlConverter != null ? p.push(this.weightUrlConverter(l)) : i.push(s + l + n);
    this.weightUrlConverter && i.push(...await Promise.all(p));
    let u = await NC(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
    return [a, Fp(u)];
  }
};
ul.URL_SCHEME_REGEX = /^https?:\/\//;
function dK(r) {
  let e = r.lastIndexOf("/"), t10 = r.lastIndexOf("?"), o = r.substring(0, e), n = t10 > e ? r.substring(t10) : "";
  return [o + "/", n];
}
function Bd(r) {
  return r.match(ul.URL_SCHEME_REGEX) != null;
}
var uN = (r, e) => {
  if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null))
    return null;
  {
    let t10 = true;
    if (Array.isArray(r) ? t10 = r.every((o) => Bd(o)) : t10 = Bd(r), t10)
      return Vd(r, e);
  }
  return null;
};
mt.registerSaveRouter(uN);
mt.registerLoadRouter(uN);
function Vd(r, e) {
  return new ul(r, e);
}
function pN(r, e) {
  return Vd(r, e);
}
var pl = class {
  constructor(e) {
    this.modelArtifacts = e;
  }
  load() {
    return this.modelArtifacts;
  }
};
var zd = class {
  constructor(e) {
    this.saveHandler = e;
  }
  save(e) {
    return this.saveHandler(e);
  }
};
var _C = class {
  constructor(e) {
    e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t10) => Promise.resolve(e.save(t10)));
  }
};
function cN(r, e, t10, o) {
  let n = arguments;
  return new _C($C(...n));
}
function $C(r, e, t10, o) {
  return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new pl(r) : (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 pl({ modelTopology: r })) : (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 pl({ modelTopology: r, weightSpecs: e, weightData: t10, trainingConfig: o }));
}
function lN(r) {
  return new zd(r);
}
function mN(r) {
  return new zd(r);
}
var fN = {};
Ge(fN, { confusionMatrix: () => dN });
function fK(r, e, t10) {
  let o = v(r, "labels", "confusionMatrix"), n = v(e, "predictions", "confusionMatrix");
  $(t10 == null || t10 > 0 && Number.isInteger(t10), () => `If provided, numClasses must be a positive integer, but got ${t10}`), $(o.rank === 1, () => `Expected the rank of labels to be 1, but got ${o.rank}`), $(n.rank === 1, () => `Expected the rank of predictions to be 1, but got ${n.rank}`), $(o.shape[0] === n.shape[0], () => `Mismatch in the number of examples: ${o.shape[0]} vs. ${n.shape[0]}. Labels and predictions should have the same number of elements.`), $(t10 > 0 && Number.isInteger(t10), () => `numClasses is required to be a positive integer, but got ${t10}`);
  let s = nl(je(o, "int32"), t10), a = nl(je(n, "int32"), t10), i = Gp(s), p = Xe(i, a);
  return je(p, "int32");
}
var dN = N({ confusionMatrix_: fK });
var gN = {};
Ge(gN, { fromPixels: () => SK, fromPixelsAsync: () => bK, toPixels: () => CK });
var vu;
function hN(r, e = 3) {
  if (e > 4)
    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
  if (r == null)
    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
  let t10 = false, o = false, n = false, s = false, a = false, i = false;
  if (r.data instanceof Uint8Array)
    t10 = true;
  else if (typeof ImageData != "undefined" && r instanceof ImageData)
    o = true;
  else if (typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement)
    n = true;
  else if (typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement)
    s = true;
  else if (r.getContext != null)
    a = true;
  else if (typeof ImageBitmap != "undefined" && r instanceof ImageBitmap)
    i = true;
  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 ${r.constructor.name}`);
  if (Kc(Zi, T.backendName) != null) {
    let f = { pixels: r }, h = { numChannels: e };
    return T.runKernel(Zi, f, h);
  }
  let [u, c] = n ? [r.videoWidth, r.videoHeight] : [r.width, r.height], l;
  if (a)
    l = r.getContext("2d").getImageData(0, 0, u, c).data;
  else if (o || t10)
    l = r.data;
  else if (s || n || i) {
    if (vu == null)
      if (typeof document == "undefined")
        if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
          vu = new OffscreenCanvas(1, 1).getContext("2d");
        else
          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
      else
        vu = document.createElement("canvas").getContext("2d", { willReadFrequently: true });
    vu.canvas.width = u, vu.canvas.height = c, vu.drawImage(r, 0, 0, u, c), l = vu.getImageData(0, 0, u, c).data;
  }
  let m;
  if (e === 4)
    m = new Int32Array(l);
  else {
    let f = u * c;
    m = new Int32Array(f * e);
    for (let h = 0; h < f; h++)
      for (let g = 0; g < e; ++g)
        m[h * e + g] = l[h * 4 + g];
  }
  return Ed(m, [c, u, e], "int32");
}
function hK(r) {
  return r != null && r.data instanceof Uint8Array;
}
function gK() {
  return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function xK(r) {
  return r != null && r.width !== 0 && r.height !== 0;
}
function yK(r) {
  return gK() && !(r instanceof ImageBitmap) && xK(r) && !hK(r);
}
async function bK(r, e = 3) {
  let t10 = null;
  if (O().getBool("WRAP_TO_IMAGEBITMAP") && yK(r)) {
    let o;
    try {
      o = await createImageBitmap(r, { premultiplyAlpha: "none" });
    } catch (n) {
      o = null;
    }
    o != null && o.width === r.width && o.height === r.height ? t10 = o : t10 = r;
  } else
    t10 = r;
  return hN(t10, e);
}
async function CK(r, e) {
  let t10 = v(r, "img", "toPixels");
  if (!(r instanceof it)) {
    let u = t10;
    t10 = je(u, "int32"), u.dispose();
  }
  if (t10.rank !== 2 && t10.rank !== 3)
    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${t10.rank}.`);
  let [o, n] = t10.shape.slice(0, 2), s = t10.rank === 2 ? 1 : t10.shape[2];
  if (s > 4 || s === 2)
    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${s}`);
  if (t10.dtype !== "float32" && t10.dtype !== "int32")
    throw new Error(`Unsupported type for toPixels: ${t10.dtype}. Please use float32 or int32 tensors.`);
  let a = await t10.data(), i = t10.dtype === "float32" ? 255 : 1, p = new Uint8ClampedArray(n * o * 4);
  for (let u = 0; u < o * n; ++u) {
    let c = [0, 0, 0, 255];
    for (let m = 0; m < s; m++) {
      let d = a[u * s + m];
      if (t10.dtype === "float32") {
        if (d < 0 || d > 1)
          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${d}.`);
      } else if (t10.dtype === "int32" && (d < 0 || d > 255))
        throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${d}.`);
      s === 1 ? (c[0] = d * i, c[1] = d * i, c[2] = d * i) : c[m] = d * i;
    }
    let l = u * 4;
    p[l + 0] = Math.round(c[0]), p[l + 1] = Math.round(c[1]), p[l + 2] = Math.round(c[2]), p[l + 3] = Math.round(c[3]);
  }
  if (e != null) {
    e.width = n, e.height = o;
    let u = e.getContext("2d"), c = new ImageData(p, n, o);
    u.putImageData(c, 0, 0);
  }
  return t10 !== r && t10.dispose(), p;
}
var SK = N({ fromPixels_: hN });
var Wd = {};
Ge(Wd, { prepareAndValidate: () => xN });
function xN(r, e) {
  let t10 = r.shape.length, o = e.shape.length;
  if (t10 < 1)
    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${t10}.`);
  if (o < 1)
    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${o}.`);
  if (e.dtype !== "int32")
    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);
  if (e.shape[o - 1] > t10)
    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${e.shape[o - 1]} vs. ${t10}`);
  if (We(r.shape) === 0)
    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`);
  let n = e.shape, s = n[n.length - 1], a = 1;
  for (let l = 0; l < n.length - 1; ++l)
    a *= n[l];
  let i = r.shape, p = n.slice();
  p.pop();
  let u = 1;
  for (let l = s; l < t10; ++l)
    u *= i[l], p.push(i[l]);
  let c = [...xs(r.shape).map((l) => l / u), 1].slice(0, s);
  return [p, a, u, c];
}
var ut = {};
Ge(ut, { assertParamsValid: () => IK, computeFlatOffset: () => _K, computeOutShape: () => kK, getNormalizedAxes: () => NK, isSliceContinous: () => TK, maskToAxes: () => vK, parseSliceParams: () => $K, sliceInfo: () => EK, startForAxis: () => kN, startIndicesWithElidedDims: () => wN, stopForAxis: () => NN, stopIndicesWithElidedDims: () => IN, stridesForAxis: () => vN, stridesWithElidedDims: () => bN });
var EC = -2;
var wK = -1;
function IK(r, e, t10) {
  let o = r.shape.length;
  $(o === e.length, () => `Error in slice${o}D: Length of begin ${e} must match the rank of the array (${o}).`), $(o === t10.length, () => `Error in slice${o}D: Length of size ${t10} must match the rank of the array (${o}).`);
  for (let n = 0; n < o; ++n)
    $(e[n] + t10[n] <= r.shape[n], () => `Error in slice${o}D: begin[${n}] + size[${n}] (${e[n] + t10[n]}) would overflow input.shape[${n}] (${r.shape[n]})`);
}
function vK(r) {
  let e = [], t10 = 0;
  for (; r > 0; )
    r & 1 && e.push(t10), r /= 2, t10++;
  return e;
}
function kK(r, e, t10) {
  let o = [];
  for (let n = 0; n < r.length; n++)
    o[n] = Math.ceil((e[n] - r[n]) / t10[n]);
  return o;
}
function bN(r, e, t10, o) {
  let n = [...r];
  for (let s = n.length; s < o.length; s++)
    n.push(1);
  for (let s = 0; s < t10; s++)
    s === 0 ? n[e] = 1 : (n.splice(e, 0, 1), n.pop());
  return n;
}
function CN(r, e, t10) {
  return t10 <= r ? t10 : t10 - (e - 1);
}
function SN(r, e) {
  let t10 = [];
  for (let o = 0; o < r; o++)
    t10.push(e + o);
  return t10;
}
function NK(r, e, t10, o, n, s, a, i, p) {
  let u = r.length, c = new Array(u), l = new Array(u), m = new Array(u);
  if (e.length && t10 > 0) {
    let d = e[0], f = t10 + 1;
    c = wN(a, d, f, o, r), l = IN(i, d, f, n, r), m = bN(s, d, f, r);
  } else
    for (let d = 0; d < u; d++)
      c[d] = kN(a, o, s, r, d, p), l[d] = NN(i, n, s, r, d, p), m[d] = vN(s, d, p);
  return { begin: c, end: l, strides: m };
}
function wN(r, e, t10, o, n) {
  let s = [...n], a = SN(t10, e);
  for (let i = 0; i < s.length; i++)
    if (a.indexOf(i) > -1)
      s[i] = 0;
    else {
      let p = CN(e, t10, i), u = o[p];
      r & 1 << p && (u = 0), s[i] = u;
    }
  return s;
}
function IN(r, e, t10, o, n) {
  let s = [...n], a = SN(t10, e);
  for (let i = 0; i < s.length; i++)
    if (a.indexOf(i) > -1)
      s[i] = Number.MAX_SAFE_INTEGER;
    else {
      let p = CN(e, t10, i), u = o[p];
      r & 1 << p && (u = Number.MAX_SAFE_INTEGER), s[i] = u;
    }
  for (let i = 0; i < s.length; i++) {
    let p = n[i];
    s[i] < 0 && (s[i] += p), s[i] = ip(0, s[i], n[i]);
  }
  return s;
}
function vN(r, e, t10) {
  let o = r[e];
  return (t10 & 1 << e || o == null) && (o = 1), o;
}
function kN(r, e, t10, o, n, s) {
  let a = e[n], i = t10[n] || 1;
  (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MIN_SAFE_INTEGER : a = Number.MAX_SAFE_INTEGER);
  let p = o[n];
  return a < 0 && (a += p), a = ip(0, a, p - 1), a;
}
function NN(r, e, t10, o, n, s) {
  let a = e[n], i = t10[n] || 1;
  (r & 1 << n || s & 1 << n || a == null) && (i > 0 ? a = Number.MAX_SAFE_INTEGER : a = Number.MIN_SAFE_INTEGER);
  let p = o[n];
  return a < 0 && (a += p), i > 0 ? a = ip(0, a, p) : a = ip(-1, a, p - 1), a;
}
function TK(r, e, t10) {
  let o = t10.length;
  for (let n = 0; n < t10.length; n++)
    if (t10[n] > 1) {
      o = n;
      break;
    }
  for (let n = o + 1; n < t10.length; n++)
    if (e[n] > 0 || t10[n] !== r[n])
      return false;
  return true;
}
function _K(r, e) {
  let t10 = r.length > 0 ? r[r.length - 1] : 1;
  for (let o = 0; o < r.length - 1; o++)
    t10 += r[o] * e[o];
  return t10;
}
function $K(r, e, t10) {
  let o, n = r.shape.length;
  typeof e == "number" ? o = [e, ...new Array(n - 1).fill(0)] : e.length < n ? o = e.concat(new Array(n - e.length).fill(0)) : o = e.slice(), o.forEach((a) => {
    $(a !== -1, () => "slice() does not support negative begin indexing.");
  });
  let s;
  return t10 == null ? s = new Array(n).fill(-1) : typeof t10 == "number" ? s = [t10, ...new Array(n - 1).fill(-1)] : t10.length < n ? s = t10.concat(new Array(n - t10.length).fill(-1)) : s = t10, s = s.map((a, i) => a >= 0 ? a : ($(a === -1, () => `Negative size values should be exactly -1 but got ${a} for the slice() size at index ${i}.`), r.shape[i] - o[i])), [o, s];
}
function EK(r, e, t10, o, n, s, a, i, p) {
  let u;
  if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && a & a - 1)
    throw new Error("Multiple ellipses in slice is not allowed.");
  let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t10.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
  for (let C = 0; C < l.dims; C++)
    c && 1 << C & i && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true);
  c || (l.ellipsisMask |= 1 << l.dims, l.dims++);
  let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
  AK(l, m);
  let d = true, f = true, h = true, g = [], x = [];
  for (let C = 0; C < r.length; ++C) {
    if (m.strides[C] === 0)
      throw Error(`strides[${C}] must be non-zero`);
    let w = !!(m.shrinkAxisMask & 1 << C), k = r[C];
    if (k === -1) {
      g.push(w ? 1 : -1);
      continue;
    }
    let _ = [m.beginMask & 1 << C, m.endMask & 1 << C], E = [m.strides[C] > 0 ? 0 : -1, m.strides[C] > 0 ? k : k - 1];
    if (w && m.strides[C] <= 0)
      throw Error("only stride 1 allowed on non-range indexing.");
    h = h && m.strides[C] === 1;
    let A = !!(m.beginMask & 1 << C && m.endMask & 1 << C);
    if (m.beginValid && m.endValid) {
      if (w) {
        let M = m.begin[C] < 0 ? k + m.begin[C] : m.begin[C];
        if (m.begin[C] = M, m.end[C] = m.begin[C] + 1, M < 0 || M >= k)
          throw Error(`slice index ${m.begin[C]} of dimension ${C} out of bounds.`);
      } else
        m.begin[C] = yN(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = yN(m.end[C], 1, m.strides[C], k, _, E);
      let P = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k;
      d = d && P, f = f && (C === 0 && m.strides[C] === 1 || P);
    } else
      d = d && m.strides[C] === 1 && A, f = f && (C === 0 && m.strides[C] === 1 || A);
    let R, D = false;
    if (m.beginValid && m.endValid ? (R = m.end[C] - m.begin[C], D = true) : w ? (R = 1, D = true) : A && k >= 0 && (m.strides[C] < 0 ? R = -k : R = k, D = true), D) {
      let P;
      R === 0 || R < 0 != m.strides[C] < 0 ? P = 0 : P = Math.trunc(R / m.strides[C]) + (R % m.strides[C] !== 0 ? 1 : 0), g.push(P);
    } else
      g.push(-1);
  }
  for (let C = 0; C < m.finalShapeGatherIndices.length; ++C) {
    let w = m.finalShapeGatherIndices[C];
    w >= 0 ? x.push(g[w]) : w === EC && x.push(1);
  }
  return { finalShapeSparse: x.filter((C, w) => m.finalShapeGatherIndices[w] !== EC), finalShape: x, isIdentity: d, sliceDim0: f, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides };
}
function AK(r, e) {
  e.beginMask = 0, e.endMask = 0, e.shrinkAxisMask = 0;
  let t10 = 0;
  e.beginValid = r.begin != null, e.endValid = r.end != null, e.begin = new Array(e.dims), e.end = new Array(e.dims), e.strides = new Array(e.dims), e.finalShapeGatherIndices = [], e.finalShapeGatherIndicesSparse = [], e.inputShapeGatherIndicesSparse = new Array(e.dims);
  for (let o = 0; o < r.dims; o++)
    if (1 << o & r.ellipsisMask) {
      let n = Math.min(e.dims - (r.dims - o) + 1 + r.numAddAxisAfterEllipsis, e.dims);
      for (; t10 < n; t10++)
        e.begin[t10] = 0, e.end[t10] = 0, e.strides[t10] = 1, e.beginMask |= 1 << t10, e.endMask |= 1 << t10, e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(-1), e.inputShapeGatherIndicesSparse[t10] = o;
    } else if (1 << o & r.newAxisMask)
      e.finalShapeGatherIndices.push(EC), e.finalShapeGatherIndicesSparse.push(-1);
    else {
      if (t10 === e.begin.length)
        throw Error(`Index out of range using input dim ${t10}; input has only ${e.dims} dims, ${e.begin.length}.`);
      r.begin != null && (e.begin[t10] = r.begin[o]), r.end != null && (e.end[t10] = r.end[o]), e.strides[t10] = r.strides[o], r.beginMask & 1 << o && (e.beginMask |= 1 << t10), r.endMask & 1 << o && (e.endMask |= 1 << t10), r.shrinkAxisMask & 1 << o ? (e.finalShapeGatherIndices.push(wK), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t10) : (e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t10] = o, t10++;
    }
}
function yN(r, e, t10, o, n, s) {
  if (n[e])
    return t10 > 0 ? s[e] : s[e + 1 & 1];
  {
    let a = r < 0 ? o + r : r;
    return a < s[0] ? s[0] : a > s[1] ? s[1] : a;
  }
}
var RK = "4.2.0";
var cl = class {
  static sgd(e) {
    return new Pa(e);
  }
  static momentum(e, t10, o = false) {
    return new wu(e, t10, o);
  }
  static rmsprop(e, t10 = 0.9, o = 0, n = null, s = false) {
    return new Iu(e, t10, o, n, s);
  }
  static adam(e = 1e-3, t10 = 0.9, o = 0.999, n = null) {
    return new Cu(e, t10, o, n);
  }
  static adadelta(e = 1e-3, t10 = 0.95, o = null) {
    return new yu(e, t10, o);
  }
  static adamax(e = 2e-3, t10 = 0.9, o = 0.999, n = null, s = 0) {
    return new Su(e, t10, o, n, s);
  }
  static adagrad(e, t10 = 0.1) {
    return new bu(e, t10);
  }
};
var YMe = cl;
var DK = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())();
function AC() {
  return new Promise((r) => DK(() => r()));
}
var S = {};
Ge(S, { ERF_A1: () => YK, ERF_A2: () => QK, ERF_A3: () => ZK, ERF_A4: () => JK, ERF_A5: () => eq, ERF_P: () => XK, PARALLELIZE_THRESHOLD: () => Ud, RowPartitionType: () => js, SELU_SCALE: () => jK, SELU_SCALEALPHA: () => qK, applyActivation: () => gu, assertAndGetBroadcastShape: () => Je, assertAxesAreInnerMostDims: () => dU, assertParamsConsistent: () => FK, assignToTypedArray: () => aq, axesAreInnerMostDims: () => uC, calculateShapes: () => l1, checkEinsumDimSizes: () => mq, checkPadOnDimRoundingMode: () => Mt, combineLocations: () => Ov, combineRaggedTensorToTensorShapes: () => PK, complexWithEvenIndex: () => oq, complexWithOddIndex: () => nq, computeConv2DInfo: () => au, computeConv3DInfo: () => rv, computeDefaultPad: () => iC, computeDilation2DInfo: () => dW, computeOptimalWindowSize: () => VK, computeOutAndReduceShapes: () => mU, computeOutShape: () => OK, computePool2DInfo: () => aC, computePool3DInfo: () => fW, convertConv2DDataFormat: () => ov, decodeEinsumEquation: () => cq, eitherStridesOrDilationsAreOne: () => mr, expandShapeToKeepDim: () => Ea, exponent: () => uq, exponents: () => iq, fromStringArrayToUint8: () => Dq, fromUint8ToStringArray: () => Rq, getAxesPermutation: () => fU, getBroadcastDims: () => Ev, getComplexWithIndex: () => sq, getEinsumComputePath: () => dq, getEinsumPermutation: () => lq, getFusedBiasGradient: () => hu, getFusedDyActivation: () => fu, getImageCenter: () => zK, getInnerMostAxes: () => gU, getPermuted: () => UK, getRaggedRank: () => LK, getReductionAxes: () => Qm, getReshaped: () => WK, getReshapedPermuted: () => GK, getRowPartitionTypesHelper: () => MK, getSliceBeginCoords: () => HK, getSliceSize: () => KK, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => xq, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => yq, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => bq, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => wq, getSparseReshapeInputOutputMismatchErrorMessage: () => vq, getSparseReshapeInputOutputMultipleErrorMessage: () => Iq, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => Cq, getSparseReshapeNegativeOutputDimErrorMessage: () => Sq, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => _q, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => kq, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => Nq, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => Tq, getUndoAxesPermutation: () => hU, isIdentityPermutation: () => fq, log: () => pz, mergeRealAndImagArrays: () => tq, prepareAndValidate: () => xN, prepareSplitSize: () => gq, segment_util: () => DC, shouldFuse: () => xu, slice_util: () => ut, splitRealAndImagArrays: () => rq, stridesOrDilationsArePositive: () => Vs, tupleValuesAreOne: () => su, upcastType: () => dt, validateDefaultValueShape: () => BK, validateInput: () => Dd, validateUpdateShape: () => bC, warn: () => Ps });
function FK(r, e) {
  let t10 = r[0].length;
  r.forEach((n, s) => {
    $(n.length === t10, () => `Error in concat${t10}D: rank of tensors[${s}] must be the same as the rank of the rest (${t10})`);
  }), $(e >= 0 && e < t10, () => `Error in concat${t10}D: axis must be between 0 and ${t10 - 1}.`);
  let o = r[0];
  r.forEach((n, s) => {
    for (let a = 0; a < t10; a++)
      $(a === e || n[a] === o[a], () => `Error in concat${t10}D: Shape of tensors[${s}] (${n}) does not match the shape of the rest (${o}) along the non-concatenated axis ${s}.`);
  });
}
function OK(r, e) {
  let t10 = r[0].slice();
  for (let o = 1; o < r.length; o++)
    t10[e] += r[o][e];
  return t10;
}
var js;
(function(r) {
  r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS";
})(js || (js = {}));
function PK(r, e, t10) {
  let o = new Array();
  if (t10 == null && e == null)
    return o;
  if (e == null)
    for (; o.length < r + t10.length; )
      o.push(-1);
  else
    o = e.slice();
  if (t10 == null)
    return o;
  if (r + t10.length !== o.length)
    throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.rank = ${r + t10.length}, but shape.rank = ${o.length}`);
  for (let n = 1; n < t10.length; ++n) {
    let s = t10[n], a = o[o.length - t10.length + n], i = o[a];
    if (s >= 0)
      if (i >= 0) {
        if (i !== s)
          throw new Error(`rt input.shape and shape=${e} are incompatible: rt input.shape[${n + r}] = ${s} but shape[${n + r}] = ${i}`);
      } else
        o[a] = s;
  }
  return o;
}
function MK(r) {
  let e = { FIRST_DIM_SIZE: js.FIRST_DIM_SIZE, VALUE_ROWIDS: js.VALUE_ROWIDS, ROW_LENGTHS: js.ROW_LENGTHS, ROW_SPLITS: js.ROW_SPLITS, ROW_LIMITS: js.ROW_LIMITS, ROW_STARTS: js.ROW_STARTS }, t10 = [];
  for (let o of r)
    if (o in e)
      t10.push(e[o]);
    else
      break;
  return t10;
}
function LK(r) {
  return r.length === 0 ? 0 : r[0] === js.FIRST_DIM_SIZE ? r.length - 1 : r.length;
}
function BK(r, e) {
  if (r == null || e == null)
    return;
  let t10 = r.length, o = e.length;
  if (t10 >= o)
    throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${e}, are incompatible: defaultValue.rank = ${t10} must be less than ragged tensor input flatValues.rank = ${o})`);
  for (let n = 0; n < Math.min(t10, o - 1); ++n) {
    let s = r[n], a = e[n + 1];
    if (s >= 0 && a >= 0 && s !== 1 && s !== a)
      throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${e} are incompatible: defaultValue.shape[${n - r.length}] = ${s} but ragged tensor input.flatValues.shape[${n - r.length}] = ${a}`);
  }
}
var Ud = 30;
function VK(r) {
  return r <= Ud ? r : cp(r, Math.floor(Math.sqrt(r)));
}
function zK(r, e, t10) {
  let o = t10 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]);
  return [o, n];
}
function WK(r, e, t10, o = true) {
  let n = [];
  if (o)
    n = n.concat(e.slice(0)), n.push(r[0] / t10), n = n.concat(r.slice(1));
  else {
    n = n.concat(r[0]);
    let s = e.length;
    for (let a = 0; a < s; ++a)
      n = n.concat([r[a + 1] / e[a], e[a]]);
    n = n.concat(r.slice(s + 1));
  }
  return n;
}
function UK(r, e, t10 = true) {
  let o = [];
  if (t10) {
    o.push(e);
    for (let n = e + 1; n < r; ++n)
      n <= 2 * e ? (o.push(n), o.push(n - (e + 1))) : o.push(n);
  } else {
    let n = [], s = [];
    for (let a = 1; a < r; ++a)
      a >= e * 2 + 1 || a % 2 === 1 ? s.push(a) : n.push(a);
    o.push(...n), o.push(0), o.push(...s);
  }
  return o;
}
function GK(r, e, t10, o = true) {
  let n = [];
  o ? n.push(r[0] / t10) : n.push(r[0] * t10);
  for (let s = 1; s < r.length; ++s)
    s <= e.length ? o ? n.push(e[s - 1] * r[s]) : n.push(r[s] / e[s - 1]) : n.push(r[s]);
  return n;
}
function HK(r, e) {
  let t10 = [0];
  for (let o = 0; o < e; ++o)
    t10.push(r[o][0]);
  return t10;
}
function KK(r, e, t10) {
  let o = r.slice(0, 1);
  for (let n = 0; n < t10; ++n)
    o.push(r[n + 1] - e[n][0] - e[n][1]);
  return o;
}
var qK = 1.7580993408473768;
var jK = 1.0507009873554805;
var XK = 0.3275911;
var YK = 0.254829592;
var QK = -0.284496736;
var ZK = 1.421413741;
var JK = -1.453152027;
var eq = 1.061405429;
function tq(r, e) {
  if (r.length !== e.length)
    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${e.length}.`);
  let t10 = new Float32Array(r.length * 2);
  for (let o = 0; o < t10.length; o += 2)
    t10[o] = r[o / 2], t10[o + 1] = e[o / 2];
  return t10;
}
function rq(r) {
  let e = new Float32Array(r.length / 2), t10 = new Float32Array(r.length / 2);
  for (let o = 0; o < r.length; o += 2)
    e[o / 2] = r[o], t10[o / 2] = r[o + 1];
  return { real: e, imag: t10 };
}
function oq(r) {
  let e = Math.ceil(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e);
  for (let n = 0; n < r.length; n += 4)
    t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
  return { real: t10, imag: o };
}
function nq(r) {
  let e = Math.floor(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e);
  for (let n = 2; n < r.length; n += 4)
    t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
  return { real: t10, imag: o };
}
function sq(r, e) {
  let t10 = r[e * 2], o = r[e * 2 + 1];
  return { real: t10, imag: o };
}
function aq(r, e, t10, o) {
  r[o * 2] = e, r[o * 2 + 1] = t10;
}
function iq(r, e) {
  let t10 = new Float32Array(r / 2), o = new Float32Array(r / 2);
  for (let n = 0; n < Math.ceil(r / 2); n++) {
    let s = (e ? 2 : -2) * Math.PI * (n / r);
    t10[n] = Math.cos(s), o[n] = Math.sin(s);
  }
  return { real: t10, imag: o };
}
function uq(r, e, t10) {
  let o = (t10 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o);
  return { real: n, imag: s };
}
var RC = "->";
var pq = /->/g;
var TN = ",";
var _N = "...";
function cq(r, e) {
  r = r.replace(/\s/g, "");
  let t10 = (r.length - r.replace(pq, "").length) / RC.length;
  if (t10 < 1)
    throw new Error("Equations without an arrow are not supported.");
  if (t10 > 1)
    throw new Error(`Equation must contain exactly one arrow ("${RC}").`);
  let [o, n] = r.split(RC);
  $(o.indexOf(_N) === -1, () => `The ellipsis notation ("${_N}") is not supported yet.`);
  let s = o.split(TN), a = s.length;
  if (e !== a)
    throw new Error(`Expected ${a} input tensors, received ${e}`);
  if (a > 2)
    throw new Error("Support for more than 2 input tensors is not implemented yet.");
  let i = [];
  for (let m = 0; m < n.length; ++m) {
    let d = n[m];
    if (!s.some((f) => f.indexOf(d) !== -1))
      throw new Error(`Output subscripts contain the label ${d} not present in the input subscripts.`);
    i.indexOf(d) === -1 && i.push(d);
  }
  for (let m = 0; m < o.length; ++m) {
    let d = o[m];
    i.indexOf(d) === -1 && d !== TN && i.push(d);
  }
  let p = new Array(s.length);
  for (let m = 0; m < a; ++m) {
    if (new Set(s[m].split("")).size !== s[m].length)
      throw new Error(`Found duplicate axes in input component ${s[m]}. Support for duplicate axes in input is not implemented yet.`);
    p[m] = [];
    for (let d = 0; d < s[m].length; ++d)
      p[m].push(i.indexOf(s[m][d]));
  }
  let u = i.length, c = n.length, l = [];
  for (let m = c; m < u; ++m)
    l.push(m);
  return { allDims: i, summedDims: l, idDims: p };
}
function lq(r, e) {
  let t10 = new Array(r);
  t10.fill(-1);
  for (let n = 0; n < e.length; ++n)
    t10[e[n]] = n;
  let o = [];
  for (let n = 0; n < r; ++n)
    t10[n] === -1 && o.push(n);
  return t10 = t10.filter((n) => n !== -1), { permutationIndices: t10, expandDims: o };
}
function mq(r, e, t10) {
  let o = new Array(r);
  for (let n = 0; n < t10.length; ++n) {
    let s = t10[n].shape;
    for (let a = 0; a < e[n].length; ++a)
      o[e[n][a]] === void 0 ? o[e[n][a]] = s[a] : $(o[e[n][a]] === s[a], () => `Expected dimension ${o[e[n][a]]} at axis ${a} of input shaped ${JSON.stringify(s)}, but got dimension ${s[a]}`);
  }
}
function dq(r, e) {
  let t10 = r, o = [], n = 0;
  r.length === 0 && t10.push(-1), n = r.length + 1;
  for (let a = 0; a < n; ++a)
    o.push([]);
  let s = [];
  for (let a = 0; a < t10.length; ++a) {
    let i = t10[a], p = hq(e, i);
    for (let u of p)
      s.indexOf(u) === -1 && (o[a].push(u), s.push(u));
  }
  return { path: t10, steps: o };
}
function fq(r) {
  return r.every((e, t10) => e === t10);
}
function hq(r, e) {
  let t10 = [];
  for (let o = 0; o < r.length; ++o)
    (r[o].length === 0 || r[o].indexOf(e) !== -1 || e === -1) && t10.push(o);
  return t10;
}
function gq(r, e, t10 = 0) {
  let o = [];
  if (typeof e == "number")
    $(r.shape[t10] % e === 0, () => "Number of splits must evenly divide the axis."), o = new Array(e).fill(r.shape[t10] / e);
  else {
    let n = e.reduce((a, i) => (i === -1 && (a += 1), a), 0);
    $(n <= 1, () => "There should be only one negative value in split array.");
    let s = e.indexOf(-1);
    if (s !== -1) {
      let a = e.reduce((i, p) => p > 0 ? i + p : i);
      e[s] = r.shape[t10] - a;
    }
    $(r.shape[t10] === e.reduce((a, i) => a + i), () => "The sum of sizes must match the size of the axis dimension."), o = e;
  }
  return o;
}
function xq(r) {
  return `Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${r}`;
}
function yq(r, e) {
  return `indices(${r}, 0) is invalid: ${e} < 0`;
}
function bq(r, e, t10) {
  return `indices(${r}, 0) is invalid: ${e} >= ${t10}`;
}
function Cq(r, e) {
  return `only one output dimension may be -1, not both ${r} and ${e}`;
}
function Sq(r, e) {
  return `size ${r} must be non-negative, not ${e}`;
}
function wq() {
  return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function Iq(r, e) {
  let t10 = We(r), o = We(e);
  return `Input to reshape is a SparseTensor with ${t10}
  dense values, but the requested shape requires a multiple of ${o}. inputShape=${r} outputShape= ${e}`;
}
function vq(r, e) {
  let t10 = We(r), o = We(e);
  return `Input to reshape is a tensor with ${t10} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`;
}
function kq() {
  return "segment ids must be >= 0";
}
function Nq() {
  return "segment ids are not increasing";
}
function Tq(r, e) {
  return `Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`;
}
function _q(r, e, t10) {
  return `Bad: indices[${r}] == ${e} out of range [0, ${t10})`;
}
var DC = {};
Ge(DC, { collectGatherOpShapeInfo: () => Aq, computeOutShape: () => Eq, segOpComputeOptimalWindowSize: () => $q });
function $q(r, e) {
  let t10 = false, o;
  for (r <= Ud ? (o = r, t10 = true) : o = cp(r, Math.floor(Math.sqrt(r))); !t10; )
    o > e || o === r ? t10 = true : o = cp(r, o + 1);
  return o;
}
function Eq(r, e, t10) {
  let o = [], n = r.length;
  for (let s = 0; s < n; s++)
    s !== e ? o.push(r[s]) : o.push(t10);
  return o;
}
function Aq(r, e, t10, o) {
  let n = e.shape.length, s = r.shape.length;
  if (o !== 0 && (o < -n || o > n))
    throw new Error(`Expect batchDims in the range of [-${n}, ${n}], but got ${o}`);
  if (o < 0 && (o += n), o > s)
    throw new Error(`batchDims (${o}) must be less than rank(x) (
    ${s}).`);
  if (t10 < o)
    throw new Error(`batchDims (${o}) must be less than or equal to axis (${t10}).`);
  for (let l = 0; l < o; ++l)
    if (r.shape[l] !== e.shape[l])
      throw new Error(`x.shape[${l}]: ${r.shape[l]} should be equal to indices.shape[${l}]: ${e.shape[l]}.`);
  let a = r.shape[t10], i = [], p = 1, u = 1, c = 1;
  for (let l = 0; l < o; ++l)
    i.push(r.shape[l]), p *= r.shape[l];
  for (let l = o; l < t10; l++)
    i.push(r.shape[l]), u *= r.shape[l];
  for (let l = o; l < n; l++)
    i.push(e.shape[l]);
  for (let l = t10 + 1; l < s; l++)
    i.push(r.shape[l]), c *= r.shape[l];
  return { batchSize: p, sliceSize: c, outerSize: u, dimSize: a, outputShape: i };
}
function Rq(r) {
  try {
    return r.map((e) => Rp(e));
  } catch (e) {
    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`);
  }
}
function Dq(r) {
  return r.map((e) => Ii(e));
}
var Vt = {};
Ge(Vt, { nonMaxSuppressionV3Impl: () => Pd, nonMaxSuppressionV4Impl: () => Md, nonMaxSuppressionV5Impl: () => Ld, whereImpl: () => Ad });
nN();
var Fq = O();
Fq.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (r) => {
  r && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
});
var ao;
(function(r) {
  r[r.DT_INVALID = 0] = "DT_INVALID", r[r.DT_FLOAT = 1] = "DT_FLOAT", r[r.DT_DOUBLE = 2] = "DT_DOUBLE", r[r.DT_INT32 = 3] = "DT_INT32", r[r.DT_UINT8 = 4] = "DT_UINT8", r[r.DT_INT16 = 5] = "DT_INT16", r[r.DT_INT8 = 6] = "DT_INT8", r[r.DT_STRING = 7] = "DT_STRING", r[r.DT_COMPLEX64 = 8] = "DT_COMPLEX64", r[r.DT_INT64 = 9] = "DT_INT64", r[r.DT_BOOL = 10] = "DT_BOOL", r[r.DT_QINT8 = 11] = "DT_QINT8", r[r.DT_QUINT8 = 12] = "DT_QUINT8", r[r.DT_QINT32 = 13] = "DT_QINT32", r[r.DT_BFLOAT16 = 14] = "DT_BFLOAT16", r[r.DT_QINT16 = 15] = "DT_QINT16", r[r.DT_QUINT16 = 16] = "DT_QUINT16", r[r.DT_UINT16 = 17] = "DT_UINT16", r[r.DT_COMPLEX128 = 18] = "DT_COMPLEX128", r[r.DT_HALF = 19] = "DT_HALF", r[r.DT_RESOURCE = 20] = "DT_RESOURCE", r[r.DT_VARIANT = 21] = "DT_VARIANT", r[r.DT_UINT32 = 22] = "DT_UINT32", r[r.DT_UINT64 = 23] = "DT_UINT64", r[r.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", r[r.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", r[r.DT_INT32_REF = 103] = "DT_INT32_REF", r[r.DT_UINT8_REF = 104] = "DT_UINT8_REF", r[r.DT_INT16_REF = 105] = "DT_INT16_REF", r[r.DT_INT8_REF = 106] = "DT_INT8_REF", r[r.DT_STRING_REF = 107] = "DT_STRING_REF", r[r.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", r[r.DT_INT64_REF = 109] = "DT_INT64_REF", r[r.DT_BOOL_REF = 110] = "DT_BOOL_REF", r[r.DT_QINT8_REF = 111] = "DT_QINT8_REF", r[r.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", r[r.DT_QINT32_REF = 113] = "DT_QINT32_REF", r[r.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", r[r.DT_QINT16_REF = 115] = "DT_QINT16_REF", r[r.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", r[r.DT_UINT16_REF = 117] = "DT_UINT16_REF", r[r.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", r[r.DT_HALF_REF = 119] = "DT_HALF_REF", r[r.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", r[r.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", r[r.DT_UINT32_REF = 122] = "DT_UINT32_REF", r[r.DT_UINT64_REF = 123] = "DT_UINT64_REF";
})(ao || (ao = {}));
var $N;
(function(r) {
  let e;
  (function(t10) {
    t10[t10.LEGACY = 0] = "LEGACY", t10[t10.V1 = 1] = "V1", t10[t10.V2 = 2] = "V2";
  })(e = r.CheckpointFormatVersion || (r.CheckpointFormatVersion = {}));
})($N || ($N = {}));
var OC = {};
function Pq(r, e) {
  let t10 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e };
  OC[r] = t10;
}
function Gd(r) {
  return OC[r];
}
function Mq(r) {
  delete OC[r];
}
function I(r, e, t10, o, n) {
  let s = e.inputParams[r];
  if (s && s.inputIndexStart !== void 0) {
    let i = s.inputIndexStart, p = s.inputIndexEnd === 0 ? void 0 : s.inputIndexEnd === void 0 ? i + 1 : s.inputIndexEnd;
    if (s.type === "tensor")
      return Gt(e.inputNames[s.inputIndexStart], t10, o, n);
    if (s.type === "tensors")
      return e.inputNames.slice(i, p).map((m) => Gt(m, t10, o, n));
    let u = Gt(e.inputNames.slice(i)[0], t10, o, n), c = u.dataSync();
    return s.type === "number" ? c[0] : y.toNestedArray(u.shape, c);
  }
  let a = e.attrParams[r];
  return a && a.value;
}
function Gt(r, e, t10, o) {
  let [n, s] = Ir(r);
  if (o != null) {
    let i = o.getHashTableHandleByName(n);
    if (i != null)
      return i;
  }
  let a = t10.currentContextIds.find((i) => !!e[Hd(n, i)]);
  return a !== void 0 ? e[Hd(n, a)][s] : void 0;
}
function EN(r, e, t10) {
  return e[Hd(r, t10.currentContextId)];
}
function as(r, e) {
  let [t10, o, n] = Ir(r);
  return [Hd(t10, e && e.currentContextId), o, n];
}
function Hd(r, e) {
  return e ? `${r}-${e}` : r;
}
function Ir(r) {
  let e = r.split(":");
  if (e.length === 1)
    return [r, 0, void 0];
  let t10 = e[0], o = e.length === 3 ? e[1] : void 0, n = Number(e[e.length - 1]);
  return [t10, n, o];
}
function ll(r, e, t10) {
  let o = I("pad", r, e, t10);
  if (o === "explicit") {
    o = I("explicitPaddings", r, e, t10);
    let n = [[0, 0], [0, 0], [0, 0], [0, 0]];
    for (let s = 0; s < 4; s++)
      n[s][0] = o[s * 2], n[s][1] = o[s * 2 + 1];
    return n;
  }
  return o;
}
function is(r) {
  return r.kept ? r : Lr(r);
}
var PC = {};
Ge(PC, { json: () => Lq });
var Lq = [{ 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: true }] }, { 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: true }] }, { 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: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }];
var MC = {};
Ge(MC, { json: () => Bq });
var Bq = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { 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: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
var LC = {};
Ge(LC, { json: () => Vq });
var Vq = [{ 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: true }, { 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: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: "TensorListConcatV2", 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" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }, { tfOpName: "TensorListResize", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "size", type: "number" }] }];
var BC = {};
Ge(BC, { json: () => zq });
var zq = [{ 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { 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: true }, { 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: true }, { 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: true }, { 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", defaultValue: 0.2 }] }, { 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: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { 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: true }, { 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" }] }];
var VC = {};
Ge(VC, { json: () => Wq });
var Wq = [{ 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: true }] }, { 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: true }, { tfName: "T", name: "dtype", type: "dtype" }] }, { 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: "RandomStandardNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { 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: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { 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: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { 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" }] }];
var zC = {};
Ge(zC, { json: () => Uq });
var Uq = [{ 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: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { 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: true }] }, { 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: true }] }];
var WC = {};
Ge(WC, { json: () => Gq });
var Gq = [{ tfOpName: "LowerBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { 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: "UpperBound", category: "evaluation", inputs: [{ start: 0, name: "sortedSequence", type: "tensor" }, { start: 1, name: "values", type: "tensor" }] }, { 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" }] }];
var UC = {};
Ge(UC, { json: () => Hq });
var Hq = [{ 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: true }, { 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" }] }];
var GC = {};
Ge(GC, { json: () => Kq });
var Kq = [{ 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: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "InitializeTable", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }, { tfOpName: "InitializeTableV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }] }];
var HC = {};
Ge(HC, { json: () => qq });
var qq = [{ 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: true }] }, { 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: true }] }, { 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" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }];
var KC = {};
Ge(KC, { json: () => jq });
var jq = [{ 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }];
var qC = {};
Ge(qC, { json: () => Xq });
var Xq = [{ 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: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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" }] }];
var jC = {};
Ge(jC, { json: () => Yq });
var Yq = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { 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: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { 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: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { 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: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { 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: 0.5 }] }, { 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: true, notSupported: true }] }];
var XC = {};
Ge(XC, { json: () => Qq });
var Qq = [{ 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: "Cumprod", 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" }] }, { 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" }] }];
var YC = {};
Ge(YC, { json: () => Zq });
var Zq = [{ 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: true }] }, { 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: true }] }, { 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: false, notSupported: true }] }];
var QC = {};
Ge(QC, { json: () => Jq });
var Jq = [{ 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: true }] }, { 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" }] }];
var ZC = {};
Ge(ZC, { json: () => e6 });
var e6 = [{ 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: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }];
var JC = {};
Ge(JC, { json: () => t6 });
var t6 = [{ 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" }] }];
var eS = {};
Ge(eS, { json: () => r6 });
var r6 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { 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: [] }];
var ml = class {
  constructor() {
    let e = [PC, MC, LC, BC, VC, zC, WC, UC, GC, HC, KC, qC, jC, XC, YC, QC, ZC, JC, eS], t10 = [].concat(...e.map((o) => o.json));
    this.opMappers = t10.reduce((o, n) => (o[n.tfOpName] = n, o), {});
  }
  static get Instance() {
    return this._instance || (this._instance = new this());
  }
  transformGraph(e, t10 = {}) {
    let o = e.node, n = [], s = [], a = [], i = o.reduce((h, g) => (h[g.name] = this.mapNode(g), g.op.startsWith("Placeholder") ? n.push(h[g.name]) : g.op === "Const" ? s.push(h[g.name]) : (g.input == null || g.input.length === 0) && a.push(h[g.name]), h), {}), p = [], u = [], c = {}, l = {};
    t10 != null && (c = this.mapSignatureEntries(t10.inputs), l = this.mapSignatureEntries(t10.outputs));
    let m = Object.keys(i);
    m.forEach((h) => {
      let g = i[h];
      g.inputNames.forEach((x, b) => {
        let [C, , w] = as(x), k = i[C];
        if (k.outputs != null) {
          let _ = k.outputs.indexOf(w);
          if (_ !== -1) {
            let E = `${C}:${_}`;
            g.inputNames[b] = E;
          }
        }
        g.inputs.push(k), k.children.push(g);
      });
    }), Object.keys(l).length === 0 ? m.forEach((h) => {
      let g = i[h];
      g.children.length === 0 && u.push(g);
    }) : Object.keys(l).forEach((h) => {
      let [g] = as(h), x = i[g];
      x != null && (x.signatureKey = l[h], u.push(x));
    }), Object.keys(c).length > 0 ? Object.keys(c).forEach((h) => {
      let [g] = as(h), x = i[g];
      x && (x.signatureKey = c[h], p.push(x));
    }) : p = n;
    let d = {};
    e.library != null && e.library.function != null && (d = e.library.function.reduce((h, g) => (h[g.signature.name] = this.mapFunction(g), h), {}));
    let f = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t10, functions: d };
    return a.length > 0 && (f.initNodes = a), f;
  }
  mapSignatureEntries(e) {
    return Object.keys(e || {}).reduce((t10, o) => (t10[e[o].name] = o, t10), {});
  }
  mapNode(e) {
    let t10 = Gd(e.op) || this.opMappers[e.op] || {};
    e.attr == null && (e.attr = {});
    let o = { name: e.name, op: e.op, category: t10.category, inputNames: (e.input || []).map((n) => n.startsWith("^") ? n.slice(1) : n), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t10.outputs };
    return t10.inputs != null && (o.inputParams = t10.inputs.reduce((n, s) => (n[s.name] = { type: s.type, inputIndexStart: s.start, inputIndexEnd: s.end }, n), {})), t10.attrs != null && (o.attrParams = t10.attrs.reduce((n, s) => {
      let a = s.type, i;
      switch (s.type) {
        case "string":
          i = Kd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Kd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "string[]":
          i = Jd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Jd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "number":
          i = jd(e.attr, s.tfName, s.defaultValue || 0), i === void 0 && s.tfDeprecatedName && (i = jd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "number[]":
          i = Zd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Zd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "bool":
          i = qd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = qd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "bool[]":
          i = tf(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = tf(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "shape":
          i = Qd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Qd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "shape[]":
          i = ef(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = ef(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "dtype":
          i = Xd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Xd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "dtype[]":
          i = Yd(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = Yd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "func":
          i = AN(e.attr, s.tfName, s.defaultValue), i === void 0 && s.tfDeprecatedName && (i = AN(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "tensor":
        case "tensors":
          break;
        default:
          throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`);
      }
      return n[s.name] = { value: i, type: a }, n;
    }, {})), o;
  }
  mapFunction(e) {
    let t10 = e.nodeDef, o = [], n = [], s = {};
    t10 != null && (s = t10.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {}));
    let a = [], i = [];
    e.signature.inputArg.forEach((l) => {
      let [m] = as(l.name), d = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: tS(l.type), type: "dtype" } }, children: [] };
      d.signatureKey = l.name, a.push(d), s[m] = d;
    }), Object.keys(s).forEach((l) => {
      let m = s[l];
      m.inputNames.forEach((d, f) => {
        let [h, , g] = as(d), x = s[h];
        if (x.outputs != null) {
          let b = x.outputs.indexOf(g);
          if (b !== -1) {
            let C = `${h}:${b}`;
            m.inputNames[f] = C;
          }
        }
        m.inputs.push(x), x.children.push(m);
      });
    });
    let u = e.ret;
    e.signature.outputArg.forEach((l) => {
      let [m, d] = as(u[l.name]), f = s[m];
      f != null && (f.defaultOutput = d, i.push(f));
    });
    let c = this.mapArgsToSignature(e);
    return { nodes: s, inputs: a, outputs: i, weights: n, placeholders: o, signature: c };
  }
  mapArgsToSignature(e) {
    return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o), t10), {}), outputs: e.signature.outputArg.reduce((t10, o) => (t10[o.name] = this.mapArgToTensorInfo(o, e.ret), t10), {}) };
  }
  mapArgToTensorInfo(e, t10) {
    let o = e.name;
    return t10 != null && (o = t10[o]), { name: o, dtype: e.type };
  }
};
function o6(r) {
  let e = O().global;
  if (typeof e.atob != "undefined")
    return e.atob(r);
  if (typeof Buffer != "undefined")
    return new Buffer(r, "base64").toString();
  throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
}
function RN(r, e) {
  let t10 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : o6(r);
  return e ? t10 : t10.toLowerCase();
}
function Kd(r, e, t10, o = false) {
  let n = r[e];
  return n != null ? RN(n.s, o) : t10;
}
function qd(r, e, t10) {
  let o = r[e];
  return o ? o.b : t10;
}
function jd(r, e, t10) {
  let o = r[e] || {}, n = o.i != null ? o.i : o.f != null ? o.f : t10;
  return typeof n == "number" ? n : parseInt(n, 10);
}
function tS(r) {
  switch (typeof r == "string" && (r = ao[r]), r) {
    case ao.DT_FLOAT:
    case ao.DT_HALF:
      return "float32";
    case ao.DT_INT32:
    case ao.DT_INT64:
    case ao.DT_INT8:
    case ao.DT_UINT8:
      return "int32";
    case ao.DT_BOOL:
      return "bool";
    case ao.DT_DOUBLE:
      return "float32";
    case ao.DT_STRING:
      return "string";
    default:
      return null;
  }
}
function AN(r, e, t10) {
  let o = r[e];
  return o && o.func ? o.func.name : t10;
}
function Xd(r, e, t10) {
  let o = r[e];
  return o && o.type ? tS(o.type) : t10;
}
function Yd(r, e, t10) {
  let o = r[e];
  return o && o.list && o.list.type ? o.list.type.map((n) => tS(n)) : t10;
}
function DN(r) {
  if (!r.unknownRank)
    return r.dim != null ? r.dim.map((e) => typeof e.size == "number" ? e.size : parseInt(e.size, 10)) : [];
}
function Qd(r, e, t10) {
  let o = r[e];
  return o && o.shape ? DN(o.shape) : t10;
}
function Zd(r, e, t10) {
  let o = r[e];
  return o ? ((o.list.f && o.list.f.length ? o.list.f : o.list.i) || []).map((n) => typeof n == "number" ? n : parseInt(n, 10)) : t10;
}
function Jd(r, e, t10, o = false) {
  let n = r[e];
  return n && n.list && n.list.s ? n.list.s.map((s) => RN(s, o)) : t10;
}
function ef(r, e, t10) {
  let o = r[e];
  return o && o.list && o.list.shape ? o.list.shape.map((n) => DN(n)) : t10;
}
function tf(r, e, t10) {
  let o = r[e];
  return o && o.list && o.list.b ? o.list.b : t10;
}
var rf = class {
  constructor(e, t10, o) {
    this.node = e, this.tensorMap = t10, this.context = o, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((n) => this.getInput(n)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((n, s) => (n[s] = this.getAttr(s), n), {}));
  }
  getInput(e) {
    return Gt(e, this.tensorMap, this.context);
  }
  getAttr(e, t10) {
    let o = this.node.rawAttrs[e];
    if (o.tensor != null)
      return Gt(e, this.tensorMap, this.context);
    if (o.i != null || o.f != null)
      return jd(this.node.rawAttrs, e, t10);
    if (o.s != null)
      return Kd(this.node.rawAttrs, e, t10);
    if (o.b != null)
      return qd(this.node.rawAttrs, e, t10);
    if (o.shape != null)
      return Qd(this.node.rawAttrs, e, t10);
    if (o.type != null)
      return Xd(this.node.rawAttrs, e, t10);
    if (o.list != null) {
      if (o.list.i != null || o.list.f != null)
        return Zd(this.node.rawAttrs, e, t10);
      if (o.list.s != null)
        return Jd(this.node.rawAttrs, e, t10);
      if (o.list.shape != null)
        return ef(this.node.rawAttrs, e, t10);
      if (o.list.b != null)
        return tf(this.node.rawAttrs, e, t10);
      if (o.list.type != null)
        return Yd(this.node.rawAttrs, e, t10);
    }
    return t10;
  }
};
var Ye = {};
Ge(Ye, { OP_SCOPE_SUFFIX: () => jb, abs: () => Yt, acos: () => G0, acosh: () => H0, add: () => be, addN: () => K0, all: () => q0, any: () => j0, argMax: () => X0, argMin: () => Y0, asin: () => Q0, asinh: () => Z0, atan: () => J0, atan2: () => ev, atanh: () => tv, avgPool: () => qm, avgPool3d: () => nv, basicLSTMCell: () => sv, batchNorm: () => _i, batchNorm2d: () => iv, batchNorm3d: () => uv, batchNorm4d: () => pv, batchToSpaceND: () => jm, bincount: () => Xm, booleanMaskAsync: () => VG, broadcastArgs: () => cv, broadcastTo: () => $i, buffer: () => le, cast: () => je, ceil: () => lv, clipByValue: () => mv, clone: () => Lr, complex: () => Tr, concat: () => xt, concat1d: () => dv, concat2d: () => fv, concat3d: () => hv, concat4d: () => gv, conv1d: () => xv, conv2d: () => Ei, conv2dTranspose: () => yv, conv3d: () => bv, conv3dTranspose: () => Sv, cos: () => wv, cosh: () => Iv, cosineWindow: () => al, cumprod: () => vv, cumsum: () => kv, denseBincount: () => Nv, depthToSpace: () => Tv, depthwiseConv2d: () => Bp, diag: () => _v, dilation2d: () => $v, div: () => He, divNoNan: () => Av, dot: () => Rv, dropout: () => ZG, einsum: () => Dv, elu: () => Jm, enclosingPowerOfTwo: () => CC, equal: () => Zm, erf: () => Fv, euclideanNorm: () => Mv, exp: () => So, expandDims: () => Ra, expm1: () => Lv, eye: () => ed, fft: () => zp, fill: () => Us, floor: () => td, floorDiv: () => Km, fused: () => SC, gather: () => rd, gatherND: () => YG, greater: () => uu, greaterEqual: () => od, ifft: () => du, imag: () => Ri, image: () => eK, inTopKAsync: () => eH, irfft: () => Td, isFinite: () => Bv, isInf: () => Vv, isNaN: () => zv, leakyRelu: () => nd, less: () => Wv, lessEqual: () => Vp, linalg: () => tK, linspace: () => Uv, localResponseNormalization: () => Gv, log: () => Da, log1p: () => sd, logSigmoid: () => Hv, logSoftmax: () => Kv, logSumExp: () => ud, logicalAnd: () => pu, logicalNot: () => pd, logicalOr: () => cd, logicalXor: () => qv, losses: () => rK, lowerBound: () => jv, matMul: () => Xe, max: () => Gs, maxPool: () => md, maxPool3d: () => Xv, maxPoolWithArgmax: () => Yv, maximum: () => dd, mean: () => cu, meshgrid: () => Qv, min: () => rl, minimum: () => fd, mirrorPad: () => Zv, mod: () => Jv, moments: () => ek, movingAverage: () => UG, mul: () => ne, multiRNNCell: () => tk, multinomial: () => rk, neg: () => Cr, norm: () => iu, notEqual: () => hd, oneHot: () => nl, ones: () => Hs, onesLike: () => ok, op: () => N, outerProduct: () => nk, pad: () => Ks, pad1d: () => sk, pad2d: () => ak, pad3d: () => ik, pad4d: () => uk, pool: () => pk, pow: () => Aa, prelu: () => xd, print: () => Hm, prod: () => ck, raggedGather: () => lk, raggedRange: () => mk, raggedTensorToTensor: () => dk, rand: () => fk, randomGamma: () => Ok, randomNormal: () => Id, randomStandardNormal: () => Pk, randomUniform: () => vd, range: () => Di, real: () => Fa, reciprocal: () => Mk, relu: () => Fi, relu6: () => kd, reshape: () => W, reverse: () => no, reverse1d: () => Lk, reverse2d: () => Bk, reverse3d: () => Vk, reverse4d: () => zk, rfft: () => Wp, round: () => Nd, rsqrt: () => Wk, scalar: () => Ce, scatterND: () => HG, searchSorted: () => ol, selu: () => Uk, separableConv2d: () => Gk, setdiff1dAsync: () => Hk, sigmoid: () => Ws, sign: () => Kk, signal: () => JH, sin: () => qk, sinh: () => jk, slice: () => Ke, slice1d: () => Xk, slice2d: () => Yk, slice3d: () => Qk, slice4d: () => Zk, softmax: () => Jk, softplus: () => id, spaceToBatchND: () => gd, sparse: () => oK, sparseToDense: () => jG, spectral: () => ZH, split: () => Oa, sqrt: () => $r, square: () => Qt, squaredDifference: () => _d, squeeze: () => Up, stack: () => Sr, step: () => $d, stridedSlice: () => e1, string: () => nK, sub: () => ke, sum: () => et, tan: () => t1, tanh: () => tl, tensor: () => nr, tensor1d: () => dr, tensor2d: () => Oi, tensor3d: () => Ed, tensor4d: () => r1, tensor5d: () => o1, tensor6d: () => n1, tile: () => Ai, topk: () => s1, transpose: () => Gp, truncatedNormal: () => a1, unique: () => i1, unsortedSegmentSum: () => u1, unstack: () => so, upperBound: () => p1, variable: () => c1, where: () => ss, whereAsync: () => Rd, zeros: () => Br, zerosLike: () => Ut });
var FN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "BiasAdd":
    case "AddV2":
    case "Add":
      return [o.add(I("a", r, e, t10), I("b", r, e, t10))];
    case "AddN":
      return [o.addN(I("tensors", r, e, t10))];
    case "FloorMod":
    case "Mod":
      return [o.mod(I("a", r, e, t10), I("b", r, e, t10))];
    case "Mul":
      return [o.mul(I("a", r, e, t10), I("b", r, e, t10))];
    case "RealDiv":
    case "Div":
      return [o.div(I("a", r, e, t10), I("b", r, e, t10))];
    case "DivNoNan":
      return [o.divNoNan(I("a", r, e, t10), I("b", r, e, t10))];
    case "FloorDiv":
      return [o.floorDiv(I("a", r, e, t10), I("b", r, e, t10))];
    case "Sub":
      return [o.sub(I("a", r, e, t10), I("b", r, e, t10))];
    case "Minimum":
      return [o.minimum(I("a", r, e, t10), I("b", r, e, t10))];
    case "Maximum":
      return [o.maximum(I("a", r, e, t10), I("b", r, e, t10))];
    case "Pow":
      return [o.pow(I("a", r, e, t10), I("b", r, e, t10))];
    case "SquaredDifference":
      return [o.squaredDifference(I("a", r, e, t10), I("b", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var ON = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Abs":
    case "ComplexAbs":
      return [o.abs(I("x", r, e, t10))];
    case "Acos":
      return [o.acos(I("x", r, e, t10))];
    case "Acosh":
      return [o.acosh(I("x", r, e, t10))];
    case "Asin":
      return [o.asin(I("x", r, e, t10))];
    case "Asinh":
      return [o.asinh(I("x", r, e, t10))];
    case "Atan":
      return [o.atan(I("x", r, e, t10))];
    case "Atan2":
      return [o.atan2(I("x", r, e, t10), I("y", r, e, t10))];
    case "Atanh":
      return [o.atanh(I("x", r, e, t10))];
    case "Ceil":
      return [o.ceil(I("x", r, e, t10))];
    case "Complex":
      return [o.complex(I("real", r, e, t10), I("imag", r, e, t10))];
    case "Cos":
      return [o.cos(I("x", r, e, t10))];
    case "Cosh":
      return [o.cosh(I("x", r, e, t10))];
    case "Elu":
      return [o.elu(I("x", r, e, t10))];
    case "Erf":
      return [o.erf(I("x", r, e, t10))];
    case "Exp":
      return [o.exp(I("x", r, e, t10))];
    case "Expm1":
      return [o.expm1(I("x", r, e, t10))];
    case "Floor":
      return [o.floor(I("x", r, e, t10))];
    case "Log":
      return [o.log(I("x", r, e, t10))];
    case "Log1p":
      return [o.log1p(I("x", r, e, t10))];
    case "Imag":
      return [o.imag(I("x", r, e, t10))];
    case "Neg":
      return [o.neg(I("x", r, e, t10))];
    case "Reciprocal":
      return [o.reciprocal(I("x", r, e, t10))];
    case "Real":
      return [o.real(I("x", r, e, t10))];
    case "Relu":
      return [o.relu(I("x", r, e, t10))];
    case "Round":
      return [o.round(I("x", r, e, t10))];
    case "Selu":
      return [o.selu(I("x", r, e, t10))];
    case "Sigmoid":
      return [o.sigmoid(I("x", r, e, t10))];
    case "Sin":
      return [o.sin(I("x", r, e, t10))];
    case "Sign":
      return [o.sign(I("x", r, e, t10))];
    case "Sinh":
      return [o.sinh(I("x", r, e, t10))];
    case "Softplus":
      return [o.softplus(I("x", r, e, t10))];
    case "Sqrt":
      return [o.sqrt(I("x", r, e, t10))];
    case "Square":
      return [o.square(I("x", r, e, t10))];
    case "Tanh":
      return [o.tanh(I("x", r, e, t10))];
    case "Tan":
      return [o.tan(I("x", r, e, t10))];
    case "ClipByValue":
      return [o.clipByValue(I("x", r, e, t10), I("clipValueMin", r, e, t10), I("clipValueMax", r, e, t10))];
    case "Relu6":
      return [o.relu6(I("x", r, e, t10))];
    case "Rsqrt":
      return [o.rsqrt(Gt(r.inputNames[0], e, t10))];
    case "Prod":
      return [o.prod(I("x", r, e, t10), I("axes", r, e, t10))];
    case "LeakyRelu":
      return [o.leakyRelu(I("x", r, e, t10), I("alpha", r, e, t10))];
    case "Prelu":
      return [o.prelu(I("x", r, e, t10), I("alpha", r, e, t10))];
    case "IsNan":
      return [o.isNaN(Gt(r.inputNames[0], e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function Vr(r, e, t10 = "") {
  if (!(typeof r == "number" || typeof e == "number")) {
    y.assert(r.length === e.length, () => t10 + ` Shapes ${r} and ${e} must match`);
    for (let o = 0; o < r.length; o++) {
      let n = r[o], s = e[o];
      y.assert(n < 0 || s < 0 || n === s, () => t10 + ` Shapes ${r} and ${e} must match`);
    }
  }
}
function PN(r) {
  return !(typeof r == "number" || r.some((e) => e < 0));
}
function Hp(r, e, t10) {
  let o = of(r, t10), n = !PN(o);
  if (n && e.length === 0)
    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);
  if (n && e.forEach((s) => {
    o = of(s.shape, o);
  }), !PN(o))
    throw new Error(`Non-fully-defined elementShape: ${o}`);
  return o;
}
function of(r, e) {
  if (typeof r == "number")
    return e;
  if (typeof e == "number")
    return r;
  if (r.length !== e.length)
    throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);
  let t10 = [];
  for (let o = 0; o < r.length; ++o) {
    let n = r[o], s = e[o];
    if (n >= 0 && s >= 0 && n !== s)
      throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);
    t10[o] = n >= 0 ? n : s;
  }
  return t10;
}
var nf = class {
  constructor(e, t10, o, n, s, a, i) {
    this.name = e, this.dtype = t10, this.maxSize = o, this.elementShape = n, this.identicalElementShapes = s, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = Ce(0), _r(this.idTensor);
  }
  get id() {
    return this.idTensor.id;
  }
  get closed() {
    return this.closed_;
  }
  clearAndClose(e) {
    this.tensors.forEach((t10) => {
      (e == null || !e.has(t10.tensor.id)) && t10.tensor.dispose();
    }), this.tensors = [], this.closed_ = true, 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 t10 = this.tensors[e];
    if (t10.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 && (t10.cleared = true), t10.read = true, t10.tensor;
  }
  readMany(e) {
    return e.map((t10) => this.read(t10));
  }
  write(e, t10) {
    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 o = this.tensors[e] || {};
    if (t10.dtype !== this.dtype)
      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
          because the value dtype is ${t10.dtype}, but TensorArray dtype is ${this.dtype}.`);
    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t10.shape), Vr(this.elementShape, t10.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), o.read)
      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);
    if (o.written)
      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);
    o.tensor = t10, _r(t10), o.written = true, this.tensors[e] = o;
  }
  writeMany(e, t10) {
    if (e.length !== t10.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: ${t10.length}.`);
    e.forEach((o, n) => this.write(o, t10[n]));
  }
  gather(e, t10) {
    if (t10 && t10 !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t10}`);
    if (e)
      e = e.slice(0, this.size());
    else {
      e = [];
      for (let n = 0; n < this.size(); n++)
        e.push(n);
    }
    if (e.length === 0)
      return nr([], [0].concat(this.elementShape));
    let o = this.readMany(e);
    return Vr(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), Sr(o, 0);
  }
  concat(e) {
    if (e && e !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);
    if (this.size() === 0)
      return nr([], [0].concat(this.elementShape));
    let t10 = [];
    for (let n = 0; n < this.size(); n++)
      t10.push(n);
    let o = this.readMany(t10);
    return Vr(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), xt(o, 0);
  }
  scatter(e, t10) {
    if (t10.dtype !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`);
    if (e.length !== t10.shape[0])
      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t10.shape[0]}`);
    let o = Math.max(...e);
    if (!this.dynamicSize && o >= this.maxSize)
      throw new Error(`Max index must be < array size (${o}  vs. ${this.maxSize})`);
    this.writeMany(e, so(t10, 0));
  }
  split(e, t10) {
    if (t10.dtype !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t10.dtype}`);
    let o = 0, n = e.map((p) => (o += p, o));
    if (o !== t10.shape[0])
      throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${o}, and tensor's shape is: ${t10.shape}`);
    if (!this.dynamicSize && e.length !== this.maxSize)
      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);
    let s = o === 0 ? 0 : t10.size / o, a = [];
    Ee(() => {
      t10 = W(t10, [1, o, s]);
      for (let p = 0; p < e.length; ++p) {
        let c = [0, p === 0 ? 0 : n[p - 1], 0], l = [1, e[p], s];
        a[p] = W(Ke(t10, c, l), this.elementShape);
      }
      return a;
    });
    let i = [];
    for (let p = 0; p < e.length; p++)
      i[p] = p;
    this.writeMany(i, a);
  }
};
var Ba = class {
  constructor(e, t10, o, n = -1) {
    this.tensors = e, this.elementShape = t10, this.elementDtype = o, e != null && e.forEach((s) => {
      if (o !== s.dtype)
        throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`);
      Vr(t10, s.shape, "TensorList shape mismatch: "), _r(s);
    }), this.idTensor = Ce(0), this.maxNumElements = n, _r(this.idTensor);
  }
  get id() {
    return this.idTensor.id;
  }
  copy() {
    return new Ba([...this.tensors], this.elementShape, this.elementDtype);
  }
  clearAndClose(e) {
    this.tensors.forEach((t10) => {
      (e == null || !e.has(t10.id)) && t10.dispose();
    }), this.tensors.length = 0, this.idTensor.dispose();
  }
  size() {
    return this.tensors.length;
  }
  stack(e, t10, o = -1) {
    if (t10 !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`);
    if (o !== -1 && this.tensors.length !== o)
      throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`);
    Vr(e, this.elementShape, "TensorList shape mismatch: ");
    let n = Hp(this.elementShape, this.tensors, e);
    return Ee(() => {
      let s = this.tensors.map((a) => W(a, n));
      return Sr(s, 0);
    });
  }
  popBack(e, t10) {
    if (t10 !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`);
    if (this.size() === 0)
      throw new Error("Trying to pop from an empty list.");
    let o = Hp(this.elementShape, this.tensors, e), n = this.tensors.pop();
    return n.kept = false, Vr(n.shape, e, "TensorList shape mismatch: "), W(n, o);
  }
  pushBack(e) {
    if (e.dtype !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);
    if (Vr(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
      throw new Error("Trying to push element into a full list.");
    _r(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}.`);
    let t10 = new Ba([], this.elementShape, this.elementDtype, this.maxNumElements);
    t10.tensors.length = e;
    for (let o = 0; o < Math.min(this.tensors.length, e); ++o)
      t10.tensors[o] = this.tensors[o];
    return t10;
  }
  getItem(e, t10, o) {
    if (o !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${o}, 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.`);
    Vr(this.tensors[e].shape, t10, "TensorList shape mismatch: ");
    let n = Hp(this.elementShape, this.tensors, t10);
    return W(this.tensors[e], n);
  }
  setItem(e, t10) {
    if (t10.dtype !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t10.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.`);
    Vr(this.elementShape, t10.shape, "TensorList shape mismatch: "), _r(t10), this.tensors[e] != null && (this.tensors[e].kept = false), this.tensors[e] = t10;
  }
  gather(e, t10, o) {
    if (t10 !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t10}, but list elements ${this.elementDtype}`);
    Vr(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size());
    let n = Hp(this.elementShape, this.tensors, o);
    return e.length === 0 ? nr([], [0].concat(n)) : Ee(() => {
      let s = e.map((a) => W(this.tensors[a], n));
      return Sr(s, 0);
    });
  }
  concat(e, t10) {
    if (e && e !== this.elementDtype)
      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
    Vr(this.elementShape, t10, "TensorList shape mismatch: ");
    let o = Hp(this.elementShape, this.tensors, t10);
    return this.size() === 0 ? nr([], [0].concat(o)) : Ee(() => {
      let n = this.tensors.map((s) => W(s, o));
      return xt(n, 0);
    });
  }
};
function MN(r, e, t10) {
  let o = r.dtype;
  if (r.shape.length < 1)
    throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);
  if (r.dtype !== t10)
    throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t10}`);
  let n = r.shape.slice(1);
  Vr(n, e, "TensorList shape mismatch: ");
  let s = so(r);
  return new Ba(s, e, o);
}
function LN(r, e, t10, o) {
  return new Ba([], r, e, o);
}
function BN(r, e, t10, o) {
  if (e.length !== r.shape[0])
    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);
  let n = Math.max(...e);
  if (o != null && o !== -1 && n >= o)
    throw new Error(`Max index must be < array size (${n}  vs. ${o})`);
  let s = new Ba([], t10, r.dtype, o), a = so(r, 0);
  return e.forEach((i, p) => {
    s.setItem(i, a[p]);
  }), s;
}
function VN(r, e, t10) {
  let o = 0, n = e.map((c) => (o += c, o));
  if (o !== r.shape[0])
    throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${o}, and tensor's shape is: ${r.shape}`);
  let s = r.shape.slice(1), a = of(s, t10), i = o === 0 ? 0 : r.size / o, p = Ee(() => {
    let c = [];
    r = W(r, [1, o, i]);
    for (let l = 0; l < e.length; ++l) {
      let d = [0, l === 0 ? 0 : n[l - 1], 0], f = [1, e[l], i];
      c[l] = W(Ke(r, d, f), a);
    }
    return r.dispose(), c;
  }), u = new Ba([], t10, r.dtype, e.length);
  for (let c = 0; c < p.length; c++)
    u.setItem(c, p[c]);
  return u;
}
var zN = async (r, e, t10) => {
  switch (r.op) {
    case "If":
    case "StatelessIf": {
      let o = I("thenBranch", r, e, t10), n = I("elseBranch", r, e, t10), s = I("cond", r, e, t10), a = I("args", r, e, t10);
      return (await s.data())[0] ? t10.functionMap[o].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap) : t10.functionMap[n].executeFunctionAsync(a, t10.tensorArrayMap, t10.tensorListMap);
    }
    case "While":
    case "StatelessWhile": {
      let o = I("body", r, e, t10), n = I("cond", r, e, t10), s = I("args", r, e, t10), a = await t10.functionMap[n].executeFunctionAsync(s, t10.tensorArrayMap, t10.tensorListMap), i = s.map((c) => c.id), p = await a[0].data();
      a.forEach((c) => {
        !c.kept && i.indexOf(c.id) === -1 && c.dispose();
      });
      let u = s;
      for (; p[0]; ) {
        let c = u;
        u = await t10.functionMap[o].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap);
        let l = u.map((d) => d.id);
        c.forEach((d) => {
          !d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose();
        });
        let m = await t10.functionMap[n].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap);
        p = await m[0].data(), m.forEach((d) => {
          !d.kept && i.indexOf(d.id) === -1 && l.indexOf(d.id) === -1 && d.dispose();
        });
      }
      return u;
    }
    case "LoopCond": {
      let o = I("pred", r, e, t10);
      return [is(o)];
    }
    case "Switch": {
      let o = I("pred", r, e, t10), n = I("data", r, e, t10);
      return n.kept || (n = is(n)), (await o.data())[0] ? [void 0, n] : [n, void 0];
    }
    case "Merge": {
      let o = r.inputNames.find((n) => Gt(n, e, t10) !== void 0);
      if (o) {
        let n = Gt(o, e, t10);
        return [is(n)];
      }
      return;
    }
    case "Enter": {
      let o = I("frameName", r, e, t10), n = I("tensor", r, e, t10);
      return t10.enterFrame(o), [is(n)];
    }
    case "Exit": {
      let o = I("tensor", r, e, t10);
      return t10.exitFrame(), [is(o)];
    }
    case "NextIteration": {
      let o = I("tensor", r, e, t10);
      return t10.nextIteration(), [is(o)];
    }
    case "TensorArrayV3": {
      let o = I("size", r, e, t10), n = I("dtype", r, e, t10), s = I("elementShape", r, e, t10), a = I("dynamicSize", r, e, t10), i = I("clearAfterRead", r, e, t10), p = I("identicalElementShapes", r, e, t10), u = I("name", r, e, t10), c = new nf(u, n, o, s, p, a, i);
      return t10.addTensorArray(c), [c.idTensor, Ce(1)];
    }
    case "TensorArrayWriteV3": {
      let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id);
      return a.write(n, s), [a.idTensor];
    }
    case "TensorArrayReadV3": {
      let o = I("tensorArrayId", r, e, t10), n = I("index", r, e, t10);
      return [t10.getTensorArray(o.id).read(n)];
    }
    case "TensorArrayGatherV3": {
      let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("dtype", r, e, t10);
      return [t10.getTensorArray(o.id).gather(n, s)];
    }
    case "TensorArrayScatterV3": {
      let o = I("tensorArrayId", r, e, t10), n = I("indices", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorArray(o.id);
      return a.scatter(n, s), [a.idTensor];
    }
    case "TensorArrayConcatV3": {
      let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id), s = I("dtype", r, e, t10);
      return [n.concat(s)];
    }
    case "TensorArraySplitV3": {
      let o = I("tensorArrayId", r, e, t10), n = I("tensor", r, e, t10), s = I("lengths", r, e, t10), a = t10.getTensorArray(o.id);
      return a.split(s, n), [a.idTensor];
    }
    case "TensorArraySizeV3": {
      let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id);
      return [Ce(n.size(), "int32")];
    }
    case "TensorArrayCloseV3": {
      let o = I("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id);
      return n.clearAndClose(), [n.idTensor];
    }
    case "TensorListSetItem": {
      let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("tensor", r, e, t10), a = t10.getTensorList(o.id);
      return a.setItem(n, s), [a.idTensor];
    }
    case "TensorListGetItem": {
      let o = I("tensorListId", r, e, t10), n = I("index", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10);
      return [t10.getTensorList(o.id).getItem(n, s, a)];
    }
    case "TensorListScatterV2":
    case "TensorListScatter": {
      let o = I("indices", r, e, t10), n = I("tensor", r, e, t10), s = I("elementShape", r, e, t10), a = I("numElements", r, e, t10), i = BN(n, o, s, a);
      return t10.addTensorList(i), [i.idTensor];
    }
    case "TensorListReserve":
    case "EmptyTensorList": {
      let o = I("elementShape", r, e, t10), n = I("elementDType", r, e, t10), s;
      r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements";
      let a = I(s, r, e, t10), i = r.op === "TensorListReserve" ? -1 : a, p = LN(o, n, a, i);
      return t10.addTensorList(p), [p.idTensor];
    }
    case "TensorListGather": {
      let o = I("tensorListId", r, e, t10), n = I("indices", r, e, t10), s = I("elementShape", r, e, t10), a = I("elementDType", r, e, t10);
      return [t10.getTensorList(o.id).gather(n, a, s)];
    }
    case "TensorListStack": {
      let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = I("numElements", r, e, t10);
      return [t10.getTensorList(o.id).stack(n, s, a)];
    }
    case "TensorListFromTensor": {
      let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10), a = MN(o, n, s);
      return t10.addTensorList(a), [a.idTensor];
    }
    case "TensorListConcat":
    case "TensorListConcatV2": {
      let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id), s = I("dtype", r, e, t10), a = I("elementShape", r, e, t10);
      return [n.concat(s, a)];
    }
    case "TensorListPushBack": {
      let o = I("tensorListId", r, e, t10), n = I("tensor", r, e, t10), s = t10.getTensorList(o.id);
      return s.pushBack(n), [s.idTensor];
    }
    case "TensorListPopBack": {
      let o = I("tensorListId", r, e, t10), n = I("elementShape", r, e, t10), s = I("elementDType", r, e, t10);
      return [t10.getTensorList(o.id).popBack(n, s)];
    }
    case "TensorListSplit": {
      let o = I("tensor", r, e, t10), n = I("elementShape", r, e, t10), s = I("lengths", r, e, t10), a = VN(o, s, n);
      return t10.addTensorList(a), [a.idTensor];
    }
    case "TensorListLength": {
      let o = I("tensorListId", r, e, t10), n = t10.getTensorList(o.id);
      return [Ce(n.size(), "int32")];
    }
    case "TensorListResize": {
      let o = I("tensorListId", r, e, t10), n = I("size", r, e, t10), a = t10.getTensorList(o.id).resize(n);
      return t10.addTensorList(a), [a.idTensor];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function WN(r, e, t10) {
  let [o, n] = I("fusedOps", r, e, t10), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = I("numArgs", r, e, t10);
  if (s) {
    if (i && u !== 2)
      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
    if (!i && s && u !== 1)
      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
  }
  if (p)
    throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
  let c = I("strides", r, e, t10), l = ll(r, e, t10), m = I("dataFormat", r, e, t10).toUpperCase(), d = I("dilations", r, e, t10), [f, h] = I("args", r, e, t10);
  a && (h = f, f = void 0);
  let g = I("leakyreluAlpha", r, e, t10);
  return { stride: c, pad: l, dataFormat: m, dilations: d, biasArg: f, preluArg: h, activationFunc: n, leakyreluAlpha: g };
}
var UN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Conv1D": {
      let n = I("stride", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilation", r, e, t10);
      return [o.conv1d(I("x", r, e, t10), I("filter", r, e, t10), n, s, a, i)];
    }
    case "Conv2D": {
      let n = I("strides", r, e, t10), s = ll(r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10);
      return [o.conv2d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, a, [i[1], i[2]])];
    }
    case "_FusedConv2D": {
      let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = WN(r, e, t10);
      return [o.fused.conv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })];
    }
    case "FusedDepthwiseConv2dNative": {
      let { stride: n, pad: s, dataFormat: a, dilations: i, biasArg: p, preluArg: u, activationFunc: c, leakyreluAlpha: l } = WN(r, e, t10);
      return [o.fused.depthwiseConv2d({ x: I("x", r, e, t10), filter: I("filter", r, e, t10), strides: [n[1], n[2]], pad: s, dataFormat: a, dilations: [i[1], i[2]], bias: p, activation: c, preluActivationWeights: u, leakyreluAlpha: l })];
    }
    case "Conv2DBackpropInput":
    case "Conv2dTranspose": {
      let n = I("outputShape", r, e, t10), s = I("strides", r, e, t10), a = ll(r, e, t10);
      return [o.conv2dTranspose(I("x", r, e, t10), I("filter", r, e, t10), n, [s[1], s[2]], a)];
    }
    case "DepthwiseConv2dNative":
    case "DepthwiseConv2d": {
      let n = I("strides", r, e, t10), s = ll(r, e, t10), a = I("dilations", r, e, t10), i = I("dataFormat", r, e, t10).toUpperCase();
      return [o.depthwiseConv2d(I("input", r, e, t10), I("filter", r, e, t10), [n[1], n[2]], s, i, [a[1], a[2]])];
    }
    case "Conv3D": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dataFormat", r, e, t10).toUpperCase(), i = I("dilations", r, e, t10);
      return [o.conv3d(I("x", r, e, t10), I("filter", r, e, t10), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])];
    }
    case "AvgPool": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
      return [o.avgPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)];
    }
    case "MaxPool": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
      return [o.maxPool(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)];
    }
    case "MaxPoolWithArgmax": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10), i = I("includeBatchInIndex", r, e, t10), { result: p, indexes: u } = o.maxPoolWithArgmax(I("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s, i);
      return [p, u];
    }
    case "AvgPool3D": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
      return [o.avgPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
    }
    case "MaxPool3D": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("kernelSize", r, e, t10);
      return [o.maxPool3d(I("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
    }
    case "Dilation2D": {
      let n = I("strides", r, e, t10), s = I("pad", r, e, t10), a = I("dilations", r, e, t10), i = n[1], p = n[2], u = a[1], c = a[2];
      return [o.dilation2d(I("x", r, e, t10), I("filter", r, e, t10), [i, p], s, [u, c], "NHWC")];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var GN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Fill": {
      let n = I("shape", r, e, t10), s = I("dtype", r, e, t10), a = I("value", r, e, t10);
      return [o.fill(n, a, s)];
    }
    case "LinSpace": {
      let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("num", r, e, t10);
      return [o.linspace(n, s, a)];
    }
    case "Multinomial": {
      let n = I("logits", r, e, t10), s = I("numSamples", r, e, t10), a = I("seed", r, e, t10);
      return [o.multinomial(n, s, a)];
    }
    case "OneHot": {
      let n = I("indices", r, e, t10), s = I("depth", r, e, t10), a = I("onValue", r, e, t10), i = I("offValue", r, e, t10), p = I("dtype", r, e, t10);
      return [o.oneHot(n, s, a, i, p)];
    }
    case "Ones":
      return [o.ones(I("shape", r, e, t10), I("dtype", r, e, t10))];
    case "OnesLike":
      return [o.onesLike(I("x", r, e, t10))];
    case "RandomStandardNormal":
      return [o.randomStandardNormal(I("shape", r, e, t10), I("dtype", r, e, t10), I("seed", r, e, t10))];
    case "RandomUniform":
      return [o.randomUniform(I("shape", r, e, t10), I("minval", r, e, t10), I("maxval", r, e, t10), I("dtype", r, e, t10))];
    case "Range": {
      let n = I("start", r, e, t10), s = I("stop", r, e, t10), a = I("step", r, e, t10);
      return [o.range(n, s, a, I("dtype", r, e, t10))];
    }
    case "TruncatedNormal": {
      let n = I("shape", r, e, t10), s = I("mean", r, e, t10), a = I("stdDev", r, e, t10), i = I("seed", r, e, t10);
      return [o.truncatedNormal(n, s, a, I("dtype", r, e, t10), i)];
    }
    case "Zeros":
      return [o.zeros(I("shape", r, e, t10), I("dtype", r, e, t10))];
    case "ZerosLike":
      return [o.zerosLike(I("x", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function rS(r, e, t10) {
  let o = I("boxes", r, e, t10), n = I("scores", r, e, t10), s = I("maxOutputSize", r, e, t10), a = I("iouThreshold", r, e, t10), i = I("scoreThreshold", r, e, t10), p = I("softNmsSigma", r, e, t10);
  return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p };
}
var HN = async (r, e, t10, o, n = Ye) => {
  switch (r.op) {
    case "NonMaxSuppressionV5": {
      let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = rS(r, e, t10), l = await n.image.nonMaxSuppressionWithScoreAsync(s, a, i, p, u, c);
      return [l.selectedIndices, l.selectedScores];
    }
    case "NonMaxSuppressionV4": {
      let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = rS(r, e, t10), c = I("padToMaxOutputSize", r, e, t10), l = await n.image.nonMaxSuppressionPaddedAsync(s, a, i, p, u, c);
      return [l.selectedIndices, l.validOutputs];
    }
    case "NonMaxSuppressionV3":
    case "NonMaxSuppressionV2": {
      let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u } = rS(r, e, t10);
      return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)];
    }
    case "Where": {
      let s = n.cast(I("condition", r, e, t10), "bool"), a = [await n.whereAsync(s)];
      return s.dispose(), a;
    }
    case "ListDiff":
      return n.setdiff1dAsync(I("x", r, e, t10), I("y", r, e, t10));
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var KN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "LowerBound": {
      let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10);
      return [o.lowerBound(n, s)];
    }
    case "TopKV2": {
      let n = I("x", r, e, t10), s = I("k", r, e, t10), a = I("sorted", r, e, t10), i = o.topk(n, s, a);
      return [i.values, i.indices];
    }
    case "UpperBound": {
      let n = I("sortedSequence", r, e, t10), s = I("values", r, e, t10);
      return [o.upperBound(n, s)];
    }
    case "Unique": {
      let n = I("x", r, e, t10), s = o.unique(n);
      return [s.values, s.indices];
    }
    case "UniqueV2": {
      let n = I("x", r, e, t10), s = I("axis", r, e, t10), a = o.unique(n, s);
      return [a.values, a.indices];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var qN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Const":
      return e[r.name];
    case "PlaceholderWithDefault":
      let n = I("default", r, e, t10);
      return [Gt(r.name, e, t10) || n];
    case "Placeholder":
      return [Gt(r.name, e, t10)];
    case "Identity":
    case "StopGradient":
    case "FakeQuantWithMinMaxVars": {
      let c = I("x", r, e, t10);
      return [is(c)];
    }
    case "IdentityN":
      return I("x", r, e, t10).map((c) => is(c));
    case "Snapshot":
      let s = I("x", r, e, t10);
      return [is(s)];
    case "Shape":
      return [o.tensor1d(I("x", r, e, t10).shape, "int32")];
    case "ShapeN":
      return I("x", r, e, t10).map((c) => o.tensor1d(c.shape));
    case "Size":
      return [o.scalar(I("x", r, e, t10).size, "int32")];
    case "Rank":
      return [o.scalar(I("x", r, e, t10).rank, "int32")];
    case "NoOp":
      return [o.scalar(1)];
    case "Print":
      let a = I("x", r, e, t10), i = I("data", r, e, t10), p = I("message", r, e, t10), u = I("summarize", r, e, t10);
      console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(p);
      for (let c = 0; c < i.length; c++)
        console.log(Array.prototype.slice.call(i[c].dataSync()).slice(0, u));
      return [a];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var sf = class {
  constructor(e, t10) {
    this.keyDType = e, this.valueDType = t10, this.handle = Ce(0), this.tensorMap = /* @__PURE__ */ new Map(), _r(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 Ce(this.size(), "int32");
  }
  async import(e, t10) {
    this.checkKeyAndValueTensor(e, t10);
    let o = await e.data();
    return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), Ee(() => {
      let n = so(t10), s = o.length, a = n.length;
      y.assert(s === a, () => `The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);
      for (let i = 0; i < s; i++) {
        let p = o[i], u = n[i];
        _r(u), this.tensorMap.set(p, u);
      }
      return this.handle;
    });
  }
  async find(e, t10) {
    this.checkKeyAndValueTensor(e, t10);
    let o = await e.data();
    return Ee(() => {
      let n = [];
      for (let s = 0; s < o.length; s++) {
        let a = o[s], i = this.findWithDefault(a, t10);
        n.push(i);
      }
      return Sr(n);
    });
  }
  findWithDefault(e, t10) {
    let o = this.tensorMap.get(e);
    return o != null ? o : t10;
  }
  checkKeyAndValueTensor(e, t10) {
    if (e.dtype !== this.keyDType)
      throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);
    if (t10.dtype !== this.valueDType)
      throw new Error(`Expect value dtype ${this.valueDType}, but got ${t10.dtype}`);
  }
};
var jN = async (r, e, t10, o) => {
  switch (r.op) {
    case "HashTable":
    case "HashTableV2": {
      let n = o.getHashTableHandleByName(r.name);
      if (n != null)
        return [n];
      {
        let s = I("keyDType", r, e, t10), a = I("valueDType", r, e, t10), i = new sf(s, a);
        return o.addHashTable(r.name, i), [i.handle];
      }
    }
    case "InitializeTable":
    case "InitializeTableV2":
    case "LookupTableImport":
    case "LookupTableImportV2": {
      let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("values", r, e, t10);
      return [await o.getHashTableById(n.id).import(s, a)];
    }
    case "LookupTableFind":
    case "LookupTableFindV2": {
      let n = I("tableHandle", r, e, t10, o), s = I("keys", r, e, t10), a = I("defaultValue", r, e, t10);
      return [await o.getHashTableById(n.id).find(s, a)];
    }
    case "LookupTableSize":
    case "LookupTableSizeV2": {
      let n = I("tableHandle", r, e, t10, o);
      return [o.getHashTableById(n.id).tensorSize()];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var XN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "ResizeBilinear": {
      let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10);
      return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)];
    }
    case "ResizeNearestNeighbor": {
      let n = I("images", r, e, t10), s = I("size", r, e, t10), a = I("alignCorners", r, e, t10), i = I("halfPixelCenters", r, e, t10);
      return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)];
    }
    case "CropAndResize": {
      let n = I("image", r, e, t10), s = I("boxes", r, e, t10), a = I("boxInd", r, e, t10), i = I("cropSize", r, e, t10), p = I("method", r, e, t10), u = I("extrapolationValue", r, e, t10);
      return [o.image.cropAndResize(n, s, a, i, p, u)];
    }
    case "ImageProjectiveTransformV3": {
      let n = I("images", r, e, t10), s = I("transforms", r, e, t10), a = I("outputShape", r, e, t10), i = I("fillValue", r, e, t10), p = I("interpolation", r, e, t10), u = I("fillMode", r, e, t10);
      return [o.image.transform(n, s, p.toLowerCase(), u.toLowerCase(), i, a)];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var YN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Equal":
      return [o.equal(I("a", r, e, t10), I("b", r, e, t10))];
    case "NotEqual":
      return [o.notEqual(I("a", r, e, t10), I("b", r, e, t10))];
    case "Greater":
      return [o.greater(I("a", r, e, t10), I("b", r, e, t10))];
    case "GreaterEqual":
      return [o.greaterEqual(I("a", r, e, t10), I("b", r, e, t10))];
    case "Less":
      return [o.less(I("a", r, e, t10), I("b", r, e, t10))];
    case "LessEqual":
      return [o.lessEqual(I("a", r, e, t10), I("b", r, e, t10))];
    case "LogicalAnd":
      return [o.logicalAnd(I("a", r, e, t10), I("b", r, e, t10))];
    case "LogicalNot":
      return [o.logicalNot(I("a", r, e, t10))];
    case "LogicalOr":
      return [o.logicalOr(I("a", r, e, t10), I("b", r, e, t10))];
    case "Select":
    case "SelectV2":
      return [o.where(I("condition", r, e, t10), I("a", r, e, t10), I("b", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var QN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "BatchMatMul":
    case "BatchMatMulV2":
    case "MatMul":
      return [o.matMul(I("a", r, e, t10), I("b", r, e, t10), I("transposeA", r, e, t10), I("transposeB", r, e, t10))];
    case "Einsum":
      return [o.einsum(I("equation", r, e, t10), ...I("tensors", r, e, t10))];
    case "Transpose":
      return [o.transpose(I("x", r, e, t10), I("perm", r, e, t10))];
    case "_FusedMatMul":
      let [n, s] = I("fusedOps", r, e, t10), a = n === "biasadd", i = s === "prelu", p = I("numArgs", r, e, t10), u = I("leakyreluAlpha", r, e, t10);
      if (a) {
        if (i && p !== 2)
          throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
        if (!i && p !== 1)
          throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
      }
      let [c, l] = I("args", r, e, t10);
      return [o.fused.matMul({ a: I("a", r, e, t10), b: I("b", r, e, t10), transposeA: I("transposeA", r, e, t10), transposeB: I("transposeB", r, e, t10), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var ZN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "EuclideanNorm":
      return [o.euclideanNorm(I("x", r, e, t10), I("axis", r, e, t10), I("keepDims", r, e, t10))];
    case "FusedBatchNorm":
    case "FusedBatchNormV2":
      return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))];
    case "FusedBatchNormV3":
      return [o.batchNorm(I("x", r, e, t10), I("mean", r, e, t10), I("variance", r, e, t10), I("offset", r, e, t10), I("scale", r, e, t10), I("epsilon", r, e, t10))];
    case "LRN":
      return [o.localResponseNormalization(I("x", r, e, t10), I("radius", r, e, t10), I("bias", r, e, t10), I("alpha", r, e, t10), I("beta", r, e, t10))];
    case "Softmax":
      return [o.softmax(I("x", r, e, t10))];
    case "LogSoftmax":
      return [o.logSoftmax(I("x", r, e, t10))];
    case "SparseToDense":
      return [o.sparseToDense(I("sparseIndices", r, e, t10), I("outputShape", r, e, t10), I("sparseValues", r, e, t10), I("defaultValue", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var JN = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "RaggedGather": {
      let { outputNestedSplits: n, outputDenseValues: s } = o.raggedGather(I("paramsNestedSplits", r, e, t10), I("paramsDenseValues", r, e, t10), I("indices", r, e, t10), I("outputRaggedRank", r, e, t10));
      return n.concat(s);
    }
    case "RaggedRange": {
      let { rtNestedSplits: n, rtDenseValues: s } = o.raggedRange(I("starts", r, e, t10), I("limits", r, e, t10), I("splits", r, e, t10));
      return [n, s];
    }
    case "RaggedTensorToTensor":
      return [o.raggedTensorToTensor(I("shape", r, e, t10), I("values", r, e, t10), I("defaultValue", r, e, t10), I("rowPartitionTensors", r, e, t10), I("rowPartitionTypes", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var eT = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Max": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.max(I("x", r, e, t10), i, p)];
    }
    case "Mean": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.mean(I("x", r, e, t10), i, p)];
    }
    case "Min": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.min(I("x", r, e, t10), i, p)];
    }
    case "Sum": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.sum(I("x", r, e, t10), i, p)];
    }
    case "All": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.all(I("x", r, e, t10), i, p)];
    }
    case "Any": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.any(I("x", r, e, t10), i, p)];
    }
    case "ArgMax": {
      let i = I("axis", r, e, t10);
      return [o.argMax(I("x", r, e, t10), i)];
    }
    case "ArgMin": {
      let i = I("axis", r, e, t10);
      return [o.argMin(I("x", r, e, t10), i)];
    }
    case "Prod": {
      let i = I("axis", r, e, t10), p = I("keepDims", r, e, t10);
      return [o.prod(I("x", r, e, t10), i, p)];
    }
    case "Cumprod": {
      let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10);
      return [o.cumprod(I("x", r, e, t10), i, p, u)];
    }
    case "Cumsum": {
      let i = I("axis", r, e, t10), p = I("exclusive", r, e, t10), u = I("reverse", r, e, t10);
      return [o.cumsum(I("x", r, e, t10), i, p, u)];
    }
    case "Bincount":
      let n = I("x", r, e, t10), s = I("weights", r, e, t10), a = I("size", r, e, t10);
      return [o.bincount(n, s, a)];
    case "DenseBincount": {
      let i = I("x", r, e, t10), p = I("weights", r, e, t10), u = I("size", r, e, t10), c = I("binaryOutput", r, e, t10);
      return [o.denseBincount(i, p, u, c)];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var tT = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "ConcatV2":
    case "Concat": {
      let n = I("n", r, e, t10), s = I("axis", r, e, t10), a = I("tensors", r, e, t10);
      return a = a.slice(0, n), [o.concat(a, s)];
    }
    case "Gather": {
      let n = I("x", r, e, t10), s = I("indices", r, e, t10);
      return [o.gather(n, o.cast(s, "int32"), 0)];
    }
    case "GatherV2": {
      let n = I("axis", r, e, t10), s = I("batchDims", r, e, t10), a = I("x", r, e, t10), i = I("indices", r, e, t10);
      return [o.gather(a, o.cast(i, "int32"), n, s)];
    }
    case "Reverse": {
      let n = I("dims", r, e, t10), s = [];
      for (let i = 0; i < n.length; i++)
        n[i] && s.push(i);
      let a = I("x", r, e, t10);
      return [o.reverse(a, s)];
    }
    case "ReverseV2": {
      let n = I("axis", r, e, t10), s = I("x", r, e, t10);
      return [o.reverse(s, n)];
    }
    case "Slice": {
      let n = I("begin", r, e, t10), s = I("size", r, e, t10);
      return [o.slice(I("x", r, e, t10), n, s)];
    }
    case "StridedSlice": {
      let n = I("begin", r, e, t10), s = I("end", r, e, t10), a = I("strides", r, e, t10), i = I("beginMask", r, e, t10), p = I("endMask", r, e, t10), u = I("ellipsisMask", r, e, t10), c = I("newAxisMask", r, e, t10), l = I("shrinkAxisMask", r, e, t10), m = I("x", r, e, t10);
      return [o.stridedSlice(m, n, s, a, i, p, u, c, l)];
    }
    case "Pack":
      return Ee(() => {
        let n = I("axis", r, e, t10), s = I("tensors", r, e, t10), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => {
          let c = y.arraysEqual(u.shape, a);
          if (!c && !y.arraysEqual(o.squeeze(u).shape, i))
            throw new Error("the input tensors shape does not match");
          return c ? u : o.reshape(u, a);
        });
        return [o.stack(p, n)];
      });
    case "Unpack": {
      let n = I("axis", r, e, t10), s = I("tensor", r, e, t10);
      return o.unstack(s, n);
    }
    case "Tile": {
      let n = I("reps", r, e, t10);
      return [o.tile(I("x", r, e, t10), n)];
    }
    case "Split":
    case "SplitV": {
      let n = I("axis", r, e, t10), s = I("numOrSizeSplits", r, e, t10), a = I("x", r, e, t10);
      return o.split(a, s, n);
    }
    case "ScatterNd": {
      let n = I("indices", r, e, t10), s = I("values", r, e, t10), a = I("shape", r, e, t10);
      return [o.scatterND(n, s, a)];
    }
    case "GatherNd": {
      let n = I("x", r, e, t10), s = I("indices", r, e, t10);
      return [o.gatherND(n, s)];
    }
    case "SparseToDense": {
      let n = I("sparseIndices", r, e, t10), s = I("outputShape", r, e, t10), a = I("sparseValues", r, e, t10), i = I("defaultValue", r, e, t10);
      return [o.sparseToDense(n, a, s, a.dtype === i.dtype ? i : o.cast(i, a.dtype))];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var rT = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "SparseFillEmptyRows": {
      let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(I("indices", r, e, t10), I("values", r, e, t10), I("denseShape", r, e, t10), I("defaultValue", r, e, t10));
      return [n, s, a, i];
    }
    case "SparseReshape": {
      let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(I("inputIndices", r, e, t10), I("inputShape", r, e, t10), I("newShape", r, e, t10));
      return [n, s];
    }
    case "SparseSegmentMean":
      return [o.sparse.sparseSegmentMean(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))];
    case "SparseSegmentSum":
      return [o.sparse.sparseSegmentSum(I("data", r, e, t10), I("indices", r, e, t10), I("segmentIds", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var oT = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "FFT":
      return [o.fft(I("x", r, e, t10))];
    case "IFFT":
      return [o.ifft(I("x", r, e, t10))];
    case "RFFT":
      return [o.rfft(I("x", r, e, t10))];
    case "IRFFT":
      return [o.irfft(I("x", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var nT = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "StringNGrams": {
      let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(I("data", r, e, t10), I("dataSplits", r, e, t10), I("separator", r, e, t10), I("nGramWidths", r, e, t10), I("leftPad", r, e, t10), I("rightPad", r, e, t10), I("padWidth", r, e, t10), I("preserveShortSequences", r, e, t10));
      return [n, s];
    }
    case "StringSplit": {
      let { indices: n, values: s, shape: a } = o.string.stringSplit(I("input", r, e, t10), I("delimiter", r, e, t10), I("skipEmpty", r, e, t10));
      return [n, s, a];
    }
    case "StringToHashBucketFast":
      return [o.string.stringToHashBucketFast(I("input", r, e, t10), I("numBuckets", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var sT = (r, e, t10, o = Ye) => {
  switch (r.op) {
    case "Cast":
      return [o.cast(I("x", r, e, t10), I("dtype", r, e, t10))];
    case "ExpandDims": {
      let n = I("axis", r, e, t10);
      return [o.expandDims(I("x", r, e, t10), n)];
    }
    case "Squeeze": {
      let n = I("axis", r, e, t10);
      return [o.squeeze(I("x", r, e, t10), n)];
    }
    case "Reshape":
      return [o.reshape(I("x", r, e, t10), I("shape", r, e, t10))];
    case "MirrorPad":
      return [o.mirrorPad(I("x", r, e, t10), I("padding", r, e, t10), I("mode", r, e, t10))];
    case "PadV2":
    case "Pad":
      return [o.pad(I("x", r, e, t10), I("padding", r, e, t10), I("constantValue", r, e, t10))];
    case "SpaceToBatchND": {
      let n = I("blockShape", r, e, t10), s = I("paddings", r, e, t10);
      return [o.spaceToBatchND(I("x", r, e, t10), n, s)];
    }
    case "BatchToSpaceND": {
      let n = I("blockShape", r, e, t10), s = I("crops", r, e, t10);
      return [o.batchToSpaceND(I("x", r, e, t10), n, s)];
    }
    case "DepthToSpace": {
      let n = I("blockSize", r, e, t10), s = I("dataFormat", r, e, t10).toUpperCase();
      return [o.depthToSpace(I("x", r, e, t10), n, s)];
    }
    case "BroadcastTo":
      return [o.broadcastTo(I("x", r, e, t10), I("shape", r, e, t10))];
    case "BroadcastArgs":
      return [o.broadcastArgs(I("s0", r, e, t10), I("s1", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function oS(r, e, t10, o, n = Ee) {
  let s = ((a, i, p) => {
    switch (a.category) {
      case "arithmetic":
        return n(() => FN(a, i, p));
      case "basic_math":
        return n(() => ON(a, i, p));
      case "control":
        return zN(a, i, p);
      case "convolution":
        return n(() => UN(a, i, p));
      case "creation":
        return n(() => GN(a, i, p));
      case "dynamic":
        return HN(a, i, p);
      case "evaluation":
        return n(() => KN(a, i, p));
      case "image":
        return n(() => XN(a, i, p));
      case "graph":
        return n(() => qN(a, i, p));
      case "logical":
        return n(() => YN(a, i, p));
      case "matrices":
        return n(() => QN(a, i, p));
      case "normalization":
        return n(() => ZN(a, i, p));
      case "ragged":
        return n(() => JN(a, i, p));
      case "reduction":
        return n(() => eT(a, i, p));
      case "slice_join":
        return n(() => tT(a, i, p));
      case "sparse":
        return n(() => rT(a, i, p));
      case "spectral":
        return n(() => oT(a, i, p));
      case "string":
        return n(() => nT(a, i, p));
      case "transformation":
        return n(() => sT(a, i, p));
      case "hash_table":
        return jN(a, i, p, o);
      case "custom":
        let u = Gd(a.op);
        if (u && u.customExecutor)
          return u.customExecutor(new rf(a, i, p));
        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()`);
    }
  })(r, e, t10);
  return y.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s);
}
var dl = class {
  constructor(e = {}, t10 = {}, o = {}, n = {}) {
    this.weightMap = e, this.tensorArrayMap = t10, this.tensorListMap = o, this.functionMap = n, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds();
  }
  newFrame(e, t10) {
    return { id: e, frameName: t10, 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 t10 = 0; t10 < this.contexts.length - 1; t10++) {
      let o = this.contexts.slice(0, this.contexts.length - t10);
      e.push(this.contextIdforContexts(o));
    }
    e.push(""), this._currentContextIds = e;
  }
  contextIdforContexts(e) {
    return e ? e.map((t10) => t10.id === 0 && t10.iterationId === 0 ? "" : `${t10.frameName}-${t10.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 t10 in this.tensorArrayMap)
      this.tensorArrayMap[t10].clearAndClose(e);
    for (let t10 in this.tensorListMap)
      this.tensorListMap[t10].clearAndClose(e);
  }
};
function nS(r, e, t10, o) {
  let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = Object.keys(r).map((m) => Ir(m)[0]), c = [];
  o != null && (c = o.map((m) => Ir(m.name)[0]));
  let l = [...e];
  for (; l.length > 0; ) {
    let m = l.pop();
    if ((sS(m) || T6(m) || _6(m)) && a == null && (a = m, i = a.children.map((d) => d.name).filter((d) => n.has(d))), n.add(m.name), t10[m.name] == null && u.indexOf(m.name) === -1 && c.indexOf(m.name) === -1) {
      if (m.inputs.length === 0) {
        s.push(m.name);
        continue;
      }
      m.inputs.forEach((d) => {
        p.has(d.name) || (p.add(d.name), l.push(d));
      });
    }
  }
  return { inputs: r, outputs: e, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i };
}
function aT(r, e, t10) {
  let { usedNodes: o, inputs: n } = t10, s = [], a = Object.keys(n).map((c) => Ir(c)[0]).map((c) => r.nodes[c]), i = r.initNodes;
  a.forEach((c) => {
    o.has(c.name) && s.push(c);
  }), r.weights.forEach((c) => {
    o.has(c.name) && s.push(c);
  }), i != null && i.forEach((c) => {
    o.has(c.name) && s.push(c);
  });
  let p = /* @__PURE__ */ new Set(), u = [];
  for (; s.length > 0; ) {
    let c = s.pop();
    p.add(c.name), e[c.name] || u.push(c), c.children.forEach((l) => {
      !p.has(l.name) && o.has(l.name) && l.inputs.every((m) => p.has(m.name)) && s.push(l);
    });
  }
  return u;
}
var v6 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
var k6 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
var N6 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
function sS(r) {
  return v6.indexOf(r.op) >= 0;
}
function T6(r) {
  return k6.indexOf(r.op) >= 0;
}
function _6(r) {
  return N6.indexOf(r.op) >= 0;
}
var ku = class {
  constructor(e, t10) {
    this.graph = e, this.parent = t10, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.keepIntermediateTensors = false, 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((o) => {
      this._functionExecutorMap[o] = new ku(e.functions[o], 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 t10 = Object.keys(e).map((o) => e[o].map((n) => n.id));
    this._weightIds = [].concat(...t10), 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 t10 = e.signatureKey || e.name;
      return e.defaultOutput ? `${t10}:${e.defaultOutput}` : t10;
    });
  }
  get functions() {
    return Object.keys(this._functions).reduce((e, t10) => (e[t10] = this._functions[t10].signature, e), {});
  }
  getCompilationKey(e, t10) {
    let o = e.map((s) => s.name).sort(), n = t10.map((s) => s.name).sort();
    return o.join(this.SEPERATOR) + "--" + n.join(this.SEPERATOR);
  }
  compile(e, t10) {
    let o = nS(e, t10, this.weightMap, this._initNodes), { missingInputs: n, dynamicNode: s, syncInputs: a } = o;
    if (s != null)
      throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);
    if (n.length > 0) {
      let i = t10.map((u) => u.name), p = Object.keys(e);
      throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${p}]. Missing the following inputs: [${n}]`);
    }
    return aT(this.graph, this.weightMap, o);
  }
  cloneAndKeepTensor(e) {
    if (e == null)
      return null;
    let t10 = e.clone();
    return _r(t10), t10;
  }
  cloneTensorList(e) {
    return e ? e.map((o) => this.cloneAndKeepTensor(o)) : null;
  }
  cloneTensorMap(e) {
    return Object.fromEntries(Object.entries(e).map(([t10, o]) => [t10, this.cloneTensorList(o)]));
  }
  execute(e, t10) {
    this.disposeIntermediateTensors(), e = this.mapInputs(e);
    let o = Object.keys(e).sort();
    this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10);
    let n = o.map((l) => this.graph.nodes[Ir(l)[0]]), s = t10.map((l) => Ir(l)[0]), a = s.map((l) => this.graph.nodes[l]);
    a.length === 0 && (a = this._outputs);
    let i = this.getCompilationKey(n, a), p = this.compiledMap.get(i);
    p == null && (p = this.compile(e, a), this.compiledMap.set(i, p));
    try {
      this.keepIntermediateTensors = O().getBool("KEEP_INTERMEDIATE_TENSORS");
    } catch (l) {
      this.keepIntermediateTensors = false, console.warn(l.message);
    }
    let u = {}, c = {};
    return Ee(() => {
      let l = new dl(this.weightMap, u, c, this.functionExecutorMap), m = Object.assign({}, this.weightMap);
      this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap)), Object.keys(e).forEach((h) => {
        let [g, x] = Ir(h), b = [];
        b[x] = e[h], m[g] = b, this.keepIntermediateTensors && (this.clonedTensorsMap[g] = this.cloneTensorList(b));
      });
      let d = this.getFrozenTensorIds(m), f = {};
      for (let h = 0; h < p.length; h++) {
        let g = p[h];
        if (!m[g.name]) {
          let x = oS(g, m, l, this._resourceManager);
          if (y.isPromise(x))
            throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);
          m[g.name] = x, this.keepIntermediateTensors && (this.clonedTensorsMap[g.name] = this.cloneTensorList(x)), this.checkTensorForDisposal(g.name, g, m, l, d, s, f);
        }
      }
      return this.parent == null && l.dispose(d), t10.map((h) => Gt(h, m, l));
    });
  }
  getFrozenTensorIds(e) {
    let t10 = [].concat.apply([], Object.keys(e).map((o) => e[o]).map((o) => o.map((n) => n.id)));
    return new Set(t10);
  }
  checkTensorForDisposal(e, t10, o, n, s, a, i) {
    t10.category === "control" || a.indexOf(e) !== -1 || (o[e].forEach((p) => {
      p != null && (i[p.id] = (i[p.id] || 0) + t10.children.length);
    }), t10.inputs.forEach((p) => {
      if (p.category !== "control") {
        let u = EN(p.name, o, n);
        u != null && u.forEach((c) => {
          if (c && !c.kept && !s.has(c.id)) {
            let l = i[c.id];
            l === 1 ? (c.dispose(), delete i[c.id]) : l != null && i[c.id]--;
          }
        });
      }
    }));
  }
  async executeAsync(e, t10) {
    return this._executeAsync(e, t10);
  }
  disposeIntermediateTensors() {
    this.clonedTensorsMap && (Object.values(this.clonedTensorsMap).forEach((e) => {
      for (let t10 of e)
        t10 && !t10.isDisposed && t10.dispose();
    }), this.clonedTensorsMap = null);
  }
  getIntermediateTensors() {
    return this.clonedTensorsMap;
  }
  async _executeAsync(e, t10, o = false, n = {}, s = {}) {
    this.disposeIntermediateTensors(), o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10));
    try {
      this.keepIntermediateTensors = O().getBool("KEEP_INTERMEDIATE_TENSORS");
    } catch (m) {
      this.keepIntermediateTensors = false, console.warn(m.message);
    }
    let a = new dl(this.weightMap, n, s, this.functionExecutorMap);
    this.keepIntermediateTensors && (this.clonedTensorsMap = this.cloneTensorMap(this.weightMap));
    let i = await this.executeWithControlFlow(e, a, t10, o), p = t10.map((m) => Gt(m, i, a)), u = p.map((m) => m.id), c = Object.keys(e).map((m) => e[m].id), l = /* @__PURE__ */ new Set([...u, ...c, ...this.weightIds]);
    return Object.values(i).forEach((m) => {
      m.forEach((d) => {
        d && !d.isDisposed && !l.has(d.id) && d.dispose();
      });
    }), this.parent == null && a.dispose(l), p;
  }
  async executeFunctionAsync(e, t10, o) {
    let n = e.reduce((s, a, i) => (s[this.inputs[i].name] = a, s), {});
    return this._executeAsync(n, this.outputNodes, true, t10, o);
  }
  async executeWithControlFlow(e, t10, o, n) {
    let s = Object.keys(e), a = s.map((C) => this.graph.nodes[Ir(C)[0]]), i = o.map((C) => Ir(C)[0]), p = i.map((C) => this.graph.nodes[C]);
    p.length === 0 && (p = this._outputs);
    let { usedNodes: u, missingInputs: c, dynamicNode: l, syncInputs: m } = nS(e, p, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((C) => ({ node: C, contexts: t10.currentContext })), f = Object.assign({}, this.weightMap);
    Object.keys(e).forEach((C) => {
      let [w, k] = Ir(C), _ = [];
      _[k] = e[C], f[w] = _;
    });
    let h = {}, g = this.getFrozenTensorIds(f), x = {};
    for (; d.length > 0; ) {
      let C = this.processStack(a, d, t10, f, x, g, i, h, u);
      await Promise.all(C);
    }
    l == null && !n && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
    let b = p.filter((C) => !sS(C) && !Gt(C.name, f, t10)).map((C) => C.name);
    if (b.length > 0) {
      let C = "";
      throw l != null && (C = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`), new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${C}`);
    }
    return f;
  }
  processStack(e, t10, o, n, s, a, i, p, u) {
    let c = [];
    for (; t10.length > 0; ) {
      let l = t10.pop();
      o.currentContext = l.contexts;
      let m = "";
      if (l.node.op === "Enter" && I("isConstant", l.node, n, o) && ([m] = as(l.node.name, o)), n[l.node.name] == null) {
        let d = oS(l.node, n, o, this._resourceManager);
        m || ([m] = as(l.node.name, o));
        let f = o.currentContext;
        y.isPromise(d) ? c.push(d.then((h) => (n[m] = h, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(h)), o.currentContext = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u), h))) : (n[m] = d, this.keepIntermediateTensors && (this.clonedTensorsMap[m] = this.cloneTensorList(d)), this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u));
      } else
        this.processChildNodes(l.node, t10, o, n, s, u);
    }
    return c;
  }
  processChildNodes(e, t10, o, n, s, a) {
    e.children.forEach((i) => {
      let [p] = as(i.name, o);
      s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Gt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Gt(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })));
    });
  }
  dispose() {
    Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t10) => t10.dispose()));
  }
  checkInputShapeAndType(e) {
    Object.keys(e).forEach((t10) => {
      let o = e[t10], [n] = Ir(t10), s = this.graph.nodes[n];
      if (s.attrParams.shape && s.attrParams.shape.value) {
        let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p);
        y.assert(i, () => `The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`);
      }
      s.attrParams.dtype && s.attrParams.dtype.value && y.assert(o.dtype === s.attrParams.dtype.value, () => `The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`);
    });
  }
  mapInputs(e) {
    var t10, o;
    let n = {};
    for (let s in e) {
      let a = (o = (t10 = this._signature) === null || t10 === void 0 ? void 0 : t10.inputs) === null || o === void 0 ? void 0 : o[s];
      a != null ? n[a.name] = e[s] : n[s] = e[s];
    }
    return n;
  }
  checkInputs(e) {
    let t10 = Object.keys(e).filter((o) => {
      let [n] = Ir(o);
      return this.graph.nodes[n] == null;
    });
    if (t10.length > 0)
      throw new Error(`The dict provided in model.execute(dict) has keys: [${t10}] that are not part of graph`);
  }
  mapOutputs(e) {
    return e.map((t10) => {
      var o, n;
      let s = (n = (o = this._signature) === null || o === void 0 ? void 0 : o.outputs) === null || n === void 0 ? void 0 : n[t10];
      return s != null ? s.name : t10;
    }, {});
  }
  checkOutputs(e) {
    e.forEach((t10) => {
      let [o] = Ir(t10);
      if (!this.graph.nodes[o])
        throw new Error(`The output '${t10}' is not found in the graph`);
    });
  }
};
var af = class {
  constructor(e = {}, t10 = {}) {
    this.hashTableNameToHandle = e, this.hashTableMap = t10;
  }
  addHashTable(e, t10) {
    this.hashTableNameToHandle[e] = t10.handle, this.hashTableMap[t10.id] = t10;
  }
  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];
  }
};
var $6 = "?tfjs-format=file";
var E6 = "model.json";
var fl = class {
  constructor(e, t10 = {}, o = La) {
    this.modelUrl = e, this.loadOptions = t10, this.version = "n/a", this.io = o, t10 == null && (this.loadOptions = {}), this.resourceManager = new af();
  }
  get modelVersion() {
    return this.version;
  }
  get inputNodes() {
    return this.executor.inputNodes;
  }
  get outputNodes() {
    return this.executor.outputNodes;
  }
  get inputs() {
    return this.executor.inputs;
  }
  get outputs() {
    return this.executor.outputs;
  }
  get weights() {
    return this.executor.weightMap;
  }
  get metadata() {
    return this.artifacts.userDefinedMetadata;
  }
  get modelSignature() {
    return this.signature;
  }
  get modelStructuredOutputKeys() {
    return this.structuredOutputKeys;
  }
  findIOHandler() {
    let e = this.modelUrl;
    if (e.load != null)
      this.handler = e;
    else if (this.loadOptions.requestInit != null)
      this.handler = this.io.browserHTTPRequest(e, this.loadOptions);
    else {
      let t10 = this.io.getLoadHandlers(e, this.loadOptions);
      if (t10.length === 0)
        t10.push(this.io.browserHTTPRequest(e, this.loadOptions));
      else if (t10.length > 1)
        throw new Error(`Found more than one (${t10.length}) load handlers for URL '${[e]}'`);
      this.handler = t10[0];
    }
  }
  load() {
    if (this.findIOHandler(), this.handler.load == null)
      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
    let e = this.handler.load();
    return y.isPromise(e) ? e.then((t10) => this.loadSync(t10)) : this.loadSync(e);
  }
  loadSync(e) {
    this.artifacts = e;
    let t10 = this.artifacts.modelTopology, o = this.artifacts.signature;
    if (this.artifacts.userDefinedMetadata != null) {
      let s = this.artifacts.userDefinedMetadata;
      s.signature != null && (o = s.signature), s.structuredOutputKeys != null && (this.structuredOutputKeys = s.structuredOutputKeys);
    }
    this.signature = o, this.version = `${t10.versions.producer}.${t10.versions.minConsumer}`;
    let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
    if (this.executor = new ku(ml.Instance.transformGraph(t10, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
      let s = ml.Instance.transformGraph(e.modelInitializer);
      this.initializer = new ku(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature;
    }
    return true;
  }
  async save(e, t10) {
    if (typeof e == "string") {
      let o = this.io.getSaveHandlers(e);
      if (o.length === 0)
        throw new Error(`Cannot find any save handlers for URL '${e}'`);
      if (o.length > 1)
        throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);
      e = o[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);
  }
  addStructuredOutputNames(e) {
    if (this.structuredOutputKeys) {
      let t10 = e instanceof it ? [e] : e, o = {};
      return t10.forEach((n, s) => o[this.structuredOutputKeys[s]] = n), o;
    }
    return e;
  }
  predict(e, t10) {
    let o = this.execute(e, this.outputNodes);
    return this.addStructuredOutputNames(o);
  }
  async predictAsync(e, t10) {
    let o = await this.executeAsync(e, this.outputNodes);
    return this.addStructuredOutputNames(o);
  }
  normalizeInputs(e) {
    var t10;
    if (!(e instanceof it) && !Array.isArray(e)) {
      let s = (t10 = this.signature) === null || t10 === void 0 ? void 0 : t10.inputs;
      if (s != null)
        for (let a in s) {
          let i = s[a];
          i.resourceId != null && (e[a] = this.resourceIdToCapturedInput[i.resourceId]);
        }
      return e;
    }
    e = Array.isArray(e) ? e : [e];
    let o = Object.keys(this.resourceIdToCapturedInput).length;
    if (e.length + o !== this.inputNodes.length)
      throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - o} non-resource placeholders, while there are ${e.length} input tensors provided.`);
    let n = 0;
    return this.inputNodes.reduce((s, a) => {
      var i, p, u;
      let c = (u = (p = (i = this.signature) === null || i === void 0 ? void 0 : i.inputs) === null || p === void 0 ? void 0 : p[a]) === null || u === void 0 ? void 0 : u.resourceId;
      return c != null ? s[a] = this.resourceIdToCapturedInput[c] : s[a] = e[n++], s;
    }, {});
  }
  normalizeOutputs(e) {
    return e = e || this.outputNodes, Array.isArray(e) ? e : [e];
  }
  executeInitializerGraph() {
    return this.initializer == null ? [] : this.initializerSignature == null ? this.initializer.execute({}, []) : this.initializer.execute({}, Object.keys(this.initializerSignature.outputs));
  }
  async executeInitializerGraphAsync() {
    return this.initializer == null ? [] : this.initializerSignature == null ? this.initializer.executeAsync({}, []) : this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs));
  }
  setResourceIdToCapturedInput(e) {
    if (this.resourceIdToCapturedInput = {}, this.initializerSignature) {
      let t10 = this.initializerSignature.outputs, o = Object.keys(t10);
      for (let n = 0; n < o.length; n++) {
        let s = o[n], a = t10[s];
        this.resourceIdToCapturedInput[a.resourceId] = e[n];
      }
    }
  }
  execute(e, t10) {
    this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(this.executeInitializerGraph()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10);
    let o = this.executor.execute(e, t10);
    return o.length > 1 ? o : o[0];
  }
  async executeAsync(e, t10) {
    this.resourceIdToCapturedInput == null && this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()), e = this.normalizeInputs(e), t10 = this.normalizeOutputs(t10);
    let o = await this.executor.executeAsync(e, t10);
    return o.length > 1 ? o : o[0];
  }
  getIntermediateTensors() {
    return this.executor.getIntermediateTensors();
  }
  disposeIntermediateTensors() {
    this.executor.disposeIntermediateTensors();
  }
  convertTensorMapToTensorsMap(e) {
    return Object.keys(e).reduce((t10, o) => (t10[o] = [e[o]], t10), {});
  }
  dispose() {
    this.executor.dispose(), this.initializer && (this.initializer.dispose(), this.resourceIdToCapturedInput && Ot(this.resourceIdToCapturedInput)), this.resourceManager.dispose();
  }
};
async function A6(r, e = {}, t10 = La) {
  if (r == null)
    throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
  e == null && (e = {}), e.fromTFHub && typeof r == "string" && (r = D6(r));
  let o = new fl(r, e, t10);
  return await o.load(), o;
}
function R6(r) {
  if (r == null)
    throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");
  let e;
  if (r instanceof Array) {
    let [o, n] = r;
    if (!o)
      throw new Error("modelJSON must be the first element of the array");
    if (!n || !(n instanceof ArrayBuffer))
      throw new Error("An ArrayBuffer of weights must be the second element of the array");
    if (!("modelTopology" in o))
      throw new Error("Model JSON is missing 'modelTopology'");
    if (!("weightsManifest" in o))
      throw new Error("Model JSON is missing 'weightsManifest'");
    let s = La.getWeightSpecs(o.weightsManifest), a = La.getModelArtifactsForJSONSync(o, s, n);
    e = La.fromMemorySync(a);
  } else if ("load" in r)
    e = r;
  else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r)
    e = La.fromMemorySync(r);
  else
    throw new Error("Unknown model format");
  let t10 = new fl(e);
  return t10.load(), t10;
}
function D6(r) {
  return r.endsWith("/") || (r = r + "/"), `${r}${E6}${$6}`;
}
var F6 = "4.2.0";
function j(r, e) {
  Array.isArray(r) || (r = [r]), r.forEach((t10) => {
    t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`);
  });
}
var O6 = Vt.whereImpl;
var Pi = class extends Zr {
  constructor() {
    super(), this.blockSize = 48, this.firstUse = true, this.data = new Fo(this, sr());
  }
  nextDataId() {
    return Pi.nextDataId++;
  }
  write(e, t10, o) {
    this.firstUse && (this.firstUse = false, O().get("IS_NODE") && S.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. 
============================`));
    let n = { id: this.nextDataId() };
    return this.data.set(n, { values: e, dtype: o, refCount: 1 }), n;
  }
  makeTensorInfo(e, t10, o) {
    let n;
    if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) {
      let s = o.map((a) => y.encodeString(a));
      n = this.write(s, e, t10);
    } else
      n = this.write(o, e, t10);
    return { dataId: n, shape: e, dtype: t10 };
  }
  refCount(e) {
    return this.data.has(e) ? this.data.get(e).refCount : 0;
  }
  incRef(e) {
    let t10 = this.data.get(e);
    t10.refCount++;
  }
  decRef(e) {
    if (this.data.has(e)) {
      let t10 = this.data.get(e);
      t10.refCount--;
    }
  }
  move(e, t10, o, n, s) {
    this.data.set(e, { values: t10, dtype: n, refCount: s });
  }
  numDataIds() {
    return this.data.numDataIds();
  }
  async read(e) {
    return this.readSync(e);
  }
  readSync(e) {
    let { dtype: t10, complexTensorInfos: o } = this.data.get(e);
    if (t10 === "complex64") {
      let n = this.readSync(o.real.dataId), s = this.readSync(o.imag.dataId);
      return S.mergeRealAndImagArrays(n, s);
    }
    return y.convertBackendValuesAndArrayBuffer(this.data.get(e).values, t10);
  }
  bufferSync(e) {
    let t10 = this.readSync(e.dataId);
    if (e.dtype === "string")
      try {
        let o = t10.map((n) => y.decodeString(n));
        return le(e.shape, e.dtype, o);
      } catch (o) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return le(e.shape, e.dtype, t10);
  }
  makeOutput(e, t10, o) {
    return sr().makeTensorFromTensorInfo(this.makeTensorInfo(t10, o, e), this);
  }
  disposeData(e, t10 = false) {
    if (this.data.has(e)) {
      if (this.data.get(e).refCount--, !t10 && this.data.get(e).refCount > 0)
        return false;
      let { complexTensorInfos: o } = this.data.get(e);
      o != null && (this.disposeData(o.real.dataId, true), this.disposeData(o.imag.dataId, true)), this.data.delete(e);
    }
    return true;
  }
  disposeIntermediateTensorInfo(e) {
    this.disposeData(e.dataId);
  }
  async time(e) {
    let t10 = y.now();
    return e(), { kernelMs: y.now() - t10 };
  }
  memory() {
    return { unreliable: true, reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."] };
  }
  where(e) {
    j([e], "where");
    let t10 = this.readSync(e.dataId);
    return O6(e.shape, t10);
  }
  dispose() {
  }
  floatPrecision() {
    return 32;
  }
  epsilon() {
    return super.epsilon();
  }
};
Pi.nextDataId = 0;
var Zp = {};
Ge(Zp, { addImpl: () => uS, bincountImpl: () => jp, bincountReduceImpl: () => uf, castImpl: () => iS, ceilImpl: () => pS, concatImpl: () => Nu, equalImpl: () => cS, expImpl: () => mS, expm1Impl: () => fS, floorImpl: () => hS, gatherNdImpl: () => pf, gatherV2Impl: () => cf, greaterEqualImpl: () => xS, greaterImpl: () => gS, lessEqualImpl: () => bS, lessImpl: () => yS, linSpaceImpl: () => lf, logImpl: () => CS, maxImpl: () => mf, maximumImpl: () => SS, minimumImpl: () => wS, multiplyImpl: () => hl, negImpl: () => IS, notEqualImpl: () => vS, prodImpl: () => kS, raggedGatherImpl: () => df, raggedRangeImpl: () => ff, raggedTensorToTensorImpl: () => hf, rangeImpl: () => _u, rsqrtImpl: () => NS, scatterImpl: () => Va, sigmoidImpl: () => OT, simpleAbsImpl: () => aS, sliceImpl: () => $u, sparseFillEmptyRowsImpl: () => gf, sparseReshapeImpl: () => xf, sparseSegmentReductionImpl: () => Qp, sqrtImpl: () => LT, squaredDifferenceImpl: () => _S, stridedSliceImpl: () => yf, stringNGramsImpl: () => Eu, stringSplitImpl: () => Au, stringToHashBucketFastImpl: () => Ru, subImpl: () => ES, tileImpl: () => bf, topKImpl: () => Cf, transposeImpl: () => Xp, uniqueImpl: () => Sf });
function aS(r) {
  let e = new Float32Array(r.length);
  for (let t10 = 0; t10 < r.length; ++t10)
    e[t10] = Math.abs(r[t10]);
  return e;
}
var P6 = (r) => {
  let { x: e } = r.inputs, t10 = r.backend;
  j(e, "abs");
  let o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId).values;
  return o = aS(n), t10.makeOutput(o, e.shape, e.dtype);
};
var iT = { kernelName: ys, backendName: "cpu", kernelFunc: P6 };
function Be(r) {
  return (e, t10, o, n, s) => {
    let a = S.assertAndGetBroadcastShape(e, t10), i = a.length, p = y.computeStrides(a), u = y.sizeFromShape(a), c = y.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, d = y.computeStrides(e), f = y.computeStrides(t10), h = S.getBroadcastDims(e, a), g = S.getBroadcastDims(t10, a);
    if (h.length + g.length === 0)
      for (let x = 0; x < c.length; ++x)
        c[x] = r(o[x % o.length], n[x % n.length]);
    else
      for (let x = 0; x < c.length; ++x) {
        let b = y.indexToLoc(x, i, p), C = b.slice(-l);
        h.forEach((E) => C[E] = 0);
        let w = y.locToIndex(C, l, d), k = b.slice(-m);
        g.forEach((E) => k[E] = 0);
        let _ = y.locToIndex(k, m, f);
        c[x] = r(o[w], n[_]);
      }
    return [c, a];
  };
}
function Ht(r) {
  let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = t10.makeTensorInfo(o.shape, "complex64"), p = t10.data.get(i.dataId);
  return p.complexTensorInfos = { real: t10.makeTensorInfo(o.shape, "float32", s), imag: t10.makeTensorInfo(n.shape, "float32", a) }, i;
}
var uT = { kernelName: ri, backendName: "cpu", kernelFunc: Ht };
function Kp(r, e, t10 = "float32") {
  if (t10 === "complex64") {
    let n = Kp(r, e, "float32"), s = Kp(r, e, "float32");
    return Ht({ inputs: { real: n, imag: s }, backend: r });
  }
  let o = y.makeZerosTypedArray(y.sizeFromShape(e), t10);
  return r.makeTensorInfo(e, t10, o);
}
function ir(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
var pT = { kernelName: mo, backendName: "cpu", kernelFunc: ir };
function Io(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.real, s = t10.data.get(n.dataId).values;
  return t10.makeTensorInfo(n.shape, n.dtype, s);
}
var cT = { kernelName: di, backendName: "cpu", kernelFunc: Io };
function iS(r, e, t10, o) {
  if (o === "int32") {
    let n = Int32Array.from(r);
    return [e, "int32", n];
  }
  if (o === "bool") {
    let n = y.toTypedArray([0], t10), [s, a] = Be((i, p) => i !== p ? 1 : 0)(e, [], r, n, "bool");
    return [a, "bool", s];
  }
  throw new Error(`Error in Cast: failed to cast ${t10} to ${o}`);
}
function vo(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return ir({ inputs: { x: n }, backend: t10 });
    let c = Kp(t10, n.shape, n.dtype), l = vo({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), m = Ht({ inputs: { real: l, imag: c }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), m;
  }
  if (n.dtype === "complex64") {
    let c = Io({ inputs: { input: n }, backend: t10 }), l = vo({ inputs: { x: c }, backend: t10, attrs: { dtype: s } });
    return t10.disposeIntermediateTensorInfo(c), l;
  }
  if (!y.hasEncodingLoss(n.dtype, s)) {
    let c = ir({ inputs: { x: n }, backend: t10 });
    return { dataId: c.dataId, shape: c.shape, dtype: s };
  }
  let a = t10.data.get(n.dataId).values, [i, p, u] = iS(a, n.shape, n.dtype, s);
  return t10.makeTensorInfo(i, p, u);
}
var lT = { kernelName: co, backendName: "cpu", kernelFunc: vo };
function Qe(r, e, t10, o) {
  return t10 == null ? ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    j([a, i], r);
    let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? S.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? S.fromUint8ToStringArray(c) : c, d = o || a.dtype, [f, h] = e(a.shape, i.shape, l, m, d);
    return p.makeTensorInfo(h, d, f);
  } : ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    if (a.dtype === "complex64" || i.dtype === "complex64") {
      let u = vo({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, d = p.data.get(l.dataId).values, f = p.data.get(m.dataId).values, h = vo({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), x = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(x.dataId).values, w = p.data.get(b.dataId).values, [k, _, E] = t10(a.shape, i.shape, d, f, C, w), A = p.makeTensorInfo(E, "float32", k), R = p.makeTensorInfo(E, "float32", _), D = Ht({ inputs: { real: A, imag: R }, backend: p });
      return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(A), p.disposeIntermediateTensorInfo(R), D;
    } else {
      let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, d] = e(a.shape, i.shape, u, c, l);
      return p.makeTensorInfo(d, l, m);
    }
  };
}
function qp(r) {
  return (e, t10, o, n, s, a) => {
    let i = S.assertAndGetBroadcastShape(e, t10), p = y.sizeFromShape(i), u = i.length, c = y.computeStrides(i), l = y.getTypedArrayFromDType("float32", p), m = y.getTypedArrayFromDType("float32", p), d = S.getBroadcastDims(e, i), f = S.getBroadcastDims(t10, i), h = S.mergeRealAndImagArrays(o, n), g = S.mergeRealAndImagArrays(s, a), x = e.length, b = y.computeStrides(e), C = t10.length, w = y.computeStrides(t10);
    if (d.length + f.length === 0)
      for (let k = 0; k < l.length; k++) {
        let _ = k % h.length, E = k % g.length, A = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]);
        l[k] = A.real, m[k] = A.imag;
      }
    else
      for (let k = 0; k < l.length; k++) {
        let _ = y.indexToLoc(k, u, c), E = _.slice(-x);
        d.forEach((M) => E[M] = 0);
        let A = y.locToIndex(E, x, b), R = _.slice(-C);
        f.forEach((M) => R[M] = 0);
        let D = y.locToIndex(R, C, w), P = r(h[A * 2], h[A * 2 + 1], g[D * 2], g[D * 2 + 1]);
        l[k] = P.real, m[k] = P.imag;
      }
    return [l, m, i];
  };
}
var uS = Be((r, e) => r + e);
var M6 = qp((r, e, t10, o) => ({ real: r + t10, imag: e + o }));
var Xs = Qe(eo, uS, M6);
var mT = { kernelName: eo, backendName: "cpu", kernelFunc: Xs };
function jp(r, e, t10, o, n) {
  let s = y.sizeFromShape(o), a = y.makeZerosTypedArray(n, t10);
  for (let i = 0; i < r.length; i++) {
    let p = r[i];
    if (p < 0)
      throw new Error("Input x must be non-negative!");
    p >= n || (s > 0 ? a[p] += e[i] : a[p] += 1);
  }
  return a;
}
function uf(r, e, t10, o = false) {
  let n = r.shape[0], s = r.shape[1], a = le([n, t10], e.dtype);
  for (let i = 0; i < n; i++)
    for (let p = 0; p < s; p++) {
      let u = r.get(i, p);
      if (u < 0)
        throw new Error("Input x must be non-negative!");
      u >= t10 || (o ? a.set(1, i, u) : e.size > 0 ? a.set(a.get(i, u) + e.get(i, p), i, u) : a.set(a.get(i, u) + 1, i, u));
    }
  return a;
}
function vr(r) {
  return (e, t10, o) => {
    let n = y.getTypedArrayFromDType(t10, e.length);
    for (let s = 0; s < e.length; ++s)
      n[s] = r(e[s], o);
    return n;
  };
}
function we(r, e, t10) {
  return ({ inputs: o, attrs: n, backend: s }) => {
    let { x: a } = o;
    if (j(a, r), a.dtype === "string" || t10 === "string")
      throw new Error("unaryKernelFunc does not support string input/output");
    let i = s, p = i.data.get(a.dataId).values, u = y.sizeFromShape(a.shape), c = t10 || a.dtype, l = y.getArrayFromDType(c, u);
    for (let m = 0; m < u; ++m)
      l[m] = e(p[m], n);
    return i.makeTensorInfo(a.shape, c, l);
  };
}
function ko(r, e, t10) {
  return ({ inputs: o, attrs: n, backend: s }) => {
    let { x: a } = o;
    if (j(a, r), a.dtype === "string" || t10 === "string")
      throw new Error("unaryKernelFunc does not support string input/output");
    let i = s, p = i.data.get(a.dataId).values, u = t10 || a.dtype, c = e(p, u, n);
    return i.makeTensorInfo(a.shape, u, c);
  };
}
var pS = vr((r) => Math.ceil(r));
var L6 = ko(Wo, pS);
var dT = { kernelName: Wo, backendName: "cpu", kernelFunc: L6 };
function Nu(r, e, t10, o) {
  let n = y.getArrayFromDType(t10, y.sizeFromShape(e));
  if (o && t10 !== "string") {
    let s = 0;
    r.forEach((a) => {
      let i = y.sizeFromShape(a.shape);
      n.set(a.vals, s), s += i;
    });
  } else {
    let s = 0;
    r.forEach((a) => {
      let i = t10 === "string" ? S.fromUint8ToStringArray(a.vals) : a.vals, p = 0;
      for (let u = 0; u < a.shape[0]; ++u) {
        let c = u * e[1] + s;
        for (let l = 0; l < a.shape[1]; ++l)
          n[c + l] = i[p++];
      }
      s += a.shape[1];
    });
  }
  return n;
}
var cS = Be((r, e) => r === e ? 1 : 0);
var lS = Qe(en, cS, null, "bool");
var fT = { kernelName: en, backendName: "cpu", kernelFunc: lS };
var mS = vr((r) => Math.exp(r));
var dS = ko(tn, mS, "float32");
var hT = { kernelName: tn, backendName: "cpu", kernelFunc: dS };
var fS = vr((r) => Math.expm1(r));
var B6 = ko(fa, fS);
var gT = { kernelName: fa, backendName: "cpu", kernelFunc: B6 };
var hS = vr((r) => Math.floor(r));
var V6 = ko(on, hS);
var xT = { kernelName: on, backendName: "cpu", kernelFunc: V6 };
function pf(r, e, t10, o, n, s, a, i, p) {
  let u = le([o, s], t10);
  for (let c = 0; c < o; c++) {
    let l = [], m = 0;
    for (let d = 0; d < n; d++) {
      let f = r[c * n + d];
      m += f * a[d], l.push(f);
    }
    if (m < 0 || m >= p / s)
      throw new Error(`Invalid indices: ${l} does not index into ${i}`);
    for (let d = 0; d < s; d++)
      u.values[c * s + d] = e.get(...e.indexToLoc(m * s + d));
  }
  return u;
}
function cf(r, e, t10) {
  let o = le(t10, r.dtype);
  for (let n = 0; n < o.size; ++n) {
    let a = o.indexToLoc(n).slice(), i = a[0], p = a[2], u = e.locToIndex([i, p]);
    a[2] = e.values[u];
    let c = r.locToIndex(a);
    0 <= c && c < r.values.length && (o.values[n] = r.values[c]);
  }
  return o;
}
var gS = Be((r, e) => r > e ? 1 : 0);
var z6 = Qe(un, gS, null, "bool");
var yT = { kernelName: un, backendName: "cpu", kernelFunc: z6 };
var xS = Be((r, e) => r >= e ? 1 : 0);
var W6 = Qe(pn, xS, null, "bool");
var bT = { kernelName: pn, backendName: "cpu", kernelFunc: W6 };
var yS = Be((r, e) => r < e ? 1 : 0);
var U6 = Qe(mn, yS, null, "bool");
var CT = { kernelName: mn, backendName: "cpu", kernelFunc: U6 };
var bS = Be((r, e) => r <= e ? 1 : 0);
var G6 = Qe(dn, bS, null, "bool");
var ST = { kernelName: dn, backendName: "cpu", kernelFunc: G6 };
function lf(r, e, t10) {
  let o = (e - r) / (t10 - 1), n = y.makeZerosTypedArray(t10, "float32");
  n[0] = r;
  for (let s = 1; s < n.length; s++)
    n[s] = n[s - 1] + o;
  return n;
}
var CS = vr((r) => Math.log(r));
var H6 = ko(fn, CS);
var wT = { kernelName: fn, backendName: "cpu", kernelFunc: H6 };
function mf(r, e, t10, o) {
  let n = y.getTypedArrayFromDType(o, y.sizeFromShape(t10));
  for (let s = 0; s < n.length; ++s) {
    let a = s * e, i = r[a];
    for (let p = 0; p < e; ++p) {
      let u = r[a + p];
      (Number.isNaN(u) || u > i) && (i = u);
    }
    n[s] = i;
  }
  return n;
}
var SS = Be((r, e) => Math.max(r, e));
var K6 = Qe(bn, SS);
var IT = { kernelName: bn, backendName: "cpu", kernelFunc: K6 };
var wS = Be((r, e) => Math.min(r, e));
var q6 = Qe(In, wS);
var vT = { kernelName: In, backendName: "cpu", kernelFunc: q6 };
var hl = Be((r, e) => r * e);
var j6 = qp((r, e, t10, o) => ({ real: r * t10 - e * o, imag: r * o + e * t10 }));
var Tu = Qe(kn, hl, j6);
var kT = { kernelName: kn, backendName: "cpu", kernelFunc: Tu };
function IS(r, e, t10) {
  let o = y.createScalarValue(-1, t10);
  return hl([], e, o, r, t10);
}
function X6(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  j(o, "neg");
  let n = t10.data.get(o.dataId).values, [s, a] = IS(n, o.shape, o.dtype);
  return t10.makeTensorInfo(a, o.dtype, s);
}
var NT = { kernelName: vs, backendName: "cpu", kernelFunc: X6 };
var vS = Be((r, e) => r !== e ? 1 : 0);
var Y6 = Qe(Nn, vS, null, "bool");
var TT = { kernelName: Nn, backendName: "cpu", kernelFunc: Y6 };
function Xp(r, e, t10, o, n) {
  let s = e.length, a = y.sizeFromShape(e), i = y.computeStrides(e), p = y.computeStrides(n), u = y.getTypedArrayFromDType(t10, y.sizeFromShape(n));
  for (let c = 0; c < a; ++c) {
    let l = y.indexToLoc(c, s, i), m = new Array(l.length);
    for (let f = 0; f < m.length; f++)
      m[f] = l[o[f]];
    let d = y.locToIndex(m, s, p);
    u[d] = r[c];
  }
  return u;
}
function St(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { perm: s } = t10;
  j(n, "transpose");
  let a = n.shape.length, i = new Array(a);
  for (let l = 0; l < i.length; l++)
    i[l] = n.shape[s[l]];
  let p = o.data.get(n.dataId).values, u = Xp(p, n.shape, n.dtype, s, i);
  return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype };
}
var _T = { kernelName: ro, backendName: "cpu", kernelFunc: St };
function kS(r, e, t10, o) {
  let [n, s] = S.computeOutAndReduceShapes(r, o), a = dt(e, "int32"), i = y.makeZerosTypedArray(y.sizeFromShape(n), a), p = y.sizeFromShape(s);
  for (let u = 0; u < i.length; ++u) {
    let c = u * p, l = 1;
    for (let m = 0; m < p; ++m)
      l *= t10[c + m];
    i[u] = l;
  }
  return { outVals: i, outShape: n, outDtype: a };
}
function Q6(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  j(n, "prod");
  let i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = S.getAxesPermutation(p, i), c = p, l = n, m = [];
  u != null && (l = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), m.push(l), c = S.getInnerMostAxes(c.length, i));
  let d = t10.data.get(l.dataId).values, { outVals: f, outShape: h, outDtype: g } = kS(l.shape, l.dtype, d, c), x = h;
  return a && (x = S.expandShapeToKeepDim(h, p)), m.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.makeTensorInfo(x, g, f);
}
var $T = { kernelName: Dn, backendName: "cpu", kernelFunc: Q6 };
function Z6(r, e, t10) {
  r.forEach((o, n) => {
    if (o < 0 || o >= t10) {
      let s = y.indexToLoc(n, e.length, y.computeStrides(e)).join(",");
      throw new Error(`indices[${s}] = ${o} is not in [0, ${t10})`);
    }
  });
}
function J6(r, e) {
  for (let t10 = 0; t10 < r.length; ++t10) {
    let o = r[t10], n = t10 === r.length - 1 ? e : r[t10 + 1].length;
    if (o.length === 0)
      throw new Error("Ragged splits may not be empty");
    if (o[0] < 0)
      throw new Error("Ragged splits must be non-negative");
    if (o[o.length - 1] > n)
      throw new Error("Ragged splits must not point past values");
    for (let s = 1; s < o.length; ++s)
      if (o[s - 1] > o[s])
        throw new Error("Ragged splits must be sorted in ascending order");
  }
}
function ej(r, e, t10, o) {
  let n = [], s = 0, a = e.length - 1 + t10.length, i = new Array(a).fill(null).map(() => [0]);
  J6(t10, o);
  let p = 1;
  for (let u = 0; u < e.length - 1; ++u) {
    p *= e[u];
    let c = e[u + 1];
    for (let l = 1; l < p + 1; ++l)
      i[u].push(l * c);
  }
  for (let u = 0; u < r.length; ++u) {
    let c = r[u], l = r[u] + 1;
    for (let m = 0; m < t10.length; ++m) {
      let d = t10[m], f = m + e.length - 1;
      if (f >= 0) {
        let h = i[f], g = h[h.length - 1] - d[c];
        for (let x = c; x < l; ++x)
          i[f].push(d[x + 1] + g);
      }
      c = d[c], l = d[l];
    }
    l !== c && (n.push([c, l]), s += l - c);
  }
  return { outSplits: i, valueSlices: n, numValues: s };
}
function tj(r) {
  let e = [];
  for (let t10 = 0; t10 < r.length; ++t10) {
    let o = r[t10].length, n = y.getArrayFromDType("int32", o);
    e.push(n), r[t10].forEach((s, a) => n[a] = s);
  }
  return e;
}
function ET(r, e) {
  let t10 = r.slice(0, e);
  for (; t10.length < e; )
    t10.push(1);
  for (let o = e; o < r.length; o++)
    t10[e - 1] *= r[o];
  return t10;
}
function rj(r, e, t10, o, n, s) {
  let a = ET(e, 2)[1], i = ET(s, 2)[1], p = 0;
  for (let u of t10)
    for (let c = u[0]; c < u[1]; ++c) {
      for (let l = 0; l < o; ++l)
        n[p * i + l] = r[c * a + l];
      ++p;
    }
}
function oj(r, e, t10, o, n) {
  let s = e.slice();
  s[0] = n;
  let a = y.getArrayFromDType(t10, y.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0];
  return rj(r, e, o, p, a, s), [a, s];
}
function df(r, e, t10, o, n, s, a, i) {
  if (r.length === 0)
    throw new Error("paramsNestedSplits must be non empty");
  if (e[0].length === 0)
    throw new Error("Split tensors must not be scalars");
  let p = e[0][0] - 1;
  if (Z6(s, a, p), o.length === 0)
    throw new Error("params.rank must be nonzero");
  let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = ej(s, a, r, u), d = tj(c), f = oj(t10, o, n, l, m);
  return [d, f[0], f[1]];
}
var AT = 2147483647;
function ff(r, e, t10, o, n, s, a) {
  if (e.length > 1)
    throw new Error("starts must be a scalar or vector");
  if (n.length > 1)
    throw new Error("limits must be a scalar or vector");
  if (a.length > 1)
    throw new Error("deltas must be a scalar or vector");
  let i = e.length === 0, p = n.length === 0, u = a.length === 0, c = [];
  i || c.push(e[0]), p || c.push(n[0]), u || c.push(a[0]);
  for (let g = 1; g < c.length; ++g)
    if (c[g] !== c[g - 1])
      throw new Error("starts, limits, and deltas must have the same shape");
  let l = c.length === 0 ? 1 : c[0], m = y.getArrayFromDType("int32", l + 1);
  m[0] = 0;
  for (let g = 0; g < l; ++g) {
    let x = i ? r[0] : r[g], b = p ? o[0] : o[g], C = u ? s[0] : s[g];
    if (C === 0)
      throw new Error("Requires delta != 0");
    let w;
    if (C > 0 && b < x || C < 0 && b > x)
      w = 0;
    else if (w = Math.ceil(Math.abs((b - x) / C)), w > AT)
      throw new Error(`Requires ((limit - start) / delta) <= ${AT}`);
    m[g + 1] = m[g] + w;
  }
  let d = m[l], f = y.getArrayFromDType(t10, d), h = 0;
  for (let g = 0; g < l; ++g) {
    let x = m[g + 1] - m[g], b = i ? r[0] : r[g], C = u ? s[0] : s[g];
    for (let w = 0; w < x; ++w)
      f[h++] = b, b += C;
  }
  return [m, f];
}
var No = S.RowPartitionType;
var Yp = class {
  constructor(e, t10, o, n, s, a, i, p, u, c) {
    this.shape = e, this.shapeShape = t10, this.values = o, this.valuesShape = n, this.valuesDType = s, this.defaultValue = a, this.defaultValueShape = i, this.rowPartitionValues = p, this.rowPartitionValuesShapes = u, this.rowPartitionTypes = S.getRowPartitionTypesHelper(c), this.raggedRank = S.getRaggedRank(this.rowPartitionTypes);
  }
  getRowPartitionTypeByDimension(e) {
    return this.rowPartitionTypes[0] === No.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e];
  }
  getRowPartitionTensor(e) {
    return this.rowPartitionTypes[0] === No.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e];
  }
  getMaxWidth(e) {
    let t10 = this.getRowPartitionTensor(e - 1);
    switch (this.getRowPartitionTypeByDimension(e - 1)) {
      case No.VALUE_ROWIDS:
        return Yp.getMaxWidthValueRowID(t10);
      case No.ROW_SPLITS:
        return Yp.getMaxWidthRowSplit(t10);
      default:
        throw new Error(`Cannot handle partition type ${No[this.getRowPartitionTypeByDimension(e - 1)]}`);
    }
  }
  static getMaxWidthRowSplit(e) {
    let t10 = e.length;
    if (t10 === 0 || t10 === 1)
      return 0;
    let o = 0;
    for (let n = 0; n < t10 - 1; ++n) {
      let s = e[n + 1] - e[n];
      s > o && (o = s);
    }
    return o;
  }
  static getMaxWidthValueRowID(e) {
    let t10 = e.length;
    if (t10 === 0)
      return 0;
    let o = 0, n = e[0], s = 0;
    for (let a = 1; a < t10; ++a) {
      let i = e[a];
      i !== n && (n = i, s = Math.max(a - o, s), o = a);
    }
    return Math.max(t10 - o, s);
  }
  tensorShapeFromTensor(e, t10, o = true) {
    if (t10.length === 0) {
      if (e[0] === -1)
        return [];
      throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.");
    }
    return DT(e, o);
  }
  calculateOutputSize(e) {
    let t10 = this.valuesShape, o = this.defaultValueShape;
    S.validateDefaultValueShape(o, t10);
    let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = S.combineRaggedTensorToTensorShapes(this.raggedRank, n, t10);
    a[0] < 0 && (a[0] = e);
    for (let i = 1; i <= this.raggedRank; ++i)
      a[i] < 0 && (a[i] = this.getMaxWidth(i));
    return a;
  }
  calculateFirstParentOutputIndex(e, t10, o) {
    let n = Math.min(e, o), s = [], a = 0;
    for (let i = 0; i < n; ++i, a += t10)
      s.push(a);
    for (let i = n; i < e; ++i)
      s.push(-1);
    return y.assert(s.length === e, () => "Final length of result must be equal to firstDimension."), s;
  }
  calculateOutputIndexRowSplit(e, t10, o, n) {
    let s = e.length, a = [];
    for (let i = 0; i < s - 1; ++i) {
      let p = e[i + 1] - e[i], u = Math.min(n, p), c = t10[i];
      c === -1 && (u = 0);
      for (let l = 0; l < u; ++l)
        a.push(c), c += o;
      for (let l = 0; l < p - u; ++l)
        a.push(-1);
    }
    if (s > 0 && a.length !== e[s - 1])
      throw new Error("Invalid row split size.");
    return a;
  }
  calculateOutputIndexValueRowID(e, t10, o, n) {
    let s = e.length, a = [];
    if (s === 0)
      return [];
    let i = 0, p = e[0];
    if (p >= t10.length)
      throw new Error(`Got currentValueRowId=${p}, which is not less than ${t10.length}`);
    let u = t10[p];
    a.push(u);
    for (let c = 1; c < s; ++c) {
      let l = e[c];
      if (l === p)
        u >= 0 && (++i, i < n ? u += o : u = -1);
      else {
        if (i = 0, p = l, l >= t10.length)
          throw new Error(`Got nextValueRowId=${l} which is not less than ${t10.length}`);
        u = t10[l];
      }
      a.push(u);
    }
    if (a.length !== e.length)
      throw new Error("Invalid row ids.");
    return a;
  }
  calculateOutputIndex(e, t10, o, n) {
    let s = this.getRowPartitionTensor(e), a = this.getRowPartitionTypeByDimension(e);
    switch (a) {
      case No.VALUE_ROWIDS:
        return this.calculateOutputIndexValueRowID(s, t10, o, n);
      case No.ROW_SPLITS:
        if (s.length - 1 > t10.length)
          throw new Error(`Row partition size is greater than output size: ${s.length - 1} > ${t10.length}`);
        return this.calculateOutputIndexRowSplit(s, t10, o, n);
      default:
        throw new Error(`Unsupported partition type: ${No[a]}`);
    }
  }
  getFirstDimensionSize() {
    let e = this.rowPartitionValues[0];
    if (this.rowPartitionTypes.length === 0)
      throw new Error("No row_partition_types given.");
    let t10 = this.rowPartitionTypes[0];
    switch (t10) {
      case No.FIRST_DIM_SIZE:
        return e[0];
      case No.VALUE_ROWIDS:
        throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");
      case No.ROW_SPLITS:
        return this.rowPartitionValuesShapes[0][0] - 1;
      default:
        throw new Error(`Cannot handle type ${No[t10]}`);
    }
  }
  compute() {
    if (this.rowPartitionValues[0].length <= 0)
      throw new Error("Invalid first partition input. Tensor requires at least one element.");
    let t10 = this.getFirstDimensionSize(), o = this.calculateOutputSize(t10), n = new Array(this.raggedRank + 1);
    n[n.length - 1] = 1;
    for (let p = n.length - 2; p >= 0; --p)
      n[p] = n[p + 1] * o[p + 1];
    let s = DT(o, false), a = y.getArrayFromDType(this.valuesDType, y.sizeFromShape(s));
    if (n[0] * o[0] > 0) {
      let p = this.calculateFirstParentOutputIndex(t10, n[0], o[0]);
      for (let u = 1; u <= this.raggedRank; ++u)
        p = this.calculateOutputIndex(u - 1, p, n[u], o[u]);
      this.setOutput(this.raggedRank, p, a, s);
    }
    return [s, a];
  }
  setOutput(e, t10, o, n) {
    if (o.length === 0)
      return;
    let s = this.values, a = o, i = n.slice();
    i = i.slice(e + 1);
    let p = y.sizeFromShape(i), u = t10.length, c = this.defaultValue;
    if (c.length !== p && c.length !== 1) {
      let f = this.defaultValueShape;
      Ee(() => {
        let h = W(c, f);
        c = $i(h, i).dataSync();
      });
    }
    let l = 0, m = 0, d = 0;
    for (let f = 0; f <= u; ++f) {
      let h = f < u ? t10[f] : -1;
      if (h === d) {
        ++d;
        continue;
      }
      if (m < d) {
        let g = s.subarray(l * p), x = a.subarray(m * p), b = (d - m) * p;
        RT(x, g, b);
      }
      if (f >= u) {
        let g = o.length;
        h = Math.floor(g / p);
      }
      if (h > d)
        if (this.defaultValue.length === 1)
          a.subarray(d * p, h * p).fill(this.defaultValue[0]), d = h;
        else
          for (; h > d; ) {
            let g = a.slice(d * p);
            RT(g, c, p), ++d;
          }
      h < 0 ? (l = f + 1, m = d) : (l = f, m = d, d = m + 1);
    }
  }
};
function RT(r, e, t10) {
  for (let o = 0; o < t10; o++)
    r[o] = e[o];
}
function DT(r, e) {
  let t10 = [];
  for (let o of r) {
    if (o < 0) {
      if (!e)
        throw new Error(`Dimension ${o} must be >= 0`);
      if (o < -1)
        throw new Error(`Dimension ${o} must be >= -1`);
      o = -1;
    }
    t10.push(o);
  }
  return t10;
}
function hf(r, e, t10, o, n, s, a, i, p, u) {
  return new Yp(r, e, t10, o, n, s, a, i, p, u).compute();
}
function _u(r, e, t10, o) {
  let n = r === e, s = r < e && t10 < 0, a = e < r && t10 > 1;
  if (n || s || a)
    return y.makeZerosTypedArray(0, o);
  let i = Math.abs(Math.ceil((e - r) / t10)), p = y.makeZerosTypedArray(i, o);
  e < r && t10 === 1 && (t10 = -1), p[0] = r;
  for (let u = 1; u < p.length; u++)
    p[u] = p[u - 1] + t10;
  return p;
}
var NS = vr((r) => 1 / Math.sqrt(r));
var nj = ko(zn, NS);
var FT = { kernelName: zn, backendName: "cpu", kernelFunc: nj };
function Va(r, e, t10, o, n, s, a, i, p, u) {
  let c = [o / n, n], l = r.values, m = e.values;
  if (o === 0)
    return le(t10, e.dtype);
  let d = le(c, e.dtype);
  typeof p == "string" || typeof p == "number" ? d.values.fill(p) : typeof p == "boolean" && d.values.fill(+p);
  for (let f = 0; f < s; f++) {
    let h = [], g = 0;
    for (let x = 0; x < a; x++) {
      let b = l[f * a + x];
      h.push(b), g += b * i[x];
    }
    if (g < 0 || g >= o / n)
      throw new Error(`Invalid indices: ${h} does not index into ${t10}`);
    for (let x = 0; x < n; x++)
      u ? d.values[g * n + x] += m[f * n + x] : d.values[g * n + x] = e.rank === 0 ? m[0] : m[f * n + x];
  }
  return d;
}
var OT = vr((r) => 1 / (1 + Math.exp(-r)));
var TS = we(Gn, (r) => 1 / (1 + Math.exp(-r)));
var PT = { kernelName: Gn, backendName: "cpu", kernelFunc: TS };
function $u(r, e, t10, o, n) {
  let s = ut.isSliceContinous(o, e, t10), a = y.sizeFromShape(t10), i = y.computeStrides(o);
  if (s) {
    let l = ut.computeFlatOffset(e, i);
    return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a);
  }
  let p = n === "string" ? S.fromUint8ToStringArray(r) : r, u = le(o, n, p), c = le(t10, n);
  for (let l = 0; l < c.size; ++l) {
    let m = c.indexToLoc(l), d = m.map((f, h) => f + e[h]);
    c.set(u.get(...d), ...m);
  }
  return n === "string" ? S.fromStringArrayToUint8(c.values) : c.values;
}
function To(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o;
  j(n, "slice");
  let [i, p] = ut.parseSliceParams(n, s, a);
  ut.assertParamsValid(n, i, p);
  let u = t10.data.get(n.dataId).values, c = $u(u, i, p, n.shape, n.dtype);
  return t10.makeTensorInfo(p, n.dtype, c);
}
var MT = { kernelName: Es, backendName: "cpu", kernelFunc: To };
function gf(r, e, t10, o, n, s, a) {
  let i = e[0], p = s[0], u = new Array(p), c = new Array(i), l = e[1];
  if (p === 0) {
    if (i !== 0)
      throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));
    let g = y.getArrayFromDType(t10, 0), x = y.getArrayFromDType(n, 0);
    return [g, [0, l], x, u, c];
  }
  let m = true, d = 0, f = new Array(p).fill(0);
  for (let g = 0; g < i; ++g) {
    let x = r[g * l];
    if (x < 0)
      throw new Error(S.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, x));
    if (x >= p)
      throw new Error(S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, x, p));
    ++f[x], m = m && x >= d, d = x;
  }
  let h = true;
  for (let g = 0; g < p; ++g) {
    let x = f[g] === 0;
    u[g] = x, h = h && !x, f[g] = Math.max(f[g], 1), g > 0 && (f[g] += f[g - 1]);
  }
  if (h && m) {
    let g = r, x = o;
    for (let b = 0; b < i; ++b)
      c[b] = b;
    return [g, [i, l], x, u, c];
  } else {
    let g = f[p - 1], x = y.getArrayFromDType(t10, g * l), b = y.getArrayFromDType(n, g), C = new Array(p).fill(0);
    for (let w = 0; w < i; ++w) {
      let k = r[w * l], _ = C[k], E = (k === 0 ? 0 : f[k - 1]) + _;
      C[k]++;
      for (let A = 0; A < l; ++A)
        x[E * l + A] = r[w * l + A];
      b[E] = o[w], c[w] = E;
    }
    for (let w = 0; w < p; ++w)
      if (C[w] === 0) {
        let _ = w === 0 ? 0 : f[w - 1];
        x[_ * l + 0] = w;
        for (let E = 1; E < l; ++E)
          x[_ * l + E] = 0;
        b[_] = a;
      }
    return [x, [g, l], b, u, c];
  }
}
function xf(r, e, t10, o, n) {
  let s = y.sizeFromShape(o), a = e[0], i = n.length, p = [], u = 1, c = -1;
  for (let g = 0; g < i; ++g) {
    let x = n[g];
    if (x === -1) {
      if (c !== -1)
        throw new Error(S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
      c = g, p.push(1);
    } else {
      if (x < 0)
        throw new Error(S.getSparseReshapeNegativeOutputDimErrorMessage(g, x));
      u *= x, p.push(x);
    }
  }
  if (c !== -1) {
    if (u <= 0)
      throw new Error(S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
    let g = Math.trunc(s / u);
    if (u * g !== s)
      throw new Error(S.getSparseReshapeInputOutputMultipleErrorMessage(o, p));
    p[c] = g;
  }
  if (y.sizeFromShape(p) !== s)
    throw new Error(S.getSparseReshapeInputOutputMismatchErrorMessage(o, p));
  let m = o.length, d = [];
  if (m > 0) {
    d[m - 1] = 1;
    for (let g = m - 2; g >= 0; --g)
      d[g] = d[g + 1] * o[g + 1];
  }
  let f = [];
  if (i > 0) {
    f[i - 1] = 1;
    for (let g = i - 2; g >= 0; --g)
      f[g] = f[g + 1] * p[g + 1];
  }
  let h = y.getArrayFromDType(t10, a * i);
  for (let g = 0; g < a; ++g) {
    let x = 0;
    for (let b = 0; b < m; ++b)
      x += r[g * m + b] * d[b];
    for (let b = 0; b < i; ++b)
      h[g * i + b] = Math.trunc(x / f[b]), x %= f[b];
  }
  return [h, [a, i], p];
}
function Qp(r, e, t10, o, n, s = false, a = 0) {
  let i = o.length, p = [e[0], r.length / e[0]], u = p[1], l = i > 0 ? n[i - 1] + 1 : 0;
  if (l < 0)
    throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let m = e.slice();
  m[0] = l;
  let d = m.reduce((C, w) => C * w, 1), f = y.getArrayFromDType(t10, d);
  if (i === 0)
    return l > 0 && f.fill(a), [f, m];
  if (l <= 0)
    throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let h = 0, g = 1, x = 0, b = n[h];
  for (; ; ) {
    let C = 0;
    if (g < i) {
      if (C = n[g], b === C) {
        ++g;
        continue;
      }
      if (b >= C)
        throw new Error(S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
    }
    if (b < 0 || b >= l)
      throw new Error(S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b, l));
    b > x && f.fill(a, x * u, b * u);
    for (let w = h; w < g; ++w) {
      let k = o[w];
      if (k < 0 || k >= p[0])
        throw new Error(S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w, o[w], p[0]));
      for (let _ = 0; _ < u; _++)
        f[b * u + _] += r[k * u + _];
    }
    if (s)
      for (let w = 0; w < u; w++)
        f[b * u + w] /= g - h;
    if (h = g, ++g, x = b + 1, b = C, g > i)
      break;
  }
  return x < l && f.fill(a, x * u, l * u), [f, m];
}
var LT = vr((r) => Math.sqrt(r));
var sj = we(Hn, (r) => Math.sqrt(r));
var BT = { kernelName: Hn, backendName: "cpu", kernelFunc: sj };
var _S = Be((r, e) => {
  let t10 = r - e;
  return t10 * t10;
});
var aj = Qe(jn, _S);
var VT = { kernelName: jn, backendName: "cpu", kernelFunc: aj };
function yf(r, e, t10, o) {
  let n = le(r, e.dtype);
  for (let s = 0; s < n.size; s++) {
    let a = n.indexToLoc(s), i = new Array(a.length);
    for (let p = 0; p < i.length; p++)
      i[p] = a[p] * t10[p] + o[p];
    n.set(e.get(...i), ...a);
  }
  return n;
}
var $S = class {
  constructor(e, t10, o, n, s, a) {
    this.separator = y.encodeString(e), this.nGramWidths = t10, this.leftPad = y.encodeString(o), this.rightPad = y.encodeString(n), this.padWidth = s, this.preserveShort = a;
  }
  getPadWidth(e) {
    return Math.min(this.padWidth < 0 ? e - 1 : this.padWidth, e - 1);
  }
  getNumNGrams(e, t10) {
    let o = this.getPadWidth(t10);
    return Math.max(0, e + 2 * o - t10 + 1);
  }
  createNGrams(e, t10, o, n, s, a) {
    for (let i = 0; i < s; ++i) {
      let p = this.getPadWidth(a), u = Math.max(0, p - i), c = Math.max(0, p - (s - (i + 1))), l = a - (u + c), m = t10 + (u > 0 ? 0 : i - p), d = 0;
      d += u * this.leftPad.length;
      for (let b = 0; b < l; ++b)
        d += e[m + b].length;
      d += c * this.rightPad.length;
      let f = u + c + l - 1;
      d += f * this.separator.length, o[n + i] = new Uint8Array(d);
      let h = o[n + i], g = 0, x = (b) => b.forEach((C) => h[g++] = C);
      for (let b = 0; b < u; ++b)
        x(this.leftPad), x(this.separator);
      for (let b = 0; b < l - 1; ++b)
        x(e[m + b]), x(this.separator);
      if (l > 0) {
        x(e[m + l - 1]);
        for (let b = 0; b < c; ++b)
          x(this.separator), x(this.rightPad);
      } else {
        for (let b = 0; b < c - 1; ++b)
          x(this.rightPad), x(this.separator);
        x(this.rightPad);
      }
    }
  }
  compute(e, t10) {
    let o = e.length, n = t10.length;
    if (n > 0) {
      let p = t10[0];
      if (p !== 0)
        throw new Error(`First split value must be 0, got ${p}`);
      for (let u = 1; u < n; ++u) {
        let c = t10[u] >= p;
        if (c = c && t10[u] <= o, !c)
          throw new Error(`Invalid split value ${t10[u]}, must be in [${p}, ${o}]`);
        p = t10[u];
      }
      if (p !== o)
        throw new Error(`Last split value must be data size. Expected ${o}, got ${p}`);
    }
    let s = n - 1, a = y.getArrayFromDType("int32", n);
    if (o === 0 || n === 0) {
      let p = new Array(o);
      for (let u = 0; u <= s; ++u)
        a[u] = 0;
      return [p, a];
    }
    a[0] = 0;
    for (let p = 1; p <= s; ++p) {
      let u = t10[p] - t10[p - 1], c = 0;
      this.nGramWidths.forEach((l) => {
        c += this.getNumNGrams(u, l);
      }), this.preserveShort && u > 0 && c === 0 && (c = 1), a[p] = a[p - 1] + c;
    }
    let i = new Array(a[s]);
    for (let p = 0; p < s; ++p) {
      let u = t10[p], c = a[p];
      if (this.nGramWidths.forEach((l) => {
        let m = t10[p + 1] - t10[p], d = this.getNumNGrams(m, l);
        this.createNGrams(e, u, i, c, d, l), c += d;
      }), this.preserveShort && c === a[p]) {
        let l = t10[p + 1] - t10[p];
        if (l === 0)
          continue;
        let m = l + 2 * this.padWidth, d = 1;
        this.createNGrams(e, u, i, c, d, m);
      }
    }
    return [i, a];
  }
};
function Eu(r, e, t10, o, n, s, a, i) {
  return new $S(t10, o, n, s, a, i).compute(r, e);
}
function ij(r, e, t10, o) {
  if (!r.length)
    return;
  if (e.length === 0) {
    for (let s = 0; s < r.length; ++s)
      o.push(r.subarray(s, s + 1));
    return;
  }
  if (e.length === 1) {
    let s = e[0], a = r.indexOf(s);
    for (; a !== -1; ) {
      let i = r.subarray(0, a);
      (!t10 || i.length !== 0) && o.push(i), r = r.subarray(a + 1), a = r.indexOf(s);
    }
    (!t10 || r.length !== 0) && o.push(r);
    return;
  }
  let n = 0;
  for (let s = 0; s < r.length + 1; s++)
    if (s === r.length || e.indexOf(r[s]) !== -1) {
      let a = r.subarray(n, s);
      (!t10 || a.length !== 0) && o.push(a), n = s + 1;
    }
}
function Au(r, e, t10) {
  let o = r.length, n = [], s = 0, a = 0, i = new Array(o);
  for (let m = 0; m < o; ++m) {
    let d = n.length;
    ij(r[m], e, t10, n);
    let f = n.length - d;
    i[m] = f, s += f, a = Math.max(a, f);
  }
  let p = y.getArrayFromDType("int32", s * 2), u = new Array(s), c = [o, a], l = 0;
  for (let m = 0; m < o; ++m)
    for (let d = 0; d < i[m]; ++d)
      p[l * 2] = m, p[l * 2 + 1] = d, u[l] = n[l], ++l;
  return [p, u, c];
}
function Ru(r, e) {
  let t10 = y.getArrayFromDType("int32", r.length);
  for (let o = 0; o < r.length; ++o)
    t10[o] = y.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned();
  return t10;
}
var ES = Be((r, e) => r - e);
var uj = qp((r, e, t10, o) => ({ real: r - t10, imag: e - o }));
var gl = Qe(Yn, ES, uj);
var zT = { kernelName: Yn, backendName: "cpu", kernelFunc: gl };
function bf(r, e) {
  let t10 = new Array(r.rank);
  for (let n = 0; n < t10.length; n++)
    t10[n] = r.shape[n] * e[n];
  let o = le(t10, r.dtype);
  for (let n = 0; n < o.values.length; ++n) {
    let s = o.indexToLoc(n), a = new Array(r.rank);
    for (let p = 0; p < a.length; p++)
      a[p] = s[p] % r.shape[p];
    let i = r.locToIndex(a);
    o.values[n] = r.values[i];
  }
  return o;
}
var xl = (r, e) => {
  let t10 = e.value - r.value;
  return t10 === 0 ? r.index - e.index : t10;
};
function WT(r, e, t10 = 0, o = r.length - 1) {
  for (; o > t10; ) {
    if (o - t10 > 600) {
      let i = o - t10 + 1, p = e - t10 + 1, u = Math.log(i), c = 0.5 * Math.exp(2 * u / 3), l = 0.5 * Math.sqrt(u * c * (i - c) / i) * Math.sign(p - i / 2), m = Math.max(t10, Math.floor(e - p * c / i + l)), d = Math.min(o, Math.floor(e + (i - p) * c / i + l));
      WT(r, e, m, d);
    }
    let n = r[e], s = t10, a = o;
    for (y.swap(r, t10, e), xl(r[o], n) > 0 && y.swap(r, t10, o); s < a; ) {
      for (y.swap(r, s, a), s++, a--; xl(r[s], n) < 0; )
        s = s + 1;
      for (; xl(r[a], n) > 0; )
        a = a - 1;
    }
    xl(r[t10], n) === 0 ? y.swap(r, t10, a) : (a = a + 1, y.swap(r, a, o)), a <= e && (t10 = a + 1), e <= a && (o = a - 1);
  }
}
function Cf(r, e, t10, o, n) {
  let s = e[e.length - 1], [a, i] = [r.length / s, s], p = y.getTypedArrayFromDType(t10, a * o), u = y.getTypedArrayFromDType("int32", a * o);
  for (let l = 0; l < a; l++) {
    let m = l * i, d = r.subarray(m, m + i), f = new Array(d.length);
    d.forEach((b, C) => f[C] = { value: b, index: C }), o < f.length && (WT(f, o), f = f.slice(0, o)), n && f.sort(xl);
    let h = l * o, g = p.subarray(h, h + o), x = u.subarray(h, h + o);
    for (let b = 0; b < o; b++)
      g[b] = f[b].value, x[b] = f[b].index;
  }
  let c = e.slice();
  return c[c.length - 1] = o, [le(c, t10, p), le(c, "int32", u)];
}
function Sf(r, e, t10, o) {
  let n = y.parseAxisParam(e, t10)[0], s = [1, t10[0], 1];
  for (let f = 0; f < n; f++)
    s[0] *= t10[f];
  s[1] = t10[n];
  for (let f = n + 1; f < t10.length; f++)
    s[2] *= t10[f];
  let a = {}, i = new Int32Array(t10[n]), p = new st(s, o, r), u = [], c = s[0] === 1 && s[2] === 1;
  for (let f = 0; f < t10[n]; f++) {
    let h;
    if (c)
      h = r[f].toString();
    else {
      let g = [];
      for (let x = 0; x < s[0]; x++)
        for (let b = 0; b < s[2]; b++)
          g.push(p.get(x, f, b));
      h = g.join(",");
    }
    if (a[h] !== void 0)
      i[f] = a[h];
    else {
      let g = Object.keys(a).length;
      a[h] = g, i[f] = g, u.push(f);
    }
  }
  let l = s.slice();
  l[1] = Object.keys(a).length;
  let m = new st(l, o);
  u.forEach((f, h) => {
    for (let g = 0; g < s[0]; g++)
      for (let x = 0; x < s[2]; x++)
        m.set(p.get(g, f, x), g, h, x);
  });
  let d = t10.slice();
  return d[n] = l[1], { outputValues: m.values, outputShape: d, indices: i };
}
var pj = "4.2.0";
Ti("cpu", () => new Pi(), 1);
var AS = we(Jo, (r) => r >= 0 ? r : Math.exp(r) - 1);
var UT = { kernelName: Jo, backendName: "cpu", kernelFunc: AS };
function RS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o;
  j([n], "leakyRelu");
  let a = y.sizeFromShape(n.shape), i = t10.data.get(n.dataId).values, p = y.getTypedArrayFromDType("float32", a);
  for (let u = 0; u < i.length; u++)
    p[u] = i[u] < 0 ? s * i[u] : i[u];
  return t10.makeTensorInfo(n.shape, "float32", p);
}
var GT = { kernelName: ln, backendName: "cpu", kernelFunc: RS };
var cj = Be((r, e) => r < 0 ? e * r : r);
function DS(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e;
  j([o, n], "prelu");
  let s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, [i, p] = cj(o.shape, n.shape, s, a, "float32");
  return t10.makeTensorInfo(p, "float32", i);
}
var HT = { kernelName: Rn, backendName: "cpu", kernelFunc: DS };
var FS = we(On, (r) => Math.max(0, r));
var KT = { kernelName: On, backendName: "cpu", kernelFunc: FS };
var OS = we(Ln, (r) => Math.min(Math.max(0, r), 6));
var qT = { kernelName: Ln, backendName: "cpu", kernelFunc: OS };
function Du(r, e, t10, o, n) {
  if (t10 === "linear")
    return ir({ inputs: { x: e }, backend: r });
  if (t10 === "relu")
    return FS({ inputs: { x: e }, backend: r });
  if (t10 === "elu")
    return AS({ inputs: { x: e }, backend: r });
  if (t10 === "relu6")
    return OS({ inputs: { x: e }, backend: r });
  if (t10 === "prelu")
    return DS({ inputs: { x: e, alpha: o }, backend: r });
  if (t10 === "leakyrelu")
    return RS({ inputs: { x: e }, backend: r, attrs: { alpha: n } });
  if (t10 === "sigmoid")
    return TS({ inputs: { x: e }, backend: r });
  throw new Error(`Activation ${t10} has not been implemented for the CPU backend.`);
}
function Le(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = y.sizeFromShape(n.shape), i = y.inferFromImplicitShape(s, a), p = y.sizeFromShape(i);
  y.assert(a === p, () => `The new shape (${i}) has ${p} elements and the old shape (${n.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), t10.incRef(n.dataId);
  let u = t10.data.get(n.dataId);
  if (u.complexTensorInfos != null) {
    let c = u.complexTensorInfos.real, l = u.complexTensorInfos.imag;
    c.shape = i, l.shape = i;
  }
  return { dataId: n.dataId, shape: i, dtype: n.dtype };
}
var jT = { kernelName: _s, backendName: "cpu", kernelFunc: Le };
function PS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  j([n, s], "matMul");
  let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = yr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]);
  y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);
  let w = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Le({ inputs: { x: n }, backend: t10, attrs: { shape: w } }), E = Le({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), A = a ? _.shape[1] : _.shape[2], R = a ? _.shape[2] : _.shape[1], D = i ? E.shape[1] : E.shape[2], P = Math.max(g, x), M = t10.data.get(_.dataId).values, L = t10.data.get(E.dataId).values, V = y.computeStrides(_.shape), z = y.computeStrides(E.shape), [U, K, H] = a ? [V[0], 1, V[1]] : [V[0], V[1], 1], [q, Z, ee] = i ? [1, z[1], z[0]] : [z[1], 1, z[0]], oe = R * D, J = le([P, R, D], _.dtype), te = J.values, ie = t10.blockSize;
  for (let ce = 0; ce < P; ce++) {
    let ye = ce % g, Ne = ce % x;
    for (let Ie = 0; Ie < R; Ie += ie) {
      let Ae = Math.min(Ie + ie, R);
      for (let Re = 0; Re < D; Re += ie) {
        let ot = Math.min(Re + ie, D);
        for (let pt = 0; pt < A; pt += ie) {
          let ze = Math.min(pt + ie, A);
          for (let ct = Ie; ct < Ae; ct++)
            for (let at = Re; at < ot; at++) {
              let ft = 0;
              for (let ht = pt; ht < ze; ht++) {
                let Rr = M[ye * U + ct * K + ht * H], Pt = L[ht * q + at * Z + Ne * ee];
                ft += Rr * Pt;
              }
              te[ce * oe + (ct * D + at)] += ft;
            }
        }
      }
    }
  }
  return t10.disposeIntermediateTensorInfo(_), t10.disposeIntermediateTensorInfo(E), t10.makeTensorInfo(C, J.dtype, J.values);
}
var XT = { kernelName: zo, backendName: "cpu", kernelFunc: PS };
function lj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m, d, f, h = [];
  m = PS({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t10 }), a && (d = Xs({ inputs: { a: m, b: a }, backend: t10 }), h.push(m), m = d), c && (f = Du(t10, m, c, i, l), h.push(m), m = f);
  for (let x of h)
    t10.disposeIntermediateTensorInfo(x);
  return m;
}
var YT = { kernelName: ho, backendName: "cpu", kernelFunc: lj };
var mj = we(aa, (r) => Math.acos(r));
var QT = { kernelName: aa, backendName: "cpu", kernelFunc: mj };
var dj = we(ia, (r) => Math.acosh(r));
var ZT = { kernelName: ia, backendName: "cpu", kernelFunc: dj };
function fj(r) {
  let { inputs: e, backend: t10 } = r, o = e;
  j(e, "addN");
  let n = o.map((i) => t10.data.get(i.dataId).values), s = le(o[0].shape, o[0].dtype), a = s.values;
  for (let i = 0; i < o.length; i++) {
    let p = n[i];
    for (let u = 0; u < a.length; u++)
      a[u] += p[u];
  }
  return t10.makeTensorInfo(s.shape, s.dtype, s.values);
}
var JT = { kernelName: Po, backendName: "cpu", kernelFunc: fj };
function hj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  j(n, "all");
  let i = y.parseAxisParam(s, n.shape), p = i, u = S.getAxesPermutation(p, n.shape.length), c = n;
  u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("all", p, c.shape.length);
  let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
  for (let x = 0; x < f.length; ++x) {
    let b = x * d, C = h[b];
    for (let w = 0; w < d; ++w) {
      let k = h[b + w];
      C = C && k;
    }
    f[x] = C;
  }
  u != null && t10.disposeIntermediateTensorInfo(c);
  let g = t10.makeTensorInfo(l, c.dtype, f);
  if (a) {
    let x = S.expandShapeToKeepDim(l, i), b = Le({ inputs: { x: g }, backend: t10, attrs: { shape: x } });
    return t10.disposeIntermediateTensorInfo(g), b;
  }
  return g;
}
var e2 = { kernelName: Mo, backendName: "cpu", kernelFunc: hj };
function gj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  j(n, "any");
  let i = y.parseAxisParam(s, n.shape), p = i, u = S.getAxesPermutation(p, n.shape.length), c = n;
  u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("any", p, c.shape.length);
  let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
  for (let x = 0; x < f.length; ++x) {
    let b = x * d, C = h[b];
    for (let w = 0; w < d; ++w) {
      let k = h[b + w];
      C = C || k;
    }
    f[x] = C;
  }
  u != null && t10.disposeIntermediateTensorInfo(c);
  let g = t10.makeTensorInfo(l, c.dtype, f);
  if (a) {
    let x = S.expandShapeToKeepDim(l, i), b = Le({ inputs: { x: g }, backend: t10, attrs: { shape: x } });
    return t10.disposeIntermediateTensorInfo(g), b;
  }
  return g;
}
var t2 = { kernelName: Lo, backendName: "cpu", kernelFunc: gj };
function xj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o;
  j(n, "argMax");
  let a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], S.assertAxesAreInnerMostDims("argMax", a, p.shape.length);
  let [c, l] = S.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values;
  for (let g = 0; g < d.length; ++g) {
    let x = g * f, b = h[x], C = 0;
    for (let w = 0; w < f; ++w) {
      let k = h[x + w];
      k > b && (b = k, C = w);
    }
    d[g] = C;
  }
  return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d);
}
var r2 = { kernelName: Bo, backendName: "cpu", kernelFunc: xj };
function yj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o;
  j(n, "argMin");
  let a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = St({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], S.assertAxesAreInnerMostDims("argMin", a, p.shape.length);
  let [c, l] = S.computeOutAndReduceShapes(p.shape, a), m = y.sizeFromShape(c), d = y.makeZerosTypedArray(m, "int32"), f = y.sizeFromShape(l), h = t10.data.get(p.dataId).values;
  for (let g = 0; g < d.length; ++g) {
    let x = g * f, b = h[x], C = 0;
    for (let w = 0; w < f; ++w) {
      let k = h[x + w];
      k < b && (b = k, C = w);
    }
    d[g] = C;
  }
  return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", d);
}
var o2 = { kernelName: ei, backendName: "cpu", kernelFunc: yj };
var bj = we(ua, (r) => Math.asin(r));
var n2 = { kernelName: ua, backendName: "cpu", kernelFunc: bj };
var Cj = we(pa, (r) => Math.asinh(r));
var s2 = { kernelName: pa, backendName: "cpu", kernelFunc: Cj };
var Sj = we(ca, (r) => Math.atan(r));
var a2 = { kernelName: ca, backendName: "cpu", kernelFunc: Sj };
var wj = Be((r, e) => Math.atan2(r, e));
var Ij = Qe(ma, wj);
var i2 = { kernelName: ma, backendName: "cpu", kernelFunc: Ij };
var vj = we(la, (r) => Math.atanh(r));
var u2 = { kernelName: la, backendName: "cpu", kernelFunc: vj };
function Jp(r, e, t10, o, n, s) {
  let a = n.strideHeight, i = n.strideWidth, p = n.dilationHeight, u = n.dilationWidth, c = n.effectiveFilterHeight, l = n.effectiveFilterWidth, m = n.padInfo.top, d = n.padInfo.left, f = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = le(n.outShape, t10), g = h.values, x = n.outShape[1] * n.outShape[2] * n.outShape[3], b = n.outShape[2] * n.outShape[3], C = n.outShape[3];
  for (let w = 0; w < n.batchSize; ++w) {
    let k = w * x, _ = w * o[0];
    for (let E = 0; E < n.inChannels; ++E)
      for (let A = 0; A < n.outHeight; ++A) {
        let R = A * a - m, D = Math.max(0, R), P = Math.min(n.inHeight, c + R), M = k + A * b;
        for (let L = 0; L < n.outWidth; ++L) {
          let V = L * i - d, z = Math.max(0, V), U = Math.min(n.inWidth, l + V), K = f, H = 0, q = 0;
          for (let ee = D; ee < P; ee += p) {
            let oe = _ + ee * o[1];
            for (let J = z; J < U; J += u) {
              let te = oe + J * o[2], ie = r[te + E];
              s === "max" && ie > K ? K = ie : s === "avg" && (H += ie, q++);
            }
            if (isNaN(K))
              break;
          }
          let Z = M + L * C + E;
          g[Z] = s === "avg" ? H / q : K;
        }
      }
  }
  return h;
}
function wf(r, e, t10, o, n = false, s = false) {
  let a = le(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, d = o.padInfo.top, f = o.padInfo.left, h = le(e, t10, r);
  for (let g = 0; g < o.batchSize; ++g)
    for (let x = 0; x < o.inChannels; ++x)
      for (let b = 0; b < o.outHeight; ++b) {
        let C = b * i - d, w = C;
        for (; w < 0; )
          w += u;
        let k = Math.min(o.inHeight, l + C);
        for (let _ = 0; _ < o.outWidth; ++_) {
          let E = _ * p - f, A = E;
          for (; A < 0; )
            A += c;
          let R = Math.min(o.inWidth, m + E), D = Number.NEGATIVE_INFINITY, P = -1;
          for (let M = w; M < k; M += u) {
            let L = M - C;
            for (let V = A; V < R; V += c) {
              let z = V - E, U = h.get(g, M, V, x);
              U > D && (D = U, n ? P = s ? ((g * o.inHeight + M) * o.inWidth + V) * o.inChannels + x : (M * o.inWidth + V) * o.inChannels + x : P = L * m + z);
            }
          }
          a.set(P, g, b, _, x);
        }
      }
  return a;
}
function If(r, e, t10, o, n, s) {
  let a = n.strideDepth, i = n.strideHeight, p = n.strideWidth, u = n.dilationDepth, c = n.dilationHeight, l = n.dilationWidth, m = n.effectiveFilterDepth, d = n.effectiveFilterHeight, f = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, x = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, C = le(n.outShape, t10), w = C.values, k = n.outShape[1] * n.outShape[2] * n.outShape[3] * n.outShape[4], _ = n.outShape[2] * n.outShape[3] * n.outShape[4], E = n.outShape[3] * n.outShape[4], A = n.outShape[4];
  for (let R = 0; R < n.batchSize; ++R) {
    let D = R * k, P = R * o[0];
    for (let M = 0; M < n.inChannels; ++M)
      for (let L = 0; L < n.outDepth; ++L) {
        let V = L * a - h, z = V;
        for (; z < 0; )
          z += u;
        let U = Math.min(n.inDepth, m + V), K = D + L * _;
        for (let H = 0; H < n.outHeight; ++H) {
          let q = H * i - g, Z = q;
          for (; Z < 0; )
            Z += c;
          let ee = Math.min(n.inHeight, d + q), oe = K + H * E;
          for (let J = 0; J < n.outWidth; ++J) {
            let te = J * p - x, ie = te;
            for (; ie < 0; )
              ie += l;
            let ce = Math.min(n.inWidth, f + te), ye = oe + J * A, Ne = b, Ie = 0, Ae = 0;
            for (let ot = z; ot < U; ot += u) {
              let pt = P + ot * o[1];
              for (let ze = Z; ze < ee; ze += c) {
                let ct = pt + ze * o[2];
                for (let at = ie; at < ce; at += l) {
                  let ft = ct + at * o[3], ht = r[ft + M];
                  if (s === "max" && ht > Ne ? Ne = ht : s === "avg" && (Ie += ht, Ae++), isNaN(Ne))
                    break;
                }
                if (isNaN(Ne))
                  break;
              }
              if (isNaN(Ne))
                break;
            }
            let Re = ye + M;
            w[Re] = s === "avg" ? Ie / Math.max(Ae, 1) : Ne;
          }
        }
      }
  }
  return C;
}
function p2(r, e) {
  let t10 = le(e.outShape, "int32"), o = e.strideDepth, n = e.strideHeight, s = e.strideWidth, a = e.dilationDepth, i = e.dilationHeight, p = e.dilationWidth, u = e.effectiveFilterDepth, c = e.effectiveFilterHeight, l = e.effectiveFilterWidth, m = e.padInfo.front, d = e.padInfo.top, f = e.padInfo.left;
  for (let h = 0; h < e.batchSize; ++h)
    for (let g = 0; g < e.inChannels; ++g)
      for (let x = 0; x < e.outDepth; ++x) {
        let b = x * o - m, C = b;
        for (; C < 0; )
          C += a;
        let w = Math.min(e.inDepth, u + b);
        for (let k = 0; k < e.outHeight; ++k) {
          let _ = k * n - d, E = _;
          for (; E < 0; )
            E += i;
          let A = Math.min(e.inHeight, c + _);
          for (let R = 0; R < e.outWidth; ++R) {
            let D = R * s - f, P = D;
            for (; P < 0; )
              P += p;
            let M = Math.min(e.inWidth, l + D), L = Number.NEGATIVE_INFINITY, V = -1;
            for (let z = C; z < w; z += a) {
              let U = z - b;
              for (let K = E; K < A; K += i) {
                let H = K - _;
                for (let q = P; q < M; q += p) {
                  let Z = q - D, ee = r.get(h, z, K, q, g);
                  ee >= L && (L = ee, V = U * c * l + H * c + Z);
                }
              }
            }
            t10.set(V, h, x, k, R, g);
          }
        }
      }
  return t10;
}
function kj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  j(n, "avgPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = S.computePool2DInfo(n.shape, s, a, u, i, p), l;
  if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
    l = ir({ inputs: { x: n }, backend: t10 });
  else {
    let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Jp(m, n.shape, n.dtype, d, c, "avg");
    l = t10.makeTensorInfo(c.outShape, n.dtype, f.values);
  }
  return l;
}
var c2 = { kernelName: Vo, backendName: "cpu", kernelFunc: kj };
function Nj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
  j(n, "avgPool3d");
  let c = S.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = If(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "avg");
  return t10.makeTensorInfo(m.shape, "float32", m.values);
}
var l2 = { kernelName: dp, backendName: "cpu", kernelFunc: Nj };
function Tj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
  j([n, s], "avgPool3DGrad");
  let c = S.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, d = c.strideWidth, f = c.filterDepth, h = c.filterHeight, g = c.filterWidth, x = c.dilationDepth, b = c.dilationHeight, C = c.dilationWidth, w = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = w - 1 - c.padInfo.front, A = _ - 1 - c.padInfo.left, R = k - 1 - c.padInfo.top, D = le(s.shape, "float32"), P = 1 / (f * h * g), M = t10.bufferSync(n);
  for (let L = 0; L < c.batchSize; ++L)
    for (let V = 0; V < c.inChannels; ++V)
      for (let z = 0; z < c.inDepth; ++z)
        for (let U = 0; U < c.inHeight; ++U)
          for (let K = 0; K < c.inWidth; ++K) {
            let H = z - E, q = U - R, Z = K - A, ee = 0;
            for (let oe = 0; oe < w; oe += x) {
              let J = (H + oe) / l;
              if (!(J < 0 || J >= c.outDepth || Math.floor(J) !== J))
                for (let te = 0; te < k; te += b) {
                  let ie = (q + te) / m;
                  if (!(ie < 0 || ie >= c.outHeight || Math.floor(ie) !== ie))
                    for (let ce = 0; ce < _; ce += C) {
                      let ye = (Z + ce) / d;
                      if (ye < 0 || ye >= c.outWidth || Math.floor(ye) !== ye)
                        continue;
                      let Ne = M.get(L, J, ie, ye, V);
                      ee += Ne;
                    }
                }
            }
            D.set(ee * P, L, z, U, K, V);
          }
  return t10.makeTensorInfo(D.shape, D.dtype, D.values);
}
var m2 = { kernelName: vm, backendName: "cpu", kernelFunc: Tj };
function _j(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
  j([n, s], "avgPoolGrad");
  let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, d = c.filterHeight, f = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, x = c.effectiveFilterHeight, b = c.effectiveFilterWidth, C = b - 1 - c.padInfo.left, w = x - 1 - c.padInfo.top, k = le(a.shape, "float32"), _ = 1 / (d * f), E = t10.data.get(n.dataId).values, A = le(n.shape, "float32", E);
  for (let R = 0; R < c.batchSize; ++R)
    for (let D = 0; D < c.inChannels; ++D)
      for (let P = 0; P < c.inHeight; ++P)
        for (let M = 0; M < c.inWidth; ++M) {
          let L = P - w, V = M - C, z = 0;
          for (let U = 0; U < x; U += h) {
            let K = (L + U) / l;
            if (!(K < 0 || K >= c.outHeight || Math.floor(K) !== K))
              for (let H = 0; H < b; H += g) {
                let q = (V + H) / m;
                if (q < 0 || q >= c.outWidth || Math.floor(q) !== q)
                  continue;
                let Z = A.get(R, K, q, D);
                z += Z;
              }
          }
          k.set(z * _, R, P, M, D);
        }
  return t10.makeTensorInfo(k.shape, k.dtype, k.values);
}
var d2 = { kernelName: mp, backendName: "cpu", kernelFunc: _j };
function $j(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e;
  y.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), j([n, i, p, s, a], "batchNorm");
  let { varianceEpsilon: u } = o;
  u == null && (u = 1e-3);
  let c = t10.data.get(n.dataId).values, l = t10.data.get(i.dataId).values, m = t10.data.get(p.dataId).values, d = s ? t10.data.get(s.dataId).values : new Float32Array([1]), f = a ? t10.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = f.length, x = d.length, b = m.length, C = l.length, w = 0, k = 0, _ = 0, E = 0;
  for (let A = 0; A < c.length; ++A)
    h[A] = f[w++] + (c[A] - l[k++]) * d[_++] / Math.sqrt(m[E++] + u), w >= g && (w = 0), k >= C && (k = 0), _ >= x && (_ = 0), E >= b && (E = 0);
  return t10.makeTensorInfo(n.shape, n.dtype, h);
}
var f2 = { kernelName: sn, backendName: "cpu", kernelFunc: $j };
function Ej(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
  j([n], "batchToSpaceND");
  let i = s.reduce((x, b) => x * b), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = Le({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = St({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = Le({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = To({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } });
  return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g;
}
var h2 = { kernelName: bs, backendName: "cpu", kernelFunc: Ej };
function Aj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = jp(i, p, s.dtype, s.shape, a);
  return t10.makeTensorInfo([a], s.dtype, u);
}
var g2 = { kernelName: ti, backendName: "cpu", kernelFunc: Aj };
function Rj(r) {
  let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, i = S.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
  return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var x2 = { kernelName: fp, backendName: "cpu", kernelFunc: Rj };
var Dj = we(lo, (r, e) => {
  let t10 = e;
  return r > t10.clipValueMax ? t10.clipValueMax : r < t10.clipValueMin ? t10.clipValueMin : r;
});
var y2 = { kernelName: lo, backendName: "cpu", kernelFunc: Dj };
var Fj = (r) => {
  let { x: e } = r.inputs, t10 = r.backend, o = new Float32Array(y.sizeFromShape(e.shape)), n = t10.data.get(e.dataId), s = n.complexTensorInfos.real, a = n.complexTensorInfos.imag, i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values;
  for (let u = 0; u < i.length; u++) {
    let c = i[u], l = p[u];
    o[u] = Math.hypot(c, l);
  }
  return t10.makeOutput(o, e.shape, "float32");
};
var b2 = { kernelName: hp, backendName: "cpu", kernelFunc: Fj };
function Ys(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.data.get(o.dataId).complexTensorInfos.imag, s = t10.data.get(n.dataId).values;
  return t10.makeTensorInfo(n.shape, n.dtype, s);
}
var C2 = { kernelName: ci, backendName: "cpu", kernelFunc: Ys };
function Mi(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape);
  S.assertParamsConsistent(a, s);
  let i = S.computeOutShape(e.map((h) => h.shape), s);
  if (y.sizeFromShape(i) === 0)
    return t10.makeTensorInfo(i, e[0].dtype, []);
  let p = e.filter((h) => y.sizeFromShape(h.shape) > 0);
  if (p.length === 1)
    return ir({ inputs: { x: p[0] }, backend: t10 });
  if (p[0].dtype === "complex64") {
    let h = p.map((w) => Io({ inputs: { input: w }, backend: t10 })), g = p.map((w) => Ys({ inputs: { input: w }, backend: t10 })), x = Mi({ inputs: h, backend: t10, attrs: { axis: s } }), b = Mi({ inputs: g, backend: t10, attrs: { axis: s } }), C = Ht({ inputs: { real: x, imag: b }, backend: t10 });
    return h.forEach((w) => t10.disposeIntermediateTensorInfo(w)), g.forEach((w) => t10.disposeIntermediateTensorInfo(w)), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), C;
  }
  let u = p.map((h) => {
    let x = [-1, y.sizeFromShape(h.shape.slice(s))];
    return Le({ inputs: { x: h }, backend: t10, attrs: { shape: x } });
  }), c = u.map((h) => ({ vals: t10.data.get(h.dataId).values, shape: h.shape }));
  i = S.computeOutShape(u.map((h) => h.shape), 1);
  let l = u[0].shape[0] === 1, m = Nu(c, i, e[0].dtype, l), d = S.computeOutShape(p.map((h) => h.shape), s), f = t10.makeTensorInfo(d, e[0].dtype, m);
  return u.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f;
}
var S2 = { kernelName: Cs, backendName: "cpu", kernelFunc: Mi };
function MS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o;
  j([n, s], "conv2d");
  let l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d = m.filterHeight, f = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, x = m.padInfo.left, b = m.padInfo.top, C = m.dataFormat === "channelsLast", w = new st(m.outShape, n.dtype), k = y.computeStrides(n.shape), _ = y.computeStrides(s.shape), E = k[0], A = C ? k[1] : k[2], R = C ? k[2] : 1, D = C ? 1 : k[1], P = w.strides[0], M = C ? w.strides[1] : w.strides[2], L = C ? w.strides[2] : 1, V = C ? 1 : w.strides[1], z = t10.data.get(n.dataId).values, U = t10.data.get(s.dataId).values, K = w.values;
  for (let H = 0; H < m.batchSize; ++H) {
    let q = H * E, Z = H * P;
    for (let ee = 0; ee < m.outHeight; ++ee) {
      let oe = Z + ee * M, J = ee * m.strideHeight - b;
      for (let te = 0; te < d; ++te) {
        let ie = J + te * h;
        if (ie < 0 || ie >= m.inHeight)
          continue;
        let ce = te * _[0], ye = q + ie * A;
        for (let Ne = 0; Ne < m.outWidth; ++Ne) {
          let Ie = oe + Ne * L, Ae = Ne * m.strideWidth - x;
          for (let Re = 0; Re < f; ++Re) {
            let ot = Ae + Re * g;
            if (ot < 0 || ot >= m.inWidth)
              continue;
            let pt = ce + Re * _[1], ze = ye + ot * R, ct = pt;
            for (let at = 0; at < m.inChannels; ++at) {
              let ft = z[ze + at * D];
              for (let ht = 0; ht < m.outChannels; ++ht)
                K[Ie + ht * V] += ft * U[ct + ht];
              ct += m.outChannels;
            }
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(w.shape, w.dtype, K);
}
var w2 = { kernelName: Uo, backendName: "cpu", kernelFunc: MS };
function Oj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o;
  j([n, s], "conv2dBackpropFilter");
  let l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: d, strideWidth: f, filterHeight: h, filterWidth: g } = m, x = m.dataFormat === "channelsLast", b = new st(m.filterShape, "float32"), C = m.padInfo.left, w = m.padInfo.top, k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = new st(n.shape, n.dtype, k), A = new st(s.shape, s.dtype, _);
  for (let R = 0; R < h; ++R) {
    let D = Math.max(0, Math.ceil((w - R) / d)), P = Math.min(m.outHeight, (m.inHeight + w - R) / d);
    for (let M = 0; M < g; ++M) {
      let L = Math.max(0, Math.ceil((C - M) / f)), V = Math.min(m.outWidth, (m.inWidth + C - M) / f);
      for (let z = 0; z < m.inChannels; ++z)
        for (let U = 0; U < m.outChannels; ++U) {
          let K = 0;
          for (let H = 0; H < m.batchSize; ++H)
            for (let q = D; q < P; ++q) {
              let Z = R + q * d - w;
              for (let ee = L; ee < V; ++ee) {
                let oe = M + ee * f - C;
                x ? K += E.get(H, Z, oe, z) * A.get(H, q, ee, U) : K += E.get(H, z, Z, oe) * A.get(H, U, q, ee);
              }
            }
          b.set(K, R, M, z, U);
        }
    }
  }
  return t10.makeTensorInfo(b.shape, b.dtype, b.values);
}
var I2 = { kernelName: oi, backendName: "cpu", kernelFunc: Oj };
function Pj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o;
  j([n, s], "conv2dBackpropInput");
  let l = y.computeStrides(s.shape), m = y.computeStrides(n.shape), d = S.convertConv2DDataFormat(u), f = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, d), h = new st(f.inShape, "float32"), g = h.values, x = t10.data.get(n.dataId).values, b = t10.data.get(s.dataId).values, [C, w, k] = l, { batchSize: _, filterHeight: E, filterWidth: A, inChannels: R, inHeight: D, inWidth: P, outChannels: M, outHeight: L, outWidth: V, strideHeight: z, strideWidth: U } = f;
  d = f.dataFormat;
  let K = E - 1 - f.padInfo.top, H = A - 1 - f.padInfo.left, q = d === "channelsLast", Z = h.strides[0], ee = q ? h.strides[1] : h.strides[2], oe = q ? h.strides[2] : 1, J = q ? 1 : h.strides[1], te = m[0], ie = q ? m[1] : m[2], ce = q ? m[2] : 1, ye = q ? 1 : m[1];
  for (let Ne = 0; Ne < _; ++Ne)
    for (let Ie = 0; Ie < R; ++Ie)
      for (let Ae = 0; Ae < D; ++Ae) {
        let Re = Ae - K, ot = Math.max(0, Math.ceil(Re / z)), pt = Math.min(L, (E + Re) / z);
        for (let ze = 0; ze < P; ++ze) {
          let ct = ze - H, at = Math.max(0, Math.ceil(ct / U)), ft = Math.min(V, (A + ct) / U), ht = 0;
          for (let Pt = ot; Pt < pt; ++Pt) {
            let qr = Pt * z - Re;
            for (let er = at; er < ft; ++er) {
              let Nt = er * U - ct, tr = te * Ne + ie * Pt + ce * er, rr = C * (E - 1 - qr) + w * (A - 1 - Nt) + k * Ie;
              for (let jr = 0; jr < M; ++jr) {
                let Xr = x[tr + ye * jr], cr = b[rr + jr];
                ht += Xr * cr;
              }
            }
          }
          let Rr = Z * Ne + ee * Ae + oe * ze + J * Ie;
          g[Rr] = ht;
        }
      }
  return t10.makeTensorInfo(h.shape, h.dtype, h.values);
}
var v2 = { kernelName: Go, backendName: "cpu", kernelFunc: Pj };
function Mj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o;
  j([n, s], "conv3d");
  let u = S.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: d, dilationHeight: f, dilationWidth: h, padInfo: g } = u, x = g.front, b = g.left, C = g.top, w = new st(u.outShape, n.dtype), k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = w.values, A = y.computeStrides(n.shape), R = y.computeStrides(s.shape);
  for (let D = 0; D < u.batchSize; ++D) {
    let P = D * A[0], M = D * w.strides[0];
    for (let L = 0; L < u.outDepth; ++L) {
      let V = M + L * w.strides[1], z = L * u.strideDepth - x;
      for (let U = 0; U < c; ++U) {
        let K = z + U * d;
        if (K < 0 || K >= u.inDepth)
          continue;
        let H = U * R[0], q = P + K * A[1];
        for (let Z = 0; Z < u.outHeight; ++Z) {
          let ee = V + Z * w.strides[2], oe = Z * u.strideHeight - C;
          for (let J = 0; J < l; ++J) {
            let te = oe + J * f;
            if (te < 0 || te >= u.inHeight)
              continue;
            let ie = H + J * R[1], ce = q + te * A[2];
            for (let ye = 0; ye < u.outWidth; ++ye) {
              let Ne = ee + ye * u.outChannels, Ie = ye * u.strideWidth - b;
              for (let Ae = 0; Ae < m; ++Ae) {
                let Re = Ie + Ae * h;
                if (Re < 0 || Re >= u.inWidth)
                  continue;
                let ot = ie + Ae * R[2], pt = ce + Re * u.inChannels, ze = ot;
                for (let ct = 0; ct < u.inChannels; ++ct) {
                  let at = k[pt + ct];
                  for (let ft = 0; ft < u.outChannels; ++ft)
                    E[Ne + ft] += at * _[ze + ft];
                  ze += u.outChannels;
                }
              }
            }
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(w.shape, w.dtype, w.values);
}
var k2 = { kernelName: gp, backendName: "cpu", kernelFunc: Mj };
function Lj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o;
  j([n, s], "conv3dBackpropFilterV2");
  let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = S.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, d = l.strideHeight, f = l.strideWidth, h = l.filterDepth, g = l.filterHeight, x = l.filterWidth, b = new st(l.filterShape, "float32"), C = b.values, [w, k, _, E] = b.strides, A = t10.data.get(s.dataId).values, [R, D, P, M] = c, L = t10.data.get(n.dataId).values, [V, z, U, K] = u, H = l.padInfo.front, q = l.padInfo.left, Z = l.padInfo.top;
  for (let ee = 0; ee < h; ++ee) {
    let oe = Math.max(0, Math.ceil((H - ee) / m)), J = Math.min(l.outDepth, (l.inDepth + H - ee) / m), te = ee * w;
    for (let ie = 0; ie < g; ++ie) {
      let ce = Math.max(0, Math.ceil((Z - ie) / d)), ye = Math.min(l.outHeight, (l.inHeight + Z - ie) / d), Ne = ie * k + te;
      for (let Ie = 0; Ie < x; ++Ie) {
        let Ae = Math.max(0, Math.ceil((q - Ie) / f)), Re = Math.min(l.outWidth, (l.inWidth + q - Ie) / f), ot = Ie * _ + Ne;
        for (let pt = 0; pt < l.inChannels; ++pt) {
          let ze = pt * E + ot;
          for (let ct = 0; ct < l.outChannels; ++ct) {
            let at = 0;
            for (let ft = 0; ft < l.batchSize; ++ft) {
              let ht = ft * V, Rr = ft * R;
              for (let Pt = oe; Pt < J; ++Pt) {
                let er = (ee + Pt * m - H) * z + ht, Nt = Pt * D + Rr;
                for (let tr = ce; tr < ye; ++tr) {
                  let jr = (ie + tr * d - Z) * U + er, Xr = tr * P + Nt;
                  for (let cr = Ae; cr < Re; ++cr) {
                    let Do = (Ie + cr * f - q) * K + jr, hs = cr * M + Xr;
                    at += L[Do + pt] * A[hs + ct];
                  }
                }
              }
            }
            C[ze + ct] = at;
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(b.shape, b.dtype, b.values);
}
var N2 = { kernelName: km, backendName: "cpu", kernelFunc: Lj };
function Bj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o;
  j([n], "conv3dBackpropInputV2");
  let u = y.computeStrides(n.shape), c = y.computeStrides(s.shape), l = S.computeConv3DInfo(p, s.shape, i, 1, a), m = new st(l.inShape, "float32"), d = m.values, [f, h, g, x] = m.strides, b = t10.data.get(n.dataId).values, [C, w, k, _] = u, E = t10.data.get(s.dataId).values, [A, R, D, P] = c, { batchSize: M, filterDepth: L, filterHeight: V, filterWidth: z, inChannels: U, inDepth: K, inHeight: H, inWidth: q, outChannels: Z, outDepth: ee, outHeight: oe, outWidth: J, strideDepth: te, strideHeight: ie, strideWidth: ce } = l, ye = L - 1 - l.padInfo.front, Ne = V - 1 - l.padInfo.top, Ie = z - 1 - l.padInfo.left;
  for (let Ae = 0; Ae < M; ++Ae)
    for (let Re = 0; Re < U; ++Re)
      for (let ot = 0; ot < K; ++ot) {
        let pt = ot - ye, ze = Math.max(0, Math.ceil(pt / te)), ct = Math.min(ee, (L + pt) / te);
        for (let at = 0; at < H; ++at) {
          let ft = at - Ne, ht = Math.max(0, Math.ceil(ft / ie)), Rr = Math.min(oe, (V + ft) / ie);
          for (let Pt = 0; Pt < q; ++Pt) {
            let qr = Pt - Ie, er = Math.max(0, Math.ceil(qr / ce)), Nt = Math.min(J, (z + qr) / ce), tr = 0;
            for (let rr = ze; rr < ct; ++rr) {
              let jr = rr * te - pt;
              for (let Xr = ht; Xr < Rr; ++Xr) {
                let cr = Xr * ie - ft;
                for (let ea = er; ea < Nt; ++ea) {
                  let Do = ea * ce - qr, hs = C * Ae + w * rr + k * Xr + _ * ea, qt = A * (L - 1 - jr) + R * (V - 1 - cr) + D * (z - 1 - Do) + P * Re;
                  for (let ta = 0; ta < Z; ++ta) {
                    let Dc = b[hs + ta], Fc = E[qt + ta];
                    tr += Dc * Fc;
                  }
                }
              }
            }
            d[f * Ae + h * ot + g * at + x * Pt + Re] = tr;
          }
        }
      }
  return t10.makeTensorInfo(m.shape, m.dtype, m.values);
}
var T2 = { kernelName: xp, backendName: "cpu", kernelFunc: Bj };
var Vj = we(Ho, (r) => Math.cos(r));
var _2 = { kernelName: Ho, backendName: "cpu", kernelFunc: Vj };
var zj = we(Ko, (r) => Math.cosh(r));
var $2 = { kernelName: Ko, backendName: "cpu", kernelFunc: zj };
function Wj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, [c, l, m, d] = n.shape, f = s.shape[0], [h, g] = i, x = le([f, h, g, d], "float32"), b = t10.data.get(s.dataId).values, C = t10.data.get(a.dataId).values, w = t10.data.get(n.dataId).values, k = y.computeStrides(n.shape), _ = y.computeStrides(x.shape);
  for (let E = 0; E < f; E++) {
    let A = E * 4, R = b[A], D = b[A + 1], P = b[A + 2], M = b[A + 3], L = C[E];
    if (L >= c)
      continue;
    let V = h > 1 ? (P - R) * (l - 1) / (h - 1) : 0, z = g > 1 ? (M - D) * (m - 1) / (g - 1) : 0;
    for (let U = 0; U < h; U++) {
      let K = h > 1 ? R * (l - 1) + U * V : 0.5 * (R + P) * (l - 1);
      if (K < 0 || K > l - 1) {
        for (let H = 0; H < g; H++)
          for (let q = 0; q < d; q++) {
            let Z = q + H * _[2] + U * _[1] + E * _[0];
            x.values[Z] = u;
          }
        continue;
      }
      if (p === "bilinear") {
        let H = Math.floor(K), q = Math.ceil(K), Z = K - H;
        for (let ee = 0; ee < g; ee++) {
          let oe = g > 1 ? D * (m - 1) + ee * z : 0.5 * (D + M) * (m - 1);
          if (oe < 0 || oe > m - 1) {
            for (let ce = 0; ce < d; ce++) {
              let ye = ce + ee * _[2] + U * _[1] + E * _[0];
              x.values[ye] = u;
            }
            continue;
          }
          let J = Math.floor(oe), te = Math.ceil(oe), ie = oe - J;
          for (let ce = 0; ce < d; ce++) {
            let ye = ce + J * k[2] + H * k[1] + L * k[0], Ne = w[ye];
            ye = ce + te * k[2] + H * k[1] + L * k[0];
            let Ie = w[ye];
            ye = ce + J * k[2] + q * k[1] + L * k[0];
            let Ae = w[ye];
            ye = ce + te * k[2] + q * k[1] + L * k[0];
            let Re = w[ye], ot = Ne + (Ie - Ne) * ie, pt = Ae + (Re - Ae) * ie;
            ye = ce + ee * _[2] + U * _[1] + E * _[0], x.values[ye] = ot + (pt - ot) * Z;
          }
        }
      } else
        for (let H = 0; H < g; ++H) {
          let q = g > 1 ? D * (m - 1) + H * z : 0.5 * (D + M) * (m - 1);
          if (q < 0 || q > m - 1) {
            for (let oe = 0; oe < d; oe++) {
              let J = oe + H * _[2] + U * _[1] + E * _[0];
              x.values[J] = u;
            }
            continue;
          }
          let Z = Math.round(q), ee = Math.round(K);
          for (let oe = 0; oe < d; oe++) {
            let J = oe + Z * k[2] + ee * k[1] + L * k[0], te = oe + H * _[2] + U * _[1] + E * _[0];
            x.values[te] = w[J];
          }
        }
    }
  }
  return t10.makeTensorInfo(x.shape, x.dtype, x.values);
}
var E2 = { kernelName: Xo, backendName: "cpu", kernelFunc: Wj };
function Uj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  j(n, "cumprod");
  let p = S.getAxesPermutation([s], n.shape.length), u = n;
  p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } }));
  let c = S.getInnerMostAxes(1, n.shape.length)[0];
  if (c !== u.shape.length - 1)
    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`);
  let l = dt(u.dtype, "int32"), m = y.makeOnesTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b;
  for (let x = 0; x < d.length; x += f)
    for (let b = 0; b < f; b++) {
      let C = h(x, b);
      if (b === 0)
        m[C] = a ? 1 : d[C];
      else {
        let w = h(x, b - 1);
        m[C] = a ? d[w] * m[w] : d[C] * m[w];
      }
    }
  let g = t10.makeTensorInfo(u.shape, l, m);
  if (p != null) {
    let x = S.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } });
    return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b;
  }
  return g;
}
var A2 = { kernelName: qo, backendName: "cpu", kernelFunc: Uj };
function Gj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  j(n, "cumsum");
  let p = S.getAxesPermutation([s], n.shape.length), u = n;
  p != null && (u = St({ inputs: { x: n }, backend: t10, attrs: { perm: p } }));
  let c = S.getInnerMostAxes(1, n.shape.length)[0];
  if (c !== u.shape.length - 1)
    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`);
  let l = dt(u.dtype, "int32"), m = y.makeZerosTypedArray(y.sizeFromShape(u.shape), l), d = t10.data.get(u.dataId).values, f = u.shape[u.shape.length - 1], h = i ? (x, b) => x + f - b - 1 : (x, b) => x + b;
  for (let x = 0; x < d.length; x += f)
    for (let b = 0; b < f; b++) {
      let C = h(x, b);
      if (b === 0)
        m[C] = a ? 0 : d[C];
      else {
        let w = h(x, b - 1);
        m[C] = a ? d[w] + m[w] : d[C] + m[w];
      }
    }
  let g = t10.makeTensorInfo(u.shape, l, m);
  if (p != null) {
    let x = S.getUndoAxesPermutation(p), b = St({ inputs: { x: g }, backend: t10, attrs: { perm: x } });
    return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b;
  }
  return g;
}
var R2 = { kernelName: jo, backendName: "cpu", kernelFunc: Gj };
function Hj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
  if (n.shape.length === 1) {
    let p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = jp(p, u, s.dtype, s.shape, a);
    return t10.makeTensorInfo([a], s.dtype, c);
  } else if (n.shape.length === 2) {
    let p = t10.bufferSync(n), u = t10.bufferSync(s), c = uf(p, u, a, i);
    return t10.makeTensorInfo(c.shape, s.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var D2 = { kernelName: ni, backendName: "cpu", kernelFunc: Hj };
function Kj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o;
  y.assert(a === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);
  let i = n.shape[0], p = n.shape[1], u = n.shape[2], c = n.shape[3], l = p * s, m = u * s, d = c / (s * s), f = t10.data.get(n.dataId).values, h = new Float32Array(i * l * m * d), g = 0;
  for (let x = 0; x < i; ++x)
    for (let b = 0; b < l; ++b) {
      let C = Math.floor(b / s), w = b % s;
      for (let k = 0; k < m; ++k) {
        let _ = Math.floor(k / s), E = k % s, A = (w * s + E) * d;
        for (let R = 0; R < d; ++R) {
          let P = R + A + c * (_ + u * (C + p * x));
          h[g++] = f[P];
        }
      }
    }
  return t10.makeTensorInfo([i, l, m, d], n.dtype, h);
}
var F2 = { kernelName: Yo, backendName: "cpu", kernelFunc: Kj };
function LS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o;
  j([n, s], "depthwiseConv2DNative");
  let c = y.computeStrides(n.shape), l = y.computeStrides(s.shape), m = p;
  m == null && (m = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);
  let d = S.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: f, filterWidth: h, dilationHeight: g, dilationWidth: x, padInfo: b } = d, C = b.left, w = b.top, k = d.outChannels / d.inChannels, _ = new st(d.outShape, n.dtype), E = t10.data.get(n.dataId).values, A = t10.data.get(s.dataId).values, R = _.values;
  for (let D = 0; D < d.batchSize; ++D) {
    let P = D * c[0], M = D * _.strides[0];
    for (let L = 0; L < d.outHeight; ++L) {
      let V = M + L * _.strides[1], z = L * d.strideHeight - w;
      for (let U = 0; U < f; ++U) {
        let K = z + U * g;
        if (K < 0 || K >= d.inHeight)
          continue;
        let H = U * l[0], q = P + K * c[1];
        for (let Z = 0; Z < d.outWidth; ++Z) {
          let ee = V + Z * _.strides[2], oe = Z * d.strideWidth - C;
          for (let J = 0; J < h; ++J) {
            let te = oe + J * x;
            if (te < 0 || te >= d.inWidth)
              continue;
            let ie = H + J * l[1], ce = q + te * d.inChannels, ye = ee, Ne = ie;
            for (let Ie = 0; Ie < d.inChannels; ++Ie) {
              let Ae = E[ce + Ie];
              for (let Re = 0; Re < k; ++Re)
                R[ye + Re] += Ae * A[Ne + Re];
              ye += k, Ne += k;
            }
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(_.shape, _.dtype, _.values);
}
var O2 = { kernelName: Qo, backendName: "cpu", kernelFunc: LS };
function qj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o;
  j([n, s], "depthwiseConv2dNativeBackpropFilter");
  let l = S.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: d, filterHeight: f, filterWidth: h } = l, g = new st(l.filterShape, "float32"), x = l.padInfo.left, b = l.padInfo.top, C = l.outChannels / l.inChannels, w = t10.data.get(n.dataId).values, k = new st(n.shape, n.dtype, w), _ = t10.data.get(s.dataId).values, E = new st(s.shape, s.dtype, _);
  for (let A = 0; A < f; ++A) {
    let R = Math.max(0, Math.ceil((b - A) / m)), D = Math.min(l.outHeight, (l.inHeight + b - A) / m);
    for (let P = 0; P < h; ++P) {
      let M = Math.max(0, Math.ceil((x - P) / d)), L = Math.min(l.outWidth, (l.inWidth + x - P) / d);
      for (let V = 0; V < l.outChannels; ++V) {
        let z = Math.trunc(V / C), U = V % C, K = 0;
        for (let H = 0; H < l.batchSize; ++H)
          for (let q = R; q < D; ++q) {
            let Z = A + q * m - b;
            for (let ee = M; ee < L; ++ee) {
              let oe = P + ee * d - x;
              K += k.get(H, Z, oe, z) * E.get(H, q, ee, V);
            }
          }
        g.set(K, A, P, z, U);
      }
    }
  }
  return t10.makeTensorInfo(g.shape, g.dtype, g.values);
}
var P2 = { kernelName: yp, backendName: "cpu", kernelFunc: qj };
function jj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o;
  j([n, s], "depthwiseConv2DNativeBackpropInput");
  let l = y.computeStrides(n.shape), m = y.computeStrides(s.shape), d = S.computeConv2DInfo(c, s.shape, a, i, p, u, true), f = new st(d.inShape, "float32"), h = f.values, [g, x, b] = f.strides, C = t10.data.get(n.dataId).values, [w, k, _] = l, E = t10.data.get(s.dataId).values, [A, R, D] = m, { batchSize: P, filterHeight: M, filterWidth: L, inChannels: V, inHeight: z, inWidth: U, outChannels: K, outHeight: H, outWidth: q, strideHeight: Z, strideWidth: ee } = d, oe = M - 1 - d.padInfo.top, J = L - 1 - d.padInfo.left, te = K / V;
  for (let ie = 0; ie < P; ++ie)
    for (let ce = 0; ce < V; ++ce)
      for (let ye = 0; ye < z; ++ye) {
        let Ne = ye - oe, Ie = Math.max(0, Math.ceil(Ne / Z)), Ae = Math.min(H, (M + Ne) / Z);
        for (let Re = 0; Re < U; ++Re) {
          let ot = Re - J, pt = Math.max(0, Math.ceil(ot / ee)), ze = Math.min(q, (L + ot) / ee), ct = 0;
          for (let at = Ie; at < Ae; ++at) {
            let ft = at * Z - Ne;
            for (let ht = pt; ht < ze; ++ht) {
              let Rr = ht * ee - ot, Pt = w * ie + k * at + _ * ht, qr = A * (M - 1 - ft) + R * (L - 1 - Rr) + D * ce;
              for (let er = 0; er < te; ++er) {
                let Nt = ce * te + er, tr = C[Pt + Nt], rr = E[qr + er];
                ct += tr * rr;
              }
            }
          }
          h[g * ie + x * ye + b * Re + ce] = ct;
        }
      }
  return t10.makeTensorInfo(f.shape, f.dtype, f.values);
}
var M2 = { kernelName: bp, backendName: "cpu", kernelFunc: jj };
function Xj(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = y.sizeFromShape(o.shape), s = t10.data.get(o.dataId).values, a = le([n, n], o.dtype), i = a.values;
  for (let u = 0; u < s.length; u++)
    i[u * n + u] = s[u];
  let p = [...o.shape, ...o.shape];
  return t10.makeTensorInfo(p, a.dtype, a.values);
}
var L2 = { kernelName: si, backendName: "cpu", kernelFunc: Xj };
var B2 = { kernelName: ai, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o, filter: n } = r, { strides: s, pad: a, dilations: i } = t10, p = e, u = p.data.get(o.dataId).values, c = o.shape.length, l = p.data.get(n.dataId).values, m = n.shape.length, { batchSize: d, inHeight: f, inWidth: h, inChannels: g, outHeight: x, outWidth: b, padInfo: C, strideHeight: w, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: A, dilationWidth: R, outShape: D } = S.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), P = y.sizeFromShape(D), M = D.length, L = y.getArrayFromDType(o.dtype, P);
  for (let z = 0; z < d; ++z)
    for (let U = 0; U < x; ++U) {
      let K = U * w - C.top;
      for (let H = 0; H < b; ++H) {
        let q = H * k - C.left;
        for (let Z = 0; Z < g; ++Z) {
          let ee = Number.MIN_SAFE_INTEGER;
          for (let J = 0; J < _; ++J) {
            let te = K + J * A;
            if (te >= 0 && te < f)
              for (let ie = 0; ie < E; ++ie) {
                let ce = q + ie * R;
                if (ce >= 0 && ce < h) {
                  let ye = y.locToIndex([z, te, ce, Z], c, y.computeStrides(o.shape)), Ne = y.locToIndex([J, ie, Z], m, y.computeStrides(n.shape)), Ie = u[ye] + l[Ne];
                  Ie > ee && (ee = Ie);
                }
              }
          }
          let oe = y.locToIndex([z, U, H, Z], M, y.computeStrides(D));
          L[oe] = ee;
        }
      }
    }
  return { dataId: p.write(y.toTypedArray(L, o.dtype), D, o.dtype), shape: D, dtype: o.dtype };
} };
var V2 = { kernelName: $b, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: A, outShape: R } = S.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
  y.assert(s.rank === R.length, () => `Error in ${$b}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);
  let D = y.toNestedArray(R, u.data.get(s.dataId).values), P = y.makeZerosNestedTypedArray(n.shape, n.dtype);
  for (let L = 0; L < m; ++L)
    for (let V = 0; V < g; ++V) {
      let z = V * C - b.top;
      for (let U = 0; U < x; ++U) {
        let K = U * w - b.left;
        for (let H = 0; H < h; ++H) {
          let q = Number.MIN_SAFE_INTEGER, Z = 0, ee = 0;
          for (let oe = 0; oe < k; ++oe) {
            let J = z + oe * E;
            if (J >= 0 && J < d)
              for (let te = 0; te < _; ++te) {
                let ie = K + te * A;
                if (ie >= 0 && ie < f) {
                  let ce = c[L][J][ie][H] + l[oe][te][H];
                  ce > q && (q = ce, Z = oe, ee = te);
                }
              }
          }
          P[Z][ee][H] += D[L][V][U][H];
        }
      }
    }
  return { dataId: u.write(y.toTypedArray(P, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
} };
var z2 = { kernelName: _b, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o, filter: n, dy: s } = r, { strides: a, pad: i, dilations: p } = t10, u = e, c = y.toNestedArray(o.shape, u.data.get(o.dataId).values), l = y.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: d, inWidth: f, inChannels: h, outHeight: g, outWidth: x, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: A, outShape: R } = S.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
  y.assert(s.rank === R.length, () => `Error in ${_b}, dy must have the same rank as output ${R.length}, but got ${s.rank}`);
  let D = y.toNestedArray(R, u.data.get(s.dataId).values), P = y.makeZerosNestedTypedArray(o.shape, o.dtype);
  for (let L = 0; L < m; ++L)
    for (let V = 0; V < g; ++V) {
      let z = V * C - b.top;
      for (let U = 0; U < x; ++U) {
        let K = U * w - b.left;
        for (let H = 0; H < h; ++H) {
          let q = Number.MIN_SAFE_INTEGER, Z = z < 0 ? 0 : z, ee = K < 0 ? 0 : K;
          for (let oe = 0; oe < k; ++oe) {
            let J = z + oe * E;
            if (J >= 0 && J < d)
              for (let te = 0; te < _; ++te) {
                let ie = K + te * A;
                if (ie >= 0 && ie < f) {
                  let ce = c[L][J][ie][H] + l[oe][te][H];
                  ce > q && (q = ce, Z = J, ee = ie);
                }
              }
          }
          P[L][Z][ee][H] += D[L][V][U][H];
        }
      }
    }
  return { dataId: u.write(y.toTypedArray(P, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
function za(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  j(n, "sum");
  let i;
  n.dtype === "bool" ? i = vo({ inputs: { x: n }, backend: t10, attrs: { dtype: "int32" } }) : i = ir({ inputs: { x: n }, backend: t10 });
  let p = i.shape.length, u = y.parseAxisParam(s, i.shape), c = S.getAxesPermutation(u, p), l = u, m = i;
  c != null && (m = St({ inputs: { x: i }, backend: t10, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, p)), S.assertAxesAreInnerMostDims("sum", l, m.shape.length);
  let [d, f] = S.computeOutAndReduceShapes(m.shape, l), h = S.upcastType(m.dtype, "int32"), g = Kp(t10, d, h), x = y.sizeFromShape(f), b = t10.data.get(g.dataId).values, C = t10.data.get(m.dataId).values;
  for (let w = 0; w < b.length; ++w) {
    let k = w * x, _ = 0;
    for (let E = 0; E < x; ++E)
      _ += C[k + E];
    b[w] = _;
  }
  if (a) {
    let w = S.expandShapeToKeepDim(g.shape, u), k = g;
    g = Le({ inputs: { x: g }, backend: t10, attrs: { shape: w } }), t10.disposeIntermediateTensorInfo(k);
  }
  return t10.disposeIntermediateTensorInfo(i), c != null && t10.disposeIntermediateTensorInfo(m), g;
}
var W2 = { kernelName: Kn, backendName: "cpu", kernelFunc: za };
function Yj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length);
  S.checkEinsumDimSizes(a.length, p, s);
  let { path: u, steps: c } = S.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = [];
  for (let h = 0; h < l; ++h) {
    for (let g of c[h]) {
      let { permutationIndices: x, expandDims: b } = S.getEinsumPermutation(d, p[g]), C;
      S.isIdentityPermutation(x) ? C = s[g] : (C = St({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(C));
      let w = C.shape.slice();
      for (let k = 0; k < b.length; ++k)
        w.splice(b[k], 0, 1);
      y.arraysEqual(C.shape, w) || (C = Le({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = Tu({ inputs: { a: C, b: m }, backend: t10 }), f.push(m));
    }
    h < l - 1 && (u[h] >= 0 && (m = za({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
  }
  for (let h of f)
    h !== m && t10.disposeIntermediateTensorInfo(h);
  return m;
}
var U2 = { kernelName: ii, backendName: "cpu", kernelFunc: Yj };
function Qj(r) {
  let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e;
  j([o, n], "eluGrad");
  let s = new Float32Array(y.sizeFromShape(n.shape)), a = t10.data.get(n.dataId).values, i = t10.data.get(o.dataId).values;
  for (let p = 0; p < a.length; ++p) {
    let u = a[p];
    u >= 1 ? s[p] = i[p] : s[p] = i[p] * (u + 1);
  }
  return t10.makeTensorInfo(n.shape, "float32", s);
}
var G2 = { kernelName: Nm, backendName: "cpu", kernelFunc: Qj };
var Zj = S.ERF_P;
var Jj = S.ERF_A1;
var eX = S.ERF_A2;
var tX = S.ERF_A3;
var rX = S.ERF_A4;
var oX = S.ERF_A5;
var nX = we(da, (r) => {
  let e = Math.sign(r), t10 = Math.abs(r), o = 1 / (1 + Zj * t10);
  return e * (1 - ((((oX * o + rX) * o + tX) * o + eX) * o + Jj) * o * Math.exp(-t10 * t10));
});
var H2 = { kernelName: da, backendName: "cpu", kernelFunc: nX };
function ec(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { input: n } = e, { dim: s } = o, a = n.shape.length, i = n.shape.slice(), p = s;
  return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Le({ inputs: { x: n }, backend: t10, attrs: { shape: i } });
}
var K2 = { kernelName: Ss, backendName: "cpu", kernelFunc: ec };
var sX = Be((r, e) => r / e);
var yl = Qe(Zo, sX);
var bl = { kernelName: Zo, backendName: "cpu", kernelFunc: yl };
function vf(r, e, t10) {
  let o = r.shape, n = o[0], s = o[1], a = t10.data.get(r.dataId), i = a.complexTensorInfos.real, p = a.complexTensorInfos.imag, u = [n, s], c = y.sizeFromShape(u), l = y.getTypedArrayFromDType("float32", c), m = y.getTypedArrayFromDType("float32", c);
  for (let g = 0; g < n; g++) {
    let x = To({ inputs: { x: i }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), b = To({ inputs: { x: p }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), C = Ht({ inputs: { real: x, imag: b }, backend: t10 }), { real: w, imag: k } = aX(C, e, t10), _ = S.mergeRealAndImagArrays(w, k);
    for (let E = 0; E < s; E++) {
      let A = S.getComplexWithIndex(_, E);
      l[g * s + E] = A.real, m[g * s + E] = A.imag;
    }
    t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(b), t10.disposeIntermediateTensorInfo(C);
  }
  let d = t10.makeTensorInfo(u, "float32", l), f = t10.makeTensorInfo(u, "float32", m), h = Ht({ inputs: { real: d, imag: f }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), h;
}
function aX(r, e, t10) {
  let o = y.sizeFromShape(r.shape), n = t10.data.get(r.dataId), s = t10.data.get(n.complexTensorInfos.real.dataId).values, a = t10.data.get(n.complexTensorInfos.imag.dataId).values;
  if (iX(o)) {
    let i = BS(s, a, o, e, t10), p = [r.shape[0], r.shape[1]];
    if (e) {
      let u = t10.makeTensorInfo(p, "float32", i.real), c = t10.makeTensorInfo(p, "float32", i.imag), l = t10.makeTensorInfo([], "float32", y.createScalarValue(o, "float32")), m = ir({ inputs: { x: l }, backend: t10 }), d = bl.kernelFunc({ inputs: { a: u, b: l }, backend: t10 }), f = bl.kernelFunc({ inputs: { a: c, b: m }, backend: t10 }), h = t10.data.get(d.dataId).values, g = t10.data.get(f.dataId).values;
      return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), { real: h, imag: g };
    }
    return i;
  } else {
    let i = S.mergeRealAndImagArrays(s, a), p = uX(i, o, e);
    return S.splitRealAndImagArrays(p);
  }
}
function iX(r) {
  return (r & r - 1) === 0;
}
function BS(r, e, t10, o, n) {
  if (t10 === 1)
    return { real: r, imag: e };
  let s = S.mergeRealAndImagArrays(r, e), a = t10 / 2, i = S.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), d = Ht({ inputs: { real: l, imag: m }, backend: n }), f = S.complexWithOddIndex(s), h = f.real, g = f.imag, x = [h.length], b = n.makeTensorInfo(x, "float32", h), C = n.makeTensorInfo(x, "float32", g), w = Ht({ inputs: { real: b, imag: C }, backend: n }), k = BS(p, u, a, o, n), _ = k.real, E = k.imag, A = [_.length], R = n.makeTensorInfo(A, "float32", _), D = n.makeTensorInfo(A, "float32", E), P = Ht({ inputs: { real: R, imag: D }, backend: n }), M = BS(h, g, a, o, n), L = M.real, V = M.imag, z = [L.length], U = n.makeTensorInfo(z, "float32", L), K = n.makeTensorInfo(z, "float32", V), H = Ht({ inputs: { real: U, imag: K }, backend: n }), q = S.exponents(t10, o), Z = [q.real.length], ee = n.makeTensorInfo(Z, "float32", q.real), oe = n.makeTensorInfo(Z, "float32", q.imag), J = Ht({ inputs: { real: ee, imag: oe }, backend: n }), te = Tu({ inputs: { a: J, b: H }, backend: n }), ie = Xs({ inputs: { a: P, b: te }, backend: n }), ce = gl({ inputs: { a: P, b: te }, backend: n }), ye = Io({ inputs: { input: ie }, backend: n }), Ne = Io({ inputs: { input: ce }, backend: n }), Ie = Ys({ inputs: { input: ie }, backend: n }), Ae = Ys({ inputs: { input: ce }, backend: n }), Re = Mi({ inputs: [ye, Ne], backend: n, attrs: { axis: 0 } }), ot = Mi({ inputs: [Ie, Ae], backend: n, attrs: { axis: 0 } }), pt = n.data.get(Re.dataId).values, ze = n.data.get(ot.dataId).values;
  return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(w), n.disposeIntermediateTensorInfo(R), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(P), n.disposeIntermediateTensorInfo(U), n.disposeIntermediateTensorInfo(K), n.disposeIntermediateTensorInfo(H), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(oe), n.disposeIntermediateTensorInfo(J), n.disposeIntermediateTensorInfo(te), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(ce), n.disposeIntermediateTensorInfo(ye), n.disposeIntermediateTensorInfo(Ie), n.disposeIntermediateTensorInfo(Ne), n.disposeIntermediateTensorInfo(Ae), n.disposeIntermediateTensorInfo(Re), n.disposeIntermediateTensorInfo(ot), { real: pt, imag: ze };
}
function uX(r, e, t10) {
  let o = new Float32Array(e * 2);
  for (let n = 0; n < e; n++) {
    let s = 0, a = 0;
    for (let i = 0; i < e; i++) {
      let p = S.exponent(n * i, e, t10), u = S.getComplexWithIndex(r, i);
      s += u.real * p.real - u.imag * p.imag, a += u.real * p.imag + u.imag * p.real;
    }
    t10 && (s /= e, a /= e), S.assignToTypedArray(o, s, a, n);
  }
  return o;
}
function pX(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Le({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = vf(i, false, t10), u = Le({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u;
}
var q2 = { kernelName: ui, backendName: "cpu", kernelFunc: pX };
function Cl(r) {
  let { backend: e, attrs: t10 } = r, { shape: o, value: n, dtype: s } = t10, a = s || y.inferDtype(n), i = y.getArrayFromDType(a, y.sizeFromShape(o));
  return cX(i, n, a), e.makeTensorInfo(o, a, i);
}
var j2 = { kernelName: ws, backendName: "cpu", kernelFunc: Cl };
function cX(r, e, t10) {
  r.fill(e);
}
var X2 = { kernelName: rn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, n = t10, s = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values;
  for (let m = 0; m < a; m++) {
    let d = m * p * i * u;
    for (let f = 0; f < i; f++) {
      let h = f * (p * u);
      for (let g = 0; g < p; g++) {
        let x = g * u;
        for (let b = 0; b < u; b++) {
          let C = Math.round(p - g - 1), w = d + h + x + b, k = c[w];
          if (C >= 0 && C < p) {
            let _ = C * u, E = d + h + _ + b;
            k = c[E];
          }
          s[w] = k;
        }
      }
    }
  }
  return { dataId: n.write(s, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
var lX = Be((r, e) => Math.floor(r / e));
var mX = Qe(nn, lX, null, "int32");
var Y2 = { kernelName: nn, backendName: "cpu", kernelFunc: mX };
function dX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = MS({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } });
  if (a) {
    let g = h;
    if (c === "NCHW" && a.shape.length === 1 && a.shape[0] !== 1) {
      let x = Le({ inputs: { x: a }, backend: t10, attrs: { shape: [a.shape[0], 1, 1] } });
      h = Xs({ inputs: { a: h, b: x }, backend: t10 }), t10.disposeIntermediateTensorInfo(x);
    } else
      h = Xs({ inputs: { a: h, b: a }, backend: t10 });
    t10.disposeIntermediateTensorInfo(g);
  }
  if (d) {
    let g = h;
    if (c === "NCHW" && d === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) {
      let x = Le({ inputs: { x: i }, backend: t10, attrs: { shape: [i.shape[0], 1, 1] } });
      h = Du(t10, h, d, x, f), t10.disposeIntermediateTensorInfo(x);
    } else
      h = Du(t10, h, d, i, f);
    t10.disposeIntermediateTensorInfo(g);
  }
  return h;
}
var Q2 = { kernelName: go, backendName: "cpu", kernelFunc: dX };
function fX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = LS({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } });
  if (a) {
    let g = h;
    h = Xs({ inputs: { a: h, b: a }, backend: t10 }), t10.disposeIntermediateTensorInfo(g);
  }
  if (d) {
    let g = h;
    h = Du(t10, h, d, i, f), t10.disposeIntermediateTensorInfo(g);
  }
  return h;
}
var Z2 = { kernelName: xo, backendName: "cpu", kernelFunc: fX };
function hX(r) {
  let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = y.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = S.prepareAndValidate(o, n);
  if (u === 0)
    return t10.makeTensorInfo(p, o.dtype, []);
  let m = t10.data.get(n.dataId).values, d = t10.bufferSync(o), f = pf(m, d, o.dtype, u, i, c, l, o.shape, s);
  return t10.makeTensorInfo(p, o.dtype, f.values);
}
var J2 = { kernelName: an, backendName: "cpu", kernelFunc: hX };
function gX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o;
  j([n, s], "gatherV2");
  let p = y.parseAxisParam(a, n.shape)[0], u = t10.data.get(s.dataId).values, c = n.shape[p];
  for (let w = 0; w < u.length; ++w) {
    let k = u[w];
    y.assert(k <= c - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${c - 1}]`);
  }
  let l = i;
  i == null && (l = 0);
  let m = y.sizeFromShape(s.shape), d = S.segment_util.collectGatherOpShapeInfo(n, s, p, l), f = Le({ inputs: { x: n }, backend: t10, attrs: { shape: [d.batchSize, d.outerSize, d.dimSize, d.sliceSize] } }), h = Le({ inputs: { x: s }, backend: t10, attrs: { shape: [d.batchSize, m / d.batchSize] } }), g = [d.batchSize, d.outerSize, m / d.batchSize, d.sliceSize], x = t10.bufferSync(h), b = t10.bufferSync(f), C = cf(b, x, g);
  return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), t10.makeTensorInfo(d.outputShape, C.dtype, C.values);
}
var e_ = { kernelName: Is, backendName: "cpu", kernelFunc: gX };
function xX(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = y.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Le({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = vf(i, true, t10), u = Le({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u;
}
var t_ = { kernelName: pi, backendName: "cpu", kernelFunc: xX };
var yX = we(ha, (r) => Number.isFinite(r) ? 1 : 0, "bool");
var r_ = { kernelName: ha, backendName: "cpu", kernelFunc: yX };
var bX = we(ga, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool");
var o_ = { kernelName: ga, backendName: "cpu", kernelFunc: bX };
var CX = we(cn, (r) => Number.isNaN(r) ? 1 : 0, "bool");
var n_ = { kernelName: cn, backendName: "cpu", kernelFunc: CX };
function SX(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = lf(o, n, s);
  return e.makeTensorInfo([a.length], "float32", a);
}
var s_ = { kernelName: li, backendName: "cpu", kernelFunc: SX };
var wX = we(xa, (r) => Math.log1p(r));
var a_ = { kernelName: xa, backendName: "cpu", kernelFunc: wX };
var IX = Be((r, e) => r && e);
var vX = Qe(hn, IX, null, "bool");
var i_ = { kernelName: hn, backendName: "cpu", kernelFunc: vX };
var kX = we(gn, (r) => r ? 0 : 1, "bool");
var u_ = { kernelName: gn, backendName: "cpu", kernelFunc: kX };
var NX = Be((r, e) => r || e);
var TX = Qe(xn, NX, null, "bool");
var p_ = { kernelName: xn, backendName: "cpu", kernelFunc: TX };
function _X(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o;
  j(n, "LRN");
  let u = n.shape[3], c = u - 1, l = t10.data.get(n.dataId).values, m = y.sizeFromShape(n.shape), d = new Float32Array(m);
  function f(h) {
    let g = h % u, x = h - g + Math.max(0, g - s), b = h - g + Math.min(g + s, c), C = 0;
    for (; x <= b; x++) {
      let w = l[x];
      C += w * w;
    }
    return C;
  }
  for (let h = 0; h < m; h++) {
    let g = f(h), x = l[h] * Math.pow(a + i * g, -p);
    d[h] = x;
  }
  return t10.makeTensorInfo(n.shape, n.dtype, d);
}
var c_ = { kernelName: mi, backendName: "cpu", kernelFunc: _X };
function $X(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o;
  j(a, "LRNGrad");
  let l = y.sizeFromShape(a.shape), m = a.shape[3], d = t10.data.get(a.dataId).values, f = t10.data.get(n.dataId).values, h = t10.data.get(s.dataId).values, g = new Float32Array(l), x = l;
  for (let b = 0; b < x; b++) {
    let C = b % m, w = b - C + Math.max(0, C - i), k = b - C + Math.min(m, C + i + 1), _ = 0;
    for (let E = w; E < k; E++)
      _ += Math.pow(f[E], 2);
    _ = u * _ + p;
    for (let E = w; E < k; E++) {
      let A = -2 * u * c * f[E] * h[b] / _;
      b === E && (A += Math.pow(_, -c)), A *= d[b], g[E] += A;
    }
  }
  return t10.makeTensorInfo(a.shape, n.dtype, g);
}
var l_ = { kernelName: Tm, backendName: "cpu", kernelFunc: $X };
function VS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = t10, p = n.shape, u = p.length, c = y.parseAxisParam(s, p), l = c, m = S.getAxesPermutation(l, u), d = i.data.get(n.dataId).values;
  if (m != null) {
    let w = new Array(u);
    for (let k = 0; k < w.length; k++)
      w[k] = p[m[k]];
    d = Xp(d, p, n.dtype, m, w), l = S.getInnerMostAxes(l.length, u), p = w;
  }
  j(n, "max"), S.assertAxesAreInnerMostDims("max", l, u);
  let [f, h] = S.computeOutAndReduceShapes(p, l), g = y.sizeFromShape(h), x = mf(d, g, f, n.dtype), b = i.write(x, f, n.dtype), C = f;
  return a && (C = S.expandShapeToKeepDim(f, c)), { dataId: b, shape: C, dtype: n.dtype };
}
var m_ = { kernelName: yn, backendName: "cpu", kernelFunc: VS };
function EX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  j(n, "maxPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = S.computePool2DInfo(n.shape, s, a, u, i, p), l;
  if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
    l = ir({ inputs: { x: n }, backend: t10 });
  else {
    let m = t10.data.get(n.dataId).values, d = y.computeStrides(n.shape), f = Jp(m, n.shape, n.dtype, d, c, "max");
    l = t10.makeTensorInfo(c.outShape, n.dtype, f.values);
  }
  return l;
}
var d_ = { kernelName: Cn, backendName: "cpu", kernelFunc: EX };
function AX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
  j(n, "maxPool3d");
  let c = S.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = If(l, n.shape, n.dtype, y.computeStrides(n.shape), c, "max");
  return t10.makeTensorInfo(m.shape, "float32", m.values);
}
var f_ = { kernelName: Cp, backendName: "cpu", kernelFunc: AX };
function RX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
  j([n, s], "maxPool3DGrad");
  let c = S.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.bufferSync(s), m = p2(l, c), d = c.strideDepth, f = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, x = c.dilationHeight, b = c.dilationWidth, C = c.effectiveFilterDepth, w = c.effectiveFilterHeight, k = c.effectiveFilterWidth, _ = C - 1 - c.padInfo.front, E = k - 1 - c.padInfo.left, A = w - 1 - c.padInfo.top, R = le(s.shape, "float32"), D = t10.bufferSync(n);
  for (let P = 0; P < c.batchSize; ++P)
    for (let M = 0; M < c.inChannels; ++M)
      for (let L = 0; L < c.inDepth; ++L)
        for (let V = 0; V < c.inHeight; ++V)
          for (let z = 0; z < c.inWidth; ++z) {
            let U = L - _, K = V - A, H = z - E, q = 0;
            for (let Z = 0; Z < C; Z += g) {
              let ee = (U + Z) / d;
              if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee))
                for (let oe = 0; oe < w; oe += x) {
                  let J = (K + oe) / f;
                  if (!(J < 0 || J >= c.outHeight || Math.floor(J) !== J))
                    for (let te = 0; te < k; te += b) {
                      let ie = (H + te) / h;
                      if (ie < 0 || ie >= c.outWidth || Math.floor(ie) !== ie)
                        continue;
                      let ce = C * w * k - 1 - m.get(P, ee, J, ie, M), ye = Z * w * k + oe * k + te, Ne = ce === ye ? 1 : 0;
                      if (Ne === 0)
                        continue;
                      let Ie = D.get(P, ee, J, ie, M);
                      q += Ie * Ne;
                    }
                }
            }
            R.set(q, P, L, V, z, M);
          }
  return t10.makeTensorInfo(R.shape, R.dtype, R.values);
}
var h_ = { kernelName: $m, backendName: "cpu", kernelFunc: RX };
function DX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
  j([s, a], "maxPoolGrad");
  let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = S.computePool2DInfo(i.shape, p, u, 1, c, l), d = t10.data.get(i.dataId).values, f = le(m.outShape, i.dtype, wf(d, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, x = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, w = m.effectiveFilterWidth, k = w - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = le(i.shape, "float32"), A = t10.data.get(n.dataId).values, R = le(n.shape, "float32", A);
  for (let D = 0; D < m.batchSize; ++D)
    for (let P = 0; P < m.inChannels; ++P)
      for (let M = 0; M < m.inHeight; ++M)
        for (let L = 0; L < m.inWidth; ++L) {
          let V = M - _, z = L - k, U = 0;
          for (let K = 0; K < C; K += x) {
            let H = (V + K) / h;
            if (!(H < 0 || H >= m.outHeight || Math.floor(H) !== H))
              for (let q = 0; q < w; q += b) {
                let Z = (z + q) / g;
                if (Z < 0 || Z >= m.outWidth || Math.floor(Z) !== Z)
                  continue;
                let ee = C * w - 1 - f.get(D, H, Z, P), oe = K * w + q, J = ee === oe ? 1 : 0;
                if (J === 0)
                  continue;
                let te = R.get(D, H, Z, P);
                U += te * J;
              }
          }
          E.set(U, D, M, L, P);
        }
  return t10.makeTensorInfo(E.shape, E.dtype, E.values);
}
var g_ = { kernelName: _m, backendName: "cpu", kernelFunc: DX };
function x_(r, e, t10, o, n) {
  let s = y.computeStrides(e), a = Jp(r, e, t10, s, n, "max"), i = wf(r, e, t10, n, true, o);
  return [a.values, i.values];
}
var y_ = { kernelName: Sp, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10;
  j(o, "MaxPoolWithArgmax");
  let u = p.data.get(o.dataId).values, c = S.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = x_(u, o.shape, o.dtype, i, c), d = p.write(l, c.outShape, o.dtype), f = p.write(m, c.outShape, o.dtype);
  return [{ dataId: d, shape: c.outShape, dtype: o.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }];
} };
function FX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = y.parseAxisParam(s, n.shape), u = S.computeOutAndReduceShapes(n.shape, i)[1], c = y.sizeFromShape(u), l = [], m = t10.makeTensorInfo([], "float32", new Float32Array([c]));
  l.push(m);
  let d = vo({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } });
  l.push(d);
  let f = yl({ inputs: { a: d, b: m }, backend: t10 });
  l.push(f);
  let h = za({ inputs: { x: f }, backend: t10, attrs: { axis: s, keepDims: a } });
  return l.forEach((g) => t10.disposeIntermediateTensorInfo(g)), h;
}
var b_ = { kernelName: Sn, backendName: "cpu", kernelFunc: FX };
function OX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  j(n, "min");
  let i = y.parseAxisParam(s, n.shape), p = i, u = S.getAxesPermutation(p, n.shape.length), c = n;
  u != null && (c = St({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = S.getInnerMostAxes(p.length, n.shape.length)), S.assertAxesAreInnerMostDims("min", p, c.shape.length);
  let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = y.sizeFromShape(m), f = y.makeZerosTypedArray(y.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
  for (let x = 0; x < f.length; ++x) {
    let b = x * d, C = h[b];
    for (let w = 0; w < d; ++w) {
      let k = h[b + w];
      (Number.isNaN(k) || k < C) && (C = k);
    }
    f[x] = C;
  }
  u != null && t10.disposeIntermediateTensorInfo(c);
  let g = t10.makeTensorInfo(l, c.dtype, f);
  if (a) {
    let x = S.expandShapeToKeepDim(l, i), b = Le({ inputs: { x: g }, backend: t10, attrs: { shape: x } });
    return t10.disposeIntermediateTensorInfo(g), b;
  }
  return g;
}
var C_ = { kernelName: wn, backendName: "cpu", kernelFunc: OX };
function PX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o;
  j(n, "mirrorPad");
  let i = s.map((C, w) => C[0] + n.shape[w] + C[1]), p = s.map((C) => C[0]), u = s.map((C, w) => C[0] + n.shape[w]), c = a === "reflect" ? 0 : 1, l = t10.data.get(n.dataId).values, m = n.shape.length, d = y.computeStrides(n.shape), f = y.sizeFromShape(i), h = i.length, g = y.computeStrides(i), x = y.getTypedArrayFromDType(n.dtype, f);
  for (let C = 0; C < f; C++) {
    let w = y.indexToLoc(C, h, g);
    for (let _ = 0; _ < h; _++)
      w[_] < p[_] ? w[_] = p[_] * 2 - w[_] - c : w[_] >= u[_] && (w[_] = (u[_] - 1) * 2 - w[_] + c);
    w = w.map((_, E) => _ - p[E]);
    let k = y.locToIndex(w, m, d);
    x[C] = l[k];
  }
  return { dataId: t10.write(x, i, n.dtype), shape: i, dtype: n.dtype };
}
var S_ = { kernelName: vn, backendName: "cpu", kernelFunc: PX };
var MX = Be((r, e) => {
  let t10 = r % e;
  return r < 0 && e < 0 || r >= 0 && e >= 0 ? t10 : (t10 + e) % e;
});
var LX = Qe(ya, MX);
var w_ = { kernelName: ya, backendName: "cpu", kernelFunc: LX };
var v_ = ap(gC());
function zS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = n.shape.length, i = s;
  if (i === -1 && (i = a - 1), i !== a - 1)
    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);
  let p = y.parseAxisParam([i], n.shape), u = VS({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: p, keepDims: false } }), c = S.expandShapeToKeepDim(u.shape, p), l = Le({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), m = gl({ inputs: { a: n, b: l }, backend: t10 }), d = dS({ inputs: { x: m }, backend: t10 }), f = za({ inputs: { x: d }, backend: t10, attrs: { axis: p, keepDims: false } }), h = Le({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = yl({ inputs: { a: d, b: h }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g;
}
var I_ = { kernelName: qn, backendName: "cpu", kernelFunc: zS };
function BX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o;
  j(n, "multinomial");
  let p = i ? n : zS({ inputs: { logits: n }, backend: t10, attrs: { dim: -1 } }), u = p.shape[0], c = p.shape[1], l = t10.data.get(p.dataId).values, m = [u, s], d = y.makeZerosTypedArray(y.sizeFromShape(m), "int32");
  for (let f = 0; f < u; ++f) {
    let h = f * c, g = new Float32Array(c - 1);
    g[0] = l[h];
    for (let C = 1; C < g.length; ++C)
      g[C] = g[C - 1] + l[h + C];
    let x = v_.alea(a.toString()), b = f * s;
    for (let C = 0; C < s; ++C) {
      let w = x();
      d[b + C] = g.length;
      for (let k = 0; k < g.length; k++)
        if (w < g[k]) {
          d[b + C] = k;
          break;
        }
    }
  }
  return i || t10.disposeIntermediateTensorInfo(p), t10.makeTensorInfo(m, "int32", d);
}
var k_ = { kernelName: wp, backendName: "cpu", kernelFunc: BX };
var VX = Vt.nonMaxSuppressionV3Impl;
function zX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o;
  j(n, "NonMaxSuppression");
  let u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, { selectedIndices: l } = VX(u, c, a, i, p);
  return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var N_ = { kernelName: Tn, backendName: "cpu", kernelFunc: zX };
var WX = Vt.nonMaxSuppressionV4Impl;
function UX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o;
  j(n, "NonMaxSuppressionPadded");
  let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, { selectedIndices: m, validOutputs: d } = WX(c, l, a, i, p, u);
  return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))];
}
var T_ = { kernelName: ba, backendName: "cpu", kernelFunc: UX };
var GX = Vt.nonMaxSuppressionV5Impl;
function HX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o;
  j(n, "NonMaxSuppressionWithScore");
  let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = GX(c, l, m, d, f, h);
  return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var __ = { kernelName: _n, backendName: "cpu", kernelFunc: HX };
function KX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o;
  j(n, "oneHot");
  let u = y.sizeFromShape(n.shape), c = new Float32Array(u * a);
  c.fill(p);
  let l = t10.data.get(n.dataId).values;
  for (let m = 0; m < u; ++m)
    l[m] >= 0 && l[m] < a && (c[m * a + l[m]] = i);
  return t10.makeTensorInfo([...n.shape, a], s, c);
}
var $_ = { kernelName: $n, backendName: "cpu", kernelFunc: KX };
function Sl(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "string")
    throw new Error("zerosLike is not supported for string tensors");
  if (o.dtype === "complex64") {
    let n = Io({ inputs: { input: o }, backend: t10 }), s = Sl({ inputs: { x: n }, backend: t10 }), a = Ys({ inputs: { input: o }, backend: t10 }), i = Sl({ inputs: { x: a }, backend: t10 }), p = Ht({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
  } else
    return Cl({ backend: t10, attrs: { shape: o.shape, value: 0, dtype: o.dtype } });
}
var E_ = { kernelName: Os, backendName: "cpu", kernelFunc: Sl };
function A_(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "string")
    throw new Error("onesLike is not supported for string tensors");
  if (o.dtype === "complex64") {
    let n = Io({ inputs: { input: o }, backend: t10 }), s = A_({ inputs: { x: n }, backend: t10 }), a = Ys({ inputs: { input: o }, backend: t10 }), i = Sl({ inputs: { x: a }, backend: t10 }), p = Ht({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
  } else
    return Cl({ backend: t10, attrs: { shape: o.shape, value: 1, dtype: o.dtype } });
}
var R_ = { kernelName: ks, backendName: "cpu", kernelFunc: A_ };
function WS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return ec({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = ec({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = Mi({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u;
}
var D_ = { kernelName: Ns, backendName: "cpu", kernelFunc: WS };
function qX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
  j(n, "pad");
  let i = s.map((b, C) => b[0] + n.shape[C] + b[1]), p = s.map((b) => b[0]), u = t10.data.get(n.dataId).values, c = y.sizeFromShape(n.shape), l = n.shape.length, m = y.computeStrides(n.shape), d = y.sizeFromShape(i), f = i.length, h = y.computeStrides(i), g = y.getTypedArrayFromDType(n.dtype, d);
  a !== 0 && g.fill(a);
  for (let b = 0; b < c; b++) {
    let w = y.indexToLoc(b, l, m).map((_, E) => _ + p[E]), k = y.locToIndex(w, f, h);
    g[k] = u[b];
  }
  return { dataId: t10.write(g, i, n.dtype), shape: i, dtype: n.dtype };
}
var kf = { kernelName: En, backendName: "cpu", kernelFunc: qX };
var jX = Be((r, e) => Math.pow(r, e));
var XX = Qe(An, jX);
var F_ = { kernelName: An, backendName: "cpu", kernelFunc: XX };
function YX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.data.get(x.dataId).values), u = n.map((x) => x.shape), c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, [m, d, f] = df(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d);
  return h.concat([g]);
}
var O_ = { kernelName: Ip, backendName: "cpu", kernelFunc: YX };
function QX(r) {
  let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = ff(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c);
  return [l, m];
}
var P_ = { kernelName: vp, backendName: "cpu", kernelFunc: QX };
function ZX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, m = i.map((g) => t10.data.get(g.dataId).values), d = i.map((g) => g.shape), [f, h] = hf(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p);
  return t10.makeTensorInfo(f, s.dtype, h);
}
var M_ = { kernelName: kp, backendName: "cpu", kernelFunc: ZX };
function JX(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, dtype: s, step: a } = t10, i = _u(o, n, a, s);
  return e.makeTensorInfo([i.length], s, i);
}
var L_ = { kernelName: Ts, backendName: "cpu", kernelFunc: JX };
var e5 = we(Fn, (r) => 1 / r);
var B_ = { kernelName: Fn, backendName: "cpu", kernelFunc: e5 };
function t5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
  j(n, "resizeBilinear");
  let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(y.sizeFromShape([l, u, c, f])), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = 0, w = x[0] / b[0], k = x[1] / b[1];
  for (let _ = 0; _ < l; _++)
    for (let E = 0; E < u; E++) {
      let A;
      a ? A = w * (E + 0.5) - 0.5 : A = w * E;
      let R = Math.max(0, Math.floor(A)), D = A - R, P = Math.min(m - 1, Math.ceil(A)), M = _ * p[0] + R * p[1], L = _ * p[0] + P * p[1];
      for (let V = 0; V < c; V++) {
        let z;
        a ? z = k * (V + 0.5) - 0.5 : z = k * V;
        let U = Math.max(0, Math.floor(z)), K = z - U, H = Math.min(d - 1, Math.ceil(z)), q = M + U * p[2], Z = L + U * p[2], ee = M + H * p[2], oe = L + H * p[2];
        for (let J = 0; J < f; J++) {
          let te = h[q + J], ie = h[Z + J], ce = h[ee + J], ye = h[oe + J], Ne = te + (ce - te) * K, Ie = ie + (ye - ie) * K, Ae = Ne + (Ie - Ne) * D;
          g[C++] = Ae;
        }
      }
    }
  return t10.makeTensorInfo([l, u, c, f], "float32", g);
}
var V_ = { kernelName: Mn, backendName: "cpu", kernelFunc: t5 };
function r5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
  j([s, n], "resizeBilinearGrad");
  let i = y.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, d] = s.shape, f = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && d > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && d > 1 ? d - 1 : d], x = h[0] / g[0], b = h[1] / g[1], C = t10.data.get(s.dataId).values, w = 0;
  for (let k = 0; k < p; k++) {
    let _ = k * i[0];
    for (let E = 0; E < m; E++) {
      let A = E * x, R = Math.floor(A), D = Math.min(Math.ceil(A), u - 1), P = _ + R * i[1], M = _ + D * i[1], L = A - R, V = 1 - L;
      for (let z = 0; z < d; z++) {
        let U = z * b, K = Math.floor(U), H = Math.min(Math.ceil(U), c - 1), q = U - K, Z = 1 - q, ee = P + K * i[2], oe = P + H * i[2], J = M + K * i[2], te = M + H * i[2], ie = V * Z, ce = V * q, ye = L * Z, Ne = L * q;
        for (let Ie = 0; Ie < l; Ie++) {
          let Ae = C[w++];
          f[ee + Ie] += Ae * ie, f[oe + Ie] += Ae * ce, f[J + Ie] += Ae * ye, f[te + Ie] += Ae * Ne;
        }
      }
    }
  }
  return t10.makeTensorInfo([p, c, u, l], "float32", f);
}
var z_ = { kernelName: Am, backendName: "cpu", kernelFunc: r5 };
function o5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
  j(n, "resizeNearestNeighbor");
  let p = y.computeStrides(n.shape), [u, c] = i, [l, m, d, f] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(l * u * c * f), x = [s && u > 1 ? m - 1 : m, s && c > 1 ? d - 1 : d], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = x[0] / b[0], w = x[1] / b[1], k = 0;
  for (let _ = 0; _ < l; _++) {
    let E = _ * p[0];
    for (let A = 0; A < u; A++) {
      let R = a ? C * (A + 0.5) : C * A, D = Math.min(m - 1, s ? Math.round(R) : Math.floor(R));
      a && (D = Math.max(0, D));
      let P = E + D * p[1];
      for (let M = 0; M < c; M++) {
        let L = a ? w * (M + 0.5) : w * M, V = Math.min(d - 1, s ? Math.round(L) : Math.floor(L));
        a && (V = Math.max(0, V));
        let z = P + V * p[2];
        for (let U = 0; U < f; U++) {
          let K = h[z + U];
          g[k++] = K;
        }
      }
    }
  }
  return t10.makeTensorInfo([l, u, c, f], n.dtype, g);
}
var W_ = { kernelName: Pn, backendName: "cpu", kernelFunc: o5 };
function n5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
  j([s, n], "resizeNearestNeighborGrad");
  let i = y.computeStrides(n.shape), p = y.computeStrides(s.shape), [u, c, l, m] = n.shape, [, d, f] = s.shape, h = new Float32Array(u * c * l * m), g = t10.data.get(s.dataId).values, x = [a && d > 1 ? c - 1 : c, a && f > 1 ? l - 1 : l], b = [a && d > 1 ? d - 1 : d, a && f > 1 ? f - 1 : f], C = x[0] / b[0], w = x[1] / b[1], k = 1 / C, _ = 1 / w, E = Math.ceil(k) * 2 + 2, A = Math.ceil(_) * 2 + 2;
  for (let R = 0; R < u; R++) {
    let D = R * i[0];
    for (let P = 0; P < c; P++) {
      let M = D + P * i[1], L = Math.floor(P * k), V = Math.floor(L - E / 2);
      for (let z = 0; z < l; z++) {
        let U = M + z * i[2], K = Math.floor(z * _), H = Math.floor(K - A / 2);
        for (let q = 0; q < m; q++) {
          let Z = 0;
          for (let ee = 0; ee < E; ee++) {
            let oe = ee + V;
            if (oe < 0 || oe >= d)
              continue;
            let J = D + oe * p[1], te = oe * C, ie = Math.min(c - 1, a ? Math.round(te) : Math.floor(te));
            if (P === ie)
              for (let ce = 0; ce < A; ce++) {
                let ye = ce + H;
                if (ye < 0 || ye >= f)
                  continue;
                let Ne = J + ye * p[2], Ie = ye * w, Ae = Math.min(l - 1, a ? Math.round(Ie) : Math.floor(Ie));
                z === Ae && (Z += g[Ne + q]);
              }
          }
          h[U + q] = Z;
        }
      }
    }
  }
  return t10.makeTensorInfo(n.shape, n.dtype, h);
}
var U_ = { kernelName: Em, backendName: "cpu", kernelFunc: n5 };
function s5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o;
  j(n, "reverse");
  let a = n.shape.length, i = y.parseAxisParam(s, n.shape);
  if (a === 0)
    return ir({ inputs: { x: n }, backend: t10 });
  let p = new st(n.shape, n.dtype), u = t10.bufferSync(n);
  for (let c = 0; c < p.size; c++) {
    let l = p.indexToLoc(c), m = l.slice();
    i.forEach((d) => m[d] = n.shape[d] - 1 - m[d]), p.set(u.get(...m), ...l);
  }
  return t10.makeTensorInfo(p.shape, p.dtype, p.values);
}
var G_ = { kernelName: Bn, backendName: "cpu", kernelFunc: s5 };
var H_ = { kernelName: ts, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = y.getTypedArrayFromDType(o.dtype, y.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [d, f] = S.getImageCenter(a, c, l), h = 255, g = Math.sin(n), x = Math.cos(n), b = i.data.get(o.dataId).values;
  for (let w = 0; w < u; w++) {
    let k = w * l * c * m;
    for (let _ = 0; _ < c; _++) {
      let E = _ * (l * m);
      for (let A = 0; A < l; A++) {
        let R = A * m;
        for (let D = 0; D < m; D++) {
          let P = [u, _, A, D], M = P[2], L = P[1], V = (M - d) * x - (L - f) * g, z = (M - d) * g + (L - f) * x;
          V = Math.round(V + d), z = Math.round(z + f);
          let U = s;
          if (typeof s != "number" && (D === 3 ? U = h : U = s[D]), V >= 0 && V < l && z >= 0 && z < c) {
            let H = z * (l * m), q = V * m, Z = k + H + q + D;
            U = b[Z];
          }
          let K = k + E + R + D;
          p[K] = U;
        }
      }
    }
  }
  return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
var a5 = we(Vn, (r) => {
  let e = Math.floor(r);
  return r - e < 0.5 ? Math.floor(r) : r - e > 0.5 ? Math.ceil(r) : e % 2 === 0 ? e : e + 1;
});
var K_ = { kernelName: Vn, backendName: "cpu", kernelFunc: a5 };
function i5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = true, d = t10.bufferSync(n), f = t10.bufferSync(s), h = Va(d, f, a, l, u, p, i, c, 0, m);
  return t10.makeTensorInfo(a, h.dtype, h.values);
}
var q_ = { kernelName: Wn, backendName: "cpu", kernelFunc: i5 };
function u5(r, e) {
  let t10 = 0, o = r.length, n = 0;
  for (; t10 < o; )
    n = Math.floor((t10 + o) / 2), r[n] < e ? t10 = n + 1 : o = n;
  return o;
}
function p5(r, e) {
  let t10 = 0, o = r.length, n = 0;
  for (; t10 < o; )
    n = Math.floor((t10 + o) / 2), r[n] <= e ? t10 = n + 1 : o = n;
  return o;
}
function j_(r, e, t10, o, n, s) {
  let a = y.getArrayFromDType("int32", t10 * n);
  for (let i = 0; i < t10; ++i) {
    let p = r.slice(i * o, (i + 1) * o), u = i * n;
    for (let c = 0; c < n; ++c)
      a[u + c] = s === "left" ? u5(p, e[c + u]) : p5(p, e[c + u]);
  }
  return a;
}
function c5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, u = j_(i, p, n.shape[0], n.shape[1], s.shape[1], a);
  return t10.makeTensorInfo(s.shape, "int32", u);
}
var X_ = { kernelName: fi, backendName: "cpu", kernelFunc: c5 };
function l5(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e;
  j([o, n, s], "select");
  let a = o.shape.length, i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = dt(n.dtype, s.dtype), l = y.makeZerosTypedArray(y.sizeFromShape(n.shape), c), m = 0, d = a === 0 || a > 1 || n.shape.length === 1 ? 1 : y.sizeFromShape(n.shape.slice(1));
  for (let f = 0; f < i.length; f++)
    for (let h = 0; h < d; h++)
      i[f] === 1 ? l[m++] = p[f] : l[m++] = u[f];
  return t10.makeTensorInfo(n.shape, c, l);
}
var Y_ = { kernelName: $s, backendName: "cpu", kernelFunc: l5 };
var m5 = S.SELU_SCALEALPHA;
var d5 = S.SELU_SCALE;
var f5 = we(Ca, (r) => r >= 0 ? d5 * r : m5 * (Math.exp(r) - 1));
var Q_ = { kernelName: Ca, backendName: "cpu", kernelFunc: f5 };
var h5 = we(wa, (r) => r < 0 ? -1 : r > 0 ? 1 : 0);
var Z_ = { kernelName: wa, backendName: "cpu", kernelFunc: h5 };
var g5 = we(Un, (r) => Math.sin(r));
var J_ = { kernelName: Un, backendName: "cpu", kernelFunc: g5 };
var x5 = we(Sa, (r) => Math.sinh(r));
var e$ = { kernelName: Sa, backendName: "cpu", kernelFunc: x5 };
var y5 = 11920928955078125e-23;
var t$ = Math.log(y5) + 2;
var b5 = we(Ia, (r) => {
  let e = r > -t$, t10 = r < t$, o = Math.exp(r), n;
  return t10 ? n = o : e ? n = r : n = Math.log(1 + o), n;
});
var r$ = { kernelName: Ia, backendName: "cpu", kernelFunc: b5 };
function C5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
  j([n], "spaceToBatchND");
  let i = y.sizeFromShape(s), p = [[0, 0]];
  p.push(...a);
  for (let _ = 1 + s.length; _ < n.shape.length; ++_)
    p.push([0, 0]);
  let u = kf.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = S.getReshaped(u.shape, s, i, false), l = S.getPermuted(c.length, s.length, false), m = S.getReshapedPermuted(u.shape, s, i, false), h = Le({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = St({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Le({ inputs: { x: b }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(b), k;
}
var o$ = { kernelName: As, backendName: "cpu", kernelFunc: C5 };
function S5(r) {
  let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e;
  if (s.shape.length !== 1)
    throw new Error(`Dense shape must be a vector, saw:
        ${s.shape}`);
  if (o.shape.length !== 2)
    throw new Error(`Indices must be a matrix, saw:
        ${o.shape}`);
  if (n.shape.length !== 1)
    throw new Error(`Values must be a vector, saw:
        ${n.shape}`);
  if (a.shape.length !== 0)
    throw new Error(`Default value must be a scalar, saw:
        ${a.shape}`);
  let i = t10.data.get(o.dataId).values, p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = t10.data.get(a.dataId).values[0], [l, m, d, f, h] = gf(i, o.shape, o.dtype, p, n.dtype, u, c);
  return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var n$ = { kernelName: hi, backendName: "cpu", kernelFunc: S5 };
function w5(r) {
  let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e;
  if (o.shape.length !== 2)
    throw new Error(`Input indices should be a matrix but received shape
        ${o.shape}`);
  if (n.shape.length !== 1)
    throw new Error(`Input shape should be a vector but received shape
        ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Target shape should be a vector but received shape ${s.shape}`);
  let a = Array.from(t10.data.get(n.dataId).values), i = t10.data.get(o.dataId).values, p = Array.from(t10.data.get(s.dataId).values), [u, c, l] = xf(i, o.shape, o.dtype, a, p);
  return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var s$ = { kernelName: va, backendName: "cpu", kernelFunc: w5 };
function I5(r) {
  let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
  if (o.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (n.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
          ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
          ${s.shape}`);
  if (n.shape[0] !== s.shape[0])
    throw new Error("segmentIds and indices should have same size.");
  let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = Qp(a, o.shape, o.dtype, i, p, true);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var a$ = { kernelName: gi, backendName: "cpu", kernelFunc: I5 };
function v5(r) {
  let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
  if (o.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (n.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
         ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
         ${s.shape}`);
  if (n.shape[0] !== s.shape[0])
    throw new Error("segmentIds and indices should have same size.");
  let a = t10.data.get(o.dataId).values, i = t10.data.get(n.dataId).values, p = t10.data.get(s.dataId).values, [u, c] = Qp(a, o.shape, o.dtype, i, p);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var i$ = { kernelName: xi, backendName: "cpu", kernelFunc: v5 };
function k5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false, f = t10.bufferSync(n), h;
  switch (s.dtype) {
    case "bool": {
      let g = t10.bufferSync(s), x = Boolean(t10.data.get(a.dataId).values[0]);
      h = Va(f, g, i, m, c, u, p, l, x, d);
      break;
    }
    case "float32": {
      let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0];
      h = Va(f, g, i, m, c, u, p, l, x, d);
      break;
    }
    case "int32": {
      let g = t10.bufferSync(s), x = t10.data.get(a.dataId).values[0];
      h = Va(f, g, i, m, c, u, p, l, x, d);
      break;
    }
    case "string": {
      let g = t10.bufferSync(s), x = y.decodeString(t10.data.get(a.dataId).values[0]);
      h = Va(f, g, i, m, c, u, p, l, x, d);
      break;
    }
    default:
      throw new Error(`Unsupported type ${s.dtype}`);
  }
  return t10.makeTensorInfo(i, h.dtype, h.values);
}
var u$ = { kernelName: yi, backendName: "cpu", kernelFunc: k5 };
function N5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice();
  return p.map((l) => {
    let m = [...c];
    m[i] = l;
    let d = To({ inputs: { x: n }, backend: t10, attrs: { begin: u, size: m } });
    return u[i] += l, d;
  });
}
var p$ = { kernelName: Rs, backendName: "cpu", kernelFunc: N5 };
var c$ = { kernelName: bi, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => {
  let { x: t10 } = r, o = e;
  j(t10, "square");
  let n = o.data.get(t10.dataId).values, s = new Float32Array(n.length);
  for (let i = 0; i < n.length; ++i) {
    let p = n[i];
    s[i] = p * p;
  }
  return { dataId: o.write(s, t10.shape, t10.dtype), shape: t10.shape, dtype: t10.dtype };
} };
var T5 = we(fo, (r, e) => {
  let t10 = e;
  return isNaN(r) ? NaN : r > 0 ? 1 : t10.alpha;
});
var l$ = { kernelName: fo, backendName: "cpu", kernelFunc: T5 };
function _5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o;
  j(n, "stridedSlice");
  let { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = Le({ inputs: { x: n }, backend: t10, attrs: { shape: f } });
  else if (g || x) {
    y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let _ = ut.computeOutShape(b, C, w), E = To({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } });
    k = Le({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(E);
  } else {
    let _ = t10.bufferSync(n), E = yf(d, _, w, b);
    k = t10.makeTensorInfo(f, E.dtype, E.values);
  }
  return k;
}
var m$ = { kernelName: Xn, backendName: "cpu", kernelFunc: _5 };
function $5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.data.get(c.dataId).values, d = t10.data.get(l.dataId).values, [f, h] = Eu(m, d, n, s, a, i, p, u);
  return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)];
}
var d$ = { kernelName: Ds, backendName: "cpu", kernelFunc: $5 };
function E5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e;
  if (s.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (s.shape.length !== 1)
    throw new Error(`Input must be a vector, got shape: ${s.shape}`);
  if (a.shape.length !== 0)
    throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);
  let i = t10.data.get(s.dataId).values, p = t10.data.get(a.dataId).values[0], [u, c, l] = Au(i, p, n), m = c.length;
  return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))];
}
var f$ = { kernelName: Ci, backendName: "cpu", kernelFunc: E5 };
function A5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e;
  if (s.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (n <= 0)
    throw new Error("Number of buckets must be at least 1");
  let a = t10.data.get(s.dataId).values, i = Ru(a, n);
  return t10.makeTensorInfo(s.shape, "int32", i);
}
var h$ = { kernelName: Si, backendName: "cpu", kernelFunc: A5 };
var R5 = we(Qn, (r) => Math.tan(r));
var g$ = { kernelName: Qn, backendName: "cpu", kernelFunc: R5 };
var D5 = we(Zn, (r) => Math.tanh(r));
var x$ = { kernelName: Zn, backendName: "cpu", kernelFunc: D5 };
function F5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
  j(n, "tile");
  let a = bf(t10.bufferSync(n), s);
  return t10.makeTensorInfo(a.shape, a.dtype, a.values);
}
var y$ = { kernelName: to, backendName: "cpu", kernelFunc: F5 };
function O5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o;
  j(n, "topk");
  let i = t10.data.get(n.dataId).values, [p, u] = Cf(i, n.shape, n.dtype, s, a);
  return [t10.makeTensorInfo(p.shape, p.dtype, p.values), t10.makeTensorInfo(u.shape, u.dtype, u.values)];
}
var b$ = { kernelName: Jn, backendName: "cpu", kernelFunc: O5 };
function P5(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = t10, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = y.computeStrides(n.shape), b = x[0], C = x[1], w = x[2], k = y.computeStrides(g), _ = k[0], E = k[1], A = k[2], R = y.getTypedArrayFromDType(n.dtype, y.sizeFromShape(g));
  R.fill(p);
  let D = o.data.get(n.dataId).values, P = o.data.get(s.dataId).values;
  for (let L = 0; L < c; ++L) {
    let V = s.shape[0] === 1 ? P : P.subarray(L * 8, L * 8 + 8);
    for (let z = 0; z < f; ++z)
      for (let U = 0; U < h; ++U)
        for (let K = 0; K < d; ++K) {
          let H, q = V[6] * U + V[7] * z + 1;
          if (q === 0)
            continue;
          let Z = (V[0] * U + V[1] * z + V[2]) / q, ee = (V[3] * U + V[4] * z + V[5]) / q, oe = C$(Z, m, i), J = C$(ee, l, i);
          switch (a) {
            case "nearest":
              H = z5(D, l, m, b, C, w, L, J, oe, K, p);
              break;
            case "bilinear":
              H = W5(D, l, m, b, C, w, L, J, oe, K, p);
              break;
            default:
              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${a}`);
          }
          let te = L * _ + z * E + U * A + K;
          R[te] = H;
        }
    return o.makeTensorInfo(g, n.dtype, R);
  }
  return { dataId: o.write(R, g, n.dtype), shape: n.shape, dtype: n.dtype };
}
var S$ = { kernelName: es, backendName: "cpu", kernelFunc: P5 };
function C$(r, e, t10) {
  switch (t10) {
    case "reflect":
      return M5(r, e);
    case "wrap":
      return L5(r, e);
    case "nearest":
      return V5(r, e);
    case "constant":
    default:
      return B5(r, e);
  }
}
function M5(r, e) {
  let t10 = r;
  if (t10 < 0)
    if (e <= 1)
      t10 = 0;
    else {
      let o = 2 * e;
      t10 < o && (t10 = o * Math.trunc(-t10 / o) + t10), t10 = t10 < -e ? t10 + o : -t10 - 1;
    }
  else if (t10 > e - 1)
    if (e <= 1)
      t10 = 0;
    else {
      let o = 2 * e;
      t10 -= o * Math.trunc(t10 / o), t10 >= e && (t10 = o - t10 - 1);
    }
  return y.clamp(0, t10, e - 1);
}
function L5(r, e) {
  let t10 = r;
  if (t10 < 0)
    if (e <= 1)
      t10 = 0;
    else {
      let o = e - 1;
      t10 += e * (Math.trunc(-t10 / o) + 1);
    }
  else if (t10 > e - 1)
    if (e <= 1)
      t10 = 0;
    else {
      let o = e - 1;
      t10 -= e * Math.trunc(t10 / o);
    }
  return y.clamp(0, t10, e - 1);
}
function B5(r, e) {
  return r;
}
function V5(r, e) {
  return y.clamp(0, r, e - 1);
}
function wl(r, e, t10, o, n, s, a, i, p, u, c) {
  let l = a * o + i * n + p * s + u;
  return 0 <= i && i < e && 0 <= p && p < t10 ? r[l] : c;
}
function z5(r, e, t10, o, n, s, a, i, p, u, c) {
  let l = Math.round(i), m = Math.round(p);
  return wl(r, e, t10, o, n, s, a, l, m, u, c);
}
function W5(r, e, t10, o, n, s, a, i, p, u, c) {
  let l = Math.floor(i), m = Math.floor(p), d = l + 1, f = m + 1, h = (f - p) * wl(r, e, t10, o, n, s, a, l, m, u, c) + (p - m) * wl(r, e, t10, o, n, s, a, l, f, u, c), g = (f - p) * wl(r, e, t10, o, n, s, a, d, m, u, c) + (p - m) * wl(r, e, t10, o, n, s, a, d, f, u, c);
  return (d - i) * h + (i - l) * g;
}
function U5(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e;
  j(s, "unique");
  let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = Sf(a, n, s.shape, s.dtype);
  return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var w$ = { kernelName: Np, backendName: "cpu", kernelFunc: U5 };
function G5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
  s < 0 && (s += n.shape.length);
  let a = n.shape.length, i = n.shape[s], p = new Array(a - 1), u = 0;
  for (let d = 0; d < a; d++)
    d !== s && (p[u++] = n.shape[d]);
  let c = new Array(a).fill(0), l = n.shape.slice();
  l[s] = 1;
  let m = new Array(i);
  for (let d = 0; d < m.length; d++) {
    c[s] = d;
    let f = To({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: l } });
    m[d] = Le({ inputs: { x: f }, backend: t10, attrs: { shape: p } }), t10.disposeIntermediateTensorInfo(f);
  }
  return m;
}
var I$ = { kernelName: Fs, backendName: "cpu", kernelFunc: G5 };
function H5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o;
  j(n, "unsortedSegmentSum");
  let i = n.shape.length, p = s.shape.length, u = [], c = [], l = i - p, m = s;
  for (let f = 0; f < l; ++f) {
    let h = ec({ inputs: { input: m }, backend: t10, attrs: { dim: f + 1 } });
    m = h, c.push(h);
  }
  for (let f = 0; f < a; ++f) {
    let h = y.createScalarValue(f, "int32"), g = t10.makeTensorInfo([], "int32", h), x = lS({ inputs: { a: g, b: m }, backend: t10 }), b = vo({ inputs: { x }, backend: t10, attrs: { dtype: "float32" } }), C = Tu({ inputs: { a: b, b: n }, backend: t10 }), w = za({ inputs: { x: C }, backend: t10, attrs: { axis: 0, keepDims: false } });
    u.push(w), c.push(g), c.push(x), c.push(b), c.push(C), c.push(w);
  }
  let d = WS({ inputs: u, backend: t10, attrs: { axis: 0 } });
  return c.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d;
}
var v$ = { kernelName: Tp, backendName: "cpu", kernelFunc: H5 };
var K5 = [YT, iT, QT, ZT, mT, JT, e2, t2, r2, o2, n2, s2, a2, i2, u2, c2, l2, m2, d2, XT, f2, h2, g2, x2, lT, dT, y2, uT, b2, S2, w2, I2, v2, k2, N2, T2, _2, $2, E2, A2, R2, D2, F2, O2, P2, M2, L2, B2, V2, z2, U2, UT, G2, fT, H2, hT, K2, gT, q2, j2, X2, xT, Y2, Q2, Z2, J2, e_, yT, bT, pT, t_, C2, r_, o_, n_, GT, CT, ST, s_, wT, a_, i_, u_, p_, c_, l_, m_, IT, d_, f_, h_, g_, y_, b_, C_, vT, S_, w_, k_, kT, NT, N_, T_, __, TT, $_, R_, D_, kf, F_, HT, $T, O_, P_, M_, L_, cT, bl, B_, KT, qT, jT, V_, z_, W_, U_, G_, H_, K_, FT, q_, X_, Y_, Q_, PT, Z_, J_, e$, MT, I_, r$, o$, n$, s$, a$, i$, u$, p$, BT, c$, VT, l$, m$, d$, f$, h$, zT, W2, g$, x$, y$, b$, S$, _T, w$, I$, v$, E_];
for (let r of K5)
  ka(r);
var nc = {};
Ge(nc, { assertNotComplex: () => us, bindCanvasToFramebuffer: () => t8, bindColorTextureToFramebuffer: () => Nl, bindTextureToProgramUniformSampler: () => nw, bindTextureUnit: () => _$, bindVertexBufferToProgramAttribute: () => Af, callAndCheck: () => pe, canBeRepresented: () => KS, createFragmentShader: () => jS, createFramebuffer: () => tw, createProgram: () => XS, createStaticIndexBuffer: () => ZS, createStaticVertexBuffer: () => QS, createTexture: () => JS, createVertexShader: () => qS, getBatchDim: () => Ua, getExtensionOrThrow: () => tc, getFramebufferErrorMessage: () => $$, getMaxTexturesInShader: () => iw, getNumChannels: () => J5, getProgramUniformLocation: () => ow, getProgramUniformLocationOrThrow: () => rw, getRowsCols: () => Ga, getShapeAs3D: () => oc, getTextureShapeFromLogicalShape: () => sw, getWebGLDisjointQueryTimerVersion: () => uw, getWebGLErrorMessage: () => T$, getWebGLMaxTextureSize: () => aw, hasExtension: () => Wr, isCapableOfRenderingToFloatTexture: () => pw, isDownloadFloatTextureEnabled: () => cw, isReshapeFree: () => Bi, isWebGLFenceEnabled: () => lw, isWebGLVersionEnabled: () => Df, linkProgram: () => YS, logShaderSourceAndInfoLog: () => Ef, resetMaxTextureSize: () => r8, resetMaxTexturesInShader: () => o8, unbindColorTextureFromFramebuffer: () => Rf, unbindTextureUnit: () => e8, validateFramebuffer: () => rc, validateProgram: () => kl, validateTextureSize: () => ew });
var Fu = {};
var Nf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function US(r, e) {
  Fu[r] = e;
}
function zr(r, e) {
  if (!(r in Fu) || e != null) {
    let o = j5(r, e);
    if (o !== null)
      Fu[r] = o;
    else
      return console.log("Could not get context for WebGL version", r), null;
  }
  let t10 = Fu[r];
  return t10 == null || t10.isContextLost() ? (delete Fu[r], zr(r)) : (t10.disable(t10.DEPTH_TEST), t10.disable(t10.STENCIL_TEST), t10.disable(t10.BLEND), t10.disable(t10.DITHER), t10.disable(t10.POLYGON_OFFSET_FILL), t10.disable(t10.SAMPLE_COVERAGE), t10.enable(t10.SCISSOR_TEST), t10.enable(t10.CULL_FACE), t10.cullFace(t10.BACK), Fu[r]);
}
function q5(r) {
  if (typeof OffscreenCanvas != "undefined" && r === 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 j5(r, e) {
  if (r !== 1 && r !== 2)
    throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
  let t10 = e == null ? q5(r) : e;
  return t10.addEventListener("webglcontextlost", (o) => {
    o.preventDefault(), delete Fu[r];
  }, false), O().getBool("SOFTWARE_WEBGL_ENABLED") && (Nf.failIfMajorPerformanceCaveat = false), r === 1 ? t10.getContext("webgl", Nf) || t10.getContext("experimental-webgl", Nf) : t10.getContext("webgl2", Nf);
}
var Li;
(function(r) {
  r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH";
})(Li || (Li = {}));
var ur;
(function(r) {
  r[r.RENDER = 0] = "RENDER", r[r.UPLOAD = 1] = "UPLOAD", r[r.PIXELS = 2] = "PIXELS", r[r.DOWNLOAD = 3] = "DOWNLOAD";
})(ur || (ur = {}));
var Zt;
(function(r) {
  r[r.UNPACKED_FLOAT16 = 0] = "UNPACKED_FLOAT16", r[r.UNPACKED_FLOAT32 = 1] = "UNPACKED_FLOAT32", r[r.PACKED_4X1_UNSIGNED_BYTE = 2] = "PACKED_4X1_UNSIGNED_BYTE", r[r.PACKED_2X2_FLOAT32 = 3] = "PACKED_2X2_FLOAT32", r[r.PACKED_2X2_FLOAT16 = 4] = "PACKED_2X2_FLOAT16";
})(Zt || (Zt = {}));
function Ou(r, e) {
  return [e, r];
}
function k$(r, e) {
  return r * e;
}
function Il(r) {
  let e = y.sizeFromShape(r), t10 = Math.ceil(e / 4);
  return y.sizeToSquarishShape(t10);
}
function Qs(r, e) {
  return [Math.max(1, Math.ceil(e / 2)), Math.max(1, Math.ceil(r / 2))];
}
function N$(r, e) {
  let [t10, o] = Qs(r, e);
  return t10 * o * 4;
}
function vl(r, e) {
  let t10 = r, o, n, s, a, i, p, u, c, l, m;
  return O().getNumber("WEBGL_VERSION") === 2 ? (o = t10.R32F, n = t10.R16F, s = t10.RGBA16F, a = t10.RGBA32F, i = t10.RED, u = 4, c = 1, l = t10.HALF_FLOAT, m = t10.FLOAT, p = t10.RGBA8) : (o = r.RGBA, n = r.RGBA, s = r.RGBA, a = t10.RGBA, i = r.RGBA, u = 4, c = 4, l = e != null ? e.HALF_FLOAT_OES : null, m = r.FLOAT, p = r.RGBA), { internalFormatFloat: o, internalFormatHalfFloat: n, internalFormatPackedHalfFloat: s, internalFormatPackedFloat: a, textureFormatFloat: i, downloadTextureFormat: p, downloadUnpackNumChannels: u, defaultNumChannels: c, textureTypeHalfFloat: l, textureTypeFloat: m };
}
function pe(r, e) {
  let t10 = e();
  return O().getBool("DEBUG") && X5(r), t10;
}
function X5(r) {
  let e = r.getError();
  if (e !== r.NO_ERROR)
    throw new Error("WebGL Error: " + T$(r, e));
}
var Y5 = 596e-10;
var Q5 = 65504;
function KS(r) {
  return !!(O().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || Y5 < Math.abs(r) && Math.abs(r) < Q5);
}
function T$(r, e) {
  switch (e) {
    case r.NO_ERROR:
      return "NO_ERROR";
    case r.INVALID_ENUM:
      return "INVALID_ENUM";
    case r.INVALID_VALUE:
      return "INVALID_VALUE";
    case r.INVALID_OPERATION:
      return "INVALID_OPERATION";
    case r.INVALID_FRAMEBUFFER_OPERATION:
      return "INVALID_FRAMEBUFFER_OPERATION";
    case r.OUT_OF_MEMORY:
      return "OUT_OF_MEMORY";
    case r.CONTEXT_LOST_WEBGL:
      return "CONTEXT_LOST_WEBGL";
    default:
      return `Unknown error code ${e}`;
  }
}
function tc(r, e) {
  return Wa(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.');
}
function qS(r, e) {
  let t10 = Wa(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
  if (pe(r, () => r.shaderSource(t10, e)), pe(r, () => r.compileShader(t10)), r.getShaderParameter(t10, r.COMPILE_STATUS) === false)
    throw console.log(r.getShaderInfoLog(t10)), new Error("Failed to compile vertex shader.");
  return t10;
}
function jS(r, e) {
  let t10 = Wa(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
  if (pe(r, () => r.shaderSource(t10, e)), pe(r, () => r.compileShader(t10)), O().get("ENGINE_COMPILE_ONLY"))
    return t10;
  if (r.getShaderParameter(t10, r.COMPILE_STATUS) === false)
    throw Ef(e, r.getShaderInfoLog(t10)), new Error("Failed to compile fragment shader.");
  return t10;
}
var Z5 = /ERROR: [0-9]+:([0-9]+):/g;
function Ef(r, e) {
  let t10 = Z5.exec(e);
  if (t10 == null) {
    console.log(`Couldn't parse line number in error: ${e}`), console.log(r);
    return;
  }
  let o = +t10[1], n = r.split(`
`), s = n.length.toString().length + 2, a = n.map((l, m) => y.rightPad((m + 1).toString(), s) + l), i = 0;
  for (let l = 0; l < a.length; l++)
    i = Math.max(a[l].length, i);
  let p = a.slice(0, o - 1), u = a.slice(o - 1, o), c = a.slice(o);
  console.log(p.join(`
`)), console.log(e.split(`
`)[0]), console.log(`%c ${y.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function XS(r) {
  return Wa(r, () => r.createProgram(), "Unable to create WebGLProgram.");
}
function YS(r, e) {
  if (pe(r, () => r.linkProgram(e)), !O().get("ENGINE_COMPILE_ONLY") && r.getProgramParameter(e, r.LINK_STATUS) === false)
    throw console.log(r.getProgramInfoLog(e)), new Error("Failed to link vertex and fragment shaders.");
}
function kl(r, e) {
  if (pe(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false)
    throw console.log(r.getProgramInfoLog(e)), new Error("Shader program validation failed.");
}
function QS(r, e) {
  let t10 = Wa(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
  return pe(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), pe(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t10;
}
function ZS(r, e) {
  let t10 = Wa(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
  return pe(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t10)), pe(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t10;
}
function J5() {
  return O().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function JS(r) {
  return Wa(r, () => r.createTexture(), "Unable to create WebGLTexture.");
}
function ew(r, e) {
  let t10 = O().getNumber("WEBGL_MAX_TEXTURE_SIZE");
  if (r <= 0 || e <= 0) {
    let o = `[${r}x${e}]`;
    throw new Error("Requested texture size " + o + " is invalid.");
  }
  if (r > t10 || e > t10) {
    let o = `[${r}x${e}]`, n = `[${t10}x${t10}]`;
    throw new Error("Requested texture size " + o + " greater than WebGL maximum on this browser / GPU " + n + ".");
  }
}
function tw(r) {
  return Wa(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Af(r, e, t10, o, n, s, a) {
  let i = r.getAttribLocation(e, t10);
  return i === -1 ? false : (pe(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), pe(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), pe(r, () => r.enableVertexAttribArray(i)), true);
}
function _$(r, e, t10) {
  E$(r, t10), pe(r, () => r.activeTexture(r.TEXTURE0 + t10)), pe(r, () => r.bindTexture(r.TEXTURE_2D, e));
}
function e8(r, e) {
  E$(r, e), pe(r, () => r.activeTexture(r.TEXTURE0 + e)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function rw(r, e, t10) {
  return Wa(r, () => r.getUniformLocation(e, t10), 'uniform "' + t10 + '" not present in program.');
}
function ow(r, e, t10) {
  return r.getUniformLocation(e, t10);
}
function nw(r, e, t10, o) {
  pe(r, () => _$(r, e, o)), pe(r, () => r.uniform1i(t10, o));
}
function t8(r) {
  pe(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), pe(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), pe(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height));
}
function Nl(r, e, t10) {
  pe(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t10)), pe(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0));
}
function Rf(r, e) {
  pe(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), pe(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0));
}
function rc(r) {
  let e = r.checkFramebufferStatus(r.FRAMEBUFFER);
  if (e !== r.FRAMEBUFFER_COMPLETE)
    throw new Error("Error binding framebuffer: " + $$(r, e));
}
function $$(r, e) {
  switch (e) {
    case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
      return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
    case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
      return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
    case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
      return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
    case r.FRAMEBUFFER_UNSUPPORTED:
      return "FRAMEBUFFER_UNSUPPORTED";
    default:
      return `unknown error ${e}`;
  }
}
function Wa(r, e, t10) {
  let o = pe(r, () => e());
  if (o == null)
    throw new Error(t10);
  return o;
}
function E$(r, e) {
  let t10 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0;
  if (o < r.TEXTURE0 || o > t10) {
    let n = `[gl.TEXTURE0, gl.TEXTURE${t10}]`;
    throw new Error(`textureUnit must be in ${n}.`);
  }
}
function Ua(r, e = 2) {
  return y.sizeFromShape(r.slice(0, r.length - e));
}
function Ga(r) {
  if (r.length === 0)
    throw Error("Cannot get rows and columns of an empty shape array.");
  return [r.length > 1 ? r[r.length - 2] : 1, r[r.length - 1]];
}
function oc(r) {
  let e = [1, 1, 1];
  return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [Ua(r), ...Ga(r)]), e;
}
function sw(r, e = false) {
  let t10 = O().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = O().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");
  o === 1 / 0 && O().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE") && (o = t10 / 2), e && (t10 = t10 * 2, o = o * 2, r = r.map((i, p) => p >= r.length - 2 ? y.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = y.squeezeShape(r).newShape);
  let n = y.sizeFromShape(r), s = null;
  r.length <= 1 && n <= t10 ? s = [1, n] : r.length === 2 && r[0] <= t10 && r[1] <= t10 ? s = r : r.length === 3 && r[0] * r[1] <= t10 && r[2] <= t10 ? s = [r[0] * r[1], r[2]] : r.length === 3 && r[0] <= t10 && r[1] * r[2] <= t10 ? s = [r[0], r[1] * r[2]] : r.length === 4 && r[0] * r[1] * r[2] <= t10 && r[3] <= t10 ? s = [r[0] * r[1] * r[2], r[3]] : r.length === 4 && r[0] <= t10 && r[1] * r[2] * r[3] <= t10 && (s = [r[0], r[1] * r[2] * r[3]]);
  let a = s != null && Math.max(...s) > o && Math.min(...s) <= (e ? 2 : 1) && Math.min(...s) > 0;
  if (s == null || a)
    if (e) {
      let i = Ua(r), p = 2, u = 2;
      r.length && ([p, u] = Ga(r)), n = i * (p / 2) * (u / 2), s = y.sizeToSquarishShape(n).map((c) => c * 2);
    } else
      s = y.sizeToSquarishShape(n);
  return s;
}
function Tf(r) {
  return r % 2 === 0;
}
function Bi(r, e) {
  if (r = r.slice(-2), e = e.slice(-2), y.arraysEqual(r, e) || !r.length || !e.length || r[0] === 0 || r[1] === 0 || e[0] === 0 || e[1] === 0)
    return true;
  if (r.length !== e.length) {
    let t10 = r.slice(-1)[0], o = e.slice(-1)[0];
    if (t10 === o || Tf(t10) && Tf(o) && (r[0] === 1 || e[0] === 1))
      return true;
  }
  return r[1] === e[1] && Tf(r[0]) && Tf(e[0]);
}
var _f;
var $f;
function aw(r) {
  if (_f == null) {
    let e = zr(r);
    _f = e.getParameter(e.MAX_TEXTURE_SIZE);
  }
  return _f;
}
function r8() {
  _f = null;
}
function o8() {
  $f = null;
}
function iw(r) {
  if ($f == null) {
    let e = zr(r);
    $f = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS);
  }
  return Math.min(16, $f);
}
function uw(r) {
  if (r === 0)
    return 0;
  let e, t10 = zr(r);
  return Wr(t10, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : Wr(t10, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e;
}
function Wr(r, e) {
  return r.getExtension(e) != null;
}
function Df(r) {
  try {
    if (zr(r) != null)
      return true;
  } catch (e) {
    return console.log("Error when getting WebGL context: ", e), false;
  }
  return false;
}
function pw(r) {
  if (r === 0)
    return false;
  let e = zr(r);
  if (r === 1) {
    if (!Wr(e, "OES_texture_float"))
      return false;
  } else if (!Wr(e, "EXT_color_buffer_float"))
    return false;
  return HS(e);
}
function cw(r) {
  if (r === 0)
    return false;
  let e = zr(r);
  if (r === 1) {
    if (!Wr(e, "OES_texture_float") || !Wr(e, "WEBGL_color_buffer_float"))
      return false;
  } else {
    if (Wr(e, "EXT_color_buffer_float"))
      return HS(e);
    let o = "EXT_color_buffer_half_float";
    if (Wr(e, o)) {
      let n = e.getExtension(o);
      return n8(e, n);
    }
    return false;
  }
  return HS(e);
}
function HS(r) {
  let e = vl(r), t10 = r.createTexture();
  r.bindTexture(r.TEXTURE_2D, t10);
  let o = 1, n = 1;
  r.texImage2D(r.TEXTURE_2D, 0, e.internalFormatFloat, o, n, 0, e.textureFormatFloat, e.textureTypeFloat, null);
  let s = r.createFramebuffer();
  r.bindFramebuffer(r.FRAMEBUFFER, s), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, t10, 0);
  let a = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE;
  return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(t10), r.deleteFramebuffer(s), a;
}
function n8(r, e) {
  let t10 = vl(r, e), o = r.createTexture();
  r.bindTexture(r.TEXTURE_2D, o);
  let n = 1, s = 1;
  r.texImage2D(r.TEXTURE_2D, 0, t10.internalFormatHalfFloat, n, s, 0, t10.textureFormatFloat, t10.textureTypeHalfFloat, null);
  let a = r.createFramebuffer();
  r.bindFramebuffer(r.FRAMEBUFFER, a), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, o, 0);
  let i = r.checkFramebufferStatus(r.FRAMEBUFFER) === r.FRAMEBUFFER_COMPLETE;
  return r.bindTexture(r.TEXTURE_2D, null), r.bindFramebuffer(r.FRAMEBUFFER, null), r.deleteTexture(o), r.deleteFramebuffer(a), i;
}
function lw(r) {
  return r !== 2 ? false : zr(r).fenceSync != null;
}
function us(r, e) {
  Array.isArray(r) || (r = [r]), r.forEach((t10) => {
    t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`);
  });
}
var Se = O();
Se.registerFlag("HAS_WEBGL", () => Se.getNumber("WEBGL_VERSION") > 0);
Se.registerFlag("WEBGL_VERSION", () => Df(2) ? 2 : Df(1) ? 1 : 0);
Se.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
Se.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Se.get("WEBGL_VERSION") === 2);
Se.registerFlag("WEBGL_CPU_FORWARD", () => true);
Se.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
Se.registerFlag("WEBGL_PACK", () => Se.getBool("HAS_WEBGL"));
Se.registerFlag("WEBGL_PACK_NORMALIZATION", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_CLIP", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_PACK_REDUCE", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_LAZILY_UNPACK", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_CONV_IM2COL", () => Se.getBool("WEBGL_PACK"));
Se.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => aw(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => iw(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
  let r = Se.getNumber("WEBGL_VERSION");
  return r === 0 ? 0 : uw(r);
});
Se.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Se.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !ki.isMobile());
Se.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => pw(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Se.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Se.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
Se.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => cw(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_FENCE_API_ENABLED", () => lw(Se.getNumber("WEBGL_VERSION")));
Se.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Se.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0);
Se.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (r) => {
  if (r < 0 && r !== -1)
    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`);
});
Se.registerFlag("WEBGL_FLUSH_THRESHOLD", () => ki.isMobile() ? 1 : -1, (r) => {
  if (r < 0 && r !== -1)
    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`);
});
Se.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
Se.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
Se.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
Se.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
Se.registerFlag("WEBGL_EXP_CONV", () => false);
Se.registerFlag("SOFTWARE_WEBGL_ENABLED", () => Se.getBool("IS_TEST"));
Se.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0);
Se.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false);
Se.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false);
Se.registerFlag("ENGINE_COMPILE_ONLY", () => false);
function wt() {
  let r, e, t10, o, n, s, a, i, p, u;
  return O().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t10 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = O().getBool("WEBGL2_ISNAN_CUSTOM") ? `
      bool isnan_custom(float val) {
        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
      }

      #define isnan(value) isnan_custom(value)
    ` : "", p = "", u = `
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `) : (r = "", e = "attribute", t10 = "varying", o = "varying", n = "texture2D", s = "gl_FragColor", a = "", 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));
      }
    `, p = `
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `, u = `
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `), { version: r, attribute: e, varyingVs: t10, varyingFs: o, texture2D: n, output: s, defineOutput: a, defineSpecialNaN: i, defineSpecialInf: p, defineRound: u };
}
function ps(r, e, t10 = "index") {
  let o = y.computeStrides(e);
  return o.map((n, s) => {
    let a = `int ${r[s]} = ${t10} / ${n}`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * ${n}` : `index -= ${r[s]} * ${n}`;
    return `${a}; ${i};`;
  }).join("");
}
function Pu(r, e, t10 = "index") {
  let o = y.computeStrides(e);
  return o.map((n, s) => {
    let a = `int ${r[s]} = ${t10} / outShapeStrides[${s}]`, i = s === o.length - 1 ? `int ${r[s + 1]} = ${t10} - ${r[s]} * outShapeStrides[${s}]` : `index -= ${r[s]} * outShapeStrides[${s}]`;
    return `${a}; ${i};`;
  }).join("");
}
function s8(r, e) {
  let t10 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t10 - 1);
  n[t10 - 2] = o[t10 - 1];
  for (let s = t10 - 3; s >= 0; --s)
    n[s] = `(${n[s + 1]} * ${o[s + 1]})`;
  return n;
}
function A$(r, e, t10 = "index") {
  let o = r.map((s, a) => a), n = s8(o, e);
  return n.map((s, a) => {
    let i = `int ${r[a]} = ${t10} / ${n[a]}`, p = a === n.length - 1 ? `int ${r[a + 1]} = ${t10} - ${r[a]} * ${n[a]}` : `index -= ${r[a]} * ${n[a]}`;
    return `${i}; ${p};`;
  }).join("");
}
function sc(r) {
  let e = y.computeStrides(r).map((t10) => t10.toString());
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
  }
`;
}
function ac() {
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`;
}
var Ff = `
  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;
  }
`;
var { getBroadcastDims: R$ } = S;
function D$(r, e, t10) {
  let o = [];
  if (r.forEach((d) => {
    let f = y.sizeFromShape(d.shapeInfo.logicalShape);
    if (d.shapeInfo.isUniform ? o.push(`uniform float ${d.name}${f > 1 ? `[${f}]` : ""};`) : (o.push(`uniform sampler2D ${d.name};`), o.push(`uniform int offset${d.name};`)), t10.enableShapeUniforms) {
      let { uniformShape: h } = Of(t10.packedInputs, d.shapeInfo.logicalShape, d.shapeInfo.texShape);
      switch (h.length) {
        case 1:
          o.push(`uniform int ${d.name}Shape;`);
          break;
        case 2:
          o.push(`uniform ivec2 ${d.name}Shape;`);
          break;
        case 3:
          o.push(`uniform ivec3 ${d.name}Shape;`);
          break;
        case 4:
          o.push(`uniform ivec4 ${d.name}Shape;`);
          break;
        default:
          break;
      }
      o.push(`uniform ivec2 ${d.name}TexShape;`);
    }
  }), t10.enableShapeUniforms) {
    switch (e.logicalShape.length) {
      case 1:
        o.push("uniform int outShape;");
        break;
      case 2:
        o.push("uniform ivec2 outShape;"), o.push("uniform int outShapeStrides;");
        break;
      case 3:
        o.push("uniform ivec3 outShape;"), o.push("uniform ivec2 outShapeStrides;");
        break;
      case 4:
        o.push("uniform ivec4 outShape;"), o.push("uniform ivec3 outShapeStrides;");
        break;
      default:
        break;
    }
    o.push("uniform ivec2 outTexShape;");
  }
  t10.customUniforms && t10.customUniforms.forEach((d) => {
    o.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
  });
  let n = o.join(`
`), s = r.map((d) => a8(d, e, t10.packedInputs, t10.enableShapeUniforms)).join(`
`), a = e.texShape, i = wt(), p = p8(i), u, c, l = m8(i);
  return e.isPacked ? (u = i8(e.logicalShape, a, t10.enableShapeUniforms), c = l8(i)) : (u = u8(e.logicalShape, a, t10.enableShapeUniforms), c = c8(i)), t10.packedInputs && (l += g8), [l, p, c, n, u, s, t10.userCode].join(`
`);
}
function uc(r, e = false) {
  let t10 = r.shapeInfo.logicalShape;
  switch (t10.length) {
    case 0:
      return _8(r, e);
    case 1:
      return E8(r, e);
    case 2:
      return R8(r, e);
    case 3:
      return F8(r, e);
    case 4:
      return P8(r, e);
    case 5:
      return M8(r);
    case 6:
      return L8(r);
    default:
      throw new Error(`${t10.length}-D input sampling is not yet supported`);
  }
}
function F$(r, e) {
  switch (r.shapeInfo.logicalShape.length) {
    case 0:
      return T8(r);
    case 1:
      return $8(r, e);
    case 2:
      return A8(r, e);
    case 3:
      return D8(r, e);
    default:
      return O8(r, e);
  }
}
function a8(r, e, t10 = false, o) {
  let n = "";
  t10 ? n += F$(r, o) : n += uc(r, o);
  let s = r.shapeInfo.logicalShape, a = e.logicalShape;
  return s.length <= a.length && (t10 ? n += B8(r, e) : n += V8(r, e)), n;
}
function i8(r, e, t10) {
  switch (r.length) {
    case 0:
      return O$();
    case 1:
      return x8(r, e, t10);
    case 2:
      return k8(r, e, t10);
    case 3:
      return b8(r, e, t10);
    default:
      return S8(r, e, t10);
  }
}
function u8(r, e, t10) {
  switch (r.length) {
    case 0:
      return O$();
    case 1:
      return y8(r, e, t10);
    case 2:
      return N8(r, e, t10);
    case 3:
      return C8(r, e, t10);
    case 4:
      return w8(r, e, t10);
    case 5:
      return I8(r, e);
    case 6:
      return v8(r, e);
    default:
      throw new Error(`${r.length}-D output sampling is not yet supported`);
  }
}
function p8(r) {
  return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${r.texture2D}(textureSampler, uv).r;
    }
  `;
}
function c8(r) {
  return `
    void setOutput(float val) {
      ${r.output} = vec4(val, 0, 0, 0);
    }
  `;
}
function l8(r) {
  return `
    void setOutput(vec4 val) {
      ${r.output} = val;
    }
  `;
}
function m8(r) {
  return `${r.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${r.varyingFs} vec2 resultUV;
    ${r.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;
    ${r.defineSpecialNaN}
    ${r.defineSpecialInf}
    ${r.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);
    }

    ${d8}
    ${f8}
    ${h8}
  `;
}
var d8 = `
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var f8 = `
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var h8 = `
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var g8 = `
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;
function O$() {
  return `
    int getOutputCoords() {
      return 0;
    }
  `;
}
function x8(r, e, t10) {
  let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
  return o[0] === 1 ? t10 ? `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    ` : `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${o[1]}.0);
      }
    ` : o[1] === 1 ? t10 ? `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    ` : `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${o[0]}.0);
      }
    ` : t10 ? `
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  ` : `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${o[0]}, ${o[1]}));
      return 2 * (resTexRC.x * ${o[1]} + resTexRC.y);
    }
  `;
}
function y8(r, e, t10) {
  return e[0] === 1 ? t10 ? `
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    ` : `
      int getOutputCoords() {
        return int(resultUV.x * ${e[1]}.0);
      }
    ` : e[1] === 1 ? t10 ? `
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    ` : `
      int getOutputCoords() {
        return int(resultUV.y * ${e[0]}.0);
      }
    ` : t10 ? `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  ` : `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      return resTexRC.x * ${e[1]} + resTexRC.y;
    }
  `;
}
function b8(r, e, t10) {
  if (t10)
    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 o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)], n = Math.ceil(r[2] / 2), s = n * Math.ceil(r[1] / 2);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${o[0]}, ${o[1]}));
      int index = resTexRC.x * ${o[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `;
}
function C8(r, e, t10) {
  if (t10)
    return `
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Pu(["r", "c", "d"], r)}
    return ivec3(r, c, d);
  }
`;
  let o = ps(["r", "c", "d"], r);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${o}
      return ivec3(r, c, d);
    }
  `;
}
function S8(r, e, t10) {
  if (t10)
    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 o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)], n = Math.ceil(r[r.length - 1] / 2), s = n * Math.ceil(r[r.length - 2] / 2), a = s, i = "", p = "b, r, c";
  for (let u = 2; u < r.length - 1; u++)
    a *= r[r.length - u - 1], i = `
      int b${u} = index / ${a};
      index -= b${u} * ${a};
    ` + i, p = `b${u}, ` + p;
  return `
    ivec${r.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${o[0]}, ${o[1]}));
      int index = resTexRC.x * ${o[1]} + resTexRC.y;

      ${i}

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

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

      return ivec${r.length}(${p});
    }
  `;
}
function w8(r, e, t10) {
  if (t10)
    return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${Pu(["r", "c", "d", "d2"], r)}
      return ivec4(r, c, d, d2);
    }
  `;
  let o = ps(["r", "c", "d", "d2"], r);
  return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${o}
      return ivec4(r, c, d, d2);
    }
  `;
}
function I8(r, e) {
  let t10 = ps(["r", "c", "d", "d2", "d3"], r);
  return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
                             ${e[1]}));

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

      ${t10}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `;
}
function v8(r, e) {
  let t10 = ps(["r", "c", "d", "d2", "d3", "d4"], r);
  return `
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;

      ${t10}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `;
}
function k8(r, e, t10) {
  let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
  if (y.arraysEqual(r, e))
    return t10 ? `
      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(${o[0]}, ${o[1]}));
      }
    `;
  let n = Math.ceil(r[1] / 2);
  return t10 ? `
    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(${o[0]}, ${o[1]}));

      int index = resTexRC.x * ${o[1]} + resTexRC.y;
      int r = 2 * (index / ${n});
      int c = imod(index, ${n}) * 2;

      return ivec2(r, c);
    }
  `;
}
function N8(r, e, t10) {
  return y.arraysEqual(r, e) ? t10 ? `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    ` : `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
      }
    ` : r[1] === 1 ? t10 ? `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    ` : `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${e[0]}, ${e[1]}));
        int index = resTexRC.x * ${e[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    ` : r[0] === 1 ? t10 ? `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    ` : `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${e[0]}, ${e[1]}));
        int index = resTexRC.x * ${e[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    ` : t10 ? `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  ` : `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      int r = index / ${r[1]};
      int c = index - r * ${r[1]};
      return ivec2(r, c);
    }
  `;
}
function Mu(r) {
  return `offset${r}`;
}
function T8(r) {
  let e = r.name, t10 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = wt();
  return `
    vec4 ${t10}() {
      return ${o.texture2D}(${e}, halfCR);
    }
  `;
}
function _8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1);
  if (r.shapeInfo.isUniform)
    return `float ${o}() {return ${t10};}`;
  let [n, s] = r.shapeInfo.texShape;
  if (n === 1 && s === 1)
    return `
      float ${o}() {
        return sampleTexture(${t10}, halfCR);
      }
    `;
  let a = Mu(t10);
  if (e)
    return `
    float ${o}() {
      vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], ${a});
      return sampleTexture(${t10}, uv);
    }
  `;
  let [i, p] = r.shapeInfo.texShape;
  return `
    float ${o}() {
      vec2 uv = uvFromFlat(${i}, ${p}, ${a});
      return sampleTexture(${t10}, uv);
    }
  `;
}
function $8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = r.shapeInfo.texShape, s = wt();
  if (e)
    return `
    vec4 ${o}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${t10}, uv);
    }
  `;
  let a = [Math.ceil(n[0] / 2), Math.ceil(n[1] / 2)];
  return `
    vec4 ${o}(int index) {
      vec2 uv = packedUVfrom1D(
        ${a[0]}, ${a[1]}, index);
      return ${s.texture2D}(${t10}, uv);
    }
  `;
}
function E8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1);
  if (r.shapeInfo.isUniform)
    return `
      float ${o}(int index) {
        ${pc(r)}
      }
    `;
  let n = r.shapeInfo.texShape, s = n[0], a = n[1];
  if (a === 1 && s === 1)
    return `
      float ${o}(int index) {
        return sampleTexture(${t10}, halfCR);
      }
    `;
  let i = Mu(t10);
  return a === 1 ? e ? `
      float ${o}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${t10}TexShape[0]));
        return sampleTexture(${t10}, uv);
      }
    ` : `
      float ${o}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);
        return sampleTexture(${t10}, uv);
      }
    ` : s === 1 ? e ? `
      float ${o}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${t10}TexShape[1]), 0.5);
        return sampleTexture(${t10}, uv);
      }
    ` : `
      float ${o}(int index) {
        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${a}.0, 0.5);
        return sampleTexture(${t10}, uv);
      }
    ` : e ? `
    float ${o}(int index) {
      vec2 uv = uvFromFlat(${t10}TexShape[0], ${t10}TexShape[1], index + ${i});
      return sampleTexture(${t10}, uv);
    }
  ` : `
    float ${o}(int index) {
      vec2 uv = uvFromFlat(${s}, ${a}, index + ${i});
      return sampleTexture(${t10}, uv);
    }
  `;
}
function A8(r, e) {
  let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = s[0], i = s[1], p = wt();
  if (s != null && y.arraysEqual(t10, s))
    return e ? `
      vec4 ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);

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

        return ${p.texture2D}(${o}, uv);
      }
    `;
  if (e)
    return `
    vec4 ${n}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${o}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${p.texture2D}(${o}, uv);
    }
  `;
  let u = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)], c = Math.ceil(t10[1] / 2);
  return `
    vec4 ${n}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
      return ${p.texture2D}(${o}, uv);
    }
  `;
}
function R8(r, e) {
  let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape;
  if (s != null && y.arraysEqual(t10, s)) {
    if (e)
      return `
      float ${n}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);
        return sampleTexture(${o}, uv);
      }
    `;
    let m = s[0], d = s[1];
    return `
    float ${n}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${m}.0);
      return sampleTexture(${o}, uv);
    }
  `;
  }
  let { newShape: a, keptDims: i } = y.squeezeShape(t10), p = a;
  if (p.length < t10.length) {
    let m = cc(r, p), d = ["row", "col"];
    return `
      ${uc(m, e)}
      float ${n}(int row, int col) {
        return ${n}(${lc(d, i)});
      }
    `;
  }
  if (r.shapeInfo.isUniform)
    return `
      float ${n}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t10[1]}, 1)));
        ${pc(r)}
      }
    `;
  let u = s[0], c = s[1], l = Mu(o);
  return c === 1 ? e ? `
      float ${n}(int row, int col) {
        float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${o}TexShape[0]));
        return sampleTexture(${o}, uv);
      }
    ` : `
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
      return sampleTexture(${o}, uv);
    }
  ` : u === 1 ? e ? `
      float ${n}(int row, int col) {
        float index = dot(vec3(row, col, ${l}), vec3(${o}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${o}TexShape[1]), 0.5);
        return sampleTexture(${o}, uv);
      }
    ` : `
    float ${n}(int row, int col) {
      float index = dot(vec3(row, col, ${l}), vec3(${t10[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${o}, uv);
    }
  ` : e ? `
      float ${n}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${o}Shape[1] + col + ${l};
        vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);
        return sampleTexture(${o}, uv);
      }
    ` : `
  float ${n}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${t10[1]} + col + ${l};
    vec2 uv = uvFromFlat(${u}, ${c}, index);
    return sampleTexture(${o}, uv);
  }
`;
}
function D8(r, e) {
  let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = r.shapeInfo.texShape, a = [Math.ceil(s[0] / 2), Math.ceil(s[1] / 2)];
  if (t10[0] === 1) {
    let m = t10.slice(1), d = [1, 2], f = cc(r, m), h = ["b", "row", "col"];
    return `
        ${F$(f, e)}
        vec4 ${n}(int b, int row, int col) {
          return ${n}(${lc(h, d)});
        }
      `;
  }
  let i = wt();
  if (e)
    return `
    vec4 ${n}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${o}TexShape[0]) / 2.0), ceil(float(${o}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${o}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${o}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${i.texture2D}(${o}, uv);
    }
  `;
  let p = a[0], u = a[1], c = Math.ceil(t10[2] / 2), l = c * Math.ceil(t10[1] / 2);
  return `
    vec4 ${n}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${p}, ${u}, ${l}, ${c}, b, row, col);
      return ${i.texture2D}(${o}, uv);
    }
  `;
}
function F8(r, e) {
  let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[1] * t10[2], a = t10[2], { newShape: i, keptDims: p } = y.squeezeShape(t10), u = i;
  if (u.length < t10.length) {
    let h = cc(r, u), g = ["row", "col", "depth"];
    return `
        ${uc(h, e)}
        float ${n}(int row, int col, int depth) {
          return ${n}(${lc(g, p)});
        }
      `;
  }
  if (r.shapeInfo.isUniform)
    return `
      float ${n}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${a}, 1)));
        ${pc(r)}
      }
    `;
  let c = r.shapeInfo.texShape, l = c[0], m = c[1], d = r.shapeInfo.flatOffset;
  if (m === s && d == null)
    return e ? `
      float ${n}(int row, int col, int depth) {
        int stride1 = ${o}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${o}TexShape[1], ${o}TexShape[0]);
        return sampleTexture(${o}, uv);
      }
    ` : `
        float ${n}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${a}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${m}.0, ${l}.0);
          return sampleTexture(${o}, uv);
        }
      `;
  if (m === a && d == null)
    return e ? `
      float ${n}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${o}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}TexShape[1], ${o}TexShape[0]);
        return sampleTexture(${o}, uv);
      }
    ` : `
    float ${n}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${t10[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${l}.0);
      return sampleTexture(${o}, uv);
    }
  `;
  let f = Mu(o);
  return e ? `
    float ${n}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${o}Shape[1] * ${o}Shape[2];
      int stride1 = ${o}Shape[2];
      int index = row * stride0 + col * stride1 + depth + ${f};
      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index);
      return sampleTexture(${o}, uv);
    }
    ` : `
      float ${n}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${a} + depth + ${f};
        vec2 uv = uvFromFlat(${l}, ${m}, index);
        return sampleTexture(${o}, uv);
      }
  `;
}
function O8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = wt();
  if (e)
    return `
    vec4 ${o}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${t10}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${t10}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${t10}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${t10}TexShape[0]) / 2.0), ceil(float(${t10}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 ${n.texture2D}(${t10}, uv);
    }
  `;
  let s = r.shapeInfo.logicalShape, a = s.length, i = r.shapeInfo.texShape, p = [Math.ceil(i[0] / 2), Math.ceil(i[1] / 2)], u = p[0], c = p[1], l = Math.ceil(s[a - 1] / 2), m = l * Math.ceil(s[a - 2] / 2), d = "int b, int row, int col", f = `b * ${m} + (row / 2) * ${l} + (col / 2)`;
  for (let h = 2; h < a - 1; h++)
    d = `int b${h}, ` + d, m *= s[a - h - 1], f = `b${h} * ${m} + ` + f;
  return `
    vec4 ${o}(${d}) {
      int index = ${f};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
      return ${n.texture2D}(${t10}, uv);
    }
  `;
}
function P8(r, e) {
  let t10 = r.shapeInfo.logicalShape, o = r.name, n = "get" + o.charAt(0).toUpperCase() + o.slice(1), s = t10[3], a = t10[2] * s, i = t10[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(t10);
  if (p.length < t10.length) {
    let b = cc(r, p), C = ["row", "col", "depth", "depth2"];
    return `
      ${uc(b, e)}
      float ${n}(int row, int col, int depth, int depth2) {
        return ${n}(${lc(C, u)});
      }
    `;
  }
  if (r.shapeInfo.isUniform)
    return `
      float ${n}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${i}, ${a}, ${s}, 1)));
        ${pc(r)}
      }
    `;
  let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1], f = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`;
  if (d === i && c == null)
    return e ? `
      float ${n}(int row, int col, int depth, int depth2) {
        ${f}
        ${h}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${o}TexShape[1], ${o}TexShape[0]);
        return sampleTexture(${o}, uv);
      }
    ` : `
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${a}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${m}.0);
        return sampleTexture(${o}, uv);
      }
    `;
  if (d === s && c == null)
    return e ? `
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${o}Shape[1] * ${o}Shape[2], ${o}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${o}TexShape[1], ${o}TexShape[0]);
        return sampleTexture(${o}, uv);
      }
    ` : `
      float ${n}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${t10[1] * t10[2]}, ${t10[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${m}.0);
        return sampleTexture(${o}, uv);
      }
    `;
  let x = Mu(o);
  return e ? `
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${h}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index + ${x});
      return sampleTexture(${o}, uv);
    }
  ` : `
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${i} + col * ${a} +
          depth * ${s} + depth2;
      vec2 uv = uvFromFlat(${m}, ${d}, index + ${x});
      return sampleTexture(${o}, uv);
    }
  `;
}
function M8(r) {
  let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = e[4], s = e[3] * n, a = e[2] * s, i = e[1] * a, { newShape: p, keptDims: u } = y.squeezeShape(e);
  if (p.length < e.length) {
    let h = cc(r, p), g = ["row", "col", "depth", "depth2", "depth3"];
    return `
      ${uc(h)}
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        return ${o}(${lc(g, u)});
      }
    `;
  }
  if (r.shapeInfo.isUniform)
    return `
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${i}, ${a}, ${s}, ${n})) +
          depth3;
        ${pc(r)}
      }
    `;
  let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], d = l[1];
  if (d === i && c == null)
    return `
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${a}, ${s}, ${n}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${m}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  if (d === n && c == null)
    return `
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${e[1] * e[2] * e[3]},
               ${e[2] * e[3]}, ${e[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${m}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  let f = Mu(t10);
  return `
    float ${o}(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 * ${a} + depth * ${s} +
          depth2 * ${n} + depth3 + ${f};
      vec2 uv = uvFromFlat(${m}, ${d}, index);
      return sampleTexture(${t10}, uv);
    }
  `;
}
function L8(r) {
  let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), { newShape: n, keptDims: s } = y.squeezeShape(e);
  if (n.length < e.length) {
    let g = cc(r, n), x = ["row", "col", "depth", "depth2", "depth3", "depth4"];
    return `
      ${uc(g)}
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${o}(${lc(x, s)});
      }
    `;
  }
  let a = e[5], i = e[4] * a, p = e[3] * i, u = e[2] * p, c = e[1] * u;
  if (r.shapeInfo.isUniform)
    return `
      float ${o}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${u}, ${p}, ${i})) +
          dot(
            vec2(depth3, depth4),
            vec2(${a}, 1)));
        ${pc(r)}
      }
    `;
  let l = r.shapeInfo.flatOffset, m = r.shapeInfo.texShape, d = m[0], f = m[1];
  if (f === c && l == null)
    return `
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${u}, ${p}, ${i}, ${a})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${d}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  if (f === a && l == null)
    return `
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${e[1] * e[2] * e[3] * e[4]},
               ${e[2] * e[3] * e[4]},
               ${e[3] * e[4]},
               ${e[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${d}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  let h = Mu(t10);
  return `
    float ${o}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${u} + depth * ${p} +
          depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
      vec2 uv = uvFromFlat(${d}, ${f}, index);
      return sampleTexture(${t10}, uv);
    }
  `;
}
function pc(r) {
  let e = r.name, t10 = y.sizeFromShape(r.shapeInfo.logicalShape);
  return t10 < 2 ? `return ${e};` : `
    for (int i = 0; i < ${t10}; i++) {
      if (i == index) {
        return ${e}[i];
      }
    }
  `;
}
function B8(r, e) {
  let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = R$(r.shapeInfo.logicalShape, e.logicalShape), p = $e(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"];
  s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${l[b + u]} = 0;`).join(`
`);
  let m = "";
  a < 2 && s > 0 ? m = "coords" : m = r.shapeInfo.logicalShape.map((b, C) => `coords.${l[C + u]}`).join(", ");
  let d = "return outputValue;", h = y.sizeFromShape(r.shapeInfo.logicalShape) === 1, x = y.sizeFromShape(e.logicalShape) === 1;
  if (s === 1 && !h && !x)
    d = `
      return vec4(outputValue.xy, outputValue.xy);
    `;
  else if (h && !x)
    a === 1 ? d = `
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      ` : d = `
        return vec4(outputValue.x);
      `;
  else if (i.length) {
    let b = s - 2, C = s - 1;
    i.indexOf(b) > -1 && i.indexOf(C) > -1 ? d = "return vec4(outputValue.x);" : i.indexOf(b) > -1 ? d = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(C) > -1 && (d = "return vec4(outputValue.xx, outputValue.zz);");
  }
  return `
    vec4 ${n}() {
      ${p} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${o}(${m});
      ${d}
    }
  `;
}
function V8(r, e) {
  let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = e.texShape, a = r.shapeInfo.texShape, i = r.shapeInfo.logicalShape.length, p = e.logicalShape.length;
  if (!r.shapeInfo.isUniform && i === p && r.shapeInfo.flatOffset == null && y.arraysEqual(a, s))
    return `
      float ${n}() {
        return sampleTexture(${t10}, resultUV);
      }
    `;
  let u = $e(p), c = R$(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, d = ["x", "y", "z", "w", "u", "v"];
  i === 0 ? m = "" : p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((h) => `coords.${d[h + l]} = 0;`).join(`
`);
  let f = "";
  return p < 2 && i > 0 ? f = "coords" : f = r.shapeInfo.logicalShape.map((h, g) => `coords.${d[g + l]}`).join(", "), `
    float ${n}() {
      ${u} coords = getOutputCoords();
      ${m}
      return get${o}(${f});
    }
  `;
}
function $e(r) {
  if (r <= 1)
    return "int";
  if (r === 2)
    return "ivec2";
  if (r === 3)
    return "ivec3";
  if (r === 4)
    return "ivec4";
  if (r === 5)
    return "ivec5";
  if (r === 6)
    return "ivec6";
  throw Error(`GPU for rank ${r} is not yet supported`);
}
function Of(r, e, t10) {
  let { newShape: o, keptDims: n } = y.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !y.arraysEqual(e, t10) && o.length < s || a;
  return { useSqueezeShape: p, uniformShape: p ? i : e, keptDims: n };
}
function cc(r, e) {
  let t10 = JSON.parse(JSON.stringify(r));
  return t10.shapeInfo.logicalShape = e, t10;
}
function lc(r, e) {
  return e.map((t10) => r[t10]).join(", ");
}
function M$(r, e, t10, o) {
  let n = t10.map((c, l) => {
    let m = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null };
    return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (m.flatOffset = c.texData.slice.flatOffset), { name: e.variableNames[l], shapeInfo: m };
  }), s = n.map((c) => c.shapeInfo), a = { logicalShape: o.shape, texShape: o.texData.texShape, isUniform: false, isPacked: o.texData.isPacked, flatOffset: null }, i = D$(n, a, e), p = jS(r.gl, i), u = r.createProgram(p);
  return O().get("ENGINE_COMPILE_ONLY") ? { program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : Object.assign({ program: e, fragmentShader: p, source: i, webGLProgram: u, inShapeInfos: s, outShapeInfo: a }, mw(r, e, u));
}
function mw(r, e, t10) {
  let o = {}, n = {}, s = {}, a = [], i, p, u, c = null, l = null;
  l = r.getUniformLocation(t10, "NAN", false), O().getNumber("WEBGL_VERSION") === 1 && (c = r.getUniformLocation(t10, "INFINITY", false));
  let m = false;
  for (let d = 0; d < e.variableNames.length; d++) {
    let f = e.variableNames[d];
    o[f] = r.getUniformLocation(t10, f, m), o[`offset${f}`] = r.getUniformLocation(t10, `offset${f}`, m), e.enableShapeUniforms && (n[`${f}Shape`] = r.getUniformLocation(t10, `${f}Shape`, m), s[`${f}TexShape`] = r.getUniformLocation(t10, `${f}TexShape`, m));
  }
  return e.enableShapeUniforms && (i = r.getUniformLocation(t10, "outShape", m), u = r.getUniformLocation(t10, "outShapeStrides", m), p = r.getUniformLocation(t10, "outTexShape", m)), e.customUniforms && e.customUniforms.forEach((d, f) => {
    a[f] = r.getUniformLocation(t10, d.name, m);
  }), { uniformLocations: o, customUniformLocations: a, infLoc: c, nanLoc: l, inShapesLocations: n, inTexShapesLocations: s, outShapeLocation: i, outShapeStridesLocation: u, outTexShapeLocation: p };
}
function P$(r, e) {
  if (r.length !== e.length)
    throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);
  r.forEach((t10, o) => {
    let n = t10.logicalShape, s = e[o], a = s.shape;
    if (!y.arraysEqual(n, a))
      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);
    if (t10.isUniform && s.isUniform)
      return;
    let i = t10.texShape, p = s.isUniform ? null : s.texData.texShape;
    if (!y.arraysEqual(i, p))
      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`);
  });
}
function L$(r, e, t10, o, n) {
  e.program.enableShapeUniforms || (P$(e.inShapeInfos, t10), P$([e.outShapeInfo], [o]));
  let s = o.texData.texture, a = o.texData.texShape;
  o.texData.isPacked ? r.setOutputPackedMatrixTexture(s.texture, a[0], a[1]) : r.setOutputMatrixTexture(s.texture, a[0], a[1]), r.setProgram(e.webGLProgram), O().getNumber("WEBGL_VERSION") === 1 && e.infLoc !== null && r.gl.uniform1f(e.infLoc, 1 / 0), e.nanLoc !== null && r.gl.uniform1f(e.nanLoc, NaN), t10.forEach((p, u) => {
    let c = e.program.variableNames[u], l = e.uniformLocations[c], m = e.uniformLocations[`offset${c}`], d = e.inShapesLocations[`${c}Shape`], f = e.inTexShapesLocations[`${c}TexShape`];
    if (d) {
      let { uniformShape: h } = Of(e.program.packedInputs, p.shape, p.texData.texShape);
      switch (h.length) {
        case 1:
          r.gl.uniform1iv(d, new Int32Array(h));
          break;
        case 2:
          r.gl.uniform2iv(d, new Int32Array(h));
          break;
        case 3:
          r.gl.uniform3iv(d, new Int32Array(h));
          break;
        case 4:
          r.gl.uniform4iv(d, new Int32Array(h));
          break;
        default:
          break;
      }
    }
    if (f && r.gl.uniform2i(f, p.texData.texShape[0], p.texData.texShape[1]), l != null) {
      if (p.isUniform) {
        if (y.sizeFromShape(p.shape) < 2)
          r.gl.uniform1f(l, p.uniformValues[0]);
        else {
          let h = p.uniformValues;
          h instanceof Float32Array || (h = new Float32Array(h)), r.gl.uniform1fv(l, h);
        }
        return;
      }
      p.texData.slice != null && m != null && r.gl.uniform1i(m, p.texData.slice.flatOffset), r.setInputMatrixTexture(p.texData.texture.texture, l, u);
    }
  });
  let i = e.outShapeLocation;
  if (i)
    switch (o.shape.length) {
      case 1:
        r.gl.uniform1iv(i, new Int32Array(o.shape));
        break;
      case 2:
        r.gl.uniform2iv(i, new Int32Array(o.shape));
        break;
      case 3:
        r.gl.uniform3iv(i, new Int32Array(o.shape));
        break;
      case 4:
        r.gl.uniform4iv(i, new Int32Array(o.shape));
        break;
      default:
        break;
    }
  if (e.outShapeStridesLocation) {
    let p = y.computeStrides(o.shape);
    switch (o.shape.length) {
      case 2:
        r.gl.uniform1iv(e.outShapeStridesLocation, new Int32Array(p));
        break;
      case 3:
        r.gl.uniform2iv(e.outShapeStridesLocation, new Int32Array(p));
        break;
      case 4:
        r.gl.uniform3iv(e.outShapeStridesLocation, new Int32Array(p));
        break;
      default:
        break;
    }
  }
  e.outTexShapeLocation && r.gl.uniform2i(e.outTexShapeLocation, o.texData.texShape[0], o.texData.texShape[1]), e.program.customUniforms && n && e.program.customUniforms.forEach((p, u) => {
    let c = e.customUniformLocations[u], l = n[u];
    if (p.type === "float")
      r.gl.uniform1fv(c, l);
    else if (p.type === "vec2")
      r.gl.uniform2fv(c, l);
    else if (p.type === "vec3")
      r.gl.uniform3fv(c, l);
    else if (p.type === "vec4")
      r.gl.uniform4fv(c, l);
    else if (p.type === "int")
      r.gl.uniform1iv(c, l);
    else if (p.type === "ivec2")
      r.gl.uniform2iv(c, l);
    else if (p.type === "ivec3")
      r.gl.uniform3iv(c, l);
    else if (p.type === "ivec4")
      r.gl.uniform4iv(c, l);
    else
      throw Error(`uniform type ${p.type} is not supported yet.`);
  }), r.executeProgram();
}
function B$(r, e, t10) {
  let o = "";
  e.concat(t10).forEach((a) => {
    let i = a.texData != null && a.texData.slice != null && a.texData.slice.flatOffset > 0;
    if (r.enableShapeUniforms && !a.isUniform) {
      let p = a.texData.texShape, { useSqueezeShape: u, uniformShape: c, keptDims: l } = Of(r.packedInputs, a.shape, p), m = "", d = "", f = "";
      if (c.length === 1 && r.packedInputs) {
        let k = [Math.ceil(p[0] / 2), Math.ceil(p[1] / 2)];
        m = `${k[0] > 1}_${k[1] > 1}`;
      } else if (c.length === 2 && !r.packedInputs)
        d = `${c[0] > 1}_${c[1] > 1}`;
      else if (c.length > 2 && !r.packedInputs) {
        let k = y.computeStrides(c);
        f = `${k[0] === p[1]}_${k[k.length - 1] === p[1]}`;
      }
      let h = a.shape.length, g = c.length === 2 && y.arraysEqual(a.shape, p), x = y.sizeFromShape(a.shape) === 1, b = S.getBroadcastDims(a.shape, t10.shape), C = !r.packedInputs && h === t10.shape.length && y.arraysEqual(p, t10.texData.texShape), w = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`;
      o += `${h}_${C}_${u ? l : ""}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${w}_${i}`;
    } else {
      let p = a.isUniform ? "uniform" : a.texData.texShape;
      o += `${a.shape}_${p}_${i}`;
    }
  });
  let n = r.userCode, s = r.constructor.name;
  return s += "_" + o + "_" + n + `${O().getNumber("WEBGL_VERSION")}`, s;
}
function lt(r) {
  return O().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4;
}
var Pf = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = Li.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t10 = wt();
    this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Pu(["r", "c", "d"], e) : ps(["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);
        }

        ${t10.output} = result;
      }
    `;
  }
};
var Mf = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = Li.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t10 = wt();
    this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Pu(["r", "c", "d"], e) : ps(["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));
        }

        ${t10.output} = result;
      }
    `;
  }
};
var Lf = class {
  constructor(e) {
    this.variableNames = ["A"], this.outTexUsage = ur.DOWNLOAD;
    let t10 = wt();
    this.outputShape = e, this.userCode = `
      ${Ff}

      void main() {
        float x = getAAtOutCoords();
        ${t10.output} = encode_float(x);
      }
    `;
  }
};
var Bf = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = ur.DOWNLOAD;
    let t10 = wt();
    this.outputShape = e, this.userCode = `
      ${Ff}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t10.output} = encode_float(x);
      }
    `;
  }
};
var U8 = { R: 0, G: 1, B: 2, A: 3 };
var Tl = class {
  constructor(e, t10 = false, o = "RGBA") {
    this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let n = wt();
    this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length);
    let s = "result";
    t10 && (s = "floor(result * 255. + 0.5)");
    let a = "";
    for (let i = 0; i < o.length; i++) {
      let p = o[i];
      a += `
          if(offset == ${i}) {
            result = values[${U8[p]}];
          }`;
    }
    this.userCode = `
      ${this.enableShapeUniforms ? ac() : sc(e)}

      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${o.length});

        flatIndex = idiv(flatIndex, ${o.length}, 1.);

        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${n.texture2D}(A, uv);
          ${a}
        }
        ${n.output} = vec4(${s}, 0., 0., 0.);
      }
    `;
  }
};
var Vf = class {
  constructor(e, t10 = false) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let o = wt();
    this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length);
    let n = "", s = "result";
    t10 && (s = "floor(result * 255. + 0.5)");
    for (let a = 0; a <= 1; a++)
      for (let i = 0; i <= 1; i++) {
        let p = a * 2 + i;
        n += `
          localCoords = coords;
          if(localCoords[2] + ${i} < ${this.enableShapeUniforms ? "outShape[2]" : `${e[2]}`}) {
          localCoords[2] += ${i};
          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 = ${o.texture2D}(A, uv);

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

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

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

          ${n}

          ${o.output} = ${s};
        }
    `;
  }
};
var $w = {};
Ge($w, { bindVertexProgramAttributeStreams: () => Sw, createBufferFromOutputTexture: () => vw, createFloat16MatrixTexture: () => xw, createFloat16PackedMatrixTexture: () => Cw, createFloat32MatrixTexture: () => gw, createIndexBuffer: () => hw, createPackedMatrixTexture: () => bw, createUnsignedBytesMatrixTexture: () => yw, createVertexBuffer: () => fw, createVertexShader: () => dw, downloadByteEncodedFloatMatrixFromOutputTexture: () => Nw, downloadFloat32MatrixFromBuffer: () => kw, downloadMatrixFromPackedOutputTexture: () => _w, downloadPackedMatrixFromBuffer: () => Tw, getInternalFormatForFloat16MatrixTexture: () => Wf, getInternalFormatForFloat16PackedMatrixTexture: () => Hf, getInternalFormatForFloat32MatrixTexture: () => zf, getInternalFormatForPackedMatrixTexture: () => Gf, getInternalFormatForUnsignedBytesMatrixTexture: () => Uf, uploadDenseMatrixToTexture: () => ww, uploadPixelDataToTexture: () => Iw });
function dw(r) {
  let e = wt(), t10 = `${e.version}
    precision highp float;
    ${e.attribute} vec3 clipSpacePos;
    ${e.attribute} vec2 uv;
    ${e.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;
  return qS(r, t10);
}
function fw(r) {
  let e = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
  return QS(r, e);
}
function hw(r) {
  let e = new Uint16Array([0, 1, 2, 2, 1, 3]);
  return ZS(r, e);
}
function _l(r, e, t10, o, n, s) {
  ew(e, t10);
  let a = JS(r), i = r.TEXTURE_2D;
  return pe(r, () => r.bindTexture(i, a)), pe(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), pe(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), pe(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), pe(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), O().getNumber("WEBGL_VERSION") === 1 ? pe(r, () => r.texImage2D(i, 0, o, e, t10, 0, n, s, null)) : pe(r, () => r.texStorage2D(i, 1, o, e, t10)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t10, e] };
}
function zf(r) {
  return r.internalFormatFloat;
}
function gw(r, e, t10, o) {
  let [n, s] = Ou(e, t10);
  return _l(r, n, s, zf(o), o.textureFormatFloat, r.FLOAT);
}
function Wf(r) {
  return r.internalFormatHalfFloat;
}
function xw(r, e, t10, o) {
  let [n, s] = Ou(e, t10);
  return _l(r, n, s, Wf(o), o.textureFormatFloat, o.textureTypeHalfFloat);
}
function Uf(r) {
  return r.downloadTextureFormat;
}
function yw(r, e, t10, o) {
  let [n, s] = Ou(e, t10);
  return _l(r, n, s, Uf(o), r.RGBA, r.UNSIGNED_BYTE);
}
function Gf(r) {
  return r.internalFormatPackedFloat;
}
function bw(r, e, t10, o) {
  let [n, s] = Qs(e, t10);
  return _l(r, n, s, Gf(o), r.RGBA, r.FLOAT);
}
function Hf(r) {
  return r.internalFormatPackedHalfFloat;
}
function Cw(r, e, t10, o) {
  let [n, s] = Qs(e, t10);
  return _l(r, n, s, Hf(o), r.RGBA, o.textureTypeHalfFloat);
}
function Sw(r, e, t10) {
  return pe(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), Af(r, e, "clipSpacePos", t10, 3, 20, 0) && Af(r, e, "uv", t10, 2, 20, 12);
}
function ww(r, e, t10, o, n, s) {
  pe(r, () => r.bindTexture(r.TEXTURE_2D, e));
  let a, i, p;
  n instanceof Uint8Array ? (a = new Uint8Array(t10 * o * 4), i = r.UNSIGNED_BYTE, p = r.RGBA) : (a = new Float32Array(t10 * o * 4), i = r.FLOAT, p = s.internalFormatPackedFloat), a.set(n), O().getNumber("WEBGL_VERSION") === 2 ? pe(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10, o, r.RGBA, i, a)) : pe(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t10, o, 0, r.RGBA, i, a)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function Iw(r, e, t10) {
  pe(r, () => r.bindTexture(r.TEXTURE_2D, e)), t10.data instanceof Uint8Array ? O().getNumber("WEBGL_VERSION") === 2 ? pe(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10.width, t10.height, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : pe(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t10.width, t10.height, 0, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : O().getNumber("WEBGL_VERSION") === 2 ? pe(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t10)) : pe(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t10)), pe(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function vw(r, e, t10, o) {
  let n = r.createBuffer();
  pe(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n));
  let i = 4 * 4 * e * t10;
  return pe(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), pe(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, 0)), pe(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n;
}
function kw(r, e, t10) {
  let o = r, n = new Float32Array(t10);
  return o.bindBuffer(o.PIXEL_PACK_BUFFER, e), o.getBufferSubData(o.PIXEL_PACK_BUFFER, 0, n), o.bindBuffer(o.PIXEL_PACK_BUFFER, null), n;
}
function Nw(r, e, t10, o) {
  let [n, s] = Ou(e, t10), a = 4, i = new Uint8Array(k$(e * t10, a));
  return pe(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer);
}
function Tw(r, e, t10, o, n, s, a, i) {
  let p = r, u = new Float32Array(N$(s, a));
  return p.bindBuffer(p.PIXEL_PACK_BUFFER, e), p.getBufferSubData(p.PIXEL_PACK_BUFFER, 0, u), p.bindBuffer(p.PIXEL_PACK_BUFFER, null), u;
}
function _w(r, e, t10) {
  let o = new Float32Array(e * t10 * 4);
  return pe(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, o)), o;
}
var Lu = class {
  constructor(e) {
    this.outputTexture = null, this.program = null, this.disposed = false, this.itemsToPoll = [];
    let t10 = O().getNumber("WEBGL_VERSION");
    if (e != null ? (this.gl = e, US(t10, e)) : this.gl = zr(t10), e = this.gl, O().getNumber("WEBGL_VERSION") === 2) {
      let s = e;
      this.createVertexArray = () => pe(s, () => s.createVertexArray()), this.bindVertexArray = (a) => pe(s, () => s.bindVertexArray(a)), this.deleteVertexArray = (a) => pe(s, () => s.deleteVertexArray(a)), this.getVertexArray = () => pe(s, () => s.getParameter(s.VERTEX_ARRAY_BINDING));
    } else if (e != null) {
      let s = e.getExtension("OES_vertex_array_object");
      if (s == null)
        throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");
      this.createVertexArray = () => pe(e, () => s.createVertexArrayOES()), this.bindVertexArray = (a) => pe(e, () => s.bindVertexArrayOES(a)), this.deleteVertexArray = (a) => pe(e, () => s.deleteVertexArrayOES(a)), this.getVertexArray = () => pe(e, () => e.getParameter(s.VERTEX_ARRAY_BINDING_OES));
    }
    let o = "WEBGL_color_buffer_float", n = "EXT_color_buffer_half_float";
    if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), O().getNumber("WEBGL_VERSION") === 1) {
      let s = "OES_texture_float", a = "OES_texture_half_float";
      if (this.textureFloatExtension = tc(this.gl, s), Wr(this.gl, a))
        this.textureHalfFloatExtension = tc(this.gl, a);
      else if (O().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(o), Wr(this.gl, n))
        this.colorBufferHalfFloatExtension = tc(this.gl, n);
      else if (O().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 (o = "EXT_color_buffer_float", Wr(this.gl, o))
      this.colorBufferFloatExtension = this.gl.getExtension(o);
    else if (Wr(this.gl, n))
      this.colorBufferHalfFloatExtension = this.gl.getExtension(n);
    else
      throw new Error("GL context does not support color renderable floats");
    this.vertexBuffer = fw(this.gl), this.indexBuffer = hw(this.gl), this.framebuffer = tw(this.gl), this.textureConfig = vl(this.gl, this.textureHalfFloatExtension);
  }
  get debug() {
    return O().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;
    pe(e, () => e.finish()), pe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), pe(e, () => e.deleteFramebuffer(this.framebuffer)), pe(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), pe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), pe(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true;
  }
  createFloat32MatrixTexture(e, t10) {
    return this.throwIfDisposed(), gw(this.gl, e, t10, this.textureConfig);
  }
  createFloat16MatrixTexture(e, t10) {
    return this.throwIfDisposed(), xw(this.gl, e, t10, this.textureConfig);
  }
  createUnsignedBytesMatrixTexture(e, t10) {
    return this.throwIfDisposed(), yw(this.gl, e, t10, this.textureConfig);
  }
  uploadPixelDataToTexture(e, t10) {
    this.throwIfDisposed(), Iw(this.gl, e, t10);
  }
  uploadDenseMatrixToTexture(e, t10, o, n) {
    this.throwIfDisposed(), ww(this.gl, e, t10, o, n, this.textureConfig);
  }
  createFloat16PackedMatrixTexture(e, t10) {
    return this.throwIfDisposed(), Cw(this.gl, e, t10, this.textureConfig);
  }
  createPackedMatrixTexture(e, t10) {
    return this.throwIfDisposed(), bw(this.gl, e, t10, this.textureConfig);
  }
  deleteMatrixTexture(e) {
    this.throwIfDisposed(), this.outputTexture === e && (Rf(this.gl, this.framebuffer), this.outputTexture = null), pe(this.gl, () => this.gl.deleteTexture(e));
  }
  downloadByteEncodedFloatMatrixFromOutputTexture(e, t10, o) {
    return this.downloadMatrixDriver(e, () => Nw(this.gl, t10, o, this.textureConfig));
  }
  downloadPackedMatrixFromBuffer(e, t10, o, n, s, a) {
    return Tw(this.gl, e, t10, o, n, s, a, this.textureConfig);
  }
  downloadFloat32MatrixFromBuffer(e, t10) {
    return kw(this.gl, e, t10);
  }
  createBufferFromTexture(e, t10, o) {
    this.bindTextureToFrameBuffer(e);
    let n = vw(this.gl, t10, o, this.textureConfig);
    return this.unbindTextureToFrameBuffer(), n;
  }
  createAndWaitForFence() {
    let e = this.createFence(this.gl);
    return this.pollFence(e);
  }
  createFence(e) {
    let t10, o;
    if (O().getBool("WEBGL_FENCE_API_ENABLED")) {
      let n = e, s = n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE, 0);
      e.flush(), o = () => {
        let a = n.clientWaitSync(s, 0, 0);
        return a === n.ALREADY_SIGNALED || a === n.CONDITION_SATISFIED;
      }, t10 = s;
    } else
      O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t10 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t10, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true;
    return { query: t10, isFencePassed: o };
  }
  downloadMatrixFromPackedTexture(e, t10, o) {
    return this.downloadMatrixDriver(e, () => _w(this.gl, t10, o));
  }
  createProgram(e) {
    this.throwIfDisposed();
    let t10 = this.gl;
    this.vertexShader == null && (this.vertexShader = dw(t10));
    let o = XS(t10);
    pe(t10, () => t10.attachShader(o, this.vertexShader)), pe(t10, () => t10.attachShader(o, e)), YS(t10, o);
    let n;
    return n = Object.assign(o, { vao: this.createVertexArray() }), this.bindVertexArray(n.vao), pe(t10, () => t10.bindBuffer(t10.ELEMENT_ARRAY_BUFFER, this.indexBuffer)), console.assert(Sw(t10, n, this.vertexBuffer), "gpgpu_util.bindVertexProgramAttributeStreams not fully successful."), this.debug && kl(t10, n), this.setProgram(n), n;
  }
  deleteProgram(e) {
    this.throwIfDisposed(), e === this.program && (this.program = null), e != null && (pe(this.gl, () => this.gl.deleteProgram(e)), this.deleteVertexArray(e.vao));
  }
  setProgram(e) {
    this.throwIfDisposed(), this.program = e, this.program != null && (this.bindVertexArray(this.program.vao), this.debug && kl(this.gl, this.program)), pe(this.gl, () => this.gl.useProgram(e));
  }
  getUniformLocation(e, t10, o = true) {
    return this.throwIfDisposed(), o ? rw(this.gl, e, t10) : ow(this.gl, e, t10);
  }
  getAttributeLocation(e, t10) {
    return this.throwIfDisposed(), pe(this.gl, () => this.gl.getAttribLocation(e, t10));
  }
  getUniformLocationNoThrow(e, t10) {
    return this.throwIfDisposed(), this.gl.getUniformLocation(e, t10);
  }
  setInputMatrixTexture(e, t10, o) {
    this.throwIfDisposed(), this.throwIfNoProgram(), nw(this.gl, e, t10, o);
  }
  setOutputMatrixTexture(e, t10, o) {
    this.setOutputMatrixTextureDriver(e, o, t10);
  }
  setOutputPackedMatrixTexture(e, t10, o) {
    this.throwIfDisposed();
    let [n, s] = Qs(t10, o);
    this.setOutputMatrixTextureDriver(e, n, s);
  }
  setOutputMatrixWriteRegion(e, t10, o, n) {
    this.setOutputMatrixWriteRegionDriver(o, e, n, t10);
  }
  setOutputPackedMatrixWriteRegion(e, t10, o, n) {
    throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
  }
  debugValidate() {
    this.program != null && kl(this.gl, this.program), rc(this.gl);
  }
  executeProgram() {
    this.throwIfDisposed(), this.throwIfNoProgram();
    let e = this.gl;
    if (this.debug) {
      let t10 = this.getVertexArray();
      console.assert(t10 === this.program.vao, "VAO changed between setProgram and executeProgram!"), this.debugValidate();
    }
    pe(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0));
  }
  blockUntilAllProgramsCompleted() {
    this.throwIfDisposed(), pe(this.gl, () => this.gl.finish());
  }
  getQueryTimerExtension() {
    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = tc(this.gl, O().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 (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
      let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.createQuery();
      return o.beginQuery(n.TIME_ELAPSED_EXT, s), s;
    }
    let e = this.getQueryTimerExtensionWebGL1(), t10 = e.createQueryEXT();
    return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t10), t10;
  }
  endQuery() {
    if (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
      let t10 = this.gl, o = this.getQueryTimerExtensionWebGL2();
      t10.endQuery(o.TIME_ELAPSED_EXT);
      return;
    }
    let e = this.getQueryTimerExtensionWebGL1();
    e.endQueryEXT(e.TIME_ELAPSED_EXT);
  }
  async waitForQueryAndGetTime(e) {
    return await y.repeatedTry(() => this.disposed || this.isQueryAvailable(e, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
  }
  getQueryTime(e, t10) {
    if (t10 === 0)
      return null;
    if (t10 === 2) {
      let o = this.gl;
      return o.getQueryParameter(e, o.QUERY_RESULT) / 1e6;
    } else {
      let o = this.getQueryTimerExtensionWebGL1();
      return o.getQueryObjectEXT(e, o.QUERY_RESULT_EXT) / 1e6;
    }
  }
  isQueryAvailable(e, t10) {
    if (t10 === 0)
      return true;
    if (t10 === 2) {
      let o = this.gl, n = this.getQueryTimerExtensionWebGL2(), s = o.getQueryParameter(e, o.QUERY_RESULT_AVAILABLE);
      return this.disjoint == null && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), s && !this.disjoint;
    } else {
      let o = this.getQueryTimerExtensionWebGL1(), n = o.getQueryObjectEXT(e, o.QUERY_RESULT_AVAILABLE_EXT);
      return this.disjoint == null && (this.disjoint = this.gl.getParameter(o.GPU_DISJOINT_EXT)), n && !this.disjoint;
    }
  }
  pollFence(e) {
    return new Promise((t10) => {
      this.addItemToPoll(() => e.isFencePassed(), () => t10());
    });
  }
  pollItems() {
    let e = G8(this.itemsToPoll.map((t10) => t10.isDoneFn));
    for (let t10 = 0; t10 <= e; ++t10) {
      let { resolveFn: o } = this.itemsToPoll[t10];
      o();
    }
    this.itemsToPoll = this.itemsToPoll.slice(e + 1);
  }
  addItemToPoll(e, t10) {
    if (this.itemsToPoll.push({ isDoneFn: e, resolveFn: t10 }), this.itemsToPoll.length > 1)
      return;
    let o;
    "setTimeoutCustom" in O().platform && (o = O().platform.setTimeoutCustom.bind(O().platform)), y.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o);
  }
  bindTextureToFrameBuffer(e) {
    this.throwIfDisposed(), Nl(this.gl, e, this.framebuffer), this.debug && rc(this.gl);
  }
  unbindTextureToFrameBuffer() {
    this.outputTexture != null ? (Nl(this.gl, this.outputTexture, this.framebuffer), this.debug && rc(this.gl)) : Rf(this.gl, this.framebuffer);
  }
  downloadMatrixDriver(e, t10) {
    this.bindTextureToFrameBuffer(e);
    let o = t10();
    return this.unbindTextureToFrameBuffer(), o;
  }
  setOutputMatrixTextureDriver(e, t10, o) {
    this.throwIfDisposed();
    let n = this.gl;
    Nl(n, e, this.framebuffer), this.debug && rc(n), this.outputTexture = e, pe(n, () => n.viewport(0, 0, t10, o)), pe(n, () => n.scissor(0, 0, t10, o));
  }
  setOutputMatrixWriteRegionDriver(e, t10, o, n) {
    this.throwIfDisposed(), pe(this.gl, () => this.gl.scissor(e, t10, o, n));
  }
  throwIfDisposed() {
    if (this.disposed)
      throw new Error("Attempted to use disposed GPGPUContext.");
  }
  throwIfNoProgram() {
    if (this.program == null)
      throw new Error("No GPU program is currently set.");
  }
};
function G8(r) {
  let e = 0;
  for (; e < r.length && r[e](); ++e)
    ;
  return e - 1;
}
var { addImpl: V$, bincountImpl: Kf, bincountReduceImpl: z$, castImpl: W$, ceilImpl: U$, concatImpl: G$, equalImpl: H$, expImpl: K$, expm1Impl: q$, floorImpl: j$, gatherNdImpl: X$, gatherV2Impl: Y$, greaterImpl: Q$, greaterEqualImpl: Z$, lessImpl: J$, lessEqualImpl: eE, linSpaceImpl: tE, logImpl: rE, maxImpl: oE, maximumImpl: nE, minimumImpl: sE, multiplyImpl: aE, negImpl: iE, notEqualImpl: uE, prodImpl: pE, raggedGatherImpl: cE, raggedRangeImpl: lE, raggedTensorToTensorImpl: mE, rangeImpl: dE, rsqrtImpl: fE, scatterImpl: hE, sigmoidImpl: gE, simpleAbsImpl: qf, sliceImpl: xE, sparseFillEmptyRowsImpl: yE, sparseReshapeImpl: bE, sparseSegmentReductionImpl: jf, sqrtImpl: CE, stridedSliceImpl: SE, stringNGramsImpl: wE, stringSplitImpl: IE, stringToHashBucketFastImpl: vE, subImpl: kE, tileImpl: NE, topKImpl: TE, transposeImpl: Bu, uniqueImpl: _E } = Zp;
function Ew(r, e) {
  return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t10) => `${r}.${t10}`);
}
function Et(r, e) {
  return e === 1 ? [r] : Ew(r, e);
}
function $E(r, e) {
  if (r === 1)
    return "rc";
  let t10 = "";
  for (let o = 0; o < r; o++)
    t10 += e[o], o < r - 1 && (t10 += ",");
  return t10;
}
var Xf = class {
  constructor(e) {
    if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = lt(this.outputShape.length), this.rank === 0)
      this.userCode = `
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;
    else {
      let t10 = Et("rc", this.rank), o = $e(this.rank), n = this.getOutOfBoundsCondition(t10), s = this.getSetup(t10), a = this.getOutput(t10);
      this.userCode = `
        void main() {
          ${o} rc = getOutputCoords();

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

            setOutput(vec4(${a}));
          }
        }
      `;
    }
  }
  getSourceCoordsArr(e) {
    let t10 = [];
    for (let o = 0; o <= 1; o++)
      for (let n = 0; n <= 1; n++) {
        let s = `${o === 0 ? "r" : "rp1"}, ${n === 0 ? "c" : "cp1"}`;
        for (let a = 2; a < this.rank; a++)
          s = `${e[e.length - 1 - a]},` + s;
        t10.push(s);
      }
    return t10;
  }
  getOutOfBoundsCondition(e) {
    if (this.rank === 1)
      return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
    let t10 = "";
    for (let o = this.rank - 2; o < this.rank; o++)
      t10 += `${e[o]} >= ${this.enableShapeUniforms ? `outShape[${o}]` : this.outputShape[o]}`, o < this.rank - 1 && (t10 += "||");
    return t10;
  }
  getSetup(e) {
    if (this.rank === 1)
      return "";
    let t10 = e.slice(-2), o = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1], n = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];
    return `
      int r = ${t10[0]};
      int c = ${t10[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${o};
      bool rEdge = rp1 >= ${n};
    `;
  }
  getOutput(e) {
    let t10 = this.getSourceCoordsArr(e);
    return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t10[0]}),
            cEdge ? 0. : getA(${t10[1]}),
            rEdge ? 0. : getA(${t10[2]}),
            rEdge || cEdge ? 0. : getA(${t10[3]})`;
  }
};
var mc = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length);
    let o = "";
    for (let n = 0; n < 4; n++) {
      let s = "thisRC = rc;";
      n % 2 === 1 && (s += "thisRC.z += 1;"), n > 1 && (s += "thisRC.y += 1;"), o += `
        ${s}
        ${n > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : ""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${n}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${n > 0 ? "}" : ""}
      `;
    }
    this.userCode = `
      ${H8(t10, this.enableShapeUniforms)}
      ${this.enableShapeUniforms ? ac() : sc(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]};

        ${o}

        setOutput(result);
      }
    `;
  }
};
function H8(r, e) {
  return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${e ? A$(["r", "c", "d"], "inputShape") : ps(["r", "c", "d"], r)}
      return ivec3(r, c, d);
    }
  `;
}
var Yf = class {
  constructor(e) {
    this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.logEnabled = false, this.usedTextures = {};
  }
  acquireTexture(e, t10, o) {
    let n = AE(t10, o), s = RE(e, n, o);
    s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []);
    let a = EE(e, n, this.gpgpu.gl, this.gpgpu.textureConfig, o);
    if (this.freeTextures[s].length > 0) {
      this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log();
      let p = this.freeTextures[s].shift();
      return this.usedTextures[s].push(p), p;
    }
    let i;
    return n === Zt.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === Zt.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === Zt.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === Zt.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === Zt.PACKED_4X1_UNSIGNED_BYTE && (i = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[s].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i;
  }
  releaseTexture(e, t10, o, n) {
    if (this.freeTextures == null)
      return;
    let s = AE(o, n), a = RE(t10, s, n);
    a in this.freeTextures || (this.freeTextures[a] = []);
    let i = EE(t10, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = O().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
    p !== -1 && this._numBytesAllocated > p ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--;
    let u = this.usedTextures[a], c = u.indexOf(e);
    if (c < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    u.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 t10 = this._numBytesFree / this._numBytesAllocated;
    console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t10)}%)`);
  }
  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((t10) => {
          this.gpgpu.deleteMatrixTexture(t10.texture);
        });
      for (let e in this.usedTextures)
        this.usedTextures[e].forEach((t10) => {
          this.gpgpu.deleteMatrixTexture(t10.texture);
        });
      this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0;
    }
  }
};
function K8(r, e) {
  let t10 = r;
  if (e === t10.R32F)
    return 4;
  if (e === t10.R16F)
    return 2;
  if (e === t10.RGBA32F)
    return 16;
  if (e === r.RGBA)
    return 16;
  if (e === t10.RGBA16F)
    return 8;
  if (e === t10.RGBA8)
    return 4;
  throw new Error(`Unknown internal format ${e}`);
}
function EE(r, e, t10, o, n) {
  let s = q8(e, o), a;
  if (n) {
    let [p, u] = Qs(r[0], r[1]);
    a = p * u;
  } else {
    let [p, u] = Ou(r[0], r[1]);
    a = p * u;
  }
  let i = K8(t10, s);
  return a * i;
}
function q8(r, e) {
  switch (r) {
    case Zt.PACKED_2X2_FLOAT32:
      return Gf(e);
    case Zt.PACKED_2X2_FLOAT16:
      return Hf(e);
    case Zt.UNPACKED_FLOAT32:
      return zf(e);
    case Zt.UNPACKED_FLOAT16:
      return Wf(e);
    case Zt.PACKED_4X1_UNSIGNED_BYTE:
      return Uf(e);
    default:
      throw new Error(`Unknown physical texture type ${r}`);
  }
}
function j8(r) {
  return O().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? Zt.PACKED_2X2_FLOAT32 : Zt.UNPACKED_FLOAT32 : r ? Zt.PACKED_2X2_FLOAT16 : Zt.UNPACKED_FLOAT16;
}
function AE(r, e) {
  if (r === ur.UPLOAD)
    return Zt.PACKED_2X2_FLOAT32;
  if (r === ur.RENDER || r == null)
    return j8(e);
  if (r === ur.DOWNLOAD || r === ur.PIXELS)
    return Zt.PACKED_4X1_UNSIGNED_BYTE;
  throw new Error(`Unknown logical texture type ${r}`);
}
function RE(r, e, t10) {
  return `${r[0]}_${r[1]}_${e}_${t10}`;
}
var Jt = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      float unaryOperation(float x) {
        ${t10}
      }

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

        setOutput(y);
      }
    `;
  }
};
var zt = "if (isnan(x)) return x;";
var DE = "return x;";
var Aw = "return abs(x);";
var FE = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var OE = zt + `
  return (x < 0.0) ? 0.0 : x;
`;
var PE = zt + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Zs = "return x;";
var ME = "return 1.0 / (1.0 + exp(-1.0 * x));";
var BE = "return x;";
var VE = `
  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;
`;
var zE = `
  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;
`;
var WE = `
  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;
`;
var UE = "return 1.0 / (1.0 + exp(-1.0 * x));";
var Er = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      vec4 unaryOperation(vec4 x) {
        ${t10}
      }

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

        setOutput(y);
      }
    `;
  }
};
var Qf = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length);
    let t10 = e.length, o = Et("rc", t10), n = $e(t10), s = $E(t10, o), a = o.slice(-2), i = t10 <= 1 ? "rc" : `vec2(${a.join(",")})`;
    this.userCode = `
      void main() {
        ${n} rc = getOutputCoords();
        vec4 packedInput = getA(${s});

        setOutput(getChannel(packedInput, ${i}));
      }
    `;
  }
};
var Y8 = Vt.whereImpl;
var Q8 = 1e-7;
var Z8 = 1e-4;
var Zf = {};
function J8(r) {
  return r in Zf || (Zf[r] = {}), Zf[r];
}
var eY = O().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var tY = 600;
function rY() {
  return O().global.screen == null ? 1024 : O().global.screen.height * O().global.screen.width * window.devicePixelRatio * tY / 1024 / 1024;
}
var Vi = class extends Zr {
  constructor(e) {
    if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !O().getBool("HAS_WEBGL"))
      throw new Error("WebGL is not supported on this device");
    let t10;
    if (e != null) {
      if (e instanceof Lu)
        t10 = e;
      else {
        let o = zr(O().getNumber("WEBGL_VERSION"), e);
        t10 = new Lu(o);
      }
      this.binaryCache = {}, this.gpgpuCreatedLocally = false;
    } else {
      let o = zr(O().getNumber("WEBGL_VERSION"));
      t10 = new Lu(o), this.binaryCache = J8(O().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
    }
    this.gpgpu = t10, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new Yf(this.gpgpu), this.numMBBeforeWarning = rY(), this.texData = new Fo(this, sr());
  }
  nextDataId() {
    return Vi.nextDataId++;
  }
  numDataIds() {
    return this.texData.numDataIds() - this.pendingDeletes;
  }
  writeTexture(e, t10, o, n, s, a) {
    let i = this.makeTensorInfo(t10, o), p = this.texData.get(i.dataId);
    p.isPacked = false, p.texture = { texture: e, texShape: [n, s] }, p.texShape = [n, s];
    let u = oc(t10), c = new Tl(u, false, a), l = this.runWebGLProgram(c, [i], o, [[n, s]]);
    return l.shape = t10, p.texture = null, this.disposeIntermediateTensorInfo(i), l.dataId;
  }
  write(e, t10, o) {
    if ((O().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || O().getBool("DEBUG")) && this.checkNumericalProblems(e), o === "complex64" && e != null)
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    let n = { id: this.nextDataId() };
    return this.texData.set(n, { shape: t10, dtype: o, values: e, usage: ur.UPLOAD, refCount: 1 }), n;
  }
  refCount(e) {
    return this.texData.has(e) ? this.texData.get(e).refCount : 0;
  }
  incRef(e) {
    let t10 = this.texData.get(e);
    t10.refCount++;
  }
  decRef(e) {
    if (this.texData.has(e)) {
      let t10 = this.texData.get(e);
      t10.refCount--;
    }
  }
  move(e, t10, o, n, s) {
    if (O().getBool("DEBUG") && this.checkNumericalProblems(t10), n === "complex64")
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    this.texData.set(e, { shape: o, dtype: n, values: t10, usage: ur.UPLOAD, refCount: s });
  }
  disposeIntermediateTensorInfo(e) {
    this.disposeData(e.dataId);
  }
  readSync(e) {
    let t10 = this.texData.get(e), { values: o, dtype: n, complexTensorInfos: s, slice: a, shape: i, isPacked: p } = t10;
    if (a != null) {
      let m;
      p ? m = new Er(i, Zs) : m = new Jt(i, Zs);
      let d = this.runWebGLProgram(m, [{ dataId: e, shape: i, dtype: n }], n), f = this.readSync(d.dataId);
      return this.disposeIntermediateTensorInfo(d), f;
    }
    if (o != null)
      return this.convertAndCacheOnCPU(e);
    if (n === "string")
      return o;
    let u = this.activeTimers != null, c;
    u && (c = y.now());
    let l;
    if (n === "complex64") {
      let m = this.readSync(s.real.dataId), d = this.readSync(s.imag.dataId);
      l = S.mergeRealAndImagArrays(m, d);
    } else
      l = this.getValuesFromTexture(e);
    return u && (this.downloadWaitMs += y.now() - c), this.convertAndCacheOnCPU(e, l);
  }
  async read(e) {
    if (this.pendingRead.has(e)) {
      let f = this.pendingRead.get(e);
      return new Promise((h) => f.push(h));
    }
    let t10 = this.texData.get(e), { values: o, shape: n, slice: s, dtype: a, complexTensorInfos: i, isPacked: p } = t10;
    if (s != null) {
      let f;
      p ? f = new Er(n, Zs) : f = new Jt(n, Zs);
      let h = this.runWebGLProgram(f, [{ dataId: e, shape: n, dtype: a }], a), g = this.read(h.dataId);
      return this.disposeIntermediateTensorInfo(h), g;
    }
    if (o != null)
      return this.convertAndCacheOnCPU(e);
    if (O().getBool("DEBUG") && !O().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && O().getNumber("WEBGL_VERSION") === 2)
      throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");
    let u = null, c;
    if (a !== "complex64" && O().get("WEBGL_BUFFER_SUPPORTED")) {
      c = this.decode(e);
      let f = this.texData.get(c.dataId);
      u = this.gpgpu.createBufferFromTexture(f.texture.texture, ...Il(n));
    }
    this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence();
    let l;
    if (a === "complex64") {
      let f = await Promise.all([this.read(i.real.dataId), this.read(i.imag.dataId)]), h = f[0], g = f[1];
      l = S.mergeRealAndImagArrays(h, g);
    } else if (u == null)
      l = this.getValuesFromTexture(e);
    else {
      let f = y.sizeFromShape(n);
      l = this.gpgpu.downloadFloat32MatrixFromBuffer(u, f);
    }
    if (c != null && this.disposeIntermediateTensorInfo(c), u != null) {
      let f = this.gpgpu.gl;
      pe(f, () => f.deleteBuffer(u));
    }
    let m = this.convertAndCacheOnCPU(e, l), d = this.pendingRead.get(e);
    return this.pendingRead.delete(e), d.forEach((f) => f(m)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && sr().removeDataId(e, this), this.pendingDeletes--), m;
  }
  readToGPU(e, t10 = {}) {
    let o = this.texData.get(e), { values: n, shape: s, slice: a, dtype: i, isPacked: p, texture: u } = o;
    if (i === "complex64")
      throw new Error("Does not support reading texture for complex64 dtype.");
    if (a != null) {
      let d;
      p ? d = new Er(s, Zs) : d = new Jt(s, Zs);
      let f = this.runWebGLProgram(d, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(f, t10);
      return this.disposeIntermediateTensorInfo(f), h;
    }
    if (u == null)
      throw n != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
    let c = this.decode(e, t10.customTexShape), l = sr().makeTensorFromTensorInfo(c), m = this.texData.get(c.dataId);
    return Object.assign({ tensorRef: l }, m.texture);
  }
  bufferSync(e) {
    let t10 = this.readSync(e.dataId);
    if (e.dtype === "string")
      try {
        let o = t10.map((n) => y.decodeString(n));
        return le(e.shape, e.dtype, o);
      } catch (o) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return le(e.shape, e.dtype, t10);
  }
  checkNumericalProblems(e) {
    if (e != null)
      for (let t10 = 0; t10 < e.length; t10++) {
        let o = e[t10];
        if (!KS(o))
          throw O().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${o} cannot be represented on this device.`);
      }
  }
  getValuesFromTexture(e) {
    let { shape: t10, dtype: o, isPacked: n } = this.texData.get(e), s = y.sizeFromShape(t10);
    if (O().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
      let m = this.decode(e), d = this.texData.get(m.dataId), f = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Il(t10)).subarray(0, s);
      return this.disposeIntermediateTensorInfo(m), f;
    }
    let a = O().getBool("WEBGL_PACK") && n === true, i = a ? oc(t10) : t10, p = a ? new Bf(i) : new Lf(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s);
    return this.disposeIntermediateTensorInfo(u), l;
  }
  timerAvailable() {
    return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
  }
  time(e) {
    let t10 = this.activeTimers, o = [], n = false;
    this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e();
    let s = y.flatten(this.activeTimers.map((p) => p.query)).filter((p) => p != null), a = y.flatten(this.activeTimers.map((p) => p.name)).filter((p) => p != null);
    this.activeTimers = t10, n && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
    return (async () => {
      if (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        let p = await Promise.all(s);
        i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", ");
      } else
        i.kernelMs = { error: "WebGL query timers are not supported in this environment." };
      return this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
    })();
  }
  memory() {
    return { unreliable: false, numBytesInGPU: this.numBytesInGPU, numBytesInGPUAllocated: this.textureManager.numBytesAllocated, numBytesInGPUFree: this.textureManager.numBytesFree };
  }
  startTimer() {
    return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: y.now(), endMs: null };
  }
  endTimer(e) {
    return O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = y.now(), e);
  }
  async getQueryTime(e) {
    if (O().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0)
      return this.gpgpu.waitForQueryAndGetTime(e);
    let t10 = e;
    return t10.endMs - t10.startMs;
  }
  disposeData(e, t10 = false) {
    if (this.pendingDisposal.has(e))
      return false;
    if (!this.texData.has(e))
      return true;
    if (t10 ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t10 && this.texData.get(e).refCount > 0)
      return false;
    if (this.pendingRead.has(e))
      return this.pendingDisposal.add(e), this.pendingDeletes++, false;
    this.releaseGPUData(e);
    let { complexTensorInfos: o } = this.texData.get(e);
    return o != null && (this.disposeData(o.real.dataId, t10), this.disposeData(o.imag.dataId, t10)), this.texData.delete(e), true;
  }
  releaseGPUData(e) {
    let { texture: t10, dtype: o, texShape: n, usage: s, isPacked: a, slice: i } = this.texData.get(e), p = i && i.origDataId || e, u = this.dataRefCount.get(p);
    u > 1 ? this.dataRefCount.set(p, u - 1) : (this.dataRefCount.delete(p), t10 != null && (this.numBytesInGPU -= this.computeBytes(n, o), this.textureManager.releaseTexture(t10, n, s, a)));
    let c = this.texData.get(e);
    c.texture = null, c.texShape = null, c.isPacked = false, c.slice = null;
  }
  getTexture(e) {
    return this.uploadToGPU(e), this.texData.get(e).texture.texture;
  }
  getDataInfo(e) {
    return this.texData.get(e);
  }
  shouldExecuteOnCPU(e, t10 = eY) {
    return O().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && y.sizeFromShape(o.shape) < t10);
  }
  getGPGPUContext() {
    return this.gpgpu;
  }
  where(e) {
    S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
    let t10 = e.dataSync();
    return Y8(e.shape, t10);
  }
  packedUnaryOp(e, t10, o) {
    let n = new Er(e.shape, t10), s = this.compileAndRun(n, [e], o);
    return sr().makeTensorFromTensorInfo(s);
  }
  abs(e) {
    if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
      let n = qf(this.texData.get(e.dataId).values);
      return this.makeOutput(e.shape, e.dtype, n);
    }
    if (O().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
      return this.packedUnaryOp(e, Aw, e.dtype);
    let t10 = new Jt(e.shape, Aw), o = this.compileAndRun(t10, [e]);
    return sr().makeTensorFromTensorInfo(o);
  }
  makeTensorInfo(e, t10, o) {
    let n;
    if (t10 === "string" && o != null && o.length > 0 && y.isString(o[0])) {
      let s = o.map((a) => y.encodeString(a));
      n = this.write(s, e, t10);
    } else
      n = this.write(o, e, t10);
    return this.texData.get(n).usage = null, { dataId: n, shape: e, dtype: t10 };
  }
  makeOutput(e, t10, o) {
    return sr().makeTensorFromTensorInfo(this.makeTensorInfo(e, t10, o), this);
  }
  unpackTensor(e) {
    let t10 = new Qf(e.shape);
    return this.runWebGLProgram(t10, [e], e.dtype);
  }
  packTensor(e) {
    let t10 = new Xf(e.shape), o = true;
    return this.runWebGLProgram(t10, [e], e.dtype, null, o);
  }
  packedReshape(e, t10) {
    let o = [Ua(e.shape), ...Ga(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [Ua(t10), ...Ga(t10)], a = new mc(s, o), i = true, p = [o], u = this.runWebGLProgram(a, [n], e.dtype, p, i);
    return { dataId: u.dataId, shape: t10, dtype: u.dtype };
  }
  decode(e, t10) {
    let o = this.texData.get(e), { isPacked: n, shape: s, dtype: a } = o;
    if (t10 != null) {
      let m = y.sizeFromShape(s), d = t10[0] * t10[1] * 4;
      y.assert(m <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
    }
    let i = oc(s), p;
    n ? p = new Mf(i) : p = new Pf(i);
    let u = true, c = [t10 != null ? t10 : Il(i)], l = this.runWebGLProgram(p, [{ shape: i, dtype: a, dataId: e }], a, c, u, t10);
    return { dtype: a, shape: s, dataId: l.dataId };
  }
  runWebGLProgram(e, t10, o, n, s = false, a) {
    let i = this.makeTensorInfo(e.outputShape, o), p = this.texData.get(i.dataId);
    if (e.packedOutput && (p.isPacked = true), e.outPackingScheme === Li.DENSE) {
      let x = a != null ? a : Il(e.outputShape);
      p.texShape = x.map((b) => b * 2);
    }
    if (e.outTexUsage != null && (p.usage = e.outTexUsage), y.sizeFromShape(i.shape) === 0)
      return p.values = y.getTypedArrayFromDType(i.dtype, 0), i;
    let u = [], c = t10.map((x) => {
      if (x.dtype === "complex64")
        throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
      let b = this.texData.get(x.dataId);
      if (b.texture == null) {
        if (!e.packedInputs && y.sizeFromShape(x.shape) <= O().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))
          return { shape: x.shape, texData: null, isUniform: true, uniformValues: b.values };
        e.packedInputs && (b.isPacked = true, b.shape = x.shape);
      }
      if (this.uploadToGPU(x.dataId), !!b.isPacked != !!e.packedInputs)
        x = b.isPacked ? this.unpackTensor(x) : this.packTensor(x), u.push(x), b = this.texData.get(x.dataId);
      else if (b.isPacked && !Bi(b.shape, x.shape)) {
        let C = x, w = x.shape;
        x.shape = b.shape, x = this.packedReshape(x, w), u.push(x), b = this.texData.get(x.dataId), C.shape = w;
      }
      return { shape: x.shape, texData: b, isUniform: false };
    });
    this.uploadToGPU(i.dataId);
    let l = { shape: i.shape, texData: p, isUniform: false }, m = B$(e, c, l), d = this.getAndSaveBinary(m, () => M$(this.gpgpu, e, c, l)), f = this.activeTimers != null, h;
    f && (h = this.startTimer()), O().get("ENGINE_COMPILE_ONLY") || L$(this.gpgpu, d, c, l, n), u.forEach((x) => this.disposeIntermediateTensorInfo(x)), f && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) }));
    let g = O().get("WEBGL_FLUSH_THRESHOLD");
    if (g > 0) {
      let x = y.now();
      x - this.lastGlFlushTime > g && (this.gpgpu.gl.flush(), this.lastGlFlushTime = x);
    }
    if (!O().getBool("WEBGL_LAZILY_UNPACK") && p.isPacked && s === false) {
      let x = this.unpackTensor(i);
      return this.disposeIntermediateTensorInfo(i), x;
    }
    return i;
  }
  compileAndRun(e, t10, o, n, s = false) {
    return o = o || t10[0].dtype, this.runWebGLProgram(e, t10, o, n, s);
  }
  getAndSaveBinary(e, t10) {
    return e in this.binaryCache || (this.binaryCache[e] = t10()), this.binaryCache[e];
  }
  getTextureManager() {
    return this.textureManager;
  }
  dispose() {
    this.disposed || (O().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t10) => {
      this.gpgpu.deleteProgram(this.binaryCache[t10].webGLProgram), delete this.binaryCache[t10];
    }), 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 = true);
  }
  floatPrecision() {
    return this.floatPrecisionValue == null && (this.floatPrecisionValue = Ee(() => {
      if (!O().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
        let e = O().getBool("DEBUG");
        O().set("DEBUG", false);
        let t10 = this.abs(Ce(1e-8)).dataSync()[0];
        if (O().set("DEBUG", e), t10 > 0)
          return 32;
      }
      return 16;
    })), this.floatPrecisionValue;
  }
  epsilon() {
    return this.floatPrecision() === 32 ? Q8 : Z8;
  }
  uploadToGPU(e) {
    let t10 = this.texData.get(e), { shape: o, dtype: n, values: s, texture: a, usage: i, isPacked: p } = t10;
    if (a != null)
      return;
    let u = this.activeTimers != null, c;
    u && (c = y.now());
    let l = t10.texShape;
    if (l == null && (l = sw(o, p), t10.texShape = l), s != null) {
      let m = oc(o), d, f = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray;
      (p || !g) && ([f, h] = Qs(l[0], l[1])), p ? d = new Vf(m, g) : d = new Tl(m, g);
      let x = g ? [h, f] : l, b = this.makeTensorInfo(x, n), C = this.texData.get(b.dataId);
      g ? C.usage = ur.PIXELS : C.usage = ur.UPLOAD, C.texShape = x, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), f, h, s);
      let w = [[h, f]], k = true, _ = this.runWebGLProgram(d, [b], n, w, k), E = this.texData.get(_.dataId);
      t10.texShape = E.texShape, t10.isPacked = E.isPacked, t10.usage = E.usage, O().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t10.texture = E.texture, t10.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += y.now() - c);
    } else {
      let m = this.acquireTexture(l, i, n, p);
      t10.texture = m;
    }
  }
  convertAndCacheOnCPU(e, t10) {
    let o = this.texData.get(e), { dtype: n } = o;
    return t10 != null && (o.values = oY(t10, n)), o.values;
  }
  acquireTexture(e, t10, o, n) {
    if (this.numBytesInGPU += this.computeBytes(e, o), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
      let s = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
      this.warnedAboutMemory = true, console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`);
    }
    return this.textureManager.acquireTexture(e, t10, n);
  }
  computeBytes(e, t10) {
    return e[0] * e[1] * y.bytesPerElement(t10);
  }
  checkCompileCompletion() {
    for (let [, e] of Object.entries(this.binaryCache))
      this.checkCompletion_(e);
  }
  async checkCompileCompletionAsync() {
    let e = [];
    if (this.gpgpu.parallelCompilationExtension) {
      for (let [, t10] of Object.entries(this.binaryCache))
        e.push(this.checkCompletionAsync_(t10));
      return Promise.all(e);
    } else {
      for (let [, t10] of Object.entries(this.binaryCache)) {
        let o = new Promise((n) => {
          try {
            this.checkCompletion_(t10), n(true);
          } catch (s) {
            throw s;
          }
        });
        e.push(o);
      }
      return Promise.all(e);
    }
  }
  async checkCompletionAsync_(e) {
    return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await AC(), this.checkCompletionAsync_(e));
  }
  checkCompletion_(e) {
    if (this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false)
      throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (Ef(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders.");
    return true;
  }
  getUniformLocations() {
    for (let [, e] of Object.entries(this.binaryCache)) {
      let { uniformLocations: t10, customUniformLocations: o, infLoc: n, nanLoc: s, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: p, outShapeStridesLocation: u, outTexShapeLocation: c } = mw(this.gpgpu, e.program, e.webGLProgram);
      e.uniformLocations = t10, e.customUniformLocations = o, e.infLoc = n, e.nanLoc = s, e.inShapesLocations = a, e.inTexShapesLocations = i, e.outShapeLocation = p, e.outShapeStridesLocation = u, e.outTexShapeLocation = c;
    }
  }
  createTensorFromGPUData(e, t10, o) {
    e.channels = e.channels || "RGBA";
    let { texture: n, height: s, width: a, channels: i } = e, p = sr().backend;
    if (!p.gpgpu.gl.isTexture(n))
      throw new Error("The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.");
    let u = p.writeTexture(n, t10, o, s, a, i);
    return sr().makeTensorFromDataId(u, t10, o, p);
  }
};
Vi.nextDataId = 0;
function oY(r, e) {
  if (e === "float32" || e === "complex64")
    return r;
  if (e === "int32" || e === "bool") {
    let t10 = e === "int32" ? new Int32Array(r.length) : new Uint8Array(r.length);
    for (let o = 0; o < t10.length; ++o)
      t10[o] = Math.round(r[o]);
    return t10;
  } else
    throw new Error(`Unknown dtype ${e}`);
}
var nY = "4.2.0";
function GE() {
  O().set("WEBGL_FORCE_F16_TEXTURES", true);
}
ki.isBrowser() && Ti("webgl", () => new Vi(), 2);
var CJe = { forceHalfFloat: GE };
var dc = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var io = class {
  constructor(e, t10, o) {
    this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      float binaryOperation(float a, float b) {
        ${e}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `;
  }
};
var Js = `
  result.r = isNaN.r ? NAN : result.r;
  result.g = isNaN.g ? NAN : result.g;
  result.b = isNaN.b ? NAN : result.b;
  result.a = isNaN.a ? NAN : result.a;
`;
var _o = class {
  constructor(e, t10, o, n = false) {
    this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = S.assertAndGetBroadcastShape(t10, o);
    let s = this.outputShape.length;
    this.enableShapeUniforms = lt(s);
    let a = "";
    if (n)
      if (s === 0 || y.sizeFromShape(this.outputShape) === 1)
        a = `
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;
      else if (a = `
          ${$e(s)} coords = getOutputCoords();
        `, s === 1)
        this.enableShapeUniforms ? a += `
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          ` : a += `
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;
      else {
        let p = Et("coords", s);
        this.enableShapeUniforms ? a += `
            bool nextRowOutOfBounds =
              (${p[s - 2]} + 1) >= outShape[${s} - 2];
            bool nextColOutOfBounds =
              (${p[s - 1]} + 1) >= outShape[${s} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          ` : a += `
            bool nextRowOutOfBounds =
              (${p[s - 2]} + 1) >= ${this.outputShape[s - 2]};
            bool nextColOutOfBounds =
              (${p[s - 1]} + 1) >= ${this.outputShape[s - 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 At(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
var HE = { kernelName: mo, backendName: "webgl", kernelFunc: At };
function Ar(r) {
  let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.texData.get(s.dataId), i = At({ inputs: { x: o }, backend: t10 }), p = At({ inputs: { x: n }, backend: t10 });
  return a.complexTensorInfos = { real: i, imag: p }, s;
}
var KE = { kernelName: ri, backendName: "webgl", kernelFunc: Ar };
var Rw = "return (a < 0.) ? b * a : a;";
var Dw = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function sY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t10.makeTensorInfo([], "float32", y.createScalarValue(s, "float32")), i = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new _o(Dw, n.shape, a.shape) : new io(Rw, n.shape, a.shape), p = t10.runWebGLProgram(i, [n, a], "float32");
  return t10.disposeIntermediateTensorInfo(a), p;
}
var qE = { kernelName: ln, backendName: "webgl", kernelFunc: sY };
var Fw = "return (a < 0.) ? b * a : a;";
var Ow = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function aY(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new _o(Ow, o.shape, n.shape) : new io(Fw, o.shape, n.shape);
  return t10.runWebGLProgram(s, [o, n], "float32");
}
var jE = { kernelName: Rn, backendName: "webgl", kernelFunc: aY };
var $o = "if (isnan(x)) return x;";
function he({ opSnippet: r, packedOpSnippet: e, cpuKernelImpl: t10, dtype: o }) {
  return ({ inputs: n, backend: s }) => {
    let { x: a } = n, i = s, p = o || a.dtype;
    if (i.shouldExecuteOnCPU([a]) && t10 != null) {
      let l = i.texData.get(a.dataId), m = t10(l.values, p);
      return i.makeTensorInfo(a.shape, p, m);
    }
    let u = O().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c;
    return u ? c = new Er(a.shape, e) : c = new Jt(a.shape, r), i.runWebGLProgram(c, [a], p);
  };
}
function tt({ opSnippet: r, packedOpSnippet: e, checkOutOfBounds: t10 = false, supportsComplex: o = false, cpuKernelImpl: n, dtype: s }) {
  return ({ inputs: a, backend: i }) => {
    let { a: p, b: u } = a, c = i;
    if (o && p.dtype === "complex64") {
      let f = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, x] = [[f.complexTensorInfos.real, h.complexTensorInfos.real], [f.complexTensorInfos.imag, h.complexTensorInfos.imag]].map((C) => {
        let [w, k] = C, _ = { dataId: w.dataId, dtype: w.dtype, shape: p.shape }, E = { dataId: k.dataId, dtype: k.dtype, shape: u.shape }, A = new io(r, p.shape, u.shape);
        return c.runWebGLProgram(A, [_, E], dt(w.dtype, k.dtype));
      }), b = Ar({ inputs: { real: g, imag: x }, backend: c });
      return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(x), b;
    }
    let l = s || dt(p.dtype, u.dtype);
    if ((p.dtype === "string" || u.dtype === "string" || c.shouldExecuteOnCPU([p, u])) && n != null) {
      let f = c.texData.get(p.dataId).values, h = c.texData.get(u.dataId).values, g = p.dtype === "string" ? S.fromUint8ToStringArray(f) : f, x = p.dtype === "string" ? S.fromUint8ToStringArray(h) : h, [b, C] = n(p.shape, u.shape, g, x, l), w = c.makeTensorInfo(C, l), k = c.texData.get(w.dataId);
      return k.values = b, w;
    }
    let m = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, d;
    return m ? d = new _o(e, p.shape, u.shape, t10) : d = new io(r, p.shape, u.shape), c.runWebGLProgram(d, [p, u], l);
  };
}
function Ha(r, e = false) {
  if (r === "linear")
    return e ? BE : DE;
  if (r === "relu")
    return e ? zE : OE;
  if (r === "elu")
    return e ? VE : FE;
  if (r === "relu6")
    return e ? WE : PE;
  if (r === "prelu")
    return e ? Ow : Fw;
  if (r === "leakyrelu")
    return e ? Dw : Rw;
  if (r === "sigmoid")
    return e ? UE : ME;
  throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`);
}
var fc = class {
  constructor(e, t10, o, n = false, s = false, a = false, i = null, p = false, u = false) {
    this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = o, this.enableShapeUniforms = lt(this.outputShape.length);
    let c = n ? e[1] : e[2], l = Math.ceil(c / 2), m = n ? "i * 2, rc.y" : "rc.y, i * 2", d = s ? "rc.z, i * 2" : "i * 2, rc.z", f = n ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], h = s ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], g = "", x = "";
    i && (p ? g = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }` : u ? g = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }` : g = `vec4 activation(vec4 x) {
          ${i}
        }`, x = "result = activation(result);");
    let b = a ? "result += getBiasAtOutCoords();" : "";
    a && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha");
    let C = "rc.x", w = "rc.x";
    e[0] < t10[0] ? C = `imod(rc.x, ${e[0]})` : t10[0] < e[0] && (w = `imod(rc.x, ${t10[0]})`), this.userCode = `
      ${g}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${l}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        int batchA = ${C};
        int batchB = ${w};
        for (int i = 0; i < ${l}; i++) {
          vec4 a = getMatrixA(batchA, ${m});
          vec4 b = getMatrixB(batchB, ${d});

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

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

        ${b}

        ${x}

        setOutput(result);
      }
    `;
  }
};
var Pw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var $l = class {
  constructor(e, t10, o) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = S.assertAndGetBroadcastShape(t10, o), 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));
      }
    `;
  }
};
var XE = "return a * b;";
function El(r) {
  let { inputs: e, backend: t10 } = r, { a: o, b: n } = e, s = S.upcastType(o.dtype, n.dtype);
  if (o.dtype === "complex64") {
    let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), u = new $l(Pw.REAL, o.shape, n.shape), c = new $l(Pw.IMAG, o.shape, n.shape), l = [{ dataId: i.complexTensorInfos.real.dataId, dtype: i.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: i.complexTensorInfos.imag.dataId, dtype: i.complexTensorInfos.imag.dtype, shape: o.shape }, { dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: n.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: n.shape }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Ar({ inputs: { real: m, imag: d }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f;
  }
  if (t10.shouldExecuteOnCPU([o, n])) {
    let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), [u, c] = aE(o.shape, n.shape, i.values, p.values, s), l = t10.makeTensorInfo(c, s), m = t10.texData.get(l.dataId);
    return m.values = u, l;
  }
  let a;
  return O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new _o(XE, o.shape, n.shape) : a = new io(XE, o.shape, n.shape), t10.runWebGLProgram(a, [o, n], s);
}
var YE = { kernelName: kn, backendName: "webgl", kernelFunc: El };
function QE(r, e, t10) {
  let o = [Ua(r.shape), ...Ga(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [Ua(e), ...Ga(e)], a = new mc(s, o), i = true, p = [o], u = t10.runWebGLProgram(a, [n], r.dtype, p, i);
  return { dataId: u.dataId, shape: e, dtype: u.dtype };
}
function re(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t10, i = y.sizeFromShape(n.shape), p = y.inferFromImplicitShape(s, i), u = y.sizeFromShape(p);
  y.assert(i === u, () => `The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);
  let c = a.texData.get(n.dataId);
  return c.isPacked && !Bi(n.shape, p) && !(c.texture !== null && Bi(c.shape, p)) ? QE(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype });
}
var ZE = { kernelName: _s, backendName: "webgl", kernelFunc: re };
var Al = class {
  constructor(e, t10) {
    this.variableNames = ["x"];
    let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e;
    this.outputShape = [n, a];
    let i = Math.floor(o / 4) * 4, p = o % 4, u = "sumValue += dot(values, ones);";
    if (t10 != null) {
      let l = 1 / t10;
      u = `sumValue += dot(values * ${y.isInt(l) ? l.toPrecision(2) : l}, ones);`;
    }
    let c = "";
    s % o > 0 && (c = `
        if (inIdx < 0 || inIdx >= ${s}) {
          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 * ${o};

        float sumValue = 0.0;

        for (int i = 0; i < ${i}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${u}
        }

        int inIdx = inOffset + ${i};
        if (${p === 1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${u}
        } else if (${p === 2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${u}
        } else if (${p === 3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${u}
        }
        setOutput(sumValue);
      }
    `;
  }
};
var Jf = class {
  constructor(e, t10) {
    this.variableNames = ["x"];
    let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e;
    this.outputShape = [n, a];
    let i = "0.0", p = "";
    t10 === "prod" ? i = "1.0" : t10 === "min" ? (i = "1.0 / 1e-20", p = "min") : t10 === "max" && (i = "-1.0 / 1e-20", p = "max");
    let u = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t10 === "sum" ? u = "sumValue" : t10 === "prod" ? u = "prodValue" : t10 === "all" ? u = "allValue" : t10 === "any" && (u = "anyValue");
    let c = Math.floor(o / 4) * 4, l = o % 4, m = `
      if (${t10 === "sum"}) {
        sumValue += dot(values, ones);
      } else if (${t10 === "prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${p}(values, minMaxValue);
        if (${t10 === "min"} || ${t10 === "max"}) {
          minMaxValue = ${p}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `, d = "vec4";
    t10 === "all" ? (i = "1.0", m = `
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `, d = "bvec4") : t10 === "any" && (i = "0.0", m = `
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `, d = "bvec4");
    let f = "";
    s % o > 0 && (f = `
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `), this.userCode = `
      const float initializationValue = ${i};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

        vec4 minMaxValue = vec4(${i});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${c}; i += 4) {
          int inIdx = inOffset + i;
          ${d} values = ${d}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${m}
        }

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

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

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

          ${m}
        }
        setOutput(${u});
      }
    `;
  }
};
function uY(r) {
  let e = [];
  for (; e.length === 0 || e[e.length - 1].outSize !== 1; ) {
    let t10 = e.length ? e[e.length - 1].outSize : r[1], o = S.computeOptimalWindowSize(t10);
    e.push({ inSize: t10, windowSize: o, outSize: Math.ceil(t10 / o) });
  }
  return e;
}
function Ur(r, e, t10, o) {
  let n = uY(r.shape), s = r;
  for (let a = 0; a < n.length; a++) {
    let { inSize: i, windowSize: p, outSize: u } = n[a], c, l;
    t10 === "mean" ? c = a === 0 ? new Al({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new Al({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new Jf({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, t10), l = s, s = o.runWebGLProgram(c, [s], e), l.dataId !== r.dataId && o.disposeIntermediateTensorInfo(l);
  }
  return s;
}
var eh = class {
  constructor(e, t10) {
    this.variableNames = ["A"];
    let o = new Array(e.length);
    for (let a = 0; a < o.length; a++)
      o[a] = e[t10[a]];
    this.outputShape = o, this.rank = o.length;
    let n = $e(this.rank), s = pY(t10);
    this.userCode = `
    void main() {
      ${n} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `;
  }
};
function pY(r) {
  let e = r.length;
  if (e > 6)
    throw Error(`Transpose for rank ${e} is not yet supported`);
  let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], o = new Array(e);
  for (let n = 0; n < r.length; n++)
    o[r[n]] = t10[n];
  return o.join();
}
var th = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true;
    let o = new Array(e.length);
    for (let c = 0; c < o.length; c++)
      o[c] = e[t10[c]];
    if (this.outputShape = o, this.rank = o.length, this.rank > 6)
      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
    let n = $e(this.rank), s = Ew("rc", this.rank), a = new Array(this.rank);
    for (let c = 0; c < t10.length; c++)
      a[t10[c]] = s[c];
    let i = `vec2(${a.slice(-2).join()})`, p = `++${s[this.rank - 1]} < ${o[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`;
    this.userCode = `
    void main() {
      ${n} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${u};
      if(${p}) {
        result[1] = ${u};
      }
      --${s[this.rank - 1]};
      if(++${s[this.rank - 2]} < ${o[this.rank - 2]}) {
        result[2] = ${u};
        if(${p}) {
          result[3] = ${u};
        }
      }
      setOutput(result);
    }
    `;
  }
};
function zi(r, e, t10) {
  let o = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new th(r.shape, e) : new eh(r.shape, e);
  return t10.runWebGLProgram(o, [r], r.dtype);
}
function JE(r, e, t10, o) {
  let n = e, s = r.shape.length, a = y.parseAxisParam(n, r.shape), i = a, p = S.getAxesPermutation(i, s), u = p != null, c = r;
  u && (c = zi(r, p, o), i = S.getInnerMostAxes(i.length, s)), S.assertAxesAreInnerMostDims("sum", i, s);
  let [l, m] = S.computeOutAndReduceShapes(c.shape, i), d = l;
  t10 && (d = S.expandShapeToKeepDim(l, a));
  let f = y.sizeFromShape(m), g = y.sizeFromShape(r.shape) / f, x = re({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: o }), b = Ta(r.dtype), C = Ur(x, b, "sum", o), w = re({ inputs: { x: C }, attrs: { shape: d }, backend: o });
  return o.disposeIntermediateTensorInfo(x), o.disposeIntermediateTensorInfo(C), u && o.disposeIntermediateTensorInfo(c), w;
}
function Vu(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return JE(n, s, a, t10);
}
var eA = { kernelName: Kn, backendName: "webgl", kernelFunc: Vu };
function yt(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i);
  for (let c = 0; c < p.length; c++)
    p[c] = n.shape[s[c]];
  let u;
  if (a.shouldExecuteOnCPU([n])) {
    let l = a.texData.get(n.dataId).values, m = Bu(l, n.shape, n.dtype, s, p);
    u = a.makeTensorInfo(p, n.dtype);
    let d = a.texData.get(u.dataId);
    d.values = m;
  } else
    u = zi(n, s, a);
  return u;
}
var tA = { kernelName: ro, backendName: "webgl", kernelFunc: yt };
var Mw = 1e3;
function zu({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) {
  let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), w = yr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]);
  y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`);
  let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = re({ inputs: { x: r }, backend: n, attrs: { shape: k } }), A = re({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), R = [E, A], D = Math.max(x, b), P = t10 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, V = p === "leakyrelu", z = p != null ? Ha(p, true) : null, U = M || L || V || z != null, K;
  if ((d === 1 || f === 1) && P > Mw && U === false) {
    let q = E, Z = A;
    t10 && (q = yt({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), R.push(q)), o && (Z = yt({ inputs: { x: A }, backend: n, attrs: { perm: [0, 2, 1] } }), R.push(Z));
    let ee = f !== 1, oe = f === 1, J = q;
    ee && (J = re({ inputs: { x: q }, backend: n, attrs: { shape: [D, P, 1] } }), R.push(J));
    let te = f === 1 ? 2 : 1, ie = Z;
    oe && (ie = re({ inputs: { x: Z }, backend: n, attrs: { shape: [D, 1, P] } }), R.push(ie));
    let ce = El({ inputs: { a: J, b: ie }, backend: n });
    K = Vu({ inputs: { x: ce }, backend: n, attrs: { axis: te, keepDims: true } }), R.push(ce);
  } else {
    let q = dt(r.dtype, e.dtype), Z = new fc(k, _, [D, d, f], t10, o, M, z, L, V), ee = [E, A];
    if (s != null && ee.push(s), L && ee.push(a), V) {
      let oe = n.makeTensorInfo([], "float32", y.createScalarValue(i, "float32"));
      ee.push(oe), R.push(oe);
    }
    K = n.runWebGLProgram(Z, ee, q);
  }
  let H = re({ inputs: { x: K }, backend: n, attrs: { shape: w } });
  R.push(K);
  for (let q of R)
    n.disposeIntermediateTensorInfo(q);
  return H;
}
function cY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
  return zu({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var rA = { kernelName: ho, backendName: "webgl", kernelFunc: cY };
var oA = "return abs(x);";
function lY(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (t10.shouldExecuteOnCPU([o]) && o.dtype !== "complex64") {
    let s = t10.texData.get(o.dataId), a = qf(s.values);
    return t10.makeTensorInfo(o.shape, o.dtype, a);
  }
  let n;
  return O().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Er(o.shape, oA) : n = new Jt(o.shape, oA), t10.runWebGLProgram(n, [o], o.dtype);
}
var nA = { kernelName: ys, backendName: "webgl", kernelFunc: lY };
var mY = zt + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
var dY = he({ opSnippet: mY });
var sA = { kernelName: aa, backendName: "webgl", kernelFunc: dY };
var fY = zt + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var hY = he({ opSnippet: fY });
var aA = { kernelName: ia, backendName: "webgl", kernelFunc: hY };
var iA = "return a + b;";
var gY = tt({ opSnippet: iA, packedOpSnippet: iA, supportsComplex: true, cpuKernelImpl: V$ });
var uA = { kernelName: eo, backendName: "webgl", kernelFunc: gY };
var rh = class {
  constructor(e, t10) {
    this.outputShape = [], this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`);
    let o = [];
    this.variableNames.forEach((s) => {
      o.push(`float v${s} = get${s}AtOutCoords();`);
    });
    let n = this.variableNames.map((s) => `v${s}`).join(" + ");
    this.userCode = `
      void main() {
        ${o.join(`
        `)}

        float result = ${n};
        setOutput(result);
      }
    `;
  }
};
var oh = class {
  constructor(e, t10) {
    this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t10.map((s, a) => `T${a}`);
    let o = [];
    this.variableNames.forEach((s) => {
      o.push(`vec4 v${s} = get${s}AtOutCoords();`);
    });
    let n = this.variableNames.map((s) => `v${s}`).join(" + ");
    this.userCode = `
      void main() {
        ${o.join(`
        `)}

        vec4 result = ${n};
        setOutput(result);
      }
    `;
  }
};
function nh(r) {
  let { inputs: e, backend: t10 } = r, o = e;
  if (o.length === 1)
    return At({ inputs: { x: o[0] }, backend: t10 });
  if (o.length > O().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
    let p = Math.floor(o.length / 2), u = nh({ inputs: o.slice(0, p), backend: t10 }), c = nh({ inputs: o.slice(p), backend: t10 });
    return nh({ inputs: [u, c], backend: t10 });
  }
  let n = o.map((p) => p.dtype).reduce((p, u) => dt(p, u)), s = o.map((p) => p.shape), i = O().getBool("WEBGL_PACK") ? new oh(o[0].shape, s) : new rh(o[0].shape, s);
  return t10.runWebGLProgram(i, o, n);
}
var pA = { kernelName: Po, backendName: "webgl", kernelFunc: nh };
function xY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n;
  c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, i)), S.assertAxesAreInnerMostDims("all", u, i);
  let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = Ur(h, h.dtype, "all", t10), x;
  if (a) {
    let b = S.expandShapeToKeepDim(m, p);
    x = re({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
  } else
    x = re({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x;
}
var cA = { kernelName: Mo, backendName: "webgl", kernelFunc: xY };
function yY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n;
  c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, i)), S.assertAxesAreInnerMostDims("any", u, i);
  let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = Ur(h, h.dtype, "any", t10), x;
  if (a) {
    let b = S.expandShapeToKeepDim(m, p);
    x = re({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
  } else
    x = re({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x;
}
var lA = { kernelName: Lo, backendName: "webgl", kernelFunc: yY };
var sh = class {
  constructor(e, t10, o) {
    this.variableNames = ["A"];
    let { windowSize: n, batchSize: s, outSize: a } = e;
    o || this.variableNames.push("bestIndicesA"), this.outputShape = [s, a];
    let i = t10 === "max" ? ">" : "<", p = o ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
    this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

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

        for (int i = 0; i < ${n}; i++) {
          int inIdx = ${p};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
  }
};
var ah = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, y.assert(e.length > 2, () => `Packed arg${o.charAt(0).toUpperCase() + o.slice(1)} supports only inputs with rank above 2.`);
    let s = e[e.length - 1], a = Math.ceil(s / t10);
    this.outputShape = e.slice(0, -1), a > 1 && this.outputShape.push(a), n || this.variableNames.push("bestIndicesA");
    let i = this.outputShape, p = i.length, u = $e(p), c = Et("coords", p), l, m;
    if (a === 1) {
      m = p + 1;
      let A = $e(m);
      l = `
        ${A} sourceLocR = ${A}(${c.join()}, 0);
        ++${c[p - 1]};
        ${A} sourceLocG = ${A}(${c.join()}, 0);
        ++${c[p - 2]};
        ${A} sourceLocA = ${A}(${c.join()}, 0);
        --${c[p - 1]};
        ${A} sourceLocB = ${A}(${c.join()}, 0);
        --${c[p - 2]};`;
    } else
      m = p, l = `
        ${u} sourceLocR = coords;
        ++${c[p - 1]};
        ${u} sourceLocG = coords;
        ++${c[p - 2]};
        ${u} sourceLocA = coords;
        --${c[p - 1]};
        ${u} sourceLocB = coords;
        --${c[p - 2]};`;
    let d = ["x", "y", "z", "w", "u", "v"].slice(0, m), f = "." + d[m - 1], h = d.map((A) => "int " + A), g = Et("sourceLocR", m - 1).concat("inIdx.r"), x = Et("sourceLocG", m - 1).concat("inIdx.g"), b = Et("sourceLocB", m - 1).concat("inIdx.b"), C = Et("sourceLocA", m - 1).concat("inIdx.a"), w = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : `
          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${x.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${C.join()})));`, _ = `vec4(
            getAChannel(${g.join()}),
            hasNextCol ? getAChannel(${x.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`, E = n ? "" : `
      float getBestIndicesAChannel(${h.join()}) {
        return getChannel(getBestIndicesA(${d.join()}),
                                          vec2(${d.slice(-2).join()}));
      }`;
    this.userCode = `
      float getAChannel(${h.join()}) {
        return getChannel(getA(${d.join()}),
                               vec2(${d.slice(-2).join()}));
      }
      ${E}
      void main() {
        ${u} coords = getOutputCoords();
        bool hasNextCol = ${c[p - 1]} < ${i[p - 1] - 1};
        bool hasNextRow = ${c[p - 2]} < ${i[p - 2] - 1};
        ${l}
        ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},
          sourceLocB${f}, sourceLocA${f}) * ${t10};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${_};

        for (int i = 0; i < ${t10}; i++) {
          inIdx = srcIdx;
          ${k}
          vec4 candidate = ${_};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `;
  }
};
function mA(r, e, t10, o = null) {
  let n = e.shape[0], s = e.shape[1];
  o != null && (n = o.shape[0], s = o.shape[1]);
  let a = S.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new sh(i, t10, o == null), u = [e];
  o != null && u.push(o);
  let c = r.runWebGLProgram(p, u, "int32");
  if (c.shape[1] === 1)
    return c;
  let l = mA(r, e, t10, c);
  return r.disposeIntermediateTensorInfo(c), l;
}
function dA(r, e, t10, o = null) {
  let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = S.computeOptimalWindowSize(s), i = new ah(n, a, t10, o == null), p = o == null ? [e] : [e, o], u = r.runWebGLProgram(i, p, "int32");
  if (u.shape.length === e.shape.length) {
    let c = dA(r, e, t10, u);
    return r.disposeIntermediateTensorInfo(u), c;
  }
  return u;
}
function ih(r, e, t10, o) {
  let n = [t10];
  if (S.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !O().getBool("WEBGL_PACK_REDUCE") || e.shape.length <= 2) {
    let s = [], a = r.texData.get(e.dataId), i = a !== null && a.isPacked, p = e;
    i && (p = r.unpackTensor(e), s.push(p));
    let [u, c] = S.computeOutAndReduceShapes(p.shape, n), l = y.sizeFromShape(c), m = re({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } });
    s.push(m);
    let d = mA(r, m, o);
    s.push(d);
    let f = re({ inputs: { x: d }, backend: r, attrs: { shape: u } });
    return s.forEach((h) => r.disposeIntermediateTensorInfo(h)), f;
  }
  return dA(r, e, o);
}
function bY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = yt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
  let c = ih(t10, p, a[0], "max");
  return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c;
}
var fA = { kernelName: Bo, backendName: "webgl", kernelFunc: bY };
function CY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = yt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
  let c = ih(t10, p, a[0], "min");
  return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c;
}
var hA = { kernelName: ei, backendName: "webgl", kernelFunc: CY };
var SY = zt + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
var wY = he({ opSnippet: SY });
var gA = { kernelName: ua, backendName: "webgl", kernelFunc: wY };
var IY = zt + "return log(x + sqrt(x * x + 1.0));";
var vY = he({ opSnippet: IY });
var xA = { kernelName: pa, backendName: "webgl", kernelFunc: vY };
var kY = zt + `
  return atan(x);
`;
var NY = he({ opSnippet: kY });
var yA = { kernelName: ca, backendName: "webgl", kernelFunc: NY };
var TY = dc + `
  return atan(a, b);
`;
var _Y = `
  vec4 result = atan(a, b);
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + Js + `
  return result;
`;
var $Y = tt({ opSnippet: TY, packedOpSnippet: _Y });
var bA = { kernelName: ma, backendName: "webgl", kernelFunc: $Y };
var EY = zt + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var AY = he({ opSnippet: EY });
var CA = { kernelName: la, backendName: "webgl", kernelFunc: AY };
var cs = class {
  constructor(e, t10, o, n = false, s = false) {
    if (this.variableNames = ["x"], t10 === "avg" && o)
      throw new Error("Cannot compute positions for average pool.");
    let a = e.filterWidth, i = e.strideHeight, p = e.strideWidth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterHeight, m = e.effectiveFilterWidth, d = e.padInfo.top, f = e.padInfo.left;
    this.outputShape = e.outShape;
    let h = t10 === "avg", g = `((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, x = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0";
    if (h || (b = "-1.0 / 1e-20"), o) {
      let A = ">=";
      this.userCode = `
        const ivec2 strides = ivec2(${i}, ${p});
        const ivec2 pads = ivec2(${d}, ${f});

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

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

            for (int wC = 0; wC < ${m};
                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 ${A} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${n ? s ? g : x : `wR * ${m} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
      return;
    }
    let C = "max", w = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t10 === "avg" && (w = "avgValue / max(count, 1.0)");
    let k = Math.floor(a / 4) * 4, _ = a % 4, E = `
      if (${h}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${C}(values, minMaxValue);
      }
    `;
    this.userCode = `
      const ivec2 strides = ivec2(${i}, ${p});
      const ivec2 pads = ivec2(${d}, ${f});
      const float initializationValue = ${b};
      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(${b});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${l};
            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 * ${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)
            );

            ${E}
          }

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

            ${E}
          } else if (${_ === 2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              initializationValue,
              initializationValue
            );

            ${E}
          } else if (${_ === 3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              getValue(batch, xR, xC + 2 * ${c}, d),
              initializationValue
            );

            ${E}
          }
        }
        setOutput(${w});
      }
    `;
  }
};
var Wi = class {
  constructor(e, t10, o, n = false, s = false) {
    if (this.variableNames = ["x"], t10 === "avg" && o)
      throw new Error("Cannot compute positions for average pool.");
    let a = e.filterWidth, i = e.strideDepth, p = e.strideHeight, u = e.strideWidth, c = e.dilationDepth, l = e.dilationHeight, m = e.dilationWidth, d = e.effectiveFilterDepth, f = e.effectiveFilterHeight, h = e.effectiveFilterWidth, g = e.padInfo.front, x = e.padInfo.top, b = e.padInfo.left;
    this.outputShape = e.outShape;
    let C = t10 === "avg", w = "0.0";
    if (C || (w = "-1.0 / 1e-20"), o) {
      let D = ">=";
      this.userCode = `
        const ivec3 strides =
            ivec3(${i}, ${p}, ${u});
        const ivec3 pads = ivec3(${g}, ${x}, ${b});

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

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${d};
              wD += ${c}) {
            int xD = xDCorner + wD;

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

            for (int wR = 0; wR < ${f};
                wR += ${l}) {
              int xR = xRCorner + wR;

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

              for (int wC = 0; wC < ${h};
                  wC += ${m}) {
                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 ${D} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${n ? s ? `(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `wD * ${f} * ${h} +
                      wR * ${h} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
      return;
    }
    let k = "max", _ = `${t10}(${t10}(${t10}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t10 === "avg" && (_ = "avgValue / max(count, 1.0)");
    let E = Math.floor(a / 4) * 4, A = a % 4, R = `
      if (${C}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${k}(values, minMaxValue);
      }
    `;
    this.userCode = `
      const ivec3 strides =
        ivec3(${i}, ${p}, ${u});
      const ivec3 pads = ivec3(${g}, ${x}, ${b});
      const float initializationValue = ${w};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${e.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

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

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${w});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${d};
            wD += ${c}) {
          int xD = xDCorner + wD;

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

          for (int wR = 0; wR < ${f};
            wR += ${l}) {
            int xR = xRCorner + wR;

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

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

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

              ${R}
            }

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

              ${R}
            } else if (${A === 2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                initializationValue,
                initializationValue
              );

              ${R}
            } else if (${A === 3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                getValue(batch, xD, xR, xC + 2 * ${m}, ch),
                initializationValue
              );

              ${R}
            }
          }
        }
        setOutput(${_});
      }
    `;
  }
};
function RY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  us(n, "avgPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = S.computePool2DInfo(n.shape, s, a, u, i, p);
  if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
    return At({ inputs: { x: n }, backend: t10 });
  let l = new cs(c, "avg", false);
  return t10.runWebGLProgram(l, [n], "float32");
}
var SA = { kernelName: Vo, backendName: "webgl", kernelFunc: RY };
function DY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o, c = [1, 1, 1], l = S.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new Wi(l, "avg", false);
  return t10.runWebGLProgram(m, [n], "float32");
}
var wA = { kernelName: dp, backendName: "webgl", kernelFunc: DY };
var uh = class {
  constructor(e) {
    this.variableNames = ["dy"], this.outputShape = e.inShape;
    let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterHeight, u = e.effectiveFilterWidth, c = p - 1 - e.padInfo.top, l = u - 1 - e.padInfo.left, m = 1 / (t10 * o);
    this.userCode = `
      const ivec2 pads = ivec2(${c}, ${l});
      const float avgMultiplier = float(${m});

      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 < ${p};
            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 < ${u};
            wC+= ${i}) {
            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(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var ph = class {
  constructor(e) {
    this.variableNames = ["dy"], this.outputShape = e.inShape;
    let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.effectiveFilterDepth, m = e.effectiveFilterHeight, d = e.effectiveFilterWidth, f = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = d - 1 - e.padInfo.left, x = 1 / (t10 * o * n);
    this.userCode = `
      const ivec3 pads = ivec3(${f}, ${h}, ${g});
      const float avgMultiplier = float(${x});

      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 < ${l};
            wD += ${p}) {
          float dyD = float(dyDCorner + wD) / ${s}.0;

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

          for (int wR = 0; wR < ${m};
              wR += ${u}) {
            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 < ${d};
                wC += ${c}) {
              float dyC = float(dyCCorner + wC) / ${i}.0;

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

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function FY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = S.computePool3DInfo(a.shape, i, p, l, u, c), d = new ph(m);
  return t10.runWebGLProgram(d, [n], a.dtype);
}
var IA = { kernelName: vm, backendName: "webgl", kernelFunc: FY };
function OY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
  us([n, s], "avgPoolGrad");
  let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = new uh(c);
  return t10.runWebGLProgram(l, [n], a.dtype);
}
var vA = { kernelName: mp, backendName: "webgl", kernelFunc: OY };
function PY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  return zu({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 });
}
var kA = { kernelName: zo, backendName: "webgl", kernelFunc: PY };
var ch = class {
  constructor(e, t10, o, n, s, a) {
    this.outputShape = [], this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t10), S.assertAndGetBroadcastShape(e, o);
    let i = "0.0";
    n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let p = "1.0";
    s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${p};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `;
  }
};
var lh = class {
  constructor(e, t10, o, n, s, a) {
    this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t10), S.assertAndGetBroadcastShape(e, o);
    let i = "vec4(0.0)";
    n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let p = "vec4(1.0)";
    s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale"), p = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${p};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${a}));

        setOutput((x - mean) * inv + offset);
      }
    `;
  }
};
var MY = ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o, mean: n, variance: s, offset: a, scale: i } = r;
  y.assert(n.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), y.assert(a == null || n.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), y.assert(i == null || n.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
  let { varianceEpsilon: p } = t10;
  p == null && (p = 1e-3);
  let u = [o, n, s], c = null;
  a != null && (c = a.shape, u.push(a));
  let l = null;
  i != null && (l = i.shape, u.push(i));
  let m = O().getBool("WEBGL_PACK_NORMALIZATION") ? new lh(o.shape, n.shape, s.shape, c, l, p) : new ch(o.shape, n.shape, s.shape, c, l, p);
  return e.runWebGLProgram(m, u, u[0].dtype);
};
var NA = { kernelName: sn, backendName: "webgl", kernelFunc: MY };
var mh = class {
  constructor(e) {
    this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
    let t10 = $e(this.rank);
    this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
    let o = LY(this.rank), n, s = e.map((a, i) => `sourceLoc.${Lw[i]} = start[${i}] + coords.${Lw[i]};`);
    n = `
        ${t10} sourceLoc;
        ${t10} coords = getOutputCoords();
        ${s.join(`
`)}
      `, this.userCode = `
      void main() {
        ${n}
        setOutput(getSource(${o}));
      }
    `;
  }
};
var Lw = ["x", "y", "z", "w", "u", "v"];
function LY(r) {
  if (r === 1)
    return "sourceLoc";
  if (r <= 6)
    return Lw.slice(0, r).map((e) => "sourceLoc." + e).join(",");
  throw Error(`Slicing for rank ${r} is not yet supported`);
}
var dh = class {
  constructor(e) {
    this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
    let t10 = $e(this.rank), o = Et("coords", this.rank), n = Et("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${n.slice(-2).join()})`, a = `getChannel(getSource(${n.join()}), ${s})`, i = `
      result.x = ${a};
      if (++${o[this.rank - 1]} < ${e[this.rank - 1]}) {
        ++${n[this.rank - 1]};
        result.y = ${a};
        --${n[this.rank - 1]};
      }
    `, p = this.rank === 1 ? "" : `
      --${o[this.rank - 1]};
      if (++${o[this.rank - 2]} < ${e[this.rank - 2]}) {
        ++${n[this.rank - 2]};
        result.z = ${a};
        if (++${o[this.rank - 1]} < ${e[this.rank - 1]}) {
          ++${n[this.rank - 1]};
          result.w = ${a};
        }
      }
    `, u = this.rank <= 4 ? `sourceLoc = coords +
            ${t10}(${e.map((c, l) => `start[${l}]`).join()});` : e.map((c, l) => `${n[l]} = ${o[l]} + start[${l}];`).join(`
`);
    this.userCode = `
      void main() {
        ${t10} coords = getOutputCoords();
        ${t10} sourceLoc;
        ${u}
        vec4 result = vec4(0.);
        ${i}
        ${p}
        setOutput(result);
      }
    `;
  }
};
function BY(r, e, t10, o) {
  let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t10, r.dtype), a = o.texData.get(s.dataId);
  Object.assign(a, n), a.refCount = 1, a.shape = t10, a.dtype = r.dtype;
  let i = ut.computeFlatOffset(e, y.computeStrides(r.shape));
  n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r.dataId };
  let p = o.dataRefCount.get(a.slice.origDataId) || 1;
  return o.dataRefCount.set(a.slice.origDataId, p + 1), s;
}
function ls(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ut.parseSliceParams(n, s, a);
  if (ut.assertParamsValid(n, i, p), y.sizeFromShape(p) === 0)
    return t10.makeTensorInfo(p, n.dtype, []);
  if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string") {
    let l = t10.texData.get(n.dataId), m = xE(l.values, i, p, n.shape, n.dtype);
    return t10.makeTensorInfo(p, n.dtype, m);
  }
  let { isPacked: u } = t10.texData.get(n.dataId), c = ut.isSliceContinous(n.shape, i, p);
  if (u || !c) {
    let l = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new dh(p) : new mh(p), m = [i];
    return t10.runWebGLProgram(l, [n], n.dtype, m);
  }
  return t10.uploadToGPU(n.dataId), BY(n, i, p, t10);
}
var TA = { kernelName: Es, backendName: "webgl", kernelFunc: ls };
var VY = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
  y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
  let i = s.reduce((b, C) => b * C), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = [], f = re({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = yt({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = ls({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } });
  return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x;
};
var _A = { kernelName: bs, backendName: "webgl", kernelFunc: VY };
function zY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), u = Kf(i, p, s.dtype, s.shape, a);
  return t10.makeTensorInfo([a], s.dtype, u);
}
var $A = { kernelName: ti, backendName: "webgl", kernelFunc: zY };
function WY(r) {
  let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.readSync(o.dataId), a = t10.readSync(n.dataId), i = S.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
  return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var EA = { kernelName: fp, backendName: "webgl", kernelFunc: WY };
var UY = "return float(a != b);";
var Bw = tt({ opSnippet: UY, cpuKernelImpl: uE, dtype: "bool" });
var AA = { kernelName: Nn, backendName: "webgl", kernelFunc: Bw };
function Ka(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId);
  return At({ inputs: { x: n.complexTensorInfos.real }, backend: t10 });
}
var RA = { kernelName: di, backendName: "webgl", kernelFunc: Ka };
var GY = "return float(int(x));";
function DA(r, e) {
  let t10 = new Jt(r.shape, GY), o = e.runWebGLProgram(t10, [r], "int32");
  return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function Vw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return At({ inputs: { x: n }, backend: t10 });
    let a = Br(n.shape), i = Vw({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = Ar({ inputs: { real: i, imag: a }, backend: t10 });
    return a.dispose(), t10.disposeIntermediateTensorInfo(i), p;
  }
  if (n.dtype === "complex64") {
    let a = Ka({ inputs: { input: n }, backend: t10 }), i = Vw({ inputs: { x: a }, backend: t10, attrs: { dtype: s } });
    return t10.disposeIntermediateTensorInfo(a), i;
  }
  if (!y.hasEncodingLoss(n.dtype, s)) {
    let a = At({ inputs: { x: n }, backend: t10 });
    return { dataId: a.dataId, shape: a.shape, dtype: s };
  }
  if (t10.shouldExecuteOnCPU([n])) {
    let a = t10.texData.get(n.dataId).values, [i, p, u] = W$(a, n.shape, n.dtype, s);
    return t10.makeTensorInfo(i, p, u);
  }
  if (s === "int32")
    return DA(n, t10);
  if (s === "bool") {
    let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = Bw({ inputs: { a: n, b: a }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(a), p;
  }
  throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var FA = { kernelName: co, backendName: "webgl", kernelFunc: Vw };
var OA = "return ceil(x);";
var HY = he({ opSnippet: OA, packedOpSnippet: OA, cpuKernelImpl: U$ });
var PA = { kernelName: Wo, backendName: "webgl", kernelFunc: HY };
var fh = 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));
      }
    `;
  }
};
var hh = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, 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 KY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i;
  O().getBool("WEBGL_PACK_CLIP") ? i = new hh(n.shape) : i = new fh(n.shape);
  let p = [[s], [a]];
  return t10.runWebGLProgram(i, [n], n.dtype, p);
}
var MA = { kernelName: lo, backendName: "webgl", kernelFunc: KY };
var gh = 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 LA(r, e) {
  return { dataId: e.dataId, dtype: e.dtype, shape: r.shape };
}
function qY(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.texData.get(o.dataId), s = new gh(o.shape), a = [LA(o, n.complexTensorInfos.real), LA(o, n.complexTensorInfos.imag)];
  return t10.runWebGLProgram(s, a, a[0].dtype);
}
var BA = { kernelName: hp, backendName: "webgl", kernelFunc: qY };
var xh = class {
  constructor(e) {
    this.outputShape = [], this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
    let t10 = new Array(e.length - 1);
    t10[0] = e[0][1];
    for (let a = 1; a < t10.length; a++)
      t10[a] = t10[a - 1] + e[a][1];
    let o = [`if (yC < ${t10[0]}) setOutput(getT0(yR, yC));`];
    for (let a = 1; a < t10.length; a++) {
      let i = t10[a - 1];
      o.push(`else if (yC < ${t10[a]}) setOutput(getT${a}(yR, yC-${i}));`);
    }
    let n = t10.length, s = t10[t10.length - 1];
    o.push(`else setOutput(getT${n}(yR, yC-${s}));`), this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${o.join(`
        `)}
      }
    `;
  }
};
var bh = class {
  constructor(e, t10) {
    this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = S.computeOutShape(e, t10);
    let o = this.outputShape, n = o.length, s = $e(n), a = Et("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n);
    this.variableNames = e.map((h, g) => `T${g}`);
    let p = new Array(e.length - 1);
    p[0] = e[0][t10];
    for (let h = 1; h < p.length; h++)
      p[h] = p[h - 1] + e[h][t10];
    let u = i[t10], c = i.slice(-2), l = i.join(), m = `if (${u} < ${p[0]}) {
        return getChannel(
            getT0(${l}), vec2(${c.join()}));
        }`;
    for (let h = 1; h < p.length; h++) {
      let g = p[h - 1];
      m += `
        if (${u} < ${p[h]}  && ${u} >= ${p[h - 1]}) {
          return getChannel(
            getT${h}(${yh(i, u, g)}),
            vec2(${yh(c, u, g)}));
        }`;
    }
    let d = p.length, f = p[p.length - 1];
    m += `
        return getChannel(
          getT${d}(${yh(i, u, f)}),
          vec2(${yh(c, u, f)}));`, this.userCode = `
      float getValue(${i.map((h) => "int " + h)}) {
        ${m}
      }

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

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

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

        ${a[n - 1]} = ${a[n - 1]} - 1;
        if (${a[n - 2]} < ${o[n - 2]} &&
            ${a[n - 1]} < ${o[n - 1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `;
  }
};
function yh(r, e, t10) {
  let o = r.indexOf(e);
  return r.map((s, a) => a === o ? `${s} - ${t10}` : s).join();
}
function Wu(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId);
  return At({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 });
}
var VA = { kernelName: ci, backendName: "webgl", kernelFunc: Wu };
function hc(r, e, t10) {
  let o = r[0].dtype;
  if (o === "complex64") {
    let d = r.map((b) => Ka({ inputs: { input: b }, backend: t10 })), f = r.map((b) => Wu({ inputs: { input: b }, backend: t10 })), h = hc(d, e, t10), g = hc(f, e, t10), x = Ar({ inputs: { real: h, imag: g }, backend: t10 });
    return d.forEach((b) => t10.disposeIntermediateTensorInfo(b)), f.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), x;
  }
  let n = t10.shouldExecuteOnCPU(r);
  if (o === "string" && (n = true), n) {
    let d = r.map((w) => {
      let _ = [-1, y.sizeFromShape(w.shape.slice(e))];
      return re({ inputs: { x: w }, backend: t10, attrs: { shape: _ } });
    }), f = d.map((w) => ({ vals: t10.readSync(w.dataId), shape: w.shape })), h = S.computeOutShape(d.map((w) => w.shape), 1), g = d[0].shape[0] === 1, x = G$(f, h, o, g), b = S.computeOutShape(r.map((w) => w.shape), e), C = t10.makeTensorInfo(b, o, x);
    return d.forEach((w) => t10.disposeIntermediateTensorInfo(w)), C;
  }
  let s = r.filter((d) => y.sizeFromShape(d.shape) > 0), a = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && s[0].shape.length > 1;
  if (s.length === 1) {
    let d = a ? new Jt(r[0].shape, Zs) : new Er(r[0].shape, Zs);
    return t10.runWebGLProgram(d, r, o);
  }
  let i = O().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
  if (s.length > i) {
    let d = [];
    for (let h = 0; h < s.length; h += i) {
      let g = s.slice(h, h + i);
      d.push(hc(g, e, t10));
    }
    let f = hc(d, e, t10);
    for (let h of d)
      t10.disposeIntermediateTensorInfo(h);
    return f;
  }
  if (a) {
    let d = new bh(s.map((f) => f.shape), e);
    return t10.runWebGLProgram(d, s, o);
  }
  let { tensors2D: p, outShape: u } = jY(s, e, t10), c = new xh(p.map((d) => d.shape)), l = t10.runWebGLProgram(c, p, o);
  p.forEach((d) => t10.disposeIntermediateTensorInfo(d));
  let m = re({ inputs: { x: l }, attrs: { shape: u }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(l), m;
}
function jY(r, e, t10) {
  let o = S.computeOutShape(r.map((s) => s.shape), e);
  return { tensors2D: r.map((s) => re({ inputs: { x: s }, attrs: { shape: [-1, y.sizeFromShape(s.shape.slice(e))] }, backend: t10 })), outShape: o };
}
function zw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
  S.assertParamsConsistent(a, s);
  let i = S.computeOutShape(e.map((u) => u.shape), s);
  if (y.sizeFromShape(i) === 0)
    return t10.makeTensorInfo(i, e[0].dtype, []);
  let p = e.filter((u) => y.sizeFromShape(u.shape) > 0);
  return p.length === 1 ? At({ inputs: { x: p[0] }, backend: t10 }) : hc(p, s, t10);
}
var zA = { kernelName: Cs, backendName: "webgl", kernelFunc: zw };
var gc = class {
  constructor(e, t10 = false, o = null, n = false, s = false) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let a = e.padInfo.top, i = e.padInfo.left, p = e.strideHeight, u = e.strideWidth, c = e.dilationHeight, l = e.dilationWidth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4, g = e.dataFormat === "channelsLast", x = g ? 1 : 2, b = g ? 2 : 3, C = g ? 3 : 1, w = "", k = "";
    o && (n ? w = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }` : s ? w = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }` : w = `
          float activation(float x) {
            ${o}
          }
        `, k = "result = activation(result);");
    let _ = t10 ? "result += getBiasAtOutCoords();" : "";
    t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = `
      ${w}

      const ivec2 strides = ivec2(${p}, ${u});
      const ivec2 pads = ivec2(${a}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${C}];

        ivec2 xRCCorner =
            ivec2(coords[${x}], coords[${b}]) * 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 < ${m}; wR++) {
          int xR = xRCorner + wR * ${c};

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

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

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

            for (int d1 = 0; d1 < ${f}; 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 (${g}) {
                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 (${h === 1}) {

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

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

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

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

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

            }
          }
        }

        float result = dotProd;
        ${_}
        ${k}
        setOutput(result);
      }
    `;
  }
};
var Ch = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let t10 = e.padInfo.front, o = e.padInfo.top, n = e.padInfo.left, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = e.dilationDepth, u = e.dilationHeight, c = e.dilationWidth, l = e.filterDepth, m = e.filterHeight, d = e.filterWidth, f = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4;
    this.userCode = `
      const ivec3 strides = ivec3(${s}, ${a}, ${i});
      const ivec3 pads = ivec3(${t10}, ${o}, ${n});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${l}; wF++) {
          int xF = xFCorner + wF * ${p};

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

          for (int wR = 0; wR < ${m}; wR++) {
            int xR = xRCorner + wR * ${u};

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

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

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

              for (int d1 = 0; d1 < ${f}; 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 (${h === 1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${f}) *
                  getW(wF, wR, wC, ${f}, d2);
              } else if (${h === 2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${f}),
                  getX(batch, xF, xR, xC, ${f} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${f}, d2),
                  getW(wF, wR, wC, ${f} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${h === 3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${f}),
                  getX(batch, xF, xR, xC, ${f} + 1),
                  getX(batch, xF, xR, xC, ${f} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${f}, d2),
                  getW(wF, wR, wC, ${f} + 1, d2),
                  getW(wF, wR, wC, ${f} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var xc = class {
  constructor(e, t10 = false, o = null, n = false, s = false) {
    this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = lt(this.outputShape.length);
    let a = e.padInfo.left, i = e.strideWidth, p = e.dilationWidth, u = e.filterHeight, c = e.filterWidth, l = c, m = `
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;
    for (let g = 0; g < c; g++)
      m += `
           vec4 xTexelC${g * 2};
           int xTexelC${g * 2}Ready;
           vec4 xTexelC${g * 2 + 1};
           int xTexelC${g * 2 + 1}Ready;
           vec4 xC${g};`;
    m += `
     for (int r = 0; r < ${u}; r++) {
      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {
       `;
    for (let g = 0; g < c; g++)
      m += `
           xTexelC${g * 2} = vec4(0.0);
           xTexelC${g * 2}Ready = 0;
           xTexelC${g * 2 + 1} = vec4(0.0);
           xTexelC${g * 2 + 1}Ready = 0;
           xC${g} = vec4(0.0);`;
    m += `
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;
    for (let g = 0; g < (l + 1) / 2; g++) {
      let x = g * 2;
      if (m += `
           xC = xCCorner + ${x * p};
           `, i === 1) {
        if (x < c && (a % 2 === 1 ? (m += `
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = 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${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }
               `, p === 1 && x > 0 ? m += `
                 xC${x} = vec4(xTexelC${x - 2}.zw, xTexelC${x}.xy);
                 ` : m += `
                   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${x} = vec4(previous.zw, xTexelC${x}.xy);
                   } else {
                     xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);
                   }
                   `) : m += `
                 if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }

                 xC${x} = xTexelC${x};
                 `, x + 1 < c)) {
          let b = a % 2 === 0 ? y.nearestLargerEven(p) : p;
          p % 2 === 0 && a % 2 === 1 || p % 2 !== 0 && a % 2 !== 1 ? (m += `
                   xCOffset = xC + imod(pads[1], 2) + ${b};

                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x + 1}Ready == 0) {
                     xTexelC${x + 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${x + 1}.zw = vec2(0.0);
                     }
                     xTexelC${x + 1}Ready = 1;
                   }
                   `, p > 1 ? m += `
                     xCOffset -= 2;
                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
                      previous = getX(batch, xR, xCOffset, d1);
                      xC${x + 1} = vec4(previous.zw, xTexelC${x + 1}.xy);
                     } else {
                      xC${x + 1} = vec4(0.0, 0.0, xTexelC${x + 1}.xy);
                     }
                     ` : m += `
                     xC${x + 1} = vec4(xTexelC${x}.zw, xTexelC${x + 1}.xy);
                     `) : b === 1 ? m += `
                     xC${x + 1} = xTexelC${x};
                     ` : m += `
                     xCOffset = xC + ${b};

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

                     xC${x + 1} = xTexelC${x + 1};
                     `;
        }
      } else
        x < c && (a % 2 === 1 ? (m += `
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {
                   xTexelC${x} = 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${x}.zw = vec2(0.0);
                   }
                   xTexelC${x}Ready = 1;
                 }

                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x + 1}Ready == 0) {
                   xTexelC${x + 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${x + 1}.zw = vec2(0.0);
                   }
                   xTexelC${x + 1}Ready = 1;
                 }

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

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

                 xC${x} = vec4(
                   xTexelC${x}.xy, xTexelC${x + 1}.xy);
               `, x + 1 < c && (m += `
                   xC${x + 1} = vec4(xTexelC${x}.zw, xTexelC${x + 1}.zw);
                 `)));
      x < c && (m += `
             wTexel = getW(r, ${x}, d1, d2);
             dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${e.inChannels}) {
               dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `, x + 1 < c && (m += `
               wTexel = getW(r, ${x + 1}, d1, d2);
               dotProd += xC${x + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${e.inChannels}) {
                 dotProd += xC${x + 1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `));
    }
    m += `
     }
   `, m += `
     }
   `, m += `
     }
   `;
    let d = "", f = "";
    o && (n ? d = `vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${o}
         }` : s ? d = `vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${o}
         }` : d = `vec4 activation(vec4 x) {
           ${o}
         }`, f = "result = activation(result);");
    let h = t10 ? "result += getBiasAtOutCoords();" : "";
    t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = `
       ${d}

       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         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);

         ${m}

         vec4 result = dotProd - vec4(0.000000000000001);
         ${h}
         ${f}
         setOutput(result);
       }
     `;
  }
};
var Sh = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec4" }, { 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 = lt(this.outputShape.length);
    let { dataFormat: o } = t10, n = wt(), s = o === "channelsLast", a = s ? 1 : 2, i = s ? 2 : 3, p = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${e[2]} && pos < ${e[1]}) {`, u = "";
    for (let c = 0; c <= 1; c++)
      for (let l = 0; l <= 1; l++)
        u += `
          blockIndex = rc.z + ${l};
          pos = rc.y + ${c};

          ${p}
            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[${i}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${s}) {
                  innerDims = vec2(d1, ch);
                  result[${c * 2 + l}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${c * 2 + l}] = getChannel(
                    getA(rc.x, ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;
    this.userCode = `
      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0);

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

        ${u}

        ${n.output} = result;
      }
    `;
  }
};
function wh(r, e) {
  let t10 = r.length;
  return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null;
}
function Ih({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
  let p = r.shape, u = o.texData.get(r.dataId), c = t10.inChannels, l = p[0] * p[1] * p[2], m = t10.outChannels, d = t10.dataFormat === "channelsLast", f = false, h = false, g, x = [];
  if (s != null) {
    let w = wh(s.shape, d);
    w != null && (s = re({ inputs: { x: s }, backend: o, attrs: { shape: w } }), x.push(s));
  }
  if (n != null) {
    let w = wh(n.shape, d);
    w != null && (n = re({ inputs: { x: n }, backend: o, attrs: { shape: w } }), x.push(n));
  }
  if (!((l === 1 || m === 1) && c > Mw) && u.isPacked && d && u.texture != null && p[2] % 2 !== 0 && y.arraysEqual(u.shape.slice(-3), p.slice(-3))) {
    let w = p[0] * p[1] * (p[2] + 1), k = { dataId: r.dataId, shape: [1, w, t10.inChannels], dtype: r.dtype }, _ = u.shape;
    u.shape = u.shape.slice(), u.shape[u.shape.length - 2]++, y.assert(Bi(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`);
    let E = re({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } });
    x.push(E);
    let A = zu({ a: k, b: E, backend: o, transposeA: f, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), R = o.texData.get(A.dataId);
    y.assert(R.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, R.shape = t10.outShape, g = At({ inputs: { x: A }, backend: o }), g.shape = t10.outShape, x.push(A);
  } else {
    let w = t10.outHeight * t10.outWidth, k = re({ inputs: { x: r }, backend: o, attrs: { shape: d ? [t10.batchSize, w, t10.inChannels] : [t10.batchSize, t10.inChannels, w] } }), _ = re({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }), E = zu({ a: d ? k : _, b: d ? _ : k, transposeA: !d, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
    g = re({ inputs: { x: E }, backend: o, attrs: { shape: t10.outShape } }), x.push(k), x.push(_), x.push(E);
  }
  for (let w of x)
    o.disposeIntermediateTensorInfo(w);
  return g;
}
function vh({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
  let { filterWidth: p, filterHeight: u, inChannels: c, outWidth: l, outHeight: m, dataFormat: d } = t10, f = d === "channelsLast", h = p * u * c, g = m * l, x = [t10.batchSize, h, g], b = true, C = false, w = [];
  if (s != null) {
    let H = wh(s.shape, f);
    H != null && (s = re({ inputs: { x: s }, backend: o, attrs: { shape: H } }), w.push(s));
  }
  if (n != null) {
    let H = wh(n.shape, f);
    H != null && (n = re({ inputs: { x: n }, backend: o, attrs: { shape: H } }), w.push(n));
  }
  let k = re({ inputs: { x: e }, backend: o, attrs: { shape: [1, h, y.sizeFromShape(e.shape) / h] } });
  w.push(k);
  let _ = new Sh(x, t10), E = [r.shape, [t10.padInfo.top, t10.padInfo.left], [t10.strideHeight, t10.strideWidth], [t10.dilationHeight, t10.dilationWidth], [t10.inChannels], [t10.filterWidth * t10.inChannels], [t10.outWidth]], A = o.runWebGLProgram(_, [r], "float32", E), R = re({ inputs: { x: A }, backend: o, attrs: { shape: x } });
  w.push(A), w.push(R);
  let D = n != null, P = s != null, M = i === "leakyrelu", L = i ? Ha(i, true) : null, V = new fc(f ? R.shape : k.shape, f ? k.shape : R.shape, f ? [t10.batchSize, g, t10.outChannels] : [t10.batchSize, t10.outChannels, g], b, C, D, L, P, M), z = f ? [R, k] : [k, R];
  if (n && z.push(n), P && z.push(s), M) {
    let H = o.makeTensorInfo([], "float32", y.createScalarValue(a, "float32"));
    z.push(H), w.push(H);
  }
  let U = o.runWebGLProgram(V, z, "float32"), K = re({ inputs: { x: U }, backend: o, attrs: { shape: t10.outShape } });
  w.push(U);
  for (let H of w)
    o.disposeIntermediateTensorInfo(H);
  return K;
}
function XY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), d;
  if (m.filterHeight === 1 && m.filterWidth === 1 && m.dilationHeight === 1 && m.dilationWidth === 1 && m.strideHeight === 1 && m.strideWidth === 1 && (m.padInfo.type === "SAME" || m.padInfo.type === "VALID"))
    d = Ih({ x: n, filter: s, convInfo: m, backend: t10 });
  else if (m.strideWidth <= 2 && l === "channelsLast" && O().getBool("WEBGL_EXP_CONV")) {
    let h = new xc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]];
    d = t10.runWebGLProgram(h, [n, s], "float32", g);
  } else if (O().getBool("WEBGL_CONV_IM2COL"))
    d = vh({ x: n, filter: s, convInfo: m, backend: t10 });
  else {
    let h = new gc(m);
    d = t10.runWebGLProgram(h, [n, s], "float32");
  }
  let f = re({ inputs: { x: d }, backend: t10, attrs: { shape: m.outShape } });
  return t10.disposeIntermediateTensorInfo(d), f;
}
var WA = { kernelName: Uo, backendName: "webgl", kernelFunc: XY };
var kh = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t10 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = 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 * ${t10} - ${n};

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

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

              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);
      }
    `;
  }
};
var Nh = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = e.dataFormat === "channelsLast", i = t10 - 1 - e.padInfo.top, p = o - 1 - e.padInfo.left, u = a ? 1 : 2, c = a ? 2 : 3, l = a ? 3 : 1;
    this.userCode = `
      const ivec2 pads = ivec2(${i}, ${p});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${l}];

        ivec2 dyCorner = ivec2(coords[${u}], coords[${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 < ${t10}; wR++) {
          float dyR = float(dyRCorner + wR) / ${n}.0;

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

          int wRPerm = ${t10} - 1 - wR;

          for (int wC = 0; wC < ${o}; wC++) {
            float dyC = float(dyCCorner + wC) / ${s}.0;

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

            int wCPerm = ${o} - 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);
      }
    `;
  }
};
var Th = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left;
    this.userCode = `
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yF = 0; yF < ${e.outDepth}; yF++) {
            int xF = wF + yF * ${t10} - ${s};

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

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

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

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

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

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var _h = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t10 = e.filterDepth, o = e.filterHeight, n = e.filterWidth, s = e.strideDepth, a = e.strideHeight, i = e.strideWidth, p = t10 - 1 - e.padInfo.front, u = o - 1 - e.padInfo.top, c = n - 1 - e.padInfo.left;
    this.userCode = `
      const ivec3 pads = ivec3(${p}, ${u}, ${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 < ${t10}; wF++) {
          float dyF = float(dyFCorner + wF) / ${s}.0;

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

          int wFPerm = ${t10} - 1 - wF;

          for (int wR = 0; wR < ${o}; 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 = ${o} - 1 - wR;

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

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

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

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function YY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new kh(m);
  return t10.runWebGLProgram(d, [n, s], "float32");
}
var UA = { kernelName: oi, backendName: "webgl", kernelFunc: YY };
function QY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(u), m = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = new Nh(m);
  return t10.runWebGLProgram(d, [n, s], "float32");
}
var GA = { kernelName: Go, backendName: "webgl", kernelFunc: QY };
function ZY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Ch(u);
  return t10.runWebGLProgram(c, [n, s], "float32");
}
var HA = { kernelName: gp, backendName: "webgl", kernelFunc: ZY };
function JY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = S.computeConv3DInfo(n.shape, p, a, 1, i), c = new Th(u);
  return t10.runWebGLProgram(c, [n, s], "float32");
}
var KA = { kernelName: km, backendName: "webgl", kernelFunc: JY };
function eQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = S.computeConv3DInfo(p, s.shape, i, 1, a), c = new _h(u);
  return t10.runWebGLProgram(c, [n, s], "float32");
}
var qA = { kernelName: xp, backendName: "webgl", kernelFunc: eQ };
var tQ = $o + `
  return cos(x);
`;
var rQ = he({ opSnippet: tQ });
var jA = { kernelName: Ho, backendName: "webgl", kernelFunc: rQ };
var oQ = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var nQ = he({ opSnippet: oQ });
var XA = { kernelName: Ko, backendName: "webgl", kernelFunc: nQ };
var $h = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
    let [a, i, p, u] = e, [c] = t10, [l, m] = o;
    this.outputShape = [c, l, m, u];
    let d = n === "bilinear" ? 1 : 0, [f, h] = [`${i - 1}.0`, `${p - 1}.0`], [g, x, b] = l > 1 ? [`${(i - 1) / (l - 1)}`, "(y2-y1) * height_ratio", `y1*${f} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${f}`], [C, w, k] = m > 1 ? [`${(p - 1) / (m - 1)}`, "(x2-x1) * width_ratio", `x1*${h} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${h}`];
    this.userCode = `
      const float height_ratio = float(${g});
      const float width_ratio = float(${C});
      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 = ${x};
        float width_scale = ${w};

        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${f} ) {
          setOutput(float(${s}));
          return;
        }
        float in_x = ${k};
        if( in_x < 0.0 || in_x > ${h} ) {
          setOutput(float(${s}));
          return;
        }

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

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

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `;
  }
};
var sQ = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new $h(n.shape, s.shape, i, p, u);
  return t10.runWebGLProgram(c, [n, s, a], "float32");
};
var YA = { kernelName: Xo, backendName: "webgl", kernelFunc: sQ };
var Uu;
(function(r) {
  r.Prod = "*", r.Sum = "+";
})(Uu || (Uu = {}));
var Rl = class {
  constructor(e, t10, o, n) {
    this.op = e, this.outputShape = t10, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }];
    let s = this.outputShape.length, a = this.op === Uu.Prod ? "1.0" : "0.0", i = o ? a : `getX(${QA(s, "coords", this.op)})`, p = this.outputShape[this.outputShape.length - 1], u = "", c = "";
    o ? (u = n ? `end != ${p - 1}` : "end != 0", c = n ? "end + 1" : "end - 1") : (u = n ? `end + pow2 < ${p}` : "end >= pow2", c = n ? "end + pow2" : "end - pow2"), this.userCode = `
      void main() {
        ${$e(s)} coords = getOutputCoords();
        int end = ${ZA(s, "coords", this.op)};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${u}) {
          int idx = ${c};
          ${ZA(s, "coords", this.op)} = idx;
          val ${this.op}= getX(${QA(s, "coords", this.op)});
        }
        setOutput(val);
      }
    `;
  }
};
function QA(r, e, t10) {
  if (r === 1)
    return `${e}`;
  if (r === 2)
    return `${e}.x, ${e}.y`;
  if (r === 3)
    return `${e}.x, ${e}.y, ${e}.z`;
  if (r === 4)
    return `${e}.x, ${e}.y, ${e}.z, ${e}.w`;
  throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function ZA(r, e, t10) {
  if (r === 1)
    return `${e}`;
  if (r === 2)
    return `${e}.y`;
  if (r === 3)
    return `${e}.z`;
  if (r === 4)
    return `${e}.w`;
  throw new Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function Eh(r, e, t10, o, n, s) {
  let a = e.shape.length, i = S.getAxesPermutation([o], a), p = e;
  i != null && (p = yt({ inputs: { x: e }, backend: t10, attrs: { perm: i } }));
  let u = S.getInnerMostAxes(1, a)[0];
  if (u !== a - 1)
    throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
  let c = p.shape[u], l = At({ inputs: { x: p }, backend: t10 });
  for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
    let d = new Rl(r, p.shape, false, s), f = [[m]], h = l;
    l = t10.runWebGLProgram(d, [l], l.dtype, f), t10.disposeIntermediateTensorInfo(h);
  }
  if (n) {
    let m = new Rl(r, p.shape, n, s), d = l;
    l = t10.runWebGLProgram(m, [l], l.dtype), t10.disposeIntermediateTensorInfo(d);
  }
  if (i != null) {
    let m = S.getUndoAxesPermutation(i), d = yt({ inputs: { x: l }, backend: t10, attrs: { perm: m } });
    return t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(p), d;
  }
  return l;
}
function aQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return Eh(Uu.Prod, n, t10, s, a, i);
}
var JA = { kernelName: qo, backendName: "webgl", kernelFunc: aQ };
function iQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return Eh(Uu.Sum, n, t10, s, a, i);
}
var eR = { kernelName: jo, backendName: "webgl", kernelFunc: iQ };
function uQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
  if (n.shape.length === 1) {
    let p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = Kf(p, u, s.dtype, s.shape, a);
    return t10.makeTensorInfo([a], s.dtype, c);
  } else if (n.shape.length === 2) {
    let p = t10.bufferSync(n), u = t10.bufferSync(s), c = z$(p, u, a, i);
    return t10.makeTensorInfo(c.shape, s.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var tR = { kernelName: ni, backendName: "webgl", kernelFunc: uQ };
var Ah = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t10, this.dataFormat = o, 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 / ${t10};
      int offset_h = imod(h, ${t10});
      int in_w = w / ${t10};
      int offset_w = imod(w, ${t10});
      int offset_d = (offset_h * ${t10} + 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 pQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = new Ah(f, s, a);
  return t10.runWebGLProgram(h, [n], n.dtype);
}
var rR = { kernelName: Yo, backendName: "webgl", kernelFunc: pQ };
var yc = class {
  constructor(e, t10 = false, o = null, n = false, s = false) {
    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 = lt(this.outputShape.length);
    let a = e.filterHeight, i = e.filterWidth, p = e.outChannels / e.inChannels, u = "", c = "";
    o && (n ? u = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }` : s ? u = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }` : u = `
          float activation(float x) {
            ${o}
          }
        `, c = "result = activation(result);");
    let l = t10 ? "result += getBiasAtOutCoords();" : "";
    t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = `
      ${u}

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

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

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

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

        float result = dotProd;
        ${l}
        ${c}
        setOutput(result);
      }
    `;
  }
};
var bc = class {
  constructor(e, t10 = false, o = null, n = false, s = false) {
    this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = lt(this.outputShape.length);
    let a = e.outChannels / e.inChannels, i = e.padInfo.left, p = e.strideWidth, u = e.dilationWidth, c = e.filterHeight, l = e.filterWidth, m = l, d = `
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;
    for (let x = 0; x < l; x++)
      d += `
          vec4 xTexelC${x * 2};
          int xTexelC${x * 2}Ready;
          vec4 xTexelC${x * 2 + 1};
          int xTexelC${x * 2 + 1}Ready;
          vec4 xC${x};`;
    d += `
    for (int r = 0; r < ${c}; r++) {
      `;
    for (let x = 0; x < l; x++)
      d += `
          xTexelC${x * 2} = vec4(0.0);
          xTexelC${x * 2}Ready = 0;
          xTexelC${x * 2 + 1} = vec4(0.0);
          xTexelC${x * 2 + 1}Ready = 0;
          xC${x} = vec4(0.0);`;
    d += `
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;
    for (let x = 0; x < (m + 1) / 2; x++) {
      let b = x * 2;
      if (d += `
          xC = xCCorner + ${b * u};
          `, p === 1) {
        if (b < l && (i % 2 === 1 ? (d += `
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = 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${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
              `, u === 1 && b > 0 ? d += `
                xC${b} = vec4(xTexelC${b - 2}.zw, xTexelC${b}.xy);
                ` : d += `
                  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${b} = vec4(previous.zw, xTexelC${b}.xy);
                  } else {
                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);
                  }
                  `) : d += `
                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }

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

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b + 1}Ready == 0) {
                    xTexelC${b + 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${b + 1}.zw = vec2(0.0);
                    }
                    xTexelC${b + 1}Ready = 1;
                  }
                  `, u > 1 ? d += `
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);
                     xC${b + 1} = vec4(previous.zw, xTexelC${b + 1}.xy);
                    } else {
                     xC${b + 1} = vec4(0.0, 0.0, xTexelC${b + 1}.xy);
                    }
                    ` : d += `
                    xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.xy);
                    `) : C === 1 ? d += `
                    xC${b + 1} = xTexelC${b};
                    ` : d += `
                    xCOffset = xC + ${C};

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

                    xC${b + 1} = xTexelC${b + 1};
                    `;
        }
      } else
        b < l && (i % 2 === 1 ? (d += `
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = 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${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b + 1}Ready == 0) {
                  xTexelC${b + 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${b + 1}.zw = vec2(0.0);
                  }
                  xTexelC${b + 1}Ready = 1;
                }

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

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

                xC${b} = vec4(
                  xTexelC${b}.xy, xTexelC${b + 1}.xy);
              `, b + 1 < l && (d += `
                  xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.zw);
                `)));
      b < l && (d += `
            wTexel = getW(r, ${b}, d1, q);
            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
          `, b + 1 < l && (d += `
              wTexel = getW(r, ${b + 1}, d1, q);
              dotProd += xC${b + 1} * vec4(wTexel.xz, wTexel.xz);
            `));
    }
    d += `
    }
  `, d += `
      }
    `;
    let f = "", h = "";
    o && (n ? f = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${o}
        }` : s ? f = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${o}
        }` : f = `vec4 activation(vec4 x) {
          ${o}
        }`, h = "result = activation(result);");
    let g = t10 ? "result += getBiasAtOutCoords();" : "";
    t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), s && this.variableNames.push("leakyreluAlpha"), this.userCode = `
      ${f}

      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);

        ${d}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${g}
        ${h}
        setOutput(result);
      }
    `;
  }
};
function cQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o, c = p;
  c == null && (c = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);
  let l = S.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m;
  O().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new bc(l) : m = new yc(l);
  let d = [[l.padInfo.top, l.padInfo.left], [l.strideHeight, l.strideWidth], [l.dilationHeight, l.dilationWidth], [l.inHeight, l.inWidth]];
  return t10.runWebGLProgram(m, [n, s], "float32", d);
}
var oR = { kernelName: Qo, backendName: "webgl", kernelFunc: cQ };
var Rh = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t10 = e.strideHeight, o = e.strideWidth, n = e.padInfo.top, s = 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 * ${t10} - ${n};

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

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

              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);
      }
    `;
  }
};
var Dh = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t10 = e.filterHeight, o = e.filterWidth, n = e.strideHeight, s = e.strideWidth, a = t10 - 1 - e.padInfo.top, i = o - 1 - e.padInfo.left, p = e.outChannels / e.inChannels;
    this.userCode = `
      const ivec2 pads = ivec2(${a}, ${i});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${t10}; wR++) {
          float dyR = float(dyRCorner + wR) / ${n}.0;

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

          int wRPerm = ${t10} - 1 - wR;

          for (int wC = 0; wC < ${o}; wC++) {
            float dyC = float(dyCCorner + wC) / ${s}.0;

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${p}; dm++) {
              int d2 = d1 * ${p} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function lQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, filterShape: c } = o, l = S.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Rh(l);
  return t10.runWebGLProgram(m, [n, s], "float32");
}
var nR = { kernelName: yp, backendName: "webgl", kernelFunc: lQ };
function mQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { strides: a, dilations: i, pad: p, dimRoundingMode: u, inputShape: c } = o, l = S.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Dh(l);
  return t10.runWebGLProgram(m, [n, s], "float32");
}
var sR = { kernelName: bp, backendName: "webgl", kernelFunc: mQ };
var Fh = 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 dQ(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = re({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new Fh(s), p = t10.runWebGLProgram(i, [a], a.dtype), u = re({ inputs: { x: p }, backend: t10, attrs: { shape: n } });
  return t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(p), u;
}
var aR = { kernelName: si, backendName: "webgl", kernelFunc: dQ };
var Oh = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let { inHeight: t10, inWidth: o, padInfo: n, strideHeight: s, strideWidth: a, filterHeight: i, filterWidth: p, dilationHeight: u, dilationWidth: c } = e, { top: l, left: m } = n;
    this.userCode = `
      const ivec2 strides = ivec2(${s}, ${a});
      const ivec2 pads = ivec2(${l}, ${m});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${i}; h++) {
          int hIn = hBeg + h * ${u};

          if (hIn >= 0 && hIn < ${t10}) {
            for (int w = 0; w < ${p}; w++) {
              int wIn = wBeg + w * ${c};

              if (wIn >= 0 && wIn < ${o}) {
                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 fQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new Oh(u);
  c = t10.runWebGLProgram(l, [n, s], "float32");
  let m = re({ inputs: { x: c }, backend: t10, attrs: { shape: u.outShape } });
  return t10.disposeIntermediateTensorInfo(c), m;
}
var iR = { kernelName: ai, backendName: "webgl", kernelFunc: fQ };
function hQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length);
  S.checkEinsumDimSizes(a.length, p, s);
  let { path: u, steps: c } = S.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = [];
  for (let h = 0; h < l; ++h) {
    for (let g of c[h]) {
      let { permutationIndices: x, expandDims: b } = S.getEinsumPermutation(d, p[g]), C;
      S.isIdentityPermutation(x) ? C = s[g] : (C = yt({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(C));
      let w = C.shape.slice();
      for (let k = 0; k < b.length; ++k)
        w.splice(b[k], 0, 1);
      y.arraysEqual(C.shape, w) || (C = re({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = El({ inputs: { a: C, b: m }, backend: t10 }), f.push(m));
    }
    h < l - 1 && (u[h] >= 0 && (m = Vu({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
  }
  for (let h of f)
    h !== m && t10.disposeIntermediateTensorInfo(h);
  return m;
}
var uR = { kernelName: ii, backendName: "webgl", kernelFunc: hQ };
var gQ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var xQ = `
  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;
`;
var yQ = he({ opSnippet: gQ, packedOpSnippet: xQ });
var pR = { kernelName: Jo, backendName: "webgl", kernelFunc: yQ };
var bQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
var CQ = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var SQ = (r) => {
  let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = O().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new _o(CQ, o.shape, n.shape) : new io(bQ, o.shape, n.shape);
  return t10.runWebGLProgram(s, [o, n], o.dtype);
};
var cR = { kernelName: Nm, backendName: "webgl", kernelFunc: SQ };
var wQ = `
  return vec4(equal(a, b));
`;
var IQ = "return float(a == b);";
var vQ = tt({ opSnippet: IQ, packedOpSnippet: wQ, dtype: "bool", cpuKernelImpl: H$ });
var lR = { kernelName: en, backendName: "webgl", kernelFunc: vQ };
var kQ = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${S.ERF_P};
  float a1 = ${S.ERF_A1};
  float a2 = ${S.ERF_A2};
  float a3 = ${S.ERF_A3};
  float a4 = ${S.ERF_A4};
  float a5 = ${S.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));
`;
var NQ = he({ opSnippet: kQ });
var mR = { kernelName: da, backendName: "webgl", kernelFunc: NQ };
var TQ = $o + `
  return exp(x);
`;
var _Q = `
  vec4 result = exp(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
var Ww = he({ opSnippet: TQ, packedOpSnippet: _Q, cpuKernelImpl: K$, dtype: "float32" });
var dR = { kernelName: tn, backendName: "webgl", kernelFunc: Ww };
function Ph(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n;
  return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), re({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var fR = { kernelName: Ss, backendName: "webgl", kernelFunc: Ph };
var hR = "return exp(x) - 1.0;";
var $Q = he({ opSnippet: hR, packedOpSnippet: hR, cpuKernelImpl: q$ });
var gR = { kernelName: fa, backendName: "webgl", kernelFunc: $Q };
var Dl = class {
  constructor(e, t10, o) {
    this.variableNames = ["real", "imag"];
    let n = t10[1];
    this.outputShape = t10;
    let s = o ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = o ? `${n}.0` : "1.0", i;
    if (e === "real")
      i = "return real * expR - imag * expI;";
    else if (e === "imag")
      i = "return real * expI + imag * expR;";
    else
      throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);
    this.userCode = `
      const float exponentMultiplier = ${s};

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

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

        float result = 0.0;

        for (int i = 0; i < ${n}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${a};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `;
  }
};
function Mh(r, e, t10) {
  let o = t10.texData.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = re({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }), p = i.shape, u = new Dl("real", p, e), c = new Dl("imag", p, e), l = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: p }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: p }], m = t10.runWebGLProgram(u, l, "float32"), d = t10.runWebGLProgram(c, l, "float32"), f = Ar({ inputs: { real: m, imag: d }, backend: t10 });
  t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d);
  let h = re({ inputs: { x: f }, backend: t10, attrs: { shape: r.shape } });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(f), h;
}
function EQ(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e;
  return Mh(o, false, t10);
}
var xR = { kernelName: ui, backendName: "webgl", kernelFunc: EQ };
var Lh = class {
  constructor(e, t10) {
    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 qa(r) {
  let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10;
  if (s = s || y.inferDtype(n), s === "string") {
    let a = y.getArrayFromDType(s, y.sizeFromShape(o));
    return a.fill(n), e.makeTensorInfo(o, s, a);
  } else {
    let a = new Lh(o, n), i = [[n]];
    return e.runWebGLProgram(a, [], s, i);
  }
}
var yR = { kernelName: ws, backendName: "webgl", kernelFunc: qa };
var Bh = class {
  constructor(e) {
    this.variableNames = ["Image"], this.outputShape = [];
    let t10 = e[2];
    this.outputShape = e, this.userCode = `
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t10} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${t10}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var bR = { kernelName: rn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => {
  let { image: t10 } = r, o = e, n = new Bh(t10.shape);
  return o.runWebGLProgram(n, [t10], t10.dtype);
} };
var CR = "return floor(x);";
var AQ = he({ opSnippet: CR, packedOpSnippet: CR, cpuKernelImpl: j$ });
var SR = { kernelName: on, backendName: "webgl", kernelFunc: AQ };
var RQ = `
  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;
  }
`;
var DQ = `
  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);
`;
var FQ = tt({ opSnippet: RQ, packedOpSnippet: DQ, dtype: "int32" });
var wR = { kernelName: nn, backendName: "webgl", kernelFunc: FQ };
var Vh = class {
  constructor(e) {
    this.variableNames = ["A"];
    let t10 = wt(), [o, n] = e;
    this.outputShape = e, this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);

        vec4 values = ${t10.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));
      }
    `;
  }
};
var zh = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
    let t10 = wt(), [o, n] = e;
    this.outputShape = e, this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${n}.0, ${o}.0);
            vec4 values = ${t10.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);
          }
        }

        ${t10.output} = result;
      }
    `;
  }
};
var IR = { kernelName: Zi, backendName: "webgl", kernelFunc: OQ };
var Cc;
var Uw = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function OQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o, a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, [p, u] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], c = [u, p], l = [u, p, s];
  if (i || a) {
    let h = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
    (Cc == null || h !== Uw) && (Uw = h, Cc = document.createElement("canvas").getContext("2d", { willReadFrequently: Uw })), Cc.canvas.width = p, Cc.canvas.height = u, Cc.drawImage(n, 0, 0, p, u), n = Cc.canvas;
  }
  let m = t10.makeTensorInfo(c, "int32");
  t10.texData.get(m.dataId).usage = ur.PIXELS, t10.gpgpu.uploadPixelDataToTexture(t10.getTexture(m.dataId), n);
  let d = O().getBool("WEBGL_PACK") ? new zh(l) : new Vh(l), f = t10.runWebGLProgram(d, [m], "int32");
  return t10.disposeData(m.dataId), f;
}
function PQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), x, b = [], C = a != null, w = i != null, k = d === "leakyrelu", _ = () => {
    let A = [n, s], R = (D, P) => {
      if (P === "NCHW" && D.shape.length === 1 && D.shape[0] !== 1) {
        let M = re({ inputs: { x: D }, backend: t10, attrs: { shape: [D.shape[0], 1, 1] } });
        return b.push(M), M;
      }
      return D;
    };
    if (C && A.push(R(a, c)), w && A.push(R(i, c)), k) {
      let D = t10.makeTensorInfo([], "float32", y.createScalarValue(f, "float32"));
      A.push(D), b.push(D);
    }
    return 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"))
    x = Ih({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f });
  else if (g.strideWidth <= 2 && h === "channelsLast" && O().getBool("WEBGL_EXP_CONV")) {
    let A = d ? Ha(d, true) : null, R = new xc(g, C, A, w, k), D = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], P = _();
    x = t10.runWebGLProgram(R, P, "float32", D);
  } else if (O().getBool("WEBGL_CONV_IM2COL"))
    x = vh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: d, preluActivationWeights: i, leakyreluAlpha: f });
  else {
    let A = d ? Ha(d, false) : null, R = new gc(g, C, A, w, k), D = _();
    x = t10.runWebGLProgram(R, D, "float32");
  }
  let E = re({ inputs: { x }, backend: t10, attrs: { shape: g.outShape } });
  return b.push(x), b.forEach((A) => t10.disposeIntermediateTensorInfo(A)), E;
}
var vR = { kernelName: go, backendName: "webgl", kernelFunc: PQ };
function MQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = [], h = c;
  h == null && (h = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);
  let g = S.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), x = O().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? Ha(m, x) : null, C = [n, s], w = a != null, k = i != null, _ = m === "leakyrelu";
  if (w && C.push(a), k && C.push(i), _) {
    let D = t10.makeTensorInfo([], "float32", y.createScalarValue(d, "float32"));
    C.push(D), f.push(D);
  }
  let E;
  x ? E = new bc(g, w, b, k, _) : E = new yc(g, w, b, k, _);
  let A = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], R = t10.runWebGLProgram(E, C, "float32", A);
  return f.forEach((D) => t10.disposeIntermediateTensorInfo(D)), R;
}
var kR = { kernelName: xo, backendName: "webgl", kernelFunc: MQ };
var Wh = class {
  constructor(e, t10, o, n) {
    this.sliceDim = e, this.strides = t10, this.paramsShape = n, this.variableNames = ["x", "indices"], this.outputShape = o;
    let s = $e(o.length), a = `
    int index;`;
    for (let i = 0; i < this.sliceDim; i++)
      a += `
          index = round(getIndices(coords[0], ${i}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[i]};
          flattenIndex += index * ${this.strides[i]};`;
    this.userCode = `
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;

          ${a}

          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `;
  }
};
function LQ(r) {
  let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = S.prepareAndValidate(o, n), m = re({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = re({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } });
  if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
    let x = t10.readSync(n.dataId), b = t10.bufferSync(o), C = X$(x, b, o.dtype, u, a, c, l, o.shape, i);
    return t10.makeTensorInfo(p, o.dtype, C.values);
  }
  let f = new Wh(a, l, [u, c], o.shape), h = t10.runWebGLProgram(f, [d, m], d.dtype), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: p } });
  return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), g;
}
var NR = { kernelName: an, backendName: "webgl", kernelFunc: LQ };
var Uh = class {
  constructor(e, t10) {
    this.variableNames = ["A", "indices"], this.outputShape = t10, this.rank = t10.length;
    let o = $e(this.rank), n = BQ(e, 2);
    this.userCode = `
      void main() {
        ${o} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${n}));
      }
    `;
  }
};
function BQ(r, e) {
  let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [];
  for (let n = 0; n < r.length; n++)
    n === 2 ? o.push("index") : o.push(`${t10[n]}`);
  return o.join();
}
function Gw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0];
  if (O().get("DEBUG")) {
    let b = t10.readSync(s.dataId), C = n.shape[p];
    for (let w = 0; w < b.length; ++w) {
      let k = b[w];
      y.assert(k <= C - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${C - 1}]`);
    }
  }
  let u = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = re({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = re({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } });
  l.push(m), l.push(d);
  let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize];
  if (t10.shouldExecuteOnCPU([n, s]) || n.dtype === "string") {
    let b = t10.bufferSync(d), C = t10.bufferSync(m), w = Y$(C, b, f);
    return l.forEach((k) => t10.disposeIntermediateTensorInfo(k)), t10.makeTensorInfo(u.outputShape, w.dtype, w.values);
  }
  let h = new Uh(m.shape, f), g = t10.runWebGLProgram(h, [m, d], m.dtype);
  l.push(g);
  let x = re({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } });
  return l.forEach((b) => t10.disposeIntermediateTensorInfo(b)), x;
}
var TR = { kernelName: Is, backendName: "webgl", kernelFunc: Gw };
var VQ = "return float(a > b);";
var zQ = `
  return vec4(greaterThan(a, b));
`;
var WQ = tt({ opSnippet: VQ, packedOpSnippet: zQ, cpuKernelImpl: Q$, dtype: "bool" });
var _R = { kernelName: un, backendName: "webgl", kernelFunc: WQ };
var UQ = "return float(a >= b);";
var GQ = `
  return vec4(greaterThanEqual(a, b));
`;
var HQ = tt({ opSnippet: UQ, packedOpSnippet: GQ, dtype: "bool", cpuKernelImpl: Z$ });
var $R = { kernelName: pn, backendName: "webgl", kernelFunc: HQ };
function KQ(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e;
  return Mh(o, true, t10);
}
var ER = { kernelName: pi, backendName: "webgl", kernelFunc: KQ };
var qQ = "return float(!isnan(x) && !isinf(x));";
var jQ = he({ opSnippet: qQ, dtype: "bool" });
var AR = { kernelName: ha, backendName: "webgl", kernelFunc: jQ };
var XQ = "return float(isinf(x));";
var YQ = he({ opSnippet: XQ, dtype: "bool" });
var RR = { kernelName: ga, backendName: "webgl", kernelFunc: YQ };
var QQ = "return float(isnan(x));";
var ZQ = he({ opSnippet: QQ, dtype: "bool" });
var DR = { kernelName: cn, backendName: "webgl", kernelFunc: ZQ };
var JQ = "return float(a < b);";
var e7 = `
  return vec4(lessThan(a, b));
`;
var t7 = tt({ opSnippet: JQ, packedOpSnippet: e7, cpuKernelImpl: J$, dtype: "bool" });
var FR = { kernelName: mn, backendName: "webgl", kernelFunc: t7 };
var r7 = "return float(a <= b);";
var o7 = `
  return vec4(lessThanEqual(a, b));
`;
var n7 = tt({ opSnippet: r7, packedOpSnippet: o7, cpuKernelImpl: eE, dtype: "bool" });
var OR = { kernelName: dn, backendName: "webgl", kernelFunc: n7 };
function s7(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = tE(o, n, s);
  return e.makeTensorInfo([a.length], "float32", a);
}
var PR = { kernelName: li, backendName: "webgl", kernelFunc: s7 };
var a7 = $o + `
  return x < 0.0 ? 0./0. : log(x);
`;
var i7 = `
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`;
var u7 = he({ opSnippet: a7, packedOpSnippet: i7, cpuKernelImpl: rE });
var MR = { kernelName: fn, backendName: "webgl", kernelFunc: u7 };
var p7 = $o + `
  return log(1.0 + x);
`;
var c7 = he({ opSnippet: p7 });
var LR = { kernelName: xa, backendName: "webgl", kernelFunc: c7 };
var l7 = "return float(a >= 1.0 && b >= 1.0);";
var m7 = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
var d7 = tt({ opSnippet: l7, packedOpSnippet: m7, dtype: "bool" });
var BR = { kernelName: hn, backendName: "webgl", kernelFunc: d7 };
var f7 = "return float(!(x >= 1.0));";
var h7 = he({ opSnippet: f7 });
var VR = { kernelName: gn, backendName: "webgl", kernelFunc: h7 };
var g7 = "return float(a >= 1.0 || b >= 1.0);";
var x7 = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
var y7 = tt({ opSnippet: g7, packedOpSnippet: x7, dtype: "bool" });
var zR = { kernelName: xn, backendName: "webgl", kernelFunc: y7 };
var Gh = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["x"], this.outputShape = [];
    let a = t10, i = e[3] - 1;
    this.outputShape = e;
    let p, u = `float(${o}) + float(${n}) * sum`;
    s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${u}) * float(-${s}));`, this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${i}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${p};
        setOutput(val);
      }
    `;
  }
};
var Hh = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
    let a = t10, i = e[3] - 1;
    this.outputShape = e;
    let p, u = `float(${o}) + float(${n}) * sum`;
    s === 0.5 ? p = `inversesqrt(${u})` : s === 1 ? p = `1.0/(${u})` : p = `exp(log(${u}) * float(-${s}));`, 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 <= ${a}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${p};
        setOutput(result);
      }
    `;
  }
};
var b7 = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = O().getBool("WEBGL_PACK_NORMALIZATION") ? new Hh(n.shape, s, a, i, p) : new Gh(n.shape, s, a, i, p);
  return t10.runWebGLProgram(u, [n], n.dtype);
};
var WR = { kernelName: mi, backendName: "webgl", kernelFunc: b7 };
var Kh = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t10, this.bias = o, this.alpha = n, this.beta = s, 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 - ${t10})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t10} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${n}) * norm + float(${o});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${n})
                * float(${s})
                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `;
  }
};
var C7 = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new Kh(n.shape, i, p, u, c);
  return t10.runWebGLProgram(l, [n, s, a], n.dtype);
};
var UR = { kernelName: Tm, backendName: "webgl", kernelFunc: C7 };
function GR(r, e, t10, o) {
  let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = re({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Ur(i, r.dtype, "max", o), u = re({ inputs: { x: p }, attrs: { shape: t10 }, backend: o });
  return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
function Hw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = c != null, m = t10.shouldExecuteOnCPU([n]), d = n;
  if (l) {
    if (m) {
      let C = t10.texData.get(d.dataId).values, w = new Array(i);
      for (let E = 0; E < w.length; E++)
        w[E] = n.shape[c[E]];
      let k = Bu(C, n.shape, n.dtype, c, w);
      d = t10.makeTensorInfo(w, n.dtype);
      let _ = t10.texData.get(d.dataId);
      _.values = k;
    } else
      d = zi(n, c, t10);
    u = S.getInnerMostAxes(u.length, i);
  }
  S.assertAxesAreInnerMostDims("max", u, i);
  let [f, h] = S.computeOutAndReduceShapes(d.shape, u), g = f;
  a && (g = S.expandShapeToKeepDim(f, p));
  let x;
  if (m) {
    let C = t10.texData.get(d.dataId).values, w = oE(C, y.sizeFromShape(h), g, n.dtype);
    x = t10.makeTensorInfo(g, n.dtype);
    let k = t10.texData.get(x.dataId);
    k.values = w;
  } else
    x = GR(d, h, g, t10);
  return l && t10.disposeIntermediateTensorInfo(d), x;
}
var HR = { kernelName: yn, backendName: "webgl", kernelFunc: Hw };
var S7 = dc + `
  return max(a, b);
`;
var w7 = `
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + Js + `
  return result;
`;
var I7 = tt({ opSnippet: S7, packedOpSnippet: w7, cpuKernelImpl: nE });
var KR = { kernelName: bn, backendName: "webgl", kernelFunc: I7 };
function v7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  us(n, "maxPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  y.assert(S.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = S.computePool2DInfo(n.shape, s, a, u, i, p);
  if (c.filterWidth === 1 && c.filterHeight === 1 && y.arraysEqual(c.inShape, c.outShape))
    return At({ inputs: { x: n }, backend: t10 });
  let l = new cs(c, "max", false);
  return t10.runWebGLProgram(l, [n], n.dtype);
}
var qR = { kernelName: Cn, backendName: "webgl", kernelFunc: v7 };
function k7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dataFormat: p, dimRoundingMode: u } = o, c = [1, 1, 1], l = S.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new Wi(l, "max", false);
  return t10.runWebGLProgram(m, [n], n.dtype);
}
var jR = { kernelName: Cp, backendName: "webgl", kernelFunc: k7 };
var qh = class {
  constructor(e) {
    this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
    let t10 = e.strideHeight, o = e.strideWidth, n = e.dilationHeight, s = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = s - 1 - e.padInfo.top, p = a - 1 - e.padInfo.left, u = s * a - 1;
    this.userCode = `
      const ivec2 pads = ivec2(${i}, ${p});

      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 < ${s};
          wR += ${n}) {
          float dyR = float(dyRCorner + wR) / ${t10}.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) / ${o}.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 = ${u} - 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);
      }
    `;
  }
};
var jh = class {
  constructor(e) {
    this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
    let t10 = e.strideDepth, o = e.strideHeight, n = e.strideWidth, s = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, p = e.effectiveFilterDepth, u = e.effectiveFilterHeight, c = e.effectiveFilterWidth, l = p - 1 - e.padInfo.front, m = u - 1 - e.padInfo.top, d = c - 1 - e.padInfo.left, f = p * u * c - 1;
    this.userCode = `
      const ivec3 pads = ivec3(${l}, ${m}, ${d});

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

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

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

        for (int wD = 0; wD < ${p};
           wD += ${s}) {
          float dyD = float(dyDCorner + wD) / ${t10}.0;

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

          for (int wR = 0; wR < ${u};
              wR += ${a}) {
            float dyR = float(dyRCorner + wR) / ${o}.0;

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

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

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

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${f} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${u} * ${c} +
                  wR * ${c} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function N7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s, { filterSize: i, strides: p, pad: u, dimRoundingMode: c } = o, l = [1, 1, 1], m = S.computePool3DInfo(a.shape, i, p, l, u, c), d = new Wi(m, "max", true), f = t10.runWebGLProgram(d, [a], a.dtype), h = new jh(m), g = t10.runWebGLProgram(h, [n, f], a.dtype);
  return t10.disposeIntermediateTensorInfo(f), g;
}
var XR = { kernelName: $m, backendName: "webgl", kernelFunc: N7 };
function T7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
  us([s, a], "maxPoolGrad");
  let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = S.computePool2DInfo(i.shape, p, u, 1, c, l), d = true, f = new cs(m, "max", d), h = t10.runWebGLProgram(f, [i], i.dtype), g = new qh(m), x = t10.runWebGLProgram(g, [n, h], i.dtype);
  return t10.disposeIntermediateTensorInfo(h), x;
}
var YR = { kernelName: _m, backendName: "webgl", kernelFunc: T7 };
function QR(r, e, t10, o) {
  let n = new cs(t10, "max", false), s = o.runWebGLProgram(n, [r], "float32");
  n = new cs(t10, "max", true, true, e);
  let a = o.runWebGLProgram(n, [r], "float32");
  return [s, a];
}
var ZR = { kernelName: Sp, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10;
  y.assert(o.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);
  let u = [1, 1];
  y.assert(S.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);
  let c = S.computePool2DInfo(o.shape, n, s, u, a), [l, m] = QR(o, i, c, p);
  return [l, m];
} };
function JR(r, e, t10, o) {
  let n = y.sizeFromShape(e), a = y.sizeFromShape(r.shape) / n, i = re({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = Ur(i, "float32", "mean", o), u = re({ inputs: { x: p }, attrs: { shape: t10 }, backend: o });
  return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
var eD = { kernelName: Sn, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o } = r, { keepDims: n, axis: s } = e, a = t10, i = o.shape.length, p = y.parseAxisParam(s, o.shape), u = p, c = S.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), d = [], f = o;
  if (l) {
    if (m) {
      let w = a.texData.get(f.dataId).values, k = new Array(i);
      for (let A = 0; A < k.length; A++)
        k[A] = o.shape[c[A]];
      let _ = Bu(w, o.shape, o.dtype, c, k);
      f = a.makeTensorInfo(k, o.dtype);
      let E = a.texData.get(f.dataId);
      E.values = _;
    } else
      f = zi(o, c, a);
    d.push(f), u = S.getInnerMostAxes(u.length, i);
  }
  S.assertAxesAreInnerMostDims("sum", u, i);
  let [h, g] = S.computeOutAndReduceShapes(f.shape, u), x = h;
  n && (x = S.expandShapeToKeepDim(h, p));
  let b = JR(f, g, x, a);
  for (let C of d)
    a.disposeIntermediateTensorInfo(C);
  return b;
} };
function _7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = y.parseAxisParam(s, n.shape), u = p, c = S.getAxesPermutation(u, i), l = n;
  c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = S.getInnerMostAxes(u.length, n.shape.length)), S.assertAxesAreInnerMostDims("min", u, i);
  let [m, d] = S.computeOutAndReduceShapes(l.shape, u), f = y.sizeFromShape(d), h = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } }), g = Ur(h, h.dtype, "min", t10), x;
  if (a) {
    let b = S.expandShapeToKeepDim(m, p);
    x = re({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
  } else
    x = re({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), x;
}
var tD = { kernelName: wn, backendName: "webgl", kernelFunc: _7 };
var $7 = dc + `
  return min(a, b);
`;
var E7 = `
  vec4 result = vec4(min(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + Js + `
  return result;
`;
var A7 = tt({ opSnippet: $7, packedOpSnippet: E7, cpuKernelImpl: sE });
var rD = { kernelName: In, backendName: "webgl", kernelFunc: A7 };
var Xh = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.outputShape = t10.map((c, l) => c[0] + e[l] + c[1]);
    let n = e.length, s = $e(n), a = t10.map((c) => c[0]).join(","), i = t10.map((c, l) => c[0] + e[l]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n), u = o === "reflect" ? 0 : 1;
    if (n === 1) {
      this.userCode = `
        int start = ${a};
        int end = ${i};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${u};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${u};
          }
          setOutput(getX(outC - start));
        }
      `;
      return;
    }
    this.userCode = `
      ${s} start = ${s}(${a});
      ${s} end = ${s}(${i});

      void main() {
        ${s} outC = getOutputCoords();
        for (int i = 0; i < ${n}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${u};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
          }
        }
        ${s} coords = outC - start;
        setOutput(getX(${p}));
      }
    `;
  }
};
var Yh = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t10.map((f, h) => f[0] + e[h] + f[1]);
    let n = e.length, s = $e(n), a = t10.map((f) => f[0]).join(","), i = t10.map((f, h) => f[0] + e[h]).join(","), p = Et("rc", n), u = Et("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, d = "";
    if (n === 1) {
      let f = `
        ${s} source = rc;
        if (source < start) {
          source = start * 2 - source - ${m};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${m};
        }
        source -= start;
      `;
      d = `
        ${s} rc = outputLoc;
        ${f}
        result[0] = getChannel(getX(${u.join()}), ${l});
        ${p[n - 1]} += 1;
        if(${c}) {
          ${f}
          result[1] = getChannel(getX(${u.join()}), ${l});
        }
      `;
    } else {
      let f = `
        ${s} source = rc;
        ${s} lt = ${s}(lessThan(source, start));
        ${s} gte = ${s}(greaterThanEqual(source, end));
        ${s} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${m}) +
                gte * ((end - 1) * 2 - source + ${m});
        source -= start;
      `;
      d = `
        ${s} rc = outputLoc;
        ${f}
        result[0] = getChannel(getX(${u.join()}), ${l});
        ${p[n - 1]} += 1;
        if(${c}) {
          ${f}
          result[1] = getChannel(getX(${u.join()}), ${l});
        }
        rc = outputLoc;
        ${p[n - 2]} += 1;
        if(${p[n - 2]} < ${this.outputShape[n - 2]}) {
          ${f}
          result[2] = getChannel(getX(${u.join()}), ${l});
          ${p[n - 1]} += 1;
          if(${c}) {
            ${f}
            result[3] = getChannel(getX(${u.join()}), ${l});
          }
        }
      `;
    }
    this.userCode = `
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${i});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `;
  }
};
var R7 = ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o } = r, { paddings: n, mode: s } = t10, a = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new Yh(o.shape, n, s) : new Xh(o.shape, n, s);
  return e.runWebGLProgram(a, [o], o.dtype);
};
var oD = { kernelName: vn, backendName: "webgl", kernelFunc: R7 };
var D7 = `if (b == 0.0) return NAN;
  return mod(a, b);`;
var F7 = `
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  ` + Js + `
  return result;
`;
var O7 = tt({ opSnippet: D7, packedOpSnippet: F7 });
var nD = { kernelName: ya, backendName: "webgl", kernelFunc: O7 };
var Qh = class {
  constructor(e, t10, o) {
    this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, o], this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

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

        for (int i = 0; i < ${t10 - 1}; i++) {
          cdf += getProbs(batch, i);

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

        // If no other event happened, last event happened.
        setOutput(float(${t10 - 1}));
      }
    `;
  }
};
var P7 = `
if (a == b) {
  return 1.0;
};
return a / b;`;
var M7 = `
  // 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;
`;
var Kw = tt({ opSnippet: P7, packedOpSnippet: M7, checkOutOfBounds: true });
var sD = { kernelName: Zo, backendName: "webgl", kernelFunc: Kw };
var aD = "return a - b;";
var qw = tt({ opSnippet: aD, packedOpSnippet: aD, supportsComplex: true, cpuKernelImpl: kE });
var iD = { kernelName: Yn, backendName: "webgl", kernelFunc: qw };
function jw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Hw({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = S.expandShapeToKeepDim(i.shape, a), u = re({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = qw({ inputs: { a: n, b: u }, backend: t10 }), l = Ww({ inputs: { x: c }, backend: t10 }), m = Vu({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = re({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = Kw({ inputs: { a: l, b: d }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(d), f;
}
var uD = { kernelName: qn, backendName: "webgl", kernelFunc: jw };
function L7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : jw({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new Qh(u, c, s), m = [[a]], d = t10.runWebGLProgram(l, [p], "int32", m);
  return i || t10.disposeIntermediateTensorInfo(p), d;
}
var pD = { kernelName: wp, backendName: "webgl", kernelFunc: L7 };
var B7 = zt + `
  return -x;
`;
var V7 = `
  vec4 result = -x;
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
function z7(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (t10.shouldExecuteOnCPU([o])) {
    let s = t10.texData.get(o.dataId), [a, i] = iE(s.values, o.shape, o.dtype);
    return t10.makeTensorInfo(i, o.dtype, a);
  }
  let n;
  return O().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new Er(o.shape, V7) : n = new Jt(o.shape, B7), t10.runWebGLProgram(n, [o], o.dtype);
}
var cD = { kernelName: vs, backendName: "webgl", kernelFunc: z7 };
var W7 = Vt.nonMaxSuppressionV3Impl;
function U7(r) {
  S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = W7(u, c, a, i, p);
  return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var lD = { kernelName: Tn, backendName: "webgl", kernelFunc: U7 };
var G7 = Vt.nonMaxSuppressionV4Impl;
function H7(r) {
  S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), { selectedIndices: m, validOutputs: d } = G7(c, l, a, i, p, u);
  return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([d]))];
}
var mD = { kernelName: ba, backendName: "webgl", kernelFunc: H7 };
var K7 = Vt.nonMaxSuppressionV5Impl;
function q7(r) {
  S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = K7(c, l, m, d, f, h);
  return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var dD = { kernelName: _n, backendName: "webgl", kernelFunc: q7 };
var Zh = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["indices"], this.outputShape = [e, t10], this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${n}), float(${o}),
                      float(index == coords.y)));
      }
    `;
  }
};
var j7 = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new Zh(u, a, i, p), l = re({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = t10.runWebGLProgram(c, [l], s);
  t10.disposeIntermediateTensorInfo(l);
  let d = [...n.shape, a], f = re({ inputs: { x: m }, backend: t10, attrs: { shape: d } });
  return t10.disposeIntermediateTensorInfo(m), f;
};
var fD = { kernelName: $n, backendName: "webgl", kernelFunc: j7 };
function Fl(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "complex64") {
    let n = Ka({ inputs: { input: o }, backend: t10 }), s = Fl({ inputs: { x: n }, backend: t10 }), a = Wu({ inputs: { input: o }, backend: t10 }), i = Fl({ inputs: { x: a }, backend: t10 }), p = Ar({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
  } else
    return qa({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 });
}
var hD = { kernelName: Os, backendName: "webgl", kernelFunc: Fl };
function gD(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "string")
    throw new Error("onesLike is not supported under string dtype");
  if (o.dtype === "complex64") {
    let n = Ka({ inputs: { input: o }, backend: t10 }), s = gD({ inputs: { x: n }, backend: t10 }), a = Wu({ inputs: { input: o }, backend: t10 }), i = Fl({ inputs: { x: a }, backend: t10 }), p = Ar({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
  } else
    return qa({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 });
}
var xD = { kernelName: ks, backendName: "webgl", kernelFunc: gD };
function X7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return Ph({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = Ph({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = zw({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u;
}
var yD = { kernelName: Ns, backendName: "webgl", kernelFunc: X7 };
var Jh = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((u, c) => u[0] + e[c] + u[1]);
    let n = e.length, s = $e(n), a = t10.map((u) => u[0]).join(","), i = t10.map((u, c) => u[0] + e[c]).join(","), p = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, n);
    if (n === 1) {
      this.userCode = `
        int start = ${a};
        int end = ${i};

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

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${p}));
        }
      }
    `;
  }
};
var eg = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t10.map((h, g) => h[0] + e[g] + h[1]);
    let n = e.length, s = $e(n), a = t10.map((h) => h[0]).join(","), i = t10.map((h, g) => h[0] + e[g]).join(","), p = Et("rc", n), u = Et("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = [`${s} rc = outputLoc;`, `${p[n - 1]} += 1;
       if(${c}) {
      `, n === 1 ? "" : `}
       rc = outputLoc;
       ${p[n - 2]} += 1;
       if(${p[n - 2]} < ${this.outputShape[n - 2]}) {`, n === 1 ? "" : `  ${p[n - 1]} += 1;
         if(${c}) {`], d = n === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))", f = "";
    for (let h = 0, g = n === 1 ? 2 : 4; h < g; h++)
      f += `
        ${m[h]}
        if (${d}) {
          result[${h}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${h}] = getChannel(getX(${u.join()}), ${l});
        }
      `;
    f += n === 1 ? "} " : "}}", this.userCode = `
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${i});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${f}
        setOutput(result);
      }
    `;
  }
};
var Xw = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
  if (y.sizeFromShape(n.shape) === 0) {
    let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
    return qa({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } });
  }
  let i = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new eg(n.shape, s, a) : new Jh(n.shape, s, a), p = [[a]];
  return t10.runWebGLProgram(i, [n], n.dtype, p);
};
var bD = { kernelName: En, backendName: "webgl", kernelFunc: Xw };
var Y7 = `
  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);
`;
var Q7 = `
  // 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;

  bvec4 isNaN1 = lessThan(a, vec4(0.0));
  bvec4 isNaN2 = lessThan(floor(b), b);
  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
  ` + Js + `
  return result;
`;
var Z7 = tt({ opSnippet: Y7, packedOpSnippet: Q7 });
var CD = { kernelName: An, backendName: "webgl", kernelFunc: Z7 };
function J7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = y.parseAxisParam(s, n.shape), c = u, l = S.getAxesPermutation(c, i), m = n;
  l != null && (m = yt({ inputs: { x: n }, backend: t10, attrs: { perm: l } }), c = S.getInnerMostAxes(c.length, i), p.push(m)), S.assertAxesAreInnerMostDims("prod", c, i);
  let d;
  if (t10.shouldExecuteOnCPU([m])) {
    let f = t10.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: x } = pE(m.shape, m.dtype, f, c);
    d = t10.makeTensorInfo(g, x, h);
  } else {
    let [f, h] = S.computeOutAndReduceShapes(m.shape, c), g = y.sizeFromShape(h), x = re({ inputs: { x: m }, backend: t10, attrs: { shape: [-1, g] } }), b = Ta(n.dtype), C = Ur(x, b, "prod", t10);
    d = re({ inputs: { x: C }, backend: t10, attrs: { shape: f } }), p.push(x), p.push(C);
  }
  if (a) {
    p.push(d);
    let f = S.expandShapeToKeepDim(d.shape, u);
    d = re({ inputs: { x: d }, backend: t10, attrs: { shape: f } });
  }
  return p.forEach((f) => t10.disposeIntermediateTensorInfo(f)), d;
}
var SD = { kernelName: Dn, backendName: "webgl", kernelFunc: J7 };
function eZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((x) => t10.readSync(x.dataId)), u = n.map((x) => x.shape), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), [m, d, f] = cE(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((x) => t10.makeTensorInfo([x.length], "int32", x)), g = t10.makeTensorInfo(f, s.dtype, d);
  return h.concat([g]);
}
var wD = { kernelName: Ip, backendName: "webgl", kernelFunc: eZ };
function tZ(r) {
  let { inputs: e, backend: t10 } = r, { starts: o, limits: n, deltas: s } = e, a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = lE(a, o.shape, o.dtype, i, n.shape, p, s.shape), l = t10.makeTensorInfo([u.length], "int32", u), m = t10.makeTensorInfo([c.length], o.dtype, c);
  return [l, m];
}
var ID = { kernelName: vp, backendName: "webgl", kernelFunc: tZ };
function rZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { shape: n, values: s, defaultValue: a, rowPartitionTensors: i } = e, { rowPartitionTypes: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), m = i.map((g) => t10.readSync(g.dataId)), d = i.map((g) => g.shape), [f, h] = mE(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, d, p);
  return t10.makeTensorInfo(f, s.dtype, h);
}
var vD = { kernelName: kp, backendName: "webgl", kernelFunc: rZ };
var Yw = (r) => {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = dE(o, n, s, a);
  return e.makeTensorInfo([i.length], a, i);
};
var kD = { kernelName: Ts, backendName: "webgl", kernelFunc: Yw };
var oZ = "return 1.0 / x;";
var nZ = he({ opSnippet: oZ });
var ND = { kernelName: Fn, backendName: "webgl", kernelFunc: nZ };
var sZ = zt + `
  return (x < 0.0) ? 0.0 : x;
`;
var aZ = `
  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;
`;
var iZ = he({ opSnippet: sZ, packedOpSnippet: aZ });
var TD = { kernelName: On, backendName: "webgl", kernelFunc: iZ };
var uZ = zt + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var pZ = `
  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;
`;
var cZ = he({ opSnippet: uZ, packedOpSnippet: pZ });
var _D = { kernelName: Ln, backendName: "webgl", kernelFunc: cZ };
var tg = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["A"], this.outputShape = [];
    let [a, i, p, u] = e;
    this.outputShape = [a, t10, o, u];
    let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m;
    s ? m = "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)" : m = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0] / l[0]},
          ${c[1] / l[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${m};

        // 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);
      }
    `;
  }
};
var rg = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
    let [a, i, p, u] = e;
    this.outputShape = [a, t10, o, u];
    let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m;
    s ? m = "(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)" : m = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0] / l[0]},
          ${c[1] / l[1]},
          ${c[1] / l[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,
                                     ${p}.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 = ${m};

        // 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 < ${u - 1};
        bool hasNextRow = coords.z < ${o - 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 lZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = O().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new rg(n.shape, p, u, s, a) : new tg(n.shape, p, u, s, a);
  return t10.runWebGLProgram(c, [n], "float32");
}
var $D = { kernelName: Mn, backendName: "webgl", kernelFunc: lZ };
var og = class {
  constructor(e, t10, o) {
    this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10;
    let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2;
    this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${c});
        const float widthScale = float(${l});

        const float invHeightScale = float(${m});
        const float invWidthScale = float(${d});

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

        // 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 >= ${i}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${n - 1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${s - 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 mZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new og(s.shape, n.shape, a);
  return t10.runWebGLProgram(i, [s], s.dtype);
}
var ED = { kernelName: Am, backendName: "webgl", kernelFunc: mZ };
var ng = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["A"], this.outputShape = [];
    let [a, i, p, u] = e;
    this.outputShape = [a, t10, o, u];
    let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d;
    s ? d = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : d = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0] / l[0]},
          ${c[1] / l[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${d};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `;
  }
};
var sg = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
    let [a, i, p, u] = e;
    this.outputShape = [a, t10, o, u];
    let c = [n && t10 > 1 ? i - 1 : i, n && o > 1 ? p - 1 : p], l = [n && t10 > 1 ? t10 - 1 : t10, n && o > 1 ? o - 1 : o], m = n ? "0.5" : "0.0", d;
    s ? d = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : d = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0] / l[0]},
          ${c[1] / l[1]},
          ${c[1] / l[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,
                                     ${p}.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 coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${u - 1};
        bool hasNextRow = coords.z < ${o - 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 dZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = O().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new sg(n.shape, p, u, s, a) : new ng(n.shape, p, u, s, a);
  return t10.runWebGLProgram(c, [n], n.dtype);
}
var AD = { kernelName: Pn, backendName: "webgl", kernelFunc: dZ };
var ag = class {
  constructor(e, t10, o) {
    this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t10;
    let [, n, s] = t10, [, a, i] = e, p = [o && a > 1 ? n - 1 : n, o && i > 1 ? s - 1 : s], u = [o && a > 1 ? a - 1 : a, o && i > 1 ? i - 1 : i], c = p[0] / u[0], l = p[1] / u[1], m = 1 / c, d = 1 / l, f = Math.ceil(m) * 2 + 2, h = Math.ceil(d) * 2 + 2;
    this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${c});
        const float widthScale = float(${l});

        const float invHeightScale = float(${m});
        const float invWidthScale = float(${d});

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

        // 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 >= ${i}) {
              continue;
            }

            float sourceFracRow =
              float(${p[0]}) *
                (float(dyR) / float(${u[0]}));

            float sourceFracCol =
                float(${p[1]}) *
                  (float(dyC) / float(${u[1]}));

            int sourceNearestRow = int(min(
                float(int(${n}) - 1),
                ${o} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

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

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

        setOutput(accumulator);
      }
    `;
  }
};
function fZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new ag(s.shape, n.shape, a);
  return t10.runWebGLProgram(i, [s], s.dtype);
}
var RD = { kernelName: Em, backendName: "webgl", kernelFunc: fZ };
var ig = class {
  constructor(e, t10) {
    this.variableNames = ["x"];
    let o = e.length;
    if (o > 4)
      throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);
    if (this.outputShape = e, o === 1) {
      this.userCode = `
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;
      return;
    }
    let n = (i) => t10.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, s = e.map((i, p) => n(p)).join(","), a = $e(o);
    this.userCode = `
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `;
  }
};
var ug = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true;
    let o = e.length;
    if (o > 4)
      throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);
    this.outputShape = e;
    let n = Et("rc", o), s = `${n[o - 1]} + 1 < ${this.outputShape[o - 1]}`, a = `${n[o - 2]} + 1 < ${this.outputShape[o - 2]}`, i = $e(o);
    o === 1 ? this.userCode = `
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      ` : this.userCode = `
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${p(n.slice())};
          if(${s}){
            result.g = ${u(n.slice())};
          }
          if(${a}) {
            result.b = ${c(n.slice())};
            if(${s}) {
              result.a = ${l(n.slice())};
            }
          }
          setOutput(result);
        }
    `;
    function p(f) {
      return m(f);
    }
    function u(f) {
      return f[o - 1] = "(" + f[o - 1] + " + 1)", m(f);
    }
    function c(f) {
      return f[o - 2] = "(" + f[o - 2] + " + 1)", m(f);
    }
    function l(f) {
      return f[o - 1] = "(" + f[o - 1] + " + 1)", f[o - 2] = "(" + f[o - 2] + " + 1)", m(f);
    }
    function m(f) {
      let h = e.map((b, C) => d(C, f)), g = h.join(","), x = h.slice(-2).join(",");
      return `getChannel(getX(${g}), vec2(${x}))`;
    }
    function d(f, h) {
      return t10.indexOf(f) !== -1 && e[f] !== 1 ? `${e[f]} - ${h[f]} - 1` : `${h[f]}`;
    }
  }
};
function hZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = y.parseAxisParam(s, n.shape);
  if (a === 0)
    return At({ inputs: { x: n }, backend: t10 });
  let p = O().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ug(n.shape, i) : new ig(n.shape, i);
  return t10.runWebGLProgram(p, [n], n.dtype);
}
var DD = { kernelName: Bn, backendName: "webgl", kernelFunc: hZ };
var pg = class {
  constructor(e, t10) {
    this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
    let o = e[1], n = e[2];
    this.outputShape = e;
    let s = "";
    typeof t10 == "number" ? s = `float outputValue = ${t10.toFixed(2)};` : s = `
        vec3 fill = vec3(${t10.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]));
          ${s}
          if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var FD = { kernelName: ts, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new pg(o.shape, s), [u, c] = S.getImageCenter(a, o.shape[1], o.shape[2]), l = [[u, c, Math.sin(n), Math.cos(n)]];
  return i.runWebGLProgram(p, [o], o.dtype, l);
} };
var gZ = `
  // 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;
    }
  }
`;
var xZ = he({ opSnippet: gZ });
var OD = { kernelName: Vn, backendName: "webgl", kernelFunc: xZ };
var yZ = "return inversesqrt(x);";
var bZ = he({ opSnippet: yZ, cpuKernelImpl: fE });
var PD = { kernelName: zn, backendName: "webgl", kernelFunc: bZ };
var Sc = class {
  constructor(e, t10, o, n, s, a, i = true) {
    this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
    let p = $e(s.length), u = $e(a.length), c = "";
    o === 1 ? c = "i" : o === 2 && (c = "i, j");
    let l = `getIndices(${c})`, m = "";
    n === 1 ? m = "i" : n === 2 && (m = "i, coords[1]");
    let d = `getUpdates(${m})`, f = t10 > 1 ? "strides[j]" : "strides";
    this.userCode = `
        ${p} strides = ${p}(${s});

        void main() {
          ${u} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${e}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${t10}; j++) {
              int index = round(${l});
              flattenedIndex += index * ${f};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `;
  }
};
function CZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = [l / u, u];
  if (l === 0)
    return t10.makeTensorInfo(a, n.dtype);
  let d = re({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = re({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = t10.makeTensorInfo([], "float32", new Float32Array([0])), g = new Sc(p, i, d.shape.length, f.shape.length, c, m), x = t10.runWebGLProgram(g, [f, d, h], f.dtype), b = re({ inputs: { x }, backend: t10, attrs: { shape: a } });
  return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(x), t10.disposeIntermediateTensorInfo(h), b;
}
var MD = { kernelName: Wn, backendName: "webgl", kernelFunc: CZ };
var cg = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [e, o];
    let s = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t10 + 1))}; ++i) { if (left >= right) break;`, i = O().getNumber("WEBGL_VERSION") === 2 ? s : a, p = n === "left" ? "<" : "<=";
    this.userCode = `
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${i}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${p} value) {
             left = mid + 1;
           } else {
             right = mid;
           }
         }
         return right;
       }

       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int valueIndex = coords[1];

         float value = getValues(batch, valueIndex);

         setOutput(float(findBound(batch, value)));
       }
     `;
  }
};
function SZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new cg(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]];
  return t10.runWebGLProgram(i, [n, s], "int32", p);
}
var LD = { kernelName: fi, backendName: "webgl", kernelFunc: SZ };
var lg = class {
  constructor(e, t10, o) {
    this.variableNames = ["c", "a", "b"], this.outputShape = t10;
    let n, s;
    if (o > 4)
      throw Error(`Where for rank ${o} is not yet supported`);
    if (o === 1)
      s = "resRC", n = "resRC";
    else {
      let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], p = [], u = [];
      for (let c = 0; c < t10.length; c++)
        u.push(`${i[c]}`), c < e && p.push(`${i[c]}`);
      n = p.join(), s = u.join();
    }
    let a = $e(o);
    this.userCode = `
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${n});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `;
  }
};
function wZ(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new lg(o.shape.length, n.shape, n.shape.length);
  return t10.runWebGLProgram(a, [o, n, s], dt(n.dtype, s.dtype));
}
var BD = { kernelName: $s, backendName: "webgl", kernelFunc: wZ };
var IZ = `
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${S.SELU_SCALEALPHA};
  float scale = ${S.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var vZ = he({ opSnippet: IZ });
var VD = { kernelName: Ca, backendName: "webgl", kernelFunc: vZ };
var kZ = $o + `
  return 1.0 / (1.0 + exp(-1.0 * x));
`;
var NZ = `
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
var TZ = he({ opSnippet: kZ, packedOpSnippet: NZ, cpuKernelImpl: gE });
var zD = { kernelName: Gn, backendName: "webgl", kernelFunc: TZ };
var _Z = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
var $Z = he({ opSnippet: _Z });
var WD = { kernelName: wa, backendName: "webgl", kernelFunc: $Z };
var EZ = $o + `
  return sin(x);
`;
var AZ = he({ opSnippet: EZ });
var UD = { kernelName: Un, backendName: "webgl", kernelFunc: AZ };
var RZ = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var DZ = he({ opSnippet: RZ });
var GD = { kernelName: Sa, backendName: "webgl", kernelFunc: DZ };
var FZ = `
  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;
`;
var OZ = he({ opSnippet: FZ });
var HD = { kernelName: Ia, backendName: "webgl", kernelFunc: OZ };
var PZ = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
  y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
  let i = s.reduce((x, b) => x * b), p = [[0, 0]];
  p.push(...a);
  for (let x = 1 + s.length; x < n.shape.length; ++x)
    p.push([0, 0]);
  let u = [], c = Xw({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = S.getReshaped(c.shape, s, i, false), m = S.getPermuted(l.length, s.length, false), d = S.getReshapedPermuted(c.shape, s, i, false), f = re({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = yt({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: d } });
  return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeIntermediateTensorInfo(x)), g;
};
var KD = { kernelName: As, backendName: "webgl", kernelFunc: PZ };
function MZ(r) {
  let { inputs: e, backend: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = e;
  if (s.shape.length !== 1)
    throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);
  if (o.shape.length !== 2)
    throw new Error(`Indices must be a matrix, saw:
         ${o.shape}`);
  if (n.shape.length !== 1)
    throw new Error(`Values must be a vector, saw:
         ${n.shape}`);
  if (a.shape.length !== 0)
    throw new Error(`Default value must be a scalar, saw:
        ${a.shape}`);
  let i = t10.readSync(o.dataId), p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = t10.readSync(a.dataId)[0], [l, m, d, f, h] = yE(i, o.shape, o.dtype, p, n.dtype, u, c);
  return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, d), t10.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var qD = { kernelName: hi, backendName: "webgl", kernelFunc: MZ };
function LZ(r) {
  let { inputs: e, backend: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = e;
  if (o.shape.length !== 2)
    throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);
  if (n.shape.length !== 1)
    throw new Error(`Input shape should be a vector but received shape ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Target shape should be a vector but received shape ${s.shape}`);
  let a = Array.from(t10.readSync(n.dataId)), i = t10.readSync(o.dataId), p = Array.from(t10.readSync(s.dataId)), [u, c, l] = bE(i, o.shape, o.dtype, a, p);
  return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var jD = { kernelName: va, backendName: "webgl", kernelFunc: LZ };
function BZ(r) {
  let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
  if (o.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (n.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
              ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);
  let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = jf(a, o.shape, o.dtype, i, p, true);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var XD = { kernelName: gi, backendName: "webgl", kernelFunc: BZ };
function VZ(r) {
  let { inputs: e, backend: t10 } = r, { data: o, indices: n, segmentIds: s } = e;
  if (o.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (n.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
             ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);
  let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = jf(a, o.shape, o.dtype, i, p);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var YD = { kernelName: xi, backendName: "webgl", kernelFunc: VZ };
function zZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false;
  if (s.dtype === "string") {
    let x = t10.bufferSync(n), b = t10.bufferSync(s), C = y.decodeString(t10.readSync(a.dataId)[0]), w = hE(x, b, i, m, c, u, p, l, C, d);
    return t10.makeTensorInfo(i, w.dtype, w.values);
  }
  let f = new Sc(u, p, n.shape.length, s.shape.length, l, [m, 1], d), h = t10.runWebGLProgram(f, [s, n, a], s.dtype), g = re({ inputs: { x: h }, backend: t10, attrs: { shape: i } });
  return t10.disposeIntermediateTensorInfo(h), g;
}
var QD = { kernelName: yi, backendName: "webgl", kernelFunc: zZ };
function WZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice();
  return p.map((m) => {
    let d = [...l];
    d[i] = m;
    let f = ls({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } });
    return c[i] += m, f;
  });
}
var ZD = { kernelName: Rs, backendName: "webgl", kernelFunc: WZ };
var JD = "return sqrt(x);";
var UZ = he({ opSnippet: JD, packedOpSnippet: JD, cpuKernelImpl: CE });
var eF = { kernelName: Hn, backendName: "webgl", kernelFunc: UZ };
var GZ = "return x * x;";
var HZ = he({ opSnippet: GZ });
var tF = { kernelName: bi, backendName: "webgl", kernelFunc: HZ };
var rF = "return (a - b) * (a - b);";
var KZ = tt({ opSnippet: rF, packedOpSnippet: rF });
var oF = { kernelName: jn, backendName: "webgl", kernelFunc: KZ };
function qZ({ inputs: r, attrs: e, backend: t10 }) {
  let { x: o } = r, n = zt + `
    return x > 0.0 ? 1.0 : float(${e.alpha});
  `, s = new Jt(o.shape, n);
  return t10.runWebGLProgram(s, [o], o.dtype);
}
var nF = { kernelName: fo, backendName: "webgl", kernelFunc: qZ };
var mg = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.outputShape = o;
    let n = o.length, s = $e(o.length), a = $e(o.length), i = "";
    if (n === 1)
      i = "coords * strides + begin";
    else {
      let p = 0;
      i = o.map((u, c) => (p++, o.length === 1 ? `coords * strides[${c}] + begin[${c}]` : `coords[${p - 1}] * strides[${c}] + begin[${c}]`)).join(",");
    }
    this.userCode = `
      ${s} begin = ${s}(${e});
      ${s} strides = ${s}(${t10});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `;
  }
};
function jZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = re({ inputs: { x: n }, backend: t10, attrs: { shape: f } });
  else if (g || x) {
    y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let E = ut.computeOutShape(b, C, w), A = ls({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: E } });
    k = re({ inputs: { x: A }, backend: t10, attrs: { shape: f } }), t10.disposeIntermediateTensorInfo(A);
  } else if (t10.shouldExecuteOnCPU([n])) {
    let A = t10.readSync(n.dataId), R = le(n.shape, n.dtype, A), D = SE(d, R, w, b);
    k = t10.makeTensorInfo(f, n.dtype, D.values);
  } else {
    let A = new mg(b, w, d);
    k = t10.runWebGLProgram(A, [n], n.dtype);
  }
  let _ = re({ inputs: { x: k }, backend: t10, attrs: { shape: f } });
  return t10.disposeIntermediateTensorInfo(k), _;
}
var sF = { kernelName: Xn, backendName: "webgl", kernelFunc: jZ };
function XZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = wE(m, d, n, s, a, i, p, u);
  return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)];
}
var aF = { kernelName: Ds, backendName: "webgl", kernelFunc: XZ };
function YZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { skipEmpty: n } = o, { input: s, delimiter: a } = e;
  if (s.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (s.shape.length !== 1)
    throw new Error(`Input must be a vector, got shape: ${s.shape}`);
  if (a.shape.length !== 0)
    throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);
  let i = t10.readSync(s.dataId), p = t10.readSync(a.dataId)[0], [u, c, l] = IE(i, p, n), m = c.length;
  return [t10.makeTensorInfo([m, 2], "int32", u), t10.makeTensorInfo([m], "string", c), t10.makeTensorInfo([2], "int32", new Int32Array(l))];
}
var iF = { kernelName: Ci, backendName: "webgl", kernelFunc: YZ };
function QZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { numBuckets: n } = o, { input: s } = e;
  if (s.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (n <= 0)
    throw new Error("Number of buckets must be at least 1");
  let a = t10.readSync(s.dataId), i = vE(a, n);
  return t10.makeTensorInfo(s.shape, "int32", i);
}
var uF = { kernelName: Si, backendName: "webgl", kernelFunc: QZ };
var ZZ = "return tan(x);";
var JZ = he({ opSnippet: ZZ });
var pF = { kernelName: Qn, backendName: "webgl", kernelFunc: JZ };
var e9 = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var t9 = he({ opSnippet: e9 });
var cF = { kernelName: Zn, backendName: "webgl", kernelFunc: t9 };
var dg = class {
  constructor(e, t10) {
    this.variableNames = ["A"];
    let o = new Array(e.length);
    for (let a = 0; a < o.length; a++)
      o[a] = e[a] * t10[a];
    this.outputShape = o, this.rank = o.length;
    let n = $e(this.rank), s = r9(e);
    this.userCode = `
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `;
  }
};
function r9(r) {
  let e = r.length;
  if (e > 5)
    throw Error(`Tile for rank ${e} is not yet supported`);
  if (e === 1)
    return `imod(resRC, ${r[0]})`;
  let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], o = [];
  for (let n = 0; n < r.length; n++)
    o.push(`imod(${t10[n]}, ${r[n]})`);
  return o.join();
}
function Qw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
  if (n.dtype === "string" || n.shape.length > 5) {
    let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = le(n.shape, n.dtype, u), l = NE(c, s);
    return t10.makeTensorInfo(l.shape, l.dtype, l.values);
  }
  let a = new dg(n.shape, s);
  return t10.runWebGLProgram(a, [n], n.dtype);
}
var lF = { kernelName: to, backendName: "webgl", kernelFunc: Qw };
var fg = 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));
         }
       }
     `;
  }
};
var hg = 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 Gu(r, e) {
  e !== null && r.disposeIntermediateTensorInfo(e);
}
function mF(r) {
  let e = 1;
  for (; e < r; )
    e *= 2;
  return e;
}
function o9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = O().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = O().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), u = n.shape, c = u[u.length - 1];
  if (t10.shouldExecuteOnCPU([n]) || c < i || s > p) {
    let D = t10.readSync(n.dataId), [P, M] = TE(D, u, n.dtype, s, a);
    return [t10.makeTensorInfo(P.shape, P.dtype, P.values), t10.makeTensorInfo(M.shape, M.dtype, M.values)];
  }
  if (s === 0)
    return u[u.length - 1] = 0, [t10.makeTensorInfo(u, n.dtype, []), t10.makeTensorInfo(u, "int32", [])];
  if (c === 1)
    return [n, qa({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t10 })];
  let l = t10.texData.get(n.dataId), m = l !== null && l.isPacked, d = m ? t10.unpackTensor(n) : n, h = y.sizeFromShape(u) / c, g = re({ inputs: { x: d }, attrs: { shape: [h, c] }, backend: t10 });
  m && Gu(t10, d);
  let x = mF(s), b = mF(c), C = null, w = () => C === null ? [g, g] : [g, C], k = (D, P, M) => {
    let L = w(), V = new fg(M), U = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [D], [P]], K = C;
    C = t10.runWebGLProgram(V, L, "int32", U), Gu(t10, K);
  };
  for (let D = 1; D < x; D *= 2) {
    let P = D * 2;
    for (let M = D; M >= 1; M /= 2)
      k(P, M, [h, b]);
  }
  for (let D = b; D > x; D /= 2) {
    let P = w(), M = new hg([h, D / 2]), V = [[c], [C === null ? 1 : 0], [x]], z = C;
    C = t10.runWebGLProgram(M, P, "int32", V), Gu(t10, z);
    let U = x / 2, K = U * 2;
    for (let H = U; H >= 1; H /= 2)
      k(K, H, C.shape);
  }
  let _ = C;
  C = ls({ inputs: { x: C }, backend: t10, attrs: { begin: 0, size: [h, s] } }), Gu(t10, _);
  let E = Gw({ inputs: { x: g, indices: C }, backend: t10, attrs: { axis: 1, batchDims: 1 } });
  Gu(t10, g);
  let A = u.slice(0, -1);
  A.push(s), _ = C, C = re({ inputs: { x: C }, attrs: { shape: A }, backend: t10 }), Gu(t10, _);
  let R = E;
  return E = re({ inputs: { x: E }, attrs: { shape: A }, backend: t10 }), Gu(t10, R), [E, C];
}
var dF = { kernelName: Jn, backendName: "webgl", kernelFunc: o9 };
var gg = class {
  constructor(e, t10, o, n, s, a) {
    this.variableNames = ["Image", "Transforms"], this.outputShape = a;
    let i = o === "nearest" ? 1 : 2, p;
    switch (n) {
      case "constant":
        p = 1;
        break;
      case "reflect":
        p = 2;
        break;
      case "wrap":
        p = 3;
        break;
      case "nearest":
        p = 4;
        break;
      default:
        p = 1;
        break;
    }
    this.userCode = `
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${p} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${p} == 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 (${p} == 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 < ${t10}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${s});
              }
              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(${s});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t10}));
                float mapY = mapCoord(inY, float(${e}));

                if (${i} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `;
  }
};
function n9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new gg(l, m, a, i, p, g);
  return t10.runWebGLProgram(x, [n, s], "float32");
}
var fF = { kernelName: es, backendName: "webgl", kernelFunc: n9 };
function s9(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e;
  us(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
  let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = _E(a, n, s.shape, s.dtype);
  return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var hF = { kernelName: Np, backendName: "webgl", kernelFunc: s9 };
function a9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
  s < 0 && (s += n.shape.length);
  let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0;
  for (let h = 0; h < i; h++)
    h !== s && (u[c++] = a.shape[h]);
  let l = [], m = new Array(i).fill(0), d = a.shape.slice();
  d[s] = 1;
  let f = new Array(p);
  for (let h = 0; h < f.length; h++) {
    m[s] = h;
    let g = ls({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = re({ inputs: { x: g }, backend: t10, attrs: { shape: u } });
    f[h] = x, l.push(g);
  }
  return l.forEach((h) => t10.disposeIntermediateTensorInfo(h)), f;
}
var gF = { kernelName: Fs, backendName: "webgl", kernelFunc: a9 };
var xg = class {
  constructor(e, t10) {
    this.variableNames = ["x", "segmentIds"];
    let o = e.windowSize, n = e.batchSize, s = e.inSize, a = e.numSegments, i = a * Math.ceil(s / o);
    this.outputShape = [n, i];
    let p = "0.0", u = "sumValue", c = Math.floor(o / 4) * 4, l = o % 4, m = `
        sumValue += dot(values, segFilter);
    `, d = "";
    s % o > 0 && (d = `
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);
    let f = "";
    s % o > 0 && (f = `
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `), this.userCode = `
      const float initializationValue = ${p};

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

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

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

          ${m}
        }

        int inIdx = inOffset + ${c};
        if (${l === 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
          );

          ${m}
        } else if (${l === 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
          );

          ${m}
        } else if (${l === 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
          );

          ${m}
        }
        setOutput(${u});
      }
    `;
  }
};
function i9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o, i = n.shape.length, p = [], u = 0, c = S.getAxesPermutation([u], i), l = n;
  c != null && (l = yt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = S.getInnerMostAxes(1, i)[0]);
  let m = S.segment_util.computeOutShape(l.shape, u, a), d = y.sizeFromShape([l.shape[u]]), f = re({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } });
  p.push(f);
  let h = Ta(n.dtype), g = (w, k, _, E, A) => {
    let R = w.shape[0], D = w.shape[1], P = S.segment_util.segOpComputeOptimalWindowSize(D, A), M = { windowSize: P, inSize: D, batchSize: R, numSegments: A }, L = new xg(M, k), V = t10.compileAndRun(L, [w, _], E);
    if (p.push(V), V.shape[1] === A)
      return V;
    let z = Yw({ backend: t10, attrs: { start: 0, stop: A, step: 1, dtype: "float32" } }), U = Qw({ inputs: { x: z }, backend: t10, attrs: { reps: [D / P] } });
    return p.push(z), p.push(U), g(V, k, U, E, A);
  }, x = g(f, "unsortedSegmentSum", s, h, a), b = re({ inputs: { x }, backend: t10, attrs: { shape: m } }), C = b;
  if (c != null) {
    p.push(b);
    let w = S.getUndoAxesPermutation(c);
    C = yt({ inputs: { x: C }, backend: t10, attrs: { perm: w } });
  }
  return p.forEach((w) => t10.disposeIntermediateTensorInfo(w)), C;
}
var xF = { kernelName: Tp, backendName: "webgl", kernelFunc: i9 };
var u9 = [rA, nA, sA, aA, uA, pA, cA, lA, fA, hA, gA, xA, yA, bA, CA, SA, wA, IA, vA, kA, NA, _A, $A, EA, FA, PA, MA, KE, BA, zA, WA, UA, GA, HA, KA, qA, jA, XA, YA, JA, eR, tR, rR, oR, nR, sR, aR, iR, uR, pR, cR, lR, mR, dR, fR, gR, xR, yR, bR, SR, wR, IR, vR, kR, NR, TR, _R, $R, HE, ER, VA, AR, RR, DR, qE, FR, OR, PR, MR, LR, BR, VR, zR, WR, UR, HR, KR, qR, jR, XR, YR, ZR, eD, tD, rD, oD, nD, pD, YE, cD, lD, mD, dD, AA, fD, xD, yD, bD, CD, jE, SD, wD, ID, vD, kD, RA, sD, ND, TD, _D, ZE, $D, ED, AD, RD, DD, FD, OD, PD, MD, LD, BD, VD, zD, WD, UD, GD, TA, uD, HD, KD, qD, jD, XD, YD, QD, ZD, eF, tF, oF, nF, sF, aF, iF, uF, iD, eA, pF, cF, lF, dF, fF, tA, hF, gF, xF, hD];
for (let r of u9)
  ka(r);
var Fe;
(function(r) {
  r[r.float32 = 0] = "float32", r[r.int32 = 1] = "int32", r[r.bool = 2] = "bool", r[r.string = 3] = "string", r[r.complex64 = 4] = "complex64";
})(Fe || (Fe = {}));
var Ui;
(function(r) {
  r[r.linear = 0] = "linear", r[r.relu = 1] = "relu", r[r.relu6 = 2] = "relu6", r[r.prelu = 3] = "prelu", r[r.leakyrelu = 4] = "leakyrelu", r[r.sigmoid = 5] = "sigmoid", r[r.elu = 6] = "elu";
})(Ui || (Ui = {}));
var yF;
function p9(r) {
  yF = r.wasm.cwrap(ho, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function c9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e;
  if (n.dtype !== "float32" || s.dtype !== "float32")
    throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
  let { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o, m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = 0;
  if (a != null) {
    let A = t10.dataIdMap.get(a.dataId);
    if (A.shape.length !== 1)
      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${A.shape.length}.`);
    f = A.id;
  }
  let h = i == null ? 0 : t10.dataIdMap.get(i.dataId).id, g = Ui[c];
  if (g == null)
    throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
  let x = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = yr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), w = t10.makeOutput([...C, x, b], n.dtype), k = t10.dataIdMap.get(w.dataId).id, _ = new Uint8Array(new Int32Array(n.shape).buffer), E = new Uint8Array(new Int32Array(s.shape).buffer);
  return yF(m, _, n.shape.length, d, E, s.shape.length, p, u, g, f, h, l || 0, k), w;
}
var bF = { kernelName: ho, backendName: "wasm", setupFunc: p9, kernelFunc: c9 };
function Ve(r, e) {
  let t10;
  function o(s) {
    t10 = s.wasm.cwrap(r, null, ["number", "number", "number"]);
  }
  function n(s) {
    let { backend: a, inputs: { x: i } } = s, p = a.dataIdMap.get(i.dataId).id, u = a.makeOutput(i.shape, e || i.dtype), c = a.dataIdMap.get(u.dataId).id;
    return y.sizeFromShape(u.shape) === 0 || t10(p, Fe[i.dtype], c), u;
  }
  return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n };
}
var CF = Ve(ys);
function rt(r, e, t10) {
  let o;
  function n(a) {
    o = a.wasm.cwrap(r, null, ["number", "array", "number", "number", "array", "number", "number", "number"]);
  }
  function s(a) {
    let { backend: i, inputs: p } = a, { a: u, b: c } = p, l = i.dataIdMap.get(u.dataId).id, m = i.dataIdMap.get(c.dataId).id, d = t10 != null ? t10 : u.dtype, f = S.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(f, d);
    if (y.sizeFromShape(f) === 0)
      return h;
    let g = new Uint8Array(new Int32Array(u.shape).buffer), x = new Uint8Array(new Int32Array(c.shape).buffer), b = i.dataIdMap.get(h.dataId).id;
    return (() => o(l, g, u.shape.length, m, x, c.shape.length, Fe[u.dtype], b))(), h;
  }
  return { kernelName: r, backendName: "wasm", setupFunc: n, kernelFunc: s };
}
var l9 = true;
var SF = rt(eo, l9);
var wF;
function m9(r) {
  wF = r.wasm.cwrap(Po, null, ["array", "number", "number", "number"]);
}
function d9(r) {
  let { inputs: e, backend: t10 } = r, o = t10.makeOutput(e[0].shape, e[0].dtype);
  if (y.sizeFromShape(o.shape) === 0)
    return o;
  let n = e.map((i) => t10.dataIdMap.get(i.dataId).id), s = new Uint8Array(new Int32Array(n).buffer), a = t10.dataIdMap.get(o.dataId).id;
  return wF(s, n.length, Fe[o.dtype], a), o;
}
var IF = { kernelName: Po, backendName: "wasm", setupFunc: m9, kernelFunc: d9 };
function Hu(r) {
  let { inputs: { x: e }, backend: t10 } = r;
  if (e.dtype === "string")
    return nr(t10.readSync(e.dataId), e.shape, e.dtype);
  let o = t10.makeOutput(e.shape, e.dtype), n = t10.typedArrayFromHeap(e);
  return t10.typedArrayFromHeap(o).set(n), o;
}
var vF = { kernelName: mo, backendName: "wasm", kernelFunc: Hu };
var kF;
function f9(r) {
  kF = r.wasm.cwrap(ro, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function uo(r) {
  let { inputs: e, backend: t10, attrs: o } = r, [n, s] = g9(e.x.shape, o.perm), a = true;
  for (let f = 0; f < s.length; f++)
    s[f] !== f && (a = false);
  let i = h9(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype };
  if (a) {
    let f = Hu({ inputs: e, backend: t10 });
    return f.shape = i, f;
  }
  let u = t10.makeOutput(i, p.dtype), c = t10.dataIdMap.get(p.dataId).id, l = t10.dataIdMap.get(u.dataId).id, m = new Uint8Array(new Int32Array(s).buffer), d = new Uint8Array(new Int32Array(p.shape).buffer);
  return kF(c, d, p.shape.length, Fe[p.dtype], l, m, s.length), u;
}
function h9(r, e) {
  let t10 = new Array(r.length);
  for (let o = 0; o < t10.length; o++)
    t10[o] = r[e[o]];
  return t10;
}
function g9(r, e) {
  let t10 = [], o = [];
  for (let n = 0; n < r.length; ++n)
    r[n] !== 1 && t10.push(r[n]), r[e[n]] !== 1 && o.push(e[n]);
  for (let n = 0; n < o.length; ++n) {
    let s = -1;
    for (let a = 0; a < o.length; ++a)
      o[a] >= n && (s === -1 || o[s] > o[a]) && (s = a);
    o[s] = n;
  }
  return [t10, o];
}
var NF = { kernelName: ro, backendName: "wasm", kernelFunc: uo, setupFunc: f9 };
function kr(r, e, t10) {
  let o = r.shape, n = r.shape.length, s = y.parseAxisParam(e, o), a = s, i = S.getAxesPermutation(a, n), p = null, u = false;
  if (i != null) {
    let c = new Array(n);
    for (let d = 0; d < c.length; d++)
      c[d] = o[i[d]];
    a = S.getInnerMostAxes(a.length, n), p = uo({ inputs: { x: r }, attrs: { perm: i }, backend: t10 });
    let l = t10.dataIdMap.get(r.dataId).id;
    t10.dataIdMap.get(p.dataId).id !== l && (u = true);
  }
  return { transposed: p, originalAxes: s, axes: a, inputWasTransposed: u };
}
var TF;
function x9(r) {
  TF = r.wasm.cwrap(Mo, null, ["number, number, number"]);
}
function y9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e);
  if (d) {
    let C = e.dataIdMap.get(c.dataId).id;
    u = c, p = C;
  }
  let f = u.shape.length;
  S.assertAxesAreInnerMostDims("all", l, f);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
  if (y.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    TF(p, x, C);
  }
  if (d && e.disposeData(c.dataId), s) {
    let C = S.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var _F = { kernelName: Mo, backendName: "wasm", setupFunc: x9, kernelFunc: y9 };
var $F;
function b9(r) {
  $F = r.wasm.cwrap(Lo, null, ["number, number, number"]);
}
function C9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e);
  if (d) {
    let C = e.dataIdMap.get(c.dataId).id;
    u = c, p = C;
  }
  let f = u.shape.length;
  S.assertAxesAreInnerMostDims("any", l, f);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
  if (y.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    $F(p, x, C);
  }
  if (d && e.disposeData(c.dataId), s) {
    let C = S.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var EF = { kernelName: Lo, backendName: "wasm", setupFunc: b9, kernelFunc: C9 };
var AF;
function S9(r) {
  AF = r.wasm.cwrap(Bo, null, ["number", "number", "number", "number", "number"]);
}
function w9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n } = o, { x: s } = t10, a = e.dataIdMap.get(s.dataId).id, i = a, p = s, { transposed: u, axes: c, inputWasTransposed: l } = kr(s, n, e);
  if (l) {
    let x = e.dataIdMap.get(u.dataId).id;
    x !== a && (p = u, i = x);
  }
  let m = p.shape.slice(0, -1), d = e.makeOutput(m, "int32"), f = e.dataIdMap.get(d.dataId).id, h = y.sizeFromShape(d.shape), g = p.shape[c[0]];
  return AF(i, Fe[p.dtype], h, g, f), l && e.disposeData(u.dataId), d;
}
var RF = { kernelName: Bo, backendName: "wasm", kernelFunc: w9, setupFunc: S9 };
var DF;
function I9(r) {
  DF = r.wasm.cwrap(Vo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function v9(r) {
  let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = S.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.strideHeight, b = c.strideWidth, C = c.inChannels;
  if (c.dataFormat !== "channelsLast")
    throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
  if (c.dilationWidth !== 1 || c.dilationHeight !== 1)
    throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);
  let w = o.makeOutput(c.outShape, "float32"), k = o.dataIdMap.get(w.dataId).id;
  return DF(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, k), w;
}
var FF = { kernelName: Vo, backendName: "wasm", setupFunc: I9, kernelFunc: v9 };
function Lt(r) {
  let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s);
  return y.assert(s === y.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var OF = { kernelName: _s, backendName: "wasm", kernelFunc: Lt };
var PF;
function k9(r) {
  PF = r.wasm.cwrap(zo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function N9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  if (n.dtype !== "float32" || s.dtype !== "float32")
    throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
  let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], d = i ? s.shape[u - 2] : s.shape[u - 1], f = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = y.sizeFromShape(f), x = y.sizeFromShape(h), C = yr.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, d]);
  y.assert(c === l, () => `Error in matMul: inner shapes (${c}) and (${l}) of Tensors with shapes ${n.shape} and ${s.shape} and transposeA=${a} and transposeB=${i} must match.`);
  let w = a ? [g, c, m] : [g, m, c], k = i ? [x, d, l] : [x, l, d], _ = Lt({ inputs: { x: n }, backend: t10, attrs: { shape: w } }), E = Lt({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), A = t10.dataIdMap.get(_.dataId).id, R = t10.dataIdMap.get(E.dataId).id, D = a ? _.shape[2] : _.shape[1], P = i ? E.shape[1] : E.shape[2], M = Math.max(g, x), L = t10.makeOutput([M, D, P], _.dtype), V = t10.dataIdMap.get(L.dataId).id, z = new Uint8Array(new Int32Array(_.shape).buffer), U = new Uint8Array(new Int32Array(E.shape).buffer);
  return PF(A, z, _.shape.length, R, U, E.shape.length, a, i, V), t10.disposeData(_.dataId), t10.disposeData(E.dataId), L.shape = C, L;
}
var MF = { kernelName: zo, backendName: "wasm", setupFunc: k9, kernelFunc: N9 };
function Eo(r) {
  let { inputs: { x: e }, attrs: { begin: t10, size: o }, backend: n } = r, [s, a] = ut.parseSliceParams(e, t10, o), i = ut.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = y.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId);
  if (i) {
    let f = ut.computeFlatOffset(s, c);
    return e.dtype === "string" ? l.stringBytes = p.slice(f, f + y.sizeFromShape(a)) : n.typedArrayFromHeap(u).set(p.subarray(f, f + y.sizeFromShape(a))), u;
  }
  if (e.dtype === "string") {
    let f = $u(p, s, a, e.shape, e.dtype);
    return l.stringBytes = f, u;
  }
  let m = n.typedArrayFromHeap(u), d = e.shape.length;
  if (d === 2)
    T9(p, c[0], m, s, a);
  else if (d === 3)
    _9(p, c[0], c[1], m, s, a);
  else if (d === 4)
    $9(p, c[0], c[1], c[2], m, s, a);
  else {
    let f = $u(p, s, a, e.shape, e.dtype);
    m.set(f);
  }
  return u;
}
function T9(r, e, t10, o, n) {
  let s = 0, a = o[0], i = o[1], p = a + n[0];
  for (let u = a; u < p; u++) {
    let c = u * e + i;
    t10.set(r.subarray(c, c + n[1]), s), s += n[1];
  }
}
function _9(r, e, t10, o, n, s) {
  let a = 0, i = n[0], p = n[1], u = n[2], c = i + s[0], l = p + s[1];
  for (let m = i; m < c; m++)
    for (let d = p; d < l; d++) {
      let f = m * e + d * t10 + u;
      o.set(r.subarray(f, f + s[2]), a), a += s[2];
    }
}
function $9(r, e, t10, o, n, s, a) {
  let i = 0, p = s[0], u = s[1], c = s[2], l = p + a[0], m = u + a[1], d = c + a[2], f = s[3];
  for (let h = p; h < l; h++)
    for (let g = u; g < m; g++)
      for (let x = c; x < d; x++) {
        let b = h * e + g * t10 + x * o + f;
        n.set(r.subarray(b, b + a[3]), i), i += a[3];
      }
}
var LF = { kernelName: Es, backendName: "wasm", kernelFunc: Eo };
function E9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((x, b) => x * b), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = Lt({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), f = uo({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), h = Lt({ inputs: { x: f }, backend: t10, attrs: { shape: c } }), g = Eo({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } });
  return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(d.dataId), g;
}
var BF = { kernelName: bs, backendName: "wasm", kernelFunc: E9 };
function ms(r) {
  let { inputs: { x: e }, attrs: { dtype: t10 }, backend: o } = r, n = o.makeOutput(e.shape, t10), s = o.typedArrayFromHeap(e);
  return o.typedArrayFromHeap(n).set(s), n;
}
var VF = { kernelName: co, backendName: "wasm", kernelFunc: ms };
var zF = Ve(Wo);
var WF;
function A9(r) {
  WF = r.wasm.cwrap(lo, null, ["number", "number", "number", "number"]);
}
function R9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i = t10.dataIdMap.get(n.dataId).id, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(p.dataId).id;
  return WF(i, s, a, u), p;
}
var UF = { kernelName: lo, backendName: "wasm", setupFunc: A9, kernelFunc: R9 };
function Zw(r) {
  let { inputs: e, backend: t10 } = r, o = y.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((d) => d.shape);
  S.assertParamsConsistent(n, o);
  let s = S.computeOutShape(e.map((d) => d.shape), o), a = e.filter((d) => y.sizeFromShape(d.shape) > 0);
  if (a.length === 1)
    return Hu({ inputs: { x: a[0] }, backend: t10 });
  let i = t10.makeOutput(s, e[0].dtype);
  if (y.sizeFromShape(s) === 0)
    return i;
  if (a[0].dtype === "string") {
    let d = a.map((C) => {
      let k = [-1, y.sizeFromShape(C.shape.slice(o))];
      return Lt({ inputs: { x: C }, backend: t10, attrs: { shape: k } });
    }), f = d.map((C) => ({ vals: t10.readSync(C.dataId), shape: C.shape }));
    s = S.computeOutShape(d.map((C) => C.shape), 1);
    let h = d[0].shape[0] === 1, g = Nu(f, s, e[0].dtype, h), x = S.computeOutShape(a.map((C) => C.shape), o);
    i.shape = x;
    let b = t10.dataIdMap.get(i.dataId);
    return b.stringBytes = S.fromStringArrayToUint8(g), d.forEach((C) => t10.disposeData(C.dataId)), i;
  }
  let p = y.sizeFromShape(a[0].shape.slice(0, o)), u = 0, c = a.map((d) => {
    let f = y.sizeFromShape(d.shape.slice(o));
    return u += f, f;
  }), l = a.map((d) => t10.typedArrayFromHeap(d)), m = t10.typedArrayFromHeap(i);
  for (let d = 0; d < p; d++) {
    let f = d * u;
    for (let h = 0; h < l.length; h++) {
      let g = c[h], x = d * g, b = l[h].subarray(x, x + g);
      m.set(b, f), f += g;
    }
  }
  return i;
}
var GF = { kernelName: Cs, backendName: "wasm", kernelFunc: Zw };
var HF;
function D9(r) {
  HF = r.wasm.cwrap(Uo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function F9(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l, dataFormat: m } = t10, d = S.convertConv2DDataFormat(m), f = S.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, d), h = f.filterHeight, g = f.filterWidth, x = f.padInfo.top, b = f.padInfo.right, C = f.padInfo.bottom, w = f.padInfo.left, k = f.dilationHeight, _ = f.dilationWidth, E = f.strideHeight, A = f.strideWidth, R = f.inChannels, D = f.outChannels, P = 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 M = o.makeOutput(f.outShape, "float32"), L = o.dataIdMap.get(M.dataId).id;
  return HF(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, x, b, C, w, P, k, _, E, A, R, D, L), M;
}
var KF = { kernelName: Uo, backendName: "wasm", setupFunc: D9, kernelFunc: F9 };
var qF;
function O9(r) {
  qF = r.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", "number", "number", "number", "number"]);
}
function P9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { dy: n, filter: s } = t10, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, inputShape: c } = o, l = 1, m = S.convertConv2DDataFormat(p), d = S.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: f, filterHeight: h, filterWidth: g, inChannels: x, inHeight: b, inWidth: C, outChannels: w, outHeight: k, outWidth: _, strideHeight: E, strideWidth: A } = d, R = h - 1 - d.padInfo.top, D = g - 1 - d.padInfo.left, P = d.dataFormat === "channelsLast", M = y.computeStrides(d.inShape), L = y.computeStrides(n.shape), [V, z, U] = y.computeStrides(s.shape), K = M[0], H = P ? M[1] : M[2], q = P ? M[2] : 1, Z = P ? 1 : M[1], ee = L[0], oe = P ? L[1] : L[2], J = P ? L[2] : 1, te = P ? 1 : L[1], ie = e.makeOutput(d.inShape, "float32"), ce = e.dataIdMap.get(ie.dataId).id, ye = e.dataIdMap.get(n.dataId).id, Ne = e.dataIdMap.get(s.dataId).id;
  return qF(ye, Ne, f, h, g, b, C, x, k, _, w, E, A, R, D, V, z, U, K, H, q, Z, ee, oe, J, te, ce), ie;
}
var jF = { kernelName: Go, backendName: "wasm", setupFunc: O9, kernelFunc: P9 };
var XF = Ve(Ho);
var YF = Ve(Ko);
var Jw;
(function(r) {
  r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest";
})(Jw || (Jw = {}));
var QF;
function M9(r) {
  QF = r.wasm.cwrap(Xo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function L9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { method: n, extrapolationValue: s, cropSize: a } = o, { image: i, boxes: p, boxInd: u } = t10, c = p.shape[0], [l, m] = a, d = [c, l, m, i.shape[3]], f = e.dataIdMap.get(i.dataId), h;
  i.dtype !== "float32" && (h = ms({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), f = e.dataIdMap.get(h.dataId));
  let g = f.id, x = e.dataIdMap.get(p.dataId).id, b = e.dataIdMap.get(u.dataId).id, C = e.makeOutput(d, "float32"), w = e.dataIdMap.get(C.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer);
  return QF(g, x, b, c, k, l, m, Jw[n], s, w), h != null && e.disposeData(h.dataId), C;
}
var ZF = { kernelName: Xo, backendName: "wasm", setupFunc: M9, kernelFunc: L9 };
var JF;
function B9(r) {
  JF = r.wasm.cwrap(qo, null, ["number", "number", "number", "number", "number", "number"]);
}
function V9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length;
  y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumprod does not support ${n.dtype} tensors in the WASM backend`);
  let u = S.getAxesPermutation([s], p), c = n;
  u !== null && (c = uo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 }));
  let l = S.getInnerMostAxes(1, p)[0];
  S.assertAxesAreInnerMostDims("cumprod", [l], p);
  let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id;
  JF(f, a ? 1 : 0, i ? 1 : 0, d, h, Fe[n.dtype]);
  let g = m;
  if (u !== null) {
    let x = S.getUndoAxesPermutation(u);
    g = uo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId);
  }
  return g;
}
var eO = { kernelName: qo, backendName: "wasm", setupFunc: B9, kernelFunc: V9 };
var tO;
function z9(r) {
  tO = r.wasm.cwrap(jo, null, ["number", "number", "number", "number", "number", "number"]);
}
function W9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length;
  y.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumsum does not support ${n.dtype} tensors in the WASM backend`);
  let u = S.getAxesPermutation([s], p), c = n;
  u !== null && (c = uo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 }));
  let l = S.getInnerMostAxes(1, p)[0];
  S.assertAxesAreInnerMostDims("cumsum", [l], p);
  let m = t10.makeOutput(c.shape, c.dtype), d = c.shape[l], f = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id;
  tO(f, a ? 1 : 0, i ? 1 : 0, d, h, Fe[n.dtype]);
  let g = m;
  if (u !== null) {
    let x = S.getUndoAxesPermutation(u);
    g = uo({ inputs: { x: m }, attrs: { perm: x }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId);
  }
  return g;
}
var rO = { kernelName: jo, backendName: "wasm", setupFunc: z9, kernelFunc: W9 };
var oO;
function U9(r) {
  oO = r.wasm.cwrap(Yo, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function G9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = e.makeOutput(f, "float32"), x = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(f).buffer), w = new Uint8Array(new Int32Array(y.computeStrides(f)).buffer), k = e.dataIdMap.get(h.dataId).id;
  return oO(x, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, w, f.length, k), h;
}
var nO = { kernelName: Yo, backendName: "wasm", setupFunc: U9, kernelFunc: G9 };
var sO;
function H9(r) {
  sO = r.wasm.cwrap(Qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function K9(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, a = o.dataIdMap.get(n.dataId).id, i = o.dataIdMap.get(s.dataId).id, { strides: p, dilations: u, pad: c, dimRoundingMode: l } = t10, m = u == null ? [1, 1] : u, d = S.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), f = d.filterHeight, h = d.filterWidth, g = d.padInfo.top, x = d.padInfo.right, b = d.padInfo.bottom, C = d.padInfo.left, w = d.dilationHeight, k = d.dilationWidth, _ = d.strideHeight, E = d.strideWidth, A = d.inChannels, R = d.outChannels, D = d.padInfo.type === "SAME" ? 1 : 0;
  if (d.dataFormat !== "channelsLast")
    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);
  let P = o.makeOutput(d.outShape, "float32"), M = o.dataIdMap.get(P.dataId).id;
  return sO(a, n.shape[0], n.shape[1], n.shape[2], i, f, h, g, x, b, C, D, w, k, _, E, A, R, M), P;
}
var aO = { kernelName: Qo, backendName: "wasm", setupFunc: H9, kernelFunc: K9 };
var iO = Ve(Jo);
var q9 = false;
var uO = rt(en, q9, "bool");
var pO = Ve(tn, "float32");
function yg(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { input: n } = e, { dim: s } = t10, a = n.shape.length, i = n.shape.slice(), p = s;
  return s < 0 && (y.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Lt({ inputs: { x: n }, backend: o, attrs: { shape: i } });
}
var cO = { kernelName: Ss, backendName: "wasm", kernelFunc: yg };
function eI(r) {
  let { attrs: { shape: e, value: t10, dtype: o }, backend: n } = r, s = n.makeOutput(e, o);
  return n.typedArrayFromHeap(s).fill(t10), s;
}
var lO = { kernelName: ws, backendName: "wasm", kernelFunc: eI };
var mO;
function j9(r) {
  mO = r.wasm.cwrap(rn, null, ["number", "number", "number", "number", "number", "number"]);
}
function X9(r) {
  let { inputs: e, backend: t10 } = r, { image: o } = e, n = t10.makeOutput(o.shape, o.dtype), s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, [i, p, u, c] = o.shape;
  return mO(s, i, p, u, c, a), n;
}
var dO = { kernelName: rn, backendName: "wasm", kernelFunc: X9, setupFunc: j9 };
var fO = Ve(on);
var Y9 = false;
var hO = rt(nn, Y9);
var gO;
function Q9(r) {
  gO = r.wasm.cwrap(sn, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function Z9(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { varianceEpsilon: n } = o, { x: s, mean: a, variance: i, offset: p, scale: u } = t10, c = e.dataIdMap.get(s.dataId).id, l = e.dataIdMap.get(a.dataId).id, m = e.dataIdMap.get(i.dataId).id, d = p != null ? e.dataIdMap.get(p.dataId).id : 0, f = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype);
  if (y.sizeFromShape(s.shape) === 0)
    return h;
  let g = e.dataIdMap.get(h.dataId).id;
  return gO(c, l, m, d, f, n, g), h;
}
var xO = { kernelName: sn, backendName: "wasm", setupFunc: Q9, kernelFunc: Z9 };
var yO;
function J9(r) {
  yO = r.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 eJ(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = S.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = Ui[d];
  if (g == null)
    throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);
  let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, w = 0;
  if (a != null) {
    let J = o.dataIdMap.get(a.dataId);
    if (J.shape.length !== 1)
      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);
    if (J.shape[0] !== C)
      throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${C})`);
    w = J.id;
  }
  let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, A = h.padInfo.right, R = h.padInfo.bottom, D = h.padInfo.left, P = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, V = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, K = h.batchSize, H = h.inHeight, q = h.inWidth;
  if (l !== "NHWC")
    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);
  let Z = o.makeOutput(h.outShape, "float32"), ee = o.dataIdMap.get(Z.dataId).id, oe = i == null ? 0 : o.dataIdMap.get(i.dataId).id;
  return yO(x, K, H, q, b, k, _, w, E, A, R, D, U, P, M, L, V, z, C, g, oe, f || 0, ee), Z;
}
var bO = { kernelName: go, backendName: "wasm", setupFunc: J9, kernelFunc: eJ };
var CO;
function tJ(r) {
  CO = r.wasm.cwrap(xo, 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 rJ(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dataFormat: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = t10, h = S.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = Ui[d];
  if (g == null)
    throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
  let x = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, w = 0;
  if (a != null) {
    let J = o.dataIdMap.get(a.dataId);
    if (J.shape.length !== 1)
      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);
    if (J.shape[0] !== C)
      throw new Error(`FusedDepthwiseConv2D bias shape (${J.shape}) does not match the number of output channels (${C})`);
    w = J.id;
  }
  let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, A = h.padInfo.right, R = h.padInfo.bottom, D = h.padInfo.left, P = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, V = h.strideWidth, z = h.inChannels, U = h.padInfo.type === "SAME" ? 1 : 0, K = h.batchSize, H = h.inHeight, q = h.inWidth;
  if (l !== "NHWC")
    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);
  let Z = o.makeOutput(h.outShape, "float32"), ee = o.dataIdMap.get(Z.dataId).id, oe = i == null ? 0 : o.dataIdMap.get(i.dataId).id;
  return CO(x, K, H, q, b, k, _, w, E, A, R, D, U, P, M, L, V, z, C, g, oe, f || 0, ee), Z;
}
var SO = { kernelName: xo, backendName: "wasm", setupFunc: tJ, kernelFunc: rJ };
var wO;
function oJ(r) {
  wO = r.wasm.cwrap(an, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function nJ(r) {
  let { backend: e, inputs: t10 } = r, { params: o, indices: n } = t10, [s, a, i, p] = Wd.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype);
  if (a === 0)
    return u;
  let c = n.shape, l = c[c.length - 1], d = e.dataIdMap.get(o.dataId).id, h = e.dataIdMap.get(n.dataId).id, g = new Uint8Array(new Int32Array(p).buffer), x = e.dataIdMap.get(u.dataId).id;
  return wO(d, Fe[o.dtype], h, a, l, i, g, x), u;
}
var IO = { kernelName: an, backendName: "wasm", setupFunc: oJ, kernelFunc: nJ };
var vO;
function sJ(r) {
  vO = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function aJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { x: n, indices: s } = t10, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p];
  for (let R = 0; R < u.length; ++R) {
    let D = u[R];
    y.assert(D <= c - 1 && D >= 0, () => `GatherV2: the index value ${D} is not in [0, ${c - 1}]`);
  }
  let l = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = Lt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), d = y.sizeFromShape(s.shape), f = Lt({ inputs: { x: s }, attrs: { shape: [l.batchSize, d / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, d / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype);
  if (y.sizeFromShape(n.shape) === 0)
    return g;
  let x = m.shape.length - 1, C = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(f.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer), A = new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);
  return vO(C, Fe[n.dtype], E, x, k, l.batchSize, A, _), e.disposeData(m.dataId), e.disposeData(f.dataId), g.shape = l.outputShape, g;
}
var kO = { kernelName: Is, backendName: "wasm", setupFunc: sJ, kernelFunc: aJ };
var iJ = false;
var NO = rt(un, iJ, "bool");
var uJ = false;
var TO = rt(pn, uJ, "bool");
var _O = Ve(cn, "bool");
var $O;
function pJ(r) {
  $O = r.wasm.cwrap(ln, null, ["number", "number", "number", "number"]);
}
function cJ(r) {
  let { inputs: { x: e }, attrs: { alpha: t10 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32");
  if (y.sizeFromShape(e.shape) !== 0) {
    let a = o.dataIdMap.get(s.dataId).id;
    $O(n, Fe[e.dtype], t10, a);
  }
  return s;
}
var EO = { kernelName: ln, backendName: "wasm", setupFunc: pJ, kernelFunc: cJ };
var lJ = false;
var AO = rt(mn, lJ, "bool");
var mJ = false;
var RO = rt(dn, mJ, "bool");
var DO = Ve(fn);
var dJ = false;
var FO = rt(hn, dJ, "bool");
var OO = Ve(gn);
var fJ = false;
var PO = rt(xn, fJ, "bool");
var hJ = false;
var MO = rt(XI, hJ, "bool");
var LO;
function gJ(r) {
  LO = r.wasm.cwrap(yn, null, ["number", "number", "number", "number"]);
}
function xJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { reductionIndices: n, keepDims: s } = o, { x: a } = t10, p = e.dataIdMap.get(a.dataId).id, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e);
  if (d) {
    let C = e.dataIdMap.get(c.dataId).id;
    u = c, p = C;
  }
  let f = u.shape.length;
  S.assertAxesAreInnerMostDims("max", l, f);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
  if (y.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    LO(p, Fe[a.dtype], x, C);
  }
  if (d && e.disposeData(c.dataId), s) {
    let C = S.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var BO = { kernelName: yn, backendName: "wasm", setupFunc: gJ, kernelFunc: xJ };
var yJ = false;
var VO = rt(bn, yJ);
var zO;
function bJ(r) {
  zO = r.wasm.cwrap(Cn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function CJ(r) {
  let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id;
  y.assert(n.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);
  let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = S.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, d = c.padInfo.top, f = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, x = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, w = c.strideWidth, k = c.inChannels, _ = c.outChannels;
  if (c.dataFormat !== "channelsLast")
    throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
  let E = o.makeOutput(c.outShape, "float32"), A = o.dataIdMap.get(E.dataId).id;
  return zO(s, n.shape[0], n.shape[1], n.shape[2], l, m, d, f, h, g, x, b, C, w, k, _, A), E;
}
var WO = { kernelName: Cn, backendName: "wasm", setupFunc: bJ, kernelFunc: CJ };
var UO;
function SJ(r) {
  UO = r.wasm.cwrap(Sn, null, ["number, number, number"]);
}
function wJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l;
  if (d) {
    let w = e.dataIdMap.get(c.dataId).id;
    w !== i && (u = c, p = w, f = S.getInnerMostAxes(f.length, u.shape.length));
  }
  S.assertAxesAreInnerMostDims("mean", f, u.shape.length);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = u;
  u.dtype !== "float32" && (b = ms({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id);
  let C = e.makeOutput(h, "float32");
  if (y.sizeFromShape(u.shape) !== 0) {
    let w = e.dataIdMap.get(C.dataId).id;
    UO(p, x, w);
  }
  if (d && e.disposeData(c.dataId), s) {
    let w = S.expandShapeToKeepDim(C.shape, m);
    C.shape = w;
  }
  return u.dtype !== "float32" && e.disposeData(b.dataId), C;
}
var GO = { kernelName: Sn, backendName: "wasm", setupFunc: SJ, kernelFunc: wJ };
var HO;
function IJ(r) {
  HO = r.wasm.cwrap(wn, null, ["number", "number", "number", "number"]);
}
function vJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e);
  if (d) {
    let C = e.dataIdMap.get(c.dataId).id;
    C !== i && (u = c, p = C);
  }
  let f = u.shape.length;
  S.assertAxesAreInnerMostDims("min", l, f);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, l), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
  if (y.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    HO(p, Fe[a.dtype], x, C);
  }
  if (d && e.disposeData(c.dataId), s) {
    let C = S.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var KO = { kernelName: wn, backendName: "wasm", setupFunc: IJ, kernelFunc: vJ };
var kJ = false;
var qO = rt(In, kJ);
var tI;
(function(r) {
  r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric";
})(tI || (tI = {}));
var jO;
function NJ(r) {
  jO = r.wasm.cwrap(vn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function TJ(r) {
  let { inputs: { x: e }, backend: t10, attrs: { paddings: o, mode: n } } = r, s = o.map((f, h) => f[0] + e.shape[h] + f[1]), a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), p = t10.dataIdMap.get(i.dataId).id, u = new Uint8Array(new Int32Array(e.shape).buffer), c = o.map((f) => f[0]), l = o.map((f) => f[1]), m = new Uint8Array(new Int32Array(c).buffer), d = new Uint8Array(new Int32Array(l).buffer);
  return jO(a, u, e.shape.length, Fe[e.dtype], m, d, tI[n], p), i;
}
var XO = { kernelName: vn, backendName: "wasm", kernelFunc: TJ, setupFunc: NJ };
var _J = true;
var YO = rt(kn, _J);
var QO = Ve(vs);
function wc(r, e) {
  let t10 = new Int32Array(r.wasm.HEAPU8.buffer, e, 4), o = t10[0], n = t10[1], s = t10[2], a = t10[3];
  return r.wasm._free(e), { pSelectedIndices: o, selectedSize: n, pSelectedScores: s, pValidOutputs: a };
}
var ZO;
function $J(r) {
  ZO = r.wasm.cwrap(Tn, "number", ["number", "number", "number", "number", "number"]);
}
function EJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a } = o, { boxes: i, scores: p } = t10, u = e.dataIdMap.get(i.dataId).id, c = e.dataIdMap.get(p.dataId).id, l = ZO(u, c, s, n, a), { pSelectedIndices: m, selectedSize: d, pSelectedScores: f, pValidOutputs: h } = wc(e, l);
  return e.wasm._free(f), e.wasm._free(h), e.makeOutput([d], "int32", m);
}
var JO = { kernelName: Tn, backendName: "wasm", setupFunc: $J, kernelFunc: EJ };
var eP;
function AJ(r) {
  eP = r.wasm.cwrap(ba, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function RJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, padToMaxOutputSize: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = eP(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = wc(e, m);
  e.wasm._free(h);
  let x = e.makeOutput([f], "int32", d), b = e.makeOutput([], "int32", g);
  return [x, b];
}
var tP = { kernelName: ba, backendName: "wasm", setupFunc: AJ, kernelFunc: RJ };
var rP;
function DJ(r) {
  rP = r.wasm.cwrap(_n, "number", ["number", "number", "number", "number", "number", "number"]);
}
function FJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { iouThreshold: n, maxOutputSize: s, scoreThreshold: a, softNmsSigma: i } = o, { boxes: p, scores: u } = t10, c = e.dataIdMap.get(p.dataId).id, l = e.dataIdMap.get(u.dataId).id, m = rP(c, l, s, n, a, i), { pSelectedIndices: d, selectedSize: f, pSelectedScores: h, pValidOutputs: g } = wc(e, m);
  e.wasm._free(g);
  let x = e.makeOutput([f], "int32", d), b = e.makeOutput([f], "float32", h);
  return [x, b];
}
var oP = { kernelName: _n, backendName: "wasm", setupFunc: DJ, kernelFunc: FJ };
var OJ = false;
var nP = rt(Nn, OJ, "bool");
var sP;
function PJ(r) {
  sP = r.wasm.cwrap($n, null, ["number", "number", "number", "number", "number"]);
}
function MJ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = t10.makeOutput([...n.shape, a], s), c = t10.dataIdMap.get(u.dataId).id, m = t10.dataIdMap.get(n.dataId).id;
  return sP(m, a, i, p, c), u;
}
var aP = { kernelName: $n, backendName: "wasm", setupFunc: PJ, kernelFunc: MJ };
function LJ(r) {
  let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype);
  return t10.typedArrayFromHeap(o).fill(1), o;
}
var iP = { kernelName: ks, backendName: "wasm", kernelFunc: LJ };
function BJ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return yg({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = yg({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = Zw({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeData(c.dataId)), u;
}
var uP = { kernelName: Ns, backendName: "wasm", kernelFunc: BJ };
var pP;
function VJ(r) {
  pP = r.wasm.cwrap(En, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function zJ(r) {
  let { inputs: { x: e }, backend: t10, attrs: { paddings: o, constantValue: n } } = r, s = o.map((h, g) => h[0] + e.shape[g] + h[1]);
  if (y.sizeFromShape(e.shape) === 0)
    return eI({ backend: t10, attrs: { shape: s, value: n, dtype: e.dtype } });
  let a = t10.dataIdMap.get(e.dataId).id, i = t10.makeOutput(s, e.dtype), u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(e.shape).buffer), l = o.map((h) => h[0]), m = o.map((h) => h[1]), d = new Uint8Array(new Int32Array(l).buffer), f = new Uint8Array(new Int32Array(m).buffer);
  return pP(a, c, e.shape.length, Fe[e.dtype], d, f, n, u), i;
}
var bg = { kernelName: En, backendName: "wasm", kernelFunc: zJ, setupFunc: VJ };
var WJ = false;
var cP = rt(An, WJ);
var lP;
function UJ(r) {
  lP = r.wasm.cwrap(Rn, null, ["number", "number", "number"]);
}
function GJ(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = t10.dataIdMap.get(o.dataId).id, a = t10.dataIdMap.get(n.dataId).id, i = s, p = o, u = p;
  p.dtype !== "float32" && (u = ms({ backend: t10, inputs: { x: o }, attrs: { dtype: "float32" } }), i = t10.dataIdMap.get(u.dataId).id);
  let c = t10.makeOutput(o.shape, "float32"), l = t10.dataIdMap.get(c.dataId).id;
  return lP(i, a, l), p.dtype !== "float32" && t10.disposeData(u.dataId), c;
}
var mP = { kernelName: Rn, backendName: "wasm", setupFunc: UJ, kernelFunc: GJ };
var dP;
function HJ(r) {
  dP = r.wasm.cwrap(Dn, null, ["number", "number", "number", "number"]);
}
function KJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l;
  if (d) {
    let C = e.dataIdMap.get(c.dataId).id;
    C !== i && (u = c, p = C, f = S.getInnerMostAxes(f.length, u.shape.length));
  }
  S.assertAxesAreInnerMostDims("prod", f, u.shape.length);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
  if (y.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    dP(p, x, Fe[b.dtype], C);
  }
  if (d && e.disposeData(c.dataId), s) {
    let C = S.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var fP = { kernelName: Dn, backendName: "wasm", setupFunc: HJ, kernelFunc: KJ };
var qJ = (r) => {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = _u(o, n, s, a), p = e.makeOutput([i.length], a);
  return e.typedArrayFromHeap(p).set(i), p;
};
var hP = { kernelName: Ts, backendName: "wasm", kernelFunc: qJ };
var jJ = true;
var gP = rt(Zo, jJ);
var xP = Ve(Fn);
var yP = Ve(On);
var bP = Ve(Ln);
var CP;
function XJ(r) {
  CP = r.wasm.cwrap(Mn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function YJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.dataIdMap.get(n.dataId), g;
  h.dtype !== "float32" && (g = ms({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId));
  let x = h.id, b = e.makeOutput(f, "float32");
  if (y.sizeFromShape(n.shape) === 0)
    return b;
  let C = e.dataIdMap.get(b.dataId).id;
  return CP(x, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && e.disposeData(g.dataId), b;
}
var SP = { kernelName: Mn, backendName: "wasm", setupFunc: XJ, kernelFunc: YJ };
var wP;
function QJ(r) {
  wP = r.wasm.cwrap(Pn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function ZJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { images: n } = t10, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, [c, l, m, d] = n.shape, f = [c, p, u, d], h = e.makeOutput(f, "float32");
  if (y.sizeFromShape(n.shape) === 0)
    return h;
  let g = e.dataIdMap.get(n.dataId), x;
  g.dtype !== "float32" && (x = ms({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(x.dataId));
  let b = g.id, C = e.dataIdMap.get(h.dataId).id;
  return wP(b, c, l, m, d, p, u, s ? 1 : 0, a ? 1 : 0, C), x != null && e.disposeData(x.dataId), h;
}
var IP = { kernelName: Pn, backendName: "wasm", setupFunc: QJ, kernelFunc: ZJ };
var vP;
function JJ(r) {
  vP = r.wasm.cwrap(Bn, null, ["number", "array", "number", "array", "number", "number"]);
}
function eee(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = y.parseAxisParam(s, n.shape);
  if (n.shape.length === 0)
    return Hu({ inputs: { x: n }, backend: t10 });
  let i = t10.makeOutput(n.shape, n.dtype), p = t10.dataIdMap.get(n.dataId).id, u = t10.dataIdMap.get(i.dataId).id, c = new Uint8Array(new Int32Array(a).buffer), l = new Uint8Array(new Int32Array(n.shape).buffer);
  vP(p, c, a.length, l, n.shape.length, u);
  let m = Lt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t10 });
  return t10.disposeData(i.dataId), m;
}
var kP = { kernelName: Bn, backendName: "wasm", kernelFunc: eee, setupFunc: JJ };
var NP;
function tee(r) {
  NP = r.wasm.cwrap(ts, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function ree(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n } = e, { radians: s, fillValue: a, center: i } = o, p = t10.makeOutput(n.shape, n.dtype), u = t10.dataIdMap.get(n.dataId).id, c = t10.dataIdMap.get(p.dataId).id, [l, m, d, f] = n.shape, [h, g] = S.getImageCenter(i, m, d), x = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, x ? 0 : b] : [...a, b], w = new Uint8Array(new Int32Array(C).buffer);
  return NP(u, l, m, d, f, s, h, g, w, C.length, c), p;
}
var TP = { kernelName: ts, backendName: "wasm", kernelFunc: ree, setupFunc: tee };
var _P = Ve(Vn);
var $P = Ve(zn);
var EP;
function oee(r) {
  EP = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function nee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { indices: n, updates: s } = t10, { shape: a } = o, i = e.makeOutput(a, s.dtype);
  if (y.sizeFromShape(a) === 0)
    return i;
  let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = sl.calculateShapes(s, n, a), f = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, x = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id;
  return EP(f, g, Fe[s.dtype], p, u, c, x, m, b), i;
}
var AP = { kernelName: Wn, backendName: "wasm", setupFunc: oee, kernelFunc: nee };
var RP;
function see(r) {
  RP = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function aee(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = t10.dataIdMap.get(o.dataId).id, i = t10.dataIdMap.get(n.dataId).id, p = t10.dataIdMap.get(s.dataId).id, u = t10.makeOutput(n.shape, n.dtype), c = t10.dataIdMap.get(u.dataId).id, l = o.shape.length, m = n.shape.length, d = l === 0 || l > 1 || m === 1 ? 1 : y.sizeFromShape(n.shape.slice(1));
  return RP(a, i, p, d, c), u;
}
var DP = { kernelName: $s, backendName: "wasm", kernelFunc: aee, setupFunc: see };
var FP;
function iee(r) {
  FP = r.wasm.cwrap(Gn, null, ["number", "number"]);
}
function uee(r) {
  let { backend: e, inputs: { x: t10 } } = r, o = e.dataIdMap.get(t10.dataId).id, n = e.makeOutput(t10.shape, t10.dtype), s = e.dataIdMap.get(n.dataId).id;
  return y.sizeFromShape(n.shape) === 0 || FP(o, s), n;
}
var OP = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: iee, kernelFunc: uee };
var PP = Ve(Un);
var MP;
function pee(r) {
  MP = r.wasm.cwrap(qn, null, ["number", "number", "number", "number"]);
}
function cee(r) {
  let { backend: e, inputs: { logits: t10 }, attrs: { dim: o } } = r, n = e.dataIdMap.get(t10.dataId).id, s = e.makeOutput(t10.shape, t10.dtype), a = e.dataIdMap.get(s.dataId).id, i = t10.shape[o], p = y.sizeFromShape(t10.shape) / i;
  return y.sizeFromShape(s.shape) === 0 || MP(n, a, i, p), s;
}
var LP = { kernelName: qn, backendName: "wasm", setupFunc: pee, kernelFunc: cee };
function lee(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = y.sizeFromShape(s), p = [[0, 0]];
  p.push(...a);
  for (let _ = 1 + s.length; _ < n.shape.length; ++_)
    p.push([0, 0]);
  let u = bg.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = S.getReshaped(u.shape, s, i, false), l = S.getPermuted(c.length, s.length, false), m = S.getReshapedPermuted(u.shape, s, i, false), h = Lt({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = uo({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Lt({ inputs: { x: b }, backend: t10, attrs: { shape: m } });
  return t10.disposeData(u.dataId), t10.disposeData(h.dataId), t10.disposeData(b.dataId), k;
}
var BP = { kernelName: As, backendName: "wasm", kernelFunc: lee };
var VP;
function mee(r) {
  VP = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function dee(r) {
  let { backend: e, inputs: t10 } = r, { indices: o, values: n, denseShape: s, defaultValue: a } = t10, i = o.shape[0], p = o.shape[1], u = e.readSync(s.dataId)[0], c = [i + u, p], l = e.dataIdMap.get(o.dataId).id, m = e.dataIdMap.get(n.dataId).id, d = e.dataIdMap.get(a.dataId).id, f = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(f.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), x = e.dataIdMap.get(g.dataId).id, b = e.makeOutput([u], "bool"), C = e.dataIdMap.get(b.dataId).id, w = e.makeOutput([i], o.dtype), k = e.dataIdMap.get(w.dataId).id, _ = e.makeOutput([4], "int32"), E = e.dataIdMap.get(_.dataId).id, A = VP(l, m, Fe[n.dtype], i, u, p, d, h, x, C, k, E), R = e.readSync(_.dataId), D;
  switch (R[0]) {
    case 1: {
      D = S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);
      break;
    }
    case 2: {
      D = S.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1], R[2]);
      break;
    }
    case 3:
      D = S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1], R[2], R[3]);
      break;
    default:
      D = "";
  }
  if (e.disposeData(_.dataId), D)
    throw e.disposeData(f.dataId), e.disposeData(g.dataId), e.disposeData(b.dataId), e.disposeData(w.dataId), new Error(D);
  let P = f, M = g;
  return A !== c[0] && (P = Eo({ inputs: { x: f }, attrs: { begin: 0, size: [A, p] }, backend: e }), M = Eo({ inputs: { x: g }, attrs: { begin: 0, size: A }, backend: e }), e.disposeData(f.dataId), e.disposeData(g.dataId)), [P, M, b, w];
}
var zP = { kernelName: hi, backendName: "wasm", setupFunc: mee, kernelFunc: dee };
var WP;
function fee(r) {
  WP = r.wasm.cwrap(va, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function hee(r) {
  let { backend: e, inputs: t10 } = r, { inputIndices: o, inputShape: n, newShape: s } = t10;
  if (o.shape.length !== 2)
    throw new Error(`Input indices should be a matrix but received shape
        ${o.shape}`);
  if (n.shape.length !== 1)
    throw new Error(`Input shape should be a vector but received shape
        ${n.shape}`);
  if (s.shape.length !== 1)
    throw new Error(`Target shape should be a vector but received shape ${s.shape}`);
  let a = e.dataIdMap.get(o.dataId).id, i = e.dataIdMap.get(n.dataId).id, p = e.dataIdMap.get(s.dataId).id, u = o.shape[0], c = y.sizeFromShape(s.shape), l = e.makeOutput([u, c], o.dtype), m = e.dataIdMap.get(l.dataId).id, d = e.makeOutput([c], s.dtype), f = e.dataIdMap.get(d.dataId).id, h = e.makeOutput([3], "int32"), g = e.dataIdMap.get(h.dataId).id;
  WP(a, i, p, u, m, f, g);
  let x = e.readSync(h.dataId), b;
  switch (x[0]) {
    case 0: {
      b = S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1], x[2]);
      break;
    }
    case 1: {
      b = S.getSparseReshapeNegativeOutputDimErrorMessage(x[1], x[2]);
      break;
    }
    case 2:
      b = S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
      break;
    case 3: {
      let C = Array.from(e.readSync(n.dataId)), w = Array.from(e.readSync(d.dataId));
      b = S.getSparseReshapeInputOutputMultipleErrorMessage(C, w);
      break;
    }
    case 4: {
      let C = Array.from(e.readSync(n.dataId)), w = Array.from(e.readSync(d.dataId));
      b = S.getSparseReshapeInputOutputMismatchErrorMessage(C, w);
      break;
    }
    default:
      b = "";
  }
  if (e.disposeData(h.dataId), b)
    throw e.disposeData(l.dataId), e.disposeData(d.dataId), new Error(b);
  return [l, d];
}
var UP = { kernelName: va, backendName: "wasm", setupFunc: fee, kernelFunc: hee };
var GP;
function Cg(r) {
  GP = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Sg(r, e) {
  let { backend: t10, inputs: o } = r, { data: n, indices: s, segmentIds: a } = o, i = s.shape[0], p = t10.readSync(a.dataId, i - 1, i)[0], c = i > 0 ? p + 1 : 0;
  if (c < 0)
    throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let l = n.shape.slice();
  l[0] = c;
  let m = t10.dataIdMap.get(n.dataId).id, d = t10.dataIdMap.get(s.dataId).id, f = t10.dataIdMap.get(a.dataId).id, h = t10.makeOutput(l, n.dtype), g = t10.dataIdMap.get(h.dataId).id, x = t10.makeOutput([4], "int32"), b = t10.dataIdMap.get(x.dataId).id;
  GP(m, Fe[n.dtype], n.shape[0], d, f, g, b, e, 0);
  let C = t10.readSync(x.dataId), w;
  switch (C[0]) {
    case 0: {
      w = S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
      break;
    }
    case 1: {
      w = S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
      break;
    }
    case 2:
      w = S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1], C[2]);
      break;
    case 3:
      w = S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1], C[2], C[3]);
      break;
    default:
      w = "";
  }
  if (t10.disposeData(x.dataId), w)
    throw t10.disposeData(h.dataId), new Error(w);
  return h;
}
function gee(r) {
  return Sg(r, true);
}
var HP = { kernelName: gi, backendName: "wasm", setupFunc: Cg, kernelFunc: gee };
function xee(r) {
  return Sg(r, false);
}
var KP = { kernelName: xi, backendName: "wasm", setupFunc: Cg, kernelFunc: xee };
function yee(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t10, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice();
  return p.map((l) => {
    let m = [...c];
    m[i] = l;
    let d = Eo({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o });
    return u[i] += l, d;
  });
}
var qP = { kernelName: Rs, backendName: "wasm", kernelFunc: yee };
var jP = Ve(Hn);
var XP = Ve(bi);
var bee = true;
var YP = rt(jn, bee);
var QP;
function Cee(r) {
  QP = r.wasm.cwrap(fo, null, ["number", "number", "number", "number"]);
}
function See(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { alpha: n } = o, { x: s } = t10, a = e.dataIdMap.get(s.dataId).id, i = e.makeOutput(s.shape, s.dtype), p = e.dataIdMap.get(i.dataId).id;
  return QP(a, n, Fe[s.dtype], p), i;
}
var ZP = { kernelName: fo, backendName: "wasm", setupFunc: Cee, kernelFunc: See };
var JP;
function wee(r) {
  JP = r.wasm.cwrap(Xn, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function Iee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { x: n } = t10, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = Lt({ inputs: { x: n }, backend: e, attrs: { shape: f } });
  else if (g || x) {
    y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let _ = ut.computeOutShape(b, C, w), E = Eo({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } });
    k = Lt({ inputs: { x: E }, backend: e, attrs: { shape: f } }), e.disposeData(E.dataId);
  } else {
    let _ = e.makeOutput(d, "float32"), E = e.dataIdMap.get(n.dataId).id, A = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), R = new Uint8Array(new Int32Array(b).buffer), D = new Uint8Array(new Int32Array(C).buffer), P = new Uint8Array(new Int32Array(w).buffer), M = new Uint8Array(new Int32Array(d).buffer), L = new Uint8Array(new Int32Array(y.computeStrides(d)).buffer), V = e.dataIdMap.get(_.dataId).id;
    JP(E, A, n.shape.length, R, D, P, M, L, d.length, V), k = Lt({ inputs: { x: _ }, backend: e, attrs: { shape: f } }), e.disposeData(_.dataId);
  }
  return k;
}
var e3 = { kernelName: Xn, backendName: "wasm", setupFunc: wee, kernelFunc: Iee };
function vee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { data: n, dataSplits: s } = t10, { separator: a, nGramWidths: i, leftPad: p, rightPad: u, padWidth: c, preserveShortSequences: l } = o, m = e.readSync(n.dataId), d = e.readSync(s.dataId), [f, h] = Eu(m, d, a, i, p, u, c, l), g = e.makeOutput([f.length], "string"), x = e.dataIdMap.get(g.dataId);
  x.stringBytes = f;
  let b = e.makeOutput(s.shape, "int32");
  return e.typedArrayFromHeap(b).set(h), [g, b];
}
var t3 = { kernelName: Ds, backendName: "wasm", kernelFunc: vee };
function kee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { input: n, delimiter: s } = t10, { skipEmpty: a } = o, i = e.readSync(n.dataId), p = e.readSync(s.dataId), [u, c, l] = Au(i, p[0], a), m = c.length, d = e.makeOutput([m, 2], "int32");
  e.typedArrayFromHeap(d).set(u);
  let h = e.makeOutput([m], "string"), g = e.dataIdMap.get(h.dataId);
  g.stringBytes = c;
  let x = e.makeOutput([2], "int32");
  return e.typedArrayFromHeap(x).set(l), [d, h, x];
}
var r3 = { kernelName: Ci, backendName: "wasm", kernelFunc: kee };
function Nee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { input: n } = t10, { numBuckets: s } = o, a = e.readSync(n.dataId), i = Ru(a, s), p = e.makeOutput(n.shape, "int32");
  return e.typedArrayFromHeap(p).set(i), p;
}
var o3 = { kernelName: Si, backendName: "wasm", kernelFunc: Nee };
var Tee = true;
var n3 = rt(Yn, Tee);
var s3;
function _ee(r) {
  s3 = r.wasm.cwrap(Kn, null, ["number", "number", "number", "number"]);
}
function $ee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { axis: n, keepDims: s } = o, { x: a } = t10, i = e.dataIdMap.get(a.dataId).id, p = i, u = a, { transposed: c, axes: l, originalAxes: m, inputWasTransposed: d } = kr(a, n, e), f = l;
  if (d) {
    let C = e.dataIdMap.get(c.dataId).id;
    C !== i && (u = c, p = C, f = S.getInnerMostAxes(f.length, u.shape.length));
  }
  S.assertAxesAreInnerMostDims("sum", f, u.shape.length);
  let [h, g] = S.computeOutAndReduceShapes(u.shape, f), x = y.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
  if (y.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    s3(p, x, Fe[b.dtype], C);
  }
  if (d && e.disposeData(c.dataId), s) {
    let C = S.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var a3 = { kernelName: Kn, backendName: "wasm", setupFunc: _ee, kernelFunc: $ee };
var i3 = Ve(Qn);
var u3 = Ve(Zn);
var p3;
function Eee(r) {
  p3 = r.wasm.cwrap(to, null, ["number", "array", "number", "array", "number", "number"]);
}
function Aee(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, s = t10.dataIdMap.get(n.dataId).id, { reps: a } = o, i = new Array(n.shape.length);
  for (let m = 0; m < i.length; m++)
    i[m] = n.shape[m] * a[m];
  let p = new Uint8Array(new Int32Array(n.shape).buffer), u = new Uint8Array(new Int32Array(i).buffer), c = t10.makeOutput(i, n.dtype), l = t10.dataIdMap.get(c.dataId).id;
  return p3(s, p, n.shape.length, u, i.length, Fe[c.dtype], l), c;
}
var c3 = { kernelName: to, backendName: "wasm", setupFunc: Eee, kernelFunc: Aee };
var l3;
function Ree(r) {
  l3 = r.wasm.cwrap(Jn, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var Dee = ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o } = r, { k: n, sorted: s } = t10, a = e.dataIdMap.get(o.dataId).id, i = new Uint8Array(new Int32Array(o.shape).buffer), p = o.shape.slice();
  p[p.length - 1] = n;
  let u = e.makeOutput(p, o.dtype), c = e.dataIdMap.get(u.dataId).id, l = e.makeOutput(p, "int32"), m = e.dataIdMap.get(l.dataId).id;
  return l3(a, i, o.shape.length, Fe[o.dtype], n, s, c, m), [u, l];
};
var m3 = { kernelName: Jn, backendName: "wasm", setupFunc: Ree, kernelFunc: Dee };
var d3;
function Fee(r) {
  d3 = r.wasm.cwrap(es, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]);
}
function Oee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { image: n, transforms: s } = t10, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Uint8Array(new Int32Array(y.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(y.computeStrides(g)).buffer), C = e.makeOutput(g, n.dtype), w = e.dataIdMap.get(C.dataId).id, _ = e.dataIdMap.get(n.dataId).id, A = e.dataIdMap.get(s.dataId).id, R = a === "nearest" ? 1 : 2, D;
  switch (i) {
    case "constant":
      D = 1;
      break;
    case "reflect":
      D = 2;
      break;
    case "wrap":
      D = 3;
      break;
    case "nearest":
      D = 4;
      break;
    default:
      D = 1;
      break;
  }
  return d3(_, A, s.shape[0] > 1, c, f, h, d, m, l, x, n.shape.length - 1, b, g.length - 1, R, D, p, w), C;
}
var f3 = { kernelName: es, backendName: "wasm", setupFunc: Fee, kernelFunc: Oee };
function Pee(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
  s < 0 && (s += n.shape.length);
  let a = n.shape[s], i = n.shape.length, p = new Array(i - 1), u = 0;
  for (let d = 0; d < i; d++)
    d !== s && (p[u++] = n.shape[d]);
  let c = new Array(a), l = new Array(i).fill(0), m = n.shape.slice();
  m[s] = 1;
  for (let d = 0; d < c.length; d++)
    l[s] = d, c[d] = Eo({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t10 });
  return c.map(({ dataId: d, dtype: f }) => ({ dataId: d, dtype: f, shape: p }));
}
var h3 = { kernelName: Fs, backendName: "wasm", kernelFunc: Pee };
function Mee(r) {
  let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype);
  return t10.typedArrayFromHeap(o).fill(0), o;
}
var g3 = { kernelName: Os, backendName: "wasm", kernelFunc: Mee };
var Lee = [bF, CF, SF, IF, _F, EF, RF, FF, MF, BF, VF, zF, UF, GF, KF, jF, XF, YF, ZF, eO, rO, nO, aO, iO, uO, pO, cO, lO, dO, fO, hO, xO, bO, SO, IO, kO, NO, TO, vF, _O, EO, AO, RO, DO, FO, OO, PO, MO, BO, VO, WO, GO, KO, qO, XO, YO, QO, JO, tP, oP, nP, aP, iP, uP, bg, cP, mP, fP, hP, gP, xP, yP, bP, OF, SP, IP, kP, TP, _P, $P, AP, DP, OP, PP, LF, LP, BP, zP, UP, HP, KP, qP, jP, XP, YP, ZP, e3, t3, r3, o3, n3, a3, i3, u3, c3, m3, f3, NF, h3, g3];
for (let r of Lee)
  ka(r);
var rI = O();
rI.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => {
  try {
    return 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]));
  } catch (r) {
    return false;
  }
});
rI.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
  if (rI.get("IS_NODE"))
    return false;
  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 (r) {
    return false;
  }
});
var cI = ap(C3());
var N3 = ap(w3());
var lI = ap(I3());
var v3 = cI.default || cI;
var Bee = lI.default || lI;
var Ll = class extends Zr {
  constructor(e) {
    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(_3), pI = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Fo(this, sr());
  }
  write(e, t10, o) {
    let n = { id: this.dataIdNextNumber++ };
    return this.move(n, e, t10, o, 1), n;
  }
  numDataIds() {
    return this.dataIdMap.numDataIds();
  }
  async time(e) {
    let t10 = y.now();
    return e(), { kernelMs: y.now() - t10 };
  }
  move(e, t10, o, n, s) {
    let a = this.dataIdNextNumber++;
    if (n === "string") {
      let c = t10;
      this.dataIdMap.set(e, { id: a, stringBytes: c, shape: o, dtype: n, memoryOffset: null, refCount: s });
      return;
    }
    let i = y.sizeFromShape(o), p = i * y.bytesPerElement(n), u = this.wasm._malloc(p);
    this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: o, dtype: n, refCount: s }), this.wasm.tfjs.registerTensor(a, i, u), t10 != null && this.wasm.HEAPU8.set(new Uint8Array(t10.buffer, t10.byteOffset, p), u);
  }
  async read(e) {
    return this.readSync(e);
  }
  readSync(e, t10, o) {
    let { memoryOffset: n, dtype: s, shape: a, stringBytes: i } = this.dataIdMap.get(e);
    if (s === "string")
      return (t10 == null || t10 === 0) && (o == null || o >= i.length) ? i : i.slice(t10, o);
    t10 = t10 || 0, o = o || y.sizeFromShape(a);
    let p = y.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t10 * p, n + o * p);
    return zee(u.buffer, s);
  }
  disposeData(e, t10 = false) {
    if (this.dataIdMap.has(e)) {
      let o = this.dataIdMap.get(e);
      if (o.refCount--, !t10 && o.refCount > 0)
        return false;
      this.wasm._free(o.memoryOffset), this.wasm.tfjs.disposeData(o.id), this.dataIdMap.delete(e);
    }
    return true;
  }
  refCount(e) {
    return this.dataIdMap.has(e) ? this.dataIdMap.get(e).refCount : 0;
  }
  incRef(e) {
    let t10 = this.dataIdMap.get(e);
    t10 != null && t10.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: false };
  }
  makeOutput(e, t10, o) {
    let n;
    if (o == null)
      n = this.write(null, e, t10);
    else {
      let s = this.dataIdNextNumber++;
      n = { id: s }, this.dataIdMap.set(n, { id: s, memoryOffset: o, shape: e, dtype: t10, refCount: 1 });
      let a = y.sizeFromShape(e);
      this.wasm.tfjs.registerTensor(s, a, o);
    }
    return { dataId: n, shape: e, dtype: t10 };
  }
  typedArrayFromHeap({ shape: e, dtype: t10, dataId: o }) {
    let n = this.wasm.HEAPU8.buffer, { memoryOffset: s } = this.dataIdMap.get(o), a = y.sizeFromShape(e);
    switch (t10) {
      case "float32":
        return new Float32Array(n, s, a);
      case "int32":
        return new Int32Array(n, s, a);
      case "bool":
        return new Uint8Array(n, s, a);
      default:
        throw new Error(`Unknown dtype ${t10}`);
    }
  }
};
function Vee(r) {
  return (e, t10) => (y.fetch(r, { credentials: "same-origin" }).then((o) => {
    o.ok || e.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => {
      WebAssembly.instantiate(n, e).then((s) => {
        t10(s.instance, s.module);
      });
    });
  }), {});
}
function k3(r, e, t10) {
  if (vg != null)
    return vg;
  let o = "tfjs-backend-wasm.wasm";
  return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), Pl != null && Pl[o] != null ? Pl[o] : t10 + o;
}
async function T3() {
  let [r, e] = await Promise.all([O().getAsync("WASM_HAS_SIMD_SUPPORT"), O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
  return new Promise((t10, o) => {
    let n = {};
    n.locateFile = (i, p) => {
      if (i.endsWith(".worker.js")) {
        let u = N3.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" });
        return URL.createObjectURL(c);
      }
      return i.endsWith(".wasm") ? k3(r, e, Ol != null ? Ol : p) : p + i;
    }, mI && (n.instantiateWasm = Vee(k3(r, e, Ol != null ? Ol : "")));
    let s = false;
    n.onAbort = () => {
      if (s || Ml)
        return;
      Ml = true, o({ 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 a;
    e && r && vg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + v3.toString()], { type: "text/javascript" }), a = v3(n)) : a = Bee(n), a.then((i) => {
      s = true, Ml = false;
      let p = null;
      i.tfjs = { init: i.cwrap("init", null, []), initWithThreadsCount: i.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: i.cwrap("get_threads_count", "number", []), registerTensor: i.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: i.cwrap("dispose_data", p, ["number"]), dispose: i.cwrap("dispose", p, []) }, t10({ wasm: i });
    }).catch(o);
  });
}
function zee(r, e) {
  switch (e) {
    case "float32":
      return new Float32Array(r);
    case "int32":
      return new Int32Array(r);
    case "bool":
      return new Uint8Array(r);
    default:
      throw new Error(`Unknown dtype ${e}`);
  }
}
var Wee = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var vg = null;
var Ol = null;
var Pl = {};
var Ml = false;
var mI = false;
function Uee(r, e = false) {
  if (nC("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Ml)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
  vg = r, mI = e;
}
function Gee(r, e = false) {
  if (Ml)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
  if (typeof r == "string")
    Ol = r;
  else {
    Pl = r;
    let t10 = Wee.filter((o) => Pl[o] == null);
    if (t10.length > 0)
      throw new Error(`There were no entries found for the following binaries: ${t10.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.`);
  }
  mI = e;
}
var _3 = -1;
var pI = -1;
function Hee(r) {
  _3 = r;
}
function Kee() {
  if (pI === -1)
    throw new Error("WASM backend not initialized.");
  return pI;
}
var qee = "4.2.0";
var jee = 2;
Ti("wasm", async () => {
  let { wasm: r } = await T3();
  return new Ll(r);
}, jee);
var ds = O();
ds.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
ds.registerFlag("WEBGPU_CPU_FORWARD", () => true);
ds.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1);
ds.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
ds.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
ds.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
ds.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
ds.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true);
ds.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false);
ds.registerFlag("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL", () => 0);
ds.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false);
var kg = class {
  constructor(e) {
    e && (this.vendor = e.vendor, this.architecture = e.architecture, this.intelGPUGeneration = this.getIntelGPUGeneration());
  }
  getIntelGPUGeneration() {
    if (this.isIntel()) {
      if (this.architecture.startsWith("gen"))
        return Number(this.architecture.match(/\d+/));
      if (this.architecture.startsWith("xe"))
        return 12;
    }
    return 0;
  }
  isIntel() {
    return this.vendor === "intel";
  }
};
var Ng = class {
  constructor(e) {
    this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
  acquireUploadBuffer(e, t10) {
    return this.acquireBuffer(e, t10, true);
  }
  acquireBuffer(e, t10, o = false) {
    let n = $3(e, t10);
    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 a = this.freeBuffers.get(n).shift();
      return this.usedBuffers.get(n).push(a), a;
    }
    this.numBytesAllocated += e;
    let s = this.device.createBuffer({ size: e, usage: t10, mappedAtCreation: o });
    return this.usedBuffers.get(n).push(s), s;
  }
  releaseBuffer(e, t10, o) {
    if (this.freeBuffers.size === 0)
      return;
    let n = $3(t10, o);
    this.freeBuffers.has(n) || this.freeBuffers.set(n, []), this.freeBuffers.get(n).push(e), this.numFreeBuffers++, this.numUsedBuffers--;
    let s = this.usedBuffers.get(n), a = s.indexOf(e);
    if (a < 0)
      throw new Error("Cannot release a buffer that was never provided by this buffer manager");
    s.splice(a, 1), this.numBytesUsed -= t10;
  }
  releaseUploadBuffer(e, t10, o) {
    e.mapAsync(GPUMapMode.WRITE).then(() => {
      this.releaseBuffer(e, t10, o);
    }, (n) => {
    });
  }
  getNumUsedBuffers() {
    return this.numUsedBuffers;
  }
  getNumFreeBuffers() {
    return this.numFreeBuffers;
  }
  dispose() {
    this.freeBuffers.forEach((e, t10) => {
      e.forEach((o) => {
        o.destroy();
      });
    }), this.usedBuffers.forEach((e, t10) => {
      e.forEach((o) => {
        o.destroy();
      });
    }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
};
function $3(r, e) {
  return `${r}_${e}`;
}
var Tg = class {
  constructor(e) {
    this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
  acquireTexture(e, t10, o, n) {
    let s = A3(o), a = e * t10 * s, i = E3(e, t10, o, n);
    if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) {
      this.numFreeTextures--;
      let u = this.freeTextures.get(i).shift();
      return this.usedTextures.get(i).push(u), u;
    }
    this.numBytesAllocated += a;
    let p = this.device.createTexture({ size: [e, t10], format: o, usage: n });
    return this.usedTextures.get(i).push(p), p;
  }
  releaseTexture(e, t10, o, n, s) {
    if (this.freeTextures.size === 0)
      return;
    let a = E3(t10, o, n, s);
    this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
    let i = this.usedTextures.get(a), p = i.indexOf(e);
    if (p < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    i.splice(p, 1);
    let u = A3(n), c = t10 * o * u;
    this.numBytesUsed -= c;
  }
  getNumUsedTextures() {
    return this.numUsedTextures;
  }
  getNumFreeTextures() {
    return this.numFreeTextures;
  }
  dispose() {
    this.freeTextures.forEach((e, t10) => {
      e.forEach((o) => {
        o.destroy();
      });
    }), this.usedTextures.forEach((e, t10) => {
      e.forEach((o) => {
        o.destroy();
      });
    }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
};
function E3(r, e, t10, o) {
  return `${r}_${e}_${t10}_${o}`;
}
function A3(r) {
  if (r === "rgba8unorm")
    return 16;
  throw new Error(`${r} is not supported!`);
}
function R3(r, e) {
  if (Math.max(...r) > 3)
    throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
  let t10 = r.length, o = r.map((s) => `${e}[${s}]`), n = new Array(t10 - 1);
  n[t10 - 2] = o[t10 - 1];
  for (let s = t10 - 3; s >= 0; --s)
    n[s] = `(${n[s + 1]} * ${o[s + 1]})`;
  return n;
}
var Ic = (r, e, t10) => t10 === "int32" ? `atomicAdd(${r}, bitcast<i32>(${e}));` : `
          {
            var oldValue = 0;
            loop {
              let newValueF32 = bitcast<f32>(oldValue) + (${e});
              let newValue = bitcast<i32>(newValueF32);
              let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);
              if res.exchanged {
                break;
              }
              oldValue = res.old_value;
            }
          }`;
var M3 = (r, e, t10, o) => {
  let n = { dtype: o.dtype, shape: o.shape }, s = Yee(t10, n, e), a = r.createShaderModule({ code: s, label: e.constructor.name });
  return r.createComputePipeline({ compute: { module: a, entryPoint: "_start" }, label: e.constructor.name, layout: "auto" });
};
function Rt(r) {
  if (r <= 1)
    return "i32";
  if (r === 2)
    return "vec2<i32>";
  if (r === 3)
    return "vec3<i32>";
  if (r === 4)
    return "vec4<i32>";
  if (r === 5)
    return "vec5";
  if (r === 6)
    return "vec6";
  throw Error(`GPU for rank ${r} is not yet supported`);
}
function Ao(r) {
  if (r === 0)
    return "x";
  if (r === 1)
    return "y";
  if (r === 2)
    return "z";
  if (r === 3)
    return "w";
  if (r === 4)
    return "u";
  if (r === 5)
    return "v";
  throw Error(`Index ${r} is not yet supported`);
}
function Q(...r) {
  let e;
  switch (r.length) {
    case 0:
      e = `
        fn main()
      `;
      break;
    case 1:
      e = `
        fn main(${r[0]} : i32)
      `;
      break;
    default:
      throw Error("Unreachable");
  }
  return e;
}
function D3(r, e) {
  let t10;
  return t10 = `
     ${Xee(e)}
      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                @builtin(global_invocation_id) GlobalId : vec3<u32>,
                @builtin(local_invocation_index) LocalIndex: u32,
                @builtin(workgroup_id) WorkgroupId : vec3<u32>,
                @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
        localId = LocalId;
        localIndex = LocalIndex;
        globalId = GlobalId;
        numWorkgroups = NumWorkgroups;
        workgroupId = WorkgroupId;
        ${r ? "main(getGlobalIndex());" : "main();"};
      }
    `, t10;
}
function Xee(r) {
  return `
  @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})
`;
}
function Yee(r, e, t10) {
  let o = [], n = t10.workgroupSize[0] * t10.workgroupSize[1] * t10.workgroupSize[2];
  if (o.push(`

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

      // Only used when the y/z dimension of workgroup size is 1.
      fn getGlobalIndex() -> i32 {
        ${B3(t10) ? "  return i32(globalId.x);" : `  return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +
                workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +
                localIndex);
        `}
      }
    `), t10.isFromPixels) {
    o.push(`
        struct Uniform {
          size            : i32,
          numChannels     : i32,
          outShapeStrides : vec2<i32>,
        };

        @group(0) @binding(0) var<storage, read_write> result: array<${vc(e.dtype, t10.isVec4)}>;
        @group(0) @binding(2) var<uniform> uniforms: Uniform;
      `);
    let f = P3(t10);
    return [F3, o.join(`
`), O3(e.shape), t10.getUserCode(), D3(f, t10)].join(`
`);
  }
  let s = "struct Uniforms { NAN : f32, INFINITY : f32, ";
  t10.variableNames.forEach((f, h) => {
    let g = Rt(r[h].shape.length);
    s += `${f.charAt(0).toLowerCase() + f.slice(1)}Shape : ${g}, `;
  });
  let a = Rt(e.shape.length);
  s += `outShape : ${a}, `;
  let i = e.shape.length - 1, p = Rt(i);
  s += `
         outShapeStrides: ${p}, `, t10.size && (s += "size : i32, "), t10.uniforms && (s += t10.uniforms), s += "};", s = nte(s), o.push(s), t10.atomic ? o.push(`
      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
    `) : o.push(`
      @group(0) @binding(0) var<storage, read_write> result: array<${vc(e.dtype, t10.isVec4)}>;
    `), t10.variableNames.forEach((f, h) => {
    o.push(`
      @group(0) @binding(${1 + h}) var<storage, read> ${f}: array<${t10.variableTypes ? t10.variableTypes[h] : vc(r[h].dtype, t10.isVec4)}>;
        `);
  }), s !== "" && o.push(`
      @group(0) @binding(${1 + t10.variableNames.length}) var<uniform> uniforms: Uniforms;
      `);
  let u = tte(e.shape, t10.dispatchLayout), c = [F3, o.join(`
`) + Qee, O3(e.shape), u, rte(e.shape.length)];
  t10.atomic || c.push(ote(e.shape, e.dtype, t10.isVec4));
  let l = r.map((f, h) => ete(f, e.shape, t10.variableTypes ? t10.variableTypes[h] === "vec4<f32>" : t10.isVec4, t10.dispatchLayout.x.length === e.shape.length)).join(`
`);
  c.push(l), c.push(t10.getUserCode());
  let m = P3(t10);
  return c.push(D3(m, t10)), c.join(`
`);
}
function L3(r, e, t10, o) {
  let n = r.shaderKey;
  if (r.isFromPixels)
    return n;
  let s = t10.map((c) => c.dtype).concat(o.dtype), a = t10.map((c) => S.getBroadcastDims(c.shape, o.shape)), i = t10.map((c) => y.arraysEqual(c.shape, o.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = B3(r) ? "flatDispatch" : "";
  return n += "_" + (r.workgroupSize ? r.workgroupSize.join(",") : "") + e.map((c) => c.length).join(",") + s.join(",") + r.variableNames.join(",") + p + i + u, n;
}
var F3 = `
  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};

  // Checks whether coordinates lie within the bounds of the shape.
  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
    return all(coord >= vec2<i32>(0)) && all(coord < shape);
  }
  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
    return all(coord >= vec3<i32>(0)) && all(coord < shape);
  }
  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
    return all(coord >= vec4<i32>(0)) && all(coord < shape);
  }

  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
    return coord;
  }
  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
    return dot(coords, vec2<i32>(shape.y, 1));
  }
  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
  }
  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
    return dot(coords, vec4<i32>(
        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
  }
  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
  }
  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
  }

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

  // NaN defination in IEEE 754-1985 is :
  //   - sign = either 0 or 1.
  //   - biased exponent = all 1 bits.
  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
  fn isnan(val: f32) -> bool {
    let floatToUint: u32 = bitcast<u32>(val);
    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
  }
  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
    let floatToUint: vec4<u32> = bitcast<vec4<u32>>(val);
    return (floatToUint & vec4<u32>(0x7fffffffu)) > vec4<u32>(0x7f800000u);
  }
`;
var Qee = `
  fn isinf(val: f32) -> bool {
    return abs(val) == uniforms.INFINITY;
  }
`;
function O3(r) {
  let e = r.length;
  if (e <= 1)
    return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
  let t10 = y.computeStrides(r), o = Rt(e), n = [];
  for (let a = 0; a < e; a++)
    n.push(`d${a}`);
  if (t10.length === 1)
    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
      return vec2<i32>(d0, d1);
    }`;
  let s;
  return s = "var index2 = index;" + t10.map((a, i) => {
    let p = `let ${n[i]} = index2 / uniforms.outShapeStrides.${Ao(i)}`, u = i === t10.length - 1 ? `let ${n[i + 1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}` : `index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Ao(i)}`;
    return `${p}; ${u};`;
  }).join(""), `
    fn getCoordsFromIndex(index : i32) -> ${o} {
      ${s}
      return ${o}(${n.join(",")});
    }
  `;
}
function Zee(r, e) {
  let t10 = r.name, o = r.shape.length, n = Rt(o), s = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = a.map((c) => `${c} : i32`).join(", ");
  if (o < 1)
    return e ? `
        fn ${s}() -> vec4<f32> {
          return vec4<f32>(${t10}[0]);
        }
      ` : `
      fn ${s}() ->f32 {
        return f32(${t10}[0]);
      }
    `;
  let p = `uniforms.${t10.charAt(0).toLowerCase() + t10.slice(1)}Shape`, u = `${o}D`;
  return o === 0 && (u = "1D"), e ? `
      fn ${s}(${i}) -> vec4<f32> {
        return vec4<f32>(${t10}[getIndexFromCoords${u}(${n}(${a.join(",")}),
          ${p}) / 4]);
      }
      ` : `
    fn ${s}(${i}) -> f32 {
      return f32(${t10}[getIndexFromCoords${u}(${n}(${a.join(",")}),
        ${p})]);
    }
   `;
}
function Jee(r, e, t10, o) {
  let n = r.name, s = n.charAt(0).toUpperCase() + n.slice(1), a = "get" + s + "ByOutput", i = r.shape.length, p = e.length, u = Rt(p);
  if (y.arraysEqual(r.shape, e) && o)
    return t10 ? `
      fn ${a}Index(globalIndex : i32) -> vec4<f32> {
        return vec4<f32>(${n}[globalIndex]);
      }

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

    fn ${a}Coords(coords : ${u}) -> f32 {
      return f32(${n}[${p > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}]);
    }
    `;
  let c = S.getBroadcastDims(r.shape, e), l = p - i, m = "";
  if (i === 0)
    return t10 ? `
    fn ${a}Index(globalIndex : i32) -> vec4<f32> {
      return get${s}();
    }

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

    fn ${a}Coords(coords : ${u}) -> f32{
      return get${s}();
    }
  `;
  p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((g) => `coords.${Ao(g + l)} = 0;`).join(`
`);
  let d = "";
  if (p < 2 && i > 0)
    d = "coords";
  else if (p > 1) {
    let g = Rt(i), x = r.shape.map((b, C) => `coords.${Ao(C + l)}`).join(", ");
    d = `${g}(${x})`;
  } else
    d = "coords";
  let f = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, h = `${i}D`;
  return t10 ? `
    fn ${a}Index(globalIndex : i32) -> vec4<f32> {
      var coords = getCoordsFromIndex(globalIndex);
      ${m}
      return ${n}[getIndexFromCoords${h}(${d}, ${f}) / 4];
    }

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

  fn ${a}Coords(coordsIn : ${u}) -> f32 {
    var coords = coordsIn;
    ${m}
    return f32(${n}[getIndexFromCoords${h}(${d}, ${f})]);
  }
`;
}
function ete(r, e, t10, o) {
  let n = Zee(r, t10);
  return r.shape.length <= e.length && (n += Jee(r, e, t10, o)), n;
}
function tte(r, e) {
  let { x: t10, y: o = [], z: n = [] } = e, s = r.length, a = t10.length + o.length + n.length;
  if (a !== s)
    return "";
  if (t10.length === s)
    return `fn getOutputCoords() -> ${Rt(s)}{
    let globalIndex = getGlobalIndex();
    return getCoordsFromIndex(globalIndex);
  }
  `;
  let i = "", p = [t10, o, n];
  for (let m = 0; m < p.length; m++) {
    let d = p[m];
    if (d.length !== 0)
      if (d.length === 1)
        i += `let d${d[0]} = i32(globalId[${m}]);`;
      else {
        let f = R3(d, "uniforms.outShape");
        i += `var index${m} = i32(globalId[${m}]);`;
        for (let h = 0; h < f.length; h++)
          i += `let d${d[h]} = index${m} / ${f[h]};`, h === f.length - 1 ? i += `let d${d[h + 1]} = index${m} - d${d[h]} * ${f[h]};` : i += `index${m} = index${m} - d${d[h]} * ${f[h]};`;
      }
  }
  let u = [];
  for (let m = 0; m < a; m++)
    u.push(`d${m}`);
  let c = Rt(a), l = `fn getOutputCoords() -> ${c} {
  ${i}
`;
  return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l;
}
function rte(r) {
  let e = "";
  switch (r) {
    case 0:
    case 1:
      e += `
        fn getOutputIndexFromCoords(coords : i32) -> i32 {
          return coords;
        }
        `;
      break;
    case 2:
      e += `
        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
        }
        `;
      break;
    case 3:
      e += `
        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
        }
        `;
      break;
    case 4:
      e += `
        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
          return dot(coords, vec4<i32>(
            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
        }
        `;
      break;
    case 5:
      e += `
        fn getOutputIndexFromCoords(coords : vec5) -> i32 {
          return coords.x * uniforms.outShapeStrides.x +
              coords.y * uniforms.outShapeStrides.y +
              coords.z * uniforms.outShapeStrides.z +
              coords.w * uniforms.outShapeStrides.w +
              coords.u;
        }
        `;
      break;
    case 6:
      e += `
        fn getOutputIndexFromCoords(coords : vec6) -> i32 {
          return coords.x * uniforms.outShapeStrides.x +
              coords.y * uniforms.outShapeStrides.y +
              coords.z * uniforms.outShapeStrides.z +
              coords.w * uniforms.outShapeStrides.w +
              coords.u * uniforms.outShapeStrides.u +
              coords.v;
        }
        `;
      break;
    default:
      y.assert(false, () => `Unsupported ${r}D shape`);
      break;
  }
  return e;
}
function B3(r) {
  return r.dispatch[1] === 1 && r.dispatch[2] === 1;
}
function vc(r, e) {
  return r === "float32" ? e ? "vec4<f32>" : "f32" : r === "int32" || r === "bool" ? e ? "vec4<i32>" : "i32" : r;
}
function ote(r, e, t10) {
  let o = r.length, n = vc(e, t10), s;
  if (t10 ? s = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
      result[flatIndex] = ${n}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
      result[flatIndex] = ${n}(value);
    }` : s = `fn setOutputAtIndex(flatIndex : i32, value : f32) {
      result[flatIndex] = ${n}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
      result[flatIndex] = ${n}(value);
    }`, o >= 2) {
    let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = Rt(o);
    t10 ? s += `
      fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : vec4<f32>) {
        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
        setOutputAtIndex(flatIndex / 4, value);
      }
      fn setOutputAtCoordsI32(${a.map((p) => `${p} : i32`).join(", ")}, value : vec4<i32>) {
        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
        setOutputAtIndexI32(flatIndex / 4, value);
      }
    ` : s += `
      fn setOutputAtCoords(${a.map((p) => `${p} : i32`).join(", ")}, value : f32) {
        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
        setOutputAtIndex(flatIndex, value);
      }
      fn setOutputAtCoordsI32(${a.map((p) => `${p} : i32`).join(", ")}, value : i32) {
        let flatIndex = getOutputIndexFromCoords(${i}(${a.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `;
  }
  return s;
}
function nte(r) {
  let e = /(\w+)\s*:\s*vec(5|6)/g;
  r = r.replace(e, (o) => "@align(16) " + o);
  let t10 = /vec(5|6)\s*,\s*(\w+)/g;
  return r = r.replace(t10, (o, n, s) => `vec${n}, @align(16) ${s}`), r;
}
function P3(r) {
  return !(r.dispatchLayout.hasOwnProperty("y") && r.dispatchLayout.y.length !== 0 || r.dispatchLayout.hasOwnProperty("z") && r.dispatchLayout.z.length !== 0);
}
var hI = {};
Ge(hI, { GPUBytesPerElement: () => _g, MatMulProgramType: () => Ro, assertNotComplex: () => fI, computeDispatch: () => Y, computeWorkPerThreadForConv2d: () => Vl, computeWorkgroupInfoForMatMul: () => dI, computeWorkgroupSizeForConv2d: () => Bl, flatDispatchLayout: () => ae, isWebGPUSupported: () => zl, tilesFitEvenlyIntoShape: () => ate });
var Ku = (r) => {
  let e = 1;
  for (let t10 = 0; t10 < r.length; t10++)
    e *= r[t10];
  return e;
};
function ate(r, e) {
  if (r.length !== e.length)
    throw new Error(`Cannot compute whether rank ${r.length} tiles fit evenly into rank ${e.length} shape - ranks must match.`);
  return e.every((t10, o) => t10 % r[o] === 0);
}
function Y(r, e, t10 = [1, 1, 1], o = [1, 1, 1]) {
  let [n, s, a] = [Math.ceil(Ku(r.x.map((i) => e[i])) / (t10[0] * o[0])), r.y ? Math.ceil(Ku(r.y.map((i) => e[i])) / (t10[1] * o[1])) : 1, r.z ? Math.ceil(Ku(r.z.map((i) => e[i])) / (t10[2] * o[2])) : 1];
  return [n, s, a];
}
function dI(r, e, t10, o = false) {
  let n = [8, 8, 1], s = [4, 4, 1];
  return o || (r <= 8 && (s[1] = 1), e <= 16 && t10 <= 16 && (n[0] = 4)), { workgroupSize: n, elementsPerThread: s };
}
function Bl(r, e, t10 = false) {
  if (t10)
    return [8, 8, 1];
  let o = Ku(r.x.map((s) => e[s])), n = Ku(r.y.map((s) => e[s]));
  return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function Vl(r, e, t10 = false) {
  if (t10)
    return [4, 4, 1];
  let o = Ku(r.x.map((s) => e[s])), n = Ku(r.y.map((s) => e[s]));
  return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1];
}
function ae(r) {
  return { x: r.map((e, t10) => t10) };
}
function _g(r) {
  if (r === "float32" || r === "int32" || r === "bool" || r === "string")
    return 4;
  if (r === "complex64")
    return 8;
  throw new Error(`Unknown dtype ${r}`);
}
function zl() {
  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
function fI(r, e) {
  Array.isArray(r) || (r = [r]), r.forEach((t10) => {
    t10 != null && y.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGPU backend.`);
  });
}
var Ro;
(function(r) {
  r[r.MatMulReduceProgram = 0] = "MatMulReduceProgram", r[r.MatMulSplitKProgram = 1] = "MatMulSplitKProgram", r[r.MatMulSmallOutputSizeProgram = 2] = "MatMulSmallOutputSizeProgram", r[r.MatMulPackedProgram = 3] = "MatMulPackedProgram", r[r.MatMulMax = 4] = "MatMulMax";
})(Ro || (Ro = {}));
var ite = O().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var ute = (r, e) => {
  let t10 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch;
  if (n.every((a) => a <= t10))
    return n;
  y.assert(n[0] > t10 && o.y === void 0 && o.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
  let s = Math.ceil(Math.sqrt(n[0]));
  return s > t10 ? (s = Math.ceil(Math.cbrt(n[0])), y.assert(s <= t10, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1];
};
var Gi = class extends Zr {
  constructor(e, t10) {
    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.dispatchNumberInEncoder = 0, this.disposed = false, this.downloadWaitMs = 0, this.tensorDataPendingDisposal = [], this.stagingPendingDisposal = [], this.uniformPendingDisposal = [], this.uploadWaitMs = 0, !zl())
      throw new Error("WebGPU is not supported on this device");
    this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = e.features.has("timestamp-query-inside-passes"), this.adapterInfo = new kg(t10), this.thresholdToIncreaseWorkgroups = this.adapterInfo.intelGPUGeneration >= 12 ? 16 : 8, this.bufferManager = new Ng(this.device), this.textureManager = new Tg(this.device), this.tensorMap = new Fo(this, sr()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), O().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 Gi.nextDataId++;
  }
  floatPrecision() {
    return 32;
  }
  defaultGpuBufferUsage() {
    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;
  }
  disposeData(e, t10 = false) {
    if (this.tensorDataPendingDisposal.indexOf(e) >= 0)
      return false;
    if (!this.tensorMap.has(e))
      return true;
    let o = this.tensorMap.get(e);
    if (this.decRef(e), !t10 && o.refCount > 0)
      return false;
    if (this.commandQueueOwnedIds.has(e))
      return this.tensorDataPendingDisposal.push(e), false;
    let { complexTensorInfos: n } = this.tensorMap.get(e);
    return n != null && (this.disposeData(n.real.dataId, t10), this.disposeData(n.imag.dataId, t10)), this.releaseResource(e), this.tensorMap.delete(e), true;
  }
  memory() {
    return { numBytesInGPU: this.bufferManager.numBytesUsed, numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, unreliable: false };
  }
  releaseResource(e) {
    let t10 = this.tensorMap.get(e);
    if (!(!t10 || !t10.resourceInfo)) {
      if (t10.external) {
        t10.resourceInfo = null;
        return;
      }
      if ("texture" in t10.resourceInfo) {
        let o = t10.resourceInfo;
        o.texture instanceof GPUTexture && this.textureManager.releaseTexture(o.texture, o.width, o.height, o.format, o.usage), o.texture = null;
      } else {
        let o = t10.resourceInfo;
        this.bufferManager.releaseBuffer(o.buffer, o.size, o.usage), o.buffer = null;
      }
      t10.resourceInfo = null;
    }
  }
  refCount(e) {
    return this.tensorMap.has(e) ? this.tensorMap.get(e).refCount : 0;
  }
  incRef(e) {
    let t10 = this.tensorMap.get(e);
    t10.refCount++;
  }
  decRef(e) {
    if (this.tensorMap.has(e)) {
      let t10 = this.tensorMap.get(e);
      t10.refCount--;
    }
  }
  write(e, t10, o) {
    if (o === "complex64" && e != null)
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    let n = { id: this.nextDataId() };
    return this.tensorMap.set(n, { dtype: o, shape: t10, values: e, refCount: 1 }), n;
  }
  move(e, t10, o, n, s) {
    if (n === "complex64")
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    this.tensorMap.set(e, { dtype: n, shape: o, values: t10, refCount: s });
  }
  submitQueue() {
    this.ensureComputePassEnded(), this.queue.submit([this.currentCommandEncoder.finish()]), this.currentCommandEncoder = null, this.dispatchNumberInEncoder = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDataPendingDisposal.forEach((e) => {
      this.releaseResource(e), this.tensorMap.delete(e);
    }), this.uniformPendingDisposal.forEach((e) => this.bufferManager.releaseBuffer(e.buffer, e.size, e.usage)), this.stagingPendingDisposal.forEach((e) => this.bufferManager.releaseUploadBuffer(e.buffer, e.size, e.usage)), this.tensorDataPendingDisposal = [], this.uniformPendingDisposal = [], this.stagingPendingDisposal = [];
  }
  ensureCommandEncoderReady() {
    this.currentCommandEncoder || (this.currentCommandEncoder = this.device.createCommandEncoder());
  }
  ensureComputePassEnded() {
    this.currentComputePass && (this.currentComputePass.end(), this.currentComputePass = null);
  }
  getComputePass() {
    return this.currentComputePass || (this.currentComputePass = this.currentCommandEncoder.beginComputePass()), this.currentComputePass;
  }
  async getBufferData(e, t10) {
    let o = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, o, 0, t10), this.submitQueue(), await o.mapAsync(GPUMapMode.READ);
    let n = o.getMappedRange().slice(0);
    return o.unmap(), o != null && this.bufferManager.releaseBuffer(o, t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), O().getBool("WEBGPU_USE_PROFILE_TOOL") && (y.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n;
  }
  convertAndCacheOnCPU(e, t10) {
    let o = this.tensorMap.get(e);
    return this.releaseResource(e), o.values = t10, o.values;
  }
  readSync(e) {
    let t10 = this.tensorMap.get(e), { values: o } = t10;
    if (o == null)
      throw new Error("WebGPU readSync is only available for CPU-resident tensors.");
    return o;
  }
  async read(e) {
    if (!this.tensorMap.has(e))
      throw new Error(`Tensor ${e} was not registered!`);
    let t10 = this.tensorMap.get(e), { values: o } = t10;
    if (o != null)
      return this.convertAndCacheOnCPU(e, o);
    let n;
    if (t10.dtype === "complex64") {
      let s = await Promise.all([this.read(t10.complexTensorInfos.real.dataId), this.read(t10.complexTensorInfos.imag.dataId)]), a = s[0], i = s[1];
      n = S.mergeRealAndImagArrays(a, i);
    } else {
      let s = t10.resourceInfo, a = await this.getBufferData(s.buffer, s.size);
      n = y.convertBackendValuesAndArrayBuffer(a, t10.dtype);
    }
    return this.convertAndCacheOnCPU(e, n), n;
  }
  copyBuffer(e, t10, o) {
    let n = this.bufferManager.acquireBuffer(t10, o);
    return this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, n, 0, t10), this.submitQueue(), n;
  }
  createTensorFromGPUData(e, t10, o) {
    let n = e.buffer;
    if (o === "complex64")
      throw new Error("Cannot write to a complex64 dtype. ");
    let s = { id: this.nextDataId() };
    this.tensorMap.set(s, { dtype: o, shape: t10, values: null, refCount: 1, external: e.zeroCopy });
    let a = this.tensorMap.get(s), i = _g(a.dtype) * y.sizeFromShape(a.shape);
    if (e.buffer.size < i)
      throw new Error(`GPUBuffer size(${e.buffer.size}) is smaller than tensor size(${i})!`);
    if ((e.buffer.usage & (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC)) !== (GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC))
      throw new Error("GPUBuffer.usage should include GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC!");
    return e.zeroCopy !== true && (n = this.copyBuffer(n, i, n.usage)), a.resourceInfo = { size: n.size, usage: n.usage, buffer: n }, sr().makeTensorFromDataId(s, t10, o, this);
  }
  readToGPU(e) {
    let t10 = this.tensorMap.get(e), { values: o, dtype: n, shape: s, resourceInfo: a } = t10;
    if (n === "complex64")
      throw new Error("Does not support reading buffer for complex64 dtype.");
    if (a == null)
      throw o != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
    let i = a.size, p = this.bufferManager.acquireBuffer(i, a.usage);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, p, 0, i), this.submitQueue();
    let u = this.makeTensorInfo(s, n), c = sr().makeTensorFromTensorInfo(u), l = this.tensorMap.get(u.dataId);
    return l.resourceInfo = { size: i, usage: this.defaultGpuBufferUsage(), buffer: p }, { tensorRef: c, buffer: p, bufSize: i };
  }
  bufferSync(e) {
    let t10 = this.readSync(e.dataId);
    if (e.dtype === "string")
      try {
        let o = t10.map((n) => y.decodeString(n));
        return le(e.shape, e.dtype, o);
      } catch (o) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return le(e.shape, e.dtype, t10);
  }
  async time(e) {
    this.supportTimeQuery || console.warn("This device doesn't support timestamp-query-inside-passes extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");
    let t10 = this.activeTimers, o = [], n = false;
    this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e();
    let s = y.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = y.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null);
    this.activeTimers = t10, n && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, p = await Promise.all(s);
    return i.kernelMs = y.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
  }
  makeTensorInfo(e, t10, o) {
    return t10 === "string" && o != null && o.length > 0 && y.isString(o[0]) && (o = o.map((s) => y.encodeString(s))), { dataId: this.write(o, e, t10), shape: e, dtype: t10 };
  }
  tensorToBinding(e) {
    if (!e)
      return null;
    let t10 = this.tensorMap.get(e.dataId);
    if ("texture" in t10.resourceInfo) {
      let n = t10.resourceInfo;
      return n.texture instanceof GPUExternalTexture ? n.texture : n.texture.createView();
    }
    let o = t10.resourceInfo;
    return { offset: 0, size: o.size, buffer: o.buffer };
  }
  async getQueryTime(e) {
    return this.supportTimeQuery ? this.getTimeFromQuerySet(e) : 0;
  }
  uploadToGPU(e) {
    let t10 = this.tensorMap.get(e);
    if (t10.resourceInfo)
      return;
    let o = _g(t10.dtype) * y.sizeFromShape(t10.shape), n = this.bufferManager.acquireBuffer(o, this.defaultGpuBufferUsage());
    if (t10.resourceInfo = { size: o, usage: this.defaultGpuBufferUsage(), buffer: n }, t10.values) {
      let s = this.bufferManager.acquireUploadBuffer(o, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC), a = s.getMappedRange();
      t10.dtype === "int32" || t10.dtype === "bool" ? new Int32Array(a).set(t10.values) : new Float32Array(a).set(t10.values), s.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(s, 0, n, 0, o);
      let i = { size: o, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, buffer: s };
      this.stagingPendingDisposal.push(i);
    }
  }
  makeUniforms(e) {
    let t10 = 0, o = 0, n = [], s = 1;
    e.forEach((u) => {
      u.data.length === 0 && (u.data = [1]);
      let c;
      switch (u.data.length) {
        case 1:
          c = 4;
          break;
        case 2:
          c = 8;
          break;
        case 3:
          c = 16;
          break;
        case 4:
          c = 16;
          break;
        case 5:
          c = 16;
          break;
        case 6:
          c = 16;
          break;
        default:
          y.assert(false, () => `Unsupported ${u.data.length}D shape`);
      }
      (o === 5 || o === 6) && (c = 16), c > s && (s = c), t10 = Math.ceil(t10 / c) * c, o = u.data.length, n.push(t10), t10 += u.data.length * 4;
    }), t10 = Math.ceil(t10 / s) * s;
    let a = new ArrayBuffer(t10);
    e.forEach((u, c) => {
      let l = n[c];
      u.type === "int32" ? new Int32Array(a, l, u.data.length).set(u.data) : u.type === "uint32" ? new Uint32Array(a, l, u.data.length).set(u.data) : new Float32Array(a, l, u.data.length).set(u.data);
    });
    let i = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
    this.queue.writeBuffer(i, 0, a, 0, t10);
    let p = { size: t10, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: i };
    return this.uniformPendingDisposal.push(p), { offset: 0, size: t10, buffer: i };
  }
  runWebGPUProgram(e, t10, o, n, s) {
    if (s || (s = this.makeTensorInfo(e.outputShape, o)), y.sizeFromShape(s.shape) === 0)
      return this.tensorMap.get(s.dataId).values = y.getTypedArrayFromDType(s.dtype, 0), s;
    this.uploadToGPU(s.dataId), e.dispatch = ute(this.device, e);
    let a = [], i = [];
    if (!e.isFromPixels) {
      a.push({ type: "float32", data: [NaN] }, { type: "float32", data: [1 / 0] }), i = t10.concat(s).map((x) => x.shape);
      let h = "int32";
      i.map((x) => {
        a.push({ type: h, data: x });
      });
      let g = y.computeStrides(s.shape);
      if (a.push({ type: h, data: g }), e.size) {
        let x = y.sizeFromShape(e.outputShape);
        a.push({ type: h, data: [e.isVec4 ? x / 4 : x] });
      }
    }
    let p = t10.map((h, g) => {
      if (h.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(h.dataId), { dtype: this.tensorMap.get(h.dataId).dtype, shape: h.shape, name: e.variableNames[g] };
    }), u = L3(e, i, p, s), c;
    u in this.pipelineCache ? c = this.pipelineCache[u] : (c = M3(this.device, e, p, s), this.pipelineCache[u] = c), n && (a = [...a, ...n]);
    let l = [this.tensorToBinding(s), ...t10.map((h) => this.tensorToBinding(h)), this.makeUniforms(a)], m = this.device.createBindGroup({ layout: c.getBindGroupLayout(0), entries: l.map((h, g) => ({ binding: g, resource: h })) });
    this.ensureCommandEncoderReady();
    let d = this.getComputePass(), f = this.activeTimers != null;
    return f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 0), d.setPipeline(c), d.setBindGroup(0, m), d.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && d.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t10.forEach((h) => {
      this.commandQueueOwnedIds.add(h.dataId);
    }), this.commandQueueOwnedIds.add(s.dataId), O().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), f && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), s;
  }
  async getTimeFromQuerySet(e) {
    let t10 = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), o = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t10, 0), this.currentCommandEncoder.copyBufferToBuffer(t10, 0, o, 0, 16), this.submitQueue(), await o.mapAsync(GPUMapMode.READ);
    let n = new BigUint64Array(o.getMappedRange()), s = Number(n[1] - n[0]);
    return o.unmap(), this.bufferManager.releaseBuffer(o, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t10, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), s / 1e6;
  }
  shouldExecuteOnCPU(e, t10 = ite) {
    return O().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resourceInfo == null && y.sizeFromShape(o.shape) < t10);
  }
  numDataIds() {
    return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;
  }
  dispose() {
    this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true);
  }
};
Gi.nextDataId = 0;
zl() && Ti("webgpu", async () => {
  O().set("CHECK_COMPUTATION_FOR_ERRORS", false);
  let r = { powerPreference: O().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t10 = {};
  e.features.has("timestamp-query-inside-passes") && (t10.requiredFeatures = ["timestamp-query-inside-passes"]);
  let o = e.limits;
  t10.requiredLimits = { maxComputeWorkgroupStorageSize: o.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: o.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: o.maxStorageBufferBindingSize };
  let n = await e.requestDevice(t10), s = await e.requestAdapterInfo();
  return new Gi(n, s);
}, 3);
var ge;
(function(r) {
  r[r.ADD = 0] = "ADD", r[r.ATAN2 = 1] = "ATAN2", r[r.COMPLEX_MULTIPLY_IMAG = 2] = "COMPLEX_MULTIPLY_IMAG", r[r.COMPLEX_MULTIPLY_REAL = 3] = "COMPLEX_MULTIPLY_REAL", r[r.DIV = 4] = "DIV", r[r.EQUAL = 5] = "EQUAL", r[r.GREATER = 6] = "GREATER", r[r.GREATER_EQUAL = 7] = "GREATER_EQUAL", r[r.INT_DIV = 8] = "INT_DIV", r[r.LESS = 9] = "LESS", r[r.LESS_EQUAL = 10] = "LESS_EQUAL", r[r.LOGICAL_AND = 11] = "LOGICAL_AND", r[r.LOGICAL_OR = 12] = "LOGICAL_OR", r[r.MAX = 13] = "MAX", r[r.MIN = 14] = "MIN", r[r.MOD = 15] = "MOD", r[r.MUL = 16] = "MUL", r[r.NOT_EQUAL = 17] = "NOT_EQUAL", r[r.POW = 18] = "POW", r[r.PRELU = 19] = "PRELU", r[r.SQUARED_DIFFERENCE = 20] = "SQUARED_DIFFERENCE", r[r.SUB = 21] = "SUB";
})(ge || (ge = {}));
var V3 = `
  if (isnan(a)) { return a; }
  if (isnan(b)) { return b; }
  `;
var $g = `
  resultTemp = select(
      resultTemp, vec4<f32>(valueForNaN),
      vec4<bool>(isNaN) | isnanVec4(a) | isnanVec4(b));
  `;
var pte = "return a + b;";
var cte = "return areal * breal - aimag * bimag;";
var lte = "return areal * bimag + aimag * breal;";
var mte = "return a / b;";
var dte = "return f32(a == b);";
var fte = "return vec4<f32>(a == b);";
var hte = "return f32(a > b);";
var gte = "return vec4<f32>(a > b);";
var xte = "return f32(a >= b);";
var yte = "return vec4<f32>(a >= b);";
var bte = `
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
`;
var Cte = `
  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);
`;
var Ste = "return f32(a < b);";
var wte = "return vec4<f32>(a < b);";
var Ite = "return f32(a <= b);";
var vte = "return vec4<f32>(a <= b);";
var kte = "return f32(a >= 1.0 && b >= 1.0);";
var Nte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`;
var Tte = "return f32(a >= 1.0 || b >= 1.0);";
var _te = `return min(vec4<f32>(a >= vec4<f32>(1.0)) +
  vec4<f32>(b >= vec4<f32>(1.0)), vec4<f32>(1.0));`;
var $te = `
  ${V3}
  if (b == 0.) {
    return uniforms.NAN;
  }
  var resultTemp = a % b;
  if ((a < 0. && b < 0.) || (a >= 0. && b > 0.)) {
    return resultTemp;
  } else {
    return (resultTemp + b) % b;
  }
`;
var Ete = `
  let isNaN = !vec4<bool>(b);
  let valueForNaN = uniforms.NAN;
  var resultTemp = vec4<f32>(a % b);
  ${$g}

  if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {
    resultTemp[0] = (resultTemp[0] + b[0]) % b[0];
  }
  if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {
    resultTemp[1] = (resultTemp[1] + b[1]) % b[1];
  }
  if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {
    resultTemp[2] = (resultTemp[2] + b[2]) % b[2];
  }
  if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {
    resultTemp[3] = (resultTemp[3] + b[3]) % b[3];
  }

  return resultTemp;
`;
var Ate = "return a * b;";
var Rte = `
  if (isnan(a) || isnan(b)) {
    return 1.0;
  }
  return f32(a != b);
`;
var Dte = `
  var resultTemp = vec4<f32>(a != b);
  let valueForNaN = 1.0;
  ${$g}

  return resultTemp;
`;
var Fte = `
  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);
`;
var Ote = `
  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
  let isModRound1 = vec4<f32>(isModRound1Bool);
  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
  var resultTemp = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  let isExpZero = b == vec4<f32>(0.0);
  if (isExpZero.r) {
    resultTemp.r = 1.0;
  }
  if (isExpZero.g) {
    resultTemp.g = 1.0;
  }
  if (isExpZero.b) {
    resultTemp.b = 1.0;
  }
  if (isExpZero.a) {
    resultTemp.a = 1.0;
  }
  let isNaN = (a < vec4<f32>(0.0)) & (floor(b) < b);
  let valueForNaN = uniforms.NAN;
  ${$g}
  return resultTemp;
`;
var Pte = "if (a < 0.0) { return b * a; }  return a;";
var Mte = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var Lte = "return (a - b) * (a - b);";
var Bte = "return a - b;";
function gI(r, e, t10 = "uniforms.NAN") {
  let o = e ? $g : V3;
  return e ? `
    let valueForNaN = ${t10};
    var resultTemp = vec4<f32>(${r}(a, b));
    ` + o + `
    return resultTemp;
  ` : o + `
    return ${r}(a, b);
  `;
}
function kc(r, e) {
  switch (r) {
    case ge.ADD:
      return pte;
    case ge.ATAN2:
      return gI("atan2", e);
    case ge.COMPLEX_MULTIPLY_IMAG:
      return lte;
    case ge.COMPLEX_MULTIPLY_REAL:
      return cte;
    case ge.DIV:
      return mte;
    case ge.EQUAL:
      return e ? fte : dte;
    case ge.GREATER:
      return e ? gte : hte;
    case ge.GREATER_EQUAL:
      return e ? yte : xte;
    case ge.INT_DIV:
      return e ? Cte : bte;
    case ge.LESS:
      return e ? wte : Ste;
    case ge.LESS_EQUAL:
      return e ? vte : Ite;
    case ge.LOGICAL_AND:
      return e ? Nte : kte;
    case ge.LOGICAL_OR:
      return e ? _te : Tte;
    case ge.MAX:
      return gI("max", e);
    case ge.MIN:
      return gI("min", e);
    case ge.MOD:
      return e ? Ete : $te;
    case ge.MUL:
      return Ate;
    case ge.NOT_EQUAL:
      return e ? Dte : Rte;
    case ge.POW:
      return e ? Ote : Fte;
    case ge.PRELU:
      return e ? Mte : Pte;
    case ge.SQUARED_DIFFERENCE:
      return Lte;
    case ge.SUB:
      return Bte;
    default:
      throw new Error(`BinaryType ${r} is not implemented!`);
  }
}
var X;
(function(r) {
  r[r.ABS = 0] = "ABS", r[r.ACOS = 1] = "ACOS", r[r.ACOSH = 2] = "ACOSH", r[r.ASIN = 3] = "ASIN", r[r.ASINH = 4] = "ASINH", r[r.ATAN = 5] = "ATAN", r[r.ATANH = 6] = "ATANH", r[r.CEIL = 7] = "CEIL", r[r.COS = 8] = "COS", r[r.COSH = 9] = "COSH", r[r.ELU = 10] = "ELU", r[r.ERF = 11] = "ERF", r[r.EXP = 12] = "EXP", r[r.EXPM1 = 13] = "EXPM1", r[r.FLOOR = 14] = "FLOOR", r[r.IS_FINITE = 15] = "IS_FINITE", r[r.IS_INF = 16] = "IS_INF", r[r.IS_NAN = 17] = "IS_NAN", r[r.LINEAR = 18] = "LINEAR", r[r.LOG = 19] = "LOG", r[r.LOG1P = 20] = "LOG1P", r[r.LOGICAL_NOT = 21] = "LOGICAL_NOT", r[r.NEG = 22] = "NEG", r[r.RELU = 23] = "RELU", r[r.RELU6 = 24] = "RELU6", r[r.LEAKYRELU = 25] = "LEAKYRELU", r[r.RECIPROCAL = 26] = "RECIPROCAL", r[r.ROUND = 27] = "ROUND", r[r.RSQRT = 28] = "RSQRT", r[r.SELU = 29] = "SELU", r[r.SIGMOID = 30] = "SIGMOID", r[r.SIGN = 31] = "SIGN", r[r.SIN = 32] = "SIN", r[r.SINH = 33] = "SINH", r[r.SOFTPLUS = 34] = "SOFTPLUS", r[r.SQRT = 35] = "SQRT", r[r.SQUARE = 36] = "SQUARE", r[r.STEP = 37] = "STEP", r[r.TAN = 38] = "TAN", r[r.TANH = 39] = "TANH", r[r.TO_INT = 40] = "TO_INT";
})(X || (X = {}));
var Vte = "return abs(a);";
var zte = `
  if (abs(a) > 1.) {
    return uniforms.NAN;
  }
  return acos(a);
`;
var Wte = `
  if (a < 1.) {
    return uniforms.NAN;
  }
  return acosh(a);
`;
var Ute = `
  if (abs(a) > 1.) {
    return uniforms.NAN;
  }
  return asin(a);
`;
var Gte = "return asinh(a);";
var Hte = `
  if (isnan(a)) {
    return uniforms.NAN;
  }
  return atan(a);
`;
var Kte = `
  if (abs(a) > 1.) {
    return uniforms.NAN;
  }
  if (a == 1.) {
    return uniforms.INFINITY;
  }
  if (a == -1.) {
    return -uniforms.INFINITY;
  }
  return atanh(a);
`;
var qte = "return ceil(a);";
var jte = "return cos(a);";
var Xte = `
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var Yte = "return exp(a) - 1.0;";
var Qte = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
var Zte = `
  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;
`;
var Jte = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  let p = ${S.ERF_P};
  let a1 = ${S.ERF_A1};
  let a2 = ${S.ERF_A2};
  let a3 = ${S.ERF_A3};
  let a4 = ${S.ERF_A4};
  let a5 = ${S.ERF_A5};

  let sign = sign(a);
  let absA = abs(a);
  let t = 1.0 / (1.0 + p * absA);
  return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));
`;
var ere = "return exp(a);";
var tre = "return floor(a);";
var rre = "return f32(!isnan(a) && !isinf(a));";
var ore = "return f32(isinf(a));";
var nre = "return f32(isnan(a));";
var sre = "return a;";
var are = `if (a < 0.0) { return uniforms.NAN; }
  return log(a);`;
var ire = `
  if (isnan(a)) { return a; }
  return log(1.0 + a);
`;
var ure = "return f32(!(a >= 1.0));";
var pre = "return -a;";
var cre = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var lre = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var mre = "return 1.0 / a;";
var dre = "return select(a, 0.0, a < 0.0);";
var fre = "return clamp(a, 0.0, 6.0);";
var hre = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var gre = `
  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`;
var xre = "return round(a);";
var yre = "return inverseSqrt(a);";
var bre = `
  if (a >= 0.0) {
    return ${S.SELU_SCALE} * a;
  } else {
    return ${S.SELU_SCALEALPHA} * (exp(a) - 1.0);
  }
`;
var Cre = "return 1.0 / (1.0 + exp(-1.0 * a));";
var Sre = "return sign(a);";
var wre = "return sin(a);";
var Ire = `
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var vre = `
  let epsilon = 1.1920928955078125e-7;
  let threshold = log(epsilon) + 2.0;

  let too_large = a > -threshold;
  let too_small = a < threshold;
  let exp_a = exp(a);

  if (too_large) {
    return a;
  } else if (too_small) {
    return exp_a;
  } else {
    return log(exp_a + 1.0);
  }
`;
var kre = "return sqrt(a);";
var Nre = "return a * a;";
var Tre = `
  if (isnan(a)) {
    return a;
  }

  return select(uniforms.stepAlpha, 1.0, a > 0.0);
`;
var _re = "return tan(a);";
var $re = `
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var Ere = "return f32(i32((a)));";
function ja(r, e) {
  switch (r) {
    case X.ABS:
      return Vte;
    case X.ACOS:
      return zte;
    case X.ACOSH:
      return Wte;
    case X.ASIN:
      return Ute;
    case X.ASINH:
      return Gte;
    case X.ATAN:
      return Hte;
    case X.ATANH:
      return Kte;
    case X.COS:
      return jte;
    case X.COSH:
      return Xte;
    case X.CEIL:
      return qte;
    case X.ELU:
      return e ? Zte : Qte;
    case X.ERF:
      return Jte;
    case X.EXP:
      return ere;
    case X.EXPM1:
      return Yte;
    case X.FLOOR:
      return tre;
    case X.IS_FINITE:
      return rre;
    case X.IS_INF:
      return ore;
    case X.IS_NAN:
      return nre;
    case X.LINEAR:
      return sre;
    case X.LOG:
      return are;
    case X.LOG1P:
      return ire;
    case X.LOGICAL_NOT:
      return ure;
    case X.NEG:
      return pre;
    case X.LEAKYRELU:
      return e ? lre : cre;
    case X.RECIPROCAL:
      return mre;
    case X.RELU:
      return e ? gre : dre;
    case X.RELU6:
      return e ? hre : fre;
    case X.ROUND:
      return xre;
    case X.RSQRT:
      return yre;
    case X.SELU:
      return bre;
    case X.SIGMOID:
      return Cre;
    case X.SIGN:
      return Sre;
    case X.SIN:
      return wre;
    case X.SINH:
      return Ire;
    case X.SOFTPLUS:
      return vre;
    case X.SQRT:
      return kre;
    case X.SQUARE:
      return Nre;
    case X.STEP:
      return Tre;
    case X.TAN:
      return _re;
    case X.TANH:
      return $re;
    case X.TO_INT:
      return Ere;
    default:
      throw new Error(`BinaryType ${r} is not implemented!`);
  }
}
var kt = (r) => {
  switch (r) {
    case 1:
      return "f32";
    case 2:
      return "vec2<f32>";
    case 3:
      return "vec3<f32>";
    case 4:
      return "vec4<f32>";
    default:
      throw new Error(`${r}-component is not supported.`);
  }
};
function pr(r, e = false, t10 = false, o = 3) {
  if (r === null)
    return "";
  let n = "";
  if (r === "linear")
    n = ja(X.LINEAR);
  else if (r === "relu")
    n = ja(X.RELU, t10);
  else if (r === "elu")
    n = ja(X.ELU, t10);
  else if (r === "relu6")
    n = ja(X.RELU6, t10);
  else if (r === "prelu")
    n = kc(ge.PRELU, t10);
  else if (r === "sigmoid")
    n = ja(X.SIGMOID, t10);
  else if (r === "leakyrelu")
    n = ja(X.LEAKYRELU, t10);
  else
    throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);
  let a = kt(t10 ? 4 : 1), i = "";
  return e ? i = `
      fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {
        let b = getPreluActivationWeightsByOutputCoords(coords);
        ${n}
      }` : i = `
      fn activation(a : ${a}, coords : vec${o}<i32>) -> ${a} {
        ${n}
      }`, i;
}
function Gr(r, e) {
  return `
      ${r ? "value = value + getBiasByOutputCoords(coords);" : ""}
      ${e ? "value = activation(value, coords);" : ""}
      `;
}
function xI(r, e, t10 = false, o = false, n = false, s = 1) {
  y.assert(r && s === 1 || !r, () => `transposeA ${r} is not compatible with component size ${s}`);
  let a = `
      ${r ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"}

    `, i = e ? "value = getB(batch, col, row);" : "value = getB(batch, row, col);";
  return `
  fn mm_readA(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {
    var value = ${kt(s)}(0.0);
    let col = colIn * ${s};
    ${t10 && n ? a : `
    ${r ? "if(row < uniforms.dimAOuter && col < uniforms.dimInner)" : "if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"}
    {
      ${a}
    }
    `}
    return value;
  }

  fn mm_readB(batch: i32, row: i32, colIn: i32) -> ${kt(s)} {
    let col = colIn * ${s};
    var value = ${kt(s)}(0.0);
    ${i}
    return value;
  }
  `;
}
function Wl(r, e, t10, o, n = false, s = false, a = false, i = 1) {
  return `
  ${xI(t10, o, n, s, a, i)}
  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${kt(i)}) {
    let col = colIn * ${i};
    ${n && s ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
    {
      var value = valueIn;
      let coords = vec3<i32>(batch, row, col);
      ${Gr(r, e)}
      setOutputAtCoords(coords[0], coords[1], coords[2], value);
    }
  }
  `;
}
var Are = (r, e) => r ? `
        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
          kStart + inputRow,
          globalRowStart / ${e} + inputCol);
        ` : `
        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
          globalRow + innerRow,
          kStart / ${e} + inputCol);
        `;
var Rre = (r, e, t10) => r ? `
        let ACached0 = mm_Asub[k * ${e}][localRow];
        let ACached1 = mm_Asub[k * ${e} + 1][localRow];
        let ACached2 = mm_Asub[k * ${e} + 2][localRow];
        ${e === 3 ? "" : `let ACached3 = mm_Asub[k * ${e} + 3][localRow];`}
        for (var i = 0; i < ${t10}; i++) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${e === 3 ? "" : "acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }` : `
        for (var i = 0; i < ${t10}; i++) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${e === 3 ? "" : "acc[i] = BCached3 * ACached.w + acc[i];"}
        }`;
function qu(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) {
  let p = e[1] * r[1], u = e[0] * r[0], c = t10 ? p : o, l = t10 ? o : p, m = c / e[0], d = o / e[1], f = r[1];
  return y.assert((t10 && m === 4 && r[1] === 4 || !t10 && (m === 3 || m === 4)) && c % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t10} is true, innerElementSize ${m} and workPerThread[1] ${r[1]} must be 4.
          Otherwise, innerElementSize ${m} must be 3 or 4.
      tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`), `
  var<workgroup> mm_Asub : array<array<vec${m}<f32>, ${c / m}>, ${l}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${u / r[0]}>, ${o}>;

  ${Q()} {
    let localRow = i32(localId.y);
    let tileRow = ${a ? "0" : `localRow * ${f}`};
    let tileCol = i32(localId.x);

    let globalRow = ${a ? "0" : `i32(globalId.y) * ${f}`};
    let globalCol = i32(globalId.x);
    let batch = ${n ? "0" : "i32(globalId.z)"};
    let batchA = ${n || !i ? "batch" : "batch % uniforms.aShape[0]"};
    let batchB = ${n || !i ? "batch" : "batch % uniforms.bShape[0]"};
    let globalRowStart = i32(workgroupId.y) * ${p};

    let numTiles = ${n ? `${Math.ceil(s / o)}` : `(uniforms.dimInner - 1) / ${o} + 1`};
    var kStart = ${n ? `i32(globalId.z) * ${s}` : "0"};

    var acc: array<vec4<f32>, ${f}>;

    // Loop over shared dimension.
    let tileRowB = localRow * ${d};
    for (var t = 0; t < numTiles; t++) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < ${f}; innerRow++) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileCol;
            ${Are(t10, m)}
        }

        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < ${d}; innerRow++) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol;
            mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);
        }
        kStart = kStart + ${o};
        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < ${o / m}; k++) {
            let BCached0 = mm_Bsub[k * ${m}][tileCol];
            let BCached1 = mm_Bsub[k * ${m} + 1][tileCol];
            let BCached2 = mm_Bsub[k * ${m} + 2][tileCol];
            ${m === 3 ? "" : `let BCached3 = mm_Bsub[k * ${m} + 3][tileCol];`}

            ${Rre(t10, m, f)}
        }

        workgroupBarrier();
    }

    for (var innerRow = 0; innerRow < ${f}; innerRow++) {
        mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
    }
  }`;
}
var z3 = (r) => r ? `
        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
          kStart + inputRow,
          globalRowStart + inputCol);
        ` : `
        mm_Asub[inputRow][inputCol] = mm_readA(batchA,
          globalRowStart + inputRow,
          kStart + inputCol);
        `;
var Dre = (r) => r ? "let ACached = mm_Asub[k][tileRow + innerRow];" : "let ACached = mm_Asub[tileRow + innerRow][k];";
function ju(r, e, t10 = false, o = 32, n = false, s = 32, a = false, i = false) {
  let p = r[1] * e[1], u = r[0] * e[0], c = t10 ? p : o, l = t10 ? o : p;
  y.assert(l % e[1] === 0 && c % e[0] === 0 && o % e[1] === 0, () => `tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);
  let m = l / e[1], d = c / e[0], f = o / e[1], h = r[1], g = r[0], x = a ? `
      let localRow = i32(localId.y);
      let localCol = i32(localId.x);
      let globalRowStart = i32(workgroupId.y) * ${p};
      let globalColStart = i32(workgroupId.x) * ${u};

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t++) {
        // Load one tile of A into local memory.
        for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {
          for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {
            ${z3(t10)}
          }
        }
        // Load one tile of B into local memory.
        for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {
              for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {
            mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
              kStart + inputRow,
              globalColStart + inputCol);
          }
        }
        kStart = kStart + ${o};
        workgroupBarrier();

        // Compute acc values for a single thread.
        var BCached : array<f32, ${g}>;
        for (var k = 0; k < ${o}; k++) {
          for (var inner = 0; inner < ${g}; inner++) {
            BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];
          }
          for (var innerRow = 0; innerRow < ${h}; innerRow++) {
            let ACached = ${t10 ? `mm_Asub[k][localRow + innerRow * ${e[1]}];` : `mm_Asub[localRow + innerRow * ${e[1]}][k];`}
            for (var innerCol = 0; innerCol < ${g}; innerCol++) {
              acc[innerRow][innerCol] = acc[innerRow][innerCol] +
                  ACached * BCached[innerCol];
            }
          }
        }
        workgroupBarrier();
      }
      for (var innerRow = 0; innerRow < ${h}; innerRow++) {
        let gRow = globalRowStart + localRow + innerRow * ${e[1]};
        for (var innerCol = 0; innerCol < ${g}; innerCol++) {
          let gCol = globalColStart + localCol + innerCol * ${e[0]};
          mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
        }
      }
      ` : `
  let tileRow = i32(localId.y) * ${h};
  let tileCol = i32(localId.x) * ${g};

  let globalRow = i32(globalId.y) * ${h};
  let globalCol = i32(globalId.x) * ${g};
  let globalRowStart = i32(workgroupId.y) * ${p};

  let tileRowA = i32(localId.y) * ${m};
  let tileColA = i32(localId.x) * ${d};
  let tileRowB = i32(localId.y) * ${f};
  // Loop over shared dimension.
  for (var t = 0; t < numTiles; t++) {
    // Load one tile of A into local memory.
    for (var innerRow = 0; innerRow < ${m}; innerRow++) {
      for (var innerCol = 0; innerCol < ${d}; innerCol++) {
        let inputRow = tileRowA + innerRow;
        let inputCol = tileColA + innerCol;
        ${z3(t10)}
      }
    }

    // Load one tile of B into local memory.
    for (var innerRow = 0; innerRow < ${f}; innerRow++) {
      for (var innerCol = 0; innerCol < ${g}; innerCol++) {
        let inputRow = tileRowB + innerRow;
        let inputCol = tileCol + innerCol;
        mm_Bsub[inputRow][inputCol] = mm_readB(batchB,
          kStart + inputRow,
          globalCol + innerCol);
      }
    }
    kStart = kStart + ${o};
    workgroupBarrier();

    // Compute acc values for a single thread.
    var BCached : array<f32, ${g}>;
    for (var k = 0; k < ${o}; k++) {
      for (var inner = 0; inner < ${g}; inner++) {
        BCached[inner] = mm_Bsub[k][tileCol + inner];
      }

      for (var innerRow = 0; innerRow < ${h}; innerRow++) {
        ${Dre(t10)}
        for (var innerCol = 0; innerCol < ${g}; innerCol++) {
          acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
        }
      }
    }

    workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < ${h}; innerRow++) {
    for (var innerCol = 0; innerCol < ${g}; innerCol++) {
      mm_write(batch, globalRow + innerRow, globalCol + innerCol,
          acc[innerRow][innerCol]);
    }
  }
  `;
  return `
    var<workgroup> mm_Asub : array<array<f32, ${c}>, ${l}>;
    var<workgroup> mm_Bsub : array<array<f32, ${u}>, ${o}>;

    ${Q()} {
      let batch = ${n ? "0" : "i32(globalId.z)"};
      let batchA = ${n || !i ? "batch" : "batch % uniforms.aShape[0]"};
      let batchB = ${n || !i ? "batch" : "batch % uniforms.bShape[0]"};
      let numTiles = ${n ? `${Math.ceil(s / o)}` : `(uniforms.dimInner - 1) / ${o} + 1`};
      var kStart = ${n ? `i32(globalId.z) * ${s}` : "0"};

      var acc : array<array<f32, ${g}>, ${h}>;

      // Without this initialization strange values show up in acc.
      for (var innerRow = 0; innerRow < ${h}; innerRow++) {
        for (var innerCol = 0; innerCol < ${g}; innerCol++) {
          acc[innerRow][innerCol] = 0.0;
        }
      }
      ${x}
    }
  `;
}
var Fre = (r) => r ? `
      mm_readA(batchA, colA, globalRow),
      mm_readA(batchA, colA + 1, globalRow),
      mm_readA(batchA, colA + 2, globalRow),
      mm_readA(batchA, colA + 3, globalRow)
  ` : `
      mm_readA(batchA, globalRow, colA),
      mm_readA(batchA, globalRow, colA + 1),
      mm_readA(batchA, globalRow, colA + 2),
      mm_readA(batchA, globalRow, colA + 3)
  `;
function Ore(r, e = false) {
  y.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`);
  let t10 = r[0] * 4;
  return `
    var<workgroup> mm_Asub : array<vec4<f32>, ${r[0]}>;

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

      let numTiles = (uniforms.dimInner - 1) / ${t10} + 1;
      let batch = i32(globalId.z);
      let batchA = batch % uniforms.aShape[0];
      let batchB = batch % uniforms.bShape[0];
      // Without this initialization strange values show up in acc.
      var acc = 0.0;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t++) {
        // Load one tile of A into local memory.
        let colA = t * ${t10} + tileCol * 4;
        mm_Asub[tileCol] = vec4<f32>(${Fre(e)});
        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < ${t10 / 4}; k++) {
          let rowB = t * ${t10} + k * 4;
          let BCached = vec4<f32>(mm_readB(batchB, rowB, globalCol),
                              mm_readB(batchB, rowB + 1, globalCol),
                              mm_readB(batchB, rowB + 2, globalCol),
                              mm_readB(batchB, rowB + 3, globalCol));

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

        workgroupBarrier();
      }

      mm_write(batch, globalRow, globalCol, acc);
    }
  `;
}
var Eg = class {
  constructor(e, t10, o = false, n = false, s = null, a = null, i = null, p = false) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = t10, this.dispatchLayout = { x: [2], y: [1], z: [0] };
    let u = o ? e[1] : e[2];
    if (this.isVec4 = (u % 4 === 0 && !o || t10[1] % 4 === 0 && o) && t10[2] % 4 === 0 && !n, this.isVectorA = t10[1] === 1 && !o, !this.isVec4 && this.isVectorA)
      this.elementsPerThread = [1, 1, 1], this.workgroupSize = [32, 1, 1];
    else {
      let m = dI(t10[1], u, t10[2], o);
      this.workgroupSize = m.workgroupSize, this.elementsPerThread = m.elementsPerThread;
    }
    this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread);
    let c = s != null, l = i != null;
    c && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = p, this.transposeA = o, this.transposeB = n, this.addBias = c, this.activation = a, this.hasPreluActivationWeights = l, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t10[1], t10[2], u), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`;
  }
  getShapeFit(e, t10, o) {
    let n = this.workgroupSize[1] * this.elementsPerThread[1], s = this.workgroupSize[0] * this.elementsPerThread[0];
    !this.isVec4 && this.isVectorA ? this.tileInner = this.workgroupSize[0] * 4 : this.tileInner = s;
    let a = e % n === 0, i = t10 % s === 0, p = o % this.tileInner === 0;
    return [a, i, p];
  }
  getUserCode() {
    return `
      ${pr(this.activation, this.hasPreluActivationWeights, this.isVec4)}
      ${Wl(this.addBias, this.activation, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)}
      ${this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.isVectorA, true) : this.isVectorA ? Ore(this.workgroupSize, this.transposeA) : ju(this.elementsPerThread, this.workgroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads, true)}
    `;
  }
};
function Pre(r) {
  return `
    var<workgroup> sumValues : array<f32, ${r}>;
    ${Q()} {
      let coords = getOutputCoords();
      let batch = coords[0];
      let batchA = batch % uniforms.aShape[0];
      let batchB = batch % uniforms.bShape[0];
      let row = coords[1];
      let col = coords[2];
      var sum = 0.0;
      let Length = uniforms.dimInner;
      for (var k = i32(localId.x); k < Length; k = k + ${r}) {
        let dataA = mm_readA(batchA, row, k);
        let dataB = mm_readB(batchB, k, col);
        sum = sum + dataA * dataB;
      }
      sumValues[localId.x] = sum;
      workgroupBarrier();

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

      if (localId.x == 0u) {
        sum = sumValues[0] + sumValues[1];
        mm_write(batch, row, col, sum);
      }
    }
  `;
}
var Ag = class {
  constructor(e, t10 = false, o = false, n = null, s = null, a = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize);
    let i = n != null, p = a != null;
    i && this.variableNames.push("bias"), p && this.variableNames.push("preluActivationWeights"), this.transposeA = t10, this.transposeB = o, this.addBias = i, this.activation = s, this.hasPreluActivationWeights = p, this.shaderKey = `matMulReduce_${this.activation}_${t10}_${o}`;
  }
  getUserCode() {
    return `
      ${pr(this.activation, this.hasPreluActivationWeights)}
      ${Wl(this.addBias, this.activation, this.transposeA, this.transposeB)}
      ${Pre(this.workgroupSize[0])}
    `;
  }
};
function Mre(r) {
  let e = r[1], t10 = r[0], o = e > t10 ? e : t10;
  return `
  var<workgroup> mm_Asub : array<array<f32, ${o}>, ${e}>;
  var<workgroup> mm_Bsub : array<array<f32, ${t10}>, ${o}>;

  // If the output size is small for matrix multiplication, avoid to use vec4
  // and handle some elements per thread to optimally utilize the ALU.
  // Read data from global memory to registers firstly, then store them into
  // shared memory, so it is instruction-Level parallelism for arithmetic
  // operations and others handle IO operations between barrier api, makes ALU
  // and load/store units work simultaneously, could improves the performance.
  ${Q()} {
    let tileRow = i32(localId.y);
    let tileCol = i32(localId.x);
    let globalRow = i32(globalId.y);
    let globalCol = i32(globalId.x);
    let batch = i32(globalId.z);
    let batchA = batch % uniforms.aShape[0];
    let batchB = batch % uniforms.bShape[0];

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

    var globalColA = tileCol;
    var globalRowB = 0;
    var regA = mm_readA(batchA, globalRow, globalColA);
    var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);
    var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);
    globalColA = globalColA + ${o};
    globalRowB = globalRowB + ${o};

    for (var t = 0; t < numTiles; t = t + 1) {
      mm_Asub[tileRow][tileCol] = regA;
      mm_Bsub[2 * tileRow][tileCol] = regB0;
      mm_Bsub[2 * tileRow + 1][tileCol] = regB1;

      workgroupBarrier();

      regA = mm_readA(batchA, globalRow, globalColA);
      regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);
      regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);
      globalColA = globalColA + ${o};
      globalRowB = globalRowB + ${o};

      for (var k = 0; k < ${o}; k = k + 1) {
        acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];
      }
      workgroupBarrier();
    }

    mm_write(batch, globalRow, globalCol, acc);
  }
  `;
}
var Rg = class {
  constructor(e, t10, o, n = false, s = false, a = null, i = null, p = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [16, 8, 1], this.outputShape = o, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(o[2] / this.workgroupSize[0]), Math.ceil(o[1] / this.workgroupSize[1]), o[0]];
    let u = a != null;
    u && this.variableNames.push("bias");
    let c = p != null;
    c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${n}_${s}`;
  }
  getUserCode() {
    return `
      ${pr(this.activation, this.hasPreluActivationWeights)}
      ${Wl(this.addBias, this.activation, this.transposeA, this.transposeB)}
      ${Mre(this.workgroupSize)}
    `;
  }
};
var Dg = class {
  constructor(e, t10, o = false, n = false) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workgroupSize = [8, 8, 1], this.atomic = true, this.isVec4 = false, this.splitedDimInner = 128, y.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] }, this.isVec4 = (o && this.outputShape[1] % 4 === 0 || !o && t10 % 4 === 0) && this.outputShape[2] % 4 === 0, this.elementsPerThread = [4, 4, this.splitedDimInner], this.isVec4 || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = Y(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t10], this.workgroupSize, this.elementsPerThread), this.transposeA = o, this.transposeB = n, this.shaderKey = `matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`;
  }
  getUserCode() {
    let e = this.isVec4 ? 4 : 1;
    return `
      ${xI(false, this.transposeB, false, false, false, e)}
      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${kt(e)}) {
        let col = colIn * ${e};
        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
          let coords = vec3<i32>(batch, row, col);
          let flatIndex = getOutputIndexFromCoords(coords);
          // The problem is that we should initialize output to zero before using.
          // Otherwise, the original value will be added to the result.
          for (var i = 0; i < ${e}; i = i + 1) {
            ${Ic("&result[flatIndex + i]", `${e > 1 ? "value[i]" : "value"}`, "float32")}
          }
        }
      }
      ${this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner) : ju(this.elementsPerThread, this.workgroupSize, this.transposeA, 32, true, this.splitedDimInner)}
    `;
  }
};
var Fg = class {
  constructor(e, t10 = null, o = null, n = null) {
    this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`;
  }
  getUserCode() {
    return `
    ${pr(this.activation, this.hasPreluActivationWeights)}
    ${Q("index")} {
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        var value = getXByOutputIndex(index);
        ${Gr(this.addBias, this.activation)}
        setOutputAtIndex(index, value);
      }
    }
    `;
  }
};
var Og = class {
  constructor(e) {
    this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "fill";
  }
  getUserCode() {
    return `
    ${Q("index")} {
      if (index < uniforms.size) {
        setOutputAtIndex(index, uniforms.value);
      }
    }
  `;
  }
};
function fr(r) {
  let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10;
  if (s = s || y.inferDtype(n), s === "string") {
    let a = y.getArrayFromDType(s, y.sizeFromShape(o));
    return a.fill(n), e.makeTensorInfo(o, s, a);
  } else {
    let a = new Og(o), i = [{ type: "float32", data: [n] }];
    return e.runWebGPUProgram(a, [], s, i);
  }
}
var W3 = { kernelName: ws, backendName: "webgpu", kernelFunc: fr };
function me(r) {
  let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = y.sizeFromShape(o.shape), a = y.inferFromImplicitShape(n, s), i = y.sizeFromShape(a);
  return y.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var U3 = { kernelName: _s, backendName: "webgpu", kernelFunc: me };
function Xu({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) {
  let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], d = t10 ? r.shape[u - 1] : r.shape[u - 2], f = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), x = y.sizeFromShape(h), b = y.sizeFromShape(g), w = yr.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([d, f]);
  y.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`);
  let k = t10 ? [x, l, d] : [x, d, l], _ = o ? [b, f, m] : [b, m, f], E = me({ inputs: { x: r }, backend: n, attrs: { shape: k } }), A = me({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), R = [E, A], D = Math.max(x, b), P = [E, A], M = [{ type: "int32", data: [d] }, { type: "int32", data: [f] }, { type: "int32", data: [l] }], L, V, z = [D, d, f], U = O().get("WEBGPU_MATMUL_PROGRAM_TYPE");
  if (U < 0) {
    let H = O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), q = H > 0 ? H : n.thresholdToIncreaseWorkgroups, Z = D * Math.ceil(d / 32) * Math.ceil(f / 32);
    Z <= q || d <= 8 && Z <= q * 2 ? D * d * f <= 128 ? U = Ro.MatMulReduceProgram : D === 1 && m >= 2e3 ? U = Ro.MatMulSplitKProgram : U = Ro.MatMulSmallOutputSizeProgram : U = Ro.MatMulPackedProgram;
  }
  switch (U) {
    case Ro.MatMulReduceProgram:
      L = new Ag(z, t10, o, s, p, a);
      break;
    case Ro.MatMulSplitKProgram: {
      if (V = fr({ backend: n, attrs: { shape: z, value: 0, dtype: r.dtype } }), L = new Dg(z, m, t10, o), s || p) {
        V = n.runWebGPUProgram(L, P, r.dtype, M, V);
        let q = new Fg(V.shape, s, p, a), Z = null, ee = [V];
        s && ee.push(s), a && ee.push(a), p === "leakyrelu" && (Z = [{ type: "float32", data: [i] }], q.uniforms += " alpha : f32,");
        let oe = n.runWebGPUProgram(q, ee, V.dtype, Z);
        R.push(V);
        let J = me({ inputs: { x: oe }, backend: n, attrs: { shape: w } });
        R.push(oe);
        for (let te of R)
          n.disposeData(te.dataId);
        return J;
      }
      break;
    }
    case Ro.MatMulSmallOutputSizeProgram:
      L = new Rg(k, _, z, t10, o, s, p, a);
      break;
    case Ro.MatMulPackedProgram:
      let H = n.adapterInfo.isIntel();
      L = new Eg(k, z, t10, o, s, p, a, H);
      break;
    default:
      throw new Error(`Unsupported MatMulProgramType ${U}.`);
  }
  s && P.push(s), a && P.push(a), p === "leakyrelu" && (M.push({ type: "float32", data: [i] }), L.uniforms += " alpha : f32,"), V = n.runWebGPUProgram(L, P, r.dtype, M, V);
  let K = me({ inputs: { x: V }, backend: n, attrs: { shape: w } });
  R.push(V);
  for (let H of R)
    n.disposeData(H.dataId);
  return K;
}
function Lre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
  return Xu({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var G3 = { kernelName: ho, backendName: "webgpu", kernelFunc: Lre };
var Ul = class {
  constructor(e, t10, o) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
  }
  getUserCode() {
    return `
      fn binaryOpComplex(
          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
        ${kc(this.op, false)}
      }

      ${Q("index")} {
        if(index < uniforms.size) {
          let areal = getARealByOutputIndex(index);
          let aimag = getAImagByOutputIndex(index);
          let breal = getBRealByOutputIndex(index);
          let bimag = getBImagByOutputIndex(index);
          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `;
  }
};
var Yu = class {
  constructor(e, t10, o) {
    this.size = true, this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = ae(this.outputShape), this.op = e, this.useSharedMemoryWithA = t10.length <= 1 && o.length > 1 && t10[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t10.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB ? (this.isVec4 = false, this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t10[0], this.shaderKey = `binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`, this.type = "shared", this.workgroupSize = [256, 1, 1], this.workPerThread = 1) : (y.arraysEqual(t10, o) && y.sizeFromShape(t10) % 4 === 0 ? (this.isVec4 = true, this.type = "vec4", this.workPerThread = 4) : (this.isVec4 = false, this.type = "plain", this.workPerThread = 1), this.shaderKey = `binary_${this.type}_${e}`, this.workgroupSize = [128, 1, 1]), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]);
  }
  getUserCode() {
    let e, t10 = this.isVec4 ? "vec4<f32>" : "f32", o = `
    fn binaryOperation(a : ${t10}, b : ${t10}) -> ${t10} {
      let isNaN = false;
      {
        ${kc(this.op, this.isVec4)}
      }
    };
    `;
    if (this.type === "shared") {
      let n = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0", s = this.useSharedMemoryWithB ? `let a = getAByOutputIndex(index);
          let b = sharedBuf[${n}];` : `let a = sharedBuf[${n}];
          let b = getBByOutputIndex(index);`;
      e = `
        ${o}
        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
        ${Q("index")} {
          // Fill in the shared memory buffer.
          let localIndex = i32(localId.x);
          if(localIndex < ${this.lastDimensionSize}) {
            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? "B" : "A"}[localIndex]);
          }
          workgroupBarrier();

          if(index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            ${s}
            setOutputAtIndex(index, binaryOperation(a, b));
          }
        }
        `;
    } else
      e = `
       ${o}
       ${Q("index")} {
         if (index < uniforms.size) {
           let a = getAByOutputIndex(index);
           let b = getBByOutputIndex(index);
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
       `;
    return e;
  }
};
function Dt(r) {
  let { inputs: e } = r, { x: t10 } = e;
  return r.backend.incRef(t10.dataId), { dataId: t10.dataId, shape: t10.shape, dtype: t10.dtype };
}
var H3 = { kernelName: mo, backendName: "webgpu", kernelFunc: Dt };
function po(r) {
  let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.tensorMap.get(s.dataId), i = Dt({ inputs: { x: o }, backend: t10 }), p = Dt({ inputs: { x: n }, backend: t10 });
  return a.complexTensorInfos = { real: i, imag: p }, s;
}
var K3 = { kernelName: ri, backendName: "webgpu", kernelFunc: po };
var Hr = class {
  constructor(e, t10, o = "") {
    this.variableNames = ["A"], this.size = true;
    let n = 128;
    this.workgroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.op = t10, o !== "" && (this.uniforms = o), this.shaderKey = `unary_${t10}`;
  }
  getUserCode() {
    return `
      fn unaryOperation(a : f32) -> f32 {
        ${ja(this.op, false)}
      }
      ${Q("index")} {
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          setOutputAtIndex(index, unaryOperation(a));
        }
      }
      `;
  }
};
function xe({ opType: r, cpuKernelImpl: e, dtype: t10 }) {
  return ({ inputs: o, backend: n }) => {
    let { x: s } = o, a = n, i = t10 || s.dtype;
    if (a.shouldExecuteOnCPU([s]) && e != null) {
      let u = a.tensorMap.get(s.dataId), c = e(u.values, i);
      return a.makeTensorInfo(s.shape, i, c);
    }
    let p = new Hr(s.shape, r);
    return a.runWebGPUProgram(p, [s], i);
  };
}
function Ze({ opType: r, cpuKernelImpl: e, supportsComplex: t10 = false, dtype: o }) {
  return ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    if (t10 && a.dtype === "complex64") {
      let l = p.tensorMap.get(a.dataId), m = p.tensorMap.get(i.dataId), d, f;
      if (r !== ge.MUL)
        [d, f] = [[l.complexTensorInfos.real, m.complexTensorInfos.real], [l.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((g) => {
          let [x, b] = g, C = { dataId: x.dataId, dtype: x.dtype, shape: a.shape }, w = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Yu(r, a.shape, i.shape);
          return p.runWebGPUProgram(k, [C, w], dt(x.dtype, b.dtype));
        });
      else {
        let g = new Ul(ge.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), x = new Ul(ge.COMPLEX_MULTIPLY_IMAG, a.shape, i.shape), b = [{ dataId: l.complexTensorInfos.real.dataId, dtype: l.complexTensorInfos.real.dtype, shape: a.shape }, { dataId: l.complexTensorInfos.imag.dataId, dtype: l.complexTensorInfos.imag.dtype, shape: a.shape }, { dataId: m.complexTensorInfos.real.dataId, dtype: m.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: m.complexTensorInfos.imag.dataId, dtype: m.complexTensorInfos.imag.dtype, shape: i.shape }];
        d = p.runWebGPUProgram(g, b, "float32"), f = p.runWebGPUProgram(x, b, "float32");
      }
      let h = po({ inputs: { real: d, imag: f }, backend: p });
      return p.disposeData(d.dataId), p.disposeData(f.dataId), h;
    }
    let u = o || dt(a.dtype, i.dtype);
    if ((a.dtype === "string" || i.dtype === "string" || p.shouldExecuteOnCPU([a, i])) && e != null) {
      let l = p.tensorMap.get(a.dataId).values, m = p.tensorMap.get(i.dataId).values, d = a.dtype === "string" ? S.fromUint8ToStringArray(l) : l, f = a.dtype === "string" ? S.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, d, f, u);
      return p.makeTensorInfo(g, u, h);
    }
    let c = new Yu(r, a.shape, i.shape);
    return p.runWebGPUProgram(c, [a, i], u);
  };
}
var { addImpl: q3, castImpl: j3, ceilImpl: X3, concatImpl: Y3, equalImpl: Q3, expImpl: Z3, expm1Impl: J3, floorImpl: eM, gatherNdImpl: tM, gatherV2Impl: rM, greaterEqualImpl: oM, greaterImpl: nM, lessEqualImpl: sM, lessImpl: aM, logImpl: iM, maxImpl: uM, maximumImpl: pM, minimumImpl: cM, multiplyImpl: lM, negImpl: mM, notEqualImpl: dM, prodImpl: fM, rangeImpl: hM, rsqrtImpl: gM, scatterImpl: xM, simpleAbsImpl: yM, sliceImpl: bM, stridedSliceImpl: CM, stringNGramsImpl: SM, subImpl: wM, tileImpl: IM, topKImpl: vM, transposeImpl: kM, uniqueImpl: iTt } = Zp;
var Bre = xe({ opType: X.ABS, cpuKernelImpl: yM });
var NM = { kernelName: ys, backendName: "webgpu", kernelFunc: Bre };
var Vre = xe({ opType: X.ACOS });
var TM = { kernelName: aa, backendName: "webgpu", kernelFunc: Vre };
var zre = xe({ opType: X.ACOSH });
var _M = { kernelName: ia, backendName: "webgpu", kernelFunc: zre };
var Wre = Ze({ opType: ge.ADD, cpuKernelImpl: q3, supportsComplex: true });
var $M = { kernelName: eo, backendName: "webgpu", kernelFunc: Wre };
var Pg = class {
  constructor(e) {
    this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
  }
  getUserCode() {
    let e = [];
    this.variableNames.forEach((n) => {
      e.push(`let v${n} = get${n}ByOutputCoords(coords);`);
    });
    let t10 = this.variableNames.map((n) => `v${n}`).join(" + ");
    return `
      ${Q("index")} {
        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndex);
            ${e.join(`
        `)}
            setOutputAtIndex(flatIndex, ${t10});
          }
        }
      }
    `;
  }
};
function Ure(r) {
  let { inputs: e, backend: t10 } = r, o = e;
  if (o.length === 1)
    return Dt({ inputs: { x: o[0] }, backend: t10 });
  let n = o.map((i) => i.dtype).reduce((i, p) => dt(i, p)), s = o.map((i) => i.shape), a = new Pg(s);
  return t10.runWebGPUProgram(a, o, n);
}
var EM = { kernelName: Po, backendName: "webgpu", kernelFunc: Ure };
var Mg = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.workgroupSize = [16, 16, 1];
    let o = new Array(e.length);
    for (let n = 0; n < o.length; n++)
      o[n] = e[t10[n]];
    this.outputShape = o, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [1, 1, 1]), this.shaderKey = "transposeShared";
  }
  getUserCode() {
    y.assert(this.workgroupSize[0] === this.workgroupSize[1], () => `Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);
    let e = this.workgroupSize[0];
    return `
      var<workgroup> tile : array<array<f32, ${this.workgroupSize[0] + 1}>, ${this.workgroupSize[0]}>;
      ${Q()} {
        var x = i32(workgroupId.x) * ${e} + i32(localId.x);
        var y = i32(workgroupId.y) * ${e} + i32(localId.y);
        let width = uniforms.outShape[0];
        let height = uniforms.outShape[1];
        if (x < width && y < height) {
          tile[localId.y][localId.x] = f32(A[y * width + x]);
        }
        workgroupBarrier();

        x = i32(workgroupId.y) * ${e} + i32(localId.x);
        y = i32(workgroupId.x) * ${e} + i32(localId.y);
        if (x < height && y < width) {
          setOutputAtIndex((y * height + x), tile[localId.x]
            [localId.y]);
        }
      }
    `;
  }
};
var Lg = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true;
    let o = new Array(e.length);
    for (let n = 0; n < o.length; n++)
      o[n] = e[t10[n]];
    this.outputShape = o, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.newDim = t10, this.shaderKey = `transpose_${t10}`;
  }
  getUserCode() {
    let e = Rt(this.outputShape.length), t10 = Gre(this.newDim);
    return `
      ${Q("index")} {
        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let resRC = getCoordsFromIndex(flatIndex);
            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
              ${e}(${t10}), uniforms.aShape)]);
          }
        }
      }
    `;
  }
};
function Gre(r) {
  let e = r.length;
  if (e > 6)
    throw Error(`Transpose for rank ${e} is not yet supported`);
  let t10 = new Array(e);
  for (let o = 0; o < r.length; o++)
    t10[r[o]] = `resRC.${Ao(o)}`;
  return t10.join();
}
function Nr(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { perm: s } = o, a = t10, i = n.shape.length, p = new Array(i);
  for (let c = 0; c < p.length; c++)
    p[c] = n.shape[s[c]];
  if (t10.shouldExecuteOnCPU([n])) {
    let l = a.tensorMap.get(n.dataId).values, m = kM(l, n.shape, n.dtype, s, p);
    return t10.makeTensorInfo(p, n.dtype, m);
  }
  if (n.shape.length === 2 && y.arraysEqual(s, [1, 0])) {
    let c = new Mg(n.shape, s);
    return a.runWebGPUProgram(c, [n], n.dtype);
  }
  let u = new Lg(n.shape, s);
  return a.runWebGPUProgram(u, [n], n.dtype);
}
var AM = { kernelName: ro, backendName: "webgpu", kernelFunc: Nr };
var Bg = class {
  constructor(e, t10) {
    this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
    let [o] = S.computeOutAndReduceShapes(this.inputShape, [1]);
    this.outputShape = o.length === 0 ? [1] : o, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t10, this.shaderKey = `reduce_${t10}`;
  }
  getUserCode() {
    let e = "", t10 = "0.0", o = this.workgroupSize[0];
    this.reduceType === "min" || this.reduceType === "max" ? (e = `
         if (isnan(candidate)) {
          bestValue = uniforms.NAN;
         } else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue)
           {  bestValue = candidate; }`, t10 = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" ? (e = " bestValue = bestValue * candidate; ", t10 = "1.0") : this.reduceType === "all" ? (e = " bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); ", t10 = "1.0") : this.reduceType === "any" && (e = " bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); ", t10 = "0.0");
    let n = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);";
    return `
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
       }

       ${`
         var<workgroup> xBestValues : array<f32, ${o}>;
       `}
       fn getOffset(outputIndex : i32) -> i32 {
         let outputCoords = getCoordsFromIndex(outputIndex);
         let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize;
          return offset;
       }
       ${Q("index")} {
         let outputIndex = index / ${o};
         let offset = getOffset(outputIndex);
         var bestValue = ${t10};
         let Length = uniforms.reduceSize;
         let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + ${o}) {
           let candidate = f32(x[offset + k]);
           ${e}
         }
         xBestValues[localId.x] = bestValue;
         workgroupBarrier();

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

         if (localId.x == 0u && outputIndex < uniforms.size) {
          ${n}
        }
       }
     `;
  }
};
function Kr(r, e, t10, o, n) {
  let s = r.shape.length, a = [], i = y.parseAxisParam(e, r.shape), p = i, u = S.getAxesPermutation(p, s), c = r;
  u != null && (c = Nr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = S.getInnerMostAxes(p.length, s), a.push(c)), S.assertAxesAreInnerMostDims(o, p, s);
  let [l, m] = S.computeOutAndReduceShapes(c.shape, p), d = l;
  t10 && (d = S.expandShapeToKeepDim(l, i));
  let f;
  if ((o === "max" || o === "prod") && n.shouldExecuteOnCPU([c])) {
    let h = n.tensorMap.get(c.dataId).values;
    switch (o) {
      case "max":
        let g = uM(h, y.sizeFromShape(m), d, r.dtype);
        f = n.makeTensorInfo(d, r.dtype, g);
        break;
      case "prod":
        let { outVals: x, outShape: b, outDtype: C } = fM(c.shape, c.dtype, h, p);
        f = n.makeTensorInfo(b, C, x);
        break;
      default:
        throw new Error(`${o} CPU implementation is not yet supported.`);
    }
  } else {
    let h = y.sizeFromShape(m), x = y.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: x, outSize: 1 }, C = o === "mean" ? "float32" : Ta(r.dtype), w = [{ type: "int32", data: [h] }], k = new Bg(b, o), _ = n.runWebGPUProgram(k, [c], C, w);
    a.push(_), f = me({ inputs: { x: _ }, attrs: { shape: d }, backend: n });
  }
  return a.forEach((h) => n.disposeData(h.dataId)), f;
}
function Hre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
  return Kr(n, a, s, "all", t10);
}
var RM = { kernelName: Mo, backendName: "webgpu", kernelFunc: Hre };
function Kre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
  return Kr(n, a, s, "any", t10);
}
var DM = { kernelName: Lo, backendName: "webgpu", kernelFunc: Kre };
var Nc = class {
  constructor(e, t10, o) {
    this.workgroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
    let n = [t10];
    this.op = o === "min" ? "<" : ">";
    let [s, a] = S.computeOutAndReduceShapes(e, n);
    this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = ae(this.outputShape), y.sizeFromShape(a) < 32 || y.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize)) : (this.type = "shared", this.dispatch = Y(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`;
  }
  getUserCode() {
    let e = this.workgroupSize[0], t10 = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Ao(this.inputShape.length - 1)}`, o = () => {
      let n = "";
      if (this.outputShape.length === 1)
        this.inputShape.length !== 1 && (n += "outputCoords,");
      else
        for (let s = 0; s < this.outputShape.length; s++)
          n += `outputCoords.${Ao(s)},`;
      return n;
    };
    return this.type === "shared" ? `
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${`
      var<workgroup> xBestIndices : array<i32, ${e}>;
      var<workgroup> xBestValues : array<f32, ${e}>;
    `}

      ${Q("index")} {
        let outputIndex = index / ${e};
        let reduceLength = ${t10()};

        var bestIndex = i32(localId.x);
        var bestValue = uniforms.infinityValue;
        let outputCoords = getCoordsFromIndex(outputIndex);
        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
            k = k + ${e}) {
          let candidate = getX(${o()} k);
          if (!isnan(candidate) && candidate ${this.op} bestValue) {
            bestValue = candidate;
            bestIndex = k;
          }
        }
        xBestValues[localId.x] = bestValue;
        xBestIndices[localId.x] = bestIndex;
        workgroupBarrier();

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

        if (localId.x == 0u && outputIndex < uniforms.size) {
          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
        }
      }
    ` : `
      ${Q("index")} {
        if (index < uniforms.size) {
          let outputCoords = getCoordsFromIndex(index);
          var bestIndex = 0;
          var bestValue = getX(${o()} 0);
          let reduceLength = ${t10()};
          for (var i = 1; i < reduceLength; i++) {
            let candidate = getX(${o()} i);
            if (candidate ${this.op} bestValue) {
              bestValue = candidate;
              bestIndex = i;
            }
          }
          setOutputAtIndexI32(index, bestIndex);
        }
      }
      `;
  }
};
function qre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = Nr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
  let c = new Nc(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l);
  return u.forEach((d) => t10.disposeData(d.dataId)), m;
}
var FM = { kernelName: Bo, backendName: "webgpu", kernelFunc: qre };
function jre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = y.parseAxisParam(s, n.shape), i = S.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = Nr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = S.getInnerMostAxes(a.length, p.shape.length)), S.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
  let c = new Nc(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l);
  return u.forEach((d) => t10.disposeData(d.dataId)), m;
}
var OM = { kernelName: ei, backendName: "webgpu", kernelFunc: jre };
var Xre = xe({ opType: X.ASIN });
var PM = { kernelName: ua, backendName: "webgpu", kernelFunc: Xre };
var Yre = xe({ opType: X.ASINH });
var MM = { kernelName: pa, backendName: "webgpu", kernelFunc: Yre };
var Qre = xe({ opType: X.ATAN });
var LM = { kernelName: ca, backendName: "webgpu", kernelFunc: Qre };
var Zre = Ze({ opType: ge.ATAN2 });
var BM = { kernelName: ma, backendName: "webgpu", kernelFunc: Zre };
var Jre = xe({ opType: X.ATANH });
var VM = { kernelName: la, backendName: "webgpu", kernelFunc: Jre };
var Gl = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workgroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `pool2D_${t10}`, this.poolType = t10;
  }
  getUserCode() {
    let e = "resultValue = max(value, resultValue);";
    this.poolType === "avg" && (e = "resultValue = resultValue + value; count = count + 1.0;");
    let t10 = "resultValue";
    return this.poolType === "avg" && (t10 = "resultValue / max(count, 1.0)"), `
      ${Q("index")} {
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
          let batch = coords[0];
          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
          let xRCorner = xRCCorner.x;
          let xCCorner = xRCCorner.y;

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

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

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

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

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

          setOutputAtIndex(index, ${t10});
        }
      }
    `;
  }
};
var Vg = class {
  constructor(e) {
    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>,", this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let batch = coords[0];
          let d = coords[3];

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

          let value = getX(batch, xRCorner, xCCorner, d);
          setOutputAtIndex(index, value);
        }
      }
    `;
  }
};
function Hl(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o;
  return Kr(n, s, a, "max", t10);
}
var zM = { kernelName: yn, backendName: "webgpu", kernelFunc: Hl };
function yI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
  return Kr(n, a, s, "mean", t10);
}
var WM = { kernelName: Sn, backendName: "webgpu", kernelFunc: yI };
function zg(r, e, t10, o) {
  if (e.filterWidth === 1 && e.filterHeight === 1 && y.arraysEqual(e.inShape, e.outShape))
    return Dt({ inputs: { x: r }, backend: o });
  if (e.filterWidth === e.inWidth && e.filterHeight === e.inHeight && e.batchSize === 1 && e.padInfo.type === "VALID") {
    let a = r.shape.length, i = me({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p;
    t10 === "avg" ? p = yI({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), p = Hl({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } }));
    let u = me({ inputs: { x: p }, backend: o, attrs: { shape: e.outShape } });
    return o.disposeData(i.dataId), o.disposeData(p.dataId), u;
  }
  let n, s = [{ type: "int32", data: [e.strideHeight, e.strideWidth] }];
  return e.filterHeight === 1 && e.filterWidth === 1 ? n = new Vg(e) : (t10 === "avg" ? n = new Gl(e, "avg") : (y.assert(t10 === "max", () => `Invalid pool type ${t10}`), n = new Gl(e, "max")), s.push({ type: "int32", data: [e.padInfo.top, e.padInfo.left] }, { type: "int32", data: [e.dilationHeight, e.dilationWidth] }, { type: "int32", data: [e.inHeight, e.inWidth] }, { type: "int32", data: [e.effectiveFilterHeight, e.effectiveFilterWidth] })), o.runWebGPUProgram(n, [r], r.dtype, s);
}
function eoe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = S.computePool2DInfo(n.shape, s, a, u, i, p);
  return zg(n, c, "avg", t10);
}
var UM = { kernelName: Vo, backendName: "webgpu", kernelFunc: eoe };
var Wg = class {
  constructor(e) {
    this.variableNames = ["dy"], this.uniforms = `stride : vec2<i32>, pads : vec2<i32>, dilation : vec2<i32>, filterDims : vec2<i32>,
       outHeight : i32, outWidth : i32, avgMultiplier : f32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "avg_pool2d_backprop";
  }
  getUserCode() {
    return `
      ${Q("index")} {
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let batch = coords[0];
        let d = coords[3];

        let dyRCCorner = vec2<i32>(coords.yz) - uniforms.pads;
        let dyRCorner = dyRCCorner.x;
        let 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.
        var dotProd = 0.0;
        for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilation[0]) {
          let dyR = f32(dyRCorner + wR) / f32(uniforms.stride[0]);

          if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {
            continue;
          }
          let idyR = i32(dyR);

          for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilation[1]) {
            let dyC = f32(dyCCorner + wC) / f32(uniforms.stride[1]);

            if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {
              continue;
            }
            let idyC = i32(dyC);

            let dyValue = getDy(batch, idyR, idyC, d);

            dotProd = dotProd + dyValue * uniforms.avgMultiplier;
          }
        }
        setOutputAtIndex(index, dotProd);
      }
    }
    `;
  }
};
function toe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
  fI([n, s], "avgPoolGrad");
  let { filterSize: i, strides: p, pad: u } = o, c = S.computePool2DInfo(a.shape, i, p, 1, u), l = new Wg(c), m = 1 / (c.filterHeight * c.filterWidth), d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.effectiveFilterHeight - 1 - c.padInfo.top, c.effectiveFilterWidth - 1 - c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] }, { type: "int32", data: [c.outHeight] }, { type: "int32", data: [c.outWidth] }, { type: "float32", data: [m] }];
  return t10.runWebGPUProgram(l, [n], a.dtype, d);
}
var GM = { kernelName: mp, backendName: "webgpu", kernelFunc: toe };
function roe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  return Xu({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 });
}
var HM = { kernelName: zo, backendName: "webgpu", kernelFunc: roe };
var Ug = class {
  constructor(e, t10) {
    this.variableNames = ["source"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.rank = t10.length, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Rt(e.length)}, `, this.shaderKey = "slice";
  }
  getUserCode() {
    let e = Rt(this.rank), t10 = ooe(this.rank), o;
    return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${bI[a]} = uniforms.start.${Ao(a)} + coords.${bI[a]};`), `
      ${Q("index")} {
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${o.join(`
`)}
          setOutputAtIndex(index, getSource(${t10}));
        }
      }
    `;
  }
};
var bI = ["x", "y", "z", "w", "u", "v"];
function ooe(r) {
  if (r === 1)
    return "sourceLoc";
  if (r <= 6)
    return bI.slice(0, r).map((e) => `sourceLoc.${e}`).join(",");
  throw Error(`Slicing for rank ${r} is not yet supported`);
}
function fs(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = ut.parseSliceParams(n, s, a);
  if (ut.assertParamsValid(n, i, p), t10.shouldExecuteOnCPU([n]) || n.dtype === "string") {
    let l = t10.tensorMap.get(n.dataId), m = bM(l.values, i, p, n.shape, n.dtype);
    return t10.makeTensorInfo(p, n.dtype, m);
  }
  if (y.sizeFromShape(p) === 0)
    return t10.makeTensorInfo(p, n.dtype, []);
  let u = new Ug(i, p), c = [{ type: "int32", data: i }];
  return t10.runWebGPUProgram(u, [n], n.dtype, c);
}
var KM = { kernelName: Es, backendName: "webgpu", kernelFunc: fs };
var noe = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
  y.assert(n.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
  let i = s.reduce((b, C) => b * C), p = S.getReshaped(n.shape, s, i), u = S.getPermuted(p.length, s.length), c = S.getReshapedPermuted(n.shape, s, i), l = S.getSliceBeginCoords(a, s.length), m = S.getSliceSize(c, a, s.length), d = [], f = me({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = Nr({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), g = me({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), x = fs({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } });
  return d.push(f), d.push(h), d.push(g), d.forEach((b) => t10.disposeData(b.dataId)), x;
};
var qM = { kernelName: bs, backendName: "webgpu", kernelFunc: noe };
var soe = `
  fn bincount_write(index: i32, value: f32) {
    ${Ic("&result[index]", "value", "float32")}
  }
`;
var aoe = `
  fn bincount_write(index: i32, value: f32) {
    atomicStore(&result[index], bitcast<i32>(value));
  }
`;
var Tc = class {
  constructor(e, t10, o = false) {
    this.outputShape = [], this.variableNames = ["x"], this.uniforms = "binCountSize : i32,", this.workgroupSize = [64, 1, 1], this.atomic = true, this.hasWeights = true, this.binaryOutput = false, this.outputShape = e, this.rank = e.length, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.binaryOutput = o, o && (this.atomic = false), this.hasWeights = t10, this.hasWeights && this.variableNames.push("w"), this.shaderKey = `bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`;
  }
  getUserCode() {
    return `
    ${this.binaryOutput ? aoe : soe}
  ${Q("index")} {
    ${this.rank === 1 ? `if (index < uniforms.xShape) {
      let indexVal = i32(getX(index));
      if (indexVal < uniforms.binCountSize) {
        let value = ${this.binaryOutput ? 1 : this.hasWeights ? "getW(index)" : "1."};
        bincount_write(indexVal, value);
      }
    }` : `let coord = getCoordsFromIndex(index);
    if (coordsInBounds2D(coord, uniforms.xShape)) {
      let indexVal = i32(getX(coord[0], coord[1]));
      if (indexVal < uniforms.binCountSize) {
        let value = ${this.binaryOutput ? 1 : this.hasWeights ? "getW(coord[0], coord[1])" : "1."};
        bincount_write(coord.x * uniforms.binCountSize + indexVal, value);
      }
    }`}
  }
  `;
  }
};
function ioe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a } = o, i = y.sizeFromShape(n.shape), u = y.sizeFromShape(s.shape) > 0, c = [a], l = s.dtype, m = fr({ backend: t10, attrs: { shape: c, value: 0, dtype: l } }), d = new Tc([i], u), f = [{ type: "int32", data: [a] }], h = u ? [n, s] : [n];
  return t10.runWebGPUProgram(d, h, l, f, m);
}
var jM = { kernelName: ti, backendName: "webgpu", kernelFunc: ioe };
var CI = Ze({ opType: ge.NOT_EQUAL, dtype: "bool", cpuKernelImpl: dM });
var XM = { kernelName: Nn, backendName: "webgpu", kernelFunc: CI };
function Xa(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId);
  return Dt({ inputs: { x: n.complexTensorInfos.real }, backend: t10 });
}
var YM = { kernelName: di, backendName: "webgpu", kernelFunc: Xa };
function QM(r, e) {
  let t10 = new Hr(r.shape, X.TO_INT), o = e.runWebGPUProgram(t10, [r], "int32");
  return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function SI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return Dt({ inputs: { x: n }, backend: t10 });
    let a = Br(n.shape), i = SI({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = po({ inputs: { real: i, imag: a }, backend: t10 });
    return a.dispose(), t10.disposeData(i.dataId), p;
  }
  if (n.dtype === "complex64") {
    let a = Xa({ inputs: { input: n }, backend: t10 }), i = SI({ inputs: { x: a }, backend: t10, attrs: { dtype: s } });
    return t10.disposeData(a.dataId), i;
  }
  if (!y.hasEncodingLoss(n.dtype, s)) {
    let a = Dt({ inputs: { x: n }, backend: t10 });
    return { dataId: a.dataId, shape: a.shape, dtype: s };
  }
  if (t10.shouldExecuteOnCPU([n])) {
    let a = t10.tensorMap.get(n.dataId).values, [i, p, u] = j3(a, n.shape, n.dtype, s);
    return t10.makeTensorInfo(i, p, u);
  }
  if (s === "int32")
    return QM(n, t10);
  if (s === "bool") {
    let a = t10.makeTensorInfo([], "bool", y.getTypedArrayFromDType("bool", 1)), p = CI({ inputs: { a: n, b: a }, backend: t10 });
    return t10.disposeData(a.dataId), p;
  }
  throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var ZM = { kernelName: co, backendName: "webgpu", kernelFunc: SI };
var uoe = xe({ opType: X.CEIL, cpuKernelImpl: X3 });
var JM = { kernelName: Wo, backendName: "webgpu", kernelFunc: uoe };
var Gg = class {
  constructor(e) {
    this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workgroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          var clampedValue = clamp(
              value, vec4<f32>(uniforms.minVal), vec4<f32>(uniforms.maxVal));
          clampedValue = select(clampedValue, value, isnanVec4(value));
          setOutputAtIndex(index, clampedValue);
        }
      }
    `;
  }
};
var Hg = class {
  constructor(e) {
    this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "clip";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          if (isnan(value)) {
            setOutputAtIndex(index, value);
            return;
          }
          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `;
  }
};
function poe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }];
  return y.sizeFromShape(n.shape) % 4 === 0 ? i = new Gg(n.shape) : i = new Hg(n.shape), t10.runWebGPUProgram(i, [n], n.dtype, p);
}
var eL = { kernelName: lo, backendName: "webgpu", kernelFunc: poe };
var Kg = class {
  constructor(e) {
    this.uniforms = "", this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
    for (let t10 = 0; t10 < this.offsetLength; t10++)
      this.uniforms += `offset${t10} : i32,`;
    this.shaderKey = "concat";
  }
  getUserCode() {
    let e = [];
    if (this.offsetLength > 0) {
      e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");
      for (let s = 1; s < this.offsetLength; s++)
        e.push(`else if (yC < uniforms.offset${[s]}){ setOutputAtCoords(coords.x, coords.y, getT${s}(yR, yC - uniforms.offset${s - 1})); }`);
      let o = this.offsetLength, n = this.offsetLength - 1;
      e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${o}(yR, yC - uniforms.offset${n})); }`);
    } else
      e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");
    return `
      ${Q("index")} {
        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndex);
            let yR = coords.x;
            let yC = coords.y;

            ${e.join(`
        `)}
          }
        }
      }
    `;
  }
};
function Qu(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId);
  return Dt({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 });
}
var tL = { kernelName: ci, backendName: "webgpu", kernelFunc: Qu };
function _c(r, e, t10) {
  let o = r[0].dtype;
  if (o === "complex64") {
    let f = r.map((C) => Xa({ inputs: { input: C }, backend: t10 })), h = r.map((C) => Qu({ inputs: { input: C }, backend: t10 })), g = _c(f, e, t10), x = _c(h, e, t10), b = po({ inputs: { real: g, imag: x }, backend: t10 });
    return f.forEach((C) => t10.disposeData(C.dataId)), h.forEach((C) => t10.disposeData(C.dataId)), t10.disposeData(g.dataId), t10.disposeData(x.dataId), b;
  }
  let n = t10.shouldExecuteOnCPU(r);
  if (o === "string" && (n = true), n) {
    let f = r.map((k) => {
      let E = [-1, y.sizeFromShape(k.shape.slice(e))];
      return me({ inputs: { x: k }, backend: t10, attrs: { shape: E } });
    }), h = f.map((k) => ({ vals: t10.readSync(k.dataId), shape: k.shape })), g = S.computeOutShape(f.map((k) => k.shape), 1), x = f[0].shape[0] === 1, b = Y3(h, g, o, x), C = S.computeOutShape(r.map((k) => k.shape), e), w = t10.makeTensorInfo(C, o, b);
    return f.forEach((k) => t10.disposeData(k.dataId)), w;
  }
  let s = t10.device.limits.maxStorageBuffersPerShaderStage - 1;
  if (r.length > s) {
    let f = [];
    for (let g = 0; g < r.length; g += s) {
      let x = r.slice(g, g + s);
      f.push(_c(x, e, t10));
    }
    let h = _c(f, e, t10);
    for (let g of f)
      t10.disposeData(g.dataId);
    return h;
  }
  let { tensors2D: a, outShape: i } = coe(r, e, t10), p = a.map((f) => f.shape), u = new Kg(p), c = [], l = new Array(p.length - 1);
  if (l.length > 0) {
    l[0] = p[0][1], c.push({ type: "int32", data: [l[0]] });
    for (let f = 1; f < l.length; f++)
      l[f] = l[f - 1] + p[f][1], c.push({ type: "int32", data: [l[f]] });
  }
  let m = t10.runWebGPUProgram(u, a, a[0].dtype, c);
  a.forEach((f) => t10.disposeData(f.dataId));
  let d = me({ inputs: { x: m }, backend: t10, attrs: { shape: i } });
  return t10.disposeData(m.dataId), d;
}
function coe(r, e, t10) {
  let o = S.computeOutShape(r.map((s) => s.shape), e);
  return { tensors2D: r.map((s) => me({ inputs: { x: s }, backend: t10, attrs: { shape: [y.sizeFromShape(s.shape.slice(0, e)), y.sizeFromShape(s.shape.slice(e))] } })), outShape: o };
}
function wI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = y.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
  S.assertParamsConsistent(a, s);
  let i = S.computeOutShape(e.map((u) => u.shape), s);
  if (y.sizeFromShape(i) === 0)
    return t10.makeTensorInfo(i, e[0].dtype, []);
  let p = e.filter((u) => y.sizeFromShape(u.shape) > 0);
  return p.length === 1 ? Dt({ inputs: { x: p[0] }, backend: t10 }) : _c(p, s, t10);
}
var rL = { kernelName: Cs, backendName: "webgpu", kernelFunc: wI };
function loe(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) {
  let c = (R) => {
    switch (R) {
      case 1:
        return "resData = x[xIndex];";
      case 3:
        return "resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";
      case 4:
        return "resData = x[xIndex / 4];";
      default:
        throw new Error(`innerElementSize ${R} is not supported.`);
    }
  }, l = (R) => {
    switch (R) {
      case 1:
        return "return W[row * uniforms.wShape[3] + colIn];";
      case 4:
        return "return W[row * uniforms.wShape[3] / 4 + colIn];";
      default:
        throw new Error(`innerElementSize ${R} is not supported.`);
    }
  }, m = r ? `
      let coord = vec4<i32>(batch, xRow, xCol, xCh);
      ` : `
      let coord = vec4<i32>(batch, xCh, xRow, xCol);
      `, d = r ? `
      let coords = vec4<i32>(
        batch,
        row / outWidth,
        row % outWidth,
        col);
      ` : `
      let coords = vec4<i32>(
        batch,
        row,
        col / outWidth,
        col % outWidth);
      `, f = r ? "uniforms.xShape[1]" : "uniforms.xShape[2]", h = r ? "uniforms.xShape[2]" : "uniforms.xShape[3]", g = r ? "row" : "col", x = r ? "col" : "row", b = `
      let inChannels = uniforms.wShape[2];
      let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
      let outRow = ${g} / outWidth;
      let outCol = ${g} % outWidth;

      let WRow = ${x} / (uniforms.filterDims[1] * inChannels);
      let WCol = ${x} / inChannels % uniforms.filterDims[1];
      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
      let xCh = ${x} % inChannels;
      var resData = ${kt(i)}(0.0);
      // The bounds checking is always needed since we use it to pad zero for
      // the 'same' padding type.
      if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {
        ${m}
        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
        ${c(i)}
      }
      return resData;`, C = r ? e && o ? `
      let col = colIn * ${i};
      ${b}` : `
      let col = colIn * ${i};
      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
        ${b}
      }
      return ${kt(i)}(0.0);` : o && t10 ? `
      let col = colIn * ${i};
      ${b}` : `
      let col = colIn * ${i};
      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
        ${b}
      }
      return ${kt(i)}(0.0);`, w = `${l(p)}`, k = kt(u), _ = r ? kt(i) : kt(p), E = r ? kt(p) : kt(i);
  return `
      ${pr(s, a, u === 4, 4)}
      fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${_} {
        ${r ? C : w}
      }

      fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${E} {
        ${r ? w : C}
      }

      fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${k}) {
        let col = colIn * ${u};
        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
        {
        var value = valueIn;
        let outWidth = ${r ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
        ${d}
        ${Gr(n, s)}
        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
        }
      }`;
}
var qg = class {
  constructor(e, t10, o, n, s = false, a = null, i = false, p = false) {
    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, this.isChannelsLast = e.dataFormat === "channelsLast", this.isVec4 = ((e.inChannels % 4 === 0 || e.inChannels % 3 === 0) && this.isChannelsLast || e.outWidth % 4 === 0 && !this.isChannelsLast) && e.outChannels % 4 === 0, this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] }, this.workgroupSize = Bl(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Vl(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 ? (this.isChannelsLast && e.inChannels % 4 !== 0 ? (this.innerElementSize = 3, this.variableTypes = ["f32", "vec4<f32>"]) : (this.innerElementSize = 4, this.variableTypes = ["vec4<f32>", "vec4<f32>"]), s && (this.variableNames.push("bias"), this.variableTypes.push("vec4<f32>")), i && (this.variableNames.push("preluActivationWeights"), this.variableTypes.push("vec4<f32>"))) : (this.innerElementSize = this.elementsPerThread[0], s && this.variableNames.push("bias"), i && this.variableNames.push("preluActivationWeights")), this.sequentialAccessByThreads = p, this.addBias = s, this.activation = a, this.hasPreluActivationWeights = i, this.tileAOuter = this.workgroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workgroupSize[0] * this.elementsPerThread[0], this.tileInner = Math.max(this.workgroupSize[0] * this.innerElementSize, this.workgroupSize[1]), this.fitAOuter = t10 % this.tileAOuter === 0, this.fitBOuter = o % this.tileBOuter === 0, this.fitInner = n % this.tileInner === 0, this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`;
  }
  getUserCode() {
    let e = this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner) : ju(this.elementsPerThread, this.workgroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t10 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];
    return `
    ${loe(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t10[0], t10[1], t10[2])}
    ${e}
  `;
  }
};
var jg = class {
  constructor(e, t10 = false, o = null, n = false) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>,", this.workgroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.addBias = t10, this.activation = o, this.hasPreluActivationWeights = n, t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`;
  }
  getUserCode() {
    return `
       ${pr(this.activation, this.hasPreluActivationWeights, false, 4)}
       fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{
         let coords = vec4<i32>(batch, row, col, chan);
         if (coordsInBounds4D(coords, uniforms.xShape)) {
           return  getX(batch, row, col, chan);
         } else {
          return 0.0;
         }
       }
       fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
         let coords = vec4<i32>(row, col, xChannel, outChannel);
         if(coordsInBounds4D(coords, uniforms.wShape)) {
           return getW(row, col, xChannel, outChannel);
          } else {
            return 0.0;
          }
       }
       fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {
         let coords = ${this.isChannelsLast ? "vec4<i32>(batch, row, col, chan);" : "vec4<i32>(batch, chan, row, col);"}
         if (coordsInBounds4D(coords, uniforms.outShape)) {
           var value = valueIn;
           ${Gr(this.addBias, this.activation)}
           setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);
         }
       }
       ${Q("index")} {
         let coords = getOutputCoords();
         let batch = coords[0];
         let outChannel = ${this.isChannelsLast ? "coords[3];" : "coords[1];"}
         let outRow = ${this.isChannelsLast ? "coords[1];" : "coords[2];"}
         let outCol = ${this.isChannelsLast ? "coords[2];" : "coords[3];"}
         var acc : f32 = 0.0;
         for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
           for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
             let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
             let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
             for (var xChannel = 0; xChannel < ${this.isChannelsLast ? "uniforms.xShape[3];" : "uniforms.xShape[1];"} xChannel = xChannel + 1) {
               ${this.isChannelsLast ? "let v = readInp(batch, xRow, xCol, xChannel);" : "let v = readInp(batch, xChannel, xRow, xCol);"}
               let f = readFilt(row, col, xChannel, outChannel);
               acc = acc + v * f;
             }
           }
         }
         writeResult(batch, outRow, outCol, outChannel, acc);
       }
     `;
  }
};
var Xg = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
       inChannels : i32,`, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = t10, this.shaderKey = `im2col_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? "coords[1]" : "coords[2]", n = this.isChannelsLast ? "coords[2]" : "coords[1]", s = this.isChannelsLast ? "getX(batch, xRow, xCol, ch)" : "getX(batch, ch, xRow, xCol)";
    return `
    ${Q("index")} {
      let coords = getCoordsFromIndex(index);
      if(index < uniforms.size) {
        let batch = coords[0];
        let row = ${o};
        let col = ${n};
        let offsetY = (row / uniforms.outWidth) * uniforms.stride[0] - uniforms.pad[0];
        let xRow = offsetY + uniforms.dilation[0] * (col / uniforms.itemsPerBlockRow);
        var value = 0.0;
        if(xRow < uniforms.xShape[${e}] && xRow >= 0) {
          let offsetX = (row % uniforms.outWidth) * uniforms.stride[1] -
              uniforms.pad[1];
          let xCol = offsetX + uniforms.dilation[1] * ((col %
              uniforms.itemsPerBlockRow) / uniforms.inChannels);
          let ch = col % uniforms.inChannels;
          if(xCol < uniforms.xShape[${t10}] && xCol >= 0) {
            value = ${s};
          }
        }
        setOutputAtIndex(index, value);
      }
    }
   `;
  }
};
function Yg(r, e) {
  let t10 = r.length;
  return t10 >= 3 ? e ? [...r.slice(0, -3), r[t10 - 3] * r[t10 - 2], r[t10 - 1]] : [...r.slice(0, -3), r[t10 - 3], r[t10 - 2] * r[t10 - 1]] : !e && t10 === 1 && r[0] > 1 ? [r[0], 1] : null;
}
function moe({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
  let p = t10.dataFormat === "channelsLast", u = !p, c = false, l = p && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = [], d, f;
  if (l) {
    let x = t10.inHeight * t10.inWidth * t10.inChannels;
    d = me({ inputs: { x: r }, backend: o, attrs: { shape: [1, t10.batchSize, x] } }), f = me({ inputs: { x: e }, backend: o, attrs: { shape: [1, x, t10.outChannels] } });
  } else
    d = me({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t10.batchSize, t10.inHeight * t10.inWidth, t10.inChannels] : [t10.batchSize, t10.inChannels, t10.inHeight * t10.inWidth] } }), f = me({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } });
  if (m.push(d), m.push(f), s != null) {
    let x = Yg(s.shape, p);
    x != null && (s = me({ inputs: { x: s }, backend: o, attrs: { shape: x } }), m.push(s));
  }
  if (n != null) {
    let x = Yg(n.shape, p);
    x != null && (n = me({ inputs: { x: n }, backend: o, attrs: { shape: x } }), m.push(n));
  }
  let h = Xu({ a: p ? d : f, b: p ? f : d, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = me({ inputs: { x: h }, backend: o, attrs: { shape: t10.outShape } });
  m.push(h);
  for (let x of m)
    o.disposeData(x.dataId);
  return g;
}
function doe({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
  let { filterWidth: p, filterHeight: u, inChannels: c, strideWidth: l, strideHeight: m, padInfo: d, outWidth: f, outHeight: h, dilationWidth: g, dilationHeight: x, dataFormat: b } = t10, C = b === "channelsLast", w = p * u * c, k = h * f, _ = C ? [t10.batchSize, k, w] : [t10.batchSize, w, k], E = new Xg(_, C), A = [{ type: "int32", data: [d.top, d.left] }, { type: "int32", data: [m, l] }, { type: "int32", data: [x, g] }, { type: "int32", data: [f] }, { type: "int32", data: [c * p] }, { type: "int32", data: [c] }], R = o.runWebGPUProgram(E, [r], r.dtype, A), D = [];
  D.push(R);
  let P = me({ inputs: { x: e }, backend: o, attrs: { shape: [1, w, -1] } });
  if (D.push(P), s != null) {
    let U = Yg(s.shape, C);
    U != null && (s = me({ inputs: { x: s }, backend: o, attrs: { shape: U } }), D.push(s));
  }
  if (n != null) {
    let U = Yg(n.shape, C);
    U != null && (n = me({ inputs: { x: n }, backend: o, attrs: { shape: U } }), D.push(n));
  }
  let V = Xu({ a: C ? R : P, b: C ? P : R, transposeA: !C, transposeB: false, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), z = me({ inputs: { x: V }, backend: o, attrs: { shape: t10.outShape } });
  D.push(V);
  for (let U of D)
    o.disposeData(U.dataId);
  return z;
}
function Qg({ x: r, filter: e, convInfo: t10, backend: o, bias: n = null, preluActivationWeights: s = null, leakyreluAlpha: a = 0, activation: i = null }) {
  let p = n != null, u = s != null, c = t10.dataFormat === "channelsLast", l = c && t10.filterHeight === t10.inHeight && t10.filterWidth === t10.inWidth && t10.padInfo.type === "VALID", m = O().getBool("WEBGPU_USE_NAIVE_CONV2D_DEBUG");
  if (!m && (l || t10.filterHeight === 1 && t10.filterWidth === 1 && t10.dilationHeight === 1 && t10.dilationWidth === 1 && t10.strideHeight === 1 && t10.strideWidth === 1 && (t10.padInfo.type === "SAME" || t10.padInfo.type === "VALID")))
    return moe({ x: r, filter: e, convInfo: t10, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
  let d = O().getNumber("WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL"), f = d > 0 ? d : o.thresholdToIncreaseWorkgroups, h = t10.batchSize * Math.ceil(t10.outHeight * t10.outWidth / 32) * Math.ceil(t10.outChannels / 32);
  if (O().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") || h <= f)
    return doe({ x: r, filter: e, convInfo: t10, backend: o, bias: n, preluActivationWeights: s, leakyreluAlpha: a, activation: i });
  let g, x = [t10.padInfo.top, t10.padInfo.left], b = [{ type: "int32", data: [t10.filterHeight, t10.filterWidth] }, { type: "int32", data: [...x] }, { type: "int32", data: [t10.strideHeight, t10.strideWidth] }, { type: "int32", data: [t10.dilationHeight, t10.dilationWidth] }];
  if (m)
    g = new jg(t10, p, i, u);
  else {
    let _ = c ? t10.outHeight * t10.outWidth : t10.outChannels, E = c ? t10.outChannels : t10.outHeight * t10.outWidth, A = t10.filterHeight * t10.filterWidth * t10.inChannels;
    b.push({ type: "int32", data: [_] }, { type: "int32", data: [E] }, { type: "int32", data: [A] });
    let R = o.adapterInfo.isIntel();
    g = new qg(t10, _, E, A, p, i, u, R);
  }
  let C = [], w = [r, e];
  p && (!c && n.shape.length === 1 && (n = me({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), C.push(n)), w.push(n)), u && (!c && s.shape.length === 1 && (s = me({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), C.push(s)), w.push(s)), i === "leakyrelu" && (b.push({ type: "float32", data: [a] }), g.uniforms += " alpha : f32,");
  let k = o.runWebGPUProgram(g, w, r.dtype, b);
  for (let _ of C)
    o.disposeData(_.dataId);
  return k;
}
function foe(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = t10, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l);
  return Qg({ x: n, filter: s, convInfo: m, backend: o });
}
var oL = { kernelName: Uo, backendName: "webgpu", kernelFunc: foe };
var Zg = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1;
    return `
    ${Q("index")} {
      if(index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let batch = coords[0];
        let d1 = coords[${o}];

        let dyCorner = vec2<i32>(coords[${e}], coords[${t10}]) - 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 = i32(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 = i32(dyC);

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

            }
          }
        }
        setOutputAtIndex(index, dotProd);
      }
    }
  `;
  }
};
var Jg = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.filterShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerFilter_${this.isChannelsLast}`;
  }
  getUserCode() {
    return `
    ${Q("index")} {
      if(index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let wR = coords[0];
        let wC = coords[1];
        let d1 = coords[2];
        let d2 = coords[3];

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;
        for (var b = 0; b < uniforms.batchSize; b = b + 1) {
          for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {
            let xR = wR + yR * uniforms.stride[0] - uniforms.pad[0];
            if (xR < 0 || xR >= uniforms.inHeight) {
              continue;
            }

            for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {
              let xC = wC + yC * uniforms.stride[1] - uniforms.pad[1];

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

              if (${this.isChannelsLast}) {
                let dyValue = getDy(b, yR, yC, d2);
                let xValue = getX(b, xR, xC, d1);
                dotProd = dotProd + xValue * dyValue;
              } else {
                let dyValue = getDy(b, d2, yR, yC);
                let xValue = getX(b, d1, xR, xC);
                dotProd = dotProd + xValue * dyValue;
              }
            }
          }
        }
        setOutputAtIndex(index, dotProd);
      }
    }
  `;
  }
};
function hoe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, dataFormat: p, dimRoundingMode: u, filterShape: c } = o, l = S.convertConv2DDataFormat(p), m = S.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), d = new Jg(m), f = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize] }, { type: "int32", data: [m.outHeight] }, { type: "int32", data: [m.outWidth] }, { type: "int32", data: [m.inHeight] }, { type: "int32", data: [m.inWidth] }];
  return t10.runWebGPUProgram(d, [n, s], n.dtype, f);
}
var nL = { kernelName: oi, backendName: "webgpu", kernelFunc: hoe };
function goe(r = 4) {
  let e = (s) => {
    switch (s) {
      case 1:
        return "return W[getIndexFromCoords4D(coord, uniforms.wShape)];";
      case 4:
        return `
            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
            let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];
            let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];
            let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];
            let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];
            return vec4<f32>(v0, v1, v2, v3);
            `;
      default:
        throw new Error(`innerElementSize ${s} is not supported.`);
    }
  }, o = `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 ${kt(r)}(0.0);
      }
      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
        return ${kt(r)}(0.0);
      }
      let coord = vec4<i32>(
          batch,
          i32(xR),
          i32(xC),
          col % uniforms.outBackprop[3]);
      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}
      }
      return ${kt(r)}(0.0);`;
  return `
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {
    let col = colIn * ${r};
    ${o}
  }

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${kt(r)} {
    let col = colIn * ${r};
    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 rowInner = row % uniforms.outBackprop[3];
      let coord = vec4<i32>(coordX, coordY, col, rowInner);
      ${e(r)}
    }
    return ${kt(r)}(0.0);
  }

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${kt(r)}) {
    let col = colIn * ${r};
    if (row < uniforms.dimAOuter && (col + ${r - 1}) < uniforms.dimBOuter) {
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;
    }
  }`;
}
var ex = 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, y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.isVec4 = e.inChannels % 4 === 0 && e.outChannels % 4 === 0, this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workgroupSize = Bl(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = Vl(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, this.elementsPerThread), this.isVec4 && (this.variableTypes = ["vec4<f32>", "f32"]), this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`;
  }
  getUserCode() {
    let e = this.isVec4 ? qu(this.elementsPerThread, this.workgroupSize) : ju(this.elementsPerThread, this.workgroupSize);
    return `
    ${goe(this.isVec4 ? 4 : 1)}
    ${e}
    `;
  }
};
function xoe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { inputShape: a, strides: i, pad: p, dataFormat: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(u), m = S.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), d = [{ type: "int32", data: [m.filterHeight, m.filterWidth] }, { type: "int32", data: [m.filterHeight - 1 - m.padInfo.top, m.filterWidth - 1 - m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.batchSize, m.outHeight, m.outWidth, m.outChannels] }], f;
  if (O().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.filterHeight <= 2 && m.filterWidth <= 2 && m.outChannels <= 16 && m.inChannels === 1)
    f = new Zg(m);
  else {
    f = new ex(m);
    let h = m.inHeight * m.inWidth, g = m.inChannels, x = m.filterHeight * m.filterWidth * m.outChannels;
    d.push({ type: "uint32", data: [h] }, { type: "uint32", data: [g] }, { type: "uint32", data: [x] });
  }
  return t10.runWebGPUProgram(f, [n, s], "float32", d);
}
var sL = { kernelName: Go, backendName: "webgpu", kernelFunc: xoe };
var yoe = xe({ opType: X.COS });
var aL = { kernelName: Ho, backendName: "webgpu", kernelFunc: yoe };
var boe = xe({ opType: X.COSH });
var iL = { kernelName: Ko, backendName: "webgpu", kernelFunc: boe };
var tx = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true;
    let [s] = t10;
    this.outputShape = [s, o[0], o[1], e], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.methodId = n === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
  }
  getUserCode() {
    let [e, t10] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [o, n, s] = 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) * ${e}`], [a, i, p] = this.cropWidthBiggerThan1 ? [`(${t10} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t10} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t10}`];
    return `
    ${Q("index")} {
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let height_ratio = f32(${o});
        let width_ratio = f32(${a});
        let b = coords[0];
        let y = coords[1];
        let x = coords[2];
        let d = coords[3];
        // get box vals
        let y1 = getBoxes(b, 0);
        let x1 = getBoxes(b, 1);
        let y2 = getBoxes(b, 2);
        let x2 = getBoxes(b, 3);
        // get image in batch index
        let bInd = i32(round(getBoxInd(b)));
        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
          return;
        }
        let height_scale = ${n};
        let width_scale = ${i};
        let in_y = ${s};
        if( in_y < 0.0 || in_y > ${e} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let in_x = ${p};
        if( in_x < 0.0 || in_x > ${t10} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
        if(${this.methodId} == 1) {
          // Compute the four integer indices.
          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
          let top = topLeft + (topRight - topLeft) * fracCR.x;
          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          let newValue = top + (bottom - top) * fracCR.y;
          setOutputAtIndex(index, newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          let sourceNearestCR = vec2<i32>(floor(
            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
          let newValue = getImage(
            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutputAtIndex(index, newValue);
        }
      }
    }
    `;
  }
};
var Coe = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n, boxes: s, boxInd: a } = e, { cropSize: i, method: p, extrapolationValue: u } = o, c = new tx(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }];
  return t10.runWebGPUProgram(c, [n, s, a], "float32", l);
};
var uL = { kernelName: Xo, backendName: "webgpu", kernelFunc: Coe };
var Zu;
(function(r) {
  r.Prod = "*", r.Sum = "+";
})(Zu || (Zu = {}));
var Kl = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true, this.workgroupSize = [128, 1, 1], this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
  }
  getUserCode() {
    let e = this.outputShape.length, t10 = this.op === Zu.Prod ? "1.0" : "0.0", o = this.exclusive ? t10 : `getX(${pL(e, "coords", this.op)})`, n = this.outputShape[this.outputShape.length - 1], s = "", a = "";
    return this.exclusive ? (s = this.reverse ? `end != ${n - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (s = this.reverse ? `end + pow2 < ${n}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), `
      ${Q("index")} {
       if (index < uniforms.size) {
         var coords = getCoordsFromIndex(index);

         let end = ${cL(e, "coords", this.op)};
         var val = ${o};
         let pow2 = i32(pow(2.0, uniforms.index));
         if (${s}) {
           let idx = ${a};
           ${cL(e, "coords", this.op)} = idx;
           val ${this.op}= getX(${pL(e, "coords", this.op)});
         }
         setOutputAtIndex(index, val);
       }
      }
    `;
  }
};
function pL(r, e, t10) {
  if (r === 1)
    return `${e}`;
  if (r === 2)
    return `${e}.x, ${e}.y`;
  if (r === 3)
    return `${e}.x, ${e}.y, ${e}.z`;
  if (r === 4)
    return `${e}.x, ${e}.y, ${e}.z, ${e}.w`;
  throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function cL(r, e, t10) {
  if (r === 1)
    return `${e}`;
  if (r === 2)
    return `${e}.y`;
  if (r === 3)
    return `${e}.z`;
  if (r === 4)
    return `${e}.w`;
  throw Error(`Cumulative ${t10} for rank ${r} is not yet supported`);
}
function rx(r, e, t10, o, n, s) {
  let a = e.shape.length, i = S.getAxesPermutation([o], a), p = e;
  i != null && (p = Nr({ inputs: { x: e }, backend: t10, attrs: { perm: i } }));
  let u = S.getInnerMostAxes(1, a)[0];
  if (u !== a - 1)
    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
  let c = p.shape[u], l = Dt({ inputs: { x: p }, backend: t10 });
  for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
    let d = new Kl(r, p.shape, false, s), f = l, h = [{ type: "float32", data: [m] }];
    l = t10.runWebGPUProgram(d, [l], l.dtype, h), t10.disposeData(f.dataId);
  }
  if (n) {
    let m = new Kl(r, p.shape, n, s), d = l, f = [{ type: "float32", data: [0] }];
    l = t10.runWebGPUProgram(m, [l], l.dtype, f), t10.disposeData(d.dataId);
  }
  if (i != null) {
    let m = S.getUndoAxesPermutation(i), d = Nr({ inputs: { x: l }, backend: t10, attrs: { perm: m } });
    return t10.disposeData(l.dataId), t10.disposeData(p.dataId), d;
  }
  return l;
}
function Soe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return rx(Zu.Prod, n, t10, s, a, i);
}
var lL = { kernelName: qo, backendName: "webgpu", kernelFunc: Soe };
function woe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return rx(Zu.Sum, n, t10, s, a, i);
}
var mL = { kernelName: jo, backendName: "webgpu", kernelFunc: woe };
function Ioe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o, p = n.shape.length === 1, c = y.sizeFromShape(s.shape) > 0, l = s.dtype, m = p ? [n.shape[0]] : [n.shape[0], n.shape[1]], d = p ? [a] : [n.shape[0], a], f = fr({ backend: t10, attrs: { shape: d, value: 0, dtype: l } }), h = new Tc(m, c, i), g = [{ type: "int32", data: [a] }], x = c ? [n, s] : [n];
  return t10.runWebGPUProgram(h, x, l, g, f);
}
var dL = { kernelName: ni, backendName: "webgpu", kernelFunc: Ioe };
var ox = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `depthToSpace_${t10}`, this.dataFormat = t10;
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let b = coords[0];
          let h = ${this.getHeightCoordString()};
          let w = ${this.getWidthCoordString()};
          let d = ${this.getDepthCoordString()};

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

          let rlt = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`;
  }
  getHeightCoordString() {
    return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
  }
  getWidthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
  }
  getDepthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
  }
  getOutputDepthSize() {
    return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
  }
  getInputSamplingString() {
    return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
  }
};
function voe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, d = c / (s * s), f = a === "NHWC" ? [i, l, m, d] : [i, d, l, m], h = [{ type: "int32", data: [s] }], g = new ox(f, a);
  return t10.runWebGPUProgram(g, [n], n.dtype, h);
}
var fL = { kernelName: Yo, backendName: "webgpu", kernelFunc: voe };
var nx = class {
  constructor(e, t10, o, n = false, s = null, a = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, inDims : vec2<i32>,", this.workgroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t10, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`;
  }
  getUserCode() {
    let e = this.filterWidth * this.filterHeight, t10 = this.workgroupSize[0] * this.workgroupSize[1] * this.workgroupSize[2], o = this.workgroupSize[1] + this.filterHeight - 1, n = this.workgroupSize[0] + this.filterWidth - 1;
    return `
      ${pr(this.activation, this.hasPreluActivation, false, 4)}

      var<workgroup> mm_Asub : array<array<f32, ${n}>, ${o}>;
      var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;
      fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {
        var value = 0.0;
        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])
        {
          value = getX(batch, channel, row, col);
        }
        return value;
      }

      ${Q()} {
        let coords = getOutputCoords();
        let batch = coords[0];
        let xRCCorner = vec2<i32>(coords.zw) - uniforms.pad;
        let channelMul = uniforms.wShape[3];
        let d1 = coords[1] / channelMul;
        let q = coords[1] % channelMul;

        let inputRowStart = xRCCorner.x;
        let inputColStart = xRCCorner.y;

        let localRow = i32(localId.y);
        let localCol = i32(localId.x);

        // Load one tile of X into local memory.
        for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {
          for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {
            let rowOffset = inputRow - localRow;
            let colOffset = inputCol - localCol;
            mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);
          }
        }

        // Load one tile of W into local memory.
        var wIndex = i32(localIndex);
        ${e < t10 ? `if (wIndex < ${e})` : `for(; wIndex < ${e}; wIndex = wIndex + ${t10})`}

        {
          let wRow = wIndex / ${this.filterWidth};
          let wCol = wIndex % ${this.filterWidth};
          mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);
        }

        workgroupBarrier();

        var value = 0.0;
        for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {
          for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {
            let xVal = mm_Asub[localRow + wR][localCol + wC];
            let wVal = mm_Bsub[wR][wC];
            value = fma(xVal, wVal, value);
          }
        }
        ${Gr(this.addBias, this.activation)}
        if (coordsInBounds4D(coords, uniforms.outShape)) {
          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
        }
      }
    `;
  }
};
var $c = class {
  constructor(e, t10 = false, o = null, n = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, inDims : vec2<i32>,", this.workgroupSize = [4, 4, 4], this.workPerThread = 4, this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [4, this.workPerThread, 1]), y.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`;
  }
  getUserCode() {
    let e = (this.workPerThread - 1) * this.convInfo.strideWidth + this.convInfo.filterWidth, t10 = this.convInfo.strideHeight, o = this.convInfo.strideWidth;
    return `
      ${pr(this.activation, this.hasPreluActivation, true, 4)}
      fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {
        var value = vec4<f32>(0.0);
        if (col >=0 && col < uniforms.inDims[1]) {
          value = getX(batch, row, col, channel);
        }
        return value;
      }

      ${Q()} {
        let batch = i32(globalId.z) / uniforms.outShape[1];
        let r = i32(globalId.z) % uniforms.outShape[1];
        let c = i32(globalId.y) * ${this.workPerThread};
        let d1 = i32(globalId.x) * 4;
        let xRCCorner = vec2<i32>(r, c) * vec2<i32>(${t10}, ${o}) - uniforms.pad;

        let xRCorner = xRCCorner.x;
        let xCCorner = xRCCorner.y;
        var xVals : array<vec4<f32>, ${e}>;
        var dotProd : array<vec4<f32>, ${this.workPerThread}>;
        for (var i = 0; i < ${this.workPerThread}; i++) {
          dotProd[i] = vec4<f32>(0.0);
        }

        // Use constant instead of uniform can give better performance.
        for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {
          let xR = xRCorner + wR;
          if (xR >=0 && xR < uniforms.inDims[0]) {
            for (var i = 0; i < ${e}; i++) {
              xVals[i] = readX(batch, xR, xCCorner + i, d1);
            }
            for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {
              let wValue = getW(wR, wC, d1, 0);
              for (var i = 0; i < ${this.workPerThread}; i++) {
                dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);
              }
            }
          }
        }

        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let coords = vec4<i32>(batch, r, c + i, d1);
          if (coordsInBounds4D(coords, uniforms.outShape)) {
            var value = dotProd[i];
            ${Gr(this.addBias, this.activation)}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
          }
        }
      }
    `;
  }
};
var Ec = class {
  constructor(e, t10 = false, o = null, n = false) {
    this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`, this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? "getX(batch, xR, xC, d1);" : "getX(batch, d1, xR, xC);";
    return `
      ${pr(this.activation, this.hasPreluActivation, false, 4)}

      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getOutputCoords();
          let batch = coords[0];
          let xRCCorner = vec2<i32>(coords.${this.isChannelsLast ? "yz" : "zw"}) * uniforms.stride - uniforms.pad;
          let d2 = coords[${this.isChannelsLast ? 3 : 1}];
          let channelMul = uniforms.wShape[3];
          let d1 = d2 / channelMul;
          let q = d2 % channelMul;

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

          // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get
          // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all
          // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.
          // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.
          var value = 0.0;

          // Extract if checking out of for loop for performance.
          if (inputRowStart >= 0 && inputColStart >= 0 &&
            inputRowEnd < uniforms.inDims[0] &&
                inputColEnd < uniforms.inDims[1]) {
              for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
                let xR = inputRowStart + wR * uniforms.dilation[0];

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

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

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

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

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

                  let xVal = ${e};
                  let wVal = getW(wR, wC, d1, q);
                  value = value + xVal * wVal;
                }
              }
            }
            ${Gr(this.addBias, this.activation)}
          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
        }
      }
    `;
  }
};
function koe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dataFormat: p, dilations: u, dimRoundingMode: c } = o, l = S.convertConv2DDataFormat(p), m = u;
  m == null && (m = [1, 1]);
  let d = S.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), f = [{ type: "int32", data: [d.padInfo.top, d.padInfo.left] }, { type: "int32", data: [d.inHeight, d.inWidth] }], h = d.dataFormat === "channelsLast", g;
  return !h && d.inHeight > 16 && d.inWidth > 16 && d.strideHeight === 1 && d.strideWidth === 1 && d.dilationWidth === 1 && d.dilationHeight === 1 && d.inChannels === d.outChannels ? g = new nx(d.outShape, d.filterHeight, d.filterWidth) : h && d.outHeight > 4 && d.outWidth > 4 && d.strideWidth <= 2 && d.inChannels === d.outChannels && d.dilationHeight === 1 && d.dilationWidth === 1 && d.inChannels % 4 === 0 ? g = new $c(d) : (g = new Ec(d), f.push({ type: "int32", data: [d.filterHeight] }, { type: "int32", data: [d.filterWidth] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.dilationHeight, d.dilationWidth] })), t10.runWebGPUProgram(g, [n, s], n.dtype, f);
}
var hL = { kernelName: Qo, backendName: "webgpu", kernelFunc: koe };
var sx = class {
  constructor(e) {
    this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, e], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "diag";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getOutputCoords();
          let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);
          setOutputAtIndex(index, value);
        }
      }
    `;
  }
};
function Noe(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = y.sizeFromShape(o.shape), a = me({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new sx(s), p = t10.runWebGPUProgram(i, [a], a.dtype), u = me({ inputs: { x: p }, backend: t10, attrs: { shape: n } });
  return t10.disposeData(a.dataId), t10.disposeData(p.dataId), u;
}
var gL = { kernelName: si, backendName: "webgpu", kernelFunc: Noe };
var ax = class {
  constructor(e) {
    this.variableNames = ["x", "w"], this.uniforms = "filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "dilation2d";
  }
  getUserCode() {
    return `
       ${Q("index")} {
         if (index < uniforms.size) {
           let neg_infinity = -3.4e38;
           let coords = getOutputCoords();
           let batch = coords.x;
           let d1 = coords.w;
           let outTopLeftCorner = coords.yz * uniforms.stride - uniforms.pad;
           let hBeg = outTopLeftCorner.x;
           let wBeg = outTopLeftCorner.y;

           var curVal = neg_infinity;
           for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {
             let hIn = hBeg + h * uniforms.dilation[0];

             if (hIn >= 0 && hIn < uniforms.xShape[1]) {
               for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {
                 let wIn = wBeg + w * uniforms.dilation[1];

                 if (wIn >= 0 && wIn < uniforms.xShape[2]) {
                   let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);
                   if (val > curVal) {
                     curVal = val;
                   }
                 }
               }
             }
           }

           setOutputAtIndex(index, curVal);
         }
       }
     `;
  }
};
function Toe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = S.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c = [u.padInfo.top, u.padInfo.left], l = [{ type: "int32", data: [u.filterHeight, u.filterWidth] }, { type: "int32", data: [...c] }, { type: "int32", data: [u.strideHeight, u.strideWidth] }, { type: "int32", data: [u.dilationHeight, u.dilationWidth] }], m = new ax(u);
  return t10.runWebGPUProgram(m, [n, s], n.dtype, l);
}
var xL = { kernelName: ai, backendName: "webgpu", kernelFunc: Toe };
var II = Ze({ opType: ge.MUL, cpuKernelImpl: lM, supportsComplex: true });
var yL = { kernelName: kn, backendName: "webgpu", kernelFunc: II };
function ql(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return Kr(n, s, a, "sum", t10);
}
var bL = { kernelName: Kn, backendName: "webgpu", kernelFunc: ql };
function _oe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = S.decodeEinsumEquation(n, s.length);
  S.checkEinsumDimSizes(a.length, p, s);
  let { path: u, steps: c } = S.getEinsumComputePath(i, p), l = c.length, m = null, d = a.length, f = [];
  for (let h = 0; h < l; ++h) {
    for (let g of c[h]) {
      let { permutationIndices: x, expandDims: b } = S.getEinsumPermutation(d, p[g]), C;
      S.isIdentityPermutation(x) ? C = s[g] : (C = Nr({ inputs: { x: s[g] }, backend: t10, attrs: { perm: x } }), f.push(C));
      let w = C.shape.slice();
      for (let k = 0; k < b.length; ++k)
        w.splice(b[k], 0, 1);
      y.arraysEqual(C.shape, w) || (C = me({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), f.push(C)), m === null ? m = C : (m = II({ inputs: { a: C, b: m }, backend: t10 }), f.push(m));
    }
    h < l - 1 && (u[h] >= 0 && (m = ql({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - d), keepDims: false } }), f.push(m)), d--);
  }
  for (let h of f)
    h !== m && t10.disposeData(h.dataId);
  return m;
}
var CL = { kernelName: ii, backendName: "webgpu", kernelFunc: _oe };
var $oe = xe({ opType: X.ELU });
var SL = { kernelName: Jo, backendName: "webgpu", kernelFunc: $oe };
var Eoe = Ze({ opType: ge.EQUAL, dtype: "bool", cpuKernelImpl: Q3 });
var wL = { kernelName: en, backendName: "webgpu", kernelFunc: Eoe };
var Aoe = xe({ opType: X.ERF });
var IL = { kernelName: da, backendName: "webgpu", kernelFunc: Aoe };
var vI = xe({ opType: X.EXP, cpuKernelImpl: Z3, dtype: "float32" });
var vL = { kernelName: tn, backendName: "webgpu", kernelFunc: vI };
function ix(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n;
  return n < 0 && (y.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), me({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var kL = { kernelName: Ss, backendName: "webgpu", kernelFunc: ix };
var Roe = xe({ opType: X.EXPM1, cpuKernelImpl: J3 });
var NL = { kernelName: fa, backendName: "webgpu", kernelFunc: Roe };
var jl = class {
  constructor(e, t10) {
    this.variableNames = ["real", "imag"], this.outputShape = [], this.uniforms = "exponentMultiplier : f32, denominator: f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.component = e, this.shaderKey = `fft_${e}`;
  }
  getUserCode() {
    return `
    fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {
      ${this.component === "real" ? "return real * expR - imag * expI;" : "return real * expI + imag * expR;"}
    }

    fn mulMatDFT(batch: i32, index: i32) -> f32 {
      let indexRatio = f32(index) / f32(uniforms.realShape[1]);
      let exponentMultiplierTimesIndexRatio =
          uniforms.exponentMultiplier * indexRatio;

      var result = 0.0;

      for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {
        // x = (-2|2 * PI / N) * index * i;
        let x = exponentMultiplierTimesIndexRatio * f32(i);
        let expR = cos(x);
        let expI = sin(x);
        let real = getReal(batch, i);
        let imag = getImag(batch, i);

        result = result +
            unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;
      }

      return result;
    }

    ${Q("index")} {
      if (index < uniforms.size) {
        let coords = getOutputCoords();
        setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));
      }
    }
  `;
  }
};
function ux(r, e, t10) {
  let o = t10.tensorMap.get(r.dataId), n = y.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = [], p = me({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } });
  i.push(p);
  let u = p.shape, c = new jl("real", u), l = new jl("imag", u), m = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: u }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: u }], d = e ? 2 * Math.PI : -2 * Math.PI, f = e ? u[1] : 1, h = [{ type: "float32", data: [d] }, { type: "float32", data: [f] }], g = t10.runWebGPUProgram(c, m, "float32", h);
  i.push(g);
  let x = t10.runWebGPUProgram(l, m, "float32", h);
  i.push(x);
  let b = po({ inputs: { real: g, imag: x }, backend: t10 });
  i.push(b);
  let C = me({ inputs: { x: b }, backend: t10, attrs: { shape: r.shape } });
  return i.forEach((w) => t10.disposeData(w.dataId)), C;
}
function Doe(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e;
  return ux(o, false, t10);
}
var TL = { kernelName: ui, backendName: "webgpu", kernelFunc: Doe };
var px = class {
  constructor(e) {
    this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "flipLeftRight";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let coordX = uniforms.xShape[2] - coords[2] - 1;
          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
          setOutputAtIndex(index, outputValue);
        }
      }
    `;
  }
};
var _L = { kernelName: rn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
  let { image: t10 } = r, o = e, n = new px(t10.shape);
  return o.runWebGPUProgram(n, [t10], t10.dtype);
} };
var Foe = xe({ opType: X.FLOOR, cpuKernelImpl: eM });
var $L = { kernelName: on, backendName: "webgpu", kernelFunc: Foe };
var Ooe = Ze({ opType: ge.INT_DIV, dtype: "int32" });
var EL = { kernelName: nn, backendName: "webgpu", kernelFunc: Ooe };
var cx = class {
  constructor(e, t10, o = false) {
    this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workgroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [t10, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${this.importVideo}`;
  }
  getUserCode() {
    let e = this.importVideo ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
    return `
      @binding(1) @group(0) var src: ${this.importVideo ? "texture_external" : "texture_2d<f32>"};
      ${Q("index")} {
        let flatIndex = index * uniforms.numChannels;
        if (flatIndex < uniforms.size) {
          let coords = getCoordsFromIndex(flatIndex);
          let values = ${e};
          for (var i = 0; i < uniforms.numChannels; i = i + 1) {
            result[flatIndex + i] = i32(floor(255.0 * values[i]));
          }
        }
      }
  `;
  }
};
var AL = { kernelName: Zi, backendName: "webgpu", kernelFunc: Poe };
var Ac;
var kI = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
var lx = /* @__PURE__ */ new Map();
function Poe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { pixels: n } = e, { numChannels: s } = o;
  if (n == null)
    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
  let a = typeof HTMLVideoElement != "undefined" && n instanceof HTMLVideoElement, i = typeof HTMLImageElement != "undefined" && n instanceof HTMLImageElement, p = typeof HTMLCanvasElement != "undefined" && n instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && n instanceof OffscreenCanvas, u = typeof ImageBitmap != "undefined" && n instanceof ImageBitmap, [c, l] = a ? [n.videoWidth, n.videoHeight] : [n.width, n.height], m = [l, c, s], d = false, f = a || i;
  if (u || p || f) {
    let b;
    if (d) {
      let D = n;
      if (!lx.has(D) || lx.get(D).expired) {
        let P = { source: D };
        lx.set(D, t10.device.importExternalTexture(P));
      }
      b = { width: c, height: l, format: null, usage: null, texture: lx.get(D) };
    } else {
      if (f) {
        let L = O().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
        (Ac == null || L !== kI) && (kI = L, Ac = document.createElement("canvas").getContext("2d", { willReadFrequently: kI })), Ac.canvas.width = c, Ac.canvas.height = l, Ac.drawImage(n, 0, 0, c, l), n = Ac.canvas;
      }
      let D = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, P = "rgba8unorm", M = t10.textureManager.acquireTexture(m[1], m[0], P, D);
      t10.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = { width: c, height: l, format: P, usage: D, texture: M };
    }
    let C = y.sizeFromShape(m), w = y.computeStrides(m), k = new cx(m, s, d), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...w] }], E = t10.makeTensorInfo([l, c], "int32"), A = t10.tensorMap.get(E.dataId);
    A.resourceInfo = b;
    let R = t10.runWebGPUProgram(k, [E], "int32", _);
    return t10.disposeData(E.dataId), R;
  }
  let h = n.data, g = h;
  if (s != null && s !== 4) {
    g = new Uint8Array(n.width * n.height * s);
    let b = h.length, C = 0;
    for (let w = 0; w < b; w++)
      w % 4 < s && (g[C++] = h[w]);
  }
  let x = t10.makeTensorInfo(m, "int32", new Int32Array(g));
  return t10.uploadToGPU(x.dataId), x;
}
var mx = class {
  constructor(e, t10, o, n, s) {
    this.uniforms = "varianceEpsilon : f32,", this.workgroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t10), S.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), n != null && (S.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("scale")), this.offsetShape = n, this.scaleShape = s, this.shaderKey = "batchNorm";
  }
  getUserCode() {
    let e = "0.0";
    this.offsetShape != null && (e = "getOffsetByOutputIndex(index)");
    let t10 = "1.0";
    return this.scaleShape != null && (t10 = "getScaleByOutputIndex(index)"), `
      ${Q("index")} {
        if (index < uniforms.size)
        {
          let xValue = getXByOutputIndex(index);
          let meanValue = getMeanByOutputIndex(index);
          let varianValue = getVarianceByOutputIndex(index);
          let offsetValue = ${e};
          let scaleValue = ${t10};
          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
        }
      }
  `;
  }
};
var RL = { kernelName: sn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o, scale: n, offset: s, mean: a, variance: i } = r, { varianceEpsilon: p } = e, u = t10, c = [o, a, i], l = null;
  s != null && (l = s.shape, c.push(s));
  let m = null;
  n != null && (m = n.shape, c.push(n));
  let d = new mx(o.shape, a.shape, i.shape, l, m), f = [{ type: "float32", data: [p] }];
  return u.runWebGPUProgram(d, c, o.dtype, f);
} };
function Moe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m, activation: d, leakyreluAlpha: f } = o, h = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h);
  return Qg({ x: n, filter: s, convInfo: g, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: f, activation: d });
}
var DL = { kernelName: go, backendName: "webgpu", kernelFunc: Moe };
function Loe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s, bias: a, preluActivationWeights: i } = e, { strides: p, pad: u, dilations: c, dimRoundingMode: l, activation: m, leakyreluAlpha: d } = o, f = c;
  f == null && (f = [1, 1]), y.assert(S.eitherStridesOrDilationsAreOne(p, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);
  let h = S.computeConv2DInfo(n.shape, s.shape, p, f, u, l, true), g = [n, s], x = a != null, b = i != null;
  x && g.push(a), b && g.push(i);
  let C = [{ type: "int32", data: [h.padInfo.top, h.padInfo.left] }, { type: "int32", data: [h.inHeight, h.inWidth] }], w;
  return h.outHeight > 4 && h.outWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? w = new $c(h, x, m, b) : (w = new Ec(h, x, m, b), C.push({ type: "int32", data: [h.filterHeight] }, { type: "int32", data: [h.filterWidth] }, { type: "int32", data: [h.strideHeight, h.strideWidth] }, { type: "int32", data: [h.dilationHeight, h.dilationWidth] })), m === "leakyrelu" && (C.push({ type: "float32", data: [d] }), w.uniforms += " alpha : f32,"), t10.runWebGPUProgram(w, g, "float32", C);
}
var FL = { kernelName: xo, backendName: "webgpu", kernelFunc: Loe };
var dx = class {
  constructor(e, t10) {
    this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Rt(e)},`;
  }
  getUserCode() {
    let e;
    return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", `
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          var flattenIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexTemp = i32(round(getIndices(coords[0], j)));
            let strideNum = ${e};
            flattenIndex = flattenIndex + indexTemp * strideNum;
          }

          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
        }
      }
      `;
  }
};
function Boe(r) {
  let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = y.sizeFromShape(o.shape), [p, u, c, l] = S.prepareAndValidate(o, n), m = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), d = me({ inputs: { x: o }, backend: t10, attrs: { shape: [y.sizeFromShape(o.shape) / c, c] } });
  if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
    let b = t10.readSync(n.dataId), C = t10.bufferSync(o), w = tM(b, C, o.dtype, u, a, c, l, o.shape, i);
    return t10.makeTensorInfo(p, o.dtype, w.values);
  }
  let f = new dx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t10.runWebGPUProgram(f, [d, m], d.dtype, h), x = me({ inputs: { x: g }, backend: t10, attrs: { shape: p } });
  return t10.disposeData(m.dataId), t10.disposeData(d.dataId), t10.disposeData(g.dataId), x;
}
var OL = { kernelName: an, backendName: "webgpu", kernelFunc: Boe };
var fx = class {
  constructor(e, t10) {
    this.variableNames = ["A", "indices"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "gather";
  }
  getUserCode() {
    let e = Voe(this.aShape);
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let indexZ = i32(getIndices(resRC.x, resRC.z));
          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
          setOutputAtIndex(index, inBounds * getA(${e}));
        }
      }
    `;
  }
};
function Voe(r) {
  let e = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], t10 = [];
  for (let o = 0; o < r.length; o++)
    o === 2 ? t10.push("indexZ") : t10.push(`${e[o]}`);
  return t10.join();
}
function NI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = y.parseAxisParam(a, n.shape)[0], u = S.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = y.sizeFromShape(s.shape), l = [], m = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), d = me({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } });
  l.push(m), l.push(d);
  let f = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize];
  if (t10.shouldExecuteOnCPU([n, s])) {
    let C = t10.tensorMap.get(d.dataId).values, w = le(d.shape, d.dtype, C), _ = t10.tensorMap.get(m.dataId).values, E = le(m.shape, m.dtype, _), A = rM(E, w, f);
    return l.forEach((R) => t10.disposeData(R.dataId)), t10.makeTensorInfo(u.outputShape, A.dtype, A.values);
  }
  let h = new fx(m.shape, f), g = t10.runWebGPUProgram(h, [m, d], m.dtype);
  l.push(g);
  let x = me({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } });
  return l.forEach((b) => t10.disposeData(b.dataId)), x;
}
var PL = { kernelName: Is, backendName: "webgpu", kernelFunc: NI };
var zoe = Ze({ opType: ge.GREATER, cpuKernelImpl: nM, dtype: "bool" });
var ML = { kernelName: un, backendName: "webgpu", kernelFunc: zoe };
var Woe = Ze({ opType: ge.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: oM });
var LL = { kernelName: pn, backendName: "webgpu", kernelFunc: Woe };
function Uoe(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e;
  return ux(o, true, t10);
}
var BL = { kernelName: pi, backendName: "webgpu", kernelFunc: Uoe };
var Goe = xe({ opType: X.IS_FINITE, dtype: "bool" });
var VL = { kernelName: ha, backendName: "webgpu", kernelFunc: Goe };
var Hoe = xe({ opType: X.IS_INF, dtype: "bool" });
var zL = { kernelName: ga, backendName: "webgpu", kernelFunc: Hoe };
var Koe = xe({ opType: X.IS_NAN, dtype: "bool" });
var WL = { kernelName: cn, backendName: "webgpu", kernelFunc: Koe };
function qoe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Hr(n.shape, X.LEAKYRELU, "alpha : f32,");
  return t10.runWebGPUProgram(i, [n], "float32", a);
}
var UL = { kernelName: ln, backendName: "webgpu", kernelFunc: qoe };
var joe = Ze({ opType: ge.LESS, dtype: "bool", cpuKernelImpl: aM });
var GL = { kernelName: mn, backendName: "webgpu", kernelFunc: joe };
var Xoe = Ze({ opType: ge.LESS_EQUAL, dtype: "bool", cpuKernelImpl: sM });
var HL = { kernelName: dn, backendName: "webgpu", kernelFunc: Xoe };
var hx = class {
  constructor(e) {
    this.variableNames = [], this.outputShape = [], this.uniforms = "start : f32, step : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "linSpace";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);
        }
      }
    `;
  }
};
function Yoe(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = (n - o) / (s - 1), i = new hx(s), p = [{ type: "float32", data: [o] }, { type: "float32", data: [a] }];
  return e.runWebGPUProgram(i, [], "float32", p);
}
var KL = { kernelName: li, backendName: "webgpu", kernelFunc: Yoe };
var Qoe = xe({ opType: X.LOG, cpuKernelImpl: iM });
var qL = { kernelName: fn, backendName: "webgpu", kernelFunc: Qoe };
var Zoe = xe({ opType: X.LOG1P });
var jL = { kernelName: xa, backendName: "webgpu", kernelFunc: Zoe };
var Joe = Ze({ opType: ge.LOGICAL_AND, dtype: "bool" });
var XL = { kernelName: hn, backendName: "webgpu", kernelFunc: Joe };
var ene = xe({ opType: X.LOGICAL_NOT });
var YL = { kernelName: gn, backendName: "webgpu", kernelFunc: ene };
var tne = Ze({ opType: ge.LOGICAL_OR });
var QL = { kernelName: xn, backendName: "webgpu", kernelFunc: tne };
var ZL = `
  var powValue = 0.0;
  let basis = uniforms.bias + uniforms.alpha * sum;
  if (uniforms.beta == 0.5) {
    powValue = inverseSqrt(basis);
  } else if (uniforms.beta == 1.0) {
    powValue = 1.0 / basis;
  } else {
    powValue = exp(log(basis) * (-uniforms.beta));
  }
`;
var gx = class {
  constructor(e) {
    this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "lrn";
  }
  getUserCode() {
    return `
    ${Q("index")} {
      if (index < uniforms.size) {
        let coords = getOutputCoords();
        let b = coords[0];
        let r = coords[1];
        let c = coords[2];
        let d = coords[3];

        let x = getX(b, r, c, d);
        var sum = 0.0;
        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {
          let idx = d + i;
          if (idx >= 0 && idx < uniforms.xShape[3]) {
            let z = getX(b, r, c, idx);
            sum = sum + z * z;
          }
        }
        ${ZL}

        setOutputAtIndex(index, x * powValue);
      }
    }
  `;
  }
};
var xx = class {
  constructor(e, t10) {
    this.outputShape = [], this.variableNames = ["x"], this.uniforms = "radius : i32, bias : f32, alpha : f32, beta : f32,", this.workgroupSize = [256, 1, 1], this.maxAllowRadius = 16, y.assert(t10 <= this.maxAllowRadius, () => `Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t10}`), this.outputShape = e, this.elementsPerWorkgroup = this.workgroupSize[0] - 2 * this.maxAllowRadius, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = Y(this.dispatchLayout, this.outputShape, [this.elementsPerWorkgroup, this.workgroupSize[1], this.workgroupSize[2]]), this.shaderKey = "lrn_shared";
  }
  getUserCode() {
    return `
    var <workgroup>lrnSub: array<f32, ${this.workgroupSize[0]}>;
    const elementsPerWorkgroup = ${this.elementsPerWorkgroup};
    const maxAllowRadius = ${this.maxAllowRadius};

    ${Q()} {
      let localDepth = i32(localId.x);
      let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;
      let xDepth = workgroupDepth + localDepth - maxAllowRadius;
      let b = i32(globalId.z) / uniforms.xShape[1];
      let r = i32(globalId.z) - b * uniforms.xShape[1];
      let c = i32(globalId.y);
      let d = workgroupDepth + localDepth;

      var x = 0.0;
      if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {
        x = getX(b, r, c, xDepth);
      }
      lrnSub[localDepth] = x;
      workgroupBarrier();

      if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {
        var sum = 0.0;
        let index = localDepth + maxAllowRadius;
        for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {
          let z = lrnSub[index + i];
          sum = sum + z * z;
        }
        ${ZL}

        setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);
      }
    } `;
  }
};
function rne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u;
  s > 16 ? u = new gx(n.shape) : u = new xx(n.shape, s);
  let c = [{ type: "int32", data: [s] }, { type: "float32", data: [a] }, { type: "float32", data: [i] }, { type: "float32", data: [p] }];
  return t10.runWebGPUProgram(u, [n], n.dtype, c);
}
var JL = { kernelName: mi, backendName: "webgpu", kernelFunc: rne };
var one = Ze({ opType: ge.MAX, cpuKernelImpl: pM });
var eB = { kernelName: bn, backendName: "webgpu", kernelFunc: one };
function nne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = S.computePool2DInfo(n.shape, s, a, u, i, p);
  return zg(n, c, "max", t10);
}
var tB = { kernelName: Cn, backendName: "webgpu", kernelFunc: nne };
function sne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return Kr(n, s, a, "min", t10);
}
var rB = { kernelName: wn, backendName: "webgpu", kernelFunc: sne };
var ane = Ze({ opType: ge.MIN, cpuKernelImpl: cM });
var oB = { kernelName: In, backendName: "webgpu", kernelFunc: ane };
var yx = class {
  constructor(e, t10, o) {
    this.uniforms = "", this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.xShape = e, t10.map((n, s) => {
      this.uniforms += ` pad${s} : vec2<i32>,`;
    }), this.offset = o === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${o}`;
  }
  getUserCode() {
    let e = this.xShape.length, t10 = this.xShape.map((u, c) => `uniforms.pad${c}[0]`).join(","), o = this.xShape.map((u, c) => `uniforms.pad${c}[0] + uniforms.xShape${e > 1 ? `[${c}]` : ""}`).join(","), n = e === 1 ? "start" : "start[i]", s = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Rt(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let start = ${i}(${t10});
          let end = ${i}(${o});
          var outC = getCoordsFromIndex(index);
          for (var i = 0; i < ${e}; i = i + 1) {
            if (${a} < ${n}) {
              ${a} = ${n} * 2 - ${a} - ${this.offset};
            } else if(${a} >= ${s}) {
              ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};
            }
          }
          let coords = outC - start;
          setOutputAtIndex(index, getX(${p}));
        }
      }
    `;
  }
};
var nB = { kernelName: vn, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o } = r, { paddings: n, mode: s } = e, a = t10, i = n.map((c) => ({ type: "int32", data: [c[0], c[1]] })), p = new yx(o.shape, n, s);
  return a.runWebGPUProgram(p, [o], o.dtype, i);
} };
var ine = Ze({ opType: ge.MOD });
var sB = { kernelName: ya, backendName: "webgpu", kernelFunc: ine };
function une(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (t10.shouldExecuteOnCPU([o])) {
    let s = t10.tensorMap.get(o.dataId), [a, i] = mM(s.values, o.shape, o.dtype);
    return t10.makeTensorInfo(i, o.dtype, a);
  }
  let n = new Hr(o.shape, X.NEG);
  return t10.runWebGPUProgram(n, [o], o.dtype);
}
var aB = { kernelName: vs, backendName: "webgpu", kernelFunc: une };
function pne(r) {
  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o, u = t10.readSync(n.dataId), c = t10.readSync(s.dataId), { selectedIndices: l } = Vt.nonMaxSuppressionV3Impl(u, c, a, i, p);
  return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var iB = { kernelName: Tn, backendName: "webgpu", kernelFunc: pne };
function cne(r) {
  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, d = i, f = p, h = u, { selectedIndices: g, selectedScores: x } = Vt.nonMaxSuppressionV5Impl(c, l, m, d, f, h);
  return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([x.length], "float32", new Float32Array(x))];
}
var uB = { kernelName: _n, backendName: "webgpu", kernelFunc: cne };
var bx = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.uniforms = "onValue : f32, offValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e, t10], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "onehot";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if(index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,
                                      f32(i32(round(getX(coords.x))) == coords.y)));
        }
      }
    `;
  }
};
function lne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = y.sizeFromShape(n.shape), c = new bx(u, a), l = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = [{ type: "float32", data: [i] }, { type: "float32", data: [p] }], d = t10.runWebGPUProgram(c, [l], s, m);
  t10.disposeData(l.dataId);
  let f = [...n.shape, a], h = me({ inputs: { x: d }, backend: t10, attrs: { shape: f } });
  return t10.disposeData(d.dataId), h;
}
var pB = { kernelName: $n, backendName: "webgpu", kernelFunc: lne };
function Xl(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "complex64") {
    let n = Xa({ inputs: { input: o }, backend: t10 }), s = Xl({ inputs: { x: n }, backend: t10 }), a = Qu({ inputs: { input: o }, backend: t10 }), i = Xl({ inputs: { x: a }, backend: t10 }), p = po({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p;
  } else
    return fr({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 });
}
var cB = { kernelName: Os, backendName: "webgpu", kernelFunc: Xl };
function lB(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "string")
    throw new Error("onesLike is not supported under string dtype");
  if (o.dtype === "complex64") {
    let n = Xa({ inputs: { input: o }, backend: t10 }), s = lB({ inputs: { x: n }, backend: t10 }), a = Qu({ inputs: { input: o }, backend: t10 }), i = Xl({ inputs: { x: a }, backend: t10 }), p = po({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeData(n.dataId), t10.disposeData(s.dataId), t10.disposeData(a.dataId), t10.disposeData(i.dataId), p;
  } else
    return fr({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 });
}
var mB = { kernelName: ks, backendName: "webgpu", kernelFunc: lB };
function mne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return ix({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    y.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), y.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = ix({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = wI({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeData(c.dataId)), u;
}
var dB = { kernelName: Ns, backendName: "webgpu", kernelFunc: mne };
var Cx = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), t10.map((o, n) => {
      this.uniforms += ` pad${n} : vec2<i32>,`;
    }), this.xShape = e, this.shaderKey = "pad";
  }
  getUserCode() {
    let e = this.xShape.length, t10 = Rt(e), o = this.xShape.map((l, m) => `uniforms.pad${m}[0]`).join(","), n = this.xShape.map((l, m) => `uniforms.pad${m}[0] + uniforms.xShape${e > 1 ? `[${m}]` : ""}`).join(","), s = e > 1 ? `${t10}(${o})` : `${o}`, a = e > 1 ? `${t10}(${n})` : `${n}`, i = e > 1 ? "any(outC < start)" : "outC < start", p = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let start = ${s};
          let end = ${a};
          let outC = getCoordsFromIndex(index);

          if (${i} || ${p}) {
            setOutputAtIndex(index, uniforms.constantValue);
          } else {
            let coords = outC - start;
            setOutputAtIndex(index, getX(${u}));
          }
        }
      }
    `;
  }
};
var TI = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
  if (s.every((u) => y.arraysEqual(u, [0, 0])))
    return Dt({ inputs: { x: n }, backend: t10 });
  if (y.sizeFromShape(n.shape) === 0) {
    let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
    return fr({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } });
  }
  let i = [{ type: "float32", data: [a] }];
  s.map((u) => i.push({ type: "int32", data: [u[0], u[1]] }));
  let p = new Cx(n.shape, s);
  return t10.runWebGPUProgram(p, [n], n.dtype, i);
};
var fB = { kernelName: En, backendName: "webgpu", kernelFunc: TI };
var dne = Ze({ opType: ge.POW });
var hB = { kernelName: An, backendName: "webgpu", kernelFunc: dne };
function fne(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = new Yu(ge.PRELU, o.shape, n.shape);
  return t10.runWebGPUProgram(s, [o, n], "float32");
}
var gB = { kernelName: Rn, backendName: "webgpu", kernelFunc: fne };
function hne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return Kr(n, s, a, "prod", t10);
}
var xB = { kernelName: Dn, backendName: "webgpu", kernelFunc: hne };
var gne = (r) => {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = hM(o, n, s, a);
  return e.makeTensorInfo([i.length], a, i);
};
var yB = { kernelName: Ts, backendName: "webgpu", kernelFunc: gne };
var _I = Ze({ opType: ge.DIV });
var bB = { kernelName: Zo, backendName: "webgpu", kernelFunc: _I };
var xne = xe({ opType: X.RECIPROCAL });
var CB = { kernelName: Fn, backendName: "webgpu", kernelFunc: xne };
var yne = xe({ opType: X.RELU });
var SB = { kernelName: On, backendName: "webgpu", kernelFunc: yne };
var bne = xe({ opType: X.RELU6 });
var wB = { kernelName: Ln, backendName: "webgpu", kernelFunc: bne };
var Sx = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "resizeBilinear";
  }
  getUserCode() {
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
          let b = coords[0];
          let d = coords[3];
          let rc = coords.yz;

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

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

          let effectiveInputOverOutputRatioRC =
              effectiveInSize / effectiveOutSize;

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

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

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

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

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

          setOutputAtIndex(index, newValue);
        }
      }
    `;
  }
};
function Cne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, size: a, halfPixelCenters: i } = o, [p, u] = a, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], f = new Sx(n.shape, p, u);
  return t10.runWebGPUProgram(f, [n], "float32", d);
}
var IB = { kernelName: Mn, backendName: "webgpu", kernelFunc: Cne };
var wx = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t10, o, e[3]], this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.halfPixelCenters = n, this.shaderKey = `resizeNearest_${n}`;
  }
  getUserCode() {
    let e;
    return this.halfPixelCenters ? e = "max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))" : e = "vec2<f32>(rc) * effectiveInputOverOutputRatioRC", `
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let b = coords[0];
          let d = coords[3];
          let rc = coords.yz;

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

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

          let effectiveInputOverOutputRatioRC =
              effectiveInSize / effectiveOutSize;

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

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

          setOutputAtIndex(index, newValue);
        }
      }
    `;
  }
};
function Sne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = s && p > 1 ? 1 : 0, l = s && u > 1 ? 1 : 0, d = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], f = new wx(n.shape, p, u, a);
  return t10.runWebGPUProgram(f, [n], n.dtype, d);
}
var vB = { kernelName: Pn, backendName: "webgpu", kernelFunc: Sne };
var Ix = class {
  constructor(e) {
    this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = " axis : vec4<i32>,", this.shaderKey = "reverse";
  }
  getUserCode() {
    return `
      
      // Using uniform variables as judging conditions, so the function has
      // coherent execution within all threads.
      fn getReverseCoords(coords : vec4<i32>) -> vec4<i32> {
        var reverseCoords = coords;
        if (uniforms.axis[0] == 1) {
          reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;
        }
        if (uniforms.axis[1] == 1) {
          reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;
        }
        if (uniforms.axis[2] == 1) {
          reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;
        }
        if (uniforms.axis[3] == 1) {
          reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;
        }

        return reverseCoords;
      }
    
      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let reverseCoords = getReverseCoords(coords);
          setOutputAtIndex(index, getX(reverseCoords[0],
              reverseCoords[1], reverseCoords[2], reverseCoords[3]));
        }
      }
    `;
  }
};
function wne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length;
  if (a === 0)
    return Dt({ inputs: { x: n }, backend: t10 });
  let i = n.shape, p = [1, 1, 1, 1];
  i.forEach((g, x) => {
    let b = x + 4 - a;
    p[b] = g;
  });
  let u = y.parseAxisParam(s, n.shape), c = [0, 0, 0, 0];
  u.forEach((g) => {
    let x = g + 4 - a;
    c[x] = 1;
  });
  let l = [{ type: "int32", data: c }], m = me({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), d = new Ix(p), f = t10.runWebGPUProgram(d, [m], m.dtype, l);
  t10.disposeData(m.dataId);
  let h = me({ inputs: { x: f }, backend: t10, attrs: { shape: i } });
  return t10.disposeData(f.dataId), h;
}
var kB = { kernelName: Bn, backendName: "webgpu", kernelFunc: wne };
var vx = class {
  constructor(e, t10) {
    this.outputShape = [], this.variableNames = ["x"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
          cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t10 == "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 `
        ${Q("index")} {
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
                uniforms.sinRadians;
            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
                uniforms.cosRadians;
            let coordX = i32(round(coordXFloat + uniforms.centerX));
            let coordY = i32(round(coordYFloat + uniforms.centerY));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `;
  }
};
var NB = { kernelName: ts, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new vx(o.shape, s), [u, c] = S.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }];
  return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l);
} };
var Ine = xe({ opType: X.ROUND });
var TB = { kernelName: Vn, backendName: "webgpu", kernelFunc: Ine };
var vne = xe({ opType: X.RSQRT, cpuKernelImpl: gM });
var _B = { kernelName: zn, backendName: "webgpu", kernelFunc: vne };
var Hi = class {
  constructor(e, t10, o, n, s, a, i, p = true) {
    this.variableNames = ["updates", "indices"], this.workgroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = ae(e), this.dispatch = Y(this.dispatchLayout, e, this.workgroupSize), this.sliceDimGreaterThanOne = t10 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;
    let u = Rt(s.length);
    this.uniforms = `sliceDim : i32, strides: ${u}, updatesSize: i32,`, this.updatesRank = n, this.indicesRank = o;
  }
  getUserCode() {
    let e = "";
    this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j");
    let t10 = `getIndices(${e})`, o = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", n = "", s = "";
    this.dispatchLayout.x.length === 1 ? (n = "flattenedIndex", s = `
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
        return index;
      }
      `) : this.dispatchLayout.x.length === 2 && (n = "vec2<i32>(flattenedIndex, coords[1])", s = `
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
        // N.B. |updates| could be a scalar tensor, conceptually representing a
        // 2D tensor with all values equal to that. By design, its size must be
        // the same as |outShape[1]| in one dimension, and |indicesShape[0]|
        // gives the other.
        let sliceSize = uniforms.outShape[1];
        let d0 = index / sliceSize;
        let d1 = index - d0 * sliceSize;
        return vec2<i32>(d0, d1);
      }
      `);
    let i = `getUpdates(${Array.from({ length: this.updatesRank }, (u, c) => `coords[${c}]`).join(", ")})`;
    return `
    ${s}
      ${Q("index")} {
        if (index < uniforms.updatesSize) {
          let coords = getUpdatesCoordsFromFlatIndex(index);
          var flattenedIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexInside = i32(round(${t10}));
            flattenedIndex = flattenedIndex + indexInside * ${o};
          }
          let updateValue =
              ${vc(this.type, false)}(${i});
          let flatIndex = getOutputIndexFromCoords(${n});

          ${this.sumDupeIndices ? Ic("&result[flatIndex]", "updateValue", this.type) : "atomicStore(&result[flatIndex], bitcast<i32>(updateValue));"}
        }
      }`;
  }
};
function kne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n, updates: s } = e, { shape: a } = o, { sliceRank: i, numUpdates: p, sliceSize: u, strides: c, outputSize: l } = S.calculateShapes(s, n, a), m = [l / u, u];
  if (l === 0)
    return t10.makeTensorInfo(a, n.dtype);
  let d = me({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), f = me({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = f.dtype, g = fr({ backend: t10, attrs: { shape: m, value: 0, dtype: h } }), x = y.sizeFromShape(f.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [x] }], C = new Hi(f.shape, i, d.shape.length, f.shape.length, c, m, h), w = t10.runWebGPUProgram(C, [f, d], h, b, g), k = me({ inputs: { x: w }, backend: t10, attrs: { shape: a } });
  return t10.disposeData(d.dataId), t10.disposeData(f.dataId), t10.disposeData(w.dataId), k;
}
var $B = { kernelName: Wn, backendName: "webgpu", kernelFunc: kne };
var kx = class {
  constructor(e, t10) {
    this.outputShape = [], this.variableNames = ["sortedSequence", "values"], this.uniforms = "numInputs : i32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.side = t10, this.shaderKey = `search_sorted_${t10}`;
  }
  getUserCode() {
    return `
      fn findBound(batch: i32, value: f32) -> i32 {
        var left = i32(0);
        var right = uniforms.numInputs;
        while (left < right) {
          var mid = (left + right) / 2;
          if (getSortedSequence(batch, mid) ${this.side === "left" ? "<" : "<="} value) {
            left = mid + 1;
          } else {
            right = mid;
          }
        }
        return right;
      }

      ${Q("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let value = getValuesByOutputIndex(index);
          setOutputAtIndexI32(index, findBound(coords[0], value));
        }
      }
    `;
  }
};
function Nne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new kx([s.shape[0], s.shape[1]], a), p = [{ type: "int32", data: [n.shape[1]] }];
  return t10.runWebGPUProgram(i, [n, s], "int32", p);
}
var EB = { kernelName: fi, backendName: "webgpu", kernelFunc: Nne };
var Nx = class {
  constructor(e, t10, o) {
    this.variableNames = ["c", "a", "b"], this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.cRank = e, this.rank = o, this.shaderKey = "select";
  }
  getUserCode() {
    let e, t10;
    if (this.rank > 4)
      throw Error(`Where for rank ${this.rank} is not yet supported`);
    if (this.rank === 1)
      t10 = "resRC", e = "resRC";
    else {
      let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [], a = [];
      for (let i = 0; i < this.outputShape.length; i++)
        a.push(`${n[i]}`), i < this.cRank && s.push(`${n[i]}`);
      e = s.join(), t10 = a.join();
    }
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let cVal = getC(${e});
          if (cVal >= 1.0) {
            setOutputAtIndex(index, getA(${t10}));
          } else {
            setOutputAtIndex(index, getB(${t10}));
          }
        }
      }
    `;
  }
};
function Tne(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new Nx(o.shape.length, n.shape, n.shape.length);
  return t10.runWebGPUProgram(a, [o, n, s], dt(n.dtype, s.dtype));
}
var AB = { kernelName: $s, backendName: "webgpu", kernelFunc: Tne };
var _ne = xe({ opType: X.SELU });
var RB = { kernelName: Ca, backendName: "webgpu", kernelFunc: _ne };
var $ne = xe({ opType: X.SIGMOID });
var DB = { kernelName: Gn, backendName: "webgpu", kernelFunc: $ne };
var Ene = xe({ opType: X.SIGN });
var FB = { kernelName: wa, backendName: "webgpu", kernelFunc: Ene };
var Ane = xe({ opType: X.SIN });
var OB = { kernelName: Un, backendName: "webgpu", kernelFunc: Ane };
var Rne = xe({ opType: X.SINH });
var PB = { kernelName: Sa, backendName: "webgpu", kernelFunc: Rne };
var $I = Ze({ opType: ge.SUB, cpuKernelImpl: wM, supportsComplex: true });
var MB = { kernelName: Yn, backendName: "webgpu", kernelFunc: $I };
function Dne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = y.parseAxisParam([s], n.shape), i = Hl({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = S.expandShapeToKeepDim(i.shape, a), u = me({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = $I({ inputs: { a: n, b: u }, backend: t10 }), l = vI({ inputs: { x: c }, backend: t10 }), m = ql({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), d = me({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), f = _I({ inputs: { a: l, b: d }, backend: t10 });
  return t10.disposeData(i.dataId), t10.disposeData(u.dataId), t10.disposeData(c.dataId), t10.disposeData(l.dataId), t10.disposeData(m.dataId), t10.disposeData(d.dataId), f;
}
var LB = { kernelName: qn, backendName: "webgpu", kernelFunc: Dne };
var Fne = xe({ opType: X.SOFTPLUS });
var BB = { kernelName: Ia, backendName: "webgpu", kernelFunc: Fne };
var One = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
  y.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
  let i = s.reduce((x, b) => x * b), p = [[0, 0]];
  p.push(...a);
  for (let x = 1 + s.length; x < n.shape.length; ++x)
    p.push([0, 0]);
  let u = [], c = TI({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = S.getReshaped(c.shape, s, i, false), m = S.getPermuted(l.length, s.length, false), d = S.getReshapedPermuted(c.shape, s, i, false), f = me({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = Nr({ inputs: { x: f }, backend: t10, attrs: { perm: m } }), g = me({ inputs: { x: h }, backend: t10, attrs: { shape: d } });
  return u.push(c), u.push(f), u.push(h), u.forEach((x) => t10.disposeData(x.dataId)), g;
};
var VB = { kernelName: As, backendName: "webgpu", kernelFunc: One };
var Tx = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.workgroupSize = [64, 1, 1], this.size = true;
    let o = new Array(e.length);
    for (let n = 0; n < o.length; n++)
      o[n] = e[n] * t10[n];
    this.outputShape = o, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
  }
  getUserCode() {
    let e = Pne(this.rank, "uniforms.");
    return `
      ${Q("index")} {
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `;
  }
};
function Pne(r, e = "") {
  if (r >= 5)
    throw Error(`Tile for rank ${r} is not yet supported`);
  if (r === 1)
    return `(resRC % ${e}aShape)`;
  let t10 = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [];
  for (let n = 0; n < r; n++)
    o.push(`(${t10[n]} % ${e}aShape[${n}])`);
  return o.join();
}
function EI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
  if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string" || n.shape.length >= 5) {
    let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => y.decodeString(m)) : p, c = le(n.shape, n.dtype, u), l = IM(c, s);
    return t10.makeTensorInfo(l.shape, l.dtype, l.values);
  }
  let a = new Tx(n.shape, s);
  return t10.runWebGPUProgram(a, [n], n.dtype);
}
var zB = { kernelName: to, backendName: "webgpu", kernelFunc: EI };
function Mne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false;
  if (s.dtype === "string") {
    let A = t10.bufferSync(n), R = t10.bufferSync(s), D = y.decodeString(t10.readSync(a.dataId)[0]), P = xM(A, R, i, m, c, u, p, l, D, d);
    return t10.makeTensorInfo(i, P.dtype, P.values);
  }
  let f = [m / c, c], h = me({ inputs: { x: n }, backend: t10, attrs: { shape: [u, p] } }), g = s.shape.length ? me({ inputs: { x: s }, backend: t10, attrs: { shape: [u, c] } }) : Dt({ inputs: { x: s }, backend: t10 }), x = g.dtype, b = t10.makeTensorInfo([], x, y.makeZerosTypedArray(1, x)), C = me({ inputs: { x: a }, backend: t10, attrs: { shape: Array(f.length).fill(1) } }), w = EI({ inputs: { x: C }, backend: t10, attrs: { reps: f } }), k = y.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }];
  switch (u) {
    case 0:
      break;
    case 1:
      {
        let A = new Hi([u, c], p, h.shape.length, g.shape.length, l, f, x, d);
        t10.runWebGPUProgram(A, [g, h], x, _, w);
      }
      break;
    default:
      {
        let A = new Hi([u, c], p, h.shape.length, b.shape.length, l, f, x, d);
        t10.runWebGPUProgram(A, [b, h], x, _, w);
      }
      {
        let A = new Hi([u, c], p, h.shape.length, g.shape.length, l, f, x);
        t10.runWebGPUProgram(A, [g, h], x, _, w);
      }
  }
  let E = me({ inputs: { x: w }, backend: t10, attrs: { shape: i } });
  return t10.disposeData(h.dataId), t10.disposeData(g.dataId), t10.disposeData(C.dataId), t10.disposeData(b.dataId), t10.disposeData(w.dataId), E;
}
var WB = { kernelName: yi, backendName: "webgpu", kernelFunc: Mne };
function Lne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = y.parseAxisParam(a, n.shape)[0], p = S.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice();
  return p.map((m) => {
    let d = [...l];
    d[i] = m;
    let f = fs({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: d } });
    return c[i] += m, f;
  });
}
var UB = { kernelName: Rs, backendName: "webgpu", kernelFunc: Lne };
var Bne = xe({ opType: X.SQRT });
var GB = { kernelName: Hn, backendName: "webgpu", kernelFunc: Bne };
var HB = { kernelName: bi, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
  let { x: t10 } = r, o = e, n = new Hr(t10.shape, X.SQUARE);
  return o.runWebGPUProgram(n, [t10], t10.dtype);
} };
var Vne = Ze({ opType: ge.SQUARED_DIFFERENCE });
var KB = { kernelName: jn, backendName: "webgpu", kernelFunc: Vne };
function zne({ inputs: r, attrs: e, backend: t10 }) {
  let { x: o } = r, n = new Hr(o.shape, X.STEP, "stepAlpha : f32,"), s = [{ type: "float32", data: [e.alpha] }];
  return t10.runWebGPUProgram(n, [o], o.dtype, s);
}
var qB = { kernelName: fo, backendName: "webgpu", kernelFunc: zne };
var _x = class {
  constructor(e) {
    this.variableNames = ["x"], this.workPerThread = 1, this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize, [this.workPerThread, 1, 1]);
    let t10 = Rt(this.outputShape.length);
    this.uniforms = `begin : ${t10},  strides : ${t10}, `, this.shaderKey = "stridedSlice";
  }
  getUserCode() {
    let e = this.outputShape.length, t10 = "";
    if (e === 1)
      t10 = "coords * uniforms.strides + uniforms.begin";
    else {
      let n = 0;
      t10 = this.outputShape.map((s, a) => (n++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${n - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",");
    }
    return `
       ${Q("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           setOutputAtIndex(index, getX(${t10}));
         }
       }
     `;
  }
};
function Wne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, end: a, strides: i, beginMask: p, endMask: u, ellipsisMask: c, newAxisMask: l, shrinkAxisMask: m } = o, { finalShapeSparse: d, finalShape: f, isIdentity: h, sliceDim0: g, isSimpleSlice: x, begin: b, end: C, strides: w } = ut.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = me({ inputs: { x: n }, backend: t10, attrs: { shape: f } });
  else if (g || x) {
    y.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let _ = ut.computeOutShape(b, C, w), E = fs({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } });
    k = me({ inputs: { x: E }, backend: t10, attrs: { shape: f } }), t10.disposeData(E.dataId);
  } else if (t10.shouldExecuteOnCPU([n])) {
    let E = t10.readSync(n.dataId), A = le(n.shape, n.dtype, E), R = CM(d, A, w, b);
    k = t10.makeTensorInfo(f, n.dtype, R.values);
  } else {
    let E = new _x(d), A = [{ type: "int32", data: b }, { type: "int32", data: w }], R = t10.runWebGPUProgram(E, [n], n.dtype, A);
    k = me({ inputs: { x: R }, backend: t10, attrs: { shape: f } }), t10.disposeData(R.dataId);
  }
  return k;
}
var jB = { kernelName: Xn, backendName: "webgpu", kernelFunc: Wne };
function Une(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { separator: n, nGramWidths: s, leftPad: a, rightPad: i, padWidth: p, preserveShortSequences: u } = o, { data: c, dataSplits: l } = e, m = t10.readSync(c.dataId), d = t10.readSync(l.dataId), [f, h] = SM(m, d, n, s, a, i, p, u);
  return [t10.makeTensorInfo([f.length], "string", f), t10.makeTensorInfo(l.shape, "int32", h)];
}
var XB = { kernelName: Ds, backendName: "webgpu", kernelFunc: Une };
var Gne = xe({ opType: X.TAN });
var YB = { kernelName: Qn, backendName: "webgpu", kernelFunc: Gne };
var Hne = xe({ opType: X.TANH });
var QB = { kernelName: Zn, backendName: "webgpu", kernelFunc: Hne };
var $x = class {
  constructor(e) {
    this.variableNames = ["x", "indices"], this.workgroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
        dir : i32, inc : i32,`, this.shaderKey = "swap";
  }
  getUserCode() {
    return `
        ${Q("index")} {
          if (index < uniforms.size) {
            let outC = getCoordsFromIndex(index);
            let batch = outC[0];
            let elemIdx = outC[1];
            // We compare elements pair-wise within a group of size 2 * inc.
            // The comparing rule for each group alternates between ascending
            // and descending. Within each group, we compare each pair at
            // positions i and i+inc. To decide whether an element at position i
            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
            // inc, it is in the first half of the group, we denote it as x0,
            // otherwise we denote it as x1.
            // For example, as shown in the Bitonic top K paper referenced
            // above, Figure5(a) shows that element[1] is in the second half of
            // the group when group size is 2, but it is in the first half of
            // the group when group size is 4.
            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
            var i = 0;
            if (isFirstInPair) {
              i = elemIdx;
            } else {
              i = elemIdx - uniforms.inc;
            }

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

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

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

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

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

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

            if (x0 >= x1) {
              setOutputAtIndex(index, f32(i0));
            } else {
              setOutputAtIndex(index, f32(i1));
            }
          }
        }
      `;
  }
};
function Rc(r, e) {
  e !== null && r.disposeData(e.dataId);
}
function ZB(r) {
  let e = 1;
  for (; e < r; )
    e *= 2;
  return e;
}
function Kne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = n.shape, p = i[i.length - 1];
  if (t10.shouldExecuteOnCPU([n])) {
    let k = t10.readSync(n.dataId), [_, E] = vM(k, i, n.dtype, s, a);
    return [t10.makeTensorInfo(_.shape, _.dtype, _.values), t10.makeTensorInfo(E.shape, E.dtype, E.values)];
  }
  if (s === 0)
    return i[i.length - 1] = 0, [t10.makeTensorInfo(i, n.dtype, []), t10.makeTensorInfo(i, "int32", [])];
  if (p === 1)
    return [n, fr({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t10 })];
  let c = y.sizeFromShape(i) / p, l = me({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t10 }), m = ZB(s), d = ZB(p), f = null, h = () => f === null ? [l, l] : [l, f], g = (k, _, E) => {
    let A = h(), R = new $x(E), P = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = f;
    f = t10.runWebGPUProgram(R, A, "int32", P), Rc(t10, M);
  };
  for (let k = 1; k < m; k *= 2) {
    let _ = k * 2;
    for (let E = k; E >= 1; E /= 2)
      g(_, E, [c, d]);
  }
  for (let k = d; k > m; k /= 2) {
    let _ = h(), E = new Ex([c, k / 2]), R = [{ type: "int32", data: [p] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [m] }], D = f;
    f = t10.runWebGPUProgram(E, _, "int32", R), Rc(t10, D);
    let P = m / 2, M = P * 2;
    for (let L = P; L >= 1; L /= 2)
      g(M, L, f.shape);
  }
  let x = f;
  f = fs({ inputs: { x: f }, backend: t10, attrs: { begin: 0, size: [c, s] } }), Rc(t10, x);
  let b = NI({ inputs: { x: l, indices: f }, backend: t10, attrs: { axis: 1, batchDims: 1 } });
  Rc(t10, l);
  let C = i.slice(0, -1);
  C.push(s), x = f, f = me({ inputs: { x: f }, attrs: { shape: C }, backend: t10 }), Rc(t10, x);
  let w = b;
  return b = me({ inputs: { x: b }, attrs: { shape: C }, backend: t10 }), Rc(t10, w), [b, f];
}
var JB = { kernelName: Jn, backendName: "webgpu", kernelFunc: Kne };
var Ax = class {
  constructor(e) {
    this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workgroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = ae(this.outputShape), this.dispatch = Y(this.dispatchLayout, this.outputShape, this.workgroupSize), this.shaderKey = "transform";
  }
  getUserCode() {
    return `
          fn mapCoord(outCoord : f32, len : f32) -> f32{
            var inCoord = outCoord;
            if(uniforms.fillModeId == 2) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  if (inCoord < sz2) {
                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
                    inCoord;
                  }
                  if (inCoord < -len) {
                    inCoord = inCoord + sz2;
                  } else {
                    inCoord = -inCoord - 1.0;
                  }
                }
              } else if (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz2 = 2.0 * len;
                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
                  if (inCoord >= len) {
                    inCoord = sz2 - inCoord - 1.0;
                  }
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } else if (uniforms.fillModeId == 3) {
              if (inCoord < 0.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
                }
              } else if (inCoord > len - 1.0) {
                if (len <= 1.0) {
                  inCoord = 0.0;
                } else {
                  let sz = len - 1.0;
                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
                }
              }
              return clamp(inCoord, 0.0, len - 1.0);
            } else if (uniforms.fillModeId == 4) {
              return clamp(outCoord, 0.0, len - 1.0);
            }
            return outCoord;
          }
          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
            channel : i32) -> f32 {
            var outputValue : f32;
            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
                outputValue = getImage(batch, coordY, coordX, channel);
            } else {
              outputValue = uniforms.fillValue;
            }
            return outputValue;
          }

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

                if (uniforms.interpolationModeId == 1) {
                  let coordY = i32(round(mapY));
                  let coordX = i32(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  let yFloor = floor(mapY);
                  let xFloor = floor(mapX);
                  let yCeil = yFloor + 1.0;
                  let xCeil = xFloor + 1.0;
                  let valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
                  let valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutputAtIndex(index, outputValue);
            }
          }
        `;
  }
};
function qne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, d] = n.shape, [f, h] = u != null ? u : [l, m], g = [c, f, h, d], x = new Ax(g), b = a === "nearest" ? 1 : 2, C;
  switch (i) {
    case "constant":
      C = 1;
      break;
    case "reflect":
      C = 2;
      break;
    case "wrap":
      C = 3;
      break;
    case "nearest":
      C = 4;
      break;
    default:
      C = 1;
      break;
  }
  let w = [{ type: "int32", data: [b] }, { type: "int32", data: [C] }, { type: "float32", data: [p] }];
  return t10.runWebGPUProgram(x, [n, s], "float32", w);
}
var eV = { kernelName: es, backendName: "webgpu", kernelFunc: qne };
function jne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { value: n } = e, { axis: s } = o;
  s < 0 && (s += n.shape.length);
  let a = n, i = a.shape.length, p = n.shape[s], u = new Array(i - 1), c = 0;
  for (let h = 0; h < i; h++)
    h !== s && (u[c++] = a.shape[h]);
  let l = [], m = new Array(i).fill(0), d = a.shape.slice();
  d[s] = 1;
  let f = new Array(p);
  for (let h = 0; h < f.length; h++) {
    m[s] = h;
    let g = fs({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: d } }), x = me({ inputs: { x: g }, backend: t10, attrs: { shape: u } });
    f[h] = x, l.push(g);
  }
  return l.forEach((h) => t10.disposeData(h.dataId)), f;
}
var tV = { kernelName: Fs, backendName: "webgpu", kernelFunc: jne };
var Xne = [G3, NM, TM, _M, $M, EM, RM, DM, FM, OM, PM, MM, LM, BM, VM, UM, GM, HM, qM, jM, ZM, JM, eL, K3, rL, oL, nL, sL, aL, iL, uL, lL, mL, dL, fL, hL, gL, xL, CL, SL, wL, IL, vL, kL, NL, TL, W3, _L, AL, $L, EL, RL, DL, FL, OL, PL, ML, LL, H3, BL, tL, VL, zL, WL, UL, GL, HL, KL, jL, qL, XL, YL, QL, JL, zM, eB, tB, WM, rB, oB, nB, sB, yL, aB, iB, uB, XM, pB, mB, dB, fB, hB, gB, xB, yB, YM, bB, CB, SB, wB, U3, IB, vB, kB, NB, TB, _B, $B, EB, AB, RB, DB, FB, OB, PB, KM, qB, jB, XB, LB, BB, VB, WB, UB, GB, HB, KB, MB, bL, YB, QB, zB, JB, eV, AM, tV, cB];
for (let r of Xne)
  ka(r);
var rV = "4.2.0";
var Yne = "4.2.0";
var Qne = "4.2.0";
var Zne = "4.2.0";
var Jne = "4.2.0";
var ese = "0.0.1-alpha.17";
var tse = { tfjs: rV, "tfjs-core": rV, "tfjs-converter": Yne, "tfjs-backend-cpu": Qne, "tfjs-backend-webgl": Zne, "tfjs-backend-wasm": Jne, "tfjs-backend-webgpu": ese };

// src/util/util.ts
function log(...msg) {
  const dt2 = new Date();
  const ts2 = `${dt2.getHours().toString().padStart(2, "0")}:${dt2.getMinutes().toString().padStart(2, "0")}:${dt2.getSeconds().toString().padStart(2, "0")}.${dt2.getMilliseconds().toString().padStart(3, "0")}`;
  if (msg)
    console.log(ts2, "Human:", ...msg);
}
function join(folder, file) {
  const separator = folder.endsWith("/") ? "" : "/";
  const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
  if (!path.toLocaleLowerCase().includes(".json"))
    throw new Error(`modelpath error: expecting json file: ${path}`);
  return path;
}
var now = () => {
  if (typeof performance !== "undefined")
    return performance.now();
  return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
};
function validate(defaults, config3, parent = "config", msgs = []) {
  for (const key of Object.keys(config3)) {
    if (typeof config3[key] === "object") {
      validate(defaults[key], config3[key], key, msgs);
    } else {
      const defined = defaults && typeof defaults[key] !== "undefined";
      if (!defined)
        msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
      const same = defaults && typeof defaults[key] === typeof config3[key];
      if (defined && !same)
        msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
    }
  }
  if (config3.debug && parent === "config" && msgs.length > 0)
    log("invalid configuration", msgs);
  return msgs;
}
function mergeDeep(...objects) {
  const isObject = (obj) => obj && typeof obj === "object";
  return objects.reduce((prev, obj) => {
    Object.keys(obj || {}).forEach((key) => {
      const pVal = prev[key];
      const oVal = obj[key];
      if (Array.isArray(pVal) && Array.isArray(oVal))
        prev[key] = pVal.concat(...oVal);
      else if (isObject(pVal) && isObject(oVal))
        prev[key] = mergeDeep(pVal, oVal);
      else
        prev[key] = oVal;
    });
    return prev;
  }, {});
}

// src/config.ts
var config = {
  backend: "",
  modelBasePath: "",
  cacheModels: true,
  validateModels: true,
  wasmPath: "",
  wasmPlatformFetch: false,
  debug: false,
  async: true,
  warmup: "full",
  cacheSensitivity: 0.7,
  skipAllowed: false,
  deallocate: false,
  flags: {},
  softwareKernels: false,
  filter: {
    enabled: true,
    equalization: false,
    width: 0,
    height: 0,
    flip: false,
    return: true,
    autoBrightness: true,
    brightness: 0,
    contrast: 0,
    sharpness: 0,
    blur: 0,
    saturation: 0,
    hue: 0,
    negative: false,
    sepia: false,
    vintage: false,
    kodachrome: false,
    technicolor: false,
    polaroid: false,
    pixelate: 0
  },
  gesture: {
    enabled: true
  },
  face: {
    enabled: true,
    detector: {
      modelPath: "blazeface.json",
      rotation: false,
      maxDetected: 1,
      skipFrames: 99,
      skipTime: 2500,
      minConfidence: 0.2,
      iouThreshold: 0.1,
      mask: false,
      return: false
    },
    mesh: {
      enabled: true,
      modelPath: "facemesh.json",
      keepInvalid: false
    },
    attention: {
      enabled: false,
      modelPath: "facemesh-attention.json"
    },
    iris: {
      enabled: true,
      modelPath: "iris.json"
    },
    emotion: {
      enabled: true,
      minConfidence: 0.1,
      skipFrames: 99,
      skipTime: 1500,
      modelPath: "emotion.json"
    },
    description: {
      enabled: true,
      modelPath: "faceres.json",
      skipFrames: 99,
      skipTime: 3e3,
      minConfidence: 0.1
    },
    antispoof: {
      enabled: false,
      skipFrames: 99,
      skipTime: 4e3,
      modelPath: "antispoof.json"
    },
    liveness: {
      enabled: false,
      skipFrames: 99,
      skipTime: 4e3,
      modelPath: "liveness.json"
    }
  },
  body: {
    enabled: true,
    modelPath: "movenet-lightning.json",
    maxDetected: -1,
    minConfidence: 0.3,
    skipFrames: 1,
    skipTime: 200
  },
  hand: {
    enabled: true,
    rotation: true,
    skipFrames: 99,
    skipTime: 1e3,
    minConfidence: 0.5,
    iouThreshold: 0.2,
    maxDetected: -1,
    landmarks: true,
    detector: {
      modelPath: "handtrack.json"
    },
    skeleton: {
      modelPath: "handlandmark-lite.json"
    }
  },
  object: {
    enabled: false,
    modelPath: "centernet.json",
    minConfidence: 0.2,
    iouThreshold: 0.4,
    maxDetected: 10,
    skipFrames: 99,
    skipTime: 2e3
  },
  segmentation: {
    enabled: false,
    modelPath: "rvm.json",
    ratio: 0.5,
    mode: "default"
  }
};

// src/image/imagefxshaders.ts
var vertexIdentity = `
  precision highp float;
  attribute vec2 pos;
  attribute vec2 uv;
  varying vec2 vUv;
  uniform float flipY;
  void main(void) {
    vUv = uv;
    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
  }
`;
var colorMatrixWithAlpha = `
  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];
  }
`;
var colorMatrixWithoutAlpha = `
  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;
  }
`;
var pixelate = `
  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);
  }
`;
var blur = `
  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;
  }
`;
var convolution = `
  precision highp float;
  varying vec2 vUv;
  uniform sampler2D texture;
  uniform vec2 px;
  uniform float m[9];
  void main(void) {
    vec4 c11 = texture2D(texture, vUv - px); // top left
    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
    vec4 c22 = texture2D(texture, vUv); // mid center
    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
    gl_FragColor = 
    c11 * m[0] + c12 * m[1] + c22 * m[2] +
    c21 * m[3] + c22 * m[4] + c23 * m[5] +
    c31 * m[6] + c32 * m[7] + c33 * m[8];
    gl_FragColor.a = c22.a;
  }
`;

// src/image/imagefx.ts
var collect = (source, prefix, collection) => {
  const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
  source.replace(r, (match2, name) => {
    collection[name] = 0;
    return match2;
  });
};
var GLProgram = class {
  constructor(gl2, vertexSource, fragmentSource) {
    __publicField(this, "uniform", {});
    __publicField(this, "attribute", {});
    __publicField(this, "gl");
    __publicField(this, "id");
    __publicField(this, "compile", (source, type) => {
      const shader = this.gl.createShader(type);
      if (!shader) {
        log("filter: could not create shader");
        return null;
      }
      this.gl.shaderSource(shader, source);
      this.gl.compileShader(shader);
      if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
        log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
        return null;
      }
      return shader;
    });
    this.gl = gl2;
    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
    this.id = this.gl.createProgram();
    if (!vertexShader || !fragmentShader)
      return;
    if (!this.id) {
      log("filter: could not create webgl program");
      return;
    }
    this.gl.attachShader(this.id, vertexShader);
    this.gl.attachShader(this.id, fragmentShader);
    this.gl.linkProgram(this.id);
    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
      return;
    }
    this.gl.useProgram(this.id);
    collect(vertexSource, "attribute", this.attribute);
    for (const a in this.attribute)
      this.attribute[a] = this.gl.getAttribLocation(this.id, a);
    collect(vertexSource, "uniform", this.uniform);
    collect(fragmentSource, "uniform", this.uniform);
    for (const u in this.uniform)
      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
  }
};
function GLImageFilter() {
  let drawCount = 0;
  let sourceTexture = null;
  let lastInChain = false;
  let currentFramebufferIndex = -1;
  let tempFramebuffers = [null, null];
  let filterChain = [];
  let vertexBuffer = null;
  let currentProgram = null;
  const fxcanvas = canvas(100, 100);
  const shaderProgramCache = {};
  const DRAW = { INTERMEDIATE: 1 };
  const gl2 = fxcanvas.getContext("webgl");
  if (!gl2) {
    log("filter: cannot get webgl context");
    return;
  }
  this.gl = gl2;
  function resize(width, height) {
    if (width === fxcanvas.width && height === fxcanvas.height)
      return;
    fxcanvas.width = width;
    fxcanvas.height = height;
    if (!vertexBuffer) {
      const vertices = 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]);
      vertexBuffer = gl2.createBuffer();
      gl2.bindBuffer(gl2.ARRAY_BUFFER, vertexBuffer);
      gl2.bufferData(gl2.ARRAY_BUFFER, vertices, gl2.STATIC_DRAW);
      gl2.pixelStorei(gl2.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
    }
    gl2.viewport(0, 0, fxcanvas.width, fxcanvas.height);
    tempFramebuffers = [null, null];
  }
  function createFramebufferTexture(width, height) {
    const fbo = gl2.createFramebuffer();
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, fbo);
    const renderbuffer = gl2.createRenderbuffer();
    gl2.bindRenderbuffer(gl2.RENDERBUFFER, renderbuffer);
    const texture = gl2.createTexture();
    gl2.bindTexture(gl2.TEXTURE_2D, texture);
    gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, width, height, 0, gl2.RGBA, gl2.UNSIGNED_BYTE, null);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.LINEAR);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.LINEAR);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
    gl2.framebufferTexture2D(gl2.FRAMEBUFFER, gl2.COLOR_ATTACHMENT0, gl2.TEXTURE_2D, texture, 0);
    gl2.bindTexture(gl2.TEXTURE_2D, null);
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, null);
    return { fbo, texture };
  }
  function getTempFramebuffer(index2) {
    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
    return tempFramebuffers[index2];
  }
  function draw(flags = 0) {
    if (!currentProgram)
      return;
    let source = null;
    let target = null;
    let flipY = false;
    if (drawCount === 0)
      source = sourceTexture;
    else
      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
    drawCount++;
    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
      target = null;
      flipY = drawCount % 2 === 0;
    } else {
      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
    }
    gl2.bindTexture(gl2.TEXTURE_2D, source);
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, target);
    gl2.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
    gl2.drawArrays(gl2.TRIANGLES, 0, 6);
  }
  function compileShader(fragmentSource) {
    if (shaderProgramCache[fragmentSource]) {
      currentProgram = shaderProgramCache[fragmentSource];
      gl2.useProgram((currentProgram ? currentProgram.id : null) || null);
      return currentProgram;
    }
    currentProgram = new GLProgram(gl2, vertexIdentity, fragmentSource);
    if (!currentProgram) {
      log("filter: could not get webgl program");
      return null;
    }
    const floatSize = Float32Array.BYTES_PER_ELEMENT;
    const vertSize = 4 * floatSize;
    gl2.enableVertexAttribArray(currentProgram.attribute["pos"]);
    gl2.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl2.FLOAT, false, vertSize, 0 * floatSize);
    gl2.enableVertexAttribArray(currentProgram.attribute["uv"]);
    gl2.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl2.FLOAT, false, vertSize, 2 * floatSize);
    shaderProgramCache[fragmentSource] = currentProgram;
    return currentProgram;
  }
  const filter = {
    colorMatrix: (matrix) => {
      const m = new Float32Array(matrix);
      m[4] /= 255;
      m[9] /= 255;
      m[14] /= 255;
      m[19] /= 255;
      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
      const program = compileShader(shader);
      if (!program)
        return;
      gl2.uniform1fv(program.uniform["m"], m);
      draw();
    },
    brightness: (brightness) => {
      const b = (brightness || 0) + 1;
      filter.colorMatrix([
        b,
        0,
        0,
        0,
        0,
        0,
        b,
        0,
        0,
        0,
        0,
        0,
        b,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    saturation: (amount) => {
      const x = (amount || 0) * 2 / 3 + 1;
      const y6 = (x - 1) * -0.5;
      filter.colorMatrix([
        x,
        y6,
        y6,
        0,
        0,
        y6,
        x,
        y6,
        0,
        0,
        y6,
        y6,
        x,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    desaturate: () => {
      filter.saturation(-1);
    },
    contrast: (amount) => {
      const v10 = (amount || 0) + 1;
      const o = -128 * (v10 - 1);
      filter.colorMatrix([
        v10,
        0,
        0,
        0,
        o,
        0,
        v10,
        0,
        0,
        o,
        0,
        0,
        v10,
        0,
        o,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    negative: () => {
      filter.contrast(-2);
    },
    hue: (rotation) => {
      rotation = (rotation || 0) / 180 * Math.PI;
      const cos = Math.cos(rotation);
      const sin = Math.sin(rotation);
      const lumR = 0.213;
      const lumG = 0.715;
      const lumB = 0.072;
      filter.colorMatrix([
        lumR + cos * (1 - lumR) + sin * -lumR,
        lumG + cos * -lumG + sin * -lumG,
        lumB + cos * -lumB + sin * (1 - lumB),
        0,
        0,
        lumR + cos * -lumR + sin * 0.143,
        lumG + cos * (1 - lumG) + sin * 0.14,
        lumB + cos * -lumB + sin * -0.283,
        0,
        0,
        lumR + cos * -lumR + sin * -(1 - lumR),
        lumG + cos * -lumG + sin * lumG,
        lumB + cos * (1 - lumB) + sin * lumB,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    desaturateLuminance: () => {
      filter.colorMatrix([
        0.2764723,
        0.929708,
        0.0938197,
        0,
        -37.1,
        0.2764723,
        0.929708,
        0.0938197,
        0,
        -37.1,
        0.2764723,
        0.929708,
        0.0938197,
        0,
        -37.1,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    sepia: () => {
      filter.colorMatrix([
        0.393,
        0.7689999,
        0.18899999,
        0,
        0,
        0.349,
        0.6859999,
        0.16799999,
        0,
        0,
        0.272,
        0.5339999,
        0.13099999,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    brownie: () => {
      filter.colorMatrix([
        0.5997023498159715,
        0.34553243048391263,
        -0.2708298674538042,
        0,
        47.43192855600873,
        -0.037703249837783157,
        0.8609577587992641,
        0.15059552388459913,
        0,
        -36.96841498319127,
        0.24113635128153335,
        -0.07441037908422492,
        0.44972182064877153,
        0,
        -7.562075277591283,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    vintagePinhole: () => {
      filter.colorMatrix([
        0.6279345635605994,
        0.3202183420819367,
        -0.03965408211312453,
        0,
        9.651285835294123,
        0.02578397704808868,
        0.6441188644374771,
        0.03259127616149294,
        0,
        7.462829176470591,
        0.0466055556782719,
        -0.0851232987247891,
        0.5241648018700465,
        0,
        5.159190588235296,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    kodachrome: () => {
      filter.colorMatrix([
        1.1285582396593525,
        -0.3967382283601348,
        -0.03992559172921793,
        0,
        63.72958762196502,
        -0.16404339962244616,
        1.0835251566291304,
        -0.05498805115633132,
        0,
        24.732407896706203,
        -0.16786010706155763,
        -0.5603416277695248,
        1.6014850761964943,
        0,
        35.62982807460946,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    technicolor: () => {
      filter.colorMatrix([
        1.9125277891456083,
        -0.8545344976951645,
        -0.09155508482755585,
        0,
        11.793603434377337,
        -0.3087833385928097,
        1.7658908555458428,
        -0.10601743074722245,
        0,
        -70.35205161461398,
        -0.231103377548616,
        -0.7501899197440212,
        1.847597816108189,
        0,
        30.950940869491138,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    polaroid: () => {
      filter.colorMatrix([
        1.438,
        -0.062,
        -0.062,
        0,
        0,
        -0.122,
        1.378,
        -0.122,
        0,
        0,
        -0.016,
        -0.016,
        1.483,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    shiftToBGR: () => {
      filter.colorMatrix([
        0,
        0,
        1,
        0,
        0,
        0,
        1,
        0,
        0,
        0,
        1,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    convolution: (matrix) => {
      const m = new Float32Array(matrix);
      const pixelSizeX = 1 / fxcanvas.width;
      const pixelSizeY = 1 / fxcanvas.height;
      const program = compileShader(convolution);
      if (!program)
        return;
      gl2.uniform1fv(program.uniform["m"], m);
      gl2.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
      draw();
    },
    detectEdges: () => {
      filter.convolution.call(this, [
        0,
        1,
        0,
        1,
        -4,
        1,
        0,
        1,
        0
      ]);
    },
    sobelX: () => {
      filter.convolution.call(this, [
        -1,
        0,
        1,
        -2,
        0,
        2,
        -1,
        0,
        1
      ]);
    },
    sobelY: () => {
      filter.convolution.call(this, [
        -1,
        -2,
        -1,
        0,
        0,
        0,
        1,
        2,
        1
      ]);
    },
    sharpen: (amount) => {
      const a = amount || 1;
      filter.convolution.call(this, [
        0,
        -1 * a,
        0,
        -1 * a,
        1 + 4 * a,
        -1 * a,
        0,
        -1 * a,
        0
      ]);
    },
    emboss: (size2) => {
      const s = size2 || 1;
      filter.convolution.call(this, [
        -2 * s,
        -1 * s,
        0,
        -1 * s,
        1,
        1 * s,
        0,
        1 * s,
        2 * s
      ]);
    },
    blur: (size2) => {
      const blurSizeX = size2 / 7 / fxcanvas.width;
      const blurSizeY = size2 / 7 / fxcanvas.height;
      const program = compileShader(blur);
      if (!program)
        return;
      gl2.uniform2f(program.uniform["px"], 0, blurSizeY);
      draw(DRAW.INTERMEDIATE);
      gl2.uniform2f(program.uniform["px"], blurSizeX, 0);
      draw();
    },
    pixelate: (size2) => {
      const blurSizeX = size2 / fxcanvas.width;
      const blurSizeY = size2 / fxcanvas.height;
      const program = compileShader(pixelate);
      if (!program)
        return;
      gl2.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
      draw();
    }
  };
  this.add = function(name) {
    const args = Array.prototype.slice.call(arguments, 1);
    const func = filter[name];
    filterChain.push({ func, args });
  };
  this.reset = function() {
    filterChain = [];
  };
  this.get = function() {
    return filterChain;
  };
  this.apply = function(image) {
    resize(image.width, image.height);
    drawCount = 0;
    if (!sourceTexture)
      sourceTexture = gl2.createTexture();
    gl2.bindTexture(gl2.TEXTURE_2D, sourceTexture);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.NEAREST);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.NEAREST);
    gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, gl2.RGBA, gl2.UNSIGNED_BYTE, image);
    for (let i = 0; i < filterChain.length; i++) {
      lastInChain = i === filterChain.length - 1;
      const f = filterChain[i];
      f.func.apply(this, f.args || []);
    }
    return fxcanvas;
  };
  this.draw = function(image) {
    this.add("brightness", 0);
    return this.apply(image);
  };
}

// src/image/enhance.ts
async function histogramEqualization(inputImage) {
  const squeeze = inputImage.shape.length === 4 ? Up(inputImage) : inputImage;
  const rgb2 = Oa(squeeze, 3, 2);
  const min = [rl(rgb2[0]), rl(rgb2[1]), rl(rgb2[2])];
  const max = [Gs(rgb2[0]), Gs(rgb2[1]), Gs(rgb2[2])];
  const absMax = await Promise.all(max.map((channel) => channel.data()));
  const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
  const maxRange = maxValue > 1 ? 255 : 1;
  const factor = maxRange / maxValue;
  let final;
  if (factor > 1) {
    const sub = [ke(rgb2[0], min[0]), ke(rgb2[1], min[1]), ke(rgb2[2], min[2])];
    const range = [ke(max[0], min[0]), ke(max[1], min[1]), ke(max[2], min[2])];
    const enh = [ne(sub[0], factor), ne(sub[1], factor), ne(sub[2], factor)];
    const stack = Sr([enh[0], enh[1], enh[2]], 2);
    final = W(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);
    Ot([...sub, ...range, ...enh]);
  } else {
    final = Ra(squeeze, 0);
  }
  Ot([...rgb2, ...min, ...max, rgb2, squeeze, inputImage]);
  return final;
}

// src/image/image.ts
var maxSize = 3840;
var inCanvas = null;
var outCanvas = null;
var tmpCanvas = null;
var fx2;
var last = {
  inputSum: 0,
  cacheDiff: 1,
  sumMethod: 0,
  inputTensor: void 0
};
function reset() {
  last.inputSum = 0;
  last.cacheDiff = 1;
  last.sumMethod = 0;
  last.inputTensor = void 0;
}
function canvas(width, height) {
  let c;
  if (env.browser) {
    if (env.worker) {
      if (typeof OffscreenCanvas === "undefined")
        throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
      c = new OffscreenCanvas(width, height);
    } else {
      if (typeof document === "undefined")
        throw new Error("canvas error: attempted to run in browser but DOM is not defined");
      c = document.createElement("canvas");
      c.width = width;
      c.height = height;
    }
  } else {
    if (typeof env.Canvas !== "undefined")
      c = new env.Canvas(width, height);
    else if (typeof globalThis.Canvas !== "undefined")
      c = new globalThis.Canvas(width, height);
  }
  return c;
}
function copy(input, output) {
  const outputCanvas = output || canvas(input.width, input.height);
  const ctx = outputCanvas.getContext("2d");
  ctx.drawImage(input, 0, 0);
  return outputCanvas;
}
async function process2(input, config3, getTensor = true) {
  var _a2, _b2, _c2;
  if (!input) {
    if (config3.debug)
      log("input error: input is missing");
    return { tensor: null, canvas: null };
  }
  if (!(input instanceof it) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
    throw new Error("input error: type not recognized");
  }
  if (input instanceof it) {
    let tensor2 = null;
    if (input["isDisposedInternal"])
      throw new Error("input error: attempted to use tensor but it is disposed");
    if (!input.shape)
      throw new Error("input error: attempted to use tensor without a shape");
    if (input.shape.length === 3) {
      if (input.shape[2] === 3) {
        tensor2 = Ra(input, 0);
      } else if (input.shape[2] === 4) {
        const rgb2 = Qk(input, [0, 0, 0], [-1, -1, 3]);
        tensor2 = Ra(rgb2, 0);
        Ot(rgb2);
      }
    } else if (input.shape.length === 4) {
      if (input.shape[3] === 3) {
        tensor2 = Lr(input);
      } else if (input.shape[3] === 4) {
        tensor2 = Zk(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
      }
    }
    if (tensor2 == null || tensor2.shape.length !== 4 || tensor2.shape[0] !== 1 || tensor2.shape[3] !== 3)
      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
    if (tensor2.dtype === "int32") {
      const cast = je(tensor2, "float32");
      Ot(tensor2);
      tensor2 = cast;
    }
    return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null };
  }
  if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
    if (config3.debug)
      log("input stream is not ready");
    return { tensor: null, canvas: inCanvas };
  }
  const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
  const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
  if (!originalWidth || !originalHeight) {
    if (config3.debug)
      log("cannot determine input dimensions");
    return { tensor: null, canvas: inCanvas };
  }
  let targetWidth = originalWidth;
  let targetHeight = originalHeight;
  if (targetWidth > maxSize) {
    targetWidth = maxSize;
    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
  }
  if (targetHeight > maxSize) {
    targetHeight = maxSize;
    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
  }
  if ((((_a2 = config3.filter) == null ? void 0 : _a2.width) || 0) > 0)
    targetWidth = config3.filter.width;
  else if ((((_b2 = config3.filter) == null ? void 0 : _b2.height) || 0) > 0)
    targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
  if ((config3.filter.height || 0) > 0)
    targetHeight = config3.filter.height;
  else if ((config3.filter.width || 0) > 0)
    targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
  if (!targetWidth || !targetHeight)
    throw new Error("input error: cannot determine dimension");
  if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
    inCanvas = canvas(targetWidth, targetHeight);
  const inCtx = inCanvas.getContext("2d");
  if (typeof ImageData !== "undefined" && input instanceof ImageData) {
    inCtx.putImageData(input, 0, 0);
  } else {
    if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
      inCtx.translate(originalWidth, 0);
      inCtx.scale(-1, 1);
      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
      inCtx.setTransform(1, 0, 0, 1, 0, 0);
    } else {
      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
    }
  }
  if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
    outCanvas = canvas(inCanvas.width, inCanvas.height);
  if (config3.filter.enabled && env.webgl.supported) {
    if (!fx2)
      fx2 = env.browser ? new GLImageFilter() : null;
    env.filter = !!fx2;
    if (!(fx2 == null ? void 0 : fx2.add)) {
      if (config3.debug)
        log("input process error: cannot initialize filters");
      env.webgl.supported = false;
      config3.filter.enabled = false;
      copy(inCanvas, outCanvas);
    } else {
      fx2.reset();
      if (config3.filter.brightness !== 0)
        fx2.add("brightness", config3.filter.brightness);
      if (config3.filter.contrast !== 0)
        fx2.add("contrast", config3.filter.contrast);
      if (config3.filter.sharpness !== 0)
        fx2.add("sharpen", config3.filter.sharpness);
      if (config3.filter.blur !== 0)
        fx2.add("blur", config3.filter.blur);
      if (config3.filter.saturation !== 0)
        fx2.add("saturation", config3.filter.saturation);
      if (config3.filter.hue !== 0)
        fx2.add("hue", config3.filter.hue);
      if (config3.filter.negative)
        fx2.add("negative");
      if (config3.filter.sepia)
        fx2.add("sepia");
      if (config3.filter.vintage)
        fx2.add("brownie");
      if (config3.filter.sepia)
        fx2.add("sepia");
      if (config3.filter.kodachrome)
        fx2.add("kodachrome");
      if (config3.filter.technicolor)
        fx2.add("technicolor");
      if (config3.filter.polaroid)
        fx2.add("polaroid");
      if (config3.filter.pixelate !== 0)
        fx2.add("pixelate", config3.filter.pixelate);
      if (((_c2 = fx2.get()) == null ? void 0 : _c2.length) > 1)
        outCanvas = fx2.apply(inCanvas);
      else
        outCanvas = fx2.draw(inCanvas);
    }
  } else {
    copy(inCanvas, outCanvas);
    if (fx2)
      fx2 = null;
    env.filter = !!fx2;
  }
  if (!getTensor)
    return { tensor: null, canvas: outCanvas };
  if (!outCanvas)
    throw new Error("canvas error: cannot create output");
  let pixels;
  let depth = 3;
  if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
    if (env.browser && gN) {
      pixels = gN ? gN.fromPixels(input) : null;
    } else {
      depth = input.data.length / input.height / input.width;
      const arr = new Uint8Array(input.data.buffer);
      pixels = nr(arr, [input.height, input.width, depth], "int32");
    }
  } else {
    if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
      tmpCanvas = canvas(outCanvas.width, outCanvas.height);
    if (gN && env.browser) {
      if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
        pixels = gN.fromPixels(outCanvas);
      } else {
        tmpCanvas = copy(outCanvas);
        pixels = gN.fromPixels(tmpCanvas);
      }
    } else {
      const tempCanvas = copy(outCanvas);
      const tempCtx = tempCanvas.getContext("2d");
      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
      depth = tempData.data.length / targetWidth / targetHeight;
      const arr = new Uint8Array(tempData.data.buffer);
      pixels = nr(arr, [targetWidth, targetHeight, depth]);
    }
  }
  if (depth === 4) {
    const rgb2 = Qk(pixels, [0, 0, 0], [-1, -1, 3]);
    Ot(pixels);
    pixels = rgb2;
  }
  if (!pixels)
    throw new Error("input error: cannot create tensor");
  const casted = je(pixels, "float32");
  const tensor = config3.filter.equalization ? await histogramEqualization(casted) : Ra(casted, 0);
  Ot([pixels, casted]);
  if (config3.filter.autoBrightness) {
    const max = Gs(tensor);
    const maxVal = await max.data();
    config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0];
    Ot(max);
  }
  return { tensor, canvas: config3.filter.return ? outCanvas : null };
}
async function skip(config3, input) {
  let skipFrame = false;
  if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160)
    return skipFrame;
  if (!last.inputTensor) {
    last.inputTensor = Lr(input);
  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
    Ot(last.inputTensor);
    last.inputTensor = Lr(input);
  } else {
    const t10 = {};
    t10.diff = ke(input, last.inputTensor);
    t10.squared = ne(t10.diff, t10.diff);
    t10.sum = et(t10.squared);
    const diffSum = await t10.sum.data();
    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
    Ot([last.inputTensor, t10.diff, t10.squared, t10.sum]);
    last.inputTensor = Lr(input);
    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
  }
  return skipFrame;
}
async function compare(config3, input1, input2) {
  const t10 = {};
  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
    if (!config3.debug)
      log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
    return 0;
  }
  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
    if (!config3.debug)
      log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
    return 0;
  }
  t10.input1 = Lr(input1);
  t10.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? eK.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : Lr(input2);
  t10.diff = ke(t10.input1, t10.input2);
  t10.squared = ne(t10.diff, t10.diff);
  t10.sum = et(t10.squared);
  const diffSum = await t10.sum.data();
  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
  Ot([t10.input1, t10.input2, t10.diff, t10.squared, t10.sum]);
  return diffRelative;
}

// src/util/env.ts
var _canvas, _image, _imageData;
var Env = class {
  constructor() {
    /** Running in Browser */
    __publicField(this, "browser");
    /** Running in NodeJS */
    __publicField(this, "node");
    /** Running in WebWorker thread */
    __publicField(this, "worker");
    /** Detected platform */
    __publicField(this, "platform", "");
    /** Detected agent */
    __publicField(this, "agent", "");
    /** List of supported backends */
    __publicField(this, "backends", []);
    /** Has any work been performed so far */
    __publicField(this, "initial");
    /** Are image filters supported? */
    __publicField(this, "filter");
    /** TFJS instance details */
    __publicField(this, "tfjs");
    /** Is offscreenCanvas supported? */
    __publicField(this, "offscreen");
    /** Are performance counter instant values or additive */
    __publicField(this, "perfadd", false);
    /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */
    __publicField(this, "tensorflow", {
      version: void 0,
      gpu: void 0
    });
    /** WASM detected capabilities */
    __publicField(this, "wasm", {
      supported: void 0,
      backend: void 0,
      simd: void 0,
      multithread: void 0
    });
    /** WebGL detected capabilities */
    __publicField(this, "webgl", {
      supported: void 0,
      backend: void 0,
      version: void 0,
      renderer: void 0,
      shader: void 0,
      vendor: void 0
    });
    /** WebGPU detected capabilities */
    __publicField(this, "webgpu", {
      supported: void 0,
      backend: void 0,
      adapter: void 0
    });
    /** CPU info */
    __publicField(this, "cpu", {
      model: void 0,
      flags: []
    });
    /** List of supported kernels for current backend */
    __publicField(this, "kernels", []);
    /** MonkeyPatch for Canvas/Image/ImageData */
    __privateAdd(this, _canvas, void 0);
    __privateAdd(this, _image, void 0);
    __privateAdd(this, _imageData, void 0);
    this.browser = typeof navigator !== "undefined";
    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
    this.tfjs = { version: tse["tfjs-core"] };
    this.offscreen = typeof OffscreenCanvas !== "undefined";
    this.initial = true;
    this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
    if (typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined") {
      const agent = navigator.userAgent || "";
      const raw = agent.match(/\(([^()]+)\)/g);
      if (raw == null ? void 0 : raw[0]) {
        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
        this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
        this.agent = agent.replace(raw[0], "");
        if (this.platform[1])
          this.agent = this.agent.replace(raw[1], "");
        this.agent = this.agent.replace(/  /g, " ");
      }
    } else if (typeof process !== "undefined") {
      this.platform = `${process.platform} ${process.arch}`;
      this.agent = `NodeJS ${process.version}`;
    }
  }
  get Canvas() {
    return __privateGet(this, _canvas);
  }
  set Canvas(val) {
    __privateSet(this, _canvas, val);
    globalThis.Canvas = val;
  }
  get Image() {
    return __privateGet(this, _image);
  }
  // @ts-ignore monkey-patch;
  set Image(val) {
    __privateSet(this, _image, val);
    globalThis.Image = val;
  }
  get ImageData() {
    return __privateGet(this, _imageData);
  }
  // @ts-ignore monkey-patch;
  set ImageData(val) {
    __privateSet(this, _imageData, val);
    globalThis.ImageData = val;
  }
  /** update backend information */
  async updateBackend() {
    this.backends = Object.keys(sr().registryFactory);
    try {
      this.tensorflow = {
        version: Pie()["binding"] ? Pie()["binding"].TF_Version : void 0,
        gpu: Pie()["binding"] ? Pie()["binding"].isUsingGpuDevice() : void 0
      };
    } catch (e) {
    }
    this.wasm.supported = typeof WebAssembly !== "undefined";
    this.wasm.backend = this.backends.includes("wasm");
    if (this.wasm.supported && this.wasm.backend) {
      this.wasm.simd = await O().getAsync("WASM_HAS_SIMD_SUPPORT");
      this.wasm.multithread = await O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
    }
    const c = canvas(100, 100);
    const gl2 = c ? c.getContext("webgl2") : void 0;
    this.webgl.supported = typeof gl2 !== "undefined";
    this.webgl.backend = this.backends.includes("webgl");
    if (this.webgl.supported && this.webgl.backend && gl2) {
      this.webgl.version = gl2.getParameter(gl2.VERSION);
      this.webgl.vendor = gl2.getParameter(gl2.VENDOR);
      this.webgl.renderer = gl2.getParameter(gl2.RENDERER);
      this.webgl.shader = gl2.getParameter(gl2.SHADING_LANGUAGE_VERSION);
    }
    this.webgpu.supported = this.browser && typeof navigator !== "undefined" && typeof navigator.gpu !== "undefined";
    this.webgpu.backend = this.backends.includes("webgpu");
    try {
      if (this.webgpu.supported) {
        const adapter = await navigator.gpu.requestAdapter();
        this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo());
      }
    } catch (e) {
      this.webgpu.supported = false;
    }
    try {
      this.kernels = Rm(Rie()).map((kernel) => kernel.kernelName.toLowerCase());
    } catch (e) {
    }
  }
  /** update cpu information */
  updateCPU() {
    const cpu = { model: "", flags: [] };
    if (this.node && this.platform.startsWith("linux")) {
    }
    if (!this.cpu)
      Object.defineProperty(this, "cpu", { value: cpu });
    else
      this.cpu = cpu;
  }
};
_canvas = new WeakMap();
_image = new WeakMap();
_imageData = new WeakMap();
var env = new Env();

// src/util/webcam.ts
var WebCam = class {
  constructor() {
    // eslint-disable-line @typescript-eslint/no-extraneous-class
    /** current webcam configuration */
    __publicField(this, "config");
    /** instance of dom element associated with webcam stream */
    __publicField(this, "element");
    /** active webcam stream */
    __publicField(this, "stream");
    /** enumerated video devices */
    __publicField(this, "devices", []);
    __publicField(this, "enumerate", async () => {
      try {
        const devices = await navigator.mediaDevices.enumerateDevices();
        this.devices = devices.filter((device) => device.kind === "videoinput");
      } catch (e) {
        this.devices = [];
      }
      return this.devices;
    });
    /** start method initializizes webcam stream and associates it with a dom video element */
    __publicField(this, "start", async (webcamConfig) => {
      var _a2, _b2;
      if (webcamConfig == null ? void 0 : webcamConfig.debug)
        this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
      if (webcamConfig == null ? void 0 : webcamConfig.crop)
        this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
      if (webcamConfig == null ? void 0 : webcamConfig.mode)
        this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
      if (webcamConfig == null ? void 0 : webcamConfig.width)
        this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
      if (webcamConfig == null ? void 0 : webcamConfig.height)
        this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
      if (webcamConfig == null ? void 0 : webcamConfig.id)
        this.config.id = webcamConfig == null ? void 0 : webcamConfig.id;
      if (webcamConfig == null ? void 0 : webcamConfig.element) {
        if (typeof webcamConfig.element === "string") {
          const el2 = document.getElementById(webcamConfig.element);
          if (el2 && el2 instanceof HTMLVideoElement) {
            this.element = el2;
          } else {
            if (this.config.debug)
              log("webcam", "cannot get dom element", webcamConfig.element);
            return;
          }
        } else if (webcamConfig.element instanceof HTMLVideoElement) {
          this.element = webcamConfig.element;
        } else {
          if (this.config.debug)
            log("webcam", "unknown dom element", webcamConfig.element);
          return;
        }
      } else {
        this.element = document.createElement("video");
      }
      const requestedConstraints = {
        audio: false,
        video: {
          facingMode: this.config.mode === "front" ? "user" : "environment",
          // @ts-ignore // resizeMode is still not defined in tslib
          resizeMode: this.config.crop ? "crop-and-scale" : "none"
        }
      };
      if (((_a2 = this.config) == null ? void 0 : _a2.width) > 0)
        requestedConstraints.video.width = { ideal: this.config.width };
      if (((_b2 = this.config) == null ? void 0 : _b2.height) > 0)
        requestedConstraints.video.height = { ideal: this.config.height };
      if (this.config.id)
        requestedConstraints.video.deviceId = this.config.id;
      this.element.addEventListener("play", () => {
        if (this.config.debug)
          log("webcam", "play");
      });
      this.element.addEventListener("pause", () => {
        if (this.config.debug)
          log("webcam", "pause");
      });
      this.element.addEventListener("click", async () => {
        if (!this.element || !this.stream)
          return;
        if (this.element.paused)
          await this.element.play();
        else
          this.element.pause();
      });
      if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
        if (this.config.debug)
          log("webcam", "no devices");
        return;
      }
      try {
        this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
      } catch (err) {
        log("webcam", err);
        return;
      }
      if (!this.stream) {
        if (this.config.debug)
          log("webcam", "no stream");
        return;
      }
      this.element.srcObject = this.stream;
      const ready = new Promise((resolve) => {
        if (!this.element)
          resolve(false);
        else
          this.element.onloadeddata = () => resolve(true);
      });
      await ready;
      await this.element.play();
      if (this.config.debug) {
        log("webcam", {
          width: this.width,
          height: this.height,
          label: this.label,
          stream: this.stream,
          track: this.track,
          settings: this.settings,
          constraints: this.constraints,
          capabilities: this.capabilities
        });
      }
    });
    /** pause webcam video method */
    __publicField(this, "pause", () => {
      if (this.element)
        this.element.pause();
    });
    /** play webcam video method */
    __publicField(this, "play", async () => {
      if (this.element)
        await this.element.play();
    });
    /** stop method stops active webcam stream track and disconnects webcam */
    __publicField(this, "stop", () => {
      if (this.config.debug)
        log("webcam", "stop");
      if (this.track)
        this.track.stop();
    });
    this.config = {
      element: void 0,
      debug: true,
      mode: "front",
      crop: false,
      width: 0,
      height: 0
    };
  }
  /** get active webcam stream track */
  get track() {
    if (!this.stream)
      return void 0;
    return this.stream.getVideoTracks()[0];
  }
  /** get webcam capabilities */
  get capabilities() {
    if (!this.track)
      return void 0;
    return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
  }
  /** get webcam constraints */
  get constraints() {
    if (!this.track)
      return void 0;
    return this.track.getConstraints ? this.track.getConstraints() : void 0;
  }
  /** get webcam settings */
  get settings() {
    if (!this.stream)
      return void 0;
    const track = this.stream.getVideoTracks()[0];
    return track.getSettings ? track.getSettings() : void 0;
  }
  /** get webcam label */
  get label() {
    if (!this.track)
      return "";
    return this.track.label;
  }
  /** is webcam paused */
  get paused() {
    var _a2;
    return ((_a2 = this.element) == null ? void 0 : _a2.paused) || false;
  }
  /** webcam current width */
  get width() {
    var _a2;
    return ((_a2 = this.element) == null ? void 0 : _a2.videoWidth) || 0;
  }
  /** webcam current height */
  get height() {
    var _a2;
    return ((_a2 = this.element) == null ? void 0 : _a2.videoHeight) || 0;
  }
};

// models/models.json
var models_exports = {};
__export(models_exports, {
  age: () => age,
  "anti-spoofing": () => anti_spoofing,
  antispoof: () => antispoof,
  blazeface: () => blazeface,
  "blazeface-back": () => blazeface_back,
  "blazeface-front": () => blazeface_front,
  "blazepose-detector": () => blazepose_detector,
  "blazepose-full": () => blazepose_full,
  "blazepose-heavy": () => blazepose_heavy,
  "blazepose-lite": () => blazepose_lite,
  centernet: () => centernet,
  default: () => models_default,
  efficientpose: () => efficientpose,
  "efficientpose-i-lite": () => efficientpose_i_lite,
  "efficientpose-ii-lite": () => efficientpose_ii_lite,
  "efficientpose-iv": () => efficientpose_iv,
  emotion: () => emotion,
  faceboxes: () => faceboxes,
  facemesh: () => facemesh,
  "facemesh-attention": () => facemesh_attention,
  "facemesh-attention-pinto": () => facemesh_attention_pinto,
  "facemesh-detection-full": () => facemesh_detection_full,
  "facemesh-detection-short": () => facemesh_detection_short,
  faceres: () => faceres,
  "faceres-deep": () => faceres_deep,
  gear: () => gear,
  gender: () => gender,
  "gender-ssrnet-imdb": () => gender_ssrnet_imdb,
  handdetect: () => handdetect,
  "handlandmark-full": () => handlandmark_full,
  "handlandmark-lite": () => handlandmark_lite,
  "handlandmark-sparse": () => handlandmark_sparse,
  handskeleton: () => handskeleton,
  handtrack: () => handtrack,
  "insightface-efficientnet-b0": () => insightface_efficientnet_b0,
  "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
  "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
  "insightface-mobilenet-emore": () => insightface_mobilenet_emore,
  "insightface-mobilenet-swish": () => insightface_mobilenet_swish,
  iris: () => iris,
  liveness: () => liveness,
  meet: () => meet,
  mobileface: () => mobileface,
  mobilefacenet: () => mobilefacenet,
  models: () => models,
  "movenet-lightning": () => movenet_lightning,
  "movenet-multipose": () => movenet_multipose,
  "movenet-thunder": () => movenet_thunder,
  nanodet: () => nanodet,
  "nanodet-e": () => nanodet_e,
  "nanodet-g": () => nanodet_g,
  "nanodet-m": () => nanodet_m,
  "nanodet-t": () => nanodet_t,
  posenet: () => posenet,
  rvm: () => rvm,
  selfie: () => selfie
});
var antispoof = 853098;
var blazeface = 538928;
var centernet = 4030290;
var emotion = 820516;
var facemesh = 1477958;
var faceres = 6978814;
var handlandmark_lite = 2023432;
var handtrack = 2964837;
var iris = 2599092;
var liveness = 592976;
var models = 0;
var movenet_lightning = 4650216;
var age = 161240;
var blazeface_back = 538928;
var blazeface_front = 402048;
var blazepose_detector = 5928856;
var blazepose_full = 6339202;
var blazepose_heavy = 27502466;
var blazepose_lite = 2726402;
var efficientpose = 5651240;
var faceboxes = 2013002;
var facemesh_attention_pinto = 2387598;
var facemesh_attention = 2382414;
var facemesh_detection_full = 1026192;
var facemesh_detection_short = 201268;
var faceres_deep = 13957620;
var gear = 1498916;
var gender_ssrnet_imdb = 161236;
var gender = 201808;
var handdetect = 3515612;
var handlandmark_full = 5431368;
var handlandmark_sparse = 5286322;
var handskeleton = 5502280;
var meet = 372228;
var mobileface = 2183192;
var mobilefacenet = 5171976;
var movenet_multipose = 9448838;
var movenet_thunder = 12477112;
var nanodet = 7574558;
var posenet = 5032780;
var rvm = 3739355;
var selfie = 212886;
var anti_spoofing = 853098;
var efficientpose_i_lite = 2269064;
var efficientpose_ii_lite = 5651240;
var efficientpose_iv = 25643252;
var insightface_efficientnet_b0 = 13013224;
var insightface_ghostnet_strides1 = 8093408;
var insightface_ghostnet_strides2 = 8049584;
var insightface_mobilenet_emore = 6938536;
var insightface_mobilenet_swish = 12168584;
var nanodet_e = 12319156;
var nanodet_g = 7574558;
var nanodet_m = 1887474;
var nanodet_t = 5294216;
var models_default = {
  antispoof,
  blazeface,
  centernet,
  emotion,
  facemesh,
  faceres,
  "handlandmark-lite": handlandmark_lite,
  handtrack,
  iris,
  liveness,
  models,
  "movenet-lightning": movenet_lightning,
  age,
  "blazeface-back": blazeface_back,
  "blazeface-front": blazeface_front,
  "blazepose-detector": blazepose_detector,
  "blazepose-full": blazepose_full,
  "blazepose-heavy": blazepose_heavy,
  "blazepose-lite": blazepose_lite,
  efficientpose,
  faceboxes,
  "facemesh-attention-pinto": facemesh_attention_pinto,
  "facemesh-attention": facemesh_attention,
  "facemesh-detection-full": facemesh_detection_full,
  "facemesh-detection-short": facemesh_detection_short,
  "faceres-deep": faceres_deep,
  gear,
  "gender-ssrnet-imdb": gender_ssrnet_imdb,
  gender,
  handdetect,
  "handlandmark-full": handlandmark_full,
  "handlandmark-sparse": handlandmark_sparse,
  handskeleton,
  meet,
  mobileface,
  mobilefacenet,
  "movenet-multipose": movenet_multipose,
  "movenet-thunder": movenet_thunder,
  nanodet,
  posenet,
  rvm,
  selfie,
  "anti-spoofing": anti_spoofing,
  "efficientpose-i-lite": efficientpose_i_lite,
  "efficientpose-ii-lite": efficientpose_ii_lite,
  "efficientpose-iv": efficientpose_iv,
  "insightface-efficientnet-b0": insightface_efficientnet_b0,
  "insightface-ghostnet-strides1": insightface_ghostnet_strides1,
  "insightface-ghostnet-strides2": insightface_ghostnet_strides2,
  "insightface-mobilenet-emore": insightface_mobilenet_emore,
  "insightface-mobilenet-swish": insightface_mobilenet_swish,
  "nanodet-e": nanodet_e,
  "nanodet-g": nanodet_g,
  "nanodet-m": nanodet_m,
  "nanodet-t": nanodet_t
};

// src/tfjs/load.ts
var options = {
  cacheModels: true,
  cacheSupported: true,
  verbose: true,
  debug: false,
  modelBasePath: ""
};
var modelStats = {};
async function httpHandler(url, init4) {
  if (options.debug)
    log("load model fetch:", url, init4);
  return fetch(url, init4);
}
function setModelLoadOptions(config3) {
  options.cacheModels = config3.cacheModels;
  options.verbose = config3.debug;
  options.modelBasePath = config3.modelBasePath;
}
async function loadModel(modelPath) {
  var _a2, _b2, _c2, _d2;
  let modelUrl = join(options.modelBasePath, modelPath || "");
  if (!modelUrl.toLowerCase().endsWith(".json"))
    modelUrl += ".json";
  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
  const cachedModelName = "indexeddb://" + shortModelName;
  modelStats[shortModelName] = {
    name: shortModelName,
    sizeFromManifest: 0,
    sizeLoadedWeights: 0,
    sizeDesired: models_exports[shortModelName],
    inCache: false,
    url: ""
  };
  options.cacheSupported = typeof indexedDB !== "undefined";
  let cachedModels = {};
  try {
    cachedModels = options.cacheSupported && options.cacheModels ? await La.listModels() : {};
  } catch (e) {
    options.cacheSupported = false;
  }
  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
  modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;
  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) };
  let model23 = new fl(modelStats[shortModelName].url, tfLoadOptions);
  let loaded = false;
  try {
    model23.findIOHandler();
    if (options.debug)
      log("model load handler:", model23["handler"]);
  } catch (err) {
    log("error finding model i/o handler:", modelUrl, err);
  }
  try {
    const artifacts = await ((_a2 = model23.handler) == null ? void 0 : _a2.load()) || null;
    modelStats[shortModelName].sizeFromManifest = ((_b2 = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b2.byteLength) || 0;
    if (artifacts)
      model23.loadSync(artifacts);
    else
      model23 = await A6(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
    modelStats[shortModelName].sizeLoadedWeights = ((_d2 = (_c2 = model23.artifacts) == null ? void 0 : _c2.weightData) == null ? void 0 : _d2.byteLength) || 0;
    if (options.verbose)
      log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
    loaded = true;
  } catch (err) {
    log("error loading model:", modelUrl, err);
  }
  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
    try {
      const saveResult = await model23.save(cachedModelName);
      if (options.debug)
        log("model saved:", cachedModelName, saveResult);
    } catch (err) {
      log("error saving model:", modelUrl, err);
    }
  }
  return model23;
}

// package.json
var version = "3.0.3";

// src/tfjs/humangl.ts
var config2 = {
  name: "humangl",
  priority: 999,
  canvas: null,
  gl: null,
  extensions: [],
  webGLattr: {
    // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2
    alpha: false,
    antialias: false,
    premultipliedAlpha: false,
    preserveDrawingBuffer: false,
    depth: false,
    stencil: false,
    failIfMajorPerformanceCaveat: false,
    // default=true
    desynchronized: true
    // default=undefined
  }
};
function extensions() {
  const gl2 = config2.gl;
  if (!gl2)
    return;
  config2.extensions = gl2.getSupportedExtensions();
}
function register(instance) {
  var _a2;
  if (instance.config.backend !== "humangl")
    return;
  if (config2.name in sr().registry && !((_a2 = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a2.getParameter(config2.gl.VERSION))) {
    log("humangl error: backend invalid context");
    instance.models.reset();
  }
  if (!Fie(config2.name)) {
    try {
      config2.canvas = canvas(100, 100);
    } catch (err) {
      log("humangl error: cannot create canvas:", err);
      return;
    }
    try {
      config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
      if (!config2.gl) {
        log("humangl error: cannot get webgl context");
        return;
      }
      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
      if (!glv2) {
        log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
        instance.config.backend = "webgl";
        return;
      }
      if (config2.canvas) {
        config2.canvas.addEventListener("webglcontextlost", (e) => {
          log("humangl error:", e.type);
          log("possible browser memory leak using webgl or conflict with multiple backend registrations");
          instance.emit("error");
          throw new Error("backend error: webgl context lost");
        });
        config2.canvas.addEventListener("webglcontextrestored", (e) => {
          log("humangl error: context restored:", e);
        });
        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
          log("humangl error: context create:", e);
        });
      }
    } catch (err) {
      log("humangl error: cannot get webgl context:", err);
      return;
    }
    try {
      US(2, config2.gl);
    } catch (err) {
      log("humangl error: cannot set webgl context:", err);
      return;
    }
    try {
      const ctx = new Lu(config2.gl);
      Ti(config2.name, () => new Vi(ctx), config2.priority);
    } catch (err) {
      log("humangl error: cannot register webgl backend:", err);
      return;
    }
    try {
      const kernels = Rm("webgl");
      kernels.forEach((kernelConfig) => {
        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
        ka(newKernelConfig);
      });
    } catch (err) {
      log("humangl error: cannot update webgl backend registration:", err);
      return;
    }
    try {
      if (O().flagRegistry.WEBGL_VERSION)
        O().set("WEBGL_VERSION", 2);
    } catch (err) {
      log("humangl error: cannot set WebGL backend flags:", err);
      return;
    }
    extensions();
    const backend = Pie();
    const current = typeof backend["gpgpu"] !== "undefined" ? backend["getGPGPUContext"]().gl : null;
    if (current) {
      if (instance.config.debug)
        log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
    } else {
      log("humangl error: no current gl context:", current, config2.gl);
    }
  }
}

// src/tfjs/constants.ts
var constants = {
  tf255: 255,
  tf1: 1,
  tf2: 2,
  tf05: 0.5,
  tf127: 127.5,
  rgb: [0.2989, 0.587, 0.114]
};
function init() {
  constants.tf255 = Ce(255, "float32");
  constants.tf1 = Ce(1, "float32");
  constants.tf2 = Ce(2, "float32");
  constants.tf05 = Ce(0.5, "float32");
  constants.tf127 = Ce(127.5, "float32");
  constants.rgb = dr([0.2989, 0.587, 0.114], "float32");
}

// src/tfjs/backend.ts
async function getBestBackend() {
  var _a2;
  await env.updateBackend();
  if ((_a2 = env.tensorflow) == null ? void 0 : _a2.version)
    return "tensorflow";
  if (env.webgpu.supported && env.webgpu.backend)
    return "webgpu";
  if (env.webgl.supported && env.webgl.backend)
    return "webgl";
  if (env.wasm.supported && env.wasm.backend)
    return "wasm";
  return "cpu";
}
function registerCustomOps(config3) {
  const newKernels = [];
  if (!env.kernels.includes("mod")) {
    const kernelMod = {
      kernelName: "Mod",
      backendName: Rie(),
      kernelFunc: (op) => Ee(() => ke(op.inputs.a, ne(He(op.inputs.a, op.inputs.b), op.inputs.b)))
    };
    ka(kernelMod);
    env.kernels.push("mod");
    newKernels.push("mod");
  }
  if (!env.kernels.includes("floormod")) {
    const kernelFloorMod = {
      kernelName: "FloorMod",
      backendName: Rie(),
      kernelFunc: (op) => Ee(() => be(ne(Km(op.inputs.a, op.inputs.b), op.inputs.b), Jv(op.inputs.a, op.inputs.b)))
    };
    ka(kernelFloorMod);
    env.kernels.push("floormod");
    newKernels.push("floormod");
  }
  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
    const kernelRotateWithOffset = {
      kernelName: "RotateWithOffset",
      backendName: Rie(),
      kernelFunc: (op) => Ee(() => {
        const backend = Rie();
        Eie("cpu");
        const t10 = eK.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
        Eie(backend);
        return t10;
      })
    };
    ka(kernelRotateWithOffset);
    env.kernels.push("rotatewithoffset");
    newKernels.push("rotatewithoffset");
  }
  if (newKernels.length > 0 && config3.debug)
    log("registered kernels:", newKernels);
}
var defaultFlags = {};
async function check(instance, force = false) {
  var _a2;
  instance.state = "backend";
  if (((_a2 = instance.config.backend) == null ? void 0 : _a2.length) === 0)
    instance.config.backend = await getBestBackend();
  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Rie() !== instance.config.backend) {
    const timeStamp = now();
    if (instance.config.backend && instance.config.backend.length > 0) {
      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
        if (instance.config.debug)
          log("running inside web worker");
      }
      if (env.browser && instance.config.backend === "tensorflow") {
        if (instance.config.debug)
          log("override: backend set to tensorflow while running in browser");
        instance.config.backend = "webgl";
      }
      if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) {
        if (instance.config.debug)
          log(`override: backend set to ${instance.config.backend} while running in nodejs`);
        instance.config.backend = "tensorflow";
      }
      if (env.browser && instance.config.backend === "webgpu") {
        if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
          log("override: backend set to webgpu but browser does not support webgpu");
          instance.config.backend = "webgl";
        } else {
          const adapter = await navigator.gpu.requestAdapter();
          if (instance.config.debug)
            log("enumerated webgpu adapter:", adapter);
          if (!adapter) {
            log("override: backend set to webgpu but browser reports no available gpu");
            instance.config.backend = "webgl";
          } else {
            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
            log("webgpu adapter info:", adapterInfo);
          }
        }
      }
      let available = Object.keys(sr().registryFactory);
      if (instance.config.backend === "humangl" && !available.includes("humangl")) {
        register(instance);
        available = Object.keys(sr().registryFactory);
      }
      if (instance.config.debug)
        log("available backends:", available);
      if (!available.includes(instance.config.backend)) {
        log(`error: backend ${instance.config.backend} not found in registry`);
        instance.config.backend = env.node ? "tensorflow" : "webgl";
        if (instance.config.debug)
          log(`override: setting backend ${instance.config.backend}`);
      }
      if (instance.config.debug)
        log("setting backend:", [instance.config.backend]);
      if (instance.config.backend === "wasm") {
        if (O().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
          O().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
        if (instance.config.debug)
          log("wasm path:", instance.config.wasmPath);
        if (typeof Gee !== "undefined")
          Gee(instance.config.wasmPath, instance.config.wasmPlatformFetch);
        else
          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
        let mt2 = false;
        let simd = false;
        try {
          mt2 = await O().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
          simd = await O().getAsync("WASM_HAS_SIMD_SUPPORT");
          if (instance.config.debug)
            log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt2 ? "multithreaded" : "singlethreaded"}`);
          if (instance.config.debug && !simd)
            log("warning: wasm simd support is not enabled");
        } catch (e) {
          log("wasm detection failed");
        }
      }
      try {
        await Eie(instance.config.backend);
        await Aie();
      } catch (err) {
        log("error: cannot set backend:", instance.config.backend, err);
        return false;
      }
      if (instance.config.debug)
        defaultFlags = JSON.parse(JSON.stringify(O().flags));
    }
    if (Rie() === "humangl" || Rie() === "webgl") {
      if (O().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
        O().set("WEBGL_USE_SHAPES_UNIFORMS", true);
      if (O().flagRegistry.WEBGL_EXP_CONV)
        O().set("WEBGL_EXP_CONV", true);
      if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) {
        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
        O().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
      }
    }
    if (Rie() === "webgpu") {
    }
    if (instance.config.debug) {
      const newFlags = O().flags;
      const updatedFlags = {};
      for (const key of Object.keys(newFlags)) {
        if (defaultFlags[key] === newFlags[key])
          continue;
        updatedFlags[key] = newFlags[key];
      }
      if (instance.config.debug && Object.keys(updatedFlags).length > 0)
        log("backend:", Rie(), "flags:", updatedFlags);
    }
    if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {
      if (instance.config.debug)
        log("flags:", instance.config["flags"]);
      for (const [key, val] of Object.entries(instance.config.flags)) {
        O().set(key, val);
      }
    }
    Iie();
    init();
    instance.performance.initBackend = Math.trunc(now() - timeStamp);
    instance.config.backend = Rie();
    await env.updateBackend();
    registerCustomOps(instance.config);
    env.initial = false;
  }
  return true;
}
function fakeOps(kernelNames, config3) {
  for (const kernelName of kernelNames) {
    const kernelConfig = {
      kernelName,
      backendName: config3.backend,
      kernelFunc: (param) => {
        var _a2;
        if (config3.debug)
          log("kernelFunc", kernelName, config3.backend, param);
        return (_a2 = param == null ? void 0 : param.inputs) == null ? void 0 : _a2.info;
      }
      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },
    };
    ka(kernelConfig);
  }
  env.kernels = Rm(Rie()).map((kernel) => kernel.kernelName.toLowerCase());
}

// src/draw/draw.ts
var draw_exports = {};
__export(draw_exports, {
  all: () => all,
  body: () => body,
  canvas: () => canvas2,
  face: () => face,
  gesture: () => gesture,
  hand: () => hand,
  init: () => init2,
  object: () => object,
  options: () => options2,
  person: () => person
});

// src/draw/primitives.ts
var getCanvasContext = (input) => {
  if (!input)
    log("draw error: invalid canvas");
  else if (!input.getContext)
    log("draw error: canvas context not defined");
  else {
    const ctx = input.getContext("2d");
    if (!ctx)
      log("draw error: cannot get canvas context");
    else
      return ctx;
  }
  return null;
};
var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target);
var colorDepth = (z, opt) => {
  if (!opt.useDepth || typeof z === "undefined")
    return opt.color;
  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
  return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt.alpha})`;
};
function labels(ctx, str, startX, startY, localOptions2) {
  const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim());
  const x = Math.max(0, startX);
  for (let i = line.length - 1; i >= 0; i--) {
    const y6 = i * localOptions2.lineHeight + startY;
    if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
      ctx.fillStyle = localOptions2.shadowColor;
      ctx.fillText(line[i], x + 5, y6 + 16);
    }
    ctx.fillStyle = localOptions2.labelColor;
    ctx.fillText(line[i], x + 4, y6 + 15);
  }
}
function point(ctx, x, y6, z, localOptions2) {
  ctx.fillStyle = colorDepth(z, localOptions2);
  ctx.beginPath();
  ctx.arc(x, y6, localOptions2.pointSize, 0, 2 * Math.PI);
  ctx.fill();
}
function rect(ctx, x, y6, width, height, localOptions2) {
  ctx.beginPath();
  ctx.lineWidth = localOptions2.lineWidth;
  if (localOptions2.useCurves) {
    const cx2 = (x + x + width) / 2;
    const cy = (y6 + y6 + height) / 2;
    ctx.ellipse(cx2, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
  } else {
    ctx.moveTo(x + localOptions2.roundRect, y6);
    ctx.lineTo(x + width - localOptions2.roundRect, y6);
    ctx.quadraticCurveTo(x + width, y6, x + width, y6 + localOptions2.roundRect);
    ctx.lineTo(x + width, y6 + height - localOptions2.roundRect);
    ctx.quadraticCurveTo(x + width, y6 + height, x + width - localOptions2.roundRect, y6 + height);
    ctx.lineTo(x + localOptions2.roundRect, y6 + height);
    ctx.quadraticCurveTo(x, y6 + height, x, y6 + height - localOptions2.roundRect);
    ctx.lineTo(x, y6 + localOptions2.roundRect);
    ctx.quadraticCurveTo(x, y6, x + localOptions2.roundRect, y6);
    ctx.closePath();
  }
  ctx.stroke();
}
function lines(ctx, points, localOptions2) {
  if (points.length < 2)
    return;
  ctx.beginPath();
  ctx.moveTo(points[0][0], points[0][1]);
  for (const pt of points) {
    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2);
    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
  }
  ctx.stroke();
  if (localOptions2.fillPolygons) {
    ctx.closePath();
    ctx.fill();
  }
}
function curves(ctx, points, localOptions2) {
  if (points.length < 2)
    return;
  ctx.lineWidth = localOptions2.lineWidth;
  if (!localOptions2.useCurves || points.length <= 2) {
    lines(ctx, points, localOptions2);
    return;
  }
  ctx.moveTo(points[0][0], points[0][1]);
  for (let i = 0; i < points.length - 2; i++) {
    const xc2 = (points[i][0] + points[i + 1][0]) / 2;
    const yc2 = (points[i][1] + points[i + 1][1]) / 2;
    ctx.quadraticCurveTo(points[i][0], points[i][1], xc2, yc2);
  }
  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
  ctx.stroke();
  if (localOptions2.fillPolygons) {
    ctx.closePath();
    ctx.fill();
  }
}
function arrow(ctx, from, to2, radius = 5) {
  let angle;
  let x;
  let y6;
  ctx.beginPath();
  ctx.moveTo(from[0], from[1]);
  ctx.lineTo(to2[0], to2[1]);
  angle = Math.atan2(to2[1] - from[1], to2[0] - from[0]);
  x = radius * Math.cos(angle) + to2[0];
  y6 = radius * Math.sin(angle) + to2[1];
  ctx.moveTo(x, y6);
  angle += 1 / 3 * (2 * Math.PI);
  x = radius * Math.cos(angle) + to2[0];
  y6 = radius * Math.sin(angle) + to2[1];
  ctx.lineTo(x, y6);
  angle += 1 / 3 * (2 * Math.PI);
  x = radius * Math.cos(angle) + to2[0];
  y6 = radius * Math.sin(angle) + to2[1];
  ctx.lineTo(x, y6);
  ctx.closePath();
  ctx.stroke();
  ctx.fill();
}

// src/draw/options.ts
var options2 = {
  color: "rgba(173, 216, 230, 0.6)",
  // 'lightblue' with light alpha channel
  labelColor: "rgba(173, 216, 230, 1)",
  // 'lightblue' with dark alpha channel
  shadowColor: "black",
  alpha: 0.5,
  font: 'small-caps 16px "Segoe UI"',
  lineHeight: 18,
  lineWidth: 4,
  pointSize: 2,
  roundRect: 8,
  drawPoints: false,
  drawLabels: true,
  drawBoxes: true,
  drawAttention: true,
  drawGestures: true,
  drawPolygons: true,
  drawGaze: true,
  fillPolygons: false,
  useDepth: true,
  useCurves: false,
  faceLabels: "",
  bodyLabels: "",
  bodyPartLabels: "",
  objectLabels: "",
  handLabels: "",
  fingerLabels: "",
  gestureLabels: ""
};

// src/face/facemeshcoords.ts
var meshAnnotations = {
  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], // 11
  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10
  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11
  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11
  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
  // 7
  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
  // 9
  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
  // 7
  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
  // 9
  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
  // 7
  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
  // 9
  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
  // 9
  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
  // 8
  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
  // 6
  rightEyeIris: [473, 474, 475, 476, 477],
  // 5
  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]
};
var meshLandmarks = {
  count: 468,
  mouth: 13,
  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
};
var blazeFaceLandmarks = {
  leftEye: 0,
  rightEye: 1,
  nose: 2,
  mouth: 3,
  leftEar: 4,
  rightEar: 5,
  symmetryLine: [3, 2]
};
var irisIndices = [
  // A mapping from facemesh model keypoints to iris model keypoints.
  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
  // 7 x 3d
  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
  // 7 x 3d
  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
  // 7 x 3d
  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
  // 7 x 3d
  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
  // 9 x 3d
  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
  // 9 x 3d
  { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
  // 9 x 3d
  { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] },
  // 8 x 3d
  { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] }
  // 6 x 3d
];
var UV468 = [
  [0.499976992607117, 0.652534008026123],
  [0.500025987625122, 0.547487020492554],
  [0.499974012374878, 0.602371990680695],
  [0.482113003730774, 0.471979022026062],
  [0.500150978565216, 0.527155995368958],
  [0.499909996986389, 0.498252987861633],
  [0.499523013830185, 0.40106201171875],
  [0.289712011814117, 0.380764007568359],
  [0.499954998493195, 0.312398016452789],
  [0.499987006187439, 0.269918978214264],
  [0.500023007392883, 0.107050001621246],
  [0.500023007392883, 0.666234016418457],
  [0.5000159740448, 0.679224014282227],
  [0.500023007392883, 0.692348003387451],
  [0.499976992607117, 0.695277988910675],
  [0.499976992607117, 0.70593398809433],
  [0.499976992607117, 0.719385027885437],
  [0.499976992607117, 0.737019002437592],
  [0.499967992305756, 0.781370997428894],
  [0.499816000461578, 0.562981009483337],
  [0.473773002624512, 0.573909997940063],
  [0.104906998574734, 0.254140973091125],
  [0.365929991006851, 0.409575998783112],
  [0.338757991790771, 0.41302502155304],
  [0.311120003461838, 0.409460008144379],
  [0.274657994508743, 0.389131009578705],
  [0.393361985683441, 0.403706014156342],
  [0.345234006643295, 0.344011008739471],
  [0.370094001293182, 0.346076011657715],
  [0.319321990013123, 0.347265005111694],
  [0.297903001308441, 0.353591024875641],
  [0.24779200553894, 0.410809993743896],
  [0.396889001131058, 0.842755019664764],
  [0.280097991228104, 0.375599980354309],
  [0.106310002505779, 0.399955987930298],
  [0.2099249958992, 0.391353011131287],
  [0.355807989835739, 0.534406006336212],
  [0.471751004457474, 0.65040397644043],
  [0.474155008792877, 0.680191993713379],
  [0.439785003662109, 0.657229006290436],
  [0.414617002010345, 0.66654098033905],
  [0.450374007225037, 0.680860996246338],
  [0.428770989179611, 0.682690978050232],
  [0.374971002340317, 0.727805018424988],
  [0.486716985702515, 0.547628998756409],
  [0.485300987958908, 0.527395009994507],
  [0.257764995098114, 0.314490020275116],
  [0.401223003864288, 0.455172002315521],
  [0.429818987846375, 0.548614978790283],
  [0.421351999044418, 0.533740997314453],
  [0.276895999908447, 0.532056987285614],
  [0.483370006084442, 0.499586999416351],
  [0.33721199631691, 0.282882988452911],
  [0.296391993761063, 0.293242990970612],
  [0.169294998049736, 0.193813979625702],
  [0.447580009698868, 0.302609980106354],
  [0.392390012741089, 0.353887975215912],
  [0.354490011930466, 0.696784019470215],
  [0.067304998636246, 0.730105042457581],
  [0.442739009857178, 0.572826027870178],
  [0.457098007202148, 0.584792017936707],
  [0.381974011659622, 0.694710969924927],
  [0.392388999462128, 0.694203019142151],
  [0.277076005935669, 0.271932005882263],
  [0.422551989555359, 0.563233017921448],
  [0.385919004678726, 0.281364023685455],
  [0.383103013038635, 0.255840003490448],
  [0.331431001424789, 0.119714021682739],
  [0.229923993349075, 0.232002973556519],
  [0.364500999450684, 0.189113974571228],
  [0.229622006416321, 0.299540996551514],
  [0.173287004232407, 0.278747975826263],
  [0.472878992557526, 0.666198015213013],
  [0.446828007698059, 0.668527007102966],
  [0.422762006521225, 0.673889994621277],
  [0.445307999849319, 0.580065965652466],
  [0.388103008270264, 0.693961024284363],
  [0.403039008378983, 0.706539988517761],
  [0.403629004955292, 0.693953037261963],
  [0.460041999816895, 0.557139039039612],
  [0.431158006191254, 0.692366003990173],
  [0.452181994915009, 0.692366003990173],
  [0.475387006998062, 0.692366003990173],
  [0.465828001499176, 0.779190003871918],
  [0.472328990697861, 0.736225962638855],
  [0.473087012767792, 0.717857003211975],
  [0.473122000694275, 0.704625964164734],
  [0.473033010959625, 0.695277988910675],
  [0.427942007780075, 0.695277988910675],
  [0.426479011774063, 0.703539967536926],
  [0.423162013292313, 0.711845993995667],
  [0.4183090031147, 0.720062971115112],
  [0.390094995498657, 0.639572978019714],
  [0.013953999616206, 0.560034036636353],
  [0.499913990497589, 0.58014702796936],
  [0.413199990987778, 0.69539999961853],
  [0.409626007080078, 0.701822996139526],
  [0.468080013990402, 0.601534962654114],
  [0.422728985548019, 0.585985004901886],
  [0.463079988956451, 0.593783974647522],
  [0.37211999297142, 0.47341400384903],
  [0.334562003612518, 0.496073007583618],
  [0.411671012639999, 0.546965003013611],
  [0.242175996303558, 0.14767599105835],
  [0.290776997804642, 0.201445996761322],
  [0.327338010072708, 0.256527006626129],
  [0.399509996175766, 0.748921036720276],
  [0.441727995872498, 0.261676013469696],
  [0.429764986038208, 0.187834024429321],
  [0.412198007106781, 0.108901023864746],
  [0.288955003023148, 0.398952007293701],
  [0.218936994671822, 0.435410976409912],
  [0.41278201341629, 0.398970007896423],
  [0.257135003805161, 0.355440020561218],
  [0.427684992551804, 0.437960982322693],
  [0.448339998722076, 0.536936044692993],
  [0.178560003638268, 0.45755398273468],
  [0.247308000922203, 0.457193970680237],
  [0.286267012357712, 0.467674970626831],
  [0.332827985286713, 0.460712015628815],
  [0.368755996227264, 0.447206974029541],
  [0.398963987827301, 0.432654976844788],
  [0.476410001516342, 0.405806005001068],
  [0.189241006970406, 0.523923993110657],
  [0.228962004184723, 0.348950982093811],
  [0.490725994110107, 0.562400996685028],
  [0.404670000076294, 0.485132992267609],
  [0.019469000399113, 0.401564002037048],
  [0.426243007183075, 0.420431017875671],
  [0.396993011236191, 0.548797011375427],
  [0.266469985246658, 0.376977026462555],
  [0.439121007919312, 0.51895797252655],
  [0.032313998788595, 0.644356966018677],
  [0.419054001569748, 0.387154996395111],
  [0.462783008813858, 0.505746960639954],
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  45,
  220,
  44,
  156,
  70,
  139,
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  122,
  245,
  139,
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  162,
  145,
  153,
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  149,
  170,
  150,
  122,
  188,
  196,
  206,
  216,
  92,
  163,
  144,
  161,
  164,
  2,
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  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,
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  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,
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  181,
  194,
  201,
  182,
  177,
  137,
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  76,
  183,
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  61,
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  186,
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  216,
  212,
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  192,
  214,
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  139,
  34,
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  218,
  79,
  237,
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  45,
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  201,
  32,
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  192,
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  235,
  59,
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  240,
  75,
  235,
  229,
  24,
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  31,
  25,
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  230,
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  229,
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  22,
  230,
  232,
  26,
  231,
  233,
  112,
  232,
  244,
  189,
  243,
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  221,
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  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,
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  381,
  384,
  382,
  280,
  347,
  330,
  269,
  303,
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  151,
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  344,
  278,
  360,
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  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,
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  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,
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  295,
  195,
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  440,
  274,
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  300,
  368,
  417,
  351,
  465,
  251,
  301,
  389,
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  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 VTX68 = [
  /* cont  */
  127,
  234,
  132,
  58,
  172,
  150,
  149,
  148,
  152,
  377,
  378,
  379,
  397,
  288,
  361,
  454,
  356,
  /* brows */
  70,
  63,
  105,
  66,
  107,
  336,
  296,
  334,
  293,
  300,
  /* nose  */
  168,
  6,
  195,
  4,
  98,
  97,
  2,
  326,
  327,
  /* eyes  */
  33,
  160,
  158,
  133,
  153,
  144,
  362,
  385,
  387,
  263,
  373,
  380,
  /* lip   */
  57,
  40,
  37,
  0,
  267,
  270,
  287,
  321,
  314,
  17,
  84,
  91,
  /* mouth */
  78,
  81,
  13,
  311,
  308,
  402,
  14,
  178
];
var VTX33 = [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];
var VTX7 = [33, 133, 362, 263, 1, 78, 308];
var UV68 = VTX68.map((x) => UV468[x]);
var UV33 = VTX33.map((x) => UV468[x]);
var UV7 = VTX7.map((x) => UV468[x]);
function connectionsToIndices(connections) {
  const indices = connections.map((connection) => connection[0]);
  indices.push(connections[connections.length - 1][1]);
  return indices;
}
var pairsLips = [
  [61, 146],
  [146, 91],
  [91, 181],
  [181, 84],
  [84, 17],
  [17, 314],
  [314, 405],
  [405, 321],
  [321, 375],
  [375, 291],
  [61, 185],
  [185, 40],
  [40, 39],
  [39, 37],
  [37, 0],
  [0, 267],
  [267, 269],
  [269, 270],
  [270, 409],
  [409, 291],
  [78, 95],
  [95, 88],
  [88, 178],
  [178, 87],
  [87, 14],
  [14, 317],
  [317, 402],
  [402, 318],
  [318, 324],
  [324, 308],
  [78, 191],
  [191, 80],
  [80, 81],
  [81, 82],
  [82, 13],
  [13, 312],
  [312, 311],
  [311, 310],
  [310, 415],
  [415, 308]
];
var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
var pairsFaceContour = [
  [10, 338],
  [338, 297],
  [297, 332],
  [332, 284],
  [284, 251],
  [251, 389],
  [389, 356],
  [356, 454],
  [454, 323],
  [323, 361],
  [361, 288],
  [288, 397],
  [397, 365],
  [365, 379],
  [379, 378],
  [378, 400],
  [400, 377],
  [377, 152],
  [152, 148],
  [148, 176],
  [176, 149],
  [149, 150],
  [150, 136],
  [136, 172],
  [172, 58],
  [58, 132],
  [132, 93],
  [93, 234],
  [234, 127],
  [127, 162],
  [162, 21],
  [21, 54],
  [54, 103],
  [103, 67],
  [67, 109],
  [109, 10]
];
var contourKeypoints = {
  lips: connectionsToIndices(pairsLips),
  leftEye: connectionsToIndices(pairsLeftEye),
  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
  leftIris: connectionsToIndices(pairsLeftIris),
  rightEye: connectionsToIndices(pairsRightEye),
  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
  rightIris: connectionsToIndices(pairsRightIris),
  faceOval: connectionsToIndices(pairsFaceContour)
};

// src/face/constants.ts
var LIPS_CONNECTIONS = [
  [61, 146],
  [146, 91],
  [91, 181],
  [181, 84],
  [84, 17],
  [17, 314],
  [314, 405],
  [405, 321],
  [321, 375],
  [375, 291],
  [61, 185],
  [185, 40],
  [40, 39],
  [39, 37],
  [37, 0],
  [0, 267],
  [267, 269],
  [269, 270],
  [270, 409],
  [409, 291],
  [78, 95],
  [95, 88],
  [88, 178],
  [178, 87],
  [87, 14],
  [14, 317],
  [317, 402],
  [402, 318],
  [318, 324],
  [324, 308],
  [78, 191],
  [191, 80],
  [80, 81],
  [81, 82],
  [82, 13],
  [13, 312],
  [312, 311],
  [311, 310],
  [310, 415],
  [415, 308]
];
var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
var FACE_OVAL_CONNECTIONS = [
  [10, 338],
  [338, 297],
  [297, 332],
  [332, 284],
  [284, 251],
  [251, 389],
  [389, 356],
  [356, 454],
  [454, 323],
  [323, 361],
  [361, 288],
  [288, 397],
  [397, 365],
  [365, 379],
  [379, 378],
  [378, 400],
  [400, 377],
  [377, 152],
  [152, 148],
  [148, 176],
  [176, 149],
  [149, 150],
  [150, 136],
  [136, 172],
  [172, 58],
  [58, 132],
  [132, 93],
  [93, 234],
  [234, 127],
  [127, 162],
  [162, 21],
  [21, 54],
  [54, 103],
  [103, 67],
  [67, 109],
  [109, 10]
];
function connectionsToIndices2(connections) {
  const indices = connections.map((connection) => connection[0]);
  indices.push(connections[connections.length - 1][1]);
  return indices;
}
var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
  lips: connectionsToIndices2(LIPS_CONNECTIONS),
  leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
  leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
  leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
  rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
  rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
  rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
  faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
};
var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
  61,
  146,
  91,
  181,
  84,
  17,
  314,
  405,
  321,
  375,
  291,
  // Lower outer.
  185,
  40,
  39,
  37,
  0,
  267,
  269,
  270,
  409,
  // Upper outer(excluding corners).
  78,
  95,
  88,
  178,
  87,
  14,
  317,
  402,
  318,
  324,
  308,
  // Lower inner.
  191,
  80,
  81,
  82,
  13,
  312,
  311,
  310,
  415,
  // Upper inner(excluding corners).
  76,
  77,
  90,
  180,
  85,
  16,
  315,
  404,
  320,
  307,
  306,
  // Lower semi - outer.
  184,
  74,
  73,
  72,
  11,
  302,
  303,
  304,
  408,
  // Upper semi - outer(excluding corners).
  62,
  96,
  89,
  179,
  86,
  15,
  316,
  403,
  319,
  325,
  292,
  // Lower semi - inner.
  183,
  42,
  41,
  38,
  12,
  268,
  271,
  272,
  407
  // Upper semi - inner(excluding corners).
];
var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
  33,
  7,
  163,
  144,
  145,
  153,
  154,
  155,
  133,
  // Lower contour.
  246,
  161,
  160,
  159,
  158,
  157,
  173,
  // upper contour (excluding corners).
  130,
  25,
  110,
  24,
  23,
  22,
  26,
  112,
  243,
  // Halo x2 lower contour.
  247,
  30,
  29,
  27,
  28,
  56,
  190,
  // Halo x2 upper contour (excluding corners).
  226,
  31,
  228,
  229,
  230,
  231,
  232,
  233,
  244,
  // Halo x3 lower contour.
  113,
  225,
  224,
  223,
  222,
  221,
  189,
  // Halo x3 upper contour (excluding corners).
  35,
  124,
  46,
  53,
  52,
  65,
  // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.
  143,
  111,
  117,
  118,
  119,
  120,
  121,
  128,
  245,
  // Halo x5 lower contour.
  156,
  70,
  63,
  105,
  66,
  107,
  55,
  193
  // Halo x5 upper contour (excluding corners) or eyebrow outer contour.
];
var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
  263,
  249,
  390,
  373,
  374,
  380,
  381,
  382,
  362,
  // Lower contour.
  466,
  388,
  387,
  386,
  385,
  384,
  398,
  // Upper contour (excluding corners).
  359,
  255,
  339,
  254,
  253,
  252,
  256,
  341,
  463,
  // Halo x2 lower contour.
  467,
  260,
  259,
  257,
  258,
  286,
  414,
  // Halo x2 upper contour (excluding corners).
  446,
  261,
  448,
  449,
  450,
  451,
  452,
  453,
  464,
  // Halo x3 lower contour.
  342,
  445,
  444,
  443,
  442,
  441,
  413,
  // Halo x3 upper contour (excluding corners).
  265,
  353,
  276,
  283,
  282,
  295,
  // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.
  372,
  340,
  346,
  347,
  348,
  349,
  350,
  357,
  465,
  // Halo x5 lower contour.
  383,
  300,
  293,
  334,
  296,
  336,
  285,
  417
  // Halo x5 upper contour (excluding corners) or eyebrow outer contour.
];

// src/draw/face.ts
var localOptions;
function drawLabels(f, ctx) {
  var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2;
  if (!localOptions.drawLabels || ((_a2 = localOptions.faceLabels) == null ? void 0 : _a2.length) === 0)
    return;
  let l = localOptions.faceLabels.slice();
  if (f.score)
    l = replace(l, "[score]", 100 * f.score);
  if (f.gender)
    l = replace(l, "[gender]", f.gender);
  if (f.genderScore)
    l = replace(l, "[genderScore]", 100 * f.genderScore);
  if (f.age)
    l = replace(l, "[age]", f.age);
  if (f.distance)
    l = replace(l, "[distance]", 100 * f.distance);
  if (f.real)
    l = replace(l, "[real]", 100 * f.real);
  if (f.live)
    l = replace(l, "[live]", 100 * f.live);
  if (f.emotion && f.emotion.length > 0) {
    const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
    if (emotion2.length > 3)
      emotion2.length = 3;
    l = replace(l, "[emotions]", emotion2.join(" "));
  }
  if ((_c2 = (_b2 = f.rotation) == null ? void 0 : _b2.angle) == null ? void 0 : _c2.roll)
    l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll));
  if ((_e = (_d2 = f.rotation) == null ? void 0 : _d2.angle) == null ? void 0 : _e.yaw)
    l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw));
  if ((_g2 = (_f2 = f.rotation) == null ? void 0 : _f2.angle) == null ? void 0 : _g2.pitch)
    l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch));
  if ((_i2 = (_h2 = f.rotation) == null ? void 0 : _h2.gaze) == null ? void 0 : _i2.bearing)
    l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing));
  labels(ctx, l, f.box[0], f.box[1], localOptions);
}
function drawIrisElipse(f, ctx) {
  var _a2, _b2, _c2, _d2;
  if (((_a2 = f.annotations) == null ? void 0 : _a2.leftEyeIris) && ((_b2 = f.annotations) == null ? void 0 : _b2.leftEyeIris[0])) {
    ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
    ctx.beginPath();
    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
    ctx.stroke();
    if (localOptions.fillPolygons) {
      ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
      ctx.fill();
    }
  }
  if (((_c2 = f.annotations) == null ? void 0 : _c2.rightEyeIris) && ((_d2 = f.annotations) == null ? void 0 : _d2.rightEyeIris[0])) {
    ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
    ctx.beginPath();
    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
    ctx.stroke();
    if (localOptions.fillPolygons) {
      ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
      ctx.fill();
    }
  }
}
function drawGazeSpheres(f, ctx) {
  var _a2;
  if (localOptions.drawGaze && ((_a2 = f.rotation) == null ? void 0 : _a2.angle) && typeof Path2D !== "undefined") {
    ctx.strokeStyle = "pink";
    const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
    const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
    const pathV = new Path2D(`
      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
      C
        ${valX} ${f.box[1]},
        ${valX} ${f.box[1] + f.box[3]},
        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
    `);
    const pathH = new Path2D(`
      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
      C 
        ${f.box[0]} ${valY},
        ${f.box[0] + f.box[2]} ${valY},
        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
    `);
    ctx.stroke(pathH);
    ctx.stroke(pathV);
  }
}
function drawGazeArrows(f, ctx) {
  var _a2;
  if (localOptions.drawGaze && ((_a2 = f.rotation) == null ? void 0 : _a2.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
    ctx.strokeStyle = "pink";
    ctx.fillStyle = "pink";
    const leftGaze = [
      f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
      f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
    ];
    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
    const rightGaze = [
      f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
      f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
    ];
    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
  }
}
function drawFacePolygons(f, ctx) {
  if (localOptions.drawPolygons && f.mesh.length >= 468) {
    ctx.lineWidth = 1;
    for (let i = 0; i < TRI468.length / 3; i++) {
      const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
      lines(ctx, points, localOptions);
    }
    drawIrisElipse(f, ctx);
  }
}
function drawFacePoints(f, ctx) {
  if (localOptions.drawPoints && f.mesh.length >= 468) {
    for (let i = 0; i < f.mesh.length; i++) {
      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);
      if (localOptions.drawAttention) {
        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions);
        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions);
        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions);
      }
    }
  }
}
function drawFaceBoxes(f, ctx) {
  if (localOptions.drawBoxes) {
    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
  }
}
function face(inCanvas2, result, drawOptions) {
  localOptions = mergeDeep(options2, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.font = localOptions.font;
  ctx.strokeStyle = localOptions.color;
  ctx.fillStyle = localOptions.color;
  for (const f of result) {
    drawFaceBoxes(f, ctx);
    drawLabels(f, ctx);
    if (f.mesh && f.mesh.length > 0) {
      drawFacePoints(f, ctx);
      drawFacePolygons(f, ctx);
      drawGazeSpheres(f, ctx);
      drawGazeArrows(f, ctx);
    }
  }
}

// src/draw/body.ts
function body(inCanvas2, result, drawOptions) {
  var _a2, _b2;
  const localOptions2 = mergeDeep(options2, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  for (let i = 0; i < result.length; i++) {
    ctx.strokeStyle = localOptions2.color;
    ctx.fillStyle = localOptions2.color;
    ctx.lineWidth = localOptions2.lineWidth;
    ctx.font = localOptions2.font;
    if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) {
      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2);
      if (localOptions2.drawLabels && ((_a2 = localOptions2.bodyLabels) == null ? void 0 : _a2.length) > 0) {
        let l = localOptions2.bodyLabels.slice();
        l = replace(l, "[score]", 100 * result[i].score);
        labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2);
      }
    }
    if (localOptions2.drawPoints && result[i].keypoints) {
      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
          continue;
        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2);
        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2);
      }
    }
    if (localOptions2.drawLabels && ((_b2 = localOptions2.bodyPartLabels) == null ? void 0 : _b2.length) > 0 && result[i].keypoints) {
      ctx.font = localOptions2.font;
      for (const pt of result[i].keypoints) {
        if (!pt.score || pt.score === 0)
          continue;
        let l = localOptions2.bodyPartLabels.slice();
        l = replace(l, "[label]", pt.part);
        l = replace(l, "[score]", 100 * pt.score);
        labels(ctx, l, pt.position[0], pt.position[1], localOptions2);
      }
    }
    if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) {
      for (const part of Object.values(result[i].annotations)) {
        for (const connected4 of part)
          curves(ctx, connected4, localOptions2);
      }
    }
  }
}

// src/draw/hand.ts
function hand(inCanvas2, result, drawOptions) {
  var _a2, _b2;
  const localOptions2 = mergeDeep(options2, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions2.font;
  for (const h of result) {
    if (localOptions2.drawBoxes) {
      ctx.strokeStyle = localOptions2.color;
      ctx.fillStyle = localOptions2.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2);
      if (localOptions2.drawLabels && ((_a2 = localOptions2.handLabels) == null ? void 0 : _a2.length) > 0) {
        let l = localOptions2.handLabels.slice();
        l = replace(l, "[label]", h.label);
        l = replace(l, "[score]", 100 * h.score);
        labels(ctx, l, h.box[0], h.box[1], localOptions2);
      }
      ctx.stroke();
    }
    if (localOptions2.drawPoints) {
      if (h.keypoints && h.keypoints.length > 0) {
        for (const pt of h.keypoints) {
          ctx.fillStyle = colorDepth(pt[2], localOptions2);
          point(ctx, pt[0], pt[1], 0, localOptions2);
        }
      }
    }
    if (localOptions2.drawLabels && h.annotations && ((_b2 = localOptions2.fingerLabels) == null ? void 0 : _b2.length) > 0) {
      for (const [part, pt] of Object.entries(h.annotations)) {
        let l = localOptions2.fingerLabels.slice();
        l = replace(l, "[label]", part);
        labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2);
      }
    }
    if (localOptions2.drawPolygons && h.annotations) {
      const addHandLine = (part) => {
        if (!part || part.length === 0 || !part[0])
          return;
        for (let i = 0; i < part.length; i++) {
          ctx.beginPath();
          const z = part[i][2] || 0;
          ctx.strokeStyle = colorDepth(i * z, localOptions2);
          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
          ctx.lineTo(part[i][0], part[i][1]);
          ctx.stroke();
        }
      };
      ctx.lineWidth = localOptions2.lineWidth;
      addHandLine(h.annotations.index);
      addHandLine(h.annotations.middle);
      addHandLine(h.annotations.ring);
      addHandLine(h.annotations.pinky);
      addHandLine(h.annotations.thumb);
    }
  }
}

// src/draw/object.ts
function object(inCanvas2, result, drawOptions) {
  var _a2;
  const localOptions2 = mergeDeep(options2, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions2.font;
  for (const h of result) {
    if (localOptions2.drawBoxes) {
      ctx.strokeStyle = localOptions2.color;
      ctx.fillStyle = localOptions2.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2);
      if (localOptions2.drawLabels && ((_a2 = localOptions2.objectLabels) == null ? void 0 : _a2.length) > 0) {
        let l = localOptions2.objectLabels.slice();
        l = replace(l, "[label]", h.label);
        l = replace(l, "[score]", 100 * h.score);
        labels(ctx, l, h.box[0], h.box[1], localOptions2);
      }
      ctx.stroke();
    }
  }
}

// src/draw/gesture.ts
function gesture(inCanvas2, result, drawOptions) {
  var _a2;
  const localOptions2 = mergeDeep(options2, drawOptions);
  if (!result || !inCanvas2)
    return;
  if (localOptions2.drawGestures && ((_a2 = localOptions2.gestureLabels) == null ? void 0 : _a2.length) > 0) {
    const ctx = getCanvasContext(inCanvas2);
    if (!ctx)
      return;
    ctx.font = localOptions2.font;
    ctx.fillStyle = localOptions2.color;
    let i = 1;
    for (let j10 = 0; j10 < result.length; j10++) {
      const [where, what] = Object.entries(result[j10]);
      if (what.length > 1 && what[1].length > 0) {
        const who = where[1] > 0 ? `#${where[1]}` : "";
        let l = localOptions2.gestureLabels.slice();
        l = replace(l, "[where]", where[0]);
        l = replace(l, "[who]", who);
        l = replace(l, "[what]", what[1]);
        labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2);
        i += 1;
      }
    }
  }
}

// src/draw/labels.ts
var defaultLabels = {
  face: `face
    confidence: [score]%
    [gender] [genderScore]%
    age: [age] years
    distance: [distance]cm
    real: [real]%
    live: [live]%
    [emotions]
    roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0
    gaze: [gaze]\xB0`,
  body: "body [score]%",
  bodyPart: "[label] [score]%",
  object: "[label] [score]%",
  hand: "[label] [score]%",
  finger: "[label]",
  gesture: "[where] [who]: [what]"
};

// src/draw/draw.ts
var drawTime = 0;
function person(inCanvas2, result, drawOptions) {
  const localOptions2 = mergeDeep(options2, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions2.font;
  for (let i = 0; i < result.length; i++) {
    if (localOptions2.drawBoxes) {
      ctx.strokeStyle = localOptions2.color;
      ctx.fillStyle = localOptions2.color;
      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2);
      if (localOptions2.drawLabels) {
        const label = `person #${i}`;
        if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
          ctx.fillStyle = localOptions2.shadowColor;
          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]);
        }
        ctx.fillStyle = localOptions2.labelColor;
        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]);
      }
      ctx.stroke();
    }
  }
}
function canvas2(input, output) {
  if (!input || !output)
    return;
  const ctx = getCanvasContext(output);
  if (!ctx)
    return;
  ctx.drawImage(input, 0, 0);
}
async function all(inCanvas2, result, drawOptions) {
  if (!(result == null ? void 0 : result.performance) || !inCanvas2)
    return null;
  const timeStamp = now();
  const localOptions2 = mergeDeep(options2, drawOptions);
  const promise = Promise.all([
    face(inCanvas2, result.face, localOptions2),
    body(inCanvas2, result.body, localOptions2),
    hand(inCanvas2, result.hand, localOptions2),
    object(inCanvas2, result.object, localOptions2),
    gesture(inCanvas2, result.gesture, localOptions2)
    // gestures do not have buffering
    // person(inCanvas, result.persons, localOptions); // already included above
  ]);
  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
  result.performance.draw = drawTime;
  return promise;
}
function init2() {
  options2.faceLabels = defaultLabels.face;
  options2.bodyLabels = defaultLabels.body;
  options2.bodyPartLabels = defaultLabels.bodyPart;
  options2.handLabels = defaultLabels.hand;
  options2.fingerLabels = defaultLabels.finger;
  options2.objectLabels = defaultLabels.object;
  options2.gestureLabels = defaultLabels.gesture;
}

// src/body/blazeposecoords.ts
var blazeposecoords_exports = {};
__export(blazeposecoords_exports, {
  connected: () => connected,
  kpt: () => kpt
});
var kpt = [
  "nose",
  //  0
  "leftEyeInside",
  //  1
  "leftEye",
  //  2
  "leftEyeOutside",
  //  3
  "rightEyeInside",
  //  4
  "rightEye",
  //  5
  "rightEyeOutside",
  //  6
  "leftEar",
  //  7
  "rightEar",
  //  8
  "leftMouth",
  //  9
  "rightMouth",
  // 10
  "leftShoulder",
  // 11
  "rightShoulder",
  // 12
  "leftElbow",
  // 13
  "rightElbow",
  // 14
  "leftWrist",
  // 15
  "rightWrist",
  // 16
  "leftPinky",
  // 17
  "rightPinky",
  // 18
  "leftIndex",
  // 19
  "rightIndex",
  // 20
  "leftThumb",
  // 21
  "rightThumb",
  // 22
  "leftHip",
  // 23
  "rightHip",
  // 24
  "leftKnee",
  // 25
  "rightKnee",
  // 26
  "leftAnkle",
  // 27
  "rightAnkle",
  // 28
  "leftHeel",
  // 29
  "rightHeel",
  // 30
  "leftFoot",
  // 31
  "rightFoot",
  // 32
  "bodyCenter",
  // 33
  "bodyTop",
  // 34
  "leftPalm",
  // 35 // z-coord not ok
  "leftHand",
  // 36 // similar to wrist but z-coord not ok
  "rightPalm",
  // 37 // z-coord not ok
  "rightHand"
  // 38 // similar to wrist but z-coord not ok
];
var connected = {
  shoulders: ["leftShoulder", "rightShoulder"],
  hips: ["rightHip", "leftHip"],
  mouth: ["leftMouth", "rightMouth"],
  leftLegUpper: ["leftHip", "leftKnee"],
  leftLegLower: ["leftKnee", "leftAnkle"],
  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
  leftTorso: ["leftShoulder", "leftHip"],
  leftArmUpper: ["leftShoulder", "leftElbow"],
  leftArmLower: ["leftElbow", "leftWrist"],
  leftHand: ["leftWrist", "leftPalm"],
  leftHandPinky: ["leftPalm", "leftPinky"],
  leftHandIndex: ["leftPalm", "leftIndex"],
  leftHandThumb: ["leftPalm", "leftThumb"],
  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
  rightLegUpper: ["rightHip", "rightKnee"],
  rightLegLower: ["rightKnee", "rightAnkle"],
  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
  rightTorso: ["rightShoulder", "rightHip"],
  rightArmUpper: ["rightShoulder", "rightElbow"],
  rightArmLower: ["rightElbow", "rightWrist"],
  rightHand: ["rightWrist", "rightPalm"],
  rightHandPinky: ["rightPalm", "rightPinky"],
  rightHandIndex: ["rightPalm", "rightIndex"],
  rightHandThumb: ["rightPalm", "rightThumb"],
  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
};

// src/body/blazeposedetector.ts
var model;
var inputSize = 224;
var anchorTensor;
var numLayers = 5;
var strides = [8, 16, 32, 32, 32];
function createAnchors() {
  const anchors3 = [];
  let layerId = 0;
  while (layerId < numLayers) {
    let anchorCount = 0;
    let lastSameStrideLayer = layerId;
    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
      anchorCount += 2;
      lastSameStrideLayer++;
    }
    const stride = strides[layerId];
    const featureMapHeight = Math.ceil(inputSize / stride);
    const featureMapWidth = Math.ceil(inputSize / stride);
    for (let y6 = 0; y6 < featureMapHeight; ++y6) {
      for (let x = 0; x < featureMapWidth; ++x) {
        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y6 + 0.5) / featureMapHeight });
        }
      }
    }
    layerId = lastSameStrideLayer;
  }
  anchorTensor = { x: dr(anchors3.map((a) => a.x)), y: dr(anchors3.map((a) => a.y)) };
}
async function loadDetector(config3) {
  if (env.initial)
    model = null;
  if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") {
    model = await loadModel(config3.body["detector"].modelPath);
    const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0;
    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
  } else if (config3.debug && model)
    log("cached model:", model["modelUrl"]);
  createAnchors();
  return model;
}
var cropFactor = [5, 5];
function decodeBoxes(boxesTensor, anchor) {
  return Ee(() => {
    const split = Oa(boxesTensor, 12, 1);
    let xCenter = Up(split[0]);
    let yCenter = Up(split[1]);
    let width = Up(split[2]);
    let height = Up(split[3]);
    xCenter = be(He(xCenter, inputSize), anchor.x);
    yCenter = be(He(yCenter, inputSize), anchor.y);
    width = ne(He(width, inputSize), cropFactor[0]);
    height = ne(He(height, inputSize), cropFactor[1]);
    const xMin = ke(xCenter, He(width, 2));
    const yMin = ke(yCenter, He(height, 2));
    const xMax = be(xMin, width);
    const yMax = be(yMin, height);
    const boxes = Sr([xMin, yMin, xMax, yMax], 1);
    return boxes;
  });
}
async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) {
  var _a2, _b2;
  const detectedBoxes = [];
  const t10 = {};
  t10.boxes = decodeBoxes(boxesTensor, anchorTensor);
  t10.scores = Ws(logitsTensor);
  t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b2 = config3.body["detector"]) == null ? void 0 : _b2.iouThreshold) || 0.1);
  const nms = await t10.nms.data();
  const scores = await t10.scores.data();
  const boxes = await t10.boxes.array();
  for (const i of Array.from(nms)) {
    const score = scores[i];
    const boxRaw = boxes[i];
    const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])];
    const detectedBox = { score, boxRaw, box };
    detectedBoxes.push(detectedBox);
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return detectedBoxes;
}
async function detectBoxes(input, config3, outputSize2) {
  const t10 = {};
  t10.res = model == null ? void 0 : model.execute(input, ["Identity"]);
  t10.logitsRaw = Ke(t10.res, [0, 0, 0], [1, -1, 1]);
  t10.boxesRaw = Ke(t10.res, [0, 0, 1], [1, -1, -1]);
  t10.logits = Up(t10.logitsRaw);
  t10.boxes = Up(t10.boxesRaw);
  const boxes = await decodeResults(t10.boxes, t10.logits, config3, outputSize2);
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return boxes;
}

// src/util/box.ts
function calc(keypoints, outputSize2 = [1, 1]) {
  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
  const min = [Math.min(...coords[0]), Math.min(...coords[1])];
  const max = [Math.max(...coords[0]), Math.max(...coords[1])];
  const box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];
  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
  return { box, boxRaw };
}
function square(keypoints, outputSize2 = [1, 1]) {
  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
  const min = [Math.min(...coords[0]), Math.min(...coords[1])];
  const max = [Math.max(...coords[0]), Math.max(...coords[1])];
  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2];
  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]);
  const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
  return { box, boxRaw };
}
function scale(box, scaleFact) {
  const dist = [box[2] * scaleFact, box[3] * scaleFact];
  const newBox = [
    box[0] - (dist[0] - box[2]) / 2,
    box[1] - (dist[1] - box[3]) / 2,
    dist[0],
    dist[1]
  ];
  return newBox;
}

// src/body/blazepose.ts
var model2;
var inputSize2 = 256;
var skipped = Number.MAX_SAFE_INTEGER;
var outputNodes = {
  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
  detector: []
};
var cache = [];
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime = 0;
var sigmoid = (x) => 1 - 1 / (1 + Math.exp(x));
var loadDetect = (config3) => loadDetector(config3);
async function loadPose(config3) {
  if (env.initial)
    model2 = null;
  if (!model2) {
    model2 = await loadModel(config3.body.modelPath);
    const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0;
    inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
  } else if (config3.debug)
    log("cached model:", model2["modelUrl"]);
  return model2;
}
function prepareImage(input, size2, cropBox) {
  var _a2, _b2;
  const t10 = {};
  if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b2 = input == null ? void 0 : input.shape) == null ? void 0 : _b2[2]))
    return input;
  let final;
  if (cropBox) {
    t10.cropped = eK.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
  }
  if (input.shape[1] !== input.shape[2]) {
    const height = [
      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
    ];
    const width = [
      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
    ];
    padding = [
      [0, 0],
      // dont touch batch
      height,
      // height before&after
      width,
      // width before&after
      [0, 0]
      // dont touch rbg
    ];
    t10.pad = Ks(t10.cropped || input, padding);
    t10.resize = eK.resizeBilinear(t10.pad, [size2, size2]);
    final = He(t10.resize, constants.tf255);
  } else if (input.shape[1] !== size2) {
    t10.resize = eK.resizeBilinear(t10.cropped || input, [size2, size2]);
    final = He(t10.resize, constants.tf255);
  } else {
    final = He(t10.cropped || input, constants.tf255);
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return final;
}
function rescaleKeypoints(keypoints, outputSize2, cropBox) {
  for (const kpt4 of keypoints) {
    kpt4.position = [
      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
      kpt4.position[2]
    ];
    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
  }
  if (cropBox) {
    const width = cropBox[2] - cropBox[0];
    const height = cropBox[3] - cropBox[1];
    for (const kpt4 of keypoints) {
      kpt4.positionRaw = [
        kpt4.positionRaw[0] / height + cropBox[1],
        // correct offset due to crop
        kpt4.positionRaw[1] / width + cropBox[0],
        // correct offset due to crop
        kpt4.positionRaw[2]
      ];
      kpt4.position = [
        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
        kpt4.positionRaw[2]
      ];
    }
  }
  return keypoints;
}
function fixKeypoints(keypoints) {
  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
}
async function detectLandmarks(input, config3, outputSize2) {
  if (!(model2 == null ? void 0 : model2["executor"]))
    return null;
  const t10 = {};
  [
    t10.ld,
    t10.segmentation,
    t10.heatmap,
    t10.world,
    t10.poseflag
    /* 1,1 */
  ] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks);
  const poseScore = (await t10.poseflag.data())[0];
  const points = await t10.ld.data();
  const distances = await t10.world.data();
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  const keypointsRelative = [];
  const depth = 5;
  for (let i = 0; i < points.length / depth; i++) {
    const score = sigmoid(points[depth * i + 3]);
    const presence = sigmoid(points[depth * i + 4]);
    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
    const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0];
    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
  }
  if (poseScore < (config3.body.minConfidence || 0))
    return null;
  fixKeypoints(keypointsRelative);
  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
  const kpts = keypoints.map((k) => k.position);
  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
  const annotations2 = {};
  for (const [name, indexes] of Object.entries(connected)) {
    const pt = [];
    for (let i = 0; i < indexes.length - 1; i++) {
      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
      if (pt0 && pt1)
        pt.push([pt0.position, pt1.position]);
    }
    annotations2[name] = pt;
  }
  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
  return body4;
}
async function predict(input, config3) {
  var _a2, _b2, _c2;
  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime;
  const skipFrame = skipped < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
    skipped++;
  } else {
    let boxes = [];
    if ((_b2 = (_a2 = config3.body) == null ? void 0 : _a2["detector"]) == null ? void 0 : _b2["enabled"]) {
      const preparedImage = prepareImage(input, 224);
      boxes = await detectBoxes(preparedImage, config3, outputSize2);
      Ot(preparedImage);
    } else {
      boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }];
    }
    for (let i = 0; i < boxes.length; i++) {
      const preparedBox = prepareImage(input, 256, (_c2 = boxes[i]) == null ? void 0 : _c2.boxRaw);
      cache.length = 0;
      const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2);
      Ot(preparedBox);
      if (!bodyResult)
        continue;
      bodyResult.id = i;
      cache.push(bodyResult);
    }
    lastTime = now();
    skipped = 0;
  }
  return cache;
}

// src/object/labels.ts
var labels2 = [
  { 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" }
];

// src/object/centernet.ts
var model3;
var inputSize3 = 0;
var last2 = [];
var lastTime2 = 0;
var skipped2 = Number.MAX_SAFE_INTEGER;
async function load(config3) {
  if (env.initial)
    model3 = null;
  if (!model3) {
    model3 = await loadModel(config3.object.modelPath);
    const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
    inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", model3["modelUrl"]);
  return model3;
}
async function process3(res, outputShape, config3) {
  if (!res)
    return [];
  const t10 = {};
  const results = [];
  const detections = await res.array();
  t10.squeeze = Up(res);
  const arr = Oa(t10.squeeze, 6, 1);
  t10.stack = Sr([arr[1], arr[0], arr[3], arr[2]], 1);
  t10.boxes = Up(t10.stack);
  t10.scores = Up(arr[4]);
  t10.classes = Up(arr[5]);
  Ot([res, ...arr]);
  t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0);
  const nms = await t10.nms.data();
  let i = 0;
  for (const id2 of Array.from(nms)) {
    const score = Math.trunc(100 * detections[0][id2][4]) / 100;
    const classVal = detections[0][id2][5];
    if (Number.isNaN(classVal))
      continue;
    const label = labels2[classVal].label;
    const [x, y6] = [
      detections[0][id2][0] / inputSize3,
      detections[0][id2][1] / inputSize3
    ];
    const boxRaw = [
      x,
      y6,
      detections[0][id2][2] / inputSize3 - x,
      detections[0][id2][3] / inputSize3 - y6
    ];
    const box = [
      Math.trunc(boxRaw[0] * outputShape[0]),
      Math.trunc(boxRaw[1] * outputShape[1]),
      Math.trunc(boxRaw[2] * outputShape[0]),
      Math.trunc(boxRaw[3] * outputShape[1])
    ];
    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return results;
}
async function predict2(input, config3) {
  if (!(model3 == null ? void 0 : model3["executor"]))
    return [];
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime2;
  const skipFrame = skipped2 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
    skipped2++;
    return last2;
  }
  skipped2 = 0;
  return new Promise(async (resolve) => {
    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
    const resize = eK.resizeBilinear(input, [inputSize3, inputSize3]);
    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
    lastTime2 = now();
    Ot(resize);
    const obj = await process3(objectT, outputSize2, config3);
    last2 = obj;
    resolve(obj);
  });
}

// src/body/efficientposecoords.ts
var efficientposecoords_exports = {};
__export(efficientposecoords_exports, {
  connected: () => connected2,
  kpt: () => kpt2
});
var kpt2 = [
  "head",
  "neck",
  "rightShoulder",
  "rightElbow",
  "rightWrist",
  "chest",
  "leftShoulder",
  "leftElbow",
  "leftWrist",
  "bodyCenter",
  "rightHip",
  "rightKnee",
  "rightAnkle",
  "leftHip",
  "leftKnee",
  "leftAnkle"
];
var connected2 = {
  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
  head: []
};

// src/body/efficientpose.ts
var model4;
var lastTime3 = 0;
var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
var skipped3 = Number.MAX_SAFE_INTEGER;
async function load2(config3) {
  if (env.initial)
    model4 = null;
  if (!model4)
    model4 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model4["modelUrl"]);
  return model4;
}
async function max2d(inputs, minScore) {
  const [width, height] = inputs.shape;
  const reshaped = W(inputs, [height * width]);
  const max = Gs(reshaped, 0);
  const newScore = (await max.data())[0];
  if (newScore > minScore) {
    const coordinates = X0(reshaped, 0);
    const mod = Jv(coordinates, width);
    const x = (await mod.data())[0];
    const div = He(coordinates, width);
    const y6 = (await div.data())[0];
    Ot([reshaped, max, coordinates, mod, div]);
    return [x, y6, newScore];
  }
  Ot([reshaped, max]);
  return [0, 0, newScore];
}
async function predict3(image, config3) {
  if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape))
    return [];
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime3;
  const skipFrame = skipped3 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
    skipped3++;
    return [cache2];
  }
  skipped3 = 0;
  return new Promise(async (resolve) => {
    const tensor = Ee(() => {
      var _a2, _b2;
      const resize = eK.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b2[1]) || 0], false);
      const enhance2 = ne(resize, constants.tf2);
      const norm = ke(enhance2, constants.tf1);
      return norm;
    });
    let resT;
    if (config3.body.enabled)
      resT = model4 == null ? void 0 : model4.execute(tensor);
    lastTime3 = now();
    Ot(tensor);
    if (resT) {
      cache2.keypoints.length = 0;
      const squeeze = Up(resT);
      Ot(resT);
      const stack = so(squeeze, 2);
      Ot(squeeze);
      for (let id2 = 0; id2 < stack.length; id2++) {
        const [x6, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence);
        if (partScore > (config3.body.minConfidence || 0)) {
          cache2.keypoints.push({
            score: Math.round(100 * partScore) / 100,
            part: kpt2[id2],
            positionRaw: [
              // normalized to 0..1
              // @ts-ignore model is not undefined here
              x6 / model4.inputs[0].shape[2],
              y10 / model4.inputs[0].shape[1]
            ],
            position: [
              // normalized to input image size
              // @ts-ignore model is not undefined here
              Math.round(image.shape[2] * x6 / model4.inputs[0].shape[2]),
              Math.round(image.shape[1] * y10 / model4.inputs[0].shape[1])
            ]
          });
        }
      }
      stack.forEach((s) => Ot(s));
    }
    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
    const x = cache2.keypoints.map((a) => a.position[0]);
    const y6 = cache2.keypoints.map((a) => a.position[1]);
    cache2.box = [
      Math.min(...x),
      Math.min(...y6),
      Math.max(...x) - Math.min(...x),
      Math.max(...y6) - Math.min(...y6)
    ];
    const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
    const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
    cache2.boxRaw = [
      Math.min(...xRaw),
      Math.min(...yRaw),
      Math.max(...xRaw) - Math.min(...xRaw),
      Math.max(...yRaw) - Math.min(...yRaw)
    ];
    for (const [name, indexes] of Object.entries(connected2)) {
      const pt = [];
      for (let i = 0; i < indexes.length - 1; i++) {
        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
          pt.push([pt0.position, pt1.position]);
      }
      cache2.annotations[name] = pt;
    }
    resolve([cache2]);
  });
}

// src/face/facemeshutil.ts
var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
var clampBox = (box, input) => box ? [
  Math.trunc(Math.max(0, box.startPoint[0])),
  Math.trunc(Math.max(0, box.startPoint[1])),
  Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
  Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
] : [0, 0, 0, 0];
var getRawBox = (box, input) => box ? [
  box.startPoint[0] / (input.shape[2] || 0),
  box.startPoint[1] / (input.shape[1] || 0),
  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
] : [0, 0, 0, 0];
var scaleBoxCoordinates = (box, factor) => {
  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
};
var cutAndResize = (box, image, cropSize) => {
  const h = image.shape[1];
  const w = image.shape[2];
  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
  const crop = eK.cropAndResize(image, [cutBox], [0], cropSize);
  const norm = He(crop, constants.tf255);
  Ot(crop);
  return norm;
};
var enlargeBox = (box, factor) => {
  const center = getBoxCenter(box);
  const size2 = getBoxSize(box);
  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
};
var squarifyBox = (box) => {
  const centers = getBoxCenter(box);
  const size2 = getBoxSize(box);
  const halfSize = Math.max(...size2) / 2;
  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
};
var calculateLandmarksBoundingBox = (landmarks) => {
  const x = landmarks.map((d) => d[0]);
  const y6 = landmarks.map((d) => d[1]);
  return { startPoint: [Math.min(...x), Math.min(...y6)], endPoint: [Math.max(...x), Math.max(...y6)], landmarks };
};
var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
var buildTranslationMatrix = (x, y6) => [[1, 0, x], [0, 1, y6], [0, 0, 1]];
var dot = (v12, v22) => {
  let product = 0;
  for (let i = 0; i < v12.length; i++)
    product += v12[i] * v22[i];
  return product;
};
var getColumnFrom2DArr = (arr, columnIndex) => {
  const column = [];
  for (let i = 0; i < arr.length; i++)
    column.push(arr[i][columnIndex]);
  return column;
};
var multiplyTransformMatrices = (mat1, mat2) => {
  const product = [];
  const size2 = mat1.length;
  for (let row = 0; row < size2; row++) {
    product.push([]);
    for (let col = 0; col < size2; col++)
      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
  }
  return product;
};
var buildRotationMatrix = (rotation, center) => {
  const cosA = Math.cos(rotation);
  const sinA = Math.sin(rotation);
  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
  const translationMatrix = buildTranslationMatrix(center[0], center[1]);
  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
};
var invertTransformMatrix = (matrix) => {
  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
  const translationComponent = [matrix[0][2], matrix[1][2]];
  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
};
var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
function generateAnchors(inputSize10) {
  const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
  const anchors3 = [];
  for (let i = 0; i < spec.strides.length; i++) {
    const stride = spec.strides[i];
    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
    const anchorsNum = spec.anchors[i];
    for (let gridY = 0; gridY < gridRows; gridY++) {
      const anchorY = stride * (gridY + 0.5);
      for (let gridX = 0; gridX < gridCols; gridX++) {
        const anchorX = stride * (gridX + 0.5);
        for (let n = 0; n < anchorsNum; n++)
          anchors3.push([anchorX, anchorY]);
      }
    }
  }
  return anchors3;
}
function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
  const boxSize = getBoxSize(box);
  const coordsScaled = coordsRaw.map((coord) => [
    // scaled around zero-point
    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
    coord[2] || 0
  ]);
  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
  const boxCenter = getBoxCenter(box);
  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
  return coordsRotated.map((coord) => [
    Math.trunc(coord[0] + offsets[0]),
    Math.trunc(coord[1] + offsets[1]),
    Math.trunc(coord[2] || 0)
  ]);
}
function correctFaceRotation(rotate, box, input, inputSize10) {
  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
  let angle = 0;
  let rotationMatrix = fixedRotationMatrix;
  let face4;
  if (rotate && env.kernels.includes("rotatewithoffset")) {
    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
    if (largeAngle) {
      const center = getBoxCenter(box);
      const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
      const rotated = eK.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);
      rotationMatrix = buildRotationMatrix(-angle, center);
      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
      Ot(rotated);
    } else {
      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
    }
  } else {
    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
  }
  return [angle, rotationMatrix, face4];
}
var findFaceCenter = (mesh) => {
  const x = mesh.map((m) => m[0]);
  const y6 = mesh.map((m) => m[1]);
  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y6) + (Math.max(...y6) - Math.min(...y6)) / 2];
};
var calculateFaceBox = (mesh, previousBox) => {
  const center = findFaceCenter(mesh);
  const boxSize = getBoxSize(previousBox);
  const calculatedBox = {
    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
  };
  return calculatedBox;
};

// src/face/blazeface.ts
var keypointsCount = 6;
var faceBoxScaleFactor = 1.4;
var model5;
var anchors = null;
var inputSize4 = 0;
var inputSizeT = null;
var size = () => inputSize4;
async function load3(config3) {
  var _a2;
  if (env.initial)
    model5 = null;
  if (!model5)
    model5 = await loadModel((_a2 = config3.face.detector) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model5["modelUrl"]);
  inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256;
  inputSizeT = Ce(inputSize4, "int32");
  anchors = Oi(generateAnchors(inputSize4));
  return model5;
}
function decodeBoxes2(boxOutputs) {
  if (!anchors || !inputSizeT)
    return Br([0, 0]);
  const t10 = {};
  t10.boxStarts = Ke(boxOutputs, [0, 1], [-1, 2]);
  t10.centers = be(t10.boxStarts, anchors);
  t10.boxSizes = Ke(boxOutputs, [0, 3], [-1, 2]);
  t10.boxSizesNormalized = He(t10.boxSizes, inputSizeT);
  t10.centersNormalized = He(t10.centers, inputSizeT);
  t10.halfBoxSize = He(t10.boxSizesNormalized, constants.tf2);
  t10.starts = ke(t10.centersNormalized, t10.halfBoxSize);
  t10.ends = be(t10.centersNormalized, t10.halfBoxSize);
  t10.startNormalized = ne(t10.starts, inputSizeT);
  t10.endNormalized = ne(t10.ends, inputSizeT);
  const boxes = fv([t10.startNormalized, t10.endNormalized], 1);
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return boxes;
}
async function getBoxes(inputImage, config3) {
  var _a2, _b2, _c2, _d2;
  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
    return [];
  const t10 = {};
  t10.resized = eK.resizeBilinear(inputImage, [inputSize4, inputSize4]);
  t10.div = He(t10.resized, constants.tf127);
  t10.normalized = ke(t10.div, constants.tf05);
  const res = model5 == null ? void 0 : model5.execute(t10.normalized);
  if (Array.isArray(res) && res.length > 2) {
    const sorted = res.sort((a, b) => a.size - b.size);
    t10.concat384 = xt([sorted[0], sorted[2]], 2);
    t10.concat512 = xt([sorted[1], sorted[3]], 2);
    t10.concat = xt([t10.concat512, t10.concat384], 1);
    t10.batch = Up(t10.concat, [0]);
  } else if (Array.isArray(res)) {
    t10.batch = Up(res[0]);
  } else {
    t10.batch = Up(res);
  }
  Ot(res);
  t10.boxes = decodeBoxes2(t10.batch);
  t10.logits = Ke(t10.batch, [0, 0], [-1, 1]);
  t10.sigmoid = Ws(t10.logits);
  t10.scores = Up(t10.sigmoid);
  t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c2 = config3.face.detector) == null ? void 0 : _c2.minConfidence) || 0);
  const nms = await t10.nms.array();
  const boxes = [];
  const scores = await t10.scores.data();
  for (let i = 0; i < nms.length; i++) {
    const confidence = scores[nms[i]];
    if (confidence > (((_d2 = config3.face.detector) == null ? void 0 : _d2.minConfidence) || 0)) {
      const b = {};
      b.bbox = Ke(t10.boxes, [nms[i], 0], [1, -1]);
      b.slice = Ke(t10.batch, [nms[i], keypointsCount - 1], [1, -1]);
      b.squeeze = Up(b.slice);
      b.landmarks = W(b.squeeze, [keypointsCount, -1]);
      const points = await b.bbox.data();
      const rawBox = {
        startPoint: [points[0], points[1]],
        endPoint: [points[2], points[3]],
        landmarks: await b.landmarks.array(),
        confidence
      };
      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4]);
      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
      const squaredBox = squarifyBox(enlargedBox);
      boxes.push(squaredBox);
      Object.keys(b).forEach((tensor) => Ot(b[tensor]));
    }
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return boxes;
}

// src/face/iris.ts
var model6;
var inputSize5 = 0;
var irisEnlarge = 2.3;
var leftOutline = meshAnnotations.leftEyeLower0;
var rightOutline = meshAnnotations.rightEyeLower0;
var eyeLandmarks = {
  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
};
var irisLandmarks = {
  upperCenter: 3,
  lowerCenter: 4,
  index: 71,
  numCoordinates: 76
};
async function load4(config3) {
  var _a2, _b2;
  if (env.initial)
    model6 = null;
  if (!model6)
    model6 = await loadModel((_a2 = config3.face.iris) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model6["modelUrl"]);
  inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b2 = model6.inputs) == null ? void 0 : _b2[0].shape) ? model6.inputs[0].shape[2] : 0;
  if (inputSize5 === -1)
    inputSize5 = 64;
  return model6;
}
function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
  for (let i = 0; i < irisIndices.length; i++) {
    const { key, indices } = irisIndices[i];
    const originalIndices = meshAnnotations[`${prefix}${key}`];
    if (!keys || keys.includes(key)) {
      for (let j10 = 0; j10 < indices.length; j10++) {
        const index2 = indices[j10];
        rawCoords[originalIndices[j10]] = [
          newCoords[index2][0],
          newCoords[index2][1],
          (newCoords[index2][2] + rawCoords[originalIndices[j10]][2]) / 2
        ];
      }
    }
  }
}
var getLeftToRightEyeDepthDifference = (rawCoords) => {
  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
  return leftEyeZ - rightEyeZ;
};
var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
  const boxSize = getBoxSize(box);
  let crop = eK.cropAndResize(face4, [[
    box.startPoint[1] / meshSize,
    box.startPoint[0] / meshSize,
    box.endPoint[1] / meshSize,
    box.endPoint[0] / meshSize
  ]], [0], [inputSize5, inputSize5]);
  if (flip && env.kernels.includes("flipleftright")) {
    const flipped = eK.flipLeftRight(crop);
    Ot(crop);
    crop = flipped;
  }
  return { box, boxSize, crop };
};
var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
  const eyeRawCoords = [];
  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
    const x = eyeData[i * 3];
    const y6 = eyeData[i * 3 + 1];
    const z = eyeData[i * 3 + 2];
    eyeRawCoords.push([
      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
      y6 / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
      z
    ]);
  }
  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
  return irisCoords.map((coord, i) => {
    let z = averageZ;
    if (i === 2) {
      z = upperCenterZ;
    } else if (i === 4) {
      z = lowerCenterZ;
    }
    return [coord[0], coord[1], z];
  });
};
async function augmentIris(rawCoords, face4, meshSize) {
  if (!(model6 == null ? void 0 : model6["executor"]))
    return rawCoords;
  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
  const combined = xt([leftEyeCrop, rightEyeCrop]);
  Ot(leftEyeCrop);
  Ot(rightEyeCrop);
  const eyePredictions = model6.execute(combined);
  Ot(combined);
  const eyePredictionsData = await eyePredictions.data();
  Ot(eyePredictions);
  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
  } else if (leftToRightEyeDepthDifference < 1) {
    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
  } else {
    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
  }
  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
  return newCoords;
}

// src/face/attention.ts
async function augment(rawCoords, results) {
  var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2;
  const t10 = {
    // all attention models produce 2d results so it needs to be later augmented with correct z-coords
    // mesh: results[0], // already have it in rawCoords // output_mesh_identity
    // flag: results[1], // already processed in parent // conv_faceflag
    lips: await ((_b2 = (_a2 = results.filter((r) => r.size === 160)) == null ? void 0 : _a2[0]) == null ? void 0 : _b2.data()),
    // 80 x 2d = 160 // output_lips
    irisL: await ((_d2 = (_c2 = results.filter((r) => r.size === 10)) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.data()),
    // 5 x 2d = 10 // output_right_iris
    eyeL: await ((_f2 = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f2.data()),
    // 71 x 2d = 142 // output_right_eye
    irisR: await ((_h2 = (_g2 = results.filter((r) => r.size === 10)) == null ? void 0 : _g2[1]) == null ? void 0 : _h2.data()),
    // 5 x 2d = 10 // output_left_iris
    eyeR: await ((_j2 = (_i2 = results.filter((r) => r.size === 142)) == null ? void 0 : _i2[1]) == null ? void 0 : _j2.data())
    // 71 x 2d = 142// output_left_eye
  };
  for (const val of Object.values(t10)) {
    if (!val)
      return rawCoords;
  }
  const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
  for (let i = 0; i < t10.irisL.length / 2; i++)
    rawCoords.push([t10.irisL[2 * i + 0], t10.irisL[2 * i + 1], irisLDepth]);
  const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
  for (let i = 0; i < t10.irisR.length / 2; i++)
    rawCoords.push([t10.irisR[2 * i + 0], t10.irisR[2 * i + 1], irisRDepth]);
  for (let i = 0; i < t10.eyeL.length / 2; i++)
    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t10.eyeL[2 * i + 0], t10.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
  for (let i = 0; i < t10.eyeR.length / 2; i++)
    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t10.eyeR[2 * i + 0], t10.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
  for (let i = 0; i < t10.lips.length / 2; i++)
    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t10.lips[2 * i + 0], t10.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
  return rawCoords;
}

// src/face/facemesh.ts
var cache3 = {
  boxes: [],
  skipped: Number.MAX_SAFE_INTEGER,
  timestamp: 0
};
var model7 = null;
var inputSize6 = 0;
async function predict4(input, config3) {
  var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2;
  const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
  const skipFrame = cache3.skipped < (((_b2 = config3.face.detector) == null ? void 0 : _b2.skipFrames) || 0);
  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
    cache3.boxes = await getBoxes(input, config3);
    cache3.timestamp = now();
    cache3.skipped = 0;
  } else {
    cache3.skipped++;
  }
  const faces = [];
  const newCache = [];
  let id2 = 0;
  const size2 = inputSize6;
  for (let i = 0; i < cache3.boxes.length; i++) {
    const box = cache3.boxes[i];
    let angle = 0;
    let rotationMatrix;
    const face4 = {
      // init face result
      id: id2++,
      mesh: [],
      meshRaw: [],
      box: [0, 0, 0, 0],
      boxRaw: [0, 0, 0, 0],
      score: 0,
      boxScore: 0,
      faceScore: 0,
      // contoursRaw: [],
      // contours: [],
      annotations: {}
    };
    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c2 = config3.face.detector) == null ? void 0 : _c2.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size());
    if (config3.filter.equalization) {
      const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
      Ot(face4.tensor);
      if (equilized)
        face4.tensor = equilized;
    }
    face4.boxScore = Math.round(100 * box.confidence) / 100;
    if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled) || !(model7 == null ? void 0 : model7["executor"])) {
      face4.box = clampBox(box, input);
      face4.boxRaw = getRawBox(box, input);
      face4.score = face4.boxScore;
      face4.mesh = box.landmarks.map((pt) => [
        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
      ]);
      face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
      for (const key of Object.keys(blazeFaceLandmarks)) {
        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
      }
    } else if (!model7) {
      if (config3.debug)
        log("face mesh detection requested, but model is not loaded");
    } else {
      if (((_f2 = config3.face.attention) == null ? void 0 : _f2.enabled) && !env.kernels.includes("atan2")) {
        config3.face.attention.enabled = false;
        Ot(face4.tensor);
        return faces;
      }
      const results = model7.execute(face4.tensor);
      const confidenceT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1);
      const faceConfidence = await confidenceT.data();
      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
      if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) {
        box.confidence = face4.faceScore;
        if (config3.face.mesh.keepInvalid) {
          face4.box = clampBox(box, input);
          face4.boxRaw = getRawBox(box, input);
          face4.score = face4.boxScore;
          face4.mesh = box.landmarks.map((pt) => [
            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
          ]);
          face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
          for (const key of Object.keys(blazeFaceLandmarks)) {
            face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
          }
        }
      } else {
        const meshT = results.find((t10) => t10.shape[t10.shape.length - 1] === 1404);
        const coordsReshaped = W(meshT, [-1, 3]);
        let rawCoords = await coordsReshaped.array();
        Ot(coordsReshaped);
        if ((_h2 = config3.face.attention) == null ? void 0 : _h2.enabled) {
          rawCoords = await augment(rawCoords, results);
        } else if ((_i2 = config3.face.iris) == null ? void 0 : _i2.enabled) {
          rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
        }
        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
        face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
        for (const key of Object.keys(meshAnnotations))
          face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
        face4.score = face4.faceScore;
        const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
        face4.box = clampBox(calculatedBox, input);
        face4.boxRaw = getRawBox(calculatedBox, input);
        newCache.push(calculatedBox);
      }
      Ot(results);
    }
    if (face4.score > (((_j2 = config3.face.detector) == null ? void 0 : _j2.minConfidence) || 1))
      faces.push(face4);
    else
      Ot(face4.tensor);
  }
  cache3.boxes = newCache;
  return faces;
}
async function load5(config3) {
  var _a2, _b2, _c2, _d2, _e, _f2;
  if (env.initial)
    model7 = null;
  if (((_a2 = config3.face.attention) == null ? void 0 : _a2.enabled) && (model7 == null ? void 0 : model7["signature"])) {
    if (Object.keys(((_b2 = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b2.outputs) || {}).length < 6)
      model7 = null;
  }
  if (!model7) {
    if ((_c2 = config3.face.attention) == null ? void 0 : _c2.enabled)
      model7 = await loadModel(config3.face.attention.modelPath);
    else
      model7 = await loadModel((_d2 = config3.face.mesh) == null ? void 0 : _d2.modelPath);
  } else if (config3.debug) {
    log("cached model:", model7["modelUrl"]);
  }
  inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f2 = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f2[0].shape[2] : 256;
  return model7;
}
var triangulation = TRI468;
var uvmap = UV468;

// src/gear/emotion.ts
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var model8;
var last3 = [];
var lastCount = 0;
var lastTime4 = 0;
var skipped4 = Number.MAX_SAFE_INTEGER;
async function load6(config3) {
  var _a2;
  if (env.initial)
    model8 = null;
  if (!model8)
    model8 = await loadModel((_a2 = config3.face.emotion) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model8["modelUrl"]);
  return model8;
}
async function predict5(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model8)
    return [];
  const skipFrame = skipped4 < (((_a2 = config3.face.emotion) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.emotion) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime4;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) {
    skipped4++;
    return last3[idx];
  }
  skipped4 = 0;
  return new Promise(async (resolve) => {
    var _a3;
    const obj = [];
    if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
      const t10 = {};
      const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
      t10.resize = eK.resizeBilinear(image, [inputSize10, inputSize10], false);
      t10.channels = ne(t10.resize, constants.rgb);
      t10.grayscale = et(t10.channels, 3, true);
      t10.grayscaleSub = ke(t10.grayscale, constants.tf05);
      t10.grayscaleMul = ne(t10.grayscaleSub, constants.tf2);
      t10.emotion = model8 == null ? void 0 : model8.execute(t10.grayscaleMul);
      lastTime4 = now();
      const data = await t10.emotion.data();
      for (let i = 0; i < data.length; i++) {
        if (data[i] > (config3.face.emotion.minConfidence || 0))
          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
      }
      obj.sort((a, b) => b.score - a.score);
      Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    }
    last3[idx] = obj;
    lastCount = count2;
    resolve(obj);
  });
}

// src/face/faceres.ts
var model9;
var last4 = [];
var lastTime5 = 0;
var lastCount2 = 0;
var skipped5 = Number.MAX_SAFE_INTEGER;
async function load7(config3) {
  var _a2;
  if (env.initial)
    model9 = null;
  if (!model9)
    model9 = await loadModel((_a2 = config3.face.description) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model9["modelUrl"]);
  return model9;
}
function enhance(input) {
  const tensor = input.image || input.tensor || input;
  if (!(model9 == null ? void 0 : model9.inputs[0].shape))
    return tensor;
  const crop = eK.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
  const norm = ne(crop, constants.tf255);
  Ot(crop);
  return norm;
}
async function predict6(image, config3, idx, count2) {
  var _a2, _b2, _c2, _d2;
  const obj = {
    age: 0,
    gender: "unknown",
    genderScore: 0,
    descriptor: []
  };
  if (!(model9 == null ? void 0 : model9["executor"]))
    return obj;
  const skipFrame = skipped5 < (((_a2 = config3.face.description) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.description) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime5;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c2 = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c2.age) > 0 && ((_d2 = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d2.genderScore) > 0) {
    skipped5++;
    return last4[idx];
  }
  skipped5 = 0;
  return new Promise(async (resolve) => {
    var _a3;
    if ((_a3 = config3.face.description) == null ? void 0 : _a3.enabled) {
      const enhanced = enhance(image);
      const resT = model9 == null ? void 0 : model9.execute(enhanced);
      lastTime5 = now();
      Ot(enhanced);
      const genderT = resT.find((t10) => t10.shape[1] === 1);
      const gender2 = await genderT.data();
      const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
      if (confidence > (config3.face.description.minConfidence || 0)) {
        obj.gender = gender2[0] <= 0.5 ? "female" : "male";
        obj.genderScore = Math.min(0.99, confidence);
      }
      const argmax = X0(resT.find((t10) => t10.shape[1] === 100), 1);
      const ageIdx = (await argmax.data())[0];
      Ot(argmax);
      const ageT = resT.find((t10) => t10.shape[1] === 100);
      const all2 = await ageT.data();
      obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
      if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
        log("faceres error:", { model: model9, result: resT });
      const desc = resT.find((t10) => t10.shape[1] === 1024);
      const descriptor = desc ? await desc.data() : [];
      obj.descriptor = Array.from(descriptor);
      resT.forEach((t10) => Ot(t10));
    }
    last4[idx] = obj;
    lastCount2 = count2;
    resolve(obj);
  });
}

// src/face/mask.ts
var expandFact = 0.1;
var alpha = 0.5;
function insidePoly(x, y6, polygon) {
  let inside = false;
  let j10 = polygon.length - 1;
  for (let i = 0; i < polygon.length; j10 = i++) {
    if (polygon[i].y > y6 !== polygon[j10].y > y6 && x < (polygon[j10].x - polygon[i].x) * (y6 - polygon[i].y) / (polygon[j10].y - polygon[i].y) + polygon[i].x)
      inside = !inside;
  }
  return inside;
}
async function mask(face4) {
  if (!face4.tensor)
    return face4.tensor;
  if (!face4.mesh || face4.mesh.length < 100)
    return face4.tensor;
  const width = face4.tensor.shape[2] || 0;
  const height = face4.tensor.shape[1] || 0;
  const buffer = await face4.tensor.buffer();
  let silhouette = [];
  for (const pt of meshAnnotations.silhouette)
    silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
  if (expandFact && expandFact > 0)
    silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
  for (let x = 0; x < width; x++) {
    for (let y6 = 0; y6 < height; y6++) {
      const inside = insidePoly(x / width, y6 / width, silhouette);
      if (!inside) {
        buffer.set(alpha * buffer.get(0, y6, x, 0), 0, y6, x, 0);
        buffer.set(alpha * buffer.get(0, y6, x, 1), 0, y6, x, 1);
        buffer.set(alpha * buffer.get(0, y6, x, 2), 0, y6, x, 2);
      }
    }
  }
  const output = buffer.toTensor();
  return output;
}

// src/face/antispoof.ts
var model10;
var cached = [];
var skipped6 = Number.MAX_SAFE_INTEGER;
var lastCount3 = 0;
var lastTime6 = 0;
async function load8(config3) {
  var _a2;
  if (env.initial)
    model10 = null;
  if (!model10)
    model10 = await loadModel((_a2 = config3.face.antispoof) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model10["modelUrl"]);
  return model10;
}
async function predict7(image, config3, idx, count2) {
  var _a2, _b2;
  if (!(model10 == null ? void 0 : model10["executor"]))
    return 0;
  const skipTime = (((_a2 = config3.face.antispoof) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime6;
  const skipFrame = skipped6 < (((_b2 = config3.face.antispoof) == null ? void 0 : _b2.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) {
    skipped6++;
    return cached[idx];
  }
  skipped6 = 0;
  return new Promise(async (resolve) => {
    const resize = eK.resizeBilinear(image, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false);
    const res = model10 == null ? void 0 : model10.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount3 = count2;
    lastTime6 = now();
    Ot([resize, res]);
    resolve(cached[idx]);
  });
}

// src/face/liveness.ts
var model11;
var cached2 = [];
var skipped7 = Number.MAX_SAFE_INTEGER;
var lastCount4 = 0;
var lastTime7 = 0;
async function load9(config3) {
  var _a2;
  if (env.initial)
    model11 = null;
  if (!model11)
    model11 = await loadModel((_a2 = config3.face.liveness) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model11["modelUrl"]);
  return model11;
}
async function predict8(image, config3, idx, count2) {
  var _a2, _b2;
  if (!(model11 == null ? void 0 : model11["executor"]))
    return 0;
  const skipTime = (((_a2 = config3.face.liveness) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime7;
  const skipFrame = skipped7 < (((_b2 = config3.face.liveness) == null ? void 0 : _b2.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) {
    skipped7++;
    return cached2[idx];
  }
  skipped7 = 0;
  return new Promise(async (resolve) => {
    const resize = eK.resizeBilinear(image, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
    const res = model11 == null ? void 0 : model11.execute(resize);
    const num = (await res.data())[0];
    cached2[idx] = Math.round(100 * num) / 100;
    lastCount4 = count2;
    lastTime7 = now();
    Ot([resize, res]);
    resolve(cached2[idx]);
  });
}

// src/gear/gear.ts
var model12;
var last5 = [];
var raceNames = ["white", "black", "asian", "indian", "other"];
var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
var lastCount5 = 0;
var lastTime8 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load10(config3) {
  var _a2;
  if (env.initial)
    model12 = null;
  if (!model12)
    model12 = await loadModel((_a2 = config3.face.gear) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model12["modelUrl"]);
  return model12;
}
async function predict9(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model12)
    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
  const skipFrame = skipped8 < (((_a2 = config3.face.gear) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.gear) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime8;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) {
    skipped8++;
    return last5[idx];
  }
  skipped8 = 0;
  return new Promise(async (resolve) => {
    var _a3, _b3;
    if (!(model12 == null ? void 0 : model12.inputs[0].shape))
      return;
    const t10 = {};
    const box = [[0, 0.1, 0.9, 0.9]];
    t10.resize = eK.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]);
    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
    if ((_a3 = config3.face.gear) == null ? void 0 : _a3.enabled)
      [t10.age, t10.gender, t10.race] = model12.execute(t10.resize, ["age_output", "gender_output", "race_output"]);
    const gender2 = await t10.gender.data();
    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
    const race = await t10.race.data();
    for (let i = 0; i < race.length; i++) {
      if (race[i] > (((_b3 = config3.face.gear) == null ? void 0 : _b3.minConfidence) || 0.2))
        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
    }
    obj.race.sort((a, b) => b.score - a.score);
    const ageDistribution = Array.from(await t10.age.data());
    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
    let age2 = ageSorted[0][0];
    for (let i = 1; i < ageSorted.length; i++)
      age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
    obj.age = Math.round(10 * age2) / 10;
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    last5[idx] = obj;
    lastCount5 = count2;
    lastTime8 = now();
    resolve(obj);
  });
}

// src/gear/ssrnet-age.ts
var model13;
var last6 = [];
var lastCount6 = 0;
var lastTime9 = 0;
var skipped9 = Number.MAX_SAFE_INTEGER;
async function load11(config3) {
  if (env.initial)
    model13 = null;
  if (!model13)
    model13 = await loadModel(config3.face["ssrnet"].modelPathAge);
  else if (config3.debug)
    log("cached model:", model13["modelUrl"]);
  return model13;
}
async function predict10(image, config3, idx, count2) {
  var _a2, _b2, _c2, _d2;
  if (!model13)
    return { age: 0 };
  const skipFrame = skipped9 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime9;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c2 = last6[idx]) == null ? void 0 : _c2.age) && ((_d2 = last6[idx]) == null ? void 0 : _d2.age) > 0) {
    skipped9++;
    return last6[idx];
  }
  skipped9 = 0;
  return new Promise(async (resolve) => {
    var _a3;
    if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape)
      return;
    const t10 = {};
    t10.resize = eK.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
    t10.enhance = ne(t10.resize, constants.tf255);
    const obj = { age: 0 };
    if ((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3.enabled)
      t10.age = model13.execute(t10.enhance);
    if (t10.age) {
      const data = await t10.age.data();
      obj.age = Math.trunc(10 * data[0]) / 10;
    }
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    last6[idx] = obj;
    lastCount6 = count2;
    lastTime9 = now();
    resolve(obj);
  });
}

// src/gear/ssrnet-gender.ts
var model14;
var last7 = [];
var lastCount7 = 0;
var lastTime10 = 0;
var skipped10 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load12(config3) {
  var _a2;
  if (env.initial)
    model14 = null;
  if (!model14)
    model14 = await loadModel((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.modelPathGender);
  else if (config3.debug)
    log("cached model:", model14["modelUrl"]);
  return model14;
}
async function predict11(image, config3, idx, count2) {
  var _a2, _b2, _c2, _d2;
  if (!model14)
    return { gender: "unknown", genderScore: 0 };
  const skipFrame = skipped10 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime10;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c2 = last7[idx]) == null ? void 0 : _c2.gender) && ((_d2 = last7[idx]) == null ? void 0 : _d2.genderScore) > 0) {
    skipped10++;
    return last7[idx];
  }
  skipped10 = 0;
  return new Promise(async (resolve) => {
    var _a3;
    if (!(model14 == null ? void 0 : model14.inputs[0].shape))
      return;
    const t10 = {};
    t10.resize = eK.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
    t10.enhance = Ee(() => {
      const [red, green, blue] = Oa(t10.resize, 3, 3);
      const redNorm = ne(red, rgb[0]);
      const greenNorm = ne(green, rgb[1]);
      const blueNorm = ne(blue, rgb[2]);
      const grayscale = K0([redNorm, greenNorm, blueNorm]);
      const normalize2 = ne(ke(grayscale, constants.tf05), 2);
      return normalize2;
    });
    const obj = { gender: "unknown", genderScore: 0 };
    if ((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3.enabled)
      t10.gender = model14.execute(t10.enhance);
    const data = await t10.gender.data();
    obj.gender = data[0] > data[1] ? "female" : "male";
    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    last7[idx] = obj;
    lastCount7 = count2;
    lastTime10 = now();
    resolve(obj);
  });
}

// src/face/mobilefacenet.ts
var model15;
var last8 = [];
var lastCount8 = 0;
var lastTime11 = 0;
var skipped11 = Number.MAX_SAFE_INTEGER;
async function load13(config3) {
  var _a2;
  if (env.initial)
    model15 = null;
  if (!model15)
    model15 = await loadModel((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model15["modelUrl"]);
  return model15;
}
async function predict12(input, config3, idx, count2) {
  var _a2, _b2;
  if (!(model15 == null ? void 0 : model15["executor"]))
    return [];
  const skipFrame = skipped11 < (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["mobilefacenet"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime11;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) {
    skipped11++;
    return last8[idx];
  }
  return new Promise(async (resolve) => {
    var _a3;
    let data = [];
    if (((_a3 = config3.face["mobilefacenet"]) == null ? void 0 : _a3.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) {
      const t10 = {};
      t10.crop = eK.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false);
      t10.data = model15.execute(t10.crop);
      const output = await t10.data.data();
      data = Array.from(output);
      Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    }
    last8[idx] = data;
    lastCount8 = count2;
    lastTime11 = now();
    resolve(data);
  });
}

// src/face/insightface.ts
var model16;
var last9 = [];
var lastCount9 = 0;
var lastTime12 = 0;
var skipped12 = Number.MAX_SAFE_INTEGER;
async function load14(config3) {
  if (env.initial)
    model16 = null;
  if (!model16)
    model16 = await loadModel(config3.face["insightface"].modelPath);
  else if (config3.debug)
    log("cached model:", model16["modelUrl"]);
  return model16;
}
async function predict13(input, config3, idx, count2) {
  var _a2, _b2;
  if (!(model16 == null ? void 0 : model16["executor"]))
    return [];
  const skipFrame = skipped12 < (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["insightface"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime12;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) {
    skipped12++;
    return last9[idx];
  }
  return new Promise(async (resolve) => {
    var _a3;
    let data = [];
    if (((_a3 = config3.face["insightface"]) == null ? void 0 : _a3.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) {
      const t10 = {};
      t10.crop = eK.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false);
      t10.data = model16.execute(t10.crop);
      const output = await t10.data.data();
      data = Array.from(output);
      Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    }
    last9[idx] = data;
    lastCount9 = count2;
    lastTime12 = now();
    resolve(data);
  });
}

// src/face/angles.ts
var calculateGaze = (face4) => {
  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
  if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
    return { bearing: 0, strength: 0 };
  const offsetIris = [0, -0.1];
  const eyeRatio = 1;
  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
  const eyeDiff = [
    // x distance between extreme point and center point normalized with eye size
    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
  ];
  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
  return { bearing, strength };
};
var calculateFaceAngle = (face4, imageSize) => {
  const normalize2 = (v10) => {
    const length = Math.sqrt(v10[0] * v10[0] + v10[1] * v10[1] + v10[2] * v10[2]);
    v10[0] /= length;
    v10[1] /= length;
    v10[2] /= length;
    return v10;
  };
  const subVectors = (a, b) => {
    const x = a[0] - b[0];
    const y6 = a[1] - b[1];
    const z = a[2] - b[2];
    return [x, y6, z];
  };
  const crossVectors = (a, b) => {
    const x = a[1] * b[2] - a[2] * b[1];
    const y6 = a[2] * b[0] - a[0] * b[2];
    const z = a[0] * b[1] - a[1] * b[0];
    return [x, y6, z];
  };
  const rotationMatrixToEulerAngle = (r) => {
    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
    let thetaX;
    let thetaY;
    let thetaZ;
    if (r10 < 1) {
      if (r10 > -1) {
        thetaZ = Math.asin(r10);
        thetaY = Math.atan2(-r20, r00);
        thetaX = Math.atan2(-r12, r11);
      } else {
        thetaZ = -Math.PI / 2;
        thetaY = -Math.atan2(r21, r22);
        thetaX = 0;
      }
    } else {
      thetaZ = Math.PI / 2;
      thetaY = Math.atan2(r21, r22);
      thetaX = 0;
    }
    if (Number.isNaN(thetaX))
      thetaX = 0;
    if (Number.isNaN(thetaY))
      thetaY = 0;
    if (Number.isNaN(thetaZ))
      thetaZ = 0;
    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
  };
  const mesh = face4.meshRaw;
  if (!mesh || mesh.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 } };
  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
  const yAxis = normalize2(subVectors(pts[1], pts[0]));
  let xAxis = normalize2(subVectors(pts[3], pts[2]));
  const zAxis = normalize2(crossVectors(xAxis, yAxis));
  xAxis = crossVectors(yAxis, zAxis);
  const matrix = [
    xAxis[0],
    xAxis[1],
    xAxis[2],
    yAxis[0],
    yAxis[1],
    yAxis[2],
    zAxis[0],
    zAxis[1],
    zAxis[2]
  ];
  const angle = rotationMatrixToEulerAngle(matrix);
  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
  return { angle, matrix, gaze };
};

// src/face/anthropometry.ts
function calculateCameraDistance(face4, width) {
  const f = face4 == null ? void 0 : face4.annotations;
  if (!f)
    return 0;
  const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;
  const cameraDistance = Math.round(1.17 / irisSize) / 100;
  return cameraDistance;
}

// src/face/face.ts
var detectFace = async (instance, input) => {
  var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2;
  let timeStamp = now();
  let ageRes;
  let gearRes;
  let genderRes;
  let emotionRes;
  let mobilefacenetRes;
  let insightfaceRes;
  let antispoofRes;
  let livenessRes;
  let descRes;
  const faceRes = [];
  instance.state = "run:face";
  const faces = await predict4(input, instance.config);
  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
  if (!input.shape || input.shape.length !== 4)
    return [];
  if (!faces)
    return [];
  for (let i = 0; i < faces.length; i++) {
    instance.analyze("Get Face");
    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
      log("Face object is disposed:", faces[i].tensor);
      continue;
    }
    if ((_a2 = instance.config.face.detector) == null ? void 0 : _a2.mask) {
      const masked = await mask(faces[i]);
      Ot(faces[i].tensor);
      if (masked)
        faces[i].tensor = masked;
    }
    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
    instance.analyze("Start Emotion:");
    if (instance.config.async) {
      emotionRes = ((_b2 = instance.config.face.emotion) == null ? void 0 : _b2.enabled) ? predict5(faces[i].tensor || nr([]), instance.config, i, faces.length) : [];
    } else {
      instance.state = "run:emotion";
      timeStamp = now();
      emotionRes = ((_c2 = instance.config.face.emotion) == null ? void 0 : _c2.enabled) ? await predict5(faces[i].tensor || nr([]), instance.config, i, faces.length) : [];
      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance.analyze("End Emotion:");
    instance.analyze("Start AntiSpoof:");
    if (instance.config.async) {
      antispoofRes = ((_d2 = instance.config.face.antispoof) == null ? void 0 : _d2.enabled) ? predict7(faces[i].tensor || nr([]), instance.config, i, faces.length) : 0;
    } else {
      instance.state = "run:antispoof";
      timeStamp = now();
      antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || nr([]), instance.config, i, faces.length) : 0;
      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance.analyze("End AntiSpoof:");
    instance.analyze("Start Liveness:");
    if (instance.config.async) {
      livenessRes = ((_f2 = instance.config.face.liveness) == null ? void 0 : _f2.enabled) ? predict8(faces[i].tensor || nr([]), instance.config, i, faces.length) : 0;
    } else {
      instance.state = "run:liveness";
      timeStamp = now();
      livenessRes = ((_g2 = instance.config.face.liveness) == null ? void 0 : _g2.enabled) ? await predict8(faces[i].tensor || nr([]), instance.config, i, faces.length) : 0;
      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance.analyze("End Liveness:");
    instance.analyze("Start GEAR:");
    if (instance.config.async) {
      gearRes = ((_h2 = instance.config.face.gear) == null ? void 0 : _h2.enabled) ? predict9(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
    } else {
      instance.state = "run:gear";
      timeStamp = now();
      gearRes = ((_i2 = instance.config.face.gear) == null ? void 0 : _i2.enabled) ? await predict9(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
      instance.performance.gear = Math.trunc(now() - timeStamp);
    }
    instance.analyze("End GEAR:");
    instance.analyze("Start SSRNet:");
    if (instance.config.async) {
      ageRes = ((_j2 = instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) ? predict10(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
      genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
    } else {
      instance.state = "run:ssrnet";
      timeStamp = now();
      ageRes = ((_l2 = instance.config.face["ssrnet"]) == null ? void 0 : _l2.enabled) ? await predict10(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
      genderRes = ((_m2 = instance.config.face["ssrnet"]) == null ? void 0 : _m2.enabled) ? await predict11(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
      instance.performance.ssrnet = Math.trunc(now() - timeStamp);
    }
    instance.analyze("End SSRNet:");
    instance.analyze("Start MobileFaceNet:");
    if (instance.config.async) {
      mobilefacenetRes = ((_n2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _n2.enabled) ? predict12(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
    } else {
      instance.state = "run:mobilefacenet";
      timeStamp = now();
      mobilefacenetRes = ((_o2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _o2.enabled) ? await predict12(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
    }
    instance.analyze("End MobileFaceNet:");
    instance.analyze("Start InsightFace:");
    if (instance.config.async) {
      insightfaceRes = ((_p2 = instance.config.face["insightface"]) == null ? void 0 : _p2.enabled) ? predict13(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
    } else {
      instance.state = "run:mobilefacenet";
      timeStamp = now();
      insightfaceRes = ((_q2 = instance.config.face["insightface"]) == null ? void 0 : _q2.enabled) ? await predict13(faces[i].tensor || nr([]), instance.config, i, faces.length) : null;
      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
    }
    instance.analyze("End InsightFace:");
    instance.analyze("Start Description:");
    if (instance.config.async) {
      descRes = predict6(faces[i].tensor || nr([]), instance.config, i, faces.length);
    } else {
      instance.state = "run:description";
      timeStamp = now();
      descRes = await predict6(faces[i].tensor || nr([]), instance.config, i, faces.length);
      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance.analyze("End Description:");
    if (instance.config.async) {
      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
    }
    instance.analyze("Finish Face:");
    if (((_r2 = instance.config.face["ssrnet"]) == null ? void 0 : _r2.enabled) && ageRes && genderRes) {
      descRes = {
        ...descRes,
        age: ageRes.age,
        gender: genderRes.gender,
        genderScore: genderRes.genderScore
      };
    }
    if (((_s2 = instance.config.face.gear) == null ? void 0 : _s2.enabled) && gearRes) {
      descRes = {
        ...descRes,
        age: gearRes.age,
        gender: gearRes.gender,
        genderScore: gearRes.genderScore,
        race: gearRes.race
      };
    }
    if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
      descRes.descriptor = mobilefacenetRes;
    }
    if (((_u2 = instance.config.face["insightface"]) == null ? void 0 : _u2.enabled) && insightfaceRes) {
      descRes.descriptor = insightfaceRes;
    }
    const irisSize = ((_v2 = instance.config.face.iris) == null ? void 0 : _v2.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0;
    const tensor = ((_w2 = instance.config.face.detector) == null ? void 0 : _w2.return) ? Up(faces[i].tensor) : null;
    Ot(faces[i].tensor);
    if (faces[i].tensor)
      delete faces[i].tensor;
    const res = {
      ...faces[i],
      id: i
    };
    if (descRes.age)
      res.age = descRes.age;
    if (descRes.gender)
      res.gender = descRes.gender;
    if (descRes.genderScore)
      res.genderScore = descRes.genderScore;
    if (descRes.descriptor)
      res.embedding = descRes.descriptor;
    if (descRes.race)
      res.race = descRes.race;
    if (emotionRes)
      res.emotion = emotionRes;
    if (antispoofRes)
      res.real = antispoofRes;
    if (livenessRes)
      res.live = livenessRes;
    if (irisSize > 0)
      res.distance = irisSize;
    if (rotation)
      res.rotation = rotation;
    if (tensor)
      res.tensor = tensor;
    faceRes.push(res);
    instance.analyze("End Face");
  }
  instance.analyze("End FaceMesh:");
  if (instance.config.async) {
    if (instance.performance.face)
      delete instance.performance.face;
    if (instance.performance.age)
      delete instance.performance.age;
    if (instance.performance.gender)
      delete instance.performance.gender;
    if (instance.performance.emotion)
      delete instance.performance.emotion;
  }
  return faceRes;
};

// src/hand/fingerdef.ts
var Finger = {
  thumb: 0,
  index: 1,
  middle: 2,
  ring: 3,
  pinky: 4,
  all: [0, 1, 2, 3, 4],
  // just for convenience
  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
  // Describes mapping of joints based on the 21 points returned by handpose.
  // [0]     Palm
  // [1-4]   Thumb
  // [5-8]   Index
  // [9-12]  Middle
  // [13-16] Ring
  // [17-20] 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: (value) => Finger.nameMapping[value],
  getPoints: (value) => Finger.pointsMapping[value]
};
var FingerCurl = {
  none: 0,
  half: 1,
  full: 2,
  nameMapping: { 0: "none", 1: "half", 2: "full" },
  getName: (value) => FingerCurl.nameMapping[value]
};
var FingerDirection = {
  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: (value) => FingerDirection.nameMapping[value]
};
var FingerGesture = class {
  constructor(name) {
    __publicField(this, "name");
    __publicField(this, "curls");
    __publicField(this, "directions");
    __publicField(this, "weights");
    __publicField(this, "weightsRelative");
    this.name = name;
    this.curls = {};
    this.directions = {};
    this.weights = [1, 1, 1, 1, 1];
    this.weightsRelative = [1, 1, 1, 1, 1];
  }
  curl(finger, curl, confidence) {
    if (typeof this.curls[finger] === "undefined")
      this.curls[finger] = [];
    this.curls[finger].push([curl, confidence]);
  }
  direction(finger, position, confidence) {
    if (!this.directions[finger])
      this.directions[finger] = [];
    this.directions[finger].push([position, confidence]);
  }
  weight(finger, weight) {
    this.weights[finger] = weight;
    const total = this.weights.reduce((a, b) => a + b, 0);
    this.weightsRelative = this.weights.map((el2) => el2 * 5 / total);
  }
  matchAgainst(detectedCurls, detectedDirections) {
    let confidence = 0;
    for (const fingerIdx in detectedCurls) {
      const detectedCurl = detectedCurls[fingerIdx];
      const expectedCurls = this.curls[fingerIdx];
      if (typeof expectedCurls === "undefined") {
        confidence += this.weightsRelative[fingerIdx];
        continue;
      }
      for (const [expectedCurl, score] of expectedCurls) {
        if (detectedCurl === expectedCurl) {
          confidence += score * this.weightsRelative[fingerIdx];
          break;
        }
      }
    }
    for (const fingerIdx in detectedDirections) {
      const detectedDirection = detectedDirections[fingerIdx];
      const expectedDirections = this.directions[fingerIdx];
      if (typeof expectedDirections === "undefined") {
        confidence += this.weightsRelative[fingerIdx];
        continue;
      }
      for (const [expectedDirection, score] of expectedDirections) {
        if (detectedDirection === expectedDirection) {
          confidence += score * this.weightsRelative[fingerIdx];
          break;
        }
      }
    }
    return confidence / 10;
  }
};

// src/hand/fingergesture.ts
var { thumb, index, middle, ring, pinky } = Finger;
var { none, half, full } = FingerCurl;
var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
var ThumbsUp = new FingerGesture("thumbs up");
ThumbsUp.curl(thumb, none, 1);
ThumbsUp.direction(thumb, verticalUp, 1);
ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
  ThumbsUp.curl(finger, full, 1);
  ThumbsUp.direction(finger, horizontalLeft, 1);
  ThumbsUp.direction(finger, horizontalRight, 1);
}
var Victory = new FingerGesture("victory");
Victory.curl(thumb, half, 0.5);
Victory.curl(thumb, none, 0.5);
Victory.direction(thumb, verticalUp, 1);
Victory.direction(thumb, diagonalUpLeft, 1);
Victory.curl(index, none, 1);
Victory.direction(index, verticalUp, 0.75);
Victory.direction(index, diagonalUpLeft, 1);
Victory.curl(middle, none, 1);
Victory.direction(middle, verticalUp, 1);
Victory.direction(middle, diagonalUpLeft, 0.75);
Victory.curl(ring, full, 1);
Victory.direction(ring, verticalUp, 0.2);
Victory.direction(ring, diagonalUpLeft, 1);
Victory.direction(ring, horizontalLeft, 0.2);
Victory.curl(pinky, full, 1);
Victory.direction(pinky, verticalUp, 0.2);
Victory.direction(pinky, diagonalUpLeft, 1);
Victory.direction(pinky, horizontalLeft, 0.2);
Victory.weight(index, 2);
Victory.weight(middle, 2);
var Point = new FingerGesture("point");
Point.curl(thumb, full, 1);
Point.curl(index, none, 0.5);
Point.curl(middle, full, 0.5);
Point.curl(ring, full, 0.5);
Point.curl(pinky, full, 0.5);
Point.weight(index, 2);
Point.weight(middle, 2);
var MiddleFinger = new FingerGesture("middle finger");
MiddleFinger.curl(thumb, none, 1);
MiddleFinger.curl(index, full, 0.5);
MiddleFinger.curl(middle, full, 0.5);
MiddleFinger.curl(ring, full, 0.5);
MiddleFinger.curl(pinky, full, 0.5);
MiddleFinger.weight(index, 2);
MiddleFinger.weight(middle, 2);
var OpenPalm = new FingerGesture("open palm");
OpenPalm.curl(thumb, none, 0.75);
OpenPalm.curl(index, none, 0.75);
OpenPalm.curl(middle, none, 0.75);
OpenPalm.curl(ring, none, 0.75);
OpenPalm.curl(pinky, none, 0.75);
var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];

// src/hand/fingerpose.ts
var minConfidence = 0.7;
var options3 = {
  // curl estimation
  HALF_CURL_START_LIMIT: 60,
  NO_CURL_START_LIMIT: 130,
  // direction estimation
  DISTANCE_VOTE_POWER: 1.1,
  SINGLE_ANGLE_VOTE_POWER: 0.9,
  TOTAL_ANGLE_VOTE_POWER: 1.6
};
function calculateSlope(point1x, point1y, point2x, point2y) {
  const value = (point1y - point2y) / (point1x - point2x);
  let slope = Math.atan(value) * 180 / Math.PI;
  if (slope <= 0)
    slope = -slope;
  else if (slope > 0)
    slope = 180 - slope;
  return slope;
}
function getSlopes(point1, point2) {
  if (!point1 || !point2)
    return [0, 0];
  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
  if (point1.length === 2)
    return slopeXY;
  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
  return [slopeXY, slopeYZ];
}
function angleOrientationAt(angle, weightageAt = 1) {
  let isVertical = 0;
  let isDiagonal = 0;
  let isHorizontal = 0;
  if (angle >= 75 && angle <= 105)
    isVertical = 1 * weightageAt;
  else if (angle >= 25 && angle <= 155)
    isDiagonal = 1 * weightageAt;
  else
    isHorizontal = 1 * weightageAt;
  return [isVertical, isDiagonal, isHorizontal];
}
function estimateFingerCurl(startPoint, midPoint, endPoint) {
  const start_mid_x_dist = startPoint[0] - midPoint[0];
  const start_end_x_dist = startPoint[0] - endPoint[0];
  const mid_end_x_dist = midPoint[0] - endPoint[0];
  const start_mid_y_dist = startPoint[1] - midPoint[1];
  const start_end_y_dist = startPoint[1] - endPoint[1];
  const mid_end_y_dist = midPoint[1] - endPoint[1];
  const start_mid_z_dist = startPoint[2] - midPoint[2];
  const start_end_z_dist = startPoint[2] - endPoint[2];
  const mid_end_z_dist = midPoint[2] - endPoint[2];
  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
  if (cos_in > 1)
    cos_in = 1;
  else if (cos_in < -1)
    cos_in = -1;
  let angleOfCurve = Math.acos(cos_in);
  angleOfCurve = 57.2958 * angleOfCurve % 180;
  let fingerCurl;
  if (angleOfCurve > options3.NO_CURL_START_LIMIT)
    fingerCurl = FingerCurl.none;
  else if (angleOfCurve > options3.HALF_CURL_START_LIMIT)
    fingerCurl = FingerCurl.half;
  else
    fingerCurl = FingerCurl.full;
  return fingerCurl;
}
function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
  let estimatedDirection;
  if (max_dist_x === Math.abs(start_end_x_dist)) {
    if (start_end_x_dist > 0)
      estimatedDirection = FingerDirection.horizontalLeft;
    else
      estimatedDirection = FingerDirection.horizontalRight;
  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
    if (start_mid_x_dist > 0)
      estimatedDirection = FingerDirection.horizontalLeft;
    else
      estimatedDirection = FingerDirection.horizontalRight;
  } else {
    if (mid_end_x_dist > 0)
      estimatedDirection = FingerDirection.horizontalLeft;
    else
      estimatedDirection = FingerDirection.horizontalRight;
  }
  return estimatedDirection;
}
function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
  let estimatedDirection;
  if (max_dist_y === Math.abs(start_end_y_dist)) {
    if (start_end_y_dist < 0)
      estimatedDirection = FingerDirection.verticalDown;
    else
      estimatedDirection = FingerDirection.verticalUp;
  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
    if (start_mid_y_dist < 0)
      estimatedDirection = FingerDirection.verticalDown;
    else
      estimatedDirection = FingerDirection.verticalUp;
  } else {
    if (mid_end_y_dist < 0)
      estimatedDirection = FingerDirection.verticalDown;
    else
      estimatedDirection = FingerDirection.verticalUp;
  }
  return estimatedDirection;
}
function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
  let estimatedDirection;
  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
  if (reqd_vertical_direction === FingerDirection.verticalUp) {
    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
      estimatedDirection = FingerDirection.diagonalUpLeft;
    else
      estimatedDirection = FingerDirection.diagonalUpRight;
  } else {
    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
      estimatedDirection = FingerDirection.diagonalDownLeft;
    else
      estimatedDirection = FingerDirection.diagonalDownRight;
  }
  return estimatedDirection;
}
function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
  const start_mid_x_dist = startPoint[0] - midPoint[0];
  const start_end_x_dist = startPoint[0] - endPoint[0];
  const mid_end_x_dist = midPoint[0] - endPoint[0];
  const start_mid_y_dist = startPoint[1] - midPoint[1];
  const start_end_y_dist = startPoint[1] - endPoint[1];
  const mid_end_y_dist = midPoint[1] - endPoint[1];
  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
  let voteVertical = 0;
  let voteDiagonal = 0;
  let voteHorizontal = 0;
  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
  if (start_end_x_y_dist_ratio > 1.5)
    voteVertical += options3.DISTANCE_VOTE_POWER;
  else if (start_end_x_y_dist_ratio > 0.66)
    voteDiagonal += options3.DISTANCE_VOTE_POWER;
  else
    voteHorizontal += options3.DISTANCE_VOTE_POWER;
  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
  let calc_start_point_x = startPoint[0];
  let calc_start_point_y = startPoint[1];
  let calc_end_point_x = endPoint[0];
  let calc_end_point_y = endPoint[1];
  if (max_dist === start_mid_dist) {
    calc_end_point_x = endPoint[0];
    calc_end_point_y = endPoint[1];
  } else if (max_dist === mid_end_dist) {
    calc_start_point_x = midPoint[0];
    calc_start_point_y = midPoint[1];
  }
  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
  const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER);
  voteVertical += votes[0];
  voteDiagonal += votes[1];
  voteHorizontal += votes[2];
  for (const fingerSlope of fingerSlopes) {
    const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER);
    voteVertical += fingerVotes[0];
    voteDiagonal += fingerVotes[1];
    voteHorizontal += fingerVotes[2];
  }
  let estimatedDirection;
  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
  } else {
    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
  }
  return estimatedDirection;
}
function estimate(landmarks) {
  const slopesXY = [];
  const slopesYZ = [];
  const fingerCurls = [];
  const fingerDirections = [];
  if (!landmarks)
    return { curls: fingerCurls, directions: fingerDirections };
  for (const finger of Finger.all) {
    const points = Finger.getPoints(finger);
    const slopeAtXY = [];
    const slopeAtYZ = [];
    for (const point2 of points) {
      const point1 = landmarks[point2[0]];
      const point22 = landmarks[point2[1]];
      const slopes = getSlopes(point1, point22);
      const slopeXY = slopes[0];
      const slopeYZ = slopes[1];
      slopeAtXY.push(slopeXY);
      slopeAtYZ.push(slopeYZ);
    }
    slopesXY.push(slopeAtXY);
    slopesYZ.push(slopeAtYZ);
  }
  for (const finger of Finger.all) {
    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
    const fingerPointsAt = Finger.getPoints(finger);
    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
    const endPoint = landmarks[fingerPointsAt[3][1]];
    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
    fingerCurls[finger] = fingerCurled;
    fingerDirections[finger] = fingerPosition;
  }
  return { curls: fingerCurls, directions: fingerDirections };
}
function analyze(keypoints) {
  if (!keypoints || keypoints.length === 0)
    return null;
  const estimatorRes = estimate(keypoints);
  const landmarks = {};
  for (const fingerIdx of Finger.all) {
    landmarks[Finger.getName(fingerIdx)] = {
      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
    };
  }
  return landmarks;
}
function match(keypoints) {
  const poses = [];
  if (!keypoints || keypoints.length === 0)
    return poses;
  const estimatorRes = estimate(keypoints);
  for (const gesture2 of fingergesture_default) {
    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
    if (confidence >= minConfidence)
      poses.push({ name: gesture2.name, confidence });
  }
  return poses;
}

// src/gesture/gesture.ts
var body2 = (res) => {
  if (!res)
    return [];
  const gestures = [];
  for (let i = 0; i < res.length; i++) {
    const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
    const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
    const nose = res[i].keypoints.find((a) => a.part === "nose");
    if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
      gestures.push({ body: i, gesture: "i give up" });
    else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
      gestures.push({ body: i, gesture: "raise left hand" });
    else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
      gestures.push({ body: i, gesture: "raise right hand" });
    const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
    const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
      gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
    }
  }
  return gestures;
};
var face2 = (res) => {
  if (!res)
    return [];
  const gestures = [];
  for (let i = 0; i < res.length; i++) {
    if (res[i].mesh && res[i].mesh.length > 450) {
      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
      if (Math.abs(zDiff / xDiff) <= 0.15)
        gestures.push({ face: i, gesture: "facing center" });
      else
        gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
      if (openLeft < 0.2)
        gestures.push({ face: i, gesture: "blink left eye" });
      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
      if (openRight < 0.2)
        gestures.push({ face: i, gesture: "blink right eye" });
      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
      if (mouthOpen > 10)
        gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
      const chinDepth = res[i].mesh[152][2] || 0;
      if (Math.abs(chinDepth) > 10)
        gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
    }
  }
  return gestures;
};
var iris2 = (res) => {
  var _a2, _b2, _c2, _d2;
  if (!res)
    return [];
  const gestures = [];
  for (let i = 0; i < res.length; i++) {
    if (!((_b2 = (_a2 = res[i].annotations) == null ? void 0 : _a2.leftEyeIris) == null ? void 0 : _b2[0]) || !((_d2 = (_c2 = res[i].annotations) == null ? void 0 : _c2.rightEyeIris) == null ? void 0 : _d2[0]))
      continue;
    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
    const areaRight = Math.abs(sizeXRight * sizeYRight);
    let center = false;
    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
    if (difference < 0.25) {
      center = true;
      gestures.push({ iris: i, gesture: "facing center" });
    }
    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
      center = false;
    if (leftIrisCenterX > rightIrisCenterX) {
      if (leftIrisCenterX > 0.05)
        gestures.push({ iris: i, gesture: "looking right" });
    } else {
      if (rightIrisCenterX > 0.05)
        gestures.push({ iris: i, gesture: "looking left" });
    }
    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
      center = false;
    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
      gestures.push({ iris: i, gesture: "looking down" });
    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
      gestures.push({ iris: i, gesture: "looking up" });
    if (center)
      gestures.push({ iris: i, gesture: "looking center" });
  }
  return gestures;
};
var hand2 = (res) => {
  if (!res)
    return [];
  const gestures = [];
  for (let i = 0; i < res.length; i++) {
    const fingers = [];
    if (res[i].annotations) {
      for (const [finger, pos] of Object.entries(res[i].annotations)) {
        if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
          fingers.push({ name: finger.toLowerCase(), position: pos[0] });
      }
    }
    if (fingers && fingers.length > 0) {
      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
      gestures.push({ hand: i, gesture: `${closest.name} forward` });
      const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
      gestures.push({ hand: i, gesture: `${highest.name} up` });
    }
    if (res[i].keypoints) {
      const poses = match(res[i].keypoints);
      for (const pose of poses)
        gestures.push({ hand: i, gesture: pose.name });
    }
  }
  return gestures;
};

// src/hand/handposeutil.ts
function getBoxSize2(box) {
  return [
    Math.abs(box.endPoint[0] - box.startPoint[0]),
    Math.abs(box.endPoint[1] - box.startPoint[1])
  ];
}
function getBoxCenter2(box) {
  return [
    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
  ];
}
function cutBoxFromImageAndResize(box, image, cropSize) {
  const h = image.shape[1];
  const w = image.shape[2];
  const boxes = [[
    box.startPoint[1] / h,
    box.startPoint[0] / w,
    box.endPoint[1] / h,
    box.endPoint[0] / w
  ]];
  return eK.cropAndResize(image, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box, factor) {
  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
  const palmLandmarks = box.palmLandmarks.map((coord) => {
    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
    return scaledCoord;
  });
  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
}
function enlargeBox2(box, factor = 1.5) {
  const center = getBoxCenter2(box);
  const size2 = getBoxSize2(box);
  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function squarifyBox2(box) {
  const centers = getBoxCenter2(box);
  const size2 = getBoxSize2(box);
  const maxEdge = Math.max(...size2);
  const halfSize = maxEdge / 2;
  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function normalizeRadians2(angle) {
  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
function computeRotation2(point1, point2) {
  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
  return normalizeRadians2(radians);
}
var buildTranslationMatrix2 = (x, y6) => [[1, 0, x], [0, 1, y6], [0, 0, 1]];
function dot2(v12, v22) {
  let product = 0;
  for (let i = 0; i < v12.length; i++) {
    product += v12[i] * v22[i];
  }
  return product;
}
function getColumnFrom2DArr2(arr, columnIndex) {
  const column = [];
  for (let i = 0; i < arr.length; i++) {
    column.push(arr[i][columnIndex]);
  }
  return column;
}
function multiplyTransformMatrices2(mat1, mat2) {
  const product = [];
  const size2 = mat1.length;
  for (let row = 0; row < size2; row++) {
    product.push([]);
    for (let col = 0; col < size2; col++) {
      product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
    }
  }
  return product;
}
function buildRotationMatrix2(rotation, center) {
  const cosA = Math.cos(rotation);
  const sinA = Math.sin(rotation);
  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
  const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
  const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
  const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
  return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
}
function invertTransformMatrix2(matrix) {
  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
  const translationComponent = [matrix[0][2], matrix[1][2]];
  const invertedTranslation = [
    -dot2(rotationComponent[0], translationComponent),
    -dot2(rotationComponent[1], translationComponent)
  ];
  return [
    rotationComponent[0].concat(invertedTranslation[0]),
    rotationComponent[1].concat(invertedTranslation[1]),
    [0, 0, 1]
  ];
}
function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
  return [
    dot2(homogeneousCoordinate, rotationMatrix[0]),
    dot2(homogeneousCoordinate, rotationMatrix[1])
  ];
}

// src/hand/handposeanchors.ts
var anchors2 = [
  { x: 0.015625, y: 0.015625 },
  { x: 0.015625, y: 0.015625 },
  { x: 0.046875, y: 0.015625 },
  { x: 0.046875, y: 0.015625 },
  { x: 0.078125, y: 0.015625 },
  { x: 0.078125, y: 0.015625 },
  { x: 0.109375, y: 0.015625 },
  { x: 0.109375, y: 0.015625 },
  { x: 0.140625, y: 0.015625 },
  { x: 0.140625, y: 0.015625 },
  { x: 0.171875, y: 0.015625 },
  { x: 0.171875, y: 0.015625 },
  { x: 0.203125, y: 0.015625 },
  { x: 0.203125, y: 0.015625 },
  { x: 0.234375, y: 0.015625 },
  { x: 0.234375, y: 0.015625 },
  { x: 0.265625, y: 0.015625 },
  { x: 0.265625, y: 0.015625 },
  { x: 0.296875, y: 0.015625 },
  { x: 0.296875, y: 0.015625 },
  { x: 0.328125, y: 0.015625 },
  { x: 0.328125, y: 0.015625 },
  { x: 0.359375, y: 0.015625 },
  { x: 0.359375, y: 0.015625 },
  { x: 0.390625, y: 0.015625 },
  { x: 0.390625, y: 0.015625 },
  { x: 0.421875, y: 0.015625 },
  { x: 0.421875, y: 0.015625 },
  { x: 0.453125, y: 0.015625 },
  { x: 0.453125, y: 0.015625 },
  { x: 0.484375, y: 0.015625 },
  { x: 0.484375, y: 0.015625 },
  { x: 0.515625, y: 0.015625 },
  { x: 0.515625, y: 0.015625 },
  { x: 0.546875, y: 0.015625 },
  { x: 0.546875, y: 0.015625 },
  { x: 0.578125, y: 0.015625 },
  { x: 0.578125, y: 0.015625 },
  { x: 0.609375, y: 0.015625 },
  { x: 0.609375, y: 0.015625 },
  { x: 0.640625, y: 0.015625 },
  { x: 0.640625, y: 0.015625 },
  { x: 0.671875, y: 0.015625 },
  { x: 0.671875, y: 0.015625 },
  { x: 0.703125, y: 0.015625 },
  { x: 0.703125, y: 0.015625 },
  { x: 0.734375, y: 0.015625 },
  { x: 0.734375, y: 0.015625 },
  { x: 0.765625, y: 0.015625 },
  { x: 0.765625, y: 0.015625 },
  { x: 0.796875, y: 0.015625 },
  { x: 0.796875, y: 0.015625 },
  { x: 0.828125, y: 0.015625 },
  { x: 0.828125, y: 0.015625 },
  { x: 0.859375, y: 0.015625 },
  { x: 0.859375, y: 0.015625 },
  { x: 0.890625, y: 0.015625 },
  { x: 0.890625, y: 0.015625 },
  { x: 0.921875, y: 0.015625 },
  { x: 0.921875, y: 0.015625 },
  { x: 0.953125, y: 0.015625 },
  { x: 0.953125, y: 0.015625 },
  { x: 0.984375, y: 0.015625 },
  { x: 0.984375, y: 0.015625 },
  { x: 0.015625, y: 0.046875 },
  { x: 0.015625, y: 0.046875 },
  { x: 0.046875, y: 0.046875 },
  { x: 0.046875, y: 0.046875 },
  { x: 0.078125, y: 0.046875 },
  { x: 0.078125, y: 0.046875 },
  { x: 0.109375, y: 0.046875 },
  { x: 0.109375, y: 0.046875 },
  { x: 0.140625, y: 0.046875 },
  { x: 0.140625, y: 0.046875 },
  { x: 0.171875, y: 0.046875 },
  { x: 0.171875, y: 0.046875 },
  { x: 0.203125, y: 0.046875 },
  { x: 0.203125, y: 0.046875 },
  { x: 0.234375, y: 0.046875 },
  { x: 0.234375, y: 0.046875 },
  { x: 0.265625, y: 0.046875 },
  { x: 0.265625, y: 0.046875 },
  { x: 0.296875, y: 0.046875 },
  { x: 0.296875, y: 0.046875 },
  { x: 0.328125, y: 0.046875 },
  { x: 0.328125, y: 0.046875 },
  { x: 0.359375, y: 0.046875 },
  { x: 0.359375, y: 0.046875 },
  { x: 0.390625, y: 0.046875 },
  { x: 0.390625, y: 0.046875 },
  { x: 0.421875, y: 0.046875 },
  { x: 0.421875, y: 0.046875 },
  { x: 0.453125, y: 0.046875 },
  { x: 0.453125, y: 0.046875 },
  { x: 0.484375, y: 0.046875 },
  { x: 0.484375, y: 0.046875 },
  { x: 0.515625, y: 0.046875 },
  { x: 0.515625, y: 0.046875 },
  { x: 0.546875, y: 0.046875 },
  { x: 0.546875, y: 0.046875 },
  { x: 0.578125, y: 0.046875 },
  { x: 0.578125, y: 0.046875 },
  { x: 0.609375, y: 0.046875 },
  { x: 0.609375, y: 0.046875 },
  { x: 0.640625, y: 0.046875 },
  { x: 0.640625, y: 0.046875 },
  { x: 0.671875, y: 0.046875 },
  { x: 0.671875, y: 0.046875 },
  { x: 0.703125, y: 0.046875 },
  { x: 0.703125, y: 0.046875 },
  { x: 0.734375, y: 0.046875 },
  { x: 0.734375, y: 0.046875 },
  { x: 0.765625, y: 0.046875 },
  { x: 0.765625, y: 0.046875 },
  { x: 0.796875, y: 0.046875 },
  { x: 0.796875, y: 0.046875 },
  { x: 0.828125, y: 0.046875 },
  { x: 0.828125, y: 0.046875 },
  { x: 0.859375, y: 0.046875 },
  { x: 0.859375, y: 0.046875 },
  { x: 0.890625, y: 0.046875 },
  { x: 0.890625, y: 0.046875 },
  { x: 0.921875, y: 0.046875 },
  { x: 0.921875, y: 0.046875 },
  { x: 0.953125, y: 0.046875 },
  { x: 0.953125, y: 0.046875 },
  { x: 0.984375, y: 0.046875 },
  { x: 0.984375, y: 0.046875 },
  { x: 0.015625, y: 0.078125 },
  { x: 0.015625, y: 0.078125 },
  { x: 0.046875, y: 0.078125 },
  { x: 0.046875, y: 0.078125 },
  { x: 0.078125, y: 0.078125 },
  { x: 0.078125, y: 0.078125 },
  { x: 0.109375, y: 0.078125 },
  { x: 0.109375, y: 0.078125 },
  { x: 0.140625, y: 0.078125 },
  { x: 0.140625, y: 0.078125 },
  { x: 0.171875, y: 0.078125 },
  { x: 0.171875, y: 0.078125 },
  { x: 0.203125, y: 0.078125 },
  { x: 0.203125, y: 0.078125 },
  { x: 0.234375, y: 0.078125 },
  { x: 0.234375, y: 0.078125 },
  { x: 0.265625, y: 0.078125 },
  { x: 0.265625, y: 0.078125 },
  { x: 0.296875, y: 0.078125 },
  { x: 0.296875, y: 0.078125 },
  { x: 0.328125, y: 0.078125 },
  { x: 0.328125, y: 0.078125 },
  { x: 0.359375, y: 0.078125 },
  { x: 0.359375, y: 0.078125 },
  { x: 0.390625, y: 0.078125 },
  { x: 0.390625, y: 0.078125 },
  { x: 0.421875, y: 0.078125 },
  { x: 0.421875, y: 0.078125 },
  { x: 0.453125, y: 0.078125 },
  { x: 0.453125, y: 0.078125 },
  { x: 0.484375, y: 0.078125 },
  { x: 0.484375, y: 0.078125 },
  { x: 0.515625, y: 0.078125 },
  { x: 0.515625, y: 0.078125 },
  { x: 0.546875, y: 0.078125 },
  { x: 0.546875, y: 0.078125 },
  { x: 0.578125, y: 0.078125 },
  { x: 0.578125, y: 0.078125 },
  { x: 0.609375, y: 0.078125 },
  { x: 0.609375, y: 0.078125 },
  { x: 0.640625, y: 0.078125 },
  { x: 0.640625, y: 0.078125 },
  { x: 0.671875, y: 0.078125 },
  { x: 0.671875, y: 0.078125 },
  { x: 0.703125, y: 0.078125 },
  { x: 0.703125, y: 0.078125 },
  { x: 0.734375, y: 0.078125 },
  { x: 0.734375, y: 0.078125 },
  { x: 0.765625, y: 0.078125 },
  { x: 0.765625, y: 0.078125 },
  { x: 0.796875, y: 0.078125 },
  { x: 0.796875, y: 0.078125 },
  { x: 0.828125, y: 0.078125 },
  { x: 0.828125, y: 0.078125 },
  { x: 0.859375, y: 0.078125 },
  { x: 0.859375, y: 0.078125 },
  { x: 0.890625, y: 0.078125 },
  { x: 0.890625, y: 0.078125 },
  { x: 0.921875, y: 0.078125 },
  { x: 0.921875, y: 0.078125 },
  { x: 0.953125, y: 0.078125 },
  { x: 0.953125, y: 0.078125 },
  { x: 0.984375, y: 0.078125 },
  { x: 0.984375, y: 0.078125 },
  { x: 0.015625, y: 0.109375 },
  { x: 0.015625, y: 0.109375 },
  { x: 0.046875, y: 0.109375 },
  { x: 0.046875, y: 0.109375 },
  { x: 0.078125, y: 0.109375 },
  { x: 0.078125, y: 0.109375 },
  { x: 0.109375, y: 0.109375 },
  { x: 0.109375, y: 0.109375 },
  { x: 0.140625, y: 0.109375 },
  { x: 0.140625, y: 0.109375 },
  { x: 0.171875, y: 0.109375 },
  { x: 0.171875, y: 0.109375 },
  { x: 0.203125, y: 0.109375 },
  { x: 0.203125, y: 0.109375 },
  { x: 0.234375, y: 0.109375 },
  { x: 0.234375, y: 0.109375 },
  { x: 0.265625, y: 0.109375 },
  { x: 0.265625, y: 0.109375 },
  { x: 0.296875, y: 0.109375 },
  { x: 0.296875, y: 0.109375 },
  { x: 0.328125, y: 0.109375 },
  { x: 0.328125, y: 0.109375 },
  { x: 0.359375, y: 0.109375 },
  { x: 0.359375, y: 0.109375 },
  { x: 0.390625, y: 0.109375 },
  { x: 0.390625, y: 0.109375 },
  { x: 0.421875, y: 0.109375 },
  { x: 0.421875, y: 0.109375 },
  { x: 0.453125, y: 0.109375 },
  { x: 0.453125, y: 0.109375 },
  { x: 0.484375, y: 0.109375 },
  { x: 0.484375, y: 0.109375 },
  { x: 0.515625, y: 0.109375 },
  { x: 0.515625, y: 0.109375 },
  { x: 0.546875, y: 0.109375 },
  { x: 0.546875, y: 0.109375 },
  { x: 0.578125, y: 0.109375 },
  { x: 0.578125, y: 0.109375 },
  { x: 0.609375, y: 0.109375 },
  { x: 0.609375, y: 0.109375 },
  { x: 0.640625, y: 0.109375 },
  { x: 0.640625, y: 0.109375 },
  { x: 0.671875, y: 0.109375 },
  { x: 0.671875, y: 0.109375 },
  { x: 0.703125, y: 0.109375 },
  { x: 0.703125, y: 0.109375 },
  { x: 0.734375, y: 0.109375 },
  { x: 0.734375, y: 0.109375 },
  { x: 0.765625, y: 0.109375 },
  { x: 0.765625, y: 0.109375 },
  { x: 0.796875, y: 0.109375 },
  { x: 0.796875, y: 0.109375 },
  { x: 0.828125, y: 0.109375 },
  { x: 0.828125, y: 0.109375 },
  { x: 0.859375, y: 0.109375 },
  { x: 0.859375, y: 0.109375 },
  { x: 0.890625, y: 0.109375 },
  { x: 0.890625, y: 0.109375 },
  { x: 0.921875, y: 0.109375 },
  { x: 0.921875, y: 0.109375 },
  { x: 0.953125, y: 0.109375 },
  { x: 0.953125, y: 0.109375 },
  { x: 0.984375, y: 0.109375 },
  { x: 0.984375, y: 0.109375 },
  { x: 0.015625, y: 0.140625 },
  { x: 0.015625, y: 0.140625 },
  { x: 0.046875, y: 0.140625 },
  { x: 0.046875, y: 0.140625 },
  { x: 0.078125, y: 0.140625 },
  { x: 0.078125, y: 0.140625 },
  { x: 0.109375, y: 0.140625 },
  { x: 0.109375, y: 0.140625 },
  { x: 0.140625, y: 0.140625 },
  { x: 0.140625, y: 0.140625 },
  { x: 0.171875, y: 0.140625 },
  { x: 0.171875, y: 0.140625 },
  { x: 0.203125, y: 0.140625 },
  { x: 0.203125, y: 0.140625 },
  { x: 0.234375, y: 0.140625 },
  { x: 0.234375, y: 0.140625 },
  { x: 0.265625, y: 0.140625 },
  { x: 0.265625, y: 0.140625 },
  { x: 0.296875, y: 0.140625 },
  { x: 0.296875, y: 0.140625 },
  { x: 0.328125, y: 0.140625 },
  { x: 0.328125, y: 0.140625 },
  { x: 0.359375, y: 0.140625 },
  { x: 0.359375, y: 0.140625 },
  { x: 0.390625, y: 0.140625 },
  { x: 0.390625, y: 0.140625 },
  { x: 0.421875, y: 0.140625 },
  { x: 0.421875, y: 0.140625 },
  { x: 0.453125, y: 0.140625 },
  { x: 0.453125, y: 0.140625 },
  { x: 0.484375, y: 0.140625 },
  { x: 0.484375, y: 0.140625 },
  { x: 0.515625, y: 0.140625 },
  { x: 0.515625, y: 0.140625 },
  { x: 0.546875, y: 0.140625 },
  { x: 0.546875, y: 0.140625 },
  { x: 0.578125, y: 0.140625 },
  { x: 0.578125, y: 0.140625 },
  { x: 0.609375, y: 0.140625 },
  { x: 0.609375, y: 0.140625 },
  { x: 0.640625, y: 0.140625 },
  { x: 0.640625, y: 0.140625 },
  { x: 0.671875, y: 0.140625 },
  { x: 0.671875, y: 0.140625 },
  { x: 0.703125, y: 0.140625 },
  { x: 0.703125, y: 0.140625 },
  { x: 0.734375, y: 0.140625 },
  { x: 0.734375, y: 0.140625 },
  { x: 0.765625, y: 0.140625 },
  { x: 0.765625, y: 0.140625 },
  { x: 0.796875, y: 0.140625 },
  { x: 0.796875, y: 0.140625 },
  { x: 0.828125, y: 0.140625 },
  { x: 0.828125, y: 0.140625 },
  { x: 0.859375, y: 0.140625 },
  { x: 0.859375, y: 0.140625 },
  { x: 0.890625, y: 0.140625 },
  { x: 0.890625, y: 0.140625 },
  { x: 0.921875, y: 0.140625 },
  { x: 0.921875, y: 0.140625 },
  { x: 0.953125, y: 0.140625 },
  { x: 0.953125, y: 0.140625 },
  { x: 0.984375, y: 0.140625 },
  { x: 0.984375, y: 0.140625 },
  { x: 0.015625, y: 0.171875 },
  { x: 0.015625, y: 0.171875 },
  { x: 0.046875, y: 0.171875 },
  { x: 0.046875, y: 0.171875 },
  { x: 0.078125, y: 0.171875 },
  { x: 0.078125, y: 0.171875 },
  { x: 0.109375, y: 0.171875 },
  { x: 0.109375, y: 0.171875 },
  { x: 0.140625, y: 0.171875 },
  { x: 0.140625, y: 0.171875 },
  { x: 0.171875, y: 0.171875 },
  { x: 0.171875, y: 0.171875 },
  { x: 0.203125, y: 0.171875 },
  { x: 0.203125, y: 0.171875 },
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  { x: 0.6875, y: 0.9375 },
  { x: 0.6875, y: 0.9375 },
  { x: 0.8125, y: 0.9375 },
  { x: 0.8125, y: 0.9375 },
  { x: 0.8125, y: 0.9375 },
  { x: 0.8125, y: 0.9375 },
  { x: 0.8125, y: 0.9375 },
  { x: 0.8125, y: 0.9375 },
  { x: 0.9375, y: 0.9375 },
  { x: 0.9375, y: 0.9375 },
  { x: 0.9375, y: 0.9375 },
  { x: 0.9375, y: 0.9375 },
  { x: 0.9375, y: 0.9375 },
  { x: 0.9375, y: 0.9375 }
];

// src/hand/handposedetector.ts
var HandDetector = class {
  constructor(model23) {
    __publicField(this, "model");
    __publicField(this, "anchors");
    __publicField(this, "anchorsTensor");
    __publicField(this, "inputSize");
    __publicField(this, "inputSizeTensor");
    __publicField(this, "doubleInputSizeTensor");
    var _a2, _b2, _c2, _d2;
    this.model = model23;
    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
    this.anchorsTensor = Oi(this.anchors);
    this.inputSize = ((_d2 = (_c2 = (_b2 = (_a2 = this == null ? void 0 : this.model) == null ? void 0 : _a2.inputs) == null ? void 0 : _b2[0]) == null ? void 0 : _c2.shape) == null ? void 0 : _d2[2]) || 0;
    this.inputSizeTensor = dr([this.inputSize, this.inputSize]);
    this.doubleInputSizeTensor = dr([this.inputSize * 2, this.inputSize * 2]);
  }
  normalizeBoxes(boxes) {
    const t10 = {};
    t10.boxOffsets = Ke(boxes, [0, 0], [-1, 2]);
    t10.boxSizes = Ke(boxes, [0, 2], [-1, 2]);
    t10.div = He(t10.boxOffsets, this.inputSizeTensor);
    t10.boxCenterPoints = be(t10.div, this.anchorsTensor);
    t10.halfBoxSizes = He(t10.boxSizes, this.doubleInputSizeTensor);
    t10.sub = ke(t10.boxCenterPoints, t10.halfBoxSizes);
    t10.startPoints = ne(t10.sub, this.inputSizeTensor);
    t10.add = be(t10.boxCenterPoints, t10.halfBoxSizes);
    t10.endPoints = ne(t10.add, this.inputSizeTensor);
    const res = fv([t10.startPoints, t10.endPoints], 1);
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    return res;
  }
  normalizeLandmarks(rawPalmLandmarks, index2) {
    const t10 = {};
    t10.reshape = W(rawPalmLandmarks, [-1, 7, 2]);
    t10.div = He(t10.reshape, this.inputSizeTensor);
    t10.landmarks = be(t10.div, this.anchors[index2] ? this.anchors[index2] : 0);
    const res = ne(t10.landmarks, this.inputSizeTensor);
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    return res;
  }
  async predict(input, config3) {
    var _a2;
    const t10 = {};
    t10.resize = eK.resizeBilinear(input, [this.inputSize, this.inputSize]);
    t10.div = He(t10.resize, constants.tf127);
    t10.image = ke(t10.div, constants.tf1);
    t10.batched = this.model.execute(t10.image);
    t10.predictions = Up(t10.batched);
    t10.slice = Ke(t10.predictions, [0, 0], [-1, 1]);
    t10.sigmoid = Ws(t10.slice);
    t10.scores = Up(t10.sigmoid);
    const scores = await t10.scores.data();
    t10.boxes = Ke(t10.predictions, [0, 1], [-1, 4]);
    t10.norm = this.normalizeBoxes(t10.boxes);
    t10.nms = await eK.nonMaxSuppressionAsync(t10.norm, t10.scores, 3 * (((_a2 = config3.hand) == null ? void 0 : _a2.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
    const nms = await t10.nms.array();
    const hands = [];
    for (const index2 of nms) {
      const p = {};
      p.box = Ke(t10.norm, [index2, 0], [1, -1]);
      p.slice = Ke(t10.predictions, [index2, 5], [1, 14]);
      p.norm = this.normalizeLandmarks(p.slice, index2);
      p.palmLandmarks = W(p.norm, [-1, 2]);
      const box = await p.box.data();
      const startPoint = box.slice(0, 2);
      const endPoint = box.slice(2, 4);
      const palmLandmarks = await p.palmLandmarks.array();
      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
      const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
      hands.push(scaled);
      Object.keys(p).forEach((tensor) => Ot(p[tensor]));
    }
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    return hands;
  }
};

// src/hand/handposepipeline.ts
var palmBoxEnlargeFactor = 5;
var handBoxEnlargeFactor = 1.65;
var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
var palmLandmarksPalmBase = 0;
var palmLandmarksMiddleFingerBase = 2;
var lastTime13 = 0;
var HandPipeline = class {
  constructor(handDetector, handPoseModel2) {
    __publicField(this, "handDetector");
    __publicField(this, "handPoseModel");
    __publicField(this, "inputSize");
    __publicField(this, "storedBoxes");
    __publicField(this, "skipped");
    __publicField(this, "detectedHands");
    var _a2, _b2, _c2;
    this.handDetector = handDetector;
    this.handPoseModel = handPoseModel2;
    this.inputSize = ((_c2 = (_b2 = (_a2 = this.handPoseModel) == null ? void 0 : _a2.inputs) == null ? void 0 : _b2[0].shape) == null ? void 0 : _c2[2]) || 0;
    this.storedBoxes = [];
    this.skipped = Number.MAX_SAFE_INTEGER;
    this.detectedHands = 0;
  }
  calculateLandmarksBoundingBox(landmarks) {
    const xs2 = landmarks.map((d) => d[0]);
    const ys2 = landmarks.map((d) => d[1]);
    const startPoint = [Math.min(...xs2), Math.min(...ys2)];
    const endPoint = [Math.max(...xs2), Math.max(...ys2)];
    return { startPoint, endPoint };
  }
  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
  }
  getBoxForHandLandmarks(landmarks) {
    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
    boxAroundHand.palmLandmarks = [];
    for (let i = 0; i < palmLandmarkIds.length; i++) {
      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
    }
    return boxAroundHand;
  }
  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
    const boxSize = getBoxSize2(box2);
    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
    const coordsScaled = rawCoords.map((coord) => [
      scaleFactor[0] * (coord[0] - this.inputSize / 2),
      scaleFactor[1] * (coord[1] - this.inputSize / 2),
      scaleFactor[2] * coord[2]
    ]);
    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
    const coordsRotated = coordsScaled.map((coord) => {
      const rotated = rotatePoint2(coord, coordsRotationMatrix);
      return [...rotated, coord[2]];
    });
    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
    const boxCenter = [...getBoxCenter2(box2), 1];
    const originalBoxCenter = [
      dot2(boxCenter, inverseRotationMatrix[0]),
      dot2(boxCenter, inverseRotationMatrix[1])
    ];
    return coordsRotated.map((coord) => [
      Math.trunc(coord[0] + originalBoxCenter[0]),
      Math.trunc(coord[1] + originalBoxCenter[1]),
      Math.trunc(coord[2])
    ]);
  }
  async estimateHands(image, config3) {
    let useFreshBox = false;
    let boxes;
    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13;
    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
    if (config3.skipAllowed && skipTime && skipFrame) {
      boxes = await this.handDetector.predict(image, config3);
      this.skipped = 0;
    }
    if (config3.skipAllowed)
      this.skipped++;
    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
      this.detectedHands = 0;
      this.storedBoxes = [...boxes];
      if (this.storedBoxes.length > 0)
        useFreshBox = true;
    }
    const hands = [];
    for (let i = 0; i < this.storedBoxes.length; i++) {
      const currentBox = this.storedBoxes[i];
      if (!currentBox)
        continue;
      if (config3.hand.landmarks) {
        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
        const palmCenter = getBoxCenter2(currentBox);
        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? eK.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
        const handImage = He(croppedInput, constants.tf255);
        Ot(croppedInput);
        Ot(rotatedImage);
        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime13 = now();
        Ot(handImage);
        const confidence = (await confidenceT.data())[0];
        Ot(confidenceT);
        if (confidence >= config3.hand.minConfidence / 4) {
          const keypointsReshaped = W(keypoints, [-1, 3]);
          const rawCoords = await keypointsReshaped.array();
          Ot(keypoints);
          Ot(keypointsReshaped);
          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
          const nextBoundingBox = this.getBoxForHandLandmarks(coords);
          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
          const result = {
            landmarks: coords,
            confidence,
            boxConfidence: currentBox.confidence,
            fingerConfidence: confidence,
            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
          };
          hands.push(result);
        } else {
          this.storedBoxes[i] = null;
        }
        Ot(keypoints);
      } else {
        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
        const result = {
          confidence: currentBox.confidence,
          boxConfidence: currentBox.confidence,
          fingerConfidence: 0,
          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
          landmarks: []
        };
        hands.push(result);
      }
    }
    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
    this.detectedHands = hands.length;
    if (hands.length > config3.hand.maxDetected)
      hands.length = config3.hand.maxDetected;
    return hands;
  }
};

// src/hand/handpose.ts
var meshAnnotations2 = {
  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]
};
var handDetectorModel;
var handPoseModel;
var handPipeline;
async function predict14(input, config3) {
  const predictions = await handPipeline.estimateHands(input, config3);
  if (!predictions)
    return [];
  const hands = [];
  for (let i = 0; i < predictions.length; i++) {
    const annotations2 = {};
    if (predictions[i].landmarks) {
      for (const key of Object.keys(meshAnnotations2)) {
        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
      }
    }
    const keypoints = predictions[i].landmarks;
    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
    let boxRaw = [0, 0, 0, 0];
    if (keypoints && keypoints.length > 0) {
      for (const pt of keypoints) {
        if (pt[0] < box[0])
          box[0] = pt[0];
        if (pt[1] < box[1])
          box[1] = pt[1];
        if (pt[0] > box[2])
          box[2] = pt[0];
        if (pt[1] > box[3])
          box[3] = pt[1];
      }
      box[2] -= box[0];
      box[3] -= box[1];
      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
    } else {
      box = predictions[i].box ? [
        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
      ] : [0, 0, 0, 0];
      boxRaw = [
        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
      ];
    }
    const landmarks = analyze(keypoints);
    hands.push({
      id: i,
      score: Math.round(100 * predictions[i].confidence) / 100,
      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
      label: "hand",
      box,
      boxRaw,
      keypoints,
      annotations: annotations2,
      landmarks
    });
  }
  return hands;
}
async function load15(config3) {
  var _a2, _b2;
  if (env.initial) {
    handDetectorModel = null;
    handPoseModel = null;
  }
  if (!handDetectorModel || !handPoseModel) {
    [handDetectorModel, handPoseModel] = await Promise.all([
      config3.hand.enabled ? loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath) : null,
      config3.hand.landmarks ? loadModel((_b2 = config3.hand.skeleton) == null ? void 0 : _b2.modelPath) : null
    ]);
  } else {
    if (config3.debug)
      log("cached model:", handDetectorModel["modelUrl"]);
    if (config3.debug)
      log("cached model:", handPoseModel["modelUrl"]);
  }
  const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
  if (handDetector && handPoseModel)
    handPipeline = new HandPipeline(handDetector, handPoseModel);
  return [handDetectorModel, handPoseModel];
}

// src/hand/handtrack.ts
var models2 = [null, null];
var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
var inputSize7 = [[0, 0], [0, 0]];
var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
var faceIndex = 4;
var boxExpandFact = 1.6;
var maxDetectorResolution = 512;
var detectorExpandFact = 1.4;
var skipped13 = Number.MAX_SAFE_INTEGER;
var lastTime14 = 0;
var outputSize = [0, 0];
var cache4 = {
  boxes: [],
  hands: []
};
var fingerMap = {
  /*
  thumb: [0, 1, 2, 3, 4],
  index: [0, 5, 6, 7, 8],
  middle: [0, 9, 10, 11, 12],
  ring: [0, 13, 14, 15, 16],
  pinky: [0, 17, 18, 19, 20],
  palm: [0],
  */
  thumb: [1, 2, 3, 4],
  index: [5, 6, 7, 8],
  middle: [9, 10, 11, 12],
  ring: [13, 14, 15, 16],
  pinky: [17, 18, 19, 20],
  base: [0],
  palm: [0, 17, 13, 9, 5, 1, 0]
};
async function loadDetect2(config3) {
  var _a2;
  if (env.initial)
    models2[0] = null;
  if (!models2[0]) {
    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
    models2[0] = await loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath);
    const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0;
    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", models2[0]["modelUrl"]);
  return models2[0];
}
async function loadSkeleton(config3) {
  var _a2;
  if (env.initial)
    models2[1] = null;
  if (!models2[1]) {
    models2[1] = await loadModel((_a2 = config3.hand.skeleton) == null ? void 0 : _a2.modelPath);
    const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0;
    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", models2[1]["modelUrl"]);
  return models2[1];
}
async function detectHands(input, config3) {
  const hands = [];
  if (!input || !models2[0])
    return hands;
  const t10 = {};
  const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
  const width = Math.round(height * ratio2 / 8) * 8;
  t10.resize = eK.resizeBilinear(input, [height, width]);
  t10.cast = je(t10.resize, "int32");
  [t10.rawScores, t10.rawBoxes] = await models2[0].executeAsync(t10.cast, modelOutputNodes);
  t10.boxes = Up(t10.rawBoxes, [0, 2]);
  t10.scores = Up(t10.rawScores, [0]);
  const classScores = so(t10.scores, 1);
  Ot(classScores[faceIndex]);
  classScores.splice(faceIndex, 1);
  t10.filtered = Sr(classScores, 1);
  Ot(classScores);
  t10.max = Gs(t10.filtered, 1);
  t10.argmax = X0(t10.filtered, 1);
  let id2 = 0;
  t10.nms = await eK.nonMaxSuppressionAsync(t10.boxes, t10.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
  const nms = await t10.nms.data();
  const scores = await t10.max.data();
  const classNum = await t10.argmax.data();
  for (const nmsIndex of Array.from(nms)) {
    const boxSlice = Ke(t10.boxes, nmsIndex, 1);
    const boxYX = await boxSlice.data();
    Ot(boxSlice);
    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
    const boxRaw = scale(boxData, detectorExpandFact);
    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
    const score = scores[nmsIndex];
    const label = classes[classNum[nmsIndex]];
    const hand3 = { id: id2++, score, box: boxFull, boxRaw, label };
    hands.push(hand3);
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  hands.sort((a, b) => b.score - a.score);
  if (hands.length > (config3.hand.maxDetected || 1))
    hands.length = config3.hand.maxDetected || 1;
  return hands;
}
async function detectFingers(input, h, config3) {
  const hand3 = {
    // initial values inherited from hand detect
    id: h.id,
    score: Math.round(100 * h.score) / 100,
    boxScore: Math.round(100 * h.score) / 100,
    fingerScore: 0,
    box: h.box,
    boxRaw: h.boxRaw,
    label: h.label,
    keypoints: [],
    landmarks: {},
    annotations: {}
  };
  if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
    const t10 = {};
    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
    t10.crop = eK.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
    t10.div = He(t10.crop, constants.tf255);
    [t10.score, t10.keypoints] = models2[1].execute(t10.div, ["Identity_1", "Identity"]);
    const rawScore = (await t10.score.data())[0];
    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
    if (score >= (config3.hand.minConfidence || 0)) {
      hand3.fingerScore = score;
      t10.reshaped = W(t10.keypoints, [-1, 3]);
      const coordsData = await t10.reshaped.array();
      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
      hand3.landmarks = analyze(hand3.keypoints);
      for (const key of Object.keys(fingerMap)) {
        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
      }
    }
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  }
  return hand3;
}
async function predict15(input, config3) {
  var _a2, _b2;
  if (!((_a2 = models2[0]) == null ? void 0 : _a2["executor"]) || !((_b2 = models2[1]) == null ? void 0 : _b2["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape)
    return [];
  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
  skipped13++;
  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14;
  const skipFrame = skipped13 < (config3.hand.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame) {
    return cache4.hands;
  }
  return new Promise(async (resolve) => {
    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14;
    const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0);
    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
    } else {
      cache4.boxes = await detectHands(input, config3);
      lastTime14 = now();
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
      skipped13 = 0;
    }
    const oldCache = [...cache4.boxes];
    cache4.boxes.length = 0;
    if (config3.cacheSensitivity > 0) {
      for (let i = 0; i < cache4.hands.length; i++) {
        const boxKpt = square(cache4.hands[i].keypoints, outputSize);
        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
          const boxScale = scale(boxKpt.box, boxExpandFact);
          const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
        }
      }
    }
    for (let i = 0; i < cache4.hands.length; i++) {
      const bbox = calc(cache4.hands[i].keypoints, outputSize);
      cache4.hands[i].box = bbox.box;
      cache4.hands[i].boxRaw = bbox.boxRaw;
    }
    resolve(cache4.hands);
  });
}

// src/result.ts
var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });

// src/body/movenetcoords.ts
var movenetcoords_exports = {};
__export(movenetcoords_exports, {
  connected: () => connected3,
  horizontal: () => horizontal,
  kpt: () => kpt3,
  relative: () => relative,
  vertical: () => vertical
});
var kpt3 = [
  // used to create part labels
  "nose",
  "leftEye",
  "rightEye",
  "leftEar",
  "rightEar",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle"
];
var horizontal = [
  // used to fix left vs right
  ["leftEye", "rightEye"],
  ["leftEar", "rightEar"],
  ["leftShoulder", "rightShoulder"],
  ["leftElbow", "rightElbow"],
  ["leftWrist", "rightWrist"],
  ["leftHip", "rightHip"],
  ["leftKnee", "rightKnee"],
  ["leftAnkle", "rightAnkle"]
];
var vertical = [
  // used to remove unlikely keypoint positions
  ["leftKnee", "leftShoulder"],
  ["rightKnee", "rightShoulder"],
  ["leftAnkle", "leftKnee"],
  ["rightAnkle", "rightKnee"]
];
var relative = [
  // used to match relative body parts
  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
];
var connected3 = {
  // used to create body outline in annotations
  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
  head: []
};

// src/util/interpolate.ts
var bufferedResult = empty();
var interpolateTime = 0;
function calc2(newResult, config3) {
  var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2;
  const t0 = now();
  if (!newResult)
    return empty();
  const elapsed = Date.now() - newResult.timestamp;
  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
  if (newResult.canvas)
    bufferedResult.canvas = newResult.canvas;
  if (newResult.error)
    bufferedResult.error = newResult.error;
  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
  } else {
    for (let i = 0; i < newResult.body.length; i++) {
      const box = newResult.body[i].box.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j10] + newBoxCoord) / bufferedFactor);
      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j10] + newBoxCoord) / bufferedFactor);
      const keypoints = newResult.body[i].keypoints.map((newKpt, j10) => {
        var _a3, _b3, _c3, _d3, _e2, _f3, _g3, _h3, _i3;
        return {
          score: newKpt.score,
          part: newKpt.part,
          position: [
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
          ],
          positionRaw: [
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
          ],
          distance: [
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _a3[0]) || 0) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c3 = newKpt.distance) == null ? void 0 : _c3[0],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _d3[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f3 = newKpt.distance) == null ? void 0 : _f3[1],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _g3[2]) || 0) + (((_h3 = newKpt.distance) == null ? void 0 : _h3[2]) || 0)) / bufferedFactor : (_i3 = newKpt.distance) == null ? void 0 : _i3[2]
          ]
        };
      });
      const annotations2 = {};
      let coords = { connected: {} };
      if ((_a2 = config3.body.modelPath) == null ? void 0 : _a2.includes("efficientpose"))
        coords = efficientposecoords_exports;
      else if ((_b2 = config3.body.modelPath) == null ? void 0 : _b2.includes("blazepose"))
        coords = blazeposecoords_exports;
      else if ((_c2 = config3.body.modelPath) == null ? void 0 : _c2.includes("movenet"))
        coords = movenetcoords_exports;
      for (const [name, indexes] of Object.entries(coords.connected)) {
        const pt = [];
        for (let j10 = 0; j10 < indexes.length - 1; j10++) {
          const pt0 = keypoints.find((kp2) => kp2.part === indexes[j10]);
          const pt1 = keypoints.find((kp2) => kp2.part === indexes[j10 + 1]);
          if (pt0 && pt1)
            pt.push([pt0.position, pt1.position]);
        }
        annotations2[name] = pt;
      }
      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
    }
  }
  if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
  } else {
    for (let i = 0; i < newResult.hand.length; i++) {
      const box = newResult.hand[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j10] + b) / bufferedFactor);
      const boxRaw = newResult.hand[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j10] + b) / bufferedFactor);
      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j10) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j10][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
      let annotations2 = {};
      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
        annotations2 = bufferedResult.hand[i].annotations;
      } else if (newResult.hand[i].annotations) {
        for (const key of Object.keys(newResult.hand[i].annotations)) {
          annotations2[key] = ((_f2 = (_e = (_d2 = newResult.hand[i]) == null ? void 0 : _d2.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f2[0]) ? newResult.hand[i].annotations[key].map((val, j10) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j10][k] + coord) / bufferedFactor)) : null;
        }
      }
      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
    }
  }
  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
  } else {
    for (let i = 0; i < newResult.face.length; i++) {
      const box = newResult.face[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j10] + b) / bufferedFactor);
      const boxRaw = newResult.face[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j10] + b) / bufferedFactor);
      if (newResult.face[i].rotation) {
        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
        rotation.matrix = (_g2 = newResult.face[i].rotation) == null ? void 0 : _g2.matrix;
        rotation.angle = {
          roll: ((bufferedFactor - 1) * (((_i2 = (_h2 = bufferedResult.face[i].rotation) == null ? void 0 : _h2.angle) == null ? void 0 : _i2.roll) || 0) + (((_k = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
          yaw: ((bufferedFactor - 1) * (((_m2 = (_l2 = bufferedResult.face[i].rotation) == null ? void 0 : _l2.angle) == null ? void 0 : _m2.yaw) || 0) + (((_o2 = (_n2 = newResult.face[i].rotation) == null ? void 0 : _n2.angle) == null ? void 0 : _o2.yaw) || 0)) / bufferedFactor,
          pitch: ((bufferedFactor - 1) * (((_q2 = (_p2 = bufferedResult.face[i].rotation) == null ? void 0 : _p2.angle) == null ? void 0 : _q2.pitch) || 0) + (((_s2 = (_r2 = newResult.face[i].rotation) == null ? void 0 : _r2.angle) == null ? void 0 : _s2.pitch) || 0)) / bufferedFactor
        };
        rotation.gaze = {
          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive
          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u2 = newResult.face[i].rotation) == null ? void 0 : _u2.gaze.bearing) || 0)) / bufferedFactor,
          strength: ((bufferedFactor - 1) * (((_v2 = bufferedResult.face[i].rotation) == null ? void 0 : _v2.gaze.strength) || 0) + (((_w2 = newResult.face[i].rotation) == null ? void 0 : _w2.gaze.strength) || 0)) / bufferedFactor
        };
        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
      } else {
        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
      }
    }
  }
  if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
  } else {
    for (let i = 0; i < newResult.object.length; i++) {
      const box = newResult.object[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j10] + b) / bufferedFactor);
      const boxRaw = newResult.object[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j10] + b) / bufferedFactor);
      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
    }
  }
  if (newResult.persons) {
    const newPersons = newResult.persons;
    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
    } else {
      for (let i = 0; i < newPersons.length; i++) {
        bufferedResult.persons[i].box = newPersons[i].box.map((box, j10) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j10] + box) / bufferedFactor);
      }
    }
  }
  if (newResult.gesture)
    bufferedResult.gesture = newResult.gesture;
  bufferedResult.width = newResult.width;
  bufferedResult.height = newResult.height;
  const t12 = now();
  interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t0) : Math.round(t12 - t0);
  if (newResult.performance)
    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
  return bufferedResult;
}

// src/segmentation/meet.ts
var model17;
async function load16(config3) {
  if (!model17 || env.initial)
    model17 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model17["modelUrl"]);
  return model17;
}
async function predict16(input, config3) {
  var _a2;
  if (!model17)
    model17 = await load16(config3);
  if (!(model17 == null ? void 0 : model17["executor"]) || !((_a2 = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a2[0].shape))
    return null;
  const t10 = {};
  t10.resize = eK.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
  t10.norm = He(t10.resize, constants.tf255);
  t10.res = model17.execute(t10.norm);
  t10.squeeze = Up(t10.res, [0]);
  [t10.bgRaw, t10.fgRaw] = so(t10.squeeze, 2);
  t10.fg = Jk(t10.fgRaw);
  t10.mul = ne(t10.fg, constants.tf255);
  t10.expand = Ra(t10.mul, 2);
  t10.output = eK.resizeBilinear(t10.expand, [input.shape[1] || 0, input.shape[2] || 0]);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      t10.input = Up(input);
      t10.concat = xt([t10.input, t10.output], -1);
      rgba = je(t10.concat, "int32");
      break;
    case "alpha":
      rgba = je(t10.output, "int32");
      break;
    default:
      rgba = nr(0);
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return rgba;
}

// src/face/match.ts
var match_exports = {};
__export(match_exports, {
  distance: () => distance,
  find: () => find,
  similarity: () => similarity
});
function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
  if (!descriptor1 || !descriptor1)
    return Number.MAX_SAFE_INTEGER;
  let sum = 0;
  for (let i = 0; i < descriptor1.length; i++) {
    const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
    sum += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
  }
  return (options4.multiplier || 20) * sum;
}
var normalizeDistance = (dist, order, min, max) => {
  if (dist === 0)
    return 1;
  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
  const norm = (1 - root / 100 - min) / (max - min);
  const clamp2 = Math.max(Math.min(norm, 1), 0);
  return clamp2;
};
function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
  const dist = distance(descriptor1, descriptor2, options4);
  return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
}
function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
  }
  let lowestDistance = Number.MAX_SAFE_INTEGER;
  let index2 = -1;
  for (let i = 0; i < descriptors.length; i++) {
    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
    if (res < lowestDistance) {
      lowestDistance = res;
      index2 = i;
    }
    if (lowestDistance < (options4.threshold || 0))
      break;
  }
  const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
  return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
}

// src/models.ts
var models_exports2 = {};
__export(models_exports2, {
  Models: () => Models,
  validateModel: () => validateModel
});

// src/body/movenetfix.ts
var maxJitter = 5e-3;
var cache5 = {
  keypoints: [],
  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
};
function bodyParts(body4) {
  for (const pair of horizontal) {
    const left = body4.keypoints.findIndex((kp2) => kp2.part === pair[0]);
    const right = body4.keypoints.findIndex((kp2) => kp2.part === pair[1]);
    if (body4.keypoints[left] && body4.keypoints[right]) {
      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
        const tmp = body4.keypoints[left];
        body4.keypoints[left] = body4.keypoints[right];
        body4.keypoints[right] = tmp;
      }
    }
  }
  for (const pair of vertical) {
    const lower = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[0]);
    const higher = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[1]);
    if (body4.keypoints[lower] && body4.keypoints[higher]) {
      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
        body4.keypoints.splice(lower, 1);
      }
    }
  }
  for (const [pair, compare2] of relative) {
    const left = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[0]);
    const right = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[1]);
    const leftTo = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === compare2[0]);
    const rightTo = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === compare2[1]);
    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
      continue;
    const distanceLeft = body4.keypoints[left] ? [
      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
    ] : [0, 0];
    const distanceRight = body4.keypoints[right] ? [
      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
    ] : [0, 0];
    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
      const tmp = body4.keypoints[left];
      body4.keypoints[left] = body4.keypoints[right];
      body4.keypoints[right] = tmp;
    }
  }
}
function jitter(keypoints) {
  for (let i = 0; i < keypoints.length; i++) {
    if (keypoints[i] && cache5.keypoints[i]) {
      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
      if (diff[0] < maxJitter && diff[1] < maxJitter) {
        keypoints[i] = cache5.keypoints[i];
      } else {
        cache5.keypoints[i] = keypoints[i];
      }
    } else {
      cache5.keypoints[i] = keypoints[i];
    }
  }
  return keypoints;
}
function padInput(input, inputSize10) {
  var _a2, _b2;
  const t10 = {};
  if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b2 = input == null ? void 0 : input.shape) == null ? void 0 : _b2[2]))
    return input;
  cache5.padding = [
    [0, 0],
    // dont touch batch
    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
    // height before&after
    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
    // width before&after
    [0, 0]
    // dont touch rbg
  ];
  t10.pad = Ks(input, cache5.padding);
  t10.resize = eK.resizeBilinear(t10.pad, [inputSize10, inputSize10]);
  const final = je(t10.resize, "int32");
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return final;
}
function rescaleBody(body4, outputSize2) {
  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
  for (const kpt4 of body4.keypoints) {
    kpt4.position = [
      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
    ];
    kpt4.positionRaw = [
      kpt4.position[0] / outputSize2[0],
      kpt4.position[1] / outputSize2[1]
    ];
  }
  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
  body4.box = rescaledBoxes.box;
  body4.boxRaw = rescaledBoxes.boxRaw;
  return body4;
}

// src/body/movenet.ts
var model18;
var inputSize8 = 0;
var skipped14 = Number.MAX_SAFE_INTEGER;
var cache6 = {
  boxes: [],
  bodies: [],
  last: 0
};
async function load17(config3) {
  var _a2;
  if (env.initial)
    model18 = null;
  if (!model18) {
    fakeOps(["size"], config3);
    model18 = await loadModel(config3.body.modelPath);
  } else if (config3.debug)
    log("cached model:", model18["modelUrl"]);
  inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a2 = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a2[0].shape) ? model18.inputs[0].shape[2] : 0;
  if (inputSize8 < 64)
    inputSize8 = 256;
  return model18;
}
function parseSinglePose(res, config3, image) {
  const kpt4 = res[0][0];
  const keypoints = [];
  let score = 0;
  for (let id2 = 0; id2 < kpt4.length; id2++) {
    score = kpt4[id2][2];
    if (score > config3.body.minConfidence) {
      const positionRaw = [kpt4[id2][1], kpt4[id2][0]];
      keypoints.push({
        score: Math.round(100 * score) / 100,
        part: kpt3[id2],
        positionRaw,
        position: [
          // normalized to input image size
          Math.round((image.shape[2] || 0) * positionRaw[0]),
          Math.round((image.shape[1] || 0) * positionRaw[1])
        ]
      });
    }
  }
  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
  const bodies = [];
  const newBox = calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);
  const annotations2 = {};
  for (const [name, indexes] of Object.entries(connected3)) {
    const pt = [];
    for (let i = 0; i < indexes.length - 1; i++) {
      const pt0 = keypoints.find((kp2) => kp2.part === indexes[i]);
      const pt1 = keypoints.find((kp2) => kp2.part === indexes[i + 1]);
      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
        pt.push([pt0.position, pt1.position]);
    }
    annotations2[name] = pt;
  }
  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
  bodyParts(body4);
  bodies.push(body4);
  return bodies;
}
function parseMultiPose(res, config3, image) {
  const bodies = [];
  for (let id2 = 0; id2 < res[0].length; id2++) {
    const kpt4 = res[0][id2];
    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
    if (totalScore > config3.body.minConfidence) {
      const keypoints = [];
      for (let i = 0; i < 17; i++) {
        const score = kpt4[3 * i + 2];
        if (score > config3.body.minConfidence) {
          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
          keypoints.push({
            part: kpt3[i],
            score: Math.round(100 * score) / 100,
            positionRaw,
            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])]
          });
        }
      }
      const newBox = calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);
      const annotations2 = {};
      for (const [name, indexes] of Object.entries(connected3)) {
        const pt = [];
        for (let i = 0; i < indexes.length - 1; i++) {
          const pt0 = keypoints.find((kp2) => kp2.part === indexes[i]);
          const pt1 = keypoints.find((kp2) => kp2.part === indexes[i + 1]);
          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
            pt.push([pt0.position, pt1.position]);
        }
        annotations2[name] = pt;
      }
      const body4 = { id: id2, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
      bodyParts(body4);
      bodies.push(body4);
    }
  }
  bodies.sort((a, b) => b.score - a.score);
  if (bodies.length > config3.body.maxDetected)
    bodies.length = config3.body.maxDetected;
  return bodies;
}
async function predict17(input, config3) {
  var _a2;
  if (!(model18 == null ? void 0 : model18["executor"]) || !((_a2 = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a2[0].shape))
    return [];
  if (!config3.skipAllowed)
    cache6.boxes.length = 0;
  skipped14++;
  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
  const skipFrame = skipped14 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame) {
    return cache6.bodies;
  }
  return new Promise(async (resolve) => {
    const t10 = {};
    skipped14 = 0;
    t10.input = padInput(input, inputSize8);
    t10.res = model18 == null ? void 0 : model18.execute(t10.input);
    cache6.last = now();
    const res = await t10.res.array();
    cache6.bodies = t10.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
    for (const body4 of cache6.bodies) {
      rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
      jitter(body4.keypoints);
    }
    Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
    resolve(cache6.bodies);
  });
}

// src/object/nanodet.ts
var model19;
var last10 = [];
var lastTime15 = 0;
var skipped15 = Number.MAX_SAFE_INTEGER;
var inputSize9 = 0;
var scaleBox = 2.5;
async function load18(config3) {
  if (!model19 || env.initial) {
    model19 = await loadModel(config3.object.modelPath);
    const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0;
    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
  } else if (config3.debug)
    log("cached model:", model19["modelUrl"]);
  return model19;
}
async function process4(res, outputShape, config3) {
  var _a2, _b2;
  let id2 = 0;
  let results = [];
  const size2 = inputSize9;
  for (const strideSize of [1, 2, 4]) {
    const baseSize = strideSize * 13;
    const scoresT = Up(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length));
    const scores = await scoresT.array();
    const featuresT = Up(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length));
    const boxesMaxT = W(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]);
    const boxIdxT = X0(boxesMaxT, 2);
    const boxIdx = await boxIdxT.array();
    for (let i = 0; i < scoresT.shape[0]; i++) {
      for (let j10 = 0; j10 < (((_b2 = scoresT.shape) == null ? void 0 : _b2[1]) || 0); j10++) {
        const score = scores[i][j10];
        if (score > (config3.object.minConfidence || 0) && j10 !== 61) {
          const cx2 = (0.5 + Math.trunc(i % baseSize)) / baseSize;
          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
          const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
          const [x, y6] = [
            cx2 - scaleBox / strideSize * boxOffset[0],
            cy - scaleBox / strideSize * boxOffset[1]
          ];
          const [w, h] = [
            cx2 + scaleBox / strideSize * boxOffset[2] - x,
            cy + scaleBox / strideSize * boxOffset[3] - y6
          ];
          let boxRaw = [x, y6, w, h];
          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
          const box = [
            // results normalized to input image pixels
            boxRaw[0] * outputShape[0],
            boxRaw[1] * outputShape[1],
            boxRaw[2] * outputShape[0],
            boxRaw[3] * outputShape[1]
          ];
          const result = {
            id: id2++,
            // strideSize,
            score: Math.round(100 * score) / 100,
            class: j10 + 1,
            label: labels2[j10].label,
            // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],
            // centerRaw: [cx, cy],
            box: box.map((a) => Math.trunc(a)),
            boxRaw
          };
          results.push(result);
        }
      }
    }
    Ot([scoresT, featuresT, boxesMaxT, boxIdxT]);
  }
  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
  const nmsScores = results.map((a) => a.score);
  let nmsIdx = [];
  if (nmsBoxes && nmsBoxes.length > 0) {
    const nms = await eK.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence);
    nmsIdx = Array.from(await nms.data());
    Ot(nms);
  }
  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
  return results;
}
async function predict18(image, config3) {
  if (!(model19 == null ? void 0 : model19["executor"]))
    return [];
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime15;
  const skipFrame = skipped15 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) {
    skipped15++;
    return last10;
  }
  skipped15 = 0;
  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
    return last10;
  return new Promise(async (resolve) => {
    const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0];
    const resizeT = eK.resizeBilinear(image, [inputSize9, inputSize9], false);
    const normT = He(resizeT, constants.tf255);
    const transposeT = Gp(normT, [0, 3, 1, 2]);
    let objectT;
    if (config3.object.enabled)
      objectT = model19.execute(transposeT);
    lastTime15 = now();
    const obj = await process4(objectT, outputSize2, config3);
    last10 = obj;
    Ot([resizeT, normT, transposeT, ...objectT]);
    resolve(obj);
  });
}

// src/body/posenetutils.ts
var partNames = [
  "nose",
  "leftEye",
  "rightEye",
  "leftEar",
  "rightEar",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle"
];
var count = partNames.length;
var partIds = partNames.reduce((result, jointName, i) => {
  result[jointName] = i;
  return result;
}, {});
var connectedPartNames = [
  ["leftHip", "leftShoulder"],
  ["leftElbow", "leftShoulder"],
  ["leftElbow", "leftWrist"],
  ["leftHip", "leftKnee"],
  ["leftKnee", "leftAnkle"],
  ["rightHip", "rightShoulder"],
  ["rightElbow", "rightShoulder"],
  ["rightElbow", "rightWrist"],
  ["rightHip", "rightKnee"],
  ["rightKnee", "rightAnkle"],
  ["leftShoulder", "rightShoulder"],
  ["leftHip", "rightHip"]
];
var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
var poseChain = [
  ["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 getBoundingBox(keypoints) {
  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y: y6 } }) => ({
    maxX: Math.max(maxX, x),
    maxY: Math.max(maxY, y6),
    minX: Math.min(minX, x),
    minY: Math.min(minY, y6)
  }), {
    maxX: Number.NEGATIVE_INFINITY,
    maxY: Number.NEGATIVE_INFINITY,
    minX: Number.POSITIVE_INFINITY,
    minY: Number.POSITIVE_INFINITY
  });
  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
}
function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
  const scaleY = height / inputResolutionHeight;
  const scaleX = width / inputResolutionWidth;
  const scalePose = (pose, i) => ({
    id: i,
    score: pose.score,
    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
    keypoints: pose.keypoints.map(({ score, part, position }) => ({
      score,
      part,
      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
    })),
    annotations: {}
  });
  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
  return scaledPoses;
}
var MaxHeap = class {
  // function call
  constructor(maxSize2, getElementValue) {
    __publicField(this, "priorityQueue");
    // don't touch
    __publicField(this, "numberOfElements");
    __publicField(this, "getElementValue");
    this.priorityQueue = new Array(maxSize2);
    this.numberOfElements = -1;
    this.getElementValue = getElementValue;
  }
  enqueue(x) {
    this.priorityQueue[++this.numberOfElements] = x;
    this.swim(this.numberOfElements);
  }
  dequeue() {
    const max = this.priorityQueue[0];
    this.exchange(0, this.numberOfElements--);
    this.sink(0);
    this.priorityQueue[this.numberOfElements + 1] = null;
    return max;
  }
  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(k) {
    while (k > 0 && this.less(Math.floor(k / 2), k)) {
      this.exchange(k, Math.floor(k / 2));
      k = Math.floor(k / 2);
    }
  }
  sink(k) {
    while (2 * k <= this.numberOfElements) {
      let j10 = 2 * k;
      if (j10 < this.numberOfElements && this.less(j10, j10 + 1))
        j10++;
      if (!this.less(k, j10))
        break;
      this.exchange(k, j10);
      k = j10;
    }
  }
  getValueAt(i) {
    return this.getElementValue(this.priorityQueue[i]);
  }
  less(i, j10) {
    return this.getValueAt(i) < this.getValueAt(j10);
  }
  exchange(i, j10) {
    const t10 = this.priorityQueue[i];
    this.priorityQueue[i] = this.priorityQueue[j10];
    this.priorityQueue[j10] = t10;
  }
};
function getOffsetPoint(y6, x, keypoint, offsets) {
  return {
    y: offsets.get(y6, x, keypoint),
    x: offsets.get(y6, x, keypoint + count)
  };
}
function getImageCoords(part, outputStride2, offsets) {
  const { heatmapY, heatmapX, id: keypoint } = part;
  const { y: y6, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
  return {
    x: part.heatmapX * outputStride2 + x,
    y: part.heatmapY * outputStride2 + y6
  };
}
function clamp(a, min, max) {
  if (a < min)
    return min;
  if (a > max)
    return max;
  return a;
}
function squaredDistance(y12, x12, y22, x22) {
  const dy = y22 - y12;
  const dx2 = x22 - x12;
  return dy * dy + dx2 * dx2;
}
function addVectors(a, b) {
  return { x: a.x + b.x, y: a.y + b.y };
}

// src/body/posenet.ts
var model20;
var poseNetOutputs = [
  "MobilenetV1/offset_2/BiasAdd",
  "MobilenetV1/heatmap_2/BiasAdd",
  "MobilenetV1/displacement_fwd_2/BiasAdd",
  "MobilenetV1/displacement_bwd_2/BiasAdd"
  /* displacementBwd */
];
var localMaximumRadius = 1;
var outputStride = 16;
var squaredNmsRadius = 50 ** 2;
function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
  const getDisplacement = (point2) => ({
    y: displacements.get(point2.y, point2.x, edgeId),
    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
  });
  const getStridedIndexNearPoint = (point2, height2, width2) => ({
    y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
    x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
  });
  const [height, width] = scores.shape;
  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
  const displacement = getDisplacement(sourceKeypointIndices);
  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
  let targetKeypoint = displacedPoint;
  for (let i = 0; i < offsetRefineStep; i++) {
    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
    targetKeypoint = addVectors(
      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
      { x: offsetPoint.x, y: offsetPoint.y }
    );
  }
  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
  return { position: targetKeypoint, part: partNames[targetId], score };
}
function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
  const numParts = scores.shape[2];
  const numEdges = edgesFwd.length;
  const keypoints = new Array(numParts);
  const rootPoint = getImageCoords(root.part, outputStride, offsets);
  keypoints[root.part.id] = {
    score: root.score,
    part: partNames[root.part.id],
    position: rootPoint
  };
  for (let edge = numEdges - 1; edge >= 0; --edge) {
    const sourceId = edgesFwd[edge];
    const targetId = edgesBwd[edge];
    if (keypoints[sourceId] && !keypoints[targetId]) {
      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
    }
  }
  for (let edge = 0; edge < numEdges; ++edge) {
    const sourceId = edgesBwd[edge];
    const targetId = edgesFwd[edge];
    if (keypoints[sourceId] && !keypoints[targetId]) {
      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
    }
  }
  return keypoints;
}
function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
  const [height, width] = scores.shape;
  let localMaximum = true;
  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
        localMaximum = false;
        break;
      }
    }
    if (!localMaximum)
      break;
  }
  return localMaximum;
}
function buildPartWithScoreQueue(minConfidence2, scores) {
  const [height, width, numKeypoints] = scores.shape;
  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
        const score = scores.get(heatmapY, heatmapX, keypointId);
        if (score < minConfidence2)
          continue;
        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
      }
    }
  }
  return queue;
}
function withinRadius(poses, { x, y: y6 }, keypointId) {
  return poses.some(({ keypoints }) => {
    var _a2;
    const correspondingKeypoint = (_a2 = keypoints[keypointId]) == null ? void 0 : _a2.position;
    if (!correspondingKeypoint)
      return false;
    return squaredDistance(y6, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
  });
}
function getInstanceScore(existingPoses, keypoints) {
  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
    if (!withinRadius(existingPoses, position, keypointId))
      result += score;
    return result;
  }, 0);
  return notOverlappedKeypointScores / keypoints.length;
}
function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
  const poses = [];
  const queue = buildPartWithScoreQueue(minConfidence2, scores);
  while (poses.length < maxDetected && !queue.empty()) {
    const root = queue.dequeue();
    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
    if (withinRadius(poses, rootImageCoords, root.part.id))
      continue;
    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
    keypoints = keypoints.filter((a) => a.score > minConfidence2);
    const score = getInstanceScore(poses, keypoints);
    const box = getBoundingBox(keypoints);
    if (score > minConfidence2)
      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
  }
  return poses;
}
async function predict19(input, config3) {
  if (!(model20 == null ? void 0 : model20["executor"]))
    return [];
  const res = Ee(() => {
    if (!model20.inputs[0].shape)
      return [];
    const resized = eK.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
    const normalized = ke(He(je(resized, "float32"), 127.5), 1);
    const results = model20.execute(normalized, poseNetOutputs);
    const results3d = results.map((y6) => Up(y6, [0]));
    results3d[1] = Ws(results3d[1]);
    return results3d;
  });
  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
  for (const t10 of res)
    Ot(t10);
  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
  if (!model20.inputs[0].shape)
    return [];
  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
  return scaled;
}
async function load19(config3) {
  if (!model20 || env.initial)
    model20 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model20["modelUrl"]);
  return model20;
}

// src/segmentation/rvm.ts
var model21;
var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
var t = {};
var ratio = 0;
function init3(config3) {
  Ot([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
  t.r1i = nr(0);
  t.r2i = nr(0);
  t.r3i = nr(0);
  t.r4i = nr(0);
  ratio = config3.segmentation.ratio || 0.5;
  t.downsample_ratio = nr(ratio);
}
async function load20(config3) {
  if (!model21 || env.initial)
    model21 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model21["modelUrl"]);
  init3(config3);
  return model21;
}
var normalize = (r) => Ee(() => {
  const squeeze = Up(r, [0]);
  const mul = ne(squeeze, constants.tf255);
  const cast = je(mul, "int32");
  return cast;
});
function getRGBA(fgr, pha) {
  const rgb2 = fgr ? normalize(fgr) : Us([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
  const a = pha ? normalize(pha) : Us([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
  const rgba = xt([rgb2, a], -1);
  Ot([rgb2, a]);
  return rgba;
}
function getState(state) {
  return Ee(() => {
    const r = {};
    r.unstack = so(state, -1);
    r.concat = xt(r.unstack, 1);
    r.split = Oa(r.concat, 4, 1);
    r.stack = xt(r.split, 2);
    r.squeeze = Up(r.stack, [0]);
    r.expand = Ra(r.squeeze, -1);
    r.add = be(r.expand, 1);
    r.mul = ne(r.add, 127.5);
    r.cast = je(r.mul, "int32");
    r.tile = Ai(r.cast, [1, 1, 3]);
    r.alpha = Us([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
    return xt([r.tile, r.alpha], -1);
  });
}
async function predict20(input, config3) {
  if (!model21)
    model21 = await load20(config3);
  if (!(model21 == null ? void 0 : model21["executor"]))
    return null;
  t.src = He(input, 255);
  if (ratio !== config3.segmentation.ratio)
    init3(config3);
  const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      rgba = getRGBA(fgr, pha);
      break;
    case "alpha":
      rgba = getRGBA(null, pha);
      break;
    case "foreground":
      rgba = getRGBA(fgr, null);
      break;
    case "state":
      rgba = getState(r1o);
      break;
    default:
      rgba = nr(0);
  }
  Ot([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
  return rgba;
}

// src/segmentation/selfie.ts
var model22;
async function load21(config3) {
  if (!model22 || env.initial)
    model22 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model22["modelUrl"]);
  return model22;
}
async function predict21(input, config3) {
  var _a2;
  if (!model22)
    model22 = await load21(config3);
  if (!(model22 == null ? void 0 : model22["executor"]) || !((_a2 = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a2[0].shape))
    return null;
  const t10 = {};
  t10.resize = eK.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false);
  t10.norm = He(t10.resize, constants.tf255);
  t10.res = model22.execute(t10.norm);
  t10.squeeze = Up(t10.res, [0]);
  t10.alpha = eK.resizeBilinear(t10.squeeze, [input.shape[1] || 0, input.shape[2] || 0]);
  t10.mul = ne(t10.alpha, constants.tf255);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      t10.input = Up(input);
      t10.concat = xt([t10.input, t10.mul], -1);
      rgba = je(t10.concat, "int32");
      break;
    case "alpha":
      rgba = je(t10.mul, "int32");
      break;
    default:
      rgba = nr(0);
  }
  Object.keys(t10).forEach((tensor) => Ot(t10[tensor]));
  return rgba;
}

// src/models.ts
function validateModel(instance, model23, name) {
  var _a2, _b2;
  if (!model23)
    return null;
  if (!((_a2 = instance == null ? void 0 : instance.config) == null ? void 0 : _a2.validateModels))
    return null;
  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
  const ops = [];
  const missing = [];
  const url = model23["modelUrl"];
  const executor = model23["executor"];
  if ((_b2 = executor == null ? void 0 : executor.graph) == null ? void 0 : _b2.nodes) {
    for (const kernel of Object.values(executor.graph.nodes)) {
      const op = kernel.op.toLowerCase();
      if (!ops.includes(op))
        ops.push(op);
    }
  } else {
    if (!executor && instance.config.debug) {
      log("model not loaded", name);
    }
  }
  for (const op of ops) {
    if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) {
      missing.push(op);
    }
  }
  if (instance.config.debug && missing.length > 0)
    log("model validation failed:", name, missing);
  return missing.length > 0 ? { name, missing, ops, url } : null;
}
var Models = class {
  constructor(currentInstance) {
    __publicField(this, "instance");
    __publicField(this, "models", {});
    this.models = {};
    this.instance = currentInstance;
  }
  stats() {
    let totalSizeFromManifest = 0;
    let totalSizeWeights = 0;
    let totalSizeLoading = 0;
    for (const m of Object.values(modelStats)) {
      totalSizeFromManifest += m.sizeFromManifest;
      totalSizeWeights += m.sizeLoadedWeights;
      totalSizeLoading += m.sizeDesired;
    }
    const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
    return {
      numLoadedModels: Object.values(modelStats).length,
      numDefinedModels: Object.keys(this.models).length,
      percentageLoaded,
      totalSizeFromManifest,
      totalSizeWeights,
      totalSizeLoading,
      modelStats: Object.values(modelStats)
    };
  }
  reset() {
    for (const model23 of Object.keys(this.models))
      this.models[model23] = null;
  }
  async load(instance) {
    var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2, _A2;
    if (env.initial)
      this.reset();
    if (instance)
      this.instance = instance;
    const m = {};
    m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null;
    m.antispoof = this.instance.config.face.enabled && ((_a2 = this.instance.config.face.antispoof) == null ? void 0 : _a2.enabled) && !this.models.antispoof ? load8(this.instance.config) : null;
    m.liveness = this.instance.config.face.enabled && ((_b2 = this.instance.config.face.liveness) == null ? void 0 : _b2.enabled) && !this.models.liveness ? load9(this.instance.config) : null;
    m.faceres = this.instance.config.face.enabled && ((_c2 = this.instance.config.face.description) == null ? void 0 : _c2.enabled) && !this.models.faceres ? load7(this.instance.config) : null;
    m.emotion = this.instance.config.face.enabled && ((_d2 = this.instance.config.face.emotion) == null ? void 0 : _d2.enabled) && !this.models.emotion ? load6(this.instance.config) : null;
    m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f2 = this.instance.config.face.attention) == null ? void 0 : _f2.enabled) && !this.models.iris ? load4(this.instance.config) : null;
    m.facemesh = this.instance.config.face.enabled && ((_g2 = this.instance.config.face.mesh) == null ? void 0 : _g2.enabled) && !this.models.facemesh ? load5(this.instance.config) : null;
    m.gear = this.instance.config.face.enabled && ((_h2 = this.instance.config.face["gear"]) == null ? void 0 : _h2.enabled) && !this.models.gear ? load10(this.instance.config) : null;
    m.ssrnetage = this.instance.config.face.enabled && ((_i2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _i2.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null;
    m.ssrnetgender = this.instance.config.face.enabled && ((_j2 = this.instance.config.face["ssrnet"]) == null ? void 0 : _j2.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null;
    m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null;
    m.insightface = this.instance.config.face.enabled && ((_l2 = this.instance.config.face["insightface"]) == null ? void 0 : _l2.enabled) && !this.models.insightface ? load14(this.instance.config) : null;
    m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m2 = this.instance.config.body.modelPath) == null ? void 0 : _m2.includes("blazepose")) ? loadPose(this.instance.config) : null;
    m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null;
    m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n2 = this.instance.config.body.modelPath) == null ? void 0 : _n2.includes("efficientpose")) ? load2(this.instance.config) : null;
    m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o2 = this.instance.config.body.modelPath) == null ? void 0 : _o2.includes("movenet")) ? load17(this.instance.config) : null;
    m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p2 = this.instance.config.body.modelPath) == null ? void 0 : _p2.includes("posenet")) ? load19(this.instance.config) : null;
    m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r2 = (_q2 = this.instance.config.hand.detector) == null ? void 0 : _q2.modelPath) == null ? void 0 : _r2.includes("handtrack")) ? loadDetect2(this.instance.config) : null;
    m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s2 = this.instance.config.hand.detector) == null ? void 0 : _s2.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton(this.instance.config) : null;
    if ((_v2 = (_u2 = this.instance.config.hand.detector) == null ? void 0 : _u2.modelPath) == null ? void 0 : _v2.includes("handdetect"))
      [m.handpose, m.handskeleton] = !this.models.handpose ? await load15(this.instance.config) : [null, null];
    m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w2 = this.instance.config.object.modelPath) == null ? void 0 : _w2.includes("centernet")) ? load(this.instance.config) : null;
    m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x2 = this.instance.config.object.modelPath) == null ? void 0 : _x2.includes("nanodet")) ? load18(this.instance.config) : null;
    m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load21(this.instance.config) : null;
    m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _z2.includes("meet")) ? load16(this.instance.config) : null;
    m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A2 = this.instance.config.segmentation.modelPath) == null ? void 0 : _A2.includes("rvm")) ? load20(this.instance.config) : null;
    for (const [model23, promise] of Object.entries(m)) {
      if (promise == null ? void 0 : promise["then"])
        promise["then"]((val) => this.models[model23] = val);
    }
    await Promise.all(Object.values(m));
  }
  list() {
    const models3 = Object.keys(this.models).map((model23) => {
      var _a2;
      return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a2 = this.models[model23]) == null ? void 0 : _a2["modelUrl"] : null };
    });
    for (const m of models3) {
      const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));
      if (!stats)
        continue;
      m.size = modelStats[stats].sizeLoadedWeights;
      m.url = modelStats[stats].url;
    }
    return models3;
  }
  loaded() {
    const list = this.list();
    const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name);
    return loaded;
  }
  validate() {
    const missing = [];
    for (const defined of Object.keys(this.models)) {
      const model23 = this.models[defined];
      if (!model23)
        continue;
      const res = validateModel(this.instance, model23, defined);
      if (res)
        missing.push(res);
    }
    return missing;
  }
};

// src/util/persons.ts
function join2(faces, bodies, hands, gestures, shape) {
  var _a2, _b2, _c2, _d2, _e, _f2;
  let id2 = 0;
  const persons = [];
  for (const face4 of faces) {
    const person2 = { id: id2++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
    for (const body4 of bodies) {
      if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
        person2.body = body4;
      }
    }
    if (person2.body) {
      for (const hand3 of hands) {
        if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
          if (person2.hands)
            person2.hands.left = hand3;
        }
        if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
          if (person2.hands)
            person2.hands.right = hand3;
        }
      }
    }
    for (const gesture2 of gestures) {
      if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
        person2.gestures.push(gesture2);
      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
        person2.gestures.push(gesture2);
      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a2 = person2.body) == null ? void 0 : _a2.id))
        person2.gestures.push(gesture2);
      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b2 = person2.hands.left) == null ? void 0 : _b2.id))
        person2.gestures.push(gesture2);
      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c2 = person2.hands.right) == null ? void 0 : _c2.id))
        person2.gestures.push(gesture2);
    }
    const x = [];
    const y6 = [];
    const extractXY = (box) => {
      if (box && box.length === 4) {
        x.push(box[0], box[0] + box[2]);
        y6.push(box[1], box[1] + box[3]);
      }
    };
    extractXY(person2.face.box);
    extractXY((_d2 = person2.body) == null ? void 0 : _d2.box);
    extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
    extractXY((_f2 = person2.hands.right) == null ? void 0 : _f2.box);
    const minX = Math.min(...x);
    const minY = Math.min(...y6);
    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y6) - minY];
    if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
    persons.push(person2);
  }
  return persons;
}

// src/sample.ts
var face3 = `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// src/warmup.ts
async function warmupBitmap(instance) {
  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
  let blob;
  let res;
  switch (instance.config.warmup) {
    case "face":
      blob = await b64toBlob(face3);
      break;
    case "body":
    case "full":
      blob = await b64toBlob(body3);
      break;
    default:
      blob = null;
  }
  if (blob) {
    const bitmap = await createImageBitmap(blob);
    res = await instance.detect(bitmap, instance.config);
    bitmap.close();
  }
  return res;
}
async function warmupCanvas(instance) {
  return new Promise((resolve) => {
    let src;
    switch (instance.config.warmup) {
      case "face":
        src = "data:image/jpeg;base64," + face3;
        break;
      case "full":
      case "body":
        src = "data:image/jpeg;base64," + body3;
        break;
      default:
        src = "";
    }
    let img;
    if (typeof Image !== "undefined")
      img = new Image();
    else if (env.Image)
      img = new env.Image();
    else {
      resolve(void 0);
      return;
    }
    img.onload = async () => {
      const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
      if (!canvas3) {
        log("Warmup: Canvas not found");
        resolve(void 0);
      } else {
        const ctx = canvas3.getContext("2d");
        if (ctx)
          ctx.drawImage(img, 0, 0);
        const tensor = await instance.image(canvas3, true);
        const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : void 0;
        resolve(res);
      }
    };
    if (src)
      img.src = src;
    else
      resolve(void 0);
  });
}
async function warmupNode(instance) {
  const atob2 = (str) => Buffer.from(str, "base64");
  let img;
  if (instance.config.warmup === "face")
    img = atob2(face3);
  else
    img = atob2(body3);
  let res;
  if ("node" in tfjs_esm_exports && Rie() === "tensorflow") {
    const data = (void 0).decodeJpeg(img);
    const expanded = Ra(data, 0);
    instance.tf.dispose(data);
    res = await instance.detect(expanded, instance.config);
    instance.tf.dispose(expanded);
  } else {
    if (instance.config.debug)
      log("Warmup tfjs-node not loaded");
  }
  return res;
}
async function runInference(instance) {
  let res;
  if (typeof createImageBitmap === "function")
    res = await warmupBitmap(instance);
  else if (typeof Image !== "undefined" || env.Canvas !== void 0)
    res = await warmupCanvas(instance);
  else
    res = await warmupNode(instance);
  return res;
}
async function runCompile(instance) {
  var _a2, _b2, _c2, _d2;
  if (!O().flagRegistry.ENGINE_COMPILE_ONLY)
    return;
  const backendType = Rie();
  const webGLBackend = Pie();
  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) {
    return;
  }
  O().set("ENGINE_COMPILE_ONLY", true);
  const numTensorsStart = sr().state.numTensors;
  const compiledModels = [];
  for (const [modelName, model23] of Object.entries(instance.models.models)) {
    if (!model23)
      continue;
    const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b2 = (_a2 = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a2[0]) == null ? void 0 : _b2.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3];
    const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d2 = (_c2 = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.dtype) ? model23.inputs[0].dtype : "float32";
    for (let dim = 0; dim < shape.length; dim++) {
      if (shape[dim] === -1)
        shape[dim] = dim === 0 ? 1 : 64;
    }
    const tensor = Br(shape, dtype);
    try {
      const res = model23.execute(tensor);
      compiledModels.push(modelName);
      if (Array.isArray(res))
        res.forEach((t10) => Ot(t10));
      else
        Ot(res);
    } catch (e) {
      if (instance.config.debug)
        log("compile fail model:", modelName);
    }
    Ot(tensor);
  }
  const kernels = await webGLBackend["checkCompileCompletionAsync"]();
  webGLBackend["getUniformLocations"]();
  if (instance.config.debug)
    log("compile pass:", { models: compiledModels, kernels: kernels.length });
  O().set("ENGINE_COMPILE_ONLY", false);
  const numTensorsEnd = sr().state.numTensors;
  if (numTensorsEnd - numTensorsStart > 0)
    log("tensor leak:", numTensorsEnd - numTensorsStart);
}
async function warmup(instance, userConfig) {
  await check(instance, false);
  const t0 = now();
  instance.state = "warmup";
  if (userConfig)
    instance.config = mergeDeep(instance.config, userConfig);
  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
    return empty();
  }
  return new Promise(async (resolve) => {
    await instance.models.load();
    await runCompile(instance);
    const res = await runInference(instance);
    const t12 = now();
    if (instance.config.debug)
      log("warmup", instance.config.warmup, Math.round(t12 - t0), "ms");
    instance.emit("warmup");
    resolve(res);
  });
}

// src/human.ts
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
var Human = class {
  // definition end
  /** Constructor for **Human** library that is futher used for all operations
   * @param userConfig - user configuration object {@link Config}
   */
  constructor(userConfig) {
    /** Current version of Human library in *semver* format */
    __publicField(this, "version");
    /** Current configuration
     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
     */
    __publicField(this, "config");
    /** Last known result of detect run
     * - Can be accessed anytime after initial detection
    */
    __publicField(this, "result");
    /** Current state of Human library
     * - Can be polled to determine operations that are currently executed
     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
     */
    __publicField(this, "state");
    /** currenty processed image tensor and canvas */
    __publicField(this, "process");
    /** Instance of TensorFlow/JS used by Human
     *  - Can be embedded or externally provided
     * [TFJS API](https://js.tensorflow.org/api/latest/)
     */
    __publicField(this, "tf");
    /** Object containing environment information used for diagnostics */
    __publicField(this, "env", env);
    /** Draw helper classes that can draw detected objects on canvas using specified draw
     * - canvas: draws input to canvas
     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
     */
    // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };
    __publicField(this, "draw", draw_exports);
    /** Face Matching
     * - similarity: compare two face descriptors and return similarity index
     * - distance: compare two face descriptors and return raw calculated differences
     * - find: compare face descriptor to array of face descriptors and return best match
     */
    __publicField(this, "match", match_exports);
    /** Currently loaded models
     * @internal
     * {@link models#Models}
    */
    __publicField(this, "models");
    /** Container for events dispatched by Human
     * Possible events:
     * - `create`: triggered when Human object is instantiated
     * - `load`: triggered when models are loaded (explicitly or on-demand)
     * - `image`: triggered when input image is processed
     * - `result`: triggered when detection is complete
     * - `warmup`: triggered when warmup is complete
     * - `error`: triggered on some errors
     */
    __publicField(this, "events");
    /** Reference face triangualtion array of 468 points, used for triangle references between points */
    __publicField(this, "faceTriangulation");
    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
    __publicField(this, "faceUVMap");
    /** Performance object that contains values for all recently performed operations */
    __publicField(this, "performance");
    // perf members are dynamically defined as needed
    __privateAdd(this, _numTensors, void 0);
    __privateAdd(this, _analyzeMemoryLeaks, void 0);
    __privateAdd(this, _checkSanity, void 0);
    /** internal function to measure tensor leaks */
    __publicField(this, "analyze", (...msg) => {
      if (!__privateGet(this, _analyzeMemoryLeaks))
        return;
      const currentTensors = this.tf.engine().state.numTensors;
      const previousTensors = __privateGet(this, _numTensors);
      __privateSet(this, _numTensors, currentTensors);
      const leaked = currentTensors - previousTensors;
      if (leaked !== 0)
        log(...msg, leaked);
    });
    /** internal function for quick sanity check on inputs @hidden */
    __privateAdd(this, _sanity, (input) => {
      if (!__privateGet(this, _checkSanity))
        return null;
      if (!input)
        return "input is not defined";
      if (this.env.node && !(input instanceof it))
        return "input must be a tensor";
      try {
        this.tf.getBackend();
      } catch (e) {
        return "backend not loaded";
      }
      return null;
    });
    /** WebCam helper methods
     *
     */
    __publicField(this, "webcam", new WebCam());
    /** emit event */
    __publicField(this, "emit", (event) => {
      var _a2;
      if ((_a2 = this.events) == null ? void 0 : _a2.dispatchEvent)
        this.events.dispatchEvent(new Event(event));
    });
    /** internal structure that keeps track of processed videos @hidden */
    __privateAdd(this, _loops, {});
    const tfVersion = (tse.tfjs || RK).replace(/-(.*)/, "");
    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
    config.modelBasePath = env.browser ? "../models/" : "file://models/";
    this.version = version;
    Object.defineProperty(this, "version", { value: version });
    this.config = JSON.parse(JSON.stringify(config));
    Object.seal(this.config);
    this.config.cacheModels = typeof indexedDB !== "undefined";
    if (userConfig)
      this.config = mergeDeep(this.config, userConfig);
    setModelLoadOptions(this.config);
    this.tf = tfjs_esm_exports;
    this.state = "idle";
    __privateSet(this, _numTensors, 0);
    __privateSet(this, _analyzeMemoryLeaks, false);
    __privateSet(this, _checkSanity, false);
    this.performance = {};
    this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
    this.models = new Models(this);
    init2();
    this.result = empty();
    this.process = { tensor: null, canvas: null };
    this.faceTriangulation = triangulation;
    this.faceUVMap = uvmap;
    validateModel(this, null, "");
    this.emit("create");
    if (this.config.debug || this.env.browser)
      log(`version: ${this.version}`);
    if (this.config.debug)
      log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
    const envTemp = JSON.parse(JSON.stringify(this.env));
    delete envTemp.kernels;
    delete envTemp.initial;
    delete envTemp.perfadd;
    if (this.config.debug)
      log("environment:", envTemp);
  }
  /** Reset configuration to default values */
  reset() {
    const currentBackend = this.config.backend;
    this.config = JSON.parse(JSON.stringify(config));
    this.config.backend = currentBackend;
    reset();
    env.initial = true;
  }
  /** Validate current configuration schema */
  validate(userConfig) {
    const msgs = validate(config, userConfig || this.config);
    if (msgs.length === 0)
      this.config = mergeDeep(this.config, userConfig);
    return msgs;
  }
  /** Utility wrapper for performance.now() */
  now() {
    return now();
  }
  /** Process input as return canvas and tensor
   *
   * @param input - any input {@link Input}
   * @param getTensor - should image processing also return tensor or just canvas
   * Returns object with `tensor` and `canvas`
   */
  image(input, getTensor = false) {
    return process2(input, this.config, getTensor);
  }
  /** Segmentation method takes any input and returns RGBA tensor
   * Note: Segmentation is not triggered as part of detect process
   *
   * @param input - {@link Input}
   * Returns tensor which contains image data in RGBA format
   */
  async segmentation(input, userConfig) {
    var _a2, _b2, _c2;
    if (userConfig)
      this.config = mergeDeep(this.config, userConfig);
    if (!this.config.segmentation.enabled)
      return null;
    const processed = await process2(input, this.config);
    if (!processed.tensor)
      return null;
    let tensor = null;
    if ((_a2 = this.config.segmentation.modelPath) == null ? void 0 : _a2.includes("rvm"))
      tensor = await predict20(processed.tensor, this.config);
    if ((_b2 = this.config.segmentation.modelPath) == null ? void 0 : _b2.includes("meet"))
      tensor = await predict16(processed.tensor, this.config);
    if ((_c2 = this.config.segmentation.modelPath) == null ? void 0 : _c2.includes("selfie"))
      tensor = await predict21(processed.tensor, this.config);
    Ot(processed.tensor);
    return tensor;
  }
  /** Compare two input tensors for pixel similarity
   * - use `human.image` to process any valid input and get a tensor that can be used for compare
   * - when passing manually generated tensors:
   *  - both input tensors must be in format [1, height, width, 3]
   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
   * - return value is pixel similarity score normalized by input resolution and rgb channels
  */
  compare(firstImageTensor, secondImageTensor) {
    return compare(this.config, firstImageTensor, secondImageTensor);
  }
  /** Explicit backend initialization
   *  - Normally done implicitly during initial load phase
   *  - Call to explictly register and initialize TFJS backend without any other operations
   *  - Use when changing backend during runtime
   */
  async init() {
    await check(this, true);
    await this.tf.ready();
    reset();
  }
  /** Load method preloads all configured models on-demand
   * - Not explicitly required as any required model is load implicitly on it's first run
   *
   * @param userConfig - {@link Config}
  */
  async load(userConfig) {
    this.state = "load";
    const timeStamp = now();
    const count2 = Object.values(this.models.models).filter((model23) => model23).length;
    if (userConfig)
      this.config = mergeDeep(this.config, userConfig);
    if (this.env.initial) {
      if (!await check(this, false))
        log("error: backend check failed");
      await Aie();
      if (this.env.browser) {
        if (this.config.debug)
          log("configuration:", this.config);
        if (this.config.debug)
          log("tf flags:", this.tf.ENV.flags);
      }
    }
    await this.models.load(this);
    if (this.env.initial && this.config.debug)
      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
    this.env.initial = false;
    const loaded = Object.values(this.models.models).filter((model23) => model23).length;
    if (loaded !== count2) {
      this.models.validate();
      this.emit("load");
    }
    const current = Math.trunc(now() - timeStamp);
    if (current > (this.performance.loadModels || 0))
      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
  }
  /** Runs interpolation using last known result and returns smoothened result
   * Interpolation is based on time since last known result so can be called independently
   *
   * @param result - {@link Result} optional use specific result set to run interpolation on
   * @returns result - {@link Result}
   */
  next(result = this.result) {
    return calc2(result, this.config);
  }
  /** Warmup method pre-initializes all configured models for faster inference
   * - can take significant time on startup
   * - only used for `webgl` and `humangl` backends
   * @param userConfig - {@link Config}
   * @returns result - {@link Result}
  */
  async warmup(userConfig) {
    const t0 = now();
    const res = await warmup(this, userConfig);
    const t12 = now();
    this.performance.warmup = Math.trunc(t12 - t0);
    return res;
  }
  /** Run detect with tensorflow profiling
   * - result object will contain total exeuction time information for top-20 kernels
   * - actual detection object can be accessed via `human.result`
  */
  async profile(input, userConfig) {
    const profile = await this.tf.profile(() => this.detect(input, userConfig));
    const kernels = {};
    let total = 0;
    for (const kernel of profile.kernels) {
      const ms2 = Number(kernel.kernelTimeMs) || 0;
      if (kernels[kernel.name])
        kernels[kernel.name] += ms2;
      else
        kernels[kernel.name] = ms2;
      total += ms2;
    }
    const kernelArr = [];
    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
    for (const kernel of kernelArr) {
      kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
      kernel.time = Math.round(1e3 * kernel.time) / 1e3;
    }
    kernelArr.sort((a, b) => b.time - a.time);
    kernelArr.length = 20;
    return kernelArr;
  }
  /** Main detection method
   * - Analyze configuration: {@link Config}
   * - Pre-process input: {@link Input}
   * - Run inference for all configured models
   * - Process and return result: {@link Result}
   *
   * @param input - {@link Input}
   * @param userConfig - {@link Config}
   * @returns result - {@link Result}
  */
  async detect(input, userConfig) {
    this.state = "detect";
    return new Promise(async (resolve) => {
      var _a2, _b2, _c2, _d2, _e, _f2, _g2, _h2, _i2, _j2, _k, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2;
      this.state = "config";
      let timeStamp;
      this.config = mergeDeep(this.config, userConfig);
      this.state = "check";
      const error = __privateGet(this, _sanity).call(this, input);
      if (error) {
        log(error, input);
        this.emit("error");
        resolve(empty(error));
      }
      const timeStart = now();
      await this.load();
      timeStamp = now();
      this.state = "image";
      const img = await process2(input, this.config);
      this.process = img;
      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      this.analyze("Get Image:");
      if (!img.tensor) {
        if (this.config.debug)
          log("could not convert input to tensor");
        this.emit("error");
        resolve(empty("could not convert input to tensor"));
        return;
      }
      this.emit("image");
      timeStamp = now();
      this.config.skipAllowed = await skip(this.config, img.tensor);
      this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed;
      if (!this.performance.totalFrames)
        this.performance.totalFrames = 0;
      if (!this.performance.cachedFrames)
        this.performance.cachedFrames = 0;
      this.performance.totalFrames++;
      if (this.config.skipAllowed)
        this.performance.cachedFrames++;
      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      this.analyze("Check Changed:");
      let faceRes = [];
      let bodyRes = [];
      let handRes = [];
      let objectRes = [];
      this.state = "detect:face";
      if (this.config.async) {
        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
        if (this.performance.face)
          delete this.performance.face;
      } else {
        timeStamp = now();
        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
        faceRes = await faceRes;
      this.analyze("Start Body:");
      this.state = "detect:body";
      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
      if (this.config.async) {
        if ((_a2 = this.config.body.modelPath) == null ? void 0 : _a2.includes("posenet"))
          bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : [];
        else if ((_b2 = this.config.body.modelPath) == null ? void 0 : _b2.includes("blazepose"))
          bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : [];
        else if ((_c2 = this.config.body.modelPath) == null ? void 0 : _c2.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : [];
        else if ((_d2 = this.config.body.modelPath) == null ? void 0 : _d2.includes("movenet"))
          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
        if (this.performance.body)
          delete this.performance.body;
      } else {
        timeStamp = now();
        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
          bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : [];
        else if ((_f2 = this.config.body.modelPath) == null ? void 0 : _f2.includes("blazepose"))
          bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : [];
        else if ((_g2 = this.config.body.modelPath) == null ? void 0 : _g2.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : [];
        else if ((_h2 = this.config.body.modelPath) == null ? void 0 : _h2.includes("movenet"))
          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      this.analyze("End Body:");
      this.analyze("Start Hand:");
      this.state = "detect:hand";
      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
      if (this.config.async) {
        if ((_j2 = (_i2 = this.config.hand.detector) == null ? void 0 : _i2.modelPath) == null ? void 0 : _j2.includes("handdetect"))
          handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : [];
        else if ((_l2 = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l2.includes("handtrack"))
          handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : [];
        if (this.performance.hand)
          delete this.performance.hand;
      } else {
        timeStamp = now();
        if ((_n2 = (_m2 = this.config.hand.detector) == null ? void 0 : _m2.modelPath) == null ? void 0 : _n2.includes("handdetect"))
          handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : [];
        else if ((_p2 = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p2.includes("handtrack"))
          handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : [];
        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      this.analyze("End Hand:");
      this.analyze("Start Object:");
      this.state = "detect:object";
      if (this.config.async) {
        if ((_q2 = this.config.object.modelPath) == null ? void 0 : _q2.includes("nanodet"))
          objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : [];
        else if ((_r2 = this.config.object.modelPath) == null ? void 0 : _r2.includes("centernet"))
          objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : [];
        if (this.performance.object)
          delete this.performance.object;
      } else {
        timeStamp = now();
        if ((_s2 = this.config.object.modelPath) == null ? void 0 : _s2.includes("nanodet"))
          objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : [];
        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
          objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : [];
        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      this.analyze("End Object:");
      this.state = "detect:await";
      if (this.config.async)
        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
      this.state = "detect:gesture";
      let gestureRes = [];
      if (this.config.gesture.enabled) {
        timeStamp = now();
        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
        if (!this.config.async)
          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
        else if (this.performance.gesture)
          delete this.performance.gesture;
      }
      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
      const shape = ((_u2 = this.process.tensor) == null ? void 0 : _u2.shape) || [0, 0, 0, 0];
      this.result = {
        face: faceRes,
        body: bodyRes,
        hand: handRes,
        gesture: gestureRes,
        object: objectRes,
        performance: this.performance,
        canvas: this.process.canvas,
        timestamp: Date.now(),
        error: null,
        width: shape[2],
        height: shape[1],
        get persons() {
          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
        }
      };
      Ot(img.tensor);
      this.emit("detect");
      this.state = "idle";
      resolve(this.result);
    });
  }
  /** Helper function
   * @param ms - sleep time in miliseconds
   */
  async sleep(ms2) {
    return new Promise((resolve) => {
      setTimeout(resolve, ms2);
    });
  }
  /** Continously detect video frames
   * @param element - HTMLVideoElement input
   * @param run - boolean run continously or stop if already running, default true
   * @param delay - number delay detection between frames for number of miliseconds, default 0
   */
  async video(element, run = true, delay = 0) {
    if (run) {
      if (!__privateGet(this, _loops)[element.id]) {
        if (this.config.debug)
          log("video start", element.id);
        __privateGet(this, _loops)[element.id] = true;
      }
      if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
        await this.detect(element);
      if (delay > 0)
        await this.sleep(delay);
      if (__privateGet(this, _loops)[element.id])
        requestAnimationFrame(() => this.video(element, run, delay));
    } else {
      if (this.config.debug)
        log("video stop", element.id);
      __privateGet(this, _loops)[element.id] = false;
    }
  }
};
_numTensors = new WeakMap();
_analyzeMemoryLeaks = new WeakMap();
_checkSanity = new WeakMap();
_sanity = new WeakMap();
_loops = new WeakMap();
export {
  Env,
  Human,
  Human as default,
  config as defaults,
  draw_exports as draw,
  empty,
  env,
  match_exports as match,
  models_exports2 as models
};
//# sourceMappingURL=human.esm.js.map