human/dist/human.esm.js

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

var __defProp = Object.defineProperty;
var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
var __export = (target, all2) => {
  for (var name in all2)
    __defProp(target, name, { get: all2[name], enumerable: true });
};
var __publicField = (obj, key, value) => {
  __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
  return value;
};
var __accessCheck = (obj, member, msg) => {
  if (!member.has(obj))
    throw TypeError("Cannot " + msg);
};
var __privateGet = (obj, member, getter) => {
  __accessCheck(obj, member, "read from private field");
  return getter ? getter.call(obj) : member.get(obj);
};
var __privateAdd = (obj, member, value) => {
  if (member.has(obj))
    throw TypeError("Cannot add the same private member more than once");
  member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
};
var __privateSet = (obj, member, value, setter) => {
  __accessCheck(obj, member, "write to private field");
  setter ? setter.call(obj, value) : member.set(obj, value);
  return value;
};

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

// src/config.ts
var config = {
  backend: "",
  modelBasePath: "",
  cacheModels: true,
  wasmPath: "",
  wasmPlatformFetch: false,
  debug: true,
  async: true,
  warmup: "full",
  cacheSensitivity: 0.7,
  skipAllowed: false,
  deallocate: false,
  filter: {
    enabled: true,
    equalization: false,
    width: 0,
    height: 0,
    flip: false,
    return: 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"
    },
    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: "mb3-centernet.json",
    minConfidence: 0.2,
    iouThreshold: 0.4,
    maxDetected: 10,
    skipFrames: 99,
    skipTime: 2e3
  },
  segmentation: {
    enabled: false,
    modelPath: "selfie.json",
    blur: 8
  }
};

// dist/tfjs.esm.js
var tfjs_esm_exports = {};
__export(tfjs_esm_exports, {
  Abs: () => co,
  Acos: () => al,
  Acosh: () => il,
  AdadeltaOptimizer: () => vb,
  AdagradOptimizer: () => xb,
  AdamOptimizer: () => wb,
  AdamaxOptimizer: () => kb,
  Add: () => Sr,
  AddN: () => Sa,
  All: () => ol,
  Any: () => ul,
  ArgMax: () => Ia,
  ArgMin: () => ll,
  Asin: () => cl,
  Asinh: () => dl,
  Atan: () => pl,
  Atan2: () => fl,
  Atanh: () => hl,
  AvgPool: () => Ca,
  AvgPool3D: () => Qd,
  AvgPool3DGrad: () => dg,
  AvgPoolGrad: () => cg,
  BackendWasm: () => Xde,
  BatchMatMul: () => Na,
  BatchToSpaceND: () => po,
  Bincount: () => pg,
  BroadcastArgs: () => hg,
  BroadcastTo: () => L$,
  Callback: () => VW,
  CallbackList: () => sB,
  Cast: () => Ta,
  Ceil: () => $a,
  ClipByValue: () => Ir,
  Complex: () => Zd,
  ComplexAbs: () => Jd,
  Concat: () => ho,
  Conv2D: () => _a,
  Conv2DBackpropFilter: () => fg,
  Conv2DBackpropInput: () => Aa,
  Conv3D: () => ep,
  Conv3DBackpropFilterV2: () => mg,
  Conv3DBackpropInputV2: () => gg,
  Cos: () => Ea,
  Cosh: () => Ra,
  CropAndResize: () => mo,
  Cumprod: () => fo,
  Cumsum: () => Da,
  CustomCallback: () => iB,
  DataStorage: () => Kd,
  DenseBincount: () => bg,
  DepthToSpace: () => go,
  DepthwiseConv2dNative: () => Fa,
  DepthwiseConv2dNativeBackpropFilter: () => yg,
  DepthwiseConv2dNativeBackpropInput: () => vg,
  Diag: () => xg,
  Dilation2D: () => tp,
  Dilation2DBackpropFilter: () => tm,
  Dilation2DBackpropInput: () => em,
  ENV: () => ok,
  EarlyStopping: () => WW,
  Einsum: () => np,
  Elu: () => Pa,
  EluGrad: () => wg,
  Environment: () => D$,
  Equal: () => bo,
  Erf: () => ml,
  Exp: () => za,
  ExpandDims: () => yo,
  Expm1: () => vo,
  FFT: () => kg,
  Fill: () => gl,
  FlipLeftRight: () => xo,
  Floor: () => Ma,
  FloorDiv: () => La,
  FromPixels: () => bd,
  FusedBatchNorm: () => Ba,
  FusedConv2D: () => ia,
  FusedDepthwiseConv2D: () => oa,
  GPGPUContext: () => Zf,
  GatherNd: () => ko,
  GatherV2: () => wo,
  GraphModel: () => j4,
  Greater: () => So,
  GreaterEqual: () => Va,
  History: () => aB,
  IFFT: () => Sg,
  Identity: () => Wa,
  Imag: () => sp,
  InputSpec: () => Dt,
  IsFinite: () => bl,
  IsInf: () => yl,
  IsNan: () => vl,
  KernelBackend: () => rl,
  LRN: () => ap,
  LRNGrad: () => Cg,
  LayerVariable: () => yz,
  LayersModel: () => dr,
  LeakyRelu: () => Ua,
  Less: () => Io,
  LessEqual: () => Co,
  LinSpace: () => Ig,
  Log: () => Ga,
  Log1p: () => xl,
  LogSoftmax: () => B$,
  LogicalAnd: () => No,
  LogicalNot: () => wl,
  LogicalOr: () => rp,
  LowerBound: () => tpe,
  MathBackendCPU: () => X0,
  MathBackendWebGL: () => K1,
  Max: () => Ha,
  MaxPool: () => ja,
  MaxPool3D: () => ip,
  MaxPool3DGrad: () => Tg,
  MaxPoolGrad: () => Ng,
  MaxPoolWithArgmax: () => $g,
  Maximum: () => qa,
  Mean: () => Ka,
  Min: () => Xa,
  Minimum: () => Ya,
  MirrorPad: () => Qa,
  Mod: () => kl,
  MomentumOptimizer: () => Sb,
  Multinomial: () => _g,
  Multiply: () => Za,
  Neg: () => To,
  NonMaxSuppressionV3: () => _o,
  NonMaxSuppressionV4: () => Sl,
  NonMaxSuppressionV5: () => Ao,
  NotEqual: () => $o,
  OP_SCOPE_SUFFIX: () => y_,
  OneHot: () => Ro,
  OnesLike: () => Eo,
  Optimizer: () => _r,
  OptimizerConstructors: () => Ur,
  Pack: () => Do,
  PadV2: () => Ja,
  Pool: () => npe,
  Pow: () => ei,
  Prelu: () => ti,
  Prod: () => ni,
  RMSPropOptimizer: () => Ib,
  RNN: () => Ar,
  Range: () => Il,
  Rank: () => l_,
  Real: () => op,
  RealDiv: () => Oa,
  Reciprocal: () => Cl,
  Reduction: () => yO,
  Relu: () => si,
  Relu6: () => ai,
  Reshape: () => Fo,
  ResizeBilinear: () => ri,
  ResizeBilinearGrad: () => Eg,
  ResizeNearestNeighbor: () => Nl,
  ResizeNearestNeighborGrad: () => Ag,
  Reverse: () => Oo,
  RotateWithOffset: () => Xo,
  Round: () => Po,
  Rsqrt: () => ii,
  SGDOptimizer: () => $p,
  ScatterNd: () => zo,
  SearchSorted: () => Rg,
  Select: () => Mo,
  Selu: () => Tl,
  Sequential: () => Kb,
  Sigmoid: () => ui,
  Sign: () => $l,
  Sin: () => oi,
  Sinh: () => Bo,
  Slice: () => Lo,
  Softmax: () => di,
  Softplus: () => _l,
  SpaceToBatchND: () => Vo,
  SparseFillEmptyRows: () => up,
  SparseReshape: () => Al,
  SparseSegmentMean: () => lp,
  SparseSegmentSum: () => cp,
  SparseToDense: () => dp,
  SplitV: () => Wo,
  Sqrt: () => li,
  Square: () => El,
  SquaredDifference: () => pi,
  Step: () => gi,
  StridedSlice: () => Uo,
  StringNGrams: () => pp,
  StringSplit: () => Dg,
  StringToHashBucketFast: () => Fg,
  Sub: () => hi,
  Sum: () => ci,
  SymbolicTensor: () => $s,
  Tan: () => Go,
  Tanh: () => fi,
  Tensor: () => et,
  TensorBuffer: () => Wt,
  Tile: () => Cr,
  TopK: () => Ho,
  Transform: () => qo,
  Transpose: () => mi,
  Unique: () => Og,
  Unpack: () => jo,
  UnsortedSegmentSum: () => hp,
  UpperBound: () => spe,
  Variable: () => vd,
  ZerosLike: () => Ko,
  _FusedMatMul: () => aa,
  abs: () => Lt,
  acos: () => JA,
  acosh: () => tE,
  add: () => ie,
  addN: () => sE,
  all: () => eS,
  any: () => gm,
  argMax: () => ju,
  argMin: () => uE,
  asin: () => cE,
  asinh: () => pE,
  atan: () => fE,
  atan2: () => gE,
  atanh: () => yE,
  avgPool: () => Xg,
  avgPool3d: () => rS,
  backend: () => qA,
  backend_util: () => C,
  basicLSTMCell: () => wpe,
  batchNorm: () => Xu,
  batchNorm2d: () => zE,
  batchNorm3d: () => LE,
  batchNorm4d: () => VE,
  batchToSpaceND: () => Yg,
  bincount: () => aS,
  booleanMaskAsync: () => Xpe,
  broadcastArgs: () => GE,
  broadcastTo: () => rd,
  broadcast_util: () => bi,
  browser: () => Fk,
  buffer: () => De,
  callbacks: () => ahe,
  cast: () => le,
  ceil: () => jE,
  clipByValue: () => Vn,
  clone: () => ur,
  complex: () => ua,
  concat: () => Ft,
  concat1d: () => YE,
  concat2d: () => ZE,
  concat3d: () => eR,
  concat4d: () => nR,
  constraints: () => FL,
  conv1d: () => iS,
  conv2d: () => da,
  conv2dTranspose: () => oS,
  conv3d: () => uS,
  conv3dTranspose: () => cR,
  copyRegisteredKernels: () => ipe,
  cos: () => Zg,
  cosh: () => cS,
  cosineWindow: () => PS,
  cumprod: () => ym,
  cumsum: () => dS,
  customGrad: () => qs,
  data: () => K4,
  denseBincount: () => gR,
  deprecationWarn: () => Zk,
  depthToSpace: () => yR,
  depthwiseConv2d: () => yp,
  deregisterOp: () => ohe,
  device_util: () => gp,
  diag: () => kpe,
  dilation2d: () => kR,
  disableDeprecationWarnings: () => cpe,
  dispose: () => Re,
  disposeVariables: () => dpe,
  div: () => xe,
  divNoNan: () => TR,
  dot: () => Spe,
  dropout: () => hF,
  einsum: () => AR,
  elu: () => vp,
  enableDebugMode: () => lpe,
  enableProdMode: () => upe,
  enclosingPowerOfTwo: () => fF,
  engine: () => ds,
  env: () => K,
  equal: () => Xn,
  erf: () => DR,
  exp: () => Yn,
  expandDims: () => Pn,
  expm1: () => zR,
  eye: () => pS,
  fft: () => fb,
  fill: () => zl,
  findBackend: () => ype,
  findBackendFactory: () => vpe,
  floor: () => xp,
  floorDiv: () => Jk,
  forceHalfFloat: () => d8,
  fused: () => fa,
  gather: () => Yu,
  gatherND: () => cF,
  gather_util: () => Pk,
  getBackend: () => gpe,
  getGradient: () => ox,
  getKernel: () => nm,
  getKernelsForBackend: () => sm,
  getThreadsCount: () => bhe,
  gpgpu_util: () => JK,
  grad: () => Npe,
  grads: () => Tpe,
  greater: () => Un,
  greaterEqual: () => Yo,
  ifft: () => Nd,
  imag: () => Jg,
  image: () => jn,
  inTopKAsync: () => Qpe,
  initializers: () => LL,
  input: () => QB,
  io: () => An,
  irfft: () => _S,
  isFinite: () => Ipe,
  isInf: () => Cpe,
  isNaN: () => KR,
  keep: () => qt,
  kernel_impls: () => ws,
  layers: () => tB,
  leakyRelu: () => eb,
  less: () => hS,
  lessEqual: () => Qo,
  linalg: () => ZO,
  linspace: () => ZR,
  loadGraphModel: () => uhe,
  loadLayersModel: () => she,
  localResponseNormalization: () => eD,
  log: () => Qn,
  log1p: () => tb,
  logSigmoid: () => Ape,
  logSoftmax: () => fS,
  logSumExp: () => fD,
  logicalAnd: () => Ds,
  logicalNot: () => rb,
  logicalOr: () => yS,
  logicalXor: () => Epe,
  losses: () => ehe,
  lowerBound: () => xD,
  matMul: () => Ve,
  math: () => mA,
  max: () => As,
  maxPool: () => ab,
  maxPool3d: () => xS,
  maxPoolWithArgmax: () => ID,
  maximum: () => $r,
  mean: () => It,
  memory: () => mm,
  meshgrid: () => Rpe,
  metrics: () => xW,
  min: () => vm,
  minimum: () => kp,
  mirrorPad: () => AD,
  mod: () => RD,
  model: () => the,
  models: () => PW,
  moments: () => ib,
  movingAverage: () => Ype,
  mul: () => V,
  multiRNNCell: () => Dpe,
  multinomial: () => zD,
  neg: () => kt,
  nextFrame: () => GS,
  norm: () => FS,
  notEqual: () => Qu,
  oneHot: () => kd,
  ones: () => Mn,
  onesLike: () => Zn,
  op: () => L,
  outerProduct: () => Fpe,
  pad: () => yi,
  pad1d: () => Ope,
  pad2d: () => Ppe,
  pad3d: () => zpe,
  pad4d: () => Mpe,
  pool: () => Lpe,
  pow: () => ha,
  prelu: () => ub,
  print: () => Z_,
  prod: () => wS,
  profile: () => ppe,
  rand: () => Bpe,
  randomGamma: () => Vpe,
  randomNormal: () => r3,
  randomUniform: () => Ll,
  range: () => Zu,
  ready: () => mpe,
  real: () => Id,
  reciprocal: () => u3,
  registerBackend: () => bp,
  registerCallbackConstructor: () => rhe,
  registerGradient: () => W$,
  registerKernel: () => Rl,
  registerOp: () => ihe,
  regularizers: () => zW,
  relu: () => Xs,
  relu6: () => kS,
  removeBackend: () => bpe,
  reshape: () => U,
  reverse: () => Jn,
  reverse1d: () => Wpe,
  reverse2d: () => Upe,
  reverse3d: () => Gpe,
  reverse4d: () => Hpe,
  rfft: () => mb,
  round: () => SS,
  rsqrt: () => IS,
  scalar: () => we,
  scatterND: () => iF,
  scatter_util: () => Mk,
  searchSorted: () => vS,
  selu: () => CS,
  separableConv2d: () => x3,
  sequential: () => nhe,
  serialization: () => re,
  setBackend: () => fpe,
  setPlatform: () => xpe,
  setThreadsCount: () => ghe,
  setWasmPath: () => fhe,
  setWasmPaths: () => mhe,
  setWebGLContext: () => H5,
  setdiff1dAsync: () => k3,
  shared: () => sv,
  sigmoid: () => Hs,
  sign: () => I3,
  signal: () => Jpe,
  sin: () => NS,
  sinh: () => TS,
  slice: () => qe,
  slice1d: () => db,
  slice2d: () => $S,
  slice3d: () => pb,
  slice4d: () => Cd,
  slice_util: () => wt,
  softmax: () => hb,
  softplus: () => Ml,
  spaceToBatchND: () => ob,
  sparse: () => Gc,
  sparseToDense: () => OS,
  spectral: () => Zpe,
  split: () => Bn,
  sqrt: () => dn,
  square: () => ct,
  squaredDifference: () => AS,
  squeeze: () => mr,
  stack: () => es,
  step: () => Sp,
  stridedSlice: () => U3,
  string: () => Uf,
  sub: () => ge,
  sum: () => ve,
  sumOutType: () => mp,
  tan: () => H3,
  tanh: () => Ku,
  tensor: () => ms,
  tensor1d: () => Zt,
  tensor2d: () => Zi,
  tensor3d: () => wA,
  tensor4d: () => qpe,
  tensor5d: () => jpe,
  tensor6d: () => Kpe,
  tensor_util: () => _s,
  test_util: () => MA,
  tidy: () => q,
  tile: () => hs,
  time: () => hpe,
  topk: () => j3,
  train: () => Bi,
  transpose: () => Ge,
  truncatedNormal: () => gb,
  unique: () => yx,
  unregisterGradient: () => ape,
  unregisterKernel: () => rpe,
  unsortedSegmentSum: () => Q3,
  unstack: () => Fs,
  upcastType: () => cn,
  upperBound: () => J3,
  util: () => w,
  valueAndGrad: () => $pe,
  valueAndGrads: () => _pe,
  variable: () => eF,
  variableGrads: () => sD,
  version: () => vhe,
  version_converter: () => lhe,
  version_core: () => ope,
  version_cpu: () => che,
  version_layers: () => yI,
  version_wasm: () => yhe,
  version_webgl: () => dhe,
  webgl: () => phe,
  webgl_util: () => G5,
  webgpu: () => Eoe,
  where: () => vn,
  whereAsync: () => RS,
  zeros: () => $t,
  zerosLike: () => je
});
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  if (t && typeof t == "object" || typeof t == "function")
    for (let r of Xw(t))
      !QT.call(e, r) && (n || r !== "default") && qd(e, r, { get: () => t[r], enumerable: !(s = XT(t, r)) || s.enumerable });
  return e;
};
var wa = (e, t) => JT(ZT(qd(e != null ? KT(YT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
var e$ = Mt({ "src/node_modules/long/src/long.js"(e, t) {
  t.exports = s;
  var n = null;
  try {
    n = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports;
  } catch (F) {
  }
  function s(F, T, z) {
    this.low = F | 0, this.high = T | 0, this.unsigned = !!z;
  }
  s.prototype.__isLong__, Object.defineProperty(s.prototype, "__isLong__", { value: true });
  function r(F) {
    return (F && F.__isLong__) === true;
  }
  s.isLong = r;
  var a = {}, i = {};
  function o(F, T) {
    var z, W, j;
    return T ? (F >>>= 0, (j = 0 <= F && F < 256) && (W = i[F], W) ? W : (z = l(F, (F | 0) < 0 ? -1 : 0, true), j && (i[F] = z), z)) : (F |= 0, (j = -128 <= F && F < 128) && (W = a[F], W) ? W : (z = l(F, F < 0 ? -1 : 0, false), j && (a[F] = z), z));
  }
  s.fromInt = o;
  function u(F, T) {
    if (isNaN(F))
      return T ? x : v;
    if (T) {
      if (F < 0)
        return x;
      if (F >= g)
        return A;
    } else {
      if (F <= -b)
        return P;
      if (F + 1 >= b)
        return E;
    }
    return F < 0 ? u(-F, T).neg() : l(F % m | 0, F / m | 0, T);
  }
  s.fromNumber = u;
  function l(F, T, z) {
    return new s(F, T, z);
  }
  s.fromBits = l;
  var c = Math.pow;
  function p(F, T, z) {
    if (F.length === 0)
      throw Error("empty string");
    if (F === "NaN" || F === "Infinity" || F === "+Infinity" || F === "-Infinity")
      return v;
    if (typeof T == "number" ? (z = T, T = false) : T = !!T, z = z || 10, z < 2 || 36 < z)
      throw RangeError("radix");
    var W;
    if ((W = F.indexOf("-")) > 0)
      throw Error("interior hyphen");
    if (W === 0)
      return p(F.substring(1), T, z).neg();
    for (var j = u(c(z, 8)), X = v, Y = 0; Y < F.length; Y += 8) {
      var Z = Math.min(8, F.length - Y), te = parseInt(F.substring(Y, Y + Z), z);
      if (Z < 8) {
        var J = u(c(z, Z));
        X = X.mul(J).add(u(te));
      } else
        X = X.mul(j), X = X.add(u(te));
    }
    return X.unsigned = T, X;
  }
  s.fromString = p;
  function d(F, T) {
    return typeof F == "number" ? u(F, T) : typeof F == "string" ? p(F, T) : l(F.low, F.high, typeof T == "boolean" ? T : F.unsigned);
  }
  s.fromValue = d;
  var h = 1 << 16, f = 1 << 24, m = h * h, g = m * m, b = g / 2, y = o(f), v = o(0);
  s.ZERO = v;
  var x = o(0, true);
  s.UZERO = x;
  var k = o(1);
  s.ONE = k;
  var I = o(1, true);
  s.UONE = I;
  var $ = o(-1);
  s.NEG_ONE = $;
  var E = l(-1, 2147483647, false);
  s.MAX_VALUE = E;
  var A = l(-1, -1, true);
  s.MAX_UNSIGNED_VALUE = A;
  var P = l(0, -2147483648, false);
  s.MIN_VALUE = P;
  var R = s.prototype;
  R.toInt = function() {
    return this.unsigned ? this.low >>> 0 : this.low;
  }, R.toNumber = function() {
    return this.unsigned ? (this.high >>> 0) * m + (this.low >>> 0) : this.high * m + (this.low >>> 0);
  }, R.toString = function(T) {
    if (T = T || 10, T < 2 || 36 < T)
      throw RangeError("radix");
    if (this.isZero())
      return "0";
    if (this.isNegative())
      if (this.eq(P)) {
        var z = u(T), W = this.div(z), j = W.mul(z).sub(this);
        return W.toString(T) + j.toInt().toString(T);
      } else
        return "-" + this.neg().toString(T);
    for (var X = u(c(T, 6), this.unsigned), Y = this, Z = ""; ; ) {
      var te = Y.div(X), J = Y.sub(te.mul(X)).toInt() >>> 0, se = J.toString(T);
      if (Y = te, Y.isZero())
        return se + Z;
      for (; se.length < 6; )
        se = "0" + se;
      Z = "" + se + Z;
    }
  }, R.getHighBits = function() {
    return this.high;
  }, R.getHighBitsUnsigned = function() {
    return this.high >>> 0;
  }, R.getLowBits = function() {
    return this.low;
  }, R.getLowBitsUnsigned = function() {
    return this.low >>> 0;
  }, R.getNumBitsAbs = function() {
    if (this.isNegative())
      return this.eq(P) ? 64 : this.neg().getNumBitsAbs();
    for (var T = this.high != 0 ? this.high : this.low, z = 31; z > 0 && (T & 1 << z) == 0; z--)
      ;
    return this.high != 0 ? z + 33 : z + 1;
  }, R.isZero = function() {
    return this.high === 0 && this.low === 0;
  }, R.eqz = R.isZero, R.isNegative = function() {
    return !this.unsigned && this.high < 0;
  }, R.isPositive = function() {
    return this.unsigned || this.high >= 0;
  }, R.isOdd = function() {
    return (this.low & 1) === 1;
  }, R.isEven = function() {
    return (this.low & 1) === 0;
  }, R.equals = function(T) {
    return r(T) || (T = d(T)), this.unsigned !== T.unsigned && this.high >>> 31 === 1 && T.high >>> 31 === 1 ? false : this.high === T.high && this.low === T.low;
  }, R.eq = R.equals, R.notEquals = function(T) {
    return !this.eq(T);
  }, R.neq = R.notEquals, R.ne = R.notEquals, R.lessThan = function(T) {
    return this.comp(T) < 0;
  }, R.lt = R.lessThan, R.lessThanOrEqual = function(T) {
    return this.comp(T) <= 0;
  }, R.lte = R.lessThanOrEqual, R.le = R.lessThanOrEqual, R.greaterThan = function(T) {
    return this.comp(T) > 0;
  }, R.gt = R.greaterThan, R.greaterThanOrEqual = function(T) {
    return this.comp(T) >= 0;
  }, R.gte = R.greaterThanOrEqual, R.ge = R.greaterThanOrEqual, R.compare = function(T) {
    if (r(T) || (T = d(T)), this.eq(T))
      return 0;
    var z = this.isNegative(), W = T.isNegative();
    return z && !W ? -1 : !z && W ? 1 : this.unsigned ? T.high >>> 0 > this.high >>> 0 || T.high === this.high && T.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(T).isNegative() ? -1 : 1;
  }, R.comp = R.compare, R.negate = function() {
    return !this.unsigned && this.eq(P) ? P : this.not().add(k);
  }, R.neg = R.negate, R.add = function(T) {
    r(T) || (T = d(T));
    var z = this.high >>> 16, W = this.high & 65535, j = this.low >>> 16, X = this.low & 65535, Y = T.high >>> 16, Z = T.high & 65535, te = T.low >>> 16, J = T.low & 65535, se = 0, ne = 0, oe = 0, ae = 0;
    return ae += X + J, oe += ae >>> 16, ae &= 65535, oe += j + te, ne += oe >>> 16, oe &= 65535, ne += W + Z, se += ne >>> 16, ne &= 65535, se += z + Y, se &= 65535, l(oe << 16 | ae, se << 16 | ne, this.unsigned);
  }, R.subtract = function(T) {
    return r(T) || (T = d(T)), this.add(T.neg());
  }, R.sub = R.subtract, R.multiply = function(T) {
    if (this.isZero())
      return v;
    if (r(T) || (T = d(T)), n) {
      var z = n.mul(this.low, this.high, T.low, T.high);
      return l(z, n.get_high(), this.unsigned);
    }
    if (T.isZero())
      return v;
    if (this.eq(P))
      return T.isOdd() ? P : v;
    if (T.eq(P))
      return this.isOdd() ? P : v;
    if (this.isNegative())
      return T.isNegative() ? this.neg().mul(T.neg()) : this.neg().mul(T).neg();
    if (T.isNegative())
      return this.mul(T.neg()).neg();
    if (this.lt(y) && T.lt(y))
      return u(this.toNumber() * T.toNumber(), this.unsigned);
    var W = this.high >>> 16, j = this.high & 65535, X = this.low >>> 16, Y = this.low & 65535, Z = T.high >>> 16, te = T.high & 65535, J = T.low >>> 16, se = T.low & 65535, ne = 0, oe = 0, ae = 0, de = 0;
    return de += Y * se, ae += de >>> 16, de &= 65535, ae += X * se, oe += ae >>> 16, ae &= 65535, ae += Y * J, oe += ae >>> 16, ae &= 65535, oe += j * se, ne += oe >>> 16, oe &= 65535, oe += X * J, ne += oe >>> 16, oe &= 65535, oe += Y * te, ne += oe >>> 16, oe &= 65535, ne += W * se + j * J + X * te + Y * Z, ne &= 65535, l(ae << 16 | de, ne << 16 | oe, this.unsigned);
  }, R.mul = R.multiply, R.divide = function(T) {
    if (r(T) || (T = d(T)), T.isZero())
      throw Error("division by zero");
    if (n) {
      if (!this.unsigned && this.high === -2147483648 && T.low === -1 && T.high === -1)
        return this;
      var z = (this.unsigned ? n.div_u : n.div_s)(this.low, this.high, T.low, T.high);
      return l(z, n.get_high(), this.unsigned);
    }
    if (this.isZero())
      return this.unsigned ? x : v;
    var W, j, X;
    if (this.unsigned) {
      if (T.unsigned || (T = T.toUnsigned()), T.gt(this))
        return x;
      if (T.gt(this.shru(1)))
        return I;
      X = x;
    } else {
      if (this.eq(P)) {
        if (T.eq(k) || T.eq($))
          return P;
        if (T.eq(P))
          return k;
        var Y = this.shr(1);
        return W = Y.div(T).shl(1), W.eq(v) ? T.isNegative() ? k : $ : (j = this.sub(T.mul(W)), X = W.add(j.div(T)), X);
      } else if (T.eq(P))
        return this.unsigned ? x : v;
      if (this.isNegative())
        return T.isNegative() ? this.neg().div(T.neg()) : this.neg().div(T).neg();
      if (T.isNegative())
        return this.div(T.neg()).neg();
      X = v;
    }
    for (j = this; j.gte(T); ) {
      W = Math.max(1, Math.floor(j.toNumber() / T.toNumber()));
      for (var Z = Math.ceil(Math.log(W) / Math.LN2), te = Z <= 48 ? 1 : c(2, Z - 48), J = u(W), se = J.mul(T); se.isNegative() || se.gt(j); )
        W -= te, J = u(W, this.unsigned), se = J.mul(T);
      J.isZero() && (J = k), X = X.add(J), j = j.sub(se);
    }
    return X;
  }, R.div = R.divide, R.modulo = function(T) {
    if (r(T) || (T = d(T)), n) {
      var z = (this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, T.low, T.high);
      return l(z, n.get_high(), this.unsigned);
    }
    return this.sub(this.div(T).mul(T));
  }, R.mod = R.modulo, R.rem = R.modulo, R.not = function() {
    return l(~this.low, ~this.high, this.unsigned);
  }, R.and = function(T) {
    return r(T) || (T = d(T)), l(this.low & T.low, this.high & T.high, this.unsigned);
  }, R.or = function(T) {
    return r(T) || (T = d(T)), l(this.low | T.low, this.high | T.high, this.unsigned);
  }, R.xor = function(T) {
    return r(T) || (T = d(T)), l(this.low ^ T.low, this.high ^ T.high, this.unsigned);
  }, R.shiftLeft = function(T) {
    return r(T) && (T = T.toInt()), (T &= 63) === 0 ? this : T < 32 ? l(this.low << T, this.high << T | this.low >>> 32 - T, this.unsigned) : l(0, this.low << T - 32, this.unsigned);
  }, R.shl = R.shiftLeft, R.shiftRight = function(T) {
    return r(T) && (T = T.toInt()), (T &= 63) === 0 ? this : T < 32 ? l(this.low >>> T | this.high << 32 - T, this.high >> T, this.unsigned) : l(this.high >> T - 32, this.high >= 0 ? 0 : -1, this.unsigned);
  }, R.shr = R.shiftRight, R.shiftRightUnsigned = function(T) {
    if (r(T) && (T = T.toInt()), T &= 63, T === 0)
      return this;
    var z = this.high;
    if (T < 32) {
      var W = this.low;
      return l(W >>> T | z << 32 - T, z >>> T, this.unsigned);
    } else
      return T === 32 ? l(z, 0, this.unsigned) : l(z >>> T - 32, 0, this.unsigned);
  }, R.shru = R.shiftRightUnsigned, R.shr_u = R.shiftRightUnsigned, R.toSigned = function() {
    return this.unsigned ? l(this.low, this.high, false) : this;
  }, R.toUnsigned = function() {
    return this.unsigned ? this : l(this.low, this.high, true);
  }, R.toBytes = function(T) {
    return T ? this.toBytesLE() : this.toBytesBE();
  }, R.toBytesLE = function() {
    var T = this.high, z = this.low;
    return [z & 255, z >>> 8 & 255, z >>> 16 & 255, z >>> 24, T & 255, T >>> 8 & 255, T >>> 16 & 255, T >>> 24];
  }, R.toBytesBE = function() {
    var T = this.high, z = this.low;
    return [T >>> 24, T >>> 16 & 255, T >>> 8 & 255, T & 255, z >>> 24, z >>> 16 & 255, z >>> 8 & 255, z & 255];
  }, s.fromBytes = function(T, z, W) {
    return W ? s.fromBytesLE(T, z) : s.fromBytesBE(T, z);
  }, s.fromBytesLE = function(T, z) {
    return new s(T[0] | T[1] << 8 | T[2] << 16 | T[3] << 24, T[4] | T[5] << 8 | T[6] << 16 | T[7] << 24, z);
  }, s.fromBytesBE = function(T, z) {
    return new s(T[4] << 24 | T[5] << 16 | T[6] << 8 | T[7], T[0] << 24 | T[1] << 16 | T[2] << 8 | T[3], z);
  };
} });
var t$ = Mt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
} });
var n$ = Mt({ "(disabled):util"() {
} });
var s$ = Mt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
  (function(n, s, r) {
    function a(l) {
      var c = this, p = u();
      c.next = function() {
        var d = 2091639 * c.s0 + c.c * 23283064365386963e-26;
        return c.s0 = c.s1, c.s1 = c.s2, c.s2 = d - (c.c = d | 0);
      }, c.c = 1, c.s0 = p(" "), c.s1 = p(" "), c.s2 = p(" "), c.s0 -= p(l), c.s0 < 0 && (c.s0 += 1), c.s1 -= p(l), c.s1 < 0 && (c.s1 += 1), c.s2 -= p(l), c.s2 < 0 && (c.s2 += 1), p = null;
    }
    function i(l, c) {
      return c.c = l.c, c.s0 = l.s0, c.s1 = l.s1, c.s2 = l.s2, c;
    }
    function o(l, c) {
      var p = new a(l), d = c && c.state, h = p.next;
      return h.int32 = function() {
        return p.next() * 4294967296 | 0;
      }, h.double = function() {
        return h() + (h() * 2097152 | 0) * 11102230246251565e-32;
      }, h.quick = h, d && (typeof d == "object" && i(d, p), h.state = function() {
        return i(p, {});
      }), h;
    }
    function u() {
      var l = 4022871197, c = function(p) {
        p = String(p);
        for (var d = 0; d < p.length; d++) {
          l += p.charCodeAt(d);
          var h = 0.02519603282416938 * l;
          l = h >>> 0, h -= l, h *= l, l = h >>> 0, h -= l, l += h * 4294967296;
        }
        return (l >>> 0) * 23283064365386963e-26;
      };
      return c;
    }
    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
      return o;
    }) : this.alea = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var r$ = Mt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
  (function(n, s, r) {
    function a(u) {
      var l = this, c = "";
      l.x = 0, l.y = 0, l.z = 0, l.w = 0, l.next = function() {
        var d = l.x ^ l.x << 11;
        return l.x = l.y, l.y = l.z, l.z = l.w, l.w ^= l.w >>> 19 ^ d ^ d >>> 8;
      }, u === (u | 0) ? l.x = u : c += u;
      for (var p = 0; p < c.length + 64; p++)
        l.x ^= c.charCodeAt(p) | 0, l.next();
    }
    function i(u, l) {
      return l.x = u.x, l.y = u.y, l.z = u.z, l.w = u.w, l;
    }
    function o(u, l) {
      var c = new a(u), p = l && l.state, d = function() {
        return (c.next() >>> 0) / 4294967296;
      };
      return d.double = function() {
        do
          var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
        while (m === 0);
        return m;
      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() {
        return i(c, {});
      }), d;
    }
    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
      return o;
    }) : this.xor128 = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var a$ = Mt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
  (function(n, s, r) {
    function a(u) {
      var l = this, c = "";
      l.next = function() {
        var d = l.x ^ l.x >>> 2;
        return l.x = l.y, l.y = l.z, l.z = l.w, l.w = l.v, (l.d = l.d + 362437 | 0) + (l.v = l.v ^ l.v << 4 ^ (d ^ d << 1)) | 0;
      }, l.x = 0, l.y = 0, l.z = 0, l.w = 0, l.v = 0, u === (u | 0) ? l.x = u : c += u;
      for (var p = 0; p < c.length + 64; p++)
        l.x ^= c.charCodeAt(p) | 0, p == c.length && (l.d = l.x << 10 ^ l.x >>> 4), l.next();
    }
    function i(u, l) {
      return l.x = u.x, l.y = u.y, l.z = u.z, l.w = u.w, l.v = u.v, l.d = u.d, l;
    }
    function o(u, l) {
      var c = new a(u), p = l && l.state, d = function() {
        return (c.next() >>> 0) / 4294967296;
      };
      return d.double = function() {
        do
          var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21);
        while (m === 0);
        return m;
      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() {
        return i(c, {});
      }), d;
    }
    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
      return o;
    }) : this.xorwow = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var i$ = Mt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
  (function(n, s, r) {
    function a(u) {
      var l = this;
      l.next = function() {
        var p = l.x, d = l.i, h, f, m;
        return h = p[d], h ^= h >>> 7, f = h ^ h << 24, h = p[d + 1 & 7], f ^= h ^ h >>> 10, h = p[d + 3 & 7], f ^= h ^ h >>> 3, h = p[d + 4 & 7], f ^= h ^ h << 7, h = p[d + 7 & 7], h = h ^ h << 13, f ^= h ^ h << 9, p[d] = f, l.i = d + 1 & 7, f;
      };
      function c(p, d) {
        var h, f, m = [];
        if (d === (d | 0))
          f = m[0] = d;
        else
          for (d = "" + d, h = 0; h < d.length; ++h)
            m[h & 7] = m[h & 7] << 15 ^ d.charCodeAt(h) + m[h + 1 & 7] << 13;
        for (; m.length < 8; )
          m.push(0);
        for (h = 0; h < 8 && m[h] === 0; ++h)
          ;
        for (h == 8 ? f = m[7] = -1 : f = m[h], p.x = m, p.i = 0, h = 256; h > 0; --h)
          p.next();
      }
      c(l, u);
    }
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      return l.x = u.x.slice(), l.i = u.i, l;
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      }, d.int32 = c.next, d.quick = d, p && (p.x && i(p, c), d.state = function() {
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  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var o$ = Mt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
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          d && (f ^= d.charCodeAt((g + 32) % d.length)), g === 0 && (b = f), f ^= f << 10, f ^= f >>> 15, f ^= f << 4, f ^= f >>> 13, g >= 0 && (b = b + 1640531527 | 0, h = y[g & 127] ^= f + b, m = h == 0 ? m + 1 : 0);
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} });
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        do
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      }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() {
        return i(c, {});
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      return o;
    }) : this.tychei = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var l$ = Mt({ "(disabled):crypto"() {
} });
var c$ = Mt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
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          F = (F + z) * a, T *= a, z = P.g(1);
        for (; F >= p; )
          F /= 2, T /= 2, z >>>= 1;
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    function b(k, I) {
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    function y(k, I) {
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    function x(k) {
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} });
var jd = Mt({ "src/node_modules/seedrandom/index.js"(e, t) {
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} });
var Yw = Mt({ "(disabled):src/node_modules/string_decoder/index.js"() {
} });
var ag = Mt({ "(disabled):fs"() {
} });
var fd = Mt({ "(disabled):path"() {
} });
var d$ = Mt({ "(disabled):worker_threads"() {
} });
var p$ = Mt({ "(disabled):perf_hooks"() {
} });
var h$ = Mt({ "(disabled):os"() {
} });
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      }, Pi = d._Less = function() {
        return (Pi = d._Less = d.asm.Less).apply(null, arguments);
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      }, f && (x = (H) => {
        var ee = new XMLHttpRequest();
        return ee.open("GET", H, false), ee.responseType = "arraybuffer", ee.send(null), new Uint8Array(ee.response);
      }), v = (H, ee, ce) => {
        var Se = new XMLHttpRequest();
        Se.open("GET", H, true), Se.responseType = "arraybuffer", Se.onload = () => {
          if (Se.status == 200 || Se.status == 0 && Se.response) {
            ee(Se.response);
            return;
          }
          ce();
        }, Se.onerror = ce, Se.send(null);
      }, k = (H) => document.title = H);
      var P = a.print || console.log.bind(console), R = a.printErr || console.warn.bind(console);
      Object.assign(a, l), l = null, a.arguments && (c = a.arguments), a.thisProgram && (p = a.thisProgram), a.quit && (d = a.quit);
      var F = 4;
      function T(H) {
        T.shown || (T.shown = {}), T.shown[H] || (T.shown[H] = 1, R(H));
      }
      function z(H, ee) {
        if (typeof WebAssembly.Function == "function") {
          for (var ce = { i: "i32", j: "i64", f: "f32", d: "f64" }, Se = { parameters: [], results: ee[0] == "v" ? [] : [ce[ee[0]]] }, Qe = 1; Qe < ee.length; ++Qe)
            Se.parameters.push(ce[ee[Qe]]);
          return new WebAssembly.Function(Se, H);
        }
        var Ze = [1, 0, 1, 96], Le = ee.slice(0, 1), ze = ee.slice(1), Tt = { i: 127, j: 126, f: 125, d: 124 };
        Ze.push(ze.length);
        for (var Qe = 0; Qe < ze.length; ++Qe)
          Ze.push(Tt[ze[Qe]]);
        Le == "v" ? Ze.push(0) : Ze = Ze.concat([1, Tt[Le]]), Ze[1] = Ze.length - 2;
        var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(Ze, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), zi = new WebAssembly.Instance(os, { e: { f: H } }), Vr = zi.exports.f;
        return Vr;
      }
      var W = [], j;
      function X() {
        if (W.length)
          return W.pop();
        try {
          Zs.grow(1);
        } catch (H) {
          throw H instanceof RangeError ? "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH." : H;
        }
        return Zs.length - 1;
      }
      function Y(H, ee) {
        for (var ce = H; ce < H + ee; ce++) {
          var Se = yu(ce);
          Se && j.set(Se, ce);
        }
      }
      var Z = 0, te = (H) => {
        Z = H;
      }, J;
      a.wasmBinary && (J = a.wasmBinary);
      var se = a.noExitRuntime || true;
      typeof WebAssembly != "object" && Pr("no native wasm support detected");
      var ne, oe = false, ae;
      function de(H, ee) {
        H || Pr(ee);
      }
      function me(H) {
        var ee = a["_" + H];
        return ee;
      }
      function ke(H, ee, ce, Se, Qe) {
        var Ze = { string: function(an) {
          var tr = 0;
          if (an != null && an !== 0) {
            var Oc = (an.length << 2) + 1;
            tr = wu(Oc), tt(an, tr, Oc);
          }
          return tr;
        }, array: function(an) {
          var tr = wu(an.length);
          return rt(an, tr), tr;
        } };
        function Le(an) {
          return ee === "string" ? Je(an) : ee === "boolean" ? Boolean(an) : an;
        }
        var ze = me(H), Tt = [], is = 0;
        if (Se)
          for (var os = 0; os < Se.length; os++) {
            var zi = Ze[ce[os]];
            zi ? (is === 0 && (is = Dc()), Tt[os] = zi(Se[os])) : Tt[os] = Se[os];
          }
        var Vr = ze.apply(null, Tt);
        function Rf(an) {
          return is !== 0 && Fc(is), Le(an);
        }
        return Vr = Rf(Vr), Vr;
      }
      function Ie(H, ee, ce, Se) {
        ce = ce || [];
        var Qe = ce.every(function(Le) {
          return Le === "number";
        }), Ze = ee !== "string";
        return Ze && Qe && !Se ? me(H) : function() {
          return ke(H, ee, ce, arguments, Se);
        };
      }
      var Ee = 1, Pe = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0;
      function Xe(H, ee, ce) {
        for (var Se = ee + ce, Qe = ee; H[Qe] && !(Qe >= Se); )
          ++Qe;
        if (Qe - ee > 16 && H.subarray && Pe)
          return Pe.decode(H.subarray(ee, Qe));
        for (var Ze = ""; ee < Qe; ) {
          var Le = H[ee++];
          if (!(Le & 128)) {
            Ze += String.fromCharCode(Le);
            continue;
          }
          var ze = H[ee++] & 63;
          if ((Le & 224) == 192) {
            Ze += String.fromCharCode((Le & 31) << 6 | ze);
            continue;
          }
          var Tt = H[ee++] & 63;
          if ((Le & 240) == 224 ? Le = (Le & 15) << 12 | ze << 6 | Tt : Le = (Le & 7) << 18 | ze << 12 | Tt << 6 | H[ee++] & 63, Le < 65536)
            Ze += String.fromCharCode(Le);
          else {
            var is = Le - 65536;
            Ze += String.fromCharCode(55296 | is >> 10, 56320 | is & 1023);
          }
        }
        return Ze;
      }
      function Je(H, ee) {
        return H ? Xe(en, H, ee) : "";
      }
      function Ye(H, ee, ce, Se) {
        if (!(Se > 0))
          return 0;
        for (var Qe = ce, Ze = ce + Se - 1, Le = 0; Le < H.length; ++Le) {
          var ze = H.charCodeAt(Le);
          if (ze >= 55296 && ze <= 57343) {
            var Tt = H.charCodeAt(++Le);
            ze = 65536 + ((ze & 1023) << 10) | Tt & 1023;
          }
          if (ze <= 127) {
            if (ce >= Ze)
              break;
            ee[ce++] = ze;
          } else if (ze <= 2047) {
            if (ce + 1 >= Ze)
              break;
            ee[ce++] = 192 | ze >> 6, ee[ce++] = 128 | ze & 63;
          } else if (ze <= 65535) {
            if (ce + 2 >= Ze)
              break;
            ee[ce++] = 224 | ze >> 12, ee[ce++] = 128 | ze >> 6 & 63, ee[ce++] = 128 | ze & 63;
          } else {
            if (ce + 3 >= Ze)
              break;
            ee[ce++] = 240 | ze >> 18, ee[ce++] = 128 | ze >> 12 & 63, ee[ce++] = 128 | ze >> 6 & 63, ee[ce++] = 128 | ze & 63;
          }
        }
        return ee[ce] = 0, ce - Qe;
      }
      function tt(H, ee, ce) {
        return Ye(H, en, ee, ce);
      }
      function Ce(H) {
        for (var ee = 0, ce = 0; ce < H.length; ++ce) {
          var Se = H.charCodeAt(ce);
          Se >= 55296 && Se <= 57343 && (Se = 65536 + ((Se & 1023) << 10) | H.charCodeAt(++ce) & 1023), Se <= 127 ? ++ee : Se <= 2047 ? ee += 2 : Se <= 65535 ? ee += 3 : ee += 4;
        }
        return ee;
      }
      var ut = typeof TextDecoder != "undefined" ? new TextDecoder("utf-16le") : void 0;
      function rt(H, ee) {
        Et.set(H, ee);
      }
      function Jt(H, ee, ce) {
        for (var Se = 0; Se < H.length; ++Se)
          Et[ee++ >> 0] = H.charCodeAt(Se);
        ce || (Et[ee >> 0] = 0);
      }
      function Nt(H, ee) {
        return H % ee > 0 && (H += ee - H % ee), H;
      }
      var In, Et, en, Cn, Nn, Yt, Dn, tn, zs;
      function Ms(H) {
        In = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Cn = new Int16Array(H), a.HEAP32 = Yt = new Int32Array(H), a.HEAPU8 = en = new Uint8Array(H), a.HEAPU16 = Nn = new Uint16Array(H), a.HEAPU32 = Dn = new Uint32Array(H), a.HEAPF32 = tn = new Float32Array(H), a.HEAPF64 = zs = new Float64Array(H);
      }
      var Ni = a.INITIAL_MEMORY || 16777216, Zs, Ls = [], fu = [], Ti = [], nn = false, oc = false, uc = 0;
      function mu() {
        return se || uc > 0;
      }
      function lc() {
        if (a.preRun)
          for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
            pc(a.preRun.shift());
        bu(Ls);
      }
      function cc() {
        nn = true, bu(fu);
      }
      function Lv() {
        oc = true;
      }
      function dc() {
        if (a.postRun)
          for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
            hc(a.postRun.shift());
        bu(Ti);
      }
      function pc(H) {
        Ls.unshift(H);
      }
      function rs(H) {
        fu.unshift(H);
      }
      function hc(H) {
        Ti.unshift(H);
      }
      var Fn = 0, $i = null, Js = null;
      function ih(H) {
        Fn++, a.monitorRunDependencies && a.monitorRunDependencies(Fn);
      }
      function fc(H) {
        if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && ($i !== null && (clearInterval($i), $i = null), Js)) {
          var ee = Js;
          Js = null, ee();
        }
      }
      a.preloadedImages = {}, a.preloadedAudios = {};
      function Pr(H) {
        a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", R(H), oe = true, ae = 1, H += ". Build with -s ASSERTIONS=1 for more info.";
        var ee = new WebAssembly.RuntimeError(H);
        throw o(ee), ee;
      }
      var oh = "data:application/octet-stream;base64,";
      function mc(H) {
        return H.startsWith(oh);
      }
      function zr(H) {
        return H.startsWith("file://");
      }
      var sn;
      sn = "tfjs-backend-wasm.wasm", mc(sn) || (sn = b(sn));
      function gu(H) {
        try {
          if (H == sn && J)
            return new Uint8Array(J);
          if (x)
            return x(H);
          throw "both async and sync fetching of the wasm failed";
        } catch (ee) {
          Pr(ee);
        }
      }
      function uh() {
        if (!J && (h || f)) {
          if (typeof fetch == "function" && !zr(sn))
            return fetch(sn, { credentials: "same-origin" }).then(function(H) {
              if (!H.ok)
                throw "failed to load wasm binary file at '" + sn + "'";
              return H.arrayBuffer();
            }).catch(function() {
              return gu(sn);
            });
          if (v)
            return new Promise(function(H, ee) {
              v(sn, function(ce) {
                H(new Uint8Array(ce));
              }, ee);
            });
        }
        return Promise.resolve().then(function() {
          return gu(sn);
        });
      }
      function lh() {
        var H = { env: Ei, wasi_snapshot_preview1: Ei };
        function ee(Le, ze) {
          var Tt = Le.exports;
          a.asm = Tt, ne = a.asm.memory, Ms(ne.buffer), Zs = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), fc("wasm-instantiate");
        }
        ih("wasm-instantiate");
        function ce(Le) {
          ee(Le.instance);
        }
        function Se(Le) {
          return uh().then(function(ze) {
            return WebAssembly.instantiate(ze, H);
          }).then(function(ze) {
            return ze;
          }).then(Le, function(ze) {
            R("failed to asynchronously prepare wasm: " + ze), Pr(ze);
          });
        }
        function Qe() {
          return !J && typeof WebAssembly.instantiateStreaming == "function" && !mc(sn) && !zr(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(Le) {
            var ze = WebAssembly.instantiateStreaming(Le, H);
            return ze.then(ce, function(Tt) {
              return R("wasm streaming compile failed: " + Tt), R("falling back to ArrayBuffer instantiation"), Se(ce);
            });
          }) : Se(ce);
        }
        if (a.instantiateWasm)
          try {
            var Ze = a.instantiateWasm(H, ee);
            return Ze;
          } catch (Le) {
            return R("Module.instantiateWasm callback failed with error: " + Le), false;
          }
        return Qe().catch(o), {};
      }
      var Bv, Vv;
      function bu(H) {
        for (; H.length > 0; ) {
          var ee = H.shift();
          if (typeof ee == "function") {
            ee(a);
            continue;
          }
          var ce = ee.func;
          typeof ce == "number" ? ee.arg === void 0 ? yu(ce)() : yu(ce)(ee.arg) : ce(ee.arg === void 0 ? null : ee.arg);
        }
      }
      function er(H) {
        return H;
      }
      function gc(H) {
        var ee = /\b_Z[\w\d_]+/g;
        return H.replace(ee, function(ce) {
          var Se = ce;
          return ce === Se ? ce : Se + " [" + ce + "]";
        });
      }
      var as = [];
      function yu(H) {
        var ee = as[H];
        return ee || (H >= as.length && (as.length = H + 1), as[H] = ee = Zs.get(H)), ee;
      }
      function Wv() {
        var H = new Error();
        if (!H.stack) {
          try {
            throw new Error();
          } catch (ee) {
            H = ee;
          }
          if (!H.stack)
            return "(no stack trace available)";
        }
        return H.stack.toString();
      }
      function _i(H, ee) {
        Zs.set(H, ee), as[H] = ee;
      }
      function ch() {
        Pr("");
      }
      function bc(H, ee, ce) {
        en.copyWithin(H, ee, ee + ce);
      }
      function yc() {
        return 2147483648;
      }
      function rn(H) {
        try {
          return ne.grow(H - In.byteLength + 65535 >>> 16), Ms(ne.buffer), 1;
        } catch (ee) {
        }
      }
      function vc(H) {
        var ee = en.length;
        H = H >>> 0;
        var ce = yc();
        if (H > ce)
          return false;
        for (var Se = 1; Se <= 4; Se *= 2) {
          var Qe = ee * (1 + 0.2 / Se);
          Qe = Math.min(Qe, H + 100663296);
          var Ze = Math.min(ce, Nt(Math.max(H, Qe), 65536)), Le = rn(Ze);
          if (Le)
            return true;
        }
        return false;
      }
      var Ai = { mappings: {}, buffers: [null, [], []], printChar: function(H, ee) {
        var ce = Ai.buffers[H];
        ee === 0 || ee === 10 ? ((H === 1 ? P : R)(Xe(ce, 0)), ce.length = 0) : ce.push(ee);
      }, varargs: void 0, get: function() {
        Ai.varargs += 4;
        var H = Yt[Ai.varargs - 4 >> 2];
        return H;
      }, getStr: function(H) {
        var ee = Je(H);
        return ee;
      }, get64: function(H, ee) {
        return H;
      } };
      function dh(H) {
        return 0;
      }
      function Uv(H, ee, ce, Se, Qe) {
      }
      function Gv(H, ee, ce, Se) {
        for (var Qe = 0, Ze = 0; Ze < ce; Ze++) {
          var Le = Yt[ee >> 2], ze = Yt[ee + 4 >> 2];
          ee += 8;
          for (var Tt = 0; Tt < ze; Tt++)
            Ai.printChar(H, en[Le + Tt]);
          Qe += ze;
        }
        return Yt[Se >> 2] = Qe, 0;
      }
      function ph(H) {
        te(H);
      }
      var xc = false, Ei = { abort: ch, emscripten_memcpy_big: bc, emscripten_resize_heap: vc, fd_close: dh, fd_seek: Uv, fd_write: Gv, setTempRet0: ph }, tT = lh(), Hv = a.___wasm_call_ctors = function() {
        return (Hv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
      }, hh = a._init = function() {
        return (hh = a._init = a.asm.init).apply(null, arguments);
      }, fh = a._init_with_threads_count = function() {
        return (fh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
      }, wc = a._get_threads_count = function() {
        return (wc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
      }, kc = a._register_tensor = function() {
        return (kc = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
      }, mh = a._dispose_data = function() {
        return (mh = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
      }, $e = a._dispose = function() {
        return ($e = a._dispose = a.asm.dispose).apply(null, arguments);
      }, gh = a._Abs = function() {
        return (gh = a._Abs = a.asm.Abs).apply(null, arguments);
      }, Sc = a._Add = function() {
        return (Sc = a._Add = a.asm.Add).apply(null, arguments);
      }, Mr = a._AddN = function() {
        return (Mr = a._AddN = a.asm.AddN).apply(null, arguments);
      }, Ri = a._All = function() {
        return (Ri = a._All = a.asm.All).apply(null, arguments);
      }, bh = a._Any = function() {
        return (bh = a._Any = a.asm.Any).apply(null, arguments);
      }, qv = a._ArgMax = function() {
        return (qv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
      }, yh = a._AvgPool = function() {
        return (yh = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
      }, jv = a._BatchMatMul = function() {
        return (jv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
      }, Lr = a._Ceil = function() {
        return (Lr = a._Ceil = a.asm.Ceil).apply(null, arguments);
      }, vh = a._ClipByValue = function() {
        return (vh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
      }, xh = a._Conv2D = function() {
        return (xh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
      }, wh = a._Conv2DBackpropInput = function() {
        return (wh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
      }, kh = a._Cos = function() {
        return (kh = a._Cos = a.asm.Cos).apply(null, arguments);
      }, Sh = a._Cosh = function() {
        return (Sh = a._Cosh = a.asm.Cosh).apply(null, arguments);
      }, Ih = a._CropAndResize = function() {
        return (Ih = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
      }, Ic = a._Cumprod = function() {
        return (Ic = a._Cumprod = a.asm.Cumprod).apply(null, arguments);
      }, Ch = a._Cumsum = function() {
        return (Ch = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
      }, Nh = a._DepthToSpace = function() {
        return (Nh = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
      }, Th = a._DepthwiseConv2dNative = function() {
        return (Th = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, $h = a._Elu = function() {
        return ($h = a._Elu = a.asm.Elu).apply(null, arguments);
      }, _h = a._Equal = function() {
        return (_h = a._Equal = a.asm.Equal).apply(null, arguments);
      }, Cc = a._Exp = function() {
        return (Cc = a._Exp = a.asm.Exp).apply(null, arguments);
      }, Ah = a._FlipLeftRight = function() {
        return (Ah = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
      }, Eh = a._Floor = function() {
        return (Eh = a._Floor = a.asm.Floor).apply(null, arguments);
      }, Br = a._FloorDiv = function() {
        return (Br = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
      }, vu = a._FusedBatchNorm = function() {
        return (vu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
      }, Rh = a._FusedConv2D = function() {
        return (Rh = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
      }, Dh = a._FusedDepthwiseConv2D = function() {
        return (Dh = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
      }, Fh = a._Gather = function() {
        return (Fh = a._Gather = a.asm.Gather).apply(null, arguments);
      }, Me = a._GatherNd = function() {
        return (Me = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
      }, Oh = a._Greater = function() {
        return (Oh = a._Greater = a.asm.Greater).apply(null, arguments);
      }, Ph = a._GreaterEqual = function() {
        return (Ph = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
      }, zh = a._LeakyRelu = function() {
        return (zh = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
      }, Mh = a._Less = function() {
        return (Mh = a._Less = a.asm.Less).apply(null, arguments);
      }, Lh = a._LessEqual = function() {
        return (Lh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
      }, Bh = a._Log = function() {
        return (Bh = a._Log = a.asm.Log).apply(null, arguments);
      }, xu = a._LogicalAnd = function() {
        return (xu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
      }, Nc = a._Max = function() {
        return (Nc = a._Max = a.asm.Max).apply(null, arguments);
      }, Tc = a._MaxPool = function() {
        return (Tc = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
      }, Vh = a._Maximum = function() {
        return (Vh = a._Maximum = a.asm.Maximum).apply(null, arguments);
      }, Wh = a._Mean = function() {
        return (Wh = a._Mean = a.asm.Mean).apply(null, arguments);
      }, Uh = a._Min = function() {
        return (Uh = a._Min = a.asm.Min).apply(null, arguments);
      }, Gh = a._Minimum = function() {
        return (Gh = a._Minimum = a.asm.Minimum).apply(null, arguments);
      }, Hh = a._MirrorPad = function() {
        return (Hh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
      }, qh = a._Multiply = function() {
        return (qh = a._Multiply = a.asm.Multiply).apply(null, arguments);
      }, gt = a._Neg = function() {
        return (gt = a._Neg = a.asm.Neg).apply(null, arguments);
      }, jh = a._NonMaxSuppressionV3 = function() {
        return (jh = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
      }, Kh = a._NonMaxSuppressionV4 = function() {
        return (Kh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
      }, Xh = a._NonMaxSuppressionV5 = function() {
        return (Xh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
      }, Di = a._NotEqual = function() {
        return (Di = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
      }, $c = a._OneHot = function() {
        return ($c = a._OneHot = a.asm.OneHot).apply(null, arguments);
      }, _c = a._PadV2 = function() {
        return (_c = a._PadV2 = a.asm.PadV2).apply(null, arguments);
      }, Ac = a._Pow = function() {
        return (Ac = a._Pow = a.asm.Pow).apply(null, arguments);
      }, Yh = a._Prelu = function() {
        return (Yh = a._Prelu = a.asm.Prelu).apply(null, arguments);
      }, Ec = a._Prod = function() {
        return (Ec = a._Prod = a.asm.Prod).apply(null, arguments);
      }, Qh = a._RealDiv = function() {
        return (Qh = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
      }, Kv = a._Relu = function() {
        return (Kv = a._Relu = a.asm.Relu).apply(null, arguments);
      }, Rc = a._Relu6 = function() {
        return (Rc = a._Relu6 = a.asm.Relu6).apply(null, arguments);
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        return (Xv = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
      }, Zh = a._Reverse = function() {
        return (Zh = a._Reverse = a.asm.Reverse).apply(null, arguments);
      }, Jh = a._RotateWithOffset = function() {
        return (Jh = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
      }, ef = a._Round = function() {
        return (ef = a._Round = a.asm.Round).apply(null, arguments);
      }, tf = a._Rsqrt = function() {
        return (tf = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
      }, nf = a._ScatterNd = function() {
        return (nf = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
      }, sf = a._SelectV2 = function() {
        return (sf = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
      }, rf = a._Sigmoid = function() {
        return (rf = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
      }, af = a._Sin = function() {
        return (af = a._Sin = a.asm.Sin).apply(null, arguments);
      }, of = a._Softmax = function() {
        return (of = a._Softmax = a.asm.Softmax).apply(null, arguments);
      }, uf = a._SparseFillEmptyRows = function() {
        return (uf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
      }, lf = a._SparseReshape = function() {
        return (lf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
      }, cf = a._SparseSegmentReduction = function() {
        return (cf = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
      }, df = a._Sqrt = function() {
        return (df = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
      }, pf = a._Square = function() {
        return (pf = a._Square = a.asm.Square).apply(null, arguments);
      }, hf = a._SquaredDifference = function() {
        return (hf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
      }, ff = a._Step = function() {
        return (ff = a._Step = a.asm.Step).apply(null, arguments);
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        return (mf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
      }, gf = a._Sub = function() {
        return (gf = a._Sub = a.asm.Sub).apply(null, arguments);
      }, bf = a._Sum = function() {
        return (bf = a._Sum = a.asm.Sum).apply(null, arguments);
      }, yf = a._Tan = function() {
        return (yf = a._Tan = a.asm.Tan).apply(null, arguments);
      }, vf = a._Tanh = function() {
        return (vf = a._Tanh = a.asm.Tanh).apply(null, arguments);
      }, xf = a._Tile = function() {
        return (xf = a._Tile = a.asm.Tile).apply(null, arguments);
      }, wf = a._TopK = function() {
        return (wf = a._TopK = a.asm.TopK).apply(null, arguments);
      }, kf = a._Transform = function() {
        return (kf = a._Transform = a.asm.Transform).apply(null, arguments);
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        return (Sf = a._Transpose = a.asm.Transpose).apply(null, arguments);
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        return (Cf = a._malloc = a.asm.malloc).apply(null, arguments);
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        return (Nf = a._free = a.asm.free).apply(null, arguments);
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        return (Fc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
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          setTimeout(function() {
            a.setStatus("");
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      a.run = Su;
      function Yv(H) {
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      var Oi;
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      var Pi;
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} });
var g$ = 1e-7;
var b$ = 1e-4;
var Kd = class {
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  set(e, t) {
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};
var rl = class {
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  time(e) {
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  read(e) {
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  numDataIds() {
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  disposeData(e, t) {
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  write(e, t, n) {
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};
function On(e) {
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}
function Qw(e) {
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    n = Math.random() * t | 0, t--, md(e, t, n);
}
function y$(e, t) {
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    s = Math.random() * n | 0, n--, md(e, n, s), md(t, n, s);
}
function Uu(e, t, n) {
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}
function v$(e) {
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}
function md(e, t, n) {
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}
function x$(e) {
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}
function w$(e, t) {
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}
function k$(e, t) {
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}
function O(e, t) {
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function pn(e, t, n = "") {
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}
function ka(e) {
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}
function ra(e, t = [], n = false) {
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      ra(e[s], t, n);
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    t.push(e);
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}
function dt(e) {
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    return 1;
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}
function S$(e) {
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}
function kr(e, t) {
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    if (e[n] !== t[n])
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}
function eo(e) {
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}
function I$(e) {
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  if (e === -1 / 0)
    return -1;
  {
    let t = Math.exp(2 * e);
    return (t - 1) / (t + 1);
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}
function C$(e) {
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}
function N$(e) {
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    t[n] = n;
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}
function Lu(e, t) {
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}
function T$(e, t = (s) => 0, n) {
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      if (e()) {
        s();
        return;
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      a++;
      let o = t(a);
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        r();
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    i();
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}
function $$(e, t) {
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        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${s} and dim ${a}`);
      s = a;
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      throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);
  if (s === -1) {
    if (t > 0 && t !== n)
      throw Error(`Size(${t}) must match the product of shape ${e}`);
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  if (n === 0)
    throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);
  if (t % n !== 0)
    throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);
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  return r[s] = t / n, r;
}
function ts(e, t) {
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}
function Zw(e, t) {
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    e[o] !== 1 && (n.push(e[o]), s.push(o));
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  return { newShape: n, keptDims: s };
}
function Jw(e, t) {
  let n = null;
  if (e == null || e === "float32")
    n = new Float32Array(t);
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    n = new Int32Array(t);
  else if (e === "bool")
    n = new Uint8Array(t);
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    throw new Error(`Unknown data type ${e}`);
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}
function ek(e, t) {
  let n = null;
  if (e == null || e === "float32")
    n = new Float32Array(t);
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    n = new Int32Array(t);
  else if (e === "bool")
    n = new Uint8Array(t);
  else if (e === "string")
    n = new Array(t);
  else
    throw new Error(`Unknown data type ${e}`);
  return n;
}
function tk(e, t) {
  for (let n = 0; n < e.length; n++) {
    let s = e[n];
    if (isNaN(s) || !isFinite(s))
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}
function nk(e) {
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}
function _$(e, t) {
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}
function Qt(e) {
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}
function Jf(e) {
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  if (e === "bool")
    return 1;
  throw new Error(`Unknown dtype ${e}`);
}
function sk(e) {
  if (e == null)
    return 0;
  let t = 0;
  return e.forEach((n) => t += n.length), t;
}
function ar(e) {
  return typeof e == "string" || e instanceof String;
}
function rk(e) {
  return typeof e == "boolean";
}
function ak(e) {
  return typeof e == "number";
}
function Xd(e) {
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}
function hr(e) {
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}
function gd(e, t) {
  for (let n = t; n < e; ++n)
    if (e % n === 0)
      return n;
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}
function lo(e) {
  let t = e.length;
  if (t < 2)
    return [];
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  n[t - 2] = e[t - 1];
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    n[s] = n[s + 1] * e[s + 1];
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}
function ik(e, t, n, s = false) {
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      r[i] = n[e + i];
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    let a = t[0], i = t.slice(1), o = i.reduce((u, l) => u * l) * (s ? 2 : 1);
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      r[u] = ik(e + u * o, i, n, s);
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}
function Xi(e, t, n = false) {
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    return t[0];
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    return [];
  if (s !== t.length)
    throw new Error(`[${e}] does not match the input size ${t.length}${n ? " for a complex tensor" : ""}.`);
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}
function ig(e, t) {
  let n = Yd(e, t);
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    n[s] = 1;
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}
function Yd(e, t) {
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    return new Uint8Array(e);
  throw new Error(`Unknown data type ${t}`);
}
function A$(e, t) {
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  if (t === "int32")
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}
function og(e) {
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}
function E$(e, t, n) {
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    s += n[r] * e[r];
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}
function R$(e, t, n) {
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    s[r] = Math.floor(e / n[r]), e -= s[r] * n[r];
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}
function ug(e) {
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}
var ix = "tfjsflags";
var D$ = class {
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  setPlatform(e, t) {
    this.platform != null && (K().getBool("IS_TEST") || K().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t;
  }
  registerFlag(e, t, n) {
    if (this.flagRegistry[e] = { evaluationFn: t, setHook: n }, this.urlFlags[e] != null) {
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      K().getBool("IS_TEST") || K().getBool("PROD") || console.warn(`Setting feature override from URL ${e}: ${s}.`), this.set(e, s);
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  async getAsync(e) {
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  get(e) {
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      return this.flags[e];
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    if (ug(t))
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    return this.flags[e] = t, this.flags[e];
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  getNumber(e) {
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  getBool(e) {
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  getFlags() {
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  get features() {
    return this.flags;
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  set(e, t) {
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      throw new Error(`Cannot set flag ${e} as it has not been registered.`);
    this.flags[e] = t, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t);
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  evaluateFlag(e) {
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      throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);
    return this.flagRegistry[e].evaluationFn();
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  setFlags(e) {
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  reset() {
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  populateURLFlags() {
    if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined")
      return;
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    ix in e && e[ix].split(",").forEach((n) => {
      let [s, r] = n.split(":");
      this.urlFlags[s] = P$(s, r);
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};
function F$(e) {
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}
function O$(e, t, n) {
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}
function P$(e, t) {
  if (t = t.toLowerCase(), t === "true" || t === "false")
    return t === "true";
  if (`${+t}` === t)
    return +t;
  throw new Error(`Could not parse value flag value ${t} for flag ${e}.`);
}
function K() {
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}
var ok = null;
function z$(e) {
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var Mf;
function uk() {
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function M$() {
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function lg(e, t) {
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var co = "Abs";
var al = "Acos";
var il = "Acosh";
var Sr = "Add";
var Sa = "AddN";
var ol = "All";
var ul = "Any";
var Ia = "ArgMax";
var ll = "ArgMin";
var cl = "Asin";
var dl = "Asinh";
var pl = "Atan";
var hl = "Atanh";
var fl = "Atan2";
var Ca = "AvgPool";
var cg = "AvgPoolGrad";
var Qd = "AvgPool3D";
var dg = "AvgPool3DGrad";
var Na = "BatchMatMul";
var po = "BatchToSpaceND";
var pg = "Bincount";
var L$ = "BroadcastTo";
var hg = "BroadcastArgs";
var Ta = "Cast";
var $a = "Ceil";
var Ir = "ClipByValue";
var Zd = "Complex";
var Jd = "ComplexAbs";
var ho = "Concat";
var _a = "Conv2D";
var fg = "Conv2DBackpropFilter";
var Aa = "Conv2DBackpropInput";
var ep = "Conv3D";
var mg = "Conv3DBackpropFilterV2";
var gg = "Conv3DBackpropInputV2";
var Ea = "Cos";
var Ra = "Cosh";
var fo = "Cumprod";
var Da = "Cumsum";
var mo = "CropAndResize";
var bg = "DenseBincount";
var go = "DepthToSpace";
var Fa = "DepthwiseConv2dNative";
var yg = "DepthwiseConv2dNativeBackpropFilter";
var vg = "DepthwiseConv2dNativeBackpropInput";
var xg = "Diag";
var tp = "Dilation2D";
var em = "Dilation2DBackpropInput";
var tm = "Dilation2DBackpropFilter";
var Oa = "RealDiv";
var np = "Einsum";
var Pa = "Elu";
var wg = "EluGrad";
var ml = "Erf";
var bo = "Equal";
var za = "Exp";
var yo = "ExpandDims";
var vo = "Expm1";
var kg = "FFT";
var gl = "Fill";
var xo = "FlipLeftRight";
var Ma = "Floor";
var La = "FloorDiv";
var Ba = "FusedBatchNorm";
var wo = "GatherV2";
var ko = "GatherNd";
var So = "Greater";
var Va = "GreaterEqual";
var Wa = "Identity";
var Sg = "IFFT";
var sp = "Imag";
var bl = "IsFinite";
var yl = "IsInf";
var vl = "IsNan";
var Ua = "LeakyRelu";
var Io = "Less";
var Co = "LessEqual";
var Ig = "LinSpace";
var Ga = "Log";
var xl = "Log1p";
var No = "LogicalAnd";
var wl = "LogicalNot";
var rp = "LogicalOr";
var B$ = "LogSoftmax";
var tpe = "LowerBound";
var ap = "LRN";
var Cg = "LRNGrad";
var Ha = "Max";
var qa = "Maximum";
var ja = "MaxPool";
var Ng = "MaxPoolGrad";
var ip = "MaxPool3D";
var Tg = "MaxPool3DGrad";
var $g = "MaxPoolWithArgmax";
var Ka = "Mean";
var Xa = "Min";
var Ya = "Minimum";
var Qa = "MirrorPad";
var kl = "Mod";
var _g = "Multinomial";
var Za = "Multiply";
var To = "Neg";
var $o = "NotEqual";
var _o = "NonMaxSuppressionV3";
var Sl = "NonMaxSuppressionV4";
var Ao = "NonMaxSuppressionV5";
var Eo = "OnesLike";
var Ro = "OneHot";
var Do = "Pack";
var Ja = "PadV2";
var npe = "Pool";
var ei = "Pow";
var ti = "Prelu";
var ni = "Prod";
var Il = "Range";
var op = "Real";
var Cl = "Reciprocal";
var si = "Relu";
var Fo = "Reshape";
var Nl = "ResizeNearestNeighbor";
var Ag = "ResizeNearestNeighborGrad";
var ri = "ResizeBilinear";
var Eg = "ResizeBilinearGrad";
var ai = "Relu6";
var Oo = "Reverse";
var Po = "Round";
var ii = "Rsqrt";
var zo = "ScatterNd";
var Rg = "SearchSorted";
var Mo = "Select";
var Tl = "Selu";
var Lo = "Slice";
var oi = "Sin";
var Bo = "Sinh";
var $l = "Sign";
var ui = "Sigmoid";
var _l = "Softplus";
var li = "Sqrt";
var ci = "Sum";
var Vo = "SpaceToBatchND";
var Wo = "SplitV";
var di = "Softmax";
var up = "SparseFillEmptyRows";
var Al = "SparseReshape";
var lp = "SparseSegmentMean";
var cp = "SparseSegmentSum";
var dp = "SparseToDense";
var pi = "SquaredDifference";
var El = "Square";
var Uo = "StridedSlice";
var pp = "StringNGrams";
var Dg = "StringSplit";
var Fg = "StringToHashBucketFast";
var hi = "Sub";
var Go = "Tan";
var fi = "Tanh";
var Cr = "Tile";
var Ho = "TopK";
var qo = "Transform";
var mi = "Transpose";
var Og = "Unique";
var jo = "Unpack";
var hp = "UnsortedSegmentSum";
var spe = "UpperBound";
var Ko = "ZerosLike";
var gi = "Step";
var bd = "FromPixels";
var Xo = "RotateWithOffset";
var aa = "_FusedMatMul";
var ia = "FusedConv2D";
var oa = "FusedDepthwiseConv2D";
function rr(...e) {
  K().getBool("IS_TEST") || K().getBool("PROD") || console.warn(...e);
}
function V$(...e) {
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}
var to = lg("kernelRegistry", () => /* @__PURE__ */ new Map());
var Gu = lg("gradRegistry", () => /* @__PURE__ */ new Map());
function nm(e, t) {
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function ox(e) {
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function sm(e) {
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  return n;
}
function Rl(e) {
  let { kernelName: t, backendName: n } = e, s = Pg(t, n);
  to.has(s) && rr(`The kernel '${t}' for backend '${n}' is already registered`), to.set(s, e);
}
function W$(e) {
  let { kernelName: t } = e;
  Gu.has(t) && K().getBool("DEBUG") && rr(`Overriding the gradient for '${t}'`), Gu.set(t, e);
}
function rpe(e, t) {
  let n = Pg(e, t);
  if (!to.has(n))
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}
function ape(e) {
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    throw new Error(`The gradient '${e}' for backend is not registered`);
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}
function ipe(e, t) {
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    Rl(r);
  });
}
function Pg(e, t) {
  return `${t}_${e}`;
}
var w = {};
Ae(w, { arraysEqual: () => kr, assert: () => O, assertNonNegativeIntegerDimensions: () => og, assertNonNull: () => ka, assertShapesMatch: () => pn, bytesFromStringArray: () => sk, bytesPerElement: () => Jf, checkConversionForErrors: () => tk, clamp: () => Uu, computeStrides: () => lo, createScalarValue: () => K$, createShuffledIndices: () => N$, decodeString: () => yd, distSquared: () => k$, encodeString: () => Fl, fetch: () => Y$, fingerPrint64: () => j$, flatten: () => ra, getArrayFromDType: () => ek, getTypedArrayFromDType: () => Jw, hasEncodingLoss: () => _$, hexToLong: () => Dl, indexToLoc: () => R$, inferDtype: () => Xd, inferFromImplicitShape: () => $$, isBoolean: () => rk, isFunction: () => hr, isInt: () => eo, isNumber: () => ak, isPromise: () => ug, isScalarShape: () => S$, isString: () => ar, isTypedArray: () => Qt, isValidDtype: () => nk, locToIndex: () => E$, makeOnesTypedArray: () => ig, makeZerosNestedTypedArray: () => A$, makeZerosTypedArray: () => Yd, nearestDivisor: () => gd, nearestLargerEven: () => v$, now: () => Hu, parseAxisParam: () => ts, randUniform: () => w$, repeatedTry: () => T$, rightPad: () => Lu, shuffle: () => Qw, shuffleCombo: () => y$, sizeFromShape: () => dt, sizeToSquarishShape: () => C$, squeezeShape: () => Zw, sum: () => x$, swap: () => md, tanh: () => I$, toNestedArray: () => Xi, toTypedArray: () => fp });
var ux = wa(e$());
var jr = ux.default || ux;
function Dl(e) {
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}
var lk = Dl("c3a5c85c97cb3127");
var Hr = Dl("b492b66fbe98f273");
var on = Dl("9ae16a3b2f90404f");
function rm(e) {
  return e.xor(e.shru(47));
}
function ck(e, t, n) {
  let s = e.slice(t, t + n);
  return jr.fromBytes(Array.from(s), true, true);
}
function lt(e, t) {
  return ck(e, t, 8);
}
function lx(e, t) {
  return ck(e, t, 4);
}
function Bt(e, t) {
  return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
}
function or(e, t, n = Dl("9ddfea08eb382d69")) {
  let s = e.xor(t).mul(n);
  s = s.xor(s.shru(47));
  let r = t.xor(s).mul(n);
  return r = r.xor(r.shru(47)), r = r.mul(n), r;
}
function U$(e, t, n, s, r, a) {
  r = r.add(e), a = Bt(a.add(r).add(s), 21);
  let i = r;
  return r = r.add(t), r = r.add(n), a = a.add(Bt(r, 44)), [r.add(s), a.add(i)];
}
function Wc(e, t, n, s) {
  return U$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
}
function G$(e, t = e.length) {
  if (t >= 8) {
    let n = on.add(t * 2), s = lt(e, 0).add(on), r = lt(e, t - 8), a = Bt(r, 37).mul(n).add(s), i = Bt(s, 25).add(r).mul(n);
    return or(a, i, n);
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  if (t >= 4) {
    let n = on.add(t * 2), s = lx(e, 0);
    return or(s.shl(3).add(t), lx(e, t - 4), n);
  }
  if (t > 0) {
    let n = e[0], s = e[t >> 1], r = e[t - 1], a = n + (s << 8), i = t + (r << 2);
    return rm(on.mul(a).xor(lk.mul(i))).mul(on);
  }
  return on;
}
function H$(e, t = e.length) {
  let n = on.add(t * 2), s = lt(e, 0).mul(Hr), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on);
  return or(Bt(s.add(r), 43).add(Bt(a, 30)).add(i), s.add(Bt(r.add(on), 18)).add(a), n);
}
function q$(e, t = e.length) {
  let n = on.add(t * 2), s = lt(e, 0).mul(on), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on), o = Bt(s.add(r), 43).add(Bt(a, 30)).add(i), u = or(o, s.add(Bt(r.add(on), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = o.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n);
  return or(Bt(l.add(c), 43).add(Bt(p, 30)).add(d), l.add(Bt(c.add(s), 18)).add(p), n);
}
function j$(e, t = e.length) {
  let n = jr.fromNumber(81, true);
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    return t <= 16 ? G$(e, t) : H$(e, t);
  if (t <= 64)
    return q$(e, t);
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  let p = Hr.add(a.and(255).shl(1));
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}
function K$(e, t) {
  return t === "string" ? Fl(e) : fp([e], t);
}
function X$(e, t) {
  return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
}
function fp(e, t) {
  if (t === "string")
    throw new Error("Cannot convert a string[] to a TypedArray");
  if (Array.isArray(e) && (e = ra(e)), K().getBool("DEBUG") && tk(e, t), X$(e, t))
    return e;
  if (t == null || t === "float32" || t === "complex64")
    return new Float32Array(e);
  if (t === "int32")
    return new Int32Array(e);
  if (t === "bool") {
    let n = new Uint8Array(e.length);
    for (let s = 0; s < n.length; ++s)
      Math.round(e[s]) !== 0 && (n[s] = 1);
    return n;
  } else
    throw new Error(`Unknown data type ${t}`);
}
function Hu() {
  return K().platform.now();
}
function Y$(e, t) {
  return K().platform.fetch(e, t);
}
function Fl(e, t = "utf-8") {
  return t = t || "utf-8", K().platform.encode(e, t);
}
function yd(e, t = "utf-8") {
  return t = t || "utf-8", K().platform.decode(e, t);
}
var Q$ = class {
  constructor(e, t) {
    this.backendTimer = e, this.logger = t, t == null && (this.logger = new J$());
  }
  profileKernel(e, t, n) {
    let s, r = () => {
      s = n();
    }, a, i = Hu();
    if (this.backendTimer.timerAvailable())
      a = this.backendTimer.time(r);
    else {
      r();
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        u.dataSync();
      a = Promise.resolve({ kernelMs: Hu() - i });
    }
    if (K().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
      for (let u = 0; u < s.length; u++) {
        let l = s[u];
        l.data().then((c) => {
          Z$(c, l.dtype, e);
        });
      }
    return { kernelName: e, outputs: s, inputs: t, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") };
  }
  logKernelProfile(e) {
    let { kernelName: t, outputs: n, timeMs: s, inputs: r, extraInfo: a } = e;
    n.forEach((i) => {
      Promise.all([i.data(), s, a]).then((o) => {
        this.logger.logKernelProfile(t, i, o[0], o[1], r, o[2]);
      });
    });
  }
};
function Z$(e, t, n) {
  if (t !== "float32")
    return false;
  for (let s = 0; s < e.length; s++) {
    let r = e[s];
    if (isNaN(r) || !isFinite(r))
      return console.warn(`Found ${r} in the result of '${n}'`), true;
  }
  return false;
}
var J$ = class {
  logKernelProfile(e, t, n, s, r, a) {
    let i = typeof s == "number" ? Lu(`${s}ms`, 9) : s.error, o = Lu(e, 25), u = t.rank, l = t.size, c = Lu(t.shape.toString(), 14), p = "";
    for (let d in r) {
      let h = r[d];
      if (h != null) {
        let f = h.shape || t.shape, m = f.length;
        p += `${d}: ${m}D ${m > 0 ? f : ""} `;
      }
    }
    console.log(`%c${o}	%c${i}	%c${u}D ${c}	%c${l}	%c${p}	%c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
  }
};
function e_(e, t, n) {
  let s = {}, r = {};
  for (let u = 0; u < t.length; u++)
    s[t[u].id] = true;
  for (let u = 0; u < e.length; u++) {
    let l = e[u], c = l.inputs;
    for (let p in c) {
      let d = c[p], h = false;
      for (let f = 0; f < t.length; f++)
        if (s[d.id]) {
          l.outputs.forEach((m) => s[m.id] = true), h = true, r[l.id] = true;
          break;
        }
      if (h)
        break;
    }
  }
  let a = {};
  a[n.id] = true;
  let i = {};
  for (let u = e.length - 1; u >= 0; u--) {
    let l = e[u], c = l.inputs;
    for (let p = 0; p < l.outputs.length; p++)
      if (a[l.outputs[p].id]) {
        for (let d in c)
          a[c[d].id] = true, i[l.id] = true;
        break;
      }
  }
  let o = [];
  for (let u = 0; u < e.length; u++) {
    let l = e[u];
    if (r[l.id] && i[l.id]) {
      let c = {};
      for (let d in l.inputs) {
        let h = l.inputs[d];
        s[h.id] && (c[d] = h);
      }
      let p = Object.assign({}, l);
      p.inputs = c, p.outputs = l.outputs, o.push(p);
    }
  }
  return o;
}
function t_(e, t, n, s) {
  for (let r = t.length - 1; r >= 0; r--) {
    let a = t[r], i = [];
    if (a.outputs.forEach((u) => {
      let l = e[u.id];
      l != null ? i.push(l) : i.push(null);
    }), a.gradient == null)
      throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);
    let o = a.gradient(i);
    for (let u in a.inputs) {
      if (!(u in o))
        throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(o)}.`);
      let l = n(() => o[u]());
      if (l.dtype !== "float32")
        throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${l.dtype}'`);
      let c = a.inputs[u];
      if (!kr(l.shape, c.shape))
        throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);
      if (e[c.id] == null)
        e[c.id] = l;
      else {
        let p = e[c.id];
        e[c.id] = s(p, l), p.dispose();
      }
    }
  }
}
var cx = 20;
var Nu = 3;
var Lf = 7;
function n_(e, t, n, s) {
  let r = lo(t), a = s_(e, t, n, r), i = t.length, o = nd(e, t, n, r, a), u = ["Tensor"];
  return s && (u.push(`  dtype: ${n}`), u.push(`  rank: ${i}`), u.push(`  shape: [${t}]`), u.push("  values:")), u.push(o.map((l) => "    " + l).join(`
`)), u.join(`
`);
}
function s_(e, t, n, s) {
  let r = dt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? Eu(e) : e;
  if (o > 1)
    for (let l = 0; l < r / a; l++) {
      let c = l * a;
      for (let p = 0; p < a; p++)
        i[p] = Math.max(i[p], Au(u[c + p], 0, n).length);
    }
  return i;
}
function Au(e, t, n) {
  let s;
  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Lf))} + ${parseFloat(e[1].toFixed(Lf))}j` : ar(e) ? s = `'${e}'` : n === "bool" ? s = dk(e) : s = parseFloat(e.toFixed(Lf)).toString(), Lu(s, t);
}
function dk(e) {
  return e === 0 ? "false" : "true";
}
function nd(e, t, n, s, r, a = true) {
  let i = n === "complex64" ? 2 : 1, o = t[0], u = t.length;
  if (u === 0) {
    if (n === "complex64") {
      let m = Eu(e);
      return [Au(m[0], 0, n)];
    }
    return n === "bool" ? [dk(e[0])] : [e[0].toString()];
  }
  if (u === 1) {
    if (o > cx) {
      let g = Nu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Nu) * i, o * i));
      return n === "complex64" && (b = Eu(b), y = Eu(y)), ["[" + b.map((v, x) => Au(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Au(v, r[o - Nu + x], n)).join(", ") + "]"];
    }
    let m = n === "complex64" ? Eu(e) : Array.from(e);
    return ["[" + m.map((g, b) => Au(g, r[b], n)).join(", ") + "]"];
  }
  let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
  if (o > cx) {
    for (let m = 0; m < Nu; m++) {
      let g = m * p, b = g + p;
      d.push(...nd(e.slice(g, b), l, n, c, r, false));
    }
    d.push("...");
    for (let m = o - Nu; m < o; m++) {
      let g = m * p, b = g + p;
      d.push(...nd(e.slice(g, b), l, n, c, r, m === o - 1));
    }
  } else
    for (let m = 0; m < o; m++) {
      let g = m * p, b = g + p;
      d.push(...nd(e.slice(g, b), l, n, c, r, m === o - 1));
    }
  let h = u === 2 ? "," : "";
  d[0] = "[" + d[0] + h;
  for (let m = 1; m < d.length - 1; m++)
    d[m] = " " + d[m] + h;
  let f = `,
`;
  for (let m = 2; m < u; m++)
    f += `
`;
  return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d;
}
function Eu(e) {
  let t = [];
  for (let n = 0; n < e.length; n += 2)
    t.push([e[n], e[n + 1]]);
  return t;
}
var Wt = class {
  constructor(e, t, n) {
    if (this.dtype = t, this.shape = e.slice(), this.size = dt(e), n != null) {
      let s = n.length;
      O(s === this.size, () => `Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`);
    }
    if (t === "complex64")
      throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
    this.values = n || ek(t, this.size), this.strides = lo(e);
  }
  set(e, ...t) {
    t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
    let n = this.locToIndex(t);
    this.values[n] = e;
  }
  get(...e) {
    e.length === 0 && (e = [0]);
    let t = 0;
    for (let s of e) {
      if (s < 0 || s >= this.shape[t]) {
        let r = `Requested out of range element at ${e}.   Buffer shape=${this.shape}`;
        throw new Error(r);
      }
      t++;
    }
    let n = e[e.length - 1];
    for (let s = 0; s < e.length - 1; ++s)
      n += this.strides[s] * e[s];
    return this.values[n];
  }
  locToIndex(e) {
    if (this.rank === 0)
      return 0;
    if (this.rank === 1)
      return e[0];
    let t = e[e.length - 1];
    for (let n = 0; n < e.length - 1; ++n)
      t += this.strides[n] * e[n];
    return t;
  }
  indexToLoc(e) {
    if (this.rank === 0)
      return [];
    if (this.rank === 1)
      return [e];
    let t = new Array(this.shape.length);
    for (let n = 0; n < t.length - 1; ++n)
      t[n] = Math.floor(e / this.strides[n]), e -= t[n] * this.strides[n];
    return t[t.length - 1] = e, t;
  }
  get rank() {
    return this.shape.length;
  }
  toTensor() {
    return cs().makeTensor(this.values, this.shape, this.dtype);
  }
};
var cs = null;
var qi = null;
var r_ = null;
function a_(e) {
  cs = e;
}
function i_(e) {
  qi = e;
}
function o_(e) {
  r_ = e;
}
var et = class {
  constructor(e, t, n, s) {
    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = dt(e), this.strides = lo(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
  }
  get rank() {
    return this.shape.length;
  }
  async buffer() {
    let e = await this.data();
    return qi.buffer(this.shape, this.dtype, e);
  }
  bufferSync() {
    return qi.buffer(this.shape, this.dtype, this.dataSync());
  }
  async array() {
    let e = await this.data();
    return Xi(this.shape, e, this.dtype === "complex64");
  }
  arraySync() {
    return Xi(this.shape, this.dataSync(), this.dtype === "complex64");
  }
  async data() {
    this.throwIfDisposed();
    let e = cs().read(this.dataId);
    if (this.dtype === "string") {
      let t = await e;
      try {
        return t.map((n) => yd(n));
      } catch (n) {
        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
      }
    }
    return e;
  }
  dataToGPU(e) {
    return this.throwIfDisposed(), cs().readToGPU(this.dataId, e);
  }
  dataSync() {
    this.throwIfDisposed();
    let e = cs().readSync(this.dataId);
    if (this.dtype === "string")
      try {
        return e.map((t) => yd(t));
      } catch (t) {
        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
      }
    return e;
  }
  async bytes() {
    this.throwIfDisposed();
    let e = await cs().read(this.dataId);
    return this.dtype === "string" ? e : new Uint8Array(e.buffer);
  }
  dispose() {
    this.isDisposed || (cs().disposeTensor(this), this.isDisposedInternal = true);
  }
  get isDisposed() {
    return this.isDisposedInternal;
  }
  throwIfDisposed() {
    if (this.isDisposed)
      throw new Error("Tensor is disposed.");
  }
  print(e = false) {
    return qi.print(this, e);
  }
  clone() {
    return this.throwIfDisposed(), qi.clone(this);
  }
  toString(e = false) {
    let t = this.dataSync();
    return n_(t, this.shape, this.dtype, e);
  }
  cast(e) {
    return this.throwIfDisposed(), qi.cast(this, e);
  }
  variable(e = true, t, n) {
    return this.throwIfDisposed(), cs().makeVariable(this, e, t, n);
  }
};
Object.defineProperty(et, Symbol.hasInstance, { value: (e) => !!e && e.data != null && e.dataSync != null && e.throwIfDisposed != null });
function u_() {
  return lg("Tensor", () => et);
}
u_();
var vd = class extends et {
  constructor(e, t, n, s) {
    super(e.shape, e.dtype, e.dataId, s), this.trainable = t, this.name = n;
  }
  assign(e) {
    if (e.dtype !== this.dtype)
      throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);
    if (!kr(e.shape, this.shape))
      throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
    cs().disposeTensor(this), this.dataId = e.dataId, cs().incRef(this, null);
  }
  dispose() {
    cs().disposeVariable(this), this.isDisposedInternal = true;
  }
};
Object.defineProperty(vd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
var _s = {};
Ae(_s, { assertTypesMatch: () => gk, getTensorsInContainer: () => zg, isTensorInList: () => d_, makeTypesMatch: () => vt });
var l_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(l_ || {});
var pk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(pk || {});
var hk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(hk || {});
var fk = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(fk || {});
var mk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(mk || {});
var c_ = { float32: fk, int32: pk, bool: hk, complex64: mk };
function cn(e, t) {
  if (e === "string" || t === "string") {
    if (e === "string" && t === "string")
      return "string";
    throw new Error(`Can not upcast ${e} with ${t}`);
  }
  return c_[e][t];
}
function mp(e) {
  return cn(e, "int32");
}
function vt(e, t) {
  if (e.dtype === t.dtype)
    return [e, t];
  let n = cn(e.dtype, t.dtype);
  return [e.cast(n), t.cast(n)];
}
function gk(e, t) {
  O(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`);
}
function d_(e, t) {
  return t.some((n) => n.id === e.id);
}
function zg(e) {
  let t = [];
  return bk(e, t, /* @__PURE__ */ new Set()), t;
}
function bk(e, t, n) {
  if (e == null)
    return;
  if (e instanceof et) {
    t.push(e);
    return;
  }
  if (!p_(e))
    return;
  let s = e;
  for (let r in s) {
    let a = s[r];
    n.has(a) || (n.add(a), bk(a, t, n));
  }
}
function p_(e) {
  return Array.isArray(e) || typeof e == "object";
}
function Bf(e) {
  return e.kernelName != null;
}
var dx = class {
  constructor() {
    this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
      return Array.from(new Set(this.kernels.map((e) => e.name)));
    } };
  }
  dispose() {
    for (let e in this.registeredVariables)
      this.registeredVariables[e].dispose();
  }
};
var am = class {
  constructor(e) {
    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new dx();
  }
  async ready() {
    if (this.pendingBackendInit != null)
      return this.pendingBackendInit.then(() => {
      });
    if (this.backendInstance != null)
      return;
    let e = this.getSortedBackends();
    for (let t = 0; t < e.length; t++) {
      let n = e[t];
      if (await this.initializeBackend(n).success) {
        await this.setBackend(n);
        return;
      }
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  get backend() {
    if (this.pendingBackendInit != null)
      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
    if (this.backendInstance == null) {
      let { name: e, asyncInit: t } = this.initializeBackendsAndReturnBest();
      if (t)
        throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
      this.setBackend(e);
    }
    return this.backendInstance;
  }
  backendNames() {
    return Object.keys(this.registryFactory);
  }
  findBackend(e) {
    if (!(e in this.registry))
      if (e in this.registryFactory) {
        let { asyncInit: t } = this.initializeBackend(e);
        if (t)
          return null;
      } else
        return null;
    return this.registry[e];
  }
  findBackendFactory(e) {
    return e in this.registryFactory ? this.registryFactory[e].factory : null;
  }
  registerBackend(e, t, n = 1) {
    return e in this.registryFactory ? (rr(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t, priority: n }, true);
  }
  async setBackend(e) {
    if (this.registryFactory[e] == null)
      throw new Error(`Backend name '${e}' not found in registry`);
    if (this.backendName = e, this.registry[e] == null) {
      this.backendInstance = null;
      let { success: t, asyncInit: n } = this.initializeBackend(e);
      if (!(n ? await t : t))
        return false;
    }
    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new Q$(this.backendInstance), true;
  }
  setupRegisteredKernels() {
    sm(this.backendName).forEach((t) => {
      t.setupFunc != null && t.setupFunc(this.backendInstance);
    });
  }
  disposeRegisteredKernels(e) {
    sm(e).forEach((n) => {
      n.disposeFunc != null && n.disposeFunc(this.registry[e]);
    });
  }
  initializeBackend(e) {
    let t = this.registryFactory[e];
    if (t == null)
      throw new Error(`Cannot initialize backend ${e}, no registration found.`);
    try {
      let n = t.factory();
      if (n && !(n instanceof rl) && typeof n.then == "function") {
        let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, rr(`Initialization of backend ${e} failed`), rr(a.stack || a.message)), false));
        return this.pendingBackendInit = r, { success: r, asyncInit: true };
      } else
        return this.registry[e] = n, { success: true, asyncInit: false };
    } catch (n) {
      return rr(`Initialization of backend ${e} failed`), rr(n.stack || n.message), { success: false, asyncInit: false };
    }
  }
  removeBackend(e) {
    if (!(e in this.registryFactory))
      throw new Error(`${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, t) => this.registryFactory[t].priority - this.registryFactory[e].priority);
  }
  initializeBackendsAndReturnBest() {
    let e = this.getSortedBackends();
    for (let t = 0; t < e.length; t++) {
      let n = e[t], { success: s, asyncInit: r } = this.initializeBackend(n);
      if (r || s)
        return { name: n, asyncInit: r };
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  moveData(e, t) {
    let n = this.state.tensorInfo.get(t), s = n.backend, r = this.readSync(t), a = s.refCount(t);
    s.disposeData(t, true), n.backend = e, e.move(t, r, n.shape, n.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
  }
  tidy(e, t) {
    let n = null;
    if (t == null) {
      if (typeof e != "function")
        throw new Error("Please provide a function to tidy()");
      t = e;
    } else {
      if (typeof e != "string" && !(e instanceof String))
        throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
      if (typeof t != "function")
        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
      n = e;
    }
    let s;
    return this.scopedRun(() => this.startScope(n), () => this.endScope(s), () => (s = t(), s instanceof Promise && console.error("Cannot return a Promise inside of tidy."), s));
  }
  scopedRun(e, t, n) {
    e();
    try {
      let s = n();
      return t(), s;
    } catch (s) {
      throw t(), s;
    }
  }
  nextTensorId() {
    return am.nextTensorId++;
  }
  nextVariableId() {
    return am.nextVariableId++;
  }
  clone(e) {
    let t = M.runKernel(Wa, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
      let i = "float32", o = { x: a }, u = { dtype: i };
      return M.runKernel(Ta, o, u);
    } }), r = [];
    return this.addTapeNode(this.state.activeScope.name, n, [t], s, r, {}), t;
  }
  runKernel(e, t, n) {
    if (this.backendName == null && this.backend, !(nm(e, this.backendName) != null))
      throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);
    return this.runKernelFunc({ kernelName: e, inputs: t, attrs: n });
  }
  shouldCheckForMemLeaks() {
    return this.ENV.getBool("IS_TEST");
  }
  checkKernelForMemLeak(e, t, n) {
    let s = this.backend.numDataIds(), r = 0;
    n.forEach((o) => {
      r += o.dtype === "complex64" ? 3 : 1;
    });
    let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = s - t - r - a;
    if (i > 0)
      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`);
  }
  runKernelFunc(e) {
    let t, n = [], s = this.isTapeOn(), r = this.state.numBytes, a = this.state.numTensors;
    this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
    let i;
    this.backendName == null && this.backend;
    let o, u = Bf(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
    if (Bf(e)) {
      let { kernelName: h, inputs: f, attrs: m } = e;
      this.backendName == null && this.backend;
      let g = nm(h, this.backendName);
      O(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), i = () => {
        let b = this.backend.numDataIds();
        o = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend });
        let y = Array.isArray(o) ? o : [o];
        this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(h, b, y);
        let v = y.map((x) => x.rank != null ? x : this.makeTensorFromTensorInfo(x));
        if (s) {
          let x = this.getTensorsForGradient(h, f, v);
          n = this.saveTensorsForBackwardMode(x);
        }
        return v;
      };
    } else {
      let { forwardFunc: h } = e, f = (m) => {
        !s || (n = m.map((g) => this.keep(this.clone(g))));
      };
      i = () => {
        let m = this.backend.numDataIds();
        o = this.tidy(() => h(this.backend, f));
        let g = Array.isArray(o) ? o : [o];
        return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, m, g), g;
      };
    }
    let { inputs: l, attrs: c } = e, p = Bf(e) ? null : e.backwardsFunc, d;
    return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
      !this.ENV.getBool("DEBUG") && !this.state.profiling ? t = i() : (d = this.profiler.profileKernel(u, l, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t = d.outputs);
    }), s && this.addTapeNode(u, l, t, p, n, c), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - r, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(l).map((h) => l[h] != null ? l[h].shape : null), outputShapes: t.map((h) => h.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(o) ? t : t[0];
  }
  saveTensorsForBackwardMode(e) {
    return e.map((n) => this.keep(this.clone(n)));
  }
  getTensorsForGradient(e, t, n) {
    let s = ox(e);
    if (s != null) {
      let r = s.inputsToSave || [], a = s.outputsToSave || [], i;
      s.saveAllInputs ? (O(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t).map((u) => t[u])) : i = r.map((u) => t[u]);
      let o = n.filter((u, l) => a[l]);
      return i.concat(o);
    }
    return [];
  }
  makeTensor(e, t, n, s) {
    if (e == null)
      throw new Error("Values passed to engine.makeTensor() are null");
    n = n || "float32", s = s || this.backend;
    let r = e;
    n === "string" && ar(e[0]) && (r = e.map((o) => Fl(o)));
    let a = s.write(r, t, n), i = new et(t, n, a, this.nextTensorId());
    if (this.trackTensor(i, s), n === "string") {
      let o = this.state.tensorInfo.get(a), u = sk(r);
      this.state.numBytes += u - o.bytes, o.bytes = u;
    }
    return i;
  }
  makeTensorFromTensorInfo(e, t) {
    let { dataId: n, shape: s, dtype: r } = e, a = new et(s, r, n, this.nextTensorId());
    return this.trackTensor(a, t), a;
  }
  makeVariable(e, t = true, n, s) {
    n = n || this.nextVariableId().toString(), s != null && s !== e.dtype && (e = e.cast(s));
    let r = new vd(e, t, n, this.nextTensorId());
    if (this.state.registeredVariables[r.name] != null)
      throw new Error(`Variable with name ${r.name} was already registered`);
    return this.state.registeredVariables[r.name] = r, this.incRef(r, this.backend), r;
  }
  trackTensor(e, t) {
    this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
    let n = 0;
    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * Jf(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof vd || this.track(e);
  }
  incRef(e, t) {
    this.trackTensor(e, t), this.backend.incRef(e.dataId);
  }
  removeDataId(e, t) {
    this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--);
  }
  disposeTensor(e) {
    if (!this.state.tensorInfo.has(e.dataId))
      return;
    let t = this.state.tensorInfo.get(e.dataId);
    if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
      let n = e.size * Jf(e.dtype);
      this.state.numBytes -= n;
    }
    t.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t.backend);
  }
  disposeVariables() {
    for (let e in this.state.registeredVariables) {
      let t = this.state.registeredVariables[e];
      this.disposeVariable(t);
    }
  }
  disposeVariable(e) {
    this.disposeTensor(e), this.state.registeredVariables[e.name] != null && delete this.state.registeredVariables[e.name];
  }
  memory() {
    let e = this.backend.memory();
    return e.numTensors = this.state.numTensors, e.numDataBuffers = this.state.numDataBuffers, e.numBytes = this.state.numBytes, this.state.numStringTensors > 0 && (e.unreliable = 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 t = this.state.numBytes, n = 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((s) => s.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t, this.state.activeProfile.newTensors = this.state.numTensors - n;
    for (let s of this.state.activeProfile.kernels)
      s.kernelTimeMs = await s.kernelTimeMs, s.extraInfo = await s.extraInfo;
    return this.state.activeProfile;
  }
  isTapeOn() {
    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
  }
  addTapeNode(e, t, n, s, r, a) {
    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = ox(e);
    o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => {
      if (l == null) {
        let p = n[c], d = Yd(p.size, p.dtype);
        return this.makeTensor(d, p.shape, p.dtype);
      }
      return l;
    }), s(u.length > 1 ? u : u[0], r, 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 t = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ };
    e && (t.name = e), this.state.scopeStack.push(t), this.state.activeScope = t;
  }
  endScope(e) {
    let t = zg(e), n = new Set(t.map((r) => r.id));
    for (let r = 0; r < this.state.activeScope.track.length; r++) {
      let a = this.state.activeScope.track[r];
      !a.kept && !n.has(a.id) && a.dispose();
    }
    let s = this.state.scopeStack.pop();
    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t.forEach((r) => {
      !r.kept && r.scopeId === s.id && this.track(r);
    });
  }
  gradients(e, t, n, s = false) {
    if (O(t.length > 0, () => "gradients() received an empty list of xs."), n != null && n.dtype !== "float32")
      throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);
    let r = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
    O(r instanceof et, () => "The result y returned by f() must be a tensor.");
    let a = e_(this.state.activeTape, t, r);
    if (!s && a.length === 0 && t.length > 0)
      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
    return this.tidy("backward", () => {
      let i = {};
      i[r.id] = n == null ? h_(r.shape) : n, t_(i, a, (u) => this.tidy(u), f_);
      let o = t.map((u) => i[u.id]);
      return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => {
        for (let l of u.saved)
          l.dispose();
      }), this.state.activeTape = null), { value: r, grads: o };
    });
  }
  customGrad(e) {
    return O(hr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
      O(t.every((i) => i instanceof et), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
      let n, s = {};
      t.forEach((i, o) => {
        s[o] = i;
      });
      let r = (i, o) => (n = e(...t, o), O(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), O(hr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (i, o) => {
        let u = n.gradFunc(i, o), l = Array.isArray(u) ? u : [u];
        O(l.length === t.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), O(l.every((p) => p instanceof et), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
        let c = {};
        return l.forEach((p, d) => {
          c[d] = () => p;
        }), c;
      };
      return this.runKernelFunc({ forwardFunc: r, backwardsFunc: a, inputs: s });
    };
  }
  readSync(e) {
    return this.state.tensorInfo.get(e).backend.readSync(e);
  }
  read(e) {
    return this.state.tensorInfo.get(e).backend.read(e);
  }
  readToGPU(e, t) {
    return this.state.tensorInfo.get(e).backend.readToGPU(e, t);
  }
  async time(e) {
    let t = Hu(), n = await this.backend.time(e);
    return n.wallMs = Hu() - t, n;
  }
  track(e) {
    return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e;
  }
  get registeredVariables() {
    return this.state.registeredVariables;
  }
  reset() {
    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new dx();
    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;
  }
};
var Mg = am;
Mg.nextTensorId = 0;
Mg.nextVariableId = 0;
function h_(e) {
  let t = ig(dt(e), "float32");
  return M.makeTensor(t, e, "float32");
}
function yk() {
  let e = uk();
  if (e._tfengine == null) {
    let t = new D$(e);
    e._tfengine = new Mg(t);
  }
  return z$(e._tfengine.ENV), a_(() => e._tfengine), e._tfengine;
}
var M = yk();
function f_(e, t) {
  let n = { a: e, b: t };
  return M.runKernel(Sr, n);
}
var gp = {};
Ae(gp, { isBrowser: () => vk, isMobile: () => b_, mockIsMobile: () => g_ });
function m_() {
  return typeof navigator != "undefined" && navigator != null;
}
var im;
function g_(e) {
  im = e;
}
function b_(e) {
  if (im !== void 0)
    return im;
  if (e || m_()) {
    if (e || (e = navigator), e.product === "ReactNative")
      return true;
    let t = e.userAgent || e.vendor || (typeof window != "undefined" ? window.opera : "");
    if (!t) {
      let n = e;
      return n.userAgentData && n.userAgentData.mobile;
    }
    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0, 4));
  }
  return false;
}
function vk() {
  return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
}
var Kn = K();
Kn.registerFlag("DEBUG", () => false, (e) => {
  e && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
});
Kn.registerFlag("IS_BROWSER", () => vk());
Kn.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
Kn.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
Kn.registerFlag("PROD", () => false);
Kn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => Kn.getBool("DEBUG"));
Kn.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
Kn.registerFlag("IS_TEST", () => false);
Kn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
Kn.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
Kn.registerFlag("ENGINE_COMPILE_ONLY", () => false);
function Rs(e, t) {
  let n = e;
  if (Qt(e))
    return t === "string" ? [] : [e.length];
  if (!Array.isArray(e))
    return [];
  let s = [];
  for (; Array.isArray(n) || Qt(n) && t !== "string"; )
    s.push(n.length), n = n[0];
  return Array.isArray(e) && K().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && xk(e, s, []), s;
}
function xk(e, t, n) {
  if (n = n || [], !Array.isArray(e) && !Qt(e)) {
    O(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);
    return;
  }
  O(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), O(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);
  let s = t.slice(1);
  for (let r = 0; r < e.length; ++r)
    xk(e[r], s, n.concat(r));
}
function px(e, t, n, s) {
  if (e !== "string_or_numeric") {
    if (e == null)
      throw new Error("Expected dtype cannot be null.");
    if (e !== "numeric" && e !== t || e === "numeric" && t === "string")
      throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`);
  }
}
function _(e, t, n, s = "numeric") {
  if (e instanceof et)
    return px(s, e.dtype, t, n), e;
  let r = Xd(e);
  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), px(s, r, t, n), e == null || !Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
    let u = e == null ? "null" : e.constructor.name;
    throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`);
  }
  let a = Rs(e, r);
  !Qt(e) && !Array.isArray(e) && (e = [e]);
  let o = r !== "string" ? fp(e, r) : ra(e, [], true);
  return M.makeTensor(o, a, r);
}
function qu(e, t, n, s = "numeric") {
  if (!Array.isArray(e))
    throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);
  return e.map((a, i) => _(a, `${t}[${i}]`, n, s));
}
var y_ = "__op";
function L(e) {
  let t = Object.keys(e);
  if (t.length !== 1)
    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);
  let n = t[0], s = e[n];
  n.endsWith("_") && (n = n.substring(0, n.length - 1)), n = n + y_;
  let r = (...a) => {
    M.startScope(n);
    try {
      let i = s(...a);
      return ug(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i;
    } catch (i) {
      throw M.endScope(null), i;
    }
  };
  return Object.defineProperty(r, "name", { value: n, configurable: true }), r;
}
function v_(e, t) {
  let n = _(e, "real", "complex"), s = _(t, "imag", "complex");
  pn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);
  let r = { real: n, imag: s };
  return M.runKernel(Zd, r);
}
var ua = L({ complex_: v_ });
function Nr(e, t, n, s) {
  if (s == null && (s = Xd(e)), s === "complex64")
    throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
  if (!Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
  if (t != null) {
    og(t);
    let r = dt(t), a = dt(n);
    O(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
    for (let i = 0; i < n.length; ++i) {
      let o = n[i], u = i === n.length - 1 ? o !== dt(t.slice(i)) : true;
      O(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
    }
  }
  return !Qt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? fp(e, s) : ra(e, [], true), M.makeTensor(e, t, s);
}
function ms(e, t, n) {
  let s = Rs(e, n);
  return Nr(e, t, s, n);
}
var om = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var xd = 4;
async function x_(e, t) {
  let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e);
  for (let i = 0; i < r.length; ++i) {
    let o = r[i], u = Array.isArray(e) ? e[i].tensor : e[o];
    if (u.dtype !== "float32" && u.dtype !== "int32" && u.dtype !== "bool" && u.dtype !== "string" && u.dtype !== "complex64")
      throw new Error(`Unsupported dtype in weight '${o}': ${u.dtype}`);
    let l = { name: o, shape: u.shape, dtype: u.dtype };
    if (u.dtype === "string") {
      let c = new Promise(async (p) => {
        let d = await u.bytes(), h = d.reduce((g, b) => g + b.length, 0) + xd * d.length, f = new Uint8Array(h), m = 0;
        for (let g = 0; g < d.length; g++) {
          let b = d[g], y = new Uint8Array(new Uint32Array([b.length]).buffer);
          f.set(y, m), m += xd, f.set(b, m), m += b.length;
        }
        p(f);
      });
      s.push(c);
    } else
      s.push(u.data());
    t != null && (l.group = t), n.push(l);
  }
  let a = await Promise.all(s);
  return { data: w_(a), specs: n };
}
function wk(e, t) {
  let n = {}, s, r = 0;
  for (let a of t) {
    let i = a.name, o = a.dtype, u = a.shape, l = dt(u), c;
    if ("quantization" in a) {
      let p = a.quantization;
      if (p.dtype === "uint8" || p.dtype === "uint16") {
        if (!("min" in p && "scale" in p))
          throw new Error(`Weight ${a.name} with quantization ${p.dtype} doesn't have corresponding metadata min and scale.`);
      } else if (p.dtype === "float16") {
        if (o !== "float32")
          throw new Error(`Weight ${a.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`);
      } else
        throw new Error(`Weight ${a.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
      let d = om[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
      if (o === "float32")
        if (p.dtype === "uint8" || p.dtype === "uint16") {
          c = new Float32Array(f.length);
          for (let m = 0; m < f.length; m++) {
            let g = f[m];
            c[m] = g * p.scale + p.min;
          }
        } else if (p.dtype === "float16")
          s === void 0 && (s = T_()), c = s(f);
        else
          throw new Error(`Unsupported quantization type ${p.dtype} for weight type float32.`);
      else if (o === "int32") {
        if (p.dtype !== "uint8" && p.dtype !== "uint16")
          throw new Error(`Unsupported quantization type ${p.dtype} for weight type int32.`);
        c = new Int32Array(f.length);
        for (let m = 0; m < f.length; m++) {
          let g = f[m];
          c[m] = Math.round(g * p.scale + p.min);
        }
      } else
        throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
      r += l * d;
    } else if (o === "string") {
      let p = dt(a.shape);
      c = [];
      for (let d = 0; d < p; d++) {
        let h = new Uint32Array(e.slice(r, r + xd))[0];
        r += xd;
        let f = new Uint8Array(e.slice(r, r + h));
        c.push(f), r += h;
      }
    } else {
      let p = om[o], d = e.slice(r, r + l * p);
      if (o === "float32")
        c = new Float32Array(d);
      else if (o === "int32")
        c = new Int32Array(d);
      else if (o === "bool")
        c = new Uint8Array(d);
      else if (o === "complex64") {
        c = new Float32Array(d);
        let h = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2);
        for (let b = 0; b < h.length; b++)
          h[b] = c[b * 2], f[b] = c[b * 2 + 1];
        let m = ms(h, u, "float32"), g = ms(f, u, "float32");
        n[i] = ua(m, g), m.dispose(), g.dispose();
      } else
        throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
      r += l * p;
    }
    o !== "complex64" && (n[i] = ms(c, u, o));
  }
  return n;
}
function w_(e) {
  if (e === null)
    throw new Error(`Invalid input value: ${JSON.stringify(e)}`);
  let t = 0, n = [];
  e.forEach((a) => {
    if (t += a.byteLength, n.push(a.byteLength === a.buffer.byteLength ? a : new a.constructor(a)), !(a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array))
      throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`);
  });
  let s = new Uint8Array(t), r = 0;
  return n.forEach((a) => {
    s.set(new Uint8Array(a.buffer), r), r += a.byteLength;
  }), s.buffer;
}
var Lg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function hx(e) {
  return Lg ? Buffer.byteLength(e) : new Blob([e]).size;
}
function k_(e) {
  if (Lg)
    return Buffer.from(e).toString("base64");
  let t = new Uint8Array(e), n = "";
  for (let s = 0, r = t.length; s < r; s++)
    n += String.fromCharCode(t[s]);
  return btoa(n);
}
function S_(e) {
  if (Lg) {
    let s = Buffer.from(e, "base64");
    return s.buffer.slice(s.byteOffset, s.byteOffset + s.byteLength);
  }
  let t = atob(e), n = new Uint8Array(t.length);
  for (let s = 0; s < t.length; ++s)
    n.set([t.charCodeAt(s)], s);
  return n.buffer;
}
function Bg(e) {
  if (e.length === 1)
    return e[0];
  let t = 0;
  e.forEach((r) => {
    t += r.byteLength;
  });
  let n = new Uint8Array(t), s = 0;
  return e.forEach((r) => {
    n.set(new Uint8Array(r), s), s += r.byteLength;
  }), n.buffer;
}
function fx(e) {
  let t = "/";
  for (e = e.trim(); e.endsWith(t); )
    e = e.slice(0, e.length - 1);
  let n = e.split(t);
  return n[n.length - 1];
}
function kk(e, t) {
  let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, weightsManifest: t };
  return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), n;
}
async function Vg(e, t) {
  let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy };
  if (e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), e.weightsManifest != null) {
    let [s, r] = await t(e.weightsManifest);
    n.weightSpecs = s, n.weightData = r;
  }
  return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), n;
}
function Ol(e) {
  if (e.modelTopology instanceof ArrayBuffer)
    throw new Error("Expected JSON model topology, received ArrayBuffer.");
  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : hx(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : hx(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
}
function I_() {
  let e = (n) => {
    let s = n << 13, r = 0;
    for (; (s & 8388608) === 0; )
      r -= 8388608, s <<= 1;
    return s &= -8388609, r += 947912704, s | r;
  }, t = new Uint32Array(2048);
  t[0] = 0;
  for (let n = 1; n < 1024; n++)
    t[n] = e(n);
  for (let n = 1024; n < 2048; n++)
    t[n] = 939524096 + (n - 1024 << 13);
  return t;
}
function C_() {
  let e = new Uint32Array(64);
  e[0] = 0, e[31] = 1199570944, e[32] = 2147483648, e[63] = 3347054592;
  for (let t = 1; t < 31; t++)
    e[t] = t << 23;
  for (let t = 33; t < 63; t++)
    e[t] = 2147483648 + (t - 32 << 23);
  return e;
}
function N_() {
  let e = new Uint32Array(64);
  for (let t = 0; t < 64; t++)
    e[t] = 1024;
  return e[0] = e[32] = 0, e;
}
function T_() {
  let e = I_(), t = C_(), n = N_();
  return (s) => {
    let r = new ArrayBuffer(4 * s.length), a = new Uint32Array(r);
    for (let i = 0; i < s.length; i++) {
      let o = s[i], u = e[n[o >> 10] + (o & 1023)] + t[o >> 10];
      a[i] = u;
    }
    return new Float32Array(r);
  };
}
var xt = class {
  constructor() {
    this.saveRouters = [], this.loadRouters = [];
  }
  static getInstance() {
    return xt.instance == null && (xt.instance = new xt()), xt.instance;
  }
  static registerSaveRouter(e) {
    xt.getInstance().saveRouters.push(e);
  }
  static registerLoadRouter(e) {
    xt.getInstance().loadRouters.push(e);
  }
  static getSaveHandlers(e) {
    return xt.getHandlers(e, "save");
  }
  static getLoadHandlers(e, t) {
    return xt.getHandlers(e, "load", t);
  }
  static getHandlers(e, t, n) {
    let s = [];
    return (t === "load" ? xt.getInstance().loadRouters : xt.getInstance().saveRouters).forEach((a) => {
      let i = a(e, n);
      i !== null && s.push(i);
    }), s;
  }
};
var $_ = (e) => xt.registerSaveRouter(e);
var __ = (e) => xt.registerLoadRouter(e);
var A_ = (e) => xt.getSaveHandlers(e);
var E_ = (e, t) => xt.getLoadHandlers(e, t);
var um = "tensorflowjs";
var lm = 1;
var Qr = "models_store";
var ir = "model_info_store";
function Sk() {
  if (!K().getBool("IS_BROWSER"))
    throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
  let e = typeof window == "undefined" ? self : window, t = e.indexedDB || e.mozIndexedDB || e.webkitIndexedDB || e.msIndexedDB || e.shimIndexedDB;
  if (t == null)
    throw new Error("The current browser does not appear to support IndexedDB.");
  return t;
}
function cm(e) {
  let t = e.result;
  t.createObjectStore(Qr, { keyPath: "modelPath" }), t.createObjectStore(ir, { keyPath: "modelPath" });
}
var la = class {
  constructor(e) {
    if (this.indexedDB = Sk(), e == null || !e)
      throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
    this.modelPath = e;
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
    return this.databaseAction(this.modelPath, e);
  }
  async load() {
    return this.databaseAction(this.modelPath);
  }
  databaseAction(e, t) {
    return new Promise((n, s) => {
      let r = this.indexedDB.open(um, lm);
      r.onupgradeneeded = () => cm(r), r.onsuccess = () => {
        let a = r.result;
        if (t == null) {
          let i = a.transaction(Qr, "readonly"), u = i.objectStore(Qr).get(this.modelPath);
          u.onsuccess = () => {
            if (u.result == null)
              return a.close(), s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
            n(u.result.modelArtifacts);
          }, u.onerror = (l) => (a.close(), s(u.error)), i.oncomplete = () => a.close();
        } else {
          let i = Ol(t), o = a.transaction(ir, "readwrite"), u = o.objectStore(ir), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
          l.onsuccess = () => {
            c = a.transaction(Qr, "readwrite");
            let d = c.objectStore(Qr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
            d.onsuccess = () => n({ modelArtifactsInfo: i }), d.onerror = (h) => {
              u = o.objectStore(ir);
              let f = u.delete(this.modelPath);
              f.onsuccess = () => (a.close(), s(d.error)), f.onerror = (m) => (a.close(), s(d.error));
            };
          }, l.onerror = (p) => (a.close(), s(l.error)), o.oncomplete = () => {
            c == null ? a.close() : c.oncomplete = () => a.close();
          };
        }
      }, r.onerror = (a) => s(r.error);
    });
  }
};
la.URL_SCHEME = "indexeddb://";
var Ik = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(la.URL_SCHEME) ? R_(e.slice(la.URL_SCHEME.length)) : null;
xt.registerSaveRouter(Ik);
xt.registerLoadRouter(Ik);
function R_(e) {
  return new la(e);
}
function D_(e) {
  return e.startsWith(la.URL_SCHEME) ? e.slice(la.URL_SCHEME.length) : e;
}
var F_ = class {
  constructor() {
    this.indexedDB = Sk();
  }
  async listModels() {
    return new Promise((e, t) => {
      let n = this.indexedDB.open(um, lm);
      n.onupgradeneeded = () => cm(n), n.onsuccess = () => {
        let s = n.result, r = s.transaction(ir, "readonly"), i = r.objectStore(ir).getAll();
        i.onsuccess = () => {
          let o = {};
          for (let u of i.result)
            o[u.modelPath] = u.modelArtifactsInfo;
          e(o);
        }, i.onerror = (o) => (s.close(), t(i.error)), r.oncomplete = () => s.close();
      }, n.onerror = (s) => t(n.error);
    });
  }
  async removeModel(e) {
    return e = D_(e), new Promise((t, n) => {
      let s = this.indexedDB.open(um, lm);
      s.onupgradeneeded = () => cm(s), s.onsuccess = () => {
        let r = s.result, a = r.transaction(ir, "readwrite"), i = a.objectStore(ir), o = i.get(e), u;
        o.onsuccess = () => {
          if (o.result == null)
            return r.close(), n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));
          {
            let l = i.delete(e), c = () => {
              u = r.transaction(Qr, "readwrite");
              let d = u.objectStore(Qr).delete(e);
              d.onsuccess = () => t(o.result.modelArtifactsInfo), d.onerror = (h) => n(o.error);
            };
            l.onsuccess = c, l.onerror = (p) => (c(), r.close(), n(o.error));
          }
        }, o.onerror = (l) => (r.close(), n(o.error)), a.oncomplete = () => {
          u == null ? r.close() : u.oncomplete = () => r.close();
        };
      }, s.onerror = (r) => n(s.error);
    });
  }
};
var Us = "/";
var ji = "tensorflowjs_models";
var Ck = "info";
var O_ = "model_topology";
var P_ = "weight_specs";
var z_ = "weight_data";
var M_ = "model_metadata";
function Nk(e) {
  return { info: [ji, e, Ck].join(Us), topology: [ji, e, O_].join(Us), weightSpecs: [ji, e, P_].join(Us), weightData: [ji, e, z_].join(Us), modelMetadata: [ji, e, M_].join(Us) };
}
function Tk(e) {
  for (let t of Object.values(e))
    window.localStorage.removeItem(t);
}
function L_(e) {
  let t = e.split(Us);
  if (t.length < 3)
    throw new Error(`Invalid key format: ${e}`);
  return t.slice(1, t.length - 1).join(Us);
}
function B_(e) {
  return e.startsWith(ca.URL_SCHEME) ? e.slice(ca.URL_SCHEME.length) : e;
}
var ca = class {
  constructor(e) {
    if (!K().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined")
      throw new Error("The current environment does not support local storage.");
    if (this.LS = window.localStorage, e == null || !e)
      throw new Error("For local storage, modelPath must not be null, undefined or empty.");
    this.modelPath = e, this.keys = Nk(this.modelPath);
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
    {
      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Ol(e);
      try {
        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, k_(e.weightData));
        let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
        return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(r)), { modelArtifactsInfo: s };
      } catch (r) {
        throw Tk(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`);
      }
    }
  }
  async load() {
    let e = JSON.parse(this.LS.getItem(this.keys.info));
    if (e == null)
      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
    if (e.modelTopologyType !== "JSON")
      throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
    let t = {}, n = JSON.parse(this.LS.getItem(this.keys.topology));
    if (n == null)
      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
    t.modelTopology = n;
    let s = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
    if (s == null)
      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
    t.weightSpecs = s;
    let r = this.LS.getItem(this.keys.modelMetadata);
    if (r != null) {
      let i = JSON.parse(r);
      t.format = i.format, t.generatedBy = i.generatedBy, t.convertedBy = i.convertedBy, i.signature != null && (t.signature = i.signature), i.userDefinedMetadata != null && (t.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t.modelInitializer = i.modelInitializer), i.trainingConfig != null && (t.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 t.weightData = S_(a), t;
  }
};
ca.URL_SCHEME = "localstorage://";
var $k = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ca.URL_SCHEME) ? V_(e.slice(ca.URL_SCHEME.length)) : null;
xt.registerSaveRouter($k);
xt.registerLoadRouter($k);
function V_(e) {
  return new ca(e);
}
var W_ = class {
  constructor() {
    O(K().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), O(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
  }
  async listModels() {
    let e = {}, t = ji + Us, n = Us + Ck;
    for (let s = 0; s < this.LS.length; ++s) {
      let r = this.LS.key(s);
      if (r.startsWith(t) && r.endsWith(n)) {
        let a = L_(r);
        e[a] = JSON.parse(this.LS.getItem(r));
      }
    }
    return e;
  }
  async removeModel(e) {
    e = B_(e);
    let t = Nk(e);
    if (this.LS.getItem(t.info) == null)
      throw new Error(`Cannot find model at path '${e}'`);
    let n = JSON.parse(this.LS.getItem(t.info));
    return Tk(t), n;
  }
};
var Yi = "://";
var zn = class {
  constructor() {
    this.managers = {};
  }
  static getInstance() {
    return zn.instance == null && (zn.instance = new zn()), zn.instance;
  }
  static registerManager(e, t) {
    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(Yi) && (e = e.slice(0, e.indexOf(Yi))), O(e.length > 0, () => "scheme must not be an empty string.");
    let n = zn.getInstance();
    O(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
  }
  static getManager(e) {
    let t = this.getInstance().managers[e];
    if (t == null)
      throw new Error(`Cannot find model manager for scheme '${e}'`);
    return t;
  }
  static getSchemes() {
    return Object.keys(this.getInstance().managers);
  }
};
function sd(e) {
  if (e.indexOf(Yi) === -1)
    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${zn.getSchemes().join(",")}`);
  return { scheme: e.split(Yi)[0], path: e.split(Yi)[1] };
}
async function _k(e, t, n = false) {
  O(e !== t, () => `Old path and new path are the same: '${e}'`);
  let s = xt.getLoadHandlers(e);
  O(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), O(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
  let r = s[0], a = xt.getSaveHandlers(t);
  O(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), O(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);
  let i = a[0], o = sd(e).scheme, u = sd(e).path, l = o === sd(e).scheme, c = await r.load();
  n && l && await zn.getManager(o).removeModel(u);
  let p = await i.save(c);
  return n && !l && await zn.getManager(o).removeModel(u), p.modelArtifactsInfo;
}
async function U_() {
  let e = zn.getSchemes(), t = {};
  for (let n of e) {
    let s = await zn.getManager(n).listModels();
    for (let r in s) {
      let a = n + Yi + r;
      t[a] = s[r];
    }
  }
  return t;
}
async function G_(e) {
  let t = sd(e);
  return zn.getManager(t.scheme).removeModel(t.path);
}
async function H_(e, t) {
  return _k(e, t, false);
}
async function q_(e, t) {
  return _k(e, t, true);
}
var j_ = class {
  fetch(e, t) {
    return fetch(e, t);
  }
  now() {
    return performance.now();
  }
  encode(e, t) {
    if (t !== "utf-8" && t !== "utf8")
      throw new Error(`Browser's encoder only supports utf-8, but got ${t}`);
    return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e);
  }
  decode(e, t) {
    return new TextDecoder(t).decode(e);
  }
};
if (K().get("IS_BROWSER")) {
  K().setPlatform("browser", new j_());
  try {
    zn.registerManager(ca.URL_SCHEME, new W_());
  } catch (e) {
  }
  try {
    zn.registerManager(la.URL_SCHEME, new F_());
  } catch (e) {
  }
}
var K_ = { importFetch: () => t$() };
var Vf;
var X_ = class {
  constructor() {
    this.util = n$(), this.textEncoder = new this.util.TextEncoder();
  }
  fetch(e, t) {
    return K().global.fetch != null ? K().global.fetch(e, t) : (Vf == null && (Vf = K_.importFetch()), Vf(e, t));
  }
  now() {
    let e = process.hrtime();
    return e[0] * 1e3 + e[1] / 1e6;
  }
  encode(e, t) {
    if (t !== "utf-8" && t !== "utf8")
      throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);
    return this.textEncoder.encode(e);
  }
  decode(e, t) {
    return e.length === 0 ? "" : new this.util.TextDecoder(t).decode(e);
  }
};
K().get("IS_NODE") && !K().get("IS_BROWSER") && K().setPlatform("node", new X_());
function De(e, t = "float32", n) {
  return t = t || "float32", og(e), new Wt(e, t, n);
}
function Y_(e, t) {
  let n = _(e, "x", "cast");
  if (!nk(t))
    throw new Error(`Failed to cast to unknown dtype ${t}`);
  if (t === "string" && n.dtype !== "string" || t !== "string" && n.dtype === "string")
    throw new Error("Only strings can be casted to strings");
  let s = { x: n }, r = { dtype: t };
  return M.runKernel(Ta, s, r);
}
var le = L({ cast_: Y_ });
function Q_(e) {
  let n = { x: _(e, "x", "clone", "string_or_numeric") };
  return M.runKernel(Wa, n);
}
var ur = L({ clone_: Q_ });
function Z_(e, t = false) {
  console.log(e.toString(t));
}
yk();
var J_ = { buffer: De, cast: le, clone: ur, print: Z_ };
i_(J_);
var An = {};
Ae(An, { browserFiles: () => iA, browserHTTPRequest: () => dA, concatenateArrayBuffers: () => Bg, copyModel: () => H_, decodeWeights: () => wk, encodeWeights: () => x_, fromMemory: () => hA, getLoadHandlers: () => E_, getModelArtifactsForJSON: () => Vg, getModelArtifactsInfoForJSON: () => Ol, getSaveHandlers: () => A_, http: () => Ug, isHTTPScheme: () => pm, listModels: () => U_, loadWeights: () => oA, moveModel: () => q_, registerLoadRouter: () => __, registerSaveRouter: () => $_, removeModel: () => G_, weightsLoaderFactory: () => Ek, withSaveHandler: () => fA });
var eA = "model";
var tA = ".json";
var nA = ".weights.bin";
function mx(e) {
  return new Promise((t) => setTimeout(t)).then(e);
}
var dm = class {
  constructor(e) {
    if (!K().getBool("IS_BROWSER"))
      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
    e.startsWith(dm.URL_SCHEME) && (e = e.slice(dm.URL_SCHEME.length)), (e == null || e.length === 0) && (e = eA), this.modelJsonFileName = e + tA, this.weightDataFileName = e + nA;
  }
  async save(e) {
    if (typeof document == "undefined")
      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
    let t = window.URL.createObjectURL(new Blob([e.weightData], { type: "application/octet-stream" }));
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
    {
      let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = kk(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
      if (a.download = this.modelJsonFileName, a.href = r, await mx(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
        let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
        i.download = this.weightDataFileName, i.href = t, await mx(() => i.dispatchEvent(new MouseEvent("click")));
      }
      return { modelArtifactsInfo: Ol(e) };
    }
  }
};
var wd = dm;
wd.URL_SCHEME = "downloads://";
var sA = class {
  constructor(e) {
    if (e == null || e.length < 1)
      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);
    this.jsonFile = e[0], this.weightsFiles = e.slice(1);
  }
  async load() {
    return new Promise((e, t) => {
      let n = new FileReader();
      n.onload = (s) => {
        let r = JSON.parse(s.target.result), a = r.modelTopology;
        if (a == null) {
          t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
          return;
        }
        if (r.weightsManifest == null) {
          t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
          return;
        }
        if (this.weightsFiles.length === 0) {
          e({ modelTopology: a });
          return;
        }
        let o = Vg(r, (u) => this.loadWeights(u));
        e(o);
      }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), n.readAsText(this.jsonFile);
    });
  }
  loadWeights(e) {
    let t = [], n = [];
    for (let a of e)
      t.push(...a.weights), n.push(...a.paths);
    let s = this.checkManifestAndWeightFiles(e), r = n.map((a) => this.loadWeightsFile(a, s[a]));
    return Promise.all(r).then((a) => [t, Bg(a)]);
  }
  loadWeightsFile(e, t) {
    return new Promise((n, s) => {
      let r = new FileReader();
      r.onload = (a) => {
        let i = a.target.result;
        n(i);
      }, r.onerror = (a) => s(`Failed to weights data from file of path '${e}'.`), r.readAsArrayBuffer(t);
    });
  }
  checkManifestAndWeightFiles(e) {
    let t = [], n = this.weightsFiles.map((r) => fx(r.name)), s = {};
    for (let r of e)
      r.paths.forEach((a) => {
        let i = fx(a);
        if (t.indexOf(i) !== -1)
          throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
        if (t.push(i), n.indexOf(i) === -1)
          throw new Error(`Weight file with basename '${i}' is not provided.`);
        s[a] = this.weightsFiles[n.indexOf(i)];
      });
    if (t.length !== this.weightsFiles.length)
      throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
    return s;
  }
};
var rA = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(wd.URL_SCHEME) ? aA(e.slice(wd.URL_SCHEME.length)) : null;
xt.registerSaveRouter(rA);
function aA(e = "model") {
  return new wd(e);
}
function iA(e) {
  return new sA(e);
}
function gx(e, t, n, s) {
  i(e), n = n == null ? 0 : n, s = s == null ? 1 : s, o(n, s);
  let r = 0, a = (u) => (u.then((l) => {
    let c = n + ++r / e.length * (s - n);
    return t(c), l;
  }), u);
  function i(u) {
    O(u != null && Array.isArray(u) && u.length > 0, () => "promises must be a none empty array");
  }
  function o(u, l) {
    O(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${u}`), O(l >= 0 && l <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${l}`), O(l >= u, () => `startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`);
  }
  return Promise.all(e.map(a));
}
async function Ak(e, t) {
  t == null && (t = {});
  let n = t.fetchFunc == null ? K().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await gx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
  return t.onProgress == null ? await Promise.all(o) : await gx(o, t.onProgress, u, l);
}
async function oA(e, t = "", n, s) {
  return Ek((i) => Ak(i, { requestInit: s }))(e, t, n);
}
function Ek(e) {
  return async (t, n = "", s) => {
    let r = t.map(() => false), a = {}, i = s != null ? s.map(() => false) : [], o = [];
    if (t.forEach((h, f) => {
      let m = 0;
      h.weights.forEach((g) => {
        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = om[b] * dt(g.shape), v = () => {
          r[f] = true, a[f] == null && (a[f] = []), a[f].push({ manifestEntry: g, groupOffset: m, sizeBytes: y });
        };
        s != null ? s.forEach((x, k) => {
          x === g.name && (v(), i[k] = true);
        }) : v(), o.push(g.name), m += y;
      });
    }), !i.every((h) => h)) {
      let h = s.filter((f, m) => !i[m]);
      throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}. 
Manifest JSON has weights with names: ${o.join(", ")}.`);
    }
    let u = r.reduce((h, f, m) => (f && h.push(m), h), []), l = [];
    u.forEach((h) => {
      t[h].paths.forEach((f) => {
        let m = n + (n.endsWith("/") ? "" : "/") + f;
        l.push(m);
      });
    });
    let c = await e(l), p = {}, d = 0;
    return u.forEach((h) => {
      let f = t[h].paths.length, m = 0;
      for (let x = 0; x < f; x++)
        m += c[d + x].byteLength;
      let g = new ArrayBuffer(m), b = new Uint8Array(g), y = 0;
      for (let x = 0; x < f; x++) {
        let k = new Uint8Array(c[d + x]);
        b.set(k, y), y += k.byteLength;
      }
      a[h].forEach((x) => {
        let k = g.slice(x.groupOffset, x.groupOffset + x.sizeBytes), I = wk(k, [x.manifestEntry]);
        for (let $ in I)
          p[$] = I[$];
      }), d += f;
    }), p;
  };
}
var uA = "application/octet-stream";
var lA = "application/json";
var Wg = class {
  constructor(e, t) {
    if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (O(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = K().platform.fetch, O(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && O(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
    this.requestInit = t.requestInit || {};
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
    let t = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
    t.body = new FormData();
    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = kk(e, n);
    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: lA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: uA }), "model.weights.bin");
    let r = await this.fetch(this.path, t);
    if (r.ok)
      return { modelArtifactsInfo: Ol(e), responses: [r] };
    throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`);
  }
  async load() {
    let e = await this.fetch(this.path, this.requestInit);
    if (!e.ok)
      throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);
    let t;
    try {
      t = await e.json();
    } catch (r) {
      let a = `Failed to parse model JSON of response from ${this.path}.`;
      throw this.path.endsWith(".pb") ? a += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository." : a += " Please make sure the server is serving valid JSON for this request.", new Error(a);
    }
    let n = t.modelTopology, s = t.weightsManifest;
    if (n == null && s == null)
      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
    return Vg(t, (r) => this.loadWeights(r));
  }
  async loadWeights(e) {
    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = cA(t), r = this.weightPathPrefix || n, a = [];
    for (let l of e)
      a.push(...l.weights);
    let i = [], o = [];
    for (let l of e)
      for (let c of l.paths)
        this.weightUrlConverter != null ? o.push(this.weightUrlConverter(c)) : i.push(r + c + s);
    this.weightUrlConverter && i.push(...await Promise.all(o));
    let u = await Ak(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
    return [a, Bg(u)];
  }
};
Wg.URL_SCHEME_REGEX = /^https?:\/\//;
function cA(e) {
  let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : "";
  return [s + "/", r];
}
function pm(e) {
  return e.match(Wg.URL_SCHEME_REGEX) != null;
}
var Rk = (e, t) => {
  if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null))
    return null;
  {
    let n = true;
    if (Array.isArray(e) ? n = e.every((s) => pm(s)) : n = pm(e), n)
      return Ug(e, t);
  }
  return null;
};
xt.registerSaveRouter(Rk);
xt.registerLoadRouter(Rk);
function Ug(e, t) {
  return new Wg(e, t);
}
function dA(e, t) {
  return Ug(e, t);
}
var Wf = class {
  constructor(e) {
    this.modelArtifacts = e;
  }
  async load() {
    return this.modelArtifacts;
  }
};
var pA = class {
  constructor(e) {
    this.saveHandler = e;
  }
  async save(e) {
    return this.saveHandler(e);
  }
};
function hA(e, t, n, s) {
  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Wf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Wf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Wf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
}
function fA(e) {
  return new pA(e);
}
var mA = {};
Ae(mA, { confusionMatrix: () => xA });
function gA(e, t, n = false, s = false) {
  let r = _(e, "a", "matMul"), a = _(t, "b", "matMul");
  [r, a] = vt(r, a);
  let i = { a: r, b: a }, o = { transposeA: n, transposeB: s };
  return M.runKernel(Na, i, o);
}
var Ve = L({ matMul_: gA });
function bA(e, t, n = 1, s = 0) {
  if (t < 2)
    throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
  let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
  return M.runKernel(Ro, a, i);
}
var kd = L({ oneHot_: bA });
function yA(e, t) {
  let n = _(e, "x", "transpose");
  if (t == null && (t = n.shape.map((a, i) => i).reverse()), O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`), t.forEach((a) => {
    O(a >= 0 && a < n.rank, () => `All entries in 'perm' must be between 0 and ${n.rank - 1} but got ${t}`);
  }), n.rank <= 1)
    return n.clone();
  let s = { x: n }, r = { perm: t };
  return M.runKernel(mi, s, r);
}
var Ge = L({ transpose_: yA });
function vA(e, t, n) {
  let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix");
  O(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), O(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), O(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), O(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), O(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
  let a = kd(le(s, "int32"), n), i = kd(le(r, "int32"), n), o = Ge(a), u = Ve(o, i);
  return le(u, "int32");
}
var xA = L({ confusionMatrix_: vA });
var bi = {};
Ae(bi, { assertAndGetBroadcastShape: () => it, getBroadcastDims: () => Dk, getReductionAxes: () => _t });
function Dk(e, t) {
  let n = e.length, s = [];
  for (let r = 0; r < n; r++) {
    let a = n - 1 - r, i = e[a] || 1;
    (t[t.length - 1 - r] || 1) > 1 && i === 1 && s.unshift(a);
  }
  return s;
}
function _t(e, t) {
  let n = [];
  for (let s = 0; s < t.length; s++) {
    let r = e[e.length - s - 1], a = t.length - s - 1, i = t[a];
    (r == null || r === 1 && i > 1) && n.unshift(a);
  }
  return n;
}
function it(e, t) {
  let n = [], s = Math.max(e.length, t.length);
  for (let r = 0; r < s; r++) {
    let a = e[e.length - r - 1];
    a == null && (a = 1);
    let i = t[t.length - r - 1];
    if (i == null && (i = 1), a === 1)
      n.unshift(i);
    else if (i === 1)
      n.unshift(a);
    else if (a !== i) {
      let o = `Operands could not be broadcast together with shapes ${e} and ${t}.`;
      throw Error(o);
    } else
      n.unshift(a);
  }
  return n;
}
var Fk = {};
Ae(Fk, { fromPixels: () => $A, fromPixelsAsync: () => NA, toPixels: () => TA });
function wA(e, t, n) {
  if (ka(e), t != null && t.length !== 3)
    throw new Error("tensor3d() requires shape to have three numbers");
  let s = Rs(e, n);
  if (s.length !== 3 && s.length !== 1)
    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
  if (s.length === 1 && t == null)
    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
  return Nr(e, t, s, n);
}
var Wr;
function Ok(e, t = 3) {
  if (t > 4)
    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
  if (e == null)
    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
  let n = false, s = false, r = false, a = false, i = false, o = false;
  if (e.data instanceof Uint8Array)
    n = true;
  else if (typeof ImageData != "undefined" && e instanceof ImageData)
    s = true;
  else if (typeof HTMLVideoElement != "undefined" && e instanceof HTMLVideoElement)
    r = true;
  else if (typeof HTMLImageElement != "undefined" && e instanceof HTMLImageElement)
    a = true;
  else if (e.getContext != null)
    i = true;
  else if (typeof ImageBitmap != "undefined" && e instanceof ImageBitmap)
    o = 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 ${e.constructor.name}`);
  if (r && r && e.readyState < 2)
    throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
  if (nm(bd, M.backendName) != null) {
    let f = { pixels: e }, m = { numChannels: t };
    return M.runKernel(bd, f, m);
  }
  let [l, c] = r ? [e.videoWidth, e.videoHeight] : [e.width, e.height], p;
  if (i)
    p = e.getContext("2d").getImageData(0, 0, l, c).data;
  else if (s || n)
    p = e.data;
  else if (a || r || o) {
    if (Wr == null)
      if (typeof document == "undefined")
        if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
          Wr = new OffscreenCanvas(1, 1).getContext("2d");
        else
          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
      else
        Wr = document.createElement("canvas").getContext("2d");
    Wr.canvas.width = l, Wr.canvas.height = c, Wr.drawImage(e, 0, 0, l, c), p = Wr.getImageData(0, 0, l, c).data;
  }
  let d;
  if (t === 4)
    d = new Int32Array(p);
  else {
    let f = l * c;
    d = new Int32Array(f * t);
    for (let m = 0; m < f; m++)
      for (let g = 0; g < t; ++g)
        d[m * t + g] = p[m * 4 + g];
  }
  return wA(d, [c, l, t], "int32");
}
function kA(e) {
  return e != null && e.data instanceof Uint8Array;
}
function SA() {
  return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function IA(e) {
  return e != null && e.width !== 0 && e.height !== 0;
}
function CA(e) {
  return SA() && !(e instanceof ImageBitmap) && IA(e) && !kA(e);
}
async function NA(e, t = 3) {
  let n = null;
  if (K().getBool("WRAP_TO_IMAGEBITMAP") && CA(e)) {
    let s;
    try {
      s = await createImageBitmap(e, { premultiplyAlpha: "none" });
    } catch (r) {
      s = null;
    }
    s != null && s.width === e.width && s.height === e.height ? n = s : n = e;
  } else
    n = e;
  return Ok(n, t);
}
async function TA(e, t) {
  let n = _(e, "img", "toPixels");
  if (!(e instanceof et)) {
    let l = n;
    n = le(l, "int32"), l.dispose();
  }
  if (n.rank !== 2 && n.rank !== 3)
    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${n.rank}.`);
  let [s, r] = n.shape.slice(0, 2), a = n.rank === 2 ? 1 : n.shape[2];
  if (a > 4 || a === 2)
    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${a}`);
  if (n.dtype !== "float32" && n.dtype !== "int32")
    throw new Error(`Unsupported type for toPixels: ${n.dtype}. Please use float32 or int32 tensors.`);
  let i = await n.data(), o = n.dtype === "float32" ? 255 : 1, u = new Uint8ClampedArray(r * s * 4);
  for (let l = 0; l < s * r; ++l) {
    let c = [0, 0, 0, 255];
    for (let d = 0; d < a; d++) {
      let h = i[l * a + d];
      if (n.dtype === "float32") {
        if (h < 0 || h > 1)
          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${h}.`);
      } else if (n.dtype === "int32" && (h < 0 || h > 255))
        throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${h}.`);
      a === 1 ? (c[0] = h * o, c[1] = h * o, c[2] = h * o) : c[d] = h * o;
    }
    let p = l * 4;
    u[p + 0] = Math.round(c[0]), u[p + 1] = Math.round(c[1]), u[p + 2] = Math.round(c[2]), u[p + 3] = Math.round(c[3]);
  }
  if (t != null) {
    t.width = r, t.height = s;
    let l = t.getContext("2d"), c = new ImageData(u, r, s);
    l.putImageData(c, 0, 0);
  }
  return n !== e && n.dispose(), u;
}
var $A = L({ fromPixels_: Ok });
var Pk = {};
Ae(Pk, { prepareAndValidate: () => zk });
function zk(e, t) {
  let n = e.shape.length, s = t.shape.length;
  if (n < 1)
    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);
  if (s < 1)
    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${s}.`);
  if (t.dtype !== "int32")
    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);
  if (t.shape[s - 1] > n)
    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[s - 1]} vs. ${n}`);
  if (dt(e.shape) === 0)
    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);
  let r = t.shape, a = r[r.length - 1], i = 1;
  for (let p = 0; p < r.length - 1; ++p)
    i *= r[p];
  let o = e.shape, u = r.slice();
  u.pop();
  let l = 1;
  for (let p = a; p < n; ++p)
    l *= o[p], u.push(o[p]);
  let c = [...lo(e.shape).map((p) => p / l), 1].slice(0, a);
  return [u, i, l, c];
}
var Mk = {};
Ae(Mk, { calculateShapes: () => Lk, validateInput: () => Hg, validateUpdateShape: () => Gg });
function Gg(e, t, n) {
  let s = t.rank > 1 ? t.shape[t.rank - 1] : 1, r = t.rank > 1 ? t.rank - 1 : 1, a = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;
  if (n.rank < r)
    throw new Error(a + ` update.rank < ${r}. `);
  if (e.length < s + (n.rank - r))
    throw new Error(a + ` Output shape length < ${s + (n.rank - r)}`);
  if (n.rank !== r + e.length - s)
    throw new Error(a + ` update.rank != ${r + e.length - s}`);
  for (let i = 0; i < r; ++i)
    if (n.shape[i] !== t.shape[i])
      throw new Error(a + ` updates.shape[${i}] (${n.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);
  for (let i = 0; i < n.rank - r; ++i)
    if (n.shape[i + r] !== e[i + s])
      throw new Error(a + ` updates.shape[${i + r}] (${n.shape[i + r]}) != shape[${i + r}] (${e[i + r]})`);
}
function Hg(e, t, n) {
  if (t.rank < 1)
    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);
  if (e.rank < 1)
    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);
  if (t.dtype !== "int32")
    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);
  if (n.length < 1)
    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);
  if (n.length === 0) {
    if (t.size === 0)
      throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);
    if (e.size === 0)
      throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`);
  }
  Gg(n, t, e);
}
function Lk(e, t, n) {
  let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
  for (let p = r; p < a; ++p)
    i *= n[p];
  let o = r < 1 ? 1 : r, u = dt(t.shape) / o, l = [...lo(n.slice(0, r)), 1], c = dt(n);
  return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
}
var wt = {};
Ae(wt, { assertParamsValid: () => AA, computeFlatOffset: () => OA, computeOutShape: () => RA, getNormalizedAxes: () => DA, isSliceContinous: () => FA, maskToAxes: () => EA, parseSliceParams: () => Kk, sliceInfo: () => PA, startForAxis: () => qk, startIndicesWithElidedDims: () => Uk, stopForAxis: () => jk, stopIndicesWithElidedDims: () => Gk, stridesForAxis: () => Hk, stridesWithElidedDims: () => Bk });
var hm = -2;
var _A = -1;
function AA(e, t, n) {
  let s = e.shape.length;
  O(s === t.length, () => `Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`), O(s === n.length, () => `Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);
  for (let r = 0; r < s; ++r)
    O(t[r] + n[r] <= e.shape[r], () => `Error in slice${s}D: begin[${r}] + size[${r}] (${t[r] + n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`);
}
function EA(e) {
  let t = [], n = 0;
  for (; e > 0; )
    e & 1 && t.push(n), e /= 2, n++;
  return t;
}
function RA(e, t, n) {
  let s = [];
  for (let r = 0; r < e.length; r++)
    s[r] = Math.ceil((t[r] - e[r]) / n[r]);
  return s;
}
function Bk(e, t, n, s) {
  let r = [...e];
  for (let a = r.length; a < s.length; a++)
    r.push(1);
  for (let a = 0; a < n; a++)
    a === 0 ? r[t] = 1 : (r.splice(t, 0, 1), r.pop());
  return r;
}
function Vk(e, t, n) {
  return n <= e ? n : n - (t - 1);
}
function Wk(e, t) {
  let n = [];
  for (let s = 0; s < e; s++)
    n.push(t + s);
  return n;
}
function DA(e, t, n, s, r, a, i, o, u) {
  let l = e.length, c = new Array(l), p = new Array(l), d = new Array(l);
  if (t.length && n > 0) {
    let h = t[0], f = n + 1;
    c = Uk(i, h, f, s, e), p = Gk(o, h, f, r, e), d = Bk(a, h, f, e);
  } else
    for (let h = 0; h < l; h++)
      c[h] = qk(i, s, a, e, h, u), p[h] = jk(o, r, a, e, h, u), d[h] = Hk(a, h, u);
  return { begin: c, end: p, strides: d };
}
function Uk(e, t, n, s, r) {
  let a = [...r], i = Wk(n, t);
  for (let o = 0; o < a.length; o++)
    if (i.indexOf(o) > -1)
      a[o] = 0;
    else {
      let u = Vk(t, n, o), l = s[u];
      e & 1 << u && (l = 0), a[o] = l;
    }
  return a;
}
function Gk(e, t, n, s, r) {
  let a = [...r], i = Wk(n, t);
  for (let o = 0; o < a.length; o++)
    if (i.indexOf(o) > -1)
      a[o] = Number.MAX_SAFE_INTEGER;
    else {
      let u = Vk(t, n, o), l = s[u];
      e & 1 << u && (l = Number.MAX_SAFE_INTEGER), a[o] = l;
    }
  for (let o = 0; o < a.length; o++) {
    let u = r[o];
    a[o] < 0 && (a[o] += u), a[o] = Uu(0, a[o], r[o]);
  }
  return a;
}
function Hk(e, t, n) {
  let s = e[t];
  return (n & 1 << t || s == null) && (s = 1), s;
}
function qk(e, t, n, s, r, a) {
  let i = t[r], o = n[r] || 1;
  (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MIN_SAFE_INTEGER : i = Number.MAX_SAFE_INTEGER);
  let u = s[r];
  return i < 0 && (i += u), i = Uu(0, i, u - 1), i;
}
function jk(e, t, n, s, r, a) {
  let i = t[r], o = n[r] || 1;
  (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MAX_SAFE_INTEGER : i = Number.MIN_SAFE_INTEGER);
  let u = s[r];
  return i < 0 && (i += u), o > 0 ? i = Uu(0, i, u) : i = Uu(-1, i, u - 1), i;
}
function FA(e, t, n) {
  let s = n.length;
  for (let r = 0; r < n.length; r++)
    if (n[r] > 1) {
      s = r;
      break;
    }
  for (let r = s + 1; r < n.length; r++)
    if (t[r] > 0 || n[r] !== e[r])
      return false;
  return true;
}
function OA(e, t) {
  let n = e.length > 0 ? e[e.length - 1] : 1;
  for (let s = 0; s < e.length - 1; s++)
    n += e[s] * t[s];
  return n;
}
function Kk(e, t, n) {
  let s, r = e.shape.length;
  typeof t == "number" ? s = [t, ...new Array(r - 1).fill(0)] : t.length < r ? s = t.concat(new Array(r - t.length).fill(0)) : s = t.slice(), s.forEach((i) => {
    O(i !== -1, () => "slice() does not support negative begin indexing.");
  });
  let a;
  return n == null ? a = new Array(r).fill(-1) : typeof n == "number" ? a = [n, ...new Array(r - 1).fill(-1)] : n.length < r ? a = n.concat(new Array(r - n.length).fill(-1)) : a = n, a = a.map((i, o) => i >= 0 ? i : (O(i === -1, () => `Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`), e.shape[o] - s[o])), [s, a];
}
function PA(e, t, n, s, r, a, i, o, u) {
  let l;
  if (s == null ? (l = new Array(t.length), l.fill(1)) : l = s, i != null && (i & i - 1) !== 0)
    throw new Error("Multiple ellipses in slice is not allowed.");
  let c = false, p = { dims: l.length, numAddAxisAfterEllipsis: 0, begin: t.slice(), end: n.slice(), strides: l.slice(), beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
  for (let v = 0; v < p.dims; v++)
    c && (1 << v & o) !== 0 && p.numAddAxisAfterEllipsis++, 1 << v & i && (c = true);
  c || (p.ellipsisMask |= 1 << p.dims, p.dims++);
  let d = { dims: e.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
  zA(p, d);
  let h = true, f = true, m = true, g = [], b = [];
  for (let v = 0; v < e.length; ++v) {
    if (d.strides[v] === 0)
      throw Error(`strides[${v}] must be non-zero`);
    let x = !!(d.shrinkAxisMask & 1 << v), k = e[v];
    if (k === -1) {
      g.push(x ? 1 : -1);
      continue;
    }
    let I = [d.beginMask & 1 << v, d.endMask & 1 << v], $ = [d.strides[v] > 0 ? 0 : -1, d.strides[v] > 0 ? k : k - 1];
    if (x && d.strides[v] <= 0)
      throw Error("only stride 1 allowed on non-range indexing.");
    m = m && d.strides[v] === 1;
    let E = !!(d.beginMask & 1 << v && d.endMask & 1 << v);
    if (d.beginValid && d.endValid) {
      if (x) {
        let F = d.begin[v] < 0 ? k + d.begin[v] : d.begin[v];
        if (d.begin[v] = F, d.end[v] = d.begin[v] + 1, F < 0 || F >= k)
          throw Error(`slice index ${d.begin[v]} of dimension ${v} out of bounds.`);
      } else
        d.begin[v] = bx(d.begin[v], 0, d.strides[v], k, I, $), d.end[v] = bx(d.end[v], 1, d.strides[v], k, I, $);
      let R = d.strides[v] === 1 && d.begin[v] === 0 && d.end[v] === k;
      h = h && R, f = f && (v === 0 && d.strides[v] === 1 || R);
    } else
      h = h && d.strides[v] === 1 && E, f = f && (v === 0 && d.strides[v] === 1 || E);
    let A, P = false;
    if (d.beginValid && d.endValid ? (A = d.end[v] - d.begin[v], P = true) : x ? (A = 1, P = true) : E && k >= 0 && (d.strides[v] < 0 ? A = -k : A = k, P = true), P) {
      let R;
      A === 0 || A < 0 != d.strides[v] < 0 ? R = 0 : R = Math.trunc(A / d.strides[v]) + (A % d.strides[v] !== 0 ? 1 : 0), g.push(R);
    } else
      g.push(-1);
  }
  for (let v = 0; v < d.finalShapeGatherIndices.length; ++v) {
    let x = d.finalShapeGatherIndices[v];
    x >= 0 ? b.push(g[x]) : x === hm && b.push(1);
  }
  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== hm), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
}
function zA(e, t) {
  t.beginMask = 0, t.endMask = 0, t.shrinkAxisMask = 0;
  let n = 0;
  t.beginValid = e.begin != null, t.endValid = e.end != null, t.begin = new Array(t.dims), t.end = new Array(t.dims), t.strides = new Array(t.dims), t.finalShapeGatherIndices = [], t.finalShapeGatherIndicesSparse = [], t.inputShapeGatherIndicesSparse = new Array(t.dims);
  for (let s = 0; s < e.dims; s++)
    if (1 << s & e.ellipsisMask) {
      let r = Math.min(t.dims - (e.dims - s) + 1 + e.numAddAxisAfterEllipsis, t.dims);
      for (; n < r; n++)
        t.begin[n] = 0, t.end[n] = 0, t.strides[n] = 1, t.beginMask |= 1 << n, t.endMask |= 1 << n, t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(-1), t.inputShapeGatherIndicesSparse[n] = s;
    } else if (1 << s & e.newAxisMask)
      t.finalShapeGatherIndices.push(hm), t.finalShapeGatherIndicesSparse.push(-1);
    else {
      if (n === t.begin.length)
        throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);
      e.begin != null && (t.begin[n] = e.begin[s]), e.end != null && (t.end[n] = e.end[s]), t.strides[n] = e.strides[s], e.beginMask & 1 << s && (t.beginMask |= 1 << n), e.endMask & 1 << s && (t.endMask |= 1 << n), e.shrinkAxisMask & 1 << s ? (t.finalShapeGatherIndices.push(_A), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
    }
}
function bx(e, t, n, s, r, a) {
  if (r[t])
    return n > 0 ? a[t] : a[t + 1 & 1];
  {
    let i = e < 0 ? s + e : e;
    return i < a[0] ? a[0] : i > a[1] ? a[1] : i;
  }
}
var re = {};
Ae(re, { Serializable: () => Xk, SerializationMap: () => Kr, registerClass: () => Tr });
var Xk = class {
  getClassName() {
    return this.constructor.className;
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
var Kr = class {
  constructor() {
    this.classNameMap = {};
  }
  static getMap() {
    return Kr.instance == null && (Kr.instance = new Kr()), Kr.instance;
  }
  static register(e) {
    Kr.getMap().classNameMap[e.className] = [e, e.fromConfig];
  }
};
function Tr(e) {
  O(e.className != null, () => "Class being registered does not have the static className property defined."), O(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), O(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Kr.register(e);
}
var MA = {};
Ae(MA, { TEST_EPSILON_FLOAT16: () => Yk, encodeStrings: () => Qk, expectArrayBuffersEqual: () => HA, expectArraysClose: () => BA, expectArraysEqual: () => WA, expectNumbersClose: () => UA, expectPromiseToFail: () => VA, expectValuesInRange: () => GA, testEpsilon: () => qg });
var LA = 1e-3;
var Yk = 0.1;
function BA(e, t, n) {
  return n == null && (n = qg()), fm(e, t, (s, r) => jg(s, r, n));
}
function qg() {
  return M.backend.floatPrecision() === 32 ? LA : Yk;
}
function fm(e, t, n) {
  let s = true;
  if ((Qt(e) || Qt(t)) && (s = false), Qt(e) && Qt(t) && (s = true), s) {
    let i = e.constructor.name, o = t.constructor.name;
    if (i !== o)
      throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${o}`);
  }
  if (Array.isArray(e) && Array.isArray(t)) {
    let i = Rs(e), o = Rs(t);
    if (!kr(i, o))
      throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`);
  }
  let r = Qt(e) ? e : ra(e), a = Qt(t) ? t : ra(t);
  if (r.length !== a.length)
    throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
Actual:   ${r}.
Expected: ${a}.`);
  for (let i = 0; i < a.length; ++i) {
    let o = r[i], u = a[i];
    if (!n(o, u))
      throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${u}.
Actual:   ${r}.
Expected: ${a}.`);
  }
}
function VA(e, t) {
  e().then(() => t.fail(), () => t());
}
function WA(e, t) {
  let n = typeof t == "string" || typeof t == "number" || typeof t == "boolean" ? [t] : t;
  return ar(e) || ar(e[0]) || ar(t) || ar(t[0]) ? fm(e, n, (s, r) => s == r) : fm(e, t, (s, r) => jg(s, r, 0));
}
function UA(e, t, n) {
  if (n == null && (n = qg()), !jg(e, t, n))
    throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`);
}
function jg(e, t, n) {
  return !isFinite(e) && !isFinite(t) ? true : !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);
}
function GA(e, t, n) {
  for (let s = 0; s < e.length; s++)
    if (e[s] < t || e[s] > n)
      throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`);
}
function HA(e, t) {
  let n = new Float32Array(e), s = new Float32Array(t);
  if (n.length !== s.length)
    throw new Error(`Expected ArrayBuffer to be of length ${s.length}, but it was ${n.length}`);
  for (let r = 0; r < s.length; r++)
    if (n[r] !== s[r])
      throw new Error(`Expected ArrayBuffer value at ${r} to be ${s[r]} but got ${n[r]} instead`);
}
function Qk(e) {
  for (let t = 0; t < e.length; t++) {
    let n = e[t];
    Array.isArray(n) ? Qk(n) : e[t] = Fl(n);
  }
  return e;
}
var ope = "0.0.0";
function upe() {
  K().set("PROD", true);
}
function lpe() {
  K().set("DEBUG", true);
}
function cpe() {
  K().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function Zk(e) {
  K().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(e + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
o_(Zk);
function dpe() {
  M.disposeVariables();
}
function ds() {
  return M;
}
function mm() {
  return M.memory();
}
function ppe(e) {
  return M.profile(e);
}
function q(e, t) {
  return M.tidy(e, t);
}
function Re(e) {
  zg(e).forEach((n) => n.dispose());
}
function qt(e) {
  return M.keep(e);
}
function hpe(e) {
  return M.time(e);
}
function fpe(e) {
  return M.setBackend(e);
}
function mpe() {
  return M.ready();
}
function gpe() {
  return M.backendName;
}
function bpe(e) {
  M.removeBackend(e);
}
function ype(e) {
  return M.findBackend(e);
}
function vpe(e) {
  return M.findBackendFactory(e);
}
function bp(e, t, n = 1) {
  return M.registerBackend(e, t, n);
}
function qA() {
  return M.backend;
}
function xpe(e, t) {
  K().setPlatform(e, t);
}
function jA(e, t) {
  let n = _(e, "a", "add"), s = _(t, "b", "add");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(Sr, r);
}
var ie = L({ add_: jA });
function KA(e, t) {
  let n = _(e, "a", "floorDiv"), s = _(t, "b", "floorDiv");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(La, r);
}
var Jk = L({ floorDiv_: KA });
function XA(e, t) {
  let n = _(e, "a", "div"), s = _(t, "b", "div");
  if ([n, s] = vt(n, s), n.dtype === "int32" && s.dtype === "int32")
    return Jk(n, s);
  let r = { a: n, b: s }, a = {};
  return M.runKernel(Oa, r, a);
}
var xe = L({ div_: XA });
function YA(e, t) {
  let n = _(e, "a", "mul"), s = _(t, "b", "mul");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(Za, r);
}
var V = L({ mul_: YA });
function QA(e) {
  let t = _(e, "x", "abs");
  if (t.dtype === "complex64") {
    let n = { x: t };
    return M.runKernel(Jd, n);
  } else {
    let n = { x: t };
    return M.runKernel(co, n);
  }
}
var Lt = L({ abs_: QA });
function ZA(e) {
  let n = { x: _(e, "x", "acos") };
  return M.runKernel(al, n);
}
var JA = L({ acos_: ZA });
function eE(e) {
  let n = { x: _(e, "x", "acosh") };
  return M.runKernel(il, n);
}
var tE = L({ acosh_: eE });
function nE(e) {
  O(Array.isArray(e), () => "The argument passed to tf.addN() must be a list of tensors"), O(e.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${e.length}`);
  let t = e.map((r, a) => _(r, `tensors${a}`, "addN")), n = t[0];
  t.forEach((r) => {
    if (r.dtype !== n.dtype)
      throw new Error("All tensors passed to tf.addN() must have the same dtype");
  }), t.forEach((r) => {
    if (!kr(r.shape, n.shape))
      throw new Error("All tensors passed to tf.addN() must have the same shape");
  });
  let s = t;
  return M.runKernel(Sa, s);
}
var sE = L({ addN_: nE });
function rE(e, t = null, n = false) {
  let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
  return M.runKernel(ol, r, a);
}
var eS = L({ all_: rE });
function aE(e, t = null, n = false) {
  let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
  return M.runKernel(ul, r, a);
}
var gm = L({ any_: aE });
function iE(e, t = 0) {
  let s = { x: _(e, "x", "argMax") }, r = { axis: t };
  return M.runKernel(Ia, s, r);
}
var ju = L({ argMax_: iE });
function oE(e, t = 0) {
  let s = { x: _(e, "x", "argMin") }, r = { axis: t };
  return M.runKernel(ll, s, r);
}
var uE = L({ argMin_: oE });
function lE(e) {
  let n = { x: _(e, "x", "asin") };
  return M.runKernel(cl, n);
}
var cE = L({ asin_: lE });
function dE(e) {
  let n = { x: _(e, "x", "asinh") };
  return M.runKernel(dl, n);
}
var pE = L({ asinh_: dE });
function hE(e) {
  let n = { x: _(e, "x", "atan") };
  return M.runKernel(pl, n);
}
var fE = L({ atan_: hE });
function mE(e, t) {
  let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(fl, r);
}
var gE = L({ atan2_: mE });
function bE(e) {
  let n = { x: _(e, "x", "atanh") };
  return M.runKernel(hl, n);
}
var yE = L({ atanh_: bE });
function vE(e, t, n, s, r = "NHWC", a) {
  let i = e[3], o = [...t, i], u = sS(r);
  return Pl(e, o, n, a, s, null, null, u);
}
function tS(e, t, n, s, r, a, i = "channelsLast") {
  let [o, u] = Sd(t), l;
  if (i === "channelsLast")
    l = [o, u, e[3], e[3]];
  else if (i === "channelsFirst")
    l = [o, u, e[1], e[1]];
  else
    throw new Error(`Unknown dataFormat ${i}`);
  return Pl(e, l, n, s, r, a, false, i);
}
function xE(e, t, n, s, r, a, i = "NDHWC") {
  let [o, u, l] = bm(t), c, p;
  if (i === "NDHWC")
    p = "channelsLast", c = [o, u, l, e[4], e[4]];
  else if (i === "NCDHW")
    p = "channelsFirst", c = [o, u, l, e[1], e[1]];
  else
    throw new Error(`Unknown dataFormat ${i}`);
  return nS(e, c, n, s, r, false, p, a);
}
function Pl(e, t, n, s, r, a, i = false, o = "channelsLast") {
  let [u, l, c, p] = [-1, -1, -1, -1];
  if (o === "channelsLast")
    [u, l, c, p] = e;
  else if (o === "channelsFirst")
    [u, p, l, c] = e;
  else
    throw new Error(`Unknown dataFormat ${o}`);
  let [d, h, , f] = t, [m, g] = Sd(n), [b, y] = Sd(s), v = Qi(d, b), x = Qi(h, y), { padInfo: k, outHeight: I, outWidth: $ } = SE(r, l, c, m, g, v, x, a, o), E = i ? f * p : f, A;
  return o === "channelsFirst" ? A = [u, E, I, $] : o === "channelsLast" && (A = [u, I, $, E]), { batchSize: u, dataFormat: o, inHeight: l, inWidth: c, inChannels: p, outHeight: I, outWidth: $, outChannels: E, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: A, filterShape: t };
}
function nS(e, t, n, s, r, a = false, i = "channelsLast", o) {
  let [u, l, c, p, d] = [-1, -1, -1, -1, -1];
  if (i === "channelsLast")
    [u, l, c, p, d] = e;
  else if (i === "channelsFirst")
    [u, d, l, c, p] = e;
  else
    throw new Error(`Unknown dataFormat ${i}`);
  let [h, f, m, , g] = t, [b, y, v] = bm(n), [x, k, I] = bm(s), $ = Qi(h, x), E = Qi(f, k), A = Qi(m, I), { padInfo: P, outDepth: R, outHeight: F, outWidth: T } = IE(r, l, c, p, b, y, v, $, E, A, o), z = a ? g * d : g, W;
  return i === "channelsFirst" ? W = [u, z, R, F, T] : i === "channelsLast" && (W = [u, R, F, T, z]), { batchSize: u, dataFormat: i, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: R, outHeight: F, outWidth: T, outChannels: z, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: $, effectiveFilterHeight: E, effectiveFilterWidth: A, dilationDepth: x, dilationHeight: k, dilationWidth: I, inShape: e, outShape: W, filterShape: t };
}
function wE(e, t, n, s, r) {
  s == null && (s = Kg(e, t, n));
  let a = e[0], i = e[1], o = ea((a - t + 2 * s) / n + 1, r), u = ea((i - t + 2 * s) / n + 1, r);
  return [o, u];
}
function kE(e, t, n, s, r, a) {
  r == null && (r = Kg(e, t, s));
  let i = e[0], o = e[1], u = e[2], l = ea((i - t + 2 * r) / s + 1, a), c = ea((o - t + 2 * r) / s + 1, a), p = ea((u - t + 2 * r) / s + 1, a);
  return [l, c, p, n];
}
function Kg(e, t, n, s = 1) {
  let r = Qi(t, s);
  return Math.floor((e[0] * (n - 1) - n + r) / 2);
}
function Sd(e) {
  return typeof e == "number" ? [e, e, e] : e.length === 2 ? [e[0], e[1], 1] : e;
}
function bm(e) {
  return typeof e == "number" ? [e, e, e] : e;
}
function Qi(e, t) {
  return t <= 1 ? e : e + (e - 1) * (t - 1);
}
function SE(e, t, n, s, r, a, i, o, u) {
  let l, c, p;
  if (typeof e == "number") {
    l = { top: e, bottom: e, left: e, right: e, type: e === 0 ? "VALID" : "NUMBER" };
    let h = wE([t, n], a, s, e, o);
    c = h[0], p = h[1];
  } else if (e === "same") {
    c = Math.ceil(t / s), p = Math.ceil(n / r);
    let d = Math.max(0, (c - 1) * s + a - t), h = Math.max(0, (p - 1) * r + i - n), f = Math.floor(d / 2), m = d - f, g = Math.floor(h / 2), b = h - g;
    l = { top: f, bottom: m, left: g, right: b, type: "SAME" };
  } else if (e === "valid")
    l = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((t - a + 1) / s), p = Math.ceil((n - i + 1) / r);
  else if (typeof e == "object") {
    let d = u === "channelsLast" ? e[1][0] : e[2][0], h = u === "channelsLast" ? e[1][1] : e[2][1], f = u === "channelsLast" ? e[2][0] : e[3][0], m = u === "channelsLast" ? e[2][1] : e[3][1];
    l = { top: d, bottom: h, left: f, right: m, type: d === 0 && h === 0 && f === 0 && m === 0 ? "VALID" : "EXPLICIT" }, c = ea((t - a + d + h) / s + 1, o), p = ea((n - i + f + m) / r + 1, o);
  } else
    throw Error(`Unknown padding parameter: ${e}`);
  return { padInfo: l, outHeight: c, outWidth: p };
}
function IE(e, t, n, s, r, a, i, o, u, l, c) {
  let p, d, h, f;
  if (typeof e == "number") {
    p = { top: e, bottom: e, left: e, right: e, front: e, back: e, type: e === 0 ? "VALID" : "NUMBER" };
    let g = kE([t, n, s, 1], o, 1, r, e, c);
    d = g[0], h = g[1], f = g[2];
  } else if (e === "same") {
    d = Math.ceil(t / r), h = Math.ceil(n / a), f = Math.ceil(s / i);
    let m = (d - 1) * r + o - t, g = (h - 1) * a + u - n, b = (f - 1) * i + l - s, y = Math.floor(m / 2), v = m - y, x = Math.floor(g / 2), k = g - x, I = Math.floor(b / 2), $ = b - I;
    p = { top: x, bottom: k, left: I, right: $, front: y, back: v, type: "SAME" };
  } else if (e === "valid")
    p = { top: 0, bottom: 0, left: 0, right: 0, front: 0, back: 0, type: "VALID" }, d = Math.ceil((t - o + 1) / r), h = Math.ceil((n - u + 1) / a), f = Math.ceil((s - l + 1) / i);
  else
    throw Error(`Unknown padding parameter: ${e}`);
  return { padInfo: p, outDepth: d, outHeight: h, outWidth: f };
}
function ea(e, t) {
  if (!t)
    return Math.trunc(e);
  switch (t) {
    case "round":
      return Math.round(e);
    case "ceil":
      return Math.ceil(e);
    case "floor":
      return Math.floor(e);
    default:
      throw new Error(`Unknown roundingMode ${t}`);
  }
}
function fr(e) {
  let [t, n, s] = Sd(e);
  return t === 1 && n === 1 && s === 1;
}
function Ps(e, t) {
  return fr(e) || fr(t);
}
function sS(e) {
  if (e === "NHWC")
    return "channelsLast";
  if (e === "NCHW")
    return "channelsFirst";
  throw new Error(`Unknown dataFormat ${e}`);
}
function hn(e, t, n) {
  if (n != null) {
    if (typeof t == "string")
      throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
    if (typeof t == "number")
      O(eo(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
    else if (typeof t == "object")
      t.forEach((s) => {
        s.forEach((r) => {
          O(eo(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
        });
      });
    else
      throw Error(`Error in ${e}: Unknown padding parameter: ${t}`);
  }
}
function CE(e, t) {
  let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
  return M.runKernel(Fo, s, r);
}
var U = L({ reshape_: CE });
function NE(e, t, n, s, r) {
  let a = _(e, "x", "avgPool", "float32"), i = 1;
  O(Ps(n, i), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);
  let o = a, u = false;
  a.rank === 3 && (u = true, o = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${o.rank}.`), hn("avgPool", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(Ca, l, c);
  return p = le(p, a.dtype), u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var Xg = L({ avgPool_: NE });
function TE(e, t, n, s, r, a = "NDHWC") {
  let i = _(e, "x", "avgPool3d", "float32"), o = i, u = false;
  i.rank === 4 && (u = true, o = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), hn("avgPool3d", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(Qd, l, c);
  return p = le(p, o.dtype), u ? U(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
}
var rS = L({ avgPool3d_: TE });
function $E(e, t = 0) {
  O(e.length >= 1, () => "Pass at least one tensor to concat");
  let n = qu(e, "tensors", "concat", "string_or_numeric");
  if (n[0].dtype === "complex64" && n.forEach((a) => {
    if (a.dtype !== "complex64")
      throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${a.dtype}. `);
  }), n.length === 1)
    return ur(n[0]);
  let s = n, r = { axis: t };
  return M.runKernel(ho, s, r);
}
var Ft = L({ concat_: $E });
function _E(e) {
  let n = { x: _(e, "x", "sigmoid", "float32") };
  return M.runKernel(ui, n);
}
var Hs = L({ sigmoid_: _E });
function AE(e, t, n) {
  let s = _(e, "x", "slice", "string_or_numeric");
  if (s.rank === 0)
    throw new Error("Slicing scalar is not possible");
  let r = { x: s }, a = { begin: t, size: n };
  return M.runKernel(Lo, r, a);
}
var qe = L({ slice_: AE });
function EE(e) {
  let n = { x: _(e, "x", "tanh", "float32") };
  return M.runKernel(fi, n);
}
var Ku = L({ tanh_: EE });
function RE(e, t, n, s, r, a) {
  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = Ve(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = qe(f, [0, 0], b), v = qe(f, [0, g], b), x = qe(f, [0, g * 2], b), k = qe(f, [0, g * 3], b), I = ie(V(Hs(y), Ku(v)), V(c, Hs(ie(i, x)))), $ = V(Ku(I), Hs(k));
  return [I, $];
}
var wpe = L({ basicLSTMCell_: RE });
function DE(e, t, n) {
  let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
  O(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), O(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), O(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
  let a = { x: s }, i = { blockShape: t, crops: n };
  return M.runKernel(po, a, i);
}
var Yg = L({ batchToSpaceND_: DE });
function FE(e) {
  let t;
  return e.rank === 0 || e.rank === 1 ? t = U(e, [1, 1, 1, e.size]) : e.rank === 2 ? t = U(e, [1, 1, e.shape[0], e.shape[1]]) : e.rank === 3 ? t = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]) : t = e, t;
}
function OE(e, t, n, s, r, a) {
  a == null && (a = 1e-3);
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  s != null && (c = _(s, "offset", "batchNorm")), O(o.rank === u.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), O(c == null || o.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), O(l == null || o.rank === l.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
  let d = { x: FE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = M.runKernel(Ba, d, h);
  return U(f, i.shape);
}
var Xu = L({ batchNorm_: OE });
function PE(e, t, n, s, r, a) {
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), Xu(i, o, u, c, l, a);
}
var zE = L({ batchNorm2d_: PE });
function ME(e, t, n, s, r, a) {
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), Xu(i, o, u, c, l, a);
}
var LE = L({ batchNorm3d_: ME });
function BE(e, t, n, s, r, a) {
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), Xu(i, o, u, c, l, a);
}
var VE = L({ batchNorm4d_: BE });
function WE(e, t, n) {
  let s = _(e, "x", "bincount"), r = _(t, "weights", "bincount");
  O(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(r.size === s.size || r.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);
  let a = { x: s, weights: r }, i = { size: n };
  return M.runKernel(pg, a, i);
}
var aS = L({ bincount_: WE });
function UE(e, t) {
  let n = _(e, "s0", "broadcastArgs", "int32"), s = _(t, "s1", "broadcastArgs", "int32");
  if (n.rank !== 1)
    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);
  if (s.rank !== 1)
    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);
  let r = { s0: n, s1: s };
  return M.runKernel(hg, r);
}
var GE = L({ broadcastArgs_: UE });
function HE(e, t) {
  let n = _(e, "broadcastTo", "x"), s = n.shape;
  if (t.some((l) => !(l > 0) || l % 1 !== 0))
    throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);
  if (t.length < n.rank)
    throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);
  if (t.length > n.rank) {
    let l = n.shape.slice();
    for (; l.length < t.length; )
      l.unshift(1);
    n = U(n, l);
  }
  let r = n.shape, a = Array.from(t);
  for (let l = t.length - 1; l >= 0; l--)
    if (r[l] === t[l])
      a[l] = 1;
    else if (n.shape[l] !== 1)
      throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);
  if (a.map((l, c) => l > 1 ? c : -1).filter((l) => l >= 0).length === 0)
    return ur(n);
  let o = { x: n }, u = { reps: a };
  return M.runKernel(Cr, o, u);
}
var rd = L({ broadcastTo_: HE });
function qE(e) {
  let n = { x: _(e, "x", "ceil", "float32") };
  return M.runKernel($a, n);
}
var jE = L({ ceil_: qE });
function KE(e, t, n) {
  let s = _(e, "x", "clipByValue");
  O(t <= n, () => `Error in clip: min (${t}) must be less than or equal to max (${n}).`);
  let r = { x: s }, a = { clipValueMin: t, clipValueMax: n };
  return M.runKernel(Ir, r, a);
}
var Vn = L({ clipByValue_: KE });
function XE(e) {
  return Ft(e, 0);
}
var YE = L({ concat1d_: XE });
function QE(e, t) {
  return Ft(e, t);
}
var ZE = L({ concat2d_: QE });
function JE(e, t) {
  return Ft(e, t);
}
var eR = L({ concat3d_: JE });
function tR(e, t) {
  return Ft(e, t);
}
var nR = L({ concat4d_: tR });
function sR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
  let o = _(e, "x", "conv2d", "float32"), u = _(t, "filter", "conv2d", "float32"), l = o, c = false;
  o.rank === 3 && (c = true, l = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(l.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`), hn("conv2d", s, i);
  let p = r === "NHWC" ? l.shape[3] : l.shape[1];
  O(p === u.shape[2], () => `Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
  let d = { x: l, filter: u }, h = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, f = M.runKernel(_a, d, h);
  return c ? U(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var da = L({ conv2d_: sR });
function rR(e, t, n, s, r = "NWC", a = 1, i) {
  let o = _(e, "x", "conv1d"), u = _(t, "filter", "conv1d"), l = o, c = false;
  o.rank === 2 && (c = true, l = U(o, [1, o.shape[0], o.shape[1]])), O(l.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${l.rank}.`), O(u.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`), hn("conv1d", s, i), O(l.shape[2] === u.shape[1], () => `Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`), O(Ps(n, a), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`), O(r === "NWC", () => `Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);
  let p = U(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = U(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = da(d, p, [1, n], s, "NHWC", [1, a], i);
  return c ? U(g, [g.shape[2], g.shape[3]]) : U(g, [g.shape[0], g.shape[2], g.shape[3]]);
}
var iS = L({ conv1d_: rR });
function aR(e, t, n, s, r, a = "NHWC", i) {
  O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
  let o = e, u = t, l = false;
  t.rank === 3 && (l = true, u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]), o = [1, e[0], e[1], e[2]]), O(o.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`), O(u.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`), O(n.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);
  let c = a === "NHWC" ? o[3] : o[1], p = a === "NHWC" ? u.shape[3] : u.shape[1];
  O(c === n.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`), O(p === n.shape[3], () => `Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`), hn("conv2dDerInput", r, i);
  let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, inputShape: o }, f = M.runKernel(Aa, d, h);
  return l ? U(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var Qg = L({ conv2DBackpropInput_: aR });
function iR(e, t, n, s, r, a) {
  let i = _(e, "x", "conv2dTranspose"), o = _(t, "filter", "conv2dTranspose");
  return Qg(n, i, o, s, r, "NHWC", a);
}
var oS = L({ conv2dTranspose_: iR });
function oR(e, t, n, s, r = "NDHWC", a = [1, 1, 1]) {
  let i = _(e, "x", "conv3d"), o = _(t, "filter", "conv3d"), u = i, l = false;
  i.rank === 4 && (l = true, u = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), O(o.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`), O(u.shape[4] === o.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`), O(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = M.runKernel(ep, c, p);
  return l ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
}
var uS = L({ conv3d_: oR });
function uR(e, t, n, s, r) {
  O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
  let a = e, i = t, o = false;
  t.rank === 4 && (o = true, i = U(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]]), a = [1, e[0], e[1], e[2], e[3]]);
  let u = a[4], l = i.shape[4];
  O(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), O(i.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`), O(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), O(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), O(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = M.runKernel(gg, c, p);
  return o ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
}
var lS = L({ conv3DBackpropInput_: uR });
function lR(e, t, n, s, r) {
  let a = _(e, "x", "conv3dTranspose"), i = _(t, "filter", "conv3dTranspose");
  return lS(n, a, i, s, r);
}
var cR = L({ conv3dTranspose_: lR });
function dR(e) {
  let n = { x: _(e, "x", "cos", "float32") };
  return M.runKernel(Ea, n);
}
var Zg = L({ cos_: dR });
function pR(e) {
  let n = { x: _(e, "x", "cosh", "float32") };
  return M.runKernel(Ra, n);
}
var cS = L({ cosh_: pR });
function hR(e, t = 0, n = false, s = false) {
  let a = { x: _(e, "x", "cumprod") }, i = { axis: t, exclusive: n, reverse: s };
  return M.runKernel(fo, a, i);
}
var ym = L({ cumprod_: hR });
function fR(e, t = 0, n = false, s = false) {
  let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
  return M.runKernel(Da, a, i);
}
var dS = L({ cumsum_: fR });
function mR(e, t, n, s = false) {
  let r = _(e, "x", "denseBincount"), a = _(t, "weights", "denseBincount");
  O(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), O(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(a.size === r.size || a.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);
  let i = { x: r, weights: a }, o = { size: n, binaryOutput: s };
  return M.runKernel(bg, i, o);
}
var gR = L({ denseBincount_: mR });
function bR(e, t, n = "NHWC") {
  let s = _(e, "x", "depthToSpace", "float32"), r = n === "NHWC" ? s.shape[1] : s.shape[2], a = n === "NHWC" ? s.shape[2] : s.shape[3], i = n === "NHWC" ? s.shape[3] : s.shape[1];
  O(t > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t}`), O(r * t >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${r} and ${t}  for depthToSpace with input shape
    ${s.shape}`), O(a * t >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${a} and ${t} for depthToSpace with input shape
        ${s.shape}`), O(i % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${i} for depthToSpace with input shape ${s.shape}`);
  let o = { x: s }, u = { blockSize: t, dataFormat: n };
  return M.runKernel(go, o, u);
}
var yR = L({ depthToSpace_: bR });
function vR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
  let o = _(e, "x", "depthwiseConv2d", "float32"), u = _(t, "filter", "depthwiseConv2d", "float32"), l = o, c = false;
  o.rank === 3 && (c = true, l = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(l.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[3] === u.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`), hn("depthwiseConv2d", s, i);
  let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, h = M.runKernel(Fa, p, d);
  return c ? U(h, [h.shape[1], h.shape[2], h.shape[3]]) : h;
}
var yp = L({ depthwiseConv2d_: vR });
function xR(e) {
  let n = { x: _(e, "x", "diag") };
  return M.runKernel(xg, n);
}
var kpe = L({ diag_: xR });
function wR(e, t, n, s, r = [1, 1], a = "NHWC") {
  let i = _(e, "x", "dilation2d"), o = _(t, "filter", "dilation2d");
  O(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), O(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), O(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
  let u = i, l = false;
  i.rank === 3 && (u = U(i, [1, i.shape[0], i.shape[1], i.shape[2]]), l = true);
  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = M.runKernel(tp, c, p);
  return l ? U(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var kR = L({ dilation2d_: wR });
function SR(e, t) {
  let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(bo, r);
}
var Xn = L({ equal_: SR });
function IR(e, t, n) {
  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = it(it(a.shape, s.shape), r.shape), o = rd(a, i), u = rd(s, i), l = rd(r, i), c = { condition: o, t: u, e: l };
  return M.runKernel(Mo, c);
}
var vn = L({ where_: IR });
function CR(e) {
  let n = { x: _(e, "x", "zerosLike") };
  return M.runKernel(Ko, n);
}
var je = L({ zerosLike_: CR });
function NR(e, t) {
  let n = _(e, "a", "div"), s = _(t, "b", "div");
  [n, s] = vt(n, s);
  let r = xe(n, s), a = je(r), i = Xn(s, a);
  return vn(i, a, r);
}
var TR = L({ divNoNan_: NR });
function $R(e, t) {
  let n = _(e, "t1", "dot"), s = _(t, "t2", "dot");
  O((n.rank === 1 || n.rank === 2) && (s.rank === 1 || s.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);
  let r = n.rank === 1 ? n.size : n.shape[1], a = s.rank === 1 ? s.size : s.shape[0];
  if (O(r === a, () => `Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`), n.rank === 1 && s.rank === 1) {
    let i = U(n, [1, -1]), o = U(s, [-1, 1]), u = Ve(i, o);
    return U(u, []);
  } else if (n.rank === 1 && s.rank === 2) {
    let i = U(n, [1, -1]), o = U(s, [s.shape[0], s.shape[1]]), u = Ve(i, o);
    return U(u, [u.size]);
  } else if (n.rank === 2 && s.rank === 1) {
    let i = U(s, [-1, 1]), o = Ve(n, i);
    return U(o, [o.size]);
  } else {
    let i = U(s, [s.shape[0], s.shape[1]]);
    return Ve(n, i);
  }
}
var Spe = L({ dot_: $R });
function _R(e, ...t) {
  let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
  return M.runKernel(np, n, s);
}
var AR = L({ einsum_: _R });
function ER(e) {
  let n = { x: _(e, "x", "elu", "float32") };
  return M.runKernel(Pa, n);
}
var vp = L({ elu_: ER });
function RR(e) {
  let t = _(e, "x", "erf");
  O(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = le(t, "float32"));
  let n = { x: t };
  return M.runKernel(ml, n);
}
var DR = L({ erf_: RR });
function FR(e) {
  let n = { x: _(e, "x", "exp") };
  return M.runKernel(za, n);
}
var Yn = L({ exp_: FR });
function OR(e, t = 0) {
  let n = _(e, "x", "expandDims", "string_or_numeric");
  O(t <= n.rank, () => "Axis must be <= rank of the tensor");
  let s = { input: n }, r = { dim: t };
  return M.runKernel(yo, s, r);
}
var Pn = L({ expandDims_: OR });
function PR(e) {
  let n = { x: _(e, "x", "expm1") };
  return M.runKernel(vo, n);
}
var zR = L({ expm1_: PR });
function MR(e, t) {
  let n = _(e, "x", "tile", "string_or_numeric");
  O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);
  let s = { x: n }, r = { reps: t };
  return M.runKernel(Cr, s, r);
}
var hs = L({ tile_: MR });
function LR(e, t, n, s = "float32") {
  t == null && (t = e);
  let r = De([e, t], s), a = e <= t ? e : t;
  for (let o = 0; o < a; ++o)
    r.set(1, o, o);
  let i = U(r.toTensor(), [e, t]);
  if (n == null)
    return i;
  if (n.length === 1)
    return hs(Pn(i, 0), [n[0], 1, 1]);
  if (n.length === 2)
    return hs(Pn(Pn(i, 0), 0), [n[0], n[1], 1, 1]);
  if (n.length === 3)
    return hs(Pn(Pn(Pn(i, 0), 0), 0), [n[0], n[1], n[2], 1, 1]);
  throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`);
}
var pS = L({ eye_: LR });
function zl(e, t, n) {
  let s = { shape: e, value: t, dtype: n };
  return M.runKernel(gl, {}, s);
}
function BR(e) {
  let n = { x: _(e, "x", "floor", "float32") };
  return M.runKernel(Ma, n);
}
var xp = L({ floor_: BR });
function VR(e, t, n = 0, s = 0) {
  let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
  return M.runKernel(wo, i, o);
}
var Yu = L({ gather_: VR });
function WR(e, t) {
  let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(So, r);
}
var Un = L({ greater_: WR });
function UR(e, t) {
  let n = _(e, "a", "greaterEqual", "string_or_numeric"), s = _(t, "b", "greaterEqual", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Va, r);
}
var Yo = L({ greaterEqual_: UR });
function GR(e) {
  let n = { input: _(e, "input", "imag") };
  return M.runKernel(sp, n);
}
var Jg = L({ imag_: GR });
function HR(e) {
  let n = { x: _(e, "x", "isFinite") };
  return M.runKernel(bl, n);
}
var Ipe = L({ isFinite_: HR });
function qR(e) {
  let n = { x: _(e, "x", "isInf") };
  return M.runKernel(yl, n);
}
var Cpe = L({ isInf_: qR });
function jR(e) {
  let n = { x: _(e, "x", "isNaN") };
  return M.runKernel(vl, n);
}
var KR = L({ isNaN_: jR });
function XR(e, t = 0.2) {
  let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
  return M.runKernel(Ua, s, r);
}
var eb = L({ leakyRelu_: XR });
function YR(e, t) {
  let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Io, r);
}
var hS = L({ less_: YR });
function QR(e, t) {
  let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Co, r);
}
var Qo = L({ lessEqual_: QR });
function ZR(e, t, n) {
  if (n <= 0)
    throw new Error("The number of values should be positive.");
  let s = { start: e, stop: t, num: n };
  return M.runKernel(Ig, {}, s);
}
function JR(e, t = 5, n = 1, s = 1, r = 0.5) {
  let a = _(e, "x", "localResponseNormalization");
  O(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${a.rank}.`), O(eo(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
  let i = a, o = false;
  a.rank === 3 && (o = true, i = U(a, [1, a.shape[0], a.shape[1], a.shape[2]]));
  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = M.runKernel(ap, u, l);
  return o ? U(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var eD = L({ localResponseNormalization_: JR });
function tD(e) {
  let n = { x: _(e, "x", "log", "float32") };
  return M.runKernel(Ga, n);
}
var Qn = L({ log_: tD });
function nD(e) {
  let n = { x: _(e, "x", "log1p") };
  return M.runKernel(xl, n);
}
var tb = L({ log1p_: nD });
function Npe(e) {
  return O(hr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
    let s = _(t, "x", "tf.grad", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grad") : null;
    return M.tidy(() => {
      let { value: a, grads: i } = M.gradients(() => e(s), [s], r);
      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), wp(i), i[0];
    });
  };
}
function Tpe(e) {
  return O(hr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
    O(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
    let s = qu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
    return M.tidy(() => {
      let { value: a, grads: i } = M.gradients(() => e(...s), s, r);
      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), wp(i), i;
    });
  };
}
function $pe(e) {
  return O(hr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
    O(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
    let { grads: s, value: r } = M.gradients(() => e(t), [t], n);
    return wp(s), { grad: s[0], value: r };
  };
}
function _pe(e) {
  return O(hr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
    O(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
    let s = M.gradients(() => e(...t), t, n);
    return n != null && pn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), wp(s.grads), s;
  };
}
function sD(e, t) {
  O(hr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof vd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
  let n = t != null;
  if (!n) {
    t = [];
    for (let l in M.registeredVariables)
      t.push(M.registeredVariables[l]);
  }
  let s = n ? t.filter((l) => !l.trainable) : null, r = t.length;
  t = t.filter((l) => l.trainable), O(t.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);
  let a = true, { value: i, grads: o } = M.gradients(e, t, null, a);
  O(o.some((l) => l != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), O(i.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);
  let u = {};
  return t.forEach((l, c) => {
    o[c] != null && (u[l.name] = o[c]);
  }), s != null && s.forEach((l) => u[l.name] = null), { value: i, grads: u };
}
function qs(e) {
  return M.customGrad(e);
}
function wp(e) {
  if (e.filter((n) => n == null).length > 0)
    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
    the f you passed encloses all operations that lead from x to y.`);
}
function rD(e) {
  let n = { x: _(e, "x", "neg") };
  return M.runKernel(To, n);
}
var kt = L({ neg_: rD });
function aD(e) {
  let n = { x: _(e, "x", "softplus") };
  return M.runKernel(_l, n);
}
var Ml = L({ softplus_: aD });
function iD(e) {
  let t = _(e, "x", "logSigmoid");
  return qs((s) => ({ value: kt(Ml(kt(s))), gradFunc: (i) => V(i, Hs(kt(s))) }))(t);
}
var Ape = L({ logSigmoid_: iD });
function oD(e, t = null, n = false) {
  let r = { x: _(e, "x", "max") }, a = { reductionIndices: t, keepDims: n };
  return M.runKernel(Ha, r, a);
}
var As = L({ max_: oD });
function uD(e, t) {
  let n = _(e, "a", "sub"), s = _(t, "b", "sub");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(hi, r);
}
var ge = L({ sub_: uD });
function lD(e, t = null, n = false) {
  let s = _(e, "x", "sum");
  s.dtype === "bool" && (s = le(s, "int32"));
  let r = { x: s }, a = { axis: t, keepDims: n };
  return M.runKernel(ci, r, a);
}
var ve = L({ sum_: lD });
function cD(e, t = -1) {
  let n = _(e, "logits", "logSoftmax");
  if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);
  return qs((r, a) => {
    let o = As(r, t, true), u = ge(r, o), l = ge(le(u, "float32"), Qn(ve(Yn(u), t, true)));
    return a([l]), { value: l, gradFunc: (p, d) => {
      let [h] = d, f = true, m = Yn(h);
      return ge(p, V(ve(p, t, f), m));
    } };
  })(n);
}
var fS = L({ logSoftmax_: cD });
function nb(e, t) {
  for (let n = 0; n < e.length; ++n)
    if (e[e.length - n - 1] !== t - 1 - n)
      return false;
  return true;
}
function mS(e, t, n) {
  let s = e.length + t.length, r = [], a = 0, i = 0;
  for (let o = 0; o < s; o++)
    n.indexOf(o) === -1 ? r.push(e[a++]) : r.push(t[i++]);
  return r;
}
function gS(e, t) {
  let n = [], s = e.length;
  for (let a = 0; a < s; a++)
    t.indexOf(a) === -1 && n.push(e[a]);
  let r = t.map((a) => e[a]);
  return [n, r];
}
function pa(e, t) {
  let n = t.map((s) => 1);
  return mS(e, n, t);
}
function dD(e, t, n) {
  O(nb(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
}
function bS(e, t) {
  if (nb(e, t))
    return null;
  let n = [];
  for (let s = 0; s < t; ++s)
    e.indexOf(s) === -1 && n.push(s);
  return e.forEach((s) => n.push(s)), n;
}
function sb(e) {
  return e.map((t, n) => [n, t]).sort((t, n) => t[1] - n[1]).map((t) => t[0]);
}
function pD(e, t) {
  let n = [];
  for (let s = t - e; s < t; ++s)
    n.push(s);
  return n;
}
function hD(e, t = null, n = false) {
  let s = _(e, "x", "logSumExp"), r = ts(t, s.shape), a = As(s, r, true), i = ge(s, a), o = Yn(i), u = ve(o, r), l = Qn(u), c = ie(U(a, l.shape), l);
  if (n) {
    let p = pa(c.shape, r);
    return U(c, p);
  }
  return c;
}
var fD = L({ logSumExp_: hD });
function mD(e, t) {
  let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
  it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(No, r);
}
var Ds = L({ logicalAnd_: mD });
function gD(e) {
  let n = { x: _(e, "x", "logicalNot", "bool") };
  return M.runKernel(wl, n);
}
var rb = L({ logicalNot_: gD });
function bD(e, t) {
  let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
  it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(rp, r);
}
var yS = L({ logicalOr_: bD });
function yD(e, t) {
  let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
  return it(n.shape, s.shape), Ds(yS(e, t), rb(Ds(e, t)));
}
var Epe = L({ logicalXor_: yD });
var Uc = 2147483648;
function vD(e, t, n = "left") {
  let s = _(e, "sortedSequence", "searchSorted"), r = _(t, "values", "searchSorted"), a = s.shape[s.shape.length - 1], i = r.shape[r.shape.length - 1], o = U(s, [-1, a]), u = U(r, [-1, i]);
  if (o.rank < 2)
    throw new Error("Sorted input argument must be at least 2-dimensional");
  if (o.shape[0] !== u.shape[0])
    throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");
  if (dt(u.shape) >= Uc)
    throw new Error(`values tensor size must less than ${Uc}`);
  if (o.shape[1] >= Uc)
    throw new Error(`trailing dim_size must less than ${Uc} for int32 output type, was ${o.shape[1]}`);
  let l = { sortedSequence: o, values: u }, c = { side: n };
  return M.runKernel(Rg, l, c);
}
var vS = L({ searchSorted_: vD });
function xD(e, t) {
  return vS(e, t, "left");
}
function wD(e, t, n, s, r) {
  let a = _(e, "x", "maxPool"), i = 1, o = a, u = false;
  a.rank === 3 && (u = true, o = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.rank}.`), O(Ps(n, i), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`), hn("maxPool", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(ja, l, c);
  return u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var ab = L({ maxPool_: wD });
function kD(e, t = [1, 1, 1], n, s, r, a = "NDHWC") {
  let i = _(e, "x", "maxPool3d"), o = i, u = false;
  i.rank === 4 && (u = true, o = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), hn("maxPool3d", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(ip, l, c);
  return u ? U(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
}
var xS = L({ maxPool3d_: kD });
function SD(e, t, n, s, r = false) {
  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = M.runKernel($g, i, o);
  return { result: u[0], indexes: u[1] };
}
var ID = L({ maxPoolWithArgmax_: SD });
function CD(e, t) {
  let n = _(e, "a", "maximum"), s = _(t, "b", "maximum");
  [n, s] = vt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(qa, r);
}
var $r = L({ maximum_: CD });
function ND(e, t = null, n = false) {
  let r = { x: _(e, "x", "mean") }, a = { axis: t, keepDims: n };
  return M.runKernel(Ka, r, a);
}
var It = L({ mean_: ND });
function $t(e, t = "float32") {
  if (t === "complex64") {
    let s = $t(e, "float32"), r = $t(e, "float32");
    return ua(s, r);
  }
  let n = Yd(dt(e), t);
  return M.makeTensor(n, e, t);
}
function Mn(e, t = "float32") {
  if (t === "complex64") {
    let s = Mn(e, "float32"), r = $t(e, "float32");
    return ua(s, r);
  }
  let n = ig(dt(e), t);
  return M.makeTensor(n, e, t);
}
function Rpe(e, t, { indexing: n = "xy" } = {}) {
  if (n !== "xy" && n !== "ij")
    throw new TypeError(`${n} is not a valid third argument to meshgrid`);
  if (e === void 0)
    return [];
  let s = _(e, "x", "meshgrid", e instanceof et ? e.dtype : "float32");
  if (t === void 0)
    return [s];
  let r = _(t, "y", "meshgrid", t instanceof et ? t.dtype : "float32"), a = dt(s.shape), i = dt(r.shape);
  return n === "xy" ? (s = U(s, [1, -1]), r = U(r, [-1, 1]), [Ve(Mn([i, 1], s.dtype), s), Ve(r, Mn([1, a], r.dtype))]) : (s = U(s, [-1, 1]), r = U(r, [1, -1]), [Ve(s, Mn([1, i], s.dtype)), Ve(Mn([a, 1], r.dtype), r)]);
}
function TD(e, t = null, n = false) {
  let r = { x: _(e, "x", "min") }, a = { axis: t, keepDims: n };
  return M.runKernel(Xa, r, a);
}
var vm = L({ min_: TD });
function $D(e, t) {
  let n = _(e, "a", "minimum"), s = _(t, "b", "minimum");
  [n, s] = vt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Ya, r);
}
var kp = L({ minimum_: $D });
function _D(e, t, n) {
  O(n === "reflect" || n === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);
  let s = _(e, "x", "mirrorPad");
  if (s.rank === 0)
    throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
  O(t.length === s.rank, () => `Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);
  let r = n === "reflect" ? 1 : 0;
  for (let o = 0; o < s.rank; o++)
    O(t[o].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), O(t[o][0] >= 0 && t[o][0] <= s.shape[o] - r && t[o][1] >= 0 && t[o][1] <= s.shape[o] - r, () => `Padding in dimension ${o} cannot be greater than or equal to ${s.shape[o] - r} or less than 0 for input of shape ${s.shape}`);
  let a = { paddings: t, mode: n }, i = { x: s };
  return M.runKernel(Qa, i, a);
}
var AD = L({ mirrorPad_: _D });
function ED(e, t) {
  let n = _(e, "a", "mod"), s = _(t, "b", "mod");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(kl, r);
}
var RD = L({ mod_: ED });
function DD(e) {
  let t = _(e, "x", "square"), n = {};
  return M.runKernel("Square", { x: t }, n);
}
var ct = L({ square_: DD });
function FD(e, t = null, n = false) {
  e = _(e, "x", "moments");
  let s = ts(t, e.shape), r = It(e, s, n), a = r.shape;
  n || (a = pa(r.shape, s));
  let i = ct(ge(le(e, "float32"), U(r, a))), o = It(i, s, n);
  return { mean: r, variance: o };
}
var ib = L({ moments_: FD });
function OD(e, t, n, s) {
  let r = _(t, "data", "multiRNNCell"), a = qu(n, "c", "multiRNNCell"), i = qu(s, "h", "multiRNNCell"), o = r, u = [];
  for (let p = 0; p < e.length; p++) {
    let d = e[p](o, a[p], i[p]);
    u.push(d[0]), u.push(d[1]), o = d[1];
  }
  let l = [], c = [];
  for (let p = 0; p < u.length; p += 2)
    l.push(u[p]), c.push(u[p + 1]);
  return [l, c];
}
var Dpe = L({ multiRNNCell_: OD });
function PD(e, t, n, s = false) {
  let r = _(e, "logits", "multinomial"), a = r.size, i = r.rank;
  if (a < 2)
    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);
  if (i > 2)
    throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);
  n = n || Math.random();
  let u = { logits: i === 1 ? U(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = M.runKernel(_g, u, l);
  return i === 1 ? U(c, [c.size]) : c;
}
var zD = L({ multinomial_: PD });
function MD(e, t) {
  let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel($o, r);
}
var Qu = L({ notEqual_: MD });
function LD(e) {
  let n = { x: _(e, "x", "onesLike") };
  return M.runKernel(Eo, n);
}
var Zn = L({ onesLike_: LD });
function BD(e, t) {
  let n = _(e, "v1", "outerProduct"), s = _(t, "v2", "outerProduct");
  O(n.rank === 1 && s.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);
  let r = U(n, [-1, 1]), a = U(s, [1, -1]);
  return Ve(r, a);
}
var Fpe = L({ outerProduct_: BD });
function VD(e, t, n = 0) {
  let s = _(e, "x", "pad");
  if (s.rank === 0)
    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
  let r = { paddings: t, constantValue: n }, a = { x: s };
  return M.runKernel(Ja, a, r);
}
var yi = L({ pad_: VD });
function WD(e, t, n = 0) {
  return O(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), yi(e, [t], n);
}
var Ope = L({ pad1d_: WD });
function UD(e, t, n = 0) {
  return O(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), yi(e, t, n);
}
var Ppe = L({ pad2d_: UD });
function GD(e, t, n = 0) {
  return O(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), yi(e, t, n);
}
var zpe = L({ pad3d_: GD });
function HD(e, t, n = 0) {
  return O(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), yi(e, t, n);
}
var Mpe = L({ pad4d_: HD });
function qD(e, t, n) {
  let s = _(e, "x", "spaceToBatchND");
  O(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), O(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), O(s.shape.reduce((i, o, u) => u > 0 && u <= t.length ? i && (o + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : i, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
  let r = { x: s }, a = { blockShape: t, paddings: n };
  return M.runKernel(Vo, r, a);
}
var ob = L({ spaceToBatchND_: qD });
function jD(e, t, n, s, r, a, i) {
  r == null && (r = [1, 1]), a == null && (a = 1), s === 0 && (s = "valid");
  let o = _(e, "x", "maxPool"), u = o, l = false;
  o.rank === 3 && (l = true, u = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
  let c = tS(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
  s === "same" ? d = XD([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
  let h = p[0] === 1 && p[1] === 1, [f, m] = KD([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : ob(u, p, f), v = (n === "avg" ? () => Xg(b, t, a, g, i) : () => ab(b, t, a, g, i))(), x = h ? v : Yg(v, p, m);
  return l ? U(x, [x.shape[1], x.shape[2], x.shape[3]]) : x;
}
function KD(e, t, n) {
  let s = n.map((c) => c[0]), r = n.map((c) => c[1]), a = e.concat(s, r), i = t.map((c, p) => (c - a[p] % c) % c), o = r.map((c, p) => c + i[p]), u = t.map((c, p) => [s[p], o[p]]), l = t.map((c, p) => [0, i[p]]);
  return [u, l];
}
function XD(e, t) {
  let s = e.map((i, o) => i + (i - 1) * (t[o] - 1)).map((i) => i - 1), r = s.map((i) => Math.floor(i / 2)), a = s.map((i, o) => i - r[o]);
  return s.map((i, o) => [r[o], a[o]]);
}
var Lpe = L({ pool_: jD });
function YD(e, t) {
  let n = _(e, "base", "pow"), s = _(t, "exp", "pow");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(ei, r);
}
var ha = L({ pow_: YD });
function QD(e, t) {
  let n = _(e, "x", "prelu"), s = _(t, "alpha", "prelu"), r = { x: n, alpha: s };
  return M.runKernel(ti, r);
}
var ub = L({ prelu_: QD });
function ZD(e, t = null, n = false) {
  let s = _(e, "x", "prod");
  s.dtype === "bool" && (s = le(s, "int32"));
  let r = { x: s }, a = { axis: t, keepDims: n };
  return M.runKernel(ni, r, a);
}
var wS = L({ prod_: ZD });
function JD(e, t, n) {
  let s = dt(e), r = null;
  if (n == null || n === "float32")
    r = new Float32Array(s);
  else if (n === "int32")
    r = new Int32Array(s);
  else if (n === "bool")
    r = new Uint8Array(s);
  else
    throw new Error(`Unknown data type ${n}`);
  for (let a = 0; a < s; a++)
    r[a] = t();
  return M.makeTensor(r, e, n);
}
var Bpe = L({ rand_: JD });
var lb = wa(jd());
var cb = class {
  constructor(e, t, n, s, r) {
    this.mean = e, this.stdDev = t, this.dtype = n, this.nextVal = NaN, this.truncated = s, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2);
    let a = r || Math.random();
    this.random = lb.alea(a.toString());
  }
  nextValue() {
    if (!isNaN(this.nextVal)) {
      let s = this.nextVal;
      return this.nextVal = NaN, s;
    }
    let e, t, n = false;
    for (; !n; ) {
      let s, r, a;
      do
        s = 2 * this.random() - 1, r = 2 * this.random() - 1, a = s * s + r * r;
      while (a >= 1 || a === 0);
      let i = Math.sqrt(-2 * Math.log(a) / a);
      e = this.mean + this.stdDev * s * i, t = this.mean + this.stdDev * r * i, (!this.truncated || this.isValidTruncated(e)) && (n = true);
    }
    return (!this.truncated || this.isValidTruncated(t)) && (this.nextVal = this.convertValue(t)), this.convertValue(e);
  }
  convertValue(e) {
    return this.dtype == null || this.dtype === "float32" ? e : Math.round(e);
  }
  isValidTruncated(e) {
    return e <= this.upper && e >= this.lower;
  }
};
var e3 = class {
  constructor(e, t, n, s) {
    this.alpha = e, this.beta = 1 / t, this.dtype = n;
    let r = s || Math.random();
    this.randu = lb.alea(r.toString()), this.randn = new cb(0, 1, n, 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, t, n, s, r, a;
    for (; ; ) {
      do
        s = this.randn.nextValue(), a = 1 + this.c * s;
      while (a <= 0);
      if (a *= a * a, e = s * s, t = 1 - 0.331 * e * e, n = 0.5 * e + this.d * (1 - a + Math.log(a)), r = this.randu(), r < t || Math.log(r) < n)
        break;
    }
    return a = 1 / this.beta * this.d * a, this.alpha < 1 && (a *= Math.pow(this.randu(), 1 / this.alpha)), this.convertValue(a);
  }
  convertValue(e) {
    return this.dtype === "float32" ? e : Math.round(e);
  }
};
var t3 = class {
  constructor(e = 0, t = 1, n, s) {
    if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t - e, this.dtype = n, s == null && (s = Math.random()), typeof s == "number" && (s = s.toString()), !this.canReturnFloat() && this.range <= 1)
      throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);
    this.random = lb.alea(s);
  }
  convertValue(e) {
    return this.canReturnFloat() ? e : Math.round(e);
  }
  nextValue() {
    return this.convertValue(this.min + this.range * this.random());
  }
};
function n3(e, t, n = 1, s = "float32", r) {
  if (n == null && (n = 1), s == null && (s = "float32"), s !== "float32" && s !== "int32")
    throw new Error(`Unsupported data type ${s}`);
  let a = new e3(t, n, s, r), i = De(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var Vpe = L({ randomGamma_: n3 });
function s3(e, t = 0, n = 1, s, r) {
  if (s != null && s === "bool")
    throw new Error(`Unsupported data type ${s}`);
  let a = new cb(t, n, s, false, r), i = De(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var r3 = L({ randomNormal_: s3 });
function a3(e, t = 0, n = 1, s = "float32", r) {
  let a = De(e, s), i = new t3(t, n, null, r);
  for (let o = 0; o < a.values.length; o++)
    a.values[o] = i.nextValue();
  return a.toTensor();
}
var Ll = L({ randomUniform_: a3 });
function Zu(e, t, n = 1, s = "float32") {
  if (n === 0)
    throw new Error("Cannot have a step of zero");
  let r = { start: e, stop: t, step: n, dtype: s };
  return M.runKernel(Il, {}, r);
}
function i3(e) {
  let n = { input: _(e, "input", "real") };
  return M.runKernel(op, n);
}
var Id = L({ real_: i3 });
function o3(e) {
  let n = { x: _(e, "x", "reciprocal") };
  return M.runKernel(Cl, n);
}
var u3 = L({ reciprocal_: o3 });
function l3(e) {
  let n = { x: _(e, "x", "relu") };
  return M.runKernel(si, n);
}
var Xs = L({ relu_: l3 });
function c3(e) {
  let n = { x: _(e, "x", "relu6") };
  return M.runKernel(ai, n);
}
var kS = L({ relu6_: c3 });
function d3(e, t) {
  let s = { x: _(e, "x", "reverse") }, r = { dims: t };
  return M.runKernel(Oo, s, r);
}
var Jn = L({ reverse_: d3 });
function p3(e) {
  let t = _(e, "x", "reverse");
  return O(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Jn(t, 0);
}
var Wpe = L({ reverse1d_: p3 });
function h3(e, t) {
  let n = _(e, "x", "reverse");
  return O(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Jn(n, t);
}
var Upe = L({ reverse2d_: h3 });
function f3(e, t) {
  let n = _(e, "x", "reverse");
  return O(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Jn(n, t);
}
var Gpe = L({ reverse3d_: f3 });
function m3(e, t) {
  let n = _(e, "x", "reverse");
  return O(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Jn(n, t);
}
var Hpe = L({ reverse4d_: m3 });
function g3(e) {
  let n = { x: _(e, "x", "round") };
  return M.runKernel(Po, n);
}
var SS = L({ round_: g3 });
function b3(e) {
  let n = { x: _(e, "x", "rsqrt", "float32") };
  return M.runKernel(ii, n);
}
var IS = L({ rsqrt_: b3 });
function we(e, t) {
  if ((Qt(e) && t !== "string" || Array.isArray(e)) && t !== "complex64")
    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
  if (t === "string" && Qt(e) && !(e instanceof Uint8Array))
    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
  return Nr(e, [], [], t);
}
function y3(e) {
  let n = { x: _(e, "x", "selu") };
  return M.runKernel(Tl, n);
}
var CS = L({ selu_: y3 });
function v3(e, t, n, s, r, a = [1, 1], i = "NHWC") {
  let o = _(e, "x", "separableConv2d"), u = _(t, "depthwiseFilter", "separableConv2d"), l = _(n, "pointwiseFilter", "separableConv2d"), c = o, p = false;
  if (o.rank === 3 && (p = true, c = U(o, [1, o.shape[0], o.shape[1], o.shape[2]])), i === "NCHW")
    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
  O(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), O(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), O(l.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);
  let d = u.shape[2], h = u.shape[3];
  O(l.shape[2] === d * h, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${d * h}, but got ${l.shape[2]}.`);
  let f = yp(c, u, s, r, i, a), g = da(f, l, 1, "valid", i);
  return p ? U(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var x3 = L({ separableConv2d_: v3 });
async function w3(e, t) {
  let n = _(e, "x", "setdiff1d"), s = _(t, "y", "setdiff1d");
  O(n.dtype === s.dtype, () => `x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`), O(n.rank === 1, () => `x should be 1D tensor, but got x (${n.shape}).`), O(s.rank === 1, () => `y should be 1D tensor, but got y (${s.shape}).`);
  let r = await n.data(), a = await s.data(), i = new Set(a), o = 0;
  for (let c = 0; c < r.length; c++)
    i.has(r[c]) || o++;
  let u = new Wt([o], n.dtype), l = new Wt([o], "int32");
  for (let c = 0, p = 0; c < r.length; c++)
    i.has(r[c]) || (u.values[p] = r[c], l.values[p] = c, p++);
  return [u.toTensor(), l.toTensor()];
}
var k3 = w3;
function S3(e) {
  let n = { x: _(e, "x", "sign") };
  return M.runKernel($l, n);
}
var I3 = L({ sign_: S3 });
function C3(e) {
  let n = { x: _(e, "x", "sin", "float32") };
  return M.runKernel(oi, n);
}
var NS = L({ sin_: C3 });
function N3(e) {
  let n = { x: _(e, "x", "sinh") };
  return M.runKernel(Bo, n);
}
var TS = L({ sinh_: N3 });
function T3(e, t, n) {
  let s = _(e, "x", "slice1d");
  return O(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), qe(s, [t], [n]);
}
var db = L({ slice1d_: T3 });
function $3(e, t, n) {
  let s = _(e, "x", "slice2d");
  return O(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
}
var $S = L({ slice2d_: $3 });
function _3(e, t, n) {
  let s = _(e, "x", "slice3d");
  return O(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
}
var pb = L({ slice3d_: _3 });
function A3(e, t, n) {
  let s = _(e, "x", "slice4d");
  return O(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
}
var Cd = L({ slice4d_: A3 });
function E3(e, t = -1) {
  let n = _(e, "logits", "softmax", "float32");
  if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);
  let s = { logits: n }, r = { dim: t };
  return M.runKernel(di, s, r);
}
var hb = L({ softmax_: E3 });
function R3(e) {
  O(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
  let t = { input: e };
  return M.runKernel(kg, t);
}
var fb = L({ fft_: R3 });
function D3(e) {
  O(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
  let t = { input: e };
  return M.runKernel(Sg, t);
}
var Nd = L({ ifft_: D3 });
function F3(e) {
  let t = e.shape[e.shape.length - 1], n = e.size / t, s;
  if (t <= 2) {
    let r = U(e, [n, t]);
    s = Nd(r);
  } else {
    let r = [n, 2 * (t - 1)], a = U(Id(e), [n, t]), i = U(Jg(e), [n, t]), o = Jn(qe(a, [0, 1], [n, t - 2]), 1), u = V(Jn(qe(i, [0, 1], [n, t - 2]), 1), we(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = U(ua(l, c), [r[0], r[1]]);
    s = Nd(p);
  }
  if (s = Id(s), e.rank === 3 && e.shape[0] !== 0) {
    let r = s, a = e.shape[0];
    s = U(s, [a, s.shape[0] / a, s.shape[1]]), r.dispose();
  }
  return s;
}
var _S = L({ irfft_: F3 });
function O3(e, t, n = 0) {
  let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
  return M.runKernel(Wo, r, a);
}
var Bn = L({ split_: O3 });
function P3(e, t) {
  O(e.dtype === "float32", () => `The dtype for rfft() must be real value but got ${e.dtype}`);
  let n = e.shape[e.shape.length - 1], s = e.size / n, r;
  if (t != null && t < n) {
    let f = e.shape.map((g) => 0), m = e.shape.map((g) => g);
    m[e.shape.length - 1] = t, r = qe(e, f, m), n = t;
  } else if (t != null && t > n) {
    let f = e.shape.map((m) => m);
    f[e.shape.length - 1] = t - n, r = Ft([e, $t(f)], e.shape.length - 1), n = t;
  } else
    r = e;
  let a = je(r), i = U(ua(r, a), [s, n]), o = fb(i), u = Math.floor(n / 2) + 1, l = Id(o), c = Jg(o), p = Bn(l, [u, n - u], l.shape.length - 1), d = Bn(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
  return h[r.shape.length - 1] = u, U(ua(p[0], d[0]), h);
}
var mb = L({ rfft_: P3 });
function z3(e) {
  let n = { x: _(e, "x", "sqrt", "float32") };
  return M.runKernel(li, n);
}
var dn = L({ sqrt_: z3 });
function M3(e, t) {
  let n = _(e, "a", "squaredDifference"), s = _(t, "b", "squaredDifference");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s }, a = {};
  return M.runKernel(pi, r, a);
}
var AS = L({ squaredDifference_: M3 });
function L3(e, t) {
  let n = _(e, "x", "squeeze");
  return U(n, Zw(n.shape, t).newShape);
}
var mr = L({ squeeze_: L3 });
function B3(e, t = 0) {
  let n = qu(e, "tensors", "stack", "string_or_numeric");
  O(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && O(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
  let s = n, r = { axis: t };
  return M.runKernel(Do, s, r);
}
var es = L({ stack_: B3 });
function V3(e, t = 0) {
  let s = { x: _(e, "x", "step") }, r = { alpha: t };
  return M.runKernel(gi, s, r);
}
var Sp = L({ step_: V3 });
function W3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
  let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
  return M.runKernel(Uo, c, p);
}
var U3 = L({ stridedSlice_: W3 });
function G3(e) {
  let n = { x: _(e, "x", "tan", "float32") };
  return M.runKernel(Go, n);
}
var H3 = L({ tan_: G3 });
function Zt(e, t) {
  ka(e);
  let n = Rs(e, t);
  if (n.length !== 1)
    throw new Error("tensor1d() requires values to be a flat/TypedArray");
  return Nr(e, null, n, t);
}
function Zi(e, t, n) {
  if (ka(e), t != null && t.length !== 2)
    throw new Error("tensor2d() requires shape to have two numbers");
  let s = Rs(e, n);
  if (s.length !== 2 && s.length !== 1)
    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
  if (s.length === 1 && t == null)
    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
  return Nr(e, t, s, n);
}
function qpe(e, t, n) {
  if (ka(e), t != null && t.length !== 4)
    throw new Error("tensor4d() requires shape to have four numbers");
  let s = Rs(e, n);
  if (s.length !== 4 && s.length !== 1)
    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
  if (s.length === 1 && t == null)
    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
  return Nr(e, t, s, n);
}
function jpe(e, t, n) {
  if (ka(e), t != null && t.length !== 5)
    throw new Error("tensor5d() requires shape to have five numbers");
  let s = Rs(e, n);
  if (s.length !== 5 && s.length !== 1)
    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
  if (s.length === 1 && t == null)
    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
  return Nr(e, t, s, n);
}
function Kpe(e, t, n) {
  if (ka(e), t != null && t.length !== 6)
    throw new Error("tensor6d() requires shape to have six numbers");
  let s = Rs(e, n);
  if (s.length !== 6 && s.length !== 1)
    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
  if (s.length === 1 && t == null)
    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
  return t = t || s, Nr(e, t, s, n);
}
function q3(e, t = 1, n = true) {
  let s = _(e, "x", "topk");
  if (s.rank === 0)
    throw new Error("topk() expects the input to be of rank 1 or higher");
  let r = s.shape[s.shape.length - 1];
  if (t < 0)
    throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);
  if (t > r)
    throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);
  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Ho, a, i);
  return { values: o, indices: u };
}
var j3 = L({ topk_: q3 });
function K3(e, t = 0, n = 1, s, r) {
  if (s != null && s === "bool")
    throw new Error("Unsupported data type $ { dtype }");
  let a = new cb(t, n, s, true, r), i = De(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var gb = L({ truncatedNormal_: K3 });
function X3(e, t = 0) {
  let n = _(e, "x", "unique", "string_or_numeric");
  O(n.rank > 0, () => "The input tensor must be at least 1D");
  let s = { x: n }, r = { axis: t }, [a, i] = M.runKernel(Og, s, r);
  return { values: a, indices: i };
}
var yx = L({ unique_: X3 });
function Y3(e, t, n) {
  let s = _(e, "x", "unsortedSegmentSum"), r = _(t, "segmentIds", "unsortedSegmentSum", "int32");
  O(eo(n), () => "numSegments must be of dtype int");
  let a = { x: s, segmentIds: r }, i = { numSegments: n };
  return M.runKernel(hp, a, i);
}
var Q3 = L({ unsortedSegmentSum_: Y3 });
function Z3(e, t = 0) {
  let n = _(e, "x", "unstack", "string_or_numeric");
  O(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
  let s = { value: n }, r = { axis: t };
  return M.runKernel(jo, s, r);
}
var Fs = L({ unstack_: Z3 });
function J3(e, t) {
  return vS(e, t, "right");
}
function eF(e, t = true, n, s) {
  return M.makeVariable(e, t, n, s);
}
function ES(e, t) {
  let n = [];
  for (let a = 0; a < t.length; a++)
    t[a] && n.push(a);
  let s = De(e, "int32"), r = De([n.length, e.length], "int32");
  for (let a = 0; a < n.length; a++) {
    let i = s.indexToLoc(n[a]), o = a * e.length;
    r.values.set(i, o);
  }
  return r.toTensor();
}
async function tF(e) {
  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = ES(t.shape, n);
  return e !== t && t.dispose(), s;
}
var RS = tF;
async function nF(e, t, n) {
  let s = _(e, "tensor", "boolMask"), r = _(t, "mask", "boolMask", "bool"), a = n == null ? 0 : n, i = r.rank, o = s.shape;
  O(i > 0, () => "mask cannot be scalar"), pn(o.slice(a, a + i), r.shape, "mask's shape must match the first K dimensions of tensor's shape,");
  let u = 1;
  for (let m = a; m < a + i; m++)
    u *= o[m];
  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = U(s, l), p = U(r, [-1]), d = await RS(p), h = mr(d, [1]), f = Yu(c, h, a);
  return e !== s && s.dispose(), t !== r && r.dispose(), h.dispose(), c.dispose(), p.dispose(), d.dispose(), f;
}
var Xpe = nF;
function sF(e, t = "euclidean", n = null, s = false) {
  e = _(e, "x", "norm");
  let r = DS(e, t, n), a = r.shape;
  if (s) {
    let i = ts(n, e.shape);
    a = pa(r.shape, i);
  }
  return U(r, a);
}
function DS(e, t, n = null) {
  if (e.rank === 0)
    return Lt(e);
  if (e.rank !== 1 && n === null)
    return DS(U(e, [-1]), t, n);
  if (e.rank === 1 || typeof n == "number" || Array.isArray(n) && n.length === 1) {
    if (t === 1)
      return ve(Lt(e), n);
    if (t === 1 / 0)
      return As(Lt(e), n);
    if (t === -1 / 0)
      return vm(Lt(e), n);
    if (t === "euclidean" || t === 2)
      return dn(ve(ha(Lt(e), we(2, "int32")), n));
    throw new Error(`Error in norm: invalid ord value: ${t}`);
  }
  if (Array.isArray(n) && n.length === 2) {
    if (t === 1)
      return As(ve(Lt(e), n[0]), n[1] - 1);
    if (t === 1 / 0)
      return As(ve(Lt(e), n[1]), n[0]);
    if (t === -1 / 0)
      return vm(ve(Lt(e), n[1]), n[0]);
    if (t === "fro" || t === "euclidean")
      return dn(ve(ct(e), n));
    throw new Error(`Error in norm: invalid ord value: ${t}`);
  }
  throw new Error(`Error in norm: invalid axis: ${n}`);
}
var FS = L({ norm_: sF });
function rF(e, t, n, s, r = true) {
  let a = _(e, "v", "movingAverage"), i = _(t, "x", "movingAverage"), o = _(n, "decay", "movingAverage");
  gk(a, i), O(kr(a.shape, i.shape), () => "Shape mismatch in v and x");
  let u = we(1), l = ge(u, o), c = V(ge(i, a), l);
  if (r) {
    O(s != null, () => "When using zeroDebias: true, step is required.");
    let p = _(s, "step", "movingAverage");
    c = xe(c, ge(u, ha(o, p)));
  }
  return ie(a, c);
}
var Ype = L({ movingAverage_: rF });
function aF(e, t, n) {
  let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
  Hg(r, s, n);
  let a = { indices: s, updates: r }, i = { shape: n };
  return M.runKernel(zo, a, i);
}
var iF = L({ scatterND_: aF });
function oF(e, t, n, s) {
  if (e.dtype !== "int32")
    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);
  if (e.rank > 2)
    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);
  let r = e.rank > 0 ? e.shape[0] : 1, a = e.rank > 1 ? e.shape[1] : 1;
  if (n.length !== a)
    throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${a}.`);
  let i = t.size;
  if (!(t.rank === 0 || t.rank === 1 && i === r))
    throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${r}]`);
  if (t.dtype !== s.dtype)
    throw new Error("sparseValues.dtype must match defaultValues.dtype");
}
function uF(e, t, n, s = 0) {
  let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense"), i = _(s, "defaultValue", "sparseToDense", a.dtype);
  oF(r, a, n, i);
  let o = { sparseIndices: r, sparseValues: a, defaultValue: i }, u = { outputShape: n };
  return M.runKernel(dp, o, u);
}
var OS = L({ sparseToDense_: uF });
function lF(e, t) {
  let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
  return M.runKernel(ko, r);
}
var cF = L({ gatherND_: lF });
function dF(e, t) {
  if (t == null)
    return e.shape.slice();
  if (kr(e.shape, t))
    return t;
  if (e.shape.length === t.length) {
    let n = [];
    for (let s = 0; s < e.shape.length; s++)
      t[s] == null && e.shape[s] != null ? n.push(e.shape[s]) : n.push(t[s]);
    return n;
  }
  return t;
}
function pF(e, t, n, s) {
  let r = _(e, "x", "dropout");
  if (O(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), O(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
    return e instanceof et ? r.clone() : r;
  let a = dF(r, n), i = 1 - t, o = xe(xp(ie(Ll(a, 0, 1, "float32", s), i)), i);
  return V(r, o);
}
var hF = L({ dropout_: pF });
function fF(e) {
  return Math.floor(Math.pow(2, Math.ceil(Math.log(e) / Math.log(2))));
}
function PS(e, t, n) {
  let s = 1 - e % 2, r = new Float32Array(e);
  for (let a = 0; a < e; ++a) {
    let i = 2 * Math.PI * a / (e + s - 1);
    r[a] = t - n * Math.cos(i);
  }
  return Zt(r, "float32");
}
async function mF(e, t, n = 1) {
  let s = _(e, "predictions", "inTopK"), r = _(t, "targets", "inTopK");
  O(s.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`), O(s.rank - 1 === r.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`), pn(s.shape.slice(0, s.shape.length - 1), r.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
  let a = s.shape[s.shape.length - 1];
  O(n > 0 && n <= a, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);
  let i = await s.data(), o = await r.data(), [u, l] = [i.length / a, a], c = Jw("bool", u);
  for (let p = 0; p < u; p++) {
    let d = p * l, h = i.subarray(d, d + l), f = [];
    for (let m = 0; m < h.length; m++)
      f.push({ value: h[m], index: m });
    f.sort((m, g) => g.value - m.value), c[p] = 0;
    for (let m = 0; m < n; m++)
      if (f[m].index === o[p]) {
        c[p] = 1;
        break;
      }
  }
  return e !== s && s.dispose(), t !== r && r.dispose(), ms(c, r.shape, "bool");
}
var Qpe = mF;
var fa = {};
Ae(fa, { conv2d: () => yF, depthwiseConv2d: () => kF, matMul: () => IF });
function gF(e, t, n, s, r, a = "NHWC", i) {
  let o = e;
  e.rank === 3 && (o = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
  let u = t;
  u.rank === 3 && (u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]])), O(o.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`), O(u.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`), O(n.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);
  let l = a === "NHWC" ? o.shape[3] : o.shape[1], c = a === "NHWC" ? u.shape[3] : u.shape[1];
  O(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), O(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), hn("conv2dDerFilter", r, i);
  let p = { x: o, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, filterShape: n };
  return M.runKernel(fg, p, d);
}
var bb = L({ conv2DBackpropFilter_: gF });
function Ip(e, t, n) {
  if (n == null || n === "linear")
    return e;
  if (n === "relu")
    return V(e, Sp(t));
  throw new Error(`Cannot compute gradient for fused activation ${n}.`);
}
function Cp(e, t) {
  let n = t, s = _t(e.shape, t.shape);
  return s.length > 0 && (n = ve(n, s)), U(n, e.shape);
}
function Np(e, t, n, s) {
  if (t === "linear")
    return e;
  if (t === "relu")
    return Xs(e);
  if (t === "elu")
    return vp(e);
  if (t === "relu6")
    return kS(e);
  if (t === "prelu")
    return ub(e, n);
  if (t === "leakyrelu")
    return eb(e, s);
  if (t === "sigmoid")
    return Hs(e);
  throw new Error(`Unknown fused activation ${t}.`);
}
var Tp = (e, t) => !(e > 0) || t === "linear";
function bF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
  if (u = u || "linear", Tp(M.state.gradientDepth, u) === false) {
    let k = da(e, t, n, s, r, a, i);
    return o != null && (k = ie(k, o)), Np(k, u, l, c);
  }
  let p = _(e, "x", "conv2d", "float32"), d = _(t, "filter", "conv2d", "float32"), h = p, f = false;
  p.rank === 3 && (f = true, h = U(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), hn("fused conv2d", s, i), O(h.shape[3] === d.shape[2], () => `Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NHWC", () => `Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);
  let m = Pl(h.shape, d.shape, n, a, s, i), g;
  o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), it(m.outShape, g.shape));
  let b;
  l != null && (b = _(l, "prelu weights", "fused conv2d"));
  let y = (k, I) => {
    let [$, E, A, P] = I, R = Ip(k, A, u);
    O(fr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
    let F = Qg(E.shape, R, $, n, s), T = bb(E, R, $.shape, n, s), z = [F, T];
    if (P != null) {
      let W = Cp(P, R);
      z.push(W);
    }
    return z;
  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
  return o == null ? qs((I, $, E) => {
    let A = M.runKernel(ia, v, x);
    return E([$, I, A]), f && (A = U(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
  })(h, d) : qs((I, $, E, A) => {
    let P = M.runKernel(ia, v, x);
    return A([$, I, P, E]), f && (P = U(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
  })(h, d, g);
}
var yF = L({ fusedConv2d_: bF });
function vF(e, t, n, s, r, a = [1, 1], i) {
  let o = e;
  e.rank === 3 && (o = U(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
  let u = t;
  u.rank === 3 && (u = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
  let l = { x: o, dy: u }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, filterShape: n };
  return M.runKernel(yg, l, c);
}
var zS = L({ depthwiseConv2dNativeBackpropFilter_: vF });
function xF(e, t, n, s, r, a = [1, 1], i) {
  let o = t, u = false;
  t.rank === 3 && (u = true, o = U(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = M.runKernel(vg, l, c);
  return u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var MS = L({ depthwiseConv2dNativeBackpropInput_: xF });
function wF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
  if (Tp(M.state.gradientDepth, u) === false) {
    let k = yp(e, t, n, s, r, a, i);
    return o != null && (k = ie(k, o)), Np(k, u, l, c);
  }
  let p = _(e, "x", "depthwiseConv2d", "float32"), d = _(t, "filter", "depthwiseConv2d", "float32"), h = p, f = false;
  p.rank === 3 && (f = true, h = U(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), hn("fused depthwiseConv2d", s, i);
  let m = Pl(h.shape, d.shape, n, a, s, i, true), g;
  o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), it(m.outShape, g.shape));
  let b;
  l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
  let y = (k, I) => {
    O(fr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
    let [$, E, A, P] = I, R = Ip(k, A, u), F = MS(E.shape, R, $, n, s, a, i), T = zS(E, R, $.shape, n, s, a, i);
    if (P != null) {
      let z = Cp(g, R);
      return [F, T, z];
    }
    return [F, T];
  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
  return o == null ? qs((I, $, E) => {
    let A = M.runKernel(oa, v, x);
    return E([$, I, A]), f && (A = U(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
  })(h, d) : qs((I, $, E, A) => {
    let P = M.runKernel(oa, v, x);
    return A([$, I, P, E]), f && (P = U(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
  })(h, d, g);
}
var kF = L({ fusedDepthwiseConv2d_: wF });
function SF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o }) {
  if (Tp(M.state.gradientDepth, a) === false) {
    let R = Ve(e, t, n, s);
    return r != null && (R = ie(R, r)), Np(R, a, i, o);
  }
  let u = _(e, "a", "fused matMul"), l = _(t, "b", "fused matMul");
  [u, l] = vt(u, l);
  let c = n ? u.shape[u.rank - 2] : u.shape[u.rank - 1], p = s ? l.shape[l.rank - 1] : l.shape[l.rank - 2], d = n ? u.shape[u.rank - 1] : u.shape[u.rank - 2], h = s ? l.shape[l.rank - 2] : l.shape[l.rank - 1], f = u.shape.slice(0, -2), m = l.shape.slice(0, -2), g = dt(f), b = dt(m);
  O(c === p, () => `Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${n} and transposeB=${s} must match.`);
  let v = it(u.shape.slice(0, -2), l.shape.slice(0, -2)).concat([d, h]), x = n ? U(u, [g, c, d]) : U(u, [g, d, c]), k = s ? U(l, [b, h, p]) : U(l, [b, p, h]), I;
  r != null && (I = _(r, "bias", "fused matMul"), [I] = vt(I, u), it(v, I.shape));
  let $;
  i != null && ($ = _(i, "prelu weights", "fused matMul"));
  let E = (R, F) => {
    let [T, z, W, j] = F, X = Ip(U(R, W.shape), W, a), Y, Z;
    if (!n && !s ? (Y = Ve(X, z, false, true), Z = Ve(T, X, true, false)) : !n && s ? (Y = Ve(X, z, false, false), Z = Ve(X, T, true, false)) : n && !s ? (Y = Ve(z, X, false, true), Z = Ve(T, X, false, false)) : (Y = Ve(z, X, true, true), Z = Ve(X, T, true, true)), r != null) {
      let te = Cp(j, X);
      return [Y, Z, te];
    } else
      return [Y, Z];
  }, A = { a: x, b: k, bias: I, preluActivationWeights: $ }, P = { transposeA: n, transposeB: s, activation: a, leakyreluAlpha: o };
  return r == null ? qs((F, T, z) => {
    let W = M.runKernel(aa, A, P);
    return z([F, T, W]), { value: U(W, v), gradFunc: E };
  })(x, k) : qs((F, T, z, W) => {
    let j = M.runKernel(aa, A, P);
    return W([F, T, j, z]), { value: U(j, v), gradFunc: E };
  })(x, k, I);
}
var IF = L({ fusedMatMul_: SF });
function CF(e) {
  return PS(e, 0.54, 0.46);
}
var NF = L({ hammingWindow_: CF });
function TF(e) {
  return PS(e, 0.5, 0.5);
}
var LS = L({ hannWindow_: TF });
function $F(e, t, n, s = false, r = 0) {
  let a = 0, i = [];
  for (; a + t <= e.size; )
    i.push(qe(e, a, t)), a += n;
  if (s)
    for (; a < e.size; ) {
      let o = a + t - e.size, u = Ft([qe(e, a, t - o), zl([o], r)]);
      i.push(u), a += n;
    }
  return i.length === 0 ? Zi([], [0, t]) : U(Ft(i), [i.length, t]);
}
var BS = L({ frame_: $F });
function _F(e, t, n, s, r = LS) {
  s == null && (s = fF(t));
  let a = BS(e, t, n), i = V(a, r(t));
  return mb(i, s);
}
var AF = L({ stft_: _F });
function EF(e, t, n, s, r = "bilinear", a = 0) {
  let i = _(e, "image", "cropAndResize"), o = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = o.shape[0];
  O(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), O(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), O(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), O(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), O(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
  let c = { image: i, boxes: o, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
  return M.runKernel(mo, c, p);
}
var RF = L({ cropAndResize_: EF });
function DF(e) {
  let t = _(e, "image", "flipLeftRight", "float32");
  O(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
  let n = { image: t };
  return M.runKernel(xo, n, {});
}
var FF = L({ flipLeftRight_: DF });
function OF(e) {
  let t = _(e, "image", "grayscaleToRGB"), n = t.rank - 1, s = t.shape[n];
  O(t.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`), O(s === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);
  let r = new Array(t.rank);
  return r.fill(1, 0, n), r[n] = 3, hs(t, r);
}
var PF = L({ grayscaleToRGB_: OF });
function zF(e, t, n = 0, s = 0.5) {
  let r = _(e, "image", "rotateWithOffset", "float32");
  O(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
  let a = { image: r }, i = { radians: t, fillValue: n, center: s };
  return M.runKernel(Xo, a, i);
}
var MF = L({ rotateWithOffset_: zF });
function Zo(e, t, n, s, r, a) {
  s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
  let i = e.shape[0];
  return n = Math.min(n, i), O(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), O(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), O(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), O(t.rank === 1, () => "scores must be a 1D tensor"), O(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), O(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
}
function LF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Zo(a, i, n, s, r);
  n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
  let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
  return M.runKernel(_o, { boxes: a, scores: i }, u);
}
var BF = L({ nonMaxSuppression_: LF });
function VF(e, t, n) {
  let s = WF(e, t, n), r = s < 0 ? -(s + 1) : s;
  e.splice(r, 0, t);
}
function WF(e, t, n) {
  return GF(e, t, n || UF);
}
function UF(e, t) {
  return e > t ? 1 : e < t ? -1 : 0;
}
function GF(e, t, n) {
  let s = 0, r = e.length, a = 0, i = false;
  for (; s < r; ) {
    a = s + (r - s >>> 1);
    let o = n(t, e[a]);
    o > 0 ? s = a + 1 : (r = a, i = !o);
  }
  return i ? s : -s - 1;
}
function VS(e, t, n, s, r) {
  return yb(e, t, n, s, r, 0);
}
function WS(e, t, n, s, r, a) {
  return yb(e, t, n, s, r, 0, false, a, true);
}
function US(e, t, n, s, r, a) {
  return yb(e, t, n, s, r, a, true);
}
function yb(e, t, n, s, r, a, i = false, o = false, u = false) {
  let l = [];
  for (let g = 0; g < t.length; g++)
    t[g] > r && l.push({ score: t[g], boxIndex: g, suppressBeginIndex: 0 });
  l.sort(vx);
  let c = a > 0 ? -0.5 / a : 0, p = [], d = [];
  for (; p.length < n && l.length > 0; ) {
    let g = l.pop(), { score: b, boxIndex: y, suppressBeginIndex: v } = g;
    if (b < r)
      break;
    let x = false;
    for (let k = p.length - 1; k >= v; --k) {
      let I = HF(e, y, p[k]);
      if (I >= s) {
        x = true;
        break;
      }
      if (g.score = g.score * qF(s, c, I), g.score <= r)
        break;
    }
    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && VF(l, g, vx));
  }
  let h = p.length, f = n - h;
  o && f > 0 && (p.push(...new Array(f).fill(0)), d.push(...new Array(f).fill(0)));
  let m = { selectedIndices: p };
  return i && (m.selectedScores = d), u && (m.validOutputs = h), m;
}
function HF(e, t, n) {
  let s = e.subarray(t * 4, t * 4 + 4), r = e.subarray(n * 4, n * 4 + 4), a = Math.min(s[0], s[2]), i = Math.min(s[1], s[3]), o = Math.max(s[0], s[2]), u = Math.max(s[1], s[3]), l = Math.min(r[0], r[2]), c = Math.min(r[1], r[3]), p = Math.max(r[0], r[2]), d = Math.max(r[1], r[3]), h = (o - a) * (u - i), f = (p - l) * (d - c);
  if (h <= 0 || f <= 0)
    return 0;
  let m = Math.max(a, l), g = Math.max(i, c), b = Math.min(o, p), y = Math.min(u, d), v = Math.max(b - m, 0) * Math.max(y - g, 0);
  return v / (h + f - v);
}
function qF(e, t, n) {
  let s = Math.exp(t * n * n);
  return n <= e ? s : 0;
}
function vx(e, t) {
  return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
}
async function jF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Zo(a, i, n, s, r);
  n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
  let u = await Promise.all([a.data(), i.data()]), l = u[0], c = u[1], { selectedIndices: p } = VS(l, c, n, s, r);
  return a !== e && a.dispose(), i !== t && i.dispose(), Zt(p, "int32");
}
var KF = jF;
function XF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Zo(i, o, n, s, r, a);
  n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(Ao, l, c);
  return { selectedIndices: p[0], selectedScores: p[1] };
}
var YF = L({ nonMaxSuppressionWithScore_: XF });
async function QF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Zo(i, o, n, s, r, a);
  n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
  let l = await Promise.all([i.data(), o.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = US(c, p, n, s, r, a);
  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(d, "int32"), selectedScores: Zt(h) };
}
var ZF = QF;
function JF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Zo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(Sl, d, h);
  return { selectedIndices: f[0], validOutputs: f[1] };
}
var eO = L({ nonMaxSuppressionPadded_: JF });
async function tO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Zo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = WS(d, h, l, c, p, a);
  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(f, "int32"), validOutputs: we(m, "int32") };
}
var nO = tO;
function sO(e, t, n = false, s = false) {
  let r = _(e, "images", "resizeBilinear");
  O(r.rank === 3 || r.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t}.`), O(s === false || n === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
  let a = r, i = false;
  r.rank === 3 && (i = true, a = U(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(ri, o, u);
  return i ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var rO = L({ resizeBilinear_: sO });
function aO(e, t, n = false, s = false) {
  let r = _(e, "images", "resizeNearestNeighbor");
  O(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), O(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), O(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
  let a = r, i = false;
  r.rank === 3 && (i = true, a = U(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Nl, o, u);
  return i ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var iO = L({ resizeNearestNeighbor_: aO });
function oO(e, t = "binary", n = false, s = 0.5) {
  let r = _(e, "image", "threshold"), a = 0.2989, i = 0.587, o = 0.114, u = r.shape[0] * r.shape[1], l = V(Zt([s]), 255), c, p, d, h;
  if (O(r.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${r.rank}.`), O(r.shape[2] === 3 || r.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`), O(r.dtype === "int32" || r.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`), O(t === "otsu" || t === "binary", () => `Method must be binary or otsu, but was ${t}`), r.shape[2] === 3) {
    [c, p, d] = Bn(r, [1, 1, 1], -1);
    let g = V(c, a), b = V(p, i), y = V(d, o);
    h = ie(ie(g, b), y);
  } else
    h = e;
  if (t === "otsu") {
    let g = aS(le(SS(h), "int32"), ms([]), 256);
    l = uO(g, u);
  }
  let f = n ? Qo(h, l) : Un(h, l);
  return le(V(f, 255), "int32");
}
function uO(e, t) {
  let n = Zt([-1]), s = Zt([0]), r = Zt([0]), a, i, o, u, l, c;
  for (let p = 0; p < e.size - 1; p++) {
    a = qe(e, 0, p + 1), i = qe(e, p + 1), l = xe(ve(a), t), c = xe(ve(i), t);
    let d = ve(V(a, Zu(0, a.size)));
    o = xe(d, ve(a));
    let h = zl(i.shape, a.size), f = ie(Zu(0, i.size), h), m = V(i, f);
    u = xe(ve(m), ve(i));
    let g = ge(o, u), b = ge(o, u), y = V(l, c);
    r = V(V(y, g), b);
    let v = Un(r, s);
    s = vn(v, r, s), n = vn(v, Zt([p]), n);
  }
  return n;
}
var lO = L({ threshold_: oO });
function cO(e, t, n = "nearest", s = "constant", r = 0, a) {
  let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
  O(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), O(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
  let u = { image: i, transforms: o }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
  return M.runKernel(qo, u, l);
}
var dO = L({ transform_: cO });
function pO(e, t, n) {
  O(t % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t}.`), O(n % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${n}.`);
  let s = _(e, "a", "bandPart");
  O(s.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${s.rank}.`);
  let r = s.shape, [a, i] = s.shape.slice(-2);
  if (!(t <= a))
    throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);
  if (!(n <= i))
    throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);
  t < 0 && (t = a), n < 0 && (n = i);
  let o = U(Zu(0, a, 1, "int32"), [-1, 1]), u = Zu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Qo(l, we(+t, "int32")), Yo(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
  return U(es(Fs(U(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
}
var hO = L({ bandPart_: pO });
function fO(e) {
  let t;
  if (Array.isArray(e)) {
    t = false, O(e != null && e.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
    let r = e[0].shape[0];
    for (let a = 1; a < e.length; ++a)
      O(e[a].shape[0] === r, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`);
  } else
    t = true, e = Bn(e, e.shape[0], 0).map((r) => mr(r, [0]));
  O(e.length <= e[0].shape[0], () => `Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);
  let n = [], s = e;
  for (let r = 0; r < e.length; ++r)
    n.push(M.tidy(() => {
      let a = s[r];
      if (r > 0)
        for (let i = 0; i < r; ++i) {
          let o = V(ve(V(n[i], a)), n[i]);
          a = ge(a, o);
        }
      return xe(a, FS(a, "euclidean"));
    }));
  return t ? es(n, 0) : n;
}
var mO = L({ gramSchmidt_: fO });
function gO(e, t = false) {
  if (O(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
    return xx(e, t);
  {
    let n = e.shape.slice(0, e.shape.length - 2).reduce((u, l) => u * l), s = Fs(U(e, [n, e.shape[e.shape.length - 2], e.shape[e.shape.length - 1]]), 0), r = [], a = [];
    s.forEach((u) => {
      let [l, c] = xx(u, t);
      r.push(l), a.push(c);
    });
    let i = U(es(r, 0), e.shape), o = U(es(a, 0), e.shape);
    return [i, o];
  }
}
function xx(e, t = false) {
  return M.tidy(() => {
    O(e.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);
    let n = e.shape[0], s = e.shape[1], r = pS(n), a = ur(e), i = Zi([[1]], [1, 1]), o = ur(i), u = n >= s ? s : n;
    for (let l = 0; l < u; ++l) {
      let c = a, p = o, d = r;
      [o, a, r] = M.tidy(() => {
        let h = qe(a, [l, l], [n - l, 1]), f = FS(h), m = qe(a, [l, l], [1, 1]), g = vn(Un(m, 0), Zi([[-1]]), Zi([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
        y.shape[0] === 1 ? o = ur(i) : o = Ft([i, qe(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
        let v = kt(xe(Ve(g, b), f)), x = qe(a, [l, 0], [n - l, s]), k = V(v, o), I = Ge(o);
        if (l === 0)
          a = ge(x, Ve(k, Ve(I, x)));
        else {
          let A = ge(x, Ve(k, Ve(I, x)));
          a = Ft([qe(a, [0, 0], [l, s]), A], 0);
        }
        let $ = Ge(k), E = qe(r, [0, l], [n, r.shape[1] - l]);
        if (l === 0)
          r = ge(E, Ve(Ve(E, o), $));
        else {
          let A = ge(E, Ve(Ve(E, o), $));
          r = Ft([qe(r, [0, 0], [n, l]), A], 1);
        }
        return [o, a, r];
      }), Re([c, p, d]);
    }
    return !t && n > s && (r = qe(r, [0, 0], [n, s]), a = qe(a, [0, 0], [s, s])), [r, a];
  });
}
var bO = L({ qr_: gO });
var yO = ((e) => (e[e.NONE = 0] = "NONE", e[e.MEAN = 1] = "MEAN", e[e.SUM = 2] = "SUM", e[e.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS", e))(yO || {});
function vO(e, t, n = 3) {
  let s = _(e, "losses", "computeWeightedLoss"), r = null;
  t != null && (r = _(t, "weights", "computeWeightedLoss"));
  let a = r == null ? s : V(s, r);
  if (n === 0)
    return a;
  if (n === 2)
    return ve(a);
  if (n === 1) {
    if (r == null)
      return It(a);
    {
      let i = s.size / r.size, o = xe(ve(a), ve(r));
      return i > 1 ? xe(o, we(i)) : o;
    }
  }
  if (n === 3) {
    if (r == null)
      return xe(ve(a), we(s.size));
    {
      let i = V(r, Mn(s.shape)), o = le(ve(Qu(i, we(0))), "float32");
      return xe(ve(a), o);
    }
  }
  throw Error(`Unknown reduction: ${n}`);
}
var Ys = L({ computeWeightedLoss_: vO });
function xO(e, t, n, s = 3) {
  let r = _(e, "labels", "absoluteDifference"), a = _(t, "predictions", "absoluteDifference"), i = null;
  n != null && (i = _(n, "weights", "absoluteDifference")), pn(r.shape, a.shape, "Error in absoluteDifference: ");
  let o = Lt(ge(r, a));
  return Ys(o, i, s);
}
var wO = L({ absoluteDifference_: xO });
function kO(e, t, n, s, r = 3) {
  let a = _(e, "labels", "cosineDistance"), i = _(t, "predictions", "cosineDistance"), o = null;
  s != null && (o = _(s, "weights", "cosineDistance")), pn(a.shape, i.shape, "Error in cosineDistance: ");
  let u = we(1), l = ge(u, ve(V(a, i), n, true));
  return Ys(l, o, r);
}
var SO = L({ cosineDistance_: kO });
function IO(e, t, n, s = 3) {
  let r = _(e, "labels", "hingeLoss"), a = _(t, "predictions", "hingeLoss"), i = null;
  n != null && (i = _(n, "weights", "hingeLoss")), pn(r.shape, a.shape, "Error in hingeLoss: ");
  let o = we(1);
  r = ge(V(we(2), r), o);
  let u = Xs(ge(o, V(r, a)));
  return Ys(u, i, s);
}
var CO = L({ hingeLoss_: IO });
function NO(e, t, n, s = 1, r = 3) {
  let a = _(e, "labels", "huberLoss"), i = _(t, "predictions", "huberLoss"), o = null;
  n != null && (o = _(n, "weights", "huberLoss")), pn(a.shape, i.shape, "Error in huberLoss: ");
  let u = we(s), l = Lt(ge(i, a)), c = kp(l, u), p = ge(l, c), d = ie(V(we(0.5), ct(c)), V(u, p));
  return Ys(d, o, r);
}
var TO = L({ huberLoss_: NO });
function $O(e, t, n, s = 1e-7, r = 3) {
  let a = _(e, "labels", "logLoss"), i = _(t, "predictions", "logLoss"), o = null;
  n != null && (o = _(n, "weights", "logLoss")), pn(a.shape, i.shape, "Error in logLoss: ");
  let u = we(1), l = we(s), c = kt(V(a, Qn(ie(i, l)))), p = V(ge(u, a), Qn(ie(ge(u, i), l))), d = ge(c, p);
  return Ys(d, o, r);
}
var _O = L({ logLoss_: $O });
function AO(e, t, n, s = 3) {
  let r = _(e, "labels", "meanSquaredError"), a = _(t, "predictions", "meanSquaredError"), i = null;
  n != null && (i = _(n, "weights", "meanSquaredError")), pn(r.shape, a.shape, "Error in meanSquaredError: ");
  let o = AS(r, a);
  return Ys(o, i, s);
}
var EO = L({ meanSquaredError_: AO });
function RO(e, t) {
  let n = _(e, "labels", "sigmoidCrossEntropyWithLogits"), s = _(t, "logits", "sigmoidCrossEntropyWithLogits");
  pn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
  let r = Xs(s), a = V(s, n), i = tb(Yn(kt(Lt(s))));
  return ie(ge(r, a), i);
}
function DO(e, t, n, s = 0, r = 3) {
  let a = _(e, "multiClassLabels", "sigmoidCrossEntropy"), i = _(t, "logits", "sigmoidCrossEntropy"), o = null;
  if (n != null && (o = _(n, "weights", "sigmoidCrossEntropy")), pn(a.shape, i.shape, "Error in sigmoidCrossEntropy: "), s > 0) {
    let l = we(s), c = we(1), p = we(0.5);
    a = ie(V(a, ge(c, l)), V(p, l));
  }
  let u = RO(a, i);
  return Ys(u, o, r);
}
var FO = L({ sigmoidCrossEntropy_: DO });
function OO(e, t, n = -1) {
  if (n === -1 && (n = t.rank - 1), n !== t.rank - 1)
    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);
  return qs((r, a, i) => {
    let u = fD(a, [n], true), l = ge(le(a, "float32"), u);
    i([r, l]);
    let c = kt(V(l, r));
    return { value: ve(c, [n]), gradFunc: (h, f) => {
      let [m, g] = f, b = pa(h.shape, [n]);
      return [V(U(h, b), ge(le(m, "float32"), Yn(g))), V(U(h, b), ge(Yn(g), le(m, "float32")))];
    } };
  })(e, t);
}
function PO(e, t, n, s = 0, r = 3) {
  let a = _(e, "onehotLabels", "softmaxCrossEntropy"), i = _(t, "logits", "softmaxCrossEntropy"), o = null;
  if (n != null && (o = _(n, "weights", "softmaxCrossEntropy")), pn(a.shape, i.shape, "Error in softmaxCrossEntropy: "), s > 0) {
    let l = we(s), c = we(1), p = we(a.shape[1]);
    a = ie(V(a, ge(c, l)), xe(l, p));
  }
  let u = OO(a, i);
  return Ys(u, o, r);
}
var zO = L({ softmaxCrossEntropy_: PO });
function MO(e, t, n, s) {
  let r = _(e, "indices", "sparseFillEmptyRows", "int32"), a = _(t, "values", "sparseFillEmptyRows"), i = _(n, "denseShape", "sparseFillEmptyRows", "int32"), o = _(s, "defaultValue", "sparseFillEmptyRows", a.dtype);
  if (r.rank !== 2)
    throw new Error(`Indices should be Tensor2D but received shape
        ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);
  if (i.rank !== 1)
    throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);
  if (o.rank !== 0)
    throw new Error(`Default value should be a scalar but received shape ${o.shape}`);
  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = M.runKernel(up, u);
  return { outputIndices: l[0], outputValues: l[1], emptyRowIndicator: l[2], reverseIndexMap: l[3] };
}
var LO = L({ sparseFillEmptyRows_: MO });
function BO(e, t, n) {
  let s = _(e, "inputIndices", "sparseReshape", "int32"), r = _(t, "inputShape", "sparseReshape", "int32"), a = _(n, "newShape", "sparseReshape", "int32");
  if (s.rank !== 2)
    throw new Error(`Input indices should be Tensor2D but received shape
        ${s.shape}`);
  if (r.rank !== 1)
    throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);
  let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel(Al, i);
  return { outputIndices: o[0], outputShape: o[1] };
}
var VO = L({ sparseReshape_: BO });
function WO(e, t, n) {
  let s = _(e, "data", "sparseSegmentMean"), r = _(t, "indices", "sparseSegmentMean", "int32"), a = _(n, "segmentIds", "sparseSegmentMean", "int32");
  if (s.rank < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.rank !== 1)
    throw new Error(`Indices should be Tensor1D but received shape
          ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);
  let i = { data: s, indices: r, segmentIds: a };
  return M.runKernel(lp, i);
}
var UO = L({ sparseSegmentMean_: WO });
function GO(e, t, n) {
  let s = _(e, "data", "sparseSegmentSum"), r = _(t, "indices", "sparseSegmentSum", "int32"), a = _(n, "segmentIds", "sparseSegmentSum", "int32");
  if (s.rank < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.rank !== 1)
    throw new Error(`Indices should be Tensor1D but received shape
         ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`Segment ids should be Tensor1D but received shape
         ${a.shape}`);
  let i = { data: s, indices: r, segmentIds: a };
  return M.runKernel(cp, i);
}
var HO = L({ sparseSegmentSum_: GO });
function qO(e, t, n, s, r, a, i, o) {
  let u = _(e, "data", "stringNGrams", "string");
  if (u.dtype !== "string")
    throw new Error("Data must be of datatype string");
  if (u.shape.length !== 1)
    throw new Error(`Data must be a vector, saw: ${u.shape}`);
  let l = _(t, "dataSplits", "stringNGrams");
  if (l.dtype !== "int32")
    throw new Error("Data splits must be of datatype int32");
  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = M.runKernel(pp, p, c);
  return { nGrams: d[0], nGramsSplits: d[1] };
}
var jO = L({ stringNGrams_: qO });
function KO(e, t, n = true) {
  let s = _(e, "input", "stringSplit", "string"), r = _(t, "delimiter", "stringSplit", "string");
  if (s.rank !== 1)
    throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);
  if (r.rank !== 0)
    throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);
  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = M.runKernel(Dg, i, a);
  return { indices: o[0], values: o[1], shape: o[2] };
}
var XO = L({ stringSplit_: KO });
function YO(e, t) {
  let n = _(e, "input", "stringToHashBucketFast", "string"), s = { numBuckets: t };
  if (t <= 0)
    throw new Error("Number of buckets must be at least 1");
  let r = { input: n };
  return M.runKernel(Fg, r, s);
}
var QO = L({ stringToHashBucketFast_: YO });
var Zpe = { fft: fb, ifft: Nd, rfft: mb, irfft: _S };
var Jpe = { hammingWindow: NF, hannWindow: LS, frame: BS, stft: AF };
var jn = { flipLeftRight: FF, grayscaleToRGB: PF, resizeNearestNeighbor: iO, resizeBilinear: rO, rotateWithOffset: MF, cropAndResize: RF, nonMaxSuppression: BF, nonMaxSuppressionAsync: KF, nonMaxSuppressionWithScore: YF, nonMaxSuppressionWithScoreAsync: ZF, nonMaxSuppressionPadded: eO, nonMaxSuppressionPaddedAsync: nO, threshold: lO, transform: dO };
var ZO = { bandPart: hO, gramSchmidt: mO, qr: bO };
var ehe = { absoluteDifference: wO, computeWeightedLoss: Ys, cosineDistance: SO, hingeLoss: CO, huberLoss: TO, logLoss: _O, meanSquaredError: EO, sigmoidCrossEntropy: FO, softmaxCrossEntropy: zO };
var Gc = { sparseFillEmptyRows: LO, sparseReshape: VO, sparseSegmentMean: UO, sparseSegmentSum: HO };
var Uf = { stringNGrams: jO, stringSplit: XO, stringToHashBucketFast: QO };
var _r = class extends Xk {
  minimize(e, t = false, n) {
    let { value: s, grads: r } = this.computeGradients(e, n);
    if (n != null) {
      let a = n.map((i) => ({ name: i.name, tensor: r[i.name] }));
      this.applyGradients(a);
    } else
      this.applyGradients(r);
    return Re(r), t ? s : (s.dispose(), null);
  }
  get iterations() {
    return this.iterations_ == null && (this.iterations_ = 0), this.iterations_;
  }
  incrementIterations() {
    this.iterations_ = this.iterations + 1;
  }
  computeGradients(e, t) {
    return sD(e, t);
  }
  dispose() {
    this.iterations_ != null && Re(this.iterations_);
  }
  async saveIterations() {
    return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: we(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 ${this.getClassName()}`);
  }
  async extractIterations(e) {
    return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
  }
};
Object.defineProperty(_r, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
var vb = class extends _r {
  constructor(e, t, n = null) {
    super(), this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = M.backend.epsilon());
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n], a = false;
      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accum_grad`, variable: q(() => je(r).variable(a)) }), this.accumulatedUpdates[s] == null && (this.accumulatedUpdates[s] = { originalName: `${n}/accum_var`, variable: q(() => je(r).variable(a)) });
      let i = Array.isArray(e) ? e[s].tensor : e[n];
      if (i == null)
        return;
      let o = this.accumulatedGrads[s].variable, u = this.accumulatedUpdates[s].variable;
      q(() => {
        let l = ie(V(o, this.rho), V(ct(i), 1 - this.rho)), c = V(xe(dn(ie(u, this.epsilon)), dn(ie(o, this.epsilon))), i), p = ie(V(u, this.rho), V(ct(c), 1 - this.rho));
        o.assign(l), u.assign(p);
        let d = ie(V(c, -this.learningRate), r);
        r.assign(d);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedUpdates != null && (Re(this.accumulatedGrads.map((e) => e.variable)), Re(this.accumulatedUpdates.map((e) => e.variable)));
  }
  async getWeights() {
    let e = [...this.accumulatedGrads, ...this.accumulatedUpdates];
    return [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = e.length / 2, n = false;
    this.accumulatedGrads = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedUpdates = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.rho, t.epsilon);
  }
};
vb.className = "Adadelta";
Tr(vb);
var xb = class extends _r {
  constructor(e, t = 0.1) {
    super(), this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n];
      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: q(() => zl(r.shape, this.initialAccumulatorValue).variable(false)) });
      let a = Array.isArray(e) ? e[s].tensor : e[n];
      if (a == null)
        return;
      let i = this.accumulatedGrads[s].variable;
      q(() => {
        let o = ie(i, ct(a));
        i.assign(o);
        let u = ie(V(xe(a, dn(ie(o, M.backend.epsilon()))), -this.learningRate), r);
        r.assign(u);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedGrads != null && Re(this.accumulatedGrads.map((e) => e.variable));
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulatedGrads.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = false;
    this.accumulatedGrads = e.map((n) => ({ originalName: n.name, variable: n.tensor.variable(t) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.initialAccumulatorValue);
  }
};
xb.className = "Adagrad";
Tr(xb);
var wb = class extends _r {
  constructor(e, t, n, s = null) {
    super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], q(() => {
      this.accBeta1 = we(t).variable(), this.accBeta2 = we(n).variable();
    }), s == null && (this.epsilon = M.backend.epsilon());
  }
  applyGradients(e) {
    let t = Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e);
    q(() => {
      let n = ge(1, this.accBeta1), s = ge(1, this.accBeta2);
      t.forEach((r, a) => {
        let i = M.registeredVariables[r], o = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: q(() => je(i).variable(o)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${r}/v`, variable: q(() => je(i).variable(o)) });
        let u = Array.isArray(e) ? e[a].tensor : e[r];
        if (u == null)
          return;
        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedSecondMoment[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = ie(V(c, this.beta2), V(ct(u), 1 - this.beta2)), h = xe(p, n), f = xe(d, s);
        l.assign(p), c.assign(d);
        let m = ie(V(xe(h, ie(dn(f), this.epsilon)), -this.learningRate), i);
        i.assign(m);
      }), this.accBeta1.assign(V(this.accBeta1, this.beta1)), this.accBeta2.assign(V(this.accBeta2, this.beta2));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Re(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Re(this.accumulatedSecondMoment.map((e) => e.variable));
  }
  async getWeights() {
    let e = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
    return [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e), q(() => {
      this.accBeta1.assign(ha(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ha(this.beta2, this.iterations_ + 1));
    });
    let t = e.length / 2, n = false;
    this.accumulatedFirstMoment = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedSecondMoment = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.beta1, t.beta2, t.epsilon);
  }
};
wb.className = "Adam";
Tr(wb);
var kb = class extends _r {
  constructor(e, t, n, s = null, r = 0) {
    super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], q(() => {
      this.iteration = we(0).variable(), this.accBeta1 = we(t).variable();
    }), s == null && (this.epsilon = M.backend.epsilon());
  }
  applyGradients(e) {
    let t = Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e);
    q(() => {
      let n = ge(1, this.accBeta1), s = xe(-this.learningRate, ie(V(this.iteration, this.decay), 1));
      t.forEach((r, a) => {
        let i = M.registeredVariables[r], o = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: je(i).variable(o) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${r}/v`, variable: je(i).variable(o) });
        let u = Array.isArray(e) ? e[a].tensor : e[r];
        if (u == null)
          return;
        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedWeightedInfNorm[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = V(c, this.beta2), h = Lt(u), f = $r(d, h);
        l.assign(p), c.assign(f);
        let m = ie(V(xe(s, n), xe(p, ie(f, this.epsilon))), i);
        i.assign(m);
      }), this.iteration.assign(ie(this.iteration, 1)), this.accBeta1.assign(V(this.accBeta1, this.beta1));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Re(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Re(this.accumulatedWeightedInfNorm.map((e) => e.variable));
  }
  async getWeights() {
    throw new Error("getWeights() is not implemented for Adamax yet.");
  }
  async setWeights(e) {
    throw new Error("setWeights() is not implemented for Adamax yet.");
  }
  getConfig() {
    return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon, decay: this.decay };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.beta1, t.beta2, t.epsilon, t.decay);
  }
};
kb.className = "Adamax";
Tr(kb);
var $p = class extends _r {
  constructor(e) {
    super(), this.learningRate = e, this.setLearningRate(e);
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = Array.isArray(e) ? e[s].tensor : e[n];
      if (r == null)
        return;
      let a = M.registeredVariables[n];
      q(() => {
        let i = ie(V(this.c, r), a);
        a.assign(i);
      });
    }), this.incrementIterations();
  }
  setLearningRate(e) {
    this.learningRate = e, this.c != null && this.c.dispose(), this.c = qt(we(-e));
  }
  dispose() {
    this.c.dispose();
  }
  async getWeights() {
    return [await this.saveIterations()];
  }
  async setWeights(e) {
    if (e = await this.extractIterations(e), e.length !== 0)
      throw new Error("SGD optimizer does not have settable weights.");
  }
  getConfig() {
    return { learningRate: this.learningRate };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate);
  }
};
$p.className = "SGD";
Tr($p);
var Sb = class extends $p {
  constructor(e, t, n = false) {
    super(e), this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = we(this.momentum);
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n];
      this.accumulations[s] == null && (this.accumulations[s] = { originalName: `${n}/momentum`, variable: q(() => je(r).variable(false)) });
      let a = this.accumulations[s].variable, i = Array.isArray(e) ? e[s].tensor : e[n];
      i != null && q(() => {
        let o, u = ie(V(this.m, a), i);
        this.useNesterov ? o = ie(V(this.c, ie(i, V(u, this.m))), r) : o = ie(V(this.c, u), r), a.assign(u), r.assign(o);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.m.dispose(), this.accumulations != null && Re(this.accumulations.map((e) => e.variable));
  }
  setMomentum(e) {
    this.momentum = e;
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulations.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = false;
    this.accumulations = e.map((n) => ({ originalName: n.name, variable: n.tensor.variable(t) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.momentum, t.useNesterov);
  }
};
Sb.className = "Momentum";
Tr(Sb);
var Ib = class extends _r {
  constructor(e, t = 0.9, n = 0, s = null, r = false) {
    if (super(), this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = M.backend.epsilon()), e == null)
      throw new Error("learningRate for RMSPropOptimizer must be defined.");
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n], a = false;
      this.accumulatedMeanSquares[s] == null && (this.accumulatedMeanSquares[s] = { originalName: `${n}/rms`, variable: q(() => je(r).variable(a)) }), this.accumulatedMoments[s] == null && (this.accumulatedMoments[s] = { originalName: `${n}/momentum`, variable: q(() => je(r).variable(a)) }), this.accumulatedMeanGrads[s] == null && this.centered && (this.accumulatedMeanGrads[s] = { originalName: `${n}/mg`, variable: q(() => je(r).variable(a)) });
      let i = Array.isArray(e) ? e[s].tensor : e[n];
      if (i == null)
        return;
      let o = this.accumulatedMeanSquares[s].variable, u = this.accumulatedMoments[s].variable;
      q(() => {
        let l = ie(V(o, this.decay), V(ct(i), 1 - this.decay));
        if (this.centered) {
          let c = this.accumulatedMeanGrads[s].variable, p = ie(V(c, this.decay), V(i, 1 - this.decay)), d = xe(V(i, this.learningRate), dn(ge(l, ie(ct(p), this.epsilon)))), h = ie(V(u, this.momentum), d);
          o.assign(l), c.assign(p), u.assign(h);
          let f = ge(r, h);
          r.assign(f);
        } else {
          let c = ie(V(o, this.decay), V(ct(i), 1 - this.decay)), p = ie(V(u, this.momentum), xe(V(i, this.learningRate), dn(ie(c, this.epsilon))));
          o.assign(c), u.assign(p);
          let d = ge(r, p);
          r.assign(d);
        }
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedMeanSquares != null && Re(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Re(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Re(this.accumulatedMoments.map((e) => e.variable));
  }
  async getWeights() {
    let e = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
    return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = this.centered ? e.length / 3 : e.length / 2, n = false;
    this.accumulatedMeanSquares = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedMoments = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.centered && (this.accumulatedMeanGrads = e.slice(t * 2, t * 3).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })));
  }
  getConfig() {
    return { learningRate: this.learningRate, decay: this.decay, momentum: this.momentum, epsilon: this.epsilon, centered: this.centered };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.decay, t.momentum, t.epsilon, t.centered);
  }
};
Ib.className = "RMSProp";
Tr(Ib);
var Ur = class {
  static sgd(e) {
    return new $p(e);
  }
  static momentum(e, t, n = false) {
    return new Sb(e, t, n);
  }
  static rmsprop(e, t = 0.9, n = 0, s = null, r = false) {
    return new Ib(e, t, n, s, r);
  }
  static adam(e = 1e-3, t = 0.9, n = 0.999, s = null) {
    return new wb(e, t, n, s);
  }
  static adadelta(e = 1e-3, t = 0.95, n = null) {
    return new vb(e, t, n);
  }
  static adamax(e = 2e-3, t = 0.9, n = 0.999, s = null, r = 0) {
    return new kb(e, t, n, s, r);
  }
  static adagrad(e, t = 0.1) {
    return new xb(e, t);
  }
};
var Bi = { sgd: Ur.sgd, momentum: Ur.momentum, adadelta: Ur.adadelta, adagrad: Ur.adagrad, rmsprop: Ur.rmsprop, adamax: Ur.adamax, adam: Ur.adam };
var JO = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
function GS() {
  return new Promise((e) => JO(() => e()));
}
var C = {};
Ae(C, { ERF_A1: () => cP, ERF_A2: () => dP, ERF_A3: () => pP, ERF_A4: () => hP, ERF_A5: () => fP, ERF_P: () => lP, PARALLELIZE_THRESHOLD: () => Cb, SELU_SCALE: () => qS, SELU_SCALEALPHA: () => HS, applyActivation: () => Np, assertAndGetBroadcastShape: () => it, assertAxesAreInnerMostDims: () => dD, assertParamsConsistent: () => eP, assignToTypedArray: () => xP, axesAreInnerMostDims: () => nb, calculateShapes: () => Lk, checkEinsumDimSizes: () => NP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => mS, complexWithEvenIndex: () => bP, complexWithOddIndex: () => yP, computeConv2DInfo: () => Pl, computeConv3DInfo: () => nS, computeDefaultPad: () => Kg, computeDilation2DInfo: () => vE, computeOptimalWindowSize: () => nP, computeOutAndReduceShapes: () => gS, computeOutShape: () => tP, computePool2DInfo: () => tS, computePool3DInfo: () => xE, convertConv2DDataFormat: () => sS, decodeEinsumEquation: () => IP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => pa, exponent: () => kP, exponents: () => wP, fromStringArrayToUint8: () => jP, fromUint8ToStringArray: () => qP, getAxesPermutation: () => bS, getBroadcastDims: () => Dk, getComplexWithIndex: () => vP, getEinsumComputePath: () => TP, getEinsumPermutation: () => CP, getFusedBiasGradient: () => Cp, getFusedDyActivation: () => Ip, getImageCenter: () => sP, getInnerMostAxes: () => pD, getPermuted: () => aP, getReductionAxes: () => _t, getReshaped: () => rP, getReshapedPermuted: () => iP, getSliceBeginCoords: () => oP, getSliceSize: () => uP, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => EP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => RP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => DP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => PP, getSparseReshapeInputOutputMismatchErrorMessage: () => MP, getSparseReshapeInputOutputMultipleErrorMessage: () => zP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => FP, getSparseReshapeNegativeOutputDimErrorMessage: () => OP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => WP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => LP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => BP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => VP, getUndoAxesPermutation: () => sb, isIdentityPermutation: () => $P, log: () => V$, mergeRealAndImagArrays: () => mP, prepareAndValidate: () => zk, prepareSplitSize: () => AP, segment_util: () => jS, shouldFuse: () => Tp, slice_util: () => wt, splitRealAndImagArrays: () => gP, tupleValuesAreOne: () => fr, upcastType: () => cn, validateInput: () => Hg, validateUpdateShape: () => Gg, warn: () => rr });
function eP(e, t) {
  let n = e[0].length;
  e.forEach((r, a) => {
    O(r.length === n, () => `Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`);
  }), O(t >= 0 && t < n, () => `Error in concat${n}D: axis must be between 0 and ${n - 1}.`);
  let s = e[0];
  e.forEach((r, a) => {
    for (let i = 0; i < n; i++)
      O(i === t || r[i] === s[i], () => `Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`);
  });
}
function tP(e, t) {
  let n = e[0].slice();
  for (let s = 1; s < e.length; s++)
    n[t] += e[s][t];
  return n;
}
var Cb = 30;
function nP(e) {
  return e <= Cb ? e : gd(e, Math.floor(Math.sqrt(e)));
}
function sP(e, t, n) {
  let s = n * (typeof e == "number" ? e : e[0]), r = t * (typeof e == "number" ? e : e[1]);
  return [s, r];
}
function rP(e, t, n, s = true) {
  let r = [];
  if (s)
    r = r.concat(t.slice(0)), r.push(e[0] / n), r = r.concat(e.slice(1));
  else {
    r = r.concat(e[0]);
    let a = t.length;
    for (let i = 0; i < a; ++i)
      r = r.concat([e[i + 1] / t[i], t[i]]);
    r = r.concat(e.slice(a + 1));
  }
  return r;
}
function aP(e, t, n = true) {
  let s = [];
  if (n) {
    s.push(t);
    for (let r = t + 1; r < e; ++r)
      r <= 2 * t ? (s.push(r), s.push(r - (t + 1))) : s.push(r);
  } else {
    let r = [], a = [];
    for (let i = 1; i < e; ++i)
      i >= t * 2 + 1 || i % 2 === 1 ? a.push(i) : r.push(i);
    s.push(...r), s.push(0), s.push(...a);
  }
  return s;
}
function iP(e, t, n, s = true) {
  let r = [];
  s ? r.push(e[0] / n) : r.push(e[0] * n);
  for (let a = 1; a < e.length; ++a)
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function yP(e) {
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function vP(e, t) {
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function xP(e, t, n, s) {
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function kP(e, t, n) {
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var Gf = "->";
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function CP(e, t) {
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function NP(e, t, n) {
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function TP(e, t) {
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function $P(e) {
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function _P(e, t) {
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function AP(e, t, n = 0) {
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function EP(e) {
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function RP(e, t) {
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function DP(e, t, n) {
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function FP(e, t) {
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function OP(e, t) {
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function PP() {
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function zP(e, t) {
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function MP(e, t) {
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function LP() {
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function VP(e, t) {
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function WP(e, t, n) {
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var jS = {};
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function UP(e, t) {
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function GP(e, t, n) {
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function HP(e, t, n, s) {
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function qP(e) {
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function jP(e) {
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var ws = {};
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var qc = {};
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var ZP = ["channelsFirst", "channelsLast"];
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        return db(e, t, n);
      case 2:
        return $S(e, [0, t], [e.shape[0], n]);
      case 3:
        return pb(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
      case 4:
        return Cd(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], n]);
      default:
        throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`);
    }
  });
}
function jc(e, t, n, s) {
  return q(() => {
    switch (e.rank) {
      case 1:
        return db(e, t, n);
      case 2:
        switch (s) {
          case 1:
            return na(e, t, n);
          case 2:
            return qf(e, t, n);
          default:
            throw new G(`The axis is not within the rank of the tensor ${s}`);
        }
      case 3:
        switch (s) {
          case 1:
            return na(e, t, n);
          case 2:
            return pb(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
          case 3:
            return qf(e, t, n);
          default:
            throw new G(`The axis is not within the rank of the tensor ${s}`);
        }
      case 4:
        switch (s) {
          case 1:
            return na(e, t, n);
          case 2:
            return Cd(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
          case 3:
            return Cd(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
          case 4:
            return qf(e, t, n);
          default:
            throw new G(`The axis is not within the rank of the tensor ${s}`);
        }
      default:
        throw new G(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`);
    }
  });
}
function $b(e, t = -1) {
  let n;
  return t < 0 && (n = e[0].rank, n !== 0 ? t = n : t = 0), t === e[0].rank && (t = -1), Ft(e, t);
}
function Cx(e, t) {
  switch (e.rank) {
    case 1:
      return YE([e, t]);
    case 2:
      return ZE([e, t], 0);
    case 3:
      return eR([e, t], 0);
    case 4:
      return nR([e, t], 0);
    default:
      throw new G(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
  }
}
function wm(e, t) {
  if (Array.isArray(t) || (t = [t]), e.rank !== t.length)
    throw new G(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);
  return hs(e, t);
}
function Ep(e, t = 0, n = 1, s, r) {
  return r3(e, t, n, s, r);
}
function Es(e, t, n, s) {
  if (e.rank < 2 || t.rank < 2)
    throw new Fe(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);
  if (t.rank >= 3) {
    let r = e.shape.slice(-1)[0], a = t.shape.slice(-2)[0];
    if (r !== a)
      throw new Fe(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`);
  }
  if (e.rank === 2 && t.rank === 2)
    return fa.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? km(e.rank, s, vs()) : null, activation: n });
  {
    let r = e.shape.slice(), a = r.pop();
    e = U(e, [-1, a]);
    let i = t.shape.slice(), o = i.pop(), u = i.pop(), l = [...i, o], c = Array.from({ length: t.rank }, (f, m) => m === 0 ? t.rank - 2 : m <= t.rank - 2 ? m - 1 : m);
    t = U(Ge(t, c), [u, -1]);
    let p = [...r, ...l], d = false, h = false;
    return U(fa.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? km(e.rank, s, vs()) : null, activation: n }), p);
  }
}
function sI(e, t, n) {
  return q(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = le(t, "int32"), Yu(e, t, n)));
}
function Wl(e) {
  return V(e, e);
}
function km(e, t, n) {
  let s = t.shape;
  if (t.rank !== 1 && t.rank !== e)
    throw new G(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);
  if (e === 5) {
    if (n === "channelsFirst")
      return s.length === 1 ? U(t, [1, s[0], 1, 1, 1]) : U(t, [1, s[3], s[0], s[1], s[2]]);
    if (n === "channelsLast")
      return s.length === 1 ? U(t, [1, 1, 1, 1, s[0]]) : U(t, [1].concat(s));
  } else if (e === 4) {
    if (n === "channelsFirst")
      return s.length === 1 ? U(t, [1, s[0], 1, 1]) : U(t, [1, s[2], s[0], s[1]]);
    if (n === "channelsLast")
      return s.length === 1 ? U(t, [1, 1, 1, s[0]]) : U(t, [1].concat(s));
  } else if (e === 3) {
    if (n === "channelsFirst")
      return s.length === 1 ? U(t, [1, s[0], 1]) : U(t, [1, s[1], s[0]]);
    if (n === "channelsLast")
      return s.length === 1 ? U(t, [1, 1, s[0]]) : U(t, [1].concat(s));
  } else if (e < 3)
    return t;
  throw new G(`Unsupported input rank by biasAdd: ${t.rank}`);
}
function ks(e, t, n) {
  return q(() => (n == null && (n = vs()), Ct(n), ie(e, km(e.rank, t, n))));
}
function cz(e, t = 1) {
  if (t !== 1)
    throw new Fe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);
  return vp(e);
}
function dz(e) {
  return q(() => xe(e, ie(Lt(e), 1)));
}
function rI(e, t, n, s) {
  return q(() => hF(e, t, n, s));
}
function pz(e) {
  return q(() => {
    let t = ie(0.5, V(0.2, e));
    return Vn(t, 0, 1);
  });
}
function Ul(e, t, n = false) {
  return n ? e() : t();
}
var hz = ["fanIn", "fanOut", "fanAvg"];
var fz = ["normal", "uniform", "truncatedNormal"];
function mz(e) {
  vi(hz, "FanMode", e);
}
function gz(e) {
  vi(fz, "Distribution", e);
}
var ns = class extends re.Serializable {
  fromConfigUsesCustomObjects() {
    return false;
  }
  getConfig() {
    return {};
  }
};
var _b = class extends ns {
  apply(e, t) {
    return $t(e, t);
  }
};
_b.className = "Zeros";
re.registerClass(_b);
var Rp = class extends ns {
  apply(e, t) {
    return Mn(e, t);
  }
};
Rp.className = "Ones";
re.registerClass(Rp);
var Ab = class extends ns {
  constructor(e) {
    if (super(), typeof e != "object")
      throw new G(`Expected argument of type ConstantConfig but got ${e}`);
    if (e.value === void 0)
      throw new G(`config must have value set but got ${e}`);
    this.value = e.value;
  }
  apply(e, t) {
    return q(() => V(we(this.value), Mn(e, t)));
  }
  getConfig() {
    return { value: this.value };
  }
};
Ab.className = "Constant";
re.registerClass(Ab);
var Eb = class extends ns {
  constructor(e) {
    super(), this.DEFAULT_MINVAL = -0.05, this.DEFAULT_MAXVAL = 0.05, this.minval = e.minval || this.DEFAULT_MINVAL, this.maxval = e.maxval || this.DEFAULT_MAXVAL, this.seed = e.seed;
  }
  apply(e, t) {
    return Ll(e, this.minval, this.maxval, t);
  }
  getConfig() {
    return { minval: this.minval, maxval: this.maxval, seed: this.seed };
  }
};
Eb.className = "RandomUniform";
re.registerClass(Eb);
var Rb = class extends ns {
  constructor(e) {
    super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
  }
  apply(e, t) {
    if (t = t || "float32", t !== "float32" && t !== "int32")
      throw new Fe(`randomNormal does not support dType ${t}.`);
    return Ep(e, this.mean, this.stddev, t, this.seed);
  }
  getConfig() {
    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
  }
};
Rb.className = "RandomNormal";
re.registerClass(Rb);
var Db = class extends ns {
  constructor(e) {
    super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
  }
  apply(e, t) {
    if (t = t || "float32", t !== "float32" && t !== "int32")
      throw new Fe(`truncatedNormal does not support dType ${t}.`);
    return gb(e, this.mean, this.stddev, t, this.seed);
  }
  getConfig() {
    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
  }
};
Db.className = "TruncatedNormal";
re.registerClass(Db);
var Fb = class extends ns {
  constructor(e) {
    super(), this.gain = e.gain != null ? e.gain : 1;
  }
  apply(e, t) {
    return q(() => {
      if (e.length !== 2 || e[0] !== e[1])
        throw new G("Identity matrix initializer can only be used for 2D square matrices.");
      return V(this.gain, pS(e[0]));
    });
  }
  getConfig() {
    return { gain: this.gain };
  }
};
Fb.className = "Identity";
re.registerClass(Fb);
function bz(e, t = "channelsLast") {
  let n, s;
  if (Ct(t), e.length === 2)
    n = e[0], s = e[1];
  else if ([3, 4, 5].indexOf(e.length) !== -1) {
    if (t === "channelsFirst") {
      let r = cr(e, 2);
      n = e[1] * r, s = e[0] * r;
    } else if (t === "channelsLast") {
      let r = cr(e, 0, e.length - 2);
      n = e[e.length - 2] * r, s = e[e.length - 1] * r;
    }
  } else {
    let r = cr(e);
    n = Math.sqrt(r), s = Math.sqrt(r);
  }
  return [n, s];
}
var xn = class extends ns {
  constructor(e) {
    if (super(), e.scale < 0)
      throw new G(`scale must be a positive float. Got: ${e.scale}`);
    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, mz(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, gz(this.distribution), this.seed = e.seed;
  }
  apply(e, t) {
    let n = bz(e), s = n[0], r = n[1], a = this.scale;
    if (this.mode === "fanIn" ? a /= Math.max(1, s) : this.mode === "fanOut" ? a /= Math.max(1, r) : a /= Math.max(1, (s + r) / 2), this.distribution === "normal") {
      let i = Math.sqrt(a);
      if (t = t || "float32", t !== "float32" && t !== "int32")
        throw new Fe(`${this.getClassName()} does not support dType ${t}.`);
      return gb(e, 0, i, t, this.seed);
    } else {
      let i = Math.sqrt(3 * a);
      return Ll(e, -i, i, t);
    }
  }
  getConfig() {
    return { scale: this.scale, mode: this.mode, distribution: this.distribution, seed: this.seed };
  }
};
xn.className = "VarianceScaling";
re.registerClass(xn);
var Dp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanAvg", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Dp.className = "GlorotUniform";
re.registerClass(Dp);
var Fp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanAvg", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Fp.className = "GlorotNormal";
re.registerClass(Fp);
var Op = class extends xn {
  constructor(e) {
    super({ scale: 2, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Op.className = "HeNormal";
re.registerClass(Op);
var Pp = class extends xn {
  constructor(e) {
    super({ scale: 2, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Pp.className = "HeUniform";
re.registerClass(Pp);
var zp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
zp.className = "LeCunNormal";
re.registerClass(zp);
var Mp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Mp.className = "LeCunNormal";
re.registerClass(Mp);
var Ob = class extends ns {
  constructor(e) {
    if (super(), this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
      throw new Fe("Random seed is not implemented for Orthogonal Initializer yet.");
  }
  apply(e, t) {
    return q(() => {
      if (e.length < 2)
        throw new Fe("Shape must be at least 2D.");
      e[0] * e[1] > 2e3 && console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0] * e[1]}) elements: Slowness may result.`);
      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = Ep(n, 0, 1, "float32"), r = ZO.gramSchmidt(s);
      return e[0] > e[1] && (r = Ge(r)), V(this.gain, r);
    });
  }
  getConfig() {
    return { gain: this.gain, seed: this.seed };
  }
};
Ob.className = "Orthogonal";
re.registerClass(Ob);
var Nx = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
function Tx(e, t = {}) {
  return Bl(e, re.SerializationMap.getMap().classNameMap, t, "initializer");
}
function yt(e) {
  return Nb(e);
}
function ht(e) {
  if (typeof e == "string") {
    let t = e in Nx ? Nx[e] : e;
    if (t === "GlorotNormal")
      return new Fp();
    if (t === "GlorotUniform")
      return new Dp();
    if (t === "HeNormal")
      return new Op();
    if (t === "HeUniform")
      return new Pp();
    if (t === "LeCunNormal")
      return new zp();
    if (t === "LeCunUniform")
      return new Mp();
    {
      let n = {};
      return n.className = t, n.config = {}, Tx(n);
    }
  } else
    return e instanceof ns ? e : Tx(e);
}
function Sm(e) {
  return Array.isArray(e) && Array.isArray(e[0]);
}
function Td(e) {
  return e.length === 0 ? [] : Array.isArray(e[0]) ? e : [e];
}
function Oe(e) {
  let t;
  if (Array.isArray(e)) {
    if (e.length !== 1)
      throw new G(`Expected Tensor length to be 1; got ${e.length}`);
    t = e[0];
  } else
    t = e;
  return t;
}
function nt(e) {
  if (Array.isArray(e) && Array.isArray(e[0])) {
    if (e.length === 1)
      return e = e, e[0];
    throw new G(`Expected exactly 1 Shape; got ${e.length}`);
  } else
    return e;
}
function $d(e) {
  let t = 0;
  for (let n of e)
    n.shape.length === 0 ? t += 1 : t += n.shape.reduce((s, r) => s * r);
  return t;
}
var $x = "Variable";
var yz = class {
  constructor(e, t = "float32", n = $x, s = true, r = null) {
    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = ZS(), n = n == null ? $x : n, this.originalName = eI(n), this.name = tI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = eF(e, this.trainable_, this.name, this.dtype);
  }
  read() {
    return this.assertNotDisposed(), this.val;
  }
  write(e) {
    return this.assertNotDisposed(), vz(this.val, e), this.val.id !== e.id && (this.val.assign(e), this.constraint != null && this.val.assign(this.constraint.apply(this.val))), this;
  }
  dispose() {
    this.assertNotDisposed(), this.val.dispose();
  }
  assertNotDisposed() {
    if (this.val.isDisposed)
      throw new Error(`LayersVariable ${this.name} is already disposed.`);
  }
  get trainable() {
    return this.trainable_;
  }
  set trainable(e) {
    this.trainable_ = e, this.val.trainable = e;
  }
};
function vz(e, t) {
  if (e.shape.toString() !== t.shape.toString())
    throw new Error("Shape mismatch: " + JSON.stringify(e.shape) + " vs. " + JSON.stringify(t.shape));
}
function Im(e) {
  return e.map((t) => t.read());
}
function Pb(e) {
  e.forEach((t) => {
    t[0].write(t[1]);
  });
}
var Dt = class {
  constructor(e) {
    this.dtype = e.dtype, this.shape = e.shape, e.shape != null ? this.ndim = e.shape.length : this.ndim = e.ndim, this.maxNDim = e.maxNDim, this.minNDim = e.minNDim, this.axes = e.axes || {};
  }
};
var $s = class {
  constructor(e, t, n, s, r, a, i) {
    this.dtype = e, this.shape = t, this.sourceLayer = n, this.inputs = s, this.callArgs = r, this.outputTensorIndex = i, this.id = ZS(), a != null && (this.originalName = eI(a), this.name = tI(this.originalName)), this.rank = t.length;
  }
};
var xz = 0;
var Lp = class {
  constructor(e, t) {
    this.callArgs = t, this.id = xz++, this.outboundLayer = e.outboundLayer, this.inboundLayers = e.inboundLayers, this.nodeIndices = e.nodeIndices, this.tensorIndices = e.tensorIndices, this.inputTensors = e.inputTensors, this.outputTensors = e.outputTensors, this.inputMasks = e.inputMasks, this.outputMasks = e.outputMasks, this.inputShapes = e.inputShapes, this.outputShapes = e.outputShapes;
    for (let n of e.inboundLayers)
      n != null && n.outboundNodes.push(this);
    e.outboundLayer.inboundNodes.push(this);
  }
  getConfig() {
    let e = [];
    for (let t of this.inboundLayers)
      t != null ? e.push(t.name) : e.push(null);
    return { outboundLayer: this.outboundLayer ? this.outboundLayer.name : null, inboundLayers: e, nodeIndices: this.nodeIndices, tensorIndices: this.tensorIndices };
  }
};
var wz = 0;
var He = class extends re.Serializable {
  constructor(e = {}) {
    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = wz++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
    let t = e.name;
    if (!t) {
      let n = this.getClassName();
      t = Vs(n) + "_" + _p(n);
    }
    if (this.name = t, this.trainable_ = e.trainable == null ? true : e.trainable, e.inputShape != null || e.batchInputShape != null) {
      let n;
      if (e.batchInputShape != null)
        n = e.batchInputShape;
      else if (e.inputShape != null) {
        let r = null;
        e.batchSize != null && (r = e.batchSize), n = [r].concat(e.inputShape);
      }
      this.batchInputShape = n;
      let s = e.dtype;
      s == null && (s = e.inputDType), s == null && (s = "float32"), this.dtype = s;
    }
    e.weights != null ? this.initialWeights = e.weights : this.initialWeights = null, this._refCount = null, this.fastWeightInitDuringBuild = false;
  }
  static nodeKey(e, t) {
    return e.name + "_ib-" + t.toString();
  }
  getNodeAtIndex(e, t) {
    if (this.inboundNodes.length === 0)
      throw new fs(`The layer has never been called and thus has no defined ${t}.`);
    if (this.inboundNodes.length <= e)
      throw new G(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);
    return this.inboundNodes[e];
  }
  getInputAt(e) {
    return bn(this.getNodeAtIndex(e, "input").inputTensors);
  }
  getOutputAt(e) {
    return bn(this.getNodeAtIndex(e, "output").outputTensors);
  }
  get input() {
    if (this.inboundNodes.length > 1)
      throw new Bs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);
    if (this.inboundNodes.length === 0)
      throw new Bs(`Layer ${this.name} is not connected, no input to return.`);
    return bn(this.getNodeAtIndex(0, "input").inputTensors);
  }
  get output() {
    if (this.inboundNodes.length === 0)
      throw new Bs(`Layer ${this.name} has no inbound nodes.`);
    if (this.inboundNodes.length > 1)
      throw new Bs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);
    return bn(this.getNodeAtIndex(0, "output").outputTensors);
  }
  get losses() {
    return this._losses;
  }
  calculateLosses() {
    return this.losses.map((e) => e());
  }
  get updates() {
    return this._updates;
  }
  get built() {
    return this._built;
  }
  set built(e) {
    this._built = e;
  }
  get trainable() {
    return this.trainable_;
  }
  set trainable(e) {
    this._trainableWeights.forEach((t) => t.trainable = e), this.trainable_ = e;
  }
  get trainableWeights() {
    return this.trainable_ ? this._trainableWeights.filter((e) => e.trainable) : [];
  }
  set trainableWeights(e) {
    this._trainableWeights = e;
  }
  get nonTrainableWeights() {
    return this.trainable ? this._trainableWeights.filter((e) => !e.trainable).concat(this._nonTrainableWeights) : this._trainableWeights.concat(this._nonTrainableWeights);
  }
  set nonTrainableWeights(e) {
    this._nonTrainableWeights = e;
  }
  get weights() {
    return this.trainableWeights.concat(this.nonTrainableWeights);
  }
  get stateful() {
    return this._stateful;
  }
  resetStates() {
    if (!this.stateful)
      throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.");
  }
  assertInputCompatibility(e) {
    if (e = pt(e), this.inputSpec == null || this.inputSpec.length === 0)
      return;
    let t = pt(this.inputSpec);
    if (e.length !== t.length)
      throw new G(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);
    for (let n = 0; n < e.length; n++) {
      let s = e[n], r = t[n];
      if (r == null)
        continue;
      let a = s.rank;
      if (r.ndim != null && a !== r.ndim)
        throw new G(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);
      if (r.maxNDim != null && a > r.maxNDim)
        throw new G(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);
      if (r.minNDim != null && a < r.minNDim)
        throw new G(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);
      if (r.dtype != null && s.dtype !== r.dtype)
        throw new G(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);
      if (r.axes) {
        let i = s.shape;
        for (let o in r.axes) {
          let u = Number(o), l = r.axes[o], c = u >= 0 ? i[u] : i[i.length + u];
          if (l != null && [l, null].indexOf(c) === -1)
            throw new G(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`);
        }
      }
      if (r.shape != null)
        for (let i = 0; i < r.shape.length; ++i) {
          let o = r.shape[i], u = s.shape[i];
          if (o != null && u != null && o !== u)
            throw new G(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`);
        }
    }
  }
  call(e, t) {
    return e;
  }
  invokeCallHook(e, t) {
    this._callHook != null && this._callHook(e, t);
  }
  setCallHook(e) {
    this._callHook = e;
  }
  clearCallHook() {
    this._callHook = null;
  }
  apply(e, t) {
    t = t || {}, this.assertNotDisposed();
    let n = pt(e), s = true;
    for (let a of n)
      if (!(a instanceof $s)) {
        s = false;
        break;
      }
    let r = true;
    for (let a of n)
      if (a instanceof $s) {
        r = false;
        break;
      }
    if (s === r)
      throw new G("Arguments to apply() must be all SymbolicTensors or all Tensors");
    return ta(this.name, () => {
      if (!this.built) {
        this.assertInputCompatibility(e);
        let a = [];
        for (let i of pt(e))
          a.push(i.shape);
        this.build(bn(a)), this.built = true, this.initialWeights && this.setWeights(this.initialWeights), this._refCount === null && r && (this._refCount = 1);
      }
      if (this.assertInputCompatibility(e), r) {
        let a = this.call(e, t), i = pt(a), o = [];
        for (let u of i)
          n.indexOf(u) !== -1 && (u = u.clone()), o.push(u);
        if (a = bn(o), this.activityRegularizer != null)
          throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
        return a;
      } else {
        let a = kz(e), i = this.computeOutputShape(a), o, u = Sz(e);
        if (this.warnOnIncompatibleInputShape(Array.isArray(e) ? a[0] : a), i != null && i.length > 0 && Array.isArray(i[0]) ? o = i.map((l, c) => new $s(u, l, this, pt(e), t, this.name, c)) : o = new $s(u, i, this, pt(e), t, this.name), this.addInboundNode(e, o, null, null, a, i, t), this._refCount++, this.activityRegularizer != null)
          throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
        return o;
      }
    });
  }
  warnOnIncompatibleInputShape(e) {
    if (this.batchInputShape != null)
      if (e.length !== this.batchInputShape.length)
        console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
      else {
        let t = false;
        this.batchInputShape.forEach((n, s) => {
          n != null && e[s] != null && e[s] !== n && (t = true);
        }), t && console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);
      }
  }
  get outputShape() {
    if (this.inboundNodes == null || this.inboundNodes.length === 0)
      throw new Bs(`The layer ${this.name} has never been called and thus has no defined output shape.`);
    let e = [];
    for (let t of this.inboundNodes) {
      let n = JSON.stringify(t.outputShapes);
      e.indexOf(n) === -1 && e.push(n);
    }
    if (e.length === 1) {
      let t = this.inboundNodes[0].outputShapes;
      return Array.isArray(t) && Array.isArray(t[0]) && t.length === 1 ? t[0] : t;
    } else
      throw new Bs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`);
  }
  countParams() {
    if (!this.built)
      throw new fs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
    return $d(this.weights);
  }
  build(e) {
    this.built = true;
  }
  getWeights(e = false) {
    return Im(e ? this.trainableWeights : this.weights);
  }
  setWeights(e) {
    q(() => {
      let t = this.weights;
      if (t.length !== e.length)
        throw new G(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);
      if (t.length === 0)
        return;
      let n = [], s = Im(t);
      for (let r = 0; r < s.length; ++r) {
        let a = s[r], i = t[r], o = e[r];
        if (!w.arraysEqual(a.shape, o.shape))
          throw new G(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);
        n.push([i, o]);
      }
      Pb(n);
    });
  }
  addWeight(e, t, n, s, r, a, i, o) {
    if (this._addedWeightNames.indexOf(e) !== -1)
      throw new G(`Duplicate weight name ${e} for layer ${this.name}`);
    this._addedWeightNames.push(e), n == null && (n = "float32"), this.fastWeightInitDuringBuild && (s = o != null ? o() : ht("zeros"));
    let u = s.apply(t, n), l = new yz(u, n, e, a, i);
    return u.dispose(), r != null && this.addLoss(() => r.apply(l.read())), a == null && (a = true), a ? this._trainableWeights.push(l) : this._nonTrainableWeights.push(l), l;
  }
  setFastWeightInitDuringBuild(e) {
    this.fastWeightInitDuringBuild = e;
  }
  addLoss(e) {
    e == null || Array.isArray(e) && e.length === 0 || (e = pt(e), this._losses !== void 0 && this._losses !== null && this.losses.push(...e));
  }
  computeOutputShape(e) {
    return e;
  }
  computeMask(e, t) {
    if (!this.supportsMasking) {
      if (t != null)
        if (Array.isArray(t))
          t.forEach((n) => {
            if (n != null)
              throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
          });
        else
          throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
      return null;
    }
    return t;
  }
  addInboundNode(e, t, n, s, r, a, i = null) {
    let o = pt(e);
    t = pt(t), n = pt(n), s = pt(s), r = Td(r), a = Td(a);
    let u = [], l = [], c = [];
    for (let p of o)
      u.push(p.sourceLayer), l.push(p.nodeIndex), c.push(p.tensorIndex);
    new Lp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
    for (let p = 0; p < t.length; p++)
      t[p].sourceLayer = this, t[p].nodeIndex = this.inboundNodes.length - 1, t[p].tensorIndex = p;
  }
  getConfig() {
    let e = { name: this.name, trainable: this.trainable };
    return this.batchInputShape != null && (e.batchInputShape = this.batchInputShape), this.dtype != null && (e.dtype = this.dtype), e;
  }
  disposeWeights() {
    return this.weights.forEach((e) => e.dispose()), this.weights.length;
  }
  assertNotDisposed() {
    if (this._refCount === 0)
      throw new Error(`Layer '${this.name}' is already disposed.`);
  }
  dispose() {
    if (!this.built)
      throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);
    if (this._refCount === null)
      throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);
    this.assertNotDisposed();
    let e = 0;
    return --this._refCount === 0 && (e = this.disposeWeights()), { refCountAfterDispose: this._refCount, numDisposedVariables: e };
  }
};
function kz(e) {
  e = pt(e);
  let t = [];
  for (let n of e)
    t.push(n.shape);
  return bn(t);
}
function Sz(e) {
  return "float32";
}
function aI(e, t, n) {
  if ((t == null || n != null && n > 0) && (t = e.sourceLayer, n = e.nodeIndex), t.inboundNodes.length === 0)
    return [e];
  {
    let s = t.inboundNodes[n];
    if (s.inboundLayers.length === 0)
      return s.inputTensors;
    {
      let r = [];
      for (let a = 0; a < s.inboundLayers.length; a++) {
        let i = s.inputTensors[a], o = s.inboundLayers[a], u = s.nodeIndices[a], l = aI(i, o, u);
        for (let c of l)
          r.indexOf(c) === -1 && r.push(c);
      }
      return r;
    }
  }
}
var Jo = class extends He {
  constructor(e) {
    if (super({ dtype: e.dtype, name: e.name != null ? e.name : _p("input").toString() }), e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
      throw new G("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
    let t = e.batchInputShape;
    if (t == null) {
      if (e.inputShape == null)
        throw new G("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
      t = [e.batchSize].concat(e.inputShape);
    } else if (e.batchSize != null)
      throw new G("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");
    let n = e.dtype || "float32";
    this.batchInputShape = t, this.dtype = n, this.inputSpec = [{ shape: t }];
    let s = new $s(this.dtype, this.batchInputShape, this, [], {}, this.name);
    s.nodeIndex = 0, s.tensorIndex = 0, new Lp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: [s], outputTensors: [s], inputMasks: [null], outputMasks: [null], inputShapes: [t], outputShapes: [t] });
  }
  apply(e, t) {
    throw new G(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
  }
  dispose() {
    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };
  }
  getConfig() {
    return { batchInputShape: this.batchInputShape, dtype: this.dtype, sparse: this.sparse, name: this.name };
  }
};
Jo.className = "InputLayer";
re.registerClass(Jo);
function iI(e) {
  if (e.batchShape == null && e.shape == null)
    throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
  if (e.batchShape != null && e.shape != null)
    throw new G("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
  let t = e.batchShape;
  e.shape != null && t == null && (t = [null].concat(e.shape));
  let n = e.dtype;
  return n == null && (n = "float32"), new Jo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
}
function Iz(e, t) {
  if (e.dtype == null || e.dtype === t.dtype)
    return t;
  try {
    return le(t, e.dtype);
  } catch (n) {
    throw new G(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`);
  }
}
var Zr = class {
  constructor(e) {
    if (this.id2Value = {}, this.id2Mask = {}, this.name2Id = {}, e instanceof Zr)
      for (let t in e.id2Value)
        this.id2Value[t] = e.id2Value[t], t in e.id2Mask && (this.id2Mask[t] = e.id2Mask[t]);
    else {
      if (e == null)
        return;
      for (let t of e)
        this.add(t.key, t.value);
    }
  }
  add(e, t, n) {
    if (this.id2Value[e.id] == null)
      this.id2Value[e.id] = Iz(e, t), this.name2Id[e.name] = e.id, n != null && (this.id2Mask[e.id] = n);
    else
      throw new G(`Duplicate key: name=${e.name}, id=${e.id}`);
    return this;
  }
  addFeed(e) {
    this.add(e.key, e.value);
  }
  hasKey(e) {
    return this.id2Value[e.id] != null;
  }
  names() {
    return Object.keys(this.name2Id);
  }
  getValue(e) {
    if (e instanceof $s) {
      if (this.id2Value[e.id] == null)
        throw new G(`Nonexistent key: ${e.name}`);
      return this.id2Value[e.id];
    } else {
      let t = this.name2Id[e];
      if (t == null)
        throw new G(`Feed dict has no SymbolicTensor name: ${e}`);
      return this.id2Value[t];
    }
  }
  getMask(e) {
    if (e instanceof $s) {
      if (this.id2Value[e.id] == null)
        throw new G(`Nonexistent key: ${e.name}`);
      return this.id2Mask[e.id];
    } else {
      let t = this.name2Id[e];
      if (t == null)
        throw new G(`Feed dict has no SymbolicTensor name: ${e}`);
      return this.id2Mask[t];
    }
  }
  disposeMasks() {
    this.id2Mask != null && Re(this.id2Mask);
  }
};
var _d = new XS();
var Ad = new XS();
function Cz(e) {
  _d != null && _d.setMaxEntries(e), Ad != null && Ad.setMaxEntries(e);
}
function Ru(e, t, n, s) {
  let r = n == null ? false : n.training, a = Array.isArray(e), i = a ? e : [e], o = i.map((f) => f.name), u = [], l = t.names();
  for (let f of o)
    l.indexOf(f) !== -1 ? u.push(t.getValue(f)) : u.push(null);
  s != null && (s.maxNumTensors = -1 / 0, s.minNumTensors = 1 / 0);
  let c = o.join(",") + "|" + t.names().sort().join(","), p = _d.get(c), d;
  if (p == null) {
    let f = Nz(i, t);
    p = f.sorted, d = f.recipientCounts, _d.put(c, p), Ad.put(c, d);
  }
  d = {}, r || Object.assign(d, Ad.get(c));
  let h = new Zr(t);
  for (let f = 0; f < p.length; ++f) {
    if (s != null) {
      let A = mm().numTensors;
      A > s.maxNumTensors && (s.maxNumTensors = A), A < s.minNumTensors && (s.minNumTensors = A);
    }
    let m = p[f], g = m.sourceLayer;
    if (g instanceof Jo)
      continue;
    let b = [], y = [], v = [], x = false;
    for (let A of m.inputs) {
      let P = h.getValue(A), R = h.getMask(A);
      b.push(P), y.push(R), R != null && (x = true), r || (d[A.name]--, d[A.name] === 0 && !t.hasKey(A) && o.indexOf(A.name) === -1 && !P.isDisposed && A.sourceLayer.stateful !== true && v.push(P));
    }
    x && (n = n || {}, n.mask = y[0]);
    let k = pt(g.apply(b, n)), I = null;
    g.supportsMasking && (I = g.computeMask(b, y));
    let $ = $z(m), E = Array.isArray($) ? $ : [$];
    for (let A = 0; A < E.length; ++A) {
      h.hasKey(E[A]) || h.add(E[A], k[A], Array.isArray(I) ? I[0] : I);
      let P = o.indexOf(E[A].name);
      P !== -1 && (u[P] = k[A]);
    }
    r || Re(v);
  }
  return h.disposeMasks(), a ? u : u[0];
}
function Nz(e, t) {
  w.assert(e != null && e.length > 0, () => "Expected at least one fetch, got none");
  let n = [], s = {};
  if (e.length === 1) {
    let r = _x(e[0], t);
    n = r.sorted, s = r.recipientMap;
  } else {
    let r = /* @__PURE__ */ new Set();
    for (let a of e) {
      let { sorted: i, recipientMap: o } = _x(a, t);
      for (let u of i)
        r.has(u.name) || (n.push(u), r.add(u.name));
      for (let u in o)
        s[u] == null && (s[u] = /* @__PURE__ */ new Set()), o[u].forEach((l) => s[u].add(l));
    }
  }
  return { sorted: n, recipientCounts: Tz(s) };
}
function Tz(e) {
  let t = {};
  for (let n in e)
    t[n] = e[n].size;
  return t;
}
function _x(e, t) {
  let n = /* @__PURE__ */ new Set(), s = [], r = {};
  for (let o of t.names())
    n.add(o);
  let a = [], i = [];
  for (a.push(e); a.length > 0; ) {
    let o = a[a.length - 1];
    if (n.has(o.name)) {
      a.pop();
      continue;
    }
    let u = i[i.length - 1] === a.length - 1;
    if (o.inputs.length === 0 || u)
      a.pop(), s.push(o), n.add(o.name), u && i.pop();
    else {
      i.push(a.length - 1);
      for (let l of o.inputs)
        r[l.name] == null && (r[l.name] = /* @__PURE__ */ new Set()), r[l.name].add(o.name), !n.has(l.name) && a.push(l);
    }
  }
  return { sorted: s, recipientMap: r };
}
function $z(e) {
  let t;
  if (e.sourceLayer.inboundNodes.length === 1)
    t = e.sourceLayer.output;
  else {
    let n = null;
    for (let s = 0; s < e.sourceLayer.inboundNodes.length; ++s)
      for (let r of e.sourceLayer.inboundNodes[s].outputTensors)
        if (r.id === e.id) {
          n = s;
          break;
        }
    t = e.sourceLayer.getOutputAt(n);
  }
  return t;
}
var _z = K();
_z.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, Cz);
var oI = { kernelName: co, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, Sp(le(n, "float32"), -1)) };
} };
var Az = { kernelName: al, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = ct(le(n, "float32")), r = dn(ge(we(1), s));
    return kt(xe(e, r));
  } };
} };
var Ez = { kernelName: il, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = dn(ge(ct(le(n, "float32")), 1));
    return xe(e, s);
  } };
} };
var Rz = { kernelName: Sr, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = e, u = _t(n.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(o, n.shape);
  }, b: () => {
    let o = e, u = _t(s.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(o, s.shape);
  } };
} };
var Dz = { kernelName: Sa, saveAllInputs: true, gradFunc: (e, t) => {
  let n = {};
  return t.forEach((s, r) => {
    n[r] = () => e.clone();
  }), n;
} };
var Fz = { kernelName: Ia, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => je(n) };
} };
var Oz = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => je(n) };
} };
var Pz = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, dn(ge(we(1), ct(le(n, "float32"))))) };
} };
var zz = { kernelName: dl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = dn(ie(we(1), ct(le(n, "float32"))));
    return xe(e, s);
  } };
} };
var Mz = { kernelName: fl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
    return l.length > 0 && (u = ve(u, l)), U(u, n.shape);
  }, b: () => {
    let o = ie(ct(n), ct(s)), u = kt(V(e, xe(n, o))), l = _t(s.shape, r);
    return l.length > 0 && (u = ve(u, l)), U(u, s.shape);
  } };
} };
var Lz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ie(ct(le(n, "float32")), 1)) };
} };
var Bz = { kernelName: hl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ge(we(1), ct(le(n, "float32")))) };
} };
function Vz(e, t, n, s, r, a) {
  let i = _(e, "dy", "avgPool3dGrad"), o = _(t, "input", "avgPool3dGrad"), u = i, l = o, c = false;
  o.rank === 4 && (c = true, u = U(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), l = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), O(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), hn("avgPool3dGrad", r, a);
  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = M.runKernel(dg, p, d);
  return c ? U(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
}
var Wz = L({ avgPool3dGrad_: Vz });
var Uz = { kernelName: Qd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
  return { x: () => Wz(e, s, r, a, i, o) };
} };
function Gz(e, t, n, s, r) {
  let a = _(e, "dy", "avgPoolGrad"), i = _(t, "input", "avgPoolGrad");
  O(i.rank === a.rank, () => `Rank of input (${i.rank}) does not match rank of dy (${a.rank})`);
  let o = i, u = a, l = false;
  i.rank === 3 && (l = true, o = U(i, [1, i.shape[0], i.shape[1], i.shape[2]]), u = U(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), O(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = M.runKernel(cg, c, p);
  return l ? U(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var Hz = L({ avgPoolGrad_: Gz });
var qz = { kernelName: Ca, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { filterSize: r, strides: a, pad: i } = n;
  return { x: () => Hz(e, s, r, a, i) };
} };
var jz = { kernelName: Na, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
  let [s, r] = t, { transposeA: a, transposeB: i } = n;
  return !a && !i ? { a: () => Ve(e, r, false, true), b: () => Ve(s, e, true, false) } : !a && i ? { a: () => Ve(e, r, false, false), b: () => Ve(e, s, true, false) } : a && !i ? { a: () => Ve(r, e, false, true), b: () => Ve(s, e, false, false) } : { a: () => Ve(r, e, true, true), b: () => Ve(e, s, true, true) };
} };
var Kz = { kernelName: po, gradFunc: (e, t, n) => {
  let { blockShape: s, crops: r } = n;
  return { x: () => ob(e, s, r) };
} };
var Xz = { kernelName: L$, gradFunc: (e, t, n) => {
  let s = n, r = s.inputShape, a = s.shape, i = Array.from(a);
  for (let u = r.length - 1; u >= 0; u--)
    if (r[u] === a[u])
      i[u] = 1;
    else if (r[u] !== 1)
      throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);
  let o = [];
  for (let u = 0; u < i.length; u++)
    i[u] > 1 && o.push(u);
  return { x: () => ve(e, o, true) };
} };
var Yz = { kernelName: Ta, gradFunc: (e) => ({ x: () => e.clone() }) };
var Qz = { kernelName: $a, gradFunc: (e) => ({ x: () => je(e) }) };
var Zz = { kernelName: Ir, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
  return { x: () => vn(Ds(Yo(s, r), Qo(s, a)), e, je(e)) };
} };
var Jz = { kernelName: Jd, inputsToSave: ["x"], gradFunc: oI.gradFunc };
var eM = { kernelName: ho, saveAllInputs: true, gradFunc: (e, t, n) => {
  let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], i = s.map((u) => u[a]);
  return Bn(e, i, a).map((u) => () => u);
} };
var tM = { kernelName: _a, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
  return O(fr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => Qg(s.shape, e, r, i, o, u), filter: () => bb(s, e, r.shape, i, o, u) };
} };
var nM = { kernelName: Aa, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
  return { dy: () => da(e, r, a, i, o, 1, u), filter: () => bb(e, s, r.shape, a, i, o, u) };
} };
function sM(e, t, n, s, r) {
  let a = e;
  e.rank === 4 && (a = U(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]));
  let i = t;
  i.rank === 4 && (i = U(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), O(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), O(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), O(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), O(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), O(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
  let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
  return M.runKernel(mg, o, u);
}
var rM = L({ conv3DBackpropFilter_: sM });
var aM = { kernelName: ep, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let { dilations: s, strides: r, pad: a } = n;
  O(fr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
  let [i, o] = t;
  return { x: () => lS(i.shape, e, o, r, a), filter: () => rM(i, e, o.shape, r, a) };
} };
var iM = { kernelName: Ea, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(kt(NS(le(n, "float32"))), e) };
} };
var oM = { kernelName: Ra, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(TS(le(n, "float32")), e) };
} };
var uM = { kernelName: Da, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
  return { x: () => {
    let o = bS([r], s.rank), u = dS(e, r, a, !i);
    return o != null && (u = Ge(u, o)), u;
  } };
} };
var lM = { kernelName: Fa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let { dilations: s, strides: r, pad: a, dimRoundingMode: i } = n, o = s == null ? [1, 1] : s;
  O(fr(o), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);
  let [u, l] = t;
  return O(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), O(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), O(Ps(r, o), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`), hn("depthwiseConv2d", a, i), { x: () => MS(u.shape, e, l, r, a, o, i), filter: () => zS(u, e, l.shape, r, a, o, i) };
} };
var cM = { kernelName: tp, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, a = { x: s, filter: r, dy: e }, i = { x: s, filter: r, dy: e };
  return { x: () => M.runKernel(em, a, n), filter: () => M.runKernel(tm, i, n) };
} };
var dM = { kernelName: Pa, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t, s = { dy: e, y: n };
  return { x: () => M.runKernel(wg, s) };
} };
var pM = { kernelName: ml, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t, s = V(Yn(kt(ct(n))), 2 / Math.sqrt(Math.PI));
  return { x: () => V(e, s) };
} };
var hM = { kernelName: za, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, n) };
} };
var fM = { kernelName: yo, inputsToSave: ["input"], gradFunc: (e, t) => {
  let [n] = t;
  return { input: () => U(e, n.shape) };
} };
var mM = { kernelName: vo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, Yn(n)) };
} };
var gM = { kernelName: Ma, gradFunc: (e) => ({ x: () => je(e) }) };
var bM = { kernelName: La, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = xe(e, le(s, "float32")), u = _t(n.shape, r);
    return u.length > 0 ? U(ve(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, le(n, "float32")), u = _t(s.shape, r);
    u.length > 0 && (o = U(ve(o, u), s.shape));
    let l = ct(s);
    return kt(xe(o, le(l, "float32")));
  } };
} };
var yM = { kernelName: Ba, inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (e, t, n) => {
  let { varianceEpsilon: s } = n, [r, a, i, o] = t, u = o == null ? we(1) : o, l = _t(a.shape, r.shape), c = [];
  if (a.rank === 1) {
    for (let x = 0; x < r.shape.length - 1; ++x)
      c.push(r.shape[x]);
    c.push(1);
  }
  let p = ge(r, a), d = V(e, u), h = IS(ie(i, we(s))), f = V(V(V(h, h), h), we(-0.5));
  return { x: () => a.rank === 1 ? U(V(V(e, hs(U(h, [1, 1, 1, a.shape[0]]), c)), u), r.shape) : U(V(V(e, h), u), r.shape), mean: () => {
    let x = V(V(h, we(-1)), d);
    return a.rank === 1 && (x = ve(x, l)), U(x, a.shape);
  }, variance: () => {
    let x = V(V(f, p), d);
    return a.rank === 1 && (x = ve(x, l)), U(x, a.shape);
  }, scale: () => {
    let x = V(p, h), k = V(e, x);
    return a.rank === 1 && (k = ve(k, l)), U(k, a.shape);
  }, offset: () => {
    let x = e;
    return a.rank === 1 && (x = ve(x, l)), U(x, a.shape);
  } };
} };
var vM = { kernelName: wo, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
  let [s, r] = t, { axis: a } = n, i = ts(a, s.shape)[0];
  return { x: () => {
    let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = Ax(0, p), m = Ax(p + 1, p + 1 + h), g = Ex([c, [l], d]), b = U(e, g), y = U(r, [l]), v = Ex([[p], f, m]), x = Ge(b, v), k = Q3(x, y, s.shape[i]), I = sb(v);
    return k = Ge(k, I), k;
  }, indices: () => r };
} };
function Ax(e, t) {
  let n = [];
  for (let s = e; s < t; ++s)
    n.push(s);
  return n;
}
function Ex(e) {
  let t = [];
  for (let n = 0; n < e.length; ++n)
    for (let s = 0; s < e[n].length; ++s)
      t.push(e[n][s]);
  return t;
}
var xM = { kernelName: Va, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => je(n), b: () => je(s) };
} };
var wM = { kernelName: Wa, gradFunc: (e) => ({ x: () => le(e, "float32") }) };
var kM = { kernelName: bl, gradFunc: (e) => ({ x: () => je(e) }) };
var SM = { kernelName: yl, gradFunc: (e) => ({ x: () => je(e) }) };
var IM = { kernelName: vl, gradFunc: (e) => ({ x: () => je(e) }) };
var CM = { kernelName: Ua, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { alpha: r } = n, a = Un(s, 0);
  return { x: () => vn(a, e, V(e, r)) };
} };
var NM = { kernelName: xl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ie(n, 1)) };
} };
var TM = { kernelName: Ga, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, le(n, "float32")) };
} };
var $M = { kernelName: B$, inputsToSave: [], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r } = n;
  return { logits: () => {
    let i = Yn(s);
    return ge(e, V(ve(e, r, true), i));
  } };
} };
function _M(e, t, n, s = 5, r = 1, a = 1, i = 0.5) {
  let o = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: i };
  return M.runKernel(Cg, o, u);
}
var AM = L({ localResponseNormalizationBackprop_: _M });
var EM = { kernelName: ap, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
  return { x: () => AM(s, r, e, a, i, o, u) };
} };
function uI(e, t, n, s) {
  return t.rank < n.rank && (t = U(t, pa(t.shape, s))), e.rank < n.rank && (e = U(e, pa(e.shape, s))), { x: () => V(e, le(Xn(n, t), e.dtype)) };
}
var Rx = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let s = n, { reductionIndices: r } = s, a = t[0], i = t[1], o = ts(r, a.shape), u = uI(e, i, a, o);
  return { x: () => u.x() };
} };
var RM = { kernelName: qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => V(e, le(Yo(n, s), "float32")), b: () => V(e, le(hS(n, s), "float32")) };
} };
function DM(e, t, n, s, r, a, i) {
  let o = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = o, p = u, d = l, h = false;
  u.rank === 4 && (h = true, c = U(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), p = U(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = U(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), O(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), O(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), O(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), hn("maxPool3dGrad", a, i);
  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = M.runKernel(Tg, f, m);
  return h ? U(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
}
var FM = L({ maxPool3dGrad_: DM });
var OM = { kernelName: ip, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
  return { x: () => FM(e, s, r, a, i, o, u) };
} };
function PM(e, t, n, s, r, a, i) {
  let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
  O(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), O(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), O(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), hn("maxPoolGrad", a, i);
  let c = { dy: o, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: i };
  return M.runKernel(Ng, c, p);
}
var zM = L({ maxPoolGrad_: PM });
var MM = { kernelName: ja, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { filterSize: a, strides: i, pad: o } = n;
  return { x: () => zM(e, s, r, a, i, o) };
} };
var LM = { kernelName: Ka, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r } = n, a = ts(r, s.shape), o = gS(s.shape, a)[1], u = dt(o);
  return { x: () => {
    let c = s.shape.slice();
    a.forEach((h) => {
      c[h] = 1;
    });
    let p = U(e, c);
    return xe(V(p, Mn(s.shape, "float32")), u);
  } };
} };
var BM = { kernelName: Xa, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let s = n, { axis: r } = s, [a, i] = t, o = ts(r, a.shape), u = uI(e, i, a, o);
  return { x: () => u.x() };
} };
var VM = { kernelName: Ya, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => V(e, le(Qo(n, s), "float32")), b: () => V(e, le(Un(n, s), "float32")) };
} };
var WM = { kernelName: Qa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
  return { x: () => qe(e, a, s.shape) };
} };
var UM = { kernelName: kl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = _t(n.shape, r);
    return o.length > 0 ? U(ve(e, o), n.shape) : e;
  }, b: () => {
    let o = V(e, kt(xp(xe(n, s)))), u = _t(s.shape, r);
    return u.length > 0 ? U(ve(o, u), s.shape) : o;
  } };
} };
var GM = { kernelName: Za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = V(e, le(s, "float32")), u = _t(n.shape, r);
    return u.length > 0 ? U(ve(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, le(n, "float32")), u = _t(s.shape, r);
    return u.length > 0 ? U(ve(o, u), s.shape) : o;
  } };
} };
var HM = { kernelName: To, gradFunc: (e) => ({ x: () => kt(e) }) };
var qM = { kernelName: Ro, inputsToSave: ["indices"], gradFunc: (e, t) => {
  let n = t[0];
  return { indices: () => $t(n.shape, "float32") };
} };
var jM = { kernelName: Eo, gradFunc: (e) => ({ x: () => je(e) }) };
var KM = { kernelName: Do, saveAllInputs: true, gradFunc: (e, t, n) => {
  let { axis: s } = n;
  return Fs(e, s).map((a) => () => a);
} };
var Dx = { kernelName: Ja, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
  return { x: () => qe(e, a, s.shape) };
} };
var XM = { kernelName: ei, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
  let [n, s, r] = t, a = n, i = s, o = it(a.shape, i.shape);
  return { a: () => {
    let c = le(i, "float32"), p = V(e, V(c, ha(a, ge(c, we(1))))), d = _t(a.shape, o);
    return d.length > 0 && (p = ve(p, d)), U(p, a.shape);
  }, b: () => {
    let c = Un(a, 0), p = vn(c, Qn(a), je(a)), d = V(e, V(r, p)), h = _t(i.shape, o);
    return h.length > 0 && (d = ve(d, h)), U(d, i.shape);
  } };
} };
var YM = { kernelName: ti, inputsToSave: ["x", "alpha"], gradFunc: (e, t) => {
  let [n, s] = t, r = Un(n, 0);
  return { x: () => vn(r, e, V(e, s)), alpha: () => {
    let a = vn(r, je(e), V(e, n)), i = _t(s.shape, e.shape);
    return i.length > 0 && (a = ve(a, i)), U(a, s.shape);
  } };
} };
function QM(e, t, n) {
  let s = e.shape.slice();
  s[n] = 1;
  let r = U(t, s), a = ym(e, n, true, false), i = ym(e, n, true, true), o = V(a, i);
  return V(r, o);
}
function ZM(e, t, n) {
  let s = e.shape.length, r = s - n.length, a = C.getAxesPermutation(n, s), i = e;
  a != null && (i = Ge(e, a));
  let o = i.shape.slice(), l = o.splice(s - n.length, n.length).reduce((d, h) => d * h, 1);
  o.push(l);
  let c = i.reshape(o), p = QM(c, t, r);
  if (p = p.reshape(i.shape), a != null) {
    let d = C.getUndoAxesPermutation(a);
    p = Ge(p, d);
  }
  return p;
}
var JM = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r } = n, a = [];
  return r == null ? a = s.shape.map((i, o) => o) : typeof r == "number" ? a = [r] : a = r, { x: () => ZM(s, e, a) };
} };
var eL = { kernelName: Oa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = xe(e, le(s, "float32")), u = _t(n.shape, r);
    return u.length > 0 ? U(ve(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, le(n, "float32")), u = _t(s.shape, r);
    u.length > 0 && (o = U(ve(o, u), s.shape));
    let l = ct(s);
    return kt(xe(o, le(l, "float32")));
  } };
} };
var tL = { kernelName: Cl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, kt(ct(n))) };
} };
var nL = { kernelName: ai, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t, s = V(Qo(n, 6), Sp(n));
  return { x: () => V(e, le(s, "float32")) };
} };
var sL = { kernelName: si, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, le(Sp(n), "float32")) };
} };
var rL = { kernelName: Fo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => U(e, n.shape) };
} };
var aL = { kernelName: ri, inputsToSave: ["images"], gradFunc: (e, t, n) => {
  let [s] = t, r = { dy: e, images: s };
  return { images: () => M.runKernel(Eg, r, n) };
} };
var iL = { kernelName: Nl, inputsToSave: ["images"], gradFunc: (e, t, n) => {
  let [s] = t, r = { dy: e, images: s };
  return { images: () => M.runKernel(Ag, r, n) };
} };
var oL = { kernelName: Oo, gradFunc: (e, t, n) => {
  let { dims: s } = n, r = ts(s, e.shape);
  return { x: () => Jn(e, r) };
} };
var uL = { kernelName: Po, gradFunc: (e) => ({ x: () => je(e) }) };
var lL = { kernelName: ii, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => kt(xe(e, V(ha(n, 1.5), 2))) };
} };
var cL = { kernelName: Mo, inputsToSave: ["condition"], gradFunc: (e, t) => {
  let [n] = t;
  return { condition: () => le(je(n), "float32"), t: () => V(e, le(n, e.dtype)), e: () => V(e, le(rb(n), e.dtype)) };
} };
var dL = { kernelName: Tl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = Un(n, we(0)), r = we(HS), a = we(qS), i = V(e, a), o = V(V(e, r), Yn(le(n, "float32")));
    return vn(s, i, o);
  } };
} };
var pL = { kernelName: ui, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, V(n, ge(we(1), n))) };
} };
var hL = { kernelName: $l, gradFunc: (e) => ({ x: () => je(e) }) };
var fL = { kernelName: oi, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(Zg(le(n, "float32")), e) };
} };
var mL = { kernelName: Bo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(cS(le(n, "float32")), e) };
} };
var gL = { kernelName: Lo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = Kk(s, r, a), l = [];
  for (let c = 0; c < e.rank; c++)
    l.push([o[c], i[c] - o[c] - u[c]]);
  return { x: () => yi(e, l) };
} };
var bL = { kernelName: di, outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s] = t, { dim: r } = n, a = true, i = V(e, s);
  return { logits: () => ge(i, V(ve(i, [r], a), s)) };
} };
var yL = { kernelName: _l, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, Hs(n)) };
} };
var Fx = { kernelName: Vo, gradFunc: (e, t, n) => {
  let { blockShape: s, paddings: r } = n;
  return { x: () => Yg(e, s, r) };
} };
var Ox = { kernelName: Wo, gradFunc: (e, t, n) => {
  let { axis: s } = n;
  return { x: () => Ft(e, s) };
} };
var vL = { kernelName: li, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, V(dn(le(n, "float32")), 2)) };
} };
var xL = { kernelName: El, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, V(le(n, "float32"), 2)) };
} };
var wL = { kernelName: pi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = we(2);
  return { a: () => V(e, V(r, ge(n, s))), b: () => V(e, V(r, ge(s, n))) };
} };
var kL = { kernelName: gi, gradFunc: (e) => ({ x: () => je(e) }) };
var SL = { kernelName: hi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = e, u = _t(n.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(o, n.shape);
  }, b: () => {
    let o = e, u = _t(s.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(kt(o), s.shape);
  } };
} };
var IL = { kernelName: ci, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, r = s.shape.slice(), { axis: a } = n;
  ts(a, s.shape).forEach((l) => {
    r[l] = 1;
  });
  let o = U(e, r), u = V(o, Mn(s.shape, "float32"));
  return { x: () => u };
} };
var CL = { kernelName: Go, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ct(Zg(n))) };
} };
var NL = { kernelName: fi, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(ge(we(1), ct(n)), e) };
} };
var TL = { kernelName: Cr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { reps: r } = n;
  return { x: () => {
    let i = je(s);
    if (s.rank === 1)
      for (let o = 0; o < r[0]; ++o)
        i = ie(i, qe(e, [o * s.shape[0]], [s.shape[0]]));
    else if (s.rank === 2)
      for (let o = 0; o < r[0]; ++o)
        for (let u = 0; u < r[1]; ++u)
          i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1]], [s.shape[0], s.shape[1]]));
    else if (s.rank === 3)
      for (let o = 0; o < r[0]; ++o)
        for (let u = 0; u < r[1]; ++u)
          for (let l = 0; l < r[2]; ++l)
            i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2]], [s.shape[0], s.shape[1], s.shape[2]]));
    else if (s.rank === 4)
      for (let o = 0; o < r[0]; ++o)
        for (let u = 0; u < r[1]; ++u)
          for (let l = 0; l < r[2]; ++l)
            for (let c = 0; c < r[3]; ++c)
              i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2], c * s.shape[3]], [s.shape[0], s.shape[1], s.shape[2], s.shape[3]]));
    else
      throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);
    return i;
  } };
} };
var $L = { kernelName: mi, gradFunc: (e, t, n) => {
  let s = n, { perm: r } = s, a = sb(r);
  return { x: () => Ge(e, a) };
} };
var _L = { kernelName: jo, gradFunc: (e, t, n) => {
  let s = n, { axis: r } = s;
  return { value: () => es(e, r) };
} };
var AL = { kernelName: hp, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => EL(e, n) };
} };
function EL(e, t) {
  let n = $r(t, je(t)), s = Yu(e, n), r = Yo(t, we(0, "int32")), a = s.rank - r.rank;
  for (let o = 0; o < a; ++o)
    r = Pn(r, o + 1);
  r = Ds(r, Mn(s.shape, "bool"));
  let i = je(s);
  return vn(r, s, i);
}
var RL = { kernelName: Ko, gradFunc: (e) => ({ x: () => je(e) }) };
var DL = [oI, Az, Ez, Rz, Dz, Fz, Oz, Pz, zz, Mz, Lz, Bz, Uz, qz, jz, Kz, Xz, Yz, Qz, Zz, Jz, eM, nM, tM, aM, iM, oM, uM, lM, cM, eL, dM, pM, hM, fM, mM, bM, gM, yM, vM, xM, wM, kM, SM, IM, CM, NM, TM, $M, EM, Rx, Rx, RM, OM, MM, LM, BM, VM, WM, UM, GM, HM, qM, jM, KM, Dx, Dx, XM, YM, JM, tL, nL, sL, rL, aL, iL, oL, uL, lL, cL, dL, pL, hL, fL, mL, gL, bL, yL, Fx, Fx, Ox, Ox, vL, wL, xL, kL, SL, IL, CL, NL, TL, $L, _L, AL, RL];
for (let e of DL)
  W$(e);
var FL = {};
Ae(FL, { maxNorm: () => OL, minMaxNorm: () => ML, nonNeg: () => zL, unitNorm: () => PL });
function zb(e, t) {
  return q(() => dn(ve(V(e, e), t, true)));
}
var Gl = class extends re.Serializable {
  getConfig() {
    return {};
  }
};
var Mb = class extends Gl {
  constructor(e) {
    super(), this.defaultMaxValue = 2, this.defaultAxis = 0, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.axis = e.axis != null ? e.axis : this.defaultAxis;
  }
  apply(e) {
    return q(() => {
      let t = zb(e, this.axis), n = Vn(t, 0, this.maxValue);
      return V(e, xe(n, ie(Rt(), t)));
    });
  }
  getConfig() {
    return { maxValue: this.maxValue, axis: this.axis };
  }
};
Mb.className = "MaxNorm";
re.registerClass(Mb);
var Lb = class extends Gl {
  constructor(e) {
    super(), this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
  }
  apply(e) {
    return q(() => xe(e, ie(Rt(), zb(e, this.axis))));
  }
  getConfig() {
    return { axis: this.axis };
  }
};
Lb.className = "UnitNorm";
re.registerClass(Lb);
var Bb = class extends Gl {
  apply(e) {
    return Xs(e);
  }
};
Bb.className = "NonNeg";
re.registerClass(Bb);
var Vb = class extends Gl {
  constructor(e) {
    super(), this.defaultMinValue = 0, this.defaultMaxValue = 1, this.defaultRate = 1, this.defaultAxis = 0, this.minValue = e.minValue != null ? e.minValue : this.defaultMinValue, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.rate = e.rate != null ? e.rate : this.defaultRate, this.axis = e.axis != null ? e.axis : this.defaultAxis;
  }
  apply(e) {
    return q(() => {
      let t = zb(e, this.axis), n = ie(V(this.rate, Vn(t, this.minValue, this.maxValue)), V(1 - this.rate, t));
      return V(e, xe(n, ie(Rt(), t)));
    });
  }
  getConfig() {
    return { minValue: this.minValue, maxValue: this.maxValue, rate: this.rate, axis: this.axis };
  }
};
Vb.className = "MinMaxNorm";
re.registerClass(Vb);
var Px = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
function Ot(e) {
  return Nb(e);
}
function zx(e, t = {}) {
  return Bl(e, re.SerializationMap.getMap().classNameMap, t, "constraint");
}
function Pt(e) {
  if (e == null)
    return null;
  if (typeof e == "string") {
    let n = { className: e in Px ? Px[e] : e, config: {} };
    return zx(n);
  } else
    return e instanceof Gl ? e : zx(e);
}
function OL(e) {
  return new Mb(e);
}
function PL(e) {
  return new Lb(e);
}
function zL() {
  return new Bb();
}
function ML(e) {
  return new Vb(e);
}
var LL = {};
Ae(LL, { constant: () => WL, glorotNormal: () => XL, glorotUniform: () => KL, heNormal: () => YL, heUniform: () => QL, identity: () => qL, leCunNormal: () => ZL, leCunUniform: () => JL, ones: () => VL, orthogonal: () => eB, randomNormal: () => GL, randomUniform: () => UL, truncatedNormal: () => HL, varianceScaling: () => jL, zeros: () => BL });
function BL() {
  return new _b();
}
function VL() {
  return new Rp();
}
function WL(e) {
  return new Ab(e);
}
function UL(e) {
  return new Eb(e);
}
function GL(e) {
  return new Rb(e);
}
function HL(e) {
  return new Db(e);
}
function qL(e) {
  return new Fb(e);
}
function jL(e) {
  return new xn(e);
}
function KL(e) {
  return new Dp(e);
}
function XL(e) {
  return new Fp(e);
}
function YL(e) {
  return new Op(e);
}
function QL(e) {
  return new Pp(e);
}
function ZL(e) {
  return new zp(e);
}
function JL(e) {
  return new Mp(e);
}
function eB(e) {
  return new Ob(e);
}
var tB = {};
Ae(tB, { Layer: () => He, RNN: () => Ar, RNNCell: () => jl, activation: () => NV, add: () => OV, alphaDropout: () => yW, average: () => PV, averagePooling1d: () => Yy, averagePooling2d: () => Qy, averagePooling3d: () => Zy, avgPool1d: () => HV, avgPool2d: () => jV, avgPool3d: () => XV, avgPooling1d: () => qV, avgPooling2d: () => KV, avgPooling3d: () => YV, batchNormalization: () => WV, bidirectional: () => cW, concatenate: () => zV, conv1d: () => bV, conv2d: () => yV, conv2dTranspose: () => vV, conv3d: () => xV, conv3dTranspose: () => wV, convLstm2d: () => iW, convLstm2dCell: () => oW, cropping2D: () => SV, dense: () => TV, depthwiseConv2d: () => CV, dot: () => VV, dropout: () => $V, elu: () => dV, embedding: () => FV, flatten: () => AV, gaussianDropout: () => bW, gaussianNoise: () => gW, globalAveragePooling1d: () => QV, globalAveragePooling2d: () => ZV, globalMaxPool1d: () => pW, globalMaxPool2d: () => hW, globalMaxPooling1d: () => e0, globalMaxPooling2d: () => t0, gru: () => eW, gruCell: () => tW, input: () => QB, inputLayer: () => cV, layerNormalization: () => UV, leakyReLU: () => hV, lstm: () => nW, lstmCell: () => sW, masking: () => vW, maxPool1d: () => fW, maxPool2d: () => mW, maxPooling1d: () => n0, maxPooling2d: () => s0, maxPooling3d: () => JV, maximum: () => MV, minimum: () => LV, multiply: () => BV, permute: () => DV, prelu: () => fV, reLU: () => pV, repeatVector: () => EV, reshape: () => RV, rnn: () => uW, separableConv2d: () => kV, simpleRNN: () => rW, simpleRNNCell: () => aW, softmax: () => mV, spatialDropout1d: () => _V, stackedRNNCells: () => lW, thresholdedReLU: () => gV, timeDistributed: () => dW, upSampling2d: () => IV, zeroPadding2d: () => GV });
async function sr(e) {
  if (e == null)
    return;
  let t = [], n = [], s = [];
  for (let r in e) {
    let a = e[r];
    if (typeof a != "number") {
      let i = a;
      t.push(i.data()), n.push(r), s.push(i);
    }
  }
  if (t.length > 0) {
    let r = await Promise.all(t);
    for (let a = 0; a < r.length; ++a)
      e[n[a]] = r[a][0];
    Re(s);
  }
}
function lI(e) {
  if (e != null)
    for (let t in e) {
      let n = e[t];
      typeof n != "number" && n.dispose();
    }
}
var nB = 125;
var so = class {
  constructor() {
    this.validationData = null;
  }
  setParams(e) {
    this.params = e;
  }
  async onEpochBegin(e, t) {
  }
  async onEpochEnd(e, t) {
  }
  async onBatchBegin(e, t) {
  }
  async onBatchEnd(e, t) {
  }
  async onTrainBegin(e) {
  }
  async onTrainEnd(e) {
  }
  setModel(e) {
  }
};
var sB = class {
  constructor(e, t = 10) {
    e == null && (e = []), this.callbacks = e, this.queueLength = t;
  }
  append(e) {
    this.callbacks.push(e);
  }
  setParams(e) {
    for (let t of this.callbacks)
      t.setParams(e);
  }
  setModel(e) {
    for (let t of this.callbacks)
      t.setModel(e);
  }
  async onEpochBegin(e, t) {
    t == null && (t = {});
    for (let n of this.callbacks)
      await n.onEpochBegin(e, t);
  }
  async onEpochEnd(e, t) {
    t == null && (t = {});
    for (let n of this.callbacks)
      await n.onEpochEnd(e, t);
  }
  async onBatchBegin(e, t) {
    t == null && (t = {});
    for (let n of this.callbacks)
      await n.onBatchBegin(e, t);
  }
  async onBatchEnd(e, t) {
    t == null && (t = {});
    for (let n of this.callbacks)
      await n.onBatchEnd(e, t);
  }
  async onTrainBegin(e) {
    e == null && (e = {});
    for (let t of this.callbacks)
      await t.onTrainBegin(e);
  }
  async onTrainEnd(e) {
    e == null && (e = {});
    for (let t of this.callbacks)
      await t.onTrainEnd(e);
  }
};
var rB = class extends so {
  constructor() {
    super();
  }
  async onEpochBegin(e) {
    this.seen = 0, this.totals = {};
  }
  async onBatchEnd(e, t) {
    t == null && (t = {});
    let n = t.size == null ? 0 : t.size;
    this.seen += n;
    for (let s in t) {
      let r = t[s];
      if (typeof r == "number")
        this.totals.hasOwnProperty(s) || (this.totals[s] = 0), this.totals[s] = this.totals[s] + r * n;
      else {
        let a;
        s in this.totals ? a = this.totals[s] : this.totals[s] = 0;
        let i = q(() => ie(this.totals[s], V(r, n)));
        this.totals[s] = i, a != null && a.dispose();
      }
    }
  }
  async onEpochEnd(e, t) {
    if (t != null)
      for (let n of this.params.metrics)
        this.totals[n] != null && (typeof this.totals[n] == "number" ? t[n] = this.totals[n] / this.seen : q(() => {
          let s = V(xe(1, this.seen), this.totals[n]);
          t[n] = s, this.totals[n].dispose(), qt(t[n]);
        }));
  }
};
var aB = class extends so {
  async onTrainBegin(e) {
    this.epoch = [], this.history = {};
  }
  async onEpochEnd(e, t) {
    t == null && (t = {}), this.epoch.push(e);
    for (let n in t)
      this.history[n] == null && (this.history[n] = []), this.history[n].push(t[n]);
  }
  async syncData() {
    let e = [], t = [], n = [];
    for (let r in this.history) {
      let a = this.history[r];
      for (let i = 0; i < a.length; ++i)
        if (typeof a[i] != "number") {
          let o = a[i];
          e.push(o.data()), t.push(r), n.push(i);
        }
    }
    let s = await Promise.all(e);
    for (let r = 0; r < s.length; ++r)
      this.history[t[r]][n[r]].dispose(), this.history[t[r]][n[r]] = s[r][0];
  }
};
var iB = class extends so {
  constructor(e, t) {
    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || GS, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = nB), this.yieldEvery === "never" && e.onYield != null)
      throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
    w.isNumber(this.yieldEvery) && (this.maybeWait = YP(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc)), this.trainBegin = e.onTrainBegin, this.trainEnd = e.onTrainEnd, this.epochBegin = e.onEpochBegin, this.epochEnd = e.onEpochEnd, this.batchBegin = e.onBatchBegin, this.batchEnd = e.onBatchEnd, this.yield = e.onYield;
  }
  async maybeWait(e, t, n) {
    let s = [];
    this.yield != null && (await sr(n), s.push(this.yield(e, t, n))), s.push(this.nextFrameFunc()), await Promise.all(s);
  }
  async onEpochBegin(e, t) {
    this.currentEpoch = e, this.epochBegin != null && (await sr(t), await this.epochBegin(e, t));
  }
  async onEpochEnd(e, t) {
    let n = [];
    this.epochEnd != null && (await sr(t), n.push(this.epochEnd(e, t))), this.yieldEvery === "epoch" && n.push(this.nextFrameFunc()), await Promise.all(n);
  }
  async onBatchBegin(e, t) {
    this.batchBegin != null && (await sr(t), await this.batchBegin(e, t));
  }
  async onBatchEnd(e, t) {
    let n = [];
    this.batchEnd != null && (await sr(t), n.push(this.batchEnd(e, t))), this.yieldEvery === "batch" ? n.push(this.nextFrameFunc()) : w.isNumber(this.yieldEvery) && n.push(this.maybeWait(this.currentEpoch, e, t)), await Promise.all(n);
  }
  async onTrainBegin(e) {
    this.trainBegin != null && (await sr(e), await this.trainBegin(e));
  }
  async onTrainEnd(e) {
    this.trainEnd != null && (await sr(e), await this.trainEnd(e));
  }
};
function cI(e, t) {
  return e == null && (e = {}), e instanceof so ? [e] : Array.isArray(e) && e[0] instanceof so ? e : pt(e).map((s) => new iB(s, t));
}
var Ss = class {
  constructor() {
  }
  static registerCallbackConstructor(e, t) {
    w.assert(e >= 0 && Number.isInteger(e), () => `Verbosity level is expected to be an integer >= 0, but got ${e}`), Ss.checkForDuplicate(t), Ss.constructors[e] == null && (Ss.constructors[e] = []), Ss.constructors[e].push(t);
  }
  static checkForDuplicate(e) {
    for (let t in Ss.constructors)
      Ss.constructors[+t].forEach((s) => {
        if (s === e)
          throw new G("Duplicate callback constructor.");
      });
  }
  static clear() {
    Ss.constructors = {};
  }
  static createCallbacks(e) {
    let t = [];
    for (let n in Ss.constructors) {
      let s = +n;
      e >= s && t.push(...Ss.constructors[s]);
    }
    return t.map((n) => new n());
  }
};
var Wb = Ss;
Wb.constructors = {};
function dI(e, t, n, s, r, a, i, o, u) {
  let l = new aB(), c = [new rB(), ...Wb.createCallbacks(t)];
  e != null && c.push(...e), c.push(l);
  let p = new sB(c);
  return p.setParams({ epochs: n, initialEpoch: s, samples: r, steps: a, batchSize: i, verbose: t, doValidation: o, metrics: u }), { callbackList: p, history: l };
}
function gs(e, t = {}, n = false) {
  return Bl(e, re.SerializationMap.getMap().classNameMap, t, "layer", n);
}
function Ed(e, t) {
  return q(() => {
    e.dtype !== "float32" && (e = le(e, "float32"));
    let n = ve(Wl(e), t, true), s = zl(n.shape, Rt()), r = dn($r(n, s));
    return xe(e, r);
  });
}
function xi(e, t) {
  return q(() => It(Wl(ge(t, e)), -1));
}
function Bp(e, t) {
  return q(() => It(Lt(ge(t, e)), -1));
}
function eu(e, t) {
  return q(() => {
    let n = ge(e, t), s = Vn(Lt(e), Rt(), Number.MAX_VALUE), r = Lt(xe(n, s));
    return V(100, It(r, -1));
  });
}
function oB(e, t) {
  return q(() => {
    let n = Vn(t, Rt(), Number.MAX_VALUE), s = Qn(ie(1, n)), r = Vn(e, Rt(), Number.MAX_VALUE), a = Qn(ie(1, r));
    return It(Wl(ge(s, a)), -1);
  });
}
function uB(e, t) {
  return q(() => {
    let n = $r(0, ge(1, V(e, t)));
    return It(Wl(n), -1);
  });
}
function lB(e, t) {
  return q(() => {
    let n = $r(0, ge(1, V(e, t)));
    return It(n, -1);
  });
}
function cB(e, t) {
  return q(() => {
    let n = ve(V(e, t), -1), s = As(V(ge(1, e), t), -1);
    return $r(0, ie(1, ge(s, n)));
  });
}
function dB(e, t) {
  return q(() => {
    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Ml(V(-2, s))), n);
    return It(r, -1);
  });
}
function Ju(e, t, n = false) {
  return q(() => {
    if (n)
      t = hb(t);
    else {
      let s = ve(t, t.shape.length - 1, true);
      t = xe(t, s);
    }
    return t = Vn(t, Rt(), 1 - Rt()), kt(ve(V(le(e, "float32"), Qn(t)), t.shape.length - 1));
  });
}
function Rd(e, t, n = false) {
  return q(() => {
    let s = le(xp(uz(e)), "int32");
    t = Vn(t, Rt(), 1 - Rt());
    let r = t.shape, a = U(kd(s, r[r.length - 1]), r);
    return Ju(a, t, n);
  });
}
function pB(e, t) {
  if (!w.arraysEqual(e.shape, t.shape))
    throw new G(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);
  return q(() => {
    let n = Xs(t), s = kt(Lt(t));
    return ie(ge(n, V(t, e)), tb(Yn(s)));
  });
}
function Vp(e, t) {
  return q(() => {
    let n;
    return n = Vn(t, Rt(), 1 - Rt()), n = Qn(xe(n, ge(1, n))), It(pB(e, n), -1);
  });
}
function hB(e, t) {
  return q(() => {
    let n = Vn(e, Rt(), 1), s = Vn(t, Rt(), 1);
    return ve(V(e, Qn(xe(n, s))), -1);
  });
}
function fB(e, t) {
  return q(() => {
    let n = Qn(ie(Rt(), t));
    return It(ge(t, V(e, n)), -1);
  });
}
function Ub(e, t) {
  return q(() => {
    let n = Ed(e, -1), s = Ed(t, -1), r = V(n, s);
    return kt(ve(r, -1));
  });
}
var Dd = { meanSquaredError: xi, meanAbsoluteError: Bp, meanAbsolutePercentageError: eu, meanSquaredLogarithmicError: oB, squaredHinge: uB, hinge: lB, categoricalHinge: cB, logcosh: dB, categoricalCrossentropy: Ju, sparseCategoricalCrossentropy: Rd, binaryCrossentropy: Vp, kullbackLeiblerDivergence: hB, poisson: fB, cosineProximity: Ub };
function jf(e) {
  if (typeof e == "string") {
    if (e in Dd)
      return Dd[e];
    let t = `Unknown loss ${e}`;
    throw e.toLowerCase().includes("softmaxcrossentropy") && (t = `Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`), new G(t);
  } else
    return e;
}
function Gb(e, t) {
  return q(() => {
    let n = V(0.5, Zn(t)), s = Ap(Un(t, n), e.dtype);
    return It(Xn(e, s), -1);
  });
}
function Hb(e, t) {
  return q(() => Ap(Xn(ju(e, -1), ju(t, -1)), "float32"));
}
function pI(e, t) {
  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 1))), "float32"));
}
function mB(e, t) {
  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 0))), "float32"));
}
function gB(e, t) {
  return q(() => le(ve(Ds(Xn(e, 0), Xn(t, 1))), "float32"));
}
function hI(e, t) {
  return q(() => {
    let n = pI(e, t), s = gB(e, t), r = ie(n, s);
    return le(vn(Un(r, 0), xe(n, r), 0), "float32");
  });
}
function bB(e, t) {
  return q(() => {
    let n = pI(e, t), s = mB(e, t), r = ie(n, s);
    return le(vn(Un(r, 0), xe(n, r), 0), "float32");
  });
}
function fI(e, t) {
  return Vp(e, t);
}
function mI(e, t) {
  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = ju(t, -1), t.dtype !== e.dtype && (t = le(t, e.dtype)), le(Xn(e, t), "float32");
}
var yB = xi;
var vB = xi;
var xB = Bp;
var wB = Bp;
var kB = eu;
var SB = eu;
var qb = Ju;
var IB = Ub;
var gI = Rd;
var Fd = { binaryAccuracy: Gb, categoricalAccuracy: Hb, precision: hI, categoricalCrossentropy: qb, sparseCategoricalCrossentropy: gI, mse: yB, MSE: vB, mae: xB, MAE: wB, mape: kB, MAPE: SB, cosine: IB };
function CB(e) {
  if (typeof e == "string" && e in Fd)
    return Fd[e];
  if (typeof e != "string" && e != null)
    return e;
  throw new G(`Unknown metric ${e}`);
}
function Kc(e) {
  if (Cs(e !== null, `Unknown LossOrMetricFn ${e}`), typeof e == "string")
    return e;
  {
    let t;
    for (let n of Object.keys(Dd))
      if (Dd[n] === e) {
        t = n;
        break;
      }
    if (t !== void 0)
      return t;
    for (let n of Object.keys(Fd))
      if (Fd[n] === e) {
        t = n;
        break;
      }
    return t !== void 0 ? t : e.name;
  }
}
function NB(e) {
  let t = { Adagrad: () => Bi.adagrad(0.01), Adadelta: () => Bi.adadelta(1, 0.95, Rt()), Adam: () => Bi.adam(1e-3, 0.9, 0.999, Rt()), Adamax: () => Bi.adamax(2e-3, 0.9, 0.999, Rt(), 0), RMSProp: () => Bi.rmsprop(1e-3, 0.9, 0, Rt()), SGD: () => Bi.sgd(0.01) };
  if (t.adagrad = t.Adagrad, t.adadelta = t.Adadelta, t.adam = t.Adam, t.adamax = t.Adamax, t.rmsprop = t.RMSProp, t.sgd = t.SGD, e in t)
    return t[e]();
  throw new G(`Unknown Optimizer ${e}`);
}
var Mx = 1 * 1024 * 1024;
function Lx(e, t, n = false) {
  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !Cm(e))
    throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
  if (n) {
    let s = JSON.stringify(e);
    s.length > Mx && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Mx}.`);
  }
}
function Cm(e) {
  if (e === null)
    return true;
  if (typeof e == "object")
    if (Object.getPrototypeOf(e) === Object.prototype) {
      let t = Object.keys(e);
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        if (typeof n != "string" || !Cm(e[n]))
          return false;
      return true;
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      for (let t of e)
        if (!Cm(t))
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      return true;
    } else
      return false;
  else {
    let t = typeof e;
    return t === "string" || t === "number" || t === "boolean";
  }
}
function TB(e, t, n, s = console.log) {
  let r = _B(e), a = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
  r ? (t = t || 90, n = n || [0.32, 0.61, 0.89, 1]) : (t = t || 115, n = n || [0.24, 0.48, 0.7, 0.8, 1]), n[n.length - 1] <= 1 && (n = n.map((c) => Math.floor(t * c)));
  let i;
  if (!r) {
    a.push("Receives inputs"), i = [];
    for (let c in e.nodesByDepth)
      i.push(...e.nodesByDepth[c]);
  }
  s("_".repeat(t)), Od(a, n, s), s("=".repeat(t));
  let o = e.layers;
  for (let c = 0; c < o.length; ++c)
    r ? AB(o[c], n, s) : EB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
  e.checkTrainableWeightsConsistency();
  let u = $B(e), l = $d(e.nonTrainableWeights);
  s(`Total params: ${u + l}`), s(`Trainable params: ${u}`), s(`Non-trainable params: ${l}`), s("_".repeat(t));
}
function $B(e) {
  let t;
  return e.collectedTrainableWeights != null ? t = $d(e.collectedTrainableWeights) : t = $d(e.trainableWeights), t;
}
function _B(e) {
  let t = true, n = [], s = [];
  for (let r in e.nodesByDepth)
    n.push(e.nodesByDepth[r]);
  for (let r of n) {
    if (r.length > 1 || r.length === 1 && r[0].inboundLayers.length > 1) {
      t = false;
      break;
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    s.push(...r);
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  if (t)
    for (let r of e.layers) {
      let a = false;
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        if (s.indexOf(i) !== -1)
          if (a) {
            t = false;
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            a = true;
      if (!t)
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}
function Od(e, t, n = console.log) {
  let s = "";
  for (let r = 0; r < e.length; ++r)
    r > 0 && (s = s.slice(0, s.length - 1) + " "), s += e[r], s = s.slice(0, t[r]), s += " ".repeat(t[r] - s.length);
  n(s);
}
function AB(e, t, n) {
  let s, r;
  try {
    r = e.inboundNodes.map((u) => JSON.stringify(u.inputShapes)).join(",");
  } catch (u) {
    r = "multiple";
  }
  try {
    s = JSON.stringify(e.outputShape);
  } catch (u) {
    s = "multiple";
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  let a = e.name, i = e.getClassName(), o = [`${a} (${i})`, r, s, e.countParams().toString()];
  Od(o, t, n);
}
function EB(e, t, n, s) {
  let r, a;
  try {
    a = e.inboundNodes.map((p) => JSON.stringify(p.inputShapes)).join(",");
  } catch (p) {
    a = "multiple";
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  try {
    r = JSON.stringify(e.outputShape);
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    r = "multiple";
  }
  let i = [];
  for (let p of e.inboundNodes)
    if (!(n != null && n.length > 0 && n.indexOf(p) === -1))
      for (let d = 0; d < p.inboundLayers.length; ++d) {
        let h = p.inboundLayers[d].name, f = p.nodeIndices[d], m = p.tensorIndices[d];
        i.push(`${h}[${f}][${m}]`);
      }
  let o = e.name, u = e.getClassName(), l = i.length === 0 ? "" : i[0], c = [`${o} (${u})`, a, r, e.countParams().toString(), l];
  Od(c, t, s);
  for (let p = 1; p < i.length; ++p)
    Od(["", "", "", "", i[p]], t, s);
}
function bI(e, t, n) {
  return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
}
function el(e, t) {
  if (e === null)
    return null;
  if (typeof e == "string")
    return Xr(e);
  if (typeof e == "number" || typeof e == "boolean")
    return e;
  if (e instanceof Array) {
    let n = [], s = e.length;
    for (let r = 0; r < s; ++r) {
      let a = e[r];
      bI(t, r, a) ? n.push(a) : n.push(el(a, t));
    }
    return n;
  } else {
    let n = {};
    for (let s of Object.keys(e)) {
      let r = e[s];
      if (s === "name" && typeof r == "string")
        n[s] = r;
      else {
        let a = Xr(s);
        n[a] = el(r, a);
      }
    }
    return n;
  }
}
function Nm(e, t) {
  if (e == null)
    return null;
  if (typeof e == "string")
    return Vs(e);
  if (typeof e == "number" || typeof e == "boolean")
    return e;
  if (e instanceof Array) {
    let n = [], s = e.length;
    for (let r = 0; r < s; ++r) {
      let a = e[r];
      bI(t, r, a) ? n.push(a) : n.push(Nm(a, t));
    }
    return n;
  } else {
    let n = {};
    for (let s of Object.keys(e)) {
      let r = e[s], a = Vs(s);
      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = Nm(r, s);
    }
    return n;
  }
}
var yI = "0.0.0";
var Is = class extends He {
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    if (super({}), this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
      let b = this.getClassName().toLowerCase();
      this.name = _p(b);
    }
    if (this.supportsMasking = false, this.trainable_ = true, Array.isArray(e.inputs) ? this.inputs = e.inputs.slice() : this.inputs = [e.inputs], Array.isArray(e.outputs) ? this.outputs = e.outputs.slice() : this.outputs = [e.outputs], lr(this.inputs).length !== this.inputs.length)
      throw new G(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((b) => b.name)}`);
    lr(this.outputs).length !== this.outputs.length && console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((b) => b.name)}`), this.inputLayers = [], this.inputLayersNodeIndices = [], this.inputLayersTensorIndices = [], this.outputLayers = [], this.outputLayersNodeIndices = [], this.outputLayersTensorIndices = [], this.layers = [], this.internalContainerRefs = [];
    for (let b of this.outputs) {
      let y = b.sourceLayer, v = b.nodeIndex, x = b.tensorIndex;
      this.outputLayers.push(y), this.outputLayersNodeIndices.push(v), this.outputLayersTensorIndices.push(x);
    }
    for (let b of this.inputs) {
      let y = b.sourceLayer, v = b.nodeIndex, x = b.tensorIndex;
      Cs(v === 0, "input layer has >1 nodes"), Cs(x === 0, "input layer has >1 tensors"), this.inputLayers.push(y), this.inputLayersNodeIndices.push(v), this.inputLayersTensorIndices.push(x);
    }
    this.inputNames = [], this.outputNames = [], this.feedInputShapes = [], this.feedInputNames = [], this.feedOutputNames = [];
    for (let b = 0; b < this.inputLayers.length; b++) {
      let y = this.inputLayers[b];
      if (!(y instanceof Jo))
        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);
      this.inputNames.push(y.name), this.feedInputShapes.push(y.batchInputShape), this.feedInputNames.push(y.name);
    }
    for (let b of this.outputLayers)
      this.outputNames.push(b.name);
    this.internalInputShapes = this.inputs.map((b) => b.shape), this.internalOutputShapes = this.outputs.map((b) => b.shape);
    let t = {}, n = {}, s = {}, r = {}, a = {}, i = [], o = (b, y, v, x, k, I) => {
      (x == null || k == null || I == null) && (x = b.sourceLayer, k = b.nodeIndex, I = b.tensorIndex);
      let $ = x.inboundNodes[k];
      if (v.indexOf($) !== -1)
        throw new fs(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);
      if (y.indexOf($) !== -1)
        return;
      this.containerNodes.add(Is.nodeKey(x, k)), x.id in a || (a[x.id] = Object.keys(a).length), v.indexOf($) === -1 && v.push($);
      let E = $.inboundLayers.length;
      for (let A = 0; A < E; A++) {
        let P = $.inputTensors[A], R = $.inboundLayers[A], F = $.nodeIndices[A], T = $.tensorIndices[A];
        o(P, y, v, R, F, T);
      }
      for (y.push($); v.indexOf($) >= 0; )
        v.splice(v.indexOf($), 1);
      i.push($);
    }, u = [], l = [];
    for (let b of this.outputs)
      o(b, u, l);
    let c = i.slice().reverse();
    for (let b of c) {
      n[b.id] = b, b.id in t || (t[b.id] = 0);
      let y = t[b.id], v = s[b.outboundLayer.id] == null ? 0 : s[b.outboundLayer.id];
      y = Math.max(y, v), s[b.outboundLayer.id] = y, r[b.outboundLayer.id] = b.outboundLayer, t[b.id] = y;
      for (let x = 0; x < b.inboundLayers.length; x++) {
        let k = b.inboundLayers[x], I = b.nodeIndices[x], $ = k.inboundNodes[I], E = t[$.id] == null ? 0 : t[$.id];
        t[$.id] = Math.max(y + 1, E), n[$.id] = $;
      }
    }
    let p = {};
    for (let b in t) {
      let y = t[b];
      y in p || (p[y] = []), p[y].push(n[b]);
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    let d = {};
    for (let b in s) {
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      y in d || (d[y] = []), d[y].push(r[b]);
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    let h = Object.keys(d).map((b) => parseInt(b, 10)).sort(Hc);
    this.layers = [];
    for (let b of h) {
      let y = d[b];
      y.sort((v, x) => {
        let k = a[v.id], I = a[x.id];
        return k < I ? -1 : k > I ? 1 : 0;
      });
      for (let v of y)
        v instanceof Is && this.internalContainerRefs.push(v), this.layers.push(v);
    }
    this.layersByDepth = d, h = Object.keys(p).map((b) => parseInt(b, 10)).sort(Hc);
    let f = this.inputs.slice(), m = [];
    for (let b of h)
      for (let y of p[b]) {
        let v = y.outboundLayer;
        if (v != null) {
          for (let x of y.inputTensors)
            if (f.indexOf(x) === -1)
              throw new fs(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);
          for (let x of y.outputTensors)
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          m.push(v.name);
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    this.nodesByDepth = p;
    let g = this.layers.map((b) => b.name);
    for (let b of g) {
      let y = g.filter((v) => v === b).length;
      if (y !== 1)
        throw new fs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(g));
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    this.outboundNodes = [], this.inboundNodes = [], new Lp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
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      throw new Error(`Container '${this.name}' is already disposed.`);
  }
  dispose() {
    this.assertNotDisposed();
    let e = { refCountAfterDispose: null, numDisposedVariables: 0 };
    if (--this._refCount === 0) {
      for (let t of this.layers)
        e.numDisposedVariables += t.dispose().numDisposedVariables;
      for (let t of this.internalContainerRefs)
        e.numDisposedVariables += t.dispose().numDisposedVariables;
    }
    return e.refCountAfterDispose = this._refCount, e;
  }
  get trainable() {
    return this.trainable_;
  }
  set trainable(e) {
    this.layers.forEach((t) => {
      t._trainableWeights.forEach((n) => n.trainable = e);
    }), this.trainable_ = e;
  }
  get trainableWeights() {
    if (this._trainableWeights.length > 0)
      throw new G("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");
    if (!this.trainable)
      return [];
    let e = [];
    for (let t of this.layers)
      e = e.concat(t.trainableWeights);
    return e;
  }
  get nonTrainableWeights() {
    let e = [];
    for (let t of this.layers)
      e.push(...t.nonTrainableWeights);
    if (!this.trainable) {
      let t = [];
      for (let n of this.layers)
        t.push(...n.trainableWeights);
      return t.concat(e);
    }
    return e;
  }
  get weights() {
    return this.trainableWeights.concat(this.nonTrainableWeights);
  }
  loadWeights(e, t = true) {
    let n = {}, s = 0;
    for (let a of this.layers)
      for (let i of a.weights) {
        if (n[i.originalName] != null)
          throw new G(`Duplicate weight name: ${i.originalName}`);
        n[i.originalName] = i, s++;
      }
    let r = [];
    for (let a in e) {
      let i = a;
      if (n[a] == null) {
        let o = a.split("/");
        i = o.slice(0, -2).concat([o[o.length - 1]]).join("/");
      }
      if (n[i] != null)
        r.push([n[i], e[a]]);
      else if (t)
        throw new G(`Provided weight data has no target variable: ${a}`);
      delete n[i];
    }
    if (t) {
      let a = [];
      for (let i in n)
        a.push(i);
      if (a.length > 0)
        throw new G(`${a.length} of ${s} weights are not set: ${a}`);
    }
    Pb(r);
  }
  updatedConfig() {
    let e = this.getConfig(), t = {};
    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${yI}`, t.backend = "TensorFlow.js", t;
  }
  toJSON(e, t = true) {
    let n = Nm(this.updatedConfig());
    return t ? JSON.stringify(n) : n;
  }
  call(e, t) {
    return q(() => {
      e = pt(e);
      let n = new Zr();
      for (let s = 0; s < this.inputs.length; ++s)
        n.add(this.inputs[s], e[s]);
      return Ru(this.outputs, n, t);
    });
  }
  computeMask(e, t) {
    return q(() => {
      e = pt(e);
      let n;
      return t == null ? n = ma(null, e.length) : n = pt(t), this.runInternalGraph(e, n)[1];
    });
  }
  computeOutputShape(e) {
    let t = Td(e);
    if (t.length !== this.inputLayers.length)
      throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);
    let n = {};
    for (let i = 0; i < t.length; i++) {
      let o = this.inputLayers[i], u = t[i], l = o.name + "_0_0";
      n[l] = u;
    }
    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(Hc);
    if (s.length > 1)
      for (let i of s) {
        let o = this.nodesByDepth[i];
        for (let u of o) {
          let l = u.outboundLayer;
          if (this.inputLayers.map((f) => f.id).indexOf(l.id) !== -1)
            continue;
          let c = [];
          for (let f = 0; f < u.inboundLayers.length; f++) {
            let m = u.inboundLayers[f], g = u.nodeIndices[f], b = u.tensorIndices[f], y = `${m.name}_${g}_${b}`, v = n[y];
            c.push(v);
          }
          let p = l.computeOutputShape(bn(c)), d = Td(p), h = l.inboundNodes.indexOf(u);
          for (let f = 0; f < d.length; f++) {
            let m = `${l.name}_${h}_${f}`;
            n[m] = d[f];
          }
        }
      }
    let r = [], a = [];
    for (let i = 0; i < this.outputLayers.length; i++) {
      let o = this.outputLayers[i], u = this.outputLayersNodeIndices[i], l = this.outputLayersTensorIndices[i], c = `${o.name}_${u}_${l}`;
      a.push(c);
    }
    for (let i = 0; i < a.length; i++) {
      let o = a[i];
      Cs(o in n), r.push(n[o]);
    }
    return bn(r);
  }
  runInternalGraph(e, t) {
    t == null && (t = ma(null, e.length));
    let n = {};
    for (let o = 0; o < this.inputs.length; ++o) {
      let u = this.inputs[o], l = e[o], c = t[o];
      n[u.id] = [l, c];
    }
    let s = Object.keys(this.nodesByDepth).map((o) => parseInt(o, 10)).sort(Hc);
    for (let o of s) {
      let u = this.nodesByDepth[o];
      for (let l of u) {
        let c = l.outboundLayer, p = l.inputTensors, d = l.outputTensors, h = new Array();
        for (let f of p)
          f.id in n && h.push(n[f.id]);
        if (h.length === p.length) {
          let f = {}, m, g, b, y;
          if (l.callArgs != null && (f = l.callArgs), h.length === 1) {
            let [v, x] = h[0];
            f.mask == null && (f.mask = x), b = pt(c.call(v, f)), y = pt(c.computeMask(v, x)), m = [v], g = [x];
          } else
            m = h.map((v) => v[0]), g = h.map((v) => v[1]), f.mask == null && (f.mask = g), b = pt(c.call(m, f)), y = pt(c.computeMask(m, g));
          if (c.activityRegularizer)
            throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
          for (let v = 0; v < d.length; ++v) {
            let x = d[v], k = b[v], I = y[v];
            n[x.id] = [k, I];
          }
        }
      }
    }
    let r = [], a = [], i = [];
    for (let o of this.outputs) {
      Cs(o.id in n, `Could not compute output ${o.name} : ${o.id}`);
      let [u, l] = n[o.id];
      i.push(u.shape), r.push(u), a.push(l);
    }
    return [r, a, i];
  }
  buildNodeConversionMap(e) {
    let t = {}, n;
    for (let s of this.layers) {
      n = s instanceof Is ? 1 : 0;
      for (let r = 0; r < s.inboundNodes.length; r++) {
        let a = Is.nodeKey(s, r);
        this.containerNodes.has(a) && (t[a] = n, n += 1);
      }
    }
    return t;
  }
  getLayer(e, t) {
    if (t != null) {
      if (this.layers.length <= t)
        throw new G(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);
      return this.layers[t];
    } else if (e == null)
      throw new G("Provide either a layer name or layer index");
    for (let n of this.layers)
      if (n.name === e)
        return n;
    throw new G(`No such layer: ${e}`);
  }
  calculateLosses() {
    return q(() => {
      let e = [];
      for (let t of this.layers)
        for (let n = 0; n < t.inboundNodes.length; ++n) {
          let s = Is.nodeKey(t, n);
          this.containerNodes.has(s) && e.push(...t.calculateLosses());
        }
      return e;
    });
  }
  getConfig() {
    let e = { name: this.name }, t = this.buildNodeConversionMap(this.layers), n = [];
    for (let a of this.layers) {
      let i = a.getClassName(), o = a.getConfig(), u = [];
      for (let c = 0; c < a.inboundNodes.length; c++) {
        let p = a.inboundNodes[c], d = Is.nodeKey(a, c), h = {};
        if (this.containerNodes.has(d)) {
          if (p.callArgs)
            try {
              JSON.stringify(p.callArgs), h = p.callArgs;
            } catch (f) {
              console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`), h = {};
            }
          if (p.inboundLayers.length > 0) {
            let f = [];
            for (let m = 0; m < p.inboundLayers.length; m++) {
              let g = p.inboundLayers[m], b = p.nodeIndices[m], y = p.tensorIndices[m], v = Is.nodeKey(g, b), x = t[v];
              x == null && (x = 0), f.push([g.name, x, y, h]);
            }
            u.push(f);
          }
        }
      }
      let l = {};
      l.name = a.name, l.className = i, l.config = o, l.inboundNodes = u, n.push(l);
    }
    e.layers = n;
    let s = [];
    for (let a = 0; a < this.inputLayers.length; a++) {
      let i = this.inputLayers[a], o = this.inputLayersNodeIndices[a], u = Is.nodeKey(i, o);
      if (!this.containerNodes.has(u))
        continue;
      let l = t[u];
      l == null && (l = 0);
      let c = this.inputLayersTensorIndices[a];
      s.push([i.name, l, c]);
    }
    e.inputLayers = s;
    let r = [];
    for (let a = 0; a < this.outputLayers.length; a++) {
      let i = this.outputLayers[a], o = this.outputLayersNodeIndices[a], u = Is.nodeKey(i, o);
      if (!this.containerNodes.has(u))
        continue;
      let l = t[u];
      l == null && (l = 0);
      let c = this.outputLayersTensorIndices[a];
      r.push([i.name, l, c]);
    }
    return e.outputLayers = r, e;
  }
  static fromConfig(e, t, n = {}, s = false) {
    let r = {}, a = {};
    function i(m, g) {
      m.name in a ? a[m.name].push(g) : a[m.name] = [g];
    }
    function o(m, g) {
      let b = [], y;
      for (let v of g) {
        let x = v[0], k = v[1], I = v[2];
        if (y = v[3] == null ? {} : v[3], !(x in r)) {
          i(m, g);
          return;
        }
        let $ = r[x];
        if ($.inboundNodes.length <= k) {
          i(m, g);
          return;
        }
        let E = $.inboundNodes[k];
        b.push(E.outputTensors[I]);
      }
      b.length > 0 && m.apply(bn(b), y);
    }
    function u(m) {
      let g = m.name, b = gs(m, t.customObjects != null ? t.customObjects : {});
      b.setFastWeightInitDuringBuild(s), r[g] = b, m.inboundNodes.forEach((v) => {
        if (!(v instanceof Array))
          throw new G(`Corrupted configuration, expected array for nodeData: ${v}`);
        i(b, v);
      });
    }
    let l = t.name, c = t.layers;
    for (let m of c)
      u(m);
    for (; !XP(a); )
      for (let m of c) {
        let g = r[m.name];
        if (g.name in a) {
          let b = a[g.name];
          delete a[g.name];
          for (let y of b)
            o(g, y);
        }
      }
    let p = [], d = [], h = t.inputLayers;
    for (let m of h) {
      let g = m[0], b = m[1], y = m[2];
      Cs(g in r);
      let x = r[g].inboundNodes[b].outputTensors;
      p.push(x[y]);
    }
    let f = t.outputLayers;
    for (let m of f) {
      let g = m[0], b = m[1], y = m[2];
      Cs(g in r);
      let x = r[g].inboundNodes[b].outputTensors;
      d.push(x[y]);
    }
    return new e({ inputs: p, outputs: d, name: l });
  }
  get stateful() {
    if (this._stateful)
      throw new G("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");
    for (let e of this.layers)
      if (e.stateful)
        return true;
    return false;
  }
  resetStates() {
    q(() => {
      this.layers.forEach((e) => {
        e.stateful && e.resetStates();
      });
    });
  }
};
function RB(e, t, n) {
  let s = t.length;
  if (e == null || Array.isArray(e) && e.length === 0)
    return t.map((r) => null);
  if (s === 1)
    return Array.isArray(e) && e.length === 1 ? e : typeof e == "object" && t[0] in e ? [e[t[0]]] : [e];
  if (Array.isArray(e)) {
    if (e.length !== s)
      throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);
    return e;
  } else if (typeof e == "object" && Object.keys(e).length > 0 && typeof e[Object.keys(e)[0]] == "object") {
    let r = [];
    return t.forEach((a) => {
      a in e ? r.push(e[a]) : r.push(null);
    }), r;
  } else
    throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`);
}
function vI(e, t) {
  return RB(e, t, "classWeight");
}
async function xI(e, t, n, s) {
  if (t != null || s != null)
    throw new Error("Support sampleWeight is not implemented yet");
  if (n != null) {
    let r = q(() => {
      if (e.shape.length === 1)
        return ur(e);
      if (e.shape.length === 2) {
        if (e.shape[1] > 1)
          return ju(e, 1);
        if (e.shape[1] === 1)
          return U(e, [e.shape[0]]);
        throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
      } else
        throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
    }), a = Array.from(await r.data());
    Re(r);
    let i = [];
    return a.forEach((o) => {
      if (n[o] == null)
        throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);
      i.push(n[o]);
    }), Zt(i, "float32");
  } else
    return null;
}
function DB(e, t) {
  return V(e, t);
}
var FB = 32;
function wI(e, t) {
  let n, s, r = t;
  n = r.xs, s = r.ys, w.assert(n != null && s != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);
  let a = Bx("input", e.inputNames, n), i = Bx("output", e.outputNames, s), o = a[0].shape[0];
  w.assert(a.length === e.inputs.length, () => `LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`), w.assert(i.length === e.outputs.length, () => `LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);
  for (let u = 0; u < a.length; u++)
    w.assert(a[u].shape[0] === o, () => `Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
  for (let u = 0; u < i.length; u++)
    w.assert(i[u].shape[0] === o, () => `Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
  return { xs: a, ys: i };
}
function Bx(e, t, n) {
  if (n instanceof et)
    return [n];
  if (Array.isArray(n))
    return w.assert(n.length === t.length, () => `Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`), n;
  {
    let s = [];
    for (let r of t) {
      if (n[r] == null)
        throw new G(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);
      s.push(n[r]);
    }
    return s;
  }
}
function OB(e) {
  if (e.length === 3)
    throw new Fe("Validation with sample weights is not implemented yet.");
  return { xs: e[0], ys: e[1] };
}
async function PB(e, t, n) {
  let s = n.batchesPerEpoch != null;
  if (w.assert(e.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."), w.assert(n != null, () => "For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."), w.assert(n.epochs != null && n.epochs > 0 && Number.isInteger(n.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`), w.assert(!s || n.batchesPerEpoch > 0 && Number.isInteger(n.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`), w.assert(n.validationSplit == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead."), e.isTraining)
    throw new Error("Cannot start training because another fit() call is ongoing.");
  e.isTraining = true;
  try {
    let r = n.validationData != null, a, i;
    if (r)
      if (Vx(n.validationData))
        w.assert(n.validationBatches == null || n.validationBatches > 0 && Number.isInteger(n.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);
      else {
        let g = OB(n.validationData);
        a = g.xs, i = g.ys;
      }
    let o = e.makeTrainFunction(), u = e.getDedupedMetricsNames(), l;
    r ? l = u.slice().concat(u.map((g) => "val_" + g)) : l = u.slice();
    let c = cI(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = dI(c, p, n.epochs, null, null, zB(t, n), null, r, l);
    d.setModel(e), e.history = h, await d.onTrainBegin(), e.stopTraining_ = false;
    let f = n.initialEpoch == null ? 0 : n.initialEpoch, m = await t.iterator();
    for (; f < n.epochs; ) {
      let g = {};
      await d.onEpochBegin(f);
      let b = 0, y = 0;
      for (s || (m = await t.iterator()); !s || b < n.batchesPerEpoch; ) {
        let v = await m.next();
        if (s && v.done) {
          console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch * n.epochs} batches). You may need to use the repeat() function when building your dataset.`);
          break;
        }
        if (v.value != null) {
          let { xs: x, ys: k } = wI(e, v.value), I = {};
          I.batch = y, I.size = x[0].shape[0], await d.onBatchBegin(y, I);
          let $ = [];
          if (n.classWeight != null) {
            let P = vI(n.classWeight, e.outputNames);
            for (let R = 0; R < P.length; ++R)
              $.push(await xI(k[R], null, P[R]));
          }
          let E = x.concat(k).concat($), A = o(E);
          Re(E);
          for (let P = 0; P < u.length; ++P) {
            let R = u[P], F = A[P];
            I[R] = F, qt(F);
          }
          await d.onBatchEnd(y, I), lI(I), y++, b++;
        }
        if (s ? b >= n.batchesPerEpoch : v.done) {
          if (r) {
            let x;
            Vx(n.validationData) ? x = pt(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = pt(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? FB : n.validationBatchSize, verbose: 0 }));
            for (let k = 0; k < e.metricsNames.length; ++k)
              g[`val_${e.metricsNames[k]}`] = x[k];
          }
          break;
        }
        if (e.stopTraining_)
          break;
      }
      if (await d.onEpochEnd(f, g), f++, e.stopTraining_)
        break;
    }
    return await d.onTrainEnd(), await e.history.syncData(), e.history;
  } finally {
    e.isTraining = false;
  }
}
function zB(e, t) {
  let n = null;
  return t.batchesPerEpoch != null ? n = t.batchesPerEpoch : Number.isFinite(e.size) && (n = e.size), n;
}
function Vx(e) {
  return typeof e.iterator == "function";
}
function MB(e) {
  return typeof e.next == "function";
}
async function LB(e, t, n) {
  n = n || {};
  let s = n.batches != null, r = e.testFunction, a = [];
  if (n.verbose > 0)
    throw new Fe("Verbose mode is not implemented yet.");
  w.assert(!s || n.batches > 0 && Number.isInteger(n.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);
  let i = MB(t) ? t : await t.iterator(), o = 0, u = 0;
  for (; !s || u < n.batches; ) {
    let l = await i.next();
    if (a = q(() => {
      if (l.value) {
        let { xs: c, ys: p } = wI(e, l.value), d = c.concat(p), h = q(() => r(d));
        if (Re(d), u === 0)
          for (let m = 0; m < h.length; ++m)
            a.push(we(0));
        let f = d[0].shape[0];
        for (let m = 0; m < h.length; ++m) {
          let g = h[m], b = a[m];
          a[m] = q(() => ie(a[m], V(f, g))), u > 0 && Re(b);
        }
        Re(h), o += f, ++u;
      }
      return a;
    }), l.done) {
      s && console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);
      break;
    }
  }
  for (let l = 0; l < a.length; ++l) {
    let c = a[l];
    a[l] = xe(a[l], o), Re(c);
  }
  return bn(a);
}
function Tm(e) {
  w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
}
function Du(e, t, n) {
  return e == null ? [null] : Array.isArray(e) ? e.map((s) => na(s, t, n - t)) : na(e, t, n - t);
}
function jb(e, t) {
  return q(() => e == null ? null : Array.isArray(e) ? e.map((n) => jb(n, t)) : sI(e, t.dtype === "int32" ? t : le(t, "int32")));
}
function $m(e, t) {
  let n = [], s = 0, r = null;
  for (; s < e; )
    r = s + t, r >= e && (r = e), n.push([s, r]), s = r;
  return n;
}
async function BB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
  r == null && (r = 32), a == null && (a = 1), c == null && (c = true), d == null && (d = 0);
  let m = false;
  if (u != null && l != null && (m = true), f != null && (m = true, h == null))
    throw new G("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
  let g = e.checkNumSamples(n, r, h, "steps_per_epoch"), b;
  g != null && (b = ys(0, g)), i == null && (i = 1);
  let { callbackList: y, history: v } = dI(o, i, a, d, g, h, r, m, p);
  y.setModel(e), e.history = v, await y.onTrainBegin(), e.stopTraining_ = false;
  for (let x = d; x < a; ++x) {
    await y.onEpochBegin(x);
    let k = {};
    if (h != null)
      throw new Fe("stepsPerEpoch mode is not implemented yet.");
    {
      if (c === "batch")
        throw new Fe("batch shuffling is not implemneted yet");
      c && w.shuffle(b);
      let I = Zt(b), $ = $m(g, r);
      for (let E = 0; E < $.length; ++E) {
        let A = {};
        if (await y.onBatchBegin(E, A), q(() => {
          let P = $[E][0], R = $[E][1], F = na(I, P, R - P);
          A.batch = E, A.size = R - P;
          let T = jb(n, F), z = t(T);
          for (let W = 0; W < s.length; ++W) {
            let j = s[W], X = z[W];
            A[j] = X, qt(X);
          }
          if (E === $.length - 1 && m) {
            let W = e.testLoop(u, l, r);
            for (let j = 0; j < s.length; ++j) {
              let X = s[j], Y = W[j];
              qt(Y), k["val_" + X] = Y;
            }
          }
        }), await y.onBatchEnd(E, A), lI(A), e.stopTraining_)
          break;
      }
      I.dispose();
    }
    if (await y.onEpochEnd(x, k), e.stopTraining_)
      break;
  }
  return await y.onTrainEnd(), await e.history.syncData(), e.history;
}
async function VB(e, t, n, s = {}) {
  if (e.isTraining)
    throw new Error("Cannot start training because another fit() call is ongoing.");
  e.isTraining = true;
  let r, a, i, o, u, l, c, p, d;
  try {
    let h = s.batchSize == null ? 32 : s.batchSize;
    Tm(h);
    let f = false, m = await e.standardizeUserData(t, n, s.sampleWeight, s.classWeight, f, h);
    r = m[0], a = m[1], d = m[2];
    let g = false, b;
    if (s.validationData != null && s.validationData.length > 0) {
      if (g = true, s.validationData.length === 2)
        u = s.validationData[0], l = s.validationData[1];
      else
        throw s.validationData.length === 3 ? new Fe("validationData including sample weights is not supported yet.") : new G(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);
      let A = true, P = await e.standardizeUserData(u, l, null, null, A, h);
      c = P[0], p = P[1], b = c.concat(p);
    } else if (s.validationSplit != null && s.validationSplit > 0 && s.validationSplit < 1) {
      g = true;
      let A = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
      c = Du(r, A, P), i = r, r = Du(r, 0, A), p = Du(a, A, P), o = a, a = Du(a, 0, A), b = c.concat(p);
    } else
      s.validationSteps != null && (g = true);
    let y = r.concat(a).concat(d);
    e.checkTrainableWeightsConsistency();
    let v = e.makeTrainFunction(), x = e.getDedupedMetricsNames(), k, I;
    g ? (e.makeTestFunction(), k = e.testFunction, I = x.slice().concat(x.map((A) => "val_" + A))) : (k = null, b = [], I = x.slice());
    let $ = cI(s.callbacks, s.yieldEvery);
    return await BB(e, v, y, x, h, s.epochs, s.verbose, $, k, b, s.shuffle, I, s.initialEpoch, null, null);
  } finally {
    e.isTraining = false, ps(r, t), ps(a, n), ps(i, t), ps(o, n), ps(c, u), ps(p, l), d != null && Re(d);
  }
}
function kI(e) {
  let t = [];
  e instanceof et && (e = [e]);
  for (let n = 0; n < e.length; ++n) {
    let s = e[n];
    if (s.rank === 1)
      t.push(Vl(s, 1));
    else {
      if (s.rank === 0)
        throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
      t.push(s);
    }
  }
  return t;
}
function ps(e, t) {
  if (e == null)
    return;
  let n = [];
  if (t instanceof et)
    n.push(t.id);
  else if (Array.isArray(t))
    t.forEach((r) => n.push(r.id));
  else if (t != null)
    for (let r in t) {
      let a = t[r];
      n.push(a.id);
    }
  let s = [];
  if (e instanceof et)
    n.indexOf(e.id) === -1 && s.push(e);
  else if (Array.isArray(e))
    e.forEach((r) => {
      n.indexOf(r.id) === -1 && s.push(r);
    });
  else if (e != null)
    for (let r in e) {
      let a = e[r];
      n.indexOf(a.id) === -1 && s.push(a);
    }
  s.forEach((r) => {
    r.isDisposed || r.dispose();
  });
}
function WB(e) {
  return e instanceof et;
}
function _m(e) {
  return Array.isArray(e);
}
function Wx(e) {
  return !WB(e) && !_m(e);
}
function Ux(e, t, n, s = true, r = "") {
  if (t == null || t.length === 0) {
    if (e != null) {
      let i = false;
      if (_m(e) && e.length > 0)
        i = true;
      else if (Wx(e)) {
        for (let o in e)
          if (e.hasOwnProperty(o)) {
            i = true;
            break;
          }
      } else
        i = true;
      if (i)
        throw new G(`Error when checking model ${r} expected no data, but got ${e}`);
    }
    return [];
  }
  if (e == null)
    return t.map((i) => null);
  let a;
  if (Wx(e)) {
    e = e, a = [];
    for (let i of t) {
      if (e[i] == null)
        throw new G(`No data provided for "${i}". Need data for each key in: ${t}`);
      a.push(e[i]);
    }
  } else if (_m(e)) {
    if (e = e, e.length !== t.length)
      throw new G(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);
    a = e;
  } else {
    if (e = e, t.length > 1)
      throw new G(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);
    a = [e];
  }
  if (a = kI(a), n != null)
    for (let i = 0; i < t.length; ++i) {
      if (n[i] == null)
        continue;
      let o = a[i];
      if (o.shape.length !== n[i].length)
        throw new G(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);
      for (let u = 0; u < n[i].length; ++u) {
        if (u === 0 && !s)
          continue;
        let l = o.shape[u], c = n[i][u];
        if (c != null && c >= 0 && l !== c)
          throw new G(`${r} expected a batch of elements where each example has shape [${n[i].slice(1, n[i].length)}] (i.e.,tensor shape [*,${n[i].slice(1, n[i].length)}]) but the ${r} received an input with ${o.shape[0]} examples, each with shape [${o.shape.slice(1, o.shape.length)}] (tensor shape [${o.shape}])`);
      }
    }
  return a;
}
function UB(e, t, n) {
  let s = lr(e.map((a) => a.shape[0]));
  s.sort();
  let r = lr(t.map((a) => a.shape[0]));
  if (r.sort(), s.length > 1)
    throw new G(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map((a) => a.shape))}`);
  if (r.length > 1)
    throw new G(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map((a) => a.shape))}`);
  if (s.length > 0 && r.length > 0 && !w.arraysEqual(s, r))
    throw new G(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`);
}
function GB(e, t, n) {
  let s = [xi, Vp, Ju];
  for (let r = 0; r < e.length; ++r) {
    let a = e[r], i = t[r], o = n[r];
    if (i != null) {
      if (i === Ju && a.shape[a.shape.length - 1] === 1)
        throw new G(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
      if (s.indexOf(i) !== -1) {
        let u = a.shape.slice(1), l = o.slice(1);
        for (let c = 0; c < u.length; ++c) {
          let p = u[c], d = l[c];
          if (d != null && p !== d)
            throw new G(`A target Tensor with shape ${a.shape} was passed for an output of shape ${o}, while using a loss function that expects targets to have the same shape as the output.`);
        }
      }
    }
  }
}
function Gx(e, t, n, s = true, r = "") {
  let a;
  if (Array.isArray(e)) {
    if (e.length !== t.length)
      throw new G(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${e.length} Tensors(s).`);
    a = e;
  } else {
    if (t.length > 1)
      throw new G(`The model expects ${t.length} ${r} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(e.shape)}.`);
    a = [e];
  }
  if (n != null)
    for (let i = 0; i < t.length; ++i) {
      if (n[i] == null)
        continue;
      let o = a[i];
      if (o.shape.length !== n[i].length)
        throw new G(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s), but got array with shape ${JSON.stringify(o.shape)}`);
      for (let u = 0; u < n[i].length; ++u) {
        if (u === 0 && !s)
          continue;
        let l = o.shape[u], c = n[i][u];
        if (c != null && c !== l)
          throw new G(`Error when checking ${r}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`);
      }
    }
}
function HB(e, t) {
  if (e == null || Array.isArray(e) && e.length === 0)
    return t.map((s) => []);
  let n;
  if (typeof e == "string" || typeof e == "function")
    n = [e];
  else if (Array.isArray(e) || typeof e == "object")
    n = e;
  else
    throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${e}`);
  if (Array.isArray(n))
    return t.map((s) => n);
  {
    let s = [];
    for (let r of t) {
      let a = n.hasOwnProperty(r) ? n[r] : [];
      Array.isArray(a) || (a = [a]), s.push(a);
    }
    return s;
  }
}
var qB = "layers-model";
var dr = class extends Is {
  constructor(e) {
    super(e), this.isTraining = false;
  }
  summary(e, t, n = console.log) {
    if (!this.built)
      throw new G("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");
    TB(this, e, t, n);
  }
  compile(e) {
    if (e.loss == null && (e.loss = []), this.loss = e.loss, typeof e.optimizer == "string")
      this.optimizer_ = NB(e.optimizer), this.isOptimizerOwned = true;
    else {
      if (!(e.optimizer instanceof _r))
        throw new G("User-defined optimizer must be an instance of tf.Optimizer.");
      this.optimizer_ = e.optimizer, this.isOptimizerOwned = false;
    }
    let t = [];
    if (!Array.isArray(e.loss) && typeof e.loss != "string" && typeof e.loss != "function") {
      e.loss = e.loss;
      for (let a in e.loss)
        if (this.outputNames.indexOf(a) === -1)
          throw new G(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);
      for (let a of this.outputNames)
        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(jf(e.loss[a]));
    } else if (Array.isArray(e.loss)) {
      if (e.loss.length !== this.outputs.length)
        throw new G(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);
      t = e.loss.map((i) => jf(i));
    } else {
      let a = jf(e.loss);
      this.outputs.forEach((i) => {
        t.push(a);
      });
    }
    this.lossFunctions = t, this.feedOutputNames = [], this.feedOutputShapes = [], this.feedLossFns = [];
    for (let a = 0; a < this.outputs.length; ++a) {
      let i = this.internalOutputShapes[a], o = this.outputNames[a];
      this.feedOutputNames.push(o), this.feedOutputShapes.push(i), this.feedLossFns.push(this.lossFunctions[a]);
    }
    let n = [];
    this.metrics = e.metrics, this.metricsNames = ["loss"], this.metricsTensors = [], ta("loss", () => {
      for (let a = 0; a < this.outputs.length; ++a) {
        if (n.indexOf(a) !== -1)
          continue;
        let i = this.lossFunctions[a];
        this.outputs.length > 1 && (this.metricsTensors.push([i, a]), this.metricsNames.push(this.outputNames[a] + "_loss"));
      }
    });
    let s = HB(e.metrics, this.outputNames), r = (a, i, o) => {
      this.outputNames.length > 1 && (i = this.outputNames[a] + "_" + i), this.metricsNames.push(i), this.metricsTensors.push([o, a]);
    };
    ta("metric", () => {
      for (let a = 0; a < this.outputs.length; ++a) {
        if (n.indexOf(a) !== -1)
          continue;
        let i = s[a];
        ((u) => {
          let l = "", c, p, d;
          for (let h of u) {
            if (typeof h == "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(h) !== -1) {
              let m = this.internalOutputShapes[a];
              m[m.length - 1] === 1 || this.lossFunctions[a] === Vp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = Gb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = fI) : this.lossFunctions[a] === Rd ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = mI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = gI) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Hb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = qb);
              let g;
              ["accuracy", "acc"].indexOf(h) !== -1 ? g = "acc" : ["crossentropy", "ce"].indexOf(h) !== -1 && (g = "ce"), d = p, c = l + g;
            } else
              d = CB(h), c = l + Kc(h);
            let f;
            ta(c, () => {
              f = d;
            }), r(a, c, f);
          }
        })(i);
      }
    }), this.collectedTrainableWeights = this.trainableWeights;
  }
  checkTrainableWeightsConsistency() {
    this.collectedTrainableWeights != null && this.trainableWeights.length !== this.collectedTrainableWeights.length && console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?");
  }
  evaluate(e, t, n = {}) {
    let s = n.batchSize == null ? 32 : n.batchSize;
    Tm(s);
    let r = true, a = this.standardizeUserDataXY(e, t, r, s);
    try {
      let i = a[0].concat(a[1]);
      this.makeTestFunction();
      let o = this.testFunction, u = this.testLoop(o, i, s, n.verbose, n.steps);
      return bn(u);
    } finally {
      ps(a[0], e), ps(a[1], t);
    }
  }
  async evaluateDataset(e, t) {
    return this.makeTestFunction(), LB(this, e, t);
  }
  checkNumSamples(e, t, n, s = "steps") {
    let r;
    if (n != null) {
      if (r = null, t != null)
        throw new G(`If ${s} is set, batchSize must be null or undefined.Got batchSize = ${t}`);
    } else if (e != null)
      Array.isArray(e) ? r = e[0].shape[0] : r = e.shape[0];
    else
      throw new G(`Either the input data should have a defined shape, or ${s} shoud be specified.`);
    return r;
  }
  execute(e, t) {
    if (Array.isArray(t) && t.length === 0)
      throw new G("`outputs` is an empty Array, which is not allowed.");
    let n = Array.isArray(t), s = n ? t : [t], r = this.retrieveSymbolicTensors(s), a = new Zr();
    if (e instanceof et && (e = [e]), Array.isArray(e)) {
      if (e.length !== this.inputs.length)
        throw new G(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
      for (let o = 0; o < this.inputs.length; ++o)
        a.add(this.inputs[o], e[o]);
    } else
      for (let o of this.inputs) {
        let u = e[o.name];
        if (u == null)
          throw new G(`No value is provided for the model's input ${o.name}`);
        a.add(o, u);
      }
    let i = Ru(r, a);
    return n ? i : i[0];
  }
  retrieveSymbolicTensors(e) {
    let t = ma(null, e.length), n = e.length;
    for (let s of this.layers) {
      let r = Array.isArray(s.output) ? s.output : [s.output], a = r.map((i) => i.name);
      for (let i = 0; i < e.length; ++i) {
        let o = a.indexOf(e[i]);
        if (o !== -1 && (t[i] = r[o], n--), n === 0)
          break;
      }
      if (n === 0)
        break;
    }
    if (n > 0) {
      let s = [];
      throw t.forEach((r, a) => {
        r == null && s.push(e[a]);
      }), new G(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`);
    }
    return t;
  }
  predictLoop(e, t = 32, n = false) {
    return q(() => {
      let s = this.checkNumSamples(e);
      if (n)
        throw new Fe("Verbose predictLoop() is not implemented yet.");
      let r = $m(s, t), a = this.outputs.map((i) => []);
      for (let i = 0; i < r.length; ++i)
        q(() => {
          let u = r[i][0], l = r[i][1], c = Du(e, u, l), p = [];
          if (Array.isArray(c))
            for (let h = 0; h < c.length; ++h)
              p.push({ key: this.inputs[h], value: c[h] });
          else
            p.push({ key: this.inputs[0], value: c });
          let d = new Zr(p);
          return Ru(this.outputs, d);
        }).forEach((u, l) => a[l].push(u));
      return bn(a.map((i) => Ft(i, 0)));
    });
  }
  predict(e, t = {}) {
    let n = kI(e);
    Gx(n, this.inputNames, this.feedInputShapes, false);
    try {
      let s = t.batchSize == null ? 32 : t.batchSize;
      return Tm(s), this.predictLoop(n, s);
    } finally {
      ps(n, e);
    }
  }
  predictOnBatch(e) {
    Gx(e, this.inputNames, this.feedInputShapes, true);
    let t = (Array.isArray(e) ? e[0] : e).shape[0];
    return this.predictLoop(e, t);
  }
  standardizeUserDataXY(e, t, n = true, s) {
    if (this.optimizer_ == null)
      throw new fs("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
    let r = [];
    for (let a = 0; a < this.feedOutputShapes.length; ++a) {
      let i = this.feedOutputShapes[a];
      this.feedLossFns[a] === Rd ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
    }
    if (e = Ux(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Ux(t, this.feedOutputNames, r, false, "target"), UB(e, t, null), GB(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
      throw new G(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);
    return [e, t];
  }
  async standardizeUserData(e, t, n, s, r = true, a) {
    let [i, o] = this.standardizeUserDataXY(e, t, r, a);
    if (n != null)
      throw new Error("sample weight is not supported yet.");
    let u = null;
    if (s != null) {
      let l = vI(s, this.outputNames);
      u = [];
      for (let c = 0; c < l.length; ++c)
        u.push(await xI(o[c], null, l[c]));
    }
    return [i, o, u];
  }
  testLoop(e, t, n, s = 0, r) {
    return q(() => {
      let a = this.checkNumSamples(t, n, r, "steps"), i = [];
      if (s > 0)
        throw new Fe("Verbose mode is not implemented yet.");
      if (r != null)
        throw new Fe("steps mode in testLoop() is not implemented yet");
      {
        let o = $m(a, n), u = Zt(ys(0, a));
        for (let l = 0; l < o.length; ++l) {
          let c = o[l][0], p = o[l][1], d = na(u, c, p - c), h = jb(t, d), f = e(h);
          if (l === 0)
            for (let m = 0; m < f.length; ++m)
              i.push(we(0));
          for (let m = 0; m < f.length; ++m) {
            let g = f[m];
            i[m] = ie(i[m], V(p - c, g));
          }
        }
        for (let l = 0; l < i.length; ++l)
          i[l] = xe(i[l], a);
      }
      return i;
    });
  }
  getDedupedMetricsNames() {
    let e = this.metricsNames, t = [];
    for (let n = 0; n < e.length; ++n) {
      let s = e[n], r = s;
      Sx(e, s) > 1 && (r += `_${Sx(e.slice(0, n), s)}`), t.push(r);
    }
    return t;
  }
  makeTrainFunction() {
    return (e) => {
      let t = [], n = e.slice(0, this.inputs.length), s = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), r = e.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2), a = [], i = () => {
        let c = [];
        for (let f = 0; f < this.inputs.length; ++f)
          c.push({ key: this.inputs[f], value: n[f] });
        let p = new Zr(c), d = Ru(this.outputs, p, { training: true }), h;
        for (let f = 0; f < this.lossFunctions.length; ++f) {
          let g = this.lossFunctions[f](s[f], d[f]);
          r[f] != null && (g = DB(g, r[f]));
          let b = It(g);
          t.push(b), f === 0 ? h = g : h = ie(h, g);
        }
        for (let f = 0; f < this.metricsTensors.length; ++f) {
          let m;
          if (this.outputs.length > 1 && f < this.outputs.length)
            m = t[f];
          else {
            let g = this.metricsTensors[f][0], b = this.metricsTensors[f][1];
            m = It(g(s[b], d[b]));
          }
          qt(m), a.push(m);
        }
        return h = It(h), this.calculateLosses().forEach((f) => {
          h = ie(h, f);
        }), h;
      }, o = this.collectedTrainableWeights.map((c) => c.read()), u = true;
      return [this.optimizer_.minimize(i, u, o)].concat(a);
    };
  }
  makeTestFunction() {
    this.testFunction = (e) => q(() => {
      let t = [], n, s = e.slice(0, this.inputs.length), r = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), a = [];
      for (let u = 0; u < this.inputs.length; ++u)
        a.push({ key: this.inputs[u], value: s[u] });
      let i = new Zr(a), o = Ru(this.outputs, i);
      for (let u = 0; u < this.lossFunctions.length; ++u) {
        let l = this.lossFunctions[u], c = It(l(r[u], o[u]));
        u === 0 ? n = c : n = ie(n, c), t.push(n);
      }
      for (let u = 0; u < this.metricsTensors.length; ++u) {
        let l = this.metricsTensors[u][0], c = this.metricsTensors[u][1], p = It(l(r[c], o[c]));
        t.push(p);
      }
      return t;
    });
  }
  async fit(e, t, n = {}) {
    return VB(this, e, t, n);
  }
  async fitDataset(e, t) {
    return PB(this, e, t);
  }
  async trainOnBatch(e, t) {
    let n = await this.standardizeUserData(e, t), s = n[0], r = n[1], i = this.makeTrainFunction()(s.concat(r)), o = [];
    for (let u of i) {
      let l = await u.data();
      o.push(l[0]);
    }
    return Re(i), ps(n[0], e), ps(n[1], t), bn(o);
  }
  getNamedWeights(e) {
    let t = [], n = e != null && e.trainableOnly, s = n ? this.trainableWeights : this.weights, r = this.getWeights(n);
    for (let a = 0; a < s.length; ++a)
      n && !s[a].trainable || t.push({ name: s[a].originalName, tensor: r[a] });
    return t;
  }
  set stopTraining(e) {
    this.stopTraining_ = e;
  }
  get stopTraining() {
    return this.stopTraining_;
  }
  get optimizer() {
    return this.optimizer_;
  }
  set optimizer(e) {
    this.optimizer_ !== e && (this.optimizer_ = e, this.isOptimizerOwned = false);
  }
  dispose() {
    let e = super.dispose();
    if (e.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
      let t = mm().numTensors;
      this.optimizer_.dispose(), e.numDisposedVariables += t - mm().numTensors;
    }
    return e;
  }
  getLossIdentifiers() {
    let e;
    if (typeof this.loss == "string")
      e = Vs(this.loss);
    else if (Array.isArray(this.loss)) {
      for (let t of this.loss)
        if (typeof t != "string")
          throw new Error("Serialization of non-string loss is not supported.");
      e = this.loss.map((t) => Vs(t));
    } else {
      let t = Object.keys(this.loss);
      e = {};
      let n = this.loss;
      for (let s of t)
        if (typeof n[s] == "string")
          e[s] = Vs(n[s]);
        else
          throw new Error("Serialization of non-string loss is not supported.");
    }
    return e;
  }
  getMetricIdentifiers() {
    if (typeof this.metrics == "string" || typeof this.metrics == "function")
      return [Vs(Kc(this.metrics))];
    if (Array.isArray(this.metrics))
      return this.metrics.map((e) => Vs(Kc(e)));
    {
      let e = {};
      for (let t in this.metrics)
        e[t] = Vs(Kc(this.metrics[t]));
      return e;
    }
  }
  getTrainingConfig() {
    return { loss: this.getLossIdentifiers(), metrics: this.getMetricIdentifiers(), optimizer_config: { class_name: this.optimizer.getClassName(), config: this.optimizer.getConfig() } };
  }
  loadTrainingConfig(e) {
    if (e.weighted_metrics != null)
      throw new Error("Loading weight_metrics is not supported yet.");
    if (e.loss_weights != null)
      throw new Error("Loading loss_weights is not supported yet.");
    if (e.sample_weight_mode != null)
      throw new Error("Loading sample_weight_mode is not supported yet.");
    let t = el(e.optimizer_config), n = gs(t), s;
    if (typeof e.loss == "string")
      s = Xr(e.loss);
    else if (Array.isArray(e.loss))
      s = e.loss.map((a) => Xr(a));
    else if (e.loss != null) {
      s = {};
      for (let a in e.loss)
        s[a] = Xr(e.loss[a]);
    }
    let r;
    if (Array.isArray(e.metrics))
      r = e.metrics.map((a) => Xr(a));
    else if (e.metrics != null) {
      r = {};
      for (let a in e.metrics)
        r[a] = Xr(e.metrics[a]);
    }
    this.compile({ loss: s, metrics: r, optimizer: n });
  }
  async save(e, t) {
    if (typeof e == "string") {
      let u = An.getSaveHandlers(e);
      if (u.length === 0)
        throw new G(`Cannot find any save handlers for URL '${e}'`);
      if (u.length > 1)
        throw new G(`Found more than one (${u.length}) save handlers for URL '${e}'`);
      e = u[0];
    }
    if (e.save == null)
      throw new G("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
    let n = await An.encodeWeights(this.getNamedWeights(t)), s = false, r = null, i = { modelTopology: this.toJSON(r, s), format: qB, generatedBy: `TensorFlow.js tfjs-layers v${yI}`, convertedBy: null };
    if ((t == null ? false : t.includeOptimizer) && this.optimizer != null) {
      i.trainingConfig = this.getTrainingConfig();
      let u = "optimizer", { data: l, specs: c } = await An.encodeWeights(await this.optimizer.getWeights(), u);
      n.specs.push(...c), n.data = An.concatenateArrayBuffers([n.data, l]);
    }
    return this.userDefinedMetadata != null && (Lx(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
  }
  setUserDefinedMetadata(e) {
    Lx(e, this.name), this.userDefinedMetadata = e;
  }
  getUserDefinedMetadata() {
    return this.userDefinedMetadata;
  }
};
dr.className = "Model";
re.registerClass(dr);
var SI = class extends dr {
};
SI.className = "Functional";
re.registerClass(SI);
async function jB(e, t) {
  "modelTopology" in e || (e = { modelTopology: e }), e = e;
  let n = e.modelTopology;
  n.model_config != null && (n = n.model_config);
  let s = el(n), r = gs(s, t);
  if (e.weightsManifest != null) {
    let a = await An.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((o) => o.originalName)), i = {};
    for (let o of r.weights)
      i[o.originalName] = a[o.originalName];
    r.loadWeights(i), Re(a);
  }
  return r;
}
async function KB(e, t) {
  if (t == null && (t = {}), typeof e == "string") {
    let n = An.getLoadHandlers(e, t);
    if (n.length === 0)
      n.push(An.browserHTTPRequest(e, t));
    else if (n.length > 1)
      throw new G(`Found more than one (${n.length}) load handlers for URL '${e}'`);
    e = n[0];
  }
  return XB(e, void 0, t);
}
async function XB(e, t, n) {
  if (n == null && (n = {}), e.load == null)
    throw new G("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
  let s = await e.load(), r = s.modelTopology;
  r.model_config != null && (r = r.model_config);
  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = gs(el(r), t, i), u = s.trainingConfig;
  if (u != null && o.loadTrainingConfig(u), s.userDefinedMetadata != null && o.setUserDefinedMetadata(s.userDefinedMetadata), s.weightData != null) {
    if (s.weightSpecs == null)
      throw new G("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
    let { modelWeights: l, optimizerWeights: c } = YB(s.weightData, s.weightSpecs);
    o.loadWeights(l, a), o.optimizer != null && c.length > 0 && await o.optimizer.setWeights(c), Re(l), Re(c.map((p) => p.tensor));
  }
  return o;
}
function YB(e, t) {
  let n = An.decodeWeights(e, t), s = {}, r = [];
  return t.forEach((a) => {
    a.group === "optimizer" ? r.push({ name: a.name, tensor: n[a.name] }) : s[a.name] = n[a.name];
  }), { modelWeights: s, optimizerWeights: r };
}
var Am = class extends dr {
  constructor(e) {
    if (super({ inputs: [], outputs: [] }), e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : _p("sequential_"), e.layers != null)
      for (let t of e.layers)
        this.add(t);
  }
  checkShape(e) {
    if (e.inboundNodes[0].outputTensors[0].shape.some((n) => n < 0))
      throw new G(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`);
  }
  add(e) {
    let t = e instanceof Am || e instanceof dr, n;
    if (t) {
      if (n = e, n.outputs.length !== 1)
        throw new G("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
      if (n.inputs.length !== 1)
        throw new G("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.");
    }
    if (this.outputs.length === 0) {
      if (e.inboundNodes.length === 0) {
        if (e.batchInputShape == null)
          throw new G("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
        let s = iI({ batchShape: e.batchInputShape, dtype: e.dtype, name: e.name + "_input" });
        e.apply(s);
      }
      if (t)
        this.outputs = n.outputs, this.inputs = n.inputs;
      else {
        if (e.inboundNodes.length !== 1)
          throw new G(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);
        if (e.inboundNodes[0].outputTensors.length !== 1)
          throw new G("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
        this.checkShape(e), this.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = aI(this.outputs[0]);
      }
      this.inboundNodes = [], new Lp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: ma(null, this.inputs.length), outputMasks: [null], inputShapes: this.inputs.map((s) => s.shape), outputShapes: this.outputs[0].shape });
    } else {
      let s = e.apply(this.outputs[0]);
      if (Array.isArray(s))
        throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
      this.checkShape(e), this.outputs = [s], this.inboundNodes[0].outputTensors = this.outputs, this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
    }
    this.layers.push(e), this.built = false;
  }
  pop() {
    if (this.layers.length === 0)
      throw new TypeError("There are no layers in the model.");
    if (this.layers.pop(), this.layers.length === 0)
      this.outputs = [], this.inboundNodes = [], this.outboundNodes = [];
    else {
      let e = this.layers.length - 1;
      this.layers[e].outboundNodes = [], this.outputs = [this.layers[e].output], this.inboundNodes[0].outputTensors = this.outputs, this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
    }
  }
  call(e, t) {
    return this.model == null && this.build(), this.model.call(e, t);
  }
  build(e) {
    if (nt(e), this.inputs.length === 0 || this.outputs.length === 0)
      throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
    this.model = new dr({ inputs: this.inputs, outputs: this.outputs[0], name: this.name + "_model" }), this.model.trainable = this.trainable, this.supportsMasking = this.model.supportsMasking, this.inputLayers = this.model.inputLayers, this.inputLayersNodeIndices = this.model.inputLayersNodeIndices, this.inputLayersTensorIndices = this.model.inputLayersTensorIndices, this.outputLayers = this.model.outputLayers, this.outputLayersNodeIndices = this.model.outputLayersNodeIndices, this.outputLayersTensorIndices = this.model.outputLayersTensorIndices, this.nodesByDepth = this.model.nodesByDepth, this.containerNodes = this.model.containerNodes, this.outputNames = this.model.outputNames, this.inputNames = this.model.inputNames, this.built = true;
  }
  countParams() {
    return this.built || this.build(), super.countParams();
  }
  summary(e, t, n = console.log) {
    this.built || this.build(), super.summary(e, t, n);
  }
  setWeights(e) {
    this.model == null && this.build(), this.model.setWeights(e);
  }
  evaluate(e, t, n = {}) {
    if (!this.built)
      throw new fs("The model needs to be compiled before being used.");
    return this.model.evaluate(e, t, n);
  }
  async evaluateDataset(e, t) {
    if (!this.built)
      throw new fs("The model needs to be compiled before being used.");
    return this.model.evaluateDataset(e, t);
  }
  predict(e, t = {}) {
    return this.model == null && this.build(), this.model.predict(e, t);
  }
  predictOnBatch(e) {
    return this.model == null && this.build(), this.model.predictOnBatch(e);
  }
  compile(e) {
    this.build(), this.model.compile(e), this.optimizer_ = this.model.optimizer, this.isOptimizerOwned = this.model.isOptimizerOwned, this.loss = this.model.loss, this.metrics = this.model.metrics, this.metricsTensors = this.model.metricsTensors, this.metricsNames = this.model.metricsNames;
  }
  get optimizer() {
    return this.model == null ? void 0 : this.model.optimizer;
  }
  set optimizer(e) {
    this.model.optimizer = e;
  }
  async fit(e, t, n = {}) {
    if (!this.built)
      throw new fs("The model needs to be compiled before being used.");
    return this.model.fit(e, t, n);
  }
  async fitDataset(e, t) {
    if (!this.built)
      throw new fs("The model needs to be compiled before being used.");
    return this.model.fitDataset(e, t);
  }
  async trainOnBatch(e, t) {
    return this.model.trainOnBatch(e, t);
  }
  static fromConfig(e, t, n = {}, s = false) {
    let r, a = {};
    if (t instanceof Array) {
      if (t[0].className == null || t[0].className === "Merge")
        throw new G("Legacy serialization format not supported yet.");
      r = t;
    } else
      w.assert(t.layers != null, () => "When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."), r = t.layers, delete t.layers, a = t;
    let i = new e(a);
    if (!(i instanceof Am))
      throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);
    for (let o of r) {
      let l = gs(o, void 0, s);
      s && l.setFastWeightInitDuringBuild(true), i.add(l);
    }
    return i;
  }
  set stopTraining(e) {
    if (this.model == null)
      throw new G("Cannot set the stopTraining property of a sequential model before it is compiled.");
    this.model.stopTraining = e;
  }
  get stopTraining() {
    if (this.model == null)
      throw new G("Cannot get the stopTraining property of a sequential model before it is compiled.");
    return this.model.stopTraining;
  }
  getConfig() {
    let e = [];
    for (let t of this.layers) {
      let n = {};
      n.className = t.getClassName(), n.config = t.getConfig(), e.push(n);
    }
    return { name: this.name, layers: e };
  }
};
var Kb = Am;
Kb.className = "Sequential";
re.registerClass(Kb);
function the(e) {
  return new dr(e);
}
function nhe(e) {
  return new Kb(e);
}
function she(e, t) {
  return t == null && (t = {}), KB(e, t);
}
function QB(e) {
  return iI(e);
}
function rhe(e, t) {
  Wb.registerCallbackConstructor(e, t);
}
var kn = class extends re.Serializable {
  getConfig() {
    return {};
  }
};
var II = class extends kn {
  apply(e, t = 1) {
    return cz(e, t);
  }
};
II.className = "elu";
re.registerClass(II);
var CI = class extends kn {
  apply(e) {
    return CS(e);
  }
};
CI.className = "selu";
re.registerClass(CI);
var NI = class extends kn {
  apply(e) {
    return Xs(e);
  }
};
NI.className = "relu";
re.registerClass(NI);
var TI = class extends kn {
  apply(e) {
    return q(() => kp(6, Xs(e)));
  }
};
TI.className = "relu6";
re.registerClass(TI);
var $I = class extends kn {
  apply(e) {
    return e;
  }
};
$I.className = "linear";
re.registerClass($I);
var _I = class extends kn {
  apply(e) {
    return Hs(e);
  }
};
_I.className = "sigmoid";
re.registerClass(_I);
var AI = class extends kn {
  apply(e) {
    return pz(e);
  }
};
AI.className = "hardSigmoid";
re.registerClass(AI);
var EI = class extends kn {
  apply(e) {
    return Ml(e);
  }
};
EI.className = "softplus";
re.registerClass(EI);
var RI = class extends kn {
  apply(e) {
    return dz(e);
  }
};
RI.className = "softsign";
re.registerClass(RI);
var DI = class extends kn {
  apply(e) {
    return Ku(e);
  }
};
DI.className = "tanh";
re.registerClass(DI);
var Xb = class extends kn {
  apply(e, t = -1) {
    return hb(e, t);
  }
};
Xb.className = "softmax";
re.registerClass(Xb);
var FI = class extends kn {
  apply(e, t = -1) {
    return fS(e, t);
  }
};
FI.className = "logSoftmax";
re.registerClass(FI);
var OI = class extends kn {
  apply(e, t = 1) {
    return q(() => V(Hs(V(e, t)), e));
  }
};
OI.className = "swish";
re.registerClass(OI);
var PI = class extends kn {
  apply(e) {
    return q(() => V(e, Ku(Ml(e))));
  }
};
PI.className = "mish";
re.registerClass(PI);
function br(e) {
  return e.getClassName();
}
function Kf(e, t = {}) {
  return Bl(e, re.SerializationMap.getMap().classNameMap, t, "activation");
}
function yr(e) {
  if (e == null) {
    let t = {};
    return t.className = "linear", t.config = {}, Kf(t);
  }
  if (typeof e == "string") {
    let t = {};
    return t.className = e, t.config = {}, Kf(t);
  } else
    return e instanceof kn ? e : Kf(e);
}
function Yb(e) {
  if (e != null && typeof e != "object")
    throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`);
}
var zI = class extends re.Serializable {
};
var Hl = class extends zI {
  constructor(e) {
    super(), Yb(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
  }
  apply(e) {
    return q(() => {
      let t = $t([1]);
      return this.hasL1 && (t = ie(t, ve(V(this.l1, Lt(e))))), this.hasL2 && (t = ie(t, ve(V(this.l2, Wl(e))))), U(t, []);
    });
  }
  getConfig() {
    return { l1: this.l1, l2: this.l2 };
  }
  static fromConfig(e, t) {
    return new e({ l1: t.l1, l2: t.l2 });
  }
};
Hl.className = "L1L2";
re.registerClass(Hl);
function ZB(e) {
  return Yb(e), new Hl({ l1: e != null ? e.l1 : null, l2: 0 });
}
function JB(e) {
  return Yb(e), new Hl({ l2: e != null ? e.l2 : null, l1: 0 });
}
var Hx = { l1l2: "L1L2" };
function at(e) {
  return Nb(e);
}
function qx(e, t = {}) {
  return Bl(e, re.SerializationMap.getMap().classNameMap, t, "regularizer");
}
function ft(e) {
  if (e == null)
    return null;
  if (typeof e == "string") {
    let n = { className: e in Hx ? Hx[e] : e, config: {} };
    return qx(n);
  } else
    return e instanceof zI ? e : qx(e);
}
var Qb = class extends He {
  constructor(e) {
    super(e == null ? {} : e), this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
  }
  call(e, t) {
    e = Oe(e);
    let n = Xs(e);
    return this.maxValue != null && (n = Vn(n, 0, this.maxValue)), n;
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { maxValue: this.maxValue }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Qb.className = "ReLU";
re.registerClass(Qb);
var Zb = class extends He {
  constructor(e) {
    super(e == null ? {} : e), this.DEFAULT_ALPHA = 0.3, e == null && (e = {}), this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
  }
  call(e, t) {
    let n = Oe(e);
    return eb(n, this.alpha);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { alpha: this.alpha }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Zb.className = "LeakyReLU";
re.registerClass(Zb);
var Jb = class extends He {
  constructor(e) {
    if (super(e == null ? {} : e), this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ht(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = ft(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
      this.sharedAxes = null;
    else if (Array.isArray(e.sharedAxes))
      this.sharedAxes = e.sharedAxes;
    else if (typeof e.sharedAxes == "number")
      this.sharedAxes = [e.sharedAxes];
    else
      throw new G(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);
  }
  build(e) {
    e = nt(e);
    let t = e.slice(1);
    if (this.sharedAxes != null)
      for (let s of this.sharedAxes)
        t[s - 1] = 1;
    this.alpha = this.addWeight("alpha", t, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
    let n = {};
    if (this.sharedAxes != null)
      for (let s = 1; s < e.length; ++s)
        n[s] = e[s];
    this.inputSpec = [new Dt({ ndim: e.length, axes: n })], this.built = true;
  }
  call(e, t) {
    return e = Oe(e), ub(e, this.alpha.read());
  }
  getConfig() {
    let e = { alphaInitializer: yt(this.alphaInitializer), alphaRegularizer: at(this.alphaRegularizer), alphaConstraint: Ot(this.alphaConstraint), sharedAxes: this.sharedAxes }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Jb.className = "PReLU";
re.registerClass(Jb);
var ey = class extends He {
  constructor(e) {
    if (super(e == null ? {} : e), this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
      throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);
    this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
  }
  call(e, t) {
    let n = Oe(e);
    return vp(n);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { alpha: this.alpha }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ey.className = "ELU";
re.registerClass(ey);
var ty = class extends He {
  constructor(e) {
    super(e == null ? {} : e), this.DEFAULT_THETA = 1, e == null && (e = {}), this.theta = e.theta == null ? this.DEFAULT_THETA : e.theta;
  }
  call(e, t) {
    let n = Oe(e);
    return V(n, le(Un(n, this.theta), "float32"));
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { theta: this.theta }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ty.className = "ThresholdedReLU";
re.registerClass(ty);
var ny = class extends He {
  constructor(e) {
    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Xb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
  }
  call(e, t) {
    let n = Oe(e);
    return this.softmax(n, this.axis);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { axis: this.axis }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ny.className = "Softmax";
re.registerClass(ny);
function Ji(e, t, n) {
  if (typeof e == "number")
    return ma(e, t);
  if (e.length !== t)
    throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);
  for (let s = 0; s < t; ++s) {
    let r = e[s];
    if (!iz(r))
      throw new G(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`);
  }
  return e;
}
function bs(e, t, n, s, r = 1) {
  if (e == null)
    return e;
  let a = t + (t - 1) * (r - 1), i;
  return n === "same" ? i = e : i = e - a + 1, Math.floor((i + s - 1) / s);
}
function Ns(e, t, n, s) {
  if (e == null)
    return null;
  if (s === "valid")
    e = e * t + gr([n - t, 0]);
  else if (s === "same")
    e = e * t;
  else
    throw new G(`Unsupport padding mode: ${s}.`);
  return e;
}
function sy(e, t) {
  return q(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 1]) : e));
}
function MI(e, t) {
  return q(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 4, 1]) : e));
}
function eV(e, t, n, s = 1, r = "valid", a, i = 1) {
  return q(() => {
    if (a == null && (a = vs()), Ct(a), e.shape.length !== 3)
      throw new G(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);
    if (t.shape.length !== 3)
      throw new G(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);
    if (n != null && n.shape.length !== 1)
      throw new G(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);
    if (a === "channelsFirst" && (e = Ge(e, [0, 2, 1])), r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
    let o = iS(e, t, s, r === "same" ? "same" : "valid", "NWC", i);
    return n != null && (o = ks(o, n)), o;
  });
}
function jx(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
  return q(() => {
    if (a == null && (a = vs()), Ct(a), e.rank !== 3 && e.rank !== 4)
      throw new G(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);
    if (t.rank !== 3 && t.rank !== 4)
      throw new G(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);
    let u = sy(e, a);
    if (r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
    return u = fa.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: i, dataFormat: "NHWC", bias: n, activation: o }), a === "channelsFirst" && (u = Ge(u, [0, 3, 1, 2])), u;
  });
}
function tV(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
  return q(() => {
    if (a == null && (a = vs()), Ct(a), e.rank !== 4 && e.rank !== 5)
      throw new G(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);
    if (t.rank !== 4 && t.rank !== 5)
      throw new G(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);
    let o = MI(e, a);
    if (r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
    return o = uS(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ks(o, n)), a === "channelsFirst" && (o = Ge(o, [0, 4, 1, 2, 3])), o;
  });
}
var ry = class extends He {
  constructor(e, t) {
    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", ry.verifyArgs(t), this.rank = e, Vt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
      throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
    if (this.kernelSize = Ji(t.kernelSize, e, "kernelSize"), this.strides = Ji(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Gn(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, Ct(this.dataFormat), this.activation = yr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Ji(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
      throw new G(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
    if (this.rank === 2) {
      if (typeof this.dilationRate == "number")
        this.dilationRate = [this.dilationRate, this.dilationRate];
      else if (this.dilationRate.length !== 2)
        throw new G(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);
    } else if (this.rank === 3) {
      if (typeof this.dilationRate == "number")
        this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];
      else if (this.dilationRate.length !== 3)
        throw new G(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);
    }
  }
  static verifyArgs(e) {
    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !Tb(e.kernelSize, "number", 1, 3))
      throw new G(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`);
  }
  getConfig() {
    let e = { kernelSize: this.kernelSize, strides: this.strides, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, activation: br(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var ql = class extends ry {
  constructor(e, t) {
    super(e, t), this.kernel = null, ql.verifyArgs(t), this.filters = t.filters, Vt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
  }
  build(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new G(`The channel dimension of the input should be defined. Found ${e[t]}`);
    let n = e[t], s = this.kernelSize.concat([n, this.filters]);
    this.kernel = this.addWeight("kernel", s, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [{ ndim: this.rank + 2, axes: { [t]: n } }], this.built = true;
  }
  call(e, t) {
    return q(() => {
      e = Oe(e);
      let n, s = this.bias == null ? null : this.bias.read(), r = QS(this.activation.getClassName());
      if (r != null && this.rank === 2)
        n = jx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
      else {
        if (this.rank === 1)
          n = eV(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
        else if (this.rank === 2)
          n = jx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
        else if (this.rank === 3)
          n = tV(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
        else
          throw new Fe("convolutions greater than 3D are not implemented yet.");
        this.activation != null && (n = this.activation.apply(n));
      }
      return n;
    });
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = [], n = this.dataFormat === "channelsLast" ? e.slice(1, e.length - 1) : e.slice(2);
    for (let r = 0; r < n.length; ++r) {
      let a = bs(n[r], this.kernelSize[r], this.padding, this.strides[r], typeof this.dilationRate == "number" ? this.dilationRate : this.dilationRate[r]);
      t.push(a);
    }
    let s = [e[0]];
    return this.dataFormat === "channelsLast" ? (s = s.concat(t), s.push(this.filters)) : (s.push(this.filters), s = s.concat(t)), s;
  }
  getConfig() {
    let e = { filters: this.filters, kernelInitializer: yt(this.kernelInitializer), kernelRegularizer: at(this.kernelRegularizer), kernelConstraint: Ot(this.kernelConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
  static verifyArgs(e) {
    if (!("filters" in e) || typeof e.filters != "number" || e.filters < 1)
      throw new G(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`);
  }
};
var LI = class extends ql {
  constructor(e) {
    super(2, e), LI.verifyArgs(e);
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, e;
  }
  static verifyArgs(e) {
    if (typeof e.kernelSize != "number" && !Tb(e.kernelSize, "number", 1, 2))
      throw new G(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`);
  }
};
var Wp = LI;
Wp.className = "Conv2D";
re.registerClass(Wp);
var BI = class extends ql {
  constructor(e) {
    super(3, e), BI.verifyArgs(e);
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, e;
  }
  static verifyArgs(e) {
    if (typeof e.kernelSize != "number" && !(Array.isArray(e.kernelSize) && (e.kernelSize.length === 1 || e.kernelSize.length === 3)))
      throw new G(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`);
  }
};
var Up = BI;
Up.className = "Conv3D";
re.registerClass(Up);
var ay = class extends Wp {
  constructor(e) {
    if (super(e), this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
      throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
  }
  build(e) {
    if (e = nt(e), e.length !== 4)
      throw new G("Input should have rank 4; Received input shape: " + JSON.stringify(e));
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new G("The channel dimension of the inputs should be defined. Found `None`.");
    let n = e[t], s = this.kernelSize.concat([this.filters, n]);
    this.kernel = this.addWeight("kernel", s, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [new Dt({ ndim: 4, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      if (n.shape.length !== 4)
        throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);
      let s = n.shape, r = s[0], a, i;
      this.dataFormat === "channelsFirst" ? (a = 2, i = 3) : (a = 1, i = 2);
      let o = s[a], u = s[i], l = this.kernelSize[0], c = this.kernelSize[1], p = this.strides[0], d = this.strides[1], h = Ns(o, p, l, this.padding), f = Ns(u, d, c, this.padding), m = [r, h, f, this.filters];
      this.dataFormat !== "channelsLast" && (n = Ge(n, [0, 2, 3, 1]));
      let g = oS(n, this.kernel.read(), m, this.strides, this.padding);
      return this.dataFormat !== "channelsLast" && (g = Ge(g, [0, 3, 1, 2])), this.bias != null && (g = ks(g, this.bias.read(), this.dataFormat)), this.activation != null && (g = this.activation.apply(g)), g;
    });
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = e.slice(), n, s, r;
    this.dataFormat === "channelsFirst" ? (n = 1, s = 2, r = 3) : (n = 3, s = 1, r = 2);
    let a = this.kernelSize[0], i = this.kernelSize[1], o = this.strides[0], u = this.strides[1];
    return t[n] = this.filters, t[s] = Ns(t[s], o, a, this.padding), t[r] = Ns(t[r], u, i, this.padding), t;
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.dilationRate, e;
  }
};
ay.className = "Conv2DTranspose";
re.registerClass(ay);
var iy = class extends Up {
  constructor(e) {
    if (super(e), this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
      throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
  }
  build(e) {
    if (e = nt(e), e.length !== 5)
      throw new G("Input should have rank 5; Received input shape: " + JSON.stringify(e));
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new G("The channel dimension of the inputs should be defined. Found `None`.");
    let n = e[t], s = this.kernelSize.concat([this.filters, n]);
    this.kernel = this.addWeight("kernel", s, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [new Dt({ ndim: 5, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      if (n.shape.length !== 5)
        throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);
      let s = n.shape, r = s[0], a, i, o;
      this.dataFormat === "channelsFirst" ? (o = 2, a = 3, i = 4) : (o = 1, a = 2, i = 3);
      let u = s[o], l = s[a], c = s[i], p = this.kernelSize[0], d = this.kernelSize[1], h = this.kernelSize[2], f = this.strides[0], m = this.strides[1], g = this.strides[2], b = Ns(u, f, p, this.padding), y = Ns(l, m, d, this.padding), v = Ns(c, g, h, this.padding), x = [r, b, y, v, this.filters];
      this.dataFormat !== "channelsLast" && (n = Ge(n, [0, 2, 3, 4, 1]));
      let k = cR(n, this.kernel.read(), x, this.strides, this.padding);
      return this.dataFormat !== "channelsLast" && (k = Ge(k, [0, 4, 1, 2, 3])), this.bias !== null && (k = ks(k, this.bias.read(), this.dataFormat)), this.activation !== null && (k = this.activation.apply(k)), k;
    });
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = e.slice(), n, s, r, a;
    this.dataFormat === "channelsFirst" ? (n = 1, s = 2, r = 3, a = 4) : (n = 4, s = 1, r = 2, a = 3);
    let i = this.kernelSize[0], o = this.kernelSize[1], u = this.kernelSize[2], l = this.strides[0], c = this.strides[1], p = this.strides[2];
    return t[n] = this.filters, t[s] = Ns(t[s], l, i, this.padding), t[r] = Ns(t[r], c, o, this.padding), t[a] = Ns(t[a], p, u, this.padding), t;
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.dilationRate, e;
  }
};
iy.className = "Conv3DTranspose";
re.registerClass(iy);
var VI = class extends ql {
  constructor(e, t) {
    if (super(e, t), this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
      throw new G("The `filters` configuration field is required by SeparableConv, but is unspecified.");
    if (t.kernelInitializer != null || t.kernelRegularizer != null || t.kernelConstraint != null)
      throw new G("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
    if (t.padding != null && t.padding !== "same" && t.padding !== "valid")
      throw new G(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);
    this.depthMultiplier = t.depthMultiplier == null ? 1 : t.depthMultiplier, this.depthwiseInitializer = ht(t.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER), this.depthwiseRegularizer = ft(t.depthwiseRegularizer), this.depthwiseConstraint = Pt(t.depthwiseConstraint), this.pointwiseInitializer = ht(t.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER), this.pointwiseRegularizer = ft(t.pointwiseRegularizer), this.pointwiseConstraint = Pt(t.pointwiseConstraint);
  }
  build(e) {
    if (e = nt(e), e.length < this.rank + 2)
      throw new G(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(e)}`);
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null || e[t] < 0)
      throw new G(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);
    let n = e[t], s = this.kernelSize.concat([n, this.depthMultiplier]), r = [];
    for (let i = 0; i < this.rank; ++i)
      r.push(1);
    r.push(n * this.depthMultiplier, this.filters);
    let a = true;
    this.depthwiseKernel = this.addWeight("depthwise_kernel", s, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, a, this.depthwiseConstraint), this.pointwiseKernel = this.addWeight("pointwise_kernel", r, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, a, this.pointwiseConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, a, this.biasConstraint) : this.bias = null, this.inputSpec = [new Dt({ ndim: this.rank + 2, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return q(() => {
      e = Oe(e);
      let n;
      if (this.rank === 1)
        throw new Fe("1D separable convolution is not implemented yet.");
      return this.rank === 2 && (this.dataFormat === "channelsFirst" && (e = Ge(e, [0, 2, 3, 1])), n = x3(e, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC")), this.useBias && (n = ks(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), this.dataFormat === "channelsFirst" && (n = Ge(n, [0, 3, 1, 2])), n;
    });
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, delete e.kernelInitializer, delete e.kernelRegularizer, delete e.kernelConstraint, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.pointwiseInitializer = yt(this.pointwiseInitializer), e.depthwiseRegularizer = at(this.depthwiseRegularizer), e.pointwiseRegularizer = at(this.pointwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseConstraint), e.pointwiseConstraint = Ot(this.pointwiseConstraint), e;
  }
};
VI.className = "SeparableConv";
var oy = class extends VI {
  constructor(e) {
    super(2, e);
  }
};
oy.className = "SeparableConv2D";
re.registerClass(oy);
var WI = class extends ql {
  constructor(e) {
    super(1, e), WI.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, delete e.dataFormat, e;
  }
  static verifyArgs(e) {
    if (typeof e.kernelSize != "number" && !Tb(e.kernelSize, "number", 1, 1))
      throw new G(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`);
  }
};
var uy = WI;
uy.className = "Conv1D";
re.registerClass(uy);
var ly = class extends He {
  constructor(e) {
    super(e), typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
  }
  computeOutputShape(e) {
    return this.dataFormat === "channelsFirst" ? [e[0], e[1], e[2] - this.cropping[0][0] - this.cropping[0][1], e[3] - this.cropping[1][0] - this.cropping[1][1]] : [e[0], e[1] - this.cropping[0][0] - this.cropping[0][1], e[2] - this.cropping[1][0] - this.cropping[1][1], e[3]];
  }
  call(e, t) {
    return q(() => {
      if (e = Oe(e), this.dataFormat === "channelsLast") {
        let n = jc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
        return jc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
      } else {
        let n = jc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
        return jc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
      }
    });
  }
  getConfig() {
    let e = { cropping: this.cropping, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ly.className = "Cropping2D";
re.registerClass(ly);
var cy = class extends He {
  constructor(e) {
    super(e), this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, sz(this.interpolation);
  }
  computeOutputShape(e) {
    if (this.dataFormat === "channelsFirst") {
      let t = e[2] == null ? null : this.size[0] * e[2], n = e[3] == null ? null : this.size[1] * e[3];
      return [e[0], e[1], t, n];
    } else {
      let t = e[1] == null ? null : this.size[0] * e[1], n = e[2] == null ? null : this.size[1] * e[2];
      return [e[0], t, n, e[3]];
    }
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e), s = n.shape;
      if (this.dataFormat === "channelsFirst") {
        n = Ge(n, [0, 2, 3, 1]);
        let r = this.size[0] * s[2], a = this.size[1] * s[3], i = this.interpolation === "nearest" ? jn.resizeNearestNeighbor(n, [r, a]) : jn.resizeBilinear(n, [r, a]);
        return Ge(i, [0, 3, 1, 2]);
      } else {
        let r = this.size[0] * s[1], a = this.size[1] * s[2];
        return this.interpolation === "nearest" ? jn.resizeNearestNeighbor(n, [r, a]) : jn.resizeBilinear(n, [r, a]);
      }
    });
  }
  getConfig() {
    let e = { size: this.size, dataFormat: this.dataFormat, interpolation: this.interpolation }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
cy.className = "UpSampling2D";
re.registerClass(cy);
function nV(e, t, n = [1, 1], s = "valid", r, a) {
  return q(() => {
    r == null && (r = vs()), Ct(r);
    let i = sy(e, r);
    if (e.rank !== 4)
      throw new G(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);
    if (t.rank !== 4)
      throw new G(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);
    return i = yp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
  });
}
var dy = class extends ry {
  constructor(e) {
    super(2, e), this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ht(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = ft(e.depthwiseRegularizer);
  }
  build(e) {
    if (e = nt(e), e.length < 4)
      throw new G(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);
    let t = this.dataFormat === "channelsFirst" ? 1 : 3;
    if (e[t] == null || e[t] < 0)
      throw new G(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);
    let n = e[t], s = [this.kernelSize[0], this.kernelSize[1], n, this.depthMultiplier];
    this.depthwiseKernel = this.addWeight("depthwise_kernel", s, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint), this.useBias ? this.bias = this.addWeight("bias", [n * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
  }
  call(e, t) {
    return q(() => {
      e = Oe(e);
      let n = nV(e, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
      return this.useBias && (n = ks(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), n;
    });
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? e[2] : e[1], n = this.dataFormat === "channelsFirst" ? e[3] : e[2], s = this.dataFormat === "channelsFirst" ? e[1] * this.depthMultiplier : e[3] * this.depthMultiplier, r = bs(t, this.kernelSize[0], this.padding, this.strides[0]), a = bs(n, this.kernelSize[1], this.padding, this.strides[1]);
    return this.dataFormat === "channelsFirst" ? [e[0], s, r, a] : [e[0], r, a, s];
  }
  getConfig() {
    let e = super.getConfig();
    return e.depthMultiplier = this.depthMultiplier, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.depthwiseRegularizer = at(this.depthwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseRegularizer), e;
  }
};
dy.className = "DepthwiseConv2D";
re.registerClass(dy);
function UI(e, t, n, s) {
  if (Array.isArray(e)) {
    if (t != null || n != null)
      throw new G("When inputs is an array, neither initialState or constants should be provided");
    s != null && (n = e.slice(e.length - s, e.length), e = e.slice(0, e.length - s)), e.length > 1 && (t = e.slice(1, e.length)), e = e[0];
  }
  function r(a) {
    return a == null || Array.isArray(a) ? a : [a];
  }
  return t = r(t), n = r(n), { inputs: e, initialState: t, constants: n };
}
function GI(e, t, n, s = false, r, a, i = false, o = false) {
  return q(() => {
    let u = t.shape.length;
    if (u < 3)
      throw new G(`Input should be at least 3D, but is ${u}D.`);
    let l = [1, 0].concat(ys(2, u));
    if (t = Ge(t, l), a != null)
      throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
    i && console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."), r != null && (r = le(le(r, "bool"), "float32"), r.rank === u - 1 && (r = Pn(r, -1)), r = Ge(r, l)), s && (t = Jn(t, 0), r != null && (r = Jn(r, 0)));
    let c = [], p, d = n, h = t.shape[0], f = Fs(t), m;
    r != null && (m = Fs(r));
    for (let b = 0; b < h; ++b) {
      let y = f[b], v = q(() => e(y, d));
      if (r == null)
        p = v[0], d = v[1];
      else {
        let x = q(() => {
          let k = m[b], I = ge(Zn(k), k), $ = ie(V(v[0], k), V(d[0], I)), E = d.map((A, P) => ie(V(v[1][P], k), V(A, I)));
          return { output: $, newStates: E };
        });
        p = x.output, d = x.newStates;
      }
      o && c.push(p);
    }
    let g;
    return o && (g = es(c, 1)), [p, g, d];
  });
}
var HI = class extends He {
  constructor(e) {
    super(e);
    let t;
    if (e.cell == null)
      throw new G("cell property is missing for the constructor of RNN.");
    if (Array.isArray(e.cell) ? t = new qp({ cells: e.cell }) : t = e.cell, t.stateSize == null)
      throw new G("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
    this.cell = t, this.returnSequences = e.returnSequences == null ? false : e.returnSequences, this.returnState = e.returnState == null ? false : e.returnState, this.goBackwards = e.goBackwards == null ? false : e.goBackwards, this._stateful = e.stateful == null ? false : e.stateful, this.unroll = e.unroll == null ? false : e.unroll, this.supportsMasking = true, this.inputSpec = [new Dt({ ndim: 3 })], this.stateSpec = null, this.states_ = null, this.numConstants = null, this.keptStates = [];
  }
  getStates() {
    if (this.states_ == null) {
      let e = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
      return ys(0, e).map((t) => null);
    } else
      return this.states_;
  }
  setStates(e) {
    this.states_ = e;
  }
  computeOutputShape(e) {
    Sm(e) && (e = e[0]), e = e;
    let t = this.cell.stateSize;
    Array.isArray(t) || (t = [t]);
    let n = t[0], s;
    if (this.returnSequences ? s = [e[0], e[1], n] : s = [e[0], n], this.returnState) {
      let r = [];
      for (let a of t)
        r.push([e[0], a]);
      return [s].concat(r);
    } else
      return s;
  }
  computeMask(e, t) {
    return q(() => {
      Array.isArray(t) && (t = t[0]);
      let n = this.returnSequences ? t : null;
      if (this.returnState) {
        let s = this.states.map((r) => null);
        return [n].concat(s);
      } else
        return n;
    });
  }
  get states() {
    if (this.states_ == null) {
      let e = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1, t = [];
      for (let n = 0; n < e; ++n)
        t.push(null);
      return t;
    } else
      return this.states_;
  }
  set states(e) {
    this.states_ = e;
  }
  build(e) {
    if (this.numConstants != null)
      throw new Fe("Constants support is not implemented in RNN yet.");
    Sm(e) && (e = e[0]), e = e;
    let n = this.stateful ? e[0] : null, s = e.slice(2);
    this.inputSpec[0] = new Dt({ shape: [n, null, ...s] });
    let r = [e[0]].concat(e.slice(2));
    this.cell.build(r);
    let a;
    if (Array.isArray(this.cell.stateSize) ? a = this.cell.stateSize : a = [this.cell.stateSize], this.stateSpec != null) {
      if (!w.arraysEqual(this.stateSpec.map((i) => i.shape[i.shape.length - 1]), a))
        throw new G(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
    } else
      this.stateSpec = a.map((i) => new Dt({ shape: [null, i] }));
    this.stateful && this.resetStates();
  }
  resetStates(e, t = false) {
    q(() => {
      if (!this.stateful)
        throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");
      let n = this.inputSpec[0].shape[0];
      if (n == null)
        throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
      if (this.states_ == null)
        Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map((s) => $t([n, s])) : this.states_ = [$t([n, this.cell.stateSize])];
      else if (e == null)
        Re(this.states_), this.keptStates != null && (Re(this.keptStates), this.keptStates = []), Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map((s) => $t([n, s])) : this.states_[0] = $t([n, this.cell.stateSize]);
      else {
        if (Array.isArray(e) || (e = [e]), e.length !== this.states_.length)
          throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);
        t === true ? this.keptStates.push(this.states_.slice()) : Re(this.states_);
        for (let s = 0; s < this.states_.length; ++s) {
          let r = e[s], a = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[s] : this.cell.stateSize, i = [n, a];
          if (!w.arraysEqual(r.shape, i))
            throw new G(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);
          this.states_[s] = r;
        }
      }
      this.states_ = this.states_.map((s) => qt(s.clone()));
    });
  }
  apply(e, t) {
    let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
    t == null && (t = {});
    let r = UI(e, n, s, this.numConstants);
    e = r.inputs, n = r.initialState, s = r.constants;
    let a = [], i = [];
    if (n != null) {
      t.initialState = n, a = a.concat(n), this.stateSpec = [];
      for (let u of n)
        this.stateSpec.push(new Dt({ shape: u.shape }));
      i = i.concat(this.stateSpec);
    }
    if (s != null && (t.constants = s, a = a.concat(s), this.numConstants = s.length), a[0] instanceof $s) {
      let u = [e].concat(a), l = this.inputSpec.concat(i), c = this.inputSpec;
      this.inputSpec = l;
      let p = super.apply(u, t);
      return this.inputSpec = c, p;
    } else
      return super.apply(e, t);
  }
  call(e, t) {
    return q(() => {
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      e = Oe(e), r == null && (this.stateful ? r = this.states_ : r = this.getInitialState(e));
      let a = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
      if (r.length !== a)
        throw new G(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);
      this.unroll && console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
      let i = { training: s }, u = GI((h, f) => {
        let m = this.cell.call([h].concat(f), i);
        return [m[0], m.slice(1)];
      }, e, r, this.goBackwards, n, null, this.unroll, this.returnSequences), l = u[0], c = u[1], p = u[2];
      this.stateful && this.resetStates(p, s);
      let d = this.returnSequences ? c : l;
      return this.returnState ? [d].concat(p) : d;
    });
  }
  getInitialState(e) {
    return q(() => {
      let t = $t(e.shape);
      return t = ve(t, [1, 2]), t = Vl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? wm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [wm(t, [1, this.cell.stateSize])] : [t];
    });
  }
  get trainableWeights() {
    return this.trainable ? this.cell.trainableWeights : [];
  }
  get nonTrainableWeights() {
    return this.trainable ? this.cell.nonTrainableWeights : this.cell.weights;
  }
  setFastWeightInitDuringBuild(e) {
    super.setFastWeightInitDuringBuild(e), this.cell != null && this.cell.setFastWeightInitDuringBuild(e);
  }
  getConfig() {
    let e = super.getConfig(), t = { returnSequences: this.returnSequences, returnState: this.returnState, goBackwards: this.goBackwards, stateful: this.stateful, unroll: this.unroll };
    this.numConstants != null && (t.numConstants = this.numConstants);
    let n = this.cell.getConfig();
    return this.getClassName() === HI.className && (t.cell = { className: this.cell.getClassName(), config: n }), { ...n, ...e, ...t };
  }
  static fromConfig(e, t, n = {}) {
    let s = t.cell, r = gs(s, n);
    return new e(Object.assign(t, { cell: r }));
  }
};
var Ar = HI;
Ar.className = "RNN";
re.registerClass(Ar);
var jl = class extends He {
};
var Gp = class extends jl {
  constructor(e) {
    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Vt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = no([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = no([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
  }
  build(e) {
    e = nt(e), this.kernel = this.addWeight("kernel", [e[e.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
  }
  call(e, t) {
    return q(() => {
      if (e = e, e.length !== 2)
        throw new G(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);
      let n = e[1];
      e = e[0];
      let s = t.training == null ? false : t.training;
      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Zn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Zn(n), rate: this.recurrentDropout, training: s, dropoutFunc: this.dropoutFunc }));
      let r, a = this.dropoutMask, i = this.recurrentDropoutMask;
      a != null ? r = Es(V(e, a), this.kernel.read()) : r = Es(e, this.kernel.read()), this.bias != null && (r = ks(r, this.bias.read())), i != null && (n = V(n, i));
      let o = ie(r, Es(n, this.recurrentKernel.read()));
      return this.activation != null && (o = this.activation.apply(o)), [o, o];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: at(this.kernelRegularizer), recurrentRegularizer: at(this.recurrentRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
    return { ...e, ...t };
  }
};
Gp.className = "SimpleRNNCell";
re.registerClass(Gp);
var py = class extends Ar {
  constructor(e) {
    e.cell = new Gp(e), super(e);
  }
  call(e, t) {
    return q(() => {
      this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null);
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
py.className = "SimpleRNN";
re.registerClass(py);
var Hp = class extends jl {
  constructor(e) {
    if (super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
      throw new G("GRUCell does not support reset_after parameter set to true.");
    this.units = e.units, Vt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = no([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = no([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
  }
  build(e) {
    e = nt(e);
    let t = e[e.length - 1];
    this.kernel = this.addWeight("kernel", [t, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
  }
  call(e, t) {
    return q(() => {
      if (e = e, e.length !== 2)
        throw new G(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);
      let n = t.training == null ? false : t.training, s = e[1];
      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 3, dropoutFunc: this.dropoutFunc }));
      let r = this.dropoutMask, a = this.recurrentDropoutMask, i, o, u;
      0 < this.dropout && this.dropout < 1 && (e = V(e, r[0]));
      let l = Es(e, this.kernel.read());
      this.useBias && (l = ks(l, this.bias.read())), 0 < this.recurrentDropout && this.recurrentDropout < 1 && (s = V(s, a[0]));
      let c = this.recurrentKernel.read(), [p, d] = Bn(c, [2 * this.units, this.units], c.rank - 1), h = Es(s, p), [f, m, g] = Bn(l, 3, l.rank - 1), [b, y] = Bn(h, 2, h.rank - 1);
      i = this.recurrentActivation.apply(ie(f, b)), o = this.recurrentActivation.apply(ie(m, y));
      let v = Es(V(o, s), d);
      u = this.activation.apply(ie(g, v));
      let x = ie(V(i, s), V(ie(1, kt(i)), u));
      return [x, x];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), recurrentActivation: br(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: at(this.kernelRegularizer), recurrentRegularizer: at(this.recurrentRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
    return { ...e, ...t };
  }
};
Hp.className = "GRUCell";
re.registerClass(Hp);
var hy = class extends Ar {
  constructor(e) {
    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Hp(e), super(e);
  }
  call(e, t) {
    return q(() => {
      this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null);
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  static fromConfig(e, t) {
    return t.implmentation === 0 && (t.implementation = 1), new e(t);
  }
};
hy.className = "GRU";
re.registerClass(hy);
var Kl = class extends jl {
  constructor(e) {
    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Vt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = no([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = no([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
  }
  build(e) {
    var t;
    e = nt(e);
    let n = e[e.length - 1];
    this.kernel = this.addWeight("kernel", [n, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
    let s;
    if (this.useBias) {
      if (this.unitForgetBias) {
        let r = this.biasInitializer, a = this.units;
        s = new (t = class extends ns {
          apply(i, o) {
            let u = r.apply([a]), l = new Rp().apply([a]), c = r.apply([a * 2]);
            return Cx(Cx(u, l), c);
          }
        }, t.className = "CustomInit", t)();
      } else
        s = this.biasInitializer;
      this.bias = this.addWeight("bias", [this.units * 4], null, s, this.biasRegularizer, true, this.biasConstraint);
    } else
      this.bias = null;
    this.built = true;
  }
  call(e, t) {
    return q(() => {
      let n = t.training == null ? false : t.training;
      if (e = e, e.length !== 3)
        throw new G(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
      let s = e[1], r = e[2];
      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 4, dropoutFunc: this.dropoutFunc }));
      let a = this.dropoutMask, i = this.recurrentDropoutMask, o, u, l, c;
      0 < this.dropout && this.dropout < 1 && (e = V(e, a[0]));
      let p = Es(e, this.kernel.read());
      0 < this.recurrentDropout && this.recurrentDropout < 1 && (s = V(s, i[0])), p = ie(p, Es(s, this.recurrentKernel.read())), this.useBias && (p = ks(p, this.bias.read()));
      let [d, h, f, m] = Bn(p, 4, p.rank - 1);
      o = this.recurrentActivation.apply(d), u = this.recurrentActivation.apply(h), l = ie(V(u, r), V(o, this.activation.apply(f))), c = this.recurrentActivation.apply(m);
      let g = V(c, this.activation.apply(l));
      return [g, g, l];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), recurrentActivation: br(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: at(this.kernelRegularizer), recurrentRegularizer: at(this.recurrentRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
    return { ...e, ...t };
  }
};
Kl.className = "LSTMCell";
re.registerClass(Kl);
var fy = class extends Ar {
  constructor(e) {
    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Kl(e), super(e);
  }
  call(e, t) {
    return q(() => {
      this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null);
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  static fromConfig(e, t) {
    return t.implmentation === 0 && (t.implementation = 1), new e(t);
  }
};
fy.className = "LSTM";
re.registerClass(fy);
var qp = class extends jl {
  constructor(e) {
    super(e), this.cells = e.cells;
  }
  get stateSize() {
    let e = [];
    for (let t of this.cells.slice().reverse())
      Array.isArray(t.stateSize) ? e.push(...t.stateSize) : e.push(t.stateSize);
    return e;
  }
  call(e, t) {
    return q(() => {
      e = e;
      let n = e.slice(1), s = [];
      for (let i of this.cells.slice().reverse())
        Array.isArray(i.stateSize) ? s.push(n.splice(0, i.stateSize.length)) : s.push(n.splice(0, 1));
      s.reverse();
      let r = [], a;
      for (let i = 0; i < this.cells.length; ++i) {
        let o = this.cells[i];
        n = s[i], i === 0 ? a = [e[0]].concat(n) : a = [a[0]].concat(n), a = o.call(a, t), r.push(a.slice(1));
      }
      n = [];
      for (let i of r.slice().reverse())
        n.push(...i);
      return [a[0]].concat(n);
    });
  }
  build(e) {
    Sm(e) && (e = e[0]), e = e;
    let t;
    this.cells.forEach((n, s) => {
      ta(`RNNCell_${s}`, () => {
        n.build(e), Array.isArray(n.stateSize) ? t = n.stateSize[0] : t = n.stateSize, e = [e[0], t];
      });
    }), this.built = true;
  }
  getConfig() {
    let e = super.getConfig(), t = (r) => ({ className: r.getClassName(), config: r.getConfig() }), s = { cells: this.cells.map(t) };
    return { ...e, ...s };
  }
  static fromConfig(e, t, n = {}) {
    let s = [];
    for (let r of t.cells)
      s.push(gs(r, n));
    return new e({ cells: s });
  }
  get trainableWeights() {
    if (!this.trainable)
      return [];
    let e = [];
    for (let t of this.cells)
      e.push(...t.trainableWeights);
    return e;
  }
  get nonTrainableWeights() {
    let e = [];
    for (let t of this.cells)
      e.push(...t.nonTrainableWeights);
    if (!this.trainable) {
      let t = [];
      for (let n of this.cells)
        t.push(...n.trainableWeights);
      return t.concat(e);
    }
    return e;
  }
  getWeights() {
    let e = [];
    for (let t of this.cells)
      e.push(...t.weights);
    return Im(e);
  }
  setWeights(e) {
    let t = [];
    for (let n of this.cells) {
      let s = n.weights.length, r = e.splice(s);
      for (let a = 0; a < n.weights.length; ++a)
        t.push([n.weights[a], r[a]]);
    }
    Pb(t);
  }
};
qp.className = "StackedRNNCells";
re.registerClass(qp);
function vr(e) {
  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : rI(t(), n), o = () => Ul(i, t, s);
  return !r || r <= 1 ? qt(o().clone()) : Array(r).fill(void 0).map(o).map((l) => qt(l.clone()));
}
var qI = class extends Ar {
  constructor(e) {
    if (e.unroll)
      throw new Fe("Unrolling is not possible with convolutional RNNs.");
    if (Array.isArray(e.cell))
      throw new Fe("It is not possible at the moment to stack convolutional cells.");
    super(e), this.inputSpec = [new Dt({ ndim: 5 })];
  }
  call(e, t) {
    return q(() => {
      if (this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null), t && t.constants)
        throw new G("ConvRNN2D cell does not support constants");
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  computeOutputShape(e) {
    let t = this.computeSingleOutputShape(e);
    return this.returnSequences || (t = [t[0], ...t.slice(2)]), this.returnState && (t = [t, ...Array(2).fill([e[0], ...t.slice(-3)])]), t;
  }
  getInitialState(e) {
    return q(() => {
      let { stateSize: t } = this.cell, n = e.shape, s = this.computeSingleOutputShape(n), r = [s[0], ...s.slice(2)], a = $t(r);
      return Array.isArray(t) ? Array(t.length).fill(a) : [a];
    });
  }
  resetStates(e, t = false) {
    q(() => {
      if (!this.stateful)
        throw new Bs("Cannot call resetStates() on an RNN Layer that is not stateful.");
      let n = this.inputSpec[0].shape, s = this.computeSingleOutputShape(n), r = [s[0], ...s.slice(2)];
      if (n[0] == null)
        throw new G("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
      if (this.getStates() == null)
        Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map(() => $t(r)) : this.states_ = [$t(r)];
      else if (e == null)
        Re(this.states_), this.keptStates != null && (Re(this.keptStates), this.keptStates = []), Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map(() => $t(r)) : this.states_[0] = $t(r);
      else {
        if (Array.isArray(e) || (e = [e]), e.length !== this.states_.length)
          throw new G(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);
        t ? this.keptStates.push(this.states_.slice()) : Re(this.states_);
        for (let i = 0; i < this.states_.length; ++i) {
          let o = e[i], u = r;
          if (!w.arraysEqual(o.shape, u))
            throw new G(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);
          this.states_[i] = o;
        }
      }
      this.states_ = this.states_.map((i) => qt(i.clone()));
    });
  }
  computeSingleOutputShape(e) {
    let { dataFormat: t, filters: n, kernelSize: s, padding: r, strides: a, dilationRate: i } = this.cell, o = t === "channelsFirst", u = e[o ? 3 : 2], l = e[o ? 4 : 3], c = bs(u, s[0], r, a[0], i[0]), p = bs(l, s[1], r, a[1], i[1]);
    return [...e.slice(0, 2), ...o ? [n, c, p] : [c, p, n]];
  }
};
qI.className = "ConvRNN2D";
var jp = class extends Kl {
  constructor(e) {
    let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: i } = e;
    super({ ...e, units: t }), this.filters = t, Vt(this.filters, "filters"), this.kernelSize = Ji(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Vt(o, "kernelSize")), this.strides = Ji(s || 1, 2, "strides"), this.strides.forEach((o) => Vt(o, "strides")), this.padding = r || "valid", Gn(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Ji(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Vt(o, "dilationRate"));
  }
  build(e) {
    var t;
    e = nt(e);
    let n = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[n] == null)
      throw new G(`The channel dimension of the input should be defined. Found ${e[n]}`);
    let s = e[n], r = 4, a = this.kernelSize.concat([s, this.filters * r]);
    this.kernel = this.addWeight("kernel", a, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
    let i = this.kernelSize.concat([this.filters, this.filters * r]);
    if (this.recurrentKernel = this.addWeight("recurrent_kernel", i, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias) {
      let o;
      if (this.unitForgetBias) {
        let u = this.biasInitializer, l = this.filters;
        o = new (t = class extends ns {
          apply(c, p) {
            let d = u.apply([l]), h = Mn([l]), f = u.apply([l * 2]);
            return $b([d, h, f]);
          }
        }, t.className = "CustomInit", t)();
      } else
        o = this.biasInitializer;
      this.bias = this.addWeight("bias", [this.filters * r], null, o, this.biasRegularizer, true, this.biasConstraint);
    }
    this.built = true;
  }
  call(e, t) {
    return q(() => {
      if (e.length !== 3)
        throw new G(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
      let n = t.training || false, s = e[0], r = e[1], a = e[2], i = 4;
      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Zn(s), rate: this.dropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
      let o = this.dropoutMask, u = (Z, te, J) => !te || !te[J] ? Z : V(te[J], Z), l = u(s, o, 0), c = u(s, o, 1), p = u(s, o, 2), d = u(s, o, 3);
      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Zn(r), rate: this.recurrentDropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
      let h = this.recurrentDropoutMask, f = u(r, h, 0), m = u(r, h, 1), g = u(r, h, 2), b = u(r, h, 3), y = 3, [v, x, k, I] = Bn(this.kernel.read(), i, y), [$, E, A, P] = this.useBias ? Bn(this.bias.read(), i) : [null, null, null, null];
      l = this.inputConv(l, v, $, this.padding), c = this.inputConv(c, x, E, this.padding), p = this.inputConv(p, k, A, this.padding), d = this.inputConv(d, I, P, this.padding);
      let [R, F, T, z] = Bn(this.recurrentKernel.read(), i, y);
      f = this.recurrentConv(f, R), m = this.recurrentConv(m, F), g = this.recurrentConv(g, T), b = this.recurrentConv(b, z);
      let W = this.recurrentActivation.apply(ie(l, f)), j = this.recurrentActivation.apply(ie(c, m)), X = ie(V(j, a), V(W, this.activation.apply(ie(p, g)))), Y = V(this.recurrentActivation.apply(ie(d, b)), this.activation.apply(X));
      return [Y, Y, X];
    });
  }
  getConfig() {
    let { units: e, ...t } = super.getConfig(), n = { filters: this.filters, kernelSize: this.kernelSize, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, strides: this.strides };
    return { ...t, ...n };
  }
  inputConv(e, t, n, s) {
    let r = da(e, t, this.strides, s || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
    return n ? ks(r, n, this.dataFormat) : r;
  }
  recurrentConv(e, t) {
    return da(e, t, 1, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
  }
};
jp.className = "ConvLSTM2DCell";
re.registerClass(jp);
var my = class extends qI {
  constructor(e) {
    let t = new jp(e);
    super({ ...e, cell: t });
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
my.className = "ConvLSTM2D";
re.registerClass(my);
var Kp = class extends He {
  constructor(e) {
    super(e), this.rate = Math.max(Math.min(e.rate, 1), 0), this.noiseShape = e.noiseShape, this.seed = e.seed, this.supportsMasking = true;
  }
  getNoiseShape(e) {
    if (this.noiseShape == null)
      return this.noiseShape;
    let t = e.shape, n = [];
    for (let s = 0; s < this.noiseShape.length; ++s)
      n.push(this.noiseShape[s] == null ? t[s] : this.noiseShape[s]);
    return n;
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      if (0 < this.rate && this.rate < 1) {
        let s = t.training == null ? false : t.training, r = this.getNoiseShape(n);
        return Ul(() => rI(n, this.rate, r, this.seed), () => n, s);
      }
      return e;
    });
  }
  getConfig() {
    let e = { rate: this.rate, noiseShape: this.noiseShape, seed: this.seed }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
  dispose() {
    return super.dispose();
  }
};
Kp.className = "Dropout";
re.registerClass(Kp);
var gy = class extends Kp {
  constructor(e) {
    super(e), this.inputSpec = [{ ndim: 3 }];
  }
  getNoiseShape(e) {
    let t = e.shape;
    return [t[0], 1, t[2]];
  }
};
gy.className = "SpatialDropout1D";
re.registerClass(gy);
var by = class extends He {
  constructor(e) {
    if (super(e), this.activation = null, this.useBias = true, this.kernel = null, this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.batchInputShape == null && e.inputShape == null && e.inputDim != null) {
      let t = null;
      e.batchSize != null && (t = e.batchSize), this.batchInputShape = [t, e.inputDim];
    }
    this.units = e.units, Vt(this.units, "units"), this.activation = yr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = Pt(e.kernelConstraint), this.biasConstraint = Pt(e.biasConstraint), this.kernelRegularizer = ft(e.kernelRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.activityRegularizer = ft(e.activityRegularizer), this.supportsMasking = true, this.inputSpec = [{ minNDim: 2 }];
  }
  build(e) {
    e = nt(e);
    let t = e[e.length - 1];
    this.kernel == null && (this.kernel = this.addWeight("kernel", [t, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint))), this.inputSpec = [{ minNDim: 2, axes: { [-1]: t } }], this.built = true;
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = e.slice();
    return t[t.length - 1] = this.units, t;
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e), s = QS(this.activation.getClassName()), r;
      return s != null ? r = Es(n, this.kernel.read(), s, this.bias ? this.bias.read() : null) : (r = Es(n, this.kernel.read()), this.bias != null && (r = ks(r, this.bias.read())), this.activation != null && (r = this.activation.apply(r))), r;
    });
  }
  getConfig() {
    let e = { units: this.units, activation: br(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: at(this.kernelRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
by.className = "Dense";
re.registerClass(by);
var yy = class extends He {
  constructor(e) {
    e = e || {}, super(e), this.inputSpec = [{ minNDim: 3 }], this.dataFormat = e.dataFormat;
  }
  computeOutputShape(e) {
    e = nt(e);
    for (let t of e.slice(1))
      if (t == null)
        throw new G(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
    return [e[0], cr(e, 1)];
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      if (this.dataFormat === "channelsFirst" && n.rank > 1) {
        let s = [0];
        for (let r = 2; r < n.rank; ++r)
          s.push(r);
        s.push(1), n = Ge(n, s);
      }
      return lz(n);
    });
  }
  getConfig() {
    let e = {};
    this.dataFormat != null && (e.dataFormat = this.dataFormat);
    let t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
yy.className = "Flatten";
re.registerClass(yy);
var vy = class extends He {
  constructor(e) {
    super(e), this.supportsMasking = true, this.activation = yr(e.activation);
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return this.activation.apply(n);
    });
  }
  getConfig() {
    let e = { activation: br(this.activation) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
vy.className = "Activation";
re.registerClass(vy);
var xy = class extends He {
  constructor(e) {
    super(e), this.n = e.n, this.inputSpec = [{ ndim: 2 }];
  }
  computeOutputShape(e) {
    return [e[0], this.n, e[1]];
  }
  call(e, t) {
    return q(() => (e = Oe(e), oz(e, this.n)));
  }
  getConfig() {
    let e = { n: this.n }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
xy.className = "RepeatVector";
re.registerClass(xy);
var wy = class extends He {
  constructor(e) {
    super(e), this.targetShape = e.targetShape;
    for (let t = 0; t < this.targetShape.length; ++t)
      this.isUnknown(this.targetShape[t]) && (this.targetShape[t] = null);
  }
  isUnknown(e) {
    return e < 0 || e == null;
  }
  fixUnknownDimension(e, t) {
    let n = "Total size of new array must be unchanged.", s = t.slice(), r = 1, a = null;
    for (let o = 0; o < s.length; ++o) {
      let u = s[o];
      if (this.isUnknown(u))
        if (a === null)
          a = o;
        else
          throw new G("Can only specifiy one unknown dimension.");
      else
        r *= u;
    }
    let i = cr(e);
    if (a !== null) {
      if (r === 0 || i % r !== 0)
        throw new G(n);
      s[a] = i / r;
    } else if (i !== r)
      throw new G(n);
    return s;
  }
  computeOutputShape(e) {
    let t = false;
    for (let n = 0; n < e.length; ++n)
      if (this.isUnknown(e[n])) {
        t = true;
        break;
      }
    return t ? e.slice(0, 1).concat(this.targetShape) : e.slice(0, 1).concat(this.fixUnknownDimension(e.slice(1), this.targetShape));
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e), s = n.shape, r = s.slice(0, 1).concat(this.fixUnknownDimension(s.slice(1), this.targetShape));
      return U(n, r);
    });
  }
  getConfig() {
    let e = { targetShape: this.targetShape }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
wy.className = "Reshape";
re.registerClass(wy);
var ky = class extends He {
  constructor(e) {
    if (super(e), e.dims == null)
      throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
    if (!Array.isArray(e.dims))
      throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);
    let t = ys(1, e.dims.length + 1);
    if (!w.arraysEqual(e.dims.slice().sort(), t))
      throw new Error("Invalid permutation `dims`: " + JSON.stringify(e.dims) + " `dims` must contain consecutive integers starting from 1.");
    this.dims = e.dims, this.dimsIncludingBatch = [0].concat(this.dims), this.inputSpec = [new Dt({ ndim: this.dims.length + 1 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = e.slice();
    return this.dims.forEach((n, s) => {
      t[s + 1] = e[n];
    }), t;
  }
  call(e, t) {
    return Ge(Oe(e), this.dimsIncludingBatch);
  }
  getConfig() {
    let e = { dims: this.dims }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ky.className = "Permute";
re.registerClass(ky);
var Sy = class extends He {
  constructor(e) {
    super(e == null ? {} : e), this.supportsMasking = true, e != null ? this.maskValue = e.maskValue == null ? 0 : e.maskValue : this.maskValue = 0;
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = super.getConfig(), t = { maskValue: this.maskValue };
    return Object.assign(t, e), t;
  }
  computeMask(e, t) {
    let n = Oe(e), s = -1;
    return gm(Qu(n, this.maskValue), s);
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e), s = -1, r = true, a = gm(Qu(n, this.maskValue), s, r);
      return V(n, le(a, n.dtype));
    });
  }
};
Sy.className = "Masking";
re.registerClass(Sy);
var Iy = class extends He {
  constructor(e) {
    if (super(e), this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
      let t = null;
      e.batchSize != null && (t = e.batchSize), e.inputLength == null ? this.batchInputShape = [t, null] : this.batchInputShape = [t].concat(pt(e.inputLength));
    }
    this.inputDim = e.inputDim, Vt(this.inputDim, "inputDim"), this.outputDim = e.outputDim, Vt(this.outputDim, "outputDim"), this.embeddingsInitializer = ht(e.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER), this.embeddingsRegularizer = ft(e.embeddingsRegularizer), this.activityRegularizer = ft(e.activityRegularizer), this.embeddingsConstraint = Pt(e.embeddingsConstraint), this.maskZero = e.maskZero, this.supportsMasking = e.maskZero, this.inputLength = e.inputLength;
  }
  build(e) {
    this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint), this.built = true;
  }
  warnOnIncompatibleInputShape(e) {
  }
  computeMask(e, t) {
    return q(() => this.maskZero ? (e = Oe(e), Qu(e, je(e))) : null);
  }
  computeOutputShape(e) {
    if (e = nt(e), this.inputLength == null)
      return [...e, this.outputDim];
    let t = pt(this.inputLength);
    if (t.length !== e.length - 1)
      throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);
    {
      let n = 0;
      for (let s = 0; s < t.length; ++s) {
        let r = t[s], a = e[s + 1];
        if (r != null && a != null && r !== a)
          throw new G(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);
        r == null && (t[n] = a), n++;
      }
    }
    return [e[0], ...t, this.outputDim];
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      n.dtype !== "int32" && (n = Ap(n, "int32"));
      let s = sI(this.embeddings.read(), U(n, [n.size]));
      return U(s, nt(this.computeOutputShape(n.shape)));
    });
  }
  getConfig() {
    let e = { inputDim: this.inputDim, outputDim: this.outputDim, embeddingsInitializer: yt(this.embeddingsInitializer), embeddingsRegularizer: at(this.embeddingsRegularizer), activityRegularizer: at(this.activityRegularizer), embeddingsConstraint: Ot(this.embeddingsConstraint), maskZero: this.maskZero, inputLength: this.inputLength }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Iy.className = "Embedding";
re.registerClass(Iy);
var wi = class extends He {
  constructor(e) {
    super(e || {}), this.supportsMasking = true;
  }
  mergeFunction(e) {
    throw new Fe();
  }
  computeElementwiseOpOutputShape(e, t) {
    if (e == null || t == null)
      return null;
    if (e.length < t.length)
      return this.computeElementwiseOpOutputShape(t, e);
    if (t.length === 0)
      return e;
    let n = e.slice(0, e.length - t.length);
    for (let s = 0; s < t.length; ++s) {
      let r = e[e.length - t.length + s], a = t[s];
      if (r == null || a == null || r < 0 || a < 0)
        n.push(null);
      else if (r === 1)
        n.push(a);
      else if (a === 1)
        n.push(r);
      else {
        if (r !== a)
          throw new G("Operands could not be broadcast together with shapes " + JSON.stringify(e) + " " + JSON.stringify(t));
        n.push(r);
      }
    }
    return n;
  }
  build(e) {
    if (Array.isArray(e) && !Array.isArray(e[0]) && (e = [nt(e)]), e = e, e.length < 2)
      throw new G(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);
    let t = [];
    for (let r of e)
      r != null && r[0] !== null && t.push(r[0]);
    if (t = lr(t), t.length > 1)
      throw new G(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);
    let n = e[0] == null ? null : e[0].slice(1);
    for (let r = 1; r < e.length; ++r) {
      let a = e[r] == null ? null : e[r].slice(1);
      n = this.computeElementwiseOpOutputShape(n, a);
    }
    let s = e.map((r) => r.length);
    e.indexOf(null) === -1 && lr(s).length === 1 ? this.reshapeRequired = false : this.reshapeRequired = true;
  }
  call(e, t) {
    return q(() => {
      if (e = e, this.reshapeRequired) {
        let n = [], s = e.map((r) => r.rank);
        if (s.indexOf(null) === -1) {
          let r = gr(s);
          for (let a of e) {
            let i = a.rank;
            for (let o = 0; o < r - i; ++o)
              a = Vl(a, 1);
            n.push(a);
          }
          return this.mergeFunction(n);
        } else {
          let r = false;
          for (let o of e) {
            let u = o.rank;
            if (u == null) {
              let l = o.shape, c = l[0], p = l.slice(1).concat([c]), d = U(o, [c].concat(cr(l.slice(1))));
              d = Ge(d, [1, 0]), d = U(d, p), n.push(d), r = true;
            } else if (u > 1) {
              let l = ys(1, u).concat([0]);
              n.push(Ge(o, l)), r = true;
            } else
              n.push(o);
          }
          let a = this.mergeFunction(n), i = a.rank;
          if (r) {
            if (i == null) {
              let o = a.shape, u = o.length, l = o[u - 1], c = [l].concat(o.slice(0, o.length - 1));
              a = U(Ge(U(a, [-1, l]), [1, 0]), c);
            } else if (i > 1) {
              let o = [i - 1].concat(ys(0, i - 1));
              a = Ge(a, o);
            }
          }
          return a;
        }
      } else
        return this.mergeFunction(e);
    });
  }
  computeOutputShape(e) {
    e = e;
    let t;
    e[0] == null ? t = null : t = e[0].slice(1);
    for (let s = 1; s < e.length; ++s) {
      let r = e[s] == null ? null : e[s].slice(1);
      t = this.computeElementwiseOpOutputShape(t, r);
    }
    let n = [];
    for (let s of e)
      s != null && s[0] !== null && n.push(s[0]);
    return n = lr(n), n.length === 1 ? t = n.concat(t) : t = [null].concat(t), t;
  }
  computeMask(e, t) {
    return q(() => {
      if (t == null)
        return null;
      if (!Array.isArray(t))
        throw new G("`mask` should be an Array");
      if (!Array.isArray(e))
        throw new G("`inputs` should be an Array");
      if (t.length !== e.length)
        throw new G(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);
      if (t.every((s) => s == null))
        return null;
      t = t.map((s) => s == null ? s : Pn(s, 0));
      let n = t[0];
      for (let s = 1; s < t.length - 1; ++s)
        n = Ds(n, t[s]);
      return n;
    });
  }
};
var Cy = class extends wi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0].clone();
      for (let n = 1; n < e.length; ++n)
        t = ie(t, e[n]);
      return t;
    });
  }
};
Cy.className = "Add";
re.registerClass(Cy);
var Ny = class extends wi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0].clone();
      for (let n = 1; n < e.length; ++n)
        t = V(t, e[n]);
      return t;
    });
  }
};
Ny.className = "Multiply";
re.registerClass(Ny);
var Ty = class extends wi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0].clone();
      for (let n = 1; n < e.length; ++n)
        t = ie(t, e[n]);
      return V(1 / e.length, t);
    });
  }
};
Ty.className = "Average";
re.registerClass(Ty);
var $y = class extends wi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0];
      for (let n = 1; n < e.length; ++n)
        t = $r(t, e[n]);
      return t;
    });
  }
};
$y.className = "Maximum";
re.registerClass($y);
var _y = class extends wi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0];
      for (let n = 1; n < e.length; ++n)
        t = kp(t, e[n]);
      return t;
    });
  }
};
_y.className = "Minimum";
re.registerClass(_y);
var Ay = class extends wi {
  constructor(e) {
    super(e), this.DEFAULT_AXIS = -1, e == null && (e = {}), this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis, this.supportsMasking = true, this.reshapeRequired = false;
  }
  build(e) {
    if (!(Array.isArray(e) && Array.isArray(e[0])) || e.length === 1)
      throw new G("A `Concatenate` layer should be called on a list of at least 2 inputs");
    e = e;
    let t = true;
    for (let s of e)
      if (s != null) {
        t = false;
        break;
      }
    if (t)
      return;
    let n = [];
    for (let s = 0; s < e.length; ++s) {
      let r = e[s].slice();
      r.splice(this.axis, 1);
      let a = false;
      for (let i of n)
        if (w.arraysEqual(i, r)) {
          a = true;
          break;
        }
      a || n.push(r);
    }
    if (n.length > 1)
      throw new G("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(e));
  }
  mergeFunction(e) {
    return q(() => $b(e, this.axis));
  }
  computeOutputShape(e) {
    if (!(Array.isArray(e) && Array.isArray(e[0])))
      throw new G("A `Concatenate` layer should be called on a list of inputs.");
    let t = e, n = t[0].slice(), s = this.axis < 0 ? n.length + this.axis : this.axis;
    for (let r of t.slice(1)) {
      if (n[s] == null || r[s] == null) {
        n[s] = null;
        break;
      }
      n[s] += r[s];
    }
    return n;
  }
  computeMask(e, t) {
    if (t == null)
      return null;
    if (!Array.isArray(t))
      throw new G("`mask` should be an array for Concatenate");
    if (!Array.isArray(e))
      throw new G("`inputs` should be an array for Concatenate");
    if (t.length !== e.length)
      throw new G(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);
    return q(() => {
      let n = true;
      if (t.forEach((a) => {
        if (a != null) {
          n = false;
          return;
        }
      }), n)
        return null;
      let s = [];
      for (let a = 0; a < e.length; ++a)
        t[a] == null ? s.push(le(Zn(e[a]), "bool")) : t[a].rank < e[a].rank ? s.push(Pn(t[a], -1)) : s.push(t[a]);
      let r = Ft(s, this.axis);
      return eS(r, -1, false);
    });
  }
  getConfig() {
    let e = { axis: this.axis }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Ay.className = "Concatenate";
re.registerClass(Ay);
function Tu(e, t) {
  for (; e < 0; )
    e += t;
  return e;
}
function sV(e, t, n) {
  if (e.shape.length > 3 || t.shape.length > 3)
    throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");
  if (w.assert(e.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`), w.assert(e.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`), typeof n == "number" && (n = [n, n]), e.dtype === "complex64" || t.dtype === "complex64")
    throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");
  let s = e.shape.length, r = t.shape.length;
  n == null && (n = [s - 1, r - 2]);
  let a = n;
  return q(() => {
    let i;
    if (s > r) {
      i = s - r;
      let u = [];
      for (let l = 0; l < i; ++l)
        u.push(1);
      t = U(t, t.shape.concat(u));
    } else if (r > s) {
      i = r - s;
      let u = [];
      for (let l = 0; l < i; ++l)
        u.push(1);
      e = U(e, e.shape.concat(u));
    } else
      i = 0;
    let o;
    if (e.shape.length === 2 && t.shape.length === 2)
      a[0] === a[1] ? o = ve(V(e, t), a[0]) : o = ve(V(Ge(e, [1, 0]), t), a[1]);
    else {
      let u = a[0] !== e.shape.length - 1, l = a[1] === t.shape.length - 1;
      o = Ve(e, t, u, l);
    }
    if (i > 0) {
      let u;
      s > r ? u = s + r - 3 : u = s - 1;
      let l = [];
      for (let c = u; c < u + i; ++c)
        l.push(c);
      o = mr(o, l);
    }
    return o.shape.length === 1 && (o = Pn(o, 1)), o;
  });
}
var Ey = class extends wi {
  constructor(e) {
    super(e), this.axes = e.axes, this.normalize = e.normalize == null ? false : e.normalize, this.supportsMasking = true, this.reshapeRequired = false;
  }
  build(e) {
    w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
    let t = e[0], n = e[1];
    if (t.length > 3 || n.length > 3)
      throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");
    let s = this.interpretAxes(t, n);
    if (t[s[0]] !== n[s[1]])
      throw new G(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`);
  }
  mergeFunction(e) {
    if (e.length !== 2)
      throw new G(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);
    let t = e[0], n = e[1], s;
    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Tu(r, e[a].shape.length)) : s = [Tu(this.axes, t.shape.length), Tu(this.axes, n.shape.length)], this.normalize && (t = Ed(t, s[0]), n = Ed(n, s[1])), sV(t, n, s);
  }
  interpretAxes(e, t) {
    let n;
    return Array.isArray(this.axes) ? n = this.axes : n = [Tu(this.axes, e.length), Tu(this.axes, t.length)], n;
  }
  computeOutputShape(e) {
    w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
    let t = e[0].slice(), n = e[1].slice();
    if (t.length > 3 || n.length > 3)
      throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");
    let s = this.interpretAxes(t, n);
    t.splice(s[0], 1), n.splice(s[1], 1), n.splice(0, 1);
    let r = t.concat(n);
    return r.length === 1 && r.push(1), r;
  }
  computeMask(e, t) {
    return null;
  }
  getConfig() {
    let e = { axes: this.axes, normalize: this.normalize }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Ey.className = "Dot";
re.registerClass(Ey);
var Ry = class extends He {
  constructor(e) {
    super(e), this.supportsMasking = true, this.stddev = e.stddev;
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = super.getConfig(), t = { stddev: this.stddev };
    return Object.assign(t, e), t;
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return Ul(() => ie(Ep(n.shape, 0, this.stddev), n), () => n, t.training || false);
    });
  }
};
Ry.className = "GaussianNoise";
re.registerClass(Ry);
var Dy = class extends He {
  constructor(e) {
    super(e), this.supportsMasking = true, this.rate = e.rate;
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = super.getConfig(), t = { rate: this.rate };
    return Object.assign(t, e), t;
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return this.rate > 0 && this.rate < 1 ? Ul(() => {
        let r = Math.sqrt(this.rate / (1 - this.rate));
        return V(n, Ep(n.shape, 1, r));
      }, () => n, t.training || false) : n;
    });
  }
};
Dy.className = "GaussianDropout";
re.registerClass(Dy);
var Fy = class extends He {
  constructor(e) {
    super(e), this.supportsMasking = true, this.rate = e.rate, this.noiseShape = e.noiseShape;
  }
  _getNoiseShape(e) {
    return this.noiseShape || Oe(e).shape;
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = super.getConfig(), t = { rate: this.rate };
    return Object.assign(t, e), t;
  }
  call(e, t) {
    return q(() => {
      if (this.rate < 1 && this.rate > 0) {
        let n = this._getNoiseShape(e);
        return Ul(() => {
          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Yo(Ll(n), this.rate);
          u = Ap(u, "float32");
          let l = ((1 - this.rate) * (1 + this.rate * o ** 2)) ** -0.5, c = -l * o * this.rate, p = ie(V(r, u), V(ie(u, -1), o));
          return ie(V(p, l), c);
        }, () => Oe(e), t.training || false);
      }
      return e;
    });
  }
};
Fy.className = "AlphaDropout";
re.registerClass(Fy);
function tl(e, t, n, s, r, a = 1e-3) {
  let i;
  if (e.rank === 2)
    i = zE(e, t, n, s, r, a);
  else if (e.rank === 3)
    i = LE(e, t, n, s, r, a);
  else if (e.rank === 4)
    i = VE(e, t, n, s, r, a);
  else
    throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);
  return i;
}
function rV(e, t, n, s, r = 1e-3) {
  return q(() => {
    let a = ib(e, s), i = a.mean, o = a.variance;
    return [tl(e, i, o, n, t, r), i, o];
  });
}
function aV(e, t, n, s, r = 1e-3) {
  return q(() => {
    let a = ib(e, s), i = a.mean, o = a.variance, u = [];
    for (let f of ys(0, e.rank))
      s.indexOf(f) !== -1 ? u.push(1) : u.push(e.shape[f]);
    let l = U(i, u), c = U(o, u), p = t == null ? null : U(t, u), d = n == null ? null : U(n, u);
    return [tl(e, l, c, d, p, r), i, o];
  });
}
function iV(e, t, n, s, r = 1e-3) {
  return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? rV(e, t, n, s, r) : aV(e, t, n, s, r);
}
var Oy = class extends He {
  constructor(e) {
    e == null && (e = {}), super(e), this.supportsMasking = true, this.axis = e.axis == null ? -1 : e.axis, this.momentum = e.momentum == null ? 0.99 : e.momentum, this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.movingMeanInitializer = ht(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ht(e.movingVarianceInitializer || "ones"), this.betaConstraint = Pt(e.betaConstraint), this.gammaConstraint = Pt(e.gammaConstraint), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer);
  }
  build(e) {
    e = nt(e);
    let t = this.axis >= 0 ? this.axis : this.axis + e.length, n = e[t];
    if (n == null)
      throw new G(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);
    this.inputSpec = [new Dt({ ndim: e.length, axes: { [t]: n } })];
    let s = [n];
    this.scale && (this.gamma = this.addWeight("gamma", s, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint)), this.center && (this.beta = this.addWeight("beta", s, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint)), this.movingMean = this.addWeight("moving_mean", s, null, this.movingMeanInitializer, null, false), this.movingVariance = this.addWeight("moving_variance", s, null, this.movingVarianceInitializer, null, false), this.built = true;
  }
  call(e, t) {
    return q(() => {
      let n = t.training == null ? false : t.training, s = Oe(e), r = s.shape, a = r.length, i = ys(0, a), o = this.axis >= 0 ? this.axis : this.axis + a;
      i.splice(o, 1);
      let u = ma(1, a);
      u[o] = r[o];
      let l = i.slice();
      l.sort();
      let c = !w.arraysEqual(l, ys(0, a).slice(0, a - 1)), p = () => {
        if (c) {
          let b = U(this.movingMean.read(), u), y = U(this.movingVariance.read(), u), v = this.center ? U(this.beta.read(), u) : null, x = this.scale ? U(this.gamma.read(), u) : null;
          return tl(s, b, y, v, x, this.epsilon);
        } else
          return tl(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
      };
      if (!n)
        return p();
      let [d, h, f] = iV(s, this.gamma.read(), this.beta.read(), i, this.epsilon), m = (b, y, v) => {
        q(() => {
          let x = 1 - v, k = b.read(), I = V(ge(k, y), x);
          b.write(ge(k, I));
        });
      };
      return (() => {
        m(this.movingMean, h, this.momentum), m(this.movingVariance, f, this.momentum);
      })(), d;
    });
  }
  getConfig() {
    let e = { axis: this.axis, momentum: this.momentum, epsilon: this.epsilon, center: this.center, scale: this.scale, betaInitializer: yt(this.betaInitializer), gammaInitializer: yt(this.gammaInitializer), movingMeanInitializer: yt(this.movingMeanInitializer), movingVarianceInitializer: yt(this.movingVarianceInitializer), betaRegularizer: at(this.betaRegularizer), gammaRegularizer: at(this.gammaRegularizer), betaConstraint: Ot(this.betaConstraint), gammaConstraint: Ot(this.gammaConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Oy.className = "BatchNormalization";
re.registerClass(Oy);
var Py = class extends He {
  constructor(e) {
    if (e == null && (e = {}), super(e), this.axis = e.axis == null ? -1 : e.axis, typeof this.axis == "number") {
      if (!Number.isInteger(this.axis))
        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
    } else if (Array.isArray(this.axis)) {
      for (let t of this.axis)
        if (!Number.isInteger(t))
          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);
    } else
      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);
    this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer), this.supportsMasking = true;
  }
  build(e) {
    e = nt(e);
    let t = e.length;
    typeof this.axis == "number" && (this.axis = [this.axis]);
    for (let r = 0; r < this.axis.length; ++r)
      this.axis[r] < 0 && (this.axis[r] += t);
    for (let r of this.axis)
      if (r < 0 || r >= t)
        throw new Error(`Invalid axis: ${r}`);
    if (this.axis.length !== lr(this.axis).length)
      throw new Error(`Found duplicate axes in: ${this.axis}`);
    let n = this.axis.map((r) => e[r]), s = true;
    this.scale ? this.gamma = this.addWeight("gamma", n, "float32", this.gammaInitializer, this.gammaRegularizer, s) : this.gamma = null, this.center ? this.beta = this.addWeight("beta", n, "float32", this.betaInitializer, this.betaRegularizer, s) : this.beta = null, this.built = true;
  }
  call(e, t) {
    let n = Oe(e), s = n.shape, r = s.length;
    return q(() => {
      let { mean: i, variance: o } = ib(n, this.axis, true), u = ma(1, r);
      for (let f of this.axis)
        u[f] = s[f];
      let l = (f) => f != null && f.shape.length !== r ? U(f, u) : f, c = l(this.gamma.read()), p = l(this.beta.read()), d = [], h = [];
      for (let f = 0; f < r; ++f)
        this.axis.indexOf(f) !== -1 ? (d.push(s[f]), h.push(1)) : (d.push(1), h.push(s[f]));
      return i = hs(i, d), o = hs(o, d), c = hs(c, h), p = hs(p, h), tl(n, i, o, p, c, this.epsilon);
    });
  }
  getConfig() {
    let e = { axis: this.axis, epsilon: this.epsilon, center: this.center, scale: this.scale, betaInitializer: yt(this.betaInitializer), gammaInitializer: yt(this.gammaInitializer), betaRegularizer: at(this.betaRegularizer), gammaRegularizer: at(this.gammaRegularizer) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Py.className = "LayerNormalization";
re.registerClass(Py);
function oV(e, t, n) {
  return q(() => {
    if (e.rank !== 4)
      throw new G(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);
    if (t == null && (t = [[1, 1], [1, 1]]), t.length !== 2 || t[0].length !== 2 || t[1].length !== 2)
      throw new G("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
    if (n == null && (n = vs()), n !== "channelsLast" && n !== "channelsFirst")
      throw new G(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
    let s;
    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], yi(e, s);
  });
}
var zy = class extends He {
  constructor(e) {
    if (e == null && (e = {}), super(e), this.dataFormat = e.dataFormat == null ? vs() : e.dataFormat, e.padding == null)
      this.padding = [[1, 1], [1, 1]];
    else if (typeof e.padding == "number")
      this.padding = [[e.padding, e.padding], [e.padding, e.padding]];
    else {
      if (e.padding = e.padding, e.padding.length !== 2)
        throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);
      let t, n;
      if (typeof e.padding[0] == "number")
        t = [e.padding[0], e.padding[0]], n = [e.padding[1], e.padding[1]];
      else {
        if (e.padding = e.padding, e.padding[0].length !== 2)
          throw new G(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);
        if (t = e.padding[0], e.padding[1].length !== 2)
          throw new G(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);
        n = e.padding[1];
      }
      this.padding = [t, n];
    }
    this.inputSpec = [new Dt({ ndim: 4 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t, n;
    return this.dataFormat === "channelsFirst" ? (e[2] != null && e[2] >= 0 ? t = e[2] + this.padding[0][0] + this.padding[0][1] : t = null, e[3] != null && e[3] >= 0 ? n = e[3] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], e[1], t, n]) : (e[1] != null && e[1] >= 0 ? t = e[1] + this.padding[0][0] + this.padding[0][1] : t = null, e[2] != null && e[2] >= 0 ? n = e[2] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], t, n, e[3]]);
  }
  call(e, t) {
    return q(() => oV(Oe(e), this.padding, this.dataFormat));
  }
  getConfig() {
    let e = { padding: this.padding, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
zy.className = "ZeroPadding2D";
re.registerClass(zy);
function Xp(e, t, n, s, r, a) {
  return q(() => {
    Ct(r), JS(a), Gn(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = sy(e, r);
    let i, o = s === "same" ? "same" : "valid";
    return a === "max" ? i = ab(e, t, n, o) : i = Xg(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
  });
}
function jI(e, t, n, s, r, a) {
  return q(() => {
    Ct(r), JS(a), Gn(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = MI(e, r);
    let i, o = s === "same" ? "same" : "valid";
    return a === "max" ? i = xS(e, t, n, o) : i = rS(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 4, 1, 2, 3])), i;
  });
}
var KI = class extends He {
  constructor(e) {
    if (e.poolSize == null && (e.poolSize = 2), super(e), typeof e.poolSize == "number")
      this.poolSize = [e.poolSize];
    else if (Array.isArray(e.poolSize) && e.poolSize.length === 1 && typeof e.poolSize[0] == "number")
      this.poolSize = e.poolSize;
    else
      throw new G(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);
    if (Vt(this.poolSize, "poolSize"), e.strides == null)
      this.strides = this.poolSize;
    else if (typeof e.strides == "number")
      this.strides = [e.strides];
    else if (Array.isArray(e.strides) && e.strides.length === 1 && typeof e.strides[0] == "number")
      this.strides = e.strides;
    else
      throw new G(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);
    Vt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, Gn(this.padding), this.inputSpec = [new Dt({ ndim: 3 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = bs(e[1], this.poolSize[0], this.padding, this.strides[0]);
    return [e[0], t, e[2]];
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t), e = Vl(Oe(e), 2);
      let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
      return mr(n, [2]);
    });
  }
  getConfig() {
    let e = { poolSize: this.poolSize, padding: this.padding, strides: this.strides }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var My = class extends KI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Xp(e, t, n, s, r, "max");
  }
};
My.className = "MaxPooling1D";
re.registerClass(My);
var Ly = class extends KI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Xp(e, t, n, s, r, "avg");
  }
};
Ly.className = "AveragePooling1D";
re.registerClass(Ly);
var XI = class extends He {
  constructor(e) {
    if (e.poolSize == null && (e.poolSize = [2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize], e.strides == null)
      this.strides = this.poolSize;
    else if (Array.isArray(e.strides)) {
      if (e.strides.length !== 2)
        throw new G(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);
      this.strides = e.strides;
    } else
      this.strides = [e.strides, e.strides];
    Vt(this.poolSize, "poolSize"), Vt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Gn(this.padding), this.inputSpec = [new Dt({ ndim: 4 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? e[2] : e[1], n = this.dataFormat === "channelsFirst" ? e[3] : e[2];
    return t = bs(t, this.poolSize[0], this.padding, this.strides[0]), n = bs(n, this.poolSize[1], this.padding, this.strides[1]), this.dataFormat === "channelsFirst" ? [e[0], e[1], t, n] : [e[0], t, n, e[3]];
  }
  call(e, t) {
    return q(() => (this.invokeCallHook(e, t), this.poolingFunction(Oe(e), this.poolSize, this.strides, this.padding, this.dataFormat)));
  }
  getConfig() {
    let e = { poolSize: this.poolSize, padding: this.padding, strides: this.strides, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var By = class extends XI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Xp(e, t, n, s, r, "max");
  }
};
By.className = "MaxPooling2D";
re.registerClass(By);
var Vy = class extends XI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Xp(e, t, n, s, r, "avg");
  }
};
Vy.className = "AveragePooling2D";
re.registerClass(Vy);
var YI = class extends He {
  constructor(e) {
    if (e.poolSize == null && (e.poolSize = [2, 2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize, e.poolSize], e.strides == null)
      this.strides = this.poolSize;
    else if (Array.isArray(e.strides)) {
      if (e.strides.length !== 3)
        throw new G(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);
      this.strides = e.strides;
    } else
      this.strides = [e.strides, e.strides, e.strides];
    Vt(this.poolSize, "poolSize"), Vt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Gn(this.padding), this.inputSpec = [new Dt({ ndim: 5 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? e[2] : e[1], n = this.dataFormat === "channelsFirst" ? e[3] : e[2], s = this.dataFormat === "channelsFirst" ? e[4] : e[3];
    return t = bs(t, this.poolSize[0], this.padding, this.strides[0]), n = bs(n, this.poolSize[1], this.padding, this.strides[1]), s = bs(s, this.poolSize[2], this.padding, this.strides[2]), this.dataFormat === "channelsFirst" ? [e[0], e[1], t, n, s] : [e[0], t, n, s, e[4]];
  }
  call(e, t) {
    return q(() => (this.invokeCallHook(e, t), this.poolingFunction(Oe(e), this.poolSize, this.strides, this.padding, this.dataFormat)));
  }
  getConfig() {
    let e = { poolSize: this.poolSize, padding: this.padding, strides: this.strides, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var Wy = class extends YI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), jI(e, t, n, s, r, "max");
  }
};
Wy.className = "MaxPooling3D";
re.registerClass(Wy);
var Uy = class extends YI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), jI(e, t, n, s, r, "avg");
  }
};
Uy.className = "AveragePooling3D";
re.registerClass(Uy);
var QI = class extends He {
  constructor(e) {
    super(e), this.inputSpec = [new Dt({ ndim: 3 })];
  }
  computeOutputShape(e) {
    return [e[0], e[2]];
  }
  call(e, t) {
    throw new Fe();
  }
};
var Gy = class extends QI {
  constructor(e) {
    super(e || {});
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return It(n, 1);
    });
  }
};
Gy.className = "GlobalAveragePooling1D";
re.registerClass(Gy);
var Hy = class extends QI {
  constructor(e) {
    super(e || {});
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return As(n, 1);
    });
  }
};
Hy.className = "GlobalMaxPooling1D";
re.registerClass(Hy);
var ZI = class extends He {
  constructor(e) {
    super(e), this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.inputSpec = [new Dt({ ndim: 4 })];
  }
  computeOutputShape(e) {
    return e = e, this.dataFormat === "channelsLast" ? [e[0], e[3]] : [e[0], e[1]];
  }
  call(e, t) {
    throw new Fe();
  }
  getConfig() {
    let e = { dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var qy = class extends ZI {
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return this.dataFormat === "channelsLast" ? It(n, [1, 2]) : It(n, [2, 3]);
    });
  }
};
qy.className = "GlobalAveragePooling2D";
re.registerClass(qy);
var jy = class extends ZI {
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return this.dataFormat === "channelsLast" ? As(n, [1, 2]) : As(n, [2, 3]);
    });
  }
};
jy.className = "GlobalMaxPooling2D";
re.registerClass(jy);
var JI = class extends He {
  constructor(e) {
    super(e), this.layer = e.layer;
  }
  build(e) {
    this.built = true;
  }
  get trainable() {
    return this.layer != null ? this.layer.trainable : false;
  }
  set trainable(e) {
    this.layer != null && (this.layer.trainable = e);
  }
  get trainableWeights() {
    return this.layer.trainableWeights;
  }
  get nonTrainableWeights() {
    return this.layer.nonTrainableWeights;
  }
  get updates() {
    return this.layer._updates;
  }
  get losses() {
    return this.layer.losses;
  }
  getWeights() {
    return this.layer.getWeights();
  }
  setWeights(e) {
    this.layer.setWeights(e);
  }
  getConfig() {
    let e = { layer: { className: this.layer.getClassName(), config: this.layer.getConfig() } }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
  setFastWeightInitDuringBuild(e) {
    super.setFastWeightInitDuringBuild(e), this.layer != null && this.layer.setFastWeightInitDuringBuild(e);
  }
  static fromConfig(e, t, n = {}) {
    let s = t.layer, r = gs(s, n);
    delete t.layer;
    let a = { layer: r };
    return Object.assign(a, t), new e(a);
  }
};
var Ky = class extends JI {
  constructor(e) {
    super(e), this.supportsMasking = true;
  }
  build(e) {
    if (e = nt(e), e.length < 3)
      throw new G(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);
    this.inputSpec = [{ shape: e }];
    let t = [e[0]].concat(e.slice(2));
    this.layer.built || (this.layer.build(t), this.layer.built = true), super.build(e);
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = [e[0]].concat(e.slice(2)), n = this.layer.computeOutputShape(t), s = e[1];
    return [n[0], s].concat(n.slice(1));
  }
  call(e, t) {
    return q(() => (e = Oe(e), GI((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
  }
};
Ky.className = "TimeDistributed";
re.registerClass(Ky);
function uV(e) {
  vi(nz, "BidirectionalMergeMode", e);
}
var lV = "concat";
var Xy = class extends JI {
  constructor(e) {
    super(e);
    let t = e.layer.getConfig(), n = {};
    n.className = e.layer.getClassName(), n.config = t, this.forwardLayer = gs(n), t.goBackwards = t.goBackwards !== true;
    let s = {};
    if (s.className = e.layer.getClassName(), s.config = t, this.backwardLayer = gs(s), this.forwardLayer.name = "forward_" + this.forwardLayer.name, this.backwardLayer.name = "backward_" + this.backwardLayer.name, this.mergeMode = e.mergeMode === void 0 ? lV : e.mergeMode, uV(this.mergeMode), e.weights)
      throw new Fe("weights support is not implemented for Bidirectional layer yet.");
    this._stateful = e.layer.stateful, this.returnSequences = e.layer.returnSequences, this.returnState = e.layer.returnState, this.supportsMasking = true, this._trainable = true, this.inputSpec = e.layer.inputSpec, this.numConstants = null;
  }
  get trainable() {
    return this._trainable;
  }
  set trainable(e) {
    this._trainable = e, this.forwardLayer != null && (this.forwardLayer.trainable = e), this.backwardLayer != null && (this.backwardLayer.trainable = e);
  }
  getWeights() {
    return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());
  }
  setWeights(e) {
    let t = e.length, n = Math.floor(t / 2);
    this.forwardLayer.setWeights(e.slice(0, n)), this.backwardLayer.setWeights(e.slice(n));
  }
  computeOutputShape(e) {
    let t = this.forwardLayer.computeOutputShape(e);
    Array.isArray(t) && Array.isArray(t[0]) || (t = [t]), t = t;
    let n, s, r;
    return this.returnState && (r = t.slice(1)), n = t[0], n = n, this.mergeMode === "concat" ? (n[n.length - 1] *= 2, s = [n]) : this.mergeMode == null ? s = [n, n.slice()] : s = [n], this.returnState ? this.mergeMode == null ? s.concat(r).concat(r.slice()) : [n].concat(r).concat(r.slice()) : bn(s);
  }
  apply(e, t) {
    let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
    t == null && (t = {});
    let r = UI(e, n, s, this.numConstants);
    if (e = r.inputs, n = r.initialState, s = r.constants, Array.isArray(e) && (n = e.slice(1), e = e[0]), (n == null || n.length === 0) && s == null)
      return super.apply(e, t);
    let a = [], i = [];
    if (n != null) {
      let u = n.length;
      if (u % 2 > 0)
        throw new G("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
      t.initialState = n, a.push(...n);
      let l = n.map((c) => new Dt({ shape: c.shape }));
      this.forwardLayer.stateSpec = l.slice(0, u / 2), this.backwardLayer.stateSpec = l.slice(u / 2), i.push(...l);
    }
    if (s != null)
      throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");
    let o = a[0] instanceof $s;
    for (let u of a)
      if (u instanceof $s !== o)
        throw new G("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");
    if (o) {
      let u = [e].concat(a), l = this.inputSpec.concat(i), c = this.inputSpec;
      this.inputSpec = l;
      let p = super.apply(u, t);
      return this.inputSpec = c, p;
    } else
      return super.apply(e, t);
  }
  call(e, t) {
    return q(() => {
      let n = t.initialState, s, r;
      if (n == null)
        s = this.forwardLayer.call(e, t), r = this.backwardLayer.call(e, t);
      else {
        let o = n.slice(0, n.length / 2), u = n.slice(n.length / 2);
        s = this.forwardLayer.call(e, Object.assign(t, { initialState: o })), r = this.backwardLayer.call(e, Object.assign(t, { initialState: u }));
      }
      let a;
      this.returnState && (Array.isArray(s) && (a = s.slice(1).concat(r.slice(1))), s = s[0], r = r[0]), this.returnSequences && (r = Jn(r, 1));
      let i;
      return this.mergeMode === "concat" ? i = $b([s, r]) : this.mergeMode === "sum" ? i = ie(s, r) : this.mergeMode === "ave" ? i = V(0.5, ie(s, r)) : this.mergeMode === "mul" ? i = V(s, r) : this.mergeMode == null && (i = [s, r]), this.returnState ? this.mergeMode == null ? i.concat(a) : [i].concat(a) : i;
    });
  }
  resetStates(e) {
    this.forwardLayer.resetStates(), this.backwardLayer.resetStates();
  }
  build(e) {
    ta(this.forwardLayer.name, () => {
      this.forwardLayer.build(e);
    }), ta(this.backwardLayer.name, () => {
      this.backwardLayer.build(e);
    }), this.built = true;
  }
  computeMask(e, t) {
    Array.isArray(t) && (t = t[0]);
    let n;
    if (this.returnSequences ? this.mergeMode == null ? n = [t, t] : n = t : this.mergeMode == null ? n = [null, null] : n = null, this.returnState) {
      let r = this.forwardLayer.states.map((a) => null);
      return Array.isArray(n) ? n.concat(r).concat(r) : [n].concat(r).concat(r);
    } else
      return n;
  }
  get trainableWeights() {
    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);
  }
  get nonTrainableWeights() {
    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);
  }
  setFastWeightInitDuringBuild(e) {
    super.setFastWeightInitDuringBuild(e), this.forwardLayer != null && this.forwardLayer.setFastWeightInitDuringBuild(e), this.backwardLayer != null && this.backwardLayer.setFastWeightInitDuringBuild(e);
  }
  getConfig() {
    let e = { mergeMode: this.mergeMode }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
  static fromConfig(e, t) {
    let n = gs(t.layer);
    if (delete t.layer, t.numConstants != null)
      throw new Fe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");
    let s = t;
    return s.layer = n, new e(s);
  }
};
Xy.className = "Bidirectional";
re.registerClass(Xy);
function cV(e) {
  return new Jo(e);
}
function dV(e) {
  return new ey(e);
}
function pV(e) {
  return new Qb(e);
}
function hV(e) {
  return new Zb(e);
}
function fV(e) {
  return new Jb(e);
}
function mV(e) {
  return new ny(e);
}
function gV(e) {
  return new ty(e);
}
function bV(e) {
  return new uy(e);
}
function yV(e) {
  return new Wp(e);
}
function vV(e) {
  return new ay(e);
}
function xV(e) {
  return new Up(e);
}
function wV(e) {
  return new iy(e);
}
function kV(e) {
  return new oy(e);
}
function SV(e) {
  return new ly(e);
}
function IV(e) {
  return new cy(e);
}
function CV(e) {
  return new dy(e);
}
function NV(e) {
  return new vy(e);
}
function TV(e) {
  return new by(e);
}
function $V(e) {
  return new Kp(e);
}
function _V(e) {
  return new gy(e);
}
function AV(e) {
  return new yy(e);
}
function EV(e) {
  return new xy(e);
}
function RV(e) {
  return new wy(e);
}
function DV(e) {
  return new ky(e);
}
function FV(e) {
  return new Iy(e);
}
function OV(e) {
  return new Cy(e);
}
function PV(e) {
  return new Ty(e);
}
function zV(e) {
  return new Ay(e);
}
function MV(e) {
  return new $y(e);
}
function LV(e) {
  return new _y(e);
}
function BV(e) {
  return new Ny(e);
}
function VV(e) {
  return new Ey(e);
}
function WV(e) {
  return new Oy(e);
}
function UV(e) {
  return new Py(e);
}
function GV(e) {
  return new zy(e);
}
function Yy(e) {
  return new Ly(e);
}
function HV(e) {
  return Yy(e);
}
function qV(e) {
  return Yy(e);
}
function Qy(e) {
  return new Vy(e);
}
function jV(e) {
  return Qy(e);
}
function KV(e) {
  return Qy(e);
}
function Zy(e) {
  return new Uy(e);
}
function XV(e) {
  return Zy(e);
}
function YV(e) {
  return Zy(e);
}
function QV(e) {
  return new Gy(e);
}
function ZV(e) {
  return new qy(e);
}
function e0(e) {
  return new Hy(e);
}
function t0(e) {
  return new jy(e);
}
function n0(e) {
  return new My(e);
}
function s0(e) {
  return new By(e);
}
function JV(e) {
  return new Wy(e);
}
function eW(e) {
  return new hy(e);
}
function tW(e) {
  return new Hp(e);
}
function nW(e) {
  return new fy(e);
}
function sW(e) {
  return new Kl(e);
}
function rW(e) {
  return new py(e);
}
function aW(e) {
  return new Gp(e);
}
function iW(e) {
  return new my(e);
}
function oW(e) {
  return new jp(e);
}
function uW(e) {
  return new Ar(e);
}
function lW(e) {
  return new qp(e);
}
function cW(e) {
  return new Xy(e);
}
function dW(e) {
  return new Ky(e);
}
var pW = e0;
var hW = t0;
var fW = n0;
var mW = s0;
function gW(e) {
  return new Ry(e);
}
function bW(e) {
  return new Dy(e);
}
function yW(e) {
  return new Fy(e);
}
function vW(e) {
  return new Sy(e);
}
var xW = {};
Ae(xW, { MAPE: () => EW, MSE: () => FW, binaryAccuracy: () => wW, binaryCrossentropy: () => kW, categoricalAccuracy: () => IW, categoricalCrossentropy: () => CW, cosineProximity: () => $W, mape: () => RW, meanAbsoluteError: () => _W, meanAbsolutePercentageError: () => AW, meanSquaredError: () => DW, mse: () => OW, precision: () => NW, recall: () => TW, sparseCategoricalAccuracy: () => SW });
function wW(e, t) {
  return Gb(e, t);
}
function kW(e, t) {
  return fI(e, t);
}
function SW(e, t) {
  return mI(e, t);
}
function IW(e, t) {
  return Hb(e, t);
}
function CW(e, t) {
  return qb(e, t);
}
function NW(e, t) {
  return hI(e, t);
}
function TW(e, t) {
  return bB(e, t);
}
function $W(e, t) {
  return Ub(e, t);
}
function _W(e, t) {
  return Bp(e, t);
}
function AW(e, t) {
  return eu(e, t);
}
function EW(e, t) {
  return eu(e, t);
}
function RW(e, t) {
  return eu(e, t);
}
function DW(e, t) {
  return xi(e, t);
}
function FW(e, t) {
  return xi(e, t);
}
function OW(e, t) {
  return xi(e, t);
}
var PW = {};
Ae(PW, { modelFromJSON: () => jB });
var zW = {};
Ae(zW, { l1: () => LW, l1l2: () => MW, l2: () => BW });
function MW(e) {
  return new Hl(e);
}
function LW(e) {
  return ZB(e);
}
function BW(e) {
  return JB(e);
}
var VW = class extends so {
  constructor() {
    super(...arguments), this.model = null;
  }
  setModel(e) {
    if (!(e instanceof dr))
      throw new Error("model must be a LayersModel, not some other Container");
    this.model = e;
  }
};
function Xc(e, t) {
  return e < t;
}
function Kx(e, t) {
  return e > t;
}
var WW = class extends VW {
  constructor(e) {
    if (super(), e == null && (e = {}), e.restoreBestWeights)
      throw new Fe("restoreBestWeights = True is not implemented in EarlyStopping yet.");
    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = Xc : this.mode === "max" ? this.monitorFunc = Kx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Kx : this.monitorFunc = Xc, this.monitorFunc === Xc && (this.minDelta *= -1);
  }
  async onTrainBegin(e) {
    this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === Xc ? 1 / 0 : -1 / 0;
  }
  async onEpochEnd(e, t) {
    await sr(t);
    let n = this.getMonitorValue(t);
    n != null && (this.monitorFunc(n - this.minDelta, this.best) ? (this.best = n, this.wait = 0) : (this.wait++, this.wait >= this.patience && (this.stoppedEpoch = e, this.model.stopTraining = true)));
  }
  async onTrainEnd(e) {
    this.stoppedEpoch > 0 && this.verbose && console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);
  }
  getMonitorValue(e) {
    e == null && (e = {});
    let t = e[this.monitor];
    return t == null && console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`), t;
  }
};
function UW(e) {
  return new WW(e);
}
var ahe = { earlyStopping: UW };
var GW = K();
GW.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
  e && 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 r0 = ((e) => (e[e.DT_INVALID = 0] = "DT_INVALID", e[e.DT_FLOAT = 1] = "DT_FLOAT", e[e.DT_DOUBLE = 2] = "DT_DOUBLE", e[e.DT_INT32 = 3] = "DT_INT32", e[e.DT_UINT8 = 4] = "DT_UINT8", e[e.DT_INT16 = 5] = "DT_INT16", e[e.DT_INT8 = 6] = "DT_INT8", e[e.DT_STRING = 7] = "DT_STRING", e[e.DT_COMPLEX64 = 8] = "DT_COMPLEX64", e[e.DT_INT64 = 9] = "DT_INT64", e[e.DT_BOOL = 10] = "DT_BOOL", e[e.DT_QINT8 = 11] = "DT_QINT8", e[e.DT_QUINT8 = 12] = "DT_QUINT8", e[e.DT_QINT32 = 13] = "DT_QINT32", e[e.DT_BFLOAT16 = 14] = "DT_BFLOAT16", e[e.DT_QINT16 = 15] = "DT_QINT16", e[e.DT_QUINT16 = 16] = "DT_QUINT16", e[e.DT_UINT16 = 17] = "DT_UINT16", e[e.DT_COMPLEX128 = 18] = "DT_COMPLEX128", e[e.DT_HALF = 19] = "DT_HALF", e[e.DT_RESOURCE = 20] = "DT_RESOURCE", e[e.DT_VARIANT = 21] = "DT_VARIANT", e[e.DT_UINT32 = 22] = "DT_UINT32", e[e.DT_UINT64 = 23] = "DT_UINT64", e[e.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", e[e.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", e[e.DT_INT32_REF = 103] = "DT_INT32_REF", e[e.DT_UINT8_REF = 104] = "DT_UINT8_REF", e[e.DT_INT16_REF = 105] = "DT_INT16_REF", e[e.DT_INT8_REF = 106] = "DT_INT8_REF", e[e.DT_STRING_REF = 107] = "DT_STRING_REF", e[e.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", e[e.DT_INT64_REF = 109] = "DT_INT64_REF", e[e.DT_BOOL_REF = 110] = "DT_BOOL_REF", e[e.DT_QINT8_REF = 111] = "DT_QINT8_REF", e[e.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", e[e.DT_QINT32_REF = 113] = "DT_QINT32_REF", e[e.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", e[e.DT_QINT16_REF = 115] = "DT_QINT16_REF", e[e.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", e[e.DT_UINT16_REF = 117] = "DT_UINT16_REF", e[e.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", e[e.DT_HALF_REF = 119] = "DT_HALF_REF", e[e.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", e[e.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", e[e.DT_UINT32_REF = 122] = "DT_UINT32_REF", e[e.DT_UINT64_REF = 123] = "DT_UINT64_REF", e))(r0 || {});
var Xx;
((e) => {
  let t;
  ((n) => {
    n[n.LEGACY = 0] = "LEGACY", n[n.V1 = 1] = "V1", n[n.V2 = 2] = "V2";
  })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
})(Xx || (Xx = {}));
var Jy = {};
function ihe(e, t) {
  let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
  Jy[e] = n;
}
function a0(e) {
  return Jy[e];
}
function ohe(e) {
  delete Jy[e];
}
function S(e, t, n, s, r) {
  let a = t.inputParams[e];
  if (a && a.inputIndexStart !== void 0) {
    let o = a.inputIndexStart, u = a.inputIndexEnd === 0 ? void 0 : a.inputIndexEnd === void 0 ? o + 1 : a.inputIndexEnd;
    if (a.type === "tensor")
      return un(t.inputNames[a.inputIndexStart], n, s, r);
    if (a.type === "tensors")
      return t.inputNames.slice(o, u).map((d) => un(d, n, s, r));
    let l = un(t.inputNames.slice(o)[0], n, s, r), c = l.dataSync();
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}
function un(e, t, n, s) {
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  if (s != null) {
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}
function HW(e, t, n) {
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}
function Ts(e, t) {
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  return [Pd(n, t && t.currentContextId), s, r];
}
function Pd(e, t) {
  return t ? `${e}-${t}` : e;
}
function _n(e) {
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  if (t.length === 1)
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function ad(e, t, n) {
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}
function Ws(e) {
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}
var i0 = {};
Ae(i0, { json: () => qW });
var qW = [{ 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 o0 = {};
Ae(o0, { json: () => jW });
var jW = [{ 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: 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[{ 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 u0 = {};
Ae(u0, { json: () => KW });
var KW = [{ 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 l0 = {};
Ae(l0, { json: () => XW });
var XW = [{ 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" }] }, { 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 c0 = {};
Ae(c0, { json: () => YW });
var YW = [{ 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", notSupported: true }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: 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 d0 = {};
Ae(d0, { json: () => QW });
var QW = [{ 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 p0 = {};
Ae(p0, { json: () => ZW });
var ZW = [{ 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 h0 = {};
Ae(h0, { json: () => JW });
var JW = [{ 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 f0 = {};
Ae(f0, { json: () => e4 });
var e4 = [{ 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 m0 = {};
Ae(m0, { json: () => t4 });
var t4 = [{ 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 g0 = {};
Ae(g0, { json: () => n4 });
var n4 = [{ 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 b0 = {};
Ae(b0, { json: () => s4 });
var s4 = [{ 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: "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 y0 = {};
Ae(y0, { json: () => r4 });
var r4 = [{ 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 v0 = {};
Ae(v0, { json: () => a4 });
var a4 = [{ 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 x0 = {};
Ae(x0, { json: () => i4 });
var i4 = [{ 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 w0 = {};
Ae(w0, { json: () => o4 });
var o4 = [{ 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 k0 = {};
Ae(k0, { json: () => u4 });
var u4 = [{ 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 S0 = {};
Ae(S0, { json: () => l4 });
var l4 = [{ 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 I0 = {};
Ae(I0, { json: () => c4 });
var c4 = [{ 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 Yx = class {
  static get Instance() {
    return this._instance || (this._instance = new this());
  }
  constructor() {
    let e = [i0, o0, u0, l0, c0, d0, p0, h0, f0, m0, g0, b0, y0, v0, x0, w0, k0, S0, I0], t = [].concat(...e.map((n) => n.json));
    this.opMappers = t.reduce((n, s) => (n[s.tfOpName] = s, n), {});
  }
  transformGraph(e, t = {}) {
    let n = e.node, s = [], r = [], a = [], i = n.reduce((f, m) => (f[m.name] = this.mapNode(m), m.op.startsWith("Placeholder") ? s.push(f[m.name]) : m.op === "Const" ? r.push(f[m.name]) : (m.input == null || m.input.length === 0) && a.push(f[m.name]), f), {}), o = [], u = [], l = {}, c = {};
    t != null && (l = this.mapSignatureEntries(t.inputs), c = this.mapSignatureEntries(t.outputs));
    let p = Object.keys(i);
    p.forEach((f) => {
      let m = i[f];
      m.inputNames.forEach((g, b) => {
        let [y, , v] = Ts(g), x = i[y];
        if (x.outputs != null) {
          let k = x.outputs.indexOf(v);
          if (k !== -1) {
            let I = `${y}:${k}`;
            m.inputNames[b] = I;
          }
        }
        m.inputs.push(x), x.children.push(m);
      });
    }), Object.keys(c).length === 0 ? p.forEach((f) => {
      let m = i[f];
      m.children.length === 0 && u.push(m);
    }) : Object.keys(c).forEach((f) => {
      let [m] = Ts(f), g = i[m];
      g != null && (g.signatureKey = c[f], u.push(g));
    }), Object.keys(l).length > 0 ? Object.keys(l).forEach((f) => {
      let [m] = Ts(f), g = i[m];
      g && (g.signatureKey = l[f], o.push(g));
    }) : o = s;
    let d = {};
    e.library != null && e.library.function != null && (d = e.library.function.reduce((f, m) => (f[m.signature.name] = this.mapFunction(m), f), {}));
    let h = { nodes: i, inputs: o, outputs: u, weights: r, placeholders: s, signature: t, functions: d };
    return a.length > 0 && (h.initNodes = a), h;
  }
  mapSignatureEntries(e) {
    return Object.keys(e || {}).reduce((t, n) => (t[e[n].name] = n, t), {});
  }
  mapNode(e) {
    let t = a0(e.op) || this.opMappers[e.op] || {};
    e.attr == null && (e.attr = {});
    let n = { name: e.name, op: e.op, category: t.category, inputNames: (e.input || []).map((s) => s.startsWith("^") ? s.slice(1) : s), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t.outputs };
    return t.inputs != null && (n.inputParams = t.inputs.reduce((s, r) => (s[r.name] = { type: r.type, inputIndexStart: r.start, inputIndexEnd: r.end }, s), {})), t.attrs != null && (n.attrParams = t.attrs.reduce((s, r) => {
      let a = r.type, i;
      switch (r.type) {
        case "string":
          i = Em(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Em(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "string[]":
          i = Mm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Mm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "number":
          i = Dm(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = Dm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "number[]":
          i = zm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = zm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "bool":
          i = Rm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Rm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "bool[]":
          i = Bm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Bm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "shape":
          i = Pm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Pm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "shape[]":
          i = Lm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Lm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "dtype":
          i = Fm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Fm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "dtype[]":
          i = Om(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Om(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "func":
          i = Qx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Qx(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "tensor":
        case "tensors":
          break;
        default:
          throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`);
      }
      return s[r.name] = { value: i, type: a }, s;
    }, {})), n;
  }
  mapFunction(e) {
    let t = e.nodeDef, n = [], s = [], r = {};
    t != null && (r = t.reduce((c, p) => (c[p.name] = this.mapNode(p), p.op === "Const" && s.push(c[p.name]), c), {}));
    let a = [], i = [];
    e.signature.inputArg.forEach((c) => {
      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: ev(c.type), type: "dtype" } }, children: [] };
      d.signatureKey = c.name, a.push(d), r[p] = d;
    }), Object.keys(r).forEach((c) => {
      let p = r[c];
      p.inputNames.forEach((d, h) => {
        let [f, , m] = Ts(d), g = r[f];
        if (g.outputs != null) {
          let b = g.outputs.indexOf(m);
          if (b !== -1) {
            let y = `${f}:${b}`;
            p.inputNames[h] = y;
          }
        }
        p.inputs.push(g), g.children.push(p);
      });
    });
    let u = e.ret;
    e.signature.outputArg.forEach((c) => {
      let [p, d] = Ts(u[c.name]), h = r[p];
      h != null && (h.defaultOutput = d, i.push(h));
    });
    let l = this.mapArgsToSignature(e);
    return { nodes: r, inputs: a, outputs: i, weights: s, placeholders: n, signature: l };
  }
  mapArgsToSignature(e) {
    return { methodName: e.signature.name, inputs: e.signature.inputArg.reduce((t, n) => (t[n.name] = this.mapArgToTensorInfo(n), t), {}), outputs: e.signature.outputArg.reduce((t, n) => (t[n.name] = this.mapArgToTensorInfo(n, e.ret), t), {}) };
  }
  mapArgToTensorInfo(e, t) {
    let n = e.name;
    return t != null && (n = t[n]), { name: n, dtype: e.type };
  }
};
function d4(e) {
  let t = K().global;
  if (typeof t.atob != "undefined")
    return t.atob(e);
  if (typeof Buffer != "undefined")
    return new Buffer(e, "base64").toString();
  throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
}
function C0(e, t) {
  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : d4(e);
  return t ? n : n.toLowerCase();
}
function Em(e, t, n, s = false) {
  let r = e[t];
  return r != null ? C0(r.s, s) : n;
}
function Rm(e, t, n) {
  let s = e[t];
  return s ? s.b : n;
}
function Dm(e, t, n) {
  let s = e[t] || {}, r = s.i != null ? s.i : s.f != null ? s.f : n;
  return typeof r == "number" ? r : parseInt(r, 10);
}
function ev(e) {
  switch (typeof e == "string" && (e = r0[e]), e) {
    case 1:
    case 19:
      return "float32";
    case 3:
    case 9:
    case 6:
    case 4:
      return "int32";
    case 10:
      return "bool";
    case 2:
      return "float32";
    case 7:
      return "string";
    default:
      return null;
  }
}
function Qx(e, t, n) {
  let s = e[t];
  return s && s.func ? s.func.name : n;
}
function Fm(e, t, n) {
  let s = e[t];
  return s && s.type ? ev(s.type) : n;
}
function Om(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.type ? s.list.type.map((r) => ev(r)) : n;
}
function N0(e) {
  if (!e.unknownRank)
    return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
}
function Pm(e, t, n) {
  let s = e[t];
  return s && s.shape ? N0(s.shape) : n;
}
function zm(e, t, n) {
  let s = e[t];
  return s ? ((s.list.f && s.list.f.length ? s.list.f : s.list.i) || []).map((r) => typeof r == "number" ? r : parseInt(r, 10)) : n;
}
function Mm(e, t, n, s = false) {
  let r = e[t];
  return r && r.list && r.list.s ? r.list.s.map((a) => C0(a, s)) : n;
}
function Lm(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.shape ? s.list.shape.map((r) => N0(r)) : n;
}
function Bm(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.b ? s.list.b : n;
}
var p4 = class {
  constructor(e, t, n) {
    this.node = e, this.tensorMap = t, this.context = n, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((s) => this.getInput(s)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((s, r) => (s[r] = this.getAttr(r), s), {}));
  }
  getInput(e) {
    return un(e, this.tensorMap, this.context);
  }
  getAttr(e, t) {
    let n = this.node.rawAttrs[e];
    if (n.tensor != null)
      return un(e, this.tensorMap, this.context);
    if (n.i != null || n.f != null)
      return Dm(this.node.rawAttrs, e, t);
    if (n.s != null)
      return Em(this.node.rawAttrs, e, t);
    if (n.b != null)
      return Rm(this.node.rawAttrs, e, t);
    if (n.shape != null)
      return Pm(this.node.rawAttrs, e, t);
    if (n.type != null)
      return Fm(this.node.rawAttrs, e, t);
    if (n.list != null) {
      if (n.list.i != null || n.list.f != null)
        return zm(this.node.rawAttrs, e, t);
      if (n.list.s != null)
        return Mm(this.node.rawAttrs, e, t);
      if (n.list.shape != null)
        return Lm(this.node.rawAttrs, e, t);
      if (n.list.b != null)
        return Bm(this.node.rawAttrs, e, t);
      if (n.list.type != null)
        return Om(this.node.rawAttrs, e, t);
    }
    return t;
  }
};
var h4 = (e, t, n) => {
  switch (e.op) {
    case "BiasAdd":
    case "AddV2":
    case "Add":
      return [ie(S("a", e, t, n), S("b", e, t, n))];
    case "AddN":
      return [sE(S("tensors", e, t, n))];
    case "FloorMod":
    case "Mod":
      return [RD(S("a", e, t, n), S("b", e, t, n))];
    case "Mul":
      return [V(S("a", e, t, n), S("b", e, t, n))];
    case "RealDiv":
    case "Div":
      return [xe(S("a", e, t, n), S("b", e, t, n))];
    case "DivNoNan":
      return [TR(S("a", e, t, n), S("b", e, t, n))];
    case "FloorDiv":
      return [Jk(S("a", e, t, n), S("b", e, t, n))];
    case "Sub":
      return [ge(S("a", e, t, n), S("b", e, t, n))];
    case "Minimum":
      return [kp(S("a", e, t, n), S("b", e, t, n))];
    case "Maximum":
      return [$r(S("a", e, t, n), S("b", e, t, n))];
    case "Pow":
      return [ha(S("a", e, t, n), S("b", e, t, n))];
    case "SquaredDifference":
      return [AS(S("a", e, t, n), S("b", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var f4 = (e, t, n) => {
  switch (e.op) {
    case "Abs":
    case "ComplexAbs":
      return [Lt(S("x", e, t, n))];
    case "Acos":
      return [JA(S("x", e, t, n))];
    case "Acosh":
      return [tE(S("x", e, t, n))];
    case "Asin":
      return [cE(S("x", e, t, n))];
    case "Asinh":
      return [pE(S("x", e, t, n))];
    case "Atan":
      return [fE(S("x", e, t, n))];
    case "Atan2":
      return [gE(S("x", e, t, n), S("y", e, t, n))];
    case "Atanh":
      return [yE(S("x", e, t, n))];
    case "Ceil":
      return [jE(S("x", e, t, n))];
    case "Complex":
      return [ua(S("real", e, t, n), S("imag", e, t, n))];
    case "Cos":
      return [Zg(S("x", e, t, n))];
    case "Cosh":
      return [cS(S("x", e, t, n))];
    case "Elu":
      return [vp(S("x", e, t, n))];
    case "Erf":
      return [DR(S("x", e, t, n))];
    case "Exp":
      return [Yn(S("x", e, t, n))];
    case "Expm1":
      return [zR(S("x", e, t, n))];
    case "Floor":
      return [xp(S("x", e, t, n))];
    case "Log":
      return [Qn(S("x", e, t, n))];
    case "Log1p":
      return [tb(S("x", e, t, n))];
    case "Imag":
      return [Jg(S("x", e, t, n))];
    case "Neg":
      return [kt(S("x", e, t, n))];
    case "Reciprocal":
      return [u3(S("x", e, t, n))];
    case "Real":
      return [Id(S("x", e, t, n))];
    case "Relu":
      return [Xs(S("x", e, t, n))];
    case "Round":
      return [SS(S("x", e, t, n))];
    case "Selu":
      return [CS(S("x", e, t, n))];
    case "Sigmoid":
      return [Hs(S("x", e, t, n))];
    case "Sin":
      return [NS(S("x", e, t, n))];
    case "Sign":
      return [I3(S("x", e, t, n))];
    case "Sinh":
      return [TS(S("x", e, t, n))];
    case "Softplus":
      return [Ml(S("x", e, t, n))];
    case "Sqrt":
      return [dn(S("x", e, t, n))];
    case "Square":
      return [ct(S("x", e, t, n))];
    case "Tanh":
      return [Ku(S("x", e, t, n))];
    case "Tan":
      return [H3(S("x", e, t, n))];
    case "ClipByValue":
      return [Vn(S("x", e, t, n), S("clipValueMin", e, t, n), S("clipValueMax", e, t, n))];
    case "Relu6":
      return [kS(S("x", e, t, n))];
    case "Rsqrt":
      return [IS(un(e.inputNames[0], t, n))];
    case "Prod":
      return [wS(S("x", e, t, n), S("axes", e, t, n))];
    case "LeakyRelu":
      return [eb(S("x", e, t, n), S("alpha", e, t, n))];
    case "Prelu":
      return [ub(S("x", e, t, n), S("alpha", e, t, n))];
    case "IsNan":
      return [KR(un(e.inputNames[0], t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function qn(e, t, n = "") {
  if (!(typeof e == "number" || typeof t == "number")) {
    w.assert(e.length === t.length, () => n + ` Shapes ${e} and ${t} must match`);
    for (let s = 0; s < e.length; s++) {
      let r = e[s], a = t[s];
      w.assert(r < 0 || a < 0 || r === a, () => n + ` Shapes ${e} and ${t} must match`);
    }
  }
}
function Zx(e) {
  return !(typeof e == "number" || e.some((t) => t < 0));
}
function $u(e, t, n) {
  let s = Vm(e, n), r = !Zx(s);
  if (r && t.length === 0)
    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);
  if (r && t.forEach((a) => {
    s = Vm(a.shape, s);
  }), !Zx(s))
    throw new Error(`Non-fully-defined elementShape: ${s}`);
  return s;
}
function Vm(e, t) {
  if (typeof e == "number")
    return t;
  if (typeof t == "number")
    return e;
  if (e.length !== t.length)
    throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);
  let n = [];
  for (let s = 0; s < e.length; ++s) {
    let r = e[s], a = t[s];
    if (r >= 0 && a >= 0 && r !== a)
      throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);
    n[s] = r >= 0 ? r : a;
  }
  return n;
}
var m4 = class {
  constructor(e, t, n, s, r, a, i) {
    this.name = e, this.dtype = t, this.maxSize = n, this.elementShape = s, this.identicalElementShapes = r, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = we(0), qt(this.idTensor);
  }
  get id() {
    return this.idTensor.id;
  }
  get closed() {
    return this.closed_;
  }
  clearAndClose(e) {
    this.tensors.forEach((t) => {
      (e == null || !e.has(t.tensor.id)) && t.tensor.dispose();
    }), this.tensors = [], this.closed_ = 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 t = this.tensors[e];
    if (t.cleared)
      throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);
    return this.clearAfterRead && (t.cleared = true), t.read = true, t.tensor;
  }
  readMany(e) {
    return e.map((t) => this.read(t));
  }
  write(e, t) {
    if (this.closed_)
      throw new Error(`TensorArray ${this.name} has already been closed.`);
    if (e < 0 || !this.dynamicSize && e >= this.maxSize)
      throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);
    let n = this.tensors[e] || {};
    if (t.dtype !== this.dtype)
      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
          because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);
    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t.shape), qn(this.elementShape, t.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), n.read)
      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);
    if (n.written)
      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);
    n.tensor = t, qt(t), n.written = true, this.tensors[e] = n;
  }
  writeMany(e, t) {
    if (e.length !== t.length)
      throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);
    e.forEach((n, s) => this.write(n, t[s]));
  }
  gather(e, t) {
    if (!!t && t !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);
    if (e)
      e = e.slice(0, this.size());
    else {
      e = [];
      for (let s = 0; s < this.size(); s++)
        e.push(s);
    }
    if (e.length === 0)
      return ms([], [0].concat(this.elementShape));
    let n = this.readMany(e);
    return qn(this.elementShape, n[0].shape, "TensorArray shape mismatch: "), es(n, 0);
  }
  concat(e) {
    if (!!e && e !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);
    if (this.size() === 0)
      return ms([], [0].concat(this.elementShape));
    let t = [];
    for (let s = 0; s < this.size(); s++)
      t.push(s);
    let n = this.readMany(t);
    return qn(this.elementShape, n[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`), Ft(n, 0);
  }
  scatter(e, t) {
    if (t.dtype !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);
    if (e.length !== t.shape[0])
      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);
    let n = Math.max(...e);
    if (!this.dynamicSize && n >= this.maxSize)
      throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);
    this.writeMany(e, Fs(t, 0));
  }
  split(e, t) {
    if (t.dtype !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);
    let n = 0, s = e.map((o) => (n += o, n));
    if (n !== t.shape[0])
      throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${t.shape}`);
    if (!this.dynamicSize && e.length !== this.maxSize)
      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);
    let r = n === 0 ? 0 : t.size / n, a = [];
    q(() => {
      t = U(t, [1, n, r]);
      for (let o = 0; o < e.length; ++o) {
        let u = o === 0 ? 0 : s[o - 1], l = [0, u, 0], c = [1, e[o], r];
        a[o] = U(qe(t, l, c), this.elementShape);
      }
      return a;
    });
    let i = [];
    for (let o = 0; o < e.length; o++)
      i[o] = o;
    this.writeMany(i, a);
  }
};
var ro = class {
  constructor(e, t, n, s = -1) {
    this.tensors = e, this.elementShape = t, this.elementDtype = n, e != null && e.forEach((r) => {
      if (n !== r.dtype)
        throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);
      qn(t, r.shape, "TensorList shape mismatch: "), qt(r);
    }), this.idTensor = we(0), this.maxNumElements = s, qt(this.idTensor);
  }
  get id() {
    return this.idTensor.id;
  }
  copy() {
    return new ro([...this.tensors], this.elementShape, this.elementDtype);
  }
  clearAndClose(e) {
    this.tensors.forEach((t) => {
      (e == null || !e.has(t.id)) && t.dispose();
    }), this.tensors.length = 0, this.idTensor.dispose();
  }
  size() {
    return this.tensors.length;
  }
  stack(e, t, n = -1) {
    if (t !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
    if (n !== -1 && this.tensors.length !== n)
      throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);
    qn(e, this.elementShape, "TensorList shape mismatch: ");
    let s = $u(this.elementShape, this.tensors, e);
    return q(() => {
      let r = this.tensors.map((a) => U(a, s));
      return es(r, 0);
    });
  }
  popBack(e, t) {
    if (t !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
    if (this.size() === 0)
      throw new Error("Trying to pop from an empty list.");
    let n = $u(this.elementShape, this.tensors, e), s = this.tensors.pop();
    return qn(s.shape, e, "TensorList shape mismatch: "), U(s, n);
  }
  pushBack(e) {
    if (e.dtype !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);
    if (qn(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
      throw new Error("Trying to push element into a full list.");
    qt(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 t = new ro([], this.elementShape, this.elementDtype, this.maxNumElements);
    t.tensors.length = e;
    for (let n = 0; n < Math.min(this.tensors.length, e); ++n)
      t.tensors[n] = this.tensors[n];
    return t;
  }
  getItem(e, t, n) {
    if (n !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);
    if (e < 0 || e > this.tensors.length)
      throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);
    if (this.tensors[e] == null)
      throw new Error(`element at index ${e} is null.`);
    qn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
    let s = $u(this.elementShape, this.tensors, t);
    return U(this.tensors[e], s);
  }
  setItem(e, t) {
    if (t.dtype !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);
    if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements)
      throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);
    qn(this.elementShape, t.shape, "TensorList shape mismatch: "), qt(t), this.tensors[e] = t;
  }
  gather(e, t, n) {
    if (t !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
    qn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
    let s = $u(this.elementShape, this.tensors, n);
    return e.length === 0 ? ms([], [0].concat(s)) : q(() => {
      let r = e.map((a) => U(this.tensors[a], s));
      return es(r, 0);
    });
  }
  concat(e, t) {
    if (!!e && e !== this.elementDtype)
      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
    qn(this.elementShape, t, "TensorList shape mismatch: ");
    let n = $u(this.elementShape, this.tensors, t);
    return this.size() === 0 ? ms([], [0].concat(n)) : q(() => {
      let s = this.tensors.map((r) => U(r, n));
      return Ft(s, 0);
    });
  }
};
function g4(e, t, n) {
  let s = e.dtype;
  if (e.shape.length < 1)
    throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);
  if (e.dtype !== n)
    throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);
  let r = e.shape.slice(1);
  qn(r, t, "TensorList shape mismatch: ");
  let a = Fs(e);
  return new ro(a, t, s);
}
function b4(e, t, n) {
  return new ro([], e, t, n);
}
function y4(e, t, n, s) {
  if (t.length !== e.shape[0])
    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);
  let r = Math.max(...t);
  if (s != null && s !== -1 && r >= s)
    throw new Error(`Max index must be < array size (${r}  vs. ${s})`);
  let a = new ro([], n, e.dtype, s), i = Fs(e, 0);
  return t.forEach((o, u) => {
    a.setItem(o, i[u]);
  }), a;
}
function v4(e, t, n) {
  let s = 0, r = t.map((c) => (s += c, s));
  if (s !== e.shape[0])
    throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${s}, and tensor's shape is: ${e.shape}`);
  let a = e.shape.slice(1), i = Vm(a, n), o = s === 0 ? 0 : e.size / s, u = q(() => {
    let c = [];
    e = U(e, [1, s, o]);
    for (let p = 0; p < t.length; ++p) {
      let d = p === 0 ? 0 : r[p - 1], h = [0, d, 0], f = [1, t[p], o];
      c[p] = U(qe(e, h, f), i);
    }
    return e.dispose(), c;
  }), l = new ro([], n, e.dtype, t.length);
  for (let c = 0; c < u.length; c++)
    l.setItem(c, u[c]);
  return l;
}
var x4 = async (e, t, n) => {
  switch (e.op) {
    case "If":
    case "StatelessIf": {
      let s = S("thenBranch", e, t, n), r = S("elseBranch", e, t, n), a = S("cond", e, t, n), i = S("args", e, t, n);
      return (await a.data())[0] ? n.functionMap[s].executeFunctionAsync(i, n.tensorArrayMap, n.tensorListMap) : n.functionMap[r].executeFunctionAsync(i, n.tensorArrayMap, n.tensorListMap);
    }
    case "While":
    case "StatelessWhile": {
      let s = S("body", e, t, n), r = S("cond", e, t, n), a = S("args", e, t, n), i = await n.functionMap[r].executeFunctionAsync(a, n.tensorArrayMap, n.tensorListMap), o = a.map((c) => c.id), u = await i[0].data();
      i.forEach((c) => {
        !c.kept && o.indexOf(c.id) === -1 && c.dispose();
      });
      let l = a;
      for (; u[0]; ) {
        let c = l;
        l = await n.functionMap[s].executeFunctionAsync(l, n.tensorArrayMap, n.tensorListMap);
        let p = l.map((h) => h.id);
        c.forEach((h) => {
          !h.kept && o.indexOf(h.id) === -1 && p.indexOf(h.id) === -1 && h.dispose();
        });
        let d = await n.functionMap[r].executeFunctionAsync(l, n.tensorArrayMap, n.tensorListMap);
        u = await d[0].data(), d.forEach((h) => {
          !h.kept && o.indexOf(h.id) === -1 && p.indexOf(h.id) === -1 && h.dispose();
        });
      }
      return l;
    }
    case "LoopCond": {
      let s = S("pred", e, t, n);
      return [Ws(s)];
    }
    case "Switch": {
      let s = S("pred", e, t, n), r = S("data", e, t, n);
      return r.kept || (r = Ws(r)), (await s.data())[0] ? [void 0, r] : [r, void 0];
    }
    case "Merge": {
      let s = e.inputNames.find((r) => un(r, t, n) !== void 0);
      if (s) {
        let r = un(s, t, n);
        return [Ws(r)];
      }
      return;
    }
    case "Enter": {
      let s = S("frameName", e, t, n), r = S("tensor", e, t, n);
      return n.enterFrame(s), [Ws(r)];
    }
    case "Exit": {
      let s = S("tensor", e, t, n);
      return n.exitFrame(), [Ws(s)];
    }
    case "NextIteration": {
      let s = S("tensor", e, t, n);
      return n.nextIteration(), [Ws(s)];
    }
    case "TensorArrayV3": {
      let s = S("size", e, t, n), r = S("dtype", e, t, n), a = S("elementShape", e, t, n), i = S("dynamicSize", e, t, n), o = S("clearAfterRead", e, t, n), u = S("identicalElementShapes", e, t, n), l = S("name", e, t, n), c = new m4(l, r, s, a, u, i, o);
      return n.addTensorArray(c), [c.idTensor, we(1)];
    }
    case "TensorArrayWriteV3": {
      let s = S("tensorArrayId", e, t, n), r = S("index", e, t, n), a = S("tensor", e, t, n), i = n.getTensorArray(s.id);
      return i.write(r, a), [i.idTensor];
    }
    case "TensorArrayReadV3": {
      let s = S("tensorArrayId", e, t, n), r = S("index", e, t, n);
      return [n.getTensorArray(s.id).read(r)];
    }
    case "TensorArrayGatherV3": {
      let s = S("tensorArrayId", e, t, n), r = S("indices", e, t, n), a = S("dtype", e, t, n);
      return [n.getTensorArray(s.id).gather(r, a)];
    }
    case "TensorArrayScatterV3": {
      let s = S("tensorArrayId", e, t, n), r = S("indices", e, t, n), a = S("tensor", e, t, n), i = n.getTensorArray(s.id);
      return i.scatter(r, a), [i.idTensor];
    }
    case "TensorArrayConcatV3": {
      let s = S("tensorArrayId", e, t, n), r = n.getTensorArray(s.id), a = S("dtype", e, t, n);
      return [r.concat(a)];
    }
    case "TensorArraySplitV3": {
      let s = S("tensorArrayId", e, t, n), r = S("tensor", e, t, n), a = S("lengths", e, t, n), i = n.getTensorArray(s.id);
      return i.split(a, r), [i.idTensor];
    }
    case "TensorArraySizeV3": {
      let s = S("tensorArrayId", e, t, n), r = n.getTensorArray(s.id);
      return [we(r.size(), "int32")];
    }
    case "TensorArrayCloseV3": {
      let s = S("tensorArrayId", e, t, n), r = n.getTensorArray(s.id);
      return r.clearAndClose(), [r.idTensor];
    }
    case "TensorListSetItem": {
      let s = S("tensorListId", e, t, n), r = S("index", e, t, n), a = S("tensor", e, t, n), i = n.getTensorList(s.id);
      return i.setItem(r, a), [i.idTensor];
    }
    case "TensorListGetItem": {
      let s = S("tensorListId", e, t, n), r = S("index", e, t, n), a = S("elementShape", e, t, n), i = S("elementDType", e, t, n);
      return [n.getTensorList(s.id).getItem(r, a, i)];
    }
    case "TensorListScatterV2":
    case "TensorListScatter": {
      let s = S("indices", e, t, n), r = S("tensor", e, t, n), a = S("elementShape", e, t, n), i = S("numElements", e, t, n), o = y4(r, s, a, i);
      return n.addTensorList(o), [o.idTensor];
    }
    case "TensorListReserve":
    case "EmptyTensorList": {
      let s = S("elementShape", e, t, n), r = S("elementDType", e, t, n), a;
      e.op === "TensorListReserve" ? a = "numElements" : a = "maxNumElements";
      let i = S(a, e, t, n), o = b4(s, r, i);
      return n.addTensorList(o), [o.idTensor];
    }
    case "TensorListGather": {
      let s = S("tensorListId", e, t, n), r = S("indices", e, t, n), a = S("elementShape", e, t, n), i = S("elementDType", e, t, n);
      return [n.getTensorList(s.id).gather(r, i, a)];
    }
    case "TensorListStack": {
      let s = S("tensorListId", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n), i = S("numElements", e, t, n);
      return [n.getTensorList(s.id).stack(r, a, i)];
    }
    case "TensorListFromTensor": {
      let s = S("tensor", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n), i = g4(s, r, a);
      return n.addTensorList(i), [i.idTensor];
    }
    case "TensorListConcat":
    case "TensorListConcatV2": {
      let s = S("tensorListId", e, t, n), r = n.getTensorList(s.id), a = S("dtype", e, t, n), i = S("elementShape", e, t, n);
      return [r.concat(a, i)];
    }
    case "TensorListPushBack": {
      let s = S("tensorListId", e, t, n), r = S("tensor", e, t, n), a = n.getTensorList(s.id);
      return a.pushBack(r), [a.idTensor];
    }
    case "TensorListPopBack": {
      let s = S("tensorListId", e, t, n), r = S("elementShape", e, t, n), a = S("elementDType", e, t, n);
      return [n.getTensorList(s.id).popBack(r, a)];
    }
    case "TensorListSplit": {
      let s = S("tensor", e, t, n), r = S("elementShape", e, t, n), a = S("lengths", e, t, n), i = v4(s, a, r);
      return n.addTensorList(i), [i.idTensor];
    }
    case "TensorListLength": {
      let s = S("tensorListId", e, t, n), r = n.getTensorList(s.id);
      return [we(r.size(), "int32")];
    }
    case "TensorListResize": {
      let s = S("tensorListId", e, t, n), r = S("size", e, t, n), i = n.getTensorList(s.id).resize(r);
      return n.addTensorList(i), [i.idTensor];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function Jx(e, t, n) {
  let [s, r] = S("fusedOps", e, t, n), a = s === "biasadd", i = !a, o = r === "prelu", u = s === "fusedbatchnorm", l = S("numArgs", e, t, n);
  if (a) {
    if (o && l !== 2)
      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
    if (!o && a && l !== 1)
      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
  }
  if (u)
    throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
  let c = S("strides", e, t, n), p = ad(e, t, n), d = S("dataFormat", e, t, n).toUpperCase(), h = S("dilations", e, t, n), [f, m] = S("args", e, t, n);
  i && (m = f, f = void 0);
  let g = S("leakyreluAlpha", e, t, n);
  return { stride: c, pad: p, dataFormat: d, dilations: h, biasArg: f, preluArg: m, activationFunc: r, leakyreluAlpha: g };
}
var w4 = (e, t, n) => {
  switch (e.op) {
    case "Conv1D": {
      let s = S("stride", e, t, n), r = S("pad", e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilation", e, t, n);
      return [iS(S("x", e, t, n), S("filter", e, t, n), s, r, a, i)];
    }
    case "Conv2D": {
      let s = S("strides", e, t, n), r = ad(e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilations", e, t, n);
      return [da(S("x", e, t, n), S("filter", e, t, n), [s[1], s[2]], r, a, [i[1], i[2]])];
    }
    case "_FusedConv2D": {
      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = Jx(e, t, n);
      return [fa.conv2d({ x: S("x", e, t, n), filter: S("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
    }
    case "FusedDepthwiseConv2dNative": {
      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = Jx(e, t, n);
      return [fa.depthwiseConv2d({ x: S("x", e, t, n), filter: S("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
    }
    case "Conv2DBackpropInput":
    case "Conv2dTranspose": {
      let s = S("outputShape", e, t, n), r = S("strides", e, t, n), a = ad(e, t, n);
      return [oS(S("x", e, t, n), S("filter", e, t, n), s, [r[1], r[2]], a)];
    }
    case "DepthwiseConv2dNative":
    case "DepthwiseConv2d": {
      let s = S("strides", e, t, n), r = ad(e, t, n), a = S("dilations", e, t, n), i = S("dataFormat", e, t, n).toUpperCase();
      return [yp(S("input", e, t, n), S("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
    }
    case "Conv3D": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilations", e, t, n);
      return [uS(S("x", e, t, n), S("filter", e, t, n), [s[1], s[2], s[3]], r, a, [i[1], i[2], i[3]])];
    }
    case "AvgPool": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
      return [Xg(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
    }
    case "MaxPool": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
      return [ab(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
    }
    case "MaxPoolWithArgmax": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n), i = S("includeBatchInIndex", e, t, n), { result: o, indexes: u } = ID(S("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, i);
      return [o, u];
    }
    case "AvgPool3D": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
      return [rS(S("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
    }
    case "MaxPool3D": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("kernelSize", e, t, n);
      return [xS(S("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
    }
    case "Dilation2D": {
      let s = S("strides", e, t, n), r = S("pad", e, t, n), a = S("dilations", e, t, n), i = s[1], o = s[2], u = a[1], l = a[2];
      return [kR(S("x", e, t, n), S("filter", e, t, n), [i, o], r, [u, l], "NHWC")];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var k4 = (e, t, n) => {
  switch (e.op) {
    case "Fill": {
      let s = S("shape", e, t, n), r = S("dtype", e, t, n), a = S("value", e, t, n);
      return [zl(s, a, r)];
    }
    case "LinSpace": {
      let s = S("start", e, t, n), r = S("stop", e, t, n), a = S("num", e, t, n);
      return [ZR(s, r, a)];
    }
    case "Multinomial": {
      let s = S("logits", e, t, n), r = S("numSamples", e, t, n), a = S("seed", e, t, n);
      return [zD(s, r, a)];
    }
    case "OneHot": {
      let s = S("indices", e, t, n), r = S("depth", e, t, n), a = S("onValue", e, t, n), i = S("offValue", e, t, n);
      return [kd(s, r, a, i)];
    }
    case "Ones":
      return [Mn(S("shape", e, t, n), S("dtype", e, t, n))];
    case "OnesLike":
      return [Zn(S("x", e, t, n))];
    case "RandomUniform":
      return [Ll(S("shape", e, t, n), S("minval", e, t, n), S("maxval", e, t, n), S("dtype", e, t, n))];
    case "Range": {
      let s = S("start", e, t, n), r = S("stop", e, t, n), a = S("step", e, t, n);
      return [Zu(s, r, a, S("dtype", e, t, n))];
    }
    case "TruncatedNormal": {
      let s = S("shape", e, t, n), r = S("mean", e, t, n), a = S("stdDev", e, t, n), i = S("seed", e, t, n);
      return [gb(s, r, a, S("dtype", e, t, n), i)];
    }
    case "Zeros":
      return [$t(S("shape", e, t, n), S("dtype", e, t, n))];
    case "ZerosLike":
      return [je(S("x", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function Xf(e, t, n) {
  let s = S("boxes", e, t, n), r = S("scores", e, t, n), a = S("maxOutputSize", e, t, n), i = S("iouThreshold", e, t, n), o = S("scoreThreshold", e, t, n), u = S("softNmsSigma", e, t, n);
  return { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u };
}
var S4 = async (e, t, n) => {
  switch (e.op) {
    case "NonMaxSuppressionV5": {
      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Xf(e, t, n), l = await jn.nonMaxSuppressionWithScoreAsync(s, r, a, i, o, u);
      return [l.selectedIndices, l.selectedScores];
    }
    case "NonMaxSuppressionV4": {
      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Xf(e, t, n), u = S("padToMaxOutputSize", e, t, n), l = await jn.nonMaxSuppressionPaddedAsync(s, r, a, i, o, u);
      return [l.selectedIndices, l.validOutputs];
    }
    case "NonMaxSuppressionV3":
    case "NonMaxSuppressionV2": {
      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Xf(e, t, n);
      return [await jn.nonMaxSuppressionAsync(s, r, a, i, o)];
    }
    case "Where": {
      let s = le(S("condition", e, t, n), "bool"), r = [await RS(s)];
      return s.dispose(), r;
    }
    case "ListDiff":
      return k3(S("x", e, t, n), S("y", e, t, n));
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var I4 = (e, t, n) => {
  switch (e.op) {
    case "LowerBound": {
      let s = S("sortedSequence", e, t, n), r = S("values", e, t, n);
      return [xD(s, r)];
    }
    case "TopKV2": {
      let s = S("x", e, t, n), r = S("k", e, t, n), a = S("sorted", e, t, n), i = j3(s, r, a);
      return [i.values, i.indices];
    }
    case "UpperBound": {
      let s = S("sortedSequence", e, t, n), r = S("values", e, t, n);
      return [J3(s, r)];
    }
    case "Unique": {
      let s = S("x", e, t, n), r = yx(s);
      return [r.values, r.indices];
    }
    case "UniqueV2": {
      let s = S("x", e, t, n), r = S("axis", e, t, n), a = yx(s, r);
      return [a.values, a.indices];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var C4 = (e, t, n) => {
  switch (e.op) {
    case "Const":
      return t[e.name];
    case "PlaceholderWithDefault":
      let s = S("default", e, t, n);
      return [un(e.name, t, n) || s];
    case "Placeholder":
      return [un(e.name, t, n)];
    case "Identity":
    case "StopGradient":
    case "FakeQuantWithMinMaxVars": {
      let l = S("x", e, t, n);
      return [Ws(l)];
    }
    case "IdentityN":
      return S("x", e, t, n).map((l) => Ws(l));
    case "Snapshot":
      let r = S("x", e, t, n);
      return [Ws(r)];
    case "Shape":
      return [Zt(S("x", e, t, n).shape, "int32")];
    case "ShapeN":
      return S("x", e, t, n).map((l) => Zt(l.shape));
    case "Size":
      return [we(S("x", e, t, n).size, "int32")];
    case "Rank":
      return [we(S("x", e, t, n).rank, "int32")];
    case "NoOp":
      return [we(1)];
    case "Print":
      let a = S("x", e, t, n), i = S("data", e, t, n), o = S("message", e, t, n), u = S("summarize", e, t, n);
      console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."), console.log(o);
      for (let l = 0; l < i.length; l++)
        console.log(Array.prototype.slice.call(i[l].dataSync()).slice(0, u));
      return [a];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var N4 = class {
  constructor(e, t) {
    this.keyDType = e, this.valueDType = t, this.handle = we(0), this.tensorMap = /* @__PURE__ */ new Map(), qt(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 we(this.size(), "int32");
  }
  async import(e, t) {
    this.checkKeyAndValueTensor(e, t);
    let n = await e.data();
    return this.tensorMap.forEach((s) => s.dispose()), this.tensorMap.clear(), q(() => {
      let s = Fs(t), r = n.length, a = s.length;
      w.assert(r === a, () => `The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);
      for (let i = 0; i < r; i++) {
        let o = n[i], u = s[i];
        qt(u), this.tensorMap.set(o, u);
      }
      return this.handle;
    });
  }
  async find(e, t) {
    this.checkKeyAndValueTensor(e, t);
    let n = await e.data();
    return q(() => {
      let s = [];
      for (let r = 0; r < n.length; r++) {
        let a = n[r], i = this.findWithDefault(a, t);
        s.push(i);
      }
      return es(s);
    });
  }
  findWithDefault(e, t) {
    let n = this.tensorMap.get(e);
    return n != null ? n : t;
  }
  checkKeyAndValueTensor(e, t) {
    if (e.dtype !== this.keyDType)
      throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);
    if (t.dtype !== this.valueDType)
      throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`);
  }
};
var T4 = async (e, t, n, s) => {
  switch (e.op) {
    case "HashTable":
    case "HashTableV2": {
      let r = S("keyDType", e, t, n), a = S("valueDType", e, t, n), i = new N4(r, a);
      return s.addHashTable(e.name, i), [i.handle];
    }
    case "LookupTableImport":
    case "LookupTableImportV2": {
      let r = S("tableHandle", e, t, n, s), a = S("keys", e, t, n), i = S("values", e, t, n);
      return [await s.getHashTableById(r.id).import(a, i)];
    }
    case "LookupTableFind":
    case "LookupTableFindV2": {
      let r = S("tableHandle", e, t, n, s), a = S("keys", e, t, n), i = S("defaultValue", e, t, n);
      return [await s.getHashTableById(r.id).find(a, i)];
    }
    case "LookupTableSize":
    case "LookupTableSizeV2": {
      let r = S("tableHandle", e, t, n, s);
      return [s.getHashTableById(r.id).tensorSize()];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var $4 = (e, t, n) => {
  switch (e.op) {
    case "ResizeBilinear": {
      let s = S("images", e, t, n), r = S("size", e, t, n), a = S("alignCorners", e, t, n), i = S("halfPixelCenters", e, t, n);
      return [jn.resizeBilinear(s, [r[0], r[1]], a, i)];
    }
    case "ResizeNearestNeighbor": {
      let s = S("images", e, t, n), r = S("size", e, t, n), a = S("alignCorners", e, t, n), i = S("halfPixelCenters", e, t, n);
      return [jn.resizeNearestNeighbor(s, [r[0], r[1]], a, i)];
    }
    case "CropAndResize": {
      let s = S("image", e, t, n), r = S("boxes", e, t, n), a = S("boxInd", e, t, n), i = S("cropSize", e, t, n), o = S("method", e, t, n), u = S("extrapolationValue", e, t, n);
      return [jn.cropAndResize(s, r, a, i, o, u)];
    }
    case "ImageProjectiveTransformV3": {
      let s = S("images", e, t, n), r = S("transforms", e, t, n), a = S("outputShape", e, t, n), i = S("fillValue", e, t, n), o = S("interpolation", e, t, n), u = S("fillMode", e, t, n);
      return [jn.transform(s, r, o.toLowerCase(), u.toLowerCase(), i, a)];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var _4 = (e, t, n) => {
  switch (e.op) {
    case "Equal":
      return [Xn(S("a", e, t, n), S("b", e, t, n))];
    case "NotEqual":
      return [Qu(S("a", e, t, n), S("b", e, t, n))];
    case "Greater":
      return [Un(S("a", e, t, n), S("b", e, t, n))];
    case "GreaterEqual":
      return [Yo(S("a", e, t, n), S("b", e, t, n))];
    case "Less":
      return [hS(S("a", e, t, n), S("b", e, t, n))];
    case "LessEqual":
      return [Qo(S("a", e, t, n), S("b", e, t, n))];
    case "LogicalAnd":
      return [Ds(S("a", e, t, n), S("b", e, t, n))];
    case "LogicalNot":
      return [rb(S("a", e, t, n))];
    case "LogicalOr":
      return [yS(S("a", e, t, n), S("b", e, t, n))];
    case "Select":
    case "SelectV2":
      return [vn(S("condition", e, t, n), S("a", e, t, n), S("b", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var A4 = (e, t, n) => {
  switch (e.op) {
    case "BatchMatMul":
    case "BatchMatMulV2":
    case "MatMul":
      return [Ve(S("a", e, t, n), S("b", e, t, n), S("transposeA", e, t, n), S("transposeB", e, t, n))];
    case "Einsum":
      return [AR(S("equation", e, t, n), ...S("tensors", e, t, n))];
    case "Transpose":
      return [Ge(S("x", e, t, n), S("perm", e, t, n))];
    case "_FusedMatMul":
      let [s, r] = S("fusedOps", e, t, n), a = s === "biasadd", i = r === "prelu", o = S("numArgs", e, t, n), u = S("leakyreluAlpha", e, t, n);
      if (a) {
        if (i && o !== 2)
          throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
        if (!i && o !== 1)
          throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
      }
      let [l, c] = S("args", e, t, n);
      return [fa.matMul({ a: S("a", e, t, n), b: S("b", e, t, n), transposeA: S("transposeA", e, t, n), transposeB: S("transposeB", e, t, n), bias: l, activation: r, preluActivationWeights: c, leakyreluAlpha: u })];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var E4 = (e, t, n) => {
  switch (e.op) {
    case "FusedBatchNorm":
    case "FusedBatchNormV2":
      return [Xu(S("x", e, t, n), S("mean", e, t, n), S("variance", e, t, n), S("offset", e, t, n), S("scale", e, t, n), S("epsilon", e, t, n))];
    case "FusedBatchNormV3":
      return [Xu(S("x", e, t, n), S("mean", e, t, n), S("variance", e, t, n), S("offset", e, t, n), S("scale", e, t, n), S("epsilon", e, t, n))];
    case "LRN":
      return [eD(S("x", e, t, n), S("radius", e, t, n), S("bias", e, t, n), S("alpha", e, t, n), S("beta", e, t, n))];
    case "Softmax":
      return [hb(S("x", e, t, n))];
    case "LogSoftmax":
      return [fS(S("x", e, t, n))];
    case "SparseToDense":
      return [OS(S("sparseIndices", e, t, n), S("outputShape", e, t, n), S("sparseValues", e, t, n), S("defaultValue", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var R4 = (e, t, n) => {
  switch (e.op) {
    case "Max": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [As(S("x", e, t, n), i, o)];
    }
    case "Mean": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [It(S("x", e, t, n), i, o)];
    }
    case "Min": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [vm(S("x", e, t, n), i, o)];
    }
    case "Sum": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [ve(S("x", e, t, n), i, o)];
    }
    case "All": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [eS(S("x", e, t, n), i, o)];
    }
    case "Any": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [gm(S("x", e, t, n), i, o)];
    }
    case "ArgMax": {
      let i = S("axis", e, t, n);
      return [ju(S("x", e, t, n), i)];
    }
    case "ArgMin": {
      let i = S("axis", e, t, n);
      return [uE(S("x", e, t, n), i)];
    }
    case "Prod": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [wS(S("x", e, t, n), i, o)];
    }
    case "Cumprod": {
      let i = S("axis", e, t, n), o = S("exclusive", e, t, n), u = S("reverse", e, t, n);
      return [ym(S("x", e, t, n), i, o, u)];
    }
    case "Cumsum": {
      let i = S("axis", e, t, n), o = S("exclusive", e, t, n), u = S("reverse", e, t, n);
      return [dS(S("x", e, t, n), i, o, u)];
    }
    case "Bincount":
      let s = S("x", e, t, n), r = S("weights", e, t, n), a = S("size", e, t, n);
      return [aS(s, r, a)];
    case "DenseBincount": {
      let i = S("x", e, t, n), o = S("weights", e, t, n), u = S("size", e, t, n), l = S("binaryOutput", e, t, n);
      return [gR(i, o, u, l)];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var D4 = (e, t, n) => {
  switch (e.op) {
    case "ConcatV2":
    case "Concat": {
      let s = S("n", e, t, n), r = S("axis", e, t, n), a = S("tensors", e, t, n);
      return a = a.slice(0, s), [Ft(a, r)];
    }
    case "Gather": {
      let s = S("x", e, t, n), r = S("indices", e, t, n);
      return [Yu(s, le(r, "int32"), 0)];
    }
    case "GatherV2": {
      let s = S("axis", e, t, n), r = S("batchDims", e, t, n), a = S("x", e, t, n), i = S("indices", e, t, n);
      return [Yu(a, le(i, "int32"), s, r)];
    }
    case "Reverse": {
      let s = S("dims", e, t, n), r = [];
      for (let i = 0; i < s.length; i++)
        s[i] && r.push(i);
      let a = S("x", e, t, n);
      return [Jn(a, r)];
    }
    case "ReverseV2": {
      let s = S("axis", e, t, n), r = S("x", e, t, n);
      return [Jn(r, s)];
    }
    case "Slice": {
      let s = S("begin", e, t, n), r = S("size", e, t, n);
      return [qe(S("x", e, t, n), s, r)];
    }
    case "StridedSlice": {
      let s = S("begin", e, t, n), r = S("end", e, t, n), a = S("strides", e, t, n), i = S("beginMask", e, t, n), o = S("endMask", e, t, n), u = S("ellipsisMask", e, t, n), l = S("newAxisMask", e, t, n), c = S("shrinkAxisMask", e, t, n), p = S("x", e, t, n);
      return [U3(p, s, r, a, i, o, u, l, c)];
    }
    case "Pack":
      return q(() => {
        let s = S("axis", e, t, n), r = S("tensors", e, t, n), a = r[0].shape, i = mr(r[0]).shape, o = r.map((u) => {
          let l = w.arraysEqual(u.shape, a);
          if (!l && !w.arraysEqual(mr(u).shape, i))
            throw new Error("the input tensors shape does not match");
          return l ? u : U(u, a);
        });
        return [es(o, s)];
      });
    case "Unpack": {
      let s = S("axis", e, t, n), r = S("tensor", e, t, n);
      return Fs(r, s);
    }
    case "Tile": {
      let s = S("reps", e, t, n);
      return [hs(S("x", e, t, n), s)];
    }
    case "Split":
    case "SplitV": {
      let s = S("axis", e, t, n), r = S("numOrSizeSplits", e, t, n), a = S("x", e, t, n);
      return Bn(a, r, s);
    }
    case "ScatterNd": {
      let s = S("indices", e, t, n), r = S("values", e, t, n), a = S("shape", e, t, n);
      return [iF(s, r, a)];
    }
    case "GatherNd": {
      let s = S("x", e, t, n), r = S("indices", e, t, n);
      return [cF(s, r)];
    }
    case "SparseToDense": {
      let s = S("sparseIndices", e, t, n), r = S("outputShape", e, t, n), a = S("sparseValues", e, t, n), i = S("defaultValue", e, t, n);
      return [OS(s, a, r, a.dtype === i.dtype ? i : le(i, a.dtype))];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var F4 = (e, t, n) => {
  switch (e.op) {
    case "SparseFillEmptyRows": {
      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = Gc.sparseFillEmptyRows(S("indices", e, t, n), S("values", e, t, n), S("denseShape", e, t, n), S("defaultValue", e, t, n));
      return [s, r, a, i];
    }
    case "SparseReshape": {
      let { outputIndices: s, outputShape: r } = Gc.sparseReshape(S("inputIndices", e, t, n), S("inputShape", e, t, n), S("newShape", e, t, n));
      return [s, r];
    }
    case "SparseSegmentMean":
      return [Gc.sparseSegmentMean(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
    case "SparseSegmentSum":
      return [Gc.sparseSegmentSum(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var O4 = (e, t, n) => {
  switch (e.op) {
    case "FFT":
      return [fb(S("x", e, t, n))];
    case "IFFT":
      return [Nd(S("x", e, t, n))];
    case "RFFT":
      return [mb(S("x", e, t, n))];
    case "IRFFT":
      return [_S(S("x", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var P4 = (e, t, n) => {
  switch (e.op) {
    case "StringNGrams": {
      let { nGrams: s, nGramsSplits: r } = Uf.stringNGrams(S("data", e, t, n), S("dataSplits", e, t, n), S("separator", e, t, n), S("nGramWidths", e, t, n), S("leftPad", e, t, n), S("rightPad", e, t, n), S("padWidth", e, t, n), S("preserveShortSequences", e, t, n));
      return [s, r];
    }
    case "StringSplit": {
      let { indices: s, values: r, shape: a } = Uf.stringSplit(S("input", e, t, n), S("delimiter", e, t, n), S("skipEmpty", e, t, n));
      return [s, r, a];
    }
    case "StringToHashBucketFast":
      return [Uf.stringToHashBucketFast(S("input", e, t, n), S("numBuckets", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var z4 = (e, t, n) => {
  switch (e.op) {
    case "Cast":
      return [le(S("x", e, t, n), S("dtype", e, t, n))];
    case "ExpandDims": {
      let s = S("axis", e, t, n);
      return [Pn(S("x", e, t, n), s)];
    }
    case "Squeeze": {
      let s = S("axis", e, t, n);
      return [mr(S("x", e, t, n), s)];
    }
    case "Reshape":
      return [U(S("x", e, t, n), S("shape", e, t, n))];
    case "MirrorPad":
      return [AD(S("x", e, t, n), S("padding", e, t, n), S("mode", e, t, n))];
    case "PadV2":
    case "Pad":
      return [yi(S("x", e, t, n), S("padding", e, t, n), S("constantValue", e, t, n))];
    case "SpaceToBatchND": {
      let s = S("blockShape", e, t, n), r = S("paddings", e, t, n);
      return [ob(S("x", e, t, n), s, r)];
    }
    case "BatchToSpaceND": {
      let s = S("blockShape", e, t, n), r = S("crops", e, t, n);
      return [Yg(S("x", e, t, n), s, r)];
    }
    case "DepthToSpace": {
      let s = S("blockSize", e, t, n), r = S("dataFormat", e, t, n).toUpperCase();
      return [yR(S("x", e, t, n), s, r)];
    }
    case "BroadcastTo":
      return [rd(S("x", e, t, n), S("shape", e, t, n))];
    case "BroadcastArgs":
      return [GE(S("s0", e, t, n), S("s1", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function ew(e, t, n, s) {
  let r = ((a, i, o) => {
    switch (a.category) {
      case "arithmetic":
        return q(() => h4(a, i, o));
      case "basic_math":
        return q(() => f4(a, i, o));
      case "control":
        return x4(a, i, o);
      case "convolution":
        return q(() => w4(a, i, o));
      case "creation":
        return q(() => k4(a, i, o));
      case "dynamic":
        return S4(a, i, o);
      case "evaluation":
        return q(() => I4(a, i, o));
      case "image":
        return q(() => $4(a, i, o));
      case "graph":
        return q(() => C4(a, i, o));
      case "logical":
        return q(() => _4(a, i, o));
      case "matrices":
        return q(() => A4(a, i, o));
      case "normalization":
        return q(() => E4(a, i, o));
      case "reduction":
        return q(() => R4(a, i, o));
      case "slice_join":
        return q(() => D4(a, i, o));
      case "sparse":
        return q(() => F4(a, i, o));
      case "spectral":
        return q(() => O4(a, i, o));
      case "string":
        return q(() => P4(a, i, o));
      case "transformation":
        return q(() => z4(a, i, o));
      case "hash_table":
        return T4(a, i, o, s);
      case "custom":
        let u = a0(a.op);
        if (u && u.customExecutor)
          return u.customExecutor(new p4(a, i, o));
        throw TypeError(`Custom op ${a.op} is not registered.`);
      default:
        throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
    }
  })(e, t, n);
  return w.isPromise(r) ? r.then((a) => [].concat(a)) : [].concat(r);
}
var tw = class {
  constructor(e = {}, t = {}, n = {}, s = {}) {
    this.weightMap = e, this.tensorArrayMap = t, this.tensorListMap = n, this.functionMap = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds();
  }
  newFrame(e, t) {
    return { id: e, frameName: t, iterationId: 0 };
  }
  set currentContext(e) {
    this.contexts !== e && (this.contexts = e, this.generateCurrentContextIds());
  }
  get currentContext() {
    return this.contexts;
  }
  get currentContextId() {
    return this._currentContextIds[0];
  }
  get currentContextIds() {
    return this._currentContextIds;
  }
  generateCurrentContextIds() {
    let e = [];
    for (let t = 0; t < this.contexts.length - 1; t++) {
      let n = this.contexts.slice(0, this.contexts.length - t);
      e.push(this.contextIdforContexts(n));
    }
    e.push(""), this._currentContextIds = e;
  }
  contextIdforContexts(e) {
    return e ? e.map((t) => t.id === 0 && t.iterationId === 0 ? "" : `${t.frameName}-${t.iterationId}`).join("/") : "";
  }
  enterFrame(e) {
    this.contexts && (this.lastId++, this.contexts = this.contexts.slice(), this.contexts.push(this.newFrame(this.lastId, e)), this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)));
  }
  exitFrame() {
    if (this.contexts && this.contexts.length > 1)
      this.contexts = this.contexts.slice(), this.contexts.splice(-1), this.currentContextIds.shift();
    else
      throw new Error("Cannot exit frame, the context is empty");
  }
  nextIteration() {
    if (this.contexts && this.contexts.length > 0) {
      this.contexts = this.contexts.slice(), this.lastId++;
      let e = Object.assign({}, this.contexts[this.contexts.length - 1]);
      e.iterationId += 1, e.id = this.lastId, this.contexts.splice(-1, 1, e), this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
    } else
      throw new Error("Cannot increase frame iteration, the context is empty");
  }
  getWeight(e) {
    return this.weightMap[e];
  }
  addTensorArray(e) {
    this.tensorArrayMap[e.id] = e;
  }
  getTensorArray(e) {
    return this.tensorArrayMap[e];
  }
  addTensorList(e) {
    this.tensorListMap[e.id] = e;
  }
  getTensorList(e) {
    return this.tensorListMap[e];
  }
  dispose(e) {
    for (let t in this.tensorArrayMap)
      this.tensorArrayMap[t].clearAndClose(e);
    for (let t in this.tensorListMap)
      this.tensorListMap[t].clearAndClose(e);
  }
};
function nw(e, t, n, s) {
  let r = /* @__PURE__ */ new Set(), a = [], i = null, o = null, u = /* @__PURE__ */ new Set(), l = Object.keys(e).map((d) => _n(d)[0]), c = [];
  s != null && (c = s.map((d) => _n(d.name)[0]));
  let p = [...t];
  for (; p.length > 0; ) {
    let d = p.pop();
    if ((T0(d) || W4(d) || U4(d)) && i == null && (i = d, o = i.children.map((h) => h.name).filter((h) => r.has(h))), r.add(d.name), n[d.name] == null && l.indexOf(d.name) === -1 && c.indexOf(d.name) === -1) {
      if (d.inputs.length === 0) {
        a.push(d.name);
        continue;
      }
      d.inputs.forEach((h) => {
        u.has(h.name) || (u.add(h.name), p.push(h));
      });
    }
  }
  return { inputs: e, outputs: t, usedNodes: r, missingInputs: a, dynamicNode: i, syncInputs: o };
}
function M4(e, t, n) {
  let { usedNodes: s, inputs: r } = n, a = [], i = Object.keys(r).map((c) => _n(c)[0]).map((c) => e.nodes[c]), o = e.initNodes;
  i.forEach((c) => {
    s.has(c.name) && a.push(c);
  }), e.weights.forEach((c) => {
    s.has(c.name) && a.push(c);
  }), o != null && o.forEach((c) => {
    s.has(c.name) && a.push(c);
  });
  let u = /* @__PURE__ */ new Set(), l = [];
  for (; a.length > 0; ) {
    let c = a.pop();
    u.add(c.name), t[c.name] || l.push(c), c.children.forEach((p) => {
      !u.has(p.name) && s.has(p.name) && p.inputs.every((d) => u.has(d.name)) && a.push(p);
    });
  }
  return l;
}
var L4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
var B4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
var V4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
function T0(e) {
  return L4.indexOf(e.op) >= 0;
}
function W4(e) {
  return B4.indexOf(e.op) >= 0;
}
function U4(e) {
  return V4.indexOf(e.op) >= 0;
}
var Wm = class {
  constructor(e, t) {
    this.graph = e, this.parent = t, 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((n) => {
      this._functionExecutorMap[n] = new Wm(e.functions[n], this);
    });
  }
  get weightIds() {
    return this.parent ? this.parent.weightIds : this._weightIds;
  }
  get functionExecutorMap() {
    return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;
  }
  get weightMap() {
    return this.parent ? this.parent.weightMap : this._weightMap;
  }
  set weightMap(e) {
    let t = Object.keys(e).map((n) => e[n].map((s) => s.id));
    this._weightIds = [].concat(...t), this._weightMap = e;
  }
  set resourceManager(e) {
    this._resourceManager = e;
  }
  get inputs() {
    return this._inputs.map((e) => ({ name: e.name, shape: e.attrParams.shape ? e.attrParams.shape.value : void 0, dtype: e.attrParams.dtype ? e.attrParams.dtype.value : void 0 }));
  }
  get outputs() {
    return this._outputs.map((e) => ({ name: e.name, shape: e.attrParams.shape ? e.attrParams.shape.value : void 0, dtype: e.attrParams.dtype ? e.attrParams.dtype.value : void 0 }));
  }
  get inputNodes() {
    return this._inputs.map((e) => e.signatureKey || e.name);
  }
  get outputNodes() {
    return this._outputs.map((e) => {
      let t = e.signatureKey || e.name;
      return e.defaultOutput ? `${t}:${e.defaultOutput}` : t;
    });
  }
  get functions() {
    return Object.keys(this._functions).reduce((e, t) => (e[t] = this._functions[t].signature, e), {});
  }
  getCompilationKey(e, t) {
    let n = e.map((r) => r.name).sort(), s = t.map((r) => r.name).sort();
    return n.join(this.SEPERATOR) + "--" + s.join(this.SEPERATOR);
  }
  compile(e, t) {
    let n = nw(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
    if (r != null)
      throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);
    if (s.length > 0) {
      let i = t.map((u) => u.name), o = Object.keys(e);
      throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`);
    }
    return M4(this.graph, this.weightMap, n);
  }
  execute(e, t) {
    e = this.mapInputs(e);
    let n = Object.keys(e).sort();
    this.checkInputs(e), this.checkInputShapeAndType(e), t = this.mapOutputs(t), this.checkOutputs(t);
    let s = n.map((c) => this.graph.nodes[_n(c)[0]]), r = t.map((c) => _n(c)[0]), a = r.map((c) => this.graph.nodes[c]);
    this.resetIntermediateTensors(), a.length === 0 && (a = this._outputs);
    let i = this.getCompilationKey(s, a), o = this.compiledMap.get(i);
    o == null && (o = this.compile(e, a), this.compiledMap.set(i, o));
    let u = {}, l = {};
    return q(() => {
      let c = new tw(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
      Object.keys(e).forEach((f) => {
        let [m, g] = _n(f), b = [];
        b[g] = e[f], p[m] = b;
      });
      let d = this.getFrozenTensorIds(p), h = {};
      for (let f = 0; f < o.length; f++) {
        let m = o[f];
        if (!p[m.name]) {
          let g = ew(m, p, c, this._resourceManager);
          if (w.isPromise(g))
            throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);
          p[m.name] = g, this.checkTensorForDisposal(m.name, m, p, c, d, r, h);
        }
      }
      return this.parent == null && c.dispose(d), t.map((f) => un(f, p, c));
    });
  }
  getFrozenTensorIds(e) {
    let t = [].concat.apply([], Object.keys(e).map((n) => e[n]).map((n) => n.map((s) => s.id)));
    return new Set(t);
  }
  checkTensorForDisposal(e, t, n, s, r, a, i) {
    t.category === "control" || a.indexOf(e) !== -1 || (n[e].forEach((o) => {
      o != null && (i[o.id] = (i[o.id] || 0) + t.children.length);
    }), t.inputs.forEach((o) => {
      if (o.category !== "control") {
        let u = HW(o.name, n, s);
        u != null && u.forEach((l) => {
          if (l && !l.kept && !r.has(l.id)) {
            let c = i[l.id];
            if (c === 1) {
              if (!this.keepTensorForDebug)
                l.dispose();
              else {
                let [p, d] = Ts(t.name, s);
                this.intermediateTensors[p] ? this.intermediateTensors[p][d] = l : (this.intermediateTensors[p] = [], this.intermediateTensors[p][d] = l);
              }
              delete i[l.id];
            } else
              c != null && i[l.id]--;
          }
        });
      }
    }));
  }
  async executeAsync(e, t) {
    return this._executeAsync(e, t);
  }
  disposeIntermediateTensors() {
    !this.intermediateTensors || (Object.keys(this.intermediateTensors).forEach((e) => this.intermediateTensors[e].forEach((t) => t.dispose())), this.disposeTensorsMap());
  }
  disposeTensorsMap() {
    !this.tensorsMap || Object.keys(this.tensorsMap).forEach((e) => {
      this.tensorsMap[e].forEach((n) => {
        n && !n.kept && !n.isDisposed && !this.keepIds.has(n.id) && n.dispose();
      });
    });
  }
  getIntermediateTensors() {
    return this.tensorsMap;
  }
  resetIntermediateTensors() {
    for (let e in this.intermediateTensors)
      this.intermediateTensors[e].forEach((t) => t.dispose()), delete this.intermediateTensors[e];
  }
  async _executeAsync(e, t, n = false, s = {}, r = {}) {
    n || (e = this.mapInputs(e), this.checkInputs(e), this.checkInputShapeAndType(e), t = this.mapOutputs(t), this.checkOutputs(t));
    try {
      this.keepTensorForDebug = K().getBool("KEEP_INTERMEDIATE_TENSORS");
    } catch (l) {
      console.warn(l.message);
    }
    this.resetIntermediateTensors();
    let a = new tw(this.weightMap, s, r, this.functionExecutorMap);
    this.tensorsMap = await this.executeWithControlFlow(e, a, t, n);
    let i = t.map((l) => un(l, this.tensorsMap, a)), o = i.map((l) => l.id), u = Object.keys(e).map((l) => e[l].id);
    return this.keepIds = /* @__PURE__ */ new Set([...o, ...u, ...this.weightIds]), this.keepTensorForDebug || this.disposeTensorsMap(), this.parent == null && a.dispose(this.keepIds), i;
  }
  async executeFunctionAsync(e, t, n) {
    let s = e.reduce((r, a, i) => (r[this.inputs[i].name] = a, r), {});
    return this._executeAsync(s, this.outputNodes, true, t, n);
  }
  async executeWithControlFlow(e, t, n, s) {
    let r = Object.keys(e), a = r.map((y) => this.graph.nodes[_n(y)[0]]), i = n.map((y) => _n(y)[0]), o = i.map((y) => this.graph.nodes[y]);
    o.length === 0 && (o = this._outputs);
    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = nw(e, o, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
    Object.keys(e).forEach((y) => {
      let [v, x] = _n(y), k = [];
      k[x] = e[y], h[v] = k;
    });
    let f = {}, m = this.getFrozenTensorIds(h), g = {};
    for (; d.length > 0; ) {
      let y = this.processStack(a, d, t, h, g, m, i, f, u);
      await Promise.all(y);
    }
    c == null && !s && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
    let b = o.filter((y) => !T0(y) && !un(y.name, h, t)).map((y) => y.name);
    if (b.length > 0) {
      let y = "";
      throw c != null && (y = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`), new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`);
    }
    return h;
  }
  processStack(e, t, n, s, r, a, i, o, u) {
    let l = [];
    for (; t.length > 0; ) {
      let c = t.pop();
      n.currentContext = c.contexts;
      let p = "";
      if (c.node.op === "Enter" && S("isConstant", c.node, s, n) && ([p] = Ts(c.node.name, n)), s[c.node.name] == null) {
        let d = ew(c.node, s, n, this._resourceManager);
        p || ([p] = Ts(c.node.name, n));
        let h = n.currentContext;
        w.isPromise(d) ? l.push(d.then((f) => (s[p] = f, n.currentContext = h, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u), f))) : (s[p] = d, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u));
      } else
        this.processChildNodes(c.node, t, n, s, r, u);
    }
    return l;
  }
  processChildNodes(e, t, n, s, r, a) {
    e.children.forEach((i) => {
      let [o] = Ts(i.name, n);
      r[o] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!un(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })) : i.inputNames.every((u) => !!un(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })));
    });
  }
  dispose() {
    Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t) => t.dispose()));
  }
  checkInputShapeAndType(e) {
    Object.keys(e).forEach((t) => {
      let n = e[t], [s] = _n(t), r = this.graph.nodes[s];
      if (r.attrParams.shape && r.attrParams.shape.value) {
        let a = r.attrParams.shape.value, i = a.length === n.shape.length && n.shape.every((o, u) => a[u] === -1 || a[u] === o);
        w.assert(i, () => `The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`);
      }
      r.attrParams.dtype && r.attrParams.dtype.value && w.assert(n.dtype === r.attrParams.dtype.value, () => `The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`);
    });
  }
  mapInputs(e) {
    let t = {};
    for (let n in e)
      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[n] != null) {
        let s = this._signature.inputs[n];
        t[s.name] = e[n];
      } else
        t[n] = e[n];
    return t;
  }
  checkInputs(e) {
    let t = Object.keys(e).filter((n) => {
      let [s] = _n(n);
      return this.graph.nodes[s] == null;
    });
    if (t.length > 0)
      throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`);
  }
  mapOutputs(e) {
    return e.map((t) => this._signature != null && this._signature.outputs != null && this._signature.outputs[t] != null ? this._signature.outputs[t].name : t, {});
  }
  checkOutputs(e) {
    e.forEach((t) => {
      let [n] = _n(t);
      if (!this.graph.nodes[n])
        throw new Error(`The output '${t}' is not found in the graph`);
    });
  }
};
var G4 = class {
  constructor(e = {}, t = {}) {
    this.hashTableNameToHandle = e, this.hashTableMap = t;
  }
  addHashTable(e, t) {
    this.hashTableNameToHandle[e] = t.handle, this.hashTableMap[t.id] = t;
  }
  getHashTableHandleByName(e) {
    return this.hashTableNameToHandle[e];
  }
  getHashTableById(e) {
    return this.hashTableMap[e];
  }
  dispose() {
    for (let e in this.hashTableMap)
      this.hashTableMap[e].clearAndClose(), delete this.hashTableMap[e];
    for (let e in this.hashTableNameToHandle)
      this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
  }
};
var H4 = "?tfjs-format=file";
var q4 = "model.json";
var j4 = class {
  constructor(e, t = {}) {
    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new G4();
  }
  get modelVersion() {
    return this.version;
  }
  get inputNodes() {
    return this.executor.inputNodes;
  }
  get outputNodes() {
    return this.executor.outputNodes;
  }
  get inputs() {
    return this.executor.inputs;
  }
  get outputs() {
    return this.executor.outputs;
  }
  get weights() {
    return this.executor.weightMap;
  }
  get metadata() {
    return this.artifacts.userDefinedMetadata;
  }
  get modelSignature() {
    return this.signature;
  }
  findIOHandler() {
    let e = this.modelUrl;
    if (e.load != null)
      this.handler = e;
    else if (this.loadOptions.requestInit != null)
      this.handler = An.browserHTTPRequest(e, this.loadOptions);
    else {
      let t = An.getLoadHandlers(e, this.loadOptions);
      if (t.length === 0)
        t.push(An.browserHTTPRequest(e, this.loadOptions));
      else if (t.length > 1)
        throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);
      this.handler = t[0];
    }
  }
  async load() {
    if (this.findIOHandler(), this.handler.load == null)
      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
    let e = await this.handler.load();
    return this.loadSync(e);
  }
  loadSync(e) {
    this.artifacts = e;
    let t = this.artifacts.modelTopology, n;
    this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null ? n = this.artifacts.userDefinedMetadata.signature : n = this.artifacts.signature, this.signature = n, this.version = `${t.versions.producer}.${t.versions.minConsumer}`;
    let s = An.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
    if (this.executor = new Wm(Yx.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
      let r = Yx.Instance.transformGraph(e.modelInitializer);
      this.initializer = new Wm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
    }
    return true;
  }
  async save(e, t) {
    if (typeof e == "string") {
      let n = An.getSaveHandlers(e);
      if (n.length === 0)
        throw new Error(`Cannot find any save handlers for URL '${e}'`);
      if (n.length > 1)
        throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);
      e = n[0];
    }
    if (e.save == null)
      throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
    return e.save(this.artifacts);
  }
  predict(e, t) {
    return this.execute(e, this.outputNodes);
  }
  normalizeInputs(e) {
    if (!(e instanceof et) && !Array.isArray(e))
      return e;
    if (e = Array.isArray(e) ? e : [e], e.length !== this.inputNodes.length)
      throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);
    return this.inputNodes.reduce((t, n, s) => (t[n] = e[s], t), {});
  }
  normalizeOutputs(e) {
    return e = e || this.outputNodes, Array.isArray(e) ? e : [e];
  }
  execute(e, t) {
    e = this.normalizeInputs(e), t = this.normalizeOutputs(t);
    let n = this.executor.execute(e, t);
    return n.length > 1 ? n : n[0];
  }
  async executeAsync(e, t) {
    e = this.normalizeInputs(e), t = this.normalizeOutputs(t);
    let n = await this.executor.executeAsync(e, t);
    return n.length > 1 ? n : n[0];
  }
  getIntermediateTensors() {
    return this.executor.getIntermediateTensors();
  }
  disposeIntermediateTensors() {
    this.executor.disposeIntermediateTensors();
  }
  convertTensorMapToTensorsMap(e) {
    return Object.keys(e).reduce((t, n) => (t[n] = [e[n]], t), {});
  }
  dispose() {
    this.executor.dispose(), this.initializer && this.initializer.dispose(), this.resourceManager.dispose();
  }
};
async function uhe(e, t = {}) {
  if (e == null)
    throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
  t == null && (t = {}), t.fromTFHub && e.load == null && (e.endsWith("/") || (e = e + "/"), e = `${e}${q4}${H4}`);
  let n = new j4(e, t);
  return await n.load(), n;
}
var lhe = "0.0.0";
var K4 = {};
Ae(K4, { CSVDataset: () => L0, Dataset: () => tu, FileDataSource: () => q0, TextLineDataset: () => M0, URLDataSource: () => j0, array: () => bU, csv: () => $U, func: () => _U, generator: () => AU, microphone: () => RU, version_data: () => DU, webcam: () => EU, zip: () => yU });
var X4 = wa(jd());
var Y4 = wa(jd());
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  if (r.recurse && r.value !== null)
    throw new Error("A deep map function may not return both a value and recurse=true.");
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function Z4(e, t = _0) {
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function $0(e, t, n = /* @__PURE__ */ new Set()) {
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      throw new Error(`Can't recurse into non-iterable type: ${s}`);
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function _0(e) {
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async function A0(e, t) {
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function ao(e) {
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}
function J4(e) {
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}
function eU(e) {
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}
function tU(e) {
  return Q4(e, nU);
}
function nU(e) {
  return e instanceof et ? { value: e.clone(), recurse: false } : ao(e) ? { value: null, recurse: true } : { value: e, recurse: false };
}
var E0 = class {
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  }
  pushAll(e) {
    for (let t of e)
      this.push(t);
  }
  pop() {
    if (this.isEmpty())
      throw new RangeError("Ring buffer is empty.");
    this.end = this.wrap(this.end - 1);
    let e = this.get(this.end);
    return this.set(this.end, void 0), e;
  }
  unshift(e) {
    if (this.isFull())
      throw new RangeError("Ring buffer is full.");
    this.begin = this.wrap(this.begin - 1), this.set(this.begin, e);
  }
  shift() {
    if (this.isEmpty())
      throw new RangeError("Ring buffer is empty.");
    let e = this.get(this.begin);
    return this.set(this.begin, void 0), this.begin = this.wrap(this.begin + 1), e;
  }
  shuffleExcise(e) {
    if (this.isEmpty())
      throw new RangeError("Ring buffer is empty.");
    let t = this.wrap(this.begin + e), n = this.get(t);
    return this.set(t, this.pop()), n;
  }
};
var R0 = class extends E0 {
  constructor() {
    super(R0.INITIAL_CAPACITY);
  }
  isFull() {
    return false;
  }
  push(e) {
    super.isFull() && this.expand(), super.push(e);
  }
  unshift(e) {
    super.isFull() && this.expand(), super.unshift(e);
  }
  expand() {
    let e = this.capacity * 2, t = new Array(e), n = this.length();
    for (let s = 0; s < n; s++)
      t[s] = this.get(this.wrap(this.begin + s));
    this.data = t, this.capacity = e, this.doubledCapacity = 2 * this.capacity, this.begin = 0, this.end = n;
  }
};
var D0 = R0;
D0.INITIAL_CAPACITY = 32;
function F0(e) {
  return new aU(e);
}
function tv(e) {
  return new iU(e);
}
function sU(e, t) {
  return new O0(e, t);
}
function rU(e, t = P0.FAIL) {
  return new mU(e, t);
}
var Gt = class {
  async toArray() {
    let e = [], t = await this.next();
    for (; !t.done; )
      e.push(t.value), t = await this.next();
    return e;
  }
  async toArrayForTest() {
    let e = this.prefetch(100), t = [], n = await e.next();
    for (; !n.done; )
      t.push(n.value), n = await e.next();
    return t;
  }
  async resolveFully() {
    let e = await this.next();
    for (; !e.done; )
      e = await this.next();
  }
  async resolveWhile(e) {
    let t = await this.next(), n = e(t.value);
    for (; !t.done && n; )
      t = await this.next(), n = e(t.value);
  }
  handleErrors(e) {
    return new hU(this, e);
  }
  filter(e) {
    return new dU(this, e);
  }
  map(e) {
    return new pU(this, e);
  }
  mapAsync(e) {
    return new sw(this, e);
  }
  serialMapAsync(e) {
    return new sw(this, e).serial();
  }
  flatmap(e) {
    return new fU(this, e);
  }
  async forEachAsync(e) {
    return this.map(e).resolveFully();
  }
  async serialForEach(e) {
    return this.serialMapAsync(e).resolveWhile((t) => t === true);
  }
  rowMajorBatch(e, t = true) {
    return new cU(this, e, t);
  }
  columnMajorBatch(e, t = true, n = _0) {
    return this.rowMajorBatch(e, t).map((r) => Z4(r, n));
  }
  concatenate(e, t) {
    return new O0(F0([this, e]), t);
  }
  take(e) {
    return e < 0 || e == null ? this : new lU(this, e);
  }
  skip(e) {
    return e < 0 || e == null ? this : new uU(this, e);
  }
  prefetch(e) {
    return new z0(this, e);
  }
  shuffle(e, t) {
    return new gU(this, e, t);
  }
  serial() {
    return new oU(this);
  }
};
var aU = class extends Gt {
  constructor(e) {
    super(), this.items = e, this.trav = 0;
  }
  summary() {
    return `Array of ${this.items.length} items`;
  }
  async next() {
    if (this.trav >= this.items.length)
      return { value: null, done: true };
    let e = this.items[this.trav];
    return this.trav++, { value: tU(e), done: false };
  }
};
var iU = class extends Gt {
  constructor(e) {
    super(), this.nextFn = e;
  }
  summary() {
    return "Function call";
  }
  async next() {
    try {
      return this.nextFn();
    } catch (e) {
      throw e.message = `Error thrown while iterating through a dataset: ${e.message}`, e;
    }
  }
};
var oU = class extends Gt {
  constructor(e) {
    super(), this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Serial`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    return this.upstream.next();
  }
};
var uU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.maxCount = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Skip`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; this.count++ < this.maxCount; ) {
      let e = await this.upstream.next();
      if (e.done)
        return e;
      Re(e.value);
    }
    return this.upstream.next();
  }
};
var lU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.maxCount = t, this.count = 0;
  }
  summary() {
    return `${this.upstream.summary()} -> Take`;
  }
  async next() {
    return this.count++ >= this.maxCount ? { value: null, done: true } : this.upstream.next();
  }
};
var cU = class extends Gt {
  constructor(e, t, n = true) {
    super(), this.upstream = e, this.batchSize = t, this.enableSmallLastBatch = n, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> RowMajorBatch`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    let e = [];
    for (; e.length < this.batchSize; ) {
      let t = await this.upstream.next();
      if (t.done)
        return this.enableSmallLastBatch && e.length > 0 ? { value: e, done: false } : { value: null, done: true };
      e.push(t.value);
    }
    return { value: e, done: false };
  }
};
var dU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Filter`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; ; ) {
      let e = await this.upstream.next();
      if (e.done || this.predicate(e.value))
        return e;
      Re(e.value);
    }
  }
};
var pU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> Map`;
  }
  async next() {
    let e = await this.upstream.next();
    if (e.done)
      return { value: null, done: true };
    let t = _s.getTensorsInContainer(e.value), n = this.transform(e.value), s = _s.getTensorsInContainer(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return { value: n, done: false };
  }
};
var hU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> handleErrors`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; ; )
      try {
        return await this.upstream.next();
      } catch (e) {
        if (!this.handler(e))
          return { value: null, done: true };
      }
  }
};
var sw = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> AsyncMap`;
  }
  async next() {
    let e = await this.upstream.next();
    if (e.done)
      return { value: null, done: true };
    let t = _s.getTensorsInContainer(e.value), n = await this.transform(e.value), s = _s.getTensorsInContainer(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return { value: n, done: false };
  }
};
var nv = class extends Gt {
  constructor() {
    super(), this.outputQueue = new D0(), this.lastRead = Promise.resolve({ value: null, done: false });
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; this.outputQueue.length() === 0; )
      if (!await this.pump())
        return { value: null, done: true };
    return { value: this.outputQueue.shift(), done: false };
  }
};
var fU = class extends nv {
  constructor(e, t) {
    super(), this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> Flatmap`;
  }
  async pump() {
    let e = await this.upstream.next();
    if (e.done)
      return false;
    let t = _s.getTensorsInContainer(e.value), n = this.transform(e.value), s = _s.getTensorsInContainer(n);
    this.outputQueue.pushAll(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return true;
  }
};
var O0 = class extends Gt {
  constructor(e, t) {
    super(), this.baseErrorHandler = t, this.lastRead = null, this.iterator = null, this.moreIterators = e;
  }
  summary() {
    return "TODO: fill in upstream of chained summaries -> Chained";
  }
  async next() {
    return this.lastRead = this.readFromChain(this.lastRead), this.lastRead;
  }
  async readFromChain(e) {
    if (await e, this.iterator == null) {
      let n = await this.moreIterators.next();
      if (n.done)
        return { value: null, done: true };
      this.iterator = n.value, this.baseErrorHandler != null && (this.iterator = this.iterator.handleErrors(this.baseErrorHandler));
    }
    let t = await this.iterator.next();
    return t.done ? (this.iterator = null, this.readFromChain(e)) : t;
  }
};
var P0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(P0 || {});
var mU = class extends Gt {
  constructor(e, t = 0) {
    super(), this.iterators = e, this.mismatchMode = t, this.count = 0, this.currentPromise = null;
  }
  summary() {
    return "{TODO: fill in upstream of zip summaries} -> Zip";
  }
  async nextState(e) {
    await e;
    let t = 0, n = 0;
    function s(a) {
      return a instanceof Gt ? { value: a.next().then((o) => (t++, o.done && n++, o.value)), recurse: false } : { value: null, recurse: true };
    }
    let r = await A0(this.iterators, s);
    if (t === n)
      return { value: null, done: true };
    if (n > 0)
      switch (this.mismatchMode) {
        case 0:
          throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);
        case 1:
          return { value: null, done: true };
        case 2:
        default:
      }
    return this.count++, { value: r, done: false };
  }
  async next() {
    return this.currentPromise = this.nextState(this.currentPromise), this.currentPromise;
  }
};
var z0 = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.bufferSize = t, this.buffer = new E0(t);
  }
  summary() {
    return `${this.upstream.summary()} -> Prefetch`;
  }
  refill() {
    for (; !this.buffer.isFull(); ) {
      let e = this.upstream.next();
      this.buffer.push(e);
    }
  }
  next() {
    return this.refill(), this.buffer.shift();
  }
};
var gU = class extends z0 {
  constructor(e, t, n) {
    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = Y4.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  randomInt(e) {
    return Math.floor(this.random() * e);
  }
  chooseIndex() {
    return this.randomInt(this.buffer.length());
  }
  async serialNext() {
    for (this.upstreamExhausted || this.refill(); !this.buffer.isEmpty(); ) {
      let e = this.chooseIndex(), t = await this.buffer.shuffleExcise(e);
      if (t.done)
        this.upstreamExhausted = true;
      else
        return this.refill(), t;
    }
    return { value: null, done: true };
  }
};
var tu = class {
  constructor() {
    this.size = null;
  }
  batch(e, t = true) {
    let n = this;
    w.assert(e > 0, () => `batchSize needs to be positive, but it is
      ${e}`);
    let s;
    return this.size === 1 / 0 || this.size == null ? s = this.size : t ? s = Math.ceil(this.size / e) : s = Math.floor(this.size / e), $n(async () => (await n.iterator()).columnMajorBatch(e, t, vU), s);
  }
  concatenate(e) {
    let t = this, n;
    return this.size === 1 / 0 || e.size === 1 / 0 ? n = 1 / 0 : this.size != null && e.size != null ? n = this.size + e.size : n = null, $n(async () => (await t.iterator()).concatenate(await e.iterator()), n);
  }
  filter(e) {
    let t = this, n;
    return this.size === 1 / 0 ? n = 1 / 0 : n = null, $n(async () => (await t.iterator()).filter((s) => q(() => e(s))), n);
  }
  async forEachAsync(e) {
    return (await this.iterator()).forEachAsync(e);
  }
  map(e) {
    let t = this;
    return $n(async () => (await t.iterator()).map((n) => q(() => e(n))), this.size);
  }
  mapAsync(e) {
    let t = this;
    return $n(async () => (await t.iterator()).mapAsync(e), this.size);
  }
  prefetch(e) {
    if (e == null)
      throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");
    let t = this;
    return $n(async () => (await t.iterator()).prefetch(e), this.size);
  }
  repeat(e) {
    let t = this, n;
    return this.size != null && e > 0 ? n = this.size * e : e === 0 ? n = 0 : this.size != null && (e === void 0 || e < 0) ? n = 1 / 0 : n = null, $n(async () => {
      let s = tv(async () => ({ value: await t.iterator(), done: false }));
      return sU(s.take(e));
    }, n);
  }
  skip(e) {
    let t = this, n;
    return this.size != null && e >= 0 && this.size >= e ? n = this.size - e : this.size != null && (this.size < e || e === void 0 || e < 0) ? n = 0 : n = null, $n(async () => (await t.iterator()).skip(e), n);
  }
  shuffle(e, t, n = true) {
    if (e == null || e < 0)
      throw this.size == null ? new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.") : new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
    let s = this, r = X4.alea(t || w.now().toString());
    return $n(async () => {
      let a = r.int32();
      return n && (a += r.int32()), (await s.iterator()).shuffle(e, a.toString());
    }, this.size);
  }
  take(e) {
    let t = this, n;
    return this.size != null && this.size > e ? n = e : this.size != null && this.size <= e ? n = this.size : n = null, $n(async () => (await t.iterator()).take(e), n);
  }
  async toArray() {
    if (this.size === 1 / 0)
      throw new Error("Can not convert infinite data stream to array.");
    return (await this.iterator()).toArray();
  }
  async toArrayForTest() {
    if (this.size === 1 / 0)
      throw new Error("Can not convert infinite data stream to array.");
    return (await this.iterator()).toArrayForTest();
  }
};
tu.MAX_BUFFER_SIZE = 1e4;
function $n(e, t = null) {
  return new class extends tu {
    constructor() {
      super(...arguments), this.size = t;
    }
    async iterator() {
      return e();
    }
  }();
}
function bU(e) {
  return $n(async () => F0(e), e.length);
}
function yU(e) {
  if (!ao(e))
    throw new Error("The argument to zip() must be an object or array.");
  let t;
  if (Array.isArray(e))
    for (let n = 0; n < e.length; n++)
      t = t == null ? e[n].size : Math.min(t, e[n].size);
  else if (e instanceof Object)
    for (let n in e)
      t = t == null ? e[n].size : Math.min(t, e[n].size);
  return $n(async () => {
    let n = await A0(e, (s) => {
      if (s instanceof tu)
        return { value: s.iterator(), recurse: false };
      if (ao(s))
        return { value: null, recurse: true };
      throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
    });
    return rU(n, 1);
  }, t);
}
function vU(e) {
  if (e === null)
    return null;
  let t = e[0];
  return J4(t) ? { value: xU(e), recurse: false } : { value: null, recurse: true };
}
function xU(e) {
  if (e.length === 0)
    throw new Error("Can't make a batch of zero elements.");
  return e[0] instanceof et ? es(e) : ms(e);
}
var M0 = class extends tu {
  constructor(e) {
    super(), this.input = e;
  }
  async iterator() {
    return (await this.input.iterator()).decodeUTF8().split(`
`).map((s) => (s.endsWith("\r") && (s = s.slice(0, -1)), s));
  }
};
var Yc = '"';
var _u = Symbol("out");
var rw = Symbol("field");
var Qc = Symbol("quote");
var Yf = Symbol("quoteafterquote");
var aw = Symbol("quoteinquote");
var L0 = class extends tu {
  constructor(e, t) {
    super(), this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new M0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
  }
  async columnNames() {
    return this.columnNamesValidated || await this.setColumnNames(), this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
  }
  async setColumnNames() {
    let e = await this.maybeReadHeaderLine();
    if (!this.fullColumnNames && !e)
      throw new Error("Column names must be provided if there is no header line.");
    this.fullColumnNames && e && w.assert(e.length === this.fullColumnNames.length, () => "The length of provided columnNames (" + this.fullColumnNames.length.toString() + ") does not match the length of the header line read from file (" + e.length.toString() + ")."), this.fullColumnNames || (this.fullColumnNames = e);
    let t = this.fullColumnNames.reduce((s, r) => (s[r] = s[r] + 1 || 1, s), {}), n = Object.keys(t).filter((s) => t[s] > 1);
    if (w.assert(n.length === 0, () => "Duplicate column names found: " + n.toString()), this.columnConfigs) {
      for (let s of Object.keys(this.columnConfigs))
        if (this.fullColumnNames.indexOf(s) === -1)
          throw new Error('The key "' + s + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ").");
    }
    this.columnNamesValidated = true;
  }
  async maybeReadHeaderLine() {
    if (this.hasHeader) {
      let t = await (await this.base.iterator()).next();
      if (t.done)
        throw new Error("No data was found for CSV parsing.");
      let n = t.value;
      return this.parseRow(n, false);
    } else
      return null;
  }
  async iterator() {
    this.columnNamesValidated || await this.setColumnNames();
    let e = await this.base.iterator();
    return this.hasHeader && (e = e.skip(1)), e.map((t) => this.makeDataElement(t));
  }
  makeDataElement(e) {
    let t = this.parseRow(e), n = {}, s = {};
    for (let r = 0; r < this.fullColumnNames.length; r++) {
      let a = this.fullColumnNames[r], i = this.columnConfigs ? this.columnConfigs[a] : null;
      if (!(this.configuredColumnsOnly && !i)) {
        let o = t[r], u = null;
        if (o === "")
          if (i && i.default !== void 0)
            u = i.default;
          else {
            if (i && (i.required || i.isLabel))
              throw new Error(`Required column ${a} is empty in this line: ${e}`);
            u = void 0;
          }
        else {
          let l = Number(o);
          if (isNaN(l))
            i && i.dtype === "bool" ? u = this.getBoolean(o) : u = o;
          else if (!i || !i.dtype)
            u = l;
          else
            switch (i.dtype) {
              case "float32":
                u = l;
                break;
              case "int32":
                u = Math.floor(l);
                break;
              case "bool":
                u = this.getBoolean(o);
                break;
              default:
                u = l;
            }
        }
        i && i.isLabel ? s[a] = u : n[a] = u;
      }
    }
    return Object.keys(s).length === 0 ? n : { xs: n, ys: s };
  }
  getBoolean(e) {
    return e === "1" || e.toLowerCase() === "true" ? 1 : 0;
  }
  parseRow(e, t = true) {
    let n = [], s = 0, r = e.length, a = _u;
    for (let i = 0; i < r; i++)
      switch (a) {
        case _u:
          switch (e.charAt(i)) {
            case Yc:
              s = i + 1, a = Qc;
              break;
            case this.delimiter:
              if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
                break;
              n.push(""), a = _u;
              break;
            default:
              a = rw, s = i;
              break;
          }
          break;
        case rw:
          switch (e.charAt(i)) {
            case this.delimiter:
              n.push(e.substring(s, i)), a = _u, s = i + 1;
              break;
            default:
          }
          break;
        case Qc:
          switch (e.charAt(i)) {
            case Yc:
              a = Yf;
              break;
            default:
          }
          break;
        case Yf:
          switch (e.charAt(i)) {
            case this.delimiter:
              n.push(e.substring(s, i - 1)), a = _u, s = i + 1;
              break;
            case Yc:
              a = Qc;
              break;
            default:
              a = aw;
              break;
          }
          break;
        case aw:
          switch (e.charAt(i)) {
            case Yc:
              a = Qc;
              break;
            default:
          }
          break;
        default:
      }
    if (a === Yf ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
      throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);
    return n;
  }
};
var B0 = class extends Gt {
  constructor(e) {
    super(), this.microphoneConfig = e, this.isClosed = false, this.fftSize = e.fftSize || 1024;
    let t = Math.log2(this.fftSize);
    if (this.fftSize < 0 || t < 4 || t > 14 || !Number.isInteger(t))
      throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);
    if (this.numFrames = e.numFramesPerSpectrogram || 43, this.sampleRateHz = e.sampleRateHz, this.columnTruncateLength = e.columnTruncateLength || this.fftSize, this.audioTrackConstraints = e.audioTrackConstraints, this.smoothingTimeConstant = e.smoothingTimeConstant || 0, this.includeSpectrogram = e.includeSpectrogram !== false, this.includeWaveform = e.includeWaveform === true, !this.includeSpectrogram && !this.includeWaveform)
      throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.");
  }
  summary() {
    return "microphone";
  }
  static async create(e = {}) {
    if (!K().get("IS_BROWSER"))
      throw new Error("microphone API is only supported in browser environment.");
    let t = new B0(e);
    return await t.start(), t;
  }
  async start() {
    try {
      this.stream = await navigator.mediaDevices.getUserMedia({ audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints, video: false });
    } catch (n) {
      throw new Error(`Error thrown while initializing video stream: ${n.message}`);
    }
    if (!this.stream)
      throw new Error("Could not obtain audio from microphone.");
    let e = window.AudioContext || window.webkitAudioContext;
    if (this.audioContext = new e(), !this.sampleRateHz)
      this.sampleRateHz = this.audioContext.sampleRate;
    else if (this.audioContext.sampleRate !== this.sampleRateHz)
      throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);
    let t = this.audioContext.createMediaStreamSource(this.stream);
    this.analyser = this.audioContext.createAnalyser(), this.analyser.fftSize = this.fftSize * 2, this.analyser.smoothingTimeConstant = this.smoothingTimeConstant, t.connect(this.analyser), this.freqData = new Float32Array(this.fftSize), this.timeData = new Float32Array(this.fftSize);
  }
  async next() {
    if (this.isClosed)
      return { value: null, done: true };
    let e, t, n = await this.getAudioData();
    if (this.includeSpectrogram) {
      let s = this.flattenQueue(n.freqDataQueue);
      e = this.getTensorFromAudioDataArray(s, [this.numFrames, this.columnTruncateLength, 1]);
    }
    if (this.includeWaveform) {
      let s = this.flattenQueue(n.timeDataQueue);
      t = this.getTensorFromAudioDataArray(s, [this.numFrames * this.fftSize, 1]);
    }
    return { value: { spectrogram: e, waveform: t }, done: false };
  }
  async capture() {
    return (await this.next()).value;
  }
  async getAudioData() {
    let e = [], t = [], n = 0;
    return new Promise((s) => {
      let r = setInterval(() => {
        this.includeSpectrogram && (this.analyser.getFloatFrequencyData(this.freqData), this.freqData[0] === -1 / 0 && s({ freqDataQueue: e, timeDataQueue: t }), e.push(this.freqData.slice(0, this.columnTruncateLength))), this.includeWaveform && (this.analyser.getFloatTimeDomainData(this.timeData), t.push(this.timeData.slice())), ++n === this.numFrames && (clearInterval(r), s({ freqDataQueue: e, timeDataQueue: t }));
      }, this.fftSize / this.sampleRateHz * 1e3);
    });
  }
  stop() {
    this.isClosed || (this.isClosed = true, this.analyser.disconnect(), this.audioContext.close(), this.stream != null && this.stream.getTracks().length > 0 && this.stream.getTracks()[0].stop());
  }
  toArray() {
    throw new Error("Can not convert infinite audio stream to array.");
  }
  getSampleRate() {
    return this.sampleRateHz;
  }
  flattenQueue(e) {
    let t = e[0].length, n = new Float32Array(e.length * t);
    return e.forEach((s, r) => n.set(s, r * t)), n;
  }
  getTensorFromAudioDataArray(e, t) {
    let n = new Float32Array(w.sizeFromShape(t));
    return n.set(e, n.length - e.length), ms(n, t);
  }
};
var V0 = class extends Gt {
  constructor(e, t) {
    if (super(), this.webcamVideoElement = e, this.webcamConfig = t, this.isClosed = true, this.resize = false, this.needToResize())
      if (this.resize = true, this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth], this.cropBoxInd = Zt([0], "int32"), this.webcamConfig.centerCrop) {
        let n = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width, s = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height, r = (1 - n) / 2, a = (1 - s) / 2, i = r + n, o = s + a;
        this.cropBox = Zi([a, r, o, i], [1, 4]);
      } else
        this.cropBox = Zi([0, 0, 1, 1], [1, 4]);
  }
  summary() {
    return "webcam";
  }
  static async create(e, t = {}) {
    if (!K().get("IS_BROWSER"))
      throw new Error("tf.data.webcam is only supported in browser environment.");
    if (!e) {
      if (e = document.createElement("video"), !t.resizeWidth || !t.resizeHeight)
        throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
      e.width = t.resizeWidth, e.height = t.resizeHeight;
    }
    let n = new V0(e, t);
    return await n.start(), n;
  }
  async start() {
    this.webcamConfig.facingMode && w.assert(this.webcamConfig.facingMode === "user" || this.webcamConfig.facingMode === "environment", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);
    try {
      this.stream = await navigator.mediaDevices.getUserMedia({ video: { deviceId: this.webcamConfig.deviceId, facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : "user", width: this.webcamVideoElement.width, height: this.webcamVideoElement.height } });
    } catch (e) {
      throw e.message = `Error thrown while initializing video stream: ${e.message}`, e;
    }
    if (!this.stream)
      throw new Error("Could not obtain video from webcam.");
    try {
      this.webcamVideoElement.srcObject = this.stream;
    } catch (e) {
      console.log(e), this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);
    }
    return this.webcamVideoElement.play(), this.isClosed = false, new Promise((e) => {
      this.webcamVideoElement.onloadedmetadata = () => {
        e();
      };
    });
  }
  async next() {
    if (this.isClosed)
      return { value: null, done: true };
    let e;
    try {
      e = Fk.fromPixels(this.webcamVideoElement);
    } catch (t) {
      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`);
    }
    if (this.resize)
      try {
        return { value: this.cropAndResizeFrame(e), done: false };
      } catch (t) {
        throw new Error(`Error thrown cropping the video: ${t.message}`);
      } finally {
        e.dispose();
      }
    else
      return { value: e, done: false };
  }
  needToResize() {
    return !!(this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight));
  }
  cropAndResizeFrame(e) {
    return q(() => {
      let t = Pn(le(e, "float32"), 0), n;
      n = jn.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
      let s = n.shape;
      return U(n, s.slice(1));
    });
  }
  async capture() {
    return (await this.next()).value;
  }
  stop() {
    this.stream.getTracks().forEach((t) => t.stop());
    try {
      this.webcamVideoElement.srcObject = null;
    } catch (t) {
      console.log(t), this.webcamVideoElement.src = null;
    }
    this.isClosed = true;
  }
  toArray() {
    throw new Error("Can not convert infinite video stream to array.");
  }
};
var W0 = class {
};
var U0 = class extends Gt {
  split(e) {
    return new wU(this, e);
  }
};
var wU = class extends U0 {
  constructor(e, t) {
    super(), this.upstream = e, this.impl = new kU(e, t);
  }
  summary() {
    return this.impl.summary();
  }
  async next() {
    return this.impl.next();
  }
};
var kU = class extends nv {
  constructor(e, t) {
    super(), this.upstream = e, this.separator = t, this.carryover = "";
  }
  summary() {
    return `${this.upstream.summary()} -> Split('${this.separator}')`;
  }
  async pump() {
    let e = await this.upstream.next();
    if (e.done)
      return this.carryover === "" ? false : (this.outputQueue.push(this.carryover), this.carryover = "", true);
    let t = e.value.split(this.separator);
    t[0] = this.carryover + t[0];
    for (let n of t.slice(0, -1))
      this.outputQueue.push(n);
    return this.carryover = t[t.length - 1], true;
  }
};
var SU = class extends Gt {
  decodeUTF8() {
    return new IU(this);
  }
};
var IU = class extends U0 {
  constructor(e) {
    super(), this.upstream = e, this.impl = new CU(e);
  }
  summary() {
    return this.impl.summary();
  }
  async next() {
    return this.impl.next();
  }
};
var CU = class extends nv {
  constructor(e) {
    if (super(), this.upstream = e, K().get("IS_BROWSER"))
      this.decoder = new TextDecoder("utf-8");
    else {
      let { StringDecoder: t } = Yw();
      this.decoder = new t("utf8");
    }
  }
  summary() {
    return `${this.upstream.summary()} -> Utf8`;
  }
  async pump() {
    let e = await this.upstream.next(), t;
    if (e.done)
      return false;
    t = e.value;
    let n;
    return K().get("IS_BROWSER") ? n = this.decoder.decode(t, { stream: true }) : n = this.decoder.write(Buffer.from(t.buffer)), this.outputQueue.push(n), true;
  }
};
var G0 = class extends SU {
  constructor(e, t = {}) {
    super(), this.file = e, this.options = t, w.assert(e instanceof Uint8Array || (K().get("IS_BROWSER") ? e instanceof File || e instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."), this.offset = t.offset || 0, this.chunkSize = t.chunkSize || 1024 * 1024;
  }
  summary() {
    return `FileChunks ${this.file}`;
  }
  async next() {
    return this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size) ? { value: null, done: true } : { value: await new Promise((t, n) => {
      let s = this.offset + this.chunkSize;
      if (this.file instanceof Uint8Array)
        t(new Uint8Array(this.file.slice(this.offset, s)));
      else {
        let r = new FileReader();
        r.onload = (i) => {
          let o = r.result;
          if (o instanceof ArrayBuffer && (o = new Uint8Array(o)), !(o instanceof Uint8Array))
            return n(new TypeError("FileReader returned unknown type."));
          t(o);
        }, r.onabort = (i) => n(new Error("Aborted")), r.onerror = (i) => n(new Error(i.type));
        let a = this.file.slice(this.offset, s);
        r.readAsArrayBuffer(a);
      }
      this.offset = s;
    }), done: false };
  }
};
async function NU(e, t = {}, n) {
  let s, r;
  typeof e == "string" ? s = e : (s = e.url, r = TU(e));
  let a = await (n || w.fetch)(s, r);
  if (a.ok) {
    let i = new Uint8Array(await a.arrayBuffer());
    return new G0(i, t);
  } else
    throw new Error(a.statusText);
}
var TU = (e) => ({ method: e.method, headers: e.headers, body: e.body, mode: e.mode, credentials: e.credentials, cache: e.cache, redirect: e.redirect, referrer: e.referrer, integrity: e.integrity });
function H0(e) {
  return typeof e == "string" && e.slice(0, 7) === "file://";
}
var q0 = class extends W0 {
  constructor(e, t = {}) {
    super(), this.input = e, this.options = t;
  }
  async iterator() {
    if (H0(this.input) && K().get("IS_NODE")) {
      let e = ag();
      this.input = e.readFileSync(this.input.slice(7));
    }
    return new G0(this.input, this.options);
  }
};
var j0 = class extends W0 {
  constructor(e, t = {}) {
    super(), this.url = e, this.fileOptions = t;
  }
  async iterator() {
    return H0(this.url) ? new q0(this.url, this.fileOptions).iterator() : NU(this.url, this.fileOptions);
  }
};
function $U(e, t = {}) {
  return new L0(new j0(e), t);
}
function _U(e) {
  let t = tv(e);
  return $n(async () => t);
}
function AU(e) {
  return $n(async () => {
    let t = await e();
    return tv(() => t.next());
  });
}
async function EU(e, t) {
  return V0.create(e, t);
}
async function RU(e) {
  return B0.create(e);
}
var DU = "0.0.0";
function be(e, t) {
  Array.isArray(e) || (e = [e]), e.forEach((n) => {
    n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the CPU backend.`);
  });
}
var FU = ws.whereImpl;
var K0 = class extends rl {
  constructor() {
    super(), this.blockSize = 48, this.firstUse = true, this.data = new Kd(this, ds());
  }
  nextDataId() {
    return K0.nextDataId++;
  }
  write(e, t, n) {
    this.firstUse && (this.firstUse = false, K().get("IS_NODE") && C.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));
    let s = { id: this.nextDataId() };
    return this.data.set(s, { values: e, dtype: n, refCount: 1 }), s;
  }
  makeTensorInfo(e, t, n) {
    let s;
    if (t === "string" && n != null && n.length > 0 && w.isString(n[0])) {
      let r = n.map((a) => w.encodeString(a));
      s = this.write(r, e, t);
    } else
      s = this.write(n, e, t);
    return { dataId: s, shape: e, dtype: t };
  }
  refCount(e) {
    return this.data.has(e) ? this.data.get(e).refCount : 0;
  }
  incRef(e) {
    let t = this.data.get(e);
    t.refCount++;
  }
  decRef(e) {
    if (this.data.has(e)) {
      let t = this.data.get(e);
      t.refCount--;
    }
  }
  move(e, t, n, s, r) {
    this.data.set(e, { values: t, dtype: s, refCount: r });
  }
  numDataIds() {
    return this.data.numDataIds();
  }
  async read(e) {
    return this.readSync(e);
  }
  readSync(e) {
    let { dtype: t, complexTensorInfos: n } = this.data.get(e);
    if (t === "complex64") {
      let s = this.readSync(n.real.dataId), r = this.readSync(n.imag.dataId);
      return C.mergeRealAndImagArrays(s, r);
    }
    return this.data.get(e).values;
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId), n = t;
    if (e.dtype === "string")
      try {
        n = t.map((s) => w.decodeString(s));
      } catch (s) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return De(e.shape, e.dtype, n);
  }
  makeOutput(e, t, n) {
    return ds().makeTensorFromTensorInfo(this.makeTensorInfo(t, n, e), this);
  }
  disposeData(e, t = false) {
    if (this.data.has(e)) {
      if (this.data.get(e).refCount--, !t && this.data.get(e).refCount > 0)
        return false;
      let { complexTensorInfos: n } = this.data.get(e);
      n != null && (this.disposeData(n.real.dataId, true), this.disposeData(n.imag.dataId, true)), this.data.delete(e);
    }
    return true;
  }
  disposeIntermediateTensorInfo(e) {
    this.disposeData(e.dataId);
  }
  async time(e) {
    let t = w.now();
    return e(), { kernelMs: w.now() - t };
  }
  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) {
    be([e], "where");
    let t = this.readSync(e.dataId);
    return FU(e.shape, t);
  }
  dispose() {
  }
  floatPrecision() {
    return 32;
  }
  epsilon() {
    return super.epsilon();
  }
};
var X0 = K0;
X0.nextDataId = 0;
var sv = {};
Ae(sv, { addImpl: () => Q0, bincountImpl: () => av, bincountReduceImpl: () => Z0, ceilImpl: () => J0, concatImpl: () => iv, equalImpl: () => eC, expImpl: () => nC, expm1Impl: () => rC, floorImpl: () => aC, gatherNdImpl: () => iC, gatherV2Impl: () => oC, greaterEqualImpl: () => lC, greaterImpl: () => uC, lessEqualImpl: () => dC, lessImpl: () => cC, linSpaceImpl: () => pC, logImpl: () => hC, maxImpl: () => fC, maximumImpl: () => mC, minimumImpl: () => gC, multiplyImpl: () => ov, negImpl: () => bC, notEqualImpl: () => yC, prodImpl: () => vC, rangeImpl: () => lv, rsqrtImpl: () => xC, sigmoidImpl: () => kG, simpleAbsImpl: () => Y0, sliceImpl: () => Ld, sparseFillEmptyRowsImpl: () => kC, sparseReshapeImpl: () => SC, sparseSegmentReductionImpl: () => cv, sqrtImpl: () => CG, squaredDifferenceImpl: () => IC, stridedSliceImpl: () => CC, stringNGramsImpl: () => NC, stringSplitImpl: () => TC, stringToHashBucketFastImpl: () => $C, subImpl: () => _C, tileImpl: () => AC, topKImpl: () => RC, transposeImpl: () => uv, uniqueImpl: () => DC });
function Y0(e) {
  let t = new Float32Array(e.length);
  for (let n = 0; n < e.length; ++n)
    t[n] = Math.abs(e[n]);
  return t;
}
var OU = (e) => {
  let { x: t } = e.inputs, n = e.backend;
  be(t, "abs");
  let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
  return s = Y0(r), n.makeOutput(s, t.shape, t.dtype);
};
var PU = { kernelName: co, backendName: "cpu", kernelFunc: OU };
function At(e) {
  return (t, n, s, r, a) => {
    let i = C.assertAndGetBroadcastShape(t, n), o = i.length, u = w.computeStrides(i), l = w.sizeFromShape(i), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = C.getBroadcastDims(t, i), g = C.getBroadcastDims(n, i);
    if (m.length + g.length === 0)
      for (let b = 0; b < c.length; ++b)
        c[b] = e(s[b % s.length], r[b % r.length]);
    else
      for (let b = 0; b < c.length; ++b) {
        let y = w.indexToLoc(b, o, u), v = y.slice(-p);
        m.forEach(($) => v[$] = 0);
        let x = w.locToIndex(v, p, h), k = y.slice(-d);
        g.forEach(($) => k[$] = 0);
        let I = w.locToIndex(k, d, f);
        c[b] = e(s[x], r[I]);
      }
    return [c, i];
  };
}
function En(e) {
  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = n.makeTensorInfo(s.shape, "complex64"), u = n.data.get(o.dataId);
  return u.complexTensorInfos = { real: n.makeTensorInfo(s.shape, "float32", a), imag: n.makeTensorInfo(r.shape, "float32", i) }, o;
}
var zU = { kernelName: Zd, backendName: "cpu", kernelFunc: En };
function Md(e, t, n = "float32") {
  if (n === "complex64") {
    let r = Md(e, t, "float32"), a = Md(e, t, "float32");
    return En({ inputs: { real: r, imag: a }, backend: e });
  }
  let s = w.makeZerosTypedArray(w.sizeFromShape(t), n);
  return e.makeTensorInfo(t, n, s);
}
function Os(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  return n.incRef(s.dataId), { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
var MU = { kernelName: Wa, backendName: "cpu", kernelFunc: Os };
function ga(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.real, a = n.data.get(r.dataId).values;
  return n.makeTensorInfo(r.shape, r.dtype, a);
}
var LU = { kernelName: op, backendName: "cpu", kernelFunc: ga };
function xr(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
  if (a === "complex64") {
    if (r.dtype === "complex64")
      return Os({ inputs: { x: r }, backend: n });
    let i = Md(n, r.shape, r.dtype), o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = En({ inputs: { real: o, imag: i }, backend: n });
    return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  }
  if (r.dtype === "complex64") {
    let i = ga({ inputs: { input: r }, backend: n }), o = xr({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
    return n.disposeIntermediateTensorInfo(i), o;
  }
  if (!w.hasEncodingLoss(r.dtype, a)) {
    let i = Os({ inputs: { x: r }, backend: n });
    return { dataId: i.dataId, shape: i.shape, dtype: a };
  }
  if (a === "int32") {
    let i = n.data.get(r.dataId).values, o = Int32Array.from(i);
    return n.makeTensorInfo(r.shape, "int32", o);
  }
  if (a === "bool") {
    let i = n.data.get(r.dataId).values, o = w.toTypedArray([0], r.dtype), [u, l] = At((c, p) => c !== p ? 1 : 0)(r.shape, [], i, o, "bool");
    return n.makeTensorInfo(l, "bool", u);
  }
  throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
}
var BU = { kernelName: Ta, backendName: "cpu", kernelFunc: xr };
function Ht(e, t, n, s) {
  return n == null ? ({ inputs: r, backend: a }) => {
    let { a: i, b: o } = r, u = a;
    be([i, o], e);
    let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = i.dtype === "string" ? C.fromUint8ToStringArray(l) : l, d = i.dtype === "string" ? C.fromUint8ToStringArray(c) : c, h = s || i.dtype, [f, m] = t(i.shape, o.shape, p, d, h);
    return u.makeTensorInfo(m, h, f);
  } : ({ inputs: r, backend: a }) => {
    let { a: i, b: o } = r, u = a;
    if (i.dtype === "complex64" || o.dtype === "complex64") {
      let l = xr({ inputs: { x: i }, backend: u, attrs: { dtype: "complex64" } }), c = u.data.get(l.dataId), p = c.complexTensorInfos.real, d = c.complexTensorInfos.imag, h = u.data.get(p.dataId).values, f = u.data.get(d.dataId).values, m = xr({ inputs: { x: o }, backend: u, attrs: { dtype: "complex64" } }), g = u.data.get(m.dataId), b = g.complexTensorInfos.real, y = g.complexTensorInfos.imag, v = u.data.get(b.dataId).values, x = u.data.get(y.dataId).values, [k, I, $] = n(i.shape, o.shape, h, f, v, x), E = u.makeTensorInfo($, "float32", k), A = u.makeTensorInfo($, "float32", I), P = En({ inputs: { real: E, imag: A }, backend: u });
      return u.disposeIntermediateTensorInfo(l), u.disposeIntermediateTensorInfo(m), u.disposeIntermediateTensorInfo(E), u.disposeIntermediateTensorInfo(A), P;
    } else {
      let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = s || i.dtype, [d, h] = t(i.shape, o.shape, l, c, p);
      return u.makeTensorInfo(h, p, d);
    }
  };
}
function rv(e) {
  return (t, n, s, r, a, i) => {
    let o = C.assertAndGetBroadcastShape(t, n), u = w.sizeFromShape(o), l = o.length, c = w.computeStrides(o), p = w.getTypedArrayFromDType("float32", u), d = w.getTypedArrayFromDType("float32", u), h = C.getBroadcastDims(t, o), f = C.getBroadcastDims(n, o), m = C.mergeRealAndImagArrays(s, r), g = C.mergeRealAndImagArrays(a, i), b = t.length, y = w.computeStrides(t), v = n.length, x = w.computeStrides(n);
    if (h.length + f.length === 0)
      for (let k = 0; k < p.length; k++) {
        let I = k % m.length, $ = k % g.length, E = e(m[I * 2], m[I * 2 + 1], g[$ * 2], g[$ * 2 + 1]);
        p[k] = E.real, d[k] = E.imag;
      }
    else
      for (let k = 0; k < p.length; k++) {
        let I = w.indexToLoc(k, l, c), $ = I.slice(-b);
        h.forEach((F) => $[F] = 0);
        let E = w.locToIndex($, b, y), A = I.slice(-v);
        f.forEach((F) => A[F] = 0);
        let P = w.locToIndex(A, v, x), R = e(m[E * 2], m[E * 2 + 1], g[P * 2], g[P * 2 + 1]);
        p[k] = R.real, d[k] = R.imag;
      }
    return [p, d, o];
  };
}
var Q0 = At((e, t) => e + t);
var VU = rv((e, t, n, s) => ({ real: e + n, imag: t + s }));
var Xl = Ht(Sr, Q0, VU);
var WU = { kernelName: Sr, backendName: "cpu", kernelFunc: Xl };
function av(e, t, n, s, r) {
  let a = w.sizeFromShape(s), i = w.makeZerosTypedArray(r, n);
  for (let o = 0; o < e.length; o++) {
    let u = e[o];
    if (u < 0)
      throw new Error("Input x must be non-negative!");
    u >= r || (a > 0 ? i[u] += t[o] : i[u] += 1);
  }
  return i;
}
function Z0(e, t, n, s = false) {
  let r = e.shape[0], a = e.shape[1], i = De([r, n], t.dtype);
  for (let o = 0; o < r; o++)
    for (let u = 0; u < a; u++) {
      let l = e.get(o, u);
      if (l < 0)
        throw new Error("Input x must be non-negative!");
      l >= n || (s ? i.set(1, o, l) : t.size > 0 ? i.set(i.get(o, l) + t.get(o, u), o, l) : i.set(i.get(o, l) + 1, o, l));
    }
  return i;
}
function Er(e) {
  return (t, n, s) => {
    let r = w.getTypedArrayFromDType(n, t.length);
    for (let a = 0; a < t.length; ++a)
      r[a] = e(t[a], s);
    return r;
  };
}
function st(e, t, n) {
  return ({ inputs: s, attrs: r, backend: a }) => {
    let { x: i } = s;
    if (be(i, e), i.dtype === "string" || n === "string")
      throw new Error("unaryKernelFunc does not support string input/output");
    let o = a, u = o.data.get(i.dataId).values, l = w.sizeFromShape(i.shape), c = n || i.dtype, p = w.getArrayFromDType(c, l);
    for (let d = 0; d < l; ++d)
      p[d] = t(u[d], r);
    return o.makeTensorInfo(i.shape, c, p);
  };
}
function nu(e, t, n) {
  return ({ inputs: s, attrs: r, backend: a }) => {
    let { x: i } = s;
    if (be(i, e), i.dtype === "string" || n === "string")
      throw new Error("unaryKernelFunc does not support string input/output");
    let o = a, u = o.data.get(i.dataId).values, l = n || i.dtype, c = t(u, l, r);
    return o.makeTensorInfo(i.shape, l, c);
  };
}
var J0 = Er((e) => Math.ceil(e));
var UU = nu($a, J0);
var GU = { kernelName: $a, backendName: "cpu", kernelFunc: UU };
function iv(e, t, n, s) {
  let r = w.getArrayFromDType(n, w.sizeFromShape(t));
  if (s && n !== "string") {
    let a = 0;
    e.forEach((i) => {
      let o = w.sizeFromShape(i.shape);
      r.set(i.vals, a), a += o;
    });
  } else {
    let a = 0;
    e.forEach((i) => {
      let o = n === "string" ? C.fromUint8ToStringArray(i.vals) : i.vals, u = 0;
      for (let l = 0; l < i.shape[0]; ++l) {
        let c = l * t[1] + a;
        for (let p = 0; p < i.shape[1]; ++p)
          r[c + p] = o[u++];
      }
      a += i.shape[1];
    });
  }
  return r;
}
var eC = At((e, t) => e === t ? 1 : 0);
var tC = Ht(bo, eC, null, "bool");
var HU = { kernelName: bo, backendName: "cpu", kernelFunc: tC };
var nC = Er((e) => Math.exp(e));
var sC = nu(za, nC, "float32");
var qU = { kernelName: za, backendName: "cpu", kernelFunc: sC };
var rC = Er((e) => Math.expm1(e));
var jU = nu(vo, rC);
var KU = { kernelName: vo, backendName: "cpu", kernelFunc: jU };
var aC = Er((e) => Math.floor(e));
var XU = nu(Ma, aC);
var YU = { kernelName: Ma, backendName: "cpu", kernelFunc: XU };
function iC(e, t, n, s, r, a, i, o, u) {
  let l = De([s, a], n);
  for (let c = 0; c < s; c++) {
    let p = [], d = 0;
    for (let h = 0; h < r; h++) {
      let f = e[c * r + h];
      d += f * i[h], p.push(f);
    }
    if (d < 0 || d >= u / a)
      throw new Error(`Invalid indices: ${p} does not index into ${o}`);
    for (let h = 0; h < a; h++)
      l.values[c * a + h] = t.get(...t.indexToLoc(d * a + h));
  }
  return l;
}
function oC(e, t, n) {
  let s = De(n, e.dtype);
  for (let r = 0; r < s.size; ++r) {
    let i = s.indexToLoc(r).slice(), o = i[0], u = i[2], l = t.locToIndex([o, u]);
    i[2] = t.values[l];
    let c = e.locToIndex(i);
    0 <= c && c < e.values.length && (s.values[r] = e.values[c]);
  }
  return s;
}
var uC = At((e, t) => e > t ? 1 : 0);
var QU = Ht(So, uC, null, "bool");
var ZU = { kernelName: So, backendName: "cpu", kernelFunc: QU };
var lC = At((e, t) => e >= t ? 1 : 0);
var JU = Ht(Va, lC, null, "bool");
var eG = { kernelName: Va, backendName: "cpu", kernelFunc: JU };
var cC = At((e, t) => e < t ? 1 : 0);
var tG = Ht(Io, cC, null, "bool");
var nG = { kernelName: Io, backendName: "cpu", kernelFunc: tG };
var dC = At((e, t) => e <= t ? 1 : 0);
var sG = Ht(Co, dC, null, "bool");
var rG = { kernelName: Co, backendName: "cpu", kernelFunc: sG };
function pC(e, t, n) {
  let s = (t - e) / (n - 1), r = w.makeZerosTypedArray(n, "float32");
  r[0] = e;
  for (let a = 1; a < r.length; a++)
    r[a] = r[a - 1] + s;
  return r;
}
var hC = Er((e) => Math.log(e));
var aG = nu(Ga, hC);
var iG = { kernelName: Ga, backendName: "cpu", kernelFunc: aG };
function fC(e, t, n, s) {
  let r = w.getTypedArrayFromDType(s, w.sizeFromShape(n));
  for (let a = 0; a < r.length; ++a) {
    let i = a * t, o = e[i];
    for (let u = 0; u < t; ++u) {
      let l = e[i + u];
      (Number.isNaN(l) || l > o) && (o = l);
    }
    r[a] = o;
  }
  return r;
}
var mC = At((e, t) => Math.max(e, t));
var oG = Ht(qa, mC);
var uG = { kernelName: qa, backendName: "cpu", kernelFunc: oG };
var gC = At((e, t) => Math.min(e, t));
var lG = Ht(Ya, gC);
var cG = { kernelName: Ya, backendName: "cpu", kernelFunc: lG };
var ov = At((e, t) => e * t);
var dG = rv((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
var Yp = Ht(Za, ov, dG);
var pG = { kernelName: Za, backendName: "cpu", kernelFunc: Yp };
function bC(e, t, n) {
  let s = w.createScalarValue(-1, n);
  return ov([], t, s, e, n);
}
function hG(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  be(s, "neg");
  let r = n.data.get(s.dataId).values, [a, i] = bC(r, s.shape, s.dtype);
  return n.makeTensorInfo(i, s.dtype, a);
}
var fG = { kernelName: To, backendName: "cpu", kernelFunc: hG };
var yC = At((e, t) => e !== t ? 1 : 0);
var mG = Ht($o, yC, null, "bool");
var gG = { kernelName: $o, backendName: "cpu", kernelFunc: mG };
function uv(e, t, n, s, r) {
  let a = t.length, i = w.sizeFromShape(t), o = w.computeStrides(t), u = w.computeStrides(r), l = w.getTypedArrayFromDType(n, w.sizeFromShape(r));
  for (let c = 0; c < i; ++c) {
    let p = w.indexToLoc(c, a, o), d = new Array(p.length);
    for (let f = 0; f < d.length; f++)
      d[f] = p[s[f]];
    let h = w.locToIndex(d, a, u);
    l[h] = e[c];
  }
  return l;
}
function wn(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { perm: a } = n;
  be(r, "transpose");
  let i = r.shape.length, o = new Array(i);
  for (let p = 0; p < o.length; p++)
    o[p] = r.shape[a[p]];
  let u = s.data.get(r.dataId).values, l = uv(u, r.shape, r.dtype, a, o);
  return { dataId: s.write(l, o, r.dtype), shape: o, dtype: r.dtype };
}
var bG = { kernelName: mi, backendName: "cpu", kernelFunc: wn };
function vC(e, t, n, s) {
  let [r, a] = C.computeOutAndReduceShapes(e, s), i = cn(t, "int32"), o = w.makeZerosTypedArray(w.sizeFromShape(r), i), u = w.sizeFromShape(a);
  for (let l = 0; l < o.length; ++l) {
    let c = l * u, p = 1;
    for (let d = 0; d < u; ++d)
      p *= n[c + d];
    o[l] = p;
  }
  return { outVals: o, outShape: r, outDtype: i };
}
function yG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  be(r, "prod");
  let o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = C.getAxesPermutation(u, o), c = u, p = r, d = [];
  l != null && (p = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), d.push(p), c = C.getInnerMostAxes(c.length, o));
  let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = vC(p.shape, p.dtype, h, c), b = m;
  return i && (b = C.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
}
var vG = { kernelName: ni, backendName: "cpu", kernelFunc: yG };
function lv(e, t, n, s) {
  let r = e === t, a = e < t && n < 0, i = t < e && n > 1;
  if (r || a || i)
    return w.makeZerosTypedArray(0, s);
  let o = Math.abs(Math.ceil((t - e) / n)), u = w.makeZerosTypedArray(o, s);
  t < e && n === 1 && (n = -1), u[0] = e;
  for (let l = 1; l < u.length; l++)
    u[l] = u[l - 1] + n;
  return u;
}
var xC = Er((e) => 1 / Math.sqrt(e));
var xG = nu(ii, xC);
var wG = { kernelName: ii, backendName: "cpu", kernelFunc: xG };
var kG = Er((e) => 1 / (1 + Math.exp(-e)));
var wC = st(ui, (e) => 1 / (1 + Math.exp(-e)));
var SG = { kernelName: ui, backendName: "cpu", kernelFunc: wC };
function Ld(e, t, n, s, r) {
  let a = wt.isSliceContinous(s, t, n), i = w.sizeFromShape(n), o = w.computeStrides(s);
  if (a) {
    let p = wt.computeFlatOffset(t, o);
    return r === "string" ? e.slice(p, p + i) : e.subarray(p, p + i);
  }
  let u = r === "string" ? C.fromUint8ToStringArray(e) : e, l = De(s, r, u), c = De(n, r);
  for (let p = 0; p < c.size; ++p) {
    let d = c.indexToLoc(p), h = d.map((f, m) => f + t[m]);
    c.set(l.get(...h), ...d);
  }
  return r === "string" ? C.fromStringArrayToUint8(c.values) : c.values;
}
function ba(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s;
  be(r, "slice");
  let [o, u] = wt.parseSliceParams(r, a, i);
  wt.assertParamsValid(r, o, u);
  let l = n.data.get(r.dataId).values, c = Ld(l, o, u, r.shape, r.dtype);
  return n.makeTensorInfo(u, r.dtype, c);
}
var IG = { kernelName: Lo, backendName: "cpu", kernelFunc: ba };
function kC(e, t, n, s, r, a, i) {
  let o = t[0], u = a[0], l = new Array(u), c = new Array(o), p = t[1];
  if (u === 0) {
    if (o !== 0)
      throw new Error(C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(o));
    let g = w.getArrayFromDType(n, 0), b = w.getArrayFromDType(r, 0);
    return [g, [0, p], b, l, c];
  }
  let d = true, h = 0, f = new Array(u).fill(0);
  for (let g = 0; g < o; ++g) {
    let b = e[g * p];
    if (b < 0)
      throw new Error(C.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, b));
    if (b >= u)
      throw new Error(C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g, b, u));
    ++f[b], d = d && b >= h, h = b;
  }
  let m = true;
  for (let g = 0; g < u; ++g) {
    let b = f[g] === 0;
    l[g] = b, m = m && !b, f[g] = Math.max(f[g], 1), g > 0 && (f[g] += f[g - 1]);
  }
  if (m && d) {
    let g = e, b = s;
    for (let y = 0; y < o; ++y)
      c[y] = y;
    return [g, [o, p], b, l, c];
  } else {
    let g = f[u - 1], b = w.getArrayFromDType(n, g * p), y = w.getArrayFromDType(r, g), v = new Array(u).fill(0);
    for (let x = 0; x < o; ++x) {
      let k = e[x * p], I = v[k], $ = (k === 0 ? 0 : f[k - 1]) + I;
      v[k]++;
      for (let E = 0; E < p; ++E)
        b[$ * p + E] = e[x * p + E];
      y[$] = s[x], c[x] = $;
    }
    for (let x = 0; x < u; ++x)
      if (v[x] === 0) {
        let I = x === 0 ? 0 : f[x - 1];
        b[I * p + 0] = x;
        for (let $ = 1; $ < p; ++$)
          b[I * p + $] = 0;
        y[I] = i;
      }
    return [b, [g, p], y, l, c];
  }
}
function SC(e, t, n, s, r) {
  let a = w.sizeFromShape(s), i = t[0], o = r.length, u = [], l = 1, c = -1;
  for (let g = 0; g < o; ++g) {
    let b = r[g];
    if (b === -1) {
      if (c !== -1)
        throw new Error(C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
      c = g, u.push(1);
    } else {
      if (b < 0)
        throw new Error(C.getSparseReshapeNegativeOutputDimErrorMessage(g, b));
      l *= b, u.push(b);
    }
  }
  if (c !== -1) {
    if (l <= 0)
      throw new Error(C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
    let g = Math.trunc(a / l);
    if (l * g !== a)
      throw new Error(C.getSparseReshapeInputOutputMultipleErrorMessage(s, u));
    u[c] = g;
  }
  if (w.sizeFromShape(u) !== a)
    throw new Error(C.getSparseReshapeInputOutputMismatchErrorMessage(s, u));
  let d = s.length, h = [];
  if (d > 0) {
    h[d - 1] = 1;
    for (let g = d - 2; g >= 0; --g)
      h[g] = h[g + 1] * s[g + 1];
  }
  let f = [];
  if (o > 0) {
    f[o - 1] = 1;
    for (let g = o - 2; g >= 0; --g)
      f[g] = f[g + 1] * u[g + 1];
  }
  let m = w.getArrayFromDType(n, i * o);
  for (let g = 0; g < i; ++g) {
    let b = 0;
    for (let y = 0; y < d; ++y)
      b += e[g * d + y] * h[y];
    for (let y = 0; y < o; ++y)
      m[g * o + y] = Math.trunc(b / f[y]), b %= f[y];
  }
  return [m, [i, o], u];
}
function cv(e, t, n, s, r, a = false, i = 0) {
  let o = s.length, u = [t[0], e.length / t[0]], l = u[1], p = o > 0 ? r[o - 1] + 1 : 0;
  if (p < 0)
    throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let d = t.slice();
  d[0] = p;
  let h = d.reduce((v, x) => v * x, 1), f = w.getArrayFromDType(n, h);
  if (o === 0)
    return p > 0 && f.fill(i), [f, d];
  if (p <= 0)
    throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let m = 0, g = 1, b = 0, y = r[m];
  for (; ; ) {
    let v = 0;
    if (g < o) {
      if (v = r[g], y === v) {
        ++g;
        continue;
      }
      if (y >= v)
        throw new Error(C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
    }
    if (y < 0 || y >= p)
      throw new Error(C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(y, p));
    y > b && f.fill(i, b * l, y * l);
    for (let x = m; x < g; ++x) {
      let k = s[x];
      if (k < 0 || k >= u[0])
        throw new Error(C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x, s[x], u[0]));
      for (let I = 0; I < l; I++)
        f[y * l + I] += e[k * l + I];
    }
    if (a)
      for (let x = 0; x < l; x++)
        f[y * l + x] /= g - m;
    if (m = g, ++g, b = y + 1, y = v, g > o)
      break;
  }
  return b < p && f.fill(i, b * l, p * l), [f, d];
}
var CG = Er((e) => Math.sqrt(e));
var NG = st(li, (e) => Math.sqrt(e));
var TG = { kernelName: li, backendName: "cpu", kernelFunc: NG };
var IC = At((e, t) => {
  let n = e - t;
  return n * n;
});
var $G = Ht(pi, IC);
var _G = { kernelName: pi, backendName: "cpu", kernelFunc: $G };
function CC(e, t, n, s) {
  let r = De(e, t.dtype);
  for (let a = 0; a < r.size; a++) {
    let i = r.indexToLoc(a), o = new Array(i.length);
    for (let u = 0; u < o.length; u++)
      o[u] = i[u] * n[u] + s[u];
    r.set(t.get(...o), ...i);
  }
  return r;
}
var AG = class {
  constructor(e, t, n, s, r, a) {
    this.separator = w.encodeString(e), this.nGramWidths = t, this.leftPad = w.encodeString(n), this.rightPad = w.encodeString(s), this.padWidth = r, this.preserveShort = a;
  }
  getPadWidth(e) {
    return Math.min(this.padWidth < 0 ? e - 1 : this.padWidth, e - 1);
  }
  getNumNGrams(e, t) {
    let n = this.getPadWidth(t);
    return Math.max(0, e + 2 * n - t + 1);
  }
  createNGrams(e, t, n, s, r, a) {
    for (let i = 0; i < r; ++i) {
      let o = this.getPadWidth(a), u = Math.max(0, o - i), l = Math.max(0, o - (r - (i + 1))), c = a - (u + l), p = t + (u > 0 ? 0 : i - o), d = 0;
      d += u * this.leftPad.length;
      for (let b = 0; b < c; ++b)
        d += e[p + b].length;
      d += l * this.rightPad.length, d += (u + l + c - 1) * this.separator.length, n[s + i] = new Uint8Array(d);
      let f = n[s + i], m = 0, g = (b) => b.forEach((y) => f[m++] = y);
      for (let b = 0; b < u; ++b)
        g(this.leftPad), g(this.separator);
      for (let b = 0; b < c - 1; ++b)
        g(e[p + b]), g(this.separator);
      if (c > 0) {
        g(e[p + c - 1]);
        for (let b = 0; b < l; ++b)
          g(this.separator), g(this.rightPad);
      } else {
        for (let b = 0; b < l - 1; ++b)
          g(this.rightPad), g(this.separator);
        g(this.rightPad);
      }
    }
  }
  compute(e, t) {
    let n = e.length, s = t.length;
    if (s > 0) {
      let o = t[0];
      if (o !== 0)
        throw new Error(`First split value must be 0, got ${o}`);
      for (let u = 1; u < s; ++u) {
        let l = t[u] >= o;
        if (l = l && t[u] <= n, !l)
          throw new Error(`Invalid split value ${t[u]}, must be in [${o}, ${n}]`);
        o = t[u];
      }
      if (o !== n)
        throw new Error(`Last split value must be data size. Expected ${n}, got ${o}`);
    }
    let r = s - 1, a = w.getArrayFromDType("int32", s);
    if (n === 0 || s === 0) {
      let o = new Array(n);
      for (let u = 0; u <= r; ++u)
        a[u] = 0;
      return [o, a];
    }
    a[0] = 0;
    for (let o = 1; o <= r; ++o) {
      let u = t[o] - t[o - 1], l = 0;
      this.nGramWidths.forEach((c) => {
        l += this.getNumNGrams(u, c);
      }), this.preserveShort && u > 0 && l === 0 && (l = 1), a[o] = a[o - 1] + l;
    }
    let i = new Array(a[r]);
    for (let o = 0; o < r; ++o) {
      let u = t[o], l = a[o];
      if (this.nGramWidths.forEach((c) => {
        let p = t[o + 1] - t[o], d = this.getNumNGrams(p, c);
        this.createNGrams(e, u, i, l, d, c), l += d;
      }), this.preserveShort && l === a[o]) {
        let c = t[o + 1] - t[o];
        if (c === 0)
          continue;
        let p = c + 2 * this.padWidth, d = 1;
        this.createNGrams(e, u, i, l, d, p);
      }
    }
    return [i, a];
  }
};
function NC(e, t, n, s, r, a, i, o) {
  return new AG(n, s, r, a, i, o).compute(e, t);
}
function EG(e, t, n, s) {
  if (!e.length)
    return;
  if (t.length === 0) {
    for (let a = 0; a < e.length; ++a)
      s.push(e.subarray(a, a + 1));
    return;
  }
  if (t.length === 1) {
    let a = t[0], i = e.indexOf(a);
    for (; i !== -1; ) {
      let o = e.subarray(0, i);
      (!n || o.length !== 0) && s.push(o), e = e.subarray(i + 1), i = e.indexOf(a);
    }
    (!n || e.length !== 0) && s.push(e);
    return;
  }
  let r = 0;
  for (let a = 0; a < e.length + 1; a++)
    if (a === e.length || t.indexOf(e[a]) !== -1) {
      let i = e.subarray(r, a);
      (!n || i.length !== 0) && s.push(i), r = a + 1;
    }
}
function TC(e, t, n) {
  let s = e.length, r = [], a = 0, i = 0, o = new Array(s);
  for (let d = 0; d < s; ++d) {
    let h = r.length;
    EG(e[d], t, n, r);
    let f = r.length - h;
    o[d] = f, a += f, i = Math.max(i, f);
  }
  let u = w.getArrayFromDType("int32", a * 2), l = new Array(a), c = [s, i], p = 0;
  for (let d = 0; d < s; ++d)
    for (let h = 0; h < o[d]; ++h)
      u[p * 2] = d, u[p * 2 + 1] = h, l[p] = r[p], ++p;
  return [u, l, c];
}
function $C(e, t) {
  let n = w.getArrayFromDType("int32", e.length);
  for (let s = 0; s < e.length; ++s)
    n[s] = w.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();
  return n;
}
var _C = At((e, t) => e - t);
var RG = rv((e, t, n, s) => ({ real: e - n, imag: t - s }));
var dv = Ht(hi, _C, RG);
var DG = { kernelName: hi, backendName: "cpu", kernelFunc: dv };
function AC(e, t) {
  let n = new Array(e.rank);
  for (let r = 0; r < n.length; r++)
    n[r] = e.shape[r] * t[r];
  let s = De(n, e.dtype);
  for (let r = 0; r < s.values.length; ++r) {
    let a = s.indexToLoc(r), i = new Array(e.rank);
    for (let u = 0; u < i.length; u++)
      i[u] = a[u] % e.shape[u];
    let o = e.locToIndex(i);
    s.values[r] = e.values[o];
  }
  return s;
}
var Fu = (e, t) => {
  let n = t.value - e.value;
  return n === 0 ? e.index - t.index : n;
};
function EC(e, t, n = 0, s = e.length - 1) {
  for (; s > n; ) {
    if (s - n > 600) {
      let o = s - n + 1, u = t - n + 1, l = Math.log(o), c = 0.5 * Math.exp(2 * l / 3), p = 0.5 * Math.sqrt(l * c * (o - c) / o) * Math.sign(u - o / 2), d = Math.max(n, Math.floor(t - u * c / o + p)), h = Math.min(s, Math.floor(t + (o - u) * c / o + p));
      EC(e, t, d, h);
    }
    let r = e[t], a = n, i = s;
    for (w.swap(e, n, t), Fu(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
      for (w.swap(e, a, i), a++, i--; Fu(e[a], r) < 0; )
        a = a + 1;
      for (; Fu(e[i], r) > 0; )
        i = i - 1;
    }
    Fu(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
  }
}
function RC(e, t, n, s, r) {
  let a = t[t.length - 1], [i, o] = [e.length / a, a], u = w.getTypedArrayFromDType(n, i * s), l = w.getTypedArrayFromDType("int32", i * s);
  for (let p = 0; p < i; p++) {
    let d = p * o, h = e.subarray(d, d + o), f = new Array(h.length);
    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (EC(f, s), f = f.slice(0, s)), r && f.sort(Fu);
    let m = p * s, g = u.subarray(m, m + s), b = l.subarray(m, m + s);
    for (let y = 0; y < s; y++)
      g[y] = f[y].value, b[y] = f[y].index;
  }
  let c = t.slice();
  return c[c.length - 1] = s, [De(c, n, u), De(c, "int32", l)];
}
function DC(e, t, n, s) {
  let r = w.parseAxisParam(t, n)[0], a = [1, n[0], 1];
  for (let f = 0; f < r; f++)
    a[0] *= n[f];
  a[1] = n[r];
  for (let f = r + 1; f < n.length; f++)
    a[2] *= n[f];
  let i = {}, o = new Int32Array(n[r]), u = new Wt(a, s, e), l = [], c = a[0] === 1 && a[2] === 1;
  for (let f = 0; f < n[r]; f++) {
    let m;
    if (c)
      m = e[f].toString();
    else {
      let g = [];
      for (let b = 0; b < a[0]; b++)
        for (let y = 0; y < a[2]; y++)
          g.push(u.get(b, f, y));
      m = g.join(",");
    }
    if (i[m] !== void 0)
      o[f] = i[m];
    else {
      let g = Object.keys(i).length;
      i[m] = g, o[f] = g, l.push(f);
    }
  }
  let p = a.slice();
  p[1] = Object.keys(i).length;
  let d = new Wt(p, s);
  l.forEach((f, m) => {
    for (let g = 0; g < a[0]; g++)
      for (let b = 0; b < a[2]; b++)
        d.set(u.get(g, f, b), g, m, b);
  });
  let h = n.slice();
  return h[r] = p[1], { outputValues: d.values, outputShape: h, indices: o };
}
var che = "0.0.0";
bp("cpu", () => new X0(), 1);
var FC = st(Pa, (e) => e >= 0 ? e : Math.exp(e) - 1);
var FG = { kernelName: Pa, backendName: "cpu", kernelFunc: FC };
function OC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s;
  be([r], "leakyRelu");
  let i = w.sizeFromShape(r.shape), o = n.data.get(r.dataId).values, u = w.getTypedArrayFromDType("float32", i);
  for (let l = 0; l < o.length; l++)
    u[l] = o[l] < 0 ? a * o[l] : o[l];
  return n.makeTensorInfo(r.shape, "float32", u);
}
var OG = { kernelName: Ua, backendName: "cpu", kernelFunc: OC };
var PG = At((e, t) => e < 0 ? t * e : e);
function PC(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t;
  be([s, r], "prelu");
  let a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, [o, u] = PG(s.shape, r.shape, a, i, "float32");
  return n.makeTensorInfo(u, "float32", o);
}
var zG = { kernelName: ti, backendName: "cpu", kernelFunc: PC };
var zC = st(si, (e) => Math.max(0, e));
var MG = { kernelName: si, backendName: "cpu", kernelFunc: zC };
var MC = st(ai, (e) => Math.min(Math.max(0, e), 6));
var LG = { kernelName: ai, backendName: "cpu", kernelFunc: MC };
function pv(e, t, n, s, r) {
  if (n === "linear")
    return Os({ inputs: { x: t }, backend: e });
  if (n === "relu")
    return zC({ inputs: { x: t }, backend: e });
  if (n === "elu")
    return FC({ inputs: { x: t }, backend: e });
  if (n === "relu6")
    return MC({ inputs: { x: t }, backend: e });
  if (n === "prelu")
    return PC({ inputs: { x: t, alpha: s }, backend: e });
  if (n === "leakyrelu")
    return OC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
  if (n === "sigmoid")
    return wC({ inputs: { x: t }, backend: e });
  throw new Error(`Activation ${n} has not been implemented for the CPU backend.`);
}
function mt(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = w.sizeFromShape(r.shape), o = w.inferFromImplicitShape(a, i), u = w.sizeFromShape(o);
  w.assert(i === u, () => `The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`), n.incRef(r.dataId);
  let l = n.data.get(r.dataId);
  if (l.complexTensorInfos != null) {
    let c = l.complexTensorInfos.real, p = l.complexTensorInfos.imag;
    c.shape = o, p.shape = o;
  }
  return { dataId: r.dataId, shape: o, dtype: r.dtype };
}
var BG = { kernelName: Fo, backendName: "cpu", kernelFunc: mt };
function LC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  be([r, a], "matMul");
  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = bi.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
  w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
  let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], I = mt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = mt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = i ? I.shape[1] : I.shape[2], A = i ? I.shape[2] : I.shape[1], P = o ? $.shape[1] : $.shape[2], R = Math.max(g, b), F = n.data.get(I.dataId).values, T = n.data.get($.dataId).values, z = w.computeStrides(I.shape), W = w.computeStrides($.shape), [j, X, Y] = i ? [z[0], 1, z[1]] : [z[0], z[1], 1], [Z, te, J] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = A * P, ne = De([R, A, P], I.dtype), oe = ne.values, ae = n.blockSize;
  for (let de = 0; de < R; de++)
    for (let me = 0; me < A; me += ae)
      for (let ke = 0; ke < P; ke += ae)
        for (let Ie = 0; Ie < E; Ie += ae) {
          let Ee = Math.min(me + ae, A), Pe = Math.min(ke + ae, P), Xe = Math.min(Ie + ae, E);
          for (let Je = me; Je < Ee; Je++)
            for (let Ye = ke; Ye < Pe; Ye++) {
              let tt = 0;
              for (let Ce = Ie; Ce < Xe; Ce++) {
                let ut = Math.min(de, g - 1) * j, rt = Math.min(de, b - 1) * J, Jt = F[ut + Je * X + Ce * Y], Nt = T[Ce * Z + Ye * te + rt];
                tt += Jt * Nt;
              }
              oe[de * se + (Je * P + Ye)] += tt;
            }
        }
  return n.disposeIntermediateTensorInfo(I), n.disposeIntermediateTensorInfo($), n.makeTensorInfo(v, ne.dtype, ne.values);
}
var VG = { kernelName: Na, backendName: "cpu", kernelFunc: LC };
function WG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d, h, f, m = [];
  d = LC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = Xl({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = pv(n, d, c, o, p), m.push(d), d = f);
  for (let b of m)
    n.disposeIntermediateTensorInfo(b);
  return d;
}
var UG = { kernelName: aa, backendName: "cpu", kernelFunc: WG };
var GG = st(al, (e) => Math.acos(e));
var HG = { kernelName: al, backendName: "cpu", kernelFunc: GG };
var qG = st(il, (e) => Math.acosh(e));
var jG = { kernelName: il, backendName: "cpu", kernelFunc: qG };
function KG(e) {
  let { inputs: t, backend: n } = e, s = t;
  be(t, "addN");
  let r = s.map((o) => n.data.get(o.dataId).values), a = De(s[0].shape, s[0].dtype), i = a.values;
  for (let o = 0; o < s.length; o++) {
    let u = r[o];
    for (let l = 0; l < i.length; l++)
      i[l] += u[l];
  }
  return n.makeTensorInfo(a.shape, a.dtype, a.values);
}
var XG = { kernelName: Sa, backendName: "cpu", kernelFunc: KG };
function YG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  be(r, "all");
  let o = w.parseAxisParam(a, r.shape), u = o, l = C.getAxesPermutation(u, r.shape.length), c = r;
  l != null && (c = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = C.getInnerMostAxes(u.length, r.shape.length)), C.assertAxesAreInnerMostDims("all", u, c.shape.length);
  let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = w.sizeFromShape(d), f = w.makeZerosTypedArray(w.sizeFromShape(p), c.dtype), m = n.data.get(c.dataId).values;
  for (let b = 0; b < f.length; ++b) {
    let y = b * h, v = m[y];
    for (let x = 0; x < h; ++x) {
      let k = m[y + x];
      v = v && k;
    }
    f[b] = v;
  }
  l != null && n.disposeIntermediateTensorInfo(c);
  let g = n.makeTensorInfo(p, c.dtype, f);
  if (i) {
    let b = C.expandShapeToKeepDim(p, o), y = mt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var QG = { kernelName: ol, backendName: "cpu", kernelFunc: YG };
function ZG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  be(r, "any");
  let o = w.parseAxisParam(a, r.shape), u = o, l = C.getAxesPermutation(u, r.shape.length), c = r;
  l != null && (c = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = C.getInnerMostAxes(u.length, r.shape.length)), C.assertAxesAreInnerMostDims("any", u, c.shape.length);
  let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = w.sizeFromShape(d), f = w.makeZerosTypedArray(w.sizeFromShape(p), c.dtype), m = n.data.get(c.dataId).values;
  for (let b = 0; b < f.length; ++b) {
    let y = b * h, v = m[y];
    for (let x = 0; x < h; ++x) {
      let k = m[y + x];
      v = v || k;
    }
    f[b] = v;
  }
  l != null && n.disposeIntermediateTensorInfo(c);
  let g = n.makeTensorInfo(p, c.dtype, f);
  if (i) {
    let b = C.expandShapeToKeepDim(p, o), y = mt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var JG = { kernelName: ul, backendName: "cpu", kernelFunc: ZG };
function eH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
  be(r, "argMax");
  let i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = wn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), i = [i[0]], C.assertAxesAreInnerMostDims("argMax", i, u.shape.length);
  let [c, p] = C.computeOutAndReduceShapes(u.shape, i), d = w.sizeFromShape(c), h = w.makeZerosTypedArray(d, "int32"), f = w.sizeFromShape(p), m = n.data.get(u.dataId).values;
  for (let g = 0; g < h.length; ++g) {
    let b = g * f, y = m[b], v = 0;
    for (let x = 0; x < f; ++x) {
      let k = m[b + x];
      k > y && (y = k, v = x);
    }
    h[g] = v;
  }
  return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
}
var tH = { kernelName: Ia, backendName: "cpu", kernelFunc: eH };
function nH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
  be(r, "argMin");
  let i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = wn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), i = [i[0]], C.assertAxesAreInnerMostDims("argMin", i, u.shape.length);
  let [c, p] = C.computeOutAndReduceShapes(u.shape, i), d = w.sizeFromShape(c), h = w.makeZerosTypedArray(d, "int32"), f = w.sizeFromShape(p), m = n.data.get(u.dataId).values;
  for (let g = 0; g < h.length; ++g) {
    let b = g * f, y = m[b], v = 0;
    for (let x = 0; x < f; ++x) {
      let k = m[b + x];
      k < y && (y = k, v = x);
    }
    h[g] = v;
  }
  return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
}
var sH = { kernelName: ll, backendName: "cpu", kernelFunc: nH };
var rH = st(cl, (e) => Math.asin(e));
var aH = { kernelName: cl, backendName: "cpu", kernelFunc: rH };
var iH = st(dl, (e) => Math.asinh(e));
var oH = { kernelName: dl, backendName: "cpu", kernelFunc: iH };
var uH = st(pl, (e) => Math.atan(e));
var lH = { kernelName: pl, backendName: "cpu", kernelFunc: uH };
var cH = At((e, t) => Math.atan2(e, t));
var dH = Ht(fl, cH);
var pH = { kernelName: fl, backendName: "cpu", kernelFunc: dH };
var hH = st(hl, (e) => Math.atanh(e));
var fH = { kernelName: hl, backendName: "cpu", kernelFunc: hH };
function hv(e, t, n, s, r, a) {
  let i = r.strideHeight, o = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = De(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
  for (let x = 0; x < r.batchSize; ++x) {
    let k = x * b, I = x * s[0];
    for (let $ = 0; $ < r.inChannels; ++$)
      for (let E = 0; E < r.outHeight; ++E) {
        let A = E * i - d, P = Math.max(0, A), R = Math.min(r.inHeight, c + A), F = k + E * y;
        for (let T = 0; T < r.outWidth; ++T) {
          let z = T * o - h, W = Math.max(0, z), j = Math.min(r.inWidth, p + z), X = f, Y = 0, Z = 0;
          for (let J = P; J < R; J += u) {
            let se = I + J * s[1];
            for (let ne = W; ne < j; ne += l) {
              let oe = se + ne * s[2], ae = e[oe + $];
              a === "max" && ae > X ? X = ae : a === "avg" && (Y += ae, Z++);
            }
            if (isNaN(X))
              break;
          }
          let te = F + T * v + $;
          g[te] = a === "avg" ? Y / Z : X;
        }
      }
  }
  return m;
}
function BC(e, t, n, s, r = false, a = false) {
  let i = De(s.outShape, "int32"), o = s.strideHeight, u = s.strideWidth, l = s.dilationHeight, c = s.dilationWidth, p = s.effectiveFilterHeight, d = s.effectiveFilterWidth, h = s.padInfo.top, f = s.padInfo.left, m = De(t, n, e);
  for (let g = 0; g < s.batchSize; ++g)
    for (let b = 0; b < s.inChannels; ++b)
      for (let y = 0; y < s.outHeight; ++y) {
        let v = y * o - h, x = v;
        for (; x < 0; )
          x += l;
        let k = Math.min(s.inHeight, p + v);
        for (let I = 0; I < s.outWidth; ++I) {
          let $ = I * u - f, E = $;
          for (; E < 0; )
            E += c;
          let A = Math.min(s.inWidth, d + $), P = Number.NEGATIVE_INFINITY, R = -1;
          for (let F = x; F < k; F += l) {
            let T = F - v;
            for (let z = E; z < A; z += c) {
              let W = z - $, j = m.get(g, F, z, b);
              j > P && (P = j, r ? R = a ? ((g * s.inHeight + F) * s.inWidth + z) * s.inChannels + b : (F * s.inWidth + z) * s.inChannels + b : R = T * d + W);
            }
          }
          i.set(R, g, y, I, b);
        }
      }
  return i;
}
function VC(e, t, n, s, r, a) {
  let i = r.strideDepth, o = r.strideHeight, u = r.strideWidth, l = r.dilationDepth, c = r.dilationHeight, p = r.dilationWidth, d = r.effectiveFilterDepth, h = r.effectiveFilterHeight, f = r.effectiveFilterWidth, m = r.padInfo.front, g = r.padInfo.top, b = r.padInfo.left, y = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, v = De(r.outShape, n), x = v.values, k = r.outShape[1] * r.outShape[2] * r.outShape[3] * r.outShape[4], I = r.outShape[2] * r.outShape[3] * r.outShape[4], $ = r.outShape[3] * r.outShape[4], E = r.outShape[4];
  for (let A = 0; A < r.batchSize; ++A) {
    let P = A * k, R = A * s[0];
    for (let F = 0; F < r.inChannels; ++F)
      for (let T = 0; T < r.outDepth; ++T) {
        let z = T * i - m, W = z;
        for (; W < 0; )
          W += l;
        let j = Math.min(r.inDepth, d + z), X = P + T * I;
        for (let Y = 0; Y < r.outHeight; ++Y) {
          let Z = Y * o - g, te = Z;
          for (; te < 0; )
            te += c;
          let J = Math.min(r.inHeight, h + Z), se = X + Y * $;
          for (let ne = 0; ne < r.outWidth; ++ne) {
            let oe = ne * u - b, ae = oe;
            for (; ae < 0; )
              ae += p;
            let de = Math.min(r.inWidth, f + oe), me = se + ne * E, ke = y, Ie = 0, Ee = 0;
            for (let Xe = W; Xe < j; Xe += l) {
              let Je = R + Xe * s[1];
              for (let Ye = te; Ye < J; Ye += c) {
                let tt = Je + Ye * s[2];
                for (let Ce = ae; Ce < de; Ce += p) {
                  let ut = tt + Ce * s[3], rt = e[ut + F];
                  if (a === "max" && rt > ke ? ke = rt : a === "avg" && (Ie += rt, Ee++), isNaN(ke))
                    break;
                }
                if (isNaN(ke))
                  break;
              }
              if (isNaN(ke))
                break;
            }
            let Pe = me + F;
            x[Pe] = a === "avg" ? Ie / Ee : ke;
          }
        }
      }
  }
  return v;
}
function mH(e, t) {
  let n = De(t.outShape, "int32"), s = t.strideDepth, r = t.strideHeight, a = t.strideWidth, i = t.dilationDepth, o = t.dilationHeight, u = t.dilationWidth, l = t.effectiveFilterDepth, c = t.effectiveFilterHeight, p = t.effectiveFilterWidth, d = t.padInfo.front, h = t.padInfo.top, f = t.padInfo.left;
  for (let m = 0; m < t.batchSize; ++m)
    for (let g = 0; g < t.inChannels; ++g)
      for (let b = 0; b < t.outDepth; ++b) {
        let y = b * s - d, v = y;
        for (; v < 0; )
          v += i;
        let x = Math.min(t.inDepth, l + y);
        for (let k = 0; k < t.outHeight; ++k) {
          let I = k * r - h, $ = I;
          for (; $ < 0; )
            $ += o;
          let E = Math.min(t.inHeight, c + I);
          for (let A = 0; A < t.outWidth; ++A) {
            let P = A * a - f, R = P;
            for (; R < 0; )
              R += u;
            let F = Math.min(t.inWidth, p + P), T = Number.NEGATIVE_INFINITY, z = -1;
            for (let W = v; W < x; W += i) {
              let j = W - y;
              for (let X = $; X < E; X += o) {
                let Y = X - I;
                for (let Z = R; Z < F; Z += u) {
                  let te = Z - P, J = e.get(m, W, X, Z, g);
                  J >= T && (T = J, z = j * c * p + Y * c + te);
                }
              }
            }
            n.set(z, m, b, k, A, g);
          }
        }
      }
  return n;
}
function gH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  be(r, "avgPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = C.computePool2DInfo(r.shape, a, i, l, o, u), p;
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    p = Os({ inputs: { x: r }, backend: n });
  else {
    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = hv(d, r.shape, r.dtype, h, c, "avg");
    p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
  }
  return p;
}
var bH = { kernelName: Ca, backendName: "cpu", kernelFunc: gH };
function yH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
  be(r, "avgPool3d");
  let c = C.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = VC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
  return n.makeTensorInfo(d.shape, "float32", d.values);
}
var vH = { kernelName: Qd, backendName: "cpu", kernelFunc: yH };
function xH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
  be([r, a], "avgPool3DGrad");
  let c = C.computePool3DInfo(a.shape, i, o, 1, u, l), p = c.strideDepth, d = c.strideHeight, h = c.strideWidth, f = c.filterDepth, m = c.filterHeight, g = c.filterWidth, b = c.dilationDepth, y = c.dilationHeight, v = c.dilationWidth, x = c.effectiveFilterDepth, k = c.effectiveFilterHeight, I = c.effectiveFilterWidth, $ = x - 1 - c.padInfo.front, E = I - 1 - c.padInfo.left, A = k - 1 - c.padInfo.top, P = De(a.shape, "float32"), R = 1 / (f * m * g), F = n.bufferSync(r);
  for (let T = 0; T < c.batchSize; ++T)
    for (let z = 0; z < c.inChannels; ++z)
      for (let W = 0; W < c.inDepth; ++W)
        for (let j = 0; j < c.inHeight; ++j)
          for (let X = 0; X < c.inWidth; ++X) {
            let Y = W - $, Z = j - A, te = X - E, J = 0;
            for (let se = 0; se < x; se += b) {
              let ne = (Y + se) / p;
              if (!(ne < 0 || ne >= c.outDepth || Math.floor(ne) !== ne))
                for (let oe = 0; oe < k; oe += y) {
                  let ae = (Z + oe) / d;
                  if (!(ae < 0 || ae >= c.outHeight || Math.floor(ae) !== ae))
                    for (let de = 0; de < I; de += v) {
                      let me = (te + de) / h;
                      if (me < 0 || me >= c.outWidth || Math.floor(me) !== me)
                        continue;
                      J += F.get(T, ne, ae, me, z);
                    }
                }
            }
            P.set(J * R, T, W, j, X, z);
          }
  return n.makeTensorInfo(P.shape, P.dtype, P.values);
}
var wH = { kernelName: dg, backendName: "cpu", kernelFunc: xH };
function kH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
  be([r, a], "avgPoolGrad");
  let { filterSize: o, strides: u, pad: l } = s, c = C.computePool2DInfo(i.shape, o, u, 1, l), p = c.strideHeight, d = c.strideWidth, h = c.filterHeight, f = c.filterWidth, m = c.dilationHeight, g = c.dilationWidth, b = c.effectiveFilterHeight, y = c.effectiveFilterWidth, v = y - 1 - c.padInfo.left, x = b - 1 - c.padInfo.top, k = De(i.shape, "float32"), I = 1 / (h * f), $ = n.data.get(r.dataId).values, E = De(r.shape, "float32", $);
  for (let A = 0; A < c.batchSize; ++A)
    for (let P = 0; P < c.inChannels; ++P)
      for (let R = 0; R < c.inHeight; ++R)
        for (let F = 0; F < c.inWidth; ++F) {
          let T = R - x, z = F - v, W = 0;
          for (let j = 0; j < b; j += m) {
            let X = (T + j) / p;
            if (!(X < 0 || X >= c.outHeight || Math.floor(X) !== X))
              for (let Y = 0; Y < y; Y += g) {
                let Z = (z + Y) / d;
                if (Z < 0 || Z >= c.outWidth || Math.floor(Z) !== Z)
                  continue;
                W += E.get(A, X, Z, P);
              }
          }
          k.set(W * I, A, R, F, P);
        }
  return n.makeTensorInfo(k.shape, k.dtype, k.values);
}
var SH = { kernelName: cg, backendName: "cpu", kernelFunc: kH };
function IH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, scale: a, offset: i, mean: o, variance: u } = t;
  w.assert(o.shape.length === u.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || o.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(a == null || o.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), be([r, o, u, a, i], "batchNorm");
  let { varianceEpsilon: l } = s;
  l == null && (l = 1e-3);
  let c = n.data.get(r.dataId).values, p = n.data.get(o.dataId).values, d = n.data.get(u.dataId).values, h = a ? n.data.get(a.dataId).values : new Float32Array([1]), f = i ? n.data.get(i.dataId).values : new Float32Array([0]), m = new Float32Array(c.length), g = f.length, b = h.length, y = d.length, v = p.length, x = 0, k = 0, I = 0, $ = 0;
  for (let E = 0; E < c.length; ++E)
    m[E] = f[x++] + (c[E] - p[k++]) * h[I++] / Math.sqrt(d[$++] + l), x >= g && (x = 0), k >= v && (k = 0), I >= b && (I = 0), $ >= y && ($ = 0);
  return n.makeTensorInfo(r.shape, r.dtype, m);
}
var CH = { kernelName: Ba, backendName: "cpu", kernelFunc: IH };
function NH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
  be([r], "batchToSpaceND");
  let o = a.reduce((b, y) => b * y), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = mt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
  return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
}
var TH = { kernelName: po, backendName: "cpu", kernelFunc: NH };
function $H(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = av(o, u, a.dtype, a.shape, i);
  return n.makeTensorInfo([i], a.dtype, l);
}
var _H = { kernelName: pg, backendName: "cpu", kernelFunc: $H };
function AH(e) {
  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = C.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
  return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
}
var EH = { kernelName: hg, backendName: "cpu", kernelFunc: AH };
var RH = st(Ir, (e, t) => {
  let n = t;
  return e > n.clipValueMax ? n.clipValueMax : e < n.clipValueMin ? n.clipValueMin : e;
});
var DH = { kernelName: Ir, backendName: "cpu", kernelFunc: RH };
var FH = (e) => {
  let { x: t } = e.inputs, n = e.backend, s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId), a = r.complexTensorInfos.real, i = r.complexTensorInfos.imag, o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values;
  for (let l = 0; l < o.length; l++) {
    let c = o[l], p = u[l];
    s[l] = Math.hypot(c, p);
  }
  return n.makeOutput(s, t.shape, "float32");
};
var OH = { kernelName: Jd, backendName: "cpu", kernelFunc: FH };
function io(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.imag, a = n.data.get(r.dataId).values;
  return n.makeTensorInfo(r.shape, r.dtype, a);
}
var PH = { kernelName: sp, backendName: "cpu", kernelFunc: io };
function oo(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = C.computeOutShape(t.map((m) => m.shape), a);
  if (w.sizeFromShape(i) === 0)
    return n.makeTensorInfo(i, t[0].dtype, []);
  let o = t.filter((m) => w.sizeFromShape(m.shape) > 0);
  if (o.length === 1)
    return Os({ inputs: { x: o[0] }, backend: n });
  let u = o.map((m) => m.shape);
  if (C.assertParamsConsistent(u, a), o[0].dtype === "complex64") {
    let m = o.map((x) => ga({ inputs: { input: x }, backend: n })), g = o.map((x) => io({ inputs: { input: x }, backend: n })), b = oo({ inputs: m, backend: n, attrs: { axis: a } }), y = oo({ inputs: g, backend: n, attrs: { axis: a } }), v = En({ inputs: { real: b, imag: y }, backend: n });
    return m.forEach((x) => n.disposeIntermediateTensorInfo(x)), g.forEach((x) => n.disposeIntermediateTensorInfo(x)), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), v;
  }
  let l = o.map((m) => {
    let g = w.sizeFromShape(m.shape.slice(a));
    return mt({ inputs: { x: m }, backend: n, attrs: { shape: [-1, g] } });
  }), c = l.map((m) => ({ vals: n.data.get(m.dataId).values, shape: m.shape }));
  i = C.computeOutShape(l.map((m) => m.shape), 1);
  let p = l[0].shape[0] === 1, d = iv(c, i, t[0].dtype, p), h = C.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
  return l.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
}
var zH = { kernelName: ho, backendName: "cpu", kernelFunc: oo };
function WC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s;
  be([r, a], "conv2d");
  let p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p), h = d.filterHeight, f = d.filterWidth, m = d.dilationHeight, g = d.dilationWidth, b = d.padInfo.left, y = d.padInfo.top, v = d.dataFormat === "channelsLast", x = new Wt(d.outShape, r.dtype), k = w.computeStrides(r.shape), I = w.computeStrides(a.shape), $ = k[0], E = v ? k[1] : k[2], A = v ? k[2] : 1, P = v ? 1 : k[1], R = x.strides[0], F = v ? x.strides[1] : x.strides[2], T = v ? x.strides[2] : 1, z = v ? 1 : x.strides[1], W = n.data.get(r.dataId).values, j = n.data.get(a.dataId).values, X = x.values;
  for (let Y = 0; Y < d.batchSize; ++Y) {
    let Z = Y * $, te = Y * R;
    for (let J = 0; J < d.outHeight; ++J) {
      let se = te + J * F, ne = J * d.strideHeight - y;
      for (let oe = 0; oe < h; ++oe) {
        let ae = ne + oe * m;
        if (ae < 0 || ae >= d.inHeight)
          continue;
        let de = oe * I[0], me = Z + ae * E;
        for (let ke = 0; ke < d.outWidth; ++ke) {
          let Ie = se + ke * T, Ee = ke * d.strideWidth - b;
          for (let Pe = 0; Pe < f; ++Pe) {
            let Xe = Ee + Pe * g;
            if (Xe < 0 || Xe >= d.inWidth)
              continue;
            let Je = de + Pe * I[1], Ye = me + Xe * A, tt = Je;
            for (let Ce = 0; Ce < d.inChannels; ++Ce) {
              let ut = W[Ye + Ce * P];
              for (let rt = 0; rt < d.outChannels; ++rt)
                X[Ie + rt * z] += ut * j[tt + rt];
              tt += d.outChannels;
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(x.shape, x.dtype, X);
}
var MH = { kernelName: _a, backendName: "cpu", kernelFunc: WC };
function LH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s;
  be([r, a], "conv2dBackpropFilter");
  let p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), { strideHeight: h, strideWidth: f, filterHeight: m, filterWidth: g } = d, b = d.dataFormat === "channelsLast", y = new Wt(d.filterShape, "float32"), v = d.padInfo.left, x = d.padInfo.top, k = n.data.get(r.dataId).values, I = n.data.get(a.dataId).values, $ = new Wt(r.shape, r.dtype, k), E = new Wt(a.shape, a.dtype, I);
  for (let A = 0; A < m; ++A) {
    let P = Math.max(0, Math.ceil((x - A) / h)), R = Math.min(d.outHeight, (d.inHeight + x - A) / h);
    for (let F = 0; F < g; ++F) {
      let T = Math.max(0, Math.ceil((v - F) / f)), z = Math.min(d.outWidth, (d.inWidth + v - F) / f);
      for (let W = 0; W < d.inChannels; ++W)
        for (let j = 0; j < d.outChannels; ++j) {
          let X = 0;
          for (let Y = 0; Y < d.batchSize; ++Y)
            for (let Z = P; Z < R; ++Z) {
              let te = A + Z * h - x;
              for (let J = T; J < z; ++J) {
                let se = F + J * f - v;
                b ? X += $.get(Y, te, se, W) * E.get(Y, Z, J, j) : X += $.get(Y, W, te, se) * E.get(Y, j, Z, J);
              }
            }
          y.set(X, A, F, W, j);
        }
    }
  }
  return n.makeTensorInfo(y.shape, y.dtype, y.values);
}
var BH = { kernelName: fg, backendName: "cpu", kernelFunc: LH };
function VH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s;
  be([r, a], "conv2dBackpropInput");
  let p = w.computeStrides(a.shape), d = w.computeStrides(r.shape), h = C.convertConv2DDataFormat(l), f = C.computeConv2DInfo(i, a.shape, o, 1, u, c, false, h), m = new Wt(f.inShape, "float32"), g = m.values, b = n.data.get(r.dataId).values, y = n.data.get(a.dataId).values, [v, x, k] = p, { batchSize: I, filterHeight: $, filterWidth: E, inChannels: A, inHeight: P, inWidth: R, outChannels: F, outHeight: T, outWidth: z, strideHeight: W, strideWidth: j } = f;
  h = f.dataFormat;
  let X = $ - 1 - f.padInfo.top, Y = E - 1 - f.padInfo.left, Z = h === "channelsLast", te = m.strides[0], J = Z ? m.strides[1] : m.strides[2], se = Z ? m.strides[2] : 1, ne = Z ? 1 : m.strides[1], oe = d[0], ae = Z ? d[1] : d[2], de = Z ? d[2] : 1, me = Z ? 1 : d[1];
  for (let ke = 0; ke < I; ++ke)
    for (let Ie = 0; Ie < A; ++Ie)
      for (let Ee = 0; Ee < P; ++Ee) {
        let Pe = Ee - X, Xe = Math.max(0, Math.ceil(Pe / W)), Je = Math.min(T, ($ + Pe) / W);
        for (let Ye = 0; Ye < R; ++Ye) {
          let tt = Ye - Y, Ce = Math.max(0, Math.ceil(tt / j)), ut = Math.min(z, (E + tt) / j), rt = 0;
          for (let Nt = Xe; Nt < Je; ++Nt) {
            let In = Nt * W - Pe;
            for (let Et = Ce; Et < ut; ++Et) {
              let en = Et * j - tt, Cn = oe * ke + ae * Nt + de * Et, Nn = v * ($ - 1 - In) + x * (E - 1 - en) + k * Ie;
              for (let Yt = 0; Yt < F; ++Yt) {
                let Dn = b[Cn + me * Yt], tn = y[Nn + Yt];
                rt += Dn * tn;
              }
            }
          }
          let Jt = te * ke + J * Ee + se * Ye + ne * Ie;
          g[Jt] = rt;
        }
      }
  return n.makeTensorInfo(m.shape, m.dtype, m.values);
}
var WH = { kernelName: Aa, backendName: "cpu", kernelFunc: VH };
function UH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s;
  be([r, a], "conv3d");
  let l = C.computeConv3DInfo(r.shape, a.shape, i, u, o), { filterDepth: c, filterHeight: p, filterWidth: d, dilationDepth: h, dilationHeight: f, dilationWidth: m, padInfo: g } = l, b = g.front, y = g.left, v = g.top, x = new Wt(l.outShape, r.dtype), k = n.data.get(r.dataId).values, I = n.data.get(a.dataId).values, $ = x.values, E = w.computeStrides(r.shape), A = w.computeStrides(a.shape);
  for (let P = 0; P < l.batchSize; ++P) {
    let R = P * E[0], F = P * x.strides[0];
    for (let T = 0; T < l.outDepth; ++T) {
      let z = F + T * x.strides[1], W = T * l.strideDepth - b;
      for (let j = 0; j < c; ++j) {
        let X = W + j * h;
        if (X < 0 || X >= l.inDepth)
          continue;
        let Y = j * A[0], Z = R + X * E[1];
        for (let te = 0; te < l.outHeight; ++te) {
          let J = z + te * x.strides[2], se = te * l.strideHeight - v;
          for (let ne = 0; ne < p; ++ne) {
            let oe = se + ne * f;
            if (oe < 0 || oe >= l.inHeight)
              continue;
            let ae = Y + ne * A[1], de = Z + oe * E[2];
            for (let me = 0; me < l.outWidth; ++me) {
              let ke = J + me * l.outChannels, Ie = me * l.strideWidth - y;
              for (let Ee = 0; Ee < d; ++Ee) {
                let Pe = Ie + Ee * m;
                if (Pe < 0 || Pe >= l.inWidth)
                  continue;
                let Xe = ae + Ee * A[2], Je = de + Pe * l.inChannels, Ye = Xe;
                for (let tt = 0; tt < l.inChannels; ++tt) {
                  let Ce = k[Je + tt];
                  for (let ut = 0; ut < l.outChannels; ++ut)
                    $[ke + ut] += Ce * I[Ye + ut];
                  Ye += l.outChannels;
                }
              }
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(x.shape, x.dtype, x.values);
}
var GH = { kernelName: ep, backendName: "cpu", kernelFunc: UH };
function HH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s;
  be([r, a], "conv3dBackpropFilterV2");
  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = C.computeConv3DInfo(r.shape, u, i, 1, o), d = p.strideDepth, h = p.strideHeight, f = p.strideWidth, m = p.filterDepth, g = p.filterHeight, b = p.filterWidth, y = new Wt(p.filterShape, "float32"), v = y.values, [x, k, I, $] = y.strides, E = n.data.get(a.dataId).values, [A, P, R, F] = c, T = n.data.get(r.dataId).values, [z, W, j, X] = l, Y = p.padInfo.front, Z = p.padInfo.left, te = p.padInfo.top;
  for (let J = 0; J < m; ++J) {
    let se = Math.max(0, Math.ceil((Y - J) / d)), ne = Math.min(p.outDepth, (p.inDepth + Y - J) / d), oe = J * x;
    for (let ae = 0; ae < g; ++ae) {
      let de = Math.max(0, Math.ceil((te - ae) / h)), me = Math.min(p.outHeight, (p.inHeight + te - ae) / h), ke = ae * k + oe;
      for (let Ie = 0; Ie < b; ++Ie) {
        let Ee = Math.max(0, Math.ceil((Z - Ie) / f)), Pe = Math.min(p.outWidth, (p.inWidth + Z - Ie) / f), Xe = Ie * I + ke;
        for (let Je = 0; Je < p.inChannels; ++Je) {
          let Ye = Je * $ + Xe;
          for (let tt = 0; tt < p.outChannels; ++tt) {
            let Ce = 0;
            for (let ut = 0; ut < p.batchSize; ++ut) {
              let rt = ut * z, Jt = ut * A;
              for (let Nt = se; Nt < ne; ++Nt) {
                let Et = (J + Nt * d - Y) * W + rt, en = Nt * P + Jt;
                for (let Cn = de; Cn < me; ++Cn) {
                  let Yt = (ae + Cn * h - te) * j + Et, Dn = Cn * R + en;
                  for (let tn = Ee; tn < Pe; ++tn) {
                    let Ms = (Ie + tn * f - Z) * X + Yt, Ni = tn * F + Dn;
                    Ce += T[Ms + Je] * E[Ni + tt];
                  }
                }
              }
            }
            v[Ye + tt] = Ce;
          }
        }
      }
    }
  }
  return n.makeTensorInfo(y.shape, y.dtype, y.values);
}
var qH = { kernelName: mg, backendName: "cpu", kernelFunc: HH };
function jH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s;
  be([r], "conv3dBackpropInputV2");
  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = C.computeConv3DInfo(u, a.shape, o, 1, i), d = new Wt(p.inShape, "float32"), h = d.values, [f, m, g, b] = d.strides, y = n.data.get(r.dataId).values, [v, x, k, I] = l, $ = n.data.get(a.dataId).values, [E, A, P, R] = c, { batchSize: F, filterDepth: T, filterHeight: z, filterWidth: W, inChannels: j, inDepth: X, inHeight: Y, inWidth: Z, outChannels: te, outDepth: J, outHeight: se, outWidth: ne, strideDepth: oe, strideHeight: ae, strideWidth: de } = p, me = T - 1 - p.padInfo.front, ke = z - 1 - p.padInfo.top, Ie = W - 1 - p.padInfo.left;
  for (let Ee = 0; Ee < F; ++Ee)
    for (let Pe = 0; Pe < j; ++Pe)
      for (let Xe = 0; Xe < X; ++Xe) {
        let Je = Xe - me, Ye = Math.max(0, Math.ceil(Je / oe)), tt = Math.min(J, (T + Je) / oe);
        for (let Ce = 0; Ce < Y; ++Ce) {
          let ut = Ce - ke, rt = Math.max(0, Math.ceil(ut / ae)), Jt = Math.min(se, (z + ut) / ae);
          for (let Nt = 0; Nt < Z; ++Nt) {
            let In = Nt - Ie, Et = Math.max(0, Math.ceil(In / de)), en = Math.min(ne, (W + In) / de), Cn = 0;
            for (let Nn = Ye; Nn < tt; ++Nn) {
              let Yt = Nn * oe - Je;
              for (let Dn = rt; Dn < Jt; ++Dn) {
                let tn = Dn * ae - ut;
                for (let zs = Et; zs < en; ++zs) {
                  let Ms = zs * de - In, Ni = v * Ee + x * Nn + k * Dn + I * zs, Zs = E * (T - 1 - Yt) + A * (z - 1 - tn) + P * (W - 1 - Ms) + R * Pe;
                  for (let Ls = 0; Ls < te; ++Ls) {
                    let fu = y[Ni + Ls], Ti = $[Zs + Ls];
                    Cn += fu * Ti;
                  }
                }
              }
            }
            h[f * Ee + m * Xe + g * Ce + b * Nt + Pe] = Cn;
          }
        }
      }
  return n.makeTensorInfo(d.shape, d.dtype, d.values);
}
var KH = { kernelName: gg, backendName: "cpu", kernelFunc: jH };
var XH = st(Ea, (e) => Math.cos(e));
var YH = { kernelName: Ea, backendName: "cpu", kernelFunc: XH };
var QH = st(Ra, (e) => Math.cosh(e));
var ZH = { kernelName: Ra, backendName: "cpu", kernelFunc: QH };
function JH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, [c, p, d, h] = r.shape, f = a.shape[0], [m, g] = o, b = De([f, m, g, h], "float32"), y = n.data.get(a.dataId).values, v = n.data.get(i.dataId).values, x = n.data.get(r.dataId).values, k = w.computeStrides(r.shape), I = w.computeStrides(b.shape);
  for (let $ = 0; $ < f; $++) {
    let E = $ * 4, A = y[E], P = y[E + 1], R = y[E + 2], F = y[E + 3], T = v[$];
    if (T >= c)
      continue;
    let z = m > 1 ? (R - A) * (p - 1) / (m - 1) : 0, W = g > 1 ? (F - P) * (d - 1) / (g - 1) : 0;
    for (let j = 0; j < m; j++) {
      let X = m > 1 ? A * (p - 1) + j * z : 0.5 * (A + R) * (p - 1);
      if (X < 0 || X > p - 1) {
        for (let Y = 0; Y < g; Y++)
          for (let Z = 0; Z < h; Z++) {
            let te = Z + Y * I[2] + j * I[1] + $ * I[0];
            b.values[te] = l;
          }
        continue;
      }
      if (u === "bilinear") {
        let Y = Math.floor(X), Z = Math.ceil(X), te = X - Y;
        for (let J = 0; J < g; J++) {
          let se = g > 1 ? P * (d - 1) + J * W : 0.5 * (P + F) * (d - 1);
          if (se < 0 || se > d - 1) {
            for (let de = 0; de < h; de++) {
              let me = de + J * I[2] + j * I[1] + $ * I[0];
              b.values[me] = l;
            }
            continue;
          }
          let ne = Math.floor(se), oe = Math.ceil(se), ae = se - ne;
          for (let de = 0; de < h; de++) {
            let me = de + ne * k[2] + Y * k[1] + T * k[0], ke = x[me];
            me = de + oe * k[2] + Y * k[1] + T * k[0];
            let Ie = x[me];
            me = de + ne * k[2] + Z * k[1] + T * k[0];
            let Ee = x[me];
            me = de + oe * k[2] + Z * k[1] + T * k[0];
            let Pe = x[me], Xe = ke + (Ie - ke) * ae, Je = Ee + (Pe - Ee) * ae;
            me = de + J * I[2] + j * I[1] + $ * I[0], b.values[me] = Xe + (Je - Xe) * te;
          }
        }
      } else
        for (let Y = 0; Y < g; ++Y) {
          let Z = g > 1 ? P * (d - 1) + Y * W : 0.5 * (P + F) * (d - 1);
          if (Z < 0 || Z > d - 1) {
            for (let se = 0; se < h; se++) {
              let ne = se + Y * I[2] + j * I[1] + $ * I[0];
              b.values[ne] = l;
            }
            continue;
          }
          let te = Math.round(Z), J = Math.round(X);
          for (let se = 0; se < h; se++) {
            let ne = se + te * k[2] + J * k[1] + T * k[0], oe = se + Y * I[2] + j * I[1] + $ * I[0];
            b.values[oe] = x[ne];
          }
        }
    }
  }
  return n.makeTensorInfo(b.shape, b.dtype, b.values);
}
var eq = { kernelName: mo, backendName: "cpu", kernelFunc: JH };
function tq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  be(r, "cumprod");
  let u = C.getAxesPermutation([a], r.shape.length), l = r;
  u != null && (l = wn({ inputs: { x: r }, backend: n, attrs: { perm: u } }));
  let c = C.getInnerMostAxes(1, r.shape.length)[0];
  if (c !== l.shape.length - 1)
    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length - 1} but got axis=${c}`);
  let p = cn(l.dtype, "int32"), d = w.makeOnesTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = o ? (b, y) => b + f - y - 1 : (b, y) => b + y;
  for (let b = 0; b < h.length; b += f)
    for (let y = 0; y < f; y++) {
      let v = m(b, y);
      if (y === 0)
        d[v] = i ? 1 : h[v];
      else {
        let x = m(b, y - 1);
        d[v] = i ? h[x] * d[x] : h[v] * d[x];
      }
    }
  let g = n.makeTensorInfo(l.shape, p, d);
  if (u != null) {
    let b = C.getUndoAxesPermutation(u), y = wn({ inputs: { x: g }, backend: n, attrs: { perm: b } });
    return n.disposeIntermediateTensorInfo(g), n.disposeIntermediateTensorInfo(l), y;
  }
  return g;
}
var nq = { kernelName: fo, backendName: "cpu", kernelFunc: tq };
function sq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  be(r, "cumsum");
  let u = C.getAxesPermutation([a], r.shape.length), l = r;
  u != null && (l = wn({ inputs: { x: r }, backend: n, attrs: { perm: u } }));
  let c = C.getInnerMostAxes(1, r.shape.length)[0];
  if (c !== l.shape.length - 1)
    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length - 1} but got axis=${c}`);
  let p = cn(l.dtype, "int32"), d = w.makeZerosTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = o ? (b, y) => b + f - y - 1 : (b, y) => b + y;
  for (let b = 0; b < h.length; b += f)
    for (let y = 0; y < f; y++) {
      let v = m(b, y);
      if (y === 0)
        d[v] = i ? 0 : h[v];
      else {
        let x = m(b, y - 1);
        d[v] = i ? h[x] + d[x] : h[v] + d[x];
      }
    }
  let g = n.makeTensorInfo(l.shape, p, d);
  if (u != null) {
    let b = C.getUndoAxesPermutation(u), y = wn({ inputs: { x: g }, backend: n, attrs: { perm: b } });
    return n.disposeIntermediateTensorInfo(g), n.disposeIntermediateTensorInfo(l), y;
  }
  return g;
}
var rq = { kernelName: Da, backendName: "cpu", kernelFunc: sq };
function aq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
  if (r.shape.length === 1) {
    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = av(u, l, a.dtype, a.shape, i);
    return n.makeTensorInfo([i], a.dtype, c);
  } else if (r.shape.length === 2) {
    let u = n.bufferSync(r), l = n.bufferSync(a), c = Z0(u, l, i, o);
    return n.makeTensorInfo(c.shape, a.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
}
var iq = { kernelName: bg, backendName: "cpu", kernelFunc: aq };
function oq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s;
  w.assert(i === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);
  let o = r.shape[0], u = r.shape[1], l = r.shape[2], c = r.shape[3], p = u * a, d = l * a, h = c / (a * a), f = n.data.get(r.dataId).values, m = new Float32Array(o * p * d * h), g = 0;
  for (let b = 0; b < o; ++b)
    for (let y = 0; y < p; ++y) {
      let v = Math.floor(y / a), x = y % a;
      for (let k = 0; k < d; ++k) {
        let I = Math.floor(k / a), $ = k % a, E = (x * a + $) * h;
        for (let A = 0; A < h; ++A) {
          let R = A + E + c * (I + l * (v + u * b));
          m[g++] = f[R];
        }
      }
    }
  return n.makeTensorInfo([o, p, d, h], r.dtype, m);
}
var uq = { kernelName: go, backendName: "cpu", kernelFunc: oq };
function UC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s;
  be([r, a], "depthwiseConv2DNative");
  let c = w.computeStrides(r.shape), p = w.computeStrides(a.shape), d = u;
  d == null && (d = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(i, d), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);
  let h = C.computeConv2DInfo(r.shape, a.shape, i, d, o, l, true), { filterHeight: f, filterWidth: m, dilationHeight: g, dilationWidth: b, padInfo: y } = h, v = y.left, x = y.top, k = h.outChannels / h.inChannels, I = new Wt(h.outShape, r.dtype), $ = n.data.get(r.dataId).values, E = n.data.get(a.dataId).values, A = I.values;
  for (let P = 0; P < h.batchSize; ++P) {
    let R = P * c[0], F = P * I.strides[0];
    for (let T = 0; T < h.outHeight; ++T) {
      let z = F + T * I.strides[1], W = T * h.strideHeight - x;
      for (let j = 0; j < f; ++j) {
        let X = W + j * g;
        if (X < 0 || X >= h.inHeight)
          continue;
        let Y = j * p[0], Z = R + X * c[1];
        for (let te = 0; te < h.outWidth; ++te) {
          let J = z + te * I.strides[2], se = te * h.strideWidth - v;
          for (let ne = 0; ne < m; ++ne) {
            let oe = se + ne * b;
            if (oe < 0 || oe >= h.inWidth)
              continue;
            let ae = Y + ne * p[1], de = Z + oe * h.inChannels, me = J, ke = ae;
            for (let Ie = 0; Ie < h.inChannels; ++Ie) {
              let Ee = $[de + Ie];
              for (let Pe = 0; Pe < k; ++Pe)
                A[me + Pe] += Ee * E[ke + Pe];
              me += k, ke += k;
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(I.shape, I.dtype, I.values);
}
var lq = { kernelName: Fa, backendName: "cpu", kernelFunc: UC };
function cq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s;
  be([r, a], "depthwiseConv2dNativeBackpropFilter");
  let p = C.computeConv2DInfo(r.shape, c, i, o, u, l, true), { strideHeight: d, strideWidth: h, filterHeight: f, filterWidth: m } = p, g = new Wt(p.filterShape, "float32"), b = p.padInfo.left, y = p.padInfo.top, v = p.outChannels / p.inChannels, x = n.data.get(r.dataId).values, k = new Wt(r.shape, r.dtype, x), I = n.data.get(a.dataId).values, $ = new Wt(a.shape, a.dtype, I);
  for (let E = 0; E < f; ++E) {
    let A = Math.max(0, Math.ceil((y - E) / d)), P = Math.min(p.outHeight, (p.inHeight + y - E) / d);
    for (let R = 0; R < m; ++R) {
      let F = Math.max(0, Math.ceil((b - R) / h)), T = Math.min(p.outWidth, (p.inWidth + b - R) / h);
      for (let z = 0; z < p.outChannels; ++z) {
        let W = Math.trunc(z / v), j = z % v, X = 0;
        for (let Y = 0; Y < p.batchSize; ++Y)
          for (let Z = A; Z < P; ++Z) {
            let te = E + Z * d - y;
            for (let J = F; J < T; ++J) {
              let se = R + J * h - b;
              X += k.get(Y, te, se, W) * $.get(Y, Z, J, z);
            }
          }
        g.set(X, E, R, W, j);
      }
    }
  }
  return n.makeTensorInfo(g.shape, g.dtype, g.values);
}
var dq = { kernelName: yg, backendName: "cpu", kernelFunc: cq };
function pq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s;
  be([r, a], "depthwiseConv2DNativeBackpropInput");
  let p = w.computeStrides(r.shape), d = w.computeStrides(a.shape), h = C.computeConv2DInfo(c, a.shape, i, o, u, l, true), f = new Wt(h.inShape, "float32"), m = f.values, [g, b, y] = f.strides, v = n.data.get(r.dataId).values, [x, k, I] = p, $ = n.data.get(a.dataId).values, [E, A, P] = d, { batchSize: R, filterHeight: F, filterWidth: T, inChannels: z, inHeight: W, inWidth: j, outChannels: X, outHeight: Y, outWidth: Z, strideHeight: te, strideWidth: J } = h, se = F - 1 - h.padInfo.top, ne = T - 1 - h.padInfo.left, oe = X / z;
  for (let ae = 0; ae < R; ++ae)
    for (let de = 0; de < z; ++de)
      for (let me = 0; me < W; ++me) {
        let ke = me - se, Ie = Math.max(0, Math.ceil(ke / te)), Ee = Math.min(Y, (F + ke) / te);
        for (let Pe = 0; Pe < j; ++Pe) {
          let Xe = Pe - ne, Je = Math.max(0, Math.ceil(Xe / J)), Ye = Math.min(Z, (T + Xe) / J), tt = 0;
          for (let Ce = Ie; Ce < Ee; ++Ce) {
            let ut = Ce * te - ke;
            for (let rt = Je; rt < Ye; ++rt) {
              let Jt = rt * J - Xe, Nt = x * ae + k * Ce + I * rt, In = E * (F - 1 - ut) + A * (T - 1 - Jt) + P * de;
              for (let Et = 0; Et < oe; ++Et) {
                let en = de * oe + Et, Cn = v[Nt + en], Nn = $[In + Et];
                tt += Cn * Nn;
              }
            }
          }
          m[g * ae + b * me + y * Pe + de] = tt;
        }
      }
  return n.makeTensorInfo(f.shape, f.dtype, f.values);
}
var hq = { kernelName: vg, backendName: "cpu", kernelFunc: pq };
function fq(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, i = De([r, r], s.dtype), o = i.values;
  for (let l = 0; l < a.length; l++)
    o[l * r + l] = a[l];
  let u = [...s.shape, ...s.shape];
  return n.makeTensorInfo(u, i.dtype, i.values);
}
var mq = { kernelName: xg, backendName: "cpu", kernelFunc: fq };
var gq = { kernelName: tp, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s, filter: r } = e, { strides: a, pad: i, dilations: o } = n, u = t, l = u.data.get(s.dataId).values, c = s.shape.length, p = u.data.get(r.dataId).values, d = r.shape.length, { batchSize: h, inHeight: f, inWidth: m, inChannels: g, outHeight: b, outWidth: y, padInfo: v, strideHeight: x, strideWidth: k, filterHeight: I, filterWidth: $, dilationHeight: E, dilationWidth: A, outShape: P } = C.computeDilation2DInfo(s.shape, r.shape, a, i, "NHWC", o), R = w.sizeFromShape(P), F = P.length, T = w.getArrayFromDType(s.dtype, R);
  for (let W = 0; W < h; ++W)
    for (let j = 0; j < b; ++j) {
      let X = j * x - v.top;
      for (let Y = 0; Y < y; ++Y) {
        let Z = Y * k - v.left;
        for (let te = 0; te < g; ++te) {
          let J = Number.MIN_SAFE_INTEGER;
          for (let ne = 0; ne < I; ++ne) {
            let oe = X + ne * E;
            if (oe >= 0 && oe < f)
              for (let ae = 0; ae < $; ++ae) {
                let de = Z + ae * A;
                if (de >= 0 && de < m) {
                  let me = w.locToIndex([W, oe, de, te], c, w.computeStrides(s.shape)), ke = w.locToIndex([ne, ae, te], d, w.computeStrides(r.shape)), Ie = l[me] + p[ke];
                  Ie > J && (J = Ie);
                }
              }
          }
          let se = w.locToIndex([W, j, Y, te], F, w.computeStrides(P));
          T[se] = J;
        }
      }
    }
  return { dataId: u.write(w.toTypedArray(T, s.dtype), P, s.dtype), shape: P, dtype: s.dtype };
} };
var bq = { kernelName: tm, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: I, dilationHeight: $, dilationWidth: E, outShape: A } = C.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
  w.assert(a.rank === A.length, () => `Error in ${tm}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
  let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(r.shape, r.dtype);
  for (let T = 0; T < d; ++T)
    for (let z = 0; z < g; ++z) {
      let W = z * v - y.top;
      for (let j = 0; j < b; ++j) {
        let X = j * x - y.left;
        for (let Y = 0; Y < m; ++Y) {
          let Z = Number.MIN_SAFE_INTEGER, te = 0, J = 0;
          for (let se = 0; se < k; ++se) {
            let ne = W + se * $;
            if (ne >= 0 && ne < h)
              for (let oe = 0; oe < I; ++oe) {
                let ae = X + oe * E;
                if (ae >= 0 && ae < f) {
                  let de = c[T][ne][ae][Y] + p[se][oe][Y];
                  de > Z && (Z = de, te = se, J = oe);
                }
              }
          }
          R[te][J][Y] += P[T][z][j][Y];
        }
      }
    }
  return { dataId: l.write(w.toTypedArray(R, s.dtype), r.shape, r.dtype), shape: r.shape, dtype: r.dtype };
} };
var yq = { kernelName: em, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: I, dilationHeight: $, dilationWidth: E, outShape: A } = C.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
  w.assert(a.rank === A.length, () => `Error in ${em}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
  let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(s.shape, s.dtype);
  for (let T = 0; T < d; ++T)
    for (let z = 0; z < g; ++z) {
      let W = z * v - y.top;
      for (let j = 0; j < b; ++j) {
        let X = j * x - y.left;
        for (let Y = 0; Y < m; ++Y) {
          let Z = Number.MIN_SAFE_INTEGER, te = W < 0 ? 0 : W, J = X < 0 ? 0 : X;
          for (let se = 0; se < k; ++se) {
            let ne = W + se * $;
            if (ne >= 0 && ne < h)
              for (let oe = 0; oe < I; ++oe) {
                let ae = X + oe * E;
                if (ae >= 0 && ae < f) {
                  let de = c[T][ne][ae][Y] + p[se][oe][Y];
                  de > Z && (Z = de, te = ne, J = ae);
                }
              }
          }
          R[T][te][J][Y] += P[T][z][j][Y];
        }
      }
    }
  return { dataId: l.write(w.toTypedArray(R, s.dtype), s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
function Yl(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  be(r, "sum");
  let o;
  r.dtype === "bool" ? o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : o = Os({ inputs: { x: r }, backend: n });
  let u = o.shape.length, l = w.parseAxisParam(a, o.shape), c = C.getAxesPermutation(l, u), p = l, d = o;
  c != null && (d = wn({ inputs: { x: o }, backend: n, attrs: { perm: c } }), p = C.getInnerMostAxes(p.length, u)), C.assertAxesAreInnerMostDims("sum", p, d.shape.length);
  let [h, f] = C.computeOutAndReduceShapes(d.shape, p), m = C.upcastType(d.dtype, "int32"), g = Md(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
  for (let x = 0; x < y.length; ++x) {
    let k = x * b, I = 0;
    for (let $ = 0; $ < b; ++$)
      I += v[k + $];
    y[x] = I;
  }
  if (i) {
    let x = C.expandShapeToKeepDim(g.shape, l), k = g;
    g = mt({ inputs: { x: g }, backend: n, attrs: { shape: x } }), n.disposeIntermediateTensorInfo(k);
  }
  return n.disposeIntermediateTensorInfo(o), c != null && n.disposeIntermediateTensorInfo(d), g;
}
var vq = { kernelName: ci, backendName: "cpu", kernelFunc: Yl };
function xq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = C.decodeEinsumEquation(r, a.length);
  C.checkEinsumDimSizes(i.length, u, a);
  let { path: l, steps: c } = C.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
  for (let m = 0; m < p; ++m) {
    for (let g of c[m]) {
      let { permutationIndices: b, expandDims: y } = C.getEinsumPermutation(h, u[g]), v;
      C.isIdentityPermutation(b) ? v = a[g] : (v = wn({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
      let x = v.shape.slice();
      for (let k = 0; k < y.length; ++k)
        x.splice(y[k], 0, 1);
      w.arraysEqual(v.shape, x) || (v = mt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Yp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = Yl({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
  }
  for (let m of f)
    m !== d && n.disposeIntermediateTensorInfo(m);
  return d;
}
var wq = { kernelName: np, backendName: "cpu", kernelFunc: xq };
function kq(e) {
  let { inputs: t, backend: n } = e, { dy: s, y: r } = t;
  be([s, r], "eluGrad");
  let a = new Float32Array(w.sizeFromShape(r.shape)), i = n.data.get(r.dataId).values, o = n.data.get(s.dataId).values;
  for (let u = 0; u < i.length; ++u) {
    let l = i[u];
    l >= 1 ? a[u] = o[u] : a[u] = o[u] * (l + 1);
  }
  return n.makeTensorInfo(r.shape, "float32", a);
}
var Sq = { kernelName: wg, backendName: "cpu", kernelFunc: kq };
var Iq = C.ERF_P;
var Cq = C.ERF_A1;
var Nq = C.ERF_A2;
var Tq = C.ERF_A3;
var $q = C.ERF_A4;
var _q = C.ERF_A5;
var Aq = st(ml, (e) => {
  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + Iq * n);
  return t * (1 - ((((_q * s + $q) * s + Tq) * s + Nq) * s + Cq) * s * Math.exp(-n * n));
});
var Eq = { kernelName: ml, backendName: "cpu", kernelFunc: Aq };
function Bd(e) {
  let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), mt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
}
var Rq = { kernelName: yo, backendName: "cpu", kernelFunc: Bd };
var Dq = At((e, t) => e / t);
var fv = Ht(Oa, Dq);
var Um = { kernelName: Oa, backendName: "cpu", kernelFunc: fv };
function GC(e, t, n) {
  let s = e.shape, r = s[0], a = s[1], i = n.data.get(e.dataId), o = i.complexTensorInfos.real, u = i.complexTensorInfos.imag, l = [r, a], c = w.sizeFromShape(l), p = w.getTypedArrayFromDType("float32", c), d = w.getTypedArrayFromDType("float32", c);
  for (let g = 0; g < r; g++) {
    let b = ba({ inputs: { x: o }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = ba({ inputs: { x: u }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), v = En({ inputs: { real: b, imag: y }, backend: n }), { real: x, imag: k } = Fq(v, t, n), I = C.mergeRealAndImagArrays(x, k);
    for (let $ = 0; $ < a; $++) {
      let E = C.getComplexWithIndex(I, $);
      p[g * a + $] = E.real, d[g * a + $] = E.imag;
    }
    n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), n.disposeIntermediateTensorInfo(v);
  }
  let h = n.makeTensorInfo(l, "float32", p), f = n.makeTensorInfo(l, "float32", d), m = En({ inputs: { real: h, imag: f }, backend: n });
  return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), m;
}
function Fq(e, t, n) {
  let s = w.sizeFromShape(e.shape), r = n.data.get(e.dataId), a = n.data.get(r.complexTensorInfos.real.dataId).values, i = n.data.get(r.complexTensorInfos.imag.dataId).values;
  if (Oq(s)) {
    let o = Gm(a, i, s, t, n), u = [e.shape[0], e.shape[1]];
    if (t) {
      let l = n.makeTensorInfo(u, "float32", o.real), c = n.makeTensorInfo(u, "float32", o.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = Um.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = Um.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
      return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), { real: m, imag: g };
    }
    return o;
  } else {
    let o = C.mergeRealAndImagArrays(a, i), u = Pq(o, s, t);
    return C.splitRealAndImagArrays(u);
  }
}
function Oq(e) {
  return (e & e - 1) === 0;
}
function Gm(e, t, n, s, r) {
  if (n === 1)
    return { real: e, imag: t };
  let a = C.mergeRealAndImagArrays(e, t), i = n / 2, o = C.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = En({ inputs: { real: p, imag: d }, backend: r }), f = C.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = En({ inputs: { real: y, imag: v }, backend: r }), k = Gm(u, l, i, s, r), I = k.real, $ = k.imag, E = [I.length], A = r.makeTensorInfo(E, "float32", I), P = r.makeTensorInfo(E, "float32", $), R = En({ inputs: { real: A, imag: P }, backend: r }), F = Gm(m, g, i, s, r), T = F.real, z = F.imag, W = [T.length], j = r.makeTensorInfo(W, "float32", T), X = r.makeTensorInfo(W, "float32", z), Y = En({ inputs: { real: j, imag: X }, backend: r }), Z = C.exponents(n, s), te = [Z.real.length], J = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = En({ inputs: { real: J, imag: se }, backend: r }), oe = Yp({ inputs: { a: ne, b: Y }, backend: r }), ae = Xl({ inputs: { a: R, b: oe }, backend: r }), de = dv({ inputs: { a: R, b: oe }, backend: r }), me = ga({ inputs: { input: ae }, backend: r }), ke = ga({ inputs: { input: de }, backend: r }), Ie = io({ inputs: { input: ae }, backend: r }), Ee = io({ inputs: { input: de }, backend: r }), Pe = oo({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = oo({ inputs: [Ie, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
  return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(R), r.disposeIntermediateTensorInfo(j), r.disposeIntermediateTensorInfo(X), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(J), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(ne), r.disposeIntermediateTensorInfo(oe), r.disposeIntermediateTensorInfo(ae), r.disposeIntermediateTensorInfo(de), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Ie), r.disposeIntermediateTensorInfo(ke), r.disposeIntermediateTensorInfo(Ee), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
}
function Pq(e, t, n) {
  let s = new Float32Array(t * 2);
  for (let r = 0; r < t; r++) {
    let a = 0, i = 0;
    for (let o = 0; o < t; o++) {
      let u = C.exponent(r * o, t, n), l = C.getComplexWithIndex(e, o);
      a += l.real * u.real - l.imag * u.imag, i += l.real * u.imag + l.imag * u.real;
    }
    n && (a /= t, i /= t), C.assignToTypedArray(s, a, i, r);
  }
  return s;
}
function zq(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = GC(o, false, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
}
var Mq = { kernelName: kg, backendName: "cpu", kernelFunc: zq };
function mv(e) {
  let { backend: t, attrs: n } = e, { shape: s, value: r, dtype: a } = n, i = a || w.inferDtype(r), o = w.getArrayFromDType(i, w.sizeFromShape(s));
  return Bq(o, r, i), t.makeTensorInfo(s, i, o);
}
var Lq = { kernelName: gl, backendName: "cpu", kernelFunc: mv };
function Bq(e, t, n) {
  e.fill(t);
}
var Vq = { kernelName: xo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, r = n, a = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [i, o, u, l] = s.shape, c = r.data.get(s.dataId).values;
  for (let d = 0; d < i; d++) {
    let h = d * u * o * l;
    for (let f = 0; f < o; f++) {
      let m = f * (u * l);
      for (let g = 0; g < u; g++) {
        let b = g * l;
        for (let y = 0; y < l; y++) {
          let v = Math.round(u - g - 1), x = h + m + b + y, k = c[x];
          if (v >= 0 && v < u) {
            let I = v * l, $ = h + m + I + y;
            k = c[$];
          }
          a[x] = k;
        }
      }
    }
  }
  return { dataId: r.write(a, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
var Wq = At((e, t) => Math.floor(e / t));
var Uq = Ht(La, Wq, null, "int32");
var Gq = { kernelName: La, backendName: "cpu", kernelFunc: Uq };
function Hq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = WC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
  if (i) {
    let g = m;
    m = Xl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
  }
  if (h) {
    let g = m;
    m = pv(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
  }
  return m;
}
var qq = { kernelName: ia, backendName: "cpu", kernelFunc: Hq };
function jq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = UC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
  if (i) {
    let g = m;
    m = Xl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
  }
  if (h) {
    let g = m;
    m = pv(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
  }
  return m;
}
var Kq = { kernelName: oa, backendName: "cpu", kernelFunc: jq };
function Xq(e) {
  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = w.sizeFromShape(s.shape), i = r.shape, o = i[i.length - 1], [u, l, c, p] = C.prepareAndValidate(s, r);
  if (l === 0)
    return n.makeTensorInfo(u, s.dtype, []);
  let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = iC(d, h, s.dtype, l, o, c, p, s.shape, a);
  return n.makeTensorInfo(u, s.dtype, f.values);
}
var Yq = { kernelName: ko, backendName: "cpu", kernelFunc: Xq };
function Qq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s;
  be([r, a], "gatherV2");
  let u = w.parseAxisParam(i, r.shape)[0], l = n.data.get(a.dataId).values, c = r.shape[u];
  for (let x = 0; x < l.length; ++x) {
    let k = l[x];
    w.assert(k <= c - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${c - 1}]`);
  }
  let p = o;
  o == null && (p = 0);
  let d = w.sizeFromShape(a.shape), h = C.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = mt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = mt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = oC(y, b, g);
  return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
}
var Zq = { kernelName: wo, backendName: "cpu", kernelFunc: Qq };
function Jq(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = GC(o, true, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
}
var e6 = { kernelName: Sg, backendName: "cpu", kernelFunc: Jq };
var t6 = st(bl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
var n6 = { kernelName: bl, backendName: "cpu", kernelFunc: t6 };
var s6 = st(yl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
var r6 = { kernelName: yl, backendName: "cpu", kernelFunc: s6 };
var a6 = st(vl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
var i6 = { kernelName: vl, backendName: "cpu", kernelFunc: a6 };
function o6(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = pC(s, r, a);
  return t.makeTensorInfo([i.length], "float32", i);
}
var u6 = { kernelName: Ig, backendName: "cpu", kernelFunc: o6 };
var l6 = st(xl, (e) => Math.log1p(e));
var c6 = { kernelName: xl, backendName: "cpu", kernelFunc: l6 };
var d6 = At((e, t) => e && t);
var p6 = Ht(No, d6, null, "bool");
var h6 = { kernelName: No, backendName: "cpu", kernelFunc: p6 };
var f6 = st(wl, (e) => e ? 0 : 1, "bool");
var m6 = { kernelName: wl, backendName: "cpu", kernelFunc: f6 };
var g6 = At((e, t) => e || t);
var b6 = Ht(rp, g6, null, "bool");
var y6 = { kernelName: rp, backendName: "cpu", kernelFunc: b6 };
function v6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s;
  be(r, "LRN");
  let l = r.shape[3], c = l - 1, p = n.data.get(r.dataId).values, d = w.sizeFromShape(r.shape), h = new Float32Array(d);
  function f(m) {
    let g = m % l, b = m - g + Math.max(0, g - a), y = m - g + Math.min(g + a, c), v = 0;
    for (; b <= y; b++) {
      let x = p[b];
      v += x * x;
    }
    return v;
  }
  for (let m = 0; m < d; m++) {
    let g = f(m), b = p[m] * Math.pow(i + o * g, -u);
    h[m] = b;
  }
  return n.makeTensorInfo(r.shape, r.dtype, h);
}
var x6 = { kernelName: ap, backendName: "cpu", kernelFunc: v6 };
function w6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s;
  be(i, "LRNGrad");
  let p = w.sizeFromShape(i.shape), d = i.shape[3], h = n.data.get(i.dataId).values, f = n.data.get(r.dataId).values, m = n.data.get(a.dataId).values, g = new Float32Array(p), b = p;
  for (let y = 0; y < b; y++) {
    let v = y % d, x = y - v + Math.max(0, v - o), k = y - v + Math.min(d, v + o + 1), I = 0;
    for (let $ = x; $ < k; $++)
      I += Math.pow(f[$], 2);
    I = l * I + u;
    for (let $ = x; $ < k; $++) {
      let E = -2 * l * c * f[$] * m[y] / I;
      y === $ && (E += Math.pow(I, -c)), E *= h[y], g[$] += E;
    }
  }
  return n.makeTensorInfo(i.shape, r.dtype, g);
}
var k6 = { kernelName: Cg, backendName: "cpu", kernelFunc: w6 };
function HC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = n, u = r.shape, l = u.length, c = w.parseAxisParam(a, u), p = c, d = C.getAxesPermutation(p, l), h = o.data.get(r.dataId).values;
  if (d != null) {
    let x = new Array(l);
    for (let k = 0; k < x.length; k++)
      x[k] = u[d[k]];
    h = uv(h, u, r.dtype, d, x), p = C.getInnerMostAxes(p.length, l), u = x;
  }
  be(r, "max"), C.assertAxesAreInnerMostDims("max", p, l);
  let [f, m] = C.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = fC(h, g, f, r.dtype), y = o.write(b, f, r.dtype), v = f;
  return i && (v = C.expandShapeToKeepDim(f, c)), { dataId: y, shape: v, dtype: r.dtype };
}
var S6 = { kernelName: Ha, backendName: "cpu", kernelFunc: HC };
function I6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  be(r, "maxPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = C.computePool2DInfo(r.shape, a, i, l, o, u), p;
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    p = Os({ inputs: { x: r }, backend: n });
  else {
    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = hv(d, r.shape, r.dtype, h, c, "max");
    p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
  }
  return p;
}
var C6 = { kernelName: ja, backendName: "cpu", kernelFunc: I6 };
function N6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
  be(r, "maxPool3d");
  let c = C.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = VC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
  return n.makeTensorInfo(d.shape, "float32", d.values);
}
var T6 = { kernelName: ip, backendName: "cpu", kernelFunc: N6 };
function $6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
  be([r, a], "maxPool3DGrad");
  let c = C.computePool3DInfo(a.shape, i, o, 1, u, l), p = n.bufferSync(a), d = mH(p, c), h = c.strideDepth, f = c.strideHeight, m = c.strideWidth, g = c.dilationDepth, b = c.dilationHeight, y = c.dilationWidth, v = c.effectiveFilterDepth, x = c.effectiveFilterHeight, k = c.effectiveFilterWidth, I = v - 1 - c.padInfo.front, $ = k - 1 - c.padInfo.left, E = x - 1 - c.padInfo.top, A = De(a.shape, "float32"), P = n.bufferSync(r);
  for (let R = 0; R < c.batchSize; ++R)
    for (let F = 0; F < c.inChannels; ++F)
      for (let T = 0; T < c.inDepth; ++T)
        for (let z = 0; z < c.inHeight; ++z)
          for (let W = 0; W < c.inWidth; ++W) {
            let j = T - I, X = z - E, Y = W - $, Z = 0;
            for (let te = 0; te < v; te += g) {
              let J = (j + te) / h;
              if (!(J < 0 || J >= c.outDepth || Math.floor(J) !== J))
                for (let se = 0; se < x; se += b) {
                  let ne = (X + se) / f;
                  if (!(ne < 0 || ne >= c.outHeight || Math.floor(ne) !== ne))
                    for (let oe = 0; oe < k; oe += y) {
                      let ae = (Y + oe) / m;
                      if (ae < 0 || ae >= c.outWidth || Math.floor(ae) !== ae)
                        continue;
                      let de = v * x * k - 1 - d.get(R, J, ne, ae, F), me = te * x * k + se * k + oe, ke = de === me ? 1 : 0;
                      if (ke === 0)
                        continue;
                      Z += P.get(R, J, ne, ae, F) * ke;
                    }
                }
            }
            A.set(Z, R, T, z, W, F);
          }
  return n.makeTensorInfo(A.shape, A.dtype, A.values);
}
var _6 = { kernelName: Tg, backendName: "cpu", kernelFunc: $6 };
function A6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
  be([a, i], "maxPoolGrad");
  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = C.computePool2DInfo(o.shape, u, l, 1, c, p), h = n.data.get(o.dataId).values, f = De(d.outShape, o.dtype, BC(h, o.shape, o.dtype, d).values), m = d.strideHeight, g = d.strideWidth, b = d.dilationHeight, y = d.dilationWidth, v = d.effectiveFilterHeight, x = d.effectiveFilterWidth, k = x - 1 - d.padInfo.left, I = v - 1 - d.padInfo.top, $ = De(o.shape, "float32"), E = n.data.get(r.dataId).values, A = De(r.shape, "float32", E);
  for (let P = 0; P < d.batchSize; ++P)
    for (let R = 0; R < d.inChannels; ++R)
      for (let F = 0; F < d.inHeight; ++F)
        for (let T = 0; T < d.inWidth; ++T) {
          let z = F - I, W = T - k, j = 0;
          for (let X = 0; X < v; X += b) {
            let Y = (z + X) / m;
            if (!(Y < 0 || Y >= d.outHeight || Math.floor(Y) !== Y))
              for (let Z = 0; Z < x; Z += y) {
                let te = (W + Z) / g;
                if (te < 0 || te >= d.outWidth || Math.floor(te) !== te)
                  continue;
                let J = v * x - 1 - f.get(P, Y, te, R), se = X * x + Z, ne = J === se ? 1 : 0;
                if (ne === 0)
                  continue;
                j += A.get(P, Y, te, R) * ne;
              }
          }
          $.set(j, P, F, T, R);
        }
  return n.makeTensorInfo($.shape, $.dtype, $.values);
}
var E6 = { kernelName: Ng, backendName: "cpu", kernelFunc: A6 };
function R6(e, t, n, s, r) {
  let a = w.computeStrides(t), i = hv(e, t, n, a, r, "max"), o = BC(e, t, n, r, true, s);
  return [i.values, o.values];
}
var D6 = { kernelName: $g, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
  be(s, "MaxPoolWithArgmax");
  let l = u.data.get(s.dataId).values, c = C.computePool2DInfo(s.shape, r, a, [1, 1], i), [p, d] = R6(l, s.shape, s.dtype, o, c), h = u.write(p, c.outShape, s.dtype), f = u.write(d, c.outShape, s.dtype);
  return [{ dataId: h, shape: c.outShape, dtype: s.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }];
} };
function F6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = w.parseAxisParam(a, r.shape), l = C.computeOutAndReduceShapes(r.shape, o)[1], c = w.sizeFromShape(l), p = [], d = n.makeTensorInfo([], "float32", new Float32Array([c]));
  p.push(d);
  let h = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
  p.push(h);
  let f = fv({ inputs: { a: h, b: d }, backend: n });
  p.push(f);
  let m = Yl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
  return p.forEach((g) => n.disposeIntermediateTensorInfo(g)), m;
}
var O6 = { kernelName: Ka, backendName: "cpu", kernelFunc: F6 };
function P6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  be(r, "min");
  let o = w.parseAxisParam(a, r.shape), u = o, l = C.getAxesPermutation(u, r.shape.length), c = r;
  l != null && (c = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = C.getInnerMostAxes(u.length, r.shape.length)), C.assertAxesAreInnerMostDims("min", u, c.shape.length);
  let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = w.sizeFromShape(d), f = w.makeZerosTypedArray(w.sizeFromShape(p), c.dtype), m = n.data.get(c.dataId).values;
  for (let b = 0; b < f.length; ++b) {
    let y = b * h, v = m[y];
    for (let x = 0; x < h; ++x) {
      let k = m[y + x];
      (Number.isNaN(k) || k < v) && (v = k);
    }
    f[b] = v;
  }
  l != null && n.disposeIntermediateTensorInfo(c);
  let g = n.makeTensorInfo(p, c.dtype, f);
  if (i) {
    let b = C.expandShapeToKeepDim(p, o), y = mt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var z6 = { kernelName: Xa, backendName: "cpu", kernelFunc: P6 };
function M6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, mode: i } = s;
  be(r, "mirrorPad");
  let o = a.map((v, x) => v[0] + r.shape[x] + v[1]), u = a.map((v) => v[0]), l = a.map((v, x) => v[0] + r.shape[x]), c = i === "reflect" ? 0 : 1, p = n.data.get(r.dataId).values, d = r.shape.length, h = w.computeStrides(r.shape), f = w.sizeFromShape(o), m = o.length, g = w.computeStrides(o), b = w.getTypedArrayFromDType(r.dtype, f);
  for (let v = 0; v < f; v++) {
    let x = w.indexToLoc(v, m, g);
    for (let I = 0; I < m; I++)
      x[I] < u[I] ? x[I] = u[I] * 2 - x[I] - c : x[I] >= l[I] && (x[I] = (l[I] - 1) * 2 - x[I] + c);
    x = x.map((I, $) => I - u[$]);
    let k = w.locToIndex(x, d, h);
    b[v] = p[k];
  }
  return { dataId: n.write(b, o, r.dtype), shape: o, dtype: r.dtype };
}
var L6 = { kernelName: Qa, backendName: "cpu", kernelFunc: M6 };
var B6 = At((e, t) => {
  let n = e % t;
  return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
});
var V6 = Ht(kl, B6);
var W6 = { kernelName: kl, backendName: "cpu", kernelFunc: V6 };
var U6 = wa(jd());
function qC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = r.shape.length, o = a;
  if (o === -1 && (o = i - 1), o !== i - 1)
    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);
  let u = w.parseAxisParam([o], r.shape), l = HC({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = C.expandShapeToKeepDim(l.shape, u), p = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = dv({ inputs: { a: r, b: p }, backend: n }), h = sC({ inputs: { x: d }, backend: n }), f = Yl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = fv({ inputs: { a: h, b: m }, backend: n });
  return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
}
var G6 = { kernelName: di, backendName: "cpu", kernelFunc: qC };
function H6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s;
  be(r, "multinomial");
  let u = o ? r : qC({ inputs: { logits: r }, backend: n, attrs: { dim: -1 } }), l = u.shape[0], c = u.shape[1], p = n.data.get(u.dataId).values, d = [l, a], h = w.makeZerosTypedArray(w.sizeFromShape(d), "int32");
  for (let f = 0; f < l; ++f) {
    let m = f * c, g = new Float32Array(c - 1);
    g[0] = p[m];
    for (let v = 1; v < g.length; ++v)
      g[v] = g[v - 1] + p[m + v];
    let b = U6.alea(i.toString()), y = f * a;
    for (let v = 0; v < a; ++v) {
      let x = b();
      h[y + v] = g.length;
      for (let k = 0; k < g.length; k++)
        if (x < g[k]) {
          h[y + v] = k;
          break;
        }
    }
  }
  return o || n.disposeIntermediateTensorInfo(u), n.makeTensorInfo(d, "int32", h);
}
var q6 = { kernelName: _g, backendName: "cpu", kernelFunc: H6 };
var j6 = ws.nonMaxSuppressionV3Impl;
function K6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s;
  be(r, "NonMaxSuppression");
  let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = j6(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var X6 = { kernelName: _o, backendName: "cpu", kernelFunc: K6 };
var Y6 = ws.nonMaxSuppressionV4Impl;
function Q6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s;
  be(r, "NonMaxSuppressionPadded");
  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = Y6(c, p, i, o, u, l);
  return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
}
var Z6 = { kernelName: Sl, backendName: "cpu", kernelFunc: Q6 };
var J6 = ws.nonMaxSuppressionV5Impl;
function ej(e) {
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s;
  be(r, "NonMaxSuppressionWithScore");
  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = J6(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var tj = { kernelName: Ao, backendName: "cpu", kernelFunc: ej };
function nj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s;
  be(r, "oneHot");
  let u = w.sizeFromShape(r.shape), l = new Float32Array(u * a);
  l.fill(o);
  let c = n.data.get(r.dataId).values;
  for (let p = 0; p < u; ++p)
    c[p] >= 0 && c[p] < a && (l[p * a + c[p]] = i);
  return n.makeTensorInfo([...r.shape, a], "int32", l);
}
var sj = { kernelName: Ro, backendName: "cpu", kernelFunc: nj };
function Vd(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "string")
    throw new Error("zerosLike is not supported for string tensors");
  if (s.dtype === "complex64") {
    let r = ga({ inputs: { input: s }, backend: n }), a = Vd({ inputs: { x: r }, backend: n }), i = io({ inputs: { input: s }, backend: n }), o = Vd({ inputs: { x: i }, backend: n }), u = En({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return mv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
}
var rj = { kernelName: Ko, backendName: "cpu", kernelFunc: Vd };
function jC(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "string")
    throw new Error("onesLike is not supported for string tensors");
  if (s.dtype === "complex64") {
    let r = ga({ inputs: { input: s }, backend: n }), a = jC({ inputs: { x: r }, backend: n }), i = io({ inputs: { input: s }, backend: n }), o = Vd({ inputs: { x: i }, backend: n }), u = En({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return mv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
}
var aj = { kernelName: Eo, backendName: "cpu", kernelFunc: jC };
function KC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Bd({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
  let a = t[0].shape, i = t[0].dtype;
  t.forEach((c) => {
    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let o = [], u = t.map((c) => {
    let p = Bd({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = oo({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
}
var ij = { kernelName: Do, backendName: "cpu", kernelFunc: KC };
function oj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
  be(r, "pad");
  let o = a.map((y, v) => y[0] + r.shape[v] + y[1]), u = a.map((y) => y[0]), l = n.data.get(r.dataId).values, c = w.sizeFromShape(r.shape), p = r.shape.length, d = w.computeStrides(r.shape), h = w.sizeFromShape(o), f = o.length, m = w.computeStrides(o), g = w.getTypedArrayFromDType(r.dtype, h);
  i !== 0 && g.fill(i);
  for (let y = 0; y < c; y++) {
    let x = w.indexToLoc(y, p, d).map((I, $) => I + u[$]), k = w.locToIndex(x, f, m);
    g[k] = l[y];
  }
  return { dataId: n.write(g, o, r.dtype), shape: o, dtype: r.dtype };
}
var XC = { kernelName: Ja, backendName: "cpu", kernelFunc: oj };
var uj = At((e, t) => Math.pow(e, t));
var lj = Ht(ei, uj);
var cj = { kernelName: ei, backendName: "cpu", kernelFunc: lj };
function dj(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = lv(s, r, i, a);
  return t.makeTensorInfo([o.length], a, o);
}
var pj = { kernelName: Il, backendName: "cpu", kernelFunc: dj };
var hj = st(Cl, (e) => 1 / e);
var fj = { kernelName: Cl, backendName: "cpu", kernelFunc: hj };
function mj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
  be(r, "resizeBilinear");
  let u = w.computeStrides(r.shape), [l, c] = o, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(w.sizeFromShape([p, l, c, f])), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = 0, x = b[0] / y[0], k = b[1] / y[1];
  for (let I = 0; I < p; I++)
    for (let $ = 0; $ < l; $++) {
      let E;
      i ? E = x * ($ + 0.5) - 0.5 : E = x * $;
      let A = Math.max(0, Math.floor(E)), P = E - A, R = Math.min(d - 1, Math.ceil(E)), F = I * u[0] + A * u[1], T = I * u[0] + R * u[1];
      for (let z = 0; z < c; z++) {
        let W;
        i ? W = k * (z + 0.5) - 0.5 : W = k * z;
        let j = Math.max(0, Math.floor(W)), X = W - j, Y = Math.min(h - 1, Math.ceil(W)), Z = F + j * u[2], te = T + j * u[2], J = F + Y * u[2], se = T + Y * u[2];
        for (let ne = 0; ne < f; ne++) {
          let oe = m[Z + ne], ae = m[te + ne], de = m[J + ne], me = m[se + ne], ke = oe + (de - oe) * X, Ie = ae + (me - ae) * X, Ee = ke + (Ie - ke) * P;
          g[v++] = Ee;
        }
      }
    }
  return n.makeTensorInfo([p, l, c, f], "float32", g);
}
var gj = { kernelName: ri, backendName: "cpu", kernelFunc: mj };
function bj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
  be([a, r], "resizeBilinearGrad");
  let o = w.computeStrides(r.shape), [u, l, c, p] = r.shape, [, d, h] = a.shape, f = new Float32Array(u * l * c * p), m = [i && d > 1 ? l - 1 : l, i && h > 1 ? c - 1 : c], g = [i && d > 1 ? d - 1 : d, i && h > 1 ? h - 1 : h], b = m[0] / g[0], y = m[1] / g[1], v = n.data.get(a.dataId).values, x = 0;
  for (let k = 0; k < u; k++) {
    let I = k * o[0];
    for (let $ = 0; $ < d; $++) {
      let E = $ * b, A = Math.floor(E), P = Math.min(Math.ceil(E), l - 1), R = I + A * o[1], F = I + P * o[1], T = E - A, z = 1 - T;
      for (let W = 0; W < h; W++) {
        let j = W * y, X = Math.floor(j), Y = Math.min(Math.ceil(j), c - 1), Z = j - X, te = 1 - Z, J = R + X * o[2], se = R + Y * o[2], ne = F + X * o[2], oe = F + Y * o[2], ae = z * te, de = z * Z, me = T * te, ke = T * Z;
        for (let Ie = 0; Ie < p; Ie++) {
          let Ee = v[x++];
          f[J + Ie] += Ee * ae, f[se + Ie] += Ee * de, f[ne + Ie] += Ee * me, f[oe + Ie] += Ee * ke;
        }
      }
    }
  }
  return n.makeTensorInfo([u, c, l, p], "float32", f);
}
var yj = { kernelName: Eg, backendName: "cpu", kernelFunc: bj };
function vj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
  be(r, "resizeNearestNeighbor");
  let u = w.computeStrides(r.shape), [l, c] = o, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(p * l * c * f), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = b[0] / y[0], x = b[1] / y[1], k = 0;
  for (let I = 0; I < p; I++) {
    let $ = I * u[0];
    for (let E = 0; E < l; E++) {
      let A = i ? v * (E + 0.5) : v * E, P = Math.min(d - 1, a ? Math.round(A) : Math.floor(A));
      i && (P = Math.max(0, P));
      let R = $ + P * u[1];
      for (let F = 0; F < c; F++) {
        let T = i ? x * (F + 0.5) : x * F, z = Math.min(h - 1, a ? Math.round(T) : Math.floor(T));
        i && (z = Math.max(0, z));
        let W = R + z * u[2];
        for (let j = 0; j < f; j++) {
          let X = m[W + j];
          g[k++] = X;
        }
      }
    }
  }
  return n.makeTensorInfo([p, l, c, f], r.dtype, g);
}
var xj = { kernelName: Nl, backendName: "cpu", kernelFunc: vj };
function wj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
  be([a, r], "resizeNearestNeighborGrad");
  let o = w.computeStrides(r.shape), u = w.computeStrides(a.shape), [l, c, p, d] = r.shape, [, h, f] = a.shape, m = new Float32Array(l * c * p * d), g = n.data.get(a.dataId).values, b = [i && h > 1 ? c - 1 : c, i && f > 1 ? p - 1 : p], y = [i && h > 1 ? h - 1 : h, i && f > 1 ? f - 1 : f], v = b[0] / y[0], x = b[1] / y[1], k = 1 / v, I = 1 / x, $ = Math.ceil(k) * 2 + 2, E = Math.ceil(I) * 2 + 2;
  for (let A = 0; A < l; A++) {
    let P = A * o[0];
    for (let R = 0; R < c; R++) {
      let F = P + R * o[1], T = Math.floor(R * k), z = Math.floor(T - $ / 2);
      for (let W = 0; W < p; W++) {
        let j = F + W * o[2], X = Math.floor(W * I), Y = Math.floor(X - E / 2);
        for (let Z = 0; Z < d; Z++) {
          let te = 0;
          for (let J = 0; J < $; J++) {
            let se = J + z;
            if (se < 0 || se >= h)
              continue;
            let ne = P + se * u[1], oe = se * v, ae = Math.min(c - 1, i ? Math.round(oe) : Math.floor(oe));
            if (R === ae)
              for (let de = 0; de < E; de++) {
                let me = de + Y;
                if (me < 0 || me >= f)
                  continue;
                let ke = ne + me * u[2], Ie = me * x, Ee = Math.min(p - 1, i ? Math.round(Ie) : Math.floor(Ie));
                W === Ee && (te += g[ke + Z]);
              }
          }
          m[j + Z] = te;
        }
      }
    }
  }
  return n.makeTensorInfo(r.shape, r.dtype, m);
}
var kj = { kernelName: Ag, backendName: "cpu", kernelFunc: wj };
function Sj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s;
  be(r, "reverse");
  let i = r.shape.length, o = w.parseAxisParam(a, r.shape);
  if (i === 0)
    return Os({ inputs: { x: r }, backend: n });
  let u = new Wt(r.shape, r.dtype), l = n.bufferSync(r);
  for (let c = 0; c < u.size; c++) {
    let p = u.indexToLoc(c), d = p.slice();
    o.forEach((h) => d[h] = r.shape[h] - 1 - d[h]), u.set(l.get(...d), ...p);
  }
  return n.makeTensorInfo(u.shape, u.dtype, u.values);
}
var Ij = { kernelName: Oo, backendName: "cpu", kernelFunc: Sj };
var Cj = { kernelName: Xo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [l, c, p, d] = s.shape, [h, f] = C.getImageCenter(i, c, p), m = 255, g = Math.sin(r), b = Math.cos(r), y = o.data.get(s.dataId).values;
  for (let x = 0; x < l; x++) {
    let k = x * p * c * d;
    for (let I = 0; I < c; I++) {
      let $ = I * (p * d);
      for (let E = 0; E < p; E++) {
        let A = E * d;
        for (let P = 0; P < d; P++) {
          let R = [l, I, E, P], F = R[2], T = R[1], z = (F - h) * b - (T - f) * g, W = (F - h) * g + (T - f) * b;
          z = Math.round(z + h), W = Math.round(W + f);
          let j = a;
          if (typeof a != "number" && (P === 3 ? j = m : j = a[P]), z >= 0 && z < p && W >= 0 && W < c) {
            let Y = W * (p * d), Z = z * d, te = k + Y + Z + P;
            j = y[te];
          }
          let X = k + $ + A + P;
          u[X] = j;
        }
      }
    }
  }
  return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
var Nj = st(Po, (e) => {
  let t = Math.floor(e);
  return e - t < 0.5 ? Math.floor(e) : e - t > 0.5 ? Math.ceil(e) : t % 2 === 0 ? t : t + 1;
});
var Tj = { kernelName: Po, backendName: "cpu", kernelFunc: Nj };
function YC(e, t, n, s, r, a, i, o, u, l) {
  let c = [s / r, r], p = e.values, d = t.values;
  if (s === 0)
    return De(n, t.dtype);
  let h = De(c, t.dtype);
  h.values.fill(u);
  for (let f = 0; f < a; f++) {
    let m = [], g = 0;
    for (let b = 0; b < i; b++) {
      let y = p[f * i + b];
      m.push(y), g += y * o[b];
    }
    if (g < 0 || g >= s / r)
      throw new Error(`Invalid indices: ${m} does not index into ${n}`);
    for (let b = 0; b < r; b++)
      l ? h.values[g * r + b] += d[f * r + b] : h.values[g * r + b] = t.rank === 0 ? d[0] : d[f * r + b];
  }
  return h;
}
function $j(e) {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = YC(h, f, i, p, l, u, o, c, 0, d);
  return n.makeTensorInfo(i, m.dtype, m.values);
}
var _j = { kernelName: zo, backendName: "cpu", kernelFunc: $j };
function Aj(e, t) {
  let n = 0, s = e.length, r = 0;
  for (; n < s; )
    r = Math.floor((n + s) / 2), e[r] < t ? n = r + 1 : s = r;
  return s;
}
function Ej(e, t) {
  let n = 0, s = e.length, r = 0;
  for (; n < s; )
    r = Math.floor((n + s) / 2), e[r] <= t ? n = r + 1 : s = r;
  return s;
}
function Rj(e, t, n, s, r, a) {
  let i = w.getArrayFromDType("int32", n * r);
  for (let o = 0; o < n; ++o) {
    let u = e.slice(o * s, (o + 1) * s), l = o * r;
    for (let c = 0; c < r; ++c)
      i[l + c] = a === "left" ? Aj(u, t[c + l]) : Ej(u, t[c + l]);
  }
  return i;
}
function Dj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = Rj(o, u, r.shape[0], r.shape[1], a.shape[1], i);
  return n.makeTensorInfo(a.shape, "int32", l);
}
var Fj = { kernelName: Rg, backendName: "cpu", kernelFunc: Dj };
function Oj(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t;
  be([s, r, a], "select");
  let i = s.shape.length, o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = cn(r.dtype, a.dtype), p = w.makeZerosTypedArray(w.sizeFromShape(r.shape), c), d = 0, h = i === 0 || i > 1 || r.shape.length === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
  for (let f = 0; f < o.length; f++)
    for (let m = 0; m < h; m++)
      o[f] === 1 ? p[d++] = u[f] : p[d++] = l[f];
  return n.makeTensorInfo(r.shape, c, p);
}
var Pj = { kernelName: Mo, backendName: "cpu", kernelFunc: Oj };
var zj = C.SELU_SCALEALPHA;
var Mj = C.SELU_SCALE;
var Lj = st(Tl, (e) => e >= 0 ? Mj * e : zj * (Math.exp(e) - 1));
var Bj = { kernelName: Tl, backendName: "cpu", kernelFunc: Lj };
var Vj = st($l, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
var Wj = { kernelName: $l, backendName: "cpu", kernelFunc: Vj };
var Uj = st(oi, (e) => Math.sin(e));
var Gj = { kernelName: oi, backendName: "cpu", kernelFunc: Uj };
var Hj = st(Bo, (e) => Math.sinh(e));
var qj = { kernelName: Bo, backendName: "cpu", kernelFunc: Hj };
var jj = 11920928955078125e-23;
var iw = Math.log(jj) + 2;
var Kj = st(_l, (e) => {
  let t = e > -iw, n = e < iw, s = Math.exp(e), r;
  return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
});
var Xj = { kernelName: _l, backendName: "cpu", kernelFunc: Kj };
function Yj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
  be([r], "spaceToBatchND");
  let o = w.sizeFromShape(a), u = [[0, 0]];
  u.push(...i);
  for (let I = 1 + a.length; I < r.shape.length; ++I)
    u.push([0, 0]);
  let l = XC.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = C.getReshaped(l.shape, a, o, false), p = C.getPermuted(c.length, a.length, false), d = C.getReshapedPermuted(l.shape, a, o, false), m = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = mt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
}
var Qj = { kernelName: Vo, backendName: "cpu", kernelFunc: Yj };
function Zj(e) {
  let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
  if (a.shape.length !== 1)
    throw new Error(`Dense shape must be a vector, saw:
        ${a.shape}`);
  if (s.shape.length !== 2)
    throw new Error(`Indices must be a matrix, saw:
        ${s.shape}`);
  if (r.shape.length !== 1)
    throw new Error(`Values must be a vector, saw:
        ${r.shape}`);
  if (i.shape.length !== 0)
    throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);
  let o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = n.data.get(i.dataId).values[0], [p, d, h, f, m] = kC(o, s.shape, s.dtype, u, r.dtype, l, c);
  return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
}
var Jj = { kernelName: up, backendName: "cpu", kernelFunc: Zj };
function e5(e) {
  let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
  if (s.shape.length !== 2)
    throw new Error(`Input indices should be a matrix but received shape
        ${s.shape}`);
  if (r.shape.length !== 1)
    throw new Error(`Input shape should be a vector but received shape
        ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
  let i = Array.from(n.data.get(r.dataId).values), o = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = SC(o, s.shape, s.dtype, i, u);
  return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
}
var t5 = { kernelName: Al, backendName: "cpu", kernelFunc: e5 };
function n5(e) {
  let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
  if (s.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
          ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
          ${a.shape}`);
  if (r.shape[0] !== a.shape[0])
    throw new Error("segmentIds and indices should have same size.");
  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = cv(i, s.shape, s.dtype, o, u, true);
  return n.makeTensorInfo(c, s.dtype, l);
}
var s5 = { kernelName: lp, backendName: "cpu", kernelFunc: n5 };
function r5(e) {
  let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
  if (s.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
         ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
         ${a.shape}`);
  if (r.shape[0] !== a.shape[0])
    throw new Error("segmentIds and indices should have same size.");
  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = cv(i, s.shape, s.dtype, o, u);
  return n.makeTensorInfo(c, s.dtype, l);
}
var a5 = { kernelName: cp, backendName: "cpu", kernelFunc: r5 };
function i5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = C.calculateShapes(a, r, o), h = false, f = n.bufferSync(r), m = n.bufferSync(a), g = n.data.get(i.dataId).values[0], b = YC(f, m, o, d, c, l, u, p, g, h);
  return n.makeTensorInfo(o, b.dtype, b.values);
}
var o5 = { kernelName: dp, backendName: "cpu", kernelFunc: i5 };
function u5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
  return u.map((p) => {
    let d = [...c];
    d[o] = p;
    let h = ba({ inputs: { x: r }, backend: n, attrs: { begin: l, size: d } });
    return l[o] += p, h;
  });
}
var l5 = { kernelName: Wo, backendName: "cpu", kernelFunc: u5 };
var c5 = { kernelName: El, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { x: n } = e, s = t;
  be(n, "square");
  let r = s.data.get(n.dataId).values, a = new Float32Array(r.length);
  for (let o = 0; o < r.length; ++o) {
    let u = r[o];
    a[o] = u * u;
  }
  return { dataId: s.write(a, n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
} };
var d5 = st(gi, (e, t) => {
  let n = t;
  return isNaN(e) ? NaN : e > 0 ? 1 : n.alpha;
});
var p5 = { kernelName: gi, backendName: "cpu", kernelFunc: d5 };
function h5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s;
  be(r, "stridedSlice");
  let { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = mt({ inputs: { x: r }, backend: n, attrs: { shape: f } });
  else if (g || b) {
    w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
    let I = wt.computeOutShape(y, v, x), $ = ba({ inputs: { x: r }, backend: n, attrs: { begin: y, size: I } });
    k = mt({ inputs: { x: $ }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo($);
  } else {
    let I = n.bufferSync(r), $ = CC(h, I, x, y);
    k = n.makeTensorInfo(f, $.dtype, $.values);
  }
  return k;
}
var f5 = { kernelName: Uo, backendName: "cpu", kernelFunc: h5 };
function m5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = NC(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var g5 = { kernelName: pp, backendName: "cpu", kernelFunc: m5 };
function b5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
  if (a.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (a.shape.length !== 1)
    throw new Error(`Input must be a vector, got shape: ${a.shape}`);
  if (i.shape.length !== 0)
    throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);
  let o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values[0], [l, c, p] = TC(o, u, r), d = c.length;
  return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
}
var y5 = { kernelName: Dg, backendName: "cpu", kernelFunc: b5 };
function v5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
  if (a.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (r <= 0)
    throw new Error("Number of buckets must be at least 1");
  let i = n.data.get(a.dataId).values, o = $C(i, r);
  return n.makeTensorInfo(a.shape, "int32", o);
}
var x5 = { kernelName: Fg, backendName: "cpu", kernelFunc: v5 };
var w5 = st(Go, (e) => Math.tan(e));
var k5 = { kernelName: Go, backendName: "cpu", kernelFunc: w5 };
var S5 = st(fi, (e) => Math.tanh(e));
var I5 = { kernelName: fi, backendName: "cpu", kernelFunc: S5 };
function C5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  be(r, "tile");
  let i = AC(n.bufferSync(r), a);
  return n.makeTensorInfo(i.shape, i.dtype, i.values);
}
var N5 = { kernelName: Cr, backendName: "cpu", kernelFunc: C5 };
function T5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s;
  be(r, "topk");
  let o = n.data.get(r.dataId).values, [u, l] = RC(o, r.shape, r.dtype, a, i);
  return [n.makeTensorInfo(u.shape, u.dtype, u.values), n.makeTensorInfo(l.shape, l.dtype, l.values)];
}
var $5 = { kernelName: Ho, backendName: "cpu", kernelFunc: T5 };
function _5(e) {
  let { inputs: t, attrs: n, backend: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = n, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = w.computeStrides(r.shape), y = b[0], v = b[1], x = b[2], k = w.getTypedArrayFromDType(r.dtype, w.sizeFromShape(g));
  k.fill(u);
  let I = s.data.get(r.dataId).values, $ = s.data.get(a.dataId).values;
  for (let A = 0; A < c; ++A) {
    let P = a.shape[0] === 1 ? $ : $.subarray(A * 8, A * 8 + 8);
    for (let R = 0; R < f; ++R)
      for (let F = 0; F < m; ++F)
        for (let T = 0; T < h; ++T) {
          let z, W = P[6] * F + P[7] * R + 1;
          if (W === 0)
            continue;
          let j = (P[0] * F + P[1] * R + P[2]) / W, X = (P[3] * F + P[4] * R + P[5]) / W, Y = ow(j, d, o), Z = ow(X, p, o);
          switch (i) {
            case "nearest":
              z = O5(I, p, d, y, v, x, A, Z, Y, T, u);
              break;
            case "bilinear":
              z = P5(I, p, d, y, v, x, A, Z, Y, T, u);
              break;
            default:
              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`);
          }
          let te = A * y + R * v + F * x + T;
          k[te] = z;
        }
    return s.makeTensorInfo(g, r.dtype, k);
  }
  return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
}
var A5 = { kernelName: qo, backendName: "cpu", kernelFunc: _5 };
function ow(e, t, n) {
  switch (n) {
    case "reflect":
      return E5(e, t);
    case "wrap":
      return R5(e, t);
    case "nearest":
      return F5(e, t);
    case "constant":
    default:
      return D5(e, t);
  }
}
function E5(e, t) {
  let n = e;
  if (n < 0)
    if (t <= 1)
      n = 0;
    else {
      let s = 2 * t;
      n < s && (n = s * Math.trunc(-n / s) + n), n = n < -t ? n + s : -n - 1;
    }
  else if (n > t - 1)
    if (t <= 1)
      n = 0;
    else {
      let s = 2 * t;
      n -= s * Math.trunc(n / s), n >= t && (n = s - n - 1);
    }
  return w.clamp(0, n, t - 1);
}
function R5(e, t) {
  let n = e;
  if (n < 0)
    if (t <= 1)
      n = 0;
    else {
      let s = t - 1;
      n += t * (Math.trunc(-n / s) + 1);
    }
  else if (n > t - 1)
    if (t <= 1)
      n = 0;
    else {
      let s = t - 1;
      n -= t * Math.trunc(n / s);
    }
  return w.clamp(0, n, t - 1);
}
function D5(e, t) {
  return e;
}
function F5(e, t) {
  return w.clamp(0, e, t - 1);
}
function Ou(e, t, n, s, r, a, i, o, u, l, c) {
  let p = i * s + o * r + u * a + l;
  return 0 <= o && o < t && 0 <= u && u < n ? e[p] : c;
}
function O5(e, t, n, s, r, a, i, o, u, l, c) {
  let p = Math.round(o), d = Math.round(u);
  return Ou(e, t, n, s, r, a, i, p, d, l, c);
}
function P5(e, t, n, s, r, a, i, o, u, l, c) {
  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Ou(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Ou(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Ou(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Ou(e, t, n, s, r, a, i, h, f, l, c);
  return (h - o) * m + (o - p) * g;
}
function z5(e) {
  let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
  be(a, "unique");
  let i = s.data.get(a.dataId).values, { outputValues: o, outputShape: u, indices: l } = DC(i, r, a.shape, a.dtype);
  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
}
var M5 = { kernelName: Og, backendName: "cpu", kernelFunc: z5 };
function L5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
  a < 0 && (a += r.shape.length);
  let i = r.shape.length, o = r.shape[a], u = new Array(i - 1), l = 0;
  for (let h = 0; h < i; h++)
    h !== a && (u[l++] = r.shape[h]);
  let c = new Array(i).fill(0), p = r.shape.slice();
  p[a] = 1;
  let d = new Array(o);
  for (let h = 0; h < d.length; h++) {
    c[a] = h;
    let f = ba({ inputs: { x: r }, backend: n, attrs: { begin: c, size: p } });
    d[h] = mt({ inputs: { x: f }, backend: n, attrs: { shape: u } }), n.disposeIntermediateTensorInfo(f);
  }
  return d;
}
var B5 = { kernelName: jo, backendName: "cpu", kernelFunc: L5 };
function V5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s;
  be(r, "unsortedSegmentSum");
  let o = r.shape.length, u = a.shape.length, l = [], c = [], p = o - u, d = a;
  for (let f = 0; f < p; ++f) {
    let m = Bd({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
    d = m, c.push(m);
  }
  for (let f = 0; f < i; ++f) {
    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = tC({ inputs: { a: g, b: d }, backend: n }), y = xr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = Yp({ inputs: { a: y, b: r }, backend: n }), x = Yl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
    l.push(x), c.push(g), c.push(b), c.push(y), c.push(v), c.push(x);
  }
  let h = KC({ inputs: l, backend: n, attrs: { axis: 0 } });
  return c.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
}
var W5 = { kernelName: hp, backendName: "cpu", kernelFunc: V5 };
var U5 = [UG, PU, HG, jG, WU, XG, QG, JG, tH, sH, aH, oH, lH, pH, fH, bH, vH, wH, SH, VG, CH, TH, _H, EH, BU, GU, DH, zU, OH, zH, MH, BH, WH, GH, qH, KH, YH, ZH, eq, nq, rq, iq, uq, lq, dq, hq, mq, gq, bq, yq, wq, FG, Sq, HU, Eq, qU, Rq, KU, Mq, Lq, Vq, YU, Gq, qq, Kq, Yq, Zq, ZU, eG, MU, e6, PH, n6, r6, i6, OG, nG, rG, u6, iG, c6, h6, m6, y6, x6, k6, S6, uG, C6, T6, _6, E6, D6, O6, z6, cG, L6, W6, q6, pG, fG, X6, Z6, tj, gG, sj, aj, ij, XC, cj, zG, vG, pj, LU, Um, fj, MG, LG, BG, gj, yj, xj, kj, Ij, Cj, Tj, wG, _j, Fj, Pj, Bj, SG, Wj, Gj, qj, IG, G6, Xj, Qj, Jj, t5, s5, a5, o5, l5, TG, c5, _G, p5, f5, g5, y5, x5, DG, vq, k5, I5, N5, $5, A5, bG, M5, B5, W5, rj];
for (let e of U5)
  Rl(e);
var G5 = {};
Ae(G5, { assertNotComplex: () => ru, bindCanvasToFramebuffer: () => nK, bindColorTextureToFramebuffer: () => od, bindTextureToProgramUniformSampler: () => d1, bindTextureUnit: () => u1, bindVertexBufferToProgramAttribute: () => Hm, callAndCheck: () => fe, canBeRepresented: () => QC, createFragmentShader: () => e1, createFramebuffer: () => o1, createProgram: () => t1, createStaticIndexBuffer: () => r1, createStaticVertexBuffer: () => s1, createTexture: () => a1, createVertexShader: () => JC, getBatchDim: () => ya, getExtensionOrThrow: () => Pu, getFramebufferErrorMessage: () => p1, getMaxTexturesInShader: () => g1, getNumChannels: () => eK, getProgramUniformLocation: () => c1, getProgramUniformLocationOrThrow: () => l1, getRowsCols: () => va, getShapeAs3D: () => ud, getTextureShapeFromLogicalShape: () => f1, getWebGLDisjointQueryTimerVersion: () => b1, getWebGLErrorMessage: () => ZC, getWebGLMaxTextureSize: () => m1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => y1, isDownloadFloatTextureEnabled: () => v1, isReshapeFree: () => nl, isWebGLFenceEnabled: () => x1, isWebGLVersionEnabled: () => jm, linkProgram: () => n1, logShaderSourceAndInfoLog: () => bv, resetMaxTextureSize: () => sK, resetMaxTexturesInShader: () => rK, unbindColorTextureFromFramebuffer: () => qm, unbindTextureUnit: () => tK, validateFramebuffer: () => zu, validateProgram: () => id, validateTextureSize: () => i1 });
var Yr = {};
var Qf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function H5(e, t) {
  Yr[e] = t;
}
function xs(e, t) {
  if (!(e in Yr) || t != null) {
    let s = j5(e, t);
    if (s !== null)
      Yr[e] = s;
    else
      return console.log("Could not get context for WebGL version", e), null;
  }
  let n = Yr[e];
  return n == null || n.isContextLost() ? (delete Yr[e], xs(e)) : (n.disable(n.DEPTH_TEST), n.disable(n.STENCIL_TEST), n.disable(n.BLEND), n.disable(n.DITHER), n.disable(n.POLYGON_OFFSET_FILL), n.disable(n.SAMPLE_COVERAGE), n.enable(n.SCISSOR_TEST), n.enable(n.CULL_FACE), n.cullFace(n.BACK), Yr[e]);
}
function q5(e) {
  if (typeof OffscreenCanvas != "undefined" && e === 2)
    return new OffscreenCanvas(300, 150);
  if (typeof document != "undefined")
    return document.createElement("canvas");
  throw new Error("Cannot create a canvas in this context");
}
function j5(e, t) {
  if (e !== 1 && e !== 2)
    throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
  let n = t == null ? q5(e) : t;
  return n.addEventListener("webglcontextlost", (s) => {
    s.preventDefault(), delete Yr[e];
  }, false), e === 1 ? n.getContext("webgl", Qf) || n.getContext("experimental-webgl", Qf) : n.getContext("webgl2", Qf);
}
function Ql(e, t) {
  return [t, e];
}
function K5(e, t) {
  return e * t;
}
function Zc(e) {
  let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
  return w.sizeToSquarishShape(n);
}
function su(e, t) {
  return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
}
function X5(e, t) {
  let [n, s] = su(e, t);
  return n * s * 4;
}
function gv(e, t) {
  let n = e, s, r, a, i, o, u, l, c, p, d;
  return K().getNumber("WEBGL_VERSION") === 2 ? (s = n.R32F, r = n.R16F, a = n.RGBA16F, i = n.RGBA32F, o = n.RED, l = 4, c = 1, p = n.HALF_FLOAT, d = n.FLOAT, u = n.RGBA8) : (s = e.RGBA, r = e.RGBA, a = e.RGBA, i = n.RGBA, o = e.RGBA, l = 4, c = 4, p = t != null ? t.HALF_FLOAT_OES : null, d = e.FLOAT, u = e.RGBA), { internalFormatFloat: s, internalFormatHalfFloat: r, internalFormatPackedHalfFloat: a, internalFormatPackedFloat: i, textureFormatFloat: o, downloadTextureFormat: u, downloadUnpackNumChannels: l, defaultNumChannels: c, textureTypeHalfFloat: p, textureTypeFloat: d };
}
function fe(e, t) {
  let n = t();
  return K().getBool("DEBUG") && Y5(e), n;
}
function Y5(e) {
  let t = e.getError();
  if (t !== e.NO_ERROR)
    throw new Error("WebGL Error: " + ZC(e, t));
}
var Q5 = 596e-10;
var Z5 = 65504;
function QC(e) {
  return !!(K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || Q5 < Math.abs(e) && Math.abs(e) < Z5);
}
function ZC(e, t) {
  switch (t) {
    case e.NO_ERROR:
      return "NO_ERROR";
    case e.INVALID_ENUM:
      return "INVALID_ENUM";
    case e.INVALID_VALUE:
      return "INVALID_VALUE";
    case e.INVALID_OPERATION:
      return "INVALID_OPERATION";
    case e.INVALID_FRAMEBUFFER_OPERATION:
      return "INVALID_FRAMEBUFFER_OPERATION";
    case e.OUT_OF_MEMORY:
      return "OUT_OF_MEMORY";
    case e.CONTEXT_LOST_WEBGL:
      return "CONTEXT_LOST_WEBGL";
    default:
      return `Unknown error code ${t}`;
  }
}
function Pu(e, t) {
  return Qs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
}
function JC(e, t) {
  let n = Qs(e, () => e.createShader(e.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
  if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), e.getShaderParameter(n, e.COMPILE_STATUS) === false)
    throw console.log(e.getShaderInfoLog(n)), new Error("Failed to compile vertex shader.");
  return n;
}
function e1(e, t) {
  let n = Qs(e, () => e.createShader(e.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
  if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), K().get("ENGINE_COMPILE_ONLY"))
    return n;
  if (e.getShaderParameter(n, e.COMPILE_STATUS) === false)
    throw bv(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
  return n;
}
var J5 = /ERROR: [0-9]+:([0-9]+):/g;
function bv(e, t) {
  let n = J5.exec(t);
  if (n == null) {
    console.log(`Couldn't parse line number in error: ${t}`), console.log(e);
    return;
  }
  let s = +n[1], r = e.split(`
`), a = r.length.toString().length + 2, i = r.map((p, d) => w.rightPad((d + 1).toString(), a) + p), o = 0;
  for (let p = 0; p < i.length; p++)
    o = Math.max(i[p].length, o);
  let u = i.slice(0, s - 1), l = i.slice(s - 1, s), c = i.slice(s);
  console.log(u.join(`
`)), console.log(t.split(`
`)[0]), console.log(`%c ${w.rightPad(l[0], o)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function t1(e) {
  return Qs(e, () => e.createProgram(), "Unable to create WebGLProgram.");
}
function n1(e, t) {
  if (fe(e, () => e.linkProgram(t)), !K().get("ENGINE_COMPILE_ONLY") && e.getProgramParameter(t, e.LINK_STATUS) === false)
    throw console.log(e.getProgramInfoLog(t)), new Error("Failed to link vertex and fragment shaders.");
}
function id(e, t) {
  if (fe(e, () => e.validateProgram(t)), e.getProgramParameter(t, e.VALIDATE_STATUS) === false)
    throw console.log(e.getProgramInfoLog(t)), new Error("Shader program validation failed.");
}
function s1(e, t) {
  let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
}
function r1(e, t) {
  let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
  return fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ELEMENT_ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
}
function eK() {
  return K().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function a1(e) {
  return Qs(e, () => e.createTexture(), "Unable to create WebGLTexture.");
}
function i1(e, t) {
  let n = K().getNumber("WEBGL_MAX_TEXTURE_SIZE");
  if (e <= 0 || t <= 0) {
    let s = `[${e}x${t}]`;
    throw new Error("Requested texture size " + s + " is invalid.");
  }
  if (e > n || t > n) {
    let s = `[${e}x${t}]`, r = `[${n}x${n}]`;
    throw new Error("Requested texture size " + s + " greater than WebGL maximum on this browser / GPU " + r + ".");
  }
}
function o1(e) {
  return Qs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Hm(e, t, n, s, r, a, i) {
  let o = e.getAttribLocation(t, n);
  return o === -1 ? false : (fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, s)), fe(e, () => e.vertexAttribPointer(o, r, e.FLOAT, false, a, i)), fe(e, () => e.enableVertexAttribArray(o)), true);
}
function u1(e, t, n) {
  h1(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
}
function tK(e, t) {
  h1(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
}
function l1(e, t, n) {
  return Qs(e, () => e.getUniformLocation(t, n), 'uniform "' + n + '" not present in program.');
}
function c1(e, t, n) {
  return e.getUniformLocation(t, n);
}
function d1(e, t, n, s) {
  fe(e, () => u1(e, t, s)), fe(e, () => e.uniform1i(n, s));
}
function nK(e) {
  fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.viewport(0, 0, e.canvas.width, e.canvas.height)), fe(e, () => e.scissor(0, 0, e.canvas.width, e.canvas.height));
}
function od(e, t, n) {
  fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, n)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, t, 0));
}
function qm(e, t) {
  fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
}
function zu(e) {
  let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
  if (t !== e.FRAMEBUFFER_COMPLETE)
    throw new Error("Error binding framebuffer: " + p1(e, t));
}
function p1(e, t) {
  switch (t) {
    case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
      return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
    case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
      return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
    case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
      return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
    case e.FRAMEBUFFER_UNSUPPORTED:
      return "FRAMEBUFFER_UNSUPPORTED";
    default:
      return `unknown error ${t}`;
  }
}
function Qs(e, t, n) {
  let s = fe(e, () => t());
  if (s == null)
    throw new Error(n);
  return s;
}
function h1(e, t) {
  let n = e.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, s = t + e.TEXTURE0;
  if (s < e.TEXTURE0 || s > n) {
    let r = `[gl.TEXTURE0, gl.TEXTURE${n}]`;
    throw new Error(`textureUnit must be in ${r}.`);
  }
}
function ya(e, t = 2) {
  return w.sizeFromShape(e.slice(0, e.length - t));
}
function va(e) {
  if (e.length === 0)
    throw Error("Cannot get rows and columns of an empty shape array.");
  return [e.length > 1 ? e[e.length - 2] : 1, e[e.length - 1]];
}
function ud(e) {
  let t = [1, 1, 1];
  return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [ya(e), ...va(e)]), t;
}
function f1(e, t = false) {
  let n = K().getNumber("WEBGL_MAX_TEXTURE_SIZE");
  t && (n = n * 2, e = e.map((r, a) => a >= e.length - 2 ? w.nearestLargerEven(e[a]) : e[a]), e.length === 1 && (e = [2, e[0]])), e.length !== 2 && (e = w.squeezeShape(e).newShape);
  let s = w.sizeFromShape(e);
  if (e.length <= 1 && s <= n)
    return [1, s];
  if (e.length === 2 && e[0] <= n && e[1] <= n)
    return e;
  if (e.length === 3 && e[0] * e[1] <= n && e[2] <= n)
    return [e[0] * e[1], e[2]];
  if (e.length === 3 && e[0] <= n && e[1] * e[2] <= n)
    return [e[0], e[1] * e[2]];
  if (e.length === 4 && e[0] * e[1] * e[2] <= n && e[3] <= n)
    return [e[0] * e[1] * e[2], e[3]];
  if (e.length === 4 && e[0] <= n && e[1] * e[2] * e[3] <= n)
    return [e[0], e[1] * e[2] * e[3]];
  if (t) {
    let r = ya(e), a = 2, i = 2;
    return e.length && ([a, i] = va(e)), s = r * (a / 2) * (i / 2), w.sizeToSquarishShape(s).map((o) => o * 2);
  }
  return w.sizeToSquarishShape(s);
}
function Jc(e) {
  return e % 2 === 0;
}
function nl(e, t) {
  if (e = e.slice(-2), t = t.slice(-2), w.arraysEqual(e, t) || !e.length || !t.length || e[0] === 0 || e[1] === 0 || t[0] === 0 || t[1] === 0)
    return true;
  if (e.length !== t.length) {
    let n = e.slice(-1)[0], s = t.slice(-1)[0];
    if (n === s || Jc(n) && Jc(s) && (e[0] === 1 || t[0] === 1))
      return true;
  }
  return e[1] === t[1] && Jc(e[0]) && Jc(t[0]);
}
var ld;
var cd;
function m1(e) {
  if (ld == null) {
    let t = xs(e);
    ld = t.getParameter(t.MAX_TEXTURE_SIZE);
  }
  return ld;
}
function sK() {
  ld = null;
}
function rK() {
  cd = null;
}
function g1(e) {
  if (cd == null) {
    let t = xs(e);
    cd = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
  }
  return Math.min(16, cd);
}
function b1(e) {
  if (e === 0)
    return 0;
  let t, n = xs(e);
  return Ln(n, "EXT_disjoint_timer_query_webgl2") && e === 2 ? t = 2 : Ln(n, "EXT_disjoint_timer_query") ? t = 1 : t = 0, t;
}
function Ln(e, t) {
  return e.getExtension(t) != null;
}
function jm(e) {
  try {
    if (xs(e) != null)
      return true;
  } catch (t) {
    return console.log("Error when getting WebGL context: ", t), false;
  }
  return false;
}
function y1(e) {
  if (e === 0)
    return false;
  let t = xs(e);
  if (e === 1) {
    if (!Ln(t, "OES_texture_float"))
      return false;
  } else if (!Ln(t, "EXT_color_buffer_float"))
    return false;
  return Km(t);
}
function v1(e) {
  if (e === 0)
    return false;
  let t = xs(e);
  if (e === 1) {
    if (!Ln(t, "OES_texture_float") || !Ln(t, "WEBGL_color_buffer_float"))
      return false;
  } else {
    if (Ln(t, "EXT_color_buffer_float"))
      return Km(t);
    let s = "EXT_color_buffer_half_float";
    if (Ln(t, s)) {
      let r = t.getExtension(s);
      return aK(t, r);
    }
    return false;
  }
  return Km(t);
}
function Km(e) {
  let t = gv(e), n = e.createTexture();
  e.bindTexture(e.TEXTURE_2D, n);
  let s = 1, r = 1;
  e.texImage2D(e.TEXTURE_2D, 0, t.internalFormatFloat, s, r, 0, t.textureFormatFloat, t.textureTypeFloat, null);
  let a = e.createFramebuffer();
  e.bindFramebuffer(e.FRAMEBUFFER, a), e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, n, 0);
  let i = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
  return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(n), e.deleteFramebuffer(a), i;
}
function aK(e, t) {
  let n = gv(e, t), s = e.createTexture();
  e.bindTexture(e.TEXTURE_2D, s);
  let r = 1, a = 1;
  e.texImage2D(e.TEXTURE_2D, 0, n.internalFormatHalfFloat, r, a, 0, n.textureFormatFloat, n.textureTypeHalfFloat, null);
  let i = e.createFramebuffer();
  e.bindFramebuffer(e.FRAMEBUFFER, i), e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, s, 0);
  let o = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
  return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(s), e.deleteFramebuffer(i), o;
}
function x1(e) {
  return e !== 2 ? false : xs(e).fenceSync != null;
}
function ru(e, t) {
  Array.isArray(e) || (e = [e]), e.forEach((n) => {
    n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the WebGL backend.`);
  });
}
var Ne = K();
Ne.registerFlag("HAS_WEBGL", () => Ne.getNumber("WEBGL_VERSION") > 0);
Ne.registerFlag("WEBGL_VERSION", () => jm(2) ? 2 : jm(1) ? 1 : 0);
Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
Ne.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Ne.get("WEBGL_VERSION") === 2);
Ne.registerFlag("WEBGL_CPU_FORWARD", () => true);
Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
Ne.registerFlag("WEBGL_PACK", () => Ne.getBool("HAS_WEBGL"));
Ne.registerFlag("WEBGL_PACK_NORMALIZATION", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_CLIP", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_PACK_REDUCE", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_LAZILY_UNPACK", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_CONV_IM2COL", () => Ne.getBool("WEBGL_PACK"));
Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => m1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => g1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
  let e = Ne.getNumber("WEBGL_VERSION");
  return e === 0 ? 0 : b1(e);
});
Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !gp.isMobile());
Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => y1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Ne.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => v1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_FENCE_API_ENABLED", () => x1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0);
Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (e) => {
  if (e < 0 && e !== -1)
    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`);
});
Ne.registerFlag("WEBGL_FLUSH_THRESHOLD", () => gp.isMobile() ? 1 : -1, (e) => {
  if (e < 0 && e !== -1)
    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`);
});
Ne.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
Ne.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
Ne.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
Ne.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
function fn() {
  let e, t, n, s, r, a, i, o, u, l;
  return K().getNumber("WEBGL_VERSION") === 2 ? (e = "#version 300 es", t = "in", n = "out", s = "in", r = "texture", a = "outputColor", i = "out vec4 outputColor;", o = `
      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)
    `, u = "", l = `
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `) : (e = "", t = "attribute", n = "varying", s = "varying", r = "texture2D", a = "gl_FragColor", i = "", o = `
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `, u = `
      uniform float INFINITY;

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

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `), { version: e, attribute: t, varyingVs: n, varyingFs: s, texture2D: r, output: a, defineOutput: i, defineSpecialNaN: o, defineSpecialInf: u, defineRound: l };
}
function ki(e, t, n = "index") {
  let s = w.computeStrides(t);
  return s.map((r, a) => {
    let i = `int ${e[a]} = ${n} / ${r}`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * ${r}` : `index -= ${e[a]} * ${r}`;
    return `${i}; ${o};`;
  }).join("");
}
function Qp(e, t, n = "index") {
  let s = w.computeStrides(t);
  return s.map((r, a) => {
    let i = `int ${e[a]} = ${n} / outShapeStrides[${a}]`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * outShapeStrides[${a}]` : `index -= ${e[a]} * outShapeStrides[${a}]`;
    return `${i}; ${o};`;
  }).join("");
}
function iK(e, t) {
  let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
  r[n - 2] = s[n - 1];
  for (let a = n - 3; a >= 0; --a)
    r[a] = `(${r[a + 1]} * ${s[a + 1]})`;
  return r;
}
function oK(e, t, n = "index") {
  let s = e.map((a, i) => i), r = iK(s, t);
  return r.map((a, i) => {
    let o = `int ${e[i]} = ${n} / ${r[i]}`, u = i === r.length - 1 ? `int ${e[i + 1]} = ${n} - ${e[i]} * ${r[i]}` : `index -= ${e[i]} * ${r[i]}`;
    return `${o}; ${u};`;
  }).join("");
}
function yv(e) {
  let t = w.computeStrides(e).map((n) => n.toString());
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`;
}
function vv() {
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`;
}
var w1 = `
  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: k1 } = C;
function uK(e, t, n) {
  let s = [];
  if (e.forEach((h) => {
    let f = w.sizeFromShape(h.shapeInfo.logicalShape);
    if (h.shapeInfo.isUniform ? s.push(`uniform float ${h.name}${f > 1 ? `[${f}]` : ""};`) : (s.push(`uniform sampler2D ${h.name};`), s.push(`uniform int offset${h.name};`)), n.enableShapeUniforms) {
      let { uniformShape: m } = xv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
      switch (m.length) {
        case 1:
          s.push(`uniform int ${h.name}Shape;`);
          break;
        case 2:
          s.push(`uniform ivec2 ${h.name}Shape;`);
          break;
        case 3:
          s.push(`uniform ivec3 ${h.name}Shape;`);
          break;
        case 4:
          s.push(`uniform ivec4 ${h.name}Shape;`);
          break;
        default:
          break;
      }
      s.push(`uniform ivec2 ${h.name}TexShape;`);
    }
  }), n.enableShapeUniforms) {
    switch (t.logicalShape.length) {
      case 1:
        s.push("uniform int outShape;");
        break;
      case 2:
        s.push("uniform ivec2 outShape;"), s.push("uniform int outShapeStrides;");
        break;
      case 3:
        s.push("uniform ivec3 outShape;"), s.push("uniform ivec2 outShapeStrides;");
        break;
      case 4:
        s.push("uniform ivec4 outShape;"), s.push("uniform ivec3 outShapeStrides;");
        break;
      default:
        break;
    }
    s.push("uniform ivec2 outTexShape;");
  }
  n.customUniforms && n.customUniforms.forEach((h) => {
    s.push(`uniform ${h.type} ${h.name}${h.arrayIndex ? `[${h.arrayIndex}]` : ""};`);
  });
  let r = s.join(`
`), a = e.map((h) => lK(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
`), i = t.texShape, o = fn(), u = pK(o), l, c, p = mK(o);
  return t.isPacked ? (l = cK(t.logicalShape, i, n.enableShapeUniforms), c = fK(o)) : (l = dK(t.logicalShape, i, n.enableShapeUniforms), c = hK(o)), n.packedInputs && (p += vK), [p, u, c, r, l, a, n.userCode].join(`
`);
}
function au(e, t = false) {
  let n = e.shapeInfo.logicalShape;
  switch (n.length) {
    case 0:
      return EK(e, t);
    case 1:
      return DK(e, t);
    case 2:
      return OK(e, t);
    case 3:
      return zK(e, t);
    case 4:
      return LK(e, t);
    case 5:
      return BK(e);
    case 6:
      return VK(e);
    default:
      throw new Error(`${n.length}-D input sampling is not yet supported`);
  }
}
function S1(e, t) {
  switch (e.shapeInfo.logicalShape.length) {
    case 0:
      return AK(e);
    case 1:
      return RK(e, t);
    case 2:
      return FK(e, t);
    case 3:
      return PK(e, t);
    default:
      return MK(e, t);
  }
}
function lK(e, t, n = false, s) {
  let r = "";
  n ? r += S1(e, s) : r += au(e, s);
  let a = e.shapeInfo.logicalShape, i = t.logicalShape;
  return a.length <= i.length && (n ? r += WK(e, t) : r += UK(e, t)), r;
}
function cK(e, t, n) {
  switch (e.length) {
    case 0:
      return I1();
    case 1:
      return xK(e, t, n);
    case 2:
      return $K(e, t, n);
    case 3:
      return kK(e, t, n);
    default:
      return IK(e, t, n);
  }
}
function dK(e, t, n) {
  switch (e.length) {
    case 0:
      return I1();
    case 1:
      return wK(e, t, n);
    case 2:
      return _K(e, t, n);
    case 3:
      return SK(e, t, n);
    case 4:
      return CK(e, t, n);
    case 5:
      return NK(e, t);
    case 6:
      return TK(e, t);
    default:
      throw new Error(`${e.length}-D output sampling is not yet supported`);
  }
}
function pK(e) {
  return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `;
}
function hK(e) {
  return `
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `;
}
function fK(e) {
  return `
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `;
}
function mK(e) {
  return `${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFs} vec2 resultUV;
    ${e.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${e.defineSpecialNaN}
    ${e.defineSpecialInf}
    ${e.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${gK}
    ${bK}
    ${yK}
  `;
}
var gK = `
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 bK = `
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 yK = `
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 vK = `
  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 I1() {
  return `
    int getOutputCoords() {
      return 0;
    }
  `;
}
function xK(e, t, n) {
  let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];
  return s[0] === 1 ? n ? `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    ` : `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${s[1]}.0);
      }
    ` : s[1] === 1 ? n ? `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    ` : `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${s[0]}.0);
      }
    ` : n ? `
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  ` : `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
    }
  `;
}
function wK(e, t, n) {
  return t[0] === 1 ? n ? `
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    ` : `
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    ` : t[1] === 1 ? n ? `
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    ` : `
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    ` : n ? `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  ` : `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `;
}
function kK(e, t, n) {
  if (n)
    return `
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;
  let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)], r = Math.ceil(e[2] / 2), a = r * Math.ceil(e[1] / 2);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      int index = resTexRC.x * ${s[1]} + resTexRC.y;

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

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

      return ivec3(b, r, c);
    }
  `;
}
function SK(e, t, n) {
  if (n)
    return `
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${Qp(["r", "c", "d"], e)}
    return ivec3(r, c, d);
  }
`;
  let s = ki(["r", "c", "d"], e);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec3(r, c, d);
    }
  `;
}
function IK(e, t, n) {
  if (n)
    return `
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;
  let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)], r = Math.ceil(e[e.length - 1] / 2), a = r * Math.ceil(e[e.length - 2] / 2), i = a, o = "", u = "b, r, c";
  for (let l = 2; l < e.length - 1; l++)
    i *= e[e.length - l - 1], o = `
      int b${l} = index / ${i};
      index -= b${l} * ${i};
    ` + o, u = `b${l}, ` + u;
  return `
    ivec${e.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));
      int index = resTexRC.x * ${s[1]} + resTexRC.y;

      ${o}

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

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

      return ivec${e.length}(${u});
    }
  `;
}
function CK(e, t, n) {
  if (n)
    return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${Qp(["r", "c", "d", "d2"], e)}
      return ivec4(r, c, d, d2);
    }
  `;
  let s = ki(["r", "c", "d", "d2"], e);
  return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec4(r, c, d, d2);
    }
  `;
}
function NK(e, t) {
  let n = ki(["r", "c", "d", "d2", "d3"], e);
  return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

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

      ${n}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `;
}
function $K(e, t, n) {
  let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];
  if (w.arraysEqual(e, t))
    return n ? `
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    ` : `
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
      }
    `;
  let r = Math.ceil(e[1] / 2);
  return n ? `
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  ` : `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${s[0]}, ${s[1]}));

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

      return ivec2(r, c);
    }
  `;
}
function _K(e, t, n) {
  return w.arraysEqual(e, t) ? n ? `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    ` : `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    ` : e[1] === 1 ? n ? `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    ` : `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    ` : e[0] === 1 ? n ? `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    ` : `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    ` : n ? `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  ` : `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${e[1]};
      int c = index - r * ${e[1]};
      return ivec2(r, c);
    }
  `;
}
function Si(e) {
  return `offset${e}`;
}
function AK(e) {
  let t = e.name, n = "get" + t.charAt(0).toUpperCase() + t.slice(1), s = fn();
  return `
    vec4 ${n}() {
      return ${s.texture2D}(${t}, halfCR);
    }
  `;
}
function EK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
  if (e.shapeInfo.isUniform)
    return `float ${s}() {return ${n};}`;
  let [r, a] = e.shapeInfo.texShape;
  if (r === 1 && a === 1)
    return `
      float ${s}() {
        return sampleTexture(${n}, halfCR);
      }
    `;
  let i = Si(n);
  if (t)
    return `
    float ${s}() {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
      return sampleTexture(${n}, uv);
    }
  `;
  let [o, u] = e.shapeInfo.texShape;
  return `
    float ${s}() {
      vec2 uv = uvFromFlat(${o}, ${u}, ${i});
      return sampleTexture(${n}, uv);
    }
  `;
}
function RK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = e.shapeInfo.texShape, a = fn();
  if (t)
    return `
    vec4 ${s}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${a.texture2D}(${n}, uv);
    }
  `;
  let i = [Math.ceil(r[0] / 2), Math.ceil(r[1] / 2)];
  return `
    vec4 ${s}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${a.texture2D}(${n}, uv);
    }
  `;
}
function DK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
  if (e.shapeInfo.isUniform)
    return `
      float ${s}(int index) {
        ${iu(e)}
      }
    `;
  let r = e.shapeInfo.texShape, a = r[0], i = r[1];
  if (i === 1 && a === 1)
    return `
      float ${s}(int index) {
        return sampleTexture(${n}, halfCR);
      }
    `;
  let o = Si(n);
  return i === 1 ? t ? `
      float ${s}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    ` : `
      float ${s}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);
        return sampleTexture(${n}, uv);
      }
    ` : a === 1 ? t ? `
      float ${s}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    ` : `
      float ${s}(int index) {
        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${n}, uv);
      }
    ` : t ? `
    float ${s}(int index) {
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});
      return sampleTexture(${n}, uv);
    }
  ` : `
    float ${s}(int index) {
      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});
      return sampleTexture(${n}, uv);
    }
  `;
}
function FK(e, t) {
  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = a[0], o = a[1], u = fn();
  if (a != null && w.arraysEqual(n, a))
    return t ? `
      vec4 ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);

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

        return ${u.texture2D}(${s}, uv);
      }
    `;
  if (t)
    return `
    vec4 ${r}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${u.texture2D}(${s}, uv);
    }
  `;
  let l = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)], c = Math.ceil(n[1] / 2);
  return `
    vec4 ${r}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
      return ${u.texture2D}(${s}, uv);
    }
  `;
}
function OK(e, t) {
  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape;
  if (a != null && w.arraysEqual(n, a)) {
    if (t)
      return `
      float ${r}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    `;
    let d = a[0], h = a[1];
    return `
    float ${r}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
      return sampleTexture(${s}, uv);
    }
  `;
  }
  let { newShape: i, keptDims: o } = w.squeezeShape(n), u = i;
  if (u.length < n.length) {
    let d = ou(e, u), h = ["row", "col"];
    return `
      ${au(d, t)}
      float ${r}(int row, int col) {
        return ${r}(${uu(h, o)});
      }
    `;
  }
  if (e.shapeInfo.isUniform)
    return `
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${iu(e)}
      }
    `;
  let l = a[0], c = a[1], p = Si(s);
  return c === 1 ? t ? `
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
        return sampleTexture(${s}, uv);
      }
    ` : `
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${s}, uv);
    }
  ` : l === 1 ? t ? `
      float ${r}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
        return sampleTexture(${s}, uv);
      }
    ` : `
    float ${r}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${s}, uv);
    }
  ` : t ? `
      float ${r}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s}Shape[1] + col + ${p};
        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
        return sampleTexture(${s}, uv);
      }
    ` : `
  float ${r}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${n[1]} + col + ${p};
    vec2 uv = uvFromFlat(${l}, ${c}, index);
    return sampleTexture(${s}, uv);
  }
`;
}
function PK(e, t) {
  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)];
  if (n[0] === 1) {
    let d = n.slice(1), h = [1, 2], f = ou(e, d), m = ["b", "row", "col"];
    return `
        ${S1(f, t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${uu(m, h)});
        }
      `;
  }
  let o = fn();
  if (t)
    return `
    vec4 ${r}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${o.texture2D}(${s}, uv);
    }
  `;
  let u = i[0], l = i[1], c = Math.ceil(n[2] / 2), p = c * Math.ceil(n[1] / 2);
  return `
    vec4 ${r}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${u}, ${l}, ${p}, ${c}, b, row, col);
      return ${o.texture2D}(${s}, uv);
    }
  `;
}
function zK(e, t) {
  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[1] * n[2], i = n[2], { newShape: o, keptDims: u } = w.squeezeShape(n), l = o;
  if (l.length < n.length) {
    let m = ou(e, l), g = ["row", "col", "depth"];
    return `
        ${au(m, t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${uu(g, u)});
        }
      `;
  }
  if (e.shapeInfo.isUniform)
    return `
      float ${r}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${a}, ${i}, 1)));
        ${iu(e)}
      }
    `;
  let c = e.shapeInfo.texShape, p = c[0], d = c[1], h = e.shapeInfo.flatOffset;
  if (d === a && h == null)
    return t ? `
      float ${r}(int row, int col, int depth) {
        int stride1 = ${s}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    ` : `
        float ${r}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${d}.0, ${p}.0);
          return sampleTexture(${s}, uv);
        }
      `;
  if (d === i && h == null)
    return t ? `
      float ${r}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    ` : `
    float ${r}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
      return sampleTexture(${s}, uv);
    }
  `;
  let f = Si(s);
  return t ? `
    float ${r}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${s}Shape[1] * ${s}Shape[2];
      int stride1 = ${s}Shape[2];
      int index = row * ${a} + col * ${i} + depth + ${f};
      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
      return sampleTexture(${s}, uv);
    }
    ` : `
      float ${r}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${a} + col * ${i} + depth + ${f};
        vec2 uv = uvFromFlat(${p}, ${d}, index);
        return sampleTexture(${s}, uv);
      }
  `;
}
function MK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = fn();
  if (t)
    return `
    vec4 ${s}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${n}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
    }
  `;
  let a = e.shapeInfo.logicalShape, i = a.length, o = e.shapeInfo.texShape, u = [Math.ceil(o[0] / 2), Math.ceil(o[1] / 2)], l = u[0], c = u[1], p = Math.ceil(a[i - 1] / 2), d = p * Math.ceil(a[i - 2] / 2), h = "int b, int row, int col", f = `b * ${d} + (row / 2) * ${p} + (col / 2)`;
  for (let m = 2; m < i - 1; m++)
    h = `int b${m}, ` + h, d *= a[i - m - 1], f = `b${m} * ${d} + ` + f;
  return `
    vec4 ${s}(${h}) {
      int index = ${f};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
      return ${r.texture2D}(${n}, uv);
    }
  `;
}
function LK(e, t) {
  let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[3], i = n[2] * a, o = n[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(n);
  if (u.length < n.length) {
    let y = ou(e, u), v = ["row", "col", "depth", "depth2"];
    return `
      ${au(y, t)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${uu(v, l)});
      }
    `;
  }
  if (e.shapeInfo.isUniform)
    return `
      float ${r}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${o}, ${i}, ${a}, 1)));
        ${iu(e)}
      }
    `;
  let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1], f = `int stride2 = ${s}Shape[3];`, m = `int stride1 = ${s}Shape[2] * stride2;`, g = `int stride0 = ${s}Shape[1] * stride1;`;
  if (h === o && c == null)
    return t ? `
      float ${r}(int row, int col, int depth, int depth2) {
        ${f}
        ${m}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    ` : `
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${i}, ${a}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${d}.0);
        return sampleTexture(${s}, uv);
      }
    `;
  if (h === a && c == null)
    return t ? `
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${s}TexShape[1], ${s}TexShape[0]);
        return sampleTexture(${s}, uv);
      }
    ` : `
      float ${r}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n[1] * n[2]}, ${n[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${d}.0);
        return sampleTexture(${s}, uv);
      }
    `;
  let b = Si(s);
  return t ? `
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${f}
      ${m}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});
      return sampleTexture(${s}, uv);
    }
  ` : `
    float ${r}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} +
          depth * ${a} + depth2;
      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
      return sampleTexture(${s}, uv);
    }
  `;
}
function BK(e) {
  let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = t[4], a = t[3] * r, i = t[2] * a, o = t[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(t);
  if (u.length < t.length) {
    let m = ou(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
    return `
      ${au(m)}
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        return ${s}(${uu(g, l)});
      }
    `;
  }
  if (e.shapeInfo.isUniform)
    return `
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${o}, ${i}, ${a}, ${r})) +
          depth3;
        ${iu(e)}
      }
    `;
  let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1];
  if (h === o && c == null)
    return `
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${a}, ${r}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;
  if (h === r && c == null)
    return `
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1] * t[2] * t[3]},
               ${t[2] * t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${h}.0, ${d}.0);
        return sampleTexture(${n}, uv);
      }
    `;
  let f = Si(n);
  return `
    float ${s}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${o} + col * ${i} + depth * ${a} +
          depth2 * ${r} + depth3 + ${f};
      vec2 uv = uvFromFlat(${d}, ${h}, index);
      return sampleTexture(${n}, uv);
    }
  `;
}
function VK(e) {
  let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), { newShape: r, keptDims: a } = w.squeezeShape(t);
  if (r.length < t.length) {
    let g = ou(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
    return `
      ${au(g)}
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${s}(${uu(b, a)});
      }
    `;
  }
  let i = t[5], o = t[4] * i, u = t[3] * o, l = t[2] * u, c = t[1] * l;
  if (e.shapeInfo.isUniform)
    return `
      float ${s}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${l}, ${u}, ${o})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${iu(e)}
      }
    `;
  let p = e.shapeInfo.flatOffset, d = e.shapeInfo.texShape, h = d[0], f = d[1];
  if (f === c && p == null)
    return `
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${l}, ${u}, ${o}, ${i})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;
  if (f === i && p == null)
    return `
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1] * t[2] * t[3] * t[4]},
               ${t[2] * t[3] * t[4]},
               ${t[3] * t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${h}.0);
        return sampleTexture(${n}, uv);
      }
    `;
  let m = Si(n);
  return `
    float ${s}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${l} + depth * ${u} +
          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};
      vec2 uv = uvFromFlat(${h}, ${f}, index);
      return sampleTexture(${n}, uv);
    }
  `;
}
function iu(e) {
  let t = e.name, n = w.sizeFromShape(e.shapeInfo.logicalShape);
  return n < 2 ? `return ${t};` : `
    for (int i = 0; i < ${n}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `;
}
function WK(e, t) {
  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, i = t.logicalShape.length, o = k1(e.shapeInfo.logicalShape, t.logicalShape), u = ot(i), l = i - a, c, p = ["x", "y", "z", "w", "u", "v"];
  a === 0 ? c = "" : i < 2 && o.length >= 1 ? c = "coords = 0;" : c = o.map((y) => `coords.${p[y + l]} = 0;`).join(`
`);
  let d = "";
  i < 2 && a > 0 ? d = "coords" : d = e.shapeInfo.logicalShape.map((y, v) => `coords.${p[v + l]}`).join(", ");
  let h = "return outputValue;", m = w.sizeFromShape(e.shapeInfo.logicalShape) === 1, b = w.sizeFromShape(t.logicalShape) === 1;
  if (a === 1 && !m && !b)
    h = `
      return vec4(outputValue.xy, outputValue.xy);
    `;
  else if (m && !b)
    i === 1 ? h = `
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      ` : h = `
        return vec4(outputValue.x);
      `;
  else if (o.length) {
    let y = a - 2, v = a - 1;
    o.indexOf(y) > -1 && o.indexOf(v) > -1 ? h = "return vec4(outputValue.x);" : o.indexOf(y) > -1 ? h = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : o.indexOf(v) > -1 && (h = "return vec4(outputValue.xx, outputValue.zz);");
  }
  return `
    vec4 ${r}() {
      ${u} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${s}(${d});
      ${h}
    }
  `;
}
function UK(e, t) {
  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = t.texShape, i = e.shapeInfo.texShape, o = e.shapeInfo.logicalShape.length, u = t.logicalShape.length;
  if (!e.shapeInfo.isUniform && o === u && e.shapeInfo.flatOffset == null && w.arraysEqual(i, a))
    return `
      float ${r}() {
        return sampleTexture(${n}, resultUV);
      }
    `;
  let l = ot(u), c = k1(e.shapeInfo.logicalShape, t.logicalShape), p = u - o, d, h = ["x", "y", "z", "w", "u", "v"];
  o === 0 ? d = "" : u < 2 && c.length >= 1 ? d = "coords = 0;" : d = c.map((m) => `coords.${h[m + p]} = 0;`).join(`
`);
  let f = "";
  return u < 2 && o > 0 ? f = "coords" : f = e.shapeInfo.logicalShape.map((m, g) => `coords.${h[g + p]}`).join(", "), `
    float ${r}() {
      ${l} coords = getOutputCoords();
      ${d}
      return get${s}(${f});
    }
  `;
}
function ot(e) {
  if (e <= 1)
    return "int";
  if (e === 2)
    return "ivec2";
  if (e === 3)
    return "ivec3";
  if (e === 4)
    return "ivec4";
  if (e === 5)
    return "ivec5";
  if (e === 6)
    return "ivec6";
  throw Error(`GPU for rank ${e} is not yet supported`);
}
function xv(e, t, n) {
  let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, i = e && a === 3 && t[0] === 1, o = i ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || i;
  return { useSqueezeShape: u, uniformShape: u ? o : t, keptDims: r };
}
function ou(e, t) {
  let n = JSON.parse(JSON.stringify(e));
  return n.shapeInfo.logicalShape = t, n;
}
function uu(e, t) {
  return t.map((n) => e[n]).join(", ");
}
function GK(e, t, n, s) {
  let r = n.map((c, p) => {
    let d = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null };
    return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (d.flatOffset = c.texData.slice.flatOffset), { name: t.variableNames[p], shapeInfo: d };
  }), a = r.map((c) => c.shapeInfo), i = { logicalShape: s.shape, texShape: s.texData.texShape, isUniform: false, isPacked: s.texData.isPacked, flatOffset: null }, o = uK(r, i, t), u = e1(e.gl, o), l = e.createProgram(u);
  return K().get("ENGINE_COMPILE_ONLY") ? { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, ...C1(e, t, l) };
}
function C1(e, t, n) {
  let s = {}, r = {}, a = {}, i = [], o, u, l, c = null, p = null;
  p = e.getUniformLocation(n, "NAN", false), K().getNumber("WEBGL_VERSION") === 1 && (c = e.getUniformLocation(n, "INFINITY", false));
  let d = false;
  for (let h = 0; h < t.variableNames.length; h++) {
    let f = t.variableNames[h];
    s[f] = e.getUniformLocation(n, f, d), s[`offset${f}`] = e.getUniformLocation(n, `offset${f}`, d), t.enableShapeUniforms && (r[`${f}Shape`] = e.getUniformLocation(n, `${f}Shape`, d), a[`${f}TexShape`] = e.getUniformLocation(n, `${f}TexShape`, d));
  }
  return t.enableShapeUniforms && (o = e.getUniformLocation(n, "outShape", d), l = e.getUniformLocation(n, "outShapeStrides", d), u = e.getUniformLocation(n, "outTexShape", d)), t.customUniforms && t.customUniforms.forEach((h, f) => {
    i[f] = e.getUniformLocation(n, h.name, d);
  }), { uniformLocations: s, customUniformLocations: i, infLoc: c, nanLoc: p, inShapesLocations: r, inTexShapesLocations: a, outShapeLocation: o, outShapeStridesLocation: l, outTexShapeLocation: u };
}
function uw(e, t) {
  if (e.length !== t.length)
    throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);
  e.forEach((n, s) => {
    let r = n.logicalShape, a = t[s], i = a.shape;
    if (!w.arraysEqual(r, i))
      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);
    if (n.isUniform && a.isUniform)
      return;
    let o = n.texShape, u = a.isUniform ? null : a.texData.texShape;
    if (!w.arraysEqual(o, u))
      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`);
  });
}
function HK(e, t, n, s, r) {
  t.program.enableShapeUniforms || (uw(t.inShapeInfos, n), uw([t.outShapeInfo], [s]));
  let a = s.texData.texture, i = s.texData.texShape;
  s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, i[0], i[1]) : e.setOutputMatrixTexture(a.texture, i[0], i[1]), e.setProgram(t.webGLProgram), K().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
    let c = t.program.variableNames[l], p = t.uniformLocations[c], d = t.uniformLocations[`offset${c}`], h = t.inShapesLocations[`${c}Shape`], f = t.inTexShapesLocations[`${c}TexShape`];
    if (h) {
      let { uniformShape: m } = xv(t.program.packedInputs, u.shape, u.texData.texShape);
      switch (m.length) {
        case 1:
          e.gl.uniform1iv(h, new Int32Array(m));
          break;
        case 2:
          e.gl.uniform2iv(h, new Int32Array(m));
          break;
        case 3:
          e.gl.uniform3iv(h, new Int32Array(m));
          break;
        case 4:
          e.gl.uniform4iv(h, new Int32Array(m));
          break;
        default:
          break;
      }
    }
    if (f && e.gl.uniform2i(f, u.texData.texShape[0], u.texData.texShape[1]), p != null) {
      if (u.isUniform) {
        if (w.sizeFromShape(u.shape) < 2)
          e.gl.uniform1f(p, u.uniformValues[0]);
        else {
          let m = u.uniformValues;
          m instanceof Float32Array || (m = new Float32Array(m)), e.gl.uniform1fv(p, m);
        }
        return;
      }
      u.texData.slice != null && d != null && e.gl.uniform1i(d, u.texData.slice.flatOffset), e.setInputMatrixTexture(u.texData.texture.texture, p, l);
    }
  });
  let o = t.outShapeLocation;
  if (o)
    switch (s.shape.length) {
      case 1:
        e.gl.uniform1iv(o, new Int32Array(s.shape));
        break;
      case 2:
        e.gl.uniform2iv(o, new Int32Array(s.shape));
        break;
      case 3:
        e.gl.uniform3iv(o, new Int32Array(s.shape));
        break;
      case 4:
        e.gl.uniform4iv(o, new Int32Array(s.shape));
        break;
      default:
        break;
    }
  if (t.outShapeStridesLocation) {
    let u = w.computeStrides(s.shape);
    switch (s.shape.length) {
      case 2:
        e.gl.uniform1iv(t.outShapeStridesLocation, new Int32Array(u));
        break;
      case 3:
        e.gl.uniform2iv(t.outShapeStridesLocation, new Int32Array(u));
        break;
      case 4:
        e.gl.uniform3iv(t.outShapeStridesLocation, new Int32Array(u));
        break;
      default:
        break;
    }
  }
  t.outTexShapeLocation && e.gl.uniform2i(t.outTexShapeLocation, s.texData.texShape[0], s.texData.texShape[1]), t.program.customUniforms && r && t.program.customUniforms.forEach((u, l) => {
    let c = t.customUniformLocations[l], p = r[l];
    if (u.type === "float")
      e.gl.uniform1fv(c, p);
    else if (u.type === "vec2")
      e.gl.uniform2fv(c, p);
    else if (u.type === "vec3")
      e.gl.uniform3fv(c, p);
    else if (u.type === "vec4")
      e.gl.uniform4fv(c, p);
    else if (u.type === "int")
      e.gl.uniform1iv(c, p);
    else if (u.type === "ivec2")
      e.gl.uniform2iv(c, p);
    else if (u.type === "ivec3")
      e.gl.uniform3iv(c, p);
    else if (u.type === "ivec4")
      e.gl.uniform4iv(c, p);
    else
      throw Error(`uniform type ${u.type} is not supported yet.`);
  }), e.executeProgram();
}
function qK(e, t, n) {
  let s = "";
  t.concat(n).forEach((i) => {
    let o = i.texData != null && i.texData.slice != null && i.texData.slice.flatOffset > 0;
    if (e.enableShapeUniforms && !i.isUniform) {
      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = xv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
      if (c.length === 1 && e.packedInputs) {
        let k = [Math.ceil(u[0] / 2), Math.ceil(u[1] / 2)];
        d = `${k[0] > 1}_${k[1] > 1}`;
      } else if (c.length === 2 && !e.packedInputs)
        h = `${c[0] > 1}_${c[1] > 1}`;
      else if (c.length > 2 && !e.packedInputs) {
        let k = w.computeStrides(c);
        f = `${k[0] === u[1]}_${k[k.length - 1] === u[1]}`;
      }
      let m = i.shape.length, g = c.length === 2 && w.arraysEqual(i.shape, u), b = w.sizeFromShape(i.shape) === 1, y = C.getBroadcastDims(i.shape, n.shape), v = !e.packedInputs && m === n.shape.length && w.arraysEqual(u, n.texData.texShape), x = e.packedInputs || c.length > 2 ? "" : `${u[0] > 1}_${u[1] > 1}`;
      s += `${m}_${v}_${l ? p : ""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`;
    } else {
      let u = i.isUniform ? "uniform" : i.texData.texShape;
      s += `${i.shape}_${u}_${o}`;
    }
  });
  let r = e.userCode, a = e.constructor.name;
  return a += "_" + s + "_" + r + `${K().getNumber("WEBGL_VERSION")}`, a;
}
function Sn(e) {
  return K().getBool("WEBGL_USE_SHAPES_UNIFORMS") && e <= 4;
}
var jK = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t = fn();
    this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Qp(["r", "c", "d"], e) : ki(["r", "c", "d"], e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${t.output} = result;
      }
    `;
  }
};
var KK = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t = fn();
    this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Qp(["r", "c", "d"], e) : ki(["r", "c", "d"], e)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${t.output} = result;
      }
    `;
  }
};
var XK = class {
  constructor(e) {
    this.variableNames = ["A"], this.outTexUsage = 3;
    let t = fn();
    this.outputShape = e, this.userCode = `
      ${w1}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `;
  }
};
var YK = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = 3;
    let t = fn();
    this.outputShape = e, this.userCode = `
      ${w1}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `;
  }
};
var QK = class {
  constructor(e, t = false) {
    this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let n = fn();
    this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
    let s = "result";
    t && (s = "floor(result * 255. + 0.5)"), this.userCode = `
      ${this.enableShapeUniforms ? vv() : yv(e)}

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

        int flatIndex = getFlatIndex(coords);
        int offset = imod(flatIndex, 4);

        flatIndex = idiv(flatIndex, 4, 1.);

        int r = flatIndex / texShape[1];
        int c = imod(flatIndex, texShape[1]);
        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
        vec4 values = ${n.texture2D}(A, uv);

        float result;

        if(offset == 0) {
          result = values[0];
        } else if(offset == 1) {
          result = values[1];
        } else if(offset == 2) {
          result = values[2];
        } else {
          result = values[3];
        }

        ${n.output} = vec4(${s}, 0., 0., 0.);
      }
    `;
  }
};
var ZK = class {
  constructor(e, t = false) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let n = fn();
    this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
    let s = "", r = "result";
    t && (r = "floor(result * 255. + 0.5)");
    for (let a = 0; a <= 1; a++)
      for (let i = 0; i <= 1; i++) {
        let o = a * 2 + i;
        s += `
          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 = ${n.texture2D}(A, uv);

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

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

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

          ${s}

          ${n.output} = ${r};
        }
    `;
  }
};
var JK = {};
Ae(JK, { bindVertexProgramAttributeStreams: () => F1, createBufferFromOutputTexture: () => z1, createFloat16MatrixTexture: () => A1, createFloat16PackedMatrixTexture: () => D1, createFloat32MatrixTexture: () => _1, createIndexBuffer: () => $1, createPackedMatrixTexture: () => R1, createUnsignedBytesMatrixTexture: () => E1, createVertexBuffer: () => T1, createVertexShader: () => N1, downloadByteEncodedFloatMatrixFromOutputTexture: () => L1, downloadFloat32MatrixFromBuffer: () => M1, downloadMatrixFromPackedOutputTexture: () => V1, downloadPackedMatrixFromBuffer: () => B1, getInternalFormatForFloat16MatrixTexture: () => kv, getInternalFormatForFloat16PackedMatrixTexture: () => Cv, getInternalFormatForFloat32MatrixTexture: () => wv, getInternalFormatForPackedMatrixTexture: () => Iv, getInternalFormatForUnsignedBytesMatrixTexture: () => Sv, uploadDenseMatrixToTexture: () => O1, uploadPixelDataToTexture: () => P1 });
function N1(e) {
  let t = fn(), n = `${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;
  return JC(e, n);
}
function T1(e) {
  let t = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
  return s1(e, t);
}
function $1(e) {
  let t = new Uint16Array([0, 1, 2, 2, 1, 3]);
  return r1(e, t);
}
function Zl(e, t, n, s, r, a) {
  i1(t, n);
  let i = a1(e), o = e.TEXTURE_2D;
  return fe(e, () => e.bindTexture(o, i)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_MIN_FILTER, e.NEAREST)), fe(e, () => e.texParameteri(o, e.TEXTURE_MAG_FILTER, e.NEAREST)), K().getNumber("WEBGL_VERSION") === 1 ? fe(e, () => e.texImage2D(o, 0, s, t, n, 0, r, a, null)) : fe(e, () => e.texStorage2D(o, 1, s, t, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null)), { texture: i, texShape: [n, t] };
}
function wv(e) {
  return e.internalFormatFloat;
}
function _1(e, t, n, s) {
  let [r, a] = Ql(t, n);
  return Zl(e, r, a, wv(s), s.textureFormatFloat, e.FLOAT);
}
function kv(e) {
  return e.internalFormatHalfFloat;
}
function A1(e, t, n, s) {
  let [r, a] = Ql(t, n);
  return Zl(e, r, a, kv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
}
function Sv(e) {
  return e.downloadTextureFormat;
}
function E1(e, t, n, s) {
  let [r, a] = Ql(t, n);
  return Zl(e, r, a, Sv(s), e.RGBA, e.UNSIGNED_BYTE);
}
function Iv(e) {
  return e.internalFormatPackedFloat;
}
function R1(e, t, n, s) {
  let [r, a] = su(t, n);
  return Zl(e, r, a, Iv(s), e.RGBA, e.FLOAT);
}
function Cv(e) {
  return e.internalFormatPackedHalfFloat;
}
function D1(e, t, n, s) {
  let [r, a] = su(t, n);
  return Zl(e, r, a, Cv(s), e.RGBA, s.textureTypeHalfFloat);
}
function F1(e, t, n) {
  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Hm(e, t, "clipSpacePos", n, 3, 20, 0) && Hm(e, t, "uv", n, 2, 20, 12);
}
function O1(e, t, n, s, r, a) {
  fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
  let i, o, u;
  r instanceof Uint8Array ? (i = new Uint8Array(n * s * 4), o = e.UNSIGNED_BYTE, u = e.RGBA) : (i = new Float32Array(n * s * 4), o = e.FLOAT, u = a.internalFormatPackedFloat), i.set(r), K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n, s, e.RGBA, o, i)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, u, n, s, 0, e.RGBA, o, i)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
}
function P1(e, t, n) {
  fe(e, () => e.bindTexture(e.TEXTURE_2D, t)), n.data instanceof Uint8Array ? K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n.width, n.height, e.RGBA, e.UNSIGNED_BYTE, n.data)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, n.width, n.height, 0, e.RGBA, e.UNSIGNED_BYTE, n.data)) : K().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, e.RGBA, e.UNSIGNED_BYTE, n)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, e.RGBA, e.UNSIGNED_BYTE, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
}
function z1(e, t, n, s) {
  let r = e.createBuffer();
  fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, r));
  let o = 4 * 4 * t * n;
  return fe(e, () => e.bufferData(e.PIXEL_PACK_BUFFER, o, e.STREAM_READ)), fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, 0)), fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, null)), r;
}
function M1(e, t, n) {
  let s = e, r = new Float32Array(n);
  return s.bindBuffer(s.PIXEL_PACK_BUFFER, t), s.getBufferSubData(s.PIXEL_PACK_BUFFER, 0, r), s.bindBuffer(s.PIXEL_PACK_BUFFER, null), r;
}
function L1(e, t, n, s) {
  let [r, a] = Ql(t, n), i = 4, o = new Uint8Array(K5(t * n, i));
  return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, o)), new Float32Array(o.buffer);
}
function B1(e, t, n, s, r, a, i, o) {
  let u = e, l = new Float32Array(X5(a, i));
  return u.bindBuffer(u.PIXEL_PACK_BUFFER, t), u.getBufferSubData(u.PIXEL_PACK_BUFFER, 0, l), u.bindBuffer(u.PIXEL_PACK_BUFFER, null), l;
}
function V1(e, t, n) {
  let s = new Float32Array(t * n * 4);
  return fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, s)), s;
}
var Zf = class {
  constructor(e) {
    this.outputTexture = null, this.program = null, this.disposed = false, this.vertexAttrsAreBound = false, this.itemsToPoll = [];
    let t = K().getNumber("WEBGL_VERSION");
    e != null ? (this.gl = e, H5(t, e)) : this.gl = xs(t);
    let n = "WEBGL_color_buffer_float", s = "EXT_color_buffer_half_float";
    if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), K().getNumber("WEBGL_VERSION") === 1) {
      let r = "OES_texture_float", a = "OES_texture_half_float";
      if (this.textureFloatExtension = Pu(this.gl, r), Ln(this.gl, a))
        this.textureHalfFloatExtension = Pu(this.gl, a);
      else if (K().get("WEBGL_FORCE_F16_TEXTURES"))
        throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
      if (this.colorBufferFloatExtension = this.gl.getExtension(n), Ln(this.gl, s))
        this.colorBufferHalfFloatExtension = Pu(this.gl, s);
      else if (K().get("WEBGL_FORCE_F16_TEXTURES"))
        throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
    } else if (n = "EXT_color_buffer_float", Ln(this.gl, n))
      this.colorBufferFloatExtension = this.gl.getExtension(n);
    else if (Ln(this.gl, s))
      this.colorBufferHalfFloatExtension = this.gl.getExtension(s);
    else
      throw new Error("GL context does not support color renderable floats");
    this.vertexBuffer = T1(this.gl), this.indexBuffer = $1(this.gl), this.framebuffer = o1(this.gl), this.textureConfig = gv(this.gl, this.textureHalfFloatExtension);
  }
  get debug() {
    return K().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;
    fe(e, () => e.finish()), fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.deleteFramebuffer(this.framebuffer)), fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), fe(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true;
  }
  createFloat32MatrixTexture(e, t) {
    return this.throwIfDisposed(), _1(this.gl, e, t, this.textureConfig);
  }
  createFloat16MatrixTexture(e, t) {
    return this.throwIfDisposed(), A1(this.gl, e, t, this.textureConfig);
  }
  createUnsignedBytesMatrixTexture(e, t) {
    return this.throwIfDisposed(), E1(this.gl, e, t, this.textureConfig);
  }
  uploadPixelDataToTexture(e, t) {
    this.throwIfDisposed(), P1(this.gl, e, t);
  }
  uploadDenseMatrixToTexture(e, t, n, s) {
    this.throwIfDisposed(), O1(this.gl, e, t, n, s, this.textureConfig);
  }
  createFloat16PackedMatrixTexture(e, t) {
    return this.throwIfDisposed(), D1(this.gl, e, t, this.textureConfig);
  }
  createPackedMatrixTexture(e, t) {
    return this.throwIfDisposed(), R1(this.gl, e, t, this.textureConfig);
  }
  deleteMatrixTexture(e) {
    this.throwIfDisposed(), this.outputTexture === e && (qm(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
  }
  downloadByteEncodedFloatMatrixFromOutputTexture(e, t, n) {
    return this.downloadMatrixDriver(e, () => L1(this.gl, t, n, this.textureConfig));
  }
  downloadPackedMatrixFromBuffer(e, t, n, s, r, a) {
    return B1(this.gl, e, t, n, s, r, a, this.textureConfig);
  }
  downloadFloat32MatrixFromBuffer(e, t) {
    return M1(this.gl, e, t);
  }
  createBufferFromTexture(e, t, n) {
    this.bindTextureToFrameBuffer(e);
    let s = z1(this.gl, t, n, this.textureConfig);
    return this.unbindTextureToFrameBuffer(), s;
  }
  createAndWaitForFence() {
    let e = this.createFence(this.gl);
    return this.pollFence(e);
  }
  createFence(e) {
    let t, n;
    if (K().getBool("WEBGL_FENCE_API_ENABLED")) {
      let s = e, r = s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE, 0);
      e.flush(), n = () => {
        let a = s.clientWaitSync(r, 0, 0);
        return a === s.ALREADY_SIGNALED || a === s.CONDITION_SATISFIED;
      }, t = r;
    } else
      K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t = this.beginQuery(), this.endQuery(), n = () => this.isQueryAvailable(t, K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : n = () => true;
    return { query: t, isFencePassed: n };
  }
  downloadMatrixFromPackedTexture(e, t, n) {
    return this.downloadMatrixDriver(e, () => V1(this.gl, t, n));
  }
  createProgram(e) {
    this.throwIfDisposed();
    let t = this.gl;
    this.vertexShader == null && (this.vertexShader = N1(t));
    let n = t1(t);
    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), n1(t, n), this.debug && id(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = F1(t, this.program, this.vertexBuffer)), n;
  }
  deleteProgram(e) {
    this.throwIfDisposed(), e === this.program && (this.program = null), e != null && fe(this.gl, () => this.gl.deleteProgram(e));
  }
  setProgram(e) {
    this.throwIfDisposed(), this.program = e, this.program != null && this.debug && id(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
  }
  getUniformLocation(e, t, n = true) {
    return this.throwIfDisposed(), n ? l1(this.gl, e, t) : c1(this.gl, e, t);
  }
  getAttributeLocation(e, t) {
    return this.throwIfDisposed(), fe(this.gl, () => this.gl.getAttribLocation(e, t));
  }
  getUniformLocationNoThrow(e, t) {
    return this.throwIfDisposed(), this.gl.getUniformLocation(e, t);
  }
  setInputMatrixTexture(e, t, n) {
    this.throwIfDisposed(), this.throwIfNoProgram(), d1(this.gl, e, t, n);
  }
  setOutputMatrixTexture(e, t, n) {
    this.setOutputMatrixTextureDriver(e, n, t);
  }
  setOutputPackedMatrixTexture(e, t, n) {
    this.throwIfDisposed();
    let [s, r] = su(t, n);
    this.setOutputMatrixTextureDriver(e, s, r);
  }
  setOutputMatrixWriteRegion(e, t, n, s) {
    this.setOutputMatrixWriteRegionDriver(n, e, s, t);
  }
  setOutputPackedMatrixWriteRegion(e, t, n, s) {
    throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
  }
  debugValidate() {
    this.program != null && id(this.gl, this.program), zu(this.gl);
  }
  executeProgram() {
    this.throwIfDisposed(), this.throwIfNoProgram();
    let e = this.gl;
    this.debug && this.debugValidate(), fe(e, () => e.drawElements(e.TRIANGLES, 6, e.UNSIGNED_SHORT, 0));
  }
  blockUntilAllProgramsCompleted() {
    this.throwIfDisposed(), fe(this.gl, () => this.gl.finish());
  }
  getQueryTimerExtension() {
    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Pu(this.gl, K().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 (K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
      let n = this.gl, s = this.getQueryTimerExtensionWebGL2(), r = n.createQuery();
      return n.beginQuery(s.TIME_ELAPSED_EXT, r), r;
    }
    let e = this.getQueryTimerExtensionWebGL1(), t = e.createQueryEXT();
    return e.beginQueryEXT(e.TIME_ELAPSED_EXT, t), t;
  }
  endQuery() {
    if (K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
      let t = this.gl, n = this.getQueryTimerExtensionWebGL2();
      t.endQuery(n.TIME_ELAPSED_EXT);
      return;
    }
    let e = this.getQueryTimerExtensionWebGL1();
    e.endQueryEXT(e.TIME_ELAPSED_EXT);
  }
  async waitForQueryAndGetTime(e) {
    return await w.repeatedTry(() => this.disposed || this.isQueryAvailable(e, K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
  }
  getQueryTime(e, t) {
    if (t === 0)
      return null;
    if (t === 2) {
      let n = this.gl;
      return n.getQueryParameter(e, n.QUERY_RESULT) / 1e6;
    } else {
      let n = this.getQueryTimerExtensionWebGL1();
      return n.getQueryObjectEXT(e, n.QUERY_RESULT_EXT) / 1e6;
    }
  }
  isQueryAvailable(e, t) {
    if (t === 0)
      return true;
    if (t === 2) {
      let n = this.gl, s = this.getQueryTimerExtensionWebGL2(), r = n.getQueryParameter(e, n.QUERY_RESULT_AVAILABLE);
      return this.disjoint == null && (this.disjoint = this.gl.getParameter(s.GPU_DISJOINT_EXT)), r && !this.disjoint;
    } else {
      let n = this.getQueryTimerExtensionWebGL1(), s = n.getQueryObjectEXT(e, n.QUERY_RESULT_AVAILABLE_EXT);
      return this.disjoint == null && (this.disjoint = this.gl.getParameter(n.GPU_DISJOINT_EXT)), s && !this.disjoint;
    }
  }
  pollFence(e) {
    return new Promise((t) => {
      this.addItemToPoll(() => e.isFencePassed(), () => t());
    });
  }
  pollItems() {
    let e = eX(this.itemsToPoll.map((t) => t.isDoneFn));
    for (let t = 0; t <= e; ++t) {
      let { resolveFn: n } = this.itemsToPoll[t];
      n();
    }
    this.itemsToPoll = this.itemsToPoll.slice(e + 1);
  }
  addItemToPoll(e, t) {
    this.itemsToPoll.push({ isDoneFn: e, resolveFn: t }), !(this.itemsToPoll.length > 1) && w.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0));
  }
  bindTextureToFrameBuffer(e) {
    this.throwIfDisposed(), od(this.gl, e, this.framebuffer), this.debug && zu(this.gl);
  }
  unbindTextureToFrameBuffer() {
    this.outputTexture != null ? (od(this.gl, this.outputTexture, this.framebuffer), this.debug && zu(this.gl)) : qm(this.gl, this.framebuffer);
  }
  downloadMatrixDriver(e, t) {
    this.bindTextureToFrameBuffer(e);
    let n = t();
    return this.unbindTextureToFrameBuffer(), n;
  }
  setOutputMatrixTextureDriver(e, t, n) {
    this.throwIfDisposed();
    let s = this.gl;
    od(s, e, this.framebuffer), this.debug && zu(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
  }
  setOutputMatrixWriteRegionDriver(e, t, n, s) {
    this.throwIfDisposed(), fe(this.gl, () => this.gl.scissor(e, t, n, s));
  }
  throwIfDisposed() {
    if (this.disposed)
      throw new Error("Attempted to use disposed GPGPUContext.");
  }
  throwIfNoProgram() {
    if (this.program == null)
      throw new Error("No GPU program is currently set.");
  }
};
function eX(e) {
  let t = 0;
  for (; t < e.length && e[t](); ++t)
    ;
  return t - 1;
}
var { addImpl: tX, bincountImpl: W1, bincountReduceImpl: nX, ceilImpl: sX, concatImpl: rX, equalImpl: aX, expImpl: iX, expm1Impl: oX, floorImpl: uX, gatherNdImpl: lX, gatherV2Impl: cX, greaterImpl: dX, greaterEqualImpl: pX, lessImpl: hX, lessEqualImpl: fX, linSpaceImpl: mX, logImpl: gX, maxImpl: bX, maximumImpl: yX, minimumImpl: vX, multiplyImpl: xX, negImpl: wX, notEqualImpl: kX, prodImpl: SX, rangeImpl: IX, rsqrtImpl: CX, sigmoidImpl: NX, simpleAbsImpl: U1, sliceImpl: TX, sparseFillEmptyRowsImpl: $X, sparseReshapeImpl: _X, sparseSegmentReductionImpl: G1, sqrtImpl: AX, stridedSliceImpl: EX, stringNGramsImpl: RX, stringSplitImpl: DX, stringToHashBucketFastImpl: FX, subImpl: OX, tileImpl: PX, topKImpl: zX, transposeImpl: Nv, uniqueImpl: MX } = sv;
function H1(e, t) {
  return ["x", "y", "z", "w", "u", "v"].slice(0, t).map((n) => `${e}.${n}`);
}
function ln(e, t) {
  return t === 1 ? [e] : H1(e, t);
}
function LX(e, t) {
  if (e === 1)
    return "rc";
  let n = "";
  for (let s = 0; s < e; s++)
    n += t[s], s < e - 1 && (n += ",");
  return n;
}
var BX = class {
  constructor(e) {
    if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = Sn(this.outputShape.length), this.rank === 0)
      this.userCode = `
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;
    else {
      let t = ln("rc", this.rank), n = ot(this.rank), s = this.getOutOfBoundsCondition(t), r = this.getSetup(t), a = this.getOutput(t);
      this.userCode = `
        void main() {
          ${n} rc = getOutputCoords();

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

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

      bool cEdge = cp1 >= ${n};
      bool rEdge = rp1 >= ${s};
    `;
  }
  getOutput(e) {
    let t = this.getSourceCoordsArr(e);
    return this.rank === 1 ? `getA(rc), (rc + 1 >= ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0` : `getA(${t[0]}),
            cEdge ? 0. : getA(${t[1]}),
            rEdge ? 0. : getA(${t[2]}),
            rEdge || cEdge ? 0. : getA(${t[3]})`;
  }
};
var q1 = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
    let n = "";
    for (let s = 0; s < 4; s++) {
      let r = "thisRC = rc;";
      s % 2 === 1 && (r += "thisRC.z += 1;"), s > 1 && (r += "thisRC.y += 1;"), n += `
        ${r}
        ${s > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : ""}
          int flatIndex = getFlatIndex(thisRC);

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

          result[${s}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${s > 0 ? "}" : ""}
      `;
    }
    this.userCode = `
      ${VX(t, this.enableShapeUniforms)}
      ${this.enableShapeUniforms ? vv() : yv(e)}

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

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms ? "outShape[1]" : e[1]};
        int cols = ${this.enableShapeUniforms ? "outShape[2]" : e[2]};

        ${n}

        setOutput(result);
      }
    `;
  }
};
function VX(e, t) {
  return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t ? oK(["r", "c", "d"], "inputShape") : ki(["r", "c", "d"], e)}
      return ivec3(r, c, d);
    }
  `;
}
var WX = 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, t, n) {
    let s = cw(t, n), r = dw(e, s, n);
    r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []);
    let a = lw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
    if (this.freeTextures[r].length > 0) {
      this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log();
      let o = this.freeTextures[r].shift();
      return this.usedTextures[r].push(o), o;
    }
    let i;
    return s === 3 ? i = this.gpgpu.createPackedMatrixTexture(e[0], e[1]) : s === 4 ? i = this.gpgpu.createFloat16PackedMatrixTexture(e[0], e[1]) : s === 1 ? i = this.gpgpu.createFloat32MatrixTexture(e[0], e[1]) : s === 0 ? i = this.gpgpu.createFloat16MatrixTexture(e[0], e[1]) : s === 2 && (i = this.gpgpu.createUnsignedBytesMatrixTexture(e[0], e[1])), this.usedTextures[r].push(i), this.numUsedTextures++, this._numBytesAllocated += a, this.log(), i;
  }
  releaseTexture(e, t, n, s) {
    if (this.freeTextures == null)
      return;
    let r = cw(n, s), a = dw(t, r, s);
    a in this.freeTextures || (this.freeTextures[a] = []);
    let i = lw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
    o !== -1 && this._numBytesAllocated > o ? (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], l = u.indexOf(e);
    if (l < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    u.splice(l, 1), this.log();
  }
  log() {
    if (!this.logEnabled)
      return;
    let e = this.numFreeTextures + this.numUsedTextures;
    console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${e})`);
    let t = this._numBytesFree / this._numBytesAllocated;
    console.log(`Bytes allocated: ${this._numBytesAllocated}`), console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * t)}%)`);
  }
  get numBytesAllocated() {
    return this._numBytesAllocated;
  }
  get numBytesFree() {
    return this._numBytesFree;
  }
  getNumUsedTextures() {
    return this.numUsedTextures;
  }
  getNumFreeTextures() {
    return this.numFreeTextures;
  }
  dispose() {
    if (this.freeTextures != null) {
      for (let e in this.freeTextures)
        this.freeTextures[e].forEach((t) => {
          this.gpgpu.deleteMatrixTexture(t.texture);
        });
      for (let e in this.usedTextures)
        this.usedTextures[e].forEach((t) => {
          this.gpgpu.deleteMatrixTexture(t.texture);
        });
      this.freeTextures = null, this.usedTextures = null, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0;
    }
  }
};
function UX(e, t) {
  let n = e;
  if (t === n.R32F)
    return 4;
  if (t === n.R16F)
    return 2;
  if (t === n.RGBA32F)
    return 16;
  if (t === e.RGBA)
    return 16;
  if (t === n.RGBA16F)
    return 8;
  if (t === n.RGBA8)
    return 4;
  throw new Error(`Unknown internal format ${t}`);
}
function lw(e, t, n, s, r) {
  let a = GX(t, s), i;
  if (r) {
    let [u, l] = su(e[0], e[1]);
    i = u * l;
  } else {
    let [u, l] = Ql(e[0], e[1]);
    i = u * l;
  }
  let o = UX(n, a);
  return i * o;
}
function GX(e, t) {
  switch (e) {
    case 3:
      return Iv(t);
    case 4:
      return Cv(t);
    case 1:
      return wv(t);
    case 0:
      return kv(t);
    case 2:
      return Sv(t);
    default:
      throw new Error(`Unknown physical texture type ${e}`);
  }
}
function HX(e) {
  return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? e ? 3 : 1 : e ? 4 : 0;
}
function cw(e, t) {
  if (e === 1)
    return 3;
  if (e === 0 || e == null)
    return HX(t);
  if (e === 3 || e === 2)
    return 2;
  throw new Error(`Unknown logical texture type ${e}`);
}
function dw(e, t, n) {
  return `${e[0]}_${e[1]}_${t}_${n}`;
}
var Gs = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
      float unaryOperation(float x) {
        ${t}
      }

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

        setOutput(y);
      }
    `;
  }
};
var ss = "if (isnan(x)) return x;";
var qX = "return x;";
var pw = "return abs(x);";
var jX = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var KX = ss + `
  return (x < 0.0) ? 0.0 : x;
`;
var XX = ss + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Wi = "return x;";
var YX = "return 1.0 / (1.0 + exp(-1.0 * x));";
var QX = "return x;";
var ZX = `
  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 JX = `
  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 e8 = `
  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 t8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
var Jr = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

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

        setOutput(y);
      }
    `;
  }
};
var n8 = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
    let t = e.length, n = ln("rc", t), s = ot(t), r = LX(t, n), a = n.slice(-2), i = t <= 1 ? "rc" : `vec2(${a.join(",")})`;
    this.userCode = `
      void main() {
        ${s} rc = getOutputCoords();
        vec4 packedInput = getA(${r});

        setOutput(getChannel(packedInput, ${i}));
      }
    `;
  }
};
var s8 = ws.whereImpl;
var r8 = 1e-7;
var a8 = 1e-4;
var ed = {};
function i8(e) {
  return e in ed || (ed[e] = {}), ed[e];
}
var o8 = K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var u8 = 600;
function l8() {
  return K().global.screen == null ? 1024 : K().global.screen.height * K().global.screen.width * window.devicePixelRatio * u8 / 1024 / 1024;
}
var j1 = class extends rl {
  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, !K().getBool("HAS_WEBGL"))
      throw new Error("WebGL is not supported on this device");
    let t;
    if (e != null) {
      if (e instanceof Zf)
        t = e;
      else {
        let n = xs(K().getNumber("WEBGL_VERSION"), e);
        t = new Zf(n);
      }
      this.binaryCache = {}, this.gpgpuCreatedLocally = false;
    } else {
      let n = xs(K().getNumber("WEBGL_VERSION"));
      t = new Zf(n), this.binaryCache = i8(K().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
    }
    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new WX(this.gpgpu), this.numMBBeforeWarning = l8(), this.texData = new Kd(this, ds());
  }
  nextDataId() {
    return j1.nextDataId++;
  }
  numDataIds() {
    return this.texData.numDataIds() - this.pendingDeletes;
  }
  write(e, t, n) {
    if ((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || K().getBool("DEBUG")) && this.checkNumericalProblems(e), n === "complex64" && e != null)
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    let s = { id: this.nextDataId() };
    return this.texData.set(s, { shape: t, dtype: n, values: e, usage: 1, refCount: 1 }), s;
  }
  refCount(e) {
    return this.texData.has(e) ? this.texData.get(e).refCount : 0;
  }
  incRef(e) {
    let t = this.texData.get(e);
    t.refCount++;
  }
  decRef(e) {
    if (this.texData.has(e)) {
      let t = this.texData.get(e);
      t.refCount--;
    }
  }
  move(e, t, n, s, r) {
    if (K().getBool("DEBUG") && this.checkNumericalProblems(t), s === "complex64")
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    this.texData.set(e, { shape: n, dtype: s, values: t, usage: 1, refCount: r });
  }
  disposeIntermediateTensorInfo(e) {
    this.disposeData(e.dataId);
  }
  readSync(e) {
    let t = this.texData.get(e), { values: n, dtype: s, complexTensorInfos: r, slice: a, shape: i, isPacked: o } = t;
    if (a != null) {
      let p;
      o ? p = new Jr(i, Wi) : p = new Gs(i, Wi);
      let d = this.runWebGLProgram(p, [{ dataId: e, shape: i, dtype: s }], s), h = this.readSync(d.dataId);
      return this.disposeIntermediateTensorInfo(d), h;
    }
    if (n != null)
      return this.convertAndCacheOnCPU(e);
    if (s === "string")
      return n;
    let u = this.activeTimers != null, l;
    u && (l = w.now());
    let c;
    if (s === "complex64") {
      let p = this.readSync(r.real.dataId), d = this.readSync(r.imag.dataId);
      c = C.mergeRealAndImagArrays(p, d);
    } else
      c = this.getValuesFromTexture(e);
    return u && (this.downloadWaitMs += w.now() - l), this.convertAndCacheOnCPU(e, c);
  }
  async read(e) {
    if (this.pendingRead.has(e)) {
      let h = this.pendingRead.get(e);
      return new Promise((f) => h.push(f));
    }
    let t = this.texData.get(e), { values: n, shape: s, slice: r, dtype: a, complexTensorInfos: i, isPacked: o } = t;
    if (r != null) {
      let h;
      o ? h = new Jr(s, Wi) : h = new Gs(s, Wi);
      let f = this.runWebGLProgram(h, [{ dataId: e, shape: s, dtype: a }], a), m = this.read(f.dataId);
      return this.disposeIntermediateTensorInfo(f), m;
    }
    if (n != null)
      return this.convertAndCacheOnCPU(e);
    if (K().getBool("DEBUG") && !K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && K().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, l;
    if (a !== "complex64" && K().get("WEBGL_BUFFER_SUPPORTED")) {
      l = this.decode(e);
      let h = this.texData.get(l.dataId);
      u = this.gpgpu.createBufferFromTexture(h.texture.texture, ...Zc(s));
    }
    this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence();
    let c;
    if (a === "complex64") {
      let h = await Promise.all([this.read(i.real.dataId), this.read(i.imag.dataId)]), f = h[0], m = h[1];
      c = C.mergeRealAndImagArrays(f, m);
    } else if (u == null)
      c = this.getValuesFromTexture(e);
    else {
      let h = w.sizeFromShape(s);
      c = this.gpgpu.downloadFloat32MatrixFromBuffer(u, h);
    }
    if (l != null && this.disposeIntermediateTensorInfo(l), u != null) {
      let h = this.gpgpu.gl;
      fe(h, () => h.deleteBuffer(u));
    }
    let p = this.convertAndCacheOnCPU(e, c), d = this.pendingRead.get(e);
    return this.pendingRead.delete(e), d.forEach((h) => h(p)), this.pendingDisposal.has(e) && (this.pendingDisposal.delete(e), this.disposeData(e) && ds().removeDataId(e, this), this.pendingDeletes--), p;
  }
  readToGPU(e, t = {}) {
    let n = this.texData.get(e), { values: s, shape: r, slice: a, dtype: i, isPacked: o, texture: u } = n;
    if (i === "complex64")
      throw new Error("Does not support reading texture for complex64 dtype.");
    if (a != null) {
      let d;
      o ? d = new Jr(r, Wi) : d = new Gs(r, Wi);
      let h = this.runWebGLProgram(d, [{ dataId: e, shape: r, dtype: i }], i), f = this.readToGPU(h, t);
      return this.disposeIntermediateTensorInfo(h), f;
    }
    if (u == null)
      throw s != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
    let l = this.decode(e, t.customTexShape), c = ds().makeTensorFromTensorInfo(l), p = this.texData.get(l.dataId);
    return { tensorRef: c, ...p.texture };
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId), n = t;
    if (e.dtype === "string")
      try {
        n = t.map((s) => w.decodeString(s));
      } catch (s) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return De(e.shape, e.dtype, n);
  }
  checkNumericalProblems(e) {
    if (e != null)
      for (let t = 0; t < e.length; t++) {
        let n = e[t];
        if (!QC(n))
          throw K().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${n} cannot be represented on this device.`);
      }
  }
  getValuesFromTexture(e) {
    let { shape: t, dtype: n, isPacked: s } = this.texData.get(e), r = w.sizeFromShape(t);
    if (K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Zc(t)).subarray(0, r);
      return this.disposeIntermediateTensorInfo(p), h;
    }
    let a = K().getBool("WEBGL_PACK") && s === true, i = a ? ud(t) : t, o = a ? new YK(i) : new XK(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
    return this.disposeIntermediateTensorInfo(u), c;
  }
  timerAvailable() {
    return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
  }
  time(e) {
    let t = this.activeTimers, n = [], s = false;
    this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
    let r = w.flatten(this.activeTimers.map((o) => o.query)).filter((o) => o != null), a = w.flatten(this.activeTimers.map((o) => o.name)).filter((o) => o != null);
    this.activeTimers = t, s && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
    return (async () => {
      if (K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        let o = await Promise.all(r);
        i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${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 K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: w.now(), endMs: null };
  }
  endTimer(e) {
    return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = w.now(), e);
  }
  async getQueryTime(e) {
    if (K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0)
      return this.gpgpu.waitForQueryAndGetTime(e);
    let t = e;
    return t.endMs - t.startMs;
  }
  disposeData(e, t = false) {
    if (this.pendingDisposal.has(e))
      return false;
    if (!this.texData.has(e))
      return true;
    if (t ? this.texData.get(e).refCount = 0 : this.texData.get(e).refCount--, !t && 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: n } = this.texData.get(e);
    return n != null && (this.disposeData(n.real.dataId, t), this.disposeData(n.imag.dataId, t)), this.texData.delete(e), true;
  }
  releaseGPUData(e) {
    let { texture: t, dtype: n, texShape: s, usage: r, isPacked: a, slice: i } = this.texData.get(e), o = i && i.origDataId || e, u = this.dataRefCount.get(o);
    u > 1 ? this.dataRefCount.set(o, u - 1) : (this.dataRefCount.delete(o), t != null && (this.numBytesInGPU -= this.computeBytes(s, n), this.textureManager.releaseTexture(t, s, r, a)));
    let l = this.texData.get(e);
    l.texture = null, l.texShape = null, l.isPacked = false, l.slice = null;
  }
  getTexture(e) {
    return this.uploadToGPU(e), this.texData.get(e).texture.texture;
  }
  getDataInfo(e) {
    return this.texData.get(e);
  }
  shouldExecuteOnCPU(e, t = o8) {
    return K().getBool("WEBGL_CPU_FORWARD") && e.every((n) => this.texData.get(n.dataId).texture == null && w.sizeFromShape(n.shape) < t);
  }
  getGPGPUContext() {
    return this.gpgpu;
  }
  where(e) {
    C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
    let t = e.dataSync();
    return s8(e.shape, t);
  }
  packedUnaryOp(e, t, n) {
    let s = new Jr(e.shape, t), r = this.compileAndRun(s, [e], n);
    return ds().makeTensorFromTensorInfo(r);
  }
  abs(e) {
    if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
      let s = U1(this.texData.get(e.dataId).values);
      return this.makeOutput(e.shape, e.dtype, s);
    }
    if (K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
      return this.packedUnaryOp(e, pw, e.dtype);
    let t = new Gs(e.shape, pw), n = this.compileAndRun(t, [e]);
    return ds().makeTensorFromTensorInfo(n);
  }
  makeTensorInfo(e, t, n) {
    let s;
    if (t === "string" && n != null && n.length > 0 && w.isString(n[0])) {
      let r = n.map((a) => w.encodeString(a));
      s = this.write(r, e, t);
    } else
      s = this.write(n, e, t);
    return this.texData.get(s).usage = null, { dataId: s, shape: e, dtype: t };
  }
  makeOutput(e, t, n) {
    return ds().makeTensorFromTensorInfo(this.makeTensorInfo(e, t, n), this);
  }
  unpackTensor(e) {
    let t = new n8(e.shape);
    return this.runWebGLProgram(t, [e], e.dtype);
  }
  packTensor(e) {
    let t = new BX(e.shape), n = true;
    return this.runWebGLProgram(t, [e], e.dtype, null, n);
  }
  packedReshape(e, t) {
    let n = [ya(e.shape), ...va(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [ya(t), ...va(t)], a = new q1(r, n), i = true, o = [n], u = this.runWebGLProgram(a, [s], e.dtype, o, i);
    return { dataId: u.dataId, shape: t, dtype: u.dtype };
  }
  decode(e, t) {
    let n = this.texData.get(e), { isPacked: s, shape: r, dtype: a } = n;
    if (t != null) {
      let p = w.sizeFromShape(r), d = t[0] * t[1] * 4;
      w.assert(p <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
    }
    let i = ud(r), o;
    s ? o = new KK(i) : o = new jK(i);
    let u = true, l = [t != null ? t : Zc(i)], c = this.runWebGLProgram(o, [{ shape: i, dtype: a, dataId: e }], a, l, u, t);
    return { dtype: a, shape: r, dataId: c.dataId };
  }
  runWebGLProgram(e, t, n, s, r = false, a) {
    let i = this.makeTensorInfo(e.outputShape, n), o = this.texData.get(i.dataId);
    if (e.packedOutput && (o.isPacked = true), e.outPackingScheme === 0) {
      let g = a != null ? a : Zc(e.outputShape);
      o.texShape = g.map((b) => b * 2);
    }
    if (e.outTexUsage != null && (o.usage = e.outTexUsage), w.sizeFromShape(i.shape) === 0)
      return o.values = w.getTypedArrayFromDType(i.dtype, 0), i;
    let u = [], l = t.map((g) => {
      if (g.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(g.dataId);
      if (b.texture == null) {
        if (!e.packedInputs && w.sizeFromShape(g.shape) <= K().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))
          return { shape: g.shape, texData: null, isUniform: true, uniformValues: b.values };
        e.packedInputs && (b.isPacked = true, b.shape = g.shape);
      }
      if (this.uploadToGPU(g.dataId), !!b.isPacked != !!e.packedInputs)
        g = b.isPacked ? this.unpackTensor(g) : this.packTensor(g), u.push(g), b = this.texData.get(g.dataId);
      else if (b.isPacked && !nl(b.shape, g.shape)) {
        let y = g, v = g.shape;
        g.shape = b.shape, g = this.packedReshape(g, v), u.push(g), b = this.texData.get(g.dataId), y.shape = v;
      }
      return { shape: g.shape, texData: b, isUniform: false };
    });
    this.uploadToGPU(i.dataId);
    let c = { shape: i.shape, texData: o, isUniform: false }, p = qK(e, l, c), d = this.getAndSaveBinary(p, () => GK(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
    h && (f = this.startTimer()), K().get("ENGINE_COMPILE_ONLY") || HK(this.gpgpu, d, l, c, s), u.forEach((g) => this.disposeIntermediateTensorInfo(g)), h && (f = this.endTimer(f), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(f) }));
    let m = K().get("WEBGL_FLUSH_THRESHOLD");
    if (m > 0) {
      let g = w.now();
      g - this.lastGlFlushTime > m && (this.gpgpu.gl.flush(), this.lastGlFlushTime = g);
    }
    if (!K().getBool("WEBGL_LAZILY_UNPACK") && o.isPacked && r === false) {
      let g = this.unpackTensor(i);
      return this.disposeIntermediateTensorInfo(i), g;
    }
    return i;
  }
  compileAndRun(e, t, n, s, r = false) {
    return n = n || t[0].dtype, this.runWebGLProgram(e, t, n, s, r);
  }
  getAndSaveBinary(e, t) {
    return e in this.binaryCache || (this.binaryCache[e] = t()), this.binaryCache[e];
  }
  getTextureManager() {
    return this.textureManager;
  }
  dispose() {
    this.disposed || (K().getBool("IS_TEST") || Object.keys(this.binaryCache).forEach((t) => {
      this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram), delete this.binaryCache[t];
    }), this.textureManager.dispose(), this.canvas != null && typeof HTMLCanvasElement != "undefined" && this.canvas instanceof HTMLCanvasElement ? this.canvas.remove() : this.canvas = null, this.gpgpuCreatedLocally && (this.gpgpu.program = null, this.gpgpu.dispose()), this.disposed = true);
  }
  floatPrecision() {
    return this.floatPrecisionValue == null && (this.floatPrecisionValue = q(() => {
      if (!K().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
        let e = K().getBool("DEBUG");
        K().set("DEBUG", false);
        let t = this.abs(we(1e-8)).dataSync()[0];
        if (K().set("DEBUG", e), t > 0)
          return 32;
      }
      return 16;
    })), this.floatPrecisionValue;
  }
  epsilon() {
    return this.floatPrecision() === 32 ? r8 : a8;
  }
  uploadToGPU(e) {
    let t = this.texData.get(e), { shape: n, dtype: s, values: r, texture: a, usage: i, isPacked: o } = t;
    if (a != null)
      return;
    let u = this.activeTimers != null, l;
    u && (l = w.now());
    let c = t.texShape;
    if (c == null && (c = f1(n, o), t.texShape = c), r != null) {
      let p = ud(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
      (o || !m) && ([h, f] = su(c[0], c[1])), o ? d = new ZK(p, m) : d = new QK(p, m);
      let g = m ? [f, h] : c, b = this.makeTensorInfo(g, s), y = this.texData.get(b.dataId);
      m ? y.usage = 2 : y.usage = 1, y.texShape = g, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), h, f, r);
      let v = [[f, h]], x = true, k = this.runWebGLProgram(d, [b], s, v, x), I = this.texData.get(k.dataId);
      t.texShape = I.texShape, t.isPacked = I.isPacked, t.usage = I.usage, K().get("ENGINE_COMPILE_ONLY") ? this.disposeData(k.dataId) : (t.texture = I.texture, t.values = null, this.texData.delete(k.dataId)), this.disposeIntermediateTensorInfo(b), u && (this.uploadWaitMs += w.now() - l);
    } else {
      let p = this.acquireTexture(c, i, s, o);
      t.texture = p;
    }
  }
  convertAndCacheOnCPU(e, t) {
    let n = this.texData.get(e), { dtype: s } = n;
    return this.releaseGPUData(e), t != null && (n.values = c8(t, s)), n.values;
  }
  acquireTexture(e, t, n, s) {
    if (this.numBytesInGPU += this.computeBytes(e, n), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
      let r = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
      this.warnedAboutMemory = true, console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`);
    }
    return this.textureManager.acquireTexture(e, t, s);
  }
  computeBytes(e, t) {
    return e[0] * e[1] * w.bytesPerElement(t);
  }
  checkCompileCompletion() {
    for (let [, e] of Object.entries(this.binaryCache))
      this.checkCompletion_(e);
  }
  async checkCompileCompletionAsync() {
    let e = [];
    if (this.gpgpu.parallelCompilationExtension) {
      for (let [, t] of Object.entries(this.binaryCache))
        e.push(this.checkCompletionAsync_(t));
      return Promise.all(e);
    } else {
      for (let [, t] of Object.entries(this.binaryCache)) {
        let n = new Promise((s) => {
          try {
            this.checkCompletion_(t), s(true);
          } catch (r) {
            throw r;
          }
        });
        e.push(n);
      }
      return Promise.all(e);
    }
  }
  async checkCompletionAsync_(e) {
    return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await GS(), 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 ? (bv(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: t, customUniformLocations: n, infLoc: s, nanLoc: r, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: o, outShapeStridesLocation: u, outTexShapeLocation: l } = C1(this.gpgpu, e.program, e.webGLProgram);
      e.uniformLocations = t, e.customUniformLocations = n, e.infLoc = s, e.nanLoc = r, e.inShapesLocations = a, e.inTexShapesLocations = i, e.outShapeLocation = o, e.outShapeStridesLocation = u, e.outTexShapeLocation = l;
    }
  }
};
var K1 = j1;
K1.nextDataId = 0;
function c8(e, t) {
  if (t === "float32" || t === "complex64")
    return e;
  if (t === "int32" || t === "bool") {
    let n = t === "int32" ? new Int32Array(e.length) : new Uint8Array(e.length);
    for (let s = 0; s < n.length; ++s)
      n[s] = Math.round(e[s]);
    return n;
  } else
    throw new Error(`Unknown dtype ${t}`);
}
var dhe = "0.0.0";
function d8() {
  K().set("WEBGL_FORCE_F16_TEXTURES", true);
}
gp.isBrowser() && bp("webgl", () => new K1(), 2);
var phe = { forceHalfFloat: d8 };
var X1 = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var uo = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.enableShapeUniforms = Sn(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 Zp = `
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;
var Jl = class {
  constructor(e, t, n, s = false) {
    this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = C.assertAndGetBroadcastShape(t, n);
    let r = this.outputShape.length;
    this.enableShapeUniforms = Sn(r);
    let a = "";
    if (s)
      if (r === 0 || w.sizeFromShape(this.outputShape) === 1)
        a = `
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;
      else if (a = `
          ${ot(r)} coords = getOutputCoords();
        `, r === 1)
        this.enableShapeUniforms ? a += `
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          ` : a += `
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;
      else {
        let o = ln("coords", r);
        this.enableShapeUniforms ? a += `
            bool nextRowOutOfBounds =
              (${o[r - 2]} + 1) >= outShape[${r} - 2];
            bool nextColOutOfBounds =
              (${o[r - 1]} + 1) >= outShape[${r} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          ` : a += `
            bool nextRowOutOfBounds =
              (${o[r - 2]} + 1) >= ${this.outputShape[r - 2]};
            bool nextColOutOfBounds =
              (${o[r - 1]} + 1) >= ${this.outputShape[r - 1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `;
      }
    this.userCode = `
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${e}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${a}

        setOutput(result);
      }
    `;
  }
};
function Rn(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  return n.incRef(s.dataId), { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
var p8 = { kernelName: Wa, backendName: "webgl", kernelFunc: Rn };
function Rr(e) {
  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.texData.get(a.dataId), o = Rn({ inputs: { x: s }, backend: n }), u = Rn({ inputs: { x: r }, backend: n });
  return i.complexTensorInfos = { real: o, imag: u }, a;
}
var h8 = { kernelName: Zd, backendName: "webgl", kernelFunc: Rr };
var Y1 = "return (a < 0.) ? b * a : a;";
var Q1 = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function f8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Jl(Q1, r.shape, i.shape) : new uo(Y1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
  return n.disposeIntermediateTensorInfo(i), u;
}
var m8 = { kernelName: Ua, backendName: "webgl", kernelFunc: f8 };
var Z1 = "return (a < 0.) ? b * a : a;";
var J1 = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function g8(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Jl(J1, s.shape, r.shape) : new uo(Z1, s.shape, r.shape);
  return n.runWebGLProgram(a, [s, r], "float32");
}
var b8 = { kernelName: ti, backendName: "webgl", kernelFunc: g8 };
var lu = "if (isnan(x)) return x;";
var y8 = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var v8 = `
  result.r = isNaN.r > 0. ? NAN : result.r;
  result.g = isNaN.g > 0. ? NAN : result.g;
  result.b = isNaN.b > 0. ? NAN : result.b;
  result.a = isNaN.a > 0. ? NAN : result.a;
`;
function Ke({ opSnippet: e, packedOpSnippet: t, cpuKernelImpl: n, dtype: s }) {
  return ({ inputs: r, backend: a }) => {
    let { x: i } = r, o = a, u = s || i.dtype;
    if (o.shouldExecuteOnCPU([i]) && n != null) {
      let p = o.texData.get(i.dataId), d = n(p.values, u);
      return o.makeTensorInfo(i.shape, u, d);
    }
    let l = K().getBool("WEBGL_PACK_UNARY_OPERATIONS") && t != null, c;
    return l ? c = new Jr(i.shape, t) : c = new Gs(i.shape, e), o.runWebGLProgram(c, [i], u);
  };
}
function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, supportsComplex: s = false, cpuKernelImpl: r, dtype: a }) {
  return ({ inputs: i, backend: o }) => {
    let { a: u, b: l } = i, c = o;
    if (s && u.dtype === "complex64") {
      let f = c.texData.get(u.dataId), m = c.texData.get(l.dataId), [g, b] = [[f.complexTensorInfos.real, m.complexTensorInfos.real], [f.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((v) => {
        let [x, k] = v, I = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, $ = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new uo(e, u.shape, l.shape);
        return c.runWebGLProgram(E, [I, $], cn(x.dtype, k.dtype));
      }), y = Rr({ inputs: { real: g, imag: b }, backend: c });
      return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(b), y;
    }
    let p = a || cn(u.dtype, l.dtype);
    if ((u.dtype === "string" || l.dtype === "string" || c.shouldExecuteOnCPU([u, l])) && r != null) {
      let f = c.texData.get(u.dataId).values, m = c.texData.get(l.dataId).values, g = u.dtype === "string" ? C.fromUint8ToStringArray(f) : f, b = u.dtype === "string" ? C.fromUint8ToStringArray(m) : m, [y, v] = r(u.shape, l.shape, g, b, p), x = c.makeTensorInfo(v, p), k = c.texData.get(x.dataId);
      return k.values = y, x;
    }
    let d = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t != null, h;
    return d ? h = new Jl(t, u.shape, l.shape, n) : h = new uo(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
  };
}
function Jp(e, t = false) {
  if (e === "linear")
    return t ? QX : qX;
  if (e === "relu")
    return t ? JX : KX;
  if (e === "elu")
    return t ? ZX : jX;
  if (e === "relu6")
    return t ? e8 : XX;
  if (e === "prelu")
    return t ? J1 : Z1;
  if (e === "leakyrelu")
    return t ? Q1 : Y1;
  if (e === "sigmoid")
    return t ? t8 : YX;
  throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`);
}
var e2 = class {
  constructor(e, t, n, s = false, r = false, a = false, i = null, o = false, u = false) {
    this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = n, this.enableShapeUniforms = Sn(this.outputShape.length);
    let l = s ? e[1] : e[2], c = Math.ceil(l / 2), p = s ? "i * 2, rc.y" : "rc.y, i * 2", d = r ? "rc.z, i * 2" : "i * 2, rc.z", h = s ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], f = r ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], m = "", g = "";
    i && (o ? m = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${i}
        }` : u ? m = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${i}
        }` : m = `vec4 activation(vec4 x) {
          ${i}
        }`, g = "result = activation(result);");
    let b = a ? "result += getBiasAtOutCoords();" : "";
    a && this.variableNames.push("bias"), o && this.variableNames.push("preluActivationWeights"), u && this.variableNames.push("leakyreluAlpha");
    let y = "rc.x", v = "rc.x";
    e[0] < t[0] ? y = `int(min(float(rc.x), ${e[0] - 1}.))` : t[0] < e[0] && (v = `int(min(float(rc.x), ${t[0] - 1}.))`), this.userCode = `
      ${m}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${c}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        for (int i = 0; i < ${c}; i++) {
          int batchA = ${y};
          int batchB = ${v};
          vec4 a = getMatrixA(batchA, ${p});
          vec4 b = getMatrixB(batchB, ${d});

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

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

        ${b}

        ${g}

        setOutput(result);
      }
    `;
  }
};
var hw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var fw = class {
  constructor(e, t, n) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.userCode = `
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${e}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `;
  }
};
var mw = "return a * b;";
function Tv(e) {
  let { inputs: t, backend: n } = e, { a: s, b: r } = t, a = C.upcastType(s.dtype, r.dtype);
  if (s.dtype === "complex64") {
    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new fw(hw.REAL, s.shape, r.shape), c = new fw(hw.IMAG, s.shape, r.shape), p = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Rr({ inputs: { real: d, imag: h }, backend: n });
    return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
  }
  if (n.shouldExecuteOnCPU([s, r])) {
    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), [l, c] = xX(s.shape, r.shape, o.values, u.values, a), p = n.makeTensorInfo(c, a), d = n.texData.get(p.dataId);
    return d.values = l, p;
  }
  let i;
  return K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Jl(mw, s.shape, r.shape) : i = new uo(mw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
}
var x8 = { kernelName: Za, backendName: "webgl", kernelFunc: Tv };
function w8(e, t, n) {
  let s = [ya(e.shape), ...va(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ya(t), ...va(t)], i = new q1(a, s), o = true, u = [s], l = n.runWebGLProgram(i, [r], e.dtype, u, o);
  return { dataId: l.dataId, shape: t, dtype: l.dtype };
}
function he(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = n, o = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, o), l = w.sizeFromShape(u);
  w.assert(o === l, () => `The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);
  let c = i.texData.get(r.dataId);
  return c.isPacked && !nl(r.shape, u) && !(c.texture !== null && nl(c.shape, u)) ? w8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
}
var k8 = { kernelName: Fo, backendName: "webgl", kernelFunc: he };
var gw = class {
  constructor(e, t) {
    this.variableNames = ["x"];
    let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
    this.outputShape = [s, a];
    let i = Math.floor(n / 4) * 4, o = n % 4, u = "sumValue += dot(values, ones);";
    if (t != null) {
      let c = 1 / t;
      u = `sumValue += dot(values * ${w.isInt(c) ? c.toPrecision(2) : c}, ones);`;
    }
    let l = "";
    r % n > 0 && (l = `
        if (inIdx < 0 || inIdx >= ${r}) {
          return 0.0;
        }
      `), this.userCode = `
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

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

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

        float sumValue = 0.0;

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

          ${u}
        }

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

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

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

          ${u}
        }
        setOutput(sumValue);
      }
    `;
  }
};
var S8 = class {
  constructor(e, t) {
    this.variableNames = ["x"];
    let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
    this.outputShape = [s, a];
    let i = "0.0", o = "";
    t === "prod" ? i = "1.0" : t === "min" ? (i = "1.0 / 1e-20", o = "min") : t === "max" && (i = "-1.0 / 1e-20", o = "max");
    let u = `${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t === "sum" ? u = "sumValue" : t === "prod" ? u = "prodValue" : t === "all" ? u = "allValue" : t === "any" && (u = "anyValue");
    let l = Math.floor(n / 4) * 4, c = n % 4, p = `
      if (${t === "sum"}) {
        sumValue += dot(values, ones);
      } else if (${t === "prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${o}(values, minMaxValue);
        if (${t === "min"} || ${t === "max"}) {
          minMaxValue = ${o}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `, d = "vec4";
    t === "all" ? (i = "1.0", p = `
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `, d = "bvec4") : t === "any" && (i = "0.0", p = `
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `, d = "bvec4");
    let h = "";
    r % n > 0 && (h = `
        if (inIdx < 0 || inIdx >= ${r}) {
          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) {
        ${h}
        return getX(batch, inIdx);
      }

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

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

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

          ${p}
        }

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

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

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

          ${p}
        }
        setOutput(${u});
      }
    `;
  }
};
function I8(e) {
  let t = [];
  for (; t.length === 0 || t[t.length - 1].outSize !== 1; ) {
    let n = t.length ? t[t.length - 1].outSize : e[1], s = C.computeOptimalWindowSize(n);
    t.push({ inSize: n, windowSize: s, outSize: Math.ceil(n / s) });
  }
  return t;
}
function Ii(e, t, n, s) {
  let r = I8(e.shape), a = e;
  for (let i = 0; i < r.length; i++) {
    let { inSize: o, windowSize: u, outSize: l } = r[i], c, p;
    n === "mean" ? c = i === 0 ? new gw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new gw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new S8({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
  }
  return a;
}
var C8 = class {
  constructor(e, t) {
    this.variableNames = ["A"];
    let n = new Array(e.length);
    for (let a = 0; a < n.length; a++)
      n[a] = e[t[a]];
    this.outputShape = n, this.rank = n.length;
    let s = ot(this.rank), r = N8(t);
    this.userCode = `
    void main() {
      ${s} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `;
  }
};
function N8(e) {
  let t = e.length;
  if (t > 6)
    throw Error(`Transpose for rank ${t} is not yet supported`);
  let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"], s = new Array(t);
  for (let r = 0; r < e.length; r++)
    s[e[r]] = n[r];
  return s.join();
}
var T8 = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true;
    let n = new Array(e.length);
    for (let l = 0; l < n.length; l++)
      n[l] = e[t[l]];
    if (this.outputShape = n, this.rank = n.length, this.rank > 6)
      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
    let s = ot(this.rank), r = H1("rc", this.rank), a = new Array(this.rank);
    for (let l = 0; l < t.length; l++)
      a[t[l]] = r[l];
    let i = `vec2(${a.slice(-2).join()})`, o = `++${r[this.rank - 1]} < ${n[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`;
    this.userCode = `
    void main() {
      ${s} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${u};
      if(${o}) {
        result[1] = ${u};
      }
      --${r[this.rank - 1]};
      if(++${r[this.rank - 2]} < ${n[this.rank - 2]}) {
        result[2] = ${u};
        if(${o}) {
          result[3] = ${u};
        }
      }
      setOutput(result);
    }
    `;
  }
};
function eh(e, t, n) {
  let s = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new T8(e.shape, t) : new C8(e.shape, t);
  return n.runWebGLProgram(s, [e], e.dtype);
}
function $8(e, t, n, s) {
  let r = t, a = e.shape.length, i = w.parseAxisParam(r, e.shape), o = i, u = C.getAxesPermutation(o, a), l = u != null, c = e;
  l && (c = eh(e, u, s), o = C.getInnerMostAxes(o.length, a)), C.assertAxesAreInnerMostDims("sum", o, a);
  let [p, d] = C.computeOutAndReduceShapes(c.shape, o), h = p;
  n && (h = C.expandShapeToKeepDim(p, i));
  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = he({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = mp(e.dtype), v = Ii(b, y, "sum", s), x = he({ inputs: { x: v }, attrs: { shape: h }, backend: s });
  return s.disposeIntermediateTensorInfo(b), s.disposeIntermediateTensorInfo(v), l && s.disposeIntermediateTensorInfo(c), x;
}
function th(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return $8(r, a, i, n);
}
var _8 = { kernelName: ci, backendName: "webgl", kernelFunc: th };
function zt(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
  for (let c = 0; c < u.length; c++)
    u[c] = r.shape[a[c]];
  let l;
  if (i.shouldExecuteOnCPU([r])) {
    let p = i.texData.get(r.dataId).values, d = Nv(p, r.shape, r.dtype, a, u);
    l = i.makeTensorInfo(u, r.dtype);
    let h = i.texData.get(l.dataId);
    h.values = d;
  } else
    l = eh(r, a, i);
  return l;
}
var A8 = { kernelName: mi, backendName: "webgl", kernelFunc: zt };
var t2 = 1e3;
function Wd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = bi.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
  w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
  let k = n ? [b, p, h] : [b, h, p], I = s ? [y, f, d] : [y, d, f], $ = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: I } }), A = [$, E], P = Math.max(b, y), R = n ? $.shape[1] : $.shape[2], F = a != null, T = i != null, z = u === "leakyrelu", W = u != null ? Jp(u, true) : null, j = F || T || z || W != null, X;
  if ((h === 1 || f === 1) && R > t2 && j === false) {
    let Z = $, te = E;
    n && (Z = zt({ inputs: { x: $ }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(Z)), s && (te = zt({ inputs: { x: E }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(te));
    let J = f !== 1, se = f === 1, ne = Z;
    J && (ne = he({ inputs: { x: Z }, backend: r, attrs: { shape: [P, R, 1] } }), A.push(ne));
    let oe = f === 1 ? 2 : 1, ae = te;
    se && (ae = he({ inputs: { x: te }, backend: r, attrs: { shape: [P, 1, R] } }), A.push(ae));
    let de = Tv({ inputs: { a: ne, b: ae }, backend: r });
    X = th({ inputs: { x: de }, backend: r, attrs: { axis: oe, keepDims: true } }), A.push(de);
  } else {
    let Z = cn(e.dtype, t.dtype), te = new e2(k, I, [P, h, f], n, s, F, W, T, z), J = [$, E];
    if (a != null && J.push(a), T && J.push(i), z) {
      let se = r.makeTensorInfo([], "float32", w.createScalarValue(o, "float32"));
      J.push(se), A.push(se);
    }
    X = r.runWebGLProgram(te, J, Z);
  }
  let Y = he({ inputs: { x: X }, backend: r, attrs: { shape: x } });
  A.push(X);
  for (let Z of A)
    r.disposeIntermediateTensorInfo(Z);
  return Y;
}
function E8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
  return Wd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
}
var R8 = { kernelName: aa, backendName: "webgl", kernelFunc: E8 };
var bw = "return abs(x);";
function D8(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s]) && s.dtype !== "complex64") {
    let a = n.texData.get(s.dataId), i = U1(a.values);
    return n.makeTensorInfo(s.shape, s.dtype, i);
  }
  let r;
  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Jr(s.shape, bw) : r = new Gs(s.shape, bw), n.runWebGLProgram(r, [s], s.dtype);
}
var F8 = { kernelName: co, backendName: "webgl", kernelFunc: D8 };
var O8 = ss + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
var P8 = Ke({ opSnippet: O8 });
var z8 = { kernelName: al, backendName: "webgl", kernelFunc: P8 };
var M8 = ss + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var L8 = Ke({ opSnippet: M8 });
var B8 = { kernelName: il, backendName: "webgl", kernelFunc: L8 };
var yw = "return a + b;";
var V8 = jt({ opSnippet: yw, packedOpSnippet: yw, supportsComplex: true, cpuKernelImpl: tX });
var W8 = { kernelName: Sr, backendName: "webgl", kernelFunc: V8 };
var U8 = class {
  constructor(e, t) {
    this.outputShape = [], this.outputShape = e, this.variableNames = t.map((r, a) => `T${a}`);
    let n = [];
    this.variableNames.forEach((r) => {
      n.push(`float v${r} = get${r}AtOutCoords();`);
    });
    let s = this.variableNames.map((r) => `v${r}`).join(" + ");
    this.userCode = `
      void main() {
        ${n.join(`
        `)}

        float result = ${s};
        setOutput(result);
      }
    `;
  }
};
var G8 = class {
  constructor(e, t) {
    this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t.map((r, a) => `T${a}`);
    let n = [];
    this.variableNames.forEach((r) => {
      n.push(`vec4 v${r} = get${r}AtOutCoords();`);
    });
    let s = this.variableNames.map((r) => `v${r}`).join(" + ");
    this.userCode = `
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${s};
        setOutput(result);
      }
    `;
  }
};
function dd(e) {
  let { inputs: t, backend: n } = e, s = t;
  if (s.length === 1)
    return Rn({ inputs: { x: s[0] }, backend: n });
  if (s.length > K().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
    let u = Math.floor(s.length / 2), l = dd({ inputs: s.slice(0, u), backend: n }), c = dd({ inputs: s.slice(u), backend: n });
    return dd({ inputs: [l, c], backend: n });
  }
  let r = s.map((u) => u.dtype).reduce((u, l) => cn(u, l)), a = s.map((u) => u.shape), o = K().getBool("WEBGL_PACK") ? new G8(s[0].shape, a) : new U8(s[0].shape, a);
  return n.runWebGLProgram(o, s, r);
}
var H8 = { kernelName: Sa, backendName: "webgl", kernelFunc: dd };
function q8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = r;
  c != null && (p = zt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, o)), C.assertAxesAreInnerMostDims("all", l, o);
  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = Ii(m, m.dtype, "all", n), b;
  if (i) {
    let y = C.expandShapeToKeepDim(d, u);
    b = he({ inputs: { x: g }, backend: n, attrs: { shape: y } });
  } else
    b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
}
var j8 = { kernelName: ol, backendName: "webgl", kernelFunc: q8 };
function K8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = r;
  c != null && (p = zt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, o)), C.assertAxesAreInnerMostDims("any", l, o);
  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = Ii(m, m.dtype, "any", n), b;
  if (i) {
    let y = C.expandShapeToKeepDim(d, u);
    b = he({ inputs: { x: g }, backend: n, attrs: { shape: y } });
  } else
    b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
}
var X8 = { kernelName: ul, backendName: "webgl", kernelFunc: K8 };
var Y8 = class {
  constructor(e, t, n) {
    this.variableNames = ["A"];
    let { windowSize: s, batchSize: r, outSize: a } = e;
    n || this.variableNames.push("bestIndicesA"), this.outputShape = [r, a];
    let i = t === "max" ? ">" : "<", o = n ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
    this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${s};

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

        for (int i = 0; i < ${s}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
  }
};
var Q8 = class {
  constructor(e, t, n, s) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, w.assert(e.length > 2, () => `Packed arg${n.charAt(0).toUpperCase() + n.slice(1)} supports only inputs with rank above 2.`);
    let r = e[e.length - 1], a = Math.ceil(r / t);
    this.outputShape = e.slice(0, -1), a > 1 && this.outputShape.push(a), s || this.variableNames.push("bestIndicesA");
    let i = this.outputShape, o = i.length, u = ot(o), l = ln("coords", o), c, p;
    if (a === 1) {
      p = o + 1;
      let $ = ot(p);
      c = `
        ${$} sourceLocR = ${$}(${l.join()}, 0);
        ++${l[o - 1]};
        ${$} sourceLocG = ${$}(${l.join()}, 0);
        ++${l[o - 2]};
        ${$} sourceLocA = ${$}(${l.join()}, 0);
        --${l[o - 1]};
        ${$} sourceLocB = ${$}(${l.join()}, 0);
        --${l[o - 2]};`;
    } else
      p = o, c = `
        ${u} sourceLocR = coords;
        ++${l[o - 1]};
        ${u} sourceLocG = coords;
        ++${l[o - 2]};
        ${u} sourceLocA = coords;
        --${l[o - 1]};
        ${u} sourceLocB = coords;
        --${l[o - 2]};`;
    let d = ["x", "y", "z", "w", "u", "v"].slice(0, p), h = "." + d[p - 1], f = d.map(($) => "int " + $), m = ln("sourceLocR", p - 1).concat("inIdx.r"), g = ln("sourceLocG", p - 1).concat("inIdx.g"), b = ln("sourceLocB", p - 1).concat("inIdx.b"), y = ln("sourceLocA", p - 1).concat("inIdx.a"), v = n === "max" ? "greaterThan" : "lessThan", x = s ? "" : `
          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                             getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${y.join()})));`, k = `vec4(
            getAChannel(${m.join()}),
            hasNextCol ? getAChannel(${g.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`, I = s ? "" : `
      float getBestIndicesAChannel(${f.join()}) {
        return getChannel(getBestIndicesA(${d.join()}),
                                          vec2(${d.slice(-2).join()}));
      }`;
    this.userCode = `
      float getAChannel(${f.join()}) {
        return getChannel(getA(${d.join()}),
                               vec2(${d.slice(-2).join()}));
      }
      ${I}
      void main() {
        ${u} coords = getOutputCoords();
        bool hasNextCol = ${l[o - 1]} < ${i[o - 1] - 1};
        bool hasNextRow = ${l[o - 2]} < ${i[o - 2] - 1};
        ${c}
        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
          sourceLocB${h}, sourceLocA${h}) * ${t};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${k};

        for (int i = 0; i < ${t}; i++) {
          inIdx = srcIdx;
          ${x}
          vec4 candidate = ${k};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${v}(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 n2(e, t, n, s = null) {
  let r = t.shape[0], a = t.shape[1];
  s != null && (r = s.shape[0], a = s.shape[1]);
  let i = C.computeOptimalWindowSize(a), o = { windowSize: i, inSize: a, batchSize: r, outSize: Math.ceil(a / i) }, u = new Y8(o, n, s == null), l = [t];
  s != null && l.push(s);
  let c = e.runWebGLProgram(u, l, "int32");
  if (c.shape[1] === 1)
    return c;
  let p = n2(e, t, n, c);
  return e.disposeIntermediateTensorInfo(c), p;
}
function s2(e, t, n, s = null) {
  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], i = C.computeOptimalWindowSize(a), o = new Q8(r, i, n, s == null), u = s == null ? [t] : [t, s], l = e.runWebGLProgram(o, u, "int32");
  if (l.shape.length === t.shape.length) {
    let c = s2(e, t, n, l);
    return e.disposeIntermediateTensorInfo(l), c;
  }
  return l;
}
function r2(e, t, n, s) {
  let r = [n];
  if (C.assertAxesAreInnerMostDims("arg" + s.charAt(0).toUpperCase() + s.slice(1), r, t.shape.length), !K().getBool("WEBGL_PACK_REDUCE") || t.shape.length <= 2) {
    let a = [], i = e.texData.get(t.dataId), o = i !== null && i.isPacked, u = t;
    o && (u = e.unpackTensor(t), a.push(u));
    let [l, c] = C.computeOutAndReduceShapes(u.shape, r), p = w.sizeFromShape(c), d = he({ inputs: { x: u }, backend: e, attrs: { shape: [-1, p] } });
    a.push(d);
    let h = n2(e, d, s);
    a.push(h);
    let f = he({ inputs: { x: h }, backend: e, attrs: { shape: l } });
    return a.forEach((m) => e.disposeIntermediateTensorInfo(m)), f;
  }
  return s2(e, t, s);
}
function Z8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = zt({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
  let c = r2(n, u, i[0], "max");
  return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
}
var J8 = { kernelName: Ia, backendName: "webgl", kernelFunc: Z8 };
function eY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = zt({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
  let c = r2(n, u, i[0], "min");
  return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
}
var tY = { kernelName: ll, backendName: "webgl", kernelFunc: eY };
var nY = ss + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
var sY = Ke({ opSnippet: nY });
var rY = { kernelName: cl, backendName: "webgl", kernelFunc: sY };
var aY = ss + "return log(x + sqrt(x * x + 1.0));";
var iY = Ke({ opSnippet: aY });
var oY = { kernelName: dl, backendName: "webgl", kernelFunc: iY };
var uY = ss + `
  return atan(x);
`;
var lY = Ke({ opSnippet: uY });
var cY = { kernelName: pl, backendName: "webgl", kernelFunc: lY };
var dY = y8 + `
  return atan(a, b);
`;
var pY = `
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + v8 + `
  return result;
`;
var hY = jt({ opSnippet: dY, packedOpSnippet: pY });
var fY = { kernelName: fl, backendName: "webgl", kernelFunc: hY };
var mY = ss + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var gY = Ke({ opSnippet: mY });
var bY = { kernelName: hl, backendName: "webgl", kernelFunc: gY };
var sl = class {
  constructor(e, t, n, s = false, r = false) {
    if (this.variableNames = ["x"], t === "avg" && n)
      throw new Error("Cannot compute positions for average pool.");
    let a = e.filterWidth, i = e.strideHeight, o = e.strideWidth, u = e.dilationHeight, l = e.dilationWidth, c = e.effectiveFilterHeight, p = e.effectiveFilterWidth, d = e.padInfo.top, h = e.padInfo.left;
    this.outputShape = e.outShape;
    let f = t === "avg", m = `((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`, g = `(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`, b = "0.0";
    if (f || (b = "-1.0 / 1e-20"), n) {
      let $ = ">=";
      this.userCode = `
        const ivec2 strides = ivec2(${i}, ${o});
        const ivec2 pads = ivec2(${d}, ${h});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

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

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

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

            for (int wC = 0; wC < ${p};
                wC += ${l}) {
              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 ${$} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${s ? r ? m : g : `wR * ${p} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
      return;
    }
    let y = "max", v = `${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t === "avg" && (v = "avgValue / count");
    let x = Math.floor(a / 4) * 4, k = a % 4, I = `
      if (${f}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${y}(values, minMaxValue);
      }
    `;
    this.userCode = `
      const ivec2 strides = ivec2(${i}, ${o});
      const ivec2 pads = ivec2(${d}, ${h});
      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 < ${c};
            wR += ${u}) {
          int xR = xRCorner + wR;

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

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

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${l}, d),
              getValue(batch, xR, xC + 2 * ${l}, d),
              getValue(batch, xR, xC + 3 * ${l}, d)
            );

            ${I}
          }

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

            ${I}
          } else if (${k === 2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${l}, d),
              initializationValue,
              initializationValue
            );

            ${I}
          } else if (${k === 3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${l}, d),
              getValue(batch, xR, xC + 2 * ${l}, d),
              initializationValue
            );

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

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

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

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

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

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

            for (int wR = 0; wR < ${h};
                wR += ${c}) {
              int xR = xRCorner + wR;

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

              for (int wC = 0; wC < ${f};
                  wC += ${p}) {
                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 ${A} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${s ? r ? `(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `wD * ${h} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
      return;
    }
    let x = "max", k = `${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t === "avg" && (k = "avgValue / count");
    let I = Math.floor(a / 4) * 4, $ = a % 4, E = `
      if (${y}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${x}(values, minMaxValue);
      }
    `;
    this.userCode = `
      const ivec3 strides =
        ivec3(${i}, ${o}, ${u});
      const ivec3 pads = ivec3(${m}, ${g}, ${b});
      const float initializationValue = ${v};
      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(${v});
        float avgValue = 0.0;
        count = 0.0;

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

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

          for (int wR = 0; wR < ${h};
            wR += ${c}) {
            int xR = xRCorner + wR;

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

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

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

              ${E}
            }

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

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

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

              ${E}
            }
          }
          setOutput(${k});
        }
      }
    `;
  }
};
function yY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  ru(r, "avgPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = C.computePool2DInfo(r.shape, a, i, l, o, u);
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    return Rn({ inputs: { x: r }, backend: n });
  let p = new sl(c, "avg", false);
  return n.runWebGLProgram(p, [r], "float32");
}
var vY = { kernelName: Ca, backendName: "webgl", kernelFunc: yY };
function xY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = C.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new $v(p, "avg", false);
  return n.runWebGLProgram(d, [r], "float32");
}
var wY = { kernelName: Qd, backendName: "webgl", kernelFunc: xY };
var kY = class {
  constructor(e) {
    this.variableNames = ["dy"], this.outputShape = e.inShape;
    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterHeight, u = e.effectiveFilterWidth, l = o - 1 - e.padInfo.top, c = u - 1 - e.padInfo.left, p = 1 / (t * n);
    this.userCode = `
      const ivec2 pads = ivec2(${l}, ${c});
      const float avgMultiplier = float(${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 < ${o};
            wR += ${a}) {
          float dyR = float(dyRCorner + wR) / ${s}.0;

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

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

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

            float dyValue = getDy(b, idyR, idyC, d);

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

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

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

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

        for (int wD = 0; wD < ${c};
            wD += ${o}) {
          float dyD = float(dyDCorner + wD) / ${r}.0;

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

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

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

            for (int wC = 0; wC < ${d};
                wC += ${l}) {
              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 IY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = C.computePool3DInfo(i.shape, o, u, p, l, c), h = new SY(d);
  return n.runWebGLProgram(h, [r], i.dtype);
}
var CY = { kernelName: dg, backendName: "webgl", kernelFunc: IY };
function NY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
  ru([r, a], "avgPoolGrad");
  let { filterSize: o, strides: u, pad: l } = s, c = C.computePool2DInfo(i.shape, o, u, 1, l), p = new kY(c);
  return n.runWebGLProgram(p, [r], i.dtype);
}
var TY = { kernelName: cg, backendName: "webgl", kernelFunc: NY };
function $Y(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  return Wd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
}
var _Y = { kernelName: Na, backendName: "webgl", kernelFunc: $Y };
var AY = class {
  constructor(e, t, n, s, r, a) {
    this.outputShape = [], this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t), C.assertAndGetBroadcastShape(e, n);
    let i = "0.0";
    s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let o = "1.0";
    r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale"), o = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${i};
        float scale = ${o};
        float inv = scale * inversesqrt(variance + float(${a}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `;
  }
};
var EY = class {
  constructor(e, t, n, s, r, a) {
    this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t), C.assertAndGetBroadcastShape(e, n);
    let i = "vec4(0.0)";
    s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let o = "vec4(1.0)";
    r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale"), o = "getScaleAtOutCoords()"), this.outputShape = e, this.userCode = `
      void main() {
        vec4 offset = ${i};
        vec4 scale = ${o};

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

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

        setOutput((x - mean) * inv + offset);
      }
    `;
  }
};
var RY = ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s, mean: r, variance: a, offset: i, scale: o } = e;
  w.assert(r.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || r.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(o == null || r.shape.length === o.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
  let { varianceEpsilon: u } = n;
  u == null && (u = 1e-3);
  let l = [s, r, a], c = null;
  i != null && (c = i.shape, l.push(i));
  let p = null;
  o != null && (p = o.shape, l.push(o));
  let d = K().getBool("WEBGL_PACK_NORMALIZATION") ? new EY(s.shape, r.shape, a.shape, c, p, u) : new AY(s.shape, r.shape, a.shape, c, p, u);
  return t.runWebGLProgram(d, l, l[0].dtype);
};
var DY = { kernelName: Ba, backendName: "webgl", kernelFunc: RY };
var FY = class {
  constructor(e) {
    this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
    let t = ot(this.rank);
    this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
    let n = OY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Xm[i]} = start[${i}] + coords.${Xm[i]};`);
    s = `
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `, this.userCode = `
      void main() {
        ${s}
        setOutput(getSource(${n}));
      }
    `;
  }
};
var Xm = ["x", "y", "z", "w", "u", "v"];
function OY(e) {
  if (e === 1)
    return "sourceLoc";
  if (e <= 6)
    return Xm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
  throw Error(`Slicing for rank ${e} is not yet supported`);
}
var PY = class {
  constructor(e) {
    this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
    let t = ot(this.rank), n = ln("coords", this.rank), s = ln("sourceLoc", this.rank), r = this.rank === 1 ? "sourceLoc" : `vec2(${s.slice(-2).join()})`, a = `getChannel(getSource(${s.join()}), ${r})`, i = `
      result.x = ${a};
      if (++${n[this.rank - 1]} < ${e[this.rank - 1]}) {
        ++${s[this.rank - 1]};
        result.y = ${a};
        --${s[this.rank - 1]};
      }
    `, o = this.rank === 1 ? "" : `
      --${n[this.rank - 1]};
      if (++${n[this.rank - 2]} < ${e[this.rank - 2]}) {
        ++${s[this.rank - 2]};
        result.z = ${a};
        if (++${n[this.rank - 1]} < ${e[this.rank - 1]}) {
          ++${s[this.rank - 1]};
          result.w = ${a};
        }
      }
    `, u = this.rank <= 4 ? `sourceLoc = coords +
            ${t}(${e.map((l, c) => `start[${c}]`).join()});` : e.map((l, c) => `${s[c]} = ${n[c]} + start[${c}];`).join(`
`);
    this.userCode = `
      void main() {
        ${t} coords = getOutputCoords();
        ${t} sourceLoc;
        ${u}
        vec4 result = vec4(0.);
        ${i}
        ${o}
        setOutput(result);
      }
    `;
  }
};
function zY(e, t, n, s) {
  let r = s.texData.get(e.dataId), a = s.makeTensorInfo(n, e.dtype), i = s.texData.get(a.dataId);
  Object.assign(i, r), i.refCount = 1, i.shape = n, i.dtype = e.dtype;
  let o = wt.computeFlatOffset(t, w.computeStrides(e.shape));
  r.slice && (o += r.slice.flatOffset), i.slice = { flatOffset: o, origDataId: r.slice && r.slice.origDataId || e.dataId };
  let u = s.dataRefCount.get(i.slice.origDataId) || 1;
  return s.dataRefCount.set(i.slice.origDataId, u + 1), a;
}
function cu(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
  if (wt.assertParamsValid(r, o, u), w.sizeFromShape(u) === 0)
    return n.makeTensorInfo(u, r.dtype, []);
  if (n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
    let p = n.texData.get(r.dataId), d = TX(p.values, o, u, r.shape, r.dtype);
    return n.makeTensorInfo(u, r.dtype, d);
  }
  let { isPacked: l } = n.texData.get(r.dataId), c = wt.isSliceContinous(r.shape, o, u);
  if (l || !c) {
    let p = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PY(u) : new FY(u), d = [o];
    return n.runWebGLProgram(p, [r], r.dtype, d);
  }
  return n.uploadToGPU(r.dataId), zY(r, o, u, n);
}
var MY = { kernelName: Lo, backendName: "webgl", kernelFunc: cu };
var LY = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
  w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
  let o = a.reduce((y, v) => y * v), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = zt({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = cu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
  return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
};
var BY = { kernelName: po, backendName: "webgl", kernelFunc: LY };
function VY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.readSync(r.dataId), u = n.readSync(a.dataId), l = W1(o, u, a.dtype, a.shape, i);
  return n.makeTensorInfo([i], a.dtype, l);
}
var WY = { kernelName: pg, backendName: "webgl", kernelFunc: VY };
function UY(e) {
  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.readSync(s.dataId), i = n.readSync(r.dataId), o = C.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
  return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
}
var GY = { kernelName: hg, backendName: "webgl", kernelFunc: UY };
var HY = "return float(a != b);";
var a2 = jt({ opSnippet: HY, cpuKernelImpl: kX, dtype: "bool" });
var qY = { kernelName: $o, backendName: "webgl", kernelFunc: a2 };
function ec(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
  return Rn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
}
var jY = { kernelName: op, backendName: "webgl", kernelFunc: ec };
var KY = "return float(int(x));";
function XY(e, t) {
  let n = new Gs(e.shape, KY), s = t.runWebGLProgram(n, [e], "int32");
  return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
function Ym(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
  if (a === "complex64") {
    if (r.dtype === "complex64")
      return Rn({ inputs: { x: r }, backend: n });
    let i = $t(r.shape), o = Ym({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Rr({ inputs: { real: o, imag: i }, backend: n });
    return i.dispose(), n.disposeIntermediateTensorInfo(o), u;
  }
  if (r.dtype === "complex64") {
    let i = ec({ inputs: { input: r }, backend: n }), o = Ym({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
    return n.disposeIntermediateTensorInfo(i), o;
  }
  if (!w.hasEncodingLoss(r.dtype, a)) {
    let i = Rn({ inputs: { x: r }, backend: n });
    return { dataId: i.dataId, shape: i.shape, dtype: a };
  }
  if (a === "int32")
    return XY(r, n);
  if (a === "bool") {
    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = a2({ inputs: { a: r, b: i }, backend: n });
    return n.disposeIntermediateTensorInfo(i), u;
  }
  throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
}
var YY = { kernelName: Ta, backendName: "webgl", kernelFunc: Ym };
var vw = "return ceil(x);";
var QY = Ke({ opSnippet: vw, packedOpSnippet: vw, cpuKernelImpl: sX });
var ZY = { kernelName: $a, backendName: "webgl", kernelFunc: QY };
var JY = 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 e9 = 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 t9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o;
  K().getBool("WEBGL_PACK_CLIP") ? o = new e9(r.shape) : o = new JY(r.shape);
  let u = [[a], [i]];
  return n.runWebGLProgram(o, [r], r.dtype, u);
}
var n9 = { kernelName: Ir, backendName: "webgl", kernelFunc: t9 };
var s9 = 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 xw(e, t) {
  return { dataId: t.dataId, dtype: t.dtype, shape: e.shape };
}
function r9(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new s9(s.shape), i = [xw(s, r.complexTensorInfos.real), xw(s, r.complexTensorInfos.imag)];
  return n.runWebGLProgram(a, i, i[0].dtype);
}
var a9 = { kernelName: Jd, backendName: "webgl", kernelFunc: r9 };
var i9 = class {
  constructor(e) {
    this.outputShape = [], this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
    let t = new Array(e.length - 1);
    t[0] = e[0][1];
    for (let a = 1; a < t.length; a++)
      t[a] = t[a - 1] + e[a][1];
    let n = [`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];
    for (let a = 1; a < t.length; a++) {
      let i = t[a - 1];
      n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`);
    }
    let s = t.length, r = t[t.length - 1];
    n.push(`else setOutput(getT${s}(yR, yC-${r}));`), this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `;
  }
};
var o9 = class {
  constructor(e, t) {
    this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = C.computeOutShape(e, t);
    let n = this.outputShape, s = n.length, r = ot(s), a = ln("coords", s), i = ["x", "y", "z", "w", "u", "v"].slice(0, s);
    this.variableNames = e.map((f, m) => `T${m}`);
    let o = new Array(e.length - 1);
    o[0] = e[0][t];
    for (let f = 1; f < o.length; f++)
      o[f] = o[f - 1] + e[f][t];
    let u = i[t], l = i.slice(-2), c = i.join(), p = `if (${u} < ${o[0]}) {
        return getChannel(
            getT0(${c}), vec2(${l.join()}));
        }`;
    for (let f = 1; f < o.length; f++) {
      let m = o[f - 1];
      p += `
        if (${u} < ${o[f]}  && ${u} >= ${o[f - 1]}) {
          return getChannel(
            getT${f}(${td(i, u, m)}),
            vec2(${td(l, u, m)}));
        }`;
    }
    let d = o.length, h = o[o.length - 1];
    p += `
        return getChannel(
          getT${d}(${td(i, u, h)}),
          vec2(${td(l, u, h)}));`, this.userCode = `
      float getValue(${i.map((f) => "int " + f)}) {
        ${p}
      }

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

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

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

        ${a[s - 1]} = ${a[s - 1]} - 1;
        if (${a[s - 2]} < ${n[s - 2]} &&
            ${a[s - 1]} < ${n[s - 1]}) {
          result.b = getValue(${a});
        }
        setOutput(result);
      }
    `;
  }
};
function td(e, t, n) {
  let s = e.indexOf(t);
  return e.map((a, i) => i === s ? `${a} - ${n}` : a).join();
}
function nh(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
  return Rn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
}
var u9 = { kernelName: sp, backendName: "webgl", kernelFunc: nh };
function Ki(e, t, n) {
  let s = e[0].dtype;
  if (s === "complex64") {
    let c = e.map((m) => ec({ inputs: { input: m }, backend: n })), p = e.map((m) => nh({ inputs: { input: m }, backend: n })), d = Ki(c, t, n), h = Ki(p, t, n), f = Rr({ inputs: { real: d, imag: h }, backend: n });
    return c.forEach((m) => n.disposeIntermediateTensorInfo(m)), p.forEach((m) => n.disposeIntermediateTensorInfo(m)), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
  }
  let r = n.shouldExecuteOnCPU(e);
  if (s === "string" && (r = true), r) {
    let c = e.map((b) => {
      let y = w.sizeFromShape(b.shape.slice(t));
      return he({ inputs: { x: b }, backend: n, attrs: { shape: [-1, y] } });
    }), p = c.map((b) => ({ vals: n.readSync(b.dataId), shape: b.shape })), d = C.computeOutShape(c.map((b) => b.shape), 1), h = c[0].shape[0] === 1, f = rX(p, d, s, h), m = C.computeOutShape(e.map((b) => b.shape), t), g = n.makeTensorInfo(m, s, f);
    return c.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
  }
  if (e.length > K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
    let c = Math.floor(e.length / 2), p = Ki(e.slice(0, c), t, n), d = Ki(e.slice(c), t, n), h = Ki([p, d], t, n);
    return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), h;
  }
  if (K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && e[0].shape.length > 1) {
    let c = new o9(e.map((p) => p.shape), t);
    return n.runWebGLProgram(c, e, s);
  }
  let { tensors2D: a, outShape: i } = l9(e, t, n), o = new i9(a.map((c) => c.shape)), u = n.runWebGLProgram(o, a, s);
  a.forEach((c) => n.disposeIntermediateTensorInfo(c));
  let l = he({ inputs: { x: u }, attrs: { shape: i }, backend: n });
  return n.disposeIntermediateTensorInfo(u), l;
}
function l9(e, t, n) {
  let s = C.computeOutShape(e.map((a) => a.shape), t);
  return { tensors2D: e.map((a) => he({ inputs: { x: a }, attrs: { shape: [-1, w.sizeFromShape(a.shape.slice(t))] }, backend: n })), outShape: s };
}
function i2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = C.computeOutShape(t.map((l) => l.shape), a);
  if (w.sizeFromShape(i) === 0)
    return n.makeTensorInfo(i, t[0].dtype, []);
  let o = t.filter((l) => w.sizeFromShape(l.shape) > 0);
  if (o.length === 1)
    return Rn({ inputs: { x: o[0] }, backend: n });
  let u = o.map((l) => l.shape);
  return C.assertParamsConsistent(u, a), Ki(o, a, n);
}
var c9 = { kernelName: ho, backendName: "webgl", kernelFunc: i2 };
var o2 = class {
  constructor(e, t = false, n = null, s = false, r = false) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let a = e.padInfo.top, i = e.padInfo.left, o = e.strideHeight, u = e.strideWidth, l = e.dilationHeight, c = e.dilationWidth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4, m = e.dataFormat === "channelsLast", g = m ? 1 : 2, b = m ? 2 : 3, y = m ? 3 : 1, v = "", x = "";
    n && (s ? v = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }` : r ? v = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }` : v = `
          float activation(float x) {
            ${n}
          }
        `, x = "result = activation(result);");
    let k = t ? "result += getBiasAtOutCoords();" : "";
    t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), r && this.variableNames.push("leakyreluAlpha"), this.userCode = `
      ${v}

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

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

        ivec2 xRCCorner =
            ivec2(coords[${g}], coords[${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 < ${p}; wR++) {
          int xR = xRCorner + wR * ${l};

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

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

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

            for (int d1 = 0; d1 < ${h}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${m}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${f === 1}) {

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

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

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

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

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

            }
          }
        }

        float result = dotProd;
        ${k}
        ${x}
        setOutput(result);
      }
    `;
  }
};
var d9 = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let t = e.padInfo.front, n = e.padInfo.top, s = e.padInfo.left, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.filterDepth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4;
    this.userCode = `
      const ivec3 strides = ivec3(${r}, ${a}, ${i});
      const ivec3 pads = ivec3(${t}, ${n}, ${s});

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

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

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

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

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

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

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

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

              for (int d1 = 0; d1 < ${h}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${f === 1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${h}) *
                  getW(wF, wR, wC, ${h}, d2);
              } else if (${f === 2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${f === 3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${h}),
                  getX(batch, xF, xR, xC, ${h} + 1),
                  getX(batch, xF, xR, xC, ${h} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${h}, d2),
                  getW(wF, wR, wC, ${h} + 1, d2),
                  getW(wF, wR, wC, ${h} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var p9 = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
    let { dataFormat: n } = t, s = fn(), r = n === "channelsLast", a = r ? 0 : 1, i = r ? 1 : 2, o = this.enableShapeUniforms ? "if(blockIndex < outShape[1] && pos < outShape[0]) {" : `if(blockIndex < ${e[1]} && pos < ${e[0]}) {`, u = "";
    for (let l = 0; l <= 1; l++)
      for (let c = 0; c <= 1; c++)
        u += `
          blockIndex = rc.y + ${c};
          pos = rc.x + ${l};

          ${o}
            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 (${r}) {
                  innerDims = vec2(d1, ch);
                  result[${l * 2 + c}] = getChannel(
                    getA(d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${l * 2 + c}] = getChannel(
                    getA(ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;
    this.userCode = `
      void main() {
        ivec2 rc = getOutputCoords();

        vec4 result = vec4(0);

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

        ${u}

        ${s.output} = result;
      }
    `;
  }
};
function u2(e, t, n, s) {
  let r = e.shape;
  if (w.assert(r.length <= 1 || r.length === 3, () => `WebGL conv2d only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${r.length}.`), r.length === 1) {
    let a = n ? t[3] : t[1];
    w.assert(r[0] === 1 || r[0] === a, () => `WebGL conv2d PReLU activation weights (${r}) is not compatible with the number of output channels (${a}).`);
  } else if (r.length === 3) {
    try {
      bi.assertAndGetBroadcastShape(r, t);
    } catch (a) {
      let i = `WebGL conv2d PReLU activation weights (${r}) is not compatible with the output shape of the conv2d (${t}).`;
      throw Error(i);
    }
    if (!n)
      return zt({ inputs: { x: e }, backend: s, attrs: { perm: [1, 2, 0] } });
  }
  return e;
}
function l2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
  let u = e.shape, l = s.texData.get(e.dataId), c = n.inChannels, p = u[0] * u[1] * u[2], d = n.outChannels, h = n.dataFormat === "channelsLast", f = false, m = false, g, b = [];
  if (r != null && (w.assert(r.shape.length <= 1, () => `WebGL conv2dByMatMul only supports 1-D Tensor bias but got the bias of rank-${r.shape.length}.`), w.assert(r.shape.length === 0 || r.shape[0] === n.outChannels, () => `WebGL conv2dByMatMul bias shape (${r.shape}) is not compatible with the number of output channels (${n.outChannels})`)), a != null) {
    let x = u2(a, n.outShape, h, s);
    x.dataId !== a.dataId && (b.push(x), a = x);
  }
  if (!((p === 1 || d === 1) && c > t2) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
    let x = u[0] * u[1] * (u[2] + 1), k = { dataId: e.dataId, shape: [1, x, n.inChannels], dtype: e.dtype }, I = l.shape;
    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(nl(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
    let $ = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
    b.push($);
    let E = Wd({ a: k, b: $, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
    w.assert(A.isPacked, () => "batchMatMul result is expected to be packed"), l.shape = I, A.shape = n.outShape, g = Rn({ inputs: { x: E }, backend: s }), g.shape = n.outShape, b.push(E);
  } else {
    let x = h ? e : zt({ inputs: { x: e }, backend: s, attrs: { perm: [0, 2, 3, 1] } }), k = x.shape, I = k[0] * k[1] * k[2], $ = he({ inputs: { x }, backend: s, attrs: { shape: [1, I, n.inChannels] } }), E = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), A = Wd({ a: $, b: E, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), P = [n.batchSize, n.outHeight, n.outWidth, n.outChannels], R = he({ inputs: { x: A }, backend: s, attrs: { shape: P } });
    g = h ? R : zt({ inputs: { x: R }, backend: s, attrs: { perm: [0, 3, 1, 2] } }), h || (b.push(x), b.push(R)), b.push($), b.push(E), b.push(A);
  }
  for (let x of b)
    s.disposeIntermediateTensorInfo(x);
  return g;
}
function c2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
  let { filterWidth: u, filterHeight: l, inChannels: c, outWidth: p, outHeight: d, dataFormat: h } = n, f = h === "channelsLast", m = u * l * c, g = d * p, b = [m, g], y = true, v = false, x = [];
  if (r != null && (w.assert(r.shape.length <= 1, () => `WebGL conv2dWithIm2Row only supports 1-D Tensor bias but got the bias of rank-${r.shape.length}.`), w.assert(r.shape.length === 0 || r.shape[0] === n.outChannels, () => `WebGL conv2dWithIm2Row bias shape (${r.shape}) is not compatible with the number of output channels (${n.outChannels})`)), a != null) {
    let J = u2(a, n.outShape, f, s);
    J.dataId !== a.dataId && (x.push(J), a = J);
  }
  let k = he({ inputs: { x: e }, backend: s, attrs: { shape: e.shape.slice(1) } }), I = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, m, w.sizeFromShape(t.shape) / m] } });
  x.push(k), x.push(I);
  let $ = new p9(b, n), E = [k.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], A = s.runWebGLProgram($, [k], "float32", E), P = he({ inputs: { x: A }, backend: s, attrs: { shape: [1, b[0], b[1]] } });
  x.push(A), x.push(P);
  let R = r != null, F = a != null, T = o === "leakyrelu", z = o ? Jp(o, true) : null, W = new e2(P.shape, I.shape, [1, g, n.outChannels], y, v, R, z, F, T), j = [P, I];
  if (r && j.push(r), F && j.push(a), T) {
    let J = s.makeTensorInfo([], "float32", w.createScalarValue(i, "float32"));
    j.push(J), x.push(J);
  }
  let X = s.runWebGLProgram(W, j, "float32"), Y = [1, d, p, n.outChannels], Z = he({ inputs: { x: X }, backend: s, attrs: { shape: Y } }), te = f ? Z : zt({ inputs: { x: Z }, backend: s, attrs: { perm: [0, 3, 1, 2] } });
  f || x.push(Z), x.push(X);
  for (let J of x)
    s.disposeIntermediateTensorInfo(J);
  return te;
}
function h9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p), h;
  if (d.filterHeight === 1 && d.filterWidth === 1 && d.dilationHeight === 1 && d.dilationWidth === 1 && d.strideHeight === 1 && d.strideWidth === 1 && (d.padInfo.type === "SAME" || d.padInfo.type === "VALID"))
    h = l2({ x: r, filter: a, convInfo: d, backend: n });
  else if (K().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
    h = c2({ x: r, filter: a, convInfo: d, backend: n });
  else {
    let m = new o2(d);
    h = n.runWebGLProgram(m, [r, a], "float32");
  }
  let f = he({ inputs: { x: h }, backend: n, attrs: { shape: d.outShape } });
  return n.disposeIntermediateTensorInfo(h), f;
}
var f9 = { kernelName: _a, backendName: "webgl", kernelFunc: h9 };
var m9 = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.dataFormat === "channelsLast";
    this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${s};

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

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

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

              if (${a}) {
                float dyValue = getDy(b, yR, yC, d2);
                float xValue = getX(b, xR, xC, d1);
                dotProd += (xValue * dyValue);
              } else {
                float dyValue = getDy(b, d2, yR, yC);
                float xValue = getX(b, d1, xR, xC);
                dotProd += (xValue * dyValue);
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var g9 = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dataFormat === "channelsLast", i = t - 1 - e.padInfo.top, o = n - 1 - e.padInfo.left, u = a ? 1 : 2, l = a ? 2 : 3, c = a ? 3 : 1;
    this.userCode = `
      const ivec2 pads = ivec2(${i}, ${o});

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

        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${s}.0;

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

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

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

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

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

            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {

              if (${a}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var b9 = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left;
    this.userCode = `
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

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

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

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

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

              for (int yC = 0; yC < ${e.outWidth}; yC++) {
                int xC = wC + yC * ${s} - ${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 y9 = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = t - 1 - e.padInfo.front, u = n - 1 - e.padInfo.top, l = s - 1 - e.padInfo.left;
    this.userCode = `
      const ivec3 pads = ivec3(${o}, ${u}, ${l});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


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

        float dotProd = 0.0;
        for (int wF = 0; wF < ${t}; wF++) {
          float dyF = float(dyFCorner + wF) / ${r}.0;

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

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

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

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

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

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

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

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

              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function v9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), h = new m9(d);
  return n.runWebGLProgram(h, [r, a], "float32");
}
var x9 = { kernelName: fg, backendName: "webgl", kernelFunc: v9 };
function w9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(l), d = C.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = new g9(d);
  return n.runWebGLProgram(h, [r, a], "float32");
}
var k9 = { kernelName: Aa, backendName: "webgl", kernelFunc: w9 };
function S9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = C.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new d9(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var I9 = { kernelName: ep, backendName: "webgl", kernelFunc: S9 };
function C9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = C.computeConv3DInfo(r.shape, u, i, 1, o), c = new b9(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var N9 = { kernelName: mg, backendName: "webgl", kernelFunc: C9 };
function T9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = C.computeConv3DInfo(u, a.shape, o, 1, i), c = new y9(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var $9 = { kernelName: gg, backendName: "webgl", kernelFunc: T9 };
var _9 = lu + `
  return cos(x);
`;
var A9 = Ke({ opSnippet: _9 });
var E9 = { kernelName: Ea, backendName: "webgl", kernelFunc: A9 };
var R9 = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var D9 = Ke({ opSnippet: R9 });
var F9 = { kernelName: Ra, backendName: "webgl", kernelFunc: D9 };
var O9 = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
    let [a, i, o, u] = e, [l] = t, [c, p] = n;
    this.outputShape = [l, c, p, u];
    let d = s === "bilinear" ? 1 : 0, [h, f] = [`${i - 1}.0`, `${o - 1}.0`], [m, g, b] = c > 1 ? [`${(i - 1) / (c - 1)}`, "(y2-y1) * height_ratio", `y1*${h} + float(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${h}`], [y, v, x] = p > 1 ? [`${(o - 1) / (p - 1)}`, "(x2-x1) * width_ratio", `x1*${f} + float(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${f}`];
    this.userCode = `
      const float height_ratio = float(${m});
      const float width_ratio = float(${y});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${a}) {
          return;
        }

        float height_scale = ${g};
        float width_scale = ${v};

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

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

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

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `;
  }
};
var P9 = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new O9(r.shape, a.shape, o, u, l);
  return n.runWebGLProgram(c, [r, a, i], "float32");
};
var z9 = { kernelName: mo, backendName: "webgl", kernelFunc: P9 };
var ww = class {
  constructor(e, t, n, s) {
    this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.op = e, this.outputShape = t;
    let r = t.length, a = this.op === "*" ? "1.0" : "0.0", i = n ? a : `getX(${kw(r, "coords", this.op)})`, o = t[t.length - 1], u = "", l = "";
    n ? (u = s ? `end != ${o - 1}` : "end != 0", l = s ? "end + 1" : "end - 1") : (u = s ? `end + pow2 < ${o}` : "end >= pow2", l = s ? "end + pow2" : "end - pow2"), this.userCode = `
      void main() {
        ${ot(r)} coords = getOutputCoords();
        int end = ${Sw(r, "coords", this.op)};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${u}) {
          int idx = ${l};
          ${Sw(r, "coords", this.op)} = idx;
          val ${this.op}= getX(${kw(r, "coords", this.op)});
        }
        setOutput(val);
      }
    `;
  }
};
function kw(e, t, n) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.x, ${t}.y`;
  if (e === 3)
    return `${t}.x, ${t}.y, ${t}.z`;
  if (e === 4)
    return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
  throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
}
function Sw(e, t, n) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.y`;
  if (e === 3)
    return `${t}.z`;
  if (e === 4)
    return `${t}.w`;
  throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
}
function d2(e, t, n, s, r, a) {
  let i = t.shape.length, o = C.getAxesPermutation([s], i), u = t;
  o != null && (u = zt({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
  let l = C.getInnerMostAxes(1, i)[0];
  if (l !== i - 1)
    throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
  let c = u.shape[l], p = Rn({ inputs: { x: u }, backend: n });
  for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
    let h = new ww(e, u.shape, false, a), f = [[d]], m = p;
    p = n.runWebGLProgram(h, [p], p.dtype, f), n.disposeIntermediateTensorInfo(m);
  }
  if (r) {
    let d = new ww(e, u.shape, r, a), h = p;
    p = n.runWebGLProgram(d, [p], p.dtype), n.disposeIntermediateTensorInfo(h);
  }
  if (o != null) {
    let d = C.getUndoAxesPermutation(o), h = zt({ inputs: { x: p }, backend: n, attrs: { perm: d } });
    return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(u), h;
  }
  return p;
}
function M9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return d2("*", r, n, a, i, o);
}
var L9 = { kernelName: fo, backendName: "webgl", kernelFunc: M9 };
function B9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return d2("+", r, n, a, i, o);
}
var V9 = { kernelName: Da, backendName: "webgl", kernelFunc: B9 };
function W9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
  if (r.shape.length === 1) {
    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = W1(u, l, a.dtype, a.shape, i);
    return n.makeTensorInfo([i], a.dtype, c);
  } else if (r.shape.length === 2) {
    let u = n.bufferSync(r), l = n.bufferSync(a), c = nX(u, l, i, o);
    return n.makeTensorInfo(c.shape, a.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
}
var U9 = { kernelName: bg, backendName: "webgl", kernelFunc: W9 };
var G9 = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t, this.dataFormat = n, this.userCode = `
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${t};
      int offset_h = imod(h, ${t});
      int in_w = w / ${t};
      int offset_w = imod(w, ${t});
      int offset_d = (offset_h * ${t} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `;
  }
  getHeightCoordString() {
    return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
  }
  getWidthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
  }
  getDepthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
  }
  getOutputDepthSize() {
    return this.dataFormat === "NHWC" ? this.outputShape[3] : this.outputShape[1];
  }
  getInputSamplingString() {
    return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
  }
};
function H9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new G9(f, a, i);
  return n.runWebGLProgram(m, [r], r.dtype);
}
var q9 = { kernelName: go, backendName: "webgl", kernelFunc: H9 };
var p2 = class {
  constructor(e, t = false, n = null, s = false, r = 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 = Sn(this.outputShape.length);
    let a = e.filterHeight, i = e.filterWidth, o = e.outChannels / e.inChannels, u = "", l = "";
    n && (s ? u = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }` : r ? u = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }` : u = `
          float activation(float x) {
            ${n}
          }
        `, l = "result = activation(result);");
    let c = t ? "result += getBiasAtOutCoords();" : "";
    t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), r && 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 / ${o};
        int q = d2 - d1 * ${o};

        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;
        ${c}
        ${l}
        setOutput(result);
      }
    `;
  }
};
var h2 = class {
  constructor(e, t = false, n = null, s = false, r = false) {
    this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
    let a = e.outChannels / e.inChannels, i = e.padInfo.left, o = e.strideWidth, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = `
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;
    for (let g = 0; g < c; g++)
      d += `
          vec4 xTexelC${g * 2};
          int xTexelC${g * 2}Ready;
          vec4 xTexelC${g * 2 + 1};
          int xTexelC${g * 2 + 1}Ready;
          vec4 xC${g};`;
    d += `
    for (int r = 0; r < ${l}; r++) {
      `;
    for (let g = 0; g < c; g++)
      d += `
          xTexelC${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);`;
    d += `
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;
    for (let g = 0; g < (p + 1) / 2; g++) {
      let b = g * 2;
      if (d += `
          xC = xCCorner + ${b * u};
          `, o === 1) {
        if (b < c && (i % 2 === 1 ? (d += `
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }
              `, u === 1 && b > 0 ? d += `
                xC${b} = vec4(xTexelC${b - 2}.zw, xTexelC${b}.xy);
                ` : d += `
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      previous.zw = vec2(0.0);
                    }

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

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

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

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${b + 1}.zw = vec2(0.0);
                    }
                    xTexelC${b + 1}Ready = 1;
                  }
                  `, u > 1 && (d += `
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                      xTexelC${b} = getX(batch, xR, xCOffset, d1);
                      xTexelC${b}Ready = 1;
                    }
                    `), d += `
                  xC${b + 1} = vec4(xTexelC${b}.zw, xTexelC${b + 1}.xy);
                  `) : y === 1 ? d += `
                    xC${b + 1} = xTexelC${b};
                    ` : d += `
                    xCOffset = xC + ${y};

                    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 < c && (i % 2 === 1 ? (d += `
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {
                  xTexelC${b} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${b}.zw = vec2(0.0);
                  }
                  xTexelC${b}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b + 1}Ready == 0) {
                  xTexelC${b + 1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= inDims[1]) {
                    xTexelC${b + 1}.zw = vec2(0.0);
                  }
                  xTexelC${b + 1}Ready = 1;
                }

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

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

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

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

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${d}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${m}
        ${f}
        setOutput(result);
      }
    `;
  }
};
function j9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
  c == null && (c = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(i, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);
  let p = C.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d;
  K().getBool("WEBGL_PACK_DEPTHWISECONV") && p.strideWidth <= 2 && p.outChannels / p.inChannels === 1 ? d = new h2(p) : d = new p2(p);
  let h = [[p.padInfo.top, p.padInfo.left], [p.strideHeight, p.strideWidth], [p.dilationHeight, p.dilationWidth], [p.inHeight, p.inWidth]];
  return n.runWebGLProgram(d, [r, a], "float32", h);
}
var K9 = { kernelName: Fa, backendName: "webgl", kernelFunc: j9 };
var X9 = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.outChannels / e.inChannels;
    this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${a} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${e.batchSize}; b++) {
          for (int yR = 0; yR < ${e.outHeight}; yR++) {
            int xR = wR + yR * ${t} - ${s};

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

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

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

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
var Y9 = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = t - 1 - e.padInfo.top, i = n - 1 - e.padInfo.left, o = 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 < ${t}; wR++) {
          float dyR = float(dyRCorner + wR) / ${s}.0;

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

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

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

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

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

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${o}; dm++) {
              int d2 = d1 * ${o} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function Q9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s, p = C.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new X9(p);
  return n.runWebGLProgram(d, [r, a], "float32");
}
var Z9 = { kernelName: yg, backendName: "webgl", kernelFunc: Q9 };
function J9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s, p = C.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new Y9(p);
  return n.runWebGLProgram(d, [r, a], "float32");
}
var eQ = { kernelName: vg, backendName: "webgl", kernelFunc: J9 };
var tQ = 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 nQ(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), i = he({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), o = new tQ(a), u = n.runWebGLProgram(o, [i], i.dtype), l = he({ inputs: { x: u }, backend: n, attrs: { shape: r } });
  return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(u), l;
}
var sQ = { kernelName: xg, backendName: "webgl", kernelFunc: nQ };
var rQ = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let { inHeight: t, inWidth: n, padInfo: s, strideHeight: r, strideWidth: a, filterHeight: i, filterWidth: o, dilationHeight: u, dilationWidth: l } = e, { top: c, left: p } = s;
    this.userCode = `
      const ivec2 strides = ivec2(${r}, ${a});
      const ivec2 pads = ivec2(${c}, ${p});
      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 < ${t}) {
            for (int w = 0; w < ${o}; w++) {
              int wIn = wBeg + w * ${l};

              if (wIn >= 0 && wIn < ${n}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `;
  }
};
function aQ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = C.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new rQ(l);
  c = n.runWebGLProgram(p, [r, a], "float32");
  let d = he({ inputs: { x: c }, backend: n, attrs: { shape: l.outShape } });
  return n.disposeIntermediateTensorInfo(c), d;
}
var iQ = { kernelName: tp, backendName: "webgl", kernelFunc: aQ };
function oQ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = C.decodeEinsumEquation(r, a.length);
  C.checkEinsumDimSizes(i.length, u, a);
  let { path: l, steps: c } = C.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
  for (let m = 0; m < p; ++m) {
    for (let g of c[m]) {
      let { permutationIndices: b, expandDims: y } = C.getEinsumPermutation(h, u[g]), v;
      C.isIdentityPermutation(b) ? v = a[g] : (v = zt({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
      let x = v.shape.slice();
      for (let k = 0; k < y.length; ++k)
        x.splice(y[k], 0, 1);
      w.arraysEqual(v.shape, x) || (v = he({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Tv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = th({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
  }
  for (let m of f)
    m !== d && n.disposeIntermediateTensorInfo(m);
  return d;
}
var uQ = { kernelName: np, backendName: "webgl", kernelFunc: oQ };
var lQ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var cQ = `
  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 dQ = Ke({ opSnippet: lQ, packedOpSnippet: cQ });
var pQ = { kernelName: Pa, backendName: "webgl", kernelFunc: dQ };
var hQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
var fQ = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var mQ = (e) => {
  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Jl(fQ, s.shape, r.shape) : new uo(hQ, s.shape, r.shape);
  return n.runWebGLProgram(a, [s, r], s.dtype);
};
var gQ = { kernelName: wg, backendName: "webgl", kernelFunc: mQ };
var bQ = `
  return vec4(equal(a, b));
`;
var yQ = "return float(a == b);";
var vQ = jt({ opSnippet: yQ, packedOpSnippet: bQ, dtype: "bool", cpuKernelImpl: aX });
var xQ = { kernelName: bo, backendName: "webgl", kernelFunc: vQ };
var wQ = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${C.ERF_P};
  float a1 = ${C.ERF_A1};
  float a2 = ${C.ERF_A2};
  float a3 = ${C.ERF_A3};
  float a4 = ${C.ERF_A4};
  float a5 = ${C.ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`;
var kQ = Ke({ opSnippet: wQ });
var SQ = { kernelName: ml, backendName: "webgl", kernelFunc: kQ };
var IQ = lu + `
  return exp(x);
`;
var CQ = `
  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 f2 = Ke({ opSnippet: IQ, packedOpSnippet: CQ, cpuKernelImpl: iX, dtype: "float32" });
var NQ = { kernelName: za, backendName: "webgl", kernelFunc: f2 };
function Qm(e) {
  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
  return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), he({ inputs: { x: a }, backend: s, attrs: { shape: o } });
}
var TQ = { kernelName: yo, backendName: "webgl", kernelFunc: Qm };
var Iw = "return exp(x) - 1.0;";
var $Q = Ke({ opSnippet: Iw, packedOpSnippet: Iw, cpuKernelImpl: oX });
var _Q = { kernelName: vo, backendName: "webgl", kernelFunc: $Q };
var Cw = class {
  constructor(e, t, n) {
    this.variableNames = ["real", "imag"];
    let s = t[1];
    this.outputShape = t;
    let r = n ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`, a = n ? `${s}.0` : "1.0", 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 = ${r};

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

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

        float result = 0.0;

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

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

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `;
  }
};
function m2(e, t, n) {
  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], i = r / a, o = he({ inputs: { x: e }, backend: n, attrs: { shape: [i, a] } }), u = o.shape, l = new Cw("real", u, t), c = new Cw("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Rr({ inputs: { real: d, imag: h }, backend: n });
  n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h);
  let m = he({ inputs: { x: f }, backend: n, attrs: { shape: e.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(f), m;
}
function AQ(e) {
  let { inputs: t, backend: n } = e, { input: s } = t;
  return m2(s, false, n);
}
var EQ = { kernelName: kg, backendName: "webgl", kernelFunc: AQ };
var RQ = class {
  constructor(e, t) {
    this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = `
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `;
  }
};
function tc(e) {
  let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
  if (a = a || w.inferDtype(r), a === "string") {
    let i = w.getArrayFromDType(a, w.sizeFromShape(s));
    return i.fill(r), t.makeTensorInfo(s, a, i);
  } else {
    let i = new RQ(s, r), o = [[r]];
    return t.runWebGLProgram(i, [], a, o);
  }
}
var DQ = { kernelName: gl, backendName: "webgl", kernelFunc: tc };
var FQ = class {
  constructor(e) {
    this.variableNames = ["Image"], this.outputShape = [];
    let t = e[2];
    this.outputShape = e, this.userCode = `
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${t} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${t}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var OQ = { kernelName: xo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
  let { image: n } = e, s = t, r = new FQ(n.shape);
  return s.runWebGLProgram(r, [n], n.dtype);
} };
var Nw = "return floor(x);";
var PQ = Ke({ opSnippet: Nw, packedOpSnippet: Nw, cpuKernelImpl: uX });
var zQ = { kernelName: Ma, backendName: "webgl", kernelFunc: PQ };
var MQ = `
  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 LQ = `
  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 BQ = jt({ opSnippet: MQ, packedOpSnippet: LQ, dtype: "int32" });
var VQ = { kernelName: La, backendName: "webgl", kernelFunc: BQ };
var WQ = class {
  constructor(e) {
    this.variableNames = ["A"];
    let t = fn(), [n, s] = e;
    this.outputShape = e, this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);

        vec4 values = ${t.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `;
  }
};
var UQ = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
    let t = fn(), [n, s] = e;
    this.outputShape = e, this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

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

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${s}.0, ${n}.0);
            vec4 values = ${t.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${t.output} = result;
      }
    `;
  }
};
var GQ = { kernelName: bd, backendName: "webgl", kernelFunc: HQ };
var Ui;
function HQ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s, i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, [u, l] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], c = [l, u], p = [l, u, a];
  (o || i) && (Ui == null && (Ui = document.createElement("canvas").getContext("2d")), Ui.canvas.width = u, Ui.canvas.height = l, Ui.drawImage(r, 0, 0, u, l), r = Ui.canvas);
  let d = n.makeTensorInfo(c, "int32");
  n.texData.get(d.dataId).usage = 2, n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId), r);
  let h = K().getBool("WEBGL_PACK") ? new UQ(p) : new WQ(p), f = n.runWebGLProgram(h, [d], "int32");
  return n.disposeData(d.dataId), f;
}
function qQ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m), b, y = [];
  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"))
    b = l2({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
  else if (K().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
    b = c2({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
  else {
    let x = i != null, k = o != null, I = h === "leakyrelu", $ = h ? Jp(h, false) : null, E = new o2(g, x, $, k, I), A = [r, a];
    if (i && A.push(i), o && A.push(o), I) {
      let P = n.makeTensorInfo([], "float32", w.createScalarValue(f, "float32"));
      A.push(P), y.push(P);
    }
    b = n.runWebGLProgram(E, A, "float32");
  }
  let v = he({ inputs: { x: b }, backend: n, attrs: { shape: g.outShape } });
  return y.push(b), y.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
}
var jQ = { kernelName: ia, backendName: "webgl", kernelFunc: qQ };
function KQ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = [], m = c;
  m == null && (m = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(u, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);
  let g = C.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = K().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? Jp(d, b) : null, v = [r, a], x = i != null, k = o != null, I = d === "leakyrelu";
  if (x && v.push(i), k && v.push(o), I) {
    let P = n.makeTensorInfo([], "float32", w.createScalarValue(h, "float32"));
    v.push(P), f.push(P);
  }
  let $;
  b ? $ = new h2(g, x, y, k, I) : $ = new p2(g, x, y, k, I);
  let E = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], A = n.runWebGLProgram($, v, "float32", E);
  return f.forEach((P) => n.disposeIntermediateTensorInfo(P)), A;
}
var XQ = { kernelName: oa, backendName: "webgl", kernelFunc: KQ };
var YQ = class {
  constructor(e, t, n) {
    this.sliceDim = e, this.strides = t, this.variableNames = ["x", "indices"], this.outputShape = n;
    let s = ot(t.length), r = ot(n.length), a = this.sliceDim > 1 ? "strides[j]" : "strides";
    this.userCode = `
        ${s} strides = ${s}(${this.strides});
         void main() {
          ${r} coords = getOutputCoords();
          int flattenIndex = 0;
          for (int j = 0; j < ${this.sliceDim}; j++) {
            int index = round(getIndices(coords[0], j));
            flattenIndex += index * ${a};
          }
          setOutput(getX(flattenIndex, coords[1]));
        }
      `;
  }
};
function QQ(e) {
  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = C.prepareAndValidate(s, r), d = he({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = he({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
  if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
    let b = n.readSync(r.dataId), y = n.bufferSync(s), v = lX(b, y, s.dtype, l, i, c, p, s.shape, o);
    return n.makeTensorInfo(u, s.dtype, v.values);
  }
  let f = new YQ(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: u } });
  return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(m), g;
}
var ZQ = { kernelName: ko, backendName: "webgl", kernelFunc: QQ };
var JQ = class {
  constructor(e, t) {
    this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
    let n = ot(this.rank), s = eZ(e, 2);
    this.userCode = `
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${s}));
      }
    `;
  }
};
function eZ(e, t) {
  let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [];
  for (let r = 0; r < e.length; r++)
    r === 2 ? s.push("index") : s.push(`${n[r]}`);
  return s.join();
}
function g2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0];
  if (K().get("DEBUG")) {
    let y = n.readSync(a.dataId), v = r.shape[u];
    for (let x = 0; x < y.length; ++x) {
      let k = y[x];
      w.assert(k <= v - 1 && k >= 0, () => `GatherV2: the index value ${k} is not in [0, ${v - 1}]`);
    }
  }
  let l = C.segment_util.collectGatherOpShapeInfo(r, a, u, o), c = w.sizeFromShape(a.shape), p = [], d = he({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = he({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
  p.push(d), p.push(h);
  let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
  if (n.shouldExecuteOnCPU([r, a]) || r.dtype === "string") {
    let y = n.bufferSync(h), v = n.bufferSync(d), x = cX(v, y, f);
    return p.forEach((k) => n.disposeIntermediateTensorInfo(k)), n.makeTensorInfo(l.outputShape, x.dtype, x.values);
  }
  let m = new JQ(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
  p.push(g);
  let b = he({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
  return p.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
}
var tZ = { kernelName: wo, backendName: "webgl", kernelFunc: g2 };
var nZ = "return float(a > b);";
var sZ = `
  return vec4(greaterThan(a, b));
`;
var rZ = jt({ opSnippet: nZ, packedOpSnippet: sZ, cpuKernelImpl: dX, dtype: "bool" });
var aZ = { kernelName: So, backendName: "webgl", kernelFunc: rZ };
var iZ = "return float(a >= b);";
var oZ = `
  return vec4(greaterThanEqual(a, b));
`;
var uZ = jt({ opSnippet: iZ, packedOpSnippet: oZ, dtype: "bool", cpuKernelImpl: pX });
var lZ = { kernelName: Va, backendName: "webgl", kernelFunc: uZ };
function cZ(e) {
  let { inputs: t, backend: n } = e, { input: s } = t;
  return m2(s, true, n);
}
var dZ = { kernelName: Sg, backendName: "webgl", kernelFunc: cZ };
var pZ = "return float(!isnan(x) && !isinf(x));";
var hZ = Ke({ opSnippet: pZ, dtype: "bool" });
var fZ = { kernelName: bl, backendName: "webgl", kernelFunc: hZ };
var mZ = "return float(isinf(x));";
var gZ = Ke({ opSnippet: mZ, dtype: "bool" });
var bZ = { kernelName: yl, backendName: "webgl", kernelFunc: gZ };
var yZ = "return float(isnan(x));";
var vZ = Ke({ opSnippet: yZ, dtype: "bool" });
var xZ = { kernelName: vl, backendName: "webgl", kernelFunc: vZ };
var wZ = "return float(a < b);";
var kZ = `
  return vec4(lessThan(a, b));
`;
var SZ = jt({ opSnippet: wZ, packedOpSnippet: kZ, cpuKernelImpl: hX, dtype: "bool" });
var IZ = { kernelName: Io, backendName: "webgl", kernelFunc: SZ };
var CZ = "return float(a <= b);";
var NZ = `
  return vec4(lessThanEqual(a, b));
`;
var TZ = jt({ opSnippet: CZ, packedOpSnippet: NZ, cpuKernelImpl: fX, dtype: "bool" });
var $Z = { kernelName: Co, backendName: "webgl", kernelFunc: TZ };
function _Z(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = mX(s, r, a);
  return t.makeTensorInfo([i.length], "float32", i);
}
var AZ = { kernelName: Ig, backendName: "webgl", kernelFunc: _Z };
var EZ = lu + `
  return x < 0.0 ? 0./0. : log(x);
`;
var RZ = `
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`;
var DZ = Ke({ opSnippet: EZ, packedOpSnippet: RZ, cpuKernelImpl: gX });
var FZ = { kernelName: Ga, backendName: "webgl", kernelFunc: DZ };
var OZ = lu + `
  return log(1.0 + x);
`;
var PZ = Ke({ opSnippet: OZ });
var zZ = { kernelName: xl, backendName: "webgl", kernelFunc: PZ };
var MZ = "return float(a >= 1.0 && b >= 1.0);";
var LZ = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
var BZ = jt({ opSnippet: MZ, packedOpSnippet: LZ, dtype: "bool" });
var VZ = { kernelName: No, backendName: "webgl", kernelFunc: BZ };
var WZ = "return float(!(x >= 1.0));";
var UZ = Ke({ opSnippet: WZ });
var GZ = { kernelName: wl, backendName: "webgl", kernelFunc: UZ };
var HZ = "return float(a >= 1.0 || b >= 1.0);";
var qZ = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
var jZ = jt({ opSnippet: HZ, packedOpSnippet: qZ, dtype: "bool" });
var KZ = { kernelName: rp, backendName: "webgl", kernelFunc: jZ };
var XZ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["x"], this.outputShape = [];
    let a = t, i = e[3] - 1;
    this.outputShape = e;
    let o, u = `float(${n}) + float(${s}) * sum`;
    r === 0.5 ? o = `inversesqrt(${u})` : r === 1 ? o = `1.0/(${u})` : o = `exp(log(${u}) * float(-${r}));`, this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${a}; j <= ${a}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${i}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${o};
        setOutput(val);
      }
    `;
  }
};
var YZ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
    let a = t, i = e[3] - 1;
    this.outputShape = e;
    let o, u = `float(${n}) + float(${s}) * sum`;
    r === 0.5 ? o = `inversesqrt(${u})` : r === 1 ? o = `1.0/(${u})` : o = `exp(log(${u}) * float(-${r}));`, this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${a};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${a}; j <= ${a}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));

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

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${o};
        setOutput(result);
      }
    `;
  }
};
var QZ = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = K().getBool("WEBGL_PACK_NORMALIZATION") ? new YZ(r.shape, a, i, o, u) : new XZ(r.shape, a, i, o, u);
  return n.runWebGLProgram(l, [r], r.dtype);
};
var ZZ = { kernelName: ap, backendName: "webgl", kernelFunc: QZ };
var JZ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t, this.bias = n, this.alpha = s, this.beta = r, this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${t})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${t} + 1)));

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

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

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

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${s})
                * float(${r})
                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${r});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `;
  }
};
var e7 = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s, p = new JZ(r.shape, o, u, l, c);
  return n.runWebGLProgram(p, [r, a, i], r.dtype);
};
var t7 = { kernelName: Cg, backendName: "webgl", kernelFunc: e7 };
function n7(e, t, n, s) {
  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = Ii(o, e.dtype, "max", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
}
function b2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = c != null, d = n.shouldExecuteOnCPU([r]), h = r;
  if (p) {
    if (d) {
      let v = n.texData.get(h.dataId).values, x = new Array(o);
      for (let $ = 0; $ < x.length; $++)
        x[$] = r.shape[c[$]];
      let k = Nv(v, r.shape, r.dtype, c, x);
      h = n.makeTensorInfo(x, r.dtype);
      let I = n.texData.get(h.dataId);
      I.values = k;
    } else
      h = eh(r, c, n);
    l = C.getInnerMostAxes(l.length, o);
  }
  C.assertAxesAreInnerMostDims("max", l, o);
  let [f, m] = C.computeOutAndReduceShapes(h.shape, l), g = f;
  i && (g = C.expandShapeToKeepDim(f, u));
  let b;
  if (d) {
    let v = n.texData.get(h.dataId).values, x = bX(v, w.sizeFromShape(m), g, r.dtype);
    b = n.makeTensorInfo(g, r.dtype);
    let k = n.texData.get(b.dataId);
    k.values = x;
  } else
    b = n7(h, m, g, n);
  return p && n.disposeIntermediateTensorInfo(h), b;
}
var s7 = { kernelName: Ha, backendName: "webgl", kernelFunc: b2 };
var r7 = X1 + `
  return max(a, b);
`;
var a7 = `
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + Zp + `
  return result;
`;
var i7 = jt({ opSnippet: r7, packedOpSnippet: a7, cpuKernelImpl: yX });
var o7 = { kernelName: qa, backendName: "webgl", kernelFunc: i7 };
function u7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  ru(r, "maxPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(C.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = C.computePool2DInfo(r.shape, a, i, l, o, u);
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    return Rn({ inputs: { x: r }, backend: n });
  let p = new sl(c, "max", false);
  return n.runWebGLProgram(p, [r], r.dtype);
}
var l7 = { kernelName: ja, backendName: "webgl", kernelFunc: u7 };
function c7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = C.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new $v(p, "max", false);
  return n.runWebGLProgram(d, [r], r.dtype);
}
var d7 = { kernelName: ip, backendName: "webgl", kernelFunc: c7 };
var p7 = class {
  constructor(e) {
    this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
    let t = e.strideHeight, n = e.strideWidth, s = e.dilationHeight, r = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = r - 1 - e.padInfo.top, o = a - 1 - e.padInfo.left, u = r * a - 1;
    this.userCode = `
      const ivec2 pads = ivec2(${i}, ${o});

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

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${r};
          wR += ${s}) {
          float dyR = float(dyRCorner + wR) / ${t}.0;

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

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

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

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${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 h7 = class {
  constructor(e) {
    this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
    let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterDepth, u = e.effectiveFilterHeight, l = e.effectiveFilterWidth, c = o - 1 - e.padInfo.front, p = u - 1 - e.padInfo.top, d = l - 1 - e.padInfo.left, h = o * u * l - 1;
    this.userCode = `
      const ivec3 pads = ivec3(${c}, ${p}, ${d});

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

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

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

        for (int wD = 0; wD < ${o};
           wD += ${r}) {
          float dyD = float(dyDCorner + wD) / ${t}.0;

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

          for (int wR = 0; wR < ${u};
              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 < ${l};
                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(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${h} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

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

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
  }
};
function f7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = C.computePool3DInfo(i.shape, o, u, p, l, c), h = new $v(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new h7(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
  return n.disposeIntermediateTensorInfo(f), g;
}
var m7 = { kernelName: Tg, backendName: "webgl", kernelFunc: f7 };
function g7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
  ru([a, i], "maxPoolGrad");
  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = C.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new sl(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new p7(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
  return n.disposeIntermediateTensorInfo(m), b;
}
var b7 = { kernelName: Ng, backendName: "webgl", kernelFunc: g7 };
function y7(e, t, n, s) {
  let r = new sl(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
  r = new sl(n, "max", true, true, t);
  let i = s.runWebGLProgram(r, [e], "float32");
  return [a, i];
}
var v7 = { kernelName: $g, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
  w.assert(s.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);
  let l = [1, 1];
  w.assert(C.eitherStridesOrDilationsAreOne(a, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);
  let c = C.computePool2DInfo(s.shape, r, a, l, i), [p, d] = y7(s, o, c, u);
  return [p, d];
} };
function x7(e, t, n, s) {
  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = Ii(o, "float32", "mean", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
}
var w7 = { kernelName: Ka, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s } = e, { keepDims: r, axis: a } = t, i = n, o = s.shape.length, u = w.parseAxisParam(a, s.shape), l = u, c = C.getAxesPermutation(l, o), p = c != null, d = i.shouldExecuteOnCPU([s]), h = [], f = s;
  if (p) {
    if (d) {
      let x = i.texData.get(f.dataId).values, k = new Array(o);
      for (let E = 0; E < k.length; E++)
        k[E] = s.shape[c[E]];
      let I = Nv(x, s.shape, s.dtype, c, k);
      f = i.makeTensorInfo(k, s.dtype);
      let $ = i.texData.get(f.dataId);
      $.values = I;
    } else
      f = eh(s, c, i);
    h.push(f), l = C.getInnerMostAxes(l.length, o);
  }
  C.assertAxesAreInnerMostDims("sum", l, o);
  let [m, g] = C.computeOutAndReduceShapes(f.shape, l), b = m;
  r && (b = C.expandShapeToKeepDim(m, u));
  let y = x7(f, g, b, i);
  for (let v of h)
    i.disposeIntermediateTensorInfo(v);
  return y;
} };
function k7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = C.getAxesPermutation(l, o), p = r;
  c != null && (p = zt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = C.getInnerMostAxes(l.length, r.shape.length)), C.assertAxesAreInnerMostDims("min", l, o);
  let [d, h] = C.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = Ii(m, m.dtype, "min", n), b;
  if (i) {
    let y = C.expandShapeToKeepDim(d, u);
    b = he({ inputs: { x: g }, backend: n, attrs: { shape: y } });
  } else
    b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
}
var S7 = { kernelName: Xa, backendName: "webgl", kernelFunc: k7 };
var I7 = X1 + `
  return min(a, b);
`;
var C7 = `
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + Zp + `
  return result;
`;
var N7 = jt({ opSnippet: I7, packedOpSnippet: C7, cpuKernelImpl: vX });
var T7 = { kernelName: Ya, backendName: "webgl", kernelFunc: N7 };
var $7 = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.outputShape = t.map((l, c) => l[0] + e[c] + l[1]);
    let s = e.length, r = ot(s), a = t.map((l) => l[0]).join(","), i = t.map((l, c) => l[0] + e[c]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s), u = n === "reflect" ? 0 : 1;
    if (s === 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 = `
      ${r} start = ${r}(${a});
      ${r} end = ${r}(${i});

      void main() {
        ${r} outC = getOutputCoords();
        for (int i = 0; i < ${s}; 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};
          }
        }
        ${r} coords = outC - start;
        setOutput(getX(${o}));
      }
    `;
  }
};
var _7 = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t.map((h, f) => h[0] + e[f] + h[1]);
    let s = e.length, r = ot(s), a = t.map((h) => h[0]).join(","), i = t.map((h, f) => h[0] + e[f]).join(","), o = ln("rc", s), u = ln("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = n === "reflect" ? 0 : 1, d = "";
    if (s === 1) {
      let h = `
        ${r} source = rc;
        if (source < start) {
          source = start * 2 - source - ${p};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${p};
        }
        source -= start;
      `;
      d = `
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${u.join()}), ${c});
        ${o[s - 1]} += 1;
        if(${l}) {
          ${h}
          result[1] = getChannel(getX(${u.join()}), ${c});
        }
      `;
    } else {
      let h = `
        ${r} source = rc;
        ${r} lt = ${r}(lessThan(source, start));
        ${r} gte = ${r}(greaterThanEqual(source, end));
        ${r} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${p}) +
                gte * ((end - 1) * 2 - source + ${p});
        source -= start;
      `;
      d = `
        ${r} rc = outputLoc;
        ${h}
        result[0] = getChannel(getX(${u.join()}), ${c});
        ${o[s - 1]} += 1;
        if(${l}) {
          ${h}
          result[1] = getChannel(getX(${u.join()}), ${c});
        }
        rc = outputLoc;
        ${o[s - 2]} += 1;
        if(${o[s - 2]} < ${this.outputShape[s - 2]}) {
          ${h}
          result[2] = getChannel(getX(${u.join()}), ${c});
          ${o[s - 1]} += 1;
          if(${l}) {
            ${h}
            result[3] = getChannel(getX(${u.join()}), ${c});
          }
        }
      `;
    }
    this.userCode = `
      const ${r} start = ${r}(${a});
      const ${r} end = ${r}(${i});

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `;
  }
};
var A7 = ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s } = e, { paddings: r, mode: a } = n, i = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new _7(s.shape, r, a) : new $7(s.shape, r, a);
  return t.runWebGLProgram(i, [s], s.dtype);
};
var E7 = { kernelName: Qa, backendName: "webgl", kernelFunc: A7 };
var R7 = `if (b == 0.0) return NAN;
  return mod(a, b);`;
var D7 = `
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  ` + Zp + `
  return result;
`;
var F7 = jt({ opSnippet: R7, packedOpSnippet: D7 });
var O7 = { kernelName: kl, backendName: "webgl", kernelFunc: F7 };
var P7 = class {
  constructor(e, t, n) {
    this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, n], this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

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

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

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

        // If no other event happened, last event happened.
        setOutput(float(${t - 1}));
      }
    `;
  }
};
var z7 = `
if (a == b) {
  return 1.0;
};
return a / b;`;
var M7 = `
  // vec4 one = vec4(equal(a, b));
  // return one + (vec4(1.0) - one) * a / b;
  vec4 result = a / b;
  if(a.x == b.x) {
    result.x = 1.;
  }
  if(a.y == b.y) {
    result.y = 1.;
  }
  if(a.z == b.z) {
    result.z = 1.;
  }
  if(a.w == b.w) {
    result.w = 1.;
  }

  return result;
`;
var y2 = jt({ opSnippet: z7, packedOpSnippet: M7, checkOutOfBounds: true });
var L7 = { kernelName: Oa, backendName: "webgl", kernelFunc: y2 };
var Tw = "return a - b;";
var v2 = jt({ opSnippet: Tw, packedOpSnippet: Tw, supportsComplex: true, cpuKernelImpl: OX });
var B7 = { kernelName: hi, backendName: "webgl", kernelFunc: v2 };
function x2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = b2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = C.expandShapeToKeepDim(o.shape, i), l = he({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = v2({ inputs: { a: r, b: l }, backend: n }), p = f2({ inputs: { x: c }, backend: n }), d = th({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = y2({ inputs: { a: p, b: h }, backend: n });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
}
var V7 = { kernelName: di, backendName: "webgl", kernelFunc: x2 };
function W7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : x2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new P7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
  return o || n.disposeIntermediateTensorInfo(u), h;
}
var U7 = { kernelName: _g, backendName: "webgl", kernelFunc: W7 };
var G7 = ss + `
  return -x;
`;
var H7 = `
  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 q7(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s])) {
    let a = n.texData.get(s.dataId), [i, o] = wX(a.values, s.shape, s.dtype);
    return n.makeTensorInfo(o, s.dtype, i);
  }
  let r;
  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Jr(s.shape, H7) : r = new Gs(s.shape, G7), n.runWebGLProgram(r, [s], s.dtype);
}
var j7 = { kernelName: To, backendName: "webgl", kernelFunc: q7 };
var K7 = ws.nonMaxSuppressionV3Impl;
function X7(e) {
  C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = K7(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var Y7 = { kernelName: _o, backendName: "webgl", kernelFunc: X7 };
var Q7 = ws.nonMaxSuppressionV4Impl;
function Z7(e) {
  C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = Q7(c, p, i, o, u, l);
  return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
}
var J7 = { kernelName: Sl, backendName: "webgl", kernelFunc: Z7 };
var eJ = ws.nonMaxSuppressionV5Impl;
function tJ(e) {
  C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = eJ(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var nJ = { kernelName: Ao, backendName: "webgl", kernelFunc: tJ };
var sJ = class {
  constructor(e, t, n, s) {
    this.variableNames = ["indices"], this.outputShape = [e, t], this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${s}), float(${n}),
                      float(index == coords.y)));
      }
    `;
  }
};
var rJ = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = w.sizeFromShape(r.shape), l = new sJ(u, a, i, o), c = he({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
  n.disposeIntermediateTensorInfo(c);
  let d = [...r.shape, a], h = he({ inputs: { x: p }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(p), h;
};
var aJ = { kernelName: Ro, backendName: "webgl", kernelFunc: rJ };
function Ud(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "complex64") {
    let r = ec({ inputs: { input: s }, backend: n }), a = Ud({ inputs: { x: r }, backend: n }), i = nh({ inputs: { input: s }, backend: n }), o = Ud({ inputs: { x: i }, backend: n }), u = Rr({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return tc({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
}
var iJ = { kernelName: Ko, backendName: "webgl", kernelFunc: Ud };
function w2(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "string")
    throw new Error("onesLike is not supported under string dtype");
  if (s.dtype === "complex64") {
    let r = ec({ inputs: { input: s }, backend: n }), a = w2({ inputs: { x: r }, backend: n }), i = nh({ inputs: { input: s }, backend: n }), o = Ud({ inputs: { x: i }, backend: n }), u = Rr({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return tc({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
}
var oJ = { kernelName: Eo, backendName: "webgl", kernelFunc: w2 };
function uJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Qm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
  let a = t[0].shape, i = t[0].dtype;
  t.forEach((c) => {
    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let o = [], u = t.map((c) => {
    let p = Qm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = i2({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
}
var lJ = { kernelName: Do, backendName: "webgl", kernelFunc: uJ };
var cJ = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((u, l) => u[0] + e[l] + u[1]);
    let s = e.length, r = ot(s), a = t.map((u) => u[0]).join(","), i = t.map((u, l) => u[0] + e[l]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s);
    if (s === 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 = `
      ${r} start = ${r}(${a});
      ${r} end = ${r}(${i});

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

      void main() {
        ${r} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${h}
        setOutput(result);
      }
    `;
  }
};
var k2 = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
  if (w.sizeFromShape(r.shape) === 0) {
    let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
    return tc({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
  }
  let o = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new dJ(r.shape, a, i) : new cJ(r.shape, a, i), u = [[i]];
  return n.runWebGLProgram(o, [r], r.dtype, u);
};
var pJ = { kernelName: Ja, backendName: "webgl", kernelFunc: k2 };
var hJ = `
  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 fJ = `
  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
  vec4 result = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  bvec4 isExpZero = equal(b, vec4(0.0));
  result.r = isExpZero.r ? 1.0 : result.r;
  result.g = isExpZero.g ? 1.0 : result.g;
  result.b = isExpZero.b ? 1.0 : result.b;
  result.a = isExpZero.a ? 1.0 : result.a;

  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
  ` + Zp + `
  return result;
`;
var mJ = jt({ opSnippet: hJ, packedOpSnippet: fJ });
var gJ = { kernelName: ei, backendName: "webgl", kernelFunc: mJ };
function bJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = [], l = w.parseAxisParam(a, r.shape), c = l, p = C.getAxesPermutation(c, o), d = r;
  p != null && (d = zt({ inputs: { x: r }, backend: n, attrs: { perm: p } }), c = C.getInnerMostAxes(c.length, o), u.push(d)), C.assertAxesAreInnerMostDims("prod", c, o);
  let h;
  if (n.shouldExecuteOnCPU([d])) {
    let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = SX(d.shape, d.dtype, f, c);
    h = n.makeTensorInfo(g, b, m);
  } else {
    let [f, m] = C.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = he({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = mp(r.dtype), v = Ii(b, y, "prod", n);
    h = he({ inputs: { x: v }, backend: n, attrs: { shape: f } }), u.push(b), u.push(v);
  }
  if (i) {
    u.push(h);
    let f = C.expandShapeToKeepDim(h.shape, l);
    h = he({ inputs: { x: h }, backend: n, attrs: { shape: f } });
  }
  return u.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
}
var yJ = { kernelName: ni, backendName: "webgl", kernelFunc: bJ };
var S2 = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = IX(s, r, a, i);
  return t.makeTensorInfo([o.length], i, o);
};
var vJ = { kernelName: Il, backendName: "webgl", kernelFunc: S2 };
var xJ = "return 1.0 / x;";
var wJ = Ke({ opSnippet: xJ });
var kJ = { kernelName: Cl, backendName: "webgl", kernelFunc: wJ };
var SJ = ss + `
  return (x < 0.0) ? 0.0 : x;
`;
var IJ = `
  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 CJ = Ke({ opSnippet: SJ, packedOpSnippet: IJ });
var NJ = { kernelName: si, backendName: "webgl", kernelFunc: CJ };
var TJ = ss + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var $J = `
  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 _J = Ke({ opSnippet: TJ, packedOpSnippet: $J });
var AJ = { kernelName: ai, backendName: "webgl", kernelFunc: _J };
var EJ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["A"], this.outputShape = [];
    let [a, i, o, u] = e;
    this.outputShape = [a, t, n, u];
    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p;
    r ? p = "(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)" : p = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${l[0] / c[0]},
          ${l[1] / c[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

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

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

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

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${p};

        // 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 < ${n - 1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `;
  }
};
function DJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = K().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new RJ(r.shape, u, l, a, i) : new EJ(r.shape, u, l, a, i);
  return n.runWebGLProgram(c, [r], "float32");
}
var FJ = { kernelName: ri, backendName: "webgl", kernelFunc: DJ };
var OJ = class {
  constructor(e, t, n) {
    this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
    let [, s, r] = t, [, a, i] = e, o = [n && a > 1 ? s - 1 : s, n && i > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && i > 1 ? i - 1 : i], l = o[0] / u[0], c = o[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
    this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

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

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

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

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${a}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

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

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${r - 1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `;
  }
};
function PJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new OJ(a.shape, r.shape, i);
  return n.runWebGLProgram(o, [a], a.dtype);
}
var zJ = { kernelName: Eg, backendName: "webgl", kernelFunc: PJ };
var MJ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["A"], this.outputShape = [];
    let [a, i, o, u] = e;
    this.outputShape = [a, t, n, u];
    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p = s ? "0.5" : "0.0", d;
    r ? d = "max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))" : d = "vec2(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${l[0] / c[0]},
          ${l[1] / c[1]});
      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);

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

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

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

        setOutput(newValue);
      }
    `;
  }
};
var LJ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
    let [a, i, o, u] = e;
    this.outputShape = [a, t, n, u];
    let l = [s && t > 1 ? i - 1 : i, s && n > 1 ? o - 1 : o], c = [s && t > 1 ? t - 1 : t, s && n > 1 ? n - 1 : n], p = s ? "0.5" : "0.0", d;
    r ? d = "max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))" : d = "vec3(yRC) * effectiveInputOverOutputRatioRC", this.userCode = `
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${l[0] / c[0]},
          ${l[1] / c[1]},
          ${l[1] / c[1]});
      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
                                     ${o}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${d};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${u - 1};
        bool hasNextRow = coords.z < ${n - 1};

        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);

        setOutput(newValue);
      }
    `;
  }
};
function BJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = K().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new LJ(r.shape, u, l, a, i) : new MJ(r.shape, u, l, a, i);
  return n.runWebGLProgram(c, [r], r.dtype);
}
var VJ = { kernelName: Nl, backendName: "webgl", kernelFunc: BJ };
var WJ = class {
  constructor(e, t, n) {
    this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
    let [, s, r] = t, [, a, i] = e, o = [n && a > 1 ? s - 1 : s, n && i > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && i > 1 ? i - 1 : i], l = o[0] / u[0], c = o[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
    this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

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

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

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

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${a}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${i}) {
              continue;
            }

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

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

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

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

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

        setOutput(accumulator);
      }
    `;
  }
};
function UJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new WJ(a.shape, r.shape, i);
  return n.runWebGLProgram(o, [a], a.dtype);
}
var GJ = { kernelName: Ag, backendName: "webgl", kernelFunc: UJ };
var HJ = class {
  constructor(e, t) {
    this.variableNames = ["x"];
    let n = e.length;
    if (n > 4)
      throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);
    if (this.outputShape = e, n === 1) {
      this.userCode = `
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${e[0]} - coord - 1));
        }
      `;
      return;
    }
    let s = (i) => t.indexOf(i) !== -1 && e[i] !== 1 ? `${e[i]} - coords[${i}] - 1` : `coords[${i}]`, r = e.map((i, o) => s(o)).join(","), a = ot(n);
    this.userCode = `
      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${r}));
      }
    `;
  }
};
var qJ = class {
  constructor(e, t) {
    this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true;
    let n = e.length;
    if (n > 4)
      throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);
    this.outputShape = e;
    let s = ln("rc", n), r = `${s[n - 1]} + 1 < ${this.outputShape[n - 1]}`, a = `${s[n - 2]} + 1 < ${this.outputShape[n - 2]}`, i = ot(n);
    n === 1 ? this.userCode = `
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${e[0]} - rc - 1),
            ${e[0]} - rc - 1);
          if(${r}){
              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
                ${e[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      ` : this.userCode = `
        void main() {
          ${i} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${o(s.slice())};
          if(${r}){
            result.g = ${u(s.slice())};
          }
          if(${a}) {
            result.b = ${l(s.slice())};
            if(${r}) {
              result.a = ${c(s.slice())};
            }
          }
          setOutput(result);
        }
    `;
    function o(h) {
      return p(h);
    }
    function u(h) {
      return h[n - 1] = "(" + h[n - 1] + " + 1)", p(h);
    }
    function l(h) {
      return h[n - 2] = "(" + h[n - 2] + " + 1)", p(h);
    }
    function c(h) {
      return h[n - 1] = "(" + h[n - 1] + " + 1)", h[n - 2] = "(" + h[n - 2] + " + 1)", p(h);
    }
    function p(h) {
      let f = e.map((b, y) => d(y, h)), m = f.join(","), g = f.slice(-2).join(",");
      return `getChannel(getX(${m}), vec2(${g}))`;
    }
    function d(h, f) {
      return t.indexOf(h) !== -1 && e[h] !== 1 ? `${e[h]} - ${f[h]} - 1` : `${f[h]}`;
    }
  }
};
function jJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = r.shape.length, o = w.parseAxisParam(a, r.shape);
  if (i === 0)
    return Rn({ inputs: { x: r }, backend: n });
  let u = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new qJ(r.shape, o) : new HJ(r.shape, o);
  return n.runWebGLProgram(u, [r], r.dtype);
}
var KJ = { kernelName: Oo, backendName: "webgl", kernelFunc: jJ };
var XJ = class {
  constructor(e, t) {
    this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
    let n = e[1], s = e[2];
    this.outputShape = e;
    let r = "";
    typeof t == "number" ? r = `float outputValue = ${t.toFixed(2)};` : r = `
        vec3 fill = vec3(${t.join(",")});
        float outputValue = fill[coords[3]];`, this.userCode = `
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${r}
          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var YJ = { kernelName: Xo, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new XJ(s.shape, a), [l, c] = C.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
  return o.runWebGLProgram(u, [s], s.dtype, p);
} };
var QJ = `
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`;
var ZJ = Ke({ opSnippet: QJ });
var JJ = { kernelName: Po, backendName: "webgl", kernelFunc: ZJ };
var eee = "return inversesqrt(x);";
var tee = Ke({ opSnippet: eee, cpuKernelImpl: CX });
var nee = { kernelName: ii, backendName: "webgl", kernelFunc: tee };
var I2 = class {
  constructor(e, t, n, s, r, a, i = true) {
    this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
    let o = ot(r.length), u = ot(a.length), l = "";
    n === 1 ? l = "i" : n === 2 && (l = "i, j");
    let c = `getIndices(${l})`, p = "";
    s === 1 ? p = "i" : s === 2 && (p = "i, coords[1]");
    let d = `getUpdates(${p})`, h = t > 1 ? "strides[j]" : "strides";
    this.userCode = `
        ${o} strides = ${o}(${r});

        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 < ${t}; j++) {
              int index = round(${c});
              flattenedIndex += index * ${h};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(getDefaultValue(), sum, float(found)));
        }
      `;
  }
};
function see(e) {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, i), d = [p / l, l];
  if (p === 0)
    return n.makeTensorInfo(i, r.dtype);
  let h = he({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = he({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new I2(u, o, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = he({ inputs: { x: b }, backend: n, attrs: { shape: i } });
  return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(m), y;
}
var ree = { kernelName: zo, backendName: "webgl", kernelFunc: see };
var aee = class {
  constructor(e, t, n, s) {
    this.variableNames = ["sortedSequence", "values"], this.customUniforms = [{ name: "numInputs", type: "int" }], this.outputShape = [e, n];
    let r = "while (left < right) {", a = `for (int i = 0; i < ${Math.ceil(Math.log2(t + 1))}; ++i) { if (left >= right) break;`, i = K().getNumber("WEBGL_VERSION") === 2 ? r : a, o = s === "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) ${o} 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 iee(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: i } = s, o = new aee(r.shape[0], r.shape[1], a.shape[1], i), u = [[r.shape[1]]];
  return n.runWebGLProgram(o, [r, a], "int32", u);
}
var oee = { kernelName: Rg, backendName: "webgl", kernelFunc: iee };
var uee = class {
  constructor(e, t, n) {
    this.variableNames = ["c", "a", "b"], this.outputShape = t;
    let s, r;
    if (n > 4)
      throw Error(`Where for rank ${n} is not yet supported`);
    if (n === 1)
      r = "resRC", s = "resRC";
    else {
      let i = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], o = [], u = [];
      for (let l = 0; l < t.length; l++)
        u.push(`${i[l]}`), l < e && o.push(`${i[l]}`);
      s = o.join(), r = u.join();
    }
    let a = ot(n);
    this.userCode = `
      void main() {
        ${a} resRC = getOutputCoords();
        float cVal = getC(${s});
        if (cVal >= 1.0) {
          setOutput(getA(${r}));
        } else {
          setOutput(getB(${r}));
        }
      }
    `;
  }
};
function lee(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new uee(s.shape.length, r.shape, r.shape.length);
  return n.runWebGLProgram(i, [s, r, a], cn(r.dtype, a.dtype));
}
var cee = { kernelName: Mo, backendName: "webgl", kernelFunc: lee };
var dee = `
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${C.SELU_SCALEALPHA};
  float scale = ${C.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var pee = Ke({ opSnippet: dee });
var hee = { kernelName: Tl, backendName: "webgl", kernelFunc: pee };
var fee = lu + `
  return 1.0 / (1.0 + exp(-1.0 * x));
`;
var mee = `
  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 gee = Ke({ opSnippet: fee, packedOpSnippet: mee, cpuKernelImpl: NX });
var bee = { kernelName: ui, backendName: "webgl", kernelFunc: gee };
var yee = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
var vee = Ke({ opSnippet: yee });
var xee = { kernelName: $l, backendName: "webgl", kernelFunc: vee };
var wee = lu + `
  return sin(x);
`;
var kee = Ke({ opSnippet: wee });
var See = { kernelName: oi, backendName: "webgl", kernelFunc: kee };
var Iee = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var Cee = Ke({ opSnippet: Iee });
var Nee = { kernelName: Bo, backendName: "webgl", kernelFunc: Cee };
var Tee = `
  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 $ee = Ke({ opSnippet: Tee });
var _ee = { kernelName: _l, backendName: "webgl", kernelFunc: $ee };
var Aee = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
  w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
  let o = a.reduce((b, y) => b * y), u = [[0, 0]];
  u.push(...i);
  for (let b = 1 + a.length; b < r.shape.length; ++b)
    u.push([0, 0]);
  let l = [], c = k2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = C.getReshaped(c.shape, a, o, false), d = C.getPermuted(p.length, a.length, false), h = C.getReshapedPermuted(c.shape, a, o, false), f = he({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = zt({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: h } });
  return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
};
var Eee = { kernelName: Vo, backendName: "webgl", kernelFunc: Aee };
function Ree(e) {
  let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
  if (a.shape.length !== 1)
    throw new Error(`Dense shape must be a vector, saw:
         ${a.shape}`);
  if (s.shape.length !== 2)
    throw new Error(`Indices must be a matrix, saw:
         ${s.shape}`);
  if (r.shape.length !== 1)
    throw new Error(`Values must be a vector, saw:
         ${r.shape}`);
  if (i.shape.length !== 0)
    throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);
  let o = n.readSync(s.dataId), u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = n.readSync(i.dataId)[0], [p, d, h, f, m] = $X(o, s.shape, s.dtype, u, r.dtype, l, c);
  return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
}
var Dee = { kernelName: up, backendName: "webgl", kernelFunc: Ree };
function Fee(e) {
  let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
  if (s.shape.length !== 2)
    throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);
  if (r.shape.length !== 1)
    throw new Error(`Input shape should be a vector but received shape ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
  let i = Array.from(n.readSync(r.dataId)), o = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = _X(o, s.shape, s.dtype, i, u);
  return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
}
var Oee = { kernelName: Al, backendName: "webgl", kernelFunc: Fee };
function Pee(e) {
  let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
  if (s.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);
  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = G1(i, s.shape, s.dtype, o, u, true);
  return n.makeTensorInfo(c, s.dtype, l);
}
var zee = { kernelName: lp, backendName: "webgl", kernelFunc: Pee };
function Mee(e) {
  let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
  if (s.shape.length < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.shape.length !== 1)
    throw new Error(`Indices should be a vector but received shape
             ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Segment ids should be a vector but received shape
             ${a.shape}`);
  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = G1(i, s.shape, s.dtype, o, u);
  return n.makeTensorInfo(c, s.dtype, l);
}
var Lee = { kernelName: cp, backendName: "webgl", kernelFunc: Mee };
function Bee(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = C.calculateShapes(a, r, o), d = false, h = new I2(l, u, r.shape.length, a.shape.length, c, [p, 1], d), f = n.runWebGLProgram(h, [a, r, i], a.dtype), m = he({ inputs: { x: f }, backend: n, attrs: { shape: o } });
  return n.disposeIntermediateTensorInfo(f), m;
}
var Vee = { kernelName: dp, backendName: "webgl", kernelFunc: Bee };
function Wee(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
  return u.map((d) => {
    let h = [...p];
    h[o] = d;
    let f = cu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
    return c[o] += d, f;
  });
}
var Uee = { kernelName: Wo, backendName: "webgl", kernelFunc: Wee };
var $w = "return sqrt(x);";
var Gee = Ke({ opSnippet: $w, packedOpSnippet: $w, cpuKernelImpl: AX });
var Hee = { kernelName: li, backendName: "webgl", kernelFunc: Gee };
var qee = "return x * x;";
var jee = Ke({ opSnippet: qee });
var Kee = { kernelName: El, backendName: "webgl", kernelFunc: jee };
var _w = "return (a - b) * (a - b);";
var Xee = jt({ opSnippet: _w, packedOpSnippet: _w });
var Yee = { kernelName: pi, backendName: "webgl", kernelFunc: Xee };
function Qee({ inputs: e, attrs: t, backend: n }) {
  let { x: s } = e, r = ss + `
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `, a = new Gs(s.shape, r);
  return n.runWebGLProgram(a, [s], s.dtype);
}
var Zee = { kernelName: gi, backendName: "webgl", kernelFunc: Qee };
var Jee = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.outputShape = n;
    let s = n.length, r = ot(n.length), a = ot(n.length), i = "";
    if (s === 1)
      i = "coords * strides + begin";
    else {
      let o = 0;
      i = n.map((u, l) => (o++, n.length === 1 ? `coords * strides[${l}] + begin[${l}]` : `coords[${o - 1}] * strides[${l}] + begin[${l}]`)).join(",");
    }
    this.userCode = `
      ${r} begin = ${r}(${e});
      ${r} strides = ${r}(${t});

      void main() {
        ${a} coords = getOutputCoords();
        setOutput(getX(${i}));
      }
    `;
  }
};
function ete(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = he({ inputs: { x: r }, backend: n, attrs: { shape: f } });
  else if (g || b) {
    w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
    let $ = wt.computeOutShape(y, v, x), E = cu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: $ } });
    k = he({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(E);
  } else if (n.shouldExecuteOnCPU([r])) {
    let E = n.readSync(r.dataId), A = De(r.shape, r.dtype, E), P = EX(h, A, x, y);
    k = n.makeTensorInfo(f, r.dtype, P.values);
  } else {
    let E = new Jee(y, x, h);
    k = n.runWebGLProgram(E, [r], r.dtype);
  }
  let I = he({ inputs: { x: k }, backend: n, attrs: { shape: f } });
  return n.disposeIntermediateTensorInfo(k), I;
}
var tte = { kernelName: Uo, backendName: "webgl", kernelFunc: ete };
function nte(e) {
  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = RX(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var ste = { kernelName: pp, backendName: "webgl", kernelFunc: nte };
function rte(e) {
  let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
  if (a.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (a.shape.length !== 1)
    throw new Error(`Input must be a vector, got shape: ${a.shape}`);
  if (i.shape.length !== 0)
    throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);
  let o = n.readSync(a.dataId), u = n.readSync(i.dataId)[0], [l, c, p] = DX(o, u, r), d = c.length;
  return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
}
var ate = { kernelName: Dg, backendName: "webgl", kernelFunc: rte };
function ite(e) {
  let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
  if (a.dtype !== "string")
    throw new Error("Input must be of datatype string");
  if (r <= 0)
    throw new Error("Number of buckets must be at least 1");
  let i = n.readSync(a.dataId), o = FX(i, r);
  return n.makeTensorInfo(a.shape, "int32", o);
}
var ote = { kernelName: Fg, backendName: "webgl", kernelFunc: ite };
var ute = "return tan(x);";
var lte = Ke({ opSnippet: ute });
var cte = { kernelName: Go, backendName: "webgl", kernelFunc: lte };
var dte = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var pte = Ke({ opSnippet: dte });
var hte = { kernelName: fi, backendName: "webgl", kernelFunc: pte };
var fte = class {
  constructor(e, t) {
    this.variableNames = ["A"];
    let n = new Array(e.length);
    for (let a = 0; a < n.length; a++)
      n[a] = e[a] * t[a];
    this.outputShape = n, this.rank = n.length;
    let s = ot(this.rank), r = mte(e);
    this.userCode = `
      void main() {
        ${s} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `;
  }
};
function mte(e) {
  let t = e.length;
  if (t > 5)
    throw Error(`Tile for rank ${t} is not yet supported`);
  if (t === 1)
    return `imod(resRC, ${e[0]})`;
  let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"], s = [];
  for (let r = 0; r < e.length; r++)
    s.push(`imod(${n[r]}, ${e[r]})`);
  return s.join();
}
function C2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  if (r.dtype === "string" || r.shape.length > 5) {
    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = PX(c, a);
    return n.makeTensorInfo(p.shape, p.dtype, p.values);
  }
  let i = new fte(r.shape, a);
  return n.runWebGLProgram(i, [r], r.dtype);
}
var gte = { kernelName: Cr, backendName: "webgl", kernelFunc: C2 };
var bte = 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 yte = 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 Gr(e, t) {
  t !== null && e.disposeIntermediateTensorInfo(t);
}
function Aw(e) {
  let t = 1;
  for (; t < e; )
    t *= 2;
  return t;
}
function vte(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), u = K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), l = r.shape, c = l[l.length - 1];
  if (n.shouldExecuteOnCPU([r]) || c < o || a > u) {
    let P = n.readSync(r.dataId), [R, F] = zX(P, l, r.dtype, a, i);
    return [n.makeTensorInfo(R.shape, R.dtype, R.values), n.makeTensorInfo(F.shape, F.dtype, F.values)];
  }
  if (a === 0)
    return l[l.length - 1] = 0, [n.makeTensorInfo(l, r.dtype, []), n.makeTensorInfo(l, "int32", [])];
  if (c === 1)
    return [r, tc({ attrs: { shape: l, dtype: "int32", value: 0 }, backend: n })];
  let p = n.texData.get(r.dataId), d = p !== null && p.isPacked, h = d ? n.unpackTensor(r) : r, m = w.sizeFromShape(l) / c, g = he({ inputs: { x: h }, attrs: { shape: [m, c] }, backend: n });
  d && Gr(n, h);
  let b = Aw(a), y = Aw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, R, F) => {
    let T = x(), z = new bte(F), j = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [R]], X = v;
    v = n.runWebGLProgram(z, T, "int32", j), Gr(n, X);
  };
  for (let P = 1; P < b; P *= 2) {
    let R = P * 2;
    for (let F = P; F >= 1; F /= 2)
      k(R, F, [m, y]);
  }
  for (let P = y; P > b; P /= 2) {
    let R = x(), F = new yte([m, P / 2]), z = [[c], [v === null ? 1 : 0], [b]], W = v;
    v = n.runWebGLProgram(F, R, "int32", z), Gr(n, W);
    let j = b / 2, X = j * 2;
    for (let Y = j; Y >= 1; Y /= 2)
      k(X, Y, v.shape);
  }
  let I = v;
  v = cu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Gr(n, I);
  let $ = g2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
  Gr(n, g);
  let E = l.slice(0, -1);
  E.push(a), I = v, v = he({ inputs: { x: v }, attrs: { shape: E }, backend: n }), Gr(n, I);
  let A = $;
  return $ = he({ inputs: { x: $ }, attrs: { shape: E }, backend: n }), Gr(n, A), [$, v];
}
var xte = { kernelName: Ho, backendName: "webgl", kernelFunc: vte };
var wte = class {
  constructor(e, t, n, s, r, a) {
    this.variableNames = ["Image", "Transforms"], this.outputShape = a;
    let i = n === "nearest" ? 1 : 2, o;
    switch (s) {
      case "constant":
        o = 1;
        break;
      case "reflect":
        o = 2;
        break;
      case "wrap":
        o = 3;
        break;
      case "nearest":
        o = 4;
        break;
      default:
        o = 1;
        break;
    }
    this.userCode = `
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${o} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${o} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${r});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${r});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${t}));
                float mapY = mapCoord(inY, float(${e}));

                if (${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 kte(e) {
  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new wte(p, d, i, o, u, g);
  return n.runWebGLProgram(b, [r, a], "float32");
}
var Ste = { kernelName: qo, backendName: "webgl", kernelFunc: kte };
function Ite(e) {
  let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
  ru(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
  let i = s.readSync(a.dataId), { outputValues: o, outputShape: u, indices: l } = MX(i, r, a.shape, a.dtype);
  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
}
var Cte = { kernelName: Og, backendName: "webgl", kernelFunc: Ite };
function Nte(e) {
  let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
  a < 0 && (a += r.shape.length);
  let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
  for (let m = 0; m < o; m++)
    m !== a && (l[c++] = i.shape[m]);
  let p = [], d = new Array(o).fill(0), h = i.shape.slice();
  h[a] = 1;
  let f = new Array(u);
  for (let m = 0; m < f.length; m++) {
    d[a] = m;
    let g = cu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
    f[m] = b, p.push(g);
  }
  return p.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
}
var Tte = { kernelName: jo, backendName: "webgl", kernelFunc: Nte };
var $te = class {
  constructor(e, t) {
    this.variableNames = ["x", "segmentIds"];
    let n = e.windowSize, s = e.batchSize, r = e.inSize, a = e.numSegments, i = a * Math.ceil(r / n);
    this.outputShape = [s, i];
    let o = "0.0", u = "sumValue", l = Math.floor(n / 4) * 4, c = n % 4, p = `
        sumValue += dot(values, segFilter);
    `, d = "";
    r % n > 0 && (d = `
        if (inIdx < 0 || inIdx >= ${r}) {
          return initializationValue;
        }
      `);
    let h = "";
    r % n > 0 && (h = `
        if (inIdx < 0 || inIdx >= ${r}) {
          return -1.0;
        }
      `), this.userCode = `
      const float initializationValue = ${o};

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

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

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

        float sumValue = 0.0;

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

          ${p}
        }

        int inIdx = inOffset + ${l};
        if (${c === 1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

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

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

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

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${p}
        }
        setOutput(${u});
      }
    `;
  }
};
function _te(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s, o = r.shape.length, u = [], l = 0, c = C.getAxesPermutation([l], o), p = r;
  c != null && (p = zt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), u.push(p), l = C.getInnerMostAxes(1, o)[0]);
  let d = C.segment_util.computeOutShape(p.shape, l, i), h = w.sizeFromShape([p.shape[l]]), f = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, h] } });
  u.push(f);
  let m = mp(r.dtype), g = (x, k, I, $, E) => {
    let A = x.shape[0], P = x.shape[1], R = C.segment_util.segOpComputeOptimalWindowSize(P, E), F = { windowSize: R, inSize: P, batchSize: A, numSegments: E }, T = new $te(F, k), z = n.compileAndRun(T, [x, I], $);
    if (u.push(z), z.shape[1] === E)
      return z;
    let W = S2({ backend: n, attrs: { start: 0, stop: E, step: 1, dtype: "float32" } }), j = C2({ inputs: { x: W }, backend: n, attrs: { reps: [P / R] } });
    return u.push(W), u.push(j), g(z, k, j, $, E);
  }, b = g(f, "unsortedSegmentSum", a, m, i), y = he({ inputs: { x: b }, backend: n, attrs: { shape: d } }), v = y;
  if (c != null) {
    u.push(y);
    let x = C.getUndoAxesPermutation(c);
    v = zt({ inputs: { x: v }, backend: n, attrs: { perm: x } });
  }
  return u.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
}
var Ate = { kernelName: hp, backendName: "webgl", kernelFunc: _te };
var Ete = [R8, F8, z8, B8, W8, H8, j8, X8, J8, tY, rY, oY, cY, fY, bY, vY, wY, CY, TY, _Y, DY, BY, WY, GY, YY, ZY, n9, h8, a9, c9, f9, x9, k9, I9, N9, $9, E9, F9, z9, L9, V9, U9, q9, K9, Z9, eQ, sQ, iQ, uQ, pQ, gQ, xQ, SQ, NQ, TQ, _Q, EQ, DQ, OQ, zQ, VQ, GQ, jQ, XQ, ZQ, tZ, aZ, lZ, p8, dZ, u9, fZ, bZ, xZ, m8, IZ, $Z, AZ, FZ, zZ, VZ, GZ, KZ, ZZ, t7, s7, o7, l7, d7, m7, b7, v7, w7, S7, T7, E7, O7, U7, x8, j7, Y7, J7, nJ, qY, aJ, oJ, lJ, pJ, gJ, b8, yJ, vJ, jY, L7, kJ, NJ, AJ, k8, FJ, zJ, VJ, GJ, KJ, YJ, JJ, nee, ree, oee, cee, hee, bee, xee, See, Nee, MY, V7, _ee, Eee, Dee, Oee, zee, Lee, Vee, Uee, Hee, Kee, Yee, Zee, tte, ste, ate, ote, B7, _8, cte, hte, gte, xte, Ste, A8, Cte, Tte, Ate, iJ];
for (let e of Ete)
  Rl(e);
var Dr = K();
Dr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
Dr.registerFlag("WEBGPU_CPU_FORWARD", () => true);
Dr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
Dr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
Dr.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
Dr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
Dr.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
Dr.registerFlag("WEBGPU_USE_IMPORT", () => false);
var Rte = "return a + b;";
var Dte = "return areal * breal - aimag * bimag;";
var Fte = "return areal * bimag + aimag * breal;";
var Ote = "return a / b;";
var Pte = "return a * b;";
var zte = "return (a - b) * (a - b);";
var Mte = "return a - b;";
var Lte = "return f32(a == b);";
var Bte = "return vec4<f32>(a == b);";
var Vte = "return f32(a > b);";
var Wte = "return vec4<f32>(a > b);";
var Ute = "return f32(a >= b);";
var Gte = "return vec4<f32>(a >= b);";
var Hte = "return f32(a < b);";
var qte = "return vec4<f32>(a < b);";
var jte = "return f32(a <= b);";
var Kte = "return vec4<f32>(a <= b);";
var Xte = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
var Yte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`;
var Qte = `
  if (isnan(a)) { return a; }
  if (isnan(b)) { return b; }
  `;
var N2 = `
  if (isNaN.r) {
    resultTemp.r = uniforms.NAN;
  }
  if (isNaN.g) {
    resultTemp.g = uniforms.NAN;
  }
  if (isNaN.b) {
    resultTemp.b = uniforms.NAN;
  }
  if (isNaN.a) {
    resultTemp.a = uniforms.NAN;
  }
  `;
var Zte = `
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
  `;
var Jte = `
  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 ene = "return f32(a != b);";
var tne = "return vec4<f32>(a != b);";
var nne = `
  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 sne = `
  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;
  ${N2}
  return resultTemp;
  `;
var rne = "if (a < 0.0) { return b * a; }  return a;";
var ane = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;
function Ew(e, t) {
  let n = t ? N2 : Qte;
  return t ? `
    var resultTemp = vec4<f32>(${e}(a, b));
    let isNaN = isnanVec4(a) | isnanVec4(b);
    ` + n + `
    return resultTemp;
  ` : n + `
    return ${e}(a, b);
  `;
}
function nc(e, t) {
  switch (e) {
    case 0:
      return Pte;
    case 1:
      return Rte;
    case 2:
      return Mte;
    case 3:
      return Ote;
    case 4:
      return t ? Bte : Lte;
    case 5:
      return t ? Wte : Vte;
    case 6:
      return t ? Gte : Ute;
    case 7:
      return t ? qte : Hte;
    case 8:
      return t ? Kte : jte;
    case 9:
      return t ? Yte : Xte;
    case 10:
      return t ? tne : ene;
    case 11:
      return zte;
    case 12:
      return t ? Jte : Zte;
    case 14:
      return t ? ane : rne;
    case 15:
      return Ew("max", t);
    case 16:
      return Ew("min", t);
    case 13:
      return t ? sne : nne;
    case 17:
      return Dte;
    case 18:
      return Fte;
    default:
      throw new Error(`BinaryType ${e} is not implemented!`);
  }
}
var ine = "return abs(a);";
var one = "return ceil(a);";
var une = "return cos(a);";
var lne = `
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var cne = "return exp(a) - 1.0;";
var dne = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
var pne = `
  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 hne = "return exp(a);";
var fne = "return floor(a);";
var mne = "return a;";
var gne = `if (a < 0.0) { return 1.0/0.0; }
  return log(a);`;
var bne = "return f32(!(a >= 1.0));";
var yne = "return -a;";
var vne = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var xne = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var wne = "return select(a, 0.0, a < 0.0);";
var kne = "return clamp(a, 0.0, 6.0);";
var Sne = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var Ine = `
  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`;
var Cne = "return 1.0/sqrt(a);";
var Nne = "return 1.0 / (1.0 + exp(-1.0 * a));";
var Tne = "return sin(a);";
var $ne = `
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var _ne = "return sqrt(a);";
var Ane = "return a * a;";
var Ene = `
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var Rne = "return f32(i32((a)));";
function qr(e, t) {
  switch (e) {
    case 0:
      return ine;
    case 2:
      return une;
    case 3:
      return lne;
    case 1:
      return one;
    case 4:
      return t ? pne : dne;
    case 5:
      return hne;
    case 6:
      return cne;
    case 7:
      return fne;
    case 8:
      return mne;
    case 9:
      return gne;
    case 10:
      return bne;
    case 11:
      return yne;
    case 14:
      return t ? xne : vne;
    case 12:
      return t ? Ine : wne;
    case 13:
      return t ? Sne : kne;
    case 15:
      return Cne;
    case 18:
      return Nne;
    case 16:
      return Tne;
    case 17:
      return $ne;
    case 19:
      return _ne;
    case 20:
      return Ane;
    case 21:
      return Ene;
    case 22:
      return Rne;
    default:
      throw new Error(`BinaryType ${e} is not implemented!`);
  }
}
function Fr(e, t = false) {
  if (e === null)
    return null;
  if (e === "linear")
    return qr(8);
  if (e === "relu")
    return qr(12, t);
  if (e === "elu")
    return qr(4, t);
  if (e === "relu6")
    return qr(13, t);
  if (e === "prelu")
    return nc(14, t);
  if (e === "sigmoid")
    return qr(18, t);
  if (e === "leakyrelu")
    return qr(14, t);
  throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
}
function Dne(e, t) {
  if (Math.max(...e) > 3)
    throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
  let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
  r[n - 2] = s[n - 1];
  for (let a = n - 3; a >= 0; --a)
    r[a] = `(${r[a + 1]} * ${s[a + 1]})`;
  return r;
}
function Ut(e) {
  if (e <= 1)
    return "i32";
  if (e === 2)
    return "vec2<i32>";
  if (e === 3)
    return "vec3<i32>";
  if (e === 4)
    return "vec4<i32>";
  if (e === 5)
    return "vec5";
  if (e === 6)
    return "vec6";
  throw Error(`GPU for rank ${e} is not yet supported`);
}
function pr(e) {
  if (e === 0)
    return "x";
  if (e === 1)
    return "y";
  if (e === 2)
    return "z";
  if (e === 3)
    return "w";
  if (e === 4)
    return "u";
  if (e === 5)
    return "v";
  throw Error(`Index ${e} is not yet supported`);
}
function pd(e, t) {
  return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
}
function _v() {
  return `
  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`;
}
function Ci() {
  return `
  ${_v()}
  fn main(@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;
`;
}
function Ue() {
  return `
    ${Ci()}
      let index = getGlobalIndex();
`;
}
function Fne(e, t, n, s = false) {
  let r = [];
  if (r.push(`
    let workGroupSizeX = ${n.workGroupSize[0]}u;
    let workGroupSizeY = ${n.workGroupSize[1]}u;
    let workGroupSizeZ = ${n.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 {
      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
        return i32(globalId.x);
      }

      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
          localId.y * workGroupSizeX + localId.x;
      let workGroupID = (globalId - localId)/vec3<u32>(
          workGroupSizeX, workGroupSizeY, workGroupSizeZ);

      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
        workGroupID.y * numWorkgroups.x + workGroupID.x) *
        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
        localInvocationIndex);
    }
  `), s === true)
    return r.push(`
      struct Uniform {
        size            : i32,
        numChannels     : i32,
        outShapeStrides : vec2<i32>,
        dispatchSize    : vec3<u32>,
      };

      @group(0) @binding(0) var<storage, write> result: array<${pd(t.dtype, n.isVec4)}>;
      @group(0) @binding(2) var<uniform> uniforms: Uniform;
    `), [Rw, r.join(`
`), Dw(t.shape), n.getUserCode()].join(`
`);
  let a = false, i = false, o = "struct Uniforms { NAN : f32, ";
  n.variableNames.forEach((m, g) => {
    let b = Ut(e[g].shape.length);
    (b === "vec5" || b === "vec6") && (i = true), (a || i) && (o += "@align(16) "), a = i, o += `${m.charAt(0).toLowerCase() + m.slice(1)}Shape : ${b}, `;
  });
  let u = Ut(t.shape.length);
  i = u === "vec5" || u === "vec6", (a || i) && (o += "@align(16) "), a = i, o += `outShape : ${u}, `;
  let l = t.shape.length - 1, c = Ut(l);
  i = c === "vec5" || c === "vec6", (a || i) && (o += "@align(16) "), a = i, o += `
       outShapeStrides: ${c}, `, n.size && (a && (o += "@align(16) "), a = false, o += "size : i32, "), n.uniforms && (a && (o += "@align(16) "), o += n.uniforms), o += "};", r.push(o), n.atomic ? r.push(`
    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
  `) : r.push(`
    @group(0) @binding(0) var<storage, write> result: array<${pd(t.dtype, n.isVec4)}>;
  `), n.variableNames.forEach((m, g) => {
    r.push(`
    @group(0) @binding(${1 + g}) var<storage, read> ${m}: array<${pd(e[g].dtype, n.isVec4)}>;
    `);
  }), o !== "" && r.push(`
    @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms: Uniforms;
    `);
  let [p, d] = Bne(t.shape, n.dispatchLayout), h = [Rw, r.join(`
`), Dw(t.shape), p, One(t.shape.length)];
  if (n.atomic || h.push(Pne(t.shape, t.dtype, n.isVec4)), d === t.shape.length) {
    let m = e.map((g) => zne(g, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
`);
    h.push(m);
  }
  return h.push(n.getUserCode()), h.join(`
`);
}
var Rw = `
  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 mod: i32 = a % b;
    if (sign < 0. && mod != 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 One(e) {
  let t = "";
  switch (e) {
    case 0:
    case 1:
      t += `
        fn getOutputIndexFromCoords(coords : i32) -> i32 {
          return coords;
        }
        `;
      break;
    case 2:
      t += `
        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
        }
        `;
      break;
    case 3:
      t += `
        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
        }
        `;
      break;
    case 4:
      t += `
        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
          return dot(coords, vec4<i32>(
            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
        }
        `;
      break;
    case 5:
      t += `
        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:
      t += `
        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:
      w.assert(false, () => `Unsupported ${e}D shape`);
      break;
  }
  return t;
}
function Pne(e, t, n) {
  let s = e.length, r = pd(t, n), a;
  if (n ? a = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
      result[flatIndex] = ${r}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
      result[flatIndex] = ${r}(value);
    }` : a = `fn setOutputAtIndex(flatIndex : i32, value : f32) {
      result[flatIndex] = ${r}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
      result[flatIndex] = ${r}(value);
    }`, s >= 2) {
    let i = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), o = Ut(s);
    n ? a += `
      fn setOutputAtCoords(${i.map((u) => `${u} : i32`).join(", ")}, value : vec4<f32>) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndex(flatIndex / 4, value);
      }
      fn setOutputAtCoordsI32(${i.map((u) => `${u} : i32`).join(", ")}, value : vec4<i32>) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex / 4, value);
      }
    ` : a += `
      fn setOutputAtCoords(${i.map((u) => `${u} : i32`).join(", ")}, value : f32) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndex(flatIndex, value);
      }
      fn setOutputAtCoordsI32(${i.map((u) => `${u} : i32`).join(", ")}, value : i32) {
        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `;
  }
  return a;
}
function zne(e, t, n, s) {
  let r = Mne(e, n);
  return e.shape.length <= t.length && (r += Lne(e, t, n, s)), r;
}
function Mne(e, t) {
  let n = e.name, s = e.shape.length, r = Ut(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), i = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), o = i.map((c) => `${c} : i32`).join(", ");
  if (s < 1)
    return t ? `
        fn ${a}() -> vec4<f32> {
          return vec4<f32>(${n}[0]);
        }
      ` : `
      fn ${a}() ->f32 {
        return f32(${n}[0]);
      }
    `;
  let u = `uniforms.${n.charAt(0).toLowerCase() + n.slice(1)}Shape`, l = `${s}D`;
  return s === 0 && (l = "1D"), t ? `
      fn ${a}(${o}) -> vec4<f32> {
        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
          ${u}) / 4]);
      }
      ` : `
    fn ${a}(${o}) -> f32 {
      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
        ${u})]);
    }
   `;
}
function Lne(e, t, n, s) {
  let r = e.name, a = r.charAt(0).toUpperCase() + r.slice(1), i = "get" + a + "ByOutput", o = e.shape.length, u = t.length, l = Ut(u);
  if (w.arraysEqual(e.shape, t) && s)
    return n ? `
        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
          return vec4<f32>(${r}[globalIndex]);
        }

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

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

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

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

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

    fn ${i}Coords(coordsIn : ${l}) -> f32 {
      var coords = coordsIn;
      ${d}
      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
    }
  `;
}
function Bne(e, t) {
  let { x: n, y: s = [], z: r = [] } = t, a = e.length;
  if (n.length === a)
    return [`fn getOutputCoords() -> ${Ut(a)}{
      let globalIndex = getGlobalIndex();
      return getCoordsFromIndex(globalIndex);
    }
    `, a];
  let i = "", o = [n, s, r], u = 0;
  for (let d = 0; d < o.length; d++) {
    let h = o[d];
    if (h.length !== 0)
      if (u += h.length, h.length === 1)
        i += `let d${h[0]} = i32(globalId[${d}]);`;
      else {
        let f = Dne(h, "uniforms.outShape");
        i += `var index${d} = i32(globalId[${d}]);`;
        for (let m = 0; m < f.length; m++)
          i += `let d${h[m]} = index${d} / ${f[m]};`, m === f.length - 1 ? i += `let d${h[m + 1]} = index${d} - d${h[m]} * ${f[m]};` : i += `index${d} = index${d} - d${h[m]} * ${f[m]};`;
      }
  }
  let l = [];
  for (let d = 0; d < u; d++)
    l.push(`d${d}`);
  let c = Ut(u), p = `fn getOutputCoords() -> ${c} {
    ${i}
  `;
  return l.length === 0 ? p += `return ${c}(0); }` : p += `return ${c}(${l.join(",")}); }`, [p, u];
}
function Dw(e) {
  let t = e.length;
  if (t <= 1)
    return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
  let n = w.computeStrides(e), s = Ut(t), r = [];
  for (let i = 0; i < t; i++)
    r.push(`d${i}`);
  if (n.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 a;
  return a = "var index2 = index;" + n.map((i, o) => {
    let u = `let ${r[o]} = index2 / uniforms.outShapeStrides.${pr(o)}`, l = o === n.length - 1 ? `let ${r[o + 1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}` : `index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${pr(o)}`;
    return `${u}; ${l};`;
  }).join(""), `
    fn getCoordsFromIndex(index : i32) -> ${s} {
      ${a}
      return ${s}(${r.join(",")});
    }
  `;
}
var T2 = {};
Ae(T2, { ArrayBufferToTypedArray: () => _2, GPUBytesPerElement: () => hd, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => Av, computeWorkGroupSizeForMatMul: () => $2, computeWorkPerThreadForConv2d: () => Ev, flatDispatchLayout: () => Be, isWebGPUSupported: () => Rv, tilesFitEvenlyIntoShape: () => js });
var sa = (e) => {
  let t = 1;
  for (let n = 0; n < e.length; n++)
    t *= e[n];
  return t;
};
function js(e, t) {
  if (e.length !== t.length)
    throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);
  return t.every((n, s) => n % e[s] === 0);
}
function _e(e, t, n = [1, 1, 1], s = [1, 1, 1]) {
  let [r, a, i] = [Math.ceil(sa(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(sa(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(sa(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
  return [r, a, i];
}
function Av(e, t) {
  let n = sa(e.x.map((r) => t[r])), s = sa(e.y.map((r) => t[r]));
  return n <= 4 ? [4, 16, 1] : s <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function $2(e, t, n) {
  return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
}
function Ev(e, t) {
  let n = sa(e.x.map((r) => t[r])), s = sa(e.y.map((r) => t[r]));
  return n <= 4 ? [1, 2, 1] : s <= 4 ? [2, 1, 1] : [2, 2, 1];
}
function Be(e) {
  return { x: e.map((t, n) => n) };
}
function hd(e) {
  if (e === "float32" || e === "int32" || e === "bool" || e === "string")
    return 4;
  if (e === "complex64")
    return 8;
  throw new Error(`Unknown dtype ${e}`);
}
function _2(e, t) {
  if (t === "float32")
    return new Float32Array(e);
  if (t === "int32")
    return new Int32Array(e);
  if (t === "bool" || t === "string")
    return Uint8Array.from(new Int32Array(e));
  throw new Error(`Unknown dtype ${t}`);
}
function Rv() {
  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
function A2(e, t, n, s) {
  return w.assert(s % 4 === 0 && e[0] === 4, () => "tileInner must be divisible by 4. And ColPerThread must be 4"), `
  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s / e[0]}>, ${t}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n / e[0]}>, ${s}>;

  let RowPerThread = ${e[1]};
  let ColPerThread = ${e[0]};
  let TileInner = ${s};

  ${Ci()}

    let tileRow = ${t === 1 ? "0" : "i32(localId.y) * RowPerThread"};
    let tileCol = i32(localId.x);

    let globalRow = ${t === 1 ? "0" : "i32(globalId.y) * RowPerThread"};
    let globalCol = i32(globalId.x);
    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;

    var acc: array<vec4<f32>, RowPerThread>;
    var ACached : vec4<f32>;
    var BCached : array<vec4<f32>, 4>;

    // Loop over shared dimension.
    var globalColA = tileCol;
    let RowPerThreadB = TileInner / i32(workGroupSizeY);
    let tileRowB = i32(localId.y) * RowPerThreadB;
    for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileCol;
            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
        }
        globalColA = globalColA + TileInner / ColPerThread;

        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol;
            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];

            for (var i = 0; i < RowPerThread; i = i + 1) {
                ACached = mm_Asub[tileRow + i][k];
                acc[i] = BCached[0] * ACached.x + acc[i];
                acc[i] = BCached[1] * ACached.y + acc[i];
                acc[i] = BCached[2] * ACached.z + acc[i];
                acc[i] = BCached[3] * ACached.w + acc[i];
            }
        }

        workgroupBarrier();
    }

    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
        mm_write(globalRow + innerRow,
                 globalCol,
                 acc[innerRow], globalId);
    }
  }`;
}
var Vne = class {
  constructor(e, t, n, s, r, a = null, i = null, o = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] }, t[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
    let u = a != null, l = o != null;
    u && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.tileAOuter = t[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, this.aShape = e, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = l, this.batchAEqualOne = s, this.batchBEqualOne = r, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
  }
  getShapeFit() {
    let e = this.aShape[2], t = this.outputShape[2], n = [this.outputShape[0], e, t], s = [this.tileAOuter, this.tileInner], r = [this.tileInner, this.tileBOuter];
    return [js(s, this.aShape.slice(1)), js(r, n.slice(1))];
  }
  getUserCode() {
    let e = this.fitA ? "return A[batch * batchASize + row * uniforms.dimInner / 4 + col]" : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
        }
        return vec4<f32>(0.0)`, t = this.fitB ? "return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
        }
        return vec4<f32>(0.0)`, n = "", s = "";
    if (this.activation) {
      let i = Fr(this.activation, this.isVec4);
      this.hasPreluActivationWeights ? n = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${i}
                }` : n = `
            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
              ${i}
            }`, s = "value = activation(value, outCoord);";
    }
    let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
      ${n}
      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
        ${this.batchAEqualOne ? `
          let batchASize = 0;
          let batch = 0;
        ` : `
          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
          let batch = i32(globalId.z);
        `}

        ${e};
      }

      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
        ${this.batchBEqualOne ? `
          let batchBSize = 0;
          let batch = 0;
          ` : `
          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
          let batch = i32(globalId.z);
       `}
        ${t};
      }

      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
        {
          var value = valueIn;
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col * 4);
          ${r}
          ${s}
          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
        }
      }
      ${A2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
    `;
  }
};
function Dv(e, t) {
  let n = t[1] * e[1], s = t[0] * e[0], r = n > s ? n : s;
  return `
    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
    ${Ci()}
      let tileRow = i32(localId.y) * ${e[1]};
      let tileCol = i32(localId.x) * ${e[0]};

      let globalRow = i32(globalId.y) * ${e[1]};
      let globalCol = i32(globalId.x) * ${e[0]};

      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;

      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
      var ACached : f32;
      var BCached : array<f32, ${e[0]}>;

      // Without this initialization strange values show up in acc.
      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
          acc[innerRow][innerCol] = 0.0;
        }
      }

      let ColPerThreadA = ${r} / ${t[0]};
      let tileColA = i32(localId.x) * ColPerThreadA;
      let RowPerThreadB = ${r} / ${t[1]};
      let tileRowB = i32(localId.y) * RowPerThreadB;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
            let inputRow = tileRow + innerRow;
            let inputCol = tileColA + innerCol;

            mm_Asub[inputRow][inputCol] = mm_readA(
                globalRow + innerRow,
                t * ${r} + inputCol, globalId);
          }
        }
        // Load one tile of B into local memory.
        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
            let inputRow = tileRowB + innerRow;
            let inputCol = tileCol + innerCol;

            mm_Bsub[inputRow][inputCol] = mm_readB(
              t * ${r} + inputRow,
              globalCol + innerCol, globalId);
          }
        }

        workgroupBarrier();

        // Compute acc values for a single thread.
        for (var k = 0; k < ${r}; k = k + 1) {
          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
            BCached[inner] = mm_Bsub[k][tileCol + inner];
          }

          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
            ACached = mm_Asub[tileRow + innerRow][k];
            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
            }
          }
        }

        workgroupBarrier();
      }

      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {

          if ((globalCol + innerCol) < uniforms.dimBOuter &&
              (globalRow + innerRow) < uniforms.dimAOuter) {
            mm_write(globalRow + innerRow,
                     globalCol + innerCol,
                     acc[innerRow][innerCol], globalId);
          }
        }
      }
    }
  `;
}
function Wne(e) {
  return `
    let TileSize = ${e[0] * 4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;

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

      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;

      // Without this initialization strange values show up in acc.
      var acc = 0.0;

      // Loop over shared dimension.
      for (var t = 0; t < numTiles; t = t + 1) {
        // Load one tile of A into local memory.
        let colA = t * TileSize + tileCol * 4;
        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
                                mm_readA(globalRow, colA + 1, globalId),
                                mm_readA(globalRow, colA + 2, globalId),
                                mm_readA(globalRow, colA + 3, globalId));
        workgroupBarrier();

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

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

        workgroupBarrier();
      }

      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
        mm_write(globalRow, globalCol, acc, globalId);
      }
    }
  `;
}
var Une = class {
  constructor(e, t, n, s, r, a = false, i = false, o = null, u = null, l = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [16, 16, 1], this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] };
    let c = a ? e[1] : e[2];
    this.workGroupSize = $2(t[1], c, t[2]), (t[1] === 1 || t[2] === 1) && (n = 1), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]), w.arraysEqual(this.dispatch, [1, 1, 1]) && (n = 1, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]));
    let p = o != null, d = l != null;
    p && this.variableNames.push("bias"), d && this.variableNames.push("preluActivationWeights"), this.workPerThread = n, this.aShape = e, this.transposeA = a, this.transposeB = i, this.addBias = p, this.activation = u, this.hasPreluActivationWeights = d, this.batchAEqualOne = s, this.batchBEqualOne = r;
    let h = this.outputShape[2], f = this.transposeB ? [this.outputShape[0], h, c] : [this.outputShape[0], c, h];
    [this.fitA, this.fitB] = this.getShapeFit(f), this.shaderKey = `matMulPacked_${this.workPerThread}_${a}_${i}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
  }
  getShapeFit(e) {
    let t = this.workGroupSize[1] * this.workPerThread, n = this.workGroupSize[0] * this.workPerThread, s = t > n ? t : n;
    this.outputShape[1] === 1 && (s *= 4), w.assert(s % this.workGroupSize[0] === 0 && s % this.workGroupSize[1] === 0, () => "tileInner must be multiple of workgroupsize.x and workgroupsize.y");
    let r = [t, s], a = [s, n];
    return [js(r, this.aShape.slice(1)), js(a, e.slice(1))];
  }
  getUserCode() {
    let e;
    this.transposeA === false ? e = this.fitA ? "return A[batch * batchASize + row * uniforms.dimInner + col];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A[batch * batchASize + row * uniforms.dimInner + col];
           }
           return 0.0;` : e = this.fitA ? "return A[batch * batchASize + col * uniforms.dimAOuter + row];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A[batch* batchASize + col * uniforms.dimAOuter + row];
           }
           return 0.0;`;
    let t;
    this.transposeB === false ? t = this.fitB ? "return B[batch * batchBSize + row * uniforms.dimBOuter + col];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B[batch * batchBSize + row * uniforms.dimBOuter + col];
           }
           return 0.0;` : t = this.fitB ? "return B[batch * batchBSize + col * uniforms.dimInner + row];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B[batch * batchBSize + col * uniforms.dimInner + row];
           }
           return 0.0;`;
    let n = "", s = "";
    if (this.activation) {
      let i = Fr(this.activation, false);
      this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${i}
            }` : n = `
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${i}
              }
            `, s = "value = activation(value, outCoord);";
    }
    let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
      ${n}

      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        ${this.batchAEqualOne ? `
        let batch = 0;
        let batchASize = 0;
        ` : `
        let batch = i32(globalId.z);
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        `}
        ${e}
      }

      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        ${this.batchBEqualOne ? `
        let batch = 0;
        let batchBSize = 0;
        ` : `
        let batch = i32(globalId.z);
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        `}
        ${t}
      }

      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        var value = valueIn;
        let batch = i32(globalId.z);
        let outCoord = vec3<i32>(batch, row, col);
        ${r}
        ${s}
        setOutputAtCoords(batch, row, col, value);
      }
      ${this.outputShape[1] > 1 ? Dv([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : Wne(this.workGroupSize)}
    `;
  }
};
function Gne() {
  return `
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${Ci()}
      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 Hne = class {
  constructor(e, t, n, s = false, r = false, a = null, i = null, o = 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 = _e(this.dispatchLayout, this.outputShape, this.workGroupSize);
    let u = a != null, l = o != null;
    u && this.variableNames.push("bias"), l && this.variableNames.push("preluActivationWeights"), this.transposeA = s, this.transposeB = r, this.addBias = u, this.activation = i, this.hasPreluActivationWeights = l, this.batchAEqualOne = t, this.batchBEqualOne = n, this.shaderKey = `matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
  }
  getUserCode() {
    let e;
    this.transposeA === false ? e = "return f32(A[batch * batchASize + row * uniforms.dimInner + col]);" : e = "return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";
    let t;
    this.transposeB === false ? t = "return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);" : t = "return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";
    let n = "", s = "";
    if (this.activation) {
      let i = Fr(this.activation, false);
      this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
               let b = getPreluActivationWeightsByOutputCoords(outCoord);
               ${i}
            }` : n = `
              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                ${i}
              }
            `, s = "value = activation(value, outCoord);";
    }
    let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
      ${n}

      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {
        ${this.batchAEqualOne ? `
          let batchASize = 0;
          let batch = 0;
          ` : `
          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
          let batch = batchIn;
          `}
        ${e}
      }

      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
        ${this.batchBEqualOne ? `
          let batch = 0;
          let batchBSize = 0;
          ` : `
          let batch = batchIn;
          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
          `}
        ${t}
      }

      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
        var value = valueIn;
        let outCoord = vec3<i32>(batch, row, col);
        ${r}
        ${s}
        setOutputAtCoords(batch, row, col, value);
      }
      ${Gne()}
    `;
  }
};
function qne(e) {
  let t = e[1] / 2, n = e[0], s = t > n ? t : n;
  return `
  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;

  // If the output size is small for matrix multiplication, avoid to use vec4
  // and handle some elements per thread to optimally utilize the ALU.
  // Introduces two shared memory buffers, some logical threads could handle
  // arithmetic operations and others handle IO operations between barrier api,
  // makes ALUs and load/store units work simultaneously, could improves
  // the performance.
  ${Ci()}
    let tileRow = i32(localId.y);
    let tileCol = i32(localId.x);
    let globalRow = i32(globalId.y);
    let globalCol = i32(globalId.x);

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

    var globalColA = tileCol;
    var globalRowB = tileRow;
    for (var t = 0; t < numTiles; t = t + 1) {
      if (t == 0) {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub1[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${s};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${s};
        }
      } else {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub1[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${s};
          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${s};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${s}; k = k + 1) {
            let subRow = tileRow - ${t};
            if (subRow < 0) {
              continue;
            }
            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
          }
        }
      }
      workgroupBarrier();
      if (t != 0) {
        t = t + 1;
      }

      if (t < numTiles) {
        if (tileRow < ${t}) {
          // Load one tile of A and B into local memory.
          // globalRow is always greater than or equal tileRow.
          mm_Asub2[tileRow][tileCol] =
              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
          globalColA = globalColA + ${s};
          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
          globalRowB = globalRowB + ${s};
        } else {
          // Compute acc values for a single thread.
          for (var k = 0; k < ${s}; k = k + 1) {
            let subRow = tileRow - ${t};
            if (subRow < 0) {
              continue;
            }
            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
          }
        }
      }
      workgroupBarrier();
    }
    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
    if (tileRow >= ${t} && writeCol >= 0) {
      mm_write(writeCol, globalCol, acc, globalId);
    }
  }
  `;
}
var jne = class {
  constructor(e, t, n, s = null, r = null, a = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 16, 1], w.assert(e[1] <= 16 || t[2] <= 16, () => "This program can be only used when A width or B Height are small"), this.outputShape = n, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(n[2] / this.workGroupSize[0]), Math.ceil(n[1] * 2 / this.workGroupSize[1]), n[0]];
    let i = s != null;
    i && this.variableNames.push("bias");
    let o = a != null;
    o && this.variableNames.push("preluActivationWeights"), this.addBias = i, this.activation = r, this.hasPreluActivationWeights = o, this.batchAEqualOne = e[0] === 1, this.batchBEqualOne = t[0] === 1, this.shaderKey = `matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
  }
  getUserCode() {
    let e = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
          return A[batch * batchASize + row * uniforms.dimInner + col];
        }
        return 0.0;`, t = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return B[batch * batchBSize + row * uniforms.dimBOuter + col];
         }
         return 0.0;`, n = "", s = "";
    if (this.activation) {
      let i = Fr(this.activation, false);
      this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            let b = getPreluActivationWeightsByOutputCoords(outCoord);
            ${i}
            }` : n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
            ${i}
        }`, s = "value = activation(value, outCoord);";
    }
    let r = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
      ${n}

      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        ${this.batchAEqualOne ? `
          let batch = 0;
          let batchASize = 0;
          ` : `
          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
          let batch = i32(globalId.z);
          `}
        ${e}
      }
      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        ${this.batchBEqualOne ? `
          let batch = 0;
          let batchBSize = 0;
          ` : `
          let batch = i32(globalId.z);
          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
          `}
        ${t}
      }
      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col);
          var value = valueIn;
          ${r}
          ${s}
          setOutputAtCoords(batch, row, col, value);
        }
      }
      ${qne(this.workGroupSize)}
    `;
  }
};
function We(e) {
  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
  return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
}
var Kne = { kernelName: Fo, backendName: "webgpu", kernelFunc: We };
function Fv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = bi.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
  w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
  let k = n ? [b, p, h] : [b, h, p], I = s ? [y, f, d] : [y, d, f], $ = We({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = We({ inputs: { x: t }, backend: r, attrs: { shape: I } }), A = [$, E], P = Math.max(b, y), R = b === 1, F = y === 1, T = p % 4 === 0 && f % 4 === 0 && !n && !s, z;
  h * f <= 32 ? z = new Hne([P, h, f], R, F, n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? z = new jne(k, I, [P, h, f], a, u, i) : T ? z = new Vne(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), R, F, a, u, i) : z = new Une(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), R, F, n, s, a, u, i);
  let W = [$, E];
  a && W.push(a), i && W.push(i);
  let j = [{ type: "int32", data: [h] }, { type: "int32", data: [f] }, { type: "int32", data: [p] }];
  u === "leakyrelu" && (j.push({ type: "float32", data: [o] }), z.uniforms += " alpha : f32,");
  let X = r.runWebGPUProgram(z, W, e.dtype, j), Y = We({ inputs: { x: X }, backend: r, attrs: { shape: x } });
  A.push(X);
  for (let Z of A)
    r.disposeData(Z.dataId);
  return Y;
}
function Xne(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
  return Fv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
}
var Yne = { kernelName: aa, backendName: "webgpu", kernelFunc: Xne };
var Fw = class {
  constructor(e, t, n) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(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)}
      }

      ${Ue()}
        if(index < uniforms.size) {
          let areal = getARealByOutputIndex(index);
          let aimag = getAImagByOutputIndex(index);
          let breal = getBRealByOutputIndex(index);
          let bimag = getBImagByOutputIndex(index);
          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `;
  }
};
var Qne = class {
  constructor(e, t, n, s) {
    this.variableNames = ["A", "B"], this.size = true;
    let r = 256;
    this.workGroupSize = [r, 1, 1], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.lastDimensionSize = s ? n[0] : t[0], this.lastDimensionSize < 256 ? this.workPerThread = 1 : this.lastDimensionSize < 512 ? this.workPerThread = 2 : this.workPerThread = 4, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.useSharedMemoryWithB = s, this.op = e, this.shaderKey = `binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;
  }
  getUserCode() {
    let e = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0", t = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);
         let b = sharedBuf[${e}];` : `let a = sharedBuf[${e}];
         let b = getBByOutputCoords(coords);`;
    return `
        fn binaryOperation(a : f32, b : f32) -> f32 {
          ${nc(this.op, false)}
        }
        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
        ${Ue()}

          // Fill in the shared memory buffer. Here we need a loop to make sure
          // that all data in A|B are uploaded when |sharedMemorySize| is larger
          // than work group size.
          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? "B" : "A"}[localIndex]);
          }
          workgroupBarrier();

          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
            let flatIndex = index * ${this.workPerThread} + i;
            if(flatIndex < uniforms.size) {
              let coords = getCoordsFromIndex(flatIndex);

              ${t}
              setOutputAtIndex(flatIndex, binaryOperation(a, b));
            }
          }
        }
        `;
  }
};
var Zne = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.workPerThread = 4, this.isVec4 = true, this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.op = e, this.shaderKey = `binaryVec4_${e}`;
  }
  getUserCode() {
    return `
      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
        ${nc(this.op, this.isVec4)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
    `;
  }
};
var E2 = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binary_${e}`, this.op = e;
  }
  getUserCode() {
    return `
      fn binaryOperation(a : f32, b : f32) -> f32 {
        ${nc(this.op, false)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
      `;
  }
};
function Ow(e, t, n) {
  if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
    return new Zne(e, t, n);
  let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
  return r || a ? new Qne(e, t, n, a) : new E2(e, t, n);
}
function Wn(e) {
  let { inputs: t } = e, { x: n } = t;
  return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
}
var Jne = { kernelName: Wa, backendName: "webgpu", kernelFunc: Wn };
function du(e) {
  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = Wn({ inputs: { x: s }, backend: n }), u = Wn({ inputs: { x: r }, backend: n });
  return i.complexTensorInfos = { real: o, imag: u }, a;
}
var ese = { kernelName: Zd, backendName: "webgpu", kernelFunc: du };
var sc = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.size = true;
    let n = 128;
    this.workGroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.op = t, this.shaderKey = `unary_${t}`;
  }
  getUserCode() {
    return `
      fn unaryOperation(a : f32) -> f32 {
        ${qr(this.op, false)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          setOutputAtIndex(index, unaryOperation(a));
        }
      }
      `;
  }
};
function Kt({ opType: e, cpuKernelImpl: t, dtype: n }) {
  return ({ inputs: s, backend: r }) => {
    let { x: a } = s, i = r, o = n || a.dtype;
    if (i.shouldExecuteOnCPU([a]) && t != null) {
      let l = i.tensorMap.get(a.dataId), c = t(l.values, o);
      return i.makeTensorInfo(a.shape, o, c);
    }
    let u = new sc(a.shape, e);
    return i.runWebGPUProgram(u, [a], o);
  };
}
function mn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype: s }) {
  return ({ inputs: r, backend: a }) => {
    let { a: i, b: o } = r, u = a;
    if (n && i.dtype === "complex64") {
      let p = u.tensorMap.get(i.dataId), d = u.tensorMap.get(o.dataId), h, f;
      if (e !== 0)
        [h, f] = [[p.complexTensorInfos.real, d.complexTensorInfos.real], [p.complexTensorInfos.imag, d.complexTensorInfos.imag]].map((g) => {
          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: o.shape }, k = Ow(e, i.shape, o.shape);
          return u.runWebGPUProgram(k, [v, x], cn(b.dtype, y.dtype));
        });
      else {
        let g = new Fw(17, i.shape, o.shape), b = new Fw(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
        h = u.runWebGPUProgram(g, y, "float32"), f = u.runWebGPUProgram(b, y, "float32");
      }
      let m = du({ inputs: { real: h, imag: f }, backend: u });
      return u.disposeData(h.dataId), u.disposeData(f.dataId), m;
    }
    let l = s || cn(i.dtype, o.dtype);
    if ((i.dtype === "string" || o.dtype === "string" || u.shouldExecuteOnCPU([i, o])) && t != null) {
      let p = u.tensorMap.get(i.dataId).values, d = u.tensorMap.get(o.dataId).values, h = i.dtype === "string" ? C.fromUint8ToStringArray(p) : p, f = i.dtype === "string" ? C.fromUint8ToStringArray(d) : d, [m, g] = t(i.shape, o.shape, h, f, l);
      return u.makeTensorInfo(g, l, m);
    }
    let c = Ow(e, i.shape, o.shape);
    return u.runWebGPUProgram(c, [i, o], l);
  };
}
var { addImpl: tse, ceilImpl: nse, concatImpl: sse, equalImpl: rse, expImpl: ase, expm1Impl: ise, floorImpl: ose, gatherNdImpl: use, gatherV2Impl: lse, greaterEqualImpl: cse, greaterImpl: dse, lessEqualImpl: pse, lessImpl: hse, logImpl: fse, maxImpl: mse, maximumImpl: gse, minimumImpl: bse, multiplyImpl: yse, negImpl: vse, notEqualImpl: xse, prodImpl: wse, rangeImpl: kse, rsqrtImpl: Sse, simpleAbsImpl: Ise, sliceImpl: Cse, stridedSliceImpl: Nse, stringNGramsImpl: Tse, subImpl: $se, tileImpl: _se, topKImpl: Ase, transposeImpl: Ese, uniqueImpl: hhe } = sv;
var Rse = Kt({ opType: 0, cpuKernelImpl: Ise });
var Dse = { kernelName: co, backendName: "webgpu", kernelFunc: Rse };
var Fse = mn({ opSnippet: 1, cpuKernelImpl: tse, supportsComplex: true });
var Ose = { kernelName: Sr, backendName: "webgpu", kernelFunc: Fse };
var Pse = class {
  constructor(e) {
    this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
  }
  getUserCode() {
    let e = [];
    this.variableNames.forEach((s) => {
      e.push(`let v${s} = get${s}ByOutputCoords(coords);`);
    });
    let t = this.variableNames.map((s) => `v${s}`).join(" + ");
    return `
      ${Ue()}
        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, ${t});
          }
        }
      }
    `;
  }
};
function zse(e) {
  let { inputs: t, backend: n } = e, s = t;
  if (s.length === 1)
    return Wn({ inputs: { x: s[0] }, backend: n });
  let r = s.map((o) => o.dtype).reduce((o, u) => cn(o, u)), a = s.map((o) => o.shape), i = new Pse(a);
  return n.runWebGPUProgram(i, s, r);
}
var Mse = { kernelName: Sa, backendName: "webgpu", kernelFunc: zse };
var R2 = class {
  constructor(e, t, n) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
    let s = [t];
    C.assertAxesAreInnerMostDims("arg" + n.charAt(0).toUpperCase() + n.slice(1), s, e.length), this.op = n === "min" ? "<" : ">";
    let [r] = C.computeOutAndReduceShapes(e, s);
    this.outputShape = r.length === 0 ? [1] : r, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.inputShape = e, this.shaderKey = `argMinMax${this.op}`;
  }
  getUserCode() {
    let e = `
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `, t = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${pr(this.inputShape.length - 1)}`, n = () => {
      let r = "";
      if (this.outputShape.length === 1)
        this.inputShape.length !== 1 && (r += "outputCoords,");
      else
        for (let a = 0; a < this.outputShape.length; a++)
          r += `outputCoords.${pr(a)},`;
      return r;
    };
    return `
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${e}

      ${Ue()}
        let outputIndex = index / i32(workGroupSizeX);
        let reduceLength = ${t()};

        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(${n()} 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]);
        }
      }
    `;
  }
};
var Lse = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.workGroupSize = [16, 16, 1];
    let n = new Array(e.length);
    for (let s = 0; s < n.length; s++)
      n[s] = e[t[s]];
    this.outputShape = n, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]), this.shaderKey = "transposeShared";
  }
  getUserCode() {
    return `
      let TILE_DIM = ${this.workGroupSize[0]};
      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
      ${_v()}
      fn main(@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 Bse = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true;
    let n = new Array(e.length);
    for (let s = 0; s < n.length; s++)
      n[s] = e[t[s]];
    this.outputShape = n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.newDim = t, this.shaderKey = `transpose_${t}`;
  }
  getUserCode() {
    let e = Ut(this.outputShape.length), t = Vse(this.newDim);
    return `
      ${Ue()}

        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(
              ${e}(${t}), uniforms.aShape)]);
          }
        }
      }
    `;
  }
};
function Vse(e) {
  let t = e.length;
  if (t > 6)
    throw Error(`Transpose for rank ${t} is not yet supported`);
  let n = new Array(t);
  for (let s = 0; s < e.length; s++)
    n[e[s]] = `resRC.${pr(s)}`;
  return n.join();
}
function Ks(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
  for (let c = 0; c < u.length; c++)
    u[c] = r.shape[a[c]];
  if (n.shouldExecuteOnCPU([r])) {
    let p = i.tensorMap.get(r.dataId).values, d = Ese(p, r.shape, r.dtype, a, u);
    return n.makeTensorInfo(u, r.dtype, d);
  }
  if (r.shape.length === 2 && w.arraysEqual(a, [1, 0])) {
    let c = new Lse(r.shape, a);
    return i.runWebGPUProgram(c, [r], r.dtype);
  }
  let l = new Bse(r.shape, a);
  return i.runWebGPUProgram(l, [r], r.dtype);
}
var Wse = { kernelName: mi, backendName: "webgpu", kernelFunc: Ks };
function Use(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = Ks({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
  let c = new R2(u.shape, i[0], "max"), p = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
  return l.forEach((h) => n.disposeData(h.dataId)), d;
}
var Gse = { kernelName: Ia, backendName: "webgpu", kernelFunc: Use };
function Hse(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = C.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = Ks({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = C.getInnerMostAxes(i.length, u.shape.length)), C.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
  let c = new R2(u.shape, i[0], "min"), p = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
  return l.forEach((h) => n.disposeData(h.dataId)), d;
}
var qse = { kernelName: ll, backendName: "webgpu", kernelFunc: Hse };
var D2 = class {
  constructor(e, t) {
    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `pool2D_${t}`, this.poolType = t;
  }
  getUserCode() {
    let e = "resultValue = max(value, resultValue);";
    this.poolType === "avg" && (e = "resultValue = resultValue + value; count = count + 1.0;");
    let t = "resultValue";
    return this.poolType === "avg" && (t = "resultValue / count"), `
      ${Ue()}
      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, ${t});
        }
      }
    `;
  }
};
var F2 = 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 = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
  }
  getUserCode() {
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let batch = coords[0];
          let d = coords[3];

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

          let value = getX(batch, xRCorner, xCCorner, d);
          setOutputAtIndex(index, value);
        }
      }
    `;
  }
};
function jse(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = C.computePool2DInfo(r.shape, a, i, l, o, u);
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    return Wn({ inputs: { x: r }, backend: n });
  let p, d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }];
  return c.filterHeight === 1 && c.filterWidth === 1 ? p = new F2(c) : (p = new D2(c, "avg"), d.push({ type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] })), n.runWebGPUProgram(p, [r], r.dtype, d);
}
var Kse = { kernelName: Ca, backendName: "webgpu", kernelFunc: jse };
function Xse(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  return Fv({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
}
var Yse = { kernelName: Na, backendName: "webgpu", kernelFunc: Xse };
var Qse = class {
  constructor(e, t) {
    this.variableNames = ["source"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.rank = t.length, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Ut(e.length)}, `, this.shaderKey = "slice";
  }
  getUserCode() {
    let e = Ut(this.rank), t = Zse(this.rank), n;
    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${Zm[a]} = uniforms.start[${a}] + coords.${Zm[a]};`), `
      ${Ue()}
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${n.join(`
`)}
          setOutputAtIndex(index, getSource(${t}));
        }
      }
    `;
  }
};
var Zm = ["x", "y", "z", "w", "u", "v"];
function Zse(e) {
  if (e === 1)
    return "sourceLoc";
  if (e <= 6)
    return Zm.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
  throw Error(`Slicing for rank ${e} is not yet supported`);
}
function pu(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
  if (wt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
    let p = n.tensorMap.get(r.dataId), d = Cse(p.values, o, u, r.shape, r.dtype);
    return n.makeTensorInfo(u, r.dtype, d);
  }
  if (w.sizeFromShape(u) === 0)
    return n.makeTensorInfo(u, r.dtype, []);
  let l = new Qse(o, u), c = [{ type: "int32", data: o }];
  return n.runWebGPUProgram(l, [r], r.dtype, c);
}
var Jse = { kernelName: Lo, backendName: "webgpu", kernelFunc: pu };
var ere = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
  w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
  let o = a.reduce((y, v) => y * v), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = [], f = We({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = Ks({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = We({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = pu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
  return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeData(y.dataId)), b;
};
var tre = { kernelName: po, backendName: "webgpu", kernelFunc: ere };
var O2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: xse });
var nre = { kernelName: $o, backendName: "webgpu", kernelFunc: O2 };
function rc(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
  return Wn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
}
var sre = { kernelName: op, backendName: "webgpu", kernelFunc: rc };
function rre(e, t) {
  let n = new sc(e.shape, 22), s = t.runWebGPUProgram(n, [e], "int32");
  return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
function Jm(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
  if (a === "complex64") {
    if (r.dtype === "complex64")
      return Wn({ inputs: { x: r }, backend: n });
    let i = $t(r.shape), o = Jm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = du({ inputs: { real: o, imag: i }, backend: n });
    return i.dispose(), n.disposeData(o.dataId), u;
  }
  if (r.dtype === "complex64") {
    let i = rc({ inputs: { input: r }, backend: n }), o = Jm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
    return n.disposeData(i.dataId), o;
  }
  if (!w.hasEncodingLoss(r.dtype, a)) {
    let i = Wn({ inputs: { x: r }, backend: n });
    return { dataId: i.dataId, shape: i.shape, dtype: a };
  }
  if (a === "int32")
    return rre(r, n);
  if (a === "bool") {
    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = O2({ inputs: { a: r, b: i }, backend: n });
    return n.disposeData(i.dataId), u;
  }
  throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
}
var are = { kernelName: Ta, backendName: "webgpu", kernelFunc: Jm };
var ire = Kt({ opType: 1, cpuKernelImpl: nse });
var ore = { kernelName: $a, backendName: "webgpu", kernelFunc: ire };
var ure = 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 = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
  }
  getUserCode() {
    return `
      ${Ue()}
        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 lre = 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 = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "clip";
  }
  getUserCode() {
    return `
      ${Ue()}
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          if (isnan(value)) {
            setOutputAtIndex(index, value);
            return;
          }
          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `;
  }
};
function cre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o, u = [{ type: "float32", data: [a] }, { type: "float32", data: [i] }];
  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new ure(r.shape) : o = new lre(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
}
var dre = { kernelName: Ir, backendName: "webgpu", kernelFunc: cre };
var pre = class {
  constructor(e) {
    this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
    for (let t = 0; t < this.offsetLength; t++)
      this.uniforms += `offset${t} : i32,`;
    this.shaderKey = "concat";
  }
  getUserCode() {
    let e = [];
    if (this.offsetLength > 0) {
      e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");
      for (let r = 1; r < this.offsetLength; r++)
        e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r - 1})); }`);
      let n = this.offsetLength, s = this.offsetLength - 1;
      e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`);
    } else
      e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");
    return `
      ${Ue()}
        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 sh(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
  return Wn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
}
var hre = { kernelName: sp, backendName: "webgpu", kernelFunc: sh };
function eg(e, t, n) {
  let s = e[0].dtype;
  if (s === "complex64") {
    let h = e.map((y) => rc({ inputs: { input: y }, backend: n })), f = e.map((y) => sh({ inputs: { input: y }, backend: n })), m = eg(h, t, n), g = eg(f, t, n), b = du({ inputs: { real: m, imag: g }, backend: n });
    return h.forEach((y) => n.disposeData(y.dataId)), f.forEach((y) => n.disposeData(y.dataId)), n.disposeData(m.dataId), n.disposeData(g.dataId), b;
  }
  let r = n.shouldExecuteOnCPU(e);
  if (s === "string" && (r = true), r) {
    let h = e.map((x) => {
      let k = w.sizeFromShape(x.shape.slice(t));
      return We({ inputs: { x }, backend: n, attrs: { shape: [-1, k] } });
    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = C.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = sse(f, m, s, g), y = C.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
    return h.forEach((x) => n.disposeData(x.dataId)), v;
  }
  let { tensors2D: a, outShape: i } = fre(e, t, n), o = a.map((h) => h.shape), u = new pre(o), l = [], c = new Array(o.length - 1);
  if (c.length > 0) {
    c[0] = o[0][1], l.push({ type: "int32", data: [c[0]] });
    for (let h = 1; h < c.length; h++)
      c[h] = c[h - 1] + o[h][1], l.push({ type: "int32", data: [c[h]] });
  }
  let p = n.runWebGPUProgram(u, a, a[0].dtype, l);
  a.forEach((h) => n.disposeData(h.dataId));
  let d = We({ inputs: { x: p }, backend: n, attrs: { shape: i } });
  return n.disposeData(p.dataId), d;
}
function fre(e, t, n) {
  let s = C.computeOutShape(e.map((a) => a.shape), t);
  return { tensors2D: e.map((a) => We({ inputs: { x: a }, backend: n, attrs: { shape: [w.sizeFromShape(a.shape.slice(0, t)), w.sizeFromShape(a.shape.slice(t))] } })), outShape: s };
}
function P2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = C.computeOutShape(t.map((l) => l.shape), a);
  if (w.sizeFromShape(i) === 0)
    return n.makeTensorInfo(i, t[0].dtype, []);
  let o = t.filter((l) => w.sizeFromShape(l.shape) > 0);
  if (o.length === 1)
    return Wn({ inputs: { x: o[0] }, backend: n });
  let u = o.map((l) => l.shape);
  return C.assertParamsConsistent(u, a), eg(o, a, n);
}
var mre = { kernelName: ho, backendName: "webgpu", kernelFunc: P2 };
var gre = class {
  constructor(e, t = false, n = null, s = 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.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.outputShape[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, [this.fitA, this.fitB] = this.getShapeFit(), this.remainder = this.convInfo.inChannels % 4 === 0, this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.remainder}`;
  }
  getShapeFit() {
    let e = [this.tileAOuter, this.tileInner], t = [this.tileInner, this.tileBOuter], n = this.outputShape[1] * this.outputShape[2], s = this.outputShape[3], r = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
    return [js(e, [n, r]), js(t, [r, s])];
  }
  getSampleAWithRemainder(e) {
    return `let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
    let divBy4Remainder${e} = flatIndex${e} % 4;
    let divBy4Index${e} = flatIndex${e} / 4;
    let curData${e} = x[divBy4Index${e}];
    if (divBy4Remainder${e} == 0) {
      temp = curData${e};
    } else {
      // TODO: This could end up being a redundant load with another one in
      // the same shader invocation. Perhaps there's an opportunity for
      // optimization
      let nextData${e} = x[divBy4Index${e} + 1];
      if (divBy4Remainder${e} == 1) {
        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
      } else if (divBy4Remainder${e} == 2) {
        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
      } else if (divBy4Remainder${e} == 3) {
        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
      }
    }
    `;
  }
  getUserCode() {
    let e = A2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner), n = `let outRow = r / uniforms.outShape[2];
        let outCol = r % uniforms.outShape[2];
        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
        let inChCoord = c % uniforms.xShape[3];
        var coord = vec4<i32>(
            batch,
            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
            inChCoord);
        var resData = vec4<f32>(0.0);
        ${this.remainder ? `// The bounds checking is always needed since we use it to pad zero for
          // the 'same' padding type.
          if (coordsInBounds4D(coord, uniforms.xShape)) {
            resData = x[getIndexFromCoords4D(coord, uniforms.xShape) / 4];
          } else {
            resData = vec4<f32>(0.0); }` : `var temp = vec4<f32>(0.0);
          ${this.getSampleAWithRemainder(1)}
          resData = temp;
          if (WCol == (uniforms.filterDims[1] - 1)) {
            coord = vec4<i32>(
              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
              ${this.getSampleAWithRemainder(2)}
            if (inChCoord == 0) {
              resData = vec4<f32>(resData.xyz, temp.x);
            } else if (inChCoord == 1) {
              resData = vec4<f32>(resData.xy, temp.xy);
            } else {
              resData = vec4<f32>(resData.x, temp.xyz);
            }
          }
          `}
        return resData;`, s = this.fitA ? `${n}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
          ${n}
         }
         return vec4<f32>(0.0);
        `, r = this.fitB ? "return W[row * uniforms.dimBOuter / 4 + col];" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W[row * uniforms.dimBOuter / 4 + col];
         }
         return vec4<f32>(0.0);
        `, a = "", i = "";
    if (this.activation) {
      let l = Fr(this.activation, this.isVec4);
      this.hasPreluActivationWeights ? a = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${l}
        }` : a = `
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          ${l}
        }`, i = "value = activation(value, outCoord);";
    }
    let o = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
        ${a}
        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          let r = row;
          let c = col * 4;
          var batch = i32(globalId.z);
          ${s}
        }

        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
          ${r}
        }

        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
          var batch = i32(globalId.z);
          var value = valueInput;
          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
          {
            let outCoord = vec4<i32>(
              batch,
              row / uniforms.outShape[2],
              row % uniforms.outShape[2],
              col * 4);
            ${o}
            ${i}
            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
              value);
          }
        }
        ${e}
      `;
  }
};
var bre = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [1], y: [2, 3], z: [0] }, this.workGroupSize = Av(this.dispatchLayout, this.outputShape), this.elementsPerThread = Ev(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`;
  }
  getShapeFit() {
    let e = this.workGroupSize[1] * this.elementsPerThread[1], t = this.workGroupSize[0] * this.elementsPerThread[0], n = e > t ? e : t;
    w.assert(n % this.workGroupSize[0] === 0 && n % this.workGroupSize[1] === 0, () => "tileInner must be multiple of workgroupsize.x and workgroupsize.y");
    let s = [e, n], r = [n, t], a = this.convInfo.outHeight * this.convInfo.outWidth, i = this.convInfo.outChannels, o = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
    return [js(s, [a, o]), js(r, [o, i])];
  }
  getUserCode() {
    let e = this.isChannelsLast ? `
    let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);
    ` : `
    let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);
    `, t = this.isChannelsLast ? `
    let outCoord = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    ` : `
    let outCoord = vec4<i32>(
      batch,
      col,
      row / outWidth,
      row % outWidth);
    `, n = Dv(this.elementsPerThread, this.workGroupSize), s = `
    let inChannels = uniforms.wShape[2];
    let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
    let outRow = row / outWidth;
    let outCol = row % outWidth;

    let WRow = col / (uniforms.filterDims[1] * inChannels);
    let WCol = col / 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];
    ${e}
    // The bounds checking is always needed since we use it to pad zero for the
    // 'same' padding type.
    if(coordsInBounds4D(coord, uniforms.xShape)) {
      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;`, r = this.fitA ? `${s}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      ${s}
    }
    return 0.0;
    `, a = this.fitB ? "return W[row * uniforms.dimBOuter + col];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W[row * uniforms.dimBOuter + col];
	 }
	 return 0.0;
	 `, i = "", o = "";
    if (this.activation) {
      let c = Fr(this.activation, false);
      this.hasPreluActivationWeights ? i = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${c}
                }` : i = `
                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
                    ${c}
                  }
                `, o = "value = activation(value, outCoord);";
    }
    let u = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
    ${i}
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      ${r}
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      ${a}
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
      ${t}
      ${u}
      ${o}
      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }
    ${n}
  `;
  }
};
function yre({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
  let u = n.dataFormat === "channelsLast", l = !u, c = false, p = u && n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID", d, h;
  if (p) {
    let g = n.inHeight * n.inWidth * n.inChannels;
    d = We({ inputs: { x: e }, backend: s, attrs: { shape: [1, n.batchSize, g] } }), h = We({ inputs: { x: t }, backend: s, attrs: { shape: [1, g, n.outChannels] } });
  } else
    d = We({ inputs: { x: e }, backend: s, attrs: { shape: u ? [n.batchSize, n.inHeight * n.inWidth, n.inChannels] : [n.batchSize, n.inChannels, n.inHeight * n.inWidth] } }), h = We({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
  let f = Fv({ a: u ? d : h, b: u ? h : d, transposeA: l, transposeB: c, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), m = We({ inputs: { x: f }, backend: s, attrs: { shape: n.outShape } });
  return s.disposeData(d.dataId), s.disposeData(h.dataId), s.disposeData(f.dataId), m;
}
function z2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
  let u = r != null, l = a != null, c = n.dataFormat === "channelsLast", p;
  if (c && n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID" || n.filterHeight === 1 && n.filterWidth === 1 && n.dilationHeight === 1 && n.dilationWidth === 1 && n.strideHeight === 1 && n.strideWidth === 1 && (n.padInfo.type === "SAME" || n.padInfo.type === "VALID"))
    return yre({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
  let h = (n.inChannels % 4 === 0 || n.inChannels === 3 && n.padInfo.type === "VALID") && n.outChannels % 4 === 0 && c, f = [n.padInfo.top, n.padInfo.left], m = [{ type: "int32", data: [n.filterHeight, n.filterWidth] }, { type: "int32", data: [...f] }, { type: "int32", data: [n.strideHeight, n.strideWidth] }, { type: "int32", data: [n.dilationHeight, n.dilationWidth] }];
  h ? p = new gre(n, u, o, l) : p = new bre(n, u, o, l);
  let g = n.outHeight * n.outWidth, b = n.outChannels, y = n.filterHeight * n.filterWidth * n.inChannels;
  m.push({ type: "int32", data: [g] }, { type: "int32", data: [b] }, { type: "int32", data: [y] });
  let v = [e, t];
  return u && v.push(r), l && v.push(a), o === "leakyrelu" && (m.push({ type: "float32", data: [i] }), p.uniforms += " alpha : f32,"), s.runWebGPUProgram(p, v, e.dtype, m);
}
function vre(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = n, p = C.convertConv2DDataFormat(u), d = C.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p);
  return z2({ x: r, filter: a, convInfo: d, backend: s });
}
var xre = { kernelName: _a, backendName: "webgpu", kernelFunc: vre };
var wre = 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, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Av(this.dispatchLayout, this.outputShape), this.elementsPerThread = Ev(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
  }
  getUserCode() {
    return `
    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      var batch = i32(globalId.z);
      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
      return 0.0;
    }
    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
      return 0.0;
    }
    let coord = vec4<i32>(
        batch,
        i32(xR),
        i32(xC),
        col % uniforms.outBackprop[3]);
    return x[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;
    }

    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
      let coordX = uniforms.filterDims.x - 1 -
          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
      let coordY = uniforms.filterDims.y - 1 -
          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
          coordX >= 0 && coordY >= 0) {
        let coord = vec4<i32>(coordX, coordY, col,
            row % uniforms.outBackprop[3]);
        return W[getIndexFromCoords4D(coord, uniforms.wShape)];
      }
      return 0.0;
    }

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }

    ${Dv(this.elementsPerThread, this.workGroupSize)}
  `;
  }
};
var kre = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? 1 : 2, t = this.isChannelsLast ? 2 : 3, n = this.isChannelsLast ? 3 : 1;
    return `
    ${Ue()} {
      if(index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let batch = coords[0];
        let d1 = coords[${n}];

        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
        let dyRCorner = dyCorner.x;
        let dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;
        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
          let wRPerm = uniforms.filterDims.x - 1 - wR;
          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
              wRPerm < 0) {
            continue;
          }
          let idyR = dyR;

          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
            let wCPerm = uniforms.filterDims.y - 1 - wC;
            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
                fract(dyC) > 0.0 || wCPerm < 0) {
              continue;
            }
            let idyC = dyC;

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

            }
          }
        }
        setOutputAtIndex(index, dotProd);
      }
    }
  `;
  }
};
function Sre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = C.convertConv2DDataFormat(l), d = C.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = [{ type: "int32", data: [d.filterHeight, d.filterWidth] }, { type: "int32", data: [d.filterHeight - 1 - d.padInfo.top, d.filterWidth - 1 - d.padInfo.left] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.batchSize, d.outHeight, d.outWidth, d.outChannels] }], f;
  if (K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))
    f = new kre(d);
  else {
    f = new wre(d);
    let m = d.inShape[1] * d.inShape[2], g = d.inShape[3], b = d.filterHeight * d.filterWidth * d.outChannels;
    h.push({ type: "uint32", data: [m] }, { type: "uint32", data: [g] }, { type: "uint32", data: [b] });
  }
  return n.runWebGPUProgram(f, [r, a], "float32", h);
}
var Ire = { kernelName: Aa, backendName: "webgpu", kernelFunc: Sre };
var Cre = Kt({ opType: 2 });
var Nre = { kernelName: Ea, backendName: "webgpu", kernelFunc: Cre };
var Tre = Kt({ opType: 3 });
var $re = { kernelName: Ra, backendName: "webgpu", kernelFunc: Tre };
var _re = class {
  constructor(e, t, n, s) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true;
    let [r] = t;
    this.outputShape = [r, n[0], n[1], e], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.methodId = s === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
  }
  getUserCode() {
    let [e, t] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [n, s, r] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, o] = this.cropWidthBiggerThan1 ? [`(${t} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t}`];
    return `
      ${Ue()}
      if (index < uniforms.size) {
        let coords = getCoordsFromIndex(index);
        let height_ratio = f32(${n});
        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 = ${s};
        let width_scale = ${i};
        let in_y = ${r};
        if( in_y < 0.0 || in_y > ${e} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let in_x = ${o};
        if( in_x < 0.0 || in_x > ${t} ) {
          setOutputAtIndex(index, uniforms.extrapolationValue);
          return;
        }
        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
        if(${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 Are = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new _re(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
  return n.runWebGPUProgram(c, [r, a, i], "float32", p);
};
var Ere = { kernelName: mo, backendName: "webgpu", kernelFunc: Are };
var Pw = class {
  constructor(e, t, n, s) {
    this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true;
    let r = 128;
    this.workGroupSize = [r, 1, 1], this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.exclusive = n, this.reverse = s, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
  }
  getUserCode() {
    let e = this.outputShape.length, t = this.op === "*" ? "1.0" : "0.0", n = this.exclusive ? t : `getX(${zw(e, "coords", this.op)})`, s = this.outputShape[this.outputShape.length - 1], r = "", a = "";
    return this.exclusive ? (r = this.reverse ? `end != ${s - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (r = this.reverse ? `end + pow2 < ${s}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), `
      ${Ue()}
       if (index < uniforms.size) {
         var coords = getCoordsFromIndex(index);

         let end = ${Mw(e, "coords", this.op)};
         var val = ${n};
         let pow2 = i32(pow(2.0, uniforms.index));
         if (${r}) {
           let idx = ${a};
           ${Mw(e, "coords", this.op)} = idx;
           val ${this.op}= getX(${zw(e, "coords", this.op)});
         }
         setOutputAtIndex(index, val);
       }
      }
    `;
  }
};
function zw(e, t, n) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.x, ${t}.y`;
  if (e === 3)
    return `${t}.x, ${t}.y, ${t}.z`;
  if (e === 4)
    return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
  throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
}
function Mw(e, t, n) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.y`;
  if (e === 3)
    return `${t}.z`;
  if (e === 4)
    return `${t}.w`;
  throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
}
function M2(e, t, n, s, r, a) {
  let i = t.shape.length, o = C.getAxesPermutation([s], i), u = t;
  o != null && (u = Ks({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
  let l = C.getInnerMostAxes(1, i)[0];
  if (l !== i - 1)
    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
  let c = u.shape[l], p = Wn({ inputs: { x: u }, backend: n });
  for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
    let h = new Pw(e, u.shape, false, a), f = p, m = [{ type: "float32", data: [d] }];
    p = n.runWebGPUProgram(h, [p], p.dtype, m), n.disposeData(f.dataId);
  }
  if (r) {
    let d = new Pw(e, u.shape, r, a), h = p, f = [{ type: "float32", data: [0] }];
    p = n.runWebGPUProgram(d, [p], p.dtype, f), n.disposeData(h.dataId);
  }
  if (o != null) {
    let d = C.getUndoAxesPermutation(o), h = Ks({ inputs: { x: p }, backend: n, attrs: { perm: d } });
    return n.disposeData(p.dataId), n.disposeData(u.dataId), h;
  }
  return p;
}
function Rre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return M2("*", r, n, a, i, o);
}
var Dre = { kernelName: fo, backendName: "webgpu", kernelFunc: Rre };
function Fre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return M2("+", r, n, a, i, o);
}
var Ore = { kernelName: Da, backendName: "webgpu", kernelFunc: Fre };
var Pre = class {
  constructor(e, t) {
    this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t}`, this.dataFormat = t;
  }
  getUserCode() {
    return `
      ${Ue()}
        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 = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`;
  }
  getHeightCoordString() {
    return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
  }
  getWidthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
  }
  getDepthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
  }
  getOutputDepthSize() {
    return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
  }
  getInputSamplingString() {
    return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
  }
};
function zre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new Pre(f, i);
  return n.runWebGPUProgram(g, [r], r.dtype, m);
}
var Mre = { kernelName: go, backendName: "webgpu", kernelFunc: zre };
var L2 = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Fr(this.activation, this.isVec4);
      this.hasPreluActivation ? e = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${r}
        }` : e = `
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
            ${r}
          }
        `, t = "dotProd[i] = activation(dotProd[i], coords);";
    }
    let n = this.addBias ? "dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);" : "";
    return `
      ${e}

      ${_v()}
      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
        let batch = 0;
        let r = i32(globalId.x);
        let c = i32(globalId.y) * 4;
        let d2 = i32(globalId.z) * 4;
        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
        let d1 = d2;
        let q = 0;

        let xRCorner = xRCCorner.x;
        let xCCorner = xRCCorner.y;

        var wVals : array<vec4<f32>, 9>;
        wVals[0] = getW(0, 0, d1, q);
        wVals[1] = getW(0, 1, d1, q);
        wVals[2] = getW(0, 2, d1, q);
        wVals[3] = getW(1, 0, d1, q);
        wVals[4] = getW(1, 1, d1, q);
        wVals[5] = getW(1, 2, d1, q);
        wVals[6] = getW(2, 0, d1, q);
        wVals[7] = getW(2, 1, d1, q);
        wVals[8] = getW(2, 2, d1, q);

        var xVals : array<array<vec4<f32>, 6>, 3>;
        for (var wR = 0; wR < 3; wR = wR + 1) {
          let xR = xRCorner + wR * uniforms.dilation[0];
          for (var wC = 0; wC < 6; wC = wC + 1) {
            let xC = xCCorner + wC * uniforms.dilation[1];
            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
              xVals[wR][wC] = vec4<f32>(0.0);
            } else {
              xVals[wR][wC] = getX(batch, xR, xC, d1);
            }
          }
        }

        var dotProd : array<vec4<f32>, 4>;
        dotProd[0] = vec4<f32>(0.0);
        dotProd[1] = vec4<f32>(0.0);
        dotProd[2] = vec4<f32>(0.0);
        dotProd[3] = vec4<f32>(0.0);

        for (var wR = 0; wR < 3; wR = wR + 1) {
          for (var wC = 0; wC < 3; wC = wC + 1) {
            let indexW = wR * 3 + wC;
            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
          }
        }

        for (var i = 0; i < 4; i = i + 1) {
          let coords = vec4<i32>(batch, r, c + i, d2);
          if (coordsInBounds4D(coords, uniforms.outShape)) {
            ${n}
            ${t}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
          }
        }
      }
    `;
  }
};
var B2 = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
      channelMul : i32,`, this.workGroupSize = [256, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise_${this.activation}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Fr(this.activation, false);
      this.hasPreluActivation ? e = `fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${r}
        }` : e = `
          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
            ${r}
          }
        `, t = "dotProd = activation(dotProd, coords);";
    }
    let n = this.addBias ? "dotProd = dotProd + getBiasByOutputCoords(coords);" : "";
    return `
      ${e}

      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
          value : f32) {
        let coord = vec4<i32>(batch, row, col, chan);
        if (coordsInBounds4D(coord, uniforms.outShape)) {
          setOutputAtCoords(batch, row, col, chan, value);
        }
      }

      ${Ci()}
        let coords = getOutputCoords();
        let batch = coords[0];
        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
        let d2 = coords[3];
        let d1 = d2 / uniforms.channelMul;
        let q = d2 - d1 * uniforms.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) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        var dotProd = 0.0;

        // Extract if checking out of for loop for performance.
        if (inputRowStart >= 0 && inputColStart >= 0 &&
          inputRowEnd < uniforms.inDims[0] &&
              inputColEnd < uniforms.inDims[1]) {
            // Here using a constant value |this.convInfo.filterHeight| instead
            // of uniform value is in order to loop unrolling.
            for (var wR = 0; wR < 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 = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + 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 = getX(batch, xR, xC, d1);
                let wVal = getW(wR, wC, d1, q);
                dotProd = dotProd + xVal * wVal;
              }
            }
          }

        ${n}
        ${t}
        writeResult(batch, coords[1], coords[2], d2, dotProd);
      }
    `;
  }
};
function Lre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
  c == null && (c = [1, 1]);
  let p = C.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new L2(p) : (h = new B2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
}
var Bre = { kernelName: Fa, backendName: "webgpu", kernelFunc: Lre };
var V2 = mn({ opSnippet: 0, cpuKernelImpl: yse, supportsComplex: true });
var Vre = { kernelName: Za, backendName: "webgpu", kernelFunc: V2 };
var Wre = class {
  constructor(e, t) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
    let [n] = C.computeOutAndReduceShapes(this.inputShape, [1]);
    this.outputShape = n.length === 0 ? [1] : n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t, this.shaderKey = `reduce_${t}`;
  }
  getUserCode() {
    let e = "", t = "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; }`, t = "f32(x[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" && (e = " bestValue = bestValue * candidate; ", t = "1.0");
    let n = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);";
    return `
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
       }

       ${`
         var<workgroup> xBestValues : array<f32, ${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()}
         let outputIndex = index / i32(workGroupSizeX);
         let offset = getOffset(outputIndex);
         var bestValue = ${t};
         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) {
          ${n}
        }
       }
     `;
  }
};
function ac(e, t, n, s, r) {
  let a = e.shape.length, i = [], o = w.parseAxisParam(t, e.shape), u = o, l = C.getAxesPermutation(u, a), c = e;
  l != null && (c = Ks({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = C.getInnerMostAxes(u.length, a), i.push(c)), C.assertAxesAreInnerMostDims(s, u, a);
  let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = p;
  n && (h = C.expandShapeToKeepDim(p, o));
  let f;
  if ((s === "max" || s === "prod") && r.shouldExecuteOnCPU([c])) {
    let m = r.tensorMap.get(c.dataId).values;
    switch (s) {
      case "max":
        let g = mse(m, w.sizeFromShape(d), h, e.dtype);
        f = r.makeTensorInfo(h, e.dtype, g);
        break;
      case "prod":
        let { outVals: b, outShape: y, outDtype: v } = wse(c.shape, c.dtype, m, u);
        f = r.makeTensorInfo(y, v, b);
        break;
      default:
        throw new Error(`${s} CPU implementation is not yet supported.`);
    }
  } else {
    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : mp(e.dtype), x = [{ type: "int32", data: [m] }], k = new Wre(y, s), I = r.runWebGPUProgram(k, [c], v, x);
    i.push(I), f = We({ inputs: { x: I }, attrs: { shape: h }, backend: r });
  }
  return i.forEach((m) => r.disposeData(m.dataId)), f;
}
function Ov(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return ac(r, a, i, "sum", n);
}
var Ure = { kernelName: ci, backendName: "webgpu", kernelFunc: Ov };
function Gre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = C.decodeEinsumEquation(r, a.length);
  C.checkEinsumDimSizes(i.length, u, a);
  let { path: l, steps: c } = C.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
  for (let m = 0; m < p; ++m) {
    for (let g of c[m]) {
      let { permutationIndices: b, expandDims: y } = C.getEinsumPermutation(h, u[g]), v;
      C.isIdentityPermutation(b) ? v = a[g] : (v = Ks({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
      let x = v.shape.slice();
      for (let k = 0; k < y.length; ++k)
        x.splice(y[k], 0, 1);
      w.arraysEqual(v.shape, x) || (v = We({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = V2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = Ov({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
  }
  for (let m of f)
    m !== d && n.disposeData(m.dataId);
  return d;
}
var Hre = { kernelName: np, backendName: "webgpu", kernelFunc: Gre };
var qre = Kt({ opType: 4 });
var jre = { kernelName: Pa, backendName: "webgpu", kernelFunc: qre };
var Kre = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: rse });
var Xre = { kernelName: bo, backendName: "webgpu", kernelFunc: Kre };
var W2 = Kt({ opType: 5, cpuKernelImpl: ase, dtype: "float32" });
var Yre = { kernelName: za, backendName: "webgpu", kernelFunc: W2 };
function tg(e) {
  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
  return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), We({ inputs: { x: a }, backend: s, attrs: { shape: o } });
}
var Qre = { kernelName: yo, backendName: "webgpu", kernelFunc: tg };
var Zre = Kt({ opType: 6, cpuKernelImpl: ise });
var Jre = { kernelName: vo, backendName: "webgpu", kernelFunc: Zre };
var eae = class {
  constructor(e) {
    this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
  }
  getUserCode() {
    return `
    ${Ue()}
      if (index < uniforms.size) {
        setOutputAtIndex(index, uniforms.value);
      }
    }
  `;
  }
};
function hu(e) {
  let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
  if (a = a || w.inferDtype(r), a === "string") {
    let i = w.getArrayFromDType(a, w.sizeFromShape(s));
    return i.fill(r), t.makeTensorInfo(s, a, i);
  } else {
    let i = new eae(s), o = [{ type: "float32", data: [r] }];
    return t.runWebGPUProgram(i, [], a, o);
  }
}
var tae = { kernelName: gl, backendName: "webgpu", kernelFunc: hu };
var nae = class {
  constructor(e) {
    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
  }
  getUserCode() {
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let coordX = uniforms.xShape[2] - coords[2] - 1;
          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
          setOutputAtIndex(index, outputValue);
        }
      }
    `;
  }
};
var sae = { kernelName: xo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { image: n } = e, s = t, r = new nae(n.shape);
  return s.runWebGPUProgram(r, [n], n.dtype);
} };
var rae = Kt({ opType: 7, cpuKernelImpl: ose });
var aae = { kernelName: Ma, backendName: "webgpu", kernelFunc: rae };
var iae = mn({ opSnippet: 12, dtype: "int32" });
var oae = { kernelName: La, backendName: "webgpu", kernelFunc: iae };
var uae = class {
  constructor(e, t = false) {
    this.outputShape = [0], this.variableNames = [], this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.useImport = t, this.shaderKey = `fromPixels_${this.useImport}`;
  }
  getUserCode() {
    let e = this.useImport ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
    return `
      @binding(1) @group(0) var src: ${this.useImport ? "texture_external" : "texture_2d<f32>"};

      ${Ue()}
        let flatIndexBase = index * uniforms.numChannels;
        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
          let flatIndex = flatIndexBase + i;
          if (flatIndex < uniforms.size) {
            let coords = getCoordsFromIndex(flatIndexBase);
            let values = ${e};
            result[flatIndex] = i32(floor(255.0 * values[i]));
          }
        }
      }
  `;
  }
};
var lae = { kernelName: bd, backendName: "webgpu", kernelFunc: cae };
var Gi;
function cae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s;
  if (r == null)
    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
  let i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, u = typeof HTMLCanvasElement != "undefined" && r instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && r instanceof OffscreenCanvas, l = typeof ImageBitmap != "undefined" && r instanceof ImageBitmap, [c, p] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], d = [p, c, a];
  if (K().getBool("WEBGPU_USE_IMPORT") && i)
    return Lw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
  if ((i || o) && (Gi == null && (Gi = document.createElement("canvas").getContext("2d")), Gi.canvas.width = c, Gi.canvas.height = p, Gi.drawImage(r, 0, 0, c, p), r = Gi.canvas), l || u || i || o)
    return Lw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
  let h = r.data, f = h;
  if (a != null && a !== 4) {
    f = new Uint8Array(r.width * r.height * a);
    let b = h.length, y = 0;
    for (let v = 0; v < b; v++)
      v % 4 < a && (f[y++] = h[v]);
  }
  let m = n.makeTensorInfo(d, "int32"), g = n.tensorMap.get(m.dataId);
  return g.values = new Int32Array(f), n.maybeReleaseBuffer(m.dataId), n.uploadToGPU(m.dataId), m;
}
function Lw(e) {
  let { externalImage: t, backend: n, attrs: s, outShape: r, useImport: a } = e, { numChannels: i } = s, o = w.sizeFromShape(r), u = w.computeStrides(r), l = new uae(r, a), c = [{ type: "uint32", data: [o] }, { type: "uint32", data: [i] }, { type: "uint32", data: [...u] }, { type: "uint32", data: [...l.dispatch] }];
  return n.runFromPixelsProgram(l, r, c, a, t);
}
var dae = class {
  constructor(e, t, n, s, r) {
    this.uniforms = "varianceEpsilon : f32,", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], C.assertAndGetBroadcastShape(e, t), C.assertAndGetBroadcastShape(e, n), this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), s != null && (C.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset")), r != null && (C.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale")), this.offsetShape = s, this.scaleShape = r, this.shaderKey = "batchNorm";
  }
  getUserCode() {
    let e = "0.0";
    this.offsetShape != null && (e = "getOffsetByOutputIndex(index)");
    let t = "1.0";
    return this.scaleShape != null && (t = "getScaleByOutputIndex(index)"), `
      ${Ue()}
        if (index < uniforms.size)
        {
          let xValue = getXByOutputIndex(index);
          let meanValue = getMeanByOutputIndex(index);
          let varianValue = getVarianceByOutputIndex(index);
          let offsetValue = ${e};
          let scaleValue = ${t};
          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
        }
      }
  `;
  }
};
var pae = { kernelName: Ba, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s, scale: r, offset: a, mean: i, variance: o } = e, { varianceEpsilon: u } = t, l = n, c = [s, i, o], p = null;
  a != null && (p = a.shape, c.push(a));
  let d = null;
  r != null && (d = r.shape, c.push(r));
  let h = new dae(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
  return l.runWebGPUProgram(h, c, s.dtype, f);
} };
function hae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
  return z2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
}
var fae = { kernelName: ia, backendName: "webgpu", kernelFunc: hae };
function mae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = c;
  f == null && (f = [1, 1]), w.assert(C.eitherStridesOrDilationsAreOne(u, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);
  let m = C.computeConv2DInfo(r.shape, a.shape, u, f, l, p, true), g = [r, a], b = i != null, y = o != null;
  b && g.push(i), y && g.push(o);
  let v = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }], x;
  return m.batchSize === 1 && m.inHeight === m.outHeight && m.inWidth === m.outWidth && m.strideHeight === 1 && m.strideWidth === 1 && m.filterHeight === m.filterWidth && m.inChannels === m.outChannels && m.dilationHeight === 1 && m.dilationWidth === 1 && m.filterHeight === 3 && m.inChannels % 4 === 0 ? x = new L2(m, b, d, y) : (x = new B2(m, b, d, y), v.push({ type: "int32", data: [m.filterHeight] }, { type: "int32", data: [m.filterWidth] }, { type: "int32", data: [m.outChannels / m.inChannels] })), d === "leakyrelu" && (v.push({ type: "float32", data: [h] }), x.uniforms += " alpha : f32,"), n.runWebGPUProgram(x, g, "float32", v);
}
var gae = { kernelName: oa, backendName: "webgpu", kernelFunc: mae };
var bae = class {
  constructor(e, t) {
    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Ut(e)},`;
  }
  getUserCode() {
    let e;
    return this.sliceDim > 1 ? e = "uniforms.strides[j]" : e = "uniforms.strides", `
        ${Ue()}
        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 yae(e) {
  let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = C.prepareAndValidate(s, r), d = We({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = We({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
  if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = use(y, v, s.dtype, l, i, c, p, s.shape, o);
    return n.makeTensorInfo(u, s.dtype, x.values);
  }
  let f = new bae(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = We({ inputs: { x: g }, backend: n, attrs: { shape: u } });
  return n.disposeData(d.dataId), n.disposeData(h.dataId), n.disposeData(g.dataId), b;
}
var vae = { kernelName: ko, backendName: "webgpu", kernelFunc: yae };
var xae = class {
  constructor(e, t) {
    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
  }
  getUserCode() {
    let e = wae(this.aShape);
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let indexZ = i32(getIndices(resRC.x, resRC.z));
          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
          setOutputAtIndex(index, inBounds * getA(${e}));
        }
      }
    `;
  }
};
function wae(e) {
  let t = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], n = [];
  for (let s = 0; s < e.length; s++)
    s === 2 ? n.push("indexZ") : n.push(`${t[s]}`);
  return n.join();
}
function U2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = C.segment_util.collectGatherOpShapeInfo(r, a, u, o), c = w.sizeFromShape(a.shape), p = [], d = We({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = We({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
  p.push(d), p.push(h);
  let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
  if (n.shouldExecuteOnCPU([r, a])) {
    let v = n.tensorMap.get(h.dataId).values, x = De(h.shape, h.dtype, v), I = n.tensorMap.get(d.dataId).values, $ = De(d.shape, d.dtype, I), E = lse($, x, f);
    return p.forEach((A) => n.disposeData(A.dataId)), n.makeTensorInfo(l.outputShape, E.dtype, E.values);
  }
  let m = new xae(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
  p.push(g);
  let b = We({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
  return p.forEach((y) => n.disposeData(y.dataId)), b;
}
var kae = { kernelName: wo, backendName: "webgpu", kernelFunc: U2 };
var Sae = mn({ opSnippet: 5, cpuKernelImpl: dse, dtype: "bool" });
var Iae = { kernelName: So, backendName: "webgpu", kernelFunc: Sae };
var Cae = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: cse });
var Nae = { kernelName: Va, backendName: "webgpu", kernelFunc: Cae };
function Tae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new sc(r.shape, 14);
  return o.uniforms = "alpha : f32,", n.runWebGPUProgram(o, [r], "float32", i);
}
var $ae = { kernelName: Ua, backendName: "webgpu", kernelFunc: Tae };
var _ae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: hse });
var Aae = { kernelName: Io, backendName: "webgpu", kernelFunc: _ae };
var Eae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: pse });
var Rae = { kernelName: Co, backendName: "webgpu", kernelFunc: Eae };
var Dae = Kt({ opType: 9, cpuKernelImpl: fse });
var Fae = { kernelName: Ga, backendName: "webgpu", kernelFunc: Dae };
var Oae = mn({ opSnippet: 9, dtype: "bool" });
var Pae = { kernelName: No, backendName: "webgpu", kernelFunc: Oae };
var zae = Kt({ opType: 10 });
var Mae = { kernelName: wl, backendName: "webgpu", kernelFunc: zae };
function G2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
  return ac(r, a, i, "max", n);
}
var Lae = { kernelName: Ha, backendName: "webgpu", kernelFunc: G2 };
var Bae = mn({ opSnippet: 15, cpuKernelImpl: gse });
var Vae = { kernelName: qa, backendName: "webgpu", kernelFunc: Bae };
function Wae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = C.computePool2DInfo(r.shape, a, i, l, o, u), p, d = [];
  if (c.filterHeight === 1 && c.filterWidth === 1) {
    if (w.arraysEqual(c.inShape, c.outShape))
      return Wn({ inputs: { x: r }, backend: n });
    p = new F2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
  } else
    p = new D2(c, "max"), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] });
  return n.runWebGPUProgram(p, [r], r.dtype, d);
}
var Uae = { kernelName: ja, backendName: "webgpu", kernelFunc: Wae };
function Gae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
  return ac(r, i, a, "mean", n);
}
var Hae = { kernelName: Ka, backendName: "webgpu", kernelFunc: Gae };
function qae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return ac(r, a, i, "min", n);
}
var jae = { kernelName: Xa, backendName: "webgpu", kernelFunc: qae };
var Kae = mn({ opSnippet: 16, cpuKernelImpl: bse });
var Xae = { kernelName: Ya, backendName: "webgpu", kernelFunc: Kae };
var Yae = class {
  constructor(e, t, n) {
    this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((s, r) => s[0] + e[r] + s[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.xShape = e, t.map((s, r) => {
      this.uniforms += ` pad${r} : vec2<i32>,`;
    }), this.offset = n === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${n}`;
  }
  getUserCode() {
    let e = this.xShape.length, t = this.xShape.map((u, l) => `uniforms.pad${l}[0]`).join(","), n = this.xShape.map((u, l) => `uniforms.pad${l}[0] + uniforms.xShape${e > 1 ? `[${l}]` : ""}`).join(","), s = e === 1 ? "start" : "start[i]", r = e === 1 ? "end" : "end[i]", a = e === 1 ? "outC" : "outC[i]", i = Ut(e), o = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let start = ${i}(${t});
          let end = ${i}(${n});
          var outC = getCoordsFromIndex(index);
          for (var i = 0; i < ${e}; i = i + 1) {
            if (${a} < ${s}) {
              ${a} = ${s} * 2 - ${a} - ${this.offset};
            } else if(${a} >= ${r}) {
              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
            }
          }
          let coords = outC - start;
          setOutputAtIndex(index, getX(${o}));
        }
      }
    `;
  }
};
var Qae = { kernelName: Qa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s } = e, { paddings: r, mode: a } = t, i = n, o = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new Yae(s.shape, r, a);
  return i.runWebGPUProgram(u, [s], s.dtype, o);
} };
function Zae(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s])) {
    let a = n.tensorMap.get(s.dataId), [i, o] = vse(a.values, s.shape, s.dtype);
    return n.makeTensorInfo(o, s.dtype, i);
  }
  let r = new sc(s.shape, 11);
  return n.runWebGPUProgram(r, [s], s.dtype);
}
var Jae = { kernelName: To, backendName: "webgpu", kernelFunc: Zae };
function eie(e) {
  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = ws.nonMaxSuppressionV3Impl(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var tie = { kernelName: _o, backendName: "webgpu", kernelFunc: eie };
function nie(e) {
  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = ws.nonMaxSuppressionV5Impl(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var sie = { kernelName: Ao, backendName: "webgpu", kernelFunc: nie };
function Gd(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "complex64") {
    let r = rc({ inputs: { input: s }, backend: n }), a = Gd({ inputs: { x: r }, backend: n }), i = sh({ inputs: { input: s }, backend: n }), o = Gd({ inputs: { x: i }, backend: n }), u = du({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
  } else
    return hu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
}
var rie = { kernelName: Ko, backendName: "webgpu", kernelFunc: Gd };
function H2(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "string")
    throw new Error("onesLike is not supported under string dtype");
  if (s.dtype === "complex64") {
    let r = rc({ inputs: { input: s }, backend: n }), a = H2({ inputs: { x: r }, backend: n }), i = sh({ inputs: { input: s }, backend: n }), o = Gd({ inputs: { x: i }, backend: n }), u = du({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
  } else
    return hu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
}
var aie = { kernelName: Eo, backendName: "webgpu", kernelFunc: H2 };
function iie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return tg({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
  let a = t[0].shape, i = t[0].dtype;
  t.forEach((c) => {
    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let o = [], u = t.map((c) => {
    let p = tg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = P2({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeData(c.dataId)), l;
}
var oie = { kernelName: Do, backendName: "webgpu", kernelFunc: iie };
var uie = class {
  constructor(e, t) {
    this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
      this.uniforms += ` pad${s} : vec2<i32>,`;
    }), this.xShape = e, this.shaderKey = "pad";
  }
  getUserCode() {
    let e = this.xShape.length, t = Ut(e), n = this.xShape.map((c, p) => `uniforms.pad${p}[0]`).join(","), s = this.xShape.map((c, p) => `uniforms.pad${p}[0] + uniforms.xShape${e > 1 ? `[${p}]` : ""}`).join(","), r = e > 1 ? `${t}(${n})` : `${n}`, a = e > 1 ? `${t}(${s})` : `${s}`, i = e > 1 ? "any(outC < start)" : "outC < start", o = e > 1 ? "any(outC >= end)" : "outC >= end", u = e > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, e) : "coords";
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let start = ${r};
          let end = ${a};
          let outC = getCoordsFromIndex(index);

          if (${i} || ${o}) {
            setOutputAtIndex(index, uniforms.constantValue);
          } else {
            let coords = outC - start;
            setOutputAtIndex(index, getX(${u}));
          }
        }
      }
    `;
  }
};
var q2 = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
  if (a.every((l) => w.arraysEqual(l, [0, 0])))
    return Wn({ inputs: { x: r }, backend: n });
  if (w.sizeFromShape(r.shape) === 0) {
    let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
    return hu({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
  }
  let o = [{ type: "float32", data: [i] }];
  a.map((l) => o.push({ type: "int32", data: [l[0], l[1]] }));
  let u = new uie(r.shape, a);
  return n.runWebGPUProgram(u, [r], r.dtype, o);
};
var lie = { kernelName: Ja, backendName: "webgpu", kernelFunc: q2 };
var cie = mn({ opSnippet: 13 });
var die = { kernelName: ei, backendName: "webgpu", kernelFunc: cie };
function pie(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new E2(14, s.shape, r.shape);
  return n.runWebGPUProgram(a, [s, r], "float32");
}
var hie = { kernelName: ti, backendName: "webgpu", kernelFunc: pie };
function fie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return ac(r, a, i, "prod", n);
}
var mie = { kernelName: ni, backendName: "webgpu", kernelFunc: fie };
var gie = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = kse(s, r, a, i);
  return t.makeTensorInfo([o.length], i, o);
};
var bie = { kernelName: Il, backendName: "webgpu", kernelFunc: gie };
var j2 = mn({ opSnippet: 3 });
var yie = { kernelName: Oa, backendName: "webgpu", kernelFunc: j2 };
var vie = Kt({ opType: 12 });
var xie = { kernelName: si, backendName: "webgpu", kernelFunc: vie };
var wie = Kt({ opType: 13 });
var kie = { kernelName: ai, backendName: "webgpu", kernelFunc: wie };
var Sie = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "resizeBilinear";
  }
  getUserCode() {
    return `
      ${Ue()}
        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 Iie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: i, halfPixelCenters: o } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [o ? 0.5 : 0] }], f = new Sie(r.shape, u, l);
  return n.runWebGPUProgram(f, [r], "float32", h);
}
var Cie = { kernelName: ri, backendName: "webgpu", kernelFunc: Iie };
var Nie = class {
  constructor(e, t, n, s) {
    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.halfPixelCenters = s, this.shaderKey = `resizeNearest_${s}`;
  }
  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()}
        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 Tie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new Nie(r.shape, u, l, i);
  return n.runWebGPUProgram(f, [r], r.dtype, h);
}
var $ie = { kernelName: Nl, backendName: "webgpu", kernelFunc: Tie };
var _ie = class {
  constructor(e, t) {
    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
          cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3<f32>,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3");
  }
  getUserCode() {
    return `
        ${Ue()}

          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));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `;
  }
};
var Aie = { kernelName: Xo, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new _ie(s.shape, a), [l, c] = C.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
  return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
} };
var Eie = Kt({ opType: 15, cpuKernelImpl: Sse });
var Rie = { kernelName: ii, backendName: "webgpu", kernelFunc: Eie };
var Die = class {
  constructor(e, t, n, s, r, a, i) {
    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Be(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;
    let o = Ut(r.length);
    this.uniforms = `sliceDim : i32, strides: ${o}, size: i32,`, this.updatesRank = s, this.indicesRank = n;
  }
  getUserCode() {
    let e = "";
    this.indicesRank === 1 ? e = "coords[0]" : this.indicesRank === 2 && (e = "coords[0], j");
    let t = `getIndices(${e})`, n = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides", s = "", r = "", a = "";
    this.updatesRank === 1 ? (s = "coords[0]", r = "flattenedIndex", a = `
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
        return index;
      }
      `) : this.updatesRank === 2 && (s = "coords[0], coords[1]", r = "vec2<i32>(flattenedIndex, coords[1])", a = `
      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
        let d0 = index / uniforms.updatesShape[1];
        let d1 = index - d0 * uniforms.updatesShape[1];
        return vec2<i32>(d0, d1);
      }
      `);
    let i = `getUpdates(${s})`, o = this.type === "int32" ? "atomicAdd(&(result[flatIndex]), i32(updateValue));" : `
     var assumed = atomicLoad(&(result[flatIndex]));
     var success = 0;
     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
       assumed = resValue[0];
       success = resValue[1];
     }
     `;
    return `
    ${a}

      ${Ue()}

        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(${t}));
            flattenedIndex = flattenedIndex + indexInside * ${n};
          }
          let updateValue = ${i};
          let flatIndex = getOutputIndexFromCoords(${r});

         ${o}
        }
      }`;
  }
};
function Fie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = C.calculateShapes(a, r, i), d = [p / l, l];
  if (p === 0)
    return n.makeTensorInfo(i, r.dtype);
  let h = We({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = We({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = hu({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new Die(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = We({ inputs: { x }, backend: n, attrs: { shape: i } });
  return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(x.dataId), k;
}
var Oie = { kernelName: zo, backendName: "webgpu", kernelFunc: Fie };
var Pie = class {
  constructor(e, t, n) {
    this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.cRank = e, this.rank = n, this.shaderKey = "select";
  }
  getUserCode() {
    let e, t;
    if (this.rank > 4)
      throw Error(`Where for rank ${this.rank} is not yet supported`);
    if (this.rank === 1)
      t = "resRC", e = "resRC";
    else {
      let s = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], r = [], a = [];
      for (let i = 0; i < this.outputShape.length; i++)
        a.push(`${s[i]}`), i < this.cRank && r.push(`${s[i]}`);
      e = r.join(), t = a.join();
    }
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          let cVal = getC(${e});
          if (cVal >= 1.0) {
            setOutputAtIndex(index, getA(${t}));
          } else {
            setOutputAtIndex(index, getB(${t}));
          }
        }
      }
    `;
  }
};
function zie(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new Pie(s.shape.length, r.shape, r.shape.length);
  return n.runWebGPUProgram(i, [s, r, a], cn(r.dtype, a.dtype));
}
var Mie = { kernelName: Mo, backendName: "webgpu", kernelFunc: zie };
var Lie = Kt({ opType: 18 });
var Bie = { kernelName: ui, backendName: "webgpu", kernelFunc: Lie };
var Vie = Kt({ opType: 16 });
var Wie = { kernelName: oi, backendName: "webgpu", kernelFunc: Vie };
var Uie = Kt({ opType: 17 });
var Gie = { kernelName: Bo, backendName: "webgpu", kernelFunc: Uie };
var K2 = mn({ opSnippet: 2, cpuKernelImpl: $se, supportsComplex: true });
var Hie = { kernelName: hi, backendName: "webgpu", kernelFunc: K2 };
function qie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = G2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = C.expandShapeToKeepDim(o.shape, i), l = We({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = K2({ inputs: { a: r, b: l }, backend: n }), p = W2({ inputs: { x: c }, backend: n }), d = Ov({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = We({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = j2({ inputs: { a: p, b: h }, backend: n });
  return n.disposeData(o.dataId), n.disposeData(l.dataId), n.disposeData(c.dataId), n.disposeData(p.dataId), n.disposeData(d.dataId), n.disposeData(h.dataId), f;
}
var jie = { kernelName: di, backendName: "webgpu", kernelFunc: qie };
var Kie = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
  w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
  let o = a.reduce((b, y) => b * y), u = [[0, 0]];
  u.push(...i);
  for (let b = 1 + a.length; b < r.shape.length; ++b)
    u.push([0, 0]);
  let l = [], c = q2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = C.getReshaped(c.shape, a, o, false), d = C.getPermuted(p.length, a.length, false), h = C.getReshapedPermuted(c.shape, a, o, false), f = We({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = Ks({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = We({ inputs: { x: m }, backend: n, attrs: { shape: h } });
  return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeData(b.dataId)), g;
};
var Xie = { kernelName: Vo, backendName: "webgpu", kernelFunc: Kie };
var Yie = class {
  constructor(e, t, n, s, r, a, i = true) {
    this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
    let o = t > 1;
    this.shaderKey = `scatter_${n}_${s}_${o}`;
    let u = Ut(r.length);
    this.uniforms = `updateSize : i32, sliceDim : i32, strides: ${u},`;
    let l = "";
    n === 1 ? l = "i" : n === 2 && (l = "i, j"), this.indicesSnippet = `getIndices(${l})`;
    let c = "";
    s === 1 ? c = "i" : s === 2 && (c = "i, coords[1]"), this.updatesSnippet = `getUpdates(${c})`, this.strideString = o ? "uniforms.strides[j]" : "uniforms.strides";
  }
  getUserCode() {
    return `
      ${Ue()}

        let globalIndex = index * ${this.workPerThread};
        if (globalIndex < uniforms.size) {
          var sum = vec4<f32>(0.0);
          var found = vec4<bool>(false);
          for (var i = 0; i < uniforms.updateSize; i = i + 1) {
            var flattenedIndex = 0;
            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
              let indexInside = i32(round(${this.indicesSnippet}));
              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
            }
            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
              let curIndex = globalIndex + innerIndex;
              let coords = getCoordsFromIndex(curIndex);
              if (flattenedIndex == coords[0]) {
                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
                found[innerIndex] = true;
              }
            }
          }
          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
            let curIndex = globalIndex + innerIndex;
            if (curIndex < uniforms.size)
            {
              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
            }
          }
        }
      }`;
  }
};
function Qie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = C.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new Yie(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = We({ inputs: { x: m }, backend: n, attrs: { shape: o } });
  return n.disposeData(m.dataId), g;
}
var Zie = { kernelName: dp, backendName: "webgpu", kernelFunc: Qie };
function Jie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
  return u.map((d) => {
    let h = [...p];
    h[o] = d;
    let f = pu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
    return c[o] += d, f;
  });
}
var eoe = { kernelName: Wo, backendName: "webgpu", kernelFunc: Jie };
var toe = Kt({ opType: 19 });
var noe = { kernelName: li, backendName: "webgpu", kernelFunc: toe };
var soe = { kernelName: El, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { x: n } = e, s = t, r = new sc(n.shape, 20);
  return s.runWebGPUProgram(r, [n], n.dtype);
} };
var roe = mn({ opSnippet: 11 });
var aoe = { kernelName: pi, backendName: "webgpu", kernelFunc: roe };
var ioe = class {
  constructor(e) {
    this.variableNames = ["x"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
    let t = Ut(this.outputShape.length);
    this.uniforms = `begin : ${t},  strides : ${t}, `, this.shaderKey = "stridedSlice";
  }
  getUserCode() {
    let e = this.outputShape.length, t = "";
    if (e === 1)
      t = "coords * uniforms.strides + uniforms.begin";
    else {
      let s = 0;
      t = this.outputShape.map((r, a) => (s++, this.outputShape.length === 1 ? `coords * uniforms.strides[${a}] + uniforms.begin[${a}]` : `coords[${s - 1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",");
    }
    return `
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           setOutputAtIndex(index, getX(${t}));
         }
       }
     `;
  }
};
function ooe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = We({ inputs: { x: r }, backend: n, attrs: { shape: f } });
  else if (g || b) {
    w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
    let I = wt.computeOutShape(y, v, x), $ = pu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: I } });
    k = We({ inputs: { x: $ }, backend: n, attrs: { shape: f } }), n.disposeData($.dataId);
  } else if (n.shouldExecuteOnCPU([r])) {
    let $ = n.readSync(r.dataId), E = De(r.shape, r.dtype, $), A = Nse(h, E, x, y);
    k = n.makeTensorInfo(f, r.dtype, A.values);
  } else {
    let $ = new ioe(h), E = [{ type: "int32", data: y }, { type: "int32", data: x }], A = n.runWebGPUProgram($, [r], r.dtype, E);
    k = We({ inputs: { x: A }, backend: n, attrs: { shape: f } }), n.disposeData(A.dataId);
  }
  return k;
}
var uoe = { kernelName: Uo, backendName: "webgpu", kernelFunc: ooe };
function loe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = Tse(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var coe = { kernelName: pp, backendName: "webgpu", kernelFunc: loe };
var doe = Kt({ opType: 21 });
var poe = { kernelName: fi, backendName: "webgpu", kernelFunc: doe };
var hoe = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.workGroupSize = [64, 1, 1], this.size = true;
    let n = new Array(e.length);
    for (let s = 0; s < n.length; s++)
      n[s] = e[s] * t[s];
    this.outputShape = n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
  }
  getUserCode() {
    let e = foe(this.rank, "uniforms.");
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `;
  }
};
function foe(e, t = "") {
  if (e >= 5)
    throw Error(`Tile for rank ${e} is not yet supported`);
  if (e === 1)
    return `(resRC % ${t}aShape)`;
  let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [];
  for (let r = 0; r < e; r++)
    s.push(`(${n[r]} % ${t}aShape[${r}])`);
  return s.join();
}
function moe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  if (n.shouldExecuteOnCPU([r]) || r.dtype === "string" || r.shape.length >= 5) {
    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = _se(c, a);
    return n.makeTensorInfo(p.shape, p.dtype, p.values);
  }
  let i = new hoe(r.shape, a);
  return n.runWebGPUProgram(i, [r], r.dtype);
}
var goe = { kernelName: Cr, backendName: "webgpu", kernelFunc: moe };
var boe = class {
  constructor(e) {
    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
        dir : i32, inc : i32,`, this.shaderKey = "swap";
  }
  getUserCode() {
    return `
        ${Ue()}
          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 yoe = class {
  constructor(e) {
    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
  }
  getUserCode() {
    return `
        ${Ue()}
          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 Hi(e, t) {
  t !== null && e.disposeData(t.dataId);
}
function Bw(e) {
  let t = 1;
  for (; t < e; )
    t *= 2;
  return t;
}
function voe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = r.shape, u = o[o.length - 1];
  if (n.shouldExecuteOnCPU([r])) {
    let k = n.readSync(r.dataId), [I, $] = Ase(k, o, r.dtype, a, i);
    return [n.makeTensorInfo(I.shape, I.dtype, I.values), n.makeTensorInfo($.shape, $.dtype, $.values)];
  }
  if (a === 0)
    return o[o.length - 1] = 0, [n.makeTensorInfo(o, r.dtype, []), n.makeTensorInfo(o, "int32", [])];
  if (u === 1)
    return [r, hu({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
  let c = w.sizeFromShape(o) / u, p = We({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Bw(a), h = Bw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, I, $) => {
    let E = m(), A = new boe($), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [I] }], F = f;
    f = n.runWebGPUProgram(A, E, "int32", R), Hi(n, F);
  };
  for (let k = 1; k < d; k *= 2) {
    let I = k * 2;
    for (let $ = k; $ >= 1; $ /= 2)
      g(I, $, [c, h]);
  }
  for (let k = h; k > d; k /= 2) {
    let I = m(), $ = new yoe([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
    f = n.runWebGPUProgram($, I, "int32", A), Hi(n, P);
    let R = d / 2, F = R * 2;
    for (let T = R; T >= 1; T /= 2)
      g(F, T, f.shape);
  }
  let b = f;
  f = pu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Hi(n, b);
  let y = U2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
  Hi(n, p);
  let v = o.slice(0, -1);
  v.push(a), b = f, f = We({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Hi(n, b);
  let x = y;
  return y = We({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Hi(n, x), [y, f];
}
var xoe = { kernelName: Ho, backendName: "webgpu", kernelFunc: voe };
var woe = 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 = Be(this.outputShape), this.dispatch = _e(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()}
            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 koe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new woe(g), y = i === "nearest" ? 1 : 2, v;
  switch (o) {
    case "constant":
      v = 1;
      break;
    case "reflect":
      v = 2;
      break;
    case "wrap":
      v = 3;
      break;
    case "nearest":
      v = 4;
      break;
    default:
      v = 1;
      break;
  }
  let x = [{ type: "int32", data: [y] }, { type: "int32", data: [v] }, { type: "float32", data: [u] }];
  return n.runWebGPUProgram(b, [r, a], "float32", x);
}
var Soe = { kernelName: qo, backendName: "webgpu", kernelFunc: koe };
function Ioe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
  a < 0 && (a += r.shape.length);
  let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
  for (let m = 0; m < o; m++)
    m !== a && (l[c++] = i.shape[m]);
  let p = [], d = new Array(o).fill(0), h = i.shape.slice();
  h[a] = 1;
  let f = new Array(u);
  for (let m = 0; m < f.length; m++) {
    d[a] = m;
    let g = pu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = We({ inputs: { x: g }, backend: n, attrs: { shape: l } });
    f[m] = b, p.push(g);
  }
  return p.forEach((m) => n.disposeData(m.dataId)), f;
}
var Coe = { kernelName: jo, backendName: "webgpu", kernelFunc: Ioe };
var Noe = [Yne, Dse, Ose, Mse, Gse, qse, Kse, Yse, tre, are, ore, dre, ese, mre, xre, Ire, Nre, $re, Ere, Dre, Ore, Mre, Bre, Hre, jre, Xre, Yre, Qre, Jre, tae, sae, lae, aae, oae, pae, fae, gae, vae, kae, Iae, Nae, Jne, hre, $ae, Aae, Rae, Fae, Pae, Mae, Lae, Vae, Uae, Hae, jae, Xae, Qae, Vre, Jae, tie, sie, nre, aie, oie, lie, die, hie, mie, bie, sre, yie, xie, kie, Kne, Cie, $ie, Aie, Rie, Oie, Mie, Bie, Wie, Gie, Jse, uoe, coe, jie, Xie, Zie, eoe, noe, soe, aoe, Hie, Ure, poe, goe, xoe, Soe, Wse, Coe, rie];
for (let e of Noe)
  Rl(e);
var Toe = 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, t) {
    return this.acquireBuffer(e, t, true);
  }
  acquireBuffer(e, t, n = false) {
    let s = Vw(e, t);
    if (this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.usedBuffers.has(s) || this.usedBuffers.set(s, []), this.numBytesUsed += e, this.numUsedBuffers++, this.freeBuffers.get(s).length > 0) {
      this.numFreeBuffers--;
      let a = this.freeBuffers.get(s).shift();
      return this.usedBuffers.get(s).push(a), a;
    }
    this.numBytesAllocated += e;
    let r = this.device.createBuffer({ mappedAtCreation: n, size: e, usage: t });
    return this.usedBuffers.get(s).push(r), r;
  }
  releaseBuffer(e, t, n) {
    if (this.freeBuffers.size === 0)
      return;
    let s = Vw(t, n);
    this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.freeBuffers.get(s).push(e), this.numFreeBuffers++, this.numUsedBuffers--;
    let r = this.usedBuffers.get(s), a = r.indexOf(e);
    if (a < 0)
      throw new Error("Cannot release a buffer that was never provided by this buffer manager");
    r.splice(a, 1), this.numBytesUsed -= t;
  }
  releaseUploadBuffer(e, t, n) {
    e.mapAsync(GPUMapMode.WRITE).then(() => {
      this.releaseBuffer(e, t, n);
    }, (s) => {
    });
  }
  getNumUsedBuffers() {
    return this.numUsedBuffers;
  }
  getNumFreeBuffers() {
    return this.numFreeBuffers;
  }
  dispose() {
    this.freeBuffers.forEach((e, t) => {
      e.forEach((n) => {
        n.destroy();
      });
    }), this.usedBuffers.forEach((e, t) => {
      e.forEach((n) => {
        n.destroy();
      });
    }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
};
function Vw(e, t) {
  return `${e}_${t}`;
}
var $oe = class {
  constructor(e) {
    this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
  acquireTexture(e, t, n, s) {
    let r = Uw(n), a = e * t * r, i = Ww(e, t, n, s);
    if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) {
      this.numFreeTextures--;
      let u = this.freeTextures.get(i).shift();
      return this.usedTextures.get(i).push(u), u;
    }
    this.numBytesAllocated += a;
    let o = this.device.createTexture({ size: [e, t], format: n, usage: s });
    return this.usedTextures.get(i).push(o), o;
  }
  releaseTexture(e, t, n, s, r) {
    if (this.freeTextures.size === 0)
      return;
    let a = Ww(t, n, s, r);
    this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
    let i = this.usedTextures.get(a), o = i.indexOf(e);
    if (o < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    i.splice(o, 1);
    let u = Uw(s), l = t * n * u;
    this.numBytesUsed -= l;
  }
  getNumUsedTextures() {
    return this.numUsedTextures;
  }
  getNumFreeTextures() {
    return this.numFreeTextures;
  }
  dispose() {
    this.freeTextures.forEach((e, t) => {
      e.forEach((n) => {
        n.destroy();
      });
    }), this.usedTextures.forEach((e, t) => {
      e.forEach((n) => {
        n.destroy();
      });
    }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
};
function Ww(e, t, n, s) {
  return `${e}_${t}_${n}_${s}`;
}
function Uw(e) {
  if (e === "rgba8unorm")
    return 16;
  throw new Error(`${e} is not supported!`);
}
var _oe = (e, t, n, s, r) => {
  let a = [s, ...n];
  return r && a.push(r), e.createBindGroup({ layout: t, entries: a.map((i, o) => ({ binding: o, resource: i })) });
};
var Gw = (e, t, n, s, r, a = false) => {
  let i = { dtype: r.dtype, shape: r.shape }, o = Fne(s, i, t, a), u = e.createShaderModule({ code: o, label: t.constructor.name });
  return e.createComputePipeline({ layout: n, compute: { module: u, entryPoint: "main" }, label: t.constructor.name });
};
function Hw(e, t, n = [], s = "", r = "") {
  return e.shaderKey + "_" + (e.workGroupSize ? e.workGroupSize.join(",") : "") + t.map((i) => i.length).join(",") + n.join(",") + e.variableNames.join(",") + s + r;
}
var Aoe = K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var qw = (e, t) => {
  let n = e.limits.maxComputeWorkgroupsPerDimension, s = t.dispatchLayout, r = t.dispatch;
  if (r.every((i) => i <= n))
    return r;
  w.assert(r[0] > n && s.y === void 0 && s.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
  let a = Math.ceil(Math.sqrt(r[0]));
  return a > n ? (a = Math.ceil(Math.cbrt(r[0])), w.assert(a <= n, () => "Total dispatch size exceeds WebGPU maximum."), [a, a, a]) : [a, a, 1];
};
var X2 = class extends rl {
  constructor(e, t = false) {
    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.textureDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, this.fromPixelTextureLayout = null, this.fromPixelImportTextureLayout = null, !Rv())
      throw new Error("WebGPU is not supported on this device");
    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new Toe(this.device), this.textureManager = new $oe(this.device), this.tensorMap = new Kd(this, ds()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), K().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 X2.nextDataId++;
  }
  floatPrecision() {
    return 32;
  }
  defaultGpuBufferUsage() {
    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;
  }
  flushDisposalQueue() {
    this.tensorDisposalQueue.forEach((e) => {
      this.maybeReleaseBuffer(e), this.tensorMap.delete(e);
    }), this.uniformDisposalQueue.forEach((e) => this.bufferManager.releaseBuffer(e.buffer, e.byteSize, e.usage)), this.stagingDisposalQueue.forEach((e) => this.bufferManager.releaseUploadBuffer(e.buffer, e.byteSize, e.usage)), this.textureDisposalQueue.forEach((e) => this.textureManager.releaseTexture(e.texture, e.width, e.height, e.format, e.usage)), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.textureDisposalQueue = [];
  }
  disposeData(e, t = false) {
    if (this.tensorMap.has(e)) {
      let n = this.tensorMap.get(e);
      if (n.refCount--, !t && n.refCount > 0)
        return false;
      if (this.commandQueueOwnedIds.has(e))
        return this.tensorDisposalQueue.push(e), false;
      this.maybeReleaseBuffer(e);
      let { complexTensorInfos: s } = this.tensorMap.get(e);
      s != null && (this.disposeData(s.real.dataId, true), this.disposeData(s.imag.dataId, true)), this.tensorMap.delete(e);
    }
    return true;
  }
  memory() {
    return { numBytesInGPU: this.bufferManager.numBytesUsed, numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated, unreliable: false };
  }
  getBufferManager() {
    return this.bufferManager;
  }
  getTextureManager() {
    return this.textureManager;
  }
  acquireBuffer(e, t = this.defaultGpuBufferUsage()) {
    return this.bufferManager.acquireBuffer(e, t);
  }
  maybeReleaseBuffer(e) {
    let t = this.tensorMap.get(e);
    t != null && t.bufferInfo.buffer != null && (this.bufferManager.releaseBuffer(t.bufferInfo.buffer, t.bufferInfo.byteSize, t.bufferInfo.usage), t.bufferInfo.buffer = null);
  }
  refCount(e) {
    return this.tensorMap.has(e) ? this.tensorMap.get(e).refCount : 0;
  }
  incRef(e) {
    let t = this.tensorMap.get(e);
    t.refCount++;
  }
  decRef(e) {
    if (this.tensorMap.has(e)) {
      let t = this.tensorMap.get(e);
      t.refCount--;
    }
  }
  write(e, t, n) {
    if (n === "complex64" && e != null)
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    let s = { id: this.nextDataId() }, r = w.sizeFromShape(t) * hd(n);
    return this.tensorMap.set(s, { dtype: n, shape: t, values: e, bufferInfo: { byteSize: r, usage: this.defaultGpuBufferUsage() }, refCount: 1 }), s;
  }
  move(e, t, n, s, r) {
    if (s === "complex64")
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    let a = w.sizeFromShape(n) * hd(s);
    this.tensorMap.set(e, { dtype: s, shape: n, values: t, bufferInfo: { byteSize: a, usage: this.defaultGpuBufferUsage() }, refCount: r });
  }
  submitQueue() {
    this.ensureComputePassEnded(), this.queue.submit([this.currentCommandEncoder.finish()]), this.currentCommandEncoder = null, this.dispatchNumberInEncoder = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.flushDisposalQueue();
  }
  getBuffer(e) {
    return this.uploadToGPU(e), this.tensorMap.get(e).bufferInfo.buffer;
  }
  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, t) {
    let n = this.acquireBuffer(t, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e, 0, n, 0, t), this.submitQueue(), await n.mapAsync(GPUMapMode.READ);
    let s = n.getMappedRange().slice(0);
    return n.unmap(), n != null && this.bufferManager.releaseBuffer(n, t, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), K().getBool("WEBGPU_USE_PROFILE_TOOL") && (w.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), s;
  }
  convertAndCacheOnCPU(e, t) {
    let n = this.tensorMap.get(e);
    return this.maybeReleaseBuffer(e), n.values = t, n.values;
  }
  readSync(e) {
    let t = this.tensorMap.get(e), { values: n } = t;
    if (n == null)
      throw new Error("WebGPU readSync is only available for CPU-resident tensors.");
    return n;
  }
  async read(e) {
    if (!this.tensorMap.has(e))
      throw new Error(`Tensor ${e} was not registered!`);
    let t = this.tensorMap.get(e), { values: n } = t;
    if (n != null)
      return this.convertAndCacheOnCPU(e, n);
    let s;
    if (t.dtype === "complex64") {
      let r = await Promise.all([this.read(t.complexTensorInfos.real.dataId), this.read(t.complexTensorInfos.imag.dataId)]), a = r[0], i = r[1];
      s = C.mergeRealAndImagArrays(a, i);
    } else {
      let r = t.values != null ? t.values : await this.getBufferData(t.bufferInfo.buffer, t.bufferInfo.byteSize);
      s = _2(r, t.dtype);
    }
    return this.convertAndCacheOnCPU(e, s), s;
  }
  readToGPU(e, t = {}) {
    let n = this.tensorMap.get(e), { values: s, dtype: r, shape: a, bufferInfo: i } = n;
    if (r === "complex64")
      throw new Error("Does not support reading buffer for complex64 dtype.");
    if (i.buffer == null)
      throw s != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
    let o = w.sizeFromShape(a) * hd(r);
    t.customBufSize != null && w.assert(t.customBufSize >= o, () => `customBufSize should be equal or larger than the source tensor size ${o} bytes.`);
    let u = t.customBufSize != null ? t.customBufSize : o, l = this.acquireBuffer(u);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(i.buffer, 0, l, 0, o), this.submitQueue();
    let c = this.makeTensorInfo(a, r), p = ds().makeTensorFromTensorInfo(c), d = this.tensorMap.get(c.dataId);
    return d.bufferInfo.buffer = l, d.bufferInfo.byteSize = u, { tensorRef: p, buffer: l, bufSize: u };
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId), n = t;
    if (e.dtype === "string")
      try {
        n = t.map((s) => w.decodeString(s));
      } catch (s) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return De(e.shape, e.dtype, n);
  }
  async time(e) {
    let t = this.activeTimers, n = [], s = false;
    this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
    let r = w.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = w.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null);
    this.activeTimers = t, s && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, o = await Promise.all(r);
    return i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
  }
  getAndSavePipeline(e, t) {
    return e in this.pipelineCache || (this.pipelineCache[e] = t()), this.pipelineCache[e];
  }
  makeTensorInfo(e, t, n) {
    let s;
    if (t === "string" && n != null && n.length > 0 && w.isString(n[0])) {
      let r = n.map((a) => w.encodeString(a));
      s = this.write(r, e, t);
    } else
      s = this.write(n, e, t);
    return { dataId: s, shape: e, dtype: t };
  }
  tensorToBinding(e) {
    if (!e)
      return null;
    let t = this.tensorMap.get(e.dataId);
    return { offset: 0, size: t.bufferInfo.byteSize, buffer: t.bufferInfo.buffer };
  }
  async getQueryTime(e) {
    return this.supportTimeQuery ? this.getTimeFromQuerySet(e) : 0;
  }
  uploadToGPU(e) {
    let t = this.tensorMap.get(e);
    if (t.bufferInfo.buffer == null && (t.bufferInfo.buffer = this.acquireBuffer(t.bufferInfo.byteSize), t.values)) {
      let n = this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC), s = n.getMappedRange();
      t.dtype === "int32" || t.dtype === "bool" ? new Int32Array(s).set(t.values) : new Float32Array(s).set(t.values), n.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(n, 0, t.bufferInfo.buffer, 0, t.bufferInfo.byteSize);
      let r = { byteSize: t.bufferInfo.byteSize, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, buffer: n };
      this.stagingDisposalQueue.push(r);
    }
  }
  makeUniforms(e) {
    let t = 0, n = 0, s = [];
    e.forEach((o) => {
      o.data.length === 0 && (o.data = [1]);
      let u;
      switch (o.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:
          w.assert(false, () => `Unsupported ${o.data.length}D shape`);
      }
      (n === 5 || n === 6) && (u = 16), t = Math.ceil(t / u) * u, n = o.data.length, s.push(t), t += o.data.length * 4;
    });
    let r = new ArrayBuffer(t);
    e.forEach((o, u) => {
      let l = s[u];
      o.type === "int32" ? new Int32Array(r, l, o.data.length).set(o.data) : o.type === "uint32" ? new Uint32Array(r, l, o.data.length).set(o.data) : new Float32Array(r, l, o.data.length).set(o.data);
    });
    let a = this.acquireBuffer(t, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
    this.queue.writeBuffer(a, 0, r, 0, t);
    let i = { byteSize: t, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: a };
    return this.uniformDisposalQueue.push(i), { offset: 0, size: t, buffer: a };
  }
  createLayout(e) {
    let t = [];
    t.push({ binding: 0, visibility: GPUShaderStage.COMPUTE, buffer: { type: "storage" } });
    for (let r = 0; r < e; r++)
      t.push({ binding: r + 1, visibility: GPUShaderStage.COMPUTE, buffer: { type: "read-only-storage" } });
    t.push({ binding: e + 1, visibility: GPUShaderStage.COMPUTE, buffer: { type: "uniform" } });
    let n = this.device.createBindGroupLayout({ entries: t }), s = this.device.createPipelineLayout({ bindGroupLayouts: [n] });
    return { bindGroupLayout: n, pipelineLayout: s };
  }
  getCachedOrCreateLayout(e) {
    return e in this.layoutCache || (this.layoutCache[e] = this.createLayout(e)), this.layoutCache[e];
  }
  runWebGPUProgram(e, t, n, s, r) {
    if (!r) {
      if (r = this.makeTensorInfo(e.outputShape, n), w.sizeFromShape(r.shape) === 0) {
        let I = this.tensorMap.get(r.dataId);
        return I.values = w.getTypedArrayFromDType(r.dtype, 0), r;
      }
      this.uploadToGPU(r.dataId);
    }
    e.dispatch = qw(this.device, e);
    let a = [{ type: "float32", data: [NaN] }], i = t.concat(r).map((I) => I.shape), o = "int32";
    i.map((I) => {
      a.push({ type: o, data: I });
    });
    let u = w.computeStrides(r.shape);
    if (a.push({ type: o, data: u }), e.size) {
      let I = w.sizeFromShape(e.outputShape);
      a.push({ type: o, data: [e.isVec4 ? I / 4 : I] });
    }
    s && (a = [...a, ...s]);
    let l = this.makeUniforms(a), c = t.map((I, $) => {
      if (I.dtype === "complex64")
        throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
      return this.uploadToGPU(I.dataId), { dtype: this.tensorMap.get(I.dataId).dtype, shape: I.shape, name: e.variableNames[$] };
    }), p = c.map((I) => I.dtype).concat(r.dtype), d = c.map((I) => C.getBroadcastDims(I.shape, r.shape)), h = c.map((I) => w.arraysEqual(I.shape, r.shape)).join("_"), f = d.map((I) => I.join("_")).join(";"), m = Hw(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => Gw(this.device, e, b, c, r)), v = this.activeTimers != null, x = _oe(this.device, g, t.map((I) => this.tensorToBinding(I)), this.tensorToBinding(r), l);
    this.ensureCommandEncoderReady();
    let k = this.getComputePass();
    return v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 0), k.setPipeline(y), k.setBindGroup(0, x), k.dispatch(e.dispatch[0], e.dispatch[1], e.dispatch[2]), v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t.forEach((I) => {
      this.commandQueueOwnedIds.add(I.dataId);
    }), this.commandQueueOwnedIds.add(r.dataId), K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), v && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), r;
  }
  getFromPixelTextureLayout(e) {
    return e ? (this.fromPixelImportTextureLayout === null && (this.fromPixelImportTextureLayout = this.createFromPixelTextureLayout(true)), this.fromPixelImportTextureLayout) : (this.fromPixelTextureLayout === null && (this.fromPixelTextureLayout = this.createFromPixelTextureLayout(false)), this.fromPixelTextureLayout);
  }
  createFromPixelTextureLayout(e) {
    let t = [];
    t.push({ binding: 0, visibility: GPUShaderStage.COMPUTE, buffer: { type: "storage" } }), e ? t.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, externalTexture: {} }) : t.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, texture: {} }), t.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} });
    let n = this.device.createBindGroupLayout({ entries: t }), s = this.device.createPipelineLayout({ bindGroupLayouts: [n] });
    return { bindGroupLayout: n, pipelineLayout: s };
  }
  copyExternalImageToTexture(e, t) {
    let n = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING, s = "rgba8unorm", r = this.textureManager.acquireTexture(t[1], t[0], s, n), a = r.createView();
    this.queue.copyExternalImageToTexture({ source: e }, { texture: r }, [t[1], t[0]]);
    let i = { width: t[1], height: t[0], format: s, usage: n, texture: r };
    return this.textureDisposalQueue.push(i), a;
  }
  runFromPixelsProgram(e, t, n, s, r) {
    e.dispatch = qw(this.device, e);
    let a = this.makeTensorInfo(t, "int32");
    if (w.sizeFromShape(a.shape) === 0) {
      let m = this.tensorMap.get(a.dataId);
      return m.values = w.getTypedArrayFromDType(a.dtype, 0), a;
    }
    this.uploadToGPU(a.dataId);
    let i = Hw(e, [a.shape]), o = this.getFromPixelTextureLayout(s), u = this.getAndSavePipeline(i, () => Gw(this.device, e, o.pipelineLayout, [], a, true)), l;
    if (s) {
      let m = { source: r };
      l = this.device.importExternalTexture(m);
    } else
      l = this.copyExternalImageToTexture(r, a.shape);
    let c = this.tensorToBinding(a), p = this.makeUniforms(n), d = this.device.createBindGroup({ layout: o.bindGroupLayout, entries: [{ binding: 0, resource: { buffer: c.buffer } }, { binding: 1, resource: l }, { binding: 2, resource: { buffer: p.buffer } }] });
    this.ensureCommandEncoderReady();
    let h = this.getComputePass(), f = this.activeTimers != null;
    return f && this.supportTimeQuery && h.writeTimestamp(this.querySet, 0), h.setPipeline(u), h.setBindGroup(0, d), h.dispatch(e.dispatch[0], e.dispatch[1], e.dispatch[2]), f && this.supportTimeQuery && h.writeTimestamp(this.querySet, 1), this.commandQueueOwnedIds.add(a.dataId), this.dispatchNumberInEncoder++, K().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), f && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), a;
  }
  async getTimeFromQuerySet(e) {
    let t = this.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), n = this.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t, 0), this.currentCommandEncoder.copyBufferToBuffer(t, 0, n, 0, 16), this.submitQueue(), await n.mapAsync(GPUMapMode.READ);
    let s = new BigUint64Array(n.getMappedRange()), r = Number(s[1] - s[0]);
    return n.unmap(), this.bufferManager.releaseBuffer(n, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), r / 1e6;
  }
  shouldExecuteOnCPU(e, t = Aoe) {
    return K().getBool("WEBGPU_CPU_FORWARD") && e.every((n) => this.tensorMap.get(n.dataId).bufferInfo.buffer == null && w.sizeFromShape(n.shape) < t);
  }
  numDataIds() {
    return this.tensorMap.numDataIds() - this.tensorDisposalQueue.length;
  }
  dispose() {
    this.disposed || (this.bufferManager.dispose(), this.textureManager.dispose(), this.disposed = true);
  }
};
var Pv = X2;
Pv.nextDataId = 0;
var Eoe = {};
Ae(Eoe, { WebGPUBackend: () => Pv, webgpu_util: () => T2 });
Rv() && bp("webgpu", async () => {
  K().set("CHECK_COMPUTATION_FOR_ERRORS", false);
  let e = { powerPreference: K().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t = await navigator.gpu.requestAdapter(e), n = t.limits, s = {}, r = t.features.has("timestamp-query");
  s.requiredLimits = { maxComputeWorkgroupStorageSize: n.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: n.maxComputeWorkgroupsPerDimension }, r ? s.requiredFeatures = ["timestamp-query"] : console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");
  let a = await t.requestDevice(s);
  return new Pv(a, r);
}, 3);
var St = ((e) => (e[e.float32 = 0] = "float32", e[e.int32 = 1] = "int32", e[e.bool = 2] = "bool", e[e.string = 3] = "string", e[e.complex64 = 4] = "complex64", e))(St || {});
var rh = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(rh || {});
var Y2;
function Roe(e) {
  Y2 = e.wasm.cwrap(aa, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Doe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t;
  if (r.dtype !== "float32" || a.dtype !== "float32")
    throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
  let { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = 0;
  if (i != null) {
    let E = n.dataIdMap.get(i.dataId);
    if (E.shape.length !== 1)
      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);
    f = E.id;
  }
  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = rh[c];
  if (g == null)
    throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = bi.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, I = new Uint8Array(new Int32Array(r.shape).buffer), $ = new Uint8Array(new Int32Array(a.shape).buffer);
  return Y2(d, I, r.shape.length, h, $, a.shape.length, u, l, g, f, m, p || 0, k), x;
}
var Foe = { kernelName: aa, backendName: "wasm", setupFunc: Roe, kernelFunc: Doe };
function Xt(e, t) {
  let n;
  function s(a) {
    n = a.wasm.cwrap(e, null, ["number", "number", "number"]);
  }
  function r(a) {
    let { backend: i, inputs: { x: o } } = a, u = i.dataIdMap.get(o.dataId).id, l = i.makeOutput(o.shape, t || o.dtype), c = i.dataIdMap.get(l.dataId).id;
    return w.sizeFromShape(l.shape) === 0 || n(u, St[o.dtype], c), l;
  }
  return { kernelName: e, backendName: "wasm", setupFunc: s, kernelFunc: r };
}
var Ooe = Xt(co);
function gn(e, t, n) {
  let s;
  function r(i) {
    s = i.wasm.cwrap(e, null, ["number", "array", "number", "number", "array", "number", "number", "number"]);
  }
  function a(i) {
    let { backend: o, inputs: u } = i, { a: l, b: c } = u, p = o.dataIdMap.get(l.dataId).id, d = o.dataIdMap.get(c.dataId).id, h = n != null ? n : l.dtype, f = C.assertAndGetBroadcastShape(l.shape, c.shape), m = o.makeOutput(f, h);
    if (w.sizeFromShape(f) === 0)
      return m;
    let g = new Uint8Array(new Int32Array(l.shape).buffer), b = new Uint8Array(new Int32Array(c.shape).buffer), y = o.dataIdMap.get(m.dataId).id;
    return (() => s(p, g, l.shape.length, d, b, c.shape.length, St[l.dtype], y))(), m;
  }
  return { kernelName: e, backendName: "wasm", setupFunc: r, kernelFunc: a };
}
var Poe = true;
var zoe = gn(Sr, Poe);
var Q2;
function Moe(e) {
  Q2 = e.wasm.cwrap(Sa, null, ["array", "number", "number", "number"]);
}
function Loe(e) {
  let { inputs: t, backend: n } = e, s = n.makeOutput(t[0].shape, t[0].dtype);
  if (w.sizeFromShape(s.shape) === 0)
    return s;
  let r = t.map((o) => n.dataIdMap.get(o.dataId).id), a = new Uint8Array(new Int32Array(r).buffer), i = n.dataIdMap.get(s.dataId).id;
  return Q2(a, r.length, St[s.dtype], i), s;
}
var Boe = { kernelName: Sa, backendName: "wasm", setupFunc: Moe, kernelFunc: Loe };
function ah(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype), r = n.typedArrayFromHeap(t);
  return n.typedArrayFromHeap(s).set(r), s;
}
var Voe = { kernelName: Wa, backendName: "wasm", kernelFunc: ah };
var Z2;
function Woe(e) {
  Z2 = e.wasm.cwrap(mi, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function wr(e) {
  let { inputs: t, backend: n, attrs: s } = e, [r, a] = Goe(t.x.shape, s.perm), i = true;
  for (let f = 0; f < a.length; f++)
    a[f] !== f && (i = false);
  let o = Uoe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
  if (i) {
    let f = ah({ inputs: t, backend: n });
    return f.shape = o, f;
  }
  let l = n.makeOutput(o, u.dtype), c = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(l.dataId).id, d = new Uint8Array(new Int32Array(a).buffer), h = new Uint8Array(new Int32Array(u.shape).buffer);
  return Z2(c, h, u.shape.length, St[u.dtype], p, d, a.length), l;
}
function Uoe(e, t) {
  let n = new Array(e.length);
  for (let s = 0; s < n.length; s++)
    n[s] = e[t[s]];
  return n;
}
function Goe(e, t) {
  let n = [], s = [];
  for (let r = 0; r < e.length; ++r)
    e[r] !== 1 && n.push(e[r]), e[t[r]] !== 1 && s.push(t[r]);
  for (let r = 0; r < s.length; ++r) {
    let a = -1;
    for (let i = 0; i < s.length; ++i)
      s[i] >= r && (a === -1 || s[a] > s[i]) && (a = i);
    s[a] = r;
  }
  return [n, s];
}
var Hoe = { kernelName: mi, backendName: "wasm", kernelFunc: wr, setupFunc: Woe };
function Or(e, t, n) {
  let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), i = a, o = C.getAxesPermutation(i, r), u = null, l = false;
  if (o != null) {
    let c = new Array(r);
    for (let h = 0; h < c.length; h++)
      c[h] = s[o[h]];
    i = C.getInnerMostAxes(i.length, r), u = wr({ inputs: { x: e }, attrs: { perm: o }, backend: n });
    let p = n.dataIdMap.get(e.dataId).id;
    n.dataIdMap.get(u.dataId).id !== p && (l = true);
  }
  return { transposed: u, originalAxes: a, axes: i, inputWasTransposed: l };
}
var J2;
function qoe(e) {
  J2 = e.wasm.cwrap(ol, null, ["number, number, number"]);
}
function joe(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    l = c, u = v;
  }
  let f = l.shape.length;
  C.assertAxesAreInnerMostDims("all", p, f);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
  if (w.sizeFromShape(l.shape) !== 0) {
    let v = t.dataIdMap.get(y.dataId).id;
    J2(u, b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Koe = { kernelName: ol, backendName: "wasm", setupFunc: qoe, kernelFunc: joe };
var eN;
function Xoe(e) {
  eN = e.wasm.cwrap(ul, null, ["number, number, number"]);
}
function Yoe(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    l = c, u = v;
  }
  let f = l.shape.length;
  C.assertAxesAreInnerMostDims("any", p, f);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
  if (w.sizeFromShape(l.shape) !== 0) {
    let v = t.dataIdMap.get(y.dataId).id;
    eN(u, b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Qoe = { kernelName: ul, backendName: "wasm", setupFunc: Xoe, kernelFunc: Yoe };
var tN;
function Zoe(e) {
  tN = e.wasm.cwrap(Ia, null, ["number", "number", "number", "number", "number"]);
}
function Joe(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = i, u = a, { transposed: l, axes: c, inputWasTransposed: p } = Or(a, r, t);
  if (p) {
    let b = t.dataIdMap.get(l.dataId).id;
    b !== i && (u = l, o = b);
  }
  let d = u.shape.slice(0, -1), h = t.makeOutput(d, "int32"), f = t.dataIdMap.get(h.dataId).id, m = w.sizeFromShape(h.shape), g = u.shape[c[0]];
  return tN(o, St[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
}
var eue = { kernelName: Ia, backendName: "wasm", kernelFunc: Joe, setupFunc: Zoe };
var nN;
function tue(e) {
  nN = e.wasm.cwrap(Ca, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function nue(e) {
  let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = C.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.strideHeight, y = c.strideWidth, v = 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 x = s.makeOutput(c.outShape, "float32"), k = s.dataIdMap.get(x.dataId).id;
  return nN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
}
var sue = { kernelName: Ca, backendName: "wasm", setupFunc: tue, kernelFunc: nue };
function yn(e) {
  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a);
  return w.assert(a === w.sizeFromShape(i), () => `new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
}
var rue = { kernelName: Fo, backendName: "wasm", kernelFunc: yn };
var sN;
function aue(e) {
  sN = e.wasm.cwrap(Na, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function iue(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  if (r.dtype !== "float32" || a.dtype !== "float32")
    throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = bi.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
  w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
  let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], I = yn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = yn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = n.dataIdMap.get(I.dataId).id, A = n.dataIdMap.get($.dataId).id, P = i ? I.shape[2] : I.shape[1], R = o ? $.shape[1] : $.shape[2], F = Math.max(g, b), T = n.makeOutput([F, P, R], I.dtype), z = n.dataIdMap.get(T.dataId).id, W = new Uint8Array(new Int32Array(I.shape).buffer), j = new Uint8Array(new Int32Array($.shape).buffer);
  return sN(E, W, I.shape.length, A, j, $.shape.length, i, o, z), n.disposeData(I.dataId), n.disposeData($.dataId), T.shape = v, T;
}
var oue = { kernelName: Na, backendName: "wasm", setupFunc: aue, kernelFunc: iue };
function xa(e) {
  let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, i] = wt.parseSliceParams(t, n, s), o = wt.isSliceContinous(t.shape, a, i), u = r.readSync(t.dataId), l = r.makeOutput(i, t.dtype), c = w.computeStrides(t.shape), p = r.dataIdMap.get(l.dataId);
  if (o) {
    let f = wt.computeFlatOffset(a, c);
    return t.dtype === "string" ? p.stringBytes = u.slice(f, f + w.sizeFromShape(i)) : r.typedArrayFromHeap(l).set(u.subarray(f, f + w.sizeFromShape(i))), l;
  }
  if (t.dtype === "string") {
    let f = Ld(u, a, i, t.shape, t.dtype);
    return p.stringBytes = f, l;
  }
  let d = r.typedArrayFromHeap(l), h = t.shape.length;
  if (h === 2)
    uue(u, c[0], d, a, i);
  else if (h === 3)
    lue(u, c[0], c[1], d, a, i);
  else if (h === 4)
    cue(u, c[0], c[1], c[2], d, a, i);
  else {
    let f = Ld(u, a, i, t.shape, t.dtype);
    d.set(f);
  }
  return l;
}
function uue(e, t, n, s, r) {
  let a = 0, i = s[0], o = s[1], u = i + r[0];
  for (let l = i; l < u; l++) {
    let c = l * t + o;
    n.set(e.subarray(c, c + r[1]), a), a += r[1];
  }
}
function lue(e, t, n, s, r, a) {
  let i = 0, o = r[0], u = r[1], l = r[2], c = o + a[0], p = u + a[1];
  for (let d = o; d < c; d++)
    for (let h = u; h < p; h++) {
      let f = d * t + h * n + l;
      s.set(e.subarray(f, f + a[2]), i), i += a[2];
    }
}
function cue(e, t, n, s, r, a, i) {
  let o = 0, u = a[0], l = a[1], c = a[2], p = u + i[0], d = l + i[1], h = c + i[2], f = a[3];
  for (let m = u; m < p; m++)
    for (let g = l; g < d; g++)
      for (let b = c; b < h; b++) {
        let y = m * t + g * n + b * s + f;
        r.set(e.subarray(y, y + i[3]), o), o += i[3];
      }
}
var due = { kernelName: Lo, backendName: "wasm", kernelFunc: xa };
function pue(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = C.getReshaped(r.shape, a, o), l = C.getPermuted(u.length, a.length), c = C.getReshapedPermuted(r.shape, a, o), p = C.getSliceBeginCoords(i, a.length), d = C.getSliceSize(c, i, a.length), h = yn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = xa({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
  return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(h.dataId), g;
}
var hue = { kernelName: po, backendName: "wasm", kernelFunc: pue };
function ic(e) {
  let { inputs: { x: t }, attrs: { dtype: n }, backend: s } = e, r = s.makeOutput(t.shape, n), a = s.typedArrayFromHeap(t);
  return s.typedArrayFromHeap(r).set(a), r;
}
var fue = { kernelName: Ta, backendName: "wasm", kernelFunc: ic };
var mue = Xt($a);
var rN;
function gue(e) {
  rN = e.wasm.cwrap(Ir, null, ["number", "number", "number", "number"]);
}
function bue(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o = n.dataIdMap.get(r.dataId).id, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(u.dataId).id;
  return rN(o, a, i, l), u;
}
var yue = { kernelName: Ir, backendName: "wasm", setupFunc: gue, kernelFunc: bue };
function aN(e) {
  let { inputs: t, backend: n } = e, s = w.parseAxisParam(e.attrs.axis, t[0].shape)[0], r = C.computeOutShape(t.map((h) => h.shape), s), a = t.filter((h) => w.sizeFromShape(h.shape) > 0);
  if (a.length === 1)
    return ah({ inputs: { x: a[0] }, backend: n });
  let i = n.makeOutput(r, t[0].dtype);
  if (w.sizeFromShape(r) === 0)
    return i;
  let o = a.map((h) => h.shape);
  if (C.assertParamsConsistent(o, s), a[0].dtype === "string") {
    let h = a.map((v) => {
      let x = w.sizeFromShape(v.shape.slice(s));
      return yn({ inputs: { x: v }, backend: n, attrs: { shape: [-1, x] } });
    }), f = h.map((v) => ({ vals: n.readSync(v.dataId), shape: v.shape }));
    r = C.computeOutShape(h.map((v) => v.shape), 1);
    let m = h[0].shape[0] === 1, g = iv(f, r, t[0].dtype, m), b = C.computeOutShape(a.map((v) => v.shape), s);
    i.shape = b;
    let y = n.dataIdMap.get(i.dataId);
    return y.stringBytes = C.fromStringArrayToUint8(g), h.forEach((v) => n.disposeData(v.dataId)), i;
  }
  let u = w.sizeFromShape(a[0].shape.slice(0, s)), l = 0, c = a.map((h) => {
    let f = w.sizeFromShape(h.shape.slice(s));
    return l += f, f;
  }), p = a.map((h) => n.typedArrayFromHeap(h)), d = n.typedArrayFromHeap(i);
  for (let h = 0; h < u; h++) {
    let f = h * l;
    for (let m = 0; m < p.length; m++) {
      let g = c[m], b = h * g, y = p[m].subarray(b, b + g);
      d.set(y, f), f += g;
    }
  }
  return i;
}
var vue = { kernelName: ho, backendName: "wasm", kernelFunc: aN };
var iN;
function xue(e) {
  iN = e.wasm.cwrap(_a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function wue(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p, dataFormat: d } = n, h = C.convertConv2DDataFormat(d), f = C.computeConv2DInfo(r.shape, a.shape, u, l, c, p, false, h), m = f.filterHeight, g = f.filterWidth, b = f.padInfo.top, y = f.padInfo.right, v = f.padInfo.bottom, x = f.padInfo.left, k = f.dilationHeight, I = f.dilationWidth, $ = f.strideHeight, E = f.strideWidth, A = f.inChannels, P = f.outChannels, R = f.padInfo.type === "SAME" ? 1 : 0;
  if (f.dataFormat !== "channelsLast")
    throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
  let F = s.makeOutput(f.outShape, "float32"), T = s.dataIdMap.get(F.dataId).id;
  return iN(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, R, k, I, $, E, A, P, T), F;
}
var kue = { kernelName: _a, backendName: "wasm", setupFunc: xue, kernelFunc: wue };
var oN;
function Sue(e) {
  oN = e.wasm.cwrap(Aa, 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 Iue(e) {
  let { backend: t, inputs: n, attrs: s } = e, { dy: r, filter: a } = n, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, inputShape: c } = s, p = 1, d = C.convertConv2DDataFormat(u), h = C.computeConv2DInfo(c, a.shape, i, p, o, l, false, d), { batchSize: f, filterHeight: m, filterWidth: g, inChannels: b, inHeight: y, inWidth: v, outChannels: x, outHeight: k, outWidth: I, strideHeight: $, strideWidth: E } = h, A = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, R = h.dataFormat === "channelsLast", F = w.computeStrides(h.inShape), T = w.computeStrides(r.shape), [z, W, j] = w.computeStrides(a.shape), X = F[0], Y = R ? F[1] : F[2], Z = R ? F[2] : 1, te = R ? 1 : F[1], J = T[0], se = R ? T[1] : T[2], ne = R ? T[2] : 1, oe = R ? 1 : T[1], ae = t.makeOutput(h.inShape, "float32"), de = t.dataIdMap.get(ae.dataId).id, me = t.dataIdMap.get(r.dataId).id, ke = t.dataIdMap.get(a.dataId).id;
  return oN(me, ke, f, m, g, y, v, b, k, I, x, $, E, A, P, z, W, j, X, Y, Z, te, J, se, ne, oe, de), ae;
}
var Cue = { kernelName: Aa, backendName: "wasm", setupFunc: Sue, kernelFunc: Iue };
var Nue = Xt(Ea);
var Tue = Xt(Ra);
var uN = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(uN || {});
var lN;
function $ue(e) {
  lN = e.wasm.cwrap(mo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function _ue(e) {
  let { backend: t, inputs: n, attrs: s } = e, { method: r, extrapolationValue: a, cropSize: i } = s, { image: o, boxes: u, boxInd: l } = n, c = u.shape[0], [p, d] = i, h = [c, p, d, o.shape[3]], f = t.dataIdMap.get(o.dataId), m;
  o.dtype !== "float32" && (m = ic({ backend: t, inputs: { x: o }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
  let g = f.id, b = t.dataIdMap.get(u.dataId).id, y = t.dataIdMap.get(l.dataId).id, v = t.makeOutput(h, "float32"), x = t.dataIdMap.get(v.dataId).id, k = new Uint8Array(new Int32Array(o.shape).buffer);
  return lN(g, b, y, c, k, p, d, uN[r], a, x), m != null && t.disposeData(m.dataId), v;
}
var Aue = { kernelName: mo, backendName: "wasm", setupFunc: $ue, kernelFunc: _ue };
var cN;
function Eue(e) {
  cN = e.wasm.cwrap(fo, null, ["number", "number", "number", "number", "number", "number"]);
}
function Rue(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
  w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumprod does not support ${r.dtype} tensors in the WASM backend`);
  let l = C.getAxesPermutation([a], u), c = r;
  l !== null && (c = wr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
  let p = C.getInnerMostAxes(1, u)[0];
  C.assertAxesAreInnerMostDims("cumprod", [p], u);
  let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
  cN(f, i ? 1 : 0, o ? 1 : 0, h, m, St[r.dtype]);
  let g = d;
  if (l !== null) {
    let b = C.getUndoAxesPermutation(l);
    g = wr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
  }
  return g;
}
var Due = { kernelName: fo, backendName: "wasm", setupFunc: Eue, kernelFunc: Rue };
var dN;
function Fue(e) {
  dN = e.wasm.cwrap(Da, null, ["number", "number", "number", "number", "number", "number"]);
}
function Oue(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
  w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumsum does not support ${r.dtype} tensors in the WASM backend`);
  let l = C.getAxesPermutation([a], u), c = r;
  l !== null && (c = wr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
  let p = C.getInnerMostAxes(1, u)[0];
  C.assertAxesAreInnerMostDims("cumsum", [p], u);
  let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
  dN(f, i ? 1 : 0, o ? 1 : 0, h, m, St[r.dtype]);
  let g = d;
  if (l !== null) {
    let b = C.getUndoAxesPermutation(l);
    g = wr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
  }
  return g;
}
var Pue = { kernelName: Da, backendName: "wasm", setupFunc: Fue, kernelFunc: Oue };
var pN;
function zue(e) {
  pN = e.wasm.cwrap(go, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function Mue(e) {
  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = t.makeOutput(f, "float32"), b = t.dataIdMap.get(r.dataId).id, y = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), v = new Uint8Array(new Int32Array(f).buffer), x = new Uint8Array(new Int32Array(w.computeStrides(f)).buffer), k = t.dataIdMap.get(m.dataId).id;
  return pN(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
}
var Lue = { kernelName: go, backendName: "wasm", setupFunc: zue, kernelFunc: Mue };
var hN;
function Bue(e) {
  hN = e.wasm.cwrap(Fa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Vue(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p } = n, d = l == null ? [1, 1] : l, h = C.computeConv2DInfo(r.shape, a.shape, u, d, c, p, true), f = h.filterHeight, m = h.filterWidth, g = h.padInfo.top, b = h.padInfo.right, y = h.padInfo.bottom, v = h.padInfo.left, x = h.dilationHeight, k = h.dilationWidth, I = h.strideHeight, $ = h.strideWidth, E = h.inChannels, A = h.outChannels, P = h.padInfo.type === "SAME" ? 1 : 0;
  if (h.dataFormat !== "channelsLast")
    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);
  let R = s.makeOutput(h.outShape, "float32"), F = s.dataIdMap.get(R.dataId).id;
  return hN(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, I, $, E, A, F), R;
}
var Wue = { kernelName: Fa, backendName: "wasm", setupFunc: Bue, kernelFunc: Vue };
var Uue = Xt(Pa);
var Gue = false;
var Hue = gn(bo, Gue, "bool");
var que = Xt(za, "float32");
function ng(e) {
  let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, i = r.shape.length, o = r.shape.slice(), u = a;
  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), yn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
}
var jue = { kernelName: yo, backendName: "wasm", kernelFunc: ng };
function fN(e) {
  let { attrs: { shape: t, value: n, dtype: s }, backend: r } = e, a = r.makeOutput(t, s);
  return r.typedArrayFromHeap(a).fill(n), a;
}
var Kue = { kernelName: gl, backendName: "wasm", kernelFunc: fN };
var mN;
function Xue(e) {
  mN = e.wasm.cwrap(xo, null, ["number", "number", "number", "number", "number", "number"]);
}
function Yue(e) {
  let { inputs: t, backend: n } = e, { image: s } = t, r = n.makeOutput(s.shape, s.dtype), a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, [o, u, l, c] = s.shape;
  return mN(a, o, u, l, c, i), r;
}
var Que = { kernelName: xo, backendName: "wasm", kernelFunc: Yue, setupFunc: Xue };
var Zue = Xt(Ma);
var Jue = false;
var ele = gn(La, Jue);
var gN;
function tle(e) {
  gN = e.wasm.cwrap(Ba, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function nle(e) {
  let { backend: t, inputs: n, attrs: s } = e, { varianceEpsilon: r } = s, { x: a, mean: i, variance: o, offset: u, scale: l } = n, c = t.dataIdMap.get(a.dataId).id, p = t.dataIdMap.get(i.dataId).id, d = t.dataIdMap.get(o.dataId).id, h = u != null ? t.dataIdMap.get(u.dataId).id : 0, f = l != null ? t.dataIdMap.get(l.dataId).id : 0, m = t.makeOutput(a.shape, a.dtype);
  if (w.sizeFromShape(a.shape) === 0)
    return m;
  let g = t.dataIdMap.get(m.dataId).id;
  return gN(c, p, d, h, f, r, g), m;
}
var sle = { kernelName: Ba, backendName: "wasm", setupFunc: tle, kernelFunc: nle };
var bN;
function rle(e) {
  bN = e.wasm.cwrap(ia, 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 ale(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = rh[h];
  if (g == null)
    throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);
  let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
  if (i != null) {
    let ne = s.dataIdMap.get(i.dataId);
    if (ne.shape.length !== 1)
      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);
    if (ne.shape[0] !== v)
      throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);
    x = ne.id;
  }
  let k = m.filterHeight, I = m.filterWidth, $ = m.padInfo.top, E = m.padInfo.right, A = m.padInfo.bottom, P = m.padInfo.left, R = m.dilationHeight, F = m.dilationWidth, T = m.strideHeight, z = m.strideWidth, W = m.inChannels, j = m.padInfo.type === "SAME" ? 1 : 0, X = m.batchSize, Y = m.inHeight, Z = m.inWidth;
  if (p !== "NHWC")
    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);
  let te = s.makeOutput(m.outShape, "float32"), J = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
  return bN(b, X, Y, Z, y, k, I, x, $, E, A, P, j, R, F, T, z, W, v, g, se, f || 0, J), te;
}
var ile = { kernelName: ia, backendName: "wasm", setupFunc: rle, kernelFunc: ale };
var yN;
function ole(e) {
  yN = e.wasm.cwrap(oa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function ule(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = rh[h];
  if (g == null)
    throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
  let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
  if (i != null) {
    let ne = s.dataIdMap.get(i.dataId);
    if (ne.shape.length !== 1)
      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);
    if (ne.shape[0] !== v)
      throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);
    x = ne.id;
  }
  let k = m.filterHeight, I = m.filterWidth, $ = m.padInfo.top, E = m.padInfo.right, A = m.padInfo.bottom, P = m.padInfo.left, R = m.dilationHeight, F = m.dilationWidth, T = m.strideHeight, z = m.strideWidth, W = m.inChannels, j = m.padInfo.type === "SAME" ? 1 : 0, X = m.batchSize, Y = m.inHeight, Z = m.inWidth;
  if (p !== "NHWC")
    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);
  let te = s.makeOutput(m.outShape, "float32"), J = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
  return yN(b, X, Y, Z, y, k, I, x, $, E, A, P, j, R, F, T, z, W, v, g, se, f || 0, J), te;
}
var lle = { kernelName: oa, backendName: "wasm", setupFunc: ole, kernelFunc: ule };
var vN;
function cle(e) {
  vN = e.wasm.cwrap(ko, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function dle(e) {
  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = Pk.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
  if (i === 0)
    return l;
  let c = r.shape, p = c[c.length - 1], h = t.dataIdMap.get(s.dataId).id, m = t.dataIdMap.get(r.dataId).id, g = new Uint8Array(new Int32Array(u).buffer), b = t.dataIdMap.get(l.dataId).id;
  return vN(h, St[s.dtype], m, i, p, o, g, b), l;
}
var ple = { kernelName: ko, backendName: "wasm", setupFunc: cle, kernelFunc: dle };
var xN;
function hle(e) {
  xN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function fle(e) {
  let { backend: t, inputs: n, attrs: s } = e, { x: r, indices: a } = n, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = t.readSync(a.dataId), c = r.shape[u];
  for (let A = 0; A < l.length; ++A) {
    let P = l[A];
    w.assert(P <= c - 1 && P >= 0, () => `GatherV2: the index value ${P} is not in [0, ${c - 1}]`);
  }
  let p = C.segment_util.collectGatherOpShapeInfo(r, a, u, o), d = yn({ inputs: { x: r }, attrs: { shape: [p.batchSize, p.outerSize, p.dimSize, p.sliceSize] }, backend: t }), h = w.sizeFromShape(a.shape), f = yn({ inputs: { x: a }, attrs: { shape: [p.batchSize, h / p.batchSize] }, backend: t }), m = [p.batchSize, p.outerSize, h / p.batchSize, p.sliceSize], g = t.makeOutput(m, r.dtype);
  if (w.sizeFromShape(r.shape) === 0)
    return g;
  let b = d.shape.length - 1, v = t.dataIdMap.get(d.dataId).id, k = t.dataIdMap.get(f.dataId).id, I = t.dataIdMap.get(g.dataId).id, $ = new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer), E = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
  return xN(v, St[r.dtype], $, b, k, p.batchSize, E, I), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
}
var mle = { kernelName: wo, backendName: "wasm", setupFunc: hle, kernelFunc: fle };
var gle = false;
var ble = gn(So, gle, "bool");
var yle = false;
var vle = gn(Va, yle, "bool");
var wN;
function xle(e) {
  wN = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number"]);
}
function wle(e) {
  let { inputs: { x: t }, attrs: { alpha: n }, backend: s } = e, r = s.dataIdMap.get(t.dataId).id, a = s.makeOutput(t.shape, "float32");
  if (w.sizeFromShape(t.shape) !== 0) {
    let i = s.dataIdMap.get(a.dataId).id;
    wN(r, St[t.dtype], n, i);
  }
  return a;
}
var kle = { kernelName: Ua, backendName: "wasm", setupFunc: xle, kernelFunc: wle };
var Sle = false;
var Ile = gn(Io, Sle, "bool");
var Cle = false;
var Nle = gn(Co, Cle, "bool");
var Tle = Xt(Ga);
var $le = false;
var _le = gn(No, $le, "bool");
var kN;
function Ale(e) {
  kN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number"]);
}
function Ele(e) {
  let { backend: t, inputs: n, attrs: s } = e, { reductionIndices: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    l = c, u = v;
  }
  let f = l.shape.length;
  C.assertAxesAreInnerMostDims("max", p, f);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
  if (w.sizeFromShape(l.shape) !== 0) {
    let v = t.dataIdMap.get(y.dataId).id;
    kN(u, St[i.dtype], b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Rle = { kernelName: Ha, backendName: "wasm", setupFunc: Ale, kernelFunc: Ele };
var Dle = false;
var Fle = gn(qa, Dle);
var SN;
function Ole(e) {
  SN = e.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Ple(e) {
  let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id;
  w.assert(r.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);
  let { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = C.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.dilationHeight, y = c.dilationWidth, v = c.strideHeight, x = c.strideWidth, k = c.inChannels, I = c.outChannels;
  if (c.dataFormat !== "channelsLast")
    throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
  let $ = s.makeOutput(c.outShape, "float32"), E = s.dataIdMap.get($.dataId).id;
  return SN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, I, E), $;
}
var zle = { kernelName: ja, backendName: "wasm", setupFunc: Ole, kernelFunc: Ple };
var IN;
function Mle(e) {
  IN = e.wasm.cwrap(Ka, null, ["number, number, number"]);
}
function Lle(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t), f = p;
  if (h) {
    let x = t.dataIdMap.get(c.dataId).id;
    x !== o && (l = c, u = x, f = C.getInnerMostAxes(f.length, l.shape.length));
  }
  C.assertAxesAreInnerMostDims("mean", f, l.shape.length);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = l;
  l.dtype !== "float32" && (y = ic({ backend: t, inputs: { x: l }, attrs: { dtype: "float32" } }), u = t.dataIdMap.get(y.dataId).id);
  let v = t.makeOutput(m, "float32");
  if (w.sizeFromShape(l.shape) !== 0) {
    let x = t.dataIdMap.get(v.dataId).id;
    IN(u, b, x);
  }
  if (h && t.disposeData(c.dataId), a) {
    let x = C.expandShapeToKeepDim(v.shape, d);
    v.shape = x;
  }
  return l.dtype !== "float32" && t.disposeData(y.dataId), v;
}
var Ble = { kernelName: Ka, backendName: "wasm", setupFunc: Mle, kernelFunc: Lle };
var CN;
function Vle(e) {
  CN = e.wasm.cwrap(Xa, null, ["number", "number", "number", "number"]);
}
function Wle(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    v !== o && (l = c, u = v);
  }
  let f = l.shape.length;
  C.assertAxesAreInnerMostDims("min", p, f);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
  if (w.sizeFromShape(l.shape) !== 0) {
    let v = t.dataIdMap.get(y.dataId).id;
    CN(u, St[i.dtype], b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Ule = { kernelName: Xa, backendName: "wasm", setupFunc: Vle, kernelFunc: Wle };
var Gle = false;
var Hle = gn(Ya, Gle);
var NN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(NN || {});
var TN;
function qle(e) {
  TN = e.wasm.cwrap(Qa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function jle(e) {
  let { inputs: { x: t }, backend: n, attrs: { paddings: s, mode: r } } = e, a = s.map((f, m) => f[0] + t.shape[m] + f[1]), i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), u = n.dataIdMap.get(o.dataId).id, l = new Uint8Array(new Int32Array(t.shape).buffer), c = s.map((f) => f[0]), p = s.map((f) => f[1]), d = new Uint8Array(new Int32Array(c).buffer), h = new Uint8Array(new Int32Array(p).buffer);
  return TN(i, l, t.shape.length, St[t.dtype], d, h, NN[r], u), o;
}
var Kle = { kernelName: Qa, backendName: "wasm", kernelFunc: jle, setupFunc: qle };
var Xle = true;
var Yle = gn(Za, Xle);
var Qle = Xt(To);
function zv(e, t) {
  let n = new Int32Array(e.wasm.HEAPU8.buffer, t, 4), s = n[0], r = n[1], a = n[2], i = n[3];
  return e.wasm._free(t), { pSelectedIndices: s, selectedSize: r, pSelectedScores: a, pValidOutputs: i };
}
var $N;
function Zle(e) {
  $N = e.wasm.cwrap(_o, "number", ["number", "number", "number", "number", "number"]);
}
function Jle(e) {
  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = $N(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = zv(t, p);
  return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
}
var ece = { kernelName: _o, backendName: "wasm", setupFunc: Zle, kernelFunc: Jle };
var _N;
function tce(e) {
  _N = e.wasm.cwrap(Sl, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function nce(e) {
  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = _N(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = zv(t, d);
  t.wasm._free(m);
  let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
  return [b, y];
}
var sce = { kernelName: Sl, backendName: "wasm", setupFunc: tce, kernelFunc: nce };
var AN;
function rce(e) {
  AN = e.wasm.cwrap(Ao, "number", ["number", "number", "number", "number", "number", "number"]);
}
function ace(e) {
  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = AN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = zv(t, d);
  t.wasm._free(g);
  let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
  return [b, y];
}
var ice = { kernelName: Ao, backendName: "wasm", setupFunc: rce, kernelFunc: ace };
var oce = false;
var uce = gn($o, oce, "bool");
var EN;
function lce(e) {
  EN = e.wasm.cwrap(Ro, null, ["number", "number", "number", "number", "number"]);
}
function cce(e) {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = n.makeOutput([...r.shape, a], "int32"), l = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(r.dataId).id;
  return EN(p, a, i, o, l), u;
}
var dce = { kernelName: Ro, backendName: "wasm", setupFunc: lce, kernelFunc: cce };
function pce(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
  return n.typedArrayFromHeap(s).fill(1), s;
}
var hce = { kernelName: Eo, backendName: "wasm", kernelFunc: pce };
function fce(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return ng({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
  let a = t[0].shape, i = t[0].dtype;
  t.forEach((c) => {
    w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
  });
  let o = [], u = t.map((c) => {
    let p = ng({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = aN({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeData(c.dataId)), l;
}
var mce = { kernelName: Do, backendName: "wasm", kernelFunc: fce };
var RN;
function gce(e) {
  RN = e.wasm.cwrap(Ja, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function bce(e) {
  let { inputs: { x: t }, backend: n, attrs: { paddings: s, constantValue: r } } = e, a = s.map((m, g) => m[0] + t.shape[g] + m[1]);
  if (w.sizeFromShape(t.shape) === 0)
    return fN({ backend: n, attrs: { shape: a, value: r, dtype: t.dtype } });
  let i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(t.shape).buffer), p = s.map((m) => m[0]), d = s.map((m) => m[1]), h = new Uint8Array(new Int32Array(p).buffer), f = new Uint8Array(new Int32Array(d).buffer);
  return RN(i, c, t.shape.length, St[t.dtype], h, f, r, l), o;
}
var DN = { kernelName: Ja, backendName: "wasm", kernelFunc: bce, setupFunc: gce };
var yce = false;
var vce = gn(ei, yce);
var FN;
function xce(e) {
  FN = e.wasm.cwrap(ti, null, ["number", "number", "number"]);
}
function wce(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, o = a, u = s, l = u;
  u.dtype !== "float32" && (l = ic({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), o = n.dataIdMap.get(l.dataId).id);
  let c = n.makeOutput(s.shape, "float32"), p = n.dataIdMap.get(c.dataId).id;
  return FN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
}
var kce = { kernelName: ti, backendName: "wasm", setupFunc: xce, kernelFunc: wce };
var ON;
function Sce(e) {
  ON = e.wasm.cwrap(ni, null, ["number", "number", "number", "number"]);
}
function Ice(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t), f = p;
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    v !== o && (l = c, u = v, f = C.getInnerMostAxes(f.length, l.shape.length));
  }
  C.assertAxesAreInnerMostDims("prod", f, l.shape.length);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
  if (w.sizeFromShape(l.shape) !== 0) {
    let v = t.dataIdMap.get(y.dataId).id;
    ON(u, b, St[y.dtype], v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Cce = { kernelName: ni, backendName: "wasm", setupFunc: Sce, kernelFunc: Ice };
var Nce = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = lv(s, r, a, i), u = t.makeOutput([o.length], i);
  return t.typedArrayFromHeap(u).set(o), u;
};
var Tce = { kernelName: Il, backendName: "wasm", kernelFunc: Nce };
var $ce = true;
var _ce = gn(Oa, $ce);
var Ace = Xt(si);
var Ece = Xt(ai);
var PN;
function Rce(e) {
  PN = e.wasm.cwrap(ri, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Dce(e) {
  let { backend: t, inputs: n, attrs: s } = e, { images: r } = n, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, [c, p, d, h] = r.shape, f = [c, u, l, h], m = t.dataIdMap.get(r.dataId), g;
  m.dtype !== "float32" && (g = ic({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
  let b = m.id, y = t.makeOutput(f, "float32");
  if (w.sizeFromShape(r.shape) === 0)
    return y;
  let v = t.dataIdMap.get(y.dataId).id;
  return PN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
}
var Fce = { kernelName: ri, backendName: "wasm", setupFunc: Rce, kernelFunc: Dce };
var zN;
function Oce(e) {
  zN = e.wasm.cwrap(Oo, null, ["number", "array", "number", "array", "number", "number"]);
}
function Pce(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = w.parseAxisParam(a, r.shape);
  if (r.shape.length === 0)
    return ah({ inputs: { x: r }, backend: n });
  let o = n.makeOutput(r.shape, r.dtype), u = n.dataIdMap.get(r.dataId).id, l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(i).buffer), p = new Uint8Array(new Int32Array(r.shape).buffer);
  zN(u, c, i.length, p, r.shape.length, l);
  let d = yn({ inputs: { x: o }, attrs: { shape: r.shape }, backend: n });
  return n.disposeData(o.dataId), d;
}
var zce = { kernelName: Oo, backendName: "wasm", kernelFunc: Pce, setupFunc: Oce };
var MN;
function Mce(e) {
  MN = e.wasm.cwrap(Xo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Lce(e) {
  let { inputs: t, backend: n, attrs: s } = e, { image: r } = t, { radians: a, fillValue: i, center: o } = s, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(r.dataId).id, c = n.dataIdMap.get(u.dataId).id, [p, d, h, f] = r.shape, [m, g] = C.getImageCenter(o, d, h), b = i === 0, y = 255, v = typeof i == "number" ? [i, i, i, b ? 0 : y] : [...i, y], x = new Uint8Array(new Int32Array(v).buffer);
  return MN(l, p, d, h, f, a, m, g, x, v.length, c), u;
}
var Bce = { kernelName: Xo, backendName: "wasm", kernelFunc: Lce, setupFunc: Mce };
var Vce = Xt(Po);
var Wce = Xt(ii);
var LN;
function Uce(e) {
  LN = e.wasm.cwrap(zo, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Gce(e) {
  let { backend: t, inputs: n, attrs: s } = e, { indices: r, updates: a } = n, { shape: i } = s, o = t.makeOutput(i, a.dtype);
  if (w.sizeFromShape(i) === 0)
    return o;
  let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Mk.calculateShapes(a, r, i), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(o.dataId).id;
  return LN(f, g, St[a.dtype], u, l, c, b, d, y), o;
}
var Hce = { kernelName: zo, backendName: "wasm", setupFunc: Uce, kernelFunc: Gce };
var BN;
function qce(e) {
  BN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function jce(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, u = n.dataIdMap.get(a.dataId).id, l = n.makeOutput(r.shape, r.dtype), c = n.dataIdMap.get(l.dataId).id, p = s.shape.length, d = r.shape.length, h = p === 0 || p > 1 || d === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
  return BN(i, o, u, h, c), l;
}
var Kce = { kernelName: Mo, backendName: "wasm", kernelFunc: jce, setupFunc: qce };
var VN;
function Xce(e) {
  VN = e.wasm.cwrap(ui, null, ["number", "number"]);
}
function Yce(e) {
  let { backend: t, inputs: { x: n } } = e, s = t.dataIdMap.get(n.dataId).id, r = t.makeOutput(n.shape, n.dtype), a = t.dataIdMap.get(r.dataId).id;
  return w.sizeFromShape(r.shape) === 0 || VN(s, a), r;
}
var Qce = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: Xce, kernelFunc: Yce };
var Zce = Xt(oi);
var WN;
function Jce(e) {
  WN = e.wasm.cwrap(di, null, ["number", "number", "number", "number"]);
}
function ede(e) {
  let { backend: t, inputs: { logits: n }, attrs: { dim: s } } = e, r = t.dataIdMap.get(n.dataId).id, a = t.makeOutput(n.shape, n.dtype), i = t.dataIdMap.get(a.dataId).id, o = n.shape[s], u = w.sizeFromShape(n.shape) / o;
  return w.sizeFromShape(a.shape) === 0 || WN(r, i, o, u), a;
}
var tde = { kernelName: di, backendName: "wasm", setupFunc: Jce, kernelFunc: ede };
function nde(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s, o = w.sizeFromShape(a), u = [[0, 0]];
  u.push(...i);
  for (let I = 1 + a.length; I < r.shape.length; ++I)
    u.push([0, 0]);
  let l = DN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = C.getReshaped(l.shape, a, o, false), p = C.getPermuted(c.length, a.length, false), d = C.getReshapedPermuted(l.shape, a, o, false), m = yn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wr({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = yn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
  return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
}
var sde = { kernelName: Vo, backendName: "wasm", kernelFunc: nde };
var UN;
function rde(e) {
  UN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function ade(e) {
  let { backend: t, inputs: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = n, o = s.shape[0], u = s.shape[1], l = t.readSync(a.dataId)[0], c = [o + l, u], p = t.dataIdMap.get(s.dataId).id, d = t.dataIdMap.get(r.dataId).id, h = t.dataIdMap.get(i.dataId).id, f = t.makeOutput(c, s.dtype), m = t.dataIdMap.get(f.dataId).id, g = t.makeOutput(c.slice(0, 1), r.dtype), b = t.dataIdMap.get(g.dataId).id, y = t.makeOutput([l], "bool"), v = t.dataIdMap.get(y.dataId).id, x = t.makeOutput([o], s.dtype), k = t.dataIdMap.get(x.dataId).id, I = t.makeOutput([4], "int32"), $ = t.dataIdMap.get(I.dataId).id, E = UN(p, d, St[r.dtype], o, l, u, h, m, b, v, k, $), A = t.readSync(I.dataId), P;
  switch (A[0]) {
    case 1: {
      P = C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);
      break;
    }
    case 2: {
      P = C.getSparseFillEmptyRowsNegativeIndexErrorMessage(A[1], A[2]);
      break;
    }
    case 3:
      P = C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(A[1], A[2], A[3]);
      break;
    default:
      P = "";
  }
  if (t.disposeData(I.dataId), P)
    throw t.disposeData(f.dataId), t.disposeData(g.dataId), t.disposeData(y.dataId), t.disposeData(x.dataId), new Error(P);
  let R = f, F = g;
  return E !== c[0] && (R = xa({ inputs: { x: f }, attrs: { begin: 0, size: [E, u] }, backend: t }), F = xa({ inputs: { x: g }, attrs: { begin: 0, size: E }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [R, F, y, x];
}
var ide = { kernelName: up, backendName: "wasm", setupFunc: rde, kernelFunc: ade };
var GN;
function ode(e) {
  GN = e.wasm.cwrap(Al, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function ude(e) {
  let { backend: t, inputs: n } = e, { inputIndices: s, inputShape: r, newShape: a } = n;
  if (s.shape.length !== 2)
    throw new Error(`Input indices should be a matrix but received shape
        ${s.shape}`);
  if (r.shape.length !== 1)
    throw new Error(`Input shape should be a vector but received shape
        ${r.shape}`);
  if (a.shape.length !== 1)
    throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
  let i = t.dataIdMap.get(s.dataId).id, o = t.dataIdMap.get(r.dataId).id, u = t.dataIdMap.get(a.dataId).id, l = s.shape[0], c = w.sizeFromShape(a.shape), p = t.makeOutput([l, c], s.dtype), d = t.dataIdMap.get(p.dataId).id, h = t.makeOutput([c], a.dtype), f = t.dataIdMap.get(h.dataId).id, m = t.makeOutput([3], "int32"), g = t.dataIdMap.get(m.dataId).id;
  GN(i, o, u, l, d, f, g);
  let b = t.readSync(m.dataId), y;
  switch (b[0]) {
    case 0: {
      y = C.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(b[1], b[2]);
      break;
    }
    case 1: {
      y = C.getSparseReshapeNegativeOutputDimErrorMessage(b[1], b[2]);
      break;
    }
    case 2:
      y = C.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
      break;
    case 3: {
      let v = Array.from(t.readSync(r.dataId)), x = Array.from(t.readSync(h.dataId));
      y = C.getSparseReshapeInputOutputMultipleErrorMessage(v, x);
      break;
    }
    case 4: {
      let v = Array.from(t.readSync(r.dataId)), x = Array.from(t.readSync(h.dataId));
      y = C.getSparseReshapeInputOutputMismatchErrorMessage(v, x);
      break;
    }
    default:
      y = "";
  }
  if (t.disposeData(m.dataId), y)
    throw t.disposeData(p.dataId), t.disposeData(h.dataId), new Error(y);
  return [p, h];
}
var lde = { kernelName: Al, backendName: "wasm", setupFunc: ode, kernelFunc: ude };
var HN;
function qN(e) {
  HN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function jN(e, t) {
  let { backend: n, inputs: s } = e, { data: r, indices: a, segmentIds: i } = s, o = a.shape[0], u = n.readSync(i.dataId, o - 1, o)[0], c = o > 0 ? u + 1 : 0;
  if (c < 0)
    throw new Error(C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
  let p = r.shape.slice();
  p[0] = c;
  let d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = n.dataIdMap.get(i.dataId).id, m = n.makeOutput(p, r.dtype), g = n.dataIdMap.get(m.dataId).id, b = n.makeOutput([4], "int32"), y = n.dataIdMap.get(b.dataId).id;
  HN(d, St[r.dtype], r.shape[0], h, f, g, y, t, 0);
  let v = n.readSync(b.dataId), x;
  switch (v[0]) {
    case 0: {
      x = C.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
      break;
    }
    case 1: {
      x = C.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
      break;
    }
    case 2:
      x = C.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(v[1], v[2]);
      break;
    case 3:
      x = C.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(v[1], v[2], v[3]);
      break;
    default:
      x = "";
  }
  if (n.disposeData(b.dataId), x)
    throw n.disposeData(m.dataId), new Error(x);
  return m;
}
function cde(e) {
  return jN(e, true);
}
var dde = { kernelName: lp, backendName: "wasm", setupFunc: qN, kernelFunc: cde };
function pde(e) {
  return jN(e, false);
}
var hde = { kernelName: cp, backendName: "wasm", setupFunc: qN, kernelFunc: pde };
function fde(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = n, o = w.parseAxisParam(i, r.shape)[0], u = C.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
  return u.map((p) => {
    let d = [...c];
    d[o] = p;
    let h = xa({ inputs: { x: r }, attrs: { begin: l, size: d }, backend: s });
    return l[o] += p, h;
  });
}
var mde = { kernelName: Wo, backendName: "wasm", kernelFunc: fde };
var gde = Xt(li);
var bde = Xt(El);
var yde = true;
var vde = gn(pi, yde);
var KN;
function xde(e) {
  KN = e.wasm.cwrap(gi, null, ["number", "number", "number", "number"]);
}
function wde(e) {
  let { backend: t, inputs: n, attrs: s } = e, { alpha: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = t.makeOutput(a.shape, a.dtype), u = t.dataIdMap.get(o.dataId).id;
  return KN(i, r, St[a.dtype], u), o;
}
var kde = { kernelName: gi, backendName: "wasm", setupFunc: xde, kernelFunc: wde };
var XN;
function Sde(e) {
  XN = e.wasm.cwrap(Uo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function Ide(e) {
  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = yn({ inputs: { x: r }, backend: t, attrs: { shape: f } });
  else if (g || b) {
    w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
    let I = wt.computeOutShape(y, v, x), $ = xa({ inputs: { x: r }, backend: t, attrs: { begin: y, size: I } });
    k = yn({ inputs: { x: $ }, backend: t, attrs: { shape: f } }), t.disposeData($.dataId);
  } else {
    let I = t.makeOutput(h, "float32"), $ = t.dataIdMap.get(r.dataId).id, E = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), A = new Uint8Array(new Int32Array(y).buffer), P = new Uint8Array(new Int32Array(v).buffer), R = new Uint8Array(new Int32Array(x).buffer), F = new Uint8Array(new Int32Array(h).buffer), T = new Uint8Array(new Int32Array(w.computeStrides(h)).buffer), z = t.dataIdMap.get(I.dataId).id;
    XN($, E, r.shape.length, A, P, R, F, T, h.length, z), k = yn({ inputs: { x: I }, backend: t, attrs: { shape: f } }), t.disposeData(I.dataId);
  }
  return k;
}
var Cde = { kernelName: Uo, backendName: "wasm", setupFunc: Sde, kernelFunc: Ide };
var Nde = true;
var Tde = gn(hi, Nde);
var YN;
function $de(e) {
  YN = e.wasm.cwrap(ci, null, ["number", "number", "number", "number"]);
}
function _de(e) {
  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t), f = p;
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    v !== o && (l = c, u = v, f = C.getInnerMostAxes(f.length, l.shape.length));
  }
  C.assertAxesAreInnerMostDims("sum", f, l.shape.length);
  let [m, g] = C.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
  if (w.sizeFromShape(l.shape) !== 0) {
    let v = t.dataIdMap.get(y.dataId).id;
    YN(u, b, St[y.dtype], v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Ade = { kernelName: ci, backendName: "wasm", setupFunc: $de, kernelFunc: _de };
var Ede = Xt(Go);
var Rde = Xt(fi);
var QN;
function Dde(e) {
  QN = e.wasm.cwrap(Cr, null, ["number", "array", "number", "array", "number", "number"]);
}
function Fde(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, a = n.dataIdMap.get(r.dataId).id, { reps: i } = s, o = new Array(r.shape.length);
  for (let d = 0; d < o.length; d++)
    o[d] = r.shape[d] * i[d];
  let u = new Uint8Array(new Int32Array(r.shape).buffer), l = new Uint8Array(new Int32Array(o).buffer), c = n.makeOutput(o, r.dtype), p = n.dataIdMap.get(c.dataId).id;
  return QN(a, u, r.shape.length, l, o.length, St[c.dtype], p), c;
}
var Ode = { kernelName: Cr, backendName: "wasm", setupFunc: Dde, kernelFunc: Fde };
var ZN;
function Pde(e) {
  ZN = e.wasm.cwrap(Ho, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var zde = ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s } = e, { k: r, sorted: a } = n, i = t.dataIdMap.get(s.dataId).id, o = new Uint8Array(new Int32Array(s.shape).buffer), u = s.shape.slice();
  u[u.length - 1] = r;
  let l = t.makeOutput(u, s.dtype), c = t.dataIdMap.get(l.dataId).id, p = t.makeOutput(u, "int32"), d = t.dataIdMap.get(p.dataId).id;
  return ZN(i, o, s.shape.length, St[s.dtype], r, a, c, d), [l, p];
};
var Mde = { kernelName: Ho, backendName: "wasm", setupFunc: Pde, kernelFunc: zde };
var JN;
function Lde(e) {
  JN = e.wasm.cwrap(qo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function Bde(e) {
  let { backend: t, inputs: n, attrs: s } = e, { image: r, transforms: a } = n, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), y = t.makeOutput(g, r.dtype), v = t.dataIdMap.get(y.dataId).id, k = t.dataIdMap.get(r.dataId).id, $ = t.dataIdMap.get(a.dataId).id, E = i === "nearest" ? 1 : 2, A;
  switch (o) {
    case "constant":
      A = 1;
      break;
    case "reflect":
      A = 2;
      break;
    case "wrap":
      A = 3;
      break;
    case "nearest":
      A = 4;
      break;
    default:
      A = 1;
      break;
  }
  return JN(k, $, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, E, A, u, v), y;
}
var Vde = { kernelName: qo, backendName: "wasm", setupFunc: Lde, kernelFunc: Bde };
function Wde(e) {
  let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
  a < 0 && (a += r.shape.length);
  let i = r.shape[a], o = r.shape.length, u = new Array(o - 1), l = 0;
  for (let h = 0; h < o; h++)
    h !== a && (u[l++] = r.shape[h]);
  let c = new Array(i), p = new Array(o).fill(0), d = r.shape.slice();
  d[a] = 1;
  for (let h = 0; h < c.length; h++)
    p[a] = h, c[h] = xa({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
  return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
}
var Ude = { kernelName: jo, backendName: "wasm", kernelFunc: Wde };
function Gde(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
  return n.typedArrayFromHeap(s).fill(0), s;
}
var Hde = { kernelName: Ko, backendName: "wasm", kernelFunc: Gde };
var qde = [Foe, Ooe, zoe, Boe, Koe, Qoe, eue, sue, oue, hue, fue, mue, yue, vue, kue, Cue, Nue, Tue, Aue, Due, Pue, Lue, Wue, Uue, Hue, que, jue, Kue, Que, Zue, ele, sle, ile, lle, ple, mle, ble, vle, Voe, kle, Ile, Nle, Tle, _le, Rle, Fle, zle, Ble, Ule, Hle, Kle, Yle, Qle, ece, sce, ice, uce, dce, hce, mce, DN, vce, kce, Cce, Tce, _ce, Ace, Ece, rue, Fce, zce, Bce, Vce, Wce, Hce, Kce, Qce, Zce, due, tde, sde, ide, lde, dde, hde, mde, gde, bde, vde, kde, Cde, Tde, Ade, Ede, Rde, Ode, Mde, Vde, Hoe, Ude, Hde];
for (let e of qde)
  Rl(e);
var sg = K();
sg.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
sg.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
  if (sg.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 (e) {
    return false;
  }
});
var jw = wa(f$());
var jde = `"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",function(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()}}})}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.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.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}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==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`;
var Kde = wa(m$());
var Xde = class extends rl {
  constructor(e) {
    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(eT), rg = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Kd(this, ds());
  }
  write(e, t, n) {
    let s = { id: this.dataIdNextNumber++ };
    return this.move(s, e, t, n, 1), s;
  }
  numDataIds() {
    return this.dataIdMap.numDataIds();
  }
  async time(e) {
    let t = w.now();
    return e(), { kernelMs: w.now() - t };
  }
  move(e, t, n, s, r) {
    let a = this.dataIdNextNumber++;
    if (s === "string") {
      let l = t;
      this.dataIdMap.set(e, { id: a, stringBytes: l, shape: n, dtype: s, memoryOffset: null, refCount: r });
      return;
    }
    let i = w.sizeFromShape(n), o = i * w.bytesPerElement(s), u = this.wasm._malloc(o);
    this.dataIdMap.set(e, { id: a, memoryOffset: u, shape: n, dtype: s, refCount: r }), this.wasm.tfjs.registerTensor(a, i, u), t != null && this.wasm.HEAPU8.set(new Uint8Array(t.buffer, t.byteOffset, o), u);
  }
  async read(e) {
    return this.readSync(e);
  }
  readSync(e, t, n) {
    let { memoryOffset: s, dtype: r, shape: a, stringBytes: i } = this.dataIdMap.get(e);
    if (r === "string")
      return (t == null || t === 0) && (n == null || n >= i.length) ? i : i.slice(t, n);
    t = t || 0, n = n || w.sizeFromShape(a);
    let o = w.bytesPerElement(r), u = this.wasm.HEAPU8.slice(s + t * o, s + n * o);
    return Zde(u.buffer, r);
  }
  disposeData(e, t = false) {
    if (this.dataIdMap.has(e)) {
      let n = this.dataIdMap.get(e);
      if (n.refCount--, !t && n.refCount > 0)
        return false;
      this.wasm._free(n.memoryOffset), this.wasm.tfjs.disposeData(n.id), this.dataIdMap.delete(e);
    }
    return true;
  }
  refCount(e) {
    return this.dataIdMap.has(e) ? this.dataIdMap.get(e).refCount : 0;
  }
  incRef(e) {
    let t = this.dataIdMap.get(e);
    t != null && t.refCount++;
  }
  floatPrecision() {
    return 32;
  }
  getMemoryOffset(e) {
    return this.dataIdMap.get(e).memoryOffset;
  }
  dispose() {
    this.wasm.tfjs.dispose(), "PThread" in this.wasm && this.wasm.PThread.terminateAllThreads(), this.wasm = null;
  }
  memory() {
    return { unreliable: false };
  }
  makeOutput(e, t, n) {
    let s;
    if (n == null)
      s = this.write(null, e, t);
    else {
      let r = this.dataIdNextNumber++;
      s = { id: r }, this.dataIdMap.set(s, { id: r, memoryOffset: n, shape: e, dtype: t, refCount: 1 });
      let a = w.sizeFromShape(e);
      this.wasm.tfjs.registerTensor(r, a, n);
    }
    return { dataId: s, shape: e, dtype: t };
  }
  typedArrayFromHeap({ shape: e, dtype: t, dataId: n }) {
    let s = this.wasm.HEAPU8.buffer, { memoryOffset: r } = this.dataIdMap.get(n), a = w.sizeFromShape(e);
    switch (t) {
      case "float32":
        return new Float32Array(s, r, a);
      case "int32":
        return new Int32Array(s, r, a);
      case "bool":
        return new Uint8Array(s, r, a);
      default:
        throw new Error(`Unknown dtype ${t}`);
    }
  }
};
function Yde(e) {
  return (t, n) => (w.fetch(e, { credentials: "same-origin" }).then((s) => {
    s.ok || t.env.a(`failed to load wasm binary file at '${e}'`), s.arrayBuffer().then((r) => {
      WebAssembly.instantiate(r, t).then((a) => {
        n(a.instance, a.module);
      });
    });
  }), {});
}
function Kw(e, t, n) {
  if (Hd != null)
    return Hd;
  let s = "tfjs-backend-wasm.wasm";
  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Vu != null && Vu[s] != null ? Vu[s] : n + s;
}
async function Qde() {
  let [e, t] = await Promise.all([K().getAsync("WASM_HAS_SIMD_SUPPORT"), K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
  return new Promise((n, s) => {
    let r = {};
    r.locateFile = (o, u) => {
      if (o.endsWith(".worker.js")) {
        let l = jde.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
        return URL.createObjectURL(c);
      }
      return o.endsWith(".wasm") ? Kw(e, t, Mu != null ? Mu : u) : u + o;
    }, Mv && (r.instantiateWasm = Yde(Kw(e, t, Mu != null ? Mu : "")));
    let a = false;
    r.onAbort = () => {
      if (a || Wu)
        return;
      Wu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
    };
    let i;
    t && e && Hd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + jw.default.toString()], { type: "text/javascript" }), i = (0, jw.default)(r)) : i = (0, Kde.default)(r), i.then((o) => {
      a = true, Wu = false;
      let u = null;
      o.tfjs = { init: o.cwrap("init", null, []), initWithThreadsCount: o.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: o.cwrap("get_threads_count", "number", []), registerTensor: o.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: o.cwrap("dispose_data", u, ["number"]), dispose: o.cwrap("dispose", u, []) }, n({ wasm: o });
    });
  });
}
function Zde(e, t) {
  switch (t) {
    case "float32":
      return new Float32Array(e);
    case "int32":
      return new Int32Array(e);
    case "bool":
      return new Uint8Array(e);
    default:
      throw new Error(`Unknown dtype ${t}`);
  }
}
var Jde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var Hd = null;
var Mu = null;
var Vu = {};
var Wu = false;
var Mv = false;
function fhe(e, t = false) {
  if (Zk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Wu)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
  Hd = e, Mv = t;
}
function mhe(e, t = false) {
  if (Wu)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
  if (typeof e == "string")
    Mu = e;
  else {
    Vu = e;
    let n = Jde.filter((s) => Vu[s] == null);
    if (n.length > 0)
      throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
  }
  Mv = t;
}
var eT = -1;
var rg = -1;
function ghe(e) {
  eT = e;
}
function bhe() {
  if (rg === -1)
    throw new Error("WASM backend not initialized.");
  return rg;
}
var yhe = "0.0.0";
var epe = 2;
bp("wasm", async () => {
  let { wasm: e } = await Qde();
  return new Xde(e);
}, epe);
var nr = "3.16.0-20220509";
var vhe = { tfjs: nr, "tfjs-core": nr, "tfjs-data": nr, "tfjs-layers": nr, "tfjs-converter": nr, "tfjs-backend-cpu": nr, "tfjs-backend-webgl": nr, "tfjs-backend-wasm": nr };

// 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, (match3, name) => {
    collection[name] = 0;
    return match3;
  });
};
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)}`);
        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)}`);
      return;
    }
    this.gl.useProgram(this.id);
    collect(vertexSource, "attribute", this.attribute);
    for (const a in this.attribute)
      this.attribute[a] = this.gl.getAttribLocation(this.id, a);
    collect(vertexSource, "uniform", this.uniform);
    collect(fragmentSource, "uniform", this.uniform);
    for (const u in this.uniform)
      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
  }
};
function GLImageFilter() {
  let drawCount = 0;
  let sourceTexture = null;
  let lastInChain = false;
  let currentFramebufferIndex = -1;
  let tempFramebuffers = [null, null];
  let filterChain = [];
  let vertexBuffer = null;
  let currentProgram = null;
  const fxcanvas = canvas(100, 100);
  const shaderProgramCache = {};
  const DRAW = { INTERMEDIATE: 1 };
  const gl2 = fxcanvas.getContext("webgl");
  if (!gl2) {
    log("filter: cannot get webgl context");
    return;
  }
  this.gl = gl2;
  function resize(width, height) {
    if (width === fxcanvas.width && height === fxcanvas.height)
      return;
    fxcanvas.width = width;
    fxcanvas.height = height;
    if (!vertexBuffer) {
      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
      vertexBuffer = gl2.createBuffer();
      gl2.bindBuffer(gl2.ARRAY_BUFFER, vertexBuffer);
      gl2.bufferData(gl2.ARRAY_BUFFER, vertices, gl2.STATIC_DRAW);
      gl2.pixelStorei(gl2.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
    }
    gl2.viewport(0, 0, fxcanvas.width, fxcanvas.height);
    tempFramebuffers = [null, null];
  }
  function createFramebufferTexture(width, height) {
    const fbo = gl2.createFramebuffer();
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, fbo);
    const renderbuffer = gl2.createRenderbuffer();
    gl2.bindRenderbuffer(gl2.RENDERBUFFER, renderbuffer);
    const texture = gl2.createTexture();
    gl2.bindTexture(gl2.TEXTURE_2D, texture);
    gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, width, height, 0, gl2.RGBA, gl2.UNSIGNED_BYTE, null);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.LINEAR);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.LINEAR);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
    gl2.framebufferTexture2D(gl2.FRAMEBUFFER, gl2.COLOR_ATTACHMENT0, gl2.TEXTURE_2D, texture, 0);
    gl2.bindTexture(gl2.TEXTURE_2D, null);
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, null);
    return { fbo, texture };
  }
  function getTempFramebuffer(index2) {
    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
    return tempFramebuffers[index2];
  }
  function draw(flags = 0) {
    if (!currentProgram)
      return;
    let source = null;
    let target = null;
    let flipY = false;
    if (drawCount === 0)
      source = sourceTexture;
    else
      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
    drawCount++;
    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
      target = null;
      flipY = drawCount % 2 === 0;
    } else {
      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
    }
    gl2.bindTexture(gl2.TEXTURE_2D, source);
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, target);
    gl2.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
    gl2.drawArrays(gl2.TRIANGLES, 0, 6);
  }
  function compileShader(fragmentSource) {
    if (shaderProgramCache[fragmentSource]) {
      currentProgram = shaderProgramCache[fragmentSource];
      gl2.useProgram((currentProgram ? currentProgram.id : null) || null);
      return currentProgram;
    }
    currentProgram = new GLProgram(gl2, vertexIdentity, fragmentSource);
    if (!currentProgram) {
      log("filter: could not get webgl program");
      return null;
    }
    const floatSize = Float32Array.BYTES_PER_ELEMENT;
    const vertSize = 4 * floatSize;
    gl2.enableVertexAttribArray(currentProgram.attribute["pos"]);
    gl2.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl2.FLOAT, false, vertSize, 0 * floatSize);
    gl2.enableVertexAttribArray(currentProgram.attribute["uv"]);
    gl2.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl2.FLOAT, false, vertSize, 2 * floatSize);
    shaderProgramCache[fragmentSource] = currentProgram;
    return currentProgram;
  }
  const filter = {
    colorMatrix: (matrix) => {
      const m = new Float32Array(matrix);
      m[4] /= 255;
      m[9] /= 255;
      m[14] /= 255;
      m[19] /= 255;
      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
      const program = compileShader(shader);
      if (!program)
        return;
      gl2.uniform1fv(program.uniform["m"], m);
      draw();
    },
    brightness: (brightness) => {
      const b = (brightness || 0) + 1;
      filter.colorMatrix([
        b,
        0,
        0,
        0,
        0,
        0,
        b,
        0,
        0,
        0,
        0,
        0,
        b,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    saturation: (amount) => {
      const x = (amount || 0) * 2 / 3 + 1;
      const y = (x - 1) * -0.5;
      filter.colorMatrix([
        x,
        y,
        y,
        0,
        0,
        y,
        x,
        y,
        0,
        0,
        y,
        y,
        x,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    desaturate: () => {
      filter.saturation(-1);
    },
    contrast: (amount) => {
      const v = (amount || 0) + 1;
      const o = -128 * (v - 1);
      filter.colorMatrix([
        v,
        0,
        0,
        0,
        o,
        0,
        v,
        0,
        0,
        o,
        0,
        0,
        v,
        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 ? mr(inputImage) : inputImage;
  const channels = Bn(squeeze, 3, 2);
  const min = [vm(channels[0]), vm(channels[1]), vm(channels[2])];
  const max = [As(channels[0]), As(channels[1]), As(channels[2])];
  const absMax = await Promise.all(max.map((channel) => channel.data()));
  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
  const sub = [ge(channels[0], min[0]), ge(channels[1], min[1]), ge(channels[2], min[2])];
  const range = [ge(max[0], min[0]), ge(max[1], min[1]), ge(max[2], min[2])];
  const fact = [xe(maxValue, range[0]), xe(maxValue, range[1]), xe(maxValue, range[2])];
  const enh = [V(sub[0], fact[0]), V(sub[1], fact[1]), V(sub[2], fact[2])];
  const rgb2 = es([enh[0], enh[1], enh[2]], 2);
  const reshape = U(rgb2, [1, squeeze.shape[0], squeeze.shape[1], 3]);
  Re([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb2, squeeze]);
  return reshape;
}

// 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 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) {
  if (!input) {
    if (config3.debug)
      log("input error: input is missing");
    return { tensor: null, canvas: null };
  }
  if (!(input instanceof et) && !(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 et) {
    let tensor = 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) {
        tensor = Pn(input, 0);
      } else if (input.shape[2] === 4) {
        const rgb2 = pb(input, [0, 0, 0], [-1, -1, 3]);
        tensor = Pn(rgb2, 0);
        Re(rgb2);
      }
    } else if (input.shape.length === 4) {
      if (input.shape[3] === 3) {
        tensor = ur(input);
      } else if (input.shape[3] === 4) {
        tensor = Cd(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
      }
    }
    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3)
      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input["shape"]}`);
    if (tensor.dtype === "int32") {
      const cast = le(tensor, "float32");
      Re(tensor);
      tensor = cast;
    }
    return { tensor, canvas: config3.filter.return ? outCanvas : null };
  } else {
    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 ((config3.filter.width || 0) > 0)
      targetWidth = config3.filter.width;
    else if ((config3.filter.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 == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : 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 == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
        inCtx.setTransform(1, 0, 0, 1, 0, 0);
      } else {
        inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
      }
    }
    if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : 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 || !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 (fx2.get() > 0)
          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 && Fk) {
        pixels = Fk ? Fk.fromPixels(input) : null;
      } else {
        depth = input["data"].length / input["height"] / input["width"];
        const arr = new Uint8Array(input["data"]["buffer"]);
        pixels = ms(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 (Fk && env.browser) {
        if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
          pixels = Fk.fromPixels(outCanvas);
        } else {
          tmpCanvas = copy(outCanvas);
          pixels = Fk.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 = ms(arr, [targetWidth, targetHeight, depth]);
      }
    }
    if (depth === 4) {
      const rgb2 = pb(pixels, [0, 0, 0], [-1, -1, 3]);
      Re(pixels);
      pixels = rgb2;
    }
    if (!pixels)
      throw new Error("input error: cannot create tensor");
    const casted = le(pixels, "float32");
    const tensor = config3.filter.equalization ? await histogramEqualization(casted) : Pn(casted, 0);
    Re([pixels, casted]);
    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] > 2048 || input.shape[2] > 2048)
    return skipFrame;
  if (!last.inputTensor) {
    last.inputTensor = ur(input);
  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
    Re(last.inputTensor);
    last.inputTensor = ur(input);
  } else {
    const t = {};
    t.diff = ge(input, last.inputTensor);
    t.squared = V(t.diff, t.diff);
    t.sum = ve(t.squared);
    const diffSum = await t.sum.data();
    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
    Re([last.inputTensor, t.diff, t.squared, t.sum]);
    last.inputTensor = ur(input);
    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
  }
  return skipFrame;
}
async function compare(config3, input1, input2) {
  const t = {};
  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;
  }
  t.input1 = ur(input1);
  t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? jn.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : ur(input2);
  t.diff = ge(t.input1, t.input2);
  t.squared = V(t.diff, t.diff);
  t.sum = ve(t.squared);
  const diffSum = await t.sum.data();
  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
  Re([t.input1, t.input2, t.diff, t.squared, t.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, "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
    });
    __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: vhe["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 && raw[0]) {
        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
        this.platform = platformMatch && 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(ds().registryFactory);
    this.wasm.supported = typeof WebAssembly !== "undefined";
    this.wasm.backend = this.backends.includes("wasm");
    if (this.wasm.supported && this.wasm.backend && gpe() === "wasm") {
      this.wasm.simd = await K().getAsync("WASM_HAS_SIMD_SUPPORT");
      this.wasm.multithread = await K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
    }
    const c = canvas(100, 100);
    const ctx = c ? c.getContext("webgl2") : void 0;
    this.webgl.supported = typeof ctx !== "undefined";
    this.webgl.backend = this.backends.includes("webgl");
    if (this.webgl.supported && this.webgl.backend && (gpe() === "webgl" || gpe() === "humangl")) {
      const gl2 = qA().gpgpu !== "undefined" ? await qA().getGPGPUContext().gl : null;
      if (gl2) {
        this.webgl.version = gl2.getParameter(gl2.VERSION);
        this.webgl.renderer = gl2.getParameter(gl2.RENDERER);
      }
    }
    this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined";
    this.webgpu.backend = this.backends.includes("webgpu");
    try {
      if (this.webgpu.supported)
        this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name;
    } catch (e) {
      this.webgpu.supported = false;
    }
    try {
      this.kernels = sm(gpe()).map((kernel) => kernel.kernelName.toLowerCase());
    } catch (e) {
    }
  }
  async 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/tfjs/load.ts
var options = {
  cacheModels: false,
  verbose: true,
  debug: false,
  modelBasePath: ""
};
async function httpHandler(url, init2) {
  if (options.debug)
    log("load model fetch:", url, init2);
  return fetch(url, init2);
}
function setModelLoadOptions(config3) {
  options.cacheModels = config3.cacheModels;
  options.verbose = config3.debug;
  options.modelBasePath = config3.modelBasePath;
}
async function loadModel(modelPath) {
  const modelUrl = join(options.modelBasePath, modelPath || "");
  const modelPathSegments = modelUrl.split("/");
  const cachedModelName = "indexeddb://" + modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
  const cachedModels = await An.listModels();
  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init2) => httpHandler(url, init2) };
  const model18 = new j4(modelCached ? cachedModelName : modelUrl, tfLoadOptions);
  let loaded = false;
  try {
    model18.findIOHandler();
    if (options.debug)
      log("model load handler:", model18.handler);
    const artifacts = await model18.handler.load();
    model18.loadSync(artifacts);
    if (options.verbose)
      log("load model:", model18["modelUrl"]);
    loaded = true;
  } catch (err) {
    log("error loading model:", modelUrl, err);
  }
  if (loaded && options.cacheModels && !modelCached) {
    try {
      const saveResult = await model18.save(cachedModelName);
      log("model saved:", cachedModelName, saveResult);
    } catch (err) {
      log("error saving model:", modelUrl, err);
    }
  }
  return model18;
}

// package.json
var version = "2.7.1";

// src/models.ts
var models_exports = {};
__export(models_exports, {
  Models: () => Models,
  load: () => load19,
  reset: () => reset,
  validate: () => validate2
});

// src/gear/gear.ts
var model;
var last2 = [];
var raceNames = ["white", "black", "asian", "indian", "other"];
var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
var lastCount = 0;
var lastTime = 0;
var skipped = Number.MAX_SAFE_INTEGER;
async function load(config3) {
  if (env.initial)
    model = null;
  if (!model)
    model = await loadModel(config3.face["gear"]);
  else if (config3.debug)
    log("cached model:", model["modelUrl"]);
  return model;
}
async function predict(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model)
    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
  const skipFrame = skipped < (((_a2 = config3.face["gear"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["gear"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last2[idx]) {
    skipped++;
    return last2[idx];
  }
  skipped = 0;
  return new Promise(async (resolve) => {
    var _a3, _b3;
    if (!(model == null ? void 0 : model.inputs[0].shape))
      return;
    const t = {};
    const box = [[0, 0.1, 0.9, 0.9]];
    t.resize = jn.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
    if ((_a3 = config3.face["gear"]) == null ? void 0 : _a3.enabled)
      [t.age, t.gender, t.race] = model.execute(t.resize, ["age_output", "gender_output", "race_output"]);
    const gender = await t.gender.data();
    obj.gender = gender[0] > gender[1] ? "male" : "female";
    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;
    const race = await t.race.data();
    for (let i = 0; i < race.length; i++) {
      if (race[i] > (((_b3 = config3.face["gear"]) == null ? void 0 : _b3.minConfidence) || 0.2))
        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
    }
    obj.race.sort((a, b) => b.score - a.score);
    const ageDistribution = Array.from(await t.age.data());
    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
    let age = ageSorted[0][0];
    for (let i = 1; i < ageSorted.length; i++)
      age += ageSorted[i][1] * (ageSorted[i][0] - age);
    obj.age = Math.round(10 * age) / 10;
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    last2[idx] = obj;
    lastCount = count2;
    lastTime = now();
    resolve(obj);
  });
}

// 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 = we(255, "float32");
  constants.tf1 = we(1, "float32");
  constants.tf2 = we(2, "float32");
  constants.tf05 = we(0.5, "float32");
  constants.tf127 = we(127.5, "float32");
  constants.rgb = Zt([0.2989, 0.587, 0.114], "float32");
}

// src/gear/ssrnet-age.ts
var model2;
var last3 = [];
var lastCount2 = 0;
var lastTime2 = 0;
var skipped2 = Number.MAX_SAFE_INTEGER;
async function load2(config3) {
  if (env.initial)
    model2 = null;
  if (!model2)
    model2 = await loadModel(config3.face["ssrnet"].modelPathAge);
  else if (config3.debug)
    log("cached model:", model2["modelUrl"]);
  return model2;
}
async function predict2(image, config3, idx, count2) {
  var _a2, _b2, _c, _d2;
  if (!model2)
    return { age: 0 };
  const skipFrame = skipped2 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime2;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d2 = last3[idx]) == null ? void 0 : _d2.age) > 0) {
    skipped2++;
    return last3[idx];
  }
  skipped2 = 0;
  return new Promise(async (resolve) => {
    if (!(model2 == null ? void 0 : model2.inputs) || !model2.inputs[0] || !model2.inputs[0].shape)
      return;
    const t = {};
    t.resize = jn.resizeBilinear(image, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
    t.enhance = V(t.resize, constants.tf255);
    const obj = { age: 0 };
    if (config3.face["ssrnet"].enabled)
      t.age = model2.execute(t.enhance);
    if (t.age) {
      const data = await t.age.data();
      obj.age = Math.trunc(10 * data[0]) / 10;
    }
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    last3[idx] = obj;
    lastCount2 = count2;
    lastTime2 = now();
    resolve(obj);
  });
}

// src/gear/ssrnet-gender.ts
var model3;
var last4 = [];
var lastCount3 = 0;
var lastTime3 = 0;
var skipped3 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load3(config3) {
  if (env.initial)
    model3 = null;
  if (!model3)
    model3 = await loadModel(config3.face["ssrnet"].modelPathGender);
  else if (config3.debug)
    log("cached model:", model3["modelUrl"]);
  return model3;
}
async function predict3(image, config3, idx, count2) {
  var _a2, _b2, _c, _d2;
  if (!model3)
    return { gender: "unknown", genderScore: 0 };
  const skipFrame = skipped3 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime3;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.gender) && ((_d2 = last4[idx]) == null ? void 0 : _d2.genderScore) > 0) {
    skipped3++;
    return last4[idx];
  }
  skipped3 = 0;
  return new Promise(async (resolve) => {
    if (!(model3 == null ? void 0 : model3.inputs[0].shape))
      return;
    const t = {};
    t.resize = jn.resizeBilinear(image, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
    t.enhance = q(() => {
      const [red, green, blue] = Bn(t.resize, 3, 3);
      const redNorm = V(red, rgb[0]);
      const greenNorm = V(green, rgb[1]);
      const blueNorm = V(blue, rgb[2]);
      const grayscale = sE([redNorm, greenNorm, blueNorm]);
      const normalize = V(ge(grayscale, constants.tf05), 2);
      return normalize;
    });
    const obj = { gender: "unknown", genderScore: 0 };
    if (config3.face["ssrnet"].enabled)
      t.gender = model3.execute(t.enhance);
    const data = await t.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(t).forEach((tensor) => Re(t[tensor]));
    last4[idx] = obj;
    lastCount3 = count2;
    lastTime3 = now();
    resolve(obj);
  });
}

// src/face/antispoof.ts
var model4;
var cached = [];
var skipped4 = Number.MAX_SAFE_INTEGER;
var lastCount4 = 0;
var lastTime4 = 0;
async function load4(config3) {
  var _a2;
  if (env.initial)
    model4 = null;
  if (!model4)
    model4 = await loadModel((_a2 = config3.face.antispoof) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model4["modelUrl"]);
  return model4;
}
async function predict4(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model4)
    return 0;
  const skipTime = (((_a2 = config3.face.antispoof) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime4;
  const skipFrame = skipped4 < (((_b2 = config3.face.antispoof) == null ? void 0 : _b2.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached[idx]) {
    skipped4++;
    return cached[idx];
  }
  skipped4 = 0;
  return new Promise(async (resolve) => {
    const resize = jn.resizeBilinear(image, [(model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[2] : 0, (model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[1] : 0], false);
    const res = model4 == null ? void 0 : model4.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount4 = count2;
    lastTime4 = now();
    Re([resize, res]);
    resolve(cached[idx]);
  });
}

// src/face/facemeshcoords.ts
var meshAnnotations = {
  silhouette: [
    10,
    338,
    297,
    332,
    284,
    251,
    389,
    356,
    454,
    323,
    361,
    288,
    397,
    365,
    379,
    378,
    400,
    377,
    152,
    148,
    176,
    149,
    150,
    136,
    172,
    58,
    132,
    93,
    234,
    127,
    162,
    21,
    54,
    103,
    67,
    109
  ],
  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],
  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],
  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
  rightEyeIris: [473, 474, 475, 476, 477],
  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
  leftEyeIris: [468, 469, 470, 471, 472],
  midwayBetweenEyes: [168],
  noseTip: [1],
  noseBottom: [2],
  noseRightCorner: [98],
  noseLeftCorner: [327],
  rightCheek: [205],
  leftCheek: [425]
};
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 = [
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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((x) => UV468[x]);
var UV33 = VTX33.map((x) => UV468[x]);
var UV7 = VTX7.map((x) => UV468[x]);
function connectionsToIndices(connections) {
  const indices = connections.map((connection) => connection[0]);
  indices.push(connections[connections.length - 1][1]);
  return indices;
}
var pairsLips = [
  [61, 146],
  [146, 91],
  [91, 181],
  [181, 84],
  [84, 17],
  [17, 314],
  [314, 405],
  [405, 321],
  [321, 375],
  [375, 291],
  [61, 185],
  [185, 40],
  [40, 39],
  [39, 37],
  [37, 0],
  [0, 267],
  [267, 269],
  [269, 270],
  [270, 409],
  [409, 291],
  [78, 95],
  [95, 88],
  [88, 178],
  [178, 87],
  [87, 14],
  [14, 317],
  [317, 402],
  [402, 318],
  [318, 324],
  [324, 308],
  [78, 191],
  [191, 80],
  [80, 81],
  [81, 82],
  [82, 13],
  [13, 312],
  [312, 311],
  [311, 310],
  [310, 415],
  [415, 308]
];
var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
var pairsFaceContour = [
  [10, 338],
  [338, 297],
  [297, 332],
  [332, 284],
  [284, 251],
  [251, 389],
  [389, 356],
  [356, 454],
  [454, 323],
  [323, 361],
  [361, 288],
  [288, 397],
  [397, 365],
  [365, 379],
  [379, 378],
  [378, 400],
  [400, 377],
  [377, 152],
  [152, 148],
  [148, 176],
  [176, 149],
  [149, 150],
  [150, 136],
  [136, 172],
  [172, 58],
  [58, 132],
  [132, 93],
  [93, 234],
  [234, 127],
  [127, 162],
  [162, 21],
  [21, 54],
  [54, 103],
  [103, 67],
  [67, 109],
  [109, 10]
];
var contourKeypoints = {
  lips: connectionsToIndices(pairsLips),
  leftEye: connectionsToIndices(pairsLeftEye),
  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
  leftIris: connectionsToIndices(pairsLeftIris),
  rightEye: connectionsToIndices(pairsRightEye),
  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
  rightIris: connectionsToIndices(pairsRightIris),
  faceOval: connectionsToIndices(pairsFaceContour)
};

// src/face/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 w10 = image.shape[2];
  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w10, box.endPoint[1] / h, box.endPoint[0] / w10];
  const crop = jn.cropAndResize(image, [cutBox], [0], cropSize);
  const norm = xe(crop, constants.tf255);
  Re(crop);
  return norm;
};
var enlargeBox = (box, factor) => {
  const center = getBoxCenter(box);
  const size2 = getBoxSize(box);
  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
};
var squarifyBox = (box) => {
  const centers = getBoxCenter(box);
  const size2 = getBoxSize(box);
  const halfSize = Math.max(...size2) / 2;
  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
};
var calculateLandmarksBoundingBox = (landmarks) => {
  const x = landmarks.map((d) => d[0]);
  const y = landmarks.map((d) => d[1]);
  return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), 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 = (x, y) => [[1, 0, x], [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 = { 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 = jn.rotateWithOffset(input, angle, 0, centerRaw);
      rotationMatrix = buildRotationMatrix(-angle, center);
      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
      Re(rotated);
    } else {
      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
    }
  } else {
    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
  }
  return [angle, rotationMatrix, face4];
}
var findFaceCenter = (mesh) => {
  const x = mesh.map((m) => m[0]);
  const y = mesh.map((m) => m[1]);
  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 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 inputSize = 0;
var inputSizeT = null;
var size = () => inputSize;
async function load5(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"]);
  inputSize = model5.inputs[0].shape ? model5.inputs[0].shape[2] : 0;
  inputSizeT = we(inputSize, "int32");
  anchors = Zi(generateAnchors(inputSize));
  return model5;
}
function decodeBounds(boxOutputs) {
  const t = {};
  t.boxStarts = qe(boxOutputs, [0, 1], [-1, 2]);
  t.centers = ie(t.boxStarts, anchors);
  t.boxSizes = qe(boxOutputs, [0, 3], [-1, 2]);
  t.boxSizesNormalized = xe(t.boxSizes, inputSizeT);
  t.centersNormalized = xe(t.centers, inputSizeT);
  t.halfBoxSize = xe(t.boxSizesNormalized, constants.tf2);
  t.starts = ge(t.centersNormalized, t.halfBoxSize);
  t.ends = ie(t.centersNormalized, t.halfBoxSize);
  t.startNormalized = V(t.starts, inputSizeT);
  t.endNormalized = V(t.ends, inputSizeT);
  const boxes = ZE([t.startNormalized, t.endNormalized], 1);
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return boxes;
}
async function getBoxes(inputImage, config3) {
  var _a2, _b2, _c, _d2;
  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
    return [];
  const t = {};
  t.resized = jn.resizeBilinear(inputImage, [inputSize, inputSize]);
  t.div = xe(t.resized, constants.tf127);
  t.normalized = ge(t.div, constants.tf05);
  const res = model5 == null ? void 0 : model5.execute(t.normalized);
  if (Array.isArray(res)) {
    const sorted = res.sort((a, b) => a.size - b.size);
    t.concat384 = Ft([sorted[0], sorted[2]], 2);
    t.concat512 = Ft([sorted[1], sorted[3]], 2);
    t.concat = Ft([t.concat512, t.concat384], 1);
    t.batch = mr(t.concat, 0);
  } else {
    t.batch = mr(res);
  }
  Re(res);
  t.boxes = decodeBounds(t.batch);
  t.logits = qe(t.batch, [0, 0], [-1, 1]);
  t.sigmoid = Hs(t.logits);
  t.scores = mr(t.sigmoid);
  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
  const nms = await t.nms.array();
  const boxes = [];
  const scores = await t.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 = qe(t.boxes, [nms[i], 0], [1, -1]);
      b.slice = qe(t.batch, [nms[i], keypointsCount - 1], [1, -1]);
      b.squeeze = mr(b.slice);
      b.landmarks = U(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) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
      const squaredBox = squarifyBox(enlargedBox);
      boxes.push(squaredBox);
      Object.keys(b).forEach((tensor) => Re(b[tensor]));
    }
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return boxes;
}

// 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 inputSize2 = 224;
var anchorTensor;
var numLayers = 5;
var strides = [8, 16, 32, 32, 32];
async 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(inputSize2 / stride);
    const featureMapWidth = Math.ceil(inputSize2 / stride);
    for (let y = 0; y < featureMapHeight; ++y) {
      for (let x = 0; x < featureMapWidth; ++x) {
        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
        }
      }
    }
    layerId = lastSameStrideLayer;
  }
  anchorTensor = { x: Zt(anchors3.map((a) => a.x)), y: Zt(anchors3.map((a) => a.y)) };
}

// src/util/box.ts
function calc(keypoints, outputSize2 = [1, 1]) {
  const coords = [keypoints.map((pt2) => pt2[0]), keypoints.map((pt2) => pt2[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((pt2) => pt2[0]), keypoints.map((pt2) => pt2[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 env2 = { initial: true };
var models = { detector: null, landmarks: null };
var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
var skipped5 = Number.MAX_SAFE_INTEGER;
var outputNodes = {
  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
  detector: []
};
var cache = null;
var cropBox;
var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
var lastTime5 = 0;
var sigmoid = (x) => 1 - 1 / (1 + Math.exp(x));
async function loadDetect(config3) {
  if (env2.initial)
    models.detector = null;
  if (!models.detector && config3.body["detector"] && config3.body["detector"]["modelPath"] || "") {
    models.detector = await loadModel(config3.body["detector"]["modelPath"]);
    const inputs = Object.values(models.detector.modelSignature["inputs"]);
    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug && models.detector)
    log("cached model:", models.detector["modelUrl"]);
  await createAnchors();
  return models.detector;
}
async function loadPose(config3) {
  if (env2.initial)
    models.landmarks = null;
  if (!models.landmarks) {
    models.landmarks = await loadModel(config3.body.modelPath);
    const inputs = Object.values(models.landmarks.modelSignature["inputs"]);
    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", models.landmarks["modelUrl"]);
  return models.landmarks;
}
async function prepareImage(input, size2) {
  const t = {};
  if (!input.shape || !input.shape[1] || !input.shape[2])
    return input;
  let final;
  if (cropBox) {
    t.cropped = jn.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]
    ];
    t.pad = yi(t.cropped || input, padding);
    t.resize = jn.resizeBilinear(t.pad, [size2, size2]);
    final = xe(t.resize, constants.tf255);
  } else if (input.shape[1] !== size2) {
    t.resize = jn.resizeBilinear(t.cropped || input, [size2, size2]);
    final = xe(t.resize, constants.tf255);
  } else {
    final = xe(t.cropped || input, constants.tf255);
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return final;
}
function rescaleKeypoints(keypoints, outputSize2) {
  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) {
    for (const kpt4 of keypoints) {
      kpt4.positionRaw = [
        kpt4.positionRaw[0] + cropBox[1],
        kpt4.positionRaw[1] + 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;
}
async 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) {
  var _a2;
  const t = {};
  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a2 = models.landmarks) == null ? void 0 : _a2.execute(input, outputNodes.landmarks);
  const poseScore = (await t.poseflag.data())[0];
  const points = await t.ld.data();
  const distances = await t.world.data();
  Object.keys(t).forEach((tensor) => Re(t[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] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], 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 pt2 = [];
    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)
        pt2.push([pt0.position, pt1.position]);
    }
    annotations2[name] = pt2;
  }
  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
  return body4;
}
async function predict5(input, config3) {
  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime5;
  const skipFrame = skipped5 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
    skipped5++;
  } else {
    const t = {};
    t.landmarks = await prepareImage(input, 256);
    cache = await detectLandmarks(t.landmarks, config3, outputSize2);
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    lastTime5 = now();
    skipped5 = 0;
  }
  return cache ? [cache] : [];
}

// src/object/labels.ts
var labels = [
  { 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 model6;
var inputSize4 = 0;
var last5 = [];
var lastTime6 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load6(config3) {
  if (env.initial)
    model6 = null;
  if (!model6) {
    model6 = await loadModel(config3.object.modelPath);
    const inputs = Object.values(model6.modelSignature["inputs"]);
    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", model6["modelUrl"]);
  return model6;
}
async function process3(res, outputShape, config3) {
  if (!res)
    return [];
  const t = {};
  const results = [];
  const detections = await res.array();
  t.squeeze = mr(res);
  const arr = Bn(t.squeeze, 6, 1);
  t.stack = es([arr[1], arr[0], arr[3], arr[2]], 1);
  t.boxes = mr(t.stack);
  t.scores = mr(arr[4]);
  t.classes = mr(arr[5]);
  Re([res, ...arr]);
  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
  const nms = await t.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];
    const label = labels[classVal].label;
    const [x, y] = [
      detections[0][id2][0] / inputSize4,
      detections[0][id2][1] / inputSize4
    ];
    const boxRaw = [
      x,
      y,
      detections[0][id2][2] / inputSize4 - x,
      detections[0][id2][3] / inputSize4 - 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(t).forEach((tensor) => Re(t[tensor]));
  return results;
}
async function predict6(input, config3) {
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime6;
  const skipFrame = skipped6 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
    skipped6++;
    return last5;
  }
  skipped6 = 0;
  return new Promise(async (resolve) => {
    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
    const resize = jn.resizeBilinear(input, [inputSize4, inputSize4]);
    const objectT = config3.object.enabled ? model6 == null ? void 0 : model6.execute(resize, ["tower_0/detections"]) : null;
    lastTime6 = now();
    Re(resize);
    const obj = await process3(objectT, outputSize2, config3);
    last5 = 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 model7;
var lastTime7 = 0;
var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
var skipped7 = Number.MAX_SAFE_INTEGER;
async function load7(config3) {
  if (env.initial)
    model7 = null;
  if (!model7)
    model7 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model7["modelUrl"]);
  return model7;
}
async function max2d(inputs, minScore) {
  const [width, height] = inputs.shape;
  const reshaped = U(inputs, [height * width]);
  const max = As(reshaped, 0);
  const newScore = (await max.data())[0];
  Re([reshaped, max]);
  if (newScore > minScore) {
    const coordinates = ju(reshaped, 0);
    const mod = RD(coordinates, width);
    const x = (await mod.data())[0];
    const div = xe(coordinates, we(width, "int32"));
    const y = (await div.data())[0];
    Re([mod, div]);
    return [x, y, newScore];
  }
  return [0, 0, newScore];
}
async function predict7(image, config3) {
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime7;
  const skipFrame = skipped7 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
    skipped7++;
    return [cache2];
  }
  skipped7 = 0;
  return new Promise(async (resolve) => {
    var _a2;
    const tensor = q(() => {
      if (!(model7 == null ? void 0 : model7.inputs[0].shape))
        return null;
      const resize = jn.resizeBilinear(image, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false);
      const enhance2 = V(resize, constants.tf2);
      const norm = ge(enhance2, constants.tf1);
      return norm;
    });
    let resT;
    if (config3.body.enabled)
      resT = model7 == null ? void 0 : model7.execute(tensor);
    lastTime7 = now();
    Re(tensor);
    if (resT) {
      cache2.keypoints.length = 0;
      const squeeze = resT.squeeze();
      Re(resT);
      const stack = squeeze.unstack(2);
      Re(squeeze);
      for (let id2 = 0; id2 < stack.length; id2++) {
        const [x10, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence);
        if (partScore > (((_a2 = config3.body) == null ? void 0 : _a2.minConfidence) || 0)) {
          cache2.keypoints.push({
            score: Math.round(100 * partScore) / 100,
            part: kpt2[id2],
            positionRaw: [
              x10 / model7.inputs[0].shape[2],
              y10 / model7.inputs[0].shape[1]
            ],
            position: [
              Math.round(image.shape[2] * x10 / model7.inputs[0].shape[2]),
              Math.round(image.shape[1] * y10 / model7.inputs[0].shape[1])
            ]
          });
        }
      }
      stack.forEach((s) => Re(s));
    }
    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
    const x = cache2.keypoints.map((a) => a.position[0]);
    const y = cache2.keypoints.map((a) => a.position[1]);
    cache2.box = [
      Math.min(...x),
      Math.min(...y),
      Math.max(...x) - Math.min(...x),
      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 pt2 = [];
      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))
          pt2.push([pt0.position, pt1.position]);
      }
      cache2.annotations[name] = pt2;
    }
    resolve([cache2]);
  });
}

// src/gear/emotion.ts
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var model8;
var last6 = [];
var lastCount5 = 0;
var lastTime8 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load8(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 predict8(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model8)
    return [];
  const skipFrame = skipped8 < (((_a2 = config3.face.emotion) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.emotion) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime8;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx] && last6[idx].length > 0) {
    skipped8++;
    return last6[idx];
  }
  skipped8 = 0;
  return new Promise(async (resolve) => {
    var _a3, _b3;
    const obj = [];
    if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
      const t = {};
      const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
      t.resize = jn.resizeBilinear(image, [inputSize10, inputSize10], false);
      t.channels = V(t.resize, constants.rgb);
      t.grayscale = ve(t.channels, 3, true);
      t.grayscaleSub = ge(t.grayscale, constants.tf05);
      t.grayscaleMul = V(t.grayscaleSub, constants.tf2);
      t.emotion = model8 == null ? void 0 : model8.execute(t.grayscaleMul);
      lastTime8 = now();
      const data = await t.emotion.data();
      for (let i = 0; i < data.length; i++) {
        if (data[i] > (((_b3 = config3.face.emotion) == null ? void 0 : _b3.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(t).forEach((tensor) => Re(t[tensor]));
    }
    last6[idx] = obj;
    lastCount5 = count2;
    resolve(obj);
  });
}

// src/face/mobilefacenet.ts
var model9;
var last7 = [];
var lastCount6 = 0;
var lastTime9 = 0;
var skipped9 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
  if (env.initial)
    model9 = null;
  if (!model9)
    model9 = await loadModel(config3.face["mobilefacenet"].modelPath);
  else if (config3.debug)
    log("cached model:", model9["modelUrl"]);
  return model9;
}
async function predict9(input, config3, idx, count2) {
  var _a2, _b2;
  if (!model9)
    return [];
  const skipFrame = skipped9 < (((_a2 = config3.face["embedding"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["embedding"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime9;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && last7[idx]) {
    skipped9++;
    return last7[idx];
  }
  return new Promise(async (resolve) => {
    var _a3;
    let data = [];
    if (((_a3 = config3.face["embedding"]) == null ? void 0 : _a3.enabled) && (model9 == null ? void 0 : model9.inputs[0].shape)) {
      const t = {};
      t.crop = jn.resizeBilinear(input, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
      t.data = model9 == null ? void 0 : model9.execute(t.crop);
      const output = await t.data.data();
      data = Array.from(output);
    }
    last7[idx] = data;
    lastCount6 = count2;
    lastTime9 = now();
    resolve(data);
  });
}

// src/face/iris.ts
var model10;
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 load10(config3) {
  var _a2;
  if (env.initial)
    model10 = null;
  if (!model10)
    model10 = await loadModel((_a2 = config3.face.iris) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model10["modelUrl"]);
  inputSize5 = model10.inputs[0].shape ? model10.inputs[0].shape[2] : 0;
  if (inputSize5 === -1)
    inputSize5 = 64;
  return model10;
}
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 = jn.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 = jn.flipLeftRight(crop);
    Re(crop);
    crop = flipped;
  }
  return { box, boxSize, crop };
};
var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
  const eyeRawCoords = [];
  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
    const x = eyeData[i * 3];
    const y = eyeData[i * 3 + 1];
    const z = eyeData[i * 3 + 2];
    eyeRawCoords.push([
      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
      z
    ]);
  }
  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
  return irisCoords.map((coord, i) => {
    let z = averageZ;
    if (i === 2) {
      z = upperCenterZ;
    } else if (i === 4) {
      z = lowerCenterZ;
    }
    return [coord[0], coord[1], z];
  });
};
async function augmentIris(rawCoords, face4, config3, meshSize) {
  if (!model10) {
    if (config3.debug)
      log("face mesh iris detection requested, but model is not loaded");
    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 = Ft([leftEyeCrop, rightEyeCrop]);
  Re(leftEyeCrop);
  Re(rightEyeCrop);
  const eyePredictions = model10.execute(combined);
  Re(combined);
  const eyePredictionsData = await eyePredictions.data();
  Re(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
var attentionDefinitions = {
  eyeLLower: [33, 7, 163, 144, 145, 153, 154, 155, 133],
  eyeRLower: [263, 249, 390, 373, 374, 380, 381, 382, 362],
  lips: [185, 96, 90, 181, 84, 17, 314, 405, 320, 307, 409, 40, 39, 73, 37, 0, 267, 269, 270, 409, 40, 88, 178, 178, 87, 14, 268, 402, 318, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324, 185, 95, 80, 81, 85, 16, 315, 404, 319, 325, 409, 40, 39, 73, 72, 0, 302, 303, 270, 408, 185, 88, 88, 81, 82, 15, 316, 403, 319, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324],
  eyeL: [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],
  eyeR: [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]
};
async function augment(rawCoords, results) {
  const t = {
    irisL: results[3].dataSync(),
    irisR: results[1].dataSync(),
    eyeL: results[0].dataSync(),
    eyeR: results[6].dataSync(),
    lips: results[5].dataSync()
  };
  const irisRDepth = attentionDefinitions.eyeRLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeRLower.length;
  for (let i = 0; i < t.irisR.length / 2; i++)
    rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);
  const irisLDepth = attentionDefinitions.eyeLLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeLLower.length;
  for (let i = 0; i < t.irisL.length / 2; i++)
    rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);
  for (let i = 0; i < t.eyeL.length / 2; i++)
    rawCoords[attentionDefinitions.eyeL[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[attentionDefinitions.eyeL[i]][2]];
  for (let i = 0; i < t.eyeR.length / 2; i++)
    rawCoords[attentionDefinitions.eyeR[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[attentionDefinitions.eyeR[i]][2]];
  for (let i = 0; i < t.lips.length / 2; i++)
    rawCoords[attentionDefinitions.lips[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[attentionDefinitions.lips[i]][2]];
  return rawCoords;
}

// src/face/facemesh.ts
var cache3 = {
  boxes: [],
  skipped: Number.MAX_SAFE_INTEGER,
  timestamp: 0
};
var model11 = null;
var inputSize6 = 0;
async function predict10(input, config3) {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2;
  const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
  const skipFrame = cache3.skipped < (((_b2 = config3.face.detector) == null ? void 0 : _b2.skipFrames) || 0);
  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
    cache3.boxes = await getBoxes(input, config3);
    cache3.timestamp = now();
    cache3.skipped = 0;
  } else {
    cache3.skipped++;
  }
  const faces = [];
  const newCache = [];
  let id2 = 0;
  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((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size());
    if ((_e2 = config3 == null ? void 0 : config3.filter) == null ? void 0 : _e2.equalization) {
      const equilized = await histogramEqualization(face4.tensor);
      Re(face4.tensor);
      face4.tensor = equilized;
    }
    face4.boxScore = Math.round(100 * box.confidence) / 100;
    if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
      face4.box = clampBox(box, input);
      face4.boxRaw = getRawBox(box, input);
      face4.score = face4.boxScore;
      face4.mesh = box.landmarks.map((pt2) => [
        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt2[0] / size(),
        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt2[1] / size()
      ]);
      face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 0), pt2[1] / (input.shape[1] || 0), (pt2[2] || 0) / inputSize6]);
      for (const key of Object.keys(blazeFaceLandmarks)) {
        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
      }
    } else if (!model11) {
      if (config3.debug)
        log("face mesh detection requested, but model is not loaded");
    } else {
      const results = model11.execute(face4.tensor);
      const confidence = results.find((t) => t.shape[t.shape.length - 1] === 1);
      const contourCoords = results.find((t) => t.shape[t.shape.length - 1] === 1404);
      const faceConfidence = await confidence.data();
      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
      const coordsReshaped = U(contourCoords, [-1, 3]);
      let rawCoords = await coordsReshaped.array();
      if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) {
        box.confidence = face4.faceScore;
      } else {
        if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) {
          rawCoords = await augment(rawCoords, results);
        } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) {
          rawCoords = await augmentIris(rawCoords, face4.tensor, config3, inputSize6);
        }
        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
        face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 0), pt2[1] / (input.shape[1] || 0), (pt2[2] || 0) / inputSize6]);
        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);
      }
      Re([...results, coordsReshaped]);
    }
    if (face4.score > (((_j2 = config3.face.detector) == null ? void 0 : _j2.minConfidence) || 1))
      faces.push(face4);
    else
      Re(face4.tensor);
  }
  cache3.boxes = newCache;
  return faces;
}
async function load11(config3) {
  var _a2, _b2, _c;
  if (env.initial)
    model11 = null;
  if (!model11) {
    if ((_a2 = config3.face.attention) == null ? void 0 : _a2.enabled)
      model11 = await loadModel((_b2 = config3.face.attention) == null ? void 0 : _b2.modelPath);
    else
      model11 = await loadModel((_c = config3.face.mesh) == null ? void 0 : _c.modelPath);
  } else if (config3.debug) {
    log("cached model:", model11["modelUrl"]);
  }
  inputSize6 = model11.inputs[0].shape ? model11.inputs[0].shape[2] : 0;
  return model11;
}
var triangulation = TRI468;
var uvmap = UV468;

// src/face/faceres.ts
var model12;
var last8 = [];
var lastTime10 = 0;
var lastCount7 = 0;
var skipped10 = Number.MAX_SAFE_INTEGER;
async function load12(config3) {
  var _a2;
  if (env.initial)
    model12 = null;
  if (!model12)
    model12 = await loadModel((_a2 = config3.face.description) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model12["modelUrl"]);
  return model12;
}
function enhance(input) {
  const tensor = input.image || input.tensor || input;
  if (!(model12 == null ? void 0 : model12.inputs[0].shape))
    return tensor;
  const crop = jn.resizeBilinear(tensor, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false);
  const norm = V(crop, constants.tf255);
  Re(crop);
  return norm;
}
async function predict11(image, config3, idx, count2) {
  var _a2, _b2, _c, _d2;
  if (!model12)
    return { age: 0, gender: "unknown", genderScore: 0, descriptor: [] };
  const skipFrame = skipped10 < (((_a2 = config3.face.description) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.description) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime10;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last8[idx]) == null ? void 0 : _c.age) && ((_d2 = last8[idx]) == null ? void 0 : _d2.age) > 0) {
    skipped10++;
    return last8[idx];
  }
  skipped10 = 0;
  return new Promise(async (resolve) => {
    var _a3, _b3;
    const obj = {
      age: 0,
      gender: "unknown",
      genderScore: 0,
      descriptor: []
    };
    if ((_a3 = config3.face.description) == null ? void 0 : _a3.enabled) {
      const enhanced = enhance(image);
      const resT = model12 == null ? void 0 : model12.execute(enhanced);
      lastTime10 = now();
      Re(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
      const gender = await genderT.data();
      const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
      if (confidence > (((_b3 = config3.face.description) == null ? void 0 : _b3.minConfidence) || 0)) {
        obj.gender = gender[0] <= 0.5 ? "female" : "male";
        obj.genderScore = Math.min(0.99, confidence);
      }
      const argmax = ju(resT.find((t) => t.shape[1] === 100), 1);
      const age = (await argmax.data())[0];
      Re(argmax);
      const ageT = resT.find((t) => t.shape[1] === 100);
      const all2 = await ageT.data();
      obj.age = Math.round(all2[age - 1] > all2[age + 1] ? 10 * age - 100 * all2[age - 1] : 10 * age + 100 * all2[age + 1]) / 10;
      const desc = resT.find((t) => t.shape[1] === 1024);
      const descriptor = desc ? await desc.data() : [];
      obj.descriptor = Array.from(descriptor);
      resT.forEach((t) => Re(t));
    }
    last8[idx] = obj;
    lastCount7 = count2;
    resolve(obj);
  });
}

// 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 w10 = image.shape[2];
  const boxes = [[
    box.startPoint[1] / h,
    box.startPoint[0] / w10,
    box.endPoint[1] / h,
    box.endPoint[0] / w10
  ]];
  return jn.cropAndResize(image, boxes, [0], cropSize);
}
function scaleBoxCoordinates2(box, factor) {
  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
  const palmLandmarks = box.palmLandmarks.map((coord) => {
    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
    return scaledCoord;
  });
  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
}
function enlargeBox2(box, factor = 1.5) {
  const center = getBoxCenter2(box);
  const size2 = getBoxSize2(box);
  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function squarifyBox2(box) {
  const centers = getBoxCenter2(box);
  const size2 = getBoxSize2(box);
  const maxEdge = Math.max(...size2);
  const halfSize = maxEdge / 2;
  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
}
function normalizeRadians2(angle) {
  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
}
function computeRotation2(point1, point2) {
  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
  return normalizeRadians2(radians);
}
var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [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 },
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  { 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 },
  { 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: 0.1875, 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(model18) {
    __publicField(this, "model");
    __publicField(this, "anchors");
    __publicField(this, "anchorsTensor");
    __publicField(this, "inputSize");
    __publicField(this, "inputSizeTensor");
    __publicField(this, "doubleInputSizeTensor");
    this.model = model18;
    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
    this.anchorsTensor = Zi(this.anchors);
    this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
    this.inputSizeTensor = Zt([this.inputSize, this.inputSize]);
    this.doubleInputSizeTensor = Zt([this.inputSize * 2, this.inputSize * 2]);
  }
  normalizeBoxes(boxes) {
    const t = {};
    t.boxOffsets = qe(boxes, [0, 0], [-1, 2]);
    t.boxSizes = qe(boxes, [0, 2], [-1, 2]);
    t.div = xe(t.boxOffsets, this.inputSizeTensor);
    t.boxCenterPoints = ie(t.div, this.anchorsTensor);
    t.halfBoxSizes = xe(t.boxSizes, this.doubleInputSizeTensor);
    t.sub = ge(t.boxCenterPoints, t.halfBoxSizes);
    t.startPoints = V(t.sub, this.inputSizeTensor);
    t.add = ie(t.boxCenterPoints, t.halfBoxSizes);
    t.endPoints = V(t.add, this.inputSizeTensor);
    const res = ZE([t.startPoints, t.endPoints], 1);
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    return res;
  }
  normalizeLandmarks(rawPalmLandmarks, index2) {
    const t = {};
    t.reshape = U(rawPalmLandmarks, [-1, 7, 2]);
    t.div = xe(t.reshape, this.inputSizeTensor);
    t.landmarks = ie(t.div, this.anchors[index2]);
    const res = V(t.landmarks, this.inputSizeTensor);
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    return res;
  }
  async predict(input, config3) {
    const t = {};
    t.resize = jn.resizeBilinear(input, [this.inputSize, this.inputSize]);
    t.div = xe(t.resize, constants.tf127);
    t.image = ge(t.div, constants.tf1);
    t.batched = this.model.execute(t.image);
    t.predictions = mr(t.batched);
    t.slice = qe(t.predictions, [0, 0], [-1, 1]);
    t.sigmoid = Hs(t.slice);
    t.scores = mr(t.sigmoid);
    const scores = await t.scores.data();
    t.boxes = qe(t.predictions, [0, 1], [-1, 4]);
    t.norm = this.normalizeBoxes(t.boxes);
    t.nms = await jn.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
    const nms = await t.nms.array();
    const hands = [];
    for (const index2 of nms) {
      const p = {};
      p.box = qe(t.norm, [index2, 0], [1, -1]);
      p.slice = qe(t.predictions, [index2, 5], [1, 14]);
      p.norm = this.normalizeLandmarks(p.slice, index2);
      p.palmLandmarks = U(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] / this.inputSize, input.shape[1] / this.inputSize]);
      hands.push(scaled);
      Object.keys(p).forEach((tensor) => Re(p[tensor]));
    }
    Object.keys(t).forEach((tensor) => Re(t[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 lastTime11 = 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");
    this.handDetector = handDetector;
    this.handPoseModel = handPoseModel2;
    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[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() - lastTime11;
    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") ? jn.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 = xe(croppedInput, constants.tf255);
        Re(croppedInput);
        Re(rotatedImage);
        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime11 = now();
        Re(handImage);
        const confidence = (await confidenceT.data())[0];
        Re(confidenceT);
        if (confidence >= config3.hand.minConfidence / 4) {
          const keypointsReshaped = U(keypoints, [-1, 3]);
          const rawCoords = await keypointsReshaped.array();
          Re(keypoints);
          Re(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;
        }
        Re(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/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 options2 = {
  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 > options2.NO_CURL_START_LIMIT)
    fingerCurl = FingerCurl.none;
  else if (angleOfCurve > options2.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 += options2.DISTANCE_VOTE_POWER;
  else if (start_end_x_y_dist_ratio > 0.66)
    voteDiagonal += options2.DISTANCE_VOTE_POWER;
  else
    voteHorizontal += options2.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, options2.TOTAL_ANGLE_VOTE_POWER);
  voteVertical += votes[0];
  voteDiagonal += votes[1];
  voteHorizontal += votes[2];
  for (const fingerSlope of fingerSlopes) {
    const fingerVotes = angleOrientationAt(fingerSlope, options2.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/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 predict12(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 pt2 of keypoints) {
        if (pt2[0] < box[0])
          box[0] = pt2[0];
        if (pt2[1] < box[1])
          box[1] = pt2[1];
        if (pt2[0] > box[2])
          box[2] = pt2[0];
        if (pt2[1] > box[3])
          box[3] = pt2[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 load13(config3) {
  var _a2, _b2;
  if (env.initial) {
    handDetectorModel = null;
    handPoseModel = null;
  }
  if (!handDetectorModel || !handPoseModel) {
    [handDetectorModel, handPoseModel] = await Promise.all([
      config3.hand.enabled ? loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath) : null,
      config3.hand.landmarks ? loadModel((_b2 = config3.hand.skeleton) == null ? void 0 : _b2.modelPath) : null
    ]);
  } else {
    if (config3.debug)
      log("cached model:", handDetectorModel["modelUrl"]);
    if (config3.debug)
      log("cached model:", handPoseModel["modelUrl"]);
  }
  const handDetector = new HandDetector(handDetectorModel);
  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 skipped11 = Number.MAX_SAFE_INTEGER;
var lastTime12 = 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 = Object.values(models2[0].modelSignature["inputs"]);
    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 = Object.values(models2[1].modelSignature["inputs"]);
    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 t = {};
  const ratio = (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 * ratio / 8) * 8;
  t.resize = jn.resizeBilinear(input, [height, width]);
  t.cast = le(t.resize, "int32");
  [t.rawScores, t.rawBoxes] = await models2[0].executeAsync(t.cast, modelOutputNodes);
  t.boxes = mr(t.rawBoxes, [0, 2]);
  t.scores = mr(t.rawScores, [0]);
  const classScores = Fs(t.scores, 1);
  Re(classScores[faceIndex]);
  classScores.splice(faceIndex, 1);
  t.filtered = es(classScores, 1);
  Re(classScores);
  t.max = As(t.filtered, 1);
  t.argmax = ju(t.filtered, 1);
  let id2 = 0;
  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
  const nms = await t.nms.data();
  const scores = await t.max.data();
  const classNum = await t.argmax.data();
  for (const nmsIndex of Array.from(nms)) {
    const boxSlice = qe(t.boxes, nmsIndex, 1);
    const boxYX = await boxSlice.data();
    Re(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(t).forEach((tensor) => Re(t[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 t = {};
    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
    t.crop = jn.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
    t.div = xe(t.crop, constants.tf255);
    [t.score, t.keypoints] = models2[1].execute(t.div, ["Identity_1", "Identity"]);
    const rawScore = (await t.score.data())[0];
    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
    if (score >= (config3.hand.minConfidence || 0)) {
      hand3.fingerScore = score;
      t.reshaped = U(t.keypoints, [-1, 3]);
      const coordsData = await t.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(t).forEach((tensor) => Re(t[tensor]));
  }
  return hand3;
}
async function predict13(input, config3) {
  var _a2, _b2;
  if (!models2[0] || !models2[1] || !((_a2 = models2[0]) == null ? void 0 : _a2.inputs[0].shape) || !((_b2 = models2[1]) == null ? void 0 : _b2.inputs[0].shape))
    return [];
  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
  skipped11++;
  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime12;
  const skipFrame = skipped11 < (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() - lastTime12;
    const skipFrameExtended = skipped11 < 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);
      lastTime12 = now();
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
      skipped11 = 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/face/liveness.ts
var model13;
var cached2 = [];
var skipped12 = Number.MAX_SAFE_INTEGER;
var lastCount8 = 0;
var lastTime13 = 0;
async function load14(config3) {
  var _a2;
  if (env.initial)
    model13 = null;
  if (!model13)
    model13 = await loadModel((_a2 = config3.face.liveness) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model13["modelUrl"]);
  return model13;
}
async function predict14(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model13)
    return 0;
  const skipTime = (((_a2 = config3.face.liveness) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime13;
  const skipFrame = skipped12 < (((_b2 = config3.face.liveness) == null ? void 0 : _b2.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && cached2[idx]) {
    skipped12++;
    return cached2[idx];
  }
  skipped12 = 0;
  return new Promise(async (resolve) => {
    const resize = jn.resizeBilinear(image, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false);
    const res = model13 == null ? void 0 : model13.execute(resize);
    const num = (await res.data())[0];
    cached2[idx] = Math.round(100 * num) / 100;
    lastCount8 = count2;
    lastTime13 = now();
    Re([resize, res]);
    resolve(cached2[idx]);
  });
}

// 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/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) {
  const t = {};
  if (!input.shape || !input.shape[1] || !input.shape[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]
  ];
  t.pad = yi(input, cache5.padding);
  t.resize = jn.resizeBilinear(t.pad, [inputSize10, inputSize10]);
  const final = le(t.resize, "int32");
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return final;
}
function rescaleBody(body4, outputSize2) {
  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 && 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((pt2) => pt2.position), outputSize2);
  body4.box = rescaledBoxes.box;
  body4.boxRaw = rescaledBoxes.boxRaw;
  return body4;
}

// src/body/movenet.ts
var model14;
var inputSize8 = 0;
var skipped13 = Number.MAX_SAFE_INTEGER;
var cache6 = {
  boxes: [],
  bodies: [],
  last: 0
};
async function load15(config3) {
  if (env.initial)
    model14 = null;
  if (!model14) {
    fakeOps(["size"], config3);
    model14 = await loadModel(config3.body.modelPath);
  } else if (config3.debug)
    log("cached model:", model14["modelUrl"]);
  inputSize8 = model14.inputs[0].shape ? model14.inputs[0].shape[2] : 0;
  if (inputSize8 < 64)
    inputSize8 = 256;
  return model14;
}
async 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((pt2) => pt2.position), [image.shape[2], image.shape[1]]);
  const annotations2 = {};
  for (const [name, indexes] of Object.entries(connected3)) {
    const pt2 = [];
    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))
        pt2.push([pt0.position, pt1.position]);
    }
    annotations2[name] = pt2;
  }
  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
  bodyParts(body4);
  bodies.push(body4);
  return bodies;
}
async 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((pt2) => pt2.position), [image.shape[2], image.shape[1]]);
      const annotations2 = {};
      for (const [name, indexes] of Object.entries(connected3)) {
        const pt2 = [];
        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))
            pt2.push([pt0.position, pt1.position]);
        }
        annotations2[name] = pt2;
      }
      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 predict15(input, config3) {
  if (!model14 || !(model14 == null ? void 0 : model14.inputs[0].shape))
    return [];
  if (!config3.skipAllowed)
    cache6.boxes.length = 0;
  skipped13++;
  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
  const skipFrame = skipped13 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame) {
    return cache6.bodies;
  }
  return new Promise(async (resolve) => {
    const t = {};
    skipped13 = 0;
    t.input = padInput(input, inputSize8);
    t.res = model14 == null ? void 0 : model14.execute(t.input);
    cache6.last = now();
    const res = await t.res.array();
    cache6.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input) : await 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(t).forEach((tensor) => Re(t[tensor]));
    resolve(cache6.bodies);
  });
}

// src/object/nanodet.ts
var model15;
var last9 = [];
var lastTime14 = 0;
var skipped14 = Number.MAX_SAFE_INTEGER;
var inputSize9 = 0;
var scaleBox = 2.5;
async function load16(config3) {
  if (!model15 || env.initial) {
    model15 = await loadModel(config3.object.modelPath);
    const inputs = Object.values(model15.modelSignature["inputs"]);
    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", model15["modelUrl"]);
  return model15;
}
async function process4(res, outputShape, config3) {
  let id2 = 0;
  let results = [];
  for (const strideSize of [1, 2, 4]) {
    q(async () => {
      const baseSize = strideSize * 13;
      const scoresT = mr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
      const featuresT = mr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
      const boxIdx = await boxesMax.argMax(2).array();
      const scores = await scoresT.array();
      for (let i = 0; i < scoresT.shape[0]; i++) {
        for (let j = 0; j < scoresT.shape[1]; j++) {
          const score = scores[i][j];
          if (score > (config3.object.minConfidence || 0) && j !== 61) {
            const cx2 = (0.5 + Math.trunc(i % baseSize)) / baseSize;
            const cy2 = (0.5 + Math.trunc(i / baseSize)) / baseSize;
            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize9));
            const [x, y] = [
              cx2 - scaleBox / strideSize * boxOffset[0],
              cy2 - scaleBox / strideSize * boxOffset[1]
            ];
            const [w10, h] = [
              cx2 + scaleBox / strideSize * boxOffset[2] - x,
              cy2 + scaleBox / strideSize * boxOffset[3] - y
            ];
            let boxRaw = [x, y, w10, 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: labels[j].label,
              box: box.map((a) => Math.trunc(a)),
              boxRaw
            };
            results.push(result);
          }
        }
      }
    });
  }
  res.forEach((t) => Re(t));
  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 jn.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
    nmsIdx = await nms.data();
    Re(nms);
  }
  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
  return results;
}
async function predict16(image, config3) {
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime14;
  const skipFrame = skipped14 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last9.length > 0) {
    skipped14++;
    return last9;
  }
  skipped14 = 0;
  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
    return last9;
  return new Promise(async (resolve) => {
    const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0];
    const resize = jn.resizeBilinear(image, [inputSize9, inputSize9], false);
    const norm = xe(resize, constants.tf255);
    const transpose = norm.transpose([0, 3, 1, 2]);
    Re(norm);
    Re(resize);
    let objectT;
    if (config3.object.enabled)
      objectT = model15.execute(transpose);
    lastTime14 = now();
    Re(transpose);
    const obj = await process4(objectT, outputSize2, config3);
    last9 = obj;
    resolve(obj);
  });
}

// src/body/posenetutils.ts
var partNames = [
  "nose",
  "leftEye",
  "rightEye",
  "leftEar",
  "rightEar",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle"
];
var count = partNames.length;
var partIds = partNames.reduce((result, jointName, i) => {
  result[jointName] = i;
  return result;
}, {});
var connectedPartNames = [
  ["leftHip", "leftShoulder"],
  ["leftElbow", "leftShoulder"],
  ["leftElbow", "leftWrist"],
  ["leftHip", "leftKnee"],
  ["leftKnee", "leftAnkle"],
  ["rightHip", "rightShoulder"],
  ["rightElbow", "rightShoulder"],
  ["rightElbow", "rightWrist"],
  ["rightHip", "rightKnee"],
  ["rightKnee", "rightAnkle"],
  ["leftShoulder", "rightShoulder"],
  ["leftHip", "rightHip"]
];
var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
var poseChain = [
  ["nose", "leftEye"],
  ["leftEye", "leftEar"],
  ["nose", "rightEye"],
  ["rightEye", "rightEar"],
  ["nose", "leftShoulder"],
  ["leftShoulder", "leftElbow"],
  ["leftElbow", "leftWrist"],
  ["leftShoulder", "leftHip"],
  ["leftHip", "leftKnee"],
  ["leftKnee", "leftAnkle"],
  ["nose", "rightShoulder"],
  ["rightShoulder", "rightElbow"],
  ["rightElbow", "rightWrist"],
  ["rightShoulder", "rightHip"],
  ["rightHip", "rightKnee"],
  ["rightKnee", "rightAnkle"]
];
function getBoundingBox(keypoints) {
  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
    maxX: Math.max(maxX, x),
    maxY: Math.max(maxY, y),
    minX: Math.min(minX, x),
    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(x) {
    this.priorityQueue[++this.numberOfElements] = x;
    this.swim(this.numberOfElements);
  }
  dequeue() {
    const max = this.priorityQueue[0];
    this.exchange(0, this.numberOfElements--);
    this.sink(0);
    this.priorityQueue[this.numberOfElements + 1] = null;
    return max;
  }
  empty() {
    return this.numberOfElements === -1;
  }
  size() {
    return this.numberOfElements + 1;
  }
  all() {
    return this.priorityQueue.slice(0, this.numberOfElements + 1);
  }
  max() {
    return this.priorityQueue[0];
  }
  swim(k) {
    while (k > 0 && this.less(Math.floor(k / 2), k)) {
      this.exchange(k, Math.floor(k / 2));
      k = Math.floor(k / 2);
    }
  }
  sink(k) {
    while (2 * k <= this.numberOfElements) {
      let 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 t = this.priorityQueue[i];
    this.priorityQueue[i] = this.priorityQueue[j];
    this.priorityQueue[j] = t;
  }
};
function getOffsetPoint(y, x, keypoint, offsets) {
  return {
    y: offsets.get(y, x, keypoint),
    x: offsets.get(y, x, keypoint + count)
  };
}
function getImageCoords(part, outputStride2, offsets) {
  const { heatmapY, heatmapX, id: keypoint } = part;
  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
  return {
    x: part.heatmapX * outputStride2 + x,
    y: part.heatmapY * outputStride2 + y
  };
}
function clamp(a, min, max) {
  if (a < min)
    return min;
  if (a > max)
    return max;
  return a;
}
function squaredDistance(y12, x12, y22, x22) {
  const dy2 = y22 - y12;
  const dx2 = x22 - x12;
  return dy2 * dy2 + dx2 * dx2;
}
function addVectors(a, b) {
  return { x: a.x + b.x, y: a.y + b.y };
}

// src/body/posenet.ts
var model16;
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, 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, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
  });
}
function getInstanceScore(existingPoses, keypoints) {
  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
    if (!withinRadius(existingPoses, position, keypointId))
      result += score;
    return result;
  }, 0);
  return notOverlappedKeypointScores / keypoints.length;
}
function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
  const poses = [];
  const queue = buildPartWithScoreQueue(minConfidence2, scores);
  while (poses.length < maxDetected && !queue.empty()) {
    const root = queue.dequeue();
    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
    if (withinRadius(poses, rootImageCoords, root.part.id))
      continue;
    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
    keypoints = keypoints.filter((a) => a.score > minConfidence2);
    const score = getInstanceScore(poses, keypoints);
    const box = getBoundingBox(keypoints);
    if (score > minConfidence2)
      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
  }
  return poses;
}
async function predict17(input, config3) {
  const res = q(() => {
    if (!model16.inputs[0].shape)
      return [];
    const resized = jn.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
    const normalized = ge(xe(le(resized, "float32"), 127.5), 1);
    const results = model16.execute(normalized, poseNetOutputs);
    const results3d = results.map((y) => mr(y, [0]));
    results3d[1] = Hs(results3d[1]);
    return results3d;
  });
  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
  for (const t of res)
    Re(t);
  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
  if (!model16.inputs[0].shape)
    return [];
  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
  return scaled;
}
async function load17(config3) {
  if (!model16 || env.initial)
    model16 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model16["modelUrl"]);
  return model16;
}

// src/segmentation/segmentation.ts
var model17;
var busy = false;
async function load18(config3) {
  if (!model17 || env.initial)
    model17 = await loadModel(config3.segmentation.modelPath);
  else if (config3.debug)
    log("cached model:", model17["modelUrl"]);
  return model17;
}
async function process5(input, background, config3) {
  var _a2, _b2;
  if (busy)
    return { data: [], canvas: null, alpha: null };
  busy = true;
  if (!model17)
    await load18(config3);
  const inputImage = await process2(input, config3);
  const width = ((_a2 = inputImage.tensor) == null ? void 0 : _a2.shape[2]) || 0;
  const height = ((_b2 = inputImage.tensor) == null ? void 0 : _b2.shape[1]) || 0;
  if (!inputImage.tensor)
    return { data: [], canvas: null, alpha: null };
  const t = {};
  t.resize = jn.resizeBilinear(inputImage.tensor, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
  Re(inputImage.tensor);
  t.norm = xe(t.resize, constants.tf255);
  t.res = model17.execute(t.norm);
  t.squeeze = mr(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = hb(t.squeeze);
    [t.bg, t.fg] = Fs(t.softmax, 2);
    t.expand = Pn(t.fg, 2);
    t.pad = Pn(t.expand, 0);
    t.crop = jn.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
    t.data = mr(t.crop, 0);
  } else {
    t.data = jn.resizeBilinear(t.squeeze, [height, width]);
  }
  const data = Array.from(await t.data.data());
  if (env.node && !env.Canvas && typeof ImageData === "undefined") {
    if (config3.debug)
      log("canvas support missing");
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    return { data, canvas: null, alpha: null };
  }
  const alphaCanvas = canvas(width, height);
  if (Fk)
    await Fk.toPixels(t.data, alphaCanvas);
  const alphaCtx = alphaCanvas.getContext("2d");
  if (config3.segmentation.blur && config3.segmentation.blur > 0)
    alphaCtx.filter = `blur(${config3.segmentation.blur}px)`;
  const alphaData = alphaCtx.getImageData(0, 0, width, height);
  const compositeCanvas = canvas(width, height);
  const compositeCtx = compositeCanvas.getContext("2d");
  if (inputImage.canvas)
    compositeCtx.drawImage(inputImage.canvas, 0, 0);
  compositeCtx.globalCompositeOperation = "darken";
  if (config3.segmentation.blur && config3.segmentation.blur > 0)
    compositeCtx.filter = `blur(${config3.segmentation.blur}px)`;
  compositeCtx.drawImage(alphaCanvas, 0, 0);
  compositeCtx.globalCompositeOperation = "source-over";
  compositeCtx.filter = "none";
  const compositeData = compositeCtx.getImageData(0, 0, width, height);
  for (let i = 0; i < width * height; i++)
    compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0];
  compositeCtx.putImageData(compositeData, 0, 0);
  let mergedCanvas = null;
  if (background && compositeCanvas) {
    mergedCanvas = canvas(width, height);
    const bgImage = await process2(background, config3);
    Re(bgImage.tensor);
    const ctxMerge = mergedCanvas.getContext("2d");
    ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
    ctxMerge.drawImage(compositeCanvas, 0, 0);
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  busy = false;
  return { data, canvas: compositeCanvas, alpha: alphaCanvas };
}

// src/models.ts
var Models = class {
  constructor() {
    __publicField(this, "ssrnetage", null);
    __publicField(this, "gear", null);
    __publicField(this, "blazeposedetect", null);
    __publicField(this, "blazepose", null);
    __publicField(this, "centernet", null);
    __publicField(this, "efficientpose", null);
    __publicField(this, "mobilefacenet", null);
    __publicField(this, "emotion", null);
    __publicField(this, "facedetect", null);
    __publicField(this, "faceiris", null);
    __publicField(this, "facemesh", null);
    __publicField(this, "faceres", null);
    __publicField(this, "ssrnetgender", null);
    __publicField(this, "handpose", null);
    __publicField(this, "handskeleton", null);
    __publicField(this, "handtrack", null);
    __publicField(this, "liveness", null);
    __publicField(this, "movenet", null);
    __publicField(this, "nanodet", null);
    __publicField(this, "posenet", null);
    __publicField(this, "segmentation", null);
    __publicField(this, "antispoof", null);
  }
};
function reset(instance) {
  for (const model18 of Object.keys(instance.models))
    instance.models[model18] = null;
}
async function load19(instance) {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2, _B2, _C2, _D2, _E2;
  if (env.initial)
    reset(instance);
  if (instance.config.hand.enabled) {
    if (!instance.models.handpose && ((_b2 = (_a2 = instance.config.hand.detector) == null ? void 0 : _a2.modelPath) == null ? void 0 : _b2.includes("handdetect")))
      [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config);
    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d2 = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d2.includes("handdetect")))
      [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config);
  }
  if (instance.config.body.enabled && !instance.models.blazepose && ((_f = (_e2 = instance.config.body) == null ? void 0 : _e2.modelPath) == null ? void 0 : _f.includes("blazepose")))
    instance.models.blazepose = loadPose(instance.config);
  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"]["modelPath"])
    instance.models.blazeposedetect = loadDetect(instance.config);
  if (instance.config.body.enabled && !instance.models.efficientpose && ((_h = (_g2 = instance.config.body) == null ? void 0 : _g2.modelPath) == null ? void 0 : _h.includes("efficientpose")))
    instance.models.efficientpose = load7(instance.config);
  if (instance.config.body.enabled && !instance.models.movenet && ((_j2 = (_i = instance.config.body) == null ? void 0 : _i.modelPath) == null ? void 0 : _j2.includes("movenet")))
    instance.models.movenet = load15(instance.config);
  if (instance.config.body.enabled && !instance.models.posenet && ((_l2 = (_k2 = instance.config.body) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("posenet")))
    instance.models.posenet = load17(instance.config);
  if (instance.config.face.enabled && !instance.models.facedetect)
    instance.models.facedetect = load5(instance.config);
  if (instance.config.face.enabled && ((_m2 = instance.config.face.antispoof) == null ? void 0 : _m2.enabled) && !instance.models.antispoof)
    instance.models.antispoof = load4(instance.config);
  if (instance.config.face.enabled && ((_n2 = instance.config.face.liveness) == null ? void 0 : _n2.enabled) && !instance.models.liveness)
    instance.models.liveness = load14(instance.config);
  if (instance.config.face.enabled && ((_o2 = instance.config.face.description) == null ? void 0 : _o2.enabled) && !instance.models.faceres)
    instance.models.faceres = load12(instance.config);
  if (instance.config.face.enabled && ((_p2 = instance.config.face.emotion) == null ? void 0 : _p2.enabled) && !instance.models.emotion)
    instance.models.emotion = load8(instance.config);
  if (instance.config.face.enabled && ((_q2 = instance.config.face.iris) == null ? void 0 : _q2.enabled) && !((_r2 = instance.config.face.attention) == null ? void 0 : _r2.enabled) && !instance.models.faceiris)
    instance.models.faceiris = load10(instance.config);
  if (instance.config.face.enabled && ((_s2 = instance.config.face.mesh) == null ? void 0 : _s2.enabled) && !instance.models.facemesh)
    instance.models.facemesh = load11(instance.config);
  if (instance.config.face.enabled && ((_t2 = instance.config.face["gear"]) == null ? void 0 : _t2.enabled) && !instance.models.gear)
    instance.models.gear = load(instance.config);
  if (instance.config.face.enabled && ((_u2 = instance.config.face["ssrnet"]) == null ? void 0 : _u2.enabled) && !instance.models.ssrnetage)
    instance.models.ssrnetage = load2(instance.config);
  if (instance.config.face.enabled && ((_v2 = instance.config.face["ssrnet"]) == null ? void 0 : _v2.enabled) && !instance.models.ssrnetgender)
    instance.models.ssrnetgender = load3(instance.config);
  if (instance.config.face.enabled && ((_w2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _w2.enabled) && !instance.models.mobilefacenet)
    instance.models.mobilefacenet = load9(instance.config);
  if (instance.config.hand.enabled && !instance.models.handtrack && ((_y2 = (_x2 = instance.config.hand.detector) == null ? void 0 : _x2.modelPath) == null ? void 0 : _y2.includes("handtrack")))
    instance.models.handtrack = loadDetect2(instance.config);
  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_A2 = (_z2 = instance.config.hand.detector) == null ? void 0 : _z2.modelPath) == null ? void 0 : _A2.includes("handtrack")))
    instance.models.handskeleton = loadSkeleton(instance.config);
  if (instance.config.object.enabled && !instance.models.centernet && ((_C2 = (_B2 = instance.config.object) == null ? void 0 : _B2.modelPath) == null ? void 0 : _C2.includes("centernet")))
    instance.models.centernet = load6(instance.config);
  if (instance.config.object.enabled && !instance.models.nanodet && ((_E2 = (_D2 = instance.config.object) == null ? void 0 : _D2.modelPath) == null ? void 0 : _E2.includes("nanodet")))
    instance.models.nanodet = load16(instance.config);
  if (instance.config.segmentation.enabled && !instance.models.segmentation)
    instance.models.segmentation = load18(instance.config);
  for await (const model18 of Object.keys(instance.models)) {
    if (instance.models[model18] && typeof instance.models[model18] !== "undefined")
      instance.models[model18] = await instance.models[model18];
  }
}
async function validate2(instance) {
  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
  for (const defined of Object.keys(instance.models)) {
    const model18 = instance.models[defined];
    if (!model18)
      continue;
    const ops = [];
    const executor = model18 == null ? void 0 : model18.executor;
    if (executor && executor.graph.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 signature not determined:", defined);
    }
    const missing = [];
    for (const op2 of ops) {
      if (!simpleOps.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:", defined, missing);
  }
}

// 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();
}
async function register(instance) {
  var _a2;
  if (instance.config.backend !== "humangl")
    return;
  if (config2.name in ds().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
    log("error: humangl backend invalid context");
    reset(instance);
  }
  if (!ype(config2.name)) {
    try {
      config2.canvas = await canvas(100, 100);
    } catch (err) {
      log("error: cannot create canvas:", err);
      return;
    }
    try {
      config2.gl = (_a2 = config2.canvas) == null ? void 0 : _a2.getContext("webgl2", config2.webGLattr);
      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
      if (!glv2) {
        log("override: using fallback webgl backend as webgl 2.0 is not detected");
        instance.config.backend = "webgl";
        return;
      }
      if (config2.canvas) {
        config2.canvas.addEventListener("webglcontextlost", async (e) => {
          log("error: humangl:", 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("error: humangl context restored:", e);
        });
        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
          log("error: humangl context create:", e);
        });
      }
    } catch (err) {
      log("error: cannot get WebGL context:", err);
      return;
    }
    try {
      H5(2, config2.gl);
    } catch (err) {
      log("error: cannot set WebGL context:", err);
      return;
    }
    try {
      const ctx = new Zf(config2.gl);
      bp(config2.name, () => new K1(ctx), config2.priority);
    } catch (err) {
      log("error: cannot register WebGL backend:", err);
      return;
    }
    try {
      const kernels = sm("webgl");
      kernels.forEach((kernelConfig) => {
        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
        Rl(newKernelConfig);
      });
    } catch (err) {
      log("error: cannot update WebGL backend registration:", err);
      return;
    }
    const current = qA().getGPGPUContext ? qA().getGPGPUContext().gl : null;
    if (current) {
      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);
    } else {
      log("error: no current gl context:", current, config2.gl);
      return;
    }
    try {
      ok.set("WEBGL_VERSION", 2);
    } catch (err) {
      log("error: cannot set WebGL backend flags:", err);
      return;
    }
    extensions();
    log("backend registered:", config2.name);
  }
}

// src/tfjs/backend.ts
function registerCustomOps() {
  if (!env.kernels.includes("mod")) {
    const kernelMod = {
      kernelName: "Mod",
      backendName: gpe(),
      kernelFunc: (op2) => q(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
    };
    Rl(kernelMod);
    env.kernels.push("mod");
  }
  if (!env.kernels.includes("floormod")) {
    const kernelMod = {
      kernelName: "FloorMod",
      backendName: gpe(),
      kernelFunc: (op2) => q(() => Jk(op2.inputs.a / op2.inputs.b) * op2.inputs.b + RD(op2.inputs.a, op2.inputs.b))
    };
    Rl(kernelMod);
    env.kernels.push("floormod");
  }
}
async function check(instance, force = false) {
  instance.state = "backend";
  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && gpe() !== 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 = "humangl";
      }
      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 = "humangl";
        } else {
          const adapter = await navigator["gpu"].requestAdapter();
          if (instance.config.debug)
            log("enumerated webgpu adapter:", adapter);
        }
      }
      if (instance.config.backend === "humangl")
        await register(instance);
      const available = Object.keys(ds().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 (instance.config.debug)
          log("wasm path:", instance.config.wasmPath);
        if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined")
          await mhe(instance.config.wasmPath, instance.config.wasmPlatformFetch);
        else
          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
        const simd = await K().getAsync("WASM_HAS_SIMD_SUPPORT");
        const mt2 = await K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
        if (instance.config.debug)
          log(`wasm execution: ${simd ? "SIMD" : "no SIMD"} ${mt2 ? "multithreaded" : "singlethreaded"}`);
        if (instance.config.debug && !simd)
          log("warning: wasm simd support is not enabled");
      }
      try {
        await fpe(instance.config.backend);
        await mpe();
        init();
      } catch (err) {
        log("error: cannot set backend:", instance.config.backend, err);
        return false;
      }
    }
    if (gpe() === "humangl") {
      ok.set("CHECK_COMPUTATION_FOR_ERRORS", false);
      ok.set("WEBGL_CPU_FORWARD", true);
      ok.set("WEBGL_USE_SHAPES_UNIFORMS", true);
      ok.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
      if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
        ok.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
      }
      if (qA().getGPGPUContext) {
        const gl2 = await qA().getGPGPUContext().gl;
        if (instance.config.debug)
          log(`gl version:${gl2.getParameter(gl2.VERSION)} renderer:${gl2.getParameter(gl2.RENDERER)}`);
      }
    }
    if (gpe() === "webgpu") {
    }
    upe();
    await mpe();
    instance.performance.initBackend = Math.trunc(now() - timeStamp);
    instance.config.backend = gpe();
    await env.updateBackend();
    registerCustomOps();
  }
  return true;
}
function fakeOps(kernelNames, config3) {
  for (const kernelName of kernelNames) {
    const kernelConfig = {
      kernelName,
      backendName: config3.backend,
      kernelFunc: () => {
        if (config3.debug)
          log("kernelFunc", kernelName, config3.backend);
      }
    };
    Rl(kernelConfig);
  }
  env.kernels = sm(gpe()).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,
  object: () => object,
  options: () => options3,
  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 colorDepth = (z, opt2) => {
  if (!opt2.useDepth || typeof z === "undefined")
    return opt2.color;
  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
  const color = `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
  return color;
};
function point(ctx, x, y, z, localOptions) {
  ctx.fillStyle = colorDepth(z, localOptions);
  ctx.beginPath();
  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
  ctx.fill();
}
function rect(ctx, x, y, width, height, localOptions) {
  ctx.beginPath();
  ctx.lineWidth = localOptions.lineWidth;
  if (localOptions.useCurves) {
    const cx2 = (x + x + width) / 2;
    const cy2 = (y + y + height) / 2;
    ctx.ellipse(cx2, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI);
  } else {
    ctx.moveTo(x + localOptions.roundRect, y);
    ctx.lineTo(x + width - localOptions.roundRect, y);
    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
    ctx.lineTo(x + width, y + height - localOptions.roundRect);
    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
    ctx.lineTo(x + localOptions.roundRect, y + height);
    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
    ctx.lineTo(x, y + localOptions.roundRect);
    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
    ctx.closePath();
  }
  ctx.stroke();
}
function lines(ctx, points, localOptions) {
  if (points.length < 2)
    return;
  ctx.beginPath();
  ctx.moveTo(points[0][0], points[0][1]);
  for (const pt2 of points) {
    ctx.strokeStyle = colorDepth(pt2[2], localOptions);
    ctx.lineTo(Math.trunc(pt2[0]), Math.trunc(pt2[1]));
  }
  ctx.stroke();
  if (localOptions.fillPolygons) {
    ctx.closePath();
    ctx.fill();
  }
}
function curves(ctx, points, localOptions) {
  if (points.length < 2)
    return;
  ctx.lineWidth = localOptions.lineWidth;
  if (!localOptions.useCurves || points.length <= 2) {
    lines(ctx, points, localOptions);
    return;
  }
  ctx.moveTo(points[0][0], points[0][1]);
  for (let i = 0; i < points.length - 2; i++) {
    const xc = (points[i][0] + points[i + 1][0]) / 2;
    const yc = (points[i][1] + points[i + 1][1]) / 2;
    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
  }
  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 (localOptions.fillPolygons) {
    ctx.closePath();
    ctx.fill();
  }
}
function arrow(ctx, from, to2, radius = 5) {
  let angle;
  let x;
  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]);
  x = radius * Math.cos(angle) + to2[0];
  y = radius * Math.sin(angle) + to2[1];
  ctx.moveTo(x, y);
  angle += 1 / 3 * (2 * Math.PI);
  x = radius * Math.cos(angle) + to2[0];
  y = radius * Math.sin(angle) + to2[1];
  ctx.lineTo(x, y);
  angle += 1 / 3 * (2 * Math.PI);
  x = radius * Math.cos(angle) + to2[0];
  y = radius * Math.sin(angle) + to2[1];
  ctx.lineTo(x, y);
  ctx.closePath();
  ctx.stroke();
  ctx.fill();
}

// src/draw/options.ts
var options3 = {
  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
};

// src/draw/face.ts
var opt;
function drawLabels(f, ctx) {
  if (opt.drawLabels) {
    const labels2 = [];
    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
    if (f.genderScore)
      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
    if (f.age)
      labels2.push(`age: ${f.age || ""}`);
    if (f.iris)
      labels2.push(`distance: ${f.iris}`);
    if (f.real)
      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
    if (f.live)
      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
    if (f.emotion && f.emotion.length > 0) {
      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
      if (emotion.length > 3)
        emotion.length = 3;
      labels2.push(emotion.join(" "));
    }
    if (f.rotation && f.rotation.angle && f.rotation.gaze) {
      if (f.rotation.angle.roll)
        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
      if (f.rotation.gaze.bearing)
        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
    }
    if (labels2.length === 0)
      labels2.push("face");
    ctx.fillStyle = opt.color;
    for (let i = labels2.length - 1; i >= 0; i--) {
      const x = Math.max(f.box[0], 0);
      const y = i * opt.lineHeight + f.box[1];
      if (opt.shadowColor && opt.shadowColor !== "") {
        ctx.fillStyle = opt.shadowColor;
        ctx.fillText(labels2[i], x + 5, y + 16);
      }
      ctx.fillStyle = opt.labelColor;
      ctx.fillText(labels2[i], x + 4, y + 15);
    }
  }
}
function drawIrisElipse(f, ctx) {
  if (f.annotations && f.annotations["leftEyeIris"] && f.annotations["leftEyeIris"][0]) {
    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.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 (opt.fillPolygons) {
      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
      ctx.fill();
    }
  }
  if (f.annotations && f.annotations["rightEyeIris"] && f.annotations["rightEyeIris"][0]) {
    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.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 (opt.fillPolygons) {
      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
      ctx.fill();
    }
  }
}
function drawGazeSpheres(f, ctx) {
  var _a2;
  if (opt.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, _b2, _c, _d2;
  if (opt.drawGaze && ((_b2 = (_a2 = f.rotation) == null ? void 0 : _a2.gaze) == null ? void 0 : _b2.strength) && ((_d2 = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d2.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 (opt.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, opt);
    }
    drawIrisElipse(f, ctx);
  }
}
function drawFacePoints(f, ctx) {
  if (opt.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], opt);
      if (opt.drawAttention) {
        if (attentionDefinitions.lips.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
        if (attentionDefinitions.eyeL.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
        if (attentionDefinitions.eyeR.includes(i))
          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
      }
    }
  }
}
function drawFaceBoxes(f, ctx) {
  if (opt.drawBoxes) {
    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
  }
}
async function face(inCanvas2, result, drawOptions) {
  opt = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.font = opt.font;
  ctx.strokeStyle = opt.color;
  ctx.fillStyle = opt.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
async function body(inCanvas2, result, drawOptions) {
  var _a2;
  const localOptions = mergeDeep(options3, 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 = localOptions.color;
    ctx.fillStyle = localOptions.color;
    ctx.lineWidth = localOptions.lineWidth;
    ctx.font = localOptions.font;
    if (localOptions.drawBoxes && result[i].box && ((_a2 = result[i].box) == null ? void 0 : _a2.length) === 4) {
      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
      if (localOptions.drawLabels) {
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
      }
    }
    if (localOptions.drawPoints && result[i].keypoints) {
      for (let pt2 = 0; pt2 < result[i].keypoints.length; pt2++) {
        if (!result[i].keypoints[pt2].score || result[i].keypoints[pt2].score === 0)
          continue;
        ctx.fillStyle = colorDepth(result[i].keypoints[pt2].position[2], localOptions);
        point(ctx, result[i].keypoints[pt2].position[0], result[i].keypoints[pt2].position[1], 0, localOptions);
      }
    }
    if (localOptions.drawLabels && result[i].keypoints) {
      ctx.font = localOptions.font;
      for (const pt2 of result[i].keypoints) {
        if (!pt2.score || pt2.score === 0)
          continue;
        ctx.fillStyle = colorDepth(pt2.position[2], localOptions);
        ctx.fillText(`${pt2.part} ${Math.trunc(100 * pt2.score)}%`, pt2.position[0] + 4, pt2.position[1] + 4);
      }
    }
    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
      for (const part of Object.values(result[i].annotations)) {
        for (const connected4 of part)
          curves(ctx, connected4, localOptions);
      }
    }
  }
}

// src/draw/hand.ts
async function hand(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (const h of result) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
      if (localOptions.drawLabels) {
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
      }
      ctx.stroke();
    }
    if (localOptions.drawPoints) {
      if (h.keypoints && h.keypoints.length > 0) {
        for (const pt2 of h.keypoints) {
          ctx.fillStyle = colorDepth(pt2[2], localOptions);
          point(ctx, pt2[0], pt2[1], 0, localOptions);
        }
      }
    }
    if (localOptions.drawLabels && h.annotations) {
      const addHandLabel = (part, title) => {
        if (!part || part.length === 0 || !part[0])
          return;
        const z = part[part.length - 1][2] || -256;
        ctx.fillStyle = colorDepth(z, localOptions);
        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
      };
      ctx.font = localOptions.font;
      addHandLabel(h.annotations["index"], "index");
      addHandLabel(h.annotations["middle"], "middle");
      addHandLabel(h.annotations["ring"], "ring");
      addHandLabel(h.annotations["pinky"], "pinky");
      addHandLabel(h.annotations["thumb"], "thumb");
      addHandLabel(h.annotations["palm"], "palm");
    }
    if (localOptions.drawPolygons && h.annotations) {
      const addHandLine = (part) => {
        if (!part || part.length === 0 || !part[0])
          return;
        for (let i = 0; i < part.length; i++) {
          ctx.beginPath();
          const z = part[i][2] || 0;
          ctx.strokeStyle = colorDepth(i * z, localOptions);
          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 = localOptions.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
async function object(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (const h of result) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
      if (localOptions.drawLabels) {
        const label = `${h.label} ${Math.round(100 * h.score)}%`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
      }
      ctx.stroke();
    }
  }
}

// src/draw/gesture.ts
async function gesture(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  if (localOptions.drawGestures) {
    const ctx = getCanvasContext(inCanvas2);
    if (!ctx)
      return;
    ctx.font = localOptions.font;
    ctx.fillStyle = localOptions.color;
    let i = 1;
    for (let j = 0; j < result.length; j++) {
      let where = [];
      let what = [];
      [where, what] = Object.entries(result[j]);
      if (what.length > 1 && what[1].length > 0) {
        const who = where[1] > 0 ? `#${where[1]}` : "";
        const label = `${where[0]} ${who}: ${what[1]}`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
        i += 1;
      }
    }
  }
}

// src/draw/draw.ts
var drawTime = 0;
async function person(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (let i = 0; i < result.length; i++) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
      if (localOptions.drawLabels) {
        const label = `person #${i}`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
      }
      ctx.stroke();
    }
  }
}
async 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 || !result.performance || !result || !inCanvas2)
    return null;
  const timeStamp = now();
  const localOptions = mergeDeep(options3, drawOptions);
  const promise = Promise.all([
    face(inCanvas2, result.face, localOptions),
    body(inCanvas2, result.body, localOptions),
    hand(inCanvas2, result.hand, localOptions),
    object(inCanvas2, result.object, localOptions),
    gesture(inCanvas2, result.gesture, localOptions)
  ]);
  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
  result.performance.draw = drawTime;
  return promise;
}

// src/face/mask.ts
var expandFact = 0.1;
var alpha = 0.5;
function insidePoly(x, 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 && x < (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 pt2 of meshAnnotations.silhouette)
    silhouette.push({ x: (face4.mesh[pt2][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt2][1] - face4.box[1]) / face4.box[3] });
  if (expandFact && expandFact > 0)
    silhouette = silhouette.map((pt2) => ({ x: pt2.x > 0.5 ? pt2.x + expandFact : pt2.x - expandFact, y: pt2.y > 0.5 ? pt2.y + expandFact : pt2.y - expandFact }));
  for (let x = 0; x < width; x++) {
    for (let y = 0; y < height; y++) {
      const inside = insidePoly(x / width, y / width, silhouette);
      if (!inside) {
        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
      }
    }
  }
  const output = buffer.toTensor();
  Re(buffer);
  return output;
}

// 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 normalize = (v) => {
    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
    v[0] /= length;
    v[1] /= length;
    v[2] /= length;
    return v;
  };
  const subVectors = (a, b) => {
    const x = a[0] - b[0];
    const y = a[1] - b[1];
    const z = a[2] - b[2];
    return [x, y, z];
  };
  const crossVectors = (a, b) => {
    const x = a[1] * b[2] - a[2] * b[1];
    const y = a[2] * b[0] - a[0] * b[2];
    const z = a[0] * b[1] - a[1] * b[0];
    return [x, y, z];
  };
  const rotationMatrixToEulerAngle = (r) => {
    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
    let thetaX;
    let thetaY;
    let thetaZ;
    if (r10 < 1) {
      if (r10 > -1) {
        thetaZ = Math.asin(r10);
        thetaY = Math.atan2(-r20, r00);
        thetaX = Math.atan2(-r12, r11);
      } else {
        thetaZ = -Math.PI / 2;
        thetaY = -Math.atan2(r21, r22);
        thetaX = 0;
      }
    } else {
      thetaZ = Math.PI / 2;
      thetaY = Math.atan2(r21, r22);
      thetaX = 0;
    }
    if (isNaN(thetaX))
      thetaX = 0;
    if (isNaN(thetaY))
      thetaY = 0;
    if (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((pt2) => [pt2[0] * imageSize[0] / size2, pt2[1] * imageSize[1] / size2, pt2[2]]);
  const y_axis = normalize(subVectors(pts[1], pts[0]));
  let x_axis = normalize(subVectors(pts[3], pts[2]));
  const z_axis = normalize(crossVectors(x_axis, y_axis));
  x_axis = crossVectors(y_axis, z_axis);
  const matrix = [
    x_axis[0],
    x_axis[1],
    x_axis[2],
    y_axis[0],
    y_axis[1],
    y_axis[2],
    z_axis[0],
    z_axis[1],
    z_axis[2]
  ];
  const angle = rotationMatrixToEulerAngle(matrix);
  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
  return { angle, matrix, gaze };
};

// src/face/face.ts
var detectFace = async (instance, input) => {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
  let timeStamp = now();
  let ageRes;
  let gearRes;
  let genderRes;
  let emotionRes;
  let mobilefacenetRes;
  let antispoofRes;
  let livenessRes;
  let descRes;
  const faceRes = [];
  instance.state = "run:face";
  const faces = await predict10(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]);
      Re(faces[i].tensor);
      faces[i].tensor = masked;
    }
    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
    instance.analyze("Start Emotion:");
    if (instance.config.async) {
      emotionRes = ((_b2 = instance.config.face.emotion) == null ? void 0 : _b2.enabled) ? predict8(faces[i].tensor || ms([]), instance.config, i, faces.length) : [];
    } else {
      instance.state = "run:emotion";
      timeStamp = now();
      emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict8(faces[i].tensor || ms([]), 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) ? predict4(faces[i].tensor || ms([]), 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 predict4(faces[i].tensor || ms([]), 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 = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict14(faces[i].tensor || ms([]), 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 predict14(faces[i].tensor || ms([]), 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 = ((_h = instance.config.face["gear"]) == null ? void 0 : _h.enabled) ? predict(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
    } else {
      instance.state = "run:gear";
      timeStamp = now();
      gearRes = ((_i = instance.config.face["gear"]) == null ? void 0 : _i.enabled) ? await predict(faces[i].tensor || ms([]), 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) ? predict2(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
      genderRes = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.enabled) ? predict3(faces[i].tensor || ms([]), 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 predict2(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
      genderRes = ((_m2 = instance.config.face["ssrnet"]) == null ? void 0 : _m2.enabled) ? await predict3(faces[i].tensor || ms([]), 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) ? predict9(faces[i].tensor || ms([]), 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 predict9(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
    }
    instance.analyze("End MobileFaceNet:");
    instance.analyze("Start Description:");
    if (instance.config.async) {
      descRes = ((_p2 = instance.config.face.description) == null ? void 0 : _p2.enabled) ? predict11(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
    } else {
      instance.state = "run:description";
      timeStamp = now();
      descRes = ((_q2 = instance.config.face.description) == null ? void 0 : _q2.enabled) ? await predict11(faces[i].tensor || ms([]), instance.config, i, faces.length) : null;
      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, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, 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 (((_t2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _t2.enabled) && mobilefacenetRes) {
      descRes.descriptor = mobilefacenetRes;
    }
    if (!((_u2 = instance.config.face.iris) == null ? void 0 : _u2.enabled)) {
    }
    const irisSize = faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0] && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
    const tensor = ((_v2 = instance.config.face.detector) == null ? void 0 : _v2.return) ? mr(faces[i].tensor) : null;
    Re(faces[i].tensor);
    if (faces[i].tensor)
      delete faces[i].tensor;
    const res = {
      ...faces[i],
      id: i
    };
    if (descRes == null ? void 0 : descRes.age)
      res.age = descRes.age;
    if (descRes == null ? void 0 : descRes.gender)
      res.gender = descRes.gender;
    if (descRes == null ? void 0 : descRes.genderScore)
      res.genderScore = descRes == null ? void 0 : descRes.genderScore;
    if (descRes == null ? void 0 : descRes.descriptor)
      res.embedding = descRes == null ? void 0 : descRes.descriptor;
    if (descRes == null ? void 0 : descRes.race)
      res.race = descRes == null ? void 0 : descRes.race;
    if (emotionRes)
      res.emotion = emotionRes;
    if (antispoofRes)
      res.real = antispoofRes;
    if (livenessRes)
      res.live = livenessRes;
    if (irisSize && irisSize !== 0)
      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
    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/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 iris = (res) => {
  if (!res)
    return [];
  const gestures = [];
  for (let i = 0; i < res.length; i++) {
    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[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/util/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
var interpolateTime = 0;
function calc2(newResult, config3) {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2;
  const t02 = now();
  if (!newResult)
    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
  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, _b3, _c2, _d3, _e3, _f2, _g3, _h2, _i2;
        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) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[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 : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g3[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
          ]
        };
      });
      const annotations2 = {};
      let coords = { connected: {} };
      if ((_b2 = (_a2 = config3.body) == null ? void 0 : _a2.modelPath) == null ? void 0 : _b2.includes("efficientpose"))
        coords = efficientposecoords_exports;
      else if ((_d2 = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d2.includes("blazepose"))
        coords = blazeposecoords_exports;
      else if ((_f = (_e2 = config3.body) == null ? void 0 : _e2.modelPath) == null ? void 0 : _f.includes("movenet"))
        coords = movenetcoords_exports;
      for (const [name, indexes] of Object.entries(coords.connected)) {
        const pt2 = [];
        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)
            pt2.push([pt0.position, pt1.position]);
        }
        annotations2[name] = pt2;
      }
      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] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][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) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k2 = (_j2 = newResult.face[i].rotation) == null ? void 0 : _j2.angle) == null ? void 0 : _k2.roll) || 0)) / bufferedFactor,
          yaw: ((bufferedFactor - 1) * (((_m2 = (_l2 = bufferedResult.face[i].rotation) == null ? void 0 : _l2.angle) == null ? void 0 : _m2.yaw) || 0) + (((_o2 = (_n2 = newResult.face[i].rotation) == null ? void 0 : _n2.angle) == null ? void 0 : _o2.yaw) || 0)) / bufferedFactor,
          pitch: ((bufferedFactor - 1) * (((_q2 = (_p2 = bufferedResult.face[i].rotation) == null ? void 0 : _p2.angle) == null ? void 0 : _q2.pitch) || 0) + (((_s2 = (_r2 = newResult.face[i].rotation) == null ? void 0 : _r2.angle) == null ? void 0 : _s2.pitch) || 0)) / bufferedFactor
        };
        rotation.gaze = {
          bearing: ((bufferedFactor - 1) * (((_u2 = (_t2 = bufferedResult.face[i].rotation) == null ? void 0 : _t2.gaze) == null ? void 0 : _u2.bearing) || 0) + (((_w2 = (_v2 = newResult.face[i].rotation) == null ? void 0 : _v2.gaze) == null ? void 0 : _w2.bearing) || 0)) / bufferedFactor,
          strength: ((bufferedFactor - 1) * (((_y2 = (_x2 = bufferedResult.face[i].rotation) == null ? void 0 : _x2.gaze) == null ? void 0 : _y2.strength) || 0) + (((_A2 = (_z2 = newResult.face[i].rotation) == null ? void 0 : _z2.gaze) == null ? void 0 : _A2.strength) || 0)) / bufferedFactor
        };
        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
      }
      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;
  const t12 = now();
  interpolateTime = env.perfadd ? interpolateTime + Math.round(t12 - t02) : Math.round(t12 - t02);
  if (newResult.performance)
    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
  return bufferedResult;
}

// src/face/match.ts
var match_exports = {};
__export(match_exports, {
  distance: () => distance,
  match: () => match2,
  similarity: () => similarity
});
function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
  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 match2(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 || descriptor.length !== descriptors[0].length) {
    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 = distance(descriptor, descriptors[i], options4);
    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/util/persons.ts
function join2(faces, bodies, hands, gestures, shape) {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2;
  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)
        (_a2 = person2.gestures) == null ? void 0 : _a2.push(gesture2);
      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
        (_b2 = person2.gestures) == null ? void 0 : _b2.push(gesture2);
      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_c = person2.body) == null ? void 0 : _c.id))
        (_d2 = person2.gestures) == null ? void 0 : _d2.push(gesture2);
      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_f = (_e2 = person2.hands) == null ? void 0 : _e2.left) == null ? void 0 : _f.id))
        (_g2 = person2.gestures) == null ? void 0 : _g2.push(gesture2);
      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id))
        (_j2 = person2.gestures) == null ? void 0 : _j2.push(gesture2);
    }
    const x = [];
    const y = [];
    const extractXY = (box) => {
      if (box && box.length === 4) {
        x.push(box[0], box[0] + box[2]);
        y.push(box[1], box[1] + box[3]);
      }
    };
    extractXY((_k2 = person2.face) == null ? void 0 : _k2.box);
    extractXY((_l2 = person2.body) == null ? void 0 : _l2.box);
    extractXY((_n2 = (_m2 = person2.hands) == null ? void 0 : _m2.left) == null ? void 0 : _n2.box);
    extractXY((_p2 = (_o2 = person2.hands) == null ? void 0 : _o2.right) == null ? void 0 : _p2.box);
    const minX = Math.min(...x);
    const minY = Math.min(...y);
    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
    if (shape && shape[1] && shape[2])
      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
    persons.push(person2);
  }
  return persons;
}

// src/sample.ts
var face3 = `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// src/warmup.ts
async function warmupBitmap(instance) {
  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
  let blob;
  let res;
  switch (instance.config.warmup) {
    case "face":
      blob = await b64toBlob(face3);
      break;
    case "body":
    case "full":
      blob = await b64toBlob(body3);
      break;
    default:
      blob = null;
  }
  if (blob) {
    const bitmap = await createImageBitmap(blob);
    res = await instance.detect(bitmap, instance.config);
    bitmap.close();
  }
  return res;
}
async function warmupCanvas(instance) {
  return new Promise((resolve) => {
    let src;
    switch (instance.config.warmup) {
      case "face":
        src = "data:image/jpeg;base64," + face3;
        break;
      case "full":
      case "body":
        src = "data:image/jpeg;base64," + body3;
        break;
      default:
        src = null;
    }
    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);
        const res = await instance.detect(tensor.tensor, instance.config);
        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) {
    const data = (void 0).decodeJpeg(img);
    const expanded = data.expandDims(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(allModels) {
  const backendType = gpe();
  const webGLBackend = qA();
  if (backendType !== "webgl" && backendType !== "humangl" || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {
    return;
  }
  K().set("ENGINE_COMPILE_ONLY", true);
  const numTensorsStart = ds().state.numTensors;
  const compiledModels = [];
  for (const [modelName, model18] of Object.entries(allModels).filter(([key, val]) => key !== null && val !== null)) {
    const shape = model18.inputs && model18.inputs[0] && model18.inputs[0].shape ? [...model18.inputs[0].shape] : [1, 64, 64, 3];
    const dtype = model18.inputs && model18.inputs[0] && model18.inputs[0].dtype ? model18.inputs[0].dtype : "float32";
    for (let dim = 0; dim < shape.length; dim++) {
      if (shape[dim] === -1)
        shape[dim] = dim === 0 ? 1 : 64;
    }
    const tensor = $t(shape, dtype);
    try {
      const res = model18.execute(tensor);
      compiledModels.push(modelName);
      if (Array.isArray(res))
        res.forEach((t) => Re(t));
      else
        Re(res);
    } catch (e) {
      log("compile fail model:", modelName);
    }
    Re(tensor);
  }
  const kernels = await webGLBackend.checkCompileCompletionAsync();
  webGLBackend.getUniformLocations();
  log("compile pass models:", compiledModels);
  log("compile pass kernels:", kernels.length);
  K().set("ENGINE_COMPILE_ONLY", false);
  const numTensorsEnd = ds().state.numTensors;
  if (numTensorsEnd - numTensorsStart > 0)
    log("tensor leak:", numTensorsEnd - numTensorsStart);
}
async function warmup(instance, userConfig) {
  const t02 = 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 { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
  }
  return new Promise(async (resolve) => {
    await runCompile(instance.models);
    const res = await runInference(instance);
    const t12 = now();
    if (instance.config.debug)
      log("warmup", instance.config.warmup, Math.round(t12 - t02), "ms");
    instance.emit("warmup");
    resolve(res);
  });
}

// src/human.ts
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity;
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");
    __publicField(this, "draw");
    __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, "gl");
    __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 et))
        return "input must be a tensor";
      try {
        this.tf.getBackend();
      } catch (e) {
        return "backend not loaded";
      }
      return null;
    });
    __publicField(this, "similarity", similarity);
    __publicField(this, "distance", distance);
    __publicField(this, "match", match2);
    __publicField(this, "emit", (event) => {
      var _a2;
      if (this.events && this.events.dispatchEvent)
        (_a2 = this.events) == null ? void 0 : _a2.dispatchEvent(new Event(event));
    });
    this.env = env;
    config.wasmPath = vhe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${ope}/dist/`;
    config.modelBasePath = env.browser ? "../models/" : "file://models/";
    config.backend = env.browser ? "humangl" : "tensorflow";
    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.draw = {
      options: options3,
      canvas: (input, output) => canvas2(input, output),
      face: (output, result, options4) => face(output, result, options4),
      body: (output, result, options4) => body(output, result, options4),
      hand: (output, result, options4) => hand(output, result, options4),
      gesture: (output, result, options4) => gesture(output, result, options4),
      object: (output, result, options4) => object(output, result, options4),
      person: (output, result, options4) => person(output, result, options4),
      all: (output, result, options4) => all(output, result, options4)
    };
    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
    this.process = { tensor: null, canvas: null };
    this.faceTriangulation = triangulation;
    this.faceUVMap = uvmap;
    this.gl = config2;
    this.emit("create");
  }
  reset() {
    const currentBackend = this.config.backend;
    this.config = JSON.parse(JSON.stringify(config));
    this.config.backend = currentBackend;
  }
  validate(userConfig) {
    return validate(config, userConfig || this.config);
  }
  now() {
    return now();
  }
  image(input, getTensor = true) {
    return process2(input, this.config, getTensor);
  }
  async segmentation(input, background) {
    return process5(input, background, this.config);
  }
  enhance(input) {
    return enhance(input);
  }
  compare(firstImageTensor, secondImageTensor) {
    return compare(this.config, firstImageTensor, secondImageTensor);
  }
  async init() {
    await check(this, true);
    await this.tf.ready();
  }
  async load(userConfig) {
    this.state = "load";
    const timeStamp = now();
    const count2 = Object.values(this.models).filter((model18) => model18).length;
    if (userConfig)
      this.config = mergeDeep(this.config, userConfig);
    if (this.env.initial) {
      if (this.config.debug)
        log(`version: ${this.version}`);
      if (this.config.debug)
        log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
      if (!await check(this))
        log("error: backend check failed");
      await mpe();
      if (this.env.browser) {
        if (this.config.debug)
          log("configuration:", this.config);
        if (this.config.debug)
          log("environment:", this.env);
        if (this.config.debug)
          log("tf flags:", this.tf.ENV["flags"]);
      }
    }
    await load19(this);
    if (this.env.initial && this.config.debug)
      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
    this.env.initial = false;
    const loaded = Object.values(this.models).filter((model18) => model18).length;
    if (loaded !== count2) {
      await validate2(this);
      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 t02 = now();
    const res = await warmup(this, userConfig);
    const t12 = now();
    this.performance.warmup = Math.trunc(t12 - t02);
    return res;
  }
  async profile(input, userConfig) {
    const profile = await this.tf.profile(() => this.detect(input, userConfig));
    const kernels = {};
    for (const kernel of profile.kernels) {
      if (kernels[kernel.name])
        kernels[kernel.name] += kernel.kernelTimeMs;
      else
        kernels[kernel.name] = kernel.kernelTimeMs;
    }
    const kernelArr = [];
    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] }));
    kernelArr.sort((a, b) => b.ms - a.ms);
    kernelArr.length = 20;
    const res = {};
    for (const kernel of kernelArr)
      res[kernel.name] = kernel.ms;
    return res;
  }
  async detect(input, userConfig) {
    this.state = "detect";
    return new Promise(async (resolve) => {
      var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
      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({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
      }
      const timeStart = now();
      await check(this);
      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({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
        return;
      }
      this.emit("image");
      timeStamp = now();
      this.config.skipAllowed = await skip(this.config, img.tensor);
      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 ? predict17(img.tensor, bodyConfig) : [];
        else if ((_b2 = this.config.body.modelPath) == null ? void 0 : _b2.includes("blazepose"))
          bodyRes = this.config.body.enabled ? predict5(img.tensor, bodyConfig) : [];
        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? predict7(img.tensor, bodyConfig) : [];
        else if ((_d2 = this.config.body.modelPath) == null ? void 0 : _d2.includes("movenet"))
          bodyRes = this.config.body.enabled ? predict15(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 predict17(img.tensor, bodyConfig) : [];
        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
          bodyRes = this.config.body.enabled ? await predict5(img.tensor, bodyConfig) : [];
        else if ((_g2 = this.config.body.modelPath) == null ? void 0 : _g2.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? await predict7(img.tensor, bodyConfig) : [];
        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
          bodyRes = this.config.body.enabled ? await predict15(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 = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j2.includes("handdetect"))
          handRes = this.config.hand.enabled ? predict12(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 ? predict13(img.tensor, handConfig) : [];
        if (this.performance.hand)
          delete this.performance.hand;
      } else {
        timeStamp = now();
        if ((_n2 = (_m2 = this.config.hand.detector) == null ? void 0 : _m2.modelPath) == null ? void 0 : _n2.includes("handdetect"))
          handRes = this.config.hand.enabled ? await predict12(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 predict13(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 ? predict16(img.tensor, this.config) : [];
        else if ((_r2 = this.config.object.modelPath) == null ? void 0 : _r2.includes("centernet"))
          objectRes = this.config.object.enabled ? predict6(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 predict16(img.tensor, this.config) : [];
        else if ((_t2 = this.config.object.modelPath) == null ? void 0 : _t2.includes("centernet"))
          objectRes = this.config.object.enabled ? await predict6(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), ...iris(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 = ((_v2 = (_u2 = this.process) == null ? void 0 : _u2.tensor) == null ? void 0 : _v2.shape) || [];
      this.result = {
        face: faceRes,
        body: bodyRes,
        hand: handRes,
        gesture: gestureRes,
        object: objectRes,
        performance: this.performance,
        canvas: this.process.canvas,
        timestamp: Date.now(),
        error: null,
        get persons() {
          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
        }
      };
      Re(img.tensor);
      this.emit("detect");
      this.state = "idle";
      resolve(this.result);
    });
  }
};
_numTensors = new WeakMap();
_analyzeMemoryLeaks = new WeakMap();
_checkSanity = new WeakMap();
_sanity = new WeakMap();
export {
  Human,
  Human as default,
  config as defaults,
  draw_exports as draw,
  env,
  match_exports as match,
  models_exports as models
};
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use backend file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2022 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2022 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the 'License');
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an 'AS IS' BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/**
 * Human main module
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */
//# sourceMappingURL=human.esm.js.map