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",
      keepInvalid: false
    },
    attention: {
      enabled: false,
      modelPath: "facemesh-attention.json"
    },
    iris: {
      enabled: true,
      modelPath: "iris.json"
    },
    emotion: {
      enabled: true,
      minConfidence: 0.1,
      skipFrames: 99,
      skipTime: 1500,
      modelPath: "emotion.json"
    },
    description: {
      enabled: true,
      modelPath: "faceres.json",
      skipFrames: 99,
      skipTime: 3e3,
      minConfidence: 0.1
    },
    antispoof: {
      enabled: false,
      skipFrames: 99,
      skipTime: 4e3,
      modelPath: "antispoof.json"
    },
    liveness: {
      enabled: false,
      skipFrames: 99,
      skipTime: 4e3,
      modelPath: "liveness.json"
    }
  },
  body: {
    enabled: true,
    modelPath: "movenet-lightning.json",
    maxDetected: -1,
    minConfidence: 0.3,
    skipFrames: 1,
    skipTime: 200
  },
  hand: {
    enabled: true,
    rotation: true,
    skipFrames: 99,
    skipTime: 1e3,
    minConfidence: 0.5,
    iouThreshold: 0.2,
    maxDetected: -1,
    landmarks: true,
    detector: {
      modelPath: "handtrack.json"
    },
    skeleton: {
      modelPath: "handlandmark-full.json"
    }
  },
  object: {
    enabled: false,
    modelPath: "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: () => po,
  Acos: () => ul,
  Acosh: () => ll,
  AdadeltaOptimizer: () => kb,
  AdagradOptimizer: () => Sb,
  AdamOptimizer: () => Ib,
  AdamaxOptimizer: () => Cb,
  Add: () => Cr,
  AddN: () => Ia,
  All: () => cl,
  Any: () => dl,
  ArgMax: () => Ca,
  ArgMin: () => pl,
  Asin: () => hl,
  Asinh: () => fl,
  Atan: () => ml,
  Atan2: () => bl,
  Atanh: () => gl,
  AvgPool: () => Na,
  AvgPool3D: () => Jd,
  AvgPool3DGrad: () => fg,
  AvgPoolGrad: () => hg,
  BackendWasm: () => npe,
  BatchMatMul: () => Ta,
  BatchToSpaceND: () => ho,
  Bincount: () => mg,
  BroadcastArgs: () => gg,
  BroadcastTo: () => V$,
  Callback: () => qW,
  CallbackList: () => uB,
  Cast: () => $a,
  Ceil: () => _a,
  ClipByValue: () => Nr,
  Complex: () => ep,
  ComplexAbs: () => tp,
  Concat: () => fo,
  Conv2D: () => Aa,
  Conv2DBackpropFilter: () => bg,
  Conv2DBackpropInput: () => Ea,
  Conv3D: () => np,
  Conv3DBackpropFilterV2: () => yg,
  Conv3DBackpropInputV2: () => vg,
  Cos: () => Ra,
  Cosh: () => Da,
  CropAndResize: () => go,
  Cumprod: () => mo,
  Cumsum: () => Fa,
  CustomCallback: () => dB,
  DataStorage: () => Yd,
  DenseBincount: () => xg,
  DepthToSpace: () => bo,
  DepthwiseConv2dNative: () => Oa,
  DepthwiseConv2dNativeBackpropFilter: () => wg,
  DepthwiseConv2dNativeBackpropInput: () => kg,
  Diag: () => Sg,
  Dilation2D: () => sp,
  Dilation2DBackpropFilter: () => rm,
  Dilation2DBackpropInput: () => sm,
  ENV: () => lk,
  EarlyStopping: () => jW,
  Einsum: () => rp,
  Elu: () => za,
  EluGrad: () => Ig,
  Environment: () => O$,
  Equal: () => yo,
  Erf: () => yl,
  Exp: () => Ma,
  ExpandDims: () => vo,
  Expm1: () => xo,
  FFT: () => Cg,
  Fill: () => vl,
  FlipLeftRight: () => wo,
  Floor: () => La,
  FloorDiv: () => Ba,
  FromPixels: () => vd,
  FusedBatchNorm: () => Va,
  FusedConv2D: () => ua,
  FusedDepthwiseConv2D: () => la,
  GPGPUContext: () => tm,
  GatherNd: () => So,
  GatherV2: () => ko,
  GraphModel: () => E0,
  Greater: () => Io,
  GreaterEqual: () => Wa,
  History: () => cB,
  IFFT: () => Ng,
  Identity: () => Ua,
  Imag: () => ap,
  InputSpec: () => Ft,
  IsFinite: () => xl,
  IsInf: () => wl,
  IsNan: () => kl,
  KernelBackend: () => ol,
  LRN: () => op,
  LRNGrad: () => $g,
  LayerVariable: () => Sz,
  LayersModel: () => pr,
  LeakyRelu: () => Ga,
  Less: () => Co,
  LessEqual: () => No,
  LinSpace: () => Tg,
  Log: () => Ha,
  Log1p: () => Sl,
  LogSoftmax: () => W$,
  LogicalAnd: () => To,
  LogicalNot: () => Il,
  LogicalOr: () => ip,
  LowerBound: () => upe,
  MathBackendCPU: () => J0,
  MathBackendWebGL: () => Q1,
  Max: () => qa,
  MaxPool: () => Ka,
  MaxPool3D: () => up,
  MaxPool3DGrad: () => Ag,
  MaxPoolGrad: () => _g,
  MaxPoolWithArgmax: () => Eg,
  Maximum: () => ja,
  Mean: () => Xa,
  Min: () => Ya,
  Minimum: () => Qa,
  MirrorPad: () => Za,
  Mod: () => Cl,
  MomentumOptimizer: () => Nb,
  Multinomial: () => Rg,
  Multiply: () => Ja,
  Neg: () => $o,
  NonMaxSuppressionV3: () => Ao,
  NonMaxSuppressionV4: () => Nl,
  NonMaxSuppressionV5: () => Eo,
  NotEqual: () => _o,
  OP_SCOPE_SUFFIX: () => x_,
  OneHot: () => Do,
  OnesLike: () => Ro,
  Optimizer: () => Er,
  OptimizerConstructors: () => Hr,
  Pack: () => Fo,
  PadV2: () => ei,
  Pool: () => lpe,
  Pow: () => ti,
  Prelu: () => ni,
  Prod: () => si,
  RMSPropOptimizer: () => Tb,
  RNN: () => Rr,
  Range: () => Tl,
  Rank: () => d_,
  Real: () => lp,
  RealDiv: () => Pa,
  Reciprocal: () => $l,
  Reduction: () => SO,
  Relu: () => ri,
  Relu6: () => ii,
  Reshape: () => Oo,
  ResizeBilinear: () => ai,
  ResizeBilinearGrad: () => Fg,
  ResizeNearestNeighbor: () => _l,
  ResizeNearestNeighborGrad: () => Dg,
  Reverse: () => Po,
  RotateWithOffset: () => Yo,
  Round: () => zo,
  Rsqrt: () => oi,
  SGDOptimizer: () => Ep,
  ScatterNd: () => Mo,
  SearchSorted: () => Og,
  Select: () => Lo,
  Selu: () => Al,
  Sequential: () => Qb,
  Sigmoid: () => li,
  Sign: () => El,
  Sin: () => ui,
  Sinh: () => Vo,
  Slice: () => Bo,
  Softmax: () => pi,
  Softplus: () => Rl,
  SpaceToBatchND: () => Wo,
  SparseFillEmptyRows: () => cp,
  SparseReshape: () => Dl,
  SparseSegmentMean: () => dp,
  SparseSegmentSum: () => pp,
  SparseToDense: () => hp,
  SplitV: () => Uo,
  Sqrt: () => ci,
  Square: () => Fl,
  SquaredDifference: () => hi,
  Step: () => gi,
  StridedSlice: () => Go,
  StringNGrams: () => fp,
  StringSplit: () => Pg,
  StringToHashBucketFast: () => zg,
  Sub: () => fi,
  Sum: () => di,
  SymbolicTensor: () => $s,
  Tan: () => Ho,
  Tanh: () => mi,
  Tensor: () => et,
  TensorBuffer: () => Wt,
  Tile: () => Tr,
  TopK: () => qo,
  Transform: () => jo,
  Transpose: () => Hs,
  Unique: () => Mg,
  Unpack: () => Ko,
  UnsortedSegmentSum: () => mp,
  UpperBound: () => cpe,
  Variable: () => wd,
  ZerosLike: () => Xo,
  _FusedMatMul: () => oa,
  abs: () => Lt,
  acos: () => aE,
  acosh: () => oE,
  add: () => ie,
  addN: () => lE,
  all: () => rS,
  any: () => vm,
  argMax: () => Yu,
  argMin: () => fE,
  asin: () => gE,
  asinh: () => yE,
  atan: () => xE,
  atan2: () => kE,
  atanh: () => IE,
  avgPool: () => Zg,
  avgPool3d: () => uS,
  backend: () => wA,
  backend_util: () => C,
  basicLSTMCell: () => $pe,
  batchNorm: () => Zu,
  batchNorm2d: () => UE,
  batchNorm3d: () => HE,
  batchNorm4d: () => jE,
  batchToSpaceND: () => Jg,
  bincount: () => lS,
  booleanMaskAsync: () => nhe,
  broadcastArgs: () => YE,
  broadcastTo: () => id,
  broadcast_util: () => Qo,
  browser: () => Lk,
  buffer: () => Ae,
  callbacks: () => phe,
  cast: () => le,
  ceil: () => JE,
  clipByValue: () => Vn,
  clone: () => lr,
  complex: () => mr,
  concat: () => Ot,
  concat1d: () => nR,
  concat2d: () => rR,
  concat3d: () => iR,
  concat4d: () => uR,
  constraints: () => LL,
  conv1d: () => cS,
  conv2d: () => pa,
  conv2dTranspose: () => dS,
  conv3d: () => pS,
  conv3dTranspose: () => gR,
  copyRegisteredKernels: () => hpe,
  cos: () => tb,
  cosh: () => fS,
  cosineWindow: () => LS,
  cumprod: () => wm,
  cumsum: () => mS,
  customGrad: () => js,
  data: () => J4,
  denseBincount: () => kR,
  deprecationWarn: () => zk,
  depthToSpace: () => IR,
  depthwiseConv2d: () => wp,
  deregisterOp: () => fhe,
  device_util: () => yp,
  diag: () => _pe,
  dilation2d: () => $R,
  disableDeprecationWarnings: () => gpe,
  dispose: () => De,
  disposeVariables: () => bpe,
  div: () => xe,
  divNoNan: () => DR,
  dot: () => Ape,
  dropout: () => yF,
  einsum: () => PR,
  elu: () => kp,
  enableDebugMode: () => mpe,
  enableProdMode: () => fpe,
  enclosingPowerOfTwo: () => vF,
  engine: () => ds,
  env: () => K,
  equal: () => Xn,
  erf: () => LR,
  euclideanNorm: () => YR,
  exp: () => Yn,
  expandDims: () => Pn,
  expm1: () => eD,
  eye: () => xS,
  fft: () => bb,
  fill: () => Bl,
  findBackend: () => Ipe,
  findBackendFactory: () => Cpe,
  floor: () => Sp,
  floorDiv: () => sS,
  forceHalfFloat: () => b8,
  fused: () => ma,
  gather: () => Ju,
  gatherND: () => mF,
  gather_util: () => Vk,
  getBackend: () => kpe,
  getGradient: () => lx,
  getKernel: () => am,
  getKernelsForBackend: () => im,
  getThreadsCount: () => Che,
  gpgpu_util: () => rX,
  grad: () => Dpe,
  grads: () => Fpe,
  greater: () => Un,
  greaterEqual: () => Zo,
  ifft: () => Td,
  imag: () => xp,
  image: () => jn,
  inTopKAsync: () => rhe,
  initializers: () => GL,
  input: () => nV,
  io: () => An,
  irfft: () => FS,
  isFinite: () => Epe,
  isInf: () => Rpe,
  isNaN: () => cD,
  keep: () => qt,
  kernel_impls: () => ws,
  layers: () => iB,
  leakyRelu: () => ab,
  less: () => wS,
  lessEqual: () => Jo,
  linalg: () => sP,
  linspace: () => fD,
  loadGraphModel: () => mhe,
  loadGraphModelSync: () => ghe,
  loadLayersModel: () => che,
  localResponseNormalization: () => gD,
  log: () => Qn,
  log1p: () => ib,
  logSigmoid: () => zpe,
  logSoftmax: () => kS,
  logSumExp: () => CD,
  logicalAnd: () => Ds,
  logicalNot: () => ob,
  logicalOr: () => SS,
  logicalXor: () => Mpe,
  losses: () => ohe,
  lowerBound: () => ED,
  matMul: () => Ve,
  math: () => yA,
  max: () => As,
  maxPool: () => ub,
  maxPool3d: () => CS,
  maxPoolWithArgmax: () => OD,
  maximum: () => Ar,
  mean: () => It,
  memory: () => gm,
  meshgrid: () => Lpe,
  metrics: () => CW,
  min: () => km,
  minimum: () => Cp,
  mirrorPad: () => BD,
  mod: () => WD,
  model: () => uhe,
  models: () => VW,
  moments: () => lb,
  movingAverage: () => she,
  mul: () => V,
  multiRNNCell: () => Bpe,
  multinomial: () => qD,
  neg: () => vt,
  nextFrame: () => jS,
  norm: () => rb,
  notEqual: () => el,
  oneHot: () => Id,
  ones: () => Mn,
  onesLike: () => Zn,
  op: () => L,
  outerProduct: () => Vpe,
  pad: () => bi,
  pad1d: () => Wpe,
  pad2d: () => Upe,
  pad3d: () => Gpe,
  pad4d: () => Hpe,
  pool: () => qpe,
  pow: () => fa,
  prelu: () => db,
  print: () => eA,
  prod: () => NS,
  profile: () => ype,
  rand: () => jpe,
  randomGamma: () => Kpe,
  randomNormal: () => p3,
  randomUniform: () => Wl,
  range: () => tl,
  ready: () => wpe,
  real: () => Xu,
  reciprocal: () => m3,
  registerBackend: () => vp,
  registerCallbackConstructor: () => dhe,
  registerGradient: () => G$,
  registerKernel: () => Ol,
  registerOp: () => hhe,
  regularizers: () => WW,
  relu: () => Ys,
  relu6: () => TS,
  removeBackend: () => Spe,
  reshape: () => U,
  reverse: () => Jn,
  reverse1d: () => Xpe,
  reverse2d: () => Ype,
  reverse3d: () => Qpe,
  reverse4d: () => Zpe,
  rfft: () => yb,
  round: () => $S,
  rsqrt: () => _S,
  scalar: () => we,
  scatterND: () => dF,
  scatter_util: () => Uk,
  searchSorted: () => IS,
  selu: () => AS,
  separableConv2d: () => T3,
  sequential: () => lhe,
  serialization: () => re,
  setBackend: () => xpe,
  setPlatform: () => Npe,
  setThreadsCount: () => Ihe,
  setWasmPath: () => khe,
  setWasmPaths: () => She,
  setWebGLContext: () => Y5,
  setdiff1dAsync: () => _3,
  shared: () => iv,
  sigmoid: () => qs,
  sign: () => E3,
  signal: () => ihe,
  sin: () => ES,
  sinh: () => RS,
  slice: () => qe,
  slice1d: () => fb,
  slice2d: () => DS,
  slice3d: () => mb,
  slice4d: () => Nd,
  slice_util: () => kt,
  softmax: () => gb,
  softplus: () => Vl,
  spaceToBatchND: () => cb,
  sparse: () => qc,
  sparseToDense: () => MS,
  spectral: () => ahe,
  split: () => Bn,
  sqrt: () => dn,
  square: () => ct,
  squaredDifference: () => OS,
  squeeze: () => br,
  stack: () => es,
  step: () => Np,
  stridedSlice: () => X3,
  string: () => qf,
  sub: () => ge,
  sum: () => ve,
  sumOutType: () => bp,
  tan: () => Q3,
  tanh: () => Qu,
  tensor: () => ms,
  tensor1d: () => Zt,
  tensor2d: () => Zi,
  tensor3d: () => $A,
  tensor4d: () => Jpe,
  tensor5d: () => ehe,
  tensor6d: () => the,
  tensor_util: () => _s,
  test_util: () => HA,
  tidy: () => q,
  tile: () => hs,
  time: () => vpe,
  topk: () => J3,
  train: () => Li,
  transpose: () => Ge,
  truncatedNormal: () => vb,
  unique: () => xx,
  unregisterGradient: () => ppe,
  unregisterKernel: () => dpe,
  unsortedSegmentSum: () => sF,
  unstack: () => Fs,
  upcastType: () => cn,
  upperBound: () => aF,
  util: () => w,
  valueAndGrad: () => Ope,
  valueAndGrads: () => Ppe,
  variable: () => iF,
  variableGrads: () => vD,
  version: () => The,
  version_converter: () => bhe,
  version_core: () => Tpe,
  version_cpu: () => yhe,
  version_layers: () => wI,
  version_wasm: () => Nhe,
  version_webgl: () => vhe,
  webgl: () => xhe,
  webgl_util: () => X5,
  webgpu: () => Moe,
  where: () => vn,
  whereAsync: () => zS,
  zeros: () => $t,
  zerosLike: () => je
});
var YT = Object.create;
var Kd = Object.defineProperty;
var QT = Object.getOwnPropertyDescriptor;
var Qw = Object.getOwnPropertyNames;
var ZT = Object.getPrototypeOf;
var JT = Object.prototype.hasOwnProperty;
var e$ = (e) => Kd(e, "__esModule", { value: true });
var Mt = (e, t) => function() {
  return t || (0, e[Qw(e)[0]])((t = { exports: {} }).exports, t), t.exports;
};
var Ee = (e, t) => {
  for (var n in t)
    Kd(e, n, { get: t[n], enumerable: true });
};
var t$ = (e, t, n, s) => {
  if (t && typeof t == "object" || typeof t == "function")
    for (let r of Qw(t))
      !JT.call(e, r) && (n || r !== "default") && Kd(e, r, { get: () => t[r], enumerable: !(s = QT(t, r)) || s.enumerable });
  return e;
};
var ka = (e, t) => t$(e$(Kd(e != null ? YT(ZT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
var n$ = 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 (O) {
  }
  function s(O, T, M) {
    this.low = O | 0, this.high = T | 0, this.unsigned = !!M;
  }
  s.prototype.__isLong__, Object.defineProperty(s.prototype, "__isLong__", { value: true });
  function r(O) {
    return (O && O.__isLong__) === true;
  }
  s.isLong = r;
  var a = {}, i = {};
  function o(O, T) {
    var M, W, j;
    return T ? (O >>>= 0, (j = 0 <= O && O < 256) && (W = i[O], W) ? W : (M = l(O, (O | 0) < 0 ? -1 : 0, true), j && (i[O] = M), M)) : (O |= 0, (j = -128 <= O && O < 128) && (W = a[O], W) ? W : (M = l(O, O < 0 ? -1 : 0, false), j && (a[O] = M), M));
  }
  s.fromInt = o;
  function u(O, T) {
    if (isNaN(O))
      return T ? x : v;
    if (T) {
      if (O < 0)
        return x;
      if (O >= g)
        return E;
    } else {
      if (O <= -b)
        return P;
      if (O + 1 >= b)
        return R;
    }
    return O < 0 ? u(-O, T).neg() : l(O % m | 0, O / m | 0, T);
  }
  s.fromNumber = u;
  function l(O, T, M) {
    return new s(O, T, M);
  }
  s.fromBits = l;
  var c = Math.pow;
  function p(O, T, M) {
    if (O.length === 0)
      throw Error("empty string");
    if (O === "NaN" || O === "Infinity" || O === "+Infinity" || O === "-Infinity")
      return v;
    if (typeof T == "number" ? (M = T, T = false) : T = !!T, M = M || 10, M < 2 || 36 < M)
      throw RangeError("radix");
    var W;
    if ((W = O.indexOf("-")) > 0)
      throw Error("interior hyphen");
    if (W === 0)
      return p(O.substring(1), T, M).neg();
    for (var j = u(c(M, 8)), X = v, Y = 0; Y < O.length; Y += 8) {
      var Z = Math.min(8, O.length - Y), te = parseInt(O.substring(Y, Y + Z), M);
      if (Z < 8) {
        var J = u(c(M, 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(O, T) {
    return typeof O == "number" ? u(O, T) : typeof O == "string" ? p(O, T) : l(O.low, O.high, typeof T == "boolean" ? T : O.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 R = l(-1, 2147483647, false);
  s.MAX_VALUE = R;
  var E = l(-1, -1, true);
  s.MAX_UNSIGNED_VALUE = E;
  var P = l(0, -2147483648, false);
  s.MIN_VALUE = P;
  var A = s.prototype;
  A.toInt = function() {
    return this.unsigned ? this.low >>> 0 : this.low;
  }, A.toNumber = function() {
    return this.unsigned ? (this.high >>> 0) * m + (this.low >>> 0) : this.high * m + (this.low >>> 0);
  }, A.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 M = u(T), W = this.div(M), j = W.mul(M).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;
    }
  }, A.getHighBits = function() {
    return this.high;
  }, A.getHighBitsUnsigned = function() {
    return this.high >>> 0;
  }, A.getLowBits = function() {
    return this.low;
  }, A.getLowBitsUnsigned = function() {
    return this.low >>> 0;
  }, A.getNumBitsAbs = function() {
    if (this.isNegative())
      return this.eq(P) ? 64 : this.neg().getNumBitsAbs();
    for (var T = this.high != 0 ? this.high : this.low, M = 31; M > 0 && (T & 1 << M) == 0; M--)
      ;
    return this.high != 0 ? M + 33 : M + 1;
  }, A.isZero = function() {
    return this.high === 0 && this.low === 0;
  }, A.eqz = A.isZero, A.isNegative = function() {
    return !this.unsigned && this.high < 0;
  }, A.isPositive = function() {
    return this.unsigned || this.high >= 0;
  }, A.isOdd = function() {
    return (this.low & 1) === 1;
  }, A.isEven = function() {
    return (this.low & 1) === 0;
  }, A.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;
  }, A.eq = A.equals, A.notEquals = function(T) {
    return !this.eq(T);
  }, A.neq = A.notEquals, A.ne = A.notEquals, A.lessThan = function(T) {
    return this.comp(T) < 0;
  }, A.lt = A.lessThan, A.lessThanOrEqual = function(T) {
    return this.comp(T) <= 0;
  }, A.lte = A.lessThanOrEqual, A.le = A.lessThanOrEqual, A.greaterThan = function(T) {
    return this.comp(T) > 0;
  }, A.gt = A.greaterThan, A.greaterThanOrEqual = function(T) {
    return this.comp(T) >= 0;
  }, A.gte = A.greaterThanOrEqual, A.ge = A.greaterThanOrEqual, A.compare = function(T) {
    if (r(T) || (T = d(T)), this.eq(T))
      return 0;
    var M = this.isNegative(), W = T.isNegative();
    return M && !W ? -1 : !M && 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;
  }, A.comp = A.compare, A.negate = function() {
    return !this.unsigned && this.eq(P) ? P : this.not().add(k);
  }, A.neg = A.negate, A.add = function(T) {
    r(T) || (T = d(T));
    var M = 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 += M + Y, se &= 65535, l(oe << 16 | ae, se << 16 | ne, this.unsigned);
  }, A.subtract = function(T) {
    return r(T) || (T = d(T)), this.add(T.neg());
  }, A.sub = A.subtract, A.multiply = function(T) {
    if (this.isZero())
      return v;
    if (r(T) || (T = d(T)), n) {
      var M = n.mul(this.low, this.high, T.low, T.high);
      return l(M, 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);
  }, A.mul = A.multiply, A.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 M = (this.unsigned ? n.div_u : n.div_s)(this.low, this.high, T.low, T.high);
      return l(M, 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;
  }, A.div = A.divide, A.modulo = function(T) {
    if (r(T) || (T = d(T)), n) {
      var M = (this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, T.low, T.high);
      return l(M, n.get_high(), this.unsigned);
    }
    return this.sub(this.div(T).mul(T));
  }, A.mod = A.modulo, A.rem = A.modulo, A.not = function() {
    return l(~this.low, ~this.high, this.unsigned);
  }, A.and = function(T) {
    return r(T) || (T = d(T)), l(this.low & T.low, this.high & T.high, this.unsigned);
  }, A.or = function(T) {
    return r(T) || (T = d(T)), l(this.low | T.low, this.high | T.high, this.unsigned);
  }, A.xor = function(T) {
    return r(T) || (T = d(T)), l(this.low ^ T.low, this.high ^ T.high, this.unsigned);
  }, A.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);
  }, A.shl = A.shiftLeft, A.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);
  }, A.shr = A.shiftRight, A.shiftRightUnsigned = function(T) {
    if (r(T) && (T = T.toInt()), T &= 63, T === 0)
      return this;
    var M = this.high;
    if (T < 32) {
      var W = this.low;
      return l(W >>> T | M << 32 - T, M >>> T, this.unsigned);
    } else
      return T === 32 ? l(M, 0, this.unsigned) : l(M >>> T - 32, 0, this.unsigned);
  }, A.shru = A.shiftRightUnsigned, A.shr_u = A.shiftRightUnsigned, A.toSigned = function() {
    return this.unsigned ? l(this.low, this.high, false) : this;
  }, A.toUnsigned = function() {
    return this.unsigned ? this : l(this.low, this.high, true);
  }, A.toBytes = function(T) {
    return T ? this.toBytesLE() : this.toBytesBE();
  }, A.toBytesLE = function() {
    var T = this.high, M = this.low;
    return [M & 255, M >>> 8 & 255, M >>> 16 & 255, M >>> 24, T & 255, T >>> 8 & 255, T >>> 16 & 255, T >>> 24];
  }, A.toBytesBE = function() {
    var T = this.high, M = this.low;
    return [T >>> 24, T >>> 16 & 255, T >>> 8 & 255, T & 255, M >>> 24, M >>> 16 & 255, M >>> 8 & 255, M & 255];
  }, s.fromBytes = function(T, M, W) {
    return W ? s.fromBytesLE(T, M) : s.fromBytesBE(T, M);
  }, s.fromBytesLE = function(T, M) {
    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, M);
  }, s.fromBytesBE = function(T, M) {
    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], M);
  };
} });
var s$ = Mt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
} });
var r$ = Mt({ "(disabled):util"() {
} });
var a$ = 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 i$ = 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 o$ = 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 u$ = 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);
    }
    function i(u, l) {
      return l.x = u.x.slice(), l.i = u.i, l;
    }
    function o(u, l) {
      u == null && (u = +new Date());
      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 && (p.x && i(p, c), d.state = function() {
        return i(c, {});
      }), d;
    }
    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
      return o;
    }) : this.xorshift7 = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var l$ = Mt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
  (function(n, s, r) {
    function a(u) {
      var l = this;
      l.next = function() {
        var p = l.w, d = l.X, h = l.i, f, m;
        return l.w = p = p + 1640531527 | 0, m = d[h + 34 & 127], f = d[h = h + 1 & 127], m ^= m << 13, f ^= f << 17, m ^= m >>> 15, f ^= f >>> 12, m = d[h] = m ^ f, l.i = h, m + (p ^ p >>> 16) | 0;
      };
      function c(p, d) {
        var h, f, m, g, b, y = [], v = 128;
        for (d === (d | 0) ? (f = d, d = null) : (d = d + "\0", f = 0, v = Math.max(v, d.length)), m = 0, g = -32; g < v; ++g)
          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);
        for (m >= 128 && (y[(d && d.length || 0) & 127] = -1), m = 127, g = 4 * 128; g > 0; --g)
          f = y[m + 34 & 127], h = y[m = m + 1 & 127], f ^= f << 13, h ^= h << 17, f ^= f >>> 15, h ^= h >>> 12, y[m] = f ^ h;
        p.w = b, p.X = y, p.i = m;
      }
      c(l, u);
    }
    function i(u, l) {
      return l.i = u.i, l.w = u.w, l.X = u.X.slice(), l;
    }
    function o(u, l) {
      u == null && (u = +new Date());
      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 && (p.X && i(p, c), d.state = function() {
        return i(c, {});
      }), d;
    }
    s && s.exports ? s.exports = o : r && r.amd ? r(function() {
      return o;
    }) : this.xor4096 = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var c$ = Mt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
  (function(n, s, r) {
    function a(u) {
      var l = this, c = "";
      l.next = function() {
        var d = l.b, h = l.c, f = l.d, m = l.a;
        return d = d << 25 ^ d >>> 7 ^ h, h = h - f | 0, f = f << 24 ^ f >>> 8 ^ m, m = m - d | 0, l.b = d = d << 20 ^ d >>> 12 ^ h, l.c = h = h - f | 0, l.d = f << 16 ^ h >>> 16 ^ m, l.a = m - d | 0;
      }, l.a = 0, l.b = 0, l.c = -1640531527, l.d = 1367130551, u === Math.floor(u) ? (l.a = u / 4294967296 | 0, l.b = u | 0) : c += u;
      for (var p = 0; p < c.length + 20; p++)
        l.b ^= c.charCodeAt(p) | 0, l.next();
    }
    function i(u, l) {
      return l.a = u.a, l.b = u.b, l.c = u.c, 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.tychei = o;
  })(e, typeof t == "object" && t, typeof define == "function" && define);
} });
var d$ = Mt({ "(disabled):crypto"() {
} });
var p$ = Mt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
  (function(n, s, r) {
    var a = 256, i = 6, o = 52, u = "random", l = r.pow(a, i), c = r.pow(2, o), p = c * 2, d = a - 1, h;
    function f(k, I, $) {
      var R = [];
      I = I == true ? { entropy: true } : I || {};
      var E = y(b(I.entropy ? [k, x(s)] : k == null ? v() : k, 3), R), P = new m(R), A = function() {
        for (var O = P.g(i), T = l, M = 0; O < c; )
          O = (O + M) * a, T *= a, M = P.g(1);
        for (; O >= p; )
          O /= 2, T /= 2, M >>>= 1;
        return (O + M) / T;
      };
      return A.int32 = function() {
        return P.g(4) | 0;
      }, A.quick = function() {
        return P.g(4) / 4294967296;
      }, A.double = A, y(x(P.S), s), (I.pass || $ || function(O, T, M, W) {
        return W && (W.S && g(W, P), O.state = function() {
          return g(P, {});
        }), M ? (r[u] = O, T) : O;
      })(A, E, "global" in I ? I.global : this == r, I.state);
    }
    function m(k) {
      var I, $ = k.length, R = this, E = 0, P = R.i = R.j = 0, A = R.S = [];
      for ($ || (k = [$++]); E < a; )
        A[E] = E++;
      for (E = 0; E < a; E++)
        A[E] = A[P = d & P + k[E % $] + (I = A[E])], A[P] = I;
      (R.g = function(O) {
        for (var T, M = 0, W = R.i, j = R.j, X = R.S; O--; )
          T = X[W = d & W + 1], M = M * a + X[d & (X[W] = X[j = d & j + T]) + (X[j] = T)];
        return R.i = W, R.j = j, M;
      })(a);
    }
    function g(k, I) {
      return I.i = k.i, I.j = k.j, I.S = k.S.slice(), I;
    }
    function b(k, I) {
      var $ = [], R = typeof k, E;
      if (I && R == "object")
        for (E in k)
          try {
            $.push(b(k[E], I - 1));
          } catch (P) {
          }
      return $.length ? $ : R == "string" ? k : k + "\0";
    }
    function y(k, I) {
      for (var $ = k + "", R, E = 0; E < $.length; )
        I[d & E] = d & (R ^= I[d & E] * 19) + $.charCodeAt(E++);
      return x(I);
    }
    function v() {
      try {
        var k;
        return h && (k = h.randomBytes) ? k = k(a) : (k = new Uint8Array(a), (n.crypto || n.msCrypto).getRandomValues(k)), x(k);
      } catch (R) {
        var I = n.navigator, $ = I && I.plugins;
        return [+new Date(), n, $, n.screen, x(s)];
      }
    }
    function x(k) {
      return String.fromCharCode.apply(0, k);
    }
    if (y(r.random(), s), typeof t == "object" && t.exports) {
      t.exports = f;
      try {
        h = d$();
      } catch (k) {
      }
    } else
      typeof define == "function" && define.amd ? define(function() {
        return f;
      }) : r["seed" + u] = f;
  })(typeof self != "undefined" ? self : e, [], Math);
} });
var Xd = Mt({ "src/node_modules/seedrandom/index.js"(e, t) {
  var n = a$(), s = i$(), r = o$(), a = u$(), i = l$(), o = c$(), u = p$();
  u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = i, u.tychei = o, t.exports = u;
} });
var Zw = Mt({ "(disabled):src/node_modules/string_decoder/index.js"() {
} });
var ug = Mt({ "(disabled):fs"() {
} });
var gd = Mt({ "(disabled):path"() {
} });
var h$ = Mt({ "(disabled):worker_threads"() {
} });
var f$ = Mt({ "(disabled):perf_hooks"() {
} });
var m$ = Mt({ "(disabled):os"() {
} });
var g$ = Mt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) {
  var n = (() => {
    var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
    return typeof __filename != "undefined" && (s = s || __filename), function(r) {
      r = r || {};
      function a() {
        return Ce.buffer != nn && rs(Ce.buffer), lc;
      }
      function i() {
        return Ce.buffer != nn && rs(Ce.buffer), cc;
      }
      function o() {
        return Ce.buffer != nn && rs(Ce.buffer), bu;
      }
      function u() {
        return Ce.buffer != nn && rs(Ce.buffer), dc;
      }
      function l() {
        return Ce.buffer != nn && rs(Ce.buffer), pc;
      }
      function c() {
        return Ce.buffer != nn && rs(Ce.buffer), hc;
      }
      function p() {
        return Ce.buffer != nn && rs(Ce.buffer), fc;
      }
      var d = typeof r != "undefined" ? r : {}, h, f;
      d.ready = new Promise(function(N, D) {
        h = N, f = D;
      });
      var m;
      typeof process != "undefined" && process.listeners && (m = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") });
      var g = Object.assign({}, d), b = [], y = "./this.program", v = (N, D) => {
        throw D;
      }, x = typeof window == "object", k = typeof importScripts == "function", I = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", $ = d.ENVIRONMENT_IS_PTHREAD || false, R = "";
      function E(N) {
        return d.locateFile ? d.locateFile(N, R) : R + N;
      }
      var P, A, O, T;
      function M(N) {
        if (N instanceof Nu)
          return;
        J("exiting due to exception: " + N);
      }
      var W, j, X;
      if (I) {
        k ? R = gd().dirname(R) + "/" : R = __dirname + "/", X = () => {
          j || (W = ug(), j = gd());
        }, P = function(B, Q) {
          return X(), B = j.normalize(B), W.readFileSync(B, Q ? void 0 : "utf8");
        }, O = (D) => {
          var B = P(D, true);
          return B.buffer || (B = new Uint8Array(B)), B;
        }, A = (D, B, Q) => {
          X(), D = j.normalize(D), W.readFile(D, function(ue, pe) {
            ue ? Q(ue) : B(pe.buffer);
          });
        }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(D) {
          if (!(D instanceof Nu))
            throw D;
        }), process.on("unhandledRejection", function(D) {
          throw D;
        }), v = (D, B) => {
          if (Lr())
            throw process.exitCode = D, B;
          M(B), process.exit(D);
        }, d.inspect = function() {
          return "[Emscripten Module object]";
        };
        let N;
        try {
          N = h$();
        } catch (D) {
          throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), D;
        }
        global.Worker = N.Worker;
      } else
        (x || k) && (k ? R = self.location.href : typeof document != "undefined" && document.currentScript && (R = document.currentScript.src), typeof s != "undefined" && s && (R = s), R.indexOf("blob:") !== 0 ? R = R.substr(0, R.replace(/[?#].*/, "").lastIndexOf("/") + 1) : R = "", I || (P = (N) => {
          var D = new XMLHttpRequest();
          return D.open("GET", N, false), D.send(null), D.responseText;
        }, k && (O = (N) => {
          var D = new XMLHttpRequest();
          return D.open("GET", N, false), D.responseType = "arraybuffer", D.send(null), new Uint8Array(D.response);
        }), A = (N, D, B) => {
          var Q = new XMLHttpRequest();
          Q.open("GET", N, true), Q.responseType = "arraybuffer", Q.onload = () => {
            if (Q.status == 200 || Q.status == 0 && Q.response) {
              D(Q.response);
              return;
            }
            B();
          }, Q.onerror = B, Q.send(null);
        }), T = (N) => document.title = N);
      I && typeof performance == "undefined" && (global.performance = f$().performance);
      var Y = console.log.bind(console), Z = console.warn.bind(console);
      I && (X(), Y = (N) => W.writeSync(1, N + `
`), Z = (N) => W.writeSync(2, N + `
`));
      var te = d.print || Y, J = d.printErr || Z;
      Object.assign(d, g), g = null, d.arguments && (b = d.arguments), d.thisProgram && (y = d.thisProgram), d.quit && (v = d.quit);
      var se = 4;
      function ne(N) {
        ne.shown || (ne.shown = {}), ne.shown[N] || (ne.shown[N] = 1, J(N));
      }
      function oe(N, D) {
        if (typeof WebAssembly.Function == "function") {
          for (var B = { i: "i32", j: "i64", f: "f32", d: "f64" }, Q = { parameters: [], results: D[0] == "v" ? [] : [B[D[0]]] }, ue = 1; ue < D.length; ++ue)
            Q.parameters.push(B[D[ue]]);
          return new WebAssembly.Function(Q, N);
        }
        var pe = [1, 0, 1, 96], ye = D.slice(0, 1), Te = D.slice(1), bt = { i: 127, j: 126, f: 125, d: 124 };
        pe.push(Te.length);
        for (var ue = 0; ue < Te.length; ++ue)
          pe.push(bt[Te[ue]]);
        ye == "v" ? pe.push(0) : pe = pe.concat([1, bt[ye]]), pe[1] = pe.length - 2;
        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Uc = new WebAssembly.Instance(ls, { e: { f: N } }), Tu = Uc.exports.f;
        return Tu;
      }
      var ae = [], de;
      function me() {
        if (ae.length)
          return ae.pop();
        try {
          Fn.grow(1);
        } catch (N) {
          throw N instanceof RangeError ? "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH." : N;
        }
        return Fn.length - 1;
      }
      function ke(N, D) {
        for (var B = N; B < N + D; B++) {
          var Q = Ei(B);
          Q && de.set(Q, B);
        }
      }
      var Ie = 0, Re = (N) => {
        Ie = N;
      }, Pe = Atomics.load, Xe = Atomics.store, Je = Atomics.compareExchange, Ye;
      d.wasmBinary && (Ye = d.wasmBinary);
      var tt = d.noExitRuntime || true;
      typeof WebAssembly != "object" && $i("no native wasm support detected");
      var Ce, ut, at = false, Jt;
      function Nt(N, D) {
        N || $i(D);
      }
      function In(N) {
        var D = d["_" + N];
        return D;
      }
      function Rt(N, D, B, Q, ue) {
        var pe = { string: function(Tn) {
          var Mi = 0;
          if (Tn != null && Tn !== 0) {
            var ox = (Tn.length << 2) + 1;
            Mi = zi(ox), Ms(Tn, Mi, ox);
          }
          return Mi;
        }, array: function(Tn) {
          var Mi = zi(Tn.length);
          return Ls(Tn, Mi), Mi;
        } };
        function ye(Tn) {
          return D === "string" ? tn(Tn) : D === "boolean" ? Boolean(Tn) : Tn;
        }
        var Te = In(N), bt = [], us = 0;
        if (Q)
          for (var ls = 0; ls < Q.length; ls++) {
            var Uc = pe[B[ls]];
            Uc ? (us === 0 && (us = Lf()), bt[ls] = Uc(Q[ls])) : bt[ls] = Q[ls];
          }
        var Tu = Te.apply(null, bt);
        function XT(Tn) {
          return us !== 0 && Lc(us), ye(Tn);
        }
        return Tu = XT(Tu), Tu;
      }
      function en(N, D, B, Q) {
        B = B || [];
        var ue = B.every(function(ye) {
          return ye === "number";
        }), pe = D !== "string";
        return pe && ue && !Q ? In(N) : function() {
          return Rt(N, D, B, arguments, Q);
        };
      }
      var Cn = 1;
      function Nn(N) {
        var D = new TextDecoder(N);
        this.decode = (B) => (B.buffer instanceof SharedArrayBuffer && (B = new Uint8Array(B)), D.decode.call(D, B));
      }
      var Yt = typeof TextDecoder != "undefined" ? new Nn("utf8") : void 0;
      function Dn(N, D, B) {
        for (var Q = D + B, ue = D; N[ue] && !(ue >= Q); )
          ++ue;
        if (ue - D > 16 && N.subarray && Yt)
          return Yt.decode(N.subarray(D, ue));
        for (var pe = ""; D < ue; ) {
          var ye = N[D++];
          if (!(ye & 128)) {
            pe += String.fromCharCode(ye);
            continue;
          }
          var Te = N[D++] & 63;
          if ((ye & 224) == 192) {
            pe += String.fromCharCode((ye & 31) << 6 | Te);
            continue;
          }
          var bt = N[D++] & 63;
          if ((ye & 240) == 224 ? ye = (ye & 15) << 12 | Te << 6 | bt : ye = (ye & 7) << 18 | Te << 12 | bt << 6 | N[D++] & 63, ye < 65536)
            pe += String.fromCharCode(ye);
          else {
            var us = ye - 65536;
            pe += String.fromCharCode(55296 | us >> 10, 56320 | us & 1023);
          }
        }
        return pe;
      }
      function tn(N, D) {
        return N ? Dn(i(), N, D) : "";
      }
      function zs(N, D, B, Q) {
        if (!(Q > 0))
          return 0;
        for (var ue = B, pe = B + Q - 1, ye = 0; ye < N.length; ++ye) {
          var Te = N.charCodeAt(ye);
          if (Te >= 55296 && Te <= 57343) {
            var bt = N.charCodeAt(++ye);
            Te = 65536 + ((Te & 1023) << 10) | bt & 1023;
          }
          if (Te <= 127) {
            if (B >= pe)
              break;
            D[B++] = Te;
          } else if (Te <= 2047) {
            if (B + 1 >= pe)
              break;
            D[B++] = 192 | Te >> 6, D[B++] = 128 | Te & 63;
          } else if (Te <= 65535) {
            if (B + 2 >= pe)
              break;
            D[B++] = 224 | Te >> 12, D[B++] = 128 | Te >> 6 & 63, D[B++] = 128 | Te & 63;
          } else {
            if (B + 3 >= pe)
              break;
            D[B++] = 240 | Te >> 18, D[B++] = 128 | Te >> 12 & 63, D[B++] = 128 | Te >> 6 & 63, D[B++] = 128 | Te & 63;
          }
        }
        return D[B] = 0, B - ue;
      }
      function Ms(N, D, B) {
        return zs(N, i(), D, B);
      }
      function Ci(N) {
        for (var D = 0, B = 0; B < N.length; ++B) {
          var Q = N.charCodeAt(B);
          Q >= 55296 && Q <= 57343 && (Q = 65536 + ((Q & 1023) << 10) | N.charCodeAt(++B) & 1023), Q <= 127 ? ++D : Q <= 2047 ? D += 2 : Q <= 65535 ? D += 3 : D += 4;
        }
        return D;
      }
      var Js = typeof TextDecoder != "undefined" ? new Nn("utf-16le") : void 0;
      function Ls(N, D) {
        a().set(N, D);
      }
      function gu(N, D, B) {
        for (var Q = 0; Q < N.length; ++Q)
          a()[D++ >> 0] = N.charCodeAt(Q);
        B || (a()[D >> 0] = 0);
      }
      function Ni(N, D) {
        return N % D > 0 && (N += D - N % D), N;
      }
      var nn, lc, cc, bu, dc, pc, Vv, hc, fc;
      $ && (nn = d.buffer);
      function rs(N) {
        nn = N, d.HEAP8 = lc = new Int8Array(N), d.HEAP16 = bu = new Int16Array(N), d.HEAP32 = pc = new Int32Array(N), d.HEAPU8 = cc = new Uint8Array(N), d.HEAPU16 = dc = new Uint16Array(N), d.HEAPU32 = Vv = new Uint32Array(N), d.HEAPF32 = hc = new Float32Array(N), d.HEAPF64 = fc = new Float64Array(N);
      }
      var mc = d.INITIAL_MEMORY || 16777216;
      if ($)
        Ce = d.wasmMemory, nn = d.buffer;
      else if (d.wasmMemory)
        Ce = d.wasmMemory;
      else if (Ce = new WebAssembly.Memory({ initial: mc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer))
        throw J("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), I && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory");
      Ce && (nn = Ce.buffer), mc = nn.byteLength, rs(nn);
      var Fn, Ti = [], er = [], lh = [], gc = [], Mr = false, ch = false, bc = 0;
      function Lr() {
        return tt || bc > 0;
      }
      function sn() {
        if (d.preRun)
          for (typeof d.preRun == "function" && (d.preRun = [d.preRun]); d.preRun.length; )
            Wv(d.preRun.shift());
        kc(Ti);
      }
      function yu() {
        Mr = true, !$ && kc(er);
      }
      function dh() {
        $ || ($e.terminateAllThreads(), ch = true);
      }
      function ph() {
        if (!$) {
          if (d.postRun)
            for (typeof d.postRun == "function" && (d.postRun = [d.postRun]); d.postRun.length; )
              vu(d.postRun.shift());
          kc(gc);
        }
      }
      function Wv(N) {
        Ti.unshift(N);
      }
      function Uv(N) {
        er.unshift(N);
      }
      function vu(N) {
        gc.unshift(N);
      }
      var tr = 0, yc = null, as = null;
      function xu(N) {
        tr++, d.monitorRunDependencies && d.monitorRunDependencies(tr);
      }
      function Gv(N) {
        if (tr--, d.monitorRunDependencies && d.monitorRunDependencies(tr), tr == 0 && (yc !== null && (clearInterval(yc), yc = null), as)) {
          var D = as;
          as = null, D();
        }
      }
      d.preloadedImages = {}, d.preloadedAudios = {};
      function $i(N) {
        $ ? postMessage({ cmd: "onAbort", arg: N }) : d.onAbort && d.onAbort(N), N = "Aborted(" + N + ")", J(N), at = true, Jt = 1, N += ". Build with -s ASSERTIONS=1 for more info.";
        var D = new WebAssembly.RuntimeError(N);
        throw f(D), D;
      }
      var hh = "data:application/octet-stream;base64,";
      function vc(N) {
        return N.startsWith(hh);
      }
      function xc(N) {
        return N.startsWith("file://");
      }
      var rn;
      rn = "tfjs-backend-wasm-threaded-simd.wasm", vc(rn) || (rn = E(rn));
      function wc(N) {
        try {
          if (N == rn && Ye)
            return new Uint8Array(Ye);
          if (O)
            return O(N);
          throw "both async and sync fetching of the wasm failed";
        } catch (D) {
          $i(D);
        }
      }
      function _i() {
        if (!Ye && (x || k)) {
          if (typeof fetch == "function" && !xc(rn))
            return fetch(rn, { credentials: "same-origin" }).then(function(N) {
              if (!N.ok)
                throw "failed to load wasm binary file at '" + rn + "'";
              return N.arrayBuffer();
            }).catch(function() {
              return wc(rn);
            });
          if (A)
            return new Promise(function(N, D) {
              A(rn, function(B) {
                N(new Uint8Array(B));
              }, D);
            });
        }
        return Promise.resolve().then(function() {
          return wc(rn);
        });
      }
      function fh() {
        var N = { env: Fc, wasi_snapshot_preview1: Fc };
        function D(ye, Te) {
          var bt = ye.exports;
          if (d.asm = bt, wh(d.asm.emscripten_tls_init), Fn = d.asm.__indirect_function_table, Uv(d.asm.__wasm_call_ctors), ut = Te, !$) {
            var us = $e.unusedWorkers.length;
            $e.unusedWorkers.forEach(function(ls) {
              $e.loadWasmModuleToWorker(ls, function() {
                --us || Gv("wasm-instantiate");
              });
            });
          }
        }
        $ || xu("wasm-instantiate");
        function B(ye) {
          D(ye.instance, ye.module);
        }
        function Q(ye) {
          return _i().then(function(Te) {
            return WebAssembly.instantiate(Te, N);
          }).then(function(Te) {
            return Te;
          }).then(ye, function(Te) {
            J("failed to asynchronously prepare wasm: " + Te), $i(Te);
          });
        }
        function ue() {
          return !Ye && typeof WebAssembly.instantiateStreaming == "function" && !vc(rn) && !xc(rn) && typeof fetch == "function" ? fetch(rn, { credentials: "same-origin" }).then(function(ye) {
            var Te = WebAssembly.instantiateStreaming(ye, N);
            return Te.then(B, function(bt) {
              return J("wasm streaming compile failed: " + bt), J("falling back to ArrayBuffer instantiation"), Q(B);
            });
          }) : Q(B);
        }
        if (d.instantiateWasm)
          try {
            var pe = d.instantiateWasm(N, D);
            return pe;
          } catch (ye) {
            return J("Module.instantiateWasm callback failed with error: " + ye), false;
          }
        return ue().catch(f), {};
      }
      var Hv, qv, mh = {};
      function kc(N) {
        for (; N.length > 0; ) {
          var D = N.shift();
          if (typeof D == "function") {
            D(d);
            continue;
          }
          var B = D.func;
          typeof B == "number" ? D.arg === void 0 ? Ei(B)() : Ei(B)(D.arg) : B(D.arg === void 0 ? null : D.arg);
        }
      }
      function Ai(N) {
        var D = Lf(), B = N();
        return Lc(D), B;
      }
      function sT(N) {
        return N;
      }
      function jv(N) {
        var D = /\b_Z[\w\d_]+/g;
        return N.replace(D, function(B) {
          var Q = B;
          return B === Q ? B : Q + " [" + B + "]";
        });
      }
      function gh(N) {
        l()[N >> 2] = 0;
        var D = $e.pthreads[N];
        delete $e.pthreads[N], D.worker.terminate(), Mf(N), $e.runningWorkers.splice($e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0;
      }
      function bh(N) {
        var D = $e.pthreads[N];
        D.worker.postMessage({ cmd: "cancel" });
      }
      function Sc(N) {
        var D = $e.pthreads[N];
        if (D) {
          l()[N >> 2] = 0;
          var B = D.worker;
          $e.returnWorkerToPool(B);
        }
      }
      function Ic(N) {
        qT(N);
      }
      function yh(N) {
        if (N instanceof Nu || N == "unwind")
          return Jt;
        v(1, N);
      }
      var $e = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
        $ ? $e.initWorker() : $e.initMainThread();
      }, initMainThread: function() {
        for (var N = 8, D = 0; D < N; ++D)
          $e.allocateUnusedWorker();
      }, initWorker: function() {
        tt = false;
      }, pthreads: {}, setExitStatus: function(N) {
        Jt = N;
      }, terminateAllThreads: function() {
        for (var N in $e.pthreads) {
          var D = $e.pthreads[N];
          D && D.worker && $e.returnWorkerToPool(D.worker);
        }
        for (var B = 0; B < $e.unusedWorkers.length; ++B) {
          var Q = $e.unusedWorkers[B];
          Q.terminate();
        }
        $e.unusedWorkers = [];
      }, returnWorkerToPool: function(N) {
        $e.runWithoutMainThreadQueuedCalls(function() {
          delete $e.pthreads[N.pthread.threadInfoStruct], $e.unusedWorkers.push(N), $e.runningWorkers.splice($e.runningWorkers.indexOf(N), 1), Mf(N.pthread.threadInfoStruct), N.pthread = void 0;
        });
      }, runWithoutMainThreadQueuedCalls: function(N) {
        l()[ix >> 2] = 0;
        try {
          N();
        } finally {
          l()[ix >> 2] = 1;
        }
      }, receiveObjectTransfer: function(N) {
      }, threadInit: function() {
        for (var N in $e.tlsInitFunctions)
          $e.tlsInitFunctions[N]();
      }, loadWasmModuleToWorker: function(N, D) {
        N.onmessage = (B) => {
          var Q = B.data, ue = Q.cmd;
          if (N.pthread && ($e.currentProxiedOperationCallerThread = N.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Mc()) {
            var pe = $e.pthreads[Q.targetThread];
            pe ? pe.worker.postMessage(Q, Q.transferList) : J('Internal error! Worker sent a message "' + ue + '" to target pthread ' + Q.targetThread + ", but that thread no longer exists!"), $e.currentProxiedOperationCallerThread = void 0;
            return;
          }
          ue === "processQueuedMainThreadWork" ? tx() : ue === "spawnThread" ? Nc(Q) : ue === "cleanupThread" ? Sc(Q.thread) : ue === "killThread" ? gh(Q.thread) : ue === "cancelThread" ? bh(Q.thread) : ue === "loaded" ? (N.loaded = true, D && D(N), N.runPthread && (N.runPthread(), delete N.runPthread)) : ue === "print" ? te("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? J("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? N.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : J("worker sent an unknown command " + ue), $e.currentProxiedOperationCallerThread = void 0;
        }, N.onerror = (B) => {
          var Q = "worker sent an error!";
          throw J(Q + " " + B.filename + ":" + B.lineno + ": " + B.message), B;
        }, I && (N.on("message", function(B) {
          N.onmessage({ data: B });
        }), N.on("error", function(B) {
          N.onerror(B);
        }), N.on("detachedExit", function() {
        })), N.postMessage({ cmd: "load", urlOrBlob: d.mainScriptUrlOrBlob || s, wasmMemory: Ce, wasmModule: ut });
      }, allocateUnusedWorker: function() {
        var N = E("tfjs-backend-wasm-threaded-simd.worker.js");
        $e.unusedWorkers.push(new Worker(N));
      }, getNewWorker: function() {
        return $e.unusedWorkers.length == 0 && ($e.allocateUnusedWorker(), $e.loadWasmModuleToWorker($e.unusedWorkers[0])), $e.unusedWorkers.pop();
      } };
      function vh() {
        var N = Mc(), D = l()[N + 44 >> 2], B = l()[N + 48 >> 2], Q = D - B;
        ax(D, Q), Lc(D);
      }
      d.establishStackSpace = vh;
      function Cc(N) {
        if ($)
          return Wr(1, 0, N);
        try {
          Ic(N);
        } catch (D) {
          yh(D);
        }
      }
      var Br = [];
      function Ei(N) {
        var D = Br[N];
        return D || (N >= Br.length && (Br.length = N + 1), Br[N] = D = Fn.get(N)), D;
      }
      function xh(N, D) {
        return Ei(N)(D);
      }
      d.invokeEntryPoint = xh;
      function Kv() {
        var N = new Error();
        if (!N.stack) {
          try {
            throw new Error();
          } catch (D) {
            N = D;
          }
          if (!N.stack)
            return "(no stack trace available)";
        }
        return N.stack.toString();
      }
      function wh(N, D, B) {
        $e.tlsInitFunctions.push(N);
      }
      function Xv(N, D) {
        Fn.set(N, D), Br[N] = D;
      }
      var Vr;
      I ? Vr = () => {
        var N = process.hrtime();
        return N[0] * 1e3 + N[1] / 1e6;
      } : $ ? Vr = () => performance.now() - d.__performance_now_clock_drift : Vr = () => performance.now();
      var kh = true;
      function Sh(N) {
        return l()[ex() >> 2] = N, N;
      }
      function Ih(N, D) {
        var B;
        if (N === 0)
          B = Date.now();
        else if ((N === 1 || N === 4) && kh)
          B = Vr();
        else
          return Sh(28), -1;
        return l()[D >> 2] = B / 1e3 | 0, l()[D + 4 >> 2] = B % 1e3 * 1e3 * 1e3 | 0, 0;
      }
      function Ch(N, D) {
        return Ih(N, D);
      }
      function Nh(N) {
        nx(N, !k, 1, !x), $e.threadInit();
      }
      function Th(N) {
        $ ? postMessage({ cmd: "cleanupThread", thread: N }) : Sc(N);
      }
      function Nc(N) {
        var D = $e.getNewWorker();
        if (!D)
          return 6;
        $e.runningWorkers.push(D);
        var B = $e.pthreads[N.pthread_ptr] = { worker: D, threadInfoStruct: N.pthread_ptr };
        D.pthread = B;
        var Q = { cmd: "run", start_routine: N.startRoutine, arg: N.arg, threadInfoStruct: N.pthread_ptr };
        return D.runPthread = () => {
          Q.time = performance.now(), D.postMessage(Q, N.transferList);
        }, D.loaded && (D.runPthread(), delete D.runPthread), 0;
      }
      function $h(N, D, B, Q) {
        if (typeof SharedArrayBuffer == "undefined")
          return J("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
        var ue = [], pe = 0;
        if ($ && (ue.length === 0 || pe))
          return sx(687865856, N, D, B, Q);
        if (pe)
          return pe;
        var ye = { startRoutine: B, pthread_ptr: N, arg: Q, transferList: ue };
        return $ ? (ye.cmd = "spawnThread", postMessage(ye, ue), 0) : Nc(ye);
      }
      function _h() {
        return 2097152;
      }
      function Ah(N, D) {
        if (N == D)
          postMessage({ cmd: "processQueuedMainThreadWork" });
        else if ($)
          postMessage({ targetThread: N, cmd: "processThreadQueue" });
        else {
          var B = $e.pthreads[N], Q = B && B.worker;
          if (!Q)
            return;
          Q.postMessage({ cmd: "processThreadQueue" });
        }
        return 1;
      }
      function Eh() {
        $i("");
      }
      function Rh() {
        I || k || ne("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
      }
      function Tc() {
        return 2147483648;
      }
      function Dh(N, D, B) {
        i().copyWithin(N, D, D + B);
      }
      function Fh() {
        return I ? m$().cpus().length : navigator.hardwareConcurrency;
      }
      function Wr(N, D) {
        var B = arguments.length - 2, Q = arguments;
        return Ai(function() {
          for (var ue = B, pe = zi(ue * 8), ye = pe >> 3, Te = 0; Te < B; Te++) {
            var bt = Q[2 + Te];
            p()[ye + Te] = bt;
          }
          return rx(N, ue, pe, D);
        });
      }
      var wu = [];
      function Oh(N, D, B) {
        wu.length = D;
        for (var Q = B >> 3, ue = 0; ue < D; ue++)
          wu[ue] = p()[Q + ue];
        var pe = N < 0, ye = pe ? mh[-N - 1] : ef[N];
        return ye.apply(null, wu);
      }
      function Ph(N) {
        try {
          return Ce.grow(N - nn.byteLength + 65535 >>> 16), rs(Ce.buffer), 1;
        } catch (D) {
        }
      }
      function zh(N) {
        var D = i().length;
        if (N = N >>> 0, N <= D)
          return false;
        var B = Tc();
        if (N > B)
          return false;
        for (var Q = 1; Q <= 4; Q *= 2) {
          var ue = D * (1 + 0.2 / Q);
          ue = Math.min(ue, N + 100663296);
          var pe = Math.min(B, Ni(Math.max(N, ue), 65536)), ye = Ph(pe);
          if (ye)
            return true;
        }
        return false;
      }
      var Me = { inEventHandler: 0, removeAllEventListeners: function() {
        for (var N = Me.eventHandlers.length - 1; N >= 0; --N)
          Me._removeHandler(N);
        Me.eventHandlers = [], Me.deferredCalls = [];
      }, registerRemoveEventListeners: function() {
        Me.removeEventListenersRegistered || (lh.push(Me.removeAllEventListeners), Me.removeEventListenersRegistered = true);
      }, deferredCalls: [], deferCall: function(N, D, B) {
        function Q(ye, Te) {
          if (ye.length != Te.length)
            return false;
          for (var bt in ye)
            if (ye[bt] != Te[bt])
              return false;
          return true;
        }
        for (var ue in Me.deferredCalls) {
          var pe = Me.deferredCalls[ue];
          if (pe.targetFunction == N && Q(pe.argsList, B))
            return;
        }
        Me.deferredCalls.push({ targetFunction: N, precedence: D, argsList: B }), Me.deferredCalls.sort(function(ye, Te) {
          return ye.precedence < Te.precedence;
        });
      }, removeDeferredCalls: function(N) {
        for (var D = 0; D < Me.deferredCalls.length; ++D)
          Me.deferredCalls[D].targetFunction == N && (Me.deferredCalls.splice(D, 1), --D);
      }, canPerformEventHandlerRequests: function() {
        return Me.inEventHandler && Me.currentEventHandler.allowsDeferredCalls;
      }, runDeferredCalls: function() {
        if (!!Me.canPerformEventHandlerRequests())
          for (var N = 0; N < Me.deferredCalls.length; ++N) {
            var D = Me.deferredCalls[N];
            Me.deferredCalls.splice(N, 1), --N, D.targetFunction.apply(null, D.argsList);
          }
      }, eventHandlers: [], removeAllHandlersOnTarget: function(N, D) {
        for (var B = 0; B < Me.eventHandlers.length; ++B)
          Me.eventHandlers[B].target == N && (!D || D == Me.eventHandlers[B].eventTypeString) && Me._removeHandler(B--);
      }, _removeHandler: function(N) {
        var D = Me.eventHandlers[N];
        D.target.removeEventListener(D.eventTypeString, D.eventListenerFunc, D.useCapture), Me.eventHandlers.splice(N, 1);
      }, registerOrRemoveHandler: function(N) {
        var D = function(ue) {
          ++Me.inEventHandler, Me.currentEventHandler = N, Me.runDeferredCalls(), N.handlerFunc(ue), Me.runDeferredCalls(), --Me.inEventHandler;
        };
        if (N.callbackfunc)
          N.eventListenerFunc = D, N.target.addEventListener(N.eventTypeString, D, N.useCapture), Me.eventHandlers.push(N), Me.registerRemoveEventListeners();
        else
          for (var B = 0; B < Me.eventHandlers.length; ++B)
            Me.eventHandlers[B].target == N.target && Me.eventHandlers[B].eventTypeString == N.eventTypeString && Me._removeHandler(B--);
      }, queueEventHandlerOnThread_iiii: function(N, D, B, Q, ue) {
        Ai(function() {
          var pe = zi(12);
          l()[pe >> 2] = B, l()[pe + 4 >> 2] = Q, l()[pe + 8 >> 2] = ue, zf(N, 637534208, D, Q, pe);
        });
      }, getTargetThreadForEventCallback: function(N) {
        switch (N) {
          case 1:
            return 0;
          case 2:
            return $e.currentProxiedOperationCallerThread;
          default:
            return N;
        }
      }, getNodeNameForTarget: function(N) {
        return N ? N == window ? "#window" : N == screen ? "#screen" : N && N.nodeName ? N.nodeName : "" : "";
      }, fullscreenEnabled: function() {
        return document.fullscreenEnabled || document.webkitFullscreenEnabled;
      } };
      function Mh(N) {
        var D = Ci(N) + 1, B = Pf(D);
        return Ms(N, B, D), B;
      }
      function Lh(N, D, B, Q) {
        Ai(function() {
          var ue = zi(12), pe = 0;
          D && (pe = Mh(D)), l()[ue >> 2] = pe, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, zf(N, 657457152, 0, pe, ue);
        });
      }
      function Bh(N, D, B, Q) {
        D = D ? tn(D) : "", Lh(N, D, B, Q);
      }
      function Vh(N) {
        return N > 2 ? tn(N) : N;
      }
      var Wh = [0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0];
      function Uh(N) {
        N = Vh(N);
        var D = Wh[N] || (typeof document != "undefined" ? document.querySelector(N) : void 0);
        return D;
      }
      function ku(N) {
        return Uh(N);
      }
      function $c(N, D, B) {
        var Q = ku(N);
        if (!Q)
          return -4;
        if (Q.canvasSharedPtr && (l()[Q.canvasSharedPtr >> 2] = D, l()[Q.canvasSharedPtr + 4 >> 2] = B), Q.offscreenCanvas || !Q.controlTransferredOffscreen) {
          Q.offscreenCanvas && (Q = Q.offscreenCanvas);
          var ue = false;
          if (Q.GLctxObject && Q.GLctxObject.GLctx) {
            var pe = Q.GLctxObject.GLctx.getParameter(2978);
            ue = pe[0] === 0 && pe[1] === 0 && pe[2] === Q.width && pe[3] === Q.height;
          }
          Q.width = D, Q.height = B, ue && Q.GLctxObject.GLctx.viewport(0, 0, D, B);
        } else if (Q.canvasSharedPtr) {
          var ye = l()[Q.canvasSharedPtr + 8 >> 2];
          return Bh(ye, N, D, B), 1;
        } else
          return -4;
        return 0;
      }
      function _c(N, D, B) {
        return $ ? Wr(2, 1, N, D, B) : $c(N, D, B);
      }
      function Gh(N, D, B) {
        var Q = ku(N);
        return Q ? $c(N, D, B) : _c(N, D, B);
      }
      function Hh() {
        throw "unwind";
      }
      function qh(N) {
        var D = N.getExtension("ANGLE_instanced_arrays");
        if (D)
          return N.vertexAttribDivisor = function(B, Q) {
            D.vertexAttribDivisorANGLE(B, Q);
          }, N.drawArraysInstanced = function(B, Q, ue, pe) {
            D.drawArraysInstancedANGLE(B, Q, ue, pe);
          }, N.drawElementsInstanced = function(B, Q, ue, pe, ye) {
            D.drawElementsInstancedANGLE(B, Q, ue, pe, ye);
          }, 1;
      }
      function jh(N) {
        var D = N.getExtension("OES_vertex_array_object");
        if (D)
          return N.createVertexArray = function() {
            return D.createVertexArrayOES();
          }, N.deleteVertexArray = function(B) {
            D.deleteVertexArrayOES(B);
          }, N.bindVertexArray = function(B) {
            D.bindVertexArrayOES(B);
          }, N.isVertexArray = function(B) {
            return D.isVertexArrayOES(B);
          }, 1;
      }
      function Kh(N) {
        var D = N.getExtension("WEBGL_draw_buffers");
        if (D)
          return N.drawBuffers = function(B, Q) {
            D.drawBuffersWEBGL(B, Q);
          }, 1;
      }
      function Xh(N) {
        return !!(N.multiDrawWebgl = N.getExtension("WEBGL_multi_draw"));
      }
      var gt = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function(D) {
        gt.lastError || (gt.lastError = D);
      }, getNewId: function(N) {
        for (var D = gt.counter++, B = N.length; B < D; B++)
          N[B] = null;
        return D;
      }, getSource: function(N, D, B, Q) {
        for (var ue = "", pe = 0; pe < D; ++pe) {
          var ye = Q ? l()[Q + pe * 4 >> 2] : -1;
          ue += tn(l()[B + pe * 4 >> 2], ye < 0 ? void 0 : ye);
        }
        return ue;
      }, createContext: function(N, D) {
        N.getContextSafariWebGL2Fixed || (N.getContextSafariWebGL2Fixed = N.getContext, N.getContext = function(ue, pe) {
          var ye = N.getContextSafariWebGL2Fixed(ue, pe);
          return ue == "webgl" == ye instanceof WebGLRenderingContext ? ye : null;
        });
        var B = N.getContext("webgl", D);
        if (!B)
          return 0;
        var Q = gt.registerContext(B, D);
        return Q;
      }, registerContext: function(N, D) {
        var B = Pf(8);
        l()[B + 4 >> 2] = Mc();
        var Q = { handle: B, attributes: D, version: D.majorVersion, GLctx: N };
        return N.canvas && (N.canvas.GLctxObject = Q), gt.contexts[B] = Q, (typeof D.enableExtensionsByDefault == "undefined" || D.enableExtensionsByDefault) && gt.initExtensions(Q), B;
      }, makeContextCurrent: function(N) {
        return gt.currentContext = gt.contexts[N], d.ctx = Dc = gt.currentContext && gt.currentContext.GLctx, !(N && !Dc);
      }, getContext: function(N) {
        return gt.contexts[N];
      }, deleteContext: function(N) {
        gt.currentContext === gt.contexts[N] && (gt.currentContext = null), typeof Me == "object" && Me.removeAllHandlersOnTarget(gt.contexts[N].GLctx.canvas), gt.contexts[N] && gt.contexts[N].GLctx.canvas && (gt.contexts[N].GLctx.canvas.GLctxObject = void 0), Jv(gt.contexts[N].handle), gt.contexts[N] = null;
      }, initExtensions: function(N) {
        if (N || (N = gt.currentContext), !N.initExtensionsDone) {
          N.initExtensionsDone = true;
          var D = N.GLctx;
          qh(D), jh(D), Kh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Xh(D);
          var B = D.getSupportedExtensions() || [];
          B.forEach(function(Q) {
            !Q.includes("lose_context") && !Q.includes("debug") && D.getExtension(Q);
          });
        }
      } }, Yh = ["default", "low-power", "high-performance"];
      function Qh(N, D) {
        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Yh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = ku(N);
        if (!pe || ue.explicitSwapControl)
          return 0;
        var ye = gt.createContext(pe, ue);
        return ye;
      }
      function Zh(N, D) {
        return Qh(N, D);
      }
      var Ri = { mappings: {}, buffers: [null, [], []], printChar: function(N, D) {
        var B = Ri.buffers[N];
        D === 0 || D === 10 ? ((N === 1 ? te : J)(Dn(B, 0)), B.length = 0) : B.push(D);
      }, varargs: void 0, get: function() {
        Ri.varargs += 4;
        var N = l()[Ri.varargs - 4 >> 2];
        return N;
      }, getStr: function(N) {
        var D = tn(N);
        return D;
      }, get64: function(N, D) {
        return N;
      } };
      function Ac(N) {
        return $ ? Wr(3, 1, N) : 0;
      }
      function Ec(N, D, B, Q, ue) {
        if ($)
          return Wr(4, 1, N, D, B, Q, ue);
      }
      function Rc(N, D, B, Q) {
        if ($)
          return Wr(5, 1, N, D, B, Q);
        for (var ue = 0, pe = 0; pe < B; pe++) {
          var ye = l()[D >> 2], Te = l()[D + 4 >> 2];
          D += 8;
          for (var bt = 0; bt < Te; bt++)
            Ri.printChar(N, i()[ye + bt]);
          ue += Te;
        }
        return l()[Q >> 2] = ue, 0;
      }
      function Jh(N) {
        Re(N);
      }
      $e.init();
      var Dc, ef = [null, Cc, _c, Ac, Ec, Rc], Yv = false, Fc = { __clock_gettime: Ch, __emscripten_init_main_thread_js: Nh, __emscripten_thread_cleanup: Th, __pthread_create_js: $h, _emscripten_default_pthread_stack_size: _h, _emscripten_notify_thread_queue: Ah, abort: Eh, emscripten_check_blocking_allowed: Rh, emscripten_get_heap_max: Tc, emscripten_get_now: Vr, emscripten_memcpy_big: Dh, emscripten_num_logical_cores: Fh, emscripten_receive_on_main_thread_js: Oh, emscripten_resize_heap: zh, emscripten_set_canvas_element_size: Gh, emscripten_unwind_to_js_event_loop: Hh, emscripten_webgl_create_context: Zh, exit: Ic, fd_close: Ac, fd_seek: Ec, fd_write: Rc, memory: Ce || d.wasmMemory, setTempRet0: Jh }, Qv = fh(), tf = d.___wasm_call_ctors = function() {
        return (tf = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments);
      }, nf = d._init = function() {
        return (nf = d._init = d.asm.init).apply(null, arguments);
      }, sf = d._init_with_threads_count = function() {
        return (sf = d._init_with_threads_count = d.asm.init_with_threads_count).apply(null, arguments);
      }, rf = d._get_threads_count = function() {
        return (rf = d._get_threads_count = d.asm.get_threads_count).apply(null, arguments);
      }, af = d._register_tensor = function() {
        return (af = d._register_tensor = d.asm.register_tensor).apply(null, arguments);
      }, of = d._dispose_data = function() {
        return (of = d._dispose_data = d.asm.dispose_data).apply(null, arguments);
      }, uf = d._dispose = function() {
        return (uf = d._dispose = d.asm.dispose).apply(null, arguments);
      }, lf = d._Abs = function() {
        return (lf = d._Abs = d.asm.Abs).apply(null, arguments);
      }, cf = d._Add = function() {
        return (cf = d._Add = d.asm.Add).apply(null, arguments);
      }, df = d._AddN = function() {
        return (df = d._AddN = d.asm.AddN).apply(null, arguments);
      }, pf = d._All = function() {
        return (pf = d._All = d.asm.All).apply(null, arguments);
      }, hf = d._Any = function() {
        return (hf = d._Any = d.asm.Any).apply(null, arguments);
      }, ff = d._ArgMax = function() {
        return (ff = d._ArgMax = d.asm.ArgMax).apply(null, arguments);
      }, mf = d._AvgPool = function() {
        return (mf = d._AvgPool = d.asm.AvgPool).apply(null, arguments);
      }, gf = d._BatchMatMul = function() {
        return (gf = d._BatchMatMul = d.asm.BatchMatMul).apply(null, arguments);
      }, bf = d._Ceil = function() {
        return (bf = d._Ceil = d.asm.Ceil).apply(null, arguments);
      }, yf = d._ClipByValue = function() {
        return (yf = d._ClipByValue = d.asm.ClipByValue).apply(null, arguments);
      }, vf = d._Conv2D = function() {
        return (vf = d._Conv2D = d.asm.Conv2D).apply(null, arguments);
      }, xf = d._Conv2DBackpropInput = function() {
        return (xf = d._Conv2DBackpropInput = d.asm.Conv2DBackpropInput).apply(null, arguments);
      }, wf = d._Cos = function() {
        return (wf = d._Cos = d.asm.Cos).apply(null, arguments);
      }, kf = d._Cosh = function() {
        return (kf = d._Cosh = d.asm.Cosh).apply(null, arguments);
      }, Sf = d._CropAndResize = function() {
        return (Sf = d._CropAndResize = d.asm.CropAndResize).apply(null, arguments);
      }, If = d._Cumprod = function() {
        return (If = d._Cumprod = d.asm.Cumprod).apply(null, arguments);
      }, Cf = d._Cumsum = function() {
        return (Cf = d._Cumsum = d.asm.Cumsum).apply(null, arguments);
      }, Nf = d._DepthToSpace = function() {
        return (Nf = d._DepthToSpace = d.asm.DepthToSpace).apply(null, arguments);
      }, Tf = d._DepthwiseConv2dNative = function() {
        return (Tf = d._DepthwiseConv2dNative = d.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, $f = d._Elu = function() {
        return ($f = d._Elu = d.asm.Elu).apply(null, arguments);
      }, _f = d._Equal = function() {
        return (_f = d._Equal = d.asm.Equal).apply(null, arguments);
      }, Af = d._Exp = function() {
        return (Af = d._Exp = d.asm.Exp).apply(null, arguments);
      }, Ef = d._FlipLeftRight = function() {
        return (Ef = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments);
      }, Oc = d._Floor = function() {
        return (Oc = d._Floor = d.asm.Floor).apply(null, arguments);
      }, Pc = d._FloorDiv = function() {
        return (Pc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
      }, Su = d._FusedBatchNorm = function() {
        return (Su = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
      }, Rf = d._FusedConv2D = function() {
        return (Rf = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
      }, Df = d._FusedDepthwiseConv2D = function() {
        return (Df = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
      }, Di = d._Gather = function() {
        return (Di = d._Gather = d.asm.Gather).apply(null, arguments);
      }, Iu = d._GatherNd = function() {
        return (Iu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
      }, Cu = d._Greater = function() {
        return (Cu = d._Greater = d.asm.Greater).apply(null, arguments);
      }, Zv = d._GreaterEqual = function() {
        return (Zv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
      }, Fi = d._LeakyRelu = function() {
        return (Fi = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments);
      }, Oi = d._Less = function() {
        return (Oi = d._Less = d.asm.Less).apply(null, arguments);
      }, Ff = d._LessEqual = function() {
        return (Ff = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
      }, H = d._Log = function() {
        return (H = d._Log = d.asm.Log).apply(null, arguments);
      }, ee = d._LogicalAnd = function() {
        return (ee = d._LogicalAnd = d.asm.LogicalAnd).apply(null, arguments);
      }, ce = d._Max = function() {
        return (ce = d._Max = d.asm.Max).apply(null, arguments);
      }, Se = d._MaxPool = function() {
        return (Se = d._MaxPool = d.asm.MaxPool).apply(null, arguments);
      }, Qe = d._Maximum = function() {
        return (Qe = d._Maximum = d.asm.Maximum).apply(null, arguments);
      }, Ze = d._Mean = function() {
        return (Ze = d._Mean = d.asm.Mean).apply(null, arguments);
      }, Le = d._Min = function() {
        return (Le = d._Min = d.asm.Min).apply(null, arguments);
      }, ze = d._Minimum = function() {
        return (ze = d._Minimum = d.asm.Minimum).apply(null, arguments);
      }, Tt = d._MirrorPad = function() {
        return (Tt = d._MirrorPad = d.asm.MirrorPad).apply(null, arguments);
      }, is = d._Multiply = function() {
        return (is = d._Multiply = d.asm.Multiply).apply(null, arguments);
      }, os = d._Neg = function() {
        return (os = d._Neg = d.asm.Neg).apply(null, arguments);
      }, Pi = d._NonMaxSuppressionV3 = function() {
        return (Pi = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments);
      }, Ur = d._NonMaxSuppressionV4 = function() {
        return (Ur = d._NonMaxSuppressionV4 = d.asm.NonMaxSuppressionV4).apply(null, arguments);
      }, Of = d._NonMaxSuppressionV5 = function() {
        return (Of = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
      }, an = d._NotEqual = function() {
        return (an = d._NotEqual = d.asm.NotEqual).apply(null, arguments);
      }, nr = d._OneHot = function() {
        return (nr = d._OneHot = d.asm.OneHot).apply(null, arguments);
      }, zc = d._PadV2 = function() {
        return (zc = d._PadV2 = d.asm.PadV2).apply(null, arguments);
      }, rT = d._Pow = function() {
        return (rT = d._Pow = d.asm.Pow).apply(null, arguments);
      }, aT = d._Prelu = function() {
        return (aT = d._Prelu = d.asm.Prelu).apply(null, arguments);
      }, iT = d._Prod = function() {
        return (iT = d._Prod = d.asm.Prod).apply(null, arguments);
      }, oT = d._RealDiv = function() {
        return (oT = d._RealDiv = d.asm.RealDiv).apply(null, arguments);
      }, uT = d._Relu = function() {
        return (uT = d._Relu = d.asm.Relu).apply(null, arguments);
      }, lT = d._Relu6 = function() {
        return (lT = d._Relu6 = d.asm.Relu6).apply(null, arguments);
      }, cT = d._ResizeBilinear = function() {
        return (cT = d._ResizeBilinear = d.asm.ResizeBilinear).apply(null, arguments);
      }, dT = d._Reverse = function() {
        return (dT = d._Reverse = d.asm.Reverse).apply(null, arguments);
      }, pT = d._RotateWithOffset = function() {
        return (pT = d._RotateWithOffset = d.asm.RotateWithOffset).apply(null, arguments);
      }, hT = d._Round = function() {
        return (hT = d._Round = d.asm.Round).apply(null, arguments);
      }, fT = d._Rsqrt = function() {
        return (fT = d._Rsqrt = d.asm.Rsqrt).apply(null, arguments);
      }, mT = d._ScatterNd = function() {
        return (mT = d._ScatterNd = d.asm.ScatterNd).apply(null, arguments);
      }, gT = d._SelectV2 = function() {
        return (gT = d._SelectV2 = d.asm.SelectV2).apply(null, arguments);
      }, bT = d._Sigmoid = function() {
        return (bT = d._Sigmoid = d.asm.Sigmoid).apply(null, arguments);
      }, yT = d._Sin = function() {
        return (yT = d._Sin = d.asm.Sin).apply(null, arguments);
      }, vT = d._Softmax = function() {
        return (vT = d._Softmax = d.asm.Softmax).apply(null, arguments);
      }, xT = d._SparseFillEmptyRows = function() {
        return (xT = d._SparseFillEmptyRows = d.asm.SparseFillEmptyRows).apply(null, arguments);
      }, wT = d._SparseReshape = function() {
        return (wT = d._SparseReshape = d.asm.SparseReshape).apply(null, arguments);
      }, kT = d._SparseSegmentReduction = function() {
        return (kT = d._SparseSegmentReduction = d.asm.SparseSegmentReduction).apply(null, arguments);
      }, ST = d._Sqrt = function() {
        return (ST = d._Sqrt = d.asm.Sqrt).apply(null, arguments);
      }, IT = d._Square = function() {
        return (IT = d._Square = d.asm.Square).apply(null, arguments);
      }, CT = d._SquaredDifference = function() {
        return (CT = d._SquaredDifference = d.asm.SquaredDifference).apply(null, arguments);
      }, NT = d._Step = function() {
        return (NT = d._Step = d.asm.Step).apply(null, arguments);
      }, TT = d._StridedSlice = function() {
        return (TT = d._StridedSlice = d.asm.StridedSlice).apply(null, arguments);
      }, $T = d._Sub = function() {
        return ($T = d._Sub = d.asm.Sub).apply(null, arguments);
      }, _T = d._Sum = function() {
        return (_T = d._Sum = d.asm.Sum).apply(null, arguments);
      }, AT = d._Tan = function() {
        return (AT = d._Tan = d.asm.Tan).apply(null, arguments);
      }, ET = d._Tanh = function() {
        return (ET = d._Tanh = d.asm.Tanh).apply(null, arguments);
      }, RT = d._Tile = function() {
        return (RT = d._Tile = d.asm.Tile).apply(null, arguments);
      }, DT = d._TopK = function() {
        return (DT = d._TopK = d.asm.TopK).apply(null, arguments);
      }, FT = d._Transform = function() {
        return (FT = d._Transform = d.asm.Transform).apply(null, arguments);
      }, OT = d._Transpose = function() {
        return (OT = d._Transpose = d.asm.Transpose).apply(null, arguments);
      }, PT = d.__FusedMatMul = function() {
        return (PT = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments);
      }, Pf = d._malloc = function() {
        return (Pf = d._malloc = d.asm.malloc).apply(null, arguments);
      }, Jv = d._free = function() {
        return (Jv = d._free = d.asm.free).apply(null, arguments);
      }, zT = d._emscripten_tls_init = function() {
        return (zT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments);
      }, ex = d.___errno_location = function() {
        return (ex = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
      }, Mc = d._pthread_self = function() {
        return (Mc = d._pthread_self = d.asm.pthread_self).apply(null, arguments);
      }, tx = d._emscripten_main_thread_process_queued_calls = function() {
        return (tx = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
      }, MT = d.__emscripten_thread_crashed = function() {
        return (MT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments);
      }, nx = d.__emscripten_thread_init = function() {
        return (nx = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
      }, LT = d._emscripten_current_thread_process_queued_calls = function() {
        return (LT = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments);
      }, BT = d._emscripten_main_browser_thread_id = function() {
        return (BT = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments);
      }, VT = d._emscripten_sync_run_in_main_thread_2 = function() {
        return (VT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments);
      }, sx = d._emscripten_sync_run_in_main_thread_4 = function() {
        return (sx = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
      }, rx = d._emscripten_run_in_main_runtime_thread_js = function() {
        return (rx = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
      }, zf = d._emscripten_dispatch_to_thread_ = function() {
        return (zf = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
      }, Mf = d.__emscripten_thread_free_data = function() {
        return (Mf = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
      }, WT = d.__emscripten_thread_exit = function() {
        return (WT = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments);
      }, UT = d._memalign = function() {
        return (UT = d._memalign = d.asm.memalign).apply(null, arguments);
      }, ax = d._emscripten_stack_set_limits = function() {
        return (ax = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
      }, Lf = d.stackSave = function() {
        return (Lf = d.stackSave = d.asm.stackSave).apply(null, arguments);
      }, Lc = d.stackRestore = function() {
        return (Lc = d.stackRestore = d.asm.stackRestore).apply(null, arguments);
      }, zi = d.stackAlloc = function() {
        return (zi = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments);
      }, GT = d.dynCall_iijjiiii = function() {
        return (GT = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments);
      }, HT = d.dynCall_jiji = function() {
        return (HT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
      }, ix = d.__emscripten_allow_main_runtime_queued_calls = 21456;
      d.cwrap = en, d.keepRuntimeAlive = Lr, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = Nu;
      var Bc;
      function Nu(N) {
        this.name = "ExitStatus", this.message = "Program terminated with exit(" + N + ")", this.status = N;
      }
      as = function N() {
        Bc || Bf(), Bc || (as = N);
      };
      function Bf(N) {
        if (N = N || b, tr > 0)
          return;
        if ($) {
          h(d), yu(), postMessage({ cmd: "loaded" });
          return;
        }
        if (sn(), tr > 0)
          return;
        function D() {
          Bc || (Bc = true, d.calledRun = true, !at && (yu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), ph()));
        }
        d.setStatus ? (d.setStatus("Running..."), setTimeout(function() {
          setTimeout(function() {
            d.setStatus("");
          }, 1), D();
        }, 1)) : D();
      }
      d.run = Bf;
      function qT(N, D) {
        if (Jt = N, !D && $)
          throw Cc(N), "unwind";
        Lr() || dh(), jT(N);
      }
      function jT(N) {
        Jt = N, Lr() || ($e.terminateAllThreads(), d.onExit && d.onExit(N), at = true), v(N, new Nu(N));
      }
      if (d.preInit)
        for (typeof d.preInit == "function" && (d.preInit = [d.preInit]); d.preInit.length > 0; )
          d.preInit.pop()();
      Bf();
      var Vc;
      m && (Vc = { uncaughtException: process.listeners("uncaughtException").filter(function(N) {
        return !m.uncaughtException.indexOf(N) > -1;
      }), unhandledRejection: process.listeners("unhandledRejection").filter(function(N) {
        return !m.unhandledRejection.indexOf(N) > -1;
      }) });
      var Wc;
      if (typeof WasmBackendModule != "undefined")
        Wc = WasmBackendModule;
      else if (typeof r != "undefined")
        Wc = r;
      else
        throw new Error("Could not find wasm module in post.js");
      if (Vc) {
        var KT = Wc._dispose;
        Wc._dispose = function() {
          KT(), Vc.uncaughtException.forEach(function(N) {
            process.removeListener("uncaughtException", N);
          }), Vc.unhandledRejection.forEach(function(N) {
            process.removeListener("unhandledRejection", N);
          });
        };
      }
      return r.ready;
    };
  })();
  typeof e == "object" && typeof t == "object" ? t.exports = n : typeof define == "function" && define.amd ? define([], function() {
    return n;
  }) : typeof e == "object" && (e.WasmBackendModuleThreadedSimd = n);
} });
var b$ = Mt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
  var n = (() => {
    var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
    return typeof __filename != "undefined" && (s = s || __filename), function(r) {
      r = r || {};
      var a = typeof r != "undefined" ? r : {}, i, o;
      a.ready = new Promise(function(H, ee) {
        i = H, o = ee;
      });
      var u;
      typeof process != "undefined" && process.listeners && (u = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") });
      var l = Object.assign({}, a), c = [], p = "./this.program", d = (H, ee) => {
        throw ee;
      }, h = typeof window == "object", f = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", g = "";
      function b(H) {
        return a.locateFile ? a.locateFile(H, g) : g + H;
      }
      var y, v, x, k;
      function I(H) {
        if (H instanceof Iu)
          return;
        A("exiting due to exception: " + H);
      }
      var $, R, E;
      m ? (f ? g = gd().dirname(g) + "/" : g = __dirname + "/", E = () => {
        R || ($ = ug(), R = gd());
      }, y = function(ee, ce) {
        return E(), ee = R.normalize(ee), $.readFileSync(ee, ce ? void 0 : "utf8");
      }, x = (H) => {
        var ee = y(H, true);
        return ee.buffer || (ee = new Uint8Array(ee)), ee;
      }, v = (H, ee, ce) => {
        E(), H = R.normalize(H), $.readFile(H, function(Se, Qe) {
          Se ? ce(Se) : ee(Qe.buffer);
        });
      }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), c = process.argv.slice(2), process.on("uncaughtException", function(H) {
        if (!(H instanceof Iu))
          throw H;
      }), process.on("unhandledRejection", function(H) {
        throw H;
      }), d = (H, ee) => {
        if (bu())
          throw process.exitCode = H, ee;
        I(ee), process.exit(H);
      }, a.inspect = function() {
        return "[Emscripten Module object]";
      }) : (h || f) && (f ? g = self.location.href : typeof document != "undefined" && document.currentScript && (g = document.currentScript.src), s && (g = s), g.indexOf("blob:") !== 0 ? g = g.substr(0, g.replace(/[?#].*/, "").lastIndexOf("/") + 1) : g = "", y = (H) => {
        var ee = new XMLHttpRequest();
        return ee.open("GET", H, false), ee.send(null), ee.responseText;
      }, 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), A = 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 O = 4;
      function T(H) {
        T.shown || (T.shown = {}), T.shown[H] || (T.shown[H] = 1, A(H));
      }
      function M(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), Pi = new WebAssembly.Instance(os, { e: { f: H } }), Ur = Pi.exports.f;
        return Ur;
      }
      var W = [], j;
      function X() {
        if (W.length)
          return W.pop();
        try {
          Js.grow(1);
        } catch (H) {
          throw H instanceof RangeError ? "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH." : H;
        }
        return Js.length - 1;
      }
      function Y(H, ee) {
        for (var ce = H; ce < H + ee; ce++) {
          var Se = xu(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" && Mr("no native wasm support detected");
      var ne, oe = false, ae;
      function de(H, ee) {
        H || Mr(ee);
      }
      function me(H) {
        var ee = a["_" + H];
        return ee;
      }
      function ke(H, ee, ce, Se, Qe) {
        var Ze = { string: function(an) {
          var nr = 0;
          if (an != null && an !== 0) {
            var zc = (an.length << 2) + 1;
            nr = Su(zc), tt(an, nr, zc);
          }
          return nr;
        }, array: function(an) {
          var nr = Su(an.length);
          return at(an, nr), nr;
        } };
        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 Pi = Ze[ce[os]];
            Pi ? (is === 0 && (is = Oc()), Tt[os] = Pi(Se[os])) : Tt[os] = Se[os];
          }
        var Ur = ze.apply(null, Tt);
        function Of(an) {
          return is !== 0 && Pc(is), Le(an);
        }
        return Ur = Of(Ur), Ur;
      }
      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 Re = 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 at(H, ee) {
        Rt.set(H, ee);
      }
      function Jt(H, ee, ce) {
        for (var Se = 0; Se < H.length; ++Se)
          Rt[ee++ >> 0] = H.charCodeAt(Se);
        ce || (Rt[ee >> 0] = 0);
      }
      function Nt(H, ee) {
        return H % ee > 0 && (H += ee - H % ee), H;
      }
      var In, Rt, en, Cn, Nn, Yt, Dn, tn, zs;
      function Ms(H) {
        In = H, a.HEAP8 = Rt = 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 Ci = a.INITIAL_MEMORY || 16777216, Js, Ls = [], gu = [], Ni = [], nn = false, lc = false, cc = 0;
      function bu() {
        return se || cc > 0;
      }
      function dc() {
        if (a.preRun)
          for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
            fc(a.preRun.shift());
        vu(Ls);
      }
      function pc() {
        nn = true, vu(gu);
      }
      function Vv() {
        lc = true;
      }
      function hc() {
        if (a.postRun)
          for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
            mc(a.postRun.shift());
        vu(Ni);
      }
      function fc(H) {
        Ls.unshift(H);
      }
      function rs(H) {
        gu.unshift(H);
      }
      function mc(H) {
        Ni.unshift(H);
      }
      var Fn = 0, Ti = null, er = null;
      function lh(H) {
        Fn++, a.monitorRunDependencies && a.monitorRunDependencies(Fn);
      }
      function gc(H) {
        if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && (Ti !== null && (clearInterval(Ti), Ti = null), er)) {
          var ee = er;
          er = null, ee();
        }
      }
      a.preloadedImages = {}, a.preloadedAudios = {};
      function Mr(H) {
        a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", A(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 ch = "data:application/octet-stream;base64,";
      function bc(H) {
        return H.startsWith(ch);
      }
      function Lr(H) {
        return H.startsWith("file://");
      }
      var sn;
      sn = "tfjs-backend-wasm.wasm", bc(sn) || (sn = b(sn));
      function yu(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) {
          Mr(ee);
        }
      }
      function dh() {
        if (!J && (h || f)) {
          if (typeof fetch == "function" && !Lr(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 yu(sn);
            });
          if (v)
            return new Promise(function(H, ee) {
              v(sn, function(ce) {
                H(new Uint8Array(ce));
              }, ee);
            });
        }
        return Promise.resolve().then(function() {
          return yu(sn);
        });
      }
      function ph() {
        var H = { env: Ai, wasi_snapshot_preview1: Ai };
        function ee(Le, ze) {
          var Tt = Le.exports;
          a.asm = Tt, ne = a.asm.memory, Ms(ne.buffer), Js = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), gc("wasm-instantiate");
        }
        lh("wasm-instantiate");
        function ce(Le) {
          ee(Le.instance);
        }
        function Se(Le) {
          return dh().then(function(ze) {
            return WebAssembly.instantiate(ze, H);
          }).then(function(ze) {
            return ze;
          }).then(Le, function(ze) {
            A("failed to asynchronously prepare wasm: " + ze), Mr(ze);
          });
        }
        function Qe() {
          return !J && typeof WebAssembly.instantiateStreaming == "function" && !bc(sn) && !Lr(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 A("wasm streaming compile failed: " + Tt), A("falling back to ArrayBuffer instantiation"), Se(ce);
            });
          }) : Se(ce);
        }
        if (a.instantiateWasm)
          try {
            var Ze = a.instantiateWasm(H, ee);
            return Ze;
          } catch (Le) {
            return A("Module.instantiateWasm callback failed with error: " + Le), false;
          }
        return Qe().catch(o), {};
      }
      var Wv, Uv;
      function vu(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 ? xu(ce)() : xu(ce)(ee.arg) : ce(ee.arg === void 0 ? null : ee.arg);
        }
      }
      function tr(H) {
        return H;
      }
      function yc(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 xu(H) {
        var ee = as[H];
        return ee || (H >= as.length && (as.length = H + 1), as[H] = ee = Js.get(H)), ee;
      }
      function Gv() {
        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) {
        Js.set(H, ee), as[H] = ee;
      }
      function hh() {
        Mr("");
      }
      function vc(H, ee, ce) {
        en.copyWithin(H, ee, ee + ce);
      }
      function xc() {
        return 2147483648;
      }
      function rn(H) {
        try {
          return ne.grow(H - In.byteLength + 65535 >>> 16), Ms(ne.buffer), 1;
        } catch (ee) {
        }
      }
      function wc(H) {
        var ee = en.length;
        H = H >>> 0;
        var ce = xc();
        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 _i = { mappings: {}, buffers: [null, [], []], printChar: function(H, ee) {
        var ce = _i.buffers[H];
        ee === 0 || ee === 10 ? ((H === 1 ? P : A)(Xe(ce, 0)), ce.length = 0) : ce.push(ee);
      }, varargs: void 0, get: function() {
        _i.varargs += 4;
        var H = Yt[_i.varargs - 4 >> 2];
        return H;
      }, getStr: function(H) {
        var ee = Je(H);
        return ee;
      }, get64: function(H, ee) {
        return H;
      } };
      function fh(H) {
        return 0;
      }
      function Hv(H, ee, ce, Se, Qe) {
      }
      function qv(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++)
            _i.printChar(H, en[Le + Tt]);
          Qe += ze;
        }
        return Yt[Se >> 2] = Qe, 0;
      }
      function mh(H) {
        te(H);
      }
      var kc = false, Ai = { abort: hh, emscripten_memcpy_big: vc, emscripten_resize_heap: wc, fd_close: fh, fd_seek: Hv, fd_write: qv, setTempRet0: mh }, sT = ph(), jv = a.___wasm_call_ctors = function() {
        return (jv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
      }, gh = a._init = function() {
        return (gh = a._init = a.asm.init).apply(null, arguments);
      }, bh = a._init_with_threads_count = function() {
        return (bh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
      }, Sc = a._get_threads_count = function() {
        return (Sc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
      }, Ic = a._register_tensor = function() {
        return (Ic = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
      }, yh = a._dispose_data = function() {
        return (yh = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
      }, $e = a._dispose = function() {
        return ($e = a._dispose = a.asm.dispose).apply(null, arguments);
      }, vh = a._Abs = function() {
        return (vh = a._Abs = a.asm.Abs).apply(null, arguments);
      }, Cc = a._Add = function() {
        return (Cc = a._Add = a.asm.Add).apply(null, arguments);
      }, Br = a._AddN = function() {
        return (Br = a._AddN = a.asm.AddN).apply(null, arguments);
      }, Ei = a._All = function() {
        return (Ei = a._All = a.asm.All).apply(null, arguments);
      }, xh = a._Any = function() {
        return (xh = a._Any = a.asm.Any).apply(null, arguments);
      }, Kv = a._ArgMax = function() {
        return (Kv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
      }, wh = a._AvgPool = function() {
        return (wh = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
      }, Xv = a._BatchMatMul = function() {
        return (Xv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
      }, Vr = a._Ceil = function() {
        return (Vr = a._Ceil = a.asm.Ceil).apply(null, arguments);
      }, kh = a._ClipByValue = function() {
        return (kh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
      }, Sh = a._Conv2D = function() {
        return (Sh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
      }, Ih = a._Conv2DBackpropInput = function() {
        return (Ih = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
      }, Ch = a._Cos = function() {
        return (Ch = a._Cos = a.asm.Cos).apply(null, arguments);
      }, Nh = a._Cosh = function() {
        return (Nh = a._Cosh = a.asm.Cosh).apply(null, arguments);
      }, Th = a._CropAndResize = function() {
        return (Th = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
      }, Nc = a._Cumprod = function() {
        return (Nc = a._Cumprod = a.asm.Cumprod).apply(null, arguments);
      }, $h = a._Cumsum = function() {
        return ($h = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
      }, _h = a._DepthToSpace = function() {
        return (_h = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
      }, Ah = a._DepthwiseConv2dNative = function() {
        return (Ah = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, Eh = a._Elu = function() {
        return (Eh = a._Elu = a.asm.Elu).apply(null, arguments);
      }, Rh = a._Equal = function() {
        return (Rh = a._Equal = a.asm.Equal).apply(null, arguments);
      }, Tc = a._Exp = function() {
        return (Tc = a._Exp = a.asm.Exp).apply(null, arguments);
      }, Dh = a._FlipLeftRight = function() {
        return (Dh = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
      }, Fh = a._Floor = function() {
        return (Fh = a._Floor = a.asm.Floor).apply(null, arguments);
      }, Wr = a._FloorDiv = function() {
        return (Wr = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
      }, wu = a._FusedBatchNorm = function() {
        return (wu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
      }, Oh = a._FusedConv2D = function() {
        return (Oh = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
      }, Ph = a._FusedDepthwiseConv2D = function() {
        return (Ph = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
      }, zh = a._Gather = function() {
        return (zh = a._Gather = a.asm.Gather).apply(null, arguments);
      }, Me = a._GatherNd = function() {
        return (Me = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
      }, Mh = a._Greater = function() {
        return (Mh = a._Greater = a.asm.Greater).apply(null, arguments);
      }, Lh = a._GreaterEqual = function() {
        return (Lh = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
      }, Bh = a._LeakyRelu = function() {
        return (Bh = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
      }, Vh = a._Less = function() {
        return (Vh = a._Less = a.asm.Less).apply(null, arguments);
      }, Wh = a._LessEqual = function() {
        return (Wh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
      }, Uh = a._Log = function() {
        return (Uh = a._Log = a.asm.Log).apply(null, arguments);
      }, ku = a._LogicalAnd = function() {
        return (ku = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
      }, $c = a._Max = function() {
        return ($c = a._Max = a.asm.Max).apply(null, arguments);
      }, _c = a._MaxPool = function() {
        return (_c = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
      }, Gh = a._Maximum = function() {
        return (Gh = a._Maximum = a.asm.Maximum).apply(null, arguments);
      }, Hh = a._Mean = function() {
        return (Hh = a._Mean = a.asm.Mean).apply(null, arguments);
      }, qh = a._Min = function() {
        return (qh = a._Min = a.asm.Min).apply(null, arguments);
      }, jh = a._Minimum = function() {
        return (jh = a._Minimum = a.asm.Minimum).apply(null, arguments);
      }, Kh = a._MirrorPad = function() {
        return (Kh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
      }, Xh = a._Multiply = function() {
        return (Xh = a._Multiply = a.asm.Multiply).apply(null, arguments);
      }, gt = a._Neg = function() {
        return (gt = a._Neg = a.asm.Neg).apply(null, arguments);
      }, Yh = a._NonMaxSuppressionV3 = function() {
        return (Yh = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
      }, Qh = a._NonMaxSuppressionV4 = function() {
        return (Qh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
      }, Zh = a._NonMaxSuppressionV5 = function() {
        return (Zh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
      }, Ri = a._NotEqual = function() {
        return (Ri = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
      }, Ac = a._OneHot = function() {
        return (Ac = a._OneHot = a.asm.OneHot).apply(null, arguments);
      }, Ec = a._PadV2 = function() {
        return (Ec = a._PadV2 = a.asm.PadV2).apply(null, arguments);
      }, Rc = a._Pow = function() {
        return (Rc = a._Pow = a.asm.Pow).apply(null, arguments);
      }, Jh = a._Prelu = function() {
        return (Jh = a._Prelu = a.asm.Prelu).apply(null, arguments);
      }, Dc = a._Prod = function() {
        return (Dc = a._Prod = a.asm.Prod).apply(null, arguments);
      }, ef = a._RealDiv = function() {
        return (ef = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
      }, Yv = a._Relu = function() {
        return (Yv = a._Relu = a.asm.Relu).apply(null, arguments);
      }, Fc = a._Relu6 = function() {
        return (Fc = a._Relu6 = a.asm.Relu6).apply(null, arguments);
      }, Qv = a._ResizeBilinear = function() {
        return (Qv = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
      }, tf = a._Reverse = function() {
        return (tf = a._Reverse = a.asm.Reverse).apply(null, arguments);
      }, nf = a._RotateWithOffset = function() {
        return (nf = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
      }, sf = a._Round = function() {
        return (sf = a._Round = a.asm.Round).apply(null, arguments);
      }, rf = a._Rsqrt = function() {
        return (rf = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
      }, af = a._ScatterNd = function() {
        return (af = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
      }, of = a._SelectV2 = function() {
        return (of = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
      }, uf = a._Sigmoid = function() {
        return (uf = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
      }, lf = a._Sin = function() {
        return (lf = a._Sin = a.asm.Sin).apply(null, arguments);
      }, cf = a._Softmax = function() {
        return (cf = a._Softmax = a.asm.Softmax).apply(null, arguments);
      }, df = a._SparseFillEmptyRows = function() {
        return (df = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
      }, pf = a._SparseReshape = function() {
        return (pf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
      }, hf = a._SparseSegmentReduction = function() {
        return (hf = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
      }, ff = a._Sqrt = function() {
        return (ff = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
      }, mf = a._Square = function() {
        return (mf = a._Square = a.asm.Square).apply(null, arguments);
      }, gf = a._SquaredDifference = function() {
        return (gf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
      }, bf = a._Step = function() {
        return (bf = a._Step = a.asm.Step).apply(null, arguments);
      }, yf = a._StridedSlice = function() {
        return (yf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
      }, vf = a._Sub = function() {
        return (vf = a._Sub = a.asm.Sub).apply(null, arguments);
      }, xf = a._Sum = function() {
        return (xf = a._Sum = a.asm.Sum).apply(null, arguments);
      }, wf = a._Tan = function() {
        return (wf = a._Tan = a.asm.Tan).apply(null, arguments);
      }, kf = a._Tanh = function() {
        return (kf = a._Tanh = a.asm.Tanh).apply(null, arguments);
      }, Sf = a._Tile = function() {
        return (Sf = a._Tile = a.asm.Tile).apply(null, arguments);
      }, If = a._TopK = function() {
        return (If = a._TopK = a.asm.TopK).apply(null, arguments);
      }, Cf = a._Transform = function() {
        return (Cf = a._Transform = a.asm.Transform).apply(null, arguments);
      }, Nf = a._Transpose = function() {
        return (Nf = a._Transpose = a.asm.Transpose).apply(null, arguments);
      }, Tf = a.__FusedMatMul = function() {
        return (Tf = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
      }, $f = a._malloc = function() {
        return ($f = a._malloc = a.asm.malloc).apply(null, arguments);
      }, _f = a._free = function() {
        return (_f = a._free = a.asm.free).apply(null, arguments);
      }, Af = a.___errno_location = function() {
        return (Af = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
      }, Ef = a._emscripten_main_thread_process_queued_calls = function() {
        return (Ef = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
      }, Oc = a.stackSave = function() {
        return (Oc = a.stackSave = a.asm.stackSave).apply(null, arguments);
      }, Pc = a.stackRestore = function() {
        return (Pc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
      }, Su = a.stackAlloc = function() {
        return (Su = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
      }, Rf = a.dynCall_iijjiiii = function() {
        return (Rf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
      }, Df = a.dynCall_jiji = function() {
        return (Df = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
      };
      a.cwrap = Ie;
      var Di;
      function Iu(H) {
        this.name = "ExitStatus", this.message = "Program terminated with exit(" + H + ")", this.status = H;
      }
      er = function H() {
        Di || Cu(), Di || (er = H);
      };
      function Cu(H) {
        if (H = H || c, Fn > 0 || (dc(), Fn > 0))
          return;
        function ee() {
          Di || (Di = true, a.calledRun = true, !oe && (pc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), hc()));
        }
        a.setStatus ? (a.setStatus("Running..."), setTimeout(function() {
          setTimeout(function() {
            a.setStatus("");
          }, 1), ee();
        }, 1)) : ee();
      }
      a.run = Cu;
      function Zv(H) {
        ae = H, bu() || (a.onExit && a.onExit(H), oe = true), d(H, new Iu(H));
      }
      if (a.preInit)
        for (typeof a.preInit == "function" && (a.preInit = [a.preInit]); a.preInit.length > 0; )
          a.preInit.pop()();
      Cu();
      var Fi;
      u && (Fi = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
        return !u.uncaughtException.indexOf(H) > -1;
      }), unhandledRejection: process.listeners("unhandledRejection").filter(function(H) {
        return !u.unhandledRejection.indexOf(H) > -1;
      }) });
      var Oi;
      if (typeof r != "undefined")
        Oi = r;
      else if (typeof WasmBackendModuleThreadedSimd != "undefined")
        Oi = WasmBackendModuleThreadedSimd;
      else
        throw new Error("Could not find wasm module in post.js");
      if (Fi) {
        var Ff = Oi._dispose;
        Oi._dispose = function() {
          Ff(), Fi.uncaughtException.forEach(function(H) {
            process.removeListener("uncaughtException", H);
          }), Fi.unhandledRejection.forEach(function(H) {
            process.removeListener("unhandledRejection", H);
          });
        };
      }
      return r.ready;
    };
  })();
  typeof e == "object" && typeof t == "object" ? t.exports = n : typeof define == "function" && define.amd ? define([], function() {
    return n;
  }) : typeof e == "object" && (e.WasmBackendModule = n);
} });
var y$ = 1e-7;
var v$ = 1e-4;
var Yd = class {
  constructor(e, t) {
    this.backend = e, this.dataMover = t, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0;
  }
  get(e) {
    return this.data.has(e) || this.dataMover.moveData(this.backend, e), this.data.get(e);
  }
  set(e, t) {
    this.dataIdsCount++, this.data.set(e, t);
  }
  has(e) {
    return this.data.has(e);
  }
  delete(e) {
    return this.dataIdsCount--, this.data.delete(e);
  }
  numDataIds() {
    return this.dataIdsCount;
  }
};
var ol = class {
  refCount(e) {
    return On("refCount");
  }
  incRef(e) {
    return On("incRef");
  }
  timerAvailable() {
    return true;
  }
  time(e) {
    return On("time");
  }
  read(e) {
    return On("read");
  }
  readSync(e) {
    return On("readSync");
  }
  readToGPU(e, t) {
    return On("readToGPU");
  }
  numDataIds() {
    return On("numDataIds");
  }
  disposeData(e, t) {
    return On("disposeData");
  }
  write(e, t, n) {
    return On("write");
  }
  move(e, t, n, s, r) {
    return On("move");
  }
  memory() {
    return On("memory");
  }
  floatPrecision() {
    return On("floatPrecision");
  }
  epsilon() {
    return this.floatPrecision() === 32 ? y$ : v$;
  }
  dispose() {
    return On("dispose");
  }
};
function On(e) {
  throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
}
function Jw(e) {
  let t = e.length, n = 0;
  for (; t > 0; )
    n = Math.random() * t | 0, t--, bd(e, t, n);
}
function x$(e, t) {
  if (e.length !== t.length)
    throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);
  let n = e.length, s = 0;
  for (; n > 0; )
    s = Math.random() * n | 0, n--, bd(e, n, s), bd(t, n, s);
}
function Hu(e, t, n) {
  return Math.max(e, Math.min(t, n));
}
function w$(e) {
  return e % 2 === 0 ? e : e + 1;
}
function bd(e, t, n) {
  let s = e[t];
  e[t] = e[n], e[n] = s;
}
function k$(e) {
  let t = 0;
  for (let n = 0; n < e.length; n++)
    t += e[n];
  return t;
}
function S$(e, t) {
  let n = Math.random();
  return t * n + (1 - n) * e;
}
function I$(e, t) {
  let n = 0;
  for (let s = 0; s < e.length; s++) {
    let r = Number(e[s]) - Number(t[s]);
    n += r * r;
  }
  return n;
}
function F(e, t) {
  if (!e)
    throw new Error(typeof t == "string" ? t : t());
}
function pn(e, t, n = "") {
  F(Ir(e, t), () => n + ` Shapes ${e} and ${t} must match`);
}
function Sa(e) {
  F(e != null, () => "The input to the tensor constructor must be a non-null value.");
}
function ia(e, t = [], n = false) {
  if (t == null && (t = []), Array.isArray(e) || Qt(e) && !n)
    for (let s = 0; s < e.length; ++s)
      ia(e[s], t, n);
  else
    t.push(e);
  return t;
}
function dt(e) {
  if (e.length === 0)
    return 1;
  let t = e[0];
  for (let n = 1; n < e.length; n++)
    t *= e[n];
  return t;
}
function C$(e) {
  return e.length === 0;
}
function Ir(e, t) {
  if (e === t)
    return true;
  if (e == null || t == null || e.length !== t.length)
    return false;
  for (let n = 0; n < e.length; n++)
    if (e[n] !== t[n])
      return false;
  return true;
}
function eo(e) {
  return e % 1 === 0;
}
function N$(e) {
  if (Math.tanh != null)
    return Math.tanh(e);
  if (e === 1 / 0)
    return 1;
  if (e === -1 / 0)
    return -1;
  {
    let t = Math.exp(2 * e);
    return (t - 1) / (t + 1);
  }
}
function T$(e) {
  let t = Math.ceil(Math.sqrt(e));
  return [t, Math.ceil(e / t)];
}
function $$(e) {
  let t = new Uint32Array(e);
  for (let n = 0; n < e; ++n)
    t[n] = n;
  return Jw(t), t;
}
function Vu(e, t) {
  return t <= e.length ? e : e + " ".repeat(t - e.length);
}
function _$(e, t = (s) => 0, n) {
  return new Promise((s, r) => {
    let a = 0, i = () => {
      if (e()) {
        s();
        return;
      }
      a++;
      let o = t(a);
      if (n != null && a >= n) {
        r();
        return;
      }
      setTimeout(i, o);
    };
    i();
  });
}
function A$(e, t) {
  let n = 1, s = -1;
  for (let a = 0; a < e.length; ++a)
    if (e[a] >= 0)
      n *= e[a];
    else if (e[a] === -1) {
      if (s !== -1)
        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${s} and dim ${a}`);
      s = a;
    } else if (e[a] < 0)
      throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`);
  if (s === -1) {
    if (t > 0 && t !== n)
      throw Error(`Size(${t}) must match the product of shape ${e}`);
    return e;
  }
  if (n === 0)
    throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`);
  if (t % n !== 0)
    throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`);
  let r = e.slice();
  return r[s] = t / n, r;
}
function ts(e, t) {
  let n = t.length;
  return e = e == null ? t.map((s, r) => r) : [].concat(e), F(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), F(e.every((s) => eo(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
}
function ek(e, t) {
  let n = [], s = [], r = t != null && Array.isArray(t) && t.length === 0, a = t == null || r ? null : ts(t, e).sort(), i = 0;
  for (let o = 0; o < e.length; ++o) {
    if (a != null) {
      if (a[i] === o && e[o] !== 1)
        throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`);
      (a[i] == null || a[i] > o) && e[o] === 1 && (n.push(e[o]), s.push(o)), a[i] <= o && i++;
    }
    e[o] !== 1 && (n.push(e[o]), s.push(o));
  }
  return { newShape: n, keptDims: s };
}
function tk(e, t) {
  let n = null;
  if (e == null || e === "float32")
    n = new Float32Array(t);
  else if (e === "int32")
    n = new Int32Array(t);
  else if (e === "bool")
    n = new Uint8Array(t);
  else
    throw new Error(`Unknown data type ${e}`);
  return n;
}
function nk(e, t) {
  let n = null;
  if (e == null || e === "float32")
    n = new Float32Array(t);
  else if (e === "int32")
    n = new Int32Array(t);
  else if (e === "bool")
    n = new Uint8Array(t);
  else if (e === "string")
    n = new Array(t);
  else
    throw new Error(`Unknown data type ${e}`);
  return n;
}
function sk(e, t) {
  for (let n = 0; n < e.length; n++) {
    let s = e[n];
    if (isNaN(s) || !isFinite(s))
      throw Error(`A tensor of type ${t} being uploaded contains ${s}.`);
  }
}
function rk(e) {
  return e === "bool" || e === "complex64" || e === "float32" || e === "int32" || e === "string";
}
function E$(e, t) {
  return !(t === "complex64" || t === "float32" && e !== "complex64" || t === "int32" && e !== "float32" && e !== "complex64" || t === "bool" && e === "bool");
}
function Qt(e) {
  return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray;
}
function nm(e) {
  if (e === "float32" || e === "int32")
    return 4;
  if (e === "complex64")
    return 8;
  if (e === "bool")
    return 1;
  throw new Error(`Unknown dtype ${e}`);
}
function ak(e) {
  if (e == null)
    return 0;
  let t = 0;
  return e.forEach((n) => t += n.length), t;
}
function ir(e) {
  return typeof e == "string" || e instanceof String;
}
function ik(e) {
  return typeof e == "boolean";
}
function ok(e) {
  return typeof e == "number";
}
function Qd(e) {
  return Array.isArray(e) ? Qd(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : ok(e) ? "float32" : ir(e) ? "string" : ik(e) ? "bool" : "float32";
}
function fr(e) {
  return !!(e && e.constructor && e.call && e.apply);
}
function yd(e, t) {
  for (let n = t; n < e; ++n)
    if (e % n === 0)
      return n;
  return e;
}
function co(e) {
  let t = e.length;
  if (t < 2)
    return [];
  let n = new Array(t - 1);
  n[t - 2] = e[t - 1];
  for (let s = t - 3; s >= 0; --s)
    n[s] = n[s + 1] * e[s + 1];
  return n;
}
function uk(e, t, n, s = false) {
  let r = new Array();
  if (t.length === 1) {
    let a = t[0] * (s ? 2 : 1);
    for (let i = 0; i < a; i++)
      r[i] = n[e + i];
  } else {
    let a = t[0], i = t.slice(1), o = i.reduce((u, l) => u * l) * (s ? 2 : 1);
    for (let u = 0; u < a; u++)
      r[u] = uk(e + u * o, i, n, s);
  }
  return r;
}
function Xi(e, t, n = false) {
  if (e.length === 0)
    return t[0];
  let s = e.reduce((r, a) => r * a) * (n ? 2 : 1);
  if (s === 0)
    return [];
  if (s !== t.length)
    throw new Error(`[${e}] does not match the input size ${t.length}${n ? " for a complex tensor" : ""}.`);
  return uk(0, e, t, n);
}
function lg(e, t) {
  let n = Zd(e, t);
  for (let s = 0; s < n.length; s++)
    n[s] = 1;
  return n;
}
function Zd(e, t) {
  if (t == null || t === "float32" || t === "complex64")
    return new Float32Array(e);
  if (t === "int32")
    return new Int32Array(e);
  if (t === "bool")
    return new Uint8Array(e);
  throw new Error(`Unknown data type ${t}`);
}
function R$(e, t) {
  let n = e.reduce((s, r) => s * r, 1);
  if (t == null || t === "float32")
    return Xi(e, new Float32Array(n));
  if (t === "int32")
    return Xi(e, new Int32Array(n));
  if (t === "bool")
    return Xi(e, new Uint8Array(n));
  throw new Error(`Unknown data type ${t}`);
}
function cg(e) {
  e.forEach((t) => {
    F(Number.isInteger(t) && t >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${e}].`);
  });
}
function D$(e, t, n) {
  if (t === 0)
    return 0;
  if (t === 1)
    return e[0];
  let s = e[e.length - 1];
  for (let r = 0; r < e.length - 1; ++r)
    s += n[r] * e[r];
  return s;
}
function F$(e, t, n) {
  if (t === 0)
    return [];
  if (t === 1)
    return [e];
  let s = new Array(t);
  for (let r = 0; r < s.length - 1; ++r)
    s[r] = Math.floor(e / n[r]), e -= s[r] * n[r];
  return s[s.length - 1] = e, s;
}
function dg(e) {
  return e && e.then && typeof e.then == "function";
}
var ux = "tfjsflags";
var O$ = class {
  constructor(e) {
    this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = P$, this.populateURLFlags();
  }
  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) {
      let s = this.urlFlags[e];
      K().getBool("IS_TEST") || K().getBool("PROD") || console.warn(`Setting feature override from URL ${e}: ${s}.`), this.set(e, s);
    }
  }
  async getAsync(e) {
    return e in this.flags ? this.flags[e] : (this.flags[e] = await this.evaluateFlag(e), this.flags[e]);
  }
  get(e) {
    if (e in this.flags)
      return this.flags[e];
    let t = this.evaluateFlag(e);
    if (dg(t))
      throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);
    return this.flags[e] = t, this.flags[e];
  }
  getNumber(e) {
    return this.get(e);
  }
  getBool(e) {
    return this.get(e);
  }
  getFlags() {
    return this.flags;
  }
  get features() {
    return this.flags;
  }
  set(e, t) {
    if (this.flagRegistry[e] == null)
      throw new Error(`Cannot set flag ${e} as it has not been registered.`);
    this.flags[e] = t, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t);
  }
  evaluateFlag(e) {
    if (this.flagRegistry[e] == null)
      throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`);
    return this.flagRegistry[e].evaluationFn();
  }
  setFlags(e) {
    this.flags = Object.assign({}, e);
  }
  reset() {
    this.flags = {}, this.urlFlags = {}, this.populateURLFlags();
  }
  populateURLFlags() {
    if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined")
      return;
    let e = this.getQueryParams(this.global.location.search);
    ux in e && e[ux].split(",").forEach((n) => {
      let [s, r] = n.split(":");
      this.urlFlags[s] = M$(s, r);
    });
  }
};
function P$(e) {
  let t = {};
  return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (n, ...s) => (z$(t, s[0], s[1]), s.join("="))), t;
}
function z$(e, t, n) {
  e[decodeURIComponent(t)] = decodeURIComponent(n || "");
}
function M$(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() {
  return lk;
}
var lk = null;
function L$(e) {
  lk = e;
}
var Vf;
function ck() {
  if (Vf == null) {
    let e;
    if (typeof window != "undefined")
      e = window;
    else if (typeof global != "undefined")
      e = global;
    else if (typeof process != "undefined")
      e = process;
    else if (typeof self != "undefined")
      e = self;
    else
      throw new Error("Could not find a global object");
    Vf = e;
  }
  return Vf;
}
function B$() {
  let e = ck();
  return e._tfGlobals == null && (e._tfGlobals = /* @__PURE__ */ new Map()), e._tfGlobals;
}
function pg(e, t) {
  let n = B$();
  if (n.has(e))
    return n.get(e);
  {
    let s = t();
    return n.set(e, s), n.get(e);
  }
}
var po = "Abs";
var ul = "Acos";
var ll = "Acosh";
var Cr = "Add";
var Ia = "AddN";
var cl = "All";
var dl = "Any";
var Ca = "ArgMax";
var pl = "ArgMin";
var hl = "Asin";
var fl = "Asinh";
var ml = "Atan";
var gl = "Atanh";
var bl = "Atan2";
var Na = "AvgPool";
var hg = "AvgPoolGrad";
var Jd = "AvgPool3D";
var fg = "AvgPool3DGrad";
var Ta = "BatchMatMul";
var ho = "BatchToSpaceND";
var mg = "Bincount";
var V$ = "BroadcastTo";
var gg = "BroadcastArgs";
var $a = "Cast";
var _a = "Ceil";
var Nr = "ClipByValue";
var ep = "Complex";
var tp = "ComplexAbs";
var fo = "Concat";
var Aa = "Conv2D";
var bg = "Conv2DBackpropFilter";
var Ea = "Conv2DBackpropInput";
var np = "Conv3D";
var yg = "Conv3DBackpropFilterV2";
var vg = "Conv3DBackpropInputV2";
var Ra = "Cos";
var Da = "Cosh";
var mo = "Cumprod";
var Fa = "Cumsum";
var go = "CropAndResize";
var xg = "DenseBincount";
var bo = "DepthToSpace";
var Oa = "DepthwiseConv2dNative";
var wg = "DepthwiseConv2dNativeBackpropFilter";
var kg = "DepthwiseConv2dNativeBackpropInput";
var Sg = "Diag";
var sp = "Dilation2D";
var sm = "Dilation2DBackpropInput";
var rm = "Dilation2DBackpropFilter";
var Pa = "RealDiv";
var rp = "Einsum";
var za = "Elu";
var Ig = "EluGrad";
var yl = "Erf";
var yo = "Equal";
var Ma = "Exp";
var vo = "ExpandDims";
var xo = "Expm1";
var Cg = "FFT";
var vl = "Fill";
var wo = "FlipLeftRight";
var La = "Floor";
var Ba = "FloorDiv";
var Va = "FusedBatchNorm";
var ko = "GatherV2";
var So = "GatherNd";
var Io = "Greater";
var Wa = "GreaterEqual";
var Ua = "Identity";
var Ng = "IFFT";
var ap = "Imag";
var xl = "IsFinite";
var wl = "IsInf";
var kl = "IsNan";
var Ga = "LeakyRelu";
var Co = "Less";
var No = "LessEqual";
var Tg = "LinSpace";
var Ha = "Log";
var Sl = "Log1p";
var To = "LogicalAnd";
var Il = "LogicalNot";
var ip = "LogicalOr";
var W$ = "LogSoftmax";
var upe = "LowerBound";
var op = "LRN";
var $g = "LRNGrad";
var qa = "Max";
var ja = "Maximum";
var Ka = "MaxPool";
var _g = "MaxPoolGrad";
var up = "MaxPool3D";
var Ag = "MaxPool3DGrad";
var Eg = "MaxPoolWithArgmax";
var Xa = "Mean";
var Ya = "Min";
var Qa = "Minimum";
var Za = "MirrorPad";
var Cl = "Mod";
var Rg = "Multinomial";
var Ja = "Multiply";
var $o = "Neg";
var _o = "NotEqual";
var Ao = "NonMaxSuppressionV3";
var Nl = "NonMaxSuppressionV4";
var Eo = "NonMaxSuppressionV5";
var Ro = "OnesLike";
var Do = "OneHot";
var Fo = "Pack";
var ei = "PadV2";
var lpe = "Pool";
var ti = "Pow";
var ni = "Prelu";
var si = "Prod";
var Tl = "Range";
var lp = "Real";
var $l = "Reciprocal";
var ri = "Relu";
var Oo = "Reshape";
var _l = "ResizeNearestNeighbor";
var Dg = "ResizeNearestNeighborGrad";
var ai = "ResizeBilinear";
var Fg = "ResizeBilinearGrad";
var ii = "Relu6";
var Po = "Reverse";
var zo = "Round";
var oi = "Rsqrt";
var Mo = "ScatterNd";
var Og = "SearchSorted";
var Lo = "Select";
var Al = "Selu";
var Bo = "Slice";
var ui = "Sin";
var Vo = "Sinh";
var El = "Sign";
var li = "Sigmoid";
var Rl = "Softplus";
var ci = "Sqrt";
var di = "Sum";
var Wo = "SpaceToBatchND";
var Uo = "SplitV";
var pi = "Softmax";
var cp = "SparseFillEmptyRows";
var Dl = "SparseReshape";
var dp = "SparseSegmentMean";
var pp = "SparseSegmentSum";
var hp = "SparseToDense";
var hi = "SquaredDifference";
var Fl = "Square";
var Go = "StridedSlice";
var fp = "StringNGrams";
var Pg = "StringSplit";
var zg = "StringToHashBucketFast";
var fi = "Sub";
var Ho = "Tan";
var mi = "Tanh";
var Tr = "Tile";
var qo = "TopK";
var jo = "Transform";
var Hs = "Transpose";
var Mg = "Unique";
var Ko = "Unpack";
var mp = "UnsortedSegmentSum";
var cpe = "UpperBound";
var Xo = "ZerosLike";
var gi = "Step";
var vd = "FromPixels";
var Yo = "RotateWithOffset";
var oa = "_FusedMatMul";
var ua = "FusedConv2D";
var la = "FusedDepthwiseConv2D";
function ar(...e) {
  K().getBool("IS_TEST") || K().getBool("PROD") || console.warn(...e);
}
function U$(...e) {
  K().getBool("IS_TEST") || K().getBool("PROD") || console.log(...e);
}
var to = pg("kernelRegistry", () => /* @__PURE__ */ new Map());
var qu = pg("gradRegistry", () => /* @__PURE__ */ new Map());
function am(e, t) {
  let n = Lg(e, t);
  return to.get(n);
}
function lx(e) {
  return qu.get(e);
}
function im(e) {
  let t = to.entries(), n = [];
  for (; ; ) {
    let { done: s, value: r } = t.next();
    if (s)
      break;
    let [a, i] = r, [o] = a.split("_");
    o === e && n.push(i);
  }
  return n;
}
function Ol(e) {
  let { kernelName: t, backendName: n } = e, s = Lg(t, n);
  to.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), to.set(s, e);
}
function G$(e) {
  let { kernelName: t } = e;
  qu.has(t) && K().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), qu.set(t, e);
}
function dpe(e, t) {
  let n = Lg(e, t);
  if (!to.has(n))
    throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);
  to.delete(n);
}
function ppe(e) {
  if (!qu.has(e))
    throw new Error(`The gradient '${e}' for backend is not registered`);
  qu.delete(e);
}
function hpe(e, t) {
  im(e).forEach((s) => {
    let r = Object.assign({}, s, { backendName: t });
    Ol(r);
  });
}
function Lg(e, t) {
  return `${t}_${e}`;
}
var w = {};
Ee(w, { arraysEqual: () => Ir, assert: () => F, assertNonNegativeIntegerDimensions: () => cg, assertNonNull: () => Sa, assertShapesMatch: () => pn, bytesFromStringArray: () => ak, bytesPerElement: () => nm, checkConversionForErrors: () => sk, clamp: () => Hu, computeStrides: () => co, createScalarValue: () => Y$, createShuffledIndices: () => $$, decodeString: () => xd, distSquared: () => I$, encodeString: () => zl, fetch: () => Z$, fingerPrint64: () => X$, flatten: () => ia, getArrayFromDType: () => nk, getTypedArrayFromDType: () => tk, hasEncodingLoss: () => E$, hexToLong: () => Pl, indexToLoc: () => F$, inferDtype: () => Qd, inferFromImplicitShape: () => A$, isBoolean: () => ik, isFunction: () => fr, isInt: () => eo, isNumber: () => ok, isPromise: () => dg, isScalarShape: () => C$, isString: () => ir, isTypedArray: () => Qt, isValidDtype: () => rk, locToIndex: () => D$, makeOnesTypedArray: () => lg, makeZerosNestedTypedArray: () => R$, makeZerosTypedArray: () => Zd, nearestDivisor: () => yd, nearestLargerEven: () => w$, now: () => ju, parseAxisParam: () => ts, randUniform: () => S$, repeatedTry: () => _$, rightPad: () => Vu, shuffle: () => Jw, shuffleCombo: () => x$, sizeFromShape: () => dt, sizeToSquarishShape: () => T$, squeezeShape: () => ek, sum: () => k$, swap: () => bd, tanh: () => N$, toNestedArray: () => Xi, toTypedArray: () => gp });
var cx = ka(n$());
var Xr = cx.default || cx;
function Pl(e) {
  return Xr.fromString(e, true, 16);
}
var dk = Pl("c3a5c85c97cb3127");
var jr = Pl("b492b66fbe98f273");
var on = Pl("9ae16a3b2f90404f");
function om(e) {
  return e.xor(e.shru(47));
}
function pk(e, t, n) {
  let s = e.slice(t, t + n);
  return Xr.fromBytes(Array.from(s), true, true);
}
function lt(e, t) {
  return pk(e, t, 8);
}
function dx(e, t) {
  return pk(e, t, 4);
}
function Bt(e, t) {
  return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
}
function ur(e, t, n = Pl("9ddfea08eb382d69")) {
  let s = e.xor(t).mul(n);
  s = s.xor(s.shru(47));
  let r = t.xor(s).mul(n);
  return r = r.xor(r.shru(47)), r = r.mul(n), r;
}
function H$(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 Gc(e, t, n, s) {
  return H$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
}
function q$(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 ur(a, i, n);
  }
  if (t >= 4) {
    let n = on.add(t * 2), s = dx(e, 0);
    return ur(s.shl(3).add(t), dx(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 om(on.mul(a).xor(dk.mul(i))).mul(on);
  }
  return on;
}
function j$(e, t = e.length) {
  let n = on.add(t * 2), s = lt(e, 0).mul(jr), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on);
  return ur(Bt(s.add(r), 43).add(Bt(a, 30)).add(i), s.add(Bt(r.add(on), 18)).add(a), n);
}
function K$(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 = ur(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 ur(Bt(l.add(c), 43).add(Bt(p, 30)).add(d), l.add(Bt(c.add(s), 18)).add(p), n);
}
function X$(e, t = e.length) {
  let n = Xr.fromNumber(81, true);
  if (t <= 32)
    return t <= 16 ? q$(e, t) : j$(e, t);
  if (t <= 64)
    return K$(e, t);
  let s = n, r = n.mul(jr).add(113), a = om(r.mul(on).add(113)).mul(on), i = [Xr.UZERO, Xr.UZERO], o = [Xr.UZERO, Xr.UZERO];
  s = s.mul(on).add(lt(e, 0));
  let u = 0, l = (t - 1 >> 6) * 64, c = l + (t - 1 & 63) - 63;
  do
    s = Bt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(jr), r = Bt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(jr), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Bt(a.add(o[0]), 33).mul(jr), i = Gc(e, u, i[1].mul(jr), s.add(o[0])), o = Gc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
  while (u !== l);
  let p = jr.add(a.and(255).shl(1));
  return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Bt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Bt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Bt(a.add(o[0]), 33).mul(p), i = Gc(e, u, i[1].mul(p), s.add(o[0])), o = Gc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], ur(ur(i[0], o[0], p).add(om(r).mul(dk)).add(a), ur(i[1], o[1], p).add(s), p);
}
function Y$(e, t) {
  return t === "string" ? zl(e) : gp([e], t);
}
function Q$(e, t) {
  return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
}
function gp(e, t) {
  if (t === "string")
    throw new Error("Cannot convert a string[] to a TypedArray");
  if (Array.isArray(e) && (e = ia(e)), K().getBool("DEBUG") && sk(e, t), Q$(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 ${t}`);
}
function ju() {
  return K().platform.now();
}
function Z$(e, t) {
  return K().platform.fetch(e, t);
}
function zl(e, t = "utf-8") {
  return t = t || "utf-8", K().platform.encode(e, t);
}
function xd(e, t = "utf-8") {
  return t = t || "utf-8", K().platform.decode(e, t);
}
var J$ = class {
  constructor(e, t) {
    this.backendTimer = e, this.logger = t, t == null && (this.logger = new t_());
  }
  profileKernel(e, t, n) {
    let s, r = () => {
      s = n();
    }, a, i = ju();
    if (this.backendTimer.timerAvailable())
      a = this.backendTimer.time(r);
    else {
      r();
      for (let u of s)
        u.dataSync();
      a = Promise.resolve({ kernelMs: ju() - i });
    }
    if (K().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
      for (let u = 0; u < s.length; u++) {
        let l = s[u];
        l.data().then((c) => {
          e_(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 e_(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 t_ = class {
  logKernelProfile(e, t, n, s, r, a) {
    let i = typeof s == "number" ? Vu(`${s}ms`, 9) : s.error, o = Vu(e, 25), u = t.rank, l = t.size, c = Vu(t.shape.toString(), 14), p = "";
    for (let d in r) {
      let h = r[d];
      if (h != null) {
        let f = h.shape || t.shape, m = f.length;
        p += `${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 n_(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 s_(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 (!Ir(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 px = 20;
var $u = 3;
var Wf = 7;
function r_(e, t, n, s) {
  let r = co(t), a = a_(e, t, n, r), i = t.length, o = rd(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 a_(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" ? Du(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], Ru(u[c + p], 0, n).length);
    }
  return i;
}
function Ru(e, t, n) {
  let s;
  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Wf))} + ${parseFloat(e[1].toFixed(Wf))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = hk(e) : s = parseFloat(e.toFixed(Wf)).toString(), Vu(s, t);
}
function hk(e) {
  return e === 0 ? "false" : "true";
}
function rd(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 = Du(e);
      return [Ru(m[0], 0, n)];
    }
    return n === "bool" ? [hk(e[0])] : [e[0].toString()];
  }
  if (u === 1) {
    if (o > px) {
      let g = $u * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - $u) * i, o * i));
      return n === "complex64" && (b = Du(b), y = Du(y)), ["[" + b.map((v, x) => Ru(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Ru(v, r[o - $u + x], n)).join(", ") + "]"];
    }
    let m = n === "complex64" ? Du(e) : Array.from(e);
    return ["[" + m.map((g, b) => Ru(g, r[b], n)).join(", ") + "]"];
  }
  let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
  if (o > px) {
    for (let m = 0; m < $u; m++) {
      let g = m * p, b = g + p;
      d.push(...rd(e.slice(g, b), l, n, c, r, false));
    }
    d.push("...");
    for (let m = o - $u; m < o; m++) {
      let g = m * p, b = g + p;
      d.push(...rd(e.slice(g, b), l, n, c, r, m === o - 1));
    }
  } else
    for (let m = 0; m < o; m++) {
      let g = m * p, b = g + p;
      d.push(...rd(e.slice(g, b), l, n, c, r, m === o - 1));
    }
  let h = u === 2 ? "," : "";
  d[0] = "[" + d[0] + h;
  for (let m = 1; m < d.length - 1; m++)
    d[m] = " " + d[m] + h;
  let f = `,
`;
  for (let m = 2; m < u; m++)
    f += `
`;
  return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d;
}
function Du(e) {
  let t = [];
  for (let n = 0; n < e.length; n += 2)
    t.push([e[n], e[n + 1]]);
  return t;
}
var Wt = class {
  constructor(e, t, n) {
    if (this.dtype = t, this.shape = e.slice(), this.size = dt(e), n != null) {
      let s = n.length;
      F(s === this.size, () => `Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`);
    }
    if (t === "complex64")
      throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
    this.values = n || nk(t, this.size), this.strides = co(e);
  }
  set(e, ...t) {
    t.length === 0 && (t = [0]), F(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
    let n = this.locToIndex(t);
    this.values[n] = e;
  }
  get(...e) {
    e.length === 0 && (e = [0]);
    let t = 0;
    for (let 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 Hi = null;
var i_ = null;
function o_(e) {
  cs = e;
}
function u_(e) {
  Hi = e;
}
function l_(e) {
  i_ = 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 = co(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 Hi.buffer(this.shape, this.dtype, e);
  }
  bufferSync() {
    return Hi.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) => xd(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) => xd(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 Hi.print(this, e);
  }
  clone() {
    return this.throwIfDisposed(), Hi.clone(this);
  }
  toString(e = false) {
    let t = this.dataSync();
    return r_(t, this.shape, this.dtype, e);
  }
  cast(e) {
    return this.throwIfDisposed(), Hi.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 c_() {
  return pg("Tensor", () => et);
}
c_();
var wd = 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 (!Ir(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(wd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
var _s = {};
Ee(_s, { assertTypesMatch: () => yk, getTensorsInContainer: () => Bg, isTensorInList: () => h_, makeTypesMatch: () => xt });
var d_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(d_ || {});
var fk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(fk || {});
var mk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(mk || {});
var gk = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(gk || {});
var bk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(bk || {});
var p_ = { float32: gk, int32: fk, bool: mk, complex64: bk };
function cn(e, t) {
  if (e === "string" || t === "string") {
    if (e === "string" && t === "string")
      return "string";
    throw new Error(`Can not upcast ${e} with ${t}`);
  }
  return p_[e][t];
}
function bp(e) {
  return cn(e, "int32");
}
function xt(e, t) {
  if (e.dtype === t.dtype)
    return [e, t];
  let n = cn(e.dtype, t.dtype);
  return [e.cast(n), t.cast(n)];
}
function yk(e, t) {
  F(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`);
}
function h_(e, t) {
  return t.some((n) => n.id === e.id);
}
function Bg(e) {
  let t = [];
  return vk(e, t, /* @__PURE__ */ new Set()), t;
}
function vk(e, t, n) {
  if (e == null)
    return;
  if (e instanceof et) {
    t.push(e);
    return;
  }
  if (!f_(e))
    return;
  let s = e;
  for (let r in s) {
    let a = s[r];
    n.has(a) || (n.add(a), vk(a, t, n));
  }
}
function f_(e) {
  return Array.isArray(e) || typeof e == "object";
}
function Uf(e) {
  return e.kernelName != null;
}
var hx = class {
  constructor() {
    this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
      return Array.from(new Set(this.kernels.map((e) => e.name)));
    } };
  }
  dispose() {
    for (let e in this.registeredVariables)
      this.registeredVariables[e].dispose();
  }
};
var um = class {
  constructor(e) {
    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new hx();
  }
  async ready() {
    if (this.pendingBackendInit != null)
      return this.pendingBackendInit.then(() => {
      });
    if (this.backendInstance != null)
      return;
    let e = this.getSortedBackends();
    for (let t = 0; t < e.length; t++) {
      let n = e[t];
      if (await this.initializeBackend(n).success) {
        await this.setBackend(n);
        return;
      }
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  get backend() {
    if (this.pendingBackendInit != null)
      throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
      this.setBackend(e);
    }
    return this.backendInstance;
  }
  backendNames() {
    return Object.keys(this.registryFactory);
  }
  findBackend(e) {
    if (!(e in this.registry))
      if (e in this.registryFactory) {
        let { asyncInit: t } = this.initializeBackend(e);
        if (t)
          return null;
      } else
        return null;
    return this.registry[e];
  }
  findBackendFactory(e) {
    return e in this.registryFactory ? this.registryFactory[e].factory : null;
  }
  registerBackend(e, t, n = 1) {
    return e in this.registryFactory ? (ar(`${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 '${e}' not found in registry`);
    if (this.backendName = e, this.registry[e] == null) {
      this.backendInstance = null;
      let { success: t, asyncInit: n } = this.initializeBackend(e);
      if (!(n ? await t : t))
        return false;
    }
    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new J$(this.backendInstance), true;
  }
  setupRegisteredKernels() {
    im(this.backendName).forEach((t) => {
      t.setupFunc != null && t.setupFunc(this.backendInstance);
    });
  }
  disposeRegisteredKernels(e) {
    im(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 ol) && 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, ar(`Initialization of backend ${e} failed`), ar(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 ar(`Initialization of backend ${e} failed`), ar(n.stack || n.message), { success: false, asyncInit: false };
    }
  }
  removeBackend(e) {
    if (!(e in this.registryFactory))
      throw new Error(`${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 um.nextTensorId++;
  }
  nextVariableId() {
    return um.nextVariableId++;
  }
  clone(e) {
    let t = z.runKernel(Ua, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
      let i = "float32", o = { x: a }, u = { dtype: i };
      return z.runKernel($a, o, u);
    } }), r = [];
    return this.addTapeNode(this.state.activeScope.name, n, [t], s, r, {}), t;
  }
  runKernel(e, t, n) {
    if (this.backendName == null && this.backend, !(am(e, this.backendName) != null))
      throw new Error(`Kernel '${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 = Uf(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
    if (Uf(e)) {
      let { kernelName: h, inputs: f, attrs: m } = e;
      this.backendName == null && this.backend;
      let g = am(h, this.backendName);
      F(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 = Uf(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 = lx(e);
    if (s != null) {
      let r = s.inputsToSave || [], a = s.outputsToSave || [], i;
      s.saveAllInputs ? (F(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" && ir(e[0]) && (r = e.map((o) => zl(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 = ak(r);
      this.state.numBytes += u - o.bytes, o.bytes = u;
    }
    return i;
  }
  makeTensorFromDataId(e, t, n, s) {
    n = n || "float32";
    let r = { dataId: e, shape: t, dtype: n };
    return this.makeTensorFromTensorInfo(r, s);
  }
  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 wd(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 * nm(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 wd || 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 * nm(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 = lx(e);
    o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => {
      if (l == null) {
        let p = n[c], d = Zd(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 = Bg(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 (F(t.length > 0, () => "gradients() received an empty list of xs."), n != null && n.dtype !== "float32")
      throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);
    let r = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
    F(r instanceof et, () => "The result y returned by f() must be a tensor.");
    let a = n_(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 ? m_(r.shape) : n, s_(i, a, (u) => this.tidy(u), g_);
      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 F(fr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
      F(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), F(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), F(fr(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];
        F(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(...)."), F(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 = ju(), n = await this.backend.time(e);
    return n.wallMs = ju() - 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 hx();
    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 Vg = um;
Vg.nextTensorId = 0;
Vg.nextVariableId = 0;
function m_(e) {
  let t = lg(dt(e), "float32");
  return z.makeTensor(t, e, "float32");
}
function xk() {
  let e = ck();
  if (e._tfengine == null) {
    let t = new O$(e);
    e._tfengine = new Vg(t);
  }
  return L$(e._tfengine.ENV), o_(() => e._tfengine), e._tfengine;
}
var z = xk();
function g_(e, t) {
  let n = { a: e, b: t };
  return z.runKernel(Cr, n);
}
var yp = {};
Ee(yp, { isBrowser: () => wk, isMobile: () => v_, mockIsMobile: () => y_ });
function b_() {
  return typeof navigator != "undefined" && navigator != null;
}
var lm;
function y_(e) {
  lm = e;
}
function v_(e) {
  if (lm !== void 0)
    return lm;
  if (e || b_()) {
    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 wk() {
  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", () => wk());
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") && kk(e, s, []), s;
}
function kk(e, t, n) {
  if (n = n || [], !Array.isArray(e) && !Qt(e)) {
    F(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);
    return;
  }
  F(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), F(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);
  let s = t.slice(1);
  for (let r = 0; r < e.length; ++r)
    kk(e[r], s, n.concat(r));
}
function fx(e, t, n, s) {
  if (e !== "string_or_numeric") {
    if (e == null)
      throw new Error("Expected dtype cannot be null.");
    if (e !== "numeric" && e !== t || e === "numeric" && t === "string")
      throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`);
  }
}
function _(e, t, n, s = "numeric") {
  if (e instanceof et)
    return fx(s, e.dtype, t, n), e;
  let r = Qd(e);
  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), fx(s, r, t, n), e == null || !Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
    let u = e == null ? "null" : e.constructor.name;
    throw new Error(`Argument '${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" ? gp(e, r) : ia(e, [], true);
  return z.makeTensor(o, a, r);
}
function Ku(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 x_ = "__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 + x_;
  let r = (...a) => {
    z.startScope(n);
    try {
      let i = s(...a);
      return dg(i) && console.error("Cannot return a Promise inside of tidy."), z.endScope(i), i;
    } catch (i) {
      throw z.endScope(null), i;
    }
  };
  return Object.defineProperty(r, "name", { value: n, configurable: true }), r;
}
function w_(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 z.runKernel(ep, r);
}
var mr = L({ complex_: w_ });
function $r(e, t, n, s) {
  if (s == null && (s = Qd(e)), s === "complex64")
    throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
  if (!Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
  if (t != null) {
    cg(t);
    let r = dt(t), a = dt(n);
    F(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
    for (let i = 0; i < n.length; ++i) {
      let o = n[i], u = i === n.length - 1 ? o !== dt(t.slice(i)) : true;
      F(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
    }
  }
  return !Qt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? gp(e, s) : ia(e, [], true), z.makeTensor(e, t, s);
}
function ms(e, t, n) {
  let s = Rs(e, n);
  return $r(e, t, s, n);
}
var cm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var kd = 4;
async function k_(e, t) {
  let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e);
  for (let i = 0; i < r.length; ++i) {
    let o = r[i], u = Array.isArray(e) ? e[i].tensor : e[o];
    if (u.dtype !== "float32" && u.dtype !== "int32" && u.dtype !== "bool" && u.dtype !== "string" && u.dtype !== "complex64")
      throw new Error(`Unsupported dtype in weight '${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) + kd * 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 += kd, 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: S_(a), specs: n };
}
function Sk(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 = cm[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 = __()), 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}': ${o}`);
      r += l * d;
    } else if (o === "string") {
      let p = dt(a.shape);
      c = [];
      for (let d = 0; d < p; d++) {
        let h = new Uint32Array(e.slice(r, r + kd))[0];
        r += kd;
        let f = new Uint8Array(e.slice(r, r + h));
        c.push(f), r += h;
      }
    } else {
      let p = cm[o], d = e.slice(r, r + l * p);
      if (o === "float32")
        c = new Float32Array(d);
      else if (o === "int32")
        c = new Int32Array(d);
      else if (o === "bool")
        c = new Uint8Array(d);
      else if (o === "complex64") {
        c = new Float32Array(d);
        let h = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2);
        for (let b = 0; b < h.length; b++)
          h[b] = c[b * 2], f[b] = c[b * 2 + 1];
        let m = ms(h, u, "float32"), g = ms(f, u, "float32");
        n[i] = mr(m, g), m.dispose(), g.dispose();
      } else
        throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
      r += l * p;
    }
    o !== "complex64" && (n[i] = ms(c, u, o));
  }
  return n;
}
function S_(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 Wg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function mx(e) {
  return Wg ? Buffer.byteLength(e) : new Blob([e]).size;
}
function I_(e) {
  if (Wg)
    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 C_(e) {
  if (Wg) {
    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 Ug(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 gx(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 Ik(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 Gg(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 Ml(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 : mx(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : mx(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
}
function N_() {
  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 T_() {
  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 $_() {
  let e = new Uint32Array(64);
  for (let t = 0; t < 64; t++)
    e[t] = 1024;
  return e[0] = e[32] = 0, e;
}
function __() {
  let e = N_(), t = T_(), 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 wt = class {
  constructor() {
    this.saveRouters = [], this.loadRouters = [];
  }
  static getInstance() {
    return wt.instance == null && (wt.instance = new wt()), wt.instance;
  }
  static registerSaveRouter(e) {
    wt.getInstance().saveRouters.push(e);
  }
  static registerLoadRouter(e) {
    wt.getInstance().loadRouters.push(e);
  }
  static getSaveHandlers(e) {
    return wt.getHandlers(e, "save");
  }
  static getLoadHandlers(e, t) {
    return wt.getHandlers(e, "load", t);
  }
  static getHandlers(e, t, n) {
    let s = [];
    return (t === "load" ? wt.getInstance().loadRouters : wt.getInstance().saveRouters).forEach((a) => {
      let i = a(e, n);
      i !== null && s.push(i);
    }), s;
  }
};
var A_ = (e) => wt.registerSaveRouter(e);
var E_ = (e) => wt.registerLoadRouter(e);
var R_ = (e) => wt.getSaveHandlers(e);
var D_ = (e, t) => wt.getLoadHandlers(e, t);
var dm = "tensorflowjs";
var pm = 1;
var Jr = "models_store";
var or = "model_info_store";
function Ck() {
  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 hm(e) {
  let t = e.result;
  t.createObjectStore(Jr, { keyPath: "modelPath" }), t.createObjectStore(or, { keyPath: "modelPath" });
}
var ca = class {
  constructor(e) {
    if (this.indexedDB = Ck(), 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(dm, pm);
      r.onupgradeneeded = () => hm(r), r.onsuccess = () => {
        let a = r.result;
        if (t == null) {
          let i = a.transaction(Jr, "readonly"), u = i.objectStore(Jr).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 = Ml(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
          l.onsuccess = () => {
            c = a.transaction(Jr, "readwrite");
            let d = c.objectStore(Jr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
            d.onsuccess = () => n({ modelArtifactsInfo: i }), d.onerror = (h) => {
              u = o.objectStore(or);
              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);
    });
  }
};
ca.URL_SCHEME = "indexeddb://";
var Nk = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ca.URL_SCHEME) ? F_(e.slice(ca.URL_SCHEME.length)) : null;
wt.registerSaveRouter(Nk);
wt.registerLoadRouter(Nk);
function F_(e) {
  return new ca(e);
}
function O_(e) {
  return e.startsWith(ca.URL_SCHEME) ? e.slice(ca.URL_SCHEME.length) : e;
}
var P_ = class {
  constructor() {
    this.indexedDB = Ck();
  }
  async listModels() {
    return new Promise((e, t) => {
      let n = this.indexedDB.open(dm, pm);
      n.onupgradeneeded = () => hm(n), n.onsuccess = () => {
        let s = n.result, r = s.transaction(or, "readonly"), i = r.objectStore(or).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 = O_(e), new Promise((t, n) => {
      let s = this.indexedDB.open(dm, pm);
      s.onupgradeneeded = () => hm(s), s.onsuccess = () => {
        let r = s.result, a = r.transaction(or, "readwrite"), i = a.objectStore(or), 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(Jr, "readwrite");
              let d = u.objectStore(Jr).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 qi = "tensorflowjs_models";
var Tk = "info";
var z_ = "model_topology";
var M_ = "weight_specs";
var L_ = "weight_data";
var B_ = "model_metadata";
function $k(e) {
  return { info: [qi, e, Tk].join(Us), topology: [qi, e, z_].join(Us), weightSpecs: [qi, e, M_].join(Us), weightData: [qi, e, L_].join(Us), modelMetadata: [qi, e, B_].join(Us) };
}
function _k(e) {
  for (let t of Object.values(e))
    window.localStorage.removeItem(t);
}
function V_(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 W_(e) {
  return e.startsWith(da.URL_SCHEME) ? e.slice(da.URL_SCHEME.length) : e;
}
var da = 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 = $k(this.modelPath);
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
    {
      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Ml(e);
      try {
        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, I_(e.weightData));
        let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
        return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(r)), { modelArtifactsInfo: s };
      } catch (r) {
        throw _k(this.keys), new Error(`Failed to save model '${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 = C_(a), t;
  }
};
da.URL_SCHEME = "localstorage://";
var Ak = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(da.URL_SCHEME) ? U_(e.slice(da.URL_SCHEME.length)) : null;
wt.registerSaveRouter(Ak);
wt.registerLoadRouter(Ak);
function U_(e) {
  return new da(e);
}
var G_ = class {
  constructor() {
    F(K().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), F(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
  }
  async listModels() {
    let e = {}, t = qi + Us, n = Us + Tk;
    for (let s = 0; s < this.LS.length; ++s) {
      let r = this.LS.key(s);
      if (r.startsWith(t) && r.endsWith(n)) {
        let a = V_(r);
        e[a] = JSON.parse(this.LS.getItem(r));
      }
    }
    return e;
  }
  async removeModel(e) {
    e = W_(e);
    let t = $k(e);
    if (this.LS.getItem(t.info) == null)
      throw new Error(`Cannot find model at path '${e}'`);
    let n = JSON.parse(this.LS.getItem(t.info));
    return _k(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) {
    F(e != null, () => "scheme must not be undefined or null."), e.endsWith(Yi) && (e = e.slice(0, e.indexOf(Yi))), F(e.length > 0, () => "scheme must not be an empty string.");
    let n = zn.getInstance();
    F(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
  }
  static getManager(e) {
    let t = this.getInstance().managers[e];
    if (t == null)
      throw new Error(`Cannot find model manager for scheme '${e}'`);
    return t;
  }
  static getSchemes() {
    return Object.keys(this.getInstance().managers);
  }
};
function ad(e) {
  if (e.indexOf(Yi) === -1)
    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${zn.getSchemes().join(",")}`);
  return { scheme: e.split(Yi)[0], path: e.split(Yi)[1] };
}
async function Ek(e, t, n = false) {
  F(e !== t, () => `Old path and new path are the same: '${e}'`);
  let s = wt.getLoadHandlers(e);
  F(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), F(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
  let r = s[0], a = wt.getSaveHandlers(t);
  F(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), F(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);
  let i = a[0], o = ad(e).scheme, u = ad(e).path, l = o === ad(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 H_() {
  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 q_(e) {
  let t = ad(e);
  return zn.getManager(t.scheme).removeModel(t.path);
}
async function j_(e, t) {
  return Ek(e, t, false);
}
async function K_(e, t) {
  return Ek(e, t, true);
}
var X_ = 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 ${t}`);
    return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e);
  }
  decode(e, t) {
    return new TextDecoder(t).decode(e);
  }
};
if (K().get("IS_BROWSER")) {
  K().setPlatform("browser", new X_());
  try {
    zn.registerManager(da.URL_SCHEME, new G_());
  } catch (e) {
  }
  try {
    zn.registerManager(ca.URL_SCHEME, new P_());
  } catch (e) {
  }
}
var Y_ = { importFetch: () => s$() };
var Gf;
var Q_ = class {
  constructor() {
    this.util = r$(), this.textEncoder = new this.util.TextEncoder();
  }
  fetch(e, t) {
    return K().global.fetch != null ? K().global.fetch(e, t) : (Gf == null && (Gf = Y_.importFetch()), Gf(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 Q_());
function Ae(e, t = "float32", n) {
  return t = t || "float32", cg(e), new Wt(e, t, n);
}
function Z_(e, t) {
  let n = _(e, "x", "cast");
  if (!rk(t))
    throw new Error(`Failed to cast to unknown dtype ${t}`);
  if (t === "string" && n.dtype !== "string" || t !== "string" && n.dtype === "string")
    throw new Error("Only strings can be casted to strings");
  let s = { x: n }, r = { dtype: t };
  return z.runKernel($a, s, r);
}
var le = L({ cast_: Z_ });
function J_(e) {
  let n = { x: _(e, "x", "clone", "string_or_numeric") };
  return z.runKernel(Ua, n);
}
var lr = L({ clone_: J_ });
function eA(e, t = false) {
  console.log(e.toString(t));
}
xk();
var tA = { buffer: Ae, cast: le, clone: lr, print: eA };
u_(tA);
var An = {};
Ee(An, { browserFiles: () => uA, browserHTTPRequest: () => hA, concatenateArrayBuffers: () => Ug, copyModel: () => j_, decodeWeights: () => Sk, encodeWeights: () => k_, fromMemory: () => mA, fromMemorySync: () => Pk, getLoadHandlers: () => D_, getModelArtifactsForJSON: () => Gg, getModelArtifactsInfoForJSON: () => Ml, getSaveHandlers: () => R_, http: () => qg, isHTTPScheme: () => mm, listModels: () => H_, loadWeights: () => lA, moveModel: () => K_, registerLoadRouter: () => E_, registerSaveRouter: () => A_, removeModel: () => q_, weightsLoaderFactory: () => Dk, withSaveHandler: () => gA, withSaveHandlerSync: () => bA });
var nA = "model";
var sA = ".json";
var rA = ".weights.bin";
function bx(e) {
  return new Promise((t) => setTimeout(t)).then(e);
}
var fm = class {
  constructor(e) {
    if (!K().getBool("IS_BROWSER"))
      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
    e.startsWith(fm.URL_SCHEME) && (e = e.slice(fm.URL_SCHEME.length)), (e == null || e.length === 0) && (e = nA), this.modelJsonFileName = e + sA, this.weightDataFileName = e + rA;
  }
  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 = Ik(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 bx(() => 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 bx(() => i.dispatchEvent(new MouseEvent("click")));
      }
      return { modelArtifactsInfo: Ml(e) };
    }
  }
};
var Sd = fm;
Sd.URL_SCHEME = "downloads://";
var aA = 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 ${this.jsonFile.name}`));
          return;
        }
        if (this.weightsFiles.length === 0) {
          e({ modelTopology: a });
          return;
        }
        let o = Gg(r, (u) => this.loadWeights(u));
        e(o);
      }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${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, Ug(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 '${e}'.`), r.readAsArrayBuffer(t);
    });
  }
  checkManifestAndWeightFiles(e) {
    let t = [], n = this.weightsFiles.map((r) => gx(r.name)), s = {};
    for (let r of e)
      r.paths.forEach((a) => {
        let i = gx(a);
        if (t.indexOf(i) !== -1)
          throw new Error(`Duplicate file basename found in weights manifest: '${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 iA = (e) => K().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(Sd.URL_SCHEME) ? oA(e.slice(Sd.URL_SCHEME.length)) : null;
wt.registerSaveRouter(iA);
function oA(e = "model") {
  return new Sd(e);
}
function uA(e) {
  return new aA(e);
}
function yx(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) {
    F(u != null && Array.isArray(u) && u.length > 0, () => "promises must be a none empty array");
  }
  function o(u, l) {
    F(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${u}`), F(l >= 0 && l <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${l}`), F(l >= u, () => `startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`);
  }
  return Promise.all(e.map(a));
}
async function Rk(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 yx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
  return t.onProgress == null ? await Promise.all(o) : await yx(o, t.onProgress, u, l);
}
async function lA(e, t = "", n, s) {
  return Dk((i) => Rk(i, { requestInit: s }))(e, t, n);
}
function Dk(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 = cm[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 = Sk(k, [x.manifestEntry]);
        for (let $ in I)
          p[$] = I[$];
      }), d += f;
    }), p;
  };
}
var cA = "application/octet-stream";
var dA = "application/json";
var Hg = class {
  constructor(e, t) {
    if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (F(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = K().platform.fetch, F(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && F(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
    this.requestInit = t.requestInit || {};
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
    let t = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
    t.body = new FormData();
    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = Ik(e, n);
    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: dA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: cA }), "model.weights.bin");
    let r = await this.fetch(this.path, t);
    if (r.ok)
      return { modelArtifactsInfo: Ml(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 Gg(t, (r) => this.loadWeights(r));
  }
  async loadWeights(e) {
    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = pA(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 Rk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
    return [a, Ug(u)];
  }
};
Hg.URL_SCHEME_REGEX = /^https?:\/\//;
function pA(e) {
  let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : "";
  return [s + "/", r];
}
function mm(e) {
  return e.match(Hg.URL_SCHEME_REGEX) != null;
}
var Fk = (e, t) => {
  if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null))
    return null;
  {
    let n = true;
    if (Array.isArray(e) ? n = e.every((s) => mm(s)) : n = mm(e), n)
      return qg(e, t);
  }
  return null;
};
wt.registerSaveRouter(Fk);
wt.registerLoadRouter(Fk);
function qg(e, t) {
  return new Hg(e, t);
}
function hA(e, t) {
  return qg(e, t);
}
var Hf = class {
  constructor(e) {
    this.modelArtifacts = e;
  }
  load() {
    return this.modelArtifacts;
  }
};
var Ok = class {
  constructor(e) {
    this.saveHandler = e;
  }
  save(e) {
    return this.saveHandler(e);
  }
};
var fA = class {
  constructor(e) {
    e.load && (this.load = () => Promise.resolve(e.load())), e.save && (this.save = (t) => Promise.resolve(e.save(t)));
  }
};
function mA(e, t, n, s) {
  let r = arguments;
  return new fA(Pk(...r));
}
function Pk(e, t, n, s) {
  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Hf(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 Hf({ 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 Hf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
}
function gA(e) {
  return new Ok(e);
}
function bA(e) {
  return new Ok(e);
}
var yA = {};
Ee(yA, { confusionMatrix: () => TA });
function vA(e, t, n = false, s = false) {
  let r = _(e, "a", "matMul"), a = _(t, "b", "matMul");
  [r, a] = xt(r, a);
  let i = { a: r, b: a }, o = { transposeA: n, transposeB: s };
  return z.runKernel(Ta, i, o);
}
var Ve = L({ matMul_: vA });
function xA(e, t, n = 1, s = 0) {
  if (t < 2)
    throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
  let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
  return z.runKernel(Do, a, i);
}
var Id = L({ oneHot_: xA });
function fpe() {
  K().set("PROD", true);
}
function mpe() {
  K().set("DEBUG", true);
}
function gpe() {
  K().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function zk(e) {
  K().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(e + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
l_(zk);
function bpe() {
  z.disposeVariables();
}
function ds() {
  return z;
}
function gm() {
  return z.memory();
}
function ype(e) {
  return z.profile(e);
}
function q(e, t) {
  return z.tidy(e, t);
}
function De(e) {
  Bg(e).forEach((n) => n.dispose());
}
function qt(e) {
  return z.keep(e);
}
function vpe(e) {
  return z.time(e);
}
function xpe(e) {
  return z.setBackend(e);
}
function wpe() {
  return z.ready();
}
function kpe() {
  return z.backendName;
}
function Spe(e) {
  z.removeBackend(e);
}
function Ipe(e) {
  return z.findBackend(e);
}
function Cpe(e) {
  return z.findBackendFactory(e);
}
function vp(e, t, n = 1) {
  return z.registerBackend(e, t, n);
}
function wA() {
  return z.backend;
}
function Npe(e, t) {
  K().setPlatform(e, t);
}
function kA(e) {
  let n = { input: _(e, "input", "imag") };
  return z.runKernel(ap, n);
}
var xp = L({ imag_: kA });
function SA(e) {
  let n = { x: _(e, "x", "neg") };
  return z.runKernel($o, n);
}
var vt = L({ neg_: SA });
function IA(e) {
  let n = { input: _(e, "input", "real") };
  return z.runKernel(lp, n);
}
var Xu = L({ real_: IA });
function CA(e, t, n) {
  let s = _(e, "x", "transpose");
  if (t == null && (t = s.shape.map((i, o) => o).reverse()), F(s.rank === t.length, () => `Error in transpose: rank of input ${s.rank} must match length of perm ${t}.`), t.forEach((i) => {
    F(i >= 0 && i < s.rank, () => `All entries in 'perm' must be between 0 and ${s.rank - 1} but got ${t}`);
  }), s.rank <= 1)
    return s.clone();
  let r = { x: s }, a = { perm: t };
  return s.dtype === "complex64" ? q(() => {
    let i = Xu(s), o = xp(s);
    return i = z.runKernel(Hs, { x: i }, a), o = z.runKernel(Hs, { x: o }, a), n && (o = vt(o)), mr(i, o);
  }) : z.runKernel(Hs, r, a);
}
var Ge = L({ transpose_: CA });
function NA(e, t, n) {
  let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix");
  F(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), F(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), F(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), F(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.`), F(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
  let a = Id(le(s, "int32"), n), i = Id(le(r, "int32"), n), o = Ge(a), u = Ve(o, i);
  return le(u, "int32");
}
var TA = L({ confusionMatrix_: NA });
var Qo = {};
Ee(Qo, { assertAndGetBroadcastShape: () => rt, getBroadcastDims: () => Mk, getReductionAxes: () => At });
function Mk(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 At(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 rt(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 Lk = {};
Ee(Lk, { fromPixels: () => OA, fromPixelsAsync: () => DA, toPixels: () => FA });
function $A(e, t, n) {
  if (Sa(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 $r(e, t, s, n);
}
var Gr;
function Bk(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 (am(vd, z.backendName) != null) {
    let f = { pixels: e }, m = { numChannels: t };
    return z.runKernel(vd, 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 (Gr == null)
      if (typeof document == "undefined")
        if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
          Gr = new OffscreenCanvas(1, 1).getContext("2d");
        else
          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
      else
        Gr = document.createElement("canvas").getContext("2d");
    Gr.canvas.width = l, Gr.canvas.height = c, Gr.drawImage(e, 0, 0, l, c), p = Gr.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 $A(d, [c, l, t], "int32");
}
function _A(e) {
  return e != null && e.data instanceof Uint8Array;
}
function AA() {
  return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function EA(e) {
  return e != null && e.width !== 0 && e.height !== 0;
}
function RA(e) {
  return AA() && !(e instanceof ImageBitmap) && EA(e) && !_A(e);
}
async function DA(e, t = 3) {
  let n = null;
  if (K().getBool("WRAP_TO_IMAGEBITMAP") && RA(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 Bk(n, t);
}
async function FA(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 OA = L({ fromPixels_: Bk });
var Vk = {};
Ee(Vk, { prepareAndValidate: () => Wk });
function Wk(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 = [...co(e.shape).map((p) => p / l), 1].slice(0, a);
  return [u, i, l, c];
}
var Uk = {};
Ee(Uk, { calculateShapes: () => Gk, validateInput: () => Kg, validateUpdateShape: () => jg });
function jg(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 Kg(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: ${e.shape}`);
  }
  jg(n, t, e);
}
function Gk(e, t, n) {
  let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
  for (let p = r; p < a; ++p)
    i *= n[p];
  let o = r < 1 ? 1 : r, u = dt(t.shape) / o, l = [...co(n.slice(0, r)), 1], c = dt(n);
  return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
}
var kt = {};
Ee(kt, { assertParamsValid: () => zA, computeFlatOffset: () => WA, computeOutShape: () => LA, getNormalizedAxes: () => BA, isSliceContinous: () => VA, maskToAxes: () => MA, parseSliceParams: () => Jk, sliceInfo: () => UA, startForAxis: () => Qk, startIndicesWithElidedDims: () => Kk, stopForAxis: () => Zk, stopIndicesWithElidedDims: () => Xk, stridesForAxis: () => Yk, stridesWithElidedDims: () => Hk });
var bm = -2;
var PA = -1;
function zA(e, t, n) {
  let s = e.shape.length;
  F(s === t.length, () => `Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`), F(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)
    F(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 MA(e) {
  let t = [], n = 0;
  for (; e > 0; )
    e & 1 && t.push(n), e /= 2, n++;
  return t;
}
function LA(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 Hk(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 qk(e, t, n) {
  return n <= e ? n : n - (t - 1);
}
function jk(e, t) {
  let n = [];
  for (let s = 0; s < e; s++)
    n.push(t + s);
  return n;
}
function BA(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 = Kk(i, h, f, s, e), p = Xk(o, h, f, r, e), d = Hk(a, h, f, e);
  } else
    for (let h = 0; h < l; h++)
      c[h] = Qk(i, s, a, e, h, u), p[h] = Zk(o, r, a, e, h, u), d[h] = Yk(a, h, u);
  return { begin: c, end: p, strides: d };
}
function Kk(e, t, n, s, r) {
  let a = [...r], i = jk(n, t);
  for (let o = 0; o < a.length; o++)
    if (i.indexOf(o) > -1)
      a[o] = 0;
    else {
      let u = qk(t, n, o), l = s[u];
      e & 1 << u && (l = 0), a[o] = l;
    }
  return a;
}
function Xk(e, t, n, s, r) {
  let a = [...r], i = jk(n, t);
  for (let o = 0; o < a.length; o++)
    if (i.indexOf(o) > -1)
      a[o] = Number.MAX_SAFE_INTEGER;
    else {
      let u = qk(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] = Hu(0, a[o], r[o]);
  }
  return a;
}
function Yk(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 = Hu(0, i, u - 1), i;
}
function Zk(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 = Hu(0, i, u) : i = Hu(-1, i, u - 1), i;
}
function VA(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 WA(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 Jk(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) => {
    F(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 : (F(i === -1, () => `Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`), e.shape[o] - s[o])), [s, a];
}
function UA(e, t, n, s, r, a, i, o, u) {
  let l;
  if (s == null ? (l = new Array(t.length), l.fill(1)) : l = s, i != null && (i & i - 1) !== 0)
    throw new Error("Multiple ellipses in slice is not allowed.");
  let c = false, p = { dims: l.length, numAddAxisAfterEllipsis: 0, begin: t.slice(), end: n.slice(), strides: l.slice(), beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
  for (let v = 0; v < p.dims; v++)
    c && (1 << v & o) !== 0 && p.numAddAxisAfterEllipsis++, 1 << v & i && (c = true);
  c || (p.ellipsisMask |= 1 << p.dims, p.dims++);
  let d = { dims: e.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
  GA(p, d);
  let h = true, f = true, m = true, g = [], b = [];
  for (let v = 0; v < e.length; ++v) {
    if (d.strides[v] === 0)
      throw Error(`strides[${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 R = !!(d.beginMask & 1 << v && d.endMask & 1 << v);
    if (d.beginValid && d.endValid) {
      if (x) {
        let O = d.begin[v] < 0 ? k + d.begin[v] : d.begin[v];
        if (d.begin[v] = O, d.end[v] = d.begin[v] + 1, O < 0 || O >= k)
          throw Error(`slice index ${d.begin[v]} of dimension ${v} out of bounds.`);
      } else
        d.begin[v] = vx(d.begin[v], 0, d.strides[v], k, I, $), d.end[v] = vx(d.end[v], 1, d.strides[v], k, I, $);
      let A = d.strides[v] === 1 && d.begin[v] === 0 && d.end[v] === k;
      h = h && A, f = f && (v === 0 && d.strides[v] === 1 || A);
    } else
      h = h && d.strides[v] === 1 && R, f = f && (v === 0 && d.strides[v] === 1 || R);
    let E, P = false;
    if (d.beginValid && d.endValid ? (E = d.end[v] - d.begin[v], P = true) : x ? (E = 1, P = true) : R && k >= 0 && (d.strides[v] < 0 ? E = -k : E = k, P = true), P) {
      let A;
      E === 0 || E < 0 != d.strides[v] < 0 ? A = 0 : A = Math.trunc(E / d.strides[v]) + (E % d.strides[v] !== 0 ? 1 : 0), g.push(A);
    } 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 === bm && b.push(1);
  }
  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== bm), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
}
function GA(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(bm), 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(PA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
    }
}
function vx(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 = {};
Ee(re, { Serializable: () => eS, SerializationMap: () => Yr, registerClass: () => _r });
var eS = class {
  getClassName() {
    return this.constructor.className;
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
var Yr = class {
  constructor() {
    this.classNameMap = {};
  }
  static getMap() {
    return Yr.instance == null && (Yr.instance = new Yr()), Yr.instance;
  }
  static register(e) {
    Yr.getMap().classNameMap[e.className] = [e, e.fromConfig];
  }
};
function _r(e) {
  F(e.className != null, () => "Class being registered does not have the static className property defined."), F(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), F(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Yr.register(e);
}
var HA = {};
Ee(HA, { TEST_EPSILON_FLOAT16: () => tS, encodeStrings: () => nS, expectArrayBuffersEqual: () => ZA, expectArraysClose: () => jA, expectArraysEqual: () => XA, expectNumbersClose: () => YA, expectPromiseToFail: () => KA, expectValuesInRange: () => QA, testEpsilon: () => Xg });
var qA = 1e-3;
var tS = 0.1;
function jA(e, t, n) {
  return n == null && (n = Xg()), ym(e, t, (s, r) => Yg(s, r, n));
}
function Xg() {
  return z.backend.floatPrecision() === 32 ? qA : tS;
}
function ym(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: ${o}`);
  }
  if (Array.isArray(e) && Array.isArray(t)) {
    let i = Rs(e), o = Rs(t);
    if (!Ir(i, o))
      throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`);
  }
  let r = Qt(e) ? e : ia(e), a = Qt(t) ? t : ia(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 KA(e, t) {
  e().then(() => t.fail(), () => t());
}
function XA(e, t) {
  let n = typeof t == "string" || typeof t == "number" || typeof t == "boolean" ? [t] : t;
  return ir(e) || ir(e[0]) || ir(t) || ir(t[0]) ? ym(e, n, (s, r) => s == r) : ym(e, t, (s, r) => Yg(s, r, 0));
}
function YA(e, t, n) {
  if (n == null && (n = Xg()), !Yg(e, t, n))
    throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`);
}
function Yg(e, t, n) {
  return !isFinite(e) && !isFinite(t) ? true : !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);
}
function QA(e, t, n) {
  for (let s = 0; s < e.length; s++)
    if (e[s] < t || e[s] > n)
      throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`);
}
function ZA(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 nS(e) {
  for (let t = 0; t < e.length; t++) {
    let n = e[t];
    Array.isArray(n) ? nS(n) : e[t] = zl(n);
  }
  return e;
}
var Tpe = "0.0.0";
function JA(e, t) {
  let n = _(e, "a", "add"), s = _(t, "b", "add");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(Cr, r);
}
var ie = L({ add_: JA });
function eE(e, t) {
  let n = _(e, "a", "floorDiv"), s = _(t, "b", "floorDiv");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(Ba, r);
}
var sS = L({ floorDiv_: eE });
function tE(e, t) {
  let n = _(e, "a", "div"), s = _(t, "b", "div");
  if ([n, s] = xt(n, s), n.dtype === "int32" && s.dtype === "int32")
    return sS(n, s);
  let r = { a: n, b: s }, a = {};
  return z.runKernel(Pa, r, a);
}
var xe = L({ div_: tE });
function nE(e, t) {
  let n = _(e, "a", "mul"), s = _(t, "b", "mul");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(Ja, r);
}
var V = L({ mul_: nE });
function sE(e) {
  let t = _(e, "x", "abs");
  if (t.dtype === "complex64") {
    let n = { x: t };
    return z.runKernel(tp, n);
  } else {
    let n = { x: t };
    return z.runKernel(po, n);
  }
}
var Lt = L({ abs_: sE });
function rE(e) {
  let n = { x: _(e, "x", "acos") };
  return z.runKernel(ul, n);
}
var aE = L({ acos_: rE });
function iE(e) {
  let n = { x: _(e, "x", "acosh") };
  return z.runKernel(ll, n);
}
var oE = L({ acosh_: iE });
function uE(e) {
  F(Array.isArray(e), () => "The argument passed to tf.addN() must be a list of tensors"), F(e.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${e.length}`);
  let t = e.map((r, a) => _(r, `tensors${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 (!Ir(r.shape, n.shape))
      throw new Error("All tensors passed to tf.addN() must have the same shape");
  });
  let s = t;
  return z.runKernel(Ia, s);
}
var lE = L({ addN_: uE });
function cE(e, t = null, n = false) {
  let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
  return z.runKernel(cl, r, a);
}
var rS = L({ all_: cE });
function dE(e, t = null, n = false) {
  let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
  return z.runKernel(dl, r, a);
}
var vm = L({ any_: dE });
function pE(e, t = 0) {
  let s = { x: _(e, "x", "argMax") }, r = { axis: t };
  return z.runKernel(Ca, s, r);
}
var Yu = L({ argMax_: pE });
function hE(e, t = 0) {
  let s = { x: _(e, "x", "argMin") }, r = { axis: t };
  return z.runKernel(pl, s, r);
}
var fE = L({ argMin_: hE });
function mE(e) {
  let n = { x: _(e, "x", "asin") };
  return z.runKernel(hl, n);
}
var gE = L({ asin_: mE });
function bE(e) {
  let n = { x: _(e, "x", "asinh") };
  return z.runKernel(fl, n);
}
var yE = L({ asinh_: bE });
function vE(e) {
  let n = { x: _(e, "x", "atan") };
  return z.runKernel(ml, n);
}
var xE = L({ atan_: vE });
function wE(e, t) {
  let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(bl, r);
}
var kE = L({ atan2_: wE });
function SE(e) {
  let n = { x: _(e, "x", "atanh") };
  return z.runKernel(gl, n);
}
var IE = L({ atanh_: SE });
function CE(e, t, n, s, r = "NHWC", a) {
  let i = e[3], o = [...t, i], u = oS(r);
  return Ll(e, o, n, a, s, null, null, u);
}
function aS(e, t, n, s, r, a, i = "channelsLast") {
  let [o, u] = Cd(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 Ll(e, l, n, s, r, a, false, i);
}
function NE(e, t, n, s, r, a, i = "NDHWC") {
  let [o, u, l] = xm(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 iS(e, c, n, s, r, false, p, a);
}
function Ll(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] = Cd(n), [b, y] = Cd(s), v = Qi(d, b), x = Qi(h, y), { padInfo: k, outHeight: I, outWidth: $ } = _E(r, l, c, m, g, v, x, a, o), R = i ? f * p : f, E;
  return o === "channelsFirst" ? E = [u, R, I, $] : o === "channelsLast" && (E = [u, I, $, R]), { batchSize: u, dataFormat: o, inHeight: l, inWidth: c, inChannels: p, outHeight: I, outWidth: $, outChannels: R, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: E, filterShape: t };
}
function iS(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] = xm(n), [x, k, I] = xm(s), $ = Qi(h, x), R = Qi(f, k), E = Qi(m, I), { padInfo: P, outDepth: A, outHeight: O, outWidth: T } = AE(r, l, c, p, b, y, v, $, R, E, o), M = a ? g * d : g, W;
  return i === "channelsFirst" ? W = [u, M, A, O, T] : i === "channelsLast" && (W = [u, A, O, T, M]), { batchSize: u, dataFormat: i, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: A, outHeight: O, outWidth: T, outChannels: M, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: $, effectiveFilterHeight: R, effectiveFilterWidth: E, dilationDepth: x, dilationHeight: k, dilationWidth: I, inShape: e, outShape: W, filterShape: t };
}
function TE(e, t, n, s, r) {
  s == null && (s = Qg(e, t, n));
  let a = e[0], i = e[1], o = na((a - t + 2 * s) / n + 1, r), u = na((i - t + 2 * s) / n + 1, r);
  return [o, u];
}
function $E(e, t, n, s, r, a) {
  r == null && (r = Qg(e, t, s));
  let i = e[0], o = e[1], u = e[2], l = na((i - t + 2 * r) / s + 1, a), c = na((o - t + 2 * r) / s + 1, a), p = na((u - t + 2 * r) / s + 1, a);
  return [l, c, p, n];
}
function Qg(e, t, n, s = 1) {
  let r = Qi(t, s);
  return Math.floor((e[0] * (n - 1) - n + r) / 2);
}
function Cd(e) {
  return typeof e == "number" ? [e, e, e] : e.length === 2 ? [e[0], e[1], 1] : e;
}
function xm(e) {
  return typeof e == "number" ? [e, e, e] : e;
}
function Qi(e, t) {
  return t <= 1 ? e : e + (e - 1) * (t - 1);
}
function _E(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 = TE([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 = na((t - a + d + h) / s + 1, o), p = na((n - i + f + m) / r + 1, o);
  } else
    throw Error(`Unknown padding parameter: ${e}`);
  return { padInfo: l, outHeight: c, outWidth: p };
}
function AE(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 = $E([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 na(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 gr(e) {
  let [t, n, s] = Cd(e);
  return t === 1 && n === 1 && s === 1;
}
function Ps(e, t) {
  return gr(e) || gr(t);
}
function oS(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")
      F(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) => {
          F(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 EE(e, t) {
  let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
  return z.runKernel(Oo, s, r);
}
var U = L({ reshape_: EE });
function RE(e, t, n, s, r) {
  let a = _(e, "x", "avgPool", "float32"), i = 1;
  F(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]])), F(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 = z.runKernel(Na, l, c);
  return p = le(p, a.dtype), u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var Zg = L({ avgPool_: RE });
function DE(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]])), F(o.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`), F(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 = z.runKernel(Jd, 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 uS = L({ avgPool3d_: DE });
function FE(e, t = 0) {
  F(e.length >= 1, () => "Pass at least one tensor to concat");
  let n = Ku(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 lr(n[0]);
  let s = n, r = { axis: t };
  return z.runKernel(fo, s, r);
}
var Ot = L({ concat_: FE });
function OE(e) {
  let n = { x: _(e, "x", "sigmoid", "float32") };
  return z.runKernel(li, n);
}
var qs = L({ sigmoid_: OE });
function PE(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 z.runKernel(Bo, r, a);
}
var qe = L({ slice_: PE });
function zE(e) {
  let n = { x: _(e, "x", "tanh", "float32") };
  return z.runKernel(mi, n);
}
var Qu = L({ tanh_: zE });
function ME(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 = Ot([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(qs(y), Qu(v)), V(c, qs(ie(i, x)))), $ = V(Qu(I), qs(k));
  return [I, $];
}
var $pe = L({ basicLSTMCell_: ME });
function LE(e, t, n) {
  let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
  F(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), F(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), F(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 z.runKernel(ho, a, i);
}
var Jg = L({ batchToSpaceND_: LE });
function BE(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 VE(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")), F(o.rank === u.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), F(c == null || o.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), F(l == null || o.rank === l.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
  let d = { x: BE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = z.runKernel(Va, d, h);
  return U(f, i.shape);
}
var Zu = L({ batchNorm_: VE });
function WE(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")), F(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), F(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), F(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && F(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && F(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), Zu(i, o, u, c, l, a);
}
var UE = L({ batchNorm2d_: WE });
function GE(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")), F(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), F(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), F(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && F(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && F(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), Zu(i, o, u, c, l, a);
}
var HE = L({ batchNorm3d_: GE });
function qE(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")), F(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), F(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), F(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && F(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && F(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), Zu(i, o, u, c, l, a);
}
var jE = L({ batchNorm4d_: qE });
function KE(e, t, n) {
  let s = _(e, "x", "bincount"), r = _(t, "weights", "bincount");
  F(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), F(n >= 0, () => `size must be non-negative, but got ${n}.`), F(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 z.runKernel(mg, a, i);
}
var lS = L({ bincount_: KE });
function XE(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 ${s.rank}`);
  let r = { s0: n, s1: s };
  return z.runKernel(gg, r);
}
var YE = L({ broadcastArgs_: XE });
function QE(e, t) {
  let n = _(e, "broadcastTo", "x"), s = n.shape;
  if (t.some((l) => !(l > 0) || l % 1 !== 0))
    throw new Error(`broadcastTo(): Invalid broadcast shape [${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 lr(n);
  let o = { x: n }, u = { reps: a };
  return z.runKernel(Tr, o, u);
}
var id = L({ broadcastTo_: QE });
function ZE(e) {
  let n = { x: _(e, "x", "ceil", "float32") };
  return z.runKernel(_a, n);
}
var JE = L({ ceil_: ZE });
function eR(e, t, n) {
  let s = _(e, "x", "clipByValue");
  F(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 z.runKernel(Nr, r, a);
}
var Vn = L({ clipByValue_: eR });
function tR(e) {
  return Ot(e, 0);
}
var nR = L({ concat1d_: tR });
function sR(e, t) {
  return Ot(e, t);
}
var rR = L({ concat2d_: sR });
function aR(e, t) {
  return Ot(e, t);
}
var iR = L({ concat3d_: aR });
function oR(e, t) {
  return Ot(e, t);
}
var uR = L({ concat4d_: oR });
function lR(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]])), F(l.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${l.rank}.`), F(u.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`), hn("conv2d", s, i);
  let p = r === "NHWC" ? l.shape[3] : l.shape[1];
  F(p === u.shape[2], () => `Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`), F(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 = z.runKernel(Aa, d, h);
  return c ? U(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var pa = L({ conv2d_: lR });
function cR(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]])), F(l.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${l.rank}.`), F(u.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`), hn("conv1d", s, i), F(l.shape[2] === u.shape[1], () => `Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`), F(Ps(n, a), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`), F(r === "NWC", () => `Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);
  let p = U(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = U(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = pa(d, p, [1, n], s, "NHWC", [1, a], i);
  return c ? U(g, [g.shape[2], g.shape[3]]) : U(g, [g.shape[0], g.shape[2], g.shape[3]]);
}
var cS = L({ conv1d_: cR });
function dR(e, t, n, s, r, a = "NHWC", i) {
  F(e.length === t.rank, () => `Length of inShape (${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]]), F(o.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`), F(u.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`), F(n.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);
  let c = a === "NHWC" ? o[3] : o[1], p = a === "NHWC" ? u.shape[3] : u.shape[1];
  F(c === n.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`), F(p === n.shape[3], () => `Error in conv2dDerInput: depth of output (${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 = z.runKernel(Ea, d, h);
  return l ? U(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var eb = L({ conv2DBackpropInput_: dR });
function pR(e, t, n, s, r, a) {
  let i = _(e, "x", "conv2dTranspose"), o = _(t, "filter", "conv2dTranspose");
  return eb(n, i, o, s, r, "NHWC", a);
}
var dS = L({ conv2dTranspose_: pR });
function hR(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]])), F(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), F(o.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`), F(u.shape[4] === o.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`), F(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), F(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = z.runKernel(np, c, p);
  return l ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
}
var pS = L({ conv3d_: hR });
function fR(e, t, n, s, r) {
  F(e.length === t.rank, () => `Length of inShape (${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];
  F(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), F(i.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`), F(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), F(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), F(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = z.runKernel(vg, c, p);
  return o ? U(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
}
var hS = L({ conv3DBackpropInput_: fR });
function mR(e, t, n, s, r) {
  let a = _(e, "x", "conv3dTranspose"), i = _(t, "filter", "conv3dTranspose");
  return hS(n, a, i, s, r);
}
var gR = L({ conv3dTranspose_: mR });
function bR(e) {
  let n = { x: _(e, "x", "cos", "float32") };
  return z.runKernel(Ra, n);
}
var tb = L({ cos_: bR });
function yR(e) {
  let n = { x: _(e, "x", "cosh", "float32") };
  return z.runKernel(Da, n);
}
var fS = L({ cosh_: yR });
function vR(e, t = 0, n = false, s = false) {
  let a = { x: _(e, "x", "cumprod") }, i = { axis: t, exclusive: n, reverse: s };
  return z.runKernel(mo, a, i);
}
var wm = L({ cumprod_: vR });
function xR(e, t = 0, n = false, s = false) {
  let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
  return z.runKernel(Fa, a, i);
}
var mS = L({ cumsum_: xR });
function wR(e, t, n, s = false) {
  let r = _(e, "x", "denseBincount"), a = _(t, "weights", "denseBincount");
  F(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), F(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), F(n >= 0, () => `size must be non-negative, but got ${n}.`), F(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 z.runKernel(xg, i, o);
}
var kR = L({ denseBincount_: wR });
function SR(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];
  F(t > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t}`), F(r * t >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${r} and ${t}  for depthToSpace with input shape
    ${s.shape}`), F(a * t >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${a} and ${t} for depthToSpace with input shape
        ${s.shape}`), F(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 z.runKernel(bo, o, u);
}
var IR = L({ depthToSpace_: SR });
function CR(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]])), F(l.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`), F(u.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`), F(l.shape[3] === u.shape[2], () => `Error in depthwiseConv2d: number of input channels (${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 = z.runKernel(Oa, p, d);
  return c ? U(h, [h.shape[1], h.shape[2], h.shape[3]]) : h;
}
var wp = L({ depthwiseConv2d_: CR });
function NR(e) {
  let n = { x: _(e, "x", "diag") };
  return z.runKernel(Sg, n);
}
var _pe = L({ diag_: NR });
function TR(e, t, n, s, r = [1, 1], a = "NHWC") {
  let i = _(e, "x", "dilation2d"), o = _(t, "filter", "dilation2d");
  F(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), F(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), F(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 = z.runKernel(sp, c, p);
  return l ? U(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var $R = L({ dilation2d_: TR });
function _R(e, t) {
  let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(yo, r);
}
var Xn = L({ equal_: _R });
function AR(e, t, n) {
  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = rt(rt(a.shape, s.shape), r.shape), o = id(a, i), u = id(s, i), l = id(r, i), c = { condition: o, t: u, e: l };
  return z.runKernel(Lo, c);
}
var vn = L({ where_: AR });
function ER(e) {
  let n = { x: _(e, "x", "zerosLike") };
  return z.runKernel(Xo, n);
}
var je = L({ zerosLike_: ER });
function RR(e, t) {
  let n = _(e, "a", "div"), s = _(t, "b", "div");
  [n, s] = xt(n, s);
  let r = xe(n, s), a = je(r), i = Xn(s, a);
  return vn(i, a, r);
}
var DR = L({ divNoNan_: RR });
function FR(e, t) {
  let n = _(e, "t1", "dot"), s = _(t, "t2", "dot");
  F((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 (F(r === a, () => `Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`), n.rank === 1 && s.rank === 1) {
    let i = U(n, [1, -1]), o = U(s, [-1, 1]), u = Ve(i, o);
    return U(u, []);
  } else if (n.rank === 1 && s.rank === 2) {
    let i = U(n, [1, -1]), o = U(s, [s.shape[0], s.shape[1]]), u = Ve(i, o);
    return U(u, [u.size]);
  } else if (n.rank === 2 && s.rank === 1) {
    let i = U(s, [-1, 1]), o = Ve(n, i);
    return U(o, [o.size]);
  } else {
    let i = U(s, [s.shape[0], s.shape[1]]);
    return Ve(n, i);
  }
}
var Ape = L({ dot_: FR });
function OR(e, ...t) {
  let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
  return z.runKernel(rp, n, s);
}
var PR = L({ einsum_: OR });
function zR(e) {
  let n = { x: _(e, "x", "elu", "float32") };
  return z.runKernel(za, n);
}
var kp = L({ elu_: zR });
function MR(e) {
  let t = _(e, "x", "erf");
  F(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 z.runKernel(yl, n);
}
var LR = L({ erf_: MR });
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 gS(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 bS(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 ha(e, t) {
  let n = t.map((s) => 1);
  return gS(e, n, t);
}
function BR(e, t, n) {
  F(nb(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
}
function yS(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 VR(e, t) {
  let n = [];
  for (let s = t - e; s < t; ++s)
    n.push(s);
  return n;
}
function WR(e, t = null, n = false) {
  let r = { x: _(e, "x", "max") }, a = { reductionIndices: t, keepDims: n };
  return z.runKernel(qa, r, a);
}
var As = L({ max_: WR });
function UR(e, t = null, n = false) {
  let r = { x: _(e, "x", "min") }, a = { axis: t, keepDims: n };
  return z.runKernel(Ya, r, a);
}
var km = L({ min_: UR });
function GR(e, t) {
  let n = _(e, "base", "pow"), s = _(t, "exp", "pow");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(ti, r);
}
var fa = L({ pow_: GR });
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 $r(e, [], [], t);
}
function HR(e) {
  let n = { x: _(e, "x", "sqrt", "float32") };
  return z.runKernel(ci, n);
}
var dn = L({ sqrt_: HR });
function qR(e) {
  let t = _(e, "x", "square"), n = {};
  return z.runKernel("Square", { x: t }, n);
}
var ct = L({ square_: qR });
function jR(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 z.runKernel(di, r, a);
}
var ve = L({ sum_: jR });
function KR(e, t = "euclidean", n = null, s = false) {
  e = _(e, "x", "norm");
  let r = vS(e, t, n), a = r.shape;
  if (s) {
    let i = ts(n, e.shape);
    a = ha(r.shape, i);
  }
  return U(r, a);
}
function vS(e, t, n = null) {
  if (e.rank === 0)
    return Lt(e);
  if (e.rank !== 1 && n === null)
    return vS(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 km(Lt(e), n);
    if (t === "euclidean" || t === 2)
      return dn(ve(fa(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 km(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 rb = L({ norm_: KR });
function XR(e, t = null, n = false) {
  return rb(e, "euclidean", t, n);
}
var YR = L({ euclideanNorm_: XR });
function QR(e) {
  let n = { x: _(e, "x", "exp") };
  return z.runKernel(Ma, n);
}
var Yn = L({ exp_: QR });
function ZR(e, t = 0) {
  let n = _(e, "x", "expandDims", "string_or_numeric");
  F(t <= n.rank, () => "Axis must be <= rank of the tensor");
  let s = { input: n }, r = { dim: t };
  return z.runKernel(vo, s, r);
}
var Pn = L({ expandDims_: ZR });
function JR(e) {
  let n = { x: _(e, "x", "expm1") };
  return z.runKernel(xo, n);
}
var eD = L({ expm1_: JR });
function tD(e, t) {
  let n = _(e, "x", "tile", "string_or_numeric");
  F(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);
  let s = { x: n }, r = { reps: t };
  return z.runKernel(Tr, s, r);
}
var hs = L({ tile_: tD });
function nD(e, t, n, s = "float32") {
  t == null && (t = e);
  let r = Ae([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 xS = L({ eye_: nD });
function Bl(e, t, n) {
  let s = { shape: e, value: t, dtype: n };
  return z.runKernel(vl, {}, s);
}
function sD(e) {
  let n = { x: _(e, "x", "floor", "float32") };
  return z.runKernel(La, n);
}
var Sp = L({ floor_: sD });
function rD(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 z.runKernel(ko, i, o);
}
var Ju = L({ gather_: rD });
function aD(e, t) {
  let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(Io, r);
}
var Un = L({ greater_: aD });
function iD(e, t) {
  let n = _(e, "a", "greaterEqual", "string_or_numeric"), s = _(t, "b", "greaterEqual", "string_or_numeric");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(Wa, r);
}
var Zo = L({ greaterEqual_: iD });
function oD(e) {
  let n = { x: _(e, "x", "isFinite") };
  return z.runKernel(xl, n);
}
var Epe = L({ isFinite_: oD });
function uD(e) {
  let n = { x: _(e, "x", "isInf") };
  return z.runKernel(wl, n);
}
var Rpe = L({ isInf_: uD });
function lD(e) {
  let n = { x: _(e, "x", "isNaN") };
  return z.runKernel(kl, n);
}
var cD = L({ isNaN_: lD });
function dD(e, t = 0.2) {
  let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
  return z.runKernel(Ga, s, r);
}
var ab = L({ leakyRelu_: dD });
function pD(e, t) {
  let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(Co, r);
}
var wS = L({ less_: pD });
function hD(e, t) {
  let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(No, r);
}
var Jo = L({ lessEqual_: hD });
function fD(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 z.runKernel(Tg, {}, s);
}
function mD(e, t = 5, n = 1, s = 1, r = 0.5) {
  let a = _(e, "x", "localResponseNormalization");
  F(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${a.rank}.`), F(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 = z.runKernel(op, u, l);
  return o ? U(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var gD = L({ localResponseNormalization_: mD });
function bD(e) {
  let n = { x: _(e, "x", "log", "float32") };
  return z.runKernel(Ha, n);
}
var Qn = L({ log_: bD });
function yD(e) {
  let n = { x: _(e, "x", "log1p") };
  return z.runKernel(Sl, n);
}
var ib = L({ log1p_: yD });
function Dpe(e) {
  return F(fr(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 z.tidy(() => {
      let { value: a, grads: i } = z.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)"), Ip(i), i[0];
    });
  };
}
function Fpe(e) {
  return F(fr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
    F(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
    let s = Ku(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
    return z.tidy(() => {
      let { value: a, grads: i } = z.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,...])"), Ip(i), i;
    });
  };
}
function Ope(e) {
  return F(fr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
    F(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), F(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
    let { grads: s, value: r } = z.gradients(() => e(t), [t], n);
    return Ip(s), { grad: s[0], value: r };
  };
}
function Ppe(e) {
  return F(fr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
    F(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), F(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
    let s = z.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,...])"), Ip(s.grads), s;
  };
}
function vD(e, t) {
  F(fr(e), () => "The f passed in variableGrads(f) must be a function"), F(t == null || Array.isArray(t) && t.every((l) => l instanceof wd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
  let n = t != null;
  if (!n) {
    t = [];
    for (let l in z.registeredVariables)
      t.push(z.registeredVariables[l]);
  }
  let s = n ? t.filter((l) => !l.trainable) : null, r = t.length;
  t = t.filter((l) => l.trainable), F(t.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);
  let a = true, { value: i, grads: o } = z.gradients(e, t, null, a);
  F(o.some((l) => l != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), F(i.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${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 js(e) {
  return z.customGrad(e);
}
function Ip(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 xD(e) {
  let n = { x: _(e, "x", "softplus") };
  return z.runKernel(Rl, n);
}
var Vl = L({ softplus_: xD });
function wD(e) {
  let t = _(e, "x", "logSigmoid");
  return js((s) => ({ value: vt(Vl(vt(s))), gradFunc: (i) => V(i, qs(vt(s))) }))(t);
}
var zpe = L({ logSigmoid_: wD });
function kD(e, t) {
  let n = _(e, "a", "sub"), s = _(t, "b", "sub");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(fi, r);
}
var ge = L({ sub_: kD });
function SD(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 js((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 kS = L({ logSoftmax_: SD });
function ID(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 = ha(c.shape, r);
    return U(c, p);
  }
  return c;
}
var CD = L({ logSumExp_: ID });
function ND(e, t) {
  let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
  rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(To, r);
}
var Ds = L({ logicalAnd_: ND });
function TD(e) {
  let n = { x: _(e, "x", "logicalNot", "bool") };
  return z.runKernel(Il, n);
}
var ob = L({ logicalNot_: TD });
function $D(e, t) {
  let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
  rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(ip, r);
}
var SS = L({ logicalOr_: $D });
function _D(e, t) {
  let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
  return rt(n.shape, s.shape), Ds(SS(e, t), ob(Ds(e, t)));
}
var Mpe = L({ logicalXor_: _D });
var Hc = 2147483648;
function AD(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) >= Hc)
    throw new Error(`values tensor size must less than ${Hc}`);
  if (o.shape[1] >= Hc)
    throw new Error(`trailing dim_size must less than ${Hc} for int32 output type, was ${o.shape[1]}`);
  let l = { sortedSequence: o, values: u }, c = { side: n };
  return z.runKernel(Og, l, c);
}
var IS = L({ searchSorted_: AD });
function ED(e, t) {
  return IS(e, t, "left");
}
function RD(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]])), F(o.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.rank}.`), F(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 = z.runKernel(Ka, l, c);
  return u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var ub = L({ maxPool_: RD });
function DD(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]])), F(o.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`), F(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 = z.runKernel(up, l, c);
  return u ? U(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
}
var CS = L({ maxPool3d_: DD });
function FD(e, t, n, s, r = false) {
  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = z.runKernel(Eg, i, o);
  return { result: u[0], indexes: u[1] };
}
var OD = L({ maxPoolWithArgmax_: FD });
function PD(e, t) {
  let n = _(e, "a", "maximum"), s = _(t, "b", "maximum");
  [n, s] = xt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(ja, r);
}
var Ar = L({ maximum_: PD });
function zD(e, t = null, n = false) {
  let r = { x: _(e, "x", "mean") }, a = { axis: t, keepDims: n };
  return z.runKernel(Xa, r, a);
}
var It = L({ mean_: zD });
function $t(e, t = "float32") {
  if (t === "complex64") {
    let s = $t(e, "float32"), r = $t(e, "float32");
    return mr(s, r);
  }
  let n = Zd(dt(e), t);
  return z.makeTensor(n, e, t);
}
function Mn(e, t = "float32") {
  if (t === "complex64") {
    let s = Mn(e, "float32"), r = $t(e, "float32");
    return mr(s, r);
  }
  let n = lg(dt(e), t);
  return z.makeTensor(n, e, t);
}
function Lpe(e, t, { indexing: n = "xy" } = {}) {
  if (n !== "xy" && n !== "ij")
    throw new TypeError(`${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 MD(e, t) {
  let n = _(e, "a", "minimum"), s = _(t, "b", "minimum");
  [n, s] = xt(n, s), n.dtype === "bool" && (n = le(n, "int32"), s = le(s, "int32")), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(Qa, r);
}
var Cp = L({ minimum_: MD });
function LD(e, t, n) {
  F(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");
  F(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++)
    F(t[o].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), F(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 z.runKernel(Za, i, a);
}
var BD = L({ mirrorPad_: LD });
function VD(e, t) {
  let n = _(e, "a", "mod"), s = _(t, "b", "mod");
  [n, s] = xt(n, s);
  let r = { a: n, b: s };
  return z.runKernel(Cl, r);
}
var WD = L({ mod_: VD });
function UD(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 = ha(r.shape, s));
  let i = ct(ge(le(e, "float32"), U(r, a))), o = It(i, s, n);
  return { mean: r, variance: o };
}
var lb = L({ moments_: UD });
function GD(e, t, n, s) {
  let r = _(t, "data", "multiRNNCell"), a = Ku(n, "c", "multiRNNCell"), i = Ku(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 Bpe = L({ multiRNNCell_: GD });
function HD(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 = z.runKernel(Rg, u, l);
  return i === 1 ? U(c, [c.size]) : c;
}
var qD = L({ multinomial_: HD });
function jD(e, t) {
  let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s };
  return z.runKernel(_o, r);
}
var el = L({ notEqual_: jD });
function KD(e) {
  let n = { x: _(e, "x", "onesLike") };
  return z.runKernel(Ro, n);
}
var Zn = L({ onesLike_: KD });
function XD(e, t) {
  let n = _(e, "v1", "outerProduct"), s = _(t, "v2", "outerProduct");
  F(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 Vpe = L({ outerProduct_: XD });
function YD(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 z.runKernel(ei, a, r);
}
var bi = L({ pad_: YD });
function QD(e, t, n = 0) {
  return F(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), bi(e, [t], n);
}
var Wpe = L({ pad1d_: QD });
function ZD(e, t, n = 0) {
  return F(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bi(e, t, n);
}
var Upe = L({ pad2d_: ZD });
function JD(e, t, n = 0) {
  return F(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bi(e, t, n);
}
var Gpe = L({ pad3d_: JD });
function e3(e, t, n = 0) {
  return F(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bi(e, t, n);
}
var Hpe = L({ pad4d_: e3 });
function t3(e, t, n) {
  let s = _(e, "x", "spaceToBatchND");
  F(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), F(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), F(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 z.runKernel(Wo, r, a);
}
var cb = L({ spaceToBatchND_: t3 });
function n3(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]])), F(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
  let c = aS(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
  s === "same" ? d = r3([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
  let h = p[0] === 1 && p[1] === 1, [f, m] = s3([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : cb(u, p, f), v = (n === "avg" ? () => Zg(b, t, a, g, i) : () => ub(b, t, a, g, i))(), x = h ? v : Jg(v, p, m);
  return l ? U(x, [x.shape[1], x.shape[2], x.shape[3]]) : x;
}
function s3(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 r3(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 qpe = L({ pool_: n3 });
function a3(e, t) {
  let n = _(e, "x", "prelu"), s = _(t, "alpha", "prelu"), r = { x: n, alpha: s };
  return z.runKernel(ni, r);
}
var db = L({ prelu_: a3 });
function i3(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 z.runKernel(si, r, a);
}
var NS = L({ prod_: i3 });
function o3(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 z.makeTensor(r, e, n);
}
var jpe = L({ rand_: o3 });
var pb = ka(Xd());
var hb = 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 = pb.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 u3 = class {
  constructor(e, t, n, s) {
    this.alpha = e, this.beta = 1 / t, this.dtype = n;
    let r = s || Math.random();
    this.randu = pb.alea(r.toString()), this.randn = new hb(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 l3 = 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 = pb.alea(s);
  }
  convertValue(e) {
    return this.canReturnFloat() ? e : Math.round(e);
  }
  nextValue() {
    return this.convertValue(this.min + this.range * this.random());
  }
};
function c3(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 u3(t, n, s, r), i = Ae(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var Kpe = L({ randomGamma_: c3 });
function d3(e, t = 0, n = 1, s, r) {
  if (s != null && s === "bool")
    throw new Error(`Unsupported data type ${s}`);
  let a = new hb(t, n, s, false, r), i = Ae(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var p3 = L({ randomNormal_: d3 });
function h3(e, t = 0, n = 1, s = "float32", r) {
  let a = Ae(e, s), i = new l3(t, n, null, r);
  for (let o = 0; o < a.values.length; o++)
    a.values[o] = i.nextValue();
  return a.toTensor();
}
var Wl = L({ randomUniform_: h3 });
function tl(e, t, n = 1, s = "float32") {
  if (n === 0)
    throw new Error("Cannot have a step of zero");
  let r = { start: e, stop: t, step: n, dtype: s };
  return z.runKernel(Tl, {}, r);
}
function f3(e) {
  let n = { x: _(e, "x", "reciprocal") };
  return z.runKernel($l, n);
}
var m3 = L({ reciprocal_: f3 });
function g3(e) {
  let n = { x: _(e, "x", "relu") };
  return z.runKernel(ri, n);
}
var Ys = L({ relu_: g3 });
function b3(e) {
  let n = { x: _(e, "x", "relu6") };
  return z.runKernel(ii, n);
}
var TS = L({ relu6_: b3 });
function y3(e, t) {
  let s = { x: _(e, "x", "reverse") }, r = { dims: t };
  return z.runKernel(Po, s, r);
}
var Jn = L({ reverse_: y3 });
function v3(e) {
  let t = _(e, "x", "reverse");
  return F(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Jn(t, 0);
}
var Xpe = L({ reverse1d_: v3 });
function x3(e, t) {
  let n = _(e, "x", "reverse");
  return F(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Jn(n, t);
}
var Ype = L({ reverse2d_: x3 });
function w3(e, t) {
  let n = _(e, "x", "reverse");
  return F(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Jn(n, t);
}
var Qpe = L({ reverse3d_: w3 });
function k3(e, t) {
  let n = _(e, "x", "reverse");
  return F(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Jn(n, t);
}
var Zpe = L({ reverse4d_: k3 });
function S3(e) {
  let n = { x: _(e, "x", "round") };
  return z.runKernel(zo, n);
}
var $S = L({ round_: S3 });
function I3(e) {
  let n = { x: _(e, "x", "rsqrt", "float32") };
  return z.runKernel(oi, n);
}
var _S = L({ rsqrt_: I3 });
function C3(e) {
  let n = { x: _(e, "x", "selu") };
  return z.runKernel(Al, n);
}
var AS = L({ selu_: C3 });
function N3(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");
  F(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), F(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), F(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), F(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), F(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];
  F(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 = wp(c, u, s, r, i, a), g = pa(f, l, 1, "valid", i);
  return p ? U(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var T3 = L({ separableConv2d_: N3 });
async function $3(e, t) {
  let n = _(e, "x", "setdiff1d"), s = _(t, "y", "setdiff1d");
  F(n.dtype === s.dtype, () => `x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`), F(n.rank === 1, () => `x should be 1D tensor, but got x (${n.shape}).`), F(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 _3 = $3;
function A3(e) {
  let n = { x: _(e, "x", "sign") };
  return z.runKernel(El, n);
}
var E3 = L({ sign_: A3 });
function R3(e) {
  let n = { x: _(e, "x", "sin", "float32") };
  return z.runKernel(ui, n);
}
var ES = L({ sin_: R3 });
function D3(e) {
  let n = { x: _(e, "x", "sinh") };
  return z.runKernel(Vo, n);
}
var RS = L({ sinh_: D3 });
function F3(e, t, n) {
  let s = _(e, "x", "slice1d");
  return F(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), qe(s, [t], [n]);
}
var fb = L({ slice1d_: F3 });
function O3(e, t, n) {
  let s = _(e, "x", "slice2d");
  return F(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
}
var DS = L({ slice2d_: O3 });
function P3(e, t, n) {
  let s = _(e, "x", "slice3d");
  return F(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
}
var mb = L({ slice3d_: P3 });
function z3(e, t, n) {
  let s = _(e, "x", "slice4d");
  return F(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
}
var Nd = L({ slice4d_: z3 });
function M3(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 z.runKernel(pi, s, r);
}
var gb = L({ softmax_: M3 });
function L3(e) {
  F(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
  let t = { input: e };
  return z.runKernel(Cg, t);
}
var bb = L({ fft_: L3 });
function B3(e) {
  F(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
  let t = { input: e };
  return z.runKernel(Ng, t);
}
var Td = L({ ifft_: B3 });
function V3(e) {
  let t = e.shape[e.shape.length - 1], n = e.size / t, s;
  if (t <= 2) {
    let r = U(e, [n, t]);
    s = Td(r);
  } else {
    let r = [n, 2 * (t - 1)], a = U(Xu(e), [n, t]), i = U(xp(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 = Ot([a, o], 1), c = Ot([i, u], 1), p = U(mr(l, c), [r[0], r[1]]);
    s = Td(p);
  }
  if (s = Xu(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 FS = L({ irfft_: V3 });
function W3(e, t, n = 0) {
  let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
  return z.runKernel(Uo, r, a);
}
var Bn = L({ split_: W3 });
function U3(e, t) {
  F(e.dtype === "float32", () => `The dtype for rfft() must be real value but got ${e.dtype}`);
  let n = e.shape[e.shape.length - 1], 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 = Ot([e, $t(f)], e.shape.length - 1), n = t;
  } else
    r = e;
  let a = je(r), i = U(mr(r, a), [s, n]), o = bb(i), u = Math.floor(n / 2) + 1, l = Xu(o), c = xp(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(mr(p[0], d[0]), h);
}
var yb = L({ rfft_: U3 });
function G3(e, t) {
  let n = _(e, "a", "squaredDifference"), s = _(t, "b", "squaredDifference");
  [n, s] = xt(n, s), rt(n.shape, s.shape);
  let r = { a: n, b: s }, a = {};
  return z.runKernel(hi, r, a);
}
var OS = L({ squaredDifference_: G3 });
function H3(e, t) {
  let n = _(e, "x", "squeeze");
  return U(n, ek(n.shape, t).newShape);
}
var br = L({ squeeze_: H3 });
function q3(e, t = 0) {
  let n = Ku(e, "tensors", "stack", "string_or_numeric");
  F(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && F(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
  let s = n, r = { axis: t };
  return z.runKernel(Fo, s, r);
}
var es = L({ stack_: q3 });
function j3(e, t = 0) {
  let s = { x: _(e, "x", "step") }, r = { alpha: t };
  return z.runKernel(gi, s, r);
}
var Np = L({ step_: j3 });
function K3(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 z.runKernel(Go, c, p);
}
var X3 = L({ stridedSlice_: K3 });
function Y3(e) {
  let n = { x: _(e, "x", "tan", "float32") };
  return z.runKernel(Ho, n);
}
var Q3 = L({ tan_: Y3 });
function Zt(e, t) {
  Sa(e);
  let n = Rs(e, t);
  if (n.length !== 1)
    throw new Error("tensor1d() requires values to be a flat/TypedArray");
  return $r(e, null, n, t);
}
function Zi(e, t, n) {
  if (Sa(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 $r(e, t, s, n);
}
function Jpe(e, t, n) {
  if (Sa(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 $r(e, t, s, n);
}
function ehe(e, t, n) {
  if (Sa(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 $r(e, t, s, n);
}
function the(e, t, n) {
  if (Sa(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, $r(e, t, s, n);
}
function Z3(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] = z.runKernel(qo, a, i);
  return { values: o, indices: u };
}
var J3 = L({ topk_: Z3 });
function eF(e, t = 0, n = 1, s, r) {
  if (s != null && s === "bool")
    throw new Error("Unsupported data type $ { dtype }");
  let a = new hb(t, n, s, true, r), i = Ae(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var vb = L({ truncatedNormal_: eF });
function tF(e, t = 0) {
  let n = _(e, "x", "unique", "string_or_numeric");
  F(n.rank > 0, () => "The input tensor must be at least 1D");
  let s = { x: n }, r = { axis: t }, [a, i] = z.runKernel(Mg, s, r);
  return { values: a, indices: i };
}
var xx = L({ unique_: tF });
function nF(e, t, n) {
  let s = _(e, "x", "unsortedSegmentSum"), r = _(t, "segmentIds", "unsortedSegmentSum", "int32");
  F(eo(n), () => "numSegments must be of dtype int");
  let a = { x: s, segmentIds: r }, i = { numSegments: n };
  return z.runKernel(mp, a, i);
}
var sF = L({ unsortedSegmentSum_: nF });
function rF(e, t = 0) {
  let n = _(e, "x", "unstack", "string_or_numeric");
  F(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
  let s = { value: n }, r = { axis: t };
  return z.runKernel(Ko, s, r);
}
var Fs = L({ unstack_: rF });
function aF(e, t) {
  return IS(e, t, "right");
}
function iF(e, t = true, n, s) {
  return z.makeVariable(e, t, n, s);
}
function PS(e, t) {
  let n = [];
  for (let a = 0; a < t.length; a++)
    t[a] && n.push(a);
  let s = Ae(e, "int32"), r = Ae([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 oF(e) {
  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = PS(t.shape, n);
  return e !== t && t.dispose(), s;
}
var zS = oF;
async function uF(e, t, n) {
  let s = _(e, "tensor", "boolMask"), r = _(t, "mask", "boolMask", "bool"), a = n == null ? 0 : n, i = r.rank, o = s.shape;
  F(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 zS(p), h = br(d, [1]), f = Ju(c, h, a);
  return e !== s && s.dispose(), t !== r && r.dispose(), h.dispose(), c.dispose(), p.dispose(), d.dispose(), f;
}
var nhe = uF;
function lF(e, t, n, s, r = true) {
  let a = _(e, "v", "movingAverage"), i = _(t, "x", "movingAverage"), o = _(n, "decay", "movingAverage");
  yk(a, i), F(Ir(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) {
    F(s != null, () => "When using zeroDebias: true, step is required.");
    let p = _(s, "step", "movingAverage");
    c = xe(c, ge(u, fa(o, p)));
  }
  return ie(a, c);
}
var she = L({ movingAverage_: lF });
function cF(e, t, n) {
  let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
  Kg(r, s, n);
  let a = { indices: s, updates: r }, i = { shape: n };
  return z.runKernel(Mo, a, i);
}
var dF = L({ scatterND_: cF });
function pF(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 hF(e, t, n, s = 0) {
  let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense", "string_or_numeric"), i = _(s, "defaultValue", "sparseToDense", a.dtype);
  pF(r, a, n, i);
  let o = { sparseIndices: r, sparseValues: a, defaultValue: i }, u = { outputShape: n };
  return z.runKernel(hp, o, u);
}
var MS = L({ sparseToDense_: hF });
function fF(e, t) {
  let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
  return z.runKernel(So, r);
}
var mF = L({ gatherND_: fF });
function gF(e, t) {
  if (t == null)
    return e.shape.slice();
  if (Ir(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 bF(e, t, n, s) {
  let r = _(e, "x", "dropout");
  if (F(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.`), F(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 = gF(r, n), i = 1 - t, o = xe(Sp(ie(Wl(a, 0, 1, "float32", s), i)), i);
  return V(r, o);
}
var yF = L({ dropout_: bF });
function vF(e) {
  return Math.floor(Math.pow(2, Math.ceil(Math.log(e) / Math.log(2))));
}
function LS(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 xF(e, t, n = 1) {
  let s = _(e, "predictions", "inTopK"), r = _(t, "targets", "inTopK");
  F(s.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`), F(s.rank - 1 === r.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`), pn(s.shape.slice(0, s.shape.length - 1), r.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
  let a = s.shape[s.shape.length - 1];
  F(n > 0 && n <= a, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);
  let i = await s.data(), o = await r.data(), [u, l] = [i.length / a, a], c = tk("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 rhe = xF;
var ma = {};
Ee(ma, { conv2d: () => SF, depthwiseConv2d: () => TF, matMul: () => _F });
function wF(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]])), F(o.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`), F(u.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`), F(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];
  F(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), F(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), hn("conv2dDerFilter", r, i);
  let p = { x: o, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, filterShape: n };
  return z.runKernel(bg, p, d);
}
var xb = L({ conv2DBackpropFilter_: wF });
function Tp(e, t, n) {
  if (n == null || n === "linear")
    return e;
  if (n === "relu")
    return V(e, Np(t));
  throw new Error(`Cannot compute gradient for fused activation ${n}.`);
}
function $p(e, t) {
  let n = t, s = At(e.shape, t.shape);
  return s.length > 0 && (n = ve(n, s)), U(n, e.shape);
}
function _p(e, t, n, s) {
  if (t === "linear")
    return e;
  if (t === "relu")
    return Ys(e);
  if (t === "elu")
    return kp(e);
  if (t === "relu6")
    return TS(e);
  if (t === "prelu")
    return db(e, n);
  if (t === "leakyrelu")
    return ab(e, s);
  if (t === "sigmoid")
    return qs(e);
  throw new Error(`Unknown fused activation ${t}.`);
}
var Ap = (e, t) => !(e > 0) || t === "linear";
function kF({ 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", Ap(z.state.gradientDepth, u) === false) {
    F(r === "NHWC", () => `Error in fused conv2d: got dataFormat of ${r} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
    let I = pa(e, t, n, s, r, a, i);
    return o != null && (I = ie(I, o)), _p(I, 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]])), F(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), F(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), hn("fused conv2d", s, i);
  let m = r === "NHWC" ? h.shape[3] : h.shape[1];
  F(d.shape[2] === m, () => `Error in conv2d: depth of input (${m}) must match input depth for filter ${d.shape[2]}.`), F(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
  let g = Ll(h.shape, d.shape, n, a, s, i), b;
  o != null && (b = _(o, "bias", "fused conv2d"), [b] = xt(b, p), r === "NHWC" ? rt(g.outShape, b.shape) : (F(b.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${b.shape.length}.`), F(b.shape.length === 0 || b.shape[0] === g.outChannels || b.shape[0] === 1, () => `Error in fused conv2d: bias shape (${b.shape}) is not compatible with the number of output channels (${g.outChannels})`)));
  let y;
  if (l != null) {
    let I = l.shape;
    if (F(I.length <= 1 || I.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${I.length}.`), I.length === 1)
      F(I[0] === 1 || I[0] === g.outChannels, () => `Error in fused conv2d: PReLU activation weights (${I}) is not compatible with the number of output channels (${g.outChannels}).`);
    else if (I.length === 3)
      try {
        rt(I, g.outShape);
      } catch ($) {
        let R = `Error in fused conv2d: PReLU activation weights (${I}) is not compatible with the output shape of the conv2d (${g.outShape}).`;
        throw Error(R);
      }
    y = _(l, "prelu weights", "fused conv2d");
  }
  let v = (I, $) => {
    F(r === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${r} but only NHWC is currently supported.`);
    let [R, E, P, A] = $, O = Tp(I, P, u);
    F(gr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
    let T = eb(E.shape, O, R, n, s), M = xb(E, O, R.shape, n, s), W = [T, M];
    if (A != null) {
      let j = $p(A, O);
      W.push(j);
    }
    return W;
  }, x = { x: h, filter: d, bias: b, preluActivationWeights: y }, k = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
  return o == null ? js(($, R, E) => {
    let P = z.runKernel(ua, x, k);
    return E([R, $, P]), f && (P = U(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: v };
  })(h, d) : js(($, R, E, P) => {
    let A = z.runKernel(ua, x, k);
    return P([R, $, A, E]), f && (A = U(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: v };
  })(h, d, b);
}
var SF = L({ fusedConv2d_: kF });
function IF(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 z.runKernel(wg, l, c);
}
var BS = L({ depthwiseConv2dNativeBackpropFilter_: IF });
function CF(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 = z.runKernel(kg, l, c);
  return u ? U(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var VS = L({ depthwiseConv2dNativeBackpropInput_: CF });
function NF({ 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 (Ap(z.state.gradientDepth, u) === false) {
    let k = wp(e, t, n, s, r, a, i);
    return o != null && (k = ie(k, o)), _p(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]])), F(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), F(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), F(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), F(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 = Ll(h.shape, d.shape, n, a, s, i, true), g;
  o != null && (g = _(o, "bias", "fused conv2d"), [g] = xt(g, p), rt(m.outShape, g.shape));
  let b;
  l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
  let y = (k, I) => {
    F(gr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
    let [$, R, E, P] = I, A = Tp(k, E, u), O = VS(R.shape, A, $, n, s, a, i), T = BS(R, A, $.shape, n, s, a, i);
    if (P != null) {
      let M = $p(g, A);
      return [O, T, M];
    }
    return [O, 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 ? js((I, $, R) => {
    let E = z.runKernel(la, v, x);
    return R([$, I, E]), f && (E = U(E, [E.shape[1], E.shape[2], E.shape[3]])), { value: E, gradFunc: y };
  })(h, d) : js((I, $, R, E) => {
    let P = z.runKernel(la, v, x);
    return E([$, I, P, R]), f && (P = U(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
  })(h, d, g);
}
var TF = L({ fusedDepthwiseConv2d_: NF });
function $F({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o }) {
  if (Ap(z.state.gradientDepth, a) === false) {
    let A = Ve(e, t, n, s);
    return r != null && (A = ie(A, r)), _p(A, a, i, o);
  }
  let u = _(e, "a", "fused matMul"), l = _(t, "b", "fused matMul");
  [u, l] = xt(u, l);
  let c = n ? u.shape[u.rank - 2] : u.shape[u.rank - 1], p = s ? l.shape[l.rank - 1] : l.shape[l.rank - 2], d = n ? u.shape[u.rank - 1] : u.shape[u.rank - 2], h = s ? l.shape[l.rank - 2] : l.shape[l.rank - 1], f = u.shape.slice(0, -2), m = l.shape.slice(0, -2), g = dt(f), b = dt(m);
  F(c === p, () => `Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${n} and transposeB=${s} must match.`);
  let v = rt(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] = xt(I, u), rt(v, I.shape));
  let $;
  i != null && ($ = _(i, "prelu weights", "fused matMul"));
  let R = (A, O) => {
    let [T, M, W, j] = O, X = Tp(U(A, W.shape), W, a), Y, Z;
    if (!n && !s ? (Y = Ve(X, M, false, true), Z = Ve(T, X, true, false)) : !n && s ? (Y = Ve(X, M, false, false), Z = Ve(X, T, true, false)) : n && !s ? (Y = Ve(M, X, false, true), Z = Ve(T, X, false, false)) : (Y = Ve(M, X, true, true), Z = Ve(X, T, true, true)), r != null) {
      let te = $p(j, X);
      return [Y, Z, te];
    } else
      return [Y, Z];
  }, E = { a: x, b: k, bias: I, preluActivationWeights: $ }, P = { transposeA: n, transposeB: s, activation: a, leakyreluAlpha: o };
  return r == null ? js((O, T, M) => {
    let W = z.runKernel(oa, E, P);
    return M([O, T, W]), { value: U(W, v), gradFunc: R };
  })(x, k) : js((O, T, M, W) => {
    let j = z.runKernel(oa, E, P);
    return W([O, T, j, M]), { value: U(j, v), gradFunc: R };
  })(x, k, I);
}
var _F = L({ fusedMatMul_: $F });
function AF(e) {
  return LS(e, 0.54, 0.46);
}
var EF = L({ hammingWindow_: AF });
function RF(e) {
  return LS(e, 0.5, 0.5);
}
var WS = L({ hannWindow_: RF });
function DF(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 = Ot([qe(e, a, t - o), Bl([o], r)]);
      i.push(u), a += n;
    }
  return i.length === 0 ? Zi([], [0, t]) : U(Ot(i), [i.length, t]);
}
var US = L({ frame_: DF });
function FF(e, t, n, s, r = WS) {
  s == null && (s = vF(t));
  let a = US(e, t, n), i = V(a, r(t));
  return yb(i, s);
}
var OF = L({ stft_: FF });
function PF(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];
  F(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), F(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), F(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), F(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), F(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), F(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 z.runKernel(go, c, p);
}
var zF = L({ cropAndResize_: PF });
function MF(e) {
  let t = _(e, "image", "flipLeftRight", "float32");
  F(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
  let n = { image: t };
  return z.runKernel(wo, n, {});
}
var LF = L({ flipLeftRight_: MF });
function BF(e) {
  let t = _(e, "image", "grayscaleToRGB"), n = t.rank - 1, s = t.shape[n];
  F(t.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`), F(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 VF = L({ grayscaleToRGB_: BF });
function WF(e, t, n = 0, s = 0.5) {
  let r = _(e, "image", "rotateWithOffset", "float32");
  F(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
  let a = { image: r }, i = { radians: t, fillValue: n, center: s };
  return z.runKernel(Yo, a, i);
}
var UF = L({ rotateWithOffset_: WF });
function eu(e, t, n, s, r, a) {
  s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
  let i = e.shape[0];
  return n = Math.min(n, i), F(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), F(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), F(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), F(t.rank === 1, () => "scores must be a 1D tensor"), F(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), F(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
}
function GF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = eu(a, i, n, s, r);
  n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
  let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
  return z.runKernel(Ao, { boxes: a, scores: i }, u);
}
var HF = L({ nonMaxSuppression_: GF });
function qF(e, t, n) {
  let s = jF(e, t, n), r = s < 0 ? -(s + 1) : s;
  e.splice(r, 0, t);
}
function jF(e, t, n) {
  return XF(e, t, n || KF);
}
function KF(e, t) {
  return e > t ? 1 : e < t ? -1 : 0;
}
function XF(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 GS(e, t, n, s, r) {
  return wb(e, t, n, s, r, 0);
}
function HS(e, t, n, s, r, a) {
  return wb(e, t, n, s, r, 0, false, a, true);
}
function qS(e, t, n, s, r, a) {
  return wb(e, t, n, s, r, a, true);
}
function wb(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(wx);
  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 = YF(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 && qF(l, g, wx));
  }
  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 YF(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 wx(e, t) {
  return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
}
async function ZF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = eu(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 } = GS(l, c, n, s, r);
  return a !== e && a.dispose(), i !== t && i.dispose(), Zt(p, "int32");
}
var JF = ZF;
function eO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = eu(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 = z.runKernel(Eo, l, c);
  return { selectedIndices: p[0], selectedScores: p[1] };
}
var tO = L({ nonMaxSuppressionWithScore_: eO });
async function nO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = eu(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 } = qS(c, p, n, s, r, a);
  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(d, "int32"), selectedScores: Zt(h) };
}
var sO = nO;
function rO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = eu(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 = z.runKernel(Nl, d, h);
  return { selectedIndices: f[0], validOutputs: f[1] };
}
var aO = L({ nonMaxSuppressionPadded_: rO });
async function iO(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = eu(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 } = HS(d, h, l, c, p, a);
  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(f, "int32"), validOutputs: we(m, "int32") };
}
var oO = iO;
function uO(e, t, n = false, s = false) {
  let r = _(e, "images", "resizeBilinear");
  F(r.rank === 3 || r.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`), F(t.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t}.`), F(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 = z.runKernel(ai, o, u);
  return i ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var lO = L({ resizeBilinear_: uO });
function cO(e, t, n = false, s = false) {
  let r = _(e, "images", "resizeNearestNeighbor");
  F(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), F(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), F(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), F(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 = z.runKernel(_l, o, u);
  return i ? U(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var dO = L({ resizeNearestNeighbor_: cO });
function pO(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 (F(r.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${r.rank}.`), F(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]}.`), F(r.dtype === "int32" || r.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`), F(t === "otsu" || t === "binary", () => `Method must be binary or otsu, but was ${t}`), r.shape[2] === 3) {
    [c, p, d] = Bn(r, [1, 1, 1], -1);
    let g = V(c, a), b = V(p, i), y = V(d, o);
    h = ie(ie(g, b), y);
  } else
    h = e;
  if (t === "otsu") {
    let g = lS(le($S(h), "int32"), ms([]), 256);
    l = hO(g, u);
  }
  let f = n ? Jo(h, l) : Un(h, l);
  return le(V(f, 255), "int32");
}
function hO(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, tl(0, a.size)));
    o = xe(d, ve(a));
    let h = Bl(i.shape, a.size), f = ie(tl(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 fO = L({ threshold_: pO });
function mO(e, t, n = "nearest", s = "constant", r = 0, a) {
  let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
  F(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), F(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), F(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 z.runKernel(jo, u, l);
}
var gO = L({ transform_: mO });
function bO(e, t, n) {
  F(t % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t}.`), F(n % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${n}.`);
  let s = _(e, "a", "bandPart");
  F(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(tl(0, a, 1, "int32"), [-1, 1]), u = tl(0, i, 1, "int32"), l = ge(o, u), c = Ds(Jo(l, we(+t, "int32")), Zo(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
  return U(es(Fs(U(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
}
var yO = L({ bandPart_: bO });
function vO(e) {
  let t;
  if (Array.isArray(e)) {
    t = false, F(e != null && e.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
    let r = e[0].shape[0];
    for (let a = 1; a < e.length; ++a)
      F(e[a].shape[0] === r, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`);
  } else
    t = true, e = Bn(e, e.shape[0], 0).map((r) => br(r, [0]));
  F(e.length <= e[0].shape[0], () => `Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);
  let n = [], s = e;
  for (let r = 0; r < e.length; ++r)
    n.push(z.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, rb(a, "euclidean"));
    }));
  return t ? es(n, 0) : n;
}
var xO = L({ gramSchmidt_: vO });
function wO(e, t = false) {
  if (F(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
    return kx(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] = kx(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 kx(e, t = false) {
  return z.tidy(() => {
    F(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 = xS(n), a = lr(e), i = Zi([[1]], [1, 1]), o = lr(i), u = n >= s ? s : n;
    for (let l = 0; l < u; ++l) {
      let c = a, p = o, d = r;
      [o, a, r] = z.tidy(() => {
        let h = qe(a, [l, l], [n - l, 1]), f = rb(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 = lr(i) : o = Ot([i, qe(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
        let v = vt(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 E = ge(x, Ve(k, Ve(I, x)));
          a = Ot([qe(a, [0, 0], [l, s]), E], 0);
        }
        let $ = Ge(k), R = qe(r, [0, l], [n, r.shape[1] - l]);
        if (l === 0)
          r = ge(R, Ve(Ve(R, o), $));
        else {
          let E = ge(R, Ve(Ve(R, o), $));
          r = Ot([qe(r, [0, 0], [n, l]), E], 1);
        }
        return [o, a, r];
      }), De([c, p, d]);
    }
    return !t && n > s && (r = qe(r, [0, 0], [n, s]), a = qe(a, [0, 0], [s, s])), [r, a];
  });
}
var kO = L({ qr_: wO });
var SO = ((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))(SO || {});
function IO(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(el(i, we(0))), "float32");
      return xe(ve(a), o);
    }
  }
  throw Error(`Unknown reduction: ${n}`);
}
var Qs = L({ computeWeightedLoss_: IO });
function CO(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 Qs(o, i, s);
}
var NO = L({ absoluteDifference_: CO });
function TO(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 Qs(l, o, r);
}
var $O = L({ cosineDistance_: TO });
function _O(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 = Ys(ge(o, V(r, a)));
  return Qs(u, i, s);
}
var AO = L({ hingeLoss_: _O });
function EO(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 = Cp(l, u), p = ge(l, c), d = ie(V(we(0.5), ct(c)), V(u, p));
  return Qs(d, o, r);
}
var RO = L({ huberLoss_: EO });
function DO(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 = vt(V(a, Qn(ie(i, l)))), p = V(ge(u, a), Qn(ie(ge(u, i), l))), d = ge(c, p);
  return Qs(d, o, r);
}
var FO = L({ logLoss_: DO });
function OO(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 = OS(r, a);
  return Qs(o, i, s);
}
var PO = L({ meanSquaredError_: OO });
function zO(e, t) {
  let n = _(e, "labels", "sigmoidCrossEntropyWithLogits"), s = _(t, "logits", "sigmoidCrossEntropyWithLogits");
  pn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
  let r = Ys(s), a = V(s, n), i = ib(Yn(vt(Lt(s))));
  return ie(ge(r, a), i);
}
function MO(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 = zO(a, i);
  return Qs(u, o, r);
}
var LO = L({ sigmoidCrossEntropy_: MO });
function BO(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 js((r, a, i) => {
    let u = CD(a, [n], true), l = ge(le(a, "float32"), u);
    i([r, l]);
    let c = vt(V(l, r));
    return { value: ve(c, [n]), gradFunc: (h, f) => {
      let [m, g] = f, b = ha(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 VO(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 = BO(a, i);
  return Qs(u, o, r);
}
var WO = L({ softmaxCrossEntropy_: VO });
function UO(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 = z.runKernel(cp, u);
  return { outputIndices: l[0], outputValues: l[1], emptyRowIndicator: l[2], reverseIndexMap: l[3] };
}
var GO = L({ sparseFillEmptyRows_: UO });
function HO(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 = z.runKernel(Dl, i);
  return { outputIndices: o[0], outputShape: o[1] };
}
var qO = L({ sparseReshape_: HO });
function jO(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 z.runKernel(dp, i);
}
var KO = L({ sparseSegmentMean_: jO });
function XO(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 z.runKernel(pp, i);
}
var YO = L({ sparseSegmentSum_: XO });
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 = z.runKernel(fp, p, c);
  return { nGrams: d[0], nGramsSplits: d[1] };
}
var ZO = L({ stringNGrams_: QO });
function JO(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 = z.runKernel(Pg, i, a);
  return { indices: o[0], values: o[1], shape: o[2] };
}
var eP = L({ stringSplit_: JO });
function tP(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 z.runKernel(zg, r, s);
}
var nP = L({ stringToHashBucketFast_: tP });
var ahe = { fft: bb, ifft: Td, rfft: yb, irfft: FS };
var ihe = { hammingWindow: EF, hannWindow: WS, frame: US, stft: OF };
var jn = { flipLeftRight: LF, grayscaleToRGB: VF, resizeNearestNeighbor: dO, resizeBilinear: lO, rotateWithOffset: UF, cropAndResize: zF, nonMaxSuppression: HF, nonMaxSuppressionAsync: JF, nonMaxSuppressionWithScore: tO, nonMaxSuppressionWithScoreAsync: sO, nonMaxSuppressionPadded: aO, nonMaxSuppressionPaddedAsync: oO, threshold: fO, transform: gO };
var sP = { bandPart: yO, gramSchmidt: xO, qr: kO };
var ohe = { absoluteDifference: NO, computeWeightedLoss: Qs, cosineDistance: $O, hingeLoss: AO, huberLoss: RO, logLoss: FO, meanSquaredError: PO, sigmoidCrossEntropy: LO, softmaxCrossEntropy: WO };
var qc = { sparseFillEmptyRows: GO, sparseReshape: qO, sparseSegmentMean: KO, sparseSegmentSum: YO };
var qf = { stringNGrams: ZO, stringSplit: eP, stringToHashBucketFast: nP };
var Er = class extends eS {
  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 De(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 vD(e, t);
  }
  dispose() {
    this.iterations_ != null && De(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 ${this.getClassName()}`);
  }
  async extractIterations(e) {
    return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
  }
};
Object.defineProperty(Er, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
var kb = class extends Er {
  constructor(e, t, n = null) {
    super(), this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = z.backend.epsilon());
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = z.registeredVariables[n], a = false;
      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accum_grad`, variable: q(() => je(r).variable(a)) }), this.accumulatedUpdates[s] == null && (this.accumulatedUpdates[s] = { originalName: `${n}/accum_var`, variable: q(() => je(r).variable(a)) });
      let i = Array.isArray(e) ? e[s].tensor : e[n];
      if (i == null)
        return;
      let o = this.accumulatedGrads[s].variable, u = this.accumulatedUpdates[s].variable;
      q(() => {
        let l = ie(V(o, this.rho), V(ct(i), 1 - this.rho)), c = V(xe(dn(ie(u, this.epsilon)), dn(ie(o, this.epsilon))), i), p = ie(V(u, this.rho), V(ct(c), 1 - this.rho));
        o.assign(l), u.assign(p);
        let d = ie(V(c, -this.learningRate), r);
        r.assign(d);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedUpdates != null && (De(this.accumulatedGrads.map((e) => e.variable)), De(this.accumulatedUpdates.map((e) => e.variable)));
  }
  async getWeights() {
    let e = [...this.accumulatedGrads, ...this.accumulatedUpdates];
    return [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = e.length / 2, n = false;
    this.accumulatedGrads = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedUpdates = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.rho, t.epsilon);
  }
};
kb.className = "Adadelta";
_r(kb);
var Sb = class extends Er {
  constructor(e, t = 0.1) {
    super(), this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = z.registeredVariables[n];
      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: q(() => Bl(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, z.backend.epsilon()))), -this.learningRate), r);
        r.assign(u);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedGrads != null && De(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);
  }
};
Sb.className = "Adagrad";
_r(Sb);
var Ib = class extends Er {
  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 = z.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 = z.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 && De(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && De(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(fa(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(fa(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);
  }
};
Ib.className = "Adam";
_r(Ib);
var Cb = class extends Er {
  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 = z.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 = z.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 = Ar(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 && De(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && De(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);
  }
};
Cb.className = "Adamax";
_r(Cb);
var Ep = class extends Er {
  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 = z.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);
  }
};
Ep.className = "SGD";
_r(Ep);
var Nb = class extends Ep {
  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 = z.registeredVariables[n];
      this.accumulations[s] == null && (this.accumulations[s] = { originalName: `${n}/momentum`, variable: q(() => je(r).variable(false)) });
      let a = this.accumulations[s].variable, i = Array.isArray(e) ? e[s].tensor : e[n];
      i != null && q(() => {
        let o, u = ie(V(this.m, a), i);
        this.useNesterov ? o = ie(V(this.c, ie(i, V(u, this.m))), r) : o = ie(V(this.c, u), r), a.assign(u), r.assign(o);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.m.dispose(), this.accumulations != null && De(this.accumulations.map((e) => e.variable));
  }
  setMomentum(e) {
    this.momentum = e;
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulations.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = false;
    this.accumulations = e.map((n) => ({ originalName: n.name, variable: n.tensor.variable(t) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.momentum, t.useNesterov);
  }
};
Nb.className = "Momentum";
_r(Nb);
var Tb = class extends Er {
  constructor(e, t = 0.9, n = 0, s = null, r = false) {
    if (super(), this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = z.backend.epsilon()), e == null)
      throw new Error("learningRate for RMSPropOptimizer must be defined.");
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = z.registeredVariables[n], a = false;
      this.accumulatedMeanSquares[s] == null && (this.accumulatedMeanSquares[s] = { originalName: `${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 && De(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && De(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && De(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);
  }
};
Tb.className = "RMSProp";
_r(Tb);
var Hr = class {
  static sgd(e) {
    return new Ep(e);
  }
  static momentum(e, t, n = false) {
    return new Nb(e, t, n);
  }
  static rmsprop(e, t = 0.9, n = 0, s = null, r = false) {
    return new Tb(e, t, n, s, r);
  }
  static adam(e = 1e-3, t = 0.9, n = 0.999, s = null) {
    return new Ib(e, t, n, s);
  }
  static adadelta(e = 1e-3, t = 0.95, n = null) {
    return new kb(e, t, n);
  }
  static adamax(e = 2e-3, t = 0.9, n = 0.999, s = null, r = 0) {
    return new Cb(e, t, n, s, r);
  }
  static adagrad(e, t = 0.1) {
    return new Sb(e, t);
  }
};
var Li = { sgd: Hr.sgd, momentum: Hr.momentum, adadelta: Hr.adadelta, adagrad: Hr.adagrad, rmsprop: Hr.rmsprop, adamax: Hr.adamax, adam: Hr.adam };
var rP = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
function jS() {
  return new Promise((e) => rP(() => e()));
}
var C = {};
Ee(C, { ERF_A1: () => mP, ERF_A2: () => gP, ERF_A3: () => bP, ERF_A4: () => yP, ERF_A5: () => vP, ERF_P: () => fP, PARALLELIZE_THRESHOLD: () => $b, SELU_SCALE: () => XS, SELU_SCALEALPHA: () => KS, applyActivation: () => _p, assertAndGetBroadcastShape: () => rt, assertAxesAreInnerMostDims: () => BR, assertParamsConsistent: () => aP, assignToTypedArray: () => CP, axesAreInnerMostDims: () => nb, calculateShapes: () => Gk, checkEinsumDimSizes: () => EP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => gS, complexWithEvenIndex: () => kP, complexWithOddIndex: () => SP, computeConv2DInfo: () => Ll, computeConv3DInfo: () => iS, computeDefaultPad: () => Qg, computeDilation2DInfo: () => CE, computeOptimalWindowSize: () => oP, computeOutAndReduceShapes: () => bS, computeOutShape: () => iP, computePool2DInfo: () => aS, computePool3DInfo: () => NE, convertConv2DDataFormat: () => oS, decodeEinsumEquation: () => _P, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => ha, exponent: () => TP, exponents: () => NP, fromStringArrayToUint8: () => ZP, fromUint8ToStringArray: () => QP, getAxesPermutation: () => yS, getBroadcastDims: () => Mk, getComplexWithIndex: () => IP, getEinsumComputePath: () => RP, getEinsumPermutation: () => AP, getFusedBiasGradient: () => $p, getFusedDyActivation: () => Tp, getImageCenter: () => uP, getInnerMostAxes: () => VR, getPermuted: () => cP, getReductionAxes: () => At, getReshaped: () => lP, getReshapedPermuted: () => dP, getSliceBeginCoords: () => pP, getSliceSize: () => hP, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => PP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => zP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => MP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => VP, getSparseReshapeInputOutputMismatchErrorMessage: () => UP, getSparseReshapeInputOutputMultipleErrorMessage: () => WP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => LP, getSparseReshapeNegativeOutputDimErrorMessage: () => BP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => jP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => GP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => HP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => qP, getUndoAxesPermutation: () => sb, isIdentityPermutation: () => DP, log: () => U$, mergeRealAndImagArrays: () => xP, prepareAndValidate: () => Wk, prepareSplitSize: () => OP, segment_util: () => YS, shouldFuse: () => Ap, slice_util: () => kt, splitRealAndImagArrays: () => wP, tupleValuesAreOne: () => gr, upcastType: () => cn, validateInput: () => Kg, validateUpdateShape: () => jg, warn: () => ar });
function aP(e, t) {
  let n = e[0].length;
  e.forEach((r, a) => {
    F(r.length === n, () => `Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`);
  }), F(t >= 0 && t < n, () => `Error in concat${n}D: axis must be between 0 and ${n - 1}.`);
  let s = e[0];
  e.forEach((r, a) => {
    for (let i = 0; i < n; i++)
      F(i === t || r[i] === s[i], () => `Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`);
  });
}
function iP(e, t) {
  let n = e[0].slice();
  for (let s = 1; s < e.length; s++)
    n[t] += e[s][t];
  return n;
}
var $b = 30;
function oP(e) {
  return e <= $b ? e : yd(e, Math.floor(Math.sqrt(e)));
}
function uP(e, t, n) {
  let s = n * (typeof e == "number" ? e : e[0]), r = t * (typeof e == "number" ? e : e[1]);
  return [s, r];
}
function lP(e, t, n, s = true) {
  let r = [];
  if (s)
    r = r.concat(t.slice(0)), r.push(e[0] / n), r = r.concat(e.slice(1));
  else {
    r = r.concat(e[0]);
    let a = t.length;
    for (let i = 0; i < a; ++i)
      r = r.concat([e[i + 1] / t[i], t[i]]);
    r = r.concat(e.slice(a + 1));
  }
  return r;
}
function cP(e, t, n = true) {
  let s = [];
  if (n) {
    s.push(t);
    for (let r = t + 1; r < e; ++r)
      r <= 2 * t ? (s.push(r), s.push(r - (t + 1))) : s.push(r);
  } else {
    let r = [], a = [];
    for (let i = 1; i < e; ++i)
      i >= t * 2 + 1 || i % 2 === 1 ? a.push(i) : r.push(i);
    s.push(...r), s.push(0), s.push(...a);
  }
  return s;
}
function dP(e, t, n, s = true) {
  let r = [];
  s ? r.push(e[0] / n) : r.push(e[0] * n);
  for (let a = 1; a < e.length; ++a)
    a <= t.length ? s ? r.push(t[a - 1] * e[a]) : r.push(e[a] / t[a - 1]) : r.push(e[a]);
  return r;
}
function pP(e, t) {
  let n = [0];
  for (let s = 0; s < t; ++s)
    n.push(e[s][0]);
  return n;
}
function hP(e, t, n) {
  let s = e.slice(0, 1);
  for (let r = 0; r < n; ++r)
    s.push(e[r + 1] - t[r][0] - t[r][1]);
  return s;
}
var KS = 1.7580993408473768;
var XS = 1.0507009873554805;
var fP = 0.3275911;
var mP = 0.254829592;
var gP = -0.284496736;
var bP = 1.421413741;
var yP = -1.453152027;
var vP = 1.061405429;
function xP(e, t) {
  if (e.length !== t.length)
    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);
  let n = new Float32Array(e.length * 2);
  for (let s = 0; s < n.length; s += 2)
    n[s] = e[s / 2], n[s + 1] = t[s / 2];
  return n;
}
function wP(e) {
  let t = new Float32Array(e.length / 2), n = new Float32Array(e.length / 2);
  for (let s = 0; s < e.length; s += 2)
    t[s / 2] = e[s], n[s / 2] = e[s + 1];
  return { real: t, imag: n };
}
function kP(e) {
  let t = Math.ceil(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
  for (let r = 0; r < e.length; r += 4)
    n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
  return { real: n, imag: s };
}
function SP(e) {
  let t = Math.floor(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
  for (let r = 2; r < e.length; r += 4)
    n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
  return { real: n, imag: s };
}
function IP(e, t) {
  let n = e[t * 2], s = e[t * 2 + 1];
  return { real: n, imag: s };
}
function CP(e, t, n, s) {
  e[s * 2] = t, e[s * 2 + 1] = n;
}
function NP(e, t) {
  let n = new Float32Array(e / 2), s = new Float32Array(e / 2);
  for (let r = 0; r < Math.ceil(e / 2); r++) {
    let a = (t ? 2 : -2) * Math.PI * (r / e);
    n[r] = Math.cos(a), s[r] = Math.sin(a);
  }
  return { real: n, imag: s };
}
function TP(e, t, n) {
  let s = (n ? 2 : -2) * Math.PI * (e / t), r = Math.cos(s), a = Math.sin(s);
  return { real: r, imag: a };
}
var jf = "->";
var $P = /->/g;
var Sx = ",";
var Ix = "...";
function _P(e, t) {
  e = e.replace(/\s/g, "");
  let n = (e.length - e.replace($P, "").length) / jf.length;
  if (n < 1)
    throw new Error("Equations without an arrow are not supported.");
  if (n > 1)
    throw new Error(`Equation must contain exactly one arrow ("${jf}").`);
  let [s, r] = e.split(jf);
  F(s.indexOf(Ix) === -1, () => `The ellipsis notation ("${Ix}") is not supported yet.`);
  let a = s.split(Sx), i = a.length;
  if (t !== i)
    throw new Error(`Expected ${i} input tensors, received ${t}`);
  if (i > 2)
    throw new Error("Support for more than 2 input tensors is not implemented yet.");
  let o = [];
  for (let d = 0; d < r.length; ++d) {
    let h = r[d];
    if (!a.some((f) => f.indexOf(h) !== -1))
      throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);
    o.indexOf(h) === -1 && o.push(h);
  }
  for (let d = 0; d < s.length; ++d) {
    let h = s[d];
    o.indexOf(h) === -1 && h !== Sx && o.push(h);
  }
  let u = new Array(a.length);
  for (let d = 0; d < i; ++d) {
    if (new Set(a[d].split("")).size !== a[d].length)
      throw new Error(`Found duplicate axes in input component ${a[d]}. Support for duplicate axes in input is not implemented yet.`);
    u[d] = [];
    for (let h = 0; h < a[d].length; ++h)
      u[d].push(o.indexOf(a[d][h]));
  }
  let l = o.length, c = r.length, p = [];
  for (let d = c; d < l; ++d)
    p.push(d);
  return { allDims: o, summedDims: p, idDims: u };
}
function AP(e, t) {
  let n = new Array(e);
  n.fill(-1);
  for (let r = 0; r < t.length; ++r)
    n[t[r]] = r;
  let s = [];
  for (let r = 0; r < e; ++r)
    n[r] === -1 && s.push(r);
  return n = n.filter((r) => r !== -1), { permutationIndices: n, expandDims: s };
}
function EP(e, t, n) {
  let s = new Array(e);
  for (let r = 0; r < n.length; ++r) {
    let a = n[r].shape;
    for (let i = 0; i < t[r].length; ++i)
      s[t[r][i]] === void 0 ? s[t[r][i]] = a[i] : F(s[t[r][i]] === a[i], () => `Expected dimension ${s[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(a)}, but got dimension ${a[i]}`);
  }
}
function RP(e, t) {
  let n = e, s = [], r = 0;
  e.length === 0 && n.push(-1), r = e.length + 1;
  for (let i = 0; i < r; ++i)
    s.push([]);
  let a = [];
  for (let i = 0; i < n.length; ++i) {
    let o = n[i], u = FP(t, o);
    for (let l of u)
      a.indexOf(l) === -1 && (s[i].push(l), a.push(l));
  }
  return { path: n, steps: s };
}
function DP(e) {
  return e.every((t, n) => t === n);
}
function FP(e, t) {
  let n = [];
  for (let s = 0; s < e.length; ++s)
    (e[s].length === 0 || e[s].indexOf(t) !== -1 || t === -1) && n.push(s);
  return n;
}
function OP(e, t, n = 0) {
  let s = [];
  if (typeof t == "number")
    F(e.shape[n] % t === 0, () => "Number of splits must evenly divide the axis."), s = new Array(t).fill(e.shape[n] / t);
  else {
    let r = t.reduce((i, o) => (o === -1 && (i += 1), i), 0);
    F(r <= 1, () => "There should be only one negative value in split array.");
    let a = t.indexOf(-1);
    if (a !== -1) {
      let i = t.reduce((o, u) => u > 0 ? o + u : o);
      t[a] = e.shape[n] - i;
    }
    F(e.shape[n] === t.reduce((i, o) => i + o), () => "The sum of sizes must match the size of the axis dimension."), s = t;
  }
  return s;
}
function PP(e) {
  return `Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${e}`;
}
function zP(e, t) {
  return `indices(${e}, 0) is invalid: ${t} < 0`;
}
function MP(e, t, n) {
  return `indices(${e}, 0) is invalid: ${t} >= ${n}`;
}
function LP(e, t) {
  return `only one output dimension may be -1, not both ${e} and ${t}`;
}
function BP(e, t) {
  return `size ${e} must be non-negative, not ${t}`;
}
function VP() {
  return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function WP(e, t) {
  let n = dt(e), s = dt(t);
  return `Input to reshape is a SparseTensor with ${n}
  dense values, but the requested shape requires a multiple of ${s}. inputShape=${e} outputShape= ${t}`;
}
function UP(e, t) {
  let n = dt(e), s = dt(t);
  return `Input to reshape is a tensor with ${n} dense values, but the requested shape has ${s}. inputShape=${e} outputShape=${t}`;
}
function GP() {
  return "segment ids must be >= 0";
}
function HP() {
  return "segment ids are not increasing";
}
function qP(e, t) {
  return `Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`;
}
function jP(e, t, n) {
  return `Bad: indices[${e}] == ${t} out of range [0, ${n})`;
}
var YS = {};
Ee(YS, { collectGatherOpShapeInfo: () => YP, computeOutShape: () => XP, segOpComputeOptimalWindowSize: () => KP });
function KP(e, t) {
  let n = false, s;
  for (e <= $b ? (s = e, n = true) : s = yd(e, Math.floor(Math.sqrt(e))); !n; )
    s > t || s === e ? n = true : s = yd(e, s + 1);
  return s;
}
function XP(e, t, n) {
  let s = [], r = e.length;
  for (let a = 0; a < r; a++)
    a !== t ? s.push(e[a]) : s.push(n);
  return s;
}
function YP(e, t, n, s) {
  let r = t.shape.length, a = e.shape.length;
  if (s !== 0 && (s < -r || s > r))
    throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${s}`);
  if (s < 0 && (s += r), s > a)
    throw new Error(`batchDims (${s}) must be less than rank(x) (
    ${a}).`);
  if (n < s)
    throw new Error(`batchDims (${s}) must be less than or equal to axis (${n}).`);
  for (let p = 0; p < s; ++p)
    if (e.shape[p] !== t.shape[p])
      throw new Error(`x.shape[${p}]: ${e.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);
  let i = e.shape[n], o = [], u = 1, l = 1, c = 1;
  for (let p = 0; p < s; ++p)
    o.push(e.shape[p]), u *= e.shape[p];
  for (let p = s; p < n; p++)
    o.push(e.shape[p]), l *= e.shape[p];
  for (let p = s; p < r; p++)
    o.push(t.shape[p]);
  for (let p = n + 1; p < a; p++)
    o.push(e.shape[p]), c *= e.shape[p];
  return { batchSize: u, sliceSize: c, outerSize: l, dimSize: i, outputShape: o };
}
function QP(e) {
  try {
    return e.map((t) => xd(t));
  } catch (t) {
    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`);
  }
}
function ZP(e) {
  return e.map((t) => zl(t));
}
var ws = {};
Ee(ws, { nonMaxSuppressionV3Impl: () => GS, nonMaxSuppressionV4Impl: () => HS, nonMaxSuppressionV5Impl: () => qS, whereImpl: () => PS });
var Bs = class extends Error {
  constructor(e) {
    super(e), Object.setPrototypeOf(this, Bs.prototype);
  }
};
var fs = class extends Error {
  constructor(e) {
    super(e), Object.setPrototypeOf(this, fs.prototype);
  }
};
var G = class extends Error {
  constructor(e) {
    super(e), Object.setPrototypeOf(this, G.prototype);
  }
};
var Fe = class extends Error {
  constructor(e) {
    super(e), Object.setPrototypeOf(this, Fe.prototype);
  }
};
var QS = class extends Error {
  constructor(e) {
    super(e), Object.setPrototypeOf(this, QS.prototype);
  }
};
var ZS = class {
  constructor(e) {
    this.maxEntries = e || 100, this.cache = /* @__PURE__ */ new Map();
  }
  get(e) {
    let t;
    return this.cache.has(e) && (t = this.cache.get(e), this.cache.delete(e), this.cache.set(e, t)), t;
  }
  put(e, t) {
    if (this.cache.has(e))
      this.cache.delete(e);
    else if (this.cache.size >= this.maxEntries) {
      let n = this.cache.keys().next().value;
      this.cache.delete(n);
    }
    this.cache.set(e, t);
  }
  getMaxEntries() {
    return this.maxEntries;
  }
  setMaxEntries(e) {
    if (e < 0)
      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${e}.`);
    if (this.maxEntries > e)
      for (let t = 0; t < this.maxEntries - e; t++) {
        let n = this.cache.keys().next().value;
        this.cache.delete(n);
      }
    this.maxEntries = e;
  }
};
function ga(e, t) {
  if (Array.isArray(e)) {
    let n = [];
    for (let s = 0; s < t; s++)
      n = n.concat(e);
    return n;
  } else {
    let n = new Array(t);
    return n.fill(e), n;
  }
}
function Cs(e, t) {
  if (!e)
    throw new QS(t);
}
function Cx(e, t) {
  let n = 0;
  for (let s of e)
    s === t && n++;
  return n;
}
function bn(e) {
  return e.length === 1 ? e[0] : e;
}
function ht(e) {
  return Array.isArray(e) ? e : [e];
}
function Vs(e) {
  let n = e.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2").replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase();
  return n[0] !== "_" ? n : "private" + n;
}
function Qr(e) {
  return e.length <= 1 || e.indexOf("_") === -1 ? e : e.replace(/[_]+(\w|$)/g, (t, n) => n.toUpperCase());
}
var Hn = {};
function _b(e) {
  if (e == null)
    return null;
  let t = {};
  return t.className = e.getClassName(), t.config = e.getConfig(), t;
}
function Sm(e) {
  if (!(e == null || typeof e != "object"))
    if (Array.isArray(e))
      e.forEach((t) => Sm(t));
    else {
      let t = Object.keys(e);
      for (let n of t) {
        let s = e[n];
        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : Sm(s));
      }
    }
}
function Ul(e, t = {}, n = {}, s = "object", r = false) {
  if (typeof e == "string") {
    let a = e, i;
    if (a in n)
      i = n[a];
    else if (a in Hn)
      i = Hn[a];
    else if (i = t[a], i == null)
      throw new G(`Unknown ${s}: ${e}. This may be due to one of the following reasons:
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
    return i;
  } else {
    let a = e;
    if (a.className == null || a.config == null)
      throw new G(`${s}: Improper config format: ${JSON.stringify(a)}.
'className' and 'config' must set.`);
    let i = a.className, o, u;
    if (i in n ? [o, u] = n[i] : i in Hn ? [o, u] = Hn.className : i in t && ([o, u] = t[i]), o == null)
      throw new G(`Unknown ${s}: ${i}. This may be due to one of the following reasons:
1. The ${s} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${s} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
    if (u != null) {
      let l = {};
      for (let h of Object.keys(Hn))
        l[h] = Hn[h];
      for (let h of Object.keys(n))
        l[h] = n[h];
      let c = a.config;
      c.customObjects = l;
      let p = { ...Hn };
      for (let h of Object.keys(n))
        Hn[h] = n[h];
      Sm(a.config);
      let d = u(o, a.config, n, r);
      return Hn = { ...p }, d;
    } else {
      let l = { ...Hn };
      for (let p of Object.keys(n))
        Hn[p] = n[p];
      let c = new o(a.config);
      return Hn = { ...l }, c;
    }
  }
}
function JP(e, t) {
  return e < t ? -1 : e > t ? 1 : 0;
}
function jc(e, t) {
  return -1 * JP(e, t);
}
function cr(e) {
  if (e == null)
    return e;
  let t = [];
  for (let n of e)
    t.indexOf(n) === -1 && t.push(n);
  return t;
}
function ez(e) {
  if (e == null)
    throw new G(`Invalid value in obj: ${JSON.stringify(e)}`);
  for (let t in e)
    if (e.hasOwnProperty(t))
      return false;
  return true;
}
function yi(e, t, n) {
  if (n != null && e.indexOf(n) < 0)
    throw new G(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`);
}
function Ab(e, t, n = 0, s = 1 / 0) {
  return Cs(n >= 0), Cs(s >= n), Array.isArray(e) && e.length >= n && e.length <= s && e.every((r) => typeof r === t);
}
function Vt(e, t) {
  Array.isArray(e) ? (w.assert(e.length > 0, () => `${t} is unexpectedly an empty array.`), e.forEach((n, s) => Vt(n, `element ${s + 1} of ${t}`))) : w.assert(Number.isInteger(e) && e > 0, () => `Expected ${t} to be a positive integer, but got ${JS(e)}.`);
}
function JS(e) {
  return e === null ? "null" : Array.isArray(e) ? "[" + e.map((t) => JS(t)).join(",") + "]" : typeof e == "string" ? `"${e}"` : `${e}`;
}
function tz(e, t, n) {
  let s = n != null ? n() : w.now(), r;
  return (...i) => {
    let o = n != null ? n() : w.now();
    return o - s < t || (s = o, r = e(...i)), r;
  };
}
function eI(e) {
  return e === "relu" ? "relu" : e === "linear" ? "linear" : e === "elu" ? "elu" : null;
}
var nz = 0;
function tI() {
  return nz++;
}
var Kc = {};
function Rp(e = "") {
  return e in Kc || (Kc[e] = 0), Kc[e] += 1, e + Kc[e].toString();
}
var sz = ["channelsFirst", "channelsLast"];
var rz = ["nearest", "bilinear"];
var az = ["valid", "same", "causal"];
var iz = ["max", "avg"];
var oz = ["sum", "mul", "concat", "ave"];
var Bi = /* @__PURE__ */ new Map();
function Ct(e) {
  yi(sz, "DataFormat", e);
}
function uz(e) {
  yi(rz, "InterpolationFormat", e);
}
function Gn(e) {
  yi(az, "PaddingMode", e);
}
function nI(e) {
  yi(iz, "PoolMode", e);
}
var Wu = [];
var Nx = "/";
function sa(e, t) {
  Wu.push(e);
  try {
    let n = t();
    return Wu.pop(), n;
  } catch (n) {
    throw Wu.pop(), n;
  }
}
function lz() {
  return Wu.length === 0 ? "" : Wu.join(Nx) + Nx;
}
function sI(e) {
  if (!aI(e))
    throw new Error("Not a valid tensor name: '" + e + "'");
  return lz() + e;
}
function rI(e) {
  if (!aI(e))
    throw new Error("Not a valid tensor name: '" + e + "'");
  Bi.has(e) || Bi.set(e, 0);
  let t = Bi.get(e);
  if (Bi.set(e, Bi.get(e) + 1), t > 0) {
    let n = `${e}_${t}`;
    return Bi.set(n, 1), n;
  } else
    return e;
}
var cz = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
function aI(e) {
  return !!e.match(cz);
}
function dz(e) {
  return e === parseInt(e.toString(), 10);
}
function dr(e, t, n) {
  t == null && (t = 0), n == null && (n = e.length);
  let s = 1;
  for (let r = t; r < n; ++r)
    s *= e[r];
  return s;
}
function no(e) {
  if (e.length === 0)
    return Number.NaN;
  let t = Number.POSITIVE_INFINITY;
  for (let n = 0; n < e.length; n++) {
    let s = e[n];
    s < t && (t = s);
  }
  return t;
}
function yr(e) {
  if (e.length === 0)
    return Number.NaN;
  let t = Number.NEGATIVE_INFINITY;
  for (let n = 0; n < e.length; n++) {
    let s = e[n];
    s > t && (t = s);
  }
  return t;
}
function ys(e, t) {
  if (t < e)
    throw new G(`end (${t}) < begin (${e}) is forbidden.`);
  let n = [];
  for (let s = e; s < t; ++s)
    n.push(s);
  return n;
}
var Kf;
function Dt() {
  return Kf == null && (Kf = wA().epsilon()), Kf;
}
function vs() {
  return "channelsLast";
}
function Dp(e, t) {
  return le(e, t);
}
function Gl(e, t = -1) {
  let n = e.shape.slice();
  return t < 0 && (t = n.length + t + 1), n.splice(t, 0, 1), U(e, n);
}
function pz(e, t) {
  return q(() => {
    if (e.shape.length !== 2)
      throw new G(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);
    let n = Gl(e, 1);
    return Im(n, [1, t, 1]);
  });
}
function hz(e) {
  let t = [dr(e.shape)];
  return U(e, t);
}
function fz(e) {
  if (e.rank <= 1)
    throw new G(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);
  let t = [e.shape[0], dr(e.shape, 1)];
  return U(e, t);
}
function ra(e, t, n) {
  return q(() => {
    switch (e.rank) {
      case 1:
        return fb(e, t, n);
      case 2:
        return DS(e, [t, 0], [n, e.shape[1]]);
      case 3:
        return mb(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
      case 4:
        return Nd(e, [t, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3]]);
      case 5:
        return qe(e, [t, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4]]);
      case 6:
        return qe(e, [t, 0, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4], e.shape[5]]);
      default:
        throw new G(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
    }
  });
}
function Xf(e, t, n) {
  return q(() => {
    switch (e.rank) {
      case 1:
        return fb(e, t, n);
      case 2:
        return DS(e, [0, t], [e.shape[0], n]);
      case 3:
        return mb(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
      case 4:
        return Nd(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 Xc(e, t, n, s) {
  return q(() => {
    switch (e.rank) {
      case 1:
        return fb(e, t, n);
      case 2:
        switch (s) {
          case 1:
            return ra(e, t, n);
          case 2:
            return Xf(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 ra(e, t, n);
          case 2:
            return mb(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
          case 3:
            return Xf(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 ra(e, t, n);
          case 2:
            return Nd(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
          case 3:
            return Nd(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
          case 4:
            return Xf(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 Eb(e, t = -1) {
  let n;
  return t < 0 && (n = e[0].rank, n !== 0 ? t = n : t = 0), t === e[0].rank && (t = -1), Ot(e, t);
}
function Tx(e, t) {
  switch (e.rank) {
    case 1:
      return nR([e, t]);
    case 2:
      return rR([e, t], 0);
    case 3:
      return iR([e, t], 0);
    case 4:
      return uR([e, t], 0);
    default:
      throw new G(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
  }
}
function Im(e, t) {
  if (Array.isArray(t) || (t = [t]), e.rank !== t.length)
    throw new G(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);
  return hs(e, t);
}
function Fp(e, t = 0, n = 1, s, r) {
  return p3(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 ma.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? Cm(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(ma.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? Cm(e.rank, s, vs()) : null, activation: n }), p);
  }
}
function iI(e, t, n) {
  return q(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = le(t, "int32"), Ju(e, t, n)));
}
function Hl(e) {
  return V(e, e);
}
function Cm(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: ${t.rank}`);
}
function ks(e, t, n) {
  return q(() => (n == null && (n = vs()), Ct(n), ie(e, Cm(e.rank, t, n))));
}
function mz(e, t = 1) {
  if (t !== 1)
    throw new Fe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);
  return kp(e);
}
function gz(e) {
  return q(() => xe(e, ie(Lt(e), 1)));
}
function oI(e, t, n, s) {
  return q(() => yF(e, t, n, s));
}
function bz(e) {
  return q(() => {
    let t = ie(0.5, V(0.2, e));
    return Vn(t, 0, 1);
  });
}
function ql(e, t, n = false) {
  return n ? e() : t();
}
var yz = ["fanIn", "fanOut", "fanAvg"];
var vz = ["normal", "uniform", "truncatedNormal"];
function xz(e) {
  yi(yz, "FanMode", e);
}
function wz(e) {
  yi(vz, "Distribution", e);
}
var ns = class extends re.Serializable {
  fromConfigUsesCustomObjects() {
    return false;
  }
  getConfig() {
    return {};
  }
};
var Rb = class extends ns {
  apply(e, t) {
    return $t(e, t);
  }
};
Rb.className = "Zeros";
re.registerClass(Rb);
var Op = class extends ns {
  apply(e, t) {
    return Mn(e, t);
  }
};
Op.className = "Ones";
re.registerClass(Op);
var Db = 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 };
  }
};
Db.className = "Constant";
re.registerClass(Db);
var Fb = 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 Wl(e, this.minval, this.maxval, t);
  }
  getConfig() {
    return { minval: this.minval, maxval: this.maxval, seed: this.seed };
  }
};
Fb.className = "RandomUniform";
re.registerClass(Fb);
var Ob = 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 Fp(e, this.mean, this.stddev, t, this.seed);
  }
  getConfig() {
    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
  }
};
Ob.className = "RandomNormal";
re.registerClass(Ob);
var Pb = 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 vb(e, this.mean, this.stddev, t, this.seed);
  }
  getConfig() {
    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
  }
};
Pb.className = "TruncatedNormal";
re.registerClass(Pb);
var zb = 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, xS(e[0]));
    });
  }
  getConfig() {
    return { gain: this.gain };
  }
};
zb.className = "Identity";
re.registerClass(zb);
function kz(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 = dr(e, 2);
      n = e[1] * r, s = e[0] * r;
    } else if (t === "channelsLast") {
      let r = dr(e, 0, e.length - 2);
      n = e[e.length - 2] * r, s = e[e.length - 1] * r;
    }
  } else {
    let r = dr(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: ${e.scale}`);
    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, xz(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, wz(this.distribution), this.seed = e.seed;
  }
  apply(e, t) {
    let n = kz(e), s = n[0], r = n[1], a = this.scale;
    if (this.mode === "fanIn" ? a /= Math.max(1, s) : this.mode === "fanOut" ? a /= Math.max(1, r) : a /= Math.max(1, (s + r) / 2), this.distribution === "normal") {
      let i = Math.sqrt(a);
      if (t = t || "float32", t !== "float32" && t !== "int32")
        throw new Fe(`${this.getClassName()} does not support dType ${t}.`);
      return vb(e, 0, i, t, this.seed);
    } else {
      let i = Math.sqrt(3 * a);
      return Wl(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 Pp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanAvg", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Pp.className = "GlorotUniform";
re.registerClass(Pp);
var zp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanAvg", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
zp.className = "GlorotNormal";
re.registerClass(zp);
var Mp = class extends xn {
  constructor(e) {
    super({ scale: 2, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Mp.className = "HeNormal";
re.registerClass(Mp);
var Lp = class extends xn {
  constructor(e) {
    super({ scale: 2, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Lp.className = "HeUniform";
re.registerClass(Lp);
var Bp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Bp.className = "LeCunNormal";
re.registerClass(Bp);
var Vp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Vp.className = "LeCunNormal";
re.registerClass(Vp);
var Mb = 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 = Fp(n, 0, 1, "float32"), r = sP.gramSchmidt(s);
      return e[0] > e[1] && (r = Ge(r)), V(this.gain, r);
    });
  }
  getConfig() {
    return { gain: this.gain, seed: this.seed };
  }
};
Mb.className = "Orthogonal";
re.registerClass(Mb);
var $x = { 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 _x(e, t = {}) {
  return Ul(e, re.SerializationMap.getMap().classNameMap, t, "initializer");
}
function yt(e) {
  return _b(e);
}
function ft(e) {
  if (typeof e == "string") {
    let t = e in $x ? $x[e] : e;
    if (t === "GlorotNormal")
      return new zp();
    if (t === "GlorotUniform")
      return new Pp();
    if (t === "HeNormal")
      return new Mp();
    if (t === "HeUniform")
      return new Lp();
    if (t === "LeCunNormal")
      return new Bp();
    if (t === "LeCunUniform")
      return new Vp();
    {
      let n = {};
      return n.className = t, n.config = {}, _x(n);
    }
  } else
    return e instanceof ns ? e : _x(e);
}
function Nm(e) {
  return Array.isArray(e) && Array.isArray(e[0]);
}
function $d(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 Ax = "Variable";
var Sz = class {
  constructor(e, t = "float32", n = Ax, s = true, r = null) {
    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = tI(), n = n == null ? Ax : n, this.originalName = sI(n), this.name = rI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = iF(e, this.trainable_, this.name, this.dtype);
  }
  read() {
    return this.assertNotDisposed(), this.val;
  }
  write(e) {
    return this.assertNotDisposed(), Iz(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 Iz(e, t) {
  if (e.shape.toString() !== t.shape.toString())
    throw new Error("Shape mismatch: " + JSON.stringify(e.shape) + " vs. " + JSON.stringify(t.shape));
}
function Tm(e) {
  return e.map((t) => t.read());
}
function Lb(e) {
  e.forEach((t) => {
    t[0].write(t[1]);
  });
}
var Ft = 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 = tI(), a != null && (this.originalName = sI(a), this.name = rI(this.originalName)), this.rank = t.length;
  }
};
var Cz = 0;
var Wp = class {
  constructor(e, t) {
    this.callArgs = t, this.id = Cz++, 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 Nz = 0;
var He = class extends re.Serializable {
  constructor(e = {}) {
    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = Nz++, 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) + "_" + Rp(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 = ht(e), this.inputSpec == null || this.inputSpec.length === 0)
      return;
    let t = ht(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 = ht(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 sa(this.name, () => {
      if (!this.built) {
        this.assertInputCompatibility(e);
        let a = [];
        for (let i of ht(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 = ht(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 = Tz(e), i = this.computeOutputShape(a), o, u = $z(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, ht(e), t, this.name, c)) : o = new $s(u, i, this, ht(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 ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
    return _d(this.weights);
  }
  build(e) {
    this.built = true;
  }
  getWeights(e = false) {
    return Tm(e ? this.trainableWeights : this.weights);
  }
  setWeights(e) {
    q(() => {
      let t = this.weights;
      if (t.length !== e.length)
        throw new G(`You called setWeights(weights) on layer "${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 = Tm(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]);
      }
      Lb(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() : ft("zeros"));
    let u = s.apply(t, n), l = new Sz(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 = ht(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 = ht(e);
    t = ht(t), n = ht(n), s = ht(s), r = $d(r), a = $d(a);
    let u = [], l = [], c = [];
    for (let p of o)
      u.push(p.sourceLayer), l.push(p.nodeIndex), c.push(p.tensorIndex);
    new Wp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
    for (let p = 0; p < t.length; p++)
      t[p].sourceLayer = this, t[p].nodeIndex = this.inboundNodes.length - 1, t[p].tensorIndex = p;
  }
  getConfig() {
    let e = { name: this.name, trainable: this.trainable };
    return this.batchInputShape != null && (e.batchInputShape = this.batchInputShape), this.dtype != null && (e.dtype = this.dtype), e;
  }
  disposeWeights() {
    return this.weights.forEach((e) => e.dispose()), this.weights.length;
  }
  assertNotDisposed() {
    if (this._refCount === 0)
      throw new Error(`Layer '${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 Tz(e) {
  e = ht(e);
  let t = [];
  for (let n of e)
    t.push(n.shape);
  return bn(t);
}
function $z(e) {
  return "float32";
}
function uI(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 = uI(i, o, u);
        for (let c of l)
          r.indexOf(c) === -1 && r.push(c);
      }
      return r;
    }
  }
}
var tu = class extends He {
  constructor(e) {
    if (super({ dtype: e.dtype, name: e.name != null ? e.name : Rp("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 Wp({ 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: ${this.name}`);
  }
  dispose() {
    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };
  }
  getConfig() {
    return { batchInputShape: this.batchInputShape, dtype: this.dtype, sparse: this.sparse, name: this.name };
  }
};
tu.className = "InputLayer";
re.registerClass(tu);
function lI(e) {
  if (e.batchShape == null && e.shape == null)
    throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
  if (e.batchShape != null && e.shape != null)
    throw new G("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
  let t = e.batchShape;
  e.shape != null && t == null && (t = [null].concat(e.shape));
  let n = e.dtype;
  return n == null && (n = "float32"), new tu({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
}
function _z(e, t) {
  if (e.dtype == null || e.dtype === t.dtype)
    return t;
  try {
    return le(t, e.dtype);
  } catch (n) {
    throw new G(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`);
  }
}
var ea = class {
  constructor(e) {
    if (this.id2Value = {}, this.id2Mask = {}, this.name2Id = {}, e instanceof ea)
      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] = _z(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 && De(this.id2Mask);
  }
};
var Ad = new ZS();
var Ed = new ZS();
function Az(e) {
  Ad != null && Ad.setMaxEntries(e), Ed != null && Ed.setMaxEntries(e);
}
function Fu(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 = Ad.get(c), d;
  if (p == null) {
    let f = Ez(i, t);
    p = f.sorted, d = f.recipientCounts, Ad.put(c, p), Ed.put(c, d);
  }
  d = {}, r || Object.assign(d, Ed.get(c));
  let h = new ea(t);
  for (let f = 0; f < p.length; ++f) {
    if (s != null) {
      let E = gm().numTensors;
      E > s.maxNumTensors && (s.maxNumTensors = E), E < s.minNumTensors && (s.minNumTensors = E);
    }
    let m = p[f], g = m.sourceLayer;
    if (g instanceof tu)
      continue;
    let b = [], y = [], v = [], x = false;
    for (let E of m.inputs) {
      let P = h.getValue(E), A = h.getMask(E);
      b.push(P), y.push(A), A != null && (x = true), r || (d[E.name]--, d[E.name] === 0 && !t.hasKey(E) && o.indexOf(E.name) === -1 && !P.isDisposed && E.sourceLayer.stateful !== true && v.push(P));
    }
    x && (n = n || {}, n.mask = y[0]);
    let k = ht(g.apply(b, n)), I = null;
    g.supportsMasking && (I = g.computeMask(b, y));
    let $ = Dz(m), R = Array.isArray($) ? $ : [$];
    for (let E = 0; E < R.length; ++E) {
      h.hasKey(R[E]) || h.add(R[E], k[E], Array.isArray(I) ? I[0] : I);
      let P = o.indexOf(R[E].name);
      P !== -1 && (u[P] = k[E]);
    }
    r || De(v);
  }
  return h.disposeMasks(), a ? u : u[0];
}
function Ez(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 = Ex(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 } = Ex(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: Rz(s) };
}
function Rz(e) {
  let t = {};
  for (let n in e)
    t[n] = e[n].size;
  return t;
}
function Ex(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 Dz(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 Fz = K();
Fz.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, Az);
var cI = { kernelName: po, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, Np(le(n, "float32"), -1)) };
} };
var Oz = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = ct(le(n, "float32")), r = dn(ge(we(1), s));
    return vt(xe(e, r));
  } };
} };
var Pz = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = dn(ge(ct(le(n, "float32")), 1));
    return xe(e, s);
  } };
} };
var zz = { kernelName: Cr, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = e, u = At(n.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(o, n.shape);
  }, b: () => {
    let o = e, u = At(s.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(o, s.shape);
  } };
} };
var Mz = { kernelName: Ia, saveAllInputs: true, gradFunc: (e, t) => {
  let n = {};
  return t.forEach((s, r) => {
    n[r] = () => e.clone();
  }), n;
} };
var Lz = { kernelName: Ca, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => je(n) };
} };
var Bz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => je(n) };
} };
var Vz = { kernelName: hl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, dn(ge(we(1), ct(le(n, "float32"))))) };
} };
var Wz = { kernelName: fl, 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 Uz = { kernelName: bl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = At(n.shape, r);
    return l.length > 0 && (u = ve(u, l)), U(u, n.shape);
  }, b: () => {
    let o = ie(ct(n), ct(s)), u = vt(V(e, xe(n, o))), l = At(s.shape, r);
    return l.length > 0 && (u = ve(u, l)), U(u, s.shape);
  } };
} };
var Gz = { kernelName: ml, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ie(ct(le(n, "float32")), 1)) };
} };
var Hz = { kernelName: gl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ge(we(1), ct(le(n, "float32")))) };
} };
function qz(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]])), F(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), F(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), hn("avgPool3dGrad", r, a);
  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = z.runKernel(fg, p, d);
  return c ? U(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
}
var jz = L({ avgPool3dGrad_: qz });
var Kz = { kernelName: Jd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
  return { x: () => jz(e, s, r, a, i, o) };
} };
function Xz(e, t, n, s, r) {
  let a = _(e, "dy", "avgPoolGrad"), i = _(t, "input", "avgPoolGrad");
  F(i.rank === a.rank, () => `Rank of input (${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]])), F(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), F(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = z.runKernel(hg, c, p);
  return l ? U(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var Yz = L({ avgPoolGrad_: Xz });
var Qz = { kernelName: Na, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { filterSize: r, strides: a, pad: i } = n;
  return { x: () => Yz(e, s, r, a, i) };
} };
var Zz = { kernelName: Ta, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
  let [s, r] = t, { transposeA: a, transposeB: i } = n;
  return !a && !i ? { a: () => Ve(e, r, false, true), b: () => Ve(s, e, true, false) } : !a && i ? { a: () => Ve(e, r, false, false), b: () => Ve(e, s, true, false) } : a && !i ? { a: () => Ve(r, e, false, true), b: () => Ve(s, e, false, false) } : { a: () => Ve(r, e, true, true), b: () => Ve(e, s, true, true) };
} };
var Jz = { kernelName: ho, gradFunc: (e, t, n) => {
  let { blockShape: s, crops: r } = n;
  return { x: () => cb(e, s, r) };
} };
var eM = { kernelName: V$, 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 tM = { kernelName: $a, gradFunc: (e) => ({ x: () => e.clone() }) };
var nM = { kernelName: _a, gradFunc: (e) => ({ x: () => je(e) }) };
var sM = { kernelName: Nr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
  return { x: () => vn(Ds(Zo(s, r), Jo(s, a)), e, je(e)) };
} };
var rM = { kernelName: tp, inputsToSave: ["x"], gradFunc: cI.gradFunc };
var aM = { kernelName: fo, saveAllInputs: true, gradFunc: (e, t, n) => {
  let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], i = s.map((u) => u[a]);
  return Bn(e, i, a).map((u) => () => u);
} };
var iM = { kernelName: Aa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
  return F(gr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => eb(s.shape, e, r, i, o, u), filter: () => xb(s, e, r.shape, i, o, u) };
} };
var oM = { kernelName: Ea, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
  return { dy: () => pa(e, r, a, i, o, 1, u), filter: () => xb(e, s, r.shape, a, i, o, u) };
} };
function uM(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]])), F(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), F(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), F(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), F(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), F(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
  let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
  return z.runKernel(yg, o, u);
}
var lM = L({ conv3DBackpropFilter_: uM });
var cM = { kernelName: np, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let { dilations: s, strides: r, pad: a } = n;
  F(gr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
  let [i, o] = t;
  return { x: () => hS(i.shape, e, o, r, a), filter: () => lM(i, e, o.shape, r, a) };
} };
var dM = { kernelName: Ra, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(vt(ES(le(n, "float32"))), e) };
} };
var pM = { kernelName: Da, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(RS(le(n, "float32")), e) };
} };
var hM = { kernelName: Fa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
  return { x: () => {
    let o = yS([r], s.rank), u = mS(e, r, a, !i);
    return o != null && (u = Ge(u, o)), u;
  } };
} };
var fM = { kernelName: Oa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let { dilations: s, strides: r, pad: a, dimRoundingMode: i } = n, o = s == null ? [1, 1] : s;
  F(gr(o), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);
  let [u, l] = t;
  return F(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), F(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), F(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), F(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: () => VS(u.shape, e, l, r, a, o, i), filter: () => BS(u, e, l.shape, r, a, o, i) };
} };
var mM = { kernelName: sp, 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: () => z.runKernel(sm, a, n), filter: () => z.runKernel(rm, i, n) };
} };
var gM = { kernelName: za, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t, s = { dy: e, y: n };
  return { x: () => z.runKernel(Ig, s) };
} };
var bM = { kernelName: yl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t, s = V(Yn(vt(ct(n))), 2 / Math.sqrt(Math.PI));
  return { x: () => V(e, s) };
} };
var yM = { kernelName: Ma, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, n) };
} };
var vM = { kernelName: vo, inputsToSave: ["input"], gradFunc: (e, t) => {
  let [n] = t;
  return { input: () => U(e, n.shape) };
} };
var xM = { kernelName: xo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, Yn(n)) };
} };
var wM = { kernelName: La, gradFunc: (e) => ({ x: () => je(e) }) };
var kM = { kernelName: Ba, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = xe(e, le(s, "float32")), u = At(n.shape, r);
    return u.length > 0 ? U(ve(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, le(n, "float32")), u = At(s.shape, r);
    u.length > 0 && (o = U(ve(o, u), s.shape));
    let l = ct(s);
    return vt(xe(o, le(l, "float32")));
  } };
} };
var SM = { kernelName: Va, 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 = At(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 = _S(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 IM = { kernelName: ko, 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 = Rx(0, p), m = Rx(p + 1, p + 1 + h), g = Dx([c, [l], d]), b = U(e, g), y = U(r, [l]), v = Dx([[p], f, m]), x = Ge(b, v), k = sF(x, y, s.shape[i]), I = sb(v);
    return k = Ge(k, I), k;
  }, indices: () => r };
} };
function Rx(e, t) {
  let n = [];
  for (let s = e; s < t; ++s)
    n.push(s);
  return n;
}
function Dx(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 CM = { kernelName: Wa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => je(n), b: () => je(s) };
} };
var NM = { kernelName: Ua, gradFunc: (e) => ({ x: () => le(e, "float32") }) };
var TM = { kernelName: xl, gradFunc: (e) => ({ x: () => je(e) }) };
var $M = { kernelName: wl, gradFunc: (e) => ({ x: () => je(e) }) };
var _M = { kernelName: kl, gradFunc: (e) => ({ x: () => je(e) }) };
var AM = { kernelName: Ga, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { alpha: r } = n, a = Un(s, 0);
  return { x: () => vn(a, e, V(e, r)) };
} };
var EM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ie(n, 1)) };
} };
var RM = { kernelName: Ha, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, le(n, "float32")) };
} };
var DM = { kernelName: W$, 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 FM(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 z.runKernel($g, o, u);
}
var OM = L({ localResponseNormalizationBackprop_: FM });
var PM = { kernelName: op, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
  return { x: () => OM(s, r, e, a, i, o, u) };
} };
function dI(e, t, n, s) {
  return t.rank < n.rank && (t = U(t, ha(t.shape, s))), e.rank < n.rank && (e = U(e, ha(e.shape, s))), { x: () => V(e, le(Xn(n, t), e.dtype)) };
}
var Fx = { kernelName: qa, 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 = dI(e, i, a, o);
  return { x: () => u.x() };
} };
var zM = { kernelName: ja, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => V(e, le(Zo(n, s), "float32")), b: () => V(e, le(wS(n, s), "float32")) };
} };
function MM(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]])), F(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), F(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), F(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), hn("maxPool3dGrad", a, i);
  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = z.runKernel(Ag, f, m);
  return h ? U(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
}
var LM = L({ maxPool3dGrad_: MM });
var BM = { kernelName: up, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
  return { x: () => LM(e, s, r, a, i, o, u) };
} };
function VM(e, t, n, s, r, a, i) {
  let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
  F(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), F(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), F(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 z.runKernel(_g, c, p);
}
var WM = L({ maxPoolGrad_: VM });
var UM = { kernelName: Ka, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { filterSize: a, strides: i, pad: o } = n;
  return { x: () => WM(e, s, r, a, i, o) };
} };
var GM = { kernelName: Xa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r } = n, a = ts(r, s.shape), o = bS(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 HM = { kernelName: Ya, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let s = n, { axis: r } = s, [a, i] = t, o = ts(r, a.shape), u = dI(e, i, a, o);
  return { x: () => u.x() };
} };
var qM = { kernelName: Qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => V(e, le(Jo(n, s), "float32")), b: () => V(e, le(Un(n, s), "float32")) };
} };
var jM = { kernelName: Za, 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 KM = { kernelName: Cl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = At(n.shape, r);
    return o.length > 0 ? U(ve(e, o), n.shape) : e;
  }, b: () => {
    let o = V(e, vt(Sp(xe(n, s)))), u = At(s.shape, r);
    return u.length > 0 ? U(ve(o, u), s.shape) : o;
  } };
} };
var XM = { kernelName: Ja, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = V(e, le(s, "float32")), u = At(n.shape, r);
    return u.length > 0 ? U(ve(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, le(n, "float32")), u = At(s.shape, r);
    return u.length > 0 ? U(ve(o, u), s.shape) : o;
  } };
} };
var YM = { kernelName: $o, gradFunc: (e) => ({ x: () => vt(e) }) };
var QM = { kernelName: Do, inputsToSave: ["indices"], gradFunc: (e, t) => {
  let n = t[0];
  return { indices: () => $t(n.shape, "float32") };
} };
var ZM = { kernelName: Ro, gradFunc: (e) => ({ x: () => je(e) }) };
var JM = { kernelName: Fo, saveAllInputs: true, gradFunc: (e, t, n) => {
  let { axis: s } = n;
  return Fs(e, s).map((a) => () => a);
} };
var Ox = { kernelName: ei, 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 eL = { kernelName: ti, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
  let [n, s, r] = t, a = n, i = s, o = rt(a.shape, i.shape);
  return { a: () => {
    let c = le(i, "float32"), p = V(e, V(c, fa(a, ge(c, we(1))))), d = At(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 = At(i.shape, o);
    return h.length > 0 && (d = ve(d, h)), U(d, i.shape);
  } };
} };
var tL = { kernelName: ni, 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 = At(s.shape, e.shape);
    return i.length > 0 && (a = ve(a, i)), U(a, s.shape);
  } };
} };
function nL(e, t, n) {
  let s = e.shape.slice();
  s[n] = 1;
  let r = U(t, s), a = wm(e, n, true, false), i = wm(e, n, true, true), o = V(a, i);
  return V(r, o);
}
function sL(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 = nL(c, t, r);
  if (p = p.reshape(i.shape), a != null) {
    let d = C.getUndoAxesPermutation(a);
    p = Ge(p, d);
  }
  return p;
}
var rL = { kernelName: si, 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: () => sL(s, e, a) };
} };
var aL = { kernelName: Pa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = xe(e, le(s, "float32")), u = At(n.shape, r);
    return u.length > 0 ? U(ve(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, le(n, "float32")), u = At(s.shape, r);
    u.length > 0 && (o = U(ve(o, u), s.shape));
    let l = ct(s);
    return vt(xe(o, le(l, "float32")));
  } };
} };
var iL = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, vt(ct(n))) };
} };
var oL = { kernelName: ii, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t, s = V(Jo(n, 6), Np(n));
  return { x: () => V(e, le(s, "float32")) };
} };
var uL = { kernelName: ri, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, le(Np(n), "float32")) };
} };
var lL = { kernelName: Oo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => U(e, n.shape) };
} };
var cL = { kernelName: ai, inputsToSave: ["images"], gradFunc: (e, t, n) => {
  let [s] = t, r = { dy: e, images: s };
  return { images: () => z.runKernel(Fg, r, n) };
} };
var dL = { kernelName: _l, inputsToSave: ["images"], gradFunc: (e, t, n) => {
  let [s] = t, r = { dy: e, images: s };
  return { images: () => z.runKernel(Dg, r, n) };
} };
var pL = { kernelName: Po, gradFunc: (e, t, n) => {
  let { dims: s } = n, r = ts(s, e.shape);
  return { x: () => Jn(e, r) };
} };
var hL = { kernelName: zo, gradFunc: (e) => ({ x: () => je(e) }) };
var fL = { kernelName: oi, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => vt(xe(e, V(fa(n, 1.5), 2))) };
} };
var mL = { kernelName: Lo, 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(ob(n), e.dtype)) };
} };
var gL = { kernelName: Al, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = Un(n, we(0)), r = we(KS), a = we(XS), i = V(e, a), o = V(V(e, r), Yn(le(n, "float32")));
    return vn(s, i, o);
  } };
} };
var bL = { kernelName: li, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, V(n, ge(we(1), n))) };
} };
var yL = { kernelName: El, gradFunc: (e) => ({ x: () => je(e) }) };
var vL = { kernelName: ui, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(tb(le(n, "float32")), e) };
} };
var xL = { kernelName: Vo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(fS(le(n, "float32")), e) };
} };
var wL = { kernelName: Bo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = Jk(s, r, a), l = [];
  for (let c = 0; c < e.rank; c++)
    l.push([o[c], i[c] - o[c] - u[c]]);
  return { x: () => bi(e, l) };
} };
var kL = { kernelName: pi, 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 SL = { kernelName: Rl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, qs(n)) };
} };
var Px = { kernelName: Wo, gradFunc: (e, t, n) => {
  let { blockShape: s, paddings: r } = n;
  return { x: () => Jg(e, s, r) };
} };
var zx = { kernelName: Uo, gradFunc: (e, t, n) => {
  let { axis: s } = n;
  return { x: () => Ot(e, s) };
} };
var IL = { kernelName: ci, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, V(dn(le(n, "float32")), 2)) };
} };
var CL = { kernelName: Fl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, V(le(n, "float32"), 2)) };
} };
var NL = { kernelName: hi, 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 TL = { kernelName: gi, gradFunc: (e) => ({ x: () => je(e) }) };
var $L = { kernelName: fi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = rt(n.shape, s.shape);
  return { a: () => {
    let o = e, u = At(n.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(o, n.shape);
  }, b: () => {
    let o = e, u = At(s.shape, r);
    return u.length > 0 && (o = ve(o, u)), U(vt(o), s.shape);
  } };
} };
var _L = { kernelName: di, 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 AL = { kernelName: Ho, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ct(tb(n))) };
} };
var EL = { kernelName: mi, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(ge(we(1), ct(n)), e) };
} };
var RL = { kernelName: Tr, 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 DL = { kernelName: Hs, gradFunc: (e, t, n) => {
  let s = n, { perm: r } = s, a = sb(r);
  return { x: () => Ge(e, a) };
} };
var FL = { kernelName: Ko, gradFunc: (e, t, n) => {
  let s = n, { axis: r } = s;
  return { value: () => es(e, r) };
} };
var OL = { kernelName: mp, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => PL(e, n) };
} };
function PL(e, t) {
  let n = Ar(t, je(t)), s = Ju(e, n), r = Zo(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 zL = { kernelName: Xo, gradFunc: (e) => ({ x: () => je(e) }) };
var ML = [cI, Oz, Pz, zz, Mz, Lz, Bz, Vz, Wz, Uz, Gz, Hz, Kz, Qz, Zz, Jz, eM, tM, nM, sM, rM, aM, oM, iM, cM, dM, pM, hM, fM, mM, aL, gM, bM, yM, vM, xM, kM, wM, SM, IM, CM, NM, TM, $M, _M, AM, EM, RM, DM, PM, Fx, Fx, zM, BM, UM, GM, HM, qM, jM, KM, XM, YM, QM, ZM, JM, Ox, Ox, eL, tL, rL, iL, oL, uL, lL, cL, dL, pL, hL, fL, mL, gL, bL, yL, vL, xL, wL, kL, SL, Px, Px, zx, zx, IL, NL, CL, TL, $L, _L, AL, EL, RL, DL, FL, OL, zL];
for (let e of ML)
  G$(e);
var LL = {};
Ee(LL, { maxNorm: () => BL, minMaxNorm: () => UL, nonNeg: () => WL, unitNorm: () => VL });
function Bb(e, t) {
  return q(() => dn(ve(V(e, e), t, true)));
}
var jl = class extends re.Serializable {
  getConfig() {
    return {};
  }
};
var Vb = class extends jl {
  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 = Bb(e, this.axis), n = Vn(t, 0, this.maxValue);
      return V(e, xe(n, ie(Dt(), t)));
    });
  }
  getConfig() {
    return { maxValue: this.maxValue, axis: this.axis };
  }
};
Vb.className = "MaxNorm";
re.registerClass(Vb);
var Wb = class extends jl {
  constructor(e) {
    super(), this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
  }
  apply(e) {
    return q(() => xe(e, ie(Dt(), Bb(e, this.axis))));
  }
  getConfig() {
    return { axis: this.axis };
  }
};
Wb.className = "UnitNorm";
re.registerClass(Wb);
var Ub = class extends jl {
  apply(e) {
    return Ys(e);
  }
};
Ub.className = "NonNeg";
re.registerClass(Ub);
var Gb = class extends jl {
  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 = Bb(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(Dt(), t)));
    });
  }
  getConfig() {
    return { minValue: this.minValue, maxValue: this.maxValue, rate: this.rate, axis: this.axis };
  }
};
Gb.className = "MinMaxNorm";
re.registerClass(Gb);
var Mx = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
function Pt(e) {
  return _b(e);
}
function Lx(e, t = {}) {
  return Ul(e, re.SerializationMap.getMap().classNameMap, t, "constraint");
}
function zt(e) {
  if (e == null)
    return null;
  if (typeof e == "string") {
    let n = { className: e in Mx ? Mx[e] : e, config: {} };
    return Lx(n);
  } else
    return e instanceof jl ? e : Lx(e);
}
function BL(e) {
  return new Vb(e);
}
function VL(e) {
  return new Wb(e);
}
function WL() {
  return new Ub();
}
function UL(e) {
  return new Gb(e);
}
var GL = {};
Ee(GL, { constant: () => jL, glorotNormal: () => eB, glorotUniform: () => JL, heNormal: () => tB, heUniform: () => nB, identity: () => QL, leCunNormal: () => sB, leCunUniform: () => rB, ones: () => qL, orthogonal: () => aB, randomNormal: () => XL, randomUniform: () => KL, truncatedNormal: () => YL, varianceScaling: () => ZL, zeros: () => HL });
function HL() {
  return new Rb();
}
function qL() {
  return new Op();
}
function jL(e) {
  return new Db(e);
}
function KL(e) {
  return new Fb(e);
}
function XL(e) {
  return new Ob(e);
}
function YL(e) {
  return new Pb(e);
}
function QL(e) {
  return new zb(e);
}
function ZL(e) {
  return new xn(e);
}
function JL(e) {
  return new Pp(e);
}
function eB(e) {
  return new zp(e);
}
function tB(e) {
  return new Mp(e);
}
function nB(e) {
  return new Lp(e);
}
function sB(e) {
  return new Bp(e);
}
function rB(e) {
  return new Vp(e);
}
function aB(e) {
  return new Mb(e);
}
var iB = {};
Ee(iB, { Layer: () => He, RNN: () => Rr, RNNCell: () => Yl, activation: () => EV, add: () => BV, alphaDropout: () => SW, average: () => VV, averagePooling1d: () => Jy, averagePooling2d: () => ev, averagePooling3d: () => tv, avgPool1d: () => YV, avgPool2d: () => ZV, avgPool3d: () => eW, avgPooling1d: () => QV, avgPooling2d: () => JV, avgPooling3d: () => tW, batchNormalization: () => jV, bidirectional: () => mW, concatenate: () => WV, conv1d: () => kV, conv2d: () => SV, conv2dTranspose: () => IV, conv3d: () => CV, conv3dTranspose: () => NV, convLstm2d: () => dW, convLstm2dCell: () => pW, cropping2D: () => $V, dense: () => RV, depthwiseConv2d: () => AV, dot: () => qV, dropout: () => DV, elu: () => gV, embedding: () => LV, flatten: () => OV, gaussianDropout: () => kW, gaussianNoise: () => wW, globalAveragePooling1d: () => nW, globalAveragePooling2d: () => sW, globalMaxPool1d: () => bW, globalMaxPool2d: () => yW, globalMaxPooling1d: () => s0, globalMaxPooling2d: () => r0, gru: () => aW, gruCell: () => iW, input: () => nV, inputLayer: () => mV, layerNormalization: () => KV, leakyReLU: () => yV, lstm: () => oW, lstmCell: () => uW, masking: () => IW, maxPool1d: () => vW, maxPool2d: () => xW, maxPooling1d: () => a0, maxPooling2d: () => i0, maxPooling3d: () => rW, maximum: () => UV, minimum: () => GV, multiply: () => HV, permute: () => MV, prelu: () => vV, reLU: () => bV, repeatVector: () => PV, reshape: () => zV, rnn: () => hW, separableConv2d: () => TV, simpleRNN: () => lW, simpleRNNCell: () => cW, softmax: () => xV, spatialDropout1d: () => FV, stackedRNNCells: () => fW, thresholdedReLU: () => wV, timeDistributed: () => gW, upSampling2d: () => _V, zeroPadding2d: () => XV });
async function rr(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];
    De(s);
  }
}
function pI(e) {
  if (e != null)
    for (let t in e) {
      let n = e[t];
      typeof n != "number" && n.dispose();
    }
}
var oB = 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 uB = 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 lB = 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 cB = 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 dB = class extends so {
  constructor(e, t) {
    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || jS, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = oB), 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 = tz(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 rr(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 rr(t), await this.epochBegin(e, t));
  }
  async onEpochEnd(e, t) {
    let n = [];
    this.epochEnd != null && (await rr(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 rr(t), await this.batchBegin(e, t));
  }
  async onBatchEnd(e, t) {
    let n = [];
    this.batchEnd != null && (await rr(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 rr(e), await this.trainBegin(e));
  }
  async onTrainEnd(e) {
    this.trainEnd != null && (await rr(e), await this.trainEnd(e));
  }
};
function hI(e, t) {
  return e == null && (e = {}), e instanceof so ? [e] : Array.isArray(e) && e[0] instanceof so ? e : ht(e).map((s) => new dB(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 Hb = Ss;
Hb.constructors = {};
function fI(e, t, n, s, r, a, i, o, u) {
  let l = new cB(), c = [new lB(), ...Hb.createCallbacks(t)];
  e != null && c.push(...e), c.push(l);
  let p = new uB(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 Ul(e, re.SerializationMap.getMap().classNameMap, t, "layer", n);
}
function Rd(e, t) {
  return q(() => {
    e.dtype !== "float32" && (e = le(e, "float32"));
    let n = ve(Hl(e), t, true), s = Bl(n.shape, Dt()), r = dn(Ar(n, s));
    return xe(e, r);
  });
}
function vi(e, t) {
  return q(() => It(Hl(ge(t, e)), -1));
}
function Up(e, t) {
  return q(() => It(Lt(ge(t, e)), -1));
}
function nu(e, t) {
  return q(() => {
    let n = ge(e, t), s = Vn(Lt(e), Dt(), Number.MAX_VALUE), r = Lt(xe(n, s));
    return V(100, It(r, -1));
  });
}
function pB(e, t) {
  return q(() => {
    let n = Vn(t, Dt(), Number.MAX_VALUE), s = Qn(ie(1, n)), r = Vn(e, Dt(), Number.MAX_VALUE), a = Qn(ie(1, r));
    return It(Hl(ge(s, a)), -1);
  });
}
function hB(e, t) {
  return q(() => {
    let n = Ar(0, ge(1, V(e, t)));
    return It(Hl(n), -1);
  });
}
function fB(e, t) {
  return q(() => {
    let n = Ar(0, ge(1, V(e, t)));
    return It(n, -1);
  });
}
function mB(e, t) {
  return q(() => {
    let n = ve(V(e, t), -1), s = As(V(ge(1, e), t), -1);
    return Ar(0, ie(1, ge(s, n)));
  });
}
function gB(e, t) {
  return q(() => {
    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Vl(V(-2, s))), n);
    return It(r, -1);
  });
}
function nl(e, t, n = false) {
  return q(() => {
    if (n)
      t = gb(t);
    else {
      let s = ve(t, t.shape.length - 1, true);
      t = xe(t, s);
    }
    return t = Vn(t, Dt(), 1 - Dt()), vt(ve(V(le(e, "float32"), Qn(t)), t.shape.length - 1));
  });
}
function Dd(e, t, n = false) {
  return q(() => {
    let s = le(Sp(hz(e)), "int32");
    t = Vn(t, Dt(), 1 - Dt());
    let r = t.shape, a = U(Id(s, r[r.length - 1]), r);
    return nl(a, t, n);
  });
}
function bB(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 = Ys(t), s = vt(Lt(t));
    return ie(ge(n, V(t, e)), ib(Yn(s)));
  });
}
function Gp(e, t) {
  return q(() => {
    let n;
    return n = Vn(t, Dt(), 1 - Dt()), n = Qn(xe(n, ge(1, n))), It(bB(e, n), -1);
  });
}
function yB(e, t) {
  return q(() => {
    let n = Vn(e, Dt(), 1), s = Vn(t, Dt(), 1);
    return ve(V(e, Qn(xe(n, s))), -1);
  });
}
function vB(e, t) {
  return q(() => {
    let n = Qn(ie(Dt(), t));
    return It(ge(t, V(e, n)), -1);
  });
}
function qb(e, t) {
  return q(() => {
    let n = Rd(e, -1), s = Rd(t, -1), r = V(n, s);
    return vt(ve(r, -1));
  });
}
var Fd = { meanSquaredError: vi, meanAbsoluteError: Up, meanAbsolutePercentageError: nu, meanSquaredLogarithmicError: pB, squaredHinge: hB, hinge: fB, categoricalHinge: mB, logcosh: gB, categoricalCrossentropy: nl, sparseCategoricalCrossentropy: Dd, binaryCrossentropy: Gp, kullbackLeiblerDivergence: yB, poisson: vB, cosineProximity: qb };
function Yf(e) {
  if (typeof e == "string") {
    if (e in Fd)
      return Fd[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 jb(e, t) {
  return q(() => {
    let n = V(0.5, Zn(t)), s = Dp(Un(t, n), e.dtype);
    return It(Xn(e, s), -1);
  });
}
function Kb(e, t) {
  return q(() => Dp(Xn(Yu(e, -1), Yu(t, -1)), "float32"));
}
function mI(e, t) {
  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 1))), "float32"));
}
function xB(e, t) {
  return q(() => le(ve(Ds(Xn(e, 1), Xn(t, 0))), "float32"));
}
function wB(e, t) {
  return q(() => le(ve(Ds(Xn(e, 0), Xn(t, 1))), "float32"));
}
function gI(e, t) {
  return q(() => {
    let n = mI(e, t), s = wB(e, t), r = ie(n, s);
    return le(vn(Un(r, 0), xe(n, r), 0), "float32");
  });
}
function kB(e, t) {
  return q(() => {
    let n = mI(e, t), s = xB(e, t), r = ie(n, s);
    return le(vn(Un(r, 0), xe(n, r), 0), "float32");
  });
}
function bI(e, t) {
  return Gp(e, t);
}
function yI(e, t) {
  return e.rank === t.rank && (e = br(e, [e.rank - 1])), t = Yu(t, -1), t.dtype !== e.dtype && (t = le(t, e.dtype)), le(Xn(e, t), "float32");
}
var SB = vi;
var IB = vi;
var CB = Up;
var NB = Up;
var TB = nu;
var $B = nu;
var Xb = nl;
var _B = qb;
var vI = Dd;
var Od = { binaryAccuracy: jb, categoricalAccuracy: Kb, precision: gI, categoricalCrossentropy: Xb, sparseCategoricalCrossentropy: vI, mse: SB, MSE: IB, mae: CB, MAE: NB, mape: TB, MAPE: $B, cosine: _B };
function AB(e) {
  if (typeof e == "string" && e in Od)
    return Od[e];
  if (typeof e != "string" && e != null)
    return e;
  throw new G(`Unknown metric ${e}`);
}
function Yc(e) {
  if (Cs(e !== null, `Unknown LossOrMetricFn ${e}`), typeof e == "string")
    return e;
  {
    let t;
    for (let n of Object.keys(Fd))
      if (Fd[n] === e) {
        t = n;
        break;
      }
    if (t !== void 0)
      return t;
    for (let n of Object.keys(Od))
      if (Od[n] === e) {
        t = n;
        break;
      }
    return t !== void 0 ? t : e.name;
  }
}
function EB(e) {
  let t = { Adagrad: () => Li.adagrad(0.01), Adadelta: () => Li.adadelta(1, 0.95, Dt()), Adam: () => Li.adam(1e-3, 0.9, 0.999, Dt()), Adamax: () => Li.adamax(2e-3, 0.9, 0.999, Dt(), 0), RMSProp: () => Li.rmsprop(1e-3, 0.9, 0, Dt()), SGD: () => Li.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 ${e}`);
}
var Bx = 1 * 1024 * 1024;
function Vx(e, t, n = false) {
  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !$m(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 > Bx && 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 <= ${Bx}.`);
  }
}
function $m(e) {
  if (e === null)
    return true;
  if (typeof e == "object")
    if (Object.getPrototypeOf(e) === Object.prototype) {
      let t = Object.keys(e);
      for (let n of t)
        if (typeof n != "string" || !$m(e[n]))
          return false;
      return true;
    } else if (Array.isArray(e)) {
      for (let t of e)
        if (!$m(t))
          return false;
      return true;
    } else
      return false;
  else {
    let t = typeof e;
    return t === "string" || t === "number" || t === "boolean";
  }
}
function RB(e, t, n, s = console.log) {
  let r = FB(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)), Pd(a, n, s), s("=".repeat(t));
  let o = e.layers;
  for (let c = 0; c < o.length; ++c)
    r ? OB(o[c], n, s) : PB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
  e.checkTrainableWeightsConsistency();
  let u = DB(e), l = _d(e.nonTrainableWeights);
  s(`Total params: ${u + l}`), s(`Trainable params: ${u}`), s(`Non-trainable params: ${l}`), s("_".repeat(t));
}
function DB(e) {
  let t;
  return e.collectedTrainableWeights != null ? t = _d(e.collectedTrainableWeights) : t = _d(e.trainableWeights), t;
}
function FB(e) {
  let t = true, n = [], s = [];
  for (let r in e.nodesByDepth)
    n.push(e.nodesByDepth[r]);
  for (let r of n) {
    if (r.length > 1 || r.length === 1 && r[0].inboundLayers.length > 1) {
      t = false;
      break;
    }
    s.push(...r);
  }
  if (t)
    for (let r of e.layers) {
      let a = false;
      for (let i of r.inboundNodes)
        if (s.indexOf(i) !== -1)
          if (a) {
            t = false;
            break;
          } else
            a = true;
      if (!t)
        break;
    }
  return t;
}
function Pd(e, t, n = console.log) {
  let s = "";
  for (let r = 0; r < e.length; ++r)
    r > 0 && (s = s.slice(0, s.length - 1) + " "), s += e[r], s = s.slice(0, t[r]), s += " ".repeat(t[r] - s.length);
  n(s);
}
function OB(e, t, n) {
  let s, r;
  try {
    r = e.inboundNodes.map((u) => JSON.stringify(u.inputShapes)).join(",");
  } catch (u) {
    r = "multiple";
  }
  try {
    s = JSON.stringify(e.outputShape);
  } catch (u) {
    s = "multiple";
  }
  let a = e.name, i = e.getClassName(), o = [`${a} (${i})`, r, s, e.countParams().toString()];
  Pd(o, t, n);
}
function PB(e, t, n, s) {
  let r, a;
  try {
    a = e.inboundNodes.map((p) => JSON.stringify(p.inputShapes)).join(",");
  } catch (p) {
    a = "multiple";
  }
  try {
    r = JSON.stringify(e.outputShape);
  } catch (p) {
    r = "multiple";
  }
  let i = [];
  for (let p of e.inboundNodes)
    if (!(n != null && n.length > 0 && n.indexOf(p) === -1))
      for (let d = 0; d < p.inboundLayers.length; ++d) {
        let h = p.inboundLayers[d].name, f = p.nodeIndices[d], m = p.tensorIndices[d];
        i.push(`${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];
  Pd(c, t, s);
  for (let p = 1; p < i.length; ++p)
    Pd(["", "", "", "", i[p]], t, s);
}
function xI(e, t, n) {
  return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
}
function sl(e, t) {
  if (e === null)
    return null;
  if (typeof e == "string")
    return Qr(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];
      xI(t, r, a) ? n.push(a) : n.push(sl(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 = Qr(s);
        n[a] = sl(r, a);
      }
    }
    return n;
  }
}
function _m(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];
      xI(t, r, a) ? n.push(a) : n.push(_m(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] = _m(r, s);
    }
    return n;
  }
}
var wI = "0.0.0";
var Is = class extends He {
  constructor(e) {
    if (super({}), this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
      let b = this.getClassName().toLowerCase();
      this.name = Rp(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], cr(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)}`);
    cr(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 tu))
        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 R = $.inboundLayers.length;
      for (let E = 0; E < R; E++) {
        let P = $.inputTensors[E], A = $.inboundLayers[E], O = $.nodeIndices[E], T = $.tensorIndices[E];
        o(P, y, v, A, O, T);
      }
      for (y.push($); 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], R = t[$.id] == null ? 0 : t[$.id];
        t[$.id] = Math.max(y + 1, R), n[$.id] = $;
      }
    }
    let p = {};
    for (let b in t) {
      let y = t[b];
      y in p || (p[y] = []), p[y].push(n[b]);
    }
    let d = {};
    for (let b in s) {
      let y = s[b];
      y in d || (d[y] = []), d[y].push(r[b]);
    }
    let h = Object.keys(d).map((b) => parseInt(b, 10)).sort(jc);
    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(jc);
    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: ${m}`);
          for (let x of y.outputTensors)
            f.push(x);
          m.push(v.name);
        }
      }
    this.nodesByDepth = p;
    let g = this.layers.map((b) => b.name);
    for (let b of g) {
      let y = g.filter((v) => v === b).length;
      if (y !== 1)
        throw new fs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(g));
    }
    this.outboundNodes = [], this.inboundNodes = [], new Wp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
  }
  assertNotDisposed() {
    if (this._refCount === 0)
      throw new Error(`Container '${this.name}' is already disposed.`);
  }
  dispose() {
    this.assertNotDisposed();
    let e = { refCountAfterDispose: null, numDisposedVariables: 0 };
    if (--this._refCount === 0) {
      for (let t of this.layers)
        e.numDisposedVariables += t.dispose().numDisposedVariables;
      for (let t of this.internalContainerRefs)
        e.numDisposedVariables += t.dispose().numDisposedVariables;
    }
    return e.refCountAfterDispose = this._refCount, e;
  }
  get trainable() {
    return this.trainable_;
  }
  set trainable(e) {
    this.layers.forEach((t) => {
      t._trainableWeights.forEach((n) => n.trainable = e);
    }), this.trainable_ = e;
  }
  get trainableWeights() {
    if (this._trainableWeights.length > 0)
      throw new 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}`);
    }
    Lb(r);
  }
  updatedConfig() {
    let e = this.getConfig(), t = {};
    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${wI}`, t.backend = "TensorFlow.js", t;
  }
  toJSON(e, t = true) {
    let n = _m(this.updatedConfig());
    return t ? JSON.stringify(n) : n;
  }
  call(e, t) {
    return q(() => {
      e = ht(e);
      let n = new ea();
      for (let s = 0; s < this.inputs.length; ++s)
        n.add(this.inputs[s], e[s]);
      return Fu(this.outputs, n, t);
    });
  }
  computeMask(e, t) {
    return q(() => {
      e = ht(e);
      let n;
      return t == null ? n = ga(null, e.length) : n = ht(t), this.runInternalGraph(e, n)[1];
    });
  }
  computeOutputShape(e) {
    let t = $d(e);
    if (t.length !== this.inputLayers.length)
      throw new G(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);
    let n = {};
    for (let i = 0; i < t.length; i++) {
      let o = this.inputLayers[i], u = t[i], l = o.name + "_0_0";
      n[l] = u;
    }
    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(jc);
    if (s.length > 1)
      for (let i of s) {
        let o = this.nodesByDepth[i];
        for (let u of o) {
          let l = u.outboundLayer;
          if (this.inputLayers.map((f) => f.id).indexOf(l.id) !== -1)
            continue;
          let c = [];
          for (let f = 0; f < u.inboundLayers.length; f++) {
            let m = u.inboundLayers[f], g = u.nodeIndices[f], b = u.tensorIndices[f], y = `${m.name}_${g}_${b}`, v = n[y];
            c.push(v);
          }
          let p = l.computeOutputShape(bn(c)), d = $d(p), h = l.inboundNodes.indexOf(u);
          for (let f = 0; f < d.length; f++) {
            let m = `${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 = ga(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(jc);
    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 = ht(c.call(v, f)), y = ht(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 = ht(c.call(m, f)), y = ht(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 R = $.inboundNodes[k];
        b.push(R.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 (; !ez(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 zB(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: ${JSON.stringify(e)}`);
}
function kI(e, t) {
  return zB(e, t, "classWeight");
}
async function SI(e, t, n, s) {
  if (t != null || s != null)
    throw new Error("Support sampleWeight is not implemented yet");
  if (n != null) {
    let r = q(() => {
      if (e.shape.length === 1)
        return lr(e);
      if (e.shape.length === 2) {
        if (e.shape[1] > 1)
          return Yu(e, 1);
        if (e.shape[1] === 1)
          return U(e, [e.shape[0]]);
        throw new Error(`Encountered unexpected last-dimension size (${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());
    De(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 MB(e, t) {
  return V(e, t);
}
var LB = 32;
function II(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 = Wx("input", e.inputNames, n), i = Wx("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 Wx(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 BB(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 VB(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 (Ux(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 = BB(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 = hI(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = fI(c, p, n.epochs, null, null, WB(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 } = II(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 = kI(n.classWeight, e.outputNames);
            for (let A = 0; A < P.length; ++A)
              $.push(await SI(k[A], null, P[A]));
          }
          let R = x.concat(k).concat($), E = o(R);
          De(R);
          for (let P = 0; P < u.length; ++P) {
            let A = u[P], O = E[P];
            I[A] = O, qt(O);
          }
          await d.onBatchEnd(y, I), pI(I), y++, b++;
        }
        if (s ? b >= n.batchesPerEpoch : v.done) {
          if (r) {
            let x;
            Ux(n.validationData) ? x = ht(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = ht(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? LB : 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 WB(e, t) {
  let n = null;
  return t.batchesPerEpoch != null ? n = t.batchesPerEpoch : Number.isFinite(e.size) && (n = e.size), n;
}
function Ux(e) {
  return typeof e.iterator == "function";
}
function UB(e) {
  return typeof e.next == "function";
}
async function GB(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 = UB(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 } = II(e, l.value), d = c.concat(p), h = q(() => r(d));
        if (De(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 && De(b);
        }
        De(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), De(c);
  }
  return bn(a);
}
function Am(e) {
  w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
}
function Ou(e, t, n) {
  return e == null ? [null] : Array.isArray(e) ? e.map((s) => ra(s, t, n - t)) : ra(e, t, n - t);
}
function Yb(e, t) {
  return q(() => e == null ? null : Array.isArray(e) ? e.map((n) => Yb(n, t)) : iI(e, t.dtype === "int32" ? t : le(t, "int32")));
}
function Em(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 HB(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 } = fI(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), $ = Em(g, r);
      for (let R = 0; R < $.length; ++R) {
        let E = {};
        if (await y.onBatchBegin(R, E), q(() => {
          let P = $[R][0], A = $[R][1], O = ra(I, P, A - P);
          E.batch = R, E.size = A - P;
          let T = Yb(n, O), M = t(T);
          for (let W = 0; W < s.length; ++W) {
            let j = s[W], X = M[W];
            E[j] = X, qt(X);
          }
          if (R === $.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(R, E), pI(E), 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 qB(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;
    Am(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 E = true, P = await e.standardizeUserData(u, l, null, null, E, 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 E = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
      c = Ou(r, E, P), i = r, r = Ou(r, 0, E), p = Ou(a, E, P), o = a, a = Ou(a, 0, E), 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((E) => "val_" + E))) : (k = null, b = [], I = x.slice());
    let $ = hI(s.callbacks, s.yieldEvery);
    return await HB(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 && De(d);
  }
}
function CI(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(Gl(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 jB(e) {
  return e instanceof et;
}
function Rm(e) {
  return Array.isArray(e);
}
function Gx(e) {
  return !jB(e) && !Rm(e);
}
function Hx(e, t, n, s = true, r = "") {
  if (t == null || t.length === 0) {
    if (e != null) {
      let i = false;
      if (Rm(e) && e.length > 0)
        i = true;
      else if (Gx(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 ${e}`);
    }
    return [];
  }
  if (e == null)
    return t.map((i) => null);
  let a;
  if (Gx(e)) {
    e = e, a = [];
    for (let i of t) {
      if (e[i] == null)
        throw new G(`No data provided for "${i}". Need data for each key in: ${t}`);
      a.push(e[i]);
    }
  } else if (Rm(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 = CI(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 KB(e, t, n) {
  let s = cr(e.map((a) => a.shape[0]));
  s.sort();
  let r = cr(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 XB(e, t, n) {
  let s = [vi, Gp, nl];
  for (let r = 0; r < e.length; ++r) {
    let a = e[r], i = t[r], o = n[r];
    if (i != null) {
      if (i === nl && 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 qx(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 YB(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: ${e}`);
  if (Array.isArray(n))
    return t.map((s) => n);
  {
    let s = [];
    for (let r of t) {
      let a = n.hasOwnProperty(r) ? n[r] : [];
      Array.isArray(a) || (a = [a]), s.push(a);
    }
    return s;
  }
}
var QB = "layers-model";
var pr = class extends Is {
  constructor(e) {
    super(e), this.isTraining = false;
  }
  summary(e, t, n = console.log) {
    if (!this.built)
      throw new G("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");
    RB(this, e, t, n);
  }
  compile(e) {
    if (e.loss == null && (e.loss = []), this.loss = e.loss, typeof e.optimizer == "string")
      this.optimizer_ = EB(e.optimizer), this.isOptimizerOwned = true;
    else {
      if (!(e.optimizer instanceof Er))
        throw new G("User-defined optimizer must be an instance of tf.Optimizer.");
      this.optimizer_ = e.optimizer, this.isOptimizerOwned = false;
    }
    let t = [];
    if (!Array.isArray(e.loss) && typeof e.loss != "string" && typeof e.loss != "function") {
      e.loss = e.loss;
      for (let a in e.loss)
        if (this.outputNames.indexOf(a) === -1)
          throw new G(`Unknown entry in loss dictionary: "${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(Yf(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) => Yf(i));
    } else {
      let a = Yf(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 = [], sa("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 = YB(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]);
    };
    sa("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] === Gp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = jb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = bI) : this.lossFunctions[a] === Dd ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = yI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = vI) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Kb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Xb);
              let g;
              ["accuracy", "acc"].indexOf(h) !== -1 ? g = "acc" : ["crossentropy", "ce"].indexOf(h) !== -1 && (g = "ce"), d = p, c = l + g;
            } else
              d = AB(h), c = l + Yc(h);
            let f;
            sa(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;
    Am(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(), GB(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 ${s} shoud be specified.`);
    return r;
  }
  execute(e, t) {
    if (Array.isArray(t) && t.length === 0)
      throw new G("`outputs` is an empty Array, which is not allowed.");
    let n = Array.isArray(t), s = n ? t : [t], r = this.retrieveSymbolicTensors(s), a = new ea();
    if (e instanceof et && (e = [e]), Array.isArray(e)) {
      if (e.length !== this.inputs.length)
        throw new G(`The number of inputs provided (${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 = Fu(r, a);
    return n ? i : i[0];
  }
  retrieveSymbolicTensors(e) {
    let t = ga(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 = Em(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 = Ou(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 ea(p);
          return Fu(this.outputs, d);
        }).forEach((u, l) => a[l].push(u));
      return bn(a.map((i) => Ot(i, 0)));
    });
  }
  predict(e, t = {}) {
    let n = CI(e);
    qx(n, this.inputNames, this.feedInputShapes, false);
    try {
      let s = t.batchSize == null ? 32 : t.batchSize;
      return Am(s), this.predictLoop(n, s);
    } finally {
      ps(n, e);
    }
  }
  predictOnBatch(e) {
    qx(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] === Dd ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
    }
    if (e = Hx(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Hx(t, this.feedOutputNames, r, false, "target"), KB(e, t, null), XB(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: ${e[0].shape[0]} sample(s).`);
    return [e, t];
  }
  async standardizeUserData(e, t, n, s, r = true, a) {
    let [i, o] = this.standardizeUserDataXY(e, t, r, a);
    if (n != null)
      throw new Error("sample weight is not supported yet.");
    let u = null;
    if (s != null) {
      let l = kI(s, this.outputNames);
      u = [];
      for (let c = 0; c < l.length; ++c)
        u.push(await SI(o[c], null, l[c]));
    }
    return [i, o, u];
  }
  testLoop(e, t, n, s = 0, r) {
    return q(() => {
      let a = this.checkNumSamples(t, n, r, "steps"), i = [];
      if (s > 0)
        throw new Fe("Verbose mode is not implemented yet.");
      if (r != null)
        throw new Fe("steps mode in testLoop() is not implemented yet");
      {
        let o = Em(a, n), u = Zt(ys(0, a));
        for (let l = 0; l < o.length; ++l) {
          let c = o[l][0], p = o[l][1], d = ra(u, c, p - c), h = Yb(t, d), f = e(h);
          if (l === 0)
            for (let m = 0; m < f.length; ++m)
              i.push(we(0));
          for (let m = 0; m < f.length; ++m) {
            let g = f[m];
            i[m] = ie(i[m], V(p - c, g));
          }
        }
        for (let l = 0; l < i.length; ++l)
          i[l] = xe(i[l], a);
      }
      return i;
    });
  }
  getDedupedMetricsNames() {
    let e = this.metricsNames, t = [];
    for (let n = 0; n < e.length; ++n) {
      let s = e[n], r = s;
      Cx(e, s) > 1 && (r += `_${Cx(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 ea(c), d = Fu(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 = MB(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 ea(a), o = Fu(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 qB(this, e, t, n);
  }
  async fitDataset(e, t) {
    return VB(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 De(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 = gm().numTensors;
      this.optimizer_.dispose(), e.numDisposedVariables += t - gm().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(Yc(this.metrics))];
    if (Array.isArray(this.metrics))
      return this.metrics.map((e) => Vs(Yc(e)));
    {
      let e = {};
      for (let t in this.metrics)
        e[t] = Vs(Yc(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 = sl(e.optimizer_config), n = gs(t), s;
    if (typeof e.loss == "string")
      s = Qr(e.loss);
    else if (Array.isArray(e.loss))
      s = e.loss.map((a) => Qr(a));
    else if (e.loss != null) {
      s = {};
      for (let a in e.loss)
        s[a] = Qr(e.loss[a]);
    }
    let r;
    if (Array.isArray(e.metrics))
      r = e.metrics.map((a) => Qr(a));
    else if (e.metrics != null) {
      r = {};
      for (let a in e.metrics)
        r[a] = Qr(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 '${e}'`);
      e = u[0];
    }
    if (e.save == null)
      throw new G("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
    let n = await An.encodeWeights(this.getNamedWeights(t)), s = false, r = null, i = { modelTopology: this.toJSON(r, s), format: QB, generatedBy: `TensorFlow.js tfjs-layers v${wI}`, 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 && (Vx(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
  }
  setUserDefinedMetadata(e) {
    Vx(e, this.name), this.userDefinedMetadata = e;
  }
  getUserDefinedMetadata() {
    return this.userDefinedMetadata;
  }
};
pr.className = "Model";
re.registerClass(pr);
var NI = class extends pr {
};
NI.className = "Functional";
re.registerClass(NI);
async function ZB(e, t) {
  "modelTopology" in e || (e = { modelTopology: e }), e = e;
  let n = e.modelTopology;
  n.model_config != null && (n = n.model_config);
  let s = sl(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), De(a);
  }
  return r;
}
async function JB(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 eV(e, void 0, t);
}
async function eV(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(sl(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 } = tV(s.weightData, s.weightSpecs);
    o.loadWeights(l, a), o.optimizer != null && c.length > 0 && await o.optimizer.setWeights(c), De(l), De(c.map((p) => p.tensor));
  }
  return o;
}
function tV(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 Dm = class extends pr {
  constructor(e) {
    if (super({ inputs: [], outputs: [] }), e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Rp("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 Dm || e instanceof pr, 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 = lI({ 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 = uI(this.outputs[0]);
      }
      this.inboundNodes = [], new Wp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: ga(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 pr({ 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 Dm))
      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 Qb = Dm;
Qb.className = "Sequential";
re.registerClass(Qb);
function uhe(e) {
  return new pr(e);
}
function lhe(e) {
  return new Qb(e);
}
function che(e, t) {
  return t == null && (t = {}), JB(e, t);
}
function nV(e) {
  return lI(e);
}
function dhe(e, t) {
  Hb.registerCallbackConstructor(e, t);
}
var kn = class extends re.Serializable {
  getConfig() {
    return {};
  }
};
var TI = class extends kn {
  apply(e, t = 1) {
    return mz(e, t);
  }
};
TI.className = "elu";
re.registerClass(TI);
var $I = class extends kn {
  apply(e) {
    return AS(e);
  }
};
$I.className = "selu";
re.registerClass($I);
var _I = class extends kn {
  apply(e) {
    return Ys(e);
  }
};
_I.className = "relu";
re.registerClass(_I);
var AI = class extends kn {
  apply(e) {
    return q(() => Cp(6, Ys(e)));
  }
};
AI.className = "relu6";
re.registerClass(AI);
var EI = class extends kn {
  apply(e) {
    return e;
  }
};
EI.className = "linear";
re.registerClass(EI);
var RI = class extends kn {
  apply(e) {
    return qs(e);
  }
};
RI.className = "sigmoid";
re.registerClass(RI);
var DI = class extends kn {
  apply(e) {
    return bz(e);
  }
};
DI.className = "hardSigmoid";
re.registerClass(DI);
var FI = class extends kn {
  apply(e) {
    return Vl(e);
  }
};
FI.className = "softplus";
re.registerClass(FI);
var OI = class extends kn {
  apply(e) {
    return gz(e);
  }
};
OI.className = "softsign";
re.registerClass(OI);
var PI = class extends kn {
  apply(e) {
    return Qu(e);
  }
};
PI.className = "tanh";
re.registerClass(PI);
var Zb = class extends kn {
  apply(e, t = -1) {
    return gb(e, t);
  }
};
Zb.className = "softmax";
re.registerClass(Zb);
var zI = class extends kn {
  apply(e, t = -1) {
    return kS(e, t);
  }
};
zI.className = "logSoftmax";
re.registerClass(zI);
var MI = class extends kn {
  apply(e, t = 1) {
    return q(() => V(qs(V(e, t)), e));
  }
};
MI.className = "swish";
re.registerClass(MI);
var LI = class extends kn {
  apply(e) {
    return q(() => V(e, Qu(Vl(e))));
  }
};
LI.className = "mish";
re.registerClass(LI);
function vr(e) {
  return e.getClassName();
}
function Qf(e, t = {}) {
  return Ul(e, re.SerializationMap.getMap().classNameMap, t, "activation");
}
function xr(e) {
  if (e == null) {
    let t = {};
    return t.className = "linear", t.config = {}, Qf(t);
  }
  if (typeof e == "string") {
    let t = {};
    return t.className = e, t.config = {}, Qf(t);
  } else
    return e instanceof kn ? e : Qf(e);
}
function Jb(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 BI = class extends re.Serializable {
};
var Kl = class extends BI {
  constructor(e) {
    super(), Jb(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, Hl(e))))), U(t, []);
    });
  }
  getConfig() {
    return { l1: this.l1, l2: this.l2 };
  }
  static fromConfig(e, t) {
    return new e({ l1: t.l1, l2: t.l2 });
  }
};
Kl.className = "L1L2";
re.registerClass(Kl);
function sV(e) {
  return Jb(e), new Kl({ l1: e != null ? e.l1 : null, l2: 0 });
}
function rV(e) {
  return Jb(e), new Kl({ l2: e != null ? e.l2 : null, l1: 0 });
}
var jx = { l1l2: "L1L2" };
function it(e) {
  return _b(e);
}
function Kx(e, t = {}) {
  return Ul(e, re.SerializationMap.getMap().classNameMap, t, "regularizer");
}
function mt(e) {
  if (e == null)
    return null;
  if (typeof e == "string") {
    let n = { className: e in jx ? jx[e] : e, config: {} };
    return Kx(n);
  } else
    return e instanceof BI ? e : Kx(e);
}
var ey = 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 = Ys(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;
  }
};
ey.className = "ReLU";
re.registerClass(ey);
var ty = 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 ab(n, this.alpha);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { alpha: this.alpha }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ty.className = "LeakyReLU";
re.registerClass(ty);
var ny = class extends He {
  constructor(e) {
    if (super(e == null ? {} : e), this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ft(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = mt(e.alphaRegularizer), this.alphaConstraint = zt(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 ${e.sharedAxes}`);
  }
  build(e) {
    e = nt(e);
    let t = e.slice(1);
    if (this.sharedAxes != null)
      for (let s of this.sharedAxes)
        t[s - 1] = 1;
    this.alpha = this.addWeight("alpha", t, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
    let n = {};
    if (this.sharedAxes != null)
      for (let s = 1; s < e.length; ++s)
        n[s] = e[s];
    this.inputSpec = [new Ft({ ndim: e.length, axes: n })], this.built = true;
  }
  call(e, t) {
    return e = Oe(e), db(e, this.alpha.read());
  }
  getConfig() {
    let e = { alphaInitializer: yt(this.alphaInitializer), alphaRegularizer: it(this.alphaRegularizer), alphaConstraint: Pt(this.alphaConstraint), sharedAxes: this.sharedAxes }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ny.className = "PReLU";
re.registerClass(ny);
var sy = class extends He {
  constructor(e) {
    if (super(e == null ? {} : e), this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
      throw new Fe(`Non-default alpha value (${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 kp(n);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { alpha: this.alpha }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
sy.className = "ELU";
re.registerClass(sy);
var ry = 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;
  }
};
ry.className = "ThresholdedReLU";
re.registerClass(ry);
var ay = class extends He {
  constructor(e) {
    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Zb().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;
  }
};
ay.className = "Softmax";
re.registerClass(ay);
function Ji(e, t, n) {
  if (typeof e == "number")
    return ga(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 (!dz(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 + yr([n - t, 0]);
  else if (s === "same")
    e = e * t;
  else
    throw new G(`Unsupport padding mode: ${s}.`);
  return e;
}
function iy(e, t) {
  return q(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 1]) : e));
}
function VI(e, t) {
  return q(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 4, 1]) : e));
}
function aV(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 = cS(e, t, s, r === "same" ? "same" : "valid", "NWC", i);
    return n != null && (o = ks(o, n)), o;
  });
}
function Xx(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 = iy(e, a);
    if (r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
    return u = ma.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 iV(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 ${e.rank}.`);
    let o = VI(e, a);
    if (r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
    return o = pS(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ks(o, n)), a === "channelsFirst" && (o = Ge(o, [0, 4, 1, 2, 3])), o;
  });
}
var oy = class extends He {
  constructor(e, t) {
    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", oy.verifyArgs(t), this.rank = e, Vt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
      throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${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 = xr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ft(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = zt(t.biasConstraint), this.biasRegularizer = mt(t.biasRegularizer), this.activityRegularizer = mt(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" && !Ab(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: vr(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), biasConstraint: Pt(this.biasConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var Xl = class extends oy {
  constructor(e, t) {
    super(e, t), this.kernel = null, Xl.verifyArgs(t), this.filters = t.filters, Vt(this.filters, "filters"), this.kernelInitializer = ft(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = zt(t.kernelConstraint), this.kernelRegularizer = mt(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 = eI(this.activation.getClassName());
      if (r != null && this.rank === 2)
        n = Xx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
      else {
        if (this.rank === 1)
          n = aV(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
        else if (this.rank === 2)
          n = Xx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
        else if (this.rank === 3)
          n = iV(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: it(this.kernelRegularizer), kernelConstraint: Pt(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 WI = class extends Xl {
  constructor(e) {
    super(2, e), WI.verifyArgs(e);
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, e;
  }
  static verifyArgs(e) {
    if (typeof e.kernelSize != "number" && !Ab(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 Hp = WI;
Hp.className = "Conv2D";
re.registerClass(Hp);
var UI = class extends Xl {
  constructor(e) {
    super(3, e), UI.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 qp = UI;
qp.className = "Conv3D";
re.registerClass(qp);
var uy = class extends Hp {
  constructor(e) {
    if (super(e), this.inputSpec = [new Ft({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
      throw new G(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
  }
  build(e) {
    if (e = nt(e), e.length !== 4)
      throw new G("Input should have rank 4; Received input shape: " + JSON.stringify(e));
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new G("The channel dimension of the inputs should be defined. Found `None`.");
    let n = e[t], s = this.kernelSize.concat([this.filters, n]);
    this.kernel = this.addWeight("kernel", s, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [new Ft({ ndim: 4, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      if (n.shape.length !== 4)
        throw new G(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 = dS(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;
  }
};
uy.className = "Conv2DTranspose";
re.registerClass(uy);
var ly = class extends qp {
  constructor(e) {
    if (super(e), this.inputSpec = [new Ft({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
      throw new G(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
  }
  build(e) {
    if (e = nt(e), e.length !== 5)
      throw new G("Input should have rank 5; Received input shape: " + JSON.stringify(e));
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new G("The channel dimension of the inputs should be defined. Found `None`.");
    let n = e[t], s = this.kernelSize.concat([this.filters, n]);
    this.kernel = this.addWeight("kernel", s, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [new Ft({ ndim: 5, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      if (n.shape.length !== 5)
        throw new G(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 = gR(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;
  }
};
ly.className = "Conv3DTranspose";
re.registerClass(ly);
var GI = class extends Xl {
  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 ${JSON.stringify(t.padding)}`);
    this.depthMultiplier = t.depthMultiplier == null ? 1 : t.depthMultiplier, this.depthwiseInitializer = ft(t.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER), this.depthwiseRegularizer = mt(t.depthwiseRegularizer), this.depthwiseConstraint = zt(t.depthwiseConstraint), this.pointwiseInitializer = ft(t.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER), this.pointwiseRegularizer = mt(t.pointwiseRegularizer), this.pointwiseConstraint = zt(t.pointwiseConstraint);
  }
  build(e) {
    if (e = nt(e), e.length < this.rank + 2)
      throw new G(`Inputs to SeparableConv${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 Ft({ 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 = T3(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 = it(this.depthwiseRegularizer), e.pointwiseRegularizer = it(this.pointwiseRegularizer), e.depthwiseConstraint = Pt(this.depthwiseConstraint), e.pointwiseConstraint = Pt(this.pointwiseConstraint), e;
  }
};
GI.className = "SeparableConv";
var cy = class extends GI {
  constructor(e) {
    super(2, e);
  }
};
cy.className = "SeparableConv2D";
re.registerClass(cy);
var HI = class extends Xl {
  constructor(e) {
    super(1, e), HI.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" && !Ab(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 dy = HI;
dy.className = "Conv1D";
re.registerClass(dy);
var py = 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 = Xc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
        return Xc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
      } else {
        let n = Xc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
        return Xc(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;
  }
};
py.className = "Cropping2D";
re.registerClass(py);
var hy = 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, uz(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;
  }
};
hy.className = "UpSampling2D";
re.registerClass(hy);
function oV(e, t, n = [1, 1], s = "valid", r, a) {
  return q(() => {
    r == null && (r = vs()), Ct(r);
    let i = iy(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 = wp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
  });
}
var fy = class extends oy {
  constructor(e) {
    super(2, e), this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ft(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = zt(e.depthwiseConstraint), this.depthwiseRegularizer = mt(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 = oV(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 = it(this.depthwiseRegularizer), e.depthwiseConstraint = Pt(this.depthwiseRegularizer), e;
  }
};
fy.className = "DepthwiseConv2D";
re.registerClass(fy);
function qI(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 jI(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)), R = d.map((E, P) => ie(V(v[1][P], k), V(E, I)));
          return { output: $, newStates: R };
        });
        p = x.output, d = x.newStates;
      }
      o && c.push(p);
    }
    let g;
    return o && (g = es(c, 1)), [p, g, d];
  });
}
var KI = 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 Xp({ 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 Ft({ 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) {
    Nm(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.");
    Nm(e) && (e = e[0]), e = e;
    let n = this.stateful ? e[0] : null, s = e.slice(2);
    this.inputSpec[0] = new Ft({ 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 Ft({ 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)
        De(this.states_), this.keptStates != null && (De(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()) : De(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 = qI(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 Ft({ 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 = jI((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 = Gl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? Im(t, [1, n]) : t) : this.cell.stateSize > 1 ? [Im(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() === KI.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 Rr = KI;
Rr.className = "RNN";
re.registerClass(Rr);
var Yl = class extends He {
};
var jp = class extends Yl {
  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 = xr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = zt(e.kernelConstraint), this.recurrentConstraint = zt(e.recurrentConstraint), this.biasConstraint = zt(e.biasConstraint), this.dropout = no([1, yr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = no([1, yr([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 = wr({ ones: () => Zn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ 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: vr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Pt(this.kernelConstraint), recurrentConstraint: Pt(this.recurrentConstraint), biasConstraint: Pt(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
    return { ...e, ...t };
  }
};
jp.className = "SimpleRNNCell";
re.registerClass(jp);
var my = class extends Rr {
  constructor(e) {
    e.cell = new jp(e), super(e);
  }
  call(e, t) {
    return q(() => {
      this.cell.dropoutMask != null && (De(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (De(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);
  }
};
my.className = "SimpleRNN";
re.registerClass(my);
var Kp = class extends Yl {
  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 = xr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = xr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = zt(e.kernelConstraint), this.recurrentConstraint = zt(e.recurrentConstraint), this.biasConstraint = zt(e.biasConstraint), this.dropout = no([1, yr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = no([1, yr([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 = wr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ 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, vt(i)), u));
      return [x, x];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: vr(this.activation), recurrentActivation: vr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Pt(this.kernelConstraint), recurrentConstraint: Pt(this.recurrentConstraint), biasConstraint: Pt(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
    return { ...e, ...t };
  }
};
Kp.className = "GRUCell";
re.registerClass(Kp);
var gy = class extends Rr {
  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 Kp(e), super(e);
  }
  call(e, t) {
    return q(() => {
      this.cell.dropoutMask != null && (De(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (De(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);
  }
};
gy.className = "GRU";
re.registerClass(gy);
var Ql = class extends Yl {
  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 = xr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = xr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ft(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = mt(e.kernelRegularizer), this.recurrentRegularizer = mt(e.recurrentRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.kernelConstraint = zt(e.kernelConstraint), this.recurrentConstraint = zt(e.recurrentConstraint), this.biasConstraint = zt(e.biasConstraint), this.dropout = no([1, yr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = no([1, yr([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 Op().apply([a]), c = r.apply([a * 2]);
            return Tx(Tx(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 = wr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ 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: vr(this.activation), recurrentActivation: vr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Pt(this.kernelConstraint), recurrentConstraint: Pt(this.recurrentConstraint), biasConstraint: Pt(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
    return { ...e, ...t };
  }
};
Ql.className = "LSTMCell";
re.registerClass(Ql);
var by = class extends Rr {
  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 Ql(e), super(e);
  }
  call(e, t) {
    return q(() => {
      this.cell.dropoutMask != null && (De(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (De(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);
  }
};
by.className = "LSTM";
re.registerClass(by);
var Xp = class extends Yl {
  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) {
    Nm(e) && (e = e[0]), e = e;
    let t;
    this.cells.forEach((n, s) => {
      sa(`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 Tm(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]]);
    }
    Lb(t);
  }
};
Xp.className = "StackedRNNCells";
re.registerClass(Xp);
function wr(e) {
  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : oI(t(), n), o = () => ql(i, t, s);
  return !r || r <= 1 ? qt(o().clone()) : Array(r).fill(void 0).map(o).map((l) => qt(l.clone()));
}
var XI = class extends Rr {
  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 Ft({ ndim: 5 })];
  }
  call(e, t) {
    return q(() => {
      if (this.cell.dropoutMask != null && (De(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (De(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)
        De(this.states_), this.keptStates != null && (De(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()) : De(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]];
  }
};
XI.className = "ConvRNN2D";
var Yp = class extends Ql {
  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 Eb([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 ${e.length}.`);
      let n = t.training || false, s = e[0], r = e[1], a = e[2], i = 4;
      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = wr({ ones: () => Zn(s), rate: this.dropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
      let o = this.dropoutMask, u = (Z, te, J) => !te || !te[J] ? Z : V(te[J], Z), l = u(s, o, 0), c = u(s, o, 1), p = u(s, o, 2), d = u(s, o, 3);
      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = wr({ ones: () => Zn(r), rate: this.recurrentDropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
      let h = this.recurrentDropoutMask, f = u(r, h, 0), m = u(r, h, 1), g = u(r, h, 2), b = u(r, h, 3), y = 3, [v, x, k, I] = Bn(this.kernel.read(), i, y), [$, R, E, P] = this.useBias ? Bn(this.bias.read(), i) : [null, null, null, null];
      l = this.inputConv(l, v, $, this.padding), c = this.inputConv(c, x, R, this.padding), p = this.inputConv(p, k, E, this.padding), d = this.inputConv(d, I, P, this.padding);
      let [A, O, T, M] = Bn(this.recurrentKernel.read(), i, y);
      f = this.recurrentConv(f, A), m = this.recurrentConv(m, O), g = this.recurrentConv(g, T), b = this.recurrentConv(b, M);
      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 = pa(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 pa(e, t, 1, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
  }
};
Yp.className = "ConvLSTM2DCell";
re.registerClass(Yp);
var yy = class extends XI {
  constructor(e) {
    let t = new Yp(e);
    super({ ...e, cell: t });
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
yy.className = "ConvLSTM2D";
re.registerClass(yy);
var Qp = 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 ql(() => oI(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();
  }
};
Qp.className = "Dropout";
re.registerClass(Qp);
var vy = class extends Qp {
  constructor(e) {
    super(e), this.inputSpec = [{ ndim: 3 }];
  }
  getNoiseShape(e) {
    let t = e.shape;
    return [t[0], 1, t[2]];
  }
};
vy.className = "SpatialDropout1D";
re.registerClass(vy);
var xy = 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 = xr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ft(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ft(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = zt(e.kernelConstraint), this.biasConstraint = zt(e.biasConstraint), this.kernelRegularizer = mt(e.kernelRegularizer), this.biasRegularizer = mt(e.biasRegularizer), this.activityRegularizer = mt(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 = eI(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: vr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Pt(this.kernelConstraint), biasConstraint: Pt(this.biasConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
xy.className = "Dense";
re.registerClass(xy);
var wy = 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], dr(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 fz(n);
    });
  }
  getConfig() {
    let e = {};
    this.dataFormat != null && (e.dataFormat = this.dataFormat);
    let t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
wy.className = "Flatten";
re.registerClass(wy);
var ky = class extends He {
  constructor(e) {
    super(e), this.supportsMasking = true, this.activation = xr(e.activation);
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return this.activation.apply(n);
    });
  }
  getConfig() {
    let e = { activation: vr(this.activation) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
ky.className = "Activation";
re.registerClass(ky);
var Sy = 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), pz(e, this.n)));
  }
  getConfig() {
    let e = { n: this.n }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Sy.className = "RepeatVector";
re.registerClass(Sy);
var Iy = 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 = dr(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;
  }
};
Iy.className = "Reshape";
re.registerClass(Iy);
var Cy = 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 Ft({ 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;
  }
};
Cy.className = "Permute";
re.registerClass(Cy);
var Ny = 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 vm(el(n, this.maskValue), s);
  }
  call(e, t) {
    return q(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e), s = -1, r = true, a = vm(el(n, this.maskValue), s, r);
      return V(n, le(a, n.dtype));
    });
  }
};
Ny.className = "Masking";
re.registerClass(Ny);
var Ty = 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(ht(e.inputLength));
    }
    this.inputDim = e.inputDim, Vt(this.inputDim, "inputDim"), this.outputDim = e.outputDim, Vt(this.outputDim, "outputDim"), this.embeddingsInitializer = ft(e.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER), this.embeddingsRegularizer = mt(e.embeddingsRegularizer), this.activityRegularizer = mt(e.activityRegularizer), this.embeddingsConstraint = zt(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), el(e, je(e))) : null);
  }
  computeOutputShape(e) {
    if (e = nt(e), this.inputLength == null)
      return [...e, this.outputDim];
    let t = ht(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 = Dp(n, "int32"));
      let s = iI(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: it(this.embeddingsRegularizer), activityRegularizer: it(this.activityRegularizer), embeddingsConstraint: Pt(this.embeddingsConstraint), maskZero: this.maskZero, inputLength: this.inputLength }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Ty.className = "Embedding";
re.registerClass(Ty);
var xi = 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 = cr(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 && cr(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 = yr(s);
          for (let a of e) {
            let i = a.rank;
            for (let o = 0; o < r - i; ++o)
              a = Gl(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(dr(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 = cr(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 $y = class extends xi {
  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;
    });
  }
};
$y.className = "Add";
re.registerClass($y);
var _y = class extends xi {
  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;
    });
  }
};
_y.className = "Multiply";
re.registerClass(_y);
var Ay = class extends xi {
  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);
    });
  }
};
Ay.className = "Average";
re.registerClass(Ay);
var Ey = class extends xi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0];
      for (let n = 1; n < e.length; ++n)
        t = Ar(t, e[n]);
      return t;
    });
  }
};
Ey.className = "Maximum";
re.registerClass(Ey);
var Ry = class extends xi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return q(() => {
      let t = e[0];
      for (let n = 1; n < e.length; ++n)
        t = Cp(t, e[n]);
      return t;
    });
  }
};
Ry.className = "Minimum";
re.registerClass(Ry);
var Dy = class extends xi {
  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(() => Eb(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 = Ot(s, this.axis);
      return rS(r, -1, false);
    });
  }
  getConfig() {
    let e = { axis: this.axis }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Dy.className = "Concatenate";
re.registerClass(Dy);
function _u(e, t) {
  for (; e < 0; )
    e += t;
  return e;
}
function uV(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 = br(o, l);
    }
    return o.shape.length === 1 && (o = Pn(o, 1)), o;
  });
}
var Fy = class extends xi {
  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) => _u(r, e[a].shape.length)) : s = [_u(this.axes, t.shape.length), _u(this.axes, n.shape.length)], this.normalize && (t = Rd(t, s[0]), n = Rd(n, s[1])), uV(t, n, s);
  }
  interpretAxes(e, t) {
    let n;
    return Array.isArray(this.axes) ? n = this.axes : n = [_u(this.axes, e.length), _u(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;
  }
};
Fy.className = "Dot";
re.registerClass(Fy);
var Oy = 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 ql(() => ie(Fp(n.shape, 0, this.stddev), n), () => n, t.training || false);
    });
  }
};
Oy.className = "GaussianNoise";
re.registerClass(Oy);
var Py = 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 ? ql(() => {
        let r = Math.sqrt(this.rate / (1 - this.rate));
        return V(n, Fp(n.shape, 1, r));
      }, () => n, t.training || false) : n;
    });
  }
};
Py.className = "GaussianDropout";
re.registerClass(Py);
var zy = 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 ql(() => {
          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Zo(Wl(n), this.rate);
          u = Dp(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;
    });
  }
};
zy.className = "AlphaDropout";
re.registerClass(zy);
function rl(e, t, n, s, r, a = 1e-3) {
  let i;
  if (e.rank === 2)
    i = UE(e, t, n, s, r, a);
  else if (e.rank === 3)
    i = HE(e, t, n, s, r, a);
  else if (e.rank === 4)
    i = jE(e, t, n, s, r, a);
  else
    throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);
  return i;
}
function lV(e, t, n, s, r = 1e-3) {
  return q(() => {
    let a = lb(e, s), i = a.mean, o = a.variance;
    return [rl(e, i, o, n, t, r), i, o];
  });
}
function cV(e, t, n, s, r = 1e-3) {
  return q(() => {
    let a = lb(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 [rl(e, l, c, d, p, r), i, o];
  });
}
function dV(e, t, n, s, r = 1e-3) {
  return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? lV(e, t, n, s, r) : cV(e, t, n, s, r);
}
var My = 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 = ft(e.betaInitializer || "zeros"), this.gammaInitializer = ft(e.gammaInitializer || "ones"), this.movingMeanInitializer = ft(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ft(e.movingVarianceInitializer || "ones"), this.betaConstraint = zt(e.betaConstraint), this.gammaConstraint = zt(e.gammaConstraint), this.betaRegularizer = mt(e.betaRegularizer), this.gammaRegularizer = mt(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 Ft({ 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 = ga(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 rl(s, b, y, v, x, this.epsilon);
        } else
          return rl(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] = dV(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: it(this.betaRegularizer), gammaRegularizer: it(this.gammaRegularizer), betaConstraint: Pt(this.betaConstraint), gammaConstraint: Pt(this.gammaConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
My.className = "BatchNormalization";
re.registerClass(My);
var Ly = 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 = ft(e.betaInitializer || "zeros"), this.gammaInitializer = ft(e.gammaInitializer || "ones"), this.betaRegularizer = mt(e.betaRegularizer), this.gammaRegularizer = mt(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 !== cr(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 } = lb(n, this.axis, true), u = ga(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 = this.scale ? l(this.gamma.read()) : null, p = this.center ? l(this.beta.read()) : null, 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 != null && (c = hs(c, h)), p != null && (p = hs(p, h)), rl(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: it(this.betaRegularizer), gammaRegularizer: it(this.gammaRegularizer) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Ly.className = "LayerNormalization";
re.registerClass(Ly);
function pV(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: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
    let s;
    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], bi(e, s);
  });
}
var By = class extends He {
  constructor(e) {
    if (e == null && (e = {}), super(e), this.dataFormat = e.dataFormat == null ? vs() : e.dataFormat, e.padding == null)
      this.padding = [[1, 1], [1, 1]];
    else if (typeof e.padding == "number")
      this.padding = [[e.padding, e.padding], [e.padding, e.padding]];
    else {
      if (e.padding = e.padding, e.padding.length !== 2)
        throw new G(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 Ft({ 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(() => pV(Oe(e), this.padding, this.dataFormat));
  }
  getConfig() {
    let e = { padding: this.padding, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
By.className = "ZeroPadding2D";
re.registerClass(By);
function Zp(e, t, n, s, r, a) {
  return q(() => {
    Ct(r), nI(a), Gn(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = iy(e, r);
    let i, o = s === "same" ? "same" : "valid";
    return a === "max" ? i = ub(e, t, n, o) : i = Zg(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
  });
}
function YI(e, t, n, s, r, a) {
  return q(() => {
    Ct(r), nI(a), Gn(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = vs()), a == null && (a = "max"), e = VI(e, r);
    let i, o = s === "same" ? "same" : "valid";
    return a === "max" ? i = CS(e, t, n, o) : i = uS(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 4, 1, 2, 3])), i;
  });
}
var QI = 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 Ft({ 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 = Gl(Oe(e), 2);
      let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
      return br(n, [2]);
    });
  }
  getConfig() {
    let e = { poolSize: this.poolSize, padding: this.padding, strides: this.strides }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var Vy = class extends QI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Zp(e, t, n, s, r, "max");
  }
};
Vy.className = "MaxPooling1D";
re.registerClass(Vy);
var Wy = class extends QI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Zp(e, t, n, s, r, "avg");
  }
};
Wy.className = "AveragePooling1D";
re.registerClass(Wy);
var ZI = 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 Ft({ 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 Uy = class extends ZI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Zp(e, t, n, s, r, "max");
  }
};
Uy.className = "MaxPooling2D";
re.registerClass(Uy);
var Gy = class extends ZI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), Zp(e, t, n, s, r, "avg");
  }
};
Gy.className = "AveragePooling2D";
re.registerClass(Gy);
var JI = 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 Ft({ 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 Hy = class extends JI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), YI(e, t, n, s, r, "max");
  }
};
Hy.className = "MaxPooling3D";
re.registerClass(Hy);
var qy = class extends JI {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return Ct(r), Gn(s), YI(e, t, n, s, r, "avg");
  }
};
qy.className = "AveragePooling3D";
re.registerClass(qy);
var e0 = class extends He {
  constructor(e) {
    super(e), this.inputSpec = [new Ft({ ndim: 3 })];
  }
  computeOutputShape(e) {
    return [e[0], e[2]];
  }
  call(e, t) {
    throw new Fe();
  }
};
var jy = class extends e0 {
  constructor(e) {
    super(e || {});
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return It(n, 1);
    });
  }
};
jy.className = "GlobalAveragePooling1D";
re.registerClass(jy);
var Ky = class extends e0 {
  constructor(e) {
    super(e || {});
  }
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return As(n, 1);
    });
  }
};
Ky.className = "GlobalMaxPooling1D";
re.registerClass(Ky);
var t0 = class extends He {
  constructor(e) {
    super(e), this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.inputSpec = [new Ft({ 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 Xy = class extends t0 {
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return this.dataFormat === "channelsLast" ? It(n, [1, 2]) : It(n, [2, 3]);
    });
  }
};
Xy.className = "GlobalAveragePooling2D";
re.registerClass(Xy);
var Yy = class extends t0 {
  call(e, t) {
    return q(() => {
      let n = Oe(e);
      return this.dataFormat === "channelsLast" ? As(n, [1, 2]) : As(n, [2, 3]);
    });
  }
};
Yy.className = "GlobalMaxPooling2D";
re.registerClass(Yy);
var n0 = 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 Qy = class extends n0 {
  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), jI((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
  }
};
Qy.className = "TimeDistributed";
re.registerClass(Qy);
function hV(e) {
  yi(oz, "BidirectionalMergeMode", e);
}
var fV = "concat";
var Zy = class extends n0 {
  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 ? fV : e.mergeMode, hV(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 = qI(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 Ft({ 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 = Eb([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) {
    sa(this.forwardLayer.name, () => {
      this.forwardLayer.build(e);
    }), sa(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);
  }
};
Zy.className = "Bidirectional";
re.registerClass(Zy);
function mV(e) {
  return new tu(e);
}
function gV(e) {
  return new sy(e);
}
function bV(e) {
  return new ey(e);
}
function yV(e) {
  return new ty(e);
}
function vV(e) {
  return new ny(e);
}
function xV(e) {
  return new ay(e);
}
function wV(e) {
  return new ry(e);
}
function kV(e) {
  return new dy(e);
}
function SV(e) {
  return new Hp(e);
}
function IV(e) {
  return new uy(e);
}
function CV(e) {
  return new qp(e);
}
function NV(e) {
  return new ly(e);
}
function TV(e) {
  return new cy(e);
}
function $V(e) {
  return new py(e);
}
function _V(e) {
  return new hy(e);
}
function AV(e) {
  return new fy(e);
}
function EV(e) {
  return new ky(e);
}
function RV(e) {
  return new xy(e);
}
function DV(e) {
  return new Qp(e);
}
function FV(e) {
  return new vy(e);
}
function OV(e) {
  return new wy(e);
}
function PV(e) {
  return new Sy(e);
}
function zV(e) {
  return new Iy(e);
}
function MV(e) {
  return new Cy(e);
}
function LV(e) {
  return new Ty(e);
}
function BV(e) {
  return new $y(e);
}
function VV(e) {
  return new Ay(e);
}
function WV(e) {
  return new Dy(e);
}
function UV(e) {
  return new Ey(e);
}
function GV(e) {
  return new Ry(e);
}
function HV(e) {
  return new _y(e);
}
function qV(e) {
  return new Fy(e);
}
function jV(e) {
  return new My(e);
}
function KV(e) {
  return new Ly(e);
}
function XV(e) {
  return new By(e);
}
function Jy(e) {
  return new Wy(e);
}
function YV(e) {
  return Jy(e);
}
function QV(e) {
  return Jy(e);
}
function ev(e) {
  return new Gy(e);
}
function ZV(e) {
  return ev(e);
}
function JV(e) {
  return ev(e);
}
function tv(e) {
  return new qy(e);
}
function eW(e) {
  return tv(e);
}
function tW(e) {
  return tv(e);
}
function nW(e) {
  return new jy(e);
}
function sW(e) {
  return new Xy(e);
}
function s0(e) {
  return new Ky(e);
}
function r0(e) {
  return new Yy(e);
}
function a0(e) {
  return new Vy(e);
}
function i0(e) {
  return new Uy(e);
}
function rW(e) {
  return new Hy(e);
}
function aW(e) {
  return new gy(e);
}
function iW(e) {
  return new Kp(e);
}
function oW(e) {
  return new by(e);
}
function uW(e) {
  return new Ql(e);
}
function lW(e) {
  return new my(e);
}
function cW(e) {
  return new jp(e);
}
function dW(e) {
  return new yy(e);
}
function pW(e) {
  return new Yp(e);
}
function hW(e) {
  return new Rr(e);
}
function fW(e) {
  return new Xp(e);
}
function mW(e) {
  return new Zy(e);
}
function gW(e) {
  return new Qy(e);
}
var bW = s0;
var yW = r0;
var vW = a0;
var xW = i0;
function wW(e) {
  return new Oy(e);
}
function kW(e) {
  return new Py(e);
}
function SW(e) {
  return new zy(e);
}
function IW(e) {
  return new Ny(e);
}
var CW = {};
Ee(CW, { MAPE: () => PW, MSE: () => LW, binaryAccuracy: () => NW, binaryCrossentropy: () => TW, categoricalAccuracy: () => _W, categoricalCrossentropy: () => AW, cosineProximity: () => DW, mape: () => zW, meanAbsoluteError: () => FW, meanAbsolutePercentageError: () => OW, meanSquaredError: () => MW, mse: () => BW, precision: () => EW, recall: () => RW, sparseCategoricalAccuracy: () => $W });
function NW(e, t) {
  return jb(e, t);
}
function TW(e, t) {
  return bI(e, t);
}
function $W(e, t) {
  return yI(e, t);
}
function _W(e, t) {
  return Kb(e, t);
}
function AW(e, t) {
  return Xb(e, t);
}
function EW(e, t) {
  return gI(e, t);
}
function RW(e, t) {
  return kB(e, t);
}
function DW(e, t) {
  return qb(e, t);
}
function FW(e, t) {
  return Up(e, t);
}
function OW(e, t) {
  return nu(e, t);
}
function PW(e, t) {
  return nu(e, t);
}
function zW(e, t) {
  return nu(e, t);
}
function MW(e, t) {
  return vi(e, t);
}
function LW(e, t) {
  return vi(e, t);
}
function BW(e, t) {
  return vi(e, t);
}
var VW = {};
Ee(VW, { modelFromJSON: () => ZB });
var WW = {};
Ee(WW, { l1: () => GW, l1l2: () => UW, l2: () => HW });
function UW(e) {
  return new Kl(e);
}
function GW(e) {
  return sV(e);
}
function HW(e) {
  return rV(e);
}
var qW = class extends so {
  constructor() {
    super(...arguments), this.model = null;
  }
  setModel(e) {
    if (!(e instanceof pr))
      throw new Error("model must be a LayersModel, not some other Container");
    this.model = e;
  }
};
function Qc(e, t) {
  return e < t;
}
function Yx(e, t) {
  return e > t;
}
var jW = class extends qW {
  constructor(e) {
    if (super(), e == null && (e = {}), e.restoreBestWeights)
      throw new Fe("restoreBestWeights = True is not implemented in EarlyStopping yet.");
    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = Qc : this.mode === "max" ? this.monitorFunc = Yx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Yx : this.monitorFunc = Qc, this.monitorFunc === Qc && (this.minDelta *= -1);
  }
  async onTrainBegin(e) {
    this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === Qc ? 1 / 0 : -1 / 0;
  }
  async onEpochEnd(e, t) {
    await rr(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 KW(e) {
  return new jW(e);
}
var phe = { earlyStopping: KW };
var XW = K();
XW.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 o0 = ((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))(o0 || {});
var Qx;
((e) => {
  let t;
  ((n) => {
    n[n.LEGACY = 0] = "LEGACY", n[n.V1 = 1] = "V1", n[n.V2 = 2] = "V2";
  })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
})(Qx || (Qx = {}));
var nv = {};
function hhe(e, t) {
  let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
  nv[e] = n;
}
function u0(e) {
  return nv[e];
}
function fhe(e) {
  delete nv[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();
    return a.type === "number" ? c[0] : w.toNestedArray(l.shape, c);
  }
  let i = t.attrParams[e];
  return i && i.value;
}
function un(e, t, n, s) {
  let [r, a] = _n(e);
  if (s != null) {
    let o = s.getHashTableHandleByName(r);
    if (o != null)
      return o;
  }
  let i = n.currentContextIds.find((o) => !!t[zd(r, o)]);
  return i !== void 0 ? t[zd(r, i)][a] : void 0;
}
function YW(e, t, n) {
  return t[zd(e, n.currentContextId)];
}
function Ts(e, t) {
  let [n, s, r] = _n(e);
  return [zd(n, t && t.currentContextId), s, r];
}
function zd(e, t) {
  return t ? `${e}-${t}` : e;
}
function _n(e) {
  let t = e.split(":");
  if (t.length === 1)
    return [e, 0, void 0];
  let n = t[0], s = t.length === 3 ? t[1] : void 0, r = Number(t[t.length - 1]);
  return [n, r, s];
}
function od(e, t, n) {
  let s = S("pad", e, t, n);
  if (s === "explicit") {
    s = S("explicitPaddings", e, t, n);
    let r = [[0, 0], [0, 0], [0, 0], [0, 0]];
    for (let a = 0; a < 4; a++)
      r[a][0] = s[a * 2], r[a][1] = s[a * 2 + 1];
    return r;
  }
  return s;
}
function Ws(e) {
  return e.kept ? e : lr(e);
}
var l0 = {};
Ee(l0, { 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 c0 = {};
Ee(c0, { json: () => ZW });
var ZW = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
var d0 = {};
Ee(d0, { json: () => JW });
var JW = [{ 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 p0 = {};
Ee(p0, { json: () => e4 });
var e4 = [{ 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 h0 = {};
Ee(h0, { json: () => t4 });
var t4 = [{ 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 f0 = {};
Ee(f0, { json: () => n4 });
var n4 = [{ 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 m0 = {};
Ee(m0, { json: () => s4 });
var s4 = [{ 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 g0 = {};
Ee(g0, { json: () => r4 });
var r4 = [{ 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 b0 = {};
Ee(b0, { json: () => a4 });
var a4 = [{ 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 y0 = {};
Ee(y0, { json: () => i4 });
var i4 = [{ 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 v0 = {};
Ee(v0, { json: () => o4 });
var o4 = [{ 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 x0 = {};
Ee(x0, { json: () => u4 });
var u4 = [{ 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 w0 = {};
Ee(w0, { json: () => l4 });
var l4 = [{ tfOpName: "EuclideanNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", defaultValue: false }] }, { tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "normalization", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }];
var k0 = {};
Ee(k0, { json: () => c4 });
var c4 = [{ 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 S0 = {};
Ee(S0, { json: () => d4 });
var d4 = [{ 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 I0 = {};
Ee(I0, { json: () => p4 });
var p4 = [{ 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 C0 = {};
Ee(C0, { json: () => h4 });
var h4 = [{ 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 N0 = {};
Ee(N0, { json: () => f4 });
var f4 = [{ 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 T0 = {};
Ee(T0, { json: () => m4 });
var m4 = [{ 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 Zx = class {
  static get Instance() {
    return this._instance || (this._instance = new this());
  }
  constructor() {
    let e = [l0, c0, d0, p0, h0, f0, m0, g0, b0, y0, v0, x0, w0, k0, S0, I0, C0, N0, T0], 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 = u0(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 = Fm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Fm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "string[]":
          i = Vm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Vm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "number":
          i = Pm(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = Pm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "number[]":
          i = Bm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Bm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "bool":
          i = Om(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Om(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "bool[]":
          i = Um(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Um(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 "shape[]":
          i = Wm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Wm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "dtype":
          i = zm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = zm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "dtype[]":
          i = Mm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Mm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "func":
          i = Jx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Jx(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "tensor":
        case "tensors":
          break;
        default:
          throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`);
      }
      return s[r.name] = { value: i, type: a }, s;
    }, {})), n;
  }
  mapFunction(e) {
    let t = e.nodeDef, n = [], s = [], r = {};
    t != null && (r = t.reduce((c, p) => (c[p.name] = this.mapNode(p), p.op === "Const" && s.push(c[p.name]), c), {}));
    let a = [], i = [];
    e.signature.inputArg.forEach((c) => {
      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: sv(c.type), type: "dtype" } }, children: [] };
      d.signatureKey = c.name, a.push(d), r[p] = d;
    }), Object.keys(r).forEach((c) => {
      let p = r[c];
      p.inputNames.forEach((d, h) => {
        let [f, , m] = Ts(d), g = r[f];
        if (g.outputs != null) {
          let b = g.outputs.indexOf(m);
          if (b !== -1) {
            let y = `${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 g4(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 $0(e, t) {
  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : g4(e);
  return t ? n : n.toLowerCase();
}
function Fm(e, t, n, s = false) {
  let r = e[t];
  return r != null ? $0(r.s, s) : n;
}
function Om(e, t, n) {
  let s = e[t];
  return s ? s.b : n;
}
function Pm(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 sv(e) {
  switch (typeof e == "string" && (e = o0[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 Jx(e, t, n) {
  let s = e[t];
  return s && s.func ? s.func.name : n;
}
function zm(e, t, n) {
  let s = e[t];
  return s && s.type ? sv(s.type) : n;
}
function Mm(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.type ? s.list.type.map((r) => sv(r)) : n;
}
function _0(e) {
  if (!e.unknownRank)
    return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
}
function Lm(e, t, n) {
  let s = e[t];
  return s && s.shape ? _0(s.shape) : n;
}
function Bm(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 Vm(e, t, n, s = false) {
  let r = e[t];
  return r && r.list && r.list.s ? r.list.s.map((a) => $0(a, s)) : n;
}
function Wm(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.shape ? s.list.shape.map((r) => _0(r)) : n;
}
function Um(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.b ? s.list.b : n;
}
var b4 = 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 Pm(this.node.rawAttrs, e, t);
    if (n.s != null)
      return Fm(this.node.rawAttrs, e, t);
    if (n.b != null)
      return Om(this.node.rawAttrs, e, t);
    if (n.shape != null)
      return Lm(this.node.rawAttrs, e, t);
    if (n.type != null)
      return zm(this.node.rawAttrs, e, t);
    if (n.list != null) {
      if (n.list.i != null || n.list.f != null)
        return Bm(this.node.rawAttrs, e, t);
      if (n.list.s != null)
        return Vm(this.node.rawAttrs, e, t);
      if (n.list.shape != null)
        return Wm(this.node.rawAttrs, e, t);
      if (n.list.b != null)
        return Um(this.node.rawAttrs, e, t);
      if (n.list.type != null)
        return Mm(this.node.rawAttrs, e, t);
    }
    return t;
  }
};
var y4 = (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 [lE(S("tensors", e, t, n))];
    case "FloorMod":
    case "Mod":
      return [WD(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 [DR(S("a", e, t, n), S("b", e, t, n))];
    case "FloorDiv":
      return [sS(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 [Cp(S("a", e, t, n), S("b", e, t, n))];
    case "Maximum":
      return [Ar(S("a", e, t, n), S("b", e, t, n))];
    case "Pow":
      return [fa(S("a", e, t, n), S("b", e, t, n))];
    case "SquaredDifference":
      return [OS(S("a", e, t, n), S("b", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var v4 = (e, t, n) => {
  switch (e.op) {
    case "Abs":
    case "ComplexAbs":
      return [Lt(S("x", e, t, n))];
    case "Acos":
      return [aE(S("x", e, t, n))];
    case "Acosh":
      return [oE(S("x", e, t, n))];
    case "Asin":
      return [gE(S("x", e, t, n))];
    case "Asinh":
      return [yE(S("x", e, t, n))];
    case "Atan":
      return [xE(S("x", e, t, n))];
    case "Atan2":
      return [kE(S("x", e, t, n), S("y", e, t, n))];
    case "Atanh":
      return [IE(S("x", e, t, n))];
    case "Ceil":
      return [JE(S("x", e, t, n))];
    case "Complex":
      return [mr(S("real", e, t, n), S("imag", e, t, n))];
    case "Cos":
      return [tb(S("x", e, t, n))];
    case "Cosh":
      return [fS(S("x", e, t, n))];
    case "Elu":
      return [kp(S("x", e, t, n))];
    case "Erf":
      return [LR(S("x", e, t, n))];
    case "Exp":
      return [Yn(S("x", e, t, n))];
    case "Expm1":
      return [eD(S("x", e, t, n))];
    case "Floor":
      return [Sp(S("x", e, t, n))];
    case "Log":
      return [Qn(S("x", e, t, n))];
    case "Log1p":
      return [ib(S("x", e, t, n))];
    case "Imag":
      return [xp(S("x", e, t, n))];
    case "Neg":
      return [vt(S("x", e, t, n))];
    case "Reciprocal":
      return [m3(S("x", e, t, n))];
    case "Real":
      return [Xu(S("x", e, t, n))];
    case "Relu":
      return [Ys(S("x", e, t, n))];
    case "Round":
      return [$S(S("x", e, t, n))];
    case "Selu":
      return [AS(S("x", e, t, n))];
    case "Sigmoid":
      return [qs(S("x", e, t, n))];
    case "Sin":
      return [ES(S("x", e, t, n))];
    case "Sign":
      return [E3(S("x", e, t, n))];
    case "Sinh":
      return [RS(S("x", e, t, n))];
    case "Softplus":
      return [Vl(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 [Qu(S("x", e, t, n))];
    case "Tan":
      return [Q3(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 [TS(S("x", e, t, n))];
    case "Rsqrt":
      return [_S(un(e.inputNames[0], t, n))];
    case "Prod":
      return [NS(S("x", e, t, n), S("axes", e, t, n))];
    case "LeakyRelu":
      return [ab(S("x", e, t, n), S("alpha", e, t, n))];
    case "Prelu":
      return [db(S("x", e, t, n), S("alpha", e, t, n))];
    case "IsNan":
      return [cD(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 ew(e) {
  return !(typeof e == "number" || e.some((t) => t < 0));
}
function Au(e, t, n) {
  let s = Gm(e, n), r = !ew(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 = Gm(a.shape, s);
  }), !ew(s))
    throw new Error(`Non-fully-defined elementShape: ${s}`);
  return s;
}
function Gm(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 x4 = 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})`), Ot(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 = Au(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 = Au(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 = Au(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 = Au(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 = Au(this.elementShape, this.tensors, t);
    return this.size() === 0 ? ms([], [0].concat(n)) : q(() => {
      let s = this.tensors.map((r) => U(r, n));
      return Ot(s, 0);
    });
  }
};
function w4(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 k4(e, t, n) {
  return new ro([], e, t, n);
}
function S4(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 I4(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 = Gm(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 C4 = 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 x4(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 = S4(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 = k4(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 = w4(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 = I4(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 tw(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 = od(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 N4 = (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 [cS(S("x", e, t, n), S("filter", e, t, n), s, r, a, i)];
    }
    case "Conv2D": {
      let s = S("strides", e, t, n), r = od(e, t, n), a = S("dataFormat", e, t, n).toUpperCase(), i = S("dilations", e, t, n);
      return [pa(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 } = tw(e, t, n);
      return [ma.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 } = tw(e, t, n);
      return [ma.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 = od(e, t, n);
      return [dS(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 = od(e, t, n), a = S("dilations", e, t, n), i = S("dataFormat", e, t, n).toUpperCase();
      return [wp(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 [pS(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 [Zg(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 [ub(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 } = OD(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 [uS(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 [CS(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 [$R(S("x", e, t, n), S("filter", e, t, n), [i, o], r, [u, l], "NHWC")];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var T4 = (e, t, n) => {
  switch (e.op) {
    case "Fill": {
      let s = S("shape", e, t, n), r = S("dtype", e, t, n), a = S("value", e, t, n);
      return [Bl(s, a, r)];
    }
    case "LinSpace": {
      let s = S("start", e, t, n), r = S("stop", e, t, n), a = S("num", e, t, n);
      return [fD(s, r, a)];
    }
    case "Multinomial": {
      let s = S("logits", e, t, n), r = S("numSamples", e, t, n), a = S("seed", e, t, n);
      return [qD(s, r, a)];
    }
    case "OneHot": {
      let s = S("indices", e, t, n), r = S("depth", e, t, n), a = S("onValue", e, t, n), i = S("offValue", e, t, n);
      return [Id(s, r, a, i)];
    }
    case "Ones":
      return [Mn(S("shape", e, t, n), S("dtype", e, t, n))];
    case "OnesLike":
      return [Zn(S("x", e, t, n))];
    case "RandomUniform":
      return [Wl(S("shape", e, t, n), S("minval", e, t, n), S("maxval", e, t, n), S("dtype", e, t, n))];
    case "Range": {
      let s = S("start", e, t, n), r = S("stop", e, t, n), a = S("step", e, t, n);
      return [tl(s, r, a, S("dtype", e, t, n))];
    }
    case "TruncatedNormal": {
      let s = S("shape", e, t, n), r = S("mean", e, t, n), a = S("stdDev", e, t, n), i = S("seed", e, t, n);
      return [vb(s, r, a, S("dtype", e, t, n), i)];
    }
    case "Zeros":
      return [$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 Zf(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 $4 = async (e, t, n) => {
  switch (e.op) {
    case "NonMaxSuppressionV5": {
      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Zf(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 } = Zf(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 } = Zf(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 zS(s)];
      return s.dispose(), r;
    }
    case "ListDiff":
      return _3(S("x", e, t, n), S("y", e, t, n));
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var _4 = (e, t, n) => {
  switch (e.op) {
    case "LowerBound": {
      let s = S("sortedSequence", e, t, n), r = S("values", e, t, n);
      return [ED(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 [aF(s, r)];
    }
    case "Unique": {
      let s = S("x", e, t, n), r = xx(s);
      return [r.values, r.indices];
    }
    case "UniqueV2": {
      let s = S("x", e, t, n), r = S("axis", e, t, n), a = xx(s, r);
      return [a.values, a.indices];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var A4 = (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 E4 = 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 R4 = 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 E4(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 D4 = (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 F4 = (e, t, n) => {
  switch (e.op) {
    case "Equal":
      return [Xn(S("a", e, t, n), S("b", e, t, n))];
    case "NotEqual":
      return [el(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 [Zo(S("a", e, t, n), S("b", e, t, n))];
    case "Less":
      return [wS(S("a", e, t, n), S("b", e, t, n))];
    case "LessEqual":
      return [Jo(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 [ob(S("a", e, t, n))];
    case "LogicalOr":
      return [SS(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 O4 = (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 [PR(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 [ma.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 P4 = (e, t, n) => {
  switch (e.op) {
    case "EuclideanNorm":
      return [YR(S("x", e, t, n), S("axis", e, t, n), S("keepDims", e, t, n))];
    case "FusedBatchNorm":
    case "FusedBatchNormV2":
      return [Zu(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 [Zu(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 [gD(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 [gb(S("x", e, t, n))];
    case "LogSoftmax":
      return [kS(S("x", e, t, n))];
    case "SparseToDense":
      return [MS(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 z4 = (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 [km(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 [rS(S("x", e, t, n), i, o)];
    }
    case "Any": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [vm(S("x", e, t, n), i, o)];
    }
    case "ArgMax": {
      let i = S("axis", e, t, n);
      return [Yu(S("x", e, t, n), i)];
    }
    case "ArgMin": {
      let i = S("axis", e, t, n);
      return [fE(S("x", e, t, n), i)];
    }
    case "Prod": {
      let i = S("axis", e, t, n), o = S("keepDims", e, t, n);
      return [NS(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 [wm(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 [mS(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 [lS(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 [kR(i, o, u, l)];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var M4 = (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), [Ot(a, r)];
    }
    case "Gather": {
      let s = S("x", e, t, n), r = S("indices", e, t, n);
      return [Ju(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 [Ju(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 [X3(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 = br(r[0]).shape, o = r.map((u) => {
          let l = w.arraysEqual(u.shape, a);
          if (!l && !w.arraysEqual(br(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 [dF(s, r, a)];
    }
    case "GatherNd": {
      let s = S("x", e, t, n), r = S("indices", e, t, n);
      return [mF(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 [MS(s, a, r, a.dtype === i.dtype ? i : le(i, a.dtype))];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var L4 = (e, t, n) => {
  switch (e.op) {
    case "SparseFillEmptyRows": {
      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = qc.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 } = qc.sparseReshape(S("inputIndices", e, t, n), S("inputShape", e, t, n), S("newShape", e, t, n));
      return [s, r];
    }
    case "SparseSegmentMean":
      return [qc.sparseSegmentMean(S("data", e, t, n), S("indices", e, t, n), S("segmentIds", e, t, n))];
    case "SparseSegmentSum":
      return [qc.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 B4 = (e, t, n) => {
  switch (e.op) {
    case "FFT":
      return [bb(S("x", e, t, n))];
    case "IFFT":
      return [Td(S("x", e, t, n))];
    case "RFFT":
      return [yb(S("x", e, t, n))];
    case "IRFFT":
      return [FS(S("x", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var V4 = (e, t, n) => {
  switch (e.op) {
    case "StringNGrams": {
      let { nGrams: s, nGramsSplits: r } = qf.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 } = qf.stringSplit(S("input", e, t, n), S("delimiter", e, t, n), S("skipEmpty", e, t, n));
      return [s, r, a];
    }
    case "StringToHashBucketFast":
      return [qf.stringToHashBucketFast(S("input", e, t, n), S("numBuckets", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var W4 = (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 [br(S("x", e, t, n), s)];
    }
    case "Reshape":
      return [U(S("x", e, t, n), S("shape", e, t, n))];
    case "MirrorPad":
      return [BD(S("x", e, t, n), S("padding", e, t, n), S("mode", e, t, n))];
    case "PadV2":
    case "Pad":
      return [bi(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 [cb(S("x", e, t, n), s, r)];
    }
    case "BatchToSpaceND": {
      let s = S("blockShape", e, t, n), r = S("crops", e, t, n);
      return [Jg(S("x", e, t, n), s, r)];
    }
    case "DepthToSpace": {
      let s = S("blockSize", e, t, n), r = S("dataFormat", e, t, n).toUpperCase();
      return [IR(S("x", e, t, n), s, r)];
    }
    case "BroadcastTo":
      return [id(S("x", e, t, n), S("shape", e, t, n))];
    case "BroadcastArgs":
      return [YE(S("s0", e, t, n), S("s1", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function nw(e, t, n, s) {
  let r = ((a, i, o) => {
    switch (a.category) {
      case "arithmetic":
        return q(() => y4(a, i, o));
      case "basic_math":
        return q(() => v4(a, i, o));
      case "control":
        return C4(a, i, o);
      case "convolution":
        return q(() => N4(a, i, o));
      case "creation":
        return q(() => T4(a, i, o));
      case "dynamic":
        return $4(a, i, o);
      case "evaluation":
        return q(() => _4(a, i, o));
      case "image":
        return q(() => D4(a, i, o));
      case "graph":
        return q(() => A4(a, i, o));
      case "logical":
        return q(() => F4(a, i, o));
      case "matrices":
        return q(() => O4(a, i, o));
      case "normalization":
        return q(() => P4(a, i, o));
      case "reduction":
        return q(() => z4(a, i, o));
      case "slice_join":
        return q(() => M4(a, i, o));
      case "sparse":
        return q(() => L4(a, i, o));
      case "spectral":
        return q(() => B4(a, i, o));
      case "string":
        return q(() => V4(a, i, o));
      case "transformation":
        return q(() => W4(a, i, o));
      case "hash_table":
        return R4(a, i, o, s);
      case "custom":
        let u = u0(a.op);
        if (u && u.customExecutor)
          return u.customExecutor(new b4(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 sw = 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 rw(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 ((A0(d) || j4(d) || K4(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 U4(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 G4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
var H4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
var q4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
function A0(e) {
  return G4.indexOf(e.op) >= 0;
}
function j4(e) {
  return H4.indexOf(e.op) >= 0;
}
function K4(e) {
  return q4.indexOf(e.op) >= 0;
}
var Hm = 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 Hm(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 = rw(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 U4(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 sw(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 = nw(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 = YW(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 sw(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 } = rw(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) => !A0(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}]. ${y}`);
    }
    return h;
  }
  processStack(e, t, n, s, r, a, i, o, u) {
    let l = [];
    for (; t.length > 0; ) {
      let c = t.pop();
      n.currentContext = c.contexts;
      let p = "";
      if (c.node.op === "Enter" && S("isConstant", c.node, s, n) && ([p] = Ts(c.node.name, n)), s[c.node.name] == null) {
        let d = nw(c.node, s, n, this._resourceManager);
        p || ([p] = Ts(c.node.name, n));
        let h = n.currentContext;
        w.isPromise(d) ? l.push(d.then((f) => (s[p] = f, n.currentContext = h, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u), f))) : (s[p] = d, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u));
      } else
        this.processChildNodes(c.node, t, n, s, r, u);
    }
    return l;
  }
  processChildNodes(e, t, n, s, r, a) {
    e.children.forEach((i) => {
      let [o] = Ts(i.name, n);
      r[o] || !a.has(i.name) || (i.op === "Merge" ? i.inputNames.some((u) => !!un(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })) : i.inputNames.every((u) => !!un(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })));
    });
  }
  dispose() {
    Object.keys(this.weightMap).forEach((e) => this.weightMap[e].forEach((t) => t.dispose()));
  }
  checkInputShapeAndType(e) {
    Object.keys(e).forEach((t) => {
      let n = e[t], [s] = _n(t), r = this.graph.nodes[s];
      if (r.attrParams.shape && r.attrParams.shape.value) {
        let a = r.attrParams.shape.value, i = a.length === n.shape.length && n.shape.every((o, u) => a[u] === -1 || a[u] === o);
        w.assert(i, () => `The shape of dict['${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 '${t}' is not found in the graph`);
    });
  }
};
var X4 = class {
  constructor(e = {}, t = {}) {
    this.hashTableNameToHandle = e, this.hashTableMap = t;
  }
  addHashTable(e, t) {
    this.hashTableNameToHandle[e] = t.handle, this.hashTableMap[t.id] = t;
  }
  getHashTableHandleByName(e) {
    return this.hashTableNameToHandle[e];
  }
  getHashTableById(e) {
    return this.hashTableMap[e];
  }
  dispose() {
    for (let e in this.hashTableMap)
      this.hashTableMap[e].clearAndClose(), delete this.hashTableMap[e];
    for (let e in this.hashTableNameToHandle)
      this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
  }
};
var Y4 = "?tfjs-format=file";
var Q4 = "model.json";
var E0 = class {
  constructor(e, t = {}) {
    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new X4();
  }
  get modelVersion() {
    return this.version;
  }
  get inputNodes() {
    return this.executor.inputNodes;
  }
  get outputNodes() {
    return this.executor.outputNodes;
  }
  get inputs() {
    return this.executor.inputs;
  }
  get outputs() {
    return this.executor.outputs;
  }
  get weights() {
    return this.executor.weightMap;
  }
  get metadata() {
    return this.artifacts.userDefinedMetadata;
  }
  get modelSignature() {
    return this.signature;
  }
  findIOHandler() {
    let e = this.modelUrl;
    if (e.load != null)
      this.handler = e;
    else if (this.loadOptions.requestInit != null)
      this.handler = An.browserHTTPRequest(e, this.loadOptions);
    else {
      let t = An.getLoadHandlers(e, this.loadOptions);
      if (t.length === 0)
        t.push(An.browserHTTPRequest(e, this.loadOptions));
      else if (t.length > 1)
        throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);
      this.handler = t[0];
    }
  }
  load() {
    if (this.findIOHandler(), this.handler.load == null)
      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
    let e = this.handler.load();
    return w.isPromise(e) ? e.then((t) => this.loadSync(t)) : this.loadSync(e);
  }
  loadSync(e) {
    this.artifacts = e;
    let t = this.artifacts.modelTopology, n;
    this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null ? n = this.artifacts.userDefinedMetadata.signature : n = this.artifacts.signature, this.signature = n, this.version = `${t.versions.producer}.${t.versions.minConsumer}`;
    let s = An.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
    if (this.executor = new Hm(Zx.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
      let r = Zx.Instance.transformGraph(e.modelInitializer);
      this.initializer = new Hm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
    }
    return true;
  }
  async save(e, t) {
    if (typeof e == "string") {
      let n = An.getSaveHandlers(e);
      if (n.length === 0)
        throw new Error(`Cannot find any save handlers for URL '${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 mhe(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 && typeof e == "string" && (e = Z4(e));
  let n = new E0(e, t);
  return await n.load(), n;
}
function ghe(e) {
  if (e == null)
    throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model");
  if (!e.load)
    throw new Error(`modelUrl IO Handler ${e} has no load function`);
  let t = new E0(e);
  return t.load(), t;
}
function Z4(e) {
  return e.endsWith("/") || (e = e + "/"), `${e}${Q4}${Y4}`;
}
var bhe = "0.0.0";
var J4 = {};
Ee(J4, { CSVDataset: () => U0, Dataset: () => su, FileDataSource: () => Y0, TextLineDataset: () => W0, URLDataSource: () => Q0, array: () => kU, csv: () => DU, func: () => FU, generator: () => OU, microphone: () => zU, version_data: () => MU, webcam: () => PU, zip: () => SU });
var eU = ka(Xd());
var tU = ka(Xd());
function nU(e, t) {
  return Md(e, t);
}
function Md(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set()) {
  if (e == null)
    return null;
  if (typeof Blob == "function" && e instanceof Blob)
    return e.slice();
  if (s.has(e))
    throw new Error("Circular references are not supported.");
  if (n.has(e))
    return n.get(e);
  let r = t(e);
  if (r.recurse && r.value !== null)
    throw new Error("A deep map function may not return both a value and recurse=true.");
  if (r.recurse)
    if (ao(e)) {
      let a = Array.isArray(e) ? [] : {};
      s.add(e);
      for (let i in e) {
        let o = e[i], u = Md(o, t, n, s);
        a[i] = u;
      }
      return s.delete(e), e.__proto__ && (a.__proto__ = e.__proto__), a;
    } else
      throw new Error(`Can't recurse into non-iterable type: ${e}`);
  else
    return n.set(e, r.value), r.value;
}
function sU(e, t = D0) {
  return R0(e, t);
}
function R0(e, t, n = /* @__PURE__ */ new Set()) {
  let s = e[0];
  if (n.has(s))
    throw new Error("Circular references are not supported.");
  let r = t(e);
  if (r.recurse && r.value !== null)
    throw new Error("A deep zip function may not return both a value and recurse=true.");
  if (r.recurse)
    if (ao(s)) {
      let a = Array.isArray(s) ? [] : {};
      n.add(s);
      for (let i in s) {
        let o = e.map((l) => l[i]), u = R0(o, t, n);
        a[i] = u;
      }
      return n.delete(s), a;
    } else
      throw new Error(`Can't recurse into non-iterable type: ${s}`);
  else
    return r.value;
}
function D0(e) {
  return e === null ? null : ao(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
}
async function F0(e, t) {
  let n = /* @__PURE__ */ new Map();
  Md(e, t, n);
  for (let r of Array.from(n.keys())) {
    let a = n.get(r);
    if (w.isPromise(a)) {
      let i = await a;
      n.set(r, i);
    }
  }
  return Md(e, t, n);
}
function ao(e) {
  let t = false;
  if (K().get("IS_BROWSER"))
    t = e instanceof TextDecoder;
  else {
    let { StringDecoder: n } = Zw();
    t = e instanceof n;
  }
  return e != null && !ArrayBuffer.isView(e) && (Array.isArray(e) || typeof e == "object" && !(e instanceof et) && !(e instanceof Promise) && !t);
}
function rU(e) {
  return e == null || aU(e) || Array.isArray(e) || typeof e == "object" && e instanceof et || w.isTypedArray(e);
}
function aU(e) {
  return e === null || typeof e != "object" && typeof e != "function";
}
function iU(e) {
  return nU(e, oU);
}
function oU(e) {
  return e instanceof et ? { value: e.clone(), recurse: false } : ao(e) ? { value: null, recurse: true } : { value: e, recurse: false };
}
var O0 = class {
  constructor(e) {
    if (this.capacity = e, this.begin = 0, this.end = 0, e == null)
      throw new RangeError("Can't create a ring buffer of unknown capacity.");
    if (e < 1)
      throw new RangeError("Can't create ring buffer of capacity < 1.");
    this.data = new Array(e), this.doubledCapacity = 2 * e;
  }
  wrap(e) {
    for (; e < 0; )
      e += this.doubledCapacity;
    return e % this.doubledCapacity;
  }
  get(e) {
    if (e < 0)
      throw new RangeError("Can't get item at a negative index.");
    return this.data[e % this.capacity];
  }
  set(e, t) {
    if (e < 0)
      throw new RangeError("Can't set item at a negative index.");
    this.data[e % this.capacity] = t;
  }
  length() {
    let e = this.end - this.begin;
    return e < 0 && (e = this.doubledCapacity + e), e;
  }
  isFull() {
    return this.length() === this.capacity;
  }
  isEmpty() {
    return this.length() === 0;
  }
  push(e) {
    if (this.isFull())
      throw new RangeError("Ring buffer is full.");
    this.set(this.end, e), this.end = this.wrap(this.end + 1);
  }
  pushAll(e) {
    for (let t of e)
      this.push(t);
  }
  pop() {
    if (this.isEmpty())
      throw new RangeError("Ring buffer is empty.");
    this.end = this.wrap(this.end - 1);
    let e = this.get(this.end);
    return this.set(this.end, void 0), e;
  }
  unshift(e) {
    if (this.isFull())
      throw new RangeError("Ring buffer is full.");
    this.begin = this.wrap(this.begin - 1), this.set(this.begin, e);
  }
  shift() {
    if (this.isEmpty())
      throw new RangeError("Ring buffer is empty.");
    let e = this.get(this.begin);
    return this.set(this.begin, void 0), this.begin = this.wrap(this.begin + 1), e;
  }
  shuffleExcise(e) {
    if (this.isEmpty())
      throw new RangeError("Ring buffer is empty.");
    let t = this.wrap(this.begin + e), n = this.get(t);
    return this.set(t, this.pop()), n;
  }
};
var P0 = class extends O0 {
  constructor() {
    super(P0.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 z0 = P0;
z0.INITIAL_CAPACITY = 32;
function M0(e) {
  return new cU(e);
}
function rv(e) {
  return new dU(e);
}
function uU(e, t) {
  return new L0(e, t);
}
function lU(e, t = B0.FAIL) {
  return new xU(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 yU(this, e);
  }
  filter(e) {
    return new gU(this, e);
  }
  map(e) {
    return new bU(this, e);
  }
  mapAsync(e) {
    return new aw(this, e);
  }
  serialMapAsync(e) {
    return new aw(this, e).serial();
  }
  flatmap(e) {
    return new vU(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 mU(this, e, t);
  }
  columnMajorBatch(e, t = true, n = D0) {
    return this.rowMajorBatch(e, t).map((r) => sU(r, n));
  }
  concatenate(e, t) {
    return new L0(M0([this, e]), t);
  }
  take(e) {
    return e < 0 || e == null ? this : new fU(this, e);
  }
  skip(e) {
    return e < 0 || e == null ? this : new hU(this, e);
  }
  prefetch(e) {
    return new V0(this, e);
  }
  shuffle(e, t) {
    return new wU(this, e, t);
  }
  serial() {
    return new pU(this);
  }
};
var cU = 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: iU(e), done: false };
  }
};
var dU = 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: ${e.message}`, e;
    }
  }
};
var pU = class extends Gt {
  constructor(e) {
    super(), this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Serial`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    return this.upstream.next();
  }
};
var hU = 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 `${this.upstream.summary()} -> Skip`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; this.count++ < this.maxCount; ) {
      let e = await this.upstream.next();
      if (e.done)
        return e;
      De(e.value);
    }
    return this.upstream.next();
  }
};
var fU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.maxCount = t, this.count = 0;
  }
  summary() {
    return `${this.upstream.summary()} -> Take`;
  }
  async next() {
    return this.count++ >= this.maxCount ? { value: null, done: true } : this.upstream.next();
  }
};
var mU = 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 `${this.upstream.summary()} -> RowMajorBatch`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    let e = [];
    for (; e.length < this.batchSize; ) {
      let t = await this.upstream.next();
      if (t.done)
        return this.enableSmallLastBatch && e.length > 0 ? { value: e, done: false } : { value: null, done: true };
      e.push(t.value);
    }
    return { value: e, done: false };
  }
};
var gU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${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;
      De(e.value);
    }
  }
};
var bU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> Map`;
  }
  async next() {
    let e = await this.upstream.next();
    if (e.done)
      return { value: null, done: true };
    let t = _s.getTensorsInContainer(e.value), n = this.transform(e.value), s = _s.getTensorsInContainer(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return { value: n, done: false };
  }
};
var yU = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${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 aw = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> AsyncMap`;
  }
  async next() {
    let e = await this.upstream.next();
    if (e.done)
      return { value: null, done: true };
    let t = _s.getTensorsInContainer(e.value), n = await this.transform(e.value), s = _s.getTensorsInContainer(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return { value: n, done: false };
  }
};
var av = class extends Gt {
  constructor() {
    super(), this.outputQueue = new z0(), this.lastRead = Promise.resolve({ value: null, done: false });
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; this.outputQueue.length() === 0; )
      if (!await this.pump())
        return { value: null, done: true };
    return { value: this.outputQueue.shift(), done: false };
  }
};
var vU = class extends av {
  constructor(e, t) {
    super(), this.upstream = e, this.transform = t;
  }
  summary() {
    return `${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 L0 = 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 B0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(B0 || {});
var xU = 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 F0(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 ${this.count}.`);
        case 1:
          return { value: null, done: true };
        case 2:
        default:
      }
    return this.count++, { value: r, done: false };
  }
  async next() {
    return this.currentPromise = this.nextState(this.currentPromise), this.currentPromise;
  }
};
var V0 = class extends Gt {
  constructor(e, t) {
    super(), this.upstream = e, this.bufferSize = t, this.buffer = new O0(t);
  }
  summary() {
    return `${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 wU = class extends V0 {
  constructor(e, t, n) {
    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = tU.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 su = 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, IU), 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 = rv(async () => ({ value: await t.iterator(), done: false }));
      return uU(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 = eU.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();
  }
};
su.MAX_BUFFER_SIZE = 1e4;
function $n(e, t = null) {
  return new class extends su {
    constructor() {
      super(...arguments), this.size = t;
    }
    async iterator() {
      return e();
    }
  }();
}
function kU(e) {
  return $n(async () => M0(e), e.length);
}
function SU(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 F0(e, (s) => {
      if (s instanceof su)
        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 lU(n, 1);
  }, t);
}
function IU(e) {
  if (e === null)
    return null;
  let t = e[0];
  return rU(t) ? { value: CU(e), recurse: false } : { value: null, recurse: true };
}
function CU(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 W0 = class extends su {
  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 Zc = '"';
var Eu = Symbol("out");
var iw = Symbol("field");
var Jc = Symbol("quote");
var Jf = Symbol("quoteafterquote");
var ow = Symbol("quoteinquote");
var U0 = class extends su {
  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 W0(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 = Eu;
    for (let i = 0; i < r; i++)
      switch (a) {
        case Eu:
          switch (e.charAt(i)) {
            case Zc:
              s = i + 1, a = Jc;
              break;
            case this.delimiter:
              if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
                break;
              n.push(""), a = Eu;
              break;
            default:
              a = iw, s = i;
              break;
          }
          break;
        case iw:
          switch (e.charAt(i)) {
            case this.delimiter:
              n.push(e.substring(s, i)), a = Eu, s = i + 1;
              break;
            default:
          }
          break;
        case Jc:
          switch (e.charAt(i)) {
            case Zc:
              a = Jf;
              break;
            default:
          }
          break;
        case Jf:
          switch (e.charAt(i)) {
            case this.delimiter:
              n.push(e.substring(s, i - 1)), a = Eu, s = i + 1;
              break;
            case Zc:
              a = Jc;
              break;
            default:
              a = ow;
              break;
          }
          break;
        case ow:
          switch (e.charAt(i)) {
            case Zc:
              a = Jc;
              break;
            default:
          }
          break;
        default:
      }
    if (a === Jf ? 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 G0 = 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 G0(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 H0 = 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 H0(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 = Lk.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: ${t.message}`);
      } finally {
        e.dispose();
      }
    else
      return { value: e, done: false };
  }
  needToResize() {
    return !!(this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight));
  }
  cropAndResizeFrame(e) {
    return q(() => {
      let t = Pn(le(e, "float32"), 0), n;
      n = jn.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
      let s = n.shape;
      return U(n, s.slice(1));
    });
  }
  async capture() {
    return (await this.next()).value;
  }
  stop() {
    this.stream.getTracks().forEach((t) => t.stop());
    try {
      this.webcamVideoElement.srcObject = null;
    } catch (t) {
      console.log(t), this.webcamVideoElement.src = null;
    }
    this.isClosed = true;
  }
  toArray() {
    throw new Error("Can not convert infinite video stream to array.");
  }
};
var q0 = class {
};
var j0 = class extends Gt {
  split(e) {
    return new NU(this, e);
  }
};
var NU = class extends j0 {
  constructor(e, t) {
    super(), this.upstream = e, this.impl = new TU(e, t);
  }
  summary() {
    return this.impl.summary();
  }
  async next() {
    return this.impl.next();
  }
};
var TU = class extends av {
  constructor(e, t) {
    super(), this.upstream = e, this.separator = t, this.carryover = "";
  }
  summary() {
    return `${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 $U = class extends Gt {
  decodeUTF8() {
    return new _U(this);
  }
};
var _U = class extends j0 {
  constructor(e) {
    super(), this.upstream = e, this.impl = new AU(e);
  }
  summary() {
    return this.impl.summary();
  }
  async next() {
    return this.impl.next();
  }
};
var AU = class extends av {
  constructor(e) {
    if (super(), this.upstream = e, K().get("IS_BROWSER"))
      this.decoder = new TextDecoder("utf-8");
    else {
      let { StringDecoder: t } = Zw();
      this.decoder = new t("utf8");
    }
  }
  summary() {
    return `${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 K0 = class extends $U {
  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 EU(e, t = {}, n) {
  let s, r;
  typeof e == "string" ? s = e : (s = e.url, r = RU(e));
  let a = await (n || w.fetch)(s, r);
  if (a.ok) {
    let i = new Uint8Array(await a.arrayBuffer());
    return new K0(i, t);
  } else
    throw new Error(a.statusText);
}
var RU = (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 X0(e) {
  return typeof e == "string" && e.slice(0, 7) === "file://";
}
var Y0 = class extends q0 {
  constructor(e, t = {}) {
    super(), this.input = e, this.options = t;
  }
  async iterator() {
    if (X0(this.input) && K().get("IS_NODE")) {
      let e = ug();
      this.input = e.readFileSync(this.input.slice(7));
    }
    return new K0(this.input, this.options);
  }
};
var Q0 = class extends q0 {
  constructor(e, t = {}) {
    super(), this.url = e, this.fileOptions = t;
  }
  async iterator() {
    return X0(this.url) ? new Y0(this.url, this.fileOptions).iterator() : EU(this.url, this.fileOptions);
  }
};
function DU(e, t = {}) {
  return new U0(new Q0(e), t);
}
function FU(e) {
  let t = rv(e);
  return $n(async () => t);
}
function OU(e) {
  return $n(async () => {
    let t = await e();
    return rv(() => t.next());
  });
}
async function PU(e, t) {
  return H0.create(e, t);
}
async function zU(e) {
  return G0.create(e);
}
var MU = "0.0.0";
function be(e, t) {
  Array.isArray(e) || (e = [e]), e.forEach((n) => {
    n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the CPU backend.`);
  });
}
var LU = ws.whereImpl;
var Z0 = class extends ol {
  constructor() {
    super(), this.blockSize = 48, this.firstUse = true, this.data = new Yd(this, ds());
  }
  nextDataId() {
    return Z0.nextDataId++;
  }
  write(e, t, n) {
    this.firstUse && (this.firstUse = false, K().get("IS_NODE") && C.warn(`
============================
Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. 
============================`));
    let 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);
    if (e.dtype === "string")
      try {
        let n = t.map((s) => w.decodeString(s));
        return Ae(e.shape, e.dtype, n);
      } catch (n) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return Ae(e.shape, e.dtype, t);
  }
  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 LU(e.shape, t);
  }
  dispose() {
  }
  floatPrecision() {
    return 32;
  }
  epsilon() {
    return super.epsilon();
  }
};
var J0 = Z0;
J0.nextDataId = 0;
var iv = {};
Ee(iv, { addImpl: () => tC, bincountImpl: () => uv, bincountReduceImpl: () => nC, ceilImpl: () => sC, concatImpl: () => lv, equalImpl: () => rC, expImpl: () => iC, expm1Impl: () => uC, floorImpl: () => lC, gatherNdImpl: () => cC, gatherV2Impl: () => dC, greaterEqualImpl: () => hC, greaterImpl: () => pC, lessEqualImpl: () => mC, lessImpl: () => fC, linSpaceImpl: () => gC, logImpl: () => bC, maxImpl: () => yC, maximumImpl: () => vC, minimumImpl: () => xC, multiplyImpl: () => cv, negImpl: () => wC, notEqualImpl: () => kC, prodImpl: () => SC, rangeImpl: () => pv, rsqrtImpl: () => IC, scatterImpl: () => Ki, sigmoidImpl: () => TG, simpleAbsImpl: () => eC, sliceImpl: () => Bd, sparseFillEmptyRowsImpl: () => NC, sparseReshapeImpl: () => TC, sparseSegmentReductionImpl: () => hv, sqrtImpl: () => AG, squaredDifferenceImpl: () => $C, stridedSliceImpl: () => _C, stringNGramsImpl: () => AC, stringSplitImpl: () => EC, stringToHashBucketFastImpl: () => RC, subImpl: () => DC, tileImpl: () => FC, topKImpl: () => PC, transposeImpl: () => dv, uniqueImpl: () => zC });
function eC(e) {
  let t = new Float32Array(e.length);
  for (let n = 0; n < e.length; ++n)
    t[n] = Math.abs(e[n]);
  return t;
}
var BU = (e) => {
  let { x: t } = e.inputs, n = e.backend;
  be(t, "abs");
  let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
  return s = eC(r), n.makeOutput(s, t.shape, t.dtype);
};
var VU = { kernelName: po, backendName: "cpu", kernelFunc: BU };
function Et(e) {
  return (t, n, s, r, a) => {
    let i = C.assertAndGetBroadcastShape(t, n), o = i.length, u = w.computeStrides(i), l = w.sizeFromShape(i), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = C.getBroadcastDims(t, i), g = C.getBroadcastDims(n, i);
    if (m.length + g.length === 0)
      for (let b = 0; b < c.length; ++b)
        c[b] = e(s[b % s.length], r[b % r.length]);
    else
      for (let b = 0; b < c.length; ++b) {
        let y = w.indexToLoc(b, o, u), v = y.slice(-p);
        m.forEach(($) => 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 WU = { kernelName: ep, backendName: "cpu", kernelFunc: En };
function Ld(e, t, n = "float32") {
  if (n === "complex64") {
    let r = Ld(e, t, "float32"), a = Ld(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 UU = { kernelName: Ua, backendName: "cpu", kernelFunc: Os };
function ba(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 GU = { kernelName: lp, backendName: "cpu", kernelFunc: ba };
function kr(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 = Ld(n, r.shape, r.dtype), o = kr({ 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 = ba({ inputs: { input: r }, backend: n }), o = kr({ 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] = Et((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 HU = { kernelName: $a, backendName: "cpu", kernelFunc: kr };
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 = kr({ 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 = kr({ 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, $] = n(i.shape, o.shape, h, f, v, x), R = u.makeTensorInfo($, "float32", k), E = u.makeTensorInfo($, "float32", I), P = En({ inputs: { real: R, imag: E }, backend: u });
      return u.disposeIntermediateTensorInfo(l), u.disposeIntermediateTensorInfo(m), u.disposeIntermediateTensorInfo(R), u.disposeIntermediateTensorInfo(E), 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 ov(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, R = e(m[I * 2], m[I * 2 + 1], g[$ * 2], g[$ * 2 + 1]);
        p[k] = R.real, d[k] = R.imag;
      }
    else
      for (let k = 0; k < p.length; k++) {
        let I = w.indexToLoc(k, l, c), $ = I.slice(-b);
        h.forEach((O) => $[O] = 0);
        let R = w.locToIndex($, b, y), E = I.slice(-v);
        f.forEach((O) => E[O] = 0);
        let P = w.locToIndex(E, v, x), A = e(m[R * 2], m[R * 2 + 1], g[P * 2], g[P * 2 + 1]);
        p[k] = A.real, d[k] = A.imag;
      }
    return [p, d, o];
  };
}
var tC = Et((e, t) => e + t);
var qU = ov((e, t, n, s) => ({ real: e + n, imag: t + s }));
var io = Ht(Cr, tC, qU);
var jU = { kernelName: Cr, backendName: "cpu", kernelFunc: io };
function uv(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 nC(e, t, n, s = false) {
  let r = e.shape[0], a = e.shape[1], i = Ae([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 Dr(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 ru(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 sC = Dr((e) => Math.ceil(e));
var KU = ru(_a, sC);
var XU = { kernelName: _a, backendName: "cpu", kernelFunc: KU };
function lv(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 rC = Et((e, t) => e === t ? 1 : 0);
var aC = Ht(yo, rC, null, "bool");
var YU = { kernelName: yo, backendName: "cpu", kernelFunc: aC };
var iC = Dr((e) => Math.exp(e));
var oC = ru(Ma, iC, "float32");
var QU = { kernelName: Ma, backendName: "cpu", kernelFunc: oC };
var uC = Dr((e) => Math.expm1(e));
var ZU = ru(xo, uC);
var JU = { kernelName: xo, backendName: "cpu", kernelFunc: ZU };
var lC = Dr((e) => Math.floor(e));
var eG = ru(La, lC);
var tG = { kernelName: La, backendName: "cpu", kernelFunc: eG };
function cC(e, t, n, s, r, a, i, o, u) {
  let l = Ae([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 dC(e, t, n) {
  let s = Ae(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 pC = Et((e, t) => e > t ? 1 : 0);
var nG = Ht(Io, pC, null, "bool");
var sG = { kernelName: Io, backendName: "cpu", kernelFunc: nG };
var hC = Et((e, t) => e >= t ? 1 : 0);
var rG = Ht(Wa, hC, null, "bool");
var aG = { kernelName: Wa, backendName: "cpu", kernelFunc: rG };
var fC = Et((e, t) => e < t ? 1 : 0);
var iG = Ht(Co, fC, null, "bool");
var oG = { kernelName: Co, backendName: "cpu", kernelFunc: iG };
var mC = Et((e, t) => e <= t ? 1 : 0);
var uG = Ht(No, mC, null, "bool");
var lG = { kernelName: No, backendName: "cpu", kernelFunc: uG };
function gC(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 bC = Dr((e) => Math.log(e));
var cG = ru(Ha, bC);
var dG = { kernelName: Ha, backendName: "cpu", kernelFunc: cG };
function yC(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 vC = Et((e, t) => Math.max(e, t));
var pG = Ht(ja, vC);
var hG = { kernelName: ja, backendName: "cpu", kernelFunc: pG };
var xC = Et((e, t) => Math.min(e, t));
var fG = Ht(Qa, xC);
var mG = { kernelName: Qa, backendName: "cpu", kernelFunc: fG };
var cv = Et((e, t) => e * t);
var gG = ov((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
var Jp = Ht(Ja, cv, gG);
var bG = { kernelName: Ja, backendName: "cpu", kernelFunc: Jp };
function wC(e, t, n) {
  let s = w.createScalarValue(-1, n);
  return cv([], t, s, e, n);
}
function yG(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  be(s, "neg");
  let r = n.data.get(s.dataId).values, [a, i] = wC(r, s.shape, s.dtype);
  return n.makeTensorInfo(i, s.dtype, a);
}
var vG = { kernelName: $o, backendName: "cpu", kernelFunc: yG };
var kC = Et((e, t) => e !== t ? 1 : 0);
var xG = Ht(_o, kC, null, "bool");
var wG = { kernelName: _o, backendName: "cpu", kernelFunc: xG };
function dv(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 = dv(u, r.shape, r.dtype, a, o);
  return { dataId: s.write(l, o, r.dtype), shape: o, dtype: r.dtype };
}
var kG = { kernelName: Hs, backendName: "cpu", kernelFunc: wn };
function SC(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 SG(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 } = SC(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 IG = { kernelName: si, backendName: "cpu", kernelFunc: SG };
function pv(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 IC = Dr((e) => 1 / Math.sqrt(e));
var CG = ru(oi, IC);
var NG = { kernelName: oi, backendName: "cpu", kernelFunc: CG };
function Ki(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 Ae(n, t.dtype);
  let h = Ae(c, t.dtype);
  typeof u == "string" || typeof u == "number" ? h.values.fill(u) : typeof u == "boolean" && 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;
}
var TG = Dr((e) => 1 / (1 + Math.exp(-e)));
var CC = st(li, (e) => 1 / (1 + Math.exp(-e)));
var $G = { kernelName: li, backendName: "cpu", kernelFunc: CC };
function Bd(e, t, n, s, r) {
  let a = kt.isSliceContinous(s, t, n), i = w.sizeFromShape(n), o = w.computeStrides(s);
  if (a) {
    let p = kt.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 = Ae(s, r, u), c = Ae(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 ya(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s;
  be(r, "slice");
  let [o, u] = kt.parseSliceParams(r, a, i);
  kt.assertParamsValid(r, o, u);
  let l = n.data.get(r.dataId).values, c = Bd(l, o, u, r.shape, r.dtype);
  return n.makeTensorInfo(u, r.dtype, c);
}
var _G = { kernelName: Bo, backendName: "cpu", kernelFunc: ya };
function NC(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 R = 0; R < p; ++R)
        b[$ * p + R] = e[x * p + R];
      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 TC(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 hv(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 AG = Dr((e) => Math.sqrt(e));
var EG = st(ci, (e) => Math.sqrt(e));
var RG = { kernelName: ci, backendName: "cpu", kernelFunc: EG };
var $C = Et((e, t) => {
  let n = e - t;
  return n * n;
});
var DG = Ht(hi, $C);
var FG = { kernelName: hi, backendName: "cpu", kernelFunc: DG };
function _C(e, t, n, s) {
  let r = Ae(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 OG = 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 AC(e, t, n, s, r, a, i, o) {
  return new OG(n, s, r, a, i, o).compute(e, t);
}
function PG(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 EC(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;
    PG(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 RC(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 DC = Et((e, t) => e - t);
var zG = ov((e, t, n, s) => ({ real: e - n, imag: t - s }));
var fv = Ht(fi, DC, zG);
var MG = { kernelName: fi, backendName: "cpu", kernelFunc: fv };
function FC(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 = Ae(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 Pu = (e, t) => {
  let n = t.value - e.value;
  return n === 0 ? e.index - t.index : n;
};
function OC(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));
      OC(e, t, d, h);
    }
    let r = e[t], a = n, i = s;
    for (w.swap(e, n, t), Pu(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
      for (w.swap(e, a, i), a++, i--; Pu(e[a], r) < 0; )
        a = a + 1;
      for (; Pu(e[i], r) > 0; )
        i = i - 1;
    }
    Pu(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 PC(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 && (OC(f, s), f = f.slice(0, s)), r && f.sort(Pu);
    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, [Ae(c, n, u), Ae(c, "int32", l)];
}
function zC(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 yhe = "0.0.0";
vp("cpu", () => new J0(), 1);
var MC = st(za, (e) => e >= 0 ? e : Math.exp(e) - 1);
var LG = { kernelName: za, backendName: "cpu", kernelFunc: MC };
function LC(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 BG = { kernelName: Ga, backendName: "cpu", kernelFunc: LC };
var VG = Et((e, t) => e < 0 ? t * e : e);
function BC(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] = VG(s.shape, r.shape, a, i, "float32");
  return n.makeTensorInfo(u, "float32", o);
}
var WG = { kernelName: ni, backendName: "cpu", kernelFunc: BC };
var VC = st(ri, (e) => Math.max(0, e));
var UG = { kernelName: ri, backendName: "cpu", kernelFunc: VC };
var WC = st(ii, (e) => Math.min(Math.max(0, e), 6));
var GG = { kernelName: ii, backendName: "cpu", kernelFunc: WC };
function Vd(e, t, n, s, r) {
  if (n === "linear")
    return Os({ inputs: { x: t }, backend: e });
  if (n === "relu")
    return VC({ inputs: { x: t }, backend: e });
  if (n === "elu")
    return MC({ inputs: { x: t }, backend: e });
  if (n === "relu6")
    return WC({ inputs: { x: t }, backend: e });
  if (n === "prelu")
    return BC({ inputs: { x: t, alpha: s }, backend: e });
  if (n === "leakyrelu")
    return LC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
  if (n === "sigmoid")
    return CC({ inputs: { x: t }, backend: e });
  throw new Error(`Activation ${n} has not been implemented for the CPU backend.`);
}
function pt(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = w.sizeFromShape(r.shape), o = w.inferFromImplicitShape(a, i), u = w.sizeFromShape(o);
  w.assert(i === u, () => `The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`), n.incRef(r.dataId);
  let l = n.data.get(r.dataId);
  if (l.complexTensorInfos != null) {
    let c = l.complexTensorInfos.real, p = l.complexTensorInfos.imag;
    c.shape = o, p.shape = o;
  }
  return { dataId: r.dataId, shape: o, dtype: r.dtype };
}
var HG = { kernelName: Oo, backendName: "cpu", kernelFunc: pt };
function UC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  be([r, a], "matMul");
  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
  w.assert(c === p, () => `Error in matMul: inner shapes (${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 = pt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), $ = pt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), R = i ? I.shape[1] : I.shape[2], E = i ? I.shape[2] : I.shape[1], P = o ? $.shape[1] : $.shape[2], A = Math.max(g, b), O = n.data.get(I.dataId).values, T = n.data.get($.dataId).values, M = w.computeStrides(I.shape), W = w.computeStrides($.shape), [j, X, Y] = i ? [M[0], 1, M[1]] : [M[0], M[1], 1], [Z, te, J] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = E * P, ne = Ae([A, E, P], I.dtype), oe = ne.values, ae = n.blockSize;
  for (let de = 0; de < A; de++)
    for (let me = 0; me < E; me += ae)
      for (let ke = 0; ke < P; ke += ae)
        for (let Ie = 0; Ie < R; Ie += ae) {
          let Re = Math.min(me + ae, E), Pe = Math.min(ke + ae, P), Xe = Math.min(Ie + ae, R);
          for (let Je = me; Je < Re; Je++)
            for (let Ye = ke; Ye < Pe; Ye++) {
              let tt = 0;
              for (let Ce = Ie; Ce < Xe; Ce++) {
                let ut = Math.min(de, g - 1) * j, at = Math.min(de, b - 1) * J, Jt = O[ut + Je * X + Ce * Y], Nt = T[Ce * Z + Ye * te + at];
                tt += Jt * Nt;
              }
              oe[de * se + (Je * P + Ye)] += tt;
            }
        }
  return n.disposeIntermediateTensorInfo(I), n.disposeIntermediateTensorInfo($), n.makeTensorInfo(v, ne.dtype, ne.values);
}
var qG = { kernelName: Ta, backendName: "cpu", kernelFunc: UC };
function jG(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 = UC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = io({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = Vd(n, d, c, o, p), m.push(d), d = f);
  for (let b of m)
    n.disposeIntermediateTensorInfo(b);
  return d;
}
var KG = { kernelName: oa, backendName: "cpu", kernelFunc: jG };
var XG = st(ul, (e) => Math.acos(e));
var YG = { kernelName: ul, backendName: "cpu", kernelFunc: XG };
var QG = st(ll, (e) => Math.acosh(e));
var ZG = { kernelName: ll, backendName: "cpu", kernelFunc: QG };
function JG(e) {
  let { inputs: t, backend: n } = e, s = t;
  be(t, "addN");
  let r = s.map((o) => n.data.get(o.dataId).values), a = Ae(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 eH = { kernelName: Ia, backendName: "cpu", kernelFunc: JG };
function tH(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 = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var nH = { kernelName: cl, backendName: "cpu", kernelFunc: tH };
function sH(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 = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var rH = { kernelName: dl, backendName: "cpu", kernelFunc: sH };
function aH(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 iH = { kernelName: Ca, backendName: "cpu", kernelFunc: aH };
function oH(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 uH = { kernelName: pl, backendName: "cpu", kernelFunc: oH };
var lH = st(hl, (e) => Math.asin(e));
var cH = { kernelName: hl, backendName: "cpu", kernelFunc: lH };
var dH = st(fl, (e) => Math.asinh(e));
var pH = { kernelName: fl, backendName: "cpu", kernelFunc: dH };
var hH = st(ml, (e) => Math.atan(e));
var fH = { kernelName: ml, backendName: "cpu", kernelFunc: hH };
var mH = Et((e, t) => Math.atan2(e, t));
var gH = Ht(bl, mH);
var bH = { kernelName: bl, backendName: "cpu", kernelFunc: gH };
var yH = st(gl, (e) => Math.atanh(e));
var vH = { kernelName: gl, backendName: "cpu", kernelFunc: yH };
function mv(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 = Ae(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 R = 0; R < r.outHeight; ++R) {
        let E = R * i - d, P = Math.max(0, E), A = Math.min(r.inHeight, c + E), O = k + R * y;
        for (let T = 0; T < r.outWidth; ++T) {
          let M = T * o - h, W = Math.max(0, M), j = Math.min(r.inWidth, p + M), X = f, Y = 0, Z = 0;
          for (let J = P; J < A; 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 = O + T * v + $;
          g[te] = a === "avg" ? Y / Z : X;
        }
      }
  }
  return m;
}
function GC(e, t, n, s, r = false, a = false) {
  let i = Ae(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 = Ae(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, R = $;
          for (; R < 0; )
            R += c;
          let E = Math.min(s.inWidth, d + $), P = Number.NEGATIVE_INFINITY, A = -1;
          for (let O = x; O < k; O += l) {
            let T = O - v;
            for (let M = R; M < E; M += c) {
              let W = M - $, j = m.get(g, O, M, b);
              j > P && (P = j, r ? A = a ? ((g * s.inHeight + O) * s.inWidth + M) * s.inChannels + b : (O * s.inWidth + M) * s.inChannels + b : A = T * d + W);
            }
          }
          i.set(A, g, y, I, b);
        }
      }
  return i;
}
function HC(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 = Ae(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], R = r.outShape[4];
  for (let E = 0; E < r.batchSize; ++E) {
    let P = E * k, A = E * s[0];
    for (let O = 0; O < r.inChannels; ++O)
      for (let T = 0; T < r.outDepth; ++T) {
        let M = T * i - m, W = M;
        for (; W < 0; )
          W += l;
        let j = Math.min(r.inDepth, d + M), 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 * R, ke = y, Ie = 0, Re = 0;
            for (let Xe = W; Xe < j; Xe += l) {
              let Je = A + 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], at = e[ut + O];
                  if (a === "max" && at > ke ? ke = at : a === "avg" && (Ie += at, Re++), isNaN(ke))
                    break;
                }
                if (isNaN(ke))
                  break;
              }
              if (isNaN(ke))
                break;
            }
            let Pe = me + O;
            x[Pe] = a === "avg" ? Ie / Re : ke;
          }
        }
      }
  }
  return v;
}
function xH(e, t) {
  let n = Ae(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 R = Math.min(t.inHeight, c + I);
          for (let E = 0; E < t.outWidth; ++E) {
            let P = E * a - f, A = P;
            for (; A < 0; )
              A += u;
            let O = Math.min(t.inWidth, p + P), T = Number.NEGATIVE_INFINITY, M = -1;
            for (let W = v; W < x; W += i) {
              let j = W - y;
              for (let X = $; X < R; X += o) {
                let Y = X - I;
                for (let Z = A; Z < O; Z += u) {
                  let te = Z - P, J = e.get(m, W, X, Z, g);
                  J >= T && (T = J, M = j * c * p + Y * c + te);
                }
              }
            }
            n.set(M, m, b, k, E, g);
          }
        }
      }
  return n;
}
function wH(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 = mv(d, r.shape, r.dtype, h, c, "avg");
    p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
  }
  return p;
}
var kH = { kernelName: Na, backendName: "cpu", kernelFunc: wH };
function SH(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 = HC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
  return n.makeTensorInfo(d.shape, "float32", d.values);
}
var IH = { kernelName: Jd, backendName: "cpu", kernelFunc: SH };
function CH(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, R = I - 1 - c.padInfo.left, E = k - 1 - c.padInfo.top, P = Ae(a.shape, "float32"), A = 1 / (f * m * g), O = n.bufferSync(r);
  for (let T = 0; T < c.batchSize; ++T)
    for (let M = 0; M < c.inChannels; ++M)
      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 - E, te = X - R, 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 += O.get(T, ne, ae, me, M);
                    }
                }
            }
            P.set(J * A, T, W, j, X, M);
          }
  return n.makeTensorInfo(P.shape, P.dtype, P.values);
}
var NH = { kernelName: fg, backendName: "cpu", kernelFunc: CH };
function TH(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 = Ae(i.shape, "float32"), I = 1 / (h * f), $ = n.data.get(r.dataId).values, R = Ae(r.shape, "float32", $);
  for (let E = 0; E < c.batchSize; ++E)
    for (let P = 0; P < c.inChannels; ++P)
      for (let A = 0; A < c.inHeight; ++A)
        for (let O = 0; O < c.inWidth; ++O) {
          let T = A - x, M = O - 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 = (M + Y) / d;
                if (Z < 0 || Z >= c.outWidth || Math.floor(Z) !== Z)
                  continue;
                W += R.get(E, X, Z, P);
              }
          }
          k.set(W * I, E, A, O, P);
        }
  return n.makeTensorInfo(k.shape, k.dtype, k.values);
}
var $H = { kernelName: hg, backendName: "cpu", kernelFunc: TH };
function _H(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 R = 0; R < c.length; ++R)
    m[R] = f[x++] + (c[R] - 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 AH = { kernelName: Va, backendName: "cpu", kernelFunc: _H };
function EH(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 = pt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = pt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ya({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
  return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
}
var RH = { kernelName: ho, backendName: "cpu", kernelFunc: EH };
function DH(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 = uv(o, u, a.dtype, a.shape, i);
  return n.makeTensorInfo([i], a.dtype, l);
}
var FH = { kernelName: mg, backendName: "cpu", kernelFunc: DH };
function OH(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 PH = { kernelName: gg, backendName: "cpu", kernelFunc: OH };
var zH = st(Nr, (e, t) => {
  let n = t;
  return e > n.clipValueMax ? n.clipValueMax : e < n.clipValueMin ? n.clipValueMin : e;
});
var MH = { kernelName: Nr, backendName: "cpu", kernelFunc: zH };
var LH = (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 BH = { kernelName: tp, backendName: "cpu", kernelFunc: LH };
function oo(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 VH = { kernelName: ap, backendName: "cpu", kernelFunc: oo };
function uo(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) => ba({ inputs: { input: x }, backend: n })), g = o.map((x) => oo({ inputs: { input: x }, backend: n })), b = uo({ inputs: m, backend: n, attrs: { axis: a } }), y = uo({ 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 pt({ 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 = lv(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 WH = { kernelName: fo, backendName: "cpu", kernelFunc: uo };
function qC(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], R = v ? k[1] : k[2], E = v ? k[2] : 1, P = v ? 1 : k[1], A = x.strides[0], O = v ? x.strides[1] : x.strides[2], T = v ? x.strides[2] : 1, M = 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 * A;
    for (let J = 0; J < d.outHeight; ++J) {
      let se = te + J * O, 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 * R;
        for (let ke = 0; ke < d.outWidth; ++ke) {
          let Ie = se + ke * T, Re = ke * d.strideWidth - b;
          for (let Pe = 0; Pe < f; ++Pe) {
            let Xe = Re + Pe * g;
            if (Xe < 0 || Xe >= d.inWidth)
              continue;
            let Je = de + Pe * I[1], Ye = me + Xe * E, tt = Je;
            for (let Ce = 0; Ce < d.inChannels; ++Ce) {
              let ut = W[Ye + Ce * P];
              for (let at = 0; at < d.outChannels; ++at)
                X[Ie + at * M] += ut * j[tt + at];
              tt += d.outChannels;
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(x.shape, x.dtype, X);
}
var UH = { kernelName: Aa, backendName: "cpu", kernelFunc: qC };
function GH(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), R = new Wt(a.shape, a.dtype, I);
  for (let E = 0; E < m; ++E) {
    let P = Math.max(0, Math.ceil((x - E) / h)), A = Math.min(d.outHeight, (d.inHeight + x - E) / h);
    for (let O = 0; O < g; ++O) {
      let T = Math.max(0, Math.ceil((v - O) / f)), M = Math.min(d.outWidth, (d.inWidth + v - O) / 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 < A; ++Z) {
              let te = E + Z * h - x;
              for (let J = T; J < M; ++J) {
                let se = O + J * f - v;
                b ? X += $.get(Y, te, se, W) * R.get(Y, Z, J, j) : X += $.get(Y, W, te, se) * R.get(Y, j, Z, J);
              }
            }
          y.set(X, E, O, W, j);
        }
    }
  }
  return n.makeTensorInfo(y.shape, y.dtype, y.values);
}
var HH = { kernelName: bg, backendName: "cpu", kernelFunc: GH };
function qH(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: R, inChannels: E, inHeight: P, inWidth: A, outChannels: O, outHeight: T, outWidth: M, strideHeight: W, strideWidth: j } = f;
  h = f.dataFormat;
  let X = $ - 1 - f.padInfo.top, Y = R - 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 < E; ++Ie)
      for (let Re = 0; Re < P; ++Re) {
        let Pe = Re - X, Xe = Math.max(0, Math.ceil(Pe / W)), Je = Math.min(T, ($ + Pe) / W);
        for (let Ye = 0; Ye < A; ++Ye) {
          let tt = Ye - Y, Ce = Math.max(0, Math.ceil(tt / j)), ut = Math.min(M, (R + tt) / j), at = 0;
          for (let Nt = Xe; Nt < Je; ++Nt) {
            let In = Nt * W - Pe;
            for (let Rt = Ce; Rt < ut; ++Rt) {
              let en = Rt * j - tt, Cn = oe * ke + ae * Nt + de * Rt, Nn = v * ($ - 1 - In) + x * (R - 1 - en) + k * Ie;
              for (let Yt = 0; Yt < O; ++Yt) {
                let Dn = b[Cn + me * Yt], tn = y[Nn + Yt];
                at += Dn * tn;
              }
            }
          }
          let Jt = te * ke + J * Re + se * Ye + ne * Ie;
          g[Jt] = at;
        }
      }
  return n.makeTensorInfo(m.shape, m.dtype, m.values);
}
var jH = { kernelName: Ea, backendName: "cpu", kernelFunc: qH };
function KH(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, R = w.computeStrides(r.shape), E = w.computeStrides(a.shape);
  for (let P = 0; P < l.batchSize; ++P) {
    let A = P * R[0], O = P * x.strides[0];
    for (let T = 0; T < l.outDepth; ++T) {
      let M = O + 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 * E[0], Z = A + X * R[1];
        for (let te = 0; te < l.outHeight; ++te) {
          let J = M + 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 * E[1], de = Z + oe * R[2];
            for (let me = 0; me < l.outWidth; ++me) {
              let ke = J + me * l.outChannels, Ie = me * l.strideWidth - y;
              for (let Re = 0; Re < d; ++Re) {
                let Pe = Ie + Re * m;
                if (Pe < 0 || Pe >= l.inWidth)
                  continue;
                let Xe = ae + Re * E[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 XH = { kernelName: np, backendName: "cpu", kernelFunc: KH };
function YH(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, R = n.data.get(a.dataId).values, [E, P, A, O] = c, T = n.data.get(r.dataId).values, [M, 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 Re = 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 at = ut * M, Jt = ut * E;
              for (let Nt = se; Nt < ne; ++Nt) {
                let Rt = (J + Nt * d - Y) * W + at, en = Nt * P + Jt;
                for (let Cn = de; Cn < me; ++Cn) {
                  let Yt = (ae + Cn * h - te) * j + Rt, Dn = Cn * A + en;
                  for (let tn = Re; tn < Pe; ++tn) {
                    let Ms = (Ie + tn * f - Z) * X + Yt, Ci = tn * O + Dn;
                    Ce += T[Ms + Je] * R[Ci + tt];
                  }
                }
              }
            }
            v[Ye + tt] = Ce;
          }
        }
      }
    }
  }
  return n.makeTensorInfo(y.shape, y.dtype, y.values);
}
var QH = { kernelName: yg, backendName: "cpu", kernelFunc: YH };
function ZH(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, [R, E, P, A] = c, { batchSize: O, filterDepth: T, filterHeight: M, 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 = M - 1 - p.padInfo.top, Ie = W - 1 - p.padInfo.left;
  for (let Re = 0; Re < O; ++Re)
    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, at = Math.max(0, Math.ceil(ut / ae)), Jt = Math.min(se, (M + ut) / ae);
          for (let Nt = 0; Nt < Z; ++Nt) {
            let In = Nt - Ie, Rt = 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 = at; Dn < Jt; ++Dn) {
                let tn = Dn * ae - ut;
                for (let zs = Rt; zs < en; ++zs) {
                  let Ms = zs * de - In, Ci = v * Re + x * Nn + k * Dn + I * zs, Js = R * (T - 1 - Yt) + E * (M - 1 - tn) + P * (W - 1 - Ms) + A * Pe;
                  for (let Ls = 0; Ls < te; ++Ls) {
                    let gu = y[Ci + Ls], Ni = $[Js + Ls];
                    Cn += gu * Ni;
                  }
                }
              }
            }
            h[f * Re + m * Xe + g * Ce + b * Nt + Pe] = Cn;
          }
        }
      }
  return n.makeTensorInfo(d.shape, d.dtype, d.values);
}
var JH = { kernelName: vg, backendName: "cpu", kernelFunc: ZH };
var eq = st(Ra, (e) => Math.cos(e));
var tq = { kernelName: Ra, backendName: "cpu", kernelFunc: eq };
var nq = st(Da, (e) => Math.cosh(e));
var sq = { kernelName: Da, backendName: "cpu", kernelFunc: nq };
function rq(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 = Ae([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 R = $ * 4, E = y[R], P = y[R + 1], A = y[R + 2], O = y[R + 3], T = v[$];
    if (T >= c)
      continue;
    let M = m > 1 ? (A - E) * (p - 1) / (m - 1) : 0, W = g > 1 ? (O - P) * (d - 1) / (g - 1) : 0;
    for (let j = 0; j < m; j++) {
      let X = m > 1 ? E * (p - 1) + j * M : 0.5 * (E + A) * (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 + O) * (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 Re = x[me];
            me = de + oe * k[2] + Z * k[1] + T * k[0];
            let Pe = x[me], Xe = ke + (Ie - ke) * ae, Je = Re + (Pe - Re) * 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 + O) * (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 aq = { kernelName: go, backendName: "cpu", kernelFunc: rq };
function iq(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 oq = { kernelName: mo, backendName: "cpu", kernelFunc: iq };
function uq(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 lq = { kernelName: Fa, backendName: "cpu", kernelFunc: uq };
function cq(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 = uv(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 = nC(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 dq = { kernelName: xg, backendName: "cpu", kernelFunc: cq };
function pq(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, R = (x * a + $) * h;
        for (let E = 0; E < h; ++E) {
          let A = E + R + c * (I + l * (v + u * b));
          m[g++] = f[A];
        }
      }
    }
  return n.makeTensorInfo([o, p, d, h], r.dtype, m);
}
var hq = { kernelName: bo, backendName: "cpu", kernelFunc: pq };
function jC(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, R = n.data.get(a.dataId).values, E = I.values;
  for (let P = 0; P < h.batchSize; ++P) {
    let A = P * c[0], O = P * I.strides[0];
    for (let T = 0; T < h.outHeight; ++T) {
      let M = O + 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 = A + X * c[1];
        for (let te = 0; te < h.outWidth; ++te) {
          let J = M + 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 Re = $[de + Ie];
              for (let Pe = 0; Pe < k; ++Pe)
                E[me + Pe] += Re * R[ke + Pe];
              me += k, ke += k;
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(I.shape, I.dtype, I.values);
}
var fq = { kernelName: Oa, backendName: "cpu", kernelFunc: jC };
function mq(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 R = 0; R < f; ++R) {
    let E = Math.max(0, Math.ceil((y - R) / d)), P = Math.min(p.outHeight, (p.inHeight + y - R) / d);
    for (let A = 0; A < m; ++A) {
      let O = Math.max(0, Math.ceil((b - A) / h)), T = Math.min(p.outWidth, (p.inWidth + b - A) / h);
      for (let M = 0; M < p.outChannels; ++M) {
        let W = Math.trunc(M / v), j = M % v, X = 0;
        for (let Y = 0; Y < p.batchSize; ++Y)
          for (let Z = E; Z < P; ++Z) {
            let te = R + Z * d - y;
            for (let J = O; J < T; ++J) {
              let se = A + J * h - b;
              X += k.get(Y, te, se, W) * $.get(Y, Z, J, M);
            }
          }
        g.set(X, R, A, W, j);
      }
    }
  }
  return n.makeTensorInfo(g.shape, g.dtype, g.values);
}
var gq = { kernelName: wg, backendName: "cpu", kernelFunc: mq };
function bq(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, [R, E, P] = d, { batchSize: A, filterHeight: O, filterWidth: T, inChannels: M, inHeight: W, inWidth: j, outChannels: X, outHeight: Y, outWidth: Z, strideHeight: te, strideWidth: J } = h, se = O - 1 - h.padInfo.top, ne = T - 1 - h.padInfo.left, oe = X / M;
  for (let ae = 0; ae < A; ++ae)
    for (let de = 0; de < M; ++de)
      for (let me = 0; me < W; ++me) {
        let ke = me - se, Ie = Math.max(0, Math.ceil(ke / te)), Re = Math.min(Y, (O + 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 < Re; ++Ce) {
            let ut = Ce * te - ke;
            for (let at = Je; at < Ye; ++at) {
              let Jt = at * J - Xe, Nt = x * ae + k * Ce + I * at, In = R * (O - 1 - ut) + E * (T - 1 - Jt) + P * de;
              for (let Rt = 0; Rt < oe; ++Rt) {
                let en = de * oe + Rt, Cn = v[Nt + en], Nn = $[In + Rt];
                tt += Cn * Nn;
              }
            }
          }
          m[g * ae + b * me + y * Pe + de] = tt;
        }
      }
  return n.makeTensorInfo(f.shape, f.dtype, f.values);
}
var yq = { kernelName: kg, backendName: "cpu", kernelFunc: bq };
function vq(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, i = Ae([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 xq = { kernelName: Sg, backendName: "cpu", kernelFunc: vq };
var wq = { kernelName: sp, 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: R, dilationWidth: E, outShape: P } = C.computeDilation2DInfo(s.shape, r.shape, a, i, "NHWC", o), A = w.sizeFromShape(P), O = P.length, T = w.getArrayFromDType(s.dtype, A);
  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 * R;
            if (oe >= 0 && oe < f)
              for (let ae = 0; ae < $; ++ae) {
                let de = Z + ae * E;
                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], O, w.computeStrides(P));
          T[se] = J;
        }
      }
    }
  return { dataId: u.write(w.toTypedArray(T, s.dtype), P, s.dtype), shape: P, dtype: s.dtype };
} };
var kq = { kernelName: rm, 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: R, outShape: E } = C.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
  w.assert(a.rank === E.length, () => `Error in ${rm}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);
  let P = w.toNestedArray(E, l.data.get(a.dataId).values), A = w.makeZerosNestedTypedArray(r.shape, r.dtype);
  for (let T = 0; T < d; ++T)
    for (let M = 0; M < g; ++M) {
      let W = M * 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 * R;
                if (ae >= 0 && ae < f) {
                  let de = c[T][ne][ae][Y] + p[se][oe][Y];
                  de > Z && (Z = de, te = se, J = oe);
                }
              }
          }
          A[te][J][Y] += P[T][M][j][Y];
        }
      }
    }
  return { dataId: l.write(w.toTypedArray(A, s.dtype), r.shape, r.dtype), shape: r.shape, dtype: r.dtype };
} };
var Sq = { kernelName: sm, 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: R, outShape: E } = C.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
  w.assert(a.rank === E.length, () => `Error in ${sm}, dy must have the same rank as output ${E.length}, but got ${a.rank}`);
  let P = w.toNestedArray(E, l.data.get(a.dataId).values), A = w.makeZerosNestedTypedArray(s.shape, s.dtype);
  for (let T = 0; T < d; ++T)
    for (let M = 0; M < g; ++M) {
      let W = M * 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 * R;
                if (ae >= 0 && ae < f) {
                  let de = c[T][ne][ae][Y] + p[se][oe][Y];
                  de > Z && (Z = de, te = ne, J = ae);
                }
              }
          }
          A[T][te][J][Y] += P[T][M][j][Y];
        }
      }
    }
  return { dataId: l.write(w.toTypedArray(A, s.dtype), s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
function Zl(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 = kr({ 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 = Ld(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 = pt({ inputs: { x: g }, backend: n, attrs: { shape: x } }), n.disposeIntermediateTensorInfo(k);
  }
  return n.disposeIntermediateTensorInfo(o), c != null && n.disposeIntermediateTensorInfo(d), g;
}
var Iq = { kernelName: di, backendName: "cpu", kernelFunc: Zl };
function Cq(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 = pt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Jp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = Zl({ 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 Nq = { kernelName: rp, backendName: "cpu", kernelFunc: Cq };
function Tq(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 $q = { kernelName: Ig, backendName: "cpu", kernelFunc: Tq };
var _q = C.ERF_P;
var Aq = C.ERF_A1;
var Eq = C.ERF_A2;
var Rq = C.ERF_A3;
var Dq = C.ERF_A4;
var Fq = C.ERF_A5;
var Oq = st(yl, (e) => {
  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + _q * n);
  return t * (1 - ((((Fq * s + Dq) * s + Rq) * s + Eq) * s + Aq) * s * Math.exp(-n * n));
});
var Pq = { kernelName: yl, backendName: "cpu", kernelFunc: Oq };
function Wd(e) {
  let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), pt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
}
var zq = { kernelName: vo, backendName: "cpu", kernelFunc: Wd };
var Mq = Et((e, t) => e / t);
var gv = Ht(Pa, Mq);
var qm = { kernelName: Pa, backendName: "cpu", kernelFunc: gv };
function KC(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 = ya({ inputs: { x: o }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = ya({ 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 } = Lq(v, t, n), I = C.mergeRealAndImagArrays(x, k);
    for (let $ = 0; $ < a; $++) {
      let R = C.getComplexWithIndex(I, $);
      p[g * a + $] = R.real, d[g * a + $] = R.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 Lq(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 (Bq(s)) {
    let o = jm(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 = qm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = qm.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 = Vq(o, s, t);
    return C.splitRealAndImagArrays(u);
  }
}
function Bq(e) {
  return (e & e - 1) === 0;
}
function jm(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 = jm(u, l, i, s, r), I = k.real, $ = k.imag, R = [I.length], E = r.makeTensorInfo(R, "float32", I), P = r.makeTensorInfo(R, "float32", $), A = En({ inputs: { real: E, imag: P }, backend: r }), O = jm(m, g, i, s, r), T = O.real, M = O.imag, W = [T.length], j = r.makeTensorInfo(W, "float32", T), X = r.makeTensorInfo(W, "float32", M), 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 = Jp({ inputs: { a: ne, b: Y }, backend: r }), ae = io({ inputs: { a: A, b: oe }, backend: r }), de = fv({ inputs: { a: A, b: oe }, backend: r }), me = ba({ inputs: { input: ae }, backend: r }), ke = ba({ inputs: { input: de }, backend: r }), Ie = oo({ inputs: { input: ae }, backend: r }), Re = oo({ inputs: { input: de }, backend: r }), Pe = uo({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = uo({ inputs: [Ie, Re], 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(E), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(A), 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(Re), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
}
function Vq(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 Wq(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 = pt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = KC(o, false, n), l = pt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
}
var Uq = { kernelName: Cg, backendName: "cpu", kernelFunc: Wq };
function bv(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 Hq(o, r, i), t.makeTensorInfo(s, i, o);
}
var Gq = { kernelName: vl, backendName: "cpu", kernelFunc: bv };
function Hq(e, t, n) {
  e.fill(t);
}
var qq = { kernelName: wo, 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 jq = Et((e, t) => Math.floor(e / t));
var Kq = Ht(Ba, jq, null, "int32");
var Xq = { kernelName: Ba, backendName: "cpu", kernelFunc: Kq };
function Yq(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 = qC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
  if (i) {
    let g = m;
    if (c === "NCHW" && i.shape.length === 1 && i.shape[0] !== 1) {
      let b = pt({ inputs: { x: i }, backend: n, attrs: { shape: [i.shape[0], 1, 1] } });
      m = io({ inputs: { a: m, b }, backend: n }), n.disposeIntermediateTensorInfo(b);
    } else
      m = io({ inputs: { a: m, b: i }, backend: n });
    n.disposeIntermediateTensorInfo(g);
  }
  if (h) {
    let g = m;
    if (c === "NCHW" && h === "prelu" && o.shape.length === 1 && o.shape[0] !== 1) {
      let b = pt({ inputs: { x: o }, backend: n, attrs: { shape: [o.shape[0], 1, 1] } });
      m = Vd(n, m, h, b, f), n.disposeIntermediateTensorInfo(b);
    } else
      m = Vd(n, m, h, o, f);
    n.disposeIntermediateTensorInfo(g);
  }
  return m;
}
var Qq = { kernelName: ua, backendName: "cpu", kernelFunc: Yq };
function Zq(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 = jC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
  if (i) {
    let g = m;
    m = io({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
  }
  if (h) {
    let g = m;
    m = Vd(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
  }
  return m;
}
var Jq = { kernelName: la, backendName: "cpu", kernelFunc: Zq };
function e6(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 = cC(d, h, s.dtype, l, o, c, p, s.shape, a);
  return n.makeTensorInfo(u, s.dtype, f.values);
}
var t6 = { kernelName: So, backendName: "cpu", kernelFunc: e6 };
function n6(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 = pt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = pt({ 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 = dC(y, b, g);
  return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
}
var s6 = { kernelName: ko, backendName: "cpu", kernelFunc: n6 };
function r6(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 = pt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = KC(o, true, n), l = pt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
}
var a6 = { kernelName: Ng, backendName: "cpu", kernelFunc: r6 };
var i6 = st(xl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
var o6 = { kernelName: xl, backendName: "cpu", kernelFunc: i6 };
var u6 = st(wl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
var l6 = { kernelName: wl, backendName: "cpu", kernelFunc: u6 };
var c6 = st(kl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
var d6 = { kernelName: kl, backendName: "cpu", kernelFunc: c6 };
function p6(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = gC(s, r, a);
  return t.makeTensorInfo([i.length], "float32", i);
}
var h6 = { kernelName: Tg, backendName: "cpu", kernelFunc: p6 };
var f6 = st(Sl, (e) => Math.log1p(e));
var m6 = { kernelName: Sl, backendName: "cpu", kernelFunc: f6 };
var g6 = Et((e, t) => e && t);
var b6 = Ht(To, g6, null, "bool");
var y6 = { kernelName: To, backendName: "cpu", kernelFunc: b6 };
var v6 = st(Il, (e) => e ? 0 : 1, "bool");
var x6 = { kernelName: Il, backendName: "cpu", kernelFunc: v6 };
var w6 = Et((e, t) => e || t);
var k6 = Ht(ip, w6, null, "bool");
var S6 = { kernelName: ip, backendName: "cpu", kernelFunc: k6 };
function I6(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 C6 = { kernelName: op, backendName: "cpu", kernelFunc: I6 };
function N6(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; $++) {
      let R = -2 * l * c * f[$] * m[y] / I;
      y === $ && (R += Math.pow(I, -c)), R *= h[y], g[$] += R;
    }
  }
  return n.makeTensorInfo(i.shape, r.dtype, g);
}
var T6 = { kernelName: $g, backendName: "cpu", kernelFunc: N6 };
function XC(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 = dv(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 = yC(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 $6 = { kernelName: qa, backendName: "cpu", kernelFunc: XC };
function _6(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 = mv(d, r.shape, r.dtype, h, c, "max");
    p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
  }
  return p;
}
var A6 = { kernelName: Ka, backendName: "cpu", kernelFunc: _6 };
function E6(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 = HC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
  return n.makeTensorInfo(d.shape, "float32", d.values);
}
var R6 = { kernelName: up, backendName: "cpu", kernelFunc: E6 };
function D6(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 = xH(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, R = x - 1 - c.padInfo.top, E = Ae(a.shape, "float32"), P = n.bufferSync(r);
  for (let A = 0; A < c.batchSize; ++A)
    for (let O = 0; O < c.inChannels; ++O)
      for (let T = 0; T < c.inDepth; ++T)
        for (let M = 0; M < c.inHeight; ++M)
          for (let W = 0; W < c.inWidth; ++W) {
            let j = T - I, X = M - R, 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(A, J, ne, ae, O), me = te * x * k + se * k + oe, ke = de === me ? 1 : 0;
                      if (ke === 0)
                        continue;
                      Z += P.get(A, J, ne, ae, O) * ke;
                    }
                }
            }
            E.set(Z, A, T, M, W, O);
          }
  return n.makeTensorInfo(E.shape, E.dtype, E.values);
}
var F6 = { kernelName: Ag, backendName: "cpu", kernelFunc: D6 };
function O6(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 = Ae(d.outShape, o.dtype, GC(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, $ = Ae(o.shape, "float32"), R = n.data.get(r.dataId).values, E = Ae(r.shape, "float32", R);
  for (let P = 0; P < d.batchSize; ++P)
    for (let A = 0; A < d.inChannels; ++A)
      for (let O = 0; O < d.inHeight; ++O)
        for (let T = 0; T < d.inWidth; ++T) {
          let M = O - I, W = T - k, j = 0;
          for (let X = 0; X < v; X += b) {
            let Y = (M + 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, A), se = X * x + Z, ne = J === se ? 1 : 0;
                if (ne === 0)
                  continue;
                j += E.get(P, Y, te, A) * ne;
              }
          }
          $.set(j, P, O, T, A);
        }
  return n.makeTensorInfo($.shape, $.dtype, $.values);
}
var P6 = { kernelName: _g, backendName: "cpu", kernelFunc: O6 };
function z6(e, t, n, s, r) {
  let a = w.computeStrides(t), i = mv(e, t, n, a, r, "max"), o = GC(e, t, n, r, true, s);
  return [i.values, o.values];
}
var M6 = { kernelName: Eg, 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] = z6(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 L6(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 = kr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
  p.push(h);
  let f = gv({ inputs: { a: h, b: d }, backend: n });
  p.push(f);
  let m = Zl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
  return p.forEach((g) => n.disposeIntermediateTensorInfo(g)), m;
}
var B6 = { kernelName: Xa, backendName: "cpu", kernelFunc: L6 };
function V6(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 = pt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var W6 = { kernelName: Ya, backendName: "cpu", kernelFunc: V6 };
function U6(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 G6 = { kernelName: Za, backendName: "cpu", kernelFunc: U6 };
var H6 = Et((e, t) => {
  let n = e % t;
  return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
});
var q6 = Ht(Cl, H6);
var j6 = { kernelName: Cl, backendName: "cpu", kernelFunc: q6 };
var K6 = ka(Xd());
function YC(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 = XC({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = C.expandShapeToKeepDim(l.shape, u), p = pt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = fv({ inputs: { a: r, b: p }, backend: n }), h = oC({ inputs: { x: d }, backend: n }), f = Zl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = pt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = gv({ 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 X6 = { kernelName: pi, backendName: "cpu", kernelFunc: YC };
function Y6(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 : YC({ 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 = K6.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: Rg, backendName: "cpu", kernelFunc: Y6 };
var Z6 = ws.nonMaxSuppressionV3Impl;
function J6(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 } = Z6(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var ej = { kernelName: Ao, backendName: "cpu", kernelFunc: J6 };
var tj = ws.nonMaxSuppressionV4Impl;
function nj(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 } = tj(c, p, i, o, u, l);
  return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
}
var sj = { kernelName: Nl, backendName: "cpu", kernelFunc: nj };
var rj = ws.nonMaxSuppressionV5Impl;
function aj(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 } = rj(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var ij = { kernelName: Eo, backendName: "cpu", kernelFunc: aj };
function oj(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 uj = { kernelName: Do, backendName: "cpu", kernelFunc: oj };
function Ud(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 = ba({ inputs: { input: s }, backend: n }), a = Ud({ inputs: { x: r }, backend: n }), i = oo({ inputs: { input: s }, backend: n }), o = Ud({ 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 bv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
}
var lj = { kernelName: Xo, backendName: "cpu", kernelFunc: Ud };
function QC(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 = ba({ inputs: { input: s }, backend: n }), a = QC({ inputs: { x: r }, backend: n }), i = oo({ inputs: { input: s }, backend: n }), o = Ud({ 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 bv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
}
var cj = { kernelName: Ro, backendName: "cpu", kernelFunc: QC };
function ZC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Wd({ 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 = Wd({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = uo({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
}
var dj = { kernelName: Fo, backendName: "cpu", kernelFunc: ZC };
function pj(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 JC = { kernelName: ei, backendName: "cpu", kernelFunc: pj };
var hj = Et((e, t) => Math.pow(e, t));
var fj = Ht(ti, hj);
var mj = { kernelName: ti, backendName: "cpu", kernelFunc: fj };
function gj(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = pv(s, r, i, a);
  return t.makeTensorInfo([o.length], a, o);
}
var bj = { kernelName: Tl, backendName: "cpu", kernelFunc: gj };
var yj = st($l, (e) => 1 / e);
var vj = { kernelName: $l, backendName: "cpu", kernelFunc: yj };
function xj(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; $++) {
      let R;
      i ? R = x * ($ + 0.5) - 0.5 : R = x * $;
      let E = Math.max(0, Math.floor(R)), P = R - E, A = Math.min(d - 1, Math.ceil(R)), O = I * u[0] + E * u[1], T = I * u[0] + A * u[1];
      for (let M = 0; M < c; M++) {
        let W;
        i ? W = k * (M + 0.5) - 0.5 : W = k * M;
        let j = Math.max(0, Math.floor(W)), X = W - j, Y = Math.min(h - 1, Math.ceil(W)), Z = O + j * u[2], te = T + j * u[2], J = O + 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, Re = ke + (Ie - ke) * P;
          g[v++] = Re;
        }
      }
    }
  return n.makeTensorInfo([p, l, c, f], "float32", g);
}
var wj = { kernelName: ai, backendName: "cpu", kernelFunc: xj };
function kj(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 R = $ * b, E = Math.floor(R), P = Math.min(Math.ceil(R), l - 1), A = I + E * o[1], O = I + P * o[1], T = R - E, M = 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 = A + X * o[2], se = A + Y * o[2], ne = O + X * o[2], oe = O + Y * o[2], ae = M * te, de = M * Z, me = T * te, ke = T * Z;
        for (let Ie = 0; Ie < p; Ie++) {
          let Re = v[x++];
          f[J + Ie] += Re * ae, f[se + Ie] += Re * de, f[ne + Ie] += Re * me, f[oe + Ie] += Re * ke;
        }
      }
    }
  }
  return n.makeTensorInfo([u, c, l, p], "float32", f);
}
var Sj = { kernelName: Fg, backendName: "cpu", kernelFunc: kj };
function Ij(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 R = 0; R < l; R++) {
      let E = i ? v * (R + 0.5) : v * R, P = Math.min(d - 1, a ? Math.round(E) : Math.floor(E));
      i && (P = Math.max(0, P));
      let A = $ + P * u[1];
      for (let O = 0; O < c; O++) {
        let T = i ? x * (O + 0.5) : x * O, M = Math.min(h - 1, a ? Math.round(T) : Math.floor(T));
        i && (M = Math.max(0, M));
        let W = A + M * 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 Cj = { kernelName: _l, backendName: "cpu", kernelFunc: Ij };
function Nj(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, R = Math.ceil(I) * 2 + 2;
  for (let E = 0; E < l; E++) {
    let P = E * o[0];
    for (let A = 0; A < c; A++) {
      let O = P + A * o[1], T = Math.floor(A * k), M = Math.floor(T - $ / 2);
      for (let W = 0; W < p; W++) {
        let j = O + W * o[2], X = Math.floor(W * I), Y = Math.floor(X - R / 2);
        for (let Z = 0; Z < d; Z++) {
          let te = 0;
          for (let J = 0; J < $; J++) {
            let se = J + M;
            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 (A === ae)
              for (let de = 0; de < R; de++) {
                let me = de + Y;
                if (me < 0 || me >= f)
                  continue;
                let ke = ne + me * u[2], Ie = me * x, Re = Math.min(p - 1, i ? Math.round(Ie) : Math.floor(Ie));
                W === Re && (te += g[ke + Z]);
              }
          }
          m[j + Z] = te;
        }
      }
    }
  }
  return n.makeTensorInfo(r.shape, r.dtype, m);
}
var Tj = { kernelName: Dg, backendName: "cpu", kernelFunc: Nj };
function $j(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 _j = { kernelName: Po, backendName: "cpu", kernelFunc: $j };
var Aj = { kernelName: Yo, 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 R = 0; R < p; R++) {
        let E = R * d;
        for (let P = 0; P < d; P++) {
          let A = [l, I, R, P], O = A[2], T = A[1], M = (O - h) * b - (T - f) * g, W = (O - h) * g + (T - f) * b;
          M = Math.round(M + h), W = Math.round(W + f);
          let j = a;
          if (typeof a != "number" && (P === 3 ? j = m : j = a[P]), M >= 0 && M < p && W >= 0 && W < c) {
            let Y = W * (p * d), Z = M * d, te = k + Y + Z + P;
            j = y[te];
          }
          let X = k + $ + E + P;
          u[X] = j;
        }
      }
    }
  }
  return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
var Ej = st(zo, (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 Rj = { kernelName: zo, backendName: "cpu", kernelFunc: Ej };
function Dj(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 = Ki(h, f, i, p, l, u, o, c, 0, d);
  return n.makeTensorInfo(i, m.dtype, m.values);
}
var Fj = { kernelName: Mo, backendName: "cpu", kernelFunc: Dj };
function Oj(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 Pj(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 zj(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" ? Oj(u, t[c + l]) : Pj(u, t[c + l]);
  }
  return i;
}
function Mj(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 = zj(o, u, r.shape[0], r.shape[1], a.shape[1], i);
  return n.makeTensorInfo(a.shape, "int32", l);
}
var Lj = { kernelName: Og, backendName: "cpu", kernelFunc: Mj };
function Bj(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 Vj = { kernelName: Lo, backendName: "cpu", kernelFunc: Bj };
var Wj = C.SELU_SCALEALPHA;
var Uj = C.SELU_SCALE;
var Gj = st(Al, (e) => e >= 0 ? Uj * e : Wj * (Math.exp(e) - 1));
var Hj = { kernelName: Al, backendName: "cpu", kernelFunc: Gj };
var qj = st(El, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
var jj = { kernelName: El, backendName: "cpu", kernelFunc: qj };
var Kj = st(ui, (e) => Math.sin(e));
var Xj = { kernelName: ui, backendName: "cpu", kernelFunc: Kj };
var Yj = st(Vo, (e) => Math.sinh(e));
var Qj = { kernelName: Vo, backendName: "cpu", kernelFunc: Yj };
var Zj = 11920928955078125e-23;
var uw = Math.log(Zj) + 2;
var Jj = st(Rl, (e) => {
  let t = e > -uw, n = e < uw, s = Math.exp(e), r;
  return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
});
var e5 = { kernelName: Rl, backendName: "cpu", kernelFunc: Jj };
function t5(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 = JC.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 = pt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = pt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
}
var n5 = { kernelName: Wo, backendName: "cpu", kernelFunc: t5 };
function s5(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] = NC(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 r5 = { kernelName: cp, backendName: "cpu", kernelFunc: s5 };
function a5(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] = TC(o, s.shape, s.dtype, i, u);
  return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
}
var i5 = { kernelName: Dl, backendName: "cpu", kernelFunc: a5 };
function o5(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] = hv(i, s.shape, s.dtype, o, u, true);
  return n.makeTensorInfo(c, s.dtype, l);
}
var u5 = { kernelName: dp, backendName: "cpu", kernelFunc: o5 };
function l5(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] = hv(i, s.shape, s.dtype, o, u);
  return n.makeTensorInfo(c, s.dtype, l);
}
var c5 = { kernelName: pp, backendName: "cpu", kernelFunc: l5 };
function d5(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;
  switch (a.dtype) {
    case "bool": {
      let g = n.bufferSync(a), b = Boolean(n.data.get(i.dataId).values[0]);
      m = Ki(f, g, o, d, c, l, u, p, b, h);
      break;
    }
    case "float32": {
      let g = n.bufferSync(a), b = n.data.get(i.dataId).values[0];
      m = Ki(f, g, o, d, c, l, u, p, b, h);
      break;
    }
    case "int32": {
      let g = n.bufferSync(a), b = n.data.get(i.dataId).values[0];
      m = Ki(f, g, o, d, c, l, u, p, b, h);
      break;
    }
    case "string": {
      let g = n.bufferSync(a), b = w.decodeString(n.data.get(i.dataId).values[0]);
      m = Ki(f, g, o, d, c, l, u, p, b, h);
      break;
    }
    default:
      throw new Error(`Unsupported type ${a.dtype}`);
  }
  return n.makeTensorInfo(o, m.dtype, m.values);
}
var p5 = { kernelName: hp, backendName: "cpu", kernelFunc: d5 };
function h5(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 = ya({ inputs: { x: r }, backend: n, attrs: { begin: l, size: d } });
    return l[o] += p, h;
  });
}
var f5 = { kernelName: Uo, backendName: "cpu", kernelFunc: h5 };
var m5 = { kernelName: Fl, 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 g5 = st(gi, (e, t) => {
  let n = t;
  return isNaN(e) ? NaN : e > 0 ? 1 : n.alpha;
});
var b5 = { kernelName: gi, backendName: "cpu", kernelFunc: g5 };
function y5(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 } = kt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = pt({ 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 = kt.computeOutShape(y, v, x), $ = ya({ inputs: { x: r }, backend: n, attrs: { begin: y, size: I } });
    k = pt({ inputs: { x: $ }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo($);
  } else {
    let I = n.bufferSync(r), $ = _C(h, I, x, y);
    k = n.makeTensorInfo(f, $.dtype, $.values);
  }
  return k;
}
var v5 = { kernelName: Go, backendName: "cpu", kernelFunc: y5 };
function x5(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] = AC(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var w5 = { kernelName: fp, backendName: "cpu", kernelFunc: x5 };
function k5(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] = EC(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 S5 = { kernelName: Pg, backendName: "cpu", kernelFunc: k5 };
function I5(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 = RC(i, r);
  return n.makeTensorInfo(a.shape, "int32", o);
}
var C5 = { kernelName: zg, backendName: "cpu", kernelFunc: I5 };
var N5 = st(Ho, (e) => Math.tan(e));
var T5 = { kernelName: Ho, backendName: "cpu", kernelFunc: N5 };
var $5 = st(mi, (e) => Math.tanh(e));
var _5 = { kernelName: mi, backendName: "cpu", kernelFunc: $5 };
function A5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  be(r, "tile");
  let i = FC(n.bufferSync(r), a);
  return n.makeTensorInfo(i.shape, i.dtype, i.values);
}
var E5 = { kernelName: Tr, backendName: "cpu", kernelFunc: A5 };
function R5(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] = PC(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 D5 = { kernelName: qo, backendName: "cpu", kernelFunc: R5 };
function F5(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 E = 0; E < c; ++E) {
    let P = a.shape[0] === 1 ? $ : $.subarray(E * 8, E * 8 + 8);
    for (let A = 0; A < f; ++A)
      for (let O = 0; O < m; ++O)
        for (let T = 0; T < h; ++T) {
          let M, W = P[6] * O + P[7] * A + 1;
          if (W === 0)
            continue;
          let j = (P[0] * O + P[1] * A + P[2]) / W, X = (P[3] * O + P[4] * A + P[5]) / W, Y = lw(j, d, o), Z = lw(X, p, o);
          switch (i) {
            case "nearest":
              M = B5(I, p, d, y, v, x, E, Z, Y, T, u);
              break;
            case "bilinear":
              M = V5(I, p, d, y, v, x, E, Z, Y, T, u);
              break;
            default:
              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`);
          }
          let te = E * y + A * v + O * x + T;
          k[te] = M;
        }
    return s.makeTensorInfo(g, r.dtype, k);
  }
  return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
}
var O5 = { kernelName: jo, backendName: "cpu", kernelFunc: F5 };
function lw(e, t, n) {
  switch (n) {
    case "reflect":
      return P5(e, t);
    case "wrap":
      return z5(e, t);
    case "nearest":
      return L5(e, t);
    case "constant":
    default:
      return M5(e, t);
  }
}
function P5(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 z5(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 M5(e, t) {
  return e;
}
function L5(e, t) {
  return w.clamp(0, e, t - 1);
}
function zu(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 B5(e, t, n, s, r, a, i, o, u, l, c) {
  let p = Math.round(o), d = Math.round(u);
  return zu(e, t, n, s, r, a, i, p, d, l, c);
}
function V5(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) * zu(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * zu(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * zu(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * zu(e, t, n, s, r, a, i, h, f, l, c);
  return (h - o) * m + (o - p) * g;
}
function W5(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 } = zC(i, r, a.shape, a.dtype);
  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
}
var U5 = { kernelName: Mg, backendName: "cpu", kernelFunc: W5 };
function G5(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 = ya({ inputs: { x: r }, backend: n, attrs: { begin: c, size: p } });
    d[h] = pt({ inputs: { x: f }, backend: n, attrs: { shape: u } }), n.disposeIntermediateTensorInfo(f);
  }
  return d;
}
var H5 = { kernelName: Ko, backendName: "cpu", kernelFunc: G5 };
function q5(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 = Wd({ 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 = aC({ inputs: { a: g, b: d }, backend: n }), y = kr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = Jp({ inputs: { a: y, b: r }, backend: n }), x = Zl({ 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 = ZC({ inputs: l, backend: n, attrs: { axis: 0 } });
  return c.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
}
var j5 = { kernelName: mp, backendName: "cpu", kernelFunc: q5 };
var K5 = [KG, VU, YG, ZG, jU, eH, nH, rH, iH, uH, cH, pH, fH, bH, vH, kH, IH, NH, $H, qG, AH, RH, FH, PH, HU, XU, MH, WU, BH, WH, UH, HH, jH, XH, QH, JH, tq, sq, aq, oq, lq, dq, hq, fq, gq, yq, xq, wq, kq, Sq, Nq, LG, $q, YU, Pq, QU, zq, JU, Uq, Gq, qq, tG, Xq, Qq, Jq, t6, s6, sG, aG, UU, a6, VH, o6, l6, d6, BG, oG, lG, h6, dG, m6, y6, x6, S6, C6, T6, $6, hG, A6, R6, F6, P6, M6, B6, W6, mG, G6, j6, Q6, bG, vG, ej, sj, ij, wG, uj, cj, dj, JC, mj, WG, IG, bj, GU, qm, vj, UG, GG, HG, wj, Sj, Cj, Tj, _j, Aj, Rj, NG, Fj, Lj, Vj, Hj, $G, jj, Xj, Qj, _G, X6, e5, n5, r5, i5, u5, c5, p5, f5, RG, m5, FG, b5, v5, w5, S5, C5, MG, Iq, T5, _5, E5, D5, O5, kG, U5, H5, j5, lj];
for (let e of K5)
  Ol(e);
var X5 = {};
Ee(X5, { assertNotComplex: () => iu, bindCanvasToFramebuffer: () => oK, bindColorTextureToFramebuffer: () => ld, bindTextureToProgramUniformSampler: () => f1, bindTextureUnit: () => d1, bindVertexBufferToProgramAttribute: () => Km, callAndCheck: () => fe, canBeRepresented: () => e1, createFragmentShader: () => s1, createFramebuffer: () => c1, createProgram: () => r1, createStaticIndexBuffer: () => o1, createStaticVertexBuffer: () => i1, createTexture: () => u1, createVertexShader: () => n1, getBatchDim: () => va, getExtensionOrThrow: () => Mu, getFramebufferErrorMessage: () => m1, getMaxTexturesInShader: () => v1, getNumChannels: () => aK, getProgramUniformLocation: () => h1, getProgramUniformLocationOrThrow: () => p1, getRowsCols: () => xa, getShapeAs3D: () => cd, getTextureShapeFromLogicalShape: () => b1, getWebGLDisjointQueryTimerVersion: () => x1, getWebGLErrorMessage: () => t1, getWebGLMaxTextureSize: () => y1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => w1, isDownloadFloatTextureEnabled: () => k1, isReshapeFree: () => al, isWebGLFenceEnabled: () => S1, isWebGLVersionEnabled: () => Ym, linkProgram: () => a1, logShaderSourceAndInfoLog: () => vv, resetMaxTextureSize: () => uK, resetMaxTexturesInShader: () => lK, unbindColorTextureFromFramebuffer: () => Xm, unbindTextureUnit: () => iK, validateFramebuffer: () => Lu, validateProgram: () => ud, validateTextureSize: () => l1 });
var Zr = {};
var em = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function Y5(e, t) {
  Zr[e] = t;
}
function xs(e, t) {
  if (!(e in Zr) || t != null) {
    let s = Z5(e, t);
    if (s !== null)
      Zr[e] = s;
    else
      return console.log("Could not get context for WebGL version", e), null;
  }
  let n = Zr[e];
  return n == null || n.isContextLost() ? (delete Zr[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), Zr[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 Z5(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 Zr[e];
  }, false), e === 1 ? n.getContext("webgl", em) || n.getContext("experimental-webgl", em) : n.getContext("webgl2", em);
}
function Jl(e, t) {
  return [t, e];
}
function J5(e, t) {
  return e * t;
}
function ed(e) {
  let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
  return w.sizeToSquarishShape(n);
}
function au(e, t) {
  return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
}
function eK(e, t) {
  let [n, s] = au(e, t);
  return n * s * 4;
}
function yv(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") && tK(e), n;
}
function tK(e) {
  let t = e.getError();
  if (t !== e.NO_ERROR)
    throw new Error("WebGL Error: " + t1(e, t));
}
var nK = 596e-10;
var sK = 65504;
function e1(e) {
  return !!(K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || nK < Math.abs(e) && Math.abs(e) < sK);
}
function t1(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 Mu(e, t) {
  return Zs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
}
function n1(e, t) {
  let n = Zs(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 s1(e, t) {
  let n = Zs(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 vv(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
  return n;
}
var rK = /ERROR: [0-9]+:([0-9]+):/g;
function vv(e, t) {
  let n = rK.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 ${w.rightPad(l[0], o)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function r1(e) {
  return Zs(e, () => e.createProgram(), "Unable to create WebGLProgram.");
}
function a1(e, t) {
  if (fe(e, () => e.linkProgram(t)), !K().get("ENGINE_COMPILE_ONLY") && e.getProgramParameter(t, e.LINK_STATUS) === false)
    throw console.log(e.getProgramInfoLog(t)), new Error("Failed to link vertex and fragment shaders.");
}
function ud(e, t) {
  if (fe(e, () => e.validateProgram(t)), e.getProgramParameter(t, e.VALIDATE_STATUS) === false)
    throw console.log(e.getProgramInfoLog(t)), new Error("Shader program validation failed.");
}
function i1(e, t) {
  let n = Zs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
}
function o1(e, t) {
  let n = Zs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
  return fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ELEMENT_ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
}
function aK() {
  return K().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function u1(e) {
  return Zs(e, () => e.createTexture(), "Unable to create WebGLTexture.");
}
function l1(e, t) {
  let n = K().getNumber("WEBGL_MAX_TEXTURE_SIZE");
  if (e <= 0 || t <= 0) {
    let s = `[${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 c1(e) {
  return Zs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Km(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 d1(e, t, n) {
  g1(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
}
function iK(e, t) {
  g1(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
}
function p1(e, t, n) {
  return Zs(e, () => e.getUniformLocation(t, n), 'uniform "' + n + '" not present in program.');
}
function h1(e, t, n) {
  return e.getUniformLocation(t, n);
}
function f1(e, t, n, s) {
  fe(e, () => d1(e, t, s)), fe(e, () => e.uniform1i(n, s));
}
function oK(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 ld(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 Xm(e, t) {
  fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
}
function Lu(e) {
  let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
  if (t !== e.FRAMEBUFFER_COMPLETE)
    throw new Error("Error binding framebuffer: " + m1(e, t));
}
function m1(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 ${t}`;
  }
}
function Zs(e, t, n) {
  let s = fe(e, () => t());
  if (s == null)
    throw new Error(n);
  return s;
}
function g1(e, t) {
  let n = e.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, s = t + e.TEXTURE0;
  if (s < e.TEXTURE0 || s > n) {
    let r = `[gl.TEXTURE0, gl.TEXTURE${n}]`;
    throw new Error(`textureUnit must be in ${r}.`);
  }
}
function va(e, t = 2) {
  return w.sizeFromShape(e.slice(0, e.length - t));
}
function xa(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 cd(e) {
  let t = [1, 1, 1];
  return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [va(e), ...xa(e)]), t;
}
function b1(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 = va(e), a = 2, i = 2;
    return e.length && ([a, i] = xa(e)), s = r * (a / 2) * (i / 2), w.sizeToSquarishShape(s).map((o) => o * 2);
  }
  return w.sizeToSquarishShape(s);
}
function td(e) {
  return e % 2 === 0;
}
function al(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 || td(n) && td(s) && (e[0] === 1 || t[0] === 1))
      return true;
  }
  return e[1] === t[1] && td(e[0]) && td(t[0]);
}
var dd;
var pd;
function y1(e) {
  if (dd == null) {
    let t = xs(e);
    dd = t.getParameter(t.MAX_TEXTURE_SIZE);
  }
  return dd;
}
function uK() {
  dd = null;
}
function lK() {
  pd = null;
}
function v1(e) {
  if (pd == null) {
    let t = xs(e);
    pd = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
  }
  return Math.min(16, pd);
}
function x1(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 Ym(e) {
  try {
    if (xs(e) != null)
      return true;
  } catch (t) {
    return console.log("Error when getting WebGL context: ", t), false;
  }
  return false;
}
function w1(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 Qm(t);
}
function k1(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 Qm(t);
    let s = "EXT_color_buffer_half_float";
    if (Ln(t, s)) {
      let r = t.getExtension(s);
      return cK(t, r);
    }
    return false;
  }
  return Qm(t);
}
function Qm(e) {
  let t = yv(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 cK(e, t) {
  let n = yv(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 S1(e) {
  return e !== 2 ? false : xs(e).fenceSync != null;
}
function iu(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", () => Ym(2) ? 2 : Ym(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", () => y1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => v1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
  let e = Ne.getNumber("WEBGL_VERSION");
  return e === 0 ? 0 : x1(e);
});
Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !yp.isMobile());
Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => w1(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", () => k1(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_FENCE_API_ENABLED", () => S1(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", () => yp.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 wi(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 eh(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}; ${o};`;
  }).join("");
}
function dK(e, t) {
  let n = e.length, s = e.map((a) => `${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 pK(e, t, n = "index") {
  let s = e.map((a, i) => i), r = dK(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 xv(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 wv() {
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`;
}
var I1 = `
  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: C1 } = C;
function hK(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 } = kv(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 ? `[${h.arrayIndex}]` : ""};`);
  });
  let r = s.join(`
`), a = e.map((h) => fK(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
`), i = t.texShape, o = fn(), u = bK(o), l, c, p = xK(o);
  return t.isPacked ? (l = mK(t.logicalShape, i, n.enableShapeUniforms), c = vK(o)) : (l = gK(t.logicalShape, i, n.enableShapeUniforms), c = yK(o)), n.packedInputs && (p += IK), [p, u, c, r, l, a, n.userCode].join(`
`);
}
function ou(e, t = false) {
  let n = e.shapeInfo.logicalShape;
  switch (n.length) {
    case 0:
      return PK(e, t);
    case 1:
      return MK(e, t);
    case 2:
      return BK(e, t);
    case 3:
      return WK(e, t);
    case 4:
      return GK(e, t);
    case 5:
      return HK(e);
    case 6:
      return qK(e);
    default:
      throw new Error(`${n.length}-D input sampling is not yet supported`);
  }
}
function N1(e, t) {
  switch (e.shapeInfo.logicalShape.length) {
    case 0:
      return OK(e);
    case 1:
      return zK(e, t);
    case 2:
      return LK(e, t);
    case 3:
      return VK(e, t);
    default:
      return UK(e, t);
  }
}
function fK(e, t, n = false, s) {
  let r = "";
  n ? r += N1(e, s) : r += ou(e, s);
  let a = e.shapeInfo.logicalShape, i = t.logicalShape;
  return a.length <= i.length && (n ? r += jK(e, t) : r += KK(e, t)), r;
}
function mK(e, t, n) {
  switch (e.length) {
    case 0:
      return T1();
    case 1:
      return CK(e, t, n);
    case 2:
      return DK(e, t, n);
    case 3:
      return TK(e, t, n);
    default:
      return _K(e, t, n);
  }
}
function gK(e, t, n) {
  switch (e.length) {
    case 0:
      return T1();
    case 1:
      return NK(e, t, n);
    case 2:
      return FK(e, t, n);
    case 3:
      return $K(e, t, n);
    case 4:
      return AK(e, t, n);
    case 5:
      return EK(e, t);
    case 6:
      return RK(e, t);
    default:
      throw new Error(`${e.length}-D output sampling is not yet supported`);
  }
}
function bK(e) {
  return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `;
}
function yK(e) {
  return `
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `;
}
function vK(e) {
  return `
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `;
}
function xK(e) {
  return `${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);
    }

    ${wK}
    ${kK}
    ${SK}
  `;
}
var wK = `
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 kK = `
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 SK = `
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 IK = `
  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 T1() {
  return `
    int getOutputCoords() {
      return 0;
    }
  `;
}
function CK(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 NK(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 TK(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 $K(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;
    ${eh(["r", "c", "d"], e)}
    return ivec3(r, c, d);
  }
`;
  let s = wi(["r", "c", "d"], e);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec3(r, c, d);
    }
  `;
}
function _K(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 AK(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;
      ${eh(["r", "c", "d", "d2"], e)}
      return ivec4(r, c, d, d2);
    }
  `;
  let s = wi(["r", "c", "d", "d2"], e);
  return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec4(r, c, d, d2);
    }
  `;
}
function EK(e, t) {
  let n = wi(["r", "c", "d", "d2", "d3"], e);
  return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

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

      ${n}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `;
}
function DK(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 FK(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 * ${e[1]};
      return ivec2(r, c);
    }
  `;
}
function ki(e) {
  return `offset${e}`;
}
function OK(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 PK(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 = ki(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 zK(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 MK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
  if (e.shapeInfo.isUniform)
    return `
      float ${s}(int index) {
        ${uu(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 = ki(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 LK(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 BK(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 = lu(e, u), h = ["row", "col"];
    return `
      ${ou(d, t)}
      float ${r}(int row, int col) {
        return ${r}(${cu(h, o)});
      }
    `;
  }
  if (e.shapeInfo.isUniform)
    return `
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${uu(e)}
      }
    `;
  let l = a[0], c = a[1], p = ki(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 VK(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 = lu(e, d), m = ["b", "row", "col"];
    return `
        ${N1(f, t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${cu(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 WK(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 = lu(e, l), g = ["row", "col", "depth"];
    return `
        ${ou(m, t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${cu(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)));
        ${uu(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 = ki(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 UK(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 GK(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 = lu(e, u), v = ["row", "col", "depth", "depth2"];
    return `
      ${ou(y, t)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${cu(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)));
        ${uu(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 = ki(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 HK(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 = lu(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
    return `
      ${ou(m)}
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        return ${s}(${cu(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;
        ${uu(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 = ki(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 qK(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 = lu(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
    return `
      ${ou(g)}
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${s}(${cu(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)));
        ${uu(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 = ki(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 uu(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 ${t}[i];
      }
    }
  `;
}
function jK(e, t) {
  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, i = t.logicalShape.length, o = C1(e.shapeInfo.logicalShape, t.logicalShape), u = ot(i), l = i - a, c, p = ["x", "y", "z", "w", "u", "v"];
  a === 0 ? c = "" : i < 2 && o.length >= 1 ? c = "coords = 0;" : c = o.map((y) => `coords.${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 KK(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 = C1(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 kv(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 lu(e, t) {
  let n = JSON.parse(JSON.stringify(e));
  return n.shapeInfo.logicalShape = t, n;
}
function cu(e, t) {
  return t.map((n) => e[n]).join(", ");
}
function XK(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 = hK(r, i, t), u = s1(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, ...$1(e, t, l) };
}
function $1(e, t, n) {
  let s = {}, r = {}, a = {}, i = [], o, u, l, c = null, p = null;
  p = e.getUniformLocation(n, "NAN", false), K().getNumber("WEBGL_VERSION") === 1 && (c = e.getUniformLocation(n, "INFINITY", false));
  let d = false;
  for (let h = 0; h < t.variableNames.length; h++) {
    let f = t.variableNames[h];
    s[f] = e.getUniformLocation(n, f, d), s[`offset${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 cw(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 ${u} must match`);
  });
}
function YK(e, t, n, s, r) {
  t.program.enableShapeUniforms || (cw(t.inShapeInfos, n), cw([t.outShapeInfo], [s]));
  let a = s.texData.texture, i = s.texData.texShape;
  s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, i[0], i[1]) : e.setOutputMatrixTexture(a.texture, i[0], i[1]), e.setProgram(t.webGLProgram), K().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
    let c = t.program.variableNames[l], p = t.uniformLocations[c], d = t.uniformLocations[`offset${c}`], h = t.inShapesLocations[`${c}Shape`], f = t.inTexShapesLocations[`${c}TexShape`];
    if (h) {
      let { uniformShape: m } = kv(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 ${u.type} is not supported yet.`);
  }), e.executeProgram();
}
function QK(e, t, n) {
  let s = "";
  t.concat(n).forEach((i) => {
    let o = i.texData != null && i.texData.slice != null && i.texData.slice.flatOffset > 0;
    if (e.enableShapeUniforms && !i.isUniform) {
      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = kv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
      if (c.length === 1 && e.packedInputs) {
        let k = [Math.ceil(u[0] / 2), Math.ceil(u[1] / 2)];
        d = `${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 ZK = 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 ? eh(["r", "c", "d"], e) : wi(["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 JK = 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 ? eh(["r", "c", "d"], e) : wi(["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 eX = class {
  constructor(e) {
    this.variableNames = ["A"], this.outTexUsage = 3;
    let t = fn();
    this.outputShape = e, this.userCode = `
      ${I1}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `;
  }
};
var tX = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = 3;
    let t = fn();
    this.outputShape = e, this.userCode = `
      ${I1}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${t.output} = encode_float(x);
      }
    `;
  }
};
var nX = 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 ? wv() : xv(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 sX = 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 ? wv() : xv(e)}

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

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

          ${s}

          ${n.output} = ${r};
        }
    `;
  }
};
var rX = {};
Ee(rX, { bindVertexProgramAttributeStreams: () => z1, createBufferFromOutputTexture: () => B1, createFloat16MatrixTexture: () => D1, createFloat16PackedMatrixTexture: () => P1, createFloat32MatrixTexture: () => R1, createIndexBuffer: () => E1, createPackedMatrixTexture: () => O1, createUnsignedBytesMatrixTexture: () => F1, createVertexBuffer: () => A1, createVertexShader: () => _1, downloadByteEncodedFloatMatrixFromOutputTexture: () => W1, downloadFloat32MatrixFromBuffer: () => V1, downloadMatrixFromPackedOutputTexture: () => G1, downloadPackedMatrixFromBuffer: () => U1, getInternalFormatForFloat16MatrixTexture: () => Iv, getInternalFormatForFloat16PackedMatrixTexture: () => Tv, getInternalFormatForFloat32MatrixTexture: () => Sv, getInternalFormatForPackedMatrixTexture: () => Nv, getInternalFormatForUnsignedBytesMatrixTexture: () => Cv, uploadDenseMatrixToTexture: () => M1, uploadPixelDataToTexture: () => L1 });
function _1(e) {
  let t = fn(), n = `${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 n1(e, n);
}
function A1(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 i1(e, t);
}
function E1(e) {
  let t = new Uint16Array([0, 1, 2, 2, 1, 3]);
  return o1(e, t);
}
function ec(e, t, n, s, r, a) {
  l1(t, n);
  let i = u1(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 Sv(e) {
  return e.internalFormatFloat;
}
function R1(e, t, n, s) {
  let [r, a] = Jl(t, n);
  return ec(e, r, a, Sv(s), s.textureFormatFloat, e.FLOAT);
}
function Iv(e) {
  return e.internalFormatHalfFloat;
}
function D1(e, t, n, s) {
  let [r, a] = Jl(t, n);
  return ec(e, r, a, Iv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
}
function Cv(e) {
  return e.downloadTextureFormat;
}
function F1(e, t, n, s) {
  let [r, a] = Jl(t, n);
  return ec(e, r, a, Cv(s), e.RGBA, e.UNSIGNED_BYTE);
}
function Nv(e) {
  return e.internalFormatPackedFloat;
}
function O1(e, t, n, s) {
  let [r, a] = au(t, n);
  return ec(e, r, a, Nv(s), e.RGBA, e.FLOAT);
}
function Tv(e) {
  return e.internalFormatPackedHalfFloat;
}
function P1(e, t, n, s) {
  let [r, a] = au(t, n);
  return ec(e, r, a, Tv(s), e.RGBA, s.textureTypeHalfFloat);
}
function z1(e, t, n) {
  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Km(e, t, "clipSpacePos", n, 3, 20, 0) && Km(e, t, "uv", n, 2, 20, 12);
}
function M1(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 L1(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 B1(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 V1(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 W1(e, t, n, s) {
  let [r, a] = Jl(t, n), i = 4, o = new Uint8Array(J5(t * n, i));
  return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, o)), new Float32Array(o.buffer);
}
function U1(e, t, n, s, r, a, i, o) {
  let u = e, l = new Float32Array(eK(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 G1(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 tm = 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, Y5(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 = Mu(this.gl, r), Ln(this.gl, a))
        this.textureHalfFloatExtension = Mu(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 = Mu(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 = A1(this.gl), this.indexBuffer = E1(this.gl), this.framebuffer = c1(this.gl), this.textureConfig = yv(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(), R1(this.gl, e, t, this.textureConfig);
  }
  createFloat16MatrixTexture(e, t) {
    return this.throwIfDisposed(), D1(this.gl, e, t, this.textureConfig);
  }
  createUnsignedBytesMatrixTexture(e, t) {
    return this.throwIfDisposed(), F1(this.gl, e, t, this.textureConfig);
  }
  uploadPixelDataToTexture(e, t) {
    this.throwIfDisposed(), L1(this.gl, e, t);
  }
  uploadDenseMatrixToTexture(e, t, n, s) {
    this.throwIfDisposed(), M1(this.gl, e, t, n, s, this.textureConfig);
  }
  createFloat16PackedMatrixTexture(e, t) {
    return this.throwIfDisposed(), P1(this.gl, e, t, this.textureConfig);
  }
  createPackedMatrixTexture(e, t) {
    return this.throwIfDisposed(), O1(this.gl, e, t, this.textureConfig);
  }
  deleteMatrixTexture(e) {
    this.throwIfDisposed(), this.outputTexture === e && (Xm(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
  }
  downloadByteEncodedFloatMatrixFromOutputTexture(e, t, n) {
    return this.downloadMatrixDriver(e, () => W1(this.gl, t, n, this.textureConfig));
  }
  downloadPackedMatrixFromBuffer(e, t, n, s, r, a) {
    return U1(this.gl, e, t, n, s, r, a, this.textureConfig);
  }
  downloadFloat32MatrixFromBuffer(e, t) {
    return V1(this.gl, e, t);
  }
  createBufferFromTexture(e, t, n) {
    this.bindTextureToFrameBuffer(e);
    let s = B1(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, () => G1(this.gl, t, n));
  }
  createProgram(e) {
    this.throwIfDisposed();
    let t = this.gl;
    this.vertexShader == null && (this.vertexShader = _1(t));
    let n = r1(t);
    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), a1(t, n), this.debug && ud(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = z1(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 && ud(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
  }
  getUniformLocation(e, t, n = true) {
    return this.throwIfDisposed(), n ? p1(this.gl, e, t) : h1(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(), f1(this.gl, e, t, n);
  }
  setOutputMatrixTexture(e, t, n) {
    this.setOutputMatrixTextureDriver(e, n, t);
  }
  setOutputPackedMatrixTexture(e, t, n) {
    this.throwIfDisposed();
    let [s, r] = au(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 && ud(this.gl, this.program), Lu(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 = Mu(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 = aX(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(), ld(this.gl, e, this.framebuffer), this.debug && Lu(this.gl);
  }
  unbindTextureToFrameBuffer() {
    this.outputTexture != null ? (ld(this.gl, this.outputTexture, this.framebuffer), this.debug && Lu(this.gl)) : Xm(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;
    ld(s, e, this.framebuffer), this.debug && Lu(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 aX(e) {
  let t = 0;
  for (; t < e.length && e[t](); ++t)
    ;
  return t - 1;
}
var { addImpl: iX, bincountImpl: H1, bincountReduceImpl: oX, ceilImpl: uX, concatImpl: lX, equalImpl: cX, expImpl: dX, expm1Impl: pX, floorImpl: hX, gatherNdImpl: fX, gatherV2Impl: mX, greaterImpl: gX, greaterEqualImpl: bX, lessImpl: yX, lessEqualImpl: vX, linSpaceImpl: xX, logImpl: wX, maxImpl: kX, maximumImpl: SX, minimumImpl: IX, multiplyImpl: CX, negImpl: NX, notEqualImpl: TX, prodImpl: $X, rangeImpl: _X, rsqrtImpl: AX, scatterImpl: EX, sigmoidImpl: RX, simpleAbsImpl: q1, sliceImpl: DX, sparseFillEmptyRowsImpl: FX, sparseReshapeImpl: OX, sparseSegmentReductionImpl: j1, sqrtImpl: PX, stridedSliceImpl: zX, stringNGramsImpl: MX, stringSplitImpl: LX, stringToHashBucketFastImpl: BX, subImpl: VX, tileImpl: WX, topKImpl: UX, transposeImpl: $v, uniqueImpl: GX } = iv;
function K1(e, t) {
  return ["x", "y", "z", "w", "u", "v"].slice(0, t).map((n) => `${e}.${n}`);
}
function ln(e, t) {
  return t === 1 ? [e] : K1(e, t);
}
function HX(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 qX = 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 X1 = 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 = `
      ${jX(t, this.enableShapeUniforms)}
      ${this.enableShapeUniforms ? wv() : xv(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 jX(e, t) {
  return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t ? pK(["r", "c", "d"], "inputShape") : wi(["r", "c", "d"], e)}
      return ivec3(r, c, d);
    }
  `;
}
var KX = 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 = pw(t, n), r = hw(e, s, n);
    r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []);
    let a = dw(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 = pw(n, s), a = hw(t, r, s);
    a in this.freeTextures || (this.freeTextures[a] = []);
    let i = dw(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 XX(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 dw(e, t, n, s, r) {
  let a = YX(t, s), i;
  if (r) {
    let [u, l] = au(e[0], e[1]);
    i = u * l;
  } else {
    let [u, l] = Jl(e[0], e[1]);
    i = u * l;
  }
  let o = XX(n, a);
  return i * o;
}
function YX(e, t) {
  switch (e) {
    case 3:
      return Nv(t);
    case 4:
      return Tv(t);
    case 1:
      return Sv(t);
    case 0:
      return Iv(t);
    case 2:
      return Cv(t);
    default:
      throw new Error(`Unknown physical texture type ${e}`);
  }
}
function QX(e) {
  return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? e ? 3 : 1 : e ? 4 : 0;
}
function pw(e, t) {
  if (e === 1)
    return 3;
  if (e === 0 || e == null)
    return QX(t);
  if (e === 3 || e === 2)
    return 2;
  throw new Error(`Unknown logical texture type ${e}`);
}
function hw(e, t, n) {
  return `${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 ZX = "return x;";
var fw = "return abs(x);";
var JX = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var e8 = ss + `
  return (x < 0.0) ? 0.0 : x;
`;
var t8 = ss + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Vi = "return x;";
var n8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
var s8 = "return x;";
var r8 = `
  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 a8 = `
  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 i8 = `
  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 o8 = "return 1.0 / (1.0 + exp(-1.0 * x));";
var ta = 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 u8 = 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 = HX(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 l8 = ws.whereImpl;
var c8 = 1e-7;
var d8 = 1e-4;
var nd = {};
function p8(e) {
  return e in nd || (nd[e] = {}), nd[e];
}
var h8 = K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var f8 = 600;
function m8() {
  return K().global.screen == null ? 1024 : K().global.screen.height * K().global.screen.width * window.devicePixelRatio * f8 / 1024 / 1024;
}
var Y1 = class extends ol {
  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 tm)
        t = e;
      else {
        let n = xs(K().getNumber("WEBGL_VERSION"), e);
        t = new tm(n);
      }
      this.binaryCache = {}, this.gpgpuCreatedLocally = false;
    } else {
      let n = xs(K().getNumber("WEBGL_VERSION"));
      t = new tm(n), this.binaryCache = p8(K().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
    }
    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new KX(this.gpgpu), this.numMBBeforeWarning = m8(), this.texData = new Yd(this, ds());
  }
  nextDataId() {
    return Y1.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 ta(i, Vi) : p = new Gs(i, Vi);
      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 ta(s, Vi) : h = new Gs(s, Vi);
      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, ...ed(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 ta(r, Vi) : d = new Gs(r, Vi);
      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);
    if (e.dtype === "string")
      try {
        let n = t.map((s) => w.decodeString(s));
        return Ae(e.shape, e.dtype, n);
      } catch (n) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return Ae(e.shape, e.dtype, t);
  }
  checkNumericalProblems(e) {
    if (e != null)
      for (let t = 0; t < e.length; t++) {
        let n = e[t];
        if (!e1(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 ${n} cannot be represented on this device.`);
      }
  }
  getValuesFromTexture(e) {
    let { shape: t, dtype: n, isPacked: s } = this.texData.get(e), r = w.sizeFromShape(t);
    if (K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...ed(t)).subarray(0, r);
      return this.disposeIntermediateTensorInfo(p), h;
    }
    let a = K().getBool("WEBGL_PACK") && s === true, i = a ? cd(t) : t, o = a ? new tX(i) : new eX(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
    return this.disposeIntermediateTensorInfo(u), c;
  }
  timerAvailable() {
    return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
  }
  time(e) {
    let t = this.activeTimers, n = [], s = false;
    this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
    let r = w.flatten(this.activeTimers.map((o) => o.query)).filter((o) => o != null), a = w.flatten(this.activeTimers.map((o) => o.name)).filter((o) => o != null);
    this.activeTimers = t, s && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
    return (async () => {
      if (K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        let o = await Promise.all(r);
        i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${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 = h8) {
    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 l8(e.shape, t);
  }
  packedUnaryOp(e, t, n) {
    let s = new ta(e.shape, t), r = this.compileAndRun(s, [e], n);
    return ds().makeTensorFromTensorInfo(r);
  }
  abs(e) {
    if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
      let s = q1(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, fw, e.dtype);
    let t = new Gs(e.shape, fw), 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 u8(e.shape);
    return this.runWebGLProgram(t, [e], e.dtype);
  }
  packTensor(e) {
    let t = new qX(e.shape), n = true;
    return this.runWebGLProgram(t, [e], e.dtype, null, n);
  }
  packedReshape(e, t) {
    let n = [va(e.shape), ...xa(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [va(t), ...xa(t)], a = new X1(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 = cd(r), o;
    s ? o = new JK(i) : o = new ZK(i);
    let u = true, l = [t != null ? t : ed(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 : ed(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 && !al(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, () => XK(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
    h && (f = this.startTimer()), K().get("ENGINE_COMPILE_ONLY") || YK(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 ? c8 : d8;
  }
  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 = b1(n, o), t.texShape = c), r != null) {
      let p = cd(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
      (o || !m) && ([h, f] = au(c[0], c[1])), o ? d = new sX(p, m) : d = new nX(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 = g8(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 jS(), 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 ? (vv(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 } = $1(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 Q1 = Y1;
Q1.nextDataId = 0;
function g8(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 vhe = "0.0.0";
function b8() {
  K().set("WEBGL_FORCE_F16_TEXTURES", true);
}
yp.isBrowser() && vp("webgl", () => new Q1(), 2);
var xhe = { forceHalfFloat: b8 };
var Z1 = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var lo = 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 th = `
  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 tc = 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 y8 = { kernelName: Ua, backendName: "webgl", kernelFunc: Rn };
function Fr(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 v8 = { kernelName: ep, backendName: "webgl", kernelFunc: Fr };
var J1 = "return (a < 0.) ? b * a : a;";
var e2 = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function x8(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 tc(e2, r.shape, i.shape) : new lo(J1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
  return n.disposeIntermediateTensorInfo(i), u;
}
var w8 = { kernelName: Ga, backendName: "webgl", kernelFunc: x8 };
var t2 = "return (a < 0.) ? b * a : a;";
var n2 = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function k8(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(n2, s.shape, r.shape) : new lo(t2, s.shape, r.shape);
  return n.runWebGLProgram(a, [s, r], "float32");
}
var S8 = { kernelName: ni, backendName: "webgl", kernelFunc: k8 };
var du = "if (isnan(x)) return x;";
var I8 = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var C8 = `
  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 ta(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 }, R = new lo(e, u.shape, l.shape);
        return c.runWebGLProgram(R, [I, $], cn(x.dtype, k.dtype));
      }), y = Fr({ 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 tc(t, u.shape, l.shape, n) : h = new lo(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
  };
}
function nh(e, t = false) {
  if (e === "linear")
    return t ? s8 : ZX;
  if (e === "relu")
    return t ? a8 : e8;
  if (e === "elu")
    return t ? r8 : JX;
  if (e === "relu6")
    return t ? i8 : t8;
  if (e === "prelu")
    return t ? n2 : t2;
  if (e === "leakyrelu")
    return t ? e2 : J1;
  if (e === "sigmoid")
    return t ? o8 : n8;
  throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`);
}
var s2 = 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 mw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var gw = 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 bw = "return a * b;";
function _v(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 gw(mw.REAL, s.shape, r.shape), c = new gw(mw.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 = Fr({ 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] = CX(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 tc(bw, s.shape, r.shape) : i = new lo(bw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
}
var N8 = { kernelName: Ja, backendName: "webgl", kernelFunc: _v };
function T8(e, t, n) {
  let s = [va(e.shape), ...xa(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [va(t), ...xa(t)], i = new X1(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 && !al(r.shape, u) && !(c.texture !== null && al(c.shape, u)) ? T8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
}
var $8 = { kernelName: Oo, backendName: "webgl", kernelFunc: he };
var yw = 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);

          ${u}
        }
        setOutput(sumValue);
      }
    `;
  }
};
var _8 = class {
  constructor(e, t) {
    this.variableNames = ["x"];
    let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
    this.outputShape = [s, a];
    let i = "0.0", o = "";
    t === "prod" ? i = "1.0" : t === "min" ? (i = "1.0 / 1e-20", o = "min") : t === "max" && (i = "-1.0 / 1e-20", o = "max");
    let u = `${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 A8(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 Si(e, t, n, s) {
  let r = A8(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 yw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new yw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new _8({ 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 E8 = 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 = R8(t);
    this.userCode = `
    void main() {
      ${s} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `;
  }
};
function R8(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 D8 = 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 ${this.rank} is not yet supported.`);
    let s = ot(this.rank), r = K1("rc", this.rank), a = new Array(this.rank);
    for (let l = 0; l < t.length; l++)
      a[t[l]] = r[l];
    let i = `vec2(${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 sh(e, t, n) {
  let s = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new D8(e.shape, t) : new E8(e.shape, t);
  return n.runWebGLProgram(s, [e], e.dtype);
}
function F8(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 = sh(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 = bp(e.dtype), v = Si(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 rh(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return F8(r, a, i, n);
}
var O8 = { kernelName: di, backendName: "webgl", kernelFunc: rh };
function _t(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 = $v(p, r.shape, r.dtype, a, u);
    l = i.makeTensorInfo(u, r.dtype);
    let h = i.texData.get(l.dataId);
    h.values = d;
  } else
    l = sh(r, a, i);
  return l;
}
var P8 = { kernelName: Hs, backendName: "webgl", kernelFunc: _t };
var r2 = 1e3;
function Gd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
  w.assert(p === d, () => `Error in matMul: inner shapes (${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 } }), R = he({ inputs: { x: t }, backend: r, attrs: { shape: I } }), E = [$, R], P = Math.max(b, y), A = n ? $.shape[1] : $.shape[2], O = a != null, T = i != null, M = u === "leakyrelu", W = u != null ? nh(u, true) : null, j = O || T || M || W != null, X;
  if ((h === 1 || f === 1) && A > r2 && j === false) {
    let Z = $, te = R;
    n && (Z = _t({ inputs: { x: $ }, backend: r, attrs: { perm: [0, 2, 1] } }), E.push(Z)), s && (te = _t({ inputs: { x: R }, backend: r, attrs: { perm: [0, 2, 1] } }), E.push(te));
    let J = f !== 1, se = f === 1, ne = Z;
    J && (ne = he({ inputs: { x: Z }, backend: r, attrs: { shape: [P, A, 1] } }), E.push(ne));
    let oe = f === 1 ? 2 : 1, ae = te;
    se && (ae = he({ inputs: { x: te }, backend: r, attrs: { shape: [P, 1, A] } }), E.push(ae));
    let de = _v({ inputs: { a: ne, b: ae }, backend: r });
    X = rh({ inputs: { x: de }, backend: r, attrs: { axis: oe, keepDims: true } }), E.push(de);
  } else {
    let Z = cn(e.dtype, t.dtype), te = new s2(k, I, [P, h, f], n, s, O, W, T, M), J = [$, R];
    if (a != null && J.push(a), T && J.push(i), M) {
      let se = r.makeTensorInfo([], "float32", w.createScalarValue(o, "float32"));
      J.push(se), E.push(se);
    }
    X = r.runWebGLProgram(te, J, Z);
  }
  let Y = he({ inputs: { x: X }, backend: r, attrs: { shape: x } });
  E.push(X);
  for (let Z of E)
    r.disposeIntermediateTensorInfo(Z);
  return Y;
}
function z8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
  return Gd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
}
var M8 = { kernelName: oa, backendName: "webgl", kernelFunc: z8 };
var vw = "return abs(x);";
function L8(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s]) && s.dtype !== "complex64") {
    let a = n.texData.get(s.dataId), i = q1(a.values);
    return n.makeTensorInfo(s.shape, s.dtype, i);
  }
  let r;
  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new ta(s.shape, vw) : r = new Gs(s.shape, vw), n.runWebGLProgram(r, [s], s.dtype);
}
var B8 = { kernelName: po, backendName: "webgl", kernelFunc: L8 };
var V8 = ss + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
var W8 = Ke({ opSnippet: V8 });
var U8 = { kernelName: ul, backendName: "webgl", kernelFunc: W8 };
var G8 = ss + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var H8 = Ke({ opSnippet: G8 });
var q8 = { kernelName: ll, backendName: "webgl", kernelFunc: H8 };
var xw = "return a + b;";
var j8 = jt({ opSnippet: xw, packedOpSnippet: xw, supportsComplex: true, cpuKernelImpl: iX });
var K8 = { kernelName: Cr, backendName: "webgl", kernelFunc: j8 };
var X8 = class {
  constructor(e, t) {
    this.outputShape = [], this.outputShape = e, this.variableNames = t.map((r, a) => `T${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 = ${s};
        setOutput(result);
      }
    `;
  }
};
var Y8 = class {
  constructor(e, t) {
    this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t.map((r, a) => `T${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 hd(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 = hd({ inputs: s.slice(0, u), backend: n }), c = hd({ inputs: s.slice(u), backend: n });
    return hd({ 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 Y8(s[0].shape, a) : new X8(s[0].shape, a);
  return n.runWebGLProgram(o, s, r);
}
var Q8 = { kernelName: Ia, backendName: "webgl", kernelFunc: hd };
function Z8(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 = _t({ 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 = Si(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: cl, backendName: "webgl", kernelFunc: Z8 };
function eY(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 = _t({ 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 = Si(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 tY = { kernelName: dl, backendName: "webgl", kernelFunc: eY };
var nY = 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 ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
  }
};
var sY = class {
  constructor(e, t, n, s) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, w.assert(e.length > 2, () => `Packed arg${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 a2(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 nY(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 = a2(e, t, n, c);
  return e.disposeIntermediateTensorInfo(c), p;
}
function i2(e, t, n, s = null) {
  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], i = C.computeOptimalWindowSize(a), o = new sY(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 = i2(e, t, n, l);
    return e.disposeIntermediateTensorInfo(l), c;
  }
  return l;
}
function o2(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 = a2(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 i2(e, t, s);
}
function rY(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 = _t({ 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 = o2(n, u, i[0], "max");
  return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
}
var aY = { kernelName: Ca, backendName: "webgl", kernelFunc: rY };
function iY(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 = _t({ 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 = o2(n, u, i[0], "min");
  return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
}
var oY = { kernelName: pl, backendName: "webgl", kernelFunc: iY };
var uY = ss + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
var lY = Ke({ opSnippet: uY });
var cY = { kernelName: hl, backendName: "webgl", kernelFunc: lY };
var dY = ss + "return log(x + sqrt(x * x + 1.0));";
var pY = Ke({ opSnippet: dY });
var hY = { kernelName: fl, backendName: "webgl", kernelFunc: pY };
var fY = ss + `
  return atan(x);
`;
var mY = Ke({ opSnippet: fY });
var gY = { kernelName: ml, backendName: "webgl", kernelFunc: mY };
var bY = I8 + `
  return atan(a, b);
`;
var yY = `
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + C8 + `
  return result;
`;
var vY = jt({ opSnippet: bY, packedOpSnippet: yY });
var xY = { kernelName: bl, backendName: "webgl", kernelFunc: vY };
var wY = ss + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var kY = Ke({ opSnippet: wY });
var SY = { kernelName: gl, backendName: "webgl", kernelFunc: kY };
var il = 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 Av = 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 E = ">=";
      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 ${E} 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, R = `
      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)
              );

              ${R}
            }

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

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

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

              ${R}
            }
          }
          setOutput(${k});
        }
      }
    `;
  }
};
function IY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  iu(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 il(c, "avg", false);
  return n.runWebGLProgram(p, [r], "float32");
}
var CY = { kernelName: Na, backendName: "webgl", kernelFunc: IY };
function NY(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 Av(p, "avg", false);
  return n.runWebGLProgram(d, [r], "float32");
}
var TY = { kernelName: Jd, backendName: "webgl", kernelFunc: NY };
var $Y = class {
  constructor(e) {
    this.variableNames = ["dy"], this.outputShape = e.inShape;
    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterHeight, u = e.effectiveFilterWidth, l = o - 1 - e.padInfo.top, c = u - 1 - e.padInfo.left, p = 1 / (t * n);
    this.userCode = `
      const ivec2 pads = ivec2(${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 _Y = 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 AY(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 _Y(d);
  return n.runWebGLProgram(h, [r], i.dtype);
}
var EY = { kernelName: fg, backendName: "webgl", kernelFunc: AY };
function RY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
  iu([r, a], "avgPoolGrad");
  let { filterSize: o, strides: u, pad: l } = s, c = C.computePool2DInfo(i.shape, o, u, 1, l), p = new $Y(c);
  return n.runWebGLProgram(p, [r], i.dtype);
}
var DY = { kernelName: hg, backendName: "webgl", kernelFunc: RY };
function FY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  return Gd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
}
var OY = { kernelName: Ta, backendName: "webgl", kernelFunc: FY };
var PY = 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 zY = 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 MY = ({ 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 zY(s.shape, r.shape, a.shape, c, p, u) : new PY(s.shape, r.shape, a.shape, c, p, u);
  return t.runWebGLProgram(d, l, l[0].dtype);
};
var LY = { kernelName: Va, backendName: "webgl", kernelFunc: MY };
var BY = 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 = VY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Zm[i]} = start[${i}] + coords.${Zm[i]};`);
    s = `
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `, this.userCode = `
      void main() {
        ${s}
        setOutput(getSource(${n}));
      }
    `;
  }
};
var Zm = ["x", "y", "z", "w", "u", "v"];
function VY(e) {
  if (e === 1)
    return "sourceLoc";
  if (e <= 6)
    return Zm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
  throw Error(`Slicing for rank ${e} is not yet supported`);
}
var WY = class {
  constructor(e) {
    this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
    let t = ot(this.rank), n = ln("coords", this.rank), s = ln("sourceLoc", this.rank), r = this.rank === 1 ? "sourceLoc" : `vec2(${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 UY(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 = kt.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 pu(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = kt.parseSliceParams(r, a, i);
  if (kt.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 = DX(p.values, o, u, r.shape, r.dtype);
    return n.makeTensorInfo(u, r.dtype, d);
  }
  let { isPacked: l } = n.texData.get(r.dataId), c = kt.isSliceContinous(r.shape, o, u);
  if (l || !c) {
    let p = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new WY(u) : new BY(u), d = [o];
    return n.runWebGLProgram(p, [r], r.dtype, d);
  }
  return n.uploadToGPU(r.dataId), UY(r, o, u, n);
}
var GY = { kernelName: Bo, backendName: "webgl", kernelFunc: pu };
var HY = (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 = _t({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ 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.disposeIntermediateTensorInfo(y)), b;
};
var qY = { kernelName: ho, backendName: "webgl", kernelFunc: HY };
function jY(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 = H1(o, u, a.dtype, a.shape, i);
  return n.makeTensorInfo([i], a.dtype, l);
}
var KY = { kernelName: mg, backendName: "webgl", kernelFunc: jY };
function XY(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 YY = { kernelName: gg, backendName: "webgl", kernelFunc: XY };
var QY = "return float(a != b);";
var u2 = jt({ opSnippet: QY, cpuKernelImpl: TX, dtype: "bool" });
var ZY = { kernelName: _o, backendName: "webgl", kernelFunc: u2 };
function nc(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: lp, backendName: "webgl", kernelFunc: nc };
var e9 = "return float(int(x));";
function t9(e, t) {
  let n = new Gs(e.shape, e9), s = t.runWebGLProgram(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 Rn({ inputs: { x: r }, backend: n });
    let i = $t(r.shape), o = Jm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Fr({ inputs: { real: o, imag: i }, backend: n });
    return i.dispose(), n.disposeIntermediateTensorInfo(o), u;
  }
  if (r.dtype === "complex64") {
    let i = nc({ inputs: { input: r }, backend: n }), o = Jm({ 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 t9(r, n);
  if (a === "bool") {
    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = u2({ 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 n9 = { kernelName: $a, backendName: "webgl", kernelFunc: Jm };
var ww = "return ceil(x);";
var s9 = Ke({ opSnippet: ww, packedOpSnippet: ww, cpuKernelImpl: uX });
var r9 = { kernelName: _a, backendName: "webgl", kernelFunc: s9 };
var a9 = 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 i9 = 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 o9(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 i9(r.shape) : o = new a9(r.shape);
  let u = [[a], [i]];
  return n.runWebGLProgram(o, [r], r.dtype, u);
}
var u9 = { kernelName: Nr, backendName: "webgl", kernelFunc: o9 };
var l9 = 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 kw(e, t) {
  return { dataId: t.dataId, dtype: t.dtype, shape: e.shape };
}
function c9(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new l9(s.shape), i = [kw(s, r.complexTensorInfos.real), kw(s, r.complexTensorInfos.imag)];
  return n.runWebGLProgram(a, i, i[0].dtype);
}
var d9 = { kernelName: tp, backendName: "webgl", kernelFunc: c9 };
var p9 = 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;

        ${n.join(`
        `)}
      }
    `;
  }
};
var h9 = class {
  constructor(e, t) {
    this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = C.computeOutShape(e, t);
    let n = this.outputShape, s = n.length, r = ot(s), a = ln("coords", s), i = ["x", "y", "z", "w", "u", "v"].slice(0, s);
    this.variableNames = e.map((f, m) => `T${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}(${sd(i, u, m)}),
            vec2(${sd(l, u, m)}));
        }`;
    }
    let d = o.length, h = o[o.length - 1];
    p += `
        return getChannel(
          getT${d}(${sd(i, u, h)}),
          vec2(${sd(l, u, h)}));`, this.userCode = `
      float getValue(${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 sd(e, t, n) {
  let s = e.indexOf(t);
  return e.map((a, i) => i === s ? `${a} - ${n}` : a).join();
}
function ah(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 f9 = { kernelName: ap, backendName: "webgl", kernelFunc: ah };
function ji(e, t, n) {
  let s = e[0].dtype;
  if (s === "complex64") {
    let c = e.map((m) => nc({ inputs: { input: m }, backend: n })), p = e.map((m) => ah({ inputs: { input: m }, backend: n })), d = ji(c, t, n), h = ji(p, t, n), f = Fr({ 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 = lX(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 = ji(e.slice(0, c), t, n), d = ji(e.slice(c), t, n), h = ji([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 h9(e.map((p) => p.shape), t);
    return n.runWebGLProgram(c, e, s);
  }
  let { tensors2D: a, outShape: i } = m9(e, t, n), o = new p9(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 m9(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 l2(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), ji(o, a, n);
}
var g9 = { kernelName: fo, backendName: "webgl", kernelFunc: l2 };
var c2 = 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}
        ${x}
        setOutput(result);
      }
    `;
  }
};
var b9 = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let t = e.padInfo.front, n = e.padInfo.top, s = e.padInfo.left, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.filterDepth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4;
    this.userCode = `
      const ivec3 strides = ivec3(${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 y9 = 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 d2({ 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 (a != null && !h && a.shape.length === 3) {
    let x = _t({ inputs: { x: a }, backend: s, attrs: { perm: [1, 2, 0] } });
    b.push(x), a = x;
  }
  if (!((p === 1 || d === 1) && c > r2) && 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(al(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 R = Gd({ a: k, b: $, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), E = s.texData.get(R.dataId);
    w.assert(E.isPacked, () => "batchMatMul result is expected to be packed"), l.shape = I, E.shape = n.outShape, g = Rn({ inputs: { x: R }, backend: s }), g.shape = n.outShape, b.push(R);
  } else {
    let x = h ? e : _t({ 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] } }), R = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), E = Gd({ a: $, b: R, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), P = [n.batchSize, n.outHeight, n.outWidth, n.outChannels], A = he({ inputs: { x: E }, backend: s, attrs: { shape: P } });
    g = h ? A : _t({ inputs: { x: A }, backend: s, attrs: { perm: [0, 3, 1, 2] } }), h || (b.push(x), b.push(A)), b.push($), b.push(R), b.push(E);
  }
  for (let x of b)
    s.disposeIntermediateTensorInfo(x);
  return g;
}
function p2({ 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 (a != null && !f && a.shape.length === 3) {
    let J = _t({ inputs: { x: a }, backend: s, attrs: { perm: [1, 2, 0] } });
    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 y9(b, n), R = [k.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], E = s.runWebGLProgram($, [k], "float32", R), P = he({ inputs: { x: E }, backend: s, attrs: { shape: [1, b[0], b[1]] } });
  x.push(E), x.push(P);
  let A = r != null, O = a != null, T = o === "leakyrelu", M = o ? nh(o, true) : null, W = new s2(P.shape, I.shape, [1, g, n.outChannels], y, v, A, M, O, T), j = [P, I];
  if (r && j.push(r), O && 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 : _t({ 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 v9(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 = d2({ x: r, filter: a, convInfo: d, backend: n });
  else if (K().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
    h = p2({ x: r, filter: a, convInfo: d, backend: n });
  else {
    let m = new c2(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 x9 = { kernelName: Aa, backendName: "webgl", kernelFunc: v9 };
var w9 = 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 k9 = 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 S9 = 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 I9 = 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 C9(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 w9(d);
  return n.runWebGLProgram(h, [r, a], "float32");
}
var N9 = { kernelName: bg, backendName: "webgl", kernelFunc: C9 };
function T9(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 k9(d);
  return n.runWebGLProgram(h, [r, a], "float32");
}
var $9 = { kernelName: Ea, backendName: "webgl", kernelFunc: T9 };
function _9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = C.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new b9(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var A9 = { kernelName: np, backendName: "webgl", kernelFunc: _9 };
function E9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = C.computeConv3DInfo(r.shape, u, i, 1, o), c = new S9(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var R9 = { kernelName: yg, backendName: "webgl", kernelFunc: E9 };
function D9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = C.computeConv3DInfo(u, a.shape, o, 1, i), c = new I9(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var F9 = { kernelName: vg, backendName: "webgl", kernelFunc: D9 };
var O9 = du + `
  return cos(x);
`;
var P9 = Ke({ opSnippet: O9 });
var z9 = { kernelName: Ra, backendName: "webgl", kernelFunc: P9 };
var M9 = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var L9 = Ke({ opSnippet: M9 });
var B9 = { kernelName: Da, backendName: "webgl", kernelFunc: L9 };
var V9 = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
    let [a, i, o, u] = e, [l] = t, [c, p] = n;
    this.outputShape = [l, c, p, u];
    let d = s === "bilinear" ? 1 : 0, [h, f] = [`${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 W9 = (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 V9(r.shape, a.shape, o, u, l);
  return n.runWebGLProgram(c, [r, a, i], "float32");
};
var U9 = { kernelName: go, backendName: "webgl", kernelFunc: W9 };
var Sw = class {
  constructor(e, t, n, s) {
    this.op = e, this.outputShape = t, this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }];
    let r = this.outputShape.length, a = this.op === "*" ? "1.0" : "0.0", i = n ? a : `getX(${Iw(r, "coords", this.op)})`, o = this.outputShape[this.outputShape.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 = ${Cw(r, "coords", this.op)};
        float val = ${i};
        int pow2 = int(pow(2.0, index));
        if (${u}) {
          int idx = ${l};
          ${Cw(r, "coords", this.op)} = idx;
          val ${this.op}= getX(${Iw(r, "coords", this.op)});
        }
        setOutput(val);
      }
    `;
  }
};
function Iw(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 new Error(`Cumulative ${n} for rank ${e} is not yet supported`);
}
function Cw(e, t, n) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.y`;
  if (e === 3)
    return `${t}.z`;
  if (e === 4)
    return `${t}.w`;
  throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`);
}
function h2(e, t, n, s, r, a) {
  let i = t.shape.length, o = C.getAxesPermutation([s], i), u = t;
  o != null && (u = _t({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
  let l = C.getInnerMostAxes(1, i)[0];
  if (l !== i - 1)
    throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
  let c = u.shape[l], p = Rn({ inputs: { x: u }, backend: n });
  for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
    let h = new Sw(e, u.shape, false, a), f = [[d]], m = p;
    p = n.runWebGLProgram(h, [p], p.dtype, f), n.disposeIntermediateTensorInfo(m);
  }
  if (r) {
    let d = new Sw(e, u.shape, r, a), h = p;
    p = n.runWebGLProgram(d, [p], p.dtype), n.disposeIntermediateTensorInfo(h);
  }
  if (o != null) {
    let d = C.getUndoAxesPermutation(o), h = _t({ inputs: { x: p }, backend: n, attrs: { perm: d } });
    return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(u), h;
  }
  return p;
}
function G9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return h2("*", r, n, a, i, o);
}
var H9 = { kernelName: mo, backendName: "webgl", kernelFunc: G9 };
function q9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return h2("+", r, n, a, i, o);
}
var j9 = { kernelName: Fa, backendName: "webgl", kernelFunc: q9 };
function K9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
  if (r.shape.length === 1) {
    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = H1(u, l, a.dtype, a.shape, i);
    return n.makeTensorInfo([i], a.dtype, c);
  } else if (r.shape.length === 2) {
    let u = n.bufferSync(r), l = n.bufferSync(a), c = oX(u, l, i, o);
    return n.makeTensorInfo(c.shape, a.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
}
var X9 = { kernelName: xg, backendName: "webgl", kernelFunc: K9 };
var Y9 = 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 Q9(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 Y9(f, a, i);
  return n.runWebGLProgram(m, [r], r.dtype);
}
var Z9 = { kernelName: bo, backendName: "webgl", kernelFunc: Q9 };
var f2 = 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}
        ${l}
        setOutput(result);
      }
    `;
  }
};
var m2 = class {
  constructor(e, t = false, n = null, s = false, r = false) {
    this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
    let a = e.outChannels / e.inChannels, i = e.padInfo.left, o = e.strideWidth, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = `
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;
    for (let g = 0; g < c; g++)
      d += `
          vec4 xTexelC${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 m2(p) : d = new f2(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 eQ = { kernelName: Oa, backendName: "webgl", kernelFunc: J9 };
var tQ = 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 nQ = 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 sQ(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 tQ(p);
  return n.runWebGLProgram(d, [r, a], "float32");
}
var rQ = { kernelName: wg, backendName: "webgl", kernelFunc: sQ };
function aQ(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 nQ(p);
  return n.runWebGLProgram(d, [r, a], "float32");
}
var iQ = { kernelName: kg, backendName: "webgl", kernelFunc: aQ };
var oQ = 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 uQ(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 oQ(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 lQ = { kernelName: Sg, backendName: "webgl", kernelFunc: uQ };
var cQ = 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 dQ(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 cQ(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 pQ = { kernelName: sp, backendName: "webgl", kernelFunc: dQ };
function hQ(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 = _t({ 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 = _v({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = rh({ 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 fQ = { kernelName: rp, backendName: "webgl", kernelFunc: hQ };
var mQ = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var gQ = `
  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 bQ = Ke({ opSnippet: mQ, packedOpSnippet: gQ });
var yQ = { kernelName: za, backendName: "webgl", kernelFunc: bQ };
var vQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
var xQ = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var wQ = (e) => {
  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = K().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new tc(xQ, s.shape, r.shape) : new lo(vQ, s.shape, r.shape);
  return n.runWebGLProgram(a, [s, r], s.dtype);
};
var kQ = { kernelName: Ig, backendName: "webgl", kernelFunc: wQ };
var SQ = `
  return vec4(equal(a, b));
`;
var IQ = "return float(a == b);";
var CQ = jt({ opSnippet: IQ, packedOpSnippet: SQ, dtype: "bool", cpuKernelImpl: cX });
var NQ = { kernelName: yo, backendName: "webgl", kernelFunc: CQ };
var TQ = `
  // 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 $Q = Ke({ opSnippet: TQ });
var _Q = { kernelName: yl, backendName: "webgl", kernelFunc: $Q };
var AQ = du + `
  return exp(x);
`;
var EQ = `
  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 g2 = Ke({ opSnippet: AQ, packedOpSnippet: EQ, cpuKernelImpl: dX, dtype: "float32" });
var RQ = { kernelName: Ma, backendName: "webgl", kernelFunc: g2 };
function eg(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 DQ = { kernelName: vo, backendName: "webgl", kernelFunc: eg };
var Nw = "return exp(x) - 1.0;";
var FQ = Ke({ opSnippet: Nw, packedOpSnippet: Nw, cpuKernelImpl: pX });
var OQ = { kernelName: xo, backendName: "webgl", kernelFunc: FQ };
var Tw = 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 b2(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 Tw("real", u, t), c = new Tw("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 = Fr({ 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 PQ(e) {
  let { inputs: t, backend: n } = e, { input: s } = t;
  return b2(s, false, n);
}
var zQ = { kernelName: Cg, backendName: "webgl", kernelFunc: PQ };
var MQ = 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 sc(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 MQ(s, r), o = [[r]];
    return t.runWebGLProgram(i, [], a, o);
  }
}
var LQ = { kernelName: vl, backendName: "webgl", kernelFunc: sc };
var BQ = 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 VQ = { kernelName: wo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
  let { image: n } = e, s = t, r = new BQ(n.shape);
  return s.runWebGLProgram(r, [n], n.dtype);
} };
var $w = "return floor(x);";
var WQ = Ke({ opSnippet: $w, packedOpSnippet: $w, cpuKernelImpl: hX });
var UQ = { kernelName: La, backendName: "webgl", kernelFunc: WQ };
var GQ = `
  float s = sign(a) * sign(b);
  int ia = round(a);
  int ib = round(b);
  if (ib != 0) {
    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
    return float(idiv(ia, ib, s));
  } else {
    return NAN;
  }
`;
var HQ = `
  ivec4 ia = round(a);
  ivec4 ib = round(b);
  bvec4 cond = notEqual(ib, ivec4(0));
  ivec4 result = ivec4(0);
  vec4 s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    result[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    result[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    result[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    result[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4(result);
`;
var qQ = jt({ opSnippet: GQ, packedOpSnippet: HQ, dtype: "int32" });
var jQ = { kernelName: Ba, backendName: "webgl", kernelFunc: qQ };
var KQ = 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 XQ = 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 YQ = { kernelName: vd, backendName: "webgl", kernelFunc: QQ };
var Wi;
function QQ(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) && (Wi == null && (Wi = document.createElement("canvas").getContext("2d")), Wi.canvas.width = u, Wi.canvas.height = l, Wi.drawImage(r, 0, 0, u, l), r = Wi.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 XQ(p) : new KQ(p), f = n.runWebGLProgram(h, [d], "int32");
  return n.disposeData(d.dataId), f;
}
function ZQ(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 = d2({ 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 = p2({ 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 ? nh(h, false) : null, R = new c2(g, x, $, k, I), E = [r, a], P = (A, O) => {
      if (O === "NCHW" && A.shape.length === 1 && A.shape[0] !== 1) {
        let T = he({ inputs: { x: A }, backend: n, attrs: { shape: [A.shape[0], 1, 1] } });
        return y.push(T), T;
      }
      return A;
    };
    if (x && E.push(P(i, c)), k && E.push(P(o, c)), I) {
      let A = n.makeTensorInfo([], "float32", w.createScalarValue(f, "float32"));
      E.push(A), y.push(A);
    }
    b = n.runWebGLProgram(R, E, "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: ua, backendName: "webgl", kernelFunc: ZQ };
function eZ(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 ? nh(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 m2(g, x, y, k, I) : $ = new f2(g, x, y, k, I);
  let R = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], E = n.runWebGLProgram($, v, "float32", R);
  return f.forEach((P) => n.disposeIntermediateTensorInfo(P)), E;
}
var tZ = { kernelName: la, backendName: "webgl", kernelFunc: eZ };
var nZ = 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 sZ(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 = fX(b, y, s.dtype, l, i, c, p, s.shape, o);
    return n.makeTensorInfo(u, s.dtype, v.values);
  }
  let f = new nZ(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 rZ = { kernelName: So, backendName: "webgl", kernelFunc: sZ };
var aZ = class {
  constructor(e, t) {
    this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
    let n = ot(this.rank), s = iZ(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 iZ(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 y2(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 = mX(v, y, f);
    return p.forEach((k) => n.disposeIntermediateTensorInfo(k)), n.makeTensorInfo(l.outputShape, x.dtype, x.values);
  }
  let m = new aZ(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 oZ = { kernelName: ko, backendName: "webgl", kernelFunc: y2 };
var uZ = "return float(a > b);";
var lZ = `
  return vec4(greaterThan(a, b));
`;
var cZ = jt({ opSnippet: uZ, packedOpSnippet: lZ, cpuKernelImpl: gX, dtype: "bool" });
var dZ = { kernelName: Io, backendName: "webgl", kernelFunc: cZ };
var pZ = "return float(a >= b);";
var hZ = `
  return vec4(greaterThanEqual(a, b));
`;
var fZ = jt({ opSnippet: pZ, packedOpSnippet: hZ, dtype: "bool", cpuKernelImpl: bX });
var mZ = { kernelName: Wa, backendName: "webgl", kernelFunc: fZ };
function gZ(e) {
  let { inputs: t, backend: n } = e, { input: s } = t;
  return b2(s, true, n);
}
var bZ = { kernelName: Ng, backendName: "webgl", kernelFunc: gZ };
var yZ = "return float(!isnan(x) && !isinf(x));";
var vZ = Ke({ opSnippet: yZ, dtype: "bool" });
var xZ = { kernelName: xl, backendName: "webgl", kernelFunc: vZ };
var wZ = "return float(isinf(x));";
var kZ = Ke({ opSnippet: wZ, dtype: "bool" });
var SZ = { kernelName: wl, backendName: "webgl", kernelFunc: kZ };
var IZ = "return float(isnan(x));";
var CZ = Ke({ opSnippet: IZ, dtype: "bool" });
var NZ = { kernelName: kl, backendName: "webgl", kernelFunc: CZ };
var TZ = "return float(a < b);";
var $Z = `
  return vec4(lessThan(a, b));
`;
var _Z = jt({ opSnippet: TZ, packedOpSnippet: $Z, cpuKernelImpl: yX, dtype: "bool" });
var AZ = { kernelName: Co, backendName: "webgl", kernelFunc: _Z };
var EZ = "return float(a <= b);";
var RZ = `
  return vec4(lessThanEqual(a, b));
`;
var DZ = jt({ opSnippet: EZ, packedOpSnippet: RZ, cpuKernelImpl: vX, dtype: "bool" });
var FZ = { kernelName: No, backendName: "webgl", kernelFunc: DZ };
function OZ(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = xX(s, r, a);
  return t.makeTensorInfo([i.length], "float32", i);
}
var PZ = { kernelName: Tg, backendName: "webgl", kernelFunc: OZ };
var zZ = du + `
  return x < 0.0 ? 0./0. : log(x);
`;
var MZ = `
  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 LZ = Ke({ opSnippet: zZ, packedOpSnippet: MZ, cpuKernelImpl: wX });
var BZ = { kernelName: Ha, backendName: "webgl", kernelFunc: LZ };
var VZ = du + `
  return log(1.0 + x);
`;
var WZ = Ke({ opSnippet: VZ });
var UZ = { kernelName: Sl, backendName: "webgl", kernelFunc: WZ };
var GZ = "return float(a >= 1.0 && b >= 1.0);";
var HZ = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
var qZ = jt({ opSnippet: GZ, packedOpSnippet: HZ, dtype: "bool" });
var jZ = { kernelName: To, backendName: "webgl", kernelFunc: qZ };
var KZ = "return float(!(x >= 1.0));";
var XZ = Ke({ opSnippet: KZ });
var YZ = { kernelName: Il, backendName: "webgl", kernelFunc: XZ };
var QZ = "return float(a >= 1.0 || b >= 1.0);";
var ZZ = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
var JZ = jt({ opSnippet: QZ, packedOpSnippet: ZZ, dtype: "bool" });
var e7 = { kernelName: ip, backendName: "webgl", kernelFunc: JZ };
var t7 = 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 * ${o};
        setOutput(val);
      }
    `;
  }
};
var n7 = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
    let a = t, i = e[3] - 1;
    this.outputShape = e;
    let o, u = `float(${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 s7 = (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 n7(r.shape, a, i, o, u) : new t7(r.shape, a, i, o, u);
  return n.runWebGLProgram(l, [r], r.dtype);
};
var r7 = { kernelName: op, backendName: "webgl", kernelFunc: s7 };
var a7 = 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 i7 = (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 a7(r.shape, o, u, l, c);
  return n.runWebGLProgram(p, [r, a, i], r.dtype);
};
var o7 = { kernelName: $g, backendName: "webgl", kernelFunc: i7 };
function u7(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 = Si(o, e.dtype, "max", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
}
function v2(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 = $v(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 = sh(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 = kX(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 = u7(h, m, g, n);
  return p && n.disposeIntermediateTensorInfo(h), b;
}
var l7 = { kernelName: qa, backendName: "webgl", kernelFunc: v2 };
var c7 = Z1 + `
  return max(a, b);
`;
var d7 = `
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + th + `
  return result;
`;
var p7 = jt({ opSnippet: c7, packedOpSnippet: d7, cpuKernelImpl: SX });
var h7 = { kernelName: ja, backendName: "webgl", kernelFunc: p7 };
function f7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  iu(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 il(c, "max", false);
  return n.runWebGLProgram(p, [r], r.dtype);
}
var m7 = { kernelName: Ka, backendName: "webgl", kernelFunc: f7 };
function g7(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 Av(p, "max", false);
  return n.runWebGLProgram(d, [r], r.dtype);
}
var b7 = { kernelName: up, backendName: "webgl", kernelFunc: g7 };
var y7 = 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 v7 = 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 x7(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 Av(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new v7(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
  return n.disposeIntermediateTensorInfo(f), g;
}
var w7 = { kernelName: Ag, backendName: "webgl", kernelFunc: x7 };
function k7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
  iu([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 il(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new y7(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
  return n.disposeIntermediateTensorInfo(m), b;
}
var S7 = { kernelName: _g, backendName: "webgl", kernelFunc: k7 };
function I7(e, t, n, s) {
  let r = new il(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
  r = new il(n, "max", true, true, t);
  let i = s.runWebGLProgram(r, [e], "float32");
  return [a, i];
}
var C7 = { kernelName: Eg, 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] = I7(s, o, c, u);
  return [p, d];
} };
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 = Si(o, "float32", "mean", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
}
var T7 = { kernelName: Xa, 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 R = 0; R < k.length; R++)
        k[R] = s.shape[c[R]];
      let I = $v(x, s.shape, s.dtype, c, k);
      f = i.makeTensorInfo(k, s.dtype);
      let $ = i.texData.get(f.dataId);
      $.values = I;
    } else
      f = sh(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 = N7(f, g, b, i);
  for (let v of h)
    i.disposeIntermediateTensorInfo(v);
  return y;
} };
function $7(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 = _t({ 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 = Si(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 _7 = { kernelName: Ya, backendName: "webgl", kernelFunc: $7 };
var A7 = Z1 + `
  return min(a, b);
`;
var E7 = `
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + th + `
  return result;
`;
var R7 = jt({ opSnippet: A7, packedOpSnippet: E7, cpuKernelImpl: IX });
var D7 = { kernelName: Qa, backendName: "webgl", kernelFunc: R7 };
var F7 = 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 O7 = 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 P7 = ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s } = e, { paddings: r, mode: a } = n, i = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new O7(s.shape, r, a) : new F7(s.shape, r, a);
  return t.runWebGLProgram(i, [s], s.dtype);
};
var z7 = { kernelName: Za, backendName: "webgl", kernelFunc: P7 };
var M7 = `if (b == 0.0) return NAN;
  return mod(a, b);`;
var L7 = `
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  ` + th + `
  return result;
`;
var B7 = jt({ opSnippet: M7, packedOpSnippet: L7 });
var V7 = { kernelName: Cl, backendName: "webgl", kernelFunc: B7 };
var W7 = 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 U7 = `
if (a == b) {
  return 1.0;
};
return a / b;`;
var G7 = `
  // 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 x2 = jt({ opSnippet: U7, packedOpSnippet: G7, checkOutOfBounds: true });
var H7 = { kernelName: Pa, backendName: "webgl", kernelFunc: x2 };
var _w = "return a - b;";
var w2 = jt({ opSnippet: _w, packedOpSnippet: _w, supportsComplex: true, cpuKernelImpl: VX });
var q7 = { kernelName: fi, backendName: "webgl", kernelFunc: w2 };
function k2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = v2({ 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 = w2({ inputs: { a: r, b: l }, backend: n }), p = g2({ inputs: { x: c }, backend: n }), d = rh({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = x2({ 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 j7 = { kernelName: pi, backendName: "webgl", kernelFunc: k2 };
function K7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : k2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new W7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
  return o || n.disposeIntermediateTensorInfo(u), h;
}
var X7 = { kernelName: Rg, backendName: "webgl", kernelFunc: K7 };
var Y7 = ss + `
  return -x;
`;
var Q7 = `
  vec4 result = -x;
  bvec4 isNaN = isnan(x);

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

  return result;
`;
function Z7(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s])) {
    let a = n.texData.get(s.dataId), [i, o] = NX(a.values, s.shape, s.dtype);
    return n.makeTensorInfo(o, s.dtype, i);
  }
  let r;
  return K().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new ta(s.shape, Q7) : r = new Gs(s.shape, Y7), n.runWebGLProgram(r, [s], s.dtype);
}
var J7 = { kernelName: $o, backendName: "webgl", kernelFunc: Z7 };
var eJ = ws.nonMaxSuppressionV3Impl;
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 } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = eJ(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var nJ = { kernelName: Ao, backendName: "webgl", kernelFunc: tJ };
var sJ = ws.nonMaxSuppressionV4Impl;
function rJ(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 } = sJ(c, p, i, o, u, l);
  return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
}
var aJ = { kernelName: Nl, backendName: "webgl", kernelFunc: rJ };
var iJ = ws.nonMaxSuppressionV5Impl;
function oJ(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 } = iJ(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var uJ = { kernelName: Eo, backendName: "webgl", kernelFunc: oJ };
var lJ = 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 cJ = (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 lJ(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 dJ = { kernelName: Do, backendName: "webgl", kernelFunc: cJ };
function Hd(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "complex64") {
    let r = nc({ inputs: { input: s }, backend: n }), a = Hd({ inputs: { x: r }, backend: n }), i = ah({ inputs: { input: s }, backend: n }), o = Hd({ inputs: { x: i }, backend: n }), u = Fr({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return sc({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
}
var pJ = { kernelName: Xo, backendName: "webgl", kernelFunc: Hd };
function S2(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 = nc({ inputs: { input: s }, backend: n }), a = S2({ inputs: { x: r }, backend: n }), i = ah({ inputs: { input: s }, backend: n }), o = Hd({ inputs: { x: i }, backend: n }), u = Fr({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return sc({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
}
var hJ = { kernelName: Ro, backendName: "webgl", kernelFunc: S2 };
function fJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return eg({ 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 = eg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = l2({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
}
var mJ = { kernelName: Fo, backendName: "webgl", kernelFunc: fJ };
var gJ = 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 bJ = 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 I2 = (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 sc({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
  }
  let o = K().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new bJ(r.shape, a, i) : new gJ(r.shape, a, i), u = [[i]];
  return n.runWebGLProgram(o, [r], r.dtype, u);
};
var yJ = { kernelName: ei, backendName: "webgl", kernelFunc: I2 };
var vJ = `
  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 xJ = `
  // 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));
  ` + th + `
  return result;
`;
var wJ = jt({ opSnippet: vJ, packedOpSnippet: xJ });
var kJ = { kernelName: ti, backendName: "webgl", kernelFunc: wJ };
function SJ(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 = _t({ 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 } = $X(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 = bp(r.dtype), v = Si(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 IJ = { kernelName: si, backendName: "webgl", kernelFunc: SJ };
var C2 = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = _X(s, r, a, i);
  return t.makeTensorInfo([o.length], i, o);
};
var CJ = { kernelName: Tl, backendName: "webgl", kernelFunc: C2 };
var NJ = "return 1.0 / x;";
var TJ = Ke({ opSnippet: NJ });
var $J = { kernelName: $l, backendName: "webgl", kernelFunc: TJ };
var _J = ss + `
  return (x < 0.0) ? 0.0 : x;
`;
var AJ = `
  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 EJ = Ke({ opSnippet: _J, packedOpSnippet: AJ });
var RJ = { kernelName: ri, backendName: "webgl", kernelFunc: EJ };
var DJ = ss + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var FJ = `
  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 OJ = Ke({ opSnippet: DJ, packedOpSnippet: FJ });
var PJ = { kernelName: ii, backendName: "webgl", kernelFunc: OJ };
var zJ = 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 MJ = 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 LJ(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 MJ(r.shape, u, l, a, i) : new zJ(r.shape, u, l, a, i);
  return n.runWebGLProgram(c, [r], "float32");
}
var BJ = { kernelName: ai, backendName: "webgl", kernelFunc: LJ };
var VJ = 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 WJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new VJ(a.shape, r.shape, i);
  return n.runWebGLProgram(o, [a], a.dtype);
}
var UJ = { kernelName: Fg, backendName: "webgl", kernelFunc: WJ };
var GJ = 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 HJ = 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 qJ(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 HJ(r.shape, u, l, a, i) : new GJ(r.shape, u, l, a, i);
  return n.runWebGLProgram(c, [r], r.dtype);
}
var jJ = { kernelName: _l, backendName: "webgl", kernelFunc: qJ };
var KJ = 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 XJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new KJ(a.shape, r.shape, i);
  return n.runWebGLProgram(o, [a], a.dtype);
}
var YJ = { kernelName: Dg, backendName: "webgl", kernelFunc: XJ };
var QJ = 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 ZJ = 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 ZJ(r.shape, o) : new QJ(r.shape, o);
  return n.runWebGLProgram(u, [r], r.dtype);
}
var eee = { kernelName: Po, backendName: "webgl", kernelFunc: JJ };
var tee = 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 < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var nee = { kernelName: Yo, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new tee(s.shape, a), [l, c] = C.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
  return o.runWebGLProgram(u, [s], s.dtype, p);
} };
var see = `
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`;
var ree = Ke({ opSnippet: see });
var aee = { kernelName: zo, backendName: "webgl", kernelFunc: ree };
var iee = "return inversesqrt(x);";
var oee = Ke({ opSnippet: iee, cpuKernelImpl: AX });
var uee = { kernelName: oi, backendName: "webgl", kernelFunc: oee };
var N2 = class {
  constructor(e, t, n, s, r, a, i = true) {
    this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
    let o = ot(r.length), u = ot(a.length), l = "";
    n === 1 ? l = "i" : n === 2 && (l = "i, j");
    let c = `getIndices(${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 lee(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 N2(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 cee = { kernelName: Mo, backendName: "webgl", kernelFunc: lee };
var dee = 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 pee(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sortedSequence: r, values: a } = t, { side: i } = s, o = new dee(r.shape[0], r.shape[1], a.shape[1], i), u = [[r.shape[1]]];
  return n.runWebGLProgram(o, [r, a], "int32", u);
}
var hee = { kernelName: Og, backendName: "webgl", kernelFunc: pee };
var fee = 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 mee(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new fee(s.shape.length, r.shape, r.shape.length);
  return n.runWebGLProgram(i, [s, r, a], cn(r.dtype, a.dtype));
}
var gee = { kernelName: Lo, backendName: "webgl", kernelFunc: mee };
var bee = `
  // 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 yee = Ke({ opSnippet: bee });
var vee = { kernelName: Al, backendName: "webgl", kernelFunc: yee };
var xee = du + `
  return 1.0 / (1.0 + exp(-1.0 * x));
`;
var wee = `
  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 kee = Ke({ opSnippet: xee, packedOpSnippet: wee, cpuKernelImpl: RX });
var See = { kernelName: li, backendName: "webgl", kernelFunc: kee };
var Iee = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
var Cee = Ke({ opSnippet: Iee });
var Nee = { kernelName: El, backendName: "webgl", kernelFunc: Cee };
var Tee = du + `
  return sin(x);
`;
var $ee = Ke({ opSnippet: Tee });
var _ee = { kernelName: ui, backendName: "webgl", kernelFunc: $ee };
var Aee = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var Eee = Ke({ opSnippet: Aee });
var Ree = { kernelName: Vo, backendName: "webgl", kernelFunc: Eee };
var Dee = `
  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 Fee = Ke({ opSnippet: Dee });
var Oee = { kernelName: Rl, backendName: "webgl", kernelFunc: Fee };
var Pee = (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 = I2({ 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 = _t({ 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 zee = { kernelName: Wo, backendName: "webgl", kernelFunc: Pee };
function Mee(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] = FX(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 Lee = { kernelName: cp, backendName: "webgl", kernelFunc: Mee };
function Bee(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] = OX(o, s.shape, s.dtype, i, u);
  return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
}
var Vee = { kernelName: Dl, backendName: "webgl", kernelFunc: Bee };
function Wee(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] = j1(i, s.shape, s.dtype, o, u, true);
  return n.makeTensorInfo(c, s.dtype, l);
}
var Uee = { kernelName: dp, backendName: "webgl", kernelFunc: Wee };
function Gee(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] = j1(i, s.shape, s.dtype, o, u);
  return n.makeTensorInfo(c, s.dtype, l);
}
var Hee = { kernelName: pp, backendName: "webgl", kernelFunc: Gee };
function qee(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;
  if (a.dtype === "string") {
    let b = n.bufferSync(r), y = n.bufferSync(a), v = w.decodeString(n.readSync(i.dataId)[0]), x = EX(b, y, o, d, c, l, u, p, v, h);
    return n.makeTensorInfo(o, x.dtype, x.values);
  }
  let f = new N2(l, u, r.shape.length, a.shape.length, p, [d, 1], h), m = n.runWebGLProgram(f, [a, r, i], a.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: o } });
  return n.disposeIntermediateTensorInfo(m), g;
}
var jee = { kernelName: hp, backendName: "webgl", kernelFunc: qee };
function Kee(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 Xee = { kernelName: Uo, backendName: "webgl", kernelFunc: Kee };
var Aw = "return sqrt(x);";
var Yee = Ke({ opSnippet: Aw, packedOpSnippet: Aw, cpuKernelImpl: PX });
var Qee = { kernelName: ci, backendName: "webgl", kernelFunc: Yee };
var Zee = "return x * x;";
var Jee = Ke({ opSnippet: Zee });
var ete = { kernelName: Fl, backendName: "webgl", kernelFunc: Jee };
var Ew = "return (a - b) * (a - b);";
var tte = jt({ opSnippet: Ew, packedOpSnippet: Ew });
var nte = { kernelName: hi, backendName: "webgl", kernelFunc: tte };
function ste({ 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 rte = { kernelName: gi, backendName: "webgl", kernelFunc: ste };
var ate = 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 ite(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 } = kt.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 $ = kt.computeOutShape(y, v, x), R = pu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: $ } });
    k = he({ inputs: { x: R }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(R);
  } else if (n.shouldExecuteOnCPU([r])) {
    let R = n.readSync(r.dataId), E = Ae(r.shape, r.dtype, R), P = zX(h, E, x, y);
    k = n.makeTensorInfo(f, r.dtype, P.values);
  } else {
    let R = new ate(y, x, h);
    k = n.runWebGLProgram(R, [r], r.dtype);
  }
  let I = he({ inputs: { x: k }, backend: n, attrs: { shape: f } });
  return n.disposeIntermediateTensorInfo(k), I;
}
var ote = { kernelName: Go, backendName: "webgl", kernelFunc: ite };
function ute(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] = MX(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var lte = { kernelName: fp, backendName: "webgl", kernelFunc: ute };
function cte(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] = LX(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 dte = { kernelName: Pg, backendName: "webgl", kernelFunc: cte };
function pte(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 = BX(i, r);
  return n.makeTensorInfo(a.shape, "int32", o);
}
var hte = { kernelName: zg, backendName: "webgl", kernelFunc: pte };
var fte = "return tan(x);";
var mte = Ke({ opSnippet: fte });
var gte = { kernelName: Ho, backendName: "webgl", kernelFunc: mte };
var bte = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var yte = Ke({ opSnippet: bte });
var vte = { kernelName: mi, backendName: "webgl", kernelFunc: yte };
var xte = 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 = wte(e);
    this.userCode = `
      void main() {
        ${s} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `;
  }
};
function wte(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 T2(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 = Ae(r.shape, r.dtype, l), p = WX(c, a);
    return n.makeTensorInfo(p.shape, p.dtype, p.values);
  }
  let i = new xte(r.shape, a);
  return n.runWebGLProgram(i, [r], r.dtype);
}
var kte = { kernelName: Tr, backendName: "webgl", kernelFunc: T2 };
var Ste = 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 Ite = 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 qr(e, t) {
  t !== null && e.disposeIntermediateTensorInfo(t);
}
function Rw(e) {
  let t = 1;
  for (; t < e; )
    t *= 2;
  return t;
}
function Cte(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), [A, O] = UX(P, l, r.dtype, a, i);
    return [n.makeTensorInfo(A.shape, A.dtype, A.values), n.makeTensorInfo(O.shape, O.dtype, O.values)];
  }
  if (a === 0)
    return l[l.length - 1] = 0, [n.makeTensorInfo(l, r.dtype, []), n.makeTensorInfo(l, "int32", [])];
  if (c === 1)
    return [r, sc({ 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 && qr(n, h);
  let b = Rw(a), y = Rw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, A, O) => {
    let T = x(), M = new Ste(O), j = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [A]], X = v;
    v = n.runWebGLProgram(M, T, "int32", j), qr(n, X);
  };
  for (let P = 1; P < b; P *= 2) {
    let A = P * 2;
    for (let O = P; O >= 1; O /= 2)
      k(A, O, [m, y]);
  }
  for (let P = y; P > b; P /= 2) {
    let A = x(), O = new Ite([m, P / 2]), M = [[c], [v === null ? 1 : 0], [b]], W = v;
    v = n.runWebGLProgram(O, A, "int32", M), qr(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 = pu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), qr(n, I);
  let $ = y2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
  qr(n, g);
  let R = l.slice(0, -1);
  R.push(a), I = v, v = he({ inputs: { x: v }, attrs: { shape: R }, backend: n }), qr(n, I);
  let E = $;
  return $ = he({ inputs: { x: $ }, attrs: { shape: R }, backend: n }), qr(n, E), [$, v];
}
var Nte = { kernelName: qo, backendName: "webgl", kernelFunc: Cte };
var Tte = class {
  constructor(e, t, n, s, r, a) {
    this.variableNames = ["Image", "Transforms"], this.outputShape = a;
    let i = n === "nearest" ? 1 : 2, o;
    switch (s) {
      case "constant":
        o = 1;
        break;
      case "reflect":
        o = 2;
        break;
      case "wrap":
        o = 3;
        break;
      case "nearest":
        o = 4;
        break;
      default:
        o = 1;
        break;
    }
    this.userCode = `
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${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 $te(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 Tte(p, d, i, o, u, g);
  return n.runWebGLProgram(b, [r, a], "float32");
}
var _te = { kernelName: jo, backendName: "webgl", kernelFunc: $te };
function Ate(e) {
  let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
  iu(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 } = GX(i, r, a.shape, a.dtype);
  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
}
var Ete = { kernelName: Mg, backendName: "webgl", kernelFunc: Ate };
function Rte(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 = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
    f[m] = b, p.push(g);
  }
  return p.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
}
var Dte = { kernelName: Ko, backendName: "webgl", kernelFunc: Rte };
var Fte = 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 Ote(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 = _t({ 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 = bp(r.dtype), g = (x, k, I, $, R) => {
    let E = x.shape[0], P = x.shape[1], A = C.segment_util.segOpComputeOptimalWindowSize(P, R), O = { windowSize: A, inSize: P, batchSize: E, numSegments: R }, T = new Fte(O, k), M = n.compileAndRun(T, [x, I], $);
    if (u.push(M), M.shape[1] === R)
      return M;
    let W = C2({ backend: n, attrs: { start: 0, stop: R, step: 1, dtype: "float32" } }), j = T2({ inputs: { x: W }, backend: n, attrs: { reps: [P / A] } });
    return u.push(W), u.push(j), g(M, k, j, $, R);
  }, 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 = _t({ inputs: { x: v }, backend: n, attrs: { perm: x } });
  }
  return u.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
}
var Pte = { kernelName: mp, backendName: "webgl", kernelFunc: Ote };
var zte = [M8, B8, U8, q8, K8, Q8, J8, tY, aY, oY, cY, hY, gY, xY, SY, CY, TY, EY, DY, OY, LY, qY, KY, YY, n9, r9, u9, v8, d9, g9, x9, N9, $9, A9, R9, F9, z9, B9, U9, H9, j9, X9, Z9, eQ, rQ, iQ, lQ, pQ, fQ, yQ, kQ, NQ, _Q, RQ, DQ, OQ, zQ, LQ, VQ, UQ, jQ, YQ, JQ, tZ, rZ, oZ, dZ, mZ, y8, bZ, f9, xZ, SZ, NZ, w8, AZ, FZ, PZ, BZ, UZ, jZ, YZ, e7, r7, o7, l7, h7, m7, b7, w7, S7, C7, T7, _7, D7, z7, V7, X7, N8, J7, nJ, aJ, uJ, ZY, dJ, hJ, mJ, yJ, kJ, S8, IJ, CJ, JY, H7, $J, RJ, PJ, $8, BJ, UJ, jJ, YJ, eee, nee, aee, uee, cee, hee, gee, vee, See, Nee, _ee, Ree, GY, j7, Oee, zee, Lee, Vee, Uee, Hee, jee, Xee, Qee, ete, nte, rte, ote, lte, dte, hte, q7, O8, gte, vte, kte, Nte, _te, P8, Ete, Dte, Pte, pJ];
for (let e of zte)
  Ol(e);
var Or = K();
Or.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
Or.registerFlag("WEBGPU_CPU_FORWARD", () => true);
Or.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
Or.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
Or.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
Or.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
Or.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
Or.registerFlag("WEBGPU_USE_IMPORT", () => false);
var Mte = "return a + b;";
var Lte = "return areal * breal - aimag * bimag;";
var Bte = "return areal * bimag + aimag * breal;";
var Vte = "return a / b;";
var Wte = "return a * b;";
var Ute = "return (a - b) * (a - b);";
var Gte = "return 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(a >= b);";
var Yte = "return vec4<f32>(a >= b);";
var Qte = "return f32(a < b);";
var Zte = "return vec4<f32>(a < b);";
var Jte = "return f32(a <= b);";
var ene = "return vec4<f32>(a <= b);";
var tne = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
var nne = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`;
var sne = `
  if (isnan(a)) { return a; }
  if (isnan(b)) { return b; }
  `;
var $2 = `
  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 rne = `
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
  `;
var ane = `
  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 ine = "return f32(a != b);";
var one = "return vec4<f32>(a != b);";
var une = `
  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 lne = `
  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;
  ${$2}
  return resultTemp;
  `;
var cne = "if (a < 0.0) { return b * a; }  return a;";
var dne = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;
function Dw(e, t) {
  let n = t ? $2 : sne;
  return t ? `
    var resultTemp = vec4<f32>(${e}(a, b));
    let isNaN = isnanVec4(a) | isnanVec4(b);
    ` + n + `
    return resultTemp;
  ` : n + `
    return ${e}(a, b);
  `;
}
function rc(e, t) {
  switch (e) {
    case 0:
      return Wte;
    case 1:
      return Mte;
    case 2:
      return Gte;
    case 3:
      return Vte;
    case 4:
      return t ? qte : Hte;
    case 5:
      return t ? Kte : jte;
    case 6:
      return t ? Yte : Xte;
    case 7:
      return t ? Zte : Qte;
    case 8:
      return t ? ene : Jte;
    case 9:
      return t ? nne : tne;
    case 10:
      return t ? one : ine;
    case 11:
      return Ute;
    case 12:
      return t ? ane : rne;
    case 14:
      return t ? dne : cne;
    case 15:
      return Dw("max", t);
    case 16:
      return Dw("min", t);
    case 13:
      return t ? lne : une;
    case 17:
      return Lte;
    case 18:
      return Bte;
    default:
      throw new Error(`BinaryType ${e} is not implemented!`);
  }
}
var pne = "return abs(a);";
var hne = "return ceil(a);";
var fne = "return cos(a);";
var mne = `
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var gne = "return exp(a) - 1.0;";
var bne = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
var yne = `
  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 vne = "return exp(a);";
var xne = "return floor(a);";
var wne = "return a;";
var kne = `if (a < 0.0) { return 1.0/0.0; }
  return log(a);`;
var Sne = "return f32(!(a >= 1.0));";
var Ine = "return -a;";
var Cne = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var Nne = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
`;
var Tne = "return select(a, 0.0, a < 0.0);";
var $ne = "return clamp(a, 0.0, 6.0);";
var _ne = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var Ane = `
  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
`;
var Ene = "return 1.0/sqrt(a);";
var Rne = "return 1.0 / (1.0 + exp(-1.0 * a));";
var Dne = "return sin(a);";
var Fne = `
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var One = "return sqrt(a);";
var Pne = "return a * a;";
var zne = `
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var Mne = "return f32(i32((a)));";
function Kr(e, t) {
  switch (e) {
    case 0:
      return pne;
    case 2:
      return fne;
    case 3:
      return mne;
    case 1:
      return hne;
    case 4:
      return t ? yne : bne;
    case 5:
      return vne;
    case 6:
      return gne;
    case 7:
      return xne;
    case 8:
      return wne;
    case 9:
      return kne;
    case 10:
      return Sne;
    case 11:
      return Ine;
    case 14:
      return t ? Nne : Cne;
    case 12:
      return t ? Ane : Tne;
    case 13:
      return t ? _ne : $ne;
    case 15:
      return Ene;
    case 18:
      return Rne;
    case 16:
      return Dne;
    case 17:
      return Fne;
    case 19:
      return One;
    case 20:
      return Pne;
    case 21:
      return zne;
    case 22:
      return Mne;
    default:
      throw new Error(`BinaryType ${e} is not implemented!`);
  }
}
function Pr(e, t = false) {
  if (e === null)
    return null;
  if (e === "linear")
    return Kr(8);
  if (e === "relu")
    return Kr(12, t);
  if (e === "elu")
    return Kr(4, t);
  if (e === "relu6")
    return Kr(13, t);
  if (e === "prelu")
    return rc(14, t);
  if (e === "sigmoid")
    return Kr(18, t);
  if (e === "leakyrelu")
    return Kr(14, t);
  throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
}
function Lne(e, t) {
  if (Math.max(...e) > 3)
    throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
  let n = e.length, s = e.map((a) => `${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 hr(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 fd(e, t) {
  return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
}
function Ev() {
  return `
  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`;
}
function Ii() {
  return `
  ${Ev()}
  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 `
    ${Ii()}
      let index = getGlobalIndex();
`;
}
function Bne(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<${fd(t.dtype, n.isVec4)}>;
      @group(0) @binding(2) var<uniform> uniforms: Uniform;
    `), [Fw, r.join(`
`), Ow(t.shape), n.getUserCode()].join(`
`);
  let a = false, i = false, o = "struct Uniforms { NAN : f32, ";
  n.variableNames.forEach((m, g) => {
    let b = Ut(e[g].shape.length);
    (b === "vec5" || b === "vec6") && (i = true), (a || i) && (o += "@align(16) "), a = i, o += `${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: ${c}, `, n.size && (a && (o += "@align(16) "), a = false, o += "size : i32, "), n.uniforms && (a && (o += "@align(16) "), o += n.uniforms), o += "};", r.push(o), n.atomic ? r.push(`
    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
  `) : r.push(`
    @group(0) @binding(0) var<storage, write> result: array<${fd(t.dtype, n.isVec4)}>;
  `), n.variableNames.forEach((m, g) => {
    r.push(`
    @group(0) @binding(${1 + g}) var<storage, read> ${m}: array<${n.variableTypes ? n.variableTypes[g] : fd(e[g].dtype, n.isVec4)}>;
      `);
  }), o !== "" && r.push(`
    @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms: Uniforms;
    `);
  let [p, d] = qne(t.shape, n.dispatchLayout), h = [Fw, r.join(`
`), Ow(t.shape), p, Vne(t.shape.length)];
  if (n.atomic || h.push(Wne(t.shape, t.dtype, n.isVec4)), d === t.shape.length) {
    let m = e.map((g, b) => Une(g, t.shape, n.variableTypes ? n.variableTypes[b] === "vec4<f32>" : n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
`);
    h.push(m);
  }
  return h.push(n.getUserCode()), h.join(`
`);
}
var Fw = `
  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 Vne(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 Wne(e, t, n) {
  let s = e.length, r = fd(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}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `;
  }
  return a;
}
function Une(e, t, n, s) {
  let r = Gne(e, n);
  return e.shape.length <= t.length && (r += Hne(e, t, n, s)), r;
}
function Gne(e, t) {
  let n = e.name, s = e.shape.length, r = Ut(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), i = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, s), o = i.map((c) => `${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 Hne(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.${hr(g + p)} = 0;`).join(`
`);
  let h = "";
  if (u < 2 && o > 0)
    h = "coords";
  else if (u > 1) {
    let g = Ut(o), b = e.shape.map((y, v) => `coords.${hr(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 qne(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[${d}]);`;
      else {
        let f = Lne(h, "uniforms.outShape");
        i += `var index${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 Ow(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 ${r[o]} = index2 / uniforms.outShapeStrides.${hr(o)}`, l = o === n.length - 1 ? `let ${r[o + 1]} = index2 - ${r[o]} * uniforms.outShapeStrides.${hr(o)}` : `index2 = index2 - ${r[o]} * uniforms.outShapeStrides.${hr(o)}`;
    return `${u}; ${l};`;
  }).join(""), `
    fn getCoordsFromIndex(index : i32) -> ${s} {
      ${a}
      return ${s}(${r.join(",")});
    }
  `;
}
var _2 = {};
Ee(_2, { ArrayBufferToTypedArray: () => E2, GPUBytesPerElement: () => md, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => Rv, computeWorkGroupSizeForMatMul: () => A2, computeWorkPerThreadForConv2d: () => Dv, flatDispatchLayout: () => Be, isWebGPUSupported: () => Fv, tilesFitEvenlyIntoShape: () => Ks });
var aa = (e) => {
  let t = 1;
  for (let n = 0; n < e.length; n++)
    t *= e[n];
  return t;
};
function Ks(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(aa(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(aa(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(aa(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
  return [r, a, i];
}
function Rv(e, t) {
  let n = aa(e.x.map((r) => t[r])), s = aa(e.y.map((r) => t[r]));
  return n <= 4 ? [4, 16, 1] : s <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function A2(e, t, n) {
  return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
}
function Dv(e, t) {
  let n = aa(e.x.map((r) => t[r])), s = aa(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 md(e) {
  if (e === "float32" || e === "int32" || e === "bool" || e === "string")
    return 4;
  if (e === "complex64")
    return 8;
  throw new Error(`Unknown dtype ${e}`);
}
function E2(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 ${t}`);
}
function Fv() {
  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
function R2(e, t, n, s, r = 4) {
  return w.assert((s % 4 === 0 || s % 3 === 0) && e[0] === 4 && (r === 3 || r === 4), () => `tileInner must be divisible by 4|3. ColPerThread must be 4.
           innerElementSize must be 3|4.`), `
  var<workgroup> mm_Asub : array<array<vec${r}<f32>, ${s / r}>, ${t}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n / e[0]}>, ${s}>;

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

  ${Ii()}

    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 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 / InnerElementSize;

        // 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 / InnerElementSize; k = k + 1) {
            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
            ${r === 3 ? "" : "BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}

            for (var i = 0; i < RowPerThread; i = i + 1) {
                let 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];
                ${r === 3 ? "" : "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 jne = 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 [Ks(s, this.aShape.slice(1)), Ks(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 = Pr(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);
        }
      }
      ${R2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
    `;
  }
};
function Ov(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}>;
    ${Ii()}
      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 Kne(e) {
  return `
    let TileSize = ${e[0] * 4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;

    ${Ii()}
      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 Xne = 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 = A2(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 [Ks(r, this.aShape.slice(1)), Ks(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 = Pr(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 ? Ov([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : Kne(this.workGroupSize)}
    `;
  }
};
function Yne() {
  return `
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${Ii()}
      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 Qne = 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 = Pr(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);
      }
      ${Yne()}
    `;
  }
};
function Zne(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.
  ${Ii()}
    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 = Pr(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);
        }
      }
      ${Zne(this.workGroupSize)}
    `;
  }
};
function We(e) {
  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
  return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
}
var ese = { kernelName: Oo, backendName: "webgpu", kernelFunc: We };
function Pv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
  w.assert(p === d, () => `Error in matMul: inner shapes (${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 } }), R = We({ inputs: { x: t }, backend: r, attrs: { shape: I } }), E = [$, R], P = Math.max(b, y), A = b === 1, O = y === 1, T = p % 4 === 0 && f % 4 === 0 && !n && !s, M;
  h * f <= 32 ? M = new Qne([P, h, f], A, O, n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? M = new Jne(k, I, [P, h, f], a, u, i) : T ? M = new jne(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), A, O, a, u, i) : M = new Xne(k, [P, h, f], K().get("WEBGPU_MATMUL_WORK_PER_THREAD"), A, O, n, s, a, u, i);
  let W = [$, R];
  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] }), M.uniforms += " alpha : f32,");
  let X = r.runWebGPUProgram(M, W, e.dtype, j), Y = We({ inputs: { x: X }, backend: r, attrs: { shape: x } });
  E.push(X);
  for (let Z of E)
    r.disposeData(Z.dataId);
  return Y;
}
function tse(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 Pv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
}
var nse = { kernelName: oa, backendName: "webgpu", kernelFunc: tse };
var Pw = 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 {
        ${rc(this.op, false)}
      }

      ${Ue()}
        if(index < uniforms.size) {
          let areal = getARealByOutputIndex(index);
          let aimag = getAImagByOutputIndex(index);
          let breal = getBRealByOutputIndex(index);
          let bimag = getBImagByOutputIndex(index);
          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
        }
      }
    `;
  }
};
var sse = class {
  constructor(e, t, n, s) {
    this.variableNames = ["A", "B"], this.size = true;
    let r = 256;
    this.workGroupSize = [r, 1, 1], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.lastDimensionSize = s ? n[0] : t[0], this.lastDimensionSize < 256 ? this.workPerThread = 1 : this.lastDimensionSize < 512 ? this.workPerThread = 2 : this.workPerThread = 4, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.useSharedMemoryWithB = s, this.op = e, this.shaderKey = `binaryShared_${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 {
          ${rc(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);

              ${t}
              setOutputAtIndex(flatIndex, binaryOperation(a, b));
            }
          }
        }
        `;
  }
};
var rse = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.workPerThread = 4, this.isVec4 = true, this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.op = e, this.shaderKey = `binaryVec4_${e}`;
  }
  getUserCode() {
    return `
      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
        ${rc(this.op, this.isVec4)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
    `;
  }
};
var D2 = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = C.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binary_${e}`, this.op = e;
  }
  getUserCode() {
    return `
      fn binaryOperation(a : f32, b : f32) -> f32 {
        ${rc(this.op, false)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
      `;
  }
};
function zw(e, t, n) {
  if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
    return new rse(e, t, n);
  let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
  return r || a ? new sse(e, t, n, a) : new D2(e, t, n);
}
function Wn(e) {
  let { inputs: t } = e, { x: n } = t;
  return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
}
var ase = { kernelName: Ua, backendName: "webgpu", kernelFunc: Wn };
function hu(e) {
  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = Wn({ inputs: { x: s }, backend: n }), u = Wn({ inputs: { x: r }, backend: n });
  return i.complexTensorInfos = { real: o, imag: u }, a;
}
var ise = { kernelName: ep, backendName: "webgpu", kernelFunc: hu };
var ac = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.size = true;
    let n = 128;
    this.workGroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.op = t, this.shaderKey = `unary_${t}`;
  }
  getUserCode() {
    return `
      fn unaryOperation(a : f32) -> f32 {
        ${Kr(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 ac(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 = zw(e, i.shape, o.shape);
          return u.runWebGPUProgram(k, [v, x], cn(b.dtype, y.dtype));
        });
      else {
        let g = new Pw(17, i.shape, o.shape), b = new Pw(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 = hu({ 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 = zw(e, i.shape, o.shape);
    return u.runWebGPUProgram(c, [i, o], l);
  };
}
var { addImpl: ose, ceilImpl: use, concatImpl: lse, equalImpl: cse, expImpl: dse, expm1Impl: pse, floorImpl: hse, gatherNdImpl: fse, gatherV2Impl: mse, greaterEqualImpl: gse, greaterImpl: bse, lessEqualImpl: yse, lessImpl: vse, logImpl: xse, maxImpl: wse, maximumImpl: kse, minimumImpl: Sse, multiplyImpl: Ise, negImpl: Cse, notEqualImpl: Nse, prodImpl: Tse, rangeImpl: $se, rsqrtImpl: _se, scatterImpl: Ase, simpleAbsImpl: Ese, sliceImpl: Rse, stridedSliceImpl: Dse, stringNGramsImpl: Fse, subImpl: Ose, tileImpl: Pse, topKImpl: zse, transposeImpl: Mse, uniqueImpl: whe } = iv;
var Lse = Kt({ opType: 0, cpuKernelImpl: Ese });
var Bse = { kernelName: po, backendName: "webgpu", kernelFunc: Lse };
var Vse = mn({ opSnippet: 1, cpuKernelImpl: ose, supportsComplex: true });
var Wse = { kernelName: Cr, backendName: "webgpu", kernelFunc: Vse };
var Use = class {
  constructor(e) {
    this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${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, ${t});
          }
        }
      }
    `;
  }
};
function Gse(e) {
  let { inputs: t, backend: n } = e, s = t;
  if (s.length === 1)
    return Wn({ inputs: { x: s[0] }, backend: n });
  let r = s.map((o) => o.dtype).reduce((o, u) => cn(o, u)), a = s.map((o) => o.shape), i = new Use(a);
  return n.runWebGPUProgram(i, s, r);
}
var Hse = { kernelName: Ia, backendName: "webgpu", kernelFunc: Gse };
var F2 = class {
  constructor(e, t, n) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "infinityValue : f32,", this.size = true;
    let s = [t];
    C.assertAxesAreInnerMostDims("arg" + n.charAt(0).toUpperCase() + n.slice(1), s, e.length), this.op = n === "min" ? "<" : ">";
    let [r] = C.computeOutAndReduceShapes(e, s);
    this.outputShape = r.length === 0 ? [1] : r, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.inputShape = e, this.shaderKey = `argMinMax${this.op}`;
  }
  getUserCode() {
    let e = `
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `, t = () => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape.${hr(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.${hr(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 qse = 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]}>;
      ${Ev()}
      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 jse = 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 = Kse(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 Kse(e) {
  let t = e.length;
  if (t > 6)
    throw Error(`Transpose for rank ${t} is not yet supported`);
  let n = new Array(t);
  for (let s = 0; s < e.length; s++)
    n[e[s]] = `resRC.${hr(s)}`;
  return n.join();
}
function Xs(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 = Mse(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 qse(r.shape, a);
    return i.runWebGPUProgram(c, [r], r.dtype);
  }
  let l = new jse(r.shape, a);
  return i.runWebGPUProgram(l, [r], r.dtype);
}
var Xse = { kernelName: Hs, backendName: "webgpu", kernelFunc: Xs };
function Yse(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 = Xs({ 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 F2(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 Qse = { kernelName: Ca, backendName: "webgpu", kernelFunc: Yse };
function Zse(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 = Xs({ 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 F2(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 Jse = { kernelName: pl, backendName: "webgpu", kernelFunc: Zse };
var O2 = 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 P2 = 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 ere(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 P2(c) : (p = new O2(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 tre = { kernelName: Na, backendName: "webgpu", kernelFunc: ere };
function nre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  return Pv({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
}
var sre = { kernelName: Ta, backendName: "webgpu", kernelFunc: nre };
var rre = 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 : ${Ut(e.length)}, `, this.shaderKey = "slice";
  }
  getUserCode() {
    let e = Ut(this.rank), t = are(this.rank), n;
    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${tg[a]} = uniforms.start[${a}] + coords.${tg[a]};`), `
      ${Ue()}
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${n.join(`
`)}
          setOutputAtIndex(index, getSource(${t}));
        }
      }
    `;
  }
};
var tg = ["x", "y", "z", "w", "u", "v"];
function are(e) {
  if (e === 1)
    return "sourceLoc";
  if (e <= 6)
    return tg.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
  throw Error(`Slicing for rank ${e} is not yet supported`);
}
function fu(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = kt.parseSliceParams(r, a, i);
  if (kt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
    let p = n.tensorMap.get(r.dataId), d = Rse(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 rre(o, u), c = [{ type: "int32", data: o }];
  return n.runWebGPUProgram(l, [r], r.dtype, c);
}
var ire = { kernelName: Bo, backendName: "webgpu", kernelFunc: fu };
var ore = (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 = Xs({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = We({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = fu({ 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 ure = { kernelName: ho, backendName: "webgpu", kernelFunc: ore };
var z2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Nse });
var lre = { kernelName: _o, backendName: "webgpu", kernelFunc: z2 };
function ic(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 cre = { kernelName: lp, backendName: "webgpu", kernelFunc: ic };
function dre(e, t) {
  let n = new ac(e.shape, 22), s = t.runWebGPUProgram(n, [e], "int32");
  return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
function ng(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 = ng({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = hu({ inputs: { real: o, imag: i }, backend: n });
    return i.dispose(), n.disposeData(o.dataId), u;
  }
  if (r.dtype === "complex64") {
    let i = ic({ inputs: { input: r }, backend: n }), o = ng({ 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 dre(r, n);
  if (a === "bool") {
    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = z2({ 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 pre = { kernelName: $a, backendName: "webgpu", kernelFunc: ng };
var hre = Kt({ opType: 1, cpuKernelImpl: use });
var fre = { kernelName: _a, backendName: "webgpu", kernelFunc: hre };
var mre = 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 gre = 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 `
      ${Ue()}
        if(index < uniforms.size) {
          let value = getAByOutputIndex(index);
          if (isnan(value)) {
            setOutputAtIndex(index, value);
            return;
          }
          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
        }
      }
    `;
  }
};
function bre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o, u = [{ type: "float32", data: [a] }, { type: "float32", data: [i] }];
  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new mre(r.shape) : o = new gre(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
}
var yre = { kernelName: Nr, backendName: "webgpu", kernelFunc: bre };
var vre = class {
  constructor(e) {
    this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = C.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${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 ih(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 xre = { kernelName: ap, backendName: "webgpu", kernelFunc: ih };
function sg(e, t, n) {
  let s = e[0].dtype;
  if (s === "complex64") {
    let h = e.map((y) => ic({ inputs: { input: y }, backend: n })), f = e.map((y) => ih({ inputs: { input: y }, backend: n })), m = sg(h, t, n), g = sg(f, t, n), b = hu({ 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 = lse(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 } = wre(e, t, n), o = a.map((h) => h.shape), u = new vre(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 wre(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 M2(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), sg(o, a, n);
}
var kre = { kernelName: fo, backendName: "webgpu", kernelFunc: M2 };
var Sre = 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.innerElementSize = this.convInfo.inChannels % 4 === 0 ? 4 : 3, this.innerElementSize === 3 ? this.variableTypes = ["f32", "vec4<f32>"] : this.variableTypes = ["vec4<f32>", "vec4<f32>"], this.addBias && (this.variableNames.push("bias"), this.variableTypes.push("vec4<f32>")), this.hasPreluActivationWeights && (this.variableNames.push("preluActivationWeights"), this.variableTypes.push("vec4<f32>")), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.workGroupSize[0] * this.innerElementSize, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`;
  }
  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 [Ks(e, [n, r]), Ks(t, [r, s])];
  }
  getUserCode() {
    let e = R2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, this.innerElementSize), t = `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];
        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];

        var resData = vec${this.innerElementSize}<f32>(0.0);
        // The bounds checking is always needed since we use it to pad zero for
        // the 'same' padding type.
        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
          var coord = vec4<i32>(
            batch,
            xRow,
            xCol,
            inChCoord);
          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
          ${this.innerElementSize === 3 ? "resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);" : "resData = x[xIndex / 4];"}
        }
        return resData;`, n = this.fitA ? `${t}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
          ${t}
         }
         return vec${this.innerElementSize}<f32>(0.0);
        `, s = 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);
        `, r = "", a = "";
    if (this.activation) {
      let u = Pr(this.activation, this.isVec4);
      this.hasPreluActivationWeights ? r = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${u}
        }` : r = `
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          ${u}
        }`, a = "value = activation(value, outCoord);";
    }
    let i = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
        ${r}
        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
          let r = row;
          let c = col * ${this.innerElementSize};
          var batch = i32(globalId.z);
          ${n}
        }

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

        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);
            ${i}
            ${a}
            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
              value);
          }
        }
        ${e}
      `;
  }
};
var Ire = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, this.isChannelsLast = e.dataFormat === "channelsLast", this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [1], y: [2, 3], z: [0] }, this.workGroupSize = Rv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Dv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${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 [Ks(s, [a, o]), Ks(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 = Ov(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 = Pr(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 Cre({ 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 = Pv({ 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 L2({ 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 Cre({ 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 === 0) && 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 Sre(n, u, o, l) : p = new Ire(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 Nre(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 L2({ x: r, filter: a, convInfo: d, backend: s });
}
var Tre = { kernelName: Aa, backendName: "webgpu", kernelFunc: Nre };
var $re = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Rv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Dv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${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;
    }

    ${Ov(this.elementsPerThread, this.workGroupSize)}
  `;
  }
};
var _re = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${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 Are(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 _re(d);
  else {
    f = new $re(d);
    let m = d.inShape[1] * d.inShape[2], g = d.inShape[3], b = d.filterHeight * d.filterWidth * d.outChannels;
    h.push({ type: "uint32", data: [m] }, { type: "uint32", data: [g] }, { type: "uint32", data: [b] });
  }
  return n.runWebGPUProgram(f, [r, a], "float32", h);
}
var Ere = { kernelName: Ea, backendName: "webgpu", kernelFunc: Are };
var Rre = Kt({ opType: 2 });
var Dre = { kernelName: Ra, backendName: "webgpu", kernelFunc: Rre };
var Fre = Kt({ opType: 3 });
var Ore = { kernelName: Da, backendName: "webgpu", kernelFunc: Fre };
var Pre = class {
  constructor(e, t, n, s) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true;
    let [r] = t;
    this.outputShape = [r, n[0], n[1], e], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.methodId = s === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${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 zre = (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 Pre(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
  return n.runWebGPUProgram(c, [r, a, i], "float32", p);
};
var Mre = { kernelName: go, backendName: "webgpu", kernelFunc: zre };
var Mw = 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}_${this.reverse}`;
  }
  getUserCode() {
    let e = this.outputShape.length, t = this.op === "*" ? "1.0" : "0.0", n = this.exclusive ? t : `getX(${Lw(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 = ${Bw(e, "coords", this.op)};
         var val = ${n};
         let pow2 = i32(pow(2.0, uniforms.index));
         if (${r}) {
           let idx = ${a};
           ${Bw(e, "coords", this.op)} = idx;
           val ${this.op}= getX(${Lw(e, "coords", this.op)});
         }
         setOutputAtIndex(index, val);
       }
      }
    `;
  }
};
function Lw(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 ${e} is not yet supported`);
}
function Bw(e, t, n) {
  if (e === 1)
    return `${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 ${e} is not yet supported`);
}
function B2(e, t, n, s, r, a) {
  let i = t.shape.length, o = C.getAxesPermutation([s], i), u = t;
  o != null && (u = Xs({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
  let l = C.getInnerMostAxes(1, i)[0];
  if (l !== i - 1)
    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
  let c = u.shape[l], p = Wn({ inputs: { x: u }, backend: n });
  for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
    let h = new Mw(e, u.shape, false, a), f = p, m = [{ type: "float32", data: [d] }];
    p = n.runWebGPUProgram(h, [p], p.dtype, m), n.disposeData(f.dataId);
  }
  if (r) {
    let d = new Mw(e, u.shape, r, a), h = p, f = [{ type: "float32", data: [0] }];
    p = n.runWebGPUProgram(d, [p], p.dtype, f), n.disposeData(h.dataId);
  }
  if (o != null) {
    let d = C.getUndoAxesPermutation(o), h = Xs({ inputs: { x: p }, backend: n, attrs: { perm: d } });
    return n.disposeData(p.dataId), n.disposeData(u.dataId), h;
  }
  return p;
}
function Lre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return B2("*", r, n, a, i, o);
}
var Bre = { kernelName: mo, backendName: "webgpu", kernelFunc: Lre };
function Vre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  return B2("+", r, n, a, i, o);
}
var Wre = { kernelName: Fa, backendName: "webgpu", kernelFunc: Vre };
var Ure = class {
  constructor(e, t) {
    this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${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 = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`;
  }
  getHeightCoordString() {
    return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
  }
  getWidthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
  }
  getDepthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
  }
  getOutputDepthSize() {
    return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
  }
  getInputSamplingString() {
    return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
  }
};
function Gre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new Ure(f, i);
  return n.runWebGPUProgram(g, [r], r.dtype, m);
}
var Hre = { kernelName: bo, backendName: "webgpu", kernelFunc: Gre };
var V2 = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Pr(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}

      ${Ev()}
      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}
            ${t}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
          }
        }
      }
    `;
  }
};
var W2 = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
      channelMul : i32,`, this.workGroupSize = [256, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise_${this.activation}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Pr(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);
        }
      }

      ${Ii()}
        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}
        ${t}
        writeResult(batch, coords[1], coords[2], d2, dotProd);
      }
    `;
  }
};
function qre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
  c == null && (c = [1, 1]);
  let p = C.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new V2(p) : (h = new W2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
}
var jre = { kernelName: Oa, backendName: "webgpu", kernelFunc: qre };
var U2 = mn({ opSnippet: 0, cpuKernelImpl: Ise, supportsComplex: true });
var Kre = { kernelName: Ja, backendName: "webgpu", kernelFunc: U2 };
var Xre = class {
  constructor(e, t) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
    let [n] = C.computeOutAndReduceShapes(this.inputShape, [1]);
    this.outputShape = n.length === 0 ? [1] : n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t, this.shaderKey = `reduce_${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) {
          ${n}
        }
       }
     `;
  }
};
function oc(e, t, n, s, r) {
  let a = e.shape.length, i = [], o = w.parseAxisParam(t, e.shape), u = o, l = C.getAxesPermutation(u, a), c = e;
  l != null && (c = Xs({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = C.getInnerMostAxes(u.length, a), i.push(c)), C.assertAxesAreInnerMostDims(s, u, a);
  let [p, d] = C.computeOutAndReduceShapes(c.shape, u), h = p;
  n && (h = C.expandShapeToKeepDim(p, o));
  let f;
  if ((s === "max" || s === "prod") && r.shouldExecuteOnCPU([c])) {
    let m = r.tensorMap.get(c.dataId).values;
    switch (s) {
      case "max":
        let g = wse(m, w.sizeFromShape(d), h, e.dtype);
        f = r.makeTensorInfo(h, e.dtype, g);
        break;
      case "prod":
        let { outVals: b, outShape: y, outDtype: v } = Tse(c.shape, c.dtype, m, u);
        f = r.makeTensorInfo(y, v, b);
        break;
      default:
        throw new Error(`${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" : bp(e.dtype), x = [{ type: "int32", data: [m] }], k = new Xre(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 zv(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return oc(r, a, i, "sum", n);
}
var Yre = { kernelName: di, backendName: "webgpu", kernelFunc: zv };
function Qre(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 = Xs({ 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 = U2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = zv({ 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 Zre = { kernelName: rp, backendName: "webgpu", kernelFunc: Qre };
var Jre = Kt({ opType: 4 });
var eae = { kernelName: za, backendName: "webgpu", kernelFunc: Jre };
var tae = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: cse });
var nae = { kernelName: yo, backendName: "webgpu", kernelFunc: tae };
var G2 = Kt({ opType: 5, cpuKernelImpl: dse, dtype: "float32" });
var sae = { kernelName: Ma, backendName: "webgpu", kernelFunc: G2 };
function rg(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 rae = { kernelName: vo, backendName: "webgpu", kernelFunc: rg };
var aae = Kt({ opType: 6, cpuKernelImpl: pse });
var iae = { kernelName: xo, backendName: "webgpu", kernelFunc: aae };
var oae = 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 mu(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 oae(s), o = [{ type: "float32", data: [r] }];
    return t.runWebGPUProgram(i, [], a, o);
  }
}
var uae = { kernelName: vl, backendName: "webgpu", kernelFunc: mu };
var lae = 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 `
      ${Ue()}
        if (index < uniforms.size) {
          let coords = getCoordsFromIndex(index);
          let coordX = uniforms.xShape[2] - coords[2] - 1;
          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
          setOutputAtIndex(index, outputValue);
        }
      }
    `;
  }
};
var cae = { kernelName: wo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { image: n } = e, s = t, r = new lae(n.shape);
  return s.runWebGPUProgram(r, [n], n.dtype);
} };
var dae = Kt({ opType: 7, cpuKernelImpl: hse });
var pae = { kernelName: La, backendName: "webgpu", kernelFunc: dae };
var hae = mn({ opSnippet: 12, dtype: "int32" });
var fae = { kernelName: Ba, backendName: "webgpu", kernelFunc: hae };
var mae = class {
  constructor(e, t = false) {
    this.outputShape = [0], this.variableNames = [], this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.useImport = t, this.shaderKey = `fromPixels_${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 gae = { kernelName: vd, backendName: "webgpu", kernelFunc: bae };
var Ui;
function bae(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 Vw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
  if ((i || o) && (Ui == null && (Ui = document.createElement("canvas").getContext("2d")), Ui.canvas.width = c, Ui.canvas.height = p, Ui.drawImage(r, 0, 0, c, p), r = Ui.canvas), l || u || i || o)
    return Vw({ 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 Vw(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 mae(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 yae = 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 = ${t};
          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
        }
      }
  `;
  }
};
var vae = { kernelName: Va, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s, scale: r, offset: a, mean: i, variance: o } = e, { varianceEpsilon: u } = t, l = n, c = [s, i, o], p = null;
  a != null && (p = a.shape, c.push(a));
  let d = null;
  r != null && (d = r.shape, c.push(r));
  let h = new yae(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
  return l.runWebGPUProgram(h, c, s.dtype, f);
} };
function xae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s;
  if (c !== "NHWC")
    throw new Error(`WebGPU backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);
  let m = C.convertConv2DDataFormat(c), g = C.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
  return L2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
}
var wae = { kernelName: ua, backendName: "webgpu", kernelFunc: xae };
function kae(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 V2(m, b, d, y) : (x = new W2(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 Sae = { kernelName: la, backendName: "webgpu", kernelFunc: kae };
var Iae = 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 Cae(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 = fse(y, v, s.dtype, l, i, c, p, s.shape, o);
    return n.makeTensorInfo(u, s.dtype, x.values);
  }
  let f = new Iae(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 Nae = { kernelName: So, backendName: "webgpu", kernelFunc: Cae };
var Tae = 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 = $ae(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 $ae(e) {
  let t = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], n = [];
  for (let s = 0; s < e.length; s++)
    s === 2 ? n.push("indexZ") : n.push(`${t[s]}`);
  return n.join();
}
function H2(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 = Ae(h.shape, h.dtype, v), I = n.tensorMap.get(d.dataId).values, $ = Ae(d.shape, d.dtype, I), R = mse($, x, f);
    return p.forEach((E) => n.disposeData(E.dataId)), n.makeTensorInfo(l.outputShape, R.dtype, R.values);
  }
  let m = new Tae(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 _ae = { kernelName: ko, backendName: "webgpu", kernelFunc: H2 };
var Aae = mn({ opSnippet: 5, cpuKernelImpl: bse, dtype: "bool" });
var Eae = { kernelName: Io, backendName: "webgpu", kernelFunc: Aae };
var Rae = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: gse });
var Dae = { kernelName: Wa, backendName: "webgpu", kernelFunc: Rae };
function Fae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new ac(r.shape, 14);
  return o.uniforms = "alpha : f32,", n.runWebGPUProgram(o, [r], "float32", i);
}
var Oae = { kernelName: Ga, backendName: "webgpu", kernelFunc: Fae };
var Pae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: vse });
var zae = { kernelName: Co, backendName: "webgpu", kernelFunc: Pae };
var Mae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: yse });
var Lae = { kernelName: No, backendName: "webgpu", kernelFunc: Mae };
var Bae = Kt({ opType: 9, cpuKernelImpl: xse });
var Vae = { kernelName: Ha, backendName: "webgpu", kernelFunc: Bae };
var Wae = mn({ opSnippet: 9, dtype: "bool" });
var Uae = { kernelName: To, backendName: "webgpu", kernelFunc: Wae };
var Gae = Kt({ opType: 10 });
var Hae = { kernelName: Il, backendName: "webgpu", kernelFunc: Gae };
function q2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
  return oc(r, a, i, "max", n);
}
var qae = { kernelName: qa, backendName: "webgpu", kernelFunc: q2 };
var jae = mn({ opSnippet: 15, cpuKernelImpl: kse });
var Kae = { kernelName: ja, backendName: "webgpu", kernelFunc: jae };
function Xae(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 P2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
  } else
    p = new O2(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 Yae = { kernelName: Ka, backendName: "webgpu", kernelFunc: Xae };
function Qae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
  return oc(r, i, a, "mean", n);
}
var Zae = { kernelName: Xa, backendName: "webgpu", kernelFunc: Qae };
function Jae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return oc(r, a, i, "min", n);
}
var eie = { kernelName: Ya, backendName: "webgpu", kernelFunc: Jae };
var tie = mn({ opSnippet: 16, cpuKernelImpl: Sse });
var nie = { kernelName: Qa, backendName: "webgpu", kernelFunc: tie };
var sie = 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 rie = { kernelName: Za, 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 sie(s.shape, r, a);
  return i.runWebGPUProgram(u, [s], s.dtype, o);
} };
function aie(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s])) {
    let a = n.tensorMap.get(s.dataId), [i, o] = Cse(a.values, s.shape, s.dtype);
    return n.makeTensorInfo(o, s.dtype, i);
  }
  let r = new ac(s.shape, 11);
  return n.runWebGPUProgram(r, [s], s.dtype);
}
var iie = { kernelName: $o, backendName: "webgpu", kernelFunc: aie };
function oie(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 uie = { kernelName: Ao, backendName: "webgpu", kernelFunc: oie };
function lie(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 cie = { kernelName: Eo, backendName: "webgpu", kernelFunc: lie };
function qd(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "complex64") {
    let r = ic({ inputs: { input: s }, backend: n }), a = qd({ inputs: { x: r }, backend: n }), i = ih({ inputs: { input: s }, backend: n }), o = qd({ inputs: { x: i }, backend: n }), u = hu({ 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 mu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
}
var die = { kernelName: Xo, backendName: "webgpu", kernelFunc: qd };
function j2(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 = ic({ inputs: { input: s }, backend: n }), a = j2({ inputs: { x: r }, backend: n }), i = ih({ inputs: { input: s }, backend: n }), o = qd({ inputs: { x: i }, backend: n }), u = hu({ 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 mu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
}
var pie = { kernelName: Ro, backendName: "webgpu", kernelFunc: j2 };
function hie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return rg({ 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 = rg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = M2({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeData(c.dataId)), l;
}
var fie = { kernelName: Fo, backendName: "webgpu", kernelFunc: hie };
var mie = 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 K2 = (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 mu({ 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 mie(r.shape, a);
  return n.runWebGPUProgram(u, [r], r.dtype, o);
};
var gie = { kernelName: ei, backendName: "webgpu", kernelFunc: K2 };
var bie = mn({ opSnippet: 13 });
var yie = { kernelName: ti, backendName: "webgpu", kernelFunc: bie };
function vie(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new D2(14, s.shape, r.shape);
  return n.runWebGPUProgram(a, [s, r], "float32");
}
var xie = { kernelName: ni, backendName: "webgpu", kernelFunc: vie };
function wie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return oc(r, a, i, "prod", n);
}
var kie = { kernelName: si, backendName: "webgpu", kernelFunc: wie };
var Sie = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = $se(s, r, a, i);
  return t.makeTensorInfo([o.length], i, o);
};
var Iie = { kernelName: Tl, backendName: "webgpu", kernelFunc: Sie };
var X2 = mn({ opSnippet: 3 });
var Cie = { kernelName: Pa, backendName: "webgpu", kernelFunc: X2 };
var Nie = Kt({ opType: 12 });
var Tie = { kernelName: ri, backendName: "webgpu", kernelFunc: Nie };
var $ie = Kt({ opType: 13 });
var _ie = { kernelName: ii, backendName: "webgpu", kernelFunc: $ie };
var Aie = 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 Eie(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 Aie(r.shape, u, l);
  return n.runWebGPUProgram(f, [r], "float32", h);
}
var Rie = { kernelName: ai, backendName: "webgpu", kernelFunc: Eie };
var Die = 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 Fie(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 Die(r.shape, u, l, i);
  return n.runWebGPUProgram(f, [r], r.dtype, h);
}
var Oie = { kernelName: _l, backendName: "webgpu", kernelFunc: Fie };
var Pie = 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));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `;
  }
};
var zie = { kernelName: Yo, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new Pie(s.shape, a), [l, c] = C.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
  return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
} };
var Mie = Kt({ opType: 15, cpuKernelImpl: _se });
var Lie = { kernelName: oi, backendName: "webgpu", kernelFunc: Mie };
var Bie = class {
  constructor(e, t, n, s, r, a, i) {
    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Be(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${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 Vie(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 = mu({ 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 Bie(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 Wie = { kernelName: Mo, backendName: "webgpu", kernelFunc: Vie };
var Uie = 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 Gie(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new Uie(s.shape.length, r.shape, r.shape.length);
  return n.runWebGPUProgram(i, [s, r, a], cn(r.dtype, a.dtype));
}
var Hie = { kernelName: Lo, backendName: "webgpu", kernelFunc: Gie };
var qie = Kt({ opType: 18 });
var jie = { kernelName: li, backendName: "webgpu", kernelFunc: qie };
var Kie = Kt({ opType: 16 });
var Xie = { kernelName: ui, backendName: "webgpu", kernelFunc: Kie };
var Yie = Kt({ opType: 17 });
var Qie = { kernelName: Vo, backendName: "webgpu", kernelFunc: Yie };
var Y2 = mn({ opSnippet: 2, cpuKernelImpl: Ose, supportsComplex: true });
var Zie = { kernelName: fi, backendName: "webgpu", kernelFunc: Y2 };
function Jie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = q2({ 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 = Y2({ inputs: { a: r, b: l }, backend: n }), p = G2({ inputs: { x: c }, backend: n }), d = zv({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = We({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = X2({ 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 eoe = { kernelName: pi, backendName: "webgpu", kernelFunc: Jie };
var toe = (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 = 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 = We({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = Xs({ 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 noe = { kernelName: Wo, backendName: "webgpu", kernelFunc: toe };
var soe = 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 roe(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;
  if (a.dtype === "string") {
    let y = n.bufferSync(r), v = n.bufferSync(a), x = w.decodeString(n.readSync(i.dataId)[0]), k = Ase(y, v, o, d, c, l, u, p, x, h);
    return n.makeTensorInfo(o, k.dtype, k.values);
  }
  let f = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: p }], m = new soe(l, u, r.shape.length, a.shape.length, p, [d, 1], h), g = n.runWebGPUProgram(m, [a, r, i], a.dtype, f), b = We({ inputs: { x: g }, backend: n, attrs: { shape: o } });
  return n.disposeData(g.dataId), b;
}
var aoe = { kernelName: hp, backendName: "webgpu", kernelFunc: roe };
function ioe(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 = fu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
    return c[o] += d, f;
  });
}
var ooe = { kernelName: Uo, backendName: "webgpu", kernelFunc: ioe };
var uoe = Kt({ opType: 19 });
var loe = { kernelName: ci, backendName: "webgpu", kernelFunc: uoe };
var coe = { kernelName: Fl, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { x: n } = e, s = t, r = new ac(n.shape, 20);
  return s.runWebGPUProgram(r, [n], n.dtype);
} };
var doe = mn({ opSnippet: 11 });
var poe = { kernelName: hi, backendName: "webgpu", kernelFunc: doe };
var hoe = 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 foe(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 } = kt.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 = kt.computeOutShape(y, v, x), $ = fu({ 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), R = Ae(r.shape, r.dtype, $), E = Dse(h, R, x, y);
    k = n.makeTensorInfo(f, r.dtype, E.values);
  } else {
    let $ = new hoe(h), R = [{ type: "int32", data: y }, { type: "int32", data: x }], E = n.runWebGPUProgram($, [r], r.dtype, R);
    k = We({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeData(E.dataId);
  }
  return k;
}
var moe = { kernelName: Go, backendName: "webgpu", kernelFunc: foe };
function goe(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] = Fse(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var boe = { kernelName: fp, backendName: "webgpu", kernelFunc: goe };
var yoe = Kt({ opType: 21 });
var voe = { kernelName: mi, backendName: "webgpu", kernelFunc: yoe };
var xoe = 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 = woe(this.rank, "uniforms.");
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `;
  }
};
function woe(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 koe(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 = Ae(r.shape, r.dtype, l), p = Pse(c, a);
    return n.makeTensorInfo(p.shape, p.dtype, p.values);
  }
  let i = new xoe(r.shape, a);
  return n.runWebGPUProgram(i, [r], r.dtype);
}
var Soe = { kernelName: Tr, backendName: "webgpu", kernelFunc: koe };
var Ioe = 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 Coe = 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 Gi(e, t) {
  t !== null && e.disposeData(t.dataId);
}
function Ww(e) {
  let t = 1;
  for (; t < e; )
    t *= 2;
  return t;
}
function Noe(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, $] = zse(k, o, r.dtype, a, i);
    return [n.makeTensorInfo(I.shape, I.dtype, I.values), n.makeTensorInfo($.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, mu({ 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 = Ww(a), h = Ww(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, I, $) => {
    let R = m(), E = new Ioe($), A = [{ 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] }], O = f;
    f = n.runWebGPUProgram(E, R, "int32", A), Gi(n, O);
  };
  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 Coe([c, k / 2]), E = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
    f = n.runWebGPUProgram($, I, "int32", E), Gi(n, P);
    let A = d / 2, O = A * 2;
    for (let T = A; T >= 1; T /= 2)
      g(O, T, f.shape);
  }
  let b = f;
  f = fu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Gi(n, b);
  let y = H2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
  Gi(n, p);
  let v = o.slice(0, -1);
  v.push(a), b = f, f = We({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Gi(n, b);
  let x = y;
  return y = We({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Gi(n, x), [y, f];
}
var Toe = { kernelName: qo, backendName: "webgpu", kernelFunc: Noe };
var $oe = 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 _oe(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 $oe(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 Aoe = { kernelName: jo, backendName: "webgpu", kernelFunc: _oe };
function Eoe(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 = fu({ 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 Roe = { kernelName: Ko, backendName: "webgpu", kernelFunc: Eoe };
var Doe = [nse, Bse, Wse, Hse, Qse, Jse, tre, sre, ure, pre, fre, yre, ise, kre, Tre, Ere, Dre, Ore, Mre, Bre, Wre, Hre, jre, Zre, eae, nae, sae, rae, iae, uae, cae, gae, pae, fae, vae, wae, Sae, Nae, _ae, Eae, Dae, ase, xre, Oae, zae, Lae, Vae, Uae, Hae, qae, Kae, Yae, Zae, eie, nie, rie, Kre, iie, uie, cie, lre, pie, fie, gie, yie, xie, kie, Iie, cre, Cie, Tie, _ie, ese, Rie, Oie, zie, Lie, Wie, Hie, jie, Xie, Qie, ire, moe, boe, eoe, noe, aoe, ooe, loe, coe, poe, Zie, Yre, voe, Soe, Toe, Aoe, Xse, Roe, die];
for (let e of Doe)
  Ol(e);
var Foe = 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 = Uw(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 = Uw(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 Uw(e, t) {
  return `${e}_${t}`;
}
var Ooe = class {
  constructor(e) {
    this.device = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
  acquireTexture(e, t, n, s) {
    let r = Hw(n), a = e * t * r, i = Gw(e, t, n, s);
    if (this.freeTextures.has(i) || this.freeTextures.set(i, []), this.usedTextures.has(i) || this.usedTextures.set(i, []), this.numBytesUsed += a, this.numUsedTextures++, this.freeTextures.get(i).length > 0) {
      this.numFreeTextures--;
      let u = this.freeTextures.get(i).shift();
      return this.usedTextures.get(i).push(u), u;
    }
    this.numBytesAllocated += a;
    let o = this.device.createTexture({ size: [e, t], format: n, usage: s });
    return this.usedTextures.get(i).push(o), o;
  }
  releaseTexture(e, t, n, s, r) {
    if (this.freeTextures.size === 0)
      return;
    let a = Gw(t, n, s, r);
    this.freeTextures.has(a) || this.freeTextures.set(a, []), this.freeTextures.get(a).push(e), this.numFreeTextures++, this.numUsedTextures--;
    let i = this.usedTextures.get(a), o = i.indexOf(e);
    if (o < 0)
      throw new Error("Cannot release a texture that was never provided by this texture manager");
    i.splice(o, 1);
    let u = Hw(s), l = t * n * u;
    this.numBytesUsed -= l;
  }
  getNumUsedTextures() {
    return this.numUsedTextures;
  }
  getNumFreeTextures() {
    return this.numFreeTextures;
  }
  dispose() {
    this.freeTextures.forEach((e, t) => {
      e.forEach((n) => {
        n.destroy();
      });
    }), this.usedTextures.forEach((e, t) => {
      e.forEach((n) => {
        n.destroy();
      });
    }), this.freeTextures = /* @__PURE__ */ new Map(), this.usedTextures = /* @__PURE__ */ new Map(), this.numUsedTextures = 0, this.numFreeTextures = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
  }
};
function Gw(e, t, n, s) {
  return `${e}_${t}_${n}_${s}`;
}
function Hw(e) {
  if (e === "rgba8unorm")
    return 16;
  throw new Error(`${e} is not supported!`);
}
var Poe = (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 qw = (e, t, n, s, r, a = false) => {
  let i = { dtype: r.dtype, shape: r.shape }, o = Bne(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 jw(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 zoe = K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var Kw = (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 Q2 = class extends ol {
  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, !Fv())
      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 Foe(this.device), this.textureManager = new Ooe(this.device), this.tensorMap = new Yd(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 Q2.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) * md(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) * md(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 = E2(r, t.dtype);
    }
    return this.convertAndCacheOnCPU(e, s), s;
  }
  readToGPU(e) {
    let t = this.tensorMap.get(e), { values: n, dtype: s, shape: r, bufferInfo: a } = t;
    if (s === "complex64")
      throw new Error("Does not support reading buffer for complex64 dtype.");
    if (a.buffer == null)
      throw n != null ? new Error("Data is not on GPU but on CPU.") : new Error("There is no data on GPU or CPU.");
    let i = w.sizeFromShape(r) * md(s), o = this.acquireBuffer(i);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(a.buffer, 0, o, 0, i), this.submitQueue();
    let u = this.makeTensorInfo(r, s), l = ds().makeTensorFromTensorInfo(u), c = this.tensorMap.get(u.dataId);
    return c.bufferInfo.buffer = o, { tensorRef: l, buffer: o, bufSize: i };
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId);
    if (e.dtype === "string")
      try {
        let n = t.map((s) => w.decodeString(s));
        return Ae(e.shape, e.dtype, n);
      } catch (n) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return Ae(e.shape, e.dtype, t);
  }
  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 = Kw(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 = jw(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => qw(this.device, e, b, c, r)), v = this.activeTimers != null, x = Poe(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.dispatchWorkgroups(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 = Kw(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 = jw(e, [a.shape]), o = this.getFromPixelTextureLayout(s), u = this.getAndSavePipeline(i, () => qw(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.dispatchWorkgroups(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 = zoe) {
    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 Mv = Q2;
Mv.nextDataId = 0;
var Moe = {};
Ee(Moe, { WebGPUBackend: () => Mv, webgpu_util: () => _2 });
Fv() && vp("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 Mv(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 oh = ((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))(oh || {});
var Z2;
function Loe(e) {
  Z2 = e.wasm.cwrap(oa, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Boe(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 R = n.dataIdMap.get(i.dataId);
    if (R.shape.length !== 1)
      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);
    f = R.id;
  }
  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = oh[c];
  if (g == null)
    throw new Error(`${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 = Qo.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 Z2(d, I, r.shape.length, h, $, a.shape.length, u, l, g, f, m, p || 0, k), x;
}
var Voe = { kernelName: oa, backendName: "wasm", setupFunc: Loe, kernelFunc: Boe };
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 Woe = Xt(po);
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 Uoe = true;
var Goe = gn(Cr, Uoe);
var J2;
function Hoe(e) {
  J2 = e.wasm.cwrap(Ia, null, ["array", "number", "number", "number"]);
}
function qoe(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 J2(a, r.length, St[s.dtype], i), s;
}
var joe = { kernelName: Ia, backendName: "wasm", setupFunc: Hoe, kernelFunc: qoe };
function uh(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 Koe = { kernelName: Ua, backendName: "wasm", kernelFunc: uh };
var eN;
function Xoe(e) {
  eN = e.wasm.cwrap(Hs, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function Sr(e) {
  let { inputs: t, backend: n, attrs: s } = e, [r, a] = Qoe(t.x.shape, s.perm), i = true;
  for (let f = 0; f < a.length; f++)
    a[f] !== f && (i = false);
  let o = Yoe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
  if (i) {
    let f = uh({ 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 eN(c, h, u.shape.length, St[u.dtype], p, d, a.length), l;
}
function Yoe(e, t) {
  let n = new Array(e.length);
  for (let s = 0; s < n.length; s++)
    n[s] = e[t[s]];
  return n;
}
function Qoe(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 Zoe = { kernelName: Hs, backendName: "wasm", kernelFunc: Sr, setupFunc: Xoe };
function zr(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 = Sr({ 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 tN;
function Joe(e) {
  tN = e.wasm.cwrap(cl, null, ["number, number, number"]);
}
function eue(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 } = zr(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;
    tN(u, b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var tue = { kernelName: cl, backendName: "wasm", setupFunc: Joe, kernelFunc: eue };
var nN;
function nue(e) {
  nN = e.wasm.cwrap(dl, null, ["number, number, number"]);
}
function sue(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 } = zr(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;
    nN(u, b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = C.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var rue = { kernelName: dl, backendName: "wasm", setupFunc: nue, kernelFunc: sue };
var sN;
function aue(e) {
  sN = e.wasm.cwrap(Ca, null, ["number", "number", "number", "number", "number"]);
}
function iue(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 } = zr(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 sN(o, St[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
}
var oue = { kernelName: Ca, backendName: "wasm", kernelFunc: iue, setupFunc: aue };
var rN;
function uue(e) {
  rN = e.wasm.cwrap(Na, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function lue(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 rN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
}
var cue = { kernelName: Na, backendName: "wasm", setupFunc: uue, kernelFunc: lue };
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: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
}
var due = { kernelName: Oo, backendName: "wasm", kernelFunc: yn };
var aN;
function pue(e) {
  aN = e.wasm.cwrap(Ta, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function hue(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  if (r.dtype !== "float32" || a.dtype !== "float32")
    throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
  w.assert(c === p, () => `Error in matMul: inner shapes (${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 } }), R = n.dataIdMap.get(I.dataId).id, E = n.dataIdMap.get($.dataId).id, P = i ? I.shape[2] : I.shape[1], A = o ? $.shape[1] : $.shape[2], O = Math.max(g, b), T = n.makeOutput([O, P, A], I.dtype), M = n.dataIdMap.get(T.dataId).id, W = new Uint8Array(new Int32Array(I.shape).buffer), j = new Uint8Array(new Int32Array($.shape).buffer);
  return aN(R, W, I.shape.length, E, j, $.shape.length, i, o, M), n.disposeData(I.dataId), n.disposeData($.dataId), T.shape = v, T;
}
var fue = { kernelName: Ta, backendName: "wasm", setupFunc: pue, kernelFunc: hue };
function wa(e) {
  let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, i] = kt.parseSliceParams(t, n, s), o = kt.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 = kt.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 = Bd(u, a, i, t.shape, t.dtype);
    return p.stringBytes = f, l;
  }
  let d = r.typedArrayFromHeap(l), h = t.shape.length;
  if (h === 2)
    mue(u, c[0], d, a, i);
  else if (h === 3)
    gue(u, c[0], c[1], d, a, i);
  else if (h === 4)
    bue(u, c[0], c[1], c[2], d, a, i);
  else {
    let f = Bd(u, a, i, t.shape, t.dtype);
    d.set(f);
  }
  return l;
}
function mue(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 gue(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 bue(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 yue = { kernelName: Bo, backendName: "wasm", kernelFunc: wa };
function vue(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 = Sr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = wa({ 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 xue = { kernelName: ho, backendName: "wasm", kernelFunc: vue };
function uc(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 wue = { kernelName: $a, backendName: "wasm", kernelFunc: uc };
var kue = Xt(_a);
var iN;
function Sue(e) {
  iN = e.wasm.cwrap(Nr, null, ["number", "number", "number", "number"]);
}
function Iue(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 iN(o, a, i, l), u;
}
var Cue = { kernelName: Nr, backendName: "wasm", setupFunc: Sue, kernelFunc: Iue };
function oN(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 uh({ 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 = lv(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 Nue = { kernelName: fo, backendName: "wasm", kernelFunc: oN };
var uN;
function Tue(e) {
  uN = e.wasm.cwrap(Aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function $ue(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, R = f.strideWidth, E = f.inChannels, P = f.outChannels, A = 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 O = s.makeOutput(f.outShape, "float32"), T = s.dataIdMap.get(O.dataId).id;
  return uN(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, A, k, I, $, R, E, P, T), O;
}
var _ue = { kernelName: Aa, backendName: "wasm", setupFunc: Tue, kernelFunc: $ue };
var lN;
function Aue(e) {
  lN = e.wasm.cwrap(Ea, 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 Eue(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: R } = h, E = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, A = h.dataFormat === "channelsLast", O = w.computeStrides(h.inShape), T = w.computeStrides(r.shape), [M, W, j] = w.computeStrides(a.shape), X = O[0], Y = A ? O[1] : O[2], Z = A ? O[2] : 1, te = A ? 1 : O[1], J = T[0], se = A ? T[1] : T[2], ne = A ? T[2] : 1, oe = A ? 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 lN(me, ke, f, m, g, y, v, b, k, I, x, $, R, E, P, M, W, j, X, Y, Z, te, J, se, ne, oe, de), ae;
}
var Rue = { kernelName: Ea, backendName: "wasm", setupFunc: Aue, kernelFunc: Eue };
var Due = Xt(Ra);
var Fue = Xt(Da);
var cN = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(cN || {});
var dN;
function Oue(e) {
  dN = e.wasm.cwrap(go, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function Pue(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 = uc({ 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 dN(g, b, y, c, k, p, d, cN[r], a, x), m != null && t.disposeData(m.dataId), v;
}
var zue = { kernelName: go, backendName: "wasm", setupFunc: Oue, kernelFunc: Pue };
var pN;
function Mue(e) {
  pN = e.wasm.cwrap(mo, null, ["number", "number", "number", "number", "number", "number"]);
}
function Lue(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 = Sr({ 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;
  pN(f, i ? 1 : 0, o ? 1 : 0, h, m, St[r.dtype]);
  let g = d;
  if (l !== null) {
    let b = C.getUndoAxesPermutation(l);
    g = Sr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
  }
  return g;
}
var Bue = { kernelName: mo, backendName: "wasm", setupFunc: Mue, kernelFunc: Lue };
var hN;
function Vue(e) {
  hN = e.wasm.cwrap(Fa, null, ["number", "number", "number", "number", "number", "number"]);
}
function Wue(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 = Sr({ 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;
  hN(f, i ? 1 : 0, o ? 1 : 0, h, m, St[r.dtype]);
  let g = d;
  if (l !== null) {
    let b = C.getUndoAxesPermutation(l);
    g = Sr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
  }
  return g;
}
var Uue = { kernelName: Fa, backendName: "wasm", setupFunc: Vue, kernelFunc: Wue };
var fN;
function Gue(e) {
  fN = e.wasm.cwrap(bo, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function Hue(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 fN(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
}
var que = { kernelName: bo, backendName: "wasm", setupFunc: Gue, kernelFunc: Hue };
var mN;
function jue(e) {
  mN = e.wasm.cwrap(Oa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Kue(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, R = h.inChannels, E = 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 A = s.makeOutput(h.outShape, "float32"), O = s.dataIdMap.get(A.dataId).id;
  return mN(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, I, $, R, E, O), A;
}
var Xue = { kernelName: Oa, backendName: "wasm", setupFunc: jue, kernelFunc: Kue };
var Yue = Xt(za);
var Que = false;
var Zue = gn(yo, Que, "bool");
var Jue = Xt(Ma, "float32");
function ag(e) {
  let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, i = r.shape.length, o = r.shape.slice(), u = a;
  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), yn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
}
var ele = { kernelName: vo, backendName: "wasm", kernelFunc: ag };
function gN(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 tle = { kernelName: vl, backendName: "wasm", kernelFunc: gN };
var bN;
function nle(e) {
  bN = e.wasm.cwrap(wo, null, ["number", "number", "number", "number", "number", "number"]);
}
function sle(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 bN(a, o, u, l, c, i), r;
}
var rle = { kernelName: wo, backendName: "wasm", kernelFunc: sle, setupFunc: nle };
var ale = Xt(La);
var ile = false;
var ole = gn(Ba, ile);
var yN;
function ule(e) {
  yN = e.wasm.cwrap(Va, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function lle(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 yN(c, p, d, h, f, r, g), m;
}
var cle = { kernelName: Va, backendName: "wasm", setupFunc: ule, kernelFunc: lle };
var vN;
function dle(e) {
  vN = e.wasm.cwrap(ua, 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 ple(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 = oh[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, R = m.padInfo.right, E = m.padInfo.bottom, P = m.padInfo.left, A = m.dilationHeight, O = m.dilationWidth, T = m.strideHeight, M = 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 vN(b, X, Y, Z, y, k, I, x, $, R, E, P, j, A, O, T, M, W, v, g, se, f || 0, J), te;
}
var hle = { kernelName: ua, backendName: "wasm", setupFunc: dle, kernelFunc: ple };
var xN;
function fle(e) {
  xN = e.wasm.cwrap(la, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function mle(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = C.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = oh[h];
  if (g == null)
    throw new Error(`${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, R = m.padInfo.right, E = m.padInfo.bottom, P = m.padInfo.left, A = m.dilationHeight, O = m.dilationWidth, T = m.strideHeight, M = 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 xN(b, X, Y, Z, y, k, I, x, $, R, E, P, j, A, O, T, M, W, v, g, se, f || 0, J), te;
}
var gle = { kernelName: la, backendName: "wasm", setupFunc: fle, kernelFunc: mle };
var wN;
function ble(e) {
  wN = e.wasm.cwrap(So, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function yle(e) {
  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = Vk.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 wN(h, St[s.dtype], m, i, p, o, g, b), l;
}
var vle = { kernelName: So, backendName: "wasm", setupFunc: ble, kernelFunc: yle };
var kN;
function xle(e) {
  kN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function wle(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 E = 0; E < l.length; ++E) {
    let P = l[E];
    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), R = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
  return kN(v, St[r.dtype], $, b, k, p.batchSize, R, I), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
}
var kle = { kernelName: ko, backendName: "wasm", setupFunc: xle, kernelFunc: wle };
var Sle = false;
var Ile = gn(Io, Sle, "bool");
var Cle = false;
var Nle = gn(Wa, Cle, "bool");
var SN;
function Tle(e) {
  SN = e.wasm.cwrap(Ga, null, ["number", "number", "number", "number"]);
}
function $le(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;
    SN(r, St[t.dtype], n, i);
  }
  return a;
}
var _le = { kernelName: Ga, backendName: "wasm", setupFunc: Tle, kernelFunc: $le };
var Ale = false;
var Ele = gn(Co, Ale, "bool");
var Rle = false;
var Dle = gn(No, Rle, "bool");
var Fle = Xt(Ha);
var Ole = false;
var Ple = gn(To, Ole, "bool");
var IN;
function zle(e) {
  IN = e.wasm.cwrap(qa, null, ["number", "number", "number", "number"]);
}
function Mle(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 } = zr(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;
    IN(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 Lle = { kernelName: qa, backendName: "wasm", setupFunc: zle, kernelFunc: Mle };
var Ble = false;
var Vle = gn(ja, Ble);
var CN;
function Wle(e) {
  CN = e.wasm.cwrap(Ka, null, ["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, 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"), R = s.dataIdMap.get($.dataId).id;
  return CN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, I, R), $;
}
var Gle = { kernelName: Ka, backendName: "wasm", setupFunc: Wle, kernelFunc: Ule };
var NN;
function Hle(e) {
  NN = e.wasm.cwrap(Xa, null, ["number, number, number"]);
}
function qle(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 } = zr(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 = uc({ 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;
    NN(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 jle = { kernelName: Xa, backendName: "wasm", setupFunc: Hle, kernelFunc: qle };
var TN;
function Kle(e) {
  TN = e.wasm.cwrap(Ya, null, ["number", "number", "number", "number"]);
}
function Xle(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 } = zr(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;
    TN(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 Yle = { kernelName: Ya, backendName: "wasm", setupFunc: Kle, kernelFunc: Xle };
var Qle = false;
var Zle = gn(Qa, Qle);
var $N = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))($N || {});
var _N;
function Jle(e) {
  _N = e.wasm.cwrap(Za, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function ece(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 _N(i, l, t.shape.length, St[t.dtype], d, h, $N[r], u), o;
}
var tce = { kernelName: Za, backendName: "wasm", kernelFunc: ece, setupFunc: Jle };
var nce = true;
var sce = gn(Ja, nce);
var rce = Xt($o);
function Lv(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 AN;
function ace(e) {
  AN = e.wasm.cwrap(Ao, "number", ["number", "number", "number", "number", "number"]);
}
function ice(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 = AN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Lv(t, p);
  return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
}
var oce = { kernelName: Ao, backendName: "wasm", setupFunc: ace, kernelFunc: ice };
var EN;
function uce(e) {
  EN = e.wasm.cwrap(Nl, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function lce(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 = EN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Lv(t, d);
  t.wasm._free(m);
  let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
  return [b, y];
}
var cce = { kernelName: Nl, backendName: "wasm", setupFunc: uce, kernelFunc: lce };
var RN;
function dce(e) {
  RN = e.wasm.cwrap(Eo, "number", ["number", "number", "number", "number", "number", "number"]);
}
function pce(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 = RN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Lv(t, d);
  t.wasm._free(g);
  let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
  return [b, y];
}
var hce = { kernelName: Eo, backendName: "wasm", setupFunc: dce, kernelFunc: pce };
var fce = false;
var mce = gn(_o, fce, "bool");
var DN;
function gce(e) {
  DN = e.wasm.cwrap(Do, null, ["number", "number", "number", "number", "number"]);
}
function bce(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 DN(p, a, i, o, l), u;
}
var yce = { kernelName: Do, backendName: "wasm", setupFunc: gce, kernelFunc: bce };
function vce(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
  return n.typedArrayFromHeap(s).fill(1), s;
}
var xce = { kernelName: Ro, backendName: "wasm", kernelFunc: vce };
function wce(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return ag({ 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 = ag({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = oN({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeData(c.dataId)), l;
}
var kce = { kernelName: Fo, backendName: "wasm", kernelFunc: wce };
var FN;
function Sce(e) {
  FN = e.wasm.cwrap(ei, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function Ice(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 gN({ 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 FN(i, c, t.shape.length, St[t.dtype], h, f, r, l), o;
}
var ON = { kernelName: ei, backendName: "wasm", kernelFunc: Ice, setupFunc: Sce };
var Cce = false;
var Nce = gn(ti, Cce);
var PN;
function Tce(e) {
  PN = e.wasm.cwrap(ni, null, ["number", "number", "number"]);
}
function $ce(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 = uc({ 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 PN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
}
var _ce = { kernelName: ni, backendName: "wasm", setupFunc: Tce, kernelFunc: $ce };
var zN;
function Ace(e) {
  zN = e.wasm.cwrap(si, null, ["number", "number", "number", "number"]);
}
function Ece(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 } = zr(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;
    zN(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 Rce = { kernelName: si, backendName: "wasm", setupFunc: Ace, kernelFunc: Ece };
var Dce = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = pv(s, r, a, i), u = t.makeOutput([o.length], i);
  return t.typedArrayFromHeap(u).set(o), u;
};
var Fce = { kernelName: Tl, backendName: "wasm", kernelFunc: Dce };
var Oce = true;
var Pce = gn(Pa, Oce);
var zce = Xt(ri);
var Mce = Xt(ii);
var MN;
function Lce(e) {
  MN = e.wasm.cwrap(ai, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Bce(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 = uc({ 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 MN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
}
var Vce = { kernelName: ai, backendName: "wasm", setupFunc: Lce, kernelFunc: Bce };
var LN;
function Wce(e) {
  LN = e.wasm.cwrap(Po, null, ["number", "array", "number", "array", "number", "number"]);
}
function Uce(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 uh({ 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);
  LN(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 Gce = { kernelName: Po, backendName: "wasm", kernelFunc: Uce, setupFunc: Wce };
var BN;
function Hce(e) {
  BN = e.wasm.cwrap(Yo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function qce(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 BN(l, p, d, h, f, a, m, g, x, v.length, c), u;
}
var jce = { kernelName: Yo, backendName: "wasm", kernelFunc: qce, setupFunc: Hce };
var Kce = Xt(zo);
var Xce = Xt(oi);
var VN;
function Yce(e) {
  VN = e.wasm.cwrap(Mo, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Qce(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 } = Uk.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 VN(f, g, St[a.dtype], u, l, c, b, d, y), o;
}
var Zce = { kernelName: Mo, backendName: "wasm", setupFunc: Yce, kernelFunc: Qce };
var WN;
function Jce(e) {
  WN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function ede(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 WN(i, o, u, h, c), l;
}
var tde = { kernelName: Lo, backendName: "wasm", kernelFunc: ede, setupFunc: Jce };
var UN;
function nde(e) {
  UN = e.wasm.cwrap(li, null, ["number", "number"]);
}
function sde(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 || UN(s, a), r;
}
var rde = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: nde, kernelFunc: sde };
var ade = Xt(ui);
var GN;
function ide(e) {
  GN = e.wasm.cwrap(pi, null, ["number", "number", "number", "number"]);
}
function ode(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 || GN(r, i, o, u), a;
}
var ude = { kernelName: pi, backendName: "wasm", setupFunc: ide, kernelFunc: ode };
function lde(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 = ON.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 = Sr({ 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 cde = { kernelName: Wo, backendName: "wasm", kernelFunc: lde };
var HN;
function dde(e) {
  HN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function pde(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, R = HN(p, d, St[r.dtype], o, l, u, h, m, b, v, k, $), E = t.readSync(I.dataId), P;
  switch (E[0]) {
    case 1: {
      P = C.getSparseFillEmptyRowsIndicesDenseShapeMismatch(E[1]);
      break;
    }
    case 2: {
      P = C.getSparseFillEmptyRowsNegativeIndexErrorMessage(E[1], E[2]);
      break;
    }
    case 3:
      P = C.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(E[1], E[2], E[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 A = f, O = g;
  return R !== c[0] && (A = wa({ inputs: { x: f }, attrs: { begin: 0, size: [R, u] }, backend: t }), O = wa({ inputs: { x: g }, attrs: { begin: 0, size: R }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [A, O, y, x];
}
var hde = { kernelName: cp, backendName: "wasm", setupFunc: dde, kernelFunc: pde };
var qN;
function fde(e) {
  qN = e.wasm.cwrap(Dl, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function mde(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;
  qN(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 gde = { kernelName: Dl, backendName: "wasm", setupFunc: fde, kernelFunc: mde };
var jN;
function KN(e) {
  jN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function XN(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;
  jN(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 bde(e) {
  return XN(e, true);
}
var yde = { kernelName: dp, backendName: "wasm", setupFunc: KN, kernelFunc: bde };
function vde(e) {
  return XN(e, false);
}
var xde = { kernelName: pp, backendName: "wasm", setupFunc: KN, kernelFunc: vde };
function wde(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 = wa({ inputs: { x: r }, attrs: { begin: l, size: d }, backend: s });
    return l[o] += p, h;
  });
}
var kde = { kernelName: Uo, backendName: "wasm", kernelFunc: wde };
var Sde = Xt(ci);
var Ide = Xt(Fl);
var Cde = true;
var Nde = gn(hi, Cde);
var YN;
function Tde(e) {
  YN = e.wasm.cwrap(gi, null, ["number", "number", "number", "number"]);
}
function $de(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 YN(i, r, St[a.dtype], u), o;
}
var _de = { kernelName: gi, backendName: "wasm", setupFunc: Tde, kernelFunc: $de };
var QN;
function Ade(e) {
  QN = e.wasm.cwrap(Go, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function Ede(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 } = kt.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 = kt.computeOutShape(y, v, x), $ = wa({ 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, R = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), E = new Uint8Array(new Int32Array(y).buffer), P = new Uint8Array(new Int32Array(v).buffer), A = new Uint8Array(new Int32Array(x).buffer), O = new Uint8Array(new Int32Array(h).buffer), T = new Uint8Array(new Int32Array(w.computeStrides(h)).buffer), M = t.dataIdMap.get(I.dataId).id;
    QN($, R, r.shape.length, E, P, A, O, T, h.length, M), k = yn({ inputs: { x: I }, backend: t, attrs: { shape: f } }), t.disposeData(I.dataId);
  }
  return k;
}
var Rde = { kernelName: Go, backendName: "wasm", setupFunc: Ade, kernelFunc: Ede };
var Dde = true;
var Fde = gn(fi, Dde);
var ZN;
function Ode(e) {
  ZN = e.wasm.cwrap(di, null, ["number", "number", "number", "number"]);
}
function Pde(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 } = zr(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;
    ZN(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 zde = { kernelName: di, backendName: "wasm", setupFunc: Ode, kernelFunc: Pde };
var Mde = Xt(Ho);
var Lde = Xt(mi);
var JN;
function Bde(e) {
  JN = e.wasm.cwrap(Tr, null, ["number", "array", "number", "array", "number", "number"]);
}
function Vde(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 JN(a, u, r.shape.length, l, o.length, St[c.dtype], p), c;
}
var Wde = { kernelName: Tr, backendName: "wasm", setupFunc: Bde, kernelFunc: Vde };
var eT;
function Ude(e) {
  eT = e.wasm.cwrap(qo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var Gde = ({ 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 eT(i, o, s.shape.length, St[s.dtype], r, a, c, d), [l, p];
};
var Hde = { kernelName: qo, backendName: "wasm", setupFunc: Ude, kernelFunc: Gde };
var tT;
function qde(e) {
  tT = e.wasm.cwrap(jo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function jde(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, R = i === "nearest" ? 1 : 2, E;
  switch (o) {
    case "constant":
      E = 1;
      break;
    case "reflect":
      E = 2;
      break;
    case "wrap":
      E = 3;
      break;
    case "nearest":
      E = 4;
      break;
    default:
      E = 1;
      break;
  }
  return tT(k, $, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, R, E, u, v), y;
}
var Kde = { kernelName: jo, backendName: "wasm", setupFunc: qde, kernelFunc: jde };
function Xde(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] = wa({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
  return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
}
var Yde = { kernelName: Ko, backendName: "wasm", kernelFunc: Xde };
function Qde(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
  return n.typedArrayFromHeap(s).fill(0), s;
}
var Zde = { kernelName: Xo, backendName: "wasm", kernelFunc: Qde };
var Jde = [Voe, Woe, Goe, joe, tue, rue, oue, cue, fue, xue, wue, kue, Cue, Nue, _ue, Rue, Due, Fue, zue, Bue, Uue, que, Xue, Yue, Zue, Jue, ele, tle, rle, ale, ole, cle, hle, gle, vle, kle, Ile, Nle, Koe, _le, Ele, Dle, Fle, Ple, Lle, Vle, Gle, jle, Yle, Zle, tce, sce, rce, oce, cce, hce, mce, yce, xce, kce, ON, Nce, _ce, Rce, Fce, Pce, zce, Mce, due, Vce, Gce, jce, Kce, Xce, Zce, tde, rde, ade, yue, ude, cde, hde, gde, yde, xde, kde, Sde, Ide, Nde, _de, Rde, Fde, zde, Mde, Lde, Wde, Hde, Kde, Zoe, Yde, Zde];
for (let e of Jde)
  Ol(e);
var ig = K();
ig.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])));
ig.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
  if (ig.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 Xw = ka(g$());
var epe = `"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 tpe = ka(b$());
var npe = class extends ol {
  constructor(e) {
    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(nT), og = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Yd(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 ape(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 ${t}`);
    }
  }
};
function spe(e) {
  return (t, n) => (w.fetch(e, { credentials: "same-origin" }).then((s) => {
    s.ok || t.env.a(`failed to load wasm binary file at '${e}'`), s.arrayBuffer().then((r) => {
      WebAssembly.instantiate(r, t).then((a) => {
        n(a.instance, a.module);
      });
    });
  }), {});
}
function Yw(e, t, n) {
  if (jd != null)
    return jd;
  let s = "tfjs-backend-wasm.wasm";
  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Uu != null && Uu[s] != null ? Uu[s] : n + s;
}
async function rpe() {
  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 = epe.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
        return URL.createObjectURL(c);
      }
      return o.endsWith(".wasm") ? Yw(e, t, Bu != null ? Bu : u) : u + o;
    }, Bv && (r.instantiateWasm = spe(Yw(e, t, Bu != null ? Bu : "")));
    let a = false;
    r.onAbort = () => {
      if (a || Gu)
        return;
      Gu = 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 && jd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Xw.default.toString()], { type: "text/javascript" }), i = (0, Xw.default)(r)) : i = (0, tpe.default)(r), i.then((o) => {
      a = true, Gu = 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 ape(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 ipe = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var jd = null;
var Bu = null;
var Uu = {};
var Gu = false;
var Bv = false;
function khe(e, t = false) {
  if (zk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Gu)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
  jd = e, Bv = t;
}
function She(e, t = false) {
  if (Gu)
    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")
    Bu = e;
  else {
    Uu = e;
    let n = ipe.filter((s) => Uu[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.`);
  }
  Bv = t;
}
var nT = -1;
var og = -1;
function Ihe(e) {
  nT = e;
}
function Che() {
  if (og === -1)
    throw new Error("WASM backend not initialized.");
  return og;
}
var Nhe = "0.0.0";
var ope = 2;
vp("wasm", async () => {
  let { wasm: e } = await rpe();
  return new npe(e);
}, ope);
var sr = "3.18.0-20220522";
var The = { tfjs: sr, "tfjs-core": sr, "tfjs-data": sr, "tfjs-layers": sr, "tfjs-converter": sr, "tfjs-backend-cpu": sr, "tfjs-backend-webgl": sr, "tfjs-backend-wasm": sr };

// 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 ? br(inputImage) : inputImage;
  const channels = Bn(squeeze, 3, 2);
  const min = [km(channels[0]), km(channels[1]), km(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]);
  De([...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 = mb(input, [0, 0, 0], [-1, -1, 3]);
        tensor = Pn(rgb2, 0);
        De(rgb2);
      }
    } else if (input.shape.length === 4) {
      if (input.shape[3] === 3) {
        tensor = lr(input);
      } else if (input.shape[3] === 4) {
        tensor = Nd(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");
      De(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 && Lk) {
        pixels = Lk ? Lk.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 (Lk && env.browser) {
        if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
          pixels = Lk.fromPixels(outCanvas);
        } else {
          tmpCanvas = copy(outCanvas);
          pixels = Lk.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 = mb(pixels, [0, 0, 0], [-1, -1, 3]);
      De(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);
    De([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 = lr(input);
  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
    De(last.inputTensor);
    last.inputTensor = lr(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;
    De([last.inputTensor, t.diff, t.squared, t.sum]);
    last.inputTensor = lr(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 = lr(input1);
  t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? jn.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : lr(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;
  De([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: The["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 && kpe() === "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 && (kpe() === "webgl" || kpe() === "humangl")) {
      const gl2 = wA().gpgpu !== "undefined" ? await wA().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 = im(kpe()).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) {
  let modelUrl = join(options.modelBasePath, modelPath || "");
  if (!modelUrl.toLowerCase().endsWith(".json"))
    modelUrl += ".json";
  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 E0(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 = "2.7.2";

// 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) => De(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) => De(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 = lE([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) => De(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();
    De([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 = [
  [0.499976992607117, 0.652534008026123],
  [0.500025987625122, 0.547487020492554],
  [0.499974012374878, 0.602371990680695],
  [0.482113003730774, 0.471979022026062],
  [0.500150978565216, 0.527155995368958],
  [0.499909996986389, 0.498252987861633],
  [0.499523013830185, 0.40106201171875],
  [0.289712011814117, 0.380764007568359],
  [0.499954998493195, 0.312398016452789],
  [0.499987006187439, 0.269918978214264],
  [0.500023007392883, 0.107050001621246],
  [0.500023007392883, 0.666234016418457],
  [0.5000159740448, 0.679224014282227],
  [0.500023007392883, 0.692348003387451],
  [0.499976992607117, 0.695277988910675],
  [0.499976992607117, 0.70593398809433],
  [0.499976992607117, 0.719385027885437],
  [0.499976992607117, 0.737019002437592],
  [0.499967992305756, 0.781370997428894],
  [0.499816000461578, 0.562981009483337],
  [0.473773002624512, 0.573909997940063],
  [0.104906998574734, 0.254140973091125],
  [0.365929991006851, 0.409575998783112],
  [0.338757991790771, 0.41302502155304],
  [0.311120003461838, 0.409460008144379],
  [0.274657994508743, 0.389131009578705],
  [0.393361985683441, 0.403706014156342],
  [0.345234006643295, 0.344011008739471],
  [0.370094001293182, 0.346076011657715],
  [0.319321990013123, 0.347265005111694],
  [0.297903001308441, 0.353591024875641],
  [0.24779200553894, 0.410809993743896],
  [0.396889001131058, 0.842755019664764],
  [0.280097991228104, 0.375599980354309],
  [0.106310002505779, 0.399955987930298],
  [0.2099249958992, 0.391353011131287],
  [0.355807989835739, 0.534406006336212],
  [0.471751004457474, 0.65040397644043],
  [0.474155008792877, 0.680191993713379],
  [0.439785003662109, 0.657229006290436],
  [0.414617002010345, 0.66654098033905],
  [0.450374007225037, 0.680860996246338],
  [0.428770989179611, 0.682690978050232],
  [0.374971002340317, 0.727805018424988],
  [0.486716985702515, 0.547628998756409],
  [0.485300987958908, 0.527395009994507],
  [0.257764995098114, 0.314490020275116],
  [0.401223003864288, 0.455172002315521],
  [0.429818987846375, 0.548614978790283],
  [0.421351999044418, 0.533740997314453],
  [0.276895999908447, 0.532056987285614],
  [0.483370006084442, 0.499586999416351],
  [0.33721199631691, 0.282882988452911],
  [0.296391993761063, 0.293242990970612],
  [0.169294998049736, 0.193813979625702],
  [0.447580009698868, 0.302609980106354],
  [0.392390012741089, 0.353887975215912],
  [0.354490011930466, 0.696784019470215],
  [0.067304998636246, 0.730105042457581],
  [0.442739009857178, 0.572826027870178],
  [0.457098007202148, 0.584792017936707],
  [0.381974011659622, 0.694710969924927],
  [0.392388999462128, 0.694203019142151],
  [0.277076005935669, 0.271932005882263],
  [0.422551989555359, 0.563233017921448],
  [0.385919004678726, 0.281364023685455],
  [0.383103013038635, 0.255840003490448],
  [0.331431001424789, 0.119714021682739],
  [0.229923993349075, 0.232002973556519],
  [0.364500999450684, 0.189113974571228],
  [0.229622006416321, 0.299540996551514],
  [0.173287004232407, 0.278747975826263],
  [0.472878992557526, 0.666198015213013],
  [0.446828007698059, 0.668527007102966],
  [0.422762006521225, 0.673889994621277],
  [0.445307999849319, 0.580065965652466],
  [0.388103008270264, 0.693961024284363],
  [0.403039008378983, 0.706539988517761],
  [0.403629004955292, 0.693953037261963],
  [0.460041999816895, 0.557139039039612],
  [0.431158006191254, 0.692366003990173],
  [0.452181994915009, 0.692366003990173],
  [0.475387006998062, 0.692366003990173],
  [0.465828001499176, 0.779190003871918],
  [0.472328990697861, 0.736225962638855],
  [0.473087012767792, 0.717857003211975],
  [0.473122000694275, 0.704625964164734],
  [0.473033010959625, 0.695277988910675],
  [0.427942007780075, 0.695277988910675],
  [0.426479011774063, 0.703539967536926],
  [0.423162013292313, 0.711845993995667],
  [0.4183090031147, 0.720062971115112],
  [0.390094995498657, 0.639572978019714],
  [0.013953999616206, 0.560034036636353],
  [0.499913990497589, 0.58014702796936],
  [0.413199990987778, 0.69539999961853],
  [0.409626007080078, 0.701822996139526],
  [0.468080013990402, 0.601534962654114],
  [0.422728985548019, 0.585985004901886],
  [0.463079988956451, 0.593783974647522],
  [0.37211999297142, 0.47341400384903],
  [0.334562003612518, 0.496073007583618],
  [0.411671012639999, 0.546965003013611],
  [0.242175996303558, 0.14767599105835],
  [0.290776997804642, 0.201445996761322],
  [0.327338010072708, 0.256527006626129],
  [0.399509996175766, 0.748921036720276],
  [0.441727995872498, 0.261676013469696],
  [0.429764986038208, 0.187834024429321],
  [0.412198007106781, 0.108901023864746],
  [0.288955003023148, 0.398952007293701],
  [0.218936994671822, 0.435410976409912],
  [0.41278201341629, 0.398970007896423],
  [0.257135003805161, 0.355440020561218],
  [0.427684992551804, 0.437960982322693],
  [0.448339998722076, 0.536936044692993],
  [0.178560003638268, 0.45755398273468],
  [0.247308000922203, 0.457193970680237],
  [0.286267012357712, 0.467674970626831],
  [0.332827985286713, 0.460712015628815],
  [0.368755996227264, 0.447206974029541],
  [0.398963987827301, 0.432654976844788],
  [0.476410001516342, 0.405806005001068],
  [0.189241006970406, 0.523923993110657],
  [0.228962004184723, 0.348950982093811],
  [0.490725994110107, 0.562400996685028],
  [0.404670000076294, 0.485132992267609],
  [0.019469000399113, 0.401564002037048],
  [0.426243007183075, 0.420431017875671],
  [0.396993011236191, 0.548797011375427],
  [0.266469985246658, 0.376977026462555],
  [0.439121007919312, 0.51895797252655],
  [0.032313998788595, 0.644356966018677],
  [0.419054001569748, 0.387154996395111],
  [0.462783008813858, 0.505746960639954],
  [0.238978996872902, 0.779744982719421],
  [0.198220998048782, 0.831938028335571],
  [0.107550002634525, 0.540755033493042],
  [0.183610007166862, 0.740257024765015],
  [0.134409993886948, 0.333683013916016],
  [0.385764002799988, 0.883153975009918],
  [0.490967005491257, 0.579378008842468],
  [0.382384985685349, 0.508572995662689],
  [0.174399003386497, 0.397670984268188],
  [0.318785011768341, 0.39623498916626],
  [0.343364000320435, 0.400596976280212],
  [0.396100014448166, 0.710216999053955],
  [0.187885001301765, 0.588537991046906],
  [0.430987000465393, 0.944064974784851],
  [0.318993002176285, 0.898285031318665],
  [0.266247987747192, 0.869701027870178],
  [0.500023007392883, 0.190576016902924],
  [0.499976992607117, 0.954452991485596],
  [0.366169989109039, 0.398822009563446],
  [0.393207013607025, 0.39553701877594],
  [0.410373002290726, 0.391080021858215],
  [0.194993004202843, 0.342101991176605],
  [0.388664990663528, 0.362284004688263],
  [0.365961998701096, 0.355970978736877],
  [0.343364000320435, 0.355356991291046],
  [0.318785011768341, 0.35834002494812],
  [0.301414996385574, 0.363156020641327],
  [0.058132998645306, 0.319076001644135],
  [0.301414996385574, 0.387449026107788],
  [0.499987989664078, 0.618434011936188],
  [0.415838003158569, 0.624195992946625],
  [0.445681989192963, 0.566076993942261],
  [0.465844005346298, 0.620640993118286],
  [0.49992299079895, 0.351523995399475],
  [0.288718998432159, 0.819945991039276],
  [0.335278987884521, 0.852819979190826],
  [0.440512001514435, 0.902418971061707],
  [0.128294005990028, 0.791940987110138],
  [0.408771991729736, 0.373893976211548],
  [0.455606997013092, 0.451801002025604],
  [0.499877005815506, 0.908990025520325],
  [0.375436991453171, 0.924192011356354],
  [0.11421000212431, 0.615022003650665],
  [0.448662012815475, 0.695277988910675],
  [0.4480200111866, 0.704632043838501],
  [0.447111994028091, 0.715808033943176],
  [0.444831997156143, 0.730794012546539],
  [0.430011987686157, 0.766808986663818],
  [0.406787008047104, 0.685672998428345],
  [0.400738000869751, 0.681069016456604],
  [0.392399996519089, 0.677703022956848],
  [0.367855995893478, 0.663918972015381],
  [0.247923001646996, 0.601333022117615],
  [0.452769994735718, 0.420849978923798],
  [0.43639200925827, 0.359887003898621],
  [0.416164010763168, 0.368713974952698],
  [0.413385987281799, 0.692366003990173],
  [0.228018000721931, 0.683571994304657],
  [0.468268007040024, 0.352671027183533],
  [0.411361992359161, 0.804327011108398],
  [0.499989002943039, 0.469825029373169],
  [0.479153990745544, 0.442654013633728],
  [0.499974012374878, 0.439637005329132],
  [0.432112008333206, 0.493588984012604],
  [0.499886006116867, 0.866917014122009],
  [0.49991300702095, 0.821729004383087],
  [0.456548988819122, 0.819200992584229],
  [0.344549000263214, 0.745438992977142],
  [0.37890899181366, 0.574010014533997],
  [0.374292999505997, 0.780184984207153],
  [0.319687992334366, 0.570737957954407],
  [0.357154995203018, 0.604269981384277],
  [0.295284003019333, 0.621580958366394],
  [0.447750002145767, 0.862477004528046],
  [0.410986006259918, 0.508723020553589],
  [0.31395098567009, 0.775308012962341],
  [0.354128003120422, 0.812552988529205],
  [0.324548006057739, 0.703992962837219],
  [0.189096003770828, 0.646299958229065],
  [0.279776990413666, 0.71465802192688],
  [0.1338230073452, 0.682700991630554],
  [0.336768001317978, 0.644733011722565],
  [0.429883986711502, 0.466521978378296],
  [0.455527991056442, 0.548622965812683],
  [0.437114000320435, 0.558896005153656],
  [0.467287987470627, 0.529924988746643],
  [0.414712011814117, 0.335219979286194],
  [0.37704598903656, 0.322777986526489],
  [0.344107985496521, 0.320150971412659],
  [0.312875986099243, 0.32233202457428],
  [0.283526003360748, 0.333190023899078],
  [0.241245999932289, 0.382785975933075],
  [0.102986000478268, 0.468762993812561],
  [0.267612010240555, 0.424560010433197],
  [0.297879010438919, 0.433175981044769],
  [0.333433985710144, 0.433878004550934],
  [0.366427004337311, 0.426115989685059],
  [0.396012008190155, 0.416696012020111],
  [0.420121014118195, 0.41022801399231],
  [0.007561000064015, 0.480777025222778],
  [0.432949006557465, 0.569517970085144],
  [0.458638995885849, 0.479089021682739],
  [0.473466008901596, 0.545744001865387],
  [0.476087987422943, 0.563830018043518],
  [0.468472003936768, 0.555056989192963],
  [0.433990985155106, 0.582361996173859],
  [0.483518004417419, 0.562983989715576],
  [0.482482999563217, 0.57784903049469],
  [0.42645001411438, 0.389798998832703],
  [0.438998997211456, 0.39649498462677],
  [0.450067013502121, 0.400434017181396],
  [0.289712011814117, 0.368252992630005],
  [0.276670008897781, 0.363372981548309],
  [0.517862021923065, 0.471948027610779],
  [0.710287988185883, 0.380764007568359],
  [0.526226997375488, 0.573909997940063],
  [0.895093023777008, 0.254140973091125],
  [0.634069979190826, 0.409575998783112],
  [0.661242008209229, 0.41302502155304],
  [0.688880026340485, 0.409460008144379],
  [0.725341975688934, 0.389131009578705],
  [0.606630027294159, 0.40370500087738],
  [0.654766023159027, 0.344011008739471],
  [0.629905998706818, 0.346076011657715],
  [0.680678009986877, 0.347265005111694],
  [0.702096998691559, 0.353591024875641],
  [0.75221198797226, 0.410804986953735],
  [0.602918028831482, 0.842862963676453],
  [0.719901978969574, 0.375599980354309],
  [0.893692970275879, 0.399959981441498],
  [0.790081977844238, 0.391354024410248],
  [0.643998026847839, 0.534487962722778],
  [0.528249025344849, 0.65040397644043],
  [0.525849997997284, 0.680191040039062],
  [0.560214996337891, 0.657229006290436],
  [0.585384011268616, 0.66654098033905],
  [0.549625992774963, 0.680860996246338],
  [0.57122802734375, 0.682691991329193],
  [0.624852001667023, 0.72809898853302],
  [0.513050019741058, 0.547281980514526],
  [0.51509702205658, 0.527251958847046],
  [0.742246985435486, 0.314507007598877],
  [0.598631024360657, 0.454979002475739],
  [0.570338010787964, 0.548575043678284],
  [0.578631997108459, 0.533622980117798],
  [0.723087012767792, 0.532054007053375],
  [0.516445994377136, 0.499638974666595],
  [0.662801027297974, 0.282917976379395],
  [0.70362401008606, 0.293271005153656],
  [0.830704987049103, 0.193813979625702],
  [0.552385985851288, 0.302568018436432],
  [0.607609987258911, 0.353887975215912],
  [0.645429015159607, 0.696707010269165],
  [0.932694971561432, 0.730105042457581],
  [0.557260990142822, 0.572826027870178],
  [0.542901992797852, 0.584792017936707],
  [0.6180260181427, 0.694710969924927],
  [0.607590973377228, 0.694203019142151],
  [0.722943007946014, 0.271963000297546],
  [0.577413976192474, 0.563166975975037],
  [0.614082992076874, 0.281386971473694],
  [0.616907000541687, 0.255886018276215],
  [0.668509006500244, 0.119913995265961],
  [0.770092010498047, 0.232020974159241],
  [0.635536015033722, 0.189248979091644],
  [0.77039098739624, 0.299556016921997],
  [0.826722025871277, 0.278755009174347],
  [0.527121007442474, 0.666198015213013],
  [0.553171992301941, 0.668527007102966],
  [0.577238023281097, 0.673889994621277],
  [0.554691970348358, 0.580065965652466],
  [0.611896991729736, 0.693961024284363],
  [0.59696102142334, 0.706539988517761],
  [0.596370995044708, 0.693953037261963],
  [0.539958000183105, 0.557139039039612],
  [0.568841993808746, 0.692366003990173],
  [0.547818005084991, 0.692366003990173],
  [0.52461302280426, 0.692366003990173],
  [0.534089982509613, 0.779141008853912],
  [0.527670979499817, 0.736225962638855],
  [0.526912987232208, 0.717857003211975],
  [0.526877999305725, 0.704625964164734],
  [0.526966989040375, 0.695277988910675],
  [0.572058022022247, 0.695277988910675],
  [0.573521018028259, 0.703539967536926],
  [0.57683801651001, 0.711845993995667],
  [0.581691026687622, 0.720062971115112],
  [0.609944999217987, 0.639909982681274],
  [0.986046016216278, 0.560034036636353],
  [0.5867999792099, 0.69539999961853],
  [0.590372025966644, 0.701822996139526],
  [0.531915009021759, 0.601536989212036],
  [0.577268004417419, 0.585934996604919],
  [0.536915004253387, 0.593786001205444],
  [0.627542972564697, 0.473352015018463],
  [0.665585994720459, 0.495950996875763],
  [0.588353991508484, 0.546862006187439],
  [0.757824003696442, 0.14767599105835],
  [0.709249973297119, 0.201507985591888],
  [0.672684013843536, 0.256581008434296],
  [0.600408971309662, 0.74900496006012],
  [0.55826598405838, 0.261672019958496],
  [0.570303976535797, 0.187870979309082],
  [0.588165998458862, 0.109044015407562],
  [0.711045026779175, 0.398952007293701],
  [0.781069993972778, 0.435405015945435],
  [0.587247014045715, 0.398931980133057],
  [0.742869973182678, 0.355445981025696],
  [0.572156012058258, 0.437651991844177],
  [0.55186802148819, 0.536570012569427],
  [0.821442008018494, 0.457556009292603],
  [0.752701997756958, 0.457181990146637],
  [0.71375697851181, 0.467626988887787],
  [0.66711300611496, 0.460672974586487],
  [0.631101012229919, 0.447153985500336],
  [0.6008620262146, 0.432473003864288],
  [0.523481011390686, 0.405627012252808],
  [0.810747981071472, 0.523926019668579],
  [0.771045982837677, 0.348959028720856],
  [0.509127020835876, 0.562718033790588],
  [0.595292985439301, 0.485023975372314],
  [0.980530977249146, 0.401564002037048],
  [0.573499977588654, 0.420000016689301],
  [0.602994978427887, 0.548687994480133],
  [0.733529984951019, 0.376977026462555],
  [0.560611009597778, 0.519016981124878],
  [0.967685997486115, 0.644356966018677],
  [0.580985009670258, 0.387160003185272],
  [0.537728011608124, 0.505385041236877],
  [0.760966002941132, 0.779752969741821],
  [0.801778972148895, 0.831938028335571],
  [0.892440974712372, 0.54076099395752],
  [0.816350996494293, 0.740260004997253],
  [0.865594983100891, 0.333687007427216],
  [0.614073991775513, 0.883246004581451],
  [0.508952975273132, 0.579437971115112],
  [0.617941975593567, 0.508316040039062],
  [0.825608015060425, 0.397674977779388],
  [0.681214988231659, 0.39623498916626],
  [0.656635999679565, 0.400596976280212],
  [0.603900015354156, 0.710216999053955],
  [0.81208598613739, 0.588539004325867],
  [0.56801301240921, 0.944564998149872],
  [0.681007981300354, 0.898285031318665],
  [0.733752012252808, 0.869701027870178],
  [0.633830010890961, 0.398822009563446],
  [0.606792986392975, 0.39553701877594],
  [0.589659988880157, 0.391062021255493],
  [0.805015981197357, 0.342108011245728],
  [0.611334979534149, 0.362284004688263],
  [0.634037971496582, 0.355970978736877],
  [0.656635999679565, 0.355356991291046],
  [0.681214988231659, 0.35834002494812],
  [0.698584973812103, 0.363156020641327],
  [0.941866993904114, 0.319076001644135],
  [0.698584973812103, 0.387449026107788],
  [0.584177017211914, 0.624107003211975],
  [0.554318010807037, 0.566076993942261],
  [0.534153997898102, 0.62064003944397],
  [0.711217999458313, 0.819975018501282],
  [0.664629995822906, 0.852871000766754],
  [0.559099972248077, 0.902631998062134],
  [0.871706008911133, 0.791940987110138],
  [0.591234028339386, 0.373893976211548],
  [0.544341027736664, 0.451583981513977],
  [0.624562978744507, 0.924192011356354],
  [0.88577002286911, 0.615028977394104],
  [0.551338016986847, 0.695277988910675],
  [0.551980018615723, 0.704632043838501],
  [0.552887976169586, 0.715808033943176],
  [0.555167973041534, 0.730794012546539],
  [0.569944024085999, 0.767035007476807],
  [0.593203008174896, 0.685675978660583],
  [0.599261999130249, 0.681069016456604],
  [0.607599973678589, 0.677703022956848],
  [0.631937980651855, 0.663500010967255],
  [0.752032995223999, 0.601315021514893],
  [0.547226011753082, 0.420395016670227],
  [0.563543975353241, 0.359827995300293],
  [0.583841025829315, 0.368713974952698],
  [0.586614012718201, 0.692366003990173],
  [0.771915018558502, 0.683578014373779],
  [0.531597018241882, 0.352482974529266],
  [0.588370978832245, 0.804440975189209],
  [0.52079701423645, 0.442565023899078],
  [0.567984998226166, 0.493479013442993],
  [0.543282985687256, 0.819254994392395],
  [0.655317008495331, 0.745514988899231],
  [0.621008992195129, 0.574018001556396],
  [0.625559985637665, 0.78031200170517],
  [0.680198013782501, 0.570719003677368],
  [0.64276397228241, 0.604337990283966],
  [0.704662978649139, 0.621529996395111],
  [0.552012026309967, 0.862591981887817],
  [0.589071989059448, 0.508637011051178],
  [0.685944974422455, 0.775357007980347],
  [0.645735025405884, 0.812640011310577],
  [0.675342977046967, 0.703978002071381],
  [0.810858011245728, 0.646304965019226],
  [0.72012197971344, 0.714666962623596],
  [0.866151988506317, 0.682704985141754],
  [0.663187026977539, 0.644596993923187],
  [0.570082008838654, 0.466325998306274],
  [0.544561982154846, 0.548375964164734],
  [0.562758982181549, 0.558784961700439],
  [0.531987011432648, 0.530140042304993],
  [0.585271000862122, 0.335177004337311],
  [0.622952997684479, 0.32277899980545],
  [0.655896008014679, 0.320163011550903],
  [0.687132000923157, 0.322345972061157],
  [0.716481983661652, 0.333200991153717],
  [0.758756995201111, 0.382786989212036],
  [0.897013008594513, 0.468769013881683],
  [0.732392013072968, 0.424547016620636],
  [0.70211398601532, 0.433162987232208],
  [0.66652500629425, 0.433866024017334],
  [0.633504986763, 0.426087975502014],
  [0.603875994682312, 0.416586995124817],
  [0.579657971858978, 0.409945011138916],
  [0.992439985275269, 0.480777025222778],
  [0.567192018032074, 0.569419980049133],
  [0.54136598110199, 0.478899002075195],
  [0.526564002037048, 0.546118021011353],
  [0.523913025856018, 0.563830018043518],
  [0.531529009342194, 0.555056989192963],
  [0.566035985946655, 0.582329034805298],
  [0.51631098985672, 0.563053965568542],
  [0.5174720287323, 0.577877044677734],
  [0.573594987392426, 0.389806985855103],
  [0.560697972774506, 0.395331978797913],
  [0.549755990505219, 0.399751007556915],
  [0.710287988185883, 0.368252992630005],
  [0.723330020904541, 0.363372981548309]
];
var TRI468 = [
  127,
  34,
  139,
  11,
  0,
  37,
  232,
  231,
  120,
  72,
  37,
  39,
  128,
  121,
  47,
  232,
  121,
  128,
  104,
  69,
  67,
  175,
  171,
  148,
  157,
  154,
  155,
  118,
  50,
  101,
  73,
  39,
  40,
  9,
  151,
  108,
  48,
  115,
  131,
  194,
  204,
  211,
  74,
  40,
  185,
  80,
  42,
  183,
  40,
  92,
  186,
  230,
  229,
  118,
  202,
  212,
  214,
  83,
  18,
  17,
  76,
  61,
  146,
  160,
  29,
  30,
  56,
  157,
  173,
  106,
  204,
  194,
  135,
  214,
  192,
  203,
  165,
  98,
  21,
  71,
  68,
  51,
  45,
  4,
  144,
  24,
  23,
  77,
  146,
  91,
  205,
  50,
  187,
  201,
  200,
  18,
  91,
  106,
  182,
  90,
  91,
  181,
  85,
  84,
  17,
  206,
  203,
  36,
  148,
  171,
  140,
  92,
  40,
  39,
  193,
  189,
  244,
  159,
  158,
  28,
  247,
  246,
  161,
  236,
  3,
  196,
  54,
  68,
  104,
  193,
  168,
  8,
  117,
  228,
  31,
  189,
  193,
  55,
  98,
  97,
  99,
  126,
  47,
  100,
  166,
  79,
  218,
  155,
  154,
  26,
  209,
  49,
  131,
  135,
  136,
  150,
  47,
  126,
  217,
  223,
  52,
  53,
  45,
  51,
  134,
  211,
  170,
  140,
  67,
  69,
  108,
  43,
  106,
  91,
  230,
  119,
  120,
  226,
  130,
  247,
  63,
  53,
  52,
  238,
  20,
  242,
  46,
  70,
  156,
  78,
  62,
  96,
  46,
  53,
  63,
  143,
  34,
  227,
  173,
  155,
  133,
  123,
  117,
  111,
  44,
  125,
  19,
  236,
  134,
  51,
  216,
  206,
  205,
  154,
  153,
  22,
  39,
  37,
  167,
  200,
  201,
  208,
  36,
  142,
  100,
  57,
  212,
  202,
  20,
  60,
  99,
  28,
  158,
  157,
  35,
  226,
  113,
  160,
  159,
  27,
  204,
  202,
  210,
  113,
  225,
  46,
  43,
  202,
  204,
  62,
  76,
  77,
  137,
  123,
  116,
  41,
  38,
  72,
  203,
  129,
  142,
  64,
  98,
  240,
  49,
  102,
  64,
  41,
  73,
  74,
  212,
  216,
  207,
  42,
  74,
  184,
  169,
  170,
  211,
  170,
  149,
  176,
  105,
  66,
  69,
  122,
  6,
  168,
  123,
  147,
  187,
  96,
  77,
  90,
  65,
  55,
  107,
  89,
  90,
  180,
  101,
  100,
  120,
  63,
  105,
  104,
  93,
  137,
  227,
  15,
  86,
  85,
  129,
  102,
  49,
  14,
  87,
  86,
  55,
  8,
  9,
  100,
  47,
  121,
  145,
  23,
  22,
  88,
  89,
  179,
  6,
  122,
  196,
  88,
  95,
  96,
  138,
  172,
  136,
  215,
  58,
  172,
  115,
  48,
  219,
  42,
  80,
  81,
  195,
  3,
  51,
  43,
  146,
  61,
  171,
  175,
  199,
  81,
  82,
  38,
  53,
  46,
  225,
  144,
  163,
  110,
  246,
  33,
  7,
  52,
  65,
  66,
  229,
  228,
  117,
  34,
  127,
  234,
  107,
  108,
  69,
  109,
  108,
  151,
  48,
  64,
  235,
  62,
  78,
  191,
  129,
  209,
  126,
  111,
  35,
  143,
  163,
  161,
  246,
  117,
  123,
  50,
  222,
  65,
  52,
  19,
  125,
  141,
  221,
  55,
  65,
  3,
  195,
  197,
  25,
  7,
  33,
  220,
  237,
  44,
  70,
  71,
  139,
  122,
  193,
  245,
  247,
  130,
  33,
  71,
  21,
  162,
  153,
  158,
  159,
  170,
  169,
  150,
  188,
  174,
  196,
  216,
  186,
  92,
  144,
  160,
  161,
  2,
  97,
  167,
  141,
  125,
  241,
  164,
  167,
  37,
  72,
  38,
  12,
  145,
  159,
  160,
  38,
  82,
  13,
  63,
  68,
  71,
  226,
  35,
  111,
  158,
  153,
  154,
  101,
  50,
  205,
  206,
  92,
  165,
  209,
  198,
  217,
  165,
  167,
  97,
  220,
  115,
  218,
  133,
  112,
  243,
  239,
  238,
  241,
  214,
  135,
  169,
  190,
  173,
  133,
  171,
  208,
  32,
  125,
  44,
  237,
  86,
  87,
  178,
  85,
  86,
  179,
  84,
  85,
  180,
  83,
  84,
  181,
  201,
  83,
  182,
  137,
  93,
  132,
  76,
  62,
  183,
  61,
  76,
  184,
  57,
  61,
  185,
  212,
  57,
  186,
  214,
  207,
  187,
  34,
  143,
  156,
  79,
  239,
  237,
  123,
  137,
  177,
  44,
  1,
  4,
  201,
  194,
  32,
  64,
  102,
  129,
  213,
  215,
  138,
  59,
  166,
  219,
  242,
  99,
  97,
  2,
  94,
  141,
  75,
  59,
  235,
  24,
  110,
  228,
  25,
  130,
  226,
  23,
  24,
  229,
  22,
  23,
  230,
  26,
  22,
  231,
  112,
  26,
  232,
  189,
  190,
  243,
  221,
  56,
  190,
  28,
  56,
  221,
  27,
  28,
  222,
  29,
  27,
  223,
  30,
  29,
  224,
  247,
  30,
  225,
  238,
  79,
  20,
  166,
  59,
  75,
  60,
  75,
  240,
  147,
  177,
  215,
  20,
  79,
  166,
  187,
  147,
  213,
  112,
  233,
  244,
  233,
  128,
  245,
  128,
  114,
  188,
  114,
  217,
  174,
  131,
  115,
  220,
  217,
  198,
  236,
  198,
  131,
  134,
  177,
  132,
  58,
  143,
  35,
  124,
  110,
  163,
  7,
  228,
  110,
  25,
  356,
  389,
  368,
  11,
  302,
  267,
  452,
  350,
  349,
  302,
  303,
  269,
  357,
  343,
  277,
  452,
  453,
  357,
  333,
  332,
  297,
  175,
  152,
  377,
  384,
  398,
  382,
  347,
  348,
  330,
  303,
  304,
  270,
  9,
  336,
  337,
  278,
  279,
  360,
  418,
  262,
  431,
  304,
  408,
  409,
  310,
  415,
  407,
  270,
  409,
  410,
  450,
  348,
  347,
  422,
  430,
  434,
  313,
  314,
  17,
  306,
  307,
  375,
  387,
  388,
  260,
  286,
  414,
  398,
  335,
  406,
  418,
  364,
  367,
  416,
  423,
  358,
  327,
  251,
  284,
  298,
  281,
  5,
  4,
  373,
  374,
  253,
  307,
  320,
  321,
  425,
  427,
  411,
  421,
  313,
  18,
  321,
  405,
  406,
  320,
  404,
  405,
  315,
  16,
  17,
  426,
  425,
  266,
  377,
  400,
  369,
  322,
  391,
  269,
  417,
  465,
  464,
  386,
  257,
  258,
  466,
  260,
  388,
  456,
  399,
  419,
  284,
  332,
  333,
  417,
  285,
  8,
  346,
  340,
  261,
  413,
  441,
  285,
  327,
  460,
  328,
  355,
  371,
  329,
  392,
  439,
  438,
  382,
  341,
  256,
  429,
  420,
  360,
  364,
  394,
  379,
  277,
  343,
  437,
  443,
  444,
  283,
  275,
  440,
  363,
  431,
  262,
  369,
  297,
  338,
  337,
  273,
  375,
  321,
  450,
  451,
  349,
  446,
  342,
  467,
  293,
  334,
  282,
  458,
  461,
  462,
  276,
  353,
  383,
  308,
  324,
  325,
  276,
  300,
  293,
  372,
  345,
  447,
  382,
  398,
  362,
  352,
  345,
  340,
  274,
  1,
  19,
  456,
  248,
  281,
  436,
  427,
  425,
  381,
  256,
  252,
  269,
  391,
  393,
  200,
  199,
  428,
  266,
  330,
  329,
  287,
  273,
  422,
  250,
  462,
  328,
  258,
  286,
  384,
  265,
  353,
  342,
  387,
  259,
  257,
  424,
  431,
  430,
  342,
  353,
  276,
  273,
  335,
  424,
  292,
  325,
  307,
  366,
  447,
  345,
  271,
  303,
  302,
  423,
  266,
  371,
  294,
  455,
  460,
  279,
  278,
  294,
  271,
  272,
  304,
  432,
  434,
  427,
  272,
  407,
  408,
  394,
  430,
  431,
  395,
  369,
  400,
  334,
  333,
  299,
  351,
  417,
  168,
  352,
  280,
  411,
  325,
  319,
  320,
  295,
  296,
  336,
  319,
  403,
  404,
  330,
  348,
  349,
  293,
  298,
  333,
  323,
  454,
  447,
  15,
  16,
  315,
  358,
  429,
  279,
  14,
  15,
  316,
  285,
  336,
  9,
  329,
  349,
  350,
  374,
  380,
  252,
  318,
  402,
  403,
  6,
  197,
  419,
  318,
  319,
  325,
  367,
  364,
  365,
  435,
  367,
  397,
  344,
  438,
  439,
  272,
  271,
  311,
  195,
  5,
  281,
  273,
  287,
  291,
  396,
  428,
  199,
  311,
  271,
  268,
  283,
  444,
  445,
  373,
  254,
  339,
  263,
  466,
  249,
  282,
  334,
  296,
  449,
  347,
  346,
  264,
  447,
  454,
  336,
  296,
  299,
  338,
  10,
  151,
  278,
  439,
  455,
  292,
  407,
  415,
  358,
  371,
  355,
  340,
  345,
  372,
  390,
  249,
  466,
  346,
  347,
  280,
  442,
  443,
  282,
  19,
  94,
  370,
  441,
  442,
  295,
  248,
  419,
  197,
  263,
  255,
  359,
  440,
  275,
  274,
  300,
  383,
  368,
  351,
  412,
  465,
  263,
  467,
  466,
  301,
  368,
  389,
  380,
  374,
  386,
  395,
  378,
  379,
  412,
  351,
  419,
  436,
  426,
  322,
  373,
  390,
  388,
  2,
  164,
  393,
  370,
  462,
  461,
  164,
  0,
  267,
  302,
  11,
  12,
  374,
  373,
  387,
  268,
  12,
  13,
  293,
  300,
  301,
  446,
  261,
  340,
  385,
  384,
  381,
  330,
  266,
  425,
  426,
  423,
  391,
  429,
  355,
  437,
  391,
  327,
  326,
  440,
  457,
  438,
  341,
  382,
  362,
  459,
  457,
  461,
  434,
  430,
  394,
  414,
  463,
  362,
  396,
  369,
  262,
  354,
  461,
  457,
  316,
  403,
  402,
  315,
  404,
  403,
  314,
  405,
  404,
  313,
  406,
  405,
  421,
  418,
  406,
  366,
  401,
  361,
  306,
  408,
  407,
  291,
  409,
  408,
  287,
  410,
  409,
  432,
  436,
  410,
  434,
  416,
  411,
  264,
  368,
  383,
  309,
  438,
  457,
  352,
  376,
  401,
  274,
  275,
  4,
  421,
  428,
  262,
  294,
  327,
  358,
  433,
  416,
  367,
  289,
  455,
  439,
  462,
  370,
  326,
  2,
  326,
  370,
  305,
  460,
  455,
  254,
  449,
  448,
  255,
  261,
  446,
  253,
  450,
  449,
  252,
  451,
  450,
  256,
  452,
  451,
  341,
  453,
  452,
  413,
  464,
  463,
  441,
  413,
  414,
  258,
  442,
  441,
  257,
  443,
  442,
  259,
  444,
  443,
  260,
  445,
  444,
  467,
  342,
  445,
  459,
  458,
  250,
  289,
  392,
  290,
  290,
  328,
  460,
  376,
  433,
  435,
  250,
  290,
  392,
  411,
  416,
  433,
  341,
  463,
  464,
  453,
  464,
  465,
  357,
  465,
  412,
  343,
  412,
  399,
  360,
  363,
  440,
  437,
  399,
  456,
  420,
  456,
  363,
  401,
  435,
  288,
  372,
  383,
  353,
  339,
  255,
  249,
  448,
  261,
  255,
  133,
  243,
  190,
  133,
  155,
  112,
  33,
  246,
  247,
  33,
  130,
  25,
  398,
  384,
  286,
  362,
  398,
  414,
  362,
  463,
  341,
  263,
  359,
  467,
  263,
  249,
  255,
  466,
  467,
  260,
  75,
  60,
  166,
  238,
  239,
  79,
  162,
  127,
  139,
  72,
  11,
  37,
  121,
  232,
  120,
  73,
  72,
  39,
  114,
  128,
  47,
  233,
  232,
  128,
  103,
  104,
  67,
  152,
  175,
  148,
  173,
  157,
  155,
  119,
  118,
  101,
  74,
  73,
  40,
  107,
  9,
  108,
  49,
  48,
  131,
  32,
  194,
  211,
  184,
  74,
  185,
  191,
  80,
  183,
  185,
  40,
  186,
  119,
  230,
  118,
  210,
  202,
  214,
  84,
  83,
  17,
  77,
  76,
  146,
  161,
  160,
  30,
  190,
  56,
  173,
  182,
  106,
  194,
  138,
  135,
  192,
  129,
  203,
  98,
  54,
  21,
  68,
  5,
  51,
  4,
  145,
  144,
  23,
  90,
  77,
  91,
  207,
  205,
  187,
  83,
  201,
  18,
  181,
  91,
  182,
  180,
  90,
  181,
  16,
  85,
  17,
  205,
  206,
  36,
  176,
  148,
  140,
  165,
  92,
  39,
  245,
  193,
  244,
  27,
  159,
  28,
  30,
  247,
  161,
  174,
  236,
  196,
  103,
  54,
  104,
  55,
  193,
  8,
  111,
  117,
  31,
  221,
  189,
  55,
  240,
  98,
  99,
  142,
  126,
  100,
  219,
  166,
  218,
  112,
  155,
  26,
  198,
  209,
  131,
  169,
  135,
  150,
  114,
  47,
  217,
  224,
  223,
  53,
  220,
  45,
  134,
  32,
  211,
  140,
  109,
  67,
  108,
  146,
  43,
  91,
  231,
  230,
  120,
  113,
  226,
  247,
  105,
  63,
  52,
  241,
  238,
  242,
  124,
  46,
  156,
  95,
  78,
  96,
  70,
  46,
  63,
  116,
  143,
  227,
  116,
  123,
  111,
  1,
  44,
  19,
  3,
  236,
  51,
  207,
  216,
  205,
  26,
  154,
  22,
  165,
  39,
  167,
  199,
  200,
  208,
  101,
  36,
  100,
  43,
  57,
  202,
  242,
  20,
  99,
  56,
  28,
  157,
  124,
  35,
  113,
  29,
  160,
  27,
  211,
  204,
  210,
  124,
  113,
  46,
  106,
  43,
  204,
  96,
  62,
  77,
  227,
  137,
  116,
  73,
  41,
  72,
  36,
  203,
  142,
  235,
  64,
  240,
  48,
  49,
  64,
  42,
  41,
  74,
  214,
  212,
  207,
  183,
  42,
  184,
  210,
  169,
  211,
  140,
  170,
  176,
  104,
  105,
  69,
  193,
  122,
  168,
  50,
  123,
  187,
  89,
  96,
  90,
  66,
  65,
  107,
  179,
  89,
  180,
  119,
  101,
  120,
  68,
  63,
  104,
  234,
  93,
  227,
  16,
  15,
  85,
  209,
  129,
  49,
  15,
  14,
  86,
  107,
  55,
  9,
  120,
  100,
  121,
  153,
  145,
  22,
  178,
  88,
  179,
  197,
  6,
  196,
  89,
  88,
  96,
  135,
  138,
  136,
  138,
  215,
  172,
  218,
  115,
  219,
  41,
  42,
  81,
  5,
  195,
  51,
  57,
  43,
  61,
  208,
  171,
  199,
  41,
  81,
  38,
  224,
  53,
  225,
  24,
  144,
  110,
  105,
  52,
  66,
  118,
  229,
  117,
  227,
  34,
  234,
  66,
  107,
  69,
  10,
  109,
  151,
  219,
  48,
  235,
  183,
  62,
  191,
  142,
  129,
  126,
  116,
  111,
  143,
  7,
  163,
  246,
  118,
  117,
  50,
  223,
  222,
  52,
  94,
  19,
  141,
  222,
  221,
  65,
  196,
  3,
  197,
  45,
  220,
  44,
  156,
  70,
  139,
  188,
  122,
  245,
  139,
  71,
  162,
  145,
  153,
  159,
  149,
  170,
  150,
  122,
  188,
  196,
  206,
  216,
  92,
  163,
  144,
  161,
  164,
  2,
  167,
  242,
  141,
  241,
  0,
  164,
  37,
  11,
  72,
  12,
  144,
  145,
  160,
  12,
  38,
  13,
  70,
  63,
  71,
  31,
  226,
  111,
  157,
  158,
  154,
  36,
  101,
  205,
  203,
  206,
  165,
  126,
  209,
  217,
  98,
  165,
  97,
  237,
  220,
  218,
  237,
  239,
  241,
  210,
  214,
  169,
  140,
  171,
  32,
  241,
  125,
  237,
  179,
  86,
  178,
  180,
  85,
  179,
  181,
  84,
  180,
  182,
  83,
  181,
  194,
  201,
  182,
  177,
  137,
  132,
  184,
  76,
  183,
  185,
  61,
  184,
  186,
  57,
  185,
  216,
  212,
  186,
  192,
  214,
  187,
  139,
  34,
  156,
  218,
  79,
  237,
  147,
  123,
  177,
  45,
  44,
  4,
  208,
  201,
  32,
  98,
  64,
  129,
  192,
  213,
  138,
  235,
  59,
  219,
  141,
  242,
  97,
  97,
  2,
  141,
  240,
  75,
  235,
  229,
  24,
  228,
  31,
  25,
  226,
  230,
  23,
  229,
  231,
  22,
  230,
  232,
  26,
  231,
  233,
  112,
  232,
  244,
  189,
  243,
  189,
  221,
  190,
  222,
  28,
  221,
  223,
  27,
  222,
  224,
  29,
  223,
  225,
  30,
  224,
  113,
  247,
  225,
  99,
  60,
  240,
  213,
  147,
  215,
  60,
  20,
  166,
  192,
  187,
  213,
  243,
  112,
  244,
  244,
  233,
  245,
  245,
  128,
  188,
  188,
  114,
  174,
  134,
  131,
  220,
  174,
  217,
  236,
  236,
  198,
  134,
  215,
  177,
  58,
  156,
  143,
  124,
  25,
  110,
  7,
  31,
  228,
  25,
  264,
  356,
  368,
  0,
  11,
  267,
  451,
  452,
  349,
  267,
  302,
  269,
  350,
  357,
  277,
  350,
  452,
  357,
  299,
  333,
  297,
  396,
  175,
  377,
  381,
  384,
  382,
  280,
  347,
  330,
  269,
  303,
  270,
  151,
  9,
  337,
  344,
  278,
  360,
  424,
  418,
  431,
  270,
  304,
  409,
  272,
  310,
  407,
  322,
  270,
  410,
  449,
  450,
  347,
  432,
  422,
  434,
  18,
  313,
  17,
  291,
  306,
  375,
  259,
  387,
  260,
  424,
  335,
  418,
  434,
  364,
  416,
  391,
  423,
  327,
  301,
  251,
  298,
  275,
  281,
  4,
  254,
  373,
  253,
  375,
  307,
  321,
  280,
  425,
  411,
  200,
  421,
  18,
  335,
  321,
  406,
  321,
  320,
  405,
  314,
  315,
  17,
  423,
  426,
  266,
  396,
  377,
  369,
  270,
  322,
  269,
  413,
  417,
  464,
  385,
  386,
  258,
  248,
  456,
  419,
  298,
  284,
  333,
  168,
  417,
  8,
  448,
  346,
  261,
  417,
  413,
  285,
  326,
  327,
  328,
  277,
  355,
  329,
  309,
  392,
  438,
  381,
  382,
  256,
  279,
  429,
  360,
  365,
  364,
  379,
  355,
  277,
  437,
  282,
  443,
  283,
  281,
  275,
  363,
  395,
  431,
  369,
  299,
  297,
  337,
  335,
  273,
  321,
  348,
  450,
  349,
  359,
  446,
  467,
  283,
  293,
  282,
  250,
  458,
  462,
  300,
  276,
  383,
  292,
  308,
  325,
  283,
  276,
  293,
  264,
  372,
  447,
  346,
  352,
  340,
  354,
  274,
  19,
  363,
  456,
  281,
  426,
  436,
  425,
  380,
  381,
  252,
  267,
  269,
  393,
  421,
  200,
  428,
  371,
  266,
  329,
  432,
  287,
  422,
  290,
  250,
  328,
  385,
  258,
  384,
  446,
  265,
  342,
  386,
  387,
  257,
  422,
  424,
  430,
  445,
  342,
  276,
  422,
  273,
  424,
  306,
  292,
  307,
  352,
  366,
  345,
  268,
  271,
  302,
  358,
  423,
  371,
  327,
  294,
  460,
  331,
  279,
  294,
  303,
  271,
  304,
  436,
  432,
  427,
  304,
  272,
  408,
  395,
  394,
  431,
  378,
  395,
  400,
  296,
  334,
  299,
  6,
  351,
  168,
  376,
  352,
  411,
  307,
  325,
  320,
  285,
  295,
  336,
  320,
  319,
  404,
  329,
  330,
  349,
  334,
  293,
  333,
  366,
  323,
  447,
  316,
  15,
  315,
  331,
  358,
  279,
  317,
  14,
  316,
  8,
  285,
  9,
  277,
  329,
  350,
  253,
  374,
  252,
  319,
  318,
  403,
  351,
  6,
  419,
  324,
  318,
  325,
  397,
  367,
  365,
  288,
  435,
  397,
  278,
  344,
  439,
  310,
  272,
  311,
  248,
  195,
  281,
  375,
  273,
  291,
  175,
  396,
  199,
  312,
  311,
  268,
  276,
  283,
  445,
  390,
  373,
  339,
  295,
  282,
  296,
  448,
  449,
  346,
  356,
  264,
  454,
  337,
  336,
  299,
  337,
  338,
  151,
  294,
  278,
  455,
  308,
  292,
  415,
  429,
  358,
  355,
  265,
  340,
  372,
  388,
  390,
  466,
  352,
  346,
  280,
  295,
  442,
  282,
  354,
  19,
  370,
  285,
  441,
  295,
  195,
  248,
  197,
  457,
  440,
  274,
  301,
  300,
  368,
  417,
  351,
  465,
  251,
  301,
  389,
  385,
  380,
  386,
  394,
  395,
  379,
  399,
  412,
  419,
  410,
  436,
  322,
  387,
  373,
  388,
  326,
  2,
  393,
  354,
  370,
  461,
  393,
  164,
  267,
  268,
  302,
  12,
  386,
  374,
  387,
  312,
  268,
  13,
  298,
  293,
  301,
  265,
  446,
  340,
  380,
  385,
  381,
  280,
  330,
  425,
  322,
  426,
  391,
  420,
  429,
  437,
  393,
  391,
  326,
  344,
  440,
  438,
  458,
  459,
  461,
  364,
  434,
  394,
  428,
  396,
  262,
  274,
  354,
  457,
  317,
  316,
  402,
  316,
  315,
  403,
  315,
  314,
  404,
  314,
  313,
  405,
  313,
  421,
  406,
  323,
  366,
  361,
  292,
  306,
  407,
  306,
  291,
  408,
  291,
  287,
  409,
  287,
  432,
  410,
  427,
  434,
  411,
  372,
  264,
  383,
  459,
  309,
  457,
  366,
  352,
  401,
  1,
  274,
  4,
  418,
  421,
  262,
  331,
  294,
  358,
  435,
  433,
  367,
  392,
  289,
  439,
  328,
  462,
  326,
  94,
  2,
  370,
  289,
  305,
  455,
  339,
  254,
  448,
  359,
  255,
  446,
  254,
  253,
  449,
  253,
  252,
  450,
  252,
  256,
  451,
  256,
  341,
  452,
  414,
  413,
  463,
  286,
  441,
  414,
  286,
  258,
  441,
  258,
  257,
  442,
  257,
  259,
  443,
  259,
  260,
  444,
  260,
  467,
  445,
  309,
  459,
  250,
  305,
  289,
  290,
  305,
  290,
  460,
  401,
  376,
  435,
  309,
  250,
  392,
  376,
  411,
  433,
  453,
  341,
  464,
  357,
  453,
  465,
  343,
  357,
  412,
  437,
  343,
  399,
  344,
  360,
  440,
  420,
  437,
  456,
  360,
  420,
  363,
  361,
  401,
  288,
  265,
  372,
  353,
  390,
  339,
  249,
  339,
  448,
  255
];
var VTX68 = [
  127,
  234,
  132,
  58,
  172,
  150,
  149,
  148,
  152,
  377,
  378,
  379,
  397,
  288,
  361,
  454,
  356,
  70,
  63,
  105,
  66,
  107,
  336,
  296,
  334,
  293,
  300,
  168,
  6,
  195,
  4,
  98,
  97,
  2,
  326,
  327,
  33,
  160,
  158,
  133,
  153,
  144,
  362,
  385,
  387,
  263,
  373,
  380,
  57,
  40,
  37,
  0,
  267,
  270,
  287,
  321,
  314,
  17,
  84,
  91,
  78,
  81,
  13,
  311,
  308,
  402,
  14,
  178
];
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);
  De(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 = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
  const anchors3 = [];
  for (let i = 0; i < spec.strides.length; i++) {
    const stride = spec.strides[i];
    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
    const anchorsNum = spec.anchors[i];
    for (let gridY = 0; gridY < gridRows; gridY++) {
      const anchorY = stride * (gridY + 0.5);
      for (let gridX = 0; gridX < gridCols; gridX++) {
        const anchorX = stride * (gridX + 0.5);
        for (let n = 0; n < anchorsNum; n++)
          anchors3.push([anchorX, anchorY]);
      }
    }
  }
  return anchors3;
}
function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
  const boxSize = getBoxSize(box);
  const coordsScaled = coordsRaw.map((coord) => [
    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
    coord[2] || 0
  ]);
  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
  const boxCenter = getBoxCenter(box);
  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
  return coordsRotated.map((coord) => [
    Math.trunc(coord[0] + offsets[0]),
    Math.trunc(coord[1] + offsets[1]),
    Math.trunc(coord[2] || 0)
  ]);
}
function correctFaceRotation(rotate, box, input, inputSize10) {
  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
  let angle = 0;
  let rotationMatrix = fixedRotationMatrix;
  let face4;
  if (rotate && env.kernels.includes("rotatewithoffset")) {
    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
    if (largeAngle) {
      const center = getBoxCenter(box);
      const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
      const rotated = jn.rotateWithOffset(input, angle, 0, centerRaw);
      rotationMatrix = buildRotationMatrix(-angle, center);
      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
      De(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 = rR([t.startNormalized, t.endNormalized], 1);
  Object.keys(t).forEach((tensor) => De(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) && res.length > 2) {
    const sorted = res.sort((a, b) => a.size - b.size);
    t.concat384 = Ot([sorted[0], sorted[2]], 2);
    t.concat512 = Ot([sorted[1], sorted[3]], 2);
    t.concat = Ot([t.concat512, t.concat384], 1);
    t.batch = br(t.concat, 0);
  } else if (Array.isArray(res)) {
    t.batch = br(res[0]);
  } else {
    t.batch = br(res);
  }
  De(res);
  t.boxes = decodeBounds(t.batch);
  t.logits = qe(t.batch, [0, 0], [-1, 1]);
  t.sigmoid = qs(t.logits);
  t.scores = br(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 = br(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) => De(b[tensor]));
    }
  }
  Object.keys(t).forEach((tensor) => De(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 = bi(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) => De(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) => De(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) => De(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 = br(res);
  const arr = Bn(t.squeeze, 6, 1);
  t.stack = es([arr[1], arr[0], arr[3], arr[2]], 1);
  t.boxes = br(t.stack);
  t.scores = br(arr[4]);
  t.classes = br(arr[5]);
  De([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) => De(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();
    De(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];
  De([reshaped, max]);
  if (newScore > minScore) {
    const coordinates = Yu(reshaped, 0);
    const mod = WD(coordinates, width);
    const x = (await mod.data())[0];
    const div = xe(coordinates, we(width, "int32"));
    const y = (await div.data())[0];
    De([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();
    De(tensor);
    if (resT) {
      cache2.keypoints.length = 0;
      const squeeze = resT.squeeze();
      De(resT);
      const stack = squeeze.unstack(2);
      De(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) => De(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) => De(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);
    De(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 z10 = eyeData[i * 3 + 2];
    eyeRawCoords.push([
      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
      z10
    ]);
  }
  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
  return irisCoords.map((coord, i) => {
    let z10 = averageZ;
    if (i === 2) {
      z10 = upperCenterZ;
    } else if (i === 4) {
      z10 = lowerCenterZ;
    }
    return [coord[0], coord[1], z10];
  });
};
async function augmentIris(rawCoords, face4, 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 = Ot([leftEyeCrop, rightEyeCrop]);
  De(leftEyeCrop);
  De(rightEyeCrop);
  const eyePredictions = model10.execute(combined);
  De(combined);
  const eyePredictionsData = await eyePredictions.data();
  De(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, _k2;
  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);
      De(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;
        if ((_h = config3.face.mesh) == null ? void 0 : _h.keepInvalid) {
          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 ((_i = config3.face.attention) == null ? void 0 : _i.enabled) {
          rawCoords = await augment(rawCoords, results);
        } else if ((_j2 = config3.face.iris) == null ? void 0 : _j2.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);
      }
      De([...results, coordsReshaped]);
    }
    if (face4.score > (((_k2 = config3.face.detector) == null ? void 0 : _k2.minConfidence) || 1))
      faces.push(face4);
    else
      De(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);
  De(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();
      De(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 = Yu(resT.find((t) => t.shape[1] === 100), 1);
      const age = (await argmax.data())[0];
      De(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) => De(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 },
  { x: 0.359375, y: 0.140625 },
  { x: 0.359375, y: 0.140625 },
  { x: 0.390625, y: 0.140625 },
  { x: 0.390625, y: 0.140625 },
  { x: 0.421875, y: 0.140625 },
  { x: 0.421875, y: 0.140625 },
  { x: 0.453125, y: 0.140625 },
  { x: 0.453125, y: 0.140625 },
  { x: 0.484375, y: 0.140625 },
  { x: 0.484375, y: 0.140625 },
  { x: 0.515625, y: 0.140625 },
  { x: 0.515625, y: 0.140625 },
  { x: 0.546875, y: 0.140625 },
  { x: 0.546875, y: 0.140625 },
  { x: 0.578125, y: 0.140625 },
  { x: 0.578125, y: 0.140625 },
  { x: 0.609375, y: 0.140625 },
  { x: 0.609375, y: 0.140625 },
  { x: 0.640625, y: 0.140625 },
  { x: 0.640625, y: 0.140625 },
  { x: 0.671875, y: 0.140625 },
  { x: 0.671875, y: 0.140625 },
  { x: 0.703125, y: 0.140625 },
  { x: 0.703125, y: 0.140625 },
  { x: 0.734375, y: 0.140625 },
  { x: 0.734375, y: 0.140625 },
  { x: 0.765625, y: 0.140625 },
  { x: 0.765625, y: 0.140625 },
  { x: 0.796875, y: 0.140625 },
  { x: 0.796875, y: 0.140625 },
  { x: 0.828125, y: 0.140625 },
  { x: 0.828125, y: 0.140625 },
  { x: 0.859375, y: 0.140625 },
  { x: 0.859375, y: 0.140625 },
  { x: 0.890625, y: 0.140625 },
  { x: 0.890625, y: 0.140625 },
  { x: 0.921875, y: 0.140625 },
  { x: 0.921875, y: 0.140625 },
  { x: 0.953125, y: 0.140625 },
  { x: 0.953125, y: 0.140625 },
  { x: 0.984375, y: 0.140625 },
  { x: 0.984375, y: 0.140625 },
  { x: 0.015625, y: 0.171875 },
  { x: 0.015625, y: 0.171875 },
  { x: 0.046875, y: 0.171875 },
  { x: 0.046875, y: 0.171875 },
  { x: 0.078125, y: 0.171875 },
  { x: 0.078125, y: 0.171875 },
  { x: 0.109375, y: 0.171875 },
  { x: 0.109375, y: 0.171875 },
  { x: 0.140625, y: 0.171875 },
  { x: 0.140625, y: 0.171875 },
  { x: 0.171875, y: 0.171875 },
  { x: 0.171875, y: 0.171875 },
  { x: 0.203125, y: 0.171875 },
  { x: 0.203125, y: 0.171875 },
  { x: 0.234375, y: 0.171875 },
  { x: 0.234375, y: 0.171875 },
  { x: 0.265625, y: 0.171875 },
  { x: 0.265625, y: 0.171875 },
  { x: 0.296875, y: 0.171875 },
  { x: 0.296875, y: 0.171875 },
  { x: 0.328125, y: 0.171875 },
  { x: 0.328125, y: 0.171875 },
  { x: 0.359375, y: 0.171875 },
  { x: 0.359375, y: 0.171875 },
  { x: 0.390625, y: 0.171875 },
  { x: 0.390625, y: 0.171875 },
  { x: 0.421875, y: 0.171875 },
  { x: 0.421875, y: 0.171875 },
  { x: 0.453125, y: 0.171875 },
  { x: 0.453125, y: 0.171875 },
  { x: 0.484375, y: 0.171875 },
  { x: 0.484375, y: 0.171875 },
  { x: 0.515625, y: 0.171875 },
  { x: 0.515625, y: 0.171875 },
  { x: 0.546875, y: 0.171875 },
  { x: 0.546875, y: 0.171875 },
  { x: 0.578125, y: 0.171875 },
  { x: 0.578125, y: 0.171875 },
  { x: 0.609375, y: 0.171875 },
  { x: 0.609375, y: 0.171875 },
  { x: 0.640625, y: 0.171875 },
  { x: 0.640625, y: 0.171875 },
  { x: 0.671875, y: 0.171875 },
  { x: 0.671875, y: 0.171875 },
  { x: 0.703125, y: 0.171875 },
  { x: 0.703125, y: 0.171875 },
  { x: 0.734375, y: 0.171875 },
  { x: 0.734375, y: 0.171875 },
  { x: 0.765625, y: 0.171875 },
  { x: 0.765625, y: 0.171875 },
  { x: 0.796875, y: 0.171875 },
  { x: 0.796875, y: 0.171875 },
  { x: 0.828125, y: 0.171875 },
  { x: 0.828125, y: 0.171875 },
  { x: 0.859375, y: 0.171875 },
  { x: 0.859375, y: 0.171875 },
  { x: 0.890625, y: 0.171875 },
  { x: 0.890625, y: 0.171875 },
  { x: 0.921875, y: 0.171875 },
  { x: 0.921875, y: 0.171875 },
  { x: 0.953125, y: 0.171875 },
  { x: 0.953125, y: 0.171875 },
  { x: 0.984375, y: 0.171875 },
  { x: 0.984375, y: 0.171875 },
  { x: 0.015625, y: 0.203125 },
  { x: 0.015625, y: 0.203125 },
  { x: 0.046875, y: 0.203125 },
  { x: 0.046875, y: 0.203125 },
  { x: 0.078125, y: 0.203125 },
  { x: 0.078125, y: 0.203125 },
  { x: 0.109375, y: 0.203125 },
  { x: 0.109375, y: 0.203125 },
  { x: 0.140625, y: 0.203125 },
  { x: 0.140625, y: 0.203125 },
  { x: 0.171875, y: 0.203125 },
  { x: 0.171875, y: 0.203125 },
  { x: 0.203125, y: 0.203125 },
  { x: 0.203125, y: 0.203125 },
  { x: 0.234375, y: 0.203125 },
  { x: 0.234375, y: 0.203125 },
  { x: 0.265625, y: 0.203125 },
  { x: 0.265625, y: 0.203125 },
  { x: 0.296875, y: 0.203125 },
  { x: 0.296875, y: 0.203125 },
  { x: 0.328125, y: 0.203125 },
  { x: 0.328125, y: 0.203125 },
  { x: 0.359375, y: 0.203125 },
  { x: 0.359375, y: 0.203125 },
  { x: 0.390625, y: 0.203125 },
  { x: 0.390625, y: 0.203125 },
  { x: 0.421875, y: 0.203125 },
  { x: 0.421875, y: 0.203125 },
  { x: 0.453125, y: 0.203125 },
  { x: 0.453125, y: 0.203125 },
  { x: 0.484375, y: 0.203125 },
  { x: 0.484375, y: 0.203125 },
  { x: 0.515625, y: 0.203125 },
  { x: 0.515625, y: 0.203125 },
  { x: 0.546875, y: 0.203125 },
  { x: 0.546875, y: 0.203125 },
  { x: 0.578125, y: 0.203125 },
  { x: 0.578125, y: 0.203125 },
  { x: 0.609375, y: 0.203125 },
  { x: 0.609375, y: 0.203125 },
  { x: 0.640625, y: 0.203125 },
  { x: 0.640625, y: 0.203125 },
  { x: 0.671875, y: 0.203125 },
  { x: 0.671875, y: 0.203125 },
  { x: 0.703125, y: 0.203125 },
  { x: 0.703125, y: 0.203125 },
  { x: 0.734375, y: 0.203125 },
  { x: 0.734375, y: 0.203125 },
  { x: 0.765625, y: 0.203125 },
  { x: 0.765625, y: 0.203125 },
  { x: 0.796875, y: 0.203125 },
  { x: 0.796875, y: 0.203125 },
  { x: 0.828125, y: 0.203125 },
  { x: 0.828125, y: 0.203125 },
  { x: 0.859375, y: 0.203125 },
  { x: 0.859375, y: 0.203125 },
  { x: 0.890625, y: 0.203125 },
  { x: 0.890625, y: 0.203125 },
  { x: 0.921875, y: 0.203125 },
  { x: 0.921875, y: 0.203125 },
  { x: 0.953125, y: 0.203125 },
  { x: 0.953125, y: 0.203125 },
  { x: 0.984375, y: 0.203125 },
  { x: 0.984375, y: 0.203125 },
  { x: 0.015625, y: 0.234375 },
  { x: 0.015625, y: 0.234375 },
  { x: 0.046875, y: 0.234375 },
  { x: 0.046875, y: 0.234375 },
  { x: 0.078125, y: 0.234375 },
  { x: 0.078125, y: 0.234375 },
  { x: 0.109375, y: 0.234375 },
  { x: 0.109375, y: 0.234375 },
  { x: 0.140625, y: 0.234375 },
  { x: 0.140625, y: 0.234375 },
  { x: 0.171875, y: 0.234375 },
  { x: 0.171875, y: 0.234375 },
  { x: 0.203125, y: 0.234375 },
  { x: 0.203125, y: 0.234375 },
  { x: 0.234375, y: 0.234375 },
  { x: 0.234375, y: 0.234375 },
  { x: 0.265625, y: 0.234375 },
  { x: 0.265625, y: 0.234375 },
  { x: 0.296875, y: 0.234375 },
  { x: 0.296875, y: 0.234375 },
  { x: 0.328125, y: 0.234375 },
  { x: 0.328125, y: 0.234375 },
  { x: 0.359375, y: 0.234375 },
  { x: 0.359375, y: 0.234375 },
  { x: 0.390625, y: 0.234375 },
  { x: 0.390625, y: 0.234375 },
  { x: 0.421875, y: 0.234375 },
  { x: 0.421875, y: 0.234375 },
  { x: 0.453125, y: 0.234375 },
  { x: 0.453125, y: 0.234375 },
  { x: 0.484375, y: 0.234375 },
  { x: 0.484375, y: 0.234375 },
  { x: 0.515625, y: 0.234375 },
  { x: 0.515625, y: 0.234375 },
  { x: 0.546875, y: 0.234375 },
  { x: 0.546875, y: 0.234375 },
  { x: 0.578125, y: 0.234375 },
  { x: 0.578125, y: 0.234375 },
  { x: 0.609375, y: 0.234375 },
  { x: 0.609375, y: 0.234375 },
  { x: 0.640625, y: 0.234375 },
  { x: 0.640625, y: 0.234375 },
  { x: 0.671875, y: 0.234375 },
  { x: 0.671875, y: 0.234375 },
  { x: 0.703125, y: 0.234375 },
  { x: 0.703125, y: 0.234375 },
  { x: 0.734375, y: 0.234375 },
  { x: 0.734375, y: 0.234375 },
  { x: 0.765625, y: 0.234375 },
  { x: 0.765625, y: 0.234375 },
  { x: 0.796875, y: 0.234375 },
  { x: 0.796875, y: 0.234375 },
  { x: 0.828125, y: 0.234375 },
  { x: 0.828125, y: 0.234375 },
  { x: 0.859375, y: 0.234375 },
  { x: 0.859375, y: 0.234375 },
  { x: 0.890625, y: 0.234375 },
  { x: 0.890625, y: 0.234375 },
  { x: 0.921875, y: 0.234375 },
  { x: 0.921875, y: 0.234375 },
  { x: 0.953125, y: 0.234375 },
  { x: 0.953125, y: 0.234375 },
  { x: 0.984375, y: 0.234375 },
  { x: 0.984375, y: 0.234375 },
  { x: 0.015625, y: 0.265625 },
  { x: 0.015625, y: 0.265625 },
  { x: 0.046875, y: 0.265625 },
  { x: 0.046875, y: 0.265625 },
  { x: 0.078125, y: 0.265625 },
  { x: 0.078125, y: 0.265625 },
  { x: 0.109375, y: 0.265625 },
  { x: 0.109375, y: 0.265625 },
  { x: 0.140625, y: 0.265625 },
  { x: 0.140625, y: 0.265625 },
  { x: 0.171875, y: 0.265625 },
  { x: 0.171875, y: 0.265625 },
  { x: 0.203125, y: 0.265625 },
  { x: 0.203125, y: 0.265625 },
  { x: 0.234375, y: 0.265625 },
  { x: 0.234375, y: 0.265625 },
  { x: 0.265625, y: 0.265625 },
  { x: 0.265625, y: 0.265625 },
  { x: 0.296875, y: 0.265625 },
  { x: 0.296875, y: 0.265625 },
  { x: 0.328125, y: 0.265625 },
  { x: 0.328125, y: 0.265625 },
  { x: 0.359375, y: 0.265625 },
  { x: 0.359375, y: 0.265625 },
  { x: 0.390625, y: 0.265625 },
  { x: 0.390625, y: 0.265625 },
  { x: 0.421875, y: 0.265625 },
  { x: 0.421875, y: 0.265625 },
  { x: 0.453125, y: 0.265625 },
  { x: 0.453125, y: 0.265625 },
  { x: 0.484375, y: 0.265625 },
  { x: 0.484375, y: 0.265625 },
  { x: 0.515625, y: 0.265625 },
  { x: 0.515625, y: 0.265625 },
  { x: 0.546875, y: 0.265625 },
  { x: 0.546875, y: 0.265625 },
  { x: 0.578125, y: 0.265625 },
  { x: 0.578125, y: 0.265625 },
  { x: 0.609375, y: 0.265625 },
  { x: 0.609375, y: 0.265625 },
  { x: 0.640625, y: 0.265625 },
  { x: 0.640625, y: 0.265625 },
  { x: 0.671875, y: 0.265625 },
  { x: 0.671875, y: 0.265625 },
  { x: 0.703125, y: 0.265625 },
  { x: 0.703125, y: 0.265625 },
  { x: 0.734375, y: 0.265625 },
  { x: 0.734375, y: 0.265625 },
  { x: 0.765625, y: 0.265625 },
  { x: 0.765625, y: 0.265625 },
  { x: 0.796875, y: 0.265625 },
  { x: 0.796875, y: 0.265625 },
  { x: 0.828125, y: 0.265625 },
  { x: 0.828125, y: 0.265625 },
  { x: 0.859375, y: 0.265625 },
  { x: 0.859375, y: 0.265625 },
  { x: 0.890625, y: 0.265625 },
  { x: 0.890625, y: 0.265625 },
  { x: 0.921875, y: 0.265625 },
  { x: 0.921875, y: 0.265625 },
  { x: 0.953125, y: 0.265625 },
  { x: 0.953125, y: 0.265625 },
  { x: 0.984375, y: 0.265625 },
  { x: 0.984375, y: 0.265625 },
  { x: 0.015625, y: 0.296875 },
  { x: 0.015625, y: 0.296875 },
  { x: 0.046875, y: 0.296875 },
  { x: 0.046875, y: 0.296875 },
  { x: 0.078125, y: 0.296875 },
  { x: 0.078125, y: 0.296875 },
  { x: 0.109375, y: 0.296875 },
  { x: 0.109375, y: 0.296875 },
  { x: 0.140625, y: 0.296875 },
  { x: 0.140625, y: 0.296875 },
  { x: 0.171875, y: 0.296875 },
  { x: 0.171875, y: 0.296875 },
  { x: 0.203125, y: 0.296875 },
  { x: 0.203125, y: 0.296875 },
  { x: 0.234375, y: 0.296875 },
  { x: 0.234375, y: 0.296875 },
  { x: 0.265625, y: 0.296875 },
  { x: 0.265625, y: 0.296875 },
  { x: 0.296875, y: 0.296875 },
  { x: 0.296875, y: 0.296875 },
  { x: 0.328125, y: 0.296875 },
  { x: 0.328125, y: 0.296875 },
  { x: 0.359375, y: 0.296875 },
  { x: 0.359375, y: 0.296875 },
  { x: 0.390625, y: 0.296875 },
  { x: 0.390625, y: 0.296875 },
  { x: 0.421875, y: 0.296875 },
  { x: 0.421875, y: 0.296875 },
  { x: 0.453125, y: 0.296875 },
  { x: 0.453125, y: 0.296875 },
  { x: 0.484375, y: 0.296875 },
  { x: 0.484375, y: 0.296875 },
  { x: 0.515625, y: 0.296875 },
  { x: 0.515625, y: 0.296875 },
  { x: 0.546875, y: 0.296875 },
  { x: 0.546875, y: 0.296875 },
  { x: 0.578125, y: 0.296875 },
  { x: 0.578125, y: 0.296875 },
  { x: 0.609375, y: 0.296875 },
  { x: 0.609375, y: 0.296875 },
  { x: 0.640625, y: 0.296875 },
  { x: 0.640625, y: 0.296875 },
  { x: 0.671875, y: 0.296875 },
  { x: 0.671875, y: 0.296875 },
  { x: 0.703125, y: 0.296875 },
  { x: 0.703125, y: 0.296875 },
  { x: 0.734375, y: 0.296875 },
  { x: 0.734375, y: 0.296875 },
  { x: 0.765625, y: 0.296875 },
  { x: 0.765625, y: 0.296875 },
  { x: 0.796875, y: 0.296875 },
  { x: 0.796875, y: 0.296875 },
  { x: 0.828125, y: 0.296875 },
  { x: 0.828125, y: 0.296875 },
  { x: 0.859375, y: 0.296875 },
  { x: 0.859375, y: 0.296875 },
  { x: 0.890625, y: 0.296875 },
  { x: 0.890625, y: 0.296875 },
  { x: 0.921875, y: 0.296875 },
  { x: 0.921875, y: 0.296875 },
  { x: 0.953125, y: 0.296875 },
  { x: 0.953125, y: 0.296875 },
  { x: 0.984375, y: 0.296875 },
  { x: 0.984375, y: 0.296875 },
  { x: 0.015625, y: 0.328125 },
  { x: 0.015625, y: 0.328125 },
  { x: 0.046875, y: 0.328125 },
  { x: 0.046875, y: 0.328125 },
  { x: 0.078125, y: 0.328125 },
  { x: 0.078125, y: 0.328125 },
  { x: 0.109375, y: 0.328125 },
  { x: 0.109375, y: 0.328125 },
  { x: 0.140625, y: 0.328125 },
  { x: 0.140625, y: 0.328125 },
  { x: 0.171875, y: 0.328125 },
  { x: 0.171875, y: 0.328125 },
  { x: 0.203125, y: 0.328125 },
  { x: 0.203125, y: 0.328125 },
  { x: 0.234375, y: 0.328125 },
  { x: 0.234375, y: 0.328125 },
  { x: 0.265625, y: 0.328125 },
  { x: 0.265625, y: 0.328125 },
  { x: 0.296875, y: 0.328125 },
  { x: 0.296875, y: 0.328125 },
  { x: 0.328125, y: 0.328125 },
  { x: 0.328125, y: 0.328125 },
  { x: 0.359375, y: 0.328125 },
  { x: 0.359375, y: 0.328125 },
  { x: 0.390625, y: 0.328125 },
  { x: 0.390625, y: 0.328125 },
  { x: 0.421875, y: 0.328125 },
  { x: 0.421875, y: 0.328125 },
  { x: 0.453125, y: 0.328125 },
  { x: 0.453125, y: 0.328125 },
  { x: 0.484375, y: 0.328125 },
  { x: 0.484375, y: 0.328125 },
  { x: 0.515625, y: 0.328125 },
  { x: 0.515625, y: 0.328125 },
  { x: 0.546875, y: 0.328125 },
  { x: 0.546875, y: 0.328125 },
  { x: 0.578125, y: 0.328125 },
  { x: 0.578125, y: 0.328125 },
  { x: 0.609375, y: 0.328125 },
  { x: 0.609375, y: 0.328125 },
  { x: 0.640625, y: 0.328125 },
  { x: 0.640625, y: 0.328125 },
  { x: 0.671875, y: 0.328125 },
  { x: 0.671875, y: 0.328125 },
  { x: 0.703125, y: 0.328125 },
  { x: 0.703125, y: 0.328125 },
  { x: 0.734375, y: 0.328125 },
  { x: 0.734375, y: 0.328125 },
  { x: 0.765625, y: 0.328125 },
  { x: 0.765625, y: 0.328125 },
  { x: 0.796875, y: 0.328125 },
  { x: 0.796875, y: 0.328125 },
  { x: 0.828125, y: 0.328125 },
  { x: 0.828125, y: 0.328125 },
  { x: 0.859375, y: 0.328125 },
  { x: 0.859375, y: 0.328125 },
  { x: 0.890625, y: 0.328125 },
  { x: 0.890625, y: 0.328125 },
  { x: 0.921875, y: 0.328125 },
  { x: 0.921875, y: 0.328125 },
  { x: 0.953125, y: 0.328125 },
  { x: 0.953125, y: 0.328125 },
  { x: 0.984375, y: 0.328125 },
  { x: 0.984375, y: 0.328125 },
  { x: 0.015625, y: 0.359375 },
  { x: 0.015625, y: 0.359375 },
  { x: 0.046875, y: 0.359375 },
  { x: 0.046875, y: 0.359375 },
  { x: 0.078125, y: 0.359375 },
  { x: 0.078125, y: 0.359375 },
  { x: 0.109375, y: 0.359375 },
  { x: 0.109375, y: 0.359375 },
  { x: 0.140625, y: 0.359375 },
  { x: 0.140625, y: 0.359375 },
  { x: 0.171875, y: 0.359375 },
  { x: 0.171875, y: 0.359375 },
  { x: 0.203125, y: 0.359375 },
  { x: 0.203125, y: 0.359375 },
  { x: 0.234375, y: 0.359375 },
  { x: 0.234375, y: 0.359375 },
  { x: 0.265625, y: 0.359375 },
  { x: 0.265625, y: 0.359375 },
  { x: 0.296875, y: 0.359375 },
  { x: 0.296875, y: 0.359375 },
  { x: 0.328125, y: 0.359375 },
  { x: 0.328125, y: 0.359375 },
  { x: 0.359375, y: 0.359375 },
  { x: 0.359375, y: 0.359375 },
  { x: 0.390625, y: 0.359375 },
  { x: 0.390625, y: 0.359375 },
  { x: 0.421875, y: 0.359375 },
  { x: 0.421875, y: 0.359375 },
  { x: 0.453125, y: 0.359375 },
  { x: 0.453125, y: 0.359375 },
  { x: 0.484375, y: 0.359375 },
  { x: 0.484375, y: 0.359375 },
  { x: 0.515625, y: 0.359375 },
  { x: 0.515625, y: 0.359375 },
  { x: 0.546875, y: 0.359375 },
  { x: 0.546875, y: 0.359375 },
  { x: 0.578125, y: 0.359375 },
  { x: 0.578125, y: 0.359375 },
  { x: 0.609375, y: 0.359375 },
  { x: 0.609375, y: 0.359375 },
  { x: 0.640625, y: 0.359375 },
  { x: 0.640625, y: 0.359375 },
  { x: 0.671875, y: 0.359375 },
  { x: 0.671875, y: 0.359375 },
  { x: 0.703125, y: 0.359375 },
  { x: 0.703125, y: 0.359375 },
  { x: 0.734375, y: 0.359375 },
  { x: 0.734375, y: 0.359375 },
  { x: 0.765625, y: 0.359375 },
  { x: 0.765625, y: 0.359375 },
  { x: 0.796875, y: 0.359375 },
  { x: 0.796875, y: 0.359375 },
  { x: 0.828125, y: 0.359375 },
  { x: 0.828125, y: 0.359375 },
  { x: 0.859375, y: 0.359375 },
  { x: 0.859375, y: 0.359375 },
  { x: 0.890625, y: 0.359375 },
  { x: 0.890625, y: 0.359375 },
  { x: 0.921875, y: 0.359375 },
  { x: 0.921875, y: 0.359375 },
  { x: 0.953125, y: 0.359375 },
  { x: 0.953125, y: 0.359375 },
  { x: 0.984375, y: 0.359375 },
  { x: 0.984375, y: 0.359375 },
  { x: 0.015625, y: 0.390625 },
  { x: 0.015625, y: 0.390625 },
  { x: 0.046875, y: 0.390625 },
  { x: 0.046875, y: 0.390625 },
  { x: 0.078125, y: 0.390625 },
  { x: 0.078125, y: 0.390625 },
  { x: 0.109375, y: 0.390625 },
  { x: 0.109375, y: 0.390625 },
  { x: 0.140625, y: 0.390625 },
  { x: 0.140625, y: 0.390625 },
  { x: 0.171875, y: 0.390625 },
  { x: 0.171875, y: 0.390625 },
  { x: 0.203125, y: 0.390625 },
  { x: 0.203125, y: 0.390625 },
  { x: 0.234375, y: 0.390625 },
  { x: 0.234375, y: 0.390625 },
  { x: 0.265625, y: 0.390625 },
  { x: 0.265625, y: 0.390625 },
  { x: 0.296875, y: 0.390625 },
  { x: 0.296875, y: 0.390625 },
  { x: 0.328125, y: 0.390625 },
  { x: 0.328125, y: 0.390625 },
  { x: 0.359375, y: 0.390625 },
  { x: 0.359375, y: 0.390625 },
  { x: 0.390625, y: 0.390625 },
  { x: 0.390625, y: 0.390625 },
  { x: 0.421875, y: 0.390625 },
  { x: 0.421875, y: 0.390625 },
  { x: 0.453125, y: 0.390625 },
  { x: 0.453125, y: 0.390625 },
  { x: 0.484375, y: 0.390625 },
  { x: 0.484375, y: 0.390625 },
  { x: 0.515625, y: 0.390625 },
  { x: 0.515625, y: 0.390625 },
  { x: 0.546875, y: 0.390625 },
  { x: 0.546875, y: 0.390625 },
  { x: 0.578125, y: 0.390625 },
  { x: 0.578125, y: 0.390625 },
  { x: 0.609375, y: 0.390625 },
  { x: 0.609375, y: 0.390625 },
  { x: 0.640625, y: 0.390625 },
  { x: 0.640625, y: 0.390625 },
  { x: 0.671875, y: 0.390625 },
  { x: 0.671875, y: 0.390625 },
  { x: 0.703125, y: 0.390625 },
  { x: 0.703125, y: 0.390625 },
  { x: 0.734375, y: 0.390625 },
  { x: 0.734375, y: 0.390625 },
  { x: 0.765625, y: 0.390625 },
  { x: 0.765625, y: 0.390625 },
  { x: 0.796875, y: 0.390625 },
  { x: 0.796875, y: 0.390625 },
  { x: 0.828125, y: 0.390625 },
  { x: 0.828125, y: 0.390625 },
  { x: 0.859375, y: 0.390625 },
  { x: 0.859375, y: 0.390625 },
  { x: 0.890625, y: 0.390625 },
  { x: 0.890625, y: 0.390625 },
  { x: 0.921875, y: 0.390625 },
  { x: 0.921875, y: 0.390625 },
  { x: 0.953125, y: 0.390625 },
  { x: 0.953125, y: 0.390625 },
  { x: 0.984375, y: 0.390625 },
  { x: 0.984375, y: 0.390625 },
  { x: 0.015625, y: 0.421875 },
  { x: 0.015625, y: 0.421875 },
  { x: 0.046875, y: 0.421875 },
  { x: 0.046875, y: 0.421875 },
  { x: 0.078125, y: 0.421875 },
  { x: 0.078125, y: 0.421875 },
  { x: 0.109375, y: 0.421875 },
  { x: 0.109375, y: 0.421875 },
  { x: 0.140625, y: 0.421875 },
  { x: 0.140625, y: 0.421875 },
  { x: 0.171875, y: 0.421875 },
  { x: 0.171875, y: 0.421875 },
  { x: 0.203125, y: 0.421875 },
  { x: 0.203125, y: 0.421875 },
  { x: 0.234375, y: 0.421875 },
  { x: 0.234375, y: 0.421875 },
  { x: 0.265625, y: 0.421875 },
  { x: 0.265625, y: 0.421875 },
  { x: 0.296875, y: 0.421875 },
  { x: 0.296875, y: 0.421875 },
  { x: 0.328125, y: 0.421875 },
  { x: 0.328125, y: 0.421875 },
  { x: 0.359375, y: 0.421875 },
  { x: 0.359375, y: 0.421875 },
  { x: 0.390625, y: 0.421875 },
  { x: 0.390625, y: 0.421875 },
  { x: 0.421875, y: 0.421875 },
  { x: 0.421875, y: 0.421875 },
  { x: 0.453125, y: 0.421875 },
  { x: 0.453125, y: 0.421875 },
  { x: 0.484375, y: 0.421875 },
  { x: 0.484375, y: 0.421875 },
  { x: 0.515625, y: 0.421875 },
  { x: 0.515625, y: 0.421875 },
  { x: 0.546875, y: 0.421875 },
  { x: 0.546875, y: 0.421875 },
  { x: 0.578125, y: 0.421875 },
  { x: 0.578125, y: 0.421875 },
  { x: 0.609375, y: 0.421875 },
  { x: 0.609375, y: 0.421875 },
  { x: 0.640625, y: 0.421875 },
  { x: 0.640625, y: 0.421875 },
  { x: 0.671875, y: 0.421875 },
  { x: 0.671875, y: 0.421875 },
  { x: 0.703125, y: 0.421875 },
  { x: 0.703125, y: 0.421875 },
  { x: 0.734375, y: 0.421875 },
  { x: 0.734375, y: 0.421875 },
  { x: 0.765625, y: 0.421875 },
  { x: 0.765625, y: 0.421875 },
  { x: 0.796875, y: 0.421875 },
  { x: 0.796875, y: 0.421875 },
  { x: 0.828125, y: 0.421875 },
  { x: 0.828125, y: 0.421875 },
  { x: 0.859375, y: 0.421875 },
  { x: 0.859375, y: 0.421875 },
  { x: 0.890625, y: 0.421875 },
  { x: 0.890625, y: 0.421875 },
  { x: 0.921875, y: 0.421875 },
  { x: 0.921875, y: 0.421875 },
  { x: 0.953125, y: 0.421875 },
  { x: 0.953125, y: 0.421875 },
  { x: 0.984375, y: 0.421875 },
  { x: 0.984375, y: 0.421875 },
  { x: 0.015625, y: 0.453125 },
  { x: 0.015625, y: 0.453125 },
  { x: 0.046875, y: 0.453125 },
  { x: 0.046875, y: 0.453125 },
  { x: 0.078125, y: 0.453125 },
  { x: 0.078125, y: 0.453125 },
  { x: 0.109375, y: 0.453125 },
  { x: 0.109375, y: 0.453125 },
  { x: 0.140625, y: 0.453125 },
  { x: 0.140625, y: 0.453125 },
  { x: 0.171875, y: 0.453125 },
  { x: 0.171875, y: 0.453125 },
  { x: 0.203125, y: 0.453125 },
  { x: 0.203125, y: 0.453125 },
  { x: 0.234375, y: 0.453125 },
  { x: 0.234375, y: 0.453125 },
  { x: 0.265625, y: 0.453125 },
  { x: 0.265625, y: 0.453125 },
  { x: 0.296875, y: 0.453125 },
  { x: 0.296875, y: 0.453125 },
  { x: 0.328125, y: 0.453125 },
  { x: 0.328125, y: 0.453125 },
  { x: 0.359375, y: 0.453125 },
  { x: 0.359375, y: 0.453125 },
  { x: 0.390625, y: 0.453125 },
  { x: 0.390625, y: 0.453125 },
  { x: 0.421875, y: 0.453125 },
  { x: 0.421875, y: 0.453125 },
  { x: 0.453125, y: 0.453125 },
  { x: 0.453125, y: 0.453125 },
  { x: 0.484375, y: 0.453125 },
  { x: 0.484375, y: 0.453125 },
  { x: 0.515625, y: 0.453125 },
  { x: 0.515625, y: 0.453125 },
  { x: 0.546875, y: 0.453125 },
  { x: 0.546875, y: 0.453125 },
  { x: 0.578125, y: 0.453125 },
  { x: 0.578125, y: 0.453125 },
  { x: 0.609375, y: 0.453125 },
  { x: 0.609375, y: 0.453125 },
  { x: 0.640625, y: 0.453125 },
  { x: 0.640625, y: 0.453125 },
  { x: 0.671875, y: 0.453125 },
  { x: 0.671875, y: 0.453125 },
  { x: 0.703125, y: 0.453125 },
  { x: 0.703125, y: 0.453125 },
  { x: 0.734375, y: 0.453125 },
  { x: 0.734375, y: 0.453125 },
  { x: 0.765625, y: 0.453125 },
  { x: 0.765625, y: 0.453125 },
  { x: 0.796875, y: 0.453125 },
  { x: 0.796875, y: 0.453125 },
  { x: 0.828125, y: 0.453125 },
  { x: 0.828125, y: 0.453125 },
  { x: 0.859375, y: 0.453125 },
  { x: 0.859375, y: 0.453125 },
  { x: 0.890625, y: 0.453125 },
  { x: 0.890625, y: 0.453125 },
  { x: 0.921875, y: 0.453125 },
  { x: 0.921875, y: 0.453125 },
  { x: 0.953125, y: 0.453125 },
  { x: 0.953125, y: 0.453125 },
  { x: 0.984375, y: 0.453125 },
  { x: 0.984375, y: 0.453125 },
  { x: 0.015625, y: 0.484375 },
  { x: 0.015625, y: 0.484375 },
  { x: 0.046875, y: 0.484375 },
  { x: 0.046875, y: 0.484375 },
  { x: 0.078125, y: 0.484375 },
  { x: 0.078125, y: 0.484375 },
  { x: 0.109375, y: 0.484375 },
  { x: 0.109375, y: 0.484375 },
  { x: 0.140625, y: 0.484375 },
  { x: 0.140625, y: 0.484375 },
  { x: 0.171875, y: 0.484375 },
  { x: 0.171875, y: 0.484375 },
  { x: 0.203125, y: 0.484375 },
  { x: 0.203125, y: 0.484375 },
  { x: 0.234375, y: 0.484375 },
  { x: 0.234375, y: 0.484375 },
  { x: 0.265625, y: 0.484375 },
  { x: 0.265625, y: 0.484375 },
  { x: 0.296875, y: 0.484375 },
  { x: 0.296875, y: 0.484375 },
  { x: 0.328125, y: 0.484375 },
  { x: 0.328125, y: 0.484375 },
  { x: 0.359375, y: 0.484375 },
  { x: 0.359375, y: 0.484375 },
  { x: 0.390625, y: 0.484375 },
  { x: 0.390625, y: 0.484375 },
  { x: 0.421875, y: 0.484375 },
  { x: 0.421875, y: 0.484375 },
  { x: 0.453125, y: 0.484375 },
  { x: 0.453125, y: 0.484375 },
  { x: 0.484375, y: 0.484375 },
  { x: 0.484375, y: 0.484375 },
  { x: 0.515625, y: 0.484375 },
  { x: 0.515625, y: 0.484375 },
  { x: 0.546875, y: 0.484375 },
  { x: 0.546875, y: 0.484375 },
  { x: 0.578125, y: 0.484375 },
  { x: 0.578125, y: 0.484375 },
  { x: 0.609375, y: 0.484375 },
  { x: 0.609375, y: 0.484375 },
  { x: 0.640625, y: 0.484375 },
  { x: 0.640625, y: 0.484375 },
  { x: 0.671875, y: 0.484375 },
  { x: 0.671875, y: 0.484375 },
  { x: 0.703125, y: 0.484375 },
  { x: 0.703125, y: 0.484375 },
  { x: 0.734375, y: 0.484375 },
  { x: 0.734375, y: 0.484375 },
  { x: 0.765625, y: 0.484375 },
  { x: 0.765625, y: 0.484375 },
  { x: 0.796875, y: 0.484375 },
  { x: 0.796875, y: 0.484375 },
  { x: 0.828125, y: 0.484375 },
  { x: 0.828125, y: 0.484375 },
  { x: 0.859375, y: 0.484375 },
  { x: 0.859375, y: 0.484375 },
  { x: 0.890625, y: 0.484375 },
  { x: 0.890625, y: 0.484375 },
  { x: 0.921875, y: 0.484375 },
  { x: 0.921875, y: 0.484375 },
  { x: 0.953125, y: 0.484375 },
  { x: 0.953125, y: 0.484375 },
  { x: 0.984375, y: 0.484375 },
  { x: 0.984375, y: 0.484375 },
  { x: 0.015625, y: 0.515625 },
  { x: 0.015625, y: 0.515625 },
  { x: 0.046875, y: 0.515625 },
  { x: 0.046875, y: 0.515625 },
  { x: 0.078125, y: 0.515625 },
  { x: 0.078125, y: 0.515625 },
  { x: 0.109375, y: 0.515625 },
  { x: 0.109375, y: 0.515625 },
  { x: 0.140625, y: 0.515625 },
  { x: 0.140625, y: 0.515625 },
  { x: 0.171875, y: 0.515625 },
  { x: 0.171875, y: 0.515625 },
  { x: 0.203125, y: 0.515625 },
  { x: 0.203125, y: 0.515625 },
  { x: 0.234375, y: 0.515625 },
  { x: 0.234375, y: 0.515625 },
  { x: 0.265625, y: 0.515625 },
  { x: 0.265625, y: 0.515625 },
  { x: 0.296875, y: 0.515625 },
  { x: 0.296875, y: 0.515625 },
  { x: 0.328125, y: 0.515625 },
  { x: 0.328125, y: 0.515625 },
  { x: 0.359375, y: 0.515625 },
  { x: 0.359375, y: 0.515625 },
  { x: 0.390625, y: 0.515625 },
  { x: 0.390625, y: 0.515625 },
  { x: 0.421875, y: 0.515625 },
  { x: 0.421875, y: 0.515625 },
  { x: 0.453125, y: 0.515625 },
  { x: 0.453125, y: 0.515625 },
  { x: 0.484375, y: 0.515625 },
  { x: 0.484375, y: 0.515625 },
  { x: 0.515625, y: 0.515625 },
  { x: 0.515625, y: 0.515625 },
  { x: 0.546875, y: 0.515625 },
  { x: 0.546875, y: 0.515625 },
  { x: 0.578125, y: 0.515625 },
  { x: 0.578125, y: 0.515625 },
  { x: 0.609375, y: 0.515625 },
  { x: 0.609375, y: 0.515625 },
  { x: 0.640625, y: 0.515625 },
  { x: 0.640625, y: 0.515625 },
  { x: 0.671875, y: 0.515625 },
  { x: 0.671875, y: 0.515625 },
  { x: 0.703125, y: 0.515625 },
  { x: 0.703125, y: 0.515625 },
  { x: 0.734375, y: 0.515625 },
  { x: 0.734375, y: 0.515625 },
  { x: 0.765625, y: 0.515625 },
  { x: 0.765625, y: 0.515625 },
  { x: 0.796875, y: 0.515625 },
  { x: 0.796875, y: 0.515625 },
  { x: 0.828125, y: 0.515625 },
  { x: 0.828125, y: 0.515625 },
  { x: 0.859375, y: 0.515625 },
  { x: 0.859375, y: 0.515625 },
  { x: 0.890625, y: 0.515625 },
  { x: 0.890625, y: 0.515625 },
  { x: 0.921875, y: 0.515625 },
  { x: 0.921875, y: 0.515625 },
  { x: 0.953125, y: 0.515625 },
  { x: 0.953125, y: 0.515625 },
  { x: 0.984375, y: 0.515625 },
  { x: 0.984375, y: 0.515625 },
  { x: 0.015625, y: 0.546875 },
  { x: 0.015625, y: 0.546875 },
  { x: 0.046875, y: 0.546875 },
  { x: 0.046875, y: 0.546875 },
  { x: 0.078125, y: 0.546875 },
  { x: 0.078125, y: 0.546875 },
  { x: 0.109375, y: 0.546875 },
  { x: 0.109375, y: 0.546875 },
  { x: 0.140625, y: 0.546875 },
  { x: 0.140625, y: 0.546875 },
  { x: 0.171875, y: 0.546875 },
  { x: 0.171875, y: 0.546875 },
  { x: 0.203125, y: 0.546875 },
  { x: 0.203125, y: 0.546875 },
  { x: 0.234375, y: 0.546875 },
  { x: 0.234375, y: 0.546875 },
  { x: 0.265625, y: 0.546875 },
  { x: 0.265625, y: 0.546875 },
  { x: 0.296875, y: 0.546875 },
  { x: 0.296875, y: 0.546875 },
  { x: 0.328125, y: 0.546875 },
  { x: 0.328125, y: 0.546875 },
  { x: 0.359375, y: 0.546875 },
  { x: 0.359375, y: 0.546875 },
  { x: 0.390625, y: 0.546875 },
  { x: 0.390625, y: 0.546875 },
  { x: 0.421875, y: 0.546875 },
  { x: 0.421875, y: 0.546875 },
  { x: 0.453125, y: 0.546875 },
  { x: 0.453125, y: 0.546875 },
  { x: 0.484375, y: 0.546875 },
  { x: 0.484375, y: 0.546875 },
  { x: 0.515625, y: 0.546875 },
  { x: 0.515625, y: 0.546875 },
  { x: 0.546875, y: 0.546875 },
  { x: 0.546875, y: 0.546875 },
  { x: 0.578125, y: 0.546875 },
  { x: 0.578125, y: 0.546875 },
  { x: 0.609375, y: 0.546875 },
  { x: 0.609375, y: 0.546875 },
  { x: 0.640625, y: 0.546875 },
  { x: 0.640625, y: 0.546875 },
  { x: 0.671875, y: 0.546875 },
  { x: 0.671875, y: 0.546875 },
  { x: 0.703125, y: 0.546875 },
  { x: 0.703125, y: 0.546875 },
  { x: 0.734375, y: 0.546875 },
  { x: 0.734375, y: 0.546875 },
  { x: 0.765625, y: 0.546875 },
  { x: 0.765625, y: 0.546875 },
  { x: 0.796875, y: 0.546875 },
  { x: 0.796875, y: 0.546875 },
  { x: 0.828125, y: 0.546875 },
  { x: 0.828125, y: 0.546875 },
  { x: 0.859375, y: 0.546875 },
  { x: 0.859375, y: 0.546875 },
  { x: 0.890625, y: 0.546875 },
  { x: 0.890625, y: 0.546875 },
  { x: 0.921875, y: 0.546875 },
  { x: 0.921875, y: 0.546875 },
  { x: 0.953125, y: 0.546875 },
  { x: 0.953125, y: 0.546875 },
  { x: 0.984375, y: 0.546875 },
  { x: 0.984375, y: 0.546875 },
  { x: 0.015625, y: 0.578125 },
  { x: 0.015625, y: 0.578125 },
  { x: 0.046875, y: 0.578125 },
  { x: 0.046875, y: 0.578125 },
  { x: 0.078125, y: 0.578125 },
  { x: 0.078125, y: 0.578125 },
  { x: 0.109375, y: 0.578125 },
  { x: 0.109375, y: 0.578125 },
  { x: 0.140625, y: 0.578125 },
  { x: 0.140625, y: 0.578125 },
  { x: 0.171875, y: 0.578125 },
  { x: 0.171875, y: 0.578125 },
  { x: 0.203125, y: 0.578125 },
  { x: 0.203125, y: 0.578125 },
  { x: 0.234375, y: 0.578125 },
  { x: 0.234375, y: 0.578125 },
  { x: 0.265625, y: 0.578125 },
  { x: 0.265625, y: 0.578125 },
  { x: 0.296875, y: 0.578125 },
  { x: 0.296875, y: 0.578125 },
  { x: 0.328125, y: 0.578125 },
  { x: 0.328125, y: 0.578125 },
  { x: 0.359375, y: 0.578125 },
  { x: 0.359375, y: 0.578125 },
  { x: 0.390625, y: 0.578125 },
  { x: 0.390625, y: 0.578125 },
  { x: 0.421875, y: 0.578125 },
  { x: 0.421875, y: 0.578125 },
  { x: 0.453125, y: 0.578125 },
  { x: 0.453125, y: 0.578125 },
  { x: 0.484375, y: 0.578125 },
  { x: 0.484375, y: 0.578125 },
  { x: 0.515625, y: 0.578125 },
  { x: 0.515625, y: 0.578125 },
  { x: 0.546875, y: 0.578125 },
  { x: 0.546875, y: 0.578125 },
  { x: 0.578125, y: 0.578125 },
  { x: 0.578125, y: 0.578125 },
  { x: 0.609375, y: 0.578125 },
  { x: 0.609375, y: 0.578125 },
  { x: 0.640625, y: 0.578125 },
  { x: 0.640625, y: 0.578125 },
  { x: 0.671875, y: 0.578125 },
  { x: 0.671875, y: 0.578125 },
  { x: 0.703125, y: 0.578125 },
  { x: 0.703125, y: 0.578125 },
  { x: 0.734375, y: 0.578125 },
  { x: 0.734375, y: 0.578125 },
  { x: 0.765625, y: 0.578125 },
  { x: 0.765625, y: 0.578125 },
  { x: 0.796875, y: 0.578125 },
  { x: 0.796875, y: 0.578125 },
  { x: 0.828125, y: 0.578125 },
  { x: 0.828125, y: 0.578125 },
  { x: 0.859375, y: 0.578125 },
  { x: 0.859375, y: 0.578125 },
  { x: 0.890625, y: 0.578125 },
  { x: 0.890625, y: 0.578125 },
  { x: 0.921875, y: 0.578125 },
  { x: 0.921875, y: 0.578125 },
  { x: 0.953125, y: 0.578125 },
  { x: 0.953125, y: 0.578125 },
  { x: 0.984375, y: 0.578125 },
  { x: 0.984375, y: 0.578125 },
  { x: 0.015625, y: 0.609375 },
  { x: 0.015625, y: 0.609375 },
  { x: 0.046875, y: 0.609375 },
  { x: 0.046875, y: 0.609375 },
  { x: 0.078125, y: 0.609375 },
  { x: 0.078125, y: 0.609375 },
  { x: 0.109375, y: 0.609375 },
  { x: 0.109375, y: 0.609375 },
  { x: 0.140625, y: 0.609375 },
  { x: 0.140625, y: 0.609375 },
  { x: 0.171875, y: 0.609375 },
  { x: 0.171875, y: 0.609375 },
  { x: 0.203125, y: 0.609375 },
  { x: 0.203125, y: 0.609375 },
  { x: 0.234375, y: 0.609375 },
  { x: 0.234375, y: 0.609375 },
  { x: 0.265625, y: 0.609375 },
  { x: 0.265625, y: 0.609375 },
  { x: 0.296875, y: 0.609375 },
  { x: 0.296875, y: 0.609375 },
  { x: 0.328125, y: 0.609375 },
  { x: 0.328125, y: 0.609375 },
  { x: 0.359375, y: 0.609375 },
  { x: 0.359375, y: 0.609375 },
  { x: 0.390625, y: 0.609375 },
  { x: 0.390625, y: 0.609375 },
  { x: 0.421875, y: 0.609375 },
  { x: 0.421875, y: 0.609375 },
  { x: 0.453125, y: 0.609375 },
  { x: 0.453125, y: 0.609375 },
  { x: 0.484375, y: 0.609375 },
  { x: 0.484375, y: 0.609375 },
  { x: 0.515625, y: 0.609375 },
  { x: 0.515625, y: 0.609375 },
  { x: 0.546875, y: 0.609375 },
  { x: 0.546875, y: 0.609375 },
  { x: 0.578125, y: 0.609375 },
  { x: 0.578125, y: 0.609375 },
  { x: 0.609375, y: 0.609375 },
  { x: 0.609375, y: 0.609375 },
  { x: 0.640625, y: 0.609375 },
  { x: 0.640625, y: 0.609375 },
  { x: 0.671875, y: 0.609375 },
  { x: 0.671875, y: 0.609375 },
  { x: 0.703125, y: 0.609375 },
  { x: 0.703125, y: 0.609375 },
  { x: 0.734375, y: 0.609375 },
  { x: 0.734375, y: 0.609375 },
  { x: 0.765625, y: 0.609375 },
  { x: 0.765625, y: 0.609375 },
  { x: 0.796875, y: 0.609375 },
  { x: 0.796875, y: 0.609375 },
  { x: 0.828125, y: 0.609375 },
  { x: 0.828125, y: 0.609375 },
  { x: 0.859375, y: 0.609375 },
  { x: 0.859375, y: 0.609375 },
  { x: 0.890625, y: 0.609375 },
  { x: 0.890625, y: 0.609375 },
  { x: 0.921875, y: 0.609375 },
  { x: 0.921875, y: 0.609375 },
  { x: 0.953125, y: 0.609375 },
  { x: 0.953125, y: 0.609375 },
  { x: 0.984375, y: 0.609375 },
  { x: 0.984375, y: 0.609375 },
  { x: 0.015625, y: 0.640625 },
  { x: 0.015625, y: 0.640625 },
  { x: 0.046875, y: 0.640625 },
  { x: 0.046875, y: 0.640625 },
  { x: 0.078125, y: 0.640625 },
  { x: 0.078125, y: 0.640625 },
  { x: 0.109375, y: 0.640625 },
  { x: 0.109375, y: 0.640625 },
  { x: 0.140625, y: 0.640625 },
  { x: 0.140625, y: 0.640625 },
  { x: 0.171875, y: 0.640625 },
  { x: 0.171875, y: 0.640625 },
  { x: 0.203125, y: 0.640625 },
  { x: 0.203125, y: 0.640625 },
  { x: 0.234375, y: 0.640625 },
  { x: 0.234375, y: 0.640625 },
  { x: 0.265625, y: 0.640625 },
  { x: 0.265625, y: 0.640625 },
  { x: 0.296875, y: 0.640625 },
  { x: 0.296875, y: 0.640625 },
  { x: 0.328125, y: 0.640625 },
  { x: 0.328125, y: 0.640625 },
  { x: 0.359375, y: 0.640625 },
  { x: 0.359375, y: 0.640625 },
  { x: 0.390625, y: 0.640625 },
  { x: 0.390625, y: 0.640625 },
  { x: 0.421875, y: 0.640625 },
  { x: 0.421875, y: 0.640625 },
  { x: 0.453125, y: 0.640625 },
  { x: 0.453125, y: 0.640625 },
  { x: 0.484375, y: 0.640625 },
  { x: 0.484375, y: 0.640625 },
  { x: 0.515625, y: 0.640625 },
  { x: 0.515625, y: 0.640625 },
  { x: 0.546875, y: 0.640625 },
  { x: 0.546875, y: 0.640625 },
  { x: 0.578125, y: 0.640625 },
  { x: 0.578125, y: 0.640625 },
  { x: 0.609375, y: 0.640625 },
  { x: 0.609375, y: 0.640625 },
  { x: 0.640625, y: 0.640625 },
  { x: 0.640625, y: 0.640625 },
  { x: 0.671875, y: 0.640625 },
  { x: 0.671875, y: 0.640625 },
  { x: 0.703125, y: 0.640625 },
  { x: 0.703125, y: 0.640625 },
  { x: 0.734375, y: 0.640625 },
  { x: 0.734375, y: 0.640625 },
  { x: 0.765625, y: 0.640625 },
  { x: 0.765625, y: 0.640625 },
  { x: 0.796875, y: 0.640625 },
  { x: 0.796875, y: 0.640625 },
  { x: 0.828125, y: 0.640625 },
  { x: 0.828125, y: 0.640625 },
  { x: 0.859375, y: 0.640625 },
  { x: 0.859375, y: 0.640625 },
  { x: 0.890625, y: 0.640625 },
  { x: 0.890625, y: 0.640625 },
  { x: 0.921875, y: 0.640625 },
  { x: 0.921875, y: 0.640625 },
  { x: 0.953125, y: 0.640625 },
  { x: 0.953125, y: 0.640625 },
  { x: 0.984375, y: 0.640625 },
  { x: 0.984375, y: 0.640625 },
  { x: 0.015625, y: 0.671875 },
  { x: 0.015625, y: 0.671875 },
  { x: 0.046875, y: 0.671875 },
  { x: 0.046875, y: 0.671875 },
  { x: 0.078125, y: 0.671875 },
  { x: 0.078125, y: 0.671875 },
  { x: 0.109375, y: 0.671875 },
  { x: 0.109375, y: 0.671875 },
  { x: 0.140625, y: 0.671875 },
  { x: 0.140625, y: 0.671875 },
  { x: 0.171875, y: 0.671875 },
  { x: 0.171875, y: 0.671875 },
  { x: 0.203125, y: 0.671875 },
  { x: 0.203125, y: 0.671875 },
  { x: 0.234375, y: 0.671875 },
  { x: 0.234375, y: 0.671875 },
  { x: 0.265625, y: 0.671875 },
  { x: 0.265625, y: 0.671875 },
  { x: 0.296875, y: 0.671875 },
  { x: 0.296875, y: 0.671875 },
  { x: 0.328125, y: 0.671875 },
  { x: 0.328125, y: 0.671875 },
  { x: 0.359375, y: 0.671875 },
  { x: 0.359375, y: 0.671875 },
  { x: 0.390625, y: 0.671875 },
  { x: 0.390625, y: 0.671875 },
  { x: 0.421875, y: 0.671875 },
  { x: 0.421875, y: 0.671875 },
  { x: 0.453125, y: 0.671875 },
  { x: 0.453125, y: 0.671875 },
  { x: 0.484375, y: 0.671875 },
  { x: 0.484375, y: 0.671875 },
  { x: 0.515625, y: 0.671875 },
  { x: 0.515625, y: 0.671875 },
  { x: 0.546875, y: 0.671875 },
  { x: 0.546875, y: 0.671875 },
  { x: 0.578125, y: 0.671875 },
  { x: 0.578125, y: 0.671875 },
  { x: 0.609375, y: 0.671875 },
  { x: 0.609375, y: 0.671875 },
  { x: 0.640625, y: 0.671875 },
  { x: 0.640625, y: 0.671875 },
  { x: 0.671875, y: 0.671875 },
  { x: 0.671875, y: 0.671875 },
  { x: 0.703125, y: 0.671875 },
  { x: 0.703125, y: 0.671875 },
  { x: 0.734375, y: 0.671875 },
  { x: 0.734375, y: 0.671875 },
  { x: 0.765625, y: 0.671875 },
  { x: 0.765625, y: 0.671875 },
  { x: 0.796875, y: 0.671875 },
  { x: 0.796875, y: 0.671875 },
  { x: 0.828125, y: 0.671875 },
  { x: 0.828125, y: 0.671875 },
  { x: 0.859375, y: 0.671875 },
  { x: 0.859375, y: 0.671875 },
  { x: 0.890625, y: 0.671875 },
  { x: 0.890625, y: 0.671875 },
  { x: 0.921875, y: 0.671875 },
  { x: 0.921875, y: 0.671875 },
  { x: 0.953125, y: 0.671875 },
  { x: 0.953125, y: 0.671875 },
  { x: 0.984375, y: 0.671875 },
  { x: 0.984375, y: 0.671875 },
  { x: 0.015625, y: 0.703125 },
  { x: 0.015625, y: 0.703125 },
  { x: 0.046875, y: 0.703125 },
  { x: 0.046875, y: 0.703125 },
  { x: 0.078125, y: 0.703125 },
  { x: 0.078125, y: 0.703125 },
  { x: 0.109375, y: 0.703125 },
  { x: 0.109375, y: 0.703125 },
  { x: 0.140625, y: 0.703125 },
  { x: 0.140625, y: 0.703125 },
  { x: 0.171875, y: 0.703125 },
  { x: 0.171875, y: 0.703125 },
  { x: 0.203125, y: 0.703125 },
  { x: 0.203125, y: 0.703125 },
  { x: 0.234375, y: 0.703125 },
  { x: 0.234375, y: 0.703125 },
  { x: 0.265625, y: 0.703125 },
  { x: 0.265625, y: 0.703125 },
  { x: 0.296875, y: 0.703125 },
  { x: 0.296875, y: 0.703125 },
  { x: 0.328125, y: 0.703125 },
  { x: 0.328125, y: 0.703125 },
  { x: 0.359375, y: 0.703125 },
  { x: 0.359375, y: 0.703125 },
  { x: 0.390625, y: 0.703125 },
  { x: 0.390625, y: 0.703125 },
  { x: 0.421875, y: 0.703125 },
  { x: 0.421875, y: 0.703125 },
  { x: 0.453125, y: 0.703125 },
  { x: 0.453125, y: 0.703125 },
  { x: 0.484375, y: 0.703125 },
  { x: 0.484375, y: 0.703125 },
  { x: 0.515625, y: 0.703125 },
  { x: 0.515625, y: 0.703125 },
  { x: 0.546875, y: 0.703125 },
  { x: 0.546875, y: 0.703125 },
  { x: 0.578125, y: 0.703125 },
  { x: 0.578125, y: 0.703125 },
  { x: 0.609375, y: 0.703125 },
  { x: 0.609375, y: 0.703125 },
  { x: 0.640625, y: 0.703125 },
  { x: 0.640625, y: 0.703125 },
  { x: 0.671875, y: 0.703125 },
  { x: 0.671875, y: 0.703125 },
  { x: 0.703125, y: 0.703125 },
  { x: 0.703125, y: 0.703125 },
  { x: 0.734375, y: 0.703125 },
  { x: 0.734375, y: 0.703125 },
  { x: 0.765625, y: 0.703125 },
  { x: 0.765625, y: 0.703125 },
  { x: 0.796875, y: 0.703125 },
  { x: 0.796875, y: 0.703125 },
  { x: 0.828125, y: 0.703125 },
  { x: 0.828125, y: 0.703125 },
  { x: 0.859375, y: 0.703125 },
  { x: 0.859375, y: 0.703125 },
  { x: 0.890625, y: 0.703125 },
  { x: 0.890625, y: 0.703125 },
  { x: 0.921875, y: 0.703125 },
  { x: 0.921875, y: 0.703125 },
  { x: 0.953125, y: 0.703125 },
  { x: 0.953125, y: 0.703125 },
  { x: 0.984375, y: 0.703125 },
  { x: 0.984375, y: 0.703125 },
  { x: 0.015625, y: 0.734375 },
  { x: 0.015625, y: 0.734375 },
  { x: 0.046875, y: 0.734375 },
  { x: 0.046875, y: 0.734375 },
  { x: 0.078125, y: 0.734375 },
  { x: 0.078125, y: 0.734375 },
  { x: 0.109375, y: 0.734375 },
  { x: 0.109375, y: 0.734375 },
  { x: 0.140625, y: 0.734375 },
  { x: 0.140625, y: 0.734375 },
  { x: 0.171875, y: 0.734375 },
  { x: 0.171875, y: 0.734375 },
  { x: 0.203125, y: 0.734375 },
  { x: 0.203125, y: 0.734375 },
  { x: 0.234375, y: 0.734375 },
  { x: 0.234375, y: 0.734375 },
  { x: 0.265625, y: 0.734375 },
  { x: 0.265625, y: 0.734375 },
  { x: 0.296875, y: 0.734375 },
  { x: 0.296875, y: 0.734375 },
  { x: 0.328125, y: 0.734375 },
  { x: 0.328125, y: 0.734375 },
  { x: 0.359375, y: 0.734375 },
  { x: 0.359375, y: 0.734375 },
  { x: 0.390625, y: 0.734375 },
  { x: 0.390625, y: 0.734375 },
  { x: 0.421875, y: 0.734375 },
  { x: 0.421875, y: 0.734375 },
  { x: 0.453125, y: 0.734375 },
  { x: 0.453125, y: 0.734375 },
  { x: 0.484375, y: 0.734375 },
  { x: 0.484375, y: 0.734375 },
  { x: 0.515625, y: 0.734375 },
  { x: 0.515625, y: 0.734375 },
  { x: 0.546875, y: 0.734375 },
  { x: 0.546875, y: 0.734375 },
  { x: 0.578125, y: 0.734375 },
  { x: 0.578125, y: 0.734375 },
  { x: 0.609375, y: 0.734375 },
  { x: 0.609375, y: 0.734375 },
  { x: 0.640625, y: 0.734375 },
  { x: 0.640625, y: 0.734375 },
  { x: 0.671875, y: 0.734375 },
  { x: 0.671875, y: 0.734375 },
  { x: 0.703125, y: 0.734375 },
  { x: 0.703125, y: 0.734375 },
  { x: 0.734375, y: 0.734375 },
  { x: 0.734375, y: 0.734375 },
  { x: 0.765625, y: 0.734375 },
  { x: 0.765625, y: 0.734375 },
  { x: 0.796875, y: 0.734375 },
  { x: 0.796875, y: 0.734375 },
  { x: 0.828125, y: 0.734375 },
  { x: 0.828125, y: 0.734375 },
  { x: 0.859375, y: 0.734375 },
  { x: 0.859375, y: 0.734375 },
  { x: 0.890625, y: 0.734375 },
  { x: 0.890625, y: 0.734375 },
  { x: 0.921875, y: 0.734375 },
  { x: 0.921875, y: 0.734375 },
  { x: 0.953125, y: 0.734375 },
  { x: 0.953125, y: 0.734375 },
  { x: 0.984375, y: 0.734375 },
  { x: 0.984375, y: 0.734375 },
  { x: 0.015625, y: 0.765625 },
  { x: 0.015625, y: 0.765625 },
  { x: 0.046875, y: 0.765625 },
  { x: 0.046875, y: 0.765625 },
  { x: 0.078125, y: 0.765625 },
  { x: 0.078125, y: 0.765625 },
  { x: 0.109375, y: 0.765625 },
  { x: 0.109375, y: 0.765625 },
  { x: 0.140625, y: 0.765625 },
  { x: 0.140625, y: 0.765625 },
  { x: 0.171875, y: 0.765625 },
  { x: 0.171875, y: 0.765625 },
  { x: 0.203125, y: 0.765625 },
  { x: 0.203125, y: 0.765625 },
  { x: 0.234375, y: 0.765625 },
  { x: 0.234375, y: 0.765625 },
  { x: 0.265625, y: 0.765625 },
  { x: 0.265625, y: 0.765625 },
  { x: 0.296875, y: 0.765625 },
  { x: 0.296875, y: 0.765625 },
  { x: 0.328125, y: 0.765625 },
  { x: 0.328125, y: 0.765625 },
  { x: 0.359375, y: 0.765625 },
  { x: 0.359375, y: 0.765625 },
  { x: 0.390625, y: 0.765625 },
  { x: 0.390625, y: 0.765625 },
  { x: 0.421875, y: 0.765625 },
  { x: 0.421875, y: 0.765625 },
  { x: 0.453125, y: 0.765625 },
  { x: 0.453125, y: 0.765625 },
  { x: 0.484375, y: 0.765625 },
  { x: 0.484375, y: 0.765625 },
  { x: 0.515625, y: 0.765625 },
  { x: 0.515625, y: 0.765625 },
  { x: 0.546875, y: 0.765625 },
  { x: 0.546875, y: 0.765625 },
  { x: 0.578125, y: 0.765625 },
  { x: 0.578125, y: 0.765625 },
  { x: 0.609375, y: 0.765625 },
  { x: 0.609375, y: 0.765625 },
  { x: 0.640625, y: 0.765625 },
  { x: 0.640625, y: 0.765625 },
  { x: 0.671875, y: 0.765625 },
  { x: 0.671875, y: 0.765625 },
  { x: 0.703125, y: 0.765625 },
  { x: 0.703125, y: 0.765625 },
  { x: 0.734375, y: 0.765625 },
  { x: 0.734375, y: 0.765625 },
  { x: 0.765625, y: 0.765625 },
  { x: 0.765625, y: 0.765625 },
  { x: 0.796875, y: 0.765625 },
  { x: 0.796875, y: 0.765625 },
  { x: 0.828125, y: 0.765625 },
  { x: 0.828125, y: 0.765625 },
  { x: 0.859375, y: 0.765625 },
  { x: 0.859375, y: 0.765625 },
  { x: 0.890625, y: 0.765625 },
  { x: 0.890625, y: 0.765625 },
  { x: 0.921875, y: 0.765625 },
  { x: 0.921875, y: 0.765625 },
  { x: 0.953125, y: 0.765625 },
  { x: 0.953125, y: 0.765625 },
  { x: 0.984375, y: 0.765625 },
  { x: 0.984375, y: 0.765625 },
  { x: 0.015625, y: 0.796875 },
  { x: 0.015625, y: 0.796875 },
  { x: 0.046875, y: 0.796875 },
  { x: 0.046875, y: 0.796875 },
  { x: 0.078125, y: 0.796875 },
  { x: 0.078125, y: 0.796875 },
  { x: 0.109375, y: 0.796875 },
  { x: 0.109375, y: 0.796875 },
  { x: 0.140625, y: 0.796875 },
  { x: 0.140625, y: 0.796875 },
  { x: 0.171875, y: 0.796875 },
  { x: 0.171875, y: 0.796875 },
  { x: 0.203125, y: 0.796875 },
  { x: 0.203125, y: 0.796875 },
  { x: 0.234375, y: 0.796875 },
  { x: 0.234375, y: 0.796875 },
  { x: 0.265625, y: 0.796875 },
  { x: 0.265625, y: 0.796875 },
  { x: 0.296875, y: 0.796875 },
  { x: 0.296875, y: 0.796875 },
  { x: 0.328125, y: 0.796875 },
  { x: 0.328125, y: 0.796875 },
  { x: 0.359375, y: 0.796875 },
  { x: 0.359375, y: 0.796875 },
  { x: 0.390625, y: 0.796875 },
  { x: 0.390625, y: 0.796875 },
  { x: 0.421875, y: 0.796875 },
  { x: 0.421875, y: 0.796875 },
  { x: 0.453125, y: 0.796875 },
  { x: 0.453125, y: 0.796875 },
  { x: 0.484375, y: 0.796875 },
  { x: 0.484375, y: 0.796875 },
  { x: 0.515625, y: 0.796875 },
  { x: 0.515625, y: 0.796875 },
  { x: 0.546875, y: 0.796875 },
  { x: 0.546875, y: 0.796875 },
  { x: 0.578125, y: 0.796875 },
  { x: 0.578125, y: 0.796875 },
  { x: 0.609375, y: 0.796875 },
  { x: 0.609375, y: 0.796875 },
  { x: 0.640625, y: 0.796875 },
  { x: 0.640625, y: 0.796875 },
  { x: 0.671875, y: 0.796875 },
  { x: 0.671875, y: 0.796875 },
  { x: 0.703125, y: 0.796875 },
  { x: 0.703125, y: 0.796875 },
  { x: 0.734375, y: 0.796875 },
  { x: 0.734375, y: 0.796875 },
  { x: 0.765625, y: 0.796875 },
  { x: 0.765625, y: 0.796875 },
  { x: 0.796875, y: 0.796875 },
  { x: 0.796875, y: 0.796875 },
  { x: 0.828125, y: 0.796875 },
  { x: 0.828125, y: 0.796875 },
  { x: 0.859375, y: 0.796875 },
  { x: 0.859375, y: 0.796875 },
  { x: 0.890625, y: 0.796875 },
  { x: 0.890625, y: 0.796875 },
  { x: 0.921875, y: 0.796875 },
  { x: 0.921875, y: 0.796875 },
  { x: 0.953125, y: 0.796875 },
  { x: 0.953125, y: 0.796875 },
  { x: 0.984375, y: 0.796875 },
  { x: 0.984375, y: 0.796875 },
  { x: 0.015625, y: 0.828125 },
  { x: 0.015625, y: 0.828125 },
  { x: 0.046875, y: 0.828125 },
  { x: 0.046875, y: 0.828125 },
  { x: 0.078125, y: 0.828125 },
  { x: 0.078125, y: 0.828125 },
  { x: 0.109375, y: 0.828125 },
  { x: 0.109375, y: 0.828125 },
  { x: 0.140625, y: 0.828125 },
  { x: 0.140625, y: 0.828125 },
  { x: 0.171875, y: 0.828125 },
  { x: 0.171875, y: 0.828125 },
  { x: 0.203125, y: 0.828125 },
  { x: 0.203125, y: 0.828125 },
  { x: 0.234375, y: 0.828125 },
  { x: 0.234375, y: 0.828125 },
  { x: 0.265625, y: 0.828125 },
  { x: 0.265625, y: 0.828125 },
  { x: 0.296875, y: 0.828125 },
  { x: 0.296875, y: 0.828125 },
  { x: 0.328125, y: 0.828125 },
  { x: 0.328125, y: 0.828125 },
  { x: 0.359375, y: 0.828125 },
  { x: 0.359375, y: 0.828125 },
  { x: 0.390625, y: 0.828125 },
  { x: 0.390625, y: 0.828125 },
  { x: 0.421875, y: 0.828125 },
  { x: 0.421875, y: 0.828125 },
  { x: 0.453125, y: 0.828125 },
  { x: 0.453125, y: 0.828125 },
  { x: 0.484375, y: 0.828125 },
  { x: 0.484375, y: 0.828125 },
  { x: 0.515625, y: 0.828125 },
  { x: 0.515625, y: 0.828125 },
  { x: 0.546875, y: 0.828125 },
  { x: 0.546875, y: 0.828125 },
  { x: 0.578125, y: 0.828125 },
  { x: 0.578125, y: 0.828125 },
  { x: 0.609375, y: 0.828125 },
  { x: 0.609375, y: 0.828125 },
  { x: 0.640625, y: 0.828125 },
  { x: 0.640625, y: 0.828125 },
  { x: 0.671875, y: 0.828125 },
  { x: 0.671875, y: 0.828125 },
  { x: 0.703125, y: 0.828125 },
  { x: 0.703125, y: 0.828125 },
  { x: 0.734375, y: 0.828125 },
  { x: 0.734375, y: 0.828125 },
  { x: 0.765625, y: 0.828125 },
  { x: 0.765625, y: 0.828125 },
  { x: 0.796875, y: 0.828125 },
  { x: 0.796875, y: 0.828125 },
  { x: 0.828125, y: 0.828125 },
  { x: 0.828125, y: 0.828125 },
  { x: 0.859375, y: 0.828125 },
  { x: 0.859375, y: 0.828125 },
  { x: 0.890625, y: 0.828125 },
  { x: 0.890625, y: 0.828125 },
  { x: 0.921875, y: 0.828125 },
  { x: 0.921875, y: 0.828125 },
  { x: 0.953125, y: 0.828125 },
  { x: 0.953125, y: 0.828125 },
  { x: 0.984375, y: 0.828125 },
  { x: 0.984375, y: 0.828125 },
  { x: 0.015625, y: 0.859375 },
  { x: 0.015625, y: 0.859375 },
  { x: 0.046875, y: 0.859375 },
  { x: 0.046875, y: 0.859375 },
  { x: 0.078125, y: 0.859375 },
  { x: 0.078125, y: 0.859375 },
  { x: 0.109375, y: 0.859375 },
  { x: 0.109375, y: 0.859375 },
  { x: 0.140625, y: 0.859375 },
  { x: 0.140625, y: 0.859375 },
  { x: 0.171875, y: 0.859375 },
  { x: 0.171875, y: 0.859375 },
  { x: 0.203125, y: 0.859375 },
  { x: 0.203125, y: 0.859375 },
  { x: 0.234375, y: 0.859375 },
  { x: 0.234375, y: 0.859375 },
  { x: 0.265625, y: 0.859375 },
  { x: 0.265625, y: 0.859375 },
  { x: 0.296875, y: 0.859375 },
  { x: 0.296875, y: 0.859375 },
  { x: 0.328125, y: 0.859375 },
  { x: 0.328125, y: 0.859375 },
  { x: 0.359375, y: 0.859375 },
  { x: 0.359375, y: 0.859375 },
  { x: 0.390625, y: 0.859375 },
  { x: 0.390625, y: 0.859375 },
  { x: 0.421875, y: 0.859375 },
  { x: 0.421875, y: 0.859375 },
  { x: 0.453125, y: 0.859375 },
  { x: 0.453125, y: 0.859375 },
  { x: 0.484375, y: 0.859375 },
  { x: 0.484375, y: 0.859375 },
  { x: 0.515625, y: 0.859375 },
  { x: 0.515625, y: 0.859375 },
  { x: 0.546875, y: 0.859375 },
  { x: 0.546875, y: 0.859375 },
  { x: 0.578125, y: 0.859375 },
  { x: 0.578125, y: 0.859375 },
  { x: 0.609375, y: 0.859375 },
  { x: 0.609375, y: 0.859375 },
  { x: 0.640625, y: 0.859375 },
  { x: 0.640625, y: 0.859375 },
  { x: 0.671875, y: 0.859375 },
  { x: 0.671875, y: 0.859375 },
  { x: 0.703125, y: 0.859375 },
  { x: 0.703125, y: 0.859375 },
  { x: 0.734375, y: 0.859375 },
  { x: 0.734375, y: 0.859375 },
  { x: 0.765625, y: 0.859375 },
  { x: 0.765625, y: 0.859375 },
  { x: 0.796875, y: 0.859375 },
  { x: 0.796875, y: 0.859375 },
  { x: 0.828125, y: 0.859375 },
  { x: 0.828125, y: 0.859375 },
  { x: 0.859375, y: 0.859375 },
  { x: 0.859375, y: 0.859375 },
  { x: 0.890625, y: 0.859375 },
  { x: 0.890625, y: 0.859375 },
  { x: 0.921875, y: 0.859375 },
  { x: 0.921875, y: 0.859375 },
  { x: 0.953125, y: 0.859375 },
  { x: 0.953125, y: 0.859375 },
  { x: 0.984375, y: 0.859375 },
  { x: 0.984375, y: 0.859375 },
  { x: 0.015625, y: 0.890625 },
  { x: 0.015625, y: 0.890625 },
  { x: 0.046875, y: 0.890625 },
  { x: 0.046875, y: 0.890625 },
  { x: 0.078125, y: 0.890625 },
  { x: 0.078125, y: 0.890625 },
  { x: 0.109375, y: 0.890625 },
  { x: 0.109375, y: 0.890625 },
  { x: 0.140625, y: 0.890625 },
  { x: 0.140625, y: 0.890625 },
  { x: 0.171875, y: 0.890625 },
  { x: 0.171875, y: 0.890625 },
  { x: 0.203125, y: 0.890625 },
  { x: 0.203125, y: 0.890625 },
  { x: 0.234375, y: 0.890625 },
  { x: 0.234375, y: 0.890625 },
  { x: 0.265625, y: 0.890625 },
  { x: 0.265625, y: 0.890625 },
  { x: 0.296875, y: 0.890625 },
  { x: 0.296875, y: 0.890625 },
  { x: 0.328125, y: 0.890625 },
  { x: 0.328125, y: 0.890625 },
  { x: 0.359375, y: 0.890625 },
  { x: 0.359375, y: 0.890625 },
  { x: 0.390625, y: 0.890625 },
  { x: 0.390625, y: 0.890625 },
  { x: 0.421875, y: 0.890625 },
  { x: 0.421875, y: 0.890625 },
  { x: 0.453125, y: 0.890625 },
  { x: 0.453125, y: 0.890625 },
  { x: 0.484375, y: 0.890625 },
  { x: 0.484375, y: 0.890625 },
  { x: 0.515625, y: 0.890625 },
  { x: 0.515625, y: 0.890625 },
  { x: 0.546875, y: 0.890625 },
  { x: 0.546875, y: 0.890625 },
  { x: 0.578125, y: 0.890625 },
  { x: 0.578125, y: 0.890625 },
  { x: 0.609375, y: 0.890625 },
  { x: 0.609375, y: 0.890625 },
  { x: 0.640625, y: 0.890625 },
  { x: 0.640625, y: 0.890625 },
  { x: 0.671875, y: 0.890625 },
  { x: 0.671875, y: 0.890625 },
  { x: 0.703125, y: 0.890625 },
  { x: 0.703125, y: 0.890625 },
  { x: 0.734375, y: 0.890625 },
  { x: 0.734375, y: 0.890625 },
  { x: 0.765625, y: 0.890625 },
  { x: 0.765625, y: 0.890625 },
  { x: 0.796875, y: 0.890625 },
  { x: 0.796875, y: 0.890625 },
  { x: 0.828125, y: 0.890625 },
  { x: 0.828125, y: 0.890625 },
  { x: 0.859375, y: 0.890625 },
  { x: 0.859375, y: 0.890625 },
  { x: 0.890625, y: 0.890625 },
  { x: 0.890625, y: 0.890625 },
  { x: 0.921875, y: 0.890625 },
  { x: 0.921875, y: 0.890625 },
  { x: 0.953125, y: 0.890625 },
  { x: 0.953125, y: 0.890625 },
  { x: 0.984375, y: 0.890625 },
  { x: 0.984375, y: 0.890625 },
  { x: 0.015625, y: 0.921875 },
  { x: 0.015625, y: 0.921875 },
  { x: 0.046875, y: 0.921875 },
  { x: 0.046875, y: 0.921875 },
  { x: 0.078125, y: 0.921875 },
  { x: 0.078125, y: 0.921875 },
  { x: 0.109375, y: 0.921875 },
  { x: 0.109375, y: 0.921875 },
  { x: 0.140625, y: 0.921875 },
  { x: 0.140625, y: 0.921875 },
  { x: 0.171875, y: 0.921875 },
  { x: 0.171875, y: 0.921875 },
  { x: 0.203125, y: 0.921875 },
  { x: 0.203125, y: 0.921875 },
  { x: 0.234375, y: 0.921875 },
  { x: 0.234375, y: 0.921875 },
  { x: 0.265625, y: 0.921875 },
  { x: 0.265625, y: 0.921875 },
  { x: 0.296875, y: 0.921875 },
  { x: 0.296875, y: 0.921875 },
  { x: 0.328125, y: 0.921875 },
  { x: 0.328125, y: 0.921875 },
  { x: 0.359375, y: 0.921875 },
  { x: 0.359375, y: 0.921875 },
  { x: 0.390625, y: 0.921875 },
  { x: 0.390625, y: 0.921875 },
  { x: 0.421875, y: 0.921875 },
  { x: 0.421875, y: 0.921875 },
  { x: 0.453125, y: 0.921875 },
  { x: 0.453125, y: 0.921875 },
  { x: 0.484375, y: 0.921875 },
  { x: 0.484375, y: 0.921875 },
  { x: 0.515625, y: 0.921875 },
  { x: 0.515625, y: 0.921875 },
  { x: 0.546875, y: 0.921875 },
  { x: 0.546875, y: 0.921875 },
  { x: 0.578125, y: 0.921875 },
  { x: 0.578125, y: 0.921875 },
  { x: 0.609375, y: 0.921875 },
  { x: 0.609375, y: 0.921875 },
  { x: 0.640625, y: 0.921875 },
  { x: 0.640625, y: 0.921875 },
  { x: 0.671875, y: 0.921875 },
  { x: 0.671875, y: 0.921875 },
  { x: 0.703125, y: 0.921875 },
  { x: 0.703125, y: 0.921875 },
  { x: 0.734375, y: 0.921875 },
  { x: 0.734375, y: 0.921875 },
  { x: 0.765625, y: 0.921875 },
  { x: 0.765625, y: 0.921875 },
  { x: 0.796875, y: 0.921875 },
  { x: 0.796875, y: 0.921875 },
  { x: 0.828125, y: 0.921875 },
  { x: 0.828125, y: 0.921875 },
  { x: 0.859375, y: 0.921875 },
  { x: 0.859375, y: 0.921875 },
  { x: 0.890625, y: 0.921875 },
  { x: 0.890625, y: 0.921875 },
  { x: 0.921875, y: 0.921875 },
  { x: 0.921875, y: 0.921875 },
  { x: 0.953125, y: 0.921875 },
  { x: 0.953125, y: 0.921875 },
  { x: 0.984375, y: 0.921875 },
  { x: 0.984375, y: 0.921875 },
  { x: 0.015625, y: 0.953125 },
  { x: 0.015625, y: 0.953125 },
  { x: 0.046875, y: 0.953125 },
  { x: 0.046875, y: 0.953125 },
  { x: 0.078125, y: 0.953125 },
  { x: 0.078125, y: 0.953125 },
  { x: 0.109375, y: 0.953125 },
  { x: 0.109375, y: 0.953125 },
  { x: 0.140625, y: 0.953125 },
  { x: 0.140625, y: 0.953125 },
  { x: 0.171875, y: 0.953125 },
  { x: 0.171875, y: 0.953125 },
  { x: 0.203125, y: 0.953125 },
  { x: 0.203125, y: 0.953125 },
  { x: 0.234375, y: 0.953125 },
  { x: 0.234375, y: 0.953125 },
  { x: 0.265625, y: 0.953125 },
  { x: 0.265625, y: 0.953125 },
  { x: 0.296875, y: 0.953125 },
  { x: 0.296875, y: 0.953125 },
  { x: 0.328125, y: 0.953125 },
  { x: 0.328125, y: 0.953125 },
  { x: 0.359375, y: 0.953125 },
  { x: 0.359375, y: 0.953125 },
  { x: 0.390625, y: 0.953125 },
  { x: 0.390625, y: 0.953125 },
  { x: 0.421875, y: 0.953125 },
  { x: 0.421875, y: 0.953125 },
  { x: 0.453125, y: 0.953125 },
  { x: 0.453125, y: 0.953125 },
  { x: 0.484375, y: 0.953125 },
  { x: 0.484375, y: 0.953125 },
  { x: 0.515625, y: 0.953125 },
  { x: 0.515625, y: 0.953125 },
  { x: 0.546875, y: 0.953125 },
  { x: 0.546875, y: 0.953125 },
  { x: 0.578125, y: 0.953125 },
  { x: 0.578125, y: 0.953125 },
  { x: 0.609375, y: 0.953125 },
  { x: 0.609375, y: 0.953125 },
  { x: 0.640625, y: 0.953125 },
  { x: 0.640625, y: 0.953125 },
  { x: 0.671875, y: 0.953125 },
  { x: 0.671875, y: 0.953125 },
  { x: 0.703125, y: 0.953125 },
  { x: 0.703125, y: 0.953125 },
  { x: 0.734375, y: 0.953125 },
  { x: 0.734375, y: 0.953125 },
  { x: 0.765625, y: 0.953125 },
  { x: 0.765625, y: 0.953125 },
  { x: 0.796875, y: 0.953125 },
  { x: 0.796875, y: 0.953125 },
  { x: 0.828125, y: 0.953125 },
  { x: 0.828125, y: 0.953125 },
  { x: 0.859375, y: 0.953125 },
  { x: 0.859375, y: 0.953125 },
  { x: 0.890625, y: 0.953125 },
  { x: 0.890625, y: 0.953125 },
  { x: 0.921875, y: 0.953125 },
  { x: 0.921875, y: 0.953125 },
  { x: 0.953125, y: 0.953125 },
  { x: 0.953125, y: 0.953125 },
  { x: 0.984375, y: 0.953125 },
  { x: 0.984375, y: 0.953125 },
  { x: 0.015625, y: 0.984375 },
  { x: 0.015625, y: 0.984375 },
  { x: 0.046875, y: 0.984375 },
  { x: 0.046875, y: 0.984375 },
  { x: 0.078125, y: 0.984375 },
  { x: 0.078125, y: 0.984375 },
  { x: 0.109375, y: 0.984375 },
  { x: 0.109375, y: 0.984375 },
  { x: 0.140625, y: 0.984375 },
  { x: 0.140625, y: 0.984375 },
  { x: 0.171875, y: 0.984375 },
  { x: 0.171875, y: 0.984375 },
  { x: 0.203125, y: 0.984375 },
  { x: 0.203125, y: 0.984375 },
  { x: 0.234375, y: 0.984375 },
  { x: 0.234375, y: 0.984375 },
  { x: 0.265625, y: 0.984375 },
  { x: 0.265625, y: 0.984375 },
  { x: 0.296875, y: 0.984375 },
  { x: 0.296875, y: 0.984375 },
  { x: 0.328125, y: 0.984375 },
  { x: 0.328125, y: 0.984375 },
  { x: 0.359375, y: 0.984375 },
  { x: 0.359375, y: 0.984375 },
  { x: 0.390625, y: 0.984375 },
  { x: 0.390625, y: 0.984375 },
  { x: 0.421875, y: 0.984375 },
  { x: 0.421875, y: 0.984375 },
  { x: 0.453125, y: 0.984375 },
  { x: 0.453125, y: 0.984375 },
  { x: 0.484375, y: 0.984375 },
  { x: 0.484375, y: 0.984375 },
  { x: 0.515625, y: 0.984375 },
  { x: 0.515625, y: 0.984375 },
  { x: 0.546875, y: 0.984375 },
  { x: 0.546875, y: 0.984375 },
  { x: 0.578125, y: 0.984375 },
  { x: 0.578125, y: 0.984375 },
  { x: 0.609375, y: 0.984375 },
  { x: 0.609375, y: 0.984375 },
  { x: 0.640625, y: 0.984375 },
  { x: 0.640625, y: 0.984375 },
  { x: 0.671875, y: 0.984375 },
  { x: 0.671875, y: 0.984375 },
  { x: 0.703125, y: 0.984375 },
  { x: 0.703125, y: 0.984375 },
  { x: 0.734375, y: 0.984375 },
  { x: 0.734375, y: 0.984375 },
  { x: 0.765625, y: 0.984375 },
  { x: 0.765625, y: 0.984375 },
  { x: 0.796875, y: 0.984375 },
  { x: 0.796875, y: 0.984375 },
  { x: 0.828125, y: 0.984375 },
  { x: 0.828125, y: 0.984375 },
  { x: 0.859375, y: 0.984375 },
  { x: 0.859375, y: 0.984375 },
  { x: 0.890625, y: 0.984375 },
  { x: 0.890625, y: 0.984375 },
  { x: 0.921875, y: 0.984375 },
  { x: 0.921875, y: 0.984375 },
  { x: 0.953125, y: 0.984375 },
  { x: 0.953125, y: 0.984375 },
  { x: 0.984375, y: 0.984375 },
  { x: 0.984375, y: 0.984375 },
  { x: 0.03125, y: 0.03125 },
  { x: 0.03125, y: 0.03125 },
  { x: 0.09375, y: 0.03125 },
  { x: 0.09375, y: 0.03125 },
  { x: 0.15625, y: 0.03125 },
  { x: 0.15625, y: 0.03125 },
  { x: 0.21875, y: 0.03125 },
  { x: 0.21875, y: 0.03125 },
  { x: 0.28125, y: 0.03125 },
  { x: 0.28125, y: 0.03125 },
  { x: 0.34375, y: 0.03125 },
  { x: 0.34375, y: 0.03125 },
  { x: 0.40625, y: 0.03125 },
  { x: 0.40625, y: 0.03125 },
  { x: 0.46875, y: 0.03125 },
  { x: 0.46875, y: 0.03125 },
  { x: 0.53125, y: 0.03125 },
  { x: 0.53125, y: 0.03125 },
  { x: 0.59375, y: 0.03125 },
  { x: 0.59375, y: 0.03125 },
  { x: 0.65625, y: 0.03125 },
  { x: 0.65625, y: 0.03125 },
  { x: 0.71875, y: 0.03125 },
  { x: 0.71875, y: 0.03125 },
  { x: 0.78125, y: 0.03125 },
  { x: 0.78125, y: 0.03125 },
  { x: 0.84375, y: 0.03125 },
  { x: 0.84375, y: 0.03125 },
  { x: 0.90625, y: 0.03125 },
  { x: 0.90625, y: 0.03125 },
  { x: 0.96875, y: 0.03125 },
  { x: 0.96875, y: 0.03125 },
  { x: 0.03125, y: 0.09375 },
  { x: 0.03125, y: 0.09375 },
  { x: 0.09375, y: 0.09375 },
  { x: 0.09375, y: 0.09375 },
  { x: 0.15625, y: 0.09375 },
  { x: 0.15625, y: 0.09375 },
  { x: 0.21875, y: 0.09375 },
  { x: 0.21875, y: 0.09375 },
  { x: 0.28125, y: 0.09375 },
  { x: 0.28125, y: 0.09375 },
  { x: 0.34375, y: 0.09375 },
  { x: 0.34375, y: 0.09375 },
  { x: 0.40625, y: 0.09375 },
  { x: 0.40625, y: 0.09375 },
  { x: 0.46875, y: 0.09375 },
  { x: 0.46875, y: 0.09375 },
  { x: 0.53125, y: 0.09375 },
  { x: 0.53125, y: 0.09375 },
  { x: 0.59375, y: 0.09375 },
  { x: 0.59375, y: 0.09375 },
  { x: 0.65625, y: 0.09375 },
  { x: 0.65625, y: 0.09375 },
  { x: 0.71875, y: 0.09375 },
  { x: 0.71875, y: 0.09375 },
  { x: 0.78125, y: 0.09375 },
  { x: 0.78125, y: 0.09375 },
  { x: 0.84375, y: 0.09375 },
  { x: 0.84375, y: 0.09375 },
  { x: 0.90625, y: 0.09375 },
  { x: 0.90625, y: 0.09375 },
  { x: 0.96875, y: 0.09375 },
  { x: 0.96875, y: 0.09375 },
  { x: 0.03125, y: 0.15625 },
  { x: 0.03125, y: 0.15625 },
  { x: 0.09375, y: 0.15625 },
  { x: 0.09375, y: 0.15625 },
  { x: 0.15625, y: 0.15625 },
  { x: 0.15625, y: 0.15625 },
  { x: 0.21875, y: 0.15625 },
  { x: 0.21875, y: 0.15625 },
  { x: 0.28125, y: 0.15625 },
  { x: 0.28125, y: 0.15625 },
  { x: 0.34375, y: 0.15625 },
  { x: 0.34375, y: 0.15625 },
  { x: 0.40625, y: 0.15625 },
  { x: 0.40625, y: 0.15625 },
  { x: 0.46875, y: 0.15625 },
  { x: 0.46875, y: 0.15625 },
  { x: 0.53125, y: 0.15625 },
  { x: 0.53125, y: 0.15625 },
  { x: 0.59375, y: 0.15625 },
  { x: 0.59375, y: 0.15625 },
  { x: 0.65625, y: 0.15625 },
  { x: 0.65625, y: 0.15625 },
  { x: 0.71875, y: 0.15625 },
  { x: 0.71875, y: 0.15625 },
  { x: 0.78125, y: 0.15625 },
  { x: 0.78125, y: 0.15625 },
  { x: 0.84375, y: 0.15625 },
  { x: 0.84375, y: 0.15625 },
  { x: 0.90625, y: 0.15625 },
  { x: 0.90625, y: 0.15625 },
  { x: 0.96875, y: 0.15625 },
  { x: 0.96875, y: 0.15625 },
  { x: 0.03125, y: 0.21875 },
  { x: 0.03125, y: 0.21875 },
  { x: 0.09375, y: 0.21875 },
  { x: 0.09375, y: 0.21875 },
  { x: 0.15625, y: 0.21875 },
  { x: 0.15625, y: 0.21875 },
  { x: 0.21875, y: 0.21875 },
  { x: 0.21875, y: 0.21875 },
  { x: 0.28125, y: 0.21875 },
  { x: 0.28125, y: 0.21875 },
  { x: 0.34375, y: 0.21875 },
  { x: 0.34375, y: 0.21875 },
  { x: 0.40625, y: 0.21875 },
  { x: 0.40625, y: 0.21875 },
  { x: 0.46875, y: 0.21875 },
  { x: 0.46875, y: 0.21875 },
  { x: 0.53125, y: 0.21875 },
  { x: 0.53125, y: 0.21875 },
  { x: 0.59375, y: 0.21875 },
  { x: 0.59375, y: 0.21875 },
  { x: 0.65625, y: 0.21875 },
  { x: 0.65625, y: 0.21875 },
  { x: 0.71875, y: 0.21875 },
  { x: 0.71875, y: 0.21875 },
  { x: 0.78125, y: 0.21875 },
  { x: 0.78125, y: 0.21875 },
  { x: 0.84375, y: 0.21875 },
  { x: 0.84375, y: 0.21875 },
  { x: 0.90625, y: 0.21875 },
  { x: 0.90625, y: 0.21875 },
  { x: 0.96875, y: 0.21875 },
  { x: 0.96875, y: 0.21875 },
  { x: 0.03125, y: 0.28125 },
  { x: 0.03125, y: 0.28125 },
  { x: 0.09375, y: 0.28125 },
  { x: 0.09375, y: 0.28125 },
  { x: 0.15625, y: 0.28125 },
  { x: 0.15625, y: 0.28125 },
  { x: 0.21875, y: 0.28125 },
  { x: 0.21875, y: 0.28125 },
  { x: 0.28125, y: 0.28125 },
  { x: 0.28125, y: 0.28125 },
  { x: 0.34375, y: 0.28125 },
  { x: 0.34375, y: 0.28125 },
  { x: 0.40625, y: 0.28125 },
  { x: 0.40625, y: 0.28125 },
  { x: 0.46875, y: 0.28125 },
  { x: 0.46875, y: 0.28125 },
  { x: 0.53125, y: 0.28125 },
  { x: 0.53125, y: 0.28125 },
  { x: 0.59375, y: 0.28125 },
  { x: 0.59375, y: 0.28125 },
  { x: 0.65625, y: 0.28125 },
  { x: 0.65625, y: 0.28125 },
  { x: 0.71875, y: 0.28125 },
  { x: 0.71875, y: 0.28125 },
  { x: 0.78125, y: 0.28125 },
  { x: 0.78125, y: 0.28125 },
  { x: 0.84375, y: 0.28125 },
  { x: 0.84375, y: 0.28125 },
  { x: 0.90625, y: 0.28125 },
  { x: 0.90625, y: 0.28125 },
  { x: 0.96875, y: 0.28125 },
  { x: 0.96875, y: 0.28125 },
  { x: 0.03125, y: 0.34375 },
  { x: 0.03125, y: 0.34375 },
  { x: 0.09375, y: 0.34375 },
  { x: 0.09375, y: 0.34375 },
  { x: 0.15625, y: 0.34375 },
  { x: 0.15625, y: 0.34375 },
  { x: 0.21875, y: 0.34375 },
  { x: 0.21875, y: 0.34375 },
  { x: 0.28125, y: 0.34375 },
  { x: 0.28125, y: 0.34375 },
  { x: 0.34375, y: 0.34375 },
  { x: 0.34375, y: 0.34375 },
  { x: 0.40625, y: 0.34375 },
  { x: 0.40625, y: 0.34375 },
  { x: 0.46875, y: 0.34375 },
  { x: 0.46875, y: 0.34375 },
  { x: 0.53125, y: 0.34375 },
  { x: 0.53125, y: 0.34375 },
  { x: 0.59375, y: 0.34375 },
  { x: 0.59375, y: 0.34375 },
  { x: 0.65625, y: 0.34375 },
  { x: 0.65625, y: 0.34375 },
  { x: 0.71875, y: 0.34375 },
  { x: 0.71875, y: 0.34375 },
  { x: 0.78125, y: 0.34375 },
  { x: 0.78125, y: 0.34375 },
  { x: 0.84375, y: 0.34375 },
  { x: 0.84375, y: 0.34375 },
  { x: 0.90625, y: 0.34375 },
  { x: 0.90625, y: 0.34375 },
  { x: 0.96875, y: 0.34375 },
  { x: 0.96875, y: 0.34375 },
  { x: 0.03125, y: 0.40625 },
  { x: 0.03125, y: 0.40625 },
  { x: 0.09375, y: 0.40625 },
  { x: 0.09375, y: 0.40625 },
  { x: 0.15625, y: 0.40625 },
  { x: 0.15625, y: 0.40625 },
  { x: 0.21875, y: 0.40625 },
  { x: 0.21875, y: 0.40625 },
  { x: 0.28125, y: 0.40625 },
  { x: 0.28125, y: 0.40625 },
  { x: 0.34375, y: 0.40625 },
  { x: 0.34375, y: 0.40625 },
  { x: 0.40625, y: 0.40625 },
  { x: 0.40625, y: 0.40625 },
  { x: 0.46875, y: 0.40625 },
  { x: 0.46875, y: 0.40625 },
  { x: 0.53125, y: 0.40625 },
  { x: 0.53125, y: 0.40625 },
  { x: 0.59375, y: 0.40625 },
  { x: 0.59375, y: 0.40625 },
  { x: 0.65625, y: 0.40625 },
  { x: 0.65625, y: 0.40625 },
  { x: 0.71875, y: 0.40625 },
  { x: 0.71875, y: 0.40625 },
  { x: 0.78125, y: 0.40625 },
  { x: 0.78125, y: 0.40625 },
  { x: 0.84375, y: 0.40625 },
  { x: 0.84375, y: 0.40625 },
  { x: 0.90625, y: 0.40625 },
  { x: 0.90625, y: 0.40625 },
  { x: 0.96875, y: 0.40625 },
  { x: 0.96875, y: 0.40625 },
  { x: 0.03125, y: 0.46875 },
  { x: 0.03125, y: 0.46875 },
  { x: 0.09375, y: 0.46875 },
  { x: 0.09375, y: 0.46875 },
  { x: 0.15625, y: 0.46875 },
  { x: 0.15625, y: 0.46875 },
  { x: 0.21875, y: 0.46875 },
  { x: 0.21875, y: 0.46875 },
  { x: 0.28125, y: 0.46875 },
  { x: 0.28125, y: 0.46875 },
  { x: 0.34375, y: 0.46875 },
  { x: 0.34375, y: 0.46875 },
  { x: 0.40625, y: 0.46875 },
  { x: 0.40625, y: 0.46875 },
  { x: 0.46875, y: 0.46875 },
  { x: 0.46875, y: 0.46875 },
  { x: 0.53125, y: 0.46875 },
  { x: 0.53125, y: 0.46875 },
  { x: 0.59375, y: 0.46875 },
  { x: 0.59375, y: 0.46875 },
  { x: 0.65625, y: 0.46875 },
  { x: 0.65625, y: 0.46875 },
  { x: 0.71875, y: 0.46875 },
  { x: 0.71875, y: 0.46875 },
  { x: 0.78125, y: 0.46875 },
  { x: 0.78125, y: 0.46875 },
  { x: 0.84375, y: 0.46875 },
  { x: 0.84375, y: 0.46875 },
  { x: 0.90625, y: 0.46875 },
  { x: 0.90625, y: 0.46875 },
  { x: 0.96875, y: 0.46875 },
  { x: 0.96875, y: 0.46875 },
  { x: 0.03125, y: 0.53125 },
  { x: 0.03125, y: 0.53125 },
  { x: 0.09375, y: 0.53125 },
  { x: 0.09375, y: 0.53125 },
  { x: 0.15625, y: 0.53125 },
  { x: 0.15625, y: 0.53125 },
  { x: 0.21875, y: 0.53125 },
  { x: 0.21875, y: 0.53125 },
  { x: 0.28125, y: 0.53125 },
  { x: 0.28125, y: 0.53125 },
  { x: 0.34375, y: 0.53125 },
  { x: 0.34375, y: 0.53125 },
  { x: 0.40625, y: 0.53125 },
  { x: 0.40625, y: 0.53125 },
  { x: 0.46875, y: 0.53125 },
  { x: 0.46875, y: 0.53125 },
  { x: 0.53125, y: 0.53125 },
  { x: 0.53125, y: 0.53125 },
  { x: 0.59375, y: 0.53125 },
  { x: 0.59375, y: 0.53125 },
  { x: 0.65625, y: 0.53125 },
  { x: 0.65625, y: 0.53125 },
  { x: 0.71875, y: 0.53125 },
  { x: 0.71875, y: 0.53125 },
  { x: 0.78125, y: 0.53125 },
  { x: 0.78125, y: 0.53125 },
  { x: 0.84375, y: 0.53125 },
  { x: 0.84375, y: 0.53125 },
  { x: 0.90625, y: 0.53125 },
  { x: 0.90625, y: 0.53125 },
  { x: 0.96875, y: 0.53125 },
  { x: 0.96875, y: 0.53125 },
  { x: 0.03125, y: 0.59375 },
  { x: 0.03125, y: 0.59375 },
  { x: 0.09375, y: 0.59375 },
  { x: 0.09375, y: 0.59375 },
  { x: 0.15625, y: 0.59375 },
  { x: 0.15625, y: 0.59375 },
  { x: 0.21875, y: 0.59375 },
  { x: 0.21875, y: 0.59375 },
  { x: 0.28125, y: 0.59375 },
  { x: 0.28125, y: 0.59375 },
  { x: 0.34375, y: 0.59375 },
  { x: 0.34375, y: 0.59375 },
  { x: 0.40625, y: 0.59375 },
  { x: 0.40625, y: 0.59375 },
  { x: 0.46875, y: 0.59375 },
  { x: 0.46875, y: 0.59375 },
  { x: 0.53125, y: 0.59375 },
  { x: 0.53125, y: 0.59375 },
  { x: 0.59375, y: 0.59375 },
  { x: 0.59375, y: 0.59375 },
  { x: 0.65625, y: 0.59375 },
  { x: 0.65625, y: 0.59375 },
  { x: 0.71875, y: 0.59375 },
  { x: 0.71875, y: 0.59375 },
  { x: 0.78125, y: 0.59375 },
  { x: 0.78125, y: 0.59375 },
  { x: 0.84375, y: 0.59375 },
  { x: 0.84375, y: 0.59375 },
  { x: 0.90625, y: 0.59375 },
  { x: 0.90625, y: 0.59375 },
  { x: 0.96875, y: 0.59375 },
  { x: 0.96875, y: 0.59375 },
  { x: 0.03125, y: 0.65625 },
  { x: 0.03125, y: 0.65625 },
  { x: 0.09375, y: 0.65625 },
  { x: 0.09375, y: 0.65625 },
  { x: 0.15625, y: 0.65625 },
  { x: 0.15625, y: 0.65625 },
  { x: 0.21875, y: 0.65625 },
  { x: 0.21875, y: 0.65625 },
  { x: 0.28125, y: 0.65625 },
  { x: 0.28125, y: 0.65625 },
  { x: 0.34375, y: 0.65625 },
  { x: 0.34375, y: 0.65625 },
  { x: 0.40625, y: 0.65625 },
  { x: 0.40625, y: 0.65625 },
  { x: 0.46875, y: 0.65625 },
  { x: 0.46875, y: 0.65625 },
  { x: 0.53125, y: 0.65625 },
  { x: 0.53125, y: 0.65625 },
  { x: 0.59375, y: 0.65625 },
  { x: 0.59375, y: 0.65625 },
  { x: 0.65625, y: 0.65625 },
  { x: 0.65625, y: 0.65625 },
  { x: 0.71875, y: 0.65625 },
  { x: 0.71875, y: 0.65625 },
  { x: 0.78125, y: 0.65625 },
  { x: 0.78125, y: 0.65625 },
  { x: 0.84375, y: 0.65625 },
  { x: 0.84375, y: 0.65625 },
  { x: 0.90625, y: 0.65625 },
  { x: 0.90625, y: 0.65625 },
  { x: 0.96875, y: 0.65625 },
  { x: 0.96875, y: 0.65625 },
  { x: 0.03125, y: 0.71875 },
  { x: 0.03125, y: 0.71875 },
  { x: 0.09375, y: 0.71875 },
  { x: 0.09375, y: 0.71875 },
  { x: 0.15625, y: 0.71875 },
  { x: 0.15625, y: 0.71875 },
  { x: 0.21875, y: 0.71875 },
  { x: 0.21875, y: 0.71875 },
  { x: 0.28125, y: 0.71875 },
  { x: 0.28125, y: 0.71875 },
  { x: 0.34375, y: 0.71875 },
  { x: 0.34375, y: 0.71875 },
  { x: 0.40625, y: 0.71875 },
  { x: 0.40625, y: 0.71875 },
  { x: 0.46875, y: 0.71875 },
  { x: 0.46875, y: 0.71875 },
  { x: 0.53125, y: 0.71875 },
  { x: 0.53125, y: 0.71875 },
  { x: 0.59375, y: 0.71875 },
  { x: 0.59375, y: 0.71875 },
  { x: 0.65625, y: 0.71875 },
  { x: 0.65625, y: 0.71875 },
  { x: 0.71875, y: 0.71875 },
  { x: 0.71875, y: 0.71875 },
  { x: 0.78125, y: 0.71875 },
  { x: 0.78125, y: 0.71875 },
  { x: 0.84375, y: 0.71875 },
  { x: 0.84375, y: 0.71875 },
  { x: 0.90625, y: 0.71875 },
  { x: 0.90625, y: 0.71875 },
  { x: 0.96875, y: 0.71875 },
  { x: 0.96875, y: 0.71875 },
  { x: 0.03125, y: 0.78125 },
  { x: 0.03125, y: 0.78125 },
  { x: 0.09375, y: 0.78125 },
  { x: 0.09375, y: 0.78125 },
  { x: 0.15625, y: 0.78125 },
  { x: 0.15625, y: 0.78125 },
  { x: 0.21875, y: 0.78125 },
  { x: 0.21875, y: 0.78125 },
  { x: 0.28125, y: 0.78125 },
  { x: 0.28125, y: 0.78125 },
  { x: 0.34375, y: 0.78125 },
  { x: 0.34375, y: 0.78125 },
  { x: 0.40625, y: 0.78125 },
  { x: 0.40625, y: 0.78125 },
  { x: 0.46875, y: 0.78125 },
  { x: 0.46875, y: 0.78125 },
  { x: 0.53125, y: 0.78125 },
  { x: 0.53125, y: 0.78125 },
  { x: 0.59375, y: 0.78125 },
  { x: 0.59375, y: 0.78125 },
  { x: 0.65625, y: 0.78125 },
  { x: 0.65625, y: 0.78125 },
  { x: 0.71875, y: 0.78125 },
  { x: 0.71875, y: 0.78125 },
  { x: 0.78125, y: 0.78125 },
  { x: 0.78125, y: 0.78125 },
  { x: 0.84375, y: 0.78125 },
  { x: 0.84375, y: 0.78125 },
  { x: 0.90625, y: 0.78125 },
  { x: 0.90625, y: 0.78125 },
  { x: 0.96875, y: 0.78125 },
  { x: 0.96875, y: 0.78125 },
  { x: 0.03125, y: 0.84375 },
  { x: 0.03125, y: 0.84375 },
  { x: 0.09375, y: 0.84375 },
  { x: 0.09375, y: 0.84375 },
  { x: 0.15625, y: 0.84375 },
  { x: 0.15625, y: 0.84375 },
  { x: 0.21875, y: 0.84375 },
  { x: 0.21875, y: 0.84375 },
  { x: 0.28125, y: 0.84375 },
  { x: 0.28125, y: 0.84375 },
  { x: 0.34375, y: 0.84375 },
  { x: 0.34375, y: 0.84375 },
  { x: 0.40625, y: 0.84375 },
  { x: 0.40625, y: 0.84375 },
  { x: 0.46875, y: 0.84375 },
  { x: 0.46875, y: 0.84375 },
  { x: 0.53125, y: 0.84375 },
  { x: 0.53125, y: 0.84375 },
  { x: 0.59375, y: 0.84375 },
  { x: 0.59375, y: 0.84375 },
  { x: 0.65625, y: 0.84375 },
  { x: 0.65625, y: 0.84375 },
  { x: 0.71875, y: 0.84375 },
  { x: 0.71875, y: 0.84375 },
  { x: 0.78125, y: 0.84375 },
  { x: 0.78125, y: 0.84375 },
  { x: 0.84375, y: 0.84375 },
  { x: 0.84375, y: 0.84375 },
  { x: 0.90625, y: 0.84375 },
  { x: 0.90625, y: 0.84375 },
  { x: 0.96875, y: 0.84375 },
  { x: 0.96875, y: 0.84375 },
  { x: 0.03125, y: 0.90625 },
  { x: 0.03125, y: 0.90625 },
  { x: 0.09375, y: 0.90625 },
  { x: 0.09375, y: 0.90625 },
  { x: 0.15625, y: 0.90625 },
  { x: 0.15625, y: 0.90625 },
  { x: 0.21875, y: 0.90625 },
  { x: 0.21875, y: 0.90625 },
  { x: 0.28125, y: 0.90625 },
  { x: 0.28125, y: 0.90625 },
  { x: 0.34375, y: 0.90625 },
  { x: 0.34375, y: 0.90625 },
  { x: 0.40625, y: 0.90625 },
  { x: 0.40625, y: 0.90625 },
  { x: 0.46875, y: 0.90625 },
  { x: 0.46875, y: 0.90625 },
  { x: 0.53125, y: 0.90625 },
  { x: 0.53125, y: 0.90625 },
  { x: 0.59375, y: 0.90625 },
  { x: 0.59375, y: 0.90625 },
  { x: 0.65625, y: 0.90625 },
  { x: 0.65625, y: 0.90625 },
  { x: 0.71875, y: 0.90625 },
  { x: 0.71875, y: 0.90625 },
  { x: 0.78125, y: 0.90625 },
  { x: 0.78125, y: 0.90625 },
  { x: 0.84375, y: 0.90625 },
  { x: 0.84375, y: 0.90625 },
  { x: 0.90625, y: 0.90625 },
  { x: 0.90625, y: 0.90625 },
  { x: 0.96875, y: 0.90625 },
  { x: 0.96875, y: 0.90625 },
  { x: 0.03125, y: 0.96875 },
  { x: 0.03125, y: 0.96875 },
  { x: 0.09375, y: 0.96875 },
  { x: 0.09375, y: 0.96875 },
  { x: 0.15625, y: 0.96875 },
  { x: 0.15625, y: 0.96875 },
  { x: 0.21875, y: 0.96875 },
  { x: 0.21875, y: 0.96875 },
  { x: 0.28125, y: 0.96875 },
  { x: 0.28125, y: 0.96875 },
  { x: 0.34375, y: 0.96875 },
  { x: 0.34375, y: 0.96875 },
  { x: 0.40625, y: 0.96875 },
  { x: 0.40625, y: 0.96875 },
  { x: 0.46875, y: 0.96875 },
  { x: 0.46875, y: 0.96875 },
  { x: 0.53125, y: 0.96875 },
  { x: 0.53125, y: 0.96875 },
  { x: 0.59375, y: 0.96875 },
  { x: 0.59375, y: 0.96875 },
  { x: 0.65625, y: 0.96875 },
  { x: 0.65625, y: 0.96875 },
  { x: 0.71875, y: 0.96875 },
  { x: 0.71875, y: 0.96875 },
  { x: 0.78125, y: 0.96875 },
  { x: 0.78125, y: 0.96875 },
  { x: 0.84375, y: 0.96875 },
  { x: 0.84375, y: 0.96875 },
  { x: 0.90625, y: 0.96875 },
  { x: 0.90625, y: 0.96875 },
  { x: 0.96875, y: 0.96875 },
  { x: 0.96875, y: 0.96875 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.0625, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.1875, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.3125, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.4375, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.5625, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.6875, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.8125, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.9375, y: 0.0625 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.0625, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.1875, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.3125, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.4375, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.5625, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.6875, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.8125, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.9375, y: 0.1875 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.0625, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.1875, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.3125, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.4375, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.5625, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.6875, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.8125, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.9375, y: 0.3125 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.0625, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.1875, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.3125, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.4375, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.5625, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.6875, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.8125, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.9375, y: 0.4375 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.0625, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.1875, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.3125, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.4375, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.5625, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.6875, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.8125, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.9375, y: 0.5625 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.0625, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.1875, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.3125, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.4375, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.5625, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.6875, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.8125, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.9375, y: 0.6875 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.0625, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.1875, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.3125, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.4375, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.5625, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.6875, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.8125, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.9375, y: 0.8125 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.0625, y: 0.9375 },
  { x: 0.1875, y: 0.9375 },
  { 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 = rR([t.startPoints, t.endPoints], 1);
    Object.keys(t).forEach((tensor) => De(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) => De(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 = br(t.batched);
    t.slice = qe(t.predictions, [0, 0], [-1, 1]);
    t.sigmoid = qs(t.slice);
    t.scores = br(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) => De(p[tensor]));
    }
    Object.keys(t).forEach((tensor) => De(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);
        De(croppedInput);
        De(rotatedImage);
        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime11 = now();
        De(handImage);
        const confidence = (await confidenceT.data())[0];
        De(confidenceT);
        if (confidence >= config3.hand.minConfidence / 4) {
          const keypointsReshaped = U(keypoints, [-1, 3]);
          const rawCoords = await keypointsReshaped.array();
          De(keypoints);
          De(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;
        }
        De(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 = br(t.rawBoxes, [0, 2]);
  t.scores = br(t.rawScores, [0]);
  const classScores = Fs(t.scores, 1);
  De(classScores[faceIndex]);
  classScores.splice(faceIndex, 1);
  t.filtered = es(classScores, 1);
  De(classScores);
  t.max = As(t.filtered, 1);
  t.argmax = Yu(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();
    De(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) => De(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) => De(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();
    De([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 = bi(input, cache5.padding);
  t.resize = jn.resizeBilinear(t.pad, [inputSize10, inputSize10]);
  const final = le(t.resize, "int32");
  Object.keys(t).forEach((tensor) => De(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) => De(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 = br(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
      const featuresT = br(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) => De(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();
    De(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]);
    De(norm);
    De(resize);
    let objectT;
    if (config3.object.enabled)
      objectT = model15.execute(transpose);
    lastTime14 = now();
    De(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) => br(y, [0]));
    results3d[1] = qs(results3d[1]);
    return results3d;
  });
  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
  for (const t of res)
    De(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);
  De(inputImage.tensor);
  t.norm = xe(t.resize, constants.tf255);
  t.res = model17.execute(t.norm);
  t.squeeze = br(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = gb(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 = br(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) => De(t[tensor]));
    return { data, canvas: null, alpha: null };
  }
  const alphaCanvas = canvas(width, height);
  if (Lk)
    await Lk.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);
    De(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) => De(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 (!Ipe(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 {
      Y5(2, config2.gl);
    } catch (err) {
      log("error: cannot set WebGL context:", err);
      return;
    }
    try {
      const ctx = new tm(config2.gl);
      vp(config2.name, () => new Q1(ctx), config2.priority);
    } catch (err) {
      log("error: cannot register WebGL backend:", err);
      return;
    }
    try {
      const kernels = im("webgl");
      kernels.forEach((kernelConfig) => {
        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
        Ol(newKernelConfig);
      });
    } catch (err) {
      log("error: cannot update WebGL backend registration:", err);
      return;
    }
    const current = wA().getGPGPUContext ? wA().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 {
      lk.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: kpe(),
      kernelFunc: (op2) => q(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
    };
    Ol(kernelMod);
    env.kernels.push("mod");
  }
  if (!env.kernels.includes("floormod")) {
    const kernelMod = {
      kernelName: "FloorMod",
      backendName: kpe(),
      kernelFunc: (op2) => q(() => sS(op2.inputs.a / op2.inputs.b) * op2.inputs.b + WD(op2.inputs.a, op2.inputs.b))
    };
    Ol(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 && kpe() !== 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 She(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"} ${mt2 ? "multithreaded" : "singlethreaded"}`);
        if (instance.config.debug && !simd)
          log("warning: wasm simd support is not enabled");
      }
      try {
        await xpe(instance.config.backend);
        await wpe();
        init();
      } catch (err) {
        log("error: cannot set backend:", instance.config.backend, err);
        return false;
      }
    }
    if (kpe() === "humangl") {
      lk.set("CHECK_COMPUTATION_FOR_ERRORS", false);
      lk.set("WEBGL_CPU_FORWARD", true);
      lk.set("WEBGL_USE_SHAPES_UNIFORMS", true);
      lk.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
      if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
        lk.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
      }
      if (wA().getGPGPUContext) {
        const gl2 = await wA().getGPGPUContext().gl;
        if (instance.config.debug)
          log(`gl version:${gl2.getParameter(gl2.VERSION)} renderer:${gl2.getParameter(gl2.RENDERER)}`);
      }
    }
    if (kpe() === "webgpu") {
    }
    fpe();
    await wpe();
    instance.performance.initBackend = Math.trunc(now() - timeStamp);
    instance.config.backend = kpe();
    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);
      }
    };
    Ol(kernelConfig);
  }
  env.kernels = im(kpe()).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 = (z10, opt2) => {
  if (!opt2.useDepth || typeof z10 === "undefined")
    return opt2.color;
  const rgb2 = Uint8ClampedArray.from([127 + 2 * z10, 127 - 2 * z10, 255]);
  const color = `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
  return color;
};
function point(ctx, x, y, z10, localOptions) {
  ctx.fillStyle = colorDepth(z10, 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 z10 = part[part.length - 1][2] || -256;
        ctx.fillStyle = colorDepth(z10, 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 z10 = part[i][2] || 0;
          ctx.strokeStyle = colorDepth(i * z10, 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();
  De(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 z10 = a[2] - b[2];
    return [x, y, z10];
  };
  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 z10 = a[0] * b[1] - a[1] * b[0];
    return [x, y, z10];
  };
  const rotationMatrixToEulerAngle = (r) => {
    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
    let thetaX;
    let thetaY;
    let thetaZ;
    if (r10 < 1) {
      if (r10 > -1) {
        thetaZ = Math.asin(r10);
        thetaY = Math.atan2(-r20, r00);
        thetaX = Math.atan2(-r12, r11);
      } else {
        thetaZ = -Math.PI / 2;
        thetaY = -Math.atan2(r21, r22);
        thetaX = 0;
      }
    } else {
      thetaZ = Math.PI / 2;
      thetaY = Math.atan2(r21, r22);
      thetaX = 0;
    }
    if (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]);
      De(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) ? br(faces[i].tensor) : null;
    De(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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==`;
var body3 = `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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 = kpe();
  const webGLBackend = wA();
  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) => De(t));
      else
        De(res);
    } catch (e) {
      log("compile fail model:", modelName);
    }
    De(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 = The["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Tpe}/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 wpe();
      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);
        }
      };
      De(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