human/dist/human.esm.js

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

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// dist/tfjs.esm.js
var tfjs_esm_exports = {};
__export(tfjs_esm_exports, {
  Abs: () => sn,
  Acos: () => Li,
  Acosh: () => Bi,
  AdadeltaOptimizer: () => xi,
  AdagradOptimizer: () => yi,
  AdamOptimizer: () => bi,
  AdamaxOptimizer: () => Ci,
  Add: () => _r,
  AddN: () => an,
  All: () => oa,
  Any: () => na,
  ArgMax: () => un,
  ArgMin: () => ja,
  Asin: () => Vi,
  Asinh: () => zi,
  Atan: () => Wi,
  Atan2: () => sa,
  Atanh: () => Ui,
  AvgPool: () => pn,
  AvgPool3D: () => ip,
  AvgPool3DGrad: () => Fm,
  AvgPoolGrad: () => Am,
  BackendWasm: () => Gl,
  BatchMatMul: () => cn,
  BatchToSpaceND: () => hs,
  Bincount: () => up,
  BroadcastArgs: () => pp,
  BroadcastTo: () => Tne,
  Cast: () => to,
  Ceil: () => ro,
  ClipByValue: () => Ro,
  Complex: () => aa,
  ComplexAbs: () => cp,
  Concat: () => gs,
  Conv2D: () => ln,
  Conv2DBackpropFilter: () => lp,
  Conv2DBackpropInput: () => mn,
  Conv3D: () => mp,
  Conv3DBackpropFilterV2: () => Dm,
  Conv3DBackpropInputV2: () => fp,
  Cos: () => fn,
  Cosh: () => dn,
  CropAndResize: () => xn,
  Cumprod: () => hn,
  Cumsum: () => gn,
  DataStorage: () => rn,
  DenseBincount: () => dp,
  DepthToSpace: () => yn,
  DepthwiseConv2dNative: () => bn,
  DepthwiseConv2dNativeBackpropFilter: () => hp,
  DepthwiseConv2dNativeBackpropInput: () => gp,
  Diag: () => xp,
  Dilation2D: () => yp,
  Dilation2DBackpropFilter: () => vb,
  Dilation2DBackpropInput: () => Sb,
  ENV: () => Cb,
  Einsum: () => Xa,
  Elu: () => In,
  EluGrad: () => Pm,
  Environment: () => Qc,
  Equal: () => oo,
  Erf: () => Gi,
  Exp: () => no,
  ExpandDims: () => xs,
  Expm1: () => wn,
  FFT: () => bp,
  Fill: () => ys,
  FlipLeftRight: () => Sn,
  Floor: () => so,
  FloorDiv: () => vn,
  FromPixels: () => Zi,
  FusedBatchNorm: () => kn,
  FusedConv2D: () => Do,
  FusedDepthwiseConv2D: () => Po,
  GPGPUContext: () => Fu,
  GatherNd: () => Tn,
  GatherV2: () => bs,
  GraphModel: () => bl,
  Greater: () => ao,
  GreaterEqual: () => io,
  IFFT: () => Cp,
  Identity: () => uo,
  Imag: () => Ya,
  IsFinite: () => Hi,
  IsInf: () => qi,
  IsNan: () => ia,
  KernelBackend: () => Jr,
  LRN: () => wp,
  LRNGrad: () => Om,
  LeakyRelu: () => Nn,
  Less: () => po,
  LessEqual: () => co,
  LinSpace: () => Ip,
  Log: () => lo,
  Log1p: () => Ki,
  LogSoftmax: () => Nne,
  LogicalAnd: () => _n,
  LogicalNot: () => En,
  LogicalOr: () => ua,
  LogicalXor: () => g0,
  LowerBound: () => _ne,
  MathBackendCPU: () => Si,
  MathBackendWebGL: () => Ni,
  Max: () => $n,
  MaxPool: () => Rn,
  MaxPool3D: () => Sp,
  MaxPool3DGrad: () => Lm,
  MaxPoolGrad: () => Mm,
  MaxPoolWithArgmax: () => vp,
  Maximum: () => mo,
  Mean: () => An,
  Min: () => Fn,
  Minimum: () => fo,
  MirrorPad: () => Dn,
  Mod: () => ji,
  MomentumOptimizer: () => Ii,
  Multinomial: () => kp,
  Multiply: () => ho,
  Neg: () => Pn,
  NonMaxSuppressionV3: () => On,
  NonMaxSuppressionV4: () => pa,
  NonMaxSuppressionV5: () => Mn,
  NotEqual: () => go,
  OP_SCOPE_SUFFIX: () => Ub,
  OneHot: () => ca,
  OnesLike: () => Cs,
  Optimizer: () => wr,
  OptimizerConstructors: () => ns,
  Pack: () => Is,
  PadV2: () => Ln,
  Pool: () => Ene,
  Pow: () => Bn,
  Prelu: () => Vn,
  Prod: () => Ao,
  RMSPropOptimizer: () => wi,
  RaggedGather: () => Tp,
  RaggedRange: () => Np,
  RaggedTensorToTensor: () => _p,
  Range: () => ws,
  Rank: () => Fb,
  Real: () => la,
  RealDiv: () => Cn,
  Reciprocal: () => ma,
  Reduction: () => Et,
  Relu: () => zn,
  Relu6: () => Gn,
  Reshape: () => Ss,
  ResizeBilinear: () => Un,
  ResizeBilinearGrad: () => Vm,
  ResizeNearestNeighbor: () => Wn,
  ResizeNearestNeighborGrad: () => Bm,
  Reverse: () => fa,
  RotateWithOffset: () => es,
  Round: () => da,
  Rsqrt: () => xo,
  SGDOptimizer: () => Us,
  ScatterNd: () => Hn,
  SearchSorted: () => Ep,
  Select: () => vs,
  Selu: () => Xi,
  Sigmoid: () => yo,
  Sign: () => Yi,
  Sin: () => Kn,
  Sinh: () => ha,
  Slice: () => qn,
  Softmax: () => Xn,
  Softplus: () => Qi,
  SpaceToBatchND: () => ks,
  SparseFillEmptyRows: () => Qa,
  SparseReshape: () => ga,
  SparseSegmentMean: () => Za,
  SparseSegmentSum: () => Ja,
  SparseToDense: () => ei,
  SplitV: () => Ts,
  Sqrt: () => bo,
  Square: () => ti,
  SquaredDifference: () => Co,
  Step: () => $s,
  StridedSlice: () => Yn,
  StringNGrams: () => Ns,
  StringSplit: () => ri,
  StringToHashBucketFast: () => oi,
  Sub: () => Io,
  Sum: () => jn,
  Tan: () => xa,
  Tanh: () => Qn,
  Tensor: () => ut,
  TensorBuffer: () => je,
  Tile: () => wo,
  TopK: () => Zn,
  Transform: () => Jn,
  Transpose: () => Mr,
  Unique: () => $p,
  Unpack: () => _s,
  UnsortedSegmentSum: () => Rp,
  UpperBound: () => $ne,
  Variable: () => ba,
  WebGPUBackend: () => Ai,
  ZerosLike: () => Es,
  _FusedMatMul: () => Fo,
  abs: () => Qt,
  acos: () => Vv,
  acosh: () => zv,
  add: () => ge,
  addN: () => Wv,
  all: () => Uv,
  any: () => Gv,
  argMax: () => Hv,
  argMin: () => qv,
  asin: () => Kv,
  asinh: () => jv,
  atan: () => Xv,
  atan2: () => Yv,
  atanh: () => Qv,
  avgPool: () => mf,
  avgPool3d: () => ek,
  backend: () => Bie,
  backend_util: () => I,
  basicLSTMCell: () => tk,
  batchNorm: () => li,
  batchNorm2d: () => ok,
  batchNorm3d: () => nk,
  batchNorm4d: () => sk,
  batchToSpaceND: () => ff,
  bincount: () => df,
  booleanMaskAsync: () => _H,
  broadcastArgs: () => ak,
  broadcastTo: () => Ls,
  broadcast_util: () => br,
  browser: () => Sv,
  buffer: () => ne,
  cast: () => qe,
  ceil: () => ik,
  clipByValue: () => uk,
  clone: () => zr,
  complex: () => Er,
  concat: () => gt,
  concat1d: () => pk,
  concat2d: () => ck,
  concat3d: () => lk,
  concat4d: () => mk,
  conv1d: () => fk,
  conv2d: () => mi,
  conv2dTranspose: () => dk,
  conv3d: () => hk,
  conv3dTranspose: () => xk,
  copyRegisteredKernels: () => Lne,
  cos: () => yk,
  cosh: () => bk,
  cosineWindow: () => hl,
  cumprod: () => Ck,
  cumsum: () => Ik,
  customGrad: () => Cr,
  denseBincount: () => wk,
  deprecationWarn: () => sC,
  depthToSpace: () => Sk,
  depthwiseConv2d: () => Gp,
  deregisterOp: () => YK,
  device_util: () => ii,
  diag: () => vk,
  dilation2d: () => kk,
  disableDeprecationWarnings: () => _ie,
  dispose: () => Ft,
  disposeVariables: () => Eie,
  div: () => We,
  divNoNan: () => Tk,
  dot: () => Nk,
  dropout: () => BH,
  einsum: () => _k,
  elu: () => xf,
  enableDebugMode: () => Nie,
  enableProdMode: () => Tie,
  enclosingPowerOfTwo: () => wC,
  engine: () => cr,
  env: () => P,
  equal: () => gf,
  erf: () => Ek,
  euclideanNorm: () => Ak,
  exp: () => Bo,
  expandDims: () => _a,
  expm1: () => Fk,
  eye: () => yf,
  fft: () => qp,
  fill: () => Bs,
  findBackend: () => Mie,
  findBackendFactory: () => Lie,
  floor: () => bf,
  floorDiv: () => cf,
  forceHalfFloat: () => pR,
  fused: () => SC,
  gather: () => Cf,
  gatherND: () => MH,
  gather_util: () => af,
  getBackend: () => Pie,
  getGradient: () => kb,
  getKernel: () => el,
  getKernelsForBackend: () => zm,
  getThreadsCount: () => nte,
  gpgpu_util: () => Sw,
  grad: () => GG,
  grads: () => HG,
  greater: () => cu,
  greaterEqual: () => If,
  ifft: () => hu,
  imag: () => ci,
  image: () => zq,
  inTopKAsync: () => zH,
  io: () => va,
  irfft: () => Gf,
  isFinite: () => Dk,
  isInf: () => Pk,
  isNaN: () => Ok,
  keep: () => So,
  kernel_impls: () => Bt,
  leakyRelu: () => wf,
  less: () => Mk,
  lessEqual: () => Hp,
  linalg: () => Wq,
  linspace: () => Lk,
  loadGraphModel: () => U6,
  loadGraphModelSync: () => G6,
  localResponseNormalization: () => Bk,
  log: () => Ea,
  log1p: () => Sf,
  logSigmoid: () => Vk,
  logSoftmax: () => zk,
  logSumExp: () => Tf,
  logicalAnd: () => lu,
  logicalNot: () => Nf,
  logicalOr: () => _f,
  logicalXor: () => Wk,
  losses: () => Uq,
  lowerBound: () => Uk,
  matMul: () => Xe,
  math: () => Cv,
  max: () => Vs,
  maxPool: () => $f,
  maxPool3d: () => Gk,
  maxPoolWithArgmax: () => Hk,
  maximum: () => Rf,
  mean: () => mu,
  memory: () => $ie,
  meshgrid: () => qk,
  min: () => fl,
  minimum: () => Af,
  mirrorPad: () => Kk,
  mod: () => jk,
  moments: () => Xk,
  movingAverage: () => $H,
  mul: () => oe,
  multiRNNCell: () => Yk,
  multinomial: () => Qk,
  neg: () => yr,
  nextFrame: () => kC,
  norm: () => pu,
  notEqual: () => Ff,
  oneHot: () => pl,
  ones: () => zs,
  onesLike: () => Zk,
  op: () => T,
  outerProduct: () => Jk,
  pad: () => Ws,
  pad1d: () => e1,
  pad2d: () => t1,
  pad3d: () => r1,
  pad4d: () => o1,
  pool: () => n1,
  pow: () => Na,
  prelu: () => Pf,
  print: () => ef,
  prod: () => s1,
  profile: () => Rie,
  raggedGather: () => a1,
  raggedRange: () => i1,
  raggedTensorToTensor: () => u1,
  rand: () => p1,
  randomGamma: () => T1,
  randomNormal: () => Vf,
  randomStandardNormal: () => N1,
  randomUniform: () => zf,
  range: () => di,
  ready: () => Die,
  real: () => ka,
  reciprocal: () => _1,
  registerBackend: () => pi,
  registerGradient: () => Pne,
  registerKernel: () => ya,
  registerOp: () => XK,
  relu: () => hi,
  relu6: () => Wf,
  removeBackend: () => Oie,
  reshape: () => z,
  reverse: () => vo,
  reverse1d: () => E1,
  reverse2d: () => $1,
  reverse3d: () => R1,
  reverse4d: () => A1,
  rfft: () => Kp,
  round: () => Uf,
  rsqrt: () => F1,
  scalar: () => be,
  scatterND: () => AH,
  scatter_util: () => cl,
  searchSorted: () => dl,
  selu: () => D1,
  separableConv2d: () => P1,
  serialization: () => Pv,
  setBackend: () => Fie,
  setPlatform: () => Vie,
  setThreadsCount: () => ote,
  setWasmPath: () => tte,
  setWasmPaths: () => rte,
  setWebGLContext: () => MI,
  setdiff1dAsync: () => O1,
  shared: () => Ad,
  sigmoid: () => Ms,
  sign: () => M1,
  signal: () => Vq,
  sin: () => L1,
  sinh: () => B1,
  slice: () => Ue,
  slice1d: () => V1,
  slice2d: () => z1,
  slice3d: () => W1,
  slice4d: () => U1,
  slice_util: () => et,
  softmax: () => G1,
  softplus: () => kf,
  spaceToBatchND: () => Df,
  sparse: () => Gq,
  sparseToDense: () => PH,
  spectral: () => Bq,
  split: () => $a,
  sqrt: () => Rr,
  square: () => Zt,
  squaredDifference: () => Hf,
  squeeze: () => jp,
  stack: () => Ir,
  step: () => qf,
  stridedSlice: () => H1,
  string: () => Hq,
  sub: () => ke,
  sum: () => tt,
  sumOutType: () => Ca,
  tan: () => q1,
  tanh: () => ml,
  tensor: () => nr,
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  tensor2d: () => gi,
  tensor3d: () => sf,
  tensor4d: () => K1,
  tensor5d: () => j1,
  tensor6d: () => X1,
  tensor_util: () => z0,
  test_util: () => Bv,
  tidy: () => Ne,
  tile: () => fi,
  time: () => Aie,
  topk: () => Y1,
  train: () => pMe,
  transpose: () => Wp,
  truncatedNormal: () => Q1,
  unique: () => Z1,
  unregisterGradient: () => Mne,
  unregisterKernel: () => One,
  unsortedSegmentSum: () => J1,
  unstack: () => ko,
  upcastType: () => ct,
  upperBound: () => eT,
  util: () => x,
  valueAndGrad: () => qG,
  valueAndGrads: () => KG,
  variable: () => tT,
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  version: () => Cne,
  version_converter: () => q6,
  version_core: () => YW,
  version_cpu: () => Ij,
  version_wasm: () => ste,
  version_webgl: () => gY,
  webgl: () => R9e,
  webgl_util: () => ic,
  webgpu_util: () => pS,
  where: () => os,
  whereAsync: () => jf,
  zeros: () => Wr,
  zerosLike: () => Gt
});
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  }
  wt.fromValue = ts;
  var C0 = 1 << 16, vz = 1 << 24, Dp = C0 * C0, S0 = Dp * Dp, I0 = S0 / 2, w0 = eu(vz), Lo = eu(0);
  wt.ZERO = Lo;
  var Ji = eu(0, true);
  wt.UZERO = Ji;
  var Fp = eu(1);
  wt.ONE = Fp;
  var v0 = eu(1, true);
  wt.UONE = v0;
  var Nb = eu(-1);
  wt.NEG_ONE = Nb;
  var k0 = St(-1, 2147483647, false);
  wt.MAX_VALUE = k0;
  var T0 = St(-1, -1, true);
  wt.MAX_UNSIGNED_VALUE = T0;
  var Lr = St(0, -2147483648, false);
  wt.MIN_VALUE = Lr;
  var ce = wt.prototype;
  ce.toInt = function() {
    return this.unsigned ? this.low >>> 0 : this.low;
  };
  ce.toNumber = function() {
    return this.unsigned ? (this.high >>> 0) * Dp + (this.low >>> 0) : this.high * Dp + (this.low >>> 0);
  };
  ce.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(Lr)) {
        var t10 = Mo(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 = Mo(Wm(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;
    }
  };
  ce.getHighBits = function() {
    return this.high;
  };
  ce.getHighBitsUnsigned = function() {
    return this.high >>> 0;
  };
  ce.getLowBits = function() {
    return this.low;
  };
  ce.getLowBitsUnsigned = function() {
    return this.low >>> 0;
  };
  ce.getNumBitsAbs = function() {
    if (this.isNegative())
      return this.eq(Lr) ? 64 : this.neg().getNumBitsAbs();
    for (var e = this.high != 0 ? this.high : this.low, t10 = 31; t10 > 0 && (e & 1 << t10) == 0; t10--)
      ;
    return this.high != 0 ? t10 + 33 : t10 + 1;
  };
  ce.isZero = function() {
    return this.high === 0 && this.low === 0;
  };
  ce.eqz = ce.isZero;
  ce.isNegative = function() {
    return !this.unsigned && this.high < 0;
  };
  ce.isPositive = function() {
    return this.unsigned || this.high >= 0;
  };
  ce.isOdd = function() {
    return (this.low & 1) === 1;
  };
  ce.isEven = function() {
    return (this.low & 1) === 0;
  };
  ce.equals = function(e) {
    return Br(e) || (e = ts(e)), this.unsigned !== e.unsigned && this.high >>> 31 === 1 && e.high >>> 31 === 1 ? false : this.high === e.high && this.low === e.low;
  };
  ce.eq = ce.equals;
  ce.notEquals = function(e) {
    return !this.eq(e);
  };
  ce.neq = ce.notEquals;
  ce.ne = ce.notEquals;
  ce.lessThan = function(e) {
    return this.comp(e) < 0;
  };
  ce.lt = ce.lessThan;
  ce.lessThanOrEqual = function(e) {
    return this.comp(e) <= 0;
  };
  ce.lte = ce.lessThanOrEqual;
  ce.le = ce.lessThanOrEqual;
  ce.greaterThan = function(e) {
    return this.comp(e) > 0;
  };
  ce.gt = ce.greaterThan;
  ce.greaterThanOrEqual = function(e) {
    return this.comp(e) >= 0;
  };
  ce.gte = ce.greaterThanOrEqual;
  ce.ge = ce.greaterThanOrEqual;
  ce.compare = function(e) {
    if (Br(e) || (e = ts(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;
  };
  ce.comp = ce.compare;
  ce.negate = function() {
    return !this.unsigned && this.eq(Lr) ? Lr : this.not().add(Fp);
  };
  ce.neg = ce.negate;
  ce.add = function(e) {
    Br(e) || (e = ts(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, f = 0;
    return f += s + u, m += f >>> 16, f &= 65535, m += n + p, l += m >>> 16, m &= 65535, l += o + i, c += l >>> 16, l &= 65535, c += t10 + a, c &= 65535, St(m << 16 | f, c << 16 | l, this.unsigned);
  };
  ce.subtract = function(e) {
    return Br(e) || (e = ts(e)), this.add(e.neg());
  };
  ce.sub = ce.subtract;
  ce.multiply = function(e) {
    if (this.isZero())
      return Lo;
    if (Br(e) || (e = ts(e)), Oo) {
      var t10 = Oo.mul(this.low, this.high, e.low, e.high);
      return St(t10, Oo.get_high(), this.unsigned);
    }
    if (e.isZero())
      return Lo;
    if (this.eq(Lr))
      return e.isOdd() ? Lr : Lo;
    if (e.eq(Lr))
      return this.isOdd() ? Lr : Lo;
    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(w0) && e.lt(w0))
      return Mo(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, f = 0, d = 0;
    return d += a * c, f += d >>> 16, d &= 65535, f += s * c, m += f >>> 16, f &= 65535, f += a * u, m += f >>> 16, f &= 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, St(f << 16 | d, l << 16 | m, this.unsigned);
  };
  ce.mul = ce.multiply;
  ce.divide = function(e) {
    if (Br(e) || (e = ts(e)), e.isZero())
      throw Error("division by zero");
    if (Oo) {
      if (!this.unsigned && this.high === -2147483648 && e.low === -1 && e.high === -1)
        return this;
      var t10 = (this.unsigned ? Oo.div_u : Oo.div_s)(this.low, this.high, e.low, e.high);
      return St(t10, Oo.get_high(), this.unsigned);
    }
    if (this.isZero())
      return this.unsigned ? Ji : Lo;
    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 v0;
      s = Ji;
    } else {
      if (this.eq(Lr)) {
        if (e.eq(Fp) || e.eq(Nb))
          return Lr;
        if (e.eq(Lr))
          return Fp;
        var a = this.shr(1);
        return o = a.div(e).shl(1), o.eq(Lo) ? e.isNegative() ? Fp : Nb : (n = this.sub(e.mul(o)), s = o.add(n.div(e)), s);
      } else if (e.eq(Lr))
        return this.unsigned ? Ji : Lo;
      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 = Lo;
    }
    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 : Wm(2, i - 48), u = Mo(o), c = u.mul(e); c.isNegative() || c.gt(n); )
        o -= p, u = Mo(o, this.unsigned), c = u.mul(e);
      u.isZero() && (u = Fp), s = s.add(u), n = n.sub(c);
    }
    return s;
  };
  ce.div = ce.divide;
  ce.modulo = function(e) {
    if (Br(e) || (e = ts(e)), Oo) {
      var t10 = (this.unsigned ? Oo.rem_u : Oo.rem_s)(this.low, this.high, e.low, e.high);
      return St(t10, Oo.get_high(), this.unsigned);
    }
    return this.sub(this.div(e).mul(e));
  };
  ce.mod = ce.modulo;
  ce.rem = ce.modulo;
  ce.not = function() {
    return St(~this.low, ~this.high, this.unsigned);
  };
  ce.and = function(e) {
    return Br(e) || (e = ts(e)), St(this.low & e.low, this.high & e.high, this.unsigned);
  };
  ce.or = function(e) {
    return Br(e) || (e = ts(e)), St(this.low | e.low, this.high | e.high, this.unsigned);
  };
  ce.xor = function(e) {
    return Br(e) || (e = ts(e)), St(this.low ^ e.low, this.high ^ e.high, this.unsigned);
  };
  ce.shiftLeft = function(e) {
    return Br(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? St(this.low << e, this.high << e | this.low >>> 32 - e, this.unsigned) : St(0, this.low << e - 32, this.unsigned);
  };
  ce.shl = ce.shiftLeft;
  ce.shiftRight = function(e) {
    return Br(e) && (e = e.toInt()), (e &= 63) === 0 ? this : e < 32 ? St(this.low >>> e | this.high << 32 - e, this.high >> e, this.unsigned) : St(this.high >> e - 32, this.high >= 0 ? 0 : -1, this.unsigned);
  };
  ce.shr = ce.shiftRight;
  ce.shiftRightUnsigned = function(e) {
    if (Br(e) && (e = e.toInt()), e &= 63, e === 0)
      return this;
    var t10 = this.high;
    if (e < 32) {
      var o = this.low;
      return St(o >>> e | t10 << 32 - e, t10 >>> e, this.unsigned);
    } else
      return e === 32 ? St(t10, 0, this.unsigned) : St(t10 >>> e - 32, 0, this.unsigned);
  };
  ce.shru = ce.shiftRightUnsigned;
  ce.shr_u = ce.shiftRightUnsigned;
  ce.toSigned = function() {
    return this.unsigned ? St(this.low, this.high, false) : this;
  };
  ce.toUnsigned = function() {
    return this.unsigned ? this : St(this.low, this.high, true);
  };
  ce.toBytes = function(e) {
    return e ? this.toBytesLE() : this.toBytesBE();
  };
  ce.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];
  };
  ce.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];
  };
  wt.fromBytes = function(e, t10, o) {
    return o ? wt.fromBytesLE(e, t10) : wt.fromBytesBE(e, t10);
  };
  wt.fromBytesLE = function(e, t10) {
    return new wt(e[0] | e[1] << 8 | e[2] << 16 | e[3] << 24, e[4] | e[5] << 8 | e[6] << 16 | e[7] << 24, t10);
  };
  wt.fromBytesBE = function(e, t10) {
    return new wt(e[4] << 24 | e[5] << 16 | e[6] << 8 | e[7], e[0] << 24 | e[1] << 16 | e[2] << 8 | e[3], t10);
  };
});
var pv = Kt(() => {
});
var cv = Kt(() => {
});
var l1 = Kt((c1, hC) => {
  (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;
  })(c1, typeof hC == "object" && hC, typeof define == "function" && define);
});
var f1 = Kt((m1, gC) => {
  (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, f = (l + m) / (1 << 21);
        while (f === 0);
        return f;
      }, 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;
  })(m1, typeof gC == "object" && gC, typeof define == "function" && define);
});
var h1 = Kt((d1, xC) => {
  (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, f = (l + m) / (1 << 21);
        while (f === 0);
        return f;
      }, 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;
  })(d1, typeof xC == "object" && xC, typeof define == "function" && define);
});
var x1 = Kt((g1, yC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this;
      i.next = function() {
        var u = i.x, c = i.i, l, m, f;
        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, f = [];
        if (c === (c | 0))
          m = f[0] = c;
        else
          for (c = "" + c, l = 0; l < c.length; ++l)
            f[l & 7] = f[l & 7] << 15 ^ c.charCodeAt(l) + f[l + 1 & 7] << 13;
        for (; f.length < 8; )
          f.push(0);
        for (l = 0; l < 8 && f[l] === 0; ++l)
          ;
        for (l == 8 ? m = f[7] = -1 : m = f[l], u.x = f, 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, f = (l + m) / (1 << 21);
        while (f === 0);
        return f;
      }, 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;
  })(g1, typeof yC == "object" && yC, typeof define == "function" && define);
});
var b1 = Kt((y1, bC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this;
      i.next = function() {
        var u = i.w, c = i.X, l = i.i, m, f;
        return i.w = u = u + 1640531527 | 0, f = c[l + 34 & 127], m = c[l = l + 1 & 127], f ^= f << 13, m ^= m << 17, f ^= f >>> 15, m ^= m >>> 12, f = c[l] = f ^ m, i.i = l, f + (u ^ u >>> 16) | 0;
      };
      function p(u, c) {
        var l, m, f, d, h, g = [], y = 128;
        for (c === (c | 0) ? (m = c, c = null) : (c = c + "\0", m = 0, y = Math.max(y, c.length)), f = 0, d = -32; d < y; ++d)
          c && (m ^= c.charCodeAt((d + 32) % c.length)), d === 0 && (h = m), m ^= m << 10, m ^= m >>> 15, m ^= m << 4, m ^= m >>> 13, d >= 0 && (h = h + 1640531527 | 0, l = g[d & 127] ^= m + h, f = l == 0 ? f + 1 : 0);
        for (f >= 128 && (g[(c && c.length || 0) & 127] = -1), f = 127, d = 4 * 128; d > 0; --d)
          m = g[f + 34 & 127], l = g[f = f + 1 & 127], m ^= m << 13, l ^= l << 17, m ^= m >>> 15, l ^= l >>> 12, g[f] = m ^ l;
        u.w = h, u.X = g, u.i = f;
      }
      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, f = (l + m) / (1 << 21);
        while (f === 0);
        return f;
      }, 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;
  })(y1, typeof bC == "object" && bC, typeof define == "function" && define);
});
var I1 = Kt((C1, CC) => {
  (function(r, e, t10) {
    function o(a) {
      var i = this, p = "";
      i.next = function() {
        var c = i.b, l = i.c, m = i.d, f = i.a;
        return c = c << 25 ^ c >>> 7 ^ l, l = l - m | 0, m = m << 24 ^ m >>> 8 ^ f, f = f - c | 0, i.b = c = c << 20 ^ c >>> 12 ^ l, i.c = l = l - m | 0, i.d = m << 16 ^ l >>> 16 ^ f, i.a = f - 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) {
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        return (p.next() >>> 0) / 4294967296;
      };
      return c.double = function() {
        do
          var l = p.next() >>> 11, m = (p.next() >>> 0) / 4294967296, f = (l + m) / (1 << 21);
        while (f === 0);
        return f;
      }, c.int32 = p.next, c.quick = c, u && (typeof u == "object" && n(u, p), c.state = function() {
        return n(p, {});
      }), c;
    }
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});
var w1 = Kt(() => {
});
var v1 = Kt((S1, Of) => {
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      return A.int32 = function() {
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          return d(R, {});
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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 y() {
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        var C;
        return l && (C = l.randomBytes) ? C = C(o) : (C = new Uint8Array(o), (r.crypto || r.msCrypto).getRandomValues(C)), b(C);
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        var w = r.navigator, k = w && w.plugins;
        return [+new Date(), r, k, r.screen, b(e)];
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    }
    function b(C) {
      return String.fromCharCode.apply(0, C);
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        l = w1();
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        return m;
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});
var IC = Kt((_we, k1) => {
  var F4 = l1(), D4 = f1(), P4 = h1(), O4 = x1(), M4 = b1(), L4 = I1(), fu = v1();
  fu.alea = F4;
  fu.xor128 = D4;
  fu.xorwow = P4;
  fu.xorshift7 = O4;
  fu.xor4096 = M4;
  fu.tychei = L4;
  k1.exports = fu;
});
var Vl = Kt(() => {
});
var Qw = Kt(() => {
});
var D3 = Kt(() => {
});
var P3 = Kt(() => {
});
var O3 = Kt(() => {
});
var M3 = Kt((Fg, Jw) => {
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          F = D3();
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        (y || 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) => {
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          return B.open("GET", F, false), B.send(null), B.responseText;
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              B(le.response);
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`), V = (F) => M.writeSync(2, F + `
`));
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        F || Xu(B);
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          ++Te;
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          var $e = F[B++];
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      function Me(F, B) {
        return F ? Ee(o(), F, B) : "";
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      function st(F, B, re, le) {
        if (!(le > 0))
          return 0;
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            var Wt = F.charCodeAt(++$e);
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            if (re >= Ze)
              break;
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              break;
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            if (re + 2 >= Ze)
              break;
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            if (re + 3 >= Ze)
              break;
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        return X;
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      function Ua() {
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          for (typeof u.preRun == "function" && (u.preRun = [u.preRun]); u.preRun.length; )
            Lc(u.preRun.shift());
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      function jt() {
        Qs = true, !w && Uc(Yr);
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      function Zs() {
        if (!w) {
          if (u.postRun)
            for (typeof u.postRun == "function" && (u.postRun = [u.postRun]); u.postRun.length; )
              KS(u.postRun.shift());
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        Yr.unshift(F);
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        pr.unshift(F);
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      }
      function Xu(F) {
        w ? postMessage({ cmd: "onAbort", arg: F }) : u.onAbort && u.onAbort(F), F = "Aborted(" + F + ")", q(F), ie = true, de = 1, F += ". Build with -sASSERTIONS for more info.";
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      var $x = "data:application/octet-stream;base64,";
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          if (F == dr && ee)
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      function Rx() {
        if (!ee && (y || b)) {
          if (typeof fetch == "function" && !Vc(dr))
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                throw "failed to load wasm binary file at '" + dr + "'";
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              return sm(dr);
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              R(dr, function(re) {
                F(new Uint8Array(re));
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          return sm(dr);
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      function Ax() {
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            var Zr = Fe.unusedWorkers.length;
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                --Zr || XS("wasm-instantiate");
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          B($e.instance, $e.module);
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            var Pe = WebAssembly.instantiateStreaming($e, F);
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        if (u.instantiateWasm)
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      var Fx, YS, Dx = {};
      function Di(F) {
        this.name = "ExitStatus", this.message = "Program terminated with exit(" + F + ")", this.status = F;
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      function Px(F) {
        var B = Fe.pthreads[F];
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        var B = Me(F);
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        X = false;
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        de = F;
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          Fe.returnWorkerToPool(F);
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          F.terminate();
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      }, returnWorkerToPool: function(F) {
        var B = F.pthread_ptr;
        delete Fe.pthreads[B], Fe.unusedWorkers.push(F), Fe.runningWorkers.splice(Fe.runningWorkers.indexOf(F), 1), F.pthread_ptr = 0, cb(B);
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            var Ze = Fe.pthreads[le.targetThread];
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            return;
          }
          Te === "processProxyingQueue" ? Gc(le.queue) : Te === "spawnThread" ? am(le) : Te === "cleanupThread" ? zc(le.thread) : Te === "killThread" ? Px(le.thread) : Te === "cancelThread" ? Ox(le.thread) : Te === "loaded" ? (F.loaded = true, B && B(F), F.runPthread && (F.runPthread(), delete F.runPthread)) : Te === "print" ? G("Thread " + le.threadId + ": " + le.text) : Te === "printErr" ? q("Thread " + le.threadId + ": " + le.text) : Te === "alert" ? alert("Thread " + le.threadId + ": " + le.text) : le.target === "setimmediate" ? F.postMessage(le) : Te === "onAbort" ? u.onAbort && u.onAbort(le.arg) : Te && q("worker sent an unknown command " + Te), Fe.currentProxiedOperationCallerThread = void 0;
        }, F.onerror = (re) => {
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        var F = _("tfjs-backend-wasm-threaded-simd.worker.js");
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          F.shift()(u);
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      function Lx(F) {
        var B = lb(), re = F();
        return vm(B), re;
      }
      function ZS(F) {
        return F;
      }
      function JS(F) {
        var B = /\b_Z[\w\d_]+/g;
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          var le = re;
          return re === le ? re : le + " [" + re + "]";
        });
      }
      function Bx() {
        var F = Sm(), B = s()[F + 44 >> 2], re = s()[F + 48 >> 2], le = B - re;
        a0(B, le), vm(B);
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      u.establishStackSpace = Bx;
      function pm(F) {
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          return Ha(2, 0, F);
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          um(F);
        } catch (B) {
          Mx(B);
        }
      }
      var Yu = [];
      function Vx(F) {
        var B = Yu[F];
        return B || (F >= Yu.length && (Yu.length = F + 1), Yu[F] = B = rr.get(F)), B;
      }
      function zx(F, B) {
        var re = Vx(F)(B);
        tn() ? Fe.setExitStatus(re) : s0(re);
      }
      u.invokeEntryPoint = zx;
      function e0() {
        var F = new Error();
        if (!F.stack) {
          try {
            throw new Error();
          } catch (B) {
            F = B;
          }
          if (!F.stack)
            return "(no stack trace available)";
        }
        return F.stack.toString();
      }
      function Wx(F) {
        Fe.tlsInitFunctions.push(F);
      }
      function Ux(F, B) {
        t10().set(F, B);
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        r0(F, !b, 1, !y), Fe.threadInitTLS();
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      function Hx(F) {
        w ? postMessage({ cmd: "cleanupThread", thread: F }) : zc(F);
      }
      function cm(F, B, re, le) {
        return w ? Ha(3, 1, F, B, re, le) : lm(F, B, re, le);
      }
      function lm(F, B, re, le) {
        if (typeof SharedArrayBuffer == "undefined")
          return q("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
        var Te = [], Ze = 0;
        if (w && (Te.length === 0 || Ze))
          return cm(F, B, re, le);
        if (Ze)
          return Ze;
        var $e = { startRoutine: re, pthread_ptr: F, arg: le, transferList: Te };
        return w ? ($e.cmd = "spawnThread", postMessage($e, Te), 0) : am($e);
      }
      function qx() {
        return 2097152;
      }
      var Kx = true;
      function jx() {
        return Kx;
      }
      function Gc(F) {
        Atomics.store(s(), F >> 2, 1), Sm() && n0(F), Atomics.compareExchange(s(), F >> 2, 1, 0);
      }
      u.executeNotifiedProxyingQueue = Gc;
      function Xx(F, B, re, le) {
        if (F == B)
          setTimeout(() => Gc(le));
        else if (w)
          postMessage({ targetThread: F, cmd: "processProxyingQueue", queue: le });
        else {
          var Te = Fe.pthreads[F];
          if (!Te)
            return;
          Te.postMessage({ cmd: "processProxyingQueue", queue: le });
        }
        return 1;
      }
      function Yx(F, B, re) {
        return -1;
      }
      function Qx() {
        Xu("");
      }
      function Pi(F) {
        Pi.shown || (Pi.shown = {}), Pi.shown[F] || (Pi.shown[F] = 1, C && (F = "warning: " + F), q(F));
      }
      function Zx() {
        C || b || Pi("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
      }
      function Jx() {
        return Date.now();
      }
      function mm() {
        return 2147483648;
      }
      function ey() {
        return mm();
      }
      var Qu;
      C ? Qu = () => {
        var F = process.hrtime();
        return F[0] * 1e3 + F[1] / 1e6;
      } : w ? Qu = () => performance.now() - u.__performance_now_clock_drift : Qu = () => performance.now();
      function ty(F, B, re) {
        o().copyWithin(F, B, B + re);
      }
      function ry() {
        return C ? O3().cpus().length : navigator.hardwareConcurrency;
      }
      function Ha(F, B) {
        var re = arguments.length - 2, le = arguments;
        return Lx(() => {
          for (var Te = re, Ze = km(Te * 8), $e = Ze >> 3, Pe = 0; Pe < re; Pe++) {
            var Wt = le[2 + Pe];
            p()[$e + Pe] = Wt;
          }
          return o0(F, Te, Ze, B);
        });
      }
      var Hc = [];
      function oy(F, B, re) {
        Hc.length = B;
        for (var le = re >> 3, Te = 0; Te < B; Te++)
          Hc[Te] = p()[le + Te];
        var Ze = F < 0, $e = Ze ? Dx[-F - 1] : ly[F];
        return $e.apply(null, Hc);
      }
      function ny(F) {
        try {
          return Q.grow(F - De.byteLength + 65535 >>> 16), Tt(Q.buffer), 1;
        } catch (B) {
        }
      }
      function sy(F) {
        var B = o().length;
        if (F = F >>> 0, F <= B)
          return false;
        var re = mm();
        if (F > re)
          return false;
        let le = (Wt, Zr) => Wt + (Zr - Wt % Zr) % Zr;
        for (var Te = 1; Te <= 4; Te *= 2) {
          var Ze = B * (1 + 0.2 / Te);
          Ze = Math.min(Ze, F + 100663296);
          var $e = Math.min(re, le(Math.max(F, Ze), 65536)), Pe = ny($e);
          if (Pe)
            return true;
        }
        return false;
      }
      function ay() {
        throw "unwind";
      }
      function fm(F) {
        return w ? Ha(4, 1, F) : 52;
      }
      function dm(F, B, re, le, Te) {
        return w ? Ha(5, 1, F, B, re, le, Te) : 70;
      }
      var iy = [null, [], []];
      function uy(F, B) {
        var re = iy[F];
        B === 0 || B === 10 ? ((F === 1 ? G : q)(Ee(re, 0)), re.length = 0) : re.push(B);
      }
      function hm(F, B, re, le) {
        if (w)
          return Ha(6, 1, F, B, re, le);
        for (var Te = 0, Ze = 0; Ze < re; Ze++) {
          var $e = a()[B >> 2], Pe = a()[B + 4 >> 2];
          B += 8;
          for (var Wt = 0; Wt < Pe; Wt++)
            uy(F, o()[$e + Wt]);
          Te += Pe;
        }
        return a()[le >> 2] = Te, 0;
      }
      function gm(F) {
        var B = u["_" + F];
        return B;
      }
      function py(F, B, re, le, Te) {
        var Ze = { string: (Dr) => {
          var tp = 0;
          if (Dr != null && Dr !== 0) {
            var p0 = (Dr.length << 2) + 1;
            tp = km(p0), pt(Dr, tp, p0);
          }
          return tp;
        }, array: (Dr) => {
          var tp = km(Dr.length);
          return Ux(Dr, tp), tp;
        } };
        function $e(Dr) {
          return B === "string" ? Me(Dr) : B === "boolean" ? Boolean(Dr) : Dr;
        }
        var Pe = gm(F), Wt = [], Zr = 0;
        if (le)
          for (var ta = 0; ta < le.length; ta++) {
            var u0 = Ze[re[ta]];
            u0 ? (Zr === 0 && (Zr = lb()), Wt[ta] = u0(le[ta])) : Wt[ta] = le[ta];
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        var mb = Pe.apply(null, Wt);
        function YV(Dr) {
          return Zr !== 0 && vm(Zr), $e(Dr);
        }
        return mb = YV(mb), mb;
      }
      function cy(F, B, re, le) {
        re = re || [];
        var Te = re.every(($e) => $e === "number" || $e === "boolean"), Ze = B !== "string";
        return Ze && Te && !le ? gm(F) : function() {
          return py(F, B, re, arguments, le);
        };
      }
      Fe.init();
      var ly = [null, Wc, pm, cm, fm, dm, hm], xm = { __emscripten_init_main_thread_js: Gx, __emscripten_thread_cleanup: Hx, __pthread_create_js: lm, _emscripten_default_pthread_stack_size: qx, _emscripten_get_now_is_monotonic: jx, _emscripten_notify_task_queue: Xx, _emscripten_set_offscreencanvas_size: Yx, abort: Qx, emscripten_check_blocking_allowed: Zx, emscripten_date_now: Jx, emscripten_get_heap_max: ey, emscripten_get_now: Qu, emscripten_memcpy_big: ty, emscripten_num_logical_cores: ry, emscripten_receive_on_main_thread_js: oy, emscripten_resize_heap: sy, emscripten_unwind_to_js_event_loop: ay, exit: um, fd_close: fm, fd_seek: dm, fd_write: hm, memory: Q || u.wasmMemory }, t0 = Ax(), my = u.___wasm_call_ctors = function() {
        return (my = u.___wasm_call_ctors = u.asm.__wasm_call_ctors).apply(null, arguments);
      }, fy = u._init = function() {
        return (fy = u._init = u.asm.init).apply(null, arguments);
      }, dy = u._init_with_threads_count = function() {
        return (dy = u._init_with_threads_count = u.asm.init_with_threads_count).apply(null, arguments);
      }, hy = u._get_threads_count = function() {
        return (hy = u._get_threads_count = u.asm.get_threads_count).apply(null, arguments);
      }, gy = u._register_tensor = function() {
        return (gy = u._register_tensor = u.asm.register_tensor).apply(null, arguments);
      }, xy = u._dispose_data = function() {
        return (xy = u._dispose_data = u.asm.dispose_data).apply(null, arguments);
      }, yy = u._dispose = function() {
        return (yy = u._dispose = u.asm.dispose).apply(null, arguments);
      }, by = u._Abs = function() {
        return (by = u._Abs = u.asm.Abs).apply(null, arguments);
      }, Cy = u._Add = function() {
        return (Cy = u._Add = u.asm.Add).apply(null, arguments);
      }, Iy = u._AddN = function() {
        return (Iy = u._AddN = u.asm.AddN).apply(null, arguments);
      }, wy = u._All = function() {
        return (wy = u._All = u.asm.All).apply(null, arguments);
      }, Sy = u._Any = function() {
        return (Sy = u._Any = u.asm.Any).apply(null, arguments);
      }, vy = u._ArgMax = function() {
        return (vy = u._ArgMax = u.asm.ArgMax).apply(null, arguments);
      }, ky = u._AvgPool = function() {
        return (ky = u._AvgPool = u.asm.AvgPool).apply(null, arguments);
      }, Ty = u._BatchMatMul = function() {
        return (Ty = u._BatchMatMul = u.asm.BatchMatMul).apply(null, arguments);
      }, Ny = u._Ceil = function() {
        return (Ny = u._Ceil = u.asm.Ceil).apply(null, arguments);
      }, _y = u._ClipByValue = function() {
        return (_y = u._ClipByValue = u.asm.ClipByValue).apply(null, arguments);
      }, Ey = u._Conv2D = function() {
        return (Ey = u._Conv2D = u.asm.Conv2D).apply(null, arguments);
      }, $y = u._Conv2DBackpropInput = function() {
        return ($y = u._Conv2DBackpropInput = u.asm.Conv2DBackpropInput).apply(null, arguments);
      }, Ry = u._Cos = function() {
        return (Ry = u._Cos = u.asm.Cos).apply(null, arguments);
      }, Ay = u._Cosh = function() {
        return (Ay = u._Cosh = u.asm.Cosh).apply(null, arguments);
      }, Fy = u._CropAndResize = function() {
        return (Fy = u._CropAndResize = u.asm.CropAndResize).apply(null, arguments);
      }, Dy = u._Cumprod = function() {
        return (Dy = u._Cumprod = u.asm.Cumprod).apply(null, arguments);
      }, Py = u._Cumsum = function() {
        return (Py = u._Cumsum = u.asm.Cumsum).apply(null, arguments);
      }, Oy = u._DepthToSpace = function() {
        return (Oy = u._DepthToSpace = u.asm.DepthToSpace).apply(null, arguments);
      }, My = u._DepthwiseConv2dNative = function() {
        return (My = u._DepthwiseConv2dNative = u.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, Ly = u._Elu = function() {
        return (Ly = u._Elu = u.asm.Elu).apply(null, arguments);
      }, By = u._Equal = function() {
        return (By = u._Equal = u.asm.Equal).apply(null, arguments);
      }, Vy = u._Exp = function() {
        return (Vy = u._Exp = u.asm.Exp).apply(null, arguments);
      }, zy = u._FlipLeftRight = function() {
        return (zy = u._FlipLeftRight = u.asm.FlipLeftRight).apply(null, arguments);
      }, Wy = u._Floor = function() {
        return (Wy = u._Floor = u.asm.Floor).apply(null, arguments);
      }, Uy = u._FloorDiv = function() {
        return (Uy = u._FloorDiv = u.asm.FloorDiv).apply(null, arguments);
      }, Gy = u._FusedBatchNorm = function() {
        return (Gy = u._FusedBatchNorm = u.asm.FusedBatchNorm).apply(null, arguments);
      }, Hy = u._FusedConv2D = function() {
        return (Hy = u._FusedConv2D = u.asm.FusedConv2D).apply(null, arguments);
      }, qy = u._FusedDepthwiseConv2D = function() {
        return (qy = u._FusedDepthwiseConv2D = u.asm.FusedDepthwiseConv2D).apply(null, arguments);
      }, Ky = u._Gather = function() {
        return (Ky = u._Gather = u.asm.Gather).apply(null, arguments);
      }, jy = u._GatherNd = function() {
        return (jy = u._GatherNd = u.asm.GatherNd).apply(null, arguments);
      }, Xy = u._Greater = function() {
        return (Xy = u._Greater = u.asm.Greater).apply(null, arguments);
      }, Yy = u._GreaterEqual = function() {
        return (Yy = u._GreaterEqual = u.asm.GreaterEqual).apply(null, arguments);
      }, Qy = u._LeakyRelu = function() {
        return (Qy = u._LeakyRelu = u.asm.LeakyRelu).apply(null, arguments);
      }, Zy = u._Less = function() {
        return (Zy = u._Less = u.asm.Less).apply(null, arguments);
      }, Jy = u._LessEqual = function() {
        return (Jy = u._LessEqual = u.asm.LessEqual).apply(null, arguments);
      }, eb = u._Log = function() {
        return (eb = u._Log = u.asm.Log).apply(null, arguments);
      }, tb = u._LogicalAnd = function() {
        return (tb = u._LogicalAnd = u.asm.LogicalAnd).apply(null, arguments);
      }, rb = u._LogicalNot = function() {
        return (rb = u._LogicalNot = u.asm.LogicalNot).apply(null, arguments);
      }, ob = u._LogicalOr = function() {
        return (ob = u._LogicalOr = u.asm.LogicalOr).apply(null, arguments);
      }, nb = u._LogicalXor = function() {
        return (nb = u._LogicalXor = u.asm.LogicalXor).apply(null, arguments);
      }, sb = u._Max = function() {
        return (sb = u._Max = u.asm.Max).apply(null, arguments);
      }, ym = u._MaxPool = function() {
        return (ym = u._MaxPool = u.asm.MaxPool).apply(null, arguments);
      }, bm = u._Maximum = function() {
        return (bm = u._Maximum = u.asm.Maximum).apply(null, arguments);
      }, qc = u._Mean = function() {
        return (qc = u._Mean = u.asm.Mean).apply(null, arguments);
      }, ab = u._Min = function() {
        return (ab = u._Min = u.asm.Min).apply(null, arguments);
      }, ib = u._Minimum = function() {
        return (ib = u._Minimum = u.asm.Minimum).apply(null, arguments);
      }, Zu = u._MirrorPad = function() {
        return (Zu = u._MirrorPad = u.asm.MirrorPad).apply(null, arguments);
      }, Cm = u._Multiply = function() {
        return (Cm = u._Multiply = u.asm.Multiply).apply(null, arguments);
      }, Ju = u._Neg = function() {
        return (Ju = u._Neg = u.asm.Neg).apply(null, arguments);
      }, ep = u._NonMaxSuppressionV3 = function() {
        return (ep = u._NonMaxSuppressionV3 = u.asm.NonMaxSuppressionV3).apply(null, arguments);
      }, ub = u._NonMaxSuppressionV4 = function() {
        return (ub = u._NonMaxSuppressionV4 = u.asm.NonMaxSuppressionV4).apply(null, arguments);
      }, U = u._NonMaxSuppressionV5 = function() {
        return (U = u._NonMaxSuppressionV5 = u.asm.NonMaxSuppressionV5).apply(null, arguments);
      }, te = u._NotEqual = function() {
        return (te = u._NotEqual = u.asm.NotEqual).apply(null, arguments);
      }, ve = u._OneHot = function() {
        return (ve = u._OneHot = u.asm.OneHot).apply(null, arguments);
      }, Ke = u._PadV2 = function() {
        return (Ke = u._PadV2 = u.asm.PadV2).apply(null, arguments);
      }, Nt = u._Pow = function() {
        return (Nt = u._Pow = u.asm.Pow).apply(null, arguments);
      }, _t = u._Prelu = function() {
        return (_t = u._Prelu = u.asm.Prelu).apply(null, arguments);
      }, He = u._Prod = function() {
        return (He = u._Prod = u.asm.Prod).apply(null, arguments);
      }, ze = u._RealDiv = function() {
        return (ze = u._RealDiv = u.asm.RealDiv).apply(null, arguments);
      }, zt = u._Relu = function() {
        return (zt = u._Relu = u.asm.Relu).apply(null, arguments);
      }, Qr = u._Relu6 = function() {
        return (Qr = u._Relu6 = u.asm.Relu6).apply(null, arguments);
      }, ea = u._ResizeBilinear = function() {
        return (ea = u._ResizeBilinear = u.asm.ResizeBilinear).apply(null, arguments);
      }, Im = u._ResizeNearestNeighbor = function() {
        return (Im = u._ResizeNearestNeighbor = u.asm.ResizeNearestNeighbor).apply(null, arguments);
      }, Kc = u._Reverse = function() {
        return (Kc = u._Reverse = u.asm.Reverse).apply(null, arguments);
      }, pb = u._RotateWithOffset = function() {
        return (pb = u._RotateWithOffset = u.asm.RotateWithOffset).apply(null, arguments);
      }, hr = u._Round = function() {
        return (hr = u._Round = u.asm.Round).apply(null, arguments);
      }, qa = u._Rsqrt = function() {
        return (qa = u._Rsqrt = u.asm.Rsqrt).apply(null, arguments);
      }, wm = u._ScatterNd = function() {
        return (wm = u._ScatterNd = u.asm.ScatterNd).apply(null, arguments);
      }, yV = u._SelectV2 = function() {
        return (yV = u._SelectV2 = u.asm.SelectV2).apply(null, arguments);
      }, bV = u._Sigmoid = function() {
        return (bV = u._Sigmoid = u.asm.Sigmoid).apply(null, arguments);
      }, CV = u._Sin = function() {
        return (CV = u._Sin = u.asm.Sin).apply(null, arguments);
      }, IV = u._Softmax = function() {
        return (IV = u._Softmax = u.asm.Softmax).apply(null, arguments);
      }, wV = u._SparseFillEmptyRows = function() {
        return (wV = u._SparseFillEmptyRows = u.asm.SparseFillEmptyRows).apply(null, arguments);
      }, SV = u._SparseReshape = function() {
        return (SV = u._SparseReshape = u.asm.SparseReshape).apply(null, arguments);
      }, vV = u._SparseSegmentReduction = function() {
        return (vV = u._SparseSegmentReduction = u.asm.SparseSegmentReduction).apply(null, arguments);
      }, kV = u._Sqrt = function() {
        return (kV = u._Sqrt = u.asm.Sqrt).apply(null, arguments);
      }, TV = u._Square = function() {
        return (TV = u._Square = u.asm.Square).apply(null, arguments);
      }, NV = u._SquaredDifference = function() {
        return (NV = u._SquaredDifference = u.asm.SquaredDifference).apply(null, arguments);
      }, _V = u._Step = function() {
        return (_V = u._Step = u.asm.Step).apply(null, arguments);
      }, EV = u._StridedSlice = function() {
        return (EV = u._StridedSlice = u.asm.StridedSlice).apply(null, arguments);
      }, $V = u._Sub = function() {
        return ($V = u._Sub = u.asm.Sub).apply(null, arguments);
      }, RV = u._Sum = function() {
        return (RV = u._Sum = u.asm.Sum).apply(null, arguments);
      }, AV = u._Tan = function() {
        return (AV = u._Tan = u.asm.Tan).apply(null, arguments);
      }, FV = u._Tanh = function() {
        return (FV = u._Tanh = u.asm.Tanh).apply(null, arguments);
      }, DV = u._Tile = function() {
        return (DV = u._Tile = u.asm.Tile).apply(null, arguments);
      }, PV = u._TopK = function() {
        return (PV = u._TopK = u.asm.TopK).apply(null, arguments);
      }, OV = u._Transform = function() {
        return (OV = u._Transform = u.asm.Transform).apply(null, arguments);
      }, MV = u._Transpose = function() {
        return (MV = u._Transpose = u.asm.Transpose).apply(null, arguments);
      }, LV = u.__FusedMatMul = function() {
        return (LV = u.__FusedMatMul = u.asm._FusedMatMul).apply(null, arguments);
      }, BV = u._malloc = function() {
        return (BV = u._malloc = u.asm.malloc).apply(null, arguments);
      }, VV = u._free = function() {
        return (VV = u._free = u.asm.free).apply(null, arguments);
      }, zV = u.__emscripten_tls_init = function() {
        return (zV = u.__emscripten_tls_init = u.asm._emscripten_tls_init).apply(null, arguments);
      }, Sm = u._pthread_self = function() {
        return (Sm = u._pthread_self = u.asm.pthread_self).apply(null, arguments);
      }, WV = u.___errno_location = function() {
        return (WV = u.___errno_location = u.asm.__errno_location).apply(null, arguments);
      }, r0 = u.__emscripten_thread_init = function() {
        return (r0 = u.__emscripten_thread_init = u.asm._emscripten_thread_init).apply(null, arguments);
      }, UV = u.__emscripten_thread_crashed = function() {
        return (UV = u.__emscripten_thread_crashed = u.asm._emscripten_thread_crashed).apply(null, arguments);
      }, GV = u._emscripten_main_thread_process_queued_calls = function() {
        return (GV = u._emscripten_main_thread_process_queued_calls = u.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
      }, HV = u._emscripten_main_browser_thread_id = function() {
        return (HV = u._emscripten_main_browser_thread_id = u.asm.emscripten_main_browser_thread_id).apply(null, arguments);
      }, o0 = u._emscripten_run_in_main_runtime_thread_js = function() {
        return (o0 = u._emscripten_run_in_main_runtime_thread_js = u.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
      }, qV = u._emscripten_dispatch_to_thread_ = function() {
        return (qV = u._emscripten_dispatch_to_thread_ = u.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
      }, n0 = u.__emscripten_proxy_execute_task_queue = function() {
        return (n0 = u.__emscripten_proxy_execute_task_queue = u.asm._emscripten_proxy_execute_task_queue).apply(null, arguments);
      }, cb = u.__emscripten_thread_free_data = function() {
        return (cb = u.__emscripten_thread_free_data = u.asm._emscripten_thread_free_data).apply(null, arguments);
      }, s0 = u.__emscripten_thread_exit = function() {
        return (s0 = u.__emscripten_thread_exit = u.asm._emscripten_thread_exit).apply(null, arguments);
      }, a0 = u._emscripten_stack_set_limits = function() {
        return (a0 = u._emscripten_stack_set_limits = u.asm.emscripten_stack_set_limits).apply(null, arguments);
      }, lb = u.stackSave = function() {
        return (lb = u.stackSave = u.asm.stackSave).apply(null, arguments);
      }, vm = u.stackRestore = function() {
        return (vm = u.stackRestore = u.asm.stackRestore).apply(null, arguments);
      }, km = u.stackAlloc = function() {
        return (km = u.stackAlloc = u.asm.stackAlloc).apply(null, arguments);
      }, KV = u.dynCall_iijjiiii = function() {
        return (KV = u.dynCall_iijjiiii = u.asm.dynCall_iijjiiii).apply(null, arguments);
      }, jV = u.dynCall_jiji = function() {
        return (jV = u.dynCall_jiji = u.asm.dynCall_jiji).apply(null, arguments);
      };
      u.keepRuntimeAlive = tn, u.wasmMemory = Q, u.cwrap = cy, u.ExitStatus = Di, u.PThread = Fe;
      var Tm;
      Js = function F() {
        Tm || i0(), Tm || (Js = F);
      };
      function i0(F) {
        if (F = F || d, Ga > 0)
          return;
        if (w) {
          c(u), jt(), postMessage({ cmd: "loaded" });
          return;
        }
        if (Ua(), Ga > 0)
          return;
        function B() {
          Tm || (Tm = true, u.calledRun = true, !ie && (jt(), c(u), u.onRuntimeInitialized && u.onRuntimeInitialized(), Zs()));
        }
        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()();
      i0();
      var Nm;
      m && (Nm = { 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 _m;
      if (typeof WasmBackendModule != "undefined")
        _m = WasmBackendModule;
      else if (typeof e != "undefined")
        _m = e;
      else
        throw new Error("Could not find wasm module in post.js");
      if (Nm) {
        var XV = _m._dispose;
        _m._dispose = function() {
          XV(), Nm.uncaughtException.forEach(function(F) {
            process.removeListener("uncaughtException", F);
          }), Nm.unhandledRejection.forEach(function(F) {
            process.removeListener("unhandledRejection", F);
          });
        };
      }
      return e.ready;
    };
  })();
  typeof Fg == "object" && typeof Jw == "object" ? Jw.exports = Zw : typeof define == "function" && define.amd ? define([], function() {
    return Zw;
  }) : typeof Fg == "object" && (Fg.WasmBackendModuleThreadedSimd = Zw);
});
var B3 = Kt((ukt, L3) => {
  L3.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"))},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.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;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 V3 = Kt((Dg, tS) => {
  var eS = (() => {
    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(U, te) {
        o = U, n = te;
      });
      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 = (U, te) => {
        throw te;
      }, c = typeof window == "object", l = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", f = "";
      function d(U) {
        return t10.locateFile ? t10.locateFile(U, f) : f + U;
      }
      var h, g, y, b;
      function C(U) {
        if (U instanceof ju)
          return;
        E("exiting due to exception: " + U);
      }
      if (m) {
        l ? f = Vl().dirname(f) + "/" : f = __dirname + "/";
        var w, k;
        typeof Em == "function" && (w = Qw(), k = Vl()), h = (U, te) => (U = k.normalize(U), w.readFileSync(U, te ? void 0 : "utf8")), y = (U) => {
          var te = h(U, true);
          return te.buffer || (te = new Uint8Array(te)), te;
        }, g = (U, te, ve) => {
          U = k.normalize(U), w.readFile(U, function(Ke, Nt) {
            Ke ? ve(Ke) : te(Nt.buffer);
          });
        }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), i = process.argv.slice(2), process.on("uncaughtException", function(U) {
          if (!(U instanceof ju))
            throw U;
        }), process.on("unhandledRejection", function(U) {
          throw U;
        }), u = (U, te) => {
          if (at())
            throw process.exitCode = U, te;
          C(te), process.exit(U);
        }, t10.inspect = function() {
          return "[Emscripten Module object]";
        };
      } else
        (c || l) && (l ? f = self.location.href : typeof document != "undefined" && document.currentScript && (f = document.currentScript.src), r && (f = r), f.indexOf("blob:") !== 0 ? f = f.substr(0, f.replace(/[?#].*/, "").lastIndexOf("/") + 1) : f = "", h = (U) => {
          var te = new XMLHttpRequest();
          return te.open("GET", U, false), te.send(null), te.responseText;
        }, l && (y = (U) => {
          var te = new XMLHttpRequest();
          return te.open("GET", U, false), te.responseType = "arraybuffer", te.send(null), new Uint8Array(te.response);
        }), g = (U, te, ve) => {
          var Ke = new XMLHttpRequest();
          Ke.open("GET", U, true), Ke.responseType = "arraybuffer", Ke.onload = () => {
            if (Ke.status == 200 || Ke.status == 0 && Ke.response) {
              te(Ke.response);
              return;
            }
            ve();
          }, Ke.onerror = ve, Ke.send(null);
        }, b = (U) => document.title = U);
      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 R = 4, A;
      t10.wasmBinary && (A = t10.wasmBinary);
      var D = t10.noExitRuntime || true;
      typeof WebAssembly != "object" && pr("no native wasm support detected");
      var O, M = false, L;
      function W(U, te) {
        U || pr(te);
      }
      var V = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0;
      function G(U, te, ve) {
        for (var Ke = te + ve, Nt = te; U[Nt] && !(Nt >= Ke); )
          ++Nt;
        if (Nt - te > 16 && U.buffer && V)
          return V.decode(U.subarray(te, Nt));
        for (var _t = ""; te < Nt; ) {
          var He = U[te++];
          if (!(He & 128)) {
            _t += String.fromCharCode(He);
            continue;
          }
          var ze = U[te++] & 63;
          if ((He & 224) == 192) {
            _t += String.fromCharCode((He & 31) << 6 | ze);
            continue;
          }
          var zt = U[te++] & 63;
          if ((He & 240) == 224 ? He = (He & 15) << 12 | ze << 6 | zt : He = (He & 7) << 18 | ze << 12 | zt << 6 | U[te++] & 63, He < 65536)
            _t += String.fromCharCode(He);
          else {
            var Qr = He - 65536;
            _t += String.fromCharCode(55296 | Qr >> 10, 56320 | Qr & 1023);
          }
        }
        return _t;
      }
      function q(U, te) {
        return U ? G(ee, U, te) : "";
      }
      function H(U, te, ve, Ke) {
        if (!(Ke > 0))
          return 0;
        for (var Nt = ve, _t = ve + Ke - 1, He = 0; He < U.length; ++He) {
          var ze = U.charCodeAt(He);
          if (ze >= 55296 && ze <= 57343) {
            var zt = U.charCodeAt(++He);
            ze = 65536 + ((ze & 1023) << 10) | zt & 1023;
          }
          if (ze <= 127) {
            if (ve >= _t)
              break;
            te[ve++] = ze;
          } else if (ze <= 2047) {
            if (ve + 1 >= _t)
              break;
            te[ve++] = 192 | ze >> 6, te[ve++] = 128 | ze & 63;
          } else if (ze <= 65535) {
            if (ve + 2 >= _t)
              break;
            te[ve++] = 224 | ze >> 12, te[ve++] = 128 | ze >> 6 & 63, te[ve++] = 128 | ze & 63;
          } else {
            if (ve + 3 >= _t)
              break;
            te[ve++] = 240 | ze >> 18, te[ve++] = 128 | ze >> 12 & 63, te[ve++] = 128 | ze >> 6 & 63, te[ve++] = 128 | ze & 63;
          }
        }
        return te[ve] = 0, ve - Nt;
      }
      function j(U, te, ve) {
        return H(U, ee, te, ve);
      }
      var Y, Z, ee, X, Q, se, ie, de, Ie;
      function Se(U) {
        Y = U, t10.HEAP8 = Z = new Int8Array(U), t10.HEAP16 = X = new Int16Array(U), t10.HEAP32 = se = new Int32Array(U), t10.HEAPU8 = ee = new Uint8Array(U), t10.HEAPU16 = Q = new Uint16Array(U), t10.HEAPU32 = ie = new Uint32Array(U), t10.HEAPF32 = de = new Float32Array(U), t10.HEAPF64 = Ie = new Float64Array(U);
      }
      var Ee = t10.INITIAL_MEMORY || 16777216, Me, st = [], pt = [], De = [], ft = false;
      function at() {
        return D;
      }
      function dt() {
        if (t10.preRun)
          for (typeof t10.preRun == "function" && (t10.preRun = [t10.preRun]); t10.preRun.length; )
            Pt(t10.preRun.shift());
        Js(st);
      }
      function It() {
        ft = true, Js(pt);
      }
      function Fr() {
        if (t10.postRun)
          for (typeof t10.postRun == "function" && (t10.postRun = [t10.postRun]); t10.postRun.length; )
            er(t10.postRun.shift());
        Js(De);
      }
      function Pt(U) {
        st.unshift(U);
      }
      function jr(U) {
        pt.unshift(U);
      }
      function er(U) {
        De.unshift(U);
      }
      var Tt = 0, tr = null, rr = null;
      function Xr(U) {
        Tt++, t10.monitorRunDependencies && t10.monitorRunDependencies(Tt);
      }
      function Yr(U) {
        if (Tt--, t10.monitorRunDependencies && t10.monitorRunDependencies(Tt), Tt == 0 && (tr !== null && (clearInterval(tr), tr = null), rr)) {
          var te = rr;
          rr = null, te();
        }
      }
      function pr(U) {
        t10.onAbort && t10.onAbort(U), U = "Aborted(" + U + ")", E(U), M = true, L = 1, U += ". Build with -sASSERTIONS for more info.";
        var te = new WebAssembly.RuntimeError(U);
        throw n(te), te;
      }
      var Qs = "data:application/octet-stream;base64,";
      function tn(U) {
        return U.startsWith(Qs);
      }
      function Ua(U) {
        return U.startsWith("file://");
      }
      var jt;
      jt = "tfjs-backend-wasm.wasm", tn(jt) || (jt = d(jt));
      function Zs(U) {
        try {
          if (U == jt && A)
            return new Uint8Array(A);
          if (y)
            return y(U);
          throw "both async and sync fetching of the wasm failed";
        } catch (te) {
          pr(te);
        }
      }
      function Lc() {
        if (!A && (c || l)) {
          if (typeof fetch == "function" && !Ua(jt))
            return fetch(jt, { credentials: "same-origin" }).then(function(U) {
              if (!U.ok)
                throw "failed to load wasm binary file at '" + jt + "'";
              return U.arrayBuffer();
            }).catch(function() {
              return Zs(jt);
            });
          if (g)
            return new Promise(function(U, te) {
              g(jt, function(ve) {
                U(new Uint8Array(ve));
              }, te);
            });
        }
        return Promise.resolve().then(function() {
          return Zs(jt);
        });
      }
      function Bc() {
        var U = { env: Wc, wasi_snapshot_preview1: Wc };
        function te(He, ze) {
          var zt = He.exports;
          t10.asm = zt, O = t10.asm.memory, Se(O.buffer), Me = t10.asm.__indirect_function_table, jr(t10.asm.__wasm_call_ctors), Yr("wasm-instantiate");
        }
        Xr("wasm-instantiate");
        function ve(He) {
          te(He.instance);
        }
        function Ke(He) {
          return Lc().then(function(ze) {
            return WebAssembly.instantiate(ze, U);
          }).then(function(ze) {
            return ze;
          }).then(He, function(ze) {
            E("failed to asynchronously prepare wasm: " + ze), pr(ze);
          });
        }
        function Nt() {
          return !A && typeof WebAssembly.instantiateStreaming == "function" && !tn(jt) && !Ua(jt) && !m && typeof fetch == "function" ? fetch(jt, { credentials: "same-origin" }).then(function(He) {
            var ze = WebAssembly.instantiateStreaming(He, U);
            return ze.then(ve, function(zt) {
              return E("wasm streaming compile failed: " + zt), E("falling back to ArrayBuffer instantiation"), Ke(ve);
            });
          }) : Ke(ve);
        }
        if (t10.instantiateWasm)
          try {
            var _t = t10.instantiateWasm(U, te);
            return _t;
          } catch (He) {
            E("Module.instantiateWasm callback failed with error: " + He), n(He);
          }
        return Nt().catch(n), {};
      }
      var KS, Ga;
      function ju(U) {
        this.name = "ExitStatus", this.message = "Program terminated with exit(" + U + ")", this.status = U;
      }
      function Js(U) {
        for (; U.length > 0; )
          U.shift()(t10);
      }
      function jS(U) {
        return U;
      }
      function XS(U) {
        var te = /\b_Z[\w\d_]+/g;
        return U.replace(te, function(ve) {
          var Ke = ve;
          return ve === Ke ? ve : Ke + " [" + ve + "]";
        });
      }
      function Xu() {
        var U = new Error();
        if (!U.stack) {
          try {
            throw new Error();
          } catch (te) {
            U = te;
          }
          if (!U.stack)
            return "(no stack trace available)";
        }
        return U.stack.toString();
      }
      function $x(U, te) {
        Z.set(U, te);
      }
      function nm() {
        pr("");
      }
      function Vc() {
        return 2147483648;
      }
      function dr() {
        return Vc();
      }
      function sm(U, te, ve) {
        ee.copyWithin(U, te, te + ve);
      }
      function Rx(U) {
        try {
          return O.grow(U - Y.byteLength + 65535 >>> 16), Se(O.buffer), 1;
        } catch (te) {
        }
      }
      function Ax(U) {
        var te = ee.length;
        U = U >>> 0;
        var ve = Vc();
        if (U > ve)
          return false;
        let Ke = (zt, Qr) => zt + (Qr - zt % Qr) % Qr;
        for (var Nt = 1; Nt <= 4; Nt *= 2) {
          var _t = te * (1 + 0.2 / Nt);
          _t = Math.min(_t, U + 100663296);
          var He = Math.min(ve, Ke(Math.max(U, _t), 65536)), ze = Rx(He);
          if (ze)
            return true;
        }
        return false;
      }
      var Fx = { varargs: void 0, get: function() {
        Fx.varargs += 4;
        var U = se[Fx.varargs - 4 >> 2];
        return U;
      }, getStr: function(U) {
        var te = q(U);
        return te;
      } };
      function YS(U) {
        return 52;
      }
      function Dx(U, te, ve, Ke, Nt) {
        return 70;
      }
      var Di = [null, [], []];
      function Px(U, te) {
        var ve = Di[U];
        te === 0 || te === 10 ? ((U === 1 ? _ : E)(G(ve, 0)), ve.length = 0) : ve.push(te);
      }
      function Ox(U, te, ve, Ke) {
        for (var Nt = 0, _t = 0; _t < ve; _t++) {
          var He = ie[te >> 2], ze = ie[te + 4 >> 2];
          te += 8;
          for (var zt = 0; zt < ze; zt++)
            Px(U, ee[He + zt]);
          Nt += ze;
        }
        return ie[Ke >> 2] = Nt, 0;
      }
      function zc(U) {
        var te = t10["_" + U];
        return te;
      }
      function am(U, te, ve, Ke, Nt) {
        var _t = { string: (hr) => {
          var qa = 0;
          if (hr != null && hr !== 0) {
            var wm = (hr.length << 2) + 1;
            qa = qc(wm), j(hr, qa, wm);
          }
          return qa;
        }, array: (hr) => {
          var qa = qc(hr.length);
          return $x(hr, qa), qa;
        } };
        function He(hr) {
          return te === "string" ? q(hr) : te === "boolean" ? Boolean(hr) : hr;
        }
        var ze = zc(U), zt = [], Qr = 0;
        if (Ke)
          for (var ea = 0; ea < Ke.length; ea++) {
            var Im = _t[ve[ea]];
            Im ? (Qr === 0 && (Qr = ym()), zt[ea] = Im(Ke[ea])) : zt[ea] = Ke[ea];
          }
        var Kc = ze.apply(null, zt);
        function pb(hr) {
          return Qr !== 0 && bm(Qr), He(hr);
        }
        return Kc = pb(Kc), Kc;
      }
      function im(U, te, ve, Ke) {
        ve = ve || [];
        var Nt = ve.every((He) => He === "number" || He === "boolean"), _t = te !== "string";
        return _t && Nt && !Ke ? zc(U) : function() {
          return am(U, te, ve, arguments, Ke);
        };
      }
      var Wc = { abort: nm, emscripten_get_heap_max: dr, emscripten_memcpy_big: sm, emscripten_resize_heap: Ax, fd_close: YS, fd_seek: Dx, fd_write: Ox }, QS = Bc(), um = t10.___wasm_call_ctors = function() {
        return (um = t10.___wasm_call_ctors = t10.asm.__wasm_call_ctors).apply(null, arguments);
      }, Mx = t10._init = function() {
        return (Mx = t10._init = t10.asm.init).apply(null, arguments);
      }, Fe = t10._init_with_threads_count = function() {
        return (Fe = t10._init_with_threads_count = t10.asm.init_with_threads_count).apply(null, arguments);
      }, Uc = t10._get_threads_count = function() {
        return (Uc = t10._get_threads_count = t10.asm.get_threads_count).apply(null, arguments);
      }, Lx = t10._register_tensor = function() {
        return (Lx = t10._register_tensor = t10.asm.register_tensor).apply(null, arguments);
      }, ZS = t10._dispose_data = function() {
        return (ZS = t10._dispose_data = t10.asm.dispose_data).apply(null, arguments);
      }, JS = t10._dispose = function() {
        return (JS = t10._dispose = t10.asm.dispose).apply(null, arguments);
      }, Bx = t10._Abs = function() {
        return (Bx = t10._Abs = t10.asm.Abs).apply(null, arguments);
      }, pm = t10._Add = function() {
        return (pm = t10._Add = t10.asm.Add).apply(null, arguments);
      }, Yu = t10._AddN = function() {
        return (Yu = t10._AddN = t10.asm.AddN).apply(null, arguments);
      }, Vx = t10._All = function() {
        return (Vx = t10._All = t10.asm.All).apply(null, arguments);
      }, zx = t10._Any = function() {
        return (zx = t10._Any = t10.asm.Any).apply(null, arguments);
      }, e0 = t10._ArgMax = function() {
        return (e0 = t10._ArgMax = t10.asm.ArgMax).apply(null, arguments);
      }, Wx = t10._AvgPool = function() {
        return (Wx = t10._AvgPool = t10.asm.AvgPool).apply(null, arguments);
      }, Ux = t10._BatchMatMul = function() {
        return (Ux = t10._BatchMatMul = t10.asm.BatchMatMul).apply(null, arguments);
      }, Gx = t10._Ceil = function() {
        return (Gx = t10._Ceil = t10.asm.Ceil).apply(null, arguments);
      }, Hx = t10._ClipByValue = function() {
        return (Hx = t10._ClipByValue = t10.asm.ClipByValue).apply(null, arguments);
      }, cm = t10._Conv2D = function() {
        return (cm = t10._Conv2D = t10.asm.Conv2D).apply(null, arguments);
      }, lm = t10._Conv2DBackpropInput = function() {
        return (lm = t10._Conv2DBackpropInput = t10.asm.Conv2DBackpropInput).apply(null, arguments);
      }, qx = t10._Cos = function() {
        return (qx = t10._Cos = t10.asm.Cos).apply(null, arguments);
      }, Kx = t10._Cosh = function() {
        return (Kx = t10._Cosh = t10.asm.Cosh).apply(null, arguments);
      }, jx = t10._CropAndResize = function() {
        return (jx = t10._CropAndResize = t10.asm.CropAndResize).apply(null, arguments);
      }, Gc = t10._Cumprod = function() {
        return (Gc = t10._Cumprod = t10.asm.Cumprod).apply(null, arguments);
      }, Xx = t10._Cumsum = function() {
        return (Xx = t10._Cumsum = t10.asm.Cumsum).apply(null, arguments);
      }, Yx = t10._DepthToSpace = function() {
        return (Yx = t10._DepthToSpace = t10.asm.DepthToSpace).apply(null, arguments);
      }, Qx = t10._DepthwiseConv2dNative = function() {
        return (Qx = t10._DepthwiseConv2dNative = t10.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, Pi = t10._Elu = function() {
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function Pr(r) {
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function c0(r) {
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function oz(r, e) {
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function nz(r) {
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function $m(r, e, t10) {
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function iz(r, e) {
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function $(r, e) {
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function ht(r, e, t10 = "") {
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function eo(r) {
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function Ve(r) {
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function uz(r) {
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function Or(r, e) {
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function ra(r) {
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function pz(r) {
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function cz(r) {
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function lz(r) {
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function Mi(r, e) {
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function mz(r, e = (n) => 0, t10, o) {
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function Ka(r, e) {
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function db(r, e) {
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function hb(r, e) {
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function gb(r, e) {
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function xb(r, e) {
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function yb(r) {
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function dz(r, e) {
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function Ut(r) {
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function Rm(r) {
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function bb(r) {
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function nn(r) {
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function l0(r) {
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function m0(r) {
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function np(r) {
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function fs(r) {
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function f0(r, e, t10, o = false) {
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    let i = typeof n == "number" ? Mi(`${n}ms`, 9) : n.error, p = Mi(e, 25), u = t10.rank, c = t10.size, l = Mi(t10.shape.toString(), 14), m = "";
    for (let f in s) {
      let d = s[f];
      if (d != null) {
        let h = d.shape || t10.shape, g = h.length;
        m += `${f}: ${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 A0(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], f = false;
      for (let d = 0; d < e.length; d++)
        if (o[m.id]) {
          u.outputs.forEach((h) => o[h.id] = true), f = true, n[u.id] = true;
          break;
        }
      if (f)
        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 f = u.inputs[m];
        o[f.id] && (c[m] = f);
      }
      let l = Object.assign({}, u);
      l.inputs = c, l.outputs = u.outputs, i.push(l);
    }
  }
  return i;
}
function F0(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 rl = 3;
var Ab = 7;
function P0(r, e, t10, o) {
  let n = ds(e), s = Dz(r, e, t10, n), a = e.length, i = Hm(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 Dz(r, e, t10, o) {
  let n = Ve(e), s = o[o.length - 1], a = new Array(s).fill(0), i = e.length, p = t10 === "complex64" ? nl(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], ol(p[c + l], 0, t10).length);
    }
  return a;
}
function ol(r, e, t10) {
  let o;
  return Array.isArray(r) ? o = `${parseFloat(r[0].toFixed(Ab))} + ${parseFloat(r[1].toFixed(Ab))}j` : nn(r) ? o = `'${r}'` : t10 === "bool" ? o = O0(r) : o = parseFloat(r.toFixed(Ab)).toString(), Mi(o, e);
}
function O0(r) {
  return r === 0 ? "false" : "true";
}
function Hm(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 = nl(r);
      return [ol(h[0], 0, t10)];
    }
    return t10 === "bool" ? [O0(r[0])] : [r[0].toString()];
  }
  if (p === 1) {
    if (i > D0) {
      let g = rl * a, y = Array.from(r.slice(0, g)), b = Array.from(r.slice((i - rl) * a, i * a));
      return t10 === "complex64" && (y = nl(y), b = nl(b)), ["[" + y.map((C, w) => ol(C, n[w], t10)).join(", ") + ", ..., " + b.map((C, w) => ol(C, n[i - rl + w], t10)).join(", ") + "]"];
    }
    return ["[" + (t10 === "complex64" ? nl(r) : Array.from(r)).map((g, y) => ol(g, n[y], t10)).join(", ") + "]"];
  }
  let u = e.slice(1), c = o.slice(1), l = o[0] * a, m = [];
  if (i > D0) {
    for (let h = 0; h < rl; h++) {
      let g = h * l, y = g + l;
      m.push(...Hm(r.slice(g, y), u, t10, c, n, false));
    }
    m.push("...");
    for (let h = i - rl; h < i; h++) {
      let g = h * l, y = g + l;
      m.push(...Hm(r.slice(g, y), u, t10, c, n, h === i - 1));
    }
  } else
    for (let h = 0; h < i; h++) {
      let g = h * l, y = g + l;
      m.push(...Hm(r.slice(g, y), u, t10, c, n, h === i - 1));
    }
  let f = p === 2 ? "," : "";
  m[0] = "[" + m[0] + f;
  for (let h = 1; h < m.length - 1; h++)
    m[h] = " " + m[h] + f;
  let d = `,
`;
  for (let h = 2; h < p; h++)
    d += `
`;
  return m[m.length - 1] = " " + m[m.length - 1] + "]" + (s ? "" : d), m;
}
function nl(r) {
  let e = [];
  for (let t10 = 0; t10 < r.length; t10 += 2)
    e.push([r[t10], r[t10 + 1]]);
  return e;
}
var je = class {
  constructor(e, t10, o) {
    if (this.dtype = t10, this.shape = e.slice(), this.size = Ve(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 || gb(t10, this.size), this.strides = ds(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 rs().makeTensor(this.values, this.shape, this.dtype);
  }
};
var rs = null;
var Mp = null;
var Pz = null;
function M0(r) {
  rs = r;
}
function L0(r) {
  Mp = r;
}
function B0(r) {
  Pz = r;
}
var ut = class {
  constructor(e, t10, o, n) {
    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t10 || "float32", this.size = Ve(e), this.strides = ds(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 Mp.buffer(this.shape, this.dtype, e);
  }
  bufferSync() {
    return Mp.buffer(this.shape, this.dtype, this.dataSync());
  }
  async array() {
    let e = await this.data();
    return Oi(this.shape, e, this.dtype === "complex64");
  }
  arraySync() {
    return Oi(this.shape, this.dataSync(), this.dtype === "complex64");
  }
  async data() {
    this.throwIfDisposed();
    let e = rs().read(this.dataId);
    if (this.dtype === "string") {
      let t10 = await e;
      try {
        return t10.map((o) => Op(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(), rs().readToGPU(this.dataId, e);
  }
  dataSync() {
    this.throwIfDisposed();
    let e = rs().readSync(this.dataId);
    if (this.dtype === "string")
      try {
        return e.map((t10) => Op(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 rs().read(this.dataId);
    return this.dtype === "string" ? e : new Uint8Array(e.buffer);
  }
  dispose() {
    this.isDisposed || (rs().disposeTensor(this), this.isDisposedInternal = true);
  }
  get isDisposed() {
    return this.isDisposedInternal;
  }
  throwIfDisposed() {
    if (this.isDisposed)
      throw new Error("Tensor is disposed.");
  }
  print(e = false) {
    return Mp.print(this, e);
  }
  clone() {
    return this.throwIfDisposed(), Mp.clone(this);
  }
  toString(e = false) {
    let t10 = this.dataSync();
    return P0(t10, this.shape, this.dtype, e);
  }
  cast(e) {
    return this.throwIfDisposed(), Mp.cast(this, e);
  }
  variable(e = true, t10, o) {
    return this.throwIfDisposed(), rs().makeVariable(this, e, t10, o);
  }
};
Object.defineProperty(ut, Symbol.hasInstance, { value: (r) => !!r && r.data != null && r.dataSync != null && r.throwIfDisposed != null });
function Oz() {
  return Zc("Tensor", () => ut);
}
Oz();
var ba = class extends ut {
  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`);
    rs().disposeTensor(this), this.dataId = e.dataId, rs().incRef(this, null);
  }
  dispose() {
    rs().disposeVariable(this), this.isDisposedInternal = true;
  }
};
Object.defineProperty(ba, Symbol.hasInstance, { value: (r) => r instanceof ut && r.assign != null && r.assign instanceof Function });
var z0 = {};
Be(z0, { assertTypesMatch: () => Lb, getTensorsInContainer: () => sl, isTensorInList: () => Lz, makeTypesMatch: () => Re });
var Fb;
(function(r) {
  r.R0 = "R0", r.R1 = "R1", r.R2 = "R2", r.R3 = "R3", r.R4 = "R4", r.R5 = "R5", r.R6 = "R6";
})(Fb || (Fb = {}));
var Db;
(function(r) {
  r.float32 = "float32", r.int32 = "int32", r.bool = "int32", r.complex64 = "complex64";
})(Db || (Db = {}));
var Pb;
(function(r) {
  r.float32 = "float32", r.int32 = "int32", r.bool = "bool", r.complex64 = "complex64";
})(Pb || (Pb = {}));
var Ob;
(function(r) {
  r.float32 = "float32", r.int32 = "float32", r.bool = "float32", r.complex64 = "complex64";
})(Ob || (Ob = {}));
var Mb;
(function(r) {
  r.float32 = "complex64", r.int32 = "complex64", r.bool = "complex64", r.complex64 = "complex64";
})(Mb || (Mb = {}));
var Mz = { float32: Ob, int32: Db, bool: Pb, complex64: Mb };
function ct(r, e) {
  if (r === "string" || e === "string") {
    if (r === "string" && e === "string")
      return "string";
    throw new Error(`Can not upcast ${r} with ${e}`);
  }
  return Mz[r][e];
}
function Ca(r) {
  return ct(r, "int32");
}
function Re(r, e) {
  if (r.dtype === e.dtype)
    return [r, e];
  let t10 = ct(r.dtype, e.dtype);
  return [r.cast(t10), e.cast(t10)];
}
function Lb(r, e) {
  $(r.dtype === e.dtype, () => `The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`);
}
function Lz(r, e) {
  return e.some((t10) => t10.id === r.id);
}
function sl(r) {
  let e = [];
  return V0(r, e, /* @__PURE__ */ new Set()), e;
}
function V0(r, e, t10) {
  if (r == null)
    return;
  if (r instanceof ut) {
    e.push(r);
    return;
  }
  if (!Bz(r))
    return;
  let o = r;
  for (let n in o) {
    let s = o[n];
    t10.has(s) || (t10.add(s), V0(s, e, t10));
  }
}
function Bz(r) {
  return Array.isArray(r) || typeof r == "object";
}
function Bb(r) {
  return r.kernelName != null;
}
var qm = class {
  constructor() {
    this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
      return Array.from(new Set(this.kernels.map((e) => e.name)));
    } };
  }
  dispose() {
    for (let e in this.registeredVariables)
      this.registeredVariables[e].dispose();
  }
};
var ai = class {
  constructor(e) {
    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new qm();
  }
  async ready() {
    if (this.pendingBackendInit != null)
      return this.pendingBackendInit.then(() => {
      });
    if (this.backendInstance != null)
      return;
    let e = this.getSortedBackends();
    for (let t10 = 0; t10 < e.length; t10++) {
      let o = e[t10];
      if (await this.initializeBackend(o).success) {
        await this.setBackend(o);
        return;
      }
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  get backend() {
    if (this.pendingBackendInit != null)
      throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
      this.setBackend(e);
    }
    return this.backendInstance;
  }
  backendNames() {
    return Object.keys(this.registryFactory);
  }
  findBackend(e) {
    if (!(e in this.registry))
      if (e in this.registryFactory) {
        let { asyncInit: t10 } = this.initializeBackend(e);
        if (t10)
          return null;
      } else
        return null;
    return this.registry[e];
  }
  findBackendFactory(e) {
    return e in this.registryFactory ? this.registryFactory[e].factory : null;
  }
  registerBackend(e, t10, o = 1) {
    return e in this.registryFactory ? (Rs(`${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 Gm(this.backendInstance), true;
  }
  setupRegisteredKernels() {
    zm(this.backendName).forEach((t10) => {
      t10.setupFunc != null && t10.setupFunc(this.backendInstance);
    });
  }
  disposeRegisteredKernels(e) {
    zm(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 Jr) && 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, Rs(`Initialization of backend ${e} failed`), Rs(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 Rs(`Initialization of backend ${e} failed`), Rs(o.stack || o.message), { success: false, asyncInit: false };
    }
  }
  removeBackend(e) {
    if (!(e in this.registryFactory))
      throw new Error(`${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 ai.nextTensorId++;
  }
  nextVariableId() {
    return ai.nextVariableId++;
  }
  clone(e) {
    let t10 = N.runKernel(uo, { x: e }), o = { x: e }, n = (a) => ({ x: () => {
      let i = "float32", p = { x: a }, u = { dtype: i };
      return N.runKernel(to, 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, !(el(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 = Bb(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
    if (Bb(e)) {
      let { kernelName: d, inputs: h, attrs: g } = e;
      this.backendName == null && this.backend;
      let y = el(d, this.backendName);
      $(y != null, () => `Cannot find registered kernel '${d}' for backend '${this.backendName}'`), i = () => {
        let b = this.backend.numDataIds();
        p = y.kernelFunc({ inputs: h, attrs: g, backend: this.backend });
        let C = Array.isArray(p) ? p : [p];
        this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(d, b, C);
        let w = C.map((k) => k.rank != null ? k : this.makeTensorFromTensorInfo(k));
        if (n) {
          let k = this.getTensorsForGradient(d, h, w);
          o = this.saveTensorsForBackwardMode(k);
        }
        return w;
      };
    } else {
      let { forwardFunc: d } = e, h = (g) => {
        !n || (o = g.map((y) => this.keep(this.clone(y))));
      };
      i = () => {
        let g = this.backend.numDataIds();
        p = this.tidy(() => d(this.backend, h));
        let y = Array.isArray(p) ? p : [p];
        return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, g, y), y;
      };
    }
    let { inputs: c, attrs: l } = e, m = Bb(e) ? null : e.backwardsFunc, f;
    return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
      !this.ENV.getBool("DEBUG") && !this.state.profiling ? t10 = i() : (f = this.profiler.profileKernel(u, c, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(f), t10 = f.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((d) => c[d] != null ? c[d].shape : null), outputShapes: t10.map((d) => d.shape), kernelTimeMs: f.timeMs, extraInfo: f.extraInfo }), Array.isArray(p) ? t10 : t10[0];
  }
  saveTensorsForBackwardMode(e) {
    return e.map((o) => this.keep(this.clone(o)));
  }
  getTensorsForGradient(e, t10, o) {
    let n = kb(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" && nn(e[0]) && (s = e.map((p) => si(p)));
    let a = n.write(s, t10, o), i = new ut(t10, o, a, this.nextTensorId());
    if (this.trackTensor(i, n), o === "string") {
      let p = this.state.tensorInfo.get(a), u = bb(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 ut(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 ba(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 * Rm(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 ba || 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 * Rm(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 = kb(e);
    p != null && (n = p.gradFunc), n != null && (i.gradient = (u) => (u = u.map((c, l) => {
      if (c == null) {
        let m = o[l], f = ap(m.size, m.dtype);
        return this.makeTensor(f, 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 = sl(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 ut, () => "The result y returned by f() must be a tensor.");
    let a = A0(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 ? Vz(s.shape) : o, F0(i, a, (u) => this.tidy(u), zz);
      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 $(fs(e), () => "The f passed in customGrad(f) must be a function."), (...t10) => {
      $(t10.every((i) => i instanceof ut), () => "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 ut, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), $(fs(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 ut), () => "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, f) => {
          l[f] = () => 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 qm();
    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;
  }
};
ai.nextTensorId = 0;
ai.nextVariableId = 0;
function Vz(r) {
  let e = jc(Ve(r), "float32");
  return N.makeTensor(e, r, "float32");
}
function Vb() {
  let r = wb();
  if (r._tfengine == null) {
    let e = new Qc(r);
    r._tfengine = new ai(e);
  }
  return h0(r._tfengine.ENV), M0(() => r._tfengine), r._tfengine;
}
var N = Vb();
function zz(r, e) {
  let t10 = { a: r, b: e };
  return N.runKernel(_r, t10);
}
var ii = {};
Be(ii, { isBrowser: () => Wb, isMobile: () => Gz, mockIsMobile: () => Uz });
function Wz() {
  return typeof navigator != "undefined" && navigator != null;
}
var zb;
function Uz(r) {
  zb = r;
}
function Gz(r) {
  if (zb !== void 0)
    return zb;
  if (r || Wz()) {
    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 Wb() {
  return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
}
var Vr = P();
Vr.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.");
});
Vr.registerFlag("IS_BROWSER", () => Wb());
Vr.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
Vr.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
Vr.registerFlag("PROD", () => false);
Vr.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => Vr.getBool("DEBUG"));
Vr.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
Vr.registerFlag("IS_TEST", () => false);
Vr.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
Vr.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
Vr.registerFlag("ENGINE_COMPILE_ONLY", () => false);
Vr.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", () => false);
Vr.registerFlag("USE_SETTIMEOUTCUSTOM", () => false);
function or(r, e) {
  let t10 = r;
  if (Ut(r))
    return e === "string" ? [] : [r.length];
  if (typeof r == "object" && "texture" in r) {
    let n = r.channels || "RGBA";
    return [r.height, r.width * n.length];
  }
  if (!Array.isArray(r))
    return [];
  let o = [];
  for (; Array.isArray(t10) || Ut(t10) && e !== "string"; )
    o.push(t10.length), t10 = t10[0];
  return Array.isArray(r) && P().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && U0(r, o, []), o;
}
function U0(r, e, t10) {
  if (t10 = t10 || [], !Array.isArray(r) && !Ut(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 ${r.length} elements`);
  let o = e.slice(1);
  for (let n = 0; n < r.length; ++n)
    U0(r[n], o, t10.concat(n));
}
function W0(r, e, t10, o) {
  if (r !== "string_or_numeric") {
    if (r == null)
      throw new Error("Expected dtype cannot be null.");
    if (r !== "numeric" && r !== e || r === "numeric" && e === "string")
      throw new Error(`Argument '${t10}' passed to '${o}' must be ${r} tensor, but got ${e} tensor`);
  }
}
function v(r, e, t10, o = "numeric") {
  if (r instanceof ut)
    return W0(o, r.dtype, e, t10), r;
  let n = np(r);
  if (n !== "string" && ["bool", "int32", "float32"].indexOf(o) >= 0 && (n = o), W0(o, n, e, t10), r == null || !Ut(r) && !Array.isArray(r) && typeof r != "number" && typeof r != "boolean" && typeof r != "string") {
    let p = r == null ? "null" : r.constructor.name;
    throw new Error(`Argument '${e}' passed to '${t10}' must be a Tensor or TensorLike, but got '${p}'`);
  }
  let s = or(r, n);
  !Ut(r) && !Array.isArray(r) && (r = [r]);
  let i = n !== "string" ? Pp(r, n) : on(r, [], true);
  return N.makeTensor(i, s, n);
}
function Ia(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 Ub = "__op";
function T(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 + Ub;
  let n = (...s) => {
    N.startScope(t10);
    try {
      let a = o(...s);
      return Yc(a) && console.error("Cannot return a Promise inside of tidy."), N.endScope(a), a;
    } catch (a) {
      throw N.endScope(null), a;
    }
  };
  return Object.defineProperty(n, "name", { value: t10, configurable: true }), n;
}
function Hz(r, e) {
  let t10 = v(r, "real", "complex"), o = v(e, "imag", "complex");
  ht(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 N.runKernel(aa, n);
}
var Er = T({ complex_: Hz });
function xr(r, e, t10, o) {
  if (o == null && (o = np(r)), o === "complex64")
    throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
  if (typeof r == "object" && "texture" in r) {
    if (o !== "float32" && o !== "int32")
      throw new Error(`Creating tensor from texture only supports 'float32'|'int32' dtype, while the dtype is ${o}.`);
    return r.channels = r.channels || "RGBA", N.backend.createTensorFromTexture(r, e || t10, o);
  }
  if (!Ut(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) {
    Xc(e);
    let n = Ve(e), s = Ve(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 !== Ve(e.slice(a)) : true;
      $(t10[a] === e[a] || !p, () => `Error creating a new Tensor. Inferred shape (${t10}) does not match the provided shape (${e}). `);
    }
  }
  return !Ut(r) && !Array.isArray(r) && (r = [r]), e = e || t10, r = o !== "string" ? Pp(r, o) : on(r, [], true), N.makeTensor(r, e, o);
}
function nr(r, e, t10) {
  let o = or(r, t10);
  return xr(r, e, o, t10);
}
var al = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var Km = 4;
async function H0(r, e) {
  let t10 = [], o = [], n = Array.isArray(r) ? r.map((a) => a.name) : Object.keys(r);
  for (let a = 0; a < n.length; ++a) {
    let i = n[a], p = Array.isArray(r) ? r[a].tensor : r[i];
    if (p.dtype !== "float32" && p.dtype !== "int32" && p.dtype !== "bool" && p.dtype !== "string" && p.dtype !== "complex64")
      throw new Error(`Unsupported dtype in weight '${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(), f = m.reduce((g, y) => g + y.length, 0) + Km * m.length, d = new Uint8Array(f), h = 0;
        for (let g = 0; g < m.length; g++) {
          let y = m[g], b = new Uint8Array(new Uint32Array([y.length]).buffer);
          d.set(b, h), h += Km, d.set(y, h), h += y.length;
        }
        l(d);
      });
      o.push(c);
    } else
      o.push(p.data());
    e != null && (u.group = e), t10.push(u);
  }
  let s = await Promise.all(o);
  return { data: qz(s), specs: t10 };
}
function jm(r, e) {
  let t10 = {}, o, n = 0;
  for (let s of e) {
    let a = s.name, i = s.dtype, p = s.shape, u = Ve(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 = al[l.dtype], f = r.slice(n, n + u * m), d = l.dtype === "uint8" ? new Uint8Array(f) : new Uint16Array(f);
      if (i === "float32")
        if (l.dtype === "uint8" || l.dtype === "uint16") {
          c = new Float32Array(d.length);
          for (let h = 0; h < d.length; h++) {
            let g = d[h];
            c[h] = g * l.scale + l.min;
          }
        } else if (l.dtype === "float16")
          o === void 0 && (o = Yz()), c = o(d);
        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 ${l.dtype} for weight type int32.`);
        c = new Int32Array(d.length);
        for (let h = 0; h < d.length; h++) {
          let g = d[h];
          c[h] = Math.round(g * l.scale + l.min);
        }
      } else
        throw new Error(`Unsupported dtype in weight '${a}': ${i}`);
      n += u * m;
    } else if (i === "string") {
      let l = Ve(s.shape);
      c = [];
      for (let m = 0; m < l; m++) {
        let f = new Uint32Array(r.slice(n, n + Km))[0];
        n += Km;
        let d = new Uint8Array(r.slice(n, n + f));
        c.push(d), n += f;
      }
    } else {
      let l = al[i], m = r.slice(n, n + u * l);
      if (i === "float32")
        c = new Float32Array(m);
      else if (i === "int32")
        c = new Int32Array(m);
      else if (i === "bool")
        c = new Uint8Array(m);
      else if (i === "complex64") {
        c = new Float32Array(m);
        let f = new Float32Array(c.length / 2), d = new Float32Array(c.length / 2);
        for (let y = 0; y < f.length; y++)
          f[y] = c[y * 2], d[y] = c[y * 2 + 1];
        let h = nr(f, p, "float32"), g = nr(d, p, "float32");
        t10[a] = Er(h, g), h.dispose(), g.dispose();
      } else
        throw new Error(`Unsupported dtype in weight '${a}': ${i}`);
      n += u * l;
    }
    i !== "complex64" && (t10[a] = nr(c, p, i));
  }
  return t10;
}
function qz(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 Gb = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function G0(r) {
  return Gb ? Buffer.byteLength(r) : new Blob([r]).size;
}
function q0(r) {
  if (Gb)
    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 K0(r) {
  if (Gb) {
    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 Lp(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 Hb(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 Xm(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 Bp(r, e) {
  let t10, o;
  return r.weightsManifest != null && ([t10, o] = await e(r.weightsManifest)), qb(r, t10, o);
}
function As(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 : G0(JSON.stringify(r.modelTopology)), weightSpecsBytes: r.weightSpecs == null ? 0 : G0(JSON.stringify(r.weightSpecs)), weightDataBytes: r.weightData == null ? 0 : r.weightData.byteLength };
}
function Ym(r) {
  let e = [];
  for (let t10 of r)
    e.push(...t10.weights);
  return e;
}
function Kz() {
  let r = (t10) => {
    let o = t10 << 13, n = 0;
    for (; (o & 8388608) === 0; )
      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 jz() {
  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 Xz() {
  let r = new Uint32Array(64);
  for (let e = 0; e < 64; e++)
    r[e] = 1024;
  return r[0] = r[32] = 0, r;
}
function Yz() {
  let r = Kz(), e = jz(), t10 = Xz();
  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 j0 = (r) => mt.registerSaveRouter(r);
var X0 = (r) => mt.registerLoadRouter(r);
var Y0 = (r) => mt.getSaveHandlers(r);
var Q0 = (r, e) => mt.getLoadHandlers(r, e);
var Kb = "tensorflowjs";
var jb = 1;
var nu = "models_store";
var ui = "model_info_store";
function Z0() {
  if (!P().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 Xb(r) {
  let e = r.result;
  e.createObjectStore(nu, { keyPath: "modelPath" }), e.createObjectStore(ui, { keyPath: "modelPath" });
}
var Fs = class {
  constructor(e) {
    if (this.indexedDB = Z0(), 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(Kb, jb);
      s.onupgradeneeded = () => Xb(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 = As(t10), p = a.transaction(ui, "readwrite"), u = p.objectStore(ui), c = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), l;
          c.onsuccess = () => {
            l = a.transaction(nu, "readwrite");
            let f = l.objectStore(nu).put({ modelPath: this.modelPath, modelArtifacts: t10, modelArtifactsInfo: i });
            f.onsuccess = () => o({ modelArtifactsInfo: i }), f.onerror = (d) => {
              u = p.objectStore(ui);
              let h = u.delete(this.modelPath);
              h.onsuccess = () => (a.close(), n(f.error)), h.onerror = (g) => (a.close(), n(f.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);
    });
  }
};
Fs.URL_SCHEME = "indexeddb://";
var J0 = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Fs.URL_SCHEME) ? Qz(r.slice(Fs.URL_SCHEME.length)) : null;
mt.registerSaveRouter(J0);
mt.registerLoadRouter(J0);
function Qz(r) {
  return new Fs(r);
}
function Zz(r) {
  return r.startsWith(Fs.URL_SCHEME) ? r.slice(Fs.URL_SCHEME.length) : r;
}
var Qm = class {
  constructor() {
    this.indexedDB = Z0();
  }
  async listModels() {
    return new Promise((e, t10) => {
      let o = this.indexedDB.open(Kb, jb);
      o.onupgradeneeded = () => Xb(o), o.onsuccess = () => {
        let n = o.result, s = n.transaction(ui, "readonly"), i = s.objectStore(ui).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 = Zz(e), new Promise((t10, o) => {
      let n = this.indexedDB.open(Kb, jb);
      n.onupgradeneeded = () => Xb(n), n.onsuccess = () => {
        let s = n.result, a = s.transaction(ui, "readwrite"), i = a.objectStore(ui), 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 f = u.objectStore(nu).delete(e);
              f.onsuccess = () => t10(p.result.modelArtifactsInfo), f.onerror = (d) => 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 wa = "/";
var Vp = "tensorflowjs_models";
var ev = "info";
var Jz = "model_topology";
var eW = "weight_specs";
var tW = "weight_data";
var rW = "model_metadata";
function tv(r) {
  return { info: [Vp, r, ev].join(wa), topology: [Vp, r, Jz].join(wa), weightSpecs: [Vp, r, eW].join(wa), weightData: [Vp, r, tW].join(wa), modelMetadata: [Vp, r, rW].join(wa) };
}
function rv(r) {
  for (let e of Object.values(r))
    window.localStorage.removeItem(e);
}
function oW(r) {
  let e = r.split(wa);
  if (e.length < 3)
    throw new Error(`Invalid key format: ${r}`);
  return e.slice(1, e.length - 1).join(wa);
}
function nW(r) {
  return r.startsWith(Ds.URL_SCHEME) ? r.slice(Ds.URL_SCHEME.length) : r;
}
var Ds = class {
  constructor(e) {
    if (!P().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined")
      throw new Error("The current environment does not support local storage.");
    if (this.LS = window.localStorage, e == null || !e)
      throw new Error("For local storage, modelPath must not be null, undefined or empty.");
    this.modelPath = e, this.keys = tv(this.modelPath);
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
    {
      let t10 = JSON.stringify(e.modelTopology), o = JSON.stringify(e.weightSpecs), n = As(e);
      try {
        this.LS.setItem(this.keys.info, JSON.stringify(n)), this.LS.setItem(this.keys.topology, t10), this.LS.setItem(this.keys.weightSpecs, o), this.LS.setItem(this.keys.weightData, q0(e.weightData));
        let s = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, initializerSignature: e.initializerSignature != null ? e.initializerSignature : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
        return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(s)), { modelArtifactsInfo: n };
      } catch (s) {
        throw rv(this.keys), new Error(`Failed to save model '${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 = K0(a), t10;
  }
};
Ds.URL_SCHEME = "localstorage://";
var ov = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Ds.URL_SCHEME) ? sW(r.slice(Ds.URL_SCHEME.length)) : null;
mt.registerSaveRouter(ov);
mt.registerLoadRouter(ov);
function sW(r) {
  return new Ds(r);
}
var Zm = class {
  constructor() {
    $(P().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), $(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
  }
  async listModels() {
    let e = {}, t10 = Vp + wa, o = wa + ev;
    for (let n = 0; n < this.LS.length; ++n) {
      let s = this.LS.key(n);
      if (s.startsWith(t10) && s.endsWith(o)) {
        let a = oW(s);
        e[a] = JSON.parse(this.LS.getItem(s));
      }
    }
    return e;
  }
  async removeModel(e) {
    e = nW(e);
    let t10 = tv(e);
    if (this.LS.getItem(t10.info) == null)
      throw new Error(`Cannot find model at path '${e}'`);
    let o = JSON.parse(this.LS.getItem(t10.info));
    return rv(t10), o;
  }
};
var zp = "://";
var Yt = class {
  constructor() {
    this.managers = {};
  }
  static getInstance() {
    return Yt.instance == null && (Yt.instance = new Yt()), Yt.instance;
  }
  static registerManager(e, t10) {
    $(e != null, () => "scheme must not be undefined or null."), e.endsWith(zp) && (e = e.slice(0, e.indexOf(zp))), $(e.length > 0, () => "scheme must not be an empty string.");
    let o = Yt.getInstance();
    $(o.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), o.managers[e] = t10;
  }
  static getManager(e) {
    let t10 = Yt.getInstance().managers[e];
    if (t10 == null)
      throw new Error(`Cannot find model manager for scheme '${e}'`);
    return t10;
  }
  static getSchemes() {
    return Object.keys(Yt.getInstance().managers);
  }
};
function Jm(r) {
  if (r.indexOf(zp) === -1)
    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Yt.getSchemes().join(",")}`);
  return { scheme: r.split(zp)[0], path: r.split(zp)[1] };
}
async function nv(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 = Jm(r).scheme, p = Jm(r).path, u = i === Jm(r).scheme, c = await n.load();
  t10 && u && await Yt.getManager(i).removeModel(p);
  let l = await a.save(c);
  return t10 && !u && await Yt.getManager(i).removeModel(p), l.modelArtifactsInfo;
}
async function sv() {
  let r = Yt.getSchemes(), e = {};
  for (let t10 of r) {
    let o = await Yt.getManager(t10).listModels();
    for (let n in o) {
      let s = t10 + zp + n;
      e[s] = o[n];
    }
  }
  return e;
}
async function av(r) {
  let e = Jm(r);
  return Yt.getManager(e.scheme).removeModel(e.path);
}
async function iv(r, e) {
  return nv(r, e, false);
}
async function uv(r, e) {
  return nv(r, e, true);
}
var Yb = 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" || !P().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));
  }
};
if (P().get("IS_BROWSER")) {
  P().setPlatform("browser", new Yb());
  try {
    Yt.registerManager(Ds.URL_SCHEME, new Zm());
  } catch (r) {
  }
  try {
    Yt.registerManager(Fs.URL_SCHEME, new Qm());
  } catch (r) {
  }
}
var aW = { importFetch: () => pv() };
var Qb;
var Zb = class {
  constructor() {
    this.util = cv(), this.textEncoder = new this.util.TextEncoder();
  }
  fetch(e, t10) {
    return P().global.fetch != null ? P().global.fetch(e, t10) : (Qb == null && (Qb = aW.importFetch()), Qb(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);
  }
};
P().get("IS_NODE") && !P().get("IS_BROWSER") && P().setPlatform("node", new Zb());
function ne(r, e = "float32", t10) {
  return e = e || "float32", Xc(r), new je(r, e, t10);
}
function iW(r, e) {
  let t10 = v(r, "x", "cast");
  if (!yb(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 N.runKernel(to, o, n);
}
var qe = T({ cast_: iW });
function uW(r) {
  let t10 = { x: v(r, "x", "clone", "string_or_numeric") };
  return N.runKernel(uo, t10);
}
var zr = T({ clone_: uW });
function ef(r, e = false) {
  console.log(r.toString(e));
}
Vb();
var pW = { buffer: ne, cast: qe, clone: zr, print: ef };
L0(pW);
var va = {};
Be(va, { browserFiles: () => mv, browserHTTPRequest: () => hv, concatenateArrayBuffers: () => Lp, copyModel: () => iv, decodeWeights: () => jm, encodeWeights: () => H0, fromMemory: () => gv, fromMemorySync: () => nC, getLoadHandlers: () => Q0, getModelArtifactsForJSON: () => Bp, getModelArtifactsForJSONSync: () => qb, getModelArtifactsInfoForJSON: () => As, getSaveHandlers: () => Y0, getWeightSpecs: () => Ym, http: () => rf, isHTTPScheme: () => tf, listModels: () => sv, loadWeights: () => fv, moveModel: () => uv, registerLoadRouter: () => X0, registerSaveRouter: () => j0, removeModel: () => av, weightsLoaderFactory: () => rC, withSaveHandler: () => xv, withSaveHandlerSync: () => yv });
var cW = "model";
var lW = ".json";
var mW = ".weights.bin";
function lv(r) {
  return new Promise((e) => setTimeout(e)).then(r);
}
var Sa = class {
  constructor(e) {
    if (!P().getBool("IS_BROWSER"))
      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
    e.startsWith(Sa.URL_SCHEME) && (e = e.slice(Sa.URL_SCHEME.length)), (e == null || e.length === 0) && (e = cW), this.modelJsonFileName = e + lW, this.weightDataFileName = e + mW;
  }
  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 = Xm(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 lv(() => 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 lv(() => i.dispatchEvent(new MouseEvent("click")));
      }
      return { modelArtifactsInfo: As(e) };
    }
  }
};
Sa.URL_SCHEME = "downloads://";
var Jb = 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 ${this.jsonFile.name}`));
          return;
        }
        if (this.weightsFiles.length === 0) {
          e({ modelTopology: a });
          return;
        }
        let p = Bp(s, (u) => this.loadWeights(u));
        e(p);
      }, o.onerror = (n) => t10(`Failed to read model topology and weights manifest JSON from file '${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, Lp(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 '${e}'.`), s.readAsArrayBuffer(t10);
    });
  }
  checkManifestAndWeightFiles(e) {
    let t10 = [], o = this.weightsFiles.map((s) => Hb(s.name)), n = {};
    for (let s of e)
      s.paths.forEach((a) => {
        let i = Hb(a);
        if (t10.indexOf(i) !== -1)
          throw new Error(`Duplicate file basename found in weights manifest: '${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 fW = (r) => P().getBool("IS_BROWSER") && !Array.isArray(r) && r.startsWith(Sa.URL_SCHEME) ? dW(r.slice(Sa.URL_SCHEME.length)) : null;
mt.registerSaveRouter(fW);
function dW(r = "model") {
  return new Sa(r);
}
function mv(r) {
  return new Jb(r);
}
function eC(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 tC(r, e) {
  e == null && (e = {});
  let t10 = e.fetchFunc == null ? P().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 eC(o, e.onProgress, n, s)).map((l) => l.arrayBuffer()), p = 0.5, u = 1;
  return e.onProgress == null ? await Promise.all(i) : await eC(i, e.onProgress, p, u);
}
async function fv(r, e = "", t10, o) {
  return rC((a) => tC(a, { requestInit: o }))(r, e, t10);
}
function rC(r) {
  return async (e, t10 = "", o) => {
    let n = e.map(() => false), s = {}, a = o != null ? o.map(() => false) : [], i = [];
    if (e.forEach((f, d) => {
      let h = 0;
      f.weights.forEach((g) => {
        let y = "quantization" in g ? g.quantization.dtype : g.dtype, b = al[y] * Ve(g.shape), C = () => {
          n[d] = true, s[d] == null && (s[d] = []), s[d].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((f) => f)) {
      let f = o.filter((d, h) => !a[h]);
      throw new Error(`Could not find weights in manifest with names: ${f.join(", ")}. 
Manifest JSON has weights with names: ${i.join(", ")}.`);
    }
    let p = n.reduce((f, d, h) => (d && f.push(h), f), []), u = [];
    p.forEach((f) => {
      e[f].paths.forEach((d) => {
        let h = t10 + (t10.endsWith("/") ? "" : "/") + d;
        u.push(h);
      });
    });
    let c = await r(u), l = {}, m = 0;
    return p.forEach((f) => {
      let d = e[f].paths.length, h = 0;
      for (let w = 0; w < d; w++)
        h += c[m + w].byteLength;
      let g = new ArrayBuffer(h), y = new Uint8Array(g), b = 0;
      for (let w = 0; w < d; w++) {
        let k = new Uint8Array(c[m + w]);
        y.set(k, b), b += k.byteLength;
      }
      s[f].forEach((w) => {
        let k = g.slice(w.groupOffset, w.groupOffset + w.sizeBytes), _ = jm(k, [w.manifestEntry]);
        for (let E in _)
          l[E] = _[E];
      }), m += d;
    }), l;
  };
}
var hW = "application/octet-stream";
var gW = "application/json";
var il = class {
  constructor(e, t10) {
    if (this.DEFAULT_METHOD = "POST", t10 == null && (t10 = {}), this.weightPathPrefix = t10.weightPathPrefix, this.onProgress = t10.onProgress, this.weightUrlConverter = t10.weightUrlConverter, t10.fetchFunc != null ? ($(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 = P().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 = Xm(e, o);
    t10.body.append("model.json", new Blob([JSON.stringify(n)], { type: gW }), "model.json"), e.weightData != null && t10.body.append("model.weights.bin", new Blob([e.weightData], { type: hW }), "model.weights.bin");
    let s = await this.fetch(this.path, t10);
    if (s.ok)
      return { modelArtifactsInfo: As(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 Bp(t10, (s) => this.loadWeights(s));
  }
  async loadWeights(e) {
    let t10 = Array.isArray(this.path) ? this.path[1] : this.path, [o, n] = xW(t10), s = this.weightPathPrefix || o, a = Ym(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 tC(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
    return [a, Lp(u)];
  }
};
il.URL_SCHEME_REGEX = /^https?:\/\//;
function xW(r) {
  let e = r.lastIndexOf("/"), t10 = r.lastIndexOf("?"), o = r.substring(0, e), n = t10 > e ? r.substring(t10) : "";
  return [o + "/", n];
}
function tf(r) {
  return r.match(il.URL_SCHEME_REGEX) != null;
}
var dv = (r, e) => {
  if (typeof fetch == "undefined" && (e == null || e.fetchFunc == null))
    return null;
  {
    let t10 = true;
    if (Array.isArray(r) ? t10 = r.every((o) => tf(o)) : t10 = tf(r), t10)
      return rf(r, e);
  }
  return null;
};
mt.registerSaveRouter(dv);
mt.registerLoadRouter(dv);
function rf(r, e) {
  return new il(r, e);
}
function hv(r, e) {
  return rf(r, e);
}
var ul = class {
  constructor(e) {
    this.modelArtifacts = e;
  }
  load() {
    return this.modelArtifacts;
  }
};
var of = class {
  constructor(e) {
    this.saveHandler = e;
  }
  save(e) {
    return this.saveHandler(e);
  }
};
var oC = class {
  constructor(e) {
    e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t10) => Promise.resolve(e.save(t10)));
  }
};
function gv(r, e, t10, o) {
  let n = arguments;
  return new oC(nC(...n));
}
function nC(r, e, t10, o) {
  return arguments.length === 1 ? r.modelTopology != null || r.weightSpecs != null ? new ul(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 ul({ 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 ul({ modelTopology: r, weightSpecs: e, weightData: t10, trainingConfig: o }));
}
function xv(r) {
  return new of(r);
}
function yv(r) {
  return new of(r);
}
var Cv = {};
Be(Cv, { confusionMatrix: () => bv });
function yW(r, e, t10 = false, o = false) {
  let n = v(r, "a", "matMul"), s = v(e, "b", "matMul");
  [n, s] = Re(n, s);
  let a = { a: n, b: s }, i = { transposeA: t10, transposeB: o };
  return N.runKernel(cn, a, i);
}
var Xe = T({ matMul_: yW });
function bW(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 N.runKernel(ca, a, i);
}
var pl = T({ oneHot_: bW });
function Tie() {
  P().set("PROD", true);
}
function Nie() {
  P().set("DEBUG", true);
}
function _ie() {
  P().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function sC(r) {
  P().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(r + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
B0(sC);
function Eie() {
  N.disposeVariables();
}
function cr() {
  return N;
}
function $ie() {
  return N.memory();
}
function Rie(r) {
  return N.profile(r);
}
function Ne(r, e) {
  return N.tidy(r, e);
}
function Ft(r) {
  sl(r).forEach((t10) => t10.dispose());
}
function So(r) {
  return N.keep(r);
}
function Aie(r) {
  return N.time(r);
}
function Fie(r) {
  return N.setBackend(r);
}
function Die() {
  return N.ready();
}
function Pie() {
  return N.backendName;
}
function Oie(r) {
  N.removeBackend(r);
}
function Mie(r) {
  return N.findBackend(r);
}
function Lie(r) {
  return N.findBackendFactory(r);
}
function pi(r, e, t10 = 1) {
  return N.registerBackend(r, e, t10);
}
function Bie() {
  return N.backend;
}
function Vie(r, e) {
  P().setPlatform(r, e);
}
function CW(r) {
  let t10 = { input: v(r, "input", "imag") };
  return N.runKernel(Ya, t10);
}
var ci = T({ imag_: CW });
function IW(r) {
  let t10 = { x: v(r, "x", "neg") };
  return N.runKernel(Pn, t10);
}
var yr = T({ neg_: IW });
function wW(r) {
  let t10 = { input: v(r, "input", "real") };
  return N.runKernel(la, t10);
}
var ka = T({ real_: wW });
function SW(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" ? Ne(() => {
    let a = ka(o), i = ci(o);
    return a = N.runKernel(Mr, { x: a }, s), i = N.runKernel(Mr, { x: i }, s), t10 && (i = yr(i)), Er(a, i);
  }) : N.runKernel(Mr, n, s);
}
var Wp = T({ transpose_: SW });
function vW(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 = pl(qe(o, "int32"), t10), a = pl(qe(n, "int32"), t10), i = Wp(s), p = Xe(i, a);
  return qe(p, "int32");
}
var bv = T({ confusionMatrix_: vW });
var br = {};
Be(br, { assertAndGetBroadcastShape: () => Je, getBroadcastDims: () => Iv, getReductionAxes: () => nf });
function Iv(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 nf(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;
}
var Sv = {};
Be(Sv, { fromPixels: () => RW, fromPixelsAsync: () => EW, toPixels: () => $W });
function sf(r, e, t10) {
  if (eo(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);
}
var su;
function wv(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 (el(Zi, N.backendName) != null) {
    let d = { pixels: r }, h = { numChannels: e };
    return N.runKernel(Zi, d, 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 (su == null)
      if (typeof document == "undefined")
        if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
          su = new OffscreenCanvas(1, 1).getContext("2d");
        else
          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
      else
        su = document.createElement("canvas").getContext("2d", { willReadFrequently: true });
    su.canvas.width = u, su.canvas.height = c, su.drawImage(r, 0, 0, u, c), l = su.getImageData(0, 0, u, c).data;
  }
  let m;
  if (e === 4)
    m = new Int32Array(l);
  else {
    let d = u * c;
    m = new Int32Array(d * e);
    for (let h = 0; h < d; h++)
      for (let g = 0; g < e; ++g)
        m[h * e + g] = l[h * 4 + g];
  }
  return sf(m, [c, u, e], "int32");
}
function kW(r) {
  return r != null && r.data instanceof Uint8Array;
}
function TW() {
  return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function NW(r) {
  return r != null && r.width !== 0 && r.height !== 0;
}
function _W(r) {
  return TW() && !(r instanceof ImageBitmap) && NW(r) && !kW(r);
}
async function EW(r, e = 3) {
  let t10 = null;
  if (P().getBool("WRAP_TO_IMAGEBITMAP") && _W(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 wv(t10, e);
}
async function $W(r, e) {
  let t10 = v(r, "img", "toPixels");
  if (!(r instanceof ut)) {
    let u = t10;
    t10 = qe(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 f = a[u * s + m];
      if (t10.dtype === "float32") {
        if (f < 0 || f > 1)
          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${f}.`);
      } else if (t10.dtype === "int32" && (f < 0 || f > 255))
        throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${f}.`);
      s === 1 ? (c[0] = f * i, c[1] = f * i, c[2] = f * i) : c[m] = f * 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 RW = T({ fromPixels_: wv });
var af = {};
Be(af, { prepareAndValidate: () => vv });
function vv(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 (Ve(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 = [...ds(r.shape).map((l) => l / u), 1].slice(0, s);
  return [p, a, u, c];
}
var cl = {};
Be(cl, { calculateShapes: () => kv, validateInput: () => uf, validateUpdateShape: () => aC });
function aC(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 uf(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}`);
  }
  aC(t10, e, r);
}
function kv(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 = Ve(e.shape) / i, u = [...ds(t10.slice(0, n)), 1], c = Ve(t10);
  return { sliceRank: n, numUpdates: p, sliceSize: a, strides: u, outputSize: c };
}
var et = {};
Be(et, { assertParamsValid: () => FW, computeFlatOffset: () => LW, computeOutShape: () => PW, getNormalizedAxes: () => OW, isSliceContinous: () => MW, maskToAxes: () => DW, parseSliceParams: () => BW, sliceInfo: () => VW, startForAxis: () => Fv, startIndicesWithElidedDims: () => $v, stopForAxis: () => Dv, stopIndicesWithElidedDims: () => Rv, stridesForAxis: () => Av, stridesWithElidedDims: () => Nv });
var iC = -2;
var AW = -1;
function FW(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 DW(r) {
  let e = [], t10 = 0;
  for (; r > 0; )
    r & 1 && e.push(t10), r /= 2, t10++;
  return e;
}
function PW(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 Nv(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 _v(r, e, t10) {
  return t10 <= r ? t10 : t10 - (e - 1);
}
function Ev(r, e) {
  let t10 = [];
  for (let o = 0; o < r; o++)
    t10.push(e + o);
  return t10;
}
function OW(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 f = e[0], d = t10 + 1;
    c = $v(a, f, d, o, r), l = Rv(i, f, d, n, r), m = Nv(s, f, d, r);
  } else
    for (let f = 0; f < u; f++)
      c[f] = Fv(a, o, s, r, f, p), l[f] = Dv(i, n, s, r, f, p), m[f] = Av(s, f, p);
  return { begin: c, end: l, strides: m };
}
function $v(r, e, t10, o, n) {
  let s = [...n], a = Ev(t10, e);
  for (let i = 0; i < s.length; i++)
    if (a.indexOf(i) > -1)
      s[i] = 0;
    else {
      let p = _v(e, t10, i), u = o[p];
      r & 1 << p && (u = 0), s[i] = u;
    }
  return s;
}
function Rv(r, e, t10, o, n) {
  let s = [...n], a = Ev(t10, e);
  for (let i = 0; i < s.length; i++)
    if (a.indexOf(i) > -1)
      s[i] = Number.MAX_SAFE_INTEGER;
    else {
      let p = _v(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] = op(0, s[i], n[i]);
  }
  return s;
}
function Av(r, e, t10) {
  let o = r[e];
  return (t10 & 1 << e || o == null) && (o = 1), o;
}
function Fv(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 = op(0, a, p - 1), a;
}
function Dv(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 = op(0, a, p) : a = op(-1, a, p - 1), a;
}
function MW(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 LW(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 BW(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 ${i}.`), r.shape[i] - o[i])), [o, s];
}
function VW(r, e, t10, o, n, s, a, i, p) {
  let u;
  if (o == null ? (u = new Array(e.length), u.fill(1)) : u = o, a != null && (a & a - 1) !== 0)
    throw new Error("Multiple ellipses in slice is not allowed.");
  let c = false, l = { dims: u.length, numAddAxisAfterEllipsis: 0, begin: e.slice(), end: t10.slice(), strides: u.slice(), beginMask: n, endMask: s, ellipsisMask: a, newAxisMask: i, shrinkAxisMask: p };
  for (let C = 0; C < l.dims; C++)
    c && (1 << C & i) !== 0 && l.numAddAxisAfterEllipsis++, 1 << C & a && (c = true);
  c || (l.ellipsisMask |= 1 << l.dims, l.dims++);
  let m = { dims: r.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
  zW(l, m);
  let f = true, d = true, h = true, g = [], y = [];
  for (let C = 0; C < r.length; ++C) {
    if (m.strides[C] === 0)
      throw Error(`strides[${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 R = !!(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] = Tv(m.begin[C], 0, m.strides[C], k, _, E), m.end[C] = Tv(m.end[C], 1, m.strides[C], k, _, E);
      let O = m.strides[C] === 1 && m.begin[C] === 0 && m.end[C] === k;
      f = f && O, d = d && (C === 0 && m.strides[C] === 1 || O);
    } else
      f = f && m.strides[C] === 1 && R, d = d && (C === 0 && m.strides[C] === 1 || R);
    let A, D = false;
    if (m.beginValid && m.endValid ? (A = m.end[C] - m.begin[C], D = true) : w ? (A = 1, D = true) : R && k >= 0 && (m.strides[C] < 0 ? A = -k : A = k, D = true), D) {
      let O;
      A === 0 || A < 0 != m.strides[C] < 0 ? O = 0 : O = Math.trunc(A / m.strides[C]) + (A % m.strides[C] !== 0 ? 1 : 0), g.push(O);
    } else
      g.push(-1);
  }
  for (let C = 0; C < m.finalShapeGatherIndices.length; ++C) {
    let w = m.finalShapeGatherIndices[C];
    w >= 0 ? y.push(g[w]) : w === iC && y.push(1);
  }
  return { finalShapeSparse: y.filter((C, w) => m.finalShapeGatherIndices[w] !== iC), finalShape: y, isIdentity: f, sliceDim0: d, isSimpleSlice: h, begin: m.begin, end: m.end, strides: m.strides };
}
function zW(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(iC), 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(AW), e.finalShapeGatherIndicesSparse.push(-1), e.shrinkAxisMask |= 1 << t10) : (e.finalShapeGatherIndices.push(t10), e.finalShapeGatherIndicesSparse.push(o)), e.inputShapeGatherIndicesSparse[t10] = o, t10++;
    }
}
function Tv(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 Pv = {};
Be(Pv, { Serializable: () => ll, SerializationMap: () => Ps, registerClass: () => $r });
var ll = class {
  getClassName() {
    return this.constructor.className;
  }
  static fromConfig(e, t10) {
    return new e(t10);
  }
};
var Ps = class {
  constructor() {
    this.classNameMap = {};
  }
  static getMap() {
    return Ps.instance == null && (Ps.instance = new Ps()), Ps.instance;
  }
  static register(e) {
    Ps.getMap().classNameMap[e.className] = [e, e.fromConfig];
  }
};
function $r(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."), Ps.register(r);
}
var Bv = {};
Be(Bv, { TEST_EPSILON_FLOAT16: () => Ov, createVideoElement: () => jW, encodeStrings: () => Lv, expectArrayBuffersEqual: () => KW, expectArraysClose: () => UW, expectArraysEqual: () => HW, expectNumbersClose: () => Mv, expectPromiseToFail: () => GW, expectValuesInRange: () => qW, play: () => XW, testEpsilon: () => pf });
var WW = 1e-3;
var Ov = 0.1;
function UW(r, e, t10) {
  return t10 == null && (t10 = pf()), uC(r, e, (o, n) => pC(o, n, t10));
}
function pf() {
  return N.backend.floatPrecision() === 32 ? WW : Ov;
}
function uC(r, e, t10) {
  let o = true;
  if ((Ut(r) || Ut(e)) && (o = false), Ut(r) && Ut(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 = Ut(r) ? r : on(r), s = Ut(e) ? e : on(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 GW(r, e) {
  r().then(() => e.fail(), () => e()), typeof expect != "undefined" && expect().nothing();
}
function HW(r, e) {
  let t10 = typeof e == "string" || typeof e == "number" || typeof e == "boolean" ? [e] : e;
  return nn(r) || nn(r[0]) || nn(e) || nn(e[0]) ? uC(r, t10, (o, n) => o == n) : uC(r, e, (o, n) => pC(o, n, 0));
}
function Mv(r, e, t10) {
  if (t10 == null && (t10 = pf()), !pC(r, e, t10))
    throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`);
  typeof expect != "undefined" && expect().nothing();
}
function pC(r, e, t10) {
  return !isFinite(r) && !isFinite(e) ? true : !(isNaN(r) || isNaN(e) || Math.abs(r - e) > t10);
}
function qW(r, e, t10) {
  for (let o = 0; o < r.length; o++)
    if (r[o] < e || r[o] > t10)
      throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t10}`);
}
function KW(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 Lv(r) {
  for (let e = 0; e < r.length; e++) {
    let t10 = r[e];
    Array.isArray(t10) ? Lv(t10) : r[e] = si(t10);
  }
  return r;
}
function jW(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 XW(r) {
  await r.play(), "requestVideoFrameCallback" in r && await new Promise((e) => {
    r.requestVideoFrameCallback(e);
  });
}
var YW = "4.0.0";
function QW(r, e) {
  let t10 = v(r, "a", "add"), o = v(e, "b", "add");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(_r, n);
}
var ge = T({ add_: QW });
function ZW(r, e) {
  let t10 = v(r, "a", "floorDiv"), o = v(e, "b", "floorDiv");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(vn, n);
}
var cf = T({ floorDiv_: ZW });
function JW(r, e) {
  let t10 = v(r, "a", "div"), o = v(e, "b", "div");
  if ([t10, o] = Re(t10, o), t10.dtype === "int32" && o.dtype === "int32")
    return cf(t10, o);
  let n = { a: t10, b: o }, s = {};
  return N.runKernel(Cn, n, s);
}
var We = T({ div_: JW });
function eU(r, e) {
  let t10 = v(r, "a", "mul"), o = v(e, "b", "mul");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(ho, n);
}
var oe = T({ mul_: eU });
function tU(r) {
  let e = v(r, "x", "abs");
  if (e.dtype === "complex64") {
    let t10 = { x: e };
    return N.runKernel(cp, t10);
  } else {
    let t10 = { x: e };
    return N.runKernel(sn, t10);
  }
}
var Qt = T({ abs_: tU });
function rU(r) {
  let t10 = { x: v(r, "x", "acos") };
  return N.runKernel(Li, t10);
}
var Vv = T({ acos_: rU });
function oU(r) {
  let t10 = { x: v(r, "x", "acosh") };
  return N.runKernel(Bi, t10);
}
var zv = T({ acosh_: oU });
function nU(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 N.runKernel(an, o);
}
var Wv = T({ addN_: nU });
function sU(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "all", "bool") }, s = { axis: e, keepDims: t10 };
  return N.runKernel(oa, n, s);
}
var Uv = T({ all_: sU });
function aU(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "any", "bool") }, s = { axis: e, keepDims: t10 };
  return N.runKernel(na, n, s);
}
var Gv = T({ any_: aU });
function iU(r, e = 0) {
  let o = { x: v(r, "x", "argMax") }, n = { axis: e };
  return N.runKernel(un, o, n);
}
var Hv = T({ argMax_: iU });
function uU(r, e = 0) {
  let o = { x: v(r, "x", "argMin") }, n = { axis: e };
  return N.runKernel(ja, o, n);
}
var qv = T({ argMin_: uU });
function pU(r) {
  let t10 = { x: v(r, "x", "asin") };
  return N.runKernel(Vi, t10);
}
var Kv = T({ asin_: pU });
function cU(r) {
  let t10 = { x: v(r, "x", "asinh") };
  return N.runKernel(zi, t10);
}
var jv = T({ asinh_: cU });
function lU(r) {
  let t10 = { x: v(r, "x", "atan") };
  return N.runKernel(Wi, t10);
}
var Xv = T({ atan_: lU });
function mU(r, e) {
  let t10 = v(r, "a", "atan2"), o = v(e, "b", "atan2");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(sa, n);
}
var Yv = T({ atan2_: mU });
function fU(r) {
  let t10 = { x: v(r, "x", "atanh") };
  return N.runKernel(Ui, t10);
}
var Qv = T({ atanh_: fU });
function dU(r, e, t10, o, n = "NHWC", s) {
  let a = r[3], i = [...e, a], p = Jv(n);
  return uu(r, i, t10, s, o, null, null, p);
}
function lC(r, e, t10, o, n, s, a = "channelsLast") {
  let [i, p] = lf(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 uu(r, u, t10, o, n, s, false, a);
}
function hU(r, e, t10, o, n, s, a = "NDHWC") {
  let [i, p, u] = cC(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 Zv(r, c, t10, o, n, false, l, s);
}
function uu(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, f, , d] = e, [h, g] = lf(t10), [y, b] = lf(o), C = Up(m, y), w = Up(f, b), { padInfo: k, outHeight: _, outWidth: E } = yU(n, u, c, h, g, C, w, s, i), R = a ? d * l : d, A;
  return i === "channelsFirst" ? A = [p, R, _, E] : i === "channelsLast" && (A = [p, _, E, R]), { batchSize: p, dataFormat: i, inHeight: u, inWidth: c, inChannels: l, outHeight: _, outWidth: E, outChannels: R, padInfo: k, strideHeight: h, strideWidth: g, filterHeight: m, filterWidth: f, effectiveFilterHeight: C, effectiveFilterWidth: w, dilationHeight: y, dilationWidth: b, inShape: r, outShape: A, filterShape: e };
}
function Zv(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 [f, d, h, , g] = e, [y, b, C] = cC(t10), [w, k, _] = cC(o), E = Up(f, w), R = Up(d, k), A = Up(h, _), { padInfo: D, outDepth: O, outHeight: M, outWidth: L } = bU(n, u, c, l, y, b, C, E, R, A, i), W = s ? g * m : g, V;
  return a === "channelsFirst" ? V = [p, W, O, M, L] : a === "channelsLast" && (V = [p, O, M, L, W]), { batchSize: p, dataFormat: a, inDepth: u, inHeight: c, inWidth: l, inChannels: m, outDepth: O, outHeight: M, outWidth: L, outChannels: W, padInfo: D, strideDepth: y, strideHeight: b, strideWidth: C, filterDepth: f, filterHeight: d, filterWidth: h, effectiveFilterDepth: E, effectiveFilterHeight: R, effectiveFilterWidth: A, dilationDepth: w, dilationHeight: k, dilationWidth: _, inShape: r, outShape: V, filterShape: e };
}
function gU(r, e, t10, o, n) {
  o == null && (o = mC(r, e, t10));
  let s = r[0], a = r[1], i = au((s - e + 2 * o) / t10 + 1, n), p = au((a - e + 2 * o) / t10 + 1, n);
  return [i, p];
}
function xU(r, e, t10, o, n, s) {
  n == null && (n = mC(r, e, o));
  let a = r[0], i = r[1], p = r[2], u = au((a - e + 2 * n) / o + 1, s), c = au((i - e + 2 * n) / o + 1, s), l = au((p - e + 2 * n) / o + 1, s);
  return [u, c, l, t10];
}
function mC(r, e, t10, o = 1) {
  let n = Up(e, o);
  return Math.floor((r[0] * (t10 - 1) - t10 + n) / 2);
}
function lf(r) {
  return typeof r == "number" ? [r, r, r] : r.length === 2 ? [r[0], r[1], 1] : r;
}
function cC(r) {
  return typeof r == "number" ? [r, r, r] : r;
}
function Up(r, e) {
  return e <= 1 ? r : r + (r - 1) * (e - 1);
}
function yU(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 f = gU([e, t10], s, o, r, i);
    c = f[0], l = f[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), f = Math.max(0, (l - 1) * n + a - t10), d = Math.floor(m / 2), h = m - d, g = Math.floor(f / 2), y = f - g;
    u = { top: d, bottom: h, left: g, right: y, 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], f = p === "channelsLast" ? r[1][1] : r[2][1], d = p === "channelsLast" ? r[2][0] : r[3][0], h = p === "channelsLast" ? r[2][1] : r[3][1];
    u = { top: m, bottom: f, left: d, right: h, type: m === 0 && f === 0 && d === 0 && h === 0 ? "VALID" : "EXPLICIT" }, c = au((e - s + m + f) / o + 1, i), l = au((t10 - a + d + h) / n + 1, i);
  } else
    throw Error(`Unknown padding parameter: ${r}`);
  return { padInfo: u, outHeight: c, outWidth: l };
}
function bU(r, e, t10, o, n, s, a, i, p, u, c) {
  let l, m, f, d;
  if (typeof r == "number") {
    l = { top: r, bottom: r, left: r, right: r, front: r, back: r, type: r === 0 ? "VALID" : "NUMBER" };
    let g = xU([e, t10, o, 1], i, 1, n, r, c);
    m = g[0], f = g[1], d = g[2];
  } else if (r === "same") {
    m = Math.ceil(e / n), f = Math.ceil(t10 / s), d = Math.ceil(o / a);
    let h = (m - 1) * n + i - e, g = (f - 1) * s + p - t10, y = (d - 1) * a + u - o, b = Math.floor(h / 2), C = h - b, w = Math.floor(g / 2), k = g - w, _ = Math.floor(y / 2), E = y - _;
    l = { top: w, bottom: k, left: _, right: E, front: b, back: C, type: "SAME" };
  } else if (r === "valid")
    l = { top: 0, bottom: 0, left: 0, right: 0, front: 0, back: 0, type: "VALID" }, m = Math.ceil((e - i + 1) / n), f = Math.ceil((t10 - p + 1) / s), d = Math.ceil((o - u + 1) / a);
  else
    throw Error(`Unknown padding parameter: ${r}`);
  return { padInfo: l, outDepth: m, outHeight: f, outWidth: d };
}
function au(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 iu(r) {
  let [e, t10, o] = lf(r);
  return e === 1 && t10 === 1 && o === 1;
}
function lr(r, e) {
  return iu(r) || iu(e);
}
function Jv(r) {
  if (r === "NHWC")
    return "channelsLast";
  if (r === "NCHW")
    return "channelsFirst";
  throw new Error(`Unknown dataFormat ${r}`);
}
function Ot(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")
      $(ra(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) => {
          $(ra(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 CU(r, e) {
  let o = { x: v(r, "x", "reshape", "string_or_numeric") }, n = { shape: e };
  return N.runKernel(Ss, o, n);
}
var z = T({ reshape_: CU });
function IU(r, e, t10, o, n) {
  let s = v(r, "x", "avgPool", "float32"), a = 1;
  $(lr(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 = z(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}.`), Ot("avgPool", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = N.runKernel(pn, u, c);
  return l = qe(l, s.dtype), p ? z(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var mf = T({ avgPool_: IU });
function wU(r, e, t10, o, n, s = "NDHWC") {
  let a = v(r, "x", "avgPool3d", "float32"), i = a, p = false;
  a.rank === 4 && (p = true, i = z(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}`), Ot("avgPool3d", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = N.runKernel(ip, u, c);
  return l = qe(l, i.dtype), p ? z(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var ek = T({ avgPool3d_: wU });
function SU(r, e = 0) {
  $(r.length >= 1, () => "Pass at least one tensor to concat");
  let t10 = Ia(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 zr(t10[0]);
  let o = t10, n = { axis: e };
  return N.runKernel(gs, o, n);
}
var gt = T({ concat_: SU });
function vU(r) {
  let t10 = { x: v(r, "x", "sigmoid", "float32") };
  return N.runKernel(yo, t10);
}
var Ms = T({ sigmoid_: vU });
function kU(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 N.runKernel(qn, n, s);
}
var Ue = T({ slice_: kU });
function TU(r) {
  let t10 = { x: v(r, "x", "tanh", "float32") };
  return N.runKernel(Qn, t10);
}
var ml = T({ tanh_: TU });
function NU(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 = gt([u, l], 1), f = Xe(m, i), d = ge(f, p), h = d.shape[0], g = d.shape[1] / 4, y = [h, g], b = Ue(d, [0, 0], y), C = Ue(d, [0, g], y), w = Ue(d, [0, g * 2], y), k = Ue(d, [0, g * 3], y), _ = ge(oe(Ms(b), ml(C)), oe(c, Ms(ge(a, w)))), E = oe(ml(_), Ms(k));
  return [_, E];
}
var tk = T({ basicLSTMCell_: NU });
function _U(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 N.runKernel(hs, s, a);
}
var ff = T({ batchToSpaceND_: _U });
function rk(r) {
  let e;
  return r.rank === 0 || r.rank === 1 ? e = z(r, [1, 1, 1, r.size]) : r.rank === 2 ? e = z(r, [1, 1, r.shape[0], r.shape[1]]) : r.rank === 3 ? e = z(r, [1, r.shape[0], r.shape[1], r.shape[2]]) : e = r, e;
}
function EU(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: rk(a), scale: u, offset: c, mean: i, variance: p }, f = { varianceEpsilon: s }, d = N.runKernel(kn, m, f);
  return z(d, a.shape);
}
var li = T({ batchNorm_: EU });
function $U(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}.`), li(a, i, p, c, u, s);
}
var ok = T({ batchNorm2d_: $U });
function RU(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}.`), li(a, i, p, c, u, s);
}
var nk = T({ batchNorm3d_: RU });
function AU(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}.`), li(a, i, p, c, u, s);
}
var sk = T({ batchNorm4d_: AU });
function FU(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 N.runKernel(up, s, a);
}
var df = T({ bincount_: FU });
function DU(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 ${o.rank}`);
  let n = { s0: t10, s1: o };
  return N.runKernel(pp, n);
}
var ak = T({ broadcastArgs_: DU });
function PU(r, e) {
  let t10 = v(r, "broadcastTo", "x"), o = t10.shape;
  if (e.some((u) => !(u > 0) || u % 1 !== 0))
    throw new Error(`broadcastTo(): Invalid broadcast shape [${e}].`);
  if (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 = z(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 zr(t10);
  let i = { x: t10 }, p = { reps: s };
  return N.runKernel(wo, i, p);
}
var Ls = T({ broadcastTo_: PU });
function OU(r) {
  let t10 = { x: v(r, "x", "ceil", "float32") };
  return N.runKernel(ro, t10);
}
var ik = T({ ceil_: OU });
function Bs(r, e, t10) {
  let o = { shape: r, value: e, dtype: t10 };
  return N.runKernel(ys, {}, o);
}
function MU(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 Bs(o.shape, e, o.dtype);
  let n = { x: o }, s = { clipValueMin: e, clipValueMax: t10 };
  return N.runKernel(Ro, n, s);
}
var uk = T({ clipByValue_: MU });
function LU(r) {
  return gt(r, 0);
}
var pk = T({ concat1d_: LU });
function BU(r, e) {
  return gt(r, e);
}
var ck = T({ concat2d_: BU });
function VU(r, e) {
  return gt(r, e);
}
var lk = T({ concat3d_: VU });
function zU(r, e) {
  return gt(r, e);
}
var mk = T({ concat4d_: zU });
function WU(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 = z(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}.`), Ot("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]}.`), $(lr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`);
  let m = { x: u, filter: p }, f = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, d = N.runKernel(ln, m, f);
  return c ? z(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var mi = T({ conv2d_: WU });
function UU(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 = z(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}.`), Ot("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]}.`), $(lr(t10, s), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${t10} and dilation '${s}'`), $(n === "NWC", () => `Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);
  let l = z(p, [1, p.shape[0], p.shape[1], p.shape[2]]), m = z(u, [u.shape[0], 1, u.shape[1], u.shape[2]]), g = mi(m, l, [1, t10], o, "NHWC", [1, s], a);
  return c ? z(g, [g.shape[2], g.shape[3]]) : z(g, [g.shape[0], g.shape[2], g.shape[3]]);
}
var fk = T({ conv1d_: UU });
function GU(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 = z(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]}.`), Ot("conv2dDerInput", n, a);
  let m = { dy: p, filter: t10 }, f = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, inputShape: i }, d = N.runKernel(mn, m, f);
  return u ? z(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var hf = T({ conv2DBackpropInput_: GU });
function HU(r, e, t10, o, n, s) {
  let a = v(r, "x", "conv2dTranspose"), i = v(e, "filter", "conv2dTranspose");
  return hf(t10, a, i, o, n, "NHWC", s);
}
var dk = T({ conv2dTranspose_: HU });
function qU(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 = z(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]}.`), $(lr(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.`);
  let c = { x: p, filter: i }, l = { strides: t10, pad: o, dataFormat: n, dilations: s }, m = N.runKernel(mp, c, l);
  return u ? z(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var hk = T({ conv3d_: qU });
function KU(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 = z(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 = N.runKernel(fp, c, l);
  return i ? z(m, [m.shape[1], m.shape[2], m.shape[3], m.shape[4]]) : m;
}
var gk = T({ conv3DBackpropInput_: KU });
function jU(r, e, t10, o, n) {
  let s = v(r, "x", "conv3dTranspose"), a = v(e, "filter", "conv3dTranspose");
  return gk(t10, s, a, o, n);
}
var xk = T({ conv3dTranspose_: jU });
function XU(r) {
  let t10 = { x: v(r, "x", "cos", "float32") };
  return N.runKernel(fn, t10);
}
var yk = T({ cos_: XU });
function YU(r) {
  let t10 = { x: v(r, "x", "cosh", "float32") };
  return N.runKernel(dn, t10);
}
var bk = T({ cosh_: YU });
function QU(r, e = 0, t10 = false, o = false) {
  let s = { x: v(r, "x", "cumprod") }, a = { axis: e, exclusive: t10, reverse: o };
  return N.runKernel(hn, s, a);
}
var Ck = T({ cumprod_: QU });
function ZU(r, e = 0, t10 = false, o = false) {
  let s = { x: v(r, "x", "cumsum") }, a = { axis: e, exclusive: t10, reverse: o };
  return N.runKernel(gn, s, a);
}
var Ik = T({ cumsum_: ZU });
function JU(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 N.runKernel(dp, a, i);
}
var wk = T({ denseBincount_: JU });
function eG(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 N.runKernel(yn, i, p);
}
var Sk = T({ depthToSpace_: eG });
function tG(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 = z(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]}.`), Ot("depthwiseConv2d", o, a);
  let m = { x: u, filter: p }, f = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a }, d = N.runKernel(bn, m, f);
  return c ? z(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var Gp = T({ depthwiseConv2d_: tG });
function rG(r) {
  let t10 = { x: v(r, "x", "diag") };
  return N.runKernel(xp, t10);
}
var vk = T({ diag_: rG });
function oG(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 = z(a, [1, a.shape[0], a.shape[1], a.shape[2]]), u = true);
  let c = { x: p, filter: i }, l = { strides: t10, pad: o, dilations: n }, m = N.runKernel(yp, c, l);
  return u ? z(m, [m.shape[1], m.shape[2], m.shape[3]]) : m;
}
var kk = T({ dilation2d_: oG });
function nG(r, e) {
  let t10 = v(r, "a", "equal", "string_or_numeric"), o = v(e, "b", "equal", "string_or_numeric");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(oo, n);
}
var gf = T({ equal_: nG });
function sG(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 = Ls(s, a), p = Ls(o, a), u = Ls(n, a), c = { condition: i, t: p, e: u };
  return N.runKernel(vs, c);
}
var os = T({ where_: sG });
function aG(r) {
  let t10 = { x: v(r, "x", "zerosLike") };
  return N.runKernel(Es, t10);
}
var Gt = T({ zerosLike_: aG });
function iG(r, e) {
  let t10 = v(r, "a", "div"), o = v(e, "b", "div");
  [t10, o] = Re(t10, o);
  let n = We(t10, o), s = Gt(n), a = gf(o, s);
  return os(a, s, n);
}
var Tk = T({ divNoNan_: iG });
function uG(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 ${s}.`), t10.rank === 1 && o.rank === 1) {
    let a = z(t10, [1, -1]), i = z(o, [-1, 1]), p = Xe(a, i);
    return z(p, []);
  } else if (t10.rank === 1 && o.rank === 2) {
    let a = z(t10, [1, -1]), i = z(o, [o.shape[0], o.shape[1]]), p = Xe(a, i);
    return z(p, [p.size]);
  } else if (t10.rank === 2 && o.rank === 1) {
    let a = z(o, [-1, 1]), i = Xe(t10, a);
    return z(i, [i.size]);
  } else {
    let a = z(o, [o.shape[0], o.shape[1]]);
    return Xe(t10, a);
  }
}
var Nk = T({ dot_: uG });
function pG(r, ...e) {
  let t10 = e.map((n, s) => v(n, `tensors${s}`, "einsum")), o = { equation: r };
  return N.runKernel(Xa, t10, o);
}
var _k = T({ einsum_: pG });
function cG(r) {
  let t10 = { x: v(r, "x", "elu", "float32") };
  return N.runKernel(In, t10);
}
var xf = T({ elu_: cG });
function lG(r) {
  let e = v(r, "x", "erf");
  $(e.dtype === "int32" || e.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), e.dtype === "int32" && (e = qe(e, "float32"));
  let t10 = { x: e };
  return N.runKernel(Gi, t10);
}
var Ek = T({ erf_: lG });
function fC(r, e) {
  for (let t10 = 0; t10 < r.length; ++t10)
    if (r[r.length - t10 - 1] !== e - 1 - t10)
      return false;
  return true;
}
function $k(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 mG(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 Ta(r, e) {
  let t10 = e.map((o) => 1);
  return $k(r, t10, e);
}
function fG(r, e, t10) {
  $(fC(e, t10), () => `${r} supports only inner-most axes for now. Got axes ${e} and rank-${t10} input.`);
}
function dG(r, e) {
  if (fC(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 hG(r) {
  return r.map((e, t10) => [t10, e]).sort((e, t10) => e[1] - t10[1]).map((e) => e[0]);
}
function gG(r, e) {
  let t10 = [];
  for (let o = e - r; o < e; ++o)
    t10.push(o);
  return t10;
}
function yG(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "max") }, s = { reductionIndices: e, keepDims: t10 };
  return N.runKernel($n, n, s);
}
var Vs = T({ max_: yG });
function bG(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "min") }, s = { axis: e, keepDims: t10 };
  return N.runKernel(Fn, n, s);
}
var fl = T({ min_: bG });
function CG(r, e) {
  let t10 = v(r, "base", "pow"), o = v(e, "exp", "pow");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(Bn, n);
}
var Na = T({ pow_: CG });
function be(r, e) {
  if ((Ut(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" && Ut(r) && !(r instanceof Uint8Array))
    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
  return xr(r, [], [], e);
}
function IG(r) {
  let t10 = { x: v(r, "x", "sqrt", "float32") };
  return N.runKernel(bo, t10);
}
var Rr = T({ sqrt_: IG });
function wG(r) {
  let e = v(r, "x", "square"), t10 = {};
  return N.runKernel("Square", { x: e }, t10);
}
var Zt = T({ square_: wG });
function SG(r, e = null, t10 = false) {
  let o = v(r, "x", "sum");
  o.dtype === "bool" && (o = qe(o, "int32"));
  let n = { x: o }, s = { axis: e, keepDims: t10 };
  return N.runKernel(jn, n, s);
}
var tt = T({ sum_: SG });
function vG(r, e = "euclidean", t10 = null, o = false) {
  r = v(r, "x", "norm");
  let n = Rk(r, e, t10), s = n.shape;
  if (o) {
    let a = Ka(t10, r.shape);
    s = Ta(n.shape, a);
  }
  return z(n, s);
}
function Rk(r, e, t10 = null) {
  if (r.rank === 0)
    return Qt(r);
  if (r.rank !== 1 && t10 === null)
    return Rk(z(r, [-1]), e, t10);
  if (r.rank === 1 || typeof t10 == "number" || Array.isArray(t10) && t10.length === 1) {
    if (e === 1)
      return tt(Qt(r), t10);
    if (e === 1 / 0)
      return Vs(Qt(r), t10);
    if (e === -1 / 0)
      return fl(Qt(r), t10);
    if (e === "euclidean" || e === 2)
      return Rr(tt(Na(Qt(r), be(2, "int32")), t10));
    throw new Error(`Error in norm: invalid ord value: ${e}`);
  }
  if (Array.isArray(t10) && t10.length === 2) {
    if (e === 1)
      return Vs(tt(Qt(r), t10[0]), t10[1] - 1);
    if (e === 1 / 0)
      return Vs(tt(Qt(r), t10[1]), t10[0]);
    if (e === -1 / 0)
      return fl(tt(Qt(r), t10[1]), t10[0]);
    if (e === "fro" || e === "euclidean")
      return Rr(tt(Zt(r), t10));
    throw new Error(`Error in norm: invalid ord value: ${e}`);
  }
  throw new Error(`Error in norm: invalid axis: ${t10}`);
}
var pu = T({ norm_: vG });
function kG(r, e = null, t10 = false) {
  return pu(r, "euclidean", e, t10);
}
var Ak = T({ euclideanNorm_: kG });
function TG(r) {
  let t10 = { x: v(r, "x", "exp") };
  return N.runKernel(no, t10);
}
var Bo = T({ exp_: TG });
function NG(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 N.runKernel(xs, o, n);
}
var _a = T({ expandDims_: NG });
function _G(r) {
  let t10 = { x: v(r, "x", "expm1") };
  return N.runKernel(wn, t10);
}
var Fk = T({ expm1_: _G });
function EG(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 N.runKernel(wo, o, n);
}
var fi = T({ tile_: EG });
function $G(r, e, t10, o = "float32") {
  e == null && (e = r);
  let n = ne([r, e], o), s = r <= e ? r : e;
  for (let i = 0; i < s; ++i)
    n.set(1, i, i);
  let a = z(n.toTensor(), [r, e]);
  if (t10 == null)
    return a;
  if (t10.length === 1)
    return fi(_a(a, 0), [t10[0], 1, 1]);
  if (t10.length === 2)
    return fi(_a(_a(a, 0), 0), [t10[0], t10[1], 1, 1]);
  if (t10.length === 3)
    return fi(_a(_a(_a(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 yf = T({ eye_: $G });
function RG(r) {
  let t10 = { x: v(r, "x", "floor", "float32") };
  return N.runKernel(so, t10);
}
var bf = T({ floor_: RG });
function AG(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 N.runKernel(bs, a, i);
}
var Cf = T({ gather_: AG });
function FG(r, e) {
  let t10 = v(r, "a", "greater", "string_or_numeric"), o = v(e, "b", "greater", "string_or_numeric");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(ao, n);
}
var cu = T({ greater_: FG });
function DG(r, e) {
  let t10 = v(r, "a", "greaterEqual", "string_or_numeric"), o = v(e, "b", "greaterEqual", "string_or_numeric");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(io, n);
}
var If = T({ greaterEqual_: DG });
function PG(r) {
  let t10 = { x: v(r, "x", "isFinite") };
  return N.runKernel(Hi, t10);
}
var Dk = T({ isFinite_: PG });
function OG(r) {
  let t10 = { x: v(r, "x", "isInf") };
  return N.runKernel(qi, t10);
}
var Pk = T({ isInf_: OG });
function MG(r) {
  let t10 = { x: v(r, "x", "isNaN") };
  return N.runKernel(ia, t10);
}
var Ok = T({ isNaN_: MG });
function LG(r, e = 0.2) {
  let o = { x: v(r, "x", "leakyRelu") }, n = { alpha: e };
  return N.runKernel(Nn, o, n);
}
var wf = T({ leakyRelu_: LG });
function BG(r, e) {
  let t10 = v(r, "a", "less", "string_or_numeric"), o = v(e, "b", "less", "string_or_numeric");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(po, n);
}
var Mk = T({ less_: BG });
function VG(r, e) {
  let t10 = v(r, "a", "lessEqual", "string_or_numeric"), o = v(e, "b", "lessEqual", "string_or_numeric");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(co, n);
}
var Hp = T({ lessEqual_: VG });
function Lk(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 N.runKernel(Ip, {}, o);
}
function zG(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}.`), $(ra(e), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);
  let a = s, i = false;
  s.rank === 3 && (i = true, a = z(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 = N.runKernel(wp, p, u);
  return i ? z(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var Bk = T({ localResponseNormalization_: zG });
function WG(r) {
  let t10 = { x: v(r, "x", "log", "float32") };
  return N.runKernel(lo, t10);
}
var Ea = T({ log_: WG });
function UG(r) {
  let t10 = { x: v(r, "x", "log1p") };
  return N.runKernel(Ki, t10);
}
var Sf = T({ log1p_: UG });
function GG(r) {
  return $(fs(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 N.tidy(() => {
      let { value: s, grads: a } = N.gradients(() => r(o), [o], n);
      return n != null && ht(s.shape, n.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), vf(a), a[0];
    });
  };
}
function HG(r) {
  return $(fs(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 = Ia(e, "args", "tf.grads", "string_or_numeric"), n = t10 != null ? v(t10, "dy", "tf.grads") : null;
    return N.tidy(() => {
      let { value: s, grads: a } = N.gradients(() => r(...o), o, n);
      return n != null && ht(s.shape, n.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), vf(a), a;
    });
  };
}
function qG(r) {
  return $(fs(r), () => "The f passed in valueAndGrad(f) must be a function"), (e, t10) => {
    $(e instanceof ut, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), $(t10 == null || t10 instanceof ut, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
    let { grads: o, value: n } = N.gradients(() => r(e), [e], t10);
    return vf(o), { grad: o[0], value: n };
  };
}
function KG(r) {
  return $(fs(r), () => "The f passed in valueAndGrads(f) must be a function"), (e, t10) => {
    $(Array.isArray(e) && e.every((n) => n instanceof ut), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), $(t10 == null || t10 instanceof ut, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
    let o = N.gradients(() => r(...e), e, t10);
    return t10 != null && ht(o.value.shape, t10.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), vf(o.grads), o;
  };
}
function dC(r, e) {
  $(fs(r), () => "The f passed in variableGrads(f) must be a function"), $(e == null || Array.isArray(e) && e.every((u) => u instanceof ba), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
  let t10 = e != null;
  if (!t10) {
    e = [];
    for (let u in N.registeredVariables)
      e.push(N.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 } = N.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 Cr(r) {
  return N.customGrad(r);
}
function vf(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 jG(r) {
  let t10 = { x: v(r, "x", "softplus") };
  return N.runKernel(Qi, t10);
}
var kf = T({ softplus_: jG });
function XG(r) {
  let e = v(r, "x", "logSigmoid");
  return Cr((o) => ({ value: yr(kf(yr(o))), gradFunc: (a) => oe(a, Ms(yr(o))) }))(e);
}
var Vk = T({ logSigmoid_: XG });
function YG(r, e) {
  let t10 = v(r, "a", "sub"), o = v(e, "b", "sub");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(Io, n);
}
var ke = T({ sub_: YG });
function QG(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 Cr((n, s) => {
    let i = Vs(n, e, true), p = ke(n, i), u = ke(qe(p, "float32"), Ea(tt(Bo(p), e, true)));
    return s([u]), { value: u, gradFunc: (l, m) => {
      let [f] = m, d = true, h = Bo(f);
      return ke(l, oe(tt(l, e, d), h));
    } };
  })(t10);
}
var zk = T({ logSoftmax_: QG });
function ZG(r, e = null, t10 = false) {
  let o = v(r, "x", "logSumExp"), n = Ka(e, o.shape), s = Vs(o, n, true), a = ke(o, s), i = Bo(a), p = tt(i, n), u = Ea(p), c = ge(z(s, u.shape), u);
  if (t10) {
    let l = Ta(c.shape, n);
    return z(c, l);
  }
  return c;
}
var Tf = T({ logSumExp_: ZG });
function JG(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 N.runKernel(_n, n);
}
var lu = T({ logicalAnd_: JG });
function e4(r) {
  let t10 = { x: v(r, "x", "logicalNot", "bool") };
  return N.runKernel(En, t10);
}
var Nf = T({ logicalNot_: e4 });
function t4(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 N.runKernel(ua, n);
}
var _f = T({ logicalOr_: t4 });
function r4(r, e) {
  let t10 = v(r, "a", "logicalXor", "bool"), o = v(e, "b", "logicalXor", "bool");
  return Je(t10.shape, o.shape), lu(_f(r, e), Nf(lu(r, e)));
}
var Wk = T({ logicalXor_: r4 });
var Ef = 2147483648;
function o4(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 = z(o, [-1, s]), p = z(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 (Ve(p.shape) >= Ef)
    throw new Error(`values tensor size must less than ${Ef}`);
  if (i.shape[1] >= Ef)
    throw new Error(`trailing dim_size must less than ${Ef} for int32 output type, was ${i.shape[1]}`);
  let u = { sortedSequence: i, values: p }, c = { side: t10 };
  return N.runKernel(Ep, u, c);
}
var dl = T({ searchSorted_: o4 });
function Uk(r, e) {
  return dl(r, e, "left");
}
function n4(r, e, t10, o, n) {
  let s = v(r, "x", "maxPool"), a = 1, i = s, p = false;
  s.rank === 3 && (p = true, i = z(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}.`), $(lr(t10, a), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${t10} and dilations '${a}'`), Ot("maxPool", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n }, l = N.runKernel(Rn, u, c);
  return p ? z(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var $f = T({ maxPool_: n4 });
function s4(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 = z(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 ${s}`), Ot("maxPool3d", o, n);
  let u = { x: i }, c = { filterSize: e, strides: t10, pad: o, dimRoundingMode: n, dataFormat: s }, l = N.runKernel(Sp, u, c);
  return p ? z(l, [l.shape[1], l.shape[2], l.shape[3], l.shape[4]]) : l;
}
var Gk = T({ maxPool3d_: s4 });
function a4(r, e, t10, o, n = false) {
  let a = { x: v(r, "x", "maxPoolWithArgmax") }, i = { filterSize: e, strides: t10, pad: o, includeBatchInIndex: n }, p = N.runKernel(vp, a, i);
  return { result: p[0], indexes: p[1] };
}
var Hk = T({ maxPoolWithArgmax_: a4 });
function i4(r, e) {
  let t10 = v(r, "a", "maximum"), o = v(e, "b", "maximum");
  [t10, o] = Re(t10, o), t10.dtype === "bool" && (t10 = qe(t10, "int32"), o = qe(o, "int32")), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(mo, n);
}
var Rf = T({ maximum_: i4 });
function u4(r, e = null, t10 = false) {
  let n = { x: v(r, "x", "mean") }, s = { axis: e, keepDims: t10 };
  return N.runKernel(An, n, s);
}
var mu = T({ mean_: u4 });
function Wr(r, e = "float32") {
  if (e === "complex64") {
    let o = Wr(r, "float32"), n = Wr(r, "float32");
    return Er(o, n);
  }
  let t10 = ap(Ve(r), e);
  return N.makeTensor(t10, r, e);
}
function zs(r, e = "float32") {
  if (e === "complex64") {
    let o = zs(r, "float32"), n = Wr(r, "float32");
    return Er(o, n);
  }
  let t10 = jc(Ve(r), e);
  return N.makeTensor(t10, r, e);
}
function qk(r, e, { indexing: t10 = "xy" } = {}) {
  if (t10 !== "xy" && t10 !== "ij")
    throw new TypeError(`${t10} is not a valid third argument to meshgrid`);
  if (r === void 0)
    return [];
  let o = v(r, "x", "meshgrid", r instanceof ut ? r.dtype : "float32");
  if (e === void 0)
    return [o];
  let n = v(e, "y", "meshgrid", e instanceof ut ? e.dtype : "float32"), s = Ve(o.shape), a = Ve(n.shape);
  return t10 === "xy" ? (o = z(o, [1, -1]), n = z(n, [-1, 1]), [Xe(zs([a, 1], o.dtype), o), Xe(n, zs([1, s], n.dtype))]) : (o = z(o, [-1, 1]), n = z(n, [1, -1]), [Xe(o, zs([1, a], o.dtype)), Xe(zs([s, 1], n.dtype), n)]);
}
function p4(r, e) {
  let t10 = v(r, "a", "minimum"), o = v(e, "b", "minimum");
  [t10, o] = Re(t10, o), t10.dtype === "bool" && (t10 = qe(t10, "int32"), o = qe(o, "int32")), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(fo, n);
}
var Af = T({ minimum_: p4 });
function c4(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 N.runKernel(Dn, a, s);
}
var Kk = T({ mirrorPad_: c4 });
function l4(r, e) {
  let t10 = v(r, "a", "mod"), o = v(e, "b", "mod");
  [t10, o] = Re(t10, o);
  let n = { a: t10, b: o };
  return N.runKernel(ji, n);
}
var jk = T({ mod_: l4 });
function m4(r, e = null, t10 = false) {
  r = v(r, "x", "moments");
  let o = Ka(e, r.shape), n = mu(r, o, t10), s = n.shape;
  t10 || (s = Ta(n.shape, o));
  let a = Zt(ke(qe(r, "float32"), z(n, s))), i = mu(a, o, t10);
  return { mean: n, variance: i };
}
var Xk = T({ moments_: m4 });
function f4(r, e, t10, o) {
  let n = v(e, "data", "multiRNNCell"), s = Ia(t10, "c", "multiRNNCell"), a = Ia(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 Yk = T({ multiRNNCell_: f4 });
function d4(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 ? z(n, [1, -1]) : n }, u = { numSamples: e, seed: t10, normalized: o }, c = N.runKernel(kp, p, u);
  return a === 1 ? z(c, [c.size]) : c;
}
var Qk = T({ multinomial_: d4 });
function h4(r, e) {
  let t10 = v(r, "a", "notEqual", "string_or_numeric"), o = v(e, "b", "notEqual", "string_or_numeric");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o };
  return N.runKernel(go, n);
}
var Ff = T({ notEqual_: h4 });
function g4(r) {
  let t10 = { x: v(r, "x", "onesLike") };
  return N.runKernel(Cs, t10);
}
var Zk = T({ onesLike_: g4 });
function x4(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 = z(t10, [-1, 1]), s = z(o, [1, -1]);
  return Xe(n, s);
}
var Jk = T({ outerProduct_: x4 });
function y4(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 N.runKernel(Ln, s, n);
}
var Ws = T({ pad_: y4 });
function b4(r, e, t10 = 0) {
  return $(e.length === 2, () => "Invalid number of paddings. Must be length of 2."), Ws(r, [e], t10);
}
var e1 = T({ pad1d_: b4 });
function C4(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."), Ws(r, e, t10);
}
var t1 = T({ pad2d_: C4 });
function I4(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."), Ws(r, e, t10);
}
var r1 = T({ pad3d_: I4 });
function w4(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."), Ws(r, e, t10);
}
var o1 = T({ pad4d_: w4 });
function S4(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 N.runKernel(ks, n, s);
}
var Df = T({ spaceToBatchND_: S4 });
function v4(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 = z(i, [1, i.shape[0], i.shape[1], i.shape[2]])), $(lr(s, n), () => `Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${n}'`);
  let c = lC(p.shape, e, s, n, o), l = [c.dilationHeight, c.dilationWidth], m;
  o === "same" ? m = T4([c.filterHeight, c.filterWidth], l) : m = [[0, 0], [0, 0]];
  let f = l[0] === 1 && l[1] === 1, [d, h] = k4([c.inHeight, c.inWidth], l, m), g = f ? o : "valid", y = f ? p : Df(p, l, d), C = (t10 === "avg" ? () => mf(y, e, s, g, a) : () => $f(y, e, s, g, a))(), w = f ? C : ff(C, l, h);
  return u ? z(w, [w.shape[1], w.shape[2], w.shape[3]]) : w;
}
function k4(r, e, t10) {
  let o = t10.map((c) => c[0]), n = t10.map((c) => c[1]), s = r.concat(o, n), a = e.map((c, l) => (c - s[l] % c) % c), i = n.map((c, l) => c + a[l]), p = e.map((c, l) => [o[l], i[l]]), u = e.map((c, l) => [0, a[l]]);
  return [p, u];
}
function T4(r, e) {
  let o = r.map((a, i) => a + (a - 1) * (e[i] - 1)).map((a) => a - 1), n = o.map((a) => Math.floor(a / 2)), s = o.map((a, i) => a - n[i]);
  return o.map((a, i) => [n[i], s[i]]);
}
var n1 = T({ pool_: v4 });
function N4(r, e) {
  let t10 = v(r, "x", "prelu"), o = v(e, "alpha", "prelu"), n = { x: t10, alpha: o };
  return N.runKernel(Vn, n);
}
var Pf = T({ prelu_: N4 });
function _4(r, e = null, t10 = false) {
  let o = v(r, "x", "prod");
  o.dtype === "bool" && (o = qe(o, "int32"));
  let n = { x: o }, s = { axis: e, keepDims: t10 };
  return N.runKernel(Ao, n, s);
}
var s1 = T({ prod_: _4 });
function E4(r, e, t10, o) {
  let n = r.map((c, l) => v(c, `tensors${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 = N.runKernel(Tp, i, p);
  return { outputNestedSplits: u.slice(0, u.length - 1), outputDenseValues: u[u.length - 1] };
}
var a1 = T({ raggedGather_: E4 });
function $4(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 = N.runKernel(Np, a);
  return { rtNestedSplits: i[0], rtDenseValues: i[1] };
}
var i1 = T({ raggedRange_: $4 });
function R4(r, e, t10, o, n) {
  let s = v(r, "shape", "raggedTensorToTensor", "int32"), a = v(e, "values", "raggedTensorToTensor"), i = v(t10, "defaultValue", "raggedTensorToTensor", a.dtype), p = o.map((l, m) => v(l, `tensors${m}`, "raggedTensorToTensor", "int32")), u = { shape: s, values: a, defaultValue: i, rowPartitionTensors: p }, c = { rowPartitionTypes: n };
  return N.runKernel(_p, u, c);
}
var u1 = T({ raggedTensorToTensor_: R4 });
function A4(r, e, t10) {
  let o = Ve(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 N.makeTensor(n, r, t10);
}
var p1 = T({ rand_: A4 });
var Bf = rp(IC());
var du = 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 = Bf.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 Mf = class {
  constructor(e, t10, o, n) {
    this.alpha = e, this.beta = 1 / t10, this.dtype = o;
    let s = n || Math.random();
    this.randu = Bf.alea(s.toString()), this.randn = new du(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 Lf = 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 = Bf.alea(n);
  }
  convertValue(e) {
    return this.canReturnFloat() ? e : Math.round(e);
  }
  nextValue() {
    return this.convertValue(this.min + this.range * this.random());
  }
};
function B4(r, e, t10 = 1, o = "float32", n) {
  if (t10 == null && (t10 = 1), o == null && (o = "float32"), o !== "float32" && o !== "int32")
    throw new Error(`Unsupported data type ${o}`);
  let s = new Mf(e, t10, o, n), a = ne(r, o);
  for (let i = 0; i < a.values.length; i++)
    a.values[i] = s.nextValue();
  return a.toTensor();
}
var T1 = T({ randomGamma_: B4 });
function V4(r, e = 0, t10 = 1, o, n) {
  if (o != null && o === "bool")
    throw new Error(`Unsupported data type ${o}`);
  let s = new du(e, t10, o, false, n), a = ne(r, o);
  for (let i = 0; i < a.values.length; i++)
    a.values[i] = s.nextValue();
  return a.toTensor();
}
var Vf = T({ randomNormal_: V4 });
function z4(r, e, t10) {
  if (e != null && e === "bool")
    throw new Error(`Unsupported data type ${e}`);
  return Vf(r, 0, 1, e, t10);
}
var N1 = T({ randomStandardNormal_: z4 });
function W4(r, e = 0, t10 = 1, o = "float32", n) {
  let s = ne(r, o), a = new Lf(e, t10, null, n);
  for (let i = 0; i < s.values.length; i++)
    s.values[i] = a.nextValue();
  return s.toTensor();
}
var zf = T({ randomUniform_: W4 });
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 N.runKernel(ws, {}, n);
}
function U4(r) {
  let t10 = { x: v(r, "x", "reciprocal") };
  return N.runKernel(ma, t10);
}
var _1 = T({ reciprocal_: U4 });
function G4(r) {
  let t10 = { x: v(r, "x", "relu") };
  return N.runKernel(zn, t10);
}
var hi = T({ relu_: G4 });
function H4(r) {
  let t10 = { x: v(r, "x", "relu6") };
  return N.runKernel(Gn, t10);
}
var Wf = T({ relu6_: H4 });
function q4(r, e) {
  let o = { x: v(r, "x", "reverse") }, n = { dims: e };
  return N.runKernel(fa, o, n);
}
var vo = T({ reverse_: q4 });
function K4(r) {
  let e = v(r, "x", "reverse");
  return $(e.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${e.rank}.`), vo(e, 0);
}
var E1 = T({ reverse1d_: K4 });
function j4(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}.`), vo(t10, e);
}
var $1 = T({ reverse2d_: j4 });
function X4(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}.`), vo(t10, e);
}
var R1 = T({ reverse3d_: X4 });
function Y4(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}.`), vo(t10, e);
}
var A1 = T({ reverse4d_: Y4 });
function Q4(r) {
  let t10 = { x: v(r, "x", "round") };
  return N.runKernel(da, t10);
}
var Uf = T({ round_: Q4 });
function Z4(r) {
  let t10 = { x: v(r, "x", "rsqrt", "float32") };
  return N.runKernel(xo, t10);
}
var F1 = T({ rsqrt_: Z4 });
function J4(r) {
  let t10 = { x: v(r, "x", "selu") };
  return N.runKernel(Xi, t10);
}
var D1 = T({ selu_: J4 });
function eH(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 = z(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], f = p.shape[3];
  $(u.shape[2] === m * f, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${m * f}, but got ${u.shape[2]}.`);
  let d = Gp(c, p, o, n, a, s), g = mi(d, u, 1, "valid", a);
  return l ? z(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var P1 = T({ separableConv2d_: eH });
async function tH(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 je([i], t10.dtype), u = new je([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 O1 = tH;
function rH(r) {
  let t10 = { x: v(r, "x", "sign") };
  return N.runKernel(Yi, t10);
}
var M1 = T({ sign_: rH });
function oH(r) {
  let t10 = { x: v(r, "x", "sin", "float32") };
  return N.runKernel(Kn, t10);
}
var L1 = T({ sin_: oH });
function nH(r) {
  let t10 = { x: v(r, "x", "sinh") };
  return N.runKernel(ha, t10);
}
var B1 = T({ sinh_: nH });
function sH(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`), Ue(o, [e], [t10]);
}
var V1 = T({ slice1d_: sH });
function aH(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`), Ue(o, e, t10);
}
var z1 = T({ slice2d_: aH });
function iH(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`), Ue(o, e, t10);
}
var W1 = T({ slice3d_: iH });
function uH(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`), Ue(o, e, t10);
}
var U1 = T({ slice4d_: uH });
function pH(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 N.runKernel(Xn, o, n);
}
var G1 = T({ softmax_: pH });
function cH(r) {
  $(r.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);
  let e = { input: r };
  return N.runKernel(bp, e);
}
var qp = T({ fft_: cH });
function lH(r) {
  $(r.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);
  let e = { input: r };
  return N.runKernel(Cp, e);
}
var hu = T({ ifft_: lH });
function mH(r) {
  let e = r.shape[r.shape.length - 1], t10 = r.size / e, o;
  if (e <= 2) {
    let n = z(r, [t10, e]);
    o = hu(n);
  } else {
    let n = [t10, 2 * (e - 1)], s = z(ka(r), [t10, e]), a = z(ci(r), [t10, e]), i = vo(Ue(s, [0, 1], [t10, e - 2]), 1), p = oe(vo(Ue(a, [0, 1], [t10, e - 2]), 1), be(-1)), u = gt([s, i], 1), c = gt([a, p], 1), l = z(Er(u, c), [n[0], n[1]]);
    o = hu(l);
  }
  if (o = ka(o), r.rank === 3 && r.shape[0] !== 0) {
    let n = o, s = r.shape[0];
    o = z(o, [s, o.shape[0] / s, o.shape[1]]), n.dispose();
  }
  return o;
}
var Gf = T({ irfft_: mH });
function fH(r, e, t10 = 0) {
  let n = { x: v(r, "x", "split") }, s = { numOrSizeSplits: e, axis: t10 };
  return N.runKernel(Ts, n, s);
}
var $a = T({ split_: fH });
function dH(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 d = r.shape.map((g) => 0), h = r.shape.map((g) => g);
    h[r.shape.length - 1] = e, n = Ue(r, d, h), t10 = e;
  } else if (e != null && e > t10) {
    let d = r.shape.map((h) => h);
    d[r.shape.length - 1] = e - t10, n = gt([r, Wr(d)], r.shape.length - 1), t10 = e;
  } else
    n = r;
  let s = Gt(n), a = z(Er(n, s), [o, t10]), i = qp(a), p = Math.floor(t10 / 2) + 1, u = ka(i), c = ci(i), l = $a(u, [p, t10 - p], u.shape.length - 1), m = $a(c, [p, t10 - p], c.shape.length - 1), f = n.shape.slice();
  return f[n.shape.length - 1] = p, z(Er(l[0], m[0]), f);
}
var Kp = T({ rfft_: dH });
function hH(r, e) {
  let t10 = v(r, "a", "squaredDifference"), o = v(e, "b", "squaredDifference");
  [t10, o] = Re(t10, o), Je(t10.shape, o.shape);
  let n = { a: t10, b: o }, s = {};
  return N.runKernel(Co, n, s);
}
var Hf = T({ squaredDifference_: hH });
function gH(r, e) {
  let t10 = v(r, "x", "squeeze", "string_or_numeric");
  return z(t10, db(t10.shape, e).newShape);
}
var jp = T({ squeeze_: gH });
function xH(r, e = 0) {
  let t10 = Ia(r, "tensors", "stack", "string_or_numeric");
  $(t10.length >= 1, () => "Pass at least one tensor to tf.stack"), t10.length > 0 && $(e <= t10[0].rank, () => "Axis must be <= rank of the tensor");
  let o = t10, n = { axis: e };
  return N.runKernel(Is, o, n);
}
var Ir = T({ stack_: xH });
function yH(r, e = 0) {
  let o = { x: v(r, "x", "step") }, n = { alpha: e };
  return N.runKernel($s, o, n);
}
var qf = T({ step_: yH });
function bH(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 N.runKernel(Yn, c, l);
}
var H1 = T({ stridedSlice_: bH });
function CH(r) {
  let t10 = { x: v(r, "x", "tan", "float32") };
  return N.runKernel(xa, t10);
}
var q1 = T({ tan_: CH });
function mr(r, e) {
  eo(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 gi(r, e, t10) {
  if (eo(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 K1(r, e, t10) {
  if (eo(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 j1(r, e, t10) {
  if (eo(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 X1(r, e, t10) {
  if (eo(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 IH(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] = N.runKernel(Zn, s, a);
  return { values: i, indices: p };
}
var Y1 = T({ topk_: IH });
function wH(r, e = 0, t10 = 1, o, n) {
  if (o != null && o === "bool")
    throw new Error("Unsupported data type $ { dtype }");
  let s = new du(e, t10, o, true, n), a = ne(r, o);
  for (let i = 0; i < a.values.length; i++)
    a.values[i] = s.nextValue();
  return a.toTensor();
}
var Q1 = T({ truncatedNormal_: wH });
function SH(r, e = 0) {
  let t10 = v(r, "x", "unique", "string_or_numeric");
  $(t10.rank > 0, () => "The input tensor must be at least 1D");
  let o = { x: t10 }, n = { axis: e }, [s, a] = N.runKernel($p, o, n);
  return { values: s, indices: a };
}
var Z1 = T({ unique_: SH });
function vH(r, e, t10) {
  let o = v(r, "x", "unsortedSegmentSum"), n = v(e, "segmentIds", "unsortedSegmentSum", "int32");
  $(ra(t10), () => "numSegments must be of dtype int");
  let s = { x: o, segmentIds: n }, a = { numSegments: t10 };
  return N.runKernel(Rp, s, a);
}
var J1 = T({ unsortedSegmentSum_: vH });
function kH(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 N.runKernel(_s, o, n);
}
var ko = T({ unstack_: kH });
function eT(r, e) {
  return dl(r, e, "right");
}
function tT(r, e = true, t10, o) {
  return N.makeVariable(r, e, t10, o);
}
function Kf(r, e) {
  let t10 = [];
  for (let s = 0; s < e.length; s++)
    e[s] && t10.push(s);
  let o = ne(r, "int32"), n = ne([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 TH(r) {
  let e = v(r, "condition", "whereAsync", "bool"), t10 = await e.data(), o = Kf(e.shape, t10);
  return r !== e && e.dispose(), o;
}
var jf = TH;
async function NH(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"), ht(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 = z(o, u), l = z(n, [-1]), m = await jf(l), f = jp(m, [1]), d = Cf(c, f, s);
  return r !== o && o.dispose(), e !== n && n.dispose(), f.dispose(), c.dispose(), l.dispose(), m.dispose(), d;
}
var _H = NH;
function EH(r, e, t10, o, n = true) {
  let s = v(r, "v", "movingAverage"), a = v(e, "x", "movingAverage"), i = v(t10, "decay", "movingAverage");
  Lb(s, a), $(Or(s.shape, a.shape), () => "Shape mismatch in v and x");
  let p = be(1), u = ke(p, i), c = oe(ke(a, s), u);
  if (n) {
    $(o != null, () => "When using zeroDebias: true, step is required.");
    let l = v(o, "step", "movingAverage");
    c = We(c, ke(p, Na(i, l)));
  }
  return ge(s, c);
}
var $H = T({ movingAverage_: EH });
function RH(r, e, t10) {
  let o = v(r, "indices", "scatterND", "int32"), n = v(e, "updates", "scatterND");
  uf(n, o, t10);
  let s = { indices: o, updates: n }, a = { shape: t10 };
  return N.runKernel(Hn, s, a);
}
var AH = T({ scatterND_: RH });
function rT(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 DH(r, e, t10, o = 0) {
  let n = v(r, "sparseIndices", "sparseToDense", "int32"), s = v(e, "sparseValues", "sparseToDense", "string_or_numeric"), a = v(o, "defaultValue", "sparseToDense", s.dtype);
  rT(n, s, t10, a);
  let i = { sparseIndices: n, sparseValues: s, defaultValue: a }, p = { outputShape: t10 };
  return N.runKernel(ei, i, p);
}
var PH = T({ sparseToDense_: DH });
function OH(r, e) {
  let t10 = v(e, "indices", "gatherND", "int32"), n = { params: v(r, "x", "gatherND", "string_or_numeric"), indices: t10 };
  return N.runKernel(Tn, n);
}
var MH = T({ gatherND_: OH });
function oT(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 LH(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 ut ? n.clone() : n;
  let s = oT(n, t10), a = 1 - e, i = We(bf(ge(zf(s, 0, 1, "float32", o), a)), a);
  return oe(n, i);
}
var BH = T({ dropout_: LH });
function wC(r) {
  return Math.floor(Math.pow(2, Math.ceil(Math.log(r) / Math.log(2))));
}
function hl(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 mr(n, "float32");
}
async function VH(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}`), ht(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 = hb("bool", p);
  for (let l = 0; l < p; l++) {
    let m = l * u, f = a.subarray(m, m + u), d = [];
    for (let h = 0; h < f.length; h++)
      d.push({ value: f[h], index: h });
    d.sort((h, g) => g.value - h.value), c[l] = 0;
    for (let h = 0; h < t10; h++)
      if (d[h].index === i[l]) {
        c[l] = 1;
        break;
      }
  }
  return r !== o && o.dispose(), e !== n && n.dispose(), nr(c, n.shape, "bool");
}
var zH = VH;
var SC = {};
Be(SC, { conv2d: () => sT, depthwiseConv2d: () => uT, matMul: () => pT });
function WH(r, e, t10, o, n, s = "NHWC", a) {
  let i = r;
  r.rank === 3 && (i = z(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let p = e;
  p.rank === 3 && (p = z(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]}).`), Ot("conv2dDerFilter", n, a);
  let l = { x: i, dy: p }, m = { strides: o, pad: n, dataFormat: s, dimRoundingMode: a, filterShape: t10 };
  return N.runKernel(lp, l, m);
}
var nT = T({ conv2DBackpropFilter_: WH });
function gu(r, e, t10) {
  if (t10 == null || t10 === "linear")
    return r;
  if (t10 === "relu")
    return oe(r, qf(e));
  throw new Error(`Cannot compute gradient for fused activation ${t10}.`);
}
function xu(r, e) {
  let t10 = e, o = nf(r.shape, e.shape);
  return o.length > 0 && (t10 = tt(t10, o)), z(t10, r.shape);
}
function yu(r, e, t10, o) {
  if (e === "linear")
    return r;
  if (e === "relu")
    return hi(r);
  if (e === "elu")
    return xf(r);
  if (e === "relu6")
    return Wf(r);
  if (e === "prelu")
    return Pf(r, t10);
  if (e === "leakyrelu")
    return wf(r, o);
  if (e === "sigmoid")
    return Ms(r);
  throw new Error(`Unknown fused activation ${e}.`);
}
var bu = (r, e) => !(r > 0) || e === "linear";
function UH({ 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", bu(N.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 _ = mi(r, e, t10, o, n, s, a);
    return i != null && (_ = ge(_, i)), yu(_, p, u, c);
  }
  let l = v(r, "x", "conv2d", "float32"), m = v(e, "filter", "conv2d", "float32"), f = l, d = false;
  l.rank === 3 && (d = true, f = z(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(f.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`), $(m.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`), Ot("fused conv2d", o, a);
  let h = n === "NHWC" ? f.shape[3] : f.shape[1];
  $(m.shape[2] === h, () => `Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`), $(lr(t10, s), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`);
  let g = uu(f.shape, m.shape, t10, s, o, a), y;
  i != null && (y = v(i, "bias", "fused conv2d"), [y] = Re(y, l), n === "NHWC" ? Je(g.outShape, y.shape) : ($(y.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${y.shape.length}.`), $(y.shape.length === 0 || y.shape[0] === g.outChannels || y.shape[0] === 1, () => `Error in fused conv2d: bias shape (${y.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 (${g.outChannels}).`);
    else if (_.length === 3)
      try {
        Je(_, g.outShape);
      } catch (E) {
        let R = `Error in fused conv2d: PReLU activation weights (${_}) is not compatible with the output shape of the conv2d (${g.outShape}).`;
        throw Error(R);
      }
    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 [R, A, D, O] = E, M = gu(_, D, p);
    $(iu(s), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
    let L = hf(A.shape, M, R, t10, o), W = nT(A, M, R.shape, t10, o), V = [L, W];
    if (O != null) {
      let G = xu(O, M);
      V.push(G);
    }
    return V;
  }, w = { x: f, filter: m, bias: y, preluActivationWeights: b }, k = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
  return i == null ? Cr((E, R, A) => {
    let D = N.runKernel(Do, w, k);
    return A([R, E, D]), d && (D = z(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: C };
  })(f, m) : Cr((E, R, A, D) => {
    let O = N.runKernel(Do, w, k);
    return D([R, E, O, A]), d && (O = z(O, [O.shape[1], O.shape[2], O.shape[3]])), { value: O, gradFunc: C };
  })(f, m, y);
}
var sT = T({ fusedConv2d_: UH });
function GH(r, e, t10, o, n, s = [1, 1], a) {
  let i = r;
  r.rank === 3 && (i = z(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let p = e;
  p.rank === 3 && (p = z(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 N.runKernel(hp, u, c);
}
var aT = T({ depthwiseConv2dNativeBackpropFilter_: GH });
function HH(r, e, t10, o, n, s = [1, 1], a) {
  let i = e, p = false;
  e.rank === 3 && (p = true, i = z(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 = N.runKernel(gp, u, c);
  return p ? z(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var iT = T({ depthwiseConv2dNativeBackpropInput_: HH });
function qH({ 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 (bu(N.state.gradientDepth, p) === false) {
    let k = Gp(r, e, t10, o, n, s, a);
    return i != null && (k = ge(k, i)), yu(k, p, u, c);
  }
  let l = v(r, "x", "depthwiseConv2d", "float32"), m = v(e, "filter", "depthwiseConv2d", "float32"), f = l, d = false;
  l.rank === 3 && (d = true, f = z(l, [1, l.shape[0], l.shape[1], l.shape[2]])), $(f.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`), $(m.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`), $(f.shape[3] === m.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`), s == null && (s = [1, 1]), $(lr(t10, s), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${t10} and dilations '${s}'`), Ot("fused depthwiseConv2d", o, a);
  let h = uu(f.shape, m.shape, t10, s, o, a, true), g;
  i != null && (g = v(i, "bias", "fused conv2d"), [g] = Re(g, l), Je(h.outShape, g.shape));
  let y;
  u != null && (y = v(u, "prelu weights", "fused depthwiseConv2d"));
  let b = (k, _) => {
    $(iu(s), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);
    let [E, R, A, D] = _, O = gu(k, A, p), M = iT(R.shape, O, E, t10, o, s, a), L = aT(R, O, E.shape, t10, o, s, a);
    if (D != null) {
      let W = xu(g, O);
      return [M, L, W];
    }
    return [M, L];
  }, C = { x: f, filter: m, bias: g, preluActivationWeights: y }, w = { strides: t10, pad: o, dataFormat: n, dilations: s, dimRoundingMode: a, activation: p, leakyreluAlpha: c };
  return i == null ? Cr((_, E, R) => {
    let A = N.runKernel(Po, C, w);
    return R([E, _, A]), d && (A = z(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: b };
  })(f, m) : Cr((_, E, R, A) => {
    let D = N.runKernel(Po, C, w);
    return A([E, _, D, R]), d && (D = z(D, [D.shape[1], D.shape[2], D.shape[3]])), { value: D, gradFunc: b };
  })(f, m, g);
}
var uT = T({ fusedDepthwiseConv2d_: qH });
function KH({ a: r, b: e, transposeA: t10 = false, transposeB: o = false, bias: n, activation: s = "linear", preluActivationWeights: a, leakyreluAlpha: i = 0.2 }) {
  if (bu(N.state.gradientDepth, s) === false) {
    let O = Xe(r, e, t10, o);
    return n != null && (O = ge(O, n)), yu(O, s, a, i);
  }
  let p = v(r, "a", "fused matMul"), u = v(e, "b", "fused matMul");
  [p, u] = Re(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], f = o ? u.shape[u.rank - 2] : u.shape[u.rank - 1], d = p.shape.slice(0, -2), h = u.shape.slice(0, -2), g = Ve(d), y = Ve(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, f]), w = t10 ? z(p, [g, c, m]) : z(p, [g, m, c]), k = o ? z(u, [y, f, l]) : z(u, [y, l, f]), _;
  n != null && (_ = v(n, "bias", "fused matMul"), [_] = Re(_, p), Je(C, _.shape));
  let E;
  a != null && (E = v(a, "prelu weights", "fused matMul"));
  let R = (O, M) => {
    let [L, W, V, G] = M, q = gu(z(O, V.shape), V, s), H, j;
    if (!t10 && !o ? (H = Xe(q, W, false, true), j = Xe(L, q, true, false)) : !t10 && o ? (H = Xe(q, W, false, false), j = Xe(q, L, true, false)) : t10 && !o ? (H = Xe(W, q, false, true), j = Xe(L, q, false, false)) : (H = Xe(W, q, true, true), j = Xe(q, L, true, true)), n != null) {
      let Y = xu(G, q);
      return [H, j, Y];
    } else
      return [H, j];
  }, A = { a: w, b: k, bias: _, preluActivationWeights: E }, D = { transposeA: t10, transposeB: o, activation: s, leakyreluAlpha: i };
  return n == null ? Cr((M, L, W) => {
    let V = N.runKernel(Fo, A, D);
    return W([M, L, V]), { value: z(V, C), gradFunc: R };
  })(w, k) : Cr((M, L, W, V) => {
    let G = N.runKernel(Fo, A, D);
    return V([M, L, G, W]), { value: z(G, C), gradFunc: R };
  })(w, k, _);
}
var pT = T({ fusedMatMul_: KH });
function jH(r) {
  return hl(r, 0.54, 0.46);
}
var cT = T({ hammingWindow_: jH });
function XH(r) {
  return hl(r, 0.5, 0.5);
}
var Xf = T({ hannWindow_: XH });
function YH(r, e, t10, o = false, n = 0) {
  let s = 0, a = [];
  for (; s + e <= r.size; )
    a.push(Ue(r, s, e)), s += t10;
  if (o)
    for (; s < r.size; ) {
      let i = s + e - r.size, p = gt([Ue(r, s, e - i), Bs([i], n)]);
      a.push(p), s += t10;
    }
  return a.length === 0 ? gi([], [0, e]) : z(gt(a), [a.length, e]);
}
var Yf = T({ frame_: YH });
function QH(r, e, t10, o, n = Xf) {
  o == null && (o = wC(e));
  let s = Yf(r, e, t10), a = oe(s, n(e));
  return Kp(a, o);
}
var lT = T({ stft_: QH });
function ZH(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 N.runKernel(xn, c, l);
}
var mT = T({ cropAndResize_: ZH });
function JH(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 N.runKernel(Sn, t10, {});
}
var fT = T({ flipLeftRight_: JH });
function eq(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, fi(e, n);
}
var dT = T({ grayscaleToRGB_: eq });
function tq(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 N.runKernel(es, s, a);
}
var hT = T({ rotateWithOffset_: tq });
function Vo(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 rq(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) {
  let s = v(r, "boxes", "nonMaxSuppression", "float32"), a = v(e, "scores", "nonMaxSuppression", "float32"), i = Vo(s, a, t10, o, n);
  t10 = i.maxOutputSize, o = i.iouThreshold, n = i.scoreThreshold;
  let p = { maxOutputSize: t10, iouThreshold: o, scoreThreshold: n };
  return N.runKernel(On, { boxes: s, scores: a }, p);
}
var gT = T({ nonMaxSuppression_: rq });
function xT(r, e, t10) {
  let o = oq(r, e, t10), n = o < 0 ? -(o + 1) : o;
  r.splice(n, 0, e);
}
function oq(r, e, t10) {
  return sq(r, e, t10 || nq);
}
function nq(r, e) {
  return r > e ? 1 : r < e ? -1 : 0;
}
function sq(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 Qf(r, e, t10, o, n) {
  return vC(r, e, t10, o, n, 0);
}
function Zf(r, e, t10, o, n, s) {
  return vC(r, e, t10, o, n, 0, false, s, true);
}
function Jf(r, e, t10, o, n, s) {
  return vC(r, e, t10, o, n, s, true);
}
function vC(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(yT);
  let c = s > 0 ? -0.5 / s : 0, l = [], m = [];
  for (; l.length < t10 && u.length > 0; ) {
    let g = u.pop(), { score: y, boxIndex: b, suppressBeginIndex: C } = g;
    if (y < n)
      break;
    let w = false;
    for (let k = l.length - 1; k >= C; --k) {
      let _ = aq(r, b, l[k]);
      if (_ >= o) {
        w = true;
        break;
      }
      if (g.score = g.score * iq(o, c, _), g.score <= n)
        break;
    }
    g.suppressBeginIndex = l.length, w || (g.score === y ? (l.push(b), m.push(g.score)) : g.score > n && xT(u, g, yT));
  }
  let f = l.length, d = t10 - f;
  i && d > 0 && (l.push(...new Array(d).fill(0)), m.push(...new Array(d).fill(0)));
  let h = { selectedIndices: l };
  return a && (h.selectedScores = m), p && (h.validOutputs = f), h;
}
function aq(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]), f = (i - s) * (p - a), d = (l - u) * (m - c);
  if (f <= 0 || d <= 0)
    return 0;
  let h = Math.max(s, u), g = Math.max(a, c), y = Math.min(i, l), b = Math.min(p, m), C = Math.max(y - h, 0) * Math.max(b - g, 0);
  return C / (f + d - C);
}
function iq(r, e, t10) {
  let o = Math.exp(e * t10 * t10);
  return t10 <= r ? o : 0;
}
function yT(r, e) {
  return r.score - e.score || r.score === e.score && e.boxIndex - r.boxIndex;
}
async function uq(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY) {
  let s = v(r, "boxes", "nonMaxSuppressionAsync"), a = v(e, "scores", "nonMaxSuppressionAsync"), i = Vo(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 } = Qf(u, c, t10, o, n);
  return s !== r && s.dispose(), a !== e && a.dispose(), mr(l, "int32");
}
var bT = uq;
function pq(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
  let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = Vo(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 = N.runKernel(Mn, u, c);
  return { selectedIndices: l[0], selectedScores: l[1] };
}
var CT = T({ nonMaxSuppressionWithScore_: pq });
async function cq(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = 0) {
  let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = Vo(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: f } = Jf(c, l, t10, o, n, s);
  return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: mr(m, "int32"), selectedScores: mr(f) };
}
var IT = cq;
function lq(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
  let a = v(r, "boxes", "nonMaxSuppression"), i = v(e, "scores", "nonMaxSuppression"), p = Vo(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, m = { boxes: a, scores: i }, f = { maxOutputSize: u, iouThreshold: c, scoreThreshold: l, padToMaxOutputSize: s }, d = N.runKernel(pa, m, f);
  return { selectedIndices: d[0], validOutputs: d[1] };
}
var wT = T({ nonMaxSuppressionPadded_: lq });
async function mq(r, e, t10, o = 0.5, n = Number.NEGATIVE_INFINITY, s = false) {
  let a = v(r, "boxes", "nonMaxSuppressionAsync"), i = v(e, "scores", "nonMaxSuppressionAsync"), p = Vo(a, i, t10, o, n, null), u = p.maxOutputSize, c = p.iouThreshold, l = p.scoreThreshold, [m, f] = await Promise.all([a.data(), i.data()]), { selectedIndices: d, validOutputs: h } = Zf(m, f, u, c, l, s);
  return a !== r && a.dispose(), i !== e && i.dispose(), { selectedIndices: mr(d, "int32"), validOutputs: be(h, "int32") };
}
var ST = mq;
function fq(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 = z(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
  let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = N.runKernel(Un, i, p);
  return a ? z(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var vT = T({ resizeBilinear_: fq });
function dq(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"), $(o === false || t10 === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
  let s = n, a = false;
  n.rank === 3 && (a = true, s = z(n, [1, n.shape[0], n.shape[1], n.shape[2]]));
  let [] = e, i = { images: s }, p = { alignCorners: t10, halfPixelCenters: o, size: e }, u = N.runKernel(Wn, i, p);
  return a ? z(u, [u.shape[1], u.shape[2], u.shape[3]]) : u;
}
var kT = T({ resizeNearestNeighbor_: dq });
function hq(r, e = "binary", t10 = false, o = 0.5) {
  let n = v(r, "image", "threshold"), s = 0.2989, a = 0.587, i = 0.114, p = n.shape[0] * n.shape[1], u = oe(mr([o]), 255), c, l, m, f;
  if ($(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] = $a(n, [1, 1, 1], -1);
    let g = oe(c, s), y = oe(l, a), b = oe(m, i);
    f = ge(ge(g, y), b);
  } else
    f = r;
  if (e === "otsu") {
    let g = df(qe(Uf(f), "int32"), nr([]), 256);
    u = gq(g, p);
  }
  let d = t10 ? Hp(f, u) : cu(f, u);
  return qe(oe(d, 255), "int32");
}
function gq(r, e) {
  let t10 = mr([-1]), o = mr([0]), n = mr([0]), s, a, i, p, u, c;
  for (let l = 0; l < r.size - 1; l++) {
    s = Ue(r, 0, l + 1), a = Ue(r, l + 1), u = We(tt(s), e), c = We(tt(a), e);
    let m = tt(oe(s, di(0, s.size)));
    i = We(m, tt(s));
    let f = Bs(a.shape, s.size), d = ge(di(0, a.size), f), h = oe(a, d);
    p = We(tt(h), tt(a));
    let g = ke(i, p), y = ke(i, p), b = oe(u, c);
    n = oe(oe(b, g), y);
    let C = cu(n, o);
    o = os(C, n, o), t10 = os(C, mr([l]), t10);
  }
  return t10;
}
var TT = T({ threshold_: hq });
function xq(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 N.runKernel(Jn, p, u);
}
var NT = T({ transform_: xq });
function yq(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 = z(di(0, s, 1, "int32"), [-1, 1]), p = di(0, a, 1, "int32"), u = ke(i, p), c = lu(Hp(u, be(+e, "int32")), If(u, be(-t10, "int32"))), l = Wr([s, a], o.dtype);
  return z(Ir(ko(z(o, [-1, s, a])).map((m) => os(c, m, l))), n);
}
var _T = T({ bandPart_: yq });
function bq(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 = $a(r, r.shape[0], 0).map((n) => jp(n, [0]));
  $(r.length <= r[0].shape[0], () => `Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);
  let t10 = [], o = r;
  for (let n = 0; n < r.length; ++n)
    t10.push(N.tidy(() => {
      let s = o[n];
      if (n > 0)
        for (let a = 0; a < n; ++a) {
          let i = oe(tt(oe(t10[a], s)), t10[a]);
          s = ke(s, i);
        }
      return We(s, pu(s, "euclidean"));
    }));
  return e ? Ir(t10, 0) : t10;
}
var ET = T({ gramSchmidt_: bq });
function Cq(r, e = false) {
  if ($(r.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`), r.rank === 2)
    return $T(r, e);
  {
    let t10 = r.shape.slice(0, r.shape.length - 2).reduce((p, u) => p * u), o = ko(z(r, [t10, r.shape[r.shape.length - 2], r.shape[r.shape.length - 1]]), 0), n = [], s = [];
    o.forEach((p) => {
      let [u, c] = $T(p, e);
      n.push(u), s.push(c);
    });
    let a = z(Ir(n, 0), r.shape), i = z(Ir(s, 0), r.shape);
    return [a, i];
  }
}
function $T(r, e = false) {
  return N.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 = yf(t10), s = zr(r), a = gi([[1]], [1, 1]), i = zr(a), p = t10 >= o ? o : t10;
    for (let u = 0; u < p; ++u) {
      let c = s, l = i, m = n;
      [i, s, n] = N.tidy(() => {
        let f = Ue(s, [u, u], [t10 - u, 1]), d = pu(f), h = Ue(s, [u, u], [1, 1]), g = os(cu(h, 0), gi([[-1]]), gi([[1]])), y = ke(h, oe(g, d)), b = We(f, y);
        b.shape[0] === 1 ? i = zr(a) : i = gt([a, Ue(b, [1, 0], [b.shape[0] - 1, b.shape[1]])], 0);
        let C = yr(We(Xe(g, y), d)), w = Ue(s, [u, 0], [t10 - u, o]), k = oe(C, i), _ = Wp(i);
        if (u === 0)
          s = ke(w, Xe(k, Xe(_, w)));
        else {
          let A = ke(w, Xe(k, Xe(_, w)));
          s = gt([Ue(s, [0, 0], [u, o]), A], 0);
        }
        let E = Wp(k), R = Ue(n, [0, u], [t10, n.shape[1] - u]);
        if (u === 0)
          n = ke(R, Xe(Xe(R, i), E));
        else {
          let A = ke(R, Xe(Xe(R, i), E));
          n = gt([Ue(n, [0, 0], [t10, u]), A], 1);
        }
        return [i, s, n];
      }), Ft([c, l, m]);
    }
    return !e && t10 > o && (n = Ue(n, [0, 0], [t10, o]), s = Ue(s, [0, 0], [o, o])), [n, s];
  });
}
var RT = T({ qr_: Cq });
var Et;
(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";
})(Et || (Et = {}));
function Iq(r, e, t10 = Et.SUM_BY_NONZERO_WEIGHTS) {
  let o = v(r, "losses", "computeWeightedLoss"), n = null;
  e != null && (n = v(e, "weights", "computeWeightedLoss"));
  let s = n == null ? o : oe(o, n);
  if (t10 === Et.NONE)
    return s;
  if (t10 === Et.SUM)
    return tt(s);
  if (t10 === Et.MEAN) {
    if (n == null)
      return mu(s);
    {
      let a = o.size / n.size, i = We(tt(s), tt(n));
      return a > 1 ? We(i, be(a)) : i;
    }
  }
  if (t10 === Et.SUM_BY_NONZERO_WEIGHTS) {
    if (n == null)
      return We(tt(s), be(o.size));
    {
      let a = oe(n, zs(o.shape)), i = qe(tt(Ff(a, be(0))), "float32");
      return We(tt(s), i);
    }
  }
  throw Error(`Unknown reduction: ${t10}`);
}
var sr = T({ computeWeightedLoss_: Iq });
function wq(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) {
  let n = v(r, "labels", "absoluteDifference"), s = v(e, "predictions", "absoluteDifference"), a = null;
  t10 != null && (a = v(t10, "weights", "absoluteDifference")), ht(n.shape, s.shape, "Error in absoluteDifference: ");
  let i = Qt(ke(n, s));
  return sr(i, a, o);
}
var AT = T({ absoluteDifference_: wq });
function Sq(r, e, t10, o, n = Et.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "labels", "cosineDistance"), a = v(e, "predictions", "cosineDistance"), i = null;
  o != null && (i = v(o, "weights", "cosineDistance")), ht(s.shape, a.shape, "Error in cosineDistance: ");
  let p = be(1), u = ke(p, tt(oe(s, a), t10, true));
  return sr(u, i, n);
}
var FT = T({ cosineDistance_: Sq });
function vq(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) {
  let n = v(r, "labels", "hingeLoss"), s = v(e, "predictions", "hingeLoss"), a = null;
  t10 != null && (a = v(t10, "weights", "hingeLoss")), ht(n.shape, s.shape, "Error in hingeLoss: ");
  let i = be(1);
  n = ke(oe(be(2), n), i);
  let p = hi(ke(i, oe(n, s)));
  return sr(p, a, o);
}
var DT = T({ hingeLoss_: vq });
function kq(r, e, t10, o = 1, n = Et.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "labels", "huberLoss"), a = v(e, "predictions", "huberLoss"), i = null;
  t10 != null && (i = v(t10, "weights", "huberLoss")), ht(s.shape, a.shape, "Error in huberLoss: ");
  let p = be(o), u = Qt(ke(a, s)), c = Af(u, p), l = ke(u, c), m = ge(oe(be(0.5), Zt(c)), oe(p, l));
  return sr(m, i, n);
}
var PT = T({ huberLoss_: kq });
function Tq(r, e, t10, o = 1e-7, n = Et.SUM_BY_NONZERO_WEIGHTS) {
  let s = v(r, "labels", "logLoss"), a = v(e, "predictions", "logLoss"), i = null;
  t10 != null && (i = v(t10, "weights", "logLoss")), ht(s.shape, a.shape, "Error in logLoss: ");
  let p = be(1), u = be(o), c = yr(oe(s, Ea(ge(a, u)))), l = oe(ke(p, s), Ea(ge(ke(p, a), u))), m = ke(c, l);
  return sr(m, i, n);
}
var OT = T({ logLoss_: Tq });
function Nq(r, e, t10, o = Et.SUM_BY_NONZERO_WEIGHTS) {
  let n = v(r, "labels", "meanSquaredError"), s = v(e, "predictions", "meanSquaredError"), a = null;
  t10 != null && (a = v(t10, "weights", "meanSquaredError")), ht(n.shape, s.shape, "Error in meanSquaredError: ");
  let i = Hf(n, s);
  return sr(i, a, o);
}
var MT = T({ meanSquaredError_: Nq });
function _q(r, e) {
  let t10 = v(r, "labels", "sigmoidCrossEntropyWithLogits"), o = v(e, "logits", "sigmoidCrossEntropyWithLogits");
  ht(t10.shape, o.shape, "Error in sigmoidCrossEntropyWithLogits: ");
  let n = hi(o), s = oe(o, t10), a = Sf(Bo(yr(Qt(o))));
  return ge(ke(n, s), a);
}
function Eq(r, e, t10, o = 0, n = Et.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")), ht(s.shape, a.shape, "Error in sigmoidCrossEntropy: "), o > 0) {
    let u = be(o), c = be(1), l = be(0.5);
    s = ge(oe(s, ke(c, u)), oe(l, u));
  }
  let p = _q(s, a);
  return sr(p, i, n);
}
var LT = T({ sigmoidCrossEntropy_: Eq });
function $q(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 Cr((n, s, a) => {
    let p = Tf(s, [t10], true), u = ke(qe(s, "float32"), p);
    a([n, u]);
    let c = yr(oe(u, n));
    return { value: tt(c, [t10]), gradFunc: (f, d) => {
      let [h, g] = d, y = Ta(f.shape, [t10]);
      return [oe(z(f, y), ke(qe(h, "float32"), Bo(g))), oe(z(f, y), ke(Bo(g), qe(h, "float32")))];
    } };
  })(r, e);
}
function Rq(r, e, t10, o = 0, n = Et.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")), ht(s.shape, a.shape, "Error in softmaxCrossEntropy: "), o > 0) {
    let u = be(o), c = be(1), l = be(s.shape[1]);
    s = ge(oe(s, ke(c, u)), We(u, l));
  }
  let p = $q(s, a);
  return sr(p, i, n);
}
var BT = T({ softmaxCrossEntropy_: Rq });
function Aq(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 = N.runKernel(Qa, p);
  return { outputIndices: u[0], outputValues: u[1], emptyRowIndicator: u[2], reverseIndexMap: u[3] };
}
var VT = T({ sparseFillEmptyRows_: Aq });
function Fq(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 = N.runKernel(ga, a);
  return { outputIndices: i[0], outputShape: i[1] };
}
var zT = T({ sparseReshape_: Fq });
function Dq(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 N.runKernel(Za, a);
}
var WT = T({ sparseSegmentMean_: Dq });
function Pq(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 N.runKernel(Ja, a);
}
var UT = T({ sparseSegmentSum_: Pq });
function Oq(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 = N.runKernel(Ns, l, c);
  return { nGrams: m[0], nGramsSplits: m[1] };
}
var GT = T({ stringNGrams_: Oq });
function Mq(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 = N.runKernel(ri, a, s);
  return { indices: i[0], values: i[1], shape: i[2] };
}
var HT = T({ stringSplit_: Mq });
function Lq(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 N.runKernel(oi, n, o);
}
var qT = T({ stringToHashBucketFast_: Lq });
var Bq = { fft: qp, ifft: hu, rfft: Kp, irfft: Gf };
var Vq = { hammingWindow: cT, hannWindow: Xf, frame: Yf, stft: lT };
var zq = { flipLeftRight: fT, grayscaleToRGB: dT, resizeNearestNeighbor: kT, resizeBilinear: vT, rotateWithOffset: hT, cropAndResize: mT, nonMaxSuppression: gT, nonMaxSuppressionAsync: bT, nonMaxSuppressionWithScore: CT, nonMaxSuppressionWithScoreAsync: IT, nonMaxSuppressionPadded: wT, nonMaxSuppressionPaddedAsync: ST, threshold: TT, transform: NT };
var Wq = { bandPart: _T, gramSchmidt: ET, qr: RT };
var Uq = { absoluteDifference: AT, computeWeightedLoss: sr, cosineDistance: FT, hingeLoss: DT, huberLoss: PT, logLoss: OT, meanSquaredError: MT, sigmoidCrossEntropy: LT, softmaxCrossEntropy: BT };
var Gq = { sparseFillEmptyRows: VT, sparseReshape: zT, sparseSegmentMean: WT, sparseSegmentSum: UT };
var Hq = { stringNGrams: GT, stringSplit: HT, stringToHashBucketFast: qT };
var wr = class extends ll {
  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 Ft(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 dC(e, t10);
  }
  dispose() {
    this.iterations_ != null && Ft(this.iterations_);
  }
  async saveIterations() {
    return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: be(this.iterations_, "int32") };
  }
  async getWeights() {
    throw new Error("getWeights() is not implemented for this optimizer yet.");
  }
  async setWeights(e) {
    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
  }
  async extractIterations(e) {
    return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
  }
};
Object.defineProperty(wr, Symbol.hasInstance, { value: (r) => r.minimize != null && r.computeGradients != null && r.applyGradients != null });
var xi = class extends wr {
  constructor(e, t10, o = null) {
    super(), this.learningRate = e, this.rho = t10, this.epsilon = o, this.accumulatedGrads = [], this.accumulatedUpdates = [], o == null && (this.epsilon = N.backend.epsilon());
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = N.registeredVariables[o], a = false;
      this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accum_grad`, variable: Ne(() => Gt(s).variable(a)) }), this.accumulatedUpdates[n] == null && (this.accumulatedUpdates[n] = { originalName: `${o}/accum_var`, variable: Ne(() => Gt(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;
      Ne(() => {
        let c = ge(oe(p, this.rho), oe(Zt(i), 1 - this.rho)), l = oe(We(Rr(ge(u, this.epsilon)), Rr(ge(p, this.epsilon))), i), m = ge(oe(u, this.rho), oe(Zt(l), 1 - this.rho));
        p.assign(c), u.assign(m);
        let f = ge(oe(l, -this.learningRate), s);
        s.assign(f);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedUpdates != null && (Ft(this.accumulatedGrads.map((e) => e.variable)), Ft(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);
  }
};
xi.className = "Adadelta";
$r(xi);
var yi = class extends wr {
  constructor(e, t10 = 0.1) {
    super(), this.learningRate = e, this.initialAccumulatorValue = t10, this.accumulatedGrads = [];
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = N.registeredVariables[o];
      this.accumulatedGrads[n] == null && (this.accumulatedGrads[n] = { originalName: `${o}/accumulator`, variable: Ne(() => Bs(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;
      Ne(() => {
        let p = ge(i, Zt(a));
        i.assign(p);
        let u = ge(oe(We(a, Rr(ge(p, N.backend.epsilon()))), -this.learningRate), s);
        s.assign(u);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedGrads != null && Ft(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);
  }
};
yi.className = "Adagrad";
$r(yi);
var bi = class extends wr {
  constructor(e, t10, o, n = null) {
    super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], Ne(() => {
      this.accBeta1 = be(t10).variable(), this.accBeta2 = be(o).variable();
    }), n == null && (this.epsilon = N.backend.epsilon());
  }
  applyGradients(e) {
    let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
    Ne(() => {
      let o = ke(1, this.accBeta1), n = ke(1, this.accBeta2);
      t10.forEach((s, a) => {
        let i = N.registeredVariables[s], p = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Ne(() => Gt(i).variable(p)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${s}/v`, variable: Ne(() => Gt(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 = ge(oe(c, this.beta1), oe(u, 1 - this.beta1)), f = ge(oe(l, this.beta2), oe(Zt(u), 1 - this.beta2)), d = We(m, o), h = We(f, n);
        c.assign(m), l.assign(f);
        let g = ge(oe(We(d, ge(Rr(h), this.epsilon)), -this.learningRate), i);
        i.assign(g);
      }), this.accBeta1.assign(oe(this.accBeta1, this.beta1)), this.accBeta2.assign(oe(this.accBeta2, this.beta2));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Ft(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Ft(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), Ne(() => {
      this.accBeta1.assign(Na(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(Na(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);
  }
};
bi.className = "Adam";
$r(bi);
var Ci = class extends wr {
  constructor(e, t10, o, n = null, s = 0) {
    super(), this.learningRate = e, this.beta1 = t10, this.beta2 = o, this.epsilon = n, this.decay = s, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], Ne(() => {
      this.iteration = be(0).variable(), this.accBeta1 = be(t10).variable();
    }), n == null && (this.epsilon = N.backend.epsilon());
  }
  applyGradients(e) {
    let t10 = Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e);
    Ne(() => {
      let o = ke(1, this.accBeta1), n = We(-this.learningRate, ge(oe(this.iteration, this.decay), 1));
      t10.forEach((s, a) => {
        let i = N.registeredVariables[s], p = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${s}/m`, variable: Gt(i).variable(p) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${s}/v`, variable: Gt(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 = ge(oe(c, this.beta1), oe(u, 1 - this.beta1)), f = oe(l, this.beta2), d = Qt(u), h = Rf(f, d);
        c.assign(m), l.assign(h);
        let g = ge(oe(We(n, o), We(m, ge(h, this.epsilon))), i);
        i.assign(g);
      }), this.iteration.assign(ge(this.iteration, 1)), this.accBeta1.assign(oe(this.accBeta1, this.beta1));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Ft(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Ft(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);
  }
};
Ci.className = "Adamax";
$r(Ci);
var Us = class extends wr {
  constructor(e) {
    super(), this.learningRate = e, this.setLearningRate(e);
  }
  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 = N.registeredVariables[o];
      Ne(() => {
        let i = ge(oe(this.c, s), a);
        a.assign(i);
      });
    }), this.incrementIterations();
  }
  setLearningRate(e) {
    this.learningRate = e, this.c != null && this.c.dispose(), this.c = So(be(-e));
  }
  dispose() {
    this.c.dispose();
  }
  async getWeights() {
    return [await this.saveIterations()];
  }
  async setWeights(e) {
    if (e = await this.extractIterations(e), e.length !== 0)
      throw new Error("SGD optimizer does not have settable weights.");
  }
  getConfig() {
    return { learningRate: this.learningRate };
  }
  static fromConfig(e, t10) {
    return new e(t10.learningRate);
  }
};
Us.className = "SGD";
$r(Us);
var Ii = class extends Us {
  constructor(e, t10, o = false) {
    super(e), this.learningRate = e, this.momentum = t10, this.useNesterov = o, this.accumulations = [], this.m = be(this.momentum);
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = N.registeredVariables[o];
      this.accumulations[n] == null && (this.accumulations[n] = { originalName: `${o}/momentum`, variable: Ne(() => Gt(s).variable(false)) });
      let a = this.accumulations[n].variable, i = Array.isArray(e) ? e[n].tensor : e[o];
      i != null && Ne(() => {
        let p, u = ge(oe(this.m, a), i);
        this.useNesterov ? p = ge(oe(this.c, ge(i, oe(u, this.m))), s) : p = ge(oe(this.c, u), s), a.assign(u), s.assign(p);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.m.dispose(), this.accumulations != null && Ft(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);
  }
};
Ii.className = "Momentum";
$r(Ii);
var wi = class extends wr {
  constructor(e, t10 = 0.9, o = 0, n = null, s = false) {
    if (super(), this.learningRate = e, this.decay = t10, this.momentum = o, this.epsilon = n, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = s, n == null && (this.epsilon = N.backend.epsilon()), e == null)
      throw new Error("learningRate for RMSPropOptimizer must be defined.");
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((o) => o.name) : Object.keys(e)).forEach((o, n) => {
      let s = N.registeredVariables[o], a = false;
      this.accumulatedMeanSquares[n] == null && (this.accumulatedMeanSquares[n] = { originalName: `${o}/rms`, variable: Ne(() => Gt(s).variable(a)) }), this.accumulatedMoments[n] == null && (this.accumulatedMoments[n] = { originalName: `${o}/momentum`, variable: Ne(() => Gt(s).variable(a)) }), this.accumulatedMeanGrads[n] == null && this.centered && (this.accumulatedMeanGrads[n] = { originalName: `${o}/mg`, variable: Ne(() => Gt(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;
      Ne(() => {
        let c = ge(oe(p, this.decay), oe(Zt(i), 1 - this.decay));
        if (this.centered) {
          let l = this.accumulatedMeanGrads[n].variable, m = ge(oe(l, this.decay), oe(i, 1 - this.decay)), f = We(oe(i, this.learningRate), Rr(ke(c, ge(Zt(m), this.epsilon)))), d = ge(oe(u, this.momentum), f);
          p.assign(c), l.assign(m), u.assign(d);
          let h = ke(s, d);
          s.assign(h);
        } else {
          let l = ge(oe(p, this.decay), oe(Zt(i), 1 - this.decay)), m = ge(oe(u, this.momentum), We(oe(i, this.learningRate), Rr(ge(l, this.epsilon))));
          p.assign(l), u.assign(m);
          let f = ke(s, m);
          s.assign(f);
        }
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedMeanSquares != null && Ft(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Ft(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Ft(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);
  }
};
wi.className = "RMSProp";
$r(wi);
var ns = class {
  static sgd(e) {
    return new Us(e);
  }
  static momentum(e, t10, o = false) {
    return new Ii(e, t10, o);
  }
  static rmsprop(e, t10 = 0.9, o = 0, n = null, s = false) {
    return new wi(e, t10, o, n, s);
  }
  static adam(e = 1e-3, t10 = 0.9, o = 0.999, n = null) {
    return new bi(e, t10, o, n);
  }
  static adadelta(e = 1e-3, t10 = 0.95, o = null) {
    return new xi(e, t10, o);
  }
  static adamax(e = 2e-3, t10 = 0.9, o = 0.999, n = null, s = 0) {
    return new Ci(e, t10, o, n, s);
  }
  static adagrad(e, t10 = 0.1) {
    return new yi(e, t10);
  }
};
var pMe = { sgd: ns.sgd, momentum: ns.momentum, adadelta: ns.adadelta, adagrad: ns.adagrad, rmsprop: ns.rmsprop, adamax: ns.adamax, adam: ns.adam };
var qq = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (r) => r())();
function kC() {
  return new Promise((r) => qq(() => r()));
}
var I = {};
Be(I, { ERF_A1: () => pK, ERF_A2: () => cK, ERF_A3: () => lK, ERF_A4: () => mK, ERF_A5: () => fK, ERF_P: () => uK, PARALLELIZE_THRESHOLD: () => ed, RowPartitionType: () => Gs, SELU_SCALE: () => iK, SELU_SCALEALPHA: () => aK, applyActivation: () => yu, assertAndGetBroadcastShape: () => Je, assertAxesAreInnerMostDims: () => fG, assertParamsConsistent: () => Kq, assignToTypedArray: () => bK, axesAreInnerMostDims: () => fC, calculateShapes: () => kv, checkEinsumDimSizes: () => kK, checkPadOnDimRoundingMode: () => Ot, combineLocations: () => $k, combineRaggedTensorToTensorShapes: () => Xq, complexWithEvenIndex: () => gK, complexWithOddIndex: () => xK, computeConv2DInfo: () => uu, computeConv3DInfo: () => Zv, computeDefaultPad: () => mC, computeDilation2DInfo: () => dU, computeOptimalWindowSize: () => Jq, computeOutAndReduceShapes: () => mG, computeOutShape: () => jq, computePool2DInfo: () => lC, computePool3DInfo: () => hU, convertConv2DDataFormat: () => Jv, decodeEinsumEquation: () => SK, eitherStridesOrDilationsAreOne: () => lr, expandShapeToKeepDim: () => Ta, exponent: () => IK, exponents: () => CK, fromStringArrayToUint8: () => qK, fromUint8ToStringArray: () => HK, getAxesPermutation: () => dG, getBroadcastDims: () => Iv, getComplexWithIndex: () => yK, getEinsumComputePath: () => TK, getEinsumPermutation: () => vK, getFusedBiasGradient: () => xu, getFusedDyActivation: () => gu, getImageCenter: () => eK, getInnerMostAxes: () => gG, getPermuted: () => rK, getRaggedRank: () => Qq, getReductionAxes: () => nf, getReshaped: () => tK, getReshapedPermuted: () => oK, getRowPartitionTypesHelper: () => Yq, getSliceBeginCoords: () => nK, getSliceSize: () => sK, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => $K, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => RK, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => AK, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => PK, getSparseReshapeInputOutputMismatchErrorMessage: () => MK, getSparseReshapeInputOutputMultipleErrorMessage: () => OK, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => FK, getSparseReshapeNegativeOutputDimErrorMessage: () => DK, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => zK, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => LK, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => BK, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => VK, getUndoAxesPermutation: () => hG, isIdentityPermutation: () => NK, log: () => Sz, mergeRealAndImagArrays: () => dK, prepareAndValidate: () => vv, prepareSplitSize: () => EK, segment_util: () => NC, shouldFuse: () => bu, slice_util: () => et, splitRealAndImagArrays: () => hK, tupleValuesAreOne: () => iu, upcastType: () => ct, validateDefaultValueShape: () => Zq, validateInput: () => uf, validateUpdateShape: () => aC, warn: () => Rs });
function Kq(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 ${s}.`);
  });
}
function jq(r, e) {
  let t10 = r[0].slice();
  for (let o = 1; o < r.length; o++)
    t10[e] += r[o][e];
  return t10;
}
var Gs;
(function(r) {
  r[r.FIRST_DIM_SIZE = 0] = "FIRST_DIM_SIZE", r[r.VALUE_ROWIDS = 1] = "VALUE_ROWIDS", r[r.ROW_LENGTHS = 2] = "ROW_LENGTHS", r[r.ROW_SPLITS = 3] = "ROW_SPLITS", r[r.ROW_LIMITS = 4] = "ROW_LIMITS", r[r.ROW_STARTS = 5] = "ROW_STARTS";
})(Gs || (Gs = {}));
function Xq(r, e, t10) {
  let o = new Array();
  if (t10 == null && e == null)
    return o;
  if (e == null)
    for (; o.length < r + t10.length; )
      o.push(-1);
  else
    o = e.slice();
  if (t10 == null)
    return o;
  if (r + t10.length !== o.length)
    throw new Error(`rt input.shape and shape=${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}] = ${i}`);
      } else
        o[a] = s;
  }
  return o;
}
function Yq(r) {
  let e = { FIRST_DIM_SIZE: Gs.FIRST_DIM_SIZE, VALUE_ROWIDS: Gs.VALUE_ROWIDS, ROW_LENGTHS: Gs.ROW_LENGTHS, ROW_SPLITS: Gs.ROW_SPLITS, ROW_LIMITS: Gs.ROW_LIMITS, ROW_STARTS: Gs.ROW_STARTS }, t10 = [];
  for (let o of r)
    if (o in e)
      t10.push(e[o]);
    else
      break;
  return t10;
}
function Qq(r) {
  return r.length === 0 ? 0 : r[0] === Gs.FIRST_DIM_SIZE ? r.length - 1 : r.length;
}
function Zq(r, e) {
  if (r == null || e == null)
    return;
  let t10 = r.length, o = e.length;
  if (t10 >= o)
    throw new Error(`defaultValue.shape=${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}] = ${a}`);
  }
}
var ed = 30;
function Jq(r) {
  return r <= ed ? r : sp(r, Math.floor(Math.sqrt(r)));
}
function eK(r, e, t10) {
  let o = t10 * (typeof r == "number" ? r : r[0]), n = e * (typeof r == "number" ? r : r[1]);
  return [o, n];
}
function tK(r, e, t10, o = true) {
  let n = [];
  if (o)
    n = n.concat(e.slice(0)), n.push(r[0] / t10), n = n.concat(r.slice(1));
  else {
    n = n.concat(r[0]);
    let s = e.length;
    for (let a = 0; a < s; ++a)
      n = n.concat([r[a + 1] / e[a], e[a]]);
    n = n.concat(r.slice(s + 1));
  }
  return n;
}
function rK(r, e, t10 = true) {
  let o = [];
  if (t10) {
    o.push(e);
    for (let n = e + 1; n < r; ++n)
      n <= 2 * e ? (o.push(n), o.push(n - (e + 1))) : o.push(n);
  } else {
    let n = [], s = [];
    for (let a = 1; a < r; ++a)
      a >= e * 2 + 1 || a % 2 === 1 ? s.push(a) : n.push(a);
    o.push(...n), o.push(0), o.push(...s);
  }
  return o;
}
function oK(r, e, t10, o = true) {
  let n = [];
  o ? n.push(r[0] / t10) : n.push(r[0] * t10);
  for (let s = 1; s < r.length; ++s)
    s <= e.length ? o ? n.push(e[s - 1] * r[s]) : n.push(r[s] / e[s - 1]) : n.push(r[s]);
  return n;
}
function nK(r, e) {
  let t10 = [0];
  for (let o = 0; o < e; ++o)
    t10.push(r[o][0]);
  return t10;
}
function sK(r, e, t10) {
  let o = r.slice(0, 1);
  for (let n = 0; n < t10; ++n)
    o.push(r[n + 1] - e[n][0] - e[n][1]);
  return o;
}
var aK = 1.7580993408473768;
var iK = 1.0507009873554805;
var uK = 0.3275911;
var pK = 0.254829592;
var cK = -0.284496736;
var lK = 1.421413741;
var mK = -1.453152027;
var fK = 1.061405429;
function dK(r, e) {
  if (r.length !== e.length)
    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${e.length}.`);
  let t10 = new Float32Array(r.length * 2);
  for (let o = 0; o < t10.length; o += 2)
    t10[o] = r[o / 2], t10[o + 1] = e[o / 2];
  return t10;
}
function hK(r) {
  let e = new Float32Array(r.length / 2), t10 = new Float32Array(r.length / 2);
  for (let o = 0; o < r.length; o += 2)
    e[o / 2] = r[o], t10[o / 2] = r[o + 1];
  return { real: e, imag: t10 };
}
function gK(r) {
  let e = Math.ceil(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e);
  for (let n = 0; n < r.length; n += 4)
    t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
  return { real: t10, imag: o };
}
function xK(r) {
  let e = Math.floor(r.length / 4), t10 = new Float32Array(e), o = new Float32Array(e);
  for (let n = 2; n < r.length; n += 4)
    t10[Math.floor(n / 4)] = r[n], o[Math.floor(n / 4)] = r[n + 1];
  return { real: t10, imag: o };
}
function yK(r, e) {
  let t10 = r[e * 2], o = r[e * 2 + 1];
  return { real: t10, imag: o };
}
function bK(r, e, t10, o) {
  r[o * 2] = e, r[o * 2 + 1] = t10;
}
function CK(r, e) {
  let t10 = new Float32Array(r / 2), o = new Float32Array(r / 2);
  for (let n = 0; n < Math.ceil(r / 2); n++) {
    let s = (e ? 2 : -2) * Math.PI * (n / r);
    t10[n] = Math.cos(s), o[n] = Math.sin(s);
  }
  return { real: t10, imag: o };
}
function IK(r, e, t10) {
  let o = (t10 ? 2 : -2) * Math.PI * (r / e), n = Math.cos(o), s = Math.sin(o);
  return { real: n, imag: s };
}
var TC = "->";
var wK = /->/g;
var KT = ",";
var jT = "...";
function SK(r, e) {
  r = r.replace(/\s/g, "");
  let t10 = (r.length - r.replace(wK, "").length) / TC.length;
  if (t10 < 1)
    throw new Error("Equations without an arrow are not supported.");
  if (t10 > 1)
    throw new Error(`Equation must contain exactly one arrow ("${TC}").`);
  let [o, n] = r.split(TC);
  $(o.indexOf(jT) === -1, () => `The ellipsis notation ("${jT}") is not supported yet.`);
  let s = o.split(KT), 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 f = n[m];
    if (!s.some((d) => d.indexOf(f) !== -1))
      throw new Error(`Output subscripts contain the label ${f} not present in the input subscripts.`);
    i.indexOf(f) === -1 && i.push(f);
  }
  for (let m = 0; m < o.length; ++m) {
    let f = o[m];
    i.indexOf(f) === -1 && f !== KT && i.push(f);
  }
  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 f = 0; f < s[m].length; ++f)
      p[m].push(i.indexOf(s[m][f]));
  }
  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 vK(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 kK(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 TK(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 = _K(e, i);
    for (let u of p)
      s.indexOf(u) === -1 && (o[a].push(u), s.push(u));
  }
  return { path: t10, steps: o };
}
function NK(r) {
  return r.every((e, t10) => e === t10);
}
function _K(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 EK(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 $K(r) {
  return `Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${r}`;
}
function RK(r, e) {
  return `indices(${r}, 0) is invalid: ${e} < 0`;
}
function AK(r, e, t10) {
  return `indices(${r}, 0) is invalid: ${e} >= ${t10}`;
}
function FK(r, e) {
  return `only one output dimension may be -1, not both ${r} and ${e}`;
}
function DK(r, e) {
  return `size ${r} must be non-negative, not ${e}`;
}
function PK() {
  return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function OK(r, e) {
  let t10 = Ve(r), o = Ve(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 MK(r, e) {
  let t10 = Ve(r), o = Ve(e);
  return `Input to reshape is a tensor with ${t10} dense values, but the requested shape has ${o}. inputShape=${r} outputShape=${e}`;
}
function LK() {
  return "segment ids must be >= 0";
}
function BK() {
  return "segment ids are not increasing";
}
function VK(r, e) {
  return `Segment id ${r} out of range [0, ${e}), possibly because segmentIds input is not sorted.`;
}
function zK(r, e, t10) {
  return `Bad: indices[${r}] == ${e} out of range [0, ${t10})`;
}
var NC = {};
Be(NC, { collectGatherOpShapeInfo: () => GK, computeOutShape: () => UK, segOpComputeOptimalWindowSize: () => WK });
function WK(r, e) {
  let t10 = false, o;
  for (r <= ed ? (o = r, t10 = true) : o = sp(r, Math.floor(Math.sqrt(r))); !t10; )
    o > e || o === r ? t10 = true : o = sp(r, o + 1);
  return o;
}
function UK(r, e, t10) {
  let o = [], n = r.length;
  for (let s = 0; s < n; s++)
    s !== e ? o.push(r[s]) : o.push(t10);
  return o;
}
function GK(r, e, t10, o) {
  let n = e.shape.length, s = r.shape.length;
  if (o !== 0 && (o < -n || o > n))
    throw new Error(`Expect batchDims in the range of [-${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 HK(r) {
  try {
    return r.map((e) => Op(e));
  } catch (e) {
    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${e}`);
  }
}
function qK(r) {
  return r.map((e) => si(e));
}
var Bt = {};
Be(Bt, { nonMaxSuppressionV3Impl: () => Qf, nonMaxSuppressionV4Impl: () => Zf, nonMaxSuppressionV5Impl: () => Jf, whereImpl: () => Kf });
var KK = P();
KK.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 To;
(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";
})(To || (To = {}));
var XT;
(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 = {}));
})(XT || (XT = {}));
var EC = {};
function XK(r, e) {
  let t10 = { tfOpName: r, category: "custom", inputs: [], attrs: [], customExecutor: e };
  EC[r] = t10;
}
function td(r) {
  return EC[r];
}
function YK(r) {
  delete EC[r];
}
function S(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 Ht(e.inputNames[s.inputIndexStart], t10, o, n);
    if (s.type === "tensors")
      return e.inputNames.slice(i, p).map((m) => Ht(m, t10, o, n));
    let u = Ht(e.inputNames.slice(i)[0], t10, o, n), c = u.dataSync();
    return s.type === "number" ? c[0] : x.toNestedArray(u.shape, c);
  }
  let a = e.attrParams[r];
  return a && a.value;
}
function Ht(r, e, t10, o) {
  let [n, s] = Sr(r);
  if (o != null) {
    let i = o.getHashTableHandleByName(n);
    if (i != null)
      return i;
  }
  let a = t10.currentContextIds.find((i) => !!e[rd(n, i)]);
  return a !== void 0 ? e[rd(n, a)][s] : void 0;
}
function YT(r, e, t10) {
  return e[rd(r, t10.currentContextId)];
}
function zo(r, e) {
  let [t10, o, n] = Sr(r);
  return [rd(t10, e && e.currentContextId), o, n];
}
function rd(r, e) {
  return e ? `${r}-${e}` : r;
}
function Sr(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 gl(r, e, t10) {
  let o = S("pad", r, e, t10);
  if (o === "explicit") {
    o = S("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 ss(r) {
  return r.kept ? r : zr(r);
}
var $C = {};
Be($C, { json: () => QK });
var QK = [{ 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 RC = {};
Be(RC, { json: () => ZK });
var ZK = [{ 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 AC = {};
Be(AC, { json: () => JK });
var JK = [{ 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 FC = {};
Be(FC, { json: () => e6 });
var e6 = [{ 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 DC = {};
Be(DC, { json: () => t6 });
var t6 = [{ 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 PC = {};
Be(PC, { json: () => r6 });
var r6 = [{ 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 OC = {};
Be(OC, { json: () => o6 });
var o6 = [{ 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 MC = {};
Be(MC, { json: () => n6 });
var n6 = [{ 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 LC = {};
Be(LC, { json: () => s6 });
var s6 = [{ 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" }] }];
var BC = {};
Be(BC, { json: () => a6 });
var a6 = [{ 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 VC = {};
Be(VC, { json: () => i6 });
var i6 = [{ 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 zC = {};
Be(zC, { json: () => u6 });
var u6 = [{ 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 WC = {};
Be(WC, { json: () => p6 });
var p6 = [{ 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 UC = {};
Be(UC, { json: () => c6 });
var c6 = [{ 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 GC = {};
Be(GC, { json: () => l6 });
var l6 = [{ 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 HC = {};
Be(HC, { json: () => m6 });
var m6 = [{ 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 qC = {};
Be(qC, { json: () => f6 });
var f6 = [{ 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 KC = {};
Be(KC, { json: () => d6 });
var d6 = [{ 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 jC = {};
Be(jC, { json: () => h6 });
var h6 = [{ 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 xl = class {
  constructor() {
    let e = [$C, RC, AC, FC, DC, PC, OC, MC, LC, BC, VC, zC, WC, UC, GC, HC, qC, KC, jC], t10 = [].concat(...e.map((o) => o.json));
    this.opMappers = t10.reduce((o, n) => (o[n.tfOpName] = n, o), {});
  }
  static get Instance() {
    return this._instance || (this._instance = new this());
  }
  transformGraph(e, t10 = {}) {
    let o = e.node, n = [], s = [], a = [], i = o.reduce((h, g) => (h[g.name] = this.mapNode(g), g.op.startsWith("Placeholder") ? n.push(h[g.name]) : g.op === "Const" ? s.push(h[g.name]) : (g.input == null || g.input.length === 0) && a.push(h[g.name]), h), {}), p = [], u = [], c = {}, l = {};
    t10 != null && (c = this.mapSignatureEntries(t10.inputs), l = this.mapSignatureEntries(t10.outputs));
    let m = Object.keys(i);
    m.forEach((h) => {
      let g = i[h];
      g.inputNames.forEach((y, b) => {
        let [C, , w] = zo(y), k = i[C];
        if (k.outputs != null) {
          let _ = k.outputs.indexOf(w);
          if (_ !== -1) {
            let E = `${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] = zo(h), y = i[g];
      y != null && (y.signatureKey = l[h], u.push(y));
    }), Object.keys(c).length > 0 ? Object.keys(c).forEach((h) => {
      let [g] = zo(h), y = i[g];
      y && (y.signatureKey = c[h], p.push(y));
    }) : p = n;
    let f = {};
    e.library != null && e.library.function != null && (f = e.library.function.reduce((h, g) => (h[g.signature.name] = this.mapFunction(g), h), {}));
    let d = { nodes: i, inputs: p, outputs: u, weights: s, placeholders: n, signature: t10, functions: f };
    return a.length > 0 && (d.initNodes = a), d;
  }
  mapSignatureEntries(e) {
    return Object.keys(e || {}).reduce((t10, o) => (t10[e[o].name] = o, t10), {});
  }
  mapNode(e) {
    let t10 = td(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 = od(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = od(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "string[]":
          i = cd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = cd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "number":
          i = sd(e.attr, s.tfName, s.defaultValue || 0), i === void 0 && !!s.tfDeprecatedName && (i = sd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "number[]":
          i = pd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = pd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "bool":
          i = nd(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = nd(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "bool[]":
          i = md(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = md(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "shape":
          i = ud(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = ud(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "shape[]":
          i = ld(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = ld(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "dtype":
          i = ad(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = ad(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "dtype[]":
          i = id(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = id(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "func":
          i = QT(e.attr, s.tfName, s.defaultValue), i === void 0 && !!s.tfDeprecatedName && (i = QT(e.attr, s.tfDeprecatedName, s.defaultValue));
          break;
        case "tensor":
        case "tensors":
          break;
        default:
          throw new Error(`Unsupported param type: ${s.type} for op: ${e.op}`);
      }
      return n[s.name] = { value: i, type: a }, n;
    }, {})), o;
  }
  mapFunction(e) {
    let t10 = e.nodeDef, o = [], n = [], s = {};
    t10 != null && (s = t10.reduce((l, m) => (l[m.name] = this.mapNode(m), m.op === "Const" && n.push(l[m.name]), l), {}));
    let a = [], i = [];
    e.signature.inputArg.forEach((l) => {
      let [m] = zo(l.name), f = { name: m, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: XC(l.type), type: "dtype" } }, children: [] };
      f.signatureKey = l.name, a.push(f), s[m] = f;
    }), Object.keys(s).forEach((l) => {
      let m = s[l];
      m.inputNames.forEach((f, d) => {
        let [h, , g] = zo(f), y = s[h];
        if (y.outputs != null) {
          let b = y.outputs.indexOf(g);
          if (b !== -1) {
            let C = `${h}:${b}`;
            m.inputNames[d] = C;
          }
        }
        m.inputs.push(y), y.children.push(m);
      });
    });
    let u = e.ret;
    e.signature.outputArg.forEach((l) => {
      let [m, f] = zo(u[l.name]), d = s[m];
      d != null && (d.defaultOutput = f, i.push(d));
    });
    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 g6(r) {
  let e = P().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 ZT(r, e) {
  let t10 = Array.isArray(r) ? String.fromCharCode.apply(null, r) : g6(r);
  return e ? t10 : t10.toLowerCase();
}
function od(r, e, t10, o = false) {
  let n = r[e];
  return n != null ? ZT(n.s, o) : t10;
}
function nd(r, e, t10) {
  let o = r[e];
  return o ? o.b : t10;
}
function sd(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 XC(r) {
  switch (typeof r == "string" && (r = To[r]), r) {
    case To.DT_FLOAT:
    case To.DT_HALF:
      return "float32";
    case To.DT_INT32:
    case To.DT_INT64:
    case To.DT_INT8:
    case To.DT_UINT8:
      return "int32";
    case To.DT_BOOL:
      return "bool";
    case To.DT_DOUBLE:
      return "float32";
    case To.DT_STRING:
      return "string";
    default:
      return null;
  }
}
function QT(r, e, t10) {
  let o = r[e];
  return o && o.func ? o.func.name : t10;
}
function ad(r, e, t10) {
  let o = r[e];
  return o && o.type ? XC(o.type) : t10;
}
function id(r, e, t10) {
  let o = r[e];
  return o && o.list && o.list.type ? o.list.type.map((n) => XC(n)) : t10;
}
function JT(r) {
  if (!r.unknownRank)
    return r.dim != null ? r.dim.map((e) => typeof e.size == "number" ? e.size : parseInt(e.size, 10)) : [];
}
function ud(r, e, t10) {
  let o = r[e];
  return o && o.shape ? JT(o.shape) : t10;
}
function pd(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 cd(r, e, t10, o = false) {
  let n = r[e];
  return n && n.list && n.list.s ? n.list.s.map((s) => ZT(s, o)) : t10;
}
function ld(r, e, t10) {
  let o = r[e];
  return o && o.list && o.list.shape ? o.list.shape.map((n) => JT(n)) : t10;
}
function md(r, e, t10) {
  let o = r[e];
  return o && o.list && o.list.b ? o.list.b : t10;
}
var fd = 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 Ht(e, this.tensorMap, this.context);
  }
  getAttr(e, t10) {
    let o = this.node.rawAttrs[e];
    if (o.tensor != null)
      return Ht(e, this.tensorMap, this.context);
    if (o.i != null || o.f != null)
      return sd(this.node.rawAttrs, e, t10);
    if (o.s != null)
      return od(this.node.rawAttrs, e, t10);
    if (o.b != null)
      return nd(this.node.rawAttrs, e, t10);
    if (o.shape != null)
      return ud(this.node.rawAttrs, e, t10);
    if (o.type != null)
      return ad(this.node.rawAttrs, e, t10);
    if (o.list != null) {
      if (o.list.i != null || o.list.f != null)
        return pd(this.node.rawAttrs, e, t10);
      if (o.list.s != null)
        return cd(this.node.rawAttrs, e, t10);
      if (o.list.shape != null)
        return ld(this.node.rawAttrs, e, t10);
      if (o.list.b != null)
        return md(this.node.rawAttrs, e, t10);
      if (o.list.type != null)
        return id(this.node.rawAttrs, e, t10);
    }
    return t10;
  }
};
var rt = {};
Be(rt, { OP_SCOPE_SUFFIX: () => Ub, abs: () => Qt, acos: () => Vv, acosh: () => zv, add: () => ge, addN: () => Wv, all: () => Uv, any: () => Gv, argMax: () => Hv, argMin: () => qv, asin: () => Kv, asinh: () => jv, atan: () => Xv, atan2: () => Yv, atanh: () => Qv, avgPool: () => mf, avgPool3d: () => ek, basicLSTMCell: () => tk, batchNorm: () => li, batchNorm2d: () => ok, batchNorm3d: () => nk, batchNorm4d: () => sk, batchToSpaceND: () => ff, bincount: () => df, booleanMaskAsync: () => _H, broadcastArgs: () => ak, broadcastTo: () => Ls, buffer: () => ne, cast: () => qe, ceil: () => ik, clipByValue: () => uk, clone: () => zr, complex: () => Er, concat: () => gt, concat1d: () => pk, concat2d: () => ck, concat3d: () => lk, concat4d: () => mk, conv1d: () => fk, conv2d: () => mi, conv2dTranspose: () => dk, conv3d: () => hk, conv3dTranspose: () => xk, cos: () => yk, cosh: () => bk, cosineWindow: () => hl, cumprod: () => Ck, cumsum: () => Ik, denseBincount: () => wk, depthToSpace: () => Sk, depthwiseConv2d: () => Gp, diag: () => vk, dilation2d: () => kk, div: () => We, divNoNan: () => Tk, dot: () => Nk, dropout: () => BH, einsum: () => _k, elu: () => xf, enclosingPowerOfTwo: () => wC, equal: () => gf, erf: () => Ek, euclideanNorm: () => Ak, exp: () => Bo, expandDims: () => _a, expm1: () => Fk, eye: () => yf, fft: () => qp, fill: () => Bs, floor: () => bf, floorDiv: () => cf, fused: () => SC, gather: () => Cf, gatherND: () => MH, greater: () => cu, greaterEqual: () => If, ifft: () => hu, imag: () => ci, image: () => zq, inTopKAsync: () => zH, irfft: () => Gf, isFinite: () => Dk, isInf: () => Pk, isNaN: () => Ok, leakyRelu: () => wf, less: () => Mk, lessEqual: () => Hp, linalg: () => Wq, linspace: () => Lk, localResponseNormalization: () => Bk, log: () => Ea, log1p: () => Sf, logSigmoid: () => Vk, logSoftmax: () => zk, logSumExp: () => Tf, logicalAnd: () => lu, logicalNot: () => Nf, logicalOr: () => _f, logicalXor: () => Wk, losses: () => Uq, lowerBound: () => Uk, matMul: () => Xe, max: () => Vs, maxPool: () => $f, maxPool3d: () => Gk, maxPoolWithArgmax: () => Hk, maximum: () => Rf, mean: () => mu, meshgrid: () => qk, min: () => fl, minimum: () => Af, mirrorPad: () => Kk, mod: () => jk, moments: () => Xk, movingAverage: () => $H, mul: () => oe, multiRNNCell: () => Yk, multinomial: () => Qk, neg: () => yr, norm: () => pu, notEqual: () => Ff, oneHot: () => pl, ones: () => zs, onesLike: () => Zk, op: () => T, outerProduct: () => Jk, pad: () => Ws, pad1d: () => e1, pad2d: () => t1, pad3d: () => r1, pad4d: () => o1, pool: () => n1, pow: () => Na, prelu: () => Pf, print: () => ef, prod: () => s1, raggedGather: () => a1, raggedRange: () => i1, raggedTensorToTensor: () => u1, rand: () => p1, randomGamma: () => T1, randomNormal: () => Vf, randomStandardNormal: () => N1, randomUniform: () => zf, range: () => di, real: () => ka, reciprocal: () => _1, relu: () => hi, relu6: () => Wf, reshape: () => z, reverse: () => vo, reverse1d: () => E1, reverse2d: () => $1, reverse3d: () => R1, reverse4d: () => A1, rfft: () => Kp, round: () => Uf, rsqrt: () => F1, scalar: () => be, scatterND: () => AH, searchSorted: () => dl, selu: () => D1, separableConv2d: () => P1, setdiff1dAsync: () => O1, sigmoid: () => Ms, sign: () => M1, signal: () => Vq, sin: () => L1, sinh: () => B1, slice: () => Ue, slice1d: () => V1, slice2d: () => z1, slice3d: () => W1, slice4d: () => U1, softmax: () => G1, softplus: () => kf, spaceToBatchND: () => Df, sparse: () => Gq, sparseToDense: () => PH, spectral: () => Bq, split: () => $a, sqrt: () => Rr, square: () => Zt, squaredDifference: () => Hf, squeeze: () => jp, stack: () => Ir, step: () => qf, stridedSlice: () => H1, string: () => Hq, sub: () => ke, sum: () => tt, tan: () => q1, tanh: () => ml, tensor: () => nr, tensor1d: () => mr, tensor2d: () => gi, tensor3d: () => sf, tensor4d: () => K1, tensor5d: () => j1, tensor6d: () => X1, tile: () => fi, topk: () => Y1, transpose: () => Wp, truncatedNormal: () => Q1, unique: () => Z1, unsortedSegmentSum: () => J1, unstack: () => ko, upperBound: () => eT, variable: () => tT, where: () => os, whereAsync: () => jf, zeros: () => Wr, zerosLike: () => Gt });
var eN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "BiasAdd":
    case "AddV2":
    case "Add":
      return [o.add(S("a", r, e, t10), S("b", r, e, t10))];
    case "AddN":
      return [o.addN(S("tensors", r, e, t10))];
    case "FloorMod":
    case "Mod":
      return [o.mod(S("a", r, e, t10), S("b", r, e, t10))];
    case "Mul":
      return [o.mul(S("a", r, e, t10), S("b", r, e, t10))];
    case "RealDiv":
    case "Div":
      return [o.div(S("a", r, e, t10), S("b", r, e, t10))];
    case "DivNoNan":
      return [o.divNoNan(S("a", r, e, t10), S("b", r, e, t10))];
    case "FloorDiv":
      return [o.floorDiv(S("a", r, e, t10), S("b", r, e, t10))];
    case "Sub":
      return [o.sub(S("a", r, e, t10), S("b", r, e, t10))];
    case "Minimum":
      return [o.minimum(S("a", r, e, t10), S("b", r, e, t10))];
    case "Maximum":
      return [o.maximum(S("a", r, e, t10), S("b", r, e, t10))];
    case "Pow":
      return [o.pow(S("a", r, e, t10), S("b", r, e, t10))];
    case "SquaredDifference":
      return [o.squaredDifference(S("a", r, e, t10), S("b", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var tN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Abs":
    case "ComplexAbs":
      return [o.abs(S("x", r, e, t10))];
    case "Acos":
      return [o.acos(S("x", r, e, t10))];
    case "Acosh":
      return [o.acosh(S("x", r, e, t10))];
    case "Asin":
      return [o.asin(S("x", r, e, t10))];
    case "Asinh":
      return [o.asinh(S("x", r, e, t10))];
    case "Atan":
      return [o.atan(S("x", r, e, t10))];
    case "Atan2":
      return [o.atan2(S("x", r, e, t10), S("y", r, e, t10))];
    case "Atanh":
      return [o.atanh(S("x", r, e, t10))];
    case "Ceil":
      return [o.ceil(S("x", r, e, t10))];
    case "Complex":
      return [o.complex(S("real", r, e, t10), S("imag", r, e, t10))];
    case "Cos":
      return [o.cos(S("x", r, e, t10))];
    case "Cosh":
      return [o.cosh(S("x", r, e, t10))];
    case "Elu":
      return [o.elu(S("x", r, e, t10))];
    case "Erf":
      return [o.erf(S("x", r, e, t10))];
    case "Exp":
      return [o.exp(S("x", r, e, t10))];
    case "Expm1":
      return [o.expm1(S("x", r, e, t10))];
    case "Floor":
      return [o.floor(S("x", r, e, t10))];
    case "Log":
      return [o.log(S("x", r, e, t10))];
    case "Log1p":
      return [o.log1p(S("x", r, e, t10))];
    case "Imag":
      return [o.imag(S("x", r, e, t10))];
    case "Neg":
      return [o.neg(S("x", r, e, t10))];
    case "Reciprocal":
      return [o.reciprocal(S("x", r, e, t10))];
    case "Real":
      return [o.real(S("x", r, e, t10))];
    case "Relu":
      return [o.relu(S("x", r, e, t10))];
    case "Round":
      return [o.round(S("x", r, e, t10))];
    case "Selu":
      return [o.selu(S("x", r, e, t10))];
    case "Sigmoid":
      return [o.sigmoid(S("x", r, e, t10))];
    case "Sin":
      return [o.sin(S("x", r, e, t10))];
    case "Sign":
      return [o.sign(S("x", r, e, t10))];
    case "Sinh":
      return [o.sinh(S("x", r, e, t10))];
    case "Softplus":
      return [o.softplus(S("x", r, e, t10))];
    case "Sqrt":
      return [o.sqrt(S("x", r, e, t10))];
    case "Square":
      return [o.square(S("x", r, e, t10))];
    case "Tanh":
      return [o.tanh(S("x", r, e, t10))];
    case "Tan":
      return [o.tan(S("x", r, e, t10))];
    case "ClipByValue":
      return [o.clipByValue(S("x", r, e, t10), S("clipValueMin", r, e, t10), S("clipValueMax", r, e, t10))];
    case "Relu6":
      return [o.relu6(S("x", r, e, t10))];
    case "Rsqrt":
      return [o.rsqrt(Ht(r.inputNames[0], e, t10))];
    case "Prod":
      return [o.prod(S("x", r, e, t10), S("axes", r, e, t10))];
    case "LeakyRelu":
      return [o.leakyRelu(S("x", r, e, t10), S("alpha", r, e, t10))];
    case "Prelu":
      return [o.prelu(S("x", r, e, t10), S("alpha", r, e, t10))];
    case "IsNan":
      return [o.isNaN(Ht(r.inputNames[0], e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function Ur(r, e, t10 = "") {
  if (!(typeof r == "number" || typeof e == "number")) {
    x.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];
      x.assert(n < 0 || s < 0 || n === s, () => t10 + ` Shapes ${r} and ${e} must match`);
    }
  }
}
function rN(r) {
  return !(typeof r == "number" || r.some((e) => e < 0));
}
function Xp(r, e, t10) {
  let o = dd(r, t10), n = !rN(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 = dd(s.shape, o);
  }), !rN(o))
    throw new Error(`Non-fully-defined elementShape: ${o}`);
  return o;
}
function dd(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 hd = 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 = be(0), So(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), Ur(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, So(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 Ur(this.elementShape, o[0].shape, "TensorArray shape mismatch: "), Ir(o, 0);
  }
  concat(e) {
    if (!!e && e !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);
    if (this.size() === 0)
      return nr([], [0].concat(this.elementShape));
    let t10 = [];
    for (let n = 0; n < this.size(); n++)
      t10.push(n);
    let o = this.readMany(t10);
    return Ur(this.elementShape, o[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`), gt(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, ko(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 = [];
    Ne(() => {
      t10 = z(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] = z(Ue(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 Ra = 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}`);
      Ur(t10, s.shape, "TensorList shape mismatch: "), So(s);
    }), this.idTensor = be(0), this.maxNumElements = n, So(this.idTensor);
  }
  get id() {
    return this.idTensor.id;
  }
  copy() {
    return new Ra([...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.`);
    Ur(e, this.elementShape, "TensorList shape mismatch: ");
    let n = Xp(this.elementShape, this.tensors, e);
    return Ne(() => {
      let s = this.tensors.map((a) => z(a, n));
      return Ir(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 = Xp(this.elementShape, this.tensors, e), n = this.tensors.pop();
    return n.kept = false, Ur(n.shape, e, "TensorList shape mismatch: "), z(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 (Ur(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
      throw new Error("Trying to push element into a full list.");
    So(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 Ra([], 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.`);
    Ur(this.tensors[e].shape, t10, "TensorList shape mismatch: ");
    let n = Xp(this.elementShape, this.tensors, t10);
    return z(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.`);
    Ur(this.elementShape, t10.shape, "TensorList shape mismatch: "), So(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}`);
    Ur(this.elementShape, o, "TensorList shape mismatch: "), e = e.slice(0, this.size());
    let n = Xp(this.elementShape, this.tensors, o);
    return e.length === 0 ? nr([], [0].concat(n)) : Ne(() => {
      let s = e.map((a) => z(this.tensors[a], n));
      return Ir(s, 0);
    });
  }
  concat(e, t10) {
    if (!!e && e !== this.elementDtype)
      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
    Ur(this.elementShape, t10, "TensorList shape mismatch: ");
    let o = Xp(this.elementShape, this.tensors, t10);
    return this.size() === 0 ? nr([], [0].concat(o)) : Ne(() => {
      let n = this.tensors.map((s) => z(s, o));
      return gt(n, 0);
    });
  }
};
function oN(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);
  Ur(n, e, "TensorList shape mismatch: ");
  let s = ko(r);
  return new Ra(s, e, o);
}
function nN(r, e, t10, o) {
  return new Ra([], r, e, o);
}
function sN(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 Ra([], t10, r.dtype, o), a = ko(r, 0);
  return e.forEach((i, p) => {
    s.setItem(i, a[p]);
  }), s;
}
function aN(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 = dd(s, t10), i = o === 0 ? 0 : r.size / o, p = Ne(() => {
    let c = [];
    r = z(r, [1, o, i]);
    for (let l = 0; l < e.length; ++l) {
      let f = [0, l === 0 ? 0 : n[l - 1], 0], d = [1, e[l], i];
      c[l] = z(Ue(r, f, d), a);
    }
    return r.dispose(), c;
  }), u = new Ra([], t10, r.dtype, e.length);
  for (let c = 0; c < p.length; c++)
    u.setItem(c, p[c]);
  return u;
}
var iN = async (r, e, t10) => {
  switch (r.op) {
    case "If":
    case "StatelessIf": {
      let o = S("thenBranch", r, e, t10), n = S("elseBranch", r, e, t10), s = S("cond", r, e, t10), a = S("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 = S("body", r, e, t10), n = S("cond", r, e, t10), s = S("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((f) => f.id);
        c.forEach((f) => {
          !f.kept && i.indexOf(f.id) === -1 && l.indexOf(f.id) === -1 && f.dispose();
        });
        let m = await t10.functionMap[n].executeFunctionAsync(u, t10.tensorArrayMap, t10.tensorListMap);
        p = await m[0].data(), m.forEach((f) => {
          !f.kept && i.indexOf(f.id) === -1 && l.indexOf(f.id) === -1 && f.dispose();
        });
      }
      return u;
    }
    case "LoopCond": {
      let o = S("pred", r, e, t10);
      return [ss(o)];
    }
    case "Switch": {
      let o = S("pred", r, e, t10), n = S("data", r, e, t10);
      return n.kept || (n = ss(n)), (await o.data())[0] ? [void 0, n] : [n, void 0];
    }
    case "Merge": {
      let o = r.inputNames.find((n) => Ht(n, e, t10) !== void 0);
      if (o) {
        let n = Ht(o, e, t10);
        return [ss(n)];
      }
      return;
    }
    case "Enter": {
      let o = S("frameName", r, e, t10), n = S("tensor", r, e, t10);
      return t10.enterFrame(o), [ss(n)];
    }
    case "Exit": {
      let o = S("tensor", r, e, t10);
      return t10.exitFrame(), [ss(o)];
    }
    case "NextIteration": {
      let o = S("tensor", r, e, t10);
      return t10.nextIteration(), [ss(o)];
    }
    case "TensorArrayV3": {
      let o = S("size", r, e, t10), n = S("dtype", r, e, t10), s = S("elementShape", r, e, t10), a = S("dynamicSize", r, e, t10), i = S("clearAfterRead", r, e, t10), p = S("identicalElementShapes", r, e, t10), u = S("name", r, e, t10), c = new hd(u, n, o, s, p, a, i);
      return t10.addTensorArray(c), [c.idTensor, be(1)];
    }
    case "TensorArrayWriteV3": {
      let o = S("tensorArrayId", r, e, t10), n = S("index", r, e, t10), s = S("tensor", r, e, t10), a = t10.getTensorArray(o.id);
      return a.write(n, s), [a.idTensor];
    }
    case "TensorArrayReadV3": {
      let o = S("tensorArrayId", r, e, t10), n = S("index", r, e, t10);
      return [t10.getTensorArray(o.id).read(n)];
    }
    case "TensorArrayGatherV3": {
      let o = S("tensorArrayId", r, e, t10), n = S("indices", r, e, t10), s = S("dtype", r, e, t10);
      return [t10.getTensorArray(o.id).gather(n, s)];
    }
    case "TensorArrayScatterV3": {
      let o = S("tensorArrayId", r, e, t10), n = S("indices", r, e, t10), s = S("tensor", r, e, t10), a = t10.getTensorArray(o.id);
      return a.scatter(n, s), [a.idTensor];
    }
    case "TensorArrayConcatV3": {
      let o = S("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id), s = S("dtype", r, e, t10);
      return [n.concat(s)];
    }
    case "TensorArraySplitV3": {
      let o = S("tensorArrayId", r, e, t10), n = S("tensor", r, e, t10), s = S("lengths", r, e, t10), a = t10.getTensorArray(o.id);
      return a.split(s, n), [a.idTensor];
    }
    case "TensorArraySizeV3": {
      let o = S("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id);
      return [be(n.size(), "int32")];
    }
    case "TensorArrayCloseV3": {
      let o = S("tensorArrayId", r, e, t10), n = t10.getTensorArray(o.id);
      return n.clearAndClose(), [n.idTensor];
    }
    case "TensorListSetItem": {
      let o = S("tensorListId", r, e, t10), n = S("index", r, e, t10), s = S("tensor", r, e, t10), a = t10.getTensorList(o.id);
      return a.setItem(n, s), [a.idTensor];
    }
    case "TensorListGetItem": {
      let o = S("tensorListId", r, e, t10), n = S("index", r, e, t10), s = S("elementShape", r, e, t10), a = S("elementDType", r, e, t10);
      return [t10.getTensorList(o.id).getItem(n, s, a)];
    }
    case "TensorListScatterV2":
    case "TensorListScatter": {
      let o = S("indices", r, e, t10), n = S("tensor", r, e, t10), s = S("elementShape", r, e, t10), a = S("numElements", r, e, t10), i = sN(n, o, s, a);
      return t10.addTensorList(i), [i.idTensor];
    }
    case "TensorListReserve":
    case "EmptyTensorList": {
      let o = S("elementShape", r, e, t10), n = S("elementDType", r, e, t10), s;
      r.op === "TensorListReserve" ? s = "numElements" : s = "maxNumElements";
      let a = S(s, r, e, t10), i = r.op === "TensorListReserve" ? -1 : a, p = nN(o, n, a, i);
      return t10.addTensorList(p), [p.idTensor];
    }
    case "TensorListGather": {
      let o = S("tensorListId", r, e, t10), n = S("indices", r, e, t10), s = S("elementShape", r, e, t10), a = S("elementDType", r, e, t10);
      return [t10.getTensorList(o.id).gather(n, a, s)];
    }
    case "TensorListStack": {
      let o = S("tensorListId", r, e, t10), n = S("elementShape", r, e, t10), s = S("elementDType", r, e, t10), a = S("numElements", r, e, t10);
      return [t10.getTensorList(o.id).stack(n, s, a)];
    }
    case "TensorListFromTensor": {
      let o = S("tensor", r, e, t10), n = S("elementShape", r, e, t10), s = S("elementDType", r, e, t10), a = oN(o, n, s);
      return t10.addTensorList(a), [a.idTensor];
    }
    case "TensorListConcat":
    case "TensorListConcatV2": {
      let o = S("tensorListId", r, e, t10), n = t10.getTensorList(o.id), s = S("dtype", r, e, t10), a = S("elementShape", r, e, t10);
      return [n.concat(s, a)];
    }
    case "TensorListPushBack": {
      let o = S("tensorListId", r, e, t10), n = S("tensor", r, e, t10), s = t10.getTensorList(o.id);
      return s.pushBack(n), [s.idTensor];
    }
    case "TensorListPopBack": {
      let o = S("tensorListId", r, e, t10), n = S("elementShape", r, e, t10), s = S("elementDType", r, e, t10);
      return [t10.getTensorList(o.id).popBack(n, s)];
    }
    case "TensorListSplit": {
      let o = S("tensor", r, e, t10), n = S("elementShape", r, e, t10), s = S("lengths", r, e, t10), a = aN(o, s, n);
      return t10.addTensorList(a), [a.idTensor];
    }
    case "TensorListLength": {
      let o = S("tensorListId", r, e, t10), n = t10.getTensorList(o.id);
      return [be(n.size(), "int32")];
    }
    case "TensorListResize": {
      let o = S("tensorListId", r, e, t10), n = S("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 uN(r, e, t10) {
  let [o, n] = S("fusedOps", r, e, t10), s = o === "biasadd", a = !s, i = n === "prelu", p = o === "fusedbatchnorm", u = S("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 = S("strides", r, e, t10), l = gl(r, e, t10), m = S("dataFormat", r, e, t10).toUpperCase(), f = S("dilations", r, e, t10), [d, h] = S("args", r, e, t10);
  a && (h = d, d = void 0);
  let g = S("leakyreluAlpha", r, e, t10);
  return { stride: c, pad: l, dataFormat: m, dilations: f, biasArg: d, preluArg: h, activationFunc: n, leakyreluAlpha: g };
}
var pN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Conv1D": {
      let n = S("stride", r, e, t10), s = S("pad", r, e, t10), a = S("dataFormat", r, e, t10).toUpperCase(), i = S("dilation", r, e, t10);
      return [o.conv1d(S("x", r, e, t10), S("filter", r, e, t10), n, s, a, i)];
    }
    case "Conv2D": {
      let n = S("strides", r, e, t10), s = gl(r, e, t10), a = S("dataFormat", r, e, t10).toUpperCase(), i = S("dilations", r, e, t10);
      return [o.conv2d(S("x", r, e, t10), S("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 } = uN(r, e, t10);
      return [o.fused.conv2d({ x: S("x", r, e, t10), filter: S("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 } = uN(r, e, t10);
      return [o.fused.depthwiseConv2d({ x: S("x", r, e, t10), filter: S("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 = S("outputShape", r, e, t10), s = S("strides", r, e, t10), a = gl(r, e, t10);
      return [o.conv2dTranspose(S("x", r, e, t10), S("filter", r, e, t10), n, [s[1], s[2]], a)];
    }
    case "DepthwiseConv2dNative":
    case "DepthwiseConv2d": {
      let n = S("strides", r, e, t10), s = gl(r, e, t10), a = S("dilations", r, e, t10), i = S("dataFormat", r, e, t10).toUpperCase();
      return [o.depthwiseConv2d(S("input", r, e, t10), S("filter", r, e, t10), [n[1], n[2]], s, i, [a[1], a[2]])];
    }
    case "Conv3D": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("dataFormat", r, e, t10).toUpperCase(), i = S("dilations", r, e, t10);
      return [o.conv3d(S("x", r, e, t10), S("filter", r, e, t10), [n[1], n[2], n[3]], s, a, [i[1], i[2], i[3]])];
    }
    case "AvgPool": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("kernelSize", r, e, t10);
      return [o.avgPool(S("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)];
    }
    case "MaxPool": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("kernelSize", r, e, t10);
      return [o.maxPool(S("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s)];
    }
    case "MaxPoolWithArgmax": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("kernelSize", r, e, t10), i = S("includeBatchInIndex", r, e, t10), { result: p, indexes: u } = o.maxPoolWithArgmax(S("x", r, e, t10), [a[1], a[2]], [n[1], n[2]], s, i);
      return [p, u];
    }
    case "AvgPool3D": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("kernelSize", r, e, t10);
      return [o.avgPool3d(S("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
    }
    case "MaxPool3D": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("kernelSize", r, e, t10);
      return [o.maxPool3d(S("x", r, e, t10), [a[1], a[2], a[3]], [n[1], n[2], n[3]], s)];
    }
    case "Dilation2D": {
      let n = S("strides", r, e, t10), s = S("pad", r, e, t10), a = S("dilations", r, e, t10), i = n[1], p = n[2], u = a[1], c = a[2];
      return [o.dilation2d(S("x", r, e, t10), S("filter", r, e, t10), [i, p], s, [u, c], "NHWC")];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var cN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Fill": {
      let n = S("shape", r, e, t10), s = S("dtype", r, e, t10), a = S("value", r, e, t10);
      return [o.fill(n, a, s)];
    }
    case "LinSpace": {
      let n = S("start", r, e, t10), s = S("stop", r, e, t10), a = S("num", r, e, t10);
      return [o.linspace(n, s, a)];
    }
    case "Multinomial": {
      let n = S("logits", r, e, t10), s = S("numSamples", r, e, t10), a = S("seed", r, e, t10);
      return [o.multinomial(n, s, a)];
    }
    case "OneHot": {
      let n = S("indices", r, e, t10), s = S("depth", r, e, t10), a = S("onValue", r, e, t10), i = S("offValue", r, e, t10), p = S("dtype", r, e, t10);
      return [o.oneHot(n, s, a, i, p)];
    }
    case "Ones":
      return [o.ones(S("shape", r, e, t10), S("dtype", r, e, t10))];
    case "OnesLike":
      return [o.onesLike(S("x", r, e, t10))];
    case "RandomStandardNormal":
      return [o.randomStandardNormal(S("shape", r, e, t10), S("dtype", r, e, t10), S("seed", r, e, t10))];
    case "RandomUniform":
      return [o.randomUniform(S("shape", r, e, t10), S("minval", r, e, t10), S("maxval", r, e, t10), S("dtype", r, e, t10))];
    case "Range": {
      let n = S("start", r, e, t10), s = S("stop", r, e, t10), a = S("step", r, e, t10);
      return [o.range(n, s, a, S("dtype", r, e, t10))];
    }
    case "TruncatedNormal": {
      let n = S("shape", r, e, t10), s = S("mean", r, e, t10), a = S("stdDev", r, e, t10), i = S("seed", r, e, t10);
      return [o.truncatedNormal(n, s, a, S("dtype", r, e, t10), i)];
    }
    case "Zeros":
      return [o.zeros(S("shape", r, e, t10), S("dtype", r, e, t10))];
    case "ZerosLike":
      return [o.zerosLike(S("x", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function YC(r, e, t10) {
  let o = S("boxes", r, e, t10), n = S("scores", r, e, t10), s = S("maxOutputSize", r, e, t10), a = S("iouThreshold", r, e, t10), i = S("scoreThreshold", r, e, t10), p = S("softNmsSigma", r, e, t10);
  return { boxes: o, scores: n, maxOutputSize: s, iouThreshold: a, scoreThreshold: i, softNmsSigma: p };
}
var lN = async (r, e, t10, o, n = rt) => {
  switch (r.op) {
    case "NonMaxSuppressionV5": {
      let { boxes: s, scores: a, maxOutputSize: i, iouThreshold: p, scoreThreshold: u, softNmsSigma: c } = YC(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 } = YC(r, e, t10), c = S("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 } = YC(r, e, t10);
      return [await n.image.nonMaxSuppressionAsync(s, a, i, p, u)];
    }
    case "Where": {
      let s = n.cast(S("condition", r, e, t10), "bool"), a = [await n.whereAsync(s)];
      return s.dispose(), a;
    }
    case "ListDiff":
      return n.setdiff1dAsync(S("x", r, e, t10), S("y", r, e, t10));
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var mN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "LowerBound": {
      let n = S("sortedSequence", r, e, t10), s = S("values", r, e, t10);
      return [o.lowerBound(n, s)];
    }
    case "TopKV2": {
      let n = S("x", r, e, t10), s = S("k", r, e, t10), a = S("sorted", r, e, t10), i = o.topk(n, s, a);
      return [i.values, i.indices];
    }
    case "UpperBound": {
      let n = S("sortedSequence", r, e, t10), s = S("values", r, e, t10);
      return [o.upperBound(n, s)];
    }
    case "Unique": {
      let n = S("x", r, e, t10), s = o.unique(n);
      return [s.values, s.indices];
    }
    case "UniqueV2": {
      let n = S("x", r, e, t10), s = S("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 fN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Const":
      return e[r.name];
    case "PlaceholderWithDefault":
      let n = S("default", r, e, t10);
      return [Ht(r.name, e, t10) || n];
    case "Placeholder":
      return [Ht(r.name, e, t10)];
    case "Identity":
    case "StopGradient":
    case "FakeQuantWithMinMaxVars": {
      let c = S("x", r, e, t10);
      return [ss(c)];
    }
    case "IdentityN":
      return S("x", r, e, t10).map((c) => ss(c));
    case "Snapshot":
      let s = S("x", r, e, t10);
      return [ss(s)];
    case "Shape":
      return [o.tensor1d(S("x", r, e, t10).shape, "int32")];
    case "ShapeN":
      return S("x", r, e, t10).map((c) => o.tensor1d(c.shape));
    case "Size":
      return [o.scalar(S("x", r, e, t10).size, "int32")];
    case "Rank":
      return [o.scalar(S("x", r, e, t10).rank, "int32")];
    case "NoOp":
      return [o.scalar(1)];
    case "Print":
      let a = S("x", r, e, t10), i = S("data", r, e, t10), p = S("message", r, e, t10), u = S("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 gd = class {
  constructor(e, t10) {
    this.keyDType = e, this.valueDType = t10, this.handle = be(0), this.tensorMap = /* @__PURE__ */ new Map(), So(this.handle);
  }
  get id() {
    return this.handle.id;
  }
  clearAndClose() {
    this.tensorMap.forEach((e) => e.dispose()), this.tensorMap.clear(), this.handle.dispose();
  }
  size() {
    return this.tensorMap.size;
  }
  tensorSize() {
    return be(this.size(), "int32");
  }
  async import(e, t10) {
    this.checkKeyAndValueTensor(e, t10);
    let o = await e.data();
    return this.tensorMap.forEach((n) => n.dispose()), this.tensorMap.clear(), Ne(() => {
      let n = ko(t10), s = o.length, a = n.length;
      x.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];
        So(u), this.tensorMap.set(p, u);
      }
      return this.handle;
    });
  }
  async find(e, t10) {
    this.checkKeyAndValueTensor(e, t10);
    let o = await e.data();
    return Ne(() => {
      let n = [];
      for (let s = 0; s < o.length; s++) {
        let a = o[s], i = this.findWithDefault(a, t10);
        n.push(i);
      }
      return Ir(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 dN = 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 = S("keyDType", r, e, t10), a = S("valueDType", r, e, t10), i = new gd(s, a);
        return o.addHashTable(r.name, i), [i.handle];
      }
    }
    case "LookupTableImport":
    case "LookupTableImportV2": {
      let n = S("tableHandle", r, e, t10, o), s = S("keys", r, e, t10), a = S("values", r, e, t10);
      return [await o.getHashTableById(n.id).import(s, a)];
    }
    case "LookupTableFind":
    case "LookupTableFindV2": {
      let n = S("tableHandle", r, e, t10, o), s = S("keys", r, e, t10), a = S("defaultValue", r, e, t10);
      return [await o.getHashTableById(n.id).find(s, a)];
    }
    case "LookupTableSize":
    case "LookupTableSizeV2": {
      let n = S("tableHandle", r, e, t10, o);
      return [o.getHashTableById(n.id).tensorSize()];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var hN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "ResizeBilinear": {
      let n = S("images", r, e, t10), s = S("size", r, e, t10), a = S("alignCorners", r, e, t10), i = S("halfPixelCenters", r, e, t10);
      return [o.image.resizeBilinear(n, [s[0], s[1]], a, i)];
    }
    case "ResizeNearestNeighbor": {
      let n = S("images", r, e, t10), s = S("size", r, e, t10), a = S("alignCorners", r, e, t10), i = S("halfPixelCenters", r, e, t10);
      return [o.image.resizeNearestNeighbor(n, [s[0], s[1]], a, i)];
    }
    case "CropAndResize": {
      let n = S("image", r, e, t10), s = S("boxes", r, e, t10), a = S("boxInd", r, e, t10), i = S("cropSize", r, e, t10), p = S("method", r, e, t10), u = S("extrapolationValue", r, e, t10);
      return [o.image.cropAndResize(n, s, a, i, p, u)];
    }
    case "ImageProjectiveTransformV3": {
      let n = S("images", r, e, t10), s = S("transforms", r, e, t10), a = S("outputShape", r, e, t10), i = S("fillValue", r, e, t10), p = S("interpolation", r, e, t10), u = S("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 gN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Equal":
      return [o.equal(S("a", r, e, t10), S("b", r, e, t10))];
    case "NotEqual":
      return [o.notEqual(S("a", r, e, t10), S("b", r, e, t10))];
    case "Greater":
      return [o.greater(S("a", r, e, t10), S("b", r, e, t10))];
    case "GreaterEqual":
      return [o.greaterEqual(S("a", r, e, t10), S("b", r, e, t10))];
    case "Less":
      return [o.less(S("a", r, e, t10), S("b", r, e, t10))];
    case "LessEqual":
      return [o.lessEqual(S("a", r, e, t10), S("b", r, e, t10))];
    case "LogicalAnd":
      return [o.logicalAnd(S("a", r, e, t10), S("b", r, e, t10))];
    case "LogicalNot":
      return [o.logicalNot(S("a", r, e, t10))];
    case "LogicalOr":
      return [o.logicalOr(S("a", r, e, t10), S("b", r, e, t10))];
    case "Select":
    case "SelectV2":
      return [o.where(S("condition", r, e, t10), S("a", r, e, t10), S("b", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var xN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "BatchMatMul":
    case "BatchMatMulV2":
    case "MatMul":
      return [o.matMul(S("a", r, e, t10), S("b", r, e, t10), S("transposeA", r, e, t10), S("transposeB", r, e, t10))];
    case "Einsum":
      return [o.einsum(S("equation", r, e, t10), ...S("tensors", r, e, t10))];
    case "Transpose":
      return [o.transpose(S("x", r, e, t10), S("perm", r, e, t10))];
    case "_FusedMatMul":
      let [n, s] = S("fusedOps", r, e, t10), a = n === "biasadd", i = s === "prelu", p = S("numArgs", r, e, t10), u = S("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] = S("args", r, e, t10);
      return [o.fused.matMul({ a: S("a", r, e, t10), b: S("b", r, e, t10), transposeA: S("transposeA", r, e, t10), transposeB: S("transposeB", r, e, t10), bias: c, activation: s, preluActivationWeights: l, leakyreluAlpha: u })];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var yN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "EuclideanNorm":
      return [o.euclideanNorm(S("x", r, e, t10), S("axis", r, e, t10), S("keepDims", r, e, t10))];
    case "FusedBatchNorm":
    case "FusedBatchNormV2":
      return [o.batchNorm(S("x", r, e, t10), S("mean", r, e, t10), S("variance", r, e, t10), S("offset", r, e, t10), S("scale", r, e, t10), S("epsilon", r, e, t10))];
    case "FusedBatchNormV3":
      return [o.batchNorm(S("x", r, e, t10), S("mean", r, e, t10), S("variance", r, e, t10), S("offset", r, e, t10), S("scale", r, e, t10), S("epsilon", r, e, t10))];
    case "LRN":
      return [o.localResponseNormalization(S("x", r, e, t10), S("radius", r, e, t10), S("bias", r, e, t10), S("alpha", r, e, t10), S("beta", r, e, t10))];
    case "Softmax":
      return [o.softmax(S("x", r, e, t10))];
    case "LogSoftmax":
      return [o.logSoftmax(S("x", r, e, t10))];
    case "SparseToDense":
      return [o.sparseToDense(S("sparseIndices", r, e, t10), S("outputShape", r, e, t10), S("sparseValues", r, e, t10), S("defaultValue", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var bN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Max": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.max(S("x", r, e, t10), i, p)];
    }
    case "Mean": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.mean(S("x", r, e, t10), i, p)];
    }
    case "Min": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.min(S("x", r, e, t10), i, p)];
    }
    case "Sum": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.sum(S("x", r, e, t10), i, p)];
    }
    case "All": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.all(S("x", r, e, t10), i, p)];
    }
    case "Any": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.any(S("x", r, e, t10), i, p)];
    }
    case "ArgMax": {
      let i = S("axis", r, e, t10);
      return [o.argMax(S("x", r, e, t10), i)];
    }
    case "ArgMin": {
      let i = S("axis", r, e, t10);
      return [o.argMin(S("x", r, e, t10), i)];
    }
    case "Prod": {
      let i = S("axis", r, e, t10), p = S("keepDims", r, e, t10);
      return [o.prod(S("x", r, e, t10), i, p)];
    }
    case "Cumprod": {
      let i = S("axis", r, e, t10), p = S("exclusive", r, e, t10), u = S("reverse", r, e, t10);
      return [o.cumprod(S("x", r, e, t10), i, p, u)];
    }
    case "Cumsum": {
      let i = S("axis", r, e, t10), p = S("exclusive", r, e, t10), u = S("reverse", r, e, t10);
      return [o.cumsum(S("x", r, e, t10), i, p, u)];
    }
    case "Bincount":
      let n = S("x", r, e, t10), s = S("weights", r, e, t10), a = S("size", r, e, t10);
      return [o.bincount(n, s, a)];
    case "DenseBincount": {
      let i = S("x", r, e, t10), p = S("weights", r, e, t10), u = S("size", r, e, t10), c = S("binaryOutput", r, e, t10);
      return [o.denseBincount(i, p, u, c)];
    }
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var CN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "ConcatV2":
    case "Concat": {
      let n = S("n", r, e, t10), s = S("axis", r, e, t10), a = S("tensors", r, e, t10);
      return a = a.slice(0, n), [o.concat(a, s)];
    }
    case "Gather": {
      let n = S("x", r, e, t10), s = S("indices", r, e, t10);
      return [o.gather(n, o.cast(s, "int32"), 0)];
    }
    case "GatherV2": {
      let n = S("axis", r, e, t10), s = S("batchDims", r, e, t10), a = S("x", r, e, t10), i = S("indices", r, e, t10);
      return [o.gather(a, o.cast(i, "int32"), n, s)];
    }
    case "Reverse": {
      let n = S("dims", r, e, t10), s = [];
      for (let i = 0; i < n.length; i++)
        n[i] && s.push(i);
      let a = S("x", r, e, t10);
      return [o.reverse(a, s)];
    }
    case "ReverseV2": {
      let n = S("axis", r, e, t10), s = S("x", r, e, t10);
      return [o.reverse(s, n)];
    }
    case "Slice": {
      let n = S("begin", r, e, t10), s = S("size", r, e, t10);
      return [o.slice(S("x", r, e, t10), n, s)];
    }
    case "StridedSlice": {
      let n = S("begin", r, e, t10), s = S("end", r, e, t10), a = S("strides", r, e, t10), i = S("beginMask", r, e, t10), p = S("endMask", r, e, t10), u = S("ellipsisMask", r, e, t10), c = S("newAxisMask", r, e, t10), l = S("shrinkAxisMask", r, e, t10), m = S("x", r, e, t10);
      return [o.stridedSlice(m, n, s, a, i, p, u, c, l)];
    }
    case "Pack":
      return Ne(() => {
        let n = S("axis", r, e, t10), s = S("tensors", r, e, t10), a = s[0].shape, i = o.squeeze(s[0]).shape, p = s.map((u) => {
          let c = x.arraysEqual(u.shape, a);
          if (!c && !x.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 = S("axis", r, e, t10), s = S("tensor", r, e, t10);
      return o.unstack(s, n);
    }
    case "Tile": {
      let n = S("reps", r, e, t10);
      return [o.tile(S("x", r, e, t10), n)];
    }
    case "Split":
    case "SplitV": {
      let n = S("axis", r, e, t10), s = S("numOrSizeSplits", r, e, t10), a = S("x", r, e, t10);
      return o.split(a, s, n);
    }
    case "ScatterNd": {
      let n = S("indices", r, e, t10), s = S("values", r, e, t10), a = S("shape", r, e, t10);
      return [o.scatterND(n, s, a)];
    }
    case "GatherNd": {
      let n = S("x", r, e, t10), s = S("indices", r, e, t10);
      return [o.gatherND(n, s)];
    }
    case "SparseToDense": {
      let n = S("sparseIndices", r, e, t10), s = S("outputShape", r, e, t10), a = S("sparseValues", r, e, t10), i = S("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 IN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "SparseFillEmptyRows": {
      let { outputIndices: n, outputValues: s, emptyRowIndicator: a, reverseIndexMap: i } = o.sparse.sparseFillEmptyRows(S("indices", r, e, t10), S("values", r, e, t10), S("denseShape", r, e, t10), S("defaultValue", r, e, t10));
      return [n, s, a, i];
    }
    case "SparseReshape": {
      let { outputIndices: n, outputShape: s } = o.sparse.sparseReshape(S("inputIndices", r, e, t10), S("inputShape", r, e, t10), S("newShape", r, e, t10));
      return [n, s];
    }
    case "SparseSegmentMean":
      return [o.sparse.sparseSegmentMean(S("data", r, e, t10), S("indices", r, e, t10), S("segmentIds", r, e, t10))];
    case "SparseSegmentSum":
      return [o.sparse.sparseSegmentSum(S("data", r, e, t10), S("indices", r, e, t10), S("segmentIds", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var wN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "FFT":
      return [o.fft(S("x", r, e, t10))];
    case "IFFT":
      return [o.ifft(S("x", r, e, t10))];
    case "RFFT":
      return [o.rfft(S("x", r, e, t10))];
    case "IRFFT":
      return [o.irfft(S("x", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var SN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "StringNGrams": {
      let { nGrams: n, nGramsSplits: s } = o.string.stringNGrams(S("data", r, e, t10), S("dataSplits", r, e, t10), S("separator", r, e, t10), S("nGramWidths", r, e, t10), S("leftPad", r, e, t10), S("rightPad", r, e, t10), S("padWidth", r, e, t10), S("preserveShortSequences", r, e, t10));
      return [n, s];
    }
    case "StringSplit": {
      let { indices: n, values: s, shape: a } = o.string.stringSplit(S("input", r, e, t10), S("delimiter", r, e, t10), S("skipEmpty", r, e, t10));
      return [n, s, a];
    }
    case "StringToHashBucketFast":
      return [o.string.stringToHashBucketFast(S("input", r, e, t10), S("numBuckets", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
var vN = (r, e, t10, o = rt) => {
  switch (r.op) {
    case "Cast":
      return [o.cast(S("x", r, e, t10), S("dtype", r, e, t10))];
    case "ExpandDims": {
      let n = S("axis", r, e, t10);
      return [o.expandDims(S("x", r, e, t10), n)];
    }
    case "Squeeze": {
      let n = S("axis", r, e, t10);
      return [o.squeeze(S("x", r, e, t10), n)];
    }
    case "Reshape":
      return [o.reshape(S("x", r, e, t10), S("shape", r, e, t10))];
    case "MirrorPad":
      return [o.mirrorPad(S("x", r, e, t10), S("padding", r, e, t10), S("mode", r, e, t10))];
    case "PadV2":
    case "Pad":
      return [o.pad(S("x", r, e, t10), S("padding", r, e, t10), S("constantValue", r, e, t10))];
    case "SpaceToBatchND": {
      let n = S("blockShape", r, e, t10), s = S("paddings", r, e, t10);
      return [o.spaceToBatchND(S("x", r, e, t10), n, s)];
    }
    case "BatchToSpaceND": {
      let n = S("blockShape", r, e, t10), s = S("crops", r, e, t10);
      return [o.batchToSpaceND(S("x", r, e, t10), n, s)];
    }
    case "DepthToSpace": {
      let n = S("blockSize", r, e, t10), s = S("dataFormat", r, e, t10).toUpperCase();
      return [o.depthToSpace(S("x", r, e, t10), n, s)];
    }
    case "BroadcastTo":
      return [o.broadcastTo(S("x", r, e, t10), S("shape", r, e, t10))];
    case "BroadcastArgs":
      return [o.broadcastArgs(S("s0", r, e, t10), S("s1", r, e, t10))];
    default:
      throw TypeError(`Node type ${r.op} is not implemented`);
  }
};
function QC(r, e, t10, o, n = Ne) {
  let s = ((a, i, p) => {
    switch (a.category) {
      case "arithmetic":
        return n(() => eN(a, i, p));
      case "basic_math":
        return n(() => tN(a, i, p));
      case "control":
        return iN(a, i, p);
      case "convolution":
        return n(() => pN(a, i, p));
      case "creation":
        return n(() => cN(a, i, p));
      case "dynamic":
        return lN(a, i, p);
      case "evaluation":
        return n(() => mN(a, i, p));
      case "image":
        return n(() => hN(a, i, p));
      case "graph":
        return n(() => fN(a, i, p));
      case "logical":
        return n(() => gN(a, i, p));
      case "matrices":
        return n(() => xN(a, i, p));
      case "normalization":
        return n(() => yN(a, i, p));
      case "reduction":
        return n(() => bN(a, i, p));
      case "slice_join":
        return n(() => CN(a, i, p));
      case "sparse":
        return n(() => IN(a, i, p));
      case "spectral":
        return n(() => wN(a, i, p));
      case "string":
        return n(() => SN(a, i, p));
      case "transformation":
        return n(() => vN(a, i, p));
      case "hash_table":
        return dN(a, i, p, o);
      case "custom":
        let u = td(a.op);
        if (u && u.customExecutor)
          return u.customExecutor(new fd(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 x.isPromise(s) ? s.then((a) => [].concat(a)) : [].concat(s);
}
var yl = 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 ZC(r, e, t10, o) {
  let n = /* @__PURE__ */ new Set(), s = [], a = null, i = null, p = /* @__PURE__ */ new Set(), u = Object.keys(r).map((m) => Sr(m)[0]), c = [];
  o != null && (c = o.map((m) => Sr(m.name)[0]));
  let l = [...e];
  for (; l.length > 0; ) {
    let m = l.pop();
    if ((JC(m) || B6(m) || V6(m)) && a == null && (a = m, i = a.children.map((f) => f.name).filter((f) => n.has(f))), 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((f) => {
        p.has(f.name) || (p.add(f.name), l.push(f));
      });
    }
  }
  return { inputs: r, outputs: e, usedNodes: n, missingInputs: s, dynamicNode: a, syncInputs: i };
}
function kN(r, e, t10) {
  let { usedNodes: o, inputs: n } = t10, s = [], a = Object.keys(n).map((c) => Sr(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 O6 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
var M6 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
var L6 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
function JC(r) {
  return O6.indexOf(r.op) >= 0;
}
function B6(r) {
  return M6.indexOf(r.op) >= 0;
}
function V6(r) {
  return L6.indexOf(r.op) >= 0;
}
var Cu = class {
  constructor(e, t10) {
    this.graph = e, this.parent = t10, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.intermediateTensors = {}, this.keepTensorForDebug = 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 Cu(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 = ZC(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 kN(this.graph, this.weightMap, o);
  }
  execute(e, t10) {
    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[Sr(l)[0]]), s = t10.map((l) => Sr(l)[0]), a = s.map((l) => this.graph.nodes[l]);
    this.resetIntermediateTensors(), 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));
    let u = {}, c = {};
    return Ne(() => {
      let l = new yl(this.weightMap, u, c, this.functionExecutorMap), m = Object.assign({}, this.weightMap);
      Object.keys(e).forEach((h) => {
        let [g, y] = Sr(h), b = [];
        b[y] = e[h], m[g] = b;
      });
      let f = this.getFrozenTensorIds(m), d = {};
      for (let h = 0; h < p.length; h++) {
        let g = p[h];
        if (!m[g.name]) {
          let y = QC(g, m, l, this._resourceManager);
          if (x.isPromise(y))
            throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);
          m[g.name] = y, this.checkTensorForDisposal(g.name, g, m, l, f, s, d);
        }
      }
      return this.parent == null && l.dispose(f), t10.map((h) => Ht(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 = YT(p.name, o, n);
        u != null && u.forEach((c) => {
          if (c && !c.kept && !s.has(c.id)) {
            let l = i[c.id];
            if (l === 1) {
              if (!this.keepTensorForDebug)
                c.dispose();
              else {
                let [m, f] = zo(t10.name, n);
                this.intermediateTensors[m] ? this.intermediateTensors[m][f] = c : (this.intermediateTensors[m] = [], this.intermediateTensors[m][f] = c);
              }
              delete i[c.id];
            } else
              l != null && i[c.id]--;
          }
        });
      }
    }));
  }
  async executeAsync(e, t10) {
    return this._executeAsync(e, t10);
  }
  disposeIntermediateTensors() {
    !this.intermediateTensors || (Object.keys(this.intermediateTensors).forEach((e) => this.intermediateTensors[e].forEach((t10) => t10.dispose())), this.disposeTensorsMap());
  }
  disposeTensorsMap() {
    !this.tensorsMap || Object.keys(this.tensorsMap).forEach((e) => {
      this.tensorsMap[e].forEach((o) => {
        o && !o.kept && !o.isDisposed && !this.keepIds.has(o.id) && o.dispose();
      });
    });
  }
  getIntermediateTensors() {
    return this.tensorsMap;
  }
  resetIntermediateTensors() {
    for (let e in this.intermediateTensors)
      this.intermediateTensors[e].forEach((t10) => t10.dispose()), delete this.intermediateTensors[e];
  }
  async _executeAsync(e, t10, o = false, n = {}, s = {}) {
    o || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t10 = this.mapOutputs(t10), this.checkOutputs(t10));
    try {
      this.keepTensorForDebug = P().getBool("KEEP_INTERMEDIATE_TENSORS");
    } catch (c) {
      console.warn(c.message);
    }
    this.resetIntermediateTensors();
    let a = new yl(this.weightMap, n, s, this.functionExecutorMap);
    this.tensorsMap = await this.executeWithControlFlow(e, a, t10, o);
    let i = t10.map((c) => Ht(c, this.tensorsMap, a)), p = i.map((c) => c.id), u = Object.keys(e).map((c) => e[c].id);
    return this.keepIds = /* @__PURE__ */ new Set([...p, ...u, ...this.weightIds]), this.keepTensorForDebug || this.disposeTensorsMap(), this.parent == null && a.dispose(this.keepIds), i;
  }
  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[Sr(C)[0]]), i = o.map((C) => Sr(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 } = ZC(e, p, this.weightMap, this._initNodes), f = [...a, ...this.graph.weights, ...this._initNodes || []].map((C) => ({ node: C, contexts: t10.currentContext })), d = Object.assign({}, this.weightMap);
    Object.keys(e).forEach((C) => {
      let [w, k] = Sr(C), _ = [];
      _[k] = e[C], d[w] = _;
    });
    let h = {}, g = this.getFrozenTensorIds(d), y = {};
    for (; f.length > 0; ) {
      let C = this.processStack(a, f, t10, d, y, 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) => !JC(C) && !Ht(C.name, d, 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}]. ${C}`);
    }
    return d;
  }
  processStack(e, t10, o, n, s, a, i, p, u) {
    let c = [];
    for (; t10.length > 0; ) {
      let l = t10.pop();
      o.currentContext = l.contexts;
      let m = "";
      if (l.node.op === "Enter" && S("isConstant", l.node, n, o) && ([m] = zo(l.node.name, o)), n[l.node.name] == null) {
        let f = QC(l.node, n, o, this._resourceManager);
        m || ([m] = zo(l.node.name, o));
        let d = o.currentContext;
        x.isPromise(f) ? c.push(f.then((h) => (n[m] = h, o.currentContext = d, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u), h))) : (n[m] = f, this.checkTensorForDisposal(m, l.node, n, o, a, i, p), this.processChildNodes(l.node, t10, o, n, s, u));
      } else
        this.processChildNodes(l.node, t10, o, n, s, u);
    }
    return c;
  }
  processChildNodes(e, t10, o, n, s, a) {
    e.children.forEach((i) => {
      let [p] = zo(i.name, o);
      s[p] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!Ht(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })) : i.inputNames.every((u) => !!Ht(u, n, o)) && (s[p] = true, t10.push({ contexts: o.currentContext, node: i })));
    });
  }
  dispose() {
    Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t10) => t10.dispose()));
  }
  checkInputShapeAndType(e) {
    Object.keys(e).forEach((t10) => {
      let o = e[t10], [n] = Sr(t10), s = this.graph.nodes[n];
      if (s.attrParams.shape && s.attrParams.shape.value) {
        let a = s.attrParams.shape.value, i = a.length === o.shape.length && o.shape.every((p, u) => a[u] === -1 || a[u] === p);
        x.assert(i, () => `The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`);
      }
      s.attrParams.dtype && s.attrParams.dtype.value && x.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 ${o.dtype}`);
    });
  }
  mapInputs(e) {
    let t10 = {};
    for (let o in e)
      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[o] != null) {
        let n = this._signature.inputs[o];
        t10[n.name] = e[o];
      } else
        t10[o] = e[o];
    return t10;
  }
  checkInputs(e) {
    let t10 = Object.keys(e).filter((o) => {
      let [n] = Sr(o);
      return this.graph.nodes[n] == null;
    });
    if (t10.length > 0)
      throw new Error(`The dict provided in model.execute(dict) has keys: [${t10}] that are not part of graph`);
  }
  mapOutputs(e) {
    return e.map((t10) => this._signature != null && this._signature.outputs != null && this._signature.outputs[t10] != null ? this._signature.outputs[t10].name : t10, {});
  }
  checkOutputs(e) {
    e.forEach((t10) => {
      let [o] = Sr(t10);
      if (!this.graph.nodes[o])
        throw new Error(`The output '${t10}' is not found in the graph`);
    });
  }
};
var xd = class {
  constructor(e = {}, t10 = {}) {
    this.hashTableNameToHandle = e, this.hashTableMap = t10;
  }
  addHashTable(e, t10) {
    this.hashTableNameToHandle[e] = t10.handle, this.hashTableMap[t10.id] = t10;
  }
  getHashTableHandleByName(e) {
    return this.hashTableNameToHandle[e];
  }
  getHashTableById(e) {
    return this.hashTableMap[e];
  }
  dispose() {
    for (let e in this.hashTableMap)
      this.hashTableMap[e].clearAndClose(), delete this.hashTableMap[e];
    for (let e in this.hashTableNameToHandle)
      this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
  }
};
var z6 = "?tfjs-format=file";
var W6 = "model.json";
var bl = class {
  constructor(e, t10 = {}, o = va) {
    this.modelUrl = e, this.loadOptions = t10, this.version = "n/a", this.io = o, t10 == null && (this.loadOptions = {}), this.resourceManager = new xd();
  }
  get modelVersion() {
    return this.version;
  }
  get inputNodes() {
    return this.executor.inputNodes;
  }
  get outputNodes() {
    return this.executor.outputNodes;
  }
  get inputs() {
    return this.executor.inputs;
  }
  get outputs() {
    return this.executor.outputs;
  }
  get weights() {
    return this.executor.weightMap;
  }
  get metadata() {
    return this.artifacts.userDefinedMetadata;
  }
  get modelSignature() {
    return this.signature;
  }
  get modelStructuredOutputKeys() {
    return this.structuredOutputKeys;
  }
  findIOHandler() {
    let e = this.modelUrl;
    if (e.load != null)
      this.handler = e;
    else if (this.loadOptions.requestInit != null)
      this.handler = this.io.browserHTTPRequest(e, this.loadOptions);
    else {
      let t10 = this.io.getLoadHandlers(e, this.loadOptions);
      if (t10.length === 0)
        t10.push(this.io.browserHTTPRequest(e, this.loadOptions));
      else if (t10.length > 1)
        throw new Error(`Found more than one (${t10.length}) load handlers for URL '${[e]}'`);
      this.handler = t10[0];
    }
  }
  load() {
    if (this.findIOHandler(), this.handler.load == null)
      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
    let e = this.handler.load();
    return x.isPromise(e) ? e.then((t10) => this.loadSync(t10)) : this.loadSync(e);
  }
  loadSync(e) {
    this.artifacts = e;
    let t10 = this.artifacts.modelTopology, o = this.artifacts.signature;
    if (this.artifacts.userDefinedMetadata != null) {
      let s = this.artifacts.userDefinedMetadata;
      s.signature != null && (o = s.signature), s.structuredOutputKeys != null && (this.structuredOutputKeys = s.structuredOutputKeys);
    }
    this.signature = o, this.version = `${t10.versions.producer}.${t10.versions.minConsumer}`;
    let n = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
    if (this.executor = new Cu(xl.Instance.transformGraph(t10, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(n), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
      let s = xl.Instance.transformGraph(e.modelInitializer);
      this.initializer = new Cu(s), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializerSignature = e.initializerSignature;
    }
    return true;
  }
  async save(e, t10) {
    if (typeof e == "string") {
      let o = this.io.getSaveHandlers(e);
      if (o.length === 0)
        throw new Error(`Cannot find any save handlers for URL '${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);
  }
  predict(e, t10) {
    let o = this.execute(e, this.outputNodes);
    if (this.structuredOutputKeys) {
      let n = o instanceof ut ? [o] : o, s = {};
      return n.forEach((a, i) => s[this.structuredOutputKeys[i]] = a), s;
    }
    return o;
  }
  normalizeInputs(e) {
    if (!(e instanceof ut) && !Array.isArray(e)) {
      if (this.signature != null && this.signature.inputs != null)
        for (let n in this.signature.inputs) {
          let s = this.signature.inputs[n];
          s.resourceId != null && (e[n] = this.resourceIdToCapturedInput[s.resourceId]);
        }
      return e;
    }
    e = Array.isArray(e) ? e : [e];
    let t10 = Object.keys(this.resourceIdToCapturedInput).length;
    if (e.length + t10 !== this.inputNodes.length)
      throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - t10} non-resource placeholders, while there are ${e.length} input tensors provided.`);
    let o = 0;
    return this.inputNodes.reduce((n, s) => {
      let a = this.signature ? this.signature.inputs[s] : null;
      return a != null && a.resourceId != null ? n[s] = this.resourceIdToCapturedInput[a.resourceId] : n[s] = e[o++], n;
    }, {});
  }
  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 = Object.keys(this.initializerSignature.outputs);
      for (let o = 0; o < t10.length; o++) {
        let n = t10[o], s = this.initializerSignature.outputs[n];
        this.resourceIdToCapturedInput[s.resourceId] = e[o];
      }
    }
  }
  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 && Ft(this.resourceIdToCapturedInput)), this.resourceManager.dispose();
  }
};
async function U6(r, e = {}, t10 = va) {
  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 = H6(r));
  let o = new bl(r, e, t10);
  return await o.load(), o;
}
function G6(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 = va.getWeightSpecs(o.weightsManifest), a = va.getModelArtifactsForJSONSync(o, s, n);
    e = va.fromMemorySync(a);
  } else if ("load" in r)
    e = r;
  else if ("modelTopology" in r && "weightSpecs" in r && "weightData" in r)
    e = va.fromMemorySync(r);
  else
    throw new Error("Unknown model format");
  let t10 = new bl(e);
  return t10.load(), t10;
}
function H6(r) {
  return r.endsWith("/") || (r = r + "/"), `${r}${W6}${z6}`;
}
var q6 = "4.0.0";
function K(r, e) {
  Array.isArray(r) || (r = [r]), r.forEach((t10) => {
    t10 != null && x.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`);
  });
}
var K6 = Bt.whereImpl;
var Si = class extends Jr {
  constructor() {
    super(), this.blockSize = 48, this.firstUse = true, this.data = new rn(this, cr());
  }
  nextDataId() {
    return Si.nextDataId++;
  }
  write(e, t10, o) {
    this.firstUse && (this.firstUse = false, P().get("IS_NODE") && I.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 && x.isString(o[0])) {
      let s = o.map((a) => x.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 I.mergeRealAndImagArrays(n, s);
    }
    return this.data.get(e).values;
  }
  bufferSync(e) {
    let t10 = this.readSync(e.dataId);
    if (e.dtype === "string")
      try {
        let o = t10.map((n) => x.decodeString(n));
        return ne(e.shape, e.dtype, o);
      } catch (o) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return ne(e.shape, e.dtype, t10);
  }
  makeOutput(e, t10, o) {
    return cr().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 = x.now();
    return e(), { kernelMs: x.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) {
    K([e], "where");
    let t10 = this.readSync(e.dataId);
    return K6(e.shape, t10);
  }
  dispose() {
  }
  floatPrecision() {
    return 32;
  }
  epsilon() {
    return super.epsilon();
  }
};
Si.nextDataId = 0;
var Ad = {};
Be(Ad, { addImpl: () => rI, bincountImpl: () => Zp, bincountReduceImpl: () => yd, castImpl: () => tI, ceilImpl: () => oI, concatImpl: () => Iu, equalImpl: () => nI, expImpl: () => aI, expm1Impl: () => uI, floorImpl: () => pI, gatherNdImpl: () => bd, gatherV2Impl: () => Cd, greaterEqualImpl: () => lI, greaterImpl: () => cI, lessEqualImpl: () => fI, lessImpl: () => mI, linSpaceImpl: () => Id, logImpl: () => dI, maxImpl: () => wd, maximumImpl: () => hI, minimumImpl: () => gI, multiplyImpl: () => Cl, negImpl: () => xI, notEqualImpl: () => yI, prodImpl: () => bI, raggedGatherImpl: () => Sd, raggedRangeImpl: () => vd, raggedTensorToTensorImpl: () => kd, rangeImpl: () => Su, rsqrtImpl: () => CI, scatterImpl: () => Aa, sigmoidImpl: () => e2, simpleAbsImpl: () => eI, sliceImpl: () => vu, sparseFillEmptyRowsImpl: () => Td, sparseReshapeImpl: () => Nd, sparseSegmentReductionImpl: () => tc, sqrtImpl: () => o2, squaredDifferenceImpl: () => wI, stridedSliceImpl: () => _d, stringNGramsImpl: () => ku, stringSplitImpl: () => Tu, stringToHashBucketFastImpl: () => Nu, subImpl: () => vI, tileImpl: () => Ed, topKImpl: () => $d, transposeImpl: () => Jp, uniqueImpl: () => Rd });
function eI(r) {
  let e = new Float32Array(r.length);
  for (let t10 = 0; t10 < r.length; ++t10)
    e[t10] = Math.abs(r[t10]);
  return e;
}
var j6 = (r) => {
  let { x: e } = r.inputs, t10 = r.backend;
  K(e, "abs");
  let o = new Float32Array(x.sizeFromShape(e.shape)), n = t10.data.get(e.dataId).values;
  return o = eI(n), t10.makeOutput(o, e.shape, e.dtype);
};
var TN = { kernelName: sn, backendName: "cpu", kernelFunc: j6 };
function Le(r) {
  return (e, t10, o, n, s) => {
    let a = I.assertAndGetBroadcastShape(e, t10), i = a.length, p = x.computeStrides(a), u = x.sizeFromShape(a), c = x.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, f = x.computeStrides(e), d = x.computeStrides(t10), h = I.getBroadcastDims(e, a), g = I.getBroadcastDims(t10, a);
    if (h.length + g.length === 0)
      for (let y = 0; y < c.length; ++y)
        c[y] = r(o[y % o.length], n[y % n.length]);
    else
      for (let y = 0; y < c.length; ++y) {
        let b = x.indexToLoc(y, i, p), C = b.slice(-l);
        h.forEach((E) => C[E] = 0);
        let w = x.locToIndex(C, l, f), k = b.slice(-m);
        g.forEach((E) => k[E] = 0);
        let _ = x.locToIndex(k, m, d);
        c[y] = r(o[w], n[_]);
      }
    return [c, a];
  };
}
function qt(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 NN = { kernelName: aa, backendName: "cpu", kernelFunc: qt };
function Yp(r, e, t10 = "float32") {
  if (t10 === "complex64") {
    let n = Yp(r, e, "float32"), s = Yp(r, e, "float32");
    return qt({ inputs: { real: n, imag: s }, backend: r });
  }
  let o = x.makeZerosTypedArray(x.sizeFromShape(e), t10);
  return r.makeTensorInfo(e, t10, o);
}
function ar(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 _N = { kernelName: uo, backendName: "cpu", kernelFunc: ar };
function Wo(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 EN = { kernelName: la, backendName: "cpu", kernelFunc: Wo };
function tI(r, e, t10, o) {
  if (o === "int32") {
    let n = Int32Array.from(r);
    return [e, "int32", n];
  }
  if (o === "bool") {
    let n = x.toTypedArray([0], t10), [s, a] = Le((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 Uo(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return ar({ inputs: { x: n }, backend: t10 });
    let c = Yp(t10, n.shape, n.dtype), l = Uo({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), m = qt({ inputs: { real: l, imag: c }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), m;
  }
  if (n.dtype === "complex64") {
    let c = Wo({ inputs: { input: n }, backend: t10 }), l = Uo({ inputs: { x: c }, backend: t10, attrs: { dtype: s } });
    return t10.disposeIntermediateTensorInfo(c), l;
  }
  if (!x.hasEncodingLoss(n.dtype, s)) {
    let c = ar({ 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] = tI(a, n.shape, n.dtype, s);
  return t10.makeTensorInfo(i, p, u);
}
var $N = { kernelName: to, backendName: "cpu", kernelFunc: Uo };
function Ye(r, e, t10, o) {
  return t10 == null ? ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    K([a, i], r);
    let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? I.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? I.fromUint8ToStringArray(c) : c, f = o || a.dtype, [d, h] = e(a.shape, i.shape, l, m, f);
    return p.makeTensorInfo(h, f, d);
  } : ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    if (a.dtype === "complex64" || i.dtype === "complex64") {
      let u = Uo({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, f = p.data.get(l.dataId).values, d = p.data.get(m.dataId).values, h = Uo({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), y = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(y.dataId).values, w = p.data.get(b.dataId).values, [k, _, E] = t10(a.shape, i.shape, f, d, C, w), R = p.makeTensorInfo(E, "float32", k), A = p.makeTensorInfo(E, "float32", _), D = qt({ inputs: { real: R, imag: A }, backend: p });
      return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(A), D;
    } else {
      let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, f] = e(a.shape, i.shape, u, c, l);
      return p.makeTensorInfo(f, l, m);
    }
  };
}
function Qp(r) {
  return (e, t10, o, n, s, a) => {
    let i = I.assertAndGetBroadcastShape(e, t10), p = x.sizeFromShape(i), u = i.length, c = x.computeStrides(i), l = x.getTypedArrayFromDType("float32", p), m = x.getTypedArrayFromDType("float32", p), f = I.getBroadcastDims(e, i), d = I.getBroadcastDims(t10, i), h = I.mergeRealAndImagArrays(o, n), g = I.mergeRealAndImagArrays(s, a), y = e.length, b = x.computeStrides(e), C = t10.length, w = x.computeStrides(t10);
    if (f.length + d.length === 0)
      for (let k = 0; k < l.length; k++) {
        let _ = k % h.length, E = k % g.length, R = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]);
        l[k] = R.real, m[k] = R.imag;
      }
    else
      for (let k = 0; k < l.length; k++) {
        let _ = x.indexToLoc(k, u, c), E = _.slice(-y);
        f.forEach((M) => E[M] = 0);
        let R = x.locToIndex(E, y, b), A = _.slice(-C);
        d.forEach((M) => A[M] = 0);
        let D = x.locToIndex(A, C, w), O = r(h[R * 2], h[R * 2 + 1], g[D * 2], g[D * 2 + 1]);
        l[k] = O.real, m[k] = O.imag;
      }
    return [l, m, i];
  };
}
var rI = Le((r, e) => r + e);
var X6 = Qp((r, e, t10, o) => ({ real: r + t10, imag: e + o }));
var Hs = Ye(_r, rI, X6);
var RN = { kernelName: _r, backendName: "cpu", kernelFunc: Hs };
function Zp(r, e, t10, o, n) {
  let s = x.sizeFromShape(o), a = x.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 yd(r, e, t10, o = false) {
  let n = r.shape[0], s = r.shape[1], a = ne([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 = x.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 (K(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 = x.sizeFromShape(a.shape), c = t10 || a.dtype, l = x.getArrayFromDType(c, u);
    for (let m = 0; m < u; ++m)
      l[m] = e(p[m], n);
    return i.makeTensorInfo(a.shape, c, l);
  };
}
function Go(r, e, t10) {
  return ({ inputs: o, attrs: n, backend: s }) => {
    let { x: a } = o;
    if (K(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 oI = vr((r) => Math.ceil(r));
var Y6 = Go(ro, oI);
var AN = { kernelName: ro, backendName: "cpu", kernelFunc: Y6 };
function Iu(r, e, t10, o) {
  let n = x.getArrayFromDType(t10, x.sizeFromShape(e));
  if (o && t10 !== "string") {
    let s = 0;
    r.forEach((a) => {
      let i = x.sizeFromShape(a.shape);
      n.set(a.vals, s), s += i;
    });
  } else {
    let s = 0;
    r.forEach((a) => {
      let i = t10 === "string" ? I.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 nI = Le((r, e) => r === e ? 1 : 0);
var sI = Ye(oo, nI, null, "bool");
var FN = { kernelName: oo, backendName: "cpu", kernelFunc: sI };
var aI = vr((r) => Math.exp(r));
var iI = Go(no, aI, "float32");
var DN = { kernelName: no, backendName: "cpu", kernelFunc: iI };
var uI = vr((r) => Math.expm1(r));
var Q6 = Go(wn, uI);
var PN = { kernelName: wn, backendName: "cpu", kernelFunc: Q6 };
var pI = vr((r) => Math.floor(r));
var Z6 = Go(so, pI);
var ON = { kernelName: so, backendName: "cpu", kernelFunc: Z6 };
function bd(r, e, t10, o, n, s, a, i, p) {
  let u = ne([o, s], t10);
  for (let c = 0; c < o; c++) {
    let l = [], m = 0;
    for (let f = 0; f < n; f++) {
      let d = r[c * n + f];
      m += d * a[f], l.push(d);
    }
    if (m < 0 || m >= p / s)
      throw new Error(`Invalid indices: ${l} does not index into ${i}`);
    for (let f = 0; f < s; f++)
      u.values[c * s + f] = e.get(...e.indexToLoc(m * s + f));
  }
  return u;
}
function Cd(r, e, t10) {
  let o = ne(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 cI = Le((r, e) => r > e ? 1 : 0);
var J6 = Ye(ao, cI, null, "bool");
var MN = { kernelName: ao, backendName: "cpu", kernelFunc: J6 };
var lI = Le((r, e) => r >= e ? 1 : 0);
var ej = Ye(io, lI, null, "bool");
var LN = { kernelName: io, backendName: "cpu", kernelFunc: ej };
var mI = Le((r, e) => r < e ? 1 : 0);
var tj = Ye(po, mI, null, "bool");
var BN = { kernelName: po, backendName: "cpu", kernelFunc: tj };
var fI = Le((r, e) => r <= e ? 1 : 0);
var rj = Ye(co, fI, null, "bool");
var VN = { kernelName: co, backendName: "cpu", kernelFunc: rj };
function Id(r, e, t10) {
  let o = (e - r) / (t10 - 1), n = x.makeZerosTypedArray(t10, "float32");
  n[0] = r;
  for (let s = 1; s < n.length; s++)
    n[s] = n[s - 1] + o;
  return n;
}
var dI = vr((r) => Math.log(r));
var oj = Go(lo, dI);
var zN = { kernelName: lo, backendName: "cpu", kernelFunc: oj };
function wd(r, e, t10, o) {
  let n = x.getTypedArrayFromDType(o, x.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 hI = Le((r, e) => Math.max(r, e));
var nj = Ye(mo, hI);
var WN = { kernelName: mo, backendName: "cpu", kernelFunc: nj };
var gI = Le((r, e) => Math.min(r, e));
var sj = Ye(fo, gI);
var UN = { kernelName: fo, backendName: "cpu", kernelFunc: sj };
var Cl = Le((r, e) => r * e);
var aj = Qp((r, e, t10, o) => ({ real: r * t10 - e * o, imag: r * o + e * t10 }));
var wu = Ye(ho, Cl, aj);
var GN = { kernelName: ho, backendName: "cpu", kernelFunc: wu };
function xI(r, e, t10) {
  let o = x.createScalarValue(-1, t10);
  return Cl([], e, o, r, t10);
}
function ij(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  K(o, "neg");
  let n = t10.data.get(o.dataId).values, [s, a] = xI(n, o.shape, o.dtype);
  return t10.makeTensorInfo(a, o.dtype, s);
}
var HN = { kernelName: Pn, backendName: "cpu", kernelFunc: ij };
var yI = Le((r, e) => r !== e ? 1 : 0);
var uj = Ye(go, yI, null, "bool");
var qN = { kernelName: go, backendName: "cpu", kernelFunc: uj };
function Jp(r, e, t10, o, n) {
  let s = e.length, a = x.sizeFromShape(e), i = x.computeStrides(e), p = x.computeStrides(n), u = x.getTypedArrayFromDType(t10, x.sizeFromShape(n));
  for (let c = 0; c < a; ++c) {
    let l = x.indexToLoc(c, s, i), m = new Array(l.length);
    for (let d = 0; d < m.length; d++)
      m[d] = l[o[d]];
    let f = x.locToIndex(m, s, p);
    u[f] = r[c];
  }
  return u;
}
function bt(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { perm: s } = t10;
  K(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 = Jp(p, n.shape, n.dtype, s, i);
  return { dataId: o.write(u, i, n.dtype), shape: i, dtype: n.dtype };
}
var KN = { kernelName: Mr, backendName: "cpu", kernelFunc: bt };
function bI(r, e, t10, o) {
  let [n, s] = I.computeOutAndReduceShapes(r, o), a = ct(e, "int32"), i = x.makeZerosTypedArray(x.sizeFromShape(n), a), p = x.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 pj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  K(n, "prod");
  let i = n.shape.length, p = x.parseAxisParam(s, n.shape), u = I.getAxesPermutation(p, i), c = p, l = n, m = [];
  u != null && (l = bt({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), m.push(l), c = I.getInnerMostAxes(c.length, i));
  let f = t10.data.get(l.dataId).values, { outVals: d, outShape: h, outDtype: g } = bI(l.shape, l.dtype, f, c), y = h;
  return a && (y = I.expandShapeToKeepDim(h, p)), m.forEach((b) => t10.disposeIntermediateTensorInfo(b)), t10.makeTensorInfo(y, g, d);
}
var jN = { kernelName: Ao, backendName: "cpu", kernelFunc: pj };
function cj(r, e, t10) {
  r.forEach((o, n) => {
    if (o < 0 || o >= t10) {
      let s = x.indexToLoc(n, e.length, x.computeStrides(e)).join(",");
      throw new Error(`indices[${s}] = ${o} is not in [0, ${t10})`);
    }
  });
}
function lj(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 mj(r, e, t10, o) {
  let n = [], s = 0, a = e.length - 1 + t10.length, i = new Array(a).fill(null).map(() => [0]);
  lj(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 f = t10[m], d = m + e.length - 1;
      if (d >= 0) {
        let h = i[d], g = h[h.length - 1] - f[c];
        for (let y = c; y < l; ++y)
          i[d].push(f[y + 1] + g);
      }
      c = f[c], l = f[l];
    }
    l !== c && (n.push([c, l]), s += l - c);
  }
  return { outSplits: i, valueSlices: n, numValues: s };
}
function fj(r) {
  let e = [];
  for (let t10 = 0; t10 < r.length; ++t10) {
    let o = r[t10].length, n = x.getArrayFromDType("int32", o);
    e.push(n), r[t10].forEach((s, a) => n[a] = s);
  }
  return e;
}
function XN(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 dj(r, e, t10, o, n, s) {
  let a = XN(e, 2)[1], i = XN(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 hj(r, e, t10, o, n) {
  let s = e.slice();
  s[0] = n;
  let a = x.getArrayFromDType(t10, x.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0];
  return dj(r, e, o, p, a, s), [a, s];
}
function Sd(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 (cj(s, a, p), o.length === 0)
    throw new Error("params.rank must be nonzero");
  let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = mj(s, a, r, u), f = fj(c), d = hj(t10, o, n, l, m);
  return [f, d[0], d[1]];
}
var YN = 2147483647;
function vd(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 = x.getArrayFromDType("int32", l + 1);
  m[0] = 0;
  for (let g = 0; g < l; ++g) {
    let y = 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 < y || C < 0 && b > y)
      w = 0;
    else if (w = Math.ceil(Math.abs((b - y) / C)), w > YN)
      throw new Error(`Requires ((limit - start) / delta) <= ${YN}`);
    m[g + 1] = m[g] + w;
  }
  let f = m[l], d = x.getArrayFromDType(t10, f), h = 0;
  for (let g = 0; g < l; ++g) {
    let y = m[g + 1] - m[g], b = i ? r[0] : r[g], C = u ? s[0] : s[g];
    for (let w = 0; w < y; ++w)
      d[h++] = b, b += C;
  }
  return [m, d];
}
var Ho = I.RowPartitionType;
var ec = 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 = I.getRowPartitionTypesHelper(c), this.raggedRank = I.getRaggedRank(this.rowPartitionTypes);
  }
  getRowPartitionTypeByDimension(e) {
    return this.rowPartitionTypes[0] === Ho.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e];
  }
  getRowPartitionTensor(e) {
    return this.rowPartitionTypes[0] === Ho.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e];
  }
  getMaxWidth(e) {
    let t10 = this.getRowPartitionTensor(e - 1);
    switch (this.getRowPartitionTypeByDimension(e - 1)) {
      case Ho.VALUE_ROWIDS:
        return ec.getMaxWidthValueRowID(t10);
      case Ho.ROW_SPLITS:
        return ec.getMaxWidthRowSplit(t10);
      default:
        throw new Error(`Cannot handle partition type ${Ho[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 ZN(e, o);
  }
  calculateOutputSize(e) {
    let t10 = this.valuesShape, o = this.defaultValueShape;
    I.validateDefaultValueShape(o, t10);
    let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = I.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 x.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 Ho.VALUE_ROWIDS:
        return this.calculateOutputIndexValueRowID(s, t10, o, n);
      case Ho.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: ${Ho[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 Ho.FIRST_DIM_SIZE:
        return e[0];
      case Ho.VALUE_ROWIDS:
        throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");
      case Ho.ROW_SPLITS:
        return this.rowPartitionValuesShapes[0][0] - 1;
      default:
        throw new Error(`Cannot handle type ${Ho[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 = ZN(o, false), a = x.getArrayFromDType(this.valuesDType, x.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 = x.sizeFromShape(i), u = t10.length, c = this.defaultValue;
    if (c.length !== p && c.length !== 1) {
      let d = this.defaultValueShape;
      Ne(() => {
        let h = z(c, d);
        c = Ls(h, i).dataSync();
      });
    }
    let l = 0, m = 0, f = 0;
    for (let d = 0; d <= u; ++d) {
      let h = d < u ? t10[d] : -1;
      if (h === f) {
        ++f;
        continue;
      }
      if (m < f) {
        let g = s.subarray(l * p), y = a.subarray(m * p), b = (f - m) * p;
        QN(y, g, b);
      }
      if (d >= u) {
        let g = o.length;
        h = Math.floor(g / p);
      }
      if (h > f)
        if (this.defaultValue.length === 1)
          a.subarray(f * p, h * p).fill(this.defaultValue[0]), f = h;
        else
          for (; h > f; ) {
            let g = a.slice(f * p);
            QN(g, c, p), ++f;
          }
      h < 0 ? (l = d + 1, m = f) : (l = d, m = f, f = m + 1);
    }
  }
};
function QN(r, e, t10) {
  for (let o = 0; o < t10; o++)
    r[o] = e[o];
}
function ZN(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 kd(r, e, t10, o, n, s, a, i, p, u) {
  return new ec(r, e, t10, o, n, s, a, i, p, u).compute();
}
function Su(r, e, t10, o) {
  let n = r === e, s = r < e && t10 < 0, a = e < r && t10 > 1;
  if (n || s || a)
    return x.makeZerosTypedArray(0, o);
  let i = Math.abs(Math.ceil((e - r) / t10)), p = x.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 CI = vr((r) => 1 / Math.sqrt(r));
var gj = Go(xo, CI);
var JN = { kernelName: xo, backendName: "cpu", kernelFunc: gj };
function Aa(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 ne(t10, e.dtype);
  let f = ne(c, e.dtype);
  typeof p == "string" || typeof p == "number" ? f.values.fill(p) : typeof p == "boolean" && f.values.fill(+p);
  for (let d = 0; d < s; d++) {
    let h = [], g = 0;
    for (let y = 0; y < a; y++) {
      let b = l[d * a + y];
      h.push(b), g += b * i[y];
    }
    if (g < 0 || g >= o / n)
      throw new Error(`Invalid indices: ${h} does not index into ${t10}`);
    for (let y = 0; y < n; y++)
      u ? f.values[g * n + y] += m[d * n + y] : f.values[g * n + y] = e.rank === 0 ? m[0] : m[d * n + y];
  }
  return f;
}
var e2 = vr((r) => 1 / (1 + Math.exp(-r)));
var II = we(yo, (r) => 1 / (1 + Math.exp(-r)));
var t2 = { kernelName: yo, backendName: "cpu", kernelFunc: II };
function vu(r, e, t10, o, n) {
  let s = et.isSliceContinous(o, e, t10), a = x.sizeFromShape(t10), i = x.computeStrides(o);
  if (s) {
    let l = et.computeFlatOffset(e, i);
    return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a);
  }
  let p = n === "string" ? I.fromUint8ToStringArray(r) : r, u = ne(o, n, p), c = ne(t10, n);
  for (let l = 0; l < c.size; ++l) {
    let m = c.indexToLoc(l), f = m.map((d, h) => d + e[h]);
    c.set(u.get(...f), ...m);
  }
  return n === "string" ? I.fromStringArrayToUint8(c.values) : c.values;
}
function qo(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o;
  K(n, "slice");
  let [i, p] = et.parseSliceParams(n, s, a);
  et.assertParamsValid(n, i, p);
  let u = t10.data.get(n.dataId).values, c = vu(u, i, p, n.shape, n.dtype);
  return t10.makeTensorInfo(p, n.dtype, c);
}
var r2 = { kernelName: qn, backendName: "cpu", kernelFunc: qo };
function Td(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(I.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));
    let g = x.getArrayFromDType(t10, 0), y = x.getArrayFromDType(n, 0);
    return [g, [0, l], y, u, c];
  }
  let m = true, f = 0, d = new Array(p).fill(0);
  for (let g = 0; g < i; ++g) {
    let y = r[g * l];
    if (y < 0)
      throw new Error(I.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, y));
    if (y >= p)
      throw new Error(I.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, y, p));
    ++d[y], m = m && y >= f, f = y;
  }
  let h = true;
  for (let g = 0; g < p; ++g) {
    let y = d[g] === 0;
    u[g] = y, h = h && !y, d[g] = Math.max(d[g], 1), g > 0 && (d[g] += d[g - 1]);
  }
  if (h && m) {
    let g = r, y = o;
    for (let b = 0; b < i; ++b)
      c[b] = b;
    return [g, [i, l], y, u, c];
  } else {
    let g = d[p - 1], y = x.getArrayFromDType(t10, g * l), b = x.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 : d[k - 1]) + _;
      C[k]++;
      for (let R = 0; R < l; ++R)
        y[E * l + R] = r[w * l + R];
      b[E] = o[w], c[w] = E;
    }
    for (let w = 0; w < p; ++w)
      if (C[w] === 0) {
        let _ = w === 0 ? 0 : d[w - 1];
        y[_ * l + 0] = w;
        for (let E = 1; E < l; ++E)
          y[_ * l + E] = 0;
        b[_] = a;
      }
    return [y, [g, l], b, u, c];
  }
}
function Nd(r, e, t10, o, n) {
  let s = x.sizeFromShape(o), a = e[0], i = n.length, p = [], u = 1, c = -1;
  for (let g = 0; g < i; ++g) {
    let y = n[g];
    if (y === -1) {
      if (c !== -1)
        throw new Error(I.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
      c = g, p.push(1);
    } else {
      if (y < 0)
        throw new Error(I.getSparseReshapeNegativeOutputDimErrorMessage(g, y));
      u *= y, p.push(y);
    }
  }
  if (c !== -1) {
    if (u <= 0)
      throw new Error(I.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
    let g = Math.trunc(s / u);
    if (u * g !== s)
      throw new Error(I.getSparseReshapeInputOutputMultipleErrorMessage(o, p));
    p[c] = g;
  }
  if (x.sizeFromShape(p) !== s)
    throw new Error(I.getSparseReshapeInputOutputMismatchErrorMessage(o, p));
  let m = o.length, f = [];
  if (m > 0) {
    f[m - 1] = 1;
    for (let g = m - 2; g >= 0; --g)
      f[g] = f[g + 1] * o[g + 1];
  }
  let d = [];
  if (i > 0) {
    d[i - 1] = 1;
    for (let g = i - 2; g >= 0; --g)
      d[g] = d[g + 1] * p[g + 1];
  }
  let h = x.getArrayFromDType(t10, a * i);
  for (let g = 0; g < a; ++g) {
    let y = 0;
    for (let b = 0; b < m; ++b)
      y += r[g * m + b] * f[b];
    for (let b = 0; b < i; ++b)
      h[g * i + b] = Math.trunc(y / d[b]), y %= d[b];
  }
  return [h, [a, i], p];
}
function tc(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(I.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let m = e.slice();
  m[0] = l;
  let f = m.reduce((C, w) => C * w, 1), d = x.getArrayFromDType(t10, f);
  if (i === 0)
    return l > 0 && d.fill(a), [d, m];
  if (l <= 0)
    throw new Error(I.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let h = 0, g = 1, y = 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(I.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
    }
    if (b < 0 || b >= l)
      throw new Error(I.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b, l));
    b > y && d.fill(a, y * u, b * u);
    for (let w = h; w < g; ++w) {
      let k = o[w];
      if (k < 0 || k >= p[0])
        throw new Error(I.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w, o[w], p[0]));
      for (let _ = 0; _ < u; _++)
        d[b * u + _] += r[k * u + _];
    }
    if (s)
      for (let w = 0; w < u; w++)
        d[b * u + w] /= g - h;
    if (h = g, ++g, y = b + 1, b = C, g > i)
      break;
  }
  return y < l && d.fill(a, y * u, l * u), [d, m];
}
var o2 = vr((r) => Math.sqrt(r));
var xj = we(bo, (r) => Math.sqrt(r));
var n2 = { kernelName: bo, backendName: "cpu", kernelFunc: xj };
var wI = Le((r, e) => {
  let t10 = r - e;
  return t10 * t10;
});
var yj = Ye(Co, wI);
var s2 = { kernelName: Co, backendName: "cpu", kernelFunc: yj };
function _d(r, e, t10, o) {
  let n = ne(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 SI = class {
  constructor(e, t10, o, n, s, a) {
    this.separator = x.encodeString(e), this.nGramWidths = t10, this.leftPad = x.encodeString(o), this.rightPad = x.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), f = 0;
      f += u * this.leftPad.length;
      for (let b = 0; b < l; ++b)
        f += e[m + b].length;
      f += c * this.rightPad.length;
      let d = u + c + l - 1;
      f += d * this.separator.length, o[n + i] = new Uint8Array(f);
      let h = o[n + i], g = 0, y = (b) => b.forEach((C) => h[g++] = C);
      for (let b = 0; b < u; ++b)
        y(this.leftPad), y(this.separator);
      for (let b = 0; b < l - 1; ++b)
        y(e[m + b]), y(this.separator);
      if (l > 0) {
        y(e[m + l - 1]);
        for (let b = 0; b < c; ++b)
          y(this.separator), y(this.rightPad);
      } else {
        for (let b = 0; b < c - 1; ++b)
          y(this.rightPad), y(this.separator);
        y(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 = x.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], f = this.getNumNGrams(m, l);
        this.createNGrams(e, u, i, c, f, l), c += f;
      }), this.preserveShort && c === a[p]) {
        let l = t10[p + 1] - t10[p];
        if (l === 0)
          continue;
        let m = l + 2 * this.padWidth, f = 1;
        this.createNGrams(e, u, i, c, f, m);
      }
    }
    return [i, a];
  }
};
function ku(r, e, t10, o, n, s, a, i) {
  return new SI(t10, o, n, s, a, i).compute(r, e);
}
function bj(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 Tu(r, e, t10) {
  let o = r.length, n = [], s = 0, a = 0, i = new Array(o);
  for (let m = 0; m < o; ++m) {
    let f = n.length;
    bj(r[m], e, t10, n);
    let d = n.length - f;
    i[m] = d, s += d, a = Math.max(a, d);
  }
  let p = x.getArrayFromDType("int32", s * 2), u = new Array(s), c = [o, a], l = 0;
  for (let m = 0; m < o; ++m)
    for (let f = 0; f < i[m]; ++f)
      p[l * 2] = m, p[l * 2 + 1] = f, u[l] = n[l], ++l;
  return [p, u, c];
}
function Nu(r, e) {
  let t10 = x.getArrayFromDType("int32", r.length);
  for (let o = 0; o < r.length; ++o)
    t10[o] = x.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned();
  return t10;
}
var vI = Le((r, e) => r - e);
var Cj = Qp((r, e, t10, o) => ({ real: r - t10, imag: e - o }));
var Il = Ye(Io, vI, Cj);
var a2 = { kernelName: Io, backendName: "cpu", kernelFunc: Il };
function Ed(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 = ne(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 wl = (r, e) => {
  let t10 = e.value - r.value;
  return t10 === 0 ? r.index - e.index : t10;
};
function i2(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)), f = Math.min(o, Math.floor(e + (i - p) * c / i + l));
      i2(r, e, m, f);
    }
    let n = r[e], s = t10, a = o;
    for (x.swap(r, t10, e), wl(r[o], n) > 0 && x.swap(r, t10, o); s < a; ) {
      for (x.swap(r, s, a), s++, a--; wl(r[s], n) < 0; )
        s = s + 1;
      for (; wl(r[a], n) > 0; )
        a = a - 1;
    }
    wl(r[t10], n) === 0 ? x.swap(r, t10, a) : (a = a + 1, x.swap(r, a, o)), a <= e && (t10 = a + 1), e <= a && (o = a - 1);
  }
}
function $d(r, e, t10, o, n) {
  let s = e[e.length - 1], [a, i] = [r.length / s, s], p = x.getTypedArrayFromDType(t10, a * o), u = x.getTypedArrayFromDType("int32", a * o);
  for (let l = 0; l < a; l++) {
    let m = l * i, f = r.subarray(m, m + i), d = new Array(f.length);
    f.forEach((b, C) => d[C] = { value: b, index: C }), o < d.length && (i2(d, o), d = d.slice(0, o)), n && d.sort(wl);
    let h = l * o, g = p.subarray(h, h + o), y = u.subarray(h, h + o);
    for (let b = 0; b < o; b++)
      g[b] = d[b].value, y[b] = d[b].index;
  }
  let c = e.slice();
  return c[c.length - 1] = o, [ne(c, t10, p), ne(c, "int32", u)];
}
function Rd(r, e, t10, o) {
  let n = x.parseAxisParam(e, t10)[0], s = [1, t10[0], 1];
  for (let d = 0; d < n; d++)
    s[0] *= t10[d];
  s[1] = t10[n];
  for (let d = n + 1; d < t10.length; d++)
    s[2] *= t10[d];
  let a = {}, i = new Int32Array(t10[n]), p = new je(s, o, r), u = [], c = s[0] === 1 && s[2] === 1;
  for (let d = 0; d < t10[n]; d++) {
    let h;
    if (c)
      h = r[d].toString();
    else {
      let g = [];
      for (let y = 0; y < s[0]; y++)
        for (let b = 0; b < s[2]; b++)
          g.push(p.get(y, d, b));
      h = g.join(",");
    }
    if (a[h] !== void 0)
      i[d] = a[h];
    else {
      let g = Object.keys(a).length;
      a[h] = g, i[d] = g, u.push(d);
    }
  }
  let l = s.slice();
  l[1] = Object.keys(a).length;
  let m = new je(l, o);
  u.forEach((d, h) => {
    for (let g = 0; g < s[0]; g++)
      for (let y = 0; y < s[2]; y++)
        m.set(p.get(g, d, y), g, h, y);
  });
  let f = t10.slice();
  return f[n] = l[1], { outputValues: m.values, outputShape: f, indices: i };
}
var Ij = "4.0.0";
pi("cpu", () => new Si(), 1);
var kI = we(In, (r) => r >= 0 ? r : Math.exp(r) - 1);
var u2 = { kernelName: In, backendName: "cpu", kernelFunc: kI };
function TI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o;
  K([n], "leakyRelu");
  let a = x.sizeFromShape(n.shape), i = t10.data.get(n.dataId).values, p = x.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 p2 = { kernelName: Nn, backendName: "cpu", kernelFunc: TI };
var wj = Le((r, e) => r < 0 ? e * r : r);
function NI(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e;
  K([o, n], "prelu");
  let s = t10.data.get(o.dataId).values, a = t10.data.get(n.dataId).values, [i, p] = wj(o.shape, n.shape, s, a, "float32");
  return t10.makeTensorInfo(p, "float32", i);
}
var c2 = { kernelName: Vn, backendName: "cpu", kernelFunc: NI };
var _I = we(zn, (r) => Math.max(0, r));
var l2 = { kernelName: zn, backendName: "cpu", kernelFunc: _I };
var EI = we(Gn, (r) => Math.min(Math.max(0, r), 6));
var m2 = { kernelName: Gn, backendName: "cpu", kernelFunc: EI };
function _u(r, e, t10, o, n) {
  if (t10 === "linear")
    return ar({ inputs: { x: e }, backend: r });
  if (t10 === "relu")
    return _I({ inputs: { x: e }, backend: r });
  if (t10 === "elu")
    return kI({ inputs: { x: e }, backend: r });
  if (t10 === "relu6")
    return EI({ inputs: { x: e }, backend: r });
  if (t10 === "prelu")
    return NI({ inputs: { x: e, alpha: o }, backend: r });
  if (t10 === "leakyrelu")
    return TI({ inputs: { x: e }, backend: r, attrs: { alpha: n } });
  if (t10 === "sigmoid")
    return II({ inputs: { x: e }, backend: r });
  throw new Error(`Activation ${t10} has not been implemented for the CPU backend.`);
}
function Oe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = x.sizeFromShape(n.shape), i = x.inferFromImplicitShape(s, a), p = x.sizeFromShape(i);
  x.assert(a === p, () => `The new shape (${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 f2 = { kernelName: Ss, backendName: "cpu", kernelFunc: Oe };
function $I(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  K([n, s], "matMul");
  let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], f = i ? s.shape[u - 2] : s.shape[u - 1], d = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = x.sizeFromShape(d), y = x.sizeFromShape(h), C = br.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, f]);
  x.assert(c === l, () => `Error in matMul: inner shapes (${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 ? [y, f, l] : [y, l, f], _ = Oe({ inputs: { x: n }, backend: t10, attrs: { shape: w } }), E = Oe({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), R = a ? _.shape[1] : _.shape[2], A = a ? _.shape[2] : _.shape[1], D = i ? E.shape[1] : E.shape[2], O = Math.max(g, y), M = t10.data.get(_.dataId).values, L = t10.data.get(E.dataId).values, W = x.computeStrides(_.shape), V = x.computeStrides(E.shape), [G, q, H] = a ? [W[0], 1, W[1]] : [W[0], W[1], 1], [j, Y, Z] = i ? [1, V[1], V[0]] : [V[1], 1, V[0]], ee = A * D, X = ne([O, A, D], _.dtype), Q = X.values, se = t10.blockSize;
  for (let ie = 0; ie < O; ie++)
    for (let de = 0; de < A; de += se)
      for (let Ie = 0; Ie < D; Ie += se)
        for (let Se = 0; Se < R; Se += se) {
          let Ee = Math.min(de + se, A), Me = Math.min(Ie + se, D), st = Math.min(Se + se, R);
          for (let pt = de; pt < Ee; pt++)
            for (let De = Ie; De < Me; De++) {
              let ft = 0;
              for (let at = Se; at < st; at++) {
                let dt = Math.min(ie, g - 1) * G, It = Math.min(ie, y - 1) * Z, Fr = M[dt + pt * q + at * H], Pt = L[at * j + De * Y + It];
                ft += Fr * Pt;
              }
              Q[ie * ee + (pt * D + De)] += ft;
            }
        }
  return t10.disposeIntermediateTensorInfo(_), t10.disposeIntermediateTensorInfo(E), t10.makeTensorInfo(C, X.dtype, X.values);
}
var d2 = { kernelName: cn, backendName: "cpu", kernelFunc: $I };
function Sj(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, f, d, h = [];
  m = $I({ inputs: { a: n, b: s }, attrs: { transposeA: p, transposeB: u }, backend: t10 }), a && (f = Hs({ inputs: { a: m, b: a }, backend: t10 }), h.push(m), m = f), c && (d = _u(t10, m, c, i, l), h.push(m), m = d);
  for (let y of h)
    t10.disposeIntermediateTensorInfo(y);
  return m;
}
var h2 = { kernelName: Fo, backendName: "cpu", kernelFunc: Sj };
var vj = we(Li, (r) => Math.acos(r));
var g2 = { kernelName: Li, backendName: "cpu", kernelFunc: vj };
var kj = we(Bi, (r) => Math.acosh(r));
var x2 = { kernelName: Bi, backendName: "cpu", kernelFunc: kj };
function Tj(r) {
  let { inputs: e, backend: t10 } = r, o = e;
  K(e, "addN");
  let n = o.map((i) => t10.data.get(i.dataId).values), s = ne(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 y2 = { kernelName: an, backendName: "cpu", kernelFunc: Tj };
function Nj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  K(n, "all");
  let i = x.parseAxisParam(s, n.shape), p = i, u = I.getAxesPermutation(p, n.shape.length), c = n;
  u != null && (c = bt({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = I.getInnerMostAxes(p.length, n.shape.length)), I.assertAxesAreInnerMostDims("all", p, c.shape.length);
  let [l, m] = I.computeOutAndReduceShapes(c.shape, p), f = x.sizeFromShape(m), d = x.makeZerosTypedArray(x.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
  for (let y = 0; y < d.length; ++y) {
    let b = y * f, C = h[b];
    for (let w = 0; w < f; ++w) {
      let k = h[b + w];
      C = C && k;
    }
    d[y] = C;
  }
  u != null && t10.disposeIntermediateTensorInfo(c);
  let g = t10.makeTensorInfo(l, c.dtype, d);
  if (a) {
    let y = I.expandShapeToKeepDim(l, i), b = Oe({ inputs: { x: g }, backend: t10, attrs: { shape: y } });
    return t10.disposeIntermediateTensorInfo(g), b;
  }
  return g;
}
var b2 = { kernelName: oa, backendName: "cpu", kernelFunc: Nj };
function _j(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  K(n, "any");
  let i = x.parseAxisParam(s, n.shape), p = i, u = I.getAxesPermutation(p, n.shape.length), c = n;
  u != null && (c = bt({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = I.getInnerMostAxes(p.length, n.shape.length)), I.assertAxesAreInnerMostDims("any", p, c.shape.length);
  let [l, m] = I.computeOutAndReduceShapes(c.shape, p), f = x.sizeFromShape(m), d = x.makeZerosTypedArray(x.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
  for (let y = 0; y < d.length; ++y) {
    let b = y * f, C = h[b];
    for (let w = 0; w < f; ++w) {
      let k = h[b + w];
      C = C || k;
    }
    d[y] = C;
  }
  u != null && t10.disposeIntermediateTensorInfo(c);
  let g = t10.makeTensorInfo(l, c.dtype, d);
  if (a) {
    let y = I.expandShapeToKeepDim(l, i), b = Oe({ inputs: { x: g }, backend: t10, attrs: { shape: y } });
    return t10.disposeIntermediateTensorInfo(g), b;
  }
  return g;
}
var C2 = { kernelName: na, backendName: "cpu", kernelFunc: _j };
function Ej(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o;
  K(n, "argMax");
  let a = x.parseAxisParam(s, n.shape), i = I.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = bt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = I.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], I.assertAxesAreInnerMostDims("argMax", a, p.shape.length);
  let [c, l] = I.computeOutAndReduceShapes(p.shape, a), m = x.sizeFromShape(c), f = x.makeZerosTypedArray(m, "int32"), d = x.sizeFromShape(l), h = t10.data.get(p.dataId).values;
  for (let g = 0; g < f.length; ++g) {
    let y = g * d, b = h[y], C = 0;
    for (let w = 0; w < d; ++w) {
      let k = h[y + w];
      k > b && (b = k, C = w);
    }
    f[g] = C;
  }
  return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", f);
}
var I2 = { kernelName: un, backendName: "cpu", kernelFunc: Ej };
function $j(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o;
  K(n, "argMin");
  let a = x.parseAxisParam(s, n.shape), i = I.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = bt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = I.getInnerMostAxes(a.length, p.shape.length)), a = [a[0]], I.assertAxesAreInnerMostDims("argMin", a, p.shape.length);
  let [c, l] = I.computeOutAndReduceShapes(p.shape, a), m = x.sizeFromShape(c), f = x.makeZerosTypedArray(m, "int32"), d = x.sizeFromShape(l), h = t10.data.get(p.dataId).values;
  for (let g = 0; g < f.length; ++g) {
    let y = g * d, b = h[y], C = 0;
    for (let w = 0; w < d; ++w) {
      let k = h[y + w];
      k < b && (b = k, C = w);
    }
    f[g] = C;
  }
  return u.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.makeTensorInfo(c, "int32", f);
}
var w2 = { kernelName: ja, backendName: "cpu", kernelFunc: $j };
var Rj = we(Vi, (r) => Math.asin(r));
var S2 = { kernelName: Vi, backendName: "cpu", kernelFunc: Rj };
var Aj = we(zi, (r) => Math.asinh(r));
var v2 = { kernelName: zi, backendName: "cpu", kernelFunc: Aj };
var Fj = we(Wi, (r) => Math.atan(r));
var k2 = { kernelName: Wi, backendName: "cpu", kernelFunc: Fj };
var Dj = Le((r, e) => Math.atan2(r, e));
var Pj = Ye(sa, Dj);
var T2 = { kernelName: sa, backendName: "cpu", kernelFunc: Pj };
var Oj = we(Ui, (r) => Math.atanh(r));
var N2 = { kernelName: Ui, backendName: "cpu", kernelFunc: Oj };
function rc(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, f = n.padInfo.left, d = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, h = ne(n.outShape, t10), g = h.values, y = 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 * y, _ = w * o[0];
    for (let E = 0; E < n.inChannels; ++E)
      for (let R = 0; R < n.outHeight; ++R) {
        let A = R * a - m, D = Math.max(0, A), O = Math.min(n.inHeight, c + A), M = k + R * b;
        for (let L = 0; L < n.outWidth; ++L) {
          let W = L * i - f, V = Math.max(0, W), G = Math.min(n.inWidth, l + W), q = d, H = 0, j = 0;
          for (let Z = D; Z < O; Z += p) {
            let ee = _ + Z * o[1];
            for (let X = V; X < G; X += u) {
              let Q = ee + X * o[2], se = r[Q + E];
              s === "max" && se > q ? q = se : s === "avg" && (H += se, j++);
            }
            if (isNaN(q))
              break;
          }
          let Y = M + L * C + E;
          g[Y] = s === "avg" ? H / j : q;
        }
      }
  }
  return h;
}
function Fd(r, e, t10, o, n = false, s = false) {
  let a = ne(o.outShape, "int32"), i = o.strideHeight, p = o.strideWidth, u = o.dilationHeight, c = o.dilationWidth, l = o.effectiveFilterHeight, m = o.effectiveFilterWidth, f = o.padInfo.top, d = o.padInfo.left, h = ne(e, t10, r);
  for (let g = 0; g < o.batchSize; ++g)
    for (let y = 0; y < o.inChannels; ++y)
      for (let b = 0; b < o.outHeight; ++b) {
        let C = b * i - f, w = C;
        for (; w < 0; )
          w += u;
        let k = Math.min(o.inHeight, l + C);
        for (let _ = 0; _ < o.outWidth; ++_) {
          let E = _ * p - d, R = E;
          for (; R < 0; )
            R += c;
          let A = Math.min(o.inWidth, m + E), D = Number.NEGATIVE_INFINITY, O = -1;
          for (let M = w; M < k; M += u) {
            let L = M - C;
            for (let W = R; W < A; W += c) {
              let V = W - E, G = h.get(g, M, W, y);
              G > D && (D = G, n ? O = s ? ((g * o.inHeight + M) * o.inWidth + W) * o.inChannels + y : (M * o.inWidth + W) * o.inChannels + y : O = L * m + V);
            }
          }
          a.set(O, g, b, _, y);
        }
      }
  return a;
}
function Dd(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, f = n.effectiveFilterHeight, d = n.effectiveFilterWidth, h = n.padInfo.front, g = n.padInfo.top, y = n.padInfo.left, b = s === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, C = ne(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], R = n.outShape[4];
  for (let A = 0; A < n.batchSize; ++A) {
    let D = A * k, O = A * o[0];
    for (let M = 0; M < n.inChannels; ++M)
      for (let L = 0; L < n.outDepth; ++L) {
        let W = L * a - h, V = W;
        for (; V < 0; )
          V += u;
        let G = Math.min(n.inDepth, m + W), q = D + L * _;
        for (let H = 0; H < n.outHeight; ++H) {
          let j = H * i - g, Y = j;
          for (; Y < 0; )
            Y += c;
          let Z = Math.min(n.inHeight, f + j), ee = q + H * E;
          for (let X = 0; X < n.outWidth; ++X) {
            let Q = X * p - y, se = Q;
            for (; se < 0; )
              se += l;
            let ie = Math.min(n.inWidth, d + Q), de = ee + X * R, Ie = b, Se = 0, Ee = 0;
            for (let st = V; st < G; st += u) {
              let pt = O + st * o[1];
              for (let De = Y; De < Z; De += c) {
                let ft = pt + De * o[2];
                for (let at = se; at < ie; at += l) {
                  let dt = ft + at * o[3], It = r[dt + M];
                  if (s === "max" && It > Ie ? Ie = It : s === "avg" && (Se += It, Ee++), isNaN(Ie))
                    break;
                }
                if (isNaN(Ie))
                  break;
              }
              if (isNaN(Ie))
                break;
            }
            let Me = de + M;
            w[Me] = s === "avg" ? Se / Ee : Ie;
          }
        }
      }
  }
  return C;
}
function _2(r, e) {
  let t10 = ne(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, f = e.padInfo.top, d = e.padInfo.left;
  for (let h = 0; h < e.batchSize; ++h)
    for (let g = 0; g < e.inChannels; ++g)
      for (let y = 0; y < e.outDepth; ++y) {
        let b = y * 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 - f, E = _;
          for (; E < 0; )
            E += i;
          let R = Math.min(e.inHeight, c + _);
          for (let A = 0; A < e.outWidth; ++A) {
            let D = A * s - d, O = D;
            for (; O < 0; )
              O += p;
            let M = Math.min(e.inWidth, l + D), L = Number.NEGATIVE_INFINITY, W = -1;
            for (let V = C; V < w; V += a) {
              let G = V - b;
              for (let q = E; q < R; q += i) {
                let H = q - _;
                for (let j = O; j < M; j += p) {
                  let Y = j - D, Z = r.get(h, V, q, j, g);
                  Z >= L && (L = Z, W = G * c * l + H * c + Y);
                }
              }
            }
            t10.set(W, h, y, k, A, g);
          }
        }
      }
  return t10;
}
function Mj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  K(n, "avgPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  x.assert(I.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = I.computePool2DInfo(n.shape, s, a, u, i, p), l;
  if (c.filterWidth === 1 && c.filterHeight === 1 && x.arraysEqual(c.inShape, c.outShape))
    l = ar({ inputs: { x: n }, backend: t10 });
  else {
    let m = t10.data.get(n.dataId).values, f = x.computeStrides(n.shape), d = rc(m, n.shape, n.dtype, f, c, "avg");
    l = t10.makeTensorInfo(c.outShape, n.dtype, d.values);
  }
  return l;
}
var E2 = { kernelName: pn, backendName: "cpu", kernelFunc: Mj };
function Lj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
  K(n, "avgPool3d");
  let c = I.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = Dd(l, n.shape, n.dtype, x.computeStrides(n.shape), c, "avg");
  return t10.makeTensorInfo(m.shape, "float32", m.values);
}
var $2 = { kernelName: ip, backendName: "cpu", kernelFunc: Lj };
function Bj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
  K([n, s], "avgPool3DGrad");
  let c = I.computePool3DInfo(s.shape, a, i, 1, p, u), l = c.strideDepth, m = c.strideHeight, f = c.strideWidth, d = c.filterDepth, h = c.filterHeight, g = c.filterWidth, y = c.dilationDepth, b = c.dilationHeight, C = c.dilationWidth, w = c.effectiveFilterDepth, k = c.effectiveFilterHeight, _ = c.effectiveFilterWidth, E = w - 1 - c.padInfo.front, R = _ - 1 - c.padInfo.left, A = k - 1 - c.padInfo.top, D = ne(s.shape, "float32"), O = 1 / (d * h * g), M = t10.bufferSync(n);
  for (let L = 0; L < c.batchSize; ++L)
    for (let W = 0; W < c.inChannels; ++W)
      for (let V = 0; V < c.inDepth; ++V)
        for (let G = 0; G < c.inHeight; ++G)
          for (let q = 0; q < c.inWidth; ++q) {
            let H = V - E, j = G - A, Y = q - R, Z = 0;
            for (let ee = 0; ee < w; ee += y) {
              let X = (H + ee) / l;
              if (!(X < 0 || X >= c.outDepth || Math.floor(X) !== X))
                for (let Q = 0; Q < k; Q += b) {
                  let se = (j + Q) / m;
                  if (!(se < 0 || se >= c.outHeight || Math.floor(se) !== se))
                    for (let ie = 0; ie < _; ie += C) {
                      let de = (Y + ie) / f;
                      if (de < 0 || de >= c.outWidth || Math.floor(de) !== de)
                        continue;
                      let Ie = M.get(L, X, se, de, W);
                      Z += Ie;
                    }
                }
            }
            D.set(Z * O, L, V, G, q, W);
          }
  return t10.makeTensorInfo(D.shape, D.dtype, D.values);
}
var R2 = { kernelName: Fm, backendName: "cpu", kernelFunc: Bj };
function Vj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
  K([n, s], "avgPoolGrad");
  let { filterSize: i, strides: p, pad: u } = o, c = I.computePool2DInfo(a.shape, i, p, 1, u), l = c.strideHeight, m = c.strideWidth, f = c.filterHeight, d = c.filterWidth, h = c.dilationHeight, g = c.dilationWidth, y = c.effectiveFilterHeight, b = c.effectiveFilterWidth, C = b - 1 - c.padInfo.left, w = y - 1 - c.padInfo.top, k = ne(a.shape, "float32"), _ = 1 / (f * d), E = t10.data.get(n.dataId).values, R = ne(n.shape, "float32", E);
  for (let A = 0; A < c.batchSize; ++A)
    for (let D = 0; D < c.inChannels; ++D)
      for (let O = 0; O < c.inHeight; ++O)
        for (let M = 0; M < c.inWidth; ++M) {
          let L = O - w, W = M - C, V = 0;
          for (let G = 0; G < y; G += h) {
            let q = (L + G) / l;
            if (!(q < 0 || q >= c.outHeight || Math.floor(q) !== q))
              for (let H = 0; H < b; H += g) {
                let j = (W + H) / m;
                if (j < 0 || j >= c.outWidth || Math.floor(j) !== j)
                  continue;
                let Y = R.get(A, q, j, D);
                V += Y;
              }
          }
          k.set(V * _, A, O, M, D);
        }
  return t10.makeTensorInfo(k.shape, k.dtype, k.values);
}
var A2 = { kernelName: Am, backendName: "cpu", kernelFunc: Vj };
function zj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, scale: s, offset: a, mean: i, variance: p } = e;
  x.assert(i.shape.length === p.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), x.assert(a == null || i.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), x.assert(s == null || i.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), K([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, f = s ? t10.data.get(s.dataId).values : new Float32Array([1]), d = a ? t10.data.get(a.dataId).values : new Float32Array([0]), h = new Float32Array(c.length), g = d.length, y = f.length, b = m.length, C = l.length, w = 0, k = 0, _ = 0, E = 0;
  for (let R = 0; R < c.length; ++R)
    h[R] = d[w++] + (c[R] - l[k++]) * f[_++] / Math.sqrt(m[E++] + u), w >= g && (w = 0), k >= C && (k = 0), _ >= y && (_ = 0), E >= b && (E = 0);
  return t10.makeTensorInfo(n.shape, n.dtype, h);
}
var F2 = { kernelName: kn, backendName: "cpu", kernelFunc: zj };
function Wj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
  K([n], "batchToSpaceND");
  let i = s.reduce((y, b) => y * b), p = I.getReshaped(n.shape, s, i), u = I.getPermuted(p.length, s.length), c = I.getReshapedPermuted(n.shape, s, i), l = I.getSliceBeginCoords(a, s.length), m = I.getSliceSize(c, a, s.length), f = Oe({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), d = bt({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), h = Oe({ inputs: { x: d }, backend: t10, attrs: { shape: c } }), g = qo({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } });
  return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), g;
}
var D2 = { kernelName: hs, backendName: "cpu", kernelFunc: Wj };
function Uj(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 = Zp(i, p, s.dtype, s.shape, a);
  return t10.makeTensorInfo([a], s.dtype, u);
}
var P2 = { kernelName: up, backendName: "cpu", kernelFunc: Uj };
function Gj(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 = I.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
  return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var O2 = { kernelName: pp, backendName: "cpu", kernelFunc: Gj };
var Hj = we(Ro, (r, e) => {
  let t10 = e;
  return r > t10.clipValueMax ? t10.clipValueMax : r < t10.clipValueMin ? t10.clipValueMin : r;
});
var M2 = { kernelName: Ro, backendName: "cpu", kernelFunc: Hj };
var qj = (r) => {
  let { x: e } = r.inputs, t10 = r.backend, o = new Float32Array(x.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 L2 = { kernelName: cp, backendName: "cpu", kernelFunc: qj };
function qs(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 B2 = { kernelName: Ya, backendName: "cpu", kernelFunc: qs };
function vi(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = x.parseAxisParam(n, e[0].shape)[0], a = e.map((h) => h.shape);
  I.assertParamsConsistent(a, s);
  let i = I.computeOutShape(e.map((h) => h.shape), s);
  if (x.sizeFromShape(i) === 0)
    return t10.makeTensorInfo(i, e[0].dtype, []);
  let p = e.filter((h) => x.sizeFromShape(h.shape) > 0);
  if (p.length === 1)
    return ar({ inputs: { x: p[0] }, backend: t10 });
  if (p[0].dtype === "complex64") {
    let h = p.map((w) => Wo({ inputs: { input: w }, backend: t10 })), g = p.map((w) => qs({ inputs: { input: w }, backend: t10 })), y = vi({ inputs: h, backend: t10, attrs: { axis: s } }), b = vi({ inputs: g, backend: t10, attrs: { axis: s } }), C = qt({ inputs: { real: y, imag: b }, backend: t10 });
    return h.forEach((w) => t10.disposeIntermediateTensorInfo(w)), g.forEach((w) => t10.disposeIntermediateTensorInfo(w)), t10.disposeIntermediateTensorInfo(y), t10.disposeIntermediateTensorInfo(b), C;
  }
  let u = p.map((h) => {
    let y = [-1, x.sizeFromShape(h.shape.slice(s))];
    return Oe({ inputs: { x: h }, backend: t10, attrs: { shape: y } });
  }), c = u.map((h) => ({ vals: t10.data.get(h.dataId).values, shape: h.shape }));
  i = I.computeOutShape(u.map((h) => h.shape), 1);
  let l = u[0].shape[0] === 1, m = Iu(c, i, e[0].dtype, l), f = I.computeOutShape(p.map((h) => h.shape), s), d = t10.makeTensorInfo(f, e[0].dtype, m);
  return u.forEach((h) => t10.disposeIntermediateTensorInfo(h)), d;
}
var V2 = { kernelName: gs, backendName: "cpu", kernelFunc: vi };
function RI(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;
  K([n, s], "conv2d");
  let l = I.convertConv2DDataFormat(p), m = I.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), f = m.filterHeight, d = m.filterWidth, h = m.dilationHeight, g = m.dilationWidth, y = m.padInfo.left, b = m.padInfo.top, C = m.dataFormat === "channelsLast", w = new je(m.outShape, n.dtype), k = x.computeStrides(n.shape), _ = x.computeStrides(s.shape), E = k[0], R = C ? k[1] : k[2], A = C ? k[2] : 1, D = C ? 1 : k[1], O = w.strides[0], M = C ? w.strides[1] : w.strides[2], L = C ? w.strides[2] : 1, W = C ? 1 : w.strides[1], V = t10.data.get(n.dataId).values, G = t10.data.get(s.dataId).values, q = w.values;
  for (let H = 0; H < m.batchSize; ++H) {
    let j = H * E, Y = H * O;
    for (let Z = 0; Z < m.outHeight; ++Z) {
      let ee = Y + Z * M, X = Z * m.strideHeight - b;
      for (let Q = 0; Q < f; ++Q) {
        let se = X + Q * h;
        if (se < 0 || se >= m.inHeight)
          continue;
        let ie = Q * _[0], de = j + se * R;
        for (let Ie = 0; Ie < m.outWidth; ++Ie) {
          let Se = ee + Ie * L, Ee = Ie * m.strideWidth - y;
          for (let Me = 0; Me < d; ++Me) {
            let st = Ee + Me * g;
            if (st < 0 || st >= m.inWidth)
              continue;
            let pt = ie + Me * _[1], De = de + st * A, ft = pt;
            for (let at = 0; at < m.inChannels; ++at) {
              let dt = V[De + at * D];
              for (let It = 0; It < m.outChannels; ++It)
                q[Se + It * W] += dt * G[ft + It];
              ft += m.outChannels;
            }
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(w.shape, w.dtype, q);
}
var z2 = { kernelName: ln, backendName: "cpu", kernelFunc: RI };
function Kj(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;
  K([n, s], "conv2dBackpropFilter");
  let l = I.convertConv2DDataFormat(p), m = I.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), { strideHeight: f, strideWidth: d, filterHeight: h, filterWidth: g } = m, y = m.dataFormat === "channelsLast", b = new je(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 je(n.shape, n.dtype, k), R = new je(s.shape, s.dtype, _);
  for (let A = 0; A < h; ++A) {
    let D = Math.max(0, Math.ceil((w - A) / f)), O = Math.min(m.outHeight, (m.inHeight + w - A) / f);
    for (let M = 0; M < g; ++M) {
      let L = Math.max(0, Math.ceil((C - M) / d)), W = Math.min(m.outWidth, (m.inWidth + C - M) / d);
      for (let V = 0; V < m.inChannels; ++V)
        for (let G = 0; G < m.outChannels; ++G) {
          let q = 0;
          for (let H = 0; H < m.batchSize; ++H)
            for (let j = D; j < O; ++j) {
              let Y = A + j * f - w;
              for (let Z = L; Z < W; ++Z) {
                let ee = M + Z * d - C;
                y ? q += E.get(H, Y, ee, V) * R.get(H, j, Z, G) : q += E.get(H, V, Y, ee) * R.get(H, G, j, Z);
              }
            }
          b.set(q, A, M, V, G);
        }
    }
  }
  return t10.makeTensorInfo(b.shape, b.dtype, b.values);
}
var W2 = { kernelName: lp, backendName: "cpu", kernelFunc: Kj };
function jj(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;
  K([n, s], "conv2dBackpropInput");
  let l = x.computeStrides(s.shape), m = x.computeStrides(n.shape), f = I.convertConv2DDataFormat(u), d = I.computeConv2DInfo(a, s.shape, i, 1, p, c, false, f), h = new je(d.inShape, "float32"), g = h.values, y = t10.data.get(n.dataId).values, b = t10.data.get(s.dataId).values, [C, w, k] = l, { batchSize: _, filterHeight: E, filterWidth: R, inChannels: A, inHeight: D, inWidth: O, outChannels: M, outHeight: L, outWidth: W, strideHeight: V, strideWidth: G } = d;
  f = d.dataFormat;
  let q = E - 1 - d.padInfo.top, H = R - 1 - d.padInfo.left, j = f === "channelsLast", Y = h.strides[0], Z = j ? h.strides[1] : h.strides[2], ee = j ? h.strides[2] : 1, X = j ? 1 : h.strides[1], Q = m[0], se = j ? m[1] : m[2], ie = j ? m[2] : 1, de = j ? 1 : m[1];
  for (let Ie = 0; Ie < _; ++Ie)
    for (let Se = 0; Se < A; ++Se)
      for (let Ee = 0; Ee < D; ++Ee) {
        let Me = Ee - q, st = Math.max(0, Math.ceil(Me / V)), pt = Math.min(L, (E + Me) / V);
        for (let De = 0; De < O; ++De) {
          let ft = De - H, at = Math.max(0, Math.ceil(ft / G)), dt = Math.min(W, (R + ft) / G), It = 0;
          for (let Pt = st; Pt < pt; ++Pt) {
            let jr = Pt * V - Me;
            for (let er = at; er < dt; ++er) {
              let Tt = er * G - ft, tr = Q * Ie + se * Pt + ie * er, rr = C * (E - 1 - jr) + w * (R - 1 - Tt) + k * Se;
              for (let Xr = 0; Xr < M; ++Xr) {
                let Yr = y[tr + de * Xr], pr = b[rr + Xr];
                It += Yr * pr;
              }
            }
          }
          let Fr = Y * Ie + Z * Ee + ee * De + X * Se;
          g[Fr] = It;
        }
      }
  return t10.makeTensorInfo(h.shape, h.dtype, h.values);
}
var U2 = { kernelName: mn, backendName: "cpu", kernelFunc: jj };
function Xj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o;
  K([n, s], "conv3d");
  let u = I.computeConv3DInfo(n.shape, s.shape, a, p, i), { filterDepth: c, filterHeight: l, filterWidth: m, dilationDepth: f, dilationHeight: d, dilationWidth: h, padInfo: g } = u, y = g.front, b = g.left, C = g.top, w = new je(u.outShape, n.dtype), k = t10.data.get(n.dataId).values, _ = t10.data.get(s.dataId).values, E = w.values, R = x.computeStrides(n.shape), A = x.computeStrides(s.shape);
  for (let D = 0; D < u.batchSize; ++D) {
    let O = D * R[0], M = D * w.strides[0];
    for (let L = 0; L < u.outDepth; ++L) {
      let W = M + L * w.strides[1], V = L * u.strideDepth - y;
      for (let G = 0; G < c; ++G) {
        let q = V + G * f;
        if (q < 0 || q >= u.inDepth)
          continue;
        let H = G * A[0], j = O + q * R[1];
        for (let Y = 0; Y < u.outHeight; ++Y) {
          let Z = W + Y * w.strides[2], ee = Y * u.strideHeight - C;
          for (let X = 0; X < l; ++X) {
            let Q = ee + X * d;
            if (Q < 0 || Q >= u.inHeight)
              continue;
            let se = H + X * A[1], ie = j + Q * R[2];
            for (let de = 0; de < u.outWidth; ++de) {
              let Ie = Z + de * u.outChannels, Se = de * u.strideWidth - b;
              for (let Ee = 0; Ee < m; ++Ee) {
                let Me = Se + Ee * h;
                if (Me < 0 || Me >= u.inWidth)
                  continue;
                let st = se + Ee * A[2], pt = ie + Me * u.inChannels, De = st;
                for (let ft = 0; ft < u.inChannels; ++ft) {
                  let at = k[pt + ft];
                  for (let dt = 0; dt < u.outChannels; ++dt)
                    E[Ie + dt] += at * _[De + dt];
                  De += u.outChannels;
                }
              }
            }
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(w.shape, w.dtype, w.values);
}
var G2 = { kernelName: mp, backendName: "cpu", kernelFunc: Xj };
function Yj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o;
  K([n, s], "conv3dBackpropFilterV2");
  let u = x.computeStrides(n.shape), c = x.computeStrides(s.shape), l = I.computeConv3DInfo(n.shape, p, a, 1, i), m = l.strideDepth, f = l.strideHeight, d = l.strideWidth, h = l.filterDepth, g = l.filterHeight, y = l.filterWidth, b = new je(l.filterShape, "float32"), C = b.values, [w, k, _, E] = b.strides, R = t10.data.get(s.dataId).values, [A, D, O, M] = c, L = t10.data.get(n.dataId).values, [W, V, G, q] = u, H = l.padInfo.front, j = l.padInfo.left, Y = l.padInfo.top;
  for (let Z = 0; Z < h; ++Z) {
    let ee = Math.max(0, Math.ceil((H - Z) / m)), X = Math.min(l.outDepth, (l.inDepth + H - Z) / m), Q = Z * w;
    for (let se = 0; se < g; ++se) {
      let ie = Math.max(0, Math.ceil((Y - se) / f)), de = Math.min(l.outHeight, (l.inHeight + Y - se) / f), Ie = se * k + Q;
      for (let Se = 0; Se < y; ++Se) {
        let Ee = Math.max(0, Math.ceil((j - Se) / d)), Me = Math.min(l.outWidth, (l.inWidth + j - Se) / d), st = Se * _ + Ie;
        for (let pt = 0; pt < l.inChannels; ++pt) {
          let De = pt * E + st;
          for (let ft = 0; ft < l.outChannels; ++ft) {
            let at = 0;
            for (let dt = 0; dt < l.batchSize; ++dt) {
              let It = dt * W, Fr = dt * A;
              for (let Pt = ee; Pt < X; ++Pt) {
                let er = (Z + Pt * m - H) * V + It, Tt = Pt * D + Fr;
                for (let tr = ie; tr < de; ++tr) {
                  let Xr = (se + tr * f - Y) * G + er, Yr = tr * O + Tt;
                  for (let pr = Ee; pr < Me; ++pr) {
                    let tn = (Se + pr * d - j) * q + Xr, Ua = pr * M + Yr;
                    at += L[tn + pt] * R[Ua + ft];
                  }
                }
              }
            }
            C[De + ft] = at;
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(b.shape, b.dtype, b.values);
}
var H2 = { kernelName: Dm, backendName: "cpu", kernelFunc: Yj };
function Qj(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o;
  K([n], "conv3dBackpropInputV2");
  let u = x.computeStrides(n.shape), c = x.computeStrides(s.shape), l = I.computeConv3DInfo(p, s.shape, i, 1, a), m = new je(l.inShape, "float32"), f = m.values, [d, h, g, y] = m.strides, b = t10.data.get(n.dataId).values, [C, w, k, _] = u, E = t10.data.get(s.dataId).values, [R, A, D, O] = c, { batchSize: M, filterDepth: L, filterHeight: W, filterWidth: V, inChannels: G, inDepth: q, inHeight: H, inWidth: j, outChannels: Y, outDepth: Z, outHeight: ee, outWidth: X, strideDepth: Q, strideHeight: se, strideWidth: ie } = l, de = L - 1 - l.padInfo.front, Ie = W - 1 - l.padInfo.top, Se = V - 1 - l.padInfo.left;
  for (let Ee = 0; Ee < M; ++Ee)
    for (let Me = 0; Me < G; ++Me)
      for (let st = 0; st < q; ++st) {
        let pt = st - de, De = Math.max(0, Math.ceil(pt / Q)), ft = Math.min(Z, (L + pt) / Q);
        for (let at = 0; at < H; ++at) {
          let dt = at - Ie, It = Math.max(0, Math.ceil(dt / se)), Fr = Math.min(ee, (W + dt) / se);
          for (let Pt = 0; Pt < j; ++Pt) {
            let jr = Pt - Se, er = Math.max(0, Math.ceil(jr / ie)), Tt = Math.min(X, (V + jr) / ie), tr = 0;
            for (let rr = De; rr < ft; ++rr) {
              let Xr = rr * Q - pt;
              for (let Yr = It; Yr < Fr; ++Yr) {
                let pr = Yr * se - dt;
                for (let Qs = er; Qs < Tt; ++Qs) {
                  let tn = Qs * ie - jr, Ua = C * Ee + w * rr + k * Yr + _ * Qs, jt = R * (L - 1 - Xr) + A * (W - 1 - pr) + D * (V - 1 - tn) + O * Me;
                  for (let Zs = 0; Zs < Y; ++Zs) {
                    let Lc = b[Ua + Zs], Bc = E[jt + Zs];
                    tr += Lc * Bc;
                  }
                }
              }
            }
            f[d * Ee + h * st + g * at + y * Pt + Me] = tr;
          }
        }
      }
  return t10.makeTensorInfo(m.shape, m.dtype, m.values);
}
var q2 = { kernelName: fp, backendName: "cpu", kernelFunc: Qj };
var Zj = we(fn, (r) => Math.cos(r));
var K2 = { kernelName: fn, backendName: "cpu", kernelFunc: Zj };
var Jj = we(dn, (r) => Math.cosh(r));
var j2 = { kernelName: dn, backendName: "cpu", kernelFunc: Jj };
function eX(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, f] = n.shape, d = s.shape[0], [h, g] = i, y = ne([d, h, g, f], "float32"), b = t10.data.get(s.dataId).values, C = t10.data.get(a.dataId).values, w = t10.data.get(n.dataId).values, k = x.computeStrides(n.shape), _ = x.computeStrides(y.shape);
  for (let E = 0; E < d; E++) {
    let R = E * 4, A = b[R], D = b[R + 1], O = b[R + 2], M = b[R + 3], L = C[E];
    if (L >= c)
      continue;
    let W = h > 1 ? (O - A) * (l - 1) / (h - 1) : 0, V = g > 1 ? (M - D) * (m - 1) / (g - 1) : 0;
    for (let G = 0; G < h; G++) {
      let q = h > 1 ? A * (l - 1) + G * W : 0.5 * (A + O) * (l - 1);
      if (q < 0 || q > l - 1) {
        for (let H = 0; H < g; H++)
          for (let j = 0; j < f; j++) {
            let Y = j + H * _[2] + G * _[1] + E * _[0];
            y.values[Y] = u;
          }
        continue;
      }
      if (p === "bilinear") {
        let H = Math.floor(q), j = Math.ceil(q), Y = q - H;
        for (let Z = 0; Z < g; Z++) {
          let ee = g > 1 ? D * (m - 1) + Z * V : 0.5 * (D + M) * (m - 1);
          if (ee < 0 || ee > m - 1) {
            for (let ie = 0; ie < f; ie++) {
              let de = ie + Z * _[2] + G * _[1] + E * _[0];
              y.values[de] = u;
            }
            continue;
          }
          let X = Math.floor(ee), Q = Math.ceil(ee), se = ee - X;
          for (let ie = 0; ie < f; ie++) {
            let de = ie + X * k[2] + H * k[1] + L * k[0], Ie = w[de];
            de = ie + Q * k[2] + H * k[1] + L * k[0];
            let Se = w[de];
            de = ie + X * k[2] + j * k[1] + L * k[0];
            let Ee = w[de];
            de = ie + Q * k[2] + j * k[1] + L * k[0];
            let Me = w[de], st = Ie + (Se - Ie) * se, pt = Ee + (Me - Ee) * se;
            de = ie + Z * _[2] + G * _[1] + E * _[0], y.values[de] = st + (pt - st) * Y;
          }
        }
      } else
        for (let H = 0; H < g; ++H) {
          let j = g > 1 ? D * (m - 1) + H * V : 0.5 * (D + M) * (m - 1);
          if (j < 0 || j > m - 1) {
            for (let ee = 0; ee < f; ee++) {
              let X = ee + H * _[2] + G * _[1] + E * _[0];
              y.values[X] = u;
            }
            continue;
          }
          let Y = Math.round(j), Z = Math.round(q);
          for (let ee = 0; ee < f; ee++) {
            let X = ee + Y * k[2] + Z * k[1] + L * k[0], Q = ee + H * _[2] + G * _[1] + E * _[0];
            y.values[Q] = w[X];
          }
        }
    }
  }
  return t10.makeTensorInfo(y.shape, y.dtype, y.values);
}
var X2 = { kernelName: xn, backendName: "cpu", kernelFunc: eX };
function tX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  K(n, "cumprod");
  let p = I.getAxesPermutation([s], n.shape.length), u = n;
  p != null && (u = bt({ inputs: { x: n }, backend: t10, attrs: { perm: p } }));
  let c = I.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=${c}`);
  let l = ct(u.dtype, "int32"), m = x.makeOnesTypedArray(x.sizeFromShape(u.shape), l), f = t10.data.get(u.dataId).values, d = u.shape[u.shape.length - 1], h = i ? (y, b) => y + d - b - 1 : (y, b) => y + b;
  for (let y = 0; y < f.length; y += d)
    for (let b = 0; b < d; b++) {
      let C = h(y, b);
      if (b === 0)
        m[C] = a ? 1 : f[C];
      else {
        let w = h(y, b - 1);
        m[C] = a ? f[w] * m[w] : f[C] * m[w];
      }
    }
  let g = t10.makeTensorInfo(u.shape, l, m);
  if (p != null) {
    let y = I.getUndoAxesPermutation(p), b = bt({ inputs: { x: g }, backend: t10, attrs: { perm: y } });
    return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b;
  }
  return g;
}
var Y2 = { kernelName: hn, backendName: "cpu", kernelFunc: tX };
function rX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  K(n, "cumsum");
  let p = I.getAxesPermutation([s], n.shape.length), u = n;
  p != null && (u = bt({ inputs: { x: n }, backend: t10, attrs: { perm: p } }));
  let c = I.getInnerMostAxes(1, n.shape.length)[0];
  if (c !== u.shape.length - 1)
    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length - 1} but got axis=${c}`);
  let l = ct(u.dtype, "int32"), m = x.makeZerosTypedArray(x.sizeFromShape(u.shape), l), f = t10.data.get(u.dataId).values, d = u.shape[u.shape.length - 1], h = i ? (y, b) => y + d - b - 1 : (y, b) => y + b;
  for (let y = 0; y < f.length; y += d)
    for (let b = 0; b < d; b++) {
      let C = h(y, b);
      if (b === 0)
        m[C] = a ? 0 : f[C];
      else {
        let w = h(y, b - 1);
        m[C] = a ? f[w] + m[w] : f[C] + m[w];
      }
    }
  let g = t10.makeTensorInfo(u.shape, l, m);
  if (p != null) {
    let y = I.getUndoAxesPermutation(p), b = bt({ inputs: { x: g }, backend: t10, attrs: { perm: y } });
    return t10.disposeIntermediateTensorInfo(g), t10.disposeIntermediateTensorInfo(u), b;
  }
  return g;
}
var Q2 = { kernelName: gn, backendName: "cpu", kernelFunc: rX };
function oX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
  if (n.shape.length === 1) {
    let p = t10.data.get(n.dataId).values, u = t10.data.get(s.dataId).values, c = Zp(p, u, s.dtype, s.shape, a);
    return t10.makeTensorInfo([a], s.dtype, c);
  } else if (n.shape.length === 2) {
    let p = t10.bufferSync(n), u = t10.bufferSync(s), c = yd(p, u, a, i);
    return t10.makeTensorInfo(c.shape, s.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`);
}
var Z2 = { kernelName: dp, backendName: "cpu", kernelFunc: oX };
function nX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o;
  x.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, f = c / (s * s), d = t10.data.get(n.dataId).values, h = new Float32Array(i * l * m * f), g = 0;
  for (let y = 0; y < i; ++y)
    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, R = (w * s + E) * f;
        for (let A = 0; A < f; ++A) {
          let O = A + R + c * (_ + u * (C + p * y));
          h[g++] = d[O];
        }
      }
    }
  return t10.makeTensorInfo([i, l, m, f], n.dtype, h);
}
var J2 = { kernelName: yn, backendName: "cpu", kernelFunc: nX };
function AI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p, dimRoundingMode: u } = o;
  K([n, s], "depthwiseConv2DNative");
  let c = x.computeStrides(n.shape), l = x.computeStrides(s.shape), m = p;
  m == null && (m = [1, 1]), x.assert(I.eitherStridesOrDilationsAreOne(a, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${m}'`);
  let f = I.computeConv2DInfo(n.shape, s.shape, a, m, i, u, true), { filterHeight: d, filterWidth: h, dilationHeight: g, dilationWidth: y, padInfo: b } = f, C = b.left, w = b.top, k = f.outChannels / f.inChannels, _ = new je(f.outShape, n.dtype), E = t10.data.get(n.dataId).values, R = t10.data.get(s.dataId).values, A = _.values;
  for (let D = 0; D < f.batchSize; ++D) {
    let O = D * c[0], M = D * _.strides[0];
    for (let L = 0; L < f.outHeight; ++L) {
      let W = M + L * _.strides[1], V = L * f.strideHeight - w;
      for (let G = 0; G < d; ++G) {
        let q = V + G * g;
        if (q < 0 || q >= f.inHeight)
          continue;
        let H = G * l[0], j = O + q * c[1];
        for (let Y = 0; Y < f.outWidth; ++Y) {
          let Z = W + Y * _.strides[2], ee = Y * f.strideWidth - C;
          for (let X = 0; X < h; ++X) {
            let Q = ee + X * y;
            if (Q < 0 || Q >= f.inWidth)
              continue;
            let se = H + X * l[1], ie = j + Q * f.inChannels, de = Z, Ie = se;
            for (let Se = 0; Se < f.inChannels; ++Se) {
              let Ee = E[ie + Se];
              for (let Me = 0; Me < k; ++Me)
                A[de + Me] += Ee * R[Ie + Me];
              de += k, Ie += k;
            }
          }
        }
      }
    }
  }
  return t10.makeTensorInfo(_.shape, _.dtype, _.values);
}
var e_ = { kernelName: bn, backendName: "cpu", kernelFunc: AI };
function sX(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;
  K([n, s], "depthwiseConv2dNativeBackpropFilter");
  let l = I.computeConv2DInfo(n.shape, c, a, i, p, u, true), { strideHeight: m, strideWidth: f, filterHeight: d, filterWidth: h } = l, g = new je(l.filterShape, "float32"), y = l.padInfo.left, b = l.padInfo.top, C = l.outChannels / l.inChannels, w = t10.data.get(n.dataId).values, k = new je(n.shape, n.dtype, w), _ = t10.data.get(s.dataId).values, E = new je(s.shape, s.dtype, _);
  for (let R = 0; R < d; ++R) {
    let A = Math.max(0, Math.ceil((b - R) / m)), D = Math.min(l.outHeight, (l.inHeight + b - R) / m);
    for (let O = 0; O < h; ++O) {
      let M = Math.max(0, Math.ceil((y - O) / f)), L = Math.min(l.outWidth, (l.inWidth + y - O) / f);
      for (let W = 0; W < l.outChannels; ++W) {
        let V = Math.trunc(W / C), G = W % C, q = 0;
        for (let H = 0; H < l.batchSize; ++H)
          for (let j = A; j < D; ++j) {
            let Y = R + j * m - b;
            for (let Z = M; Z < L; ++Z) {
              let ee = O + Z * f - y;
              q += k.get(H, Y, ee, V) * E.get(H, j, Z, W);
            }
          }
        g.set(q, R, O, V, G);
      }
    }
  }
  return t10.makeTensorInfo(g.shape, g.dtype, g.values);
}
var t_ = { kernelName: hp, backendName: "cpu", kernelFunc: sX };
function aX(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;
  K([n, s], "depthwiseConv2DNativeBackpropInput");
  let l = x.computeStrides(n.shape), m = x.computeStrides(s.shape), f = I.computeConv2DInfo(c, s.shape, a, i, p, u, true), d = new je(f.inShape, "float32"), h = d.values, [g, y, b] = d.strides, C = t10.data.get(n.dataId).values, [w, k, _] = l, E = t10.data.get(s.dataId).values, [R, A, D] = m, { batchSize: O, filterHeight: M, filterWidth: L, inChannels: W, inHeight: V, inWidth: G, outChannels: q, outHeight: H, outWidth: j, strideHeight: Y, strideWidth: Z } = f, ee = M - 1 - f.padInfo.top, X = L - 1 - f.padInfo.left, Q = q / W;
  for (let se = 0; se < O; ++se)
    for (let ie = 0; ie < W; ++ie)
      for (let de = 0; de < V; ++de) {
        let Ie = de - ee, Se = Math.max(0, Math.ceil(Ie / Y)), Ee = Math.min(H, (M + Ie) / Y);
        for (let Me = 0; Me < G; ++Me) {
          let st = Me - X, pt = Math.max(0, Math.ceil(st / Z)), De = Math.min(j, (L + st) / Z), ft = 0;
          for (let at = Se; at < Ee; ++at) {
            let dt = at * Y - Ie;
            for (let It = pt; It < De; ++It) {
              let Fr = It * Z - st, Pt = w * se + k * at + _ * It, jr = R * (M - 1 - dt) + A * (L - 1 - Fr) + D * ie;
              for (let er = 0; er < Q; ++er) {
                let Tt = ie * Q + er, tr = C[Pt + Tt], rr = E[jr + er];
                ft += tr * rr;
              }
            }
          }
          h[g * se + y * de + b * Me + ie] = ft;
        }
      }
  return t10.makeTensorInfo(d.shape, d.dtype, d.values);
}
var r_ = { kernelName: gp, backendName: "cpu", kernelFunc: aX };
function iX(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = x.sizeFromShape(o.shape), s = t10.data.get(o.dataId).values, a = ne([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 o_ = { kernelName: xp, backendName: "cpu", kernelFunc: iX };
var n_ = { kernelName: yp, 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: f, inHeight: d, inWidth: h, inChannels: g, outHeight: y, outWidth: b, padInfo: C, strideHeight: w, strideWidth: k, filterHeight: _, filterWidth: E, dilationHeight: R, dilationWidth: A, outShape: D } = I.computeDilation2DInfo(o.shape, n.shape, s, a, "NHWC", i), O = x.sizeFromShape(D), M = D.length, L = x.getArrayFromDType(o.dtype, O);
  for (let V = 0; V < f; ++V)
    for (let G = 0; G < y; ++G) {
      let q = G * w - C.top;
      for (let H = 0; H < b; ++H) {
        let j = H * k - C.left;
        for (let Y = 0; Y < g; ++Y) {
          let Z = Number.MIN_SAFE_INTEGER;
          for (let X = 0; X < _; ++X) {
            let Q = q + X * R;
            if (Q >= 0 && Q < d)
              for (let se = 0; se < E; ++se) {
                let ie = j + se * A;
                if (ie >= 0 && ie < h) {
                  let de = x.locToIndex([V, Q, ie, Y], c, x.computeStrides(o.shape)), Ie = x.locToIndex([X, se, Y], m, x.computeStrides(n.shape)), Se = u[de] + l[Ie];
                  Se > Z && (Z = Se);
                }
              }
          }
          let ee = x.locToIndex([V, G, H, Y], M, x.computeStrides(D));
          L[ee] = Z;
        }
      }
    }
  return { dataId: p.write(x.toTypedArray(L, o.dtype), D, o.dtype), shape: D, dtype: o.dtype };
} };
var s_ = { kernelName: vb, 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 = x.toNestedArray(o.shape, u.data.get(o.dataId).values), l = x.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: f, inWidth: d, inChannels: h, outHeight: g, outWidth: y, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: A } = I.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
  x.assert(s.rank === A.length, () => `Error in ${vb}, dy must have the same rank as output ${A.length}, but got ${s.rank}`);
  let D = x.toNestedArray(A, u.data.get(s.dataId).values), O = x.makeZerosNestedTypedArray(n.shape, n.dtype);
  for (let L = 0; L < m; ++L)
    for (let W = 0; W < g; ++W) {
      let V = W * C - b.top;
      for (let G = 0; G < y; ++G) {
        let q = G * w - b.left;
        for (let H = 0; H < h; ++H) {
          let j = Number.MIN_SAFE_INTEGER, Y = 0, Z = 0;
          for (let ee = 0; ee < k; ++ee) {
            let X = V + ee * E;
            if (X >= 0 && X < f)
              for (let Q = 0; Q < _; ++Q) {
                let se = q + Q * R;
                if (se >= 0 && se < d) {
                  let ie = c[L][X][se][H] + l[ee][Q][H];
                  ie > j && (j = ie, Y = ee, Z = Q);
                }
              }
          }
          O[Y][Z][H] += D[L][W][G][H];
        }
      }
    }
  return { dataId: u.write(x.toTypedArray(O, o.dtype), n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
} };
var a_ = { kernelName: Sb, 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 = x.toNestedArray(o.shape, u.data.get(o.dataId).values), l = x.toNestedArray(n.shape, u.data.get(n.dataId).values), { batchSize: m, inHeight: f, inWidth: d, inChannels: h, outHeight: g, outWidth: y, padInfo: b, strideHeight: C, strideWidth: w, filterHeight: k, filterWidth: _, dilationHeight: E, dilationWidth: R, outShape: A } = I.computeDilation2DInfo(o.shape, n.shape, a, i, "NHWC", p);
  x.assert(s.rank === A.length, () => `Error in ${Sb}, dy must have the same rank as output ${A.length}, but got ${s.rank}`);
  let D = x.toNestedArray(A, u.data.get(s.dataId).values), O = x.makeZerosNestedTypedArray(o.shape, o.dtype);
  for (let L = 0; L < m; ++L)
    for (let W = 0; W < g; ++W) {
      let V = W * C - b.top;
      for (let G = 0; G < y; ++G) {
        let q = G * w - b.left;
        for (let H = 0; H < h; ++H) {
          let j = Number.MIN_SAFE_INTEGER, Y = V < 0 ? 0 : V, Z = q < 0 ? 0 : q;
          for (let ee = 0; ee < k; ++ee) {
            let X = V + ee * E;
            if (X >= 0 && X < f)
              for (let Q = 0; Q < _; ++Q) {
                let se = q + Q * R;
                if (se >= 0 && se < d) {
                  let ie = c[L][X][se][H] + l[ee][Q][H];
                  ie > j && (j = ie, Y = X, Z = se);
                }
              }
          }
          O[L][Y][Z][H] += D[L][W][G][H];
        }
      }
    }
  return { dataId: u.write(x.toTypedArray(O, o.dtype), o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
function Fa(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  K(n, "sum");
  let i;
  n.dtype === "bool" ? i = Uo({ inputs: { x: n }, backend: t10, attrs: { dtype: "int32" } }) : i = ar({ inputs: { x: n }, backend: t10 });
  let p = i.shape.length, u = x.parseAxisParam(s, i.shape), c = I.getAxesPermutation(u, p), l = u, m = i;
  c != null && (m = bt({ inputs: { x: i }, backend: t10, attrs: { perm: c } }), l = I.getInnerMostAxes(l.length, p)), I.assertAxesAreInnerMostDims("sum", l, m.shape.length);
  let [f, d] = I.computeOutAndReduceShapes(m.shape, l), h = I.upcastType(m.dtype, "int32"), g = Yp(t10, f, h), y = x.sizeFromShape(d), 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 * y, _ = 0;
    for (let E = 0; E < y; ++E)
      _ += C[k + E];
    b[w] = _;
  }
  if (a) {
    let w = I.expandShapeToKeepDim(g.shape, u), k = g;
    g = Oe({ inputs: { x: g }, backend: t10, attrs: { shape: w } }), t10.disposeIntermediateTensorInfo(k);
  }
  return t10.disposeIntermediateTensorInfo(i), c != null && t10.disposeIntermediateTensorInfo(m), g;
}
var i_ = { kernelName: jn, backendName: "cpu", kernelFunc: Fa };
function uX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = I.decodeEinsumEquation(n, s.length);
  I.checkEinsumDimSizes(a.length, p, s);
  let { path: u, steps: c } = I.getEinsumComputePath(i, p), l = c.length, m = null, f = a.length, d = [];
  for (let h = 0; h < l; ++h) {
    for (let g of c[h]) {
      let { permutationIndices: y, expandDims: b } = I.getEinsumPermutation(f, p[g]), C;
      I.isIdentityPermutation(y) ? C = s[g] : (C = bt({ inputs: { x: s[g] }, backend: t10, attrs: { perm: y } }), d.push(C));
      let w = C.shape.slice();
      for (let k = 0; k < b.length; ++k)
        w.splice(b[k], 0, 1);
      x.arraysEqual(C.shape, w) || (C = Oe({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), d.push(C)), m === null ? m = C : (m = wu({ inputs: { a: C, b: m }, backend: t10 }), d.push(m));
    }
    h < l - 1 && (u[h] >= 0 && (m = Fa({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - f), keepDims: false } }), d.push(m)), f--);
  }
  for (let h of d)
    h !== m && t10.disposeIntermediateTensorInfo(h);
  return m;
}
var u_ = { kernelName: Xa, backendName: "cpu", kernelFunc: uX };
function pX(r) {
  let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e;
  K([o, n], "eluGrad");
  let s = new Float32Array(x.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 p_ = { kernelName: Pm, backendName: "cpu", kernelFunc: pX };
var cX = I.ERF_P;
var lX = I.ERF_A1;
var mX = I.ERF_A2;
var fX = I.ERF_A3;
var dX = I.ERF_A4;
var hX = I.ERF_A5;
var gX = we(Gi, (r) => {
  let e = Math.sign(r), t10 = Math.abs(r), o = 1 / (1 + cX * t10);
  return e * (1 - ((((hX * o + dX) * o + fX) * o + mX) * o + lX) * o * Math.exp(-t10 * t10));
});
var c_ = { kernelName: Gi, backendName: "cpu", kernelFunc: gX };
function oc(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 && (x.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Oe({ inputs: { x: n }, backend: t10, attrs: { shape: i } });
}
var l_ = { kernelName: xs, backendName: "cpu", kernelFunc: oc };
var xX = Le((r, e) => r / e);
var Sl = Ye(Cn, xX);
var vl = { kernelName: Cn, backendName: "cpu", kernelFunc: Sl };
function Pd(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 = x.sizeFromShape(u), l = x.getTypedArrayFromDType("float32", c), m = x.getTypedArrayFromDType("float32", c);
  for (let g = 0; g < n; g++) {
    let y = qo({ inputs: { x: i }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), b = qo({ inputs: { x: p }, backend: t10, attrs: { begin: [g, 0], size: [1, s] } }), C = qt({ inputs: { real: y, imag: b }, backend: t10 }), { real: w, imag: k } = yX(C, e, t10), _ = I.mergeRealAndImagArrays(w, k);
    for (let E = 0; E < s; E++) {
      let R = I.getComplexWithIndex(_, E);
      l[g * s + E] = R.real, m[g * s + E] = R.imag;
    }
    t10.disposeIntermediateTensorInfo(y), t10.disposeIntermediateTensorInfo(b), t10.disposeIntermediateTensorInfo(C);
  }
  let f = t10.makeTensorInfo(u, "float32", l), d = t10.makeTensorInfo(u, "float32", m), h = qt({ inputs: { real: f, imag: d }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(d), h;
}
function yX(r, e, t10) {
  let o = x.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 (bX(o)) {
    let i = FI(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", x.createScalarValue(o, "float32")), m = ar({ inputs: { x: l }, backend: t10 }), f = vl.kernelFunc({ inputs: { a: u, b: l }, backend: t10 }), d = vl.kernelFunc({ inputs: { a: c, b: m }, backend: t10 }), h = t10.data.get(f.dataId).values, g = t10.data.get(d.dataId).values;
      return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(d), { real: h, imag: g };
    }
    return i;
  } else {
    let i = I.mergeRealAndImagArrays(s, a), p = CX(i, o, e);
    return I.splitRealAndImagArrays(p);
  }
}
function bX(r) {
  return (r & r - 1) === 0;
}
function FI(r, e, t10, o, n) {
  if (t10 === 1)
    return { real: r, imag: e };
  let s = I.mergeRealAndImagArrays(r, e), a = t10 / 2, i = I.complexWithEvenIndex(s), p = i.real, u = i.imag, c = [p.length], l = n.makeTensorInfo(c, "float32", p), m = n.makeTensorInfo(c, "float32", u), f = qt({ inputs: { real: l, imag: m }, backend: n }), d = I.complexWithOddIndex(s), h = d.real, g = d.imag, y = [h.length], b = n.makeTensorInfo(y, "float32", h), C = n.makeTensorInfo(y, "float32", g), w = qt({ inputs: { real: b, imag: C }, backend: n }), k = FI(p, u, a, o, n), _ = k.real, E = k.imag, R = [_.length], A = n.makeTensorInfo(R, "float32", _), D = n.makeTensorInfo(R, "float32", E), O = qt({ inputs: { real: A, imag: D }, backend: n }), M = FI(h, g, a, o, n), L = M.real, W = M.imag, V = [L.length], G = n.makeTensorInfo(V, "float32", L), q = n.makeTensorInfo(V, "float32", W), H = qt({ inputs: { real: G, imag: q }, backend: n }), j = I.exponents(t10, o), Y = [j.real.length], Z = n.makeTensorInfo(Y, "float32", j.real), ee = n.makeTensorInfo(Y, "float32", j.imag), X = qt({ inputs: { real: Z, imag: ee }, backend: n }), Q = wu({ inputs: { a: X, b: H }, backend: n }), se = Hs({ inputs: { a: O, b: Q }, backend: n }), ie = Il({ inputs: { a: O, b: Q }, backend: n }), de = Wo({ inputs: { input: se }, backend: n }), Ie = Wo({ inputs: { input: ie }, backend: n }), Se = qs({ inputs: { input: se }, backend: n }), Ee = qs({ inputs: { input: ie }, backend: n }), Me = vi({ inputs: [de, Ie], backend: n, attrs: { axis: 0 } }), st = vi({ inputs: [Se, Ee], backend: n, attrs: { axis: 0 } }), pt = n.data.get(Me.dataId).values, De = n.data.get(st.dataId).values;
  return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(w), n.disposeIntermediateTensorInfo(A), n.disposeIntermediateTensorInfo(D), n.disposeIntermediateTensorInfo(O), n.disposeIntermediateTensorInfo(G), n.disposeIntermediateTensorInfo(q), n.disposeIntermediateTensorInfo(H), n.disposeIntermediateTensorInfo(Z), n.disposeIntermediateTensorInfo(ee), n.disposeIntermediateTensorInfo(X), n.disposeIntermediateTensorInfo(Q), n.disposeIntermediateTensorInfo(se), n.disposeIntermediateTensorInfo(ie), n.disposeIntermediateTensorInfo(de), n.disposeIntermediateTensorInfo(Se), n.disposeIntermediateTensorInfo(Ie), n.disposeIntermediateTensorInfo(Ee), n.disposeIntermediateTensorInfo(Me), n.disposeIntermediateTensorInfo(st), { real: pt, imag: De };
}
function CX(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 = I.exponent(n * i, e, t10), u = I.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), I.assignToTypedArray(o, s, a, n);
  }
  return o;
}
function IX(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = x.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Oe({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Pd(i, false, t10), u = Oe({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u;
}
var m_ = { kernelName: bp, backendName: "cpu", kernelFunc: IX };
function kl(r) {
  let { backend: e, attrs: t10 } = r, { shape: o, value: n, dtype: s } = t10, a = s || x.inferDtype(n), i = x.getArrayFromDType(a, x.sizeFromShape(o));
  return wX(i, n, a), e.makeTensorInfo(o, a, i);
}
var f_ = { kernelName: ys, backendName: "cpu", kernelFunc: kl };
function wX(r, e, t10) {
  r.fill(e);
}
var d_ = { kernelName: Sn, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, n = t10, s = x.getTypedArrayFromDType(o.dtype, x.sizeFromShape(o.shape)), [a, i, p, u] = o.shape, c = n.data.get(o.dataId).values;
  for (let m = 0; m < a; m++) {
    let f = m * p * i * u;
    for (let d = 0; d < i; d++) {
      let h = d * (p * u);
      for (let g = 0; g < p; g++) {
        let y = g * u;
        for (let b = 0; b < u; b++) {
          let C = Math.round(p - g - 1), w = f + h + y + b, k = c[w];
          if (C >= 0 && C < p) {
            let _ = C * u, E = f + h + _ + b;
            k = c[E];
          }
          s[w] = k;
        }
      }
    }
  }
  return { dataId: n.write(s, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
var SX = Le((r, e) => Math.floor(r / e));
var vX = Ye(vn, SX, null, "int32");
var h_ = { kernelName: vn, backendName: "cpu", kernelFunc: vX };
function kX(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: f, leakyreluAlpha: d } = o, h = RI({ 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 y = Oe({ inputs: { x: a }, backend: t10, attrs: { shape: [a.shape[0], 1, 1] } });
      h = Hs({ inputs: { a: h, b: y }, backend: t10 }), t10.disposeIntermediateTensorInfo(y);
    } else
      h = Hs({ inputs: { a: h, b: a }, backend: t10 });
    t10.disposeIntermediateTensorInfo(g);
  }
  if (f) {
    let g = h;
    if (c === "NCHW" && f === "prelu" && i.shape.length === 1 && i.shape[0] !== 1) {
      let y = Oe({ inputs: { x: i }, backend: t10, attrs: { shape: [i.shape[0], 1, 1] } });
      h = _u(t10, h, f, y, d), t10.disposeIntermediateTensorInfo(y);
    } else
      h = _u(t10, h, f, i, d);
    t10.disposeIntermediateTensorInfo(g);
  }
  return h;
}
var g_ = { kernelName: Do, backendName: "cpu", kernelFunc: kX };
function TX(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: f, leakyreluAlpha: d } = o, h = AI({ inputs: { x: n, filter: s }, backend: t10, attrs: { strides: p, pad: u, dataFormat: c, dilations: l, dimRoundingMode: m } });
  if (a) {
    let g = h;
    h = Hs({ inputs: { a: h, b: a }, backend: t10 }), t10.disposeIntermediateTensorInfo(g);
  }
  if (f) {
    let g = h;
    h = _u(t10, h, f, i, d), t10.disposeIntermediateTensorInfo(g);
  }
  return h;
}
var x_ = { kernelName: Po, backendName: "cpu", kernelFunc: TX };
function NX(r) {
  let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = x.sizeFromShape(o.shape), a = n.shape, i = a[a.length - 1], [p, u, c, l] = I.prepareAndValidate(o, n);
  if (u === 0)
    return t10.makeTensorInfo(p, o.dtype, []);
  let m = t10.data.get(n.dataId).values, f = t10.bufferSync(o), d = bd(m, f, o.dtype, u, i, c, l, o.shape, s);
  return t10.makeTensorInfo(p, o.dtype, d.values);
}
var y_ = { kernelName: Tn, backendName: "cpu", kernelFunc: NX };
function _X(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o;
  K([n, s], "gatherV2");
  let p = x.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];
    x.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 = x.sizeFromShape(s.shape), f = I.segment_util.collectGatherOpShapeInfo(n, s, p, l), d = Oe({ inputs: { x: n }, backend: t10, attrs: { shape: [f.batchSize, f.outerSize, f.dimSize, f.sliceSize] } }), h = Oe({ inputs: { x: s }, backend: t10, attrs: { shape: [f.batchSize, m / f.batchSize] } }), g = [f.batchSize, f.outerSize, m / f.batchSize, f.sliceSize], y = t10.bufferSync(h), b = t10.bufferSync(d), C = Cd(b, y, g);
  return t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), t10.makeTensorInfo(f.outputShape, C.dtype, C.values);
}
var b_ = { kernelName: bs, backendName: "cpu", kernelFunc: _X };
function EX(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = x.sizeFromShape(o.shape), s = o.shape[o.shape.length - 1], a = n / s, i = Oe({ inputs: { x: o }, backend: t10, attrs: { shape: [a, s] } }), p = Pd(i, true, t10), u = Oe({ inputs: { x: p }, backend: t10, attrs: { shape: o.shape } });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(p), u;
}
var C_ = { kernelName: Cp, backendName: "cpu", kernelFunc: EX };
var $X = we(Hi, (r) => Number.isFinite(r) ? 1 : 0, "bool");
var I_ = { kernelName: Hi, backendName: "cpu", kernelFunc: $X };
var RX = we(qi, (r) => Math.abs(r) === 1 / 0 ? 1 : 0, "bool");
var w_ = { kernelName: qi, backendName: "cpu", kernelFunc: RX };
var AX = we(ia, (r) => Number.isNaN(r) ? 1 : 0, "bool");
var S_ = { kernelName: ia, backendName: "cpu", kernelFunc: AX };
function FX(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = Id(o, n, s);
  return e.makeTensorInfo([a.length], "float32", a);
}
var v_ = { kernelName: Ip, backendName: "cpu", kernelFunc: FX };
var DX = we(Ki, (r) => Math.log1p(r));
var k_ = { kernelName: Ki, backendName: "cpu", kernelFunc: DX };
var PX = Le((r, e) => r && e);
var OX = Ye(_n, PX, null, "bool");
var T_ = { kernelName: _n, backendName: "cpu", kernelFunc: OX };
var MX = we(En, (r) => r ? 0 : 1, "bool");
var N_ = { kernelName: En, backendName: "cpu", kernelFunc: MX };
var LX = Le((r, e) => r || e);
var BX = Ye(ua, LX, null, "bool");
var __ = { kernelName: ua, backendName: "cpu", kernelFunc: BX };
function VX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o;
  K(n, "LRN");
  let u = n.shape[3], c = u - 1, l = t10.data.get(n.dataId).values, m = x.sizeFromShape(n.shape), f = new Float32Array(m);
  function d(h) {
    let g = h % u, y = h - g + Math.max(0, g - s), b = h - g + Math.min(g + s, c), C = 0;
    for (; y <= b; y++) {
      let w = l[y];
      C += w * w;
    }
    return C;
  }
  for (let h = 0; h < m; h++) {
    let g = d(h), y = l[h] * Math.pow(a + i * g, -p);
    f[h] = y;
  }
  return t10.makeTensorInfo(n.shape, n.dtype, f);
}
var E_ = { kernelName: wp, backendName: "cpu", kernelFunc: VX };
function zX(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;
  K(a, "LRNGrad");
  let l = x.sizeFromShape(a.shape), m = a.shape[3], f = t10.data.get(a.dataId).values, d = t10.data.get(n.dataId).values, h = t10.data.get(s.dataId).values, g = new Float32Array(l), y = l;
  for (let b = 0; b < y; 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(d[E], 2);
    _ = u * _ + p;
    for (let E = w; E < k; E++) {
      let R = -2 * u * c * d[E] * h[b] / _;
      b === E && (R += Math.pow(_, -c)), R *= f[b], g[E] += R;
    }
  }
  return t10.makeTensorInfo(a.shape, n.dtype, g);
}
var $_ = { kernelName: Om, backendName: "cpu", kernelFunc: zX };
function DI(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 = x.parseAxisParam(s, p), l = c, m = I.getAxesPermutation(l, u), f = 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]];
    f = Jp(f, p, n.dtype, m, w), l = I.getInnerMostAxes(l.length, u), p = w;
  }
  K(n, "max"), I.assertAxesAreInnerMostDims("max", l, u);
  let [d, h] = I.computeOutAndReduceShapes(p, l), g = x.sizeFromShape(h), y = wd(f, g, d, n.dtype), b = i.write(y, d, n.dtype), C = d;
  return a && (C = I.expandShapeToKeepDim(d, c)), { dataId: b, shape: C, dtype: n.dtype };
}
var R_ = { kernelName: $n, backendName: "cpu", kernelFunc: DI };
function WX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  K(n, "maxPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  x.assert(I.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = I.computePool2DInfo(n.shape, s, a, u, i, p), l;
  if (c.filterWidth === 1 && c.filterHeight === 1 && x.arraysEqual(c.inShape, c.outShape))
    l = ar({ inputs: { x: n }, backend: t10 });
  else {
    let m = t10.data.get(n.dataId).values, f = x.computeStrides(n.shape), d = rc(m, n.shape, n.dtype, f, c, "max");
    l = t10.makeTensorInfo(c.outShape, n.dtype, d.values);
  }
  return l;
}
var A_ = { kernelName: Rn, backendName: "cpu", kernelFunc: WX };
function UX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p, dataFormat: u } = o;
  K(n, "maxPool3d");
  let c = I.computePool3DInfo(n.shape, s, a, 1, i, p, u), l = t10.data.get(n.dataId).values, m = Dd(l, n.shape, n.dtype, x.computeStrides(n.shape), c, "max");
  return t10.makeTensorInfo(m.shape, "float32", m.values);
}
var F_ = { kernelName: Sp, backendName: "cpu", kernelFunc: UX };
function GX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = o;
  K([n, s], "maxPool3DGrad");
  let c = I.computePool3DInfo(s.shape, a, i, 1, p, u), l = t10.bufferSync(s), m = _2(l, c), f = c.strideDepth, d = c.strideHeight, h = c.strideWidth, g = c.dilationDepth, y = 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, R = w - 1 - c.padInfo.top, A = ne(s.shape, "float32"), D = t10.bufferSync(n);
  for (let O = 0; O < c.batchSize; ++O)
    for (let M = 0; M < c.inChannels; ++M)
      for (let L = 0; L < c.inDepth; ++L)
        for (let W = 0; W < c.inHeight; ++W)
          for (let V = 0; V < c.inWidth; ++V) {
            let G = L - _, q = W - R, H = V - E, j = 0;
            for (let Y = 0; Y < C; Y += g) {
              let Z = (G + Y) / f;
              if (!(Z < 0 || Z >= c.outDepth || Math.floor(Z) !== Z))
                for (let ee = 0; ee < w; ee += y) {
                  let X = (q + ee) / d;
                  if (!(X < 0 || X >= c.outHeight || Math.floor(X) !== X))
                    for (let Q = 0; Q < k; Q += b) {
                      let se = (H + Q) / h;
                      if (se < 0 || se >= c.outWidth || Math.floor(se) !== se)
                        continue;
                      let ie = C * w * k - 1 - m.get(O, Z, X, se, M), de = Y * w * k + ee * k + Q, Ie = ie === de ? 1 : 0;
                      if (Ie === 0)
                        continue;
                      let Se = D.get(O, Z, X, se, M);
                      j += Se * Ie;
                    }
                }
            }
            A.set(j, O, L, W, V, M);
          }
  return t10.makeTensorInfo(A.shape, A.dtype, A.values);
}
var D_ = { kernelName: Lm, backendName: "cpu", kernelFunc: GX };
function HX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
  K([s, a], "maxPoolGrad");
  let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = I.computePool2DInfo(i.shape, p, u, 1, c, l), f = t10.data.get(i.dataId).values, d = ne(m.outShape, i.dtype, Fd(f, i.shape, i.dtype, m).values), h = m.strideHeight, g = m.strideWidth, y = m.dilationHeight, b = m.dilationWidth, C = m.effectiveFilterHeight, w = m.effectiveFilterWidth, k = w - 1 - m.padInfo.left, _ = C - 1 - m.padInfo.top, E = ne(i.shape, "float32"), R = t10.data.get(n.dataId).values, A = ne(n.shape, "float32", R);
  for (let D = 0; D < m.batchSize; ++D)
    for (let O = 0; O < m.inChannels; ++O)
      for (let M = 0; M < m.inHeight; ++M)
        for (let L = 0; L < m.inWidth; ++L) {
          let W = M - _, V = L - k, G = 0;
          for (let q = 0; q < C; q += y) {
            let H = (W + q) / h;
            if (!(H < 0 || H >= m.outHeight || Math.floor(H) !== H))
              for (let j = 0; j < w; j += b) {
                let Y = (V + j) / g;
                if (Y < 0 || Y >= m.outWidth || Math.floor(Y) !== Y)
                  continue;
                let Z = C * w - 1 - d.get(D, H, Y, O), ee = q * w + j, X = Z === ee ? 1 : 0;
                if (X === 0)
                  continue;
                let Q = A.get(D, H, Y, O);
                G += Q * X;
              }
          }
          E.set(G, D, M, L, O);
        }
  return t10.makeTensorInfo(E.shape, E.dtype, E.values);
}
var P_ = { kernelName: Mm, backendName: "cpu", kernelFunc: HX };
function O_(r, e, t10, o, n) {
  let s = x.computeStrides(e), a = rc(r, e, t10, s, n, "max"), i = Fd(r, e, t10, n, true, o);
  return [a.values, i.values];
}
var M_ = { kernelName: vp, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10;
  K(o, "MaxPoolWithArgmax");
  let u = p.data.get(o.dataId).values, c = I.computePool2DInfo(o.shape, n, s, [1, 1], a), [l, m] = O_(u, o.shape, o.dtype, i, c), f = p.write(l, c.outShape, o.dtype), d = p.write(m, c.outShape, o.dtype);
  return [{ dataId: f, shape: c.outShape, dtype: o.dtype }, { dataId: d, shape: c.outShape, dtype: "int32" }];
} };
function qX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = x.parseAxisParam(s, n.shape), u = I.computeOutAndReduceShapes(n.shape, i)[1], c = x.sizeFromShape(u), l = [], m = t10.makeTensorInfo([], "float32", new Float32Array([c]));
  l.push(m);
  let f = Uo({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } });
  l.push(f);
  let d = Sl({ inputs: { a: f, b: m }, backend: t10 });
  l.push(d);
  let h = Fa({ inputs: { x: d }, backend: t10, attrs: { axis: s, keepDims: a } });
  return l.forEach((g) => t10.disposeIntermediateTensorInfo(g)), h;
}
var L_ = { kernelName: An, backendName: "cpu", kernelFunc: qX };
function KX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  K(n, "min");
  let i = x.parseAxisParam(s, n.shape), p = i, u = I.getAxesPermutation(p, n.shape.length), c = n;
  u != null && (c = bt({ inputs: { x: n }, backend: t10, attrs: { perm: u } }), p = I.getInnerMostAxes(p.length, n.shape.length)), I.assertAxesAreInnerMostDims("min", p, c.shape.length);
  let [l, m] = I.computeOutAndReduceShapes(c.shape, p), f = x.sizeFromShape(m), d = x.makeZerosTypedArray(x.sizeFromShape(l), c.dtype), h = t10.data.get(c.dataId).values;
  for (let y = 0; y < d.length; ++y) {
    let b = y * f, C = h[b];
    for (let w = 0; w < f; ++w) {
      let k = h[b + w];
      (Number.isNaN(k) || k < C) && (C = k);
    }
    d[y] = C;
  }
  u != null && t10.disposeIntermediateTensorInfo(c);
  let g = t10.makeTensorInfo(l, c.dtype, d);
  if (a) {
    let y = I.expandShapeToKeepDim(l, i), b = Oe({ inputs: { x: g }, backend: t10, attrs: { shape: y } });
    return t10.disposeIntermediateTensorInfo(g), b;
  }
  return g;
}
var B_ = { kernelName: Fn, backendName: "cpu", kernelFunc: KX };
function jX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, mode: a } = o;
  K(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, f = x.computeStrides(n.shape), d = x.sizeFromShape(i), h = i.length, g = x.computeStrides(i), y = x.getTypedArrayFromDType(n.dtype, d);
  for (let C = 0; C < d; C++) {
    let w = x.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 = x.locToIndex(w, m, f);
    y[C] = l[k];
  }
  return { dataId: t10.write(y, i, n.dtype), shape: i, dtype: n.dtype };
}
var V_ = { kernelName: Dn, backendName: "cpu", kernelFunc: jX };
var XX = Le((r, e) => {
  let t10 = r % e;
  return r < 0 && e < 0 || r >= 0 && e >= 0 ? t10 : (t10 + e) % e;
});
var YX = Ye(ji, XX);
var z_ = { kernelName: ji, backendName: "cpu", kernelFunc: YX };
var U_ = rp(IC());
function PI(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 = x.parseAxisParam([i], n.shape), u = DI({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: p, keepDims: false } }), c = I.expandShapeToKeepDim(u.shape, p), l = Oe({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), m = Il({ inputs: { a: n, b: l }, backend: t10 }), f = iI({ inputs: { x: m }, backend: t10 }), d = Fa({ inputs: { x: f }, backend: t10, attrs: { axis: p, keepDims: false } }), h = Oe({ inputs: { x: d }, backend: t10, attrs: { shape: c } }), g = Sl({ inputs: { a: f, b: h }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(h), g;
}
var W_ = { kernelName: Xn, backendName: "cpu", kernelFunc: PI };
function QX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o;
  K(n, "multinomial");
  let p = i ? n : PI({ 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], f = x.makeZerosTypedArray(x.sizeFromShape(m), "int32");
  for (let d = 0; d < u; ++d) {
    let h = d * 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 y = U_.alea(a.toString()), b = d * s;
    for (let C = 0; C < s; ++C) {
      let w = y();
      f[b + C] = g.length;
      for (let k = 0; k < g.length; k++)
        if (w < g[k]) {
          f[b + C] = k;
          break;
        }
    }
  }
  return i || t10.disposeIntermediateTensorInfo(p), t10.makeTensorInfo(m, "int32", f);
}
var G_ = { kernelName: kp, backendName: "cpu", kernelFunc: QX };
var ZX = Bt.nonMaxSuppressionV3Impl;
function JX(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p } = o;
  K(n, "NonMaxSuppression");
  let u = t10.data.get(n.dataId).values, c = t10.data.get(s.dataId).values, { selectedIndices: l } = ZX(u, c, a, i, p);
  return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var H_ = { kernelName: On, backendName: "cpu", kernelFunc: JX };
var e5 = Bt.nonMaxSuppressionV4Impl;
function t5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o;
  K(n, "NonMaxSuppressionPadded");
  let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, { selectedIndices: m, validOutputs: f } = e5(c, l, a, i, p, u);
  return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([f]))];
}
var q_ = { kernelName: pa, backendName: "cpu", kernelFunc: t5 };
var r5 = Bt.nonMaxSuppressionV5Impl;
function o5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o;
  K(n, "NonMaxSuppressionWithScore");
  let c = t10.data.get(n.dataId).values, l = t10.data.get(s.dataId).values, m = a, f = i, d = p, h = u, { selectedIndices: g, selectedScores: y } = r5(c, l, m, f, d, h);
  return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([y.length], "float32", new Float32Array(y))];
}
var K_ = { kernelName: Mn, backendName: "cpu", kernelFunc: o5 };
function n5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o;
  K(n, "oneHot");
  let u = x.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 j_ = { kernelName: ca, backendName: "cpu", kernelFunc: n5 };
function Tl(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 = Wo({ inputs: { input: o }, backend: t10 }), s = Tl({ inputs: { x: n }, backend: t10 }), a = qs({ inputs: { input: o }, backend: t10 }), i = Tl({ inputs: { x: a }, backend: t10 }), p = qt({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
  } else
    return kl({ backend: t10, attrs: { shape: o.shape, value: 0, dtype: o.dtype } });
}
var X_ = { kernelName: Es, backendName: "cpu", kernelFunc: Tl };
function Y_(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 = Wo({ inputs: { input: o }, backend: t10 }), s = Y_({ inputs: { x: n }, backend: t10 }), a = qs({ inputs: { input: o }, backend: t10 }), i = Tl({ inputs: { x: a }, backend: t10 }), p = qt({ inputs: { real: s, imag: i }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(n), t10.disposeIntermediateTensorInfo(s), t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(i), p;
  } else
    return kl({ backend: t10, attrs: { shape: o.shape, value: 1, dtype: o.dtype } });
}
var Q_ = { kernelName: Cs, backendName: "cpu", kernelFunc: Y_ };
function OI(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return oc({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    x.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), x.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = oc({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = vi({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u;
}
var Z_ = { kernelName: Is, backendName: "cpu", kernelFunc: OI };
function s5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
  K(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 = x.sizeFromShape(n.shape), l = n.shape.length, m = x.computeStrides(n.shape), f = x.sizeFromShape(i), d = i.length, h = x.computeStrides(i), g = x.getTypedArrayFromDType(n.dtype, f);
  a !== 0 && g.fill(a);
  for (let b = 0; b < c; b++) {
    let w = x.indexToLoc(b, l, m).map((_, E) => _ + p[E]), k = x.locToIndex(w, d, h);
    g[k] = u[b];
  }
  return { dataId: t10.write(g, i, n.dtype), shape: i, dtype: n.dtype };
}
var Od = { kernelName: Ln, backendName: "cpu", kernelFunc: s5 };
var a5 = Le((r, e) => Math.pow(r, e));
var i5 = Ye(Bn, a5);
var J_ = { kernelName: Bn, backendName: "cpu", kernelFunc: i5 };
function u5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((y) => t10.data.get(y.dataId).values), u = n.map((y) => y.shape), c = t10.data.get(s.dataId).values, l = t10.data.get(a.dataId).values, [m, f, d] = Sd(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((y) => t10.makeTensorInfo([y.length], "int32", y)), g = t10.makeTensorInfo(d, s.dtype, f);
  return h.concat([g]);
}
var eE = { kernelName: Tp, backendName: "cpu", kernelFunc: u5 };
function p5(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] = vd(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 tE = { kernelName: Np, backendName: "cpu", kernelFunc: p5 };
function c5(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), f = i.map((g) => g.shape), [d, h] = kd(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, f, p);
  return t10.makeTensorInfo(d, s.dtype, h);
}
var rE = { kernelName: _p, backendName: "cpu", kernelFunc: c5 };
function l5(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, dtype: s, step: a } = t10, i = Su(o, n, a, s);
  return e.makeTensorInfo([i.length], s, i);
}
var oE = { kernelName: ws, backendName: "cpu", kernelFunc: l5 };
var m5 = we(ma, (r) => 1 / r);
var nE = { kernelName: ma, backendName: "cpu", kernelFunc: m5 };
function f5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
  K(n, "resizeBilinear");
  let p = x.computeStrides(n.shape), [u, c] = i, [l, m, f, d] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(x.sizeFromShape([l, u, c, d])), y = [s && u > 1 ? m - 1 : m, s && c > 1 ? f - 1 : f], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = 0, w = y[0] / b[0], k = y[1] / b[1];
  for (let _ = 0; _ < l; _++)
    for (let E = 0; E < u; E++) {
      let R;
      a ? R = w * (E + 0.5) - 0.5 : R = w * E;
      let A = Math.max(0, Math.floor(R)), D = R - A, O = Math.min(m - 1, Math.ceil(R)), M = _ * p[0] + A * p[1], L = _ * p[0] + O * p[1];
      for (let W = 0; W < c; W++) {
        let V;
        a ? V = k * (W + 0.5) - 0.5 : V = k * W;
        let G = Math.max(0, Math.floor(V)), q = V - G, H = Math.min(f - 1, Math.ceil(V)), j = M + G * p[2], Y = L + G * p[2], Z = M + H * p[2], ee = L + H * p[2];
        for (let X = 0; X < d; X++) {
          let Q = h[j + X], se = h[Y + X], ie = h[Z + X], de = h[ee + X], Ie = Q + (ie - Q) * q, Se = se + (de - se) * q, Ee = Ie + (Se - Ie) * D;
          g[C++] = Ee;
        }
      }
    }
  return t10.makeTensorInfo([l, u, c, d], "float32", g);
}
var sE = { kernelName: Un, backendName: "cpu", kernelFunc: f5 };
function d5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
  K([s, n], "resizeBilinearGrad");
  let i = x.computeStrides(n.shape), [p, u, c, l] = n.shape, [, m, f] = s.shape, d = new Float32Array(p * u * c * l), h = [a && m > 1 ? u - 1 : u, a && f > 1 ? c - 1 : c], g = [a && m > 1 ? m - 1 : m, a && f > 1 ? f - 1 : f], y = 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 R = E * y, A = Math.floor(R), D = Math.min(Math.ceil(R), u - 1), O = _ + A * i[1], M = _ + D * i[1], L = R - A, W = 1 - L;
      for (let V = 0; V < f; V++) {
        let G = V * b, q = Math.floor(G), H = Math.min(Math.ceil(G), c - 1), j = G - q, Y = 1 - j, Z = O + q * i[2], ee = O + H * i[2], X = M + q * i[2], Q = M + H * i[2], se = W * Y, ie = W * j, de = L * Y, Ie = L * j;
        for (let Se = 0; Se < l; Se++) {
          let Ee = C[w++];
          d[Z + Se] += Ee * se, d[ee + Se] += Ee * ie, d[X + Se] += Ee * de, d[Q + Se] += Ee * Ie;
        }
      }
    }
  }
  return t10.makeTensorInfo([p, c, u, l], "float32", d);
}
var aE = { kernelName: Vm, backendName: "cpu", kernelFunc: d5 };
function h5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o;
  K(n, "resizeNearestNeighbor");
  let p = x.computeStrides(n.shape), [u, c] = i, [l, m, f, d] = n.shape, h = t10.data.get(n.dataId).values, g = new Float32Array(l * u * c * d), y = [s && u > 1 ? m - 1 : m, s && c > 1 ? f - 1 : f], b = [s && u > 1 ? u - 1 : u, s && c > 1 ? c - 1 : c], C = y[0] / b[0], w = y[1] / b[1], k = 0;
  for (let _ = 0; _ < l; _++) {
    let E = _ * p[0];
    for (let R = 0; R < u; R++) {
      let A = a ? C * (R + 0.5) : C * R, D = Math.min(m - 1, s ? Math.round(A) : Math.floor(A));
      a && (D = Math.max(0, D));
      let O = E + D * p[1];
      for (let M = 0; M < c; M++) {
        let L = a ? w * (M + 0.5) : w * M, W = Math.min(f - 1, s ? Math.round(L) : Math.floor(L));
        a && (W = Math.max(0, W));
        let V = O + W * p[2];
        for (let G = 0; G < d; G++) {
          let q = h[V + G];
          g[k++] = q;
        }
      }
    }
  }
  return t10.makeTensorInfo([l, u, c, d], n.dtype, g);
}
var iE = { kernelName: Wn, backendName: "cpu", kernelFunc: h5 };
function g5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o;
  K([s, n], "resizeNearestNeighborGrad");
  let i = x.computeStrides(n.shape), p = x.computeStrides(s.shape), [u, c, l, m] = n.shape, [, f, d] = s.shape, h = new Float32Array(u * c * l * m), g = t10.data.get(s.dataId).values, y = [a && f > 1 ? c - 1 : c, a && d > 1 ? l - 1 : l], b = [a && f > 1 ? f - 1 : f, a && d > 1 ? d - 1 : d], C = y[0] / b[0], w = y[1] / b[1], k = 1 / C, _ = 1 / w, E = Math.ceil(k) * 2 + 2, R = Math.ceil(_) * 2 + 2;
  for (let A = 0; A < u; A++) {
    let D = A * i[0];
    for (let O = 0; O < c; O++) {
      let M = D + O * i[1], L = Math.floor(O * k), W = Math.floor(L - E / 2);
      for (let V = 0; V < l; V++) {
        let G = M + V * i[2], q = Math.floor(V * _), H = Math.floor(q - R / 2);
        for (let j = 0; j < m; j++) {
          let Y = 0;
          for (let Z = 0; Z < E; Z++) {
            let ee = Z + W;
            if (ee < 0 || ee >= f)
              continue;
            let X = D + ee * p[1], Q = ee * C, se = Math.min(c - 1, a ? Math.round(Q) : Math.floor(Q));
            if (O === se)
              for (let ie = 0; ie < R; ie++) {
                let de = ie + H;
                if (de < 0 || de >= d)
                  continue;
                let Ie = X + de * p[2], Se = de * w, Ee = Math.min(l - 1, a ? Math.round(Se) : Math.floor(Se));
                V === Ee && (Y += g[Ie + j]);
              }
          }
          h[G + j] = Y;
        }
      }
    }
  }
  return t10.makeTensorInfo(n.shape, n.dtype, h);
}
var uE = { kernelName: Bm, backendName: "cpu", kernelFunc: g5 };
function x5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o;
  K(n, "reverse");
  let a = n.shape.length, i = x.parseAxisParam(s, n.shape);
  if (a === 0)
    return ar({ inputs: { x: n }, backend: t10 });
  let p = new je(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((f) => m[f] = n.shape[f] - 1 - m[f]), p.set(u.get(...m), ...l);
  }
  return t10.makeTensorInfo(p.shape, p.dtype, p.values);
}
var pE = { kernelName: fa, backendName: "cpu", kernelFunc: x5 };
var cE = { kernelName: es, backendName: "cpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = x.getTypedArrayFromDType(o.dtype, x.sizeFromShape(o.shape)), [u, c, l, m] = o.shape, [f, d] = I.getImageCenter(a, c, l), h = 255, g = Math.sin(n), y = 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 R = 0; R < l; R++) {
        let A = R * m;
        for (let D = 0; D < m; D++) {
          let O = [u, _, R, D], M = O[2], L = O[1], W = (M - f) * y - (L - d) * g, V = (M - f) * g + (L - d) * y;
          W = Math.round(W + f), V = Math.round(V + d);
          let G = s;
          if (typeof s != "number" && (D === 3 ? G = h : G = s[D]), W >= 0 && W < l && V >= 0 && V < c) {
            let H = V * (l * m), j = W * m, Y = k + H + j + D;
            G = b[Y];
          }
          let q = k + E + A + D;
          p[q] = G;
        }
      }
    }
  }
  return { dataId: i.write(p, o.shape, o.dtype), shape: o.shape, dtype: o.dtype };
} };
var y5 = we(da, (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 lE = { kernelName: da, backendName: "cpu", kernelFunc: y5 };
function b5(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 } = I.calculateShapes(s, n, a), m = true, f = t10.bufferSync(n), d = t10.bufferSync(s), h = Aa(f, d, a, l, u, p, i, c, 0, m);
  return t10.makeTensorInfo(a, h.dtype, h.values);
}
var mE = { kernelName: Hn, backendName: "cpu", kernelFunc: b5 };
function C5(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 I5(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 fE(r, e, t10, o, n, s) {
  let a = x.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" ? C5(p, e[c + u]) : I5(p, e[c + u]);
  }
  return a;
}
function w5(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 = fE(i, p, n.shape[0], n.shape[1], s.shape[1], a);
  return t10.makeTensorInfo(s.shape, "int32", u);
}
var dE = { kernelName: Ep, backendName: "cpu", kernelFunc: w5 };
function S5(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e;
  K([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 = ct(n.dtype, s.dtype), l = x.makeZerosTypedArray(x.sizeFromShape(n.shape), c), m = 0, f = a === 0 || a > 1 || n.shape.length === 1 ? 1 : x.sizeFromShape(n.shape.slice(1));
  for (let d = 0; d < i.length; d++)
    for (let h = 0; h < f; h++)
      i[d] === 1 ? l[m++] = p[d] : l[m++] = u[d];
  return t10.makeTensorInfo(n.shape, c, l);
}
var hE = { kernelName: vs, backendName: "cpu", kernelFunc: S5 };
var v5 = I.SELU_SCALEALPHA;
var k5 = I.SELU_SCALE;
var T5 = we(Xi, (r) => r >= 0 ? k5 * r : v5 * (Math.exp(r) - 1));
var gE = { kernelName: Xi, backendName: "cpu", kernelFunc: T5 };
var N5 = we(Yi, (r) => r < 0 ? -1 : r > 0 ? 1 : 0);
var xE = { kernelName: Yi, backendName: "cpu", kernelFunc: N5 };
var _5 = we(Kn, (r) => Math.sin(r));
var yE = { kernelName: Kn, backendName: "cpu", kernelFunc: _5 };
var E5 = we(ha, (r) => Math.sinh(r));
var bE = { kernelName: ha, backendName: "cpu", kernelFunc: E5 };
var $5 = 11920928955078125e-23;
var CE = Math.log($5) + 2;
var R5 = we(Qi, (r) => {
  let e = r > -CE, t10 = r < CE, o = Math.exp(r), n;
  return t10 ? n = o : e ? n = r : n = Math.log(1 + o), n;
});
var IE = { kernelName: Qi, backendName: "cpu", kernelFunc: R5 };
function A5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
  K([n], "spaceToBatchND");
  let i = x.sizeFromShape(s), p = [[0, 0]];
  p.push(...a);
  for (let _ = 1 + s.length; _ < n.shape.length; ++_)
    p.push([0, 0]);
  let u = Od.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = I.getReshaped(u.shape, s, i, false), l = I.getPermuted(c.length, s.length, false), m = I.getReshapedPermuted(u.shape, s, i, false), h = Oe({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = bt({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Oe({ inputs: { x: b }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(b), k;
}
var wE = { kernelName: ks, backendName: "cpu", kernelFunc: A5 };
function F5(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, f, d, h] = Td(i, o.shape, o.dtype, p, n.dtype, u, c);
  return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, f), t10.makeTensorInfo([d.length], "bool", new Uint8Array(d.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var SE = { kernelName: Qa, backendName: "cpu", kernelFunc: F5 };
function D5(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] = Nd(i, o.shape, o.dtype, a, p);
  return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var vE = { kernelName: ga, backendName: "cpu", kernelFunc: D5 };
function P5(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] = tc(a, o.shape, o.dtype, i, p, true);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var kE = { kernelName: Za, backendName: "cpu", kernelFunc: P5 };
function O5(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] = tc(a, o.shape, o.dtype, i, p);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var TE = { kernelName: Ja, backendName: "cpu", kernelFunc: O5 };
function M5(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 } = I.calculateShapes(s, n, i), f = false, d = t10.bufferSync(n), h;
  switch (s.dtype) {
    case "bool": {
      let g = t10.bufferSync(s), y = Boolean(t10.data.get(a.dataId).values[0]);
      h = Aa(d, g, i, m, c, u, p, l, y, f);
      break;
    }
    case "float32": {
      let g = t10.bufferSync(s), y = t10.data.get(a.dataId).values[0];
      h = Aa(d, g, i, m, c, u, p, l, y, f);
      break;
    }
    case "int32": {
      let g = t10.bufferSync(s), y = t10.data.get(a.dataId).values[0];
      h = Aa(d, g, i, m, c, u, p, l, y, f);
      break;
    }
    case "string": {
      let g = t10.bufferSync(s), y = x.decodeString(t10.data.get(a.dataId).values[0]);
      h = Aa(d, g, i, m, c, u, p, l, y, f);
      break;
    }
    default:
      throw new Error(`Unsupported type ${s.dtype}`);
  }
  return t10.makeTensorInfo(i, h.dtype, h.values);
}
var NE = { kernelName: ei, backendName: "cpu", kernelFunc: M5 };
function L5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = x.parseAxisParam(a, n.shape)[0], p = I.prepareSplitSize(n, s, i), u = new Array(n.shape.length).fill(0), c = n.shape.slice();
  return p.map((l) => {
    let m = [...c];
    m[i] = l;
    let f = qo({ inputs: { x: n }, backend: t10, attrs: { begin: u, size: m } });
    return u[i] += l, f;
  });
}
var _E = { kernelName: Ts, backendName: "cpu", kernelFunc: L5 };
var EE = { kernelName: ti, backendName: "cpu", kernelFunc: ({ inputs: r, backend: e }) => {
  let { x: t10 } = r, o = e;
  K(t10, "square");
  let n = o.data.get(t10.dataId).values, s = new Float32Array(n.length);
  for (let i = 0; i < n.length; ++i) {
    let p = n[i];
    s[i] = p * p;
  }
  return { dataId: o.write(s, t10.shape, t10.dtype), shape: t10.shape, dtype: t10.dtype };
} };
var B5 = we($s, (r, e) => {
  let t10 = e;
  return isNaN(r) ? NaN : r > 0 ? 1 : t10.alpha;
});
var $E = { kernelName: $s, backendName: "cpu", kernelFunc: B5 };
function V5(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;
  K(n, "stridedSlice");
  let { finalShapeSparse: f, finalShape: d, isIdentity: h, sliceDim0: g, isSimpleSlice: y, begin: b, end: C, strides: w } = et.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = Oe({ inputs: { x: n }, backend: t10, attrs: { shape: d } });
  else if (g || y) {
    x.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let _ = et.computeOutShape(b, C, w), E = qo({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } });
    k = Oe({ inputs: { x: E }, backend: t10, attrs: { shape: d } }), t10.disposeIntermediateTensorInfo(E);
  } else {
    let _ = t10.bufferSync(n), E = _d(f, _, w, b);
    k = t10.makeTensorInfo(d, E.dtype, E.values);
  }
  return k;
}
var RE = { kernelName: Yn, backendName: "cpu", kernelFunc: V5 };
function z5(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, f = t10.data.get(l.dataId).values, [d, h] = ku(m, f, n, s, a, i, p, u);
  return [t10.makeTensorInfo([d.length], "string", d), t10.makeTensorInfo(l.shape, "int32", h)];
}
var AE = { kernelName: Ns, backendName: "cpu", kernelFunc: z5 };
function W5(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] = Tu(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 FE = { kernelName: ri, backendName: "cpu", kernelFunc: W5 };
function U5(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 = Nu(a, n);
  return t10.makeTensorInfo(s.shape, "int32", i);
}
var DE = { kernelName: oi, backendName: "cpu", kernelFunc: U5 };
var G5 = we(xa, (r) => Math.tan(r));
var PE = { kernelName: xa, backendName: "cpu", kernelFunc: G5 };
var H5 = we(Qn, (r) => Math.tanh(r));
var OE = { kernelName: Qn, backendName: "cpu", kernelFunc: H5 };
function q5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
  K(n, "tile");
  let a = Ed(t10.bufferSync(n), s);
  return t10.makeTensorInfo(a.shape, a.dtype, a.values);
}
var ME = { kernelName: wo, backendName: "cpu", kernelFunc: q5 };
function K5(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o;
  K(n, "topk");
  let i = t10.data.get(n.dataId).values, [p, u] = $d(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 LE = { kernelName: Zn, backendName: "cpu", kernelFunc: K5 };
function j5(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, f] = n.shape, [d, h] = u != null ? u : [l, m], g = [c, d, h, f], y = x.computeStrides(n.shape), b = y[0], C = y[1], w = y[2], k = x.computeStrides(g), _ = k[0], E = k[1], R = k[2], A = x.getTypedArrayFromDType(n.dtype, x.sizeFromShape(g));
  A.fill(p);
  let D = o.data.get(n.dataId).values, O = o.data.get(s.dataId).values;
  for (let L = 0; L < c; ++L) {
    let W = s.shape[0] === 1 ? O : O.subarray(L * 8, L * 8 + 8);
    for (let V = 0; V < d; ++V)
      for (let G = 0; G < h; ++G)
        for (let q = 0; q < f; ++q) {
          let H, j = W[6] * G + W[7] * V + 1;
          if (j === 0)
            continue;
          let Y = (W[0] * G + W[1] * V + W[2]) / j, Z = (W[3] * G + W[4] * V + W[5]) / j, ee = BE(Y, m, i), X = BE(Z, l, i);
          switch (a) {
            case "nearest":
              H = J5(D, l, m, b, C, w, L, X, ee, q, p);
              break;
            case "bilinear":
              H = e8(D, l, m, b, C, w, L, X, ee, q, p);
              break;
            default:
              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${a}`);
          }
          let Q = L * _ + V * E + G * R + q;
          A[Q] = H;
        }
    return o.makeTensorInfo(g, n.dtype, A);
  }
  return { dataId: o.write(A, g, n.dtype), shape: n.shape, dtype: n.dtype };
}
var VE = { kernelName: Jn, backendName: "cpu", kernelFunc: j5 };
function BE(r, e, t10) {
  switch (t10) {
    case "reflect":
      return X5(r, e);
    case "wrap":
      return Y5(r, e);
    case "nearest":
      return Z5(r, e);
    case "constant":
    default:
      return Q5(r, e);
  }
}
function X5(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 x.clamp(0, t10, e - 1);
}
function Y5(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 x.clamp(0, t10, e - 1);
}
function Q5(r, e) {
  return r;
}
function Z5(r, e) {
  return x.clamp(0, r, e - 1);
}
function Nl(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 J5(r, e, t10, o, n, s, a, i, p, u, c) {
  let l = Math.round(i), m = Math.round(p);
  return Nl(r, e, t10, o, n, s, a, l, m, u, c);
}
function e8(r, e, t10, o, n, s, a, i, p, u, c) {
  let l = Math.floor(i), m = Math.floor(p), f = l + 1, d = m + 1, h = (d - p) * Nl(r, e, t10, o, n, s, a, l, m, u, c) + (p - m) * Nl(r, e, t10, o, n, s, a, l, d, u, c), g = (d - p) * Nl(r, e, t10, o, n, s, a, f, m, u, c) + (p - m) * Nl(r, e, t10, o, n, s, a, f, d, u, c);
  return (f - i) * h + (i - l) * g;
}
function t8(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e;
  K(s, "unique");
  let a = o.data.get(s.dataId).values, { outputValues: i, outputShape: p, indices: u } = Rd(a, n, s.shape, s.dtype);
  return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var zE = { kernelName: $p, backendName: "cpu", kernelFunc: t8 };
function r8(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 f = 0; f < a; f++)
    f !== s && (p[u++] = n.shape[f]);
  let c = new Array(a).fill(0), l = n.shape.slice();
  l[s] = 1;
  let m = new Array(i);
  for (let f = 0; f < m.length; f++) {
    c[s] = f;
    let d = qo({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: l } });
    m[f] = Oe({ inputs: { x: d }, backend: t10, attrs: { shape: p } }), t10.disposeIntermediateTensorInfo(d);
  }
  return m;
}
var WE = { kernelName: _s, backendName: "cpu", kernelFunc: r8 };
function o8(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, segmentIds: s } = e, { numSegments: a } = o;
  K(n, "unsortedSegmentSum");
  let i = n.shape.length, p = s.shape.length, u = [], c = [], l = i - p, m = s;
  for (let d = 0; d < l; ++d) {
    let h = oc({ inputs: { input: m }, backend: t10, attrs: { dim: d + 1 } });
    m = h, c.push(h);
  }
  for (let d = 0; d < a; ++d) {
    let h = x.createScalarValue(d, "int32"), g = t10.makeTensorInfo([], "int32", h), y = sI({ inputs: { a: g, b: m }, backend: t10 }), b = Uo({ inputs: { x: y }, backend: t10, attrs: { dtype: "float32" } }), C = wu({ inputs: { a: b, b: n }, backend: t10 }), w = Fa({ inputs: { x: C }, backend: t10, attrs: { axis: 0, keepDims: false } });
    u.push(w), c.push(g), c.push(y), c.push(b), c.push(C), c.push(w);
  }
  let f = OI({ inputs: u, backend: t10, attrs: { axis: 0 } });
  return c.forEach((d) => t10.disposeIntermediateTensorInfo(d)), f;
}
var UE = { kernelName: Rp, backendName: "cpu", kernelFunc: o8 };
var n8 = [h2, TN, g2, x2, RN, y2, b2, C2, I2, w2, S2, v2, k2, T2, N2, E2, $2, R2, A2, d2, F2, D2, P2, O2, $N, AN, M2, NN, L2, V2, z2, W2, U2, G2, H2, q2, K2, j2, X2, Y2, Q2, Z2, J2, e_, t_, r_, o_, n_, s_, a_, u_, u2, p_, FN, c_, DN, l_, PN, m_, f_, d_, ON, h_, g_, x_, y_, b_, MN, LN, _N, C_, B2, I_, w_, S_, p2, BN, VN, v_, zN, k_, T_, N_, __, E_, $_, R_, WN, A_, F_, D_, P_, M_, L_, B_, UN, V_, z_, G_, GN, HN, H_, q_, K_, qN, j_, Q_, Z_, Od, J_, c2, jN, eE, tE, rE, oE, EN, vl, nE, l2, m2, f2, sE, aE, iE, uE, pE, cE, lE, JN, mE, dE, hE, gE, t2, xE, yE, bE, r2, W_, IE, wE, SE, vE, kE, TE, NE, _E, n2, EE, s2, $E, RE, AE, FE, DE, a2, i_, PE, OE, ME, LE, VE, KN, zE, WE, UE, X_];
for (let r of n8)
  ya(r);
var ic = {};
Be(ic, { assertNotComplex: () => as, bindCanvasToFramebuffer: () => f8, bindColorTextureToFramebuffer: () => Rl, bindTextureToProgramUniformSampler: () => ZI, bindTextureUnit: () => KE, bindVertexBufferToProgramAttribute: () => Wd, callAndCheck: () => me, canBeRepresented: () => VI, createFragmentShader: () => WI, createFramebuffer: () => XI, createProgram: () => UI, createStaticIndexBuffer: () => qI, createStaticVertexBuffer: () => HI, createTexture: () => KI, createVertexShader: () => zI, getBatchDim: () => Pa, getExtensionOrThrow: () => nc, getFramebufferErrorMessage: () => jE, getMaxTexturesInShader: () => tw, getNumChannels: () => l8, getProgramUniformLocation: () => QI, getProgramUniformLocationOrThrow: () => YI, getRowsCols: () => Oa, getShapeAs3D: () => ac, getTextureShapeFromLogicalShape: () => JI, getWebGLDisjointQueryTimerVersion: () => rw, getWebGLErrorMessage: () => qE, getWebGLMaxTextureSize: () => ew, hasExtension: () => Hr, isCapableOfRenderingToFloatTexture: () => ow, isDownloadFloatTextureEnabled: () => nw, isReshapeFree: () => Ti, isWebGLFenceEnabled: () => sw, isWebGLVersionEnabled: () => Gd, linkProgram: () => GI, logShaderSourceAndInfoLog: () => zd, resetMaxTextureSize: () => d8, resetMaxTexturesInShader: () => h8, unbindColorTextureFromFramebuffer: () => Ud, unbindTextureUnit: () => m8, validateFramebuffer: () => sc, validateProgram: () => $l, validateTextureSize: () => jI });
var Eu = {};
var Md = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function MI(r, e) {
  Eu[r] = e;
}
function Gr(r, e) {
  if (!(r in Eu) || e != null) {
    let o = a8(r, e);
    if (o !== null)
      Eu[r] = o;
    else
      return console.log("Could not get context for WebGL version", r), null;
  }
  let t10 = Eu[r];
  return t10 == null || t10.isContextLost() ? (delete Eu[r], Gr(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), Eu[r]);
}
function s8(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 a8(r, e) {
  if (r !== 1 && r !== 2)
    throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
  let t10 = e == null ? s8(r) : e;
  return t10.addEventListener("webglcontextlost", (o) => {
    o.preventDefault(), delete Eu[r];
  }, false), P().getBool("SOFTWARE_WEBGL_ENABLED") && (Md.failIfMajorPerformanceCaveat = false), r === 1 ? t10.getContext("webgl", Md) || t10.getContext("experimental-webgl", Md) : t10.getContext("webgl2", Md);
}
var ki;
(function(r) {
  r[r.DENSE = 0] = "DENSE", r[r.SHARED_BATCH = 1] = "SHARED_BATCH";
})(ki || (ki = {}));
var ir;
(function(r) {
  r[r.RENDER = 0] = "RENDER", r[r.UPLOAD = 1] = "UPLOAD", r[r.PIXELS = 2] = "PIXELS", r[r.DOWNLOAD = 3] = "DOWNLOAD";
})(ir || (ir = {}));
var Jt;
(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";
})(Jt || (Jt = {}));
function $u(r, e) {
  return [e, r];
}
function GE(r, e) {
  return r * e;
}
function _l(r) {
  let e = x.sizeFromShape(r), t10 = Math.ceil(e / 4);
  return x.sizeToSquarishShape(t10);
}
function Ks(r, e) {
  return [Math.max(1, Math.ceil(e / 2)), Math.max(1, Math.ceil(r / 2))];
}
function HE(r, e) {
  let [t10, o] = Ks(r, e);
  return t10 * o * 4;
}
function El(r, e) {
  let t10 = r, o, n, s, a, i, p, u, c, l, m;
  return P().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 me(r, e) {
  let t10 = e();
  return P().getBool("DEBUG") && i8(r), t10;
}
function i8(r) {
  let e = r.getError();
  if (e !== r.NO_ERROR)
    throw new Error("WebGL Error: " + qE(r, e));
}
var u8 = 596e-10;
var p8 = 65504;
function VI(r) {
  return !!(P().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || r === 0 || u8 < Math.abs(r) && Math.abs(r) < p8);
}
function qE(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 nc(r, e) {
  return Da(r, () => r.getExtension(e), 'Extension "' + e + '" not supported on this browser.');
}
function zI(r, e) {
  let t10 = Da(r, () => r.createShader(r.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
  if (me(r, () => r.shaderSource(t10, e)), me(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 WI(r, e) {
  let t10 = Da(r, () => r.createShader(r.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
  if (me(r, () => r.shaderSource(t10, e)), me(r, () => r.compileShader(t10)), P().get("ENGINE_COMPILE_ONLY"))
    return t10;
  if (r.getShaderParameter(t10, r.COMPILE_STATUS) === false)
    throw zd(e, r.getShaderInfoLog(t10)), new Error("Failed to compile fragment shader.");
  return t10;
}
var c8 = /ERROR: [0-9]+:([0-9]+):/g;
function zd(r, e) {
  let t10 = c8.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) => x.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 ${x.rightPad(u[0], i)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function UI(r) {
  return Da(r, () => r.createProgram(), "Unable to create WebGLProgram.");
}
function GI(r, e) {
  if (me(r, () => r.linkProgram(e)), !P().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 $l(r, e) {
  if (me(r, () => r.validateProgram(e)), r.getProgramParameter(e, r.VALIDATE_STATUS) === false)
    throw console.log(r.getProgramInfoLog(e)), new Error("Shader program validation failed.");
}
function HI(r, e) {
  let t10 = Da(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
  return me(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), me(r, () => r.bufferData(r.ARRAY_BUFFER, e, r.STATIC_DRAW)), t10;
}
function qI(r, e) {
  let t10 = Da(r, () => r.createBuffer(), "Unable to create WebGLBuffer");
  return me(r, () => r.bindBuffer(r.ELEMENT_ARRAY_BUFFER, t10)), me(r, () => r.bufferData(r.ELEMENT_ARRAY_BUFFER, e, r.STATIC_DRAW)), t10;
}
function l8() {
  return P().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function KI(r) {
  return Da(r, () => r.createTexture(), "Unable to create WebGLTexture.");
}
function jI(r, e) {
  let t10 = P().getNumber("WEBGL_MAX_TEXTURE_SIZE");
  if (r <= 0 || e <= 0) {
    let o = `[${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 XI(r) {
  return Da(r, () => r.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Wd(r, e, t10, o, n, s, a) {
  let i = r.getAttribLocation(e, t10);
  return i === -1 ? false : (me(r, () => r.bindBuffer(r.ARRAY_BUFFER, o)), me(r, () => r.vertexAttribPointer(i, n, r.FLOAT, false, s, a)), me(r, () => r.enableVertexAttribArray(i)), true);
}
function KE(r, e, t10) {
  XE(r, t10), me(r, () => r.activeTexture(r.TEXTURE0 + t10)), me(r, () => r.bindTexture(r.TEXTURE_2D, e));
}
function m8(r, e) {
  XE(r, e), me(r, () => r.activeTexture(r.TEXTURE0 + e)), me(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function YI(r, e, t10) {
  return Da(r, () => r.getUniformLocation(e, t10), 'uniform "' + t10 + '" not present in program.');
}
function QI(r, e, t10) {
  return r.getUniformLocation(e, t10);
}
function ZI(r, e, t10, o) {
  me(r, () => KE(r, e, o)), me(r, () => r.uniform1i(t10, o));
}
function f8(r) {
  me(r, () => r.bindFramebuffer(r.FRAMEBUFFER, null)), me(r, () => r.viewport(0, 0, r.canvas.width, r.canvas.height)), me(r, () => r.scissor(0, 0, r.canvas.width, r.canvas.height));
}
function Rl(r, e, t10) {
  me(r, () => r.bindFramebuffer(r.FRAMEBUFFER, t10)), me(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, e, 0));
}
function Ud(r, e) {
  me(r, () => r.bindFramebuffer(r.FRAMEBUFFER, e)), me(r, () => r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, null, 0));
}
function sc(r) {
  let e = r.checkFramebufferStatus(r.FRAMEBUFFER);
  if (e !== r.FRAMEBUFFER_COMPLETE)
    throw new Error("Error binding framebuffer: " + jE(r, e));
}
function jE(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 ${e}`;
  }
}
function Da(r, e, t10) {
  let o = me(r, () => e());
  if (o == null)
    throw new Error(t10);
  return o;
}
function XE(r, e) {
  let t10 = r.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, o = e + r.TEXTURE0;
  if (o < r.TEXTURE0 || o > t10) {
    let n = `[gl.TEXTURE0, gl.TEXTURE${t10}]`;
    throw new Error(`textureUnit must be in ${n}.`);
  }
}
function Pa(r, e = 2) {
  return x.sizeFromShape(r.slice(0, r.length - e));
}
function Oa(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 ac(r) {
  let e = [1, 1, 1];
  return r.length === 0 || r.length === 1 && r[0] === 1 || (e = [Pa(r), ...Oa(r)]), e;
}
function JI(r, e = false) {
  let t10 = P().getNumber("WEBGL_MAX_TEXTURE_SIZE"), o = P().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");
  o === 1 / 0 && P().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 ? x.nearestLargerEven(r[p]) : r[p]), r.length === 1 && (r = [2, r[0]])), r.length !== 2 && (r = x.squeezeShape(r).newShape);
  let n = x.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 = Pa(r), p = 2, u = 2;
      r.length && ([p, u] = Oa(r)), n = i * (p / 2) * (u / 2), s = x.sizeToSquarishShape(n).map((c) => c * 2);
    } else
      s = x.sizeToSquarishShape(n);
  return s;
}
function Ld(r) {
  return r % 2 === 0;
}
function Ti(r, e) {
  if (r = r.slice(-2), e = e.slice(-2), x.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 || Ld(t10) && Ld(o) && (r[0] === 1 || e[0] === 1))
      return true;
  }
  return r[1] === e[1] && Ld(r[0]) && Ld(e[0]);
}
var Bd;
var Vd;
function ew(r) {
  if (Bd == null) {
    let e = Gr(r);
    Bd = e.getParameter(e.MAX_TEXTURE_SIZE);
  }
  return Bd;
}
function d8() {
  Bd = null;
}
function h8() {
  Vd = null;
}
function tw(r) {
  if (Vd == null) {
    let e = Gr(r);
    Vd = e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS);
  }
  return Math.min(16, Vd);
}
function rw(r) {
  if (r === 0)
    return 0;
  let e, t10 = Gr(r);
  return Hr(t10, "EXT_disjoint_timer_query_webgl2") && r === 2 ? e = 2 : Hr(t10, "EXT_disjoint_timer_query") ? e = 1 : e = 0, e;
}
function Hr(r, e) {
  return r.getExtension(e) != null;
}
function Gd(r) {
  try {
    if (Gr(r) != null)
      return true;
  } catch (e) {
    return console.log("Error when getting WebGL context: ", e), false;
  }
  return false;
}
function ow(r) {
  if (r === 0)
    return false;
  let e = Gr(r);
  if (r === 1) {
    if (!Hr(e, "OES_texture_float"))
      return false;
  } else if (!Hr(e, "EXT_color_buffer_float"))
    return false;
  return BI(e);
}
function nw(r) {
  if (r === 0)
    return false;
  let e = Gr(r);
  if (r === 1) {
    if (!Hr(e, "OES_texture_float") || !Hr(e, "WEBGL_color_buffer_float"))
      return false;
  } else {
    if (Hr(e, "EXT_color_buffer_float"))
      return BI(e);
    let o = "EXT_color_buffer_half_float";
    if (Hr(e, o)) {
      let n = e.getExtension(o);
      return g8(e, n);
    }
    return false;
  }
  return BI(e);
}
function BI(r) {
  let e = El(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 g8(r, e) {
  let t10 = El(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 sw(r) {
  return r !== 2 ? false : Gr(r).fenceSync != null;
}
function as(r, e) {
  Array.isArray(r) || (r = [r]), r.forEach((t10) => {
    t10 != null && x.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the WebGL backend.`);
  });
}
var Ce = P();
Ce.registerFlag("HAS_WEBGL", () => Ce.getNumber("WEBGL_VERSION") > 0);
Ce.registerFlag("WEBGL_VERSION", () => Gd(2) ? 2 : Gd(1) ? 1 : 0);
Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
Ce.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Ce.get("WEBGL_VERSION") === 2);
Ce.registerFlag("WEBGL_CPU_FORWARD", () => true);
Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
Ce.registerFlag("WEBGL_PACK", () => Ce.getBool("HAS_WEBGL"));
Ce.registerFlag("WEBGL_PACK_NORMALIZATION", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_CLIP", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_PACK_REDUCE", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_LAZILY_UNPACK", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_CONV_IM2COL", () => Ce.getBool("WEBGL_PACK"));
Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => ew(Ce.getNumber("WEBGL_VERSION")));
Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => tw(Ce.getNumber("WEBGL_VERSION")));
Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
  let r = Ce.getNumber("WEBGL_VERSION");
  return r === 0 ? 0 : rw(r);
});
Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !ii.isMobile());
Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => ow(Ce.getNumber("WEBGL_VERSION")));
Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Ce.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => nw(Ce.getNumber("WEBGL_VERSION")));
Ce.registerFlag("WEBGL_FENCE_API_ENABLED", () => sw(Ce.getNumber("WEBGL_VERSION")));
Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0);
Ce.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}.`);
});
Ce.registerFlag("WEBGL_FLUSH_THRESHOLD", () => ii.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}.`);
});
Ce.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
Ce.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
Ce.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
Ce.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
Ce.registerFlag("WEBGL_EXP_CONV", () => false);
Ce.registerFlag("SOFTWARE_WEBGL_ENABLED", () => Ce.getBool("IS_TEST"));
Ce.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", () => 1 / 0);
Ce.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", () => false);
Ce.registerFlag("WEBGL2_ISNAN_CUSTOM", () => false);
function Ct() {
  let r, e, t10, o, n, s, a, i, p, u;
  return P().getNumber("WEBGL_VERSION") === 2 ? (r = "#version 300 es", e = "in", t10 = "out", o = "in", n = "texture", s = "outputColor", a = "out vec4 outputColor;", i = P().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 is(r, e, t10 = "index") {
  let o = x.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 Ru(r, e, t10 = "index") {
  let o = x.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}; ${i};`;
  }).join("");
}
function x8(r, e) {
  let t10 = r.length, o = r.map((s) => `${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 YE(r, e, t10 = "index") {
  let o = r.map((s, a) => a), n = x8(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 uc(r) {
  let e = x.computeStrides(r).map((t10) => t10.toString());
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
  }
`;
}
function pc() {
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`;
}
var Hd = `
  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: QE } = I;
function ZE(r, e, t10) {
  let o = [];
  if (r.forEach((f) => {
    let d = x.sizeFromShape(f.shapeInfo.logicalShape);
    if (f.shapeInfo.isUniform ? o.push(`uniform float ${f.name}${d > 1 ? `[${d}]` : ""};`) : (o.push(`uniform sampler2D ${f.name};`), o.push(`uniform int offset${f.name};`)), t10.enableShapeUniforms) {
      let { uniformShape: h } = qd(t10.packedInputs, f.shapeInfo.logicalShape, f.shapeInfo.texShape);
      switch (h.length) {
        case 1:
          o.push(`uniform int ${f.name}Shape;`);
          break;
        case 2:
          o.push(`uniform ivec2 ${f.name}Shape;`);
          break;
        case 3:
          o.push(`uniform ivec3 ${f.name}Shape;`);
          break;
        case 4:
          o.push(`uniform ivec4 ${f.name}Shape;`);
          break;
        default:
          break;
      }
      o.push(`uniform ivec2 ${f.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((f) => {
    o.push(`uniform ${f.type} ${f.name}${f.arrayIndex ? `[${f.arrayIndex}]` : ""};`);
  });
  let n = o.join(`
`), s = r.map((f) => y8(f, e, t10.packedInputs, t10.enableShapeUniforms)).join(`
`), a = e.texShape, i = Ct(), p = I8(i), u, c, l = v8(i);
  return e.isPacked ? (u = b8(e.logicalShape, a, t10.enableShapeUniforms), c = S8(i)) : (u = C8(e.logicalShape, a, t10.enableShapeUniforms), c = w8(i)), t10.packedInputs && (l += _8), [l, p, c, n, u, s, t10.userCode].join(`
`);
}
function lc(r, e = false) {
  let t10 = r.shapeInfo.logicalShape;
  switch (t10.length) {
    case 0:
      return V8(r, e);
    case 1:
      return W8(r, e);
    case 2:
      return G8(r, e);
    case 3:
      return q8(r, e);
    case 4:
      return j8(r, e);
    case 5:
      return X8(r);
    case 6:
      return Y8(r);
    default:
      throw new Error(`${t10.length}-D input sampling is not yet supported`);
  }
}
function JE(r, e) {
  switch (r.shapeInfo.logicalShape.length) {
    case 0:
      return B8(r);
    case 1:
      return z8(r, e);
    case 2:
      return U8(r, e);
    case 3:
      return H8(r, e);
    default:
      return K8(r, e);
  }
}
function y8(r, e, t10 = false, o) {
  let n = "";
  t10 ? n += JE(r, o) : n += lc(r, o);
  let s = r.shapeInfo.logicalShape, a = e.logicalShape;
  return s.length <= a.length && (t10 ? n += Q8(r, e) : n += Z8(r, e)), n;
}
function b8(r, e, t10) {
  switch (r.length) {
    case 0:
      return e$();
    case 1:
      return E8(r, e, t10);
    case 2:
      return M8(r, e, t10);
    case 3:
      return R8(r, e, t10);
    default:
      return F8(r, e, t10);
  }
}
function C8(r, e, t10) {
  switch (r.length) {
    case 0:
      return e$();
    case 1:
      return $8(r, e, t10);
    case 2:
      return L8(r, e, t10);
    case 3:
      return A8(r, e, t10);
    case 4:
      return D8(r, e, t10);
    case 5:
      return P8(r, e);
    case 6:
      return O8(r, e);
    default:
      throw new Error(`${r.length}-D output sampling is not yet supported`);
  }
}
function I8(r) {
  return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${r.texture2D}(textureSampler, uv).r;
    }
  `;
}
function w8(r) {
  return `
    void setOutput(float val) {
      ${r.output} = vec4(val, 0, 0, 0);
    }
  `;
}
function S8(r) {
  return `
    void setOutput(vec4 val) {
      ${r.output} = val;
    }
  `;
}
function v8(r) {
  return `${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);
    }

    ${k8}
    ${T8}
    ${N8}
  `;
}
var k8 = `
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var T8 = `
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var N8 = `
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var _8 = `
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;
function e$() {
  return `
    int getOutputCoords() {
      return 0;
    }
  `;
}
function E8(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 $8(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 R8(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 A8(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;
    ${Ru(["r", "c", "d"], r)}
    return ivec3(r, c, d);
  }
`;
  let o = is(["r", "c", "d"], r);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${o}
      return ivec3(r, c, d);
    }
  `;
}
function F8(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 D8(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;
      ${Ru(["r", "c", "d", "d2"], r)}
      return ivec4(r, c, d, d2);
    }
  `;
  let o = is(["r", "c", "d", "d2"], r);
  return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${e[0]}, ${e[1]}));
      int index = resTexRC.x * ${e[1]} + resTexRC.y;
      ${o}
      return ivec4(r, c, d, d2);
    }
  `;
}
function P8(r, e) {
  let t10 = is(["r", "c", "d", "d2", "d3"], r);
  return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
                             ${e[1]}));

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

      ${t10}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `;
}
function O8(r, e) {
  let t10 = is(["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 M8(r, e, t10) {
  let o = [Math.ceil(e[0] / 2), Math.ceil(e[1] / 2)];
  if (x.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 L8(r, e, t10) {
  return x.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 * ${r[1]};
      return ivec2(r, c);
    }
  `;
}
function Au(r) {
  return `offset${r}`;
}
function B8(r) {
  let e = r.name, t10 = "get" + e.charAt(0).toUpperCase() + e.slice(1), o = Ct();
  return `
    vec4 ${t10}() {
      return ${o.texture2D}(${e}, halfCR);
    }
  `;
}
function V8(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 = Au(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 z8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = r.shapeInfo.texShape, s = Ct();
  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 W8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1);
  if (r.shapeInfo.isUniform)
    return `
      float ${o}(int index) {
        ${mc(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 = Au(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 U8(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 = Ct();
  if (s != null && x.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 G8(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 && x.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], f = s[1];
    return `
    float ${n}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
      return sampleTexture(${o}, uv);
    }
  `;
  }
  let { newShape: a, keptDims: i } = x.squeezeShape(t10), p = a;
  if (p.length < t10.length) {
    let m = fc(r, p), f = ["row", "col"];
    return `
      ${lc(m, e)}
      float ${n}(int row, int col) {
        return ${n}(${dc(f, i)});
      }
    `;
  }
  if (r.shapeInfo.isUniform)
    return `
      float ${n}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${t10[1]}, 1)));
        ${mc(r)}
      }
    `;
  let u = s[0], c = s[1], l = Au(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 H8(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), f = [1, 2], d = fc(r, m), h = ["b", "row", "col"];
    return `
        ${JE(d, e)}
        vec4 ${n}(int b, int row, int col) {
          return ${n}(${dc(h, f)});
        }
      `;
  }
  let i = Ct();
  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 q8(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 } = x.squeezeShape(t10), u = i;
  if (u.length < t10.length) {
    let h = fc(r, u), g = ["row", "col", "depth"];
    return `
        ${lc(h, e)}
        float ${n}(int row, int col, int depth) {
          return ${n}(${dc(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)));
        ${mc(r)}
      }
    `;
  let c = r.shapeInfo.texShape, l = c[0], m = c[1], f = r.shapeInfo.flatOffset;
  if (m === s && f == 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 && f == 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 d = Au(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 + ${d};
      vec2 uv = uvFromFlat(${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 + ${d};
        vec2 uv = uvFromFlat(${l}, ${m}, index);
        return sampleTexture(${o}, uv);
      }
  `;
}
function K8(r, e) {
  let t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), n = Ct();
  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), f = "int b, int row, int col", d = `b * ${m} + (row / 2) * ${l} + (col / 2)`;
  for (let h = 2; h < a - 1; h++)
    f = `int b${h}, ` + f, m *= s[a - h - 1], d = `b${h} * ${m} + ` + d;
  return `
    vec4 ${o}(${f}) {
      int index = ${d};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
      return ${n.texture2D}(${t10}, uv);
    }
  `;
}
function j8(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 } = x.squeezeShape(t10);
  if (p.length < t10.length) {
    let b = fc(r, p), C = ["row", "col", "depth", "depth2"];
    return `
      ${lc(b, e)}
      float ${n}(int row, int col, int depth, int depth2) {
        return ${n}(${dc(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)));
        ${mc(r)}
      }
    `;
  let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], f = l[1], d = `int stride2 = ${o}Shape[3];`, h = `int stride1 = ${o}Shape[2] * stride2;`, g = `int stride0 = ${o}Shape[1] * stride1;`;
  if (f === i && c == null)
    return e ? `
      float ${n}(int row, int col, int depth, int depth2) {
        ${d}
        ${h}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${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}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${o}, uv);
      }
    `;
  if (f === 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]}, ${t10[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${o}, uv);
      }
    `;
  let y = Au(o);
  return e ? `
    float ${n}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${d}
      ${h}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${o}TexShape[0], ${o}TexShape[1], index + ${y});
      return sampleTexture(${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}, ${f}, index + ${y});
      return sampleTexture(${o}, uv);
    }
  `;
}
function X8(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 } = x.squeezeShape(e);
  if (p.length < e.length) {
    let h = fc(r, p), g = ["row", "col", "depth", "depth2", "depth3"];
    return `
      ${lc(h)}
      float ${o}(int row, int col, int depth, int depth2, int depth3) {
        return ${o}(${dc(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;
        ${mc(r)}
      }
    `;
  let c = r.shapeInfo.flatOffset, l = r.shapeInfo.texShape, m = l[0], f = l[1];
  if (f === 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}, ${n}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  if (f === 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]}, ${e[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  let d = Au(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 + ${d};
      vec2 uv = uvFromFlat(${m}, ${f}, index);
      return sampleTexture(${t10}, uv);
    }
  `;
}
function Y8(r) {
  let e = r.shapeInfo.logicalShape, t10 = r.name, o = "get" + t10.charAt(0).toUpperCase() + t10.slice(1), { newShape: n, keptDims: s } = x.squeezeShape(e);
  if (n.length < e.length) {
    let g = fc(r, n), y = ["row", "col", "depth", "depth2", "depth3", "depth4"];
    return `
      ${lc(g)}
      float ${o}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${o}(${dc(y, 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)));
        ${mc(r)}
      }
    `;
  let l = r.shapeInfo.flatOffset, m = r.shapeInfo.texShape, f = m[0], d = m[1];
  if (d === 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}, ${a})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${f}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  if (d === 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]},
               ${e[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${f}.0);
        return sampleTexture(${t10}, uv);
      }
    `;
  let h = Au(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 + ${h};
      vec2 uv = uvFromFlat(${f}, ${d}, index);
      return sampleTexture(${t10}, uv);
    }
  `;
}
function mc(r) {
  let e = r.name, t10 = x.sizeFromShape(r.shapeInfo.logicalShape);
  return t10 < 2 ? `return ${e};` : `
    for (int i = 0; i < ${t10}; i++) {
      if (i == index) {
        return ${e}[i];
      }
    }
  `;
}
function Q8(r, e) {
  let t10 = r.name, o = t10.charAt(0).toUpperCase() + t10.slice(1), n = "get" + o + "AtOutCoords", s = r.shapeInfo.logicalShape.length, a = e.logicalShape.length, i = QE(r.shapeInfo.logicalShape, e.logicalShape), p = _e(a), u = a - s, c, l = ["x", "y", "z", "w", "u", "v"];
  s === 0 ? c = "" : a < 2 && i.length >= 1 ? c = "coords = 0;" : c = i.map((b) => `coords.${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 f = "return outputValue;", h = x.sizeFromShape(r.shapeInfo.logicalShape) === 1, y = x.sizeFromShape(e.logicalShape) === 1;
  if (s === 1 && !h && !y)
    f = `
      return vec4(outputValue.xy, outputValue.xy);
    `;
  else if (h && !y)
    a === 1 ? f = `
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      ` : f = `
        return vec4(outputValue.x);
      `;
  else if (i.length) {
    let b = s - 2, C = s - 1;
    i.indexOf(b) > -1 && i.indexOf(C) > -1 ? f = "return vec4(outputValue.x);" : i.indexOf(b) > -1 ? f = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : i.indexOf(C) > -1 && (f = "return vec4(outputValue.xx, outputValue.zz);");
  }
  return `
    vec4 ${n}() {
      ${p} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${o}(${m});
      ${f}
    }
  `;
}
function Z8(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 && x.arraysEqual(a, s))
    return `
      float ${n}() {
        return sampleTexture(${t10}, resultUV);
      }
    `;
  let u = _e(p), c = QE(r.shapeInfo.logicalShape, e.logicalShape), l = p - i, m, f = ["x", "y", "z", "w", "u", "v"];
  i === 0 ? m = "" : p < 2 && c.length >= 1 ? m = "coords = 0;" : m = c.map((h) => `coords.${f[h + l]} = 0;`).join(`
`);
  let d = "";
  return p < 2 && i > 0 ? d = "coords" : d = r.shapeInfo.logicalShape.map((h, g) => `coords.${f[g + l]}`).join(", "), `
    float ${n}() {
      ${u} coords = getOutputCoords();
      ${m}
      return get${o}(${d});
    }
  `;
}
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 ${r} is not yet supported`);
}
function qd(r, e, t10) {
  let { newShape: o, keptDims: n } = x.squeezeShape(e), s = e.length, a = r && s === 3 && e[0] === 1, i = a ? e.slice(1) : o, p = !r && s > 1 && !x.arraysEqual(e, t10) && o.length < s || a;
  return { useSqueezeShape: p, uniformShape: p ? i : e, keptDims: n };
}
function fc(r, e) {
  let t10 = JSON.parse(JSON.stringify(r));
  return t10.shapeInfo.logicalShape = e, t10;
}
function dc(r, e) {
  return e.map((t10) => r[t10]).join(", ");
}
function r$(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 = ZE(n, a, e), p = WI(r.gl, i), u = r.createProgram(p);
  return P().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 }, aw(r, e, u));
}
function aw(r, e, t10) {
  let o = {}, n = {}, s = {}, a = [], i, p, u, c = null, l = null;
  l = r.getUniformLocation(t10, "NAN", false), P().getNumber("WEBGL_VERSION") === 1 && (c = r.getUniformLocation(t10, "INFINITY", false));
  let m = false;
  for (let f = 0; f < e.variableNames.length; f++) {
    let d = e.variableNames[f];
    o[d] = r.getUniformLocation(t10, d, m), o[`offset${d}`] = r.getUniformLocation(t10, `offset${d}`, m), e.enableShapeUniforms && (n[`${d}Shape`] = r.getUniformLocation(t10, `${d}Shape`, m), s[`${d}TexShape`] = r.getUniformLocation(t10, `${d}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((f, d) => {
    a[d] = r.getUniformLocation(t10, f.name, m);
  }), { uniformLocations: o, customUniformLocations: a, infLoc: c, nanLoc: l, inShapesLocations: n, inTexShapesLocations: s, outShapeLocation: i, outShapeStridesLocation: u, outTexShapeLocation: p };
}
function t$(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 (!x.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 (!x.arraysEqual(i, p))
      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${p} must match`);
  });
}
function o$(r, e, t10, o, n) {
  e.program.enableShapeUniforms || (t$(e.inShapeInfos, t10), t$([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), P().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${c}`], f = e.inShapesLocations[`${c}Shape`], d = e.inTexShapesLocations[`${c}TexShape`];
    if (f) {
      let { uniformShape: h } = qd(e.program.packedInputs, p.shape, p.texData.texShape);
      switch (h.length) {
        case 1:
          r.gl.uniform1iv(f, new Int32Array(h));
          break;
        case 2:
          r.gl.uniform2iv(f, new Int32Array(h));
          break;
        case 3:
          r.gl.uniform3iv(f, new Int32Array(h));
          break;
        case 4:
          r.gl.uniform4iv(f, new Int32Array(h));
          break;
        default:
          break;
      }
    }
    if (d && r.gl.uniform2i(d, p.texData.texShape[0], p.texData.texShape[1]), l != null) {
      if (p.isUniform) {
        if (x.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 = x.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 ${p.type} is not supported yet.`);
  }), r.executeProgram();
}
function n$(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 } = qd(r.packedInputs, a.shape, p), m = "", f = "", d = "";
      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)
        f = `${c[0] > 1}_${c[1] > 1}`;
      else if (c.length > 2 && !r.packedInputs) {
        let k = x.computeStrides(c);
        d = `${k[0] === p[1]}_${k[k.length - 1] === p[1]}`;
      }
      let h = a.shape.length, g = c.length === 2 && x.arraysEqual(a.shape, p), y = x.sizeFromShape(a.shape) === 1, b = I.getBroadcastDims(a.shape, t10.shape), C = !r.packedInputs && h === t10.shape.length && x.arraysEqual(p, t10.texData.texShape), w = r.packedInputs || c.length > 2 ? "" : `${p[0] > 1}_${p[1] > 1}`;
      o += `${h}_${C}_${u ? l : ""}_${c.length}_${y}_${b}_${g}_${m}_${f}_${d}_${w}_${i}`;
    } else {
      let p = a.isUniform ? "uniform" : a.texData.texShape;
      o += `${a.shape}_${p}_${i}`;
    }
  });
  let n = r.userCode, s = r.constructor.name;
  return s += "_" + o + "_" + n + `${P().getNumber("WEBGL_VERSION")}`, s;
}
function lt(r) {
  return P().getBool("WEBGL_USE_SHAPES_UNIFORMS") && r <= 4;
}
var Kd = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = ki.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t10 = Ct();
    this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Ru(["r", "c", "d"], e) : is(["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 jd = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = ki.DENSE, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t10 = Ct();
    this.outputShape = e, this.enableShapeUniforms = lt(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Ru(["r", "c", "d"], e) : is(["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 Xd = class {
  constructor(e) {
    this.variableNames = ["A"], this.outTexUsage = ir.DOWNLOAD;
    let t10 = Ct();
    this.outputShape = e, this.userCode = `
      ${Hd}

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

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t10.output} = encode_float(x);
      }
    `;
  }
};
var tY = { R: 0, G: 1, B: 2, A: 3 };
var Al = class {
  constructor(e, t10 = false, o = "RGBA") {
    this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let n = Ct();
    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 == ${i}) {
            result = values[${tY[p]}];
          }`;
    }
    this.userCode = `
      ${this.enableShapeUniforms ? pc() : uc(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 Qd = class {
  constructor(e, t10 = false) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let o = Ct();
    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 ? pc() : uc(e)}

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

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

          ${n}

          ${o.output} = ${s};
        }
    `;
  }
};
var Sw = {};
Be(Sw, { bindVertexProgramAttributeStreams: () => hw, createBufferFromOutputTexture: () => yw, createFloat16MatrixTexture: () => lw, createFloat16PackedMatrixTexture: () => dw, createFloat32MatrixTexture: () => cw, createIndexBuffer: () => pw, createPackedMatrixTexture: () => fw, createUnsignedBytesMatrixTexture: () => mw, createVertexBuffer: () => uw, createVertexShader: () => iw, downloadByteEncodedFloatMatrixFromOutputTexture: () => Cw, downloadFloat32MatrixFromBuffer: () => bw, downloadMatrixFromPackedOutputTexture: () => ww, downloadPackedMatrixFromBuffer: () => Iw, getInternalFormatForFloat16MatrixTexture: () => Jd, getInternalFormatForFloat16PackedMatrixTexture: () => rh, getInternalFormatForFloat32MatrixTexture: () => Zd, getInternalFormatForPackedMatrixTexture: () => th, getInternalFormatForUnsignedBytesMatrixTexture: () => eh, uploadDenseMatrixToTexture: () => gw, uploadPixelDataToTexture: () => xw });
function iw(r) {
  let e = Ct(), t10 = `${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 zI(r, t10);
}
function uw(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 HI(r, e);
}
function pw(r) {
  let e = new Uint16Array([0, 1, 2, 2, 1, 3]);
  return qI(r, e);
}
function Fl(r, e, t10, o, n, s) {
  jI(e, t10);
  let a = KI(r), i = r.TEXTURE_2D;
  return me(r, () => r.bindTexture(i, a)), me(r, () => r.texParameteri(i, r.TEXTURE_WRAP_S, r.CLAMP_TO_EDGE)), me(r, () => r.texParameteri(i, r.TEXTURE_WRAP_T, r.CLAMP_TO_EDGE)), me(r, () => r.texParameteri(i, r.TEXTURE_MIN_FILTER, r.NEAREST)), me(r, () => r.texParameteri(i, r.TEXTURE_MAG_FILTER, r.NEAREST)), P().getNumber("WEBGL_VERSION") === 1 ? me(r, () => r.texImage2D(i, 0, o, e, t10, 0, n, s, null)) : me(r, () => r.texStorage2D(i, 1, o, e, t10)), me(r, () => r.bindTexture(r.TEXTURE_2D, null)), { texture: a, texShape: [t10, e] };
}
function Zd(r) {
  return r.internalFormatFloat;
}
function cw(r, e, t10, o) {
  let [n, s] = $u(e, t10);
  return Fl(r, n, s, Zd(o), o.textureFormatFloat, r.FLOAT);
}
function Jd(r) {
  return r.internalFormatHalfFloat;
}
function lw(r, e, t10, o) {
  let [n, s] = $u(e, t10);
  return Fl(r, n, s, Jd(o), o.textureFormatFloat, o.textureTypeHalfFloat);
}
function eh(r) {
  return r.downloadTextureFormat;
}
function mw(r, e, t10, o) {
  let [n, s] = $u(e, t10);
  return Fl(r, n, s, eh(o), r.RGBA, r.UNSIGNED_BYTE);
}
function th(r) {
  return r.internalFormatPackedFloat;
}
function fw(r, e, t10, o) {
  let [n, s] = Ks(e, t10);
  return Fl(r, n, s, th(o), r.RGBA, r.FLOAT);
}
function rh(r) {
  return r.internalFormatPackedHalfFloat;
}
function dw(r, e, t10, o) {
  let [n, s] = Ks(e, t10);
  return Fl(r, n, s, rh(o), r.RGBA, o.textureTypeHalfFloat);
}
function hw(r, e, t10) {
  return me(r, () => r.bindBuffer(r.ARRAY_BUFFER, t10)), Wd(r, e, "clipSpacePos", t10, 3, 20, 0) && Wd(r, e, "uv", t10, 2, 20, 12);
}
function gw(r, e, t10, o, n, s) {
  me(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), P().getNumber("WEBGL_VERSION") === 2 ? me(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10, o, r.RGBA, i, a)) : me(r, () => r.texImage2D(r.TEXTURE_2D, 0, p, t10, o, 0, r.RGBA, i, a)), me(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function xw(r, e, t10) {
  me(r, () => r.bindTexture(r.TEXTURE_2D, e)), t10.data instanceof Uint8Array ? P().getNumber("WEBGL_VERSION") === 2 ? me(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, t10.width, t10.height, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : me(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, t10.width, t10.height, 0, r.RGBA, r.UNSIGNED_BYTE, t10.data)) : P().getNumber("WEBGL_VERSION") === 2 ? me(r, () => r.texSubImage2D(r.TEXTURE_2D, 0, 0, 0, r.RGBA, r.UNSIGNED_BYTE, t10)) : me(r, () => r.texImage2D(r.TEXTURE_2D, 0, r.RGBA, r.RGBA, r.UNSIGNED_BYTE, t10)), me(r, () => r.bindTexture(r.TEXTURE_2D, null));
}
function yw(r, e, t10, o) {
  let n = r.createBuffer();
  me(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, n));
  let i = 4 * 4 * e * t10;
  return me(r, () => r.bufferData(r.PIXEL_PACK_BUFFER, i, r.STREAM_READ)), me(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, 0)), me(r, () => r.bindBuffer(r.PIXEL_PACK_BUFFER, null)), n;
}
function bw(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 Cw(r, e, t10, o) {
  let [n, s] = $u(e, t10), a = 4, i = new Uint8Array(GE(e * t10, a));
  return me(r, () => r.readPixels(0, 0, n, s, o.downloadTextureFormat, r.UNSIGNED_BYTE, i)), new Float32Array(i.buffer);
}
function Iw(r, e, t10, o, n, s, a, i) {
  let p = r, u = new Float32Array(HE(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 ww(r, e, t10) {
  let o = new Float32Array(e * t10 * 4);
  return me(r, () => r.readPixels(0, 0, t10, e, r.RGBA, r.FLOAT, o)), o;
}
var Fu = class {
  constructor(e) {
    this.outputTexture = null, this.program = null, this.disposed = false, this.vertexAttrsAreBound = false, this.itemsToPoll = [];
    let t10 = P().getNumber("WEBGL_VERSION");
    e != null ? (this.gl = e, MI(t10, e)) : this.gl = Gr(t10);
    let o = "WEBGL_color_buffer_float", n = "EXT_color_buffer_half_float";
    if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), P().getNumber("WEBGL_VERSION") === 1) {
      let s = "OES_texture_float", a = "OES_texture_half_float";
      if (this.textureFloatExtension = nc(this.gl, s), Hr(this.gl, a))
        this.textureHalfFloatExtension = nc(this.gl, a);
      else if (P().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), Hr(this.gl, n))
        this.colorBufferHalfFloatExtension = nc(this.gl, n);
      else if (P().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", Hr(this.gl, o))
      this.colorBufferFloatExtension = this.gl.getExtension(o);
    else if (Hr(this.gl, n))
      this.colorBufferHalfFloatExtension = this.gl.getExtension(n);
    else
      throw new Error("GL context does not support color renderable floats");
    this.vertexBuffer = uw(this.gl), this.indexBuffer = pw(this.gl), this.framebuffer = XI(this.gl), this.textureConfig = El(this.gl, this.textureHalfFloatExtension);
  }
  get debug() {
    return P().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;
    me(e, () => e.finish()), me(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), me(e, () => e.deleteFramebuffer(this.framebuffer)), me(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), me(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), me(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true;
  }
  createFloat32MatrixTexture(e, t10) {
    return this.throwIfDisposed(), cw(this.gl, e, t10, this.textureConfig);
  }
  createFloat16MatrixTexture(e, t10) {
    return this.throwIfDisposed(), lw(this.gl, e, t10, this.textureConfig);
  }
  createUnsignedBytesMatrixTexture(e, t10) {
    return this.throwIfDisposed(), mw(this.gl, e, t10, this.textureConfig);
  }
  uploadPixelDataToTexture(e, t10) {
    this.throwIfDisposed(), xw(this.gl, e, t10);
  }
  uploadDenseMatrixToTexture(e, t10, o, n) {
    this.throwIfDisposed(), gw(this.gl, e, t10, o, n, this.textureConfig);
  }
  createFloat16PackedMatrixTexture(e, t10) {
    return this.throwIfDisposed(), dw(this.gl, e, t10, this.textureConfig);
  }
  createPackedMatrixTexture(e, t10) {
    return this.throwIfDisposed(), fw(this.gl, e, t10, this.textureConfig);
  }
  deleteMatrixTexture(e) {
    this.throwIfDisposed(), this.outputTexture === e && (Ud(this.gl, this.framebuffer), this.outputTexture = null), me(this.gl, () => this.gl.deleteTexture(e));
  }
  downloadByteEncodedFloatMatrixFromOutputTexture(e, t10, o) {
    return this.downloadMatrixDriver(e, () => Cw(this.gl, t10, o, this.textureConfig));
  }
  downloadPackedMatrixFromBuffer(e, t10, o, n, s, a) {
    return Iw(this.gl, e, t10, o, n, s, a, this.textureConfig);
  }
  downloadFloat32MatrixFromBuffer(e, t10) {
    return bw(this.gl, e, t10);
  }
  createBufferFromTexture(e, t10, o) {
    this.bindTextureToFrameBuffer(e);
    let n = yw(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 (P().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
      P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t10 = this.beginQuery(), this.endQuery(), o = () => this.isQueryAvailable(t10, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : o = () => true;
    return { query: t10, isFencePassed: o };
  }
  downloadMatrixFromPackedTexture(e, t10, o) {
    return this.downloadMatrixDriver(e, () => ww(this.gl, t10, o));
  }
  createProgram(e) {
    this.throwIfDisposed();
    let t10 = this.gl;
    this.vertexShader == null && (this.vertexShader = iw(t10));
    let o = UI(t10);
    return me(t10, () => t10.attachShader(o, this.vertexShader)), me(t10, () => t10.attachShader(o, e)), GI(t10, o), this.debug && $l(t10, o), this.vertexAttrsAreBound || (this.setProgram(o), this.vertexAttrsAreBound = hw(t10, this.program, this.vertexBuffer)), o;
  }
  deleteProgram(e) {
    this.throwIfDisposed(), e === this.program && (this.program = null), e != null && me(this.gl, () => this.gl.deleteProgram(e));
  }
  setProgram(e) {
    this.throwIfDisposed(), this.program = e, this.program != null && this.debug && $l(this.gl, this.program), me(this.gl, () => this.gl.useProgram(e));
  }
  getUniformLocation(e, t10, o = true) {
    return this.throwIfDisposed(), o ? YI(this.gl, e, t10) : QI(this.gl, e, t10);
  }
  getAttributeLocation(e, t10) {
    return this.throwIfDisposed(), me(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(), ZI(this.gl, e, t10, o);
  }
  setOutputMatrixTexture(e, t10, o) {
    this.setOutputMatrixTextureDriver(e, o, t10);
  }
  setOutputPackedMatrixTexture(e, t10, o) {
    this.throwIfDisposed();
    let [n, s] = Ks(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 && $l(this.gl, this.program), sc(this.gl);
  }
  executeProgram() {
    this.throwIfDisposed(), this.throwIfNoProgram();
    let e = this.gl;
    this.debug && this.debugValidate(), me(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0));
  }
  blockUntilAllProgramsCompleted() {
    this.throwIfDisposed(), me(this.gl, () => this.gl.finish());
  }
  getQueryTimerExtension() {
    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = nc(this.gl, P().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 (P().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 (P().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 x.repeatedTry(() => this.disposed || this.isQueryAvailable(e, P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, P().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 = rY(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 P().platform && (o = P().platform.setTimeoutCustom.bind(P().platform)), x.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0), () => 0, null, o);
  }
  bindTextureToFrameBuffer(e) {
    this.throwIfDisposed(), Rl(this.gl, e, this.framebuffer), this.debug && sc(this.gl);
  }
  unbindTextureToFrameBuffer() {
    this.outputTexture != null ? (Rl(this.gl, this.outputTexture, this.framebuffer), this.debug && sc(this.gl)) : Ud(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;
    Rl(n, e, this.framebuffer), this.debug && sc(n), this.outputTexture = e, me(n, () => n.viewport(0, 0, t10, o)), me(n, () => n.scissor(0, 0, t10, o));
  }
  setOutputMatrixWriteRegionDriver(e, t10, o, n) {
    this.throwIfDisposed(), me(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 rY(r) {
  let e = 0;
  for (; e < r.length && r[e](); ++e)
    ;
  return e - 1;
}
var { addImpl: s$, bincountImpl: oh, bincountReduceImpl: a$, castImpl: i$, ceilImpl: u$, concatImpl: p$, equalImpl: c$, expImpl: l$, expm1Impl: m$, floorImpl: f$, gatherNdImpl: d$, gatherV2Impl: h$, greaterImpl: g$, greaterEqualImpl: x$, lessImpl: y$, lessEqualImpl: b$, linSpaceImpl: C$, logImpl: I$, maxImpl: w$, maximumImpl: S$, minimumImpl: v$, multiplyImpl: k$, negImpl: T$, notEqualImpl: N$, prodImpl: _$, raggedGatherImpl: E$, raggedRangeImpl: $$, raggedTensorToTensorImpl: R$, rangeImpl: A$, rsqrtImpl: F$, scatterImpl: D$, sigmoidImpl: P$, simpleAbsImpl: nh, sliceImpl: O$, sparseFillEmptyRowsImpl: M$, sparseReshapeImpl: L$, sparseSegmentReductionImpl: sh, sqrtImpl: B$, stridedSliceImpl: V$, stringNGramsImpl: z$, stringSplitImpl: W$, stringToHashBucketFastImpl: U$, subImpl: G$, tileImpl: H$, topKImpl: q$, transposeImpl: Du, uniqueImpl: K$ } = Ad;
function vw(r, e) {
  return ["x", "y", "z", "w", "u", "v"].slice(0, e).map((t10) => `${r}.${t10}`);
}
function $t(r, e) {
  return e === 1 ? [r] : vw(r, e);
}
function j$(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 ah = 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 = $t("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 hc = 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 = `
      ${oY(t10, this.enableShapeUniforms)}
      ${this.enableShapeUniforms ? pc() : uc(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 oY(r, e) {
  return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${e ? YE(["r", "c", "d"], "inputShape") : is(["r", "c", "d"], r)}
      return ivec3(r, c, d);
    }
  `;
}
var ih = class {
  constructor(e) {
    this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.logEnabled = false, this.usedTextures = {};
  }
  acquireTexture(e, t10, o) {
    let n = Y$(t10, o), s = Q$(e, n, o);
    s in this.freeTextures || (this.freeTextures[s] = []), s in this.usedTextures || (this.usedTextures[s] = []);
    let a = X$(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 === Jt.PACKED_2X2_FLOAT32 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : n === Jt.PACKED_2X2_FLOAT16 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : n === Jt.UNPACKED_FLOAT32 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : n === Jt.UNPACKED_FLOAT16 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : n === Jt.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 = Y$(o, n), a = Q$(t10, s, n);
    a in this.freeTextures || (this.freeTextures[a] = []);
    let i = X$(t10, s, this.gpgpu.gl, this.gpgpu.textureConfig, n), p = P().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
    p !== -1 && this._numBytesAllocated > p ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--;
    let u = this.usedTextures[a], c = u.indexOf(e);
    if (c < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    u.splice(c, 1), this.log();
  }
  log() {
    if (!this.logEnabled)
      return;
    let e = this.numFreeTextures + this.numUsedTextures;
    console.log("Free/Used", `${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 nY(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 ${e}`);
}
function X$(r, e, t10, o, n) {
  let s = sY(e, o), a;
  if (n) {
    let [p, u] = Ks(r[0], r[1]);
    a = p * u;
  } else {
    let [p, u] = $u(r[0], r[1]);
    a = p * u;
  }
  let i = nY(t10, s);
  return a * i;
}
function sY(r, e) {
  switch (r) {
    case Jt.PACKED_2X2_FLOAT32:
      return th(e);
    case Jt.PACKED_2X2_FLOAT16:
      return rh(e);
    case Jt.UNPACKED_FLOAT32:
      return Zd(e);
    case Jt.UNPACKED_FLOAT16:
      return Jd(e);
    case Jt.PACKED_4X1_UNSIGNED_BYTE:
      return eh(e);
    default:
      throw new Error(`Unknown physical texture type ${r}`);
  }
}
function aY(r) {
  return P().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? r ? Jt.PACKED_2X2_FLOAT32 : Jt.UNPACKED_FLOAT32 : r ? Jt.PACKED_2X2_FLOAT16 : Jt.UNPACKED_FLOAT16;
}
function Y$(r, e) {
  if (r === ir.UPLOAD)
    return Jt.PACKED_2X2_FLOAT32;
  if (r === ir.RENDER || r == null)
    return aY(e);
  if (r === ir.DOWNLOAD || r === ir.PIXELS)
    return Jt.PACKED_4X1_UNSIGNED_BYTE;
  throw new Error(`Unknown logical texture type ${r}`);
}
function Q$(r, e, t10) {
  return `${r[0]}_${r[1]}_${e}_${t10}`;
}
var fr = 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 Vt = "if (isnan(x)) return x;";
var Z$ = "return x;";
var kw = "return abs(x);";
var J$ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var eR = Vt + `
  return (x < 0.0) ? 0.0 : x;
`;
var tR = Vt + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Pu = "return x;";
var rR = "return 1.0 / (1.0 + exp(-1.0 * x));";
var nR = "return x;";
var sR = `
  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 aR = `
  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 iR = `
  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 uR = "return 1.0 / (1.0 + exp(-1.0 * x));";
var No = 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 uh = 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 = $t("rc", t10), n = _e(t10), s = j$(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 uY = Bt.whereImpl;
var pY = 1e-7;
var cY = 1e-4;
var ph = {};
function lY(r) {
  return r in ph || (ph[r] = {}), ph[r];
}
var mY = P().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var fY = 600;
function dY() {
  return P().global.screen == null ? 1024 : P().global.screen.height * P().global.screen.width * window.devicePixelRatio * fY / 1024 / 1024;
}
var Ni = class extends Jr {
  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, !P().getBool("HAS_WEBGL"))
      throw new Error("WebGL is not supported on this device");
    let t10;
    if (e != null) {
      if (e instanceof Fu)
        t10 = e;
      else {
        let o = Gr(P().getNumber("WEBGL_VERSION"), e);
        t10 = new Fu(o);
      }
      this.binaryCache = {}, this.gpgpuCreatedLocally = false;
    } else {
      let o = Gr(P().getNumber("WEBGL_VERSION"));
      t10 = new Fu(o), this.binaryCache = lY(P().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
    }
    this.gpgpu = t10, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new ih(this.gpgpu), this.numMBBeforeWarning = dY(), this.texData = new rn(this, cr());
  }
  nextDataId() {
    return Ni.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 = ac(t10), c = new Al(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 ((P().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || P().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: ir.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 (P().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: ir.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 No(i, Pu) : m = new fr(i, Pu);
      let f = this.runWebGLProgram(m, [{ dataId: e, shape: i, dtype: n }], n), d = this.readSync(f.dataId);
      return this.disposeIntermediateTensorInfo(f), d;
    }
    if (o != null)
      return this.convertAndCacheOnCPU(e);
    if (n === "string")
      return o;
    let u = this.activeTimers != null, c;
    u && (c = x.now());
    let l;
    if (n === "complex64") {
      let m = this.readSync(s.real.dataId), f = this.readSync(s.imag.dataId);
      l = I.mergeRealAndImagArrays(m, f);
    } else
      l = this.getValuesFromTexture(e);
    return u && (this.downloadWaitMs += x.now() - c), this.convertAndCacheOnCPU(e, l);
  }
  async read(e) {
    if (this.pendingRead.has(e)) {
      let d = this.pendingRead.get(e);
      return new Promise((h) => d.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 d;
      p ? d = new No(n, Pu) : d = new fr(n, Pu);
      let h = this.runWebGLProgram(d, [{ 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 (P().getBool("DEBUG") && !P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && P().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" && P().get("WEBGL_BUFFER_SUPPORTED")) {
      c = this.decode(e);
      let d = this.texData.get(c.dataId);
      u = this.gpgpu.createBufferFromTexture(d.texture.texture, ..._l(n));
    }
    this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence();
    let l;
    if (a === "complex64") {
      let d = await Promise.all([this.read(i.real.dataId), this.read(i.imag.dataId)]), h = d[0], g = d[1];
      l = I.mergeRealAndImagArrays(h, g);
    } else if (u == null)
      l = this.getValuesFromTexture(e);
    else {
      let d = x.sizeFromShape(n);
      l = this.gpgpu.downloadFloat32MatrixFromBuffer(u, d);
    }
    if (c != null && this.disposeIntermediateTensorInfo(c), u != null) {
      let d = this.gpgpu.gl;
      me(d, () => d.deleteBuffer(u));
    }
    let m = this.convertAndCacheOnCPU(e, l), f = this.pendingRead.get(e);
    return this.pendingRead.delete(e), f.forEach((d) => d(m)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && cr().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 f;
      p ? f = new No(s, Pu) : f = new fr(s, Pu);
      let d = this.runWebGLProgram(f, [{ dataId: e, shape: s, dtype: i }], i), h = this.readToGPU(d, t10);
      return this.disposeIntermediateTensorInfo(d), 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 = cr().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) => x.decodeString(n));
        return ne(e.shape, e.dtype, o);
      } catch (o) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return ne(e.shape, e.dtype, t10);
  }
  checkNumericalProblems(e) {
    if (e != null)
      for (let t10 = 0; t10 < e.length; t10++) {
        let o = e[t10];
        if (!VI(o))
          throw P().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 ${o} cannot be represented on this device.`);
      }
  }
  getValuesFromTexture(e) {
    let { shape: t10, dtype: o, isPacked: n } = this.texData.get(e), s = x.sizeFromShape(t10);
    if (P().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
      let m = this.decode(e), f = this.texData.get(m.dataId), d = this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture, ..._l(t10)).subarray(0, s);
      return this.disposeIntermediateTensorInfo(m), d;
    }
    let a = P().getBool("WEBGL_PACK") && n === true, i = a ? ac(t10) : t10, p = a ? new Yd(i) : new Xd(i), u = this.runWebGLProgram(p, [{ shape: i, dtype: o, dataId: e }], "float32"), c = this.texData.get(u.dataId), l = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture, c.texShape[0], c.texShape[1]).subarray(0, s);
    return this.disposeIntermediateTensorInfo(u), l;
  }
  timerAvailable() {
    return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
  }
  time(e) {
    let t10 = this.activeTimers, o = [], n = false;
    this.programTimersStack == null ? (this.programTimersStack = o, n = true) : this.activeTimers.push(o), this.activeTimers = o, e();
    let s = x.flatten(this.activeTimers.map((p) => p.query)).filter((p) => p != null), a = x.flatten(this.activeTimers.map((p) => p.name)).filter((p) => p != null);
    this.activeTimers = t10, n && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
    return (async () => {
      if (P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        let p = await Promise.all(s);
        i.kernelMs = x.sum(p), i.getExtraProfileInfo = () => p.map((u, c) => ({ name: a[c], ms: u })).map((u) => `${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 P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: x.now(), endMs: null };
  }
  endTimer(e) {
    return P().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = x.now(), e);
  }
  async getQueryTime(e) {
    if (P().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 = mY) {
    return P().getBool("WEBGL_CPU_FORWARD") && e.every((o) => this.texData.get(o.dataId).texture == null && x.sizeFromShape(o.shape) < t10);
  }
  getGPGPUContext() {
    return this.gpgpu;
  }
  where(e) {
    I.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
    let t10 = e.dataSync();
    return uY(e.shape, t10);
  }
  packedUnaryOp(e, t10, o) {
    let n = new No(e.shape, t10), s = this.compileAndRun(n, [e], o);
    return cr().makeTensorFromTensorInfo(s);
  }
  abs(e) {
    if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
      let n = nh(this.texData.get(e.dataId).values);
      return this.makeOutput(e.shape, e.dtype, n);
    }
    if (P().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
      return this.packedUnaryOp(e, kw, e.dtype);
    let t10 = new fr(e.shape, kw), o = this.compileAndRun(t10, [e]);
    return cr().makeTensorFromTensorInfo(o);
  }
  makeTensorInfo(e, t10, o) {
    let n;
    if (t10 === "string" && o != null && o.length > 0 && x.isString(o[0])) {
      let s = o.map((a) => x.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 cr().makeTensorFromTensorInfo(this.makeTensorInfo(e, t10, o), this);
  }
  unpackTensor(e) {
    let t10 = new uh(e.shape);
    return this.runWebGLProgram(t10, [e], e.dtype);
  }
  packTensor(e) {
    let t10 = new ah(e.shape), o = true;
    return this.runWebGLProgram(t10, [e], e.dtype, null, o);
  }
  packedReshape(e, t10) {
    let o = [Pa(e.shape), ...Oa(e.shape)], n = { dtype: e.dtype, shape: o, dataId: e.dataId }, s = [Pa(t10), ...Oa(t10)], a = new hc(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 = x.sizeFromShape(s), f = t10[0] * t10[1] * 4;
      x.assert(m <= f, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
    }
    let i = ac(s), p;
    n ? p = new jd(i) : p = new Kd(i);
    let u = true, c = [t10 != null ? t10 : _l(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 === ki.DENSE) {
      let y = a != null ? a : _l(e.outputShape);
      p.texShape = y.map((b) => b * 2);
    }
    if (e.outTexUsage != null && (p.usage = e.outTexUsage), x.sizeFromShape(i.shape) === 0)
      return p.values = x.getTypedArrayFromDType(i.dtype, 0), i;
    let u = [], c = t10.map((y) => {
      if (y.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(y.dataId);
      if (b.texture == null) {
        if (!e.packedInputs && x.sizeFromShape(y.shape) <= P().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))
          return { shape: y.shape, texData: null, isUniform: true, uniformValues: b.values };
        e.packedInputs && (b.isPacked = true, b.shape = y.shape);
      }
      if (this.uploadToGPU(y.dataId), !!b.isPacked != !!e.packedInputs)
        y = b.isPacked ? this.unpackTensor(y) : this.packTensor(y), u.push(y), b = this.texData.get(y.dataId);
      else if (b.isPacked && !Ti(b.shape, y.shape)) {
        let C = y, w = y.shape;
        y.shape = b.shape, y = this.packedReshape(y, w), u.push(y), b = this.texData.get(y.dataId), C.shape = w;
      }
      return { shape: y.shape, texData: b, isUniform: false };
    });
    this.uploadToGPU(i.dataId);
    let l = { shape: i.shape, texData: p, isUniform: false }, m = n$(e, c, l), f = this.getAndSaveBinary(m, () => r$(this.gpgpu, e, c, l)), d = this.activeTimers != null, h;
    d && (h = this.startTimer()), P().get("ENGINE_COMPILE_ONLY") || o$(this.gpgpu, f, c, l, n), u.forEach((y) => this.disposeIntermediateTensorInfo(y)), d && (h = this.endTimer(h), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(h) }));
    let g = P().get("WEBGL_FLUSH_THRESHOLD");
    if (g > 0) {
      let y = x.now();
      y - this.lastGlFlushTime > g && (this.gpgpu.gl.flush(), this.lastGlFlushTime = y);
    }
    if (!P().getBool("WEBGL_LAZILY_UNPACK") && p.isPacked && s === false) {
      let y = this.unpackTensor(i);
      return this.disposeIntermediateTensorInfo(i), y;
    }
    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 || (P().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 = Ne(() => {
      if (!P().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
        let e = P().getBool("DEBUG");
        P().set("DEBUG", false);
        let t10 = this.abs(be(1e-8)).dataSync()[0];
        if (P().set("DEBUG", e), t10 > 0)
          return 32;
      }
      return 16;
    })), this.floatPrecisionValue;
  }
  epsilon() {
    return this.floatPrecision() === 32 ? pY : cY;
  }
  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 = x.now());
    let l = t10.texShape;
    if (l == null && (l = JI(o, p), t10.texShape = l), s != null) {
      let m = ac(o), f, d = l[1], h = l[0], g = s instanceof Uint8Array || s instanceof Uint8ClampedArray;
      (p || !g) && ([d, h] = Ks(l[0], l[1])), p ? f = new Qd(m, g) : f = new Al(m, g);
      let y = g ? [h, d] : l, b = this.makeTensorInfo(y, n), C = this.texData.get(b.dataId);
      g ? C.usage = ir.PIXELS : C.usage = ir.UPLOAD, C.texShape = y, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), d, h, s);
      let w = [[h, d]], k = true, _ = this.runWebGLProgram(f, [b], n, w, k), E = this.texData.get(_.dataId);
      t10.texShape = E.texShape, t10.isPacked = E.isPacked, t10.usage = E.usage, P().get("ENGINE_COMPILE_ONLY") ? this.disposeData(_.dataId) : (t10.texture = E.texture, t10.values = null, this.texData.delete(_.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += x.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 this.releaseGPUData(e), t10 != null && (o.values = hY(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] * x.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 kC(), 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 ? (zd(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 } = aw(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;
    }
  }
  createTensorFromTexture(e, t10, o) {
    let { texture: n, height: s, width: a, channels: i } = e, p = cr().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 cr().makeTensorFromDataId(u, t10, o, p);
  }
};
Ni.nextDataId = 0;
function hY(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 gY = "4.0.0";
function pR() {
  P().set("WEBGL_FORCE_F16_TEXTURES", true);
}
ii.isBrowser() && pi("webgl", () => new Ni(), 2);
var R9e = { forceHalfFloat: pR };
var gc = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var _o = class {
  constructor(e, t10, o) {
    this.variableNames = ["A", "B"], this.outputShape = I.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 Ko = class {
  constructor(e, t10, o, n = false) {
    this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = I.assertAndGetBroadcastShape(t10, o);
    let s = this.outputShape.length;
    this.enableShapeUniforms = lt(s);
    let a = "";
    if (n)
      if (s === 0 || x.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 = $t("coords", s);
        this.enableShapeUniforms ? a += `
            bool nextRowOutOfBounds =
              (${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 Rt(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 cR = { kernelName: uo, backendName: "webgl", kernelFunc: Rt };
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 = Rt({ inputs: { x: o }, backend: t10 }), p = Rt({ inputs: { x: n }, backend: t10 });
  return a.complexTensorInfos = { real: i, imag: p }, s;
}
var lR = { kernelName: aa, backendName: "webgl", kernelFunc: Ar };
var Tw = "return (a < 0.) ? b * a : a;";
var Nw = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function xY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = t10.makeTensorInfo([], "float32", x.createScalarValue(s, "float32")), i = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ko(Nw, n.shape, a.shape) : new _o(Tw, n.shape, a.shape), p = t10.runWebGLProgram(i, [n, a], "float32");
  return t10.disposeIntermediateTensorInfo(a), p;
}
var mR = { kernelName: Nn, backendName: "webgl", kernelFunc: xY };
var _w = "return (a < 0.) ? b * a : a;";
var Ew = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function yY(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ko(Ew, o.shape, n.shape) : new _o(_w, o.shape, n.shape);
  return t10.runWebGLProgram(s, [o, n], "float32");
}
var fR = { kernelName: Vn, backendName: "webgl", kernelFunc: yY };
var jo = "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 = P().getBool("WEBGL_PACK_UNARY_OPERATIONS") && e != null, c;
    return u ? c = new No(a.shape, e) : c = new fr(a.shape, r), i.runWebGLProgram(c, [a], p);
  };
}
function ot({ 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 d = c.texData.get(p.dataId), h = c.texData.get(u.dataId), [g, y] = [[d.complexTensorInfos.real, h.complexTensorInfos.real], [d.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 }, R = new _o(r, p.shape, u.shape);
        return c.runWebGLProgram(R, [_, E], ct(w.dtype, k.dtype));
      }), b = Ar({ inputs: { real: g, imag: y }, backend: c });
      return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(y), b;
    }
    let l = s || ct(p.dtype, u.dtype);
    if ((p.dtype === "string" || u.dtype === "string" || c.shouldExecuteOnCPU([p, u])) && n != null) {
      let d = c.texData.get(p.dataId).values, h = c.texData.get(u.dataId).values, g = p.dtype === "string" ? I.fromUint8ToStringArray(d) : d, y = p.dtype === "string" ? I.fromUint8ToStringArray(h) : h, [b, C] = n(p.shape, u.shape, g, y, l), w = c.makeTensorInfo(C, l), k = c.texData.get(w.dataId);
      return k.values = b, w;
    }
    let m = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") && e != null, f;
    return m ? f = new Ko(e, p.shape, u.shape, t10) : f = new _o(r, p.shape, u.shape), c.runWebGLProgram(f, [p, u], l);
  };
}
function Ma(r, e = false) {
  if (r === "linear")
    return e ? nR : Z$;
  if (r === "relu")
    return e ? aR : eR;
  if (r === "elu")
    return e ? sR : J$;
  if (r === "relu6")
    return e ? iR : tR;
  if (r === "prelu")
    return e ? Ew : _w;
  if (r === "leakyrelu")
    return e ? Nw : Tw;
  if (r === "sigmoid")
    return e ? uR : rR;
  throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`);
}
var xc = 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", f = s ? "rc.z, i * 2" : "i * 2, rc.z", d = n ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], h = s ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], g = "", y = "";
    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}
        }`, y = "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 = `int(min(float(rc.x), ${e[0] - 1}.))` : t10[0] < e[0] && (w = `int(min(float(rc.x), ${t10[0] - 1}.))`), this.userCode = `
      ${g}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${l}.0;

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

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

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

        ${b}

        ${y}

        setOutput(result);
      }
    `;
  }
};
var $w = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var Dl = class {
  constructor(e, t10, o) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = I.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 dR = "return a * b;";
function Pl(r) {
  let { inputs: e, backend: t10 } = r, { a: o, b: n } = e, s = I.upcastType(o.dtype, n.dtype);
  if (o.dtype === "complex64") {
    let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), u = new Dl($w.REAL, o.shape, n.shape), c = new Dl($w.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"), f = t10.runWebGLProgram(c, l, "float32"), d = Ar({ inputs: { real: m, imag: f }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(f), d;
  }
  if (t10.shouldExecuteOnCPU([o, n])) {
    let i = t10.texData.get(o.dataId), p = t10.texData.get(n.dataId), [u, c] = k$(o.shape, n.shape, i.values, p.values, s), l = t10.makeTensorInfo(c, s), m = t10.texData.get(l.dataId);
    return m.values = u, l;
  }
  let a;
  return P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? a = new Ko(dR, o.shape, n.shape) : a = new _o(dR, o.shape, n.shape), t10.runWebGLProgram(a, [o, n], s);
}
var hR = { kernelName: ho, backendName: "webgl", kernelFunc: Pl };
function gR(r, e, t10) {
  let o = [Pa(r.shape), ...Oa(r.shape)], n = { dtype: r.dtype, shape: o, dataId: r.dataId }, s = [Pa(e), ...Oa(e)], a = new hc(s, o), i = true, p = [o], u = t10.runWebGLProgram(a, [n], r.dtype, p, i);
  return { dataId: u.dataId, shape: e, dtype: u.dtype };
}
function J(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { shape: s } = o, a = t10, i = x.sizeFromShape(n.shape), p = x.inferFromImplicitShape(s, i), u = x.sizeFromShape(p);
  x.assert(i === u, () => `The new shape (${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 && !Ti(n.shape, p) && !(c.texture !== null && Ti(c.shape, p)) ? gR(n, p, a) : (a.incRef(n.dataId), { dataId: n.dataId, shape: p, dtype: n.dtype });
}
var xR = { kernelName: Ss, backendName: "webgl", kernelFunc: J };
var Ol = 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 * ${x.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);

          ${u}
        }
        setOutput(sumValue);
      }
    `;
  }
};
var ch = class {
  constructor(e, t10) {
    this.variableNames = ["x"];
    let { windowSize: o, batchSize: n, inSize: s, outSize: a } = e;
    this.outputShape = [n, a];
    let i = "0.0", p = "";
    t10 === "prod" ? i = "1.0" : t10 === "min" ? (i = "1.0 / 1e-20", p = "min") : t10 === "max" && (i = "-1.0 / 1e-20", p = "max");
    let u = `${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);
          }
        }
      }
    `, f = "vec4";
    t10 === "all" ? (i = "1.0", m = `
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `, f = "bvec4") : t10 === "any" && (i = "0.0", m = `
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `, f = "bvec4");
    let d = "";
    s % o > 0 && (d = `
        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) {
        ${d}
        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;
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${m}
        }

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

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

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

          ${m}
        }
        setOutput(${u});
      }
    `;
  }
};
function CY(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 = I.computeOptimalWindowSize(t10);
    e.push({ inSize: t10, windowSize: o, outSize: Math.ceil(t10 / o) });
  }
  return e;
}
function qr(r, e, t10, o) {
  let n = CY(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 Ol({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }, i) : new Ol({ windowSize: p, inSize: i, batchSize: r.shape[0], outSize: u }) : c = new ch({ 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 lh = 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 = IY(t10);
    this.userCode = `
    void main() {
      ${n} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `;
  }
};
function IY(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 mh = 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 ${this.rank} is not yet supported.`);
    let n = _e(this.rank), s = vw("rc", this.rank), a = new Array(this.rank);
    for (let c = 0; c < t10.length; c++)
      a[t10[c]] = s[c];
    let i = `vec2(${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 _i(r, e, t10) {
  let o = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new mh(r.shape, e) : new lh(r.shape, e);
  return t10.runWebGLProgram(o, [r], r.dtype);
}
function yR(r, e, t10, o) {
  let n = e, s = r.shape.length, a = x.parseAxisParam(n, r.shape), i = a, p = I.getAxesPermutation(i, s), u = p != null, c = r;
  u && (c = _i(r, p, o), i = I.getInnerMostAxes(i.length, s)), I.assertAxesAreInnerMostDims("sum", i, s);
  let [l, m] = I.computeOutAndReduceShapes(c.shape, i), f = l;
  t10 && (f = I.expandShapeToKeepDim(l, a));
  let d = x.sizeFromShape(m), g = x.sizeFromShape(r.shape) / d, y = J({ inputs: { x: c }, attrs: { shape: [g, d] }, backend: o }), b = Ca(r.dtype), C = qr(y, b, "sum", o), w = J({ inputs: { x: C }, attrs: { shape: f }, backend: o });
  return o.disposeIntermediateTensorInfo(y), o.disposeIntermediateTensorInfo(C), u && o.disposeIntermediateTensorInfo(c), w;
}
function Ou(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return yR(n, s, a, t10);
}
var bR = { kernelName: jn, backendName: "webgl", kernelFunc: Ou };
function xt(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 = Du(l, n.shape, n.dtype, s, p);
    u = a.makeTensorInfo(p, n.dtype);
    let f = a.texData.get(u.dataId);
    f.values = m;
  } else
    u = _i(n, s, a);
  return u;
}
var CR = { kernelName: Mr, backendName: "webgl", kernelFunc: xt };
var Rw = 1e3;
function Mu({ 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], f = t10 ? r.shape[u - 1] : r.shape[u - 2], d = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), y = x.sizeFromShape(h), b = x.sizeFromShape(g), w = br.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([f, d]);
  x.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 ? [y, l, f] : [y, f, l], _ = o ? [b, d, m] : [b, m, d], E = J({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = J({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), A = [E, R], D = Math.max(y, b), O = t10 ? E.shape[1] : E.shape[2], M = s != null, L = a != null, W = p === "leakyrelu", V = p != null ? Ma(p, true) : null, G = M || L || W || V != null, q;
  if ((f === 1 || d === 1) && O > Rw && G === false) {
    let j = E, Y = R;
    t10 && (j = xt({ inputs: { x: E }, backend: n, attrs: { perm: [0, 2, 1] } }), A.push(j)), o && (Y = xt({ inputs: { x: R }, backend: n, attrs: { perm: [0, 2, 1] } }), A.push(Y));
    let Z = d !== 1, ee = d === 1, X = j;
    Z && (X = J({ inputs: { x: j }, backend: n, attrs: { shape: [D, O, 1] } }), A.push(X));
    let Q = d === 1 ? 2 : 1, se = Y;
    ee && (se = J({ inputs: { x: Y }, backend: n, attrs: { shape: [D, 1, O] } }), A.push(se));
    let ie = Pl({ inputs: { a: X, b: se }, backend: n });
    q = Ou({ inputs: { x: ie }, backend: n, attrs: { axis: Q, keepDims: true } }), A.push(ie);
  } else {
    let j = ct(r.dtype, e.dtype), Y = new xc(k, _, [D, f, d], t10, o, M, V, L, W), Z = [E, R];
    if (s != null && Z.push(s), L && Z.push(a), W) {
      let ee = n.makeTensorInfo([], "float32", x.createScalarValue(i, "float32"));
      Z.push(ee), A.push(ee);
    }
    q = n.runWebGLProgram(Y, Z, j);
  }
  let H = J({ inputs: { x: q }, backend: n, attrs: { shape: w } });
  A.push(q);
  for (let j of A)
    n.disposeIntermediateTensorInfo(j);
  return H;
}
function wY(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 Mu({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var IR = { kernelName: Fo, backendName: "webgl", kernelFunc: wY };
var wR = "return abs(x);";
function SY(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 = nh(s.values);
    return t10.makeTensorInfo(o.shape, o.dtype, a);
  }
  let n;
  return P().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new No(o.shape, wR) : n = new fr(o.shape, wR), t10.runWebGLProgram(n, [o], o.dtype);
}
var SR = { kernelName: sn, backendName: "webgl", kernelFunc: SY };
var vY = Vt + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
var kY = he({ opSnippet: vY });
var vR = { kernelName: Li, backendName: "webgl", kernelFunc: kY };
var TY = Vt + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var NY = he({ opSnippet: TY });
var kR = { kernelName: Bi, backendName: "webgl", kernelFunc: NY };
var TR = "return a + b;";
var _Y = ot({ opSnippet: TR, packedOpSnippet: TR, supportsComplex: true, cpuKernelImpl: s$ });
var NR = { kernelName: _r, backendName: "webgl", kernelFunc: _Y };
var fh = 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 = ${n};
        setOutput(result);
      }
    `;
  }
};
var dh = class {
  constructor(e, t10) {
    this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t10.map((s, a) => `T${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 hh(r) {
  let { inputs: e, backend: t10 } = r, o = e;
  if (o.length === 1)
    return Rt({ inputs: { x: o[0] }, backend: t10 });
  if (o.length > P().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
    let p = Math.floor(o.length / 2), u = hh({ inputs: o.slice(0, p), backend: t10 }), c = hh({ inputs: o.slice(p), backend: t10 });
    return hh({ inputs: [u, c], backend: t10 });
  }
  let n = o.map((p) => p.dtype).reduce((p, u) => ct(p, u)), s = o.map((p) => p.shape), i = P().getBool("WEBGL_PACK") ? new dh(o[0].shape, s) : new fh(o[0].shape, s);
  return t10.runWebGLProgram(i, o, n);
}
var _R = { kernelName: an, backendName: "webgl", kernelFunc: hh };
function EY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = x.parseAxisParam(s, n.shape), u = p, c = I.getAxesPermutation(u, i), l = n;
  c != null && (l = xt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = I.getInnerMostAxes(u.length, i)), I.assertAxesAreInnerMostDims("all", u, i);
  let [m, f] = I.computeOutAndReduceShapes(l.shape, u), d = x.sizeFromShape(f), h = J({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } }), g = qr(h, h.dtype, "all", t10), y;
  if (a) {
    let b = I.expandShapeToKeepDim(m, p);
    y = J({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
  } else
    y = J({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), y;
}
var ER = { kernelName: oa, backendName: "webgl", kernelFunc: EY };
function $Y(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = x.parseAxisParam(s, n.shape), u = p, c = I.getAxesPermutation(u, i), l = n;
  c != null && (l = xt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = I.getInnerMostAxes(u.length, i)), I.assertAxesAreInnerMostDims("any", u, i);
  let [m, f] = I.computeOutAndReduceShapes(l.shape, u), d = x.sizeFromShape(f), h = J({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } }), g = qr(h, h.dtype, "any", t10), y;
  if (a) {
    let b = I.expandShapeToKeepDim(m, p);
    y = J({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
  } else
    y = J({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), y;
}
var $R = { kernelName: na, backendName: "webgl", kernelFunc: $Y };
var gh = 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 ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
  }
};
var xh = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, x.assert(e.length > 2, () => `Packed arg${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 = $t("coords", p), l, m;
    if (a === 1) {
      m = p + 1;
      let R = _e(m);
      l = `
        ${R} sourceLocR = ${R}(${c.join()}, 0);
        ++${c[p - 1]};
        ${R} sourceLocG = ${R}(${c.join()}, 0);
        ++${c[p - 2]};
        ${R} sourceLocA = ${R}(${c.join()}, 0);
        --${c[p - 1]};
        ${R} sourceLocB = ${R}(${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 f = ["x", "y", "z", "w", "u", "v"].slice(0, m), d = "." + f[m - 1], h = f.map((R) => "int " + R), g = $t("sourceLocR", m - 1).concat("inIdx.r"), y = $t("sourceLocG", m - 1).concat("inIdx.g"), b = $t("sourceLocB", m - 1).concat("inIdx.b"), C = $t("sourceLocA", m - 1).concat("inIdx.a"), w = o === "max" ? "greaterThan" : "lessThan", k = n ? "" : `
          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${y.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${C.join()})));`, _ = `vec4(
            getAChannel(${g.join()}),
            hasNextCol ? getAChannel(${y.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${C.join()}) : 0.)`, E = n ? "" : `
      float getBestIndicesAChannel(${h.join()}) {
        return getChannel(getBestIndicesA(${f.join()}),
                                          vec2(${f.slice(-2).join()}));
      }`;
    this.userCode = `
      float getAChannel(${h.join()}) {
        return getChannel(getA(${f.join()}),
                               vec2(${f.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};
        ${l}
        ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
          sourceLocB${d}, sourceLocA${d}) * ${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 RR(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 = I.computeOptimalWindowSize(s), i = { windowSize: a, inSize: s, batchSize: n, outSize: Math.ceil(s / a) }, p = new gh(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 = RR(r, e, t10, c);
  return r.disposeIntermediateTensorInfo(c), l;
}
function AR(r, e, t10, o = null) {
  let n = o != null ? o.shape : e.shape, s = n[n.length - 1], a = I.computeOptimalWindowSize(s), i = new xh(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 = AR(r, e, t10, u);
    return r.disposeIntermediateTensorInfo(u), c;
  }
  return u;
}
function yh(r, e, t10, o) {
  let n = [t10];
  if (I.assertAxesAreInnerMostDims("arg" + o.charAt(0).toUpperCase() + o.slice(1), n, e.shape.length), !P().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] = I.computeOutAndReduceShapes(p.shape, n), l = x.sizeFromShape(c), m = J({ inputs: { x: p }, backend: r, attrs: { shape: [-1, l] } });
    s.push(m);
    let f = RR(r, m, o);
    s.push(f);
    let d = J({ inputs: { x: f }, backend: r, attrs: { shape: u } });
    return s.forEach((h) => r.disposeIntermediateTensorInfo(h)), d;
  }
  return AR(r, e, o);
}
function RY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = x.parseAxisParam(s, n.shape), i = I.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = xt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = I.getInnerMostAxes(a.length, p.shape.length)), I.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
  let c = yh(t10, p, a[0], "max");
  return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c;
}
var FR = { kernelName: un, backendName: "webgl", kernelFunc: RY };
function AY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = x.parseAxisParam(s, n.shape), i = I.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = xt({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = I.getInnerMostAxes(a.length, p.shape.length)), I.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
  let c = yh(t10, p, a[0], "min");
  return u.forEach((l) => t10.disposeIntermediateTensorInfo(l)), c;
}
var DR = { kernelName: ja, backendName: "webgl", kernelFunc: AY };
var FY = Vt + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
var DY = he({ opSnippet: FY });
var PR = { kernelName: Vi, backendName: "webgl", kernelFunc: DY };
var PY = Vt + "return log(x + sqrt(x * x + 1.0));";
var OY = he({ opSnippet: PY });
var OR = { kernelName: zi, backendName: "webgl", kernelFunc: OY };
var MY = Vt + `
  return atan(x);
`;
var LY = he({ opSnippet: MY });
var MR = { kernelName: Wi, backendName: "webgl", kernelFunc: LY };
var BY = gc + `
  return atan(a, b);
`;
var VY = `
  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 zY = ot({ opSnippet: BY, packedOpSnippet: VY });
var LR = { kernelName: sa, backendName: "webgl", kernelFunc: zY };
var WY = Vt + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var UY = he({ opSnippet: WY });
var BR = { kernelName: Ui, backendName: "webgl", kernelFunc: UY };
var us = 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, f = e.padInfo.top, d = e.padInfo.left;
    this.outputShape = e.outShape;
    let h = t10 === "avg", g = `((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, y = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0";
    if (h || (b = "-1.0 / 1e-20"), o) {
      let R = ">=";
      this.userCode = `
        const ivec2 strides = ivec2(${i}, ${p});
        const ivec2 pads = ivec2(${f}, ${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
          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 ${R} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${n ? s ? g : y : `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 / count");
    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}, ${p});
      const ivec2 pads = ivec2(${f}, ${d});
      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 Ei = 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, f = e.effectiveFilterDepth, d = e.effectiveFilterHeight, h = e.effectiveFilterWidth, g = e.padInfo.front, y = 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}, ${y}, ${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 < ${f};
              wD += ${c}) {
            int xD = xDCorner + wD;

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

            for (int wR = 0; wR < ${d};
                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 * ${d} * ${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 / count");
    let E = Math.floor(a / 4) * 4, R = a % 4, A = `
      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}, ${y}, ${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 < ${f};
            wD += ${c}) {
          int xD = xDCorner + wD;

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

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

              ${A}
            }

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

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

              ${A}
            } else if (${R === 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
              );

              ${A}
            }
          }
          setOutput(${_});
        }
      }
    `;
  }
};
function GY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  as(n, "avgPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  x.assert(I.eitherStridesOrDilationsAreOne(a, u), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = I.computePool2DInfo(n.shape, s, a, u, i, p);
  if (c.filterWidth === 1 && c.filterHeight === 1 && x.arraysEqual(c.inShape, c.outShape))
    return Rt({ inputs: { x: n }, backend: t10 });
  let l = new us(c, "avg", false);
  return t10.runWebGLProgram(l, [n], "float32");
}
var VR = { kernelName: pn, backendName: "webgl", kernelFunc: GY };
function HY(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 = I.computePool3DInfo(n.shape, s, a, c, i, p, u), m = new Ei(l, "avg", false);
  return t10.runWebGLProgram(m, [n], "float32");
}
var zR = { kernelName: ip, backendName: "webgl", kernelFunc: HY };
var bh = 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 Ch = 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, f = e.effectiveFilterWidth, d = l - 1 - e.padInfo.front, h = m - 1 - e.padInfo.top, g = f - 1 - e.padInfo.left, y = 1 / (t10 * o * n);
    this.userCode = `
      const ivec3 pads = ivec3(${d}, ${h}, ${g});
      const float avgMultiplier = float(${y});

      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 < ${f};
                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 qY(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 = I.computePool3DInfo(a.shape, i, p, l, u, c), f = new Ch(m);
  return t10.runWebGLProgram(f, [n], a.dtype);
}
var WR = { kernelName: Fm, backendName: "webgl", kernelFunc: qY };
function KY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s } = e, a = s;
  as([n, s], "avgPoolGrad");
  let { filterSize: i, strides: p, pad: u } = o, c = I.computePool2DInfo(a.shape, i, p, 1, u), l = new bh(c);
  return t10.runWebGLProgram(l, [n], a.dtype);
}
var UR = { kernelName: Am, backendName: "webgl", kernelFunc: KY };
function jY(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  return Mu({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 });
}
var GR = { kernelName: cn, backendName: "webgl", kernelFunc: jY };
var Ih = class {
  constructor(e, t10, o, n, s, a) {
    this.outputShape = [], this.variableNames = ["x", "mean", "variance"], I.assertAndGetBroadcastShape(e, t10), I.assertAndGetBroadcastShape(e, o);
    let i = "0.0";
    n != null && (I.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let p = "1.0";
    s != null && (I.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 wh = class {
  constructor(e, t10, o, n, s, a) {
    this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], I.assertAndGetBroadcastShape(e, t10), I.assertAndGetBroadcastShape(e, o);
    let i = "vec4(0.0)";
    n != null && (I.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let p = "vec4(1.0)";
    s != null && (I.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 XY = ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o, mean: n, variance: s, offset: a, scale: i } = r;
  x.assert(n.shape.length === s.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), x.assert(a == null || n.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), x.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 = P().getBool("WEBGL_PACK_NORMALIZATION") ? new wh(o.shape, n.shape, s.shape, c, l, p) : new Ih(o.shape, n.shape, s.shape, c, l, p);
  return e.runWebGLProgram(m, u, u[0].dtype);
};
var HR = { kernelName: kn, backendName: "webgl", kernelFunc: XY };
var Sh = 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 = YY(this.rank), n, s = e.map((a, i) => `sourceLoc.${Aw[i]} = start[${i}] + coords.${Aw[i]};`);
    n = `
        ${t10} sourceLoc;
        ${t10} coords = getOutputCoords();
        ${s.join(`
`)}
      `, this.userCode = `
      void main() {
        ${n}
        setOutput(getSource(${o}));
      }
    `;
  }
};
var Aw = ["x", "y", "z", "w", "u", "v"];
function YY(r) {
  if (r === 1)
    return "sourceLoc";
  if (r <= 6)
    return Aw.slice(0, r).map((e) => "sourceLoc." + e).join(",");
  throw Error(`Slicing for rank ${r} is not yet supported`);
}
var vh = 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 = $t("coords", this.rank), n = $t("sourceLoc", this.rank), s = this.rank === 1 ? "sourceLoc" : `vec2(${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}
        ${p}
        setOutput(result);
      }
    `;
  }
};
function QY(r, e, t10, o) {
  let n = o.texData.get(r.dataId), s = o.makeTensorInfo(t10, r.dtype), a = o.texData.get(s.dataId);
  Object.assign(a, n), a.refCount = 1, a.shape = t10, a.dtype = r.dtype;
  let i = et.computeFlatOffset(e, x.computeStrides(r.shape));
  n.slice && (i += n.slice.flatOffset), a.slice = { flatOffset: i, origDataId: n.slice && n.slice.origDataId || r.dataId };
  let p = o.dataRefCount.get(a.slice.origDataId) || 1;
  return o.dataRefCount.set(a.slice.origDataId, p + 1), s;
}
function ps(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = et.parseSliceParams(n, s, a);
  if (et.assertParamsValid(n, i, p), x.sizeFromShape(p) === 0)
    return t10.makeTensorInfo(p, n.dtype, []);
  if (t10.shouldExecuteOnCPU([n]) || n.dtype === "string") {
    let l = t10.texData.get(n.dataId), m = O$(l.values, i, p, n.shape, n.dtype);
    return t10.makeTensorInfo(p, n.dtype, m);
  }
  let { isPacked: u } = t10.texData.get(n.dataId), c = et.isSliceContinous(n.shape, i, p);
  if (u || !c) {
    let l = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new vh(p) : new Sh(p), m = [i];
    return t10.runWebGLProgram(l, [n], n.dtype, m);
  }
  return t10.uploadToGPU(n.dataId), QY(n, i, p, t10);
}
var qR = { kernelName: qn, backendName: "webgl", kernelFunc: ps };
var ZY = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
  x.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 = I.getReshaped(n.shape, s, i), u = I.getPermuted(p.length, s.length), c = I.getReshapedPermuted(n.shape, s, i), l = I.getSliceBeginCoords(a, s.length), m = I.getSliceSize(c, a, s.length), f = [], d = J({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = xt({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), g = J({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), y = ps({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } });
  return f.push(d), f.push(h), f.push(g), f.forEach((b) => t10.disposeIntermediateTensorInfo(b)), y;
};
var KR = { kernelName: hs, backendName: "webgl", kernelFunc: ZY };
function JY(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 = oh(i, p, s.dtype, s.shape, a);
  return t10.makeTensorInfo([a], s.dtype, u);
}
var jR = { kernelName: up, backendName: "webgl", kernelFunc: JY };
function eQ(r) {
  let { inputs: e, backend: t10 } = r, { s0: o, s1: n } = e, s = t10.readSync(o.dataId), a = t10.readSync(n.dataId), i = I.assertAndGetBroadcastShape(Array.from(s), Array.from(a));
  return t10.makeTensorInfo([i.length], "int32", Int32Array.from(i));
}
var XR = { kernelName: pp, backendName: "webgl", kernelFunc: eQ };
var tQ = "return float(a != b);";
var Fw = ot({ opSnippet: tQ, cpuKernelImpl: N$, dtype: "bool" });
var YR = { kernelName: go, backendName: "webgl", kernelFunc: Fw };
function La(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId);
  return Rt({ inputs: { x: n.complexTensorInfos.real }, backend: t10 });
}
var QR = { kernelName: la, backendName: "webgl", kernelFunc: La };
var rQ = "return float(int(x));";
function ZR(r, e) {
  let t10 = new fr(r.shape, rQ), o = e.runWebGLProgram(t10, [r], "int32");
  return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function Dw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return Rt({ inputs: { x: n }, backend: t10 });
    let a = Wr(n.shape), i = Dw({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = Ar({ inputs: { real: i, imag: a }, backend: t10 });
    return a.dispose(), t10.disposeIntermediateTensorInfo(i), p;
  }
  if (n.dtype === "complex64") {
    let a = La({ inputs: { input: n }, backend: t10 }), i = Dw({ inputs: { x: a }, backend: t10, attrs: { dtype: s } });
    return t10.disposeIntermediateTensorInfo(a), i;
  }
  if (!x.hasEncodingLoss(n.dtype, s)) {
    let a = Rt({ inputs: { x: n }, backend: t10 });
    return { dataId: a.dataId, shape: a.shape, dtype: s };
  }
  if (t10.shouldExecuteOnCPU([n])) {
    let a = t10.texData.get(n.dataId).values, [i, p, u] = i$(a, n.shape, n.dtype, s);
    return t10.makeTensorInfo(i, p, u);
  }
  if (s === "int32")
    return ZR(n, t10);
  if (s === "bool") {
    let a = t10.makeTensorInfo([], "bool", x.getTypedArrayFromDType("bool", 1)), p = Fw({ inputs: { a: n, b: a }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(a), p;
  }
  throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`);
}
var JR = { kernelName: to, backendName: "webgl", kernelFunc: Dw };
var eA = "return ceil(x);";
var oQ = he({ opSnippet: eA, packedOpSnippet: eA, cpuKernelImpl: u$ });
var tA = { kernelName: ro, backendName: "webgl", kernelFunc: oQ };
var kh = 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 Th = 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 nQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i;
  P().getBool("WEBGL_PACK_CLIP") ? i = new Th(n.shape) : i = new kh(n.shape);
  let p = [[s], [a]];
  return t10.runWebGLProgram(i, [n], n.dtype, p);
}
var rA = { kernelName: Ro, backendName: "webgl", kernelFunc: nQ };
var Nh = 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 oA(r, e) {
  return { dataId: e.dataId, dtype: e.dtype, shape: r.shape };
}
function sQ(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = t10.texData.get(o.dataId), s = new Nh(o.shape), a = [oA(o, n.complexTensorInfos.real), oA(o, n.complexTensorInfos.imag)];
  return t10.runWebGLProgram(s, a, a[0].dtype);
}
var nA = { kernelName: cp, backendName: "webgl", kernelFunc: sQ };
var _h = class {
  constructor(e) {
    this.outputShape = [], this.outputShape = I.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 $h = class {
  constructor(e, t10) {
    this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = I.computeOutShape(e, t10);
    let o = this.outputShape, n = o.length, s = _e(n), a = $t("coords", n), i = ["x", "y", "z", "w", "u", "v"].slice(0, n);
    this.variableNames = e.map((h, g) => `T${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}(${Eh(i, u, g)}),
            vec2(${Eh(c, u, g)}));
        }`;
    }
    let f = p.length, d = p[p.length - 1];
    m += `
        return getChannel(
          getT${f}(${Eh(i, u, d)}),
          vec2(${Eh(c, u, d)}));`, this.userCode = `
      float getValue(${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 Eh(r, e, t10) {
  let o = r.indexOf(e);
  return r.map((s, a) => a === o ? `${s} - ${t10}` : s).join();
}
function Lu(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.texData.get(o.dataId);
  return Rt({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 });
}
var sA = { kernelName: Ya, backendName: "webgl", kernelFunc: Lu };
function yc(r, e, t10) {
  let o = r[0].dtype;
  if (o === "complex64") {
    let l = r.map((g) => La({ inputs: { input: g }, backend: t10 })), m = r.map((g) => Lu({ inputs: { input: g }, backend: t10 })), f = yc(l, e, t10), d = yc(m, e, t10), h = Ar({ inputs: { real: f, imag: d }, backend: t10 });
    return l.forEach((g) => t10.disposeIntermediateTensorInfo(g)), m.forEach((g) => t10.disposeIntermediateTensorInfo(g)), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(d), h;
  }
  let n = t10.shouldExecuteOnCPU(r);
  if (o === "string" && (n = true), n) {
    let l = r.map((b) => {
      let w = [-1, x.sizeFromShape(b.shape.slice(e))];
      return J({ inputs: { x: b }, backend: t10, attrs: { shape: w } });
    }), m = l.map((b) => ({ vals: t10.readSync(b.dataId), shape: b.shape })), f = I.computeOutShape(l.map((b) => b.shape), 1), d = l[0].shape[0] === 1, h = p$(m, f, o, d), g = I.computeOutShape(r.map((b) => b.shape), e), y = t10.makeTensorInfo(g, o, h);
    return l.forEach((b) => t10.disposeIntermediateTensorInfo(b)), y;
  }
  let s = P().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
  if (r.length > s) {
    let l = [];
    for (let f = 0; f < r.length; f += s) {
      let d = r.slice(f, f + s);
      l.push(yc(d, e, t10));
    }
    let m = yc(l, e, t10);
    for (let f of l)
      t10.disposeIntermediateTensorInfo(f);
    return m;
  }
  if (P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && r[0].shape.length > 1) {
    let l = new $h(r.map((m) => m.shape), e);
    return t10.runWebGLProgram(l, r, o);
  }
  let { tensors2D: a, outShape: i } = aQ(r, e, t10), p = new _h(a.map((l) => l.shape)), u = t10.runWebGLProgram(p, a, o);
  a.forEach((l) => t10.disposeIntermediateTensorInfo(l));
  let c = J({ inputs: { x: u }, attrs: { shape: i }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(u), c;
}
function aQ(r, e, t10) {
  let o = I.computeOutShape(r.map((s) => s.shape), e);
  return { tensors2D: r.map((s) => J({ inputs: { x: s }, attrs: { shape: [-1, x.sizeFromShape(s.shape.slice(e))] }, backend: t10 })), outShape: o };
}
function Pw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = x.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
  I.assertParamsConsistent(a, s);
  let i = I.computeOutShape(e.map((u) => u.shape), s);
  if (x.sizeFromShape(i) === 0)
    return t10.makeTensorInfo(i, e[0].dtype, []);
  let p = e.filter((u) => x.sizeFromShape(u.shape) > 0);
  return p.length === 1 ? Rt({ inputs: { x: p[0] }, backend: t10 }) : yc(p, s, t10);
}
var aA = { kernelName: gs, backendName: "webgl", kernelFunc: Pw };
var bc = 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, f = e.filterWidth, d = Math.floor(e.inChannels / 4) * 4, h = e.inChannels % 4, g = e.dataFormat === "channelsLast", y = 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[${y}], coords[${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 < ${f}; wC++) {
            int xC = xCCorner + wC * ${l};

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

            for (int d1 = 0; d1 < ${d}; 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, ${d}) *
                    getW(wR, wC, ${d}, d2);
              } else {
                dotProd +=
                    getX(batch, ${d}, xR, xC) *
                    getW(wR, wC, ${d}, d2);
              }

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

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

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

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

            }
          }
        }

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

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

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

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

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

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

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

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

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

                 xC${y} = vec4(
                   xTexelC${y}.xy, xTexelC${y + 1}.xy);
               `, y + 1 < c && (m += `
                   xC${y + 1} = vec4(xTexelC${y}.zw, xTexelC${y + 1}.zw);
                 `)));
      y < c && (m += `
             wTexel = getW(r, ${y}, d1, d2);
             dotProd += xC${y}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${e.inChannels}) {
               dotProd += xC${y}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `, y + 1 < c && (m += `
               wTexel = getW(r, ${y + 1}, d1, d2);
               dotProd += xC${y + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${e.inChannels}) {
                 dotProd += xC${y + 1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `));
    }
    m += `
     }
   `, m += `
     }
   `, m += `
     }
   `;
    let f = "", d = "";
    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}
         }`, d = "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 = `
       ${f}

       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}
         ${d}
         setOutput(result);
       }
     `;
  }
};
var Ah = 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 = Ct(), 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 Fh(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 Dh({ 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, f = t10.dataFormat === "channelsLast", d = false, h = false, g, y = [];
  if (s != null) {
    let w = Fh(s.shape, f);
    w != null && (s = J({ inputs: { x: s }, backend: o, attrs: { shape: w } }), y.push(s));
  }
  if (n != null) {
    let w = Fh(n.shape, f);
    w != null && (n = J({ inputs: { x: n }, backend: o, attrs: { shape: w } }), y.push(n));
  }
  if (!((l === 1 || m === 1) && c > Rw) && u.isPacked && f && u.texture != null && p[2] % 2 !== 0 && x.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]++, x.assert(Ti(u.shape, k.shape), () => `packed reshape ${u.shape} to ${k.shape} isn't free`);
    let E = J({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } });
    y.push(E);
    let R = Mu({ a: k, b: E, backend: o, transposeA: d, transposeB: h, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), A = o.texData.get(R.dataId);
    x.assert(A.isPacked, () => "batchMatMul result is expected to be packed"), u.shape = _, A.shape = t10.outShape, g = Rt({ inputs: { x: R }, backend: o }), g.shape = t10.outShape, y.push(R);
  } else {
    let w = t10.outHeight * t10.outWidth, k = J({ inputs: { x: r }, backend: o, attrs: { shape: f ? [t10.batchSize, w, t10.inChannels] : [t10.batchSize, t10.inChannels, w] } }), _ = J({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } }), E = Mu({ a: f ? k : _, b: f ? _ : k, transposeA: !f, transposeB: h, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
    g = J({ inputs: { x: E }, backend: o, attrs: { shape: t10.outShape } }), y.push(k), y.push(_), y.push(E);
  }
  for (let w of y)
    o.disposeIntermediateTensorInfo(w);
  return g;
}
function Ph({ 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: f } = t10, d = f === "channelsLast", h = p * u * c, g = m * l, y = [t10.batchSize, h, g], b = true, C = false, w = [];
  if (s != null) {
    let H = Fh(s.shape, d);
    H != null && (s = J({ inputs: { x: s }, backend: o, attrs: { shape: H } }), w.push(s));
  }
  if (n != null) {
    let H = Fh(n.shape, d);
    H != null && (n = J({ inputs: { x: n }, backend: o, attrs: { shape: H } }), w.push(n));
  }
  let k = J({ inputs: { x: e }, backend: o, attrs: { shape: [1, h, x.sizeFromShape(e.shape) / h] } });
  w.push(k);
  let _ = new Ah(y, 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]], R = o.runWebGLProgram(_, [r], "float32", E), A = J({ inputs: { x: R }, backend: o, attrs: { shape: y } });
  w.push(R), w.push(A);
  let D = n != null, O = s != null, M = i === "leakyrelu", L = i ? Ma(i, true) : null, W = new xc(d ? A.shape : k.shape, d ? k.shape : A.shape, d ? [t10.batchSize, g, t10.outChannels] : [t10.batchSize, t10.outChannels, g], b, C, D, L, O, M), V = d ? [A, k] : [k, A];
  if (n && V.push(n), O && V.push(s), M) {
    let H = o.makeTensorInfo([], "float32", x.createScalarValue(a, "float32"));
    V.push(H), w.push(H);
  }
  let G = o.runWebGLProgram(W, V, "float32"), q = J({ inputs: { x: G }, backend: o, attrs: { shape: t10.outShape } });
  w.push(G);
  for (let H of w)
    o.disposeIntermediateTensorInfo(H);
  return q;
}
function iQ(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 = I.convertConv2DDataFormat(p), m = I.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l), f;
  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"))
    f = Dh({ x: n, filter: s, convInfo: m, backend: t10 });
  else if (m.strideWidth <= 2 && l === "channelsLast" && P().getBool("WEBGL_EXP_CONV")) {
    let h = new Cc(m), g = [[m.padInfo.top, m.padInfo.left], [m.strideHeight, m.strideWidth], [m.dilationHeight, m.dilationWidth], [m.inHeight, m.inWidth]];
    f = t10.runWebGLProgram(h, [n, s], "float32", g);
  } else if (P().getBool("WEBGL_CONV_IM2COL"))
    f = Ph({ x: n, filter: s, convInfo: m, backend: t10 });
  else {
    let h = new bc(m);
    f = t10.runWebGLProgram(h, [n, s], "float32");
  }
  let d = J({ inputs: { x: f }, backend: t10, attrs: { shape: m.outShape } });
  return t10.disposeIntermediateTensorInfo(f), d;
}
var iA = { kernelName: ln, backendName: "webgl", kernelFunc: iQ };
var Oh = 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 Mh = 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 Lh = 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 Bh = 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 uQ(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 = I.convertConv2DDataFormat(p), m = I.computeConv2DInfo(n.shape, c, a, 1, i, u, false, l), f = new Oh(m);
  return t10.runWebGLProgram(f, [n, s], "float32");
}
var uA = { kernelName: lp, backendName: "webgl", kernelFunc: uQ };
function pQ(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 = I.convertConv2DDataFormat(u), m = I.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), f = new Mh(m);
  return t10.runWebGLProgram(f, [n, s], "float32");
}
var pA = { kernelName: mn, backendName: "webgl", kernelFunc: pQ };
function cQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = I.computeConv3DInfo(n.shape, s.shape, a, p, i), c = new Rh(u);
  return t10.runWebGLProgram(c, [n, s], "float32");
}
var cA = { kernelName: mp, backendName: "webgl", kernelFunc: cQ };
function lQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, dy: s } = e, { strides: a, pad: i, filterShape: p } = o, u = I.computeConv3DInfo(n.shape, p, a, 1, i), c = new Lh(u);
  return t10.runWebGLProgram(c, [n, s], "float32");
}
var lA = { kernelName: Dm, backendName: "webgl", kernelFunc: lQ };
function mQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, filter: s } = e, { pad: a, strides: i, inputShape: p } = o, u = I.computeConv3DInfo(p, s.shape, i, 1, a), c = new Bh(u);
  return t10.runWebGLProgram(c, [n, s], "float32");
}
var mA = { kernelName: fp, backendName: "webgl", kernelFunc: mQ };
var fQ = jo + `
  return cos(x);
`;
var dQ = he({ opSnippet: fQ });
var fA = { kernelName: fn, backendName: "webgl", kernelFunc: dQ };
var hQ = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var gQ = he({ opSnippet: hQ });
var dA = { kernelName: dn, backendName: "webgl", kernelFunc: gQ };
var Vh = 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 f = n === "bilinear" ? 1 : 0, [d, h] = [`${i - 1}.0`, `${p - 1}.0`], [g, y, b] = l > 1 ? [`${(i - 1) / (l - 1)}`, "(y2-y1) * height_ratio", `y1*${d} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${d}`], [C, w, k] = m > 1 ? [`${(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 = ${y};
        float width_scale = ${w};

        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${d} ) {
          setOutput(float(${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(${f} == 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 xQ = (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 Vh(n.shape, s.shape, i, p, u);
  return t10.runWebGLProgram(c, [n, s, a], "float32");
};
var hA = { kernelName: xn, backendName: "webgl", kernelFunc: xQ };
var Bu;
(function(r) {
  r.Prod = "*", r.Sum = "+";
})(Bu || (Bu = {}));
var Ml = 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 === Bu.Prod ? "1.0" : "0.0", i = o ? a : `getX(${gA(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 = ${xA(s, "coords", this.op)};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${u}) {
          int idx = ${c};
          ${xA(s, "coords", this.op)} = idx;
          val ${this.op}= getX(${gA(s, "coords", this.op)});
        }
        setOutput(val);
      }
    `;
  }
};
function gA(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 ${r} is not yet supported`);
}
function xA(r, e, t10) {
  if (r === 1)
    return `${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 ${r} is not yet supported`);
}
function zh(r, e, t10, o, n, s) {
  let a = e.shape.length, i = I.getAxesPermutation([o], a), p = e;
  i != null && (p = xt({ inputs: { x: e }, backend: t10, attrs: { perm: i } }));
  let u = I.getInnerMostAxes(1, a)[0];
  if (u !== a - 1)
    throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
  let c = p.shape[u], l = Rt({ inputs: { x: p }, backend: t10 });
  for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
    let f = new Ml(r, p.shape, false, s), d = [[m]], h = l;
    l = t10.runWebGLProgram(f, [l], l.dtype, d), t10.disposeIntermediateTensorInfo(h);
  }
  if (n) {
    let m = new Ml(r, p.shape, n, s), f = l;
    l = t10.runWebGLProgram(m, [l], l.dtype), t10.disposeIntermediateTensorInfo(f);
  }
  if (i != null) {
    let m = I.getUndoAxesPermutation(i), f = xt({ inputs: { x: l }, backend: t10, attrs: { perm: m } });
    return t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(p), f;
  }
  return l;
}
function yQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return zh(Bu.Prod, n, t10, s, a, i);
}
var yA = { kernelName: hn, backendName: "webgl", kernelFunc: yQ };
function bQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return zh(Bu.Sum, n, t10, s, a, i);
}
var bA = { kernelName: gn, backendName: "webgl", kernelFunc: bQ };
function CQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, weights: s } = e, { size: a, binaryOutput: i } = o;
  if (n.shape.length === 1) {
    let p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = oh(p, u, s.dtype, s.shape, a);
    return t10.makeTensorInfo([a], s.dtype, c);
  } else if (n.shape.length === 2) {
    let p = t10.bufferSync(n), u = t10.bufferSync(s), c = a$(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 CA = { kernelName: dp, backendName: "webgl", kernelFunc: CQ };
var Wh = 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 IQ(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, f = c / (s * s), d = a === "NHWC" ? [i, l, m, f] : [i, f, l, m], h = new Wh(d, s, a);
  return t10.runWebGLProgram(h, [n], n.dtype);
}
var IA = { kernelName: yn, backendName: "webgl", kernelFunc: IQ };
var Ic = 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}
        ${c}
        setOutput(result);
      }
    `;
  }
};
var wc = class {
  constructor(e, t10 = false, o = null, n = false, s = false) {
    this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = lt(this.outputShape.length);
    let a = e.outChannels / e.inChannels, i = e.padInfo.left, p = e.strideWidth, u = e.dilationWidth, c = e.filterHeight, l = e.filterWidth, m = l, f = `
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;
    for (let y = 0; y < l; y++)
      f += `
          vec4 xTexelC${y * 2};
          int xTexelC${y * 2}Ready;
          vec4 xTexelC${y * 2 + 1};
          int xTexelC${y * 2 + 1}Ready;
          vec4 xC${y};`;
    f += `
    for (int r = 0; r < ${c}; r++) {
      `;
    for (let y = 0; y < l; y++)
      f += `
          xTexelC${y * 2} = vec4(0.0);
          xTexelC${y * 2}Ready = 0;
          xTexelC${y * 2 + 1} = vec4(0.0);
          xTexelC${y * 2 + 1}Ready = 0;
          xC${y} = vec4(0.0);`;
    f += `
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;
    for (let y = 0; y < (m + 1) / 2; y++) {
      let b = y * 2;
      if (f += `
          xC = xCCorner + ${b * u};
          `, p === 1) {
        if (b < l && (i % 2 === 1 ? (f += `
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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 ? f += `
                xC${b} = vec4(xTexelC${b - 2}.zw, xTexelC${b}.xy);
                ` : f += `
                  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);
                  }
                  `) : f += `
                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 ? x.nearestLargerEven(u) : u;
          u % 2 === 0 && i % 2 === 1 || u % 2 !== 0 && i % 2 !== 1 ? (f += `
                  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 ? f += `
                    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);
                    }
                    ` : f += `
                    xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.xy);
                    `) : C === 1 ? f += `
                    xC${b + 1} = xTexelC${b};
                    ` : f += `
                    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 ? (f += `
                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${b + 1}.zw);
              `, b + 1 < l && (f += `
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${b + 1} = vec4(xTexelC${b + 1}.xy, final.xy);
                `)) : (f += `
                if(xC >= 0 && xC < inDims[1] && xTexelC${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${b + 1}.xy);
              `, b + 1 < l && (f += `
                  xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.zw);
                `)));
      b < l && (f += `
            wTexel = getW(r, ${b}, d1, q);
            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
          `, b + 1 < l && (f += `
              wTexel = getW(r, ${b + 1}, d1, q);
              dotProd += xC${b + 1} * vec4(wTexel.xz, wTexel.xz);
            `));
    }
    f += `
    }
  `, f += `
      }
    `;
    let d = "", h = "";
    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}
        }`, 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 = `
      ${d}

      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);

        ${f}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${g}
        ${h}
        setOutput(result);
      }
    `;
  }
};
function wQ(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]), x.assert(I.eitherStridesOrDilationsAreOne(a, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);
  let l = I.computeConv2DInfo(n.shape, s.shape, a, c, i, u, true), m;
  P().getBool("WEBGL_PACK_DEPTHWISECONV") && l.strideWidth <= 2 && l.outChannels / l.inChannels === 1 ? m = new wc(l) : m = new Ic(l);
  let f = [[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", f);
}
var wA = { kernelName: bn, backendName: "webgl", kernelFunc: wQ };
var Uh = 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 Gh = 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 SQ(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 = I.computeConv2DInfo(n.shape, c, a, i, p, u, true), m = new Uh(l);
  return t10.runWebGLProgram(m, [n, s], "float32");
}
var SA = { kernelName: hp, backendName: "webgl", kernelFunc: SQ };
function vQ(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 = I.computeConv2DInfo(c, s.shape, a, i, p, u, true), m = new Gh(l);
  return t10.runWebGLProgram(m, [n, s], "float32");
}
var vA = { kernelName: gp, backendName: "webgl", kernelFunc: vQ };
var Hh = 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 kQ(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e, n = [...o.shape, ...o.shape], s = x.sizeFromShape(o.shape), a = J({ inputs: { x: o }, backend: t10, attrs: { shape: [s] } }), i = new Hh(s), p = t10.runWebGLProgram(i, [a], a.dtype), u = J({ inputs: { x: p }, backend: t10, attrs: { shape: n } });
  return t10.disposeIntermediateTensorInfo(a), t10.disposeIntermediateTensorInfo(p), u;
}
var kA = { kernelName: xp, backendName: "webgl", kernelFunc: kQ };
var qh = 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 TQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, filter: s } = e, { strides: a, pad: i, dilations: p } = o, u = I.computeDilation2DInfo(n.shape, s.shape, a, i, "NHWC", p), c, l = new qh(u);
  c = t10.runWebGLProgram(l, [n, s], "float32");
  let m = J({ inputs: { x: c }, backend: t10, attrs: { shape: u.outShape } });
  return t10.disposeIntermediateTensorInfo(c), m;
}
var TA = { kernelName: yp, backendName: "webgl", kernelFunc: TQ };
function NQ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = I.decodeEinsumEquation(n, s.length);
  I.checkEinsumDimSizes(a.length, p, s);
  let { path: u, steps: c } = I.getEinsumComputePath(i, p), l = c.length, m = null, f = a.length, d = [];
  for (let h = 0; h < l; ++h) {
    for (let g of c[h]) {
      let { permutationIndices: y, expandDims: b } = I.getEinsumPermutation(f, p[g]), C;
      I.isIdentityPermutation(y) ? C = s[g] : (C = xt({ inputs: { x: s[g] }, backend: t10, attrs: { perm: y } }), d.push(C));
      let w = C.shape.slice();
      for (let k = 0; k < b.length; ++k)
        w.splice(b[k], 0, 1);
      x.arraysEqual(C.shape, w) || (C = J({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), d.push(C)), m === null ? m = C : (m = Pl({ inputs: { a: C, b: m }, backend: t10 }), d.push(m));
    }
    h < l - 1 && (u[h] >= 0 && (m = Ou({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - f), keepDims: false } }), d.push(m)), f--);
  }
  for (let h of d)
    h !== m && t10.disposeIntermediateTensorInfo(h);
  return m;
}
var NA = { kernelName: Xa, backendName: "webgl", kernelFunc: NQ };
var _Q = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var EQ = `
  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 $Q = he({ opSnippet: _Q, packedOpSnippet: EQ });
var _A = { kernelName: In, backendName: "webgl", kernelFunc: $Q };
var RQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
var AQ = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var FQ = (r) => {
  let { inputs: e, backend: t10 } = r, { dy: o, y: n } = e, s = P().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ko(AQ, o.shape, n.shape) : new _o(RQ, o.shape, n.shape);
  return t10.runWebGLProgram(s, [o, n], o.dtype);
};
var EA = { kernelName: Pm, backendName: "webgl", kernelFunc: FQ };
var DQ = `
  return vec4(equal(a, b));
`;
var PQ = "return float(a == b);";
var OQ = ot({ opSnippet: PQ, packedOpSnippet: DQ, dtype: "bool", cpuKernelImpl: c$ });
var $A = { kernelName: oo, backendName: "webgl", kernelFunc: OQ };
var MQ = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${I.ERF_P};
  float a1 = ${I.ERF_A1};
  float a2 = ${I.ERF_A2};
  float a3 = ${I.ERF_A3};
  float a4 = ${I.ERF_A4};
  float a5 = ${I.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 LQ = he({ opSnippet: MQ });
var RA = { kernelName: Gi, backendName: "webgl", kernelFunc: LQ };
var BQ = jo + `
  return exp(x);
`;
var VQ = `
  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 Ow = he({ opSnippet: BQ, packedOpSnippet: VQ, cpuKernelImpl: l$, dtype: "float32" });
var AA = { kernelName: no, backendName: "webgl", kernelFunc: Ow };
function Kh(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { dim: n } = t10, { input: s } = e, a = s.shape.length, i = s.shape.slice(), p = n;
  return n < 0 && (x.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), J({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var FA = { kernelName: xs, backendName: "webgl", kernelFunc: Kh };
var DA = "return exp(x) - 1.0;";
var zQ = he({ opSnippet: DA, packedOpSnippet: DA, cpuKernelImpl: m$ });
var PA = { kernelName: wn, backendName: "webgl", kernelFunc: zQ };
var Ll = 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 jh(r, e, t10) {
  let o = t10.texData.get(r.dataId), n = x.sizeFromShape(r.shape), s = r.shape[r.shape.length - 1], a = n / s, i = J({ inputs: { x: r }, backend: t10, attrs: { shape: [a, s] } }), p = i.shape, u = new Ll("real", p, e), c = new Ll("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"), f = t10.runWebGLProgram(c, l, "float32"), d = Ar({ inputs: { real: m, imag: f }, backend: t10 });
  t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(f);
  let h = J({ inputs: { x: d }, backend: t10, attrs: { shape: r.shape } });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(d), h;
}
function WQ(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e;
  return jh(o, false, t10);
}
var OA = { kernelName: bp, backendName: "webgl", kernelFunc: WQ };
var Xh = 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 Ba(r) {
  let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10;
  if (s = s || x.inferDtype(n), s === "string") {
    let a = x.getArrayFromDType(s, x.sizeFromShape(o));
    return a.fill(n), e.makeTensorInfo(o, s, a);
  } else {
    let a = new Xh(o, n), i = [[n]];
    return e.runWebGLProgram(a, [], s, i);
  }
}
var MA = { kernelName: ys, backendName: "webgl", kernelFunc: Ba };
var Yh = 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 < ${t10}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var LA = { kernelName: Sn, backendName: "webgl", kernelFunc: ({ inputs: r, backend: e }) => {
  let { image: t10 } = r, o = e, n = new Yh(t10.shape);
  return o.runWebGLProgram(n, [t10], t10.dtype);
} };
var BA = "return floor(x);";
var UQ = he({ opSnippet: BA, packedOpSnippet: BA, cpuKernelImpl: f$ });
var VA = { kernelName: so, backendName: "webgl", kernelFunc: UQ };
var GQ = `
  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 HQ = `
  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 qQ = ot({ opSnippet: GQ, packedOpSnippet: HQ, dtype: "int32" });
var zA = { kernelName: vn, backendName: "webgl", kernelFunc: qQ };
var Qh = class {
  constructor(e) {
    this.variableNames = ["A"];
    let t10 = Ct(), [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 = Ct(), [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 WA = { kernelName: Zi, backendName: "webgl", kernelFunc: KQ };
var Sc;
var Mw = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function KQ(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 = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
    (Sc == null || h !== Mw) && (Mw = h, Sc = document.createElement("canvas").getContext("2d", { willReadFrequently: Mw })), Sc.canvas.width = p, Sc.canvas.height = u, Sc.drawImage(n, 0, 0, p, u), n = Sc.canvas;
  }
  let m = t10.makeTensorInfo(c, "int32");
  t10.texData.get(m.dataId).usage = ir.PIXELS, t10.gpgpu.uploadPixelDataToTexture(t10.getTexture(m.dataId), n);
  let f = P().getBool("WEBGL_PACK") ? new Zh(l) : new Qh(l), d = t10.runWebGLProgram(f, [m], "int32");
  return t10.disposeData(m.dataId), d;
}
function jQ(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: f, leakyreluAlpha: d } = o, h = I.convertConv2DDataFormat(c), g = I.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h), y, b = [], C = a != null, w = i != null, k = f === "leakyrelu", _ = () => {
    let R = [n, s], A = (D, O) => {
      if (O === "NCHW" && D.shape.length === 1 && D.shape[0] !== 1) {
        let M = J({ inputs: { x: D }, backend: t10, attrs: { shape: [D.shape[0], 1, 1] } });
        return b.push(M), M;
      }
      return D;
    };
    if (C && R.push(A(a, c)), w && R.push(A(i, c)), k) {
      let D = t10.makeTensorInfo([], "float32", x.createScalarValue(d, "float32"));
      R.push(D), b.push(D);
    }
    return R;
  };
  if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID"))
    y = Dh({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: f, preluActivationWeights: i, leakyreluAlpha: d });
  else if (g.strideWidth <= 2 && h === "channelsLast" && P().getBool("WEBGL_EXP_CONV")) {
    let R = f ? Ma(f, true) : null, A = new Cc(g, C, R, w, k), D = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], O = _();
    y = t10.runWebGLProgram(A, O, "float32", D);
  } else if (P().getBool("WEBGL_CONV_IM2COL"))
    y = Ph({ x: n, filter: s, convInfo: g, backend: t10, bias: a, activation: f, preluActivationWeights: i, leakyreluAlpha: d });
  else {
    let R = f ? Ma(f, false) : null, A = new bc(g, C, R, w, k), D = _();
    y = t10.runWebGLProgram(A, D, "float32");
  }
  let E = J({ inputs: { x: y }, backend: t10, attrs: { shape: g.outShape } });
  return b.push(y), b.forEach((R) => t10.disposeIntermediateTensorInfo(R)), E;
}
var UA = { kernelName: Do, backendName: "webgl", kernelFunc: jQ };
function XQ(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: f } = o, d = [], h = c;
  h == null && (h = [1, 1]), x.assert(I.eitherStridesOrDilationsAreOne(p, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${h}'`);
  let g = I.computeConv2DInfo(n.shape, s.shape, p, h, u, l, true), y = P().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, b = m ? Ma(m, y) : 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", x.createScalarValue(f, "float32"));
    C.push(D), d.push(D);
  }
  let E;
  y ? E = new wc(g, w, b, k, _) : E = new Ic(g, w, b, k, _);
  let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], A = t10.runWebGLProgram(E, C, "float32", R);
  return d.forEach((D) => t10.disposeIntermediateTensorInfo(D)), A;
}
var GA = { kernelName: Po, backendName: "webgl", kernelFunc: XQ };
var Jh = 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 YQ(r) {
  let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = x.sizeFromShape(o.shape), [p, u, c, l] = I.prepareAndValidate(o, n), m = J({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), f = J({ inputs: { x: o }, backend: t10, attrs: { shape: [x.sizeFromShape(o.shape) / c, c] } });
  if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
    let y = t10.readSync(n.dataId), b = t10.bufferSync(o), C = d$(y, b, o.dtype, u, a, c, l, o.shape, i);
    return t10.makeTensorInfo(p, o.dtype, C.values);
  }
  let d = new Jh(a, l, [u, c], o.shape), h = t10.runWebGLProgram(d, [f, m], f.dtype), g = J({ inputs: { x: h }, backend: t10, attrs: { shape: p } });
  return t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(h), g;
}
var HA = { kernelName: Tn, backendName: "webgl", kernelFunc: YQ };
var eg = class {
  constructor(e, t10) {
    this.variableNames = ["A", "indices"], this.outputShape = t10, this.rank = t10.length;
    let o = _e(this.rank), n = QQ(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 QQ(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 Lw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = x.parseAxisParam(a, n.shape)[0];
  if (P().get("DEBUG")) {
    let b = t10.readSync(s.dataId), C = n.shape[p];
    for (let w = 0; w < b.length; ++w) {
      let k = b[w];
      x.assert(k <= C - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${C - 1}]`);
    }
  }
  let u = I.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = x.sizeFromShape(s.shape), l = [], m = J({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), f = J({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } });
  l.push(m), l.push(f);
  let d = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize];
  if (t10.shouldExecuteOnCPU([n, s]) || n.dtype === "string") {
    let b = t10.bufferSync(f), C = t10.bufferSync(m), w = h$(C, b, d);
    return l.forEach((k) => t10.disposeIntermediateTensorInfo(k)), t10.makeTensorInfo(u.outputShape, w.dtype, w.values);
  }
  let h = new eg(m.shape, d), g = t10.runWebGLProgram(h, [m, f], m.dtype);
  l.push(g);
  let y = J({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } });
  return l.forEach((b) => t10.disposeIntermediateTensorInfo(b)), y;
}
var qA = { kernelName: bs, backendName: "webgl", kernelFunc: Lw };
var ZQ = "return float(a > b);";
var JQ = `
  return vec4(greaterThan(a, b));
`;
var e7 = ot({ opSnippet: ZQ, packedOpSnippet: JQ, cpuKernelImpl: g$, dtype: "bool" });
var KA = { kernelName: ao, backendName: "webgl", kernelFunc: e7 };
var t7 = "return float(a >= b);";
var r7 = `
  return vec4(greaterThanEqual(a, b));
`;
var o7 = ot({ opSnippet: t7, packedOpSnippet: r7, dtype: "bool", cpuKernelImpl: x$ });
var jA = { kernelName: io, backendName: "webgl", kernelFunc: o7 };
function n7(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e;
  return jh(o, true, t10);
}
var XA = { kernelName: Cp, backendName: "webgl", kernelFunc: n7 };
var s7 = "return float(!isnan(x) && !isinf(x));";
var a7 = he({ opSnippet: s7, dtype: "bool" });
var YA = { kernelName: Hi, backendName: "webgl", kernelFunc: a7 };
var i7 = "return float(isinf(x));";
var u7 = he({ opSnippet: i7, dtype: "bool" });
var QA = { kernelName: qi, backendName: "webgl", kernelFunc: u7 };
var p7 = "return float(isnan(x));";
var c7 = he({ opSnippet: p7, dtype: "bool" });
var ZA = { kernelName: ia, backendName: "webgl", kernelFunc: c7 };
var l7 = "return float(a < b);";
var m7 = `
  return vec4(lessThan(a, b));
`;
var f7 = ot({ opSnippet: l7, packedOpSnippet: m7, cpuKernelImpl: y$, dtype: "bool" });
var JA = { kernelName: po, backendName: "webgl", kernelFunc: f7 };
var d7 = "return float(a <= b);";
var h7 = `
  return vec4(lessThanEqual(a, b));
`;
var g7 = ot({ opSnippet: d7, packedOpSnippet: h7, cpuKernelImpl: b$, dtype: "bool" });
var eF = { kernelName: co, backendName: "webgl", kernelFunc: g7 };
function x7(r) {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, num: s } = t10, a = C$(o, n, s);
  return e.makeTensorInfo([a.length], "float32", a);
}
var tF = { kernelName: Ip, backendName: "webgl", kernelFunc: x7 };
var y7 = jo + `
  return x < 0.0 ? 0./0. : log(x);
`;
var b7 = `
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`;
var C7 = he({ opSnippet: y7, packedOpSnippet: b7, cpuKernelImpl: I$ });
var rF = { kernelName: lo, backendName: "webgl", kernelFunc: C7 };
var I7 = jo + `
  return log(1.0 + x);
`;
var w7 = he({ opSnippet: I7 });
var oF = { kernelName: Ki, backendName: "webgl", kernelFunc: w7 };
var S7 = "return float(a >= 1.0 && b >= 1.0);";
var v7 = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
var k7 = ot({ opSnippet: S7, packedOpSnippet: v7, dtype: "bool" });
var nF = { kernelName: _n, backendName: "webgl", kernelFunc: k7 };
var T7 = "return float(!(x >= 1.0));";
var N7 = he({ opSnippet: T7 });
var sF = { kernelName: En, backendName: "webgl", kernelFunc: N7 };
var _7 = "return float(a >= 1.0 || b >= 1.0);";
var E7 = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
var $7 = ot({ opSnippet: _7, packedOpSnippet: E7, dtype: "bool" });
var aF = { kernelName: ua, backendName: "webgl", kernelFunc: $7 };
var tg = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["x"], this.outputShape = [];
    let a = t10, i = e[3] - 1;
    this.outputShape = e;
    let p, u = `float(${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 * ${p};
        setOutput(val);
      }
    `;
  }
};
var rg = class {
  constructor(e, t10, o, n, s) {
    this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
    let a = t10, i = e[3] - 1;
    this.outputShape = e;
    let p, u = `float(${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 R7 = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { depthRadius: s, bias: a, alpha: i, beta: p } = o, u = P().getBool("WEBGL_PACK_NORMALIZATION") ? new rg(n.shape, s, a, i, p) : new tg(n.shape, s, a, i, p);
  return t10.runWebGLProgram(u, [n], n.dtype);
};
var iF = { kernelName: wp, backendName: "webgl", kernelFunc: R7 };
var og = 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 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `;
  }
};
var A7 = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, y: s, dy: a } = e, { depthRadius: i, bias: p, alpha: u, beta: c } = o, l = new og(n.shape, i, p, u, c);
  return t10.runWebGLProgram(l, [n, s, a], n.dtype);
};
var uF = { kernelName: Om, backendName: "webgl", kernelFunc: A7 };
function pF(r, e, t10, o) {
  let n = x.sizeFromShape(e), a = x.sizeFromShape(r.shape) / n, i = J({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = qr(i, r.dtype, "max", o), u = J({ inputs: { x: p }, attrs: { shape: t10 }, backend: o });
  return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
function Bw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o, i = n.shape.length, p = x.parseAxisParam(s, n.shape), u = p, c = I.getAxesPermutation(u, i), l = c != null, m = t10.shouldExecuteOnCPU([n]), f = n;
  if (l) {
    if (m) {
      let C = t10.texData.get(f.dataId).values, w = new Array(i);
      for (let E = 0; E < w.length; E++)
        w[E] = n.shape[c[E]];
      let k = Du(C, n.shape, n.dtype, c, w);
      f = t10.makeTensorInfo(w, n.dtype);
      let _ = t10.texData.get(f.dataId);
      _.values = k;
    } else
      f = _i(n, c, t10);
    u = I.getInnerMostAxes(u.length, i);
  }
  I.assertAxesAreInnerMostDims("max", u, i);
  let [d, h] = I.computeOutAndReduceShapes(f.shape, u), g = d;
  a && (g = I.expandShapeToKeepDim(d, p));
  let y;
  if (m) {
    let C = t10.texData.get(f.dataId).values, w = w$(C, x.sizeFromShape(h), g, n.dtype);
    y = t10.makeTensorInfo(g, n.dtype);
    let k = t10.texData.get(y.dataId);
    k.values = w;
  } else
    y = pF(f, h, g, t10);
  return l && t10.disposeIntermediateTensorInfo(f), y;
}
var cF = { kernelName: $n, backendName: "webgl", kernelFunc: Bw };
var F7 = gc + `
  return max(a, b);
`;
var D7 = `
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + js + `
  return result;
`;
var P7 = ot({ opSnippet: F7, packedOpSnippet: D7, cpuKernelImpl: S$ });
var lF = { kernelName: mo, backendName: "webgl", kernelFunc: P7 };
function O7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e;
  as(n, "maxPool");
  let { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1;
  x.assert(I.eitherStridesOrDilationsAreOne(a, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);
  let c = I.computePool2DInfo(n.shape, s, a, u, i, p);
  if (c.filterWidth === 1 && c.filterHeight === 1 && x.arraysEqual(c.inShape, c.outShape))
    return Rt({ inputs: { x: n }, backend: t10 });
  let l = new us(c, "max", false);
  return t10.runWebGLProgram(l, [n], n.dtype);
}
var mF = { kernelName: Rn, backendName: "webgl", kernelFunc: O7 };
function M7(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 = I.computePool3DInfo(n.shape, s, a, c, i, u, p), m = new Ei(l, "max", false);
  return t10.runWebGLProgram(m, [n], n.dtype);
}
var fF = { kernelName: Sp, backendName: "webgl", kernelFunc: M7 };
var ng = 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 sg = 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, f = c - 1 - e.padInfo.left, d = p * u * c - 1;
    this.userCode = `
      const ivec3 pads = ivec3(${l}, ${m}, ${f});

      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 = ${d} -
                  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 L7(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 = I.computePool3DInfo(a.shape, i, p, l, u, c), f = new Ei(m, "max", true), d = t10.runWebGLProgram(f, [a], a.dtype), h = new sg(m), g = t10.runWebGLProgram(h, [n, d], a.dtype);
  return t10.disposeIntermediateTensorInfo(d), g;
}
var dF = { kernelName: Lm, backendName: "webgl", kernelFunc: L7 };
function B7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { dy: n, input: s, output: a } = e, i = s;
  as([s, a], "maxPoolGrad");
  let { filterSize: p, strides: u, pad: c, dimRoundingMode: l } = o, m = I.computePool2DInfo(i.shape, p, u, 1, c, l), f = true, d = new us(m, "max", f), h = t10.runWebGLProgram(d, [i], i.dtype), g = new ng(m), y = t10.runWebGLProgram(g, [n, h], i.dtype);
  return t10.disposeIntermediateTensorInfo(h), y;
}
var hF = { kernelName: Mm, backendName: "webgl", kernelFunc: B7 };
function gF(r, e, t10, o) {
  let n = new us(t10, "max", false), s = o.runWebGLProgram(n, [r], "float32");
  n = new us(t10, "max", true, true, e);
  let a = o.runWebGLProgram(n, [r], "float32");
  return [s, a];
}
var xF = { kernelName: vp, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { x: o } = r, { filterSize: n, strides: s, pad: a, includeBatchInIndex: i } = e, p = t10;
  x.assert(o.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);
  let u = [1, 1];
  x.assert(I.eitherStridesOrDilationsAreOne(s, u), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);
  let c = I.computePool2DInfo(o.shape, n, s, u, a), [l, m] = gF(o, i, c, p);
  return [l, m];
} };
function yF(r, e, t10, o) {
  let n = x.sizeFromShape(e), a = x.sizeFromShape(r.shape) / n, i = J({ inputs: { x: r }, attrs: { shape: [a, n] }, backend: o }), p = qr(i, "float32", "mean", o), u = J({ inputs: { x: p }, attrs: { shape: t10 }, backend: o });
  return o.disposeIntermediateTensorInfo(i), o.disposeIntermediateTensorInfo(p), u;
}
var bF = { kernelName: An, 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 = x.parseAxisParam(s, o.shape), u = p, c = I.getAxesPermutation(u, i), l = c != null, m = a.shouldExecuteOnCPU([o]), f = [], d = o;
  if (l) {
    if (m) {
      let w = a.texData.get(d.dataId).values, k = new Array(i);
      for (let R = 0; R < k.length; R++)
        k[R] = o.shape[c[R]];
      let _ = Du(w, o.shape, o.dtype, c, k);
      d = a.makeTensorInfo(k, o.dtype);
      let E = a.texData.get(d.dataId);
      E.values = _;
    } else
      d = _i(o, c, a);
    f.push(d), u = I.getInnerMostAxes(u.length, i);
  }
  I.assertAxesAreInnerMostDims("sum", u, i);
  let [h, g] = I.computeOutAndReduceShapes(d.shape, u), y = h;
  n && (y = I.expandShapeToKeepDim(h, p));
  let b = yF(d, g, y, a);
  for (let C of f)
    a.disposeIntermediateTensorInfo(C);
  return b;
} };
function V7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = x.parseAxisParam(s, n.shape), u = p, c = I.getAxesPermutation(u, i), l = n;
  c != null && (l = xt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), u = I.getInnerMostAxes(u.length, n.shape.length)), I.assertAxesAreInnerMostDims("min", u, i);
  let [m, f] = I.computeOutAndReduceShapes(l.shape, u), d = x.sizeFromShape(f), h = J({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, d] } }), g = qr(h, h.dtype, "min", t10), y;
  if (a) {
    let b = I.expandShapeToKeepDim(m, p);
    y = J({ inputs: { x: g }, backend: t10, attrs: { shape: b } });
  } else
    y = J({ inputs: { x: g }, backend: t10, attrs: { shape: m } });
  return t10.disposeIntermediateTensorInfo(h), t10.disposeIntermediateTensorInfo(g), c != null && t10.disposeIntermediateTensorInfo(l), y;
}
var CF = { kernelName: Fn, backendName: "webgl", kernelFunc: V7 };
var z7 = gc + `
  return min(a, b);
`;
var W7 = `
  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 U7 = ot({ opSnippet: z7, packedOpSnippet: W7, cpuKernelImpl: v$ });
var IF = { kernelName: fo, backendName: "webgl", kernelFunc: U7 };
var ag = 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 ig = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t10.map((d, h) => d[0] + e[h] + d[1]);
    let n = e.length, s = _e(n), a = t10.map((d) => d[0]).join(","), i = t10.map((d, h) => d[0] + e[h]).join(","), p = $t("rc", n), u = $t("source", n), c = `${p[n - 1]} < ${this.outputShape[n - 1]}`, l = n === 1 ? "source" : `vec2(${u.slice(-2).join()})`, m = o === "reflect" ? 0 : 1, f = "";
    if (n === 1) {
      let d = `
        ${s} source = rc;
        if (source < start) {
          source = start * 2 - source - ${m};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${m};
        }
        source -= start;
      `;
      f = `
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${u.join()}), ${l});
        ${p[n - 1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${u.join()}), ${l});
        }
      `;
    } else {
      let d = `
        ${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;
      `;
      f = `
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${u.join()}), ${l});
        ${p[n - 1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${u.join()}), ${l});
        }
        rc = outputLoc;
        ${p[n - 2]} += 1;
        if(${p[n - 2]} < ${this.outputShape[n - 2]}) {
          ${d}
          result[2] = getChannel(getX(${u.join()}), ${l});
          ${p[n - 1]} += 1;
          if(${c}) {
            ${d}
            result[3] = getChannel(getX(${u.join()}), ${l});
          }
        }
      `;
    }
    this.userCode = `
      const ${s} start = ${s}(${a});
      const ${s} end = ${s}(${i});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${f}
        setOutput(result);
      }
    `;
  }
};
var G7 = ({ inputs: r, backend: e, attrs: t10 }) => {
  let { x: o } = r, { paddings: n, mode: s } = t10, a = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ig(o.shape, n, s) : new ag(o.shape, n, s);
  return e.runWebGLProgram(a, [o], o.dtype);
};
var wF = { kernelName: Dn, backendName: "webgl", kernelFunc: G7 };
var H7 = `if (b == 0.0) return NAN;
  return mod(a, b);`;
var q7 = `
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  ` + js + `
  return result;
`;
var K7 = ot({ opSnippet: H7, packedOpSnippet: q7 });
var SF = { kernelName: ji, backendName: "webgl", kernelFunc: K7 };
var ug = 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 j7 = `
if (a == b) {
  return 1.0;
};
return a / b;`;
var X7 = `
  // 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 Vw = ot({ opSnippet: j7, packedOpSnippet: X7, checkOutOfBounds: true });
var vF = { kernelName: Cn, backendName: "webgl", kernelFunc: Vw };
var kF = "return a - b;";
var zw = ot({ opSnippet: kF, packedOpSnippet: kF, supportsComplex: true, cpuKernelImpl: G$ });
var TF = { kernelName: Io, backendName: "webgl", kernelFunc: zw };
function Ww(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = x.parseAxisParam([s], n.shape), i = Bw({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = I.expandShapeToKeepDim(i.shape, a), u = J({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = zw({ inputs: { a: n, b: u }, backend: t10 }), l = Ow({ inputs: { x: c }, backend: t10 }), m = Ou({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), f = J({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), d = Vw({ inputs: { a: l, b: f }, backend: t10 });
  return t10.disposeIntermediateTensorInfo(i), t10.disposeIntermediateTensorInfo(u), t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), t10.disposeIntermediateTensorInfo(m), t10.disposeIntermediateTensorInfo(f), d;
}
var NF = { kernelName: Xn, backendName: "webgl", kernelFunc: Ww };
function Y7(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { numSamples: s, seed: a, normalized: i } = o, p = i ? n : Ww({ inputs: { logits: n }, backend: t10, attrs: { dim: n.shape.length - 1 } }), u = p.shape[0], c = p.shape[1], l = new ug(u, c, s), m = [[a]], f = t10.runWebGLProgram(l, [p], "int32", m);
  return i || t10.disposeIntermediateTensorInfo(p), f;
}
var _F = { kernelName: kp, backendName: "webgl", kernelFunc: Y7 };
var Q7 = Vt + `
  return -x;
`;
var Z7 = `
  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 J7(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (t10.shouldExecuteOnCPU([o])) {
    let s = t10.texData.get(o.dataId), [a, i] = T$(s.values, o.shape, o.dtype);
    return t10.makeTensorInfo(i, o.dtype, a);
  }
  let n;
  return P().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? n = new No(o.shape, Z7) : n = new fr(o.shape, Q7), t10.runWebGLProgram(n, [o], o.dtype);
}
var EF = { kernelName: Pn, backendName: "webgl", kernelFunc: J7 };
var eZ = Bt.nonMaxSuppressionV3Impl;
function tZ(r) {
  I.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 } = eZ(u, c, a, i, p);
  return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var $F = { kernelName: On, backendName: "webgl", kernelFunc: tZ };
var rZ = Bt.nonMaxSuppressionV4Impl;
function oZ(r) {
  I.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, padToMaxOutputSize: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), { selectedIndices: m, validOutputs: f } = rZ(c, l, a, i, p, u);
  return [t10.makeTensorInfo([m.length], "int32", new Int32Array(m)), t10.makeTensorInfo([], "int32", new Int32Array([f]))];
}
var RF = { kernelName: pa, backendName: "webgl", kernelFunc: oZ };
var nZ = Bt.nonMaxSuppressionV5Impl;
function sZ(r) {
  I.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: e, backend: t10, attrs: o } = r, { boxes: n, scores: s } = e, { maxOutputSize: a, iouThreshold: i, scoreThreshold: p, softNmsSigma: u } = o, c = t10.readSync(n.dataId), l = t10.readSync(s.dataId), m = a, f = i, d = p, h = u, { selectedIndices: g, selectedScores: y } = nZ(c, l, m, f, d, h);
  return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([y.length], "float32", new Float32Array(y))];
}
var AF = { kernelName: Mn, backendName: "webgl", kernelFunc: sZ };
var pg = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["indices"], this.outputShape = [e, t10], this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${n}), float(${o}),
                      float(index == coords.y)));
      }
    `;
  }
};
var aZ = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { indices: n } = e, { dtype: s, depth: a, onValue: i, offValue: p } = o, u = x.sizeFromShape(n.shape), c = new pg(u, a, i, p), l = J({ inputs: { x: n }, backend: t10, attrs: { shape: [u] } }), m = t10.runWebGLProgram(c, [l], s);
  t10.disposeIntermediateTensorInfo(l);
  let f = [...n.shape, a], d = J({ inputs: { x: m }, backend: t10, attrs: { shape: f } });
  return t10.disposeIntermediateTensorInfo(m), d;
};
var FF = { kernelName: ca, backendName: "webgl", kernelFunc: aZ };
function Bl(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "complex64") {
    let n = La({ inputs: { input: o }, backend: t10 }), s = Bl({ inputs: { x: n }, backend: t10 }), a = Lu({ inputs: { input: o }, backend: t10 }), i = Bl({ 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 Ba({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 });
}
var DF = { kernelName: Es, backendName: "webgl", kernelFunc: Bl };
function PF(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 = La({ inputs: { input: o }, backend: t10 }), s = PF({ inputs: { x: n }, backend: t10 }), a = Lu({ inputs: { input: o }, backend: t10 }), i = Bl({ 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 Ba({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 });
}
var OF = { kernelName: Cs, backendName: "webgl", kernelFunc: PF };
function iZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return Kh({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    x.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), x.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = Kh({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = Pw({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeIntermediateTensorInfo(c)), u;
}
var MF = { kernelName: Is, backendName: "webgl", kernelFunc: iZ };
var cg = 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 lg = 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 = $t("rc", n), u = $t("source", n), c = `${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}) {`], f = n === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))", d = "";
    for (let h = 0, g = n === 1 ? 2 : 4; h < g; h++)
      d += `
        ${m[h]}
        if (${f}) {
          result[${h}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${h}] = getChannel(getX(${u.join()}), ${l});
        }
      `;
    d += n === 1 ? "} " : "}}", 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 Uw = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
  if (x.sizeFromShape(n.shape) === 0) {
    let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
    return Ba({ backend: t10, attrs: { shape: u, value: a, dtype: n.dtype } });
  }
  let i = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new lg(n.shape, s, a) : new cg(n.shape, s, a), p = [[a]];
  return t10.runWebGLProgram(i, [n], n.dtype, p);
};
var LF = { kernelName: Ln, backendName: "webgl", kernelFunc: Uw };
var uZ = `
  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 pZ = `
  // 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 cZ = ot({ opSnippet: uZ, packedOpSnippet: pZ });
var BF = { kernelName: Bn, backendName: "webgl", kernelFunc: cZ };
function lZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o, i = n.shape.length, p = [], u = x.parseAxisParam(s, n.shape), c = u, l = I.getAxesPermutation(c, i), m = n;
  l != null && (m = xt({ inputs: { x: n }, backend: t10, attrs: { perm: l } }), c = I.getInnerMostAxes(c.length, i), p.push(m)), I.assertAxesAreInnerMostDims("prod", c, i);
  let f;
  if (t10.shouldExecuteOnCPU([m])) {
    let d = t10.texData.get(m.dataId).values, { outVals: h, outShape: g, outDtype: y } = _$(m.shape, m.dtype, d, c);
    f = t10.makeTensorInfo(g, y, h);
  } else {
    let [d, h] = I.computeOutAndReduceShapes(m.shape, c), g = x.sizeFromShape(h), y = J({ inputs: { x: m }, backend: t10, attrs: { shape: [-1, g] } }), b = Ca(n.dtype), C = qr(y, b, "prod", t10);
    f = J({ inputs: { x: C }, backend: t10, attrs: { shape: d } }), p.push(y), p.push(C);
  }
  if (a) {
    p.push(f);
    let d = I.expandShapeToKeepDim(f.shape, u);
    f = J({ inputs: { x: f }, backend: t10, attrs: { shape: d } });
  }
  return p.forEach((d) => t10.disposeIntermediateTensorInfo(d)), f;
}
var VF = { kernelName: Ao, backendName: "webgl", kernelFunc: lZ };
function mZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { paramsNestedSplits: n, paramsDenseValues: s, indices: a } = e, { outputRaggedRank: i } = o, p = n.map((y) => t10.readSync(y.dataId)), u = n.map((y) => y.shape), c = t10.readSync(s.dataId), l = t10.readSync(a.dataId), [m, f, d] = E$(p, u, c, s.shape, s.dtype, l, a.shape, i), h = m.map((y) => t10.makeTensorInfo([y.length], "int32", y)), g = t10.makeTensorInfo(d, s.dtype, f);
  return h.concat([g]);
}
var zF = { kernelName: Tp, backendName: "webgl", kernelFunc: mZ };
function fZ(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] = $$(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 WF = { kernelName: Np, backendName: "webgl", kernelFunc: fZ };
function dZ(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)), f = i.map((g) => g.shape), [d, h] = R$(u, n.shape, c, s.shape, s.dtype, l, a.shape, m, f, p);
  return t10.makeTensorInfo(d, s.dtype, h);
}
var UF = { kernelName: _p, backendName: "webgl", kernelFunc: dZ };
var Gw = (r) => {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = A$(o, n, s, a);
  return e.makeTensorInfo([i.length], a, i);
};
var GF = { kernelName: ws, backendName: "webgl", kernelFunc: Gw };
var hZ = "return 1.0 / x;";
var gZ = he({ opSnippet: hZ });
var HF = { kernelName: ma, backendName: "webgl", kernelFunc: gZ };
var xZ = Vt + `
  return (x < 0.0) ? 0.0 : x;
`;
var yZ = `
  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 bZ = he({ opSnippet: xZ, packedOpSnippet: yZ });
var qF = { kernelName: zn, backendName: "webgl", kernelFunc: bZ };
var CZ = Vt + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var IZ = `
  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 wZ = he({ opSnippet: CZ, packedOpSnippet: IZ });
var KF = { kernelName: Gn, backendName: "webgl", kernelFunc: wZ };
var mg = 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 fg = 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 SZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = P().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new fg(n.shape, p, u, s, a) : new mg(n.shape, p, u, s, a);
  return t10.runWebGLProgram(c, [n], "float32");
}
var jF = { kernelName: Un, backendName: "webgl", kernelFunc: SZ };
var dg = 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, f = 1 / l, d = Math.ceil(m) * 2 + 2, h = Math.ceil(f) * 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(${m});
        const float invWidthScale = float(${f});

        const int winHeight = int(${d});
        const int winWidth = int(${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 vZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new dg(s.shape, n.shape, a);
  return t10.runWebGLProgram(i, [s], s.dtype);
}
var XF = { kernelName: Vm, backendName: "webgl", kernelFunc: vZ };
var hg = 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", f;
    s ? f = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : f = "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 = ${f};

        // 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 gg = 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", f;
    s ? f = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : f = "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 = ${f};

        // 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 kZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n } = e, { alignCorners: s, halfPixelCenters: a, size: i } = o, [p, u] = i, c = P().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new gg(n.shape, p, u, s, a) : new hg(n.shape, p, u, s, a);
  return t10.runWebGLProgram(c, [n], n.dtype);
}
var YF = { kernelName: Wn, backendName: "webgl", kernelFunc: kZ };
var xg = 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, f = 1 / l, d = Math.ceil(m) * 2 + 2, h = Math.ceil(f) * 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(${m});
        const float invWidthScale = float(${f});

        const int winHeight = int(${d});
        const int winWidth = int(${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 TZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { images: n, dy: s } = e, { alignCorners: a } = o, i = new xg(s.shape, n.shape, a);
  return t10.runWebGLProgram(i, [s], s.dtype);
}
var QF = { kernelName: Bm, backendName: "webgl", kernelFunc: TZ };
var yg = 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 bg = 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 = $t("rc", o), s = `${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 = ${l(n.slice())};
            }
          }
          setOutput(result);
        }
    `;
    function p(d) {
      return m(d);
    }
    function u(d) {
      return d[o - 1] = "(" + d[o - 1] + " + 1)", m(d);
    }
    function c(d) {
      return d[o - 2] = "(" + d[o - 2] + " + 1)", m(d);
    }
    function l(d) {
      return d[o - 1] = "(" + d[o - 1] + " + 1)", d[o - 2] = "(" + d[o - 2] + " + 1)", m(d);
    }
    function m(d) {
      let h = e.map((b, C) => f(C, d)), g = h.join(","), y = h.slice(-2).join(",");
      return `getChannel(getX(${g}), vec2(${y}))`;
    }
    function f(d, h) {
      return t10.indexOf(d) !== -1 && e[d] !== 1 ? `${e[d]} - ${h[d]} - 1` : `${h[d]}`;
    }
  }
};
function NZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = n.shape.length, i = x.parseAxisParam(s, n.shape);
  if (a === 0)
    return Rt({ inputs: { x: n }, backend: t10 });
  let p = P().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new bg(n.shape, i) : new yg(n.shape, i);
  return t10.runWebGLProgram(p, [n], n.dtype);
}
var ZF = { kernelName: fa, backendName: "webgl", kernelFunc: NZ };
var Cg = 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 JF = { kernelName: es, backendName: "webgl", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new Cg(o.shape, s), [u, c] = I.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 _Z = `
  // 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 EZ = he({ opSnippet: _Z });
var eD = { kernelName: da, backendName: "webgl", kernelFunc: EZ };
var $Z = "return inversesqrt(x);";
var RZ = he({ opSnippet: $Z, cpuKernelImpl: F$ });
var tD = { kernelName: xo, backendName: "webgl", kernelFunc: RZ };
var vc = 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(${c})`, m = "";
    n === 1 ? m = "i" : n === 2 && (m = "i, coords[1]");
    let f = `getUpdates(${m})`, d = 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 * ${d};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${f};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `;
  }
};
function AZ(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 } = I.calculateShapes(s, n, a), m = [l / u, u];
  if (l === 0)
    return t10.makeTensorInfo(a, n.dtype);
  let f = J({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), d = J({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = t10.makeTensorInfo([], "float32", new Float32Array([0])), g = new vc(p, i, f.shape.length, d.shape.length, c, m), y = t10.runWebGLProgram(g, [d, f, h], d.dtype), b = J({ inputs: { x: y }, backend: t10, attrs: { shape: a } });
  return t10.disposeIntermediateTensorInfo(f), t10.disposeIntermediateTensorInfo(d), t10.disposeIntermediateTensorInfo(y), t10.disposeIntermediateTensorInfo(h), b;
}
var rD = { kernelName: Hn, backendName: "webgl", kernelFunc: AZ };
var Ig = 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 = P().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 FZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sortedSequence: n, values: s } = e, { side: a } = o, i = new Ig(n.shape[0], n.shape[1], s.shape[1], a), p = [[n.shape[1]]];
  return t10.runWebGLProgram(i, [n, s], "int32", p);
}
var oD = { kernelName: Ep, backendName: "webgl", kernelFunc: FZ };
var wg = 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 DZ(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new wg(o.shape.length, n.shape, n.shape.length);
  return t10.runWebGLProgram(a, [o, n, s], ct(n.dtype, s.dtype));
}
var nD = { kernelName: vs, backendName: "webgl", kernelFunc: DZ };
var PZ = `
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${I.SELU_SCALEALPHA};
  float scale = ${I.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var OZ = he({ opSnippet: PZ });
var sD = { kernelName: Xi, backendName: "webgl", kernelFunc: OZ };
var MZ = jo + `
  return 1.0 / (1.0 + exp(-1.0 * x));
`;
var LZ = `
  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 BZ = he({ opSnippet: MZ, packedOpSnippet: LZ, cpuKernelImpl: P$ });
var aD = { kernelName: yo, backendName: "webgl", kernelFunc: BZ };
var VZ = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
var zZ = he({ opSnippet: VZ });
var iD = { kernelName: Yi, backendName: "webgl", kernelFunc: zZ };
var WZ = jo + `
  return sin(x);
`;
var UZ = he({ opSnippet: WZ });
var uD = { kernelName: Kn, backendName: "webgl", kernelFunc: UZ };
var GZ = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var HZ = he({ opSnippet: GZ });
var pD = { kernelName: ha, backendName: "webgl", kernelFunc: HZ };
var qZ = `
  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 KZ = he({ opSnippet: qZ });
var cD = { kernelName: Qi, backendName: "webgl", kernelFunc: KZ };
var jZ = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
  x.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
  let i = s.reduce((y, b) => y * b), p = [[0, 0]];
  p.push(...a);
  for (let y = 1 + s.length; y < n.shape.length; ++y)
    p.push([0, 0]);
  let u = [], c = Uw({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = I.getReshaped(c.shape, s, i, false), m = I.getPermuted(l.length, s.length, false), f = I.getReshapedPermuted(c.shape, s, i, false), d = J({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = xt({ inputs: { x: d }, backend: t10, attrs: { perm: m } }), g = J({ inputs: { x: h }, backend: t10, attrs: { shape: f } });
  return u.push(c), u.push(d), u.push(h), u.forEach((y) => t10.disposeIntermediateTensorInfo(y)), g;
};
var lD = { kernelName: ks, backendName: "webgl", kernelFunc: jZ };
function XZ(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:
        ${a.shape}`);
  let i = t10.readSync(o.dataId), p = t10.readSync(n.dataId), u = t10.readSync(s.dataId), c = t10.readSync(a.dataId)[0], [l, m, f, d, h] = M$(i, o.shape, o.dtype, p, n.dtype, u, c);
  return [t10.makeTensorInfo(m, o.dtype, l), t10.makeTensorInfo([m[0]], n.dtype, f), t10.makeTensorInfo([d.length], "bool", new Uint8Array(d.map((g) => Number(g)))), t10.makeTensorInfo([h.length], o.dtype, new Int32Array(h))];
}
var mD = { kernelName: Qa, backendName: "webgl", kernelFunc: XZ };
function YZ(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 ${s.shape}`);
  let a = Array.from(t10.readSync(n.dataId)), i = t10.readSync(o.dataId), p = Array.from(t10.readSync(s.dataId)), [u, c, l] = L$(i, o.shape, o.dtype, a, p);
  return [t10.makeTensorInfo(c, o.dtype, u), t10.makeTensorInfo([l.length], s.dtype, new Int32Array(l))];
}
var fD = { kernelName: ga, backendName: "webgl", kernelFunc: YZ };
function QZ(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] = sh(a, o.shape, o.dtype, i, p, true);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var dD = { kernelName: Za, backendName: "webgl", kernelFunc: QZ };
function ZZ(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
             ${s.shape}`);
  let a = t10.readSync(o.dataId), i = t10.readSync(n.dataId), p = t10.readSync(s.dataId), [u, c] = sh(a, o.shape, o.dtype, i, p);
  return t10.makeTensorInfo(c, o.dtype, u);
}
var hD = { kernelName: Ja, backendName: "webgl", kernelFunc: ZZ };
function JZ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = I.calculateShapes(s, n, i), f = false;
  if (s.dtype === "string") {
    let y = t10.bufferSync(n), b = t10.bufferSync(s), C = x.decodeString(t10.readSync(a.dataId)[0]), w = D$(y, b, i, m, c, u, p, l, C, f);
    return t10.makeTensorInfo(i, w.dtype, w.values);
  }
  let d = new vc(u, p, n.shape.length, s.shape.length, l, [m, 1], f), h = t10.runWebGLProgram(d, [s, n, a], s.dtype), g = J({ inputs: { x: h }, backend: t10, attrs: { shape: i } });
  return t10.disposeIntermediateTensorInfo(h), g;
}
var gD = { kernelName: ei, backendName: "webgl", kernelFunc: JZ };
function e9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = x.parseAxisParam(a, n.shape)[0], p = I.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice();
  return p.map((m) => {
    let f = [...l];
    f[i] = m;
    let d = ps({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: f } });
    return c[i] += m, d;
  });
}
var xD = { kernelName: Ts, backendName: "webgl", kernelFunc: e9 };
var yD = "return sqrt(x);";
var t9 = he({ opSnippet: yD, packedOpSnippet: yD, cpuKernelImpl: B$ });
var bD = { kernelName: bo, backendName: "webgl", kernelFunc: t9 };
var r9 = "return x * x;";
var o9 = he({ opSnippet: r9 });
var CD = { kernelName: ti, backendName: "webgl", kernelFunc: o9 };
var ID = "return (a - b) * (a - b);";
var n9 = ot({ opSnippet: ID, packedOpSnippet: ID });
var wD = { kernelName: Co, backendName: "webgl", kernelFunc: n9 };
function s9({ inputs: r, attrs: e, backend: t10 }) {
  let { x: o } = r, n = Vt + `
    return x > 0.0 ? 1.0 : float(${e.alpha});
  `, s = new fr(o.shape, n);
  return t10.runWebGLProgram(s, [o], o.dtype);
}
var SD = { kernelName: $s, backendName: "webgl", kernelFunc: s9 };
var Sg = class {
  constructor(e, t10, o) {
    this.variableNames = ["x"], this.outputShape = o;
    let n = o.length, s = _e(o.length), a = _e(o.length), i = "";
    if (n === 1)
      i = "coords * strides + begin";
    else {
      let p = 0;
      i = o.map((u, c) => (p++, o.length === 1 ? `coords * strides[${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 a9(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: f, finalShape: d, isIdentity: h, sliceDim0: g, isSimpleSlice: y, begin: b, end: C, strides: w } = et.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = J({ inputs: { x: n }, backend: t10, attrs: { shape: d } });
  else if (g || y) {
    x.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let E = et.computeOutShape(b, C, w), R = ps({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: E } });
    k = J({ inputs: { x: R }, backend: t10, attrs: { shape: d } }), t10.disposeIntermediateTensorInfo(R);
  } else if (t10.shouldExecuteOnCPU([n])) {
    let R = t10.readSync(n.dataId), A = ne(n.shape, n.dtype, R), D = V$(f, A, w, b);
    k = t10.makeTensorInfo(d, n.dtype, D.values);
  } else {
    let R = new Sg(b, w, f);
    k = t10.runWebGLProgram(R, [n], n.dtype);
  }
  let _ = J({ inputs: { x: k }, backend: t10, attrs: { shape: d } });
  return t10.disposeIntermediateTensorInfo(k), _;
}
var vD = { kernelName: Yn, backendName: "webgl", kernelFunc: a9 };
function i9(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), f = t10.readSync(l.dataId), [d, h] = z$(m, f, n, s, a, i, p, u);
  return [t10.makeTensorInfo([d.length], "string", d), t10.makeTensorInfo(l.shape, "int32", h)];
}
var kD = { kernelName: Ns, backendName: "webgl", kernelFunc: i9 };
function u9(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: ${a.shape}`);
  let i = t10.readSync(s.dataId), p = t10.readSync(a.dataId)[0], [u, c, l] = W$(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 TD = { kernelName: ri, backendName: "webgl", kernelFunc: u9 };
function p9(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 = U$(a, n);
  return t10.makeTensorInfo(s.shape, "int32", i);
}
var ND = { kernelName: oi, backendName: "webgl", kernelFunc: p9 };
var c9 = "return tan(x);";
var l9 = he({ opSnippet: c9 });
var _D = { kernelName: xa, backendName: "webgl", kernelFunc: l9 };
var m9 = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var f9 = he({ opSnippet: m9 });
var ED = { kernelName: Qn, backendName: "webgl", kernelFunc: f9 };
var vg = 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 = d9(e);
    this.userCode = `
      void main() {
        ${n} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `;
  }
};
function d9(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]}, ${r[n]})`);
  return o.join();
}
function Hw(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reps: s } = o;
  if (n.dtype === "string" || n.shape.length > 5) {
    let p = t10.readSync(n.dataId), u = n.dtype === "string" ? p.map((m) => x.decodeString(m)) : p, c = ne(n.shape, n.dtype, u), l = H$(c, s);
    return t10.makeTensorInfo(l.shape, l.dtype, l.values);
  }
  let a = new vg(n.shape, s);
  return t10.runWebGLProgram(a, [n], n.dtype);
}
var $D = { kernelName: wo, backendName: "webgl", kernelFunc: Hw };
var kg = 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 Tg = 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 Vu(r, e) {
  e !== null && r.disposeIntermediateTensorInfo(e);
}
function RD(r) {
  let e = 1;
  for (; e < r; )
    e *= 2;
  return e;
}
function h9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { k: s, sorted: a } = o, i = P().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), p = P().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), [O, M] = q$(D, u, n.dtype, s, a);
    return [t10.makeTensorInfo(O.shape, O.dtype, O.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, Ba({ attrs: { shape: u, dtype: "int32", value: 0 }, backend: t10 })];
  let l = t10.texData.get(n.dataId), m = l !== null && l.isPacked, f = m ? t10.unpackTensor(n) : n, h = x.sizeFromShape(u) / c, g = J({ inputs: { x: f }, attrs: { shape: [h, c] }, backend: t10 });
  m && Vu(t10, f);
  let y = RD(s), b = RD(c), C = null, w = () => C === null ? [g, g] : [g, C], k = (D, O, M) => {
    let L = w(), W = new kg(M), G = [[c], [C === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [D], [O]], q = C;
    C = t10.runWebGLProgram(W, L, "int32", G), Vu(t10, q);
  };
  for (let D = 1; D < y; D *= 2) {
    let O = D * 2;
    for (let M = D; M >= 1; M /= 2)
      k(O, M, [h, b]);
  }
  for (let D = b; D > y; D /= 2) {
    let O = w(), M = new Tg([h, D / 2]), W = [[c], [C === null ? 1 : 0], [y]], V = C;
    C = t10.runWebGLProgram(M, O, "int32", W), Vu(t10, V);
    let G = y / 2, q = G * 2;
    for (let H = G; H >= 1; H /= 2)
      k(q, H, C.shape);
  }
  let _ = C;
  C = ps({ inputs: { x: C }, backend: t10, attrs: { begin: 0, size: [h, s] } }), Vu(t10, _);
  let E = Lw({ inputs: { x: g, indices: C }, backend: t10, attrs: { axis: 1, batchDims: 1 } });
  Vu(t10, g);
  let R = u.slice(0, -1);
  R.push(s), _ = C, C = J({ inputs: { x: C }, attrs: { shape: R }, backend: t10 }), Vu(t10, _);
  let A = E;
  return E = J({ inputs: { x: E }, attrs: { shape: R }, backend: t10 }), Vu(t10, A), [E, C];
}
var AD = { kernelName: Zn, backendName: "webgl", kernelFunc: h9 };
var Ng = 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 (${i} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `;
  }
};
function g9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { image: n, transforms: s } = e, { interpolation: a, fillMode: i, fillValue: p, outputShape: u } = o, [c, l, m, f] = n.shape, [d, h] = u != null ? u : [l, m], g = [c, d, h, f], y = new Ng(l, m, a, i, p, g);
  return t10.runWebGLProgram(y, [n, s], "float32");
}
var FD = { kernelName: Jn, backendName: "webgl", kernelFunc: g9 };
function x9(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { axis: n } = t10, { x: s } = e;
  as(s, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
  let a = o.readSync(s.dataId), { outputValues: i, outputShape: p, indices: u } = K$(a, n, s.shape, s.dtype);
  return [o.makeTensorInfo(p, s.dtype, i), o.makeTensorInfo([u.length], "int32", u)];
}
var DD = { kernelName: $p, backendName: "webgl", kernelFunc: x9 };
function y9(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), f = a.shape.slice();
  f[s] = 1;
  let d = new Array(p);
  for (let h = 0; h < d.length; h++) {
    m[s] = h;
    let g = ps({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: f } }), y = J({ inputs: { x: g }, backend: t10, attrs: { shape: u } });
    d[h] = y, l.push(g);
  }
  return l.forEach((h) => t10.disposeIntermediateTensorInfo(h)), d;
}
var PD = { kernelName: _s, backendName: "webgl", kernelFunc: y9 };
var _g = 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);
    `, f = "";
    s % o > 0 && (f = `
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);
    let d = "";
    s % o > 0 && (d = `
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `), this.userCode = `
      const float initializationValue = ${p};

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

      float getSegmentIdAtIndex(int inIdx) {
        ${d}
        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 b9(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 = I.getAxesPermutation([u], i), l = n;
  c != null && (l = xt({ inputs: { x: n }, backend: t10, attrs: { perm: c } }), p.push(l), u = I.getInnerMostAxes(1, i)[0]);
  let m = I.segment_util.computeOutShape(l.shape, u, a), f = x.sizeFromShape([l.shape[u]]), d = J({ inputs: { x: l }, backend: t10, attrs: { shape: [-1, f] } });
  p.push(d);
  let h = Ca(n.dtype), g = (w, k, _, E, R) => {
    let A = w.shape[0], D = w.shape[1], O = I.segment_util.segOpComputeOptimalWindowSize(D, R), M = { windowSize: O, inSize: D, batchSize: A, numSegments: R }, L = new _g(M, k), W = t10.compileAndRun(L, [w, _], E);
    if (p.push(W), W.shape[1] === R)
      return W;
    let V = Gw({ backend: t10, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), G = Hw({ inputs: { x: V }, backend: t10, attrs: { reps: [D / O] } });
    return p.push(V), p.push(G), g(W, k, G, E, R);
  }, y = g(d, "unsortedSegmentSum", s, h, a), b = J({ inputs: { x: y }, backend: t10, attrs: { shape: m } }), C = b;
  if (c != null) {
    p.push(b);
    let w = I.getUndoAxesPermutation(c);
    C = xt({ inputs: { x: C }, backend: t10, attrs: { perm: w } });
  }
  return p.forEach((w) => t10.disposeIntermediateTensorInfo(w)), C;
}
var OD = { kernelName: Rp, backendName: "webgl", kernelFunc: b9 };
var C9 = [IR, SR, vR, kR, NR, _R, ER, $R, FR, DR, PR, OR, MR, LR, BR, VR, zR, WR, UR, GR, HR, KR, jR, XR, JR, tA, rA, lR, nA, aA, iA, uA, pA, cA, lA, mA, fA, dA, hA, yA, bA, CA, IA, wA, SA, vA, kA, TA, NA, _A, EA, $A, RA, AA, FA, PA, OA, MA, LA, VA, zA, WA, UA, GA, HA, qA, KA, jA, cR, XA, sA, YA, QA, ZA, mR, JA, eF, tF, rF, oF, nF, sF, aF, iF, uF, cF, lF, mF, fF, dF, hF, xF, bF, CF, IF, wF, SF, _F, hR, EF, $F, RF, AF, YR, FF, OF, MF, LF, BF, fR, VF, zF, WF, UF, GF, QR, vF, HF, qF, KF, xR, jF, XF, YF, QF, ZF, JF, eD, tD, rD, oD, nD, sD, aD, iD, uD, pD, qR, NF, cD, lD, mD, fD, dD, hD, gD, xD, bD, CD, wD, SD, vD, kD, TD, ND, TF, bR, _D, ED, $D, AD, FD, CR, DD, PD, OD, DF];
for (let r of C9)
  ya(r);
var Ae;
(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";
})(Ae || (Ae = {}));
var $i;
(function(r) {
  r[r.linear = 0] = "linear", r[r.relu = 1] = "relu", r[r.relu6 = 2] = "relu6", r[r.prelu = 3] = "prelu", r[r.leakyrelu = 4] = "leakyrelu", r[r.sigmoid = 5] = "sigmoid", r[r.elu = 6] = "elu";
})($i || ($i = {}));
var MD;
function I9(r) {
  MD = r.wasm.cwrap(Fo, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function w9(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, f = t10.dataIdMap.get(s.dataId).id, d = 0;
  if (a != null) {
    let R = t10.dataIdMap.get(a.dataId);
    if (R.shape.length !== 1)
      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);
    d = R.id;
  }
  let h = i == null ? 0 : t10.dataIdMap.get(i.dataId).id, g = $i[c];
  if (g == null)
    throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
  let y = p ? n.shape[2] : n.shape[1], b = u ? s.shape[1] : s.shape[2], C = br.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)), w = t10.makeOutput([...C, y, 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 MD(m, _, n.shape.length, f, E, s.shape.length, p, u, g, d, h, l || 0, k), w;
}
var LD = { kernelName: Fo, backendName: "wasm", setupFunc: I9, kernelFunc: w9 };
function Qe(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 x.sizeFromShape(u.shape) === 0 || t10(p, Ae[i.dtype], c), u;
  }
  return { kernelName: r, backendName: "wasm", setupFunc: o, kernelFunc: n };
}
var BD = Qe(sn);
function nt(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, f = t10 != null ? t10 : u.dtype, d = I.assertAndGetBroadcastShape(u.shape, c.shape), h = i.makeOutput(d, f);
    if (x.sizeFromShape(d) === 0)
      return h;
    let g = new Uint8Array(new Int32Array(u.shape).buffer), y = new Uint8Array(new Int32Array(c.shape).buffer), b = i.dataIdMap.get(h.dataId).id;
    return (() => o(l, g, u.shape.length, m, y, c.shape.length, Ae[u.dtype], b))(), h;
  }
  return { kernelName: r, backendName: "wasm", setupFunc: n, kernelFunc: s };
}
var S9 = true;
var VD = nt(_r, S9);
var zD;
function v9(r) {
  zD = r.wasm.cwrap(an, null, ["array", "number", "number", "number"]);
}
function k9(r) {
  let { inputs: e, backend: t10 } = r, o = t10.makeOutput(e[0].shape, e[0].dtype);
  if (x.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 zD(s, n.length, Ae[o.dtype], a), o;
}
var WD = { kernelName: an, backendName: "wasm", setupFunc: v9, kernelFunc: k9 };
function zu(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 UD = { kernelName: uo, backendName: "wasm", kernelFunc: zu };
var GD;
function T9(r) {
  GD = r.wasm.cwrap(Mr, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function Eo(r) {
  let { inputs: e, backend: t10, attrs: o } = r, [n, s] = _9(e.x.shape, o.perm), a = true;
  for (let d = 0; d < s.length; d++)
    s[d] !== d && (a = false);
  let i = N9(e.x.shape, o.perm), p = { dataId: e.x.dataId, shape: n, dtype: e.x.dtype };
  if (a) {
    let d = zu({ inputs: e, backend: t10 });
    return d.shape = i, d;
  }
  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), f = new Uint8Array(new Int32Array(p.shape).buffer);
  return GD(c, f, p.shape.length, Ae[p.dtype], l, m, s.length), u;
}
function N9(r, e) {
  let t10 = new Array(r.length);
  for (let o = 0; o < t10.length; o++)
    t10[o] = r[e[o]];
  return t10;
}
function _9(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 HD = { kernelName: Mr, backendName: "wasm", kernelFunc: Eo, setupFunc: T9 };
function kr(r, e, t10) {
  let o = r.shape, n = r.shape.length, s = x.parseAxisParam(e, o), a = s, i = I.getAxesPermutation(a, n), p = null, u = false;
  if (i != null) {
    let c = new Array(n);
    for (let f = 0; f < c.length; f++)
      c[f] = o[i[f]];
    a = I.getInnerMostAxes(a.length, n), p = Eo({ 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 qD;
function E9(r) {
  qD = r.wasm.cwrap(oa, null, ["number, number, number"]);
}
function $9(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: f } = kr(a, n, e);
  if (f) {
    let C = e.dataIdMap.get(c.dataId).id;
    u = c, p = C;
  }
  let d = u.shape.length;
  I.assertAxesAreInnerMostDims("all", l, d);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, l), y = x.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
  if (x.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    qD(p, y, C);
  }
  if (f && e.disposeData(c.dataId), s) {
    let C = I.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var KD = { kernelName: oa, backendName: "wasm", setupFunc: E9, kernelFunc: $9 };
var jD;
function R9(r) {
  jD = r.wasm.cwrap(na, null, ["number, number, number"]);
}
function A9(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: f } = kr(a, n, e);
  if (f) {
    let C = e.dataIdMap.get(c.dataId).id;
    u = c, p = C;
  }
  let d = u.shape.length;
  I.assertAxesAreInnerMostDims("any", l, d);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, l), y = x.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
  if (x.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    jD(p, y, C);
  }
  if (f && e.disposeData(c.dataId), s) {
    let C = I.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var XD = { kernelName: na, backendName: "wasm", setupFunc: R9, kernelFunc: A9 };
var YD;
function F9(r) {
  YD = r.wasm.cwrap(un, null, ["number", "number", "number", "number", "number"]);
}
function D9(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 y = e.dataIdMap.get(u.dataId).id;
    y !== a && (p = u, i = y);
  }
  let m = p.shape.slice(0, -1), f = e.makeOutput(m, "int32"), d = e.dataIdMap.get(f.dataId).id, h = x.sizeFromShape(f.shape), g = p.shape[c[0]];
  return YD(i, Ae[p.dtype], h, g, d), l && e.disposeData(u.dataId), f;
}
var QD = { kernelName: un, backendName: "wasm", kernelFunc: D9, setupFunc: F9 };
var ZD;
function P9(r) {
  ZD = r.wasm.cwrap(pn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function O9(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 = I.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, f = c.padInfo.top, d = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, y = 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 ZD(s, n.shape[0], n.shape[1], n.shape[2], l, m, f, d, h, g, y, b, C, k), w;
}
var JD = { kernelName: pn, backendName: "wasm", setupFunc: P9, kernelFunc: O9 };
function Mt(r) {
  let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = x.sizeFromShape(o.shape), a = x.inferFromImplicitShape(n, s);
  return x.assert(s === x.sizeFromShape(a), () => `new shape: ${a}, old shape: ${o.shape}. New shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var eP = { kernelName: Ss, backendName: "wasm", kernelFunc: Mt };
var tP;
function M9(r) {
  tP = r.wasm.cwrap(cn, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function L9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  if (n.dtype !== "float32" || s.dtype !== "float32")
    throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
  let p = n.shape.length, u = s.shape.length, c = a ? n.shape[p - 2] : n.shape[p - 1], l = i ? s.shape[u - 1] : s.shape[u - 2], m = a ? n.shape[p - 1] : n.shape[p - 2], f = i ? s.shape[u - 2] : s.shape[u - 1], d = n.shape.slice(0, -2), h = s.shape.slice(0, -2), g = x.sizeFromShape(d), y = x.sizeFromShape(h), C = br.assertAndGetBroadcastShape(n.shape.slice(0, -2), s.shape.slice(0, -2)).concat([m, f]);
  x.assert(c === l, () => `Error in matMul: inner shapes (${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 ? [y, f, l] : [y, l, f], _ = Mt({ inputs: { x: n }, backend: t10, attrs: { shape: w } }), E = Mt({ inputs: { x: s }, backend: t10, attrs: { shape: k } }), R = t10.dataIdMap.get(_.dataId).id, A = t10.dataIdMap.get(E.dataId).id, D = a ? _.shape[2] : _.shape[1], O = i ? E.shape[1] : E.shape[2], M = Math.max(g, y), L = t10.makeOutput([M, D, O], _.dtype), W = t10.dataIdMap.get(L.dataId).id, V = new Uint8Array(new Int32Array(_.shape).buffer), G = new Uint8Array(new Int32Array(E.shape).buffer);
  return tP(R, V, _.shape.length, A, G, E.shape.length, a, i, W), t10.disposeData(_.dataId), t10.disposeData(E.dataId), L.shape = C, L;
}
var rP = { kernelName: cn, backendName: "wasm", setupFunc: M9, kernelFunc: L9 };
function Xo(r) {
  let { inputs: { x: e }, attrs: { begin: t10, size: o }, backend: n } = r, [s, a] = et.parseSliceParams(e, t10, o), i = et.isSliceContinous(e.shape, s, a), p = n.readSync(e.dataId), u = n.makeOutput(a, e.dtype), c = x.computeStrides(e.shape), l = n.dataIdMap.get(u.dataId);
  if (i) {
    let d = et.computeFlatOffset(s, c);
    return e.dtype === "string" ? l.stringBytes = p.slice(d, d + x.sizeFromShape(a)) : n.typedArrayFromHeap(u).set(p.subarray(d, d + x.sizeFromShape(a))), u;
  }
  if (e.dtype === "string") {
    let d = vu(p, s, a, e.shape, e.dtype);
    return l.stringBytes = d, u;
  }
  let m = n.typedArrayFromHeap(u), f = e.shape.length;
  if (f === 2)
    B9(p, c[0], m, s, a);
  else if (f === 3)
    V9(p, c[0], c[1], m, s, a);
  else if (f === 4)
    z9(p, c[0], c[1], c[2], m, s, a);
  else {
    let d = vu(p, s, a, e.shape, e.dtype);
    m.set(d);
  }
  return u;
}
function B9(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 V9(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 f = p; f < l; f++) {
      let d = m * e + f * t10 + u;
      o.set(r.subarray(d, d + s[2]), a), a += s[2];
    }
}
function z9(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], f = c + a[2], d = s[3];
  for (let h = p; h < l; h++)
    for (let g = u; g < m; g++)
      for (let y = c; y < f; y++) {
        let b = h * e + g * t10 + y * o + d;
        n.set(r.subarray(b, b + a[3]), i), i += a[3];
      }
}
var oP = { kernelName: qn, backendName: "wasm", kernelFunc: Xo };
function W9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o, i = s.reduce((y, b) => y * b), p = I.getReshaped(n.shape, s, i), u = I.getPermuted(p.length, s.length), c = I.getReshapedPermuted(n.shape, s, i), l = I.getSliceBeginCoords(a, s.length), m = I.getSliceSize(c, a, s.length), f = Mt({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), d = Eo({ inputs: { x: f }, backend: t10, attrs: { perm: u } }), h = Mt({ inputs: { x: d }, backend: t10, attrs: { shape: c } }), g = Xo({ inputs: { x: h }, backend: t10, attrs: { begin: l, size: m } });
  return t10.disposeData(f.dataId), t10.disposeData(d.dataId), t10.disposeData(f.dataId), g;
}
var nP = { kernelName: hs, backendName: "wasm", kernelFunc: W9 };
function cs(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 sP = { kernelName: to, backendName: "wasm", kernelFunc: cs };
var aP = Qe(ro);
var iP;
function U9(r) {
  iP = r.wasm.cwrap(Ro, null, ["number", "number", "number", "number"]);
}
function G9(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 iP(i, s, a, u), p;
}
var uP = { kernelName: Ro, backendName: "wasm", setupFunc: U9, kernelFunc: G9 };
function qw(r) {
  let { inputs: e, backend: t10 } = r, o = x.parseAxisParam(r.attrs.axis, e[0].shape)[0], n = e.map((f) => f.shape);
  I.assertParamsConsistent(n, o);
  let s = I.computeOutShape(e.map((f) => f.shape), o), a = e.filter((f) => x.sizeFromShape(f.shape) > 0);
  if (a.length === 1)
    return zu({ inputs: { x: a[0] }, backend: t10 });
  let i = t10.makeOutput(s, e[0].dtype);
  if (x.sizeFromShape(s) === 0)
    return i;
  if (a[0].dtype === "string") {
    let f = a.map((C) => {
      let k = [-1, x.sizeFromShape(C.shape.slice(o))];
      return Mt({ inputs: { x: C }, backend: t10, attrs: { shape: k } });
    }), d = f.map((C) => ({ vals: t10.readSync(C.dataId), shape: C.shape }));
    s = I.computeOutShape(f.map((C) => C.shape), 1);
    let h = f[0].shape[0] === 1, g = Iu(d, s, e[0].dtype, h), y = I.computeOutShape(a.map((C) => C.shape), o);
    i.shape = y;
    let b = t10.dataIdMap.get(i.dataId);
    return b.stringBytes = I.fromStringArrayToUint8(g), f.forEach((C) => t10.disposeData(C.dataId)), i;
  }
  let p = x.sizeFromShape(a[0].shape.slice(0, o)), u = 0, c = a.map((f) => {
    let d = x.sizeFromShape(f.shape.slice(o));
    return u += d, d;
  }), l = a.map((f) => t10.typedArrayFromHeap(f)), m = t10.typedArrayFromHeap(i);
  for (let f = 0; f < p; f++) {
    let d = f * u;
    for (let h = 0; h < l.length; h++) {
      let g = c[h], y = f * g, b = l[h].subarray(y, y + g);
      m.set(b, d), d += g;
    }
  }
  return i;
}
var pP = { kernelName: gs, backendName: "wasm", kernelFunc: qw };
var cP;
function H9(r) {
  cP = r.wasm.cwrap(ln, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function q9(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, f = I.convertConv2DDataFormat(m), d = I.computeConv2DInfo(n.shape, s.shape, p, u, c, l, false, f), h = d.filterHeight, g = d.filterWidth, y = d.padInfo.top, b = d.padInfo.right, C = d.padInfo.bottom, w = d.padInfo.left, k = d.dilationHeight, _ = d.dilationWidth, E = d.strideHeight, R = d.strideWidth, A = d.inChannels, D = d.outChannels, O = d.padInfo.type === "SAME" ? 1 : 0;
  if (d.dataFormat !== "channelsLast")
    throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);
  let M = o.makeOutput(d.outShape, "float32"), L = o.dataIdMap.get(M.dataId).id;
  return cP(a, n.shape[0], n.shape[1], n.shape[2], i, h, g, y, b, C, w, O, k, _, E, R, A, D, L), M;
}
var lP = { kernelName: ln, backendName: "wasm", setupFunc: H9, kernelFunc: q9 };
var mP;
function K9(r) {
  mP = r.wasm.cwrap(mn, 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 j9(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 = I.convertConv2DDataFormat(p), f = I.computeConv2DInfo(c, s.shape, a, l, i, u, false, m), { batchSize: d, filterHeight: h, filterWidth: g, inChannels: y, inHeight: b, inWidth: C, outChannels: w, outHeight: k, outWidth: _, strideHeight: E, strideWidth: R } = f, A = h - 1 - f.padInfo.top, D = g - 1 - f.padInfo.left, O = f.dataFormat === "channelsLast", M = x.computeStrides(f.inShape), L = x.computeStrides(n.shape), [W, V, G] = x.computeStrides(s.shape), q = M[0], H = O ? M[1] : M[2], j = O ? M[2] : 1, Y = O ? 1 : M[1], Z = L[0], ee = O ? L[1] : L[2], X = O ? L[2] : 1, Q = O ? 1 : L[1], se = e.makeOutput(f.inShape, "float32"), ie = e.dataIdMap.get(se.dataId).id, de = e.dataIdMap.get(n.dataId).id, Ie = e.dataIdMap.get(s.dataId).id;
  return mP(de, Ie, d, h, g, b, C, y, k, _, w, E, R, A, D, W, V, G, q, H, j, Y, Z, ee, X, Q, ie), se;
}
var fP = { kernelName: mn, backendName: "wasm", setupFunc: K9, kernelFunc: j9 };
var dP = Qe(fn);
var hP = Qe(dn);
var Kw;
(function(r) {
  r[r.bilinear = 0] = "bilinear", r[r.nearest = 1] = "nearest";
})(Kw || (Kw = {}));
var gP;
function X9(r) {
  gP = r.wasm.cwrap(xn, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function Y9(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, f = [c, l, m, i.shape[3]], d = e.dataIdMap.get(i.dataId), h;
  i.dtype !== "float32" && (h = cs({ backend: e, inputs: { x: i }, attrs: { dtype: "float32" } }), d = e.dataIdMap.get(h.dataId));
  let g = d.id, y = e.dataIdMap.get(p.dataId).id, b = e.dataIdMap.get(u.dataId).id, C = e.makeOutput(f, "float32"), w = e.dataIdMap.get(C.dataId).id, k = new Uint8Array(new Int32Array(i.shape).buffer);
  return gP(g, y, b, c, k, l, m, Kw[n], s, w), h != null && e.disposeData(h.dataId), C;
}
var xP = { kernelName: xn, backendName: "wasm", setupFunc: X9, kernelFunc: Y9 };
var yP;
function Q9(r) {
  yP = r.wasm.cwrap(hn, null, ["number", "number", "number", "number", "number", "number"]);
}
function Z9(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length;
  x.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumprod does not support ${n.dtype} tensors in the WASM backend`);
  let u = I.getAxesPermutation([s], p), c = n;
  u !== null && (c = Eo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 }));
  let l = I.getInnerMostAxes(1, p)[0];
  I.assertAxesAreInnerMostDims("cumprod", [l], p);
  let m = t10.makeOutput(c.shape, c.dtype), f = c.shape[l], d = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id;
  yP(d, a ? 1 : 0, i ? 1 : 0, f, h, Ae[n.dtype]);
  let g = m;
  if (u !== null) {
    let y = I.getUndoAxesPermutation(u);
    g = Eo({ inputs: { x: m }, attrs: { perm: y }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId);
  }
  return g;
}
var bP = { kernelName: hn, backendName: "wasm", setupFunc: Q9, kernelFunc: Z9 };
var CP;
function J9(r) {
  CP = r.wasm.cwrap(gn, null, ["number", "number", "number", "number", "number", "number"]);
}
function eJ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o, p = n.shape.length;
  x.assert(n.dtype === "float32" || n.dtype === "int32", () => `cumsum does not support ${n.dtype} tensors in the WASM backend`);
  let u = I.getAxesPermutation([s], p), c = n;
  u !== null && (c = Eo({ inputs: { x: n }, attrs: { perm: u }, backend: t10 }));
  let l = I.getInnerMostAxes(1, p)[0];
  I.assertAxesAreInnerMostDims("cumsum", [l], p);
  let m = t10.makeOutput(c.shape, c.dtype), f = c.shape[l], d = t10.dataIdMap.get(c.dataId).id, h = t10.dataIdMap.get(m.dataId).id;
  CP(d, a ? 1 : 0, i ? 1 : 0, f, h, Ae[n.dtype]);
  let g = m;
  if (u !== null) {
    let y = I.getUndoAxesPermutation(u);
    g = Eo({ inputs: { x: m }, attrs: { perm: y }, backend: t10 }), t10.disposeData(c.dataId), t10.disposeData(m.dataId);
  }
  return g;
}
var IP = { kernelName: gn, backendName: "wasm", setupFunc: J9, kernelFunc: eJ };
var wP;
function tJ(r) {
  wP = r.wasm.cwrap(yn, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function rJ(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, f = c / (s * s), d = a === "NHWC" ? [i, l, m, f] : [i, f, l, m], h = e.makeOutput(d, "float32"), y = e.dataIdMap.get(n.dataId).id, b = new Uint8Array(new Int32Array(x.computeStrides(n.shape)).buffer), C = new Uint8Array(new Int32Array(d).buffer), w = new Uint8Array(new Int32Array(x.computeStrides(d)).buffer), k = e.dataIdMap.get(h.dataId).id;
  return wP(y, s, a === "NHWC" ? 1 : 0, b, n.shape.length - 1, C, w, d.length, k), h;
}
var SP = { kernelName: yn, backendName: "wasm", setupFunc: tJ, kernelFunc: rJ };
var vP;
function oJ(r) {
  vP = r.wasm.cwrap(bn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function nJ(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, f = I.computeConv2DInfo(n.shape, s.shape, p, m, c, l, true), d = f.filterHeight, h = f.filterWidth, g = f.padInfo.top, y = f.padInfo.right, b = f.padInfo.bottom, C = f.padInfo.left, w = f.dilationHeight, k = f.dilationWidth, _ = f.strideHeight, E = f.strideWidth, R = f.inChannels, A = f.outChannels, D = f.padInfo.type === "SAME" ? 1 : 0;
  if (f.dataFormat !== "channelsLast")
    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
  let O = o.makeOutput(f.outShape, "float32"), M = o.dataIdMap.get(O.dataId).id;
  return vP(a, n.shape[0], n.shape[1], n.shape[2], i, d, h, g, y, b, C, D, w, k, _, E, R, A, M), O;
}
var kP = { kernelName: bn, backendName: "wasm", setupFunc: oJ, kernelFunc: nJ };
var TP = Qe(In);
var sJ = false;
var NP = nt(oo, sJ, "bool");
var _P = Qe(no, "float32");
function Eg(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 && (x.assert(-(a + 1) <= s, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + s + 1), i.splice(p, 0, 1), Mt({ inputs: { x: n }, backend: o, attrs: { shape: i } });
}
var EP = { kernelName: xs, backendName: "wasm", kernelFunc: Eg };
function jw(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 $P = { kernelName: ys, backendName: "wasm", kernelFunc: jw };
var RP;
function aJ(r) {
  RP = r.wasm.cwrap(Sn, null, ["number", "number", "number", "number", "number", "number"]);
}
function iJ(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 RP(s, i, p, u, c, a), n;
}
var AP = { kernelName: Sn, backendName: "wasm", kernelFunc: iJ, setupFunc: aJ };
var FP = Qe(so);
var uJ = false;
var DP = nt(vn, uJ);
var PP;
function pJ(r) {
  PP = r.wasm.cwrap(kn, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function cJ(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, f = p != null ? e.dataIdMap.get(p.dataId).id : 0, d = u != null ? e.dataIdMap.get(u.dataId).id : 0, h = e.makeOutput(s.shape, s.dtype);
  if (x.sizeFromShape(s.shape) === 0)
    return h;
  let g = e.dataIdMap.get(h.dataId).id;
  return PP(c, l, m, f, d, n, g), h;
}
var OP = { kernelName: kn, backendName: "wasm", setupFunc: pJ, kernelFunc: cJ };
var MP;
function lJ(r) {
  MP = r.wasm.cwrap(Do, 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 mJ(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: f, leakyreluAlpha: d } = t10, h = I.computeConv2DInfo(n.shape, s.shape, p, c, u, m), g = $i[f];
  if (g == null)
    throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);
  let y = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, w = 0;
  if (a != null) {
    let X = o.dataIdMap.get(a.dataId);
    if (X.shape.length !== 1)
      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);
    if (X.shape[0] !== C)
      throw new Error(`FusedConv2D bias shape (${X.shape}) does not match the number of output channels (${C})`);
    w = X.id;
  }
  let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, A = h.padInfo.bottom, D = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, W = h.strideWidth, V = h.inChannels, G = h.padInfo.type === "SAME" ? 1 : 0, q = h.batchSize, H = h.inHeight, j = h.inWidth;
  if (l !== "NHWC")
    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);
  let Y = o.makeOutput(h.outShape, "float32"), Z = o.dataIdMap.get(Y.dataId).id, ee = i == null ? 0 : o.dataIdMap.get(i.dataId).id;
  return MP(y, q, H, j, b, k, _, w, E, R, A, D, G, O, M, L, W, V, C, g, ee, d || 0, Z), Y;
}
var LP = { kernelName: Do, backendName: "wasm", setupFunc: lJ, kernelFunc: mJ };
var BP;
function fJ(r) {
  BP = r.wasm.cwrap(Po, 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 dJ(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: f, leakyreluAlpha: d } = t10, h = I.computeConv2DInfo(n.shape, s.shape, p, c, u, m, true), g = $i[f];
  if (g == null)
    throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
  let y = o.dataIdMap.get(n.dataId).id, b = o.dataIdMap.get(s.dataId).id, C = h.outChannels, w = 0;
  if (a != null) {
    let X = o.dataIdMap.get(a.dataId);
    if (X.shape.length !== 1)
      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${X.shape.length}.`);
    if (X.shape[0] !== C)
      throw new Error(`FusedDepthwiseConv2D bias shape (${X.shape}) does not match the number of output channels (${C})`);
    w = X.id;
  }
  let k = h.filterHeight, _ = h.filterWidth, E = h.padInfo.top, R = h.padInfo.right, A = h.padInfo.bottom, D = h.padInfo.left, O = h.dilationHeight, M = h.dilationWidth, L = h.strideHeight, W = h.strideWidth, V = h.inChannels, G = h.padInfo.type === "SAME" ? 1 : 0, q = h.batchSize, H = h.inHeight, j = h.inWidth;
  if (l !== "NHWC")
    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);
  let Y = o.makeOutput(h.outShape, "float32"), Z = o.dataIdMap.get(Y.dataId).id, ee = i == null ? 0 : o.dataIdMap.get(i.dataId).id;
  return BP(y, q, H, j, b, k, _, w, E, R, A, D, G, O, M, L, W, V, C, g, ee, d || 0, Z), Y;
}
var VP = { kernelName: Po, backendName: "wasm", setupFunc: fJ, kernelFunc: dJ };
var zP;
function hJ(r) {
  zP = r.wasm.cwrap(Tn, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function gJ(r) {
  let { backend: e, inputs: t10 } = r, { params: o, indices: n } = t10, [s, a, i, p] = af.prepareAndValidate(o, n), u = e.makeOutput(s, o.dtype);
  if (a === 0)
    return u;
  let c = n.shape, l = c[c.length - 1], f = e.dataIdMap.get(o.dataId).id, h = e.dataIdMap.get(n.dataId).id, g = new Uint8Array(new Int32Array(p).buffer), y = e.dataIdMap.get(u.dataId).id;
  return zP(f, Ae[o.dtype], h, a, l, i, g, y), u;
}
var WP = { kernelName: Tn, backendName: "wasm", setupFunc: hJ, kernelFunc: gJ };
var UP;
function xJ(r) {
  UP = r.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function yJ(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { x: n, indices: s } = t10, { axis: a, batchDims: i } = o, p = x.parseAxisParam(a, n.shape)[0], u = e.readSync(s.dataId), c = n.shape[p];
  for (let A = 0; A < u.length; ++A) {
    let D = u[A];
    x.assert(D <= c - 1 && D >= 0, () => `GatherV2: the index value ${D} is not in [0, ${c - 1}]`);
  }
  let l = I.segment_util.collectGatherOpShapeInfo(n, s, p, i), m = Mt({ inputs: { x: n }, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] }, backend: e }), f = x.sizeFromShape(s.shape), d = Mt({ inputs: { x: s }, attrs: { shape: [l.batchSize, f / l.batchSize] }, backend: e }), h = [l.batchSize, l.outerSize, f / l.batchSize, l.sliceSize], g = e.makeOutput(h, n.dtype);
  if (x.sizeFromShape(n.shape) === 0)
    return g;
  let y = m.shape.length - 1, C = e.dataIdMap.get(m.dataId).id, k = e.dataIdMap.get(d.dataId).id, _ = e.dataIdMap.get(g.dataId).id, E = new Uint8Array(new Int32Array(x.computeStrides(m.shape)).buffer), R = new Uint8Array(new Int32Array(x.computeStrides(h)).buffer);
  return UP(C, Ae[n.dtype], E, y, k, l.batchSize, R, _), e.disposeData(m.dataId), e.disposeData(d.dataId), g.shape = l.outputShape, g;
}
var GP = { kernelName: bs, backendName: "wasm", setupFunc: xJ, kernelFunc: yJ };
var bJ = false;
var HP = nt(ao, bJ, "bool");
var CJ = false;
var qP = nt(io, CJ, "bool");
var KP;
function IJ(r) {
  KP = r.wasm.cwrap(Nn, null, ["number", "number", "number", "number"]);
}
function wJ(r) {
  let { inputs: { x: e }, attrs: { alpha: t10 }, backend: o } = r, n = o.dataIdMap.get(e.dataId).id, s = o.makeOutput(e.shape, "float32");
  if (x.sizeFromShape(e.shape) !== 0) {
    let a = o.dataIdMap.get(s.dataId).id;
    KP(n, Ae[e.dtype], t10, a);
  }
  return s;
}
var jP = { kernelName: Nn, backendName: "wasm", setupFunc: IJ, kernelFunc: wJ };
var SJ = false;
var XP = nt(po, SJ, "bool");
var vJ = false;
var YP = nt(co, vJ, "bool");
var QP = Qe(lo);
var kJ = false;
var ZP = nt(_n, kJ, "bool");
var JP = Qe(En);
var TJ = false;
var eO = nt(ua, TJ, "bool");
var NJ = false;
var tO = nt(g0, NJ, "bool");
var rO;
function _J(r) {
  rO = r.wasm.cwrap($n, null, ["number", "number", "number", "number"]);
}
function EJ(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: f } = kr(a, n, e);
  if (f) {
    let C = e.dataIdMap.get(c.dataId).id;
    u = c, p = C;
  }
  let d = u.shape.length;
  I.assertAxesAreInnerMostDims("max", l, d);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, l), y = x.sizeFromShape(g), b = e.makeOutput(h, a.dtype);
  if (x.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    rO(p, Ae[a.dtype], y, C);
  }
  if (f && e.disposeData(c.dataId), s) {
    let C = I.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var oO = { kernelName: $n, backendName: "wasm", setupFunc: _J, kernelFunc: EJ };
var $J = false;
var nO = nt(mo, $J);
var sO;
function RJ(r) {
  sO = r.wasm.cwrap(Rn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function AJ(r) {
  let { inputs: e, attrs: t10, backend: o } = r, n = e.x, s = o.dataIdMap.get(n.dataId).id;
  x.assert(n.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);
  let { filterSize: a, strides: i, pad: p, dimRoundingMode: u } = t10, c = I.computePool2DInfo(n.shape, a, i, 1, p, u), l = c.filterHeight, m = c.filterWidth, f = c.padInfo.top, d = c.padInfo.right, h = c.padInfo.bottom, g = c.padInfo.left, y = c.dilationHeight, b = c.dilationWidth, C = c.strideHeight, w = c.strideWidth, k = c.inChannels, _ = c.outChannels;
  if (c.dataFormat !== "channelsLast")
    throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
  let E = o.makeOutput(c.outShape, "float32"), R = o.dataIdMap.get(E.dataId).id;
  return sO(s, n.shape[0], n.shape[1], n.shape[2], l, m, f, d, h, g, y, b, C, w, k, _, R), E;
}
var aO = { kernelName: Rn, backendName: "wasm", setupFunc: RJ, kernelFunc: AJ };
var iO;
function FJ(r) {
  iO = r.wasm.cwrap(An, null, ["number, number, number"]);
}
function DJ(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: f } = kr(a, n, e), d = l;
  if (f) {
    let w = e.dataIdMap.get(c.dataId).id;
    w !== i && (u = c, p = w, d = I.getInnerMostAxes(d.length, u.shape.length));
  }
  I.assertAxesAreInnerMostDims("mean", d, u.shape.length);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, d), y = x.sizeFromShape(g), b = u;
  u.dtype !== "float32" && (b = cs({ backend: e, inputs: { x: u }, attrs: { dtype: "float32" } }), p = e.dataIdMap.get(b.dataId).id);
  let C = e.makeOutput(h, "float32");
  if (x.sizeFromShape(u.shape) !== 0) {
    let w = e.dataIdMap.get(C.dataId).id;
    iO(p, y, w);
  }
  if (f && e.disposeData(c.dataId), s) {
    let w = I.expandShapeToKeepDim(C.shape, m);
    C.shape = w;
  }
  return u.dtype !== "float32" && e.disposeData(b.dataId), C;
}
var uO = { kernelName: An, backendName: "wasm", setupFunc: FJ, kernelFunc: DJ };
var pO;
function PJ(r) {
  pO = r.wasm.cwrap(Fn, null, ["number", "number", "number", "number"]);
}
function OJ(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: f } = kr(a, n, e);
  if (f) {
    let C = e.dataIdMap.get(c.dataId).id;
    C !== i && (u = c, p = C);
  }
  let d = u.shape.length;
  I.assertAxesAreInnerMostDims("min", l, d);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, l), y = x.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
  if (x.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    pO(p, Ae[a.dtype], y, C);
  }
  if (f && e.disposeData(c.dataId), s) {
    let C = I.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var cO = { kernelName: Fn, backendName: "wasm", setupFunc: PJ, kernelFunc: OJ };
var MJ = false;
var lO = nt(fo, MJ);
var Xw;
(function(r) {
  r[r.reflect = 0] = "reflect", r[r.symmetric = 1] = "symmetric";
})(Xw || (Xw = {}));
var mO;
function LJ(r) {
  mO = r.wasm.cwrap(Dn, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function BJ(r) {
  let { inputs: { x: e }, backend: t10, attrs: { paddings: o, mode: n } } = r, s = o.map((d, h) => d[0] + e.shape[h] + d[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((d) => d[0]), l = o.map((d) => d[1]), m = new Uint8Array(new Int32Array(c).buffer), f = new Uint8Array(new Int32Array(l).buffer);
  return mO(a, u, e.shape.length, Ae[e.dtype], m, f, Xw[n], p), i;
}
var fO = { kernelName: Dn, backendName: "wasm", kernelFunc: BJ, setupFunc: LJ };
var VJ = true;
var dO = nt(ho, VJ);
var hO = Qe(Pn);
function kc(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 gO;
function zJ(r) {
  gO = r.wasm.cwrap(On, "number", ["number", "number", "number", "number", "number"]);
}
function WJ(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 = gO(u, c, s, n, a), { pSelectedIndices: m, selectedSize: f, pSelectedScores: d, pValidOutputs: h } = kc(e, l);
  return e.wasm._free(d), e.wasm._free(h), e.makeOutput([f], "int32", m);
}
var xO = { kernelName: On, backendName: "wasm", setupFunc: zJ, kernelFunc: WJ };
var yO;
function UJ(r) {
  yO = r.wasm.cwrap(pa, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function GJ(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 = yO(c, l, s, n, a, i), { pSelectedIndices: f, selectedSize: d, pSelectedScores: h, pValidOutputs: g } = kc(e, m);
  e.wasm._free(h);
  let y = e.makeOutput([d], "int32", f), b = e.makeOutput([], "int32", g);
  return [y, b];
}
var bO = { kernelName: pa, backendName: "wasm", setupFunc: UJ, kernelFunc: GJ };
var CO;
function HJ(r) {
  CO = r.wasm.cwrap(Mn, "number", ["number", "number", "number", "number", "number", "number"]);
}
function qJ(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 = CO(c, l, s, n, a, i), { pSelectedIndices: f, selectedSize: d, pSelectedScores: h, pValidOutputs: g } = kc(e, m);
  e.wasm._free(g);
  let y = e.makeOutput([d], "int32", f), b = e.makeOutput([d], "float32", h);
  return [y, b];
}
var IO = { kernelName: Mn, backendName: "wasm", setupFunc: HJ, kernelFunc: qJ };
var KJ = false;
var wO = nt(go, KJ, "bool");
var SO;
function jJ(r) {
  SO = r.wasm.cwrap(ca, null, ["number", "number", "number", "number", "number"]);
}
function XJ(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 SO(m, a, i, p, c), u;
}
var vO = { kernelName: ca, backendName: "wasm", setupFunc: jJ, kernelFunc: XJ };
function YJ(r) {
  let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype);
  return t10.typedArrayFromHeap(o).fill(1), o;
}
var kO = { kernelName: Cs, backendName: "wasm", kernelFunc: YJ };
function QJ(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return Eg({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    x.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), x.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = Eg({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = qw({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeData(c.dataId)), u;
}
var TO = { kernelName: Is, backendName: "wasm", kernelFunc: QJ };
var NO;
function ZJ(r) {
  NO = r.wasm.cwrap(Ln, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function JJ(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 (x.sizeFromShape(e.shape) === 0)
    return jw({ 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]), f = new Uint8Array(new Int32Array(l).buffer), d = new Uint8Array(new Int32Array(m).buffer);
  return NO(a, c, e.shape.length, Ae[e.dtype], f, d, n, u), i;
}
var $g = { kernelName: Ln, backendName: "wasm", kernelFunc: JJ, setupFunc: ZJ };
var eee = false;
var _O = nt(Bn, eee);
var EO;
function tee(r) {
  EO = r.wasm.cwrap(Vn, null, ["number", "number", "number"]);
}
function ree(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 = cs({ 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 EO(i, a, l), p.dtype !== "float32" && t10.disposeData(u.dataId), c;
}
var $O = { kernelName: Vn, backendName: "wasm", setupFunc: tee, kernelFunc: ree };
var RO;
function oee(r) {
  RO = r.wasm.cwrap(Ao, null, ["number", "number", "number", "number"]);
}
function nee(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: f } = kr(a, n, e), d = l;
  if (f) {
    let C = e.dataIdMap.get(c.dataId).id;
    C !== i && (u = c, p = C, d = I.getInnerMostAxes(d.length, u.shape.length));
  }
  I.assertAxesAreInnerMostDims("prod", d, u.shape.length);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, d), y = x.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
  if (x.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    RO(p, y, Ae[b.dtype], C);
  }
  if (f && e.disposeData(c.dataId), s) {
    let C = I.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var AO = { kernelName: Ao, backendName: "wasm", setupFunc: oee, kernelFunc: nee };
var see = (r) => {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = Su(o, n, s, a), p = e.makeOutput([i.length], a);
  return e.typedArrayFromHeap(p).set(i), p;
};
var FO = { kernelName: ws, backendName: "wasm", kernelFunc: see };
var aee = true;
var DO = nt(Cn, aee);
var PO = Qe(zn);
var OO = Qe(Gn);
var MO;
function iee(r) {
  MO = r.wasm.cwrap(Un, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function uee(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, f] = n.shape, d = [c, p, u, f], h = e.dataIdMap.get(n.dataId), g;
  h.dtype !== "float32" && (g = cs({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), h = e.dataIdMap.get(g.dataId));
  let y = h.id, b = e.makeOutput(d, "float32");
  if (x.sizeFromShape(n.shape) === 0)
    return b;
  let C = e.dataIdMap.get(b.dataId).id;
  return MO(y, c, l, m, f, p, u, s ? 1 : 0, a ? 1 : 0, C), g != null && e.disposeData(g.dataId), b;
}
var LO = { kernelName: Un, backendName: "wasm", setupFunc: iee, kernelFunc: uee };
var BO;
function pee(r) {
  BO = r.wasm.cwrap(Wn, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function cee(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, f] = n.shape, d = [c, p, u, f], h = e.makeOutput(d, "float32");
  if (x.sizeFromShape(n.shape) === 0)
    return h;
  let g = e.dataIdMap.get(n.dataId), y;
  g.dtype !== "float32" && (y = cs({ backend: e, inputs: { x: n }, attrs: { dtype: "float32" } }), g = e.dataIdMap.get(y.dataId));
  let b = g.id, C = e.dataIdMap.get(h.dataId).id;
  return BO(b, c, l, m, f, p, u, s ? 1 : 0, a ? 1 : 0, C), y != null && e.disposeData(y.dataId), h;
}
var VO = { kernelName: Wn, backendName: "wasm", setupFunc: pee, kernelFunc: cee };
var zO;
function lee(r) {
  zO = r.wasm.cwrap(fa, null, ["number", "array", "number", "array", "number", "number"]);
}
function mee(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dims: s } = o, a = x.parseAxisParam(s, n.shape);
  if (n.shape.length === 0)
    return zu({ 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);
  zO(p, c, a.length, l, n.shape.length, u);
  let m = Mt({ inputs: { x: i }, attrs: { shape: n.shape }, backend: t10 });
  return t10.disposeData(i.dataId), m;
}
var WO = { kernelName: fa, backendName: "wasm", kernelFunc: mee, setupFunc: lee };
var UO;
function fee(r) {
  UO = r.wasm.cwrap(es, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function dee(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, f, d] = n.shape, [h, g] = I.getImageCenter(i, m, f), y = a === 0, b = 255, C = typeof a == "number" ? [a, a, a, y ? 0 : b] : [...a, b], w = new Uint8Array(new Int32Array(C).buffer);
  return UO(u, l, m, f, d, s, h, g, w, C.length, c), p;
}
var GO = { kernelName: es, backendName: "wasm", kernelFunc: dee, setupFunc: fee };
var HO = Qe(da);
var qO = Qe(xo);
var KO;
function hee(r) {
  KO = r.wasm.cwrap(Hn, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function gee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { indices: n, updates: s } = t10, { shape: a } = o, i = e.makeOutput(a, s.dtype);
  if (x.sizeFromShape(a) === 0)
    return i;
  let { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = cl.calculateShapes(s, n, a), d = e.dataIdMap.get(n.dataId).id, g = e.dataIdMap.get(s.dataId).id, y = new Uint8Array(new Int32Array(l).buffer), b = e.dataIdMap.get(i.dataId).id;
  return KO(d, g, Ae[s.dtype], p, u, c, y, m, b), i;
}
var jO = { kernelName: Hn, backendName: "wasm", setupFunc: hee, kernelFunc: gee };
var XO;
function xee(r) {
  XO = r.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function yee(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, f = l === 0 || l > 1 || m === 1 ? 1 : x.sizeFromShape(n.shape.slice(1));
  return XO(a, i, p, f, c), u;
}
var YO = { kernelName: vs, backendName: "wasm", kernelFunc: yee, setupFunc: xee };
var QO;
function bee(r) {
  QO = r.wasm.cwrap(yo, null, ["number", "number"]);
}
function Cee(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 x.sizeFromShape(n.shape) === 0 || QO(o, s), n;
}
var ZO = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: bee, kernelFunc: Cee };
var JO = Qe(Kn);
var e3;
function Iee(r) {
  e3 = r.wasm.cwrap(Xn, null, ["number", "number", "number", "number"]);
}
function wee(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 = x.sizeFromShape(t10.shape) / i;
  return x.sizeFromShape(s.shape) === 0 || e3(n, a, i, p), s;
}
var t3 = { kernelName: Xn, backendName: "wasm", setupFunc: Iee, kernelFunc: wee };
function See(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o, i = x.sizeFromShape(s), p = [[0, 0]];
  p.push(...a);
  for (let _ = 1 + s.length; _ < n.shape.length; ++_)
    p.push([0, 0]);
  let u = $g.kernelFunc({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), c = I.getReshaped(u.shape, s, i, false), l = I.getPermuted(c.length, s.length, false), m = I.getReshapedPermuted(u.shape, s, i, false), h = Mt({ inputs: { x: u }, backend: t10, attrs: { shape: c } }), b = Eo({ inputs: { x: h }, backend: t10, attrs: { perm: l } }), k = Mt({ inputs: { x: b }, backend: t10, attrs: { shape: m } });
  return t10.disposeData(u.dataId), t10.disposeData(h.dataId), t10.disposeData(b.dataId), k;
}
var r3 = { kernelName: ks, backendName: "wasm", kernelFunc: See };
var o3;
function vee(r) {
  o3 = r.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function kee(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, f = e.dataIdMap.get(a.dataId).id, d = e.makeOutput(c, o.dtype), h = e.dataIdMap.get(d.dataId).id, g = e.makeOutput(c.slice(0, 1), n.dtype), y = 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, R = o3(l, m, Ae[n.dtype], i, u, p, f, h, y, C, k, E), A = e.readSync(_.dataId), D;
  switch (A[0]) {
    case 1: {
      D = I.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);
      break;
    }
    case 2: {
      D = I.getSparseFillEmptyRowsNegativeIndexErrorMessage(A[1], A[2]);
      break;
    }
    case 3:
      D = I.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(A[1], A[2], A[3]);
      break;
    default:
      D = "";
  }
  if (e.disposeData(_.dataId), D)
    throw e.disposeData(d.dataId), e.disposeData(g.dataId), e.disposeData(b.dataId), e.disposeData(w.dataId), new Error(D);
  let O = d, M = g;
  return R !== c[0] && (O = Xo({ inputs: { x: d }, attrs: { begin: 0, size: [R, p] }, backend: e }), M = Xo({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: e }), e.disposeData(d.dataId), e.disposeData(g.dataId)), [O, M, b, w];
}
var n3 = { kernelName: Qa, backendName: "wasm", setupFunc: vee, kernelFunc: kee };
var s3;
function Tee(r) {
  s3 = r.wasm.cwrap(ga, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function Nee(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 = x.sizeFromShape(s.shape), l = e.makeOutput([u, c], o.dtype), m = e.dataIdMap.get(l.dataId).id, f = e.makeOutput([c], s.dtype), d = e.dataIdMap.get(f.dataId).id, h = e.makeOutput([3], "int32"), g = e.dataIdMap.get(h.dataId).id;
  s3(a, i, p, u, m, d, g);
  let y = e.readSync(h.dataId), b;
  switch (y[0]) {
    case 0: {
      b = I.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1], y[2]);
      break;
    }
    case 1: {
      b = I.getSparseReshapeNegativeOutputDimErrorMessage(y[1], y[2]);
      break;
    }
    case 2:
      b = I.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
      break;
    case 3: {
      let C = Array.from(e.readSync(n.dataId)), w = Array.from(e.readSync(f.dataId));
      b = I.getSparseReshapeInputOutputMultipleErrorMessage(C, w);
      break;
    }
    case 4: {
      let C = Array.from(e.readSync(n.dataId)), w = Array.from(e.readSync(f.dataId));
      b = I.getSparseReshapeInputOutputMismatchErrorMessage(C, w);
      break;
    }
    default:
      b = "";
  }
  if (e.disposeData(h.dataId), b)
    throw e.disposeData(l.dataId), e.disposeData(f.dataId), new Error(b);
  return [l, f];
}
var a3 = { kernelName: ga, backendName: "wasm", setupFunc: Tee, kernelFunc: Nee };
var i3;
function Rg(r) {
  i3 = r.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Ag(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(I.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let l = n.shape.slice();
  l[0] = c;
  let m = t10.dataIdMap.get(n.dataId).id, f = t10.dataIdMap.get(s.dataId).id, d = t10.dataIdMap.get(a.dataId).id, h = t10.makeOutput(l, n.dtype), g = t10.dataIdMap.get(h.dataId).id, y = t10.makeOutput([4], "int32"), b = t10.dataIdMap.get(y.dataId).id;
  i3(m, Ae[n.dtype], n.shape[0], f, d, g, b, e, 0);
  let C = t10.readSync(y.dataId), w;
  switch (C[0]) {
    case 0: {
      w = I.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
      break;
    }
    case 1: {
      w = I.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
      break;
    }
    case 2:
      w = I.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(C[1], C[2]);
      break;
    case 3:
      w = I.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(C[1], C[2], C[3]);
      break;
    default:
      w = "";
  }
  if (t10.disposeData(y.dataId), w)
    throw t10.disposeData(h.dataId), new Error(w);
  return h;
}
function _ee(r) {
  return Ag(r, true);
}
var u3 = { kernelName: Za, backendName: "wasm", setupFunc: Rg, kernelFunc: _ee };
function Eee(r) {
  return Ag(r, false);
}
var p3 = { kernelName: Ja, backendName: "wasm", setupFunc: Rg, kernelFunc: Eee };
function $ee(r) {
  let { inputs: e, attrs: t10, backend: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = t10, i = x.parseAxisParam(a, n.shape)[0], p = I.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 f = Xo({ inputs: { x: n }, attrs: { begin: u, size: m }, backend: o });
    return u[i] += l, f;
  });
}
var c3 = { kernelName: Ts, backendName: "wasm", kernelFunc: $ee };
var l3 = Qe(bo);
var m3 = Qe(ti);
var Ree = true;
var f3 = nt(Co, Ree);
var d3;
function Aee(r) {
  d3 = r.wasm.cwrap($s, null, ["number", "number", "number", "number"]);
}
function Fee(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 d3(a, n, Ae[s.dtype], p), i;
}
var h3 = { kernelName: $s, backendName: "wasm", setupFunc: Aee, kernelFunc: Fee };
var g3;
function Dee(r) {
  g3 = r.wasm.cwrap(Yn, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function Pee(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: f, finalShape: d, isIdentity: h, sliceDim0: g, isSimpleSlice: y, begin: b, end: C, strides: w } = et.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = Mt({ inputs: { x: n }, backend: e, attrs: { shape: d } });
  else if (g || y) {
    x.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let _ = et.computeOutShape(b, C, w), E = Xo({ inputs: { x: n }, backend: e, attrs: { begin: b, size: _ } });
    k = Mt({ inputs: { x: E }, backend: e, attrs: { shape: d } }), e.disposeData(E.dataId);
  } else {
    let _ = e.makeOutput(f, "float32"), E = e.dataIdMap.get(n.dataId).id, R = new Uint8Array(new Int32Array(x.computeStrides(n.shape)).buffer), A = new Uint8Array(new Int32Array(b).buffer), D = new Uint8Array(new Int32Array(C).buffer), O = new Uint8Array(new Int32Array(w).buffer), M = new Uint8Array(new Int32Array(f).buffer), L = new Uint8Array(new Int32Array(x.computeStrides(f)).buffer), W = e.dataIdMap.get(_.dataId).id;
    g3(E, R, n.shape.length, A, D, O, M, L, f.length, W), k = Mt({ inputs: { x: _ }, backend: e, attrs: { shape: d } }), e.disposeData(_.dataId);
  }
  return k;
}
var x3 = { kernelName: Yn, backendName: "wasm", setupFunc: Dee, kernelFunc: Pee };
function Oee(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), f = e.readSync(s.dataId), [d, h] = ku(m, f, a, i, p, u, c, l), g = e.makeOutput([d.length], "string"), y = e.dataIdMap.get(g.dataId);
  y.stringBytes = d;
  let b = e.makeOutput(s.shape, "int32");
  return e.typedArrayFromHeap(b).set(h), [g, b];
}
var y3 = { kernelName: Ns, backendName: "wasm", kernelFunc: Oee };
function Mee(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] = Tu(i, p[0], a), m = c.length, f = e.makeOutput([m, 2], "int32");
  e.typedArrayFromHeap(f).set(u);
  let h = e.makeOutput([m], "string"), g = e.dataIdMap.get(h.dataId);
  g.stringBytes = c;
  let y = e.makeOutput([2], "int32");
  return e.typedArrayFromHeap(y).set(l), [f, h, y];
}
var b3 = { kernelName: ri, backendName: "wasm", kernelFunc: Mee };
function Lee(r) {
  let { backend: e, inputs: t10, attrs: o } = r, { input: n } = t10, { numBuckets: s } = o, a = e.readSync(n.dataId), i = Nu(a, s), p = e.makeOutput(n.shape, "int32");
  return e.typedArrayFromHeap(p).set(i), p;
}
var C3 = { kernelName: oi, backendName: "wasm", kernelFunc: Lee };
var Bee = true;
var I3 = nt(Io, Bee);
var w3;
function Vee(r) {
  w3 = r.wasm.cwrap(jn, null, ["number", "number", "number", "number"]);
}
function zee(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: f } = kr(a, n, e), d = l;
  if (f) {
    let C = e.dataIdMap.get(c.dataId).id;
    C !== i && (u = c, p = C, d = I.getInnerMostAxes(d.length, u.shape.length));
  }
  I.assertAxesAreInnerMostDims("sum", d, u.shape.length);
  let [h, g] = I.computeOutAndReduceShapes(u.shape, d), y = x.sizeFromShape(g), b = e.makeOutput(h, u.dtype);
  if (x.sizeFromShape(u.shape) !== 0) {
    let C = e.dataIdMap.get(b.dataId).id;
    w3(p, y, Ae[b.dtype], C);
  }
  if (f && e.disposeData(c.dataId), s) {
    let C = I.expandShapeToKeepDim(b.shape, m);
    b.shape = C;
  }
  return b;
}
var S3 = { kernelName: jn, backendName: "wasm", setupFunc: Vee, kernelFunc: zee };
var v3 = Qe(xa);
var k3 = Qe(Qn);
var T3;
function Wee(r) {
  T3 = r.wasm.cwrap(wo, null, ["number", "array", "number", "array", "number", "number"]);
}
function Uee(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 T3(s, p, n.shape.length, u, i.length, Ae[c.dtype], l), c;
}
var N3 = { kernelName: wo, backendName: "wasm", setupFunc: Wee, kernelFunc: Uee };
var _3;
function Gee(r) {
  _3 = r.wasm.cwrap(Zn, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var Hee = ({ 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 _3(a, i, o.shape.length, Ae[o.dtype], n, s, c, m), [u, l];
};
var E3 = { kernelName: Zn, backendName: "wasm", setupFunc: Gee, kernelFunc: Hee };
var $3;
function qee(r) {
  $3 = r.wasm.cwrap(Jn, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "array", "number", "number", "number", "number", "number"]);
}
function Kee(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, f] = n.shape, [d, h] = u != null ? u : [l, m], g = [c, d, h, f], y = new Uint8Array(new Int32Array(x.computeStrides(n.shape)).buffer), b = new Uint8Array(new Int32Array(x.computeStrides(g)).buffer), C = e.makeOutput(g, n.dtype), w = e.dataIdMap.get(C.dataId).id, _ = e.dataIdMap.get(n.dataId).id, R = e.dataIdMap.get(s.dataId).id, A = 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 $3(_, R, s.shape[0] > 1, c, d, h, f, m, l, y, n.shape.length - 1, b, g.length - 1, A, D, p, w), C;
}
var R3 = { kernelName: Jn, backendName: "wasm", setupFunc: qee, kernelFunc: Kee };
function jee(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 f = 0; f < i; f++)
    f !== s && (p[u++] = n.shape[f]);
  let c = new Array(a), l = new Array(i).fill(0), m = n.shape.slice();
  m[s] = 1;
  for (let f = 0; f < c.length; f++)
    l[s] = f, c[f] = Xo({ inputs: { x: n }, attrs: { begin: l, size: m }, backend: t10 });
  return c.map(({ dataId: f, dtype: d }) => ({ dataId: f, dtype: d, shape: p }));
}
var A3 = { kernelName: _s, backendName: "wasm", kernelFunc: jee };
function Xee(r) {
  let { inputs: { x: e }, backend: t10 } = r, o = t10.makeOutput(e.shape, e.dtype);
  return t10.typedArrayFromHeap(o).fill(0), o;
}
var F3 = { kernelName: Es, backendName: "wasm", kernelFunc: Xee };
var Yee = [LD, BD, VD, WD, KD, XD, QD, JD, rP, nP, sP, aP, uP, pP, lP, fP, dP, hP, xP, bP, IP, SP, kP, TP, NP, _P, EP, $P, AP, FP, DP, OP, LP, VP, WP, GP, HP, qP, UD, jP, XP, YP, QP, ZP, JP, eO, tO, oO, nO, aO, uO, cO, lO, fO, dO, hO, xO, bO, IO, wO, vO, kO, TO, $g, _O, $O, AO, FO, DO, PO, OO, eP, LO, VO, WO, GO, HO, qO, jO, YO, ZO, JO, oP, t3, r3, n3, a3, u3, p3, c3, l3, m3, f3, h3, x3, y3, b3, C3, I3, S3, v3, k3, N3, E3, R3, HD, A3, F3];
for (let r of Yee)
  ya(r);
var Yw = P();
Yw.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;
  }
});
Yw.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
  if (Yw.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 oS = rp(M3());
var U3 = rp(B3());
var nS = rp(V3());
var z3 = oS.default || oS;
var Qee = nS.default || nS;
var Gl = class extends Jr {
  constructor(e) {
    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(H3), rS = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new rn(this, cr());
  }
  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 = x.now();
    return e(), { kernelMs: x.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 = x.sizeFromShape(o), p = i * x.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 || x.sizeFromShape(a);
    let p = x.bytesPerElement(s), u = this.wasm.HEAPU8.slice(n + t10 * p, n + o * p);
    return Jee(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 = x.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 = x.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 ${t10}`);
    }
  }
};
function Zee(r) {
  return (e, t10) => (x.fetch(r, { credentials: "same-origin" }).then((o) => {
    o.ok || e.env.a(`failed to load wasm binary file at '${r}'`), o.arrayBuffer().then((n) => {
      WebAssembly.instantiate(n, e).then((s) => {
        t10(s.instance, s.module);
      });
    });
  }), {});
}
function W3(r, e, t10) {
  if (Pg != null)
    return Pg;
  let o = "tfjs-backend-wasm.wasm";
  return r && e ? o = "tfjs-backend-wasm-threaded-simd.wasm" : r && (o = "tfjs-backend-wasm-simd.wasm"), Wl != null && Wl[o] != null ? Wl[o] : t10 + o;
}
async function G3() {
  let [r, e] = await Promise.all([P().getAsync("WASM_HAS_SIMD_SUPPORT"), P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
  return new Promise((t10, o) => {
    let n = {};
    n.locateFile = (i, p) => {
      if (i.endsWith(".worker.js")) {
        let u = U3.wasmWorkerContents.replace(/\n/g, "\\n"), c = new Blob([u], { type: "application/javascript" });
        return URL.createObjectURL(c);
      }
      return i.endsWith(".wasm") ? W3(r, e, zl != null ? zl : p) : p + i;
    }, sS && (n.instantiateWasm = Zee(W3(r, e, zl != null ? zl : "")));
    let s = false;
    n.onAbort = () => {
      if (s || Ul)
        return;
      Ul = 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 && Pg == null ? (n.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + z3.toString()], { type: "text/javascript" }), a = z3(n)) : a = Qee(n), a.then((i) => {
      s = true, Ul = 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 Jee(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 ete = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var Pg = null;
var zl = null;
var Wl = {};
var Ul = false;
var sS = false;
function tte(r, e = false) {
  if (sC("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Ul)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
  Pg = r, sS = e;
}
function rte(r, e = false) {
  if (Ul)
    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")
    zl = r;
  else {
    Wl = r;
    let t10 = ete.filter((o) => Wl[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.`);
  }
  sS = e;
}
var H3 = -1;
var rS = -1;
function ote(r) {
  H3 = r;
}
function nte() {
  if (rS === -1)
    throw new Error("WASM backend not initialized.");
  return rS;
}
var ste = "4.0.0";
var ate = 2;
pi("wasm", async () => {
  let { wasm: r } = await G3();
  return new Gl(r);
}, ate);
var Va = P();
Va.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
Va.registerFlag("WEBGPU_CPU_FORWARD", () => true);
Va.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE", () => -1);
Va.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
Va.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
Va.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
Va.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
Va.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE", () => true);
Va.registerFlag("WEBGPU_USE_NAIVE_CONV2D_DEBUG", () => false);
var Og = class {
  constructor(e) {
    e && (this.vendor = e.vendor);
  }
  isIntel() {
    return this.vendor === "intel";
  }
};
var Mg = 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 = q3(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 = q3(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 q3(r, e) {
  return `${r}_${e}`;
}
var Lg = class {
  constructor(e) {
    this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
  acquireTexture(e, t10, o, n) {
    let s = j3(o), a = e * t10 * s, i = K3(e, t10, o, n);
    if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) {
      this.numFreeTextures--;
      let u = this.freeTextures.get(i).shift();
      return this.usedTextures.get(i).push(u), u;
    }
    this.numBytesAllocated += a;
    let p = this.device.createTexture({ size: [e, t10], format: o, usage: n });
    return this.usedTextures.get(i).push(p), p;
  }
  releaseTexture(e, t10, o, n, s) {
    if (this.freeTextures.size === 0)
      return;
    let a = K3(t10, o, n, s);
    this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
    let i = this.usedTextures.get(a), p = i.indexOf(e);
    if (p < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    i.splice(p, 1);
    let u = j3(n), c = t10 * o * u;
    this.numBytesUsed -= c;
  }
  getNumUsedTextures() {
    return this.numUsedTextures;
  }
  getNumFreeTextures() {
    return this.numFreeTextures;
  }
  dispose() {
    this.freeTextures.forEach((e, t10) => {
      e.forEach((o) => {
        o.destroy();
      });
    }), this.usedTextures.forEach((e, t10) => {
      e.forEach((o) => {
        o.destroy();
      });
    }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
};
function K3(r, e, t10, o) {
  return `${r}_${e}_${t10}_${o}`;
}
function j3(r) {
  if (r === "rgba8unorm")
    return 16;
  throw new Error(`${r} is not supported!`);
}
function X3(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 Z3 = (r, e, t10, o) => {
  let n = { dtype: o.dtype, shape: o.shape }, s = ite(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 At(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 Yo(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 ue(...r) {
  let e;
  switch (r.length) {
    case 0:
      e = `
        ${Ri()}
        fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                  @builtin(global_invocation_id) GlobalId : vec3<u32>,
                  @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
          localId = LocalId;
          globalId = GlobalId;
          numWorkgroups = NumWorkgroups;
          main();
        }

        fn main()
      `;
      break;
    case 1:
      e = `
        ${Ri()}
        fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                  @builtin(global_invocation_id) GlobalId : vec3<u32>,
                  @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
          localId = LocalId;
          globalId = GlobalId;
          numWorkgroups = NumWorkgroups;
          main(getGlobalIndex());
        }

        fn main(${r[0]} : i32)
      `;
      break;
    default:
      throw Error("Unreachable");
  }
  return e;
}
function Ri() {
  return `
  @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`;
}
function ite(r, e, t10) {
  let o = [];
  if (o.push(`
      const workGroupSizeX = ${t10.workGroupSize[0]}u;
      const workGroupSizeY = ${t10.workGroupSize[1]}u;
      const workGroupSizeZ = ${t10.workGroupSize[2]}u;

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

      // Only used when the y/z dimension of workgroup size is 1.
      fn getGlobalIndex() -> i32 {
        ${eM(t10) ? "  return i32(globalId.x);" : `  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
                   localId.y * workGroupSizeX + localId.x;
               let workGroupID = (globalId - localId)/vec3<u32>(
                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);

               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
                   workGroupID.y * numWorkgroups.x + workGroupID.x) *
                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
                   localInvocationIndex);
        `}
      }
    `), t10.isFromPixels)
    return o.push(`
        struct Uniform {
          size            : i32,
          numChannels     : i32,
          outShapeStrides : vec2<i32>,
        };

        @group(0) @binding(0) var<storage, read_write> result: array<${Tc(e.dtype, t10.isVec4)}>;
        @group(0) @binding(2) var<uniform> uniforms: Uniform;
      `), [Y3, o.join(`
`), Q3(e.shape), t10.getUserCode()].join(`
`);
  let n = "struct Uniforms { NAN : f32, ";
  t10.variableNames.forEach((m, f) => {
    let d = At(r[f].shape.length);
    n += `${m.charAt(0).toLowerCase() + m.slice(1)}Shape : ${d}, `;
  });
  let s = At(e.shape.length);
  n += `outShape : ${s}, `;
  let a = e.shape.length - 1, i = At(a);
  n += `
         outShapeStrides: ${i}, `, t10.size && (n += "size : i32, "), t10.uniforms && (n += t10.uniforms), n += "};", n = dte(n), o.push(n), t10.atomic ? o.push(`
      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
    `) : o.push(`
      @group(0) @binding(0) var<storage, read_write> result: array<${Tc(e.dtype, t10.isVec4)}>;
    `), t10.variableNames.forEach((m, f) => {
    o.push(`
      @group(0) @binding(${1 + f}) var<storage, read> ${m}: array<${t10.variableTypes ? t10.variableTypes[f] : Tc(r[f].dtype, t10.isVec4)}>;
        `);
  }), n !== "" && o.push(`
      @group(0) @binding(${1 + t10.variableNames.length}) var<uniform> uniforms: Uniforms;
      `);
  let p = lte(e.shape, t10.dispatchLayout), u = [Y3, o.join(`
`), Q3(e.shape), p, mte(e.shape.length)];
  t10.atomic || u.push(fte(e.shape, e.dtype, t10.isVec4));
  let c = r.map((m, f) => cte(m, e.shape, t10.variableTypes ? t10.variableTypes[f] === "vec4<f32>" : t10.isVec4, t10.dispatchLayout.x.length === e.shape.length)).join(`
`);
  return u.push(c), u.push(t10.getUserCode()), u.join(`
`);
}
function J3(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) => I.getBroadcastDims(c.shape, o.shape)), i = t10.map((c) => x.arraysEqual(c.shape, o.shape)).join("_"), p = a.map((c) => c.join("_")).join(";"), u = eM(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 Y3 = `
  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> {
    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
  }
`;
function Q3(r) {
  let e = r.length;
  if (e <= 1)
    return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
  let t10 = x.computeStrides(r), o = At(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 ${n[i]} = index2 / uniforms.outShapeStrides.${Yo(i)}`, u = i === t10.length - 1 ? `let ${n[i + 1]} = index2 - ${n[i]} * uniforms.outShapeStrides.${Yo(i)}` : `index2 = index2 - ${n[i]} * uniforms.outShapeStrides.${Yo(i)}`;
    return `${p}; ${u};`;
  }).join(""), `
    fn getCoordsFromIndex(index : i32) -> ${o} {
      ${s}
      return ${o}(${n.join(",")});
    }
  `;
}
function ute(r, e) {
  let t10 = r.name, o = r.shape.length, n = At(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 pte(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 = At(p);
  if (x.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 = I.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.${Yo(g + l)} = 0;`).join(`
`);
  let f = "";
  if (p < 2 && i > 0)
    f = "coords";
  else if (p > 1) {
    let g = At(i), y = r.shape.map((b, C) => `coords.${Yo(C + l)}`).join(", ");
    f = `${g}(${y})`;
  } else
    f = "coords";
  let d = `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}(${f}, ${d}) / 4];
    }

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

  fn ${a}Coords(coordsIn : ${u}) -> f32 {
    var coords = coordsIn;
    ${m}
    return f32(${n}[getIndexFromCoords${h}(${f}, ${d})]);
  }
`;
}
function cte(r, e, t10, o) {
  let n = ute(r, t10);
  return r.shape.length <= e.length && (n += pte(r, e, t10, o)), n;
}
function lte(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() -> ${At(s)}{
    let globalIndex = getGlobalIndex();
    return getCoordsFromIndex(globalIndex);
  }
  `;
  let i = "", p = [t10, o, n];
  for (let m = 0; m < p.length; m++) {
    let f = p[m];
    if (f.length !== 0)
      if (f.length === 1)
        i += `let d${f[0]} = i32(globalId[${m}]);`;
      else {
        let d = X3(f, "uniforms.outShape");
        i += `var index${m} = i32(globalId[${m}]);`;
        for (let h = 0; h < d.length; h++)
          i += `let d${f[h]} = index${m} / ${d[h]};`, h === d.length - 1 ? i += `let d${f[h + 1]} = index${m} - d${f[h]} * ${d[h]};` : i += `index${m} = index${m} - d${f[h]} * ${d[h]};`;
      }
  }
  let u = [];
  for (let m = 0; m < a; m++)
    u.push(`d${m}`);
  let c = At(a), l = `fn getOutputCoords() -> ${c} {
  ${i}
`;
  return u.length === 0 ? l += `return ${c}(0); }` : l += `return ${c}(${u.join(",")}); }`, l;
}
function mte(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:
      x.assert(false, () => `Unsupported ${r}D shape`);
      break;
  }
  return e;
}
function eM(r) {
  return r.dispatch[1] === 1 && r.dispatch[2] === 1;
}
function Tc(r, e) {
  return r === "float32" ? e ? "vec4<f32>" : "f32" : r === "int32" || r === "bool" ? e ? "vec4<i32>" : "i32" : r;
}
function fte(r, e, t10) {
  let o = r.length, n = Tc(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] = ${n}(value);
    }`, o >= 2) {
    let a = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, o), i = At(o);
    t10 ? s += `
      fn setOutputAtCoords(${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}(${a.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `;
  }
  return s;
}
function dte(r) {
  let e = /(\w+)\s*:\s*vec(5|6)/g;
  r = r.replace(e, (o) => "@align(16) " + o);
  let t10 = /vec(5|6)\s*,\s*(\w+)/g;
  return r = r.replace(t10, (o, n, s) => `vec${n}, @align(16) ${s}`), r;
}
var pS = {};
Be(pS, { ArrayBufferToTypedArray: () => uS, GPUBytesPerElement: () => iS, MatMulProgramType: () => Qo, computeDispatch: () => ae, computeWorkGroupInfoForMatMul: () => aS, computeWorkGroupSizeForConv2d: () => Hl, computeWorkPerThreadForConv2d: () => ql, flatDispatchLayout: () => fe, isWebGPUSupported: () => Kl, tilesFitEvenlyIntoShape: () => gte });
var Wu = (r) => {
  let e = 1;
  for (let t10 = 0; t10 < r.length; t10++)
    e *= r[t10];
  return e;
};
function gte(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 ae(r, e, t10 = [1, 1, 1], o = [1, 1, 1]) {
  let [n, s, a] = [Math.ceil(Wu(r.x.map((i) => e[i])) / (t10[0] * o[0])), r.y ? Math.ceil(Wu(r.y.map((i) => e[i])) / (t10[1] * o[1])) : 1, r.z ? Math.ceil(Wu(r.z.map((i) => e[i])) / (t10[2] * o[2])) : 1];
  return [n, s, a];
}
function aS(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 Hl(r, e, t10 = false) {
  if (t10)
    return [8, 8, 1];
  let o = Wu(r.x.map((s) => e[s])), n = Wu(r.y.map((s) => e[s]));
  return o <= 4 ? [4, 16, 1] : n <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function ql(r, e, t10 = false) {
  if (t10)
    return [4, 4, 1];
  let o = Wu(r.x.map((s) => e[s])), n = Wu(r.y.map((s) => e[s]));
  return o <= 4 ? [1, 2, 1] : n <= 4 ? [2, 1, 1] : [2, 2, 1];
}
function fe(r) {
  return { x: r.map((e, t10) => t10) };
}
function iS(r) {
  if (r === "float32" || r === "int32" || r === "bool" || r === "string")
    return 4;
  if (r === "complex64")
    return 8;
  throw new Error(`Unknown dtype ${r}`);
}
function uS(r, e) {
  if (e === "float32")
    return new Float32Array(r);
  if (e === "int32")
    return new Int32Array(r);
  if (e === "bool" || e === "string")
    return Uint8Array.from(new Int32Array(r));
  throw new Error(`Unknown dtype ${e}`);
}
function Kl() {
  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
var Qo;
(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";
})(Qo || (Qo = {}));
var xte = P().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var yte = (r, e) => {
  let t10 = r.limits.maxComputeWorkgroupsPerDimension, o = e.dispatchLayout, n = e.dispatch;
  if (n.every((a) => a <= t10))
    return n;
  x.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])), x.assert(s <= t10, () => "Total dispatch size exceeds WebGPU maximum."), [s, s, s]) : [s, s, 1];
};
var Ai = class extends Jr {
  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, !Kl())
      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"), this.adapterInfo = new Og(t10), this.bufferManager = new Mg(this.device), this.textureManager = new Lg(this.device), this.tensorMap = new rn(this, cr()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), P().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 Ai.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 ("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), P().getBool("WEBGPU_USE_PROFILE_TOOL") && (x.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 = I.mergeRealAndImagArrays(a, i);
    } else {
      let s = t10.resourceInfo, a = await this.getBufferData(s.buffer, s.size);
      n = uS(a, t10.dtype);
    }
    return this.convertAndCacheOnCPU(e, n), n;
  }
  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 = cr().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) => x.decodeString(n));
        return ne(e.shape, e.dtype, o);
      } catch (o) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return ne(e.shape, e.dtype, t10);
  }
  async time(e) {
    this.supportTimeQuery || console.warn("This device doesn't support timestamp-query 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 = x.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = x.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 = x.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 && x.isString(o[0]) && (o = o.map((s) => x.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 = iS(t10.dtype) * x.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 = [];
    e.forEach((p) => {
      p.data.length === 0 && (p.data = [1]);
      let u;
      switch (p.data.length) {
        case 1:
          u = 4;
          break;
        case 2:
          u = 8;
          break;
        case 3:
          u = 16;
          break;
        case 4:
          u = 16;
          break;
        case 5:
          u = 16;
          break;
        case 6:
          u = 16;
          break;
        default:
          x.assert(false, () => `Unsupported ${p.data.length}D shape`);
      }
      (o === 5 || o === 6) && (u = 16), t10 = Math.ceil(t10 / u) * u, o = p.data.length, n.push(t10), t10 += p.data.length * 4;
    });
    let s = new ArrayBuffer(t10);
    e.forEach((p, u) => {
      let c = n[u];
      p.type === "int32" ? new Int32Array(s, c, p.data.length).set(p.data) : p.type === "uint32" ? new Uint32Array(s, c, p.data.length).set(p.data) : new Float32Array(s, c, p.data.length).set(p.data);
    });
    let a = this.bufferManager.acquireBuffer(t10, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
    this.queue.writeBuffer(a, 0, s, 0, t10);
    let i = { size: t10, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: a };
    return this.uniformPendingDisposal.push(i), { offset: 0, size: t10, buffer: a };
  }
  runWebGPUProgram(e, t10, o, n, s) {
    if (s || (s = this.makeTensorInfo(e.outputShape, o)), x.sizeFromShape(s.shape) === 0)
      return this.tensorMap.get(s.dataId).values = x.getTypedArrayFromDType(s.dtype, 0), s;
    this.uploadToGPU(s.dataId), e.dispatch = yte(this.device, e);
    let a = [], i = [];
    if (!e.isFromPixels) {
      a.push({ type: "float32", data: [NaN] }), i = t10.concat(s).map((y) => y.shape);
      let h = "int32";
      i.map((y) => {
        a.push({ type: h, data: y });
      });
      let g = x.computeStrides(s.shape);
      if (a.push({ type: h, data: g }), e.size) {
        let y = x.sizeFromShape(e.outputShape);
        a.push({ type: h, data: [e.isVec4 ? y / 4 : y] });
      }
    }
    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 = J3(e, i, p, s), c;
    u in this.pipelineCache ? c = this.pipelineCache[u] : (c = Z3(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 f = this.getComputePass(), d = this.activeTimers != null;
    return d && this.supportTimeQuery && f.writeTimestamp(this.querySet, 0), f.setPipeline(c), f.setBindGroup(0, m), f.dispatchWorkgroups(e.dispatch[0], e.dispatch[1], e.dispatch[2]), d && this.supportTimeQuery && f.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t10.forEach((h) => {
      this.commandQueueOwnedIds.add(h.dataId);
    }), this.commandQueueOwnedIds.add(s.dataId), P().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), d && 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 = xte) {
    return P().getBool("WEBGPU_CPU_FORWARD") && e.every((o) => this.tensorMap.get(o.dataId).resourceInfo == null && x.sizeFromShape(o.shape) < t10);
  }
  numDataIds() {
    return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;
  }
  dispose() {
    this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true);
  }
};
Ai.nextDataId = 0;
Kl() && pi("webgpu", async () => {
  P().set("CHECK_COMPUTATION_FOR_ERRORS", false);
  let r = { powerPreference: P().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, e = await navigator.gpu.requestAdapter(r), t10 = e.limits, o = {}, n = e.features.has("timestamp-query");
  o.requiredLimits = { maxComputeWorkgroupStorageSize: t10.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: t10.maxComputeWorkgroupsPerDimension, maxStorageBufferBindingSize: t10.maxStorageBufferBindingSize }, n && (o.requiredFeatures = ["timestamp-query"]);
  let s = await e.requestDevice(o), a = await e.requestAdapterInfo();
  return new Ai(s, a);
}, 3);
var ye;
(function(r) {
  r[r.MUL = 0] = "MUL", r[r.ADD = 1] = "ADD", r[r.ATAN2 = 2] = "ATAN2", r[r.SUB = 3] = "SUB", r[r.DIV = 4] = "DIV", r[r.EQUAL = 5] = "EQUAL", r[r.GREATER = 6] = "GREATER", r[r.GREATER_EQUAL = 7] = "GREATER_EQUAL", r[r.LESS = 8] = "LESS", r[r.LESS_EQUAL = 9] = "LESS_EQUAL", r[r.LOGICAL_AND = 10] = "LOGICAL_AND", r[r.NOT_EQUAL = 11] = "NOT_EQUAL", r[r.SQUARED_DIFFERENCE = 12] = "SQUARED_DIFFERENCE", r[r.INT_DIV = 13] = "INT_DIV", r[r.POW = 14] = "POW", r[r.PRELU = 15] = "PRELU", r[r.MAX = 16] = "MAX", r[r.MIN = 17] = "MIN", r[r.COMPLEX_MULTIPLY_REAL = 18] = "COMPLEX_MULTIPLY_REAL", r[r.COMPLEX_MULTIPLY_IMAG = 19] = "COMPLEX_MULTIPLY_IMAG";
})(ye || (ye = {}));
var bte = `
  if (isnan(a)) { return a; }
  if (isnan(b)) { return b; }
  `;
var tM = `
  if (isNaN.r) {
    resultTemp.r = valueForNaN;
  }
  if (isNaN.g) {
    resultTemp.g = valueForNaN;
  }
  if (isNaN.b) {
    resultTemp.b = valueForNaN;
  }
  if (isNaN.a) {
    resultTemp.a = valueForNaN;
  }
  `;
var rM = `
  let isNaN = isnanVec4(a) | isnanVec4(b);
  ${tM}
  `;
var Cte = "return a + b;";
var Ite = "return areal * breal - aimag * bimag;";
var wte = "return areal * bimag + aimag * breal;";
var Ste = "return a / b;";
var vte = "return a * b;";
var kte = "return (a - b) * (a - b);";
var Tte = "return a - b;";
var Nte = "return f32(a == b);";
var _te = "return vec4<f32>(a == b);";
var Ete = "return f32(a > b);";
var $te = "return vec4<f32>(a > b);";
var Rte = "return f32(a >= b);";
var Ate = "return vec4<f32>(a >= b);";
var Fte = "return f32(a < b);";
var Dte = "return vec4<f32>(a < b);";
var Pte = "return f32(a <= b);";
var Ote = "return vec4<f32>(a <= b);";
var Mte = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
var Lte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`;
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 Vte = `
  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 zte = `
  if (isnan(a) || isnan(b)) {
    return 1.0;
  }
  return f32(a != b);
`;
var Wte = `
  var resultTemp = vec4<f32>(a != b);
  let valueForNaN = 1.0;
  ${rM}

  return resultTemp;
`;
var Ute = `
  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 Gte = `
  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;
  ${tM}
  return resultTemp;
  `;
var Hte = "if (a < 0.0) { return b * a; }  return a;";
var qte = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;
function cS(r, e, t10 = "uniforms.NAN") {
  let o = e ? rM : bte;
  return e ? `
    let valueForNaN = ${t10};
    var resultTemp = vec4<f32>(${r}(a, b));
    ` + o + `
    return resultTemp;
  ` : o + `
    return ${r}(a, b);
  `;
}
function Nc(r, e) {
  switch (r) {
    case ye.MUL:
      return vte;
    case ye.ADD:
      return Cte;
    case ye.ATAN2:
      return cS("atan2", e);
    case ye.SUB:
      return Tte;
    case ye.DIV:
      return Ste;
    case ye.EQUAL:
      return e ? _te : Nte;
    case ye.GREATER:
      return e ? $te : Ete;
    case ye.GREATER_EQUAL:
      return e ? Ate : Rte;
    case ye.LESS:
      return e ? Dte : Fte;
    case ye.LESS_EQUAL:
      return e ? Ote : Pte;
    case ye.LOGICAL_AND:
      return e ? Lte : Mte;
    case ye.NOT_EQUAL:
      return e ? Wte : zte;
    case ye.SQUARED_DIFFERENCE:
      return kte;
    case ye.INT_DIV:
      return e ? Vte : Bte;
    case ye.PRELU:
      return e ? qte : Hte;
    case ye.MAX:
      return cS("max", e);
    case ye.MIN:
      return cS("min", e);
    case ye.POW:
      return e ? Gte : Ute;
    case ye.COMPLEX_MULTIPLY_REAL:
      return Ite;
    case ye.COMPLEX_MULTIPLY_IMAG:
      return wte;
    default:
      throw new Error(`BinaryType ${r} is not implemented!`);
  }
}
var pe;
(function(r) {
  r[r.ABS = 0] = "ABS", r[r.CEIL = 1] = "CEIL", r[r.COS = 2] = "COS", r[r.COSH = 3] = "COSH", r[r.ELU = 4] = "ELU", r[r.EXP = 5] = "EXP", r[r.EXPM1 = 6] = "EXPM1", r[r.FLOOR = 7] = "FLOOR", r[r.IS_NAN = 8] = "IS_NAN", r[r.LINEAR = 9] = "LINEAR", r[r.LOG = 10] = "LOG", r[r.LOGICAL_NOT = 11] = "LOGICAL_NOT", r[r.NEG = 12] = "NEG", r[r.RELU = 13] = "RELU", r[r.RELU6 = 14] = "RELU6", r[r.LEAKYRELU = 15] = "LEAKYRELU", r[r.RECIPROCAL = 16] = "RECIPROCAL", r[r.RSQRT = 17] = "RSQRT", r[r.SIN = 18] = "SIN", r[r.SINH = 19] = "SINH", r[r.SIGMOID = 20] = "SIGMOID", r[r.SQRT = 21] = "SQRT", r[r.SQUARE = 22] = "SQUARE", r[r.TANH = 23] = "TANH", r[r.TO_INT = 24] = "TO_INT";
})(pe || (pe = {}));
var Kte = "return abs(a);";
var jte = "return ceil(a);";
var Xte = "return cos(a);";
var Yte = `
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var Qte = "return exp(a) - 1.0;";
var Zte = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
var Jte = `
  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 ere = "return exp(a);";
var tre = "return floor(a);";
var rre = "return f32(isnan(a));";
var ore = "return a;";
var nre = `if (a < 0.0) { return uniforms.NAN; }
  return log(a);`;
var sre = "return f32(!(a >= 1.0));";
var are = "return -a;";
var ire = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var ure = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var pre = "return 1.0 / a;";
var cre = "return select(a, 0.0, a < 0.0);";
var lre = "return clamp(a, 0.0, 6.0);";
var mre = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var fre = `
  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`;
var dre = "return 1.0/sqrt(a);";
var hre = "return 1.0 / (1.0 + exp(-1.0 * a));";
var gre = "return sin(a);";
var xre = `
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var yre = "return sqrt(a);";
var bre = "return a * a;";
var Cre = `
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var Ire = "return f32(i32((a)));";
function za(r, e) {
  switch (r) {
    case pe.ABS:
      return Kte;
    case pe.COS:
      return Xte;
    case pe.COSH:
      return Yte;
    case pe.CEIL:
      return jte;
    case pe.ELU:
      return e ? Jte : Zte;
    case pe.EXP:
      return ere;
    case pe.EXPM1:
      return Qte;
    case pe.FLOOR:
      return tre;
    case pe.IS_NAN:
      return rre;
    case pe.LINEAR:
      return ore;
    case pe.LOG:
      return nre;
    case pe.LOGICAL_NOT:
      return sre;
    case pe.NEG:
      return are;
    case pe.LEAKYRELU:
      return e ? ure : ire;
    case pe.RECIPROCAL:
      return pre;
    case pe.RELU:
      return e ? fre : cre;
    case pe.RELU6:
      return e ? mre : lre;
    case pe.RSQRT:
      return dre;
    case pe.SIGMOID:
      return hre;
    case pe.SIN:
      return gre;
    case pe.SINH:
      return xre;
    case pe.SQRT:
      return yre;
    case pe.SQUARE:
      return bre;
    case pe.TANH:
      return Cre;
    case pe.TO_INT:
      return Ire;
    default:
      throw new Error(`BinaryType ${r} is not implemented!`);
  }
}
var vt = (r) => {
  switch (r) {
    case 1:
      return "f32";
    case 2:
      return "vec2<f32>";
    case 3:
      return "vec3<f32>";
    case 4:
      return "vec4<f32>";
    default:
      throw new Error(`${r}-component is not supported.`);
  }
};
function ur(r, e = false, t10 = false, o = 3) {
  if (r === null)
    return "";
  let n = "";
  if (r === "linear")
    n = za(pe.LINEAR);
  else if (r === "relu")
    n = za(pe.RELU, t10);
  else if (r === "elu")
    n = za(pe.ELU, t10);
  else if (r === "relu6")
    n = za(pe.RELU6, t10);
  else if (r === "prelu")
    n = Nc(ye.PRELU, t10);
  else if (r === "sigmoid")
    n = za(pe.SIGMOID, t10);
  else if (r === "leakyrelu")
    n = za(pe.LEAKYRELU, t10);
  else
    throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);
  let a = vt(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 Kr(r, e) {
  return `
      ${r ? "value = value + getBiasByOutputCoords(coords);" : ""}
      ${e ? "value = activation(value, coords);" : ""}
      `;
}
function lS(r, e, t10, o, n = false, s = false, a = false, i = 1) {
  x.assert(t10 && i === 1 || !t10, () => `transposeA ${t10} is not compatible with component size ${i}`);
  let p = `
      let batch = ${r ? "0" : "batchIn"};
      ${t10 ? "value = getA(batch, col, row);" : "value = getA(batch, row, col);"}

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

  fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${vt(i)} {
    let col = colIn * ${i};
    let batch = ${e ? "0" : "batchIn"};
    var value = ${vt(i)}(0.0);
    ${u}
    return value;
  }
  `;
}
function jl(r, e, t10, o, n, s, a = false, i = false, p = false, u = 1) {
  return `
  ${lS(t10, o, n, s, a, i, p, u)}
  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${vt(u)}) {
    let col = colIn * ${u};
    ${a && i ? "" : "if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
    {
      var value = valueIn;
      let coords = vec3<i32>(batch, row, col);
      ${Kr(r, e)}
      setOutputAtCoords(coords[0], coords[1], coords[2], value);
    }
  }
  `;
}
var wre = (r) => r ? `
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / InnerElementSize + inputCol);
        ` : `
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / InnerElementSize + inputCol);
        `;
var Sre = (r, e) => r ? `
        let ACached0 = mm_Asub[k * InnerElementSize][localRow];
        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];
        ${e === 3 ? "" : "let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];"}
        for (var i = 0; i < RowPerThread; i = i + 1) {
          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 < RowPerThread; i = i + 1) {
          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 Uu(r, e, t10 = false, o = 32, n = false, s = 32, a = false) {
  let i = e[1] * r[1], p = e[0] * r[0], u = t10 ? i : o, c = t10 ? o : i, l = u / e[0], m = o / e[1];
  return x.assert((t10 && l === 4 && r[1] === 4 || !t10 && (l === 3 || l === 4)) && u % e[0] === 0 && o % e[1] === 0 && r[0] === 4, () => `If transposeA ${t10} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.
          Otherwise, innerElementSize ${l} must be 3 or 4.
      tileAWidth ${u} must be divisible by workGroupSize[0]${e[0]}. tileInner ${o} must be divisible by workGroupSize[1] ${e[1]}. ColPerThread ${r[0]} must be 4.`), `
  var<workgroup> mm_Asub : array<array<vec${l}<f32>, ${u / l}>, ${c}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${p / r[0]}>, ${o}>;

  const RowPerThread = ${r[1]};
  const ColPerThread = ${r[0]};
  const InnerElementSize = ${l};
  const TileInner = ${o};

  @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
  fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
            @builtin(global_invocation_id) GlobalId : vec3<u32>,
            @builtin(num_workgroups) NumWorkgroups: vec3<u32>,
            @builtin(workgroup_id) workgroupId: vec3<u32>) {
    localId = LocalId;
    globalId = GlobalId;
    numWorkgroups = NumWorkgroups;

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

    let globalRow = ${a ? "0" : "i32(globalId.y) * RowPerThread"};
    let globalCol = i32(globalId.x);
    let batch = ${n ? "0" : "i32(globalId.z)"};
    let globalRowStart = i32(workgroupId.y) * ${i};

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

    var acc: array<vec4<f32>, RowPerThread>;

    // Loop over shared dimension.
    let tileRowB = localRow * ${m};
    for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileCol;
            ${wre(t10)}
        }

        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < ${m}; innerRow = innerRow + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol;
            mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);
        }
        kStart = kStart + TileInner;
        workgroupBarrier();

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

            ${Sre(t10, l)}
        }

        workgroupBarrier();
    }

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

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

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

  let globalRow = i32(globalId.y) * RowPerThread;
  let globalCol = i32(globalId.x) * ColPerThread;
  let globalRowStart = i32(workgroupId.y) * ${i};

  let tileRowA = i32(localId.y) * ${l};
  let tileColA = i32(localId.x) * ${m};
  let tileRowB = i32(localId.y) * ${f};
  // Loop over shared dimension.
  for (var t = 0; t < numTiles; t = t + 1) {
    // Load one tile of A into local memory.
    for (var innerRow = 0; innerRow < ${l}; innerRow = innerRow + 1) {
      for (var innerCol = 0; innerCol < ${m}; innerCol = innerCol + 1) {
        let inputRow = tileRowA + innerRow;
        let inputCol = tileColA + innerCol;
        ${oM(t10)}
      }
    }

    // Load one tile of B into local memory.
    for (var innerRow = 0; innerRow < ${f}; innerRow = innerRow + 1) {
      for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
        let inputRow = tileRowB + innerRow;
        let inputCol = tileCol + innerCol;
        mm_Bsub[inputRow][inputCol] = mm_readB(batch,
          kStart + inputRow,
          globalCol + innerCol);
      }
    }
    kStart = kStart + TileInner;
    workgroupBarrier();

    // Compute acc values for a single thread.
    var BCached : array<f32, ColPerThread>;
    for (var k = 0; k < TileInner; k = k + 1) {
      for (var inner = 0; inner < ColPerThread; inner = inner + 1) {
        BCached[inner] = mm_Bsub[k][tileCol + inner];
      }

      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
        ${vre(t10)}
        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
          acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
        }
      }
    }

    workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
      mm_write(batch, globalRow + innerRow, globalCol + innerCol,
          acc[innerRow][innerCol]);
    }
  }
  `;
  return `
    var<workgroup> mm_Asub : array<array<f32, ${u}>, ${c}>;
    var<workgroup> mm_Bsub : array<array<f32, ${p}>, ${o}>;
    const RowPerThread = ${r[1]};
    const ColPerThread = ${r[0]};
    const TileInner = ${o};

    @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
    fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
              @builtin(global_invocation_id) GlobalId : vec3<u32>,
              @builtin(num_workgroups) NumWorkgroups: vec3<u32>,
              @builtin(workgroup_id) workgroupId: vec3<u32>) {
      localId = LocalId;
      globalId = GlobalId;
      numWorkgroups = NumWorkgroups;

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

      var acc : array<array<f32, ColPerThread>, RowPerThread>;

      // Without this initialization strange values show up in acc.
      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
          acc[innerRow][innerCol] = 0.0;
        }
      }
      ${d}
    }
  `;
}
var kre = (r) => r ? `
      mm_readA(batch, colA, globalRow),
      mm_readA(batch, colA + 1, globalRow),
      mm_readA(batch, colA + 2, globalRow),
      mm_readA(batch, colA + 3, globalRow)
  ` : `
      mm_readA(batch, globalRow, colA),
      mm_readA(batch, globalRow, colA + 1),
      mm_readA(batch, globalRow, colA + 2),
      mm_readA(batch, globalRow, colA + 3)
  `;
function Tre(r, e = false) {
  return x.assert(r[1] === 1 && r[2] === 1, () => `A linear work group size is required. But got ${r}.`), `
    const TileSize = ${r[0] * 4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${r[0]}>;

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

      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
      let batch = i32(globalId.z);
      // Without this initialization strange values show up in acc.
      var acc = 0.0;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        let colA = t * TileSize + tileCol * 4;
        mm_Asub[tileCol] = vec4<f32>(${kre(e)});
        workgroupBarrier();

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

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

        workgroupBarrier();
      }

      mm_write(batch, globalRow, globalCol, acc);
    }
  `;
}
var Bg = class {
  constructor(e, t10, o, n, s = false, a = false, i = null, p = null, u = null, c = 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 l = s ? e[1] : e[2];
    if (this.isVec4 = (l % 4 === 0 && !s || t10[1] % 4 === 0 && s) && t10[2] % 4 === 0 && !a, this.isVectorA = t10[1] === 1 && !s, !this.isVec4 && this.isVectorA)
      this.elementsPerThread = [1, 1, 1], this.workGroupSize = [32, 1, 1];
    else {
      let d = aS(t10[1], l, t10[2], s);
      this.workGroupSize = d.workGroupSize, this.elementsPerThread = d.elementsPerThread;
    }
    this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
    let m = i != null, f = u != null;
    m && this.variableNames.push("bias"), f && this.variableNames.push("preluActivationWeights"), this.sequentialAccessByThreads = c, this.transposeA = s, this.transposeB = a, this.addBias = m, this.activation = p, this.hasPreluActivationWeights = f, this.batchAEqualOne = o, this.batchBEqualOne = n, [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(t10[1], t10[2], l), this.shaderKey = `matMulPacked_${this.elementsPerThread}_${s}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}_${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 `
      ${ur(this.activation, this.hasPreluActivationWeights, this.isVec4)}
      ${jl(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)}
      ${this.isVec4 ? Uu(this.elementsPerThread, this.workGroupSize, this.transposeA, this.tileInner, false, null, this.isVectorA) : this.isVectorA ? Tre(this.workGroupSize, this.transposeA) : Gu(this.elementsPerThread, this.workGroupSize, this.transposeA, this.tileInner, false, null, this.sequentialAccessByThreads)}
    `;
  }
};
function Nre() {
  return `
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${ue()} {
      let coords = getOutputCoords();
      let batch = coords[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 + i32(workGroupSizeX)) {
        let dataA = mm_readA(batch, row, k);
        let dataB = mm_readB(batch, k, col);
        sum = sum + dataA * dataB;
      }
      sumValues[localId.x] = sum;
      workgroupBarrier();

      for(var currentSize = workGroupSizeX / 2u; 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 Vg = 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 = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize);
    let u = a != null, c = p != null;
    u && this.variableNames.push("bias"), c && this.variableNames.push("preluActivationWeights"), this.transposeA = n, this.transposeB = s, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = c, this.batchAEqualOne = t10, this.batchBEqualOne = o, this.shaderKey = `matMulReduce_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
  }
  getUserCode() {
    return `
      ${ur(this.activation, this.hasPreluActivationWeights)}
      ${jl(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)}
      ${Nre()}
    `;
  }
};
function _re(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.
  ${ue()} {
    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);

    // 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(batch, globalRow, globalColA);
    var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);
    var regB1 = mm_readB(batch, 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(batch, globalRow, globalColA);
      regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);
      regB1 = mm_readB(batch, 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 zg = 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.batchAEqualOne = e[0] === 1, this.batchBEqualOne = t10[0] === 1, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${n}_${s}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
  }
  getUserCode() {
    return `
      ${ur(this.activation, this.hasPreluActivationWeights)}
      ${jl(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)}
      ${_re(this.workGroupSize)}
    `;
  }
};
var Wg = class {
  constructor(e, t10, o, n, s = false, a = false) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.atomic = true, this.isVec4 = false, this.splitedDimInner = 128, x.assert(e[0] === 1, () => "MatMulSplitKProgram only supports batch = 1."), this.outputShape = e, this.dispatchLayout = { x: [2], y: [1], z: [0, 3] }, this.isVec4 = (s && this.outputShape[1] % 4 === 0 || !s && t10 % 4 === 0) && this.outputShape[2] % 4 === 0, this.elementsPerThread = [4, 4, this.splitedDimInner], this.isVec4 || (this.outputShape[1] < 16 && (this.elementsPerThread[1] = 1), this.outputShape[2] < 16 && (this.elementsPerThread[0] = 1)), this.dispatch = ae(this.dispatchLayout, [this.outputShape[0], this.outputShape[1], this.outputShape[2], t10], this.workGroupSize, this.elementsPerThread), this.transposeA = s, this.transposeB = a, this.batchAEqualOne = o, this.batchBEqualOne = n, this.shaderKey = `matMulSplitK_${s}_${a}_${o}_${n}_${this.elementsPerThread}_${this.isVec4}`;
  }
  getUserCode() {
    let e = (n) => `
      for (var i = 0; i < ${n}; i = i + 1)
      {
        var oldValue = atomicLoad(&(result[flatIndex + i]));
        var exchanged = false;
        for (; !exchanged;) {
          let newValueF32 = bitcast<f32>(oldValue) + ${n > 1 ? "value[i]" : "value"};
          let newValue = bitcast<i32>(newValueF32);
          let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue);
          oldValue = res.old_value;
          exchanged = res.exchanged;
        }
      }
      `, t10 = this.isVec4 ? 4 : 1;
    return `
      ${lS(this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, false, false, false, t10)}
      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${vt(t10)}) {
        let col = colIn * ${t10};
        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.
          ${e(t10)}
        }
      }
      ${this.isVec4 ? Uu(this.elementsPerThread, this.workGroupSize, this.transposeA, 32, true, this.splitedDimInner) : Gu(this.elementsPerThread, this.workGroupSize, this.transposeA, 32, true, this.splitedDimInner)}
    `;
  }
};
var Ug = class {
  constructor(e, t10 = null, o = null, n = null) {
    this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.addBias = t10 != null, this.hasPreluActivationWeights = n != null, this.activation = o, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.shaderKey = `biasActivation_${o}`;
  }
  getUserCode() {
    return `
    ${ur(this.activation, this.hasPreluActivationWeights)}
    ${ue("index")} {
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        var value = getXByOutputIndex(index);
        ${Kr(this.addBias, this.activation)}
        setOutputAtIndex(index, value);
      }
    }
    `;
  }
};
var Gg = class {
  constructor(e) {
    this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
  }
  getUserCode() {
    return `
    ${ue("index")} {
      if (index < uniforms.size) {
        setOutputAtIndex(index, uniforms.value);
      }
    }
  `;
  }
};
function $o(r) {
  let { backend: e, attrs: t10 } = r, { shape: o, value: n } = t10, { dtype: s } = t10;
  if (s = s || x.inferDtype(n), s === "string") {
    let a = x.getArrayFromDType(s, x.sizeFromShape(o));
    return a.fill(n), e.makeTensorInfo(o, s, a);
  } else {
    let a = new Gg(o), i = [{ type: "float32", data: [n] }];
    return e.runWebGPUProgram(a, [], s, i);
  }
}
var nM = { kernelName: ys, backendName: "webgpu", kernelFunc: $o };
function xe(r) {
  let { inputs: e, attrs: t10 } = r, { x: o } = e, { shape: n } = t10, s = x.sizeFromShape(o.shape), a = x.inferFromImplicitShape(n, s), i = x.sizeFromShape(a);
  return x.assert(s === i, () => `The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`), r.backend.incRef(o.dataId), { dataId: o.dataId, shape: a, dtype: o.dtype };
}
var sM = { kernelName: Ss, backendName: "webgpu", kernelFunc: xe };
function _c({ a: r, b: e, transposeA: t10, transposeB: o, backend: n, bias: s = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: p = null }) {
  let u = r.shape.length, c = e.shape.length, l = t10 ? r.shape[u - 2] : r.shape[u - 1], m = o ? e.shape[c - 1] : e.shape[c - 2], f = t10 ? r.shape[u - 1] : r.shape[u - 2], d = o ? e.shape[c - 2] : e.shape[c - 1], h = r.shape.slice(0, -2), g = e.shape.slice(0, -2), y = x.sizeFromShape(h), b = x.sizeFromShape(g), w = br.assertAndGetBroadcastShape(r.shape.slice(0, -2), e.shape.slice(0, -2)).concat([f, d]);
  x.assert(l === m, () => `Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t10} and transposeB=${o} must match.`);
  let k = t10 ? [y, l, f] : [y, f, l], _ = o ? [b, d, m] : [b, m, d], E = xe({ inputs: { x: r }, backend: n, attrs: { shape: k } }), R = xe({ inputs: { x: e }, backend: n, attrs: { shape: _ } }), A = [E, R], D = Math.max(y, b), O = y === 1, M = b === 1, L = [E, R], W = [{ type: "int32", data: [f] }, { type: "int32", data: [d] }, { type: "int32", data: [l] }], V, G, q = [D, f, d], H = P().get("WEBGPU_MATMUL_PROGRAM_TYPE");
  switch (H < 0 && (f * d <= 128 ? H = Qo.MatMulReduceProgram : D === 1 && f <= 128 && d <= 48 && m >= 2e3 ? H = Qo.MatMulSplitKProgram : f <= 16 && (d <= 512 || m >= 2 * d) || d <= 16 && (f <= 512 || l >= 2 * f) ? H = Qo.MatMulSmallOutputSizeProgram : H = Qo.MatMulPackedProgram), H) {
    case Qo.MatMulReduceProgram:
      V = new Vg(q, O, M, t10, o, s, p, a);
      break;
    case Qo.MatMulSplitKProgram: {
      if (G = $o({ backend: n, attrs: { shape: q, value: 0, dtype: r.dtype } }), V = new Wg(q, m, O, M, t10, o), s || p) {
        G = n.runWebGPUProgram(V, L, r.dtype, W, G);
        let Z = new Ug(G.shape, s, p, a), ee = null, X = [G];
        s && X.push(s), a && X.push(a), p === "leakyrelu" && (ee = [{ type: "float32", data: [i] }], Z.uniforms += " alpha : f32,");
        let Q = n.runWebGPUProgram(Z, X, G.dtype, ee);
        A.push(G);
        let se = xe({ inputs: { x: Q }, backend: n, attrs: { shape: w } });
        A.push(Q);
        for (let ie of A)
          n.disposeData(ie.dataId);
        return se;
      }
      break;
    }
    case Qo.MatMulSmallOutputSizeProgram:
      V = new zg(k, _, q, t10, o, s, p, a);
      break;
    case Qo.MatMulPackedProgram:
      let Y = n.adapterInfo.isIntel();
      V = new Bg(k, q, O, M, t10, o, s, p, a, Y);
      break;
    default:
      throw new Error(`Unsupported MatMulProgramType ${H}.`);
  }
  s && L.push(s), a && L.push(a), p === "leakyrelu" && (W.push({ type: "float32", data: [i] }), V.uniforms += " alpha : f32,"), G = n.runWebGPUProgram(V, L, r.dtype, W, G);
  let j = xe({ inputs: { x: G }, backend: n, attrs: { shape: w } });
  A.push(G);
  for (let Y of A)
    n.disposeData(Y.dataId);
  return j;
}
function Ere(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s, bias: a, preluActivationWeights: i } = e, { transposeA: p, transposeB: u, activation: c, leakyreluAlpha: l } = o;
  return _c({ a: n, b: s, transposeA: p, transposeB: u, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: l, activation: c });
}
var aM = { kernelName: Fo, backendName: "webgpu", kernelFunc: Ere };
var Xl = class {
  constructor(e, t10, o) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = I.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
  }
  getUserCode() {
    return `
      fn binaryOpComplex(
          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
        ${Nc(this.op, false)}
      }

      ${ue("index")} {
        if(index < uniforms.size) {
          let areal = getARealByOutputIndex(index);
          let aimag = getAImagByOutputIndex(index);
          let breal = getBRealByOutputIndex(index);
          let bimag = getBImagByOutputIndex(index);
          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `;
  }
};
var Hu = class {
  constructor(e, t10, o) {
    this.size = true, this.variableNames = ["A", "B"], this.outputShape = I.assertAndGetBroadcastShape(t10, o), this.dispatchLayout = fe(this.outputShape), this.op = e, this.useSharedMemoryWithA = t10.length <= 1 && o.length > 1 && t10[0] < 128, this.useSharedMemoryWithB = o.length <= 1 && t10.length > 1 && o[0] < 128, this.useSharedMemoryWithA || this.useSharedMemoryWithB ? (this.isVec4 = false, this.lastDimensionSize = this.useSharedMemoryWithB ? o[0] : t10[0], this.shaderKey = `binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`, this.type = "shared", this.workGroupSize = [256, 1, 1], this.workPerThread = 1) : (x.arraysEqual(t10, o) && x.sizeFromShape(t10) % 4 === 0 ? (this.isVec4 = true, this.type = "vec4", this.workPerThread = 4) : (this.isVec4 = false, this.type = "plain", this.workPerThread = 1), this.shaderKey = `binary_${this.type}_${e}`, this.workGroupSize = [128, 1, 1]), this.dispatch = ae(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} {
      ${Nc(this.op, this.isVec4)}
    };
    `;
    if (this.type === "shared") {
      let n = this.lastDimensionSize > 1 ? `coords[${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}>;
        ${ue("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}
       ${ue("index")} {
         if (index < uniforms.size) {
           let a = getAByOutputIndex(index);
           let b = getBByOutputIndex(index);
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
       `;
    return e;
  }
};
function Lt(r) {
  let { inputs: e } = r, { x: t10 } = e;
  return r.backend.incRef(t10.dataId), { dataId: t10.dataId, shape: t10.shape, dtype: t10.dtype };
}
var iM = { kernelName: uo, backendName: "webgpu", kernelFunc: Lt };
function ls(r) {
  let { inputs: e, backend: t10 } = r, { real: o, imag: n } = e, s = t10.makeTensorInfo(o.shape, "complex64"), a = t10.tensorMap.get(s.dataId), i = Lt({ inputs: { x: o }, backend: t10 }), p = Lt({ inputs: { x: n }, backend: t10 });
  return a.complexTensorInfos = { real: i, imag: p }, s;
}
var uM = { kernelName: aa, backendName: "webgpu", kernelFunc: ls };
var Zo = class {
  constructor(e, t10) {
    this.variableNames = ["A"], this.size = true;
    let o = 128;
    this.workGroupSize = [o, 1, 1], this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.op = t10, this.shaderKey = `unary_${t10}`;
  }
  getUserCode() {
    return `
      fn unaryOperation(a : f32) -> f32 {
        ${za(this.op, false)}
      }
      ${ue("index")} {
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          setOutputAtIndex(index, unaryOperation(a));
        }
      }
      `;
  }
};
function Ge({ 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 Zo(s.shape, r);
    return a.runWebGPUProgram(p, [s], i);
  };
}
function it({ 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), f, d;
      if (r !== ye.MUL)
        [f, d] = [[l.complexTensorInfos.real, m.complexTensorInfos.real], [l.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((g) => {
          let [y, b] = g, C = { dataId: y.dataId, dtype: y.dtype, shape: a.shape }, w = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, k = new Hu(r, a.shape, i.shape);
          return p.runWebGPUProgram(k, [C, w], ct(y.dtype, b.dtype));
        });
      else {
        let g = new Xl(ye.COMPLEX_MULTIPLY_REAL, a.shape, i.shape), y = new Xl(ye.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 }];
        f = p.runWebGPUProgram(g, b, "float32"), d = p.runWebGPUProgram(y, b, "float32");
      }
      let h = ls({ inputs: { real: f, imag: d }, backend: p });
      return p.disposeData(f.dataId), p.disposeData(d.dataId), h;
    }
    let u = o || ct(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, f = a.dtype === "string" ? I.fromUint8ToStringArray(l) : l, d = a.dtype === "string" ? I.fromUint8ToStringArray(m) : m, [h, g] = e(a.shape, i.shape, f, d, u);
      return p.makeTensorInfo(g, u, h);
    }
    let c = new Hu(r, a.shape, i.shape);
    return p.runWebGPUProgram(c, [a, i], u);
  };
}
var FS = {};
Be(FS, { addImpl: () => dS, bincountImpl: () => lM, bincountReduceImpl: () => mM, castImpl: () => fS, ceilImpl: () => hS, concatImpl: () => fM, equalImpl: () => gS, expImpl: () => xS, expm1Impl: () => yS, floorImpl: () => bS, gatherNdImpl: () => dM, gatherV2Impl: () => hM, greaterEqualImpl: () => IS, greaterImpl: () => CS, lessEqualImpl: () => SS, lessImpl: () => wS, linSpaceImpl: () => gM, logImpl: () => vS, maxImpl: () => xM, maximumImpl: () => kS, minimumImpl: () => TS, multiplyImpl: () => Ql, negImpl: () => yM, notEqualImpl: () => NS, prodImpl: () => bM, raggedGatherImpl: () => IM, raggedTensorToTensorImpl: () => vM, rangeImpl: () => kM, rsqrtImpl: () => ES, scatterImpl: () => TM, sigmoidImpl: () => NM, simpleAbsImpl: () => pM, sliceImpl: () => _M, sparseFillEmptyRowsImpl: () => EM, sparseReshapeImpl: () => $M, sparseSegmentReductionImpl: () => RM, sqrtImpl: () => AM, squaredDifferenceImpl: () => $S, stridedSliceImpl: () => FM, stringNGramsImpl: () => DM, stringSplitImpl: () => PM, stringToHashBucketFastImpl: () => OM, subImpl: () => AS, tileImpl: () => MM, topKImpl: () => BM, transposeImpl: () => _S, uniqueImpl: () => VM });
function Xs(r, e) {
  Array.isArray(r) || (r = [r]), r.forEach((t10) => {
    t10 != null && x.assert(t10.dtype !== "complex64", () => `${e} does not support complex64 tensors in the CPU backend.`);
  });
}
function pM(r) {
  let e = new Float32Array(r.length);
  for (let t10 = 0; t10 < r.length; ++t10)
    e[t10] = Math.abs(r[t10]);
  return e;
}
function kt(r) {
  return (e, t10, o, n, s) => {
    let a = I.assertAndGetBroadcastShape(e, t10), i = a.length, p = x.computeStrides(a), u = x.sizeFromShape(a), c = x.getTypedArrayFromDType(s, u), l = e.length, m = t10.length, f = x.computeStrides(e), d = x.computeStrides(t10), h = I.getBroadcastDims(e, a), g = I.getBroadcastDims(t10, a);
    if (h.length + g.length === 0)
      for (let y = 0; y < c.length; ++y)
        c[y] = r(o[y % o.length], n[y % n.length]);
    else
      for (let y = 0; y < c.length; ++y) {
        let b = x.indexToLoc(y, i, p), C = b.slice(-l);
        h.forEach((E) => C[E] = 0);
        let w = x.locToIndex(C, l, f), k = b.slice(-m);
        g.forEach((E) => k[E] = 0);
        let _ = x.locToIndex(k, m, d);
        c[y] = r(o[w], n[_]);
      }
    return [c, a];
  };
}
function Ec(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;
}
function Hg(r, e, t10 = "float32") {
  if (t10 === "complex64") {
    let n = Hg(r, e, "float32"), s = Hg(r, e, "float32");
    return Ec({ inputs: { real: n, imag: s }, backend: r });
  }
  let o = x.makeZerosTypedArray(x.sizeFromShape(e), t10);
  return r.makeTensorInfo(e, t10, o);
}
function mS(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  return t10.incRef(o.dataId), { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function cM(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);
}
function fS(r, e, t10, o) {
  if (o === "int32") {
    let n = Int32Array.from(r);
    return [e, "int32", n];
  }
  if (o === "bool") {
    let n = x.toTypedArray([0], t10), [s, a] = kt((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 Yl(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return mS({ inputs: { x: n }, backend: t10 });
    let c = Hg(t10, n.shape, n.dtype), l = Yl({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), m = Ec({ inputs: { real: l, imag: c }, backend: t10 });
    return t10.disposeIntermediateTensorInfo(c), t10.disposeIntermediateTensorInfo(l), m;
  }
  if (n.dtype === "complex64") {
    let c = cM({ inputs: { input: n }, backend: t10 }), l = Yl({ inputs: { x: c }, backend: t10, attrs: { dtype: s } });
    return t10.disposeIntermediateTensorInfo(c), l;
  }
  if (!x.hasEncodingLoss(n.dtype, s)) {
    let c = mS({ 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] = fS(a, n.shape, n.dtype, s);
  return t10.makeTensorInfo(i, p, u);
}
function Dt(r, e, t10, o) {
  return t10 == null ? ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    Xs([a, i], r);
    let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = a.dtype === "string" ? I.fromUint8ToStringArray(u) : u, m = a.dtype === "string" ? I.fromUint8ToStringArray(c) : c, f = o || a.dtype, [d, h] = e(a.shape, i.shape, l, m, f);
    return p.makeTensorInfo(h, f, d);
  } : ({ inputs: n, backend: s }) => {
    let { a, b: i } = n, p = s;
    if (a.dtype === "complex64" || i.dtype === "complex64") {
      let u = Yl({ inputs: { x: a }, backend: p, attrs: { dtype: "complex64" } }), c = p.data.get(u.dataId), l = c.complexTensorInfos.real, m = c.complexTensorInfos.imag, f = p.data.get(l.dataId).values, d = p.data.get(m.dataId).values, h = Yl({ inputs: { x: i }, backend: p, attrs: { dtype: "complex64" } }), g = p.data.get(h.dataId), y = g.complexTensorInfos.real, b = g.complexTensorInfos.imag, C = p.data.get(y.dataId).values, w = p.data.get(b.dataId).values, [k, _, E] = t10(a.shape, i.shape, f, d, C, w), R = p.makeTensorInfo(E, "float32", k), A = p.makeTensorInfo(E, "float32", _), D = Ec({ inputs: { real: R, imag: A }, backend: p });
      return p.disposeIntermediateTensorInfo(u), p.disposeIntermediateTensorInfo(h), p.disposeIntermediateTensorInfo(R), p.disposeIntermediateTensorInfo(A), D;
    } else {
      let u = p.data.get(a.dataId).values, c = p.data.get(i.dataId).values, l = o || a.dtype, [m, f] = e(a.shape, i.shape, u, c, l);
      return p.makeTensorInfo(f, l, m);
    }
  };
}
function $c(r) {
  return (e, t10, o, n, s, a) => {
    let i = I.assertAndGetBroadcastShape(e, t10), p = x.sizeFromShape(i), u = i.length, c = x.computeStrides(i), l = x.getTypedArrayFromDType("float32", p), m = x.getTypedArrayFromDType("float32", p), f = I.getBroadcastDims(e, i), d = I.getBroadcastDims(t10, i), h = I.mergeRealAndImagArrays(o, n), g = I.mergeRealAndImagArrays(s, a), y = e.length, b = x.computeStrides(e), C = t10.length, w = x.computeStrides(t10);
    if (f.length + d.length === 0)
      for (let k = 0; k < l.length; k++) {
        let _ = k % h.length, E = k % g.length, R = r(h[_ * 2], h[_ * 2 + 1], g[E * 2], g[E * 2 + 1]);
        l[k] = R.real, m[k] = R.imag;
      }
    else
      for (let k = 0; k < l.length; k++) {
        let _ = x.indexToLoc(k, u, c), E = _.slice(-y);
        f.forEach((M) => E[M] = 0);
        let R = x.locToIndex(E, y, b), A = _.slice(-C);
        d.forEach((M) => A[M] = 0);
        let D = x.locToIndex(A, C, w), O = r(h[R * 2], h[R * 2 + 1], g[D * 2], g[D * 2 + 1]);
        l[k] = O.real, m[k] = O.imag;
      }
    return [l, m, i];
  };
}
var dS = kt((r, e) => r + e);
var $re = $c((r, e, t10, o) => ({ real: r + t10, imag: e + o }));
var LTt = Dt(_r, dS, $re);
function lM(r, e, t10, o, n) {
  let s = x.sizeFromShape(o), a = x.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 mM(r, e, t10, o = false) {
  let n = r.shape[0], s = r.shape[1], a = ne([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 Tr(r) {
  return (e, t10, o) => {
    let n = x.getTypedArrayFromDType(t10, e.length);
    for (let s = 0; s < e.length; ++s)
      n[s] = r(e[s], o);
    return n;
  };
}
function qg(r, e, t10) {
  return ({ inputs: o, attrs: n, backend: s }) => {
    let { x: a } = o;
    if (Xs(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 = x.sizeFromShape(a.shape), c = t10 || a.dtype, l = x.getArrayFromDType(c, u);
    for (let m = 0; m < u; ++m)
      l[m] = e(p[m], n);
    return i.makeTensorInfo(a.shape, c, l);
  };
}
function Jo(r, e, t10) {
  return ({ inputs: o, attrs: n, backend: s }) => {
    let { x: a } = o;
    if (Xs(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 hS = Tr((r) => Math.ceil(r));
var YTt = Jo(ro, hS);
function fM(r, e, t10, o) {
  let n = x.getArrayFromDType(t10, x.sizeFromShape(e));
  if (o && t10 !== "string") {
    let s = 0;
    r.forEach((a) => {
      let i = x.sizeFromShape(a.shape);
      n.set(a.vals, s), s += i;
    });
  } else {
    let s = 0;
    r.forEach((a) => {
      let i = t10 === "string" ? I.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 gS = kt((r, e) => r === e ? 1 : 0);
var oNt = Dt(oo, gS, null, "bool");
var xS = Tr((r) => Math.exp(r));
var uNt = Jo(no, xS, "float32");
var yS = Tr((r) => Math.expm1(r));
var fNt = Jo(wn, yS);
var bS = Tr((r) => Math.floor(r));
var yNt = Jo(so, bS);
function dM(r, e, t10, o, n, s, a, i, p) {
  let u = ne([o, s], t10);
  for (let c = 0; c < o; c++) {
    let l = [], m = 0;
    for (let f = 0; f < n; f++) {
      let d = r[c * n + f];
      m += d * a[f], l.push(d);
    }
    if (m < 0 || m >= p / s)
      throw new Error(`Invalid indices: ${l} does not index into ${i}`);
    for (let f = 0; f < s; f++)
      u.values[c * s + f] = e.get(...e.indexToLoc(m * s + f));
  }
  return u;
}
function hM(r, e, t10) {
  let o = ne(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 CS = kt((r, e) => r > e ? 1 : 0);
var NNt = Dt(ao, CS, null, "bool");
var IS = kt((r, e) => r >= e ? 1 : 0);
var ANt = Dt(io, IS, null, "bool");
var wS = kt((r, e) => r < e ? 1 : 0);
var MNt = Dt(po, wS, null, "bool");
var SS = kt((r, e) => r <= e ? 1 : 0);
var WNt = Dt(co, SS, null, "bool");
function gM(r, e, t10) {
  let o = (e - r) / (t10 - 1), n = x.makeZerosTypedArray(t10, "float32");
  n[0] = r;
  for (let s = 1; s < n.length; s++)
    n[s] = n[s - 1] + o;
  return n;
}
var vS = Tr((r) => Math.log(r));
var XNt = Jo(lo, vS);
function xM(r, e, t10, o) {
  let n = x.getTypedArrayFromDType(o, x.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 kS = kt((r, e) => Math.max(r, e));
var r2t = Dt(mo, kS);
var TS = kt((r, e) => Math.min(r, e));
var i2t = Dt(fo, TS);
var Ql = kt((r, e) => r * e);
var Rre = $c((r, e, t10, o) => ({ real: r * t10 - e * o, imag: r * o + e * t10 }));
var m2t = Dt(ho, Ql, Rre);
function yM(r, e, t10) {
  let o = x.createScalarValue(-1, t10);
  return Ql([], e, o, r, t10);
}
var NS = kt((r, e) => r !== e ? 1 : 0);
var I2t = Dt(go, NS, null, "bool");
function _S(r, e, t10, o, n) {
  let s = e.length, a = x.sizeFromShape(e), i = x.computeStrides(e), p = x.computeStrides(n), u = x.getTypedArrayFromDType(t10, x.sizeFromShape(n));
  for (let c = 0; c < a; ++c) {
    let l = x.indexToLoc(c, s, i), m = new Array(l.length);
    for (let d = 0; d < m.length; d++)
      m[d] = l[o[d]];
    let f = x.locToIndex(m, s, p);
    u[f] = r[c];
  }
  return u;
}
function bM(r, e, t10, o) {
  let [n, s] = I.computeOutAndReduceShapes(r, o), a = ct(e, "int32"), i = x.makeZerosTypedArray(x.sizeFromShape(n), a), p = x.sizeFromShape(s);
  for (let u = 0; u < i.length; ++u) {
    let c = u * p, l = 1;
    for (let m = 0; m < p; ++m)
      l *= t10[c + m];
    i[u] = l;
  }
  return { outVals: i, outShape: n, outDtype: a };
}
function Are(r, e, t10) {
  r.forEach((o, n) => {
    if (o < 0 || o >= t10) {
      let s = x.indexToLoc(n, e.length, x.computeStrides(e)).join(",");
      throw new Error(`indices[${s}] = ${o} is not in [0, ${t10})`);
    }
  });
}
function Fre(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 Dre(r, e, t10, o) {
  let n = [], s = 0, a = e.length - 1 + t10.length, i = new Array(a).fill(null).map(() => [0]);
  Fre(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 f = t10[m], d = m + e.length - 1;
      if (d >= 0) {
        let h = i[d], g = h[h.length - 1] - f[c];
        for (let y = c; y < l; ++y)
          i[d].push(f[y + 1] + g);
      }
      c = f[c], l = f[l];
    }
    l !== c && (n.push([c, l]), s += l - c);
  }
  return { outSplits: i, valueSlices: n, numValues: s };
}
function Pre(r) {
  let e = [];
  for (let t10 = 0; t10 < r.length; ++t10) {
    let o = r[t10].length, n = x.getArrayFromDType("int32", o);
    e.push(n), r[t10].forEach((s, a) => n[a] = s);
  }
  return e;
}
function CM(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 Ore(r, e, t10, o, n, s) {
  let a = CM(e, 2)[1], i = CM(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 Mre(r, e, t10, o, n) {
  let s = e.slice();
  s[0] = n;
  let a = x.getArrayFromDType(t10, x.sizeFromShape(s)), i = r.length, p = i === 0 ? 0 : i / e[0];
  return Ore(r, e, o, p, a, s), [a, s];
}
function IM(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 (Are(s, a, p), o.length === 0)
    throw new Error("params.rank must be nonzero");
  let u = o[0], { outSplits: c, valueSlices: l, numValues: m } = Dre(s, a, r, u), f = Pre(c), d = Mre(t10, o, n, l, m);
  return [f, d[0], d[1]];
}
var en = I.RowPartitionType;
var Rc = 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 = I.getRowPartitionTypesHelper(c), this.raggedRank = I.getRaggedRank(this.rowPartitionTypes);
  }
  getRowPartitionTypeByDimension(e) {
    return this.rowPartitionTypes[0] === en.FIRST_DIM_SIZE ? this.rowPartitionTypes[e + 1] : this.rowPartitionTypes[e];
  }
  getRowPartitionTensor(e) {
    return this.rowPartitionTypes[0] === en.FIRST_DIM_SIZE ? this.rowPartitionValues[e + 1] : this.rowPartitionValues[e];
  }
  getMaxWidth(e) {
    let t10 = this.getRowPartitionTensor(e - 1);
    switch (this.getRowPartitionTypeByDimension(e - 1)) {
      case en.VALUE_ROWIDS:
        return Rc.getMaxWidthValueRowID(t10);
      case en.ROW_SPLITS:
        return Rc.getMaxWidthRowSplit(t10);
      default:
        throw new Error(`Cannot handle partition type ${en[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 SM(e, o);
  }
  calculateOutputSize(e) {
    let t10 = this.valuesShape, o = this.defaultValueShape;
    I.validateDefaultValueShape(o, t10);
    let n = this.tensorShapeFromTensor(this.shape, this.shapeShape), a = I.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 x.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 en.VALUE_ROWIDS:
        return this.calculateOutputIndexValueRowID(s, t10, o, n);
      case en.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: ${en[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 en.FIRST_DIM_SIZE:
        return e[0];
      case en.VALUE_ROWIDS:
        throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");
      case en.ROW_SPLITS:
        return this.rowPartitionValuesShapes[0][0] - 1;
      default:
        throw new Error(`Cannot handle type ${en[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 = SM(o, false), a = x.getArrayFromDType(this.valuesDType, x.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 = x.sizeFromShape(i), u = t10.length, c = this.defaultValue;
    if (c.length !== p && c.length !== 1) {
      let d = this.defaultValueShape;
      Ne(() => {
        let h = z(c, d);
        c = Ls(h, i).dataSync();
      });
    }
    let l = 0, m = 0, f = 0;
    for (let d = 0; d <= u; ++d) {
      let h = d < u ? t10[d] : -1;
      if (h === f) {
        ++f;
        continue;
      }
      if (m < f) {
        let g = s.subarray(l * p), y = a.subarray(m * p), b = (f - m) * p;
        wM(y, g, b);
      }
      if (d >= u) {
        let g = o.length;
        h = Math.floor(g / p);
      }
      if (h > f)
        if (this.defaultValue.length === 1)
          a.subarray(f * p, h * p).fill(this.defaultValue[0]), f = h;
        else
          for (; h > f; ) {
            let g = a.slice(f * p);
            wM(g, c, p), ++f;
          }
      h < 0 ? (l = d + 1, m = f) : (l = d, m = f, f = m + 1);
    }
  }
};
function wM(r, e, t10) {
  for (let o = 0; o < t10; o++)
    r[o] = e[o];
}
function SM(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 vM(r, e, t10, o, n, s, a, i, p, u) {
  return new Rc(r, e, t10, o, n, s, a, i, p, u).compute();
}
function kM(r, e, t10, o) {
  let n = r === e, s = r < e && t10 < 0, a = e < r && t10 > 1;
  if (n || s || a)
    return x.makeZerosTypedArray(0, o);
  let i = Math.abs(Math.ceil((e - r) / t10)), p = x.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 ES = Tr((r) => 1 / Math.sqrt(r));
var U2t = Jo(xo, ES);
function TM(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 ne(t10, e.dtype);
  let f = ne(c, e.dtype);
  typeof p == "string" || typeof p == "number" ? f.values.fill(p) : typeof p == "boolean" && f.values.fill(+p);
  for (let d = 0; d < s; d++) {
    let h = [], g = 0;
    for (let y = 0; y < a; y++) {
      let b = l[d * a + y];
      h.push(b), g += b * i[y];
    }
    if (g < 0 || g >= o / n)
      throw new Error(`Invalid indices: ${h} does not index into ${t10}`);
    for (let y = 0; y < n; y++)
      u ? f.values[g * n + y] += m[d * n + y] : f.values[g * n + y] = e.rank === 0 ? m[0] : m[d * n + y];
  }
  return f;
}
var NM = Tr((r) => 1 / (1 + Math.exp(-r)));
var Y2t = qg(yo, (r) => 1 / (1 + Math.exp(-r)));
function _M(r, e, t10, o, n) {
  let s = et.isSliceContinous(o, e, t10), a = x.sizeFromShape(t10), i = x.computeStrides(o);
  if (s) {
    let l = et.computeFlatOffset(e, i);
    return n === "string" ? r.slice(l, l + a) : r.subarray(l, l + a);
  }
  let p = n === "string" ? I.fromUint8ToStringArray(r) : r, u = ne(o, n, p), c = ne(t10, n);
  for (let l = 0; l < c.size; ++l) {
    let m = c.indexToLoc(l), f = m.map((d, h) => d + e[h]);
    c.set(u.get(...f), ...m);
  }
  return n === "string" ? I.fromStringArrayToUint8(c.values) : c.values;
}
function EM(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(I.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));
    let g = x.getArrayFromDType(t10, 0), y = x.getArrayFromDType(n, 0);
    return [g, [0, l], y, u, c];
  }
  let m = true, f = 0, d = new Array(p).fill(0);
  for (let g = 0; g < i; ++g) {
    let y = r[g * l];
    if (y < 0)
      throw new Error(I.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, y));
    if (y >= p)
      throw new Error(I.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, y, p));
    ++d[y], m = m && y >= f, f = y;
  }
  let h = true;
  for (let g = 0; g < p; ++g) {
    let y = d[g] === 0;
    u[g] = y, h = h && !y, d[g] = Math.max(d[g], 1), g > 0 && (d[g] += d[g - 1]);
  }
  if (h && m) {
    let g = r, y = o;
    for (let b = 0; b < i; ++b)
      c[b] = b;
    return [g, [i, l], y, u, c];
  } else {
    let g = d[p - 1], y = x.getArrayFromDType(t10, g * l), b = x.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 : d[k - 1]) + _;
      C[k]++;
      for (let R = 0; R < l; ++R)
        y[E * l + R] = r[w * l + R];
      b[E] = o[w], c[w] = E;
    }
    for (let w = 0; w < p; ++w)
      if (C[w] === 0) {
        let _ = w === 0 ? 0 : d[w - 1];
        y[_ * l + 0] = w;
        for (let E = 1; E < l; ++E)
          y[_ * l + E] = 0;
        b[_] = a;
      }
    return [y, [g, l], b, u, c];
  }
}
function $M(r, e, t10, o, n) {
  let s = x.sizeFromShape(o), a = e[0], i = n.length, p = [], u = 1, c = -1;
  for (let g = 0; g < i; ++g) {
    let y = n[g];
    if (y === -1) {
      if (c !== -1)
        throw new Error(I.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
      c = g, p.push(1);
    } else {
      if (y < 0)
        throw new Error(I.getSparseReshapeNegativeOutputDimErrorMessage(g, y));
      u *= y, p.push(y);
    }
  }
  if (c !== -1) {
    if (u <= 0)
      throw new Error(I.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
    let g = Math.trunc(s / u);
    if (u * g !== s)
      throw new Error(I.getSparseReshapeInputOutputMultipleErrorMessage(o, p));
    p[c] = g;
  }
  if (x.sizeFromShape(p) !== s)
    throw new Error(I.getSparseReshapeInputOutputMismatchErrorMessage(o, p));
  let m = o.length, f = [];
  if (m > 0) {
    f[m - 1] = 1;
    for (let g = m - 2; g >= 0; --g)
      f[g] = f[g + 1] * o[g + 1];
  }
  let d = [];
  if (i > 0) {
    d[i - 1] = 1;
    for (let g = i - 2; g >= 0; --g)
      d[g] = d[g + 1] * p[g + 1];
  }
  let h = x.getArrayFromDType(t10, a * i);
  for (let g = 0; g < a; ++g) {
    let y = 0;
    for (let b = 0; b < m; ++b)
      y += r[g * m + b] * f[b];
    for (let b = 0; b < i; ++b)
      h[g * i + b] = Math.trunc(y / d[b]), y %= d[b];
  }
  return [h, [a, i], p];
}
function RM(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(I.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let m = e.slice();
  m[0] = l;
  let f = m.reduce((C, w) => C * w, 1), d = x.getArrayFromDType(t10, f);
  if (i === 0)
    return l > 0 && d.fill(a), [d, m];
  if (l <= 0)
    throw new Error(I.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let h = 0, g = 1, y = 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(I.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
    }
    if (b < 0 || b >= l)
      throw new Error(I.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b, l));
    b > y && d.fill(a, y * u, b * u);
    for (let w = h; w < g; ++w) {
      let k = o[w];
      if (k < 0 || k >= p[0])
        throw new Error(I.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w, o[w], p[0]));
      for (let _ = 0; _ < u; _++)
        d[b * u + _] += r[k * u + _];
    }
    if (s)
      for (let w = 0; w < u; w++)
        d[b * u + w] /= g - h;
    if (h = g, ++g, y = b + 1, b = C, g > i)
      break;
  }
  return y < l && d.fill(a, y * u, l * u), [d, m];
}
var AM = Tr((r) => Math.sqrt(r));
var c_t = qg(bo, (r) => Math.sqrt(r));
var $S = kt((r, e) => {
  let t10 = r - e;
  return t10 * t10;
});
var h_t = Dt(Co, $S);
function FM(r, e, t10, o) {
  let n = ne(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 RS = class {
  constructor(e, t10, o, n, s, a) {
    this.separator = x.encodeString(e), this.nGramWidths = t10, this.leftPad = x.encodeString(o), this.rightPad = x.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), f = 0;
      f += u * this.leftPad.length;
      for (let b = 0; b < l; ++b)
        f += e[m + b].length;
      f += c * this.rightPad.length;
      let d = u + c + l - 1;
      f += d * this.separator.length, o[n + i] = new Uint8Array(f);
      let h = o[n + i], g = 0, y = (b) => b.forEach((C) => h[g++] = C);
      for (let b = 0; b < u; ++b)
        y(this.leftPad), y(this.separator);
      for (let b = 0; b < l - 1; ++b)
        y(e[m + b]), y(this.separator);
      if (l > 0) {
        y(e[m + l - 1]);
        for (let b = 0; b < c; ++b)
          y(this.separator), y(this.rightPad);
      } else {
        for (let b = 0; b < c - 1; ++b)
          y(this.rightPad), y(this.separator);
        y(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 = x.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], f = this.getNumNGrams(m, l);
        this.createNGrams(e, u, i, c, f, l), c += f;
      }), this.preserveShort && c === a[p]) {
        let l = t10[p + 1] - t10[p];
        if (l === 0)
          continue;
        let m = l + 2 * this.padWidth, f = 1;
        this.createNGrams(e, u, i, c, f, m);
      }
    }
    return [i, a];
  }
};
function DM(r, e, t10, o, n, s, a, i) {
  return new RS(t10, o, n, s, a, i).compute(r, e);
}
function Lre(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 PM(r, e, t10) {
  let o = r.length, n = [], s = 0, a = 0, i = new Array(o);
  for (let m = 0; m < o; ++m) {
    let f = n.length;
    Lre(r[m], e, t10, n);
    let d = n.length - f;
    i[m] = d, s += d, a = Math.max(a, d);
  }
  let p = x.getArrayFromDType("int32", s * 2), u = new Array(s), c = [o, a], l = 0;
  for (let m = 0; m < o; ++m)
    for (let f = 0; f < i[m]; ++f)
      p[l * 2] = m, p[l * 2 + 1] = f, u[l] = n[l], ++l;
  return [p, u, c];
}
function OM(r, e) {
  let t10 = x.getArrayFromDType("int32", r.length);
  for (let o = 0; o < r.length; ++o)
    t10[o] = x.fingerPrint64(r[o]).modulo(e).getLowBitsUnsigned();
  return t10;
}
var AS = kt((r, e) => r - e);
var Bre = $c((r, e, t10, o) => ({ real: r - t10, imag: e - o }));
var __t = Dt(Io, AS, Bre);
function MM(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 = ne(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 Zl = (r, e) => {
  let t10 = e.value - r.value;
  return t10 === 0 ? r.index - e.index : t10;
};
function LM(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)), f = Math.min(o, Math.floor(e + (i - p) * c / i + l));
      LM(r, e, m, f);
    }
    let n = r[e], s = t10, a = o;
    for (x.swap(r, t10, e), Zl(r[o], n) > 0 && x.swap(r, t10, o); s < a; ) {
      for (x.swap(r, s, a), s++, a--; Zl(r[s], n) < 0; )
        s = s + 1;
      for (; Zl(r[a], n) > 0; )
        a = a - 1;
    }
    Zl(r[t10], n) === 0 ? x.swap(r, t10, a) : (a = a + 1, x.swap(r, a, o)), a <= e && (t10 = a + 1), e <= a && (o = a - 1);
  }
}
function BM(r, e, t10, o, n) {
  let s = e[e.length - 1], [a, i] = [r.length / s, s], p = x.getTypedArrayFromDType(t10, a * o), u = x.getTypedArrayFromDType("int32", a * o);
  for (let l = 0; l < a; l++) {
    let m = l * i, f = r.subarray(m, m + i), d = new Array(f.length);
    f.forEach((b, C) => d[C] = { value: b, index: C }), o < d.length && (LM(d, o), d = d.slice(0, o)), n && d.sort(Zl);
    let h = l * o, g = p.subarray(h, h + o), y = u.subarray(h, h + o);
    for (let b = 0; b < o; b++)
      g[b] = d[b].value, y[b] = d[b].index;
  }
  let c = e.slice();
  return c[c.length - 1] = o, [ne(c, t10, p), ne(c, "int32", u)];
}
function VM(r, e, t10, o) {
  let n = x.parseAxisParam(e, t10)[0], s = [1, t10[0], 1];
  for (let d = 0; d < n; d++)
    s[0] *= t10[d];
  s[1] = t10[n];
  for (let d = n + 1; d < t10.length; d++)
    s[2] *= t10[d];
  let a = {}, i = new Int32Array(t10[n]), p = new je(s, o, r), u = [], c = s[0] === 1 && s[2] === 1;
  for (let d = 0; d < t10[n]; d++) {
    let h;
    if (c)
      h = r[d].toString();
    else {
      let g = [];
      for (let y = 0; y < s[0]; y++)
        for (let b = 0; b < s[2]; b++)
          g.push(p.get(y, d, b));
      h = g.join(",");
    }
    if (a[h] !== void 0)
      i[d] = a[h];
    else {
      let g = Object.keys(a).length;
      a[h] = g, i[d] = g, u.push(d);
    }
  }
  let l = s.slice();
  l[1] = Object.keys(a).length;
  let m = new je(l, o);
  u.forEach((d, h) => {
    for (let g = 0; g < s[0]; g++)
      for (let y = 0; y < s[2]; y++)
        m.set(p.get(g, d, y), g, h, y);
  });
  let f = t10.slice();
  return f[n] = l[1], { outputValues: m.values, outputShape: f, indices: i };
}
var { addImpl: zM, castImpl: WM, ceilImpl: UM, concatImpl: GM, equalImpl: HM, expImpl: qM, expm1Impl: KM, floorImpl: jM, gatherNdImpl: XM, gatherV2Impl: YM, greaterEqualImpl: QM, greaterImpl: ZM, lessEqualImpl: JM, lessImpl: eL, logImpl: tL, maxImpl: rL, maximumImpl: oL, minimumImpl: nL, multiplyImpl: sL, negImpl: aL, notEqualImpl: iL, prodImpl: uL, rangeImpl: pL, rsqrtImpl: cL, scatterImpl: lL, simpleAbsImpl: mL, sliceImpl: fL, stridedSliceImpl: dL, stringNGramsImpl: hL, subImpl: gL, tileImpl: xL, topKImpl: yL, transposeImpl: bL, uniqueImpl: _Et } = FS;
var Vre = Ge({ opType: pe.ABS, cpuKernelImpl: mL });
var CL = { kernelName: sn, backendName: "webgpu", kernelFunc: Vre };
var zre = it({ opType: ye.ADD, cpuKernelImpl: zM, supportsComplex: true });
var IL = { kernelName: _r, backendName: "webgpu", kernelFunc: zre };
var Kg = 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 = fe(this.outputShape), this.dispatch = ae(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 `
      ${ue("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, ${t10});
          }
        }
      }
    `;
  }
};
function Wre(r) {
  let { inputs: e, backend: t10 } = r, o = e;
  if (o.length === 1)
    return Lt({ inputs: { x: o[0] }, backend: t10 });
  let n = o.map((i) => i.dtype).reduce((i, p) => ct(i, p)), s = o.map((i) => i.shape), a = new Kg(s);
  return t10.runWebGPUProgram(a, o, n);
}
var wL = { kernelName: an, backendName: "webgpu", kernelFunc: Wre };
var Ac = class {
  constructor(e, t10, o) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
    let n = [t10];
    this.op = o === "min" ? "<" : ">";
    let [s, a] = I.computeOutAndReduceShapes(e, n);
    this.outputShape = s.length === 0 ? [1] : s, this.dispatchLayout = fe(this.outputShape), x.sizeFromShape(a) < 32 || x.sizeFromShape(s) > 1e3 ? (this.type = "plain", this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize)) : (this.type = "shared", this.dispatch = ae(this.dispatchLayout, this.outputShape, [1, 1, 1])), this.inputShape = e, this.shaderKey = `argMinMax_${this.op}_${this.type}`;
  }
  getUserCode() {
    let e = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${Yo(this.inputShape.length - 1)}`, t10 = () => {
      let o = "";
      if (this.outputShape.length === 1)
        this.inputShape.length !== 1 && (o += "outputCoords,");
      else
        for (let n = 0; n < this.outputShape.length; n++)
          o += `outputCoords.${Yo(n)},`;
      return o;
    };
    return this.type === "shared" ? `
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${`
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `}

      ${ue("index")} {
        let outputIndex = index / i32(workGroupSizeX);
        let reduceLength = ${e()};

        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 + i32(workGroupSizeX)) {
          let candidate = getX(${t10()} 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), workGroupSizeX);
        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]);
        }
      }
    ` : `
      ${ue("index")} {
        if (index < uniforms.size) {
          let outputCoords = getCoordsFromIndex(index);
          var bestIndex = 0;
          var bestValue = getX(${t10()} 0);
          let reduceLength = ${e()};
          for (var i = 1; i < reduceLength; i++) {
            let candidate = getX(${t10()} i);
            if (candidate ${this.op} bestValue) {
              bestValue = candidate;
              bestIndex = i;
            }
          }
          setOutputAtIndexI32(index, bestIndex);
        }
      }
      `;
  }
};
var jg = 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 = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]), this.shaderKey = "transposeShared";
  }
  getUserCode() {
    return `
      const TILE_DIM = ${this.workGroupSize[0]};
      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
      ${Ri()}
      fn _start(@builtin(local_invocation_id) localId : vec3<u32>,
                @builtin(workgroup_id) workgroupId : vec3<u32>) {
        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
        let width = uniforms.outShape[0];
        let height = uniforms.outShape[1];
        if (x < width && y < height) {
          tile[localId.y][localId.x] = A[y * width + x];
        }
        workgroupBarrier();

        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
        if (x < height && y < width) {
          setOutputAtIndex((y * height + x), tile[localId.x]
            [localId.y]);
        }
      }
    `;
  }
};
var Xg = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.newDim = t10, this.shaderKey = `transpose_${t10}`;
  }
  getUserCode() {
    let e = At(this.outputShape.length), t10 = Ure(this.newDim);
    return `
      ${ue("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 Ure(r) {
  let e = r.length;
  if (e > 6)
    throw Error(`Transpose for rank ${e} is not yet supported`);
  let t10 = new Array(e);
  for (let o = 0; o < r.length; o++)
    t10[r[o]] = `resRC.${Yo(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 = bL(l, n.shape, n.dtype, s, p);
    return t10.makeTensorInfo(p, n.dtype, m);
  }
  if (n.shape.length === 2 && x.arraysEqual(s, [1, 0])) {
    let c = new jg(n.shape, s);
    return a.runWebGPUProgram(c, [n], n.dtype);
  }
  let u = new Xg(n.shape, s);
  return a.runWebGPUProgram(u, [n], n.dtype);
}
var SL = { kernelName: Mr, backendName: "webgpu", kernelFunc: Nr };
function Gre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = x.parseAxisParam(s, n.shape), i = I.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = Nr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = I.getInnerMostAxes(a.length, p.shape.length)), I.assertAxesAreInnerMostDims("argMax", [a[0]], p.shape.length);
  let c = new Ac(p.shape, a[0], "max"), l = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l);
  return u.forEach((f) => t10.disposeData(f.dataId)), m;
}
var vL = { kernelName: un, backendName: "webgpu", kernelFunc: Gre };
function Hre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s } = o, a = x.parseAxisParam(s, n.shape), i = I.getAxesPermutation(a, n.shape.length), p = n, u = [];
  i != null && (p = Nr({ inputs: { x: n }, backend: t10, attrs: { perm: i } }), u.push(p), a = I.getInnerMostAxes(a.length, p.shape.length)), I.assertAxesAreInnerMostDims("argMin", [a[0]], p.shape.length);
  let c = new Ac(p.shape, a[0], "min"), l = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], m = t10.runWebGPUProgram(c, [p], "int32", l);
  return u.forEach((f) => t10.disposeData(f.dataId)), m;
}
var kL = { kernelName: ja, backendName: "webgpu", kernelFunc: Hre };
var qre = it({ opType: ye.ATAN2 });
var TL = { kernelName: sa, backendName: "webgpu", kernelFunc: qre };
var Jl = 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 = fe(this.outputShape), this.dispatch = ae(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 / count"), `
      ${ue("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 Yg = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
  }
  getUserCode() {
    return `
      ${ue("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);
        }
      }
    `;
  }
};
var Qg = 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] = I.computeOutAndReduceShapes(this.inputShape, [1]);
    this.outputShape = o.length === 0 ? [1] : o, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t10, this.shaderKey = `reduce_${t10}`;
  }
  getUserCode() {
    let e = "", t10 = "0.0";
    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");
    let o = 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, ${this.workGroupSize[0]}>;
       `}
       fn getOffset(outputIndex : i32) -> i32 {
         let outputCoords = getCoordsFromIndex(outputIndex);
         let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize;
          return offset;
       }
       ${ue("index")} {
         let outputIndex = index / i32(workGroupSizeX);
         let offset = getOffset(outputIndex);
         var bestValue = ${t10};
         let Length = uniforms.reduceSize;
         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x[offset + k]);
           ${e}
         }
         xBestValues[localId.x] = bestValue;
         workgroupBarrier();

         var reduceSize = min(u32(Length), workGroupSizeX);
         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) {
          ${o}
        }
       }
     `;
  }
};
function Ys(r, e, t10, o, n) {
  let s = r.shape.length, a = [], i = x.parseAxisParam(e, r.shape), p = i, u = I.getAxesPermutation(p, s), c = r;
  u != null && (c = Nr({ inputs: { x: r }, attrs: { perm: u }, backend: n }), p = I.getInnerMostAxes(p.length, s), a.push(c)), I.assertAxesAreInnerMostDims(o, p, s);
  let [l, m] = I.computeOutAndReduceShapes(c.shape, p), f = l;
  t10 && (f = I.expandShapeToKeepDim(l, i));
  let d;
  if ((o === "max" || o === "prod") && n.shouldExecuteOnCPU([c])) {
    let h = n.tensorMap.get(c.dataId).values;
    switch (o) {
      case "max":
        let g = rL(h, x.sizeFromShape(m), f, r.dtype);
        d = n.makeTensorInfo(f, r.dtype, g);
        break;
      case "prod":
        let { outVals: y, outShape: b, outDtype: C } = uL(c.shape, c.dtype, h, p);
        d = n.makeTensorInfo(b, C, y);
        break;
      default:
        throw new Error(`${o} CPU implementation is not yet supported.`);
    }
  } else {
    let h = x.sizeFromShape(m), y = x.sizeFromShape(c.shape) / h, b = { windowSize: h, inSize: h, batchSize: y, outSize: 1 }, C = o === "mean" ? "float32" : Ca(r.dtype), w = [{ type: "int32", data: [h] }], k = new Qg(b, o), _ = n.runWebGPUProgram(k, [c], C, w);
    a.push(_), d = xe({ inputs: { x: _ }, attrs: { shape: f }, backend: n });
  }
  return a.forEach((h) => n.disposeData(h.dataId)), d;
}
function em(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { reductionIndices: s, keepDims: a } = o;
  return Ys(n, s, a, "max", t10);
}
var NL = { kernelName: $n, backendName: "webgpu", kernelFunc: em };
function DS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { keepDims: s, axis: a } = o;
  return Ys(n, a, s, "mean", t10);
}
var _L = { kernelName: An, backendName: "webgpu", kernelFunc: DS };
function Zg(r, e, t10, o) {
  if (e.filterWidth === 1 && e.filterHeight === 1 && x.arraysEqual(e.inShape, e.outShape))
    return Lt({ 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 = xe({ inputs: { x: r }, backend: o, attrs: { shape: [r.shape[a - 3] * r.shape[a - 2], r.shape[a - 1]] } }), p;
    t10 === "avg" ? p = DS({ inputs: { x: i }, backend: o, attrs: { axis: 0, keepDims: false } }) : (x.assert(t10 === "max", () => `Invalid pool type ${t10}`), p = em({ inputs: { x: i }, backend: o, attrs: { reductionIndices: 0, keepDims: false } }));
    let u = xe({ 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 Yg(e) : (t10 === "avg" ? n = new Jl(e, "avg") : (x.assert(t10 === "max", () => `Invalid pool type ${t10}`), n = new Jl(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 Kre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = I.computePool2DInfo(n.shape, s, a, u, i, p);
  return Zg(n, c, "avg", t10);
}
var EL = { kernelName: pn, backendName: "webgpu", kernelFunc: Kre };
function jre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { a: n, b: s } = e, { transposeA: a, transposeB: i } = o;
  return _c({ a: n, b: s, transposeA: a, transposeB: i, backend: t10 });
}
var $L = { kernelName: cn, backendName: "webgpu", kernelFunc: jre };
var Jg = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${At(e.length)}, `, this.shaderKey = "slice";
  }
  getUserCode() {
    let e = At(this.rank), t10 = Xre(this.rank), o;
    return this.start.length === 1 ? o = this.outputShape.map((s, a) => "sourceLoc = uniforms.start + coords;") : o = this.outputShape.map((s, a) => `sourceLoc.${PS[a]} = uniforms.start.${Yo(a)} + coords.${PS[a]};`), `
      ${ue("index")} {
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${o.join(`
`)}
          setOutputAtIndex(index, getSource(${t10}));
        }
      }
    `;
  }
};
var PS = ["x", "y", "z", "w", "u", "v"];
function Xre(r) {
  if (r === 1)
    return "sourceLoc";
  if (r <= 6)
    return PS.slice(0, r).map((e) => `sourceLoc.${e}`).join(",");
  throw Error(`Slicing for rank ${r} is not yet supported`);
}
function ms(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { begin: s, size: a } = o, [i, p] = et.parseSliceParams(n, s, a);
  if (et.assertParamsValid(n, i, p), t10.shouldExecuteOnCPU([n]) || n.dtype === "string") {
    let l = t10.tensorMap.get(n.dataId), m = fL(l.values, i, p, n.shape, n.dtype);
    return t10.makeTensorInfo(p, n.dtype, m);
  }
  if (x.sizeFromShape(p) === 0)
    return t10.makeTensorInfo(p, n.dtype, []);
  let u = new Jg(i, p), c = [{ type: "int32", data: i }];
  return t10.runWebGPUProgram(u, [n], n.dtype, c);
}
var RL = { kernelName: qn, backendName: "webgpu", kernelFunc: ms };
var Yre = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, crops: a } = o;
  x.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 = I.getReshaped(n.shape, s, i), u = I.getPermuted(p.length, s.length), c = I.getReshapedPermuted(n.shape, s, i), l = I.getSliceBeginCoords(a, s.length), m = I.getSliceSize(c, a, s.length), f = [], d = xe({ inputs: { x: n }, backend: t10, attrs: { shape: p } }), h = Nr({ inputs: { x: d }, backend: t10, attrs: { perm: u } }), g = xe({ inputs: { x: h }, backend: t10, attrs: { shape: c } }), y = ms({ inputs: { x: g }, backend: t10, attrs: { begin: l, size: m } });
  return f.push(d), f.push(h), f.push(g), f.forEach((b) => t10.disposeData(b.dataId)), y;
};
var AL = { kernelName: hs, backendName: "webgpu", kernelFunc: Yre };
var OS = it({ opType: ye.NOT_EQUAL, dtype: "bool", cpuKernelImpl: iL });
var FL = { kernelName: go, backendName: "webgpu", kernelFunc: OS };
function Wa(r) {
  let { inputs: e, backend: t10 } = r, { input: o } = e, n = t10.tensorMap.get(o.dataId);
  return Lt({ inputs: { x: n.complexTensorInfos.real }, backend: t10 });
}
var DL = { kernelName: la, backendName: "webgpu", kernelFunc: Wa };
function PL(r, e) {
  let t10 = new Zo(r.shape, pe.TO_INT), o = e.runWebGPUProgram(t10, [r], "int32");
  return { dataId: o.dataId, shape: o.shape, dtype: o.dtype };
}
function MS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { dtype: s } = o;
  if (s === "complex64") {
    if (n.dtype === "complex64")
      return Lt({ inputs: { x: n }, backend: t10 });
    let a = Wr(n.shape), i = MS({ inputs: { x: n }, backend: t10, attrs: { dtype: "float32" } }), p = ls({ inputs: { real: i, imag: a }, backend: t10 });
    return a.dispose(), t10.disposeData(i.dataId), p;
  }
  if (n.dtype === "complex64") {
    let a = Wa({ inputs: { input: n }, backend: t10 }), i = MS({ inputs: { x: a }, backend: t10, attrs: { dtype: s } });
    return t10.disposeData(a.dataId), i;
  }
  if (!x.hasEncodingLoss(n.dtype, s)) {
    let a = Lt({ 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] = WM(a, n.shape, n.dtype, s);
    return t10.makeTensorInfo(i, p, u);
  }
  if (s === "int32")
    return PL(n, t10);
  if (s === "bool") {
    let a = t10.makeTensorInfo([], "bool", x.getTypedArrayFromDType("bool", 1)), p = OS({ 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 OL = { kernelName: to, backendName: "webgpu", kernelFunc: MS };
var Qre = Ge({ opType: pe.CEIL, cpuKernelImpl: UM });
var ML = { kernelName: ro, backendName: "webgpu", kernelFunc: Qre };
var ex = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
  }
  getUserCode() {
    return `
      ${ue("index")} {
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          var clampedValue : vec4<f32>;
          for (var i = 0; i < 4; i = i + 1) {
            if (isnan(value[i])) {
              clampedValue[i] = value[i];
            } else {
              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
            }
          }

          setOutputAtIndex(index, clampedValue);
        }
      }
    `;
  }
};
var tx = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "clip";
  }
  getUserCode() {
    return `
      ${ue("index")} {
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          if (isnan(value)) {
            setOutputAtIndex(index, value);
            return;
          }
          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `;
  }
};
function Zre(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { clipValueMin: s, clipValueMax: a } = o, i, p = [{ type: "float32", data: [s] }, { type: "float32", data: [a] }];
  return x.sizeFromShape(n.shape) % 4 === 0 ? i = new ex(n.shape) : i = new tx(n.shape), t10.runWebGPUProgram(i, [n], n.dtype, p);
}
var LL = { kernelName: Ro, backendName: "webgpu", kernelFunc: Zre };
var rx = class {
  constructor(e) {
    this.uniforms = "", this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = I.computeOutShape(e, 1), this.variableNames = e.map((t10, o) => `T${o}`), this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(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 `
      ${ue("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 Lt({ inputs: { x: n.complexTensorInfos.imag }, backend: t10 });
}
var BL = { kernelName: Ya, backendName: "webgpu", kernelFunc: qu };
function Fc(r, e, t10) {
  let o = r[0].dtype;
  if (o === "complex64") {
    let d = r.map((C) => Wa({ inputs: { input: C }, backend: t10 })), h = r.map((C) => qu({ inputs: { input: C }, backend: t10 })), g = Fc(d, e, t10), y = Fc(h, e, t10), b = ls({ inputs: { real: g, imag: y }, backend: t10 });
    return d.forEach((C) => t10.disposeData(C.dataId)), h.forEach((C) => t10.disposeData(C.dataId)), t10.disposeData(g.dataId), t10.disposeData(y.dataId), b;
  }
  let n = t10.shouldExecuteOnCPU(r);
  if (o === "string" && (n = true), n) {
    let d = r.map((k) => {
      let E = [-1, x.sizeFromShape(k.shape.slice(e))];
      return xe({ inputs: { x: k }, backend: t10, attrs: { shape: E } });
    }), h = d.map((k) => ({ vals: t10.readSync(k.dataId), shape: k.shape })), g = I.computeOutShape(d.map((k) => k.shape), 1), y = d[0].shape[0] === 1, b = GM(h, g, o, y), C = I.computeOutShape(r.map((k) => k.shape), e), w = t10.makeTensorInfo(C, o, b);
    return d.forEach((k) => t10.disposeData(k.dataId)), w;
  }
  let s = t10.device.limits.maxStorageBuffersPerShaderStage - 1;
  if (r.length > s) {
    let d = [];
    for (let g = 0; g < r.length; g += s) {
      let y = r.slice(g, g + s);
      d.push(Fc(y, e, t10));
    }
    let h = Fc(d, e, t10);
    for (let g of d)
      t10.disposeData(g.dataId);
    return h;
  }
  let { tensors2D: a, outShape: i } = Jre(r, e, t10), p = a.map((d) => d.shape), u = new rx(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 d = 1; d < l.length; d++)
      l[d] = l[d - 1] + p[d][1], c.push({ type: "int32", data: [l[d]] });
  }
  let m = t10.runWebGPUProgram(u, a, a[0].dtype, c);
  a.forEach((d) => t10.disposeData(d.dataId));
  let f = xe({ inputs: { x: m }, backend: t10, attrs: { shape: i } });
  return t10.disposeData(m.dataId), f;
}
function Jre(r, e, t10) {
  let o = I.computeOutShape(r.map((s) => s.shape), e);
  return { tensors2D: r.map((s) => xe({ inputs: { x: s }, backend: t10, attrs: { shape: [x.sizeFromShape(s.shape.slice(0, e)), x.sizeFromShape(s.shape.slice(e))] } })), outShape: o };
}
function LS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o, s = x.parseAxisParam(n, e[0].shape)[0], a = e.map((u) => u.shape);
  I.assertParamsConsistent(a, s);
  let i = I.computeOutShape(e.map((u) => u.shape), s);
  if (x.sizeFromShape(i) === 0)
    return t10.makeTensorInfo(i, e[0].dtype, []);
  let p = e.filter((u) => x.sizeFromShape(u.shape) > 0);
  return p.length === 1 ? Lt({ inputs: { x: p[0] }, backend: t10 }) : Fc(p, s, t10);
}
var VL = { kernelName: gs, backendName: "webgpu", kernelFunc: LS };
function eoe(r, e, t10, o, n = false, s = null, a = false, i = 4, p = 4, u = 4) {
  let c = (A) => {
    switch (A) {
      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 ${A} is not supported.`);
    }
  }, l = (A) => {
    switch (A) {
      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 ${A} is not supported.`);
    }
  }, m = r ? `
      let coord = vec4<i32>(batch, xRow, xCol, xCh);
      ` : `
      let coord = vec4<i32>(batch, xCh, xRow, xCol);
      `, f = r ? `
      let coords = vec4<i32>(
        batch,
        row / outWidth,
        row % outWidth,
        col);
      ` : `
      let coords = vec4<i32>(
        batch,
        row,
        col / outWidth,
        col % outWidth);
      `, d = r ? "uniforms.xShape[1]" : "uniforms.xShape[2]", h = r ? "uniforms.xShape[2]" : "uniforms.xShape[3]", g = r ? "row" : "col", y = 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 = ${y} / (uniforms.filterDims[1] * inChannels);
      let WCol = ${y} / 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 = ${y} % inChannels;
      var resData = ${vt(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 < ${d} && 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 ${vt(i)}(0.0);` : o && t10 ? `
      let col = colIn * ${i};
      ${b}` : `
      let col = colIn * ${i};
      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
        ${b}
      }
      return ${vt(i)}(0.0);`, w = `${l(p)}`, k = vt(u), _ = r ? vt(i) : vt(p), E = r ? vt(p) : vt(i);
  return `
      ${ur(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]"};
        ${f}
        ${Kr(n, s)}
        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
        }
      }`;
}
var ox = 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 = Hl(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = ql(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = ae(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 ? Uu(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner) : Gu(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner, false, null, this.sequentialAccessByThreads), t10 = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];
    return `
    ${eoe(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, t10[0], t10[1], t10[2])}
    ${e}
  `;
  }
};
var nx = class {
  constructor(e, t10 = false, o = null, n = false) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims: vec2<i32>, pad: vec2<i32>, stride: vec2<i32>, dilation: vec2<i32>,", this.workGroupSize = [4, 4, 8], this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [2], y: [1], z: [0, 3] } : { x: [3], y: [2], z: [0, 1] }, this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.addBias = t10, this.activation = o, this.hasPreluActivationWeights = n, t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.shaderKey = `conv2dnaive_${this.activation}_${this.isChannelsLast}`;
  }
  getUserCode() {
    return `
       ${ur(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;
           ${Kr(this.addBias, this.activation)}
           setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);
         }
       }
       ${ue("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);
       }
     `;
  }
};
function zL(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 toe({ 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 = [], f, d;
  if (l) {
    let y = t10.inHeight * t10.inWidth * t10.inChannels;
    f = xe({ inputs: { x: r }, backend: o, attrs: { shape: [1, t10.batchSize, y] } }), d = xe({ inputs: { x: e }, backend: o, attrs: { shape: [1, y, t10.outChannels] } });
  } else
    f = xe({ inputs: { x: r }, backend: o, attrs: { shape: p ? [t10.batchSize, t10.inHeight * t10.inWidth, t10.inChannels] : [t10.batchSize, t10.inChannels, t10.inHeight * t10.inWidth] } }), d = xe({ inputs: { x: e }, backend: o, attrs: { shape: [1, t10.inChannels, t10.outChannels] } });
  if (m.push(f), m.push(d), s != null) {
    let y = zL(s.shape, p);
    y != null && (s = xe({ inputs: { x: s }, backend: o, attrs: { shape: y } }), m.push(s));
  }
  if (n != null) {
    let y = zL(n.shape, p);
    y != null && (n = xe({ inputs: { x: n }, backend: o, attrs: { shape: y } }), m.push(n));
  }
  let h = _c({ a: p ? f : d, b: p ? d : f, transposeA: u, transposeB: c, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a }), g = xe({ inputs: { x: h }, backend: o, attrs: { shape: t10.outShape } });
  m.push(h);
  for (let y of m)
    o.disposeData(y.dataId);
  return g;
}
function sx({ 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 = P().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 toe({ x: r, filter: e, convInfo: t10, backend: o, bias: n, activation: i, preluActivationWeights: s, leakyreluAlpha: a });
  let f, d = [t10.padInfo.top, t10.padInfo.left], h = [{ type: "int32", data: [t10.filterHeight, t10.filterWidth] }, { type: "int32", data: [...d] }, { type: "int32", data: [t10.strideHeight, t10.strideWidth] }, { type: "int32", data: [t10.dilationHeight, t10.dilationWidth] }];
  if (m)
    f = new nx(t10, p, i, u);
  else {
    let C = c ? t10.outHeight * t10.outWidth : t10.outChannels, w = c ? t10.outChannels : t10.outHeight * t10.outWidth, k = t10.filterHeight * t10.filterWidth * t10.inChannels;
    h.push({ type: "int32", data: [C] }, { type: "int32", data: [w] }, { type: "int32", data: [k] });
    let _ = o.adapterInfo.isIntel();
    f = new ox(t10, C, w, k, p, i, u, _);
  }
  let g = [], y = [r, e];
  p && (!c && n.shape.length === 1 && (n = xe({ inputs: { x: n }, backend: o, attrs: { shape: [n.shape[0], 1, 1] } }), g.push(n)), y.push(n)), u && (!c && s.shape.length === 1 && (s = xe({ inputs: { x: s }, backend: o, attrs: { shape: [s.shape[0], 1, 1] } }), g.push(s)), y.push(s)), i === "leakyrelu" && (h.push({ type: "float32", data: [a] }), f.uniforms += " alpha : f32,");
  let b = o.runWebGPUProgram(f, y, r.dtype, h);
  for (let C of g)
    o.disposeData(C.dataId);
  return b;
}
function roe(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 = I.convertConv2DDataFormat(p), m = I.computeConv2DInfo(n.shape, s.shape, a, u, i, c, false, l);
  return sx({ x: n, filter: s, convInfo: m, backend: o });
}
var WL = { kernelName: ln, backendName: "webgpu", kernelFunc: roe };
function ooe(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 ${vt(r)}(0.0);
      }
      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
        return ${vt(r)}(0.0);
      }
      let coord = vec4<i32>(
          batch,
          i32(xR),
          i32(xC),
          col % uniforms.outBackprop[3]);
      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}
      }
      return ${vt(r)}(0.0);`;
  return `
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${vt(r)} {
    let col = colIn * ${r};
    ${o}
  }

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${vt(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 ${vt(r)}(0.0);
  }

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${vt(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 ax = 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, x.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 = Hl(this.dispatchLayout, this.outputShape, this.isVec4), this.elementsPerThread = ql(this.dispatchLayout, this.outputShape, this.isVec4), this.dispatch = ae(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 ? Uu(this.elementsPerThread, this.workGroupSize) : Gu(this.elementsPerThread, this.workGroupSize);
    return `
    ${ooe(this.isVec4 ? 4 : 1)}
    ${e}
    `;
  }
};
var ix = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? 1 : 2, t10 = this.isChannelsLast ? 2 : 3, o = this.isChannelsLast ? 3 : 1;
    return `
    ${ue("index")} {
      if(index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let batch = coords[0];
        let d1 = coords[${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);
      }
    }
  `;
  }
};
function noe(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 = I.convertConv2DDataFormat(u), m = I.computeConv2DInfo(a, s.shape, i, 1, p, c, false, l), f = [{ 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] }], d;
  if (P().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE") || m.filterHeight <= 2 && m.filterWidth <= 2 && m.outChannels <= 16 && m.inChannels === 1)
    d = new ix(m);
  else {
    d = new ax(m);
    let h = m.inHeight * m.inWidth, g = m.inChannels, y = m.filterHeight * m.filterWidth * m.outChannels;
    f.push({ type: "uint32", data: [h] }, { type: "uint32", data: [g] }, { type: "uint32", data: [y] });
  }
  return t10.runWebGPUProgram(d, [n, s], "float32", f);
}
var UL = { kernelName: mn, backendName: "webgpu", kernelFunc: noe };
var soe = Ge({ opType: pe.COS });
var GL = { kernelName: fn, backendName: "webgpu", kernelFunc: soe };
var aoe = Ge({ opType: pe.COSH });
var HL = { kernelName: dn, backendName: "webgpu", kernelFunc: aoe };
var ux = 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 = fe(this.outputShape), this.dispatch = ae(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 `
    ${ue("index")} {
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let height_ratio = f32(${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 ioe = (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 ux(n.shape[3], s.shape, i, p), l = [{ type: "float32", data: [u] }];
  return t10.runWebGPUProgram(c, [n, s, a], "float32", l);
};
var qL = { kernelName: xn, backendName: "webgpu", kernelFunc: ioe };
var Ku;
(function(r) {
  r.Prod = "*", r.Sum = "+";
})(Ku || (Ku = {}));
var tm = class {
  constructor(e, t10, o, n) {
    this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = t10, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.exclusive = o, this.reverse = n, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
  }
  getUserCode() {
    let e = this.outputShape.length, t10 = this.op === Ku.Prod ? "1.0" : "0.0", o = this.exclusive ? t10 : `getX(${KL(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"), `
      ${ue("index")} {
       if (index < uniforms.size) {
         var coords = getCoordsFromIndex(index);

         let end = ${jL(e, "coords", this.op)};
         var val = ${o};
         let pow2 = i32(pow(2.0, uniforms.index));
         if (${s}) {
           let idx = ${a};
           ${jL(e, "coords", this.op)} = idx;
           val ${this.op}= getX(${KL(e, "coords", this.op)});
         }
         setOutputAtIndex(index, val);
       }
      }
    `;
  }
};
function KL(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 ${r} is not yet supported`);
}
function jL(r, e, t10) {
  if (r === 1)
    return `${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 ${r} is not yet supported`);
}
function px(r, e, t10, o, n, s) {
  let a = e.shape.length, i = I.getAxesPermutation([o], a), p = e;
  i != null && (p = Nr({ inputs: { x: e }, backend: t10, attrs: { perm: i } }));
  let u = I.getInnerMostAxes(1, a)[0];
  if (u !== a - 1)
    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length - 1} but got axis=${o}`);
  let c = p.shape[u], l = Lt({ inputs: { x: p }, backend: t10 });
  for (let m = 0; m <= Math.ceil(Math.log2(c)) - 1; m++) {
    let f = new tm(r, p.shape, false, s), d = l, h = [{ type: "float32", data: [m] }];
    l = t10.runWebGPUProgram(f, [l], l.dtype, h), t10.disposeData(d.dataId);
  }
  if (n) {
    let m = new tm(r, p.shape, n, s), f = l, d = [{ type: "float32", data: [0] }];
    l = t10.runWebGPUProgram(m, [l], l.dtype, d), t10.disposeData(f.dataId);
  }
  if (i != null) {
    let m = I.getUndoAxesPermutation(i), f = Nr({ inputs: { x: l }, backend: t10, attrs: { perm: m } });
    return t10.disposeData(l.dataId), t10.disposeData(p.dataId), f;
  }
  return l;
}
function uoe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return px(Ku.Prod, n, t10, s, a, i);
}
var XL = { kernelName: hn, backendName: "webgpu", kernelFunc: uoe };
function poe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, exclusive: a, reverse: i } = o;
  return px(Ku.Sum, n, t10, s, a, i);
}
var YL = { kernelName: gn, backendName: "webgpu", kernelFunc: poe };
var cx = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t10}`, this.dataFormat = t10;
  }
  getUserCode() {
    return `
      ${ue("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 = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`;
  }
  getHeightCoordString() {
    return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
  }
  getWidthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
  }
  getDepthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
  }
  getOutputDepthSize() {
    return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
  }
  getInputSamplingString() {
    return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
  }
};
function coe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockSize: s, dataFormat: a } = o, i = n.shape[0], p = a === "NHWC" ? n.shape[1] : n.shape[2], u = a === "NHWC" ? n.shape[2] : n.shape[3], c = a === "NHWC" ? n.shape[3] : n.shape[1], l = p * s, m = u * s, f = c / (s * s), d = a === "NHWC" ? [i, l, m, f] : [i, f, l, m], h = [{ type: "int32", data: [s] }], g = new cx(d, a);
  return t10.runWebGPUProgram(g, [n], n.dtype, h);
}
var QL = { kernelName: yn, backendName: "webgpu", kernelFunc: coe };
var lx = class {
  constructor(e, t10, o, n = false, s = null, a = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [16, 16, 1], this.outputShape = e, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), n && this.variableNames.push("bias"), a && this.variableNames.push("preluActivationWeights"), this.addBias = n, this.activation = s, this.hasPreluActivation = a, this.filterHeight = t10, this.filterWidth = o, this.shaderKey = `depthwiseNCHW_${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 `
      ${ur(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;
      }

      ${Ri()}
      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                @builtin(global_invocation_id) GlobalId : vec3<u32>,
                @builtin(local_invocation_index) LocalIndex: u32,
                @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
        localId = LocalId;
        globalId = GlobalId;
        let localIndex = i32(LocalIndex);
        numWorkgroups = NumWorkgroups;
        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 = 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);
          }
        }
        ${Kr(this.addBias, this.activation)}
        if (coordsInBounds4D(coords, uniforms.outShape)) {
          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
        }
      }
    `;
  }
};
var Dc = class {
  constructor(e, t10 = false, o = null, n = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.workPerThread = 4, this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [3], y: [2], z: [0, 1] }, this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [4, this.workPerThread, 1]), x.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwiseVec4_${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;
    return `
      ${ur(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;
      }

      const strideHeight = ${this.convInfo.strideHeight};
      const strideWidth = ${this.convInfo.strideWidth};
      ${Ri()}
      fn _start(@builtin(global_invocation_id) globalId: vec3<u32>) {
        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>(strideHeight, strideWidth) - 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 * strideWidth + 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];
            ${Kr(this.addBias, this.activation)}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
          }
        }
      }
    `;
  }
};
var Pc = class {
  constructor(e, t10 = false, o = null, n = false) {
    this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`, this.workGroupSize = [256, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", t10 && this.variableNames.push("bias"), n && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t10, this.activation = o, this.hasPreluActivation = n, this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? "getX(batch, xR, xC, d1);" : "getX(batch, d1, xR, xC);";
    return `
      ${ur(this.activation, this.hasPreluActivation, false, 4)}

      ${ue()} {
        let coords = getOutputCoords();
        let batch = coords[0];
        let xRCCorner = vec2<i32>(coords.${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;
              }
            }
          }
          ${Kr(this.addBias, this.activation)}
        if (coordsInBounds4D(coords, uniforms.outShape)) {
          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
        }
      }
    `;
  }
};
function loe(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 = I.convertConv2DDataFormat(p), m = u;
  m == null && (m = [1, 1]);
  let f = I.computeConv2DInfo(n.shape, s.shape, a, m, i, c, true, l), d = [{ type: "int32", data: [f.padInfo.top, f.padInfo.left] }, { type: "int32", data: [f.inHeight, f.inWidth] }], h = f.dataFormat === "channelsLast", g;
  return !h && f.inHeight > 16 && f.inWidth > 16 && f.strideHeight === 1 && f.strideWidth === 1 && f.dilationWidth === 1 && f.dilationHeight === 1 && f.inChannels === f.outChannels ? g = new lx(f.outShape, f.filterHeight, f.filterWidth) : h && f.inHeight > 4 && f.inWidth > 4 && f.strideWidth <= 2 && f.inChannels === f.outChannels && f.dilationHeight === 1 && f.dilationWidth === 1 && f.inChannels % 4 === 0 ? g = new Dc(f) : (g = new Pc(f), d.push({ type: "int32", data: [f.filterHeight] }, { type: "int32", data: [f.filterWidth] }, { type: "int32", data: [f.strideHeight, f.strideWidth] }, { type: "int32", data: [f.dilationHeight, f.dilationWidth] })), t10.runWebGPUProgram(g, [n, s], n.dtype, d);
}
var ZL = { kernelName: bn, backendName: "webgpu", kernelFunc: loe };
var BS = it({ opType: ye.MUL, cpuKernelImpl: sL, supportsComplex: true });
var JL = { kernelName: ho, backendName: "webgpu", kernelFunc: BS };
function rm(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return Ys(n, s, a, "sum", t10);
}
var eB = { kernelName: jn, backendName: "webgpu", kernelFunc: rm };
function moe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { equation: n } = o, s = e, { allDims: a, summedDims: i, idDims: p } = I.decodeEinsumEquation(n, s.length);
  I.checkEinsumDimSizes(a.length, p, s);
  let { path: u, steps: c } = I.getEinsumComputePath(i, p), l = c.length, m = null, f = a.length, d = [];
  for (let h = 0; h < l; ++h) {
    for (let g of c[h]) {
      let { permutationIndices: y, expandDims: b } = I.getEinsumPermutation(f, p[g]), C;
      I.isIdentityPermutation(y) ? C = s[g] : (C = Nr({ inputs: { x: s[g] }, backend: t10, attrs: { perm: y } }), d.push(C));
      let w = C.shape.slice();
      for (let k = 0; k < b.length; ++k)
        w.splice(b[k], 0, 1);
      x.arraysEqual(C.shape, w) || (C = xe({ inputs: { x: C }, backend: t10, attrs: { shape: w } }), d.push(C)), m === null ? m = C : (m = BS({ inputs: { a: C, b: m }, backend: t10 }), d.push(m));
    }
    h < l - 1 && (u[h] >= 0 && (m = rm({ inputs: { x: m }, backend: t10, attrs: { axis: u[h] - (a.length - f), keepDims: false } }), d.push(m)), f--);
  }
  for (let h of d)
    h !== m && t10.disposeData(h.dataId);
  return m;
}
var tB = { kernelName: Xa, backendName: "webgpu", kernelFunc: moe };
var foe = Ge({ opType: pe.ELU });
var rB = { kernelName: In, backendName: "webgpu", kernelFunc: foe };
var doe = it({ opType: ye.EQUAL, dtype: "bool", cpuKernelImpl: HM });
var oB = { kernelName: oo, backendName: "webgpu", kernelFunc: doe };
var VS = Ge({ opType: pe.EXP, cpuKernelImpl: qM, dtype: "float32" });
var nB = { kernelName: no, backendName: "webgpu", kernelFunc: VS };
function mx(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 && (x.assert(-(a + 1) <= n, () => `Axis must be in the interval [${-(a + 1)}, ${a}]`), p = a + n + 1), i.splice(p, 0, 1), xe({ inputs: { x: s }, backend: o, attrs: { shape: i } });
}
var sB = { kernelName: xs, backendName: "webgpu", kernelFunc: mx };
var hoe = Ge({ opType: pe.EXPM1, cpuKernelImpl: KM });
var aB = { kernelName: wn, backendName: "webgpu", kernelFunc: hoe };
var fx = class {
  constructor(e) {
    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
  }
  getUserCode() {
    return `
      ${ue("index")} {
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let coordX = uniforms.xShape[2] - coords[2] - 1;
          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
          setOutputAtIndex(index, outputValue);
        }
      }
    `;
  }
};
var iB = { kernelName: Sn, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
  let { image: t10 } = r, o = e, n = new fx(t10.shape);
  return o.runWebGPUProgram(n, [t10], t10.dtype);
} };
var goe = Ge({ opType: pe.FLOOR, cpuKernelImpl: jM });
var uB = { kernelName: so, backendName: "webgpu", kernelFunc: goe };
var xoe = it({ opType: ye.INT_DIV, dtype: "int32" });
var pB = { kernelName: vn, backendName: "webgpu", kernelFunc: xoe };
var dx = class {
  constructor(e, t10, o = false) {
    this.isFromPixels = true, this.outputShape = [0], this.variableNames = [], this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [t10, 1, 1]), this.importVideo = o, this.shaderKey = `fromPixels_${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>"};
      ${ue("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 cB = { kernelName: Zi, backendName: "webgpu", kernelFunc: yoe };
var Oc;
var zS = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
var hx = /* @__PURE__ */ new Map();
function yoe(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], f = false, d = a || i;
  if (u || p || d) {
    let b;
    if (f) {
      let D = n;
      if (!hx.has(D) || hx.get(D).expired) {
        let O = { source: D };
        hx.set(D, t10.device.importExternalTexture(O));
      }
      b = { width: c, height: l, format: null, usage: null, texture: hx.get(D) };
    } else {
      if (d) {
        let L = P().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
        (Oc == null || L !== zS) && (zS = L, Oc = document.createElement("canvas").getContext("2d", { willReadFrequently: zS })), Oc.canvas.width = c, Oc.canvas.height = l, Oc.drawImage(n, 0, 0, c, l), n = Oc.canvas;
      }
      let D = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, O = "rgba8unorm", M = t10.textureManager.acquireTexture(m[1], m[0], O, D);
      t10.queue.copyExternalImageToTexture({ source: n }, { texture: M }, [m[1], m[0]]), b = { width: c, height: l, format: O, usage: D, texture: M };
    }
    let C = x.sizeFromShape(m), w = x.computeStrides(m), k = new dx(m, s, f), _ = [{ type: "uint32", data: [C] }, { type: "uint32", data: [s] }, { type: "uint32", data: [...w] }], E = t10.makeTensorInfo([l, c], "int32"), R = t10.tensorMap.get(E.dataId);
    R.resourceInfo = b;
    let A = t10.runWebGPUProgram(k, [E], "int32", _);
    return t10.disposeData(E.dataId), A;
  }
  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 y = t10.makeTensorInfo(m, "int32", new Int32Array(g));
  return t10.uploadToGPU(y.dataId), y;
}
var gx = class {
  constructor(e, t10, o, n, s) {
    this.uniforms = "varianceEpsilon : f32,", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], I.assertAndGetBroadcastShape(e, t10), I.assertAndGetBroadcastShape(e, o), this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), n != null && (I.assertAndGetBroadcastShape(e, n), this.variableNames.push("offset")), s != null && (I.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)"), `
      ${ue("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 lB = { kernelName: kn, 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 f = new gx(o.shape, a.shape, i.shape, l, m), d = [{ type: "float32", data: [p] }];
  return u.runWebGPUProgram(f, c, o.dtype, d);
} };
function boe(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: f, leakyreluAlpha: d } = o, h = I.convertConv2DDataFormat(c), g = I.computeConv2DInfo(n.shape, s.shape, p, l, u, m, false, h);
  return sx({ x: n, filter: s, convInfo: g, backend: t10, bias: a, preluActivationWeights: i, leakyreluAlpha: d, activation: f });
}
var mB = { kernelName: Do, backendName: "webgpu", kernelFunc: boe };
function Coe(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: f } = o, d = c;
  d == null && (d = [1, 1]), x.assert(I.eitherStridesOrDilationsAreOne(p, d), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${d}'`);
  let h = I.computeConv2DInfo(n.shape, s.shape, p, d, u, l, true), g = [n, s], y = a != null, b = i != null;
  y && 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.inHeight > 4 && h.inWidth > 4 && h.strideWidth <= 2 && h.inChannels === h.outChannels && h.dilationHeight === 1 && h.dilationWidth === 1 && h.inChannels % 4 === 0 ? w = new Dc(h, y, m, b) : (w = new Pc(h, y, 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: [f] }), w.uniforms += " alpha : f32,"), t10.runWebGPUProgram(w, g, "float32", C);
}
var fB = { kernelName: Po, backendName: "webgpu", kernelFunc: Coe };
var xx = class {
  constructor(e, t10) {
    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${At(e)},`;
  }
  getUserCode() {
    let e;
    return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", `
      ${ue("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 Ioe(r) {
  let { inputs: e, backend: t10 } = r, { params: o, indices: n } = e, s = n.shape, a = s[s.length - 1], i = x.sizeFromShape(o.shape), [p, u, c, l] = I.prepareAndValidate(o, n), m = xe({ inputs: { x: n }, backend: t10, attrs: { shape: [u, a] } }), f = xe({ inputs: { x: o }, backend: t10, attrs: { shape: [x.sizeFromShape(o.shape) / c, c] } });
  if (t10.shouldExecuteOnCPU([o, n]) || o.dtype === "string") {
    let b = t10.readSync(n.dataId), C = t10.bufferSync(o), w = XM(b, C, o.dtype, u, a, c, l, o.shape, i);
    return t10.makeTensorInfo(p, o.dtype, w.values);
  }
  let d = new xx(a, [u, c]), h = [{ type: "int32", data: [a] }, { type: "int32", data: l }], g = t10.runWebGPUProgram(d, [f, m], f.dtype, h), y = xe({ inputs: { x: g }, backend: t10, attrs: { shape: p } });
  return t10.disposeData(m.dataId), t10.disposeData(f.dataId), t10.disposeData(g.dataId), y;
}
var dB = { kernelName: Tn, backendName: "webgpu", kernelFunc: Ioe };
var yx = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
  }
  getUserCode() {
    let e = woe(this.aShape);
    return `
      ${ue("index")} {
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let indexZ = i32(getIndices(resRC.x, resRC.z));
          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
          setOutputAtIndex(index, inBounds * getA(${e}));
        }
      }
    `;
  }
};
function woe(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 WS(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n, indices: s } = e, { axis: a, batchDims: i } = o, p = x.parseAxisParam(a, n.shape)[0], u = I.segment_util.collectGatherOpShapeInfo(n, s, p, i), c = x.sizeFromShape(s.shape), l = [], m = xe({ inputs: { x: n }, backend: t10, attrs: { shape: [u.batchSize, u.outerSize, u.dimSize, u.sliceSize] } }), f = xe({ inputs: { x: s }, backend: t10, attrs: { shape: [u.batchSize, c / u.batchSize] } });
  l.push(m), l.push(f);
  let d = [u.batchSize, u.outerSize, c / u.batchSize, u.sliceSize];
  if (t10.shouldExecuteOnCPU([n, s])) {
    let C = t10.tensorMap.get(f.dataId).values, w = ne(f.shape, f.dtype, C), _ = t10.tensorMap.get(m.dataId).values, E = ne(m.shape, m.dtype, _), R = YM(E, w, d);
    return l.forEach((A) => t10.disposeData(A.dataId)), t10.makeTensorInfo(u.outputShape, R.dtype, R.values);
  }
  let h = new yx(m.shape, d), g = t10.runWebGPUProgram(h, [m, f], m.dtype);
  l.push(g);
  let y = xe({ inputs: { x: g }, backend: t10, attrs: { shape: u.outputShape } });
  return l.forEach((b) => t10.disposeData(b.dataId)), y;
}
var hB = { kernelName: bs, backendName: "webgpu", kernelFunc: WS };
var Soe = it({ opType: ye.GREATER, cpuKernelImpl: ZM, dtype: "bool" });
var gB = { kernelName: ao, backendName: "webgpu", kernelFunc: Soe };
var voe = it({ opType: ye.GREATER_EQUAL, dtype: "bool", cpuKernelImpl: QM });
var xB = { kernelName: io, backendName: "webgpu", kernelFunc: voe };
var koe = Ge({ opType: pe.IS_NAN, dtype: "bool" });
var yB = { kernelName: ia, backendName: "webgpu", kernelFunc: koe };
function Toe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { alpha: s } = o, a = [{ type: "float32", data: [s] }], i = new Zo(n.shape, pe.LEAKYRELU);
  return i.uniforms = "alpha : f32,", t10.runWebGPUProgram(i, [n], "float32", a);
}
var bB = { kernelName: Nn, backendName: "webgpu", kernelFunc: Toe };
var Noe = it({ opType: ye.LESS, dtype: "bool", cpuKernelImpl: eL });
var CB = { kernelName: po, backendName: "webgpu", kernelFunc: Noe };
var _oe = it({ opType: ye.LESS_EQUAL, dtype: "bool", cpuKernelImpl: JM });
var IB = { kernelName: co, backendName: "webgpu", kernelFunc: _oe };
var Eoe = Ge({ opType: pe.LOG, cpuKernelImpl: tL });
var wB = { kernelName: lo, backendName: "webgpu", kernelFunc: Eoe };
var $oe = it({ opType: ye.LOGICAL_AND, dtype: "bool" });
var SB = { kernelName: _n, backendName: "webgpu", kernelFunc: $oe };
var Roe = Ge({ opType: pe.LOGICAL_NOT });
var vB = { kernelName: En, backendName: "webgpu", kernelFunc: Roe };
var Aoe = it({ opType: ye.MAX, cpuKernelImpl: oL });
var kB = { kernelName: mo, backendName: "webgpu", kernelFunc: Aoe };
function Foe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { filterSize: s, strides: a, pad: i, dimRoundingMode: p } = o, u = 1, c = I.computePool2DInfo(n.shape, s, a, u, i, p);
  return Zg(n, c, "max", t10);
}
var TB = { kernelName: Rn, backendName: "webgpu", kernelFunc: Foe };
function Doe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return Ys(n, s, a, "min", t10);
}
var NB = { kernelName: Fn, backendName: "webgpu", kernelFunc: Doe };
var Poe = it({ opType: ye.MIN, cpuKernelImpl: nL });
var _B = { kernelName: fo, backendName: "webgpu", kernelFunc: Poe };
var bx = class {
  constructor(e, t10, o) {
    this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.xShape = e, t10.map((n, s) => {
      this.uniforms += ` pad${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 = At(e), p = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
    return `
      ${ue("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 EB = { kernelName: Dn, 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 bx(o.shape, n, s);
  return a.runWebGPUProgram(p, [o], o.dtype, i);
} };
function Ooe(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (t10.shouldExecuteOnCPU([o])) {
    let s = t10.tensorMap.get(o.dataId), [a, i] = aL(s.values, o.shape, o.dtype);
    return t10.makeTensorInfo(i, o.dtype, a);
  }
  let n = new Zo(o.shape, pe.NEG);
  return t10.runWebGPUProgram(n, [o], o.dtype);
}
var $B = { kernelName: Pn, backendName: "webgpu", kernelFunc: Ooe };
function Moe(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 } = Bt.nonMaxSuppressionV3Impl(u, c, a, i, p);
  return t10.makeTensorInfo([l.length], "int32", new Int32Array(l));
}
var RB = { kernelName: On, backendName: "webgpu", kernelFunc: Moe };
function Loe(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, f = i, d = p, h = u, { selectedIndices: g, selectedScores: y } = Bt.nonMaxSuppressionV5Impl(c, l, m, f, d, h);
  return [t10.makeTensorInfo([g.length], "int32", new Int32Array(g)), t10.makeTensorInfo([y.length], "float32", new Float32Array(y))];
}
var AB = { kernelName: Mn, backendName: "webgpu", kernelFunc: Loe };
function om(r) {
  let { inputs: e, backend: t10 } = r, { x: o } = e;
  if (o.dtype === "complex64") {
    let n = Wa({ inputs: { input: o }, backend: t10 }), s = om({ inputs: { x: n }, backend: t10 }), a = qu({ inputs: { input: o }, backend: t10 }), i = om({ inputs: { x: a }, backend: t10 }), p = ls({ 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 $o({ attrs: { shape: o.shape, dtype: o.dtype, value: o.dtype === "string" ? "" : 0 }, backend: t10 });
}
var FB = { kernelName: Es, backendName: "webgpu", kernelFunc: om };
function DB(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 = Wa({ inputs: { input: o }, backend: t10 }), s = DB({ inputs: { x: n }, backend: t10 }), a = qu({ inputs: { input: o }, backend: t10 }), i = om({ inputs: { x: a }, backend: t10 }), p = ls({ 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 $o({ attrs: { shape: o.shape, dtype: o.dtype, value: 1 }, backend: t10 });
}
var PB = { kernelName: Cs, backendName: "webgpu", kernelFunc: DB };
function Boe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { axis: n } = o;
  if (e.length === 1)
    return mx({ inputs: { input: e[0] }, backend: t10, attrs: { dim: n } });
  let s = e[0].shape, a = e[0].dtype;
  e.forEach((c) => {
    x.assertShapesMatch(s, c.shape, "All tensors passed to stack must have matching shapes"), x.assert(a === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let i = [], p = e.map((c) => {
    let l = mx({ inputs: { input: c }, backend: t10, attrs: { dim: n } });
    return i.push(l), l;
  }), u = LS({ inputs: p, backend: t10, attrs: { axis: n } });
  return i.forEach((c) => t10.disposeData(c.dataId)), u;
}
var OB = { kernelName: Is, backendName: "webgpu", kernelFunc: Boe };
var Cx = class {
  constructor(e, t10) {
    this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t10.map((o, n) => o[0] + e[n] + o[1]), this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), t10.map((o, n) => {
      this.uniforms += ` pad${n} : vec2<i32>,`;
    }), this.xShape = e, this.shaderKey = "pad";
  }
  getUserCode() {
    let e = this.xShape.length, t10 = At(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 `
      ${ue("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 US = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { paddings: s, constantValue: a } = o;
  if (s.every((u) => x.arraysEqual(u, [0, 0])))
    return Lt({ inputs: { x: n }, backend: t10 });
  if (x.sizeFromShape(n.shape) === 0) {
    let u = s.map((c, l) => c[0] + n.shape[l] + c[1]);
    return $o({ 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 MB = { kernelName: Ln, backendName: "webgpu", kernelFunc: US };
var Voe = it({ opType: ye.POW });
var LB = { kernelName: Bn, backendName: "webgpu", kernelFunc: Voe };
function zoe(r) {
  let { inputs: e, backend: t10 } = r, { x: o, alpha: n } = e, s = new Hu(ye.PRELU, o.shape, n.shape);
  return t10.runWebGPUProgram(s, [o, n], "float32");
}
var BB = { kernelName: Vn, backendName: "webgpu", kernelFunc: zoe };
function Woe(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { axis: s, keepDims: a } = o;
  return Ys(n, s, a, "prod", t10);
}
var VB = { kernelName: Ao, backendName: "webgpu", kernelFunc: Woe };
var Uoe = (r) => {
  let { backend: e, attrs: t10 } = r, { start: o, stop: n, step: s, dtype: a } = t10, i = pL(o, n, s, a);
  return e.makeTensorInfo([i.length], a, i);
};
var zB = { kernelName: ws, backendName: "webgpu", kernelFunc: Uoe };
var GS = it({ opType: ye.DIV });
var WB = { kernelName: Cn, backendName: "webgpu", kernelFunc: GS };
var Goe = Ge({ opType: pe.RECIPROCAL });
var UB = { kernelName: ma, backendName: "webgpu", kernelFunc: Goe };
var Hoe = Ge({ opType: pe.RELU });
var GB = { kernelName: zn, backendName: "webgpu", kernelFunc: Hoe };
var qoe = Ge({ opType: pe.RELU6 });
var HB = { kernelName: Gn, backendName: "webgpu", kernelFunc: qoe };
var Ix = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "resizeBilinear";
  }
  getUserCode() {
    return `
      ${ue("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 Koe(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, f = [{ type: "float32", data: [c, l] }, { type: "float32", data: [i ? 0.5 : 0] }], d = new Ix(n.shape, p, u);
  return t10.runWebGPUProgram(d, [n], "float32", f);
}
var qB = { kernelName: Un, backendName: "webgpu", kernelFunc: Koe };
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 = fe(this.outputShape), this.dispatch = ae(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", `
      ${ue("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 joe(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, f = [{ type: "float32", data: [c, l] }, { type: "float32", data: [s ? 0.5 : 0] }], d = new wx(n.shape, p, u, a);
  return t10.runWebGPUProgram(d, [n], n.dtype, f);
}
var KB = { kernelName: Wn, backendName: "webgpu", kernelFunc: joe };
var Sx = class {
  constructor(e, t10) {
    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(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 `
        ${ue("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));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `;
  }
};
var jB = { kernelName: es, backendName: "webgpu", kernelFunc: ({ inputs: r, attrs: e, backend: t10 }) => {
  let { image: o } = r, { radians: n, fillValue: s, center: a } = e, i = t10, p = new Sx(o.shape, s), [u, c] = I.getImageCenter(a, o.shape[1], o.shape[2]), l = [{ type: "float32", data: [u] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(n)] }, { type: "float32", data: [Math.cos(n)] }];
  return typeof s == "number" ? l.push({ type: "float32", data: [Number.parseFloat(s.toFixed(2))] }) : l.push({ type: "float32", data: s }), i.runWebGPUProgram(p, [o], o.dtype, l);
} };
var Xoe = Ge({ opType: pe.RSQRT, cpuKernelImpl: cL });
var XB = { kernelName: xo, backendName: "webgpu", kernelFunc: Xoe };
var Fi = class {
  constructor(e, t10, o, n, s, a, i, p = true) {
    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.sumDupeIndices = p, this.dispatchLayout = fe(e), this.dispatch = ae(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t10 > 1, this.shaderKey = `scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}`;
    let u = At(s.length);
    this.uniforms = `sliceDim : i32, strides: ${u}, size: i32,`, this.updatesRank = n, this.indicesRank = o;
  }
  getUserCode() {
    let e = "";
    this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j");
    let t10 = `getIndices(${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(${Array.from({ length: this.updatesRank }, (c, l) => `coords[${l}]`).join(", ")})`, p = (c, l) => {
      let m = `atomicAdd(${c}, bitcast<i32>(${l}))`;
      this.type === "float32" && (m = `
          {
            var oldBits = 0;
            var newBits = bitcast<i32>(${l});
            loop {
              let info = atomicCompareExchangeWeak(${c}, oldBits, newBits);
              if (info.exchanged) {
                break;
              }
              oldBits = info.old_value;
              let oldValue = bitcast<f32>(oldBits);
              let newValue = oldValue + (${l});
              newBits = bitcast<i32>(newValue);
            }
          }
        `);
      let f = `atomicStore(${c}, bitcast<i32>(${l}));`;
      return this.sumDupeIndices ? m : f;
    };
    return `
    ${s}

      ${ue("index")} {
        if (index < uniforms.size) {
          let coords = getUpdatesCoordsFromFlatIndex(index);
          var flattenedIndex = 0;
          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
            let indexInside = i32(round(${t10}));
            flattenedIndex = flattenedIndex + indexInside * ${o};
          }
          let updateValue =
              ${Tc(this.type, false)}(${i});
          let flatIndex = getOutputIndexFromCoords(${n});

          ${p("&result[flatIndex]", "updateValue")};
        }
      }`;
  }
};
function Yoe(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 } = I.calculateShapes(s, n, a), m = [l / u, u];
  if (l === 0)
    return t10.makeTensorInfo(a, n.dtype);
  let f = xe({ inputs: { x: n }, backend: t10, attrs: { shape: [p, i] } }), d = xe({ inputs: { x: s }, backend: t10, attrs: { shape: [p, u] } }), h = d.dtype, g = $o({ backend: t10, attrs: { shape: m, value: 0, dtype: h } }), y = x.sizeFromShape(d.shape), b = [{ type: "int32", data: [i] }, { type: "int32", data: c }, { type: "int32", data: [y] }], C = new Fi(d.shape, i, f.shape.length, d.shape.length, c, m, h), w = t10.runWebGPUProgram(C, [d, f], h, b, g), k = xe({ inputs: { x: w }, backend: t10, attrs: { shape: a } });
  return t10.disposeData(f.dataId), t10.disposeData(d.dataId), t10.disposeData(w.dataId), k;
}
var YB = { kernelName: Hn, backendName: "webgpu", kernelFunc: Yoe };
var vx = class {
  constructor(e, t10, o) {
    this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t10, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(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 `
      ${ue("index")} {
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let cVal = getC(${e});
          if (cVal >= 1.0) {
            setOutputAtIndex(index, getA(${t10}));
          } else {
            setOutputAtIndex(index, getB(${t10}));
          }
        }
      }
    `;
  }
};
function Qoe(r) {
  let { inputs: e, backend: t10 } = r, { condition: o, t: n, e: s } = e, a = new vx(o.shape.length, n.shape, n.shape.length);
  return t10.runWebGPUProgram(a, [o, n, s], ct(n.dtype, s.dtype));
}
var QB = { kernelName: vs, backendName: "webgpu", kernelFunc: Qoe };
var Zoe = Ge({ opType: pe.SIGMOID });
var ZB = { kernelName: yo, backendName: "webgpu", kernelFunc: Zoe };
var Joe = Ge({ opType: pe.SIN });
var JB = { kernelName: Kn, backendName: "webgpu", kernelFunc: Joe };
var ene = Ge({ opType: pe.SINH });
var eV = { kernelName: ha, backendName: "webgpu", kernelFunc: ene };
var HS = it({ opType: ye.SUB, cpuKernelImpl: gL, supportsComplex: true });
var tV = { kernelName: Io, backendName: "webgpu", kernelFunc: HS };
function tne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { logits: n } = e, { dim: s } = o, a = x.parseAxisParam([s], n.shape), i = em({ inputs: { x: n }, backend: t10, attrs: { reductionIndices: a, keepDims: false } }), p = I.expandShapeToKeepDim(i.shape, a), u = xe({ inputs: { x: i }, backend: t10, attrs: { shape: p } }), c = HS({ inputs: { a: n, b: u }, backend: t10 }), l = VS({ inputs: { x: c }, backend: t10 }), m = rm({ inputs: { x: l }, backend: t10, attrs: { axis: a, keepDims: false } }), f = xe({ inputs: { x: m }, backend: t10, attrs: { shape: p } }), d = GS({ inputs: { a: l, b: f }, 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(f.dataId), d;
}
var rV = { kernelName: Xn, backendName: "webgpu", kernelFunc: tne };
var rne = (r) => {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { blockShape: s, paddings: a } = o;
  x.assert(n.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
  let i = s.reduce((y, b) => y * b), p = [[0, 0]];
  p.push(...a);
  for (let y = 1 + s.length; y < n.shape.length; ++y)
    p.push([0, 0]);
  let u = [], c = US({ inputs: { x: n }, backend: t10, attrs: { paddings: p, constantValue: 0 } }), l = I.getReshaped(c.shape, s, i, false), m = I.getPermuted(l.length, s.length, false), f = I.getReshapedPermuted(c.shape, s, i, false), d = xe({ inputs: { x: c }, backend: t10, attrs: { shape: l } }), h = Nr({ inputs: { x: d }, backend: t10, attrs: { perm: m } }), g = xe({ inputs: { x: h }, backend: t10, attrs: { shape: f } });
  return u.push(c), u.push(d), u.push(h), u.forEach((y) => t10.disposeData(y.dataId)), g;
};
var oV = { kernelName: ks, backendName: "webgpu", kernelFunc: rne };
var kx = 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 = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
  }
  getUserCode() {
    let e = one(this.rank, "uniforms.");
    return `
      ${ue("index")} {
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `;
  }
};
function one(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 qS(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) => x.decodeString(m)) : p, c = ne(n.shape, n.dtype, u), l = xL(c, s);
    return t10.makeTensorInfo(l.shape, l.dtype, l.values);
  }
  let a = new kx(n.shape, s);
  return t10.runWebGPUProgram(a, [n], n.dtype);
}
var nV = { kernelName: wo, backendName: "webgpu", kernelFunc: qS };
function nne(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 } = I.calculateShapes(s, n, i), f = false;
  if (s.dtype === "string") {
    let R = t10.bufferSync(n), A = t10.bufferSync(s), D = x.decodeString(t10.readSync(a.dataId)[0]), O = lL(R, A, i, m, c, u, p, l, D, f);
    return t10.makeTensorInfo(i, O.dtype, O.values);
  }
  let d = [m / c, c], h = xe({ inputs: { x: n }, backend: t10, attrs: { shape: [u, p] } }), g = s.shape.length ? xe({ inputs: { x: s }, backend: t10, attrs: { shape: [u, c] } }) : Lt({ inputs: { x: s }, backend: t10 }), y = g.dtype, b = t10.makeTensorInfo([], y, x.makeZerosTypedArray(1, y)), C = xe({ inputs: { x: a }, backend: t10, attrs: { shape: Array(d.length).fill(1) } }), w = qS({ inputs: { x: C }, backend: t10, attrs: { reps: d } }), k = x.sizeFromShape([u, c]), _ = [{ type: "int32", data: [p] }, { type: "int32", data: l }, { type: "int32", data: [k] }];
  switch (u) {
    case 0:
      break;
    case 1:
      {
        let R = new Fi([u, c], p, h.shape.length, g.shape.length, l, d, y, f);
        t10.runWebGPUProgram(R, [g, h], y, _, w);
      }
      break;
    default:
      {
        let R = new Fi([u, c], p, h.shape.length, b.shape.length, l, d, y, f);
        t10.runWebGPUProgram(R, [b, h], y, _, w);
      }
      {
        let R = new Fi([u, c], p, h.shape.length, g.shape.length, l, d, y);
        t10.runWebGPUProgram(R, [g, h], y, _, w);
      }
  }
  let E = xe({ 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 sV = { kernelName: ei, backendName: "webgpu", kernelFunc: nne };
function sne(r) {
  let { inputs: e, backend: t10, attrs: o } = r, { x: n } = e, { numOrSizeSplits: s, axis: a } = o, i = x.parseAxisParam(a, n.shape)[0], p = I.prepareSplitSize(n, s, i), u = n.shape.length, c = new Array(u).fill(0), l = n.shape.slice();
  return p.map((m) => {
    let f = [...l];
    f[i] = m;
    let d = ms({ inputs: { x: n }, backend: t10, attrs: { begin: c, size: f } });
    return c[i] += m, d;
  });
}
var aV = { kernelName: Ts, backendName: "webgpu", kernelFunc: sne };
var ane = Ge({ opType: pe.SQRT });
var iV = { kernelName: bo, backendName: "webgpu", kernelFunc: ane };
var uV = { kernelName: ti, backendName: "webgpu", kernelFunc: ({ inputs: r, backend: e }) => {
  let { x: t10 } = r, o = e, n = new Zo(t10.shape, pe.SQUARE);
  return o.runWebGPUProgram(n, [t10], t10.dtype);
} };
var ine = it({ opType: ye.SQUARED_DIFFERENCE });
var pV = { kernelName: Co, backendName: "webgpu", kernelFunc: ine };
var Tx = class {
  constructor(e) {
    this.variableNames = ["x"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
    let t10 = At(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 `
       ${ue("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           setOutputAtIndex(index, getX(${t10}));
         }
       }
     `;
  }
};
function une(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: f, finalShape: d, isIdentity: h, sliceDim0: g, isSimpleSlice: y, begin: b, end: C, strides: w } = et.sliceInfo(n.shape, s, a, i, p, u, c, l, m), k;
  if (h)
    k = xe({ inputs: { x: n }, backend: t10, attrs: { shape: d } });
  else if (g || y) {
    x.assert(n.shape.length >= 1, () => `Input must have rank at least 1, got: ${n.shape.length}`);
    let _ = et.computeOutShape(b, C, w), E = ms({ inputs: { x: n }, backend: t10, attrs: { begin: b, size: _ } });
    k = xe({ inputs: { x: E }, backend: t10, attrs: { shape: d } }), t10.disposeData(E.dataId);
  } else if (t10.shouldExecuteOnCPU([n])) {
    let E = t10.readSync(n.dataId), R = ne(n.shape, n.dtype, E), A = dL(f, R, w, b);
    k = t10.makeTensorInfo(d, n.dtype, A.values);
  } else {
    let E = new Tx(f), R = [{ type: "int32", data: b }, { type: "int32", data: w }], A = t10.runWebGPUProgram(E, [n], n.dtype, R);
    k = xe({ inputs: { x: A }, backend: t10, attrs: { shape: d } }), t10.disposeData(A.dataId);
  }
  return k;
}
var cV = { kernelName: Yn, backendName: "webgpu", kernelFunc: une };
function pne(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), f = t10.readSync(l.dataId), [d, h] = hL(m, f, n, s, a, i, p, u);
  return [t10.makeTensorInfo([d.length], "string", d), t10.makeTensorInfo(l.shape, "int32", h)];
}
var lV = { kernelName: Ns, backendName: "webgpu", kernelFunc: pne };
var cne = Ge({ opType: pe.TANH });
var mV = { kernelName: Qn, backendName: "webgpu", kernelFunc: cne };
var Nx = class {
  constructor(e) {
    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
        dir : i32, inc : i32,`, this.shaderKey = "swap";
  }
  getUserCode() {
    return `
        ${ue("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 _x = class {
  constructor(e) {
    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = fe(this.outputShape), this.dispatch = ae(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
  }
  getUserCode() {
    return `
        ${ue("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 Mc(r, e) {
  e !== null && r.disposeData(e.dataId);
}
function fV(r) {
  let e = 1;
  for (; e < r; )
    e *= 2;
  return e;
}
function lne(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] = yL(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, $o({ attrs: { shape: i, dtype: "int32", value: 0 }, backend: t10 })];
  let c = x.sizeFromShape(i) / p, l = xe({ inputs: { x: n }, attrs: { shape: [c, p] }, backend: t10 }), m = fV(s), f = fV(p), d = null, h = () => d === null ? [l, l] : [l, d], g = (k, _, E) => {
    let R = h(), A = new Nx(E), O = [{ type: "int32", data: [p] }, { type: "int32", data: [d === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [_] }], M = d;
    d = t10.runWebGPUProgram(A, R, "int32", O), Mc(t10, M);
  };
  for (let k = 1; k < m; k *= 2) {
    let _ = k * 2;
    for (let E = k; E >= 1; E /= 2)
      g(_, E, [c, f]);
  }
  for (let k = f; k > m; k /= 2) {
    let _ = h(), E = new _x([c, k / 2]), A = [{ type: "int32", data: [p] }, { type: "int32", data: [d === null ? 1 : 0] }, { type: "int32", data: [m] }], D = d;
    d = t10.runWebGPUProgram(E, _, "int32", A), Mc(t10, D);
    let O = m / 2, M = O * 2;
    for (let L = O; L >= 1; L /= 2)
      g(M, L, d.shape);
  }
  let y = d;
  d = ms({ inputs: { x: d }, backend: t10, attrs: { begin: 0, size: [c, s] } }), Mc(t10, y);
  let b = WS({ inputs: { x: l, indices: d }, backend: t10, attrs: { axis: 1, batchDims: 1 } });
  Mc(t10, l);
  let C = i.slice(0, -1);
  C.push(s), y = d, d = xe({ inputs: { x: d }, attrs: { shape: C }, backend: t10 }), Mc(t10, y);
  let w = b;
  return b = xe({ inputs: { x: b }, attrs: { shape: C }, backend: t10 }), Mc(t10, w), [b, d];
}
var dV = { kernelName: Zn, backendName: "webgpu", kernelFunc: lne };
var Ex = 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 = fe(this.outputShape), this.dispatch = ae(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;
          }

          ${ue("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 mne(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, f] = n.shape, [d, h] = u != null ? u : [l, m], g = [c, d, h, f], y = new Ex(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(y, [n, s], "float32", w);
}
var hV = { kernelName: Jn, backendName: "webgpu", kernelFunc: mne };
function fne(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), f = a.shape.slice();
  f[s] = 1;
  let d = new Array(p);
  for (let h = 0; h < d.length; h++) {
    m[s] = h;
    let g = ms({ inputs: { x: a }, backend: t10, attrs: { begin: m, size: f } }), y = xe({ inputs: { x: g }, backend: t10, attrs: { shape: u } });
    d[h] = y, l.push(g);
  }
  return l.forEach((h) => t10.disposeData(h.dataId)), d;
}
var gV = { kernelName: _s, backendName: "webgpu", kernelFunc: fne };
var dne = [aM, CL, IL, wL, vL, kL, TL, EL, $L, AL, OL, ML, LL, uM, VL, WL, UL, GL, HL, qL, XL, YL, QL, ZL, tB, rB, oB, nB, sB, aB, nM, iB, cB, uB, pB, lB, mB, fB, dB, hB, gB, xB, iM, BL, yB, bB, CB, IB, wB, SB, vB, NL, kB, TB, _L, NB, _B, EB, JL, $B, RB, AB, FL, PB, OB, MB, LB, BB, VB, zB, DL, WB, UB, GB, HB, sM, qB, KB, jB, XB, YB, QB, ZB, JB, eV, RL, cV, lV, rV, oV, sV, aV, iV, uV, pV, tV, eB, mV, nV, dV, hV, SL, gV, FB];
for (let r of dne)
  ya(r);
var xV = "4.0.0";
var hne = "4.0.0";
var gne = "4.0.0";
var xne = "4.0.0";
var yne = "4.0.0";
var bne = "0.0.1-alpha.14";
var Cne = { tfjs: xV, "tfjs-core": xV, "tfjs-converter": hne, "tfjs-backend-cpu": gne, "tfjs-backend-webgl": xne, "tfjs-backend-wasm": yne, "tfjs-backend-webgpu": bne };

// src/util/util.ts
function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
  if (msg)
    console.log(ts, "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: true,
      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-full.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 x6 = (amount || 0) * 2 / 3 + 1;
      const y = (x6 - 1) * -0.5;
      filter.colorMatrix([
        x6,
        y,
        y,
        0,
        0,
        y,
        x6,
        y,
        0,
        0,
        y,
        y,
        x6,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    desaturate: () => {
      filter.saturation(-1);
    },
    contrast: (amount) => {
      const v6 = (amount || 0) + 1;
      const o = -128 * (v6 - 1);
      filter.colorMatrix([
        v6,
        0,
        0,
        0,
        o,
        0,
        v6,
        0,
        0,
        o,
        0,
        0,
        v6,
        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 ? jp(inputImage) : inputImage;
  const rgb2 = $a(squeeze, 3, 2);
  const min = [fl(rgb2[0]), fl(rgb2[1]), fl(rgb2[2])];
  const max = [Vs(rgb2[0]), Vs(rgb2[1]), Vs(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 = [oe(sub[0], factor), oe(sub[1], factor), oe(sub[2], factor)];
    const stack = Ir([enh[0], enh[1], enh[2]], 2);
    final = z(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);
    Ft([...sub, ...range, ...enh]);
  } else {
    final = _a(squeeze, 0);
  }
  Ft([...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, _b, _c2;
  if (!input) {
    if (config3.debug)
      log("input error: input is missing");
    return { tensor: null, canvas: null };
  }
  if (!(input instanceof ut) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(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 is not recognized");
  }
  if (input instanceof ut) {
    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 = _a(input, 0);
      } else if (input.shape[2] === 4) {
        const rgb2 = W1(input, [0, 0, 0], [-1, -1, 3]);
        tensor2 = _a(rgb2, 0);
        Ft(rgb2);
      }
    } else if (input.shape.length === 4) {
      if (input.shape[3] === 3) {
        tensor2 = zr(input);
      } else if (input.shape[3] === 4) {
        tensor2 = U1(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 = qe(tensor2, "float32");
      Ft(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 ((((_b = config3.filter) == null ? void 0 : _b.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 && Sv) {
      pixels = Sv ? Sv.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 (Sv && env.browser) {
      if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
        pixels = Sv.fromPixels(outCanvas);
      } else {
        tmpCanvas = copy(outCanvas);
        pixels = Sv.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 = W1(pixels, [0, 0, 0], [-1, -1, 3]);
    Ft(pixels);
    pixels = rgb2;
  }
  if (!pixels)
    throw new Error("input error: cannot create tensor");
  const casted = qe(pixels, "float32");
  const tensor = config3.filter.equalization ? await histogramEqualization(casted) : _a(casted, 0);
  Ft([pixels, casted]);
  if (config3.filter.autoBrightness) {
    const max = Vs(tensor);
    const maxVal = await max.data();
    config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0];
    Ft(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 = zr(input);
  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
    Ft(last.inputTensor);
    last.inputTensor = zr(input);
  } else {
    const t10 = {};
    t10.diff = ke(input, last.inputTensor);
    t10.squared = oe(t10.diff, t10.diff);
    t10.sum = tt(t10.squared);
    const diffSum = await t10.sum.data();
    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
    Ft([last.inputTensor, t10.diff, t10.squared, t10.sum]);
    last.inputTensor = zr(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 = zr(input1);
  t10.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? zq.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : zr(input2);
  t10.diff = ke(t10.input1, t10.input2);
  t10.squared = oe(t10.diff, t10.diff);
  t10.sum = tt(t10.squared);
  const diffSum = await t10.sum.data();
  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
  Ft([t10.input1, t10.input2, t10.diff, t10.squared, t10.sum]);
  return diffRelative;
}

// src/util/env.ts
var Env = class {
  constructor() {
    __publicField(this, "browser");
    __publicField(this, "node");
    __publicField(this, "worker");
    __publicField(this, "platform", "");
    __publicField(this, "agent", "");
    __publicField(this, "backends", []);
    __publicField(this, "initial");
    __publicField(this, "filter");
    __publicField(this, "tfjs");
    __publicField(this, "offscreen");
    __publicField(this, "perfadd", false);
    __publicField(this, "tensorflow", {
      version: void 0,
      gpu: void 0
    });
    __publicField(this, "wasm", {
      supported: void 0,
      backend: void 0,
      simd: void 0,
      multithread: void 0
    });
    __publicField(this, "webgl", {
      supported: void 0,
      backend: void 0,
      version: void 0,
      renderer: void 0,
      shader: void 0,
      vendor: void 0
    });
    __publicField(this, "webgpu", {
      supported: void 0,
      backend: void 0,
      adapter: void 0
    });
    __publicField(this, "cpu", {
      model: void 0,
      flags: []
    });
    __publicField(this, "kernels", []);
    __publicField(this, "Canvas");
    __publicField(this, "Image");
    __publicField(this, "ImageData");
    this.browser = typeof navigator !== "undefined";
    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
    this.tfjs = { version: Cne["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") {
      const raw = navigator.userAgent.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 = navigator.userAgent.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}`;
    }
  }
  async updateBackend() {
    this.backends = Object.keys(cr().registryFactory);
    try {
      this.tensorflow = {
        version: Bie()["binding"] ? Bie()["binding"].TF_Version : void 0,
        gpu: Bie()["binding"] ? Bie()["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 P().getAsync("WASM_HAS_SIMD_SUPPORT");
      this.wasm.multithread = await P().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.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 = zm(Pie()).map((kernel) => kernel.kernelName.toLowerCase());
    } catch (e) {
    }
  }
  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;
  }
};
var env = new Env();

// src/util/webcam.ts
var WebCam = class {
  constructor() {
    __publicField(this, "config");
    __publicField(this, "element");
    __publicField(this, "stream");
    __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;
    });
    __publicField(this, "start", async (webcamConfig) => {
      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",
          resizeMode: this.config.crop ? "crop-and-scale" : "none",
          width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },
          height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight }
        }
      };
      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
        });
      }
    });
    __publicField(this, "pause", () => {
      if (this.element)
        this.element.pause();
    });
    __publicField(this, "play", async () => {
      if (this.element)
        await this.element.play();
    });
    __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 track() {
    if (!this.stream)
      return void 0;
    return this.stream.getVideoTracks()[0];
  }
  get capabilities() {
    if (!this.track)
      return void 0;
    return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
  }
  get constraints() {
    if (!this.track)
      return void 0;
    return this.track.getConstraints ? this.track.getConstraints() : void 0;
  }
  get settings() {
    if (!this.stream)
      return void 0;
    const track = this.stream.getVideoTracks()[0];
    return track.getSettings ? track.getSettings() : void 0;
  }
  get label() {
    if (!this.track)
      return "";
    return this.track.label;
  }
  get paused() {
    var _a2;
    return ((_a2 = this.element) == null ? void 0 : _a2.paused) || false;
  }
  get width() {
    var _a2;
    return ((_a2 = this.element) == null ? void 0 : _a2.videoWidth) || 0;
  }
  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-detect": () => blazepose_detect,
  "blazepose-detector2d": () => blazepose_detector2d,
  "blazepose-detector3d": () => blazepose_detector3d,
  "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-alt": () => facemesh_attention_alt,
  "facemesh-detection-full": () => facemesh_detection_full,
  "facemesh-detection-short": () => facemesh_detection_short,
  "facemesh-orig": () => facemesh_orig,
  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_full = 5431368;
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_detector2d = 7499400;
var blazepose_detector3d = 5928856;
var blazepose_full = 6338290;
var blazepose_heavy = 27501554;
var blazepose_lite = 2725490;
var efficientpose = 5651240;
var faceboxes = 2013002;
var facemesh_attention_alt = 2387598;
var facemesh_attention = 2382414;
var facemesh_detection_full = 1026192;
var facemesh_detection_short = 201268;
var facemesh_orig = 2955780;
var faceres_deep = 13957620;
var gear = 1498916;
var gender_ssrnet_imdb = 161236;
var gender = 201808;
var handdetect = 3515612;
var handlandmark_lite = 2023432;
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 blazepose_detect = 5928804;
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-full": handlandmark_full,
  handtrack,
  iris,
  liveness,
  models,
  "movenet-lightning": movenet_lightning,
  age,
  "blazeface-back": blazeface_back,
  "blazeface-front": blazeface_front,
  "blazepose-detector2d": blazepose_detector2d,
  "blazepose-detector3d": blazepose_detector3d,
  "blazepose-full": blazepose_full,
  "blazepose-heavy": blazepose_heavy,
  "blazepose-lite": blazepose_lite,
  efficientpose,
  faceboxes,
  "facemesh-attention-alt": facemesh_attention_alt,
  "facemesh-attention": facemesh_attention,
  "facemesh-detection-full": facemesh_detection_full,
  "facemesh-detection-short": facemesh_detection_short,
  "facemesh-orig": facemesh_orig,
  "faceres-deep": faceres_deep,
  gear,
  "gender-ssrnet-imdb": gender_ssrnet_imdb,
  gender,
  handdetect,
  "handlandmark-lite": handlandmark_lite,
  "handlandmark-sparse": handlandmark_sparse,
  handskeleton,
  meet,
  mobileface,
  mobilefacenet,
  "movenet-multipose": movenet_multipose,
  "movenet-thunder": movenet_thunder,
  nanodet,
  posenet,
  rvm,
  selfie,
  "blazepose-detect": blazepose_detect,
  "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, _b, _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 va.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 bl(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 = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
    if (artifacts)
      model23.loadSync(artifacts);
    else
      model23 = await U6(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 = "3.0.0";

// src/tfjs/humangl.ts
var config2 = {
  name: "humangl",
  priority: 999,
  canvas: null,
  gl: null,
  extensions: [],
  webGLattr: {
    alpha: false,
    antialias: false,
    premultipliedAlpha: false,
    preserveDrawingBuffer: false,
    depth: false,
    stencil: false,
    failIfMajorPerformanceCaveat: false,
    desynchronized: true
  }
};
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 cr().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 (!Mie(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 {
      MI(2, config2.gl);
    } catch (err) {
      log("humangl error: cannot set webgl context:", err);
      return;
    }
    try {
      const ctx = new Fu(config2.gl);
      pi(config2.name, () => new Ni(ctx), config2.priority);
    } catch (err) {
      log("humangl error: cannot register webgl backend:", err);
      return;
    }
    try {
      const kernels = zm("webgl");
      kernels.forEach((kernelConfig) => {
        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
        ya(newKernelConfig);
      });
    } catch (err) {
      log("humangl error: cannot update webgl backend registration:", err);
      return;
    }
    try {
      if (P().flagRegistry.WEBGL_VERSION)
        P().set("WEBGL_VERSION", 2);
    } catch (err) {
      log("humangl error: cannot set WebGL backend flags:", err);
      return;
    }
    extensions();
    const backend = Bie();
    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 = be(255, "float32");
  constants.tf1 = be(1, "float32");
  constants.tf2 = be(2, "float32");
  constants.tf05 = be(0.5, "float32");
  constants.tf127 = be(127.5, "float32");
  constants.rgb = mr([0.2989, 0.587, 0.114], "float32");
}

// src/tfjs/backend.ts
async function getBestBackend() {
  await env.updateBackend();
  if (!env.browser)
    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: Pie(),
      kernelFunc: (op2) => Ne(() => ke(op2.inputs.a, oe(We(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
    };
    ya(kernelMod);
    env.kernels.push("mod");
    newKernels.push("mod");
  }
  if (!env.kernels.includes("floormod")) {
    const kernelFloorMod = {
      kernelName: "FloorMod",
      backendName: Pie(),
      kernelFunc: (op2) => Ne(() => ge(oe(cf(op2.inputs.a, op2.inputs.b), op2.inputs.b), jk(op2.inputs.a, op2.inputs.b)))
    };
    ya(kernelFloorMod);
    env.kernels.push("floormod");
    newKernels.push("floormod");
  }
  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
    const kernelRotateWithOffset = {
      kernelName: "RotateWithOffset",
      backendName: Pie(),
      kernelFunc: (op2) => Ne(() => {
        const backend = Pie();
        Fie("cpu");
        const t10 = zq.rotateWithOffset(op2.inputs.image, op2.attrs.radians, op2.attrs.fillValue, op2.attrs.center);
        Fie(backend);
        return t10;
      })
    };
    ya(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 && Pie() !== 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(cr().registryFactory);
      if (instance.config.backend === "humangl" && !available.includes("humangl")) {
        register(instance);
        available = Object.keys(cr().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 (P().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
          P().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
        if (instance.config.debug)
          log("wasm path:", instance.config.wasmPath);
        if (typeof rte !== "undefined")
          rte(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 P().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
          simd = await P().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 Fie(instance.config.backend);
        await Die();
      } catch (err) {
        log("error: cannot set backend:", instance.config.backend, err);
        return false;
      }
      if (instance.config.debug)
        defaultFlags = JSON.parse(JSON.stringify(P().flags));
    }
    if (Pie() === "humangl" || Pie() === "webgl") {
      if (P().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
        P().set("WEBGL_USE_SHAPES_UNIFORMS", true);
      if (P().flagRegistry.WEBGL_EXP_CONV)
        P().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);
        P().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
      }
    }
    if (Pie() === "webgpu") {
    }
    if (instance.config.debug) {
      const newFlags = P().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:", Pie(), "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)) {
        P().set(key, val);
      }
    }
    Tie();
    init();
    instance.performance.initBackend = Math.trunc(now() - timeStamp);
    instance.config.backend = Pie();
    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;
      }
    };
    ya(kernelConfig);
  }
  env.kernels = zm(Pie()).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 = (z10, opt) => {
  if (!opt.useDepth || typeof z10 === "undefined")
    return opt.color;
  const rgb2 = Uint8ClampedArray.from([127 + 2 * z10, 127 - 2 * z10, 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 x6 = Math.max(0, startX);
  for (let i = line.length - 1; i >= 0; i--) {
    const y = i * localOptions2.lineHeight + startY;
    if (localOptions2.shadowColor && localOptions2.shadowColor !== "") {
      ctx.fillStyle = localOptions2.shadowColor;
      ctx.fillText(line[i], x6 + 5, y + 16);
    }
    ctx.fillStyle = localOptions2.labelColor;
    ctx.fillText(line[i], x6 + 4, y + 15);
  }
}
function point(ctx, x6, y, z10, localOptions2) {
  ctx.fillStyle = colorDepth(z10, localOptions2);
  ctx.beginPath();
  ctx.arc(x6, y, localOptions2.pointSize, 0, 2 * Math.PI);
  ctx.fill();
}
function rect(ctx, x6, y, width, height, localOptions2) {
  ctx.beginPath();
  ctx.lineWidth = localOptions2.lineWidth;
  if (localOptions2.useCurves) {
    const cx2 = (x6 + x6 + width) / 2;
    const cy = (y + y + height) / 2;
    ctx.ellipse(cx2, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
  } else {
    ctx.moveTo(x6 + localOptions2.roundRect, y);
    ctx.lineTo(x6 + width - localOptions2.roundRect, y);
    ctx.quadraticCurveTo(x6 + width, y, x6 + width, y + localOptions2.roundRect);
    ctx.lineTo(x6 + width, y + height - localOptions2.roundRect);
    ctx.quadraticCurveTo(x6 + width, y + height, x6 + width - localOptions2.roundRect, y + height);
    ctx.lineTo(x6 + localOptions2.roundRect, y + height);
    ctx.quadraticCurveTo(x6, y + height, x6, y + height - localOptions2.roundRect);
    ctx.lineTo(x6, y + localOptions2.roundRect);
    ctx.quadraticCurveTo(x6, y, x6 + localOptions2.roundRect, y);
    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 x6;
  let y;
  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]);
  x6 = radius * Math.cos(angle) + to2[0];
  y = radius * Math.sin(angle) + to2[1];
  ctx.moveTo(x6, y);
  angle += 1 / 3 * (2 * Math.PI);
  x6 = radius * Math.cos(angle) + to2[0];
  y = radius * Math.sin(angle) + to2[1];
  ctx.lineTo(x6, y);
  angle += 1 / 3 * (2 * Math.PI);
  x6 = radius * Math.cos(angle) + to2[0];
  y = radius * Math.sin(angle) + to2[1];
  ctx.lineTo(x6, y);
  ctx.closePath();
  ctx.stroke();
  ctx.fill();
}

// src/draw/options.ts
var options2 = {
  color: "rgba(173, 216, 230, 0.6)",
  labelColor: "rgba(173, 216, 230, 1)",
  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: [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],
  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
  rightEyeIris: [473, 474, 475, 476, 477],
  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
  leftEyeIris: [468, 469, 470, 471, 472],
  midwayBetweenEyes: [168],
  noseTip: [1],
  noseBottom: [2],
  noseRightCorner: [98],
  noseLeftCorner: [327],
  rightCheek: [205],
  leftCheek: [425]
};
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 = [
  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
  { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
  { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] },
  { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] }
];
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],
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  [0.33721199631691, 0.282882988452911],
  [0.296391993761063, 0.293242990970612],
  [0.169294998049736, 0.193813979625702],
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  [0.457098007202148, 0.584792017936707],
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  [0.392388999462128, 0.694203019142151],
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  [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],
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  [0.475387006998062, 0.692366003990173],
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  [0.473122000694275, 0.704625964164734],
  [0.473033010959625, 0.695277988910675],
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  [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],
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var VTX68 = [
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  17,
  84,
  91,
  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((x6) => UV468[x6]);
var UV33 = VTX33.map((x6) => UV468[x6]);
var UV7 = VTX7.map((x6) => UV468[x6]);
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,
  185,
  40,
  39,
  37,
  0,
  267,
  269,
  270,
  409,
  78,
  95,
  88,
  178,
  87,
  14,
  317,
  402,
  318,
  324,
  308,
  191,
  80,
  81,
  82,
  13,
  312,
  311,
  310,
  415,
  76,
  77,
  90,
  180,
  85,
  16,
  315,
  404,
  320,
  307,
  306,
  184,
  74,
  73,
  72,
  11,
  302,
  303,
  304,
  408,
  62,
  96,
  89,
  179,
  86,
  15,
  316,
  403,
  319,
  325,
  292,
  183,
  42,
  41,
  38,
  12,
  268,
  271,
  272,
  407
];
var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
  33,
  7,
  163,
  144,
  145,
  153,
  154,
  155,
  133,
  246,
  161,
  160,
  159,
  158,
  157,
  173,
  130,
  25,
  110,
  24,
  23,
  22,
  26,
  112,
  243,
  247,
  30,
  29,
  27,
  28,
  56,
  190,
  226,
  31,
  228,
  229,
  230,
  231,
  232,
  233,
  244,
  113,
  225,
  224,
  223,
  222,
  221,
  189,
  35,
  124,
  46,
  53,
  52,
  65,
  143,
  111,
  117,
  118,
  119,
  120,
  121,
  128,
  245,
  156,
  70,
  63,
  105,
  66,
  107,
  55,
  193
];
var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
  263,
  249,
  390,
  373,
  374,
  380,
  381,
  382,
  362,
  466,
  388,
  387,
  386,
  385,
  384,
  398,
  359,
  255,
  339,
  254,
  253,
  252,
  256,
  341,
  463,
  467,
  260,
  259,
  257,
  258,
  286,
  414,
  446,
  261,
  448,
  449,
  450,
  451,
  452,
  453,
  464,
  342,
  445,
  444,
  443,
  442,
  441,
  413,
  265,
  353,
  276,
  283,
  282,
  295,
  372,
  340,
  346,
  347,
  348,
  349,
  350,
  357,
  465,
  383,
  300,
  293,
  334,
  296,
  336,
  285,
  417
];

// src/draw/face.ts
var localOptions;
function drawLabels(f, ctx) {
  var _a2, _b, _c2, _d2, _e2, _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 = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c2.roll)
    l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll));
  if ((_e2 = (_d2 = f.rotation) == null ? void 0 : _d2.angle) == null ? void 0 : _e2.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, _b, _c2, _d2;
  if (((_a2 = f.annotations) == null ? void 0 : _a2.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.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, _b;
  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 && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.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, _b;
  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 && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.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 z10 = part[i][2] || 0;
          ctx.strokeStyle = colorDepth(i * z10, 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 j = 0; j < result.length; j++) {
      const [where, what] = Object.entries(result[j]);
      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)
  ]);
  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",
  "leftEyeInside",
  "leftEye",
  "leftEyeOutside",
  "rightEyeInside",
  "rightEye",
  "rightEyeOutside",
  "leftEar",
  "rightEar",
  "leftMouth",
  "rightMouth",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftPinky",
  "rightPinky",
  "leftIndex",
  "rightIndex",
  "leftThumb",
  "rightThumb",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle",
  "leftHeel",
  "rightHeel",
  "leftFoot",
  "rightFoot",
  "bodyCenter",
  "bodyTop",
  "leftPalm",
  "leftHand",
  "rightPalm",
  "rightHand"
];
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 y = 0; y < featureMapHeight; ++y) {
      for (let x6 = 0; x6 < featureMapWidth; ++x6) {
        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
          anchors3.push({ x: (x6 + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
        }
      }
    }
    layerId = lastSameStrideLayer;
  }
  anchorTensor = { x: mr(anchors3.map((a) => a.x)), y: mr(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 Ne(() => {
    const split = $a(boxesTensor, 12, 1);
    let xCenter = jp(split[0]);
    let yCenter = jp(split[1]);
    let width = jp(split[2]);
    let height = jp(split[3]);
    xCenter = ge(We(xCenter, inputSize), anchor.x);
    yCenter = ge(We(yCenter, inputSize), anchor.y);
    width = oe(We(width, inputSize), cropFactor[0]);
    height = oe(We(height, inputSize), cropFactor[1]);
    const xMin = ke(xCenter, We(width, 2));
    const yMin = ke(yCenter, We(height, 2));
    const xMax = ge(xMin, width);
    const yMax = ge(yMin, height);
    const boxes = Ir([xMin, yMin, xMax, yMax], 1);
    return boxes;
  });
}
async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) {
  var _a2, _b;
  const detectedBoxes = [];
  const t10 = {};
  t10.boxes = decodeBoxes(boxesTensor, anchorTensor);
  t10.scores = Ms(logitsTensor);
  t10.nms = await zq.nonMaxSuppressionAsync(t10.boxes, t10.scores, 1, ((_a2 = config3.body["detector"]) == null ? void 0 : _a2.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.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) => Ft(t10[tensor]));
  return detectedBoxes;
}
async function detectBoxes(input, config3, outputSize2) {
  const t10 = {};
  t10.res = model == null ? void 0 : model.execute(input, ["Identity"]);
  t10.logitsRaw = Ue(t10.res, [0, 0, 0], [1, -1, 1]);
  t10.boxesRaw = Ue(t10.res, [0, 0, 1], [1, -1, -1]);
  t10.logits = jp(t10.logitsRaw);
  t10.boxes = jp(t10.boxesRaw);
  const boxes = await decodeResults(t10.boxes, t10.logits, config3, outputSize2);
  Object.keys(t10).forEach((tensor) => Ft(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 = (x6) => 1 - 1 / (1 + Math.exp(x6));
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, _b;
  const t10 = {};
  if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
    return input;
  let final;
  if (cropBox) {
    t10.cropped = zq.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],
      height,
      width,
      [0, 0]
    ];
    t10.pad = Ws(t10.cropped || input, padding);
    t10.resize = zq.resizeBilinear(t10.pad, [size2, size2]);
    final = We(t10.resize, constants.tf255);
  } else if (input.shape[1] !== size2) {
    t10.resize = zq.resizeBilinear(t10.cropped || input, [size2, size2]);
    final = We(t10.resize, constants.tf255);
  } else {
    final = We(t10.cropped || input, constants.tf255);
  }
  Object.keys(t10).forEach((tensor) => Ft(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],
        kpt4.positionRaw[1] / width + cropBox[0],
        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] = 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) => Ft(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, _b, _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 ((_b = (_a2 = config3.body) == null ? void 0 : _a2["detector"]) == null ? void 0 : _b["enabled"]) {
      const preparedImage = prepareImage(input, 224);
      boxes = await detectBoxes(preparedImage, config3, outputSize2);
      Ft(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);
      Ft(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 = jp(res);
  const arr = $a(t10.squeeze, 6, 1);
  t10.stack = Ir([arr[1], arr[0], arr[3], arr[2]], 1);
  t10.boxes = jp(t10.stack);
  t10.scores = jp(arr[4]);
  t10.classes = jp(arr[5]);
  Ft([res, ...arr]);
  t10.nms = await zq.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 [x6, y] = [
      detections[0][id2][0] / inputSize3,
      detections[0][id2][1] / inputSize3
    ];
    const boxRaw = [
      x6,
      y,
      detections[0][id2][2] / inputSize3 - x6,
      detections[0][id2][3] / inputSize3 - y
    ];
    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) => Ft(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 = zq.resizeBilinear(input, [inputSize3, inputSize3]);
    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
    lastTime2 = now();
    Ft(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 = z(inputs, [height * width]);
  const max = Vs(reshaped, 0);
  const newScore = (await max.data())[0];
  if (newScore > minScore) {
    const coordinates = Hv(reshaped, 0);
    const mod = jk(coordinates, width);
    const x6 = (await mod.data())[0];
    const div = We(coordinates, width);
    const y = (await div.data())[0];
    Ft([reshaped, max, coordinates, mod, div]);
    return [x6, y, newScore];
  }
  Ft([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 = Ne(() => {
      var _a2, _b;
      const resize = zq.resizeBilinear(image, [((_a2 = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a2[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false);
      const enhance2 = oe(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();
    Ft(tensor);
    if (resT) {
      cache2.keypoints.length = 0;
      const squeeze = jp(resT);
      Ft(resT);
      const stack = ko(squeeze, 2);
      Ft(squeeze);
      for (let id2 = 0; id2 < stack.length; id2++) {
        const [x10, y6, 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: [
              x10 / model4.inputs[0].shape[2],
              y6 / model4.inputs[0].shape[1]
            ],
            position: [
              Math.round(image.shape[2] * x10 / model4.inputs[0].shape[2]),
              Math.round(image.shape[1] * y6 / model4.inputs[0].shape[1])
            ]
          });
        }
      }
      stack.forEach((s) => Ft(s));
    }
    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
    const x6 = cache2.keypoints.map((a) => a.position[0]);
    const y = cache2.keypoints.map((a) => a.position[1]);
    cache2.box = [
      Math.min(...x6),
      Math.min(...y),
      Math.max(...x6) - Math.min(...x6),
      Math.max(...y) - Math.min(...y)
    ];
    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 = zq.cropAndResize(image, [cutBox], [0], cropSize);
  const norm = We(crop, constants.tf255);
  Ft(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 x6 = landmarks.map((d) => d[0]);
  const y = landmarks.map((d) => d[1]);
  return { startPoint: [Math.min(...x6), Math.min(...y)], endPoint: [Math.max(...x6), Math.max(...y)], 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 = (x6, y) => [[1, 0, x6], [0, 1, y], [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) => [
    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 = zq.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);
      rotationMatrix = buildRotationMatrix(-angle, center);
      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
      Ft(rotated);
    } else {
      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
    }
  } else {
    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
  }
  return [angle, rotationMatrix, face4];
}
var findFaceCenter = (mesh) => {
  const x6 = mesh.map((m) => m[0]);
  const y = mesh.map((m) => m[1]);
  return [Math.min(...x6) + (Math.max(...x6) - Math.min(...x6)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 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 = be(inputSize4, "int32");
  anchors = gi(generateAnchors(inputSize4));
  return model5;
}
function decodeBoxes2(boxOutputs) {
  if (!anchors || !inputSizeT)
    return Wr([0, 0]);
  const t10 = {};
  t10.boxStarts = Ue(boxOutputs, [0, 1], [-1, 2]);
  t10.centers = ge(t10.boxStarts, anchors);
  t10.boxSizes = Ue(boxOutputs, [0, 3], [-1, 2]);
  t10.boxSizesNormalized = We(t10.boxSizes, inputSizeT);
  t10.centersNormalized = We(t10.centers, inputSizeT);
  t10.halfBoxSize = We(t10.boxSizesNormalized, constants.tf2);
  t10.starts = ke(t10.centersNormalized, t10.halfBoxSize);
  t10.ends = ge(t10.centersNormalized, t10.halfBoxSize);
  t10.startNormalized = oe(t10.starts, inputSizeT);
  t10.endNormalized = oe(t10.ends, inputSizeT);
  const boxes = ck([t10.startNormalized, t10.endNormalized], 1);
  Object.keys(t10).forEach((tensor) => Ft(t10[tensor]));
  return boxes;
}
async function getBoxes(inputImage, config3) {
  var _a2, _b, _c2, _d2;
  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
    return [];
  const t10 = {};
  t10.resized = zq.resizeBilinear(inputImage, [inputSize4, inputSize4]);
  t10.div = We(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 = gt([sorted[0], sorted[2]], 2);
    t10.concat512 = gt([sorted[1], sorted[3]], 2);
    t10.concat = gt([t10.concat512, t10.concat384], 1);
    t10.batch = jp(t10.concat, [0]);
  } else if (Array.isArray(res)) {
    t10.batch = jp(res[0]);
  } else {
    t10.batch = jp(res);
  }
  Ft(res);
  t10.boxes = decodeBoxes2(t10.batch);
  t10.logits = Ue(t10.batch, [0, 0], [-1, 1]);
  t10.sigmoid = Ms(t10.logits);
  t10.scores = jp(t10.sigmoid);
  t10.nms = await zq.nonMaxSuppressionAsync(t10.boxes, t10.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.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 = Ue(t10.boxes, [nms[i], 0], [1, -1]);
      b.slice = Ue(t10.batch, [nms[i], keypointsCount - 1], [1, -1]);
      b.squeeze = jp(b.slice);
      b.landmarks = z(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) => Ft(b[tensor]));
    }
  }
  Object.keys(t10).forEach((tensor) => Ft(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, _b;
  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"]) && ((_b = model6.inputs) == null ? void 0 : _b[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 j = 0; j < indices.length; j++) {
        const index2 = indices[j];
        rawCoords[originalIndices[j]] = [
          newCoords[index2][0],
          newCoords[index2][1],
          (newCoords[index2][2] + rawCoords[originalIndices[j]][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 = zq.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 = zq.flipLeftRight(crop);
    Ft(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 x6 = eyeData[i * 3];
    const y = eyeData[i * 3 + 1];
    const z10 = eyeData[i * 3 + 2];
    eyeRawCoords.push([
      (flip ? 1 - x6 / inputSize5 : x6 / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
      z10
    ]);
  }
  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 z10 = averageZ;
    if (i === 2) {
      z10 = upperCenterZ;
    } else if (i === 4) {
      z10 = lowerCenterZ;
    }
    return [coord[0], coord[1], z10];
  });
};
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 = gt([leftEyeCrop, rightEyeCrop]);
  Ft(leftEyeCrop);
  Ft(rightEyeCrop);
  const eyePredictions = model6.execute(combined);
  Ft(combined);
  const eyePredictionsData = await eyePredictions.data();
  Ft(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, _b, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2;
  const t10 = {
    lips: await ((_b = (_a2 = results.filter((r) => r.size === 160)) == null ? void 0 : _a2[0]) == null ? void 0 : _b.data()),
    irisL: await ((_d2 = (_c2 = results.filter((r) => r.size === 10)) == null ? void 0 : _c2[0]) == null ? void 0 : _d2.data()),
    eyeL: await ((_f2 = (_e2 = results.filter((r) => r.size === 142)) == null ? void 0 : _e2[0]) == null ? void 0 : _f2.data()),
    irisR: await ((_h2 = (_g2 = results.filter((r) => r.size === 10)) == null ? void 0 : _g2[1]) == null ? void 0 : _h2.data()),
    eyeR: await ((_j2 = (_i2 = results.filter((r) => r.size === 142)) == null ? void 0 : _i2[1]) == null ? void 0 : _j2.data())
  };
  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, _b, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2;
  if (!(model7 == null ? void 0 : model7["executor"]))
    return [];
  const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
  const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.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 = {
      id: id2++,
      mesh: [],
      meshRaw: [],
      box: [0, 0, 0, 0],
      boxRaw: [0, 0, 0, 0],
      score: 0,
      boxScore: 0,
      faceScore: 0,
      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;
      Ft(face4.tensor);
      if (equilized)
        face4.tensor = equilized;
    }
    face4.boxScore = Math.round(100 * box.confidence) / 100;
    if (!((_e2 = config3.face.mesh) == null ? void 0 : _e2.enabled)) {
      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;
        Ft(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 = z(meshT, [-1, 3]);
        let rawCoords = await coordsReshaped.array();
        Ft(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);
      }
      Ft(results);
    }
    if (face4.score > (((_j2 = config3.face.detector) == null ? void 0 : _j2.minConfidence) || 1))
      faces.push(face4);
    else
      Ft(face4.tensor);
  }
  cache3.boxes = newCache;
  return faces;
}
async function load5(config3) {
  var _a2, _b, _c2, _d2, _e2, _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(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.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"] && ((_e2 = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e2[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, _b;
  if (!model8)
    return [];
  const skipFrame = skipped4 < (((_a2 = config3.face.emotion) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.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 = zq.resizeBilinear(image, [inputSize10, inputSize10], false);
      t10.channels = oe(t10.resize, constants.rgb);
      t10.grayscale = tt(t10.channels, 3, true);
      t10.grayscaleSub = ke(t10.grayscale, constants.tf05);
      t10.grayscaleMul = oe(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) => Ft(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 = zq.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
  const norm = oe(crop, constants.tf255);
  Ft(crop);
  return norm;
}
async function predict6(image, config3, idx, count2) {
  var _a2, _b, _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 = (((_b = config3.face.description) == null ? void 0 : _b.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();
      Ft(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 = Hv(resT.find((t10) => t10.shape[1] === 100), 1);
      const ageIdx = (await argmax.data())[0];
      Ft(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) => Ft(t10));
    }
    last4[idx] = obj;
    lastCount2 = count2;
    resolve(obj);
  });
}

// src/face/mask.ts
var expandFact = 0.1;
var alpha = 0.5;
function insidePoly(x6, y, polygon) {
  let inside = false;
  let j = polygon.length - 1;
  for (let i = 0; i < polygon.length; j = i++) {
    if (polygon[i].y > y !== polygon[j].y > y && x6 < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].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 x6 = 0; x6 < width; x6++) {
    for (let y = 0; y < height; y++) {
      const inside = insidePoly(x6 / width, y / width, silhouette);
      if (!inside) {
        buffer.set(alpha * buffer.get(0, y, x6, 0), 0, y, x6, 0);
        buffer.set(alpha * buffer.get(0, y, x6, 1), 0, y, x6, 1);
        buffer.set(alpha * buffer.get(0, y, x6, 2), 0, y, x6, 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, _b;
  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 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) {
    skipped6++;
    return cached[idx];
  }
  skipped6 = 0;
  return new Promise(async (resolve) => {
    const resize = zq.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();
    Ft([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, _b;
  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 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) {
    skipped7++;
    return cached2[idx];
  }
  skipped7 = 0;
  return new Promise(async (resolve) => {
    const resize = zq.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();
    Ft([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, _b;
  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 = (((_b = config3.face.gear) == null ? void 0 : _b.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, _b2;
    if (!(model12 == null ? void 0 : model12.inputs[0].shape))
      return;
    const t10 = {};
    const box = [[0, 0.1, 0.9, 0.9]];
    t10.resize = zq.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] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.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) => Ft(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, _b, _c2, _d2;
  if (!model13)
    return { age: 0 };
  const skipFrame = skipped9 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.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 = zq.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
    t10.enhance = oe(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) => Ft(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, _b, _c2, _d2;
  if (!model14)
    return { gender: "unknown", genderScore: 0 };
  const skipFrame = skipped10 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.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 = zq.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
    t10.enhance = Ne(() => {
      const [red, green, blue] = $a(t10.resize, 3, 3);
      const redNorm = oe(red, rgb[0]);
      const greenNorm = oe(green, rgb[1]);
      const blueNorm = oe(blue, rgb[2]);
      const grayscale = Wv([redNorm, greenNorm, blueNorm]);
      const normalize2 = oe(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) => Ft(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, _b;
  if (!(model15 == null ? void 0 : model15["executor"]))
    return [];
  const skipFrame = skipped11 < (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.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 = zq.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) => Ft(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, _b;
  if (!(model16 == null ? void 0 : model16["executor"]))
    return [];
  const skipFrame = skipped12 < (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.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 = zq.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) => Ft(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 = [
    (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 = (v6) => {
    const length = Math.sqrt(v6[0] * v6[0] + v6[1] * v6[1] + v6[2] * v6[2]);
    v6[0] /= length;
    v6[1] /= length;
    v6[2] /= length;
    return v6;
  };
  const subVectors = (a, b) => {
    const x6 = a[0] - b[0];
    const y = a[1] - b[1];
    const z10 = a[2] - b[2];
    return [x6, y, z10];
  };
  const crossVectors = (a, b) => {
    const x6 = a[1] * b[2] - a[2] * b[1];
    const y = a[2] * b[0] - a[0] * b[2];
    const z10 = a[0] * b[1] - a[1] * b[0];
    return [x6, y, z10];
  };
  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, _b, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _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]);
      Ft(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 = ((_b = instance.config.face.emotion) == null ? void 0 : _b.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 = ((_e2 = instance.config.face.antispoof) == null ? void 0 : _e2.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 = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.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 = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.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) ? jp(faces[i].tensor) : null;
    Ft(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],
  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
  pointsMapping: {
    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
  },
  getName: (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 = {
  HALF_CURL_START_LIMIT: 60,
  NO_CURL_START_LIMIT: 130,
  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, _b, _c2, _d2;
  if (!res)
    return [];
  const gestures = [];
  for (let i = 0; i < res.length; i++) {
    if (!((_b = (_a2 = res[i].annotations) == null ? void 0 : _a2.leftEyeIris) == null ? void 0 : _b[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 zq.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 = (x6, y) => [[1, 0, x6], [0, 1, y], [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 },
  { x: 0.234375, y: 0.171875 },
  { x: 0.234375, y: 0.171875 },
  { x: 0.265625, y: 0.171875 },
  { x: 0.265625, y: 0.171875 },
  { x: 0.296875, y: 0.171875 },
  { x: 0.296875, y: 0.171875 },
  { x: 0.328125, y: 0.171875 },
  { x: 0.328125, y: 0.171875 },
  { x: 0.359375, y: 0.171875 },
  { x: 0.359375, y: 0.171875 },
  { x: 0.390625, y: 0.171875 },
  { x: 0.390625, y: 0.171875 },
  { x: 0.421875, y: 0.171875 },
  { x: 0.421875, y: 0.171875 },
  { x: 0.453125, y: 0.171875 },
  { x: 0.453125, y: 0.171875 },
  { x: 0.484375, y: 0.171875 },
  { x: 0.484375, y: 0.171875 },
  { x: 0.515625, y: 0.171875 },
  { x: 0.515625, y: 0.171875 },
  { x: 0.546875, y: 0.171875 },
  { x: 0.546875, y: 0.171875 },
  { x: 0.578125, y: 0.171875 },
  { x: 0.578125, y: 0.171875 },
  { x: 0.609375, y: 0.171875 },
  { x: 0.609375, y: 0.171875 },
  { x: 0.640625, y: 0.171875 },
  { x: 0.640625, y: 0.171875 },
  { x: 0.671875, y: 0.171875 },
  { x: 0.671875, y: 0.171875 },
  { x: 0.703125, y: 0.171875 },
  { x: 0.703125, y: 0.171875 },
  { x: 0.734375, y: 0.171875 },
  { x: 0.734375, y: 0.171875 },
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  { x: 0.84375, y: 0.90625 },
  { x: 0.84375, y: 0.90625 },
  { x: 0.90625, y: 0.90625 },
  { x: 0.90625, y: 0.90625 },
  { x: 0.96875, y: 0.90625 },
  { x: 0.96875, y: 0.90625 },
  { x: 0.03125, y: 0.96875 },
  { x: 0.03125, y: 0.96875 },
  { x: 0.09375, y: 0.96875 },
  { x: 0.09375, y: 0.96875 },
  { x: 0.15625, y: 0.96875 },
  { x: 0.15625, y: 0.96875 },
  { x: 0.21875, y: 0.96875 },
  { x: 0.21875, y: 0.96875 },
  { x: 0.28125, y: 0.96875 },
  { x: 0.28125, y: 0.96875 },
  { x: 0.34375, y: 0.96875 },
  { x: 0.34375, y: 0.96875 },
  { x: 0.40625, y: 0.96875 },
  { x: 0.40625, y: 0.96875 },
  { x: 0.46875, y: 0.96875 },
  { x: 0.46875, y: 0.96875 },
  { x: 0.53125, y: 0.96875 },
  { x: 0.53125, y: 0.96875 },
  { x: 0.59375, y: 0.96875 },
  { x: 0.59375, y: 0.96875 },
  { x: 0.65625, y: 0.96875 },
  { x: 0.65625, y: 0.96875 },
  { x: 0.71875, y: 0.96875 },
  { x: 0.71875, y: 0.96875 },
  { x: 0.78125, y: 0.96875 },
  { x: 0.78125, y: 0.96875 },
  { x: 0.84375, y: 0.96875 },
  { x: 0.84375, y: 0.96875 },
  { x: 0.90625, y: 0.96875 },
  { x: 0.90625, y: 0.96875 },
  { x: 0.96875, y: 0.96875 },
  { x: 0.96875, y: 0.96875 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.1875, y: 0.9375 },
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  { x: 0.1875, y: 0.9375 },
  { x: 0.1875, y: 0.9375 },
  { x: 0.1875, y: 0.9375 },
  { x: 0.1875, y: 0.9375 },
  { x: 0.3125, y: 0.9375 },
  { x: 0.3125, y: 0.9375 },
  { x: 0.3125, y: 0.9375 },
  { x: 0.3125, y: 0.9375 },
  { x: 0.3125, y: 0.9375 },
  { x: 0.3125, y: 0.9375 },
  { x: 0.4375, y: 0.9375 },
  { x: 0.4375, y: 0.9375 },
  { x: 0.4375, y: 0.9375 },
  { x: 0.4375, y: 0.9375 },
  { x: 0.4375, y: 0.9375 },
  { x: 0.4375, y: 0.9375 },
  { x: 0.5625, y: 0.9375 },
  { x: 0.5625, y: 0.9375 },
  { x: 0.5625, y: 0.9375 },
  { x: 0.5625, y: 0.9375 },
  { x: 0.5625, y: 0.9375 },
  { x: 0.5625, y: 0.9375 },
  { x: 0.6875, y: 0.9375 },
  { x: 0.6875, y: 0.9375 },
  { x: 0.6875, y: 0.9375 },
  { x: 0.6875, y: 0.9375 },
  { 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, _b, _c2, _d2;
    this.model = model23;
    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
    this.anchorsTensor = gi(this.anchors);
    this.inputSize = ((_d2 = (_c2 = (_b = (_a2 = this == null ? void 0 : this.model) == null ? void 0 : _a2.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c2.shape) == null ? void 0 : _d2[2]) || 0;
    this.inputSizeTensor = mr([this.inputSize, this.inputSize]);
    this.doubleInputSizeTensor = mr([this.inputSize * 2, this.inputSize * 2]);
  }
  normalizeBoxes(boxes) {
    const t10 = {};
    t10.boxOffsets = Ue(boxes, [0, 0], [-1, 2]);
    t10.boxSizes = Ue(boxes, [0, 2], [-1, 2]);
    t10.div = We(t10.boxOffsets, this.inputSizeTensor);
    t10.boxCenterPoints = ge(t10.div, this.anchorsTensor);
    t10.halfBoxSizes = We(t10.boxSizes, this.doubleInputSizeTensor);
    t10.sub = ke(t10.boxCenterPoints, t10.halfBoxSizes);
    t10.startPoints = oe(t10.sub, this.inputSizeTensor);
    t10.add = ge(t10.boxCenterPoints, t10.halfBoxSizes);
    t10.endPoints = oe(t10.add, this.inputSizeTensor);
    const res = ck([t10.startPoints, t10.endPoints], 1);
    Object.keys(t10).forEach((tensor) => Ft(t10[tensor]));
    return res;
  }
  normalizeLandmarks(rawPalmLandmarks, index2) {
    const t10 = {};
    t10.reshape = z(rawPalmLandmarks, [-1, 7, 2]);
    t10.div = We(t10.reshape, this.inputSizeTensor);
    t10.landmarks = ge(t10.div, this.anchors[index2] ? this.anchors[index2] : 0);
    const res = oe(t10.landmarks, this.inputSizeTensor);
    Object.keys(t10).forEach((tensor) => Ft(t10[tensor]));
    return res;
  }
  async predict(input, config3) {
    var _a2;
    const t10 = {};
    t10.resize = zq.resizeBilinear(input, [this.inputSize, this.inputSize]);
    t10.div = We(t10.resize, constants.tf127);
    t10.image = ke(t10.div, constants.tf1);
    t10.batched = this.model.execute(t10.image);
    t10.predictions = jp(t10.batched);
    t10.slice = Ue(t10.predictions, [0, 0], [-1, 1]);
    t10.sigmoid = Ms(t10.slice);
    t10.scores = jp(t10.sigmoid);
    const scores = await t10.scores.data();
    t10.boxes = Ue(t10.predictions, [0, 1], [-1, 4]);
    t10.norm = this.normalizeBoxes(t10.boxes);
    t10.nms = await zq.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 = Ue(t10.norm, [index2, 0], [1, -1]);
      p.slice = Ue(t10.predictions, [index2, 5], [1, 14]);
      p.norm = this.normalizeLandmarks(p.slice, index2);
      p.palmLandmarks = z(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) => Ft(p[tensor]));
    }
    Object.keys(t10).forEach((tensor) => Ft(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, _b, _c2;
    this.handDetector = handDetector;
    this.handPoseModel = handPoseModel2;
    this.inputSize = ((_c2 = (_b = (_a2 = this.handPoseModel) == null ? void 0 : _a2.inputs) == null ? void 0 : _b[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") ? zq.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 = We(croppedInput, constants.tf255);
        Ft(croppedInput);
        Ft(rotatedImage);
        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime13 = now();
        Ft(handImage);
        const confidence = (await confidenceT.data())[0];
        Ft(confidenceT);
        if (confidence >= config3.hand.minConfidence / 4) {
          const keypointsReshaped = z(keypoints, [-1, 3]);
          const rawCoords = await keypointsReshaped.array();
          Ft(keypoints);
          Ft(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;
        }
        Ft(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, _b;
  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((_b = config3.hand.skeleton) == null ? void 0 : _b.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: [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 = zq.resizeBilinear(input, [height, width]);
  t10.cast = qe(t10.resize, "int32");
  [t10.rawScores, t10.rawBoxes] = await models2[0].executeAsync(t10.cast, modelOutputNodes);
  t10.boxes = jp(t10.rawBoxes, [0, 2]);
  t10.scores = jp(t10.rawScores, [0]);
  const classScores = ko(t10.scores, 1);
  Ft(classScores[faceIndex]);
  classScores.splice(faceIndex, 1);
  t10.filtered = Ir(classScores, 1);
  Ft(classScores);
  t10.max = Vs(t10.filtered, 1);
  t10.argmax = Hv(t10.filtered, 1);
  let id2 = 0;
  t10.nms = await zq.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 = Ue(t10.boxes, nmsIndex, 1);
    const boxYX = await boxSlice.data();
    Ft(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) => Ft(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 = {
    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 = zq.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
    t10.div = We(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 = z(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) => Ft(t10[tensor]));
  }
  return hand3;
}
async function predict15(input, config3) {
  var _a2, _b;
  if (!((_a2 = models2[0]) == null ? void 0 : _a2["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["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 = [
  "nose",
  "leftEye",
  "rightEye",
  "leftEar",
  "rightEar",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle"
];
var horizontal = [
  ["leftEye", "rightEye"],
  ["leftEar", "rightEar"],
  ["leftShoulder", "rightShoulder"],
  ["leftElbow", "rightElbow"],
  ["leftWrist", "rightWrist"],
  ["leftHip", "rightHip"],
  ["leftKnee", "rightKnee"],
  ["leftAnkle", "rightAnkle"]
];
var vertical = [
  ["leftKnee", "leftShoulder"],
  ["rightKnee", "rightShoulder"],
  ["leftAnkle", "leftKnee"],
  ["rightAnkle", "rightKnee"]
];
var relative = [
  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
];
var connected3 = {
  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, _b, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _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, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
      const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
        var _a3, _b2, _c3, _d3, _e3, _f3, _g3, _h3, _i3;
        return {
          score: newKpt.score,
          part: newKpt.part,
          position: [
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
          ],
          positionRaw: [
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
          ],
          distance: [
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a3[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c3 = newKpt.distance) == null ? void 0 : _c3[0],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d3[1]) || 0) + (((_e3 = newKpt.distance) == null ? void 0 : _e3[1]) || 0)) / bufferedFactor : (_f3 = newKpt.distance) == null ? void 0 : _f3[1],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].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 ((_b = config3.body.modelPath) == null ? void 0 : _b.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 j = 0; j < indexes.length - 1; j++) {
          const pt0 = keypoints.find((kp2) => kp2.part === indexes[j]);
          const pt1 = keypoints.find((kp2) => kp2.part === indexes[j + 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, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
      const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + 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, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][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 = (_e2 = (_d2 = newResult.hand[i]) == null ? void 0 : _d2.annotations) == null ? void 0 : _e2[key]) == null ? void 0 : _f2[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][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, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
      const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + 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) + (((_k2 = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k2.roll) || 0)) / bufferedFactor,
          yaw: ((bufferedFactor - 1) * (((_m = (_l2 = bufferedResult.face[i].rotation) == null ? void 0 : _l2.angle) == null ? void 0 : _m.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 = {
          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, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
      const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + 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, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + 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 = zq.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 = We(t10.resize, constants.tf255);
  t10.res = model17.execute(t10.norm);
  t10.squeeze = jp(t10.res, [0]);
  [t10.bgRaw, t10.fgRaw] = ko(t10.squeeze, 2);
  t10.fg = G1(t10.fgRaw);
  t10.mul = oe(t10.fg, constants.tf255);
  t10.expand = _a(t10.mul, 2);
  t10.output = zq.resizeBilinear(t10.expand, [input.shape[1] || 0, input.shape[2] || 0]);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      t10.input = jp(input);
      t10.concat = gt([t10.input, t10.output], -1);
      rgba = qe(t10.concat, "int32");
      break;
    case "alpha":
      rgba = qe(t10.output, "int32");
      break;
    default:
      rgba = nr(0);
  }
  Object.keys(t10).forEach((tensor) => Ft(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, _b;
  const t10 = {};
  if (!((_a2 = input == null ? void 0 : input.shape) == null ? void 0 : _a2[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
    return input;
  cache5.padding = [
    [0, 0],
    [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],
    [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],
    [0, 0]
  ];
  t10.pad = Ws(input, cache5.padding);
  t10.resize = zq.resizeBilinear(t10.pad, [inputSize10, inputSize10]);
  const final = qe(t10.resize, "int32");
  Object.keys(t10).forEach((tensor) => Ft(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: [
          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) => Ft(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, _b;
  let id2 = 0;
  let results = [];
  const size2 = inputSize9;
  for (const strideSize of [1, 2, 4]) {
    const baseSize = strideSize * 13;
    const scoresT = jp(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length));
    const scores = await scoresT.array();
    const featuresT = jp(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length));
    const boxesMaxT = z(featuresT, [-1, 4, (((_a2 = featuresT.shape) == null ? void 0 : _a2[1]) || 0) / 4]);
    const boxIdxT = Hv(boxesMaxT, 2);
    const boxIdx = await boxIdxT.array();
    for (let i = 0; i < scoresT.shape[0]; i++) {
      for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) {
        const score = scores[i][j];
        if (score > (config3.object.minConfidence || 0) && j !== 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 [x6, y] = [
            cx2 - scaleBox / strideSize * boxOffset[0],
            cy - scaleBox / strideSize * boxOffset[1]
          ];
          const [w, h] = [
            cx2 + scaleBox / strideSize * boxOffset[2] - x6,
            cy + scaleBox / strideSize * boxOffset[3] - y
          ];
          let boxRaw = [x6, y, w, h];
          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
          const box = [
            boxRaw[0] * outputShape[0],
            boxRaw[1] * outputShape[1],
            boxRaw[2] * outputShape[0],
            boxRaw[3] * outputShape[1]
          ];
          const result = {
            id: id2++,
            score: Math.round(100 * score) / 100,
            class: j + 1,
            label: labels2[j].label,
            box: box.map((a) => Math.trunc(a)),
            boxRaw
          };
          results.push(result);
        }
      }
    }
    Ft([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 zq.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence);
    nmsIdx = Array.from(await nms.data());
    Ft(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 = zq.resizeBilinear(image, [inputSize9, inputSize9], false);
    const normT = We(resizeT, constants.tf255);
    const transposeT = Wp(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;
    Ft([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: x6, y } }) => ({
    maxX: Math.max(maxX, x6),
    maxY: Math.max(maxY, y),
    minX: Math.min(minX, x6),
    minY: Math.min(minY, y)
  }), {
    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 {
  constructor(maxSize2, getElementValue) {
    __publicField(this, "priorityQueue");
    __publicField(this, "numberOfElements");
    __publicField(this, "getElementValue");
    this.priorityQueue = new Array(maxSize2);
    this.numberOfElements = -1;
    this.getElementValue = getElementValue;
  }
  enqueue(x6) {
    this.priorityQueue[++this.numberOfElements] = x6;
    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 j = 2 * k;
      if (j < this.numberOfElements && this.less(j, j + 1))
        j++;
      if (!this.less(k, j))
        break;
      this.exchange(k, j);
      k = j;
    }
  }
  getValueAt(i) {
    return this.getElementValue(this.priorityQueue[i]);
  }
  less(i, j) {
    return this.getValueAt(i) < this.getValueAt(j);
  }
  exchange(i, j) {
    const t10 = this.priorityQueue[i];
    this.priorityQueue[i] = this.priorityQueue[j];
    this.priorityQueue[j] = t10;
  }
};
function getOffsetPoint(y, x6, keypoint, offsets) {
  return {
    y: offsets.get(y, x6, keypoint),
    x: offsets.get(y, x6, keypoint + count)
  };
}
function getImageCoords(part, outputStride2, offsets) {
  const { heatmapY, heatmapX, id: keypoint } = part;
  const { y, x: x6 } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
  return {
    x: part.heatmapX * outputStride2 + x6,
    y: part.heatmapY * outputStride2 + y
  };
}
function clamp(a, min, max) {
  if (a < min)
    return min;
  if (a > max)
    return max;
  return a;
}
function squaredDistance(y1, x12, y22, x22) {
  const dy = y22 - y1;
  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"];
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: x6, y }, keypointId) {
  return poses.some(({ keypoints }) => {
    var _a2;
    const correspondingKeypoint = (_a2 = keypoints[keypointId]) == null ? void 0 : _a2.position;
    if (!correspondingKeypoint)
      return false;
    return squaredDistance(y, x6, 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 = Ne(() => {
    if (!model20.inputs[0].shape)
      return [];
    const resized = zq.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]);
    const normalized = ke(We(qe(resized, "float32"), 127.5), 1);
    const results = model20.execute(normalized, poseNetOutputs);
    const results3d = results.map((y) => jp(y, [0]));
    results3d[1] = Ms(results3d[1]);
    return results3d;
  });
  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
  for (const t10 of res)
    Ft(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) {
  Ft([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) => Ne(() => {
  const squeeze = jp(r, [0]);
  const mul = oe(squeeze, constants.tf255);
  const cast = qe(mul, "int32");
  return cast;
});
function getRGBA(fgr, pha) {
  const rgb2 = fgr ? normalize(fgr) : Bs([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
  const a = pha ? normalize(pha) : Bs([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
  const rgba = gt([rgb2, a], -1);
  Ft([rgb2, a]);
  return rgba;
}
function getState(state) {
  return Ne(() => {
    const r = {};
    r.unstack = ko(state, -1);
    r.concat = gt(r.unstack, 1);
    r.split = $a(r.concat, 4, 1);
    r.stack = gt(r.split, 2);
    r.squeeze = jp(r.stack, [0]);
    r.expand = _a(r.squeeze, -1);
    r.add = ge(r.expand, 1);
    r.mul = oe(r.add, 127.5);
    r.cast = qe(r.mul, "int32");
    r.tile = fi(r.cast, [1, 1, 3]);
    r.alpha = Bs([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
    return gt([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 = We(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);
  }
  Ft([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 = zq.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 = We(t10.resize, constants.tf255);
  t10.res = model22.execute(t10.norm);
  t10.squeeze = jp(t10.res, [0]);
  t10.alpha = zq.resizeBilinear(t10.squeeze, [input.shape[1] || 0, input.shape[2] || 0]);
  t10.mul = oe(t10.alpha, constants.tf255);
  let rgba;
  switch (config3.segmentation.mode || "default") {
    case "default":
      t10.input = jp(input);
      t10.concat = gt([t10.input, t10.mul], -1);
      rgba = qe(t10.concat, "int32");
      break;
    case "alpha":
      rgba = qe(t10.mul, "int32");
      break;
    default:
      rgba = nr(0);
  }
  Object.keys(t10).forEach((tensor) => Ft(t10[tensor]));
  return rgba;
}

// src/models.ts
function validateModel(instance, model23, name) {
  var _a2, _b;
  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 ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
    for (const kernel of Object.values(executor.graph.nodes)) {
      const op2 = kernel.op.toLowerCase();
      if (!ops.includes(op2))
        ops.push(op2);
    }
  } else {
    if (!executor && instance.config.debug) {
      log("model not loaded", name);
    }
  }
  for (const op2 of ops) {
    if (!simpleOps.includes(op2) && !ignoreOps.includes(op2) && !instance.env.kernels.includes(op2) && !instance.env.kernels.includes(op2.replace("_", "")) && !instance.env.kernels.includes(op2.replace("native", "")) && !instance.env.kernels.includes(op2.replace("v2", ""))) {
      missing.push(op2);
    }
  }
  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() {
    var _a2, _b, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _n2, _o2, _p2, _q2, _r2, _s2, _t, _u2, _v2, _w2, _x2, _y, _z2, _A2;
    if (env.initial)
      this.reset();
    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 && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.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 && ((_e2 = this.instance.config.face.iris) == null ? void 0 : _e2.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 && ((_k2 = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k2.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 && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.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;
    await Promise.all([...Object.values(m)]);
    for (const model23 of Object.keys(m))
      this.models[model23] = m[model23] || this.models[model23] || null;
  }
  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, _b, _c2, _d2, _e2, _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"] === ((_b = person2.hands.left) == null ? void 0 : _b.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 x6 = [];
    const y = [];
    const extractXY = (box) => {
      if (box && box.length === 4) {
        x6.push(box[0], box[0] + box[2]);
        y.push(box[1], box[1] + box[3]);
      }
    };
    extractXY(person2.face.box);
    extractXY((_d2 = person2.body) == null ? void 0 : _d2.box);
    extractXY((_e2 = person2.hands.left) == null ? void 0 : _e2.box);
    extractXY((_f2 = person2.hands.right) == null ? void 0 : _f2.box);
    const minX = Math.min(...x6);
    const minY = Math.min(...y);
    person2.box = [minX, minY, Math.max(...x6) - minX, Math.max(...y) - 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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AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
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KCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgo/8AAEQgBAAEAAwEhAAIRAQMRAf/E
AB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAE
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tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEB
AQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXET
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nhXI67c8qa7Lw3c+adjcEDGK1paSRhVV4s6A0or0jyRRQ1AHX0V553hRQBz+vNtt5z3xXzX8Qbdm
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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
      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 && Pie() === "tensorflow") {
    const data = (void 0).decodeJpeg(img);
    const expanded = _a(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, _b, _c2, _d2;
  if (!P().flagRegistry.ENGINE_COMPILE_ONLY)
    return;
  const backendType = Pie();
  const webGLBackend = Bie();
  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) {
    return;
  }
  P().set("ENGINE_COMPILE_ONLY", true);
  const numTensorsStart = cr().state.numTensors;
  const compiledModels = [];
  for (const [modelName, model23] of Object.entries(instance.models).filter(([key, val]) => key !== null && val !== null)) {
    const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a2 = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a2[0]) == null ? void 0 : _b.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 = Wr(shape, dtype);
    try {
      const res = model23.execute(tensor);
      compiledModels.push(modelName);
      if (Array.isArray(res))
        res.forEach((t10) => Ft(t10));
      else
        Ft(res);
    } catch (e) {
      if (instance.config.debug)
        log("compile fail model:", modelName);
    }
    Ft(tensor);
  }
  const kernels = await webGLBackend["checkCompileCompletionAsync"]();
  webGLBackend["getUniformLocations"]();
  if (instance.config.debug)
    log("compile pass:", { models: compiledModels, kernels: kernels.length });
  P().set("ENGINE_COMPILE_ONLY", false);
  const numTensorsEnd = cr().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 {
  constructor(userConfig) {
    __publicField(this, "version");
    __publicField(this, "config");
    __publicField(this, "result");
    __publicField(this, "state");
    __publicField(this, "process");
    __publicField(this, "tf");
    __publicField(this, "env", env);
    __publicField(this, "draw", draw_exports);
    __publicField(this, "match", match_exports);
    __publicField(this, "models");
    __publicField(this, "events");
    __publicField(this, "faceTriangulation");
    __publicField(this, "faceUVMap");
    __publicField(this, "performance");
    __privateAdd(this, _numTensors, void 0);
    __privateAdd(this, _analyzeMemoryLeaks, void 0);
    __privateAdd(this, _checkSanity, void 0);
    __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);
    });
    __privateAdd(this, _sanity, (input) => {
      if (!__privateGet(this, _checkSanity))
        return null;
      if (!input)
        return "input is not defined";
      if (this.env.node && !(input instanceof ut))
        return "input must be a tensor";
      try {
        this.tf.getBackend();
      } catch (e) {
        return "backend not loaded";
      }
      return null;
    });
    __publicField(this, "webcam", new WebCam());
    __publicField(this, "emit", (event) => {
      var _a2;
      if ((_a2 = this.events) == null ? void 0 : _a2.dispatchEvent)
        this.events.dispatchEvent(new Event(event));
    });
    __privateAdd(this, _loops, {});
    const tfVersion = (Cne.tfjs || YW).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() {
    const currentBackend = this.config.backend;
    this.config = JSON.parse(JSON.stringify(config));
    this.config.backend = currentBackend;
    reset();
    env.initial = true;
  }
  validate(userConfig) {
    const msgs = validate(config, userConfig || this.config);
    if (msgs.length === 0)
      this.config = mergeDeep(this.config, userConfig);
    return msgs;
  }
  now() {
    return now();
  }
  image(input, getTensor = false) {
    return process2(input, this.config, getTensor);
  }
  async segmentation(input, userConfig) {
    var _a2, _b, _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 ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.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);
    Ft(processed.tensor);
    return tensor;
  }
  compare(firstImageTensor, secondImageTensor) {
    return compare(this.config, firstImageTensor, secondImageTensor);
  }
  async init() {
    await check(this, true);
    await this.tf.ready();
    reset();
  }
  async load(userConfig) {
    this.state = "load";
    const timeStamp = now();
    const count2 = Object.values(this.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 Die();
      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();
    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).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;
  }
  next(result = this.result) {
    return calc2(result, this.config);
  }
  async warmup(userConfig) {
    const t0 = now();
    const res = await warmup(this, userConfig);
    const t12 = now();
    this.performance.warmup = Math.trunc(t12 - t0);
    return res;
  }
  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;
  }
  async detect(input, userConfig) {
    this.state = "detect";
    return new Promise(async (resolve) => {
      var _a2, _b, _c2, _d2, _e2, _f2, _g2, _h2, _i2, _j2, _k2, _l2, _m, _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 ((_b = this.config.body.modelPath) == null ? void 0 : _b.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 ((_e2 = this.config.body.modelPath) == null ? void 0 : _e2.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 = (_k2 = this.config.hand.detector) == null ? void 0 : _k2.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 = (_m = this.config.hand.detector) == null ? void 0 : _m.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);
        }
      };
      Ft(img.tensor);
      this.emit("detect");
      this.state = "idle";
      resolve(this.result);
    });
  }
  async sleep(ms2) {
    return new Promise((resolve) => {
      setTimeout(resolve, ms2);
    });
  }
  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